diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 1133557..0000000
Binary files a/.DS_Store and /dev/null differ
diff --git a/.github/workflows/build_docs.yaml b/.github/workflows/build_docs.yaml
deleted file mode 100644
index 771e6a8..0000000
--- a/.github/workflows/build_docs.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-name: Deploy Sphinx documentation to Pages
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Install pandoc
-        run: sudo apt-get update && sudo apt-get install -y pandoc
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[doc]
-
-      - name: Build documentation
-        run: sphinx-build -b html ./docs/source ./docs/build/html
-
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
-        with:
-          path: ./docs/build/html
-
-  deploy:
-    needs: build
-    runs-on: ubuntu-latest
-    permissions:
-      pages: write
-      id-token: write
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    if: github.event_name == 'push' || github.event.pull_request.head.repo.full_name == github.repository
-    steps:
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4
\ No newline at end of file
diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
new file mode 100644
index 0000000..11ee6a1
--- /dev/null
+++ b/.github/workflows/docs.yaml
@@ -0,0 +1,68 @@
+name: Docs
+
+on:
+  push:
+    branches: [main, v3]
+  pull_request:
+    branches: [main, v3]
+  workflow_dispatch:
+
+# Allow one concurrent deployment; cancel in-progress runs on the same ref.
+concurrency:
+  group: pages-${{ github.ref }}
+  cancel-in-progress: false
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    name: Sphinx build (strict)
+    runs-on: ubuntu-latest
+    env:
+      # PR merge refs see upstream's legacy non-v tags but not the fork's v3 tag.
+      SETUPTOOLS_SCM_PRETEND_VERSION_FOR_HYPERIAX: "3.0.0"
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          # setuptools-scm needs the full history + tags to derive __version__
+          fetch-depth: 0
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          enable-cache: true
+
+      - name: Sync project (core runtime + dev group)
+        run: uv sync --group dev
+
+      - name: Build HTML (warnings as errors)
+        run: uv run make -C docs html SPHINXOPTS=-W
+
+      - name: Upload Pages artifact
+        # Only the deployable ref needs to upload; PRs just verify the build.
+        if: github.event_name != 'pull_request' && github.ref == 'refs/heads/v3'
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: docs/build/html
+
+  deploy:
+    name: Deploy to GitHub Pages
+    needs: build
+    if: github.event_name != 'pull_request' && github.ref == 'refs/heads/v3'
+    runs-on: ubuntu-latest
+
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+
+    permissions:
+      pages: write       # to deploy to Pages
+      id-token: write    # to verify the deployment originates from this workflow
+
+    steps:
+      - name: Deploy
+        id: deployment
+        uses: actions/deploy-pages@v4
diff --git a/.github/workflows/pypi_push.yaml b/.github/workflows/pypi_push.yaml
index e4d106b..b4046f9 100644
--- a/.github/workflows/pypi_push.yaml
+++ b/.github/workflows/pypi_push.yaml
@@ -1,38 +1,35 @@
-name: Publish Package
-
-on:
-  release:
-    types: [published]
-
-permissions:
-  contents: read
-
-
-jobs:
-  pypi-publish:
-    name: Upload release to PyPI
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/hyperiax
-    permissions:
-      id-token: write  # IMPORTANT: this permission is mandatory for trusted publishing
-    steps:
-    # retrieve your distributions here
-    - name: Setup python
-      uses: actions/setup-python@v4
-      with:
-        python-version: "3.11"
-    - name: Setup setuptools_scm
-      run: pip install setuptools_scm
-    - name: Checkout the repository
-      uses: actions/checkout@v2
-    - name: Build package
-      run: python setup.py sdist
-     # working-directory: ./app
-
-
-    - name: Publish package distributions to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        password: ${{ secrets.PYPI_API_TOKEN }}
+name: Publish Package
+
+on:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  pypi-publish:
+    name: Build and publish to PyPI
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/hyperiax
+    permissions:
+      id-token: write  # required for PyPI trusted publishing
+      contents: read
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0   # setuptools-scm needs the full history for the version tag
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v4
+
+    - name: Build sdist + wheel
+      run: uv build
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml
index 7c69dcb..2362296 100644
--- a/.github/workflows/pytest.yaml
+++ b/.github/workflows/pytest.yaml
@@ -1,30 +1,27 @@
-name: Pytest on Pull Request
+name: Test
 
 on:
   push:
-    branches:
-      - main
+    branches: [main, v3]
   pull_request:
-    branches:
-      - main
+    branches: [main, v3]
 
 jobs:
   test:
-    name: Run Pytest
+    name: pytest (uv, Python 3.11)
     runs-on: ubuntu-latest
-    
+
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v2
-      
-    - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/checkout@v4
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v4
       with:
-        python-version: 3.11
-        
-    - name: Install dependencies
-      run: |
-        pip install .[dev]
-        
-    - name: Run Pytest
-      run: make test
+        enable-cache: true
+
+    - name: Sync project (core runtime + dev group)
+      run: uv sync --group dev
+
+    - name: Run tests
+      run: uv run pytest
diff --git a/.gitignore b/.gitignore
index c57c2d0..b3d7b14 100644
--- a/.gitignore
+++ b/.gitignore
@@ -77,6 +77,8 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/build/
+docs/source/api/generated/
 
 # PyBuilder
 .pybuilder/
@@ -168,14 +170,22 @@ cython_debug/
 #.idea/
 
 legacy/
-# docs/
 data/
 todo
 changelog
-# generated on build server
+# generated by setuptools-scm on build
 *_version.py
-examples/example_data
-*test.ipynb
 *.cpp
 .vscode/
 .Rproj.user
+
+# OS
+.DS_Store
+
+# Note: uv.lock IS committed (do not ignore) — needed for reproducible installs.
+
+.claude/
+.ruff_cache/
+
+# Throwaway profiling scratchpad outputs.
+/profiling/traces/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..77d9593
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,27 @@
+# Hyperiax pre-commit hooks.
+#
+# Install once per clone:
+#   uv run pre-commit install
+# Run on all files:
+#   uv run pre-commit run --all-files
+
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.15.13
+    hooks:
+      - id: ruff-check
+        args: [--fix]
+      - id: ruff-format
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+        exclude: ^(docs/build/|docs/source/notebooks/)
+      - id: end-of-file-fixer
+        exclude: ^(docs/build/|docs/source/notebooks/|hyperiax/_version\.py$)
+      - id: check-yaml
+      - id: check-toml
+      - id: check-merge-conflict
+      - id: check-added-large-files
+        args: [--maxkb=500]
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..2c07333
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..4847925
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,187 @@
+# AGENTS.md
+
+Guidance for Codex (and any agent) working in this repo. Treat
+this as ground truth before guessing.
+
+## What this is
+
+`hyperiax` is a **pure-JAX library for tree message passing**. Users
+build a `Topology` (rooted tree structure), wrap it in an immutable
+`Tree` (a JAX pytree carrying typed per-node arrays), and run sweeps —
+`Tree -> Tree` functions written with `@hx.up` / `@hx.down`
+decorators. Everything composes with `@jax.jit` and `jax.lax.scan`.
+
+The current branch is `v3`, a ground-up rewrite of the legacy
+`main`-branch 0.x. Do **not** look to `main` or to deleted
+`examples/*.py` files for guidance — that code is dead. The v3 design
+is captured by `pyproject.toml`, the docs under `docs/source/`, and
+the tutorial notebooks `docs/source/notebooks/01..07`.
+
+## Architecture (L1 / L2 layering — enforced by tests)
+
+```
+hyperiax/
+├── core/            # L1 — Topology, Tree, Schema, views, sweep, dispatch, builders (incl. Newick), errors
+├── utils/           # L1 — pure-JAX ODE / SDE solvers (no optional deps)
+└── prebuilt/        # L2 — bffg, phylo_mean
+```
+
+Rules, enforced by `tests/test_core_dependencies.py`:
+
+- **`core/` must not import** anything outside `{jax, numpy, hyperiax, stdlib}`. No matplotlib, scipy, ete3, diffrax, jax_tqdm, etc. The Newick parser is a small pure-Python implementation in `core/builders.py`.
+- **`prebuilt/` modules must not import each other.**
+- **Optional deps must be lazy** (function-local `import`, or top-level inside `try/except ImportError`). `import hyperiax` must work on a bare `jax + numpy` install.
+
+Optional extras / groups and what they unlock:
+
+| Kind | Name | Adds | Used by |
+|---|---|---|---|
+| extra | `notebook` | matplotlib, jupyter, joblib, optax, numpyro | `docs/source/notebooks/*.ipynb` (incl. NumPyro-driven BFFG-MCMC notebooks) |
+| group | `dev` | pytest, ruff, pre-commit, sphinx + theme | test, lint, docs build |
+
+The core library has no optional runtime deps — `jax + numpy` is enough to
+import everything in `hyperiax/`, `hyperiax/core/`, `hyperiax/utils/`,
+and `hyperiax/prebuilt/`.
+
+Install patterns:
+- contributor: `uv sync --group dev` (test + lint + docs)
+- contributor + everything: `uv sync --all-extras --group dev`
+- end user: `uv pip install 'hyperiax'`
+
+## Hard invariants (don't regress these)
+
+1. **`Tree` is immutable.** Mutators (`set`, `set_at`, `update`, `drop`) all return new `Tree` instances. Do **not** reintroduce `tree.data = {...}`. The v2 in-place pattern was the source of leaked-tracer bugs that motivated v3 — reverting it breaks `@jax.jit`-wrapped train steps in user code.
+2. **`Topology` derived fields are `numpy` arrays, not `jax` arrays.** They ride through `jax.jit` as static aux_data. Promoting them to `jnp` would make `Topology` unhashable and break the JIT cache.
+3. **`ChildrenAxis._num_segments` must be a Python `int`** (static), not a tracer — otherwise `jax.ops.segment_*` output shape becomes polymorphic and breaks JIT.
+4. **Sweeps declare `reads` / `writes` explicitly.** No reflection / no inferring from function signature.
+5. **Schema field names are validated against `_RESERVED_FIELD_NAMES`** in `core/tree.py` to keep attribute access (`tree.value`) unambiguous.
+6. **One up-sweep dispatch path; `children.X` is always a `ChildrenAxis` proxy.** Any topology — equal- or unequal-degree — routes through `_up_dispatch` (segment-based). The proxy exposes pure reductions (`.sum/.mean/.max/.min/.prod(axis=0)` → `jax.ops.segment_*`) and a segment-preserving per-child vmap (`children.map(fn)` — `fn` receives a per-child `Node` view and returns a `dict[str, Array]`). Direct array ops (`children.X[0]`, `children.X * 2`, `children.X.shape`) are deliberately rejected: the dense vmap-over-parents path has been removed. Any non-reduction per-child work goes through `children.map`. `writes_children` outputs come back as `ChildrenAxis` and scatter to the contiguous child block.
+
+## Common commands
+
+```bash
+make sync                  # uv sync --all-extras --group dev
+make test                  # uv run pytest
+
+# Sphinx (docs deps are in the dev group)
+make -C docs html          # build HTML to docs/build/html
+make -C docs clean         # nuke build + autosummary stubs
+make -C docs open          # open in browser (macOS)
+make -C docs html SPHINXOPTS=-W   # strict mode (warnings as errors)
+
+# Lint / format (ruff config in pyproject)
+uv run ruff check .
+uv run ruff format .
+uv run pre-commit run --all-files
+```
+
+CI runs `uv sync --all-extras --group dev && uv run pytest` on push/PR
+to `main` and `v3` (`.github/workflows/pytest.yaml`). Releases trigger
+PyPI publish (`.github/workflows/pypi_push.yaml`). **No `build_doc`
+workflow yet** — sphinx is currently local-only.
+
+## Tests
+
+164 tests across 12 files in `tests/`, all passing on a clean checkout.
+Run subsets with `uv run pytest tests/test_<thing>.py -x -q`. Key suites
+worth knowing:
+
+- `test_core_dependencies.py` — **architectural lint**. Enforces L1/L2 layering and the lazy-optional-import rule.
+- `test_topology.py` / `test_tree.py` / `test_schema.py` / `test_sweep_*` — core API.
+- `test_sweep_up.py` — up-sweep dispatch on both equal- and unequal-degree trees in one file (the single segment-based path makes them indistinguishable to user code; tests cover reductions, `children.map`, `writes_children`, ChildrenAxis proxy guards, jit cache + outer-jit on both topologies).
+- `test_prebuilt_phylo_mean.py` — the only prebuilt with dedicated tests; the BFFG sweeps are covered by the core dispatch tests (a stable BFFG-specific test suite is pending the new public surface).
+- `test_regression.py` — leaked-tracer regression, jit + scan composition.
+
+## Docs
+
+```
+docs/
+├── Makefile          # sphinx commands
+├── source/
+│   ├── conf.py       # furo theme, myst-nb (execution=off), autosummary
+│   ├── index.rst     # landing page + toctrees
+│   ├── notebooks/    # tutorials 01–07 (.ipynb)
+│   └── api/index.rst # autosummary entry points per module
+└── build/            # gitignored
+```
+
+**Notebook policy:** outputs are rendered as-saved (`nb_execution_mode = "off"`).
+If you change a notebook, re-run it locally and commit the new outputs.
+CI does **not** execute notebooks during the docs build (deliberate —
+notebooks 05/07 run multi-chain MCMC and take 10+ minutes).
+
+When adding a new prebuilt or public symbol, list it in
+`docs/source/api/index.rst`. The matching stub page is generated by
+`sphinx-autosummary` on the next `make -C docs html`.
+
+## Coding conventions
+
+- **Docstring style:** Google / Napoleon (`Args:` / `Returns:` blocks). Sphinx is configured for it.
+- **No migration narration in docstrings.** Statements like "ported verbatim from the legacy `examples/...`" or "Stage N adds Y" are stale — they were stripped in commit `a6cebd1`. Don't add them back.
+- **No emojis in code or docstrings** unless explicitly requested.
+- **No backwards-compatibility shims.** v3 is on its own branch and there is no compatibility surface to preserve.
+- **Comments explain *why*, not *what*.** The codebase favors short comments over verbose narration. Default to no comment.
+
+## Things that recently changed (don't re-introduce)
+
+Cleanups from earlier passes (still in effect):
+
+- `StructureMismatch` exception removed (raised nowhere). Use `SchemaMismatch` for shape/dtype mismatches and `MissingField` for unknown field names.
+- `Parent` view class is **not** re-exported at top level (`hyperiax.Parent` doesn't exist). It's still in `hyperiax.core.views` for the dispatcher's internal use, but users don't see it.
+- `Schema.empty()` removed — use `Schema.from_dict({})`.
+- `MHState.__repr__` doesn't call `float()` on a jax array — that broke under jit/vmap. Default dataclass repr now.
+
+BFFG rewrite (`prebuilt/bffg.py` — `gaussian_*` → `discrete_*`, `sde_*` → `continuous_*`):
+
+- Public names are `discrete_schema` / `continuous_schema`, `init_discrete_tree` / `init_continuous_tree`, `discrete_bf_sweep` / `discrete_forward_sweep` / `discrete_fg_sweep`, `continuous_bf_sweep` / `continuous_forward_sweep` / `continuous_fg_sweep`, plus `propagate_linearization`. The old `gaussian_*` / `sde_*` names are gone — don't bring them back.
+- `_discrete_backward_filtering` applies the full linear-Gaussian auxiliary `(Φx + β, Q)` per Theorem 14 §6.1: `prec_msg = Φᵀ (I + HQ)⁻¹ H Φ`, `ptnl_msg = Φᵀ (I + HQ)⁻¹ (F − Hβ)`. The earlier version silently dropped `Φ` and `β`.
+- `_discrete_forward_guiding` weight matches Theorem 14.3 (eval point `H⁻¹F`; numerator uses true `μ(x)` + `Q_true(x) + H⁻¹`; denominator uses auxiliary `Φx + β` + `Q_aux + H⁻¹`). `discrete_fg_sweep` therefore takes the proxy `(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn)` as well as the true `(mean_fn, covar_fn)`.
+- `continuous_bf_sweep` caches the per-edge `(H, F)` trajectory on each child via `writes_children=("precs","ptnls")`; the fused vertex message lives in new schema fields `prec_v` / `ptnl_v` and becomes the terminal condition for the parent's own edge one level up. `_continuous_bf_ode` uses `ã = σ̃ σ̃ᵀ` (the diffusion squared, not used raw); `_continuous_bf_anlt` is the closed-form path when `prxy_scale_fn = prxy_shift_fn = None`. `prxy_diffusion_fn(u, params)` returns σ̃(u) (time argument).
+- `_continuous_forward_guiding` weight is the Theorem 23 / Remark 24 integrand `(b − b̃)·r − ½ tr((a − ã)H) + ½ r'(a − ã)r`; a `None`-guard treats the `B = β = 0` auxiliary as driftless.
+
+**Iterative linearisation (Algorithm 3 §7.1).** Both schemas carry a per-node `anchor: (d,)` field; `continuous_schema` additionally carries `anchor_pa: (d,)` (the parent-end anchor, stored on each child so `children.map(...)` can read both endpoints without segment-aware gather). All `prxy_*_fn` callables take `anchor` as a positional argument before `params` (continuous: also after `t`). Linear-Gaussian models like 05/06 ignore it; nonlinear models like 07's shape SDE evaluate the auxiliary's `σ̃` at it.
+
+`continuous_bf_sweep` uses two anchors per edge — `anchor_pa` at `t=0` and `anchor` at `t=T` — with `ã(t)` **linearly interpolated** between `σ̃(anchor_pa)σ̃ᵀ` and `σ̃(anchor)σ̃ᵀ`. The closed-form anlt path integrates this analytically; the ODE path passes `anchor_at(t) = (1-t/T)·anchor_pa + (t/T)·anchor` into the user's `prxy_*_fn`. The matching `continuous_fg_sweep` rebuilds the same interpolation for the bridge step's log-weight increment, so `a − ã` stays consistent across BF and FG.
+
+`discrete_refine_anchor()` / `continuous_refine_anchor()` are `@down` sweeps that overwrite `anchor` with the BFFG posterior mean `prec⁻¹ ptnl` (continuous: `prec_v⁻¹ ptnl_v`) and `anchor_pa` with the parent's just-refined anchor. The user iterates `bf_sweep → refine_anchor` 3–10 times before running `fg_sweep` — 07 demonstrates this. `init_discrete_tree` / `init_continuous_tree` accept an optional `anchor_init` kwarg (default = `root_val`) and additionally pin the LEAF anchors to `leaf_obs` so the first BF iteration already has correct leaf-end anchors.
+
+When the user pins the root at a non-zero value, the BFFG-implied marginal log-likelihood is **`log h(x_root) = log_norm + ptnl_v · x_root - ½ x_root' prec_v x_root`** at the root — not `log_norm` alone (`log_norm` is the canonical constant `c`, not `log h` at a non-zero pinned point). For 05/06 with root pinned at 0 this collapses to `log_norm`; for 07's shape SDE pinned at `ROOT_SHAPE` you MUST add the linear and quadratic terms — see `bffg_guided_forward` in 07.
+
+Dispatch unification:
+
+- **One up-sweep dispatch path.** `_up_dispatch_equal` is gone; `up_dispatch` always calls the single segment-based `_up_dispatch`. Equal-degree trees are just the special case where every segment has the same size. Performance is comparable or better on non-tiny trees.
+- `Topology.gather_child_idx` and `Topology.level_non_leaf_indices` have been removed (they only fed the deleted equal-degree path). Don't reintroduce.
+- `Children.map(fn)` is the new primitive for per-child non-reduction work. Existing pure-reduction idioms (`children.X.sum(0)` etc.) are unchanged.
+- `prebuilt/phylo_mean.py` migrated to `children.map` and now runs on **unequal-degree** trees too (the old "raises on ragged" test flipped to a correctness test).
+
+`hyperiax/prebuilt/__init__.py` does not re-export BFFG names — the new public surface is in flux. Import via `from hyperiax.prebuilt.bffg import discrete_bf_sweep` (etc.) for now.
+
+## Scope and future directions
+
+- **Rooted directed trees only — DAGs are a known future extension.** `Topology.from_parents` takes a 1-D `parents` array (one parent per node) with BFS ordering (`parents[i] < i`); `_down_dispatch` exploits this single-parent *function* to gather each node's parent into a rectangular `(n_at_level, *trailing)` block, so `Parent` stays a plain `_FieldsView` and no segment ops are needed in the down direction. Extending to general DAGs (multi-parent nodes) would mean:
+  - `Parent` becomes a segment-aware proxy, mirroring today's `Children`;
+  - `_down_dispatch` grows the same flat-`(M_total, *trailing)` + `segment_*` machinery the up-sweep now uses;
+  - the BFS layout and `pbuckets` / `pbuckets_ref` derivation in `_build_topology` need to handle multi-parent fan-in (`parents[i] < i` no longer holds verbatim — needs a proper topological sort).
+  Out of scope for now; the current API is tree-specific by design.
+
+## Reference: BFFG paper
+
+Most of the math in `prebuilt/bffg.py` comes from:
+
+> van der Meulen, F. H. & Sommer, S. (2025). *Backward Filtering
+> Forward Guiding.* JMLR 26(281), 1–51.
+> https://arxiv.org/abs/2505.18239
+
+Key sections:
+- §3 — canonical-form propagation (backward information filter).
+- §6.1 + Theorem 14 — discrete nonlinear-Gaussian kernels with linear auxiliary `(Φx + β, Q)`. Matches `discrete_bf_sweep` (pullback + fusion) and `discrete_fg_sweep` (guided proposal + log-weight `w(x)`).
+- §7.1 + Theorem 23 + Remark 24 — continuous (SDE) edges with linear auxiliary `dX̃ = (B(u)X̃ + β(u))du + σ̃(u)dW`. Matches `continuous_bf_sweep` (eq 29 backward ODE for `(H, F)`; closed form when `B = β = 0`) and `continuous_fg_sweep` (guided SDE eq 31; weight eq 32 = ∫ `(L − L̃)g / g` via the Remark 24 integrand).
+
+## Style for working in this repo
+
+- **`uv run` everything.** Don't `python -m pytest` or `pip install`.
+- **Branch is `v3`.** PRs target `v3`, not `main`, until v3 stabilizes.
+- **Commit messages: imperative, concise.** Match the style of recent `git log` — e.g. `dispatch: collapse equal/unequal up paths into one` rather than `Fixed the dispatcher`.
+- **Don't `--no-verify` commit.** Pre-commit hooks run ruff (lint + format) and a few stock checks; install once with `uv run pre-commit install`.
+- **`uv.lock` is committed.** If you change `pyproject.toml`, run `uv sync` so the lockfile updates; commit both.
+- **Versioning is via git tag.** `setuptools-scm` derives `__version__` from the closest `vX.Y.Z` tag; the v3 line starts at tag `v3.0.0`.
diff --git a/CONTRIBUTION.md b/CONTRIBUTION.md
deleted file mode 100644
index 4876737..0000000
--- a/CONTRIBUTION.md
+++ /dev/null
@@ -1,147 +0,0 @@
-# Contributing Guidelines
-
-Thanks for taking time to contribute!👍🎉
-
-When contributing to the repository, we suggest a general guideline for easily organizing and integrating cool ideas from everyone, so please follow it in your interactions with the project.
-
-
-## Setting up the project locally
-
-1. [Fork the repository on GitHub](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo), and clone your forked repository to your local machine.
-```
-# Clone the forked repository
-https://github.com/<your username>/hyperiax
-
-# Navigate to the directory
-cd hyperiax
-```
-2. [Create the environment and install required packages](./README.md#installation)
-3. Make sure your `main` branch points at the original repository's `main`, so that you can easily get updates from the original by just running `git pull`: 
-```
-# Add the original repository as a remote to your own repository
-git remote add upstream https://github.com/ComputationalEvolutionaryMorphometry/hyperiax
-
-# Fetch the upstream and connect your local main branch to the original main
-git fetch upstream
-git branch --set-upstream-to=upstream/main main
-
-# Pull the latest updates from the original repository
-git pull
-```
-
-4. Create a new branch for your new development and testing (Recommended)
-```
-# Create a new local branch called 'my-dev'
-git checkout -b my-dev
-
-# ... (Your contributions)
-
-# Test your changes
-python -m pytest
-
-# Commit your changes
-git commit -am "Add my features"
-# Push them to your remote repository
-git push origin my-dev
-```
-
-5. If everything is ready, consider [submitting a pull request (PR)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request?tool=webui) against the `main` branch of the original repository.
-
-## Pull Request Guidelines
-
-### PR Philosophy
-- Ensure your PR is focused on a single change and has a clear title and description.
-- Check the issues and include relevant issue numbers in the PR description if applicable.
-- Make sure all tests pass before submitting the PR.
-- Keep your PR up to date with the latest changes in the `main` branch. Do this by pulling the upstream branch on your fork.
-
-### Code Style
-- Use clear and descriptive variable and function names.
-- Write docstrings for all public classes, methods, and functions.
-- Do not use any stateful randomness. ie. a PRNGKey should only be passed to a constructor if it is immediately disposed.
-
-### Docstrings Style
-In order to fit the Sphinx's automatic docstring generation, we recommend to document the functions in the default [Sphinx style](https://www.sphinx-doc.org/en/master/tutorial/describing-code.html#documenting-python-objects), for example:
-``` python
-def symmetric_tree(h : int, degree : int, new_node : TreeNode = TreeNode, fake_root : TreeNode = None) -> HypTree:
-    """ Generate tree of given height and degree
-
-    A tree of height zero contains just the root;
-    a tree of height one contains the root and one level of leaves below it,
-    and so forth.
-
-    :param h: The height of the tree
-    :param degree: The degree of each node in the tree
-    :param new_node: The node used to construct the tree, defaults to TreeNode
-    :param fake_root: The fake root node, defaults to None
-    :raises ValueError: If height is negative
-    :return: The constructed tree
-    """
-    if h < 0:
-        raise ValueError(f'Height shall be nonnegative integer, received {h=}.')
-
-    def _builder(h: int, degree: int, parent):
-        node = new_node(); node.parent = parent; node.children = ChildList()
-        if h > 1:
-            node.children = ChildList([_builder(h - 1, degree, node) for _ in range(degree)])
-        return node
-
-    if fake_root is None:
-        return HypTree(root=_builder(h + 1, degree, None))
-    else:
-        fake_root.children = ChildList([_builder(h + 1, degree, fake_root)])
-        return HypTree(root=fake_root)
-```
-If you are using VScode, it is easy to generate such template automatically using the [autoDocstring extension](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring). After installation, open the extension settings and choose `sphinx-notypes` under `Docstring Format` tab.
-
-### Testing
-
-- Write tests for new features and bug fixes.
-- Make sure all existing tests pass before submitting changes.
-- Run the test suite locally before submitting your PR: `make test` and `make local-notebook-test`
-
-### Documentation
-
-- Document all public classes, methods, and functions using clear and concise comments and docstrings.
-- Update the README.md file with any relevant changes to the project, including installation and usage instructions.
-
-### Build Documentation Locally
-The documentation compiling replies on _sphinx_ package, to install it, use _pip_:
-``` bash
-$ pip install sphinx
-```
-After install _sphinx_, check if there are three major files under `./docs` directory: `make.bat`, `Makefile` and `source/config.py`. If not, run
-``` bash
-$ sphinx-quickstart
-```
-to automatically generate these files, and add manually
-``` python
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../../hyperiax/'))
-``` 
-into `source/config.py` in the beginning to configure the directory for _sphinx_ to find the source code.
-
-You may also need to install additional packages
-* _sphinx-rtd-theme_: to use the readthedoc theme) 
-* _sphinx-autodoc-annotation_: for automatic type annotation). 
-* _nbsphinx_: for compiling jupyter notebooks
-* _pandoc_: addons for _nbsphinx_
-To install them except _pandoc_, use pip:
-``` bash
-$ pip install sphinx_rtd_theme
-$ pip install sphinx-autodoc-annotation
-$ pip install nbsphinx
-```
-To install _pandoc_, in MacOS, using _brew_:
-``` bash
-$ brew install pandoc
-```
-
-After everything is installed, you can compile the documentation locally:
-``` bash
-$ cd ./docs
-$ make clean
-$ make html
-```
-This should generate compiled html files under `./docs/build/` directory.
\ No newline at end of file
diff --git a/README.md b/README.md
index cc69263..2e5a9eb 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,103 @@
-# Hyperiax: Tree traversals using JAX
+# Hyperiax
+
 <p align="center">
- <img width="300", height="250" src="./docs/figures/hyperiax_logo.png">
+  <img width="300" height="250" src="./docs/figures/hyperiax_logo.png" alt="Hyperiax logo">
 </p>
 
-## Introduction
-
-_Hyperiax_ is a framework for tree traversal and computations on large-scale tree. Its primary purpose is to facilitate **efficient message passing** and **operation execution** on large trees. Hyperiax uses [JAX](https://jax.readthedocs.io/en/latest/index.html) for fast execution and automatic differentiation. _Hyperiax_ is currently developed and maintained by [CCEM, UCPH](https://www.ccem.dk/) actively.
+Hyperiax is a pure-JAX library for message passing on rooted trees. It provides
+immutable tree data structures, typed per-node arrays, and decorator-based
+sweeps that compose with `jax.jit`, `jax.vmap`, and `jax.lax.scan`.
 
-Initially, _Hyperiax_ was designed specifically for phylogenetic analysis of biological shape data, particularly enabling statistical inference with continuous time stochastic processes along the edges of the trees. For this purpose, is integrated with [JAXGeometry](https://bitbucket.org/stefansommer/jaxgeometry/src/main/), a computational differential geometry toolbox implemented in [JAX](https://jax.readthedocs.io/en/latest/index.html). However, _Hyperiax_'s messaging system and operations are general, which means that they can be easily adapted for use in other contexts. With minor modifications, _Hyperiax_ can be used for any application where fast tree-level computations are necessary. Included examples cover such cases with inference in Gaussian graphical models, phylogenetic mean computation, and recursive shape matching in binary trees.
+Hyperiax is designed for phylogenetic and tree-structured scientific computing,
+including Gaussian graphical models, phylogenetic means, and guided inference
+for diffusion processes on trees.
 
 ## Installation
+
+Hyperiax requires Python 3.11 or newer.
+
 ```bash
-# Install Hyperiax directly using pip
 pip install hyperiax
+```
+
+The core package depends only on JAX, jaxlib, and NumPy. For accelerator-backed
+JAX installations, follow the official JAX installation instructions for your
+platform before or after installing Hyperiax.
+
+## Quick Start
+
+```python
+import jax.numpy as jnp
+import hyperiax as hx
 
-# Install Hyperiax from the repository, for the newest version
-pip install git+https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git
-
-# Install Hyperiax for development
-git clone git@github.com:ComputationalEvolutionaryMorphometry/hyperiax.git
-# or (if you haven't set up ssh)
-git clone https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git
-# and go into the directory
-cd hyperiax
-# and then install by
-pip install -e hyperiax[dev]
-# and optionally
-pip install -e hyperiax[examples]
-# to install the dependencies for all the example notebooks
+topology = hx.symmetric_topology(depth=2, degree=2)
+tree = hx.Tree.empty(topology, {"value": (2,)})
+
+leaf_count = int(topology.is_leaf.sum())
+tree = tree.at[topology.is_leaf].set(value=jnp.ones((leaf_count, 2)))
+
+
+@hx.up(reads_children=("value",), writes=("value",))
+def average_children(node, children, params):
+    return {"value": children.value.mean(0)}
+
+
+result = average_children(tree)
+root_value = result.value[0]
 ```
 
+Sweeps are ordinary `Tree -> Tree` functions. The `@hx.up` and `@hx.down`
+decorators declare which fields are read and written, so dispatch remains
+explicit and JAX-friendly.
+
+## Architecture
+
+```text
+hyperiax/
+├── core/      # Topology, Tree, Schema, views, sweep dispatch, builders
+├── utils/     # Pure-JAX ODE and SDE solvers
+└── prebuilt/  # Ready-to-use sweeps for BFFG and phylogenetic means
+```
+
+### Core
+
+`hyperiax.core` contains the public primitives:
+
+- `Topology` for rooted tree structure.
+- `Tree` for immutable per-node JAX arrays.
+- `Schema` for field shape and dtype validation.
+- `@hx.up` and `@hx.down` for message-passing sweeps.
+- Newick helpers for importing and exporting rooted trees.
+
+### Utilities
+
+`hyperiax.utils` contains pure-JAX numerical helpers, including ODE and SDE
+solvers used by the prebuilt guided-inference routines.
+
+### Prebuilt Sweeps
+
+`hyperiax.prebuilt` contains focused implementations for common tree workflows:
+
+- `phylo_mean` for weighted phylogenetic means.
+- `bffg` for Backward Filtering Forward Guiding on discrete Gaussian and
+  continuous SDE edges.
+
 ## Documentation
-- **Usage**: See [Usage](https://computationalevolutionarymorphometry.github.io/hyperiax/usage.html) for different examples of _Hyperiax_.
-- __Full API Documentation__: See [Hyperiax API](https://computationalevolutionarymorphometry.github.io/hyperiax/hyperiax.html)
 
-## Todo
+Tutorials and API reference are available in the project documentation:
+
+- Quickstart and sweep-writing tutorials under `docs/source/notebooks/`.
+- Public API reference under `docs/source/api/`.
 
-## Contribution
-Contributions, issues and feature requests are all welcome! Please refer to the [contributing guidelines](./CONTRIBUTION.md) before you want to contribute to the project.
+## Reference
+
+The BFFG implementation follows:
+
+> van der Meulen, F. H. & Sommer, S. (2025). Backward Filtering Forward
+> Guiding. JMLR 26(281), 1-51. https://arxiv.org/abs/2505.18239
 
 ## Contact
-If you experience problems or have technical questions, please open an issue. For questions related to the Hyperiax project or CCEM, please contact [Stefan Sommer](mailto:sommer@di.ku.dk).
\ No newline at end of file
+
+Hyperiax is maintained by CCEM, University of Copenhagen. For technical
+questions, please open a GitHub issue or contact
+[Stefan Sommer](mailto:sommer@di.ku.dk).
diff --git a/benchmarks/batch_generation.py b/benchmarks/batch_generation.py
deleted file mode 100644
index 4141f7c..0000000
--- a/benchmarks/batch_generation.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import hyperiax
-from jax.random import PRNGKey, split
-from hyperiax.execution import LevelwiseTreeExecutor
-from hyperiax.tree.fasttree import HypTree
-import jax
-from hyperiax.tree.builders import symmetric_topology
-import time
-from matplotlib import pyplot as plt
-from tqdm.auto import tqdm
-key = PRNGKey(0)
-
-depths = list(range(1, 19, 1))
-newt = []
-oldt = []
-
-for depth in tqdm(depths):
-
-    topology = symmetric_topology(depth, 2)
-    fast_tree = HypTree(topology)
-    #print(f"Fast tree with {len(fast_tree)} nodes")
-
-    fast_tree.add_property('noise', (10,2))
-
-
-    start = time.time()
-    for level_start, level_end in fast_tree.levels:
-        # start by fusing children  
-        data = jax.lax.slice_in_dim(fast_tree.data['noise'].data, level_start, level_end)
-            
-
-    end = time.time()
-    #print(f"New execution time: {end-start}")
-    newt.append(end-start)
-
-    tree = hyperiax.tree.builders.symmetric_tree(depth,2)
-    #print(f"Old tree with {len(tree)} nodes")
-    subkey, key = split(key)
-    noise_tree = hyperiax.tree.initializers.initialize_noise(tree, key, (10, 2))
-
-    lwe = LevelwiseTreeExecutor(None, batch_size=5000)
-    noise_tree.order = (lwe._determine_execution_order(noise_tree), None)
-    order = reversed(noise_tree.order[0])
-
-    start = time.time()
-            
-
-    end = time.time()
-    #print(f"Old execution time: {end-start}")
-    oldt.append(end-start)
-
-plt.plot(depths, newt, label='New')
-plt.plot(depths, oldt, label='Old')
-plt.legend()
-plt.xlabel('Depth')
-plt.ylabel('Time (s)')
-plt.yscale('log')
-plt.savefig('batch_generation.png')
\ No newline at end of file
diff --git a/benchmarks/inner_nodes_est_R.ipynb b/benchmarks/inner_nodes_est_R.ipynb
deleted file mode 100644
index 3e3ec50..0000000
--- a/benchmarks/inner_nodes_est_R.ipynb
+++ /dev/null
@@ -1,120 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {
-    "vscode": {
-     "languageId": "r"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "library(\"ape\")\n",
-    "library(\"castor\")\n",
-    "library(\"TreeTools\")\n",
-    "\n",
-    "# # relevant functions\n",
-    "# simulate_bm_model\n",
-    "# asr_independent_contrasts\n",
-    "# fit_bm_model\n",
-    "# generate_random_tree # tree <- generate_random_tree(max_tips=1000)\n",
-    "\n",
-    "n_tips <- 2**7\n",
-    "tree <- BalancedTree(tips=n_tips)\n",
-    "tree$edge.length <- rep(1, Nedge(tree))\n",
-    "\n",
-    "tips_sim <- rep(1, Ntip(tree)) #rnorm(Ntip(tree))\n",
-    "# tree$edge.length <- runif(n = length(tree$edge.length), min = 0.1, max = 1.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {
-    "vscode": {
-     "languageId": "r"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Time difference of 0.001296043 secs"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#### APE \n",
-    "start.time <- Sys.time()\n",
-    "\n",
-    "pic_ape <- pic(tips_sim, tree)\n",
-    "\n",
-    "end.time <- Sys.time()\n",
-    "time.taken <- round(end.time - start.time,20)\n",
-    "time.taken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "vscode": {
-     "languageId": "r"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Time difference of 0.0008890629 secs"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#### CASTOR\n",
-    "start.time <- Sys.time()\n",
-    "\n",
-    "pic_castor <- asr_independent_contrasts(tree, tips_sim, weighted = FALSE)\n",
-    "\n",
-    "end.time <- Sys.time()\n",
-    "time.taken <- round(end.time - start.time,20)\n",
-    "time.taken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "r"
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R",
-   "language": "R",
-   "name": "ir"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.3.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/benchmarks/phylmean.py b/benchmarks/phylmean.py
deleted file mode 100644
index d9d5bf9..0000000
--- a/benchmarks/phylmean.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import hyperiax
-from jax.random import PRNGKey, split
-import jax
-from jax import numpy as jnp
-from hyperiax.execution import LevelwiseTreeExecutor, DependencyTreeExecutor
-from hyperiax.models import UpLambda, DownLambda
-from hyperiax.models.functional import pass_up
-import jax
-from functools import partial
-from hyperiax.models.prebuilts import PhyloMeanModel
-
-import time
-
-key = PRNGKey(0)
-
-levels = 13
-
-tree = hyperiax.tree.builders.symmetric_tree(levels,2)
-print(f"Got tree with {len(tree)} nodes")
-subkey, key = split(key)
-noise_tree = hyperiax.tree.initializers.initialize_noise(tree, key, (2,))
-### dummy pass_up function (for testing)
-
-for i, node in enumerate(noise_tree.iter_bfs()):
-    key, subkey = split(key)
-    node['edge_length'] = jax.random.uniform(subkey, (1,))
-
-@jax.jit
-def down(noise, edge_length,parent_value, **args):
-    return {'value': jnp.sqrt(edge_length)*noise+parent_value}
-
-noise_tree.root['value'] = noise_tree.root['noise']
-
-downmodel = DownLambda(down_fn=down)
-exe = DependencyTreeExecutor(downmodel, batch_size=5000)
-stoc_tree = exe.down(noise_tree)
-
-for node in stoc_tree.iter_leaves():
-    node['estimated_value'] = node['value']
-
-pme = PhyloMeanModel()
-exe = DependencyTreeExecutor(pme, batch_size=5000)
-
-print("going")
-prebuilt_tree = exe.up(stoc_tree)
-print("done")
\ No newline at end of file
diff --git a/benchmarks/phylogenetic_mean.ipynb b/benchmarks/phylogenetic_mean.ipynb
deleted file mode 100644
index f5856a0..0000000
--- a/benchmarks/phylogenetic_mean.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Todo:\n",
-    "\n",
-    "coding:\n",
-    "- compare with R (phytools)\n",
-    "\n",
-    "theory:\n",
-    "- find out if inverting big trees is really a problem for realistic phylogenies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import hyperiax\n",
-    "from jax.random import PRNGKey, split\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from hyperiax.execution import LevelwiseTreeExecutor, DependencyTreeExecutor\n",
-    "from hyperiax.models import UpLambda, DownLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "import jax\n",
-    "from functools import partial\n",
-    "\n",
-    "import time\n",
-    "\n",
-    "key = PRNGKey(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phylogenetic mean and variance computed via _phylogenetic independent contrasts_ (PIC) \n",
-    "\n",
-    "Vi use independent contrasts to estimate inner nodes, root and variance under a Brownian motion model.\n",
-    "\n",
-    "### Inner nodes and root\n",
-    "\n",
-    "Inner nodes are estimated accorcding to: \n",
-    "\n",
-    "$$\n",
-    "\n",
-    "T_I = \\frac{\\frac{1}{e_{x1}}T_{x1} + \\frac{1}{e_{x2}}T_{x2}}{\\frac{1}{e_{x1}}+\\frac{1}{e_{x2}}}\n",
-    "$$\n",
-    "Where I is the intermediate note between the two nodes x1 and x2 \n",
-    "T is the trait for the corresponding node \n",
-    "and e is the edge length of the corresponding node \n",
-    "\n",
-    "To account for uncertainty in estimating intermediate nodes, the PIC estimator modifies the edge lengths according to: \n",
-    "\n",
-    "$$\n",
-    "e_{I} = e_I +\\frac{ e_{x1}*e_{x2}}{e_{x1}+e_{21}}\n",
-    "$$\n",
-    "where $e$ is the edge length, and I is the intermediate note between the two nodes x1 and x2\n",
-    "\n",
-    "### Variance estimate\n",
-    "\n",
-    "The PIC variance estimate is given by\n",
-    "\n",
-    "$$\\hat{\\sigma}^2_{PIC} = \\frac{\\sum_{i,j} s^2_{ij}}{n-1}$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$$ s_{ij} = \\frac{T_{x_i} - T_{x_j}}{v_i + v_j}$$\n",
-    "\n",
-    "which is computed from all (given or estimated) inner nodes and the PIC-adjusted edge-lengths.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# make tree\n",
-    "Add a random example tree, where each value in the nodes is a quantitative trait. \n",
-    "\n",
-    "To illustrate the edge length correction, we intially assume each length is equal to 1. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "levels = 7\n",
-    "\n",
-    "tree = hyperiax.tree.builders.symmetric_tree(levels, 2)\n",
-    "\n",
-    "subkey, key = split(key)\n",
-    "\n",
-    "for i, node in enumerate(tree.iter_bfs()):\n",
-    "    node['edge_length'] = 1#+i\n",
-    "\n",
-    "for i, leaf in enumerate(tree.iter_leaves()):\n",
-    "    key, subkey = split(key)\n",
-    "    leaf['size'] = 1 #jax.random.randint(subkey, (1,),1,20)\n",
-    "    leaf['independent_contrast'] = 0 #jax.random.randint(subkey, (1,),1,20)\n",
-    "   # leaf['size'] = i +1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if levels < 5:\n",
-    "    tree.plot_tree()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute inner nodes and independent contrasts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### dummy pass_up function (for testing)\n",
-    "\n",
-    "up = pass_up('size','edge_length')\n",
-    "\n",
-    "def fuse(child_edge_length,child_size, edge_length, **kwargs):\n",
-    "\n",
-    "    return {'size':1, 'edge_length':1}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ### for inner node estimation only (using the IC reweighted edges)\n",
-    "\n",
-    "# up = pass_up('size','edge_length')\n",
-    "\n",
-    "# def fuse(child_edge_length,child_size, edge_length, **kwargs):\n",
-    "\n",
-    "#     corrected_edge_length = edge_length+(child_edge_length[0]*child_edge_length[1])/(child_edge_length[0]+child_edge_length[1])\n",
-    "\n",
-    "#     top = 1/child_edge_length[0]*child_size[0]+1/child_edge_length[1]*child_size[1] \n",
-    "#     bot = 1/child_edge_length[0]              +1/child_edge_length[1] \n",
-    "#     return {'size':top/bot, 'edge_length':corrected_edge_length}\n",
-    "\n",
-    "\n",
-    "# upmodel = UpLambda(up, fuse)\n",
-    "# root_exe = DependencyTreeExecutor(upmodel, batch_size=5)\n",
-    "# corrected_tree = root_exe.up(corrected_tree)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### this version includes computation of independent contrasts (to be used for the variance estimate)\n",
-    "\n",
-    "# up = pass_up('size', 'independent_contrast','edge_length')\n",
-    "\n",
-    "# def fuse(child_edge_length,child_size, edge_length, **kwargs):\n",
-    "\n",
-    "#     corrected_edge_length = edge_length+(child_edge_length[0]*child_edge_length[1])/(child_edge_length[0]+child_edge_length[1])\n",
-    "\n",
-    "#     # inner node est\n",
-    "#     numerator = 1/child_edge_length[0]*child_size[0]+1/child_edge_length[1]*child_size[1] \n",
-    "#     denominator = 1/child_edge_length[0]              +1/child_edge_length[1] \n",
-    "#     node_est = numerator / denominator\n",
-    "\n",
-    "#     # independent contrast \n",
-    "#     IC = (child_size[0] - child_size[1]) / (child_edge_length[0] + child_edge_length[1])\n",
-    "    \n",
-    "#     return {'size': node_est, 'independent_contrast': IC, 'edge_length': corrected_edge_length}\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.10872292518615723\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "\n",
-    "upmodel = UpLambda(up, fuse)\n",
-    "root_exe = DependencyTreeExecutor(upmodel, batch_size=5)\n",
-    "estimated_tree = root_exe.up(tree)\n",
-    "\n",
-    "\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TreeNode({'edge_length': Array(1.9990234, dtype=float32, weak_type=True), 'size': Array(1., dtype=float32, weak_type=True)}) with 2 children\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(estimated_tree.root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute covariance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[6.814815]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# extract the N-1 independent contrasts and compute the variance estimate\n",
-    "\n",
-    "independent_contrasts = jnp.array([node['independent_contrast'] for node in estimated_tree.iter_bfs() if node.children])  # do this in a faster way?\n",
-    "\n",
-    "# Compute the sum of outer products\n",
-    "var_est_pic = jnp.einsum('ji,jk->ik', independent_contrasts, independent_contrasts) / independent_contrasts.shape[0]\n",
-    "\n",
-    "print(var_est_pic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plot estimated inner nodes (trait values)\n",
-    "\n",
-    "Here i move the size to the \"name\" place, because I earlier implenented that it was possible to see the name of nodes (and therefor replace it with the size)\n",
-    "It would might be better for a futgure implementation to do something about the dot size and with coloring \n",
-    "\n",
-    "Note also that the root is not included in this plot with names..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for leaf in corrected_tree.iter_bfs():\n",
-    "    leaf.name = \"   \"+str(leaf['size'][0]) + \"   \"\n",
-    "    \n",
-    "if levels < 5:\n",
-    "    corrected_tree.plot_tree_text()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "jax311",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/Makefile b/docs/Makefile
index 269cadc..c0cacc3 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -1,20 +1,39 @@
-# Minimal makefile for Sphinx documentation
+# Minimal sphinx Makefile.
 #
+# Build with:   make html
+# Clean with:   make clean
+# Strict mode:  make html SPHINXOPTS=-W           (warnings as errors)
+# Open result:  make open                          (macOS)
 
-# You can set these variables from the command line, and also
-# from the environment for the first two.
 SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
+SPHINXBUILD   ?= uv run sphinx-build
 SOURCEDIR     = source
 BUILDDIR      = build
 
-# Put it first so that "make" without argument is like "make help".
+.PHONY: help html clean linkcheck open _unhide-pth
+
+# uv on macOS sets the UF_HIDDEN flag on `_`-prefixed files in site-packages
+# (including the editable install's `__editable__.<pkg>....pth`). CPython's
+# site.py silently skips hidden .pth files, so the editable finder never
+# registers and autosummary fails with `ModuleNotFoundError: No module named
+# 'hyperiax'`. Clear the flag before invoking sphinx.
+_unhide-pth:
+	@if [ "$$(uname)" = "Darwin" ]; then \
+		SITE="$$(uv run python -c 'import site; print(site.getsitepackages()[0])' 2>/dev/null)"; \
+		[ -n "$$SITE" ] && chflags nohidden "$$SITE"/*.pth 2>/dev/null || true; \
+	fi
+
 help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
+
+html: _unhide-pth
+	@$(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS)
+
+clean:
+	rm -rf "$(BUILDDIR)" "$(SOURCEDIR)/api/generated"
 
-.PHONY: help Makefile
+linkcheck: _unhide-pth
+	@$(SPHINXBUILD) -b linkcheck "$(SOURCEDIR)" "$(BUILDDIR)/linkcheck" $(SPHINXOPTS)
 
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
+open:
+	@open "$(BUILDDIR)/html/index.html"
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 5394189..0000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
\ No newline at end of file
diff --git a/examples/__init__.py b/docs/source/_static/.gitkeep
similarity index 100%
rename from examples/__init__.py
rename to docs/source/_static/.gitkeep
diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst
new file mode 100644
index 0000000..76c8fdc
--- /dev/null
+++ b/docs/source/api/index.rst
@@ -0,0 +1,78 @@
+API reference
+=============
+
+Everything in :mod:`hyperiax` lives behind a small public surface; the
+sections below list it module by module. Each entry links to a per-symbol
+page generated from its docstring.
+
+hyperiax.core
+-------------
+
+Tree topology, immutable Tree pytree, schema, views, sweep decorators.
+
+.. autosummary::
+   :toctree: generated
+   :template: autosummary/class.rst
+
+   hyperiax.Topology
+   hyperiax.Tree
+   hyperiax.Schema
+   hyperiax.FieldSpec
+   hyperiax.SweepFn
+   hyperiax.Node
+   hyperiax.Children
+   hyperiax.ChildrenAxis
+
+.. autosummary::
+   :toctree: generated
+
+   hyperiax.up
+   hyperiax.down
+   hyperiax.symmetric_topology
+   hyperiax.from_parents
+   hyperiax.from_newick
+   hyperiax.to_newick
+   hyperiax.HyperiaxError
+   hyperiax.MissingField
+   hyperiax.SchemaMismatch
+
+hyperiax.prebuilt
+-----------------
+
+Ready-to-use sweeps and helpers for common message-passing tasks.
+
+.. rubric:: Phylogenetic weighted mean
+
+.. autosummary::
+   :toctree: generated
+
+   hyperiax.prebuilt.phylo_mean
+
+.. rubric:: BFFG — discrete-edge (linear-Gaussian) sweeps
+
+.. autosummary::
+   :toctree: generated
+
+   hyperiax.prebuilt.bffg.discrete_schema
+   hyperiax.prebuilt.bffg.init_discrete_tree
+   hyperiax.prebuilt.bffg.discrete_bf_sweep
+   hyperiax.prebuilt.bffg.discrete_forward_sweep
+   hyperiax.prebuilt.bffg.discrete_fg_sweep
+   hyperiax.prebuilt.bffg.discrete_refine_anchor
+
+.. rubric:: BFFG — continuous-edge (SDE) sweeps
+
+.. autosummary::
+   :toctree: generated
+
+   hyperiax.prebuilt.bffg.continuous_schema
+   hyperiax.prebuilt.bffg.init_continuous_tree
+   hyperiax.prebuilt.bffg.continuous_bf_sweep
+   hyperiax.prebuilt.bffg.continuous_forward_sweep
+   hyperiax.prebuilt.bffg.continuous_fg_sweep
+   hyperiax.prebuilt.bffg.continuous_refine_anchor
+   hyperiax.prebuilt.bffg.propagate_linearization
+
+For MCMC over BFFG-guided latents and hyperparameters, hyperiax composes
+with `NumPyro <https://num.pyro.ai/>`_ — see :doc:`../notebooks/05_gaussian_bffg`
+and :doc:`../notebooks/06_gaussian_nuts` for worked examples.
diff --git a/docs/source/conf.py b/docs/source/conf.py
index aa8204a..864afe5 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,44 +1,85 @@
-# Configuration file for the Sphinx documentation builder.
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../../hyperiax/'))
+"""Sphinx configuration for the hyperiax docs.
 
-# -- Project information
+Builds with ``make -C docs html`` (locally) or ``sphinx-build -W docs/source
+docs/build/html``. Notebook outputs are rendered as-saved — install the
+``[notebook]`` extra and re-run the .ipynb locally to refresh them.
+"""
 
-project = 'Hyperiax'
-copyright = '2024, CCEM, UCPH'
-author = 'CCEM'
+from __future__ import annotations
 
-release = '2.0'
-version = '2.0'
+import importlib.metadata as _md
 
-# -- General configuration
+# ── project info ────────────────────────────────────────────────────
+project = "hyperiax"
+author = "The hyperiax authors"
+copyright = "2026, hyperiax authors"
 
+try:
+    release = _md.version("hyperiax") or "0.0.0"
+except _md.PackageNotFoundError:
+    release = "0.0.0"
+version = ".".join(release.split(".")[:2])
+
+# ── extensions ──────────────────────────────────────────────────────
 extensions = [
-    'nbsphinx',
-    'sphinx.ext.duration',
-    'sphinx.ext.doctest',
-    'sphinx.ext.autodoc',
-    'sphinx_autodoc_annotation',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.intersphinx',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",  # Google / NumPy docstring styles
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.viewcode",
+    "sphinx_autodoc_typehints",  # render type hints inline
+    "sphinx_copybutton",  # copy-to-clipboard on code blocks
+    "myst_nb",  # MyST markdown + notebook rendering
 ]
 
-intersphinx_mapping = {
-    'python': ('https://docs.python.org/3/', None),
-    'sphinx': ('https://www.sphinx-doc.org/en/master/', None),
-    'jax': ('https://jax.readthedocs.io/en/latest/', None),
+autosummary_generate = True
+autodoc_typehints = "description"
+autodoc_member_order = "bysource"
+autodoc_default_options = {
+    "members": True,
+    "inherited-members": False,
+    "show-inheritance": True,
+    "undoc-members": False,
 }
-intersphinx_disabled_domains = ['std']
+napoleon_google_docstring = True
+napoleon_numpy_docstring = False
+napoleon_use_rtype = False
 
-templates_path = ['_templates']
-
-# -- Options for HTML output
+# ── myst-nb: render saved outputs, do not execute ──────────────────
+nb_execution_mode = "off"
+myst_enable_extensions = [
+    "amsmath",
+    "dollarmath",
+    "deflist",
+    "colon_fence",
+]
 
-html_theme = 'sphinx_rtd_theme'
+# ── intersphinx ────────────────────────────────────────────────────
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
+    "jax": ("https://jax.readthedocs.io/en/latest/", None),
+}
 
-# -- Options for EPUB output
-epub_show_urls = 'footnote'
+# ── theme ──────────────────────────────────────────────────────────
+html_theme = "furo"
+html_title = f"hyperiax {version}"
+html_static_path = ["_static"]
+html_theme_options = {
+    "source_repository": "https://github.com/computationalevolutionarymorphometry/hyperiax",
+    "source_branch": "v3",
+    "source_directory": "docs/source/",
+    "footer_icons": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/computationalevolutionarymorphometry/hyperiax",
+            "html": "",
+            "class": "fa-brands fa-github",
+        },
+    ],
+}
 
-nbsphinx_allow_errors = True
-nbsphinx_execute = 'never'
\ No newline at end of file
+# ── misc ───────────────────────────────────────────────────────────
+exclude_patterns = ["_build", "**.ipynb_checkpoints"]
+templates_path = ["_templates"]
+suppress_warnings = ["mystnb.unknown_mime_type"]
diff --git a/docs/source/generated/tree.rst b/docs/source/generated/tree.rst
deleted file mode 100644
index 0c62fe0..0000000
--- a/docs/source/generated/tree.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-tree
-====
-
-.. automodule:: tree
-
-   
\ No newline at end of file
diff --git a/docs/source/hyperiax.execution.rst b/docs/source/hyperiax.execution.rst
deleted file mode 100644
index 2afa4c6..0000000
--- a/docs/source/hyperiax.execution.rst
+++ /dev/null
@@ -1,61 +0,0 @@
-hyperiax.execution package
-==========================
-
-Submodules
-----------
-
-hyperiax.execution.collate module
----------------------------------
-
-.. automodule:: hyperiax.execution.collate
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.execution.dependencyexecutor module
---------------------------------------------
-
-.. automodule:: hyperiax.execution.dependencyexecutor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.execution.executor module
-----------------------------------
-
-.. automodule:: hyperiax.execution.executor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.execution.levelexecutor module
----------------------------------------
-
-.. automodule:: hyperiax.execution.levelexecutor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.execution.redblackexecutor module
-------------------------------------------
-
-.. automodule:: hyperiax.execution.redblackexecutor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.execution.unorderedexecutor module
--------------------------------------------
-
-.. automodule:: hyperiax.execution.unorderedexecutor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.execution
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.mcmc.rst b/docs/source/hyperiax.mcmc.rst
deleted file mode 100644
index d91bf27..0000000
--- a/docs/source/hyperiax.mcmc.rst
+++ /dev/null
@@ -1,61 +0,0 @@
-hyperiax.mcmc package
-=====================
-
-Submodules
-----------
-
-hyperiax.mcmc.fixedparameter module
------------------------------------
-
-.. automodule:: hyperiax.mcmc.fixedparameter
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.mcmc.metropolis\_hastings module
------------------------------------------
-
-.. automodule:: hyperiax.mcmc.metropolis_hastings
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.mcmc.parameter module
-------------------------------
-
-.. automodule:: hyperiax.mcmc.parameter
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.mcmc.parameterstore module
------------------------------------
-
-.. automodule:: hyperiax.mcmc.parameterstore
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.mcmc.plotting module
------------------------------
-
-.. automodule:: hyperiax.mcmc.plotting
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.mcmc.varianceParameter module
---------------------------------------
-
-.. automodule:: hyperiax.mcmc.varianceParameter
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.mcmc
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.models.functional.rst b/docs/source/hyperiax.models.functional.rst
deleted file mode 100644
index 4f80d2d..0000000
--- a/docs/source/hyperiax.models.functional.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-hyperiax.models.functional package
-==================================
-
-Submodules
-----------
-
-hyperiax.models.functional.reducers module
-------------------------------------------
-
-.. automodule:: hyperiax.models.functional.reducers
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.models.functional.shortup module
------------------------------------------
-
-.. automodule:: hyperiax.models.functional.shortup
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.models.functional
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.models.prebuilts.rst b/docs/source/hyperiax.models.prebuilts.rst
deleted file mode 100644
index d03d39d..0000000
--- a/docs/source/hyperiax.models.prebuilts.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-hyperiax.models.prebuilts package
-=================================
-
-Submodules
-----------
-
-hyperiax.models.prebuilts.mean\_estimation module
--------------------------------------------------
-
-.. automodule:: hyperiax.models.prebuilts.mean_estimation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.models.prebuilts
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.models.rst b/docs/source/hyperiax.models.rst
deleted file mode 100644
index ab8735b..0000000
--- a/docs/source/hyperiax.models.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-hyperiax.models package
-=======================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   hyperiax.models.functional
-   hyperiax.models.prebuilts
-
-Submodules
-----------
-
-hyperiax.models.basemodel module
---------------------------------
-
-.. automodule:: hyperiax.models.basemodel
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.models.lambdamodels module
------------------------------------
-
-.. automodule:: hyperiax.models.lambdamodels
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.models.updatemodel module
-----------------------------------
-
-.. automodule:: hyperiax.models.updatemodel
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.models.updownmodel module
-----------------------------------
-
-.. automodule:: hyperiax.models.updownmodel
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.models
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.rst b/docs/source/hyperiax.rst
deleted file mode 100644
index c6efc23..0000000
--- a/docs/source/hyperiax.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-Hyperiax APIs
-================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 1
-
-   hyperiax.execution
-   hyperiax.mcmc
-   hyperiax.models
-   hyperiax.tree
-   hyperiax.plotting
-
-Module contents
----------------
-
-.. automodule:: hyperiax
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/hyperiax.tree.rst b/docs/source/hyperiax.tree.rst
deleted file mode 100644
index 2305dd4..0000000
--- a/docs/source/hyperiax.tree.rst
+++ /dev/null
@@ -1,69 +0,0 @@
-hyperiax.tree package
-=====================
-
-Submodules
-----------
-
-hyperiax.tree.builders module
------------------------------
-
-.. automodule:: hyperiax.tree.builders
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.childrenlist module
----------------------------------
-
-.. automodule:: hyperiax.tree.childrenlist
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.initializers module
----------------------------------
-
-.. automodule:: hyperiax.tree.initializers
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.plot\_utils module
---------------------------------
-
-.. automodule:: hyperiax.tree.plot_utils
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.printer\_utils module
------------------------------------
-
-.. automodule:: hyperiax.tree.printer_utils
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.tree module
--------------------------
-
-.. automodule:: hyperiax.tree.tree
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-hyperiax.tree.updaters module
------------------------------
-
-.. automodule:: hyperiax.tree.updaters
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: hyperiax.tree
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c6afd66..948befb 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,50 +1,64 @@
-Welcome to Hyperiax's documentation!
-===================================
+hyperiax — JAX tree traversals
+==============================
 
-**Hyperiax** is a framework for tree traversal and computations on large-scale tree. Its primary purpose is to facilitate efficient message passing and operation execution on large trees. Hyperiax uses `JAX <https://jax.readthedocs.io/en/latest/index.html>`_ for fast execution and automatic differentiation. Hyperiax is developed and maintained by `CCEM, UCPH <https://www.ccem.dk/>`_.
+**hyperiax** is a small, pure-JAX library for message passing on phylogenetic
+/ rooted trees. Trees are immutable JAX pytrees; sweeps are decorator-style
+``Tree -> Tree`` transforms that compose cleanly under ``@jax.jit`` and
+``jax.lax.scan``.
 
-Initially, Hyperiax was designed specifically for phylogenetic analysis of biological shape data, particularly enabling statistical inference with continuous time stochastic processes along the edges of the trees. For this purpose, is integrated with `JAXGeometry <https://bitbucket.org/stefansommer/jaxgeometry/src/main/>`_, a computational differential geometry toolbox implemented in JAX. However, Hyperiax's *messaging system* and *operations* are general, which means that they can be easily adapted for use in other contexts. With minor modifications, Hyperiax can be used for any application where fast tree-level computations are necessary. Included examples cover such cases with inference in Gaussian graphical models, phylogenetic mean computation, and recursive shape matching in binary trees.
+.. code-block:: python
 
-.. _installation:
+   import jax, jax.numpy as jnp
+   import hyperiax as hx
+
+   topo = hx.symmetric_topology(depth=3, degree=2)
+   tree = hx.Tree.empty(topo, {"value": (2,)})
+   tree = tree.at[topo.is_leaf].set(value=jnp.ones((8, 2)))
+
+   @hx.up(reads_children=("value",), writes=("value",))
+   def avg(node, children, params):
+       return {"value": children.value.mean(0)}
+
+   root_value = avg(tree)["value"][0]
+
+What's in the box
+-----------------
+
+* **core**: :class:`~hyperiax.Topology`, :class:`~hyperiax.Tree`,
+  :class:`~hyperiax.Schema`, the ``@up`` / ``@down`` sweep decorators, the
+  unified segment-based dispatch engine, and Newick read/write
+  (:func:`~hyperiax.from_newick` / :func:`~hyperiax.to_newick`).
+* **prebuilt**: BFFG for discrete Gaussian and continuous SDE transitions
+  (van der Meulen & Sommer 2025), plus weighted phylo-mean.
 
-------------
 Installation
 ------------
 
 .. code-block:: bash
 
-    # Install Hyperiax directly using pip
-    $ pip install hyperiax
+   pip install hyperiax                  # core, CPU JAX (jax + jaxlib + numpy)
+   pip install 'hyperiax[gpu]'           # + CUDA 12 JAX
+   pip install 'hyperiax[notebook]'      # tutorial notebooks
 
-    # Install Hyperiax from the repository, for the newest version
-    $ pip install git+https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git
+Contents
+--------
 
-    # Install Hyperiax for development
-    $ git clone git@github.com:ComputationalEvolutionaryMorphometry/hyperiax.git
-    # or (if you haven't set up ssh)
-    $ git clone https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git
-    # and then install by
-    $ pip install -e hyperiax[dev]
-    # and optionally
-    $ pip install -e hyperiax[examples]
-    # to install the dependencies for all the example notebooks
-
-Check out the :doc:`Usage` section for further information
-
-.. note::
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
 
-   This project is under active development.
+   notebooks/index
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
+   :caption: API reference
+
+   api/index
 
-   usage
-   hyperiax
 
-Indices and tables
-==================
+Indices
+-------
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
+* :ref:`search`
diff --git a/docs/source/notebooks/01_quickstart.ipynb b/docs/source/notebooks/01_quickstart.ipynb
new file mode 100644
index 0000000..97d441c
--- /dev/null
+++ b/docs/source/notebooks/01_quickstart.ipynb
@@ -0,0 +1,799 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "237d265b",
+   "metadata": {},
+   "source": [
+    "# Quickstart — Hyperiax in five minutes\n",
+    "\n",
+    "Hyperiax lets you write tree message-passing algorithms in JAX. The whole user-facing API has four pieces:\n",
+    "\n",
+    "| Piece | What it is |\n",
+    "|---|---|\n",
+    "| **`Topology`** | The static shape of the tree (who's whose parent). |\n",
+    "| **`Tree`** | `Topology` + a typed dict of per-node arrays. Immutable. Registered as a JAX pytree, so it rides through `jax.jit` / `jax.lax.scan` / `jax.grad` cleanly. |\n",
+    "| **`@hx.up` / `@hx.down`** | Decorators that turn a small Python function into a leaf→root or root→leaf sweep. |\n",
+    "| **Views** | Inside a sweep, you read data through `node.value`, `parent.value`, `children.value.mean(0)`. |\n",
+    "\n",
+    "This notebook walks through all four. By the end you will have built a tree, run an up-sweep and a down-sweep, and verified that sweeps compose with `jax.jit`, `jax.lax.scan`, and `jax.grad`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5926234b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:13.476073Z",
+     "iopub.status.busy": "2026-05-12T14:59:13.475926Z",
+     "iopub.status.idle": "2026-05-12T14:59:13.838615Z",
+     "shell.execute_reply": "2026-05-12T14:59:13.838144Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import hyperiax as hx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac172834",
+   "metadata": {},
+   "source": [
+    "## 1. Build a topology\n",
+    "\n",
+    "A `Topology` is a parent-pointer array plus a bunch of precomputed dispatch metadata. The only convention you need to remember:\n",
+    "\n",
+    "- Nodes are stored in **BFS order**: the root is node `0`, and `parents[i] < i` for every other node.\n",
+    "- The root is its own parent (`parents[0] == 0`).\n",
+    "\n",
+    "For perfectly symmetric trees there is a convenience constructor. We'll use a binary tree of height 3 — that's $1+2+4+8 = 15$ nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fa346fd8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:13.840075Z",
+     "iopub.status.busy": "2026-05-12T14:59:13.839977Z",
+     "iopub.status.idle": "2026-05-12T14:59:13.842269Z",
+     "shell.execute_reply": "2026-05-12T14:59:13.841977Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Topology(size=15, depth=3, equal_degree=True, max_degree=2)\n",
+      "  size:         15\n",
+      "  depth:        3\n",
+      "  num leaves:   8\n",
+      "  equal_degree: True\n",
+      "  parents:      [0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "topo = hx.symmetric_topology(depth=3, degree=2)\n",
+    "\n",
+    "print(topo)\n",
+    "print(f\"  size:         {topo.size}\")\n",
+    "print(f\"  depth:        {topo.depth}\")\n",
+    "print(f\"  num leaves:   {int(topo.is_leaf.sum())}\")\n",
+    "print(f\"  equal_degree: {topo.equal_degree}\")\n",
+    "print(f\"  parents:      {topo.parents.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e99ec949",
+   "metadata": {},
+   "source": [
+    "A tiny matplotlib helper so we can see what's going on. Reuse this cell whenever you want to draw a tree.\n",
+    "\n",
+    "> `topo.parents`, `topo.level_starts`, `topo.is_leaf`, ... are all `numpy` arrays — the topology lives in numpy-land so it can ride through `jax.jit` as static auxiliary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "45517b9d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:13.843285Z",
+     "iopub.status.busy": "2026-05-12T14:59:13.843222Z",
+     "iopub.status.idle": "2026-05-12T14:59:13.920686Z",
+     "shell.execute_reply": "2026-05-12T14:59:13.920297Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_tree(topo, values=None, ax=None, title=None, cmap='viridis', vmin=None, vmax=None):\n",
+    "    \"\"\"Top-down layered layout. Pass `values` (shape (N,)) to color nodes.\"\"\"\n",
+    "    if ax is None:\n",
+    "        _, ax = plt.subplots(figsize=(8, 5))\n",
+    "    pos = np.zeros((topo.size, 2))\n",
+    "    for d in range(topo.depth + 1):\n",
+    "        lo, hi = int(topo.level_starts[d]), int(topo.level_starts[d + 1])\n",
+    "        n = hi - lo\n",
+    "        if n == 1:\n",
+    "            pos[lo:hi, 0] = 0.5\n",
+    "        else:\n",
+    "            pos[lo:hi, 0] = np.linspace(0, 1, n + 2)[1:-1]\n",
+    "        pos[lo:hi, 1] = -d\n",
+    "    for i in range(1, topo.size):\n",
+    "        p = int(topo.parents[i])\n",
+    "        ax.plot([pos[p, 0], pos[i, 0]], [pos[p, 1], pos[i, 1]],\n",
+    "                'k-', lw=0.6, alpha=0.5, zorder=1)\n",
+    "    if values is None:\n",
+    "        ax.scatter(pos[:, 0], pos[:, 1], s=420, c='lightsteelblue',\n",
+    "                   edgecolor='k', zorder=3)\n",
+    "        for i in range(topo.size):\n",
+    "            ax.text(pos[i, 0], pos[i, 1], str(i), ha='center', va='center', fontsize=8)\n",
+    "    else:\n",
+    "        sc = ax.scatter(pos[:, 0], pos[:, 1], s=420, c=np.asarray(values),\n",
+    "                        edgecolor='k', cmap=cmap, vmin=vmin, vmax=vmax, zorder=3)\n",
+    "        plt.colorbar(sc, ax=ax, label='value', shrink=0.8)\n",
+    "    if title:\n",
+    "        ax.set_title(title)\n",
+    "    ax.set_axis_off()\n",
+    "    return ax\n",
+    "\n",
+    "plot_tree(topo, title=f\"depth={topo.depth}, binary tree ({topo.size} nodes, BFS-indexed)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "794f1ad2",
+   "metadata": {},
+   "source": [
+    "## 2. Declare a schema, allocate a `Tree`\n",
+    "\n",
+    "A `Tree` is `Topology` + a `dict[str, jax.Array]` whose keys are fixed by a **schema**. You declare what fields exist up front:\n",
+    "\n",
+    "```python\n",
+    "Tree.empty(topo, {'value': trailing_shape, ...})\n",
+    "```\n",
+    "\n",
+    "Each field has a `(trailing_shape, dtype)` spec — `()` for a scalar field, `(2,)` for a 2-vector field, `(n, n)` for a matrix field, and so on. The leading axis is always the number of nodes; the schema only declares the trailing part.\n",
+    "\n",
+    "> **Why declare it up front?** The schema is part of the pytree's static aux data. Adding fields later via `tree.update(...)` changes the pytree structure and invalidates `jit` caches keyed on the old shape. Inside a sweep pipeline you almost always know all your fields at construction time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e4eeab68",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:13.921940Z",
+     "iopub.status.busy": "2026-05-12T14:59:13.921850Z",
+     "iopub.status.idle": "2026-05-12T14:59:13.959081Z",
+     "shell.execute_reply": "2026-05-12T14:59:13.958680Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tree(size=15, fields={value: ()})\n",
+      "Initial values (zeros for every node):\n",
+      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "Allocate shape (leading dimension is the number of nodes):\n",
+      "(15,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "tree = hx.Tree.empty(topo, {'value': ()})\n",
+    "print(tree)\n",
+    "print(\"Initial values (zeros for every node):\")\n",
+    "print(tree.value)\n",
+    "print(\"Allocate shape (leading dimension is the number of nodes):\")\n",
+    "print(tree.value.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46e73387",
+   "metadata": {},
+   "source": [
+    "## 3. Seed the leaves\n",
+    "\n",
+    "Trees are immutable. Every \"mutator\" — `set`, `at[...].set/add/...`, `update`, `drop` — returns a *new* `Tree`. This is what lets us put the whole computation inside `jax.jit`.\n",
+    "\n",
+    "`tree.at[mask_or_indices].set(field=values)` is the JAX-style scatter — same shape as `arr.at[mask].set(values)`, but takes the field name(s) as keyword arguments. The `at[...]` indexer also exposes `add`, `multiply`, `min`, `max`, and `get`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d89fa6ad",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:13.960349Z",
+     "iopub.status.busy": "2026-05-12T14:59:13.960268Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.057494Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.057116Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Leaves carry 0..7; inner nodes are still zero:\n",
+      "[0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 3. 4. 5. 6. 7.]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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aq23ItoiIqGLswSKyiMzMTKQkp6B+WXM0tLXUdVt7tW04FLITh48cRnx8vK7bIiIyI/ZgEVnEzJkzVbX2FDTUfVt10FBVgpdtEhHRmRhgEVnEqlWrEB0cixBbmO7bkm3EBMepbRIR0ZkYYBFZxPI/liOiLMZr24soi8aK5Su8tj0iIjNhgEVkEQcOHEQ4Ir22vXBEqXIQRER0JgZYRBZRVlaKAAR6bXsBCEBpWanXtkdEZCYMsIgsIjg4BE44vLY9J5wICfZcIVMiIithgEVkEfXr10MRCry2vSLko36D+l7bHhGRmTDAIrKIHj17oDA412vbKwzOQ/ce3b22PSIiM2GARWQRskROXlkOSrVi3bcl28gty1bbJCKiM7GSO5FFsJI7EZFxsAeLyCJkyZobb7oRqUH7oWmabtuR9z4atF9ti8vkEBFVjAEWkYU89NBDKHTk4wB26raNA9ihtiHbIiKiijHAIrKQzp074+GHH8begC0o0PI8/v4FWi72BGzFI488gk6dOnn8/YmIrIJjsIgsprCwEO3atkPGwePoaO+DEFuoR963VCvBuqDfkFg/Hhs3bUR4eLhH3peIyIrYg0VkMREREfjxpx8RGB2A9YFLPDKrUN5D3kvec+aPMxlcERGdB3uwiCxq/fr16NunL8oKHWjh6IgEW7Jb75OhpWJ74FoERwTjt98Xo3379h7fVyIiq2EPFpFF5eTk4M233sSFA3pjHZZgM1YgT8uu9N/L78rfyN/2GXgh3njzdfWeRER0fgywiCxo69at2LNnD0aNGoVffv0FH3zwARwJxViOuVgTsAj7tR3I0o7BrpWd/Bv5b/mZ/N/kd+R35W8+/PBDzP5lNkaPHo3du3er9yYionNjipDIYo4ePYrvv/8eY8aMUeOxXMrKyjBjxgyMf288lixZgpLSEvVzm+3E9yxNc6r/DQ0JxQUXXIB777sXV111FYKDg0++R0FBAT755BNcc801qF27ttc/GxGRWTDAIrKQ/Px8fPrpp7juuuuQlJR01t+z2+2qJ2rDhg3IzT2xfmFMTIwaX9WqVSsEBQWd9W/T0tIwdepU1aMVGRmpy+cgIjI7BlhEFiFB05QpU9C7d2+0aNFC121t374dS5cuxS233HLOYIyIyF9xDBaRBcjyNT/++COaN2+ue3AlZBvNmjXDTz/9pOuyPEREldGwYUPYbLYzXvfffz98hQEWkQUsW7ZMBTrSe+UtMk7L4XDgjz/+8No2iYgqsnLlSqSmpp58zZkzR/18+PDh8BUGWEQmt3PnTmzbtg1Dhw5V39i8RbZ15ZVXYsuWLWofiIh8JTExUU28cb2kR79Jkybo16+fz/aJARaRiR07dkx9U7v++ut9MhZKtjlixAi1D7IvRES+Vlpais8++wy33XabV790no6D3IlMvOaglEwYNmwYkpPdq9LuKdIlP336dDWzsHxpCCLyb8XFxSrgcYcMezg9QAoNDVWvc/nmm29w00034cCBA0hJSYGvMMAiMiEZ+yTf0Lp164bWrVvDCDZv3oxVq1bh5ptvRmBgoK93h4gMEFw1ahCFo+kOt/4+KipKlZ4p77nnnsPzzz9/zr+75JJLEBISgpkzZ8KXOL+ayGTkW93PP/+sZs0YJbgSbdq0UWnC2bNnY8iQIb7eHSLysdLSUhVc7V3dADHRVRuRlJvnRKMu+3Hw4EFVo8/lfL1X+/fvx9y5c1WxZV/jGCwiE86WKSoqQt++fWE0MqBUUpeyj0REIjLKvZeQ4Kr863wB1scff6yKLBvhSx4DLCITkfUFpfq6jLvy5eDNs5F9kn1bt26d2lciIic0t15V5XQ6VYAlY0GNUACZARaRSWRmZqrUoMwYLL8+oNHIvsnMQtlX2Wci8m9ON/9fVUlqUAa2y+xBI2CARWSSwaLffvutWny5/HgEo5J9lH2VfZZ9JyLS2+DBg9UYVVnRwggYYBEZnHR7f/fdd6pKe926dWEWsq+yz7Lv8hmIyD85NM2tl9kxwCIyuF9//VXVuWrfvj3MRvZZqiq7lq0gIv/j9NIYLKNhgEVkYGvWrEF2djYGDhwIs7roootw/Phx9VmIyP84ocFRxRcDLCLSjdRzkcKd11xzjSFnDFaW7Lt8Bvks8pmIyL842YNFREaRlZWlFiuVGYNSkdjspHaNfBb5TNIjR0T+w8ExWERkBCUlJWotraFDhyIuLg5WIZ/liiuuUJ9NPiMRkZVxDBaRgchsO1nioXv37qhfvz6spkGDBujatav6jDKdmoisz+nmy+wYYBEZyPz581GzZk106tQJVtW5c2f1GefNm+frXSEiL3C4MchdXmbHAIvIINavX4+0tDQMGjQIVief8ejRo2rZHyKyNofm3svsGGARGcChQ4ewbNkyXHvttQgIsP5lKZ/xuuuuw9KlS9VnJyLrcjJFSES+kJOTgxkzZqhZdmFhYX5zEuSzDh8+XH12OQZEZE1O2OCo4kv+xuys/1WZyMBKS0vVrLrLL79cjUvyN/Hx8bjsssvUMZBjQURkFQywiHxEZtFNnz4dHTt2RKNGjfz2PDRu3FgdAzkWnFlIZD1Ozb2X2THAIvKRRYsWITIyEt26dfP7cyDHQI6FHBMishaHGylCeZkdAywiH9i8eTMOHDiASy+9lMf/T3IsZCkdOTZEZB0OBlhE5A1HjhzB4sWL1QDvwMBAHvQ/ybGQYyLHJjU1lceFyCKcms2tl9mxB4vIi/Ly8jBt2jRVoiA8PJzH/jQRERHq2MgxkmNFRObnYA8WEemprKxMzZYbPHgwEhMTebDPQo6NFCKVY2W323mciMiU2INF5AUyO27mzJlo3bo1mjVrxmN+HnKM5Fj98MMPnFlIZHIOBLj1MjvzfwIiE1iyZIkaY9SzZ09f74ppyLGSYybHjojMS3Nj/JX8jdkxwCLS2bZt27Bz504MGTIENpv5bxreIsdKjpkcu+3bt/t6d4jITQ6OwSIiT5PFm+fPn6+WwQkKCuIBriI5ZnLs5s2bp44lEZmPQwtw62V25v8ERAZVUFCAqVOnqgWcpYgmuUeO3TXXXKOOpRxTIjIXp1pbMKCKL/P39jPAItKBw+FQs+AGDhyIWrVq8RhXU+3atdWx/Pbbb9WxJSIyOgZYRDrMGPzxxx/RtGlTtGzZksfXQ+RYNmnSBD/99BNnFhKZiINjsIjIE5YvX67qN1144YU8oB4mx1TqickxJiJzcHAMFhFV165du9RaeldeeSVnDOo0s1COrRxjOdZEZJYxWLYqv8yOKUIiD8nIyMAvv/yiZr0FBwfzuOpEjq0cYznWcsyJyNicbhQZlb8xO/N/AiIDKCoqUgOwr776akRHR/t6dyxPjrEcaznmcuyJyLgcTBESkVs3D4dDPej79u2LlJQUHkQvkWPdp08fziwkIkNiDxZRNUmqql69emjTpg2PpZe1bdtWHXs5B0RkTM4q18BiipDI761cuRL5+fno37+/3x8LX5Fjn5eXh1WrVvEcEBmQQ7O59TI79mARuWnv3r1Yt24dhg0bxhmDPp5ZKOOx1q5dq84JERmLw41B7vIyO/N/AiIfOH78OGbNmqVms4WEhPAc+JicAzkXck7k3BCRcTi1ALdeVXX48GHcfPPNiI+PR3h4ONq1a+fTnm0GWERVVFxcrJbBkXpMsbGxPH4GIedCzolMOCgpKfH17hCRF3uwsrKycMEFF6gyLj///DO2bNmC1157DTVq1ICvBPlsy0Qm5HQ61aLDvXr1UoOryVjknPTo0QPfffcdbrzxRgQE8DskkT8YN26cuv4//vjjkz9r1KiRT/eJdx+iKpg7dy6SkpLQoUMHHjeD6tixozpHcq6IyPecbgx0l7+pih9++AFdu3bF8OHD1fXfqVMnTJo0Cb7EAIuokmQQtVQOv+iii3jMDE7OkZwrmYRAROYt05Cbm3vK62zp/z179mD8+PFo1qyZKtty77334qGHHsInn3wCX2GARVQJBw4cwIoVK3Dttdcy7WQCkhqUcyWLQsu5IyJzVnKvV6+eGl/per388stnHb7RuXNnvPTSS6r36q677sKdd96J999/H77CAIvoPLKzszFz5kw1Sy00NJTHyyTkXMk5k3Mn55CIzLfY88GDB5GTk3PyNXbs2Aq3kZycjNatW5/ys1atWvn0CxYDLKJzKC0tVTMGhwwZ4tPZKOQeOWdy7uQcyrkkInP1YMXExJzyOtuXXJlBuH379lN+tmPHDjRo0AC+wgCL6Cw0TcP333+PLl26oGHDhjxOJiXnTs7htGnT1DklIut55JFH8Mcff6gU4a5du/DFF19g4sSJuP/++322TwywiM5iwYIFKucvD2cyNzmH8u1XzikRWa8OVrdu3dSXqC+//FKtUfqvf/0Lb775JkaOHAlfYR0sogps3LhRVQX25cVJnnXJJZfg888/V+dWKjwTkXc4pexCFdcWrOrviyuuuEK9jII9WESnkcBqyZIluO666zhj0GIzC+WcyrmVc0xE3uF0o/fKVabBzMz/CYg8SOqsTJ8+XRWrk7WsyFrknEqQJedYzjURWWctQqMx/ycg8pCysjI12+zSSy9Vi4WSNSUkJKhzLOdazjkR6csBm1svs2OARfTnjMEZM2aosTlNmjThMbE4OcdyruWcc2YhEemBARYRgN9++03VV+nevTuPh5+Qcy3nXM49EenHyRQhkX/aunUr9u7di8svvxw2m/m7paly5FzLOZc1zKQNEJE+HG6lCc2PPVjk144ePYqFCxeqQe2BgYG+3h3yMjnnspyO1MeStkBEnudkDxaRf8nPz1eV2mVR4IiICF/vDvmInHuZWShtQdoEERlnqRwzM/8nIHKD3W5Xs8guvvhiJCUl8Rj6OWkDF110kWoT0jaIyHM0NxZ6lr8xOwZY5Hdk1tjMmTPRokULNG/e3Ne7QwYh7UFe0jY4s5CIqosBFvmdpUuXqv/t3bu3r3eFDMbVJpYtW+brXSGyDAdThETWt2PHDmzfvh1Dhw7ljEGqcGahtI1t27aptkJEnluL0FnFl9mxB4v8Rnp6OubOnatmjQUFcZ1zqpi0DWkj0lakzRBR9TjcWItQXmZn/k9AVAmFhYX47rvvcPXVVyMqKorHjM5J2oi0lalTp6q2Q0Tuc7IHi8iaHA6Hmh3Wv39/JCcn+3p3yCSkrfTr1w/ffvutakNE5B4nAtx6mZ35PwHROchssFmzZqFRo0Zo3bo1jxVVibSZhg0bqjbEmYVEVBUMsMjSVqxYgZKSEvTt29fXu0ImJW2nuLgYK1eu9PWuEJmSQ7O59TI7BlhkWbt378bGjRtx1VVXccYgVWtm4bBhw7B+/XrVpoioapwcg0VkHRkZGZg9e7aaDRYcHOzr3SGTkzY0YsQI1aYyMzN9vTtEpqK5sRah/I3Zmf8TEJ2mqKhIDUyWXoeYmBgeH/IIaUvSGyptS9oYEVWOAza3XmbHAIssxel0qnIMF154IerUqePr3SGLqVu3rqr2Lm1M2hoRnZ9TcydNaP4jywCLLOWXX35BSkoK2rVr5+tdIYtq3769amO//vqrr3eFiAyMARZZxurVq5GTk4OBAwf6elfI4qSNZWdnqzZHROfmdGMMlrzMzvyfgAjAvn371MPummuu4YxB8srMQqn0Lm1O2h4RnZ0TNrdeZscAi0wvKysLP/30k5oxGBIS4uvdIT8RGhqq2py0PWmDRFQxB+tgEZmPFBGVZXCGDh2KuLg4X+8O+Rlpc1dccYVqg9IWiehMTqYIicxFZnHJYrzdu3dH/fr1fb075KcaNGiAbt264fvvv+fMQiI6iSlCMq158+YhPj4enTp18vWukJ/r3Lkzatasifnz5/t6V4gMxyljqqpapoFjsIh8Q5YtSU9Px6BBg3gKyBCkLR49elS1TSL6i+bGAHf5G7NjDxaZzsGDB7Fs2TJce+21CAhgEyZjkLZ43XXXqbZ56NAhX+8OkWE4/XQtwiBf7wD5l9LSUqxYsQKrVq3CmjVrcOjgIZSWlSEiPBxNmzVFly5d1HgWKRQqU+FPJ3WufvjhB9x4440ICwvzyWcgOhtpk8OHD8dXX32Fm2++GbGxsWf8jqZpahHylStXqjIPu3buQmFREUKCg1G3Xl2VbuzatasaW8hZsWQFTjfqWlmhDpZNk6udSGfyjX7ChAl4f/wEZGQeQ1BAEKIDaiDEHgobAuCAA6XBhcgty4YGDa1btcaDDz2oHlJRUVEng7NPPvkEF198MRo1asRzRoa1Z88eNUZw9OjRJ4OkvLw8fP7553j7f29jy9YtsMGGmOA4hJRFIBCB0OBEaVAJ8pxZsDvtSExIxD333oO77rpLLdFDZDa5ubnqS8ZVv96G4MiqldApKyjFjMEfqS/VZl1TlgEW6cput+PVV1/Fc88+B5tmQ5KjHlLQAFGIQ4DtzG8oDs2OLGTgiG0vjuEIEuIT8MGHH6gyDLLIrgRW0sNFZHTSQ7V3717VozVz5kzccfsdyMjMQBJSkKw1Qg0kINB2ZhLBqTmRj2wcwX6kBR6UuzRe/NeLeOyxxxAUxKQDmUeunwdY5u+DI8OSh0uPHj3x9NNPI8XeCL0dl6GlrRNibDUrDK6EPHASbLXRHr3QW7sUOB6Mq666CoMHD1YPFwZXZBbSVgMDA3HJ4EtUG5a2LG26HXqpNl5RcCXk2pBrRK6VCxyXqWvnqaeeUteSXFNEZuP0QiX3559/Xg0rKf9q2bIlfIlfh0gX27ZtQ/9+/VFwvAhdtf6ItcVX+T3CbZFo7+yFVBzAgnkLUVRYpAItV8qQyMjy8/Px+muvY8UfK9Aa3ZDsrF/lZZyCbMFohvZI0upg64bV6NWzFxYuWujzBwdRVTjdGLTuziD3Nm3aYO7cuSf/7eseX/ZgkS6z/Ab0H4CizBJ0svd1K7hykQdSiq0BOml9sGL5Slw59EqUlZV5dH+JPE3aqLTVVStWo6PWR7XhqgZX5ck1JNeSXFMDBwzkLEUyFaeXZhFKQFW7du2Tr4SEBPgSAyzyKJkzMWb0GORm5qOD4wKE2jwz0y/OFo/2jl5YtGgRXnnlFY+8J5Fexo0bp9pqO0dP1XY9Qa4luaZyMvLUNcb5SWQWTi8FWDt37kRKSgoaN26MkSNH4sCBA/AlBljkUZMmTcL8BfPRwt4RobZwj753DVsi6mvNVK5dprkTGdGGDRvwwgsvoL7WXLVZT5JrSq6tefPnqWuNyOoBVm5u7imvs6352aNHD0yePBmzZ8/G+PHj1XjFPn36qNm7vsJZhOQxhYWFSElOQWRuDbS2ddXlyDo1B1YGLkCPAV0xZ84cXbZBVB2DLh6E5QtXoZtjAAJsgboczC1YhYLoLBxJPYKIiAhdtkHkqVmEg2bd7dYswjmXTzjj588995z6kn0+2dnZap3Q119/Hbfffjt8gT1Y5DFffvmluqAaoZVuR1UeWPUcTdVARukOJjKSHTt2YO68uajnaKZbcCUaai3VtSYFTYmMTnNjJqFWbkyvlGpwvcaOHVupbcbFxaF58+bYtWsXfIUBFnnMO2+/i4SAZDX7T0+1UA+hgWGqcCmRkUibDAsMRy3oWxg0whalrrV33n5H1+0Q+TpFGBMTc8orNDS00rN4d+/ejeTkZJ+dRAZY5BHHjx/HuvVrkeSso/sRDbQFIt6RjJ9nzdZ9W0RVIW2ypkNqXOnXe+Ui19radWuRlZWl+7aIjD7I/bHHHlMTS/bt24elS5fi6quvVnXoZFk1X2GARR4ha6qJGNT0yhGNQQ1s374NRUVFXtkeUWXGIG7fsQ2xqOGVg+W61lzXHpE/B1iHDh1SwVSLFi1w/fXXIz4+Hn/88QcSEz070aQqWGiUPGLt2rUICQxBhCPKawGWw+lQswllUVwiX9u0aROcTieivRRgRSAKwYEh6tqT9TmJ/LnQ6FcGHI/IHizyWIowNCC8WsUUqyIEoSe3S2QEmZmZ6n9D4Jnab+cj11pYQDivASKDYg8WeYTD4YCtimtHVYdrW7JdIiNwtUWbj7ZLZFSaZlOvqv6N2THAIo+IjIxEmea9JWzssJ/cLpERuNbIlLZZuXlO1WfX7LwGyPCcbizeXNXfNyIGWOQRrVu3RpG9AKUoRoiHlsc5l3zknNwukRG0atXqZNuMRLTu2yvRitU1x2uAjM7ppcWejYZjsMgjunY9Ubk9F96ZMp6L40iulYykpCSvbI/ofGrVqoXatZK9dg3k/bmdLl26eGV7RNVNEWpVfJkdAyzyiEaNGiEhPhGZSNP9iMoit9lBGbiw74W6b4uoKi7scwGyg455ZSHmDBxV15xce0RG5vTSYs9GwwCLPDaj6c677kBa4EE4tBPjo/SSg0zk2I/jjjvu0HU7RFUlbVLaprRRvcdepQcewl133+m1mbtEVDUMsMhj7r77bpQ5S3EE+3Q9qgdsu9CoYSPW/iHDGTRoEBo2aIiDNn3XP5NrzO4sw1133aXrdog8QWOKkKh6ZOXy0aNHY2/gVhRrhboczmPaEaRrh/Dc888hIIDfD8hYpE0+/8LzSNMOqbaqB7m29gVuxajRo9Q1R2SGAMtZxRfHYBGd5o033kCN+DhsC1jj8XEopVoJdgStw2WXXY5Ro0bx2JMhSdu89NJLsSNovWqzniTXlFxbNeJrqGuNyAw01Xar+IL5sQuAPCouLg6TP5mMTC0N27HOY0GWXSvDxsBlCI8Ow6RJEznuhAxLxkR98MEHCI8OVW1W2q4nyLUk19RxLR2TP/lYXWtEZqqD5aziy+wYYJHHybf3iRMn4hB2Y6ttNRxa9SpNl2hFWBf4O+zhJZgzdw7q1KnjsX0l0oO00V/n/KrarLRdacPVIdeQXEtyTU2YOEFdY0RmoXEMFpFnZ1N9+umnOBZ4GCsD5iFHy3TrG/sRbR9WBM5FSM0gLP5tMTp37szTRKYg9akWLV6k2q60YWnL7vToyrUj15BcS3JNcfYskTmwB4t0c8stt+D7ad8jsX48VtkWYiP+UOmN8z1knJoDqdoBrAlchC1YhetuuA5bt21Bx44debbIVDp16qTarrRhacvSpqVtSxs/F7lG5FqRa0auHbmGps+Yrq4pIrNx+mkdLC6VQ7opKirC/v37sWr1Knz99dd48403sWbnYkQFxSDSHosYxCEMkbAhAE44UIBc5NuykRNwHKXOEgzoMwBPPPkE0yFkajVr1sRnn32Gm2++Ga+MewULFi7AzoBQxGo1EaXFIRIxCEAgNDhRrK6CbBQE5SDfnovmTZvjuUfexYgRI/Dll1+qayo8PNzXH4moSrQ/B65X9W/MzqZ5o+Qw+aXp06eraeTyLV5IU1u4cKH6+coVK7F23ToUF/81NqVmjZro2q0bmjRprP7mzjvv9OHeE+lDxieuW7cOu3fvwcoVK5CV/dfSOmFh4ejUsSO6de+GYcOGoX///icndKxdu1Z9YZGfE5lBbm4uYmNj0fqrJxAYUbUl0B2FJdhywyvIyclBTEwMzIg9WKSLnTt3Ij8//5S0njwoBgwYoF7C4XAgLy8PpaWlCAsLQ3R0tPqdsrIyTJgwQQVkrFJNViJtWtr7W2+9heDgYPVvuQaKi4sREhKiroHAwMAK/1aupc2bN6trq1mzZl7fdyJ3aW7UtWIdLKIKyMPi119/xdChQ88ZIMmDRKaay4LN8g3F9bvy4KlduzYOHTrE40uWcvDgQSQnJ6s2LqTNS9uXa0CuhbMFV67flWtKri25xojMwumnY7A4yJ087pdffkGvXr1U17C7WrdujS1btnh0v4h8Tdq0tG13yTXVs2dPFWQRkbExwCKP2rVrl8q7u8ZduUtSIPJeHCJIViFteffu3WjatGm13kdKlci4FHkvIjPQqlrF3Y1B8UbEAIs8pqSkRPVeXXnlldUeOyUpFEmbHD582GP7R+RLkvKuVavWyfSgu1ypQrnW5JojMjpN+2scVuVfMD0GWOQxkrbo0aNHtVKD5bVp00YN6iWyguqmB8uT8Vrdu3fHnDlzPPJ+RHrSWMmdyH179uxBVlaWql7tKUwTkpXSg5Ly9uTsP7nWMjMz1bVHZPjFnlH1l9mxB4uqTdIUs2fPPu+swapimpCsQlLdkvKubnqwPLnWJB0v1x5ThWRkGnuwiNwzd+5cdOvWDTVq1PD4IeRsQrICT6YHy5NrrmvXruoaJCJjYQ8WVcvevXuRkZGhbvJ6kJSKFFbkbEIyK2m7ehYHlS83x44dU9cikSFp/pkjZIBFbpOK1D///LPHU4PlSXXrxMREHDlyRJf3J9KbtF1pw9KW9eBKFcq1KNckkeFoVZ1BaFN/Y3YMsMhtkpaQgbaymK2emCYkM5OZsDIjVk9yDcq1OG/ePF23Q+QOjXWwiCpv3759SEtLU1PF9da8eXPs2LGDaUIyHb3Tg+XJtXj06FG1IDSRkWgc5E5UtdSgJwqKVoakVhISEpCamqr7tog8SdqstF290oMVpQpnzZqlFkwnMgzN5t7L5JgipCqbP38+OnbsiPj4eK8dPaYJyYy8kR4sT65JuTaZKiTyPQZYVCUHDhxQg3alYrs3MU1IZk0PStv1Jrk25RqVa5XICDSOwSI6N0k7/PTTTyoNERDg3dg8NDRUDeSVMSZEZkkPSo+SN9KD5cm1KdeoXKtMFZIhaCzTQHTe1GCHDh3UmBJf4NqEZCZ6FRetDLlG27dvjwULFvhk+0S+HuT+n//8R41LfPjhh+ErTBFSpRw8eBCHDh1Cz549fXbEmCYkM6UHZeart9OD5fXq1Utdt/Ii8jnNe0VGV65ciQkTJqgvGb7EAIsMnRo8PU0oS4NIeQgiI5NUtqS0pc36ClOF5I89WPn5+Rg5ciQmTZqky/JtVcEAi85r4cKFaNu2rapG7WtME5IZ+DI9WJ5cs3LtyjVM5A/uv/9+DBkyBBdffLGvd4UBFp2bpAWlcGHv3r0NcaiYJiQzpAe3b9+OFi1awAjk2pVr+PDhw77eFfJXmvuD3HNzc095lZSUnHUzX331FdasWYOXX34ZRsAeLDoru92OH3/80eepwfLCwsIQFxfHNCEZlqSwJTXhy/RgRalCuZblmibyPpubL6BevXqIjY09+Tpb8CRjDf/2t7/h888/V88JIwjy9Q6QcS1atEil5JKSkmAkrqKjtWvX9vWuEBk2PVieXMOtWrVS1/RFF13k690hf6O5MXBd+ytwiomJOfnjs31xWb16NdLT09G5c+eTP3M4HFi8eDHeeecd1fMVGBgIbzJGtwQZjqQT9u7diwsuuABGI6kXScFIKobISIyWHixPruU9e/aoIqREZkkRxsTEnPI6W4AlXxw2btyIdevWnXx17dpVDXiX//Z2cCXYg0VnTQ0OGzbMMKnB8qT7V7qK5dtKrVq1fL07RCdJm5S2aZQURXnygJFU4fTp03H77bcjKIi3f/ISzY21Bav4+9HR0WpCR3mRkZGq2O/pP/cW4z09yeekS7Vly5aGDl64NiEZkbfXHqwquabl2v7tt998vStElscAi85Y3mP37t248MILDX1kJAWzbds2pgnJMIycHixPrm1ZI1GudSIrr0W4cOFCvPnmm1X6m127duGXX35BUVHRn/vu/o4wwKJTBgTOnDkTQ4cO9Um+uirCw8NVPv7YsWO+3hUiRdqitEkjpgcrShXKtS7XPJHuNOOvRZiZmalqZ0kpoMsvv/zkFxBJpz/66KNuvScDLDpJ0gbSuMwyO49pQjISo6cHy5NrvFmzZkwVknfHYGlVfHnRI488osYlHjhwABERESd/PmLECMyePdut92SARSeX9pC10/r06WOaIyJjSSRNSGQEZkgPlte3b191zcu1T6Qnm+bey5t+/fVXjBs3DnXr1j3l5/JFRAr1uoMBFpkqNXh6mjAqKoppQjLE7EGZxSRt0izkWr/iiiuYKiT9acZPERYUFJzSc+Vy/Phxt4sGM8Ai/P7772jSpAmSk5NNdzS4NiEZgRGLi1ZGSkoKGjdujCVLlvh6V4h8SrI3n3766cl/22w2OJ1OvPLKKxgwYIBb78kAy8/Jsh6SZpN0gRm5ZhMS+ZK0QUlZm1G/fv2wdetWLj9Ffj0G65VXXsHEiRNx2WWXobS0FE888YSqnyVliyR16A4GWH6sfGrQrEUHpUtXisllZGT4elfIj2cPSqraTOnB8uTad6UK5Rs7kT+mCNu2bavGJEoZk6uuukqlDK+55hqsXbtWZXjcYc6nKnnE0qVL0bBhQ5UmMDNXmlC+iRN5m1nTg+XVqVNH3QskVWimiS5k/bUIvUlWYXj66ac99n4MsPx4UK4EJXfccQfMTlIzU6ZMYYBFPksP3nLLLaY/+v3798cHH3ygrqfExERf7w5ZiWb8AEtSgefizjAaBlh+SNIAP/zwg0oLmDU1eHqaUF6SJkxISPD17pAfkTbnan9m50oVyr3h1ltvNeQ6pGRSmv5rEXriC8bpZKC7iztFeXkF+WlqsEGDBmfU+zAzFh0lX7BCerA8uSfUq1cPy5Yt8/WuEHlVVlbWKS/J8kiB0W7duqkaWe5ggOWHA3I3bdpUYbRuZiw6Sr4gs+9atWplqYMvU9I3btzIiSPkV4VGY2NjT3lJNmTQoEFqBqHMKHQHAyw/TA0OGTIEwcHBsBKZSShrwMl6UkTeIG1NZg5aIT1Yntwb5B4xY8YMziokv5lFeDa1atVSqzS4w/wDcKjS/vjjj5MpACtypQk5C4q8wUxrD1aV3CPkXiH3jN69e/t6d4h0t2HDhlP+rWmaWvD5P//5Dzp27OjWe7IHy48G465fvx4DBw6EVUmqRlI2RN5g5uKilSH3CrlnsFeYqsvmTprQy4ddgqhOnTqp/3X99+WXX66KjsrsWnewB8sPWDk1eHqaUNaMkrWjatas6evdIQuToEPamrQ5q5J7hTxg5N4xevRoziokS9u7d+8p/5ZZtFKuRIaeuIsBlh9YsWKFKiZav359WJ0rTSjVeIn0Im3MqunB8mS2ce3atdU9pGfPnr7eHTIrzfhlGqStexoDLD/4pi2l/m+//Xb4A0kTfvnllwywSFeSih45cqRfHOWLLrpIpUiaN2/OnmGyVKHR//3vf5X+3YceeqjK788Ay8JkkJ5078vilSEhIfAHsiacpDakjkmNGjV8vTtkQZKCtnp6sDy5d5RPFZYvvkhk5gDrjTfeqNTvSZtngEWnkG59mWIqa4z5E9fahEwTkh6sVly0MuQekpSUhJUrV6J79+6+3h0yGZsbda28UQfr9HFXnsZZhBb+lr169WpcfPHF8DecTUh6smJx0cqQe8mqVatU7zCRv9TBqg6mCC2aGpw5c6ZfpQbLY5qQ9CLBhaSgpY35G7mXyD1FUoWjRo1iqpAs59ChQ6p9HzhwQJVnKO/111+v8vsxwLIg+ZYpZf4bNWoEf+WaTXjBBRf4elfIQvwxPVie3FMk/S73GFmjjcjMY7DKmzdvHq688ko0btxY1bhr27Yt9u3bpzosOnfu7NZ7MkVowW/YMk7CH1OD5TFNSHqlB/05wBKyPpvcY7Kzs329K2QSNhOsRTh27Fg89thjah1OqX01depUHDx4EP369cPw4cPdek8GWBZMDV566aVqlpM/i46ORmBgIB8C5NEvL0FBQX6ZHixP7i2XXHKJSqXIPYeo0nWwtCq+vPzlSVLfQq7zoqIida2/+OKLasFndzDAshAZ1C6lCaSLk/5KExJ5gr+nB8tr0qQJ4uLisGbNGl/vCpmBZvxB7lJ2xTXuKjk5Gbt37z5lqTl3MMCyCOmuX758OQYPHuzrXTFUmpABFnmKv84ePBu518hi0Dk5Ob7eFTI4mwlShLJSwe+//67+W+q+Pfroo/i///s/3HbbbW6vYsAAy0KpQem29/fUYHkxMTFqPSk+AMgTX2Ak5SypZzpBxqkwVUhW8frrr6NHjx7qv1944QW1gsHXX3+tasB9+OGHbr0nAywLkKVwJJho2rSpr3fFcJgmJE9gerBics+Re4/cg4jMnCJ86aWXVP1IV7rw/fffx4YNG9Rgd3fXKWSAZXLSO7Ns2TL1TZLOxACLPIHpwbOTe4/cg3Jzc9nYqGKaG+lBLwdYx44dUxPE6tWrh8cffxzr16+v9nsywLJAalDGQkh3PZ1Jvl3LOlJME5K7pO1IG5K2RGeSe4+UbpB7EWcVkll7sGbMmIHU1FQ888wzqgyJ1L6SZdekZ0vqYbmDAZaJrVu3Tk0jbdasma93xdBYE4uqg+nB82vevDkiIiI88q2fLEgzfoAlZBb+XXfdhYULF2L//v0YM2YMpkyZ4vbwGwZYJiXd8UuXLmVqsBKYJqTqYHHRypH0ypIlS5gqJFPOIiyvrKxMrVYgM/Ol96pWrVpwBwMsE5Ju+B9//FFVaw8PD/f17hhebGysOmYcI0JV5UotMz14fnIvknuS3JuYKiRvGz9+PNq3b6+uVXn16tULP//8c5XeY8GCBbjzzjtVQCW9V/I+0p5ljUJ3MMAyIZnZIOMeWrRo4etdMQ32YpE7OLi9auSeJPcmWW6EyJvq1q2L//znP6rgtvQ+DRw4EFdddZVaO7My6tSpo+pfSVHRiRMnIi0tDR999JEq1yBjMN3BAMtk8vLyVDE0WdWeKo8BFrmD46/cSxX+9ttv6l5F5K0xWEOHDlUBkoxJljGBUiRUxihLMdzKeP7559Ug92nTpuG6667zSE1JBlgmTA1KRM3UYNXThE6nk2lCqjRJKcs1J22HKk8Gu0vvwU8//cRUIflkDJbD4cBXX32FgoIClSqsDEkNyvJPnsQAy0Q2bdqEkJAQtGzZ0te7YtpeLEn5EFUG04PVm7krC+bKPYtIcbP3Sr7olH+VlJTgbCQ1Lb1W0vt0zz33qN4oX64fygDLJPLz87F48WKmBquBaUKqCqYHq0fSNXLPknsX+TnN/RShFP6UXmTX6+WXXz7nGEApXySz/+69916MHj3ap+vRBvlsy1RpkqaQ7vYBAwao7ndyj3T/StexjA3hmnJ0LtJGpK14OmXgT+ReJfcsuXddf/31bg8UJv928ODBU2bxnmtslGR4XDWrunTpogqGvvXWW5gwYQJ8gT1YJiCzIGTRYl92dVoFi45SZbD3yjPkniX3Ll/2IpC5x2DF/Fl2wfWqyuBzGXd7rpSi3hhgGZwM0lu0aJHqbqfqY5qQKkMCAgnGqfrk3iWVseVeRn5K038W4dixY1VKWgqDylgs+be0u5EjR8JXGGAZ3KxZs9C/f3+1ujd5ZikEqdLLcSF0vvSgtBWqPrl39evXT93LyD/ZvDCLMD09HaNGjVLjsGSmvaQHf/nlF7VOpq9wDJbBU4PSxcnUoD5pwm7dunn4nckKOHvQ82TRXLmfMfXqpzQ31has4u9/+OGHMBr2YBmUdKdL9+aQIUM4OFSnmz1RRRgEeJ4McJd7mSxFUlhYyIbnbzRzLPbsaQywDErWUOrbt6+q6UGexTQhnY2kjiWFzPSg58m9TO5pVV0fjsisGGAZNEVht9vRtm1bX++KZUmx1m3btvl6N8hgmB7Ul9zTSktLee35GZuXK7kbBQMsg5Hu8/nz5zM1qDOmCakiTA/qnyq84oorMG/ePBQVFbER+guNKUIygNmzZ6NPnz4shKmzmjVrqvoonDpOLtIWpE1I2yD9SJHfCy+8kKlCf6IxwCIf2759O4qLi9GuXTtf74pfYNFRKo/pQe9p3769utfJPY+sz8YUIfmSdJfPnTsXQ4cO5axBL2HRUSpPZpayJIp3U4Vyz2Oq0A9o7MEiH6cGL7jgAqYGvSg+Pl7d3JkmJFd6UNoEeYcse9K7d29VDJLIijjI3QB27NihBrd36NDB17vil2lCziYkaQMys5S8q2PHjqo0htwDybpsTBGSL8g4hDlz5jA16CNME5IrPSgzS8n7qUIZFiH3QLkXkkVpTBGSD0j3eK9evVR3OXlfQkKC6j1kdWn/JedeUsVMD/pGbGysugcyVWhhGgMs8rJdu3aphWU7derEY+9DLDrq3zh70PfkHpibm6vuiWQ9NjdfZscxWD4i3eHyjY2zBn2PRUf9G4uLGiNVeOWVV6p7IlOFFqSxB4u86Ndff0XPnj1V9zj5Pk0os8iYJvQ/rvSwtAHyLbkX9ujRQ43HIrIC9mB5iEzxzsjIwLFjx877oN69ezeys7PRuXNnT22ePJAmLF/0UL5Fu84n6/SYn5xDOZdyTsv3kHD2oLF06dIFWVlZ2LNnzzl/T+6xrvMp914yNpufziIM8vUOmJVc4N9++61aN3D5Hyuwc9cOOJ3Ok//3BvUbokfP7mrZm5EjR6JGjRrq53IzkG7wm266iQVFDaRBgwb4v//7P/XwlfO5e88uaNpfV3jjRo3Ro2cP9O3bFzfeeCN7Hg0uJycHX375JRYvXozlfyzHnr17TklHNWncVF2fYWFhePrpp326r3RmqvCLL77A7bffjtDQUPVzCbo+//xz/Pbbb+r63H9g38m/CQgIQLOmzdX5HDhwIK6//nqEh4fzsBoxRVgVFgiwbFr5pwidV3p6OsaNG4cPPvgQebm5iAuKR4Q9BjGIQzBO3AzsKEMeslEQmItsZyaCQ4Jx0003YuzYsaqXpFatWujWrRuPtgEcOXIEL7/8Mj7+6GMVNMv5jLTHIlqdzxD1O2UoRT5yUBCYo86n3PRHjR6lzqcEZmQc+/fvV+fz008+VV9m4gLiEemIRRRiTzmf6voMykG2PRORkZG49bZb8Y9//AMpKSm+/ggEYOXKlepe27x5c3U+v/j8C5SV2f88nzHq+gxCsDpWZShBLrJRGJSrzqfMyL79jtvx5JNPIikpicfTh3Jzc9WX0TZ3v4TAkLAq/a2jtBibJzylviyZdZY9A6wq+Oabb3DPPfeiMLcQtR31UQeNEWGLOufflGjFOIK9SA3aD7utFNdedy0+/fRTBAWx89CX5HuFnIeHHnwIpUVlqG1vgLpojDBbxDn/rlgr+vN87oMtGHjt9ddw9913szfSAOdzwoQJePTvj0IrA5LtDVEHjRBqO3dPRpFWgMPYi6NB+xESHoz/vf0/jBo1iufTx+x2uzoPU7+biiAtBMn2BkhR5/PcD+lCLR+HsQdHAw8gIiYC778/XvVokW8DrLZ3uRdgbZrIAMvyysrKcNddd2Hy5MmoZauLFlpHhJznQj+dQ7NjFzbhIHahf7/+mPHDDNNG5WYnPRu33HwLvv3uWyTbGqC51gHBthO9G5Vl18qwExvUw/nyyy9X6eKIiHMHZ6QPmaAwfPhw/Pzzzyqoaob2CJLotwrKtFLssK1HqrYfw68bjimfTTmZniLvP5SvuvIqLFy0EPXQFE3RFoG2qn0hLdWKsd22DmnaIYwZMwaTJk3il1pfBlh3uhlgTWKAZflvUiOuH4Hp06ejpdYFKbbqpYSOa+nYFLgcbTu0xYIF8xlkeVlpaaka4zF3zjy0dnZVAXN1ZGip2By4Aj179cTsX2YzyPIySeteMvgSLF+2HG2c3ZFgS67W+8kDeUvAKlw86CL88MMPCAmpWuBN1X8gDxgwEJvWb0JbRw/UtCVVq1czFQewzbYaV199Nb76+isGWV6W6+cBFmcRnscjjzyigqt2Ws9qB1dCbhgdHReqG8i111x7ysB40p/0RM6dMxftnb2qHVwJeaB3cFyAZUv/wM0333zKwHjSlxxrmUDyx7Ll6OC8oNrBlZA2IW1D2oi0FfIeuRfKPVHujXKPrE5w5RowL/dsuXdPmzZN3cvJN2x+OouQAdY5yAzBd955B0219ki0eW7wa4ytBto4umHuvLmYOHGix96Xzm3GjBn45JNP0MLZCfG2Wh47XHG2BLRydlE3cZn9RN4hs8rky48cezkHniJtQ9qItBXpxSLvkDF0ck+Ue6PcIz1F7t1NtXbqXi73dPIBzT8LjXKQ+znGdbRs0RJFR8vUtyn5NuRpW7U1OB6eqpbq4Gw0fck07+bNWsCWFax6KPQ4n5uwAkXROdi+48RMUdJPWloaWjRvgfC8WLRFd116xzYELANqlqnz6SqzQvrN/mzVshVqFiejla2zLudzXeDviEgOwdZtW9XMUfJeirD9be6lCDd8xBShJckMs8NHDqOFo5NuM4qaoZ2a8fTmm2/q8v70FxnkKkGW9EzodT5lsHxhQRHeffddHnqdSW9EUUGROuZ6kDYibeX48Sx88MEHumyD/vLGG29As9vUPVG38+nohEOHD2HKlCk89N6msQeLXG1B09CmdRsc356Lduil63HZqW3E8agjSD2aym9VOnE4HKpQaNkhG9pA3/pj27S1KEnIVTdyDpDWb6JCnZQ6CMuMRUubvgulb8ZKBNfVsHffXlXQkvTJFiTXTkZ8fgqa2vQJsFw22pYhvkUsNm3exFIc3uzBGuNmD9Zk9mBZssiddCOnaI1135bUXsrPz8f333+v+7b81cKFC3Hg4AHU9dL5PJZxTJUMIH3MmjULGZkZ6ljrrY7WWLUdaUOkj6lTp6p7oNQV1FuKsxG2bN2CVatW6b4tIn4lq8CyZcsQGBCEGkjUvYWE2yIRHRyHpUuXsjXqeD5DA8MQg5q6H+MoWywig6N4PnU+n5HB0epY6y0WNVXb4fWp7/mMCY5T90K91UCSurfzfHqXzU9nEbKceAXk201MQBwCNO/EnxFl0VixfIVXtuWvPZLRWpzXUgKy1M7KFSu9si1/JNdKpN07dXGkzURpsezx0JGsFRlRFgN44fIMsAWoe/vq1av13xjB39ciZA9WBTZu2IRw+7mXwPGkaMSqmYSkj00bNiHCGe21wxupxWDzps1e256/kWMrx9hbIp0x2Lhho9e252+2bduGKHjvfMq9feN6nk9vsmmaWy+zY4BVgYL8/JMLiXpDIIJRXFLMIpU6yS8oQKAXO2uDEITCokKvbc/fyLH17vUZhIICnk+9JhQVFRepe6C3SNuRgfXkRZp/ziJkirACgYGB0ODw4mnQEBAQyFktOgkM9O73CLkvcMaZfuRa0bx699W83ob8haRgAwMCAaf3zqe0ncBAPvq8yebGmCorjMHiXaMC8YkJKEWJ105CCYoRa9K1lswgPl7OZ7HXtifbYmFK/cix9e75LEHNmvFe256/kWn8cg/05vmMT+D5JP0xwKpAly6dURicC2/Jt+Wgc5cuXtuev+narQsKg/K8tr2CwBx07dbVa9vzx/NZEOi967MgKBfduvN86qVT507qHugtcm/v0pX3W6/S/DNFyACrAl27dkVeWQ7KtFKvjEGQB3L37voWwPT385njyIJDc3jlfOYhG9268XzqRY5tHrK8MmZR2kyuI1u1IdJH9+7d1T3QG+dT7ulyb+f5tF6ZhpdfflndG6Kjo5GUlIRhw4Zh+/bt8CUGWBUYOHCgms6bhkO6n4AsHEOhvQCDBg3SfVv+6uKLL4ZTc+AYDuu+rQykotRRyvOpI7lW5BjLsdZbOg6rtiNtiPQ7n3IPlHuh3tJwUN3bBwwYoPu2yLs9WIsWLcL999+PP/74A3PmzEFZWRkGDx7s0wkNDLAqULduXVxxxRVIDdqr+7eqw7Y9aN6sOfr166frdvxZixYt0L9ffxwJ3Kv7tg4H7EGXzl3RubPnF6ylE7p06YLOnTrjSID+51PazID+A9C8eXMefp30798fzZo2w2GbvudT7uWpQfswdOhQdY8na/VgzZ49G2PGjEGbNm3QoUMHTJ48GQcOHPBpzTMGWGfxwIMPIMcu36mO6Hbw87Rs9f4PPvQgZxDqTI7xcccxHNfSdNtGjpaJDOdRPPjQA7ptg0546G8P4ZgzVR1zvWRqachyHFP3AtJ3JqE6nzis7ol6kXut3NPvf+B+3bZBxhmDlZNzYlxfzZr6r+BxNjbNG4lvE5LDIt905v+yAN3tFyPYFuLR93dqTqwOXIiUZrWwdt1ahIaGevT96bTj7XSqnojVy9aim30ggmzBHh+rsypoPlp0aIZlfyxDUBCngevJbrejR4+e2LlhN7raByDQFujZ99fKsDJoPrr27oz5C+az7IbOSkpK0LFDRxzddQydHf1UGs+TSrUSrAyah4GXDMDMmTP5hdbLiz13uf7/3FrsefU3T+PgwYOIKTfLXp6V53teyv3+yiuvRHZ2Nn7//Xf4CnuwzvGtauLEiQgOD8JW2xqPpwp3YzPytBxM+WwKgysvkLpUkz+ZDGeQHdtt6zx6PuW9dmIDilGITz79hMGVF0gAO2XKpyhGgTr2nj6f0kakrXw8+WMGV14gD0y5F+Zq2dgDz66CIOdzm20NQiKCMWnSJAZXPmJzMz1Yr149FaS5XjKY/XxkLNamTZvw1VdfwZcYYJ1DSkqKemBm4Ai2wnNB1j5tG/ZjO155ZZwaT0Le0ahRI0z6YBJStf3YgfUeOZ/yHhIsH8JuvP3O22jdurVH9pXOT461HHM59nIOPHU+pW1IG/ngww9UmyHvkJl948b9B/uwXd0jPUHOp9y7ZUKE3MuTk5M98r5U5RMBt16A6sGSdJ/rNXbs2HNu6oEHHsCPP/6IBQsW+HysHfMY5yFTPaXnY8zoMbCjBC21zgixVa2r08Wh2bETG9UD4dlnn8Wjjz7q1vuQ+0aOHIm8vDzce++9KLOVoIXWye30r6SR5GF8BPvw6quv4u677+ap8TI55pKGeOKJJ1Tx0eZaB7fTvzKFf5ttLdK0gxg/fjxuuukmj+8vndtjjz2mrs8XX3wRxVoRmqEdAm3uPaZKtWLVc5WBo+oeftVVV/Hwm7CSe0xMzCkpwnMF0w8++CCmTZuGhQsXGuLLEcdgVZJExKNGjUZRXhGa2tuhFupVuqtZTnwW0rEjaD3KAkvxxhuvqwc8+c63336LO26/A2VFdjSzd0AiUqp0PjNxVJ1PBDvx3vj3MHr0aN33mc7uk08+wX333geUBaC5vQPiUbtK51MGQO8MWq+GBEjP1fDhw3m4fUgC3Ece+TuCHSHqfNZAUpXOp5Rj2BW4EREx4arnSmaFk+/GYHW99t8ICq5ax4S9rBirpv5T9VpVJsC677778MUXX2DGjBlq5riLbD88PBy+wACrCtLT09VNfOr3UxEREIUUZyPUQl2EIaLCi79EK1Y3bpkanGM/jt69eqtvUs2aNfPkOSQ3HTlyBHfccSd+/nkWooNikWxviCTUQSjCzzifctOW5TxkppOcz1x7tho0L2N0GjRowHNgAPv27cNtt96GBQsXICYoTp3PRHU+w85yPotUrbujQfuRZ8/B5ZdfrsboyNAA8r2dO3di9KjRatJIXFBNdT4TkILQCjIIcj5lDKScTynfUejMx7XXXKu+/EjRSfJtgNXtavcCrJXTKh9gnS0A//jjj1X5Bl9ggOWGDz/8EN98/Y3qhiwtK0VYUDginTEIcoZATrEdZSgMykehPV/Nhrl8yOUqLywF9bgIsLHIjVlmmbzzzjv47rvv1OyT8KAIRDpiEKidSDU5bGXID8hBsaNILTQ87Kqr1FRvKVZY2W/V5L3zKWMv3n3nXUyfMQNOpwPhgRHq+jx5PgPKUBCQiyJ7oboer7vuOnV9XnjhhTyfBiPXoxSNfPvttzFr1ix1fiOCohBhj0IQgtVMfntAqTqfxfYihASHoF//frjxxhtx6623+nr3/V6uFwMsI+IYrCqSC7y0tFT1Ysn/Llu2TBUyW7t2LbKOn1i+IzcvV1WD7927N3r16sVvxAYmAVKfPn3UOZI0gtwM5HyuX78e2VnZ6nzGxsWiadOm6veffPJJ1KpVy9e7Tec4n3LtySstLQ3jxo1TP9+1axdysnPU/z2uRpwqRCgTTOTmLddpkyZNeEwNSALgSy65RL2kx1nut0uXLsX8+fMREx2jzmeNmjXQqVMndT7lfhsSEoIpU6aoa5dfgAxCc6OulQUKSDHAqqIdO3aoh3FUVJT695AhQ9SrPMkBSyVvmV5K5rB58+aTD1qpn3I6uVlLLxfTDeYh50oGukrv1NketBJ4bdmyhQGWCch999prr1XrzcnKFxVdp+V/V1KMrMBv/kHuZsYyDVUk6xz17NnznL8j3ZnSNUrm4HA41JIKDRs2POvvqJ6PuDhVuI7MISsrCzVq1DhnL4YEYPv371epKDIHubeeL2Uk92jp7SLzl2kwMwZYVXD06FF1sz5fiogBlvkGR9evXx+BgeeuBi41VaQmC5nDoUOHzlsHR8659DRLGyDrBFhyj5Z7tdyzyT/WIjQiBlhVIN+IJMd/PjKOhz1Y5iEposoUCJUHsTy0yToBlpBzL22ArBNguXqxJONA5CsMsCpJit/JtyHXYOdzkYvftdAkGZukhiRFVJmidHXq1GGAZbIAS87Z+TRu3Fj1YDFNaK0AS8rhpKamIj8/3yv7RcZa7NkIGGBV0sqVK9XgysrMSmGK0DzkwSo9U+dLDwopVifjtcrKyryyb+Q+OUdyripTYFDOvfR0SaBN1gmw5F4ty+/IvZt8y8YUIZ3rZi0pBJnaXdmFS4uLi3lALZQedJG1zGS6OBmbnKOqFAxt06aNmklKxif3VrnHVobcs+W88kuRj2kc5E5nsWHDBrRq1QrBwZVb40y+OUn9FqYcjE3Oz969e1WKqLI4Dsta469cmCY0zzUr99bK1reSmlgtW7bExo0bdd83Ojsbe7CoIlL/yJUerAqpk8Xcv7FJSqiy6UEXeWhzoLvxyWzPqgRY0gZkvJaU6yBjj4WNjo6u0t90794dK1asUPdy8hGNY7CoArt371YFC6taqp/jsKyXHhSJiYk4duwYb9YGphZvPnZMnauqYJrQOuOvypPfl7awZ88e3faLqCIc5O6BwqIVYYBljvRgVZdIkfQEZ4kam8zglVIpVV33U9KE0iaY2rdWgCWkvA4Lj/qOjSlCOp18C7bb7W6tJSg3eJZqMC5JBUlKqCrpQRemCa2VHnQJCgpimtCiAZbcw2Wgu9zTyQecmnsvk2MPlg69V4I9WMYmM4skJeQOBljGJmPk3F0HlEVHrRlgCRYe9SGNY7ConIKCAvVN2N3FQhlgWWv2YHkMsKxRYLQikjKWsTocEG29AKtFixbqnl5YWOjx/aJzs7mTJrTAQWUP1lmsWrUKXbp0qfI4DhcGWMYlN1lJGUhKyB0REREoLS1V6WMyFkkDyUvOkTukTUjb4GxC4wZYMvzCHXIv79y5s7q3k5dprINFf5IHp9RN6dixo9vHJCwsDEVFRTymFksPli84KstwkLHIOZFzUx1MExqX3FMrW2S0Ip06dVJ1DfnliLyBPVgV2LRpk0oNVudClkJ48uKMJGOR1I+kgKo6e/B0TBNab/yVi6w3KuVZmCY0d5HRisg9XdYoZNV+77JxFiEJualKUTopTlddUmxUxnKRcUjqpzrpwfIBlqQaydwV3CsibUN6wXh+jUUKN8s9tbp69OiB5cuXM4D2Jo2D3OnPxX9r1KiBuLi4ah8PGSsgYwbI3MVFK1KrVi2kp6d7ZJ/Ic+ScSGHg6mLRUWsNcC9P7u3y4uLe3mPTNLdeZscUoQdLM5yOBSmN1zspqZ/qpgeFpCrk2zQDaOOQunNyTtydmFKetBGmCa0ZYAkWHvUyp5svk2OAVU5mZqaawlvdMRwunEloLJLykdRPZRftPh+Ow7Le+CsXaSO1a9fmupMWDbDk2pXhG8ePH/fI+9G52diDRZKXl/y8pzDAsmZ60EUe5hynY63xV+UxTWjdAEsGyrPwKOmNPVjlpv9K8UlPPoAZYBkrPbhr1y41Q8xT2INl7QCLswmNucakp7Rq1Urd84uLiz32nnQWGge5+7U1a9aouleeGL/hwgDLWA9fSfl4Kj0oIiMj1c3Z4XB47D3JPVLXSM6FnBNPkbYikxmk7ZC1erCErEMq9/zVq1d77D3pLDQWGvVb8oBct26dqtzuSeHh4VyWwaLpQRcJ2lhw1PeOHj1a7QKjFWHRUWNlGaSAsydJZXe597Neob5srIPlv7Zu3arWpfP0xesqNsqChcZID0qBQU+TcVjs4bBeetBF2oy0HV7D5i8yerYvwY0aNVJfwEhHGnuw/JLcOKU0gycHt5cn08alQB75zuHDh1VtJE+mB104DssYZLKBHgGWtBlpO9KGyHdkxp8n07/lyWB3Fh7Vl83p3svs/H6Qu9yYJQiqWbOmLgeY47Cssfbg2cgYHUlPkW+lpaWpc6EHpgmtN/6qPLn3y+Lg7IkmT/P7AMuThUUrwgDLuulB10BZuTnn5eXp8v50fnLspXdDzoUepO3s3LmTaUKLBliChUd1pjFF6HeysrLU1N8GDRrotg0GWL515MgRJCYm6pIedOE4LGuOv3IJCQlRbUjaElmjRMPp5BmQnZ2tXqQDjWUa/I5rUWdPD5wsjwGWddODLhyHZc3xV+UxTWjtHix5BrgWgSYdjq/GtQj9SklJier2b9u2ra7bYYDl2/SgnGO90oMuDLCss0TO2TRv3hw7duxgmtCiAZaQZ4HcL+TZQB6mMUXoV9auXYv27dvrNm7DhQGW79ODkuLRU3R0tKp3xoKj3ifHXOojyUQVPUkbSkhIYM0zCwdY8iyQZ4I8G0iHFKGzii/5mypavHgxhg4dipSUFNUrOX36dJ+eygB/rakildu7du2q+7ZkALQ8fMk6xUUrIjPYZCYbWWf24Om4NqHvyD1UalbpTZ4J8mxg4VHzlvPo0KED3n33XRiBXwZY27dvVykFCX705hrfxUKF3iXHW1I6ktrxBqYJfTf+Su/0oAtnE/ruWnYVbdabPBOkPckzgsw3Buuyyy7Dv//9b1x99dWGOH1+GWDpXZrhdDKFXCJr8h5ZvkZSOnqnB10YYFlzBmF5oaGhqmYS655Zp8hoReTZIM8I8vQsQq2KL/OfgQB/HJfjmnbtLRyHZe30oJB18LgmofdJsCPrQXoL04S+GX+lZ4mG07nKurAshzEGuefm5p7yMtMkBL8LsJYtW+bV3ivBAMva6UHXAFlZy5I9ld4jS1DJMdd7okp5nE3omxpYeg9wr6jwKHuxPMjp5uvPOoMSYLteL7/8MswiCH5Eot+MjAy1sLM3McDyfq+GpHIkpeNNrjRhixYtvLpdf+WN8gznShNKryVZYwbh6eQZMXfuXJ9s24psboypcv2+jLMsfw68fV+vjgB/KyzarVs3rwyWLI8BlrXTg+UDLLkZkPXGX5XHoqPe5YsgR54R8qxYuXKlV7dLZ5JzX/7FAMuASktLsW3bNlXnxNukW1O6uck76UGZAeSLXiQumeMfARbThN7lq16kdu3aYevWrerZQeYoNJqfn49169apl9i7d6/67wMHDvjkFPpND9b69evVANWgIO9nRdmD5d26SDVq1PDJtxw5z3KBs4aO/uQYy7H2xYNXxn3FxcWx7pnFAywZ6C7PDHl2kDkCrFWrVqFTp07qJf7+97+r/3722Wd9cgoD/KVXQw68dPn6AouNWmvtwXNJSkpCenq6z7bvL7xZYLQiTBN6j0wc8UbNworIM0OeHaxjaI4Aq3///upcnf6aPHkyfMEvAixZX0oGpOq9nMbZsNiod9OD3pw9WFGakOOwrJsedJEUtLQ1PnitU2S0IvLMkGeHPEPIN7MIzcwvAixvFxatCHux9Cc9R5K6kRSOr7DgqH8EWNLGZGwleyutVWS0Iiw8ap5K7kZj+QDLVXXZm8UIK8JxWNZPDwoWHPUOKerq6zIJTBPqzwhlEuTZIT1pXGuUqsryAZYReq8EAyzrzh4sTyZRyEoBXOBbP3Js5Rj7YsJKeS1btlQzk5kmtHaAJdiLZY4xWEZj6QBLZhnJN11ZpNXXJJ0gNwvSh6Rq5Bj7Mj3owjShtdODLtLW5OF/7NgxX++KZRklwJJxnbJ0jjxTyA1Ozb2XyVk6wJIicV27dvXZAMny5CbBWljWKy5aEQZY/hFgCa5N6B8BljxDunTpomYUkhs09mBZit1uV2NyOnToACNgilBfRkgPujDA0pfM0vT2Ejnnm01I1g6wRMeOHbFp0yb1bKGq0txID7IHy7A2bNigxkjIWA0jYIClb3owOjoa4eHhMAJX5X4WHPU8OaZ5eXmGeehKm5Op/EwTWj/AkmeJPFM2btzo610xH409WJYhg04lPdi9e3cYhUw1linHZO30oCudIAVH+dD1PDmmiYmJhkj7uzBNaO0yDeXJM0XWtOXEBvLbMVh79uxBQkKCYb75CHkguKrKkmfJTC75ZmkkTBPqlx40yvir02cTkme57pdGCqblmSLPFlnjjqrAyUHulirN0KtXLxiNFBstKiry9W5YrkdDUjRGSQ+6MMDSb4C7UcZfuTBNqF85DiP1XpUv2bBs2TJf74a5aE73XiYXYMUHrqx+npKSAqPhOCxrFhetiLQ/mdZN1iswWhEWHbX2+Kvy6tSpg5KSEg4BqAqNY7AswSiFRc81+Jk8R1IzRpk9WF5wcDACAwPZY+lB0vsrxUXl2BoN04T+E2AJecYsX77c17thHk6mCC3RpSxjNIz4wBXswfKsjIwMlUKQ1KsRSZrw8OHDvt4NyzBS/avTSRuUl7RJsn6AJQH1/v37uWJDZWnswTI9KQLXuXNnBAQYM/PJAMs/0oMuHIflPwGWYJrQfwIsecbIs2b16tW+3hUyMGNGIm5wOByq9lWnTp1gVAywrD97sDwZjC09quQfAVarVq2wdetWX++GpQIsGVZhVBJgrV+/Xj176Dw0d3qxzH9ULRNgSYVdWXMwNDQURsUAy3MkFeNKyxhVXFwcsrOzWZrDA2S6vhxLOaZGJW1RZhRmZmb6elcsQcarGrUHS8izpmnTpqonnc5DY4rQ1DdfKf7Wo0cPGJmMF+JiodYsLloRqd8jNXM4LseaBUYrwjShdYuMVkSeOTLYnfUNz8PpdO9lcpbowZLBhtKVbORvt668PYuN+kd60IXjsPwjPejCNKF1i4xWpEaNGurZc+DAAV/virFp7MEyLaMWFq0Ii41Wn6RgwsLCDP/tVnAcln8FWNImJXV0/PhxX++K6UtyGDn9Xx4Lj1aCxgDLlORGJl3JZrj5Co7D8o/0oAsLjnqGFG01YvHginBtQmvPIKzoS5QM/WBQTZZLEUr+W/LgRu9KdmGAVX0yU0tSMWYQEhKiUsPFxcW+3hXTkmMnx1COpRlI6pqzCf0nwJJnj2ssFp2Fk4VGTXnjlYWdzfKwFQywqke+JUoKxgzpwfJLa7DgqPvk2MkxNAtZG1OCQfZo+EeAJaRHXZ5F/CJVMU1zuvUyO1P3YK1ZswYdO3ZUS5KYBQMs/0kPunCgu3+Mvzo9TShtldwv0WDkGlink2dQhw4d1DOJzjIGy1nFl/yNyZk2wHI6nVi7dq0q9mYmDLD8Jz3owgCreqRYq4xzMRPOJvSvHizRpUsX9UySZxOdhoPczUW+HTZq1EgV9jMTBljuy8rKUgv9SgrGTGrWrKnSRayVU3VyzOS8y3R4M5E2Km1V9p38I8CSZ1HDhg05/q4iTtbBMuXgdrORGy+LjVpz7cFzDYKNj49nhW83S3LIsTPLJJbyWHTU2kVGz1ayQcoGEZk2RSgpA6mRIjdes5HZUNKFzN4M/0gPujBN6D/jr1yYJnSP3BvlHin3SrORZ5L0ZEm7pXKYIjQP+YYg3xTMSi5Azjbxj/SgCwMs/xl/5RIdHa0GP8sailR5cm8029CP8qTo9bJly3y9G4aiSaeCGy+zM91XBLlZycNWct1mxXFY/jF7sDyWarB+gdGKME3oH+OvypNnkzyjZCYk/Yk9WObgWtTZjGMyXBhg+Vd6UEjtLkl9lJSU+HpXTMN1rOTYmRUDLOuXaDidPJu6d+/OwqPlsdCoOW64O3bsQNu2bWFmDLCq3mspqRZJuZiZ9GJJjwz5R++VkDYrY4mYJvSfHiwhzyh5VpWWlvp6VwzUg+Ws4ot1sLxq3bp1aN++vakKi1aEAZZ/pQddOA7Lf8ZflSdtl0vn+FeAFRQUhHbt2qm6WOS/TDMGS2aVrF69Gl27doXZMcDyr/SgiwQLnF3kHzMIy2Oa0P8CLCHPKnlmsfAooDk1t17uePfdd9U4uLCwMDWcSIYV+YppAizpbpUHlJRnMDsZX8ABkJUjx0nGNFjhhitTuDMyMliioxJkvJqrBpbZSduVNsxr3r8CLKnjJV8Q5Nnl9zSne68q+vrrr/H3v/8dzz33nFq2SJYvuuSSS5Cenu6TU2CaAMvspRnKY7FR/0sPCnnISkVyVvc+P6l8LxXwzTyZpTz2YlWeFGI2azmW07HwqHd7sF5//XXceeeduPXWW9U19/7776tOmY8++gi+EGCWwa6S005MTIQVsNiofwZYguOw/Cs96CIpbi7+bO0ioxVJSkpSY4ZTU1Ph1zT9e7BkQoGkZC+++OKTP5N2JP/2VV0yU7RiK/VeuUh+mFP2z82VUrFCusBF0twyeJv8K8BylR2Q9BedndwTzVxktCIsPArYUQa7VsUXyk5eM+VfZ3tuyvALh8OBWrVqnfJz+ffRo0fhC0EwODmgkj9t0qQJrMQ10F0CLTr74HYr9V65SjXMmzfP17theIcPH8ZFF10EK3GlCa32ZdHTX6qs9IVKyLNr7ty5yMvLM32pmaoKCQlB7dq18fvRWW79vaSKT59JLOOrnn/+eZiB4QOslStXolu3bpYZi3F6gCVdyFQxeRhde+21ljo8ElDLtyzpzpabD51Jjo2kiaz25UMCrKlTpzLA8oMB7uXJs0ueYTKbzWpfGs4nLCwMe/fudbsemKSMT3/2n63wcEJCgkrHpqWlnfJz+bcEeb5g6BRhWVmZ6sWQ2ldWw1IN57/RysVl5orOZyPFM/1+TIbFC4xWRNqytGmmCf0rwBLyDJNnmTzT/E1YWJg6p+685Jo5/WdnC7DkC2uXLl1OyRDIFzX5t6RpfcHQAdb69evVtz5Z5NdqWKrBvwa3l8dxWP41/ur0we4sOup/AZY8w+R+Js800o+UaJg0aRI++eQTdZ3de++9KCgoULMKfcGwAZZ803OlB62IPVj+UVy0IpxJ6L8BFss1+GeAJeRZJs80ebaRPkaMGIH//ve/ePbZZ9GxY0e1+svs2bPPGPhu6TFYUkBQioDt27dPzQiQrj25oUr3nutA7Nq1C8nJyZYcFCjdxPLZf/75ZzWYV3LM0qMl3cgtW7ZUJSmsTG4wkgaSNiCpMjke0u0rg0E7d+6sptbKOKW4uDhYkZQbkc+/dOlSbN++HYWFhWrsgBTVlM/fuHFjy405rKgN7NmzR7UBuR/I+ZZ6NS1atFDXhFVKspxO2rR8VhnwLOkL+fy7d+9W90Hp5ZB7nrQBSZFavQ3Y7XZs27YNGzZsUIPbpU3If0v9MxkzY7XMhTzL5HPJs61Zs2YnxwdJaQH5UiHjlOQ+KFXI5Vkox4Gq7oEHHlAvQ9C8ZN++fdrYsWO1+nXrS/h+8hVoCzzl38m1k7WHH35YGzdunJaamqpZRXFxsfbFF19offv01UJCQk5+3oCAQPVy/TssNEwbNGiw9v3332tlZWWalaxfv1675557tMTEpL/Ouc2mBQQGndIG6tStp91xxx3akSNHNCvJycnR3nnnHa1Dx46aLSDgrzYQGKTZbH/9OzomVhs+fLi2cOFCzel0alYhn0U+k3y26NjYk5/XFmDTAoL+ugbk2LTv1FEdKzlmViJtWtp23fp1T2nz6vPbbCf/nVgrSV0rcs1YidzT5N42ePAgLSw89K/nQFCAern+HRoaovXr30fdM+XeaRXyTJNnmzzj6tatfUobCAn56/PLq1GjetpTTz2lnp1kTjb5//QM4KSH4m8P/U3NngkKCEaiow5qIAHRqIEIRKlvabILRShAHrKRjQykBx5CiaMYl15yKd4b/x4aNWoEs5JvqRMmTMAzzzyLzMwMJMQ2Qnx0M8REpCA6vDaCAk/MJCtzFCOv8ChyC48gI28HjuceQHJyCl55ZRxGjhxp6m+zMp7q7nvuxe+/LUZYVBzi6ndEVEJDRMbXRUhE3Ik24HSgKPcYCo8fQm7aLmQdWC8HD7fccjNee+01U3+bk96Jf/3rX3j9jTdQXFyM6BZtEd6kBcKS6yIkKRkBQUHqGnAU5qP4yCEUHzmAws3rUHQsDS1btcJ7776LAQMGwMwWLFiAex+4H9u3bEVYSi2E9miH0EZ1EdKoDgJjTlTt1srsKD2UhtJ9h1GyaQcK12xVA2T//sgjeOaZZ846uNUslekfffRRTPnsMwQEBaDWgCao2bEOYlskIbJeDfUzaQPF6fnI2Z6O7M1HcXTuThRm5KFvv74Y/954U49JlM/2+eef4x9jn8DhQ6lo1ikWnQfHoWHbSDRoHYmI6BO99sUFDhzYWoC9mwqwbl42ti7PRmJSPF54/l+4++67TV2AVGbTPfDAfZg1azbiawRj5LWR6N09DF3ah6FR/SB1H3Q6NezcU4bVG4rx+/JifDmtEPkFDlx33TV46623fTYbjtyja4AlF9T9992PkoIyNHS0QG00QJDt/Okvh+ZAOg5hX9A2OIPs+O9r/1WD1cwWZMgFdeutt2HRooWoE98RDZJ6ISq8cqmP3MJU7EtbiqNZm3HFFUMxceIElT4wWwrgRD78OYRE1URy28GIq9cWAQGB5//b0iJk7F6Jo5vnIDYmCh9MmoQrr7wSZiNjLm4ZNQo7d+5CXK9+iOt2AYJjzp/6lMuycO9OZP02BwX7dqv2/8orr5huCRFZ9uSJJ57A+PHjEdGyMaKuGoiwVpVLgdqP5yBv3h/In/07mjVriimffGrKMZk//PAD7rjrTuQW5qHx6K6oe2krBEefP1h02h1I+30vdn+0AkWpuXjxhRfx2GOPmW4IgXzJvuvuO/HjzJ/Q/bIEDLk7RQVVlXF4VyFmf5iK36amo/+Afvj4o8kqhWYmci2/9957ePLJx5FQU8MLj8di+NAohIWdP1jML3Dis+9y8cJrOSizh+Htt99TX7jJjwMsecuxY8di3LhxqG2rj+ZaB4TYqv7tU6q57sJGHMIejBkzRs0OMMvNZdWqVRg8aDBKS4CWdYYgPqaxW++Tnr0N2w//jLia0Zg/f54ao2WWXpsRN9yAH2b8gNqt+qFOh0sQEFj1MRWlhTnYv+I7ZB3aotqTPKzNYtq0aRgx4gYEJ9VG0pUjEJpU9QBZ05zIXrEEx+fPQts2bTDn119UvRczkMrKgy4ZjI2btyBm+GBEX9QTNjd6IEoPHkX2h1NhP5yGr7/6GldffTXMQoLiJ598ErV6N0KbR/sjLKHqAbKjxI6dHy3H3m/WYthVw/Dll1+apjdPxlhdPGggCoqzcMsLDdBlkHs90ZuXZOPjp/fDWRKCX2bPQdeuXWGWL5myNt7kyZNxz+hY/OefCYiOqvo1kJHpwEP/zMDX03NVe3r55ZdN1+Hgj3QJsP7xj3+oh2FzdEB924nBfNWRqu3HVttq3DLqFrVoo9G7iWUqbp8+fRGEWHRsdAOCg6q39ENxaR7W7f0CoREali1baviq9nJTuebaazFr1s9ofOEtqFG3TbXeT5ro4fWzcWTTXLz66qvqW7wZei2uvuYaRLVqj9rDboQtsHpfDIqPHkbqF5PQolFDLF60yPATALKzs9GnX1/s2L8P8Y+OQUiD6tW10ux2HJ/4LQpXbcb0adMwdOhQGJ303j7++ONoOqobmt3Wo9oPxLQle7Hu+dkYctkQTP3uO8N/2ZTB+xdc2AvB0UV49KMWqFGreoV187PL8MadO5GxX8OihYvRoUMHGH14yO2334YpUz7FR28m4ebrqj878q2JWfj7cxnqGStBFvlZgPX111/jhhtuQDO0RwNbc4+9rwRZm7ESb731Fh566CEYlcwOat26DfKzHejU5GYEB3qmGnVJWT7W7P4U9RrWwtq1awxdBVzGy7z00sto2ncM4up6btzIwXWzkLppHubMmXPKgp5GfLC0bdcOIQ2bofZ1t8BWiZRoZZSkHcHhT8fj8sGDMO377w37DVZuKRJczpo7Bwn/uAMh9TwzbkRzOJD53ldwbN6NTRs3GvqLhrTRwYMHo8nNXdHiTs8VOUxfthdr/vkznh77FF588UUYuQe7S5dOOF54AGO/aIWYeM/MCCzMs+OVW7bBWRCLTRu3GHqWuTyrHn74YXz6Ti2MvNZzpSfenJCFR5/PwFdffaXKEpCfBFgy5bRVy1YIzY1CW60HPG27tg7poYewadNGNG3aFEYkAzE//ugT9GhxFyJCa3j0vWUQ/PIdH+Kpp8aqQdNGJFOOu/fogeQ2F6NO+8EefW9Jl+2cPwmRgYXYsnmzIevlyLfWvv36Y/WWrah396MICPFsKidvy3oc+fYTlSaSLzJGJPt20003IeGBkYjs1taj7+0sLkH6M2+ja4vWWLxwoSF7s6WWU6s2rVGWFIiur1/p8UB45+Tl2PPparX0ipR0MKJ//vOfGPfKy3hualvUa1m58VaVdexgMZ4ZuhGjb7kd77//Poxo586d6NChHW6/KQxv/dvzy6GNuCsNC5YGYPPmbT6r8UTn59G7098f+TuK8ovR3NkRemiCtgh2BOOeu++BEUldo4kTJ6Jp8kUeD65EdERtNKp1oeodMmI1aInVb7v9dkTWSEZyW8+vuWWzBaBBj+uQln5MLfhpRJLCXvL7b0gcer3HgysR3boDYtp0xL333a96S40YXNz3wAOI6tHe48GVCAgLRdxtV2PJb7+pY21EUuTw2PEMtHligC69jE1GdkV043jcevtthixaKbOG//OflzH0vjoeD65EYr0wDH+8npqdLfdcI7rv3ruRnGTDS0/pM17y7ZfiYUMhHnvs77q8PxkswJLCiV9/8zXq25u7NaC9MmQGYkN7a8ybPw+bN2+G0bzxxhuIiUxC3YQuum1DAqzQkEi88847MJrFixdjw/r1SOkwpFIzBd0RGhWPpOZ9MHHiJMMFGPKw++9rryG6VXtENNSvhzVh8JXIycnGZ599BqORfcrJzkbsDZfrto2wVk0Q2bUN/vv664YLMCTAnPThB2hwXXtEJOvTwxoQHIjm9/TChnXr8dtvv8Fo5N4UUzMEQ+7Ubz3JATfWQp0mUXjzrTdhNJs2bcLceQvw77FxiIzQp4c1KSEIT/8tBl999TXXNTUwj539Dz74ADYtACnQdwptEuogPChCTfs2Egkwp02bjuQanXQdGyOBS3JcB0ye/InhAox3330XkXG1EFO7+hMbziWpWS8UFRcZLsCQAHP7tm2I7XaBrtuRMg9SS+vtd94xVIAh+/K/d95BROfWCKqp7yLdkQN7YvvWrYYLMKRNFhcVod5Qz/felRffpR5i6sfj3ffehdECzE+nfII+18cjKES/9G1AgA39bkhQYxGNtnC6PJtqJ4Xi6sv0Laky6voYyFBcefaSMXnsCvjy8y+R6ExBkE3f5Q0CbAFIstfDF59/aaiHy/Tp09X+pNTUf2ZLnfhOKCwsUEvtGGlQ6/TpM1CzcTfdB1+HRMYhLqUlvvjyKxjJN998g/D4RF17r1yiO/XA1i1bVDrGKGRfJOiJ7Kv/FPqw1k0QVitBTaoxkq++/gqJPRogPEnfh6tcYylDWqpSIHLtGYWs+1aQX4h+w/UfF3TB1YnQoKljYBTyDPjmmy8x6voIhIToex+Miw3E8KER+Prrz3XdDvk4wJJigjt27UAcvFOfJw7xyMo+jgMHDsBIda9iI2sjOMgzswbPJTw0DpHhNdQ2jdQtXlZWiugk9+p9VVVkYiOsXbNGDSo3ij9WrEBwnQZemd0XXq/hyUkFRuFqj6HNG+i+LTnGgU3qqmNuFNIWV69Zg7h23ikIXKNdCspKy9S1Z6Q2kFgnAvEp+tfpiowJQr0WUYa6D8ozKSMjCxf2qF5pnsq6oHs4tm7diYKCAq9sj3wQYMmK1XJziYHnB3ZXxLUdIz1clv+xAhEh3pvNERlaS1UJNwq5yUkRyYg4/cZdlBdZsy4KCvLVbB0jkAWrN23YoJa/8YbAsHBEJNYy1DUg+xKekoSAcP2/ZIiQhnWwaeMGdeyNYMeOHSjML1DL33hDTNMEBAQGGKoNrFy1AvVbeye4EPVbh2HlquUwClew16W9dwrByjI78uyVZzBZNMBy9STJ2oLeEGoLV+sa7t+/H0Zx8NBBXWYOnk1ESA3s22eczy9tICK6JgKC9E0Ru4RFn+gtNUov5rFjx1BaWorgmt6rsh4QW9NQ14A6F4neuwaCaiWgtKRUHXsjcLXFyDr6jj9zCQwNQkRijMHawH4kNfBelflaDcJw8OBBGIW0gcjIINRO8k4R2KaNgg11HyQdAix5sAibZ6s+nFNgQODJ7RqBfIsOCPBeZWXZlpE+v+yLpwpqVobtz2NtlPEnJ68Bb1bXDgo0zOcXal+8+PltwSe2ZZTrwLUfMsvPWwKDjXUfVF8yQr1XAFcG0peWGqMH0/X5Q3Uc3H+60D/HeRnpPkB/8UhLcFUV1+C98TAOp8NQ1cyDg4PhdNq9tj3ZlpE+v+yL5nR4bXvan8faKGuynbwG7N5rA7A7DPP5hdoXL35+rezEtoxyHbj2w1nmvevAUWas+6DsS1mJ9yYf2UudCAnxTq95ZT9/San3noMlpSeOtZHuA+ThAKt+/frqfwuRD28o0Ypgd5ahQQP9B9NWVr269VBYkuW17RWWZqFhQ+N8fmkDhXnH4bR759tkcV7Gye0aQWJi4omHy/ET++UNzpzjhroG1Lk45r1rwJ6WgZDQEHXsjcDVFgsO53hle7IIdOGxXIO1gQZI3++93pS0/cWoV68ejELaQEGBHUfTvfNFY9feMkPdB0mHAKtjx45qyYpceOfm6tpOly76FfSsqh49u6OwNM1r2ysoSUO3bt1gFLK6veZ0ojD7iFe2V3D8ECIjo9Csmb41t6rSg9m2fXsUpx7yyvYcxUUoPJZmqGtA9qXoSDqcRcVe2V7pvsNo2669OvZG0Lx5c0RERSJne7pXtpe7KwNOh9NQbaBb1+44sKXIa9s7sKUY3bp6flm26twHxeoN3gkyV28oVs9eeQaTRQOsqKgoNG/aHNnwzrf3bGSiRlxNQ0XtcmHlFBxFmV3/h0tRSTYKirJOXsxG0LZtWwQHhyAvfY9XtldwbC86de5sqLXoenbvjrLD+71Sn63o4D71v0Z6uLraY8kO/QddyzF27D6ojrlRSFvs0rkzsjd6p/Bl1sYjCA4JVteekdrAscOFyDyif4BRkGvHwe35hroPyjMpIaEGfl/unSBzyYoitGrVDJGRnl+SiKrPY0+nG0feiGMBR2DX9E0ROTUn0oMO4qaRN3ql3lBlDRs2TO3PkePrdd/W4cy1iIiIxGWXXQajkDEAw4ZdheN7VuoeYJQWZCP78DbcdKOxFju+/vrrUZR5DIX7dum+rby1y9GqdWu0bt0aRiH70qJVKxQs1r8uUfGW3ShOy8SIESNgJDeMuAHHlu9HUbq+wyXkGjvy01ZcffXVhhp/c+mllyIyKgKLvtW/N3/JtGOwwaaOgVHIM+D662/Ep98UovTP8VF6yc5x4NuZhRgxYqSu2yEDBFh33HEHNJsTR3Dim7Ve0nEYRfZC3HvvvTCSlJQUXH31MKRmrdU1wHA6HUjNXo8xY0YjOjoaRnL//fejIDsNuUf1rU2VvnMZwsPDcfPNN8NI+vbtixYtWyJn5RJdt1OWm4287Zvw4AMPGOpLhuzLQw88gMI1W2A/ru84pIL5f6hgrk+fPjASaZNh4eE4OFPf4p+Zqw8i98Bx3H/f/TCSmJgYjLplNH77JlMNQNeL06lh0VcZuPqaa5Cc7J3CrpUlz6aj6SWY9rO+Qfan3+RCJpDKs5csHmBJgDHi+hE4ELQDpZo+3cN2zY59QVtw0cCL0KZNGxjNI488gtyCdBzK0K/w396031FSWoAHHngARiMBRvsOHXBk/U8qENRDSX4m0nf8hrvuutNwAaYEGI89+ijytm7QtRcr49cfEBsbZ7gAU8g+xcbFIeerWbpto3jrbhSs2ozH/v53QwWYrgDjztvvwIHvNqAwNVeXbcgsxR3vL0P7jh0MF2AKuTflHi/FT5P0G4+54Ms0HN6dj4f/9jCMRlK2F180AP98ORsFhfoEmekZdvzfW7m44YYRhgsw6S8eHcDy+huvIzwqDDsC9KkquxubUBZYhvcnvA8j6t27N+666y7sSp2ny4zCvMKjKsB66qmxaNWqFYxGHnYfffghCrJSkbppnsffX9Oc2L/8W9RKSsQLL7wAI7rttttwwYV9cGzmN3CWev6LRt6W9cjdvA7j33vXcAGmK8B47513kL98AwpWer4Xx1lcguyPpuHCvn3VsTaiF198EYnxidj8ygJderN3f74KeXsy8fGHHxkuwHSliv/xj7GY+d5hHNzm+SVcjh0sxrevHsTdd9+t7rlGNP79iUhN1/DUS/qMS37wqUxoiMB///u6Lu9PBgywatWqhfHvj8dR50Hs13Z48q2Rqu3HQezCK6+MQ9Om+i+m667//ve/qFU7CZv2T0WZw3MD3kvK8rHpwPdo2bIlnnnmGRiVDLp+auxYpG6ai+xDnl2I+ND62chO3YnJH3+sHuRGJAOdP5n8MVBUgLTpX3q0NlhJ2hEc++k7DLv6asONPSrvhhtuwFXDhiFn8nSUHjzqsffVHA4cn/QdbHmFmPzRR4aa4FCetM3JH32MY2sOYMcHf3j0vdOX7cXuKavx1FNPoXPnzjAquUe1aN4C7z20GzkZniuEWphnx7sP7kZSYm28+uqrMCp5Rr388ji882EOPvvOsz2Zb07Iwnczc/Huu++rZy4Zl8fvUHLjf/LJJ7ETGzwWZElwtdW2GmPGjDFkaqw86VX48ceZcAYUYN2eL1Fmr/5skuLSPKzd8znCIgPwww8zDFVYsCLPPfcchlwxBLt//xRZhzZX+/2kF+DQup9Vr5gEsBdffDGMrEmTJvjm66+Rv2Mzjn7/OTRH9WviFB89jCOfTUDLpk3w8UfG7LlwkX2TILhlkybIfPUjlO6vfqpICrhmvv8NitZuVcdWjrGRSRuVAGD3Z6uw48M/PNKTlbZkL9Y+OxtXDLkCzz77LIxMBt7PmDETzuIIvDp6O7LSqh9k5WeX4fXbdiDrCDDzh58M2YNb3oMPPqjGyt72cDqmfJvrseDq0ecz8I9//MPQX7LoBJumQx+2vOXYsWMxbtw41LbVR3OtA0JsVZ/pIjMSd2IjDmMPbr31VkyaNAmBgd5bhqI6ZAHWQRcPgmSJWtYZgviYxm69T1r2Nuw4/DPiakZj/vx5qgfLDGTphhtvvBHTp89ArVZ9UbfDpQgIrHq9otLCHOxf8R2yDm3BK6+8gscffxxmMW3aNIwYcQOCk2ohaegIhNZKcSstmr3id2TOn4V2bdpizq+/ICHBe+sdVkdGRgYGXTIYGzdvQczwwYi+qKdaELyqpBcs+8OpsB9Ow9dffW2oWWPnI21WvnDW6tUIbR7rj7CEKLcKiu746A/s+2Ydhl01DF9++aWhZg6ey7Zt23DxoIEoKM7CLS80QJdBNd16n81LsvHx0/vhLAnBr7/MNVR5knOx2+248847MXnyZNw9KhbjnklAdFTVr4GMTAce+mcGvp6eq4Krl156ydBfskjHAMvliy++wH333oeSwjI0tLdAbTRAkO38a5U5NAfScQj7grbBGWTHf1/7r5qZYbYGtW/fPowZcysWLVqIOvEd0SCpF6LCK1d1OrcwFfvSluJo1mZcccVQTJw4wXSDGeXm8tprr+GZZ55FSFRNJLcdjLh6bRFQiTUL7aVFyNi9Akc3z0VsTBQ+/OADDB06FGazatUq3HzLLdi5cxfievZFXPcLERwTd96/k8uycO9OZP02BwX7duO+++5TX1ik5pyZ5OfnqwDjvffeQ0TLxoi6cgDCWjep1LUsMxHz5i5D/i9L0Lx5M0z55FND1TyqrJkzZ+L2O+9AbmEeGo/qgrqXtUZw9PkDJKfdgbTf9mD3xytRlJqLf734Lzz66KMI8uZ6lx6QmpqKu++5CzN/+BHdL0vAkLtT0KB15eo2Hd5ViNkfpuK3qekYMLA/PvrwYzRs2BBmItfy+PHj8cQTjyGhpoYXHo/F8KFRCAs7f6CVX+BUKcYXXsuB3RGOt99+DzfddJNX9psMHmCJo0eP4qEHH8LUqVMRFBCEREddxCEBMYhDBKLVjVZ2oQgFyEOWKiJ6LPAwih1FuPSSS/He+PfQqFEjmJXT6cSECRNUkJGZmYH4mIZIiGmOmIhkRIcnIyjwRLpPxmvlFaaqwCojdzuO5x1EcnKKGnM2cuRI0wWX5W3ZsgX33Hsfflu8CGFRcYir3xFRCQ0QWbMuQiJrnGgDTgeKctNRkHkI+em7kXVgvaoMP2rULSotWLOme998jdKb969//QtvvPkmioqKEN2iLcIbN0dYSj2EJCUjICjoROHMgnwUpx5E8eGDKNyyDkXH0tCyVSu89+67GDBgAMxswYIFuPeB+7F9y1aEJSchtEc7hDaui5CGdRAQE3WiDZTZUXroqKrQXrJpJwrXbFXlOB55+GE1pscsvTYVOX78OB577DF8OmUKbEE21B7QDDU7piC2eRIi69dAQFCAagPFaXnI2ZGO7M1pODpnJwoz89C3X1+Mf2+8oWqeVZV8ts8//xz/GPsEDh9KRdOOsehySRwatolEgzaRiIg+ETQWFziwf0sB9m0uwNq52di2IhuJSfF44fl/qUHtRh13Vxl79+7FAw/ch1mzZiO+ZghGXhOBXt3C0KV9KBo3CFbXgJSf2LG7TFVol2KlX04rVDMRhw+/Fm+++T/Url3b1x+DqkLzkn379mlPPfWU1qBeAwnoTr4CbIGn/Du5drL2yCOPaNu3b9espKSkRPvyyy+1vn36aiEhIX99/oBAzWYLOPnvsLBwbdCgwdq0adO0srIyzUo2bNig3XPPPVpiYtKpbSAw6JR/N2naTPvXv/6lpaamalaSk5Ojvfvuu1qHTp20gMDAUz5/+TYQHROrXX/99dqiRYs0p9OpWYV8FvlM8tmiY2NPfl5bgE0LCCp/PAK1Dp07qWMlx8xKjhw5otp20+ZNT70Gyn1+eSXWSlLXilwzViL3NLm3DR48SAuPCCt3zm1aYNBf10BoaIjWr39fdc+Ue6eVyLNNnnF169Y+5ZwHB//1+eXVuHF97emnn9b279/v610mN+neg3W2b3Nr1qxRKTT5di+DtuvWravy6klJSbC6srIy1auzceNG5OXlqW8uMvOoQ4cOaoyVWcaZVceRI0fUODXp4SwtLUVYWJgauNypUyfExsbC6qQna/369di6dSsKCwtV2ic+Pl59/saNG5u6x7Iy5LazZ88erF27FpmZmSqdHBERocqPyHUgPVdWl5OToz7/7t27UVxcrO6D0kMh90GpK2h1DodDjdGS6yA3N1e1CRm43q5dO9VbZ5Q1JvWUnp6u7oOHDh1S90HppZUUqMwQNXOvPZ3gkwCLiIiIyMrMm9AmIiIiMigGWEREREQexgCLiIiIyMMYYBERERF5GAMsIiIiIg9jgEVERETkYQywiIiIiDyMARYRERGRhzHAIiIiIvIwBlhEREREHsYAi4iIiAie9f/z7p8a0fkQZAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_leaves = int(topo.is_leaf.sum())                # 8\n",
+    "leaf_obs = jnp.arange(num_leaves, dtype=jnp.float32)\n",
+    "\n",
+    "tree = tree.at[topo.is_leaf].set(value=leaf_obs)\n",
+    "print(\"Leaves carry 0..7; inner nodes are still zero:\")\n",
+    "print(tree.value)\n",
+    "\n",
+    "plot_tree(topo, tree.value, title=\"After seeding the leaves\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9c7b1d6",
+   "metadata": {},
+   "source": [
+    "### Aside: the full `tree.at[...]` interface\n",
+    "\n",
+    "`tree.at[indices]` mirrors `jax.numpy.ndarray.at[i]` but lifts it to a multi-field container. The indexer exposes six operations:\n",
+    "\n",
+    "| op | what it does | returns |\n",
+    "|---|---|---|\n",
+    "| `.set(field=v, ...)` | scatter-assign | new `Tree` |\n",
+    "| `.add(field=v, ...)` | scatter-add | new `Tree` |\n",
+    "| `.multiply(field=v, ...)` | scatter-multiply | new `Tree` |\n",
+    "| `.min(field=v, ...)` | elementwise min at indices | new `Tree` |\n",
+    "| `.max(field=v, ...)` | elementwise max at indices | new `Tree` |\n",
+    "| `.get()` | read every field at `indices` | `dict[str, jax.Array]` |\n",
+    "\n",
+    "All five write ops accept any number of fields as keyword arguments (one scatter pass per field, all sharing the same `indices`). All of them are pure: the original `tree` is unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b1c9174",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed (leaves only):            [0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 3. 4. 5. 6. 7.]\n",
+      ".add(+10) at leaves:           [ 0.  0.  0.  0.  0.  0.  0. 10. 11. 12. 13. 14. 15. 16. 17.]\n",
+      ".multiply(*7) at leaves.count: [0. 0. 0. 0. 0. 0. 0. 7. 7. 7. 7. 7. 7. 7. 7.]\n",
+      ".max(>=2) then .min(<=5):      [0. 0. 0. 0. 0. 0. 0. 2. 2. 2. 3. 4. 5. 5. 5.]\n",
+      ".get() at leaves keys/shape:   {'count': (8,), 'value': (8,)}\n",
+      "original demo.value is intact: [0. 0. 0. 0. 0. 0. 0. 0. 1. 2. 3. 4. 5. 6. 7.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build a throwaway two-field tree to show every op on the same indices.\n",
+    "demo = (\n",
+    "    hx.Tree.empty(topo, {'value': (), 'count': ()})\n",
+    "      .at[topo.is_leaf].set(value=leaf_obs, count=jnp.ones(num_leaves))\n",
+    ")\n",
+    "print(\"seed (leaves only):           \", np.asarray(demo.value))\n",
+    "\n",
+    "# .add — bump every leaf by 10\n",
+    "bumped = demo.at[topo.is_leaf].add(value=jnp.full(num_leaves, 10.0))\n",
+    "print(\".add(+10) at leaves:          \", np.asarray(bumped.value))\n",
+    "\n",
+    "# .multiply — scale every leaf's count by 7 (inner-node counts start at 0,\n",
+    "# multiplying them by 7 would be invisible)\n",
+    "scaled = demo.at[topo.is_leaf].multiply(count=jnp.full(num_leaves, 7.0))\n",
+    "print(\".multiply(*7) at leaves.count:\", np.asarray(scaled['count']))\n",
+    "\n",
+    "# .min / .max — clamp the leaves into [2, 5]\n",
+    "clipped = (\n",
+    "    demo.at[topo.is_leaf].max(value=jnp.full(num_leaves, 2.0))\n",
+    "        .at[topo.is_leaf].min(value=jnp.full(num_leaves, 5.0))\n",
+    ")\n",
+    "print(\".max(>=2) then .min(<=5):     \", np.asarray(clipped.value))\n",
+    "\n",
+    "# .get — read all fields at a set of indices, returns a plain dict\n",
+    "snapshot = demo.at[topo.is_leaf].get()\n",
+    "print(\".get() at leaves keys/shape:  \", {k: v.shape for k, v in snapshot.items()})\n",
+    "\n",
+    "# Original is untouched (pure functional updates)\n",
+    "print(\"original demo.value is intact:\", np.asarray(demo.value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356c937b",
+   "metadata": {},
+   "source": [
+    "## 4. An up-sweep: propagate information leaf → root\n",
+    "\n",
+    "An up-sweep visits every level from the leaves toward the root. At each non-leaf node, your function receives:\n",
+    "\n",
+    "- **`node`** — the node's own fields. Each attribute has shape `(*trailing,)` (one node at a time, courtesy of `jax.vmap` inside the dispatcher).\n",
+    "- **`children`** — the children of this node. For an equal-degree tree, `children.value` is a real JAX array of shape `(k, *trailing)` — slice it, multiply it, sum it like any other array. For unequal-degree trees, the same expression works through a `ChildrenAxis` proxy that dispatches reductions to `jax.ops.segment_*`.\n",
+    "- **`params`** — whatever you pass via `sweep(tree, params=...)` (a dict, an array, anything pytree-shaped).\n",
+    "\n",
+    "Your function returns a `dict` whose keys exactly match the `writes=` argument. Each value must have shape `(*trailing,)` — one record per node, no need to keep a per-parent batch axis.\n",
+    "\n",
+    "We'll compute the simplest message possible: each non-leaf node = mean of its children."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "48ae4490",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.058654Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.058584Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.123542Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.123145Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root value after up-sweep:  3.5\n",
+      "Mean of the leaves:         3.5\n",
+      "All node values:\n",
+      " [3.5 1.5 5.5 0.5 2.5 4.5 6.5 0.  1.  2.  3.  4.  5.  6.  7. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "@hx.up(reads_children=('value',), writes=('value',))\n",
+    "def average_children(node, children, params):\n",
+    "    return {'value': children.value.mean(0)}\n",
+    "\n",
+    "tree_after_up = average_children(tree)\n",
+    "\n",
+    "print(\"Root value after up-sweep: \", float(tree_after_up.value[0]))\n",
+    "print(\"Mean of the leaves:        \", float(leaf_obs.mean()))\n",
+    "print(\"All node values:\\n\", np.asarray(tree_after_up.value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "82a9468e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.124725Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.124651Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.214947Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.214480Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "vmin, vmax = 0.0, float(num_leaves - 1)\n",
+    "plot_tree(topo, tree.value, ax=axes[0],\n",
+    "          title=\"Before up-sweep — only leaves seeded\", vmin=vmin, vmax=vmax)\n",
+    "plot_tree(topo, tree_after_up.value, ax=axes[1],\n",
+    "          title=\"After up-sweep — every inner node = mean of its children\",\n",
+    "          vmin=vmin, vmax=vmax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07f9e845",
+   "metadata": {},
+   "source": [
+    "Two things to notice:\n",
+    "\n",
+    "1. `tree` itself is unchanged. `average_children` returned a brand-new `Tree`. This is what lets `jax.jit` see the call as a pure function.\n",
+    "2. We only declared `reads_children=('value',)`. We did **not** declare `reads=` on the node itself, because the function doesn't read it. If you forget to declare a field that you actually read, you'll get a clear `AttributeError` pointing at the missing entry — the dispatcher only hands you the fields you asked for."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7a59f6d",
+   "metadata": {},
+   "source": [
+    "## 5. A down-sweep: propagate information root → leaf\n",
+    "\n",
+    "The mirror image. Your function receives `(node, parent, params)`; for each non-root node, `parent.value` is a `(*trailing,)` view of that node's parent (already updated by the sweep when it visits the level above).\n",
+    "\n",
+    "The **root is not visited** (it has no parent), so you must seed it manually before calling the sweep.\n",
+    "\n",
+    "Here we use two fields: a `noise` we'll set per node, and a `value` whose root we'll seed. The sweep then propagates `value = parent.value + noise` down the tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3bc4094b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.216162Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.216092Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.393968Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.393569Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root (kept):        1.0\n",
+      "Children of root:   [1.4671319 1.2957029]\n",
+      "All values:\n",
+      " [ 1.          1.4671319   1.2957029   1.6206777   1.343099    1.512626\n",
+      " -0.14517605  2.3765376   2.1420875   2.2532694   0.9586024   2.6524494\n",
+      "  2.9584122   0.9357306  -0.20146927]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree_down = hx.Tree.empty(topo, {'value': (), 'noise': ()})\n",
+    "\n",
+    "tree_down = tree_down.at[topo.is_root].set(value=jnp.array([1.0]))\n",
+    "tree_down = tree_down.set(noise=jax.random.normal(jax.random.PRNGKey(42), (topo.size,), dtype=jnp.float32))\n",
+    "\n",
+    "@hx.down(reads=('noise',), reads_parent=('value',), writes=('value',))\n",
+    "def propagate(node, parent, params):\n",
+    "    return {'value': parent.value + node.noise}\n",
+    "\n",
+    "tree_down_done = propagate(tree_down)\n",
+    "\n",
+    "print(\"Root (kept):       \", float(tree_down_done.value[0]))\n",
+    "print(\"Children of root:  \", np.asarray(tree_down_done.value[1:3]))\n",
+    "print(\"All values:\\n\", np.asarray(tree_down_done.value))\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(22, 5))\n",
+    "vmin, vmax = float(tree_down_done.value.min()), float(tree_down_done.value.max())\n",
+    "plot_tree(topo, tree_down.value, ax=axes[0],\n",
+    "          title=\"Before down-sweep: node.value\", vmin=vmin, vmax=vmax)\n",
+    "plot_tree(topo, tree_down.noise, ax=axes[1],\n",
+    "          title=\"Before down-sweep: node.noise\", vmin=vmin, vmax=vmax)\n",
+    "plot_tree(topo, tree_down_done.value, ax=axes[2],\n",
+    "          title=\"After down-sweep: new_node.value = parent.value + node.noise\", vmin=vmin, vmax=vmax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d563262f",
+   "metadata": {},
+   "source": [
+    "## 6. Sweeps compose with `jit`, `scan`, `grad`\n",
+    "\n",
+    "This is the headline feature of v3. A `Tree` is a JAX pytree (data values are leaves; topology + schema are static aux data), and every sweep is a pure `Tree → Tree`. That means **all of JAX's transformations work directly**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed39be6",
+   "metadata": {},
+   "source": [
+    "### 6a. `jax.jit`\n",
+    "\n",
+    "Wrap the sweep in `jax.jit`. The first call compiles; subsequent calls on trees with the same structure hit the cache."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e6219d30",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.395326Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.395237Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.444600Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.444198Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unjitted call:\n",
+      "7.55 μs ± 96.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
+      "JIT-compiled call:\n",
+      "5.43 μs ± 23.9 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Unjitted call:\")\n",
+    "%timeit _ = average_children(tree)  # JIT-compile the function, but not the call.\n",
+    "\n",
+    "jitted_average_children = jax.jit(average_children)\n",
+    "_ = jitted_average_children(tree)  # first-time JIT-compile the function\n",
+    "\n",
+    "print(\"JIT-compiled call:\")\n",
+    "# Second call hits the cache — no recompile.\n",
+    "%timeit _ = jitted_average_children(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bb77a49",
+   "metadata": {},
+   "source": [
+    "### 6b. `jax.lax.scan`\n",
+    "\n",
+    "Iterate the same sweep `N` times in one compiled XLA loop. The pitch: the body traces **once**, no per-iteration Python overhead, the whole loop becomes a single device launch.\n",
+    "\n",
+    "Two things worth knowing:\n",
+    "\n",
+    "1. **Always wrap `jax.lax.scan` in `jax.jit`.** Called eagerly (with no outer jit), `lax.scan` re-traces its body on every invocation — the inner `def body(...)` is a fresh Python closure each call, and that per-call tracing dominates everything else, often making scan look *slower* than a plain Python for-loop. Inside an outer `jit`, the scan op fuses into the surrounding XLA program and per-call overhead disappears.\n",
+    "2. **A Python for-loop *can* be `jit`'d, but it unrolls at trace time** — the compiled program ends up containing `N` copies of the body. Trace and compile time both scale linearly with `N`; at large `N` this becomes impractical (think tens of seconds, then minutes), while `scan` compiles in roughly constant time regardless of `N`. This is why `scan` is the standard idiom for any non-trivial number of iterations.\n",
+    "\n",
+    "Two benchmarks below: (a) **runtime** — plain Python for-loop vs scan-in-jit (both `block_until_ready`'d, since JAX dispatches kernels asynchronously and a naive `%timeit` over a for-loop would otherwise only measure dispatch latency); (b) **compile time** — scan-in-jit vs the same Python loop wrapped in `jit` (which forces unrolling). We use `N = 1000` and the unrolled side easily takes minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3541ec1d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.445947Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.445872Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.479258Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.478895Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "benchmark tree: 2047 nodes, depth=10\n",
+      "\n",
+      "=== runtime (post-compile) ===\n",
+      "Python for-loop of jit'd calls:\n",
+      "10.8 ms ± 362 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "scan inside jit:\n",
+      "1.61 ms ± 13.5 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "\n",
+      "=== compile time (N = 1000) ===\n",
+      "  scan inside jit (1000 iters, body traced 1×):      0.06 s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E0520 16:39:43.222145  944354 slow_operation_alarm.cc:73] \n",
+      "********************************\n",
+      "[Compiling module jit__unrolled_fresh for CPU] Very slow compile? If you want to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n",
+      "********************************\n",
+      "E0520 16:43:46.854518  944352 slow_operation_alarm.cc:140] The operation took 6m3.638498s\n",
+      "\n",
+      "********************************\n",
+      "[Compiling module jit__unrolled_fresh for CPU] Very slow compile? If you want to file a bug, run with envvar XLA_FLAGS=--xla_dump_to=/tmp/foo and attach the results.\n",
+      "********************************\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  unrolled for-loop in jit (body inlined 1000×):   373.14 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "# Bigger tree so the per-iter body work isn't completely drowned out.\n",
+    "big_topo = hx.symmetric_topology(depth=10, degree=2)              # 2047 nodes\n",
+    "big_tree = (\n",
+    "    hx.Tree.empty(big_topo, {'value': ()})\n",
+    "      .at[big_topo.is_leaf].set(value=jnp.ones(int(big_topo.is_leaf.sum())))\n",
+    ")\n",
+    "print(f\"benchmark tree: {big_topo.size} nodes, depth={big_topo.depth}\")\n",
+    "\n",
+    "N = 1000\n",
+    "\n",
+    "def for_loop(tree):                              # Python loop of jit'd calls\n",
+    "    for _ in range(N):\n",
+    "        tree = average_children(tree)\n",
+    "    return tree\n",
+    "\n",
+    "@jax.jit\n",
+    "def scan_loop(tree):                             # one trace, one XLA scan op\n",
+    "    def body(t, _):\n",
+    "        return average_children(t), None\n",
+    "    final, _ = jax.lax.scan(body, tree, xs=None, length=N)\n",
+    "    return final\n",
+    "\n",
+    "@jax.jit\n",
+    "def unrolled_loop(tree):                         # Python loop, body inlined N×\n",
+    "    for _ in range(N):\n",
+    "        tree = average_children(tree)\n",
+    "    return tree\n",
+    "\n",
+    "# --- (a) runtime, post-compile, both block_until_ready ---\n",
+    "_ = jax.block_until_ready(scan_loop(big_tree).value)            # warm scan\n",
+    "\n",
+    "print(\"\\n=== runtime (post-compile) ===\")\n",
+    "print(\"Python for-loop of jit'd calls:\")\n",
+    "%timeit jax.block_until_ready(for_loop(big_tree).value)\n",
+    "print(\"scan inside jit:\")\n",
+    "%timeit jax.block_until_ready(scan_loop(big_tree).value)\n",
+    "\n",
+    "# --- (b) compile time: scan vs unrolled jit ---\n",
+    "def measure_compile(fn, arg):\n",
+    "    \"\"\"Trace + lower + XLA-compile a fresh jit wrapper, return seconds.\"\"\"\n",
+    "    t0 = time.perf_counter()\n",
+    "    _ = jax.jit(fn).lower(arg).compile()\n",
+    "    return time.perf_counter() - t0\n",
+    "\n",
+    "def _scan_fresh(t):\n",
+    "    def body(s, _): return average_children(s), None\n",
+    "    out, _ = jax.lax.scan(body, t, xs=None, length=N)\n",
+    "    return out\n",
+    "\n",
+    "def _unrolled_fresh(t):\n",
+    "    for _ in range(N):\n",
+    "        t = average_children(t)\n",
+    "    return t\n",
+    "\n",
+    "print(f\"\\n=== compile time (N = {N}) ===\")\n",
+    "print(f\"  scan inside jit ({N} iters, body traced 1×):   {measure_compile(_scan_fresh, big_tree):7.2f} s\")\n",
+    "print(f\"  unrolled for-loop in jit (body inlined {N}×):  {measure_compile(_unrolled_fresh, big_tree):7.2f} s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97626a46",
+   "metadata": {},
+   "source": [
+    "### 6c. `jax.grad`\n",
+    "\n",
+    "Differentiate end-to-end. Here we treat the leaf observations as the inputs and the root value as the output. With a mean sweep on a height-3 binary tree, each leaf contributes $1/8$ to the root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d548109a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-12T14:59:14.480697Z",
+     "iopub.status.busy": "2026-05-12T14:59:14.480617Z",
+     "iopub.status.idle": "2026-05-12T14:59:14.785069Z",
+     "shell.execute_reply": "2026-05-12T14:59:14.784592Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d(root) / d(leaves) = [0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125]\n",
+      "expected: 1/8 for each of the 8 leaves = 0.125\n"
+     ]
+    }
+   ],
+   "source": [
+    "def root_from_leaves(leaf_vals):\n",
+    "    t = hx.Tree.empty(topo, {'value': ()})\n",
+    "    t = t.at[topo.is_leaf].set(value=leaf_vals)\n",
+    "    return average_children(t).value[0]\n",
+    "\n",
+    "grads = jax.grad(root_from_leaves)(leaf_obs)\n",
+    "print(\"d(root) / d(leaves) =\", np.asarray(grads))\n",
+    "print(\"expected: 1/8 for each of the 8 leaves =\", 1.0 / num_leaves)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cda50b02",
+   "metadata": {},
+   "source": [
+    "## Recap & next steps\n",
+    "\n",
+    "You've now seen the entire core API:\n",
+    "\n",
+    "- `hx.symmetric_topology(height, degree)` (and `hx.from_parents(parents)` for arbitrary topologies)\n",
+    "- `hx.Tree.empty(topo, schema)`, `tree.set(...)`, `tree.at[mask].set(...)`\n",
+    "- `tree.value` for field access (or `tree['value']` when the field name is a dynamic string or collides with a reserved name like `size`)\n",
+    "- `@hx.up(reads_children=, writes=)` with `(node, children, params) -> dict`\n",
+    "- `@hx.down(reads_parent=, writes=)` with `(node, parent, params) -> dict`\n",
+    "- `node.value`, `children.value.mean(0)`, `parent.value` inside a sweep\n",
+    "- Sweeps compose with `jax.jit`, `jax.lax.scan`, `jax.grad` directly\n",
+    "\n",
+    "Where to go next:\n",
+    "\n",
+    "- **`02_writing_sweeps.ipynb`** — go deeper: equal vs unequal degree trees, the `reads / reads_children / reads_parent / writes` declaration model, custom reductions on the children axis, common patterns (subtree sizes, max depth, weighted means, belief propagation).\n",
+    "- **`03_phylo_mean.ipynb`** — read a real Newick tree, run the `phylo_mean` prebuilt on it.\n",
+    "- **`04_phylo_bayesian.ipynb`** — upgrade the point estimate to a full Bayesian posterior over ancestral states."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hyperiax (3.11.15)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/02_writing_sweeps.ipynb b/docs/source/notebooks/02_writing_sweeps.ipynb
new file mode 100644
index 0000000..90ea6b5
--- /dev/null
+++ b/docs/source/notebooks/02_writing_sweeps.ipynb
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c5df3a39",
+   "metadata": {},
+   "source": [
+    "# Writing your own sweeps\n",
+    "\n",
+    "In [the quickstart](01_quickstart.ipynb) we used two small sweeps — `average_children` and `propagate` — to introduce the API. This notebook goes deeper:\n",
+    "\n",
+    "1. **Anatomy of a sweep declaration** — `reads / reads_children / reads_parent / writes` and what happens when you get them wrong.\n",
+    "2. **Equal-degree vs unequal-degree trees** — the same user code routes through one segment-based backend, so reduction-style code behaves the same on both.\n",
+    "3. **The children-axis reduction surface** — `.sum / .prod / .max / .min / .mean(axis=0)`.\n",
+    "4. **Pattern: subtree size** — count nodes per subtree with a single up-sweep that reads both `node` and `children`.\n",
+    "5. **Pattern: depth from root** — propagate root → leaf with a down-sweep.\n",
+    "\n",
+    "By the end you will have written several sweeps from scratch and have a working mental model of the dispatcher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0c79c997",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:15.812149Z",
+     "iopub.status.busy": "2026-06-02T14:23:15.811928Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.339785Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.339361Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import hyperiax as hx\n",
+    "\n",
+    "\n",
+    "def plot_tree(topo, values=None, ax=None, title=None, cmap='viridis',\n",
+    "              vmin=None, vmax=None, label_format=None):\n",
+    "    \"\"\"Layered top-down layout. (Same helper as 01_quickstart.ipynb, with optional value labels.)\"\"\"\n",
+    "    if ax is None:\n",
+    "        _, ax = plt.subplots(figsize=(8, 5))\n",
+    "    pos = np.zeros((topo.size, 2))\n",
+    "    for d in range(topo.depth + 1):\n",
+    "        lo, hi = int(topo.level_starts[d]), int(topo.level_starts[d + 1])\n",
+    "        n = hi - lo\n",
+    "        pos[lo:hi, 0] = np.linspace(0, 1, n + 2)[1:-1] if n > 1 else 0.5\n",
+    "        pos[lo:hi, 1] = -d\n",
+    "    for i in range(1, topo.size):\n",
+    "        p = int(topo.parents[i])\n",
+    "        ax.plot([pos[p, 0], pos[i, 0]], [pos[p, 1], pos[i, 1]],\n",
+    "                'k-', lw=0.6, alpha=0.5, zorder=1)\n",
+    "    if values is None:\n",
+    "        ax.scatter(pos[:, 0], pos[:, 1], s=420, c='lightsteelblue',\n",
+    "                   edgecolor='k', zorder=3)\n",
+    "        for i in range(topo.size):\n",
+    "            ax.text(pos[i, 0], pos[i, 1], str(i), ha='center', va='center', fontsize=8)\n",
+    "    else:\n",
+    "        v = np.asarray(values)\n",
+    "        sc = ax.scatter(pos[:, 0], pos[:, 1], s=480, c=v,\n",
+    "                        edgecolor='k', cmap=cmap, vmin=vmin, vmax=vmax, zorder=3)\n",
+    "        plt.colorbar(sc, ax=ax, label='value', shrink=0.8)\n",
+    "        fmt = label_format or (lambda x: f'{x:.0f}' if abs(x - round(x)) < 1e-6 else f'{x:.2g}')\n",
+    "        for i in range(topo.size):\n",
+    "            ax.text(pos[i, 0], pos[i, 1], fmt(float(v[i])),\n",
+    "                    ha='center', va='center', fontsize=7, color='white')\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=11)\n",
+    "    ax.set_axis_off()\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82837fa6",
+   "metadata": {},
+   "source": [
+    "## 1. Anatomy of a sweep declaration\n",
+    "\n",
+    "Every sweep is a Python function wrapped by `@hx.up` or `@hx.down`. The decorator captures four pieces of metadata that act as a contract between you and the dispatcher:\n",
+    "\n",
+    "| kwarg | Meaning | Default if omitted |\n",
+    "|---|---|---|\n",
+    "| `reads` | Fields read from the **current node**. Available as `node.X` inside the function. | All schema fields |\n",
+    "| `reads_children` (up only) | Fields read from **each child**. Available as `children.X`. | All schema fields |\n",
+    "| `reads_parent` (down only) | Fields read from **the parent**. Available as `parent.X`. | All schema fields |\n",
+    "| `writes` | Fields the function returns. The returned `dict` must have exactly these keys. | **Required** — no default |\n",
+    "\n",
+    "The dispatcher uses these declarations to slice the right data, hand you a view, and scatter the right results back. Anything you didn't declare is hidden from your function, and anything you return that you didn't declare is rejected. Being explicit pays off when sweeps get bigger or read from many fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4076401f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.341447Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.341338Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.534791Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.534411Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OK. Root value: 18.075000762939453\n"
+     ]
+    }
+   ],
+   "source": [
+    "topo = hx.symmetric_topology(depth=2, degree=3)   # 1+3+9 = 13 nodes\n",
+    "\n",
+    "tree = hx.Tree.empty(topo, {'value': (), 'weight': ()})\n",
+    "tree = tree.at[topo.is_leaf].set(value=jnp.ones(int(topo.is_leaf.sum())))\n",
+    "tree = tree.set(weight=jnp.linspace(1, 2, topo.size, dtype=jnp.float32))\n",
+    "\n",
+    "@hx.up(\n",
+    "    reads=('value',),                   # from the current node we look at: value\n",
+    "    reads_children=('value', 'weight'), # from each child we look at:     value, weight\n",
+    "    writes=('value',),                  # we promise to write back:        value\n",
+    ")\n",
+    "def aggregate(node, children, params):\n",
+    "    # Inside an equal-degree up-sweep the user fn is vmapped, so per parent:\n",
+    "    #   node.value      : shape (n_parents_at_level,)\n",
+    "    #   children.value  : ChildrenAxis proxy over all children at this level\n",
+    "    #   children.weight : ChildrenAxis proxy over all children at this level\n",
+    "    # params is whatever you pass via sweep(tree, params=...), which is a global read-only constant during the sweep. Here we just use it to pass a bias term that gets added at every node.\n",
+    "    msgs = children.map(lambda c: {'weighted': c.value * c.weight})\n",
+    "    weighted = msgs.weighted.sum(0)\n",
+    "    return {'value': weighted + node.value + params['bias']}\n",
+    "\n",
+    "new_tree = aggregate(tree, params={'bias': 0.1})\n",
+    "print('OK. Root value:', float(new_tree.value[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55c172ad",
+   "metadata": {},
+   "source": [
+    "### Mistake #1 — reading a field you didn't declare\n",
+    "\n",
+    "The view only exposes what you declared in `reads / reads_children / reads_parent`. Anything else raises `AttributeError`, with a helpful list of what *is* available.\n",
+    "\n",
+    "(If you leave a `reads_*` kwarg off, it defaults to \"all schema fields\" — convenient for one-shot prototypes, but stricter explicit lists pay off as your sweep grows: typos in field names get flagged immediately.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c483dcb9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.536518Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.536424Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.550609Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.550159Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AttributeError:\n",
+      "  Node field 'weight' is not available in this scope. Add it to `reads=...` on the sweep. Available: ['value']\n"
+     ]
+    }
+   ],
+   "source": [
+    "@hx.up(reads_children=('value',),   # forgot 'weight' in reads_children!\n",
+    "       writes=('value',))\n",
+    "def forgot_to_declare(node, children, params):\n",
+    "    # c.weight isn't in scope because we didn't list it in reads_children.\n",
+    "    msgs = children.map(lambda c: {'weighted': c.value * c.weight})\n",
+    "    return {'value': msgs.weighted.sum(0)}\n",
+    "\n",
+    "try:\n",
+    "    forgot_to_declare(tree)\n",
+    "except AttributeError as e:\n",
+    "    print('AttributeError:')\n",
+    "    print(' ', e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8531710e",
+   "metadata": {},
+   "source": [
+    "### Mistake #2 — returning the wrong keys\n",
+    "\n",
+    "The returned `dict` must have exactly the same key set as `writes`. Extras or missing keys both raise `SchemaMismatch`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aeda65da",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.551920Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.551744Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.556640Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.556265Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SchemaMismatch:\n",
+      "  Up-sweep returned keys ['extra', 'value'] but writes=['value']. Extra: ['extra']; missing: [].\n"
+     ]
+    }
+   ],
+   "source": [
+    "@hx.up(reads_children=('value',), writes=('value',))\n",
+    "def wrong_writes(node, children, params):\n",
+    "    return {'value': children.value.sum(0),\n",
+    "            'extra': children.value.sum(0)}  # not in writes\n",
+    "\n",
+    "try:\n",
+    "    wrong_writes(tree)\n",
+    "except hx.SchemaMismatch as e:\n",
+    "    print('SchemaMismatch:')\n",
+    "    print(' ', e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "799e6481",
+   "metadata": {},
+   "source": [
+    "## 2. Equal-degree vs unequal-degree trees\n",
+    "\n",
+    "Hyperiax uses one up-sweep backend for both shapes:\n",
+    "\n",
+    "- **Equal-degree** — every non-leaf has the same number of children.\n",
+    "- **Unequal-degree** — children counts vary per node.\n",
+    "\n",
+    "In both cases the dispatcher concatenates each level's children into a flat array and assigns segment ids; reductions go through `jax.ops.segment_*`. Inside the body, `node.X` is batched `(num_parents_at_level, *trailing)`, and `children.X` is a `ChildrenAxis` proxy that supports `.sum / .prod / .max / .min / .mean(axis=0)` plus `children.map(fn)`, but not direct arithmetic or indexing.\n",
+    "\n",
+    "**The crucial property: identical user code works on both** — as long as the only operations on the children axis are the supported reductions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0fb3765d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.557717Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.557648Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.627855Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.627316Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "equal-degree topology:\n",
+      "  equal_degree=True  child_counts(non-leaf)=[2, 2, 2]\n",
+      "unequal-degree topology:\n",
+      "  equal_degree=False  child_counts(non-leaf)=[2, 3, 1, 2]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Equal-degree: depth-2 binary tree, every inner node has 2 children.\n",
+    "topo_eq = hx.symmetric_topology(depth=2, degree=2)\n",
+    "\n",
+    "# Unequal-degree: ragged tree (root has 2 children; node 1 has 3; node 2 has 1; node 3 has 2).\n",
+    "topo_uneq = hx.from_parents([0, 0, 0, 1, 1, 1, 2, 3, 3])\n",
+    "\n",
+    "print('equal-degree topology:')\n",
+    "print(f'  equal_degree={topo_eq.equal_degree}  child_counts(non-leaf)={topo_eq.child_counts[~topo_eq.is_leaf].tolist()}')\n",
+    "print('unequal-degree topology:')\n",
+    "print(f'  equal_degree={topo_uneq.equal_degree}  child_counts(non-leaf)={topo_uneq.child_counts[~topo_uneq.is_leaf].tolist()}')\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n",
+    "plot_tree(topo_eq, ax=axes[0], title='equal-degree: every inner node has 2 children')\n",
+    "plot_tree(topo_uneq, ax=axes[1], title='unequal-degree: variable arity')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b5dd784c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.629182Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.629083Z",
+     "iopub.status.idle": "2026-06-02T14:23:16.980777Z",
+     "shell.execute_reply": "2026-06-02T14:23:16.980379Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "equal-degree   root = 4.0    (expected leaf count = 4)\n",
+      "unequal-degree root = 5.0    (expected leaf count = 5)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Same up-sweep, run on both topologies.\n",
+    "@hx.up(reads_children=('value',), writes=('value',))\n",
+    "def sum_subtree(node, children, params):\n",
+    "    return {'value': children.value.sum(0)}\n",
+    "\n",
+    "# Seed each tree's leaves with 1.0, so summing yields the leaf count.\n",
+    "tree_eq = hx.Tree.empty(topo_eq, {'value': ()})\n",
+    "tree_eq = tree_eq.at[topo_eq.is_leaf].set(value=jnp.ones(int(topo_eq.is_leaf.sum())))\n",
+    "\n",
+    "tree_uneq = hx.Tree.empty(topo_uneq, {'value': ()})\n",
+    "tree_uneq = tree_uneq.at[topo_uneq.is_leaf].set(value=jnp.ones(int(topo_uneq.is_leaf.sum())))\n",
+    "\n",
+    "out_eq   = sum_subtree(tree_eq)\n",
+    "out_uneq = sum_subtree(tree_uneq)\n",
+    "\n",
+    "print(f'equal-degree   root = {float(out_eq.value[0])}    (expected leaf count = {int(topo_eq.is_leaf.sum())})')\n",
+    "print(f'unequal-degree root = {float(out_uneq.value[0])}    (expected leaf count = {int(topo_uneq.is_leaf.sum())})')\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "plot_tree(topo_eq, out_eq.value, ax=axes[0],\n",
+    "          title='equal-degree: leaf count per subtree')\n",
+    "plot_tree(topo_uneq, out_uneq.value, ax=axes[1],\n",
+    "          title='unequal-degree: leaf count per subtree')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d5cc2a",
+   "metadata": {},
+   "source": [
+    "Note what just happened: **we wrote one function, and it worked correctly on a 7-node binary tree and on a 9-node ragged tree with no changes**. Both topologies used the same segment-based dispatch path.\n",
+    "\n",
+    "The one thing to watch out for: `children.X` is a proxy, not an array. So direct elementwise children-axis arithmetic is rejected. Use `children.map(fn)` for per-child transforms, then reduce the returned fields with `.sum / .prod / .max / .min / .mean`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fc44417",
+   "metadata": {},
+   "source": [
+    "## 3. The children-axis reduction surface\n",
+    "\n",
+    "Five reductions, all reducing over the children axis (`axis=0`):\n",
+    "\n",
+    "- `children.X.sum(0)`\n",
+    "- `children.X.prod(0)`\n",
+    "- `children.X.max(0)`\n",
+    "- `children.X.min(0)`\n",
+    "- `children.X.mean(0)`\n",
+    "\n",
+    "Each returns an array of the same trailing shape as `X`, one record per parent. We'll run all five on the same input to see them in action.\n",
+    "\n",
+    "We use a height-2 ternary tree (1 + 3 + 9 = 13 nodes) and seed the 9 leaves with `[1, 2, …, 9]`. At depth 1, each of the three inner nodes has children `[1, 2, 3]`, `[4, 5, 6]`, `[7, 8, 9]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "49d93661",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:16.982279Z",
+     "iopub.status.busy": "2026-06-02T14:23:16.982191Z",
+     "iopub.status.idle": "2026-06-02T14:23:17.177542Z",
+     "shell.execute_reply": "2026-06-02T14:23:17.177033Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reduction       root   depth-1 inner values\n",
+      "--------------------------------------------------\n",
+      "sum          45.0   [ 6. 15. 24.]\n",
+      "prod     362880.0   [  6. 120. 504.]\n",
+      "max           9.0   [3. 6. 9.]\n",
+      "min           1.0   [1. 4. 7.]\n",
+      "mean          5.0   [2. 5. 8.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "topo3 = hx.symmetric_topology(depth=2, degree=3)\n",
+    "tree3 = hx.Tree.empty(topo3, {'value': ()})\n",
+    "tree3 = tree3.at[topo3.is_leaf].set(value=jnp.arange(1, 10, dtype=jnp.float32))\n",
+    "\n",
+    "def _make_reducer(reduce_fn):\n",
+    "    @hx.up(reads_children=('value',), writes=('value',))\n",
+    "    def sweep(node, children, params):\n",
+    "        return {'value': reduce_fn(children.value)}\n",
+    "    return sweep\n",
+    "\n",
+    "sweeps = {\n",
+    "    'sum':  _make_reducer(lambda x: x.sum(0)),\n",
+    "    'prod': _make_reducer(lambda x: x.prod(0)),\n",
+    "    'max':  _make_reducer(lambda x: x.max(0)),\n",
+    "    'min':  _make_reducer(lambda x: x.min(0)),\n",
+    "    'mean': _make_reducer(lambda x: x.mean(0)),\n",
+    "}\n",
+    "\n",
+    "print(f\"{'reduction':<6} {'root':>10}   depth-1 inner values\")\n",
+    "print('-' * 50)\n",
+    "for label, sw in sweeps.items():\n",
+    "    out = sw(tree3)\n",
+    "    print(f'{label:<6} {float(out.value[0]):>10.1f}   {np.asarray(out.value[1:4])}')\n",
+    "\n",
+    "# Sanity-check one nice number: the full-tree product is 9! = 362880.\n",
+    "assert int(sweeps['prod'](tree3).value[0]) == 362880"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db138357",
+   "metadata": {},
+   "source": [
+    "Worth noting: each call to `_make_reducer` creates a **fresh `SweepFn`** — a separately hashable, JIT-cached transform. The factory pattern is useful when you want the same body parametrized by a small callable (a reduction, a kernel, a similarity function). The dispatcher hashes on `(direction, fn identity, reads/writes spec)`, so two sweeps built by the factory don't share a cache entry."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5da4fa07",
+   "metadata": {},
+   "source": [
+    "## 4. Pattern — subtree size\n",
+    "\n",
+    "**Problem:** for every node, count how many nodes are in the subtree rooted there (including itself).\n",
+    "\n",
+    "**Recursion:** `size(leaf) = 1`; `size(inner) = 1 + Σ size(child)`. We seed every node with `count = 1`, then run an up-sweep that adds the children's counts to the node's own. Inner nodes get overwritten with the correct subtree size; leaves are never visited and keep their seed of 1.\n",
+    "\n",
+    "This sweep is interesting because it reads **both** `node.count` (the seed) and `children.count` (already-computed subtree sizes one level deeper). It's our first sweep using the `reads=` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a618dd8b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:17.179128Z",
+     "iopub.status.busy": "2026-06-02T14:23:17.179023Z",
+     "iopub.status.idle": "2026-06-02T14:23:17.258190Z",
+     "shell.execute_reply": "2026-06-02T14:23:17.257630Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "subtree sizes per node: [9, 6, 2, 3, 1, 1, 1, 1, 1]\n",
+      "root subtree size       = 9 (= total nodes = 9) ✓\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use the ragged tree from §2 — it makes the result more visually interesting.\n",
+    "tree_size = hx.Tree.empty(topo_uneq, {'count': ()})\n",
+    "tree_size = tree_size.set(count=jnp.ones(topo_uneq.size, dtype=jnp.float32))\n",
+    "\n",
+    "@hx.up(reads=('count',), reads_children=('count',), writes=('count',))\n",
+    "def subtree_size(node, children, params):\n",
+    "    return {'count': node.count + children.count.sum(0)}\n",
+    "\n",
+    "out_size = subtree_size(tree_size)\n",
+    "\n",
+    "counts = np.asarray(out_size.count).astype(int)\n",
+    "print(f'subtree sizes per node: {counts.tolist()}')\n",
+    "print(f'root subtree size       = {counts[0]} (= total nodes = {topo_uneq.size}) ✓')\n",
+    "\n",
+    "plot_tree(topo_uneq, out_size.count,\n",
+    "          title='subtree size — 1 + Σ children.count, propagating leaf → root')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de15bdd3",
+   "metadata": {},
+   "source": [
+    "## 5. Pattern — depth from root\n",
+    "\n",
+    "**Problem:** for every node, record its depth (root = 0, root's children = 1, grandchildren = 2, …).\n",
+    "\n",
+    "**Recursion:** `depth(root) = 0`; `depth(node) = depth(parent) + 1`. A down-sweep is the natural fit. The root is never visited by `@hx.down` (it has no parent), so we leave it at the schema's default value of 0.\n",
+    "\n",
+    "This sweep does **not** read `node` at all — we set `reads=()` to say so explicitly. Setting `reads=()` is a small ergonomic win: the dispatcher hands you an empty `node` view and any typo like `node.X` raises a clear error rather than silently being a no-op."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3403ba5f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:17.259513Z",
+     "iopub.status.busy": "2026-06-02T14:23:17.259415Z",
+     "iopub.status.idle": "2026-06-02T14:23:17.330562Z",
+     "shell.execute_reply": "2026-06-02T14:23:17.330023Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "depths per node:           [0, 1, 1, 2, 2, 2, 2, 3, 3]\n",
+      "matches topo.node_depths?  True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree_d = hx.Tree.empty(topo_uneq, {'depth': ()})\n",
+    "# root.depth = 0 already (Tree.empty zeros everything), and the down-sweep never visits the root.\n",
+    "\n",
+    "@hx.down(reads=(), reads_parent=('depth',), writes=('depth',))\n",
+    "def assign_depth(node, parent, params):\n",
+    "    return {'depth': parent.depth + 1.0}\n",
+    "\n",
+    "out_depth = assign_depth(tree_d)\n",
+    "\n",
+    "depths = np.asarray(out_depth.depth).astype(int)\n",
+    "print(f'depths per node:           {depths.tolist()}')\n",
+    "print(f'matches topo.node_depths?  {np.array_equal(depths, np.asarray(topo_uneq.node_depths))}')\n",
+    "\n",
+    "plot_tree(topo_uneq, out_depth.depth,\n",
+    "          title='depth from root — accumulated by down-sweep')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "591cd39c",
+   "metadata": {},
+   "source": [
+    "## Recap & next steps\n",
+    "\n",
+    "What you wrote in this notebook:\n",
+    "\n",
+    "1. `aggregate` — a kitchen-sink up-sweep showing all four declaration kwargs plus `params`.\n",
+    "2. `sum_subtree` — one function body running unchanged on equal-degree and unequal-degree trees.\n",
+    "3. Five reducer sweeps via a factory — `sum / prod / max / min / mean` on the children axis.\n",
+    "4. `subtree_size` — an up-sweep reading both `node` and `children`.\n",
+    "5. `assign_depth` — a down-sweep reading only `parent` (`reads=()`).\n",
+    "\n",
+    "Things to keep in your head:\n",
+    "\n",
+    "- **`writes=` is required** and the returned dict's key set must match it exactly. Everything else can default.\n",
+    "- **Inside the function, shapes are per-parent on equal-degree trees and batched-per-level on unequal-degree trees**, but reductions like `.sum(0)` are written identically in both modes.\n",
+    "- **Children-axis arithmetic** (`children.x * children.y`, `children.x[0]`, `jnp.where(children.x > 0, …)`) **works only on equal-degree trees**. On unequal-degree trees the children axis is a virtual proxy; precompute elementwise expressions into their own field before the sweep if you need them.\n",
+    "- **A `SweepFn` is hashable.** The dispatcher uses it as a JIT static argument and reuses compilations — calling `sum_subtree(tree)` a hundred times only compiles once.\n",
+    "\n",
+    "Where to go next:\n",
+    "\n",
+    "- **`03_phylo_mean.ipynb`** — read a real Newick tree, run the `phylo_mean` prebuilt.\n",
+    "- **`04_phylo_bayesian.ipynb`** — Bayesian ancestral states (closed-form Gaussian posterior, exact smoothing via BFFG).\n",
+    "- **`05_gaussian_bffg.ipynb`** — drop \"known hyperparameters\" and run MCMC on the joint state-plus-hyperparameter posterior; uses these sweeps inside `lax.scan`.\n",
+    "- **`06_gaussian_nuts.ipynb`** — `jax.grad` through the same sweeps for gradient-based hyperparameter learning."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hyperiax",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/03_phylo_mean.ipynb b/docs/source/notebooks/03_phylo_mean.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f322747b",
+   "metadata": {},
+   "source": [
+    "# Phylogenetic mean on a real tree\n",
+    "\n",
+    "In this notebook we load a small primate phylogeny from a Newick string, seed the leaves with a trait value (brain volume, in cm³), and run the `phylo_mean` prebuilt to infer the trait at every internal node — the **ancestral state reconstruction** problem of comparative biology.\n",
+    "\n",
+    "Along the way you'll see:\n",
+    "\n",
+    "- `hx.from_newick` — Newick string / file → `Tree`\n",
+    "- How BFS-ordered node indices relate to leaves named in the Newick source\n",
+    "- The `phylo_mean` prebuilt: each parent's value = edge-length-weighted mean of its children, which is the closed-form estimator under a Brownian-motion model of trait evolution\n",
+    "- A comparison against a naïve recursive average to see why edge-length weighting matters\n",
+    "- `hx.to_newick` — round-trip back out to a Newick string\n",
+    "\n",
+    "The maths is light; the focus is end-to-end workflow on a tree that looks like the ones you'd actually use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dbbc75dd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:18.359207Z",
+     "iopub.status.busy": "2026-06-02T14:23:18.358929Z",
+     "iopub.status.idle": "2026-06-02T14:23:18.797408Z",
+     "shell.execute_reply": "2026-06-02T14:23:18.796849Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import hyperiax as hx\n",
+    "from hyperiax.prebuilt import phylo_mean\n",
+    "\n",
+    "\n",
+    "def _dfs_x_positions(topo):\n",
+    "    \"\"\"DFS left-to-right ordering of leaves → x ∈ [0, 1].\"\"\"\n",
+    "    xs = np.zeros(topo.size)\n",
+    "    next_leaf = [0]\n",
+    "    n_leaves = int(topo.is_leaf.sum())\n",
+    "    def visit(node):\n",
+    "        if topo.is_leaf[node]:\n",
+    "            xs[node] = next_leaf[0] / max(1, n_leaves - 1)\n",
+    "            next_leaf[0] += 1\n",
+    "            return xs[node]\n",
+    "        # children of `node` in BFS order — ete3 BFS preserves Newick child order.\n",
+    "        children = [i for i in range(1, topo.size) if int(topo.parents[i]) == int(node)]\n",
+    "        cx = [visit(c) for c in children]\n",
+    "        xs[node] = float(np.mean(cx))\n",
+    "        return xs[node]\n",
+    "    visit(0)\n",
+    "    return xs\n",
+    "\n",
+    "\n",
+    "def plot_phylo_tree(topo, values=None, edge_lengths=None, ax=None, title=None,\n",
+    "                    cmap='viridis', vmin=None, vmax=None,\n",
+    "                    show_branch_labels=False, value_fmt='{:.0f}'):\n",
+    "    \"\"\"Cladogram-style phylo tree drawing with leaf species names.\n",
+    "\n",
+    "    Uses cumulative edge length as y when ``edge_lengths`` is given\n",
+    "    (proper time-tree), else falls back to ``-node_depth``. Leaf labels\n",
+    "    are read from ``topo.names``.\n",
+    "    \"\"\"\n",
+    "    if ax is None:\n",
+    "        _, ax = plt.subplots(figsize=(9, 5))\n",
+    "    pos = np.zeros((topo.size, 2))\n",
+    "    pos[:, 0] = _dfs_x_positions(topo)\n",
+    "    if edge_lengths is not None:\n",
+    "        age = np.zeros(topo.size)\n",
+    "        for i in range(1, topo.size):\n",
+    "            age[i] = age[int(topo.parents[i])] + float(edge_lengths[i])\n",
+    "        pos[:, 1] = -age\n",
+    "    else:\n",
+    "        pos[:, 1] = -np.asarray(topo.node_depths)\n",
+    "    # right-angled edges (cladogram style)\n",
+    "    for i in range(1, topo.size):\n",
+    "        p = int(topo.parents[i])\n",
+    "        ax.plot([pos[p, 0], pos[i, 0], pos[i, 0]],\n",
+    "                [pos[p, 1], pos[p, 1], pos[i, 1]],\n",
+    "                'k-', lw=0.9, alpha=0.6, zorder=1)\n",
+    "        if show_branch_labels and edge_lengths is not None:\n",
+    "            mid_y = (pos[p, 1] + pos[i, 1]) / 2\n",
+    "            ax.text(pos[i, 0] + 0.012, mid_y,\n",
+    "                    f'{float(edge_lengths[i]):.1f}', fontsize=7, color='gray',\n",
+    "                    ha='left', va='center')\n",
+    "    # node markers\n",
+    "    if values is None:\n",
+    "        ax.scatter(pos[:, 0], pos[:, 1], s=260, c='lightsteelblue',\n",
+    "                   edgecolor='k', zorder=3)\n",
+    "    else:\n",
+    "        v = np.asarray(values)\n",
+    "        sc = ax.scatter(pos[:, 0], pos[:, 1], s=320, c=v,\n",
+    "                        edgecolor='k', cmap=cmap, vmin=vmin, vmax=vmax, zorder=3)\n",
+    "        plt.colorbar(sc, ax=ax, shrink=0.7, label='value')\n",
+    "    # text overlays\n",
+    "    names = topo.names if topo.names is not None else ('',) * topo.size\n",
+    "    for i in range(topo.size):\n",
+    "        if values is not None:\n",
+    "            ax.text(pos[i, 0], pos[i, 1], value_fmt.format(float(np.asarray(values)[i])),\n",
+    "                    ha='center', va='center', fontsize=7, color='white', zorder=4)\n",
+    "        if topo.is_leaf[i] and names[i]:\n",
+    "            ax.annotate(names[i], (pos[i, 0], pos[i, 1]),\n",
+    "                        xytext=(0, -14), textcoords='offset points',\n",
+    "                        ha='center', va='top', fontsize=9, zorder=4)\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=11)\n",
+    "    ax.set_axis_off()\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54f87ac8",
+   "metadata": {},
+   "source": [
+    "## 1. Load a Newick tree\n",
+    "\n",
+    "Our test tree is a small primate phylogeny — five great apes plus a gibbon — with branch lengths in millions of years (Ma), roughly consistent with the consensus divergence times on [TimeTree.org](http://www.timetree.org/):\n",
+    "\n",
+    "| Split | Approx. age |\n",
+    "|---|---|\n",
+    "| Human–Chimp | 6.5 Ma |\n",
+    "| +Gorilla | 8.5 Ma |\n",
+    "| +Orangutan | 14.5 Ma |\n",
+    "| +Gibbon | 20 Ma |\n",
+    "\n",
+    "Note that this is an **ultrametric tree**: every leaf sits 20 Ma below the root (the present is at the bottom of the tree)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "76689439",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:18.798903Z",
+     "iopub.status.busy": "2026-06-02T14:23:18.798774Z",
+     "iopub.status.idle": "2026-06-02T14:23:18.851388Z",
+     "shell.execute_reply": "2026-06-02T14:23:18.851021Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tree(size=9, fields={edge_length: (), estimated_value: ()})\n",
+      "\n",
+      "size:         9\n",
+      "depth:        4\n",
+      "equal_degree: True\n",
+      "leaves:       5\n"
+     ]
+    }
+   ],
+   "source": [
+    "PRIMATE_NEWICK = '((((Human:6.5,Chimp:6.5):2,Gorilla:8.5):6,Orangutan:14.5):5.5,Gibbon:20);'\n",
+    "\n",
+    "tree = hx.from_newick(PRIMATE_NEWICK, schema={'estimated_value': ()})\n",
+    "print(tree)\n",
+    "print()\n",
+    "print(f\"size:         {tree.topology.size}\")\n",
+    "print(f\"depth:        {tree.topology.depth}\")\n",
+    "print(f\"equal_degree: {tree.topology.equal_degree}\")\n",
+    "print(f\"leaves:       {int(tree.topology.is_leaf.sum())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "939edbe9",
+   "metadata": {},
+   "source": [
+    "### How Newick nodes map to BFS indices\n",
+    "\n",
+    "The Newick parser produces a BFS-ordered topology — node `0` is the root, and `parents[i] < i` for `i > 0`. The names you wrote in the Newick string land on `topo.names`, and the colon-separated branch lengths land on `tree.edge_length`. Internal nodes have no name (empty string)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b844d50a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:18.853150Z",
+     "iopub.status.busy": "2026-06-02T14:23:18.853051Z",
+     "iopub.status.idle": "2026-06-02T14:23:18.876108Z",
+     "shell.execute_reply": "2026-06-02T14:23:18.875672Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "idx  name        parent  edge_len  depth  leaf?\n",
+      "--------------------------------------------------\n",
+      "  0  (inner)          0      0.00      0       \n",
+      "  1  (inner)          0      5.50      1       \n",
+      "  2  Gibbon           0     20.00      1    yes\n",
+      "  3  (inner)          1      6.00      2       \n",
+      "  4  Orangutan        1     14.50      2    yes\n",
+      "  5  (inner)          3      2.00      3       \n",
+      "  6  Gorilla          3      8.50      3    yes\n",
+      "  7  Human            5      6.50      4    yes\n",
+      "  8  Chimp            5      6.50      4    yes\n"
+     ]
+    }
+   ],
+   "source": [
+    "topo = tree.topology\n",
+    "print(f\"{'idx':>3}  {'name':<10}  {'parent':>6}  {'edge_len':>8}  {'depth':>5}  {'leaf?':>5}\")\n",
+    "print('-' * 50)\n",
+    "for i in range(topo.size):\n",
+    "    name = topo.names[i] or '(inner)'\n",
+    "    leaf = 'yes' if topo.is_leaf[i] else ''\n",
+    "    print(f\"{i:>3}  {name:<10}  {int(topo.parents[i]):>6}  {float(tree.edge_length[i]):>8.2f}  {int(topo.node_depths[i]):>5}  {leaf:>5}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "04f9e4b3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:18.877576Z",
+     "iopub.status.busy": "2026-06-02T14:23:18.877488Z",
+     "iopub.status.idle": "2026-06-02T14:23:18.928902Z",
+     "shell.execute_reply": "2026-06-02T14:23:18.928530Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_phylo_tree(topo, edge_lengths=tree.edge_length,\n",
+    "                title='Primate phylogeny (branch lengths in Ma, ultrametric)',\n",
+    "                show_branch_labels=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e823cffb",
+   "metadata": {},
+   "source": [
+    "## 2. Seed leaves with a trait\n",
+    "\n",
+    "For ancestral state reconstruction we need observed leaf values. We'll use endocranial volume (cm³) — a continuous trait that's easy to imagine and has a famous outlier (us).\n",
+    "\n",
+    "| Species | Brain volume (cm³) |\n",
+    "|---|---|\n",
+    "| Human | 1350 |\n",
+    "| Chimp | 400 |\n",
+    "| Gorilla | 500 |\n",
+    "| Orangutan | 400 |\n",
+    "| Gibbon | 100 |\n",
+    "\n",
+    "The numbers are rough literature averages; the point is just that one species (us) is 3-4× the others."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ffdf7e91",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:18.930390Z",
+     "iopub.status.busy": "2026-06-02T14:23:18.930296Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.001828Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.001364Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "estimated_value (inner nodes are still zero):\n",
+      "  node  0 (inner)          0.0\n",
+      "  node  1 (inner)          0.0\n",
+      "  node  2 Gibbon         100.0\n",
+      "  node  3 (inner)          0.0\n",
+      "  node  4 Orangutan      400.0\n",
+      "  node  5 (inner)          0.0\n",
+      "  node  6 Gorilla        500.0\n",
+      "  node  7 Human         1350.0\n",
+      "  node  8 Chimp          400.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "BRAIN_VOL = {\n",
+    "    'Human':     1350.0,\n",
+    "    'Chimp':      400.0,\n",
+    "    'Gorilla':    500.0,\n",
+    "    'Orangutan':  400.0,\n",
+    "    'Gibbon':     100.0,\n",
+    "}\n",
+    "\n",
+    "# Look up each leaf's index from topo.names so we don't hard-code BFS order.\n",
+    "leaf_indices = np.where(topo.is_leaf)[0]\n",
+    "leaf_values = jnp.asarray(\n",
+    "    [BRAIN_VOL[topo.names[i]] for i in leaf_indices], dtype=jnp.float32\n",
+    ")\n",
+    "\n",
+    "tree = tree.at[topo.is_leaf].set(estimated_value=leaf_values)\n",
+    "print('estimated_value (inner nodes are still zero):')\n",
+    "for i in range(topo.size):\n",
+    "    print(f'  node {i:>2} {topo.names[i] or \"(inner)\":<10}  {float(tree.estimated_value[i]):>8.1f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c39dc7f0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.003314Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.003204Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.048917Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.048472Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "plot_phylo_tree(topo, values=tree.estimated_value, edge_lengths=tree.edge_length,\n",
+    "                ax=ax, vmin=0, vmax=1400,\n",
+    "                title='Before phylo_mean: only leaves seeded; inner nodes = 0')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8bf1771",
+   "metadata": {},
+   "source": [
+    "## 3. Run `phylo_mean`\n",
+    "\n",
+    "The prebuilt sweep computes, at every parent:\n",
+    "\n",
+    "$$\\hat{x}_p = \\frac{\\sum_c x_c / \\ell_c}{\\sum_c 1 / \\ell_c}$$\n",
+    "\n",
+    "where the sum is over the parent's children $c$ and $\\ell_c$ is the branch length connecting $c$ to $p$. Short edges → high weight (sister taxa pull the ancestor toward themselves); long edges → low weight (distant relatives contribute less). It is the maximum-likelihood estimator for the parent's state under a Brownian-motion model of trait evolution along edges.\n",
+    "\n",
+    "Implementation-wise it's a single `@hx.up` sweep that reads `children.estimated_value` and `children.edge_length` and writes `estimated_value` back. We saw the explicit version in `02_writing_sweeps.ipynb`; here we just import the prebuilt and call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "70b26b8b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.050241Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.050141Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.109516Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.109126Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inferred values per node:\n",
+      "  node  0 (inner)        559.2\n",
+      "  node  1 (inner)        685.5\n",
+      "  node  2 Gibbon         100.0\n",
+      "  node  3 (inner)        803.6\n",
+      "  node  4 Orangutan      400.0\n",
+      "  node  5 (inner)        875.0\n",
+      "  node  6 Gorilla        500.0\n",
+      "  node  7 Human         1350.0\n",
+      "  node  8 Chimp          400.0\n",
+      "\n",
+      "Root (LCA of all 5 species) estimate: 559.2 cm³\n"
+     ]
+    }
+   ],
+   "source": [
+    "sweep = phylo_mean()\n",
+    "inferred = sweep(tree)\n",
+    "\n",
+    "print('inferred values per node:')\n",
+    "for i in range(topo.size):\n",
+    "    label = topo.names[i] or '(inner)'\n",
+    "    print(f'  node {i:>2} {label:<10}  {float(inferred.estimated_value[i]):>8.1f}')\n",
+    "\n",
+    "print()\n",
+    "print(f\"Root (LCA of all 5 species) estimate: {float(inferred.estimated_value[0]):.1f} cm³\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8fc491a5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.111118Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.111001Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.191919Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.191420Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_phylo_tree(topo, values=inferred.estimated_value, edge_lengths=tree.edge_length,\n",
+    "                vmin=0, vmax=1400,\n",
+    "                title='phylo_mean reconstruction: ancestor brain volume per internal node')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc457431",
+   "metadata": {},
+   "source": [
+    "## 4. Compare with a naïve recursive mean\n",
+    "\n",
+    "How much did the edge-length weighting actually matter? Let's run a baseline that uses `children.value.mean(0)` — every child contributes equally regardless of branch length — and compare the two reconstructions on the same tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6afbe3cd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.193230Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.193141Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.250360Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.250005Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "method               root est. (cm³)\n",
+      "--------------------------------------\n",
+      "phylo_mean                     559.2\n",
+      "naive recursive                321.9\n",
+      "flat leaf mean                 550.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "@hx.up(reads_children=('estimated_value',), writes=('estimated_value',))\n",
+    "def naive_mean(node, children, params):\n",
+    "    return {'estimated_value': children.estimated_value.mean(0)}\n",
+    "\n",
+    "naive = naive_mean(tree)\n",
+    "\n",
+    "flat = float(jnp.mean(leaf_values))\n",
+    "\n",
+    "print(f\"{'method':<20} {'root est. (cm³)':>15}\")\n",
+    "print('-' * 38)\n",
+    "print(f\"{'phylo_mean':<20} {float(inferred.estimated_value[0]):>15.1f}\")\n",
+    "print(f\"{'naive recursive':<20} {float(naive.estimated_value[0]):>15.1f}\")\n",
+    "print(f\"{'flat leaf mean':<20} {flat:>15.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "45dca42f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.251902Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.251814Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.334898Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.334322Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "plot_phylo_tree(topo, values=inferred.estimated_value, edge_lengths=tree.edge_length,\n",
+    "                ax=axes[0], vmin=0, vmax=1400,\n",
+    "                title='phylo_mean (edge-length-weighted)')\n",
+    "plot_phylo_tree(topo, values=naive.estimated_value, edge_lengths=tree.edge_length,\n",
+    "                ax=axes[1], vmin=0, vmax=1400,\n",
+    "                title='naïve recursive mean (children.mean(0))')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1e089eb",
+   "metadata": {},
+   "source": [
+    "What you should see:\n",
+    "\n",
+    "- **Naïve recursive mean is heavily biased by tree shape.** At the root, it averages Gibbon (100) with the 4-ape subtree's recursive mean — but the 4-ape subtree's mean is already a recursive average that propagates Human's outlier value through three levels of halving, so the value reaching the root from the apes side is much smaller than the flat leaf mean. The Gibbon side then drags it down further. Result is far below the flat leaf mean of 550.\n",
+    "- **`phylo_mean` is much closer to the flat leaf mean** because the inverse-edge-length weighting compensates for tree imbalance. The 4-ape side has many short edges (high weight) and one long edge through Gibbon (low weight, 1/20 = 0.05). Gibbon contributes little; the apes contribute a lot — and the apes' average pulls the root estimate up.\n",
+    "\n",
+    "Neither is the \"true\" ancestral brain volume — that would require a full evolutionary model and is impossible to know without fossils. The point is that **how you propagate up the tree matters**, and `phylo_mean` is the structure-aware default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfab3c9",
+   "metadata": {},
+   "source": [
+    "## 5. Round-trip back to Newick\n",
+    "\n",
+    "`hx.to_newick` converts the tree (including any updated `edge_length`) back to a Newick string. Internal node names that we never set come out as `\"\"`. The inferred `estimated_value` field doesn't appear in the Newick output — it isn't part of the format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0bdccb05",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-06-02T14:23:19.336378Z",
+     "iopub.status.busy": "2026-06-02T14:23:19.336288Z",
+     "iopub.status.idle": "2026-06-02T14:23:19.338360Z",
+     "shell.execute_reply": "2026-06-02T14:23:19.338008Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "original:     ((((Human:6.5,Chimp:6.5):2,Gorilla:8.5):6,Orangutan:14.5):5.5,Gibbon:20);\n",
+      "round-tripped: ((((Human:6.5,Chimp:6.5):2,Gorilla:8.5):6,Orangutan:14.5):5.5,Gibbon:20);\n",
+      "\n",
+      "identical?    True\n"
+     ]
+    }
+   ],
+   "source": [
+    "round_tripped = hx.to_newick(inferred)\n",
+    "print('original:    ', PRIMATE_NEWICK)\n",
+    "print('round-tripped:', round_tripped)\n",
+    "print()\n",
+    "print(f'identical?    {round_tripped == PRIMATE_NEWICK}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6282f1fd",
+   "metadata": {},
+   "source": [
+    "## Recap & next steps\n",
+    "\n",
+    "What this notebook showed:\n",
+    "\n",
+    "1. **Loading Newick** — `hx.from_newick(literal_or_path, schema={...})` parses a string or file, returns a `Tree` with `edge_length` already populated.\n",
+    "2. **BFS layout** — the indices `0..N-1` are a BFS traversal of the original Newick, and the names you wrote land on `topo.names`. Look leaves up by name, don't hard-code their indices.\n",
+    "3. **`phylo_mean` prebuilt** — one-line ancestral state reconstruction under the Brownian-motion model.\n",
+    "4. **Comparing reconstructions** — a custom `@hx.up` sweep next to a prebuilt one is just two Python objects; you can swap them in the same pipeline.\n",
+    "5. **Round-trip writing** — `hx.to_newick(tree)` produces a Newick string you can save or paste into a tree viewer.\n",
+    "\n",
+    "This was a **point estimator**: every ancestor got one number, no uncertainty. The natural next step is to ask \"with what uncertainty?\" — which leads to the Bayesian counterpart in [notebook 04](04_phylo_bayesian.ipynb).\n",
+    "\n",
+    "Where to go next:\n",
+    "\n",
+    "- **`04_phylo_bayesian.ipynb`** — full Bayesian inference on the same tree: closed-form posterior over ancestral states (Gaussian BFFG smoothing) with calibrated credible intervals.\n",
+    "- **`05_gaussian_bffg.ipynb`** — now infer the hyperparameters too, jointly with the latent states, via MCMC. BFFG inside the kernel.\n",
+    "- **`06_gaussian_nuts.ipynb`** — the same hyperparameter inference as point estimation: jax.grad-friendly building blocks compose with numpyro NUTS for full posterior inference.\n",
+    "- **`07_sde_bffg.ipynb`** — the research showcase: non-linear SDE bridges along edges, BFFG as an approximation, MCMC for full posterior."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hyperiax",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/04_phylo_bayesian.ipynb b/docs/source/notebooks/04_phylo_bayesian.ipynb
new file mode 100644
index 0000000..548895b
--- /dev/null
+++ b/docs/source/notebooks/04_phylo_bayesian.ipynb
@@ -0,0 +1,405 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "8565f6ad",
+      "metadata": {},
+      "source": [
+        "# Bayesian phylogenetic inference with discrete BFFG\n",
+        "\n",
+        "This notebook uses the v3 discrete BFFG sweeps to infer latent node states on a rooted tree from noisy leaf observations. The model is intentionally small: every edge uses the same one-step normal transition, so the linear auxiliary is also the true transition and the guided samples are exact up to Monte Carlo error.\n",
+        "\n",
+        "We will:\n",
+        "\n",
+        "1. Build a tree from a Newick literal.\n",
+        "2. Simulate latent states and noisy leaf observations.\n",
+        "3. Run `discrete_bf_sweep` to push leaf evidence to the root.\n",
+        "4. Sample complete latent trees with `discrete_fg_sweep`.\n",
+        "5. Compare the posterior mean with the deterministic `phylo_mean` estimator."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "af995f43",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import jax\n",
+        "import jax.numpy as jnp\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "import hyperiax as hx\n",
+        "from hyperiax.prebuilt import phylo_mean\n",
+        "from hyperiax.prebuilt.bffg import (\n",
+        "    discrete_bf_sweep,\n",
+        "    discrete_fg_sweep,\n",
+        "    discrete_forward_sweep,\n",
+        "    discrete_schema,\n",
+        "    init_discrete_tree,\n",
+        ")\n",
+        "\n",
+        "jax.config.update(\"jax_enable_x64\", True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f67cfa7d",
+      "metadata": {},
+      "source": [
+        "## 1. Tree and schema\n",
+        "\n",
+        "`hx.from_newick` returns a `Tree` whose topology carries the rooted BFS order and node names. We pass the BFFG schema as extra fields; `from_newick` also keeps an `edge_length` field, which we use later for the deterministic mean comparison."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "dea41cc5",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nodes: 11, leaves: 7\n",
+            "leaves: Human, Chimpanzee, Gorilla, Macaque, Baboon, Orangutan, Gibbon\n"
+          ]
+        }
+      ],
+      "source": [
+        "PRIMATE_NEWICK = \"\"\"\n",
+        "(\n",
+        "  Human:1.0,\n",
+        "  Chimpanzee:1.0,\n",
+        "  (\n",
+        "    Gorilla:1.0,\n",
+        "    (Orangutan:1.0, Gibbon:1.0)AsianApes:1.0\n",
+        "  )GreatApes:1.0,\n",
+        "  (Macaque:1.0, Baboon:1.0)OldWorldMonkeys:1.0\n",
+        ")Primates;\n",
+        "\"\"\"\n",
+        "\n",
+        "D = 1\n",
+        "schema = {**discrete_schema(D), \"estimated_value\": (D,)}\n",
+        "tree = hx.from_newick(PRIMATE_NEWICK, schema=schema)\n",
+        "topo = tree.topology\n",
+        "\n",
+        "# This tutorial uses a unit-edge model. Set edge lengths to one so the\n",
+        "# deterministic phylo_mean comparison uses the same branch weights.\n",
+        "tree = tree.set(edge_length=jnp.ones(topo.size))\n",
+        "\n",
+        "leaf_idx = np.where(topo.is_leaf)[0]\n",
+        "leaf_names = [topo.names[i] for i in leaf_idx]\n",
+        "print(f\"nodes: {topo.size}, leaves: {len(leaf_idx)}\")\n",
+        "print(\"leaves:\", \", \".join(leaf_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12c3e079",
+      "metadata": {},
+      "source": [
+        "## 2. Linear normal edge model\n",
+        "\n",
+        "For each edge, the latent child state is\n",
+        "\n",
+        "$$X_c \\mid X_p = x \\sim N(x, \\sigma^2 I).$$\n",
+        "\n",
+        "The BFFG auxiliary has call signature `(anchor, params)`. Because this model is already linear, the callables ignore the anchor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "8ae643ff",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "SIGMA_SQ = 1.0\n",
+        "OBS_VAR = 0.08\n",
+        "ROOT_TRUTH = jnp.array([0.4])\n",
+        "PARAMS = {\"sigma_sq\": jnp.array(SIGMA_SQ)}\n",
+        "\n",
+        "\n",
+        "def mean_fn(x_parent, params):\n",
+        "    return x_parent\n",
+        "\n",
+        "\n",
+        "def covar_fn(x_parent, params):\n",
+        "    return params[\"sigma_sq\"] * jnp.eye(D, dtype=x_parent.dtype)\n",
+        "\n",
+        "\n",
+        "def prxy_scale_fn(anchor, params):\n",
+        "    return jnp.eye(D, dtype=anchor.dtype)\n",
+        "\n",
+        "\n",
+        "def prxy_shift_fn(anchor, params):\n",
+        "    return jnp.zeros((D,), dtype=anchor.dtype)\n",
+        "\n",
+        "\n",
+        "def prxy_covar_fn(anchor, params):\n",
+        "    return params[\"sigma_sq\"] * jnp.eye(D, dtype=anchor.dtype)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e57f7266",
+      "metadata": {},
+      "source": [
+        "## 3. Simulate observations\n",
+        "\n",
+        "`discrete_forward_sweep` is a down-sweep. The root is pinned first, and every non-root node consumes its own pre-stored standard normal `z`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "ed0abb10",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Human           latent=-0.754  obs=-0.901\n",
+            "Chimpanzee      latent= 0.354  obs= 0.264\n",
+            "Gorilla         latent= 2.133  obs= 2.336\n",
+            "Macaque         latent= 0.115  obs= 0.199\n",
+            "Baboon          latent=-0.372  obs=-0.430\n",
+            "Orangutan       latent= 0.305  obs= 0.152\n",
+            "Gibbon          latent= 0.678  obs= 0.783\n"
+          ]
+        }
+      ],
+      "source": [
+        "key = jax.random.PRNGKey(7)\n",
+        "key_path, key_obs, key_samples = jax.random.split(key, 3)\n",
+        "\n",
+        "forward = discrete_forward_sweep(mean_fn, covar_fn)\n",
+        "truth = tree.at[topo.is_root].set(val=ROOT_TRUTH[None, :])\n",
+        "truth = truth.set(z=jax.random.normal(key_path, (topo.size, D)))\n",
+        "truth = forward(truth, params=PARAMS)\n",
+        "\n",
+        "leaf_truth = truth.val[topo.is_leaf]\n",
+        "leaf_obs = leaf_truth + jnp.sqrt(OBS_VAR) * jax.random.normal(key_obs, leaf_truth.shape)\n",
+        "\n",
+        "for name, latent, obs in zip(leaf_names, leaf_truth[:, 0], leaf_obs[:, 0]):\n",
+        "    print(f\"{name:15s} latent={float(latent): .3f}  obs={float(obs): .3f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7fabbe15",
+      "metadata": {},
+      "source": [
+        "## 4. Backward filter\n",
+        "\n",
+        "`init_discrete_tree` converts noisy leaf observations into canonical leaf messages and seeds the anchor field. The backward filter then pulls every child message through the auxiliary transition and sums messages at each parent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "8da2b9fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "root posterior mean:  0.094\n",
+            "root posterior sd:    0.567\n",
+            "true root value:      0.400\n"
+          ]
+        }
+      ],
+      "source": [
+        "obs_tree = init_discrete_tree(tree, leaf_obs, OBS_VAR, d=D)\n",
+        "\n",
+        "bf = discrete_bf_sweep(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn)\n",
+        "up_out = bf(obs_tree, params=PARAMS)\n",
+        "\n",
+        "root_prec = up_out.prec[0]\n",
+        "root_ptnl = up_out.ptnl[0]\n",
+        "root_post_mean = jnp.linalg.solve(root_prec, root_ptnl)\n",
+        "root_post_covar = jnp.linalg.inv(root_prec)\n",
+        "\n",
+        "print(f\"root posterior mean: {float(root_post_mean[0]): .3f}\")\n",
+        "print(f\"root posterior sd:   {float(jnp.sqrt(root_post_covar[0, 0])): .3f}\")\n",
+        "print(f\"true root value:     {float(ROOT_TRUTH[0]): .3f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "05ab8038",
+      "metadata": {},
+      "source": [
+        "## 5. Forward-guided posterior samples\n",
+        "\n",
+        "The root message is the marginal posterior for the root under a flat root prior. We first sample the root from that one-dimensional normal and then run `discrete_fg_sweep` top-down to sample the rest of the latent tree."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "f4e459ac",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "mean absolute leaf error: 0.049\n",
+            "posterior sd range:       0.267 to 0.672\n"
+          ]
+        }
+      ],
+      "source": [
+        "fg = discrete_fg_sweep(\n",
+        "    mean_fn,\n",
+        "    covar_fn,\n",
+        "    prxy_scale_fn,\n",
+        "    prxy_shift_fn,\n",
+        "    prxy_covar_fn,\n",
+        ")\n",
+        "root_chol = jnp.linalg.cholesky(root_post_covar)\n",
+        "\n",
+        "\n",
+        "def one_sample(sample_key):\n",
+        "    key_root, key_noise = jax.random.split(sample_key)\n",
+        "    root = root_post_mean + root_chol @ jax.random.normal(key_root, (D,))\n",
+        "    t = up_out.at[topo.is_root].set(val=root[None, :])\n",
+        "    t = t.set(z=jax.random.normal(key_noise, (topo.size, D)))\n",
+        "    return fg(t, params=PARAMS).val\n",
+        "\n",
+        "\n",
+        "sample_keys = jax.random.split(key_samples, 512)\n",
+        "samples = jax.vmap(one_sample)(sample_keys)\n",
+        "post_mean = samples.mean(axis=0).squeeze(-1)\n",
+        "post_sd = samples.std(axis=0).squeeze(-1)\n",
+        "\n",
+        "print(f\"mean absolute leaf error: {float(jnp.mean(jnp.abs(post_mean[topo.is_leaf] - leaf_obs[:, 0]))):.3f}\")\n",
+        "print(f\"posterior sd range:       {float(post_sd.min()):.3f} to {float(post_sd.max()):.3f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1dc3f318",
+      "metadata": {},
+      "source": [
+        "Plotting the posterior mean and one-standard-deviation bands shows the uncertainty contracts near observed leaves and is wider at internal nodes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "3db9df75",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 900x380 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.arange(topo.size)\n",
+        "fig, ax = plt.subplots(figsize=(9, 3.8))\n",
+        "ax.errorbar(x, np.asarray(post_mean), yerr=np.asarray(post_sd), fmt=\"o\", capsize=3, label=\"posterior\")\n",
+        "ax.scatter(leaf_idx, np.asarray(leaf_obs[:, 0]), marker=\"x\", color=\"tab:red\", label=\"leaf obs\")\n",
+        "ax.scatter(x, np.asarray(truth.val[:, 0]), marker=\".\", color=\"black\", label=\"latent truth\")\n",
+        "ax.set_xlabel(\"BFS node index\")\n",
+        "ax.set_ylabel(\"state\")\n",
+        "ax.legend(loc=\"best\")\n",
+        "ax.grid(alpha=0.25)\n",
+        "plt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "68b6243c",
+      "metadata": {},
+      "source": [
+        "## 6. Deterministic mean comparison\n",
+        "\n",
+        "`phylo_mean` is a simple edge-weighted averaging sweep. With unit edge lengths it matches a quick deterministic baseline, while BFFG gives full posterior uncertainty and samples every latent node."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "ad4ec750",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "node  name              phylo_mean  bffg_mean  bffg_sd\n",
+            "   0  Primates              0.162      0.068    0.565\n",
+            "   3  GreatApes             1.402      0.953    0.672\n",
+            "   4  OldWorldMonkeys      -0.115     -0.036    0.637\n",
+            "   6  AsianApes             0.467      0.643    0.631\n"
+          ]
+        }
+      ],
+      "source": [
+        "mean_tree = tree.at[topo.is_leaf].set(estimated_value=leaf_obs)\n",
+        "mean_out = phylo_mean()(mean_tree)\n",
+        "\n",
+        "rows = []\n",
+        "for node in range(topo.size):\n",
+        "    if topo.is_leaf[node]:\n",
+        "        continue\n",
+        "    name = topo.names[node] or f\"node_{node}\"\n",
+        "    rows.append((node, name, float(mean_out.estimated_value[node, 0]), float(post_mean[node]), float(post_sd[node])))\n",
+        "\n",
+        "print(\"node  name              phylo_mean  bffg_mean  bffg_sd\")\n",
+        "for node, name, det, mean, sd in rows:\n",
+        "    print(f\"{node:4d}  {name:16s} {det:10.3f} {mean:10.3f} {sd:8.3f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "29addab6",
+      "metadata": {},
+      "source": [
+        "## Takeaways\n",
+        "\n",
+        "- `discrete_schema` declares all fields needed by the discrete BFFG pipeline.\n",
+        "- `init_discrete_tree` seeds canonical leaf messages from noisy observations.\n",
+        "- `discrete_bf_sweep` is the bottom-up evidence pass.\n",
+        "- `discrete_fg_sweep` is the top-down posterior sampler once the root state is sampled or pinned.\n",
+        "- Nonlinear models use the same sweeps, but iterate `discrete_bf_sweep` with `discrete_refine_anchor` before sampling."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "hyperiax",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/05_gaussian_bffg.ipynb b/docs/source/notebooks/05_gaussian_bffg.ipynb
new file mode 100644
index 0000000..e7e0949
--- /dev/null
+++ b/docs/source/notebooks/05_gaussian_bffg.ipynb
@@ -0,0 +1,1159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5370effd",
+   "metadata": {},
+   "source": [
+    "# Gaussian MCMC — BFFG with a hand-rolled kernel\n",
+    "\n",
+    "This notebook is about the **BFFG prebuilt** (`hyperiax.prebuilt.bffg`) and how to wrap it into a custom MCMC kernel that NumPyro's `MCMC` driver can run. The setup is a depth-4 binary Gaussian tree (31 nodes, 16 leaves, unit edges):\n",
+    "\n",
+    "$$x_v \\mid x_{\\text{pa}(v)} \\sim \\mathcal{N}(x_{\\text{pa}(v)},\\, \\sigma^2 I), \\qquad y_\\ell = x_\\ell + \\mathcal{N}(0, \\tau^2),$$\n",
+    "\n",
+    "and we want the joint posterior of the latent paths and the hyperparameters $(\\sigma^2, \\tau^2)$.\n",
+    "\n",
+    "## What BFFG gives you\n",
+    "\n",
+    "Following van der Meulen & Sommer (2025) §6.1 / Theorem 14, BFFG provides:\n",
+    "\n",
+    "1. A **backward filter** (`discrete_bf_sweep`) that propagates canonical-form messages $(H_v, F_v)$ from the leaves up to the root.\n",
+    "2. A **guided forward sampler** (`discrete_fg_sweep`) that draws each $x_v$ conditional on its parent *and* the downstream leaf observations, accumulating a per-edge **importance-weight correction** $\\log w_v$.\n",
+    "3. In the linear-Gaussian case where the auxiliary process equals the true process, $\\sum \\log w_v \\equiv 0$ (Theorem-14 collapse) — we verify this empirically below.\n",
+    "\n",
+    "The result is a pure $(z, \\log\\theta) \\mapsto (x, \\sum \\log w)$ map. To turn it into MCMC we need a *kernel* that proposes moves in $z$ and $\\log\\theta$ and accepts them against the BFFG-implied target.\n",
+    "\n",
+    "## What we'll build\n",
+    "\n",
+    "A two-block Metropolis-within-Gibbs kernel:\n",
+    "\n",
+    "- **pCN block on $z$** — Crank-Nicolson proposal $z' = \\sqrt{1-\\beta^2}\\,z + \\beta\\,\\varepsilon$. Reversible w.r.t. the standard-normal prior on $z$; in the Theorem-14-collapsed regime its accept probability is exactly 1.\n",
+    "- **RW block on $\\log\\theta$** — symmetric Gaussian random walk; standard Metropolis acceptance against the joint potential.\n",
+    "\n",
+    "The kernel is a NumPyro `MCMCKernel` subclass — about 30 lines. NumPyro's `MCMC` driver handles warmup, chains, and the JIT plumbing. *NumPyro acts purely as the engine here; the proposal logic is ours.*\n",
+    "\n",
+    "(Notebook [`06_gaussian_nuts.ipynb`](06_gaussian_nuts.ipynb) keeps the model identical but swaps this hand-rolled kernel for NumPyro's built-in **NUTS**, which uses gradients of the same potential. That's a separate story.)\n",
+    "\n",
+    "## Outline\n",
+    "\n",
+    "1. Setup — tree, schema, synthetic data, constants, BFFG-guided forward map\n",
+    "2. Closed-form marginal $\\log p(y \\mid \\theta)$ (the ground-truth landmark)\n",
+    "3. BFFG mechanics walkthrough — backward filter, guided forward, one trajectory\n",
+    "4. Empirical Theorem-14 collapse — $\\sum \\log w \\equiv 0$ to machine precision\n",
+    "5. NumPyro model wrapping BFFG\n",
+    "6. Custom `RWpCNKernel` — implementation walkthrough\n",
+    "7. Run the chain — 4 chains × 8000 samples\n",
+    "8. Verification — chain histogram vs analytic grid posterior\n",
+    "9. Non-linear teaser — state-dependent covariance breaks the collapse\n",
+    "10. Recap and bridges to notebooks 06 (NUTS) and 07 (SDE bridges)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82d4e8d9",
+   "metadata": {},
+   "source": [
+    "## 1. Setup\n",
+    "\n",
+    "We build the example from scratch so this notebook is self-contained: a depth-4 binary tree, the linear-Gaussian model, synthetic leaf observations, the closed-form marginal likelihood, and the BFFG-guided forward map."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f492f5fe",
+   "metadata": {},
+   "source": [
+    "### Imports + tree topology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "050cb66a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:23.455478Z",
+     "iopub.status.busy": "2026-05-28T20:08:23.455325Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.119102Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.118626Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/vbd402/Projects/hyperiax/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tree: 31 nodes, 16 leaves, depth 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import time\n",
+    "from collections import namedtuple\n",
+    "\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from numpyro.infer import MCMC\n",
+    "from numpyro.infer.mcmc import MCMCKernel\n",
+    "from numpyro.infer.util import initialize_model\n",
+    "from numpyro.diagnostics import effective_sample_size, gelman_rubin\n",
+    "\n",
+    "import hyperiax as hx\n",
+    "from hyperiax.prebuilt.bffg import (\n",
+    "    discrete_bf_sweep,\n",
+    "    discrete_fg_sweep,\n",
+    "    discrete_forward_sweep,\n",
+    "    discrete_schema,\n",
+    "    init_discrete_tree,\n",
+    ")\n",
+    "\n",
+    "D = 1                                          # scalar latent at every node\n",
+    "topo = hx.symmetric_topology(depth=4, degree=2)\n",
+    "N_NODES = topo.size                            # 31\n",
+    "N_LEAVES = int(topo.is_leaf.sum())             # 16\n",
+    "SCHEMA = discrete_schema(d=D)                  # {val, z, ptnl, prec, log_corr}\n",
+    "empty = hx.Tree.empty(topo, SCHEMA)\n",
+    "ROOT_VAL = jnp.zeros((D,))                     # pin root at 0\n",
+    "\n",
+    "print(f\"tree: {N_NODES} nodes, {N_LEAVES} leaves, depth {topo.depth}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daa5a443",
+   "metadata": {},
+   "source": [
+    "### True parameters + MCMC constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a59dfe26",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.120871Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.120736Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.122979Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.122572Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "truth: sigma^2 = 0.5, tau^2 = 0.1\n",
+      "prior: N(0, 2.0^2) on each log theta\n",
+      "kernel constants: pCN beta = 0.2, RW scale = 0.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "SIGMA_SQ_TRUE = 0.5    # transition variance\n",
+    "TAU_SQ_TRUE = 0.1      # observation variance\n",
+    "PRIOR_STD = 2.0        # N(0, PRIOR_STD^2) prior on each log theta\n",
+    "BETA_PCN = 0.20        # pCN step size\n",
+    "RW_SCALE = 0.80        # random-walk step size in log-theta\n",
+    "\n",
+    "print(f\"truth: sigma^2 = {SIGMA_SQ_TRUE}, tau^2 = {TAU_SQ_TRUE}\")\n",
+    "print(f\"prior: N(0, {PRIOR_STD}^2) on each log theta\")\n",
+    "print(f\"kernel constants: pCN beta = {BETA_PCN}, RW scale = {RW_SCALE}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6506e2b",
+   "metadata": {},
+   "source": [
+    "### Linear-Gaussian model + matching auxiliary\n",
+    "\n",
+    "`mean_fn` and `covar_fn` are the true transition $\\mathcal{N}(x_{\\text{pa}}, \\sigma^2 I)$. The auxiliary triple `(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn) = (I, 0, \\sigma^2 I)` matches the truth exactly — this is the regime where Theorem 14 §6.1 collapses the importance weight to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a42208fa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.124160Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.124074Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.126567Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.125872Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def mean_fn(x_pa, params):\n",
+    "    return x_pa  # Phi = I\n",
+    "\n",
+    "\n",
+    "def covar_fn(x_pa, params):\n",
+    "    return params[\"sigma_sq\"] * jnp.eye(D)  # state-independent\n",
+    "\n",
+    "\n",
+    "def prxy_scale_fn(anchor, params):\n",
+    "    return jnp.eye(D)  # Phi = I (auxiliary matches truth)\n",
+    "\n",
+    "\n",
+    "def prxy_shift_fn(anchor, params):\n",
+    "    return jnp.zeros(D)  # beta = 0\n",
+    "\n",
+    "\n",
+    "def prxy_covar_fn(anchor, params):\n",
+    "    return params[\"sigma_sq\"] * jnp.eye(D)  # Q matches truth\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8590ff90",
+   "metadata": {},
+   "source": [
+    "### Synthetic data — forward simulation under the truth\n",
+    "\n",
+    "`discrete_forward_sweep` walks root → leaves drawing each $x_v \\sim \\mathcal{N}(x_{\\text{pa}}, \\sigma^2 I)$ via reparameterisation `x_v = x_pa + sqrt(sigma_sq) * z_v`. The leaves are then perturbed with $\\mathcal{N}(0, \\tau^2)$ to give the observations the rest of the notebook treats as data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8d24d07c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.127730Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.127643Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.585832Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.585355Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first 4 leaf observations: [-3.2318456 -1.7759498 -3.105866  -1.8258889]\n"
+     ]
+    }
+   ],
+   "source": [
+    "_sweep_forward = discrete_forward_sweep(mean_fn, covar_fn)\n",
+    "_k_path, _k_obs = jax.random.split(jax.random.PRNGKey(202605), 2)\n",
+    "\n",
+    "_gt = empty.at[topo.is_root].set(val=ROOT_VAL)\n",
+    "_gt = _gt.set(z=jax.random.normal(_k_path, (N_NODES, D)))\n",
+    "_gt = _sweep_forward(_gt, params={\"sigma_sq\": SIGMA_SQ_TRUE})\n",
+    "\n",
+    "leaf_truth = _gt.val[topo.is_leaf]\n",
+    "_obs_noise = jnp.sqrt(TAU_SQ_TRUE) * jax.random.normal(_k_obs, (N_LEAVES, D))\n",
+    "leaf_obs = leaf_truth + _obs_noise          # (N_LEAVES, D)\n",
+    "print(f\"first 4 leaf observations: {np.asarray(leaf_obs[:4, 0])}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "033ba89d",
+   "metadata": {},
+   "source": [
+    "### Closed-form marginal $\\log p(y \\mid \\theta)$ via the MRCA-depth kernel\n",
+    "\n",
+    "For our depth-4 binary tree with unit edges and $x_{\\text{root}} = 0$, the leaves are jointly multivariate normal:\n",
+    "\n",
+    "$$y \\sim \\mathcal{N}\\!\\bigl(0,\\;\\; \\sigma^2 K + \\tau^2 I\\bigr), \\qquad K_{ij} = \\text{depth}\\bigl(\\text{MRCA}(i, j)\\bigr).$$\n",
+    "\n",
+    "$K$ is computed once from the topology. This will be the ground-truth landmark for verifying the BFFG-MCMC chain at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ce87a9e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.587227Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.587131Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.757807Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.757387Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log p(y | theta_true) = -24.4213\n"
+     ]
+    }
+   ],
+   "source": [
+    "def _root_to_mrca_depth(topo):\n",
+    "    # (n_leaves, n_leaves): depth of MRCA(leaf_i, leaf_j) above the root.\n",
+    "    parents = np.asarray(topo.parents)\n",
+    "    node_depths = np.asarray(topo.node_depths)\n",
+    "\n",
+    "    def path_to_root(node):\n",
+    "        path = [int(node)]\n",
+    "        while path[-1] != 0:\n",
+    "            path.append(int(parents[path[-1]]))\n",
+    "        return path[::-1]\n",
+    "\n",
+    "    leaf_idx = np.where(np.asarray(topo.is_leaf))[0]\n",
+    "    paths = [path_to_root(int(li)) for li in leaf_idx]\n",
+    "    L = len(leaf_idx)\n",
+    "    K = np.zeros((L, L), dtype=np.float32)\n",
+    "    for i, pi in enumerate(paths):\n",
+    "        for j, pj in enumerate(paths):\n",
+    "            mrca = 0\n",
+    "            for a, b in zip(pi, pj):\n",
+    "                if a == b:\n",
+    "                    mrca = a\n",
+    "                else:\n",
+    "                    break\n",
+    "            K[i, j] = float(node_depths[mrca])\n",
+    "    return K\n",
+    "\n",
+    "\n",
+    "MRCA_K = jnp.asarray(_root_to_mrca_depth(topo))\n",
+    "Y_VEC = leaf_obs.squeeze(-1)\n",
+    "I_LEAVES = jnp.eye(N_LEAVES)\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def marginal_loglik(log_sigma_sq, log_tau_sq):\n",
+    "    Sigma = jnp.exp(log_sigma_sq) * MRCA_K + jnp.exp(log_tau_sq) * I_LEAVES\n",
+    "    return jax.scipy.stats.multivariate_normal.logpdf(\n",
+    "        Y_VEC, mean=jnp.zeros(N_LEAVES), cov=Sigma\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "print(f\"log p(y | theta_true) = {float(marginal_loglik(jnp.log(SIGMA_SQ_TRUE), jnp.log(TAU_SQ_TRUE))):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb9720df",
+   "metadata": {},
+   "source": [
+    "### BFFG-guided forward map\n",
+    "\n",
+    "The three sweeps `init_discrete_tree → discrete_bf_sweep → discrete_fg_sweep` compose into a pure $(z, \\log\\theta) \\mapsto (x, \\sum \\log w)$ map. We define the sweeps and the wrapper once here; the rest of the notebook walks through what each piece does (§ 3) and then plugs the wrapper into the MCMC target (§ 5–6)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "104dde02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.759308Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.759178Z",
+     "iopub.status.idle": "2026-05-28T20:08:24.761898Z",
+     "shell.execute_reply": "2026-05-28T20:08:24.761485Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bf_sweep = discrete_bf_sweep(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn)\n",
+    "fg_sweep = discrete_fg_sweep(\n",
+    "    mean_fn, covar_fn, prxy_scale_fn, prxy_shift_fn, prxy_covar_fn\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def bffg_guided_forward(z, log_theta):\n",
+    "    # init -> backward filter -> guided forward.\n",
+    "    # Returns (per-node sampled value x, total Theorem-14 log_corr).\n",
+    "    sigma_sq = jnp.exp(log_theta[0])\n",
+    "    tau_sq = jnp.exp(log_theta[1])\n",
+    "    params = {\"sigma_sq\": sigma_sq}\n",
+    "\n",
+    "    t = init_discrete_tree(empty, leaf_obs, obs_var=tau_sq, d=D, root_val=ROOT_VAL)\n",
+    "    t = bf_sweep(t, params=params)\n",
+    "    t = t.set(z=z[:, None])\n",
+    "    t = fg_sweep(t, params=params)\n",
+    "    return t.val.squeeze(-1), t.log_corr.sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d3b5bcb",
+   "metadata": {},
+   "source": [
+    "## 2. The log-likelihood landscape\n",
+    "\n",
+    "A quick visual of `marginal_loglik` shows that the closed-form $\\log p(y \\mid \\theta)$ has its peak around $(\\sigma^2, \\tau^2)_\\text{MLE}$ near the truth, but is fairly flat in $\\tau^2$ — with only 16 leaves, $\\tau^2$ is weakly identified. We'll see exactly this shape in the posterior at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d1627ad5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:24.763263Z",
+     "iopub.status.busy": "2026-05-28T20:08:24.763156Z",
+     "iopub.status.idle": "2026-05-28T20:08:25.104885Z",
+     "shell.execute_reply": "2026-05-28T20:08:25.104400Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log p(y | theta_true) = -24.4213\n"
+     ]
+    }
+   ],
+   "source": [
+    "log_sigmas = jnp.linspace(-2.5, 1.5, 41)\n",
+    "log_taus = jnp.linspace(-4.0, 1.0, 41)\n",
+    "slice_s = jax.vmap(lambda ls: marginal_loglik(ls, jnp.log(TAU_SQ_TRUE)))(log_sigmas)\n",
+    "slice_t = jax.vmap(lambda lt: marginal_loglik(jnp.log(SIGMA_SQ_TRUE), lt))(log_taus)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 3.5))\n",
+    "ax[0].plot(np.exp(log_sigmas), np.asarray(slice_s))\n",
+    "ax[0].axvline(SIGMA_SQ_TRUE, ls=\"--\", color=\"k\", lw=1, label=\"truth\")\n",
+    "ax[0].set_xlabel(r\"$\\sigma^2$\"); ax[0].set_ylabel(r\"$\\log p(y \\mid \\theta)$\")\n",
+    "ax[0].set_title(r\"slice at $\\tau^2 = \\tau^2_{\\text{true}}$\"); ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(np.exp(log_taus), np.asarray(slice_t))\n",
+    "ax[1].axvline(TAU_SQ_TRUE, ls=\"--\", color=\"k\", lw=1, label=\"truth\")\n",
+    "ax[1].set_xlabel(r\"$\\tau^2$\"); ax[1].set_ylabel(r\"$\\log p(y \\mid \\theta)$\")\n",
+    "ax[1].set_title(r\"slice at $\\sigma^2 = \\sigma^2_{\\text{true}}$\"); ax[1].legend()\n",
+    "plt.tight_layout(); plt.show()\n",
+    "print(f\"log p(y | theta_true) = {float(marginal_loglik(jnp.log(SIGMA_SQ_TRUE), jnp.log(TAU_SQ_TRUE))):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0549f9cb",
+   "metadata": {},
+   "source": [
+    "## 3. BFFG mechanics — what the three sweeps do\n",
+    "\n",
+    "The wrapper `bffg_guided_forward` is short but does a lot inside. Let's expand it one sweep at a time:\n",
+    "\n",
+    "- **`init_discrete_tree(tree, leaf_obs, obs_var=tau_sq, d, root_val)`** — seeds the leaf canonical messages $H_\\ell = 1/\\tau^2 \\cdot I$, $F_\\ell = y_\\ell / \\tau^2$ and pins the root.\n",
+    "- **`discrete_bf_sweep`** — the backward filter. Propagates $(H_v, F_v)$ child → parent at every level: $H_v = \\sum_c \\Phi^\\top C_c^{-1} \\Phi$, $F_v = \\sum_c \\Phi^\\top C_c^{-1}(F_c - H_c \\beta)$ with $C_c = I + H_c Q$.\n",
+    "- **`discrete_fg_sweep`** — the guided forward. Given a parent state and standard-normal noise $z_v$, samples $x_v$ from the canonical-form proposal $\\mathcal{N}^{\\text{can}}(F_v + Q^{-1}\\mu,\\, H_v + Q^{-1})$ and writes the per-edge importance weight $\\log w_v$ to `log_corr`.\n",
+    "\n",
+    "The linear-auxiliary-equals-truth choice we made in § 1 means every $\\log w_v$ vanishes by Theorem 14 §6.1 — we verify that empirically in § 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "604aca48",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:25.106237Z",
+     "iopub.status.busy": "2026-05-28T20:08:25.106132Z",
+     "iopub.status.idle": "2026-05-28T20:08:25.859961Z",
+     "shell.execute_reply": "2026-05-28T20:08:25.859347Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "after init_discrete_tree:\n",
+      "  prec at leaves = 1/tau^2 = 10.0000\n",
+      "  ptnl at leaves = y/tau^2 (first three) = [-32.318455 -17.759499 -31.058659]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "after discrete_bf_sweep (root canonical message):\n",
+      "  H_root = 2.1053\n",
+      "  F_root = -2.2076\n"
+     ]
+    }
+   ],
+   "source": [
+    "params_true = {\"sigma_sq\": SIGMA_SQ_TRUE}\n",
+    "\n",
+    "t = init_discrete_tree(empty, leaf_obs, obs_var=TAU_SQ_TRUE, d=D, root_val=ROOT_VAL)\n",
+    "print(\"after init_discrete_tree:\")\n",
+    "print(f\"  prec at leaves = 1/tau^2 = {float(t.prec[topo.is_leaf][0, 0, 0]):.4f}\")\n",
+    "print(f\"  ptnl at leaves = y/tau^2 (first three) = {np.asarray(t.ptnl[topo.is_leaf, 0])[:3]}\")\n",
+    "\n",
+    "t = bf_sweep(t, params=params_true)\n",
+    "print(\"\\nafter discrete_bf_sweep (root canonical message):\")\n",
+    "print(f\"  H_root = {float(t.prec[0, 0, 0]):.4f}\")\n",
+    "print(f\"  F_root = {float(t.ptnl[0, 0]):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "157715d4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:25.861528Z",
+     "iopub.status.busy": "2026-05-28T20:08:25.861436Z",
+     "iopub.status.idle": "2026-05-28T20:08:26.313480Z",
+     "shell.execute_reply": "2026-05-28T20:08:26.313136Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sum log_corr along the whole tree = 0.00e+00  (Theorem-14 collapse)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Guided forward draw: seed z ~ N(0, I), then run the down sweep.\n",
+    "key = jax.random.PRNGKey(0)\n",
+    "z = jax.random.normal(key, (N_NODES, 1))\n",
+    "t = t.set(z=z)\n",
+    "t = fg_sweep(t, params=params_true)\n",
+    "\n",
+    "parents = np.asarray(topo.parents)\n",
+    "\n",
+    "\n",
+    "def path_to_leaf(leaf_idx: int) -> list[int]:\n",
+    "    path = [int(leaf_idx)]\n",
+    "    while path[-1] != 0:\n",
+    "        path.append(int(parents[path[-1]]))\n",
+    "    return path[::-1]\n",
+    "\n",
+    "\n",
+    "leaf_idx = int(np.where(np.asarray(topo.is_leaf))[0][0])\n",
+    "path = path_to_leaf(leaf_idx)\n",
+    "x_path = np.asarray(t.val[path, 0])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 3.5))\n",
+    "ax.plot(range(len(path)), x_path, \"o-\", label=\"guided sample\")\n",
+    "ax.axhline(float(leaf_obs[0, 0]), ls=\"--\", color=\"r\", lw=1, label=f\"y_{leaf_idx} (observed leaf)\")\n",
+    "ax.set_xlabel(\"depth (root -> leaf)\")\n",
+    "ax.set_ylabel(r\"$x_v$\")\n",
+    "ax.set_title(f\"BFFG-guided trajectory along path 0 -> {leaf_idx}\")\n",
+    "ax.legend(); plt.tight_layout(); plt.show()\n",
+    "print(f\"sum log_corr along the whole tree = {float(t.log_corr.sum()):.2e}  (Theorem-14 collapse)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42fbdb92",
+   "metadata": {},
+   "source": [
+    "## 4. Empirical Theorem-14 collapse\n",
+    "\n",
+    "Theorem 14 §6.1 guarantees that when the auxiliary process *is* the true process (our setting), the importance weight $w(x) \\equiv 1$ for all sampled paths, so $\\sum \\log w \\equiv 0$. Across 500 independent noise draws at $\\theta_{\\text{true}}$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4f8142f5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:26.315036Z",
+     "iopub.status.busy": "2026-05-28T20:08:26.314925Z",
+     "iopub.status.idle": "2026-05-28T20:08:27.108918Z",
+     "shell.execute_reply": "2026-05-28T20:08:27.108538Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500 draws at theta_true: max |sum log_corr| = 0.00e+00\n",
+      "all under 1e-5? True\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def sum_logw_at(z, log_theta):\n",
+    "    _, sum_log_corr = bffg_guided_forward(z, log_theta)\n",
+    "    return sum_log_corr\n",
+    "\n",
+    "\n",
+    "keys = jax.random.split(jax.random.PRNGKey(1), 500)\n",
+    "zs = jax.vmap(lambda k: jax.random.normal(k, (N_NODES,)))(keys)\n",
+    "lt_true = jnp.log(jnp.array([SIGMA_SQ_TRUE, TAU_SQ_TRUE]))\n",
+    "sums = jax.vmap(lambda z: sum_logw_at(z, lt_true))(zs)\n",
+    "sums = np.asarray(sums)\n",
+    "\n",
+    "print(f\"500 draws at theta_true: max |sum log_corr| = {np.max(np.abs(sums)):.2e}\")\n",
+    "print(f\"all under 1e-5? {bool(np.all(np.abs(sums) < 1e-5))}\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 3))\n",
+    "ax.hist(sums, bins=40, color=\"steelblue\", edgecolor=\"white\")\n",
+    "ax.axvline(0, color=\"r\", ls=\"--\", lw=1)\n",
+    "ax.set_xlabel(r\"$\\sum_v \\log w_v$  (per draw)\")\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_title(\"500 BFFG-guided draws at θ_true — all collapse to 0\")\n",
+    "plt.tight_layout(); plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "785aad03",
+   "metadata": {},
+   "source": [
+    "This is the empirical signal that the **pCN block in our kernel will always accept** (its acceptance ratio collapses to $\\exp(\\Delta \\sum \\log w) = \\exp(0) = 1$). Any deviation from $\\equiv 0$ would indicate a bug in the BFFG implementation. The non-linear teaser at the end of the notebook shows what happens when this assumption breaks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2d39afe",
+   "metadata": {},
+   "source": [
+    "## 5. NumPyro model wrapping BFFG\n",
+    "\n",
+    "The model has two latent sites: the hyperparameters $\\log \\theta = [\\log \\sigma^2, \\log \\tau^2]$ and the per-node noise field $z \\in \\mathbb{R}^{N_\\text{nodes}}$. The BFFG-implied log-density plugs in via `numpyro.factor`:\n",
+    "\n",
+    "$$\\text{factor}_\\text{bffg} \\;=\\; \\log p(y \\mid \\theta) \\;+\\; \\sum_v \\log w_v(z, \\theta).$$\n",
+    "\n",
+    "The first term is the closed-form marginal (works as the BFFG $\\log g_r(0; \\theta)$ in this linear case); the second is the BFFG importance correction (identically zero here). `numpyro.factor` adds this to the joint potential the sampler sees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "52c182c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:27.110770Z",
+     "iopub.status.busy": "2026-05-28T20:08:27.110662Z",
+     "iopub.status.idle": "2026-05-28T20:08:28.008184Z",
+     "shell.execute_reply": "2026-05-28T20:08:28.007757Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "potential_fn ready; U(theta_true, z0) = 76.9254\n",
+      "sites: {'log_theta': (2,), 'z': (31,)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def model():\n",
+    "    log_theta = numpyro.sample(\n",
+    "        \"log_theta\", dist.Normal(jnp.zeros(2), PRIOR_STD).to_event(1)\n",
+    "    )\n",
+    "    z = numpyro.sample(\"z\", dist.Normal(jnp.zeros(N_NODES), 1.0).to_event(1))\n",
+    "    _, sum_log_corr = bffg_guided_forward(z, log_theta)\n",
+    "    log_g_r = marginal_loglik(log_theta[0], log_theta[1])\n",
+    "    numpyro.factor(\"bffg\", log_g_r + sum_log_corr)\n",
+    "\n",
+    "\n",
+    "# initialize_model traces the model once and hands back a callable\n",
+    "# potential_fn(u) = -log_joint(u). Both sites have R support (Normal priors),\n",
+    "# so the unconstrained and constrained representations coincide.\n",
+    "mi = initialize_model(jax.random.PRNGKey(0), model, validate_grad=False)\n",
+    "potential_fn = mi.potential_fn\n",
+    "z0 = jax.random.normal(jax.random.PRNGKey(0), (N_NODES,))\n",
+    "U0 = float(potential_fn({\"log_theta\": lt_true, \"z\": z0}))\n",
+    "print(f\"potential_fn ready; U(theta_true, z0) = {U0:.4f}\")\n",
+    "print(f\"sites: {dict((k, tuple(v.shape)) for k, v in mi.param_info.z.items())}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11f5ce90",
+   "metadata": {},
+   "source": [
+    "## 6. Custom `RWpCNKernel`\n",
+    "\n",
+    "A `MCMCKernel` subclass needs three methods: `init` (initialise per-chain state), `sample` (one Metropolis step), and `postprocess_fn` (transform unconstrained → constrained if needed; identity here). Inside `sample` we do two sequential blocks per call.\n",
+    "\n",
+    "### The pCN prior-cancellation line\n",
+    "\n",
+    "`potential_fn` already includes the $z$-prior $\\log \\mathcal{N}(z; 0, I)$ because we declared $z$ with `numpyro.sample(...)`. But pCN's proposal $z' = \\sqrt{1-\\beta^2}\\,z + \\beta\\,\\varepsilon$ is **reversible** w.r.t. $\\mathcal{N}(0, I)$, which means a correct accept ratio should use the *likelihood* only — not the prior. We subtract the $z$-prior delta back out:\n",
+    "\n",
+    "$$\\log\\alpha_\\text{pCN} \\;=\\; -(U' - U) \\;-\\; \\bigl(\\log\\mathcal{N}(z'; 0, I) - \\log\\mathcal{N}(z; 0, I)\\bigr).$$\n",
+    "\n",
+    "That's the one extra line vs. a textbook pCN. The RW block on $\\log\\theta$ keeps $z$ fixed, so the $z$-prior cancels in $\\Delta U$ and no correction is needed there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c8e57bd4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:28.009731Z",
+     "iopub.status.busy": "2026-05-28T20:08:28.009643Z",
+     "iopub.status.idle": "2026-05-28T20:08:28.014900Z",
+     "shell.execute_reply": "2026-05-28T20:08:28.014389Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "RWpCNState = namedtuple(\"RWpCNState\", [\"u\", \"potential_energy\", \"accept\", \"rng_key\"])\n",
+    "\n",
+    "\n",
+    "def _std_normal_logpdf(x):\n",
+    "    # log N(x; 0, I), up to an additive constant that cancels in deltas.\n",
+    "    return -0.5 * jnp.sum(x ** 2)\n",
+    "\n",
+    "\n",
+    "class RWpCNKernel(MCMCKernel):\n",
+    "    sample_field = \"u\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        potential_fn,\n",
+    "        *,\n",
+    "        beta,\n",
+    "        rw_scale,\n",
+    "        noise_site=\"z\",\n",
+    "        param_sites=(\"log_theta\",),\n",
+    "    ):\n",
+    "        self._potential_fn = potential_fn\n",
+    "        self._beta = beta\n",
+    "        self._pcn_old = jnp.sqrt(1.0 - beta ** 2)\n",
+    "        self._rw_scale = rw_scale\n",
+    "        self._noise_site = noise_site\n",
+    "        self._param_sites = tuple(param_sites)\n",
+    "\n",
+    "    def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\n",
+    "        if init_params is None:\n",
+    "            raise ValueError(\"RWpCNKernel needs explicit init_params (a dict per site).\")\n",
+    "        return RWpCNState(init_params, self._potential_fn(init_params), jnp.zeros(2), rng_key)\n",
+    "\n",
+    "    def postprocess_fn(self, model_args, model_kwargs):\n",
+    "        # Real-support sites: unconstrained == constrained.\n",
+    "        return lambda x, *a, **k: x\n",
+    "\n",
+    "    def sample(self, state, model_args, model_kwargs):\n",
+    "        u = dict(state.u)\n",
+    "        U = state.potential_energy\n",
+    "        k_pcn, k_pcn_acc, k_rw, k_rw_acc, k_next = jax.random.split(state.rng_key, 5)\n",
+    "\n",
+    "        # ── pCN block on the noise site (parameters fixed) ──\n",
+    "        z = u[self._noise_site]\n",
+    "        z_prop = self._pcn_old * z + self._beta * jax.random.normal(k_pcn, z.shape)\n",
+    "        U_prop = self._potential_fn({**u, self._noise_site: z_prop})\n",
+    "        log_alpha = (\n",
+    "            -(U_prop - U)\n",
+    "            - (_std_normal_logpdf(z_prop) - _std_normal_logpdf(z))  # prior-cancel\n",
+    "        )\n",
+    "        acc_z = jnp.log(jax.random.uniform(k_pcn_acc)) < log_alpha\n",
+    "        u[self._noise_site] = jnp.where(acc_z, z_prop, z)\n",
+    "        U = jnp.where(acc_z, U_prop, U)  # re-cache before the RW block\n",
+    "\n",
+    "        # ── RW block on the parameter sites (noise fixed) ──\n",
+    "        rw_keys = jax.random.split(k_rw, len(self._param_sites))\n",
+    "        u_prop = dict(u)\n",
+    "        for site, key in zip(self._param_sites, rw_keys):\n",
+    "            u_prop[site] = u[site] + self._rw_scale * jax.random.normal(key, u[site].shape)\n",
+    "        U_prop = self._potential_fn(u_prop)\n",
+    "        acc_th = jnp.log(jax.random.uniform(k_rw_acc)) < -(U_prop - U)  # symmetric\n",
+    "        for site in self._param_sites:\n",
+    "            u[site] = jnp.where(acc_th, u_prop[site], u[site])\n",
+    "        U = jnp.where(acc_th, U_prop, U)\n",
+    "\n",
+    "        return RWpCNState(u, U, jnp.array([acc_z, acc_th], dtype=jnp.float32), k_next)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a7f51a",
+   "metadata": {},
+   "source": [
+    "## 7. Run the chain\n",
+    "\n",
+    "4 chains × 8000 samples with overdispersed inits. NumPyro 0.21's vectorized chain mode doesn't auto-vmap a custom kernel, so we loop 4 independent single-chain runs — JAX caches the compiled program across runs, so the overhead is one compile.\n",
+    "\n",
+    "In the linear-Gaussian regime we expect:\n",
+    "\n",
+    "- **pCN acceptance ≈ 1.0** — the visible signature of Theorem-14 collapse.\n",
+    "- **RW acceptance** in $[0.30, 0.55]$ — the optimal range for a 2-D Gaussian random walk in $\\log\\theta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ef626114",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:28.016182Z",
+     "iopub.status.busy": "2026-05-28T20:08:28.016114Z",
+     "iopub.status.idle": "2026-05-28T20:08:34.053724Z",
+     "shell.execute_reply": "2026-05-28T20:08:34.053198Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RW/pCN: 5.9s   4 chains x 8000 samples\n",
+      "  acc pCN = 1.0000  (Theorem-14 collapse -> ~1.0)\n",
+      "  acc RW  = 0.4633  (target band 0.30-0.55)\n",
+      "  Rhat    = [0.99996847 1.000542  ]\n",
+      "  ESS     = [3448.88762301 1151.18399045]  (of 32000 draws)\n"
+     ]
+    }
+   ],
+   "source": [
+    "N_WARMUP, N_SAMPLES, N_CHAINS = 1000, 8000, 4\n",
+    "\n",
+    "INIT_THETAS = np.array(\n",
+    "    [[-1.5, -2.0], [1.0, -1.0], [0.3, 0.5], [-0.5, 0.0]], dtype=np.float32\n",
+    ")\n",
+    "INIT_ZS = np.asarray(jax.random.normal(jax.random.PRNGKey(33), (N_CHAINS, N_NODES)))\n",
+    "\n",
+    "kernel = RWpCNKernel(potential_fn, beta=BETA_PCN, rw_scale=RW_SCALE)\n",
+    "chains, accs = [], []\n",
+    "t0 = time.perf_counter()\n",
+    "for c in range(N_CHAINS):\n",
+    "    mc = MCMC(\n",
+    "        kernel,\n",
+    "        num_warmup=N_WARMUP,\n",
+    "        num_samples=N_SAMPLES,\n",
+    "        num_chains=1,\n",
+    "        progress_bar=False,\n",
+    "    )\n",
+    "    init = {\"log_theta\": jnp.asarray(INIT_THETAS[c]), \"z\": jnp.asarray(INIT_ZS[c])}\n",
+    "    mc.run(jax.random.PRNGKey(7 + c), init_params=init, extra_fields=(\"accept\",))\n",
+    "    chains.append(np.asarray(mc.get_samples()[\"log_theta\"]))\n",
+    "    accs.append(np.asarray(mc.get_extra_fields()[\"accept\"]))\n",
+    "elapsed = time.perf_counter() - t0\n",
+    "\n",
+    "lt = np.stack(chains)  # (chains, samples, 2)\n",
+    "acc = np.stack(accs)   # (chains, samples, 2): [pCN, RW]\n",
+    "\n",
+    "print(f\"RW/pCN: {elapsed:.1f}s   {N_CHAINS} chains x {N_SAMPLES} samples\")\n",
+    "print(f\"  acc pCN = {acc[..., 0].mean():.4f}  (Theorem-14 collapse -> ~1.0)\")\n",
+    "print(f\"  acc RW  = {acc[..., 1].mean():.4f}  (target band 0.30-0.55)\")\n",
+    "print(f\"  Rhat    = {gelman_rubin(lt)}\")\n",
+    "print(f\"  ESS     = {effective_sample_size(lt)}  (of {N_CHAINS * N_SAMPLES} draws)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5639d4ea",
+   "metadata": {},
+   "source": [
+    "## 8. Verification vs the analytic grid posterior\n",
+    "\n",
+    "The closed-form $\\log p(y \\mid \\theta) + \\log \\pi(\\log \\theta)$ normalised and marginalised on a 200×200 grid is the cleanest possible reference — no MCMC bias, no sampler tuning. The chain histograms should hug the red curves to within Monte-Carlo noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "86928447",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:34.055056Z",
+     "iopub.status.busy": "2026-05-28T20:08:34.054980Z",
+     "iopub.status.idle": "2026-05-28T20:08:34.580861Z",
+     "shell.execute_reply": "2026-05-28T20:08:34.580464Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigma_sq  (truth = 0.5)\n",
+      "  analytic : mean=0.4669  median=0.4136  95% CI=[0.1364, 1.0678]\n",
+      "  RW/pCN   : mean=0.4676  median=0.4222  95% CI=[0.1384, 1.0574]\n",
+      "\n",
+      "tau_sq  (truth = 0.1)\n",
+      "  analytic : mean=0.3721  median=0.2584  95% CI=[0.0166, 1.3592]\n",
+      "  RW/pCN   : mean=0.3791  median=0.2602  95% CI=[0.0135, 1.4392]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_grid = 200\n",
+    "log_s2 = jnp.linspace(-3.0, 2.0, n_grid)\n",
+    "log_t2 = jnp.linspace(-5.0, 1.0, n_grid)\n",
+    "dls = float(log_s2[1] - log_s2[0])\n",
+    "dlt = float(log_t2[1] - log_t2[0])\n",
+    "LS, LT = jnp.meshgrid(log_s2, log_t2, indexing=\"ij\")\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def log_unnorm(ls, lt):\n",
+    "    return (\n",
+    "        marginal_loglik(ls, lt)\n",
+    "        + jax.scipy.stats.norm.logpdf(ls, 0.0, PRIOR_STD)\n",
+    "        + jax.scipy.stats.norm.logpdf(lt, 0.0, PRIOR_STD)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "LP = jax.vmap(jax.vmap(log_unnorm))(LS, LT)\n",
+    "P = jnp.exp(LP - LP.max())\n",
+    "P = P / (P.sum() * dls * dlt)\n",
+    "P_s2 = P.sum(1) * dlt\n",
+    "P_t2 = P.sum(0) * dls\n",
+    "s2g, t2g = jnp.exp(log_s2), jnp.exp(log_t2)\n",
+    "p_s2 = P_s2 / s2g  # density in sigma^2 space (Jacobian)\n",
+    "p_t2 = P_t2 / t2g\n",
+    "\n",
+    "\n",
+    "def grid_summary(natg, plog, dl):\n",
+    "    cdf = jnp.cumsum(plog) * dl\n",
+    "    q = lambda lv: float(jnp.interp(lv, cdf, natg))\n",
+    "    return dict(\n",
+    "        mean=float(jnp.sum(natg * plog) * dl),\n",
+    "        median=q(0.5),\n",
+    "        ci95=(q(0.025), q(0.975)),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "ana = {\n",
+    "    \"sigma_sq\": grid_summary(s2g, P_s2, dls),\n",
+    "    \"tau_sq\": grid_summary(t2g, P_t2, dlt),\n",
+    "}\n",
+    "samples = np.exp(lt.reshape(-1, 2))\n",
+    "\n",
+    "\n",
+    "def chain_summary(idx):\n",
+    "    q = np.quantile(samples[:, idx], [0.025, 0.5, 0.975])\n",
+    "    return dict(mean=float(samples[:, idx].mean()), median=float(q[1]), ci95=(float(q[0]), float(q[2])))\n",
+    "\n",
+    "\n",
+    "def fmt(s):\n",
+    "    return (\n",
+    "        f\"mean={s['mean']:.4f}  median={s['median']:.4f}  \"\n",
+    "        f\"95% CI=[{s['ci95'][0]:.4f}, {s['ci95'][1]:.4f}]\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "for k, idx, truth in ((\"sigma_sq\", 0, SIGMA_SQ_TRUE), (\"tau_sq\", 1, TAU_SQ_TRUE)):\n",
+    "    print(f\"{k}  (truth = {truth})\")\n",
+    "    print(f\"  analytic : {fmt(ana[k])}\")\n",
+    "    print(f\"  RW/pCN   : {fmt(chain_summary(idx))}\")\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2cc6da72",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:34.582123Z",
+     "iopub.status.busy": "2026-05-28T20:08:34.582047Z",
+     "iopub.status.idle": "2026-05-28T20:08:34.788920Z",
+     "shell.execute_reply": "2026-05-28T20:08:34.788440Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4.2))\n",
+    "panels = [\n",
+    "    (r\"$\\sigma^2$\", SIGMA_SQ_TRUE, s2g, p_s2, 2.5, 0),\n",
+    "    (r\"$\\tau^2$\", TAU_SQ_TRUE, t2g, p_t2, 2.0, 1),\n",
+    "]\n",
+    "for name, truth, grid_x, dens, xmax, idx in panels:\n",
+    "    a = ax[idx]\n",
+    "    a.hist(samples[:, idx], bins=80, density=True, alpha=0.6, color=\"darkorange\", label=\"RW/pCN\")\n",
+    "    a.plot(np.asarray(grid_x), np.asarray(dens), \"r-\", lw=2, label=\"analytic\")\n",
+    "    a.axvline(truth, color=\"k\", ls=\"--\", lw=1, label=\"truth\")\n",
+    "    a.set_xlim(0, xmax)\n",
+    "    a.set_xlabel(name)\n",
+    "    a.legend(fontsize=8)\n",
+    "ax[0].set_ylabel(\"posterior density\")\n",
+    "fig.suptitle(\"RW/pCN posterior vs analytic grid (linear-Gaussian)\")\n",
+    "plt.tight_layout(); plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b047c88",
+   "metadata": {},
+   "source": [
+    "## 9. Non-linear teaser — what breaks when the auxiliary differs from the truth\n",
+    "\n",
+    "So far the auxiliary process exactly matches the truth, so the importance weight collapses. Once we make the **true** transition covariance state-dependent — say, $Q_\\text{true}(x) = \\sigma^2(1 + 0.3\\,x^2)\\,I$ — the auxiliary $Q = \\sigma^2 I$ no longer matches, and $\\log w$ acquires genuine spread. Two consequences for our kernel:\n",
+    "\n",
+    "1. The pCN accept ratio is now $\\exp(\\Delta \\sum \\log w)$, which is no longer $\\equiv 1$. The pCN block does real work — proposals that increase $\\sum \\log w$ are accepted more often than proposals that decrease it.\n",
+    "2. The BFFG correction $\\sum \\log w$ shows up explicitly in the target, so the chain converges to the *true* posterior in spite of the linearised auxiliary.\n",
+    "\n",
+    "This is the setting of [notebook 07](07_kunita_shape.ipynb), where continuous-time SDE bridges (via `continuous_*` sweeps) replace the unit-edge discrete kernels here. Below we only demonstrate the first part — the spread of $\\sum \\log w$ — and leave the full nonlinear inference to 07."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e73db7d6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:34.790578Z",
+     "iopub.status.busy": "2026-05-28T20:08:34.790492Z",
+     "iopub.status.idle": "2026-05-28T20:08:35.391269Z",
+     "shell.execute_reply": "2026-05-28T20:08:35.390767Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "linear:     std(sum log_corr) = 0.00e+00\n",
+      "non-linear: std(sum log_corr) = 7.82e-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "def covar_fn_nl(x_pa, params):\n",
+    "    return params[\"sigma_sq\"] * (1.0 + 0.3 * (x_pa ** 2)) * jnp.eye(1)\n",
+    "\n",
+    "\n",
+    "fg_nl = discrete_fg_sweep(\n",
+    "    mean_fn, covar_fn_nl, prxy_scale_fn, prxy_shift_fn, prxy_covar_fn\n",
+    ")\n",
+    "bf = discrete_bf_sweep(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn)\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def bffg_nl_guided_forward(z, log_theta):\n",
+    "    sigma_sq = jnp.exp(log_theta[0])\n",
+    "    tau_sq = jnp.exp(log_theta[1])\n",
+    "    params = {\"sigma_sq\": sigma_sq}\n",
+    "    t = init_discrete_tree(empty, leaf_obs, obs_var=tau_sq, d=1, root_val=ROOT_VAL)\n",
+    "    t = bf(t, params=params)\n",
+    "    t = t.set(z=z[:, None])\n",
+    "    t = fg_nl(t, params=params)\n",
+    "    return t.val.squeeze(-1), t.log_corr.sum()\n",
+    "\n",
+    "\n",
+    "sums_nl = np.asarray(jax.vmap(lambda z: bffg_nl_guided_forward(z, lt_true)[1])(zs))\n",
+    "sums_lin = sums  # from §4\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.2))\n",
+    "ax.hist(sums_lin, bins=40, alpha=0.7, label=\"linear (auxiliary = truth)\", color=\"steelblue\")\n",
+    "ax.hist(sums_nl, bins=40, alpha=0.6, label=r\"non-linear $Q_{\\text{true}}(x)$\", color=\"darkorange\")\n",
+    "ax.set_xlabel(r\"$\\sum_v \\log w_v$\")\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_title(\"Theorem-14 collapse breaks when truth ≠ auxiliary\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout(); plt.show()\n",
+    "print(f\"linear:     std(sum log_corr) = {sums_lin.std():.2e}\")\n",
+    "print(f\"non-linear: std(sum log_corr) = {sums_nl.std():.2e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97257d74",
+   "metadata": {},
+   "source": [
+    "## Recap\n",
+    "\n",
+    "1. **`hyperiax.prebuilt.bffg`** ships three building blocks: `init_discrete_tree`, `discrete_bf_sweep`, `discrete_fg_sweep`. Together they make a pure $(z, \\log\\theta) \\mapsto (x, \\sum \\log w)$ map.\n",
+    "2. **Theorem-14 collapse holds empirically** to machine precision across 500 noise draws — the pCN block of any kernel built on top will accept 100% of proposals in this regime.\n",
+    "3. **A custom NumPyro `MCMCKernel`** (~30 lines, including a one-line pCN prior cancellation) plugs BFFG straight into NumPyro's `MCMC` driver. 4 chains × 8000 samples reproduce the analytic posterior to within ≈0.005 on means.\n",
+    "4. **State-dependent transitions break the collapse** — `sum log_corr` acquires genuine spread, the pCN accept ratio leaves the trivial 1.0 regime, and the BFFG correction does real work. That's the setting of notebook 07.\n",
+    "\n",
+    "## Where to go next\n",
+    "\n",
+    "- [`06_gaussian_nuts.ipynb`](06_gaussian_nuts.ipynb) — same model, same `numpyro.factor`, but the kernel is NumPyro's built-in **NUTS**. NUTS needs `jax.grad` of the potential, so the fact that it just runs against the BFFG pipeline is itself a demonstration of hyperiax's end-to-end differentiability.\n",
+    "- [`07_kunita_shape.ipynb`](07_kunita_shape.ipynb) — replace unit-edge discrete kernels with continuous-time SDE bridges via the `continuous_*` sweeps. The closed-form marginal disappears; BFFG becomes an approximation whose correction $\\sum \\log w$ actively keeps the chain on the true posterior.\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "- van der Meulen, F. H. & Sommer, S. (2025). *Backward Filtering Forward Guiding.* JMLR **26**(281), 1–51. — Theorem 14 (nonlinear-Gaussian importance weight); Algorithm 1 (MCMC with parameter estimation). [arXiv:2505.18239](https://arxiv.org/abs/2505.18239)\n",
+    "- Cotter, S. L., Roberts, G. O., Stuart, A. M., White, D. (2013). *MCMC methods for functions: modifying old algorithms to make them faster.* Statistical Science **28**(3), 424–446. — pCN proposal.\n",
+    "- Phan, D., Pradhan, N., Jankowiak, M. (2019). *Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro.* [arXiv:1912.11554](https://arxiv.org/abs/1912.11554)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/06_gaussian_nuts.ipynb b/docs/source/notebooks/06_gaussian_nuts.ipynb
new file mode 100644
index 0000000..dc21580
--- /dev/null
+++ b/docs/source/notebooks/06_gaussian_nuts.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cfa39cf2",
+   "metadata": {},
+   "source": [
+    "# Gaussian NUTS — and what its existence proves\n",
+    "\n",
+    "This notebook keeps the model and data of [`05_gaussian_bffg.ipynb`](05_gaussian_bffg.ipynb) **completely unchanged**. Same depth-4 binary Gaussian tree, same synthetic leaves, same priors, same `bffg_guided_forward + marginal_loglik` composed inside a NumPyro `model()` via `numpyro.factor`. The only thing that changes is the kernel: out goes the hand-rolled RW + pCN, in comes NumPyro's built-in **NUTS**.\n",
+    "\n",
+    "## Why this is interesting\n",
+    "\n",
+    "NUTS is a Hamiltonian Monte Carlo variant that uses gradients of the potential\n",
+    "\n",
+    "$$U(z, \\log\\theta) \\;=\\; -\\log p(\\log\\theta) - \\log p(z) - \\bigl[\\log g_r(0;\\theta) + \\textstyle\\sum_v \\log w_v\\bigr]$$\n",
+    "\n",
+    "to simulate trajectories through the parameter space. *No gradients, no NUTS.* The potential here threads through every piece of the BFFG pipeline — `init_discrete_tree`, `discrete_bf_sweep`, `discrete_fg_sweep`, the importance-weight accumulator, and `marginal_loglik`. The fact that NUTS *runs* against this potential is therefore a demonstration that **every one of those components is end-to-end `jax.grad`-friendly** — i.e. that hyperiax is JAX-pure in the strict sense.\n",
+    "\n",
+    "This is the whole story of the notebook. We do one tiny `jax.grad` sanity cell to make the implicit point explicit, then run NUTS and verify it targets the same posterior 05's hand-rolled chain did.\n",
+    "\n",
+    "## Outline\n",
+    "\n",
+    "1. Setup — same model as notebook 05, inlined here for self-containment\n",
+    "2. Differentiability sanity — `jax.grad` through `bffg_guided_forward`, finite-difference cross-check\n",
+    "3. NumPyro model (brief recap from 05)\n",
+    "4. NUTS — `MCMC(NUTS(model), ...)`\n",
+    "5. Verification — NUTS posterior vs analytic grid\n",
+    "6. The gradient dividend — ESS gain over 05's RW/pCN\n",
+    "7. Recap and bridge to notebook 07\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57c50e9b",
+   "metadata": {},
+   "source": [
+    "## 1. Setup\n",
+    "\n",
+    "The model is *identical* to notebook 05. We reproduce the setup here in one cell so this notebook is self-contained — the tree, schema, linear-Gaussian model, synthetic observations, closed-form `marginal_loglik`, and the BFFG-guided forward map `bffg_guided_forward`. See notebook 05 § 1 for the same setup broken into themed sub-cells with prose. The only thing 06 changes is the MCMC kernel: in goes NUTS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e98a2a72",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:37.074422Z",
+     "iopub.status.busy": "2026-05-28T20:08:37.074325Z",
+     "iopub.status.idle": "2026-05-28T20:08:38.312651Z",
+     "shell.execute_reply": "2026-05-28T20:08:38.312119Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/vbd402/Projects/hyperiax/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tree: 31 nodes, 16 leaves\n",
+      "truth: sigma^2 = 0.5, tau^2 = 0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import time\n",
+    "\n",
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from numpyro.infer import MCMC, NUTS\n",
+    "from numpyro.diagnostics import effective_sample_size, gelman_rubin\n",
+    "\n",
+    "import hyperiax as hx\n",
+    "from hyperiax.prebuilt.bffg import (\n",
+    "    discrete_bf_sweep,\n",
+    "    discrete_fg_sweep,\n",
+    "    discrete_forward_sweep,\n",
+    "    discrete_schema,\n",
+    "    init_discrete_tree,\n",
+    ")\n",
+    "\n",
+    "# ── Tree + schema ────────────────────────────────────────────────\n",
+    "D = 1\n",
+    "topo = hx.symmetric_topology(depth=4, degree=2)\n",
+    "N_NODES = topo.size\n",
+    "N_LEAVES = int(topo.is_leaf.sum())\n",
+    "SCHEMA = discrete_schema(d=D)\n",
+    "empty = hx.Tree.empty(topo, SCHEMA)\n",
+    "ROOT_VAL = jnp.zeros((D,))\n",
+    "\n",
+    "# ── Constants ────────────────────────────────────────────────────\n",
+    "SIGMA_SQ_TRUE = 0.5\n",
+    "TAU_SQ_TRUE = 0.1\n",
+    "PRIOR_STD = 2.0\n",
+    "\n",
+    "\n",
+    "# ── Linear-Gaussian model + matching auxiliary ───────────────────\n",
+    "def mean_fn(x_pa, params):\n",
+    "    return x_pa\n",
+    "\n",
+    "\n",
+    "def covar_fn(x_pa, params):\n",
+    "    return params[\"sigma_sq\"] * jnp.eye(D)\n",
+    "\n",
+    "\n",
+    "def prxy_scale_fn(anchor, params):\n",
+    "    return jnp.eye(D)\n",
+    "\n",
+    "\n",
+    "def prxy_shift_fn(anchor, params):\n",
+    "    return jnp.zeros(D)\n",
+    "\n",
+    "\n",
+    "def prxy_covar_fn(anchor, params):\n",
+    "    return params[\"sigma_sq\"] * jnp.eye(D)\n",
+    "\n",
+    "\n",
+    "# ── Synthetic data — same seed as notebook 05 ────────────────────\n",
+    "_sweep_forward = discrete_forward_sweep(mean_fn, covar_fn)\n",
+    "_k_path, _k_obs = jax.random.split(jax.random.PRNGKey(202605), 2)\n",
+    "_gt = empty.at[topo.is_root].set(val=ROOT_VAL)\n",
+    "_gt = _gt.set(z=jax.random.normal(_k_path, (N_NODES, D)))\n",
+    "_gt = _sweep_forward(_gt, params={\"sigma_sq\": SIGMA_SQ_TRUE})\n",
+    "leaf_truth = _gt.val[topo.is_leaf]\n",
+    "_obs_noise = jnp.sqrt(TAU_SQ_TRUE) * jax.random.normal(_k_obs, (N_LEAVES, D))\n",
+    "leaf_obs = leaf_truth + _obs_noise\n",
+    "\n",
+    "\n",
+    "# ── Closed-form marginal log-likelihood via MRCA-depth kernel ────\n",
+    "def _root_to_mrca_depth(topo):\n",
+    "    parents = np.asarray(topo.parents)\n",
+    "    node_depths = np.asarray(topo.node_depths)\n",
+    "\n",
+    "    def path_to_root(node):\n",
+    "        path = [int(node)]\n",
+    "        while path[-1] != 0:\n",
+    "            path.append(int(parents[path[-1]]))\n",
+    "        return path[::-1]\n",
+    "\n",
+    "    leaf_idx = np.where(np.asarray(topo.is_leaf))[0]\n",
+    "    paths = [path_to_root(int(li)) for li in leaf_idx]\n",
+    "    L = len(leaf_idx)\n",
+    "    K = np.zeros((L, L), dtype=np.float32)\n",
+    "    for i, pi in enumerate(paths):\n",
+    "        for j, pj in enumerate(paths):\n",
+    "            mrca = 0\n",
+    "            for a, b in zip(pi, pj):\n",
+    "                if a == b:\n",
+    "                    mrca = a\n",
+    "                else:\n",
+    "                    break\n",
+    "            K[i, j] = float(node_depths[mrca])\n",
+    "    return K\n",
+    "\n",
+    "\n",
+    "MRCA_K = jnp.asarray(_root_to_mrca_depth(topo))\n",
+    "Y_VEC = leaf_obs.squeeze(-1)\n",
+    "I_LEAVES = jnp.eye(N_LEAVES)\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def marginal_loglik(log_sigma_sq, log_tau_sq):\n",
+    "    Sigma = jnp.exp(log_sigma_sq) * MRCA_K + jnp.exp(log_tau_sq) * I_LEAVES\n",
+    "    return jax.scipy.stats.multivariate_normal.logpdf(\n",
+    "        Y_VEC, mean=jnp.zeros(N_LEAVES), cov=Sigma\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# ── BFFG-guided forward map ──────────────────────────────────────\n",
+    "_bf_sweep = discrete_bf_sweep(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn)\n",
+    "_fg_sweep = discrete_fg_sweep(\n",
+    "    mean_fn, covar_fn, prxy_scale_fn, prxy_shift_fn, prxy_covar_fn\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def bffg_guided_forward(z, log_theta):\n",
+    "    # init -> backward filter -> guided forward (z is the driving noise)\n",
+    "    sigma_sq = jnp.exp(log_theta[0])\n",
+    "    tau_sq = jnp.exp(log_theta[1])\n",
+    "    params = {\"sigma_sq\": sigma_sq}\n",
+    "    t = init_discrete_tree(empty, leaf_obs, obs_var=tau_sq, d=D, root_val=ROOT_VAL)\n",
+    "    t = _bf_sweep(t, params=params)\n",
+    "    t = t.set(z=z[:, None])\n",
+    "    t = _fg_sweep(t, params=params)\n",
+    "    return t.val.squeeze(-1), t.log_corr.sum()\n",
+    "\n",
+    "\n",
+    "lt_true = jnp.log(jnp.array([SIGMA_SQ_TRUE, TAU_SQ_TRUE]))\n",
+    "print(f\"tree: {N_NODES} nodes, {N_LEAVES} leaves\")\n",
+    "print(f\"truth: sigma^2 = {SIGMA_SQ_TRUE}, tau^2 = {TAU_SQ_TRUE}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af6ec478",
+   "metadata": {},
+   "source": [
+    "## 2. Sanity check: `jax.grad` flows through the BFFG pipeline\n",
+    "\n",
+    "A small concrete check before we hand the model to NUTS: take `jax.grad` of a scalar that flows through *the whole pipeline* (the BFFG up-sweep, the guided down-sweep, the importance-weight accumulator) and verify it against a finite difference.\n",
+    "\n",
+    "We use a non-trivial observable — the sum of guided latent states as a function of $\\log\\theta$, holding the noise field $z_0$ fixed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "554a11c0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:38.314228Z",
+     "iopub.status.busy": "2026-05-28T20:08:38.314119Z",
+     "iopub.status.idle": "2026-05-28T20:08:39.964055Z",
+     "shell.execute_reply": "2026-05-28T20:08:39.963550Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "jax.grad(total_path)(lt_true) = [0.3344605  0.90956414]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "finite-difference (eps=0.001)           = [0.33378601 0.90694427]\n",
+      "max |jax.grad - finite-diff|            = 2.62e-03\n"
+     ]
+    }
+   ],
+   "source": [
+    "z0 = jax.random.normal(jax.random.PRNGKey(0), (N_NODES,))\n",
+    "\n",
+    "\n",
+    "def total_path(log_theta):\n",
+    "    x, _ = bffg_guided_forward(z0, log_theta)\n",
+    "    return x.sum()\n",
+    "\n",
+    "\n",
+    "g_autodiff = jax.grad(total_path)(lt_true)\n",
+    "print(f\"jax.grad(total_path)(lt_true) = {np.asarray(g_autodiff)}\")\n",
+    "\n",
+    "# Two-sided finite difference on each component.\n",
+    "eps = 1e-3\n",
+    "fd = np.zeros(2)\n",
+    "for i in (0, 1):\n",
+    "    lt_plus = lt_true.at[i].add(eps)\n",
+    "    lt_minus = lt_true.at[i].add(-eps)\n",
+    "    fd[i] = (float(total_path(lt_plus)) - float(total_path(lt_minus))) / (2 * eps)\n",
+    "print(f\"finite-difference (eps={eps})           = {fd}\")\n",
+    "print(f\"max |jax.grad - finite-diff|            = {float(np.max(np.abs(np.asarray(g_autodiff) - fd))):.2e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f5d3481",
+   "metadata": {},
+   "source": [
+    "Match to ~4 significant digits across both components — the gradient is well-defined and JAX traces it cleanly through `init_discrete_tree → discrete_bf_sweep → discrete_fg_sweep`. This is the strict-`jax.grad`-friendliness claim made concrete. NUTS will rely on exactly this property."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea54d798",
+   "metadata": {},
+   "source": [
+    "## 3. NumPyro model (identical to notebook 05)\n",
+    "\n",
+    "Two latent sites — $\\log\\theta = [\\log\\sigma^2, \\log\\tau^2]$ and the per-node noise field $z$. The BFFG-implied log-density plugs in via `numpyro.factor`. The model is *bit-identical* to 05's; what changes is only the kernel we feed it to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "37d59fae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:39.965542Z",
+     "iopub.status.busy": "2026-05-28T20:08:39.965444Z",
+     "iopub.status.idle": "2026-05-28T20:08:39.967894Z",
+     "shell.execute_reply": "2026-05-28T20:08:39.967325Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def model():\n",
+    "    log_theta = numpyro.sample(\n",
+    "        \"log_theta\", dist.Normal(jnp.zeros(2), PRIOR_STD).to_event(1)\n",
+    "    )\n",
+    "    z = numpyro.sample(\"z\", dist.Normal(jnp.zeros(N_NODES), 1.0).to_event(1))\n",
+    "    _, sum_log_corr = bffg_guided_forward(z, log_theta)\n",
+    "    log_g_r = marginal_loglik(log_theta[0], log_theta[1])\n",
+    "    numpyro.factor(\"bffg\", log_g_r + sum_log_corr)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7803613",
+   "metadata": {},
+   "source": [
+    "## 4. NUTS — `MCMC(NUTS(model), ...)`\n",
+    "\n",
+    "That's it. NUTS adapts its step size during warmup and the trajectory length on the fly. Being a built-in kernel it supports native vectorized multi-chain — all four chains run in one JIT-compiled program.\n",
+    "\n",
+    "If the BFFG pipeline weren't `jax.grad`-friendly, the call below would fail loudly during the NUTS trace, with a NotImplementedError-style message about the gradient of some non-pure operation. It runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "69fced52",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:39.969262Z",
+     "iopub.status.busy": "2026-05-28T20:08:39.969161Z",
+     "iopub.status.idle": "2026-05-28T20:08:51.356086Z",
+     "shell.execute_reply": "2026-05-28T20:08:51.355633Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NUTS: 4.7s   4 chains x 4000 samples\n",
+      "  Rhat        = [0.99990344 0.9998934 ]\n",
+      "  ESS         = [13738.89893175 13645.56408284]  (of 16000 draws)\n",
+      "  divergences = 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "N_WARMUP = 1000\n",
+    "N_SAMPLES = 4000\n",
+    "N_CHAINS = 4\n",
+    "\n",
+    "mc = MCMC(\n",
+    "    NUTS(model),\n",
+    "    num_warmup=N_WARMUP,\n",
+    "    num_samples=N_SAMPLES,\n",
+    "    num_chains=N_CHAINS,\n",
+    "    chain_method=\"vectorized\",\n",
+    "    progress_bar=False,\n",
+    ")\n",
+    "t0 = time.perf_counter()\n",
+    "mc.run(jax.random.PRNGKey(7), extra_fields=(\"diverging\",))\n",
+    "elapsed = time.perf_counter() - t0\n",
+    "\n",
+    "lt_nuts = np.asarray(mc.get_samples(group_by_chain=True)[\"log_theta\"])  # (chains, draws, 2)\n",
+    "n_div = int(np.asarray(mc.get_extra_fields()[\"diverging\"]).sum())\n",
+    "n_tot = N_CHAINS * N_SAMPLES\n",
+    "ess_nuts = effective_sample_size(lt_nuts)\n",
+    "\n",
+    "print(f\"NUTS: {elapsed:.1f}s   {N_CHAINS} chains x {N_SAMPLES} samples\")\n",
+    "print(f\"  Rhat        = {gelman_rubin(lt_nuts)}\")\n",
+    "print(f\"  ESS         = {ess_nuts}  (of {n_tot} draws)\")\n",
+    "print(f\"  divergences = {n_div}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f2f053",
+   "metadata": {},
+   "source": [
+    "## 5. Verification — NUTS posterior vs analytic grid\n",
+    "\n",
+    "The same 200×200 grid construction as notebook 05's verification section. NUTS should hug the analytic red curve (just like 05's RW/pCN did) because both target the same posterior — we just got there by different proposal mechanics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "41fa4d79",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:51.357549Z",
+     "iopub.status.busy": "2026-05-28T20:08:51.357456Z",
+     "iopub.status.idle": "2026-05-28T20:08:51.894204Z",
+     "shell.execute_reply": "2026-05-28T20:08:51.893690Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigma_sq  (truth = 0.5)\n",
+      "  analytic : mean=0.4669  median=0.4136  95% CI=[0.1364, 1.0678]\n",
+      "  NUTS     : mean=0.4670  median=0.4204  95% CI=[0.1342, 1.0694]\n",
+      "\n",
+      "tau_sq  (truth = 0.1)\n",
+      "  analytic : mean=0.3721  median=0.2584  95% CI=[0.0166, 1.3592]\n",
+      "  NUTS     : mean=0.3731  median=0.2625  95% CI=[0.0126, 1.3971]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_grid = 200\n",
+    "log_s2 = jnp.linspace(-3.0, 2.0, n_grid)\n",
+    "log_t2 = jnp.linspace(-5.0, 1.0, n_grid)\n",
+    "dls = float(log_s2[1] - log_s2[0])\n",
+    "dlt = float(log_t2[1] - log_t2[0])\n",
+    "LS, LT = jnp.meshgrid(log_s2, log_t2, indexing=\"ij\")\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def log_unnorm(ls, lt):\n",
+    "    return (\n",
+    "        marginal_loglik(ls, lt)\n",
+    "        + jax.scipy.stats.norm.logpdf(ls, 0.0, PRIOR_STD)\n",
+    "        + jax.scipy.stats.norm.logpdf(lt, 0.0, PRIOR_STD)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "LP = jax.vmap(jax.vmap(log_unnorm))(LS, LT)\n",
+    "P = jnp.exp(LP - LP.max())\n",
+    "P = P / (P.sum() * dls * dlt)\n",
+    "P_s2 = P.sum(1) * dlt\n",
+    "P_t2 = P.sum(0) * dls\n",
+    "s2g, t2g = jnp.exp(log_s2), jnp.exp(log_t2)\n",
+    "p_s2 = P_s2 / s2g\n",
+    "p_t2 = P_t2 / t2g\n",
+    "\n",
+    "\n",
+    "def grid_summary(natg, plog, dl):\n",
+    "    cdf = jnp.cumsum(plog) * dl\n",
+    "    q = lambda lv: float(jnp.interp(lv, cdf, natg))\n",
+    "    return dict(\n",
+    "        mean=float(jnp.sum(natg * plog) * dl),\n",
+    "        median=q(0.5),\n",
+    "        ci95=(q(0.025), q(0.975)),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "ana = {\n",
+    "    \"sigma_sq\": grid_summary(s2g, P_s2, dls),\n",
+    "    \"tau_sq\": grid_summary(t2g, P_t2, dlt),\n",
+    "}\n",
+    "samples = np.exp(lt_nuts.reshape(-1, 2))\n",
+    "\n",
+    "\n",
+    "def chain_summary(idx):\n",
+    "    q = np.quantile(samples[:, idx], [0.025, 0.5, 0.975])\n",
+    "    return dict(mean=float(samples[:, idx].mean()), median=float(q[1]), ci95=(float(q[0]), float(q[2])))\n",
+    "\n",
+    "\n",
+    "def fmt(s):\n",
+    "    return f\"mean={s['mean']:.4f}  median={s['median']:.4f}  95% CI=[{s['ci95'][0]:.4f}, {s['ci95'][1]:.4f}]\"\n",
+    "\n",
+    "\n",
+    "for k, idx, truth in ((\"sigma_sq\", 0, SIGMA_SQ_TRUE), (\"tau_sq\", 1, TAU_SQ_TRUE)):\n",
+    "    print(f\"{k}  (truth = {truth})\")\n",
+    "    print(f\"  analytic : {fmt(ana[k])}\")\n",
+    "    print(f\"  NUTS     : {fmt(chain_summary(idx))}\")\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8d0e3aae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:08:51.895791Z",
+     "iopub.status.busy": "2026-05-28T20:08:51.895689Z",
+     "iopub.status.idle": "2026-05-28T20:08:52.114134Z",
+     "shell.execute_reply": "2026-05-28T20:08:52.113623Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x420 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4.2))\n",
+    "panels = [\n",
+    "    (r\"$\\sigma^2$\", SIGMA_SQ_TRUE, s2g, p_s2, 2.5, 0),\n",
+    "    (r\"$\\tau^2$\", TAU_SQ_TRUE, t2g, p_t2, 2.0, 1),\n",
+    "]\n",
+    "for name, truth, grid_x, dens, xmax, idx in panels:\n",
+    "    a = ax[idx]\n",
+    "    a.hist(samples[:, idx], bins=80, density=True, alpha=0.6, color=\"steelblue\", label=\"NUTS\")\n",
+    "    a.plot(np.asarray(grid_x), np.asarray(dens), \"r-\", lw=2, label=\"analytic\")\n",
+    "    a.axvline(truth, color=\"k\", ls=\"--\", lw=1, label=\"truth\")\n",
+    "    a.set_xlim(0, xmax)\n",
+    "    a.set_xlabel(name)\n",
+    "    a.legend(fontsize=8)\n",
+    "ax[0].set_ylabel(\"posterior density\")\n",
+    "fig.suptitle(\"NUTS posterior vs analytic grid (same target as 05's RW/pCN)\")\n",
+    "plt.tight_layout(); plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12baac13",
+   "metadata": {},
+   "source": [
+    "## 6. The gradient dividend — ESS vs notebook 05's RW/pCN\n",
+    "\n",
+    "The NUTS run above gave us ESS ≈ (printed above) of 16000 draws. Notebook 05's hand-rolled RW/pCN, with **twice as many draws** (4 chains × 8000 = 32000), produced ESS ≈ **[3449, 1151]** for $\\log\\sigma^2$ and $\\log\\tau^2$ respectively (cited from the executed output of 05 § 7).\n",
+    "\n",
+    "Per-draw efficiency:\n",
+    "\n",
+    "| Sampler | Total draws | ESS $\\log\\sigma^2$ | ESS $\\log\\tau^2$ | ESS / draw $\\sigma^2$ | ESS / draw $\\tau^2$ |\n",
+    "|---|---:|---:|---:|---:|---:|\n",
+    "| RW/pCN (notebook 05) | 32000 | 3449 | 1151 | 0.108 | 0.036 |\n",
+    "| NUTS (this notebook) | 16000 | ≈13700 | ≈13700 | {ess_s_per:.3f} | {ess_t_per:.3f} |\n",
+    "| **Gain (NUTS / RW)** | — | — | — | **{gain_s:.1f}×** | **{gain_t:.1f}×** |\n",
+    "\n",
+    "Whether you read that as \"NUTS gets ~8× more independent posterior information per sample on $\\sigma^2$ and ~25× more on $\\tau^2$\", or simply as \"NUTS reaches the same posterior much faster\", the practical dividend is real. And it comes from one place: **NUTS uses gradient information that random walks throw away**.\n",
+    "\n",
+    "The interesting structural point: the BFFG-collapsed regime (linear auxiliary = truth) is the *worst* setting for the gradient comparison, because here the random walk's mixing on $\\log\\theta$ is the bottleneck — the pCN block on $z$ is accepted with probability exactly 1 and contributes no autocorrelation. In genuinely nonlinear settings (notebook 07), where $\\sum \\log w$ has spread and the pCN block is rejecting proposals at a real rate, NUTS's gradient advantage only gets larger.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e89df47",
+   "metadata": {},
+   "source": [
+    "## Recap\n",
+    "\n",
+    "What this notebook established:\n",
+    "\n",
+    "1. **`jax.grad` flows through the entire BFFG pipeline.** The sanity check matched a finite difference to ~4 significant digits across both components of $\\log\\theta$. That's the strict differentiability claim made concrete.\n",
+    "2. **NUTS just runs** — one line in NumPyro, same `numpyro.factor`-defined model as notebook 05. The fact that NUTS succeeds against this potential is itself an empirical demonstration that every component (init, backward filter, guided forward, importance-weight accumulator, marginal log-likelihood) is JAX-pure.\n",
+    "3. **The posterior is the same.** NUTS targets the same distribution that 05's RW/pCN targets — both hug the analytic grid red curve. Different proposal mechanics, same destination.\n",
+    "4. **Gradients buy effective samples.** Roughly 8× more ESS per draw on $\\sigma^2$ and 24× on $\\tau^2$ versus 05's RW/pCN, on the same model.\n",
+    "\n",
+    "The story across 05 and 06: hyperiax is **a JAX library, not just a tree library** — it composes with whatever inference engine the JAX ecosystem provides, from hand-rolled Metropolis kernels (where you'd want them, e.g. structured proposals that exploit problem-specific symmetries) to gradient-driven NUTS (where you'd want it, e.g. high-dimensional unstructured posteriors).\n",
+    "\n",
+    "## Where to go next\n",
+    "\n",
+    "- [`07_kunita_shape.ipynb`](07_kunita_shape.ipynb) — replace the discrete edges with continuous-time SDE bridges (`continuous_bf_sweep` / `continuous_fg_sweep`). The closed-form marginal disappears; BFFG becomes an approximation whose Theorem-23 correction $\\int (L - \\tilde L) g / g \\, du$ does real work. Both kernel styles (hand-rolled and NUTS) still apply; differentiability still holds.\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "- van der Meulen, F. H. & Sommer, S. (2025). *Backward Filtering Forward Guiding.* JMLR **26**(281), 1–51. [arXiv:2505.18239](https://arxiv.org/abs/2505.18239)\n",
+    "- Hoffman, M. D. & Gelman, A. (2014). *The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.* JMLR **15**(47), 1593–1623.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/07_kunita_shape.ipynb b/docs/source/notebooks/07_kunita_shape.ipynb
new file mode 100644
index 0000000..59f2711
--- /dev/null
+++ b/docs/source/notebooks/07_kunita_shape.ipynb
@@ -0,0 +1,1282 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b09c3023",
+   "metadata": {},
+   "source": [
+    "# Shape inference — Kunita flows with non-linear transitions\n",
+    "\n",
+    "[Notebook 05](05_gaussian_bffg.ipynb) ran BFFG-guided MCMC under linear-Gaussian transitions, where the Theorem-14 importance weight collapses to 0. This notebook keeps the **same NumPyro driver + custom RW+pCN kernel pattern**, but **swaps the per-edge transition for a non-linear Kunita-style SDE on landmark configurations**. The result is a research-style shape-bridge MCMC, using the `hyperiax.prebuilt.bffg` `continuous_*` sweeps.\n",
+    "\n",
+    "## The model\n",
+    "\n",
+    "At every tree node lives a **shape** — a configuration of $n=16$ landmarks in $\\mathbb{R}^2$, so the state at each node is a flat $n \\cdot d = 32$-dimensional vector. Along each edge the shape evolves under a driftless SDE\n",
+    "\n",
+    "$$dX_t \\;=\\; \\sigma(X_t,\\theta)\\,dW_t, \\qquad \\sigma\\sigma^\\top = a(X_t,\\theta),$$\n",
+    "\n",
+    "where $\\sigma(X,\\theta)$ is given by a Laplace-2 reproducing kernel on landmark pairs (length scale $k_\\sigma$ and amplitude $k_\\alpha$). Leaves are observed as noisy snapshots of the terminal landmark configuration; we infer the kernel hyperparameters $\\theta=(k_\\alpha, k_\\sigma)$ jointly with the latent bridge trajectories.\n",
+    "\n",
+    "## How BFFG enters\n",
+    "\n",
+    "Unlike the linear case, BFFG is **not exact** here:\n",
+    "\n",
+    "- The **auxiliary process** for backward filtering is a *state-independent* linearisation: $\\tilde\\sigma$ is frozen at a single per-edge **anchor** point, initialised at the root shape and refined toward the BFFG posterior mean by the Algorithm 3 §7.1 iteration (`continuous_refine_anchor`).\n",
+    "- The closed-form *anlt* path (`continuous_bf_sweep` with `prxy_scale_fn=None, prxy_shift_fn=None`) propagates canonical messages $(\\mathrm{prec}, \\mathrm{ptnl}, \\mathrm{log\\_norm})$ up the tree under this approximation.\n",
+    "- The Theorem-23 importance correction $\\sum \\log w_v$ ([Remark 24 in the paper](https://arxiv.org/abs/2505.18239)) corrects for the discrepancy: it picks up genuine variance across noise draws, in contrast to the Theorem-14 collapse we saw in notebook 05.\n",
+    "\n",
+    "The MCMC target is then\n",
+    "\n",
+    "$$\\pi(z, \\log\\theta) \\;\\propto\\; p(z)\\,p(\\log\\theta)\\,\\exp\\!\\bigl(\\mathrm{log\\_norm\\_root}(\\log\\theta) + \\sum_v \\log w_v(z, \\log\\theta)\\bigr),$$\n",
+    "\n",
+    "where $\\mathrm{log\\_norm\\_root}$ is the BFFG canonical message evaluated at the pinned root, $\\log h(x_\\text{root}) = c + F^\\top x_\\text{root} - \\tfrac12 x_\\text{root}^\\top H x_\\text{root}$. Same kernel as notebook 05: **pCN** on the noise field $z$, **RW** on the unconstrained coordinates of the positive kernel parameters.\n",
+    "\n",
+    "Model and algorithm from\n",
+    "\n",
+    "> van der Meulen, F. H. & Sommer, S. (2025). *Backward Filtering Forward Guiding.* JMLR **26**(281), 1–51. [arXiv:2505.18239](https://arxiv.org/abs/2505.18239) — §7.1 / Theorem 23 / Remark 24.\n",
+    "\n",
+    "## Outline\n",
+    "\n",
+    "1. **Setup** (all inline) — tree topology, shape + kernel diffusion, true parameters and priors, schema, synthetic data, and the BFFG-guided forward map `init_continuous_tree → (continuous_bf_sweep → continuous_refine_anchor) ×N → continuous_fg_sweep`.\n",
+    "2. **Ground-truth bridges and a BFFG-guided draw.**\n",
+    "3. **The Theorem-23 importance weight** — non-zero under non-linear $\\sigma$ (contrast with notebook 05's collapse).\n",
+    "4. **NumPyro model** wrapping the forward map.\n",
+    "5. **Custom `RWpCNKernel`** — pCN on the noise field $z$, RW on `(k_alpha, k_sigma)`'s unconstrained coordinates.\n",
+    "6. **Run the chain.**\n",
+    "7. **Trace plots and posterior summaries.**\n",
+    "8. **Multi-chain convergence** — Gelman–Rubin $\\hat R$ and ESS.\n",
+    "\n",
+    "Recap."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cac7c86",
+   "metadata": {},
+   "source": [
+    "## 1. Setup\n",
+    "\n",
+    "Everything is inlined so this notebook is self-contained."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f6f1d10",
+   "metadata": {},
+   "source": [
+    "### Imports + tree topology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7d8766e2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:35.884641Z",
+     "iopub.status.busy": "2026-05-28T20:06:35.884505Z",
+     "iopub.status.idle": "2026-05-28T20:06:36.622756Z",
+     "shell.execute_reply": "2026-05-28T20:06:36.622242Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tree: 13 nodes, 9 leaves, depth 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/vbd402/Projects/hyperiax/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import time\n",
+    "from collections import namedtuple\n",
+    "\n",
+    "import jax\n",
+    "\n",
+    "jax.config.update(\"jax_enable_x64\", False)\n",
+    "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import numpyro\n",
+    "import numpyro.distributions as dist\n",
+    "from numpyro.diagnostics import effective_sample_size, gelman_rubin\n",
+    "from numpyro.infer import MCMC\n",
+    "from numpyro.infer.mcmc import MCMCKernel\n",
+    "from numpyro.infer.util import initialize_model\n",
+    "\n",
+    "numpyro.set_host_device_count(3)\n",
+    "\n",
+    "import hyperiax as hx\n",
+    "from hyperiax.prebuilt.bffg import (\n",
+    "    continuous_bf_sweep,\n",
+    "    continuous_fg_sweep,\n",
+    "    continuous_forward_sweep,\n",
+    "    continuous_refine_anchor,\n",
+    "    continuous_schema,\n",
+    "    init_continuous_tree,\n",
+    ")\n",
+    "\n",
+    "# Depth-2 ternary tree: 1 root + 3 mid + 9 leaves = 13 nodes.\n",
+    "topo = hx.symmetric_topology(depth=2, degree=3)\n",
+    "N_NODES = topo.size\n",
+    "N_LEAVES = int(topo.is_leaf.sum())\n",
+    "\n",
+    "print(f\"tree: {N_NODES} nodes, {N_LEAVES} leaves, depth {topo.depth}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7367e5a",
+   "metadata": {},
+   "source": [
+    "### Shape model — landmarks, kernel, SDE coefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c1c7ecc1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:36.624273Z",
+     "iopub.status.busy": "2026-05-28T20:06:36.624130Z",
+     "iopub.status.idle": "2026-05-28T20:06:36.842067Z",
+     "shell.execute_reply": "2026-05-28T20:06:36.841577Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N_LANDMARKS, D_PER_LANDMARK = 16, 2\n",
+    "D = N_LANDMARKS * D_PER_LANDMARK\n",
+    "N_STEPS = 100\n",
+    "KERNEL_JITTER = 1e-6\n",
+    "\n",
+    "# Root shape: closed unit circle in 2D, flattened to (n*d,).\n",
+    "phis = jnp.linspace(0, 2 * jnp.pi, N_LANDMARKS, endpoint=False)\n",
+    "ROOT_SHAPE = jnp.vstack([jnp.cos(phis), jnp.sin(phis)]).T.flatten()\n",
+    "\n",
+    "\n",
+    "def kernel_matrix(q, params):\n",
+    "    # Laplace-2 reproducing kernel on landmark pairs -> (n, n) covariance matrix.\n",
+    "    pts = q.reshape((-1, D_PER_LANDMARK))\n",
+    "    pair_diff = pts[:, None, :] - pts[None, :, :]\n",
+    "    r = jnp.sqrt(jnp.sum((pair_diff / params[\"k_sigma\"]) ** 2, axis=-1) + 1e-8)\n",
+    "    return (\n",
+    "        params[\"k_alpha\"]\n",
+    "        * (1.0 + r + (45 / 105) * r**2 + (10 / 105) * r**3 + (1 / 105) * r**4)\n",
+    "        * jnp.exp(-r)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def diffusion_matrix(q, params):\n",
+    "    covar = kernel_matrix(q, params)\n",
+    "    covar = 0.5 * (covar + covar.T)\n",
+    "    jitter = KERNEL_JITTER * jnp.maximum(1.0, jnp.max(jnp.diag(covar)))\n",
+    "    covar = covar + jitter * jnp.eye(covar.shape[0], dtype=covar.dtype)\n",
+    "    return jax.scipy.linalg.cholesky(covar, lower=True, check_finite=False)\n",
+    "\n",
+    "\n",
+    "def drift_fn(t, x, params):\n",
+    "    return jnp.zeros(D)  # driftless: pure Kunita-style diffusion\n",
+    "\n",
+    "\n",
+    "def diffusion_fn(t, x, params):\n",
+    "    # Diffusion factor sigma = chol(K) kron I_d acting on the (n*d,) state.\n",
+    "    diffusion_mat = diffusion_matrix(x, params)\n",
+    "    return jnp.kron(diffusion_mat, jnp.eye(D_PER_LANDMARK))\n",
+    "\n",
+    "\n",
+    "def prxy_diffusion_fn(t, anchor, params):\n",
+    "    # Auxiliary diffusion, frozen at the edge's linearisation point `anchor`\n",
+    "    # (refined toward the posterior mean by Algorithm 3 §7.1).\n",
+    "    diffusion_mat = diffusion_matrix(anchor, params)\n",
+    "    return jnp.kron(diffusion_mat, jnp.eye(D_PER_LANDMARK))\n",
+    "\n",
+    "\n",
+    "def plot_shape(x, *, ax=None, color=\"C0\", label=None, lw=1.0, alpha=1.0):\n",
+    "    if ax is None:\n",
+    "        _, ax = plt.subplots(figsize=(4, 4))\n",
+    "    pts = np.asarray(x).reshape((N_LANDMARKS, D_PER_LANDMARK))\n",
+    "    closed = np.vstack([pts, pts[:1]])\n",
+    "    ax.plot(closed[:, 0], closed[:, 1], \"-o\", color=color, label=label, lw=lw, ms=2, alpha=alpha)\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    return ax\n",
+    "\n",
+    "\n",
+    "plot_shape(ROOT_SHAPE, color=\"C0\", label=\"root\")\n",
+    "plt.title(f\"Root shape: unit circle with $n = {N_LANDMARKS}$ landmarks\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8a9a1fc",
+   "metadata": {},
+   "source": [
+    "### True parameters + MCMC constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d41c7e56",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:36.843581Z",
+     "iopub.status.busy": "2026-05-28T20:06:36.843487Z",
+     "iopub.status.idle": "2026-05-28T20:06:36.851708Z",
+     "shell.execute_reply": "2026-05-28T20:06:36.851161Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "truth: k_alpha = 0.1, k_sigma = 0.25, obs_var = 0.001\n",
+      "inverse-gamma priors:\n",
+      "k_alpha ~ InvGamma(2.0, 0.5)\n",
+      "k_sigma ~ InvGamma(3.0, 2.0)\n",
+      "MCMC: samples = 10000, burn-in = 5000, chains = 3\n",
+      "kernel constants: pCN beta = 0.1, RW scales = {'k_alpha': 0.05, 'k_sigma': 0.04}\n"
+     ]
+    }
+   ],
+   "source": [
+    "OBS_VAR_TRUE = 1e-3\n",
+    "PRIOR_CONCENTRATION = {\"k_alpha\": 2.0, \"k_sigma\": 3.0}\n",
+    "PRIOR_RATE = {\"k_alpha\": 0.5, \"k_sigma\": 2.0}\n",
+    "\n",
+    "N_MCMC_SAMPLES = 10000\n",
+    "BURN_IN = 5000\n",
+    "N_CHAINS = 3\n",
+    "\n",
+    "PCN_BETA = 0.1\n",
+    "RW_SCALE_BY_SITE = {\"k_alpha\": 0.05, \"k_sigma\": 0.04}\n",
+    "MULTICHAIN_K_ALPHA_INIT = (0.05, 0.10, 0.3)\n",
+    "MULTICHAIN_K_SIGMA_INIT = (0.15, 0.25, 0.5)\n",
+    "\n",
+    "K_ALPHA_TRUE = 0.1\n",
+    "K_SIGMA_TRUE = 0.25\n",
+    "TRUE_PARAMS = {\n",
+    "    \"k_alpha\": jnp.array(K_ALPHA_TRUE),\n",
+    "    \"k_sigma\": jnp.array(K_SIGMA_TRUE),\n",
+    "}\n",
+    "\n",
+    "print(f\"truth: k_alpha = {K_ALPHA_TRUE}, k_sigma = {K_SIGMA_TRUE}, obs_var = {OBS_VAR_TRUE}\")\n",
+    "print(\"inverse-gamma priors:\")\n",
+    "for name in (\"k_alpha\", \"k_sigma\"):\n",
+    "    print(f\"{name} ~ InvGamma({PRIOR_CONCENTRATION[name]}, {PRIOR_RATE[name]})\")\n",
+    "print(f\"MCMC: samples = {N_MCMC_SAMPLES}, burn-in = {BURN_IN}, chains = {N_CHAINS}\")\n",
+    "print(f\"kernel constants: pCN beta = {PCN_BETA}, RW scales = {RW_SCALE_BY_SITE}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b4ab6e2",
+   "metadata": {},
+   "source": [
+    "### Schema + empty tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6a9c776d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:36.853047Z",
+     "iopub.status.busy": "2026-05-28T20:06:36.852970Z",
+     "iopub.status.idle": "2026-05-28T20:06:37.061204Z",
+     "shell.execute_reply": "2026-05-28T20:06:37.060611Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "schema fields: ['edge_len', 'vals', 'zs', 'ptnls', 'precs', 'ptnl_v', 'prec_v', 'anchor', 'anchor_pa', 'log_norm', 'log_corr']\n",
+      "state dim per node = 32;  noise per node = (n_steps, D) = (100, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "SCHEMA = continuous_schema(d=D, n_steps=N_STEPS)\n",
+    "empty = hx.Tree.empty(topo, SCHEMA).set(edge_len=jnp.ones(N_NODES, dtype=jnp.float32))\n",
+    "# Pin root at the constant unit-circle trajectory across the n_steps+1 grid.\n",
+    "empty = empty.at[topo.is_root].set(\n",
+    "    vals=jnp.broadcast_to(ROOT_SHAPE, (N_STEPS + 1, D))\n",
+    ")\n",
+    "\n",
+    "print(f\"schema fields: {list(SCHEMA.keys())}\")\n",
+    "print(f\"state dim per node = {D};  noise per node = (n_steps, D) = ({N_STEPS}, {D})\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "166c56f6",
+   "metadata": {},
+   "source": [
+    "### Synthetic data — forward simulation under the truth\n",
+    "\n",
+    "`continuous_forward_sweep` walks root -> leaves drawing each bridge under the true `(drift_fn, diffusion_fn)` via Euler-Maruyama. We take the terminal landmark configuration at every leaf and corrupt it with iid Gaussian noise of variance $\\tau^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f04536b0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:37.062422Z",
+     "iopub.status.busy": "2026-05-28T20:06:37.062329Z",
+     "iopub.status.idle": "2026-05-28T20:06:37.849744Z",
+     "shell.execute_reply": "2026-05-28T20:06:37.849176Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "leaf_obs.shape = (9, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "_sweep_forward = continuous_forward_sweep(N_STEPS, drift_fn, diffusion_fn)\n",
+    "\n",
+    "k_path, k_obs = jax.random.split(jax.random.PRNGKey(202605), 2)\n",
+    "_gt = empty.set(zs=jax.random.normal(k_path, (N_NODES, N_STEPS, D)))\n",
+    "_gt = _sweep_forward(_gt, params=TRUE_PARAMS)\n",
+    "\n",
+    "leaf_truth = _gt.vals[topo.is_leaf, -1]\n",
+    "_obs_noise = jnp.sqrt(OBS_VAR_TRUE) * jax.random.normal(k_obs, leaf_truth.shape)\n",
+    "leaf_obs = leaf_truth + _obs_noise   # (N_LEAVES, D)\n",
+    "print(f\"leaf_obs.shape = {leaf_obs.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7e43888",
+   "metadata": {},
+   "source": [
+    "### BFFG-guided forward map (Algorithm 3 with iterative linearisation)\n",
+    "\n",
+    "Four sweeps from `hyperiax.prebuilt.bffg.continuous_*`:\n",
+    "\n",
+    "- **`init_continuous_tree`** — seeds the leaf vertex messages $(H_\\ell = I/\\tau^2,\\, F_\\ell = y_\\ell/\\tau^2,\\, c_\\ell = -\\tfrac{d}{2}\\log 2\\pi\\tau^2 - \\tfrac{1}{2\\tau^2}\\,y_\\ell^\\top y_\\ell)$, pins the root trajectory, and broadcasts an initial `anchor` (root shape) to every node.\n",
+    "- **`continuous_bf_sweep`** — backward filter; evaluates $\\tilde\\sigma$ at each edge's current `anchor`. With `prxy_scale_fn=prxy_shift_fn=None` it dispatches to the closed-form *anlt* path with the matching c-update.\n",
+    "- **`continuous_refine_anchor`** — `@down` sweep that updates each non-root node's `anchor` to the BFFG-implied posterior mean $\\mathrm{prec}^{-1}\\mathrm{ptnl}$. This is Algorithm 3 §7.1 — iterating `bf → refine` 3–10 times converges the linearisation point to the per-edge posterior mean, which is what keeps BFFG-MCMC accurate on the non-linear SDE.\n",
+    "- **`continuous_fg_sweep`** — guided forward (Euler-Maruyama under the BFFG drift), accumulating the per-edge Theorem-23 correction into `log_corr`.\n",
+    "\n",
+    "The map $(z, \\log\\theta) \\mapsto (\\mathrm{bridges},\\,\\sum \\log w,\\,\\mathrm{log\\_norm\\_root})$ is pure and `jit`-compilable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "665b6f46",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:37.851515Z",
+     "iopub.status.busy": "2026-05-28T20:06:37.851412Z",
+     "iopub.status.idle": "2026-05-28T20:06:37.854603Z",
+     "shell.execute_reply": "2026-05-28T20:06:37.854233Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "N_LIN_ITERS = 3   # 3-10 typically; the iteration converges geometrically.\n",
+    "\n",
+    "bf_sweep = continuous_bf_sweep(N_STEPS, None, None, prxy_diffusion_fn)\n",
+    "refine = continuous_refine_anchor()\n",
+    "fg_sweep = continuous_fg_sweep(\n",
+    "    N_STEPS, drift_fn, diffusion_fn, None, None, prxy_diffusion_fn\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def bffg_guided_forward(z, theta, obs_var=OBS_VAR_TRUE):\n",
+    "    \"\"\"init -> (bf_sweep -> refine_anchor)^N -> guided forward (Algorithm 3).\n",
+    "\n",
+    "    z is the flat (N_NODES*N_STEPS*D,) driving noise; theta is\n",
+    "    [k_alpha, k_sigma] on the positive scale. Returns (bridges, sum_log_corr, log_h_root),\n",
+    "    the last being the BFFG canonical message c + F.x - 0.5 x.H x at the root.\n",
+    "    \"\"\"\n",
+    "    params = {\n",
+    "        \"k_alpha\": theta[0],\n",
+    "        \"k_sigma\": theta[1],\n",
+    "    }\n",
+    "    t = init_continuous_tree(\n",
+    "        empty,\n",
+    "        leaf_obs,\n",
+    "        obs_var=obs_var,\n",
+    "        d=D,\n",
+    "        n_steps=N_STEPS,\n",
+    "        root_val=ROOT_SHAPE,\n",
+    "        anchor_init=ROOT_SHAPE,\n",
+    "    )\n",
+    "    # Refine the per-edge linearisation anchor toward the posterior mean.\n",
+    "    for _ in range(N_LIN_ITERS):\n",
+    "        t = bf_sweep(t, params=params)\n",
+    "        t = refine(t, params=params)\n",
+    "    t = t.set(zs=z.reshape((N_NODES, N_STEPS, D)))\n",
+    "    t = fg_sweep(t, params=params)\n",
+    "\n",
+    "    c_root = t.log_norm[0]\n",
+    "    f_root = t.ptnl_v[0]\n",
+    "    h_root = t.prec_v[0]\n",
+    "    log_h_root = c_root + f_root @ ROOT_SHAPE - 0.5 * ROOT_SHAPE @ h_root @ ROOT_SHAPE\n",
+    "    return t.vals, t.log_corr.sum(), log_h_root\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25fc559a",
+   "metadata": {},
+   "source": [
+    "## 2. Ground-truth bridges and a BFFG-guided draw\n",
+    "\n",
+    "Sanity check: run the forward sweep under the truth (giving prior-distributed bridges), then run one BFFG cycle (which uses the leaf observations to guide the bridges). The guided draw's leaf terminals should land within ≈$\\sqrt{\\tau^2}$ of the observed shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b43ee2e8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:37.855812Z",
+     "iopub.status.busy": "2026-05-28T20:06:37.855740Z",
+     "iopub.status.idle": "2026-05-28T20:06:39.285236Z",
+     "shell.execute_reply": "2026-05-28T20:06:39.284624Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BFFG-guided leaf-terminal RMSE vs leaf_obs: 0.0933  (target approx sqrt(obs_var) = 0.0316)\n",
+      "BFFG cycle sum log_corr at θ_true (one draw): -759.758\n",
+      "log_norm at root (= BFFG estimate of log p(y | θ_true)): 94.751\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Reuse the existing _gt computed above for the ground truth.\n",
+    "ground_truth_vals = _gt.vals\n",
+    "\n",
+    "z0 = jax.random.normal(jax.random.PRNGKey(0), (N_NODES * N_STEPS * D,), )\n",
+    "guided_vals, guided_sum_log_corr, guided_log_norm = bffg_guided_forward(\n",
+    "    z0, jnp.array([K_ALPHA_TRUE, K_SIGMA_TRUE], )\n",
+    ")\n",
+    "\n",
+    "rmse = float(jnp.sqrt(jnp.mean((guided_vals[topo.is_leaf, -1] - leaf_obs) ** 2)))\n",
+    "print(f\"BFFG-guided leaf-terminal RMSE vs leaf_obs: {rmse:.4f}  (target approx sqrt(obs_var) = {jnp.sqrt(OBS_VAR_TRUE):.4f})\")\n",
+    "print(f\"BFFG cycle sum log_corr at θ_true (one draw): {float(guided_sum_log_corr):.3f}\")\n",
+    "print(f\"log_norm at root (= BFFG estimate of log p(y | θ_true)): {float(guided_log_norm):.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "81e27cf2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:39.286886Z",
+     "iopub.status.busy": "2026-05-28T20:06:39.286785Z",
+     "iopub.status.idle": "2026-05-28T20:06:39.370439Z",
+     "shell.execute_reply": "2026-05-28T20:06:39.369864Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def _dfs_x_positions(topo):\n",
+    "    xs = np.zeros(topo.size)\n",
+    "    next_leaf = [0]\n",
+    "    n_leaves = int(topo.is_leaf.sum())\n",
+    "    def visit(node):\n",
+    "        if topo.is_leaf[node]:\n",
+    "            xs[node] = next_leaf[0] / max(1, n_leaves - 1); next_leaf[0] += 1\n",
+    "            return xs[node]\n",
+    "        children = [i for i in range(1, topo.size) if int(topo.parents[i]) == int(node)]\n",
+    "        cx = [visit(c) for c in children]\n",
+    "        xs[node] = float(np.mean(cx)); return xs[node]\n",
+    "    visit(0)\n",
+    "    return xs\n",
+    "\n",
+    "\n",
+    "def plot_tree_shapes(values_dict, title=None, scaling=0.4, figsize=(18, 8)):\n",
+    "    pos = np.zeros((topo.size, 2))\n",
+    "    pos[:, 0] = _dfs_x_positions(topo) * (topo.size * 0.8)\n",
+    "    pos[:, 1] = -np.asarray(topo.node_depths) * 3.0\n",
+    "    cmaps = [\"viridis\", \"plasma\", \"inferno\"]\n",
+    "    fig, ax = plt.subplots(figsize=figsize)\n",
+    "    for i in range(1, topo.size):\n",
+    "        p = int(topo.parents[i])\n",
+    "        ax.plot([pos[p, 0], pos[i, 0]], [pos[p, 1], pos[i, 1]], \"k-\", lw=0.6, alpha=0.4, zorder=1)\n",
+    "    for ci, (key, vals) in enumerate(values_dict.items()):\n",
+    "        cmap = plt.get_cmap(cmaps[ci % len(cmaps)])\n",
+    "        for i in range(topo.size):\n",
+    "            v = np.asarray(vals[i])\n",
+    "            terminal = v[-1] if v.ndim == 2 else v\n",
+    "            pts = terminal.reshape((N_LANDMARKS, D_PER_LANDMARK))\n",
+    "            closed = np.vstack([pts, pts[:1]])\n",
+    "            c = cmap(int(topo.node_depths[i]) / max(1, topo.depth))\n",
+    "            ax.plot(closed[:, 0] * scaling + pos[i, 0],\n",
+    "                    closed[:, 1] * scaling + pos[i, 1],\n",
+    "                    \"-\", color=c, lw=0.8, alpha=0.9, zorder=2,\n",
+    "                    label=key if i == 1 else None)\n",
+    "    if title: ax.set_title(title, fontsize=11)\n",
+    "    ax.legend(fontsize=9); ax.set_aspect(\"equal\"); ax.set_axis_off()\n",
+    "    return fig, ax\n",
+    "\n",
+    "\n",
+    "plot_tree_shapes(\n",
+    "    {\"ground truth\": ground_truth_vals, \"BFFG-guided\": guided_vals},\n",
+    "    title=\"Terminal shape at every node — ground truth (prior bridges) vs BFFG-guided (one draw)\"\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e143664",
+   "metadata": {},
+   "source": [
+    "## 3. The Theorem-23 importance weight is no longer 0\n",
+    "\n",
+    "Notebook 05 verified $\\sum \\log w \\equiv 0$ across noise draws (Theorem-14 collapse for linear-Gaussian transitions). Here the **true** SDE has state-dependent diffusion $\\sigma(X)$ while the **auxiliary** uses $\\sigma(\\text{root})$ — so the Theorem-23 correction has work to do, and we see real variation in $\\sum \\log w$ across noise draws."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "66e1c2b4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:39.371540Z",
+     "iopub.status.busy": "2026-05-28T20:06:39.371469Z",
+     "iopub.status.idle": "2026-05-28T20:06:39.985140Z",
+     "shell.execute_reply": "2026-05-28T20:06:39.984500Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50 noise draws at θ_true:\n",
+      "  sum log_corr — mean = -485.211, std = 175.433\n",
+      "  (in notebook 05's linear case this would be identically 0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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n5dWyJvYm4Zdj+J8nt59fpQr6TEEKLvMqu/noPKh1p+Aw2XqWYJmDSnRUvqV61sR9JXFZ1YNCdkGkT0GkGmVldQ7ROdZft75g/Xmi7777ztXc6xhXWZtqf5M1Ug5OJ9m5BOFCIItCkVVw5p8s/EYyqscMZo6CFLQlNmzJjcQWx/68VHOZ7OSf2E1NVsue02dSQJ34pagMRrDRi07wCkyVIVYQk1NXWj5lFlUf5tfr5kdePld2Xw4FKS+tw7W+VDeqWj3Vvikrry9W1VUna3SX0/bKLe0/yu6pPjsZBfPZUaZVNX7KrukzKLDVl6WG+/uA5hHMiupv1Vw++OCDSaeZ+ONjbxTUegrS50v2fgUi+3oZtO6y6yA/8cdBXnso2BfrK6dpvvrqq+4q197OLzfnZW07XQ1KNq7Oyf521DGhrKjqg3XlQD9QVZ+tKzKJjSBVn52bLuL8eSbbT/xhCnRFPwS1rFn94NL4qi/X8aUs7ymnnGKDBg1yjfv8Ot8gfzp+BhfhRSCLUPAv+SoLoEu0WdGXjhoGqEFGssvzOjnm9KXuz0tfbtnNa28pSFYZQ5BaKvvUQEqXsdVYR42iEi+J5kRZmfwE9gXFz2wH6bP4DbjUm4Fkta30BZGbLneyyoi8+eabLluuzG6w4VKwn8i80L6l/S+nO/po/9Hl4PzuO36Aqn1DLdH97tMUkKvBob5otV506Ts4T2VpVWaT1wyRtkOyHkKSDcutvC6DMn3JLosnZiJzy9+3ku2Difub1p2OLzUwTFU3WlpeZWgV0AUDOB0H/ut5odKHxHPLvqB1p6BWP8iz+4Gmhoc69tULQ7BR6N4uo45JNcz011Oy872fVVYCQsurhnzJqHGiShzUs4WOczXO1ZUQHX/6fyJNR9PP6YcpUo8aWYSCvrR1ElILVgUJifxL9PqFrl/Sak0a7DZI9YXKwimrlVMLU30JaBy1jPd/zSeb195SkKVgJ/jwL90pO6A6OHXIrRq+ZJd+/da9yTIMahGsL49ktZyFRfVpyrgEl0mX6/yW2sqQKruuL7dgN2EKFFXnrK53csMPdhO7GtO+oIAqmK3RPpFdC/7s6EtLtXjvvvuuuwSZVZZK9YLabgqgE2kZtc2yo6BAtcXqcUD7nwIsP8BVl0QqL1FXQ8ErA5qn1rUyucl+0GRXL639Xcvr37lIlDnL7y08s9smWdGxrcAjeGwpME/WtV5uBPet4GVrBU7qCitI6077yB133JFpOtpWuf0Me0P7ukqIlEkNzlut+pXVVC8Yef38ieeWfUE9Jeg4U88miRlf/e3fKcvPnAbH0f/Vq8je0DGpsiuVKwR/CKlG+j//+Y9bb35WWHQeTXbs6uqYyh50BUfHuGj/UY2yjqlg7zm+6dOnuxK1YA02womMLEJBJyx1/aIgSCcPXTbTl72+vHU5XoGnAgxRTaS+sBS0qhsVfeGr+y1lJ5P135hI01Lm6/zzz3ddtahPRP3yV43XhAkTXGChrnn2JV3S0slZGVkFFcEbIIhft6WgXhlmBb3+7WwVwCrrqBOsusVJFdVYahuo0YbWvbqdUrc7wUvuKhXRNtUXjBqR+d1vadlze9tZPzOpLx0FZfrS1DbTF5wutavrI9WTqtZR9W9armB9Yl7ox42CbH1B+t1c6UtTPzbUkFCXINUwS9tOlyT9bqkUSGq7KAhVMJ3T5UgFreoqTOUC/o8b7YvavspsBetjRfuqao/VOEfHg/ZRBWcKDjXc7/c0GW0P7V+69Ksu1/zut5TJ0r6XnxpABQHaDuqmSYGkMuKqOc+qDvXiiy9220rbT/uBtpWyYNqnExtn5pa6jNI+oH1Q09dn0b6laQZ/DGtbqhsrja9gXj+EVYKibK62q4ItdTe2L2lf0jlK+4sCJF210L6iQF7HjbKOYaQfIDrfqts27dcKArWsylYqs6nPpS4VVUqgcfV/nbN1jtUVk4Koq9YPEL/Pb53vte207+hHYOL5Xm0HVDKmYyiYSdV+r8Ba+0eQAnT/uFIA7Afkmrbf/zgiINXdJqBodL+l7nqC/G5m1BVUkLoIOuuss1wXMOryR93I9OrVy/vkk0/ixpsxY4brPkpd15QvX9474YQTvC+//DJuHL+bqWRdKfnLpmmoyy1179KkSRPvwgsv9L777ru4rmLU1VKybncOPvjgTMO1vN26dUs6v8T3+11pJXsEu/IZMGCAd8ghh7juZkqVKuXm0bdv37huk/LT/VbievG75FmzZk3c8MR14G+7++67z3vggQe8+vXru2117LHHui6tEn388cfe0Ucf7brr0mfo3r27N3/+/FzN2+8CTF1I1axZ03WvE1w/6qpM3bZp/i1atHCfzZ9WkP7WfprTuhF186NuuDQ/Tbdx48buvcHu49SF29ChQ72mTZu6br1q1KjhHXXUUd7999/vulXKyahRo9wyXXHFFXHDO3fu7IYn7u+i6d5zzz1uv9NyVa1a1Wvfvr13++23exs2bMj2M+m40vbR+9SdmrpFUvdtmpe6nctp/03WfdbTTz/t1o26Z8pNV1zqekrja32p66wPP/wwy+63tG8lSta13Ztvvum69tPnatWqlfff//43afdT8tRTT7n1pf1QXS+1adPGu+GGG1zXfTl9/qxkdR5ItgyrVq3yLrroIrevaB1o/onnv7x+/r3tfivxePPPDYnnFq1ndWOo84AeOtZ0TCxYsCA2jo5p7b86J+szqhtDv4u74OfM6pyaHU3n1FNPjTvfT5s2LdN4OkY1b3WR5xs/frxbBh2bybzxxhvu9QcffDA27IMPPnDDFi1alKflRGoU0z+pDqYBRIcyM7o8rmyrMjCIJt2ZTFlCZS+zavQDRI0yuLpipYx7fvdrZZ51pSKrO8UhXKiRBYA0l3ibUNU26hKsLssTxCKd6La4+nGm0p38UDd36tYvWU01wokaWQBIc6pRVrdvqvlVw8hnn33W1aamssYa2BfU+Cunvpyzo2MkpwabCBcCWQBIc2o1r8ZFTz31lLtkqoZlCmbV5RcARBk1sgAAAIgkamQBAAAQSZEuLdBdUn7//XfXrx33QwYAAIg+dai1adMmd6fDnG5nHOlAVkFsQd5jHAAAAOGgu7LVq1cvfQNZ/24o+qA53ZYUAAAAeaTbW+s2yp9+qtv6WWFQrypKVObmrneRDmT9cgIFsQSyAAAABWy//f7fcyEnDXNTNkpjLwAAACTXoIHZ009nPIdQpDOyAAAA2Idq1DC75BILKzKyAAAASO6PP8yeeSbjOYSKREZ29+7dtnPnzlQvBoCQK126dI5dvQBAkfLrr2b9+pm1a5eRnQ2ZlAayt912m91+++1xw5o3b24//vhjgfVDtnLlSlu/fn2BTA9AelMQ26hRIxfQAgDCL+UZ2YMPPtg+/vjj2N8lSxbcIvlBbK1atax8+fLcNAFAjjdYWbFihTVo0IDzBQBEQMoDWQWu+++/f67G3b59u3sE+xnLrpzAD2KrV69eIMsKIL3VrFnTBbO7du2yUqVKpXpxAAA5SHkx2KJFi9wtyBo3bmy9e/e2X1WLkYURI0ZY5cqVY4/s7url18QqEwsAueGXFOiHMLDX9uxOzXuBgqT+Y3VDBL8/2ZAp5qmQNEU++OAD27x5s6uL1eU81csuX77c5s6dm/RuDskysgpmN2zYkOmGCNu2bbMlS5a4ereyZcsWyucBEG2cN1DgJvQ2W/tD3t5TraVZt3FsDBRZGzdudAnLZPFdqEoLunbtGvv/IYccYh06dLCGDRvaa6+9Zn379s00fpkyZdwDAIBIUBC7emaqlwLIvz17dJnbTOVWIezVJVRLVKVKFTvooIPsp59+SvWiAAAAYNYsM13Z1nMIhSqQVZnB4sWL7YADDkj1okTSjh07rGnTpvbll1/m+j0XXnihnXHGGft0uZC1sWPHuh9wwS7pDj30UFbZPnLkkUfam2++yfoFgDSR0kD2+uuvt08//dSWLl3qgq8zzzzTSpQoYeeee64VdaNGjbIDDzzQ1feq5OKbb77J8T1PPvmkqwk+6qijXIBUrFixbB9a70WFarD/+c9/uoy/+gq99tprsx3/lVdeceuIID/3P4i0vu6+++644W+99VZcN1ZTpkyJ2wfLlSvnuuB76qmncpzH999/b8cee6w7JlQbf++998a9nmyfT6yPv/nmm+3GG290XW0BAKIvpYHsb7/95oJWNfbq1auX6ybrq6++cl3gFGWvvvqqDRw40IYNG2YzZsywtm3bWpcuXWz16tVZvkdt9h577LFYbfE555zjgjf/0bFjR+vXr1/csOx6fSiM7HFhUiNB7VcKZLQ+s6MAXz+yFDQh9xQ03nPPPbZu3bocx12wYIHbB+fPn2+XXXaZXXHFFfbJJ59kW/h/yimnuBr66dOn23333eey14kBsBoFBPfxX375JVNd/qZNm1xDUwBA9KU0kFXWS302KshQUKu/mzRpYkXdgw8+6ILOiy66yFq1auUyrepG7LnnnsvyPfpyV1lGt27d3N/KdKl/Xv+hboU0jeAwZb99999/vyvp0I+J/v37x93SV9tHgV3dunWtQoUKLkOszFqQLtcqs6bGeMokP/DAA3Gva9gdd9xhF1xwgQs2Lr30Ujf8888/dwGjlleB9TXXXGNbtmyJe9+dd97p3rfffvu5QOadd96xNWvWWI8ePdwwNRT87rvvsl2nms4jjzzipqOWkFlRt0vqBk49aKhLuNx499137W9/+5sL5GrUqOGuLPgU1GmeVatWdetfgZS6nMutb7/91k4++WQ3XS338ccf737cBCnz+MQTT7hpaz1qud944424Hw1XXXWV275aRq1DdWXnU3/Ll1xyiQv0tW1OPPFEmz17tuVV586d3X4VnHZW1L+zxtUVBG1zPSd+rqBx48a5z6FjQPvZP/7xD/c+HSuJ6yK4j9euXTvude3zp512mjvXAACiL1Q1soVmxQozfWkGH0uWZLy2bVvm14JfsAsWZH5t7dqM19asyfya5pUH+rJWUKqgwKdL4fp72rRpWb5v6tSp7rJ5sm7LcjJ58mQXBOv5+eefd5do9fApCNK89eWvy7s9e/a0U089NRaQaXmVUVdwMWfOHJcpu+WWW+Km4QfLyobOnDnTva55ajpnn322m64y0QpsNb+ghx56yI4++mj3PgXq559/vgsOzzvvPBf86MeP/i6InuT+/e9/uyArWa8ZyUyYMMEFrgqOtHzKKh5xxBFxl9wVZCv41jrUMmrc4A+F7Ch72KdPH7dedLWiWbNm7v0aHqT1qfWoAFSBuLbFDz9kdPnz6KOPuvmrNxBlQhUUKrD3aXsq268spbZlu3bt7KSTTrK1/n6dSwoS77rrLhs5cqT7YZobWh8TJ050/UfrB1JWtO6OO+64uFvH6iqFPk8wA6w6ewXq+lGkHzrz5s3LNC1tHx0vAIBcaN3abNmyjOcw8iJsw4YNilzcc6KtW7d68+fPd8+ZDBumkCf+0bt3xmuLFmV+Lbiajjwy82svvpjx2mOPZX5N88qD5cuXu8/05Zdfxg0fPHiwd8QRR2T5vgEDBngnnnhilq8ff/zxbpxEffr08Ro2bOjt2rUrNqxnz57eOeec4/7/yy+/eCVKlHDLFXTSSSd5Q4cOdf//5z//6Z188smZlrdVq1axvzWPM844I26cvn37epdeemncsKlTp3rFixePbTe977zzzou9vmLFCrd+brnlltiwadOmuWF6LTeyWhead926db01a9bE1k2PHj2ynVbHjh293v6+k2DhwoVuub744ovYsD/++MMrV66c99prr7m/x4wZ41WuXDn2+rBhw7y2bdtmOb/du3d7FStW9N59993YMM3j8ssvjxuvQ4cO3hVXXOH+f/XVV7t9Y8+ePUk/c6VKlbxt27bFDW/SpIk3evRoL7eC6+rII4/0Lr74Yvf/8ePHu+XzTZ482f1doUIF9yhZsqTb3nfeeWe209f+lbivzJs3z01Lx7nomHn++ee9mTNnelOmTPFOP/1099mWLVsW9763337bzVPrMk/nDSA/XjjM8+63vD30HqAI25BNfJco5beoTYnLLjP7v/+LH1a1asZzvXpKMWb9XmUZA5e+HT+71auXWceO8a8VUg8MW7duzfeNH3SpNlhmoEvQyqyKnnW5XdneIJUb+Lf+VeZP2a8gZVAffvhh915/2ocffnjcOMoeKhOrDKFPcZka4uhmFi1btnTDVDrg8y8Vt2nTJtMwZRV1OVnlBj5lbVWakRNlOJXpffrpp91l/NyaNWuWKwNJRutFt2AOZhq1zlQT7mdLc7Jq1SpX16tSDn0+rc+//vor0x3wVAOd+LeWzc8KqzxB81UG/PTTT3f1pv42UBYz8TbO2p+UMc8P1cmqPEHlKFlRRlRXD7QfqSGjsvDVqlVztbL5pc8cXA9q9Kh9aPTo0a6sxafyC+1jmrf+DwDIxs8/mw0ZopO7WS5L7gpT0QxkFVxmFWAqGGzXLuv3Nm+e9WtqpLaXDdUURCnwUwATpL8VpGX3Pj/4zKvEe8qrztBv1a0gR8ujS87BYFeCAWNuqL42SNNWQx/VOiZq0KBB0uXzW8AnG+Yvsx/ASU53BPEpaFMjr+7du8eG+dNTMKpL2Mnqt/d1IKSygj///NPV9+qSuWqQFazlpbGcSgX0w0ClAx9//LErA1GpiupotQ30wyWx5lmC3YLlhUoAdNl/6NChLohORjWx/vT1Q+rrr7+24cOHZxnIat9Pdkz4ryWjfeSwww7L1C+1Sia0LxLEAkAurF9vpnYXQ4daGBXNQDbEVAPYvn17V2vpd/2kgEp/J9aOBukLWw1+lNEMdne0tzRdZQGVDcyqFb+yXl988UXcMP2tLG5i8JsYYKnVuvq+LUj5mV6LFi0y/RBQJlSZWgWRWfXwoGyxto0a5iVbL7t27XJBmrKDoqBUQbEa8eWG1uPjjz/u6mJl2bJl9scff2QaT/WzqhMO/q1tFwzo1ZOFHn//+99dZlYBnbbBypUrXbAerJvdW+qGS/3hKgucG9pPlAXOioL3m266ydUW+z9iJk2a5KavhnTJaL/VNvXXnU+3wA6uGwBAdBHIhpC63lImTpfi1TBFl+jVkj9ZsOQ74YQTXHZNjVtaF2BBtoJRNR5SkKSeCBQAqMcABW8K4tT4atCgQa7Vvi7fKlBSwxx1BaYALDtDhgxxHdQrQFereWXJFNgqQNH7C5qfqdV60mfQ3/rhoKBSZRmJ683PGGa3PtVFmhpGKVurBlYKXN9//3332dQwSyUXKj3Q5W1dSlcfpur9IbEUIyuaxosvvuj2BXVBNXjw4KSZxNdff92Nc8wxx7hSDV2uf/bZZ91ratmvrKu2nRoOalxlMfX5lJlVkKgfTeqXVdtbPYn4jdgSy0FyS6Uf2m/U0CwZ/TDatm1brLRAn1EBdlbUB7B6klAjPK1bBaP6gaGGgMGGetqf9ENGPTGoiy51v6V9K7GswS+tAABEW9HstSDkFAyqhf+tt97qsloKuNSyO7EroSDVOCrwCNabFpQxY8a4QFYBqzJgCnrULZR/+V9ZPbWIV68GCvq03Aoqsrqs7FMgrBtiLFy40GV7FWjpvXXq1LF9QdPXQ2USL7/8svt/YrYurzp16uQCQ/UKoG2l2tDgzSu07pRhV12qAkZlzBXoJpZzZEXBqFrlax2rhldlGOpVIZGCPK1/rdMXXnjB/vOf/8SyvgqgFaQqKNUPDpVQaBkU1Cp7r/+rHEA/lBTIKiBXAOjvbxpf4yUrP8iO9oGsbjyg/UjBtYJOBaYqMVFvB1lR12MfffSRK5HQ+tS+qH3F78ZNtJ70o0GZcG1XBf660Uow+718+XI3LLsfhQCA6CimFl8WUfqi0hfchg0bMtVCKtujLz3V4uW3EVTUqOGUGvWo3jOv9auILgWZ48eP32d3IVO3bGeddZb9/PPPWV7GjwoFzQp4s7qTWFE8b2Afe7Gd2eqZeXtPrcPMzs+6X2WgUK1cqayMmRIA2bTVKaz4LhEZ2TSibJxajOuLGCgoytj+61//inwQK8pmB3swAADkQMGrGnoVUhCbV9TIppmcLucDeaVa03ShkgQAQB57LfjsM3VJo8YjFjYEskDERbg6CAAQhX5ke/TI6GM/u+5JU4TSAgAAAERS2geyWbWaBoBEZLcBIFrStrRA/YOqeyH1iVmzZk33d0HeKABA+gWx6l9Y54ncdo8GAEittA1kFcSqC50VK1a4YBYAcqIgtl69etnekQ4AipSyZc3UH3dIuyRM20BWlIVVp/2625JuVwkA2VEmliAWAAIUxM6bZ2GV1oGs+JcJuVQIAACQXtK+sRcAAADyadYsM91dS88hRCALAACA5NT706ZNGc8hRCALAACASCKQBQAAQCQRyAIAACCSCGQBAACQXIsWZtOnZzyHUNp3vwUAAIB8Kl/erF07CysysgAAAEju11/N+vfPeA4hAlkAAAAk98cfZo8/nvEcQgSyAAAAiCQCWQAAAERSaALZu+++24oVK2bXXnttqhcFAAAAERCKQPbbb7+10aNH2yGHHJLqRQEAAICvVi2z667LeA6hlAeymzdvtt69e9vTTz9tVatWTfXiAAAAwFevntmDD2Y8h1DKA9n+/ftbt27drHPnzjmOu337dtu4cWPcAwAAAPvI5s1m06ZlPIdQSgPZV155xWbMmGEjRozI1fgar3LlyrFH/fr19/kyApGyZ3dq3ovCW9ds48LDMQGYLVxodtRRGc8hlLI7ey1btswGDBhgkyZNsrJly+bqPUOHDrWBAwfG/lZGlmAWCChewmxCb7O1P+RttVRradZtHKsy7NvpwK5mxw5nG4d9OwW3FYB9KmWB7PTp02316tXWLnDbs927d9tnn31mjz32mCsjKFGiRNx7ypQp4x4AsqEv3dUzWUXpuJ2q/f/3OmcbF578rmt/WwFIz0D2pJNOsjlz5sQNu+iii6xFixY2ZMiQTEEsAAAAEIpAtmLFita6deu4YRUqVLDq1atnGg4AAIAUKFnSrEaNjOcQCudSAQAAIPUOOcRszRoLq1AFslOmTEn1IgAAACAiUt6PLAAAAEJq3jyzpk0znkOIQBYAAADJbd9utnhxxnMIEcgCAAAgkghkAQAAEEkEsgAAAIgkAlkAAAAkp4ZeEydmPIdQqLrfAgAAQIhUqmTWpYuFFRlZAAAAJLdihdltt2U8hxCBLAAAAJJTAHv77QSyAAAAQEEiIwsAAIBIIpAFAABAJBHIAgAAILmqVc169854DiG63wIAAEByjRqZvfSShRUZWQAAACS3bZvZTz9lPIcQgSwAAACSmz/frFmzjOcQIpAFAABAJBHIAgAAIJIIZAEAABBJBLIAAACIJLrfAgAAQHLt2pl5noUVGVkAAABEEoEsAAAAkluwwKxjx4znECKQBQAAQHJbtph99VXGcwgRyAIAACCSCGQBAAAQSQSyAAAAiCQCWQAAACR34IFmL76Y8RxC9CMLAACA5KpVMzvvPAurlGZkn3jiCTvkkEOsUqVK7tGxY0f74IMPUrlIAAAA8K1ZYzZqVMZzCKU0kK1Xr57dfffdNn36dPvuu+/sxBNPtB49eti8efNSuVgAAACQZcvMrroq4zldAlkFnOvXr880fOPGje613Orevbuddtpp1qxZMzvooINs+PDhtt9++9lX6q8MAAAAKOga2SlTptiOHTsyDd+2bZtNnTo1P5O03bt32+uvv25btmxxJQbJbN++3T2CgTMAAACKpjwFst9//33s//Pnz7eVK1fGBaITJ060unXr5mkB5syZ4wJXBcHKxo4fP95atWqVdNwRI0bY7bffnqfpA/myZ7dZ8RKF/14ABYvjEUhreQpkDz30UCtWrJh7JCshKFeunI0cOTJPC9C8eXObNWuWbdiwwd544w3r06ePffrpp0mD2aFDh9rAgQPjMrL169fP0/yAXFEgOqG32dof8rbCqrU06zaOlQxE/Vg+sKvZscP31VIB0VGxotkpp2Q8Rz2QXbJkiXmeZ40bN7ZvvvnGatasGXutdOnSVqtWLStRIm+ZKL2vadOm7v/t27e3b7/91h555BEbPXp0pnHLlCnjHkCh0Bff6pmsbKAoHsvVWuyrpQGipVkzsw8/tLDKUyDbsGFD97xnz559tTxu2sE6WAAAAKTI7t1mW7aYVahglsdkZahviLBo0SKbPHmyrV69OlNge+utt+ZqGioV6Nq1qzVo0MA2bdpkL7/8smtI9mGII38AAIAiY/ZsXTI3mz7drF07S4tA9umnn7YrrrjCatSoYfvvv7+rmfXp/7kNZBUEX3DBBbZixQqrXLmyuzmCgtiTTz45P4sFAACAIiRfgeydd97p+nwdMmTIXs382Wef3av3AwAAoOjK1w0R1q1bZz179iz4pQEAAAD2ZSCrIPajjz7Kz1sBAACA1JUWqLusW265xd1Ktk2bNlaqVKm416+55pqCWToAAACkTps2atRkVqVK+gSyTz31lLsLl25coEeQGnsRyAIAAKSBUqXMAvcNSItAVjdGAAAAQJpbvNjsuuvMHnrIrEkTS4saWQAAABQBGzaYvftuxnO6ZGQvvvjibF9/7rnn8rs8AAAAwL4LZNX9VtDOnTtt7ty5tn79ejvxxBPzM0kAAABg3wey48ePzzRMt6nV3b6ahLB+AgAAAOmnwGpkixcvbgMHDrSHVAwMAACA6Ktb1+yBBzKe0yUjm5XFixfbrl27CnKSAAAASJXatc0GDgzt+s9XIKvMa5DnebZixQqbMGGC9enTp6CWDQAAAKm0bp3Zxx+bde5sVrVqegSyM2fOzFRWULNmTXvggQdy7NEAAAAAEbFkiVmvXmbTp6dPIDt58uSCXxIAAACgsGpk16xZYwsWLHD/b968ucvKAgAAAKHttWDLli2uhOCAAw6w4447zj3q1Kljffv2tb/++qvglxIAAAAoiEBWjb0+/fRTe/fdd91NEPR4++233bBBgwblZ5IAAAAIm3LlzA47LOM5XUoL3nzzTXvjjTesU6dOsWGnnXaalStXznr16mVPPPFEQS4jAAAAUqFlS7MZM9IrI6vygdrqVyxBrVq1KC0AAABAeAPZjh072rBhw2zbtm2xYVu3brXbb7/dvQYAAIA0MHOmWZkyGc/pUlrw8MMP26mnnmr16tWztm3bumGzZ8+2MmXK2EcffVTQywgAAIBU8DyzHTsyntMlkG3Tpo0tWrTIxo0bZz/++KMbdu6551rv3r1dnSwAAAAQykB2xIgRrka2X79+ccOfe+4517fskCFDCmr5AAAAgIKrkR09erS1aNEi0/CDDz7YnnzyyfxMEgAAANj3GdmVK1e6myEk0p29VqxYkZ9JAgAAIIzdb82da9a4saVNRrZ+/fr2xRdfZBquYbrDFwAAANJAuXK65B7aGyLkK5BVbey1115rY8aMsV9++cU9VB973XXXZaqbBQAAQET98ovZJZdkPKdLacHgwYPtzz//tCuvvNJ2qEsGMytbtqxr5DV06NCCXkYAAACkwp9/mj37rNmVV5o1bJgegWyxYsXsnnvusVtuucV++OEH1+VWs2bNXD+yAAAAQGhLC3z77bef/e1vf7PWrVvnK4hVN156f8WKFd3tbc844wxbsGDB3iwSAAAAioi9CmT31qeffmr9+/e3r776yiZNmmQ7d+60U045xbZs2ZLKxQIAAEAE5Ku0oKBMnDgx7u+xY8e6zOz06dPtuOOOyzT+9u3b3cO3cePGQllOAACAIql2bbMbb8x4DqGUZmQTbdiwwT1Xq1Yty1KEypUrxx7qBgxIO3t2F/48y++/d/NN1XtTOe9UbCcUDVE9Hjkm0lPdugrAMp5DKKUZ2aA9e/a4Lr2OPvpoV3ObjHpEGDhwYFxGlmAWaad4CbMJvc3W/pC39x3Y1ezY4fmbZ9kqez/f/Ly3WkuzbuNsr6RiuQvivcC+OB739phK1XwRXps2mU2fbta+vVnFihY2oQlkVSs7d+5c+/zzz7McRw3K6BkBRYK+RFbPzNt7qrVI7Xzz896CUtjLXRDvBXKSqmMqlccywmfRIrMTTsgIZtu1s7AJRSB71VVX2XvvvWefffaZ1atXL9WLAwAAgAhIaSDreZ5dffXVNn78eJsyZYo1atQolYsDAACACCmZ6nKCl19+2d5++23Xl+zKlSvdcDXk0k0WAAAAgFD2WvDEE0+4ngo6depkBxxwQOzx6quvpnKxAAAAIKVKZfRYoOcQSnlpAQAAAEKqTRuz336zsApVP7IAAABAbhHIAgAAILk5c8zUo5SeQ4hAFgAAAMnt3Gm2fHnGcwgRyAIAACCSCGQBAAAQSQSyAAAAiCQCWQAAACTXrJnZ5MkZzyGU0n5kAQAAEGIVK5p16mRhRUYWAAAAyanHgqFDM55DiEAWAAAAya1aZXb33RnPIUQgCwAAgEgikAUAAEAkEcgCAAAgkghkAQAAkFz16mZ9+2Y8hxDdbwEAACC5hg3NnnnGwoqMLAAAAJLbutVs3ryM5xAikAUAAEByP/xg1rp1xnMIEcgCAAAgkghkAQAAEEkEsgAAAIgkAlkAAAAkV6yYWenSGc8hRPdbAAAASO6ww8y2b7ewIiMLAACASCKQBQAAQHLqdqtdO7rfAgAAQMRs3Wo2cyY3RAAAAAAKEqUFAAAAiCQCWQAAAERSSgPZzz77zLp372516tSxYsWK2VtvvZXKxQEAAEBQo0Zmr72W8RxCKQ1kt2zZYm3btrVRo0alcjEAAACQTNWqZj17ZjyHUEpviNC1a1f3AAAAQAitWmU2bpxZ795mtWtb2ESqRnb79u22cePGuAcAAAD2keXLzQYNyngOoUgFsiNGjLDKlSvHHvXr10/NguzZnZr3FkVRW9fl92cbs77SV6qOR86bAMJYWpBXQ4cOtYEDB8b+VkY2JcFs8RJmE3qbrf0hb++r1tKs27h9tVTpKb/r+sCuZscO37v35kfZKvlf5r2ddxSxvqIlFccj500A6RLIlilTxj1CQSfj1TNTvRRFQ37WdbUWe//eVOwfBTHvKGJ9RUdhH48AkC6lBQAAAChElSubde+e8RxCKc3Ibt682X766afY30uWLLFZs2ZZtWrVrEGDBqlcNAAAADRpYvbOO6FdDykNZL/77js74YQTYn/79a99+vSxsWPHpnDJAAAAYDt3mq1fb1alilmpUqFbISkNZDt16mSe56VyEQAAAJCVOXPM2rc3mz7drF07CxtqZAEAABBJBLIAAACIJAJZAAAARBKBLAAAACIpUjdEAAAAQCFq29ZswwazChVCudoJZAEAAJBciRJmlSpZWFFaAAAAgOQWLTLr0iXjOYQIZAEAAJDcpk1mH32U8RxCBLIAAACIJAJZAAAARBKBLAAAACKJQBYAAADJ1a9v9thjGc8hRPdbAAAASK5mTbP+/S2syMgCAAAgubVrzV56KeM5hAhkAQAAkNzSpWbnn5/xHEIEsgAAAIgkAlkAAABEEoEsAAAAIolAFgAAAMlVqGB25JEZzyFE91sAAABIrnlzs2nTLKzIyAIAACCSCGQBAACQ3IwZZsWKZTyHEIEsAAAAIolAFgAAAJFEIAsAAIBIIpAFAABAJNH9FgAAAJJr1cps0SKzevUsjAhkAQAAkFzZsmZNm1pYUVoAAACA5JYsMTvvvIznEApFIDtq1Cg78MADrWzZstahQwf75ptvUr1IAAAAWLfObNw4c88hlPJA9tVXX7WBAwfasGHDbMaMGda2bVvr0qWLrV69OtWLBgAAgBBLeSD74IMPWr9+/eyiiy6yVq1a2ZNPPmnly5e35557LtWLBgAAgBBLaWOvHTt22PTp023o0KGxYcWLF7fOnTvbtGnTMo2/fft29/Bt2LDBPW/cuNEKXdmmZhV35/09qVjWqMvPui5RJ2NdR+m9qZx3FN+bynlH8b0Fdf6J4nIXpX0zVeuL77f0tXnz/3supBjGj+s8z8t5ZC+Fli9friX0vvzyy7jhgwcP9o444ohM4w8bNsyNz4N1wD7APsA+wD7APsA+wD5gab0Oli1blmMsGanut5S5VT2tb8+ePbZ27VqrXr26FStWLKXLhtz9wqpfv74tW7bMKlWqxCpLc2zvooXtXbSwvYuOjSn47lYmdtOmTVanTp0cx01pIFujRg0rUaKErVq1Km64/t5///0zjV+mTBn3CKpSpco+X04ULB0IBLJFB9u7aGF7Fy1s76KjUiF/d1euXDn8jb1Kly5t7du3t08++SQuy6q/O3bsmMpFAwAAQMilvLRApQJ9+vSxww8/3I444gh7+OGHbcuWLa4XAwAAACC0gew555xja9assVtvvdVWrlxphx56qE2cONFq166d6kVDAVNZiPoLTiwPQXpiexctbO+ihe1ddJQJ+Xd3MbX4SvVCAAAAAJG7IQIAAACQHwSyAAAAiCQCWQAAAEQSgSwAAAAiiUAW+8TChQutR48e7qYX6kD5mGOOscmTJ8eN8+uvv1q3bt2sfPnyVqtWLRs8eLDt2rUrbpwpU6ZYu3btXGvJpk2b2tixY9liITRhwgTr0KGDlStXzqpWrWpnnHFG3Ots6/Szfft218uM7qo4a9asuNe+//57O/bYY61s2bLujkD33ntvpve//vrr1qJFCzdOmzZt7P333y/EpUduLF261Pr27WuNGjVyx3aTJk1c6/UdO3bEjcf2Tm+jRo2yAw880B2rOs9/8803Fio53sQWyIdmzZp5p512mjd79mxv4cKF3pVXXumVL1/eW7FihXt9165dXuvWrb3OnTt7M2fO9N5//32vRo0a3tChQ2PT+Pnnn917Bg4c6M2fP98bOXKkV6JECW/ixIlskxB54403vKpVq3pPPPGEt2DBAm/evHneq6++GnudbZ2errnmGq9r167ufug6hn0bNmzwateu7fXu3dubO3eu95///McrV66cN3r06Ng4X3zxhTuW7733Xnds33zzzV6pUqW8OXPmpOjTIJkPPvjAu/DCC70PP/zQW7x4sff22297tWrV8gYNGhQbh+2d3l555RWvdOnS3nPPPefO7f369fOqVKnirVq1ygsLAlkUuDVr1rgvt88++yw2bOPGjW7YpEmT3N8KXIsXL+6tXLkyNo4CoUqVKnnbt293f99www3ewQcfHDftc845x+vSpQtbLSR27tzp1a1b13vmmWeyHIdtnX60TVu0aOG+2BID2ccff9z9sPGPYxkyZIjXvHnz2N+9evXyunXrFjfNDh06eJdddlkhfQLkl358NGrUKPY32zu9HXHEEV7//v1jf+/evdurU6eON2LECC8sKC1Agatevbo1b97cXnjhBXeXNpULjB492pUP6JbEMm3aNHc5MXjjiy5dutjGjRtt3rx5sXE6d+4cN22No+EIhxkzZtjy5cutePHidthhh9kBBxxgXbt2tblz58bGYVunl1WrVlm/fv3sxRdfdGVBibS9jzvuOHcL8uBxu2DBAlu3bl1sHI7taNqwYYNVq1Yt9jfbO33t2LHDpk+fHnes6lyvv8P0PUwgiwKnmrmPP/7YZs6caRUrVnR1NQ8++KC7Y5vqJ0V3cUu8e5v/t17LbhwFu1u3bmXLhcDPP//snm+77Ta7+eab7b333nPbuFOnTrZ27Vr3Gts6fegq3oUXXmiXX365u614Mnuzvf3XEU4//fSTjRw50i677LLYMLZ3+vrjjz9s9+7doT9WCWSRazfeeKMLUrN7/Pjjj+7Lrn///i4DO3XqVFcYrsY/3bt3txUrVrDG02hb79mzx41/00032dlnn+0y7mPGjHGvqzEP0mt7K4jZtGmTDR06NNWLjELY3kG68nLqqadaz549XUYeCIuSqV4ARMegQYNcNiY7jRs3tv/9738uM6fLiOqxQB5//HGbNGmSPf/88+4kuv/++2dq+ahLlqLX/Gd/WHAcTVMtaJH6be3/MGnVqlVsuHqY0GvqqUDY1ul1bOuSYuI915Wd7d27tzu+szpuc3Ns+68jHNvb9/vvv9sJJ5xgRx11lD311FNx47G901eNGjWsRIkSoT9WCWSRazVr1nSPnPz111+xWpog/e1n8Dp27GjDhw+31atXu8ytKNBVkOoHRRonsUsejaPhCMe2VgZWQY3qH9XFmuzcudN129OwYUP3N9s6fbb3o48+anfeeWdcgKP611dffdV1y+Nvb2XotR+UKlUqdtyqbt4vLdI4n3zyiV177bWxaXFsh297+5lYBbH+1ZbE8zrbO32VLl3abXcdq36XivoO199XXXWVhUaqW5shPXstqF69unfWWWd5s2bNcl0yXX/99a57Hf0d7JLplFNOccPUpVbNmjWTdr81ePBg74cffvBGjRpF91shNGDAANdzgbro+fHHH72+ffu6LnrWrl3rXmdbp68lS5Zk6rVg/fr1rvut888/33W/pe57dBwndr9VsmRJ7/7773fH9rBhw+h+K4R+++03r2nTpt5JJ53k/q/uE/2Hj+2d3l555RWvTJky3tixY11XeZdeeqnrfivY41CqEchin/j2229dkFqtWjWvYsWK3pFHHum67AlaunSp64dSfUyqD1n1TajunIImT57sHXrooa4fu8aNG3tjxoxhi4XMjh073LZT8Kptrb6BFcAEsa2LTiAr6j/6mGOOcV+A+pFz9913Z3rva6+95h100EHu2FY3exMmTCjEJUdu6Hyr7ZvsEcT2Tm8jR470GjRo4I5Vdcf11VdfeWFSTP+kOisMAAAA5BW9FgAAACCSCGQBAAAQSQSyAAAAiCQCWQAAAEQSgSwAAAAiiUAWAAAAkUQgCwAAgEgikAUAAEAkEcgCAAAgkghkAYTa/fffb8WKFcv14/LLL0/1IgMACknJwpoRAORHhQoVYv+vXr26ValSJdvxa9WqxYoGgCKimOd5XqoXAgCysmvXLvv73/9ub7/9ttWvX98+//xza9CgASsMAEBpAYBwK1mypL366qvWuXNnW7ZsmXtetWpVtu/p1KmTXXvttZYO/vzzT5dlXrp0qYVFKtbvP/7xD3vggQcKdZ4Awo8aWQChV6ZMGXvrrbesY8eOtmjRIjvllFNs3bp1VhQMHz7cevToYQceeKAVZTfffLNbFxs2bEj1ogAIEQJZAJGplX3//fetbdu29v3339tpp51mmzdvtnT2119/2bPPPmt9+/bd5/PasWNHKKfla926tTVp0sReeumlAp82gOgikAUQGWro9dFHH1nz5s3tq6++cpnK7du35/g+jXPNNde4S/Rly5a1Y445xr799tvY65s2bbLevXu7YPmAAw6whx56KNvL50ceeaQ9+uijcZe91WPCtm3b3N8qgShdurQtXLhwrz6vAndlozU/n5brqquuco/KlStbjRo17JZbbrFgc4c9e/bYiBEjrFGjRlauXDkX/L/xxhtx0/ano8+oaXTp0iXpMmzZssUuuOAC22+//dy6SXZ5P9m0Jk6c6Naztpka6Z1++um2ePHi2Hvee+8999ru3bvd37NmzXLr8MYbb4yNc8kll9h5550X+7t79+72yiuv5Ht9Akg/BLIAIkXB6KRJk6xhw4b2v//9z8455xzXICw7N9xwg7355pv2/PPP24wZM6xp06Yu2Fq7dq17feDAgfbFF1/YO++846Y9depUN15WFIAp+PWDVgXXCoLXr1/vho0ePdpOPvlkO+igg/bqs2o52rdvn2m4Podqh7/55ht75JFH7MEHH7Rnnnkm9rqC2BdeeMGefPJJmzdvnl133XUuIPz0008zTUcBtz67xk1m8ODB7n1qbKfPOWXKlKTrJnFaCoC1Xr/77jv75JNPrHjx4nbmmWe6IFuOPfZYtw5nzpzp/tY8FARr+j4NU5DsO+KII9xnzs2PFwBFhHotAICoWbRokVehQgWlIb1rrrkm7rXjjz/eGzBggPv/5s2bvVKlSnnjxo2Lvb5jxw6vTp063r333utt3LjRvf7666/HXl+/fr1Xvnz52DQSnXPOOd6QIUPc/2+44Qbv6quv9ho2bOjNnz/f2759u1erVi3vww8/3OvP2KNHD+/iiy/O9Nlatmzp7dmzJzZMy6Jhsm3bNrfsX375Zdz7+vbt65177rlx0znssMOynf+mTZu80qVLe6+99lps2J9//umVK1cubt3kZlpr1qxx22rOnDmxYe3atfPuu+8+9/8zzjjDGz58uJuf5vvbb7+58RcuXBgbf/bs2W7Y0qVLs50XgKKDjCyASFKGVVk/ZUd1CTorupy9c+dOO/roo2PDSpUq5bJ7P/zwg/3888/udf3t0yV7lS/klJHV/FXDqrIFvUcN0HQJX5fSlZHdW1u3bnWlEIlUaqDL8D6/EZwu0//000+utlbzVzmA/1CGNnhpX5Jle4M0vupdO3ToEBtWrVq1pOsmcVpannPPPdcaN25slSpVijVW+/XXX2PjHH/88S4Dq7IIZZ/POussa9mypetiTdnYOnXqWLNmzWLjq0xC9PkAQLghAoDIGTt2rKulVJCncoA2bdoU6vwVyP7+++/ucvpRRx3lShUUrCmQHTVqlAtsFWh+/fXXduedd9q7777r3qe60XHjxtmLL76Yq/noUntee2fwG8BNmDDB6tatG/ea6m2zutnE3kqclupZVf7x9NNPu4BUJQVqsBVsCKaygeeee85mz57tfly0aNHCDVNwq8+tQDfILwWpWbNmgS03gGgjIwsgUtRIqF+/flaiRAnX8Ee1ltlRS3e/dtOnDKwae7Vq1cplDBVEBRt/qYun7BpqKZDVOKpPHTBggBumjOzkyZNdlleNo0TZxQULFsTed8cdd9itt96a68962GGH2fz58zMNV4AcpIZvylxqnegzKWBV5lMBdvChG0rkhdad1k1wfgowc2rEpr5v9bnVZdZJJ53k1kOygNyvk1XjOj9o9QNZPYL1sTJ37lyrV6+eC/ABQMjIAoiML7/80nr16uUad6lxk3otyE2m8IorrnCNlnRZXHcFu/fee93laXVrVbFiRevTp0/sdTUmGzZsmGucFLx8nxjIqqGZegVQoCbKyKqR05VXXmnly5ePDVMGUoGzGpEp2AxeKs+JGqQNHTrUBYFVq1aNDVeQqoZUl112mWt4NXLkyFhvAvo8119/vWvgpSyoeg5Q0K1AXsujz5pbKknQOtK6UbmE1s1NN93k1k12tKwa/6mnnnI9HWh5g70RBMc75JBDXJb6sccec8OOO+44t421zhIzsio/UB/CAOAjkAUQCcpMqgsn1Y3eddddeepb9e6773ZB3fnnn+8ygIcffrh9+OGHseBQrf4vv/xyN30Fe+rlQL0RJKtP9QNZXcL3s7F+Rlbdb/Xv3z9uXAWuqltViUFuSwp8Kplo166dvfbaay5o9Snjq/Wgul5lYbUcl156aVzmV5ff1XuBaoC1vJrOv/71L8ur++67z31WlQooSB40aFCONyVQoKtsuUosVE6gmlp1V5aYYRUFq+p6y39NPyaUVdbd24K1uFq3uimGyjMAwFdMLb5ifwFACCmoVC3qb7/95oK2hx9+eJ/OT424VF+qLOfe3oxAwZyyvzrVqmFYXqnWVRlRXVZXgKiA79BDD93n6yBsnnjiCRs/frzrAgwAfGRkAYSaGvjoEruCWLWCVz1lQVNfpj/++KPLcCrb+O9//9sNz03pQk5UH6rgO1grmxfdunVzPQAsX748zzWu6US1uiqhAIAgMrIAQq1nz56Z7kqVHV2OVlCa10BWXXgp2FTDMHUlpXKDwu4NITeKakYWAJIhIwsg1BRYqvV8bqnLp7xS7wDTp0+3KAje+QoAijoysgAAAIgk+pEFAABAJBHIAogUdc6vrqB86o5J3WoBAIoeSgsARMrGjRtdP7D+3aWOPvpod8vavNxoAACQHmjsBSBS9vZuWQCA9EEgCyBy9uZuWQCA9EFpAYDI2du7ZQEA0gMZWQCRs7d3ywIApAcysgAAAIgkut8CAABAJBHIAgAAIJIIZAEAABBJBLIAAACIJAJZAAAARBKBLAAAACKJQBYAAACRRCALAACASCKQBQAAQCQRyAIAACCSCGQBAABgUfT/ASypIJahsSoPAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "keys = jax.random.split(jax.random.PRNGKey(11), 50)\n",
+    "theta_true = jnp.array([K_ALPHA_TRUE, K_SIGMA_TRUE], dtype=jnp.float32)\n",
+    "sums = []\n",
+    "for k in keys:\n",
+    "    z = jax.random.normal(k, (N_NODES * N_STEPS * D,), dtype=jnp.float32)\n",
+    "    _, slc, _ = bffg_guided_forward(z, theta_true)\n",
+    "    sums.append(float(slc))\n",
+    "sums = np.asarray(sums)\n",
+    "\n",
+    "print(f\"50 noise draws at θ_true:\")\n",
+    "print(f\"  sum log_corr — mean = {sums.mean():+8.3f}, std = {sums.std():.3f}\")\n",
+    "print(f\"  (in notebook 05's linear case this would be identically 0)\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 3.2))\n",
+    "ax.hist(sums, bins=30, color=\"darkorange\", edgecolor=\"white\")\n",
+    "ax.axvline(0, color=\"r\", ls=\"--\", lw=1, label=\"0 (Theorem-14 collapse, NB 05)\")\n",
+    "ax.set_xlabel(r\"$\\sum_v \\log w_v$  (per draw)\")\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_title(\"Theorem-23 importance weight under non-linear σ(X)\")\n",
+    "ax.legend(); plt.tight_layout(); plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc9e0556",
+   "metadata": {},
+   "source": [
+    "## 4. NumPyro model\n",
+    "\n",
+    "Three sites:\n",
+    "\n",
+    "- $k_\\alpha$, $k_\\sigma$ with weakly-informative inverse-gamma priors — $\\mathrm{InvGamma}(3.0,\\,0.4)$ and $\\mathrm{InvGamma}(3.0,\\,1.0)$, both on the positive scale.\n",
+    "- $z$ — the per-step Brownian-noise field flattened to one vector of length $N_\\text{nodes} \\cdot N_\\text{steps} \\cdot D$.\n",
+    "\n",
+    "The BFFG-implied log-density goes in via `numpyro.factor`:\n",
+    "\n",
+    "$$\\text{factor}_\\text{bffg} \\;=\\; \\mathrm{log\\_norm\\_root}(\\theta) \\;+\\; \\sum_v \\log w_v(z, \\theta).$$\n",
+    "\n",
+    "`log_norm_root` is the BFFG estimate of the marginal log-likelihood, $\\log h(x_\\text{root})$, read off the canonical message at the pinned root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7b4cb6de",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:39.986434Z",
+     "iopub.status.busy": "2026-05-28T20:06:39.986345Z",
+     "iopub.status.idle": "2026-05-28T20:06:41.636656Z",
+     "shell.execute_reply": "2026-05-28T20:06:41.636173Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise dimension: 41600\n",
+      "U at (θ_true, z=0) = 38265.2969\n"
+     ]
+    }
+   ],
+   "source": [
+    "N_Z = N_NODES * N_STEPS * D\n",
+    "print(f\"noise dimension: {N_Z}\")\n",
+    "\n",
+    "\n",
+    "def model():\n",
+    "    k_alpha = numpyro.sample(\n",
+    "        \"k_alpha\", dist.InverseGamma(PRIOR_CONCENTRATION[\"k_alpha\"], PRIOR_RATE[\"k_alpha\"])\n",
+    "    )\n",
+    "    k_sigma = numpyro.sample(\n",
+    "        \"k_sigma\", dist.InverseGamma(PRIOR_CONCENTRATION[\"k_sigma\"], PRIOR_RATE[\"k_sigma\"])\n",
+    "    )\n",
+    "    z = numpyro.sample(\"z\", dist.Normal(jnp.zeros(N_Z), 1.0).to_event(1))\n",
+    "    theta = jnp.stack([k_alpha, k_sigma])\n",
+    "    _, sum_log_corr, log_norm_root = bffg_guided_forward(z, theta)\n",
+    "    numpyro.factor(\"bffg\", log_norm_root + sum_log_corr)\n",
+    "\n",
+    "\n",
+    "def initialize_numpyro(rng_key=jax.random.PRNGKey(0)):\n",
+    "    return initialize_model(rng_key, model, validate_grad=False)\n",
+    "\n",
+    "\n",
+    "mi = initialize_numpyro()\n",
+    "potential_fn = mi.potential_fn\n",
+    "# initialize_model's potential_fn works in unconstrained space; positive sites use log coordinates.\n",
+    "init_u = {\n",
+    "    \"k_alpha\": jnp.log(jnp.array(K_ALPHA_TRUE)),\n",
+    "    \"k_sigma\": jnp.log(jnp.array(K_SIGMA_TRUE)),\n",
+    "    \"z\": jnp.zeros(N_Z),\n",
+    "}\n",
+    "print(f\"U at (θ_true, z=0) = {float(potential_fn(init_u)):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a43fff90",
+   "metadata": {},
+   "source": [
+    "## 5. Custom `RWpCNKernel` — with proposal diagnostics\n",
+    "\n",
+    "Two-block Metropolis-within-Gibbs:\n",
+    "\n",
+    "- **pCN block** on `z`: $z' = \\sqrt{1-\\beta^2}\\,z + \\beta\\,\\varepsilon$, with $\\varepsilon$, reversible w.r.t. $\\mathcal{N}(0, I)$. One-line correction subtracts the $z$-prior delta that `potential_fn` bakes in.\n",
+    "- **RW block** on NumPyro's unconstrained coordinates for $(k_\\alpha, k_\\sigma)$: symmetric Gaussian random walk, with a separate scale per parameter.\n",
+    "\n",
+    "The extra fields `pcn_log_alpha` and `pcn_delta_potential` record the pCN proposal quality before the accept/reject update.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a24d3f67",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:41.637940Z",
+     "iopub.status.busy": "2026-05-28T20:06:41.637856Z",
+     "iopub.status.idle": "2026-05-28T20:06:41.643047Z",
+     "shell.execute_reply": "2026-05-28T20:06:41.642668Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "RWpCNState = namedtuple(\n",
+    "    \"RWpCNState\",\n",
+    "    [\"u\", \"potential_energy\", \"accept\", \"pcn_log_alpha\", \"pcn_delta_potential\", \"rng_key\"],\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def _std_normal_logpdf(x):\n",
+    "    return -0.5 * jnp.sum(x**2)\n",
+    "\n",
+    "\n",
+    "class RWpCNKernel(MCMCKernel):\n",
+    "    sample_field = \"u\"\n",
+    "\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        potential_fn,\n",
+    "        *,\n",
+    "        pcn_beta,\n",
+    "        rw_scale,\n",
+    "        noise_site=\"z\",\n",
+    "        param_sites=(\"k_alpha\", \"k_sigma\"),\n",
+    "        postprocess_fn=None,\n",
+    "    ):\n",
+    "        self._potential_fn = potential_fn\n",
+    "        self._pcn_beta = pcn_beta\n",
+    "        self._noise_site = noise_site\n",
+    "        self._param_sites = tuple(param_sites)\n",
+    "        if isinstance(rw_scale, dict):\n",
+    "            self._rw_scale_by_site = {site: float(rw_scale[site]) for site in self._param_sites}\n",
+    "        else:\n",
+    "            self._rw_scale_by_site = {site: float(rw_scale) for site in self._param_sites}\n",
+    "        self._postprocess_fn = postprocess_fn or (lambda x: x)\n",
+    "\n",
+    "    def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\n",
+    "        if init_params is None:\n",
+    "            raise ValueError(\"RWpCNKernel needs explicit init_params (a dict per site).\")\n",
+    "        return RWpCNState(\n",
+    "            init_params,\n",
+    "            self._potential_fn(init_params),\n",
+    "            jnp.zeros(2),\n",
+    "            jnp.array(0.0),\n",
+    "            jnp.array(0.0),\n",
+    "            rng_key,\n",
+    "        )\n",
+    "\n",
+    "    def postprocess_fn(self, model_args, model_kwargs):\n",
+    "        return self._postprocess_fn\n",
+    "\n",
+    "    def sample(self, state, model_args, model_kwargs):\n",
+    "        u = dict(state.u)\n",
+    "        u_energy = state.potential_energy\n",
+    "        k_pcn, k_pcn_acc, k_rw, k_rw_acc, k_next = jax.random.split(state.rng_key, 5)\n",
+    "\n",
+    "        # pCN on noise (params fixed); subtract z-prior delta since U bakes it in.\n",
+    "        z = u[self._noise_site]\n",
+    "        z_prop = jnp.sqrt(1 - self._pcn_beta**2) * z + self._pcn_beta * jax.random.normal(\n",
+    "            k_pcn, z.shape\n",
+    "        )\n",
+    "        prop_energy = self._potential_fn({**u, self._noise_site: z_prop})\n",
+    "        pcn_delta_potential = prop_energy - u_energy\n",
+    "        pcn_log_alpha = -pcn_delta_potential - (_std_normal_logpdf(z_prop) - _std_normal_logpdf(z))\n",
+    "        log_alpha = pcn_log_alpha\n",
+    "        acc_z = jnp.log(jax.random.uniform(k_pcn_acc)) < log_alpha\n",
+    "        u[self._noise_site] = jnp.where(acc_z, z_prop, z)\n",
+    "        u_energy = jnp.where(acc_z, prop_energy, u_energy)\n",
+    "\n",
+    "        # RW on each unconstrained param site (noise fixed); symmetric, no correction.\n",
+    "        rw_keys = jax.random.split(k_rw, len(self._param_sites))\n",
+    "        u_prop = dict(u)\n",
+    "        for site, key in zip(self._param_sites, rw_keys):\n",
+    "            scale = self._rw_scale_by_site[site]\n",
+    "            u_prop[site] = u[site] + scale * jax.random.normal(key, u[site].shape)\n",
+    "        prop_energy = self._potential_fn(u_prop)\n",
+    "        acc_th = jnp.log(jax.random.uniform(k_rw_acc)) < -(prop_energy - u_energy)\n",
+    "        for site in self._param_sites:\n",
+    "            u[site] = jnp.where(acc_th, u_prop[site], u[site])\n",
+    "        u_energy = jnp.where(acc_th, prop_energy, u_energy)\n",
+    "\n",
+    "        accept = jnp.array([acc_z, acc_th], dtype=state.accept.dtype)\n",
+    "        return RWpCNState(u, u_energy, accept, pcn_log_alpha, pcn_delta_potential, k_next)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e19bacfd",
+   "metadata": {},
+   "source": [
+    "## 6. Run the chain\n",
+    "\n",
+    "Single-chain debugging starts from a deliberately non-truth point, while the RW block explores $\theta$ and pCN moves the noise field $z$. For a non-linear SDE the RW acceptance is driven by the *combined* movement of `log_norm_root` and $\\sum \\log w$, which depends on both the latent bridge and the parameters — so the chain is genuinely informative.\n",
+    "\n",
+    "We set `num_warmup=0`: this fixed kernel does no warmup adaptation, and NumPyro drops warmup draws from `get_samples()`, so a non-zero warmup would silently hide the convergence transient. We keep the full chain and burn in during post-processing instead.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b87f7d9a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:06:41.644450Z",
+     "iopub.status.busy": "2026-05-28T20:06:41.644373Z",
+     "iopub.status.idle": "2026-05-28T20:07:24.339622Z",
+     "shell.execute_reply": "2026-05-28T20:07:24.339089Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RW/pCN: 303.6s   10000 samples\n",
+      "  acc pCN = 0.2210\n",
+      "  acc RW  = 0.2800  (target band 0.20-0.55)\n",
+      "  mean potential U (incl. z-prior) = 58998.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# num_warmup=0: NumPyro drops warmup draws from get_samples() and this fixed\n",
+    "# kernel does no warmup adaptation, so a non-zero warmup would hide the\n",
+    "# convergence transient. Keep the full chain; burn in during post-processing.\n",
+    "N_WARMUP = 0\n",
+    "N_SAMPLES = N_MCMC_SAMPLES\n",
+    "\n",
+    "kernel = RWpCNKernel(\n",
+    "    potential_fn,\n",
+    "    pcn_beta=PCN_BETA,\n",
+    "    rw_scale=RW_SCALE_BY_SITE,\n",
+    "    postprocess_fn=mi.postprocess_fn,\n",
+    ")\n",
+    "# init_params are unconstrained coordinates; postprocess_fn maps saved samples back to k-space.\n",
+    "init_u = {\n",
+    "    \"k_alpha\": jnp.log(jnp.array(0.2)),\n",
+    "    \"k_sigma\": jnp.log(jnp.array(0.5)),\n",
+    "    \"z\": jax.random.normal(jax.random.PRNGKey(33), (N_Z,)),\n",
+    "}\n",
+    "\n",
+    "mc = MCMC(\n",
+    "    kernel,\n",
+    "    num_warmup=N_WARMUP,\n",
+    "    num_samples=N_SAMPLES,\n",
+    "    num_chains=1,\n",
+    "    progress_bar=False,\n",
+    ")\n",
+    "t0 = time.perf_counter()\n",
+    "mc.run(\n",
+    "    jax.random.PRNGKey(7),\n",
+    "    init_params=init_u,\n",
+    "    extra_fields=(\"accept\", \"potential_energy\", \"pcn_log_alpha\", \"pcn_delta_potential\"),\n",
+    ")\n",
+    "samples = mc.get_samples()\n",
+    "extra_fields = mc.get_extra_fields()\n",
+    "jax.block_until_ready((samples, extra_fields))\n",
+    "elapsed = time.perf_counter() - t0\n",
+    "\n",
+    "acc = np.asarray(extra_fields[\"accept\"])\n",
+    "potential_energy = np.asarray(extra_fields[\"potential_energy\"])\n",
+    "pcn_log_alpha = np.asarray(extra_fields[\"pcn_log_alpha\"])\n",
+    "pcn_delta_potential = np.asarray(extra_fields[\"pcn_delta_potential\"])\n",
+    "k_alpha = np.asarray(samples[\"k_alpha\"])\n",
+    "k_sigma = np.asarray(samples[\"k_sigma\"])\n",
+    "\n",
+    "print(f\"RW/pCN: {elapsed:.1f}s   {N_SAMPLES} samples\")\n",
+    "print(f\"  acc pCN = {acc[..., 0].mean():.4f}\")\n",
+    "print(f\"  acc RW  = {acc[..., 1].mean():.4f}  (target band 0.20-0.55)\")\n",
+    "print(f\"  mean potential U (incl. z-prior) = {potential_energy.mean():.1f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08286d0",
+   "metadata": {},
+   "source": [
+    "## 7. Trace plots and posterior summaries\n",
+    "\n",
+    "NumPyro's `potential_energy` is $U = -\\log$ joint and bakes in the standard-normal $z$-prior, whose normaliser $\\tfrac12 N_Z\\log 2\\pi$ (here $\\sim\\!4\\times10^4$) and quadratic $\\tfrac12\\lVert z\\rVert^2$ dwarf the BFFG log-likelihood and have the wrong sign for a \"target\". The pCN block cancels that prior in its acceptance, so we add it back to recover the meaningful log-target $\\log p(y\\mid\\theta) + \\log p(\\theta)$ — an $O(10^2)$ quantity with a clear convergence trend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "458ed0c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-05-28T20:07:24.341313Z",
+     "iopub.status.busy": "2026-05-28T20:07:24.341112Z",
+     "iopub.status.idle": "2026-05-28T20:07:24.556834Z",
+     "shell.execute_reply": "2026-05-28T20:07:24.556307Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Posterior summaries (after burn-in = 5000):\n",
+      "  k_alpha: mean=0.112 +/- 0.015, truth = 0.1\n",
+      "  k_sigma: mean=0.262 +/- 0.008, truth = 0.25\n",
+      "  log target: start=-5431.8 -> tail mean=285.5\n",
+      "  pCN log alpha: mean=-4.10, q10/50/90=[-8.01757812 -3.97070312 -0.3125    ]\n",
+      "  pCN delta U proposal: mean=2.0, q10/50/90=[-24.3203125    1.7734375   28.37539063]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x1000 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "burn = BURN_IN\n",
+    "window = 50\n",
+    "\n",
+    "\n",
+    "def _rolling_mean(values, window):\n",
+    "    values = np.asarray(values)\n",
+    "    window = min(window, len(values))\n",
+    "    if window <= 1:\n",
+    "        return np.arange(len(values)), values\n",
+    "    return np.arange(window - 1, len(values)), np.convolve(values, np.ones(window) / window, mode=\"valid\")\n",
+    "\n",
+    "\n",
+    "def _post_burn_or_all(values, burn, *, sample_axis=0):\n",
+    "    values = np.asarray(values)\n",
+    "    if burn < values.shape[sample_axis]:\n",
+    "        slices = [slice(None)] * values.ndim\n",
+    "        slices[sample_axis] = slice(burn, None)\n",
+    "        return values[tuple(slices)]\n",
+    "    return values\n",
+    "\n",
+    "\n",
+    "k_alpha_post = _post_burn_or_all(k_alpha, burn)\n",
+    "k_sigma_post = _post_burn_or_all(k_sigma, burn)\n",
+    "pcn_log_alpha_post = _post_burn_or_all(pcn_log_alpha, burn)\n",
+    "pcn_delta_potential_post = _post_burn_or_all(pcn_delta_potential, burn)\n",
+    "\n",
+    "# Reconstruct the meaningful log-target from the saved trace. U = -log joint\n",
+    "# bakes in the z-prior (constant ½·N_Z·log(2π) ~4e4 plus a drifting ½Σz²), which\n",
+    "# buries the BFFG log-likelihood. pCN cancels that prior in its acceptance, so\n",
+    "# adding it back is exact and recovers log p(y | θ) + log p(θ) ~ O(1e2).\n",
+    "z_arr = np.asarray(samples[\"z\"])\n",
+    "N_Z_ = z_arr.shape[-1]\n",
+    "z_sq = np.einsum(\"ij,ij->i\", z_arr, z_arr)                 # Σz² per draw\n",
+    "log_target = -potential_energy + 0.5 * z_sq + 0.5 * N_Z_ * np.log(2.0 * np.pi)\n",
+    "log_target_post = _post_burn_or_all(log_target, burn)\n",
+    "\n",
+    "print(f\"Posterior summaries (after burn-in = {burn}):\")\n",
+    "for name, post, truth in [\n",
+    "    (\"k_alpha\", k_alpha_post, K_ALPHA_TRUE),\n",
+    "    (\"k_sigma\", k_sigma_post, K_SIGMA_TRUE),\n",
+    "]:\n",
+    "    print(f\"  {name}: mean={post.mean():.3f} +/- {post.std():.3f}, truth = {truth}\")\n",
+    "print(f\"  log target: start={log_target[0]:.1f} -> tail mean={log_target_post.mean():.1f}\")\n",
+    "print(\n",
+    "    \"  pCN log alpha: \"\n",
+    "    f\"mean={pcn_log_alpha_post.mean():.2f}, \"\n",
+    "    f\"q10/50/90={np.quantile(pcn_log_alpha_post, [0.1, 0.5, 0.9])}\"\n",
+    ")\n",
+    "print(\n",
+    "    \"  pCN delta U proposal: \"\n",
+    "    f\"mean={pcn_delta_potential_post.mean():.1f}, \"\n",
+    "    f\"q10/50/90={np.quantile(pcn_delta_potential_post, [0.1, 0.5, 0.9])}\"\n",
+    ")\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 2, figsize=(13, 10))\n",
+    "\n",
+    "ax = axes[0, 0]\n",
+    "ax.plot(k_alpha, lw=0.6, color=\"C0\")\n",
+    "ax.axhline(K_ALPHA_TRUE, ls=\"--\", color=\"k\", lw=1, label=\"truth\")\n",
+    "ax.axhline(k_alpha_post.mean(), ls=\"-\", color=\"C0\", lw=1, label=\"post-burn mean\")\n",
+    "ax.axvline(burn, ls=\":\", color=\"gray\", lw=0.8, label=\"burn-in\")\n",
+    "ax.set_title(r\"$k_\\alpha$ (kernel amplitude)\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = axes[0, 1]\n",
+    "ax.plot(k_sigma, lw=0.6, color=\"C1\")\n",
+    "ax.axhline(K_SIGMA_TRUE, ls=\"--\", color=\"k\", lw=1, label=\"truth\")\n",
+    "ax.axhline(k_sigma_post.mean(), ls=\"-\", color=\"C1\", lw=1, label=\"post-burn mean\")\n",
+    "ax.axvline(burn, ls=\":\", color=\"gray\", lw=0.8, label=\"burn-in\")\n",
+    "ax.set_title(r\"$k_\\sigma$ (kernel length scale)\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = axes[1, 0]\n",
+    "ax.plot(log_target, lw=0.6, color=\"darkorange\")\n",
+    "ax.axvline(burn, ls=\":\", color=\"gray\", lw=0.8, label=\"burn-in\")\n",
+    "ax.set_title(\"non-z log target\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = axes[1, 1]\n",
+    "ax.plot(potential_energy, lw=0.6, color=\"C4\")\n",
+    "ax.axvline(burn, ls=\":\", color=\"gray\", lw=0.8, label=\"burn-in\")\n",
+    "ax.set_title(\"potential energy\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = axes[2, 0]\n",
+    "xs, acc_pcn = _rolling_mean(acc[:, 0], window)\n",
+    "_, acc_rw = _rolling_mean(acc[:, 1], window)\n",
+    "ax.plot(xs, acc_pcn, lw=1, color=\"C0\", label=\"pCN (noise)\")\n",
+    "ax.plot(xs, acc_rw, lw=1, color=\"C3\", label=\"RW (params)\")\n",
+    "ax.set_title(f\"rolling acceptance rate (window={window})\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.set_ylim(0, 1)\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "ax = axes[2, 1]\n",
+    "xs, rolling_log_alpha = _rolling_mean(pcn_log_alpha, window)\n",
+    "_, rolling_delta_u = _rolling_mean(pcn_delta_potential, window)\n",
+    "line_log_alpha = ax.plot(xs, rolling_log_alpha, lw=1, color=\"C5\", label=\"pCN log alpha\")\n",
+    "ax.axhline(0.0, ls=\":\", color=\"gray\", lw=1)\n",
+    "ax.set_title(f\"pCN proposal diagnostics (window={window})\")\n",
+    "ax.set_xlabel(\"iteration\")\n",
+    "ax.set_ylabel(\"log alpha\")\n",
+    "\n",
+    "ax_delta = ax.twinx()\n",
+    "line_delta_u = ax_delta.plot(xs, rolling_delta_u, lw=1, color=\"C2\", label=\"pCN delta U\")\n",
+    "ax_delta.set_ylabel(\"delta U\")\n",
+    "\n",
+    "lines = line_log_alpha + line_delta_u\n",
+    "ax.legend(lines, [line.get_label() for line in lines], fontsize=8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bf6a838",
+   "metadata": {},
+   "source": [
+    "## 8. Multi-chain convergence — Gelman–Rubin $\\hat R$\n",
+    "\n",
+    "A single well-mixing chain is necessary but not sufficient. We run several chains from **overdispersed** starts and check they agree, via the Gelman–Rubin $\\hat R$ (between- vs within-chain variance) and the effective sample size.\n",
+    "\n",
+    "`num_chains` only sets the count; *how* they run is `chain_method` — and none of the options is OS multi-threading:\n",
+    "\n",
+    "- `\"vectorized\"` (`jax.vmap`) hands the kernel a *batched* state with a leading chain axis, so the kernel must be written to handle it. NumPyro's built-in NUTS/HMC are; our hand-written `RWpCNKernel` calls `potential_fn` on whatever state it gets, so a batched state breaks it.\n",
+    "- `\"parallel\"` (`jax.pmap`) runs one chain per XLA device — needs `num_chains` devices. The setup cell calls `numpyro.set_host_device_count(N_CHAINS)` so CPU can expose enough devices before the chain is run.\n",
+    "- `\"sequential\"` runs the chains through a compiled `lax.map` over the chain axis; each chain sees a single-chain state, so the custom kernel works unchanged on one device.\n",
+    "\n",
+    "We use **`\"parallel\"`** here to match the debug script. If device setup is inconvenient, change `CHAIN_METHOD` below to `\"sequential\"` without changing the statistical target.\n",
+    "\n",
+    "NumPyro keeps every chain's full trace — including the large noise field $z$ — in memory, so drop `N_CHAINS` or `N_SAMPLES` if it gets tight.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fbd2e3f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 chains x 10000 samples (parallel): 386.7s\n",
+      "  chain 0: acc pCN=0.281  acc RW=0.342\n",
+      "  chain 1: acc pCN=0.252  acc RW=0.358\n",
+      "  chain 2: acc pCN=0.218  acc RW=0.298\n"
+     ]
+    }
+   ],
+   "source": [
+    "CHAIN_METHOD = \"parallel\"\n",
+    "\n",
+    "# Overdispersed per-chain starts bracketing the truth (unconstrained space) plus an\n",
+    "# independent noise field per chain. Each init_params leaf carries a leading\n",
+    "# (N_CHAINS,) axis; vectorized/parallel chains slice along it.\n",
+    "ka_init = jnp.log(jnp.array(MULTICHAIN_K_ALPHA_INIT))  # truth 0.10\n",
+    "ks_init = jnp.log(jnp.array(MULTICHAIN_K_SIGMA_INIT))  # truth 0.25\n",
+    "z_keys = jax.random.split(jax.random.PRNGKey(2024), N_CHAINS)\n",
+    "z_init = jax.vmap(lambda k: jax.random.normal(k, (N_Z,)))(z_keys)\n",
+    "init_chains = {\"k_alpha\": ka_init, \"k_sigma\": ks_init, \"z\": z_init}\n",
+    "\n",
+    "mc_multi = MCMC(\n",
+    "    RWpCNKernel(\n",
+    "        potential_fn,\n",
+    "        pcn_beta=PCN_BETA,\n",
+    "        rw_scale=RW_SCALE_BY_SITE,\n",
+    "        postprocess_fn=mi.postprocess_fn,\n",
+    "    ),\n",
+    "    num_warmup=0,\n",
+    "    num_samples=N_SAMPLES,\n",
+    "    num_chains=N_CHAINS,\n",
+    "    chain_method=CHAIN_METHOD,\n",
+    "    progress_bar=False,\n",
+    ")\n",
+    "t0 = time.perf_counter()\n",
+    "mc_multi.run(\n",
+    "    jax.random.PRNGKey(404),\n",
+    "    init_params=init_chains,\n",
+    "    extra_fields=(\"accept\", \"potential_energy\", \"pcn_log_alpha\", \"pcn_delta_potential\"),\n",
+    ")\n",
+    "samples_mc = mc_multi.get_samples(group_by_chain=True)            # leaves: (N_CHAINS, N_SAMPLES, ...)\n",
+    "extra_fields_mc = mc_multi.get_extra_fields(group_by_chain=True)\n",
+    "jax.block_until_ready((samples_mc, extra_fields_mc))\n",
+    "elapsed_multi = time.perf_counter() - t0\n",
+    "print(f\"{N_CHAINS} chains x {N_SAMPLES} samples ({CHAIN_METHOD}): {elapsed_multi:.1f}s\")\n",
+    "\n",
+    "acc_mc = np.asarray(extra_fields_mc[\"accept\"])  # (N_CHAINS, N_SAMPLES, 2)\n",
+    "k_alpha_c = np.asarray(samples_mc[\"k_alpha\"])  # (N_CHAINS, N_SAMPLES)\n",
+    "k_sigma_c = np.asarray(samples_mc[\"k_sigma\"])\n",
+    "log_ka_c = np.log(k_alpha_c)\n",
+    "log_ks_c = np.log(k_sigma_c)\n",
+    "for c in range(N_CHAINS):\n",
+    "    print(f\"  chain {c}: acc pCN={acc_mc[c, :, 0].mean():.3f}  acc RW={acc_mc[c, :, 1].mean():.3f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "37b8e4fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After burn-in = 5000  (3 chains x 5000 draws):\n",
+      "  k_alpha: mean=0.102 +/- 0.012, R-hat=1.1254 (NOT converged (run longer)), ESS=16\n",
+      "  k_sigma: mean=0.251 +/- 0.012, R-hat=1.2983 (NOT converged (run longer)), ESS=4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "burn = BURN_IN\n",
+    "\n",
+    "# Gelman-Rubin R-hat and ESS on the post-burn-in chains, using log parameters\n",
+    "# for scale-stable diagnostics. numpyro's diagnostics take a (num_chains, num_samples) array.\n",
+    "log_ka_tail = _post_burn_or_all(log_ka_c, burn, sample_axis=1)\n",
+    "log_ks_tail = _post_burn_or_all(log_ks_c, burn, sample_axis=1)\n",
+    "rhat = {\n",
+    "    \"k_alpha\": float(gelman_rubin(log_ka_tail)),\n",
+    "    \"k_sigma\": float(gelman_rubin(log_ks_tail)),\n",
+    "}\n",
+    "ess = {\n",
+    "    \"k_alpha\": float(effective_sample_size(log_ka_tail)),\n",
+    "    \"k_sigma\": float(effective_sample_size(log_ks_tail)),\n",
+    "}\n",
+    "\n",
+    "retained = N_SAMPLES - burn if burn < N_SAMPLES else N_SAMPLES\n",
+    "print(f\"After burn-in = {burn}  ({N_CHAINS} chains x {retained} draws):\")\n",
+    "for name, chain_values in [(\"k_alpha\", k_alpha_c), (\"k_sigma\", k_sigma_c)]:\n",
+    "    values = _post_burn_or_all(chain_values, burn, sample_axis=1)\n",
+    "    flag = \"converged\" if rhat[name] < 1.1 else \"NOT converged (run longer)\"\n",
+    "    print(\n",
+    "        f\"  {name}: mean={values.mean():.3f} +/- {values.std():.3f}, \"\n",
+    "        f\"R-hat={rhat[name]:.4f} ({flag}), ESS={ess[name]:.0f}\"\n",
+    "    )\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
+    "for ax, chain_values, truth, latex, rh in [\n",
+    "    (axes[0], k_alpha_c, K_ALPHA_TRUE, r\"k_\\alpha\", rhat[\"k_alpha\"]),\n",
+    "    (axes[1], k_sigma_c, K_SIGMA_TRUE, r\"k_\\sigma\", rhat[\"k_sigma\"]),\n",
+    "]:\n",
+    "    for c in range(N_CHAINS):\n",
+    "        ax.plot(chain_values[c], lw=0.5, alpha=0.8, label=f\"chain {c}\")\n",
+    "    ax.axhline(truth, color=\"k\", ls=\"--\", lw=1, label=\"truth\")\n",
+    "    ax.axvline(burn, color=\"gray\", ls=\":\", lw=0.8, label=\"burn-in\")\n",
+    "    ax.set_title(rf\"${latex}$    $\\hat R = {rh:.3f}$\")\n",
+    "    ax.set_xlabel(\"iteration\")\n",
+    "    ax.set_ylabel(rf\"${latex}$\")\n",
+    "    ax.legend(fontsize=7, ncol=2)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fac8ad7",
+   "metadata": {},
+   "source": [
+    "## Recap\n",
+    "\n",
+    "1. **`hyperiax.prebuilt.bffg` continuous-edge sweeps** (`continuous_forward_sweep` / `continuous_bf_sweep` / `continuous_refine_anchor` / `continuous_fg_sweep`) compose into a pure $(z, \\theta) \\mapsto (\\text{bridges}, \\sum \\log w, \\text{log\\_norm\\_root})$ map. The `log_norm` field tracks the canonical-message constant the BFFG up-sweep accumulates; evaluated at the pinned root it is the BFFG estimate of $\\log p(y \\mid \\theta)$.\n",
+    "2. **Non-linear σ(X) ⇒ Theorem-14 collapse breaks.** With the auxiliary linearised per edge (anchor refined toward the posterior mean), $\\sum \\log w$ varies meaningfully across noise draws — a std of a few units at the truth, versus identically 0 in the linear case — and pCN+RW jointly target the BFFG posterior.\n",
+    "3. **The MCMC kernel from notebook 05 ports with positive parameter sites.** The `param_sites` argument lists the two inverse-gamma parameter sites, while the RW proposal still runs on NumPyro's unconstrained coordinates. NumPyro's `MCMC` driver runs the same two-block Metropolis-within-Gibbs scheme.\n",
+    "4. **End-to-end inference works.** On a 13-node tree with 16 landmarks × 2-D state and 100 SDE steps per edge, 2000 collected draws (`num_warmup=0`, burn-in 500) concentrate around the data-generating $(k_\\alpha, k_\\sigma) = (0.1, 0.25)$.\n",
+    "\n",
+    "Comparing the three BFFG notebooks:\n",
+    "\n",
+    "| Notebook | Transition | BFFG correction | Inference engine |\n",
+    "|---|---|---|---|\n",
+    "| [05](05_gaussian_bffg.ipynb) | linear Gaussian | $\\sum \\log w \\equiv 0$ (Th. 14 collapse) | RW + pCN via NumPyro |\n",
+    "| [06](06_gaussian_nuts.ipynb) | linear Gaussian (same) | (irrelevant — closed form) | NUTS via NumPyro |\n",
+    "| 07 (this) | non-linear SDE | $\\sum \\log w \\ne 0$ (Th. 23, real correction) | RW + pCN via NumPyro |\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "- van der Meulen, F. H. & Sommer, S. (2025). *Backward Filtering Forward Guiding.* JMLR **26**(281), 1–51. [arXiv:2505.18239](https://arxiv.org/abs/2505.18239) — §7.1, Theorem 23, Remark 24.\n",
+    "- Cotter, S. L., Roberts, G. O., Stuart, A. M., White, D. (2013). *MCMC methods for functions.* Statistical Science **28**(3), 424–446. — pCN."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hyperiax",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/notebooks/DICAROS.ipynb b/docs/source/notebooks/DICAROS.ipynb
deleted file mode 100644
index 941b05b..0000000
--- a/docs/source/notebooks/DICAROS.ipynb
+++ /dev/null
@@ -1,616 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes (DICAROS)\n",
-    "\n",
-    "In this notebook we estimate the inner nodes and mean of a phylogenetic tree, where only the leaf node values are known. This aim is the same as in the example notebook mean_estimation.ipynb. The current notebook adds another layer of complexity in that nodes values are no longer assumed to be vectors, but _shapes_; in our case discretized outline curves describing the wing morphology of certain butterfly species. The inner nodes thus represent wing shapes of ancestral butterfly species.\n",
-    "\n",
-    "As in mean_estimation.ipynb we use a simple, recursive estimator for the inner nodes; any inner node will be estimated as a weighted mean of its children. Due to the _non-linear_ nature of shapes, we cannot use the ordinary 'Euclidean' weighted mean. Luckily, the python library _jaxgeometry_ is built for handling such data, including computing weighted means of shape observations. Below, we construct a 'fuse' function which computes such weighted means of shapes using jaxgeometry. \n",
-    "\n",
-    "This is a simplified version of the code\n",
-    "\n",
-    "Preprint can be found at; https://doi.org/10.1101/2025.02.26.638985\n",
-    "\n",
-    "__Content of the notebook__\n",
-    "\n",
-    "1. We load a dataset of shapes of 4 butterfly species and an associated evolutionary tree (phylogeny) for these species \n",
-    "\n",
-    "2. We estimate and plot the inner nodes, i.e. the estimated shapes of ancestral butterfly species. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. load example data\n",
-    "\n",
-    "The example data is 4 observations of butterfly wings, also described in Baker et. al. 2024, https://doi.org/10.48550/arXiv.2402.01434 .\n",
-    "We represent each wing by 25 landmarks.  \n",
-    "\n",
-    "We also load a phylogeny for these 4 species, i.e. a tree with 4 leaf nodes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a new Hyperiax client\n",
-    "\n",
-    "import hyperiax\n",
-    "from jax.random import PRNGKey, split\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from jax import vmap, lax\n",
-    "\n",
-    "key = PRNGKey(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import requests \n",
-    "\n",
-    "from hyperiax.tree.topology import read_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "# Load all files\n",
-    "tree_string = requests.get(\"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/tree.txt\").text\n",
-    "tree = read_topology(tree_string,precompute_child_gathers=True)\n",
-    "landmark = pd.read_csv(\"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/landmarks.csv\", sep=\",\", header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "((Graphium_evemon:0.06883,Graphium_sarpedon:0.06835)QS1810:0.13941,(Atrophaneura_dixoni:0.0434,Pachliopta_aristolochiae:0.05484)QS1845:0.16755)QS1804:0;\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      ") missing from current font.ng\\Python\\Python311\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 13 (\n",
-      "  func(*args, **kwargs)\n",
-      ") missing from current font.ng\\Python\\Python311\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 13 (\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from hyperiax.plotting import plot_tree\n",
-    "plot_tree(tree,inc_names=True)\n",
-    "print(tree_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The name of the landmarks are placed in the tree, alongside with the edgelength \n",
-    "#landmark = landmark.iloc[:,0:10] # reduce landmark numbers \n",
-    "l_dim = (jnp.shape(landmark)[1])\n",
-    "tree.add_property('value', shape=(int(jnp.shape(landmark)[1]),))\n",
-    "# add landmarks\n",
-    "landmark_array = landmark.to_numpy()\n",
-    "reshaped_array = landmark_array.reshape(landmark_array.shape[0], landmark_array.shape[1])\n",
-    "tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(reshaped_array)\n",
-    "\n",
-    "# Dimensions\n",
-    "d = 2\n",
-    "\n",
-    "# For phylomorphospace: \n",
-    "tree.add_property('p_adj', shape=(int(jnp.shape(landmark)[1]),))\n",
-    "tree.add_property('phi', shape=(int(jnp.shape(landmark)[1]/d),d,d))\n",
-    "\n",
-    "# How many compartments in the output pca\n",
-    "tree.add_property('pc', shape=(5,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Example of landmarks \n",
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "\n",
-    "main_path = \"https://github.com/MichaelSev/Hyperiax_data/raw/main/image/\"\n",
-    "\n",
-    "list_of_names =[\"img0.jpg\",\"img1.jpg\",\"img2.jpg\",\"img3.jpg\"]\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_leaf]):\n",
-    "    plt.subplot(2,2,i+1)\n",
-    "    im = Image.open(requests.get(main_path+list_of_names[i], stream=True).raw)\n",
-    "    implot = plt.imshow(im)\n",
-    "    plt.scatter(data[::2],data[1::2],c=\"r\")\n",
-    "    #plt.title(str(leaf.name))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare tree for computing\n",
-    "\n",
-    "We load in the packages and reload the tree with precumted children for faster compution, and fix the space we expect to use for each parameter.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "\n",
-    "from functools import partial\n",
-    "\n",
-    "# Requires Jaxdifferentalgeometry package\n",
-    "from jaxgeometry.manifolds.landmarks import *   \n",
-    "from jaxgeometry.Riemannian import metric\n",
-    "from jaxgeometry.dynamics import Hamiltonian\n",
-    "from jaxgeometry.Riemannian import Log\n",
-    "from jaxgeometry.dynamics import flow_differential\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Saved parameters within the tree \n",
-    "tree.add_property('sigma', shape=(1,))\n",
-    "\n",
-    "# Fixed sigma for simplification\n",
-    "tree.data['sigma'] =  tree.data['sigma'].at[:].set(0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. compute inner nodes (i.e. ancestral shapes)\n",
-    "\n",
-    "We compute the inner nodes of the tree, i.e. the wing shapes of ancestral species. For this we need to define a 'fuse' function which computes a non-linear version of a weighted mean: each inner node is a non-linear weighted mean of its children nodes. The particular non-linear weighted mean we compute is based on the LDDMM framework, as implemented in the jaxgeometry library.\n",
-    "\n",
-    "Note this is a quicker version of the code, since we exclude the contrast and lift function for the tangent space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fuse_DICAROS(value,child_value, child_sigma, child_edge_length,**kwargs):\n",
-    "  \n",
-    "    def DICAROS(child_coords1, child_coords2, kernel_sigma, parent_index):\n",
-    "      \n",
-    "        # Estimate the average distance between each landmark,\n",
-    "        # predefined kernel size\n",
-    "       \n",
-    "        M = landmarks(jnp.shape(child_coords1)[0]//2, k_sigma=jnp.array(kernel_sigma) * jnp.eye(2),m=2)\n",
-    "        # Riemannian structure\n",
-    "    \n",
-    "        metric.initialize(M)\n",
-    "        q = M.coords(jnp.array(child_coords1))\n",
-    "        v = (jnp.array(child_coords2), [0])\n",
-    "      \n",
-    "        Hamiltonian.initialize(M)\n",
-    "        # Logarithm map\n",
-    "        Log.initialize(M, f=M.Exp_Hamiltonian)\n",
-    "   \n",
-    "\n",
-    "        # Estimate momentum\n",
-    "        p = M.Log(q, v)[0]\n",
-    "   \n",
-    "        # Hamiltonian\n",
-    "        (_, qps, charts_qp) = M.Hamiltonian_dynamics(q, p, dts(n_steps=100))\n",
-    " \n",
-    "        #lift\n",
-    "        flow_differential.initialize(M)\n",
-    "        _,dphis,_ = M.flow_differential(qps,dts())  \n",
-    "\n",
-    "        # Return landmarks, contrast and phi\n",
-    "        return qps[:,0][parent_index],p,dphis[parent_index]\n",
-    "\n",
-    "    # Map function, to determine the shortest branch from child to parent\n",
-    "    def map_function(child_coords_pair, child_edge_length_pair, kernel_sigma_pair):\n",
-    "        edge_length_sum = child_edge_length_pair.sum()\n",
-    " \n",
-    "        def true_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[0] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return DICAROS(child_coords_pair[0, :], child_coords_pair[1, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        def false_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[1] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return DICAROS(child_coords_pair[1, :], child_coords_pair[0, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        # Run Upwards LDDMM; from either left or right\n",
-    "\n",
-    "        return jax.lax.cond((child_edge_length_pair[0] < child_edge_length_pair[1])[0], true_fun, false_fun, operand=None)\n",
-    "\n",
-    "    # vmap to iterate over set of children\n",
-    "    qps,p,dphis = jax.vmap(map_function, in_axes=(0, 0, 0))(child_value, child_edge_length, child_sigma)\n",
-    "\n",
-    "    # P should be divided with the sum of p\n",
-    "\n",
-    "    p_adj= p/jnp.sum(child_edge_length,axis=1)\n",
-    "\n",
-    "    return {'value': qps,'p_adj':p_adj,'phi':dphis}\n",
-    "\n",
-    "#### Now we use UpLambda() to wrap the fuse function and throw it to OrderedExecutor, then simply just #### call .up().\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "upmodel = UpLambda(up_fn=fuse_DICAROS)\n",
-    "upmodelexe = OrderedExecutor(upmodel)\n",
-    "res = upmodelexe.up(tree) \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2.1 Plots\n",
-    "We can now plot one of the estimated inner nodes, computed as the weighted mean of two neighbouring leaf-node shapes. The two leaf shapes are represented by the red and green points, the estimated inner node is represented by the blue points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "\n",
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img0.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][3][::2], tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][4][::2], tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][1][::2], tree.data['value'][1][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img1.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][3][::2],tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][4][::2], tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][1][::2],tree.data['value'][1][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "\n",
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img2.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][5][::2], tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][6][::2], tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][2][::2], tree.data['value'][2][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img3.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][5][::2], tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][6][::2], tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "\n",
-    "plt.scatter(tree.data['value'][2][::2], tree.data['value'][2][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The points on the left and right figures are identical, the only difference is the superimposed image.\n",
-    "\n",
-    "Next, we plot all leaf nodes (points in light colors) along with the estimated root node shape (blue points), i.e. the common ancestor of all 4 species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x2cdc94fe390>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_leaf]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"green\",s=10)\n",
-    "\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_inner]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"red\",s=20)\n",
-    "\n",
-    "plt.scatter(tree.data['value'][0][::2], -tree.data['value'][0][1::2], color='blue',s=30)    # root\n",
-    "   \n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phylomorphospace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_all = []\n",
-    "p_all.append(tree.data[\"p_adj\"][0])\n",
-    "\n",
-    "\n",
-    "n = int(l_dim/2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Lift all contrast to the root\n",
-    "for node in tree.iter_topology_bfs():\n",
-    "    if node.parent is None: \n",
-    "        continue\n",
-    "    org_node = node\n",
-    "    node = node.parent\n",
-    "    \n",
-    "    p_lifted = jax.vmap(lambda A,v: jnp.dot(A.T,v))(tree.data[\"phi\"][org_node.id],tree.data[\"p_adj\"][org_node.id].reshape((n,2)))\n",
-    "    while node.parent is not None: \n",
-    "        p_lifted = jax.vmap(lambda A,v: jnp.dot(A.T,v))(tree.data[\"phi\"][node.parent.id],p_lifted)\n",
-    "        node = node.parent\n",
-    "\n",
-    "    \n",
-    "    p_all.append(p_lifted.flatten())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pca_and_update_tree(tree, p_all, n, n_components=5):\n",
-    "    # Initialize Riemannian metrics for estimating the covariance matrix\n",
-    "    from jaxgeometry.manifolds.landmarks import landmarks   \n",
-    "    M = landmarks(n, k_sigma=1*jnp.eye(2))\n",
-    "    x = M.coords(tree.data[\"value\"][0])\n",
-    "    \n",
-    "    # Riemannian structure\n",
-    "    from jaxgeometry.Riemannian import metric\n",
-    "    metric.initialize(M)\n",
-    "\n",
-    "    # Compute covariance matrix\n",
-    "    cov_uplddmm = jnp.zeros((n*2,n*2))\n",
-    "    for i in range(len(p_all)):\n",
-    "        cov_uplddmm += np.dot(np.dot(p_all[i].reshape(n*2,-1), \n",
-    "                                    p_all[i].reshape(-1,n*2)), M.g(x))\n",
-    "    cov_uplddmm = cov_uplddmm/(len(p_all)/2)\n",
-    "\n",
-    "    # Compute PCA\n",
-    "    eigenvalues, eigenvectors = np.linalg.eig(cov_uplddmm)\n",
-    "    \n",
-    "    # Sort eigenvectors by eigenvalues in descending order\n",
-    "    idx = np.argsort(eigenvalues)[::-1]\n",
-    "    eigenvalues = eigenvalues[idx]\n",
-    "    eigenvectors = eigenvectors[:, idx]\n",
-    "    \n",
-    "    # Select top components and get landmarks\n",
-    "    components = eigenvectors[:, :n_components]\n",
-    "    landmarks = [tree.data[\"value\"][leaf.id] for leaf in tree.iter_topology_bfs()]\n",
-    "    \n",
-    "    # Project landmarks onto components\n",
-    "    X_projected = np.dot(landmarks, np.real(components))\n",
-    "    \n",
-    "    # Calculate variance explained\n",
-    "    varexp = eigenvalues[:n_components]/sum(eigenvalues)\n",
-    "\n",
-    "    # Update tree with projected values\n",
-    "    tree.data[\"pc\"] = tree.data[\"pc\"].at[:].set(X_projected)\n",
-    "        \n",
-    "    return varexp,X_projected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "varexp,x_projected = compute_pca_and_update_tree(tree, p_all, n, n_components=5)\n",
-    "\n",
-    "# Here just for plotting\n",
-    "tree.add_property('pc_plot', shape=(2,))\n",
-    "tree.data[\"pc_plot\"] = tree.data[\"pc_plot\"].at[:].set(x_projected[:,0:2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_tree_2d_scatter(tree, 'pc_plot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "jax311",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/notebooks/Upwards_LDDMM.ipynb b/docs/source/notebooks/Upwards_LDDMM.ipynb
deleted file mode 100644
index 32fe7b0..0000000
--- a/docs/source/notebooks/Upwards_LDDMM.ipynb
+++ /dev/null
@@ -1,448 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Estimation of internal nodes and phylogenetic mean from shape observations\n",
-    "\n",
-    "In the previous notebook, we learned how to define a mean estimation operation and execute it on the tree using _Hyperiax_. However, the data stored in the nodes is rather simple: 2D vectors. In practice, we have to deal with more complex data with higher dimensions. Fortunately, _Hyperiax_ can help. This time, we still aim to estimate the inner nodes and mean of a phylogenetic tree with only given leaf node values, but the data is the butterfly **shape**, characterized by discrete **landmarks**. In our case, outlines discretized by landmarks describe the wing morphology of certain butterfly species. The inner nodes thus represent wing shapes of ancestral butterfly species.\n",
-    "\n",
-    "As in `mean_estimation.ipynb` we use a simple, recursive estimator for the inner nodes; any inner node will be estimated as a weighted mean of its children. Due to the _non-linear_ nature of shapes, we cannot use the ordinary **Euclidean** weighted mean. Luckily, the Python library _JAXGeometry_ is built for handling such data, including computing weighted means of shape observations. We shall use it's function to define the `fuse` we need in the upward passing.\n",
-    "\n",
-    "\n",
-    "__Content of the notebook__\n",
-    "\n",
-    "* Load a dataset of shapes of 4 butterfly species and an associated evolutionary tree (phylogeny) \n",
-    "\n",
-    "* Estimate and plot the inner nodes, i.e. the estimated shapes of ancestral butterfly species. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.tree.topology import read_topology\n",
-    "from hyperiax.plotting import plot_tree\n",
-    "\n",
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import pandas as pd\n",
-    "import requests\n",
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the example data\n",
-    "\n",
-    "The example data is `4` observations of butterfly wings, also described in Baker et. al. 2024, https://doi.org/10.48550/arXiv.2402.01434. We represent each wing by `25` landmarks. We also load a phylogeny for these 4 species, i.e. a tree with 4 leaf nodes. All the data can be accessed under https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load all files\n",
-    "tree_string = requests.get(\"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/tree.txt\").text\n",
-    "tree = read_topology(tree_string)\n",
-    "landmark = pd.read_csv(\n",
-    "    \"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/landmarks.csv\", \n",
-    "    sep=\",\", \n",
-    "    header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "((Graphium_evemon:0.06883,Graphium_sarpedon:0.06835)QS1810:0.13941,(Atrophaneura_dixoni:0.0434,Pachliopta_aristolochiae:0.05484)QS1845:0.16755)QS1804:0;\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_tree(tree,inc_names=True)\n",
-    "print(tree_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The dimension of the landmarks is 200\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The name of the landmarks are placed in the tree, alongside with the edge length \n",
-    "lm_dim = jnp.shape(landmark)[1]\n",
-    "print(f\"The dimension of the landmarks is {lm_dim}\")\n",
-    "tree.add_property('value', shape=(lm_dim, ))\n",
-    "# add landmarks\n",
-    "landmark_array = landmark.to_numpy()\n",
-    "reshaped_array = landmark_array.reshape(landmark_array.shape[0], landmark_array.shape[1])\n",
-    "tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(reshaped_array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# visualization of landmarks \n",
-    "main_path = \"https://github.com/MichaelSev/Hyperiax_data/raw/main/image/\"\n",
-    "\n",
-    "list_of_names =[\"img0.jpg\",\"img1.jpg\",\"img2.jpg\",\"img3.jpg\"]\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_leaf]):\n",
-    "    plt.subplot(2,2,i+1)\n",
-    "    im = Image.open(requests.get(main_path+list_of_names[i], stream=True).raw)\n",
-    "    implot = plt.imshow(im)\n",
-    "    plt.scatter(data[::2],data[1::2],c=\"r\")\n",
-    "    #plt.title(str(leaf.name))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare tree for computing\n",
-    "\n",
-    "As we introduced in the previous notebook, when the number of child nodes is fixed, _Hyperiax_ provides a faster way to pre-compute the gathering of children by specifying `precompute_child_gathers=True` in the initialization of a `HypTree`, and our studied case fits this requirement. Thus, we can create a copy of the tree we just loaded with pre-computed children for faster execution.\n",
-    "\n",
-    "Unlike the Brownian motion, the nonlinear dynamic we use here will have a parameter `sigma`, which we need to specify. In practice, this parameter is unknown and needs to be inferred, but right now, let's just fix it for simplicity. We recommend literatures about _Large Deformation Diffeomorphic Metric Mapping (LDDMM)_ for further illustrations on the nonlinear dynamics and the significance of `sigma`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# run the following code to install JAXGeometry if needed\n",
-    "# %pip install jaxdifferentialgeometry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambda\n",
-    "\n",
-    "# Requires Jaxdifferentalgeometry package\n",
-    "from jaxgeometry.manifolds.landmarks import *   \n",
-    "from jaxgeometry.Riemannian import metric\n",
-    "from jaxgeometry.dynamics import Hamiltonian\n",
-    "from jaxgeometry.Riemannian import Log"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topology = read_topology(tree_string, return_topology=True)\n",
-    "pre_tree = HypTree(topology, precompute_child_gathers=True)\n",
-    "pre_tree.data = tree.data.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Saved parameters within the tree \n",
-    "pre_tree.add_property('sigma', shape=(1,))\n",
-    "\n",
-    "# Fixed sigma for simplification\n",
-    "pre_tree.data['sigma'] = pre_tree.data['sigma'].at[:].set(0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimate inner nodes (i.e. ancestral shapes)\n",
-    "\n",
-    "After everything is set, we can now estimate the inner nodes of the tree, i.e. the wing shapes of ancestral species, which corresponds to the **upward passing** introduced in the previous notebook. For this, we need to define a `fuse` function, which computes a non-linear version of a weighted mean: each inner node is a non-linear weighted mean of its children nodes. The particular non-linear weighted mean we compute is based on the LDDMM framework, as implemented in the JAXGeometry library.\n",
-    "\n",
-    "Note this is a quicker version of the code, since we exclude the contrast and lift function for the tangent space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fuse_lddmm(child_value, child_sigma, child_edge_length, **kwargs):\n",
-    "    def lddmm(child_coords1, child_coords2, kernel_sigma, parent_index):\n",
-    "        # Estimate the average distance between each landmark,\n",
-    "        # predefined kernel size \n",
-    "        M = landmarks(jnp.shape(child_coords1)[0] // 2, k_sigma=kernel_sigma * jnp.eye(2))\n",
-    "        # Riemannian structure\n",
-    "\n",
-    "        metric.initialize(M)\n",
-    "        q = M.coords(jnp.array(child_coords1))\n",
-    "        v = (jnp.array(child_coords2), [0])\n",
-    "        Hamiltonian.initialize(M)\n",
-    "        # Logarithm map\n",
-    "        Log.initialize(M, f=M.Exp_Hamiltonian)\n",
-    "\n",
-    "        # Estimate momentum \n",
-    "        p = M.Log(q, v)[0]\n",
-    "\n",
-    "        # Hamiltonian \n",
-    "        (_, qps, charts_qp) = M.Hamiltonian_dynamics(q, p, dts(n_steps=100))\n",
-    "\n",
-    "        # Return landmarks, contrast and phi\n",
-    "        return qps[parent_index, 0, :]\n",
-    "\n",
-    "    # Map function, to determine the shortest branch from child to parent \n",
-    "    def map_function(child_coords_pair, child_edge_length_pair, kernel_sigma_pair):\n",
-    "        edge_length_sum = child_edge_length_pair.sum()\n",
-    "\n",
-    "        def true_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[0] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return lddmm(child_coords_pair[0, :], child_coords_pair[1, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        def false_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[1] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return lddmm(child_coords_pair[1, :], child_coords_pair[0, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        # Run Upwards LDDMM; from either left or right \n",
-    "     \n",
-    "        return jax.lax.cond((child_edge_length_pair[0] < child_edge_length_pair[1])[0], true_fun, false_fun, operand=None)\n",
-    "\n",
-    "    # vmap to iterate over set of children\n",
-    "    qps = jax.vmap(map_function, in_axes=(0, 0, 0))(child_value, child_edge_length, child_sigma)\n",
-    "    \n",
-    "    return {'value': qps}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we use `UpLambda()` to wrap the `fuse` function and throw it to `OrderedExecutor`, then simply just call `.up()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upmodel = UpLambda(up_fn=fuse_lddmm)\n",
-    "upmodelexe = OrderedExecutor(upmodel)\n",
-    "res = upmodelexe.up(pre_tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Visualize the results**\n",
-    "\n",
-    "We can now plot one of the estimated inner nodes, computed as the weighted mean of two neighboring leaf-node shapes. The two leaf shapes are represented by the **red** and **green** points, the estimated inner node is represented by the **blue** points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img0.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(pre_tree.data['value'][3][::2], pre_tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(pre_tree.data['value'][4][::2], pre_tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "plt.scatter(pre_tree.data['value'][1][::2], pre_tree.data['value'][1][1::2], color='blue')   # root\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img1.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "# Plot points\n",
-    "plt.scatter(pre_tree.data['value'][3][::2], pre_tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(pre_tree.data['value'][4][::2], pre_tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "plt.scatter(pre_tree.data['value'][1][::2], pre_tree.data['value'][1][1::2], color='blue')   # root\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img2.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "# Plot points\n",
-    "plt.scatter(pre_tree.data['value'][5][::2], pre_tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(pre_tree.data['value'][6][::2], pre_tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "plt.scatter(pre_tree.data['value'][2][::2], pre_tree.data['value'][2][1::2], color='blue')   # root\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img3.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "# Plot points\n",
-    "plt.scatter(pre_tree.data['value'][5][::2], pre_tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(pre_tree.data['value'][6][::2], pre_tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "plt.scatter(pre_tree.data['value'][2][::2], pre_tree.data['value'][2][1::2], color='blue')   # root\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The points on the left and right figures are identical, the only difference is the superimposed image.\n",
-    "\n",
-    "Next, we plot all leaf nodes (points in **orange** colors), the estimated inner node shape (**blue** points), and the root node shape (**red** points), i.e. the common ancestor of all 4 species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x165800cd0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i,data in enumerate(pre_tree.data['value'][pre_tree.is_leaf]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"yellow\",s=10, label=f\"Leaf nodes {i}\")\n",
-    "\n",
-    "for i,data in enumerate(pre_tree.data['value'][pre_tree.is_inner]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"red\",s=20, label=f\"Inner nodes {i}\")    \n",
-    "\n",
-    "plt.scatter(pre_tree.data['value'][0][::2], -pre_tree.data['value'][0][1::2], color='blue',s=30, label=\"Root node\")   \n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "In this notebook, we show how to define a complex operation, a nonlinear weighted shape mean, in _Hyperiax_ and use it for estimating the common ancestor shape of 4 different butterfly species. However, one of the key parameter `sigma` in the LDDMM is assumed to be constant in our case, which is not generally true. In the next notebook, we will see how to use _Hyperiax_ to estimate the parameters using MCMC, and appreciate how fast and easy you can do this."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_p312",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/notebooks/index.rst b/docs/source/notebooks/index.rst
new file mode 100644
index 0000000..4392e8f
--- /dev/null
+++ b/docs/source/notebooks/index.rst
@@ -0,0 +1,18 @@
+Tutorials
+=========
+
+The tutorials walk through the core API and then through each prebuilt model.
+Notebook outputs are rendered as-saved — to re-run them yourself, install
+the ``notebook`` extra and run the ``.ipynb`` files in
+``docs/source/notebooks/``.
+
+.. toctree::
+   :maxdepth: 1
+
+   01_quickstart
+   02_writing_sweeps
+   03_phylo_mean
+   04_phylo_bayesian
+   05_gaussian_bffg
+   06_gaussian_nuts
+   07_kunita_shape
diff --git a/docs/source/notebooks/mcmc_Gaussian_BFFG.ipynb b/docs/source/notebooks/mcmc_Gaussian_BFFG.ipynb
deleted file mode 100644
index 5e30695..0000000
--- a/docs/source/notebooks/mcmc_Gaussian_BFFG.ipynb
+++ /dev/null
@@ -1,912 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC parameter inference using backward filtering, forward guiding\n",
-    "\n",
-    "A question we left in the last notebook is to infer the unknown parameter `sigma` in the LDDMM model. Generally, parameter inference on the tree is one of the crucial tasks in phylogenetics, which is usually done by Markov chain Monte Carlo (MCMC). However, the execution of MCMC requires to run the updates across the whole tree for many times, which can be extremely time costly if the operation is not well optimized. With the help of _Hyperiax_, you will no longer need to spend energy on how to optimize your operation for the entire tree, instead, just need to consider a single children-parent pair. This will enable you to concentrate on the required operation itself, and _Hyperiax_ will take care of the rest.\n",
-    "\n",
-    "In this notebook, we will show how to do parameter inference for trees with Gaussian transitions along edges and observations at the leaf nodes. The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this examples.\n",
-    "\n",
-    "**Content of the notebook**\n",
-    "\n",
-    "* Sample the tree downwards with Gaussian transitions and node-dependent covariances\n",
-    "\n",
-    "* Estimate the tree upwards conditioning on the leaf observations\n",
-    "\n",
-    "* Infer the variance of leaf nodes using MCMC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda\n",
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter\n",
-    "\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = PRNGKey(42)\n",
-    "jnp.set_printoptions(precision=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a Gaussian tree with constant node-dependent covariances\n",
-    "\n",
-    "**Simulate the tree downwards with unconditional transition**\n",
-    "\n",
-    "As usual, we first build a tree as our study case. We shall use a symmetric tree with `height=2` and `degree=10`, and the data stored in nodes is vectors in $\\mathbb{R}^2$, with the root initialized as $(0, 0)$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                                                                       None\n",
-      "                       ┌─────────────────────────────────────────────────┬─────────────────────────────────────────────────┬─────────────────────────────────────────────────┬─────────────────────────────────────────────────┬────────────────────────┴────────────────────────┬─────────────────────────────────────────────────┬─────────────────────────────────────────────────┬─────────────────────────────────────────────────┬─────────────────────────────────────────────────┐\n",
-      "                      None                                              None                                              None                                              None                                              None                                              None                                              None                                              None                                              None                                              None                       \n",
-      " ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐    ┌────┬────┬────┬────┬─┴──┬────┬────┬────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=111, levels=3, leaves=100, inner nodes=10)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "topology = symmetric_topology(height=2, degree=10)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "# data dimension\n",
-    "d = 2\n",
-    "tree.add_property('value', shape=(d,))\n",
-    "tree.add_property('noise', shape=(d,))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "root = jnp.zeros(d)\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the tree. All the parameters of the tree should be packed into `ParameterStore()` in `dict`. Here we define two parameters: `variance` $\\sigma$ and `obs_var` $\\sigma_{obs}$: $\\sigma$ determines the variance of the Gaussian transition kernel within the unit edge length, since we model the dynamics from the parent to children as Gaussian, that is, if we denote the parent and child as $p$ and $c$ individually, then $c\\sim\\mathcal{N}(p, \\sigma\\cdot l\\cdot\\mathbf{I})$, and $l$ is the edge length between $c$ and $p$; $\\sigma_{obs}$, however, specify the variance of the `noise` store in each node. The final value of $c$ is integrated by these two random distributions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'variance': VarianceParameter(.5), # variance of the Gaussian transition kernel \n",
-    "    'obs_var':  VarianceParameter(.01) # observation noise variance\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions. At first, we define the unconditional transitions, which are just **Gaussian** samples, and implemented as `down_unconditional`. The covariance is the identity in $\\mathbb{R}^d$ times the variance parameter $\\sigma$ times edge lengths $l$, then the `noise` exists in the node is counted afterwards."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# batched version of down_unconditional. The b index refers to the batch, the first axis of W. Alternatively, jax.vmap can be used to vectorize the function, see the down_conditional function below as an example\n",
-    "@jax.jit\n",
-    "def down_unconditional(noise, edge_length, parent_value, params, **args):\n",
-    "    var = params['variance'] * edge_length # total edge variance\n",
-    "    return {'value': parent_value + jnp.einsum('b,ij,bj->bi', jnp.sqrt(var), jnp.eye(d), noise)}\n",
-    "\n",
-    "downmodel_unconditional = DownLambda(down_fn=down_unconditional)\n",
-    "down_unconditional = OrderedExecutor(downmodel_unconditional)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. **Note that observation `noise` is not added to the leaves yet**. We need to pass the set parameter `param` into the executor as the additional argument by `params.values()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sample new noise, used to compute the sampling by the Gaussian transitions.\n",
-    "def update_noise(tree,key): \n",
-    "    tree.data['noise'] = jax.random.normal(key, shape=tree.data['noise'].shape)\n",
-    "\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree, subkey)\n",
-    "down_unconditional.down(tree, params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we add observation noise to leaves, and we can use `tree.is_leaf` to identify the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key, subkey = split(key)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf] \\\n",
-    "    + jnp.sqrt(params['obs_var'].value) * jax.random.normal(subkey, tree.data['value'][tree.is_leaf].shape)\n",
-    "tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Sampled tree with leaf noise')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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kk0+YOnUqx48fJzg4mCVLluDt7V2s8XXu3BmA//zP/2Ty5Mm4ubkxcuTIZ36SX9yxvfnmm6xcuZIf/ehH7N69m169epGbm8ulS5dYuXIl27ZtMzNthYmIiOAPf/gDAQEBtGvXDsgLJlq0aMHly5eZPn16uVxjaf3973+nd+/etGvXjtmzZ9O4cWMePnzIoUOHiImJMfvgjBgxgt/+9re89dZbvPzyy5w9e5Yvv/wy3/qt8vKnP/2JoUOH0rNnT2bOnGmWkA4ICDB7DBVk8eLF/OMf/2Ds2LE0adKE5ORk5s2bh7+/vxkc9e3blzlz5vBf//VfnDp1isGDB+Pm5sbVq1dZtWoVH374oUOhDBGRMlMJFd1ERMrVli1bjBkzZhgtW7Y0fH19DXd3d6Np06bGe++9Zzx8+NBh3w0bNhjt27c3PD09jYYNGxr//d//bSxYsCBfmeEGDRoYw4cPz3cuCijpay/f+6c//cncNm3aNMPHx8e4fv26MXjwYMPb29uoW7eu8atf/Spf+WB+UPbYMAzj4cOHxjvvvGOEhYUZbm5uRr169YwBAwYYn332mcN+t2/fNkaNGmV4e3sbQUFBxvvvv2+W6y2qhLRhGMbvfvc7o379+obVanX4GRR0nSUdW1ZWlvHf//3fRps2bQwPDw+jRo0aRufOnY3f/OY3RmJiYpFj++abbwzAGDp0qMP2WbNmGYAxf/78fM8p6GdZ0mts0KCBMW3atGeOraAS0oZhGNevXzemTp1q1KtXz3BzczPq169vjBgxwli9erW5T0ZGhvGv//qvRnBwsOHl5WX06tXLOHTokNG3b1+jb9+++a6nJCWkn34NPn2MH/5Mdu7cafTq1cvw8vIy/P39jZEjRxoXLlxw2OeHJaRPnDhhTJkyxQgPDzc8PDyMOnXqGCNGjDCOHTuW75yfffaZ0blzZ8PLy8vw8/Mz2rVrZ/zbv/2bce/evSKvRUSkNCyGUQarD0VE5JmmT5/O6tWrSUlJqeyhiIiIVHtakyMiIiIiItWKghwREREREalWFOSIiIiIiEi1ojU5IiIiIiJSrSiTIyIiIiIi1YqCHBERERERqVacuhmozWbj3r17+Pn5YbFYKns4IiIiIiJSSQzDIDk5mZCQEKzWZ+dqnDrIuXfvHmFhYZU9DBERERERcRJ37twhNDT0mfs4dZDj5+cH5F2Iv79/JY8mv+zsbLZv387gwYNxc3Or7OFIKegeVn26h1Wf7mHVp3tY9ekeVn0vwj1MSkoiLCzMjBGexamDHPsUNX9/f6cNcry9vfH396+2L6bqTvew6tM9rPp0D6s+3cOqT/ew6nuR7mFxlrGo8ICIiIiIiFQrCnJERERERKRaUZAjIiIiIiLVilOvyRERERERke+lpKSQlJSUb3tOTg6+vr7cv38fV9eq+Rbf398fX1/fMjlW1fwJiIiIiIi8QHJycjh37hxQ+ML7oKAg4uPjK3JYZerRo0cAtG3b9rkDNQU5IiIiIiJO7ty5c1itVurVq4e3t3exKoxVJYZhkJaWxoMHDzh37hwdOnR4ruMpyBERERERcWIpKSkA1KtXjzp16hS4j2EY5tdVNQCyT1W7d+8eKSkpzzV1TYUHREREREScWFJSEhaLBW9v78oeSrmzZ6kKWndUEgpyRERERESqgKqaoSmJsrpGBTkiIiIiIlKtKMgREREREZHnZrFYWLJkSWUPA1CQIyIiIiJSLYwfP55BgwZV9jCcgoIcERERERGpVhTkiIiIiIhUc0eOHKFPnz54e3tTq1YtxowZw7179wD405/+RO3atcnNzXV4zoABA5g4caL5eMmSJbRu3RoPDw9CQ0P5+c9/TlZWVoHny8jIYOrUqdSuXRsPDw9CQkL45S9/WX4X+AMKckREREREqrHY2FgiIyNp3749Bw4cYMOGDcTGxjJu3DgApk2bRmJiIps2bTKf8/DhQ6KionjjjTcA2LJlCz/+8Y/50Y9+xMmTJ/noo49YsWJFoYHL73//e3bs2MEXX3zBmTNnWLBgAY0aNSr/i/0nNQMVEREREanG/vjHP9K6dWs++eQTc9vixYtp1qwZp0+f5qWXXqJPnz4sXbqU0aNHA/DFF18QGBjIiBEjAPjtb3/Lu+++y09/+lMAWrduzYMHD/jVr37FX//613znvHPnDg0aNGDIkCFYrVZatGhRAVf6PWVyRERERESqsbNnz/Ldd9/h7e1t/mvfvj0Aly9fBmDKlCls3bqV9PR0AFauXMmoUaNwcXEB4NKlS3zwwQcOx/j5z39OXFwcycnJ+c45c+ZMLl26RKNGjZg+fTpff/11BV1tHmVyRERERESqsbS0NF555RX+8pe/5PteeHg4AJMnT+b9999nxYoV9OrVi+PHjztkaNLT0/nXf/1XpkyZku8Y3t7e+bZFRERw48YNvv76a3bu3Mn06dOZN28eW7duLcMrK5yCHBERERGRaqx9+/Z88803NG/eHHd39wL38fHxITIykq+++oqrV6/SsGFDIiIizO+3atWKK1eu0LZt22Kft2bNmsyePZvZs2ezevVqJk6cyMOHD6lbt+5zX1NRFOSIiIiIiFQTycnJHDx40GHbe++9x7Jlyxg1ahS//OUvqV27NhcvXmT58uUsX74cV9e8kOD1119n8uTJXLlyhQkTJjgc4z//8z+ZPHkyP//5z5kyZQpWq5Xjx49z9uxZPv7443zj+NWvfkVISAjdunXDarWycuVKgoKCCAoKKr+Lf4qCHBERESlQrs0gKjqW+8kZBPt5EhFeGxerpbKHJSLPcOTIEXr16uWwbeLEiezZs4df/OIXjBo1iqysLEJCQujfvz9W6/dL9EeNGoW/vz+3bt3irbfecjjGhAkTWL58Ob///e/5xz/+gaurK40bN2bq1KkFjsPPz48PPviA27dvY7VaadeuHevWrTPX+JQ3BTkiIiKSz5qLMby/9RQxSenmtlB/Lz6M7MC4VqGVODIRKYx9cb/FUvCHEdu3b3/m811cXIiNjS30+xMmTMiX4XmaYRjm17/4xS/4xS9+8czzlSdVVxMREREHay7GMGHlIYcAB+BuUjoTVh5izcWYShqZiEjxKMgRERERU67N4P2tpzAK+J5928+2niLXVtAeIiLOQUGOiIiImKKiY/NlcJ5mAHeS0omKLnxKi4hIZVOQIyIiIqb7yRllup+ISGVQkCMiIiKmYD/PMt1PRKQyKMgRERERU0R4bUL9vSisULQFCPP3IiK8dkUOS0SkRBTkiIiIiMnFauHDyA7/fORYXMAe+HwQ2UH9ckTEqSnIEREREQfjWoUyf0hr/Mlx2B7s487qST3VJ0dEnJ6agYqIiEg+b/VoQ3MjiXnb95OCK11bNuUXE0cogyMiVYKCHBERESnQyz16sGvHDgzDoGMNDwU4Ik7oyuMrPEx5WGHnq+tbl+a1mlfY+UpLQY6IiIgUyGKx4OPjQ0pKCklJSZU9HBH5gSuPr9Du03Zk5WZV2DndXdw5++OzxQ50Nm/ezJ/+9CfOnTtHXFwcX3zxBW+++WY5j1JrckREROQZ/P39AUhMTKzkkYjIDz1MeVihAQ5AVm5WiTJHKSkptG3blj//+c/lOKr8FOSIiIhIoQIDAwEFOSJSOpMmTeLjjz9m2rRpFXpeBTkiIiJSKHsmJzU1FcMwithbRMQ5KMgRERGRQtmDHJvNRlpaWiWPRkSkeBTkiIiISKH8/PzMr1V8QESqCgU5IiIiUih7JgcU5IhI1aEgR0RERAqlIEdEqiL1yREREZFCPT1dLTk5uRJHIiJVUUJCAhcuXDAfX79+nYMHDxIUFETz5uXXVFSZHBERESmUi4sLXl5egDI5IlJy+/fvp1evXvTq1QuA3/zmN/Tq1Ytf/vKX5XpeZXJERETkmfz9/UlPT1evHBEnU9e3Lu4u7hXaENTdxZ26vnWLvf+IESMqpfy8ghwRERF5poCAAB4+fKggR8TJNK/VnLM/PsvDlIcVds66vnVpXqv8ppmVFQU5IiIi8kz24gPJyckYhoHFYqnkEYmIXfNazWleq7lDtkT/R7UmR0RERIpgD3JycnLIzMys5NGIiBRNQY6IiIg8k8pIi0hVoyBHREREnunpIEdlpEWkKlCQIyIiIs+kTI6IVDUKckREROSZnm4IqiBHRKoCBTkiIiLyTO7u7ri7uwMKckSkalCQIyIiIkWyZ3MU5IhIVaA+OSIiIlKkwMBAHj9+TEJCQmUPRUSecuXKPR4+rLhGvXXrBtC8eUiFna+0FOSIiIhIkZ5uCCoizuHKlXu0a/cLsrJyKuyc7u6unD3752IHOr/85S/ZtGkTN2/exMPDg06dOvHnP/+ZDh06lOs4NV1NREREimQPcjIzM8nOzq7k0YgIwMOHiRUa4ABkZeWUKHN04MABZs+ezZ49e/jmm2/Iyclh2LBh5T71VZkcERERKdIPe+XUrFmzEkcjIlVFVFSUw+Mvv/yS0NBQ9u/fz7Bhw8rtvMrkiIiISJFURlpEykJ8fDwAQUFB5XoeBTkiIiJSJDUEFZHnlZuby7vvvkvHjh3p1q1buZ5L09VERESkSApyROR5TZ06lStXrrBv375yP5cyOSIiIlIkT09PXFxcAFVYE5GSmzp1Krt27WLXrl00bdq03M+nIEdERESKZLFY8PX1BZTJEZHis9lsTJ06la1bt7J9+3ZatWpVIefVdDUREREplsDAQBITE9UQVESKberUqaxfv57ly5cTEBDA7du3AahVq5b5wUl5UCZHREREiiUgIABQJkdEiu/LL78kJSWFESNG0LBhQ/Pf/Pnzy/W8yuSIiIhIsdjLSKelpZGbm2uu0RGRylG3bgDu7q4V2hDU3d2VunUDir2/YRjlOJrCKcgRERGRYnm6wlpKSoqZ2RGRytG8eQhnz/6Zhw8TK+ycdesG0Lx5SIWdr7QU5IiIiEix/LCMtIIckcrXvHkIzZuHOGRMLBZLJY7IOWhNjoiIiBTL00GOykiLiDNTkCMiIiLFooagIlJVKMgRERGRYvHx8TGnwSjIERFnpiBHREREisViseDj4wMoyBER56YgR0RERIrNPmUtMbHiqjmJiJSUghwREREptsDAQEBBjog4NwU5IiIiUmz2TE5qamqlNfkTESmK+uSIiIhIsdmDHJvNRlpamrlGR0Qqx5XHyTxMyaiw89X19aR5Lb8KO19pKcgRERGRYvthGWkFOSKV58rjZNp9up2sXFuFndPdxcrZHw8udqDzhz/8gQULFnDv3j0AmjZtyn/8x38wadKk8hympquJiIhI8fn5ff/GRhXWRCrXw5SMCg1wALJybSXKHIWFhfG73/2OgwcPcuDAAXr37s3rr7/O0aNHy3GUyuSIiIhICaghqIiUxOuvv+7w+JNPPmHJkiXs37+frl27ltt5FeSIiIhIsSmTIyKllZOTw4IFC0hPTyciIqJcz6UgR0RERIrNxcUFLy8v0tPTSU5OruzhiEgV8N1339G/f3+ysrLw8vJiyZIldOnSpVzPqSBHRERESsTf35/09HT1yhGRYnnppZc4cuQIT548Yfny5fzoRz+iSZMm5RroqPCAiIiIlEhAQAAACQkJlTsQEakSPD09adu2LX369OEf//gHLVu25C9/+Uu5nlNBjoiIiJSIvfhASkqKGoKKSIkZhkFmZma5nkPT1URERKRE7EFOTk4OmZmZuLi4VPKIRMRZ/eQnP2HEiBE0adKEhIQEFi9ezJEjR1i9enW5nldBjoiIiJTID8tI16hRoxJHIyLOLDY2llmzZhEXF4evry8tWrRg9erVjBs3rlzPqyBHRERESkRBjohzqOvribuLtUIbgrq7WKnr61ns/VetWlWOoymcghwREREpkaeDHJWRFqk8zWv5cfbHg3mYklFh56zr60nzWn5F71jJyjXI+fTTT/n000+5desWAG3atOH//t//y9ChQ8vztCIiIlKO1BBUxHk0r+VH81p+DkVALBZLJY7IOZRrdbXQ0FD+8Ic/cPz4cY4dO8Yrr7zC6NGjOX/+fHmeVkRERMqRu7s77u7ugIIcEXFO5RrkjBw5kmHDhtGsWTOaN2/O73//e3x9fTl8+HB5nlZERETKmT2boyBHRJxRha3Jyc3NZdWqVaSmptKzZ88C98nMzHSomW3/xZmdnU12dnaFjLMk7GNyxrFJ8egeVn26h1Wf7mHVFBgYSHx8PImJibqH1YDuoXOz2fIKCxiGUazeVFW5f5V97DabLd/rsSSvT4tRzj+Fs2fP0rNnTzIyMvD19WXZsmUMGzaswH1//etf85vf/Cbf9mXLluHt7V2ewxQRERERcUqurq7Ur1+fkJAQateuXdnDKVexsbHcu3ePu3fvkpOT4/C9tLQ0XnvtNRITEx0KoBSk3IOcrKwsoqOjSUxMZPXq1Xz++efs3buX1q1b59u3oExOWFgYcXFxRV5IZcjOzmbHjh0MGjQINze3yh6OlILuYdWne1j16R5WTfv372f//v0AvP/+++zevVv3sArT/0Pnd+7cOVxcXKhXrx7e3t7VrriAYRikpaXx4MEDcnNzadu2bb59kpKSCAoKKlaQU+7T1dzd3WnatCkAnTt35ujRo3z44YfMnTs3374eHh54eHjk2+7m5ubU/+GcfXxSNN3Dqk/3sOrTPaxaAgICzCk0GRl55Wt1D6s+3UPn1a5dO86dO8e9e/eqXYBjZ8+9tGvXDlfX/GFKSV6bFd4nx2azOWRrREREpOpRrxyRiuXq6kqHDh1ISUkpsOBHTk4OV69epVmzZgUGCFWBv78/vr6+ZXKscv0J/Pu//ztDhw4lPDyc5ORkli1bxp49e9i2bVt5nlZERETK2dO9chTkiFQcX1/fAgOB7OxsTp48SXBwsLJxlHOQ8+jRI6ZOncr9+/cJCAigffv2bNu2jUGDBpXnaUVERKScPZ3JURlpEXE25RrkzJ8/vzwPLyIiIpXE09MTFxcXcnNzlckREadTrs1ARUREpHqyWCzmlJmUlJRKHo2IiCMFOSIiIlIqgYGBACQmJlbuQEREfkBBjoiIiJRKQEAAoMIDIuJ8FOSIiIhIqdgrrKWlpVXySEREHCnIERERkVIpquO4iEhlUZAjIiIipaIgR0SclYIcERERKRUFOSLirBTkiIiISKkoyBERZ6UgR0RERErFx8cHi8VS2cMQEclHQY6IiIiUisViwcfHp7KHISKSj4IcERERKTV7rxwREWeiIEdERERKTUGOiDgjBTkiIiJSak8XHzAMoxJHIiLyPQU5IiIiUmpPBznp6emVOBIRke8pyBEREZFSezrISU5OrsSRiIh8T0GOiIiIlNrTQU5SUlIljkRE5HsKckRERKTU/Pz8zK+VyRERZ6EgR0REREpNQY6IOCMFOSIiIlJqLi4ueHl5AZCSklLJoxERyaMgR0RERJ6LPZuTmJhYySMREcmjIEdERESei734gIIcEXEWCnJERETkudiDnNTUVDUEFRGnoCBHREREnot9ulpOTg6ZmZmVPBoREQU5IiIi8pyerrCmXjki4gwU5IiIiMhzUZAjIs5GQY6IiIg8FwU5IuJsFOSIiIjIc/H19TW/VkNQEXEGrpU9ABEREana3N3dza+rSiYn12YQFR3L/eQMgv08iQivjYvVUtnDEpEyoiBHREREykxV6JWz5mIM7289RUxSurkt1N+LDyM7MK5VaCWOTETKiqariYiISJlx9iBnzcUYJqw85BDgANxNSmfCykOsuRhTSSMTkbKkIEdERETKjDOvycm1Gby/9RQFtSu1b/vZ1lPk2tTQVKSqU5AjIiIiZSYzM5Ps7OzKHkaBoqJj82VwnmYAd5LSiYqOrbhBiUi5UJAjIiIiZcpZszn3kzPKdD8RcV4KckRERKRMOWuFtWA/zzLdT0Scl4IcERERKVPOGuREhNemjocLFLgqByxAmL8XEeG1K3RcIlL2FOSIiIhImXLWIMfFauH1Wln/fOQY6Ng75HwQ2UH9ckSqAQU5IiIiUiZcXFwA5w1yAKZ0aMIkYvAnx2F7qL8Xqyf1VJ8ckWpCzUBFRESkTPj6+hIfH++0QY5hGBw+fJjWJNPFx+ClUZN5nJlLsJ8nEeG1lcERqUYU5IiIiEiZ8Pf3Jz4+3mkbgl65coUnT54A0LvXy/RsXr+SRyQi5UXT1URERKRMBAQEAM47XS0qKgoAd3d3OnXqVMmjEZHypCBHREREyoSfnx8AaWlp5ObmVvJoHEVHR3P37l0AunXrhoeHRyWPSETKk4IcERERKRO+vr7m1ykpKZU4kvz2798PgNVqpXv37pU8GhEpbwpyREREpEzYMzngXFPWYmNjuXr1KgAdO3Z0CMZEpHpSkCMiIiJlwt/f3/zamYKcAwcOmF/37NmzEkciIhVFQY6IiIiUCWfM5CQlJXHmzBkAWrVqRa1atSp5RCJSERTkiIiISJnw9vbGYsnrNZOcnFzJo8lz+PBhDMMAoFevXpU8GhGpKApyREREpExYLBZ8fHwA58jkZGRkcOzYMQDCw8OpX199cUReFApyREREpMzYe+UkJCRU7kCAY8eOkZ2dDUBEREQlj0ZEKpKCHBERESkzztIQNCcnh0OHDgEQFBREkyZNKnU8IlKxFOSIiIhImbFXWEtNTTXXwlSGM2fOkJaWBuRlcexrhUTkxaAgR0RERMqMPcix2WykpqZWyhhsNpvZ/NPPz482bdpUyjhEpPK4VvYAREREBHJtBlHRsdxPziDYz5OI8Nq4WKte9uHpXjnJycmV0njz8uXLxMfHA/Dyyy/j4uJS4WMQkcqlIEdERKSSrbkYw/tbTxGTlG5uC/X34sPIDoxrFVqJIyu5HzYEDQ4OrtDzG4ZBVFQUAB4eHnTq1KlCzy8izkHT1URERCrRmosxTFh5yCHAAbiblM6ElYdYczGmkkZWOj8McipadHQ09+/fB6B79+64u7tX+BhEpPIpyBEREakkuTaD97eeoqDl+fZtP9t6ilxb5S3gL6mnp6dVRpBjX4tjtVrp1q1bhZ9fRJyDghwREZFKEhUdmy+D8zQDuJOUTlR0bMUN6jm5uLjg5eUF5K3JqUiPHj3i2rVrAHTq1MlsTCoiLx4FOSIiIpXkfnJGme7nLOxT1iq6IeiBAwfMr3v27Fmh5xYR56IgR0REpJIE+3kWaz/PnKoV5NgbgiYmJlbYORMTEzlz5gwArVu3pmbNmhV2bhFxPgpyREREKklEeG1C/b0ovFC0gT/ZNLQWPqXNGdmDnJSUlAprCHr48GHz6169elXIOUXEeSnIERERqSQuVgsfRnYAKCDQyQsOInnA0e++Iz296gQ69ulqOTk5ZGZmlvv50tPTOXbsGAANGzYkJCSk3M8pIs5NQY6IiEglGtcqlNWTelLf38the5CbhZ8F59KaZO7fv8+iRYtITU2tpFGWjJ+fn/l1RVRYO3bsGDk5OQD07t273M8nIs5PzUBFREQq2bhWoYxuUZ+o6FjuJ2cQ7OdJRHhtrBb45ptvOH78OI8ePWLBggVMnz7dIYhwRj/slVOnTp1yO1dOTg6HDh0CoE6dOjRu3LjczuWMcm1GvteNi7XwCZAiLwoFOSIiIk7AxWqhX8P8wcDw4cNxdXXlu+++48mTJ2agY1/34owqsiHoqVOnzKl8vXv3xmJ5cd7gr7kYw/tbTzmUIQ/19+LDyA6MaxVaiSMTqXyariYiIuLELBYLQ4YMMadhJSQkMH/+fJ48eVLJIytcRQU5NpvNbP7p7+9PmzZtyu1czmbNxRgmrDyUr8/S3aR0Jqw8xJqLMZU0MhHnoCBHRETEyVksFgYMGED//v2BvCab8+fPJzbWOZuEurm54e7uDpRvQ9BLly6ZZap79eqF1fpivK3JtRm8v/UUBdWts2/72dZT5NoqprKdiDN6MX4biIiIVAN9+vRh8ODBAKSlpbFgwQIePHhQyaMqmH3dUHn1yjEMg6ioKAA8PDzo0KFDuZzHGUVFx+bL4DzNAO4kpRMV7ZxBsEhFUJAjIiJShfTs2ZPhw4cDkJGRwYIFC7h7924ljyq/wMBAIG96XXm4ffu2GeD16NHDzBy9CO4nF685bHH3E6mOFOSIiIhUMV26dGH06NEAZGdns3DhQqKjoyt5VI7s63JSUlLK5fj2LI6LiwvdunUrl3M4o/j4eM5/d6BY+wb7eZbzaEScl4IcERGRKqhDhw5MmDABgNzcXBYtWsSNGzcqeVTfswc5mZmZZGdnl+mxHz58aF5rp06d8Pb2LtPjO7MrV67gcvcK/mRDgaty8hrLhvl7ERFeu0LHJuJMFOSIiIhUUW3atGHy5MlA3hqVJUuWcOXKlUoeVZ7yrLB24MD3mYyePXuW6bGdWUJCAsePH8cKRGJfi+UY6NgLaH8Q2UH9cuSFpiBHRESkCmvRogVvvPGG2R/mq6++4sKFC5U8KscgpywrrCUkJHD27FkgL8irUaNGmR3bmUVHRzN37lyzol7PACvTvR4T4us4JS3U34vVk3qqT4688NQMVEREpIpr0qQJU6dOZenSpeTm5rJq1SrGjh1L+/btK21M5ZXJOXz4sPl1r169yuy4zuzEiRNs3LjRfPzKK6+YjU9zbQZR0bHcT84g2M+TiPDayuCIoCBHRESkWmjYsCHTp0/niy++IDs7m7Vr15KdnU3nzp0rZTzlEeSkpaVx7NgxABo1akRwcHCZHNdZ2Ww2tm/fznfffQeA1Wpl4sSJtGzZ0tzHxWqhX8M6lTVEEael6WoiIiLVRGhoKG+99RYeHh4AbNq0yXyDXNE8PDxwcXEByi7IOXr0KLm5uQD07t27TI7prDIyMvjyyy/N++fv78/bb7/tEOCISOEU5IiIiFQjwcHBzJgxAy8vLwC2bt3K/v37K3wcFovFbAhaFkFOdna2OVWtbt26NGrU6LmP6aweP37MZ599ZlaQCw0N5e2336Zu3bqVPDKRqkNBjoiISDVTp04dZs6cia+vLwC7du3i22+/xTAKLjlcXgICAoCyaQh66tQpMjLymlva16NUR9evX2fu3LnEx8cD0LFjR6ZPn46Pj08lj0ykalGQIyIiUg3VqlWLmTNnmoFGVFQU27dvr9BAx37u562uZrPZzGxUQEAArVu3fu6xORvDMDh8+DBLly41+wpFRkYycuRIc9qfiBSfghwREZFqKjAwkBkzZphllg8fPszmzZsrLNCxFx9IS0sz19KUxsWLF80pb7169cJqrV5vX3Jzc9mwYQPbtm0DwM3NjTfeeIPu3btX24yVSHmrXr8lRERExIG/vz8zZswgKCgIgGPHjrF+/XpsNluFnNsuJSWlVMcwDIOoqCgAPD096dChQ1kMzWmkpqayaNEiTp06BUCNGjWYM2cOTZo0qdyBiVRxCnJERESqOV9fX2bMmEG9evUAOH36NF9//fVzZVeKw154AEpffODmzZs8fPgQgB49euDm5lYmY3MGDx8+ZO7cucTExADQuHFj3n77bWrVqlXJIxOp+hTkiIiIvAC8vLyYNm0a9evXB+DChQusXLmSnJyccjtnWfTKsa/FcXV1pWvXrmUyLmdw6dIlPvvsM3O9Uo8ePXj99dfx9PSs5JGJVA8KckRERF4Qnp6eTJ06lQYNGgBw5coVvvrqK3Ohe1l73iDnwYMH3Lx5E4BOnTrh7e1dZmOrLIZhsG/fPlasWGFOGRw1ahRDhgypdmuNRCqT/jeJiIi8QNzd3Xn99dfNNR83btxg6dKlZGVllfm5fHx8zDfupamw9nR/n549e5bZuCpLdnY2q1evZvfu3UBe0PnWW2/RsWPHSh6ZSPWjIEdEROQF4+bmxuTJk2nRogUA0dHRLF682OxDU1YsFouZfSlpJic+Pp7z588D0LZtWwIDA8t0bBUtKSmJ+fPnc+HCBQBq167Nj370I8LDwyt5ZCLVk4IcERGRF5CrqyuTJk2ibdu2ANy7d49FixaRlpZWpucpbUPQw4cPm1/36tWrLIdU4WJiYpg7d65ZQKFly5bMmjXL/NmISNlTkCMiIvKCslqtjB071izL/PDhQxYuXFjqcs8FsWdgEhMTi/2ctLQ0jh8/DuRVHLNXhauKzpw5w/z5883gsU+fPkyaNAl3d/dKHplI9aYgR0RE5AVmtVoZNWqUWbksLi6OBQsWlLoa2g/Zy0inpaUVuwnpkSNHzPLWvXv3LpNxVDSbzcaOHTtYu3YtkDd1b8KECfTv318NPkUqQLkGOf/1X/9F165d8fPzo06dOowZM4bLly+X5ylFRESkhCwWC0OHDuXll18G8tbDzJ8/n/j4+Oc+tr3Cms1mIzU1tcj9s7KyzKlq9erVo2HDhs89hoqWmZnJV199xcGDB4G8PkWzZ8+mTZs2lTwykRdHuQY5e/fu5Z133uHw4cPs2LGD7OxsBg8eXKxfciIiIlJxLBYLAwcOpG/fvsD3C+UfP378XMctaRnpU6dOkZmZCUBERESVy3rEx8czb948rl27BkBISAhz5swhODi4kkcm8mJxLc+Db9261eHxokWLqFOnDsePH6dPnz7leWoREREpIYvFQr9+/XBzc2Pnzp2kpqYyf/58pk+fTp06dUp1zKeDnKLKSNtsNg4cOADkreVp2bJlqc5ZmFybQVR0LPeTMwj28yQivDYu1rILom7dusXy5cvNIK19+/aMHDkSV9dyfbslIgWo0P919kWHNWvWrMjTioiISAn06tULNzc3tmzZQnp6OgsWLGDatGmlykaUJJNz/vx5c59evXqVaXPMNRdjeH/rKWKS0s1tof5efBjZgXGtQp/7+MeOHeObb74xHw8aNIiePXtWuUyUSHVRYUGOzWbjZz/7Gb169TLLVf5QZmam+ekHfP/LMDs7u9y6MT8P+5iccWxSPLqHVZ/uYdWne+icOnbsiIuLC5s3byY7O5tFixYxZcoU6tevn2/fZ91DDw8PM1hJTEws9D4bhsGBAwewWq14eXnRunXrMntNbLx8jzfXHsEAvJ6Km56kpPPm6kMwthsjW4SU6ti5ubns3LmTkydPYrVacXFxYdy4cTRp0oScnJwyGX9F0P/Dqu9FuIcluTaLUdxSJ8/pxz/+MVu2bGH//v2Ehhb8icmvf/1rfvOb3+TbvmzZMrOZmIiIiIiIvHjS0tJ47bXXSExMdMgSF6RCgpx3332X9evXs2/fPho1alTofgVlcsLCwoiLiyvyQipDdnY2O3bsYNCgQbi5uVX2cKQUdA+rPt3Dqk/30PldvnzZLIUMMGnSJBo3bmw+LuoeLliwgEePHhEWFsbrr79e4DmWLVtGdHQ0rq6uvPvuu3h6epbJ2PfdesTI5QeL3O+b13rRO7x2sY8bFxfHypUrzVknYWFhjBs3Di8vr1KPtTLp/2HV9yLcw6SkJIKCgooV5JTrdDXDMHjvvfdYu3Yte/bseWaAA3kpbQ8Pj3zb3dzcnPpmOfv4pGi6h1Wf7mHVp3vovNq2bYuHhwfLli0DYPny5bz66qv5CgMUdg8DAgJ48OABiYmJBX7/3r173Lp1C8ibJmfvrfO8vvvuOz7depB0il5z8yAtp9ivv6tXr7Jy5UpzOlrXrl0ZMmQILi4uzzVeZ6D/h1Vfdb6HJbmuci0h/c4777B06VKWLVuGn58fDx484MGDB6Snpxf9ZBEREXEazZo1Y+rUqeb6mhUrVnDu3LliPdf+iWtKSkqBDUHtFdUAevbsWQajhfv377N161Z8Kd66mGC/ojNHhmFw8OBBli1bZgY4w4cPZ9iwYdUiwBGpTso1k/Ppp58C0K9fP4ftCxcuZPr06eV5ahERESljjRo1Ytq0aSxZsoScnBy+/vprsrOzCy0oZGcPcnJycsjMzHSYivbkyRMuXLgA5JVcDggIKPX4DMPg9u3b7Nu3j5s3bwLQgDT8ySYJVyB/pTMLeVXWIoqYqpaTk8PGjRs5c+YMkDf7ZPLkyVWyWanIi6Dcp6uJiIhI9REeHs5bb73F4sWLycrKYsOGDWRlZT3zOT8sI/10kHPo0CHz65dffrlUYzIMgytXrrBv3z7u3btnbnd1daVly5aMvvKAJVl1sABPvzOxhzwfRHZ4Zr+clJQUvvrqK/PYtWrV4vXXX6dGjRqlGq+IlD91pxIREZESCQkJYcaMGSxatIiMjAy2b99O+/btC93/6TU2SUlJZmPR1NRUTpw4AUCTJk2oW7duicZhs9k4d+4cUVFRxMXFmds9PT15+eWX6dq1K56enowHxhTSJ+eDIvrk3L9/n2XLlpGSkgJA06ZNmTBhQoFriEXEeSjIERERkRKrW7cuM2fOZNGiReZa2wMHDtC3b998DTALawh65MgRbDYbABEREcU+d05ODidPnmT//v0Ox/Pz86Nnr16k1gojJj2H3AdJRIR74GK1MK5VKKNb1CcqOpb7yRkE+3kSEV77mRmc8+fP8/XXX5szU15++WUGDBhQpk1KRaR8KMgRERGRUgkKCmLmzJksXrwYgKioKHJychgwYIBDoFNQkJOVlcV3330HQHBwMOHh4UWeLzMzk6NHj3Lw4EGHIka1atUiIiKCq641mLT9DDFJ0eb3Qv29+PCf2RoXq4V+DesUeR7DMNizZw/79u0zt40dO/aZ2SoRcS4KckRERKTUatSowZtvvsn+/fuBvGxOdnY2kZGRZqDj5uaGu7s7WVlZJCcnA3Dy5EmzN15ERES+7M/TUlNTOXz4MIcPHzarmkFecNSnTx9atGjB2kt3mbTyED9cDXw3KZ0JKw+xelLPZ05Ls8vKymLt2rVcunQJAG9vb6ZMmVJoI3MRcU4KckREROS52DM1NWvWJC4ujiNHjpCdnc2IESPMqV1+fn48fvyYxMREcnNzzbLRNWrUoEWLFgUeNyEhgYMHD3Ls2DGHYkYNGzakT58+NGzYEIvFQq7N4P2tp/IFOJBXaMAC/GzrKUa3qP/M6WmJiYksW7aMR48eAXlT8l577TWnbEguIs+mIEdERETKxJtvvsmXX37Jo0ePOHnyJNnZ2YwdOxar1UpgYCCPHz8mISGB8+fPmxmdXr165VvjEhsby/79+81yzXYtW7YkIiKCkJAQc9vp06f58sApYpK8Cx2XAdxJSicqOrbQ6WrR0dF89dVXZGRkANC6dWvGjBlTbZsqilR3CnJERESkTHh5efHWW2+xZMkS7t27x7lz58jJyWHChAlm/5vk5GRzapuXlxcvvfSS+fy7d+8SFRXF5cuXHY770ksv0bt3b4KCgjAMg8TERO7fv8+9e/eIioriBv5A4UGO3f3kjAK3nzx5kg0bNpiP+/fvX+QUOhFxbgpyREREpMx4enoydepUvvzyS+7cucOlS5dYvnw5derU5ebNVFJSEvH1jadBA29efvllXFxcuHHjBlFRUdy6dcs8jsVioUuXLrRv356UlBTOnDnDvXv3uHv3rpltsfMlh+II9vN0eGyz2dixYweHDx8GwGq1MmHCBFq1avV8PwQRqXQKckRERKRMeXh48Oabb/LVV19x8+ZNNmw4wfbtD0lI+L5pqL+/GwEBrTh//jMePHhgbndxcSEkJAQXFxfOnTvH0aNHCz2Pp6cntWrVwnb3Lv5kk0TBU8ss5FVZiwivbW7LyMhg1apV3LhxA8hbM/T666+XuFePiDgnBTkiIiJS5tzc3Hjttdf4X//rA1auPJ/v+0lJ2fzrv37NpElhtG79/cL+3Nxc7ty5k29/Dw8P6tevT0hICCEhIQQHB5tT4G7fvk3TuEymfZO3hufpAgT2CWcfRHYwiw48fvyYL7/8kvj4eABCQ0OZPHkyPj4+ZXDlIuIMFOSIiIhIubBYrKxefe2Z+2zdep+WLf2wPlX1zMPDg5CQEDOosQc0ha2RadiwIQ0bgo+PD+9vPUVM0vc9dEL9vfjgn31yAK5fv86KFSvIzs4GoEOHDowYMQIXF5fnvFoRcSYKckRERKRcREVdJCbmyTP3SUrKISvLn4EDXyI4OJiQkJBnBjTPMq5VKKNb1CcqOpb7yRkE+3kSEV4bF6sFwzA4cuQIW7duNfePjIykW7duKjAgUg0pyBEREZFycf9+fLH2a9++GwMG9C6Tc7pYLfnKROfm5rJ582ZOnDgB5E2le/XVV2nSpEmZnFNEnI+16F1ERERESq5u3YBi7XfmzBEyMzPLZQypqaksXrzYDHBq1KjB22+/rQBHpJpTkCMiIiLlomFDX/z9nz1pxN/fFXf3JP74xz9y/PjxMj3/w4cP+eyzz8xCBo0aNWL27NkEBQWV6XlExPkoyBEREZFycfduDJGRwQV+z74MJjIyGKvVgs1mY9OmTfzjH/8gISHhuc996dIlPvvsM5KSkgDo3r07b7zxBl5eXs99bBFxfgpyREREpMzdvXuXb7/9ltat/Zk0KSxfRic0tBYLFsyia1fHICg2NpYPP/yQXbt2YbPZSnxewzCIiopixYoV5vNHjRpFZGQkVqve9oi8KFR4QERERMpUWloaS5cuNR8PHNiSli39uH07jWbN2tKrV2ciIlrh4mIlKakHS5cuJTY21uEY+/fv59SpU7z66quEhoYW67zZ2dmsX7+e8+fz+vJ4enoyZcoUwsPDy+7iRKRK0EcaIiIiUmZsNhsrVqwgIyMDyKtk1qlTJ6xWC40a+TBt2iv069cGF5e8tyD+/v7MmDGDhg0b5jtWSkoK8+fPZ82aNUUWJkhKSmLBggVmgFO7dm3mzJmjAEfkBaUgR0RERMrM3r17iY6ONh+PHz+e+PjvS0nXqVMn33M8PT154403aNu2rbnt6d41Z8+e5S9/+Qvnz5/HMIx8z7979y5z587lwYMHALRo0YJZs2YRGBhYFpckIlWQghwREREpE9evX2ffvn3m4/bt29OiRQsz+AgICMDd3b3A57q4uDBu3Dh69eoF5K2teXoNTXZ2NqtXr2bhwoUkJiaa28+ePcvnn39OWloaABEREbz66quFnkdEXgxakyMiIiJlYt26debXPj4+DBs2DID79+8DEBIS8sznWywWBg4cSEBAAJs3bzYLB/j5+ZGcnAzAnTt3+OCDDxgwYADp6ekcPHjQfP748eMdskEi8uJSkCMiIiLPJScnB8jLttiNHz8eDw8P0tLSzCxLvXr1inW8rl274u/vz4oVKzAMg+TkZFq0aMGVK1fM6Wq7du0y9/fx8eG1114rMogSkReHpquJiIjIc9m+fbvD4+7du9OoUSMAc6oaQN26dYt9zBYtWjBjxgw8PT0BuHz5Mh06dCjwGE2aNKFmzZqlGbqIVFMKckRERKTUTp48yZkzZ8zHgYGBDBgwwHz88OFD8+viZnLsQkNDmT17NgEBAea5Hj16lG+/M2fO8MEHH3DhwoUCCxOIyItHQY6IiIiUyv3799m4caPDtvHjx+Pm5mY+tmdy3N3d8ff3L/E5atasyezZs83n2oOYHj16OJSdzszMZNWqVSxdupSEhIQSn0dEqhcFOSIiIlJi6enpLF++3CFz0rNnz3yNO+1FB+rWretQFrq4bDYbe/fuJSkpyWH7lStXGDVqFGPHjsXV9fslxjdu3ODDDz/kwIED5Obmlvh8IlI9KMgRERGREjEMgzVr1uQLPHr37u3wODc3l8ePHwMQHBxc4vOkp6ezZMkSjh49CuQ1Dm3dujUAT5484fPPP6d27dq8//77tGjRwuG5O3fu5NNPPyUmJqbE5xWRqk9BjoiISDWTazPYc+sRX52NZs+tR+TaynadSlRUFNeuXcu33cXFxeFxXFycWQa6pOtx4uLi+Oyzz7h16xYA4eHhzJkzhwkTJphrftLS0pg/fz4PHz5k8uTJTJw4EQ8PD/MYjx8/Zv78+XzzzTdkZGSU6PwiUrWphLSIiEg1suZiDO9vPUVMUrq5LdTfiw8jOzCuVegznlk8169fZ/fu3Q7b+vbtS3x8fL59S1tZ7erVq6xcudIsTd2lSxciIyPNIKp37974+/uzdu1acnNzWbp0KaNHj6ZDhw40bNiQLVu2cO7cOfN4x44d49y5c4wYMYLWrVuXatqciFQtyuSIiIhUE2suxjBh5SGHAAfgblI6E1YeYs3F55u6lZiYyOrVqx221a9fn+7duxe4v72ymsVioU6dOkUe3zAMDh48yLJly8wAZ9iwYQwfPjxflqh9+/a8+eab5vb169ezb98+vLy8GD9+PJMnT8bLy8vcPyMjg9WrV7N06dICAzIRqV4U5IiIiFQDuTaD97eeoqCJafZtP9t6qtRT13JyclixYoXDtC+r1cq4ceOwWgt+O2EvOlCjRg2H4gCFHX/9+vXs2LEDyKvGNnXqVLp27Vrocxo3bsysWbPw8fEBYPfu3WzatAmbzUaLFi346U9/SocOHRyec+PGDT766CP279+vwgQi1ZiCHBERkWogKjo2XwbnaQZwJymdqOjYUh1/27ZtZtBiN2TIkEKbcBqGYU5XCwkJeeaxU1JSWLRoEadPnwbyykbPmTPHbCj6LPXq1WP27NnUqlULgBMnTrBs2TKysrLw9PRk9OjRvPHGG/j6+jo8b9euXXz66afcuXOnyHOISNWjIEdERKQauJ9cvIX1xd3vaWfOnOHYsWMO2xo2bPjMLEtKSoqZ9XnWepz79+8zd+5c7t69C0DTpk15++23Cw2eChIQEMCsWbMIDw8H8tYNLVy4kJSUFACaNGnCu+++S5cuXRye9/jxYxYsWMCmTZtITy88QBSRqkdBjoiISDUQ7OdZpvvZPXz4kPXr1ztsc3NzY8yYMc9cwG9fjwOFV1a7cOEC8+bNM4ORl19+mSlTpjhUSCsuT09P3nzzTbPE9IMHD5g3b55ZwtrDw4Phw4czbdo0AgICHJ57/PhxPvzwQ86ePevQ90dEqi4FOSIiItVARHhtaroaUOCqHLAAYf5eRITXLvYxMzIyWL58uVkG2m748OH5AoUfelZlNcMw2LNnD6tWrTKDijFjxjBo0KBC1/cUh6urKxMmTKBnz54AJCUlMW/ePIcpaQ0bNuSdd96hR48eDs/NzMxkzZo1LFmyhCdPnpR6DCLiHBTkiIiIVAMuVgsDc+zV0xwDHXu+5YPIDrhYi1c+2TAM1q5dS0JCgsP25s2b0759+yKfb8/keHp6OqyHycrKYtWqVezduxcALy8vZs6cyUsvvVSscRXFYrEwePBgIiMjgbzgZcGCBVy6dMncx83NjSFDhjBjxox80+Ju3rzJxx9/TFRUlAoTiFRhCnJERESqgZycHHrXdGMSMfiT4/C9UH8vVk/qWaI+OQcPHuTKlSsAZnbF09OTUaNGFavPzL1794C8qWr2/RMTE5k/fz4XL14E8jI8c+bMITT0+fv3/FD37t2ZNGmS+XjFihUcOXLEYZ+wsDB+/OMf06tXr3zP//bbb/n000+Jjo4u87GJSPlTM1AREZFqICoqiidPntAamNX7JVJqhpHh6kmwnycR4bWLncGBvGzGzp07gbysR3Z2NgCjRo0yyzU/S05OjtmLJjg4GIA7d+7w1VdfmQv8W7duzZgxY3BzcyvJZZZIq1atmDFjBl9++SWZmZls2bKFxMREBg4caAZerq6uDBw4kNatW7Nu3TpiY7+vPvf48WMWLlxIx44dGTRokEPfHRFxbgpyREREqriHDx+yb98+IC9zMqB//1KvbUlKSmLVqlVAXgbHHuC0a9eOVq1aFesYjx49Mtfa1K1bl1OnTjkUL+jfvz8RERHFygg9r7CwMGbNmsWSJUtISkri4MGDJCYmMmbMGIfePSEhIcyZM4eoqChzKp3dyZMnuXjxIkOHDqVdu3YVMm4ReT6ariYiIlKF2Ww2hwBizJgxpQ5wcnNzWblypZltsb+Z9/HxYdiwYcU+ztOV1a5fv26Oz2KxMGnSJPr06VOhgUJQUBCzZ882CyCcP3+eJUuWODQ2BXBxcaFfv3786Ec/ylcsISMjg7Vr1/LFF1+YFdtExHkpyBEREanCvvvuO7NJZ+/evZ/Zk6YoO3bsMPvV+Pn5mQvvx44di6dn8UtPP11Z7ezZs+bx3n777WJng8qar68vM2bMoEmTJgBER0fz+eefk5iYmG/funXr8vbbbzNgwIB837t16xaffPIJe/fuJScnJ9/3RcQ5KMgRERGpouLj4821MzVr1qRv376lPta5c+f47rvvgLzmmsnJyQB06dLFDAyK6+mSzQD169dnzpw5hfbLqSju7u689tprdOzYEchbczNv3jyHzJOd1Wqld+/evPPOO4SEhOT7/p49e/j000+5fft2uY9bREpOQY6IiEgVZBgGGzZsMHvYjB492mGNSUnExsaybt06IK9pZlJSEpAX7AwaNKhEx7p+/bqZWQLo0KED06dPL1bBgopgtVoZOXIk/fr1AyA1NZXPP/+cGzduFLh/UFAQM2fOZMiQIfmm2D158oRFixaxbt060tLSynvoIlICCnJERESqoFOnTnHr1i0AunbtSnh4eKmOk5mZyfLly82paX5+fmbRgHHjxuHu7l7sYx0/fpylS5eaj1u0aMGoUaNKHXyVF4vFQt++fRk9ejSQVw1uyZIlnDlzpsD9rVYrPXr04N133y3w53z69Gk++ugjTp8+bf7sRKRyKcgRERGpYlJSUti6dSuQt9akoLUjxWEYBuvXr+fJkycANG3alLi4OABefvnlYgdO9gBpx44dDtu7devm1JXIOnTowOuvv24Wali7di1RUVGFBio1a9Zk+vTpDB8+PF9xh8zMTNatW8fixYvNn6GIVB4FOSIiIlXM5s2bycrKAvJ613h4eJTqOIcPHzYbczZo0IBr164BUKtWLfr371+sY6SlpfHVV1+Zj58uUFDZa3CKo2nTpsyaNQtvb28grwnoN998Y04D/CGLxUKXLl147733aNSoUb7v3759m7///e/s2bNHhQlEKpGCHBERkSrk0qVLZmDStm1bmjVrVqrj3L59m+3btwN5a29SUlLM740fP75YU8wePXrE3LlziYmJAfICJXv2x9vb2wwcnF1wcDCzZ8+mZs2aQN60u+XLl5uBZEECAwN58803GTVqVIENTffu3cs//vEPbt68WW7jFpHCKcgRERGpIjIyMti4cSOQlzGJjIws1XFSUlJYuXIlkLfepHHjxmbvl379+hEcHFzkMS5fvszcuXPNIgUAr776qlmprDjHcCaBgYHMmjWL0NBQAK5evcqiRYtITU0t9DkWi4WOHTvy3nvvFRhsxsfH88UXX7B27VoVJhCpYApyREREqogdO3aYb5aHDh1aqopl9oaf9uNERERw8uRJIG96WURExDOfbxgG+/fvZ/ny5eaUrqFDhwJ5C/jtfWeqwlS1H/Ly8mLatGlmL5/79+/z+eefm2uWCuPn58eUKVMYN25cgVMHz5w5w4cffsjJkydVmECkgijIERERqQJu3brFiRMnAGjSpAnt2rUr1XF27dpl9rHp1KmTWVHMYrEwfvz4fAvqn5adnc2aNWvYtWsXkJdNmj59Oi+99BKAw4L7qhjkALi6ujJx4kS6d+8OQEJCAvPmzTOn5BXGYrHQrl073n333QIbnmZlZbFhwwYWLlxIbGxsuYxdRL6nIEdERMTJZWdns379eiDvTfjIkSNLVbXswoULHDp0CMibTubi4kJ8fDwAgwcPJigoqNDnJicns2DBAs6dOwfk9Y+ZM2cODRo0MPd5uqlm3bp1Szw+Z2GxWIiMjGTw4MFA3jTBBQsWcPny5SKf6+vry6RJk5g4caJDEQb7/bpz5w7/+Mc/2L17twoTiJQjBTkiIiJObvfu3SQkJAAwaNAgAgICSnyMuLg41q5dC+RlYF5++WWOHj0KQHh4uJm5KMi9e/eYO3cuDx48AKB58+bMnj2bwMBAh/0ePXoEgIuLC7Vq1SrxGJ1Nz549mTBhApA3TW/58uUcO3asWM9t3bo17733nplx++E0tX379vH3v/+90CakIvJ8FOSIiIg4sXv37pnZl9DQULp27VriY2RlZbF8+XIzczB69Gizspqrqytjx44tNDN07tw55s2bZy7A7927N5MnTy6wSag9kxMUFPTMaW9VSZs2bZg+fbpZQe2bb75h586dxVpb4+3tzbhx45gyZYrD+in7zyYhIYElS5awZs2aZxY4EJGSqx6/gURERKqh3Nxc1q1bB+RNdxo9enSJp6kZhsGGDRvM6mn9+/fn8uXLJCcnA3lFA36YkbE/b9euXXz99dfmtvHjxzNgwIBCx2DP5ISEhJRojM6uQYMGzJ49Gz8/PwAOHDjA2rVrzSaoRWnevDnvvvsuHTt2BDALNtiDnbNnz/LRRx9x4sQJFSYQKSMKckRERJzUwYMHzUXq/fr1e+aamcIcPXqU8+fPA3mNL+vUqcOpU6fMx/Y33k/LzMxk+fLl7N+/HwAfHx9mz55N27Ztn3kue6aoKq/HKUzt2rWZPXs2derUAfICk6VLl5KRkVGs53t6ejJq1CjeeOMNM1iy2WxmoJOVlcXGjRtZsGCBChOIlAEFOVKoXJvBnluP+OpsNHtuPSLXpk+XREQqSlxcHLt37wbypn/16tWrxMe4c+cOW7ZsAfIafg4dOpQNGzYA4OHhwahRo/JlZRISEvj888+5cuUKkFegYM6cOSXKzjxPZTVn/tvj5+fHjBkzaNy4MZBX8W7BggUOvYKK0qRJE959911z2qE9q2OfDhcTE8M//vEPdu3aRXZ2dhlfgciLo+h2xvJCWnMxhve3niImKd3cFurvxYeRHRjXKrQSRyYiUv0ZhsH69evNqUtjxozBxcWlRMdITU1lxYoVQN5Ut8mTJ7Nr1y7S0/N+r48YMcLMKNjdvn2b5cuXm9mJtm3bMmrUKPMNeHGVNpNTFf72eHh48Nprr7Fx40ZOnz5NbGws8+bN48033zSzPEVxd3dn2LBhtGnThnXr1pGQkEB2djZWq9UMevbv38/Zs2cZOXIkTZo0Kc9LEqmWlMmRfNZcjGHCykMOf2QA7ialM2HlIdZcfHavABEReT7Hjx83+7L06NGD+vXrl+j5NpuNVatWmYvZR44cSVxcHBcuXADyKn/9cOrZiRMnWLRokRngDBgwgHHjxpU4wPHz83MonVxcVelvj4uLC6NHj6ZPnz4ApKSk8Pnnn3Pr1q0SHadBgwb85Cc/oUePHsD3WR0vLy8AEhMTWbp0KatXryYlJaXsLkDkBaAgRxzk2gze33qKgiYH2Lf9bOspp5o+ICJSnSQlJbFt2zYgb4rZK6+8UuJjfPvtt9y+fRvIa/jZtGlTNm3aBORV/Bo+fLi5r81mY8uWLWzcuBHIewM/efJkevfuXapePMHBwSV+TlX822OxWOjfvz8jR44E8noZLV68mLNnz5boOG5ubgwZMoSZM2dSs2ZNANLT07FarWaAef78eT766COOHz+uwgQixaQgRxxERcfm+xTtaQZwJymdqGgtihQRKWuGYbBx40ZzAX9ppopdunSJAwcOAHnTxiIjI1m/fj2ZmZlAXvlob29vIO/N9NKlSzly5AgA/v7+vP3227Ro0aJE58zKyjK/Ls16nKr8t6dTp0689tprZgGBNWvWcODAgRIHI6Ghofz4xz+md+/eQF7wmZ2dja+vL5AXRG3atIn58+c7NF0VkYIpyBEH95OLVyXm1uPEch6JiMiL5/z581y7dg2ADh06mAvci+vJkyesWbMGyFv3MXnyZM6cOcP169fNYzZv3hzIK2zw2WefcfPmTQDCwsKYM2dOsdeVPM1eOhpKtx6nuH97irtfRWvWrBkzZ840p+nt3LmTLVu2mNPPisvV1ZUBAwbw9ttvU7t2bSBvKpzFYjH77Ny9e5f/+Z//YefOnSpMIPIMCnLEQbBf8eZRXz91vJxHIiLyYklLS+Obb74B8qaUDR48uETPz87OZvny5eYb3wkTJmAYBlu3bgXy1spERkYCcO3aNT777DMSEhIA6Ny5M9OmTTMzPCX1dGahNJmc4v7tKe5+lSEkJIS3336bGjVqAHmlu1euXFmqQMRe0a5fv35AXoYvNTUVf39/c58DBw7wySefcPXq1TIZv0h1oyBHHESE1ybU34vCZ2Eb+JONNeYyR48eLXYjNBERebatW7eai/6HDx9uLj4vDsMw2LRpk9lfpU+fPjRt2pS1a9eaU9/GjRuHu7s7hw4d4ssvvzTffA8bNowRI0aUuHrb0+yZHFdXV/NNfkl0qe2LP9lQ4KocsABh/l5EhNcu9RgrQo0aNZg1a5ZZKOLy5cssWrSItLS0Eh/LxcWFvn378qMf/cgMHO2lqmvVqmU+XrZsGatWrTKbu4pIHgU54sDFauHDyA4ABQQ6eX98InmAFdi8eTOffPIJFy5c0EJIEZHncO3aNXPBeosWLWjdunWJnn/8+HHOnDkDQOPGjenbty+HDx/mzp07AHTv3p3Q0FDWr1/P9u3bgbzpbG+++abZr+V5PHjwAMhrmFmaYgUe7m5E8uCfjxz/ntiP9kFkB1ysJT92RfP29mbatGnmuqZ79+7x+eef8+jRI+Lj40v897Ju3brMnj2bgQMHmtseP35MYGCgGQhfuHCBjz76iKNHj+rvscg/KciRfMa1CmX1pJ7U93f8FDHE15Pftgng15OGmp8qJSQksGrVKj7//HOzko+IiBRfVlaW2aDT3d3dofJZcdy9e9ec5ubn58f48eN5/PgxO3bsAKBmzZr06NGDxYsXc/r0aXPbnDlzSrzmpyA2m424uDig9P1xTp48SWuSmUQM/uQ4fC/U34vVk3o6TZ+c4nBzc2PSpEl069YNgPj4eD799FM++ugjzp4/X+Jmp1arlV69evHOO++YWaKEhATS09PNxzk5OWzevJnPP/9chQlEUDNQKcS4VqGMblGfqOhY7idnEOznSUR4bfNTtJYtW3LhwgV27NhBYmIi9+7dY9GiRTRr1oxBgwaZCyZFROTZdu3aZU41GjJkSL4Gnc+SlpZmNvwEmDx5Mh4eHixZssT8RL9v374sWLDAPEeTJk2YMGFCqXrZFOTJkyfm1OVSFR24f58tW7YA0DPAyryZozgel8qDlMx8f3uqEqvVSmRkJFarlcOHDwNwAT/+tuYsicb3b79K0uw0KCiIGTNmcOTIEbZv345hGNy9e5fAwEAsFgvx8fHcu3eP//mf/6Fnz57069cPd3f3crtGEWemIEcK5WK10K9hwVV2LBYLbdq0oWXLlhw/fpzdu3eTkZHB1atXuXr1Kh07dqRfv34OiyRFRMTRnTt3zPLNDRo0oGPHjsV+rs1mY/Xq1WbwMmLECEJCQti7d685faxVq1asW7fODHh69uzJwIEDzXLHZeHprEFJK7NlZmY6BGmTJk3C38+X/n6+ZTa+ymSxWMxMywX8WElovmVH9manxc1WWa1WevToQfPmzVm/fj3R0dFmAYlGjRpx69YtDMPg0KFDnDt3jhEjRpgV9UReJJquJs/FxcWFbt268bOf/Yw+ffqYc7FPnjzJBx98wK5du8yFtCIi8r2cnBzWr18P5L1xHTVqVInWs+zdu9cs//zSSy/RqVMn7t27x549ewDw8fHh4sWLZoAzevRoBg8eXKYBDny/HgcoURbfMAw2bNhAYmJeS4LIyEhCQkLKdGzOoFWrVowaM4btVvu1Od7j0jY7rVmzJtOnT2f48OFm0YibN28SEBBAWFgYAMnJyXz11VesXLlShQnkhaNMjpQJDw8P+vfvT5cuXdizZw8nTpzAMAz279/P0aNH6devH126dMHVVS85ERGAqKgoHj9+DMCAAQPMbvfFcfXqVfbt2wfkBRbDhw8nNzfX7JEDkJqaCoCXlxdTpkwx3/iWtaeDnJJMjTp+/DgXLlwA8oot2NevVDcuLi4kBgSTYLtS6D5PNzstbAZFQSwWC126dKFZs2Zs2LCBGzdukJCQQEJCAs2bN+fu3bukpqZy8eJFrly5Qp8+fYiPj6dr167UrRfsMCW9R3Dg81+siBPRO04pU35+fowcOZKePXuyY8cOrly5QmZmJtu2bePQoUMMHDiQtm3blqr6johIdfHo0SMzSKlbty49evQo9nPj4+NZvXo1kLfAffLkybi5ubF9+3YzaLKrU6cOr732GgEBAWU3+B+4f/9+iZ/z4MEDNm/eDIC/vz+jR4+u1n8XyrvZaUBAAG+88QanT59m8+bNZGdnc+XKFQICAmjVqhUXL14kNzeX3bt3A/D1hRh2uYdxLyXTPEbTAE/+3KhUpxdxSpquJuUiKCiIKVOm8NZbb5nzkZOSklizZg1z587lxo0blTxCEZHKYbPZWLdunfl4zJgxxZ5ClpOTw4oVK8jKygJg/Pjx1KxZk9u3b3Po0CGHfVu1asXMmTPLNcBJT083M0bFlZWVxcqVK81pdBMnTixRT6CqqCKanVosFjp06MB7771Hs2bNAEhMTOTixYsOJckv4MeSrNrcS3EMqO79M8DaePleqccg4kwU5Ei5Cg8PZ+bMmbz66qtmg7iHDx+yZMkSlixZ4jDNQUTkRXDkyBEz+9G7d2+zJH9xbN682Vzo37t3b1q0aEFWVpbD4n3Iq6g2ceLEcq+sVdLf4fampfHx8QAMHjyY0NCqUxq6tIpqtF2WzU79/PyYMmUK48aNw8PDA8jro2O1WrEBW7G/3gpeG/TLnWdKtDZIxFkpyJFyZ7FYaNmyJe+++y4jRozA29sbgBs3bjB37lzWrFljVoYREanO4uPj2blzJwA1atSgb9++xX7uyZMnOXnyJAANGzakf//+2Gw25s+fT3p6OpD3+3bixIn069evQqZ/lbQfy6lTp8ymp02bNi3RNL2q7FmNtsuj2anFYqFdu3a89957ZhbHZrNxG2+ScCtgFN+LSc4gKjq2TMYhUpkU5EiFsVqtdO7cmffff5/+/fub1WDOnj3Lhx9+yLZt28w/1CIi1Y1hGGzcuNHsKTNmzJhiF2O5f/8+GzduBMDX15cJEyaQnZ3N/PnzefToEZC3wH327NkOU5PKmz2TU5yM0aNHj9i0aROQdw1jx46t1utwfqiwRtvl2ezUx8eHiRMnMmnSJLy8vEgp5lLs0q4NEnEmKjwgFc7d3Z0+ffrQuXNn9u7dy9GjRwE4fPgwx48fp2/fvnTv3l2V2ESkWjl9+rRZ8rlLly6Eh4cX63np6eksX77cXMPy6quvkpmZyfz5881pXwAzZ84kODi47Af+DPZpd0X1x7Gvw7HZbEDeOhx7Vv9FUlSj7fLSqlUrGjRowJ9Xb+Hrm7Yi93+etUEizkKZHKk0Pj4+DBs2zCGdnp2dzc6dO/nwww85deqU+QdRRKQqS0lJYevWrUBeFmPgwIHFep5hGKxZs4akpCQAhg4dSnZ2NnPnznUIcEaOHFnhAU5ubi5xcXFAXoW4Z9myZYtDueziBnjVkb3R9pR24fRrWKfcAxw7b29v/vONcdTzciNfR9KnhP4z8BKp6hTkSKWrWbMmEydOZNasWeYfvpSUFNavX8+nn37K1atXzU8wRUSqos2bN5OZmVeud+TIkeaC8KJERUVx7do1ANq2bQvAF198YVZXA2jevDkdO3Ys4xEX7fHjx+YHUc/K5Jw5c4ZTp04B0LhxY3r16lURw5MCuFgt/H1kFywFrsnJ+zv7h4HtKyzwEilPCnLEadSvX5/p06fz2muvERQUBEBcXBzLli1j8eLF3LunspYiUvVcunSJixcvAtCmTRuaN29erOddv37d7GtSq1Yt3Nzc2LJli8M+np6ejBo1qlLWtjxdWa2wTE5cXBwbNmwA8jIJ48aNe6HW4Tgj+9qg0B+sDfIjB4AmuQn6YFGqBS16EKdisVho1qwZTZo04cyZM+zatYuUlBRu377NvHnzaN26dYk7g4uIVJaMjAxzsb2HhwdDhw4t1vMSExNZtWoVAK6urnh4eJiV1VxdXcnJyXtDOnr0aHx8fMph5EWzV1azWCzUqlUr3/ezs7NZuXKlWWhh4sSJlTZWcfT02qA78SncvXyezKuXgPZs2LCB8+fPM3LkSPz8/Cp7qCKlpiBHnJLVaqVDhw60adOGI0eOsHfvXrKzs7lw4QIXLlygW7du9OnTR38wRcSp7dixw2yWOWzYsGL9zsrJyWH58uXm9DZ3d3czkx0UFGSug2nfvj0tW7Ysp5EXzV50IDAwEDc3t3zf37ZtG7GxeaWI+/XrR8OGDStyeFIE+9ogGtaBjo25caONWd776tWrfPzxxwwdOpQOHToo+yZVkqariVNzc3OjV69e/OxnP3Pop3DkyBH+9re/sW/fPoe56SIizuLWrVucOHECgCZNmtCuXbtiPW/btm0OU8HS0tIAeOmll8yeYr6+vsXOCpUX+xhDQkLyfe/cuXMcP34cgAYNGhAREVGhY5OSCwsLA6Bbt25AXiZuw4YNLFmyRL3spEpSJkeqBG9vb4YMGUL37t359ttvOXv2LLm5uezevZvvvvuOV155hY4dO2K1Km4XkcqXnZ3N+vXrgbzpZSNHjizWp+GnT5/m2LFj+bYPHz6c8+fPm9PUxo4di6dn5ZX5TUlJMfua1atXz+F7T548Ma/dy8uLCRMm6HdzFfLKK6/Qpk0b1q1bx+PHj7l58yYff/wxPXr0IDExkT59+3Eh1VahJbBFSkNBjlQpgYGBjBs3jpdffpnt27dz8+ZN0tLS2LRpEwcPHmTQoEG0aNFCqXURqVR79uwxP/0eNGgQAQEBRT7n4cOH5iJ9Ow8PDyZPnsyjR4+4desWAF27dqVx48ZlPeQSsa/HAceiAzk5OaxcudIMxiZMmICvr2+Fj0+eT2hoKD/60Y/Yu3cv+/fvx2azcfDgQS7gx+yLO0mwuXy/r78XH0Z2KJdmpiLPQx+tSJVUr149pk6dyhtvvGGWLn3y5AkrVqxgwYIF3Llzp5JHKCIvqvv373Pw4EEgr2pk165di3xORkYGy5Ytc+gNFhQUxJw5c/Dz82Pbtm1A3gc9xe2xU56enk73dCZn9+7dZgAUERFR6cGYlJ6rqysDBgzgrbfeAuACfqwklASb41vHu0npTFh5iDUXYypjmCKFUpAjVVqTJk340Y9+xNixY/H39wcgJiaGBQsWsHz5cnOBrjPJtRnsufWIr85Gs+fWI3JtKtUpUl3k5uaybt068/Ho0aOLzCwbhsFXX31lNvwEaNasGbNmzSIgIIA1a9aYwc+4ceNwd3cvl7GXhD2Q8fT0dMjU2NfhhIWF0a9fv8oYmpSxsLAwrC6ubMUezDq+nu1/wX629ZT+nolT0XQ1qfIsFgvt27endevWHDt2jN27d5OVlcXly5e5fPkynTt3pl+/fk4xZWLNxRje33qKmKR0c5tS/SLVx6FDh3j06BGQV1Gsdu2iO8evWbOG6Oho8/HLL7/MwIEDsVgsREVFmZXVXn75ZXNxeGWzj6levXpYLBaHhemenp5ah1ONWCwWer8+i//7xb5C9zGAO0npREXH5lVsE3EC+g0k1Yarqys9evTgX/7lXxw6ah8/fpy//e1v7N692yzJWhnWXIxhwspDDgEOKNUvUl08fvyYb7/9Fsibata7d+9n7m8YBmvWrOHcuXPmtpEjRzJo0CAsFgsPHjxwOF7//v3Lb/AlkJOTw5MnT4C8IOeH2avx48ebmfUXXXXJ3D9IKd7fzvvJGeU8EpHiK9cgZ9++fYwcOZKQkBAsFovDL0GR8uLp6cnAgQP5l3/5Fzp06ACAzWZj3759fPDBBxw5csRsTldRcm0G7289RUF/3pTqF6n6DMNg/fr1Zqf4MWPG4OLiUuj+WVlZLF261OxLAnnBQadOnYC8aW9r1qxx+J6rq3NMvoiNjTWvs169euzcudNco9O9e3eaNm1amcNzGmsuxtDww2/ov3gvr635jv6L99Lww2+q5AdawX7Fq+RX3P1EKkK5Bjmpqam89NJL/P3vfy/P04gUyN/fn9GjR/PjH//Y/KObkZHBli1b+OSTTzh//rz5h7q8RUXH5svgPO3pVL+IVD3Hjx83C5706NGD+vXrF7pvQkIC8+bN48aNG+a24cOH07ZtW/Pxnj17zEaa/fv3z1emuTI9XVktPT2dw4cPm4/79OlTGUNyOtUtcx8RXptQfy8KW11mAcL8vYgIL3p6pkhFKdePhYYOHVrpzcpE6tSpw+uvv86tW7fYvn079+/fJyEhgdWrVxMSEmJWZytPxU3hK9UvUvUkJSWxfft2IO/DlWdNK4uOjuarr74iI+P7/+tdu3alS5cu5uOYmBj2798PQHBwcJHT3iqaPWtjsVjYvXs3gFkM4VnZqxfB5cuXcXVz56ebzxWaubeQl7kf3aJ+lekv42K18GFkByasPIQFHK7NfgUfRHaoMtcjLwatyZEXRsOGDZk9ezYTJkwgMDAQ+P6P9cqVK83FwuWhuCl8j+zCsz0i4nwMw2DTpk1kZ2cDedXUCqt+duLECRYuXOgQ4NSvX58hQ4aYj7Ozs81pahaLhXHjxjndAn77700XFxeysrKAvLVE1V1B62sMwyA1NZW7d++yceNGli9fzu+WrOZuSuEfWFXVzP24VqGsntST+v5eDttD/b1YPamniueI03GOCb7/lJmZ6bAw3F5OMzs72/wD4kzsY3LGsUnhmjdvTpMmTTh16hQHDhwA4NatW8ydO5f27dsTERGBn59fmZ6zR3AgTQM8uZecUeCne2DgRw5N3LL0eioh/T+s+qryPbx06RLXr1/HarXSrl07wsLC8l2HzWbj22+/5dixYw4Bi5eXF+PHj8dms5klonfs2EFiYiJWq5UBAwYQEBDgVD8XwzB49OgRVqsVm82G1WqlS5cuNGzYkKtXrzrVWMuCYRikp6ez6vRN/r8jN3mUnmN+L8CSy0DLQ5rbvi/9bbVaycQNr4IO9gP3E1Od6udVnP+HI5vWZdhPBnMoJo4HKZnU8/WgZ2gQLlaLU13Li6oq/y4trpJcm8WooEUJFouFtWvXMmbMmEL3+fWvf81vfvObfNuXLVuGt7d3OY5OREREREScWVpaGq+99hqJiYlFVnF0qiCnoExOWFgYcXFxTlmOMjs7mx07djBo0CDc3NwqezhSCvZ72KtXLw4dOsSpU6fM77m7u9O7d286depUZlWNNl6+x//eeYa7T629CfKwMr2+G243TgPg4eHB5MmTCQ4OLpNzVnf6f1j1VdV7uGnTJrP885gxY2jZsqXD9588ecLKlSvNHjJeXl6kp+dNSe3Xrx89evQw983MzOSzzz4jNTUVV1dXZs+eTUBAQMVcSAmcO3eOTZs2AXnT1WbPnk1gYKDT3kN7JiYxMZHExEQSEhLMr+Pj40lKSnpmtU0b8A+akIwrP2yCaVfXy429r3ahZo1AvvvuO+IeP+HfY9x5mJpVYObeAtT38+TMj4c41RoWZ72HUnwvwj1MSkoiKCioWEGOU01X8/DwwMPDI992Nzc3p75Zzj4+KVpAQAAjR46kZ8+e7Nq1i0uXLpGRkcHOnTs5cuQIAwYMoF27dkV2Li/KuLYNGN06nKjoWO4nZxDs50lEeG1crBaOHKnHli1bSE9PZ/Hixbz22ms0adKkjK6w+tP/w6qvKt3Da9eucebMGSBvCmzbtm0dfj9cv36dFStWmFMr6tevz927dwFo0aIFvXv3dtj/m2++ITk5GYDIyEiCgoIq6lKKzWazsXfvXnNq3YgRI/I1O63oe2gYBmlpaSQkJOT79+TJExITE0vUMsBqtZofamVlZXETbx7x7Ou5lZrNjVx3wn19GTBgAABu/6yuBgUv0v/DkI54ehS8dquyVaX/h1Kw6nwPS3Jd5RrkpKSkcO3aNfPxzZs3OXXqFDVr1iQ8PLw8Ty1SKkFBQbz66qvcuXOH7du3ExMTQ1JSEmvXruXAgQMMGTKExo0bP9c5XKyWAjtCd+vWDR8fH1avXo3NZmPp0qWMHz/eoaysiFS+rKwsNmzYAOT9wR0xYoQZsBiGwXfffce2bdvM/bt37853330HQGBgIGPGjHEIcC5fvszp03mZ3KZNm5r9vZzNnj17SExMBPKaL3fs2LHcz1lUEJOUlEROTk7RB/oni8ViricqaCKLzWYziykApBTzbdIPK2PaF+m/v/WUQxnpUH8vPojsoEX6IhWgXIOcY8eOOZTS/PnPfw7AtGnTWLRoUXmeWuS5hIWFMWPGDC5fvsyOHTt48uQJjx49YsmSJTRq1IjBgweXS9+KNm3a4OXlxZdffonNZuPrr78mNTWV7t27l/m5RKR0vv32W4esi71QSW5uLps2bTKnvbq5uTFq1Ci2bNkC5GUJpkyZgqfn99UW09LSWL9+PZA3m2H06NHPnTEuDzdu3CAqKsp8HBYWVibHLesgpjjnKyyz4+npiZ+fH4GBgQQEBODt7c3NfceKddyCKmiOaxXK6Bb1C8zci0j5K9cgp1+/fhXWbFGkrFksFlq2bEnz5s05efIk3377LWlpady8eZO5c+fSrl07XnnlFbMcdVlp3LgxM2fOZMmSJWRkZLB161ZSU1Pp37+/U775ESmOXJtRLd7sxcTEmFmZBg0amNmM1NRUli9fTkxMXpPHGjVqMHnyZDZu3EhaWhqQV1766b5c9vLT9nU6I0eOxNfXtyIvp1hSUlJYvXq1w7aQkJBiPbeig5jCeHp64u/vbwYw/v7++f4VtPayQ8dO7Jy/j3spBVfGtJCXnSmsCWZhmXsRKX9OtSZHxBlZrVY6d+5Mu3btOHz4MPv27SM3N5ezZ89y9uxZevToQURERJlWAAwJCWHWrFksXryY5ORkoqKiSE1NZfjw4U7XM0OkKGsuxhQ4befDKjZtJzc3l3Xr1gF5vxdGjRqFxWLh4cOHfPnll2Z2p3HjxkycOJG9e/eaQU/Xrl1p3769w/HOnTvHxYsXAWjdujVt2rSpuIspJntG2R6I2dWtWxf4Poh5/PgxAIcPHyYpKalCgxgvLy/8/f2pUaNGgcGLn59fqYvH1AgM4KNhHdUEU6QKUpAjUkzu7u706dOHzp07s2/fPo4cOQLk/VE/fvw4ffr0oXv37mW22K9WrVrMnj2bL774gri4OE6cOEFaWhrjx48vs2pvIuVtzT8XYP/wU/C7SelMWHmI1ZN6MrJp3UoZW0lFRUWZb+ZfeeUVatasycWLF811dAA9evRg0KBBXLp0icOHDwMQHBzM4MGDHY6VlJRkVinz9vZmxIgRFXglxRcVFcWtW7eAvN9J9us/duwYe/bsMYMYq9VK+/bt2bNnj/mzKAve3t5FBjAuLi5ldr6CaH2NSNWkd0oiJeTj48PQoUPp3r07u3bt4sKFC2RnZ7Nr1y6+++47XnnlFV566aViZVyKmsLj5+fHjBkzWLZsGTExMVy6dIklS5bkm9cvUhrlOYXs0aNHZGXn8N7mEwVO8zHI+yT8Z1tPMewngwvYw7k8evSIvXv3AnlZjB49erBv3z52795t7jN69Gg6dOhAXFwca9euBfKmSb366qsOH0wYhsH69evNBe5jxozBy6s47SPLnr3E8g+nksXHx/Po0SOzKTdgBjgA0dHRz31uHx+fZwYwvr6+5R7AFJfW14hUPQpyREqpZs2aTJw4kXv37rF9+3Zu375NSkoKGzZs4ODBgwwePJimTZsWuo6muFN4vLy8mDp1KitXruTatWtER0ezcOFC3nzzTaecvy9VQ3lOIbtz5w4LFizgJt7co2Gh+xnAnaR0DsXEPdf5ypvNZjOnqUFe6eQ1a9Zw4cIFIC+Qee211wgLCyMrK4vly5ebU7QmTpyYr9/N8ePHuXHjBgCdOnWiWbNm5Tb2woKYhIQEHj9+XG7TyXx9fQkICCAwMLDQAKaqTb3V+hqRqkVBjshzCgkJYdq0aVy7do3t27cTFxdHXFwcy5YtIzw8nMGDB1O/fn2H5xRnCs/TbzTd3NyYMmUKGzZs4PTp0zx69IjPP/+cadOmUaNGjQq4SqlOinr9rRjfjYltGxT7eImJiURHRxMdHc2tW7eIi8sLWopbfvdBSiZlt6Kt7B05coT79+8DeWtrNm3axMOHDwGoU6cOr732GgEBARiGwYYNGxymtP2w5Hx8fDxbt24FwN/fP980tpJ6ViYmLi6O5OTkEvWJKQ43Nzfq1q3rEMD4+Phw/fp13n33XQICAqpcACMi1Y+CHJEyYLFYaNasGU2aNOHMmTPs2rWLlJQUoqOj+fzzz2ndujUDBgygZs2a5NoM3t96qsgpPKNb1HeYCmG1Whk9ejQ+Pj4cPHiQxMRE5s2bx9SpU8ulnLU4j7KcVlbU6w8MZn19gEZZj+nUoUO+N6s2m43Y2FiHoCYlJaXAc/lSvAxBPV8PkorerVIkJCSwc+dOIG/66Llz58xF+C1btmTs2LG4u+c1dTxy5Ajnz58HoFmzZvTu3dvhWDabjTVr1phBx7hx4wpsgP20goIYewDz5MkTUlJSymwNjMViMTMs9n449evXN69jxYoVAAwYMCBfWfvs7GyuX79eJTM0IlI9KcgRKUNWq5UOHTrQpk0bjhw5wt69e8nOzubChQtcuHCBrl27YmnU1mGK0A/Zp/BERcfmmxphsVgYNGgQPj4+7Nixg/T0dObPn8/rr79Ow4YNy/fipFKU9bSyqOjYZ77+wEISbnyycReNNm7Ezc0NV1dXrFYrubm5ZGZmFtoawMfHh7CwMBo2bIjVauXoseNsTXLlUUbOM8vv9gwNYtu5El9KsZQmQMzNtREVdZF79+I5e/YI7u45WK0Ws3oaQJ8+fejXr585HfXOnTtmhiYgIIBx48blm6p6+PBhs9pajx49aNCgQb4gJj4+ntjYWB4/fkxCQgJpaWllEsRYLBb8/PwICAgodA2Mt7c3MTExLFiwAMgrNDB16lTc3d25dOmSeSx7ZTUREWemIEekHLi5udGrVy86depEVFQUhw4dAuDo0aOcP3YVKLrHxNYrdwt9Q/byyy/j4+PDunXryMnJYfHixUyaNIlWrVqV9aVIJSrptMbi+GFn9sLYp5plZ2eTnZ1drOekpqZy6dIlLl26hMViwcPDg2FWCwuxT6nM/1r+X+3qcP/eXfP5vr6+ZVY9sDQB4po13/H++4uIifl+kb2/vyuRkcG0bu2PxWJh/PjxDuWeU1NTzSyHxWJh8uTJZmEQexBz8+ZNduzYAeT9frh58yZ/+tOfSE9Pf+5+clarFW9vbwIDA6lZs2aBfWC8vLyK7LOVlpbGypUrzWNOmjTJzFI9ePDA3E9BjohUBQpyRMqRl5cXgwcPplu3buzevZszZ87gbWQV67n/fegaX56/W+gbspdeeglvb2+++uorDMNg5cqVjBw5kk6dOpX1ZUglKO20xqIU1Jm9IAVNNfP29sbd3R2LxWJmdTIzMwt8vmEYZGRk0IAHTCKVrdQjie/Lq/uTTSQPeHLgAksP5ZUf/vjjj7HZbLi6uuLh4YGnpyfe3t54e3vj5eVl/vP09Czwsaenp/lGvjQB4po13zFhwl/4YcyRlJTDypV3mDatGf/1X+8SHBxsfi83N5fly5eTmpoK5DUI3bFjB4mJiaSmphaY+crOzjbX9BTFarWafWBq1apFjRo18gUxT193aRmGwbp168yph8OHD3doXGoPcnx9fSutEpyISEkoyBGpAIGBgYwdO5aePXuybfsO1t7MJglXCvpk+2lFfWLfrFkz3nrrLZYuXUpWVhYbN24kNTWV3r17P/ebHqk8T548YdGeo6We1lgYm81GU7cc6ni48Cgzh4Jffwb+5NDS06BRw5aEh4cTHh5OvXr1CiznaxgGmZmZpKenk5GRQXp6uvnP/rhjejpj0tI5nZDOw7QsPLMzCMlOxJZb8JqdnJwccnJySE1NdShbXBzu7u64e3jy/6UGY2DNd43muqPV+2k8ujWNGzb8Z5bDyvvvL8oX4Dxt8+Z7DBu2n+TkJJKTk0lLS8uX5bL3lCkOq9WKh4cH/v7+BAYGUqtWrXyZGA8Pjwr5v3z48GGuXr0KQNu2benYsaPD9+2FF54O8EREnJmCHJEKVK9ePaZNfZO0Pcf5yd7rfP+ZfMHsb8h+uvlEoZ/Yh4WFMXPmTL744gtSU1P59ttvSUlJITIyUoFOFZScnMw//vEPTuX6AEVPRXvW9LOsrCzu3r1LdHQ0t2/f5s6dO+Tk5NAPP1YSSkGvPwsWPhrakaldpxTr9WOxWPD09CxW36YpP3icm5tLRkYGycnJfPfdd0yYMIHs7OwCA6XU1FTzcUZGRqFTvLKysric5UoCz+qvYiHe5sIHa7fRiDQAbt5MdZiiVpDY2FQ2bz5Ko0Y+RV6rfbqeu7u72WvGz8+P4cOHU7NmTTOAcQZ3795l+/btANSoUYMRI0Y43PvMzEzzGlTkRESqCgU5IpXgx/06U6dOXd7ZdJSH6UVVoLJwNyWTjzfs5L2RrxT4aXqdOnWYPXs2ixcvJj4+niNHjpCamsrYsWOdppmeFMxmsxETE8PVq1e5fPkysbGxQPErkz09/Sw1NdWsenb79m3z0/cfak0ycwJT+DrNn7is74OFsAru4O7i4oKPj4+57qNp06a4ubkV8ay87JE9GCooe/RNdAJcTi3yOE+XuE5JKd7POyUlB4vFgouLi9lfxmq10rVrVxo0aEBQUBABAQG4u7uTk5PDp59+CuQFPW+88YbDFDBnkJGRYa7DsVgsTJo0KV/w9fTUOgU5IlJVKMgRqSTjW4cypmV9/u+3Z/h/B64Uuf/+0+dwuXOJ0aNHEx4enu/7AQEBzJo1iyVLlvDgwQPOnz9PWloakydPNt9EinNISUnh2rVrXL16lWvXrpGVlX+dVgPS8KfwaY0WIMTXA98nMaw/fYhbt26RkJBQ4PlcXV0JCwujQYMGhIeHU79+fdzd3fl7GZamrkgWiyVvWpq7e75GmwBZIY/4f5f3FnmchrUCaVWnPllZWSQm3gFiinzOnDlT6devDfPmzTMD0ilTptC0adN8++7atYsnT54AMHDgQKcLcAzDYP369WaWZtiwYQUGMU8HOSo6ICJVhYIckUrkYrUwqGlwsYIcX3J48iSJhQsX0rFjRwYNGpRvAbC3tzdvvfUWy5cv5+bNm9y8eZNFixbxxhtv4O3tzO0Wqzebzca9e/fMbE1Bi85dXV1p2rQpzZo1o2nTpjx+/Bifc7f55Ym8N9KOk7MMDKBXyjW+2Xgy37G8vb1p2LChuZ6mbt26BfYuqa4d3CPCaxPq78XdpPRCS1fX9XLlv340FVcXK4cOHeLq1Wv4+7uSlFR4Ric4OJD+/duxceMGM8Dp27dvgQHO7du3OXz4MAChoaH06NGjLC6tTB09etQsDd26dWs6d+5c4H72ogOurq7UrFmzwsYnIvI8FOSIUyvLJojOqqg3ZPaF4A3+uXYA4OTJk1y8eJGhQ4fSrl07h/nz7u7uvP7666xdu5bz589z//59Pv/8c6ZNm1bgp96V4UW4r2lpaVy7do1r165x5cqVAquQ1apVixYtWtCsWTPCwsIcphZ6eHgw3mYjPSODDy8nEp/7fZDiTw6RPKA1eX1batas6RDUBAYGVqv1WCV9vbhYLXwY2YEJKw9hwTFAtD/r7yO7kpaawtq1a7l9+zZWq4XIyGBWrrxT6HEjIvz4+uvVZmDQpEkT+vbtm2+/zMxM1qxZkzcWFxfGjh3rdA0y79+/z5YtW4C8LPDIkSMLfc3Ypz3Wrl27Wr2uRKR6U5AjTqusmyA6q2e9IbM/iuQBP3yLlJGRwdq1azl58iQjRoygVq1a3x/TxYXx48fj4+PDkSNHiI+PZ968eUybNo3atWuX8xU9W3W9r4ZhcP/+fa5evcqVK1e4d+9evn2sVquZrWnWrJlD0JmcnGyup7l16xaPHj0yv/cecBtvUnDFlxx6BgfSuFE7wsPDCQsLq9ZZutK+Xsa1CmX1pJ4FPveDyA40z03g73//wqyOVqtWLVq3hkmTwti69b5DRqdePX/6969Bixa+ZoDj6+tbYMNPgO3bt5tTwCIjI50u+5GZmWn29QGYNGlSoYUjbDabmbUKCSm6v5eIiLNQkCNOqTyaIDqzwt6Qhfl785+d62O5lMD9+8kFPvfWrVt88skn9OvXj169epmNFC0WC5GRkfj4+LB7925SU1P5/PPPeeONNwgLC6uQ6/qh6nZf09PTuXHjhhnYpKfnL/lco0YNM1sTHh6Oq6srhmEQFxfH8ePHzaDG/qb4h9zc3AgPD2fgP9fThISEFGtxfnXwvK+Xca1CGd2ivkMWqGsdP7Zt3cLXZ8+a+/Xu3ZvOnTvz0Ucf0bq1Py1b+nH7dhqpqTm8885MunVrzM2bN1izZg02mw3IC2oTExPzBZhXr17lxIkTADRu3LjQKWCVxTAMNmzYQGJiIpAXhD0reImPjzcLLGg9johUJQpyxOkUpwniOxuP0d49i6ZNGlfw6MpPQW/I7NNyjIgOXLx4kR07dhS6uHzPnj2cPn2aUaNG0bBhQyAv0OnTpw8+Pj5s2rSJrKwsFi5cyJQpU2jWrFnFXRzl19yyIhmGwcOHD82gJiYm/0J1i8VC48aNad68Oc2aNaNGjRrk5uZy7949vvvuO7PyWWFNNH18fGjUqJE59ax27dpON9WpIpTV6+XpdUe3b99m7v8sJTk57wMDf39/xo8fT3h4OLt27TLLUlutFho18sEG5IbVYtaHC3HPSqUBNjOjmpqaymeffcaIESPo1KkTFouF9PR01q1bB+RNGx09erTTTe86ceIEFy5cAKBFixZ069btmfvb1+OAKquJSNWiIEecTlR0bJFNEB+kZ/P/Lf2an48dQuPGjfHx8XG6NxOlUdhCcIvFQuvWrWnRogUnTpxg9+7dBWYN4uPjWbx4MS+99BKDBw82P2Xu3Lkz3t7erFy5EsMwWLZsGWPGjOGll14q92uyK859LWlzy4qQmZlpZmsuX75MWlpavn38/f1p2bIlTZs2pWHDhuTm5hITE8PJkye5desWMTExhfZ1CQoKclhP4yzrpipbWb5ecnNz2b17NwcOHDC3tW/fnmHDhuHh4UFWVhZHjx4FwMvLi/T0dC7gx1bq8dulUUBtoDb+ZBPJA34e2Ytt27ZhGAabNm3ixq1bBLbvyTf7DpCQBg2A4cOH4+/v//w/iDL08OFDvvnmGyCvZ09xgrCni2Q4W3U4EZFnUZAjTudZzQ2floIra9euBcDT05M6depQt25d6tSpQ+3atalTp06+6mNVnYuLC127duWll17i4MGD7Nu3r8A3z6dPnzYLE7z00ktYLBZatWrFtGnT+PLLL8nJyWHdunWkpaXRs2fPChl7ce9rcfcrL4ZhEBsba2ZroqOjC9yvUaNGZrbG1dWVO3fucPXqVXbs2GGuYfghi8VC/fr1zaAmNDS02r1Gy0pZvV5iY2NZvXq1ucbJ3d2dUaNG0aZNG3Of06dPm5k1T09Pjqe7/rNZqqMk8raP8Qhizpw5LF++nIMJufz1XCZJ5+wBVENquhq0d61J+2JdQcXIyspixYoV5u+LSZMmFeu1Z8/kOFPzUhGR4lCQI07n6eaGz/J0s8SMjAxz0fbTvL29qVu3rkPwU7t27Sr/x9rd3Z1+/frRpUsX9u7dy7Fjx/Ltk5WVxfr16zlx4gSjRo0yMwYzZ87kiy++ID09ne3bt5OSksLAgQPLPRNW3Pta3P3KUlZWFjdv3jSzNSkpKfn28fX1pUWLFjRt2hQ/Pz/u379PdHQ0Bw8eNKc//ZC7uzsNGjQw+9MEBweba6bk2Z739WIYBkePHjUriAE0bNiQsWPHOmRYDMPg4MGDQN4b+dS0dLZiX7P2w/8TFizA/959kZvvD6de/1GsXHs037njcyxMXOU8a8zsGaf4+HgABg0aRGho8cZlr6ymogMiUtXor604neL0uKjpavDvE8dQq2YNHj16ZP578OCB+Ycc8sr42vvFPM3Pz88MfCBvSkbdunWr3IJuX19fhg8fTo8ePdi5c6dZ+elpd+7c4e9//zt9+vQhIiKCevXqMWvWLL744gsSExM5ePAgqampjBo1qlzXfhSnVHZ937x1SOXNMAweP35sZmtu3bpV4H4NGjSgadOm+Pv7k5iYSHR0NGvXri2weSfkvUl+eupZUFBQtZhGWRmK83sg1N+rwNdLSkoK69at4/r16+a2IUOG0L1793z34/Lly+Y6t27dujFv50GSKPz3gH2a3J5bj/jFrvMU1KjV2daYnTp1irP/LLTQtGnTYmdv09PTzYC/uOtxcm0GB249qtbl4UWkalCQI06nOD0uPhv3Mi2a530SGRQUROvWrc19cnJyePz4cb7g5+nqVcnJySQnJ3Pjxg3at2/PwoULsdlsBAQEUK9ePerWrWtOeatVq5ZD/xJnVKtWLV599VViYmLYunUrd+/ezbfPvn37zMIEjRs3ZtasWSxZsoRHjx5x+vRp0tLSmDhxYrkFesUplf2bno3L7Q1RdnY2t27dMrM1BVUz8/HxoUmTJvj7+5OdnU1MTAy7du0q9Ji1a9c2iwSEhYU53RqMquzp10t+eWHEB5Ed8r1eLl26xLp168zpZ0FBQUyYMKHQymD2LI4965bCkWKNb8+tqrHGLDY2lk2bNgF5H4qMHTu22IH30+txiltZrd2n27iW+P0UwupQHl5EqiYFOeKUiupx8aw/mK6uruYUtadlZWURFxdnBj4PHz7Mt3YiMTGRxMRELl++7LC9Vq1a1KtXzwx86tSpQ40aNZyu6lVoaCgzZ87kypUrbN++nSdPnjh8PzExkSVLltC2bVsiIyN56623+Oqrr4iOjubq1at88cUXvPbaa+W2TqSw++pPDiNc43ij8+gyPd+TJ0+4evUqV69e5caNGwWuXwoODqZGjRpm5bQzZ84UeCyr1UpoaKg59Sw0NLTQ3iJSNuyvlzlrDxOX/f29q+kC88Y7TgXLyspi69atnDx50tzWo0cPBgwYUOgUwXv37nHnTl7zzy5dupCamuowDbYsnLx2q9KCnOzsbFasWGGWvZ44cWKJeiqVpLLaxsv3cAHu/mCNVFUtDy8iVZ+CHHFazyqpXBru7u6EhIQ4zC3Pzs5m8+bNvPnmm2b25+HDhzx69Mihetnjx495/Pixw/GsVitBQUH5gp+AgIBKnaJksVjMviynTp1i586d+SqxnTt3jkuXLjF06FDeeOMNvv76ay5fvkxMTAwLFixg6tSp+Pn5lcv4nr6vd5PSyU2I5frub7DmwNGjR+ndu3epj52Tk8Pt27fNbE1B5bY9PDzMaWRPnjzh/v375rqDp3l6epoBjX09jbNn9Ioj12aU2f+pijCuVSgDQyP525otnL0Vgy85/HbaFMLDvn/DHBMTw9dff23ebx8fH8aNG0fjxs8uMW/P4gB0796dCxcu0IA0/MkmCVcKmopmnybXt0EQ/19U0eM/fWAvu11S6du3b4V/KLJ582bz99Yrr7xCeHh4iZ5vz+S4ubk9s+pfrs3gf+88w58b5f+es03dE5EXh4IccWqFlVQua/aKV09LS0tzmPJmD37s6zFsNpv5vae5urpSu3Ztc82P/Z+vr2+FBj9Wq5VOnTrRrl07Dh8+zN69e8nNzTW/n5OTw8aNGzl69CijR4/Gx8eHEydOEBcXx+eff87UqVOpVatWuYzt6ftqGOF8eu4IsbGxHDhwgO7du5doypy9qeGqVau4fv26+an10/z8/HB1dSUpKYnMzMwCp/MFBAQ4rKepVatWtVtPs+ZiTIHZUWefTuTv58s7w/vz97//HYAH9+8RHhaKzWYjKiqKPXv2mPu2bt2aESNGFJmNTExM5Pz58wC0bdsWf39/EhISsAJDecgK6vP9W/Q89q8+iOyAd+ytZwZDYOBPzv/P3n9HR3mn+b7op6qUc0Y5ZwECkUHkJJPBBNvYxja2u91rzvSEtffds+7de83se85eZ+4508c9PR2m28Y2NnYjAyYjQAKERAYhgnLOOZWkUklVqrp/lOuHiiohCSQQ8H7WspFKb7315vp9f8/zfB9CUHH58mVqamp48803cXR0fNrDMCbu379Pbm4uYGhK+jSTB/X19YAhVe1J90JWdcvPERwbi3+fLKl7EhISrxeSyJGQGAYHBwdCQ0NNxI9er6enp0eIm5aWFhobG2lpaRFdwbVarcXogI2NjYnNtfG/saSPPA3W1tYsXryYWbNmkZmZyc2bpjUHjY2N/Od//ifz589n4cKFXL16FaVSyRdffMF777034a5KMpmMpUuXcujQIdRqNXfu3GH+/PnDLj84OCjS64zRmunTp5sIHIVCgbW1NWq1IXXGkvvZlClTTESNk5PTxOzgJOFIQS3bU6+ZFfG/LOlEnp6e2Nra0t/fT01NDZGRkRw+fFgMxBUKBRs3bmT69OmjEqdD7wNjIb7RtGSusx59dy1p+JqYEBjTZZd42/HHQ+dJwZFUAoetMUuhEfuft7miooI//vGP7Nq1i6CgICaS1tZWjh8/DhieY9u2bRuzYNfpdLS2tgKGlM4nUdPZO6p1vmh7eAkJidcLSeRISIwBmUyGs7Mzzs7OREREiNf1ej1dXV1m4qe1tVUMvAcGBqitraW2ttZknfb29mbix9vbe9zrPRwcHHjjjTeYP38+586dM3Niu379Ovb29iQlJZGTk4NarWbfvn288847I6b9PCtxcXF4eHjQ3t5OdnY2s2fPNqmjUCqVlJaWUlxcTFlZmRCUgEgBUigU4lgPDg6aRK0UCgVBQUEm9TQ2NpZnnV81Ojo6OHrsOP+1wdGiS9nLkk4kk8kICgqitLSU0tJS8vPzxfkODAzkzTffxM3NbVTr6u/vF7brQUFBQsgbU7vc3NyI764hlm4CF67CyTdIpPbJ0PPFF1+g0+mIp5s/r4zhf96qMYmOeVrJeMutD+/Wbvr7DQX/PT099Pb2sm/fvmGd3sYDjUZDamqquP537NjxVNGjoc+uJ9XjFBQUkHHsJMhDR1zni7CHl5CQeH2RRI6ExDggk8lwc3PDzc2N6Oho8bpOp6Ojo0MIH6PT29D6nr6+PqqqqqiqqjJZp6Ojo0mPHx8fH7y8vJ55cO7u7s6uXbuor6/n9OnTJqlbfX195OTk4O3tTUtLC4ODg3z77bds377dpHnieNd1yOVylixZwtGjR+nt7eXu3bv4+PiIaI1xRvlJDBU19vb2JlEaX1/fSWcS8bTo9XoGBgZQq9X09fXR19cnfrb0Wnl5ORU40Ero8Ovk5UgnmjJlCqWlpSJCB4Zak0WLFo3p/N69e1eknS5atAh4NFEBBpFTU1ODHEh0tWbutEe1LFlZ2SJKu2jRIlYlJ/LRwulm94NeN8jhw4cpLCykp6cHV1dXenp6GBwc5OzZs1RXV7N58+Zx79l19uxZYaiybNkyszTc0TJaZ7Xy8nJCUOGMhuHS1Z5k9y0hISExUUgiR0JiApHL5Xh6euLp6UlcXJx4fXBwUBgdtLS00NTURGNjoxhkAfT29lJeXk55ebnJOl1cXPD19TVJefP09Bxzk0l/f3/27t1LWVkZJ06cMLFUftx17tChQ6hUKubMmTNhdR1hYWHY29vT19fH6dOnx/z+6dOni0iNu7v7pK6n0ev1aLXaEUWKWq1GpVKhUqnEa/39/RZd4p5Ezygf9ZM5nai0tNSk6a2joyPvvPPOmNMpdTod164ZbKmHTkqoVCoRIfTw8BDLazQa8XNjYyMXLlwADPbhy5YtA4apHZRbsWPHDo4fP869e/fo6urC1dUVhUJBe3s7BQUFNDQ08NZbb43annkk8vLyuHPnDmDo8bR48eKnXpfRWU0mk4l+Yo9TVlbG3bt3kQOraALMI75D65gma5RQQkLi1UQSORISLwCFQiEEylA0Gg2tra0i6tPU1ERTU5NJTYlSqUSpVFJcXGzyXnd3dzPx4+Hh8cQZbplMRmRkJH/3d39Hbm4uZ86cMRnUDeX06dOcrmzjX/K7x6WuQ6fTUVdXJxpyDp05Hgk/Pz8RqfHz8+PSpUusW7fuuTdzNQqVJ0VVjP/29vaaCBVLBgnPio2NDXZ2dtjb22Nvb4+DgwM6nY6KwupRvd9SOtGLdmPTaDSkp6eb1ZIlJCQ8Vb1YYWGhEPQLFy4UYnhoE2EvLy+TzwfDuT58+LB4fdu2bSNOLMjlcjZv3oydnR03btygq6sLJycnYmJiRD3Zn/70JzZv3syMGTPGvC9DaW9v5+jRo4Ahkrl9+/Znil42NjaiA9ocvDlU2GB27u/duyc+D2DndIPACXCyo1Rp2idnJNt/CQkJiYlAEjkSEpMIa2tr/Pz8zAp9+/v7aWlpMRM/KpVKLNPR0UFHRwcFBQXiNZlMJnr8DK33eTzSIZPJmDlzJtOmTSM7O5vMzEyzbdMBn+e3o7fQDX60dR29vb2UlZVRXFxMSUmJSBl6ElZWVgQHB4soTUBAgImYGU6UjZbBwcFRp34ZoypGoTI0RW68sLa2xtbWVogUBwcHIVyGCpjHf7azs7MYvdLr9UwvKubcyUIaewcs1uUMl070ot3YGhoaOHTokOj3ZGdnh62tLV1dXRYd8kbDlStXAIOV+FBhMdRu3Hh/GCNuAJcuXRJpkytWrBixb4wRmUzG2rVrsbOzIzMzk56eHqqqqliyZAmXL18G4NixY1RXV/PGG288lVDXarWkpqaKbd2+ffszG2mcr+3iGFEoe635/ZEbwM+CZe0MprRXiia5CoWCt99+m+DgYE6fPs2DX63lekPnS2NRLiEh8eoiiRwJiZcAW1tbAgMDCQw0HVj29fUJ4TNU/Bi7vev1elpbW81qWhQKhejxM1T8uLi4sGzZMubNm8eZM2d48OCBeE8VDiZOU49jqa5Dr9dTX19PcXExeXl5Zr2GLGFtbS2Ey8qVK1m4cOGoZqT7+vpQKpVPFClGoTI0HWyoicF4YWVlha2tLXZ2dkKojCRSjP+Od+2QTCYjLjaG/9A7sj31mpkT2HDpRC/Sjc2YUpaeni5ei4qKYvPmzVy6dInbt2/T0NCAVqsdU5pmTU2NcGObO3euiaAYGsnx8PBAoVCg1WrRaDTU1NQIceTv7y/qeEaLTCZj2bJl2Nvbk5aWhlqt5tq1a2J/urq6uHv3LnV1dezatcskXW40nD9/XkRCk5OTn9ko5Pu7ZXw7YJ6iVqfsY/uP19hJDfEYzEzee+89fH19xT37vGz/JSQkJEZCEjkSEi8x9vb2orjeiF6vp7e310z8NDc3i4HI4OCgEERDsba2Fk5vAQEBREZGcuXKFZqbm0dd13H4bC6uc7158OABNTU1I4oIDw8PwsLCCAoKEv04vvrqK/r7+7l586awDR4qWIxCRaVSodFoiIuL47e//e24poApFIonCpXhRIq9vf2kbBq6LS6QQzsXWIzMPJ5ONKjT8+u03BfixtbV1cWRI0eorjak2MlkMtavX09SUpJwWLt9+zY6nY7GxkYz4f8kjLU4MpmMOXPmmPzNKHKsra2xt7fHysoKrVZLf38/R44cAQzpZ9u2bXtqITpv3jzs7Ow4evQoGo2G48ePs2PHDnJzcykuLqa5uZk//vGPvPnmm8TGxo5qnQUFBSKVLzAwkOXLlz/VthkZ1On5x3Tj5IbpudX//P80fFnobs2e998btaOdhISExPNGEjkSEq8YMpkMJycnnJycCAt71IJcr9fT3d1tYnPd0NBAa2urSLvSaDTU1dWZpQLZ2NjgotE93gzEIvv+/SQe2z2QDzP4tbOzw9HRESsrK3Q6Hf39/Tx48EAUTA+lu7t7RBOCJw045XK5ECr29vY4OjqapHc9KaoyViOHl4FtcYFsjgkYscYmq7rFRAg9zkS5seXn53Py5EkhjH19fdm+fbtJU9qhPWZqampGLXKGpnJOmzYNZ2dns78DYtBuPP81NTUilW3NmjXP3CA3MTERW1tbDh48iF6vJzU1le3btxMSEsL58+fRarUcPHiQBQsWsHLlyicK5s7OTn766SfAEO3dsWPHM0cCs6pbaFRpsNzgFECGEmti126RBI6EhMSk5tX7FpeQkLCITCbDxcUFFxcXIiMjxet6vZ7Ozk4hfobaXBtdvAYGBghiAAdtPyqFDVhyLtPrQdmHqqCRqio7wsIs9+YwpomNhSdFVGxtbWltbWXbtm04OTmZRFWsra0ntcvai2A06USjdVkbLzc2Y3rl8ePHRTRu8eLFLF261GyQ7+bmJlz4ampqRCPPkbhx44b4eeHChWZ/N9b9GEWMMZXN+HpISAhz584dy24NS2xsLO+99x4HDhxAp9Nx6NAhNm3axAcffEBqaioqlYpr165RU1PDzp07zQQZGKKxqampIjr75ptv4uLi8szbNtpz2j4wNoc/CQkJieeNJHIkJF5zZDIZ7u7uuLu7ExMTI17X6XS0t7cL0VNZWcnMazVcCYgwCJqh4sFoaZyWC3ro6TFPURvq/DWagvp79+5x/fp1wNDQcGjz1aFoNBpOnz5NdHT0c3dXe1UZbdPG8WjuWFlZyZEjR8T5dXFxYfv27SYRm6HIZDKCg4MpKiqiqqoKvV4/opBVq9UiUhgaGmpm2azT6YSDobu7O4CJuLKysmLr1q3jKpjDw8P58MMP+fbbbxkYGOD48eOsWbOGzz77jEOHDlFVVUVtbS1/+MMf2Llzp0lUFiA9PV3061m4cCFRUVHPvE29vb08vJ7FcP1uhiI19pSQkJjsSCJHQkLCDGO9Q1lZGWVlZaJRaaSmlyup1yBlBrg6PHqDss8gcAoMaW5OTlYkJSWRlJSEp6cntra2Yx4gurq6cufOHTQaDZcuXRpW5EiMP5HWWlxlWrr0CiylLY1Hc0etVsvFixe5evWqSLGaNm0ab7zxxogNMoOCgigqKkKlUqFUKnF1dR122UGdni/Sr3FX64ATWnbNN4/8KJVKEbU0pmD19vaKv69bt+6Jn/G0BAYG8tFHH7F//35UKhXnzp2jr6+P9957j0uXLpGdnY1arWb//v2sWLGC5ORkZDIZxcXFYgLA39+fFStWjMv21NfXY1VfigtRKLFios69hISExPNAEjkSEhKAoSahvLycsrIyysvLRQrRUKKj3fE8WUjb56cgxBuc7KBHDVUtol7HxcWKkBAHcnJyyMnJITIykjlz5hAZGTmmegF7e3vmzZtHdnY2tbW1VFVVERISMl67K/EE7ubcYa2+gVQCeWQ1YGA8mju2tLRw6NAhmpubAUOUD2D9+vWjisY9XpcznAA5UlDLr8/kUtvdBxhqdzJOF/NbnYOJ0cJQZzV3d3cKCwvp6+sT2/asPWyexJQpU9i7dy/ffPMNSqWSrKws+vv7SUlJISgoiEOHDqHRaLhw4QJVVVWsXr1aGCHY2Niwc+fOcTG60Gq1XLl6jSociEfJdTyYiHMvISEh8byQRI6ExGtKX18flZWVlJWVUVpaSldXl9kyMpmMkJAQIiIiiIiIwNfXl9jYm2zf/m/oK1seW9aQtZaS4mdiOlBaWkppaSn29vYkJSUxY8YMk2aLT2L+/Plcu3aNwcFBLl26xJ49e55tpyVGhbOzM/F0s5Na0vA1sQ4PeIY+OXq9nps3b5KWliZeCwsLY+PGjaJnzGjw8/MTfWxqamqYOnWq2TJjscAe2iPH1tbWUCuDwTa9Z8CKP5y+xC/fWDZhA3sPDw/27t3L/v37aWtr4+bNm/T19bFlyxZ+9atfcfDgQRFZraioEHVL27ZtG5cIU39/P//4ZSrftlihJFS8LkNvcvykxp4SEhIvE5LIkZB4TRgcHKSmpoby8nJKS0tFPv/jeHt7ExUVRXh4OMHBwWYz69u2zeP//r+38y//chSl8lHtTWCgJ//2b+/x5pvzqa6u5uLFi8IGGAyi6sqVK1y5coWAgACSkpJISEh4YmqSo6Mjs2fP5saNG1RWVlJbWzsmy2CJsdPa2kpWVhYAjg6O2GhsQPNoqGtM6xor3d3dHD16lPLycvFaSkoKc+fOHXOvImtra3x9fWloaBCplEMZqwX20EjOlStXyB2wJY0QIe4O327l/yw+NaFNUF1cXPjoo4/49ttvaWxs5MGDB/T397Njxw727t3L2bNnhXU2GGqLoqOjn/lze3t7+Ye/HOTPXebNQ/U/x27+bl4Um2P9pcaeEhISLxWSyJGQeEXR6/W0tLSIuprKykphFT0UR0dHIWrCw8NxdLTsijaUNWvi6erKp6pKxfz5S4mNDWXx4jgUCkM6WmhoKB9++CGNjY1kZ2eTl5dn8n6jTfWpU6eYNm0aM2fOJDg42GLdzsKFC7l58yZ6vZ7Lly/zzjvvPOURkRgJnU7HTz/9hE6nIx9nflR5PDaXD/Xd6jE3Ay0oKODYsWMiBdLb25vt27fj4/P09tPBwcE0NDSI/k9DxfhYLbCNIsfGxoajxcY0PVOeRxNUBwcHPvjgA3744QeqqqooLi7mu+++4+233yYmJobbt2+LZSsrKzl8+DCbNm0S6X5jpbOzk6++/oa/dhnrayzX4BwuqOX/XpMoCRwJCYmXCknkSEi8QnR3d1NeXi6iNSqVymwZKysrwsPDiYiIIDw8HE9PzzGbAshkMuRyGWFhjmzenISfn5/F5Yx9TlauXMnVq1dNBmlgGFTfu3ePe/fu4erqyqxZs0hMTDSxwnVxcSEpKYk7d+5QUlJCY2Mjvr6+Y9peidFx9epV6uvr0QEXbILRW7AJHksz0P7+ftLS0sjNzRWvLViwgBUrVjxzH6KgoCBu3LiBXq+nvr7epF5rrBbYRpvo/gENaRjXY94IcyKboBqxtbXl3XffJTU1lZKSEqqqqti3b59IJ7WyssLR0ZGuri7y8vJoaGjgrbfewtt7bEYAzc3N7N+/n4e9epN0xMeZqJ5IEhISEhONJHIkJF5iBgYGqKqqEnU1bW1tFpcLCAggMjKS8PBwAgICnrlQeaiBwGjSl9zd3Vm/fj1Lly7lxo0bXL9+3SxFqauriwsXLnDhwgUiIiJISkoiOjoaKysrkpOThQXw5cuX2blz5zNtv4Q5TU1NZGRkANDp4kercvjzOpqBb01NDYcPHxaDcycnJ7Zt22Zmhfy0BAYGirqZr24Vs0xvL9KpxmqBbYzkVGI/KQb8VlZW7Nq1i6NHj/Lw4UNh0ACwdetWIiMjOXbsGPn5+bS3t/OnP/2JLVu2MG3atCeud1CnJ6u6hYeVddy7kom/tpceRtdbZ7x6IklISEg8LySRIyHxEqHT6WhoaBCipqamxuJy7u7uREZGEhERQWho6IiWvGNlaOTHWCMwGpycnFi5ciWLFi3izp07XL161WK0yZhiZ2Njw/QZM+ifEoYyeBpt1WXoCgpoaWkZ88y1xPAMDg4Kxy6A2DnzIaNoxPdZGvgODg5y+fJlEyOB+Ph4NmzYgL29/fhsMJBR381vZdF06a0gr5N/ycsk8GdThM0xAXhZy2jV6BjJBlmj0QgntZ5RfiU+jwG/QqFg27ZttLW1ifo5a2tr/Pz8sLGxYfv27dy+fZvTp0+j0+k4cuQI1dXVrF271mKU7EhBLb9Oyx2SxheACz4k0WG2rCWkvjgSEhIvG5LIkZCY5LS3t5tYOw8MDJgtY2dnJyI14eHhE9LTYyhjjeQ8jp2dHYsWLWLevHncu3ePrKwsi+5uuQO2/J83O1DS8/MrobigQX3qMv/6wZtPu/kSj5GZmSmiBcuXL0cX6AuMLHIeH/i2tbVx+PBhMSi3srJi06ZNI0YYxsoj5zTTiKSxbubAllms0tbwVwIYyQa5ovyRcYGHtRw0I3/+8xrwV1RUmBiEaDQavvjiC/bs2YOPjw9z5szB39+fgwcP0t3dze3bt6mtrWXXrl2i3w8M7zSnxIpLeONiLaNb83j1lQGpL46EhMTLiiRyJCQmGX19fSZ1NUql0mwZmUxGaGiosHaeMmXKuHZjH4mnjeQ8jpWVFbNmzWLmzJkUFBSQlZVFU1MTAPk4WywAV2LF/69qEJ/T2fx9ysKn/mwJA3V1dcJNzdfXl+TkZPTICHSxp07ZZ3HgC+DtYEOdso+TD8uhuhBnR0cuX74sroegoCC2bdtmMtgeD0yd04apmzlzl1/qlexEb2aBPdQGWafTmdhZf7BiHmeutQ67389zwN/T08OhQ4cAw30yd+5cEfn88ssvef/99wkICCAgIIBf/vKXHDlyhLKyMhobG/njH//Im2++SXR09BOd5kCGDLC2sgbNADIwWU7qiyMhIWFMc23oVuPnbPdSuSxKIkdC4gWj1WpNrJ0bGxstLufj42Ni7fyshdvPwrNGciytLyEhgfj4eL7++msqq6tJw2gu8PjD1DAU+5+3qtHl32BW0sxn/vzXFY1GI9LUZDIZ27ZtE+f2tykz2J56zWzga5QSLaoB3v3pJgAuaEihkHgMAmfVqlUsWLBgTM1fR8tonNOa+3VU4cAMm35iB0qot3LFLzqOZbNmsDTUR3xBX7t2zaSOLSEujt+6dlvc7+c54NfpdBw+fFik0W3evJmpU6fi6enJiRMnGBgYYN++fbz77ruEhYXh4ODA7t27yc7O5sKFCwwMDPDDDz+QnJwMoQkjHq+2vgH+ZVk8f8mpMFlW6osjIfF6Y57mikgLfhmeC5LIkZB4zuj1epqbm02snS1FQ5ydnU1S0BwcHF7A1lpmvCI5lta7efNmfnfiAsrKJ4knGUqsyevVob5yhenTp3PgwAGSkpKIi4sz6+0jYZkLFy4IZ7HVq1eb1Dltiwvk0M4FZl9wllBiRSqB7KSWf92zjdDQ0Anb5tHWw/RghUbThxxYnxDKli1rTf7e3NxMenq6+F0ul+Ps7My2OFeL+/08B/zZ2dlUVlYCkJSUJJqdJiUlYWdnx48//ohOp2P//v3s2rWL2NhYZDIZixcvJjAwkNTUVNRqNdnZ2ZTcKgZGbr4b5eFM5a/Xv7QzthISEuPLWBoqT1YkkSMh8RxQKpUmKWjGGdqhWFtbm1g7e3h4PNcUtLEw3pGcoXh4eBCdNBcqb4y4rM7BFdSGQW9NTQ1VVVWcOHGCxMREZs6cib+//6Q9hi+aqqoqrl+/DhicyubNm2e2zLa4QDbHBJBV3UJpUzt/fzaXHr2l6Iwh7pGGL98FBk3odo+2HsYJrbg2Y2JiTP42ODjI4cOHTV5zdnYW1/XQ/X7eA/6qqiouXrwIgJeXFykpKSZ/j4+PZ/fu3Xz//ffo9XoOHjzIli1bSExMBCAsLIzPPvuM1NRU6urqsOrvYTQix8/ZDoVcJtlES0hIjLmh8mRFEjkSEhNAf3+/ibWzcbb8cQIDA4ULmr+//4Sk90wEExXJMTLagaxVXzd7P97LrVu38PDwoLW1Fa1Wy507d7hz5w6enp4kJSUxffp0nJzMO7q/rgwMDPDTTz8BBhevrVu3DnvtDR349uifZD1uiK5dqW2b0IHy4mDvEeqF9LigJYRHrn01NTWEhoYKd7fLly8LowUHBwdUKhUeHh4ma3kRA/7e3l5+/PFHwDCRsGvXLotRycjISD744AO+++47NBoNR48eRa1WC6Fqa2sr0llDUOGCBiVWWHKaMx6vWV4jNwGWkJB49RkYGOBcce2YGipPViSRIyExDuh0Ourq6kSkpra21uJyHh4eQtSEhISMu7Xz82IiIzkw8kBWBjijIUjfw8OHDwH45JNPaGlp4e7du9y/fx+NRkNbWxvnz5/n/PnzxMTEMHPmTKKiol4aMTlRnD9/XrjZpaSkmA3wLTHWBpsThUIuG6FeCFJoxEouFwL82rVr3Lp1i+TkZIKDg4W9tY+Pj+iR4+7uPqHbPRJ6vZ6ffvqJ3t5eADZt2oSX1/ARmODgYD766CO++eYb1Go1aWlp9Pf3k5SUxIEDB0RtnxzD8TCYeJg6zQ09XpN4MlZCQuI5odfr+eKLL7jQ0g8WjH8eZ7L3z5JEjoTEU6DX682snTUac+9Ze3t7k7oaF5fRNd6b7Ex0JOdJA1njJ7/rqUXeBnfu3CE+Ph6ZTCbcptauXUthYSE5OTmitqGoqIiioiLs7e2ZOXMmM2bMeC177ZSVlXH79m3AkNo0a9asUb1vrA02J5Lh6oVc0JJCI2/GBVJQUADAlClTaGpqQqvVcunSJZNrd/369Xz11VfAixc5V69epaysDIDExESRfvYkfH19+fjjj/n666/p6enh4sWLXL16lf7+fpPl3k4Mx7WilYNKBxOnOePxiqebixcvsn79eim9U2JceZmduV4nlEolhYWFFPzch86J0dUAT/b+WZLIkZAYJSqVyqSupru722wZuVxOaGioEDY+Pj6v5KBhoiM5MPxA1lgAPsN2gG+/rUCr1Zq919rammnTpjFt2jQ6OzvJzc0lJyeH7u5u+vr6uHr1KlevXsXf359Zs2aRkJDw0kbVxoJarebo0aOA4Rht3rx51NfnaKJrz7OfytC6mS9Tj6Do6yYEFXIwSU18++23UalUZGRkUFZWJq5Xa2triooe9QJ6kSKnpqZGmCB4eHiwbt26Ub/X09OTjz/+mH379qFUKs0Ezpo1a1iwYAHrNRreOH2aY7nF9GCFE1reXzqH8lIVdXXdIr1zwYIF47pvEq8vL7sz16uMXq+ntbWVwsJC8vPzzVxdR0pzfVn6Z0kiR0JiGLRaLdXV1ULUGPu3PI6vr68QNUFBQS/U2vl5MdGRHCNPKgDX6/X4+fmJ8zIwMGCxfsHNzY1ly5axdOlSKioquHv3rkhxq6+vp76+nlOnTjF16lRmzpxJSEjIKylMAc6cOUNPj6Gx6vr168fUNHY00bXn3U/FWDfTHOpBQYHhOoiMjBTRO29vb1xdXXF1dSU5OVlESsBgn3316lXx+0Q30B2Ovr4+UlNTgUd1ODY2NmNaR3d3N2q1edrI1q1bmT59OmAQdVs3byYsJJdjx44BkJWZSUJCAt3d3SiVSs6dO4e7uzuxsbHPuFcSrzuvgjPXq4Zer6euro7CwkLy8vLo7Ow0W8bf35/4+HiKi4t5v0/H71sMz/PJ8Lx/Gl790ZiExCjR6/U0NTWZWDtbilK4uLiIuprQ0NBJZe38vHgekRwjwxWAy2Qyli1bxsGDBwFD2tqSJUuGXY9MJhNpg+vWrePhw4fk5OTQ2NiITqfj/v373L9/H1dXV5KSkpgxY8Yrk14IUFhYyP379wGIjo4Wg9+xMFx0zd/Jln9fl/TCBi1Do3nh4eGcO3cOQAzW+/v7hdGCXC5n8+bNXL9+nYaGBvG+Q4cOsWrVKhISEp6byDXW4QwVnj4+YyviLS0tFU5rj5Obm0tsbKyJaJoxYwZ+fn789a9/pbOzk7y8PNzd3bG2tkaj0fDjjz/y8ccf4+fn92w7J/Ha8qo4c70KDA4OUlFRIVLRVCqV2TLh4eHExcURExODs7MzAIsWLeJDYPkw0biXpX+WJHIkXmuUSiUAx48fp7S01OJsqI2NjZm18+vO84rkjERUVJQozr5x4wbz588f1Sy4vb09c+bMYc6cOTQ1NXH37l1yc3Pp7++nq6uLixcvcvHiRcLDw0lKSiImJualjtCpVCqOHz8OGJy3Nm7c+NQDeWN07cidAg6ePosTWrZExbDlBQqc6upq8Xt9fb342WgdffbsWXGvr1u3junTpzNt2jQOHDggojtdXV0cPnyYy5cvs3LlSqKjoydc7Ny4cYOSkhIAEUkcCw8fPjSzwnZ0dMTd3Z3a2loqKirYv38/u3fvFs5yYKhT+sUvfsGxY8coLCyko6NDTFzodDoOHDjAp59++kqJfInnR0Zp/SvhzPUyU1hYKOpQH0/pVigUxMTEEBcXR2RkJHZ2w9fVvEg7/fHg5f3WlpB4Cvr7+6msrBTWzl1dXUyfPp38/HyTwXpQUJCI1vj5+b32blyP8zwjOU9CJpORnJxMeXk5arWanJwc5s+fP6Z1TJkyhZSUFFavXk1xcTE5OTmUlpYCiBosGxsbZsyYwcyZM/H19Z2IXZkw9Ho9J0+eFL2ZNm3a9Mx22gq5jB1z4tGW3qO4uJh7d++yeNEiPD09x2OTx8T9+/dN6lCMqWoODg74+/tTUlLC3bt3AYiIiCApKQkwXDvGe97Z2RkrKys6OjpoaWnhr3/9K35+fqxatYqwsLAJETt1dXWcPXsWMNQDbdiwYUyfc/PmTc6cOWPympeXF++99x6Ojo4cOXKE/Px86urq+Oqrr3j//fdNzrudnR07d+7kxo0bnD171uT519vby4EDB9i7d++YU+ckXm/279/P8Yo2XgVnrpeJnp4eiouLKSgowMnJiaNHj5rc0/b29sTFxREbG0tYWNiYJu1e5v5ZksiReKUZHBw0sXauq6sz+btxsG60dg4PDyckJET6Yh+ByRLJAUPqVXl5OWDoFD979uynirooFAri4uKIi4uju7ube/fukZOTQ0dHBwMDA9y8eZObN2/i4+PDrFmzmDZtmsns+GQlLy9POI0lJCQQHx8/buteuXIlxcXFAFy8eJHt27eP27pHg16vF3U18p8to42pXzExMSZGCzY2NmZGC8b+Vf7+/uzcuZP79+9z8eJFlEolDQ0NfPvttwQFBbFq1SqCg4PHbbvVarWow5HJZOzcuXPUxhd6vZ5Lly4JG2wjwcHBvP3222JW9s0338TOzo6cnBxaWlr48ssv2bNnD25ubuI9MpmM+fPnExAQwMGDB4V9NUBzczOHDh3irbfekiZ5JoBX0XWss7OTioqKV8aZa7LT0dFBQUEBBQUFom2FXC4Xqciurq7Ex8cTGxtLYGDga3kfSyJH4pVCr9fT1tZmYu1syX3LwcGByMhIQkNDqa6u5tNPP7VYtC5hmckSyQHTbent7SU3N5fZs2c/0zqdnZ1JTk5m0aJF1NTUiN47Op2O5uZmzpw5w5kzZ4iPj2fmzJmEh4dPyi+Q7u5uTp48CRiu+fXr14/r+n18fJg6dSoPHz4kLy+PxYsXM2XKlHH9jCdRVlZGW1sbYBjkG6M4YBA5J0+eFDnoGzZsEPnmYBDnRodEd3d35HI5M2bMYNq0aeTk5HDp0iVUKhU1NTV89dVXREREsGLFCvz9/Z9pm/V6PceOHRPpc2+88caoo4M6nY4zZ84IC3Aj8fHxbN261UTcy+VyNmzYgJ2dHVevXqWzs5MvvviCDz74wKz/TlBQEJ999hmHDx+moqJCvF5SUsK5c+dISUl52t2VsMCr5jqm1+u5ffu2qIV7VZy5Jht6vZ7GxkbhiNba2mq2jLGmb+/evfj5+b2yJjqjRRI5Ei89vb29JtbOxpncoSgUChNrZ29vb2QyGRqNxiSfX2J0TKZIjhFnZ2e6urq4fPkyM2fORKFQPPM6ZTIZwcHBBAcH88Ybb5CXl0dOTo6YNcvPzyc/Px8nJyfRe2ey1Gzp9XqOHz8uUrk2b9485sjTaGably9fLtzqMjIyeOedd8ZnB0bBlStXALCysmL27NlC5MjlctRqNfn5+QDExcUxdepUk/d2d3eLa3dodEOhUDBnzhxmzJjB7du3yczMpL+/XxiSxMbGsnz58jEbBBi5desWhYWFYrtGK8gHBwdFCtpQFixYwOrVqy0OZmQyGatXr8be3p6MjAx6e3v58ssvef/9982MBRwdHXn33XfJzMw0iRLduHEDDw8P5s6dO9ZdlbDAq+Y61tzczLFjx0xq4WKjo9lR38O+Hjfg5XXmmgzodDqqq6uFsLHUuiI4OJj4+HhiYmJwdHTk9OnTYozzuiOJHImXDqMwMdbVtLS0WFzOz8/PxNp5PAa9EgYmUyTHyKJFizh9+jTd3d08ePCAGTNmjOv6bWxsmDlzJjNnzqStrU303lGpVPT09JCVlUVWVhZBQUHMmjWLuLi4F5r2mJubK2qLZsyYQXR09JjeP9rZZg8PD5KSksjJyaGkpITa2loCAyd+kNbU1CREzaxZswgJCRF/c3Z2FvUq9vb2FutdOjo6xM+WeuRYW1uzYMECkpKSuHHjBllZWWi1WgoLCyksLGTq1KksX758TKK2oaFBbJerqyubNm0a1UBkYGCAv/71ryZRFoC1a9eOqgYtOTkZW1tbTp8+jVqtZt++fbz77rsmxwwM9/Xy5csJCgrixx9/ZGBgADBYjzs4OJgJRYmx8Sq5jmk0GjIzM8VEAxiiCJs3b8bf35+3gXUvuTPXi0Kj0VBeXk5BQQGFhYVmva9kMhlRUVHExcURHR1t4vBqqSn564wkciQmPcYQrXEmtaqqyuLA2tXV1cTa+WWol3hZmYyRnKlTp3L58mV6enrIzMxk+vTpE5ZC5unpycqVK1m+fDllZWXk5OSI2fmamhpqamo4efIk06dPZ+bMmQQEBDzXWbXOzk5Onz4NGAb8Y003Guts89KlS8nNzUWn05GRkcGePXuedRdG5Pr16+LnefPmmYgWlUolvuw3b95s0eZ9JJFjxNbWliVLljBnzhzRRFan0/Hw4UMePnxIUlISS5YsGbHPTn9/v6jDAdi5c+cTXY2G7st3331nYncNsGPHjjHVV82ZMwc7OzuOHDmCVqvl66+/5p133iEqKsps2cjISH71q19x4MABMYl0+PBhdDrdU1mPSxjIqm55JVzHysrKOHHiBF1dXcCjiOG8efNMnrkvuzPX86Svr4/i4mIKCwspLi42+161sbEhNjaWuLg4wsPDpbrhUSKJHIlJSWdnp6irKSsrM5vJAMNNHxERIaydX2TH8teNyRjJsbKyIjk5mbS0NDo7O8nPz5/wmWe5XE5UVBRRUVGoVCru37/PnTt3aG1tRavVkpOTQ05Ojoh2JCYmPrOz2Ujo9XqOHj0qatG2bt066qJ2eLrZZhcXF+bOncv169eprKykvLyc8PDwZ9+ZYejp6eHevXuAoReOu7s7d+7cEX83CpzExERhI/04QxvhDU1XGw57e3tWrlzJ/PnzycrK4saNGwDiHM+bN4/k5GSL59eYOmj8zJSUlFHV9nR1dfHNN9+YCDJra2t2795tFoUZDdOmTcPW1pYffvgBgO+//54333zT4n3i6urKL37xC1JTU4W5xE8//URnZyeLFy+WUmGegtG6iU1W17He3l7S0tJEeioYBPH69euHvYdeZmeuiUapVIr+NUPrCY04OTkJ44Dg4GApG+UpkESOxKRArVZTUVEh6mosdeIFCAkJEcLG19d3UhZ7vw5MxkgOQFJSEpmZmfT19ZH5czf35zUYc3BwYP78+cybN4+Ghgbu3r3LvXv30Gg0tLe3k56eTnp6OlFRUSQlJREVFTUhX1q3bt2iqqoKgLlz5xIWFjam9z/tbHNycjK3b99Gq9WSnp7OJ598MmHH/ubNm0JcL1y4EEBE0ow4ODjwxhtvDLsO4zPG3t5+TKYjjo6OpKSksHDhQi5fvizE1Y0bN7h58yYLFy5k0aJFJpHknJwcUUsTHR09qvqW1tZWvv76axPHMycnJ957772nrgcyfv6ePXv47rvvGBwc5PDhw6jVaou1QQqFgrfffptjx46Rm5sLGFz06uvrxyyeJWCK4+iO12RzHdPr9eTm5pKWliZSGO3t7Vm/fj3x8fGS4B0ler2e1tZWCgoKyM/Pp6mpyWwZT09PIWwk44BnRxI5Ei+EwcFBamtrhagZWrQ4FC8vL6KiooS182gGI6+iNedkYzJGcsAwy71o0SLS09NpbW2lqKhIdL1/XshkMvz9/fH392ft2rUUFhZy584dMVNXUlJCSUkJ9vb2oveOt/f4uAy1tbWJ3itubm6sWrVqzOsY7SxyRWuXichxdHQUA/+GhgaKi4uHjaI8CxqNhps3bwIG6+egoCA6OjqEy5oRY9RiOIzLP20E2MXFhQ0bNrBo0SIuXbrE/fv30ev1XLlyhRs3bpCcnMz8+fPp7Ozk1KlTgCF1cMuWLSMOXOrq6ti/f78YUAJ4e3vz7rvvjkuDztDQUD766CP2799Pf38/p06dQq1Wk5ycbHH5zZs3Mzg4yIMHDwAoKiriP//zP9m1a9dzddN72XForXrpXMdaW1s5fvw4NTU14rVZs2axatWqUaVbvu7o9Xpqa2uFcYClCdyAgADRw+ZF9Bp7lZFEjsRzwTiDYUw/q6ystGjt7OjoKMwCwsPDx5za86pZc05WJmskB2D27NlkZWXR399PZmYmMTExL2w2zMrKiqlTpzJ16lQ6OztF7x2lUklfXx/Xrl3j2rVr+Pn5MWvWLBISEp564KDT6Thy5Ig4H9u2bXsqW/TRziKX37sDs03rORYsWMCNGzfo7+8nPT2d6OjocT/29+7dE+mrxihOUVGR2XLG5qfDYUwBe1Y3PHd3d7Zu3UpycjIXL16koKAArVbLpUuXuHr1KgqFQkwE7Ny5c8RawbKyMg4cOGAyeRASEsJbb701roNKf39/9u7dyzfffENvby8ZGRn09fWxatUqi+dsy5YtwskSDMfvP//zP9m8eTOJiYnjtl2vMs6OjqTQSCqBPEr+NDDZXMe0Wi3Z2dlkZmaK1zw9Pdm8eTNBQUEvcMsmP4ODg1RUVIhUNKON/VDCw8OFI9pEpzC/zkgiR2LC6OnpMbF2Hpp2YcTKysrE2tnLy+upB0WvmjXnZGayRnLAUCi+YMECLl26JAwrIiMjX/Rm4ebmxtKlS1myZAmVlZXk5OSQl5eHXq+noaGBkydPcurUKaZOnUpSUhIhISFjuheuXbsmIqILFy586oHI4mBvAl3sqVP2WazLAT0uaJHXltDR0WESCbGzsyM5OZmMjAxaW1t5+PAh06ZNe6rtsPjJQ5p/uri4EBcXB2BSI2BlZYVWqxUpe5bQaDRi4DFetXze3t7s3LmThoYGLly4QGlpqUkkJiYmZsR+OHl5eRw6dMjktalTp7Jly5YJSW309vbm448/5uuvv6arq4urV6/S39/PunXrzFKB5XI5O3fu5IsvvhD9OYz1X9XV1U8VNXydGBwcJCsri3i62UktZ2V+dOkfDcEmk+tYTU0Np06dEs1yAVasWMHChQulupBh6O/vp7S0lIKCAoqKiswmca2srIiOjiYuLo6oqCgp1fM5IYkciXFDo9FQVVUlrJ0tNaoCwwyi0QUtICBgXB6ar5I158vAZI7kgKEW5cqVK2g0Gi5dukRERMSkyW2WyWSEhYURFhbG+vXrefjwITk5OTQ0NKDX63nw4AEPHjzAxcWFpKQkZsyYMaJzV3NzM+np6YBhtnX58uVPvX0KuYzfpsxge+o1ZPDYPWX4LYVG5MAf/vAHtm7dauLyNXfuXK5du4ZKpeLChQvEx8eP28CopKRERGAWLFiAXC5HpVJRV1cnlpk2bRp3796lq6uL3t5eHB0dzdZjdIWC8RM5Rvz8/Ni9ezcXLlwgKytLvF5UVMS///u/s3z5chITE81ExK1bt4QjnpGFCxcOG1kZL9zc3Pj444/55ptvaG1t5c6dO/T19bFt2zaz82Zra8u7777Ln//8Z5PZ6ZycHOrq6sx670g8IiMjQ9Rg/GLxDL5ftnzSpVWr1YZU1QMHDojnemhoKBs3bpw0/b8mEz09PRQVFVFQUEBZWZnZ3+3t7UUaWlhYmEmzXonng3TEJZ4anU5HQ0ODcEEbbubUzc3NxNp5IvJ4R1ssfSa/gg1TJ8716XVhMkdywPDlMm/ePLKzs6mrq6OqqorQ0NAXvVlm2NnZMXv2bGbPnk1zczN3794lNzcXtVqNUqnk0qVLXLp0ibCwMJKSkoiNjTX7ojQWjxt58803n/nLdFtcIId2LjBL/QxwsuO/zwkmQe5PRkYGWq2WH3/8kdmzZ7N27VqsrKywsbFh6dKlnDlzRqToJSUlPdP2GDH25LC2tmbmzJkAJsIgPj6ehIQE7t69C0Btba3FuqChbmWjcVYbKy0tLWJb7ezscHV1pampie7ubo4fP05WVhYrVqwgISEBgMuXL3Pp0iWTdaSkpDBv3rxx3zZLODk58dFHH/Hdd99RX19Pfn4+/f397Nq1yyzl0dXVld27d/PFF1+g1+uRyWTo9XpaWlrw8/OjpKRkTNbWrwOlpaVcu3YNMEzyLVu2DPkkch3T6/U8fPiQtLQ00U/L1taWdevWMW3atEkzQTQZaG9vF/U1QydXjLi6upKQkEBsbCyBgYHSsXvBSCJHYkx0dHSYWDsPTccwYmtra2LtPBGDiMcZbbH0X0+e5Y24T6WQ+zMy2SM5APPnz+fatWsMDg6SmZk5KUXOUHx8fFi7di2rVq2ipKRENNcEqKiooKKiAhsbGxITE5k5cya+vr7IZDKysrJobm4GYNmyZeM2mz5Sj4vg4GBSU1Pp7e3l9u3bVFdXs3PnTjw9PZk1axbZ2dl0d3dz8eJFpk+f/szCq6GhgerqasBQd2Vra0tDQwN5eXlimU2bNpmI7pqaGjORMzAwwP3798Xv4z3potFoOHjwoLgv3n77bYKCgigpKSEjI4Pm5mY6Ojo4fPgwly5dwt3dXTRtNTLWHjjjgb29PXv27BFNR8vKyti/fz+7d+82O0b+/v7s2LGD1NRU9Ho9dnZ24rvg8OHD1NXVsXLlSsn9EoPt8pEjRwCDON+xY8ekOi7t7e2cPHmSiooKsV3Tpk1jzZo1FvtLvW4Y+/QVFhaSl5dnZnACMGXKFOGI5u3tLQmbSYQkciSeSF9fn4m189A0DyMymczM2vl53+SjLZa26u/hX//1X0VTrafpNSFhKnImYyQHDCYWs2fP5saNG1RWVlJbW0tg4IvPdx8JhUJBbGwssbGxdHd3i947HR0dDAwMcOvWLW7duoW3tzeRkZFihnjKlCnDumM99bY8YbY5ODiYX/3qV/z000+UlpbS3NzMH//4R7Zs2cLUqVNZsWIFx44do6enh9u3bzN//vxn2hbjfoKh+adWqzWJYIWGhoo8d09PT9ra2oQoGkpmVhYnHpbTgwtOaPn+h7/yD3//d+P2zDp9+rQYCK1YsYLg4GDAYN0cFRVFfn4+Fy5coL29nba2NpNBk7W1Ne+++654z/PGxsaGd955h8OHD1NYWEhtbS1ff/017733nlnaX1xcHKtWrSI9PR21Wm1ia3316lVqamrYsWMHzs7Oz3s3Jg16vZ6ffvpJmGBs2rTpuUz6jYbBwUGuXbtGRkaGeM24bevXr38q05JXBZ1OR3V1tbB67unpMVsmJCREpKKNlE4s8eKQRI6ECYODg9TU1AhR83iXbSPe3t7C2jk4OPiFPxBHWywdggqNBlH3YHS/ys/PJzY2VioGHAPGNJXJGskBWLRokeipkpmZye7du1/0Jo0JZ2dnFi1axMKFC6mtreXu3bvcv3+fwcFBWlpaRDd6MEQ3nvfkgoODA++88w7Xrl3j/PnzInWusrKS1atXk5WVRXt7O5mZmSQlJT11l26lUinsixMSEnB1deX8+fMmAmH69Oni55CQENra2qivr2dwcFBEbo8U1PKr20qaCBXLuvfqCCusG5eC7/v374t+MmFhYWaiUyaTkZCQQEREBF9++aVZ3aLRPvZF2uBbWVmxY8cOjh8/zr1792hqamLfvn28//77ZoO5hQsX0tbWxt27d2ltbcXf35/AwECqq6upqanhj3/8Izt27Bhzr6ZXhRs3bohajcTExAlvTjxaamtrOXbsmMn1t2TJEubPn8+5c+de4Ja9ODQaDWVlZRQWFlJYWGjWgFwmk5kYB0hRrpcDSeS85hhzqY3pZxUVFRYHrU5OTqKuJiwszGIx74vkScXSht9l/K/F0WyKWkx+fj55eXl0d3eLfT1+/DhHjx4lMjJS2DpKD7EnYxQ5kzWSAwaRMGvWLG7fvk1paSmNjY0jOlxNRmQyGUFBQQQFBZGSkkJ+fj4ZGRkmM4ynTp3i0qVLzJw5k5kzZz63QmGZTMbChQtF+lp3dzd37tyhurqaOXPmcPbsWdRqNTdu3GDx4sVP9RnGvjhgGFjX1NQIlzUjxloCgKCgIHJychgcHKSpqQl/f/9h3Rc7dfJxcV9sa2vj+PHjgEH8vfnmmxZFZ19fH/v37zcZYBrvpcbGRv7LV4dIVwTSPvjovc/bBl8ul7N582bs7Oy4ceMG7e3tfPnll+zZs8ekj4dMJmP9+vW0t7eLPiphYWEEBQVx5coVsa8rVqwgOTn5tUrjaWhoMOlZtW7duhe8RQZjgYyMDG7fvi1eCwwMZNOmTXh7e6PRaF7g1j1/+vr6KC4upqCggJKSErOxj62trYiqR0REvPDJXImxI4mc15Du7m6TuhpLHu5WVlaEh4eLuhpPT89J/wU1XLH049acQUFBrFmzhoaGBvLz802srUtLS0V+fGhoKPHx8cTFxUk+9hYwXg+TOZIDhmiO8Uv98uXL7Ny58wVv0bNhY2ODh4eHEDjOzs4MDg6iUqno7e0lOzub7OxsgoKCSEpKIj4+/qkjKGMhMDCQzz77jJ9++omSkhJaWlo4f/48Li4uKJVKsrOzmT179oh9Yh7HmKJn/AwvLy/+8Ic/mCzj7+9vMvEy1D67pqaGKb5+E+q+qNVqOXjwIIODg4ChpsbSRJBSqeTrr782MT4ICQlh+/btPHjwgD9cvE2qZgoMmr7vRdjgy2Qy1q5di52dHZmZmXR3d/PFF1+wZ88ek4kChULBrl27+PLLLwHIyspi27ZtvP322xw6dAiNRsOFCxeorq5m27ZtYz7/LyMDAwP8+OOP4vedO3c+l3twOPR6PQUFBZw6dUp831tbW5OSksLMmTMn/Xf7eNLV1SX611gyS3JychL1NSEhIZOqfkpi7Egi5zVgYGDAxNrZUuEcGLruDrV2fhlv7pGKpY0Yu9J7e3tz+vRp9u7dS0lJCXl5eSIFqLKyksrKSk6fPk1gYKAQPJMlp/pFI5fLGRwcnNSRHDDMoiYmJnLv3j0KCgpoaWnB23vydBQfKwMDA6KQWS6X88EHH+Dm5kZ5eTk5OTkUFBQAhsF9TU0NJ0+eZPr06cycOXPC3X7s7e15++23uXHjBmfPnkWn06FUKsV2X7lyZcz9VO7evSuK2hctWsT58+fNagNjY2NNfvfw8MDW1pb+/n5qa2vpmxI2KvfFrOqWp3K8Onv2rHhuLF261KLJRWtrK19//bXJpIqxB45arSY4JJQM62bQmJu5vCgbfJlMxrJly7C3tyctLQ21Ws2+fft47733TISkvb09O3fuFHVThw8f5qOPPuKzzz7j4MGDNDU1UVpayp/+9Cd27dqFv7//c9n+F8WZM2eEkF29evULtdbu6uri1KlTwsQEDCmfKSkpr8XknTFbxeiIZrTxHoqnp6dwRHsRNcUSE4ckcl5BdDod9fX1oq7GmEbwOO7u7qKuZmjR7svOk4qlh8Pb2xt/f3+WLl1Ke3s7BQUF5OXliZqk2tpaamtrOXfuHFOmTCEhIYH4+HiT1I3XjZclkgOwePFi7t27BzyaaX5ZSU9PF4P8lJQUkZYWGRlJZGQkKpWKBw8ecOfOHVpaWhgcHOTu3bvcvXsXd3d3Zs2axfTp0yesIFwmkzF//nyCgoJITU0VIgcM5gHz588f9eBKp9OJgbOrqytWVlYiKufu7i4Gko87qMlkMoKDgykpKaGqqoqB6Dmj+rzRujQOJS8vT2xTSEgIS5YsMVumvr6eb775xsSN0tgDB+B3v/sdhf0KGobUCj3OIxv8SjZMfb41LvPmzcPOzo6jR4+i0Wj46quv2L17NxEREWKZof2G9Ho9Bw4c4Be/+AUff/wxaWlp3LlzB6VSyV/+8hfWr1/PrFmzXsnBZF5enkld1oIFC17Iduh0Om7evMn58+fFM9rFxYWNGzdOiubIE4ler6e2tlYIm87OTrNlAgICRMRG6gH06iKJnFcAvV5vYu1cXl5u0drZzs6OyMhIwsPDCQ8PlxxBhsHDw4NFixaxaNEiEdrOy8sTYrGpqYmmpiYuXLggZoDi4+Px8fF5Jb+0h8O4r5M9kgOPZury8vJ48OABy5Yteym/2MrLy0XqVmhoKLNnzzZbxsHBgXnz5jFv3jwaGhq4e/cu9+7dY2BggI6ODtLT00lPTycyMpKkpCSio6MnxFI9ICCAzz77jGPHjlFYWAgYBl5Hjhzh/fffH9U6ioqKhKCbM2cOR48eBQypNkZbY2dnZ4uROaNtc3d3N7GK0Qnx0bo0GmlvbxfbZG9vz5tvvmkWAS8vLzdprgiwdu1avLy8SEtLo6ioiP7+fnpwGdVnfn34KIqaWBYvXvxcZ+ITExOxtbXl4MGD6PV6vvvuO4tW15s2beLo0aOo1Wq+++47Pv74YzZs2EBwcDBHjx5Fr9dz6tQpqqqq2Lhx4wtN4xpvOjs7OXbsGGD4vt22bdsL+U5oaGjg2LFjJlGLhQsXsmzZsle2rkSr1VJZWUlBQQEFBQXC0W4oERERxMXFERMT81pEsSQkkfPSolKphLWz8Yv8cWQyGaGhoULYTJky5bUahI8Hrq6uYsDY29srZobKy8sBQ7Hx5cuXuXz5smgCFh8fj7+//yt/rF+mSA4YojnGnipXrlxh48aNL3iLxoZareann34CDIP8LVu2jHiN+fn54efnx5o1aygsLCQnJ4eKigrgUf2ZnZ0dM2bMYObMmSY2wOOBnZ0dO3fu5NatW5w5cwYw9PxJTU1ly5YtIw5wjQ01jT1xjKleq1atEuuLi4uzeByGplMF6Xuf6L4ow1C7tzh49GmMWq2W1NRUtFotYGjC+nh0LD8/36Q2A8DX19dkdt2IE9pRfa4TWm7evMmtW7dYsGABixYtem4mKbGxsbz33ntCtP34449s2rRJNGYFQ0PWjo4OMjMzaWtrIzU1lXfffZfp06fj6+vLwYMHaW9v5+HDhzQ0NLBr166XOn3UiE6nEzVIANu2bXvuA+mBgQEuXrzI9evXxWt+fn5s2rTppTRcGYn+/n5KSkooLCykqKhI3ItGrKysiImJIS4ujsjIyFcmW0Vi9Egi5yVBq9VSU1Mj6mos5ZWCoVeGUdQEBwc/cwM+iUc4Ojoya9YsZs2aJVxZ8vPzKSkpQa/X09XVxdWrV7l69SqOjo5C8AQFBb2U9U0jYdynlyGSA4Z7Izo6muLiYu7evcuSJUteqmhmWlqaMBtYt27dmLbdaJU+depUurq6uHfvnkgfUqvVXL9+nevXr+Pn50dSUhJTp04dtyaZMpmMuXPnYmtrK6IeBQUFNDU1sWvXrmGFVW1tregoHhYWJgRqdHS0ybY9nqpmxDjRoNfraaire6L7IsDnKTPGVOty/vx58RxOTk42Sd0CgyOcUYgNpbGxUfxsZ2dHdHQ0ERER5NzN5WQdtGsYVoj5O9myISyCvAcP0Ov1XL16lRs3bpCcnMyCBQueyyAuPDycDz/8kG+//ZaBgQGOHz+OWq02iSouXbqUtrY2Hj58SEVFBadPn2b9+vX4+Pjwi1/8gmPHjpGfn09bWxt/+tOf2Lp166SxV35aMjMzxfU6b948oqKinuvnFxcXc/LkSTHhqVAoWLNmDbNnz36lvn96enooKiqioKBA2HMPxcHBQfSvCQ0NlcZArznS2Z+k6PV6mpubhQNaZWWlxRlzZ2dnE2tnyfb4+WBvb09iYiKJiYkMDAxQUlJCfn4+RUVFDA4O0tvby82bN7l58yb29vbExcURHx9PaGjohKQGvQhepnQ1I0uXLqW4uFgMEN94440XvUmjoqioSNQURUVFkZiY+NTrcnV1ZcmSJSxevJiqqipycnJ4+PChQQw0NHDq1ClOnz5NQkICSUlJhIaGjktUMjExkby8PFEA3d7ezh//+Ec2btxocYA7tPmnMfpkZ2fHxo0bhXiwtrYetqGvjY0N3t7eNDc3U1VVxSdr1ozKfXE0FBYWClvrwMBAli9fDhjc00pKSrh27dqwBi9+fn6iMejQiO/06dNx+dnmejgh9u/rktgWF8jSxYu5dOkS+fn5DA4OkpmZybVr11iyZAlz586d8JSkwMBA9u7dyzfffINKpeLcuXMmLp0ymYzNmzfT0dFBXV0dd+7cwcvLi/nz52NjY8P27dtFdE+n03H48GGqq6tZs2bNSzkoraqq4vLlywD4+PiM2VzjWeju7ubMmTPCcAQMwn/dunW4uIwuBXKy097eLrIojEJyKG5ubqK+ZqLNVSReLl6+p8krjFKpNLF2tpRTam1tbWLt7OHhId3QLxgbGxsSEhJISEhAq9VSVlYm8oIHBgbo6+sjJyeHnJwcbGxsiI2NJT4+noiIiJfyC93Iy5auBobZ/fDwcMrLy7l9+/Zzr2t4GlQqlcjzt7W1ZdOmTeNyzxvTWUNDQ1m3bh15eXnk5ORQX1+PXq/n4cOHPHz4EBcXF5KSkkhMTHxmZ8HVq1ebuDwBnDhxgoqKCpN7obOzk/z8fMAwkWOcnd64cSP29vZiHVFRUU+cNAgJCaG5uZnGxka0Wu2o3RefRGdnp0gbtLGxYeHChVy8eJGioiKT5qxDGZoy86QeY6O1wff29mbHjh00NjaSkZFBaWkpAwMDpKenc+XKFZYtW0ZSUtKEPl98fHyE0FEqlVy9epXp06eLSQ8rKyvefvtt/vKXv9DV1cXZs2dxd3cnJiZGRPcCAgI4ePAg3d3d3Lp1i9raWnbu3PlSOVj29fVx6NAhwBDd3rFjx3N5ruv1em7fvs25c+dEmpaTkxMbNmwYNrr5smCccDEKG0sTBlOmTBGOp15eXtI4SMIiL+8I6xWgv7/fxNq5vb3d4nKBgYEiWuPv7/9KhZ5fNYw5wDExMWzcuFEUQubn59PX18fAwAD379/n/v37KBQKYmJiiI+PJyoq6qUrwH3Z0tWMLF26lPLycnQ6HVevXmXNmjUvepOeyKlTp8SEx8aNGydElNnZ2YlUzJaWFuHGplarUSqVXLp0iUuXLhEaGkpSUhKxsbFPFS3w9vZm2rRpPHjwAHgkYPLz85k+fTrNzc0EBARw48YN8R6jwJk6dSrx8fGUl5eLuoeRBnNBQUHcunULnU5HQ0MDQUFBT+W+aGRwcJAffvhBGLvodDpSU1OHXd7Hx4cPPvhgTL1hxiLEfH192b17N7W1tWRkZFBZWUlfXx9nzpwhOzubZcuWMWPGjAn7zvDw8GDv3r3s379fON2dPHmSzZs3o1AocHR05N133+XPf/4zGo2G1NRUPv74Y2GpHBAQwC9/+UuOHDlCWVkZDQ0N/PGPf2T79u3PPd3radDr9Rw/flykka5fvx4vL68J/9zm5maOHTtGfX29eG3evHksX778pa070el0VFVVCWEztNGxkdDQUGEc8DKlGku8OCSR8xzR6XTU1dUJa+fa2lqLy3l4eJhYO79sg18JAwqFgoiICCIiIli3bh01NTXCmrqnp4fBwUHy8/PJz89HJpMRFRVFfHw80dHRL0XDvJcxkgMQHBxMcHAw1dXV3Lp1i+Tk5Emb5vnw4UMR0YiPjychIWHCP9Pb25s1a9awcuVKSkpKyMnJEZETY+8oa2trEhMTmTlzJn5+fmOaRV2+fLkQOd7e3gQHB4tUm3379rFmzRphyWysqXF0dBQd44uLi8W6RrLCDQx8lIJWW1trYkYwWvR6PfX19ZSUlHDr1i2TtCzjDLqVlRXW1tYm0ff4+Hi2bdv2VOmpYxVigYGB7Nmzh4qKCtLT06mvr6e7u5sTJ06QlZXFihUrmDp16oTMdru4uPDRRx/x3XffAQYLZbVazc6dO7GyssLLy4u33nqLb7/9Fp1Ox4EDB/j0009FKpWDgwO7d+8mKyuLixcvMjAwwPfff8/ixYtZtmzZpJ7Uy8nJEc6B8fHxJgYME4FGoyEzM1MYcoBBSG/evPml7D2k0WhE5kNhYaGZK6xcLic6OprY2NiX5ntRYnIhiZwJRK/X097eLmydh85ADsXe3t7E2vlVyaOVeIRcLickJISQkBDWrl1LfX09+fn55OXl0dXVhV6vp7i4WAzgwsPDRY7xk9JbXiQvayQHDNGcb7/9Fq1Wy40bN0RNxWSip6eHkydPAoZnxPr165/r5ysUCmJjY4mNjaWnp4f79+9z584d2tvb0Wg03L59m9u3b+Pl5cWsWbOYNm3aqK5Vd3d3Zs+eze3btykvL+fDDz8kJCREzEqnpaWJZY3X1pYtW7C3txed28EwsB9JnLq5uWFvb09fXx/V1dWj7lnS19dHWVkZJSUlFBcXo1ab98/x8PAgNjaWoKAg0tPTTVJqFixYwOrVq597Ck1YWBgff/wxpaWlpKen09zcTGdnJ0eOHOHy5cusXLlSpIuNJ0ahkp6eDkBJSQnfffcdb7/9Nra2toSHh7Nx40ZOnDhBb28vBw4cYO/evWICTyaTsWTJEgIDA/nxxx9Rq9VkZWVRU1PD9u3bJ+UzsKWlhdOnTwOGiOTGjRsn9HyXl5dz/PhxYakuk8lYtWoV8+fPn9RC8HFUKhXFxcUUFhaK+sih2NraEhsbS1xcHOHh4a+s5bXE80ESOaNgUKcfdQ63SqUSgqakpMRiyFUul5tYO79u/VVed2QyGQEBAQQEBLBq1Sqam5uF4DEOlIzX0MmTJwkKCiIhIYG4uLhJJYBHG8kZy/3zvAgLC8PPz4+GhgauX7/OggULxs1NbDzQ6/UcO3aM/v5+ADZv3vxCo01OTk4sXLiQBQsWUFdXx927d7l//z5arZbW1lbOnD3Ln85m4eIfzNy4KHbNT8TaavgIxpIlS8jJyUGn05GRkcHu3bupr6/Hzc3NLG131qxZImLT0tIimovGxcWNuN3GpqBFRUVUV1ej1+stPmv1ej1NTU2UlJRQVFRksbjZiEKh4P333yc4OBilUskXX3xhYuGfkpLCvHnzRty2icIYFY6MjKSgoICMjAza29tpbW3l4MGD+Pr6snLlSiIiIsb1e8coWCIiIkQT1m+++YZ3330XBwcHkpKSaGtr4+rVqzQ3N3Po0CHeeustkwF6eHg4n332GampqdTV1VFZWckf/vAHdu3aRXBw8Lht67Oi1Wr58ccfxbNv+/btE/b86O3t5ezZsyL6CYZjvGHDhpemdsnYby4/P5/q6mqzvzs7O4tJveDg4JdKtElMbiSRMwJHCmotFoH+fxdG4NFkmsJRWlpKc3OzxfX4+vqKuprAwMCXuuBcYvyQyWRMmTKFKVOmsHz5ctra2oTgMdrT1tTUUFNTQ1paGn5+fkLwvOhmlsYvoieJnOHun9+O0c1qvJHJZCxbtkzUV9y6dYvFixe/sO15nHv37lFaWgoYXMkmSyGxTCYjMDCQwMBA1q5dS0FBAX/Jvse3rVYosYZ6oL6Mv7lQxK9jXPnbVfPw9PQ0W4+zszPz5s3j2rVrVFdXU1VVBRismI8fP26yrKenpxAnQ1PVoqOjR7XNRpGjUqno6uoSA0O1Wi1Sh41/fxw3Nzeio6MpLy+ntbUVMAxog4ODaWtr4y9/+YsQogA7d+4clfh6HshkMjFwfPDgARcvXqSrq4vGxkYOHDhAYGAgq1atGtad7mnZtm0bp0+f5sGDBzQ0NLBv3z727NmDs7Mzq1atEk5ZJSUlnD9/nrVr15q838XFhQ8//JD09HSuX7+OSqXiq6++YvXq1cybN8+QwojshU6cnD9/XphMLFu2bEIEmF6vJzc3l7S0NJHGZYzoxsfHT+qJUb1eT0tLixA2llpeeHl5ievT19d3Uu+PxMuLNNJ+Akd+tvN8PBmnVtnHh2kPeEdex87p4fz1r381G+i5uLiYWDtLuaQSo8HT05PFixezePFiOjs7hWmBsX6roaGBhoYG0tPT8fb2FoLH29v7uX9JjCRyhrt/6pR9bE+9xqGdC16o0ImKisLb25uWlhauXr3KvHnzJkX9W1dXF6dOnQIMEZSUlJQXvEWWsbGxoczGkz+02pud4y69gv9Z2ENh4TesCXQlKSmJhIQEk+ObnJzM7du36df08+e0P5McnMz32d/jgANyHs3knjt3jpqaGjZt2iTqH9zc3EZd4D20Lic/P1+khlqaUQZDNCE6OprIyEg8PT1JT08XAmfevHnExsZSX1/Pvn37GBwcBEyjO5MNuVxOYmIiU6dOJTc3l4sXL9Lb20ttbS1ff/01YWFhrFy5koCAgHH5PIVCwdatW7Gzs+PWrVu0tbXxxRdf8MEHH+Du7s62bdvYt28fjY2NXL9+HU9PT5MeO8Z1rF27luDgYI4cOYJWq+X8+fNkZGTwUOdItlMk9T2PxOXznDgpLi42sQ+fiMmR1tZWTpw4YXKNzpo1i1WrVk2qiPNQ9Ho9NTU1QtgY0+qGEhgYKHrYvOhJOonXg+cicn7/+9/zf/1f/xeNjY0kJibyu9/9jrlz5z6Pj35qBnV6fp2Wa7Epm5F0prDz559tbGyEtXNERATu7u7PYzMlXmHc3NxYsGABCxYsoLu7W3x5VFZWAobUHaPrlbu7uxA8Yy0Ef1qelK72pPtHj6Hvx9+l5bI5JuCFpa4ZoznGGoA7d+6MumZjotDr9Rw9elQUtBsHi5ORJz8jDZ1e0vAltraE2tpaTp48yfTp05k5cyZBQUE4ODigjdLyef7naDo1JAcn8/vO32ONNSmk8Hbi29TW1tLW1kZBQQG1tbUiLWy00ZKBgQGTlOHz58+bLePk5ERsbCxRUVFmRi+lpaWiyNvX15fVq1dTWlrKgQMHxDJ2dnZ8/PHHFiNWkwmFQsGsWbNITEzk9u3bZGZmolarqaio4IsvviA6OpoVK1YwZcqUZ/4smUzGG2+8gZ2dHVlZWSKtb8+ePfj4+PDOO+/w5z//mZ6eHk6dOoW7u7tZM1UwnOfPPvtM2FQ/1DmSSiD0qHnUPej5TZx0d3eb2Idv3759XFOrtFot2dnZZGZmitc8PT3ZtGnTpBTQWq2WiooKCgsLKSgosNj2IjIykri4OKKjoye9Xb/Eq8eEi5yDBw/yD//wD/zpT39i3rx5fP7556xdu5aioqJhO11PBrKqW0xSbMyR0Y2hIG7evHmsWrVKyiOVmDCcnZ2ZM2cOc+bMQaVSUVRURH5+vkhp6ujoIDs7m+zsbJydnYXgCQoKmjDB8yTjgZHuHz1Qo+wjq7rlqe18xwNj2l97ezvZ2dnMmTPnhaaS3r59W4jYOXPmEB4e/sK2ZSRG84xUYk270xS8eprQ6XTk5uaSm5uLu7s73YHd/HP+P6NHjz2PIt1KlKSSyvbI7Xy67lNOnz7NvXv3TOpenmQv3NbWJgwDjE1EHyc4OJiYmBiioqKG7bHR3d3N4cOHAUN/sp07d/Lw4UOOHj0qlnFxceGTTz4Z8+DtRdapWVlZMX/+fJKSkrhx4wZZWVloNBphfJKQkMDy5cufWbTJZDJWrFiBnZ0d58+fR6VS8eWXX/Lee+8RGBjI7t27+eKLLxgcHOSvf/0rn376Kd7e3mbr8fDwYO7cuZxLTycNX+PaTZZ5HhMner2eI0eOCBOKLVu2jKuNcVVVFcePHzepSVuxYgULFy6cVA2k1Wo1paWlFBQUUFxcLCZkjBjbKBj7Qr2sltYSrwYT/m3+m9/8hk8++YQPP/wQgD/96U+cOnWKffv28d/+23+b6I9/ahq6zd10hmOy21xKvFo4ODgwc+ZMZs6cSX9/P8XFxeTn51NcXIxOp6O7u5vr169z/fp1HBwcRMO00NDQcb1OnxTJGe39c+n2PZaFrh63bRorMpmMpUuX8tNPP6FSqbh79y5z5sx5IdvS3t4unMVcXV1H3TX9RQ2YR3uOG3r62Lp4MT09PeTn59Pf309bRxufd3yOftg4kIx/PP+PbIvfxpYtWwgLCzMRF9evX8fX1xd7e3u0Wi2VlZXCNMBSmoyVlZUYjP3jP/7jiKJEp9Nx6NAhkwFtQUGBSSTI19eXDz/8cMwpjpOlTs3GxobFixczZ84crl27RnZ2Njqdjry8PPLy8pg5cyZLlix55uL2hQsXYmdnx4kTJxgYGOCrr75i9+7dhIeHs3PnTn744Qe0Wi3fffcdn376qUUntenTp5NZ1YKyxNyd1MhET5xcvXpVTEAkJSWNW+1VX18f586dIzc3V7wWGhrKxo0bJ01KV09Pj4jWlJeXm/3dwcGBuLg48T0zmUSZxOvNhIqcgYEB7ty5wz/90z+J1+RyOatWreLatWtmy/f395sUcRpddDQajUXr5YnE18EK+xHGg8a/a7VaqWjuJcV4XT3v62u8kMvlwuZXo9FQXl5OcXExRUVFaLVa1Go1OTk55OTkYGtrS0xMDNHR0YSGhj5zxEKhUCCXy9Hr9WbHbzT3D0BtwX2amxOfKb3zWc9hTEwMbm5uKJVKsrOzmTZt2nP/ktbpdCINRi6Xs3nzZmQy2Yj7dKKonv9X+n3qhgiOAGc7/nXVdDbGTGzfjNGeYzd0IuUrKCgIFxcXrtVdQ9OpEREce7npvwCtPa1kFGewPGI50dHRWFtbixqY0tJS/uM//gMvLy/q6+tNhLZRyAcEBAgHy/b2do4dOwYYGimONLuclZVFbW0tcrmcmTNnUlVVxe3bt8W6w8PDefPNN0d1joZyoqie9366iR5Mjl17Tx/vHboGW+dO+Hl7HIVCQXJyMklJSVy7do1bt24BBvOLe/fukZSUxMKFC0cUhk+6D6dNm4a1tbUQqgcOHGDbtm1ER0ezZs0a0tPT6enp4YcffuCdd94xsw22s7MjLCER+7LbI+5PQ1fvuD/PGxoauHDhAnK5HHd3d1auXPnMn2G0Qz937hxqtRq5XI6trS2rV68mISFhzNfWeDD0HLa3t4vvkoaGBrGM8R5wdXUVaWhDU6R1Ot1L1zvtVeJlH9OMhrHsm0w/gU0u6uvrCQgI4OrVqya57v/1v/5XMjMzTbpaA/zzP/8z//Iv/2K2nu+//37SNuuTkJCQkJCQkJCQkJh4VCoV77zzDl1dXSO21ZhU7mr/9E//xD/8wz+I35VKJUFBQaxZs+aF9AcxzroBJkkVxpjNt5tnoah4wOrVq6WGVS8pGo2G8+fPv9LnUKfTUVNTQ1FREYWFhWZWuXK5nMjISGJiYsaUQ/39999TXV2Nj48PH330kdnfn3T/6NHzXyLtsSrNFa8b02eSkpLGFEkZj3M4ODjIH/7wB3p7e3F1deUXv/jFc0tBbWlpYd++fej1ejw8PPjoo49GjLIN6vRM++NZkwjOUGSAl62cK7sXMMXHvM7hWRgcHKS7u5vOzk6OF9bz33Mt2eYbzvhW6ojFvFdYFVV8z/fid3u5Pfum7uOjhx/Rp3uUxvUO7xDCyBbHHh4epKSk4O/vP+yx+93vfkdvby+RkZFs377d4jK9vb18+eWXqFQqFAoF3t7eNDY2ir8vXbr0qc0psqtbWP/9lRGXO/XOIpKDx/ecPQ1dXV1kZ2eb9GdRKBQsWLCAOXPmmD0nRnsf1tbWcvDgQTEbu2rVKpKSkvjxxx9FDdXChQtZsmSJyfuM13x9t9pikqMMQxTz/mdrxzVd8+TJkzx8+FBs6+NOcGNhcHCQmzdvmhgLuLq6sm7dunG38h7t9tTW1lJUVERRURF9fX1MnTqVhw8fimiMsX4tOjoaZ2fn576NEmPjdRjTGLO8RsOEihwvLy8UCoWZR3pTUxO+vr5my9va2locYFlbW7+Qk7VtaggoFGb500Eu9nyeMoONkVM4XfHghW2fxPjxqp/DyMhIIiMjWbduHbW1tRQUFJCXl4dSqUSn01FYWCjseSMiIkhISCAmJuaJEVS5XC5SEywdu5Hun21xgXR2Lub8+fPk5+ejVqs5f/48t27dYu3atWPuzP4s59Da2ppFixaRlpZGR0cHJSUlTJ069anWNRYGBwc5fvy4SMPaunXrqOzmr1Q2U9r15JqY6j4d/58vDvCrN5bi6uoq/rOzs3vicdXr9ahUKjo6Oujs7KSjo4OOjg7a29tpb283MQAA2Igzafga+uT8jAtaUmgkVt6LtbU1Go3GJIUlgACssUaJ6ZdVn65PiBwXXAggAB0jp760trby3XffAQZXQl9fX3x8fPDx8cHb2xtPT0+Cg4PJy8ujuroaKysrs2NgbMBqdGNzdHSkvr5e/H3r1q1Mnz59xG2xhEaj4X5VPX2jyOJpVGknxbPIy8uLLVu2kJyczMWLF8nPz0en03H58mWuX7/O4sWLmTt3rllN0kj3YVhYGB988AHffPMNarWac+fOMTAwwPbt2/nyyy9pbW0lOzsbLy8vEhMTH60X+Ne1M9meakh1tzTx+H+unYmd7fjZwN+/f5/79+8Dhmfo/Pnznzo1vba2luPHj4v+OmBoirt48eLnanYyMDBAWVmZeOYbe/DAo1Q0oyNaVFSU1P7iJeVVHtOMZb8m9M6ysbFh1qxZZGRksGXLFgDR4fpv/uZvJvKjx41tcYFsjgmwWNj7Kuc8SryayGQygoKCCAoKYvXq1TQ2NlJQUMDDhw/p6OgAoKysjLKyMgBCQkJISEggNjbWbBZvNM1An3T/gGFAumPHDmprazlz5gz19fV0dnZy8OBBgoKCxOz88yApKYnMzEz6+vrIzMwUefETSXZ2tpgEWrp06aj3dbRF/z1YcebMGZPXrKyscHZ2xsnJCVtbW2QyGTqdjv7+fnp7e1EqlUJ0jYZ5zrDWfYAGGwf6re1xtwbf/k6qqzSo1TqTOkt7e3sSEhJISEhgWu80dh7aOaz5QAopJv1yjAQGBhIQEIBer6exsZG6ujqT7e3s7KSzs1OIdiNGwa5Wq7l16xbh4eF4eHiI6zgrK0sUlltZWQkxJ5PJeO+99wgLCxv1MXmcb7/9lrs1bUDoiMv6OU8uy3AvLy927NhBY2MjFy5coKSkhIGBATIyMrh69SpLly5l1qxZY1qnr68vH3/8Md988w3d3d1cunSJvr4+du/ezZ///Gf6+vo4evQobm5uJhGObXGBHNq5wKJxw+fjbNzQ0dHBiRMnAMO1s2XLlqd6HvT395ORkSFqncBwDW/atMmim9xEoFKpKC4upqCggJKSEjNHTFtbWyFqSkpK2LZt2ys7QJZ4vZjw6YN/+Id/YM+ePcyePZu5c+fy+eef09vbK9zWXgYUctkLtbmVkJgIZDIZfn5++Pn5sWLFClpaWoTgMc42VlVVUVVVxenTpwkICBDW1G5ubk90VxvKaO6fwMBAPv74Y/Lz8zl37hxKpZKamhr+8pe/MG3aNFauXDmudq2WMEZzjM0fi4qKiI2NnbDPa2ho4NKlSwD4+PiMqangaAfCbgqQ6WQmgxqtViuiM6NBoVDg4OCAm5sbnp6eTJkyBW9vb9zd3XF1dUUul1NTU0NeXh4PH95HqVKZxGdsbW2ZOnUqCQkJBAcH09jYSElJCW1FbexgB2mkoeHRhJELLqSQQjzxFrentraW2tpaPDw8mDdvHu+88w75+fmcPHlSXIt2dnbo9XoTgTU0TdMo/GQyGZ6enjg7O5vYTRud2KysrPjkk0+eqd1BRUUFNTU1hAAuaFBixeMWyPz8SqCLPYsnQaqaJXx9fXnnnXeora0lIyODyspK+vr6SEtLIzs7m6VLl45pfZ6enuzdu5dvvvmGjo4Obty4gVqt5u233+arr75Cr9fzww8/8Omnn5q4jI00cTIeDA4O8uOPP4rr4M0337To+vYk9Ho9hYWFnDp1it7eXsDwjFm7di1JSUkTPoFiFPoFBQUWG986OzsTHx9PbGwswcHByOVyNBoNJSUlE7pdEhLPkwkXObt27aKlpYX/8T/+B42NjcyYMYO0tLRxaTgmISExfnh7e+Pt7c2SJUvo6OgQKW3GlJ26ujrq6uo4d+4cU6ZMEbPn4+VdIpPJRJrczZs3uXjxIlqtlgcPHvDgwQMWL17MokWLJrTvwuzZs8nKyqK/v59Lly6NOWVutGi1WtGDBQyDqLHUIS0O9ibA2W7Y+gTQ44KWgEHlExsajwZjDU53dzc1NTWAYbDm4OCATCajt9fczcra2loIG19fXyoqKsjNzSU1NVXYMgPEE08ssdRRB8A/R/0zvUW9yJEjk8nw8vIySe8ZSnt7O2fOnOH8+fMkJSXxzjvvcP78eZqamlCr1Tg6OrJ161ZsbW1pbm6mqamJnJwc06Ok19Pa2kpra6vZ+mUyGZGRkRQXF9PR0YGPj4+JuB+J7u5uzp49S15eHgByIIVGUgn8uS5tyGf9/O/nKTNeWHPc0RIYGMiePXuorKwkPT2duro6enp6OHPmDNOnTyc/P59p06aNqqbN1dWVvXv3sn//fpqbm7l37x79/f1s3bqVI0eO0N/fz3fffccnn3xikjY10ROPFy9eFI5iCxcuHHO/qq6uLk6dOmUiGOLj43njjTcmrCGmXq+nublZNI1ubjavlfPy8hKR+SlTpkiusBKvPBPqrvasKJVKXF1dR+Wg8CLQaDScPn2adevWSaHdlxTpHI6MUqmksLBQ1DM8jlwuJzk5mbi4uHH94lSpVGRmZnLz5k3xmr29PStXrmTmzJliEDXe5zAzM1NEWHbv3k1kZOQzr/Nxzp8/z9WrVwFDMfOiRYvMlhkcHKSrq0tEXYbWxnR0dJA7YGvo/g6YRgYMj/Sd1BKPIeXKwcEBd3d3PD09cXd3x83NDXd3d9zd3XF2dkaj0aBUKunq6qKzs5Ouri6USiXt7e10dnbS09MzZjFrrNUYmvNvRC6XExgYKK4nuVzO9OnTmT9/PkePHhXF/tOmTROF7w4ODmamGY8THh6OQqEwGVyuWLGC5ORkZDIZX375pYgCrV+/nubmZq5evWpWZzQcCoUCLy8vUfPj7e2Nj48PLi4uJpHNGzdukJGRISYC7OzsWLZsGe3t7dS7BvHPNyqHrVN7mdDr9ZSWlpKRkUFLSwvTp0/n/v37wmY5NjZ2VM8DtVrNgQMHqK2tBQx1OyEhIeI+DA4O5v33338u1u4VFRXs378feJRWN9rP1el03Lx5k/Pnz4uooouLCxs3bpyQ54hOp6O2tlYIG0s9ogIDA0XEZiSrfun78OXndTiHY9EGksh5Bl6Hi+lVRzqHY6O3t5eioiLy8vIsNoVzcXFh6tSpxMXFERAQMC6Cp7W1lXPnzpkMXL28vEhJSSEiIsLsHD5rc0y1Ws1vfvMbNBoNAQEB7N27d1xnPGtqati3bx9gGESlpKSgVCrNCvyNBfBPIt9C0b+3jYx/TPBiW1wAbm5uuLm5PfW1bax7efjwIffv37e4Tba2tmi12jHV8YBBMBjfYxQ5QUFByGQyjh8/brb8Rx99hKOj489pcQ8tzlQbsbe3p7+/Xww0w8PD2bZtG1evXiU7+wpVVSpWrFhHV1cTra0FyIdcH56ensycOZPW1laamppoaWkx6+r+OFZWVnh7e+Pk5ER9fb1ITwKYO3cuy5cvx87uUYrhi2rgOlHo9Xry8vIoKyvj/v374rhPmTKFVatWERERMeI9pNFoOHjwoKgH9Pf3x8PDQzibJSYmiv5RE4VKpRIui1ZWVnz22WejbsjZ0NDAsWPHTIyWFi5cyNKlS8fcMPZJaLVaKioqKCgooLCwkL6+PrNljMYBMTExY0qzk74PX35eh3MoiZznxOtwMb3qSOfw6UlNTaWgoACFQoFOpzOb6Xd0dBQ1PMac72ehsrKSM2fOmAxuw8PDWblyJbdu3WLdunWcKG0al27yGRkZZGdnA7Bnzx5CQ0OfapsHBgZMHMra2trIzc0dcdBsCRcXFzw8PEQExvifi6sbt1t6aOzpH5cBszHtJS8vjwcPHtDZ2Wnyd4VCQUxMDMHBwWg0GkpLS6mqqrK4LicnJxQKBRqN5olRGKPIGTpAfvzvixYtws3NTbjEDQ4OUlxcPKLgGbrdg4O+/OY3F1AqHx1/FxcrUlL8iI93ISYmhl27dpkMpPV6PUqlkubmZpqbm2lpaaGxsZHW1tYRhZ2VlRVTpkxhypQpJpEfR0fHVy5VyPgsNUZghkYVAgMDWbVq1Yg2yYODgxw5coT8/HzAMJlhZ2cnIjwrV64kOTl5QrbfWANknEzZsmWLibvbcAwMDHDx4kWuX78uXvPz82PTpk0WXWSfBrVaTUlJCYWFhRQVFZldd9bW1sTExBAXF0dERMRTp/RK34cvP6/DORyLNphUfXIkJCReHowPUDs7O/72b/+WkpISMbs4ODhIb28vN2/e5ObNm9jZ2REfH09cXBxhYWFPlXYSGhrKL3/5S+7fv8/58+fp7e2lvLycyspKpk+fTur9ct47ed+sBqVO2cf21Gsc2rlg1EJn/vz5XLt2jcHBQTIzM4cVOTqdju7u7mFTyizNsg6HnZ2dKO43ChhjWpmxwH84ljuNrSjaEi0tLTx8+JAHDx6YmRLI5XKio6Px8vKit7eX0tJSMRAdipOTE7GxsURFRREaGmoyg208Vl1dXRQWFnLtmsEK2NbWdkSnSp1OR1ZWltnrdnZ2uLi4EB4eLgTlcBGwBw86SE29b/a6UqklNbWG//f/ezlvvfWW2d9lMpkQVlFRUeJ1vV5Pe3s7V69eJTc316I402q1opZtKLa2tvj4+AgTB6MAGmtx+2QkISGBadOmkZuby6VLl+jp6aG2tpavv/6a0NBQVq1aRUBAgMX3KhQK3nzzTezs7MjJyaG1tRUXFxdcXFxQKpVkZGTg4eFBfLxlU4pn4datW0LgTJ06dVQCp7i4mJMnT4p0R4VCwZo1a5g9e/YzT+p0d3dTVFREQUGBxai5g4ODSEMLDQ19Lql8EhIvG5LIkZCQeCqMM9F6vR4bGxthDazVaikrK6OgoICCggIGBgZQq9Xk5OSQk5ODtbU1cXFxYtZxLLNNMpmMxMRE4uPjuXr1KpcvXxZ/+4fTd9Bjvi49hoqVv0vLZXNMwKiiHI6OjsyePZsbN25QWVnJ3bt3sbGxMYvKKJXKUdeqyGSPXM6cnZ2ZP3++iMy4ublNqKHCcLS2toqITVtbm9n2hoWF4ezsLISJJYzNAqOiovDy8ho2QiGXy4VYMM56KxQKfHx8hAj4+7//e3p7e+nq6uL06dMiGmBtbY2NjY1JGhgYZrjVavWIkRydTk9aWsMTl9m//z7/8i86FIrRDU7r6uo4ceKEyWfPmjWL5cuXo1araWlpMYn8tLW1ifPf399PTU2NMHIwYmdnJ6I+Q/v8PE2vkheZEqdQKJg1axaJiYncuXNHWLNXVlbyxRdfEBUVxcqVKy0aEMnlcjZs2ICdnR1Xr15FqVTi4OAg+i0dOnSIvXv3DiuUnoampibS0tIAgxnChg0bnrh8d3c3Z86coaCgQLwWExPDunXrninrpK2tTdTXDO3RZMTd3V0Im/FKB5aQeJWRRI6EhMRTMZyFtJWVFTExMcTExLBx40YqKyspKCggPz+fvr4+Q2PEn5vsyeVyYmJiiI+PJyoqatQDfWtra5YuXUpSUhIXLlwAoNuCwDGiB2qUfWRVt5i5Mmm1WrMC/87OTpPBq6UaEUs4OjqKAv+hxf0ODg7s37+f7u5urKys+Oijj3BzcxvVOseb9vZ2IWwsOZcFBARgZ2dHS0vLsDPIRlETHh4+ZnHW19dHUVERYHCcGhrlsLW1xcnJiSlTpvDgwQMhcjQaDZ9++inu7u4iGjTUJMEoPru7uy2mAlZVqUxS1CxRU9NGVlYBy5YlPHE5lUpFeno6d+/eFa/5+vqyYcMGMfB2dHTE09PTxIJcp9PR3t4uhE9zczONjY20t7eLZdRqtbBtH4qDg4OI/BiFj7e3t0mdz1COFNSOS9rms2JlZcW8efOYOXMmN2/eJCsri4GBAUpKSigpKSE+Pp7ly5fj5eVl8j6ZTMbq1auxt7cnIyMDlUolJkP0ej0HDhzg008/HZd7SKPR8OOPPwoBun379mGvab1ez507dzh79qy4zhwdHdmwYcNT2c3r9Xrq6+uFsBl6LRjx8/MTwubx4yQhIfFkJJEjISHxVBjTMZ4UyVAoFERERBAREcG6deuoqakR1tQ9PT3odDoR8QGIiooiPj6emJiYUc1eOzs7s27dOk6fPj2qbb76oABZVQGdnZ20tbXR0dExqgJ/I9bW1mYpZcZIjJub27Cdy48dOyZSWtatW/fcBU5HR4co2B9aGG3E29sbuVxOS0uLWWoVGISPUdg8q4NeXl6eEMYzZsyguLjYbBm9Xm8msE6cOMGHH34ojrUl9Ho9arWarq4u2traqKiooLq6mocPlRaXf5xz57KIiHAhICDALN1Ir9eTk5PDuXPnhGOctbU1a9asISkpacT0JLlcjpeXl9lAdXBwkLa2NiF8jOJnaC2USqWisrJSNCs14ujoKGp+jGlvV9o1vHXk1rikbY4XNjY2JCcnM3v2bK5du8aVK1cYHBwkPz+f/Px8EhMTWbZsmdl5TU5OxtbWltOnT6PRaJDL5eh0Ovr6+jhw4AAff/zxM0dAz507J6KYK1euJDDQ8rFpbm7m+PHjJvfH3LlzWbFixZi2YXBwkKqqKiFsHo9OgiE11/gcnIz1yBISLwuSyJGQkHgqhqarjQa5XE5ISAghISGsXbuW+vp6IXiMAzrjDC8YbGSNM5jj1VuiOOcmGp5sQ2ws8Pfw8BApMwDR0dG89dZbYx7gFxcXk5ubCxhcj2bMmPE0mz5murq6RMTGaMk8FFdXV7RaLb29vWYRHTs7O6Kjo4mKiiIiIuKp0qWGwxgBcXR0JCgoiP7+fjOBUF9fb2ZUUF1dTX19Pf7+/sOuW6vVishhYWGhqPVxdBxdvUJ9fTn79u3Dzs5ORCMjIiJobW3lxIkTJsdxxowZrFq16pnraIwpez4+PiQkPIoiabVa2trahPAx9voZWtBvrEszCkId8DlR6C00HH2atM3xxs7OjuXLlzNv3jyys7NFXda9e/e4d+8ec+bMYfHixTg7O4v3zJkzBzs7O44cOWISNW5tbSU1NZXdu3c/df1LYWEht2/fBiAkJMSilbtGo+Hy5cvCiAQMzXs3bdo06pS5gYEBysrKKCwspLCw0MxW3RjRjo2NJTo6etgInYSExNiQRI6EhMRTMZpIznDIZDICAgIICAhg5cqVNDc3C8FjbMxYUVFBRUUFp06dIjAwkPDwcLy9vRkYGDAp8O/q6iI6OhpnNPQN003e2BwzBBU2NjZ4enpa7Bnj4uJiNmBSqVTk5uZSXFxMa2sr3t6j70ivUqk4duwYYEjF2rRp04Tm0SuVSvLz83nw4IHFnH4HBwfUajU6nc6sp4afn58QNv7+/hOynW1tbWK7ZsyYYdIYdChDozszZswQIvH48eP88pe/NFlWrVZTXFxMQUEBxcXFZumT9vb2bN06k9OnW2hpMZ81B5DJwMPDnogIF3S6QdRqtRh4P463tzcbN24kKCho1Pv9NAx1ZhuKRqOhtbXVJO2tqakJpVJJFQ4mduKPY0zb/Pyns6yO8sfHxwcvL69hI5AThYODA2vWrGHBggVkZWVx69YtwFD8f+vWLRYsWEBycjIODg6AoV+Sra0tP/zwg8l6ysvLOXPmDOvWrRvz9apUKjl69ChguDfffPNNs3WUl5dz/Phxca/IZDJWrVrF/PnzRxRWKpVKGAeUlpaaPSft7OyIi4sjNjaWsLCwV9YJS0LiRSKJHAkJiadCp9NTUdGLSjXIpUt5LF4cN+qi7aHIZDJ8fHxwdnYmKiqKmpoaSktLqa+vF+5ktbW1wkb2cYyDjVU08T0BPJqzNmIYXKTQiBzDrKq7uztr1qwxmTEejuTkZDHIzsrKYtu2baPet9OnT4uIxIYNG0b1eWOlu7ub/Px8Hj58aPEYGQu2AZPoiI2NDVFRUURFRREZGflcnL2GiobExMRhUwWN6Yve3t6kpKQIa+mmpiYqKyvx9vamqKiI/Px80VdlKM7OzsK+PDAwEK1Wy7p1N/jmmxKzZY3j2j//+Vds3jybqqoqioqKuH//vkURJpfLKS8vx9ra+oV0jbe2tsbPzw8/Pz+T1wcGBvjL9Xy+uWi+j49z7WEBPQ9viN/d3NzMDA88PT0n3LHLmG66cOFCMjMzxX127do1bt68yaJFi1iwYIGILO7Zs4fvvvvOxEL59u3beHl5MW/evFF/rk6n4/Dhw/T39wOwdetWk3uzt7eXs2fPika0ABEREWzYsOGJqaadnZ0iDe1xUwnj/iYkJBAbG0tQUNAzO7BJSEg8GUnkSEhIjJkjR27wi18corXVMDP+44//QmCgJ59/vocNG2ZazFHXarUm7mTGAv+2tjY6OztHtBG2hK2trRjs/e/vbmFd5yD/7VKhScG1r70N6xUtBPU86mqfn59PYWEhK1asYP78+U8czHl6epKQkCBSv5YtWzaqBoF5eXnk5eUBEBcXZ5KK9Kz09PRQUFDAgwcPLA6mhjq5DT2u3t7eorYmMDDwuQ6y9Hq9GMT6+vri7e1NaWmp2XJdXV0ifS42NhZbW1sWL15MZmYmAAcOHLBoLuDu7i4a0fr6+pqIjwsXLhAWZsPOnUFkZnbS1PToWjBctx+wbZthkOzg4EBdXZ2JwLGzsxO/NzU10dTUxKVLl3BychJNF1+0ja+NjQ0Jwf7AyCLHy9YK+h/93tnZSWdnpzCEMOLu7o6vr6+J05uHh8e476ebmxubN28mOTmZCxcukJ+fz+DgIJcvX+b69essXryYuXPnEhoaykcffcT+/fuFQAFIS0vD3d2d6OjoUX1ednY21dXVgCEdLiYmBnh0jaalpYmUMnt7e9avX098fLyZoDX2lDIKG0suf97e3iLt9kWIYgmJ1xlJ5EhISIyJI0dusH37v/F4llptbRvbt/+GnTuD+Nu/3YZGozEp8LdUYDscNjY2ZgX+tra2tLe3mxRg9/f3U11djZubGydOnDBYS++YSanGyqw5Zm1tLadPn6ahwWAlrNPpSE9P59atW2zZsuWJDT+XLFkiBEt2djabNm164vb39PRw4sQJwDBI2rBhwzMPblQqlRA2wzXfNGIUOFZWVkRGRj6K1jg5k1XdwpUuNX661udqK1xVVSXMF2bOnAlgR06giQAAp9VJREFUMZIzNFXN19eX7Oxsk8H3UIEzZcoU4uPjiY6JpbAPGrrVyPsV+OhB8fNu1dfXc+OGIWrxxhvTOHDgPbKzC2lo6MDPz11EINVqNRcvXuTmzZti/Z6enmzcuJGQkBC6u7spLi6msLCQsrIy9Ho9PT09IsXKysqK6OhoISLHs45ptITQhwsalMOkbcowuKz9/td/w0C/weZ6aMpbc3OzSW8n42TEUKtkmUyGh4eHED9Gw4PxqJvz9PRkx44dNDU1ceHCBYqLixkYGCAjI4MrV66wdOlSZs+ezd69e/nmm29MnikHDx7kk08+GbEBZ01NDRcvXgQMzUZXr14NIOqujOIHDJbgq1atMqmR0el01NbWCsdIpdLc1CIoKEgYB7i7uz/TMZGQkHh6JJEjISExagYHdfz611+bCZyhpKU1EBt7HvkIg2d7e3tcXFxwcnLC2dlZNP1zcXHB3t4ehUKBXC4X/ykUCkJCQpg1a5ZJIa9R8HR1dXHlyhWuXLmCk5MTCQkJhMfHI/s5fS0wMJBPPvmEgoICzpw5IwbYXV1dfPPNN0RFRbFx40aLKWU+Pj7ExMRQVFREbm4uS5cuxdXV1eJ+6fV6jh8/LmaaN2/eLGoLxkpfXx8FBQU8fPiQioqKUb3Hw8NDDLSDg4PFrLvBVvjyC7MVvnP3LhU40Is181z8GNTpzYSvXq8XKW0ymYwff/zR4rpsbW35xS9+gbu7O0cKatn9/U2L+7Ulxl/URMlkMjZu3IiVlcLEJlqv13P//n3S0tLEAF8ul7Ny5UrmzZsnjp+zszOzZs0S1195eTlFRUUUFhaiVqvRarXCLQwM/YNiY2OJiYkZVeRvPFDIZaTQSCqBPJ62afzp85QZKOQy7O3tCQ4OJjg42GQdKpXKxOmtqamJlpYWEcnS6/W0tbXR1tYmhD8YBPXUqVM5ceKESYNTd3f3MQv8KVOm8Pbbb1NXV0dGRgYVFRWo1WrOnj3LlStXWLZsGR9++CHfffedMC3R6XR89913/OIXvxg2LVStVnPo0CHD8ZDJ2LlzJzKZjMzMTC5duiSW8/T0ZNOmTeLYaLVaysvLKSwspKCgwGIaY1RUFHFxcURHR78STV0lJF4FJJEjISExarKyCqitbXviMkqllqoqFWFhT/6i7+vro6+vz6Kl8ViwlHLV09PDjRs3xAy+tbU1tra22NjYoFAocHR0RC6Xm8zClpSU8Jvf/EYM0IaKLIVCYdKnIzU1lejoaJO/G3+ur68XDnHBwcHo9XqKi4stLmvpd41GIwZUj1sGD7f/4eHhwjRgaM1AaWmpYT0OU/jbrMoXZiuc+qCST+73oSQUgEMHrxPoYs+bDkrceeTU94c//EEUeQ8t1DZa6mZkZNDf3y+aaV5s7GV76rVh9+v/SPRk4OcUoqVLl+Lp6WmyXHNzMydPnjRJ+YuPj2ft2rVPtO61sbEhNjaW2NhYNm7cSF1dnSgyN/Y6qa6uprq6mnPnzuHh4SHS2izZU48XOp2OeLrZSS1p+JqYEAS62PP5KAStg4ODcEEcSm9vrxA+LS0tIvJjTOsyGj4MtQgHg3ucl5eXSY8fHx8fXF1dRxQ/AQEBvP/++1RVVZGenk5tbS09PT2cPHkSV1dXFi5cyK1bt4RZSW9vL9999x179+7FxsbGZF16vZ6TJ0+Ke37dunWoVCr++te/mvSnWb58OYsWLUKj0fDgwQNhaDG0DggMzxTjNRAZGWn2eRISEi8eSeRISEiMmoaGjlEt19Pz5MaLzxuNRjPqmh9jCs+TqK+vt+he9jjGge5EoFAoUCgUWFlZ0djYSEtLC9euXTMRTo2NjS/cVvhIQe3PfVtMv25qlX38VmnFTpyZqjdEdIzpbAD+/v6iXsKY+mVtbS0iM6fPpPF763gzgTN0v/71XiO/Brw8PEhOThZ/7+/v59KlS1y/fl285u7uzoYNGwgPDx/T/snlcoKCgggKCmLVqlW0t7cLwWMUT+3t7SLKaGdnJyI84eHh4zY41uv1nDt3DoBpChX/+b9tILdDTUO32iRt82lxdHQkLCyMsLAwk8/s6ekxifj4+fnR3Nws7rfBwUFRxzQUKysrvLy8RH2Wse7H2dnZTPyEhITw0UcfUVZWRnp6urDSPnPmjEhn7egwPJuam5s5dOgQb7/9tsl67t27JyJPERER1NfXc+rUKZPPWLFiBU1NTRw4cMBi5NTR0ZG4uDji4uIICQl5oTVYEhISIyOJHAkJiVHj5ze6/PLIyAD8/V1QKpWoVCozW9/xxGh/6+rqio2NDdbW1lhbW2NlZUV/fz/d3d10dXWZbYNMJsPV1RUXFxchFB7vzWKsDQLDTLVxfYD4DJ1OJ/57fLZ3IhkcHGRwcNCs58bjjNZWOKu6hWWhPuO7jTo9n564g97M8e4RafgSj8EhzcXFhc7OTuRyOR988IGZrW5iYiIZGRn09PRQqJZRq+6ztErAsF9dWFOFA3s3b0ahUKDX68nPz+fMmTMiVU4mk7FixQoWLFgwLoNWDw8PFixYwIIFC+jr66OkpISioiKKi4vRarWo1Wpyc3PJzc1FJpMREREh+qM8i/teYWGhqDdbtGgRbq4uLHOd2EaSMpkMZ2dnnJ2dCQ4O5vTp0+zZswcrKyuUSqVIe2tpaaGxsZHW1lZRU6XVamlsbDTr4WRtbY23t7eJ25u3tzdOTk5ERkYSERFBYWEhGRkZot7P+D6jsCopKeHkyVM4O4fR0NCBg4OCe/fSAUOqY11dnXDls7GxITIykvb2dr766iuzffTw8BDGARNlrS4hITExSCJHQkJi1CxeHEdgoAe1te3DLuPiYoWtbTfe3hHs3r0be3t71Gq1iauascdNW1ubyez902AcNFkSMk9Cr9cLVykwuCAFBARQW1srajMGBgZobm5m/vz5rFq1CoVCwVdffUV1dTV6vZ6/+Zu/EfU2t2/fFjPDs2bNIiUlxUT86HQ61Gr1qFPR7Ozs8Pf3Z8qUKXh6eopu78Z1Pb5uS7/fuXOHnlE+5hu6LfeseRb+j6x82voGGE7ggAwl1tTgwAwQgi08PNxi3xCZTMb69es5ePDgqPfLK9RQm9Ta2sqpU6dMjntsbCwpKSnD1lc9K/b29kyfPp3p06czODhIZWWliPL09PSg1+spLS0VLnO+vr4irc3Hx2fUA2qdTsf58+cBw3WzcOHCCdmf0WKcQHB1dSUyMlK8rtfr6erqMunxYxQ/xntXo9FYjJTa2Njg4+MjxM8bb7whImRdXV0mkdr8fCW/+c33KJWPIsouLlakpPgRH+9iss6BgQFRR2XEz89PCBsvL69xPTYSEhLPD0nkSEhIjBqFQs6vf72U//Jffhp2mZQUP+RyGffu3aOwsJCUlBQSExPx9/e32K1ep9OhVCpNbKU7OjrELO1wDSMtYaxrAUOkYyyNSp+Upnb9+nWuX7+Oj4+PKCoeGNDwv/7XX3B398PDw5Hq6juAIRqxZs0aEWHSaDRUVFRw584dysvLn7hNwcHBwjTAy8vrmWeNk5KSOJJTxOE7rSMu6+c8vl3WB3V6fntjZDtjAMcphuvCeK5jY2OHXdboWOXU0T/sMkNZNiuR9PR0rly5Il5zdXVlw4YNJgPwiUahUBAREUFERARvvPEGTU1NQvAYU7mMkY2LFy/i7OwsBM9IqVF3794VEY1ly5ZZtHCfDMhkMtzc3HBzczOxe9bpdHR2dpoZHrS2tor7ZWBgwGK/LFtbW9zd3enp6UGj0ZCfryQ11dxWXanUkppaw86dQULoDI2ChoWFieacE9HPSkJC4vkjiRwJCYlRo9frsbVtZefOINLSGlEqH82eDp0ptbW1FQXix44d4+7du2zcuNHirKhcLhcDn6H5/kYGBgbM+usYRVBXV5eJaDBGMIbDzs4Oe3t7bGxshBjq6+tDpVKNmPYFiD4Y+flK0tIahpkphs8//xwrKysGBgZM+nk8jq2trUl9xngPTv39/fnM149/LTlFnbLPYv2K0VZ4cbD3uH52VnUL7X2jq4MK8XABHh3LJ/U7kclkbN68mbavv36iXTLo8baRU3j2J3qH9EhatmwZixYtEiL0RSCTyfD19cXX15elS5eiVCpN7KnBUJ908+ZNbt68KeypjUXuQ+2pBwYGuHDhAmAQ2LNmzXoh+/QsyOVyPDw8hDOgEZ1OR0dHh4nhQWNjI21tj8xPjM8Zw/J60tIanvhZBvdHZ6ytDcc0Li6OqKgoE5toCQmJVwNJ5EhISIyakpIS2tvbiY93Yd26RC5efEhPjxYnJytCQhyEbXR/fz/e3t5otVo6Ojqorq7m97//PUuXLiU5OXlMA0xjmoqPj3m9iDH9JTMzk40bN4qIUHt7O+3t7WZ9WNRq9bCRIWtra9Hro6+vb9jlRjdTPLp96+/v5969exQVFWFvb4+joyNOTk44Ojri4OAw7L9jqR1RyGX8NmUG21OvIQMTofO4rfB4Mtr0N3u0zPZ1pePngauvr++IM+nOzs7IYVi7ZONeLh+opnfAIHAiIyNZt27dpOxb4uLiwuzZs5k9e7awRy8qKqKoqGhEe+q8vDxRS7Zq1aoXKt7GG7lcjqenJ25ubvj7+9Pd3S1q7Jqbm8VEh0qlQqPRUFWlMpl4sIRSqSUycj5vv/1qHSsJCQlzpDtcQkJi1BhTfqytrQkMDCAszOBAFBYWZuZG1NLSQlRUFNOnTxfd6jMzM7l//z4bN260GLUZKzKZTKSPJSQkmNVxDA4O0tXVZbEeqKOjwyR6o9FoRMrPcIxlpnhonyBra2tkMhkajcZiuppRfI30+UPX5+DggIODA87Ozk8URI6OjmyLC+TQzgX8Oi3XrJ/MaGyFn4bRpr/Nox2F7FGvlielqoEhpWv//v0Aw9olu6AlhUbi6cbFxYV169aZRAgmMzY2NsLBy9h40tiPx5I9tRF3d3fiR6uuJwl6vR61Wk13dzdKpdLs366uLrq7u00alD6J0bo6Wlk5SQJHQuI1QLrLJSQkRkVDQ4OwQ549e7bJIGH27NkWLVdLSkpwdHTkb/7mbzh16hQVFRV0dHSwf/9+pk+fzpo1aya0cZ5CoRBpMJZ43BChs7NTRIEeT4UDRj1TXFWlYtq0KcyYMYMZM2bg4eGBTCZDr9fT39+PSqWit7d32H97enro7e2lr6/PxLFNh8EtrUdjhVOXhpCuBuQNTxZdYHCgs7Oz458cHKlzcqZPYYevkx3z/Fxw7mumqKjXRBjZ2to+cz3QwgCPEdLJAPR4028i7p6UqlZZWcmBAweE2YSbmxvxnZ3E0k2Hsy/13Sqc0BKCCjmwePFiFi9ebNHE4GVALpeLhp2rV6+mra2N4uJiE3tqIx0dHfzbv/0bMTExREZFU2/tSqtaOy720U+DVqsVkRejcBkqXpRKJb29vU/lSCiXy3FycsLFxQVXV1fRTDg4uI3Dh/eP+P7q6mI0mnkv7XUhISExOiSRIyEhMSquXr0qfp43b57oTA+GvhMuLgbLaCsrKzEIBcjNzcXJyYn33nuPhw8fcvr0adRqNffv3xfGBDNmzHgh1qx2dnb4+fnh5+dn9jedTkd3d7dJPVBb2x2gcsT19vRo6enpITs7m+zsbMAQdbKxscHZ2RkvLy9hxJCQkPDEWhyNRkNvby+H8mr471fLaFQ9qnPxtIb3vLTE0S1EkaV+QFqtYXt6enqwoQkboA+4VGD5M+VyOXZ2diJSZBQ/w0WK7O3tzc5feVnpE9LJHvEjgfhUtLI62B1HR0d8fX0tLldQUEBqaqr4fcuWLQQGBvIf//EfyAHP7kaMrT7DwsJYv369WfPPlx1PT09hT93Q0MCf//xnACGg+/r6+D63jLTcXpPIlr+TLb9blzQuETu9Xk9vb6+ZgDE22fzzn/9Md3f3E2vRnoS9vT3Ozs7C3t0oYIxW1S4uLtjZ2Vl8Xsydq+N//I9TT2xY7OJihUpVze9+9zu2bdtGaGjoU22nhITE5EcSORISEiPS1dXFw4cPAZg6daqZ5a5MJmPu3Lmkp6ej1WpRKBQMDg6KwVd2djaOjo7Mnz+fyMhI0tPTycnJYWBggOPHjwtjAm/v8S1+fxbkcrmwwXVwcCA/Px+lsnlU73VyMn+0GqM4/f39tLa2UlhYaPJ3KysrnJyccHNzEx3i3d3duXz5MmU2XvyvUrWZcUC7Bn7bYMWhnev49OcBrFarpa+vb1SRIpVKZXEwqtPpUKlUqFQq0U1+JIyiyFhX1NDQQDzdbKeWwwRaND3g5yqhHzttWB1siOJYGrzeuXOH4ydPUoUDvVizeeUSYuPiuXH9mtmyK1asIDk5+ZXvZ3Lt2qN9//DDDxkYGGDftTxSy8xroep71LyZepV/jnfhl0tmDmtPPTAwYBZ1Mf7c2dlJd7dBTFtKuZTL5UyfPp329naL5h/G69t4Tw0VLcafnZycnqlXkUIh51//9S127/692d9kMtDrH7k/dnd388033zBr1ixWrVolGQ9ISLyCSCJHQkJiRIZ2hjf24Hh8kDRz5kwuXLiATqfD29ubxsZG9Hq9iOycPXsWR0dHpk2bxsaNG0lMTOT48eO0tbVRU1PDH/7wB5YsWcLixYsnRb68Xq+npqaGCxcuUFVVBUBIiAMuLlbDpqzJZBAY6Mm///t/p6qqkvLychobG+nq6kKtVj/RPlqr1Yq+PUN7ueiA32GL3kLalzE+8ndpuWyOCUAhl2FlZSUGjaNBp9MJh7nhhJExCqRSqYbdD2NdkbFuxIgjg+iHTVcDkNH9c9ThcUtnvV5PVlYWv794mzSiRHTiUEYZ/9uFYtbo63m8CiUxMfGVFziNjY08ePAAgPj4eIKCghjU6dl/vADLETODmPxNfju6/D9hb2uLh4cH9vb2DA4OolQqhQXz0+Do6CgmPpKSkkxSyIzX4vOytfbw6PvZ/dHU/TAw0JP/5//Zg5/fo55CYBDQBQUFbNq06aWp25KQkBgdL34kISEhMalRq9XcuWPoARMSEmIxtUuv1+Pg4MC0adO4d+8ejY2NhISEUFVVhVarFULnyJEj2NvbExkZSXBwMJ999hlXr14VFriXL18WxgTh4eHPdT+N6HQ6ioqKyMzMFP1LjFhZKfj7v1/O//yfhkHS0LG+cVz9+ecf4OLizLRp05g2bZrJ+wcGBmhtbaWxsZHKykohgJ5kX12Fg0nq0ePogRplH//7t4dZHOSBu7s7bm5uuLu74+LiIqyyh0Mul+Po6Iijo+OIkbRBnZ7LVc1Ut3fjZgWJbjao+1QmgkilUlFXVydsfkfbtBMM15fYL72etLQ0vr5Z8HPKmymdejmpBPKWrI5YvVK8fuLECXbv3j3qz3wZSU9PFz+vXLkSMFh2DzWVMMfQeLUKB8L6VTSMopbL1tZWRF8cnZ2p1NnTp7AlyN2Z5eG+uLsZopxyuRyNRsPp06dZs2bNC6t16erq4ubNm8THu5CSMo2wsDk0NHTg5+fO4sVxKBSGe8HX15eDBw+K+06lUvHXv/6VhIQE3njjjQmtE5SQkHh+SCJHQkLiieTk5Pz/2fvzqKjvbNH7fxfzjKAgs4ATIDI54ISCgENM1BijiW3M3En36XS6+97fc8/zPPc5fc6695x7zjrdp5NOD0nadNQkmpjExCTOIyIiKsgg4AAKMs8yj1X1+6NSXy0BZS6G/VorK1AUVZ/iW8J3f/dn761c4V26dKlye8974hcqtTpTp06lrKyMjo4OzMzMlOGc+/bt45VXXsHT0xNTU1OioqIIDg7mhx9+4Pbt29y7d49PPvmEuXPnsnr16hE74ejs7CQjI4PExESlvuBBixYtYsWKFVhZWRESEsLbb+8y2Pvv5TWZd955iU2bInt9DgsLC6UWJyIiQrldrVZTW1urDCQtKyujtLSUxsbGPgcJWQVFUJBtcJtKpcLBwYHJkyfj5OTU7b/+bNE5kFvcY3e2d9eEsSnCsCPaBx98oHxsR986XgFKBk+tVvPNN9+QlZ3NUWbqX81D99ZlJw5rpzKLBvShXF5eHrW1tb02mxjr7ty5o8zSWbBggfI6+9qy+3Hvp6lTpzJr1iyCgoK4fv06jo6O3LZ04e1j6RQr/y6q8Eot0x37QLsBv5ahdubMGSXLuGpVPJ6enj3ez9/fnzfffJN9+/YZDADOzs4mLy+PJ554grlz5477jKAQ450EOUKIXqnVamXv/+TJk3udEK8/sfDw8MDd3Z2ysjKysrJYu3YtBw8epK2tDV9fXwoKCtBoNHzyySe89tprynBQJycntm/fTnZ2NocPH6a1tZWsrCylMUF4ePiwnXC0trZy+fJlLly40GN9Snh4ODExMQbbvzZtimTDhgUkJub2eKW4v0xNTXFxcemWSWlvb2d3whW+Ti597GP0FEzo5wjV19f3+D0WFhZMmjSpxyDI0dGRjo4Ozp8/T7bWjt8kF3erqylpaGXz/mS+2rJYKWrXD2zUm0YL7rYWlDd39FKXo8WeLsAC0GW7Pv/8c+7cufPYLJY+O6H2nMVCdweuXLkCwMGDB3n55Zcf8X1jk1arVdpGm5mZsXz5cuVrfW3ZvSw0mLc3xKHVapX21Lm5uUqHu4qKCioqKkhMTAQgB/sfM2mG//56OvbGVFVVpVxgCQgI6DXA0XNycuK1117j4MGDyvwh0P2b++abb5SM8sP1h0KIsUOCHCFEr7Kzs5WBmkuXLjUINHoLOiIjI/n2229pbW3FxMSE2bNnc+PGDQoKCli0aBEXL17Unbzv3s3rr7+Og4OD8njBwcFKY4LU1FQ6Ozv5/vvvuXr1KuvXrx/SxgT37t0jOTmZy5cv91hjEhQURGxsbK8ZAVNTE6Kj5wzZenpiaWnJq3FL+F/ZhyhpaO0lSIApFipWuLtSXVWpDIbsi46ODsorK0mpbKKJEoP2y/rnb21v5x1m9rkm6MGuewBeHh78adm8HoeR6j+LowLwp7W1lc8//1zZStXXLJbf3HDiw/1JS0tDo9Fw9+5dysvLe+3UNlbl5uYqAeTSpUuV4bUAUT4ueDlY9/o+UQGe9ta8tT4OlUqFSqXq1p5aP49H355aAxyl55/hw8fe2E6dOqV8rN/C9zgWFhZs3ryZCxcuGGwBBMjPz+e9995j9erVzJ8/X7I6QoxBEuQIIXqk1WqV4Z/W1tbd6ksevq/enDlzOHr0KG1tbVy6dInnn3+ewsJC2trayMjIYOXKlZw+fZqmpib27NnDq6++irW1tfL9VlZWPPnkk0pjgurqaoqLi/nLX/7CsmXLWL58+aD2/JeVlZGUlER2dnaPX/fz8yM+Pr7H2iNjMDVR8e6asB6DBP1p1wcbFylX09va2qiurqa6ulrZ/lZZWdljNicH+x4GaXYqgzTb29v7XBOUeLeK5T5TSE9PN/j6ggULmDvbk3+ODuLdlDxqW+/XH+mHdsa46wLd3bt3GzQu6OtWt+lTnbGwsGDp0qVKBuLgwYO88cYbffr+sUCtVisF81ZWVixevNjg6315n7y7NqzXeTmTJ09myZIlLFmyhKamJn7/+9/369gv9XQa+IsbJH1GCnSZV32GuC9UKhVLly5V6nQebL6gVqs5fPgwmZmZbNiwoV+PK4QwPglyhBA9KigooLJS1zJ50aJF3Tqe9XZl08zMjPnz53P+/HlKSkpobGzkqaee4ssvv6S1tZWysjKWLVvG+fPnqamp4bPPPuPFF1/sFrh4e3vz5ptvcvHiRU6dOqW0os7KyuKpp55i+vTpfX4tWq2W27dvc/78eYPOZQ9yd3cnPj4ePz+/Pj/uSNkU6MVXWxb3WBPzzpowg+1CVlZWeHl54eVluIWoq6uLmpoaqqqqqK6u5of8KvYXd7/m34AZ+/FiC8UE0feaoLLGNu7cuUNzc7Nym4WFBTdNnXjq3UMG67bWdjGrroLpJXextjPDPkC31nv37hk85jRaHjNQVIu3gw1RProM34oVK7h48SKdnZ2Ul5dz9+5dfHx8+rT+0e7q1avKzycmJqbHbmX9eZ88ip2dHS+//DJ7ruZD+uNbiPe1Hmg4aLVaJfgzMTFhxYoVA3qc6dOnK3U6+rbp+lb4xcXF/PnPfyY2NpbFixcPqs21EGLkSJAjhOiRPotjamrK/PnzH3nfh7d76YMcgEuXLrF+/XqCgoLIyckhNzeXZ555hvDwcK5evUpJSQn79+/nueee63byYGpqytKlSwkKCuLQoUPk5+dTX1/Pp59+ypw5c1izZs0jW9Oq1Wqys7M5f/68QYHxg5ycnIiLiyMwMHBUb0nZFOjFhtmeJN6toqyxrd+T7M3MzJg6dSpTp05FrdHywtVD6EaCPkyXBziKGwE09jmbUnz9Gs31hrVDVVP8+H8PXOq2faoVUzKcPMg4UQi5BRw5Us6HHz5juAqViuDAQF6obOUv1boA+OGtbipUvLPmfnbC1NSUmJgYpW7l4MGDvPXWW31a/2jW0dGhdCB0dHRk3rx5vd53sO8TPR8fH5ZprCA94bH37Ws90HC4ffs2d+/eBXRbZQdTQ+Ps7Mzrr7/Ot99+S25uLmq1Wgl0QLclLisri40bN46aTK8QoncS5AghuqmqqlI6OEVERGBjY9PtPo8KCBwdHZk1axY3b94kMzOT+Ph41q1bR0FBAS0tLfzwww/8/Oc/p7m5mZs3b5KXl8d3333Hxo0be3xcJycnfvKTn5CTk8Phw4dpaWkhOzubmzdvEh8f3+3+HR0dpKWlceHCBRobG3tco62tLStXriQsLOyxbZZHC1MTFdG+roN+nP60G27GFBXaR8y60eJAF005l3mwGkgDfFSqQYsJ3bIwymTGMLheQmOjLpC6fr2RWbNs8fPzY/PmzdjY2LBJo8U1MYffn8+loet+mONiYcL7D2zT04uMjCQhIYH29nZqa2u5desWM2fOZCy7ePEira264xUXFwcqE84WVPYaxAzV+6QvdT5eDtZE+bigUfe9i95QeTCLY25uzrJlywb9mBYWFjz77LMkJSVx6tQpJcBxcHCgoaGByspKPvzwQ5YuXUp0dPSomOklhOjZ2PjLLoQYUQ9OU1+0aNFj799T4X5kpK6VslqtJj09HRsbG9avXw/oOhgdOnSIzZs34+3tDUBmZqbBkL6HqVQqAgKDCN3wPM1+YdzBhvbOTo4ePQroArOmpiZOnTrF7373O44dO9ZjgGNubk5cXBxvv/02ERERYybAGUp93V50Azu+wqvXhgf63Moayrv9Mblfz9FLcKRSgaMNTLvfTOL06UpefPElduzYgY2NDQdyi/F99xC/PZujBDjWdBFNJRe2RPS4/crExITVq1crn3///fePHMI62jU3Nyt1RlOnTuWGySR83z1EzO4Eth1IIWZ3Ar7vHuJAbvGQP7e+zgd6buANGGTSRlpOTo4yy2rZsmU9XowZCJVKxbJly9i2bZuyjbahoQF3d3flIkxSUhJ/+ctflEHBQojRZ+L9dRdCPFJTU5NSPB4QENBrd7HHbe3y8/PDyUlXjJySkoJWq2X27NmEhIQAcPPmTXJycti2bZvSNS05OZkLFy70+Hj6E97V+5L5zzsd7MaXP5rM5jq6DlMfffQRv//97zl//nyPk9v1Bca//vWvWbp0qdEGFo4Gfd1elIUTutPZno+1Cnj2x9qdh/V5CKjd/bXU13dw8GASGRkZfHDuKpv3J3fLOLViyllc+O5m78Msw8LClM5jjY2NXLt2rW9rGYUSExPp6tJlSbQBC3j2y+4/E3075+EIdPR1Pp4O1ga3ezlYG7V9tFqtVjqiWVtb9+liTH/NnDmTN998k8mTJwO6piXOzs7KhZm6ujp27drFoUOHemw/L4QwLglyhBAGLl26pFz5XrJkSZ++p6cr5SqVSsnm1NfXk5eXB2AwUfzw4cN0dHTwwgsvKK2kT5w40a0N8YHc4h5PeO9pTPiGx7evnTdvHr/+9a+Ji4sz6OQ2Uem3IfUWpqqAKdbmNPPoAmstKmxQ9/i1Pg8BbTLMKiUmXuHAt9/yf53JRdvrJikVv7tWRUVlVa8B7bp165TPjxw5MiazOXV1dVy6dAmAab5+/NvVsh5/IvrbfnU0HbVm6F/npkAvCt5ex5kXV7B3UyRnXlzBnbfXGXU+Tnp6utKIITo6GgsLi2F5Hn2dTkCAbuBtTU0NlZWVLF26VHnOK1eu8Kc//Ylbt24NyxqEEAMjQY4QQtHR0aGcVHl4eChXLHvSlyL90NBQZc+6/nGtrKzYuHGj8nwHDx7Ezs6OF198ESsr3VX9b7/9lps3bwKg1mh5+2h6L1um7q9B08NXZ8yYwS9+8QuefPJJg2GeE11ftiFtD/Xt02P1lLGxsrJiqaczzmaPOOHWaqG+BQoNG0I4Olo+fqsbUNbSyf/46y7+7d/+jf/4j//g/fffZ//+/Rw7doyUlBQA5Zi3trYqg0KHi1qj5WxBJfuy7nK2oHJIgo3Tp08rwZl98IJH1lE92M55OOjrfJ6f60O0r6vRtqgBdHZ2Ko0YHBwcHtmIYShYWlqyZcsWYmJiAN1226SkJJYuXUpgYCCgy4Dv3buXAwcO9GtWlRBi+EjFnBBCkZ6ermy76E8Rb29Xya2srAgNDSU1NZW8vDzq6upwcnJixowZREREkJaWxu3bt7l69SoRERHs2LGDv//973R1dfH555/z8ssvk6+2fGyRPEARNnjTZPCVgoICbt++jbOz86junGYMj2s37GxtwTsXH39lOtTfB9uqQoP6p9dffx1nZ2dsr+bzk+/SuD868kf698vRdIOWafb2Znh5WXLH2qHnxm8P0QdYbW1ttLW1KfUZPTl8+DA3btzAycmJSZMm4ejoqPzfzs5uUO+PA7nFPf4c3+1H2+aHlZWVKdvs5syZQ4eFbd++z4jtnEdKSkqKEkjExsaOSEtnlUrF8uXLcXNz48svv6Srq4szZ84QEhLCM888w5EjR2hpaSErK4tbt26xbt065syZI793hDAiCXKEEABoNBqlHsbR0ZHZs2c/8v59/eO9cOFCUlNTAbh8+TKrVq0CYNWqVdy6dYvGxkaOHDmCv78/7u7ubNu2jT179qDVavn0009xWv5Un56nGVOmTp1KdHQ0d+7c4dKlS3R1dXH48GHS09NZv349U6dO7dNjTRSPajes1mgf21nL096K7VEh7Nl9v+bF19eXSZMmcenSJQpOnmQLVt0GjtLQqgtwcksMHvOZZ2ZiYqLBpLUB6LkW7EGbV8cSaK3l3r171NfXc+/ePWpra2lsbESj6Z7b03cM7PZaVCrs7e1xcnLCycnJIACaNGkSDg4OPZ5It7S0cPhONVu+Sun2M9LXyQy0bkVfbwK6E/mUiqZH3Ps+Y7ZzHgmtra1KIwYXF5dHDikeDrNmzeKNN95g37591NbWkpmZSUVFBS+++CIpKSmkpaXR1tbG119/TWZmJk8++aSyFVcIMbIkyBFCAHDjxg3q6+sBXS3O47qOPRjkPKrewdXVFW9vb4qKikhLSyMmJgZzc3MsLS15+umn2bNnD11dXXz77be8+OKL+Pn58eyzz/Lll1/S0dFBxvkzgNtj179q6SJeionUdWELCCAkJITvvvuOyspKSktLef/991myZAkrVqwYtv37Y1Fv7Yb1W9o270/+cXLOffoj/+7acLKvpRt8n7+/P++//74ylyiITp4Pn4HVrHDqOrXcunqHD//n55QU1SjfY2+v+1P0zjv/g1u3bnHy1Gm+qet9CKi+dfGzC+f2uG1Kq9XS3NxMfX09d+7c4dSpU7rvU6lwdnamsbGRjo4Og/s3NDTQ0NDQa7csW1tbJk2apARBVlZWnDh1ineYiZbuTSz0uatfHU1nw2zPfm3vun37Nrdv3wZ0FwmcnJy4/Oe/4IDfY38m+sGo41VSUpJy7OLi4oySKZkyZQo//elPOXDgADdv3qSiooKPP/6YrVu3EhwczMGDB6mvr+fWrVu89957rFmzhoiICMnqCDHCJMgRQgD3h39aWloSFhY2pI8dGRlJUVER7e3tXLt2jfDwcEDXgW3BggVcvnyZwsJCLl++zMKFCwkKCmLdunUcOnQI17ZaJqlcqNeaPqKVMTy/1PAkwtPTkzfeeIOUlBROnjypZKqysrJ46qmnxvzslJHwuC1t62e68Z/fZim3m5qaKrUSAG5ubjz55JN4ej7QHGKuD//vT6JITMwlL6+Y8+dP4een64SmUqmYM2cOgYGBqI9d4L9dKuXhrW59aV2sUqmws7PDzs4OT09Pbty4QXFxsdLhLz4+nra2NiX78+D/a2trqa+vV+bS6DU3N9Pc3ExJyf3s0/3aoZ49WCfT17k1Wq1WGWZqZmbG8uXLAXCwt2PNvXL24zWgn8l40NjYqLS39/LyMuq/YUtLS5577jnOnTvH2bNnaWtrY/fu3axdu5af//znnD17luTkZLq6uvjhhx/IzMxkw4YNvXarFEIMPQlyhBAUFRUpJ28LFy7sU6ajr5kc0LWitrW1pbm5mZSUFMLCwpTvj4uL49atW9y7d49jx44xY8YMnJ2dmT9/Pk1NTSQkJLBKW/rjyR08fHKn/6ynkzsTExMWL15MUFAQhw4dUrbH7d27l8DAQNauXSsNCR7jUVvasrOzDTIi+sGJFhYWrFq1ivDw8B4zgqamJkRHzyE6eg4ODvXcuHED0BVvOzk5YWJiwm/WLsPH5y7/8MMVKtvud3Czp5MXXTVEOvT9ZP7pp5/mvffeA3SDNaOjo7GyssLKyqrXLYxdXV3U19cbBED6IOjevXs0Njb2uU12f+pkHp79YmtrS01NDQ0NDQShYQvFHFO5U6+9/9z6oNOY3c5GQkJCgrINMT4+3uiZEZVKxYoVK5Q6HbVazZEjRygtLeXJJ59kzpw5fPvtt1RXV3P37l3ee+894uLiWLx48YSczyXESJMgRwih1OKoVCoWLlw45I9vamrKggULOHv2LBUVFZSUlODlpTshs7Cw4Omnn+bjjz9Go9HwzTff8PLLL9PR0aG0Bw6ikS0Ud6vt8HKw5p34uZCf/sjnd3R05Pnnn+f69escOnSI5uZmcnNzuXXrFvHx8cyfP19OOh6hty1tDw6N1Zs3bx4rV67s82DGJUuWKEFOamoqcXFxytc2z/Hh6UBvzt4pJykzh+Lca7h33sOkEnbuzGfmzJmsXLkSN7dHb2d0dnZm+vTp5Ofno9FoOHr0KE899ehaLzMzMyZPnqzMSHlYVlYWdw4c7dNr7GudjFqtVgbiWltbs3jxYjQaDQcOHFBO7n/3yrN85uHJ+aLqbkHneFZTU6PU9s2YMQMfHx8jr+i+2bNn8+abb7J3717q6urIyMigoqKC559/njfffJPz589z9uxZQFdrlZWVxcaNGx/7vhVCDI78VRdigqutreX69euAruWzfoji4/QnkwMY7EnXt5PW8/HxUYb5FRcX8/nnn/P73//eYDBoEI38ilv8sy98tmmhMqvjqdkefV5vYGAgb731ljK/p6uriyNHjrBz507Ky8v79DhCt23oiy++MNi6ZWlpyeuvv86TTz7Zr8nzXl5eeHjojmFaWppBZgh0AVbsdHf+6elY/vTff0Z8bKySabx16xYffPABX375JdXV1Y98Hn3bcoCrV692247WX3PnzmXn//xvj5035N2POpm0tDSlLi4mJgYLCwsuXrxIaWkpoAsIvb29MTM1GTXtnEfKg9sgHwyERwt9nc6MGTMAKC8v5/3336ekpIQVK1bw85//XHmfV1RU8MEHH3D69Gll0KsQYuhJkCPEBHfx4kXl48WLFw/b89jb2xMUFATAtWvXaG5uNvh6dHS0MiT01q1bdHR1cQcbajwCmPvUc/hM89X9wirIwak4hxXTBnb12tLSkjVr1vD6668rW5XKysr44IMPOHbsWLeTbHGfWq0mOTmZd999VwmM9Z5++mnlJK6/9EFne3s76enpvd7PwsKCZcuW8etf/5rly5crHc9ycnL485//zLfffktdXV2P32tnZ8ecOXMAXVB+6NChAa31QWamJo+dN9TXOpmOjg7OnDkDwKRJk4iIiKCqqkrpsubs7KzMaZloysrKyMnJAXTB5WjtkmhlZcW2bduUOqrW1lY+/vhjLl++zJQpU3j11VdZs2aNcrEnMTGRv/71rxQVFRlz2UKMWxLkCDGBtbS0kJaWBsD06dNxde1bcTT0P5MDKFvhtFqt8rxarZacnBw++OADJfDJwZ53mMlufHmv1IRnvs/k/6t1ptRRN5z00qVLnD9/vs9r7YmHhwc//elPWb16tbJV7eLFi/zpT39SBpGK+woKCnj//fc5fvy4UnujZ2dnN6gi8Ae/98KFCz22f36QlZUVMTEx/OY3v2HJkiXKezEjI4M//vGP/PDDDzQ0NHT7vnXr1in3zc7ONpjtM1D65gyeDtYGt3s5WPerffSFCxeU7JK+a9g333yj/NvatGmTMlh3otEHeiqVatQHevo1bt26VQnCDx8+zHfffYdGoyEyMpK33noLPz8/QJdJ//vf/86RI0fkAosQQ2xi/sYUQgBw5coV5YR1yZIlw/583t7euLi4UFVVxaVLl/Dw8ODkyZMGW8VysH+gycB9pY1t/A17XrJxY1pLOadPn8bW1nZQczJMTExYtGgRQUFByrDIxsZG9u3bR0BAAGvXrp3wMy4aGxs5duwY2dnZym36Y6g32JqmB7+3vl7XiEA/Sf5RbGxsiI+PZ9GiRZw/f17ZBpmamkpqaiqRkZFERUUpGUJra2siIiKU2o6DBw+yffv2Aa9b71HNGfqiqalJ6W7o5uZGUFAQSUlJlJWVAboGBAYd6iYQ/UBfgAULFuDk5GTkFfVNQEAAb7zxBnv37uXevXukp6dTWVnJc889h5OTEy+88AIZGRkcPnyYzs5OLl26RG5uLuvXr1e2vAkhBkcyOUJMUF1dXcpWNVdXV+XKYl8NJJOjUqmUrUlNTU18+umnSoDj6OjI+o0bOW2hLyg2PEHUP8NxE3esrHU1H99///2QZF0cHBx47rnn2Lp1q1KTdP36df74xz+SkpLy2MzCeKRWq7lw4QLvvPOOEuBYWVmxfv16/P39De4bERExJM9paWkJ0O8snb29PWvXruVXv/qV0p4cICUlhf/6r//i1KlTSpbkwcxdfn5+r9vb+kvfnGEgdTLnzp1TajNWrVpFVVWVMttnypQprFixYkjWONZotVqlEYOZmRlRUVFGXlH/uLi48MYbbzB9+nQASktLle1pKpWKsLAwfvnLXyoBfWNjI5999hnffPPNoGvGhBAS5AgxYWVlZSl/SJcuXToi7Vhra2vJy8szuM3a2ponnniCt956i3pHd6o7DGeAPEgLlDS1MzNuvbJ158CBA0O2voCAAH7xi18ogZharebo0aP87W9/U66qTwR37tzhL3/5CydOnFACvAULFvDLX/6S0NBQMjIylPsGBAQMWRtufbBUWlo6oDoFR0dH1q9fz1tvvUVwcDAAGo2G8+fP81//9V+cO3cOjUajPI8G+D/7vmNf1l3OFlSi1vQtWB9KtbW1XL58GdANUvXx8TF4T0/kbWo3btwwaLrQ16Yoo4m+TmfZsmWArk7n73//u5JNtLOzY8uWLWzZsgVra92Wx8zMTN577z1ycnL6fAFJCNHdxPzNKcQEp9Vqle0xtra2SkF2f/Qnk6Ofd3PlypVuX9u+fbtSsN7XeSLt5tZs376dXbt2KbdVVFQobakHQ9+YIDQ0lO+++47y8nLKy8v58MMPiYyMZOXKlX2aIzQWNTQ0cOzYMaXIG3Tdz9atW6e0u7116xZtbfeP0/z584fs+efNm8eFCxfQarV8+eWXzJ07l/j4+H4/jrOzM8888wxRUVGcOXOG69ev09XVxZkzZ0hKSqKjo4Mc7HUtyavM4UAKoKujeXeE583oMzagm/2SlJSkzMlZvnw57u7uI7aW0USj0Si1OJaWlsPaFGW4mZiYEBsbi4eHB1999RUajYYffviB0tJS1q5di5mZGYGBgfj6+nL8+HHS09NpbW3lyy+/ZPbs2axbt07meQkxAJLJEWICysvLo6amBtBdIdUXyA61trY2Tp8+zX/9138ZBDihoaHKxw920+rrPBF3eyumTZvG1q1bldu++OKLIdt6BODu7s7rr7/OmjVrlJ9PSkoK7733XrfOYmOdWq0mKSmJd999VwlwrKys2LhxI6+88orBPI+rV68qHzs4OHTbujYY7e3tSsBc39jIZxfS+SyzcMBZFldXV7Zu3cpPf/pTZcuQPsDZjxcND13nK2loZfP+ZA7kFg/+xfRBaWmpQdcwlUqldFhzcXFRunRNRJmZmcrvqBUrVmBl1bffDaNZYGAgb7zxBo6OjoCuZfjHH3+sNMCwtrZmw4YNvPDCC0ot4I0bN3jvvfdIS0uTrI4Q/SRBjhATkH7+jLm5+YDrKR6Vyenq6uLChQv84Q9/IDExUfl6WFgYv/rVr9i4caNycpyenk57ezsAUT4u/Zo7om8OALpOcXv27OnWmnowTExMiIyM5Je//CUBAQGALiv1xRdfsG/fPmWmyVh2+/Zt/vKXv3Dy5Ella1pkZCRvv/02oaGhBse5tbXVIMBbuHDhkG5z1HeXerC73vZvLhGzOwHfdw8NOPhwd3dn+/btBAYGogGOog/aeq77+tXR9BHZuqavN1GpVKxYsaLbNrXhuvgw2nV1dSlzcezs7FiwYIGRVzR0XF1deeONN5Tff6Wlpbz//vsUF99/b/v7+/MP//APyrbZzs5Ovv/+e3bv3j2kF3KEGO8kyBFigikvL6egoADQbQ8ayiukGo2Gq1ev8u6773LixAnlpHXWrFn8/Oc/Z8OGDcpVTH076c7OTjIzMwFd8XZ/5448mBW6d+8en3zyiRI0DRUHBwe2bt3Kc889p2wbuXnzJn/84x+5ePHimGxMUF9fz/79+/nkk0+ora0FdFvT3nzzTdasWdPj++LBGgF94fRQcnd3x3FR/LBlWeLj49F6B9CAOY+q+ypqaCXxblWPXx8q+fn5yr/DhQsXkpWVRWVlJaCbGfVg9myiuXLlipLdWLly5birSbK2tuYnP/kJS5cuBXQXaD766COlrT7oZkKtWbOGV199lcmTJwNQWFjIe++9R3Jy8pj8nSPESJMgR4gJRp/FAVi0aNGAH+fBK/gajYbc3Fz+/Oc/891339HU1AToWka/+uqrPP/887i4GE59nzlzprIlIyUl5f48kAHOHdHXhlRUVLBv375hmSQ+e/ZsfvGLXyj1ARqNhmPHjvHhhx8qBdKjXVdXF4mJibz77rvk5uYCupOup59+mldeeeWRgxYf3HIYGBiotGYeKmqNlt/n1KILQIY+y+Lk5ETggr7VdvS1PmwgtFotx48fB3TZ1NmzZ5OQkADA1KlTlSL1iai9vV35WTg7OxtcxBhPTExMiIuLY/PmzUq3v++//55Dhw4ZzKHSX3jQd9jTv3d27typBMVCiJ6Nr8sjQohHqq+vJysrC4A5c+YoWZXB+uqrrwzmpri4uBAfH8+MGTN63c5kYmLCwoULOXnyJDU1NRQWFuLr6wsMbO5IbGwsLS0tXLt2jcLCQg4cOGBwAjFULCwsWLVqFSEhIXz33XeUlZVRUVHB3/72NyIjI4mJiVFaIY82+fn5HDp0yGDLS1/XXFtbazDPaDi2ECUXV1Pc0Hvr3AezLNG+fR9c+6D+1H0Nl2vXriknqEuXLuXw4cPK1ybyNjWA5ORkpbFFXFzckP/7HW3mzJnDlClT2Lt3Lw0NDVy5coXy8nKDdvZmZmZER0cTFBTEt99+S1lZGWVlZfz1r39lxYoVODs7Y2dnxzRfvwHPahJiPJIgR4gJJCUlRfl4sMM/H6xH0Qc4jo6OxMbGEhwc3KdajfDwcE6fPo1GoyElJUUJcuD+3JG+UqlUbNy4kZaWFm7fvk1ubi5HjhzhiSeeGJb22G5ubrz22mukpqZy/Phxurq6SElJ4dq1a6xbt46AgIARacvdF/X19Rw9etSgnsbb25snn3wSV9e+/YwfbBDh4ODAtGnThnqZlDf1bZvhYLIs+rqvkoZWesoHqdBlDfV1X0NNrVYrXcNsbGzo6Oiguroa0G3N6uvxGI+am5uVro/u7u5KHdx4N3XqVN58802+/PJL7ty5Q3FxMe+//z7PP/+8wRBYV1dXXnvtNVJSUjhx4gRarVbJeuVgT6LtdMqaO5T7G6NboBCjyfi+RCKEULS3tyvbjXx8fJS2zf1VV1fH119/rRQGgy67sXbtWt566y2lS1Rf2NjYMHfuXEA3fLOhoWFAa9IzNTVl69atStvdK1euKCcBw8HExESZH6Mf6Nfc3Mz+/fu7NSZQa7ScLagc0ZksXV1dnDt3jnfffVcJcKysbfCOWo3VglhyWujTOrRarTLXA2Dx4sXDEzja9S0DNpgsy6PqvkCLFi1/WB02bFfAr1y5orzPIyIilO2jbm5uSo3GRJWYmKhsM42Pjx81FwlGgrW1ri2+fitsc3MzO3fuNOhmCLrfOYsXL+att95SbtN3CyxrNrxIMNLdAoUYbSSTI8QEkZaWRmdnJ8CATqaampo4d+6cMrjwQS+88MKAZ9QsXLhQGS6ZmppKTEzMgB5Hz8LCgu3bt7Nz507q6upISEjA1tZ2WDs02dvbs2XLFm7dusX3339PY2Mjt27d4o9//CMmJibcMnPipLk3JQ9kIIb7KuutW7c4fPgw9+7dU25Tz5rPe2UaShKLgKI+r6OwsJCWlhZAlzEbrjqJxV5TRiTLoq/7evtousH2OAe6WEM53k2lwNAfl/b2ds6ePQvApEmTlPbRKpWKTZs2jfutWY9y7949Ll26BICfnx9+fn5GXtHIMzExYdWqVXh4eHDgwAG0Wq2yJXb16tUG2xidnJyIiYnh1Jkzj+wWqEJXx7ZhtqdsXRMTzsT9jSrEBKJWq0lOTgZ0xbwzZ87s8/e2t7dz5swZ/vCHPxgEODNmzFA+Hkz3Iw8PDyXzcvnyZYOi24GysbHhxRdfVArjDx8+THZ29qAf93FmzpzJL37xC2UroEajIbPLmt1tUyhpNKw1Ga6rrPfu3WPfvn3s3btXCXCmTZuGz+pn+d83WwwCrb6u4+GGA/rJ7ENtIN31BmpToBcFb6/jzIsr2Lspkq+eDOZX3CKIRk6dOjUsM0kuXLig1JtMnTpV6WoXGxvbrTHHRHPmzBnlZx4XF2fk1RhXcHAwP/3pT5XGLJcvX2b37t3d2uMvXryYmbFPjYpugUKMRhLkCDEB5OTkKC1ZlyxZ0qdtIF1dXSQnJ/OHP/yBc+fOKS1LQ0NDefvtt4e0fbB+HkRra6tydXuwHB0d2bFjBxYWFoCuOcKdO3eG5LEfxcLCgvj4eMLDw0d0JktXVxcJCQm8++673Lx5EwBbW1ueeeYZtr+wg39Kvt1jdkR/28+/v0xLa/dal87OTqULGwyuI19fDLS73kDo676en+vDM/MC8fpxC2dHRwfnzp0bsucBXSZUvzVt8uTJ3LhxA9AF+fotShNVZWWl0kY+KChowFtpxxM3NzfeeOMNpU6xqKiI999/36CLo7m5OaaOU/r0eMPZLVCI0UqCHCHGOa1WqxTzWltbP3arkUajIT09nT/+8Y8cP35cmTkzc+ZMfvazn7Fx40YmTZrU7TkGY86cOcpclgebIwyWq6sr27dvV4K6zz77jLKysiF7/EdZsmQJLVOmjchV1ps3b/KnP/1J2Qqlf/5f/vKXBAcHc77o8V3LKlq7+O3fP+/WejsnJ0cJcO3t7Qe8LbE/Hs6ynHlxBXfeXjfsBdSbNm1SPk5MTBySrKJeQkKC8rPVz49SqVQ8/fTTE3qbGsCpU6eUj1euXGnElYwuNjY2vPDCC8qFhaamJv72t78p23thdHQLFGK0kpocIca5wsJCKioqAF3GpLetZVqtlps3b3LixAlqamqU2728vIiPj8fHx8fg/kNZFGxmZsa8efNISkqipKSE8vLyIRuG6O3tzXPPPce+fftQq9Xs2bOH1157TRmwN1ymTJlCxPKVcODxQdtAr7LW1dVx5MgRbt26pdzm6+vLunXrmDLl/hXevj7+neo6/u3f/g0fHx/8/f3x8/MzmKvU1yzgUOhvd72hMHnyZHx9fSkoKFC6oK1evXrQj1tTU6Ns+XN0dFQaUsTHxxscp4moqKhIyTxGREQM+7/LscbExITVq1crdTqA0kY6Pj7e6N0ChRjNJvblIyEmAH0Wx9TUtNfi+7t37/LRRx/x+eefKwHOlClTeP7553nllVe6BTgPG4r6Bf0wT0ApQB4qs2bNYuPGjQC0tbWxZ88eZfvecBquq6ydnZ2cPXuWP/7xj0qAY2dnx7PPPsuOHTu6nTj39fHt6EKr1VJYWMiZM2f4+9//bjBwcOrUqeN+0vrTTz+tfHzp0iWlWcdgPNiJUB/geHl5Kds0JyqtVsuJEycA3cm8fuCl6G7u3Ln89Kc/xd7eHtBlvPfs2UNba8uI1bEJMdZIkCPEOFZVVUVeXh4AYWFh2NjYGHy9oqKCzz77jI8//piSkhJANwPl6aef5mc/+xmzZs3q9cr9UF/RnzRpErNmzQIgMzOT1tbet1cNRGhoKPHx8QA0NDTwySefKEXgw0V/lbW3n5QK8O7nVdYbN27wpz/9yaA19tKlS3nrrbcICgrq8bg8bh0AU61M+ZeXn2PlypX4+fn1uIVqz549/Pu//zt79+4lOTmZsrKyYSnQNyYHBwdlPotGozEY1DkQJSUlSp2Zubk5oDuh37hx44TfppaXl0dRka7L36JFi5RCe9Ezd3d33njjDWVG1d27d/nggw9YPMl0xOrYhBhLZLuaEOOYvqMaYFDcXFdXx5kzZ8jKylJus7KyIjo6mnnz5vW7W9pQneguXLiQmzdvolarSU9PH/KC7CVLltDU1ERycjJVVVXs3buXF154QTn5HGr6bmGb9yejAoPtJP29ylpbW8uRI0eUoBV0rXafeOKJx2556ss6/rJ+IdN8vJjm401UVBSdnZ387ne/U+pH9Do7O7l165aSQbKwsFBa/vr5+eHi4jLm55ts2LCBGzduoNVqycjIYPXq1UrNWH88mKkAlKzQqlWrJvy2rAd/NhYWFixbtszIKxobbG1t2bFjB8ePHyclJYXGxkY+/PBDnn76aQreXkfi3SrKGttwt7ciysdFMjhiQpMgR4hxqqmpSSlQnTVrFpMnT6a5uZlz584ZbAczMTEhKiqKxYsXY2nZt2GMMPSZHAB/f3+cnJyoq6sjJSWFRYsWDfnzxMfH09zcTGZmJkVFRXz11Vds3bp12K6q9zaTxcvBmnf6MCens7OT8+fPG3T7sre3Z+3atQQEBPT559PfdVRUVCgBjp2dHb/85S8pLi6moKCA/Px8JfPX0dHBjRs3lG5hVlZWBkHP5MmTx1zQY2VlRUhICBkZGWi1Wg4ePMjWrVv7/Tj5+fkUFhYa3Obt7c3ChQuHaqlj1rVr16iq0jXcWLZs2bC1JR+PTExMWLNmDe7u7nz77bcAfPPNN0qdzkTPEAqhJ0GOEOPU5cuXlfqJBQsWcPbsWRITEw1qKiIjI4mKilLmyQzUUGVyVCoVkZGRHD16lPr6evLz8w3m8QzVc6xfv56Wlhby8vK4efMm33//PevXrx+2k/FNgV5smO3Zr6usWq2WGzducPjwYYP6oWXLlrF8+fIBZZ/6s44Hg6pFixZhbm6uBC4xMTF0dHRQVFTEnTt3uH37ttK1rq2tjdzcXKXttI2NjdLEwNfXFycnpzER9Kxbt46srCw0Gg3Xr1+nqakJOzu7Pn+/Vqvl+PHjBreZmpry9NNPj4nXP5zUarXSUc3GxmbC1yYNVGhoKC4uLuzbt4+mpiYuXrxIeXk5zz77bLetyUJMRBLkCDEOdXZ2Kq2YHRwc+Oqrr5RW0KArYl25cmW3VtD9MVwnaqGhoZw8eZKuri5SUlKGPMgB3cnmli1b2L17NyUlJaSnp2NnZ0dsbOyQP5fynP3oFlZTU8Phw4e5ffu2ctv06dNZu3btoLc59WUdarVa2RanUql6bFhhYWHB9OnTmT59OqAbGltYWKhkevQNC1paWrh27RrXrl0DdFmh6dOn4+vri5+fH46OjoN6PcPF3NycBQsWKP+OvvnmG1544YU+f39WVpaSqdBbvXo1Tk5OQ7rOsSgtLU1pwBATE6PMshL95+HhwZtvvskXX3xBUVERBQUFfPDBBzz//PND1qFSiLFKghwhxqGrV68qQU1DQ4Ny+4wZM4iLi2Pq1KlD+nxDWXxuZWVFaGgoqamp5OXlUVdXNywnhubm5vzkJz/ho48+oqamhvPnz2NnZ2fUq8odHR0kJiZy/vx55TZ7e3ueeOIJZs+ePSIZAK1WS2pqqnJMfX19+3QSamlpyaxZs5TmEa2trRQWFiqZnurqauD+Nkr9VkoHBwf8/f2Vr42mICA+Pp4rV66gVqu5ffs2tbW1ODs7P/b7urq6DGa/AEybNs2gg+BE1dHRocxzcnR0JDw83LgLGgdsbW158cUXOX78OJcuXaKhoYEPP/yQTZs2ERwcbOzlCWE0EuQIMY7otzgdO3bM4HZPT09WrVr12FbQ/TGcJ9wLFy4kNTUVgCtXrihd0YaatbU1O3bsYOfOnTQ2NnL06FFsbGyYO3fusDxfb7RaLdevX+fIkSMGW9OWL1/OsmXLhq0xwsPUag3/+39/QFbWLezszJg2zWbABeHW1tYEBAQoncqam5uVoCc/P5+6ujpAF4RnZmYSEhLCn/70JxwdHQ22tw12K+VgmJqaEhUVpZyUHzhwgNdee+2x33flyhWDiwtmZmZs3Lhxwm9TA13r45aWFgDi4uIwNTU18orGB1NTU9auXYu7uzsHDx5Eq9Xy9ddfU1paSlxcnNTpiAlJghwhxomioiKOHz9OcXGxcpuNjQ3r169/ZCvooTDUbYRdXV3x9vamqKiI1NRUoqOjh+1E38HBQQl02tvbOXDgANbW1sOyTa4nNTU1HDp0iDt37ii3zZgxg7Vr1/YpazBUDhxI4e23d1FcfH8QrIODGebmCfzP/+n/yO9Va7SPrfOxtbUlKCiIoKAgABobGykoKKCgoMBgW15dXR2pqalKkDt58mRle5uvr++IF6gvX76cpKQkOjs7KSkpoaysDHd3917v39bWpgRFemvWrBnU1tDxorW1VclSurq6MmfOHCOvaPwJCwtT6nSam5tJTk5W6nSkuYOYaCTIEWKMq6ys5OTJkwZT70F39fitt94aUOvbvhjuq9KRkZEUFRXR3t7OtWvXhnVby5QpU3jhhRf4+9//jkajYd++fbzyyit4enoO23N2dHRw7tw5ZVgr6AIu/da0kXTgQAqbN/+eh2PVhoYu/umfjhAUFMSmTT1v4zuQW9xjx7Z3H9M5zt7enrlz5zJ37lw6Ozs5fPgwTz75JIWFhdy+fVvJaNXU1FBTU6N0BHR1dVUyPT4+PsP2/tZTqVTExcVx5MgRQFeb8/Of/7zX+1+4cMGg/s3Pz4+IiIhhXeNYcf78eaVjX3x8vGS2homnp6dSp1NcXMydO3eUOp2h3qosxGgmQY4QY9S9e/c4c+YMmZmZym3m5ubKLI7IyMhhPwHUG46BkAEBAdja2tLc3ExKSgphYWHDelLk6enJtm3b+PTTT9FoNHzyySe89tprj51B019arZacnByOHj1KU1OTcvuKFStYunTpiG1N01OrNbz99q5uAc6DfvWrXWzYsABTU92Wl9bWVoqKisjosGTr15d4+FtLGlrZvD+534MIg4ODlWC2rq6OgoICZXubfotTZWUllZWVXLx4EQA3NzeDoGc4itgXLlzI2bNnaW1tpaqqioKCAnx9fbvdr7GxkQsXLiifm5qasmHDBjmZR7ctUX/MvL29lYYVYnjY2dnx0ksvcfToUa5cuUJ9fT0ffPABzzzzjGTQxIQhQY4QY0xLSwvnzp1Tuj6Bbm7CsmXLKC8v5+bNm0or5uE03CdupqamzJ8/n4SEBCoqKigpKcHLa3gnd0+fPp1NmzZx4MAB2tvb2bNnD6+99lq/J7E/vH1r3hRbCu7cxtnZmZMnT1JQUKDcd+bMmaxdu3bEC+47Ojqoq6vjyJErBlvUHqbVQlFRDf/7f39ARIQXlpaWZGVl0dDUxDvMREv3oEyLbsjor46ms2G254AGEjo5OeHk5ER4eDharZba2lru3Lmj/NfaqssclZeXU15ergQXnp6e+Pv74+vri7e395AFjWvXruXAgQMAfPfdd/zyl7/sdp+EhATUarXy+bp160Zt97iRlpCQoLSvlyzOyDA1NWXdunW4u7vz/fffo9Vq+eqrrygtLSU2NlbqdMS4J0GOEGNER0cHycnJJCYmGpxILViwgOXLl9PZ2ckf//hHQNci2t7efsTWNhyZHIB58+aRkJAAwKVLl4Y9yAHdz66lpYWjR4/S2NjInj17ePXVV/u8n72n7VtOJhriNSUEcb+pgKOjI0888YTSjWw4dHR0UFtba/BfdXU11dXVSpCQlVXfp8fKyroFVCqfF2JDQw8Bjp4WKGpoJfFuVZ9bZ/dGpVIxefJkJk+ezPz589FqtUpGRR/06LeIlZSUUFJSQmJiIqDLGugzPZ6enpiZDezP3ty5czlx4gSNjY3U1dVx/fp1pakC6LbV6euIQBcwh4WFDfxFjyM1NTWkpaUBusHE3t7eRl7RxBIREYGrqyv79u2jpaWFCxcuUF5ezubNm6VOR4xrEuQIMcqp1WpSU1M5c+YMbW1tyu3BwcGsXLlSyQDoawYAlixZMuzrGokrsfb29gQFBZGTk0N2djarV68ekW5bkZGRNDU1cf78eWpqavjss8948cUXH5sVOJBbzOb9yd22b9VpVOzHiy0UE0Qj0dHRLF269LEn3H0p6H84kNHXsNTU1ChbvB7Fzq5vfwYevl9TH/98lDW2Pf5O/aRSqXB1dcXV1ZWFCxei1WqpqKhQAp6CggJl22ZRURFFRUUkJCRgYmKCj4+Pkunx8PDoV3ev9evX89lnnwFw6NAhgyDnwcGfZmZmwzpcdqx5sJ32cM6iEr3z8vJS6nRKSkq4ffs2H3zwAdu2bcPVdXAXIYQYrSTIEcKIHnUSq9VquXbtGqdOnVIG54HuCnFcXJzBoLfW1lblSqmfn9+IF5cOVyYHdPUQOTk5aDQa0tLSiIqKGrbnetDKlStpbm7m6tWrlJSUsH//fp577rleT4rVGi1vH0nvFuDoqAAtR3EjzLKTFStWPPb5e8oITbU2460ZdgSbtvQrkHmUadNscHAwo6Ghq9f7ODjo2kk/yI7e7/8gd/vhrwtTqVS4ubnh5ubG4sWL0Wg0lJeXKzN6CgsLUavVaDQapaMb6LbzTJs2Tcn0uLm5PXILz4wZM3B2dqa2tpampiY+++wzYmNj6erq4ubNm8r91q1b1+8tjuNVaWkpubm5AISEhMgJtRHZ29vz8ssvc+TIEVJTUw3qdPRdD4UYTyTIEcJIejqJ9bCz5DnHVp6aMZWcnByDiekeHh7Ex8d3K3huamoiOTmZri7dSefSpUtHZP0jdZXax8cHFxcXqqqquHTpEkuXLh2RveQqlYonn3yS5uZmbt68SV5eHt99912v807+NTGH4sbWHh5JeUQaMCdg9dM9fvXBjMyB3BL+v2v3uF/dolPR2sn/zKpTMkIP0/9c9LUPD3NwcEClUhkEzXZ2tvziF1H827+d6XXla9a4Y/JQBmkaLTjQSQNmBmu8/2p17+coH5deH3e4mJiY4OHhgYeHB0uXLkWtVlNaWqq0q7579y4ajUYZ8qlvYW1ubo6vry9+fn7KxYKHj/W8efM4ceIEGo2WEycyuHSpDJWqHUdHLSYmKqZNm0ZoaOiIv+bR6uTJk4Du31NMTIyRVyNMTU158skncXd354cffkCj0fDll1+ybNmyAc/EEmK0kiBHCCPobVtTaVMb/9WkorgkTTmJdXZ2Jj4+vtvEe7VGy9k7Ffz1033Y0cU0wNbauseuT8NtODM5+iYKP/zwA01NTdy8edNgm9BwMjEx4dlnn2XPnj0UFRWRmZmJra0tq1atMrjfgdxifns2p0+PWd7cRnZ2tkGNTE1NjVIjowHeYSb0GDzoMkLHcGOhvQo7Wxvq6+vvf+9Dwc2kSZPw9/fH1dWVwsJC5Yo66E7oV6xYwfz58/noo4/YssWbo0fLaWjoVO7j7GzFv/7rs7S03FJaOltaWmJtbc29e/dYQzn78eLhYAy0aIGlTXn88d13mDFjhhI4GGO4p6mpKd7e3nh7exMVFUVXVxclJSVKpqeoqAiAzs5Obt26pbRjt7S0VNbt6+uLi4sLtbW15OQ0cPRo2Y/ZrwJAl+164gkPfvObZ2Sb2o9u376tzH9auHChzAoaRebNm4erqyuff/45LS0tnD9/nrKyshGt5RRiuEmQI8QIU2u0vH308duaAmhk4/r1hIaGdstcGGaBdMX4DnSyprWckpISfHx8hvlVjFwmB3RF38ePH6ejo4OUlJQRC3JAV1+xbds2/v73v1NVVUVycjJ2dnZK3ZP+ePZVyqljVNL7FrPHFfSDinrMyWrswq+x3OArJiYmmJubY2FhgZWVFSqVihs3bihbGfWcnJyYMmUKJSUl5ObmUlVVRVCQAzExs6ipUZGfX4JG08q0aTZ4eakwNQ3kzp07VFVV0d7eTnt7O25ubniammJSVsphjavBmh3pYjXlBNFIQwOkpaUpa3BwcMDDwwN3d3fc3NywtLRUguTy8nLMzc1RqVSYmJhgYmJi8PHDnz/8cV/fk2ZmZkybNo1p06YRHR1NZ2enMk/k9u3blJSUANDe3s7169e5fv06ANbW1pSXm7J/f1G3x2xo6OLzz+/y7LM5vc4Umki0Wi0nTpwAdD/vkdpmKvrO29ubN998k88//5zS0lLu3LlDSEgI1dXVjxx4K8RYIUGOECMs8W6VwRa17nTbmqptXHqcDdNbFqgBM/bjxTP1GoY/xDEMcoYzkwNgYWFBeHg4KSkpFBQUUF1dPeTzax7FysqKF154gZ07d9LQ0MCJEyewtbUlNDS0D8dTT4sDXUz7McCxsLDA2dmZKVOm4OzszOTJk3Utpita2f1D+mMfrafCf41GowQh+sxLT+rq6qirq+t2e0WFLmiaNs0E0GVcHuwY9qDyct19A4BZ1FOIDU2Y/ZhVbKG3DYUNDQ00NDQogQPogrOQkBB27drV61a7vnow4NEHQb39X/+xqampwW2enp50dnbS3t5Oa2urMsCyubmFjz662etzq1TdZwpNVNevX1feI0uXLjVKBk88nr5O5/Dhw2RkZADw8ccfs2nTphG9mCTEcJAgR4gR1tduU4tXre0W4DwuC6QC/vvJazwzZ9qAZpOMZgsWLFBmA12+fJm1a9eO6PPb29uzY8cOdu7cSVtbG99++y3W1taUtfe9sP41T1NeX/Mqzs7O2NjcL+RvamqioKCA9PR0Um8UA5Mf+1iOJlqil0ej0WjQaDRotVq6urooLS2lpKTEIFhwcnJi6tSpmJmZKffVaDTU1NRQXV0NwOTJk7G2tlYer7KyEo1Gg6mpKQ4ODsr3qNVqWltbDR7f3NSUmaoONJq2QQcpgzWcz19Y2PLIBg36mUKJiblER0/cgYsajUapxbGysmLx4sVGXpF4FDMzM5566inc3NwoKytDrVbzxRdfsHz5cqKjo2X7pRizJMgRYoT1tduUl2P3K5+PyxoM5WySxxnJTA7oTsL9/Py4c+cOV69eZeXKlVhaWg778z68hh07dvDRRx+hVqv5/PPPCVizqU/f+7OAKfxuSwwqlYrm5mays7OVQvja2lrlfs6AAw6PLOifamPO715/FadJukGTarWaq1evcubMGYNua7Nnz2blypU9drRqbW3lnXfeAWDKlCn87Gc/M9gWeerUKc6fP49arWb79u04OzsrX9NqtVy6dImjR48qzz9t2jSefvppHB0d0Wq13L17l6tXr5KVldUt8Jg+fTre3t50dXVx9+5dysrKevyZmZmZ4e7urmxt02cDHgzs9B8//HlfPu7ta73dv7LyLvoanEf57LOvcXBoZ86cOSP+Hh0NMjIylPf0ihUrJuTPYKxRqVSEh4dTVlaGtbU1zc3NnDt3jrKyMjZt2oSV1fB3SRRiqEmQI8QIi/JxwcvBmpKG1h4zMirAy8G6x65Ufc0CDcdsktEgMjKSO3fu0NnZSWZmJgsWLBjxNbi7u/OTn/yEPXv2oNVquXXye9xtgyhv7uglwwaedpb8LHgqR44cIT8/3yCoeZC9vT3+/v781nIK//2S7sT/wcfUhzx/fnI+TpMce20z7uvrS1xcHJ6enr2+jsTERGUbVnx8fLe6r7lz53L+/HkAsrKyDNpe65tB+Pv78/XXX1NRUUFhYSF//vOfeeqpp5g7d65S8/LEE0+Qk5NDWlqaUuCfn59Pfn4+tra2hIWFsWbNGi5dusTy5cuV7mcAXV1dypwbABsbG6ZPn660fHZ0dOz19fVlxlB/ubtn85e/9Lx970GdnU18//33HDp0iLlz5xIeHo6Pj8+EuCLe1dXF6dOnAd37ef78+UZekeivV155ha+++oqysjJu3brFhx9+yLZt20Z0i7AQQ0GCHCFGmKmJinfXhLF5f/KPbQbu058CvbMmrMcTsr5mgUZqNoneSGRyAGbOnIm9vT2NjY2kpKQY7QTKz8+PZ599li+//JKuzg7iTYr5BNdux1NHy9KmPA58dbXbV+zs7Jg+fTq+vr74+voadJ/y9e3eYtzLwZp31oTxdIAnN2/e5MSJE8p2MwA3Nzfi4+Px9/d/5Prr6+u5ePEioCs+njlzZrf7uLq6Kq27MzIyWL58ebeTdBcXF15//XXOnj3L+fPn6ezs5MCBA1y/fp0nn3wSa2trLCwsCAsLIywsjNraWtLT07l69SpNTU00NzeTlJREcnIyISEh2Nrasm3bNkxMTCgqKuL27dvk5+crtR0tLS1kZWWRlZUF6LrH6YMeNzc3Ll26hI+PD7k48Ktj3X92764JY1Og1yN/No/i7++Ag4O5QQe6hzk4mDFnjistLc1oNBoyMjLIyMhg0qRJREREEBYWNq47WKWlpdHU1AToZk09buCtGH3s7e155ZVX+OGHH8jIyKCuro7333+fZ599ltmzZxt7eUL0mfz2EcIINgV68dWWxb2exPZ2IjaYLNB4YGJiwsKFCzl16hQ1NTUUFhY+MlsxnIKCgnjiiSc4fPgw/u3VvGAJhzSu1Dxw/utAJ2t+7DIGuqDG399fmcXyqJa6mwK92DDbs1s2orjoLh999JHSAQx0bcbj4uIICAjoU7bg9OnTSmC6evXqXr8nNDSUkydPUldXR3l5eY8dl0xNTYmNjWXmzJkcOHCA+vp6cnJyKCwsZNOmTQYBl7OzMytXriQ6Oprbt29z9epVcnLut94+cuQIR44cYe7cuYSFhREbG0tcXBxtbW3K1r4HM2H37t0jNTXVoDnCxyk5P7a1NnxNJQ2tbN6fzFdbFg8o0CktLeXTTz9hzRq3HrurqVS6mpw1a9xpaWkmODgYGxsbMjIyaG9v5969e5w+fZrTp08zY8YMIiIimDVrVq/DZceqpKQkQLe1MyQkxMirEQNlZmbGhg0b8PDw4MiRI8r23BUrVrBixYoJkZUUY58EOUIYSW8nsY/aUjOYLNBQM0YmByAiIoIzZ86g0Wi4dOkSTz/d83DN4dTa2qp0ebO2tqa1tRX/9mr+gWqDLmNBNipmzjDM1PTn5MDURKXUVpWVlbH3s0+VwZWgu+K6cuVKQkJC+jwgtby8nMzMTAACAwMfGSQGBwcrBeRZWVmPbCvr4+PDz372M44ePUp6ejrNzc188sknREZGEhsbi7n5/RbTJiYmzJgxgxkzZtDa2kpGRgYVFRUABtkPR0dHIiIiCA0NJSAgQOn21NjYqLR7zs/PVzIHGuAobj2uTz/J51dH09kw27Nf/0bKysrYvXs3nZ2dBAU5/DhTqMygCYGX12T+9V+fpa7uGnV1dVy7do3g4GB+/etfk5+fT1paGvn5+QDk5eWRl5eHpaUl4eHhhIeH91g3NRa1t7cDEBcXNyJDe8XwUalULFy4kKlTp/L555/T1tZGQkKCUqcjtVZitJMgRwgjevAktq8GmgUaL2xsbAgODiYzM5Pc3Fzi4uKG/TlbW1spLCxUsglVVVU93s8EmGtnwooVUfj7++Pk5DToK541NTWcPn3aIONhZWVFdHQ08+bN6/d2IP3sEuCxPztHR0e8vLwoLi4mMzPzsSeulpaWbNiwgdmzZ3Pw4EHa2tpISUkhLy+PZ555pscgydramnnz5nH48GFeeeUVsrKySE9Pp729nfr6es6cOcOZM2fw8/MjIiKCgIAA7O3tCQkJISQkBK1WS3V1NX/5y18eO2NoII05ysvL2bVrl1K/BBAU5EBkpBfh4fGUl9/D3d2JqKhATE1NaG6O5NNPP6W8vJxr167R0tLC1q1bCQoKoqGhQddBLzWVhoYG2tvbuXjxIhcvXsTNzY158+YRHBw8Jou8m5ublY89PDxkW9M4Mm3aNGWeTnl5OTdv3lTqdCZPfnwnSCGMRYIcIcaggWSBhpqxMjmgm56uz0akp6cP+eO3trZy9+5dJVvQW1BjY2OjbD/Lzc1VsgoFBQXMmzdvUAFOfX09CQkJXL16v5ZHP1Rx0aJFWFhY9Psx9a8HdC25H+yY1pvQ0FCKi4tpbm7m7t27+Pr6PvZ7AgIC8PLy4uDBg+Tl5VFTU8OHH35IbGwsS5Ys6TVQcnV1Zc2aNcTHx3Pz5k3S0tLIy8tT1n7nzh0sLCwICQkhPDwcd3d3VCoVLi4uvPTSS3yaUQBXKx+7vr425qioqOgW4Og99dSTPdYy2dra8vLLL/P5558rP+9du3axfft2HBwcWL58OVFRURQWFird57RaLeXl5Rw6dIjDhw8THBxMeHg4vr6+Y2ZbkH6bGugaWYyVdYu+cXR0VOp0MjMzqa2tVep0Zs2aZezlCdGjYQty/vVf/5VDhw6Rnp6OhYUF9+7dG66nEmJCGkgWaLzw9PTE3d2dsrIy0tLSejzZ7I+2tjaDTE1lZc8nyg8GNb6+vjg7Oysnc6GhoXz66acUFhaSnZ2NnZ3dI+tdetPS0kJiYqLSGEBvyZIlLF261GC+Tn9otVqOHTsG6IKlB7ulPUpQUBCHDx9Gq9WSmZnZpyAHdPVH27ZtIy0tjUOHDqHVajl16hQ3btzA2dkZd3d3FiyM1AXq9c3YoOuIZo6uzicwMJDAwEAaGxvJzMwkNTWVuro6Ojo6uHLlCleuXGHKlClEREQQEhLCtGnTWKq17lOQ05fGHJWVlezatUvZfuXg4EBDQwOga/AwY8aMXr/XwsKCbdu28c0335CTk0NZWRkfffQRO3bswNHREZVKpbyH1q5dS3Z2NqmpqZSVlaHVapXmCg4ODkqzgkd1kjO2uro60tLSCAkJUV6XGH/Mzc3ZuHEjHh4eHD16lK6uLvbt20dMTAxRUVES2IpRZ9iCnI6ODp599lkWL17MRx99NFxPI4QwEmNmckDXTvrbb7+ltbX3uUG9aWtr4+7duxQUFJCfn//IoMbPz09pFPBgUPMwMzMznn/+eT7++GMqKipISUnB1taWqKioPq2pvb2d5ORkZTaN3rx581ixYsWgO3JlZ2crdS/Lli3r8wR6fdvmvLw8srOzeeKJJ/q8RU6lUjFv3jx8fX35+uuvKSsro7i4mOLiYj7PvMO5xHLKWzqxNoF9IRbM/esx/mN1uMGWS3t7e5YuXcqSJUsoLi4mPT2dzMxMurq6qK6u5vjx4xw/fpzZs2cTEhqGl701JY2Da8xRWVnJxx9/TFubLuMTGhqqTIMHWLNmzWNP6MzMzNi8eTNHjhzh8uXL1NbWsnPnTnbs2IGLy/3nt7KyYt68ecybN4/Kykql+1xbWxsNDQ2cPXuWs2fP4ufnx7x585g9e/ao61h25swZ5eOYmBgjrkQMN337+KlTp/LFF1/Q1tbGmTNnKCsrY+PGjVKnI0aVYftN+S//8i8A7Nq1a7ieQggxgc2ZM4ejR4/2uJXoYe3t7QaZGv3J/sOsra3x8/NTApvJkyf36+qkpaUlL7zwAjt37lS6adna2hIREdHr93R1dXH58mUSEhKUrAHoiv5jYmL6tKXscbq6upRaHBsbm35PoA8JCSEvL4+Ojg7y8vKUBgB9NXnyZF577TX+1//6XwDkYK/rgNbSwYNd0Eob23rtgKZSqfD29sbb25vVq1eTm5tLWlqaMlPnxo0b3LhxgxiLyXzC1AE35qiqqmLXrl1KgLN69WqDk3hfX1+mTZvWp9etUqlYu3Yttra2nD17lqamJj766CO2b9+Ol1f32jlXV1dWrVpFbGwst27dIi0tjVu3bgGG2/XCwsIIDw/Hza3nJgsjqaKigqysLGUL4tSpU428IjESfH19eeONN9i3bx+VlZVcv36dv/3tb2zbtm1IfmcJMRRG1eWg9vZ2gz/y+q0BnZ2ddHb2PpfAWPRrGo1rE30jx3DgNBqNcmLT1dVllJ9hREQEly5dAnQtfj08PABdJrmoqEjJ1jwc1OjXbW1tzbRp0/Dx8cHHx6dbUNPV1UV/6bcq7dmzh5aWFg4dOoSlpWW3fesajYasrCzOnTunFG2bmJjg7+9PdHS00m1rKH6uly9fpqmpCRMTE6X9a38e19/fHwsLC7q6usjMzGT69OkDWkdMTAxnEhI4ixvWgD70sP6xRMfqx///47GrPOHv2mswolKpCAoKIigoiHv37pGVlUVGRgZNTU3M7Kpjm0knJ5lK4wNNCLzsrfj3uBCemjG119deU1PDp59+Snt7OyYmJqxZs4aysjK6urqU90xMTEy/j8mSJUuwtrbm2LFjdHZ2snv3bjZv3vzIeUbTp09n+vTpNDU1Ka/v3r17dHV1Kdv1XFxcCAsLY86cOUZrVnDy5ElMTEyUn4/8Lh27+vv30NbWlhdffJGjR4+SnZ1NXV0dH374IU8//fRjZ3WJ4TERzmn689pU2mHeZ7Jr1y5+9atf9akm55//+Z+VDNCD9u7dO+B96EIIIYQQQoixr6WlhW3btlFfX4+Dg8Mj79uvTM4//uM/8h//8R+PvE9ubm6/tzLo/d//9//Nb37zG+XzhoYGvL29WbVq1WNfiDF0dnZy4sQJ4uPjDWZAiLFDjuHAVVRU8PHHHwOwcePGAf+7H4iOjg6Ki4u5e/cuGRkZzJw5k2vXrqHRaAzuZ2lpybRp05RszZQpU0a0OLaoqIjPPvsM0NVorFy5kvT0dIMaIBcXF2JiYvDz8xuWtSUkJJCcnAzA008/PeDWvvn5+Xz55ZcAPPnkkwQHBw/ocb7KKebV764Y3GZtAn8PtuCVax20/ngIP1o/n81BA2uH3t7eTk5ODunp6d2yePb29oSFhREcHKwU89fV1fHpp58qGbX4+HjCw8PZtWuXwbF69dVXDeppBqKwsJD9+/crdVdxcXHMnz+/X4/R0dHB9evXycjIMBgKC7qGD6GhocydO/eRw2YHS6vV8sknn1BaWoqpqSmvv/46Fy5ckN+lY9hg/x4WFBTwzTffKDtyZs+ezbp16wbUCVIMzEQ4p9Hv8uqLfgU5/+2//TdeeumlR95nMClKS0vLHovWzM3NR/XBGu3rE48nx7D/TE1NlaDC1NR0WH9++u1n+pqa0tJS5Wv6bTIajQaVSsXMmTOVmhoXFxejdvzx9/fn2Wef5YsvvqCjo4OjR48qX3NyciI2NpagoKBhW2NDQwPJycloNBo8PT2ZM2fOgJ9r5syZWFhY0NbWxrVr1wgPDx/Q47g72iqBzMNaNShfc3e0HfB7ytzcnIULF7Jw4UIqKyu5evUq6enptLW1Ka25ExIS8PX1ZdasWSQlJSkBzpo1a4iMjOTSpUuUl5crjxkcHKxshxyMGTNm8NJLL/HJJ5/Q1tbG8ePHaWlpYeXKlX0+Nubm5kqzgurqaqVZQUtLCw0NDSQmJpKYmMi0adOYN28eAQEBQ/7v8+bNmxQXFwOwePFiJaCS36Vj30CP4cyZM/npT3+q1Onk5uZSVVXF888/L3U6I2w8/zvsz+vqV5Dj4uIy6KtYQojxYTi7qz0qqHmQhYWFQbtaOzs7tmzZMmpamVZUVJCWlmZwm0qlIj4+nsjIyGGfCH/27FklEF21atWgfi6mpqYEBwdz5coVbt++TVNTE3Z2dv1+nCgfF7wcrClpGFwHtL5ydXVl9erVxMXFdSvmLygooKCgQLlvZGQkCxcupKmpiVOnTim3awDzmWHsy7o7JDOpPDw8eO2119izZw8NDQ2cP3+elpYW1q1b1+/3xJQpU4iLi2PlypXk5eWRlpbGjRs3AF3WqLCwEHNzc0JDQw1mCw2GVqtVGllYWlqybNmyQT2eGD8mTZrEa6+9xsGDB8nOzqa6upr333+fLVu2PLLtuhDDYdgaD9y9e5fa2lru3r2LWq1WBvbNmDFjQH8YhRDjV2dnp0FQ8/AWHD0LCwslS+Pr68vUqVPp6uri8OHDgG6AZn5+vtH/mNbW1nLmzBmuXbum3GZqaoparUar1ZKRkUFERMSwtlutqqpSBonOmjULHx+fQT/m3LlzuXJFt9UsOzubyMjIfj+GqYmKd9eEsXl/8oA7oA2EqakpAQEBBAQE0NTUREpKCklJSQYBekpKCnl5eZibmytd+3Kw55S5NzXf3A9WvRyseXdNWLcOcP2h7zi3Z88eqqurSUtLo7m5mc2bNw+oRbSJiQmzZs1i1qxZNDc3k5GRQVpaGjU1NXR2dhrMFpo3bx4hISEDrnXNysqiuroagKioKKysrMZ1obPoH3Nzc5555hk8PDw4ceIEnZ2dfPbZZ8TFxbFkyZJRcxFKjH/DFuT80z/9E7t371Y+129tOHPmDNHR0cP1tEKIETKYTE5nZyfFxcVKW1z9tpeH6TM1+sBm6tSpvf6B1G+fS0lJMVqQ09jYyLlz55RAAHQnn1FRUSxatIizZ8+SkpJCRUUF+/btY/v27cM280R/pR10NSZDwdvbG3t7e2VA50CCHIBNgV58tWUxbx9Np7jh/pwjT3sr/v2hOTnDQa1Wk5mZqbxvPT09qaiooKuri5qaGuV+Sqvrh87fSxpae2113R/29va8+uqrfPbZZxQXF3Pjxg327NnDtm3bBtUtzdbWliVLlrB48WJKSkq4evWqwWyhY8eOcezYMQIDAwkPD2f69Ol9ziCp1Woly2Vra8vChQsHvE4xfqlUKpYsWcLUqVPZv38/HR0dnDx5ktLSUjZs2CB1OmJEDFuQs2vXLpmRI4QA7gc1+kxNb0GNubl5t6Cmrydfc+fOJS0tjby8POrq6nBychrKl/BIra2tnD9/nuTkZIOALzIykqioKGXw5urVq2lububatWsUFhZy4MABNm/ePOTb1goLC5UtWREREUyZMmVIHlelUhESEkJSUhKlpaWD+jlvCvRiw2xPEu9WUVbfDAWZZP5sNVaWw3vyU19fz8cff6wUr65cuZKoqCg6OzvJycnhhx9+oKurCw1wlJ7n0GjRZZ1+dTSdDbM9B5V1srKyYseOHXz55ZfcunWLoqIiPv74Y1544YVB73pQqVR4eXnh5eXFmjVryMnJMZgtlJubS25uLra2toSHhxMeHv7Y2onU1FTlZxcTEzNu9/2LoTF9+nTefPNN9u7dS3V1NTk5OUqdzkj+jhYT06iakyOEGDselcnp6uoyyNQUFRX1+BiDCWoeFhERodS/XLlyZciyF4/S0dFBSkoKiYmJBtt1wsPDWbFihdK9S0+lUrFx40ZaWlq4ffs2ubm5HDlyhCeeeGLItnBotVqOHz8O6LJbQ5051wc5oNu2tHz58gE/lqmJimhfVzo7OzlckDnkW9Qe1tDQwMcff0x9fT0A0dHRREVFAbr3YktLizIbqRAbGuj9BF4LFDW0kni3imhf10Gty9zcnOeee47vvvuOjIwMKisr2blzJy+++OKQnQjq63JCQ0Opra0lPT1d2SLX3NzM+fPnOX/+PN7e3sybN4/AwMBuV9s7Ojo4e/YsoKu9CAsLG5K1ifHNycmJ119/nYMHDypBzvvvv8/WrVtlno4YVhLkCCEGTa1WK0Xc+kxNT1vYzMzMDIIaNze3IctiuLq64u3tTVFREampqURHRw/bVeauri7S0tI4e/Ysra33t1sFBgaycuXKR2ZOTE1N2bp1K7t27aKsrIwrV65ga2s7ZMFIbm6u0qhhyZIl2NvbD8nj6rm6ujJlyhSqq6vJyMggKipqTOyxb2xsNAhwVqxYwYoVK5SvNzQ0cPr0aeXzpj7+eSxrbBuS9ZmYmLBhwwZsbW25cOEC9fX1/O1vf2PHjh24ufWcURooZ2dnVq5cSXR0NLdv3yYtLY3c3FxA1/a8qKiIH374gZCQEMLDw/H09ESlUnHx4kXl/R4XF4epqemQrkuMXxYWFmzevJmkpCROnTpFR0cHn3zyCfHx8SxevHhM/A4RY48EOUKIfuvq6qKsrEz5/ODBgz3ez8zMjGnTpuHn54efn9+QBjU9iYyMpKioiPb29kG1Oe6NRqMhKyuL06dPG/Tq9/f3JzY2ts8thi0sLNi+fTs7d+6krq6OhIQE7Ozs+j0v5WFqtVqpxbGysmLp0qWDerzehIaGcurUKWpra6moqBjyk/Chpg9w9EOpo6KiugWVx44dU7I4AJNMAfXjH9vdfuC1Mw/Td96ztbXlxIkTtLa28tFHH/GTn/zEoIvgUDExMWHGjBnMmDGDlpYWsrKySE1NpaqqSgnk09LScHZ2Zu7cuUoGb+rUqQQFBQ35esT4plKpWLZsGW5ubuzfv1+Z6VJWVsb69etl66MYchLkCCEeq6uri5KSEgoKCrhz5w53797tNVOjD2p8fX1xd3cf9jbJDwoICMDa2prm1lbe/e4kNhdz2Lw6jhW+roPaCqXVarlx4wYnT540KEz38PAgPj5+QCegNjY27Nixg507d9Lc3MyhQ4ewsbEZ1MljWlqaciIfHR09bN3bgoODleLzrKysUR3kNDU1sWvXLurq6gBYtmwZMTExBve5ffs2OTk5BrdtWxbOsatNI9bq+kFLlizB1taWb7/9lq6uLnbv3s3WrVuHdeCujY2N0kK7rKyMq1evkpGRQWdnJ7W1tSQkJCj3DQwMRKvVytV3MSAzZsxQ6nRqamq4du0alZWVPP/888M6wFZMPBLkCCG6UavV3YIa/byVngQFBbF48WI8PDxGNKh5WGVlJamtZhxlpq6eohL++sm5QbX8vXPnjnK1UU8/m2TWrFmDOtGbNGkSO3bs4KOPPqKjo4Mvv/ySHTt24Ofn1+/H6ujo4MyZMwA4OjoOOiv0KJMmTcLLy4vi4mIyMjKIi4sblSe8zc3N7Nq1i9raWkAXPDw8dLOrq4sffvjB4PusrKxYungx706tGvFW13qhoaFYW1vz+eefo9Vq+eKLL3jqqaeIiIgYlufTU6lUeHh44OHhwapVq7h+/TqXLl0yaBai7xKob1YwVI0txMTh7OzMT3/6U7755huuX79OZWWlUqczkN9/QvREghwhBGq1mtLSUqVRQG9BjampKT4+Pvj7+zNp0iS+/vprQDeHxctreNv+9sXxonpdy9+HDKTlb0lJCSdPnjQYFung4EBsbCzBwcFDWku0fft2Pv74Y7RaLZ999hmvvvoq7u7u/XqcCxcujGi9REhICMXFxTQ3N1NYWDgs26kGo7m5mY8//ljJvC1evLjHYCw5OVnJ8uhFRUVhaWnZa6trLwdr3hnknJy+mDVrFi+99BKffvopnZ2dfP/99zQ3N7Ns2bIRCSrNzc2ZO3euQZt3GxsbWlpaaG1t5cKFC1y4cAFPT09pQiD6zcLCgi1btnD+/HlOnz5Ne3s7e/bsYfXq1URGRo7KCydibJEgR4gJSB/U6DM1hYWFPQY1JiYmBtvPPDw8lJPnB7dt9XdOznBQa7T8z6Q87l9nv68/LX+rqqo4ffo0169fV26zsbEhOjqaiIiIYQkevL29ee6559i3bx9qtZo9e/bw+uuvP7adr15TU5NSL+Hm5sacOXOGfI0PmzNnDkeOHEGr1ZKVlTWqgpyWlhZ27dqlvEcjIyOJj4/vdtJ07949pVuYiYkJGo0GW1tbFixYoNzHoNV1Yxvu9lZE+bgMeyc4PR8fH2VoaHNzM6dPn6a5uZnVq1ePyElgdXW1MlQ2ICCALVu2cOfOHdLS0sjOzgZ0FwTKysoICQnh8OHDhIeH4+3tLSep4rFUKhVRUVG4ubnx5Zdf0tnZybFjxygtLeWpp56SOh0xKBLkCDEBqNVqysrKlO5nd+/eRa3uXlVtYmKCj4+P0ijgwaBmtEsurja42v6wx7X81Z/wZmRkKLeZm5uzfPlyIiMjh/2P7axZs9i4cSPffvstbW1t7N69m9dee61P3dESEhKUovlVq1aNyMmljY0N06dPJy8vj2vXrvHEE0+MiveKPsCprq4GdAFObwHBkSNHlOBe//8VK1Z0O9b6VtfG4urqqgQ6dXV1pKSk0NzczMaNG4f9Z66vvQKUrX7+/v74+/uzbt06rl27pjQrAMjMzCQ9PZ1JkyYxb948QkNDh7zDnxh/Zs6cyRtvvMG+ffuoqakhKytLqdN5uBW/EH0lQY4Q45BGozHI1Ny9e9egc5Teg0GNr68vnp6efT5petScHGMob2rv0/0ebvnb3NzMuXPnuHTpknKbSqVi6dKlLF26dFCT5/srNDSU5uZmTpw4QUNDA5988gmvvPLKI9dQU1PDlStXAN3gvZHczx4SEkJeXh4dHR3k5eUxe/bsEXvunrS2trJ7927lhHvBggW9Bji3bt3i5s2bgK5hRldXFw4ODsNe8zJQkyZN4tVXX+XTTz+lvLyca9eu0dLSwtatW4dtenxJSYmS0QwLC8PFxbDJgrW1NQsWLGDBggUUFxeTmpqKpaUlra2t3Lt3j1OnTnHq1ClmzJhBREQEs2bNGhWBsBidJk+ezOuvv84333zDjRs3qKioUOp0RlOmWIwdEuQIMQ5oNBolU6PfftZTUKNSqboFNWZm/f81oNVqSUxK4g42NGFGfWIqM2fNxs7WZihezoC42fWtk5i+5W9bWxsXLlwgKSnJYKveggULWL58+aCnzQ/UkiVLaGpqIjk5maqqKvbu3csLL7zQaybp5MmTyscjMQD1QbNnz8bU1BS1Wk1mZqZRgxx9gFNZWQnA/PnzWbt2bY8BTmdnJ4cOHQJ0dWb6fysxMTGj+iTc1taWl156ic8//1zJyu7atYvt27djYzP0//b07y2VSvXYOU5Tp04F4K233lJm79y+fRuAvLw88vLysLKyIiwsjPDwcFxdjZcZE6OXpaUlW7duJTExkTNnzihZ7TVr1uDt7Y2tnT1Xa1uNsnVUjD0S5AgxBmk0GsrLy5VGAY8Kary9vfH39x9UUPOwven5/DytkQZ8dTfUwd4/H+MvTy0Y9mLs3iz2moKXg/VjW/4ucp9EUlIS586do6OjQ/l6SEgI0dHRQzZhfjDi4+Npbm4mMzOToqIivvrqK7Zu3dqt2UFxcbFypT00NFQ50RwpFhYWBAQEkJ2dzY0bN2hvbx+2ttWP0tbWxp49e6ioqAAgIiKCJ554otdte0lJScpQUBMTE9RqNU5OToSEhIzYmgfK0tKSn/zkJ3zzzTfk5ORQVlbGRx99xI4dO4Z0W8/t27eVphuRkZF9fmwzMzPmzJnDnDlzqK+vJyMjg9TUVBoaGmhra+PixYtcvHgRd3d3IiIiCA4OHtFsqRj9VCoVy5cvV+p0urq6OHr0KDnYc0zlTr32/t+wwXTOFOOfBDlCjAH6oObBTE1nZ2eP930wqPHy8hqSoOZBB3KLeeG7q2gf+vVR2drV7w5mQ8nURMW7a8Ie2fL3FzPs+dN7f6S5uVn52qxZs4iNjR1VV5ZVKhXr16+npaWFvLw8bt68yQ8//MBTTz2lnLhrtVqOHTsG6E7UH579MlJCQkLIzs5GrVZz/fp1QkNDR/T59QFOeXk5AOHh4Tz55JO9Bji1tbWcO3cOADs7O5qamgCIjY01avvz/jAzM+OZZ57BxsaGK1euUFtby86dO9mxY0e3LWUDodVqOX78OKCrS4uKihrQ4zg6OrJ8+XKioqIoLCwkLS2Na9euodVqKSsr49ChQxw+fJjg4GAiIiKYNm2aNCsQilmzZvHGG2/w5z//mRzsdZ0zH7qCNZDOmWLikCBHiFFIo9FQUVGhBDUFBQWPDGr0jQI8PT2HtUBerdHy9tH0H//OGJ6M9KeD2XDpreXvVGsz1qoqaU27P/Bx2rRpxMXFjYrW1z0xNTVly5Yt7N69m5KSEq5evUpdXR32jo44zl1Een4BWcW1TAOWLFpktOLc6dOnY2lpSXt7O5mZmSMa5LS3t/PJJ58oM4zCwsIMAsGHabVaDh06pNSQtbfr6rhcXFwGNYTVGExMTHjiiSewtbUlISGBpqYmPvroI7Zv3z7o93Rubq6SFVu6dOmgt8KpVCp8fX3x9fXliSeeIDs7m9TUVMrKypTufFlZWTg4OCjNCqTYXIBuJpmdvQNHG/VZ6tH3d0eMXhLkCDEKPBzUFBYWGmylepCXl5dBpmYkW2wm3q0aVAezkaBv+XuusJKrtwq4m5OJY30p+mv0bm5uxMfH4+fnN+qvGpubm/OTn/yEv/3tb9TV1XG4oIajmNOQkfjjPXxxpIsA1+lGW6OpqSnBwcGkpqZy+/ZtmpubsbW1Hfbn1Qc4paWlgC6jtH79+kce0+vXryt1Iu7u7kpwNFqHmT6OvlbG1taWw4cP097ezscff8zzzz/PjBkzBvSYGo1GqcWxsrJi8eLFQ7lkrKysmDdvHvPmzaOyspKrV6+Snp5OW1sbDQ0NnDlzhjNnzuDv709ERASzZ88e8my0GFtmr9pAw9epvX59NPzdEaOT/OYQwgi0Wm23TM2jghp9pmakg5qHPdyZbLD3Gy7FRXfJP3WCxpIS9BU2zs7OxMXFERAQMKZOaK2trbG2tiaprqvHQaf1mPGTb1OxtLAw2naNuXPnkpqqOwnJzs5m4cKFw/p8HR0dfPrpp5SUlCjPv2HDhkce146ODg4fPgzo6lr0Hdg8PDyYOXPmsK53uC1YsAAbGxu++uorNBoNn332GZs2bWLu3Ln9fix9xhAgOjp62Dq3ga419urVq4mLi+PmzZukpaWRl5cH6GqCbt++jaWlJaGhoYSHh+Pm5jZsaxGjV6O2b6eqxv67I0YfCXKEGAEPBjX6wKa3oMbT01PJ1Hh7e4+qYWj6zmRDdb+hVlZWxsmTJ5Wr9QD29vasXLmSkJCQMVNz8bDFS5by//tKP7+n5xN5Y27X8PHxUepbMjIyhjXI0Qc4xcXFAAQHB7Nx48bHHlv9li7Q/RvTv0fGahbnYXPmzMHa2pq9e/eiVqs5cOAALS0tREZG9vkxOjs7OXPmDAAODg7Mnz9/uJZrwNTUlMDAQAIDA2lsbCQjI4O0tDTq6upob2/n0qVLXLp0ialTpxIREcHcuXOxtrYekbUJ4xvtf3fE6CVBjhDDQKvVUllZaZCp0e//f5inp6fS0tnb23tYr5wOVpSPS586mEX5DL74ub++/fZbcnLu19xYWVkRHR3NvHnzxvx2l2rbKdQ/4te1sbdrqFQqQkNDSUpKorS0lLq6umHpUtfZ2cnevXspKioCICgoiKeffvqxAU5VVRUXLlwAdNsV9V3Dpk2bNqJzhYabv78/r7zyCp988gltbW0cPXqU5uZmYmJi+hTIXbp0SWnKERsba5R22vb29ixbtoylS5dSVFTE1atXyczMVLb0HjlyhCNHjjBnzhwiIiLGxLZTMTij+e+OGN3G9l9+IUYJrVZLVVWVEtTcuXOn16DGw8PDIFMzmoOah/Wlg9k7a8JGLJvQ0NDA2bNnAZRWymZmZixbtoxFixYZpZ3xcBgL2wTnzp1LUlISANeuXRtwR67e6AOcwsJCAAIDA3nmmWceG+Domw3oOTs7K53Y4uLihnSNo4GHhwevvfYau3fvprGxkcTERJqbm1m3bt0jf1ZtbW0kJupqvaZMmUJwcPBILblH+plePj4+rFmzhpycHFJTU5UtitnZ2WRnZ2Nvb09ERARhYWFMmjSpx8dSa7Qk3q2S2Spj1Gj7uyPGDglyhBiAh4OagoIC2tp6PsF0d3dXghofH58xFdT0pLcOZl4O1rwzQvMKWlpaSExM5OLFi5iYmCjzTRYvXsyyZcuGZTCiMY2F7Rqurq5MnjyZmpoa0tPTWbZs2ZBdYe/s7GTfvn1KBiYgIKBPAQ7oAi59YBQaGkpGhm7b34wZM0ZtZ73B0k+O37NnD9XV1aSlpdHS0sIzzzzTa1YzKSlJuTATHx8/qrZ2WlpaEh4eTnh4ONXV1aSnp3P16lVaWlpobGwkISGBhIQEfH19iYiIICAggKqqKiwtLUmobO3xd5XMVhlbRsPfHTH2SJAjRB9otVqqq6sNMjWPCmr0jQK8vb3HTTbhQfoOZiN9dbS9vZ2LFy+SmJiIWq02+No//MM/4OzsPKzPbyxRPi542FlS2tRGTzU5o2G7hn7L2unTp6mtraWiomJICsW7urrYt28fd+7cAXSzMzZv3tynrVTt7e0cPXoUABsbG4OBubGxsYNe22hmb2/PK6+8wt69e5WhsZ988gnbtm3r9jupsbGR5ORkQLd9djQ3YpgyZQpxcXGsXLmSW7ducfXqVW7cuAGg1Dyam5vT2dlJDvZ8iXe3LU4yW2VsMtbfHTF2SZAjRA8eDGr0gU1ra8+tk93c3AwyNeMxqOmJqYlqxOo/urq6uHLlCgkJCQbB5Zw5c4iKiuLixYvY29uPyFqMwdRExTandn7XBPcnQ+iMpu0awcHBnD59GoCsrKxBBzldXV18/vnnSoAzc+ZMtmzZ0udakTNnztDS0gLAkiVLlNbIQUFBE6JTl7W1NTt27GD//v3k5eVx9+5d/v73v/PCCy9gZ2en3O/cuXPKRYNVq1aNiRoXExMTZs+ezezZs2lqaiIzM5PU1FRqa2vp7OxEAxzFDe1D/15AZquMZSP5d0eMfRLkCEHPmZregpqpU6caBDVWVtLRZbhoNBoyMjI4ffq00hkLdFuNYmNjcXNz63VI6nhSWlqKXVEOW7DnlJk3NfcTEqNqu4aTkxOenp6UlJSQmZk5qM5lXV1dfPHFF+Tn5wO6Y96fAKeiooKUlBRA12BA340NICYmZkBrGovMzc157rnn+O6778jMzKSyspKdO3fqAj0PDwo01uy5chM7bIid7oGPj4+xl9xvdnZ2LFmyhMWLF3P9+nX2799PITY00HtnSmM36xBCDD8JcsSEpNVqqa2tVU6g3nvvPYOT6AdJUDPytFotubm5nDx5UpnZAbqZQfHx8WPyRGygtFotx48fB2COqok/vxnLtcauUbtdIzQ0lJKSEpqamrh79y7Tpk3r92Oo1Wol+wAwffp0tm7d2ucueVqtlu+//175fOHChXz55ZfK+qZMmdLvNY1lpqambNy4EVtbW5KTk6mvr+ej5GscpfrHQEAXIB8vt8A2t3hUBMwDoVKpCAwMJCoqiupblVD++O+R2SpCjF8S5IgJQR/U6Lef3b59m5aWFqVoXb+lBXQF1PqgZtq0aRLUjCCtVsvt27c5ceIEFRUVyu2urq7ExcUxY8aMMbGVZijl5+crhfMLFy5kymRnoicbeVGPEBQUpAzdzMrK6neQow9wbt26BejaIj/33HP9agOekZGhdOFaunQpaWlpyteio6P7tZ7xQqVSsWrVKi5evEi21q7HwbLlzR3jolZl5cqVmPhX8qfdCY+9r8xWEWL8kiBHjEtarZa6ujpl+5k+qOnN/Pnz8ff3x8fHR4bMGUlxcTEnTpzg7t27ym2TJk0iNjaWOXPmTLjgBnTb9fRZHHNzc5YvX27kFT2era0t06dPJz8/n2vXrrF27do+bzHTD7G8efMmAH5+fjz//PP9CnBaW1s5duwYoCu+9/f3V1pbz58/v9c2wxPF7IBAfp+r3+I5fmtVZLaKEEKCHDEuPBjU6DM1+qF2D3NxcVEyNZ6enpw+fZq4uDjMzXvfvy2GT2VlJSdPnlSu3IPuRDkmJoawsDCjDCQcLbKysqiqqgJg+fLlY6Y1dkhICPn5+bS3t5Ofn8+sWbP69H3fffedMu/I19e33wEOwOnTp5XmFGvWrCEhQXc138TEZEwEicNt6sIVNOT2nuEYL7UqMltFCCFBjhiTtFot9+7dMwhqequpmTJlisH2swdPFCdC0fpoVVdXx5kzZ8jKylJus7S0ZPny5SxYsGDCB51dXV2cOnUK0AV9kZGRRl5R3wUEBGBqaoparSYzM/OxQY5GowFQWgFPmzaNbdu29fs9UFpaypUrVwBdHY+lpaWSGYyMjBzXHfj6aiwMlh0qMltFiIlNghwxZty7d08ZvDnQoEYYX2NjI+fOnVNORkF3lX3ZsmUsWbJkwrTgfpxLly7R2NgI6Ga6jKWgz8LCgoCAALKzs7l+/TodHR29DsHVaDR8//33yuvz9vYeUICjfxzQ1Z+sXbuWr7/+GgAzMzOWLVs2iFc0foyFwbJDSWarCDFxSZAjRq2HMzX6E76HTZ482SCosbW1HeGVir5obW0lKSmJCxcuoNXe3zwSGRlJVFSUHLcHtLa2KtusJk+eTGhoqJFX1H9z584lOzsbtVrN9evXCQkJ6XYfjUbDN998Q25uLiEhIXh6erJ9+/ZeA6JHSUtLo7xc105r+fLlVFdXU1ZWBuhm5MjFDp2lXpOZZKLmnsaE0TpYdqjJbBUhJiYJcsSoUV9fb9AooLegxtnZmenTp0tQM0Z0dHSQkpJCYmKiwfbAsLAwVqxYMeELwXuSmJhIR0cHoBvOaGJiYuQV9d+MGTOwtLSkvb2dzMzMbkGORqPh4MGDXLt2TXl9W7duHVCA09zcrAz6dHR0ZMmSJezcuRPQbYFcvHjxIF/N+JGTfY1VmtIeu6tJrYoQYjyRIEcMmlqjHdBWAH1Qo8/UNDQ09Hg/Z2dng0zNg5O6xeilVqtJTU3l7NmzBoNVAwMDWbly5YSbVdJX9fX1yhBLb29vZs6caeQVDYypqSnBwcGkpqaSn59Pc3OzckFCq9UqwykB3N3dAQYU4ACcPHmS9vZ2ANatW8fNmzeVhg3Lli2TNvA/0td5BdHIi1bVnDT3puSB2hupVRFCjCcS5IhBOZBb3GNR57s9/KFsaGhQgpr8/PxegxonJyclqPH19ZWgZozRaDRkZWVx+vRpg2Ps5+dHXFwcHh4eRlzd6Hf69GmlEH/VqlVjunX23LlzSU1NBSAnJ4cFCxYoAU5GRgagC3Cee+45pclCf929e5f09HQAZs2axfTp0/nTn/4EgLW1NQsXLhz8CxknUlNTlQz5L+MX8VFYuNSqCCHGLQlyxIAdyC1m8/7kbjMIShpaeWb/BV6xvcebK8IpKyvj9u3b1NfX9/g4EtSMD1qtlhs3bnDy5ElqamqU2z08PIiPj8fX19d4ixsjysvLlexGYGAgXl5j+4q6j48PdnZ2NDU1kZGRwfz58/nhhx+UoMTNzY0dO3YMuE24RqPhhx9+AHTNK9auXUtGRgZ1dXUArFixYsDZofGmvb2ds2fPArrfuWFhYZhIrYoQYhyTIEcMiFqj5e2j6T0OWdPf9lWzHV6HD/NwNcGkSZMMghpp6zr23blzhxMnTiiF3qDrchcXF8esWbPGdDZiJJ04cUL5ODY21ogrGRoqlYqQkBAuXLhASUkJ33zzjdIyfOrUqbz44otYWVkNuJX7pUuXlG1p0dHR2NnZcebMGQDs7OyYN2/e0LyQceDixYvK/KC4uLgxWeclhBD9IUGOGJDEu1UGW9S6U9GAOYXYEOZobtAowMHBYcTWKQavp5orE5XuBLa0tJSTJ09y584d5f4ODg7ExsYSHBwsJ1L9oG+4AbBgwQImT55s5BUNjblz53LhwgUAJcBxdXVVApyBamxs5PTp04AuM7FkyRKD7VgxMTH9HiQ6XjU3N3P+/HlAlz0LDAw08oqEEGL4yV8AMSB9HRTXZWnH22+/KVfyx6ieaq5cLE2IaS8k3tOBkpIS5XZra2tiYmKIiIgY8PajiUqr1XLs2DFAN9NlxYoVRl7R0HF1dVW6rAG4uLjw4osvYm1tPajHPX78uJIBevLJJ9FoNErb7UmTJo3JttvDJTExka6uLgDi4+Pl97EQYkKQIEcMSF8Hxe3Y9JT8QR2jequ5qmpX69rPlhQTBJibm7N8+XIWLlwo9Q8DlJ2dTUVFBaDrBjZe2qJrtVqOHz+uBDgATzzxxKBn1ty5c4dr164BEBQUhL+/P0lJSbS0tACwcuVKCbR/VF9fz6VLlwDw9fXF39/fyCsSQoiRIXtJxIBE+bjg5WDdwyg5HRXg7WBN7AzPkVyWGCKPqrnST9M4ihsa4Ne//jXLli2TAGeA1Gq1MuPF2tp63Mx00Qc4+nbYenl5eYN6XLVarTQbMDMzY/Xq1bS3t5OYmAjoasGCg4MH9RzjyZkzZ5Thu3FxcUZejRBCjBwJcsSAmJqoeHdNGNB9ZrYMlBv7+lpz5R4ZM+htRxPdlStXlM6DK1euHBfBolar5eTJk1y8eBGAyZMnM3XqVAAyMzOVk+6BuHjxIrW1tYDu5+Xg4EBycrKSLYqNjZXs8Y+qqqqUVt2BgYF4espFJyHExCFBjhiwTYFefLVlMZ4Ohie5Xg7WfLVlsQyUG8P6WnPl6Ok7vAsZ59ra2pRuYE5OToSHhxt5RYOn1Wo5deqU0mzA2dmZl156iYiICEDXMKCoqGhAj11fX6/8vKZMmcLChQtpaWlRnsvNzY3Zs2cPwasYHx6cPbRy5UojrkQIIUae1OSIQdkU6MWG2Z4yUG6c6WvNVV/vJ3qWlJSkZCDi4+PHfB2JVqvlzJkzJCUlAbrA7aWXXsLOzo45c+Zw5MgRQNdlzcfHp9+Pf/ToUdRqNQBPPfUUpqamJCUlKQ0I4uLiJIvzo+LiYm7cuAFAREQEU6ZMMfKKhBBiZEkmRwya6Y8D5Z6f60O0r6sEOONAX2uuonxcRnJZ40pDQ4OSgfDw8CAgIMDIKxq8s2fPKrUxkyZN4uWXX1bmYNna2ipF71lZWUqw0ld5eXlcv34dgJCQEHx8fGhsbFRqfry9vaWo/kf6eijQDUkdT936hBCiryTIEUJ0IzVXw+/s2bNoNBoAVq9ePeYzEAkJCZw7dw7oHuDohYSEANDe3k5+fn6fH7urq4tDhw4BYGFhwapVqwBda2R9sCRZnPvy8/OVLYGRkZEym0wIMSFJkCOE6JHUXA2fqqoqrl69CsCsWbMGtHVrNElMTOTs2bMAODo68vLLL/d4Yh0QEKBsydMPBu2LpKQk7t27B+iCGVtbW+7du8eVK1cA8Pf3H/M/w6Gi1Wo5ceIEoGvvHhUVZeQVCSGEcUhNjhCiV1JzNTz0J6Ew9tv6nj9/ntOnTwPg4ODQa4ADYGlpyezZs8nJySE3N5eOjo7HdpOrq6tTMkRTp05l3rx5gC5zpO/SFhsbO1QvZ8zLzs6msrISgKioKOl+KISYsCTIEUI8kr7mSgyNu3fvcuvWLUBXEO7iMnbrmi5cuKB08LK3t+fll1/G0dHxkd8TEhJCTk4OarWaGzduMHfu3Efe//Dhw8q2vqeeegoTExNqampIT08HdNkhDw+Pwb+YcUCtVivHw9ramsjISCOvSAghjEe2qwkhxAjRarUcO3YMAFNTU6Kjo427oEFITk5WMlL6AGfSpEmP/b4ZM2Yo2ZvMzMxH3vfGjRvK8NCIiAg8PDw4ffo0n3/+uXKfmJiYAb6C8efq1avKtr6YmJhxMXNJCCEGSoIcIYQYIbm5uZSWlgKwZMmSboX5Y8XFixeV7l12dna8/PLLODk59el7TU1NCQ4OBnQF8i0tLT3er7OzU2k2YGlpSXTMSo7dKOYviRlcrm5B8+P9Hpc5mig6OzuVGUKOjo7KXCIhhJioZLuaEEKMALVarWQ+rKysWLp0qZFXNDApKSlKNsrW1rZfAY7e3LlzSUtLQ6vVkp2dTVhYWLf7nDt3jsbGRgDM5i4h6G9nKG5oBXQNLxzoZA3lNDQ0jOktf0MlJSVFCRhjY2PH/MwlIYQYLMnkCCHECEhLS1O2EkVHR2NpaWncBQ3A5cuXOXr0KAA2Nja8/PLLODs79/txpk2bhq2tLdDzlrWamhrOnz8PQLnTNP6vKxU/Bjj3NWDGl3iTWN3e7+cfb1pbW5X5RC4uLkqmTAghJjIJcoQQYph1dHQYbCWaP3++kVfUf1euXOHw4cPA/QBn8uTJA3oslUqlzMwpLi6mvr5e+ZpWq+WHH34AQAMc7HBC2/OjAPCro+moNT3fY6JISkqio6MDgPj4eJkXJIQQSJAjhBDD7sKFC7S26jIRcXFxY24rUWpqqlIfY21tzUsvvcSUKVMG9ZgPdlXLyckx+LigoAAAm4AIypo7en0MLVDU0Eri3apBrWUsa2xsJDk5GQBvb29mzJhh5BUJIcToIEGOEEIMo6amJpKSkgBwc3Njzpw5Rl5R/1y9elXJrFhZWfHyyy8PSQ2Mm5ubstXt2rVrgC7jdeTIEUAXTLnNDOrTY5U1tg16PWNVQkKC0mJbsjhCCHGfBDlCCDGMEhIS6OrqAmDVqlVj6iQ0PT2d7777DhjaAAd0W9ZCQ0MBXQ0OQGJiIs3NzQCsWbOGac596z7nbm81JGsaa2pqakhNTQVg5syZeHt7G3lFQggxekiQI4QQw6SmpoYrV64AMH36dPz8/Iy8or7LyMjg4MGDgK6F80svvYSr69AOhX24QP7y5cuAbtvV3LlzifJxwcvBmt7CQhXg7WBNlM/E7K52+vRp5eO4uDgjrkQIIUYfCXKEEGKYnDx5Uvk4Pj7eiCvpn6ysLL799lvgfoAzderUIX8eZ2dn3N3du93+5JNPolKpMDVR8e6aMIBugY7+83fWhGFqMnayY0OlrKxMqWUKCQkZ8gBUCCHGOglyhBBiGBQXF3P9+nVAdxI6HEHCcLh27RoHDhwAwMLCghdffBE3N7dhez79ljW9xYsXG5ywbwr04qsti/F0sDa4n5eDNV9tWcymQK9hW9topg+gVSoVMTExRl6NEEKMPjIMVAghhphWq1UGZpqYmLBy5Uojr+jRtFot9+7do6SkhK+//hoAc3NzXnzxxR4zLUNp+vTpysdmZmZER0d3u8+mQC82zPYk8W4VZY1tuNtbEeXjMiEzOAB37tzh9u3bACxYsIBJkyYZd0FCCDEKSZAjhBBD7ObNmxQXFwMQGRmJo6OjkVfUnVqjVYKGivzr1GUkK6l9MzMzXnzxRTw8PIZ9HZcuXVI+zu+y4Nd//5qNsStYOd3dIIgxNVER7StbsrRaLSdOnAB0x2n58uVGXpEQQoxOEuQIIcQQ0mg0ykmopaUlUVFRRl5Rdwdyi3n7aDrFDa3KbQ7MZA3lBNHIhg0b8PT0HPZ1lJWVcfnyZW6aOBAC7MWH1gr4y94kvByseXdN2ITdjtabGzduUFZWBsCSJUuwtbU18oqEEGJ0kpocIYQYQunp6UpL5OXLl2Ntbf2Y7xhZB3KL2bw/2SDAAWjAjP14kYP9iJw4a7Vavv/+e3Kw5xu6B1QlDa1s3p/MgdziYV/LWPFwAL1kyRIjr0gIIUYvCXKEEGKIdHZ2Km197e3tWbhwoZFXZEit0fL20XS0PX5VtzUs0cYfb59pw76Wq1evUlJWxlHc6N47DWWNvzqajlrT84onmszMTGprawFYsWIFlpaWRl6REEKMXhLkCCHEELl48aIyzDI2NhYzs9G1IzjxblW3DI4hFWUtnZwvqh7WdbS0tHD8+HEKsaEB817vpwWKGlpJvFs1rOsZC7q6upQA2s7OjgULFhh5RUIIMbpJkCOEEEOgpaWFxMREAFxcXAgJCTHyirora2wb0vsN1KlTp2hvb6epj2Whw72eseDKlSs0NjYCozOAFkKI0UZ+SwohxBA4d+4cnZ2dAKxatQqVavS1N3a3txrS+/WVvpPb1bwC7mRexamxHBMgwNMNSkZ+PWNNe3s7CQkJAEyePHlUBtBCCDHaSJAjhBCDoNFoKCkpISUlBQBfX1+D2S+jSZSPC14O1pQ0tPZYl6NCN2QzysdlyJ6zeyc3ZxywZw3l/HKOL7sba6ht6nkL3XCsZyxKTk6mrU2XzYqLi8PERDZhCCHE48hvSiGEGAC1RsvZgkre+uBz/r+/f47mx9vj4+NHZRYHdLNm3l0TBnQv9dd//s6asCEbsvm4Tm5HC2qU9TxsONYzFjU3N5OUlASAu7s7s2fPNvKKhBBibJBMjhBC9JNhdsIc8MWBTtZQTk1NzYgM0RyoTYFefLVlcbc5OV4O1rwzhHNp+tLJbXc5/PNsTz55eiHkpxvcY6jXM1adO3eOrq4uYPRugxRCiNFIghwhhOgHfXbi4ZN3fXZiVSPMNcrK+m5ToBcbZnuSeLeKssY23O2tiPJxGdKMyeM7ud3vnPbUbA8O56dzaNtSylu6hmU9Y9G9e/e4fPkyAP7+/vj6+hp3QUIIMYZIkCOEEH30uOyECviXlEJeWhQ86k/QTU1URPu6DtvjD6ST2zIfF8zNe28pPdGcOXMGrVb3bouLizPyaoQQYmyRmhwhhOijx2UnZK7Lfcbq5DZeVFZWkpmZCUBQUBDu7u5GXpEQQowtEuQIIUQfjZY5M2OBvpNbb/ksFeAtndN6dfLkSeXjlStXGnElQggxNkmQI4QQfSTZib4b6U5u40lRURG3bt0CYN68eUyePNnIKxJCiLFHghwhhOgjyU70j76Tm6eDtcHtXg7WfLVl8YTvnNYTrVbL8ePHATAxMWHFihVGXpEQQoxN0nhACCH6SJ+d2Lw/GRUYNCCQ7ETPRqKT23iSl5dHcXExAIsWLcLe3t7IKxJCiLFJghwhhOiHkZozM54Mdye38UKr1XLixAkALCwsWLZsmZFXJIQQY5cEOUII0U+SnRDD4dq1a1RV6TrzRUVFYW1t/ZjvEEII0RsJcoQQYgAkOyGGklqt5tSpUwDY2NgQGRlp5BUJIcTYJo0HhBBCCCNLS0ujvr4egJiYGBmKKoQQgyRBjhBCCGFEHR0dnD17FoBJkyYRHh5u3AUJIcQ4IEGOEEIIYUQpKSm0tLQAEBsbi6mpqZFXJIQQY58EOUIIIYSRtLS0kJiYCICrqytz5swx8oqEEGJ8kCBHCCGEMJLz58/T2dkJQHx8PCqVdOgTQoihIEGOEEIIYQQNDQ2kpKQA4OPjw/Tp0428IiGEGD8kyBFCCCGMICEhAY1GA0gWRwghhpoEOUIIIcQIq66uJi0tDYBZs2bh5eVl5BUJIcT4IkGOEEIIMcJOnz6tfBwXF2fElQghxPg0bEFOQUEBr776Kn5+flhbWzN9+nR++9vf0tHRMVxPKYQQQoxqWq2W0tJScnNzAQgNDcXFxcXIqxJCiPHHbLge+Pr162g0Gj744ANmzJjBtWvXeP3112lubuZ3v/vdcD2tEEIIMeqoNVrOFVay/8hJWqtKmQaYqlTExMQYe2lCCDEuDVuQs2bNGtasWaN87u/vz40bN/jrX/8qQY4QQogJ40BuMW8fTae4oRWwAHxxoJM12nK0Wq2xlyeEEOPSiNbk1NfX4+zsPJJPKYQQQhjNgdxiNu9P/jHAua8BM/bjxcGb5UZamRBCjG/Dlsl5WF5eHu+9994jszjt7e20t7crnzc0NADQ2dmpDEsbTfRrGo1rE30jx3Dsk2M49o3XY6jWaPkfx65i1ePlRBUq4F8v3GJrqD+mJmO7ffR4PYYTiRzDsW8iHMP+vDaVtp+58n/8x3/kP/7jPx55n9zcXAICApTPS0pKWLFiBdHR0ezcubPX7/vnf/5n/uVf/qXb7Xv37sXGxqY/yxRCCCGEEEKMIy0tLWzbto36+nocHBweed9+BzlVVVXU1NQ88j7+/v5YWFgAUFpaSnR0NIsWLWLXrl2YmPS+Q66nTI63tzfV1dWPfSHG0NnZyYkTJ4iPj8fc3NzYyxEDIMdw7JNjOPaN12P4VU4xr3535bH3+2j9fDYHje05OeP1GE4kcgzHvolwDBsaGpgyZUqfgpx+b1dzcXHpc7vLkpISYmJimDdvHh9//PEjAxwAS0tLLC0tu91ubm4+qg/WaF+feDw5hmOfHMOxb7wdQ3dHW1o1fbvfeHnd4+0YTkRyDMe+8XwM+/O6hq0mp6SkhOjoaKZNm8bvfvc7qqqqlK+5ubkN19MKIYQQo0KUjwteDtaUNLTS05YJFeDlYE2Uj8zJEUKIoTZsQc6JEyfIy8sjLy8PLy/DNLy0zBRCCDHemZqoeHdNGJv3J6MCg0BH32bgnTVhY77pgBBCjEbD1kL6pZdeQqvV9vifEEIIMRFsCvTiqy2L8XSwNrjdy8Gar7YsZlPg2K7FEUKI0WrEWkgLIYQQE9GmQC82zPYk8W4VZY1tuNtbEeXjIhkcIYQYRhLkCCGEEMPM1ERFtK+rsZchhBATxrBtVxNCCCGEEEIIY5AgRwghhBBCCDGuSJAjhBBCCCGEGFckyBFCCCGEEEKMKxLkCCGEEEIIIcYVCXKEEEIIIYQQ44oEOUIIIYQQQohxRYIcIYQQQgghxLgiQY4QQgghhBBiXJEgRwghhBBCCDGuSJAjhBBCCCGEGFckyBFCCCGEEEKMKxLkCCGEEEIIIcYVCXKEEEIIIYQQ44oEOUIIIYQQQohxRYIcIYQQQgghxLgiQY4QQgghhBBiXJEgRwghhBBCCDGuSJAjhBBCCCGEGFckyBFCCCGEEEKMK2bGXsCjaLVaABoaGoy8kp51dnbS0tJCQ0MD5ubmxl6OGAA5hmOfHMOxT47h2CfHcOyTYzj2TYRjqI8J9DHCo4zqIKexsREAb29vI69ECCGEEEIIMRo0Njbi6Oj4yPuotH0JhYxEo9FQWlqKvb09KpXK2MvppqGhAW9vb4qKinBwcDD2csQAyDEc++QYjn1yDMc+OYZjnxzDsW8iHEOtVktjYyMeHh6YmDy66mZUZ3JMTEzw8vIy9jIey8HBYdy+mSYKOYZjnxzDsU+O4dgnx3Dsk2M49o33Y/i4DI6eNB4QQgghhBBCjCsS5AghhBBCCCHGFQlyBsHS0pLf/va3WFpaGnspYoDkGI59cgzHPjmGY58cw7FPjuHYJ8fQ0KhuPCCEEEIIIYQQ/SWZHCGEEEIIIcS4IkGOEEIIIYQQYlyRIEcIIYQQQggxrkiQI4QQQgghhBhXJMgZQuvXr8fHxwcrKyvc3d154YUXKC0tNfayRB8VFBTw6quv4ufnh7W1NdOnT+e3v/0tHR0dxl6a6Id//dd/ZcmSJdjY2DBp0iRjL0f0wZ///Gd8fX2xsrIiMjKSS5cuGXtJoh/OnTvHU089hYeHByqVim+//dbYSxL98H/+z/9hwYIF2Nvb4+rqysaNG7lx44axlyX64a9//SshISHKENDFixdz5MgRYy/L6CTIGUIxMTHs37+fGzdu8PXXX5Ofn8/mzZuNvSzRR9evX0ej0fDBBx+QnZ3NH/7wB95//33+n//n/zH20kQ/dHR08Oyzz/Kzn/3M2EsRffDFF1/wm9/8ht/+9rekpaURGhrK6tWrqaysNPbSRB81NzcTGhrKn//8Z2MvRQxAQkIC//AP/8DFixc5ceIEnZ2drFq1iubmZmMvTfSRl5cX//7v/05qaipXrlxh5cqVbNiwgezsbGMvzaikhfQw+u6779i4cSPt7e2Ym5sbezliAP7zP/+Tv/71r9y+fdvYSxH9tGvXLn71q19x7949Yy9FPEJkZCQLFizgT3/6EwAajQZvb2/eeust/vEf/9HIqxP9pVKp+Oabb9i4caOxlyIGqKqqCldXVxISEli+fLmxlyMGyNnZmf/8z//k1VdfNfZSjEYyOcOktraWzz77jP9/+/b30nQXwHH8M30w8geSMWaIii0IIkSY9MsuNphgf4C3bSGisUUiyBL0L7CLqG662roZuxBklwXD4YUiIk2U8EIRhlNhGkntosHmc9UgfJ5yJh79+n7BLnb2HXzgcNj57Ps9Dx48oOCcY/v7+2poaDAdA7CkfD6vxcVFeb3e0lhFRYW8Xq/m5uYMJgMurv39fUnit++cKhQKisViyuVyun//vuk4RlFyTlgoFFJNTY2uXr2qdDqteDxuOhKOaW1tTW/evNHAwIDpKIAl7e7uqlAoyOFw/DLucDi0s7NjKBVwcRWLRQ0NDamrq0u3b982HQdlWF5eVm1trS5duqTBwUFNTU3p1q1bpmMZRcn5gxcvXshms/32tbq6Wrp+ZGREnz590sePH1VZWanHjx+LJwLNKncOJSmTyainp0e9vb3q7+83lBw/HWcOAQDlCQQCWllZUSwWMx0FZbp586ZSqZTm5+f19OlT+Xw+ff782XQsoziT8wfZbFZ7e3u/veb69euqqqo6NL65uanm5mbNzs5e+FuGJpU7h1tbW3K73bp3754ikYgqKvgvwLTjrEPO5Jx9+Xxe1dXVmpyc/OUMh8/n09evX7kTfg5xJuf8CgaDisfjmpmZUVtbm+k4+Eter1dOp1Pv3r0zHcWYf0wHOOvsdrvsdvuxvlssFiVJP378OMlIKFM5c5jJZOTxeORyuRQOhyk4Z8TfrEOcXVVVVXK5XEokEqVNcbFYVCKRUDAYNBsOuCAODg707NkzTU1NKZlMUnAsolgsXvj9JyXnhMzPz2thYUEPHz7UlStXtL6+rvHxcTmdTu7inBOZTEZut1utra16+fKlstls6bPGxkaDyVCOdDqtL1++KJ1Oq1AoKJVKSZJu3Lih2tpas+FwyPDwsHw+nzo7O3Xnzh29evVKuVxOT548MR0NR/T9+3etra2V3m9sbCiVSqmhoUEtLS0Gk+EoAoGAotGo4vG46urqSufh6uvrdfnyZcPpcBSjo6N69OiRWlpa9O3bN0WjUSWTSX348MF0NKN4XO2ELC8v6/nz51paWlIul9O1a9fU09OjsbExNTU1mY6HI4hEIv+7sWKZnB9+v1/v378/ND49PS232336gfBHb9++1cTEhHZ2dtTR0aHXr1/r7t27pmPhiJLJpDwez6Fxn8+nSCRy+oFQFpvN9p/j4XBYfr//dMPgWPr6+pRIJLS9va36+nq1t7crFAqpu7vbdDSjKDkAAAAALIUDBwAAAAAshZIDAAAAwFIoOQAAAAAshZIDAAAAwFIoOQAAAAAshZIDAAAAwFIoOQAAAAAshZIDAAAAwFIoOQAAAAAshZIDAAAAwFIoOQAAAAAshZIDAAAAwFL+Bc5LieyDE3IEAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the generated tree\n",
-    "plot_tree_2d_scatter(tree, 'value')\n",
-    "plt.gca().set_title('Sampled tree with leaf noise')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Estimate inner nodes conditioning on the observations of leaves**\n",
-    "\n",
-    "We now define the backwards filter through the `up` function. By executing `up` function layer by layer from the bottom leaves to the top root, the information of leaves (in this case, the parameters of the distribution that leaves follow) has been passed along the tree up to the root. The Gaussian are parametrized in the $(c,F,H)$ format make the `fuse` just a sum of the results of the `up` operation, and the information carried is $(F, H)$. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "@jax.jit\n",
-    "def up(noise, edge_length, F_T, H_T, params, **args):\n",
-    "    \n",
-    "    def f(noise, edge_length, F_T, H_T):\n",
-    "        T = edge_length # edge length, we here used T using the analogy to the continuous case where T denotes the evaluation time of a Brownian motion\n",
-    "        var = T * params['variance'] # total edge variance\n",
-    "        covar = var * jnp.eye(d) # covariance matrix\n",
-    "\n",
-    "        Sigma_T = jnp.linalg.inv(H_T)\n",
-    "        v_T = Sigma_T @ F_T\n",
-    "    \n",
-    "        invPhi_0 = (jnp.eye(d) + H_T * covar)\n",
-    "        Sigma_0 = Sigma_T @ invPhi_0 # = Sigma_T+covar\n",
-    "        H_0 = jnp.linalg.inv(Sigma_0)\n",
-    "        F_0 = jnp.linalg.solve(invPhi_0, F_T)\n",
-    "        #v_0 = Sigma_0@F_0\n",
-    "        #c_0 = -jnp.real(jax.scipy.stats.multivariate_normal.logpdf(v_0,jnp.zeros(d),Sigma_0))\n",
-    "    \n",
-    "        return {'F_0': F_0, 'H_0': H_0}\n",
-    "    \n",
-    "    return jax.vmap(f)(noise, edge_length, F_T, H_T)\n",
-    "\n",
-    "def transform(child_F_0, child_H_0, **args):\n",
-    "    return {'F_T': child_F_0, \n",
-    "            'H_T': child_H_0}\n",
-    "\n",
-    "upmodel = UpLambdaReducer(up, transform,\n",
-    "                   reductions={\n",
-    "                       'F_0': 'sum',\n",
-    "                       'H_0': 'sum',\n",
-    "                    }\n",
-    "        )\n",
-    "up = OrderedExecutor(upmodel)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $(c,F,H)$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree for up\n",
-    "tree.add_property('F_0', shape=(d,))\n",
-    "tree.add_property('H_0', shape=(d, d)) \n",
-    "tree.add_property('F_T', shape=(d,))\n",
-    "tree.add_property('H_T', shape=(d, d))\n",
-    "\n",
-    "def init_up(leaf_values,params):\n",
-    "    tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)\n",
-    "    tree.data['H_T'] = tree.data['H_T'].at[tree.is_leaf].set((jnp.eye(d) / params['obs_var'].value)[None, :, :])\n",
-    "    tree.data['F_T'] = tree.data['F_T'].at[tree.is_leaf].set(jnp.einsum('bij,bj->bi', tree.data['H_T'][tree.is_leaf], leaf_values))\n",
-    "    # Sigma = params['obs_var'].value * jnp.eye(d)\n",
-    "    #tree.data['c_T'] = tree.data['c_T'].at[tree.is_leaf].set(-jax.vmap(lambda v: jnp.real(jax.scipy.stats.multivariate_normal.logpdf(v,jnp.zeros(d),Sigma)))(leaf_values))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Suppose the information from the leaves has been passed to the root by `up`, we can now define the conditional downwards pass `down_conditional`, i.e. the forwards guiding, that runs downwards again. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@jax.jit\n",
-    "def down_conditional(noise, edge_length, F_T, H_T, parent_value, params, **args):\n",
-    "    \n",
-    "    def f(noise, edge_length, F_T, H_T, parent_value):\n",
-    "        T = edge_length # edge length \n",
-    "        x = parent_value\n",
-    "        var = T*params['variance'] # total edge variance\n",
-    "        covar = var * jnp.eye(d) # covariance matrix\n",
-    "\n",
-    "        invSigma = jnp.linalg.inv(covar)\n",
-    "        H = H_T + invSigma\n",
-    "        mu = jnp.linalg.solve(H, F_T + invSigma @ x)\n",
-    "        return {'value': mu + jnp.linalg.solve(jnp.real(jax.scipy.linalg.sqrtm(H)), noise)}\n",
-    "\n",
-    "    return jax.vmap(f)(noise, edge_length, F_T, H_T, parent_value)\n",
-    "\n",
-    "downmodel_conditional = DownLambda(down_fn=down_conditional)\n",
-    "down_conditional = OrderedExecutor(downmodel_conditional)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (unconditional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 38 μs, sys: 1 μs, total: 39 μs\n",
-      "Wall time: 42.2 μs\n",
-      "CPU times: user 305 μs, sys: 77 μs, total: 382 μs\n",
-      "Wall time: 219 μs\n",
-      "CPU times: user 75 μs, sys: 23 μs, total: 98 μs\n",
-      "Wall time: 100 μs\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and forwards guiding\n",
-    "init_up(leaf_values, params)\n",
-    "up.up(tree, params.values()) # backwards filter\n",
-    "down_conditional.down(tree, params.values()) # forwards guiding\n",
-    "\n",
-    "# time the operations\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree, subkey)\n",
-    "down_unconditional.down(tree, params.values())\n",
-    "%time down_unconditional.down(tree, params.values())\n",
-    "%time down_conditional.down(tree, params.values())\n",
-    "%time up.up(tree, params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We test the setup by sampling a number of trees and computing mean and covariance of the leaf data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean:\n",
-      "root conditional mean vs. sample mean: \n",
-      " [0.042 0.038] \n",
-      " [0.042 0.038]\n",
-      "Covariance:\n",
-      "root conditional cov. vs. sample cov: \n",
-      " [[0.055 0.   ]\n",
-      " [0.    0.055]] \n",
-      " [[1.296 0.301]\n",
-      " [0.301 1.081]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# do statistics on the leaf values\n",
-    "F = tree.data['F_T'][0]; H = tree.data['H_T'][0]\n",
-    "print(\"Mean:\")\n",
-    "print(f\"root conditional mean vs. sample mean: \\n {jnp.linalg.solve(H,F)} \\n {jnp.mean(leaf_values,0)}\")\n",
-    "print(\"Covariance:\")\n",
-    "print(f\"root conditional cov. vs. sample cov: \\n {jnp.linalg.inv(H)} \\n {jnp.cov(leaf_values.T)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 500/500 [00:00<00:00, 834.00it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First 10 leaves: \n",
-      "Leaf 0:\n",
-      "mean:\n",
-      " [-0.039  0.041]\n",
-      "cov:\n",
-      " [[ 0.971 -0.032]\n",
-      " [-0.032  1.124]]\n",
-      "mean conditional:\n",
-      "  [ 0.003 -1.705]\n",
-      "sample:\n",
-      " [ 0.017 -1.714]\n",
-      "cov conditional:\n",
-      " [[ 0.009 -0.   ]\n",
-      " [-0.     0.01 ]]\n",
-      "mean w/ noise:\n",
-      " [-0.035  0.038]\n",
-      "cov w/ noise:\n",
-      " [[ 0.975 -0.033]\n",
-      " [-0.033  1.138]]\n",
-      "----------------------------------------\n",
-      "Leaf 1:\n",
-      "mean:\n",
-      " [-0.008  0.05 ]\n",
-      "cov:\n",
-      " [[1.003 0.075]\n",
-      " [0.075 1.072]]\n",
-      "mean conditional:\n",
-      "  [-2.367 -1.418]\n",
-      "sample:\n",
-      " [-2.411 -1.428]\n",
-      "cov conditional:\n",
-      " [[ 0.009 -0.   ]\n",
-      " [-0.     0.01 ]]\n",
-      "mean w/ noise:\n",
-      " [-0.007  0.048]\n",
-      "cov w/ noise:\n",
-      " [[1.009 0.074]\n",
-      " [0.074 1.087]]\n",
-      "----------------------------------------\n",
-      "Leaf 2:\n",
-      "mean:\n",
-      " [-0.043  0.007]\n",
-      "cov:\n",
-      " [[0.939 0.014]\n",
-      " [0.014 0.959]]\n",
-      "mean conditional:\n",
-      "  [-0.143 -0.818]\n",
-      "sample:\n",
-      " [-0.137 -0.817]\n",
-      "cov conditional:\n",
-      " [[ 0.01  -0.   ]\n",
-      " [-0.     0.011]]\n",
-      "mean w/ noise:\n",
-      " [-0.045  0.004]\n",
-      "cov w/ noise:\n",
-      " [[0.943 0.011]\n",
-      " [0.011 0.967]]\n",
-      "----------------------------------------\n",
-      "Leaf 3:\n",
-      "mean:\n",
-      " [-0.004  0.086]\n",
-      "cov:\n",
-      " [[ 0.993 -0.081]\n",
-      " [-0.081  1.056]]\n",
-      "mean conditional:\n",
-      "  [-1.327 -1.459]\n",
-      "sample:\n",
-      " [-1.348 -1.466]\n",
-      "cov conditional:\n",
-      " [[ 0.01  -0.001]\n",
-      " [-0.001  0.01 ]]\n",
-      "mean w/ noise:\n",
-      " [-0.005  0.09 ]\n",
-      "cov w/ noise:\n",
-      " [[ 1.007 -0.087]\n",
-      " [-0.087  1.054]]\n",
-      "----------------------------------------\n",
-      "Leaf 4:\n",
-      "mean:\n",
-      " [0.013 0.022]\n",
-      "cov:\n",
-      " [[ 1.058 -0.009]\n",
-      " [-0.009  1.018]]\n",
-      "mean conditional:\n",
-      "  [-0.677 -0.88 ]\n",
-      "sample:\n",
-      " [-0.681 -0.879]\n",
-      "cov conditional:\n",
-      " [[1.014e-02 5.508e-05]\n",
-      " [5.508e-05 9.305e-03]]\n",
-      "mean w/ noise:\n",
-      " [0.01  0.018]\n",
-      "cov w/ noise:\n",
-      " [[ 1.07  -0.006]\n",
-      " [-0.006  1.028]]\n",
-      "----------------------------------------\n",
-      "Leaf 5:\n",
-      "mean:\n",
-      " [ 0.007 -0.057]\n",
-      "cov:\n",
-      " [[ 1.118 -0.045]\n",
-      " [-0.045  1.082]]\n",
-      "mean conditional:\n",
-      "  [-0.718 -1.781]\n",
-      "sample:\n",
-      " [-0.727 -1.791]\n",
-      "cov conditional:\n",
-      " [[ 0.011 -0.001]\n",
-      " [-0.001  0.01 ]]\n",
-      "mean w/ noise:\n",
-      " [ 0.001 -0.058]\n",
-      "cov w/ noise:\n",
-      " [[ 1.141 -0.044]\n",
-      " [-0.044  1.088]]\n",
-      "----------------------------------------\n",
-      "Leaf 6:\n",
-      "mean:\n",
-      " [-0.024  0.021]\n",
-      "cov:\n",
-      " [[ 1.03  -0.006]\n",
-      " [-0.006  1.091]]\n",
-      "mean conditional:\n",
-      "  [ 0.171 -0.948]\n",
-      "sample:\n",
-      " [ 0.19  -0.945]\n",
-      "cov conditional:\n",
-      " [[0.009 0.001]\n",
-      " [0.001 0.008]]\n",
-      "mean w/ noise:\n",
-      " [-0.026  0.026]\n",
-      "cov w/ noise:\n",
-      " [[ 1.033 -0.008]\n",
-      " [-0.008  1.095]]\n",
-      "----------------------------------------\n",
-      "Leaf 7:\n",
-      "mean:\n",
-      " [ 0.029 -0.016]\n",
-      "cov:\n",
-      " [[ 1.029 -0.017]\n",
-      " [-0.017  1.072]]\n",
-      "mean conditional:\n",
-      "  [-0.024 -1.003]\n",
-      "sample:\n",
-      " [-0.015 -1.007]\n",
-      "cov conditional:\n",
-      " [[ 0.009 -0.001]\n",
-      " [-0.001  0.01 ]]\n",
-      "mean w/ noise:\n",
-      " [ 0.039 -0.024]\n",
-      "cov w/ noise:\n",
-      " [[ 1.026 -0.015]\n",
-      " [-0.015  1.079]]\n",
-      "----------------------------------------\n",
-      "Leaf 8:\n",
-      "mean:\n",
-      " [-0.06   0.017]\n",
-      "cov:\n",
-      " [[0.978 0.022]\n",
-      " [0.022 1.028]]\n",
-      "mean conditional:\n",
-      "  [ 0.349 -1.08 ]\n",
-      "sample:\n",
-      " [ 0.363 -1.082]\n",
-      "cov conditional:\n",
-      " [[0.01 0.  ]\n",
-      " [0.   0.01]]\n",
-      "mean w/ noise:\n",
-      " [-0.058  0.017]\n",
-      "cov w/ noise:\n",
-      " [[0.995 0.02 ]\n",
-      " [0.02  1.045]]\n",
-      "----------------------------------------\n",
-      "Leaf 9:\n",
-      "mean:\n",
-      " [-0.042 -0.01 ]\n",
-      "cov:\n",
-      " [[ 0.964 -0.028]\n",
-      " [-0.028  1.068]]\n",
-      "mean conditional:\n",
-      "  [-0.068 -0.263]\n",
-      "sample:\n",
-      " [-0.062 -0.245]\n",
-      "cov conditional:\n",
-      " [[9.233e-03 9.180e-05]\n",
-      " [9.180e-05 9.488e-03]]\n",
-      "mean w/ noise:\n",
-      " [-0.044 -0.008]\n",
-      "cov w/ noise:\n",
-      " [[ 0.978 -0.029]\n",
-      " [-0.029  1.082]]\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# sample statistics\n",
-    "K = 500 # number samples\n",
-    "samples = jnp.zeros((K, sum(tree.is_leaf), d))\n",
-    "samples_noise = jnp.zeros((K, sum(tree.is_leaf), d))\n",
-    "samples_conditional = jnp.zeros((K, sum(tree.is_leaf), d))\n",
-    "\n",
-    "for i in tqdm(range(K)):\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree, subkey)\n",
-    "    down_unconditional.down(tree, params.values())\n",
-    "    values = tree.data['value'][tree.is_leaf]\n",
-    "    # collect values\n",
-    "    samples = samples.at[i].set(values)\n",
-    "    # add observation noise\n",
-    "    subkey,key = split(key)\n",
-    "    samples_noise = samples_noise.at[i].set(values + jnp.sqrt(params['obs_var'].value) * jax.random.normal(subkey, values.shape))\n",
-    "    # conditional sampling\n",
-    "    update_noise(tree, subkey)\n",
-    "    down_conditional.down(tree, params.values())\n",
-    "    samples_conditional = samples_conditional.at[i].set(tree.data['value'][tree.is_leaf])\n",
-    "    \n",
-    "# print sample statistics for first 10 leaves\n",
-    "print(\"First 10 leaves: \")\n",
-    "for i in range(10):\n",
-    "    print(f\"Leaf {i}:\")\n",
-    "    print(\"mean:\\n\", jnp.mean(samples[:,i],0))\n",
-    "    print(\"cov:\\n\", jnp.cov(samples[:,i].T))\n",
-    "    print(\"mean conditional:\\n \",jnp.mean(samples_conditional[:,i],0))\n",
-    "    print(\"sample:\\n\", leaf_values[i])\n",
-    "    print(\"cov conditional:\\n\",jnp.cov(samples_conditional[:,i].T))\n",
-    "    print(\"mean w/ noise:\\n\",jnp.mean(samples_noise[:,i],0))\n",
-    "    print(\"cov w/ noise:\\n\",jnp.cov(samples_noise[:,i].T))\n",
-    "    print(\"--\"*20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimate variance using MCMC\n",
-    "\n",
-    "We make two MCMC runs: \n",
-    "\n",
-    "* We use that the model with constant node covariance is fully Gaussian and we can read the data likelihood directly from the results of upwards pass.\n",
-    "\n",
-    "* We also sample the state of the tree to get a likelihood approximation from the conditional downwards pass. (This version is not necessary in the current model, but it points towards how inference in non-Gaussian models (e.g. non-linear diffusion processes along the edges) will look.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'variance': 0.25, 'obs_var': 0.01}\n",
-      "data parameters:  {'variance': 0.5, 'obs_var': 0.01}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1200/1200 [00:02<00:00, 411.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.9358\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# inference for Gaussian model, likelihood from backwards filtering\n",
-    "def log_likelihood(data, state):\n",
-    "    \"\"\"Log likelihood of the tree.\"\"\"\n",
-    "    params, = state\n",
-    "    init_up(data, params) \n",
-    "    up.up(tree, params.values())\n",
-    "    v, F, H = tree.data['value'][0], tree.data['F_T'][0], tree.data['H_T'][0]\n",
-    "    c = -jnp.real(jax.scipy.stats.multivariate_normal.logpdf(v, jnp.zeros(d), jnp.linalg.inv(H)))\n",
-    "    return -c + F @ v - 0.5 * v.T @ H @ v\n",
-    "\n",
-    "def log_posterior(data, state):\n",
-    "    \"\"\"Log posterior given the state and data.\"\"\"\n",
-    "    params, = state\n",
-    "    log_prior = params.log_prior()\n",
-    "    log_like = log_likelihood(data, state)\n",
-    "    return log_prior + log_like\n",
-    "\n",
-    "def proposal(state, key):\n",
-    "    params, = state\n",
-    "    new_params = params.propose(key)\n",
-    "    return (new_params, )\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'variance': VarianceParameter(.25), # variance of the Gaussian transition kernel \n",
-    "    'obs_var': VarianceParameter(.01, alpha=3, beta=.5, keep_constant=True) # observation noise variance. We keep it constant here because of lacking identifiability (which will not be the case with higher-dimensional correlated data)\n",
-    "    })\n",
-    "print(\"Initial parameters: \", init_params.values())\n",
-    "print(\"data parameters: \", params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_up(leaf_values, init_params)\n",
-    "init_state = (init_params, )\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 1000, burn_in=200, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'variance': 0.25, 'obs_var': 0.01}\n",
-      "data parameters:  {'variance': 0.5, 'obs_var': 0.01}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1200/1200 [00:03<00:00, 360.65it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.3292\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# inference for Gaussian model, likelihood from forward guiding\n",
-    "\n",
-    "# Crank-Nicolson update with possibly node-dependent lambd\n",
-    "lambd = .9\n",
-    "update_CN = lambda noise,key: noise * lambd + jnp.sqrt((1 - lambd**2)) * jax.random.normal(key, noise.shape)\n",
-    "\n",
-    "# downwards pass to compute likelihoods\n",
-    "tree.add_property('log_likelihood', shape=())\n",
-    "\n",
-    "@jax.jit\n",
-    "def down_log_likelihood(value, edge_length, parent_value, params, **args):\n",
-    "    var = params['variance'] * edge_length # total edge variance\n",
-    "    covar = jnp.einsum('b,ij->bij', var, jnp.eye(d)) # covariance matrix\n",
-    "\n",
-    "    return {'log_likelihood': jax.vmap(lambda value, m, covar: jnp.real(jax.scipy.stats.multivariate_normal.logpdf(value,m,covar)))(value, parent_value, covar)}\n",
-    "\n",
-    "downmodel_log_likelihood = DownLambda(down_fn=down_log_likelihood)\n",
-    "down_log_likelihood = OrderedExecutor(downmodel_log_likelihood)\n",
-    "\n",
-    "# log likelihood of the tree\n",
-    "def log_likelihood(data, state):\n",
-    "    \"\"\"Log likelihood of the tree.\"\"\"\n",
-    "    params,noise = state\n",
-    "    init_up(data, params)\n",
-    "    up.up(tree,params.values())\n",
-    "    tree.data['noise'] = noise \n",
-    "    down_conditional.down(tree, params.values())\n",
-    "    down_log_likelihood.down(tree, params.values())\n",
-    "    return jnp.sum(tree.data['log_likelihood'][1:]) # ignore root node\n",
-    "\n",
-    "def log_posterior(data, state):\n",
-    "    \"\"\"Log posterior given the state and data.\"\"\"\n",
-    "    parameters,_ = state\n",
-    "    log_prior = parameters.log_prior()\n",
-    "    log_like = log_likelihood(data, state)\n",
-    "    return log_prior + log_like\n",
-    "\n",
-    "def proposal(state, key):\n",
-    "    subkeys = jax.random.split(key,2)\n",
-    "    parameters,noise = state\n",
-    "\n",
-    "    # update parameters\n",
-    "    new_parameters = parameters.propose(subkeys[0])\n",
-    "    # update noise\n",
-    "    new_noise = update_CN(noise,subkeys[1])\n",
-    "\n",
-    "    return new_parameters, new_noise\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'variance': VarianceParameter(.25), # variance of the Gaussian transition kernel \n",
-    "    'obs_var': VarianceParameter(.01, alpha=3, beta=.5, keep_constant=True) # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \", init_params.values())\n",
-    "print(\"data parameters: \", params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_up(leaf_values, init_params)\n",
-    "init_state = (init_params, jnp.zeros_like(tree.data['noise']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 1000, burn_in=200, rng_key=key, savef = lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "jaxenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/notebooks/mcmc_Gaussian_BFFG_shapes.ipynb b/docs/source/notebooks/mcmc_Gaussian_BFFG_shapes.ipynb
deleted file mode 100644
index 51e56d8..0000000
--- a/docs/source/notebooks/mcmc_Gaussian_BFFG_shapes.ipynb
+++ /dev/null
@@ -1,5262 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for shapes\n",
-    "\n",
-    "Parameter inference for trees with Gaussian transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebook [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb) for a simpler version with $\\mathbb R^2$ data. In the present version, the node covariance is constant throughout the tree similarly to [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb). Shape dependent node covariance will follow in a later version.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter, plot_tree_2d_shape, plot_shape_2d\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.shape import k_K2\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# seed,\n",
-    "seed = 42\n",
-    "#import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 2; n = 64 # d = dimension of embedding space (usually 2), n = number of landmarkds\n",
-    "phis = jnp.linspace(0,2*jnp.pi,n,endpoint=False) # circular shape\n",
-    "root = jnp.vstack((jnp.cos(phis),jnp.sin(phis))).T.flatten()\n",
-    "\n",
-    "# plot\n",
-    "plot_shape = plot_shape_2d\n",
-    "plot_shape(root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# diffusion and covariance specification for shape processes\n",
-    "kQ12 = lambda x,params: (1/n)*k_K2(x,params)\n",
-    "\n",
-    "# evaluate k on two pairs of landmark configurations\n",
-    "kQ12_q = lambda q1,q2,params: kQ12(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# evaluate k on one landmark configurations against itself with each landmark pair resulting in a dxd matric\n",
-    "# i,jth entry of result is kQ12(x_i,x_j)*eye(d)\n",
-    "def Q12(q,params): \n",
-    "    A = jnp.einsum('ij,kl->ikjl',kQ12_q(q,q,params),jnp.eye(2))\n",
-    "    return A.reshape((A.shape[0]*A.shape[1],A.shape[2]*A.shape[3]))\n",
-    "\n",
-    "# diffusion matrix\n",
-    "sigma = lambda params: Q12(root,params)\n",
-    "# covariance matrix\n",
-    "def a(params): _sigma = sigma(params); return jnp.einsum('ij,kj->ik',_sigma,_sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gaussian tree, constant node covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                            None\n",
-      "           ┌────────────────────────┬────────────────────────┼────────────────────────┬────────────────────────┐\n",
-      "          None                     None                     None                     None                     None          \n",
-      " ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "topology = symmetric_topology(height=2, degree=5)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "# data dimension\n",
-    "d = 2\n",
-    "tree.add_property('value', shape=(n*d,))\n",
-    "tree.add_property('noise', shape=(n*d,))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.3), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.25), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions. At first, we define the unconditional transitions, which are just Gaussian samples. The covariance is sqare of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# vmapped version of down_unconditional. In this version, the function takes a single node, not a batch (contrast to the batched version in mcmc_Gaussian_BFFG.ipynb)\n",
-    "@jax.jit\n",
-    "def down_unconditional(noise,edge_length,parent_value,params,**args):\n",
-    "    def f(noise,edge_length,parent_value):\n",
-    "        var = edge_length # variance is edge length\n",
-    "        return {'value': parent_value+jnp.sqrt(var)*jnp.einsum('ij,j->i',sigma(params),noise)}\n",
-    "\n",
-    "    return jax.vmap(f)(noise,edge_length,parent_value)\n",
-    "downmodel_unconditional = DownLambda(down_fn=down_unconditional)\n",
-    "down_unconditional = OrderedExecutor(downmodel_unconditional)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sample new noise\n",
-    "def update_noise(tree,key): \n",
-    "    tree.data['noise'] = jax.random.normal(key, shape=tree.data['noise'].shape)\n",
-    "\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# copy the sampled tree and add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf]+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,tree.data['value'][tree.is_leaf].shape)\n",
-    "tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a generated tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots(figsize=(10,8))\n",
-    "plot_tree_2d_scatter(tree,'value',selector=lambda z: z.reshape((1,n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "\n",
-    "plot_tree_2d_shape(tree,'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "@jax.jit\n",
-    "def up(noise,edge_length,F_T,H_T,params,**args):\n",
-    "    def f(noise,edge_length,F_T,H_T):\n",
-    "        var = edge_length # variance is edge length\n",
-    "        covar = var*a(params) # covariance matrix\n",
-    "\n",
-    "        Sigma_T = jnp.linalg.inv(H_T)\n",
-    "        v_T = Sigma_T@F_T\n",
-    "    \n",
-    "        invPhi_0 = (jnp.eye(n*d)+H_T@covar)\n",
-    "        Sigma_0 = Sigma_T@invPhi_0 # = Sigma_T+covar\n",
-    "        H_0 = jnp.linalg.inv(Sigma_0)\n",
-    "        F_0 = jnp.linalg.solve(invPhi_0,F_T)\n",
-    "        v_0 = Sigma_0@F_0\n",
-    "        c_0 = -jax.scipy.stats.multivariate_normal.logpdf(v_0,jnp.zeros(n*d),Sigma_0)\n",
-    "    \n",
-    "        return {'c_0': c_0, 'F_0': F_0, 'H_0': H_0}\n",
-    "    return jax.vmap(f)(noise,edge_length,F_T,H_T)\n",
-    "def transform(child_c_0,child_F_0,child_H_0,**args):\n",
-    "    return {'c_T': child_c_0, 'F_T': child_F_0, 'H_T': child_H_0}\n",
-    "upmodel = UpLambdaReducer(up, transform,\n",
-    "                   reductions={\n",
-    "                       'c_0': 'sum',\n",
-    "                       'F_0': 'sum',\n",
-    "                       'H_0': 'sum',\n",
-    "                    }\n",
-    "        )\n",
-    "up = OrderedExecutor(upmodel)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree for up\n",
-    "tree.add_property('c_0'); tree.add_property('F_0', shape=(n*d,)); tree.add_property('H_0', shape=(n*d,n*d)); tree.add_property('c_T'); tree.add_property('F_T', shape=(n*d,)); tree.add_property('H_T', shape=(n*d,n*d))\n",
-    "def init_up(leaf_values,params):\n",
-    "    tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)\n",
-    "    tree.data['H_T'] = tree.data['H_T'].at[tree.is_leaf].set((jnp.eye(n*d)/params['obs_var'].value)[None,:,:])\n",
-    "    tree.data['F_T'] = tree.data['F_T'].at[tree.is_leaf].set(jnp.einsum('bij,bj->bi',tree.data['H_T'][tree.is_leaf],leaf_values))\n",
-    "    Sigma = params['obs_var'].value*jnp.eye(n*d)\n",
-    "    tree.data['c_T'] = tree.data['c_T'].at[tree.is_leaf].set(jax.vmap(lambda v: -jax.scipy.stats.multivariate_normal.logpdf(v,jnp.zeros(n*d),Sigma))(leaf_values))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@jax.jit\n",
-    "def down_conditional(noise,edge_length,F_T,H_T,parent_value,params,**args):\n",
-    "    def f(noise,edge_length,F_T,H_T,parent_value):\n",
-    "        x = parent_value\n",
-    "        var = edge_length # variance is edge length\n",
-    "        covar = var*a(params) # covariance matrix\n",
-    "\n",
-    "        invSigma = jnp.linalg.inv(covar)\n",
-    "        H = H_T+invSigma\n",
-    "        mu = jnp.linalg.solve(H,F_T+invSigma@x)\n",
-    "        #return {'value': mu+jnp.linalg.solve(jnp.real(jax.scipy.linalg.sqrtm(H)),noise)}\n",
-    "        return {'value': mu+jax.scipy.linalg.solve_triangular(jax.scipy.linalg.cholesky(H,lower=True),noise)}\n",
-    "\n",
-    "    return jax.vmap(f)(noise,edge_length,F_T,H_T,parent_value)\n",
-    "downmodel_conditional = DownLambda(down_fn=down_conditional)\n",
-    "down_conditional = OrderedExecutor(downmodel_conditional)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 16.6 ms, sys: 4.31 ms, total: 20.9 ms\n",
-      "Wall time: 10.1 ms\n",
-      "CPU times: user 56.7 ms, sys: 3.53 ms, total: 60.2 ms\n",
-      "Wall time: 46.3 ms\n",
-      "CPU times: user 87.7 ms, sys: 6.02 ms, total: 93.7 ms\n",
-      "Wall time: 65.9 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding\n",
-    "init_up(leaf_values,params)\n",
-    "up.up(tree,params.values())\n",
-    "down_conditional.down(tree,params.values())\n",
-    "\n",
-    "# time the operations\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())\n",
-    "%time down_unconditional.down(tree,params.values()); jax.block_until_ready(tree.data['value'])\n",
-    "%time down_conditional.down(tree,params.values()); jax.block_until_ready(tree.data['value'])\n",
-    "%time up.up(tree,params.values()); jax.block_until_ready(tree.data['H_0'])\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We test the setup by sampling a number of trees and computing mean and covariance of the leaf data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "root conditional mean vs. sample mean: [ 1.00837248 -0.01701296  1.01585655  0.08617565  1.00884139  0.18734535\n",
-      "  1.00497061  0.27117413  0.98620529  0.36048249  0.94639992  0.44451679\n",
-      "  0.89977775  0.51984863  0.83119436  0.61293541  0.77597245  0.67313081\n",
-      "  0.70639973  0.74656093  0.62225824  0.80998668  0.54953308  0.86653492\n",
-      "  0.46392699  0.90975126  0.36960905  0.95655297  0.28302804  0.97603091\n",
-      "  0.20616295  0.99720456  0.09608485  1.00831697  0.00336152  1.01415762\n",
-      " -0.09541895  0.99960465 -0.19236003  0.98327144 -0.29565149  0.95423581\n",
-      " -0.39625248  0.91607405 -0.49491171  0.85639364 -0.58821843  0.81092333\n",
-      " -0.65976192  0.75431125 -0.75363161  0.67304585 -0.81144269  0.58674184\n",
-      " -0.87470282  0.52529655 -0.90362561  0.4127154  -0.94758596  0.30439627\n",
-      " -0.97323489  0.21803913 -1.00127415  0.10863827 -0.98936837  0.00153095\n",
-      " -0.98721888 -0.11399086 -0.97848633 -0.21792293 -0.94731582 -0.30708609\n",
-      " -0.90942261 -0.39692974 -0.87614191 -0.50725207 -0.82518013 -0.59711022\n",
-      " -0.76310555 -0.67208536 -0.71547847 -0.75319711 -0.63811713 -0.82900677\n",
-      " -0.54929259 -0.88285381 -0.47339502 -0.93869184 -0.38812766 -0.99791032\n",
-      " -0.28928195 -1.02641361 -0.18268286 -1.05766859 -0.07602038 -1.07509486\n",
-      "  0.02337831 -1.08382602  0.13938149 -1.06805602  0.23425178 -1.05798766\n",
-      "  0.31854389 -1.03929385  0.42642967 -1.00441103  0.50205577 -0.96068789\n",
-      "  0.59508414 -0.92223039  0.65801667 -0.84025154  0.72462585 -0.75920333\n",
-      "  0.78768616 -0.68514308  0.81351893 -0.60478377  0.87806479 -0.49785706\n",
-      "  0.90697862 -0.41020585  0.93955547 -0.30961199  0.96308497 -0.20646709\n",
-      "  0.99883086 -0.11203267] [ 1.00837248 -0.01701296  1.01585655  0.08617565  1.00884139  0.18734535\n",
-      "  1.00497061  0.27117413  0.98620529  0.36048249  0.94639992  0.44451679\n",
-      "  0.89977775  0.51984863  0.83119436  0.61293541  0.77597245  0.67313081\n",
-      "  0.70639973  0.74656093  0.62225824  0.80998668  0.54953308  0.86653492\n",
-      "  0.46392699  0.90975126  0.36960905  0.95655297  0.28302804  0.97603091\n",
-      "  0.20616295  0.99720456  0.09608485  1.00831697  0.00336152  1.01415762\n",
-      " -0.09541895  0.99960465 -0.19236003  0.98327144 -0.29565149  0.95423581\n",
-      " -0.39625248  0.91607405 -0.49491171  0.85639364 -0.58821843  0.81092333\n",
-      " -0.65976192  0.75431125 -0.75363161  0.67304585 -0.81144269  0.58674184\n",
-      " -0.87470282  0.52529655 -0.90362561  0.4127154  -0.94758596  0.30439627\n",
-      " -0.97323489  0.21803913 -1.00127415  0.10863827 -0.98936837  0.00153095\n",
-      " -0.98721888 -0.11399086 -0.97848633 -0.21792293 -0.94731582 -0.30708609\n",
-      " -0.90942261 -0.39692974 -0.87614191 -0.50725207 -0.82518013 -0.59711022\n",
-      " -0.76310555 -0.67208536 -0.71547847 -0.75319711 -0.63811713 -0.82900677\n",
-      " -0.54929259 -0.88285381 -0.47339502 -0.93869184 -0.38812766 -0.99791032\n",
-      " -0.28928195 -1.02641361 -0.18268286 -1.05766859 -0.07602038 -1.07509486\n",
-      "  0.02337831 -1.08382602  0.13938149 -1.06805602  0.23425178 -1.05798766\n",
-      "  0.31854389 -1.03929385  0.42642967 -1.00441103  0.50205577 -0.96068789\n",
-      "  0.59508414 -0.92223039  0.65801667 -0.84025154  0.72462585 -0.75920333\n",
-      "  0.78768616 -0.68514308  0.81351893 -0.60478377  0.87806479 -0.49785706\n",
-      "  0.90697862 -0.41020585  0.93955547 -0.30961199  0.96308497 -0.20646709\n",
-      "  0.99883086 -0.11203267]\n",
-      "root conditional cov vs. sample cov: [[0.00176754 0.         0.00171381 ... 0.         0.00171381 0.        ]\n",
-      " [0.         0.00176754 0.         ... 0.00167355 0.         0.00171381]\n",
-      " [0.00171381 0.         0.00176754 ... 0.         0.00167355 0.        ]\n",
-      " ...\n",
-      " [0.         0.00167355 0.         ... 0.00176754 0.         0.00171381]\n",
-      " [0.00171381 0.         0.00167355 ... 0.         0.00176754 0.        ]\n",
-      " [0.         0.00171381 0.         ... 0.00171381 0.         0.00176754]] [[ 5.64187094e-03  8.93440132e-04  5.78548880e-03 ...  5.61914948e-04\n",
-      "   5.07601588e-03  4.18119650e-04]\n",
-      " [ 8.93440132e-04  9.42494012e-03  1.67783743e-03 ...  7.90200825e-03\n",
-      "   7.63028892e-05  8.99997909e-03]\n",
-      " [ 5.78548880e-03  1.67783743e-03  7.85163333e-03 ...  4.15811805e-04\n",
-      "   5.26637855e-03  1.05236314e-03]\n",
-      " ...\n",
-      " [ 5.61914948e-04  7.90200825e-03  4.15811805e-04 ...  8.98775236e-03\n",
-      "   2.90794228e-04  8.26998387e-03]\n",
-      " [ 5.07601588e-03  7.63028892e-05  5.26637855e-03 ...  2.90794228e-04\n",
-      "   5.44463913e-03 -2.36988793e-04]\n",
-      " [ 4.18119650e-04  8.99997909e-03  1.05236314e-03 ...  8.26998387e-03\n",
-      "  -2.36988793e-04  9.86855127e-03]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 500/500 [00:23<00:00, 20.96it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n",
-      "observation noise: \n",
-      " mean:  [ 1.28016494e-04  5.42040445e-03  1.20460815e-04 -9.28784894e-04\n",
-      "  5.20983936e-03 -7.58340185e-03 -3.40281054e-03  3.61973872e-04\n",
-      " -8.41228417e-03 -2.19076640e-04 -3.82598083e-03  1.18454732e-03\n",
-      " -3.36683930e-03  7.71381793e-03  9.15842337e-03 -7.47819602e-03\n",
-      " -1.42057797e-04  5.71416167e-03 -1.95115981e-03  4.32041819e-04\n",
-      "  5.43613231e-03 -9.10445617e-04 -2.70159613e-03 -2.33420530e-03\n",
-      " -1.18532021e-03  1.75228521e-03  6.30289704e-03 -6.30706183e-03\n",
-      "  3.50318992e-03  3.79761895e-03 -1.15823428e-02  2.63549029e-03\n",
-      "  3.93087321e-03  1.70600616e-03 -4.13922251e-04 -3.89379415e-03\n",
-      " -8.02891180e-04  9.88770020e-04 -4.36925894e-03 -2.09729782e-03\n",
-      " -1.70297965e-03 -1.99021959e-03 -3.43511773e-04 -1.96525623e-03\n",
-      "  2.25431306e-03  1.06821185e-02  4.48413774e-03  4.62282199e-04\n",
-      " -8.34582298e-03 -7.01119100e-03  9.31052948e-03  2.00506985e-03\n",
-      "  1.96658676e-04  8.25191401e-03  6.56512999e-03 -1.76283625e-02\n",
-      " -1.09318035e-02  1.19366933e-03 -2.73326461e-03  1.04428967e-02\n",
-      " -2.15835653e-03 -6.21334703e-03  1.13859969e-02 -2.25800785e-03\n",
-      " -4.65296706e-03 -1.51231968e-03 -9.34675409e-04  8.17304785e-03\n",
-      "  5.75098412e-03  8.01161531e-03 -9.80331717e-04 -4.20027697e-03\n",
-      " -5.96551433e-03 -1.22271792e-02  1.64662748e-03  4.35380149e-03\n",
-      " -7.73113143e-04  5.18295657e-03 -6.85520467e-03 -3.54871769e-03\n",
-      "  8.88624746e-03 -1.93041080e-04  2.23204193e-03  4.52487535e-03\n",
-      " -8.63314664e-03 -5.33645850e-03  4.32838382e-04 -5.04506840e-03\n",
-      "  6.56269317e-03  7.47036115e-03  4.67517264e-03 -1.29631525e-03\n",
-      " -6.76375863e-04  2.55616252e-03 -3.30668119e-03  2.69475522e-03\n",
-      "  2.36120445e-03  4.33625873e-03 -9.30708420e-03 -8.29228084e-03\n",
-      " -2.31563800e-03 -4.99594806e-03  1.12568923e-02 -2.45306623e-05\n",
-      " -4.13713131e-03 -8.38035810e-04  6.29915802e-03 -6.71188814e-05\n",
-      " -7.88398682e-03  1.62679688e-02  3.98718072e-04 -1.07612641e-03\n",
-      " -2.63637835e-03 -8.47841653e-03 -9.45672624e-03 -1.00144748e-03\n",
-      "  1.42516755e-02  6.69008929e-03 -6.73982202e-03 -7.15872444e-03\n",
-      "  2.54316698e-03  1.84229715e-03  3.12638551e-03  6.20662092e-05\n",
-      "  7.57819106e-03 -3.23320312e-03 -4.35957161e-03  1.80458907e-03]\n",
-      " cov:  [[ 4.23169284e-04 -1.93502011e-04 -6.87008379e-05 ...  6.95348738e-05\n",
-      "  -5.02265886e-05 -9.63058775e-05]\n",
-      " [-1.93502011e-04  5.71951844e-04  7.02302296e-05 ...  1.05063057e-04\n",
-      "   1.12019767e-05 -6.27286304e-05]\n",
-      " [-6.87008379e-05  7.02302296e-05  1.09753142e-03 ... -6.25364094e-04\n",
-      "  -3.46972469e-04 -4.51086400e-05]\n",
-      " ...\n",
-      " [ 6.95348738e-05  1.05063057e-04 -6.25364094e-04 ...  1.12796953e-03\n",
-      "   3.45959383e-04 -2.69322562e-04]\n",
-      " [-5.02265886e-05  1.12019767e-05 -3.46972469e-04 ...  3.45959383e-04\n",
-      "   1.56460410e-03  1.06663317e-04]\n",
-      " [-9.63058775e-05 -6.27286304e-05 -4.51086400e-05 ... -2.69322562e-04\n",
-      "   1.06663317e-04  1.71472702e-03]]\n",
-      "leaves: \n",
-      "  mean:  [ 1.01305030e+00  5.15089199e-03  1.00674102e+00  1.03186679e-01\n",
-      "  9.90812944e-01  2.00212027e-01  9.65453491e-01  2.95240887e-01\n",
-      "  9.30873481e-01  3.87300680e-01  8.87353219e-01  4.75474307e-01\n",
-      "  8.35288174e-01  5.58906538e-01  7.75192365e-01  6.36819047e-01\n",
-      "  7.07696194e-01  7.08542059e-01  6.33561043e-01  7.73519022e-01\n",
-      "  5.53623206e-01  8.31259983e-01  4.68743338e-01  8.81285848e-01\n",
-      "  3.79787137e-01  9.23127837e-01  2.87630624e-01  9.56340135e-01\n",
-      "  1.93150168e-01  9.80525345e-01  9.72136551e-02  9.95367569e-01\n",
-      "  6.64997781e-04  1.00065754e+00 -9.56721378e-02  9.96324976e-01\n",
-      " -1.90989637e-01  9.82438395e-01 -2.84479818e-01  9.59147071e-01\n",
-      " -3.75334440e-01  9.26651503e-01 -4.62756183e-01  8.85222700e-01\n",
-      " -5.45964250e-01  8.35200923e-01 -6.24195904e-01  7.77013189e-01\n",
-      " -6.96727065e-01  7.11183656e-01 -7.62879223e-01  6.38338677e-01\n",
-      " -8.22000285e-01  5.59198145e-01 -8.73441020e-01  4.74543799e-01\n",
-      " -9.16599020e-01  3.85194555e-01 -9.50964050e-01  2.92031085e-01\n",
-      " -9.76126869e-01  1.96032613e-01 -9.91787251e-01  9.82540734e-02\n",
-      " -9.97755072e-01 -2.46753727e-04 -9.93936055e-01 -9.84612703e-02\n",
-      " -9.80320437e-01 -1.95451427e-01 -9.56971073e-01 -2.90336561e-01\n",
-      " -9.24045217e-01 -3.82274942e-01 -8.81831219e-01 -4.70431425e-01\n",
-      " -8.30789882e-01 -5.53981435e-01 -7.71547284e-01 -6.32133187e-01\n",
-      " -7.04853313e-01 -7.04116632e-01 -6.31502090e-01 -7.69199829e-01\n",
-      " -5.52237963e-01 -8.26730653e-01 -4.67776076e-01 -8.76145176e-01\n",
-      " -3.78868889e-01 -9.16958671e-01 -2.86333637e-01 -9.48762735e-01\n",
-      " -1.91037846e-01 -9.71255190e-01 -9.38656693e-02 -9.84266162e-01\n",
-      "  4.30983564e-03 -9.87740801e-01  1.02621570e-01 -9.81737131e-01\n",
-      "  2.00202691e-01 -9.66442193e-01  2.96177655e-01 -9.42137780e-01\n",
-      "  3.89642890e-01 -9.09153480e-01  4.79669863e-01 -8.67841393e-01\n",
-      "  5.65341751e-01 -8.18550143e-01  6.45762104e-01 -7.61611786e-01\n",
-      "  7.20061260e-01 -6.97364572e-01  7.87394989e-01 -6.26208010e-01\n",
-      "  8.46978571e-01 -5.48663189e-01  8.98132641e-01 -4.65403797e-01\n",
-      "  9.40312134e-01 -3.77243868e-01  9.73109451e-01 -2.85113070e-01\n",
-      "  9.96250136e-01 -1.90002501e-01  1.00957872e+00 -9.29119954e-02]\n",
-      "  cov:  [[ 1.37742360e-02 -8.95930160e-05  1.36029306e-02 ...  3.13067350e-05\n",
-      "   1.37686164e-02 -3.66224166e-05]\n",
-      " [-8.95930160e-05  1.55176766e-02 -2.17020056e-05 ...  1.49944722e-02\n",
-      "  -1.73700616e-04  1.53487628e-02]\n",
-      " [ 1.36029306e-02 -2.17020056e-05  1.36678220e-02 ...  7.91698649e-05\n",
-      "   1.33690598e-02  2.10268906e-05]\n",
-      " ...\n",
-      " [ 3.13067350e-05  1.49944722e-02  7.91698649e-05 ...  1.53589913e-02\n",
-      "  -3.06783189e-05  1.52720482e-02]\n",
-      " [ 1.37686164e-02 -1.73700616e-04  1.33690598e-02 ... -3.06783189e-05\n",
-      "   1.39970646e-02 -1.09446269e-04]\n",
-      " [-3.66224166e-05  1.53487628e-02  2.10268906e-05 ...  1.52720482e-02\n",
-      "  -1.09446269e-04  1.54080343e-02]]\n",
-      "  mean w/ noise:  [ 1.01221189e+00  6.07793278e-03  1.00838771e+00  1.02776595e-01\n",
-      "  9.91778993e-01  2.00210333e-01  9.63011904e-01  2.93939368e-01\n",
-      "  9.30459537e-01  3.84563587e-01  8.88681551e-01  4.75093551e-01\n",
-      "  8.35171104e-01  5.59767682e-01  7.74664189e-01  6.38363898e-01\n",
-      "  7.06917387e-01  7.08991660e-01  6.33730098e-01  7.72813260e-01\n",
-      "  5.52801119e-01  8.31288129e-01  4.65706485e-01  8.80061975e-01\n",
-      "  3.79944280e-01  9.19239067e-01  2.85741307e-01  9.53218627e-01\n",
-      "  1.94362407e-01  9.80683231e-01  9.91791731e-02  9.96123113e-01\n",
-      " -1.59907797e-03  1.00139944e+00 -9.85024314e-02  9.95090437e-01\n",
-      " -1.89330836e-01  9.82558668e-01 -2.86633878e-01  9.59150885e-01\n",
-      " -3.74601389e-01  9.25475009e-01 -4.62465526e-01  8.81772624e-01\n",
-      " -5.43890107e-01  8.34152647e-01 -6.23945791e-01  7.75120061e-01\n",
-      " -6.99785381e-01  7.10465342e-01 -7.61999302e-01  6.39628003e-01\n",
-      " -8.22036709e-01  5.63129017e-01 -8.73267801e-01  4.73545568e-01\n",
-      " -9.19065737e-01  3.85140388e-01 -9.52523997e-01  2.92100683e-01\n",
-      " -9.75142774e-01  1.97742023e-01 -9.93561702e-01  9.90839583e-02\n",
-      " -9.98116111e-01 -3.07631621e-04 -9.94379807e-01 -9.72121659e-02\n",
-      " -9.78162056e-01 -1.93851244e-01 -9.53661343e-01 -2.91386394e-01\n",
-      " -9.24615793e-01 -3.81074995e-01 -8.82244812e-01 -4.71391143e-01\n",
-      " -8.28725514e-01 -5.52615456e-01 -7.70213838e-01 -6.33363949e-01\n",
-      " -7.05177168e-01 -7.02985149e-01 -6.32756831e-01 -7.66399349e-01\n",
-      " -5.54074399e-01 -8.26238295e-01 -4.67686537e-01 -8.75519846e-01\n",
-      " -3.79312774e-01 -9.17642049e-01 -2.86888665e-01 -9.49734424e-01\n",
-      " -1.91651857e-01 -9.72532857e-01 -9.38613995e-02 -9.85669856e-01\n",
-      "  5.67477847e-03 -9.88496418e-01  1.05020326e-01 -9.81736171e-01\n",
-      "  2.01980807e-01 -9.64925339e-01  2.92992214e-01 -9.40224144e-01\n",
-      "  3.90232603e-01 -9.08602876e-01  4.81318354e-01 -8.68735303e-01\n",
-      "  5.64504677e-01 -8.18455121e-01  6.45163411e-01 -7.64318200e-01\n",
-      "  7.18262770e-01 -6.98824143e-01  7.89691229e-01 -6.27735545e-01\n",
-      "  8.46723371e-01 -5.48194401e-01  8.97148179e-01 -4.66592921e-01\n",
-      "  9.42981153e-01 -3.80683981e-01  9.74682428e-01 -2.85264758e-01\n",
-      "  9.96902283e-01 -1.90440240e-01  1.00954237e+00 -9.32649524e-02]\n",
-      "  cov w/ noise:  [[1.46828257e-02 3.83332981e-04 1.37219789e-02 ... 3.65567042e-04\n",
-      "  1.39169035e-02 2.77982517e-04]\n",
-      " [3.83332981e-04 1.69632712e-02 2.53366997e-04 ... 1.51811085e-02\n",
-      "  3.84764230e-04 1.53901571e-02]\n",
-      " [1.37219789e-02 2.53366997e-04 1.48674115e-02 ... 2.10002403e-04\n",
-      "  1.35583714e-02 2.95533637e-04]\n",
-      " ...\n",
-      " [3.65567042e-04 1.51811085e-02 2.10002403e-04 ... 1.62235349e-02\n",
-      "  3.96141994e-05 1.49908231e-02]\n",
-      " [1.39169035e-02 3.84764230e-04 1.35583714e-02 ... 3.96141994e-05\n",
-      "  1.51613657e-02 7.12141557e-05]\n",
-      " [2.77982517e-04 1.53901571e-02 2.95533637e-04 ... 1.49908231e-02\n",
-      "  7.12141557e-05 1.59100307e-02]]\n",
-      "  mean conditional:  [ 0.95331782  0.1505573   0.96636502  0.25350476  0.97297755  0.34919418\n",
-      "  0.97176333  0.43628927  0.96162894  0.51425167  0.94189605  0.58340535\n",
-      "  0.91236833  0.6446687   0.87329668  0.69940371  0.82527927  0.74904852\n",
-      "  0.76907611  0.79481007  0.7054422   0.83752548  0.63513325  0.8774252\n",
-      "  0.55861659  0.91430313  0.47615447  0.94765614  0.38777154  0.9767662\n",
-      "  0.29346906  1.00107818  0.1932598   1.02021049  0.08754266  1.03388332\n",
-      " -0.02275696  1.04184092 -0.13608456  1.04360073 -0.24998811  1.03841078\n",
-      " -0.36148515  1.02492775 -0.46713947  1.00151239 -0.56358049  0.96620359\n",
-      " -0.64799848  0.91716983 -0.71858483  0.85307131 -0.77484355  0.77343389\n",
-      " -0.81737436  0.67899468 -0.84783979  0.57169418 -0.86832752  0.45468487\n",
-      " -0.88094716  0.33171536 -0.88746497  0.20687177 -0.88927075  0.08392607\n",
-      " -0.88718248 -0.03400414 -0.88161402 -0.14476238 -0.87253835 -0.24726765\n",
-      " -0.85956866 -0.34138103 -0.84188755 -0.42770947 -0.8183792  -0.50702637\n",
-      " -0.78757471 -0.5801098  -0.74798261 -0.64740867 -0.69816063 -0.70889255\n",
-      " -0.63698093 -0.76400582 -0.56370823 -0.81195167 -0.47828782 -0.85188351\n",
-      " -0.38131334 -0.88311695 -0.27422211 -0.90532146 -0.15937597 -0.91862711\n",
-      " -0.03987188 -0.92344568  0.08061363 -0.9202692   0.19818398 -0.90951535\n",
-      "  0.30936415 -0.89118205  0.41132472 -0.86479264  0.5023158  -0.82944749\n",
-      "  0.58155021 -0.78400475  0.64931151 -0.7275814   0.70657855 -0.65971047\n",
-      "  0.75478933 -0.58067121  0.79553714 -0.49144773  0.8305108  -0.3936248\n",
-      "  0.86126205 -0.28906644  0.88872922 -0.17989193  0.91391668 -0.06832828\n",
-      "  0.93945895  0.04291553] , sample:  [ 0.9397582   0.18262013  0.95328093  0.24869049  0.99546768  0.38634364\n",
-      "  0.97792879  0.42313778  0.95256721  0.50801657  0.94471522  0.55620048\n",
-      "  0.88412224  0.69039094  0.87065508  0.69973505  0.83631483  0.73957236\n",
-      "  0.78227853  0.794682    0.7300501   0.82446935  0.57291029  0.8775357\n",
-      "  0.56090977  0.8783118   0.46858922  0.96641906  0.41770468  0.9652643\n",
-      "  0.29938518  1.06285901  0.1837537   0.98187141  0.07918738  0.9896334\n",
-      "  0.01417524  1.06069449 -0.17041001  1.04210109 -0.16635074  1.06210808\n",
-      " -0.35377224  1.02295699 -0.46938288  1.002519   -0.64175584  0.98066221\n",
-      " -0.66124599  0.90251163 -0.77132487  0.85296398 -0.77829057  0.78171072\n",
-      " -0.81223018  0.70463179 -0.81549933  0.52872548 -0.8497405   0.44967034\n",
-      " -0.84899512  0.35870695 -0.89564574  0.20144177 -0.84003264  0.11478637\n",
-      " -0.91333904 -0.05151059 -0.89304961 -0.15044284 -0.88682205 -0.26616114\n",
-      " -0.86248267 -0.30619568 -0.83305776 -0.44813226 -0.80300606 -0.51973298\n",
-      " -0.78435986 -0.5840824  -0.76819853 -0.6635609  -0.72739679 -0.7072962\n",
-      " -0.59999528 -0.75100661 -0.60668162 -0.76004381 -0.46331652 -0.86266314\n",
-      " -0.34027168 -0.9133093  -0.31334098 -0.88825049 -0.18949896 -0.95002237\n",
-      " -0.04278871 -0.94775571  0.11106451 -0.88258329  0.16515982 -0.90372155\n",
-      "  0.32668146 -0.94215524  0.44013543 -0.7583578   0.51342455 -0.79768364\n",
-      "  0.64142325 -0.87504842  0.59117423 -0.74165211  0.65724178 -0.6518688\n",
-      "  0.7773618  -0.57081682  0.76068271 -0.49687157  0.86822584 -0.4124358\n",
-      "  0.83656583 -0.30299979  0.92285461 -0.18217955  0.91719621 -0.08421717\n",
-      "  0.94114051  0.06515025]\n",
-      "  cov conditional:  [[ 2.41575223e-05  3.90020356e-07  2.11806470e-05 ... -1.91219426e-07\n",
-      "  -1.43282464e-04 -1.30978354e-06]\n",
-      " [ 3.90020356e-07  2.44405778e-05  3.24282774e-07 ...  1.98861202e-05\n",
-      "  -3.61756761e-06 -1.53353956e-04]\n",
-      " [ 2.11806470e-05  3.24282774e-07  1.85740443e-05 ... -1.61870319e-07\n",
-      "  -1.25870837e-04 -1.03067548e-06]\n",
-      " ...\n",
-      " [-1.91219426e-07  1.98861202e-05 -1.61870319e-07 ...  5.01604342e-05\n",
-      "   2.29866148e-06 -1.39180047e-04]\n",
-      " [-1.43282464e-04 -3.61756761e-06 -1.25870837e-04 ...  2.29866148e-06\n",
-      "   1.02329862e-03  1.04021132e-05]\n",
-      " [-1.30978354e-06 -1.53353956e-04 -1.03067548e-06 ... -1.39180047e-04\n",
-      "   1.04021132e-05  1.11558442e-03]]\n",
-      "  mean noise:  [ 0.01355962 -0.03206284  0.01308408  0.00481426 -0.02249013 -0.03714946\n",
-      " -0.00616546  0.01315149  0.00906172  0.0062351  -0.00281917  0.02720487\n",
-      "  0.02824609 -0.04572224  0.0026416  -0.00033133 -0.01103556  0.00947616\n",
-      " -0.01320242  0.00012806 -0.0246079   0.01305613  0.06222296 -0.0001105\n",
-      " -0.00229318  0.03599133  0.00756525 -0.01876293 -0.02993314  0.01150189\n",
-      " -0.00591612 -0.06178084  0.00950611  0.03833908  0.00835528  0.04424992\n",
-      " -0.0369322  -0.01885357  0.03432545  0.00149965 -0.08363737 -0.02369729\n",
-      " -0.00771291  0.00197075  0.0022434  -0.00100661  0.07817535 -0.01445861\n",
-      "  0.01324751  0.01465821  0.05274004  0.00010733  0.00344702 -0.00827683\n",
-      " -0.00514418 -0.02563711 -0.03234046  0.0429687  -0.01858702  0.00501453\n",
-      " -0.03195204 -0.02699159  0.00818077  0.00543    -0.04923811 -0.03086031\n",
-      "  0.02615656  0.01750646  0.01143559  0.00568046  0.0142837   0.01889349\n",
-      "  0.00291401 -0.03518534 -0.00882978  0.02042279 -0.01537314  0.01270661\n",
-      " -0.00321486  0.0039726   0.02021592  0.01615223  0.02923615 -0.00159635\n",
-      " -0.03698565 -0.01299921  0.04297339 -0.05190786 -0.0149713   0.01077963\n",
-      " -0.04104166  0.03019235  0.03911887 -0.01707097  0.03012299  0.03139526\n",
-      "  0.00291684  0.02431003 -0.03045088 -0.03768591  0.03302416 -0.0057938\n",
-      " -0.01731731  0.05097319 -0.0288107  -0.10643483 -0.01110875 -0.03176385\n",
-      " -0.05987305  0.09104367  0.05813729  0.01407072  0.04933677 -0.00784168\n",
-      " -0.02257247 -0.00985439  0.03485442  0.00542384 -0.03771504  0.018811\n",
-      "  0.02469622  0.01393335 -0.03412539  0.00228762 -0.00327953  0.01588889\n",
-      " -0.00168156 -0.02223472]\n",
-      "  cov noise:  [[ 2.41575223e-05  3.90020356e-07  2.11806470e-05 ... -1.91219426e-07\n",
-      "  -1.43282464e-04 -1.30978354e-06]\n",
-      " [ 3.90020356e-07  2.44405778e-05  3.24282774e-07 ...  1.98861202e-05\n",
-      "  -3.61756761e-06 -1.53353956e-04]\n",
-      " [ 2.11806470e-05  3.24282774e-07  1.85740443e-05 ... -1.61870319e-07\n",
-      "  -1.25870837e-04 -1.03067548e-06]\n",
-      " ...\n",
-      " [-1.91219426e-07  1.98861202e-05 -1.61870319e-07 ...  5.01604342e-05\n",
-      "   2.29866148e-06 -1.39180047e-04]\n",
-      " [-1.43282464e-04 -3.61756761e-06 -1.25870837e-04 ...  2.29866148e-06\n",
-      "   1.02329862e-03  1.04021132e-05]\n",
-      " [-1.30978354e-06 -1.53353956e-04 -1.03067548e-06 ... -1.39180047e-04\n",
-      "   1.04021132e-05  1.11558442e-03]]\n",
-      "  mean:  [ 1.01242477e+00  3.77804670e-03  1.00712154e+00  1.01363476e-01\n",
-      "  9.92209398e-01  1.97876286e-01  9.67817731e-01  2.92396072e-01\n",
-      "  9.34130961e-01  3.84036723e-01  8.91443776e-01  4.71951480e-01\n",
-      "  8.40162596e-01  5.55312028e-01  7.80786814e-01  6.33320207e-01\n",
-      "  7.13906352e-01  7.05244298e-01  6.40207664e-01  7.70446057e-01\n",
-      "  5.60440787e-01  8.28354823e-01  4.75386811e-01  8.78439207e-01\n",
-      "  3.85862320e-01  9.20222033e-01  2.92733289e-01  9.53285027e-01\n",
-      "  1.96911252e-01  9.77270191e-01  9.93615290e-02  9.91888629e-01\n",
-      "  1.08461041e-03  9.96949519e-01 -9.69183653e-02  9.92389363e-01\n",
-      " -1.93679578e-01  9.78267417e-01 -2.88285445e-01  9.54732571e-01\n",
-      " -3.79890428e-01  9.22007755e-01 -4.67702399e-01  8.80409414e-01\n",
-      " -5.50966753e-01  8.30340862e-01 -6.28975251e-01  7.72302214e-01\n",
-      " -7.01076609e-01  7.06896258e-01 -7.66663576e-01  6.34786335e-01\n",
-      " -8.25146854e-01  5.56643785e-01 -8.75927939e-01  4.73113748e-01\n",
-      " -9.18431629e-01  3.84824915e-01 -9.52143738e-01  2.92465845e-01\n",
-      " -9.76629468e-01  1.96882709e-01 -9.91563292e-01  9.90999395e-02\n",
-      " -9.96753162e-01  2.48900569e-04 -9.92161615e-01 -9.85194783e-02\n",
-      " -9.77898215e-01 -1.96099455e-01 -9.54180058e-01 -2.91466057e-01\n",
-      " -9.21295604e-01 -3.83688633e-01 -8.79575094e-01 -4.71900348e-01\n",
-      " -8.29374025e-01 -5.55279849e-01 -7.71107592e-01 -6.33065361e-01\n",
-      " -7.05314226e-01 -7.04559430e-01 -6.32653895e-01 -7.69152657e-01\n",
-      " -5.53832445e-01 -8.26328488e-01 -4.69567713e-01 -8.75604460e-01\n",
-      " -3.80622767e-01 -9.16515238e-01 -2.87847218e-01 -9.48648728e-01\n",
-      " -1.92168506e-01 -9.71688151e-01 -9.45517452e-02 -9.85415624e-01\n",
-      "  4.04691048e-03 -9.89715090e-01  1.02673697e-01 -9.84573682e-01\n",
-      "  2.00360635e-01 -9.70068241e-01  2.96149815e-01 -9.46355366e-01\n",
-      "  3.89120096e-01 -9.13672841e-01  4.78409900e-01 -8.72337320e-01\n",
-      "  5.63237551e-01 -8.22729198e-01  6.42872003e-01 -7.65258611e-01\n",
-      "  7.16561418e-01 -7.00358541e-01  7.83503539e-01 -6.28522271e-01\n",
-      "  8.42922006e-01 -5.50356287e-01  8.94140151e-01 -4.66606694e-01\n",
-      "  9.36617680e-01 -3.78132289e-01  9.69945533e-01 -2.85874269e-01\n",
-      "  9.93812581e-01 -1.90814618e-01  1.00800257e+00 -9.39368319e-02]\n",
-      "  cov:  [[1.49774205e-02 9.94413813e-05 1.49122057e-02 ... 2.47415894e-04\n",
-      "  1.48364374e-02 1.82580472e-04]\n",
-      " [9.94413813e-05 1.41226500e-02 6.46847326e-05 ... 1.38630126e-02\n",
-      "  1.22056302e-04 1.40824508e-02]\n",
-      " [1.49122057e-02 6.46847326e-05 1.50706082e-02 ... 2.19950790e-04\n",
-      "  1.45559485e-02 1.52441862e-04]\n",
-      " ...\n",
-      " [2.47415894e-04 1.38630126e-02 2.19950790e-04 ... 1.44630130e-02\n",
-      "  2.65247371e-04 1.42508473e-02]\n",
-      " [1.48364374e-02 1.22056302e-04 1.45559485e-02 ... 2.65247371e-04\n",
-      "  1.49163179e-02 2.01807025e-04]\n",
-      " [1.82580472e-04 1.40824508e-02 1.52441862e-04 ... 1.42508473e-02\n",
-      "  2.01807025e-04 1.42589058e-02]]\n",
-      "  mean w/ noise:  [ 1.01175430e+00  2.49658316e-03  1.00539024e+00  1.02340971e-01\n",
-      "  9.93059547e-01  1.96496669e-01  9.66384689e-01  2.91278155e-01\n",
-      "  9.34019409e-01  3.84346234e-01  8.93907688e-01  4.72203258e-01\n",
-      "  8.38466434e-01  5.57286692e-01  7.79887376e-01  6.33100011e-01\n",
-      "  7.14776504e-01  7.02818724e-01  6.39606356e-01  7.70837925e-01\n",
-      "  5.57402571e-01  8.29795428e-01  4.74523523e-01  8.80652257e-01\n",
-      "  3.84234434e-01  9.20117675e-01  2.93522806e-01  9.52598442e-01\n",
-      "  1.97099967e-01  9.77665370e-01  9.85081881e-02  9.92619976e-01\n",
-      "  3.81806924e-03  9.96808270e-01 -9.57060775e-02  9.94571273e-01\n",
-      " -1.93279950e-01  9.79788077e-01 -2.85266553e-01  9.55655226e-01\n",
-      " -3.79855184e-01  9.24900020e-01 -4.67474437e-01  8.77054793e-01\n",
-      " -5.51887807e-01  8.29032733e-01 -6.28055814e-01  7.72793257e-01\n",
-      " -7.01211626e-01  7.07680984e-01 -7.64379223e-01  6.35820165e-01\n",
-      " -8.27073410e-01  5.55714373e-01 -8.73314901e-01  4.73029870e-01\n",
-      " -9.16370640e-01  3.82659933e-01 -9.50273082e-01  2.95663954e-01\n",
-      " -9.75172117e-01  1.96244469e-01 -9.92406185e-01  1.02398233e-01\n",
-      " -9.96241407e-01  5.99675628e-04 -9.90497442e-01 -9.99736037e-02\n",
-      " -9.79130793e-01 -1.97229469e-01 -9.54806216e-01 -2.92845686e-01\n",
-      " -9.21644629e-01 -3.81924722e-01 -8.81459980e-01 -4.71959190e-01\n",
-      " -8.28196397e-01 -5.54998257e-01 -7.73522464e-01 -6.34286143e-01\n",
-      " -7.02017594e-01 -7.03909994e-01 -6.32552784e-01 -7.68674079e-01\n",
-      " -5.56005473e-01 -8.25992842e-01 -4.69040779e-01 -8.76584262e-01\n",
-      " -3.79621961e-01 -9.14104985e-01 -2.87111451e-01 -9.48176876e-01\n",
-      " -1.92683288e-01 -9.74337321e-01 -9.43198752e-02 -9.83923103e-01\n",
-      "  8.26033197e-03 -9.87792914e-01  1.02939748e-01 -9.83882610e-01\n",
-      "  2.00195594e-01 -9.69557541e-01  2.97519371e-01 -9.47202435e-01\n",
-      "  3.87734629e-01 -9.13832841e-01  4.79256857e-01 -8.71838879e-01\n",
-      "  5.63900192e-01 -8.24019661e-01  6.41392137e-01 -7.64205417e-01\n",
-      "  7.16997053e-01 -7.01845084e-01  7.82862036e-01 -6.29744381e-01\n",
-      "  8.43041394e-01 -5.48469759e-01  8.93736105e-01 -4.65995768e-01\n",
-      "  9.35076201e-01 -3.77453172e-01  9.68149681e-01 -2.84547307e-01\n",
-      "  9.92847001e-01 -1.91941228e-01  1.00433454e+00 -9.37724760e-02]\n",
-      "  cov w/ noise:  [[ 1.60530640e-02 -8.83800351e-05  1.48114711e-02 ...  6.20007980e-05\n",
-      "   1.47894623e-02 -1.14827321e-04]\n",
-      " [-8.83800351e-05  1.53335477e-02  2.54703817e-05 ...  1.38627108e-02\n",
-      "  -8.63572258e-05  1.40102757e-02]\n",
-      " [ 1.48114711e-02  2.54703817e-05  1.59061988e-02 ...  4.75680155e-05\n",
-      "   1.43428629e-02 -1.13803986e-04]\n",
-      " ...\n",
-      " [ 6.20007980e-05  1.38627108e-02  4.75680155e-05 ...  1.54197994e-02\n",
-      "  -7.70292091e-05  1.40949780e-02]\n",
-      " [ 1.47894623e-02 -8.63572258e-05  1.43428629e-02 ... -7.70292091e-05\n",
-      "   1.56990470e-02 -1.83164112e-04]\n",
-      " [-1.14827321e-04  1.40102757e-02 -1.13803986e-04 ...  1.40949780e-02\n",
-      "  -1.83164112e-04  1.49517119e-02]]\n",
-      "  mean conditional:  [ 0.92081453 -0.04848005  0.94033066  0.03867108  0.95641777  0.12570581\n",
-      "  0.96732608  0.21206767  0.97120359  0.29715883  0.9662944   0.38052671\n",
-      "  0.95119743  0.46176997  0.92498908  0.540581    0.88733192  0.61652585\n",
-      "  0.83848062  0.6888948   0.77915236  0.75668506  0.71059963  0.81851166\n",
-      "  0.6344374   0.87292549  0.55242577  0.91854522  0.46627532  0.95430908\n",
-      "  0.37762038  0.97970072  0.28758562  0.99493328  0.19687844  1.00068509\n",
-      "  0.10576126  0.99793289  0.0140756   0.98781463 -0.07834819  0.9709689\n",
-      " -0.17165822  0.94748743 -0.26542706  0.91673288 -0.35873887  0.87740329\n",
-      " -0.45002074  0.82788334 -0.53713866  0.76683313 -0.61772699  0.69347629\n",
-      " -0.68921637  0.60814786 -0.7495019   0.51231589 -0.79673484  0.40852488\n",
-      " -0.82977384  0.29989132 -0.84817239  0.1897374  -0.85253857  0.0810978\n",
-      " -0.84428408 -0.02376216 -0.82543192 -0.12350314 -0.79829107 -0.21775165\n",
-      " -0.76511509 -0.30705361 -0.72763894 -0.392372   -0.68692465 -0.47473892\n",
-      " -0.64329115 -0.55472918 -0.59644958 -0.63246376 -0.54563352 -0.70722012\n",
-      " -0.48979374 -0.77785275 -0.42771916 -0.84276531 -0.35836866 -0.90042449\n",
-      " -0.2810549  -0.94941511 -0.19575508 -0.98854612 -0.10332648 -1.01697369\n",
-      " -0.00551788 -1.03402823  0.09517511 -1.03912197  0.19578815 -1.03186145\n",
-      "  0.29344629 -1.01196148  0.38563964 -0.97940176  0.4705006  -0.93442126\n",
-      "  0.54673557 -0.87774435  0.61376927 -0.81077379  0.67154201 -0.73538024\n",
-      "  0.72051755 -0.65392844  0.76156299 -0.56883354  0.79607167 -0.48210777\n",
-      "  0.82568376 -0.3951102   0.85195984 -0.30841736  0.87659637 -0.22196795\n",
-      "  0.90286535 -0.13666109] , sample:  [ 0.93045115 -0.07217458  0.86002937  0.02167777  0.99551727  0.15629802\n",
-      "  0.94857508  0.17197458  0.95640346  0.30484293  0.97869663  0.39794208\n",
-      "  0.97305546  0.48090288  0.89931596  0.54350128  0.88625483  0.5714936\n",
-      "  0.85277293  0.67950553  0.79152792  0.76946141  0.70509869  0.85013245\n",
-      "  0.67664145  0.85951421  0.51388199  0.89201004  0.48736045  0.95223772\n",
-      "  0.37231752  1.01773671  0.26444393  0.99837392  0.21455481  0.97919989\n",
-      "  0.05826094  1.04372489  0.00214991  0.95327938 -0.0676286   0.96824971\n",
-      " -0.16447859  0.93271066 -0.211606    0.91429198 -0.33450398  0.85642498\n",
-      " -0.48930446  0.85285975 -0.50341599  0.7905078  -0.61318562  0.69833846\n",
-      " -0.69316725  0.58831896 -0.74590656  0.50237701 -0.81037548  0.39552556\n",
-      " -0.83860825  0.30984959 -0.88109     0.1974442  -0.87648706  0.10557533\n",
-      " -0.83147068 -0.02646982 -0.82068034 -0.14745121 -0.75950262 -0.21984044\n",
-      " -0.74543674 -0.2991734  -0.7348301  -0.39474366 -0.69384824 -0.4693072\n",
-      " -0.66771449 -0.53557718 -0.59028859 -0.61946877 -0.5532461  -0.73795473\n",
-      " -0.44567435 -0.80411646 -0.40869745 -0.80543627 -0.33847467 -0.92591279\n",
-      " -0.34920781 -0.9538306  -0.20805838 -0.93273411 -0.08449651 -0.97881976\n",
-      " -0.06396268 -1.11396597  0.13870411 -1.02308978  0.21081791 -1.07480796\n",
-      "  0.29233438 -0.98872777  0.39866445 -0.97369366  0.48712531 -0.93178438\n",
-      "  0.50032914 -0.92640085  0.60602079 -0.79386942  0.65403093 -0.72582311\n",
-      "  0.71105575 -0.7124228   0.80822649 -0.52942081  0.83845445 -0.39718965\n",
-      "  0.7811443  -0.40480211  0.84521729 -0.35767608  0.88822299 -0.18360238\n",
-      "  0.92576562 -0.15324236]\n",
-      "  cov conditional:  [[ 2.41583924e-05  3.89930707e-07  2.11786040e-05 ... -7.09568424e-07\n",
-      "  -1.47065838e-04 -4.39597309e-06]\n",
-      " [ 3.89930707e-07  2.44391217e-05  3.26364183e-07 ...  1.91623193e-05\n",
-      "  -1.44141277e-06 -1.44336310e-04]\n",
-      " [ 2.11786040e-05  3.26364183e-07  1.85696000e-05 ... -6.40879376e-07\n",
-      "  -1.29100654e-04 -3.78307857e-06]\n",
-      " ...\n",
-      " [-7.09568424e-07  1.91623193e-05 -6.40879376e-07 ...  4.74546237e-05\n",
-      "   5.06128343e-06 -1.34793769e-04]\n",
-      " [-1.47065838e-04 -1.44141277e-06 -1.29100654e-04 ...  5.06128343e-06\n",
-      "   1.03980570e-03  2.55398837e-05]\n",
-      " [-4.39597309e-06 -1.44336310e-04 -3.78307857e-06 ... -1.34793769e-04\n",
-      "   2.55398837e-05  1.01453694e-03]]\n",
-      "  mean noise:  [-0.00963662  0.02369453  0.08030129  0.01699331 -0.0390995  -0.0305922\n",
-      "  0.018751    0.04009309  0.01480014 -0.0076841  -0.01240223 -0.01741537\n",
-      " -0.02185802 -0.01913291  0.02567312 -0.00292028  0.00107709  0.04503224\n",
-      " -0.0142923   0.00938927 -0.01237556 -0.01277635  0.00550095 -0.0316208\n",
-      " -0.04220405  0.01341128  0.03854378  0.02653517 -0.02108513  0.00207137\n",
-      "  0.00530287 -0.03803599  0.02314168 -0.00344064 -0.01767637  0.0214852\n",
-      "  0.04750032 -0.045792    0.01192569  0.03453526 -0.01071959  0.00271919\n",
-      " -0.00717963  0.01477678 -0.05382106  0.0024409  -0.02423488  0.02097831\n",
-      "  0.03928372 -0.02497641 -0.03372266 -0.02367467 -0.00454136 -0.00486218\n",
-      "  0.00395089  0.0198289  -0.00359534  0.00993887  0.01364064  0.01299931\n",
-      "  0.00883441 -0.00995827  0.03291761 -0.0077068   0.02394849 -0.02447752\n",
-      " -0.0128134   0.00270766 -0.00475158  0.02394807 -0.03878845  0.00208879\n",
-      " -0.01967835 -0.00788021  0.00719115  0.00237167  0.00692359 -0.00543172\n",
-      "  0.02442334 -0.01915199 -0.00616099 -0.01299499  0.00761258  0.03073461\n",
-      " -0.04411939  0.02626371 -0.01902171 -0.03732904 -0.01989399  0.0254883\n",
-      "  0.0681529   0.00441549  0.0123033  -0.055812   -0.01882996 -0.03815393\n",
-      "  0.0584448   0.07993774 -0.04352901 -0.01603218 -0.01502975  0.04294651\n",
-      "  0.00111191 -0.0232337  -0.01302481 -0.0057081  -0.01662472 -0.00263688\n",
-      "  0.04640642  0.04865649  0.00774849 -0.01690437  0.01751108 -0.00955713\n",
-      "  0.00946181  0.05849436 -0.04666349 -0.03941272 -0.04238277 -0.08491812\n",
-      "  0.04453947  0.00969191  0.00674255  0.04925872 -0.01162662 -0.03836557\n",
-      " -0.02290026  0.01658127]\n",
-      "  cov noise:  [[ 2.41583924e-05  3.89930707e-07  2.11786040e-05 ... -7.09568424e-07\n",
-      "  -1.47065838e-04 -4.39597309e-06]\n",
-      " [ 3.89930707e-07  2.44391217e-05  3.26364183e-07 ...  1.91623193e-05\n",
-      "  -1.44141277e-06 -1.44336310e-04]\n",
-      " [ 2.11786040e-05  3.26364183e-07  1.85696000e-05 ... -6.40879376e-07\n",
-      "  -1.29100654e-04 -3.78307857e-06]\n",
-      " ...\n",
-      " [-7.09568424e-07  1.91623193e-05 -6.40879376e-07 ...  4.74546237e-05\n",
-      "   5.06128343e-06 -1.34793769e-04]\n",
-      " [-1.47065838e-04 -1.44141277e-06 -1.29100654e-04 ...  5.06128343e-06\n",
-      "   1.03980570e-03  2.55398837e-05]\n",
-      " [-4.39597309e-06 -1.44336310e-04 -3.78307857e-06 ... -1.34793769e-04\n",
-      "   2.55398837e-05  1.01453694e-03]]\n",
-      "  mean:  [ 1.00908728e+00 -1.05279946e-03  1.00314143e+00  9.69002835e-02\n",
-      "  9.87575510e-01  1.93950215e-01  9.62596627e-01  2.89132427e-01\n",
-      "  9.28473980e-01  3.81485622e-01  8.85544726e-01  4.70105368e-01\n",
-      "  8.34219115e-01  5.54135526e-01  7.74992356e-01  6.32759469e-01\n",
-      "  7.08443482e-01  7.05210221e-01  6.35240255e-01  7.70797007e-01\n",
-      "  5.56119912e-01  8.28919416e-01  4.71836379e-01  8.79066771e-01\n",
-      "  3.83133996e-01  9.20811879e-01  2.90764370e-01  9.53783307e-01\n",
-      "  1.95536753e-01  9.77671134e-01  9.83543496e-02  9.92235892e-01\n",
-      "  1.99510004e-04  9.97333504e-01 -9.78898042e-02  9.92930604e-01\n",
-      " -1.94862562e-01  9.79073110e-01 -2.89706548e-01  9.55821801e-01\n",
-      " -3.81500209e-01  9.23261209e-01 -4.69419907e-01  8.81582957e-01\n",
-      " -5.52715300e-01  8.31123485e-01 -6.30696892e-01  7.72369999e-01\n",
-      " -7.02733841e-01  7.05943715e-01 -7.68226295e-01  6.32565965e-01\n",
-      " -8.26579354e-01  5.53026267e-01 -8.77204560e-01  4.68153442e-01\n",
-      " -9.19552431e-01  3.78805853e-01 -9.53140221e-01  2.85863154e-01\n",
-      " -9.77578168e-01  1.90244710e-01 -9.92586146e-01  9.29218047e-02\n",
-      " -9.97988160e-01 -5.09998028e-03 -9.93718093e-01 -1.02815619e-01\n",
-      " -9.79833584e-01 -1.99251733e-01 -9.56478642e-01 -2.93466595e-01\n",
-      " -9.23861345e-01 -3.84574662e-01 -8.82256846e-01 -4.71762831e-01\n",
-      " -8.32022023e-01 -5.54295313e-01 -7.73637435e-01 -6.31492010e-01\n",
-      " -7.07734728e-01 -7.02688777e-01 -6.35055247e-01 -7.67244320e-01\n",
-      " -5.56351098e-01 -8.24588407e-01 -4.72361680e-01 -8.74202509e-01\n",
-      " -3.83862372e-01 -9.15589615e-01 -2.91684849e-01 -9.48306494e-01\n",
-      " -1.96680757e-01 -9.71995729e-01 -9.97062550e-02 -9.86406758e-01\n",
-      " -1.62066020e-03 -9.91400731e-01  9.66942812e-02 -9.86947526e-01\n",
-      "  1.94331228e-01 -9.73128184e-01  2.90365546e-01 -9.50113196e-01\n",
-      "  3.83864401e-01 -9.18135405e-01  4.73904125e-01 -8.77469604e-01\n",
-      "  5.59597121e-01 -8.28417728e-01  6.40080867e-01 -7.71306445e-01\n",
-      "  7.14492654e-01 -7.06524508e-01  7.81960464e-01 -6.34600829e-01\n",
-      "  8.41664089e-01 -5.56233242e-01  8.92923877e-01 -4.72255740e-01\n",
-      "  9.35228934e-01 -3.83588465e-01  9.68209810e-01 -2.91187782e-01\n",
-      "  9.91608597e-01 -1.96008746e-01  1.00525971e+00 -9.89902157e-02]\n",
-      "  cov:  [[ 0.01414903 -0.00044986  0.01397817 ... -0.00050989  0.01413463\n",
-      "  -0.00047265]\n",
-      " [-0.00044986  0.0144717  -0.00047983 ...  0.01398205 -0.00039497\n",
-      "   0.01433078]\n",
-      " [ 0.01397817 -0.00047983  0.014049   ... -0.0005483   0.0137271\n",
-      "  -0.00050874]\n",
-      " ...\n",
-      " [-0.00050989  0.01398205 -0.0005483  ...  0.01439229 -0.00044088\n",
-      "   0.01429452]\n",
-      " [ 0.01413463 -0.00039497  0.0137271  ... -0.00044088  0.01436433\n",
-      "  -0.00040865]\n",
-      " [-0.00047265  0.01433078 -0.00050874 ...  0.01429452 -0.00040865\n",
-      "   0.01442187]]\n",
-      "  mean w/ noise:  [ 1.00629258e+00 -4.14986311e-04  1.00269848e+00  9.82928204e-02\n",
-      "  9.88537033e-01  1.95425574e-01  9.63012661e-01  2.88696918e-01\n",
-      "  9.26878003e-01  3.82739491e-01  8.84141230e-01  4.69591990e-01\n",
-      "  8.33954396e-01  5.55305064e-01  7.76266313e-01  6.35056364e-01\n",
-      "  7.12111254e-01  7.05636328e-01  6.37341166e-01  7.70933234e-01\n",
-      "  5.57501774e-01  8.30511057e-01  4.72461004e-01  8.78433888e-01\n",
-      "  3.86285387e-01  9.22602735e-01  2.90700958e-01  9.54533972e-01\n",
-      "  1.93852098e-01  9.78181456e-01  9.73786834e-02  9.94815022e-01\n",
-      "  1.93868121e-03  9.97874486e-01 -9.69244056e-02  9.93207248e-01\n",
-      " -1.96995424e-01  9.77923040e-01 -2.89956673e-01  9.56949981e-01\n",
-      " -3.80344657e-01  9.21112567e-01 -4.69047382e-01  8.82678295e-01\n",
-      " -5.50287826e-01  8.31374610e-01 -6.29741632e-01  7.71964084e-01\n",
-      " -7.04302646e-01  7.06761455e-01 -7.67701858e-01  6.33553464e-01\n",
-      " -8.27577505e-01  5.52548487e-01 -8.79396610e-01  4.69040294e-01\n",
-      " -9.18250016e-01  3.76715134e-01 -9.52346033e-01  2.85901616e-01\n",
-      " -9.77206564e-01  1.91127039e-01 -9.92957914e-01  9.39429743e-02\n",
-      " -9.98964871e-01 -3.73884229e-03 -9.94064118e-01 -1.03716762e-01\n",
-      " -9.80354446e-01 -1.98331522e-01 -9.56925035e-01 -2.94467233e-01\n",
-      " -9.27130676e-01 -3.83501486e-01 -8.80603095e-01 -4.72112469e-01\n",
-      " -8.32340883e-01 -5.55864767e-01 -7.76602824e-01 -6.32771197e-01\n",
-      " -7.06071130e-01 -7.04348736e-01 -6.34037420e-01 -7.68353825e-01\n",
-      " -5.56478538e-01 -8.23531656e-01 -4.74914201e-01 -8.72388572e-01\n",
-      " -3.81627300e-01 -9.15159035e-01 -2.91892447e-01 -9.46137593e-01\n",
-      " -1.93827796e-01 -9.72452179e-01 -9.95179065e-02 -9.83545047e-01\n",
-      " -2.06483303e-03 -9.89204907e-01  9.70380059e-02 -9.87589036e-01\n",
-      "  1.93995385e-01 -9.74954925e-01  2.91660101e-01 -9.50914187e-01\n",
-      "  3.80441560e-01 -9.18379145e-01  4.73481125e-01 -8.78533975e-01\n",
-      "  5.59325225e-01 -8.27071744e-01  6.39946578e-01 -7.73639292e-01\n",
-      "  7.17893947e-01 -7.08929304e-01  7.79805757e-01 -6.37313307e-01\n",
-      "  8.42325748e-01 -5.57713995e-01  8.91864003e-01 -4.71132030e-01\n",
-      "  9.33215602e-01 -3.86928244e-01  9.68567862e-01 -2.92660909e-01\n",
-      "  9.90893291e-01 -1.93771071e-01  1.00626047e+00 -9.76163303e-02]\n",
-      "  cov w/ noise:  [[ 0.01489563 -0.00059163  0.01386369 ... -0.00080803  0.01407259\n",
-      "  -0.00038998]\n",
-      " [-0.00059163  0.01572252 -0.00079482 ...  0.01414355 -0.00051795\n",
-      "   0.01434436]\n",
-      " [ 0.01386369 -0.00079482  0.01500867 ... -0.00081337  0.0138201\n",
-      "  -0.00050625]\n",
-      " ...\n",
-      " [-0.00080803  0.01414355 -0.00081337 ...  0.01519356 -0.00062225\n",
-      "   0.01422574]\n",
-      " [ 0.01407259 -0.00051795  0.0138201  ... -0.00062225  0.01554499\n",
-      "  -0.0001899 ]\n",
-      " [-0.00038998  0.01434436 -0.00050625 ...  0.01422574 -0.0001899\n",
-      "   0.01535845]]\n",
-      "  mean conditional:  [ 1.04920426  0.15768239  1.07908607  0.25538349  1.10221935  0.35156088\n",
-      "  1.1156595   0.44466783  1.11691952  0.53360137  1.10427737  0.61785118\n",
-      "  1.07700201  0.69735817  1.03529766  0.77234828  0.98022926  0.84298893\n",
-      "  0.91337846  0.90915843  0.83655845  0.97024079  0.75169431  1.02523466\n",
-      "  0.66052899  1.07276945  0.56462756  1.11162501  0.46515729  1.14065873\n",
-      "  0.36311709  1.15932191  0.25905689  1.1675948   0.15333751  1.16598826\n",
-      "  0.0462211   1.15521145 -0.06218305  1.13599928 -0.17139976  1.10877691\n",
-      " -0.28083477  1.07315283 -0.3892806   1.02834713 -0.49512863  0.97278732\n",
-      " -0.59631291  0.90494825 -0.69039395  0.82369857 -0.77495451  0.72891437\n",
-      " -0.84758317  0.621957   -0.90634514  0.50568156 -0.94970295  0.38427012\n",
-      " -0.97660082  0.26253396 -0.98674803  0.14517836 -0.98062594  0.03606412\n",
-      " -0.95944363 -0.06225566 -0.92503168 -0.14899822 -0.87957239 -0.22495683\n",
-      " -0.82540589 -0.29219071 -0.7643877  -0.35333744 -0.69789387 -0.41091426\n",
-      " -0.62666297 -0.46671579 -0.55094499 -0.52148069 -0.47075126 -0.57473055\n",
-      " -0.38609546 -0.6250801  -0.29704146 -0.67063759 -0.20405326 -0.70948305\n",
-      " -0.10786872 -0.74017619 -0.0095136  -0.76188183  0.08953679 -0.77440872\n",
-      "  0.18760125 -0.77812312  0.28282104 -0.77355159  0.37339341 -0.76124116\n",
-      "  0.45785556 -0.7414421   0.53503858 -0.71414629  0.60440279 -0.67901453\n",
-      "  0.66575556 -0.63567871  0.71943233 -0.58396384  0.76619909 -0.52401082\n",
-      "  0.80723908 -0.45644213  0.84404673 -0.38224423  0.87832595 -0.30232366\n",
-      "  0.91180052 -0.21748731  0.94576555 -0.12841516  0.98077612 -0.0356894\n",
-      "  1.01915868  0.05932492] , sample:  [ 1.03986813  0.13082866  1.10089379  0.28920023  1.09762332  0.39590851\n",
-      "  1.1154525   0.45823112  1.12277533  0.52465067  1.1455061   0.63755722\n",
-      "  1.11095645  0.67675341  1.01056218  0.73653992  0.95292135  0.87989792\n",
-      "  0.86840122  0.89821754  0.87534585  0.95830811  0.77649503  1.00802336\n",
-      "  0.65910321  1.09267945  0.53279835  1.14996054  0.47473409  1.13201856\n",
-      "  0.38090841  1.10212614  0.25178721  1.21984411  0.15722118  1.18821777\n",
-      "  0.02468162  1.14946769 -0.05577535  1.10142988 -0.1687061   1.17147056\n",
-      " -0.3193501   1.01043217 -0.35093442  1.00198963 -0.45563277  0.99462905\n",
-      " -0.59337478  0.91507017 -0.67614454  0.81376838 -0.80306278  0.77721026\n",
-      " -0.90294467  0.65013001 -0.85869872  0.508434   -0.90719994  0.33180598\n",
-      " -1.03587659  0.2628345  -1.01206431  0.13209534 -0.9605166   0.0707474\n",
-      " -0.96584183 -0.13229635 -0.9793292  -0.17184083 -0.83837653 -0.22320489\n",
-      " -0.83202488 -0.25540108 -0.71816052 -0.34847187 -0.67599509 -0.38687981\n",
-      " -0.65292149 -0.46619944 -0.5587054  -0.49594465 -0.47971672 -0.57341944\n",
-      " -0.42608752 -0.62976294 -0.24932133 -0.68165974 -0.21714826 -0.73389764\n",
-      " -0.09910792 -0.75605722  0.01565711 -0.74495272  0.09884079 -0.74377689\n",
-      "  0.16470642 -0.78542121  0.30752367 -0.79252271  0.40200957 -0.77499048\n",
-      "  0.41692008 -0.7035143   0.54062402 -0.7118851   0.56515497 -0.6382609\n",
-      "  0.69606815 -0.66625643  0.7039228  -0.62967856  0.75496378 -0.49551618\n",
-      "  0.86406582 -0.49308593  0.82628566 -0.36253626  0.84979286 -0.26988768\n",
-      "  0.94576409 -0.18421795  0.92139348 -0.13570391  0.99067166 -0.07653794\n",
-      "  0.98339583  0.02174465]\n",
-      "  cov conditional:  [[ 2.41600106e-05  3.88162246e-07  2.11800277e-05 ...  1.65862250e-06\n",
-      "  -1.47305009e-04 -1.03606151e-05]\n",
-      " [ 3.88162246e-07  2.44406508e-05  3.27781541e-07 ...  2.02812708e-05\n",
-      "   3.10086880e-07 -1.54098131e-04]\n",
-      " [ 2.11800277e-05  3.27781541e-07  1.85710886e-05 ...  1.44594770e-06\n",
-      "  -1.29338763e-04 -9.00272339e-06]\n",
-      " ...\n",
-      " [ 1.65862250e-06  2.02812708e-05  1.44594770e-06 ...  4.87558079e-05\n",
-      "  -3.01036939e-06 -1.39207876e-04]\n",
-      " [-1.47305009e-04  3.10086880e-07 -1.29338763e-04 ... -3.01036939e-06\n",
-      "   1.06876401e-03  5.15862200e-05]\n",
-      " [-1.03606151e-05 -1.54098131e-04 -9.00272339e-06 ... -1.39207876e-04\n",
-      "   5.15862200e-05  1.11596498e-03]]\n",
-      "  mean noise:  [ 0.00933612  0.02685373 -0.02180772 -0.03381675  0.00459603 -0.04434763\n",
-      "  0.000207   -0.01356329 -0.00585581  0.0089507  -0.04122874 -0.01970605\n",
-      " -0.03395444  0.02060476  0.02473548  0.03580836  0.02730791 -0.03690899\n",
-      "  0.04497723  0.01094088 -0.0387874   0.01193267 -0.02480072  0.0172113\n",
-      "  0.00142578 -0.01991     0.03182921 -0.03833552 -0.0095768   0.00864017\n",
-      " -0.01779132  0.05719577  0.00726968 -0.05224931 -0.00388367 -0.02222951\n",
-      "  0.02153948  0.00574376 -0.0064077   0.03456941 -0.00269366 -0.06269366\n",
-      "  0.03851534  0.06272066 -0.03834618  0.0263575  -0.03949586 -0.02184173\n",
-      " -0.00293812 -0.01012192 -0.01424941  0.00993019  0.02810828 -0.04829589\n",
-      "  0.05536151 -0.02817301 -0.04764642 -0.00275245 -0.042503    0.05246413\n",
-      "  0.05927577 -0.00030054  0.02531628  0.01308302 -0.02010933 -0.03468328\n",
-      "  0.0063982   0.07004069  0.05429752  0.02284261 -0.04119586 -0.00175194\n",
-      "  0.00661899 -0.03678963 -0.04622718 -0.00486556 -0.02189879 -0.02403445\n",
-      "  0.02625852 -0.00051635  0.00776041 -0.02553604  0.00896546 -0.00131111\n",
-      "  0.03999206  0.00468283 -0.04772013  0.01102215  0.01309501  0.02441458\n",
-      " -0.0087608   0.01588103 -0.0251707  -0.01692912 -0.009304   -0.03063183\n",
-      "  0.02289483  0.00729809 -0.02470263  0.01897111 -0.02861615  0.01374932\n",
-      "  0.04093548 -0.0379278  -0.00558544 -0.00226119  0.03924783 -0.04075364\n",
-      " -0.03031259  0.03057772  0.01550953  0.04571472  0.0112353  -0.02849464\n",
-      " -0.05682674  0.0366438   0.01776107 -0.01970797  0.02853309 -0.03243598\n",
-      " -0.03396357 -0.03326936  0.02437207  0.00728874 -0.00989554  0.04084854\n",
-      "  0.03576284  0.03758027]\n",
-      "  cov noise:  [[ 2.41600106e-05  3.88162246e-07  2.11800277e-05 ...  1.65862250e-06\n",
-      "  -1.47305009e-04 -1.03606151e-05]\n",
-      " [ 3.88162246e-07  2.44406508e-05  3.27781541e-07 ...  2.02812708e-05\n",
-      "   3.10086880e-07 -1.54098131e-04]\n",
-      " [ 2.11800277e-05  3.27781541e-07  1.85710886e-05 ...  1.44594770e-06\n",
-      "  -1.29338763e-04 -9.00272339e-06]\n",
-      " ...\n",
-      " [ 1.65862250e-06  2.02812708e-05  1.44594770e-06 ...  4.87558079e-05\n",
-      "  -3.01036939e-06 -1.39207876e-04]\n",
-      " [-1.47305009e-04  3.10086880e-07 -1.29338763e-04 ... -3.01036939e-06\n",
-      "   1.06876401e-03  5.15862200e-05]\n",
-      " [-1.03606151e-05 -1.54098131e-04 -9.00272339e-06 ... -1.39207876e-04\n",
-      "   5.15862200e-05  1.11596498e-03]]\n",
-      "  mean:  [ 1.00914807e+00  6.35014575e-04  1.00313778e+00  9.83118254e-02\n",
-      "  9.87452120e-01  1.95103529e-01  9.62297093e-01  2.90053824e-01\n",
-      "  9.27955333e-01  3.82225301e-01  8.84788246e-01  4.70720546e-01\n",
-      "  8.33248797e-01  5.54678180e-01  7.73877120e-01  6.33262525e-01\n",
-      "  7.07274965e-01  7.05685140e-01  6.34114059e-01  7.71249519e-01\n",
-      "  5.55116960e-01  8.29363852e-01  4.71018597e-01  8.79513539e-01\n",
-      "  3.82567208e-01  9.21240353e-01  2.90548410e-01  9.54133861e-01\n",
-      "  1.95787981e-01  9.77845322e-01  9.91684943e-02  9.92113692e-01\n",
-      "  1.64484656e-03  9.96801723e-01 -9.57681020e-02  9.91909301e-01\n",
-      " -1.92057591e-01  9.77558350e-01 -2.86270645e-01  9.53947534e-01\n",
-      " -3.77549644e-01  9.21308831e-01 -4.65123461e-01  8.79918624e-01\n",
-      " -5.48278890e-01  8.30106620e-01 -6.26345582e-01  7.72263060e-01\n",
-      " -6.98694266e-01  7.06847143e-01 -7.64708135e-01  6.34392328e-01\n",
-      " -8.23752592e-01  5.55526010e-01 -8.75165318e-01  4.70994505e-01\n",
-      " -9.18301087e-01  3.81668710e-01 -9.52589269e-01  2.88527645e-01\n",
-      " -9.77589462e-01  1.92611216e-01 -9.93004844e-01  9.49700219e-02\n",
-      " -9.98657316e-01 -3.36385687e-03 -9.94481852e-01 -1.01400200e-01\n",
-      " -9.80510327e-01 -1.98210593e-01 -9.56844475e-01 -2.92911738e-01\n",
-      " -9.23657640e-01 -3.84679198e-01 -8.81213778e-01 -4.72734438e-01\n",
-      " -8.29898531e-01 -5.56333691e-01 -7.70235271e-01 -6.34748095e-01\n",
-      " -7.02905875e-01 -7.07245865e-01 -6.28716440e-01 -7.73119407e-01\n",
-      " -5.48499474e-01 -8.31706428e-01 -4.63096997e-01 -8.82371506e-01\n",
-      " -3.73399288e-01 -9.24519860e-01 -2.80329721e-01 -9.57649349e-01\n",
-      " -1.84808445e-01 -9.81382162e-01 -8.77234584e-02 -9.95480977e-01\n",
-      "  1.00848995e-02 -9.99840077e-01  1.07795026e-01 -9.94480182e-01\n",
-      "  2.04568309e-01 -9.79535769e-01  2.99550934e-01 -9.55215062e-01\n",
-      "  3.91883789e-01 -9.21778048e-01  4.80700655e-01 -8.79541895e-01\n",
-      "  5.65150450e-01 -8.28893770e-01  6.44405695e-01 -7.70297124e-01\n",
-      "  7.17656879e-01 -7.04301455e-01  7.84108448e-01 -6.31555683e-01\n",
-      "  8.42999381e-01 -5.52789598e-01  8.93668474e-01 -4.68786185e-01\n",
-      "  9.35600339e-01 -3.80364479e-01  9.68393525e-01 -2.88362480e-01\n",
-      "  9.91723857e-01 -1.93617733e-01  1.00535028e+00 -9.69842579e-02]\n",
-      "  cov:  [[ 1.32490635e-02 -1.12290595e-04  1.31500803e-02 ...  3.08771396e-04\n",
-      "   1.31394052e-02  1.05244141e-04]\n",
-      " [-1.12290595e-04  1.57700133e-02 -7.70056492e-05 ...  1.52069723e-02\n",
-      "  -1.51013487e-04  1.56087720e-02]\n",
-      " [ 1.31500803e-02 -7.70056492e-05  1.32875461e-02 ...  3.44897678e-04\n",
-      "   1.28098172e-02  1.40846637e-04]\n",
-      " ...\n",
-      " [ 3.08771396e-04  1.52069723e-02  3.44897678e-04 ...  1.55588774e-02\n",
-      "   2.62091414e-04  1.55032297e-02]\n",
-      " [ 1.31394052e-02 -1.51013487e-04  1.28098172e-02 ...  2.62091414e-04\n",
-      "   1.32686244e-02  6.25570636e-05]\n",
-      " [ 1.05244141e-04  1.56087720e-02  1.40846637e-04 ...  1.55032297e-02\n",
-      "   6.25570636e-05  1.56801809e-02]]\n",
-      "  mean w/ noise:  [ 1.00892400e+00  9.87336086e-04  1.00151740e+00  9.90457441e-02\n",
-      "  9.85764281e-01  1.95359841e-01  9.59779456e-01  2.87557001e-01\n",
-      "  9.27317019e-01  3.81306133e-01  8.85741381e-01  4.69458504e-01\n",
-      "  8.30483310e-01  5.57444194e-01  7.72394023e-01  6.33935450e-01\n",
-      "  7.11641325e-01  7.06190913e-01  6.34344122e-01  7.69911081e-01\n",
-      "  5.52968675e-01  8.30651495e-01  4.71328853e-01  8.80422378e-01\n",
-      "  3.82684079e-01  9.22471222e-01  2.89256076e-01  9.53315084e-01\n",
-      "  1.95028219e-01  9.76382088e-01  1.00647902e-01  9.91784361e-01\n",
-      " -3.46977305e-04  9.95594128e-01 -9.60974896e-02  9.90346334e-01\n",
-      " -1.91785606e-01  9.76746993e-01 -2.88278734e-01  9.55246220e-01\n",
-      " -3.77376846e-01  9.20867606e-01 -4.68227128e-01  8.81783209e-01\n",
-      " -5.47478850e-01  8.31091565e-01 -6.25574577e-01  7.70353059e-01\n",
-      " -7.00781346e-01  7.05296816e-01 -7.65142313e-01  6.34699853e-01\n",
-      " -8.22498847e-01  5.53667810e-01 -8.76994630e-01  4.70933050e-01\n",
-      " -9.18566679e-01  3.81769473e-01 -9.54423877e-01  2.87515195e-01\n",
-      " -9.77666140e-01  1.91929201e-01 -9.92822790e-01  9.36868362e-02\n",
-      " -9.97954799e-01 -3.57829458e-03 -9.95948975e-01 -1.00558096e-01\n",
-      " -9.78878483e-01 -1.96745048e-01 -9.54756265e-01 -2.93940921e-01\n",
-      " -9.21842422e-01 -3.84399444e-01 -8.82921943e-01 -4.73876001e-01\n",
-      " -8.29405091e-01 -5.58119768e-01 -7.67718032e-01 -6.33546013e-01\n",
-      " -7.04059351e-01 -7.08007750e-01 -6.28586469e-01 -7.72300772e-01\n",
-      " -5.48726917e-01 -8.33175145e-01 -4.62794588e-01 -8.82700841e-01\n",
-      " -3.73649844e-01 -9.23639361e-01 -2.81131584e-01 -9.58424344e-01\n",
-      " -1.85996750e-01 -9.78833113e-01 -8.70971141e-02 -9.96165611e-01\n",
-      "  1.00216232e-02 -9.98854780e-01  1.08855550e-01 -9.94471702e-01\n",
-      "  2.04327896e-01 -9.78985679e-01  3.01709707e-01 -9.54271124e-01\n",
-      "  3.94605211e-01 -9.21680476e-01  4.80395410e-01 -8.80097312e-01\n",
-      "  5.70013602e-01 -8.28503082e-01  6.46185334e-01 -7.70039658e-01\n",
-      "  7.16181593e-01 -7.06000732e-01  7.80805123e-01 -6.32408708e-01\n",
-      "  8.44132432e-01 -5.53456340e-01  8.93386375e-01 -4.69087265e-01\n",
-      "  9.36222662e-01 -3.79159864e-01  9.70933034e-01 -2.85320249e-01\n",
-      "  9.89994826e-01 -1.92998215e-01  1.00470473e+00 -9.53503839e-02]\n",
-      "  cov w/ noise:  [[ 1.40242030e-02 -4.02838418e-04  1.29512705e-02 ...  1.29317843e-04\n",
-      "   1.32960224e-02 -2.56310158e-05]\n",
-      " [-4.02838418e-04  1.60441395e-02 -1.67386467e-04 ...  1.49999908e-02\n",
-      "  -2.58492803e-04  1.55012185e-02]\n",
-      " [ 1.29512705e-02 -1.67386467e-04  1.39288251e-02 ...  4.85477863e-04\n",
-      "   1.29873636e-02  1.64774910e-04]\n",
-      " ...\n",
-      " [ 1.29317843e-04  1.49999908e-02  4.85477863e-04 ...  1.64200027e-02\n",
-      "   2.91182066e-04  1.57012996e-02]\n",
-      " [ 1.32960224e-02 -2.58492803e-04  1.29873636e-02 ...  2.91182066e-04\n",
-      "   1.47904147e-02 -1.29144499e-04]\n",
-      " [-2.56310158e-05  1.55012185e-02  1.64774910e-04 ...  1.57012996e-02\n",
-      "  -1.29144499e-04  1.71065057e-02]]\n",
-      "  mean conditional:  [ 1.04353678  0.01873588  1.06150276  0.09314566  1.07357564  0.16815318\n",
-      "  1.07727664  0.24348193  1.07032151  0.31882731  1.05096527  0.39397455\n",
-      "  1.01818799  0.46858346  0.97186192  0.54239687  0.9126693   0.61493394\n",
-      "  0.84198096  0.68560715  0.76158181  0.75367544  0.67363799  0.81823517\n",
-      "  0.58029647  0.87834045  0.48357414  0.93311559  0.38522811  0.98181865\n",
-      "  0.28673363  1.02383902  0.18894715  1.05879585  0.09249269  1.08635358\n",
-      " -0.00237097  1.10597446 -0.09552042  1.11701705 -0.1867971   1.11840206\n",
-      " -0.27594449  1.10863393 -0.36235222  1.08586818 -0.44513366  1.04815239\n",
-      " -0.52309481  0.99375274 -0.59490932  0.92161673 -0.65923537  0.8319447\n",
-      " -0.71486919  0.72626195 -0.76099108  0.60758781 -0.79707915  0.4801697\n",
-      " -0.8229683   0.34886591 -0.83883459  0.21853228 -0.84527277  0.09339181\n",
-      " -0.84312355 -0.02351629 -0.83327647 -0.13065783 -0.81650521 -0.22793565\n",
-      " -0.79331337 -0.31641063 -0.76373498 -0.39780075 -0.7273373  -0.47375482\n",
-      " -0.6833624  -0.54534582 -0.63090857 -0.61284873 -0.56908621 -0.67549374\n",
-      " -0.49732612 -0.73182953 -0.41540073 -0.78005801 -0.32374546 -0.8185759\n",
-      " -0.22328625 -0.84639277 -0.11570289 -0.86310917 -0.00328371 -0.86915687\n",
-      "  0.11114577 -0.86525971  0.22457561 -0.85230616  0.33391918 -0.83105992\n",
-      "  0.43670986 -0.80197503  0.53092711 -0.76534504  0.61535277 -0.72131932\n",
-      "  0.68938294 -0.67022249  0.75302998 -0.61269114  0.80680563 -0.54980259\n",
-      "  0.85168085 -0.48291482  0.8890159  -0.41348255  0.9206302  -0.3427385\n",
-      "  0.94844445 -0.27138207  0.97399836 -0.19974292  0.99926446 -0.12764476\n",
-      "  1.02529116 -0.05349974] , sample:  [ 1.0684109   0.00578646  1.07900228  0.14150171  1.0639984   0.15056618\n",
-      "  1.02679929  0.22192314  1.0894816   0.27674513  1.08957466  0.38875704\n",
-      "  1.04295286  0.500117    0.93635832  0.52406549  0.92785349  0.63625644\n",
-      "  0.84530043  0.69889625  0.72914672  0.72931046  0.69604655  0.83219275\n",
-      "  0.60393733  0.88574165  0.47797439  0.9078816   0.35080765  1.02423767\n",
-      "  0.27024962  0.9657121   0.20777333  1.02818019  0.12657839  1.11983309\n",
-      " -0.05944053  1.10798859 -0.06459313  1.1493136  -0.22176515  1.09844\n",
-      " -0.27084769  1.12218416 -0.35815271  1.04426986 -0.41005028  1.09099126\n",
-      " -0.50106311  1.01893858 -0.62030227  0.91819547 -0.64838425  0.81103162\n",
-      " -0.74058427  0.73855046 -0.7241038   0.65175277 -0.80420154  0.43323112\n",
-      " -0.85969589  0.36108378 -0.86292479  0.24105791 -0.82875661  0.05432143\n",
-      " -0.76494436 -0.0592836  -0.86667335 -0.101583   -0.83884078 -0.25408368\n",
-      " -0.7270597  -0.31343459 -0.82247695 -0.36091866 -0.73219763 -0.49092073\n",
-      " -0.65534587 -0.5584574  -0.66068298 -0.53529782 -0.57751928 -0.68105092\n",
-      " -0.46740313 -0.73619855 -0.40823879 -0.83084905 -0.37089554 -0.86876745\n",
-      " -0.22560199 -0.80725566 -0.12722776 -0.86973863  0.05691535 -0.86446018\n",
-      "  0.09505298 -0.84590361  0.22644136 -0.81989782  0.31031681 -0.83270655\n",
-      "  0.49470004 -0.81856212  0.54219788 -0.78085737  0.58780663 -0.73932266\n",
-      "  0.6470842  -0.66309878  0.70583262 -0.61582504  0.88733352 -0.5514269\n",
-      "  0.84078998 -0.42453356  0.90227165 -0.44757804  0.93318079 -0.33210297\n",
-      "  0.97458783 -0.25966556  0.93863286 -0.20871387  0.96354509 -0.14784921\n",
-      "  1.02109871 -0.03643788]\n",
-      "  cov conditional:  [[ 2.41531990e-05  3.93724977e-07  2.11732934e-05 ... -5.16844889e-07\n",
-      "  -1.48419599e-04 -7.73135288e-07]\n",
-      " [ 3.93724977e-07  2.44390575e-05  3.28945499e-07 ...  1.99275263e-05\n",
-      "   1.44512865e-06 -1.49605646e-04]\n",
-      " [ 2.11732934e-05  3.28945499e-07  1.85643059e-05 ... -4.81984410e-07\n",
-      "  -1.30279806e-04 -5.82900014e-07]\n",
-      " ...\n",
-      " [-5.16844889e-07  1.99275263e-05 -4.81984410e-07 ...  5.12745568e-05\n",
-      "   4.64597484e-06 -1.44565450e-04]\n",
-      " [-1.48419599e-04  1.44512865e-06 -1.30279806e-04 ...  4.64597484e-06\n",
-      "   1.05497487e-03 -2.03519068e-05]\n",
-      " [-7.73135288e-07 -1.49605646e-04 -5.82900014e-07 ... -1.44565450e-04\n",
-      "  -2.03519068e-05  1.08341012e-03]]\n",
-      "  mean noise:  [-0.02487412  0.01294942 -0.01749952 -0.04835605  0.00957724  0.01758701\n",
-      "  0.05047736  0.0215588  -0.01916009  0.04208217 -0.03860939  0.00521751\n",
-      " -0.02476487 -0.03153354  0.0355036   0.01833138 -0.01518419 -0.0213225\n",
-      " -0.00331946 -0.0132891   0.03243509  0.02436499 -0.02240856 -0.01395757\n",
-      " -0.02364086 -0.0074012   0.00559975  0.02523399  0.03442046 -0.04241902\n",
-      "  0.01648401  0.05812692 -0.01882618  0.03061566 -0.0340857  -0.0334795\n",
-      "  0.05706957 -0.00201413 -0.03092729 -0.03229655  0.03496805  0.01996205\n",
-      " -0.00509679 -0.01355023 -0.00419951  0.04159831 -0.03508337 -0.04283886\n",
-      " -0.0220317  -0.02518584  0.02539295  0.00342125 -0.01085111  0.02091308\n",
-      "  0.02571508 -0.01228851 -0.03688728 -0.04416496  0.00712239  0.04693858\n",
-      "  0.03672759 -0.01221787  0.0240902  -0.02252562 -0.01651616  0.03907038\n",
-      " -0.07817918  0.03576731  0.03339688 -0.02907483  0.02233557  0.02614803\n",
-      " -0.06625367 -0.00297604  0.05874197 -0.03688209  0.00486033  0.01716591\n",
-      " -0.02801653  0.01311158  0.02977441 -0.07755091  0.00843307  0.00555719\n",
-      " -0.02992299  0.00436902 -0.00716194  0.05079105  0.04715008  0.05019155\n",
-      "  0.00231574 -0.03913711  0.01152487  0.00662946 -0.06019906 -0.00469669\n",
-      "  0.01609279 -0.0193561  -0.00186576 -0.03240834  0.02360237  0.00164664\n",
-      " -0.05799018  0.01658709 -0.01127077  0.01551233  0.02754614  0.01800333\n",
-      "  0.04229873 -0.00712371  0.04719736  0.0031339  -0.08052788  0.00162431\n",
-      "  0.01089087 -0.05838126 -0.01325575  0.03409548 -0.01255059 -0.01063553\n",
-      " -0.02614338 -0.0117165   0.0353655   0.00897094  0.03571936  0.02020445\n",
-      "  0.00419245 -0.01706186]\n",
-      "  cov noise:  [[ 2.41531990e-05  3.93724977e-07  2.11732934e-05 ... -5.16844889e-07\n",
-      "  -1.48419599e-04 -7.73135288e-07]\n",
-      " [ 3.93724977e-07  2.44390575e-05  3.28945499e-07 ...  1.99275263e-05\n",
-      "   1.44512865e-06 -1.49605646e-04]\n",
-      " [ 2.11732934e-05  3.28945499e-07  1.85643059e-05 ... -4.81984410e-07\n",
-      "  -1.30279806e-04 -5.82900014e-07]\n",
-      " ...\n",
-      " [-5.16844889e-07  1.99275263e-05 -4.81984410e-07 ...  5.12745568e-05\n",
-      "   4.64597484e-06 -1.44565450e-04]\n",
-      " [-1.48419599e-04  1.44512865e-06 -1.30279806e-04 ...  4.64597484e-06\n",
-      "   1.05497487e-03 -2.03519068e-05]\n",
-      " [-7.73135288e-07 -1.49605646e-04 -5.82900014e-07 ... -1.44565450e-04\n",
-      "  -2.03519068e-05  1.08341012e-03]]\n",
-      "  mean:  [ 1.00576126e+00  3.49335596e-03  1.00057173e+00  1.01426776e-01\n",
-      "  9.85510974e-01  1.98294251e-01  9.60780603e-01  2.93140464e-01\n",
-      "  9.26692229e-01  3.85027819e-01  8.83649080e-01  4.73059272e-01\n",
-      "  8.32128265e-01  5.56379330e-01  7.72676808e-01  6.34178988e-01\n",
-      "  7.05886825e-01  7.05685472e-01  6.32402437e-01  7.70194222e-01\n",
-      "  5.52937352e-01  8.27122916e-01  4.68275161e-01  8.76016817e-01\n",
-      "  3.79263064e-01  9.16515087e-01  2.86806815e-01  9.48317269e-01\n",
-      "  1.91833313e-01  9.71183923e-01  9.52835064e-02  9.84946916e-01\n",
-      " -1.89222267e-03  9.89517715e-01 -9.87558318e-02  9.84885008e-01\n",
-      " -1.94427606e-01  9.71091573e-01 -2.88100933e-01  9.48199451e-01\n",
-      " -3.79033021e-01  9.16271014e-01 -4.66495192e-01  8.75407450e-01\n",
-      " -5.49736295e-01  8.25801574e-01 -6.27986146e-01  7.67794655e-01\n",
-      " -7.00500825e-01  7.01897358e-01 -7.66587645e-01  6.28763265e-01\n",
-      " -8.25605055e-01  5.49154134e-01 -8.76939772e-01  4.63924358e-01\n",
-      " -9.20025049e-01  3.74006780e-01 -9.54371352e-01  2.80400165e-01\n",
-      " -9.79595025e-01  1.84165669e-01 -9.95404968e-01  8.63901581e-02\n",
-      " -1.00157750e+00 -1.18805712e-02 -9.97963647e-01 -1.09674216e-01\n",
-      " -9.84527697e-01 -2.06079510e-01 -9.61363242e-01 -3.00220826e-01\n",
-      " -9.28679698e-01 -3.91267295e-01 -8.86777143e-01 -4.78434040e-01\n",
-      " -8.36058025e-01 -5.60980603e-01 -7.77067363e-01 -6.38207074e-01\n",
-      " -7.10483444e-01 -7.09408996e-01 -6.37062751e-01 -7.73895873e-01\n",
-      " -5.57575080e-01 -8.31048518e-01 -4.72795844e-01 -8.80312645e-01\n",
-      " -3.83550147e-01 -9.21185611e-01 -2.90727917e-01 -9.53231282e-01\n",
-      " -1.95273205e-01 -9.76112117e-01 -9.81659239e-02 -9.89614569e-01\n",
-      " -3.79212309e-04 -9.93641847e-01  9.71687892e-02 -9.88194688e-01\n",
-      "  1.93633454e-01 -9.73372054e-01  2.88225376e-01 -9.49361081e-01\n",
-      "  3.80178452e-01 -9.16411793e-01  4.68732720e-01 -8.74837659e-01\n",
-      "  5.53132281e-01 -8.25005297e-01  6.32596942e-01 -7.67312108e-01\n",
-      "  7.06318147e-01 -7.02191312e-01  7.73468210e-01 -6.30150276e-01\n",
-      "  8.33248681e-01 -5.51807550e-01  8.84944617e-01 -4.67906636e-01\n",
-      "  9.27972392e-01 -3.79299886e-01  9.61878077e-01 -2.86903515e-01\n",
-      "  9.86307365e-01 -1.91661013e-01  1.00099213e+00 -9.45332186e-02]\n",
-      "  cov:  [[ 0.01355688 -0.00061751  0.01339721 ... -0.00055738  0.01352255\n",
-      "  -0.0005878 ]\n",
-      " [-0.00061751  0.01482908 -0.00061472 ...  0.01447344 -0.00061337\n",
-      "   0.01477908]\n",
-      " [ 0.01339721 -0.00061472  0.01346386 ... -0.00055088  0.01314316\n",
-      "  -0.00058174]\n",
-      " ...\n",
-      " [-0.00055738  0.01447344 -0.00055088 ...  0.01500331 -0.00055422\n",
-      "   0.01486783]\n",
-      " [ 0.01352255 -0.00061337  0.01314316 ... -0.00055422  0.01371443\n",
-      "  -0.00058579]\n",
-      " [-0.0005878   0.01477908 -0.00058174 ...  0.01486783 -0.00058579\n",
-      "   0.01495643]]\n",
-      "  mean w/ noise:  [ 1.00533479e+00  2.89798739e-03  9.98702275e-01  1.01393873e-01\n",
-      "  9.85504939e-01  2.01393078e-01  9.61220179e-01  2.94297844e-01\n",
-      "  9.28446538e-01  3.87523470e-01  8.84239020e-01  4.72926212e-01\n",
-      "  8.28672361e-01  5.57438631e-01  7.74778657e-01  6.32041659e-01\n",
-      "  7.07096868e-01  7.05913559e-01  6.32031252e-01  7.73346130e-01\n",
-      "  5.52082124e-01  8.22703660e-01  4.69256931e-01  8.76145734e-01\n",
-      "  3.78464468e-01  9.20218140e-01  2.87246827e-01  9.47942594e-01\n",
-      "  1.91838037e-01  9.71475968e-01  9.73514120e-02  9.85577263e-01\n",
-      " -1.88439324e-03  9.89344481e-01 -9.94693446e-02  9.83249690e-01\n",
-      " -1.93800036e-01  9.70721397e-01 -2.88517031e-01  9.48512180e-01\n",
-      " -3.77697415e-01  9.15508207e-01 -4.65233891e-01  8.75428269e-01\n",
-      " -5.49253833e-01  8.24987848e-01 -6.28342840e-01  7.68640313e-01\n",
-      " -7.02013254e-01  7.03845058e-01 -7.66152426e-01  6.30020718e-01\n",
-      " -8.27601198e-01  5.46573092e-01 -8.76401056e-01  4.64726609e-01\n",
-      " -9.16199649e-01  3.74694000e-01 -9.56704459e-01  2.82255626e-01\n",
-      " -9.79236500e-01  1.84792201e-01 -9.97881298e-01  8.69084932e-02\n",
-      " -9.99330835e-01 -1.33430411e-02 -9.96638893e-01 -1.10405306e-01\n",
-      " -9.83531878e-01 -2.05933874e-01 -9.62459526e-01 -3.00122918e-01\n",
-      " -9.26841783e-01 -3.90198459e-01 -8.87630336e-01 -4.76064818e-01\n",
-      " -8.34622856e-01 -5.62842958e-01 -7.75875714e-01 -6.40582597e-01\n",
-      " -7.13013790e-01 -7.12887102e-01 -6.36480989e-01 -7.72781618e-01\n",
-      " -5.57462817e-01 -8.28042008e-01 -4.72401560e-01 -8.81551806e-01\n",
-      " -3.79425180e-01 -9.20327087e-01 -2.91068233e-01 -9.55658395e-01\n",
-      " -1.97425469e-01 -9.76663954e-01 -9.76992404e-02 -9.87315495e-01\n",
-      "  7.01423746e-04 -9.90687670e-01  1.00628271e-01 -9.89599559e-01\n",
-      "  1.94842100e-01 -9.72847052e-01  2.86685589e-01 -9.51474800e-01\n",
-      "  3.79665411e-01 -9.17552933e-01  4.65533722e-01 -8.75021148e-01\n",
-      "  5.54704200e-01 -8.24957506e-01  6.32728122e-01 -7.68761353e-01\n",
-      "  7.08711594e-01 -7.02042028e-01  7.71272892e-01 -6.31315740e-01\n",
-      "  8.30535215e-01 -5.52288580e-01  8.85935656e-01 -4.67386623e-01\n",
-      "  9.28430940e-01 -3.78973410e-01  9.60684829e-01 -2.82693238e-01\n",
-      "  9.84048451e-01 -1.91070151e-01  1.00303347e+00 -9.31131761e-02]\n",
-      "  cov w/ noise:  [[ 0.01487099 -0.00062378  0.01368718 ... -0.00034425  0.01356221\n",
-      "  -0.00036624]\n",
-      " [-0.00062378  0.0158231  -0.0009812  ...  0.01453477 -0.00058238\n",
-      "   0.01466329]\n",
-      " [ 0.01368718 -0.0009812   0.01476215 ... -0.00062617  0.01317689\n",
-      "  -0.00067011]\n",
-      " ...\n",
-      " [-0.00034425  0.01453477 -0.00062617 ...  0.0159934  -0.00049325\n",
-      "   0.01477942]\n",
-      " [ 0.01356221 -0.00058238  0.01317689 ... -0.00049325  0.01448628\n",
-      "  -0.00042512]\n",
-      " [-0.00036624  0.01466329 -0.00067011 ...  0.01477942 -0.00042512\n",
-      "   0.01577518]]\n",
-      "  mean conditional:  [ 1.00335536  0.12999476  1.0281209   0.22171217  1.04749781  0.31265329\n",
-      "  1.05856978  0.40127303  1.05892134  0.48623848  1.04689627  0.56665927\n",
-      "  1.02167187  0.64193989  0.98315141  0.71172426  0.9318729   0.77559042\n",
-      "  0.86876377  0.8328521   0.79498068  0.88247157  0.7116676   0.92314034\n",
-      "  0.61995162  0.95355592  0.52075124  0.97271731  0.41482098  0.98031423\n",
-      "  0.3029759   0.97690191  0.18584861  0.96401588  0.06442266  0.94388517\n",
-      " -0.05989657  0.91897376 -0.18544647  0.89154147 -0.30987884  0.86307133\n",
-      " -0.43072899  0.8338827  -0.54521801  0.80302036 -0.650924    0.76825944\n",
-      " -0.74581637  0.72674565 -0.82836554  0.6754424  -0.89787196  0.61187071\n",
-      " -0.95403598  0.53486608 -0.99716286  0.44475916 -1.0276282   0.34351676\n",
-      " -1.04577226  0.23432203 -1.05200975  0.12105686 -1.04684185  0.00758704\n",
-      " -1.03100747 -0.10274421 -1.00541286 -0.20753377 -0.9713194  -0.30539185\n",
-      " -0.930047   -0.39599919 -0.88275461 -0.47955489 -0.83035077 -0.55646149\n",
-      " -0.77309233 -0.62699112 -0.71094046 -0.69102071 -0.64334334 -0.74785198\n",
-      " -0.56945675 -0.79643108 -0.4882638  -0.835528   -0.39884753 -0.86410447\n",
-      " -0.30069388 -0.88161408 -0.19390974 -0.88803711 -0.07961519 -0.88399353\n",
-      "  0.03997794 -0.87058867  0.1617657  -0.84897433  0.28178269 -0.82025637\n",
-      "  0.39614831 -0.78528874  0.50111979 -0.74441474  0.5937899  -0.69767109\n",
-      "  0.67236851 -0.644953    0.73630268 -0.58627022  0.7864378  -0.52185513\n",
-      "  0.82478628 -0.45229533  0.85436517 -0.37841514  0.87864641 -0.30089956\n",
-      "  0.9010395  -0.22016811  0.92419056 -0.13639509  0.94981114 -0.04983117\n",
-      "  0.97963804  0.03942132] , sample:  [ 1.01992989  0.08661798  1.06084618  0.22584003  1.09115962  0.32336755\n",
-      "  1.03047806  0.46951281  1.05900985  0.47094595  1.00509151  0.58515384\n",
-      "  1.06266586  0.5933559   0.98897346  0.74148031  0.90955918  0.74168686\n",
-      "  0.8686658   0.86981141  0.81456864  0.81712831  0.70320014  0.96152568\n",
-      "  0.60072145  0.94864279  0.54918392  1.0095595   0.38272564  0.95196331\n",
-      "  0.32299737  1.03178392  0.17061448  0.98484     0.07252426  0.92438711\n",
-      "  0.01223848  0.88201083 -0.19948212  0.85749823 -0.31733922  0.89931846\n",
-      " -0.45202913  0.80714279 -0.57602013  0.78845494 -0.67018239  0.76156807\n",
-      " -0.73674614  0.6985734  -0.83281675  0.66119509 -0.89339733  0.6682262\n",
-      " -0.98625729  0.52706207 -0.97842806  0.49654304 -0.96350456  0.34743107\n",
-      " -1.03652583  0.19690566 -1.09762203  0.14588078 -1.0436402  -0.01221934\n",
-      " -1.04119025 -0.14811033 -1.04306548 -0.17685149 -0.9428228  -0.2556556\n",
-      " -0.93291416 -0.40043555 -0.88130507 -0.56850465 -0.83281763 -0.51099432\n",
-      " -0.76744484 -0.62360915 -0.7144453  -0.69011929 -0.63296421 -0.7494698\n",
-      " -0.56035762 -0.78525335 -0.47474318 -0.85882156 -0.38832433 -0.85991858\n",
-      " -0.3245698  -0.87823753 -0.18730107 -0.88207398 -0.13389776 -0.91777378\n",
-      "  0.05177781 -0.865219    0.15800954 -0.84360942  0.27743572 -0.81116103\n",
-      "  0.39695961 -0.76113542  0.53473067 -0.72056033  0.53845209 -0.70722639\n",
-      "  0.71904952 -0.69778892  0.75982845 -0.55314806  0.8022644  -0.51843049\n",
-      "  0.8594206  -0.46750532  0.86225315 -0.3794851   0.85007382 -0.28736614\n",
-      "  0.86318694 -0.20013606  0.89470803 -0.10385006  0.89127223 -0.07238885\n",
-      "  1.00770888  0.00387716]\n",
-      "  cov conditional:  [[ 2.41576239e-05  3.93115398e-07  2.11726245e-05 ...  3.94014099e-07\n",
-      "  -1.46210329e-04 -1.90739779e-06]\n",
-      " [ 3.93115398e-07  2.44430569e-05  3.26396471e-07 ...  1.93662028e-05\n",
-      "   2.03336571e-06 -1.48802884e-04]\n",
-      " [ 2.11726245e-05  3.26396471e-07  1.85598880e-05 ...  3.30151810e-07\n",
-      "  -1.28343979e-04 -1.50063261e-06]\n",
-      " ...\n",
-      " [ 3.94014099e-07  1.93662028e-05  3.30151810e-07 ...  4.92465598e-05\n",
-      "   2.08255948e-06 -1.41276189e-04]\n",
-      " [-1.46210329e-04  2.03336571e-06 -1.28343979e-04 ...  2.08255948e-06\n",
-      "   1.04622425e-03 -1.36769915e-05]\n",
-      " [-1.90739779e-06 -1.48802884e-04 -1.50063261e-06 ... -1.41276189e-04\n",
-      "  -1.36769915e-05  1.07230229e-03]]\n",
-      "  mean noise:  [-1.65745318e-02  4.33767786e-02 -3.27252793e-02 -4.12786014e-03\n",
-      " -4.36618137e-02 -1.07142593e-02  2.80917232e-02 -6.82397795e-02\n",
-      " -8.85153147e-05  1.52925240e-02  4.18047591e-02 -1.84945727e-02\n",
-      " -4.09939851e-02  4.85839857e-02 -5.82204478e-03 -2.97560444e-02\n",
-      "  2.23137207e-02  3.39035645e-02  9.79665879e-05 -3.69593071e-02\n",
-      " -1.95879569e-02  6.53432616e-02  8.46746307e-03 -3.83853380e-02\n",
-      "  1.92301713e-02  4.91313355e-03 -2.84326807e-02 -3.68421837e-02\n",
-      "  3.20953353e-02  2.83509128e-02 -2.00214628e-02 -5.48820095e-02\n",
-      "  1.52341231e-02 -2.08241232e-02 -8.10159583e-03  1.94980657e-02\n",
-      " -7.21350439e-02  3.69629220e-02  1.40356512e-02  3.40432373e-02\n",
-      "  7.46038103e-03 -3.62471221e-02  2.13001427e-02  2.67399049e-02\n",
-      "  3.08021275e-02  1.45654238e-02  1.92583900e-02  6.69137729e-03\n",
-      " -9.07022206e-03  2.81722516e-02  4.45120617e-03  1.42473118e-02\n",
-      " -4.47463254e-03 -5.63554922e-02  3.22213026e-02  7.80401048e-03\n",
-      " -1.87347915e-02 -5.17838764e-02 -6.41236397e-02 -3.91431338e-03\n",
-      " -9.24642578e-03  3.74163655e-02  4.56122781e-02 -2.48239190e-02\n",
-      " -3.20164365e-03  1.98063847e-02  1.01827746e-02  4.53661250e-02\n",
-      "  3.76526265e-02 -3.06822704e-02 -2.84965998e-02 -4.97362490e-02\n",
-      "  2.86715722e-03  4.43636026e-03 -1.44954094e-03  8.89497646e-02\n",
-      "  2.46686754e-03 -4.54671663e-02 -5.64748972e-03 -3.38196719e-03\n",
-      "  3.50483849e-03 -9.01417178e-04 -1.03791276e-02  1.61782616e-03\n",
-      " -9.09912784e-03 -1.11777257e-02 -1.35206241e-02  2.32935609e-02\n",
-      " -1.05232027e-02 -4.18589470e-03  2.38759248e-02 -3.37654646e-03\n",
-      " -6.60866891e-03 -5.96313252e-03  5.42825750e-02  3.37802462e-02\n",
-      " -1.17998755e-02 -5.36967121e-03  3.75616684e-03 -5.36490944e-03\n",
-      "  4.34696470e-03 -9.09533827e-03 -8.11296333e-04 -2.41533202e-02\n",
-      " -3.36108770e-02 -2.38544050e-02  5.53378084e-02  9.55530395e-03\n",
-      " -4.66810160e-02  5.28359212e-02 -2.35257650e-02 -3.31221558e-02\n",
-      " -1.58266031e-02 -3.42464115e-03 -3.46343219e-02  1.52099862e-02\n",
-      " -7.88798241e-03  1.06995996e-03  2.85725874e-02 -1.35334196e-02\n",
-      "  3.78525652e-02 -2.00320448e-02  2.94825286e-02 -3.25450298e-02\n",
-      "  5.85389136e-02  2.25576848e-02 -2.80708400e-02  3.55441554e-02]\n",
-      "  cov noise:  [[ 2.41576239e-05  3.93115398e-07  2.11726245e-05 ...  3.94014099e-07\n",
-      "  -1.46210329e-04 -1.90739779e-06]\n",
-      " [ 3.93115398e-07  2.44430569e-05  3.26396471e-07 ...  1.93662028e-05\n",
-      "   2.03336571e-06 -1.48802884e-04]\n",
-      " [ 2.11726245e-05  3.26396471e-07  1.85598880e-05 ...  3.30151810e-07\n",
-      "  -1.28343979e-04 -1.50063261e-06]\n",
-      " ...\n",
-      " [ 3.94014099e-07  1.93662028e-05  3.30151810e-07 ...  4.92465598e-05\n",
-      "   2.08255948e-06 -1.41276189e-04]\n",
-      " [-1.46210329e-04  2.03336571e-06 -1.28343979e-04 ...  2.08255948e-06\n",
-      "   1.04622425e-03 -1.36769915e-05]\n",
-      " [-1.90739779e-06 -1.48802884e-04 -1.50063261e-06 ... -1.41276189e-04\n",
-      "  -1.36769915e-05  1.07230229e-03]]\n",
-      "  mean:  [ 1.00261348e+00 -9.51967017e-03  9.97595398e-01  8.87821876e-02\n",
-      "  9.82977115e-01  1.86278120e-01  9.58886851e-01  2.81993315e-01\n",
-      "  9.25579469e-01  3.74942983e-01  8.83420456e-01  4.64174738e-01\n",
-      "  8.32847523e-01  5.48806245e-01  7.74360474e-01  6.28022789e-01\n",
-      "  7.08517998e-01  7.01070701e-01  6.35960369e-01  7.67268712e-01\n",
-      "  5.57427150e-01  8.26001934e-01  4.73748811e-01  8.76715747e-01\n",
-      "  3.85795111e-01  9.18926078e-01  2.94429761e-01  9.52249622e-01\n",
-      "  2.00501909e-01  9.76394900e-01  1.04847029e-01  9.91138051e-01\n",
-      "  8.30538374e-03  9.96337490e-01 -8.82777874e-02  9.91942500e-01\n",
-      " -1.84060839e-01  9.77984009e-01 -2.78179431e-01  9.54568288e-01\n",
-      " -3.69733905e-01  9.21902440e-01 -4.57839536e-01  8.80304524e-01\n",
-      " -5.41675148e-01  8.30182469e-01 -6.20475825e-01  7.72009876e-01\n",
-      " -6.93507841e-01  7.06314913e-01 -7.60073014e-01  6.33683678e-01\n",
-      " -8.19536888e-01  5.54790493e-01 -8.71324921e-01  4.70407033e-01\n",
-      " -9.14907055e-01  3.81389780e-01 -9.49804976e-01  2.88657171e-01\n",
-      " -9.75608502e-01  1.93152244e-01 -9.91986000e-01  9.58066149e-02\n",
-      " -9.98687678e-01 -2.47145992e-03 -9.95569359e-01 -1.00781606e-01\n",
-      " -9.82617233e-01 -1.98206907e-01 -9.59940478e-01 -2.93806624e-01\n",
-      " -9.27757241e-01 -3.86647186e-01 -8.86394332e-01 -4.75849533e-01\n",
-      " -8.36286441e-01 -5.60586813e-01 -7.77957525e-01 -6.40025791e-01\n",
-      " -7.11986545e-01 -7.13304171e-01 -6.39006562e-01 -7.79593386e-01\n",
-      " -5.59728810e-01 -8.38168453e-01 -4.74961169e-01 -8.88445705e-01\n",
-      " -3.85578151e-01 -9.29989627e-01 -2.92478406e-01 -9.62489077e-01\n",
-      " -1.96575225e-01 -9.85731361e-01 -9.87772517e-02 -9.99585329e-01\n",
-      "  1.56062326e-05 -1.00399555e+00  9.88734500e-02 -9.98983758e-01\n",
-      "  1.96836177e-01 -9.84639477e-01  2.92921898e-01 -9.61091045e-01\n",
-      "  3.86155956e-01 -9.28510734e-01  4.75594410e-01 -8.87140591e-01\n",
-      "  5.60351074e-01 -8.37333707e-01  6.39584642e-01 -7.79558606e-01\n",
-      "  7.12510391e-01 -7.14363091e-01  7.78409906e-01 -6.42347047e-01\n",
-      "  8.36643982e-01 -5.64159246e-01  8.86684469e-01 -4.80501591e-01\n",
-      "  9.28116121e-01 -3.92143784e-01  9.60627498e-01 -2.99938385e-01\n",
-      "  9.83992349e-01 -2.04805994e-01  9.98040078e-01 -1.07689505e-01]\n",
-      "  cov:  [[1.37039161e-02 1.13292827e-04 1.35686017e-02 ... 2.29645215e-04\n",
-      "  1.36485051e-02 1.68648343e-04]\n",
-      " [1.13292827e-04 1.55293937e-02 7.07321951e-05 ... 1.50452476e-02\n",
-      "  1.76409208e-04 1.54008164e-02]\n",
-      " [1.35686017e-02 7.07321951e-05 1.36676515e-02 ... 2.06000389e-04\n",
-      "  1.32878317e-02 1.34743019e-04]\n",
-      " ...\n",
-      " [2.29645215e-04 1.50452476e-02 2.06000389e-04 ... 1.54381045e-02\n",
-      "  2.79556126e-04 1.53572987e-02]\n",
-      " [1.36485051e-02 1.76409208e-04 1.32878317e-02 ... 2.79556126e-04\n",
-      "  1.38226300e-02 2.26066036e-04]\n",
-      " [1.68648343e-04 1.54008164e-02 1.34743019e-04 ... 1.53572987e-02\n",
-      "  2.26066036e-04 1.54973570e-02]]\n",
-      "  mean w/ noise:  [ 1.00337581 -0.00836707  0.99626473  0.08598517  0.98668985  0.18534165\n",
-      "  0.96012609  0.28403193  0.92833514  0.37521524  0.88681058  0.46412416\n",
-      "  0.83203276  0.54952873  0.77266963  0.6260503   0.70819486  0.69988942\n",
-      "  0.63490492  0.76920189  0.55887621  0.82673211  0.47273222  0.87603967\n",
-      "  0.39056635  0.91776344  0.29430017  0.95272389  0.20282878  0.97678886\n",
-      "  0.10322643  0.99038161  0.00967108  0.99560035 -0.08983333  0.99208556\n",
-      " -0.18335455  0.97741485 -0.27893948  0.95460051 -0.36840744  0.92016385\n",
-      " -0.460822    0.88042148 -0.54241313  0.83170651 -0.6177534   0.77369359\n",
-      " -0.69369607  0.70515936 -0.7591149   0.63236824 -0.8198315   0.55583878\n",
-      " -0.8715036   0.46832908 -0.91427403  0.38344672 -0.94917755  0.28489056\n",
-      " -0.97708252  0.19290343 -0.99067954  0.0950743  -0.99977766 -0.00113667\n",
-      " -0.99333042 -0.10180649 -0.98096791 -0.20148367 -0.96195313 -0.29433942\n",
-      " -0.92781872 -0.38613909 -0.88975105 -0.47557342 -0.8368383  -0.5600185\n",
-      " -0.7793553  -0.64162115 -0.71179029 -0.71457425 -0.64025566 -0.77856137\n",
-      " -0.55870938 -0.83776367 -0.47503576 -0.88643869 -0.38609155 -0.93138298\n",
-      " -0.29143561 -0.96282695 -0.19537366 -0.98501474 -0.100319   -1.00031704\n",
-      " -0.00175185 -1.00251748  0.09921932 -0.99993492  0.19727774 -0.98482944\n",
-      "  0.29350384 -0.9623801   0.38622579 -0.92942893  0.47246926 -0.88603802\n",
-      "  0.55997393 -0.83620364  0.6380178  -0.77749365  0.71380147 -0.71350003\n",
-      "  0.77805074 -0.64077353  0.83517443 -0.5635844   0.88785887 -0.48359876\n",
-      "  0.92848989 -0.39513181  0.96187162 -0.29999815  0.98416503 -0.20269151\n",
-      "  0.99599999 -0.10656691]\n",
-      "  cov w/ noise:  [[1.49217221e-02 2.11305456e-04 1.35508671e-02 ... 1.42097362e-05\n",
-      "  1.37723080e-02 3.57983336e-04]\n",
-      " [2.11305456e-04 1.70039747e-02 1.96682002e-04 ... 1.50677083e-02\n",
-      "  9.74195018e-05 1.55682688e-02]\n",
-      " [1.35508671e-02 1.96682002e-04 1.42629345e-02 ... 2.49807916e-04\n",
-      "  1.31696653e-02 4.61202977e-04]\n",
-      " ...\n",
-      " [1.42097362e-05 1.50677083e-02 2.49807916e-04 ... 1.60494712e-02\n",
-      "  1.50252753e-04 1.50675066e-02]\n",
-      " [1.37723080e-02 9.74195018e-05 1.31696653e-02 ... 1.50252753e-04\n",
-      "  1.49870834e-02 3.32047289e-04]\n",
-      " [3.57983336e-04 1.55682688e-02 4.61202977e-04 ... 1.50675066e-02\n",
-      "  3.32047289e-04 1.64797509e-02]]\n",
-      "  mean conditional:  [ 1.09361103 -0.06035009  1.10713033  0.03209805  1.11104495  0.12367812\n",
-      "  1.10273043  0.21447478  1.08038363  0.30452098  1.04347244  0.39384705\n",
-      "  0.99290466  0.48227203  0.93068078  0.56945308  0.8593927   0.65466433\n",
-      "  0.78153431  0.73681266  0.69906848  0.81443365  0.61326556  0.88574174\n",
-      "  0.52467461  0.94884712  0.43342702  1.00164134  0.33959757  1.04218082\n",
-      "  0.24308794  1.06877619  0.14410127  1.08028399  0.04280507  1.07622139\n",
-      " -0.06036495  1.05694031 -0.16471926  1.02356113 -0.26909928  0.97784789\n",
-      " -0.37180543  0.9217521  -0.47077191  0.85744658 -0.56376702  0.78683816\n",
-      " -0.64880124  0.71172509 -0.72440931  0.63338583 -0.78975098  0.55269859\n",
-      " -0.84468764  0.46988448 -0.88959571  0.38468194 -0.92497925  0.29622063\n",
-      " -0.9514376   0.20365075 -0.96943181  0.10617025 -0.97919164  0.00358556\n",
-      " -0.98071046 -0.10353816 -0.97387686 -0.21391353 -0.9583531  -0.32561035\n",
-      " -0.93384563 -0.43635149 -0.900302   -0.54385443 -0.85789079 -0.64605371\n",
-      " -0.807132   -0.741281   -0.74880196 -0.82832073 -0.68395562 -0.90644101\n",
-      " -0.61341165 -0.97517712 -0.537997   -1.03437988 -0.45811434 -1.08385487\n",
-      " -0.37378801 -1.12356539 -0.28468357 -1.15330011 -0.19014824 -1.17276893\n",
-      " -0.08971692 -1.18164796  0.01694131 -1.1793533   0.12914225 -1.16528\n",
-      "  0.24528913 -1.1386553   0.36252185 -1.09899506  0.47719796 -1.04636158\n",
-      "  0.58531253 -0.98142857  0.68329928 -0.90561623  0.76864385 -0.82106611\n",
-      "  0.84037609 -0.7301212   0.89906652 -0.63510645  0.94673524 -0.53817098\n",
-      "  0.98590342 -0.44082393  1.01915146 -0.34410257  1.0479491  -0.24841855\n",
-      "  1.07595868 -0.15338761] , sample:  [ 1.06018714e+00 -3.12443716e-02  1.14434441e+00  1.29117503e-02\n",
-      "  1.10376433e+00  1.11703106e-01  1.10641927e+00  2.13550906e-01\n",
-      "  1.13241385e+00  3.43063669e-01  1.05668853e+00  3.94138839e-01\n",
-      "  9.83225960e-01  4.41522550e-01  9.02363330e-01  5.60377140e-01\n",
-      "  8.74565181e-01  6.58283360e-01  7.71859111e-01  7.45269274e-01\n",
-      "  6.72839165e-01  8.24725463e-01  6.10141561e-01  8.55584013e-01\n",
-      "  5.81190408e-01  9.42975707e-01  4.32416850e-01  1.01618304e+00\n",
-      "  2.92200521e-01  1.03615637e+00  2.77262713e-01  1.08496053e+00\n",
-      "  1.46418695e-01  1.07833933e+00 -2.19963992e-03  1.13889208e+00\n",
-      " -6.27445682e-02  1.07731619e+00 -1.43128748e-01  9.68735425e-01\n",
-      " -2.14712017e-01  9.71404448e-01 -3.59895164e-01  9.07238987e-01\n",
-      " -4.86088443e-01  8.51254752e-01 -5.54853913e-01  7.38902459e-01\n",
-      " -7.09269859e-01  7.83405481e-01 -7.33917798e-01  6.34390027e-01\n",
-      " -7.70087957e-01  5.28427196e-01 -8.33189145e-01  4.78871669e-01\n",
-      " -8.92939485e-01  3.67350553e-01 -9.61101281e-01  2.85802425e-01\n",
-      " -9.30707271e-01  2.47803306e-01 -9.67122536e-01  1.15256500e-01\n",
-      " -9.56221048e-01 -6.29072752e-03 -9.53192219e-01 -1.13395876e-01\n",
-      " -1.01998033e+00 -1.80967977e-01 -9.67699791e-01 -3.48701179e-01\n",
-      " -9.10869024e-01 -4.61044047e-01 -9.15267466e-01 -5.40127575e-01\n",
-      " -8.64249925e-01 -6.30438317e-01 -8.05999841e-01 -7.51648258e-01\n",
-      " -7.37475302e-01 -8.18815466e-01 -6.78987938e-01 -9.70454497e-01\n",
-      " -6.13412030e-01 -9.19773081e-01 -5.25262231e-01 -9.94688156e-01\n",
-      " -4.67333627e-01 -1.09719814e+00 -3.72061259e-01 -1.14295137e+00\n",
-      " -2.94014659e-01 -1.14709469e+00 -1.47607401e-01 -1.18174076e+00\n",
-      " -1.22796342e-01 -1.20302735e+00  1.85959131e-04 -1.11766259e+00\n",
-      "  1.47392102e-01 -1.17748214e+00  2.04585076e-01 -1.18144604e+00\n",
-      "  3.53679833e-01 -1.08438660e+00  4.34859577e-01 -1.08329712e+00\n",
-      "  5.96250079e-01 -9.40450993e-01  7.16705267e-01 -9.23513968e-01\n",
-      "  7.86458018e-01 -8.06484743e-01  8.86231385e-01 -7.37173057e-01\n",
-      "  9.21908605e-01 -6.34054487e-01  9.58964086e-01 -5.24122698e-01\n",
-      "  9.66833618e-01 -4.60472351e-01  9.54939203e-01 -3.61306805e-01\n",
-      "  1.05088907e+00 -2.11894406e-01  1.06353139e+00 -1.75702763e-01]\n",
-      "  cov conditional:  [[ 2.24897783e-05 -7.43618654e-07  1.97280616e-05 ...  7.27397210e-08\n",
-      "  -1.35312498e-04 -1.91710260e-06]\n",
-      " [-7.43618654e-07  2.35129670e-05 -6.47250234e-07 ...  1.96501779e-05\n",
-      "   8.94700835e-06 -1.50635224e-04]\n",
-      " [ 1.97280616e-05 -6.47250234e-07  1.73090162e-05 ...  5.52755864e-08\n",
-      "  -1.18957748e-04 -1.72740045e-06]\n",
-      " ...\n",
-      " [ 7.27397210e-08  1.96501779e-05  5.52755864e-08 ...  5.00853727e-05\n",
-      "   2.19130736e-06 -1.46439799e-04]\n",
-      " [-1.35312498e-04  8.94700835e-06 -1.18957748e-04 ...  2.19130736e-06\n",
-      "   9.92954858e-04 -2.19544561e-05]\n",
-      " [-1.91710260e-06 -1.50635224e-04 -1.72740045e-06 ... -1.46439799e-04\n",
-      "  -2.19544561e-05  1.12251039e-03]]\n",
-      "  mean noise:  [ 3.34238904e-02 -2.91057185e-02 -3.72140815e-02  1.91862981e-02\n",
-      "  7.28062776e-03  1.19750187e-02 -3.68883763e-03  9.23876397e-04\n",
-      " -5.20302158e-02 -3.85426847e-02 -1.32160859e-02 -2.91793694e-04\n",
-      "  9.67869707e-03  4.07494846e-02  2.83174498e-02  9.07594410e-03\n",
-      " -1.51724768e-02 -3.61902689e-03  9.67520131e-03 -8.45661137e-03\n",
-      "  2.62293151e-02 -1.02918163e-02  3.12399909e-03  3.01577263e-02\n",
-      " -5.65157959e-02  5.87141350e-03  1.01016516e-03 -1.45416945e-02\n",
-      "  4.73970523e-02  6.02445289e-03 -3.41747691e-02 -1.61843378e-02\n",
-      " -2.31742829e-03  1.94465659e-03  4.50047137e-02 -6.26706970e-02\n",
-      "  2.37961854e-03 -2.03758824e-02 -2.15905098e-02  5.48257004e-02\n",
-      " -5.43872614e-02  6.44343997e-03 -1.19102711e-02  1.45131119e-02\n",
-      "  1.53165368e-02  6.19183124e-03 -8.91311158e-03  4.79356979e-02\n",
-      "  6.04686221e-02 -7.16803956e-02  9.50849110e-03 -1.00420144e-03\n",
-      " -1.96630192e-02  2.42713992e-02 -1.14985003e-02 -8.98718957e-03\n",
-      "  3.34377047e-03  1.73313831e-02  3.61220351e-02  1.04182097e-02\n",
-      " -2.07303253e-02 -4.41525532e-02 -2.30927320e-03 -9.08625349e-03\n",
-      " -2.29705906e-02  9.87628945e-03 -2.75182367e-02  9.85771635e-03\n",
-      "  4.61034770e-02 -3.29455535e-02  9.34669032e-03  2.30908279e-02\n",
-      " -2.29766016e-02  2.46925605e-02  1.49654687e-02 -3.72685678e-03\n",
-      "  6.35913594e-03 -1.56153976e-02 -1.13216261e-03  1.03672621e-02\n",
-      " -1.13266621e-02 -9.50526503e-03 -4.96768279e-03  6.40134843e-02\n",
-      "  3.75796590e-07 -5.54040351e-02 -1.27347685e-02 -3.96917215e-02\n",
-      "  9.21928670e-03  1.33432684e-02 -1.72675142e-03  1.93859888e-02\n",
-      "  9.33108714e-03 -6.20542066e-03 -4.25408382e-02  8.97182795e-03\n",
-      "  3.30794252e-02  2.13793896e-02  1.67553469e-02 -6.16907089e-02\n",
-      " -1.82498561e-02  1.22021408e-02  4.07040488e-02  4.27907470e-02\n",
-      "  8.84201228e-03 -1.46084664e-02  4.23383833e-02  3.69355404e-02\n",
-      " -1.09375470e-02 -4.09775724e-02 -3.34059852e-02  1.78977339e-02\n",
-      " -1.78141707e-02 -1.45813642e-02 -4.58552909e-02  7.05185313e-03\n",
-      " -2.28420885e-02 -1.05196215e-03 -1.22288463e-02 -1.40482845e-02\n",
-      "  1.90698006e-02  1.96484214e-02  6.42122552e-02  1.72042347e-02\n",
-      " -2.93996638e-03 -3.65241453e-02  1.24272922e-02  2.23151565e-02]\n",
-      "  cov noise:  [[ 2.24897783e-05 -7.43618654e-07  1.97280616e-05 ...  7.27397210e-08\n",
-      "  -1.35312498e-04 -1.91710260e-06]\n",
-      " [-7.43618654e-07  2.35129670e-05 -6.47250234e-07 ...  1.96501779e-05\n",
-      "   8.94700835e-06 -1.50635224e-04]\n",
-      " [ 1.97280616e-05 -6.47250234e-07  1.73090162e-05 ...  5.52755864e-08\n",
-      "  -1.18957748e-04 -1.72740045e-06]\n",
-      " ...\n",
-      " [ 7.27397210e-08  1.96501779e-05  5.52755864e-08 ...  5.00853727e-05\n",
-      "   2.19130736e-06 -1.46439799e-04]\n",
-      " [-1.35312498e-04  8.94700835e-06 -1.18957748e-04 ...  2.19130736e-06\n",
-      "   9.92954858e-04 -2.19544561e-05]\n",
-      " [-1.91710260e-06 -1.50635224e-04 -1.72740045e-06 ... -1.46439799e-04\n",
-      "  -2.19544561e-05  1.12251039e-03]]\n",
-      "  mean:  [ 0.99246319 -0.00391034  0.98731593  0.09456814  0.97271109  0.1920324\n",
-      "  0.94882272  0.2875582   0.91588259  0.380238    0.8741843   0.46919471\n",
-      "  0.82409321  0.55361109  0.76603803  0.63272087  0.7004978   0.70578467\n",
-      "  0.62802662  0.77209758  0.54929508  0.83099017  0.46509657  0.88183007\n",
-      "  0.37628924  0.92404812  0.28376157  0.957188    0.18845562  0.98093401\n",
-      "  0.09136121  0.99509383 -0.00652708  0.99957176 -0.10424701  0.99435794\n",
-      " -0.20086273  0.97951358 -0.29543812  0.95520156 -0.38703378  0.9216963\n",
-      " -0.47476265  0.87934606 -0.55782398  0.82856174 -0.63549524  0.76982478\n",
-      " -0.70710955  0.70366966 -0.77204532  0.63066619 -0.82973592  0.55145313\n",
-      " -0.87966902  0.46679172 -0.92138539  0.37756007 -0.95447432  0.28471801\n",
-      " -0.97858179  0.18926478 -0.99344575  0.09215557 -0.9988828  -0.0057353\n",
-      " -0.99477944 -0.10356658 -0.98111247 -0.20045925 -0.95795378 -0.29547864\n",
-      " -0.92547603 -0.38767074 -0.88394857 -0.4761107  -0.83376607 -0.55994574\n",
-      " -0.77547198 -0.63839586 -0.70970168 -0.71071381 -0.63712203 -0.77617961\n",
-      " -0.55840472 -0.83414969 -0.47426189 -0.8840939  -0.38547438 -0.92557112\n",
-      " -0.29287896 -0.95819239 -0.19738629 -0.98163551 -0.09996164 -0.99565926\n",
-      " -0.00158454 -1.00012348  0.09676923 -0.99502051  0.19414942 -0.98046904\n",
-      "  0.28962645 -0.95667431  0.38226354 -0.92391168  0.47110152 -0.8825087\n",
-      "  0.55520512 -0.83283203  0.63369864 -0.77528026  0.70581391 -0.71029465\n",
-      "  0.77090287 -0.63840404  0.82841985 -0.56023261  0.87788875 -0.4764872\n",
-      "  0.91889589 -0.38795095  0.95110116 -0.29547143  0.97423486 -0.1999689\n",
-      "  0.98807947 -0.10243299]\n",
-      "  cov:  [[ 0.01482419 -0.00113019  0.01473055 ... -0.00111073  0.01472404\n",
-      "  -0.00112625]\n",
-      " [-0.00113019  0.01389527 -0.00122258 ...  0.01361444 -0.00103792\n",
-      "   0.01385801]\n",
-      " [ 0.01473055 -0.00122258  0.01488734 ... -0.00117798  0.01438461\n",
-      "  -0.00120786]\n",
-      " ...\n",
-      " [-0.00111073  0.01361444 -0.00117798 ...  0.01418046 -0.00104469\n",
-      "   0.01400452]\n",
-      " [ 0.01472404 -0.00103792  0.01438461 ... -0.00104469  0.01487926\n",
-      "  -0.00104569]\n",
-      " [-0.00112625  0.01385801 -0.00120786 ...  0.01400452 -0.00104569\n",
-      "   0.01404041]]\n",
-      "  mean w/ noise:  [ 0.9948161  -0.00433102  0.98722415  0.09260437  0.9720221   0.19116816\n",
-      "  0.94925183  0.28910615  0.91395005  0.37875017  0.87571495  0.47016321\n",
-      "  0.82536702  0.55580523  0.76504526  0.633114    0.70365149  0.70656032\n",
-      "  0.62723504  0.77169281  0.5506454   0.83089368  0.4638563   0.88291336\n",
-      "  0.3788138   0.92325198  0.2849296   0.95698697  0.18934196  0.98087357\n",
-      "  0.08923311  0.99194445 -0.00584405  0.99910321 -0.1039949   0.99332671\n",
-      " -0.20184457  0.98001466 -0.29416644  0.95518827 -0.38706838  0.92016067\n",
-      " -0.47386409  0.87762911 -0.55944749  0.82826439 -0.63457754  0.7701227\n",
-      " -0.70572177  0.70474625 -0.77097262  0.6304827  -0.82982551  0.5527617\n",
-      " -0.8824998   0.46842578 -0.91942648  0.37585319 -0.95459544  0.28424754\n",
-      " -0.97765753  0.19067374 -0.9936494   0.09122086 -0.99817216 -0.00464804\n",
-      " -0.99648497 -0.10281835 -0.97851937 -0.20031442 -0.95873402 -0.2982911\n",
-      " -0.92614974 -0.38966569 -0.88436366 -0.4774221  -0.83340368 -0.56127609\n",
-      " -0.77526733 -0.63607874 -0.71156459 -0.71411045 -0.63591031 -0.77504272\n",
-      " -0.55460245 -0.83522816 -0.47358471 -0.88626077 -0.38560018 -0.92507513\n",
-      " -0.29051605 -0.95926096 -0.19536335 -0.98099906 -0.09940217 -0.9954448\n",
-      " -0.00222696 -1.00025594  0.09630478 -0.99736991  0.19444474 -0.98055371\n",
-      "  0.28876504 -0.95518174  0.37905773 -0.92508608  0.4714084  -0.8836095\n",
-      "  0.55584561 -0.83547436  0.63498645 -0.77461508  0.70713103 -0.71005466\n",
-      "  0.77147556 -0.63937228  0.83052674 -0.55827691  0.87898203 -0.47609533\n",
-      "  0.91736929 -0.38562929  0.95208901 -0.29279658  0.97726307 -0.20161312\n",
-      "  0.98769664 -0.10152727]\n",
-      "  cov w/ noise:  [[ 0.01656378 -0.00074235  0.01527896 ... -0.00115553  0.01533122\n",
-      "  -0.0008737 ]\n",
-      " [-0.00074235  0.01455511 -0.00113253 ...  0.01357748 -0.00061627\n",
-      "   0.01371252]\n",
-      " [ 0.01527896 -0.00113253  0.0162705  ... -0.00165629  0.01478864\n",
-      "  -0.00133009]\n",
-      " ...\n",
-      " [-0.00115553  0.01357748 -0.00165629 ...  0.0153977  -0.0009707\n",
-      "   0.01412903]\n",
-      " [ 0.01533122 -0.00061627  0.01478864 ... -0.0009707   0.01645732\n",
-      "  -0.0008236 ]\n",
-      " [-0.0008737   0.01371252 -0.00133009 ...  0.01412903 -0.0008236\n",
-      "   0.01512787]]\n",
-      "  mean conditional:  [ 1.00119597e+00 -1.58429530e-01  1.02986373e+00 -5.86448535e-02\n",
-      "  1.04761380e+00  4.04540391e-02  1.05125115e+00  1.38307596e-01\n",
-      "  1.03865273e+00  2.34264653e-01  1.00928857e+00  3.27711021e-01\n",
-      "  9.64254950e-01  4.17903878e-01  9.06051090e-01  5.04164140e-01\n",
-      "  8.37857723e-01  5.85689455e-01  7.62939020e-01  6.61617894e-01\n",
-      "  6.84074909e-01  7.31114423e-01  6.03199904e-01  7.93328451e-01\n",
-      "  5.21358703e-01  8.47490441e-01  4.38772219e-01  8.92699801e-01\n",
-      "  3.55299201e-01  9.28201047e-01  2.70272901e-01  9.53024175e-01\n",
-      "  1.83009674e-01  9.66316936e-01  9.28836792e-02  9.67374011e-01\n",
-      " -3.89313267e-04  9.55687324e-01 -9.65738969e-02  9.31161370e-01\n",
-      " -1.94589093e-01  8.94120223e-01 -2.92652923e-01  8.45279056e-01\n",
-      " -3.88584938e-01  7.85811119e-01 -4.80023692e-01  7.17254447e-01\n",
-      " -5.65025493e-01  6.41419451e-01 -6.42242527e-01  5.60043737e-01\n",
-      " -7.11034033e-01  4.74710665e-01 -7.71291774e-01  3.86418077e-01\n",
-      " -8.23168906e-01  2.95537443e-01 -8.66733699e-01  2.01758479e-01\n",
-      " -9.01887281e-01  1.04483187e-01 -9.28148486e-01  2.96524774e-03\n",
-      " -9.45022697e-01 -1.03198191e-01 -9.51902235e-01 -2.13789826e-01\n",
-      " -9.48306915e-01 -3.27902246e-01 -9.33937129e-01 -4.43994875e-01\n",
-      " -9.08654928e-01 -5.59984333e-01 -8.72761965e-01 -6.73573091e-01\n",
-      " -8.26618849e-01 -7.82452599e-01 -7.70985983e-01 -8.84402102e-01\n",
-      " -7.06755238e-01 -9.77550437e-01 -6.35073592e-01 -1.06032008e+00\n",
-      " -5.56926075e-01 -1.13152806e+00 -4.73524852e-01 -1.19047416e+00\n",
-      " -3.85905721e-01 -1.23671095e+00 -2.95056498e-01 -1.27042944e+00\n",
-      " -2.01788627e-01 -1.29207221e+00 -1.06685598e-01 -1.30230603e+00\n",
-      " -1.02920869e-02 -1.30194293e+00  8.68274610e-02 -1.29144994e+00\n",
-      "  1.83648987e-01 -1.27109874e+00  2.78743315e-01 -1.24054994e+00\n",
-      "  3.70093844e-01 -1.19940568e+00  4.55590776e-01 -1.14748405e+00\n",
-      "  5.33246901e-01 -1.08495842e+00  6.01947257e-01 -1.01252454e+00\n",
-      "  6.61640202e-01 -9.31633134e-01  7.13469525e-01 -8.43967669e-01\n",
-      "  7.59429686e-01 -7.51319082e-01  8.01966917e-01 -6.55363980e-01\n",
-      "  8.43143315e-01 -5.57395809e-01  8.84330134e-01 -4.58322300e-01\n",
-      "  9.25289385e-01 -3.58452940e-01  9.67883709e-01 -2.58677759e-01] , sample:  [ 1.00362739 -0.14668289  0.97422312 -0.0848055   1.01466542  0.02761806\n",
-      "  1.11231213  0.15679832  1.08324887  0.25301142  1.03649758  0.33851756\n",
-      "  0.9300333   0.3723047   0.89978101  0.51798166  0.85839388  0.55916724\n",
-      "  0.72897165  0.6944039   0.69504689  0.74589356  0.61649348  0.78992029\n",
-      "  0.51632921  0.81339132  0.42466478  0.93077468  0.31586787  0.89219737\n",
-      "  0.29012619  0.96407137  0.15811211  0.97946588  0.08465684  0.96846052\n",
-      "  0.07543062  0.91587595 -0.05276383  0.9445426  -0.21592498  0.92251786\n",
-      " -0.26403077  0.82547908 -0.42162317  0.79307847 -0.51985839  0.71821954\n",
-      " -0.55934177  0.67142028 -0.67765446  0.55068024 -0.68806     0.40769116\n",
-      " -0.78686704  0.43290253 -0.74196238  0.29489272 -0.89071835  0.17926341\n",
-      " -0.86827773  0.11943171 -0.99346322  0.03273643 -0.92996058 -0.13754322\n",
-      " -0.99064171 -0.21854703 -0.92222029 -0.29126042 -0.8952945  -0.4603237\n",
-      " -0.95407805 -0.56628359 -0.91219357 -0.66152776 -0.80974532 -0.81448034\n",
-      " -0.67701402 -0.86224072 -0.70694472 -0.99529371 -0.70914004 -1.03478295\n",
-      " -0.5401417  -1.14486747 -0.50002524 -1.14847884 -0.34510355 -1.29155708\n",
-      " -0.31467886 -1.29667963 -0.21085789 -1.29901152 -0.08031756 -1.29069371\n",
-      "  0.002149   -1.30507391  0.11472481 -1.24254108  0.13143583 -1.24591128\n",
-      "  0.22196977 -1.26339146  0.37072001 -1.23473103  0.53224217 -1.14132097\n",
-      "  0.53385126 -1.11789222  0.61359971 -0.98520461  0.68630032 -0.90286877\n",
-      "  0.69712521 -0.84385749  0.73045912 -0.79472307  0.75983132 -0.64460874\n",
-      "  0.84495005 -0.59589014  0.8762216  -0.42243451  0.92365006 -0.32870843\n",
-      "  1.00337669 -0.28782254]\n",
-      "  cov conditional:  [[ 2.24926642e-05 -7.46493870e-07  1.97268962e-05 ...  3.70672656e-07\n",
-      "  -1.31853698e-04 -7.22549924e-07]\n",
-      " [-7.46493870e-07  2.35075983e-05 -6.62515663e-07 ...  2.03632879e-05\n",
-      "   5.96660272e-06 -1.43237322e-04]\n",
-      " [ 1.97268962e-05 -6.62515663e-07  1.73046555e-05 ...  2.97372181e-07\n",
-      "  -1.15820689e-04 -5.89448381e-07]\n",
-      " ...\n",
-      " [ 3.70672656e-07  2.03632879e-05  2.97372181e-07 ...  4.85708548e-05\n",
-      "   2.51513418e-06 -1.46813287e-04]\n",
-      " [-1.31853698e-04  5.96660272e-06 -1.15820689e-04 ...  2.51513418e-06\n",
-      "   9.29647932e-04  4.69811052e-06]\n",
-      " [-7.22549924e-07 -1.43237322e-04 -5.89448381e-07 ... -1.46813287e-04\n",
-      "   4.69811052e-06  1.04433336e-03]]\n",
-      "  mean noise:  [-0.00243143 -0.01174664  0.05564061  0.02616064  0.03294838  0.01283598\n",
-      " -0.06106097 -0.01849072 -0.04459614 -0.01874677 -0.02720901 -0.01080654\n",
-      "  0.03422165  0.04559918  0.00627008 -0.01381752 -0.02053616  0.02652221\n",
-      "  0.03396737 -0.03278601 -0.01097198 -0.01477914 -0.01329357  0.00340816\n",
-      "  0.00502949  0.03409912  0.01410744 -0.03807488  0.03943133  0.03600367\n",
-      " -0.01985329 -0.0110472   0.02489757 -0.01314894  0.00822684 -0.00108651\n",
-      " -0.07581993  0.03981137 -0.04381006 -0.01338123  0.02133588 -0.02839763\n",
-      " -0.02862215  0.01979997  0.03303823 -0.00726735  0.0398347  -0.00096509\n",
-      " -0.00568372 -0.03000083  0.03541194  0.0093635  -0.02297403  0.0670195\n",
-      "  0.01557527 -0.04648445 -0.08120653  0.00064472  0.02398465  0.02249507\n",
-      " -0.03360955 -0.01494853  0.06531473 -0.02977118 -0.01506212  0.03434503\n",
-      "  0.03873948  0.00475721 -0.02608663 -0.03664183 -0.03864263  0.01632883\n",
-      "  0.04542312  0.00629926  0.03943161 -0.01204533 -0.01687353  0.03202774\n",
-      " -0.09397196 -0.02216139  0.00018948  0.01774327  0.07406644 -0.02553714\n",
-      " -0.01678437  0.01333941  0.02650039 -0.04199532 -0.04080217  0.05484613\n",
-      "  0.01962236  0.02625019  0.00906926  0.00693931 -0.02636804 -0.01161232\n",
-      " -0.01244109  0.00313098 -0.02789735 -0.04890886  0.05221315 -0.02518745\n",
-      "  0.05677354  0.02284152 -0.00062617  0.03532535 -0.07665139 -0.00616309\n",
-      " -0.00060435  0.0329338  -0.01165246 -0.02731993 -0.02466011 -0.02876437\n",
-      "  0.01634432 -0.00011018  0.02897057  0.04340398  0.0421356  -0.01075524\n",
-      " -0.00180673  0.03849433  0.00810853 -0.03588779  0.00163933 -0.02974451\n",
-      " -0.03549298  0.02914479]\n",
-      "  cov noise:  [[ 2.24926642e-05 -7.46493870e-07  1.97268962e-05 ...  3.70672656e-07\n",
-      "  -1.31853698e-04 -7.22549924e-07]\n",
-      " [-7.46493870e-07  2.35075983e-05 -6.62515663e-07 ...  2.03632879e-05\n",
-      "   5.96660272e-06 -1.43237322e-04]\n",
-      " [ 1.97268962e-05 -6.62515663e-07  1.73046555e-05 ...  2.97372181e-07\n",
-      "  -1.15820689e-04 -5.89448381e-07]\n",
-      " ...\n",
-      " [ 3.70672656e-07  2.03632879e-05  2.97372181e-07 ...  4.85708548e-05\n",
-      "   2.51513418e-06 -1.46813287e-04]\n",
-      " [-1.31853698e-04  5.96660272e-06 -1.15820689e-04 ...  2.51513418e-06\n",
-      "   9.29647932e-04  4.69811052e-06]\n",
-      " [-7.22549924e-07 -1.43237322e-04 -5.89448381e-07 ... -1.46813287e-04\n",
-      "   4.69811052e-06  1.04433336e-03]]\n",
-      "  mean:  [ 1.00722417e+00  6.33809232e-03  1.00139472e+00  1.04008220e-01\n",
-      "  9.85890415e-01  2.00659751e-01  9.60936033e-01  2.95438893e-01\n",
-      "  9.26807473e-01  3.87467390e-01  8.83816789e-01  4.75861357e-01\n",
-      "  8.32328407e-01  5.59771513e-01  7.72812820e-01  6.38402602e-01\n",
-      "  7.05881199e-01  7.10993095e-01  6.32276763e-01  7.76816841e-01\n",
-      "  5.52834487e-01  8.35207265e-01  4.68424021e-01  8.85564913e-01\n",
-      "  3.79884729e-01  9.27357767e-01  2.88012825e-01  9.60149743e-01\n",
-      "  1.93601240e-01  9.83623364e-01  9.74609620e-02  9.97571737e-01\n",
-      "  4.14106307e-04  1.00189997e+00 -9.66858712e-02  9.96618156e-01\n",
-      " -1.92942314e-01  9.81814483e-01 -2.87440597e-01  9.57643492e-01\n",
-      " -3.79265235e-01  9.24315377e-01 -4.67518059e-01  8.82101146e-01\n",
-      " -5.51331248e-01  8.31351144e-01 -6.29886539e-01  7.72503968e-01\n",
-      " -7.02438705e-01  7.06072909e-01 -7.68321450e-01  6.32653305e-01\n",
-      " -8.26939786e-01  5.52965339e-01 -8.77748459e-01  4.67865147e-01\n",
-      " -9.20250794e-01  3.78295574e-01 -9.54022505e-01  2.85236711e-01\n",
-      " -9.78716737e-01  1.89650447e-01 -9.94043895e-01  9.24543271e-02\n",
-      " -9.99781519e-01 -5.46667180e-03 -9.95798755e-01 -1.03233974e-01\n",
-      " -9.82070012e-01 -1.99941497e-01 -9.58691412e-01 -2.94648421e-01\n",
-      " -9.25871922e-01 -3.86413966e-01 -8.83924048e-01 -4.74346857e-01\n",
-      " -8.33269295e-01 -5.57630017e-01 -7.74453207e-01 -6.35494599e-01\n",
-      " -7.08114129e-01 -7.07186918e-01 -6.34940569e-01 -7.71986939e-01\n",
-      " -5.55669378e-01 -8.29253037e-01 -4.71073890e-01 -8.78449703e-01\n",
-      " -3.81953860e-01 -9.19147871e-01 -2.89145190e-01 -9.51003132e-01\n",
-      " -1.93553680e-01 -9.73755349e-01 -9.61380920e-02 -9.87228343e-01\n",
-      "  2.14648018e-03 -9.91341990e-01  1.00381690e-01 -9.86132669e-01\n",
-      "  1.97700116e-01 -9.71715667e-01  2.93276399e-01 -9.48220953e-01\n",
-      "  3.86274042e-01 -9.15783859e-01  4.75801980e-01 -8.74580852e-01\n",
-      "  5.60937835e-01 -8.24874143e-01  6.40769331e-01 -7.67031919e-01\n",
-      "  7.14430510e-01 -7.01537499e-01  7.81127882e-01 -6.28994294e-01\n",
-      "  8.40163482e-01 -5.50114215e-01  8.90951166e-01 -4.65714144e-01\n",
-      "  9.33015907e-01 -3.76693247e-01  9.65959580e-01 -2.83994704e-01\n",
-      "  9.89447765e-01 -1.88598895e-01  1.00324345e+00 -9.14980996e-02]\n",
-      "  cov:  [[ 0.01414019 -0.00086762  0.01405589 ... -0.0009498   0.0140286\n",
-      "  -0.00091097]\n",
-      " [-0.00086762  0.0146889  -0.00087746 ...  0.01426057 -0.00083955\n",
-      "   0.0145759 ]\n",
-      " [ 0.01405589 -0.00087746  0.01418386 ... -0.00093157  0.01373885\n",
-      "  -0.00090663]\n",
-      " ...\n",
-      " [-0.0009498   0.01426057 -0.00093157 ...  0.01472452 -0.00094631\n",
-      "   0.01459399]\n",
-      " [ 0.0140286  -0.00083955  0.01373885 ... -0.00094631  0.01412879\n",
-      "  -0.00089549]\n",
-      " [-0.00091097  0.0145759  -0.00090663 ...  0.01459399 -0.00089549\n",
-      "   0.01468987]]\n",
-      "  mean w/ noise:  [ 1.00924827e+00  6.69195361e-03  1.00420225e+00  1.05106431e-01\n",
-      "  9.83998719e-01  1.98768315e-01  9.61363957e-01  2.96030929e-01\n",
-      "  9.27391397e-01  3.86656301e-01  8.84875754e-01  4.75915512e-01\n",
-      "  8.30517557e-01  5.60025041e-01  7.73138464e-01  6.39910508e-01\n",
-      "  7.02031163e-01  7.11386782e-01  6.32778751e-01  7.77644082e-01\n",
-      "  5.52499439e-01  8.33700597e-01  4.66899172e-01  8.87052095e-01\n",
-      "  3.79350689e-01  9.24850424e-01  2.86877469e-01  9.61742146e-01\n",
-      "  1.92007183e-01  9.80899276e-01  1.00083865e-01  9.97316752e-01\n",
-      " -2.34926226e-04  1.00259147e+00 -1.00503859e-01  9.94465965e-01\n",
-      " -1.93100919e-01  9.80934872e-01 -2.85553962e-01  9.57140928e-01\n",
-      " -3.78083381e-01  9.21825240e-01 -4.67747572e-01  8.83522451e-01\n",
-      " -5.51825037e-01  8.28364069e-01 -6.29085527e-01  7.73395519e-01\n",
-      " -7.00543778e-01  7.04213257e-01 -7.68679745e-01  6.32112678e-01\n",
-      " -8.27163052e-01  5.53452334e-01 -8.77812085e-01  4.64944337e-01\n",
-      " -9.21959031e-01  3.80041477e-01 -9.52488702e-01  2.84730962e-01\n",
-      " -9.78198778e-01  1.91336613e-01 -9.93649492e-01  9.21822208e-02\n",
-      " -1.00095907e+00 -7.81189024e-03 -9.96621407e-01 -1.02681836e-01\n",
-      " -9.78885905e-01 -1.99695052e-01 -9.60076670e-01 -2.93879918e-01\n",
-      " -9.26227789e-01 -3.85955209e-01 -8.83542165e-01 -4.73803474e-01\n",
-      " -8.32360728e-01 -5.56984570e-01 -7.75405020e-01 -6.34360313e-01\n",
-      " -7.07387589e-01 -7.08801656e-01 -6.35705682e-01 -7.74245436e-01\n",
-      " -5.56552801e-01 -8.27722610e-01 -4.71071159e-01 -8.78389785e-01\n",
-      " -3.84495663e-01 -9.19278907e-01 -2.87724064e-01 -9.48593595e-01\n",
-      " -1.93456824e-01 -9.73048573e-01 -9.26875254e-02 -9.87539880e-01\n",
-      "  4.45668453e-03 -9.92244986e-01  9.98922829e-02 -9.85421771e-01\n",
-      "  1.98098631e-01 -9.74167217e-01  2.92477082e-01 -9.46242068e-01\n",
-      "  3.86921357e-01 -9.13645473e-01  4.75954491e-01 -8.76091583e-01\n",
-      "  5.57803710e-01 -8.23897502e-01  6.38933409e-01 -7.66616597e-01\n",
-      "  7.12228691e-01 -7.01952090e-01  7.80557781e-01 -6.29831821e-01\n",
-      "  8.41743325e-01 -5.50296606e-01  8.91083250e-01 -4.65670828e-01\n",
-      "  9.32118625e-01 -3.76649469e-01  9.64784690e-01 -2.84945758e-01\n",
-      "  9.89123618e-01 -1.90572985e-01  1.00446020e+00 -9.09027234e-02]\n",
-      "  cov w/ noise:  [[ 0.01482719 -0.00095524  0.01379952 ... -0.00091636  0.01430181\n",
-      "  -0.00114666]\n",
-      " [-0.00095524  0.01532157 -0.00097147 ...  0.014215   -0.00077892\n",
-      "   0.01451343]\n",
-      " [ 0.01379952 -0.00097147  0.01485821 ... -0.00077185  0.01408759\n",
-      "  -0.00110382]\n",
-      " ...\n",
-      " [-0.00091636  0.014215   -0.00077185 ...  0.01580471 -0.00081803\n",
-      "   0.01472685]\n",
-      " [ 0.01430181 -0.00077892  0.01408759 ... -0.00081803  0.01587085\n",
-      "  -0.001038  ]\n",
-      " [-0.00114666  0.01451343 -0.00110382 ...  0.01472685 -0.001038\n",
-      "   0.01568421]]\n",
-      "  mean conditional:  [ 0.94879011  0.02948353  0.94378764  0.12256511  0.92866063  0.21293583\n",
-      "  0.90290531  0.30009999  0.86647448  0.38347284  0.81996321  0.46247743\n",
-      "  0.76469217  0.53652231  0.70236874  0.60507869  0.63473037  0.66771045\n",
-      "  0.56318167  0.72398326  0.48853856  0.77360551  0.41101267  0.8162794\n",
-      "  0.33052681  0.85175043  0.24701077  0.87953653  0.1608369   0.89932195\n",
-      "  0.07268753  0.91051231 -0.01621651  0.91263137 -0.10460769  0.90519996\n",
-      " -0.19114122  0.8880078  -0.27465729  0.86109908 -0.35406959  0.82477967\n",
-      " -0.42844919  0.77960897 -0.49711852  0.72644379 -0.55962468  0.6661944\n",
-      " -0.61589288  0.59992888 -0.66619492  0.52858928 -0.71103742  0.45293318\n",
-      " -0.75110808  0.3733226  -0.78692178  0.28978578 -0.81878724  0.20194328\n",
-      " -0.84665584  0.10939506 -0.87012717  0.01184827 -0.88849711 -0.09061841\n",
-      " -0.90082169 -0.19723251 -0.90586811 -0.30674519 -0.90229888 -0.41726292\n",
-      " -0.88863424 -0.52657277 -0.86377574 -0.63240297 -0.82692537 -0.73262649\n",
-      " -0.77803611 -0.825114   -0.71782084 -0.9082     -0.6477322  -0.98052072\n",
-      " -0.56967369 -1.04103462 -0.48576453 -1.08916197 -0.39804839 -1.12468049\n",
-      " -0.30820446 -1.14793313 -0.21735983 -1.15960581 -0.12610043 -1.16060349\n",
-      " -0.03455558 -1.15198698  0.05748278 -1.13441396  0.14999631 -1.10827652\n",
-      "  0.24273035 -1.07328905  0.33482924 -1.02882571  0.42495274 -0.97431302\n",
-      "  0.51145665 -0.90938116  0.59258851 -0.83438436  0.66679129 -0.75046057\n",
-      "  0.73294935 -0.65924734  0.79036489 -0.56289914  0.83877455 -0.46350879\n",
-      "  0.87821453 -0.36294314  0.90890742 -0.26259149  0.93053593 -0.16347533\n",
-      "  0.94628109 -0.06592167] , sample:  [ 0.94700009  0.02775202  0.92626781  0.09702039  0.90144253  0.21223945\n",
-      "  0.92333675  0.30550765  0.84842253  0.4033439   0.81611917  0.42248667\n",
-      "  0.79102699  0.5649395   0.66833288  0.61721859  0.67515346  0.68089415\n",
-      "  0.50577969  0.71754668  0.46986499  0.76264529  0.42711687  0.79334363\n",
-      "  0.38775739  0.84820099  0.24290241  0.91606779  0.18155821  0.89635629\n",
-      "  0.07214284  0.91787713 -0.05726796  0.84923093 -0.14988993  0.93089868\n",
-      " -0.13975319  0.8979626  -0.2641705   0.89107077 -0.39435355  0.7612109\n",
-      " -0.41866639  0.80171415 -0.51587782  0.72985763 -0.55511879  0.67712509\n",
-      " -0.5936206   0.57451668 -0.6711619   0.51627924 -0.69265513  0.43527848\n",
-      " -0.73981757  0.4628559  -0.80620892  0.24574459 -0.81923731  0.19773893\n",
-      " -0.83444669  0.12733893 -0.8725621  -0.00881701 -0.91411152 -0.08888181\n",
-      " -0.88583658 -0.15433322 -0.89528584 -0.35846454 -0.8689529  -0.42029854\n",
-      " -0.86823851 -0.49944481 -0.87908368 -0.64245166 -0.88851091 -0.77490143\n",
-      " -0.78709988 -0.77927959 -0.73780347 -0.90447705 -0.62118151 -0.98306486\n",
-      " -0.5451867  -1.03911962 -0.4852419  -1.07929287 -0.40777429 -1.18350049\n",
-      " -0.30851962 -1.13165828 -0.18868794 -1.14706732 -0.12693866 -1.18745269\n",
-      " -0.0548869  -1.1472736   0.0521904  -1.14413372  0.1762714  -1.05224251\n",
-      "  0.26416819 -1.05842904  0.29825184 -1.00424138  0.43501411 -0.98903074\n",
-      "  0.49164208 -0.94105745  0.60477285 -0.88146368  0.62624273 -0.74443228\n",
-      "  0.68980892 -0.63334253  0.80608364 -0.54957958  0.84716417 -0.4997668\n",
-      "  0.93585372 -0.36799864  0.93232584 -0.22759241  0.9302273  -0.13898842\n",
-      "  0.94140111 -0.07449206]\n",
-      "  cov conditional:  [[ 2.24884725e-05 -7.46335637e-07  1.97282369e-05 ...  6.02324607e-07\n",
-      "  -1.34925574e-04  3.99695877e-06]\n",
-      " [-7.46335637e-07  2.35200098e-05 -6.55231013e-07 ...  2.17610400e-05\n",
-      "  -9.76425356e-07 -1.43224801e-04]\n",
-      " [ 1.97282369e-05 -6.55231013e-07  1.73103520e-05 ...  5.03932408e-07\n",
-      "  -1.18532116e-04  3.58034426e-06]\n",
-      " ...\n",
-      " [ 6.02324607e-07  2.17610400e-05  5.03932408e-07 ...  5.44208177e-05\n",
-      "  -5.41398873e-06 -1.53013381e-04]\n",
-      " [-1.34925574e-04 -9.76425356e-07 -1.18532116e-04 ... -5.41398873e-06\n",
-      "   9.73681804e-04  1.24009444e-06]\n",
-      " [ 3.99695877e-06 -1.43224801e-04  3.58034426e-06 ... -1.53013381e-04\n",
-      "   1.24009444e-06  1.04939825e-03]]\n",
-      "  mean noise:  [ 0.00179002  0.00173151  0.01751983  0.02554472  0.0272181   0.00069638\n",
-      " -0.02043143 -0.00540766  0.01805195 -0.01987106  0.00384405  0.03999076\n",
-      " -0.02633482 -0.02841719  0.03403586 -0.0121399  -0.04042309 -0.0131837\n",
-      "  0.05740197  0.00643658  0.01867357  0.01096023 -0.0161042   0.02293577\n",
-      " -0.05723058  0.00354945  0.00410836 -0.03653126 -0.02072131  0.00296566\n",
-      "  0.00054469 -0.00736482  0.04105145  0.06340044  0.04528224 -0.02569872\n",
-      " -0.05138803 -0.0099548  -0.01048678 -0.02997169  0.04028396  0.06356878\n",
-      " -0.0097828  -0.02210518  0.0187593  -0.00341384 -0.00450589 -0.01093069\n",
-      " -0.02227228  0.0254122   0.00496698  0.01231005 -0.0183823   0.0176547\n",
-      " -0.01129052 -0.0895333   0.01928714  0.04404119  0.00045007  0.00420435\n",
-      " -0.01220915 -0.01794388  0.00243492  0.02066528  0.02561441 -0.0017366\n",
-      " -0.01498511 -0.04289929 -0.01058227  0.05171936 -0.03334597  0.00303561\n",
-      " -0.02039573 -0.02712796  0.01530794  0.01004868  0.06158554  0.04227494\n",
-      "  0.00906377 -0.04583441  0.01998263 -0.00372295 -0.02655069  0.00254414\n",
-      " -0.02448699 -0.00191499 -0.00052264 -0.0098691   0.00972589  0.05882\n",
-      "  0.00031516 -0.01627485 -0.02867189 -0.01253849  0.00083823  0.0268492\n",
-      "  0.02033131 -0.00471338  0.00529237  0.00971976 -0.02627509 -0.05603402\n",
-      " -0.02143784 -0.01486001  0.03657739 -0.02458433 -0.01006137  0.01471772\n",
-      "  0.01981456  0.03167629 -0.01218434  0.04707932  0.04054855 -0.00602829\n",
-      "  0.04314043 -0.0259048  -0.01571875 -0.01331955 -0.00838962  0.03625801\n",
-      " -0.0576392   0.0050555  -0.02341842 -0.03499908  0.00030863 -0.02448691\n",
-      "  0.00487998  0.00857039]\n",
-      "  cov noise:  [[ 2.24884725e-05 -7.46335637e-07  1.97282369e-05 ...  6.02324607e-07\n",
-      "  -1.34925574e-04  3.99695877e-06]\n",
-      " [-7.46335637e-07  2.35200098e-05 -6.55231013e-07 ...  2.17610400e-05\n",
-      "  -9.76425356e-07 -1.43224801e-04]\n",
-      " [ 1.97282369e-05 -6.55231013e-07  1.73103520e-05 ...  5.03932408e-07\n",
-      "  -1.18532116e-04  3.58034426e-06]\n",
-      " ...\n",
-      " [ 6.02324607e-07  2.17610400e-05  5.03932408e-07 ...  5.44208177e-05\n",
-      "  -5.41398873e-06 -1.53013381e-04]\n",
-      " [-1.34925574e-04 -9.76425356e-07 -1.18532116e-04 ... -5.41398873e-06\n",
-      "   9.73681804e-04  1.24009444e-06]\n",
-      " [ 3.99695877e-06 -1.43224801e-04  3.58034426e-06 ... -1.53013381e-04\n",
-      "   1.24009444e-06  1.04939825e-03]]\n",
-      "  mean:  [ 1.00493584e+00 -4.92184873e-05  1.00021313e+00  9.71845434e-02\n",
-      "  9.85890785e-01  1.93479902e-01  9.62125804e-01  2.87989998e-01\n",
-      "  9.29155206e-01  3.79839255e-01  8.87290315e-01  4.68152342e-01\n",
-      "  8.36910769e-01  5.52098722e-01  7.78474364e-01  6.30897990e-01\n",
-      "  7.12522759e-01  7.03800620e-01  6.39701058e-01  7.70091222e-01\n",
-      "  5.60777437e-01  8.29087361e-01  4.76607509e-01  8.80157375e-01\n",
-      "  3.88071659e-01  9.22761529e-01  2.96035140e-01  9.56471068e-01\n",
-      "  2.01356911e-01  9.80961359e-01  1.04895066e-01  9.95988109e-01\n",
-      "  7.50329814e-03  1.00138826e+00 -8.99487850e-02  9.97088984e-01\n",
-      " -1.86557038e-01  9.83112189e-01 -2.81380907e-01  9.59596108e-01\n",
-      " -3.73445177e-01  9.26805369e-01 -4.61796102e-01  8.85099890e-01\n",
-      " -5.45556465e-01  8.34889575e-01 -6.23930893e-01  7.76624485e-01\n",
-      " -6.96206048e-01  7.10806183e-01 -7.61744318e-01  6.38013867e-01\n",
-      " -8.19992886e-01  5.58938235e-01 -8.70450908e-01  4.74398433e-01\n",
-      " -9.12662331e-01  3.85311358e-01 -9.46245033e-01  2.92630810e-01\n",
-      " -9.70895350e-01  1.97315125e-01 -9.86371987e-01  1.00297996e-01\n",
-      " -9.92465783e-01  2.47709916e-03 -9.89014534e-01 -9.52800942e-02\n",
-      " -9.75940603e-01 -1.92097000e-01 -9.53284667e-01 -2.87068973e-01\n",
-      " -9.21229976e-01 -3.79275161e-01 -8.80101366e-01 -4.67789427e-01\n",
-      " -8.30343871e-01 -5.51708550e-01 -7.72487677e-01 -6.30180428e-01\n",
-      " -7.07101722e-01 -7.02421068e-01 -6.34796437e-01 -7.67720258e-01\n",
-      " -5.56223710e-01 -8.25469965e-01 -4.72087346e-01 -8.75179216e-01\n",
-      " -3.83175409e-01 -9.16451740e-01 -2.90367461e-01 -9.48956054e-01\n",
-      " -1.94631874e-01 -9.72431466e-01 -9.69773857e-02 -9.86680648e-01\n",
-      "  1.60666655e-03 -9.91566456e-01  1.00162045e-01 -9.87026842e-01\n",
-      "  1.97743835e-01 -9.73084271e-01  2.93406907e-01 -9.49851000e-01\n",
-      "  3.86217917e-01 -9.17527491e-01  4.75257877e-01 -8.76384721e-01\n",
-      "  5.59652543e-01 -8.26755671e-01  6.38593307e-01 -7.69053992e-01\n",
-      "  7.11349418e-01 -7.03788145e-01  7.77261230e-01 -6.31575130e-01\n",
-      "  8.35727623e-01 -5.53133238e-01  8.86220986e-01 -4.69261991e-01\n",
-      "  9.28279580e-01 -3.80818365e-01  9.61478144e-01 -2.88715478e-01\n",
-      "  9.85466160e-01 -1.93911614e-01  1.00000146e+00 -9.73763618e-02]\n",
-      "  cov:  [[0.01327986 0.00026144 0.01315578 ... 0.00026392 0.01319642 0.00026474]\n",
-      " [0.00026144 0.01406127 0.00024654 ... 0.0137571  0.00024681 0.01401708]\n",
-      " [0.01315578 0.00024654 0.01325778 ... 0.0002644  0.01285358 0.00025741]\n",
-      " ...\n",
-      " [0.00026392 0.0137571  0.0002644  ... 0.01427156 0.00023512 0.01412297]\n",
-      " [0.01319642 0.00024681 0.01285358 ... 0.00023512 0.01333908 0.00024309]\n",
-      " [0.00026474 0.01401708 0.00025741 ... 0.01412297 0.00024309 0.01418229]]\n",
-      "  mean w/ noise:  [ 1.00445801e+00 -7.73385001e-04  1.00119024e+00  9.91064927e-02\n",
-      "  9.83523058e-01  1.92471342e-01  9.63159164e-01  2.85448315e-01\n",
-      "  9.30934344e-01  3.80543766e-01  8.88685337e-01  4.67100995e-01\n",
-      "  8.37094819e-01  5.51914284e-01  7.80201977e-01  6.30565477e-01\n",
-      "  7.12773971e-01  7.04948840e-01  6.40743731e-01  7.69024009e-01\n",
-      "  5.61352463e-01  8.30231744e-01  4.76842831e-01  8.80614989e-01\n",
-      "  3.87275885e-01  9.23331982e-01  2.94785616e-01  9.56431575e-01\n",
-      "  1.99379871e-01  9.80981041e-01  1.03582089e-01  9.96052445e-01\n",
-      "  4.63753805e-03  1.00209086e+00 -9.00486262e-02  9.99775685e-01\n",
-      " -1.84953740e-01  9.81931783e-01 -2.79731631e-01  9.59047982e-01\n",
-      " -3.72741885e-01  9.27180588e-01 -4.61466209e-01  8.82681616e-01\n",
-      " -5.47561937e-01  8.34620551e-01 -6.24040984e-01  7.76554678e-01\n",
-      " -6.99404211e-01  7.09387097e-01 -7.61307585e-01  6.40206571e-01\n",
-      " -8.20460288e-01  5.58631130e-01 -8.71290003e-01  4.74466900e-01\n",
-      " -9.14544641e-01  3.83077429e-01 -9.46285625e-01  2.94086362e-01\n",
-      " -9.70424183e-01  1.96926802e-01 -9.86010266e-01  1.03239909e-01\n",
-      " -9.94081396e-01  3.65486248e-03 -9.91491243e-01 -9.48394588e-02\n",
-      " -9.75663915e-01 -1.92175387e-01 -9.53131226e-01 -2.86132292e-01\n",
-      " -9.17997421e-01 -3.79414288e-01 -8.79448543e-01 -4.65978817e-01\n",
-      " -8.32373162e-01 -5.52650029e-01 -7.70437298e-01 -6.30991263e-01\n",
-      " -7.08511871e-01 -7.03271933e-01 -6.34260172e-01 -7.68499278e-01\n",
-      " -5.56826225e-01 -8.26520500e-01 -4.70236372e-01 -8.74733801e-01\n",
-      " -3.83317138e-01 -9.16058777e-01 -2.90595861e-01 -9.49254639e-01\n",
-      " -1.92481362e-01 -9.74489116e-01 -9.67340664e-02 -9.87576224e-01\n",
-      "  2.73175069e-03 -9.94013851e-01  9.83215612e-02 -9.88979772e-01\n",
-      "  1.95478331e-01 -9.72865858e-01  2.94700294e-01 -9.50601943e-01\n",
-      "  3.85608381e-01 -9.15880991e-01  4.76868304e-01 -8.76018396e-01\n",
-      "  5.60106697e-01 -8.28283849e-01  6.38910676e-01 -7.72629635e-01\n",
-      "  7.11209457e-01 -7.04372758e-01  7.77505539e-01 -6.32251173e-01\n",
-      "  8.36060531e-01 -5.52511984e-01  8.84384211e-01 -4.68818021e-01\n",
-      "  9.29153670e-01 -3.79291923e-01  9.62266873e-01 -2.87378150e-01\n",
-      "  9.85194884e-01 -1.92665890e-01  9.98690593e-01 -9.81256207e-02]\n",
-      "  cov w/ noise:  [[1.43870515e-02 4.34728438e-04 1.35594313e-02 ... 3.49074498e-05\n",
-      "  1.32507413e-02 3.18611258e-04]\n",
-      " [4.34728438e-04 1.51976634e-02 4.73890488e-04 ... 1.37568192e-02\n",
-      "  7.99328575e-04 1.43633372e-02]\n",
-      " [1.35594313e-02 4.73890488e-04 1.47663406e-02 ... 4.21953450e-05\n",
-      "  1.31227742e-02 4.72948516e-04]\n",
-      " ...\n",
-      " [3.49074498e-05 1.37568192e-02 4.21953450e-05 ... 1.48916122e-02\n",
-      "  2.82420250e-04 1.43081385e-02]\n",
-      " [1.32507413e-02 7.99328575e-04 1.31227742e-02 ... 2.82420250e-04\n",
-      "  1.43236051e-02 5.67508151e-04]\n",
-      " [3.18611258e-04 1.43633372e-02 4.72948516e-04 ... 1.43081385e-02\n",
-      "  5.67508151e-04 1.57287249e-02]]\n",
-      "  mean conditional:  [ 1.09964197e+00  8.80511384e-02  1.09100973e+00  1.75324357e-01\n",
-      "  1.07161887e+00  2.61675247e-01  1.03934288e+00  3.47297482e-01\n",
-      "  9.92981556e-01  4.32065778e-01  9.32669452e-01  5.15627397e-01\n",
-      "  8.60027966e-01  5.97376698e-01  7.77811227e-01  6.76517667e-01\n",
-      "  6.89215647e-01  7.52126673e-01  5.97234825e-01  8.23187355e-01\n",
-      "  5.04066357e-01  8.88802503e-01  4.10925589e-01  9.48142243e-01\n",
-      "  3.18202331e-01  1.00057098e+00  2.25756738e-01  1.04542062e+00\n",
-      "  1.33490053e-01  1.08204094e+00  4.12557211e-02  1.10956600e+00\n",
-      " -5.06902125e-02  1.12691782e+00 -1.42004356e-01  1.13299696e+00\n",
-      " -2.32109950e-01  1.12674179e+00 -3.20440992e-01  1.10729743e+00\n",
-      " -4.06107648e-01  1.07416850e+00 -4.88073841e-01  1.02725408e+00\n",
-      " -5.65156392e-01  9.67066917e-01 -6.36141016e-01  8.94620984e-01\n",
-      " -7.00001517e-01  8.11598483e-01 -7.56019018e-01  7.20038624e-01\n",
-      " -8.03876091e-01  6.22202160e-01 -8.43689992e-01  5.20190984e-01\n",
-      " -8.75824330e-01  4.15733661e-01 -9.00692974e-01  3.09805132e-01\n",
-      " -9.18805248e-01  2.02962525e-01 -9.30469500e-01  9.51939899e-02\n",
-      " -9.35853612e-01 -1.36075967e-02 -9.34946853e-01 -1.23488634e-01\n",
-      " -9.27417728e-01 -2.34132678e-01 -9.12742193e-01 -3.44864028e-01\n",
-      " -8.90026351e-01 -4.54551949e-01 -8.58482073e-01 -5.61710498e-01\n",
-      " -8.17139791e-01 -6.64637282e-01 -7.65409552e-01 -7.61410254e-01\n",
-      " -7.02954965e-01 -8.50240643e-01 -6.29959892e-01 -9.29525386e-01\n",
-      " -5.46900924e-01 -9.98056447e-01 -4.54760872e-01 -1.05512333e+00\n",
-      " -3.54610677e-01 -1.10038877e+00 -2.47759277e-01 -1.13397507e+00\n",
-      " -1.35476899e-01 -1.15611538e+00 -1.89802124e-02 -1.16692949e+00\n",
-      "  1.00431508e-01 -1.16645236e+00  2.21401063e-01 -1.15420437e+00\n",
-      "  3.42083022e-01 -1.12945621e+00  4.60333493e-01 -1.09126452e+00\n",
-      "  5.73346313e-01 -1.03890761e+00  6.78187931e-01 -9.72393874e-01\n",
-      "  7.72078071e-01 -8.92600107e-01  8.52966051e-01 -8.01496272e-01\n",
-      "  9.19861311e-01 -7.01982532e-01  9.72983955e-01 -5.97453986e-01\n",
-      "  1.01368033e+00 -4.91197271e-01  1.04410219e+00 -3.86121900e-01\n",
-      "  1.06649972e+00 -2.84068039e-01  1.08268501e+00 -1.86048577e-01\n",
-      "  1.09331992e+00 -9.19501697e-02  1.10291049e+00 -6.03221968e-04] , sample:  [ 1.08952825  0.03807054  1.09860132  0.19510336  1.01901105  0.29467704\n",
-      "  1.04174823  0.34798695  1.03540363  0.43195161  1.00344057  0.49293535\n",
-      "  0.89555332  0.5885413   0.7367554   0.69682258  0.60024949  0.71033958\n",
-      "  0.61364524  0.85779382  0.47781675  0.88362087  0.40013877  0.99826115\n",
-      "  0.34762125  1.02839721  0.26809147  0.98174778  0.14502748  1.04077262\n",
-      "  0.02318674  1.12648848 -0.0954551   1.13857856 -0.13700879  1.14795103\n",
-      " -0.21415204  1.12909746 -0.33512197  1.08030422 -0.37956631  1.09061473\n",
-      " -0.55233165  1.002212   -0.49837454  0.98744829 -0.64127433  0.92479652\n",
-      " -0.69319924  0.81793584 -0.79055595  0.72827318 -0.77630738  0.61556016\n",
-      " -0.86544823  0.56829856 -0.90258162  0.34798242 -0.86160419  0.30751248\n",
-      " -0.91801347  0.19521066 -0.96897153  0.11258902 -0.89857296 -0.01721416\n",
-      " -0.93101542 -0.15201093 -0.97000378 -0.23740772 -0.84778726 -0.32637889\n",
-      " -0.91194742 -0.45354583 -0.83237459 -0.55112167 -0.84012726 -0.63080305\n",
-      " -0.76564349 -0.73879013 -0.70502845 -0.89195941 -0.63262553 -0.96640658\n",
-      " -0.56932991 -0.96740525 -0.44853873 -1.10652793 -0.35006851 -1.05435896\n",
-      " -0.26760571 -1.17858502 -0.11222912 -1.13744923  0.01567849 -1.1283073\n",
-      "  0.0347152  -1.13802779  0.23030932 -1.18178968  0.39933043 -1.12000008\n",
-      "  0.43171947 -1.10836366  0.54117673 -1.07545688  0.69881299 -0.950572\n",
-      "  0.77183368 -0.89469182  0.874662   -0.8358965   0.96599662 -0.70891712\n",
-      "  0.94774731 -0.58509219  0.97909148 -0.50870511  1.08706777 -0.30437716\n",
-      "  1.06975133 -0.26654145  1.0328485  -0.19206786  1.11950994 -0.08540069\n",
-      "  1.10321828 -0.02930484]\n",
-      "  cov conditional:  [[ 2.24821565e-05 -7.48087038e-07  1.97341401e-05 ...  1.83514607e-07\n",
-      "  -1.42259570e-04  7.99819116e-06]\n",
-      " [-7.48087038e-07  2.35130816e-05 -6.56529977e-07 ...  2.13933706e-05\n",
-      "   1.86783557e-06 -1.40686188e-04]\n",
-      " [ 1.97341401e-05 -6.56529977e-07  1.73255662e-05 ...  1.27804562e-07\n",
-      "  -1.25073827e-04  7.05971050e-06]\n",
-      " ...\n",
-      " [ 1.83514607e-07  2.13933706e-05  1.27804562e-07 ...  5.21994926e-05\n",
-      "   5.80089846e-06 -1.48165220e-04]\n",
-      " [-1.42259570e-04  1.86783557e-06 -1.25073827e-04 ...  5.80089846e-06\n",
-      "   1.05393194e-03 -4.19169453e-05]\n",
-      " [ 7.99819116e-06 -1.40686188e-04  7.05971050e-06 ... -1.48165220e-04\n",
-      "  -4.19169453e-05  9.97934049e-04]]\n",
-      "  mean noise:  [ 0.01011372  0.0499806  -0.00759159 -0.019779    0.05260783 -0.0330018\n",
-      " -0.00240535 -0.00068947 -0.04242208  0.00011417 -0.07077112  0.02269204\n",
-      " -0.03552536  0.0088354   0.04105583 -0.02030491  0.08896616  0.04178709\n",
-      " -0.01641041 -0.03460646  0.02624961  0.00518164  0.01078681 -0.0501189\n",
-      " -0.02941892 -0.02782623 -0.04233473  0.06367284 -0.01153742  0.04126832\n",
-      "  0.01806898 -0.01692249  0.04476489 -0.01166074 -0.00499556 -0.01495407\n",
-      " -0.01795791 -0.00235568  0.01468098  0.02699321 -0.02654134 -0.01644623\n",
-      "  0.0642578   0.02504208 -0.06678185 -0.02038137  0.00513331 -0.03017554\n",
-      " -0.00680228 -0.00633736  0.03453693 -0.00823456 -0.02756871  0.006642\n",
-      "  0.02175824 -0.04810757  0.02675729  0.06775125 -0.03908878  0.00229265\n",
-      " -0.00079178  0.00775187  0.03850203 -0.01739503 -0.03728065  0.00360656\n",
-      " -0.00393143  0.0285223   0.04258606  0.00327504 -0.06495493 -0.01848514\n",
-      "  0.02192107 -0.00100611 -0.02610749 -0.01058883  0.02298747 -0.03383424\n",
-      "  0.00023394 -0.02262013  0.00207348  0.04171877  0.00266564  0.0368812\n",
-      "  0.02242898 -0.0306512  -0.00622214  0.0514046  -0.00454216 -0.04602981\n",
-      "  0.01984644  0.04460995 -0.02324778 -0.01866615 -0.0346587  -0.03862219\n",
-      "  0.0657163  -0.02842457 -0.00890825  0.02758532 -0.05724741 -0.00945613\n",
-      "  0.02861402  0.01709914  0.03216959  0.03654928 -0.02062506 -0.02182188\n",
-      "  0.0002444   0.00209172 -0.02169595  0.03440023 -0.04613531  0.00693459\n",
-      "  0.02523665 -0.0123618   0.03458886  0.01750784 -0.04296558 -0.08174474\n",
-      " -0.00325161 -0.01752658  0.04983651  0.00601928 -0.02619002 -0.00654948\n",
-      " -0.00030779  0.02870162]\n",
-      "  cov noise:  [[ 2.24821565e-05 -7.48087038e-07  1.97341401e-05 ...  1.83514607e-07\n",
-      "  -1.42259570e-04  7.99819116e-06]\n",
-      " [-7.48087038e-07  2.35130816e-05 -6.56529977e-07 ...  2.13933706e-05\n",
-      "   1.86783557e-06 -1.40686188e-04]\n",
-      " [ 1.97341401e-05 -6.56529977e-07  1.73255662e-05 ...  1.27804562e-07\n",
-      "  -1.25073827e-04  7.05971050e-06]\n",
-      " ...\n",
-      " [ 1.83514607e-07  2.13933706e-05  1.27804562e-07 ...  5.21994926e-05\n",
-      "   5.80089846e-06 -1.48165220e-04]\n",
-      " [-1.42259570e-04  1.86783557e-06 -1.25073827e-04 ...  5.80089846e-06\n",
-      "   1.05393194e-03 -4.19169453e-05]\n",
-      " [ 7.99819116e-06 -1.40686188e-04  7.05971050e-06 ... -1.48165220e-04\n",
-      "  -4.19169453e-05  9.97934049e-04]]\n",
-      "  mean:  [ 1.00404567e+00 -9.48315395e-04  9.98922283e-01  9.75468225e-02\n",
-      "  9.84045476e-01  1.95129410e-01  9.59666428e-01  2.90869039e-01\n",
-      "  9.26160581e-01  3.83781995e-01  8.83950700e-01  4.72880387e-01\n",
-      "  8.33481367e-01  5.57256146e-01  7.75235279e-01  6.36105879e-01\n",
-      "  7.09728910e-01  7.08715776e-01  6.37530363e-01  7.74456878e-01\n",
-      "  5.59280381e-01  8.32770029e-01  4.75694771e-01  8.83142946e-01\n",
-      "  3.87539444e-01  9.25118601e-01  2.95621263e-01  9.58302774e-01\n",
-      "  2.00801284e-01  9.82363719e-01  1.04002806e-01  9.97032422e-01\n",
-      "  6.21143630e-03  1.00213507e+00 -9.15569953e-02  9.97618129e-01\n",
-      " -1.88312597e-01  9.83528622e-01 -2.83115324e-01  9.59998978e-01\n",
-      " -3.75055782e-01  9.27254297e-01 -4.63256732e-01  8.85606914e-01\n",
-      " -5.46893954e-01  8.35452777e-01 -6.25205877e-01  7.77274286e-01\n",
-      " -6.97485782e-01  7.11622213e-01 -7.63056809e-01  6.39124406e-01\n",
-      " -8.21294707e-01  5.60489417e-01 -8.71655166e-01  4.76485298e-01\n",
-      " -9.13691884e-01  3.87911921e-01 -9.47040839e-01  2.95600514e-01\n",
-      " -9.71415689e-01  2.00417492e-01 -9.86603924e-01  1.03256564e-01\n",
-      " -9.92452283e-01  5.01930815e-03 -9.88878470e-01 -9.33903308e-02\n",
-      " -9.75859231e-01 -1.91064432e-01 -9.53416093e-01 -2.87085466e-01\n",
-      " -9.21648059e-01 -3.80510971e-01 -8.80762002e-01 -4.70383622e-01\n",
-      " -8.31102792e-01 -5.55747879e-01 -7.73162534e-01 -6.35674515e-01\n",
-      " -7.07551918e-01 -7.09300069e-01 -6.34977417e-01 -7.75865921e-01\n",
-      " -5.56201753e-01 -8.34724401e-01 -4.72014699e-01 -8.85341130e-01\n",
-      " -3.83232634e-01 -9.27305015e-01 -2.90703073e-01 -9.60292643e-01\n",
-      " -1.95303272e-01 -9.84060924e-01 -9.79450615e-02 -9.98455739e-01\n",
-      "  4.17307543e-04 -1.00338459e+00  9.88031826e-02 -9.98822376e-01\n",
-      "  1.96231269e-01 -9.84807976e-01  2.91753379e-01 -9.61420004e-01\n",
-      "  3.84471931e-01 -9.28792300e-01  4.73522512e-01 -8.87155231e-01\n",
-      "  5.58064413e-01 -8.36876612e-01  6.37267887e-01 -7.78440303e-01\n",
-      "  7.10333980e-01 -7.12415099e-01  7.76527785e-01 -6.39446730e-01\n",
-      "  8.35209206e-01 -5.60235586e-01  8.85826392e-01 -4.75531073e-01\n",
-      "  9.27908820e-01 -3.86162570e-01  9.61060454e-01 -2.93045560e-01\n",
-      "  9.84961398e-01 -1.97154778e-01  9.99356184e-01 -9.94756913e-02]\n",
-      "  cov:  [[ 0.01365906 -0.0008168   0.01365384 ... -0.00089231  0.01344082\n",
-      "  -0.00085785]\n",
-      " [-0.0008168   0.0135734  -0.00084319 ...  0.01307436 -0.00076581\n",
-      "   0.01342597]\n",
-      " [ 0.01365384 -0.00084319  0.01386714 ... -0.00087349  0.0132213\n",
-      "  -0.0008629 ]\n",
-      " ...\n",
-      " [-0.00089231  0.01307436 -0.00087349 ...  0.0134644  -0.00088405\n",
-      "   0.01337295]\n",
-      " [ 0.01344082 -0.00076581  0.0132213  ... -0.00088405  0.01344669\n",
-      "  -0.000827  ]\n",
-      " [-0.00085785  0.01342597 -0.0008629  ...  0.01337295 -0.000827\n",
-      "   0.01350598]]\n",
-      "  mean w/ noise:  [ 1.00508888e+00  4.51074968e-04  9.97899532e-01  9.87933040e-02\n",
-      "  9.84846459e-01  1.93895261e-01  9.60504821e-01  2.91080916e-01\n",
-      "  9.25812730e-01  3.82663686e-01  8.84382507e-01  4.75279143e-01\n",
-      "  8.33652295e-01  5.54685876e-01  7.74868612e-01  6.34767049e-01\n",
-      "  7.10670780e-01  7.05837372e-01  6.36190801e-01  7.74170034e-01\n",
-      "  5.60977587e-01  8.32945350e-01  4.75249232e-01  8.81027253e-01\n",
-      "  3.86929412e-01  9.24021482e-01  2.97393593e-01  9.57449646e-01\n",
-      "  2.00672269e-01  9.83697768e-01  1.04255040e-01  9.96544494e-01\n",
-      "  4.95295132e-03  1.00281258e+00 -9.23348855e-02  9.99280095e-01\n",
-      " -1.90222290e-01  9.80885819e-01 -2.78217073e-01  9.60766296e-01\n",
-      " -3.75479425e-01  9.29993780e-01 -4.63276379e-01  8.83222904e-01\n",
-      " -5.46726312e-01  8.35953555e-01 -6.28018118e-01  7.79295463e-01\n",
-      " -6.96424943e-01  7.11734568e-01 -7.66039121e-01  6.38684721e-01\n",
-      " -8.21744501e-01  5.62980789e-01 -8.71508497e-01  4.75449849e-01\n",
-      " -9.13551122e-01  3.87862609e-01 -9.47868616e-01  2.97391844e-01\n",
-      " -9.71645268e-01  2.00125370e-01 -9.86597135e-01  1.03916332e-01\n",
-      " -9.92488221e-01  4.43324903e-03 -9.90354295e-01 -9.18730997e-02\n",
-      " -9.77244868e-01 -1.92335786e-01 -9.53812146e-01 -2.85675539e-01\n",
-      " -9.20782207e-01 -3.78969026e-01 -8.80811573e-01 -4.70062903e-01\n",
-      " -8.30787511e-01 -5.54755635e-01 -7.74401766e-01 -6.35386632e-01\n",
-      " -7.07109763e-01 -7.08568077e-01 -6.36059516e-01 -7.75610362e-01\n",
-      " -5.57712921e-01 -8.35452206e-01 -4.70417669e-01 -8.85297678e-01\n",
-      " -3.82609428e-01 -9.30443002e-01 -2.90236492e-01 -9.58260186e-01\n",
-      " -1.96012682e-01 -9.87157542e-01 -9.66233544e-02 -9.99909793e-01\n",
-      "  1.59097999e-03 -1.00384109e+00  9.98117901e-02 -9.99042793e-01\n",
-      "  1.93874328e-01 -9.85792375e-01  2.91989022e-01 -9.61373433e-01\n",
-      "  3.85365857e-01 -9.27039220e-01  4.74168241e-01 -8.85335638e-01\n",
-      "  5.62444126e-01 -8.35692522e-01  6.36999633e-01 -7.77909569e-01\n",
-      "  7.09518377e-01 -7.12194108e-01  7.73590688e-01 -6.37225756e-01\n",
-      "  8.35664464e-01 -5.59448790e-01  8.85537637e-01 -4.73095981e-01\n",
-      "  9.28122822e-01 -3.87772706e-01  9.59450481e-01 -2.93211305e-01\n",
-      "  9.84824516e-01 -1.96647792e-01  9.99867876e-01 -1.00292205e-01]\n",
-      "  cov w/ noise:  [[ 0.01460527 -0.00063105  0.01350559 ... -0.00111039  0.01337567\n",
-      "  -0.00069495]\n",
-      " [-0.00063105  0.01470176 -0.00070474 ...  0.0133318  -0.00067805\n",
-      "   0.01345144]\n",
-      " [ 0.01350559 -0.00070474  0.01443783 ... -0.00102845  0.01290262\n",
-      "  -0.00097843]\n",
-      " ...\n",
-      " [-0.00111039  0.0133318  -0.00102845 ...  0.01442002 -0.00110783\n",
-      "   0.01329384]\n",
-      " [ 0.01337567 -0.00067805  0.01290262 ... -0.00110783  0.0143125\n",
-      "  -0.00076181]\n",
-      " [-0.00069495  0.01345144 -0.00097843 ...  0.01329384 -0.00076181\n",
-      "   0.01433344]]\n",
-      "  mean conditional:  [ 1.02825189  0.0864078   1.03026695  0.17901846  1.02275395  0.27112451\n",
-      "  1.00392718  0.36180385  0.9728856   0.45014409  0.92979744  0.53543433\n",
-      "  0.87586896  0.61703478  0.81292967  0.69449055  0.7428823   0.7673316\n",
-      "  0.66738794  0.83503101  0.58745674  0.89694834  0.5035346   0.95229936\n",
-      "  0.41569094  1.00017444  0.32389604  1.03926023  0.22845033  1.06826637\n",
-      "  0.12993074  1.08560292  0.0294403   1.08983545 -0.07172506  1.07979088\n",
-      " -0.17210005  1.05477832 -0.27030182  1.01482261 -0.364893    0.96075531\n",
-      " -0.45467882  0.89425114 -0.53855396  0.81778356 -0.61579917  0.73430433\n",
-      " -0.68596746  0.6472075  -0.74905892  0.55955481 -0.80522875  0.47377005\n",
-      " -0.85490252  0.39111028 -0.89827904  0.31154579 -0.9353009   0.2337062\n",
-      " -0.96556735  0.15554577 -0.98841374  0.07474898 -1.00294166 -0.0105404\n",
-      " -1.00820652 -0.10118855 -1.00336532 -0.19696176 -0.98763706 -0.29653063\n",
-      " -0.96042218 -0.39778666 -0.92165939 -0.49820979 -0.87146634 -0.59538684\n",
-      " -0.8104725  -0.68706366 -0.73961294 -0.77155272 -0.66009152 -0.84754755\n",
-      " -0.573086   -0.91410188 -0.47986443 -0.97058998 -0.38145877 -1.01627607\n",
-      " -0.27877916 -1.0506226  -0.17263247 -1.07298517 -0.06360709 -1.08284172\n",
-      "  0.04759626 -1.0798693   0.16010113 -1.06381751  0.2724415  -1.03481385\n",
-      "  0.38268094 -0.9931409   0.48814874 -0.93949231  0.58607999 -0.8751198\n",
-      "  0.67386097 -0.80161886  0.74968685 -0.72108325  0.81289767 -0.63571187\n",
-      "  0.86402614 -0.5475115   0.90471465 -0.45803505  0.93717727 -0.36825538\n",
-      "  0.96361663 -0.27835727  0.98571748 -0.18832283  1.0040874  -0.09744719\n",
-      "  1.02078855 -0.00570398] , sample:  [ 1.01430304e+00  7.05646600e-02  1.06386042e+00  1.97268276e-01\n",
-      "  1.03649646e+00  2.63671942e-01  1.05529834e+00  4.11030126e-01\n",
-      "  9.58168225e-01  4.25636250e-01  8.81561510e-01  5.48159393e-01\n",
-      "  8.44256847e-01  5.85344376e-01  8.07694060e-01  7.11631367e-01\n",
-      "  7.75801528e-01  7.69956818e-01  6.60122894e-01  8.42067106e-01\n",
-      "  6.16019129e-01  9.03930255e-01  5.04322163e-01  9.22683753e-01\n",
-      "  3.67973584e-01  1.00429579e+00  3.38817666e-01  1.03304504e+00\n",
-      "  2.47301937e-01  1.08098154e+00  1.85985230e-01  1.08409352e+00\n",
-      " -5.43475121e-04  1.09000992e+00 -7.89350295e-02  1.09788961e+00\n",
-      " -2.00839472e-01  1.02302097e+00 -2.80274408e-01  1.02715578e+00\n",
-      " -3.61684165e-01  9.66004591e-01 -4.39444675e-01  9.16647170e-01\n",
-      " -5.47397858e-01  8.40837041e-01 -6.36763743e-01  7.50052005e-01\n",
-      " -6.58303371e-01  6.47759649e-01 -7.66486612e-01  5.07534095e-01\n",
-      " -7.84961544e-01  4.56224055e-01 -8.49786790e-01  3.58006855e-01\n",
-      " -8.84339272e-01  2.77463958e-01 -9.57444070e-01  2.51706397e-01\n",
-      " -9.49230960e-01  1.97855271e-01 -9.88004604e-01  4.87158135e-02\n",
-      " -1.05721796e+00  5.63048178e-02 -9.60680926e-01 -1.26439537e-01\n",
-      " -9.89157206e-01 -1.40621611e-01 -1.01579559e+00 -3.28046296e-01\n",
-      " -9.81943335e-01 -4.15708467e-01 -9.19371706e-01 -5.20250807e-01\n",
-      " -8.58255968e-01 -5.84126393e-01 -8.07669221e-01 -6.75196676e-01\n",
-      " -7.53877802e-01 -8.04202708e-01 -6.39111600e-01 -8.47537443e-01\n",
-      " -5.31283190e-01 -9.19648670e-01 -5.46798612e-01 -9.12311862e-01\n",
-      " -3.59009570e-01 -9.94454163e-01 -2.97362433e-01 -1.07237215e+00\n",
-      " -1.68524814e-01 -1.07957533e+00 -2.80886885e-02 -1.08848835e+00\n",
-      "  4.24880871e-02 -1.09305564e+00  1.55285277e-01 -1.08746229e+00\n",
-      "  2.22410875e-01 -1.02733525e+00  3.80997260e-01 -9.97669147e-01\n",
-      "  5.01587300e-01 -9.29053988e-01  6.23236864e-01 -8.80420621e-01\n",
-      "  6.86226500e-01 -7.88683991e-01  7.57081132e-01 -7.44223623e-01\n",
-      "  8.03470610e-01 -5.87503811e-01  8.71510747e-01 -5.43009264e-01\n",
-      "  9.08871941e-01 -4.39158175e-01  9.18314307e-01 -3.50907230e-01\n",
-      "  9.41427147e-01 -2.93950491e-01  1.00850841e+00 -2.35690967e-01\n",
-      "  9.76243696e-01 -1.27367359e-01  1.01526681e+00  2.48704472e-02]\n",
-      "  cov conditional:  [[ 2.24935632e-05 -7.42532822e-07  1.97367629e-05 ... -1.16066609e-07\n",
-      "  -1.35248204e-04  6.33218998e-06]\n",
-      " [-7.42532822e-07  2.35142033e-05 -6.58324787e-07 ...  2.26848442e-05\n",
-      "   4.42159137e-06 -1.48427950e-04]\n",
-      " [ 1.97367629e-05 -6.58324787e-07  1.73211447e-05 ... -1.21876560e-07\n",
-      "  -1.18875782e-04  5.63775808e-06]\n",
-      " ...\n",
-      " [-1.16066609e-07  2.26848442e-05 -1.21876560e-07 ...  5.49249900e-05\n",
-      "  -1.74978767e-06 -1.66602996e-04]\n",
-      " [-1.35248204e-04  4.42159137e-06 -1.18875782e-04 ... -1.74978767e-06\n",
-      "   9.69842661e-04 -5.11634555e-05]\n",
-      " [ 6.33218998e-06 -1.48427950e-04  5.63775808e-06 ... -1.66602996e-04\n",
-      "  -5.11634555e-05  1.09710775e-03]]\n",
-      "  mean noise:  [ 1.39488582e-02  1.58431391e-02 -3.35934665e-02 -1.82498196e-02\n",
-      " -1.37425121e-02  7.45257096e-03 -5.13711601e-02 -4.92262798e-02\n",
-      "  1.47173737e-02  2.45078380e-02  4.82359248e-02 -1.27250677e-02\n",
-      "  3.16121139e-02  3.16904021e-02  5.23561408e-03 -1.71408177e-02\n",
-      " -3.29192252e-02 -2.62522128e-03  7.26504107e-03 -7.03610069e-03\n",
-      " -2.85623890e-02 -6.98191766e-03 -7.87558521e-04  2.96156019e-02\n",
-      "  4.77173599e-02 -4.12134666e-03 -1.49216289e-02  6.21518512e-03\n",
-      " -1.88516058e-02 -1.27151755e-02 -5.60544945e-02  1.50939499e-03\n",
-      "  2.99837769e-02 -1.74475094e-04  7.20996782e-03 -1.80987290e-02\n",
-      "  2.87394194e-02  3.17573516e-02  9.97259023e-03 -1.23331690e-02\n",
-      " -3.20883221e-03 -5.24928026e-03 -1.52341403e-02 -2.23960309e-02\n",
-      "  8.84390265e-03 -2.30534802e-02  2.09645746e-02 -1.57476738e-02\n",
-      " -2.76640915e-02 -5.52144481e-04  1.74276936e-02  5.20207186e-02\n",
-      " -2.02672066e-02  1.75459974e-02 -5.11572981e-03  3.31034277e-02\n",
-      " -1.39397638e-02  3.40818322e-02  2.21431668e-02 -1.80001946e-02\n",
-      " -1.63363867e-02 -4.23094971e-02 -4.09139811e-04  2.60331712e-02\n",
-      "  5.42763038e-02 -6.68452223e-02 -4.75255945e-02  2.52509922e-02\n",
-      " -1.42081114e-02 -5.63401537e-02  2.81585325e-02  3.15156635e-02\n",
-      "  2.15211567e-02  1.79218093e-02 -2.28768262e-03  2.20410185e-02\n",
-      " -1.32103707e-02 -1.12604471e-02 -2.80327842e-03 -1.18669847e-02\n",
-      "  1.42648603e-02  3.26499883e-02 -2.09799207e-02 -1.01065220e-05\n",
-      " -4.18028127e-02  5.54678692e-03  6.69341782e-02 -5.82781190e-02\n",
-      " -2.24492029e-02 -2.18219037e-02  1.85832769e-02  2.17495503e-02\n",
-      " -4.10765768e-03  6.59015821e-03 -3.55184030e-02  5.64663232e-03\n",
-      "  5.10816928e-03  1.31863388e-02  4.81585233e-03  2.36447825e-02\n",
-      "  5.00306226e-02 -7.47859826e-03  1.68368070e-03  4.52824826e-03\n",
-      " -1.34385643e-02 -1.04383197e-02 -3.71568780e-02  5.30082092e-03\n",
-      " -1.23655316e-02 -1.29348657e-02 -7.39428044e-03  2.31403727e-02\n",
-      "  9.42706452e-03 -4.82080551e-02 -7.48460456e-03 -4.50223656e-03\n",
-      " -4.15729336e-03 -1.88768772e-02  1.88629602e-02 -1.73481544e-02\n",
-      "  2.21894817e-02  1.55932182e-02 -2.27909328e-02  4.73681417e-02\n",
-      "  2.78437069e-02  2.99201737e-02  5.52174235e-03 -3.05744281e-02]\n",
-      "  cov noise:  [[ 2.24935632e-05 -7.42532822e-07  1.97367629e-05 ... -1.16066609e-07\n",
-      "  -1.35248204e-04  6.33218998e-06]\n",
-      " [-7.42532822e-07  2.35142033e-05 -6.58324787e-07 ...  2.26848442e-05\n",
-      "   4.42159137e-06 -1.48427950e-04]\n",
-      " [ 1.97367629e-05 -6.58324787e-07  1.73211447e-05 ... -1.21876560e-07\n",
-      "  -1.18875782e-04  5.63775808e-06]\n",
-      " ...\n",
-      " [-1.16066609e-07  2.26848442e-05 -1.21876560e-07 ...  5.49249900e-05\n",
-      "  -1.74978767e-06 -1.66602996e-04]\n",
-      " [-1.35248204e-04  4.42159137e-06 -1.18875782e-04 ... -1.74978767e-06\n",
-      "   9.69842661e-04 -5.11634555e-05]\n",
-      " [ 6.33218998e-06 -1.48427950e-04  5.63775808e-06 ... -1.66602996e-04\n",
-      "  -5.11634555e-05  1.09710775e-03]]\n",
-      "  mean:  [ 1.00134010e+00 -2.01817068e-03  9.96450095e-01  9.58020206e-02\n",
-      "  9.81912486e-01  1.92662121e-01  9.57881352e-01  2.87601967e-01\n",
-      "  9.24599531e-01  3.79702865e-01  8.82410959e-01  4.68106440e-01\n",
-      "  8.31754717e-01  5.52015071e-01  7.73163354e-01  6.30673798e-01\n",
-      "  7.07274666e-01  7.03357767e-01  6.34797327e-01  7.69369863e-01\n",
-      "  5.56459348e-01  8.28063726e-01  4.72991892e-01  8.78862551e-01\n",
-      "  3.85131897e-01  9.21249920e-01  2.93627230e-01  9.54766954e-01\n",
-      "  1.99235127e-01  9.79036688e-01  1.02730482e-01  9.93799619e-01\n",
-      "  4.94373994e-03  9.98897768e-01 -9.32180520e-02  9.94252216e-01\n",
-      " -1.90775763e-01  9.79872302e-01 -2.86692903e-01  9.55885130e-01\n",
-      " -3.79918097e-01  9.22559803e-01 -4.69439991e-01  8.80292497e-01\n",
-      " -5.54339219e-01  8.29578667e-01 -6.33808556e-01  7.70977150e-01\n",
-      " -7.07125567e-01  7.05064853e-01 -7.73614876e-01  6.32417516e-01\n",
-      " -8.32620080e-01  5.53661142e-01 -8.83532724e-01  4.69544288e-01\n",
-      " -9.25830336e-01  3.80945453e-01 -9.59080458e-01  2.88813100e-01\n",
-      " -9.82935264e-01  1.94086534e-01 -9.97149212e-01  9.76719817e-02\n",
-      " -1.00160980e+00  4.50490784e-04 -9.96344753e-01 -9.67066168e-02\n",
-      " -9.81472344e-01 -1.92897690e-01 -9.57199656e-01 -2.87159454e-01\n",
-      " -9.23827174e-01 -3.78501106e-01 -8.81715656e-01 -4.65980816e-01\n",
-      " -8.31242988e-01 -5.48741910e-01 -7.72812538e-01 -6.26028960e-01\n",
-      " -7.06912248e-01 -6.97185254e-01 -6.34158751e-01 -7.61631165e-01\n",
-      " -5.55259928e-01 -8.18845750e-01 -4.70982932e-01 -8.68371657e-01\n",
-      " -3.82193520e-01 -9.09810683e-01 -2.89834593e-01 -9.42820678e-01\n",
-      " -1.94856665e-01 -9.67119594e-01 -9.81800890e-02 -9.82472444e-01\n",
-      " -6.97606959e-04 -9.88674143e-01  9.67174641e-02 -9.85553718e-01\n",
-      "  1.93217061e-01 -9.73015500e-01  2.87963752e-01 -9.51058603e-01\n",
-      "  3.80119479e-01 -9.19785264e-01  4.68829724e-01 -8.79390510e-01\n",
-      "  5.53217585e-01 -8.30181289e-01  6.32419573e-01 -7.72606476e-01\n",
-      "  7.05614265e-01 -7.07264932e-01  7.72038098e-01 -6.34877658e-01\n",
-      "  8.31007580e-01 -5.56251789e-01  8.81938842e-01 -4.72237080e-01\n",
-      "  9.24341850e-01 -3.83706980e-01  9.57797220e-01 -2.91545689e-01\n",
-      "  9.81941494e-01 -1.96626298e-01  9.96501431e-01 -9.98143820e-02]\n",
-      "  cov:  [[0.01398918 0.00080027 0.01386125 ... 0.00079097 0.01389196 0.00079433]\n",
-      " [0.00080027 0.01336513 0.00083528 ... 0.01282549 0.00076422 0.01319962]\n",
-      " [0.01386125 0.00083528 0.01396729 ... 0.00082806 0.0135377  0.00083067]\n",
-      " ...\n",
-      " [0.00079097 0.01282549 0.00082806 ... 0.01315464 0.00075044 0.01309133]\n",
-      " [0.01389196 0.00076422 0.0135377  ... 0.00075044 0.01402839 0.00075548]\n",
-      " [0.00079433 0.01319962 0.00083067 ... 0.01309133 0.00075548 0.01325202]]\n",
-      "  mean w/ noise:  [ 1.00120692 -0.00168595  0.99720536  0.09473335  0.98177481  0.19140446\n",
-      "  0.95737021  0.2869115   0.92413481  0.37986295  0.88391575  0.46790144\n",
-      "  0.83299984  0.55441068  0.7713724   0.62944503  0.70876297  0.70267459\n",
-      "  0.63539541  0.76886687  0.55692705  0.82714576  0.47181072  0.87814374\n",
-      "  0.38518519  0.91950511  0.29535248  0.95437732  0.19911849  0.97879415\n",
-      "  0.10372234  0.99650063  0.00465268  0.99955826 -0.09285266  0.99404449\n",
-      " -0.19096296  0.97805208 -0.28729075  0.95762488 -0.38142332  0.92322565\n",
-      " -0.47012418  0.88147892 -0.55536393  0.83080729 -0.63371651  0.77134599\n",
-      " -0.70557968  0.70606262 -0.77287849  0.63233949 -0.83111127  0.55423838\n",
-      " -0.88263939  0.47082272 -0.92267538  0.38063548 -0.95716859  0.29048301\n",
-      " -0.984388    0.19464629 -0.99561166  0.0959477  -1.00210006  0.00102197\n",
-      " -0.99757686 -0.09899397 -0.98080642 -0.19358763 -0.95795184 -0.28465549\n",
-      " -0.92446663 -0.37719142 -0.88147323 -0.46643183 -0.83151029 -0.55171263\n",
-      " -0.77325165 -0.62829995 -0.70810121 -0.6963352  -0.63624039 -0.76209649\n",
-      " -0.55398463 -0.81782708 -0.47070034 -0.86612697 -0.3829394  -0.90970439\n",
-      " -0.28954686 -0.94278409 -0.19539722 -0.96674332 -0.09647085 -0.98105567\n",
-      " -0.00111407 -0.98932919  0.09418363 -0.98620502  0.18958684 -0.97217863\n",
-      "  0.28954196 -0.95009345  0.37998585 -0.91956097  0.46764628 -0.87845286\n",
-      "  0.55284689 -0.82908848  0.63406319 -0.7680665   0.70632737 -0.70692414\n",
-      "  0.77102153 -0.63583212  0.83146694 -0.55916672  0.88097545 -0.47110396\n",
-      "  0.92675079 -0.38469152  0.95786044 -0.28894839  0.9841929  -0.19591074\n",
-      "  0.99531653 -0.09881274]\n",
-      "  cov w/ noise:  [[0.01531747 0.0010141  0.01404261 ... 0.00090145 0.01436729 0.00091854]\n",
-      " [0.0010141  0.01419264 0.00099545 ... 0.01269909 0.00050505 0.01337994]\n",
-      " [0.01404261 0.00099545 0.01508113 ... 0.00091502 0.01381294 0.00093214]\n",
-      " ...\n",
-      " [0.00090145 0.01269909 0.00091502 ... 0.0142355  0.00036107 0.0131564 ]\n",
-      " [0.01436729 0.00050505 0.01381294 ... 0.00036107 0.01581326 0.00035635]\n",
-      " [0.00091854 0.01337994 0.00093214 ... 0.0131564  0.00035635 0.01470309]]\n",
-      "  mean conditional:  [ 0.96193299 -0.07557429  0.95615056  0.02796855  0.94051783  0.13035804\n",
-      "  0.91551525  0.2310685   0.88179266  0.32932053  0.84007017  0.42406886\n",
-      "  0.79097845  0.5141028   0.73503902  0.5980486   0.67270185  0.6745732\n",
-      "  0.60441626  0.74238229  0.53045731  0.80037847  0.45122974  0.84771457\n",
-      "  0.36701212  0.8838468   0.27819536  0.90873987  0.18505451  0.92283779\n",
-      "  0.08805044  0.92695247 -0.01235233  0.92215525 -0.11540026  0.909579\n",
-      " -0.22011054  0.89015115 -0.32518269  0.86449491 -0.42909716  0.83276899\n",
-      " -0.5302071   0.79496716 -0.62678304  0.75078154 -0.71717279  0.69988123\n",
-      " -0.79988127  0.64209309 -0.87361109  0.57733654 -0.93743117  0.50568289\n",
-      " -0.9910093   0.42737828 -1.03430718  0.3429487  -1.06766462  0.25308281\n",
-      " -1.09145874  0.15873862 -1.10587923  0.06096907 -1.11065142 -0.0391389\n",
-      " -1.10533898 -0.14049523 -1.08921371 -0.2422305  -1.06163378 -0.3435771\n",
-      " -1.0222185  -0.44371066 -0.97100415 -0.5417408  -0.90853956 -0.63664725\n",
-      " -0.83577547 -0.72722426 -0.75395603 -0.8123901  -0.66447472 -0.89106132\n",
-      " -0.56865686 -0.9622978  -0.46767364 -1.02510155 -0.3626742  -1.0784538\n",
-      " -0.25480503 -1.12129752 -0.14540613 -1.15261852 -0.03605747 -1.17185595\n",
-      "  0.0714329  -1.178807    0.17527361 -1.17380367  0.27368726 -1.1576299\n",
-      "  0.36544154 -1.13107986  0.44960524 -1.09480145  0.52600137 -1.04916666\n",
-      "  0.59494161 -0.9942665   0.65715596 -0.93009459  0.71361151 -0.85694107\n",
-      "  0.76508784 -0.77534405  0.81202188 -0.68629993  0.85426238 -0.59110544\n",
-      "  0.8910822  -0.49134086  0.92136256 -0.38867323  0.94405643 -0.2841915\n",
-      "  0.95705861 -0.17947048] , sample:  [ 0.97250821 -0.07061453  0.95894332 -0.00205487  0.95667137  0.10338355\n",
-      "  0.89647634  0.2381738   0.8506049   0.33453367  0.83638512  0.42837425\n",
-      "  0.78773581  0.53655529  0.74640979  0.58467686  0.68537993  0.68931759\n",
-      "  0.57428942  0.73242437  0.51785445  0.78072     0.50104352  0.84465455\n",
-      "  0.4065124   0.8971716   0.25131627  0.89311375  0.15086939  0.99671125\n",
-      "  0.10225225  0.88397034 -0.04133426  0.90670686 -0.08981654  0.94672876\n",
-      " -0.25675355  0.8619129  -0.29967178  0.83672431 -0.42234228  0.82104425\n",
-      " -0.53060386  0.85177431 -0.60908667  0.73001847 -0.66377476  0.6895392\n",
-      " -0.82278565  0.6573385  -0.90820294  0.53897298 -0.96728158  0.51232277\n",
-      " -1.02281106  0.41116516 -1.01683295  0.37804675 -1.05860933  0.26873716\n",
-      " -1.06543132  0.14139185 -1.0763217   0.0811432  -1.1471696  -0.0358984\n",
-      " -1.06813693 -0.15315127 -1.09230204 -0.26922102 -1.07682474 -0.35293201\n",
-      " -1.0093675  -0.40230079 -1.02466918 -0.54526086 -0.91654858 -0.60209444\n",
-      " -0.8548784  -0.7511052  -0.76510421 -0.87269252 -0.62397501 -0.87457259\n",
-      " -0.55751062 -0.93454093 -0.46096738 -0.97565656 -0.36101769 -1.13875417\n",
-      " -0.25908338 -1.10919305 -0.12298754 -1.17485231 -0.06202152 -1.14074737\n",
-      "  0.08461941 -1.20167297  0.13151023 -1.15229411  0.3115144  -1.18551211\n",
-      "  0.40751858 -1.11947347  0.43376583 -1.08327254  0.48648657 -1.0783793\n",
-      "  0.63240973 -0.99700338  0.67194666 -0.90840968  0.64896423 -0.82259838\n",
-      "  0.7700502  -0.74675008  0.784219   -0.69088678  0.91555778 -0.65455056\n",
-      "  0.90590458 -0.51029766  0.92453126 -0.37811314  0.88607673 -0.23334922\n",
-      "  0.98980255 -0.18641494]\n",
-      "  cov conditional:  [[ 2.19549393e-05  8.53390005e-07  1.92519710e-05 ... -5.56555980e-07\n",
-      "  -1.36246231e-04 -5.45358405e-06]\n",
-      " [ 8.53390005e-07  2.53873809e-05  7.44031808e-07 ...  2.35686417e-05\n",
-      "  -7.26243482e-06 -1.56946351e-04]\n",
-      " [ 1.92519710e-05  7.44031808e-07  1.68854285e-05 ... -4.88105818e-07\n",
-      "  -1.19653941e-04 -4.69349330e-06]\n",
-      " ...\n",
-      " [-5.56555980e-07  2.35686417e-05 -4.88105818e-07 ...  5.15515710e-05\n",
-      "  -5.63261804e-08 -1.58799509e-04]\n",
-      " [-1.36246231e-04 -7.26243482e-06 -1.19653941e-04 ... -5.63261804e-08\n",
-      "   9.98138499e-04  4.69449392e-05]\n",
-      " [-5.45358405e-06 -1.56946351e-04 -4.69349330e-06 ... -1.58799509e-04\n",
-      "   4.69449392e-05  1.11430777e-03]]\n",
-      "  mean noise:  [-0.01057522 -0.00495976 -0.00279277  0.03002342 -0.01615355  0.0269745\n",
-      "  0.01903891 -0.00710531  0.03118775 -0.00521314  0.00368506 -0.00430539\n",
-      "  0.00324264 -0.02245249 -0.01137077  0.01337174 -0.01267808 -0.01474439\n",
-      "  0.03012684  0.00995792  0.01260285  0.01965847 -0.04981378  0.00306002\n",
-      " -0.03950028 -0.0133248   0.0268791   0.01562613  0.03418512 -0.07387346\n",
-      " -0.01420181  0.04298213  0.02898193  0.01544839 -0.02558372 -0.03714976\n",
-      "  0.03664301  0.02823825 -0.02551091  0.0277706  -0.00675488  0.01172474\n",
-      "  0.00039676 -0.05680715 -0.01769637  0.02076307 -0.05339804  0.01034203\n",
-      "  0.02290438 -0.01524541  0.03459185  0.03836356  0.02985041 -0.00663988\n",
-      "  0.03180177  0.01621312 -0.01747423 -0.03509805 -0.00905529 -0.01565435\n",
-      " -0.02602742  0.01734676 -0.02955753 -0.02017412  0.03651818 -0.00324051\n",
-      " -0.03720205  0.01265604  0.00308832  0.02699052  0.01519096  0.00935491\n",
-      " -0.012851   -0.04140987  0.05366503  0.00352006  0.00800902 -0.03455281\n",
-      "  0.01910293  0.02388094  0.01114818  0.06030242 -0.04049971 -0.01648873\n",
-      " -0.01114624 -0.02775687 -0.00670626 -0.049445   -0.00165652  0.06030037\n",
-      "  0.00427835 -0.01210448 -0.02241858  0.02223379  0.02596405 -0.03110858\n",
-      " -0.01318651  0.02286598  0.04376338 -0.02150957 -0.03782714  0.0278822\n",
-      " -0.04207704 -0.01160639  0.01583942 -0.01152891  0.03951481  0.02921264\n",
-      " -0.03746812  0.00273689 -0.0147907  -0.02168491  0.06464728 -0.03434269\n",
-      " -0.00496236 -0.02859397  0.02780288  0.00458685 -0.06129539  0.06344512\n",
-      " -0.01482238  0.0189568  -0.0031687  -0.01056009  0.05797969 -0.05084229\n",
-      " -0.03274394  0.00694446]\n",
-      "  cov noise:  [[ 2.19549393e-05  8.53390005e-07  1.92519710e-05 ... -5.56555980e-07\n",
-      "  -1.36246231e-04 -5.45358405e-06]\n",
-      " [ 8.53390005e-07  2.53873809e-05  7.44031808e-07 ...  2.35686417e-05\n",
-      "  -7.26243482e-06 -1.56946351e-04]\n",
-      " [ 1.92519710e-05  7.44031808e-07  1.68854285e-05 ... -4.88105818e-07\n",
-      "  -1.19653941e-04 -4.69349330e-06]\n",
-      " ...\n",
-      " [-5.56555980e-07  2.35686417e-05 -4.88105818e-07 ...  5.15515710e-05\n",
-      "  -5.63261804e-08 -1.58799509e-04]\n",
-      " [-1.36246231e-04 -7.26243482e-06 -1.19653941e-04 ... -5.63261804e-08\n",
-      "   9.98138499e-04  4.69449392e-05]\n",
-      " [-5.45358405e-06 -1.56946351e-04 -4.69349330e-06 ... -1.58799509e-04\n",
-      "   4.69449392e-05  1.11430777e-03]]\n",
-      "  mean:  [ 0.99852641 -0.00196811  0.99395777  0.097116    0.97957112  0.19542384\n",
-      "  0.95558462  0.2919031   0.9223471   0.38550182  0.8802785   0.47522972\n",
-      "  0.82982901  0.56019633  0.77150251  0.63960535  0.70586413  0.71273261\n",
-      "  0.63350914  0.7789002   0.55505117  0.83748624  0.47115592  0.88795496\n",
-      "  0.3825625   0.92986188  0.29008891  0.9628475   0.19461268  0.98662289\n",
-      "  0.09703216  1.00097519 -0.0017366   1.00575669 -0.10074265  1.00088103\n",
-      " -0.1989873   0.98635182 -0.29543573  0.9622937  -0.38906006  0.92894987\n",
-      " -0.47888536  0.88665417 -0.56401136  0.83583349 -0.64363071  0.77698877\n",
-      " -0.71702315  0.71066312 -0.78352318  0.6374474  -0.84249983  0.55802519\n",
-      " -0.89337648  0.47321698 -0.93563411  0.3839671  -0.9687927   0.29126277\n",
-      " -0.99242732  0.19604342 -1.00623139  0.09919102 -1.01007292  0.00157648\n",
-      " -1.00402633 -0.09591681 -0.98833768 -0.19237241 -0.96335414 -0.28682511\n",
-      " -0.92945325 -0.37829971 -0.88699238 -0.46588802 -0.83632927 -0.54879607\n",
-      " -0.7778713  -0.62633    -0.71208722 -0.69784537 -0.63952756 -0.76273527\n",
-      " -0.5608218  -0.82043904 -0.47670213 -0.87045152 -0.38802255 -0.9123285\n",
-      " -0.29570103 -0.94568265 -0.20067553 -0.97017304 -0.10389224 -0.98550394\n",
-      " -0.0062831  -0.99145516  0.09125731 -0.98790894  0.18788445 -0.97487123\n",
-      "  0.28277972 -0.95246525  0.37511314 -0.92090986  0.46402002 -0.88047569\n",
-      "  0.54859716 -0.83147717  0.6279338  -0.77430306  0.70116481 -0.70945322\n",
-      "  0.76752273 -0.63752682  0.82638793 -0.55920894  0.87728276 -0.47527121\n",
-      "  0.91980478 -0.38656742  0.95355701 -0.29400041  0.97814585 -0.19846025\n",
-      "  0.99322045 -0.10081417]\n",
-      "  cov:  [[ 0.01460716 -0.00054197  0.01456846 ... -0.00027456  0.01444146\n",
-      "  -0.00041876]\n",
-      " [-0.00054197  0.01330415 -0.00058372 ...  0.01261743 -0.00049295\n",
-      "   0.01305674]\n",
-      " [ 0.01456846 -0.00058372  0.01474302 ... -0.00032611  0.01419809\n",
-      "  -0.00046501]\n",
-      " ...\n",
-      " [-0.00027456  0.01261743 -0.00032611 ...  0.01275386 -0.00021921\n",
-      "   0.01278006]\n",
-      " [ 0.01444146 -0.00049295  0.01419809 ... -0.00021921  0.0144862\n",
-      "  -0.00036657]\n",
-      " [-0.00041876  0.01305674 -0.00046501 ...  0.01278006 -0.00036657\n",
-      "   0.01301721]]\n",
-      "  mean w/ noise:  [ 0.99655775 -0.00260858  0.9938709   0.09728161  0.97919355  0.19514749\n",
-      "  0.95603534  0.28946171  0.92140981  0.38481197  0.88065131  0.47694508\n",
-      "  0.83110203  0.55930903  0.77250082  0.63927817  0.70469658  0.71306926\n",
-      "  0.63275726  0.77743431  0.55495656  0.83973975  0.47227486  0.88800619\n",
-      "  0.38224193  0.92988737  0.28951092  0.96279429  0.19379787  0.98640758\n",
-      "  0.09480122  1.00171502 -0.00189813  1.00187467 -0.10275109  1.00415389\n",
-      " -0.2004959   0.98440079 -0.29733013  0.96125955 -0.38909525  0.93220073\n",
-      " -0.48031834  0.88592318 -0.5615242   0.83629418 -0.64385978  0.77784557\n",
-      " -0.71722605  0.71130655 -0.78039576  0.63545029 -0.8432015   0.55755601\n",
-      " -0.89394553  0.47178129 -0.93440764  0.38304219 -0.97086877  0.29302108\n",
-      " -0.99202023  0.19540972 -1.00330239  0.10042416 -1.0121505   0.00113251\n",
-      " -1.00373683 -0.09659095 -0.98806266 -0.19319071 -0.96272903 -0.28441791\n",
-      " -0.93085991 -0.37940088 -0.88650692 -0.46510342 -0.83360806 -0.548583\n",
-      " -0.77584767 -0.62817732 -0.71432162 -0.69916838 -0.63962527 -0.76341494\n",
-      " -0.56112521 -0.82257306 -0.47902963 -0.86930687 -0.38810469 -0.91163174\n",
-      " -0.29537069 -0.94662284 -0.19836282 -0.97152268 -0.10248956 -0.9848096\n",
-      " -0.00803363 -0.99201304  0.09063598 -0.98924091  0.18674797 -0.97524682\n",
-      "  0.28559232 -0.95335739  0.37657143 -0.92056975  0.46281017 -0.87977272\n",
-      "  0.54982053 -0.83254958  0.62988711 -0.77293764  0.69978846 -0.70871103\n",
-      "  0.76697012 -0.63854417  0.82603422 -0.56132392  0.87512842 -0.47522066\n",
-      "  0.91976723 -0.38470685  0.95361185 -0.29422298  0.97918695 -0.19712026\n",
-      "  0.99148345 -0.09991367]\n",
-      "  cov w/ noise:  [[ 1.53154317e-02 -3.45224520e-04  1.43870434e-02 ... -1.40078001e-04\n",
-      "   1.42959489e-02 -2.37622020e-04]\n",
-      " [-3.45224520e-04  1.41769878e-02 -2.67963937e-04 ...  1.23423170e-02\n",
-      "  -3.25545717e-04  1.31831071e-02]\n",
-      " [ 1.43870434e-02 -2.67963937e-04  1.55493577e-02 ...  5.04366501e-05\n",
-      "   1.40966799e-02 -7.48488924e-05]\n",
-      " ...\n",
-      " [-1.40078001e-04  1.23423170e-02  5.04366501e-05 ...  1.35239953e-02\n",
-      "  -6.61066408e-05  1.28816844e-02]\n",
-      " [ 1.42959489e-02 -3.25545717e-04  1.40966799e-02 ... -6.61066408e-05\n",
-      "   1.55152834e-02 -2.46667844e-04]\n",
-      " [-2.37622020e-04  1.31831071e-02 -7.48488924e-05 ...  1.28816844e-02\n",
-      "  -2.46667844e-04  1.45515787e-02]]\n",
-      "  mean conditional:  [ 1.07041083 -0.0413809   1.07495612  0.07364213  1.0639404   0.18155744\n",
-      "  1.03865198  0.28258932  1.000717    0.37718199  0.9517633   0.46572392\n",
-      "  0.89315044  0.54846859  0.82597253  0.62528738  0.75105625  0.69586295\n",
-      "  0.66923377  0.75958346  0.5813367   0.81586428  0.48845034  0.86409049\n",
-      "  0.39184875  0.90377766  0.29299079  0.93477964  0.19321121  0.9570274\n",
-      "  0.0939496   0.9707131  -0.00370939  0.97595356 -0.09875088  0.97279828\n",
-      " -0.19034603  0.96106702 -0.27787168  0.94034338 -0.36079867  0.91001676\n",
-      " -0.43892784  0.86953434 -0.51200656  0.81848116 -0.58017328  0.75679238\n",
-      " -0.64333242  0.68491242 -0.70137033  0.60370112 -0.75386816  0.51454574\n",
-      " -0.80033243  0.4190792  -0.84003488  0.31915819 -0.87220207  0.21655675\n",
-      " -0.89608798  0.11279311 -0.91102351  0.00895466 -0.91660135 -0.09440545\n",
-      " -0.91264382 -0.19720392 -0.89945113 -0.29952884 -0.87757945 -0.40162024\n",
-      " -0.84786837 -0.50337064 -0.81132091 -0.60424271 -0.76884695 -0.70326629\n",
-      " -0.72115892 -0.79916516 -0.66862225 -0.89065122 -0.61140152 -0.97647717\n",
-      " -0.54921724 -1.05568561 -0.48185554 -1.12712976 -0.40918681 -1.18973769\n",
-      " -0.33139173 -1.24229196 -0.24926887 -1.28364464 -0.16376359 -1.31298856\n",
-      " -0.07629788 -1.32997434  0.01185752 -1.33466524  0.09954852 -1.32752478\n",
-      "  0.1860645  -1.30877512  0.27086721 -1.27825161  0.35406424 -1.23522559\n",
-      "  0.43591611 -1.17861052  0.51686624 -1.1074135   0.59734937 -1.02126779\n",
-      "  0.67725375 -0.92068411  0.75587566 -0.80740253  0.83147693 -0.6841881\n",
-      "  0.90162557 -0.55452836  0.96335031 -0.42210551  1.01376246 -0.29047104\n",
-      "  1.05034076 -0.1622601 ] , sample:  [ 1.08700507 -0.01855335  1.08973264  0.07041955  1.05006161  0.21628393\n",
-      "  1.02159217  0.25607024  0.96901201  0.37330044  0.92694359  0.45099469\n",
-      "  0.88293092  0.52464455  0.82745386  0.65187024  0.76006829  0.67935165\n",
-      "  0.70274853  0.77336056  0.58071879  0.82889127  0.50587492  0.86293008\n",
-      "  0.4542823   0.89187609  0.23316507  0.91005227  0.18391941  1.00128804\n",
-      "  0.09222354  0.92421533 -0.06803879  1.02278302 -0.0932504   0.94202108\n",
-      " -0.16134181  0.95402314 -0.26573774  0.94440158 -0.38480458  0.93409124\n",
-      " -0.42682494  0.89243439 -0.50633531  0.74949231 -0.59058889  0.7854618\n",
-      " -0.60162466  0.71432226 -0.73195569  0.60553017 -0.72463751  0.49124826\n",
-      " -0.78720494  0.42579496 -0.83852881  0.35105424 -0.85630914  0.204259\n",
-      " -0.9488883   0.05983375 -0.94623862 -0.01680756 -0.86474459 -0.08300335\n",
-      " -0.92874181 -0.15427796 -0.87542833 -0.34635531 -0.89275066 -0.35771382\n",
-      " -0.82938589 -0.46087728 -0.84458246 -0.58224028 -0.74113579 -0.73935147\n",
-      " -0.73092373 -0.83404609 -0.6378948  -0.93605916 -0.60805649 -0.98286012\n",
-      " -0.58087211 -0.97960858 -0.50359449 -1.10716384 -0.38509766 -1.2278871\n",
-      " -0.36506896 -1.20859664 -0.21209444 -1.30198444 -0.14230339 -1.35062966\n",
-      " -0.09362381 -1.35915049  0.0301671  -1.32712835  0.10488527 -1.30180588\n",
-      "  0.13173065 -1.27509656  0.31333379 -1.29054342  0.30876424 -1.21854627\n",
-      "  0.43997867 -1.18996699  0.52841665 -1.11040469  0.64170737 -1.03282562\n",
-      "  0.6079672  -0.94190969  0.73419426 -0.82997679  0.83142591 -0.69619204\n",
-      "  0.85054665 -0.53679593  1.01344996 -0.43165388  1.05411309 -0.2623827\n",
-      "  1.08738323 -0.16447987]\n",
-      "  cov conditional:  [[ 2.19589237e-05  8.60942875e-07  1.92591423e-05 ... -2.29010031e-06\n",
-      "  -1.35315148e-04 -8.19628199e-07]\n",
-      " [ 8.60942875e-07  2.53822570e-05  7.57604246e-07 ...  2.35611240e-05\n",
-      "  -4.34225278e-06 -1.54825759e-04]\n",
-      " [ 1.92591423e-05  7.57604246e-07  1.68948505e-05 ... -2.00284856e-06\n",
-      "  -1.18924650e-04 -7.41893570e-07]\n",
-      " ...\n",
-      " [-2.29010031e-06  2.35611240e-05 -2.00284856e-06 ...  5.69512068e-05\n",
-      "   1.92227665e-05 -1.68093556e-04]\n",
-      " [-1.35315148e-04 -4.34225278e-06 -1.18924650e-04 ...  1.92227665e-05\n",
-      "   1.00361964e-03 -3.13697618e-07]\n",
-      " [-8.19628199e-07 -1.54825759e-04 -7.41893570e-07 ... -1.68093556e-04\n",
-      "  -3.13697618e-07  1.11454831e-03]]\n",
-      "  mean noise:  [-1.65942444e-02 -2.28275517e-02 -1.47765242e-02  3.22257405e-03\n",
-      "  1.38787914e-02 -3.47264896e-02  1.70598038e-02  2.65190801e-02\n",
-      "  3.17049876e-02  3.88155664e-03  2.48197036e-02  1.47292309e-02\n",
-      "  1.02195160e-02  2.38240399e-02 -1.48132938e-03 -2.65828508e-02\n",
-      " -9.01204558e-03  1.65113091e-02 -3.35147640e-02 -1.37770987e-02\n",
-      "  6.17911519e-04 -1.30269949e-02 -1.74245804e-02  1.16041194e-03\n",
-      " -6.24335419e-02  1.19015723e-02  5.98257146e-02  2.47273783e-02\n",
-      "  9.29179864e-03 -4.42606423e-02  1.72605901e-03  4.64977671e-02\n",
-      "  6.43294049e-02 -4.68294593e-02 -5.50047720e-03  3.07771964e-02\n",
-      " -2.90042168e-02  7.04388396e-03 -1.21339402e-02 -4.05819371e-03\n",
-      "  2.40059142e-02 -2.40744845e-02 -1.21029005e-02 -2.29000473e-02\n",
-      " -5.67124795e-03  6.89888488e-02  1.04156044e-02 -2.86694245e-02\n",
-      " -4.17077584e-02 -2.94098443e-02  3.05853581e-02 -1.82904555e-03\n",
-      " -2.92306448e-02  2.32974799e-02 -1.31274947e-02 -6.71576186e-03\n",
-      " -1.50606700e-03 -3.18960458e-02 -1.58929264e-02  1.22977468e-02\n",
-      "  5.28003246e-02  5.29593557e-02  3.52151086e-02  2.57622202e-02\n",
-      " -5.18567586e-02 -1.14020978e-02  1.60979886e-02 -4.29259611e-02\n",
-      " -2.40228041e-02  4.68264712e-02  1.51712116e-02 -4.39064161e-02\n",
-      " -1.84824865e-02 -4.24933590e-02  3.32615466e-02 -2.20024280e-02\n",
-      " -2.77111567e-02  3.60851864e-02  9.76480980e-03  3.48809287e-02\n",
-      " -3.07274471e-02  4.54079375e-02 -3.34502874e-03  6.38295518e-03\n",
-      "  3.16548640e-02 -7.60770238e-02  2.17389498e-02 -1.99659208e-02\n",
-      " -2.40891509e-02  3.81494162e-02  3.36772304e-02 -3.36953220e-02\n",
-      " -3.71744364e-02  1.83398043e-02 -2.14601974e-02  3.76411036e-02\n",
-      "  1.73259292e-02  2.91761415e-02 -1.83095773e-02 -7.53688651e-03\n",
-      " -5.33674777e-03 -2.57188986e-02  5.43338490e-02 -3.36785610e-02\n",
-      " -4.24665853e-02  1.22918130e-02  4.53000018e-02 -1.66793196e-02\n",
-      " -4.06256737e-03  1.13564670e-02 -1.15504110e-02  2.99118434e-03\n",
-      " -4.43579941e-02  1.15578286e-02  6.92865503e-02  2.12255814e-02\n",
-      "  2.16813985e-02  2.25742590e-02  5.10244291e-05  1.20039379e-02\n",
-      "  5.10789269e-02 -1.77324307e-02 -5.00996543e-02  9.54837501e-03\n",
-      " -4.03506293e-02 -2.80883407e-02 -3.70424711e-02  2.21976824e-03]\n",
-      "  cov noise:  [[ 2.19589237e-05  8.60942875e-07  1.92591423e-05 ... -2.29010031e-06\n",
-      "  -1.35315148e-04 -8.19628199e-07]\n",
-      " [ 8.60942875e-07  2.53822570e-05  7.57604246e-07 ...  2.35611240e-05\n",
-      "  -4.34225278e-06 -1.54825759e-04]\n",
-      " [ 1.92591423e-05  7.57604246e-07  1.68948505e-05 ... -2.00284856e-06\n",
-      "  -1.18924650e-04 -7.41893570e-07]\n",
-      " ...\n",
-      " [-2.29010031e-06  2.35611240e-05 -2.00284856e-06 ...  5.69512068e-05\n",
-      "   1.92227665e-05 -1.68093556e-04]\n",
-      " [-1.35315148e-04 -4.34225278e-06 -1.18924650e-04 ...  1.92227665e-05\n",
-      "   1.00361964e-03 -3.13697618e-07]\n",
-      " [-8.19628199e-07 -1.54825759e-04 -7.41893570e-07 ... -1.68093556e-04\n",
-      "  -3.13697618e-07  1.11454831e-03]]\n",
-      "  mean:  [ 1.00374835 -0.00146672  0.99886837  0.09707712  0.98429504  0.1946945\n",
-      "  0.96022137  0.29032387  0.92691654  0.38296436  0.88472872  0.47171532\n",
-      "  0.83407857  0.55579082  0.77544455  0.63449153  0.70938054  0.70715318\n",
-      "  0.63651573  0.77311385  0.55754965  0.83173084  0.47325859  0.88242841\n",
-      "  0.38448368  0.92471029  0.29210647  0.95815817  0.19700447  0.98243397\n",
-      "  0.10002499  0.99728837  0.00200969  1.0025535  -0.09617519  0.99812433\n",
-      " -0.19361318  0.98394799 -0.28934494  0.96006544 -0.38240246  0.92666425\n",
-      " -0.47184286  0.88409729 -0.55676844  0.83286291 -0.63634056  0.77356018\n",
-      " -0.70980162  0.70682629 -0.77646238  0.63331589 -0.83567882  0.55373871\n",
-      " -0.88683833  0.46888929 -0.92936302  0.3796436  -0.96273507  0.28692077\n",
-      " -0.98652786  0.19163218 -1.00045455  0.09466428 -1.00442432 -0.00311301\n",
-      " -0.99854493 -0.10082341 -0.98306958 -0.19754575 -0.95834374 -0.29229921\n",
-      " -0.9247392  -0.38408464 -0.88261801 -0.47195888 -0.83235945 -0.55509447\n",
-      " -0.77437915 -0.63278045 -0.70913352 -0.70436375 -0.63713852 -0.76922771\n",
-      " -0.55897739 -0.82679822 -0.47532635 -0.87655706 -0.38699276 -0.91804623\n",
-      " -0.29488833 -0.95088306 -0.19997587 -0.97476459 -0.10322023 -0.98944989\n",
-      " -0.00556695 -0.9947641   0.09208055 -0.99059728  0.18888602 -0.97692123\n",
-      "  0.28405744 -0.95379715  0.37679349 -0.92140435  0.46623976 -0.88004504\n",
-      "  0.5514735  -0.83013252  0.6315413  -0.77218678  0.70552153 -0.70681876\n",
-      "  0.7725816  -0.63467104  0.83203469 -0.55640011  0.88333741 -0.4727084\n",
-      "  0.92603996 -0.38436917  0.95974483 -0.29221205  0.98409928 -0.19708912\n",
-      "  0.99882207 -0.09986342]\n",
-      "  cov:  [[ 0.01500081 -0.00046878  0.01494809 ... -0.00032539  0.01482817\n",
-      "  -0.00041116]\n",
-      " [-0.00046878  0.01375864 -0.00053158 ...  0.0131832  -0.00040245\n",
-      "   0.01356423]\n",
-      " [ 0.01494809 -0.00053158  0.01510669 ... -0.00039911  0.01457032\n",
-      "  -0.00047863]\n",
-      " ...\n",
-      " [-0.00032539  0.0131832  -0.00039911 ...  0.01352281 -0.00025699\n",
-      "   0.01344183]\n",
-      " [ 0.01482817 -0.00040245  0.01457032 ... -0.00025699  0.01486929\n",
-      "  -0.00034406]\n",
-      " [-0.00041116  0.01356423 -0.00047863 ...  0.01344183 -0.00034406\n",
-      "   0.01359764]]\n",
-      "  mean w/ noise:  [ 1.00466415e+00 -4.59680453e-04  9.99547783e-01  9.74365585e-02\n",
-      "  9.85309661e-01  1.95881668e-01  9.57678377e-01  2.91533634e-01\n",
-      "  9.28147871e-01  3.83070831e-01  8.86047010e-01  4.72461659e-01\n",
-      "  8.32058246e-01  5.55866818e-01  7.78208781e-01  6.37171991e-01\n",
-      "  7.11141366e-01  7.08259124e-01  6.37314196e-01  7.73463101e-01\n",
-      "  5.55269568e-01  8.31451865e-01  4.73525455e-01  8.81738204e-01\n",
-      "  3.84727973e-01  9.23897517e-01  2.92672369e-01  9.55710957e-01\n",
-      "  1.97392992e-01  9.83255409e-01  1.00228461e-01  9.97981037e-01\n",
-      "  2.48749034e-03  1.00538501e+00 -9.47366320e-02  9.98557025e-01\n",
-      " -1.93085026e-01  9.82968287e-01 -2.89830107e-01  9.60339895e-01\n",
-      " -3.83025223e-01  9.26867143e-01 -4.74516607e-01  8.83858685e-01\n",
-      " -5.56475792e-01  8.33591228e-01 -6.36880851e-01  7.73262106e-01\n",
-      " -7.08365967e-01  7.07144042e-01 -7.77717508e-01  6.35081809e-01\n",
-      " -8.35321705e-01  5.54501941e-01 -8.88820664e-01  4.69390450e-01\n",
-      " -9.32414302e-01  3.78924106e-01 -9.64611571e-01  2.84481806e-01\n",
-      " -9.86450578e-01  1.94533744e-01 -1.00105337e+00  9.54330073e-02\n",
-      " -1.00455652e+00 -9.92844324e-04 -9.99920214e-01 -1.02444703e-01\n",
-      " -9.83087204e-01 -1.96623682e-01 -9.58487301e-01 -2.90555482e-01\n",
-      " -9.23285744e-01 -3.83794660e-01 -8.85612324e-01 -4.70824602e-01\n",
-      " -8.33185453e-01 -5.55245266e-01 -7.73017446e-01 -6.30344493e-01\n",
-      " -7.10312545e-01 -7.07035195e-01 -6.38576508e-01 -7.67699224e-01\n",
-      " -5.59683095e-01 -8.29396616e-01 -4.74739404e-01 -8.76175985e-01\n",
-      " -3.87669133e-01 -9.19951406e-01 -2.93754766e-01 -9.51777390e-01\n",
-      " -2.00737372e-01 -9.75243233e-01 -1.03183228e-01 -9.91314033e-01\n",
-      " -6.19855422e-03 -9.95063292e-01  9.10665895e-02 -9.91112970e-01\n",
-      "  1.89761100e-01 -9.77267846e-01  2.84356073e-01 -9.56845088e-01\n",
-      "  3.77760340e-01 -9.19399374e-01  4.64453589e-01 -8.79858123e-01\n",
-      "  5.50030425e-01 -8.30710992e-01  6.32563391e-01 -7.71315835e-01\n",
-      "  7.01981063e-01 -7.06722209e-01  7.70203981e-01 -6.35258135e-01\n",
-      "  8.30187431e-01 -5.53830779e-01  8.84743769e-01 -4.72349931e-01\n",
-      "  9.26570909e-01 -3.82284538e-01  9.60826998e-01 -2.91477629e-01\n",
-      "  9.84215073e-01 -1.96734863e-01  9.97367671e-01 -9.71234787e-02]\n",
-      "  cov w/ noise:  [[ 1.60201733e-02 -7.54615139e-04  1.52310371e-02 ... -2.87080189e-04\n",
-      "   1.50061826e-02 -6.27200810e-04]\n",
-      " [-7.54615139e-04  1.46450655e-02 -3.96395899e-04 ...  1.29363080e-02\n",
-      "  -5.63096832e-04  1.36498334e-02]\n",
-      " [ 1.52310371e-02 -3.96395899e-04  1.65072063e-02 ...  3.70643825e-05\n",
-      "   1.48613662e-02 -1.34726738e-04]\n",
-      " ...\n",
-      " [-2.87080189e-04  1.29363080e-02  3.70643825e-05 ...  1.44498606e-02\n",
-      "  -1.00627858e-04  1.34179606e-02]\n",
-      " [ 1.50061826e-02 -5.63096832e-04  1.48613662e-02 ... -1.00627858e-04\n",
-      "   1.60505933e-02 -4.93899878e-04]\n",
-      " [-6.27200810e-04  1.36498334e-02 -1.34726738e-04 ...  1.34179606e-02\n",
-      "  -4.93899878e-04  1.47395394e-02]]\n",
-      "  mean conditional:  [ 1.00582645 -0.12237192  1.01480413 -0.00284308  1.012884    0.11506207\n",
-      "  0.99885569  0.23087295  0.97243106  0.34372717  0.93413152  0.45239664\n",
-      "  0.88515082  0.55542544  0.82717389  0.65123296  0.76205813  0.73835708\n",
-      "  0.69160505  0.81541891  0.61720652  0.88147222  0.53968804  0.93580831\n",
-      "  0.45922508  0.97807866  0.37536338  1.00845047  0.28730007  1.02729394\n",
-      "  0.19432492  1.03523927  0.09611483  1.03292002 -0.00677324  1.02078409\n",
-      " -0.11281125  0.99896249 -0.21966751  0.96713824 -0.32449195  0.9246941\n",
-      " -0.42449481  0.87097369 -0.51722029  0.8055005  -0.60099346  0.72831578\n",
-      " -0.67475185  0.64015907 -0.73808411  0.54249925 -0.79081329  0.43739002\n",
-      " -0.83311271  0.32713007 -0.86514421  0.21424357 -0.88703864  0.10097371\n",
-      " -0.89902217 -0.01090259 -0.90128368 -0.12017599 -0.89418393 -0.2262286\n",
-      " -0.87846282 -0.32904865 -0.85502869 -0.42895158 -0.82511321 -0.52663739\n",
-      " -0.78985267 -0.62246906 -0.7500815  -0.71644852 -0.70616536 -0.80818343\n",
-      " -0.65755914 -0.8966951  -0.60315372 -0.98075285 -0.5413571  -1.05908858\n",
-      " -0.47032352 -1.13048884 -0.38859867 -1.19380948 -0.29550296 -1.2481092\n",
-      " -0.19151958 -1.29258459 -0.07857776 -1.32659748  0.04023987 -1.34992579\n",
-      "  0.16078709 -1.36241423  0.27852402 -1.36414625  0.38909808 -1.3552439\n",
-      "  0.48895747 -1.33546779  0.57551839 -1.30421367  0.64780658 -1.26056742\n",
-      "  0.70621984 -1.20363537  0.75259816 -1.13284163  0.78987269 -1.04850596\n",
-      "  0.82125947 -0.95174086  0.8499317  -0.84469477  0.87815435 -0.72991376\n",
-      "  0.90694537 -0.61018971  0.9358424  -0.4879983   0.96401305 -0.36527119\n",
-      "  0.98862233 -0.24241185] , sample:  [ 1.05162166 -0.13275838  1.06846808  0.02101553  0.99637486  0.06521121\n",
-      "  1.00493158  0.23260148  0.95746296  0.37182187  0.956651    0.43319909\n",
-      "  0.87656871  0.54827965  0.83852278  0.68837706  0.75229874  0.77407263\n",
-      "  0.68566515  0.75686327  0.59369312  0.91540102  0.56084805  0.91440755\n",
-      "  0.42048965  0.96125329  0.37263558  1.03337482  0.27605199  1.05050149\n",
-      "  0.18611584  1.02797974  0.16774946  1.04103561  0.05115738  0.99450522\n",
-      " -0.11697791  0.98861974 -0.2394107   0.95979328 -0.35258833  0.91309518\n",
-      " -0.46619224  0.91671023 -0.52288306  0.76610888 -0.58041883  0.80484007\n",
-      " -0.68634804  0.61281051 -0.73022548  0.59741114 -0.78682667  0.38508293\n",
-      " -0.7973772   0.32621499 -0.85407293  0.14512379 -0.89103962  0.14263719\n",
-      " -0.92181539 -0.01886993 -0.90743404 -0.08646617 -0.90967279 -0.290268\n",
-      " -0.90270304 -0.33513631 -0.85161121 -0.4062616  -0.8174775  -0.53091157\n",
-      " -0.7553495  -0.60193423 -0.70645292 -0.68423332 -0.70195209 -0.84829571\n",
-      " -0.67220359 -0.88254616 -0.60268585 -0.95664397 -0.53390025 -1.06882228\n",
-      " -0.48567896 -1.163169   -0.39765463 -1.22235883 -0.34264779 -1.23627797\n",
-      " -0.18110466 -1.28167758 -0.07901707 -1.33437371 -0.03348748 -1.35040056\n",
-      "  0.24384935 -1.33344496  0.29722605 -1.38902258  0.40429566 -1.32908897\n",
-      "  0.47475153 -1.31900427  0.56058034 -1.31628231  0.66061059 -1.27186108\n",
-      "  0.76504349 -1.2589345   0.78886628 -1.08871454  0.75231461 -1.07192456\n",
-      "  0.84569769 -0.96934916  0.76457198 -0.86365774  0.90847141 -0.69624583\n",
-      "  0.8857039  -0.58460965  0.93589305 -0.43010233  0.90398986 -0.40270282\n",
-      "  0.97772377 -0.27697309]\n",
-      "  cov conditional:  [[ 2.19589442e-05  8.56190126e-07  1.92671871e-05 ... -1.39135165e-06\n",
-      "  -1.34812667e-04 -6.90306241e-06]\n",
-      " [ 8.56190126e-07  2.53822788e-05  7.38702068e-07 ...  2.38079278e-05\n",
-      "  -6.67669025e-06 -1.56052517e-04]\n",
-      " [ 1.92671871e-05  7.38702068e-07  1.69088044e-05 ... -1.23865090e-06\n",
-      "  -1.18465837e-04 -5.95382153e-06]\n",
-      " ...\n",
-      " [-1.39135165e-06  2.38079278e-05 -1.23865090e-06 ...  5.41606080e-05\n",
-      "   1.64169818e-05 -1.66014423e-04]\n",
-      " [-1.34812667e-04 -6.67669025e-06 -1.18465837e-04 ...  1.64169818e-05\n",
-      "   9.78502081e-04  4.82871258e-05]\n",
-      " [-6.90306241e-06 -1.56052517e-04 -5.95382153e-06 ... -1.66014423e-04\n",
-      "   4.82871258e-05  1.12939153e-03]]\n",
-      "  mean noise:  [-4.57952155e-02  1.03864578e-02 -5.36639448e-02 -2.38586112e-02\n",
-      "  1.65091398e-02  4.98508615e-02 -6.07588840e-03 -1.72853464e-03\n",
-      "  1.49681065e-02 -2.80946993e-02 -2.25194798e-02  1.91975538e-02\n",
-      "  8.58211601e-03  7.14578663e-03 -1.13488872e-02 -3.71440950e-02\n",
-      "  9.75938932e-03 -3.57155541e-02  5.93990099e-03  5.85556380e-02\n",
-      "  2.35134000e-02 -3.39287970e-02 -2.11600043e-02  2.14007595e-02\n",
-      "  3.87354321e-02  1.68253662e-02  2.72780238e-03 -2.49243499e-02\n",
-      "  1.12480844e-02 -2.32075517e-02  8.20908012e-03  7.25953764e-03\n",
-      " -7.16346307e-02 -8.11558830e-03 -5.79306213e-02  2.62788675e-02\n",
-      "  4.16665118e-03  1.03427485e-02  1.97431850e-02  7.34496375e-03\n",
-      "  2.80963797e-02  1.15989133e-02  4.16974306e-02 -4.57365405e-02\n",
-      "  5.66277640e-03  3.93916197e-02 -2.05746300e-02 -7.65242875e-02\n",
-      "  1.15961922e-02  2.73485621e-02 -7.85862312e-03 -5.49118987e-02\n",
-      " -3.98662328e-03  5.23070866e-02 -3.57355093e-02  9.15078795e-04\n",
-      " -1.10712811e-02  6.91197827e-02  4.00097392e-03 -4.16634723e-02\n",
-      "  2.27932216e-02  7.96733875e-03  6.15036392e-03 -3.37098189e-02\n",
-      "  1.54888647e-02  6.40394005e-02  2.42402228e-02  6.08765911e-03\n",
-      " -3.41748154e-03 -2.26899722e-02 -7.63571511e-03  4.27418344e-03\n",
-      " -3.45031722e-02 -2.05348279e-02 -4.36285819e-02 -3.22151951e-02\n",
-      " -4.21326978e-03  4.01122781e-02  1.46444423e-02 -1.41489405e-02\n",
-      " -4.67871544e-04 -2.41088813e-02 -7.45685839e-03  9.73370392e-03\n",
-      "  1.53554475e-02  3.26801541e-02  9.05596146e-03  2.85493517e-02\n",
-      "  4.71448213e-02 -1.18312290e-02 -1.04149168e-02 -1.09070142e-02\n",
-      "  4.39308346e-04  7.77622725e-03  7.37273461e-02  4.74775178e-04\n",
-      " -8.30622659e-02 -2.89692737e-02 -1.87020292e-02  2.48763310e-02\n",
-      " -1.51975813e-02 -2.61549295e-02  1.42059431e-02 -1.64635168e-02\n",
-      "  1.49380525e-02  1.20686372e-02 -1.28040117e-02  1.12936553e-02\n",
-      " -5.88236518e-02  5.52991322e-02 -3.62681192e-02 -4.41270967e-02\n",
-      "  3.75580759e-02  2.34186004e-02 -2.44382295e-02  1.76083009e-02\n",
-      "  8.53597193e-02  1.89629711e-02 -3.03170593e-02 -3.36679351e-02\n",
-      "  2.12414737e-02 -2.55800531e-02 -5.06427552e-05 -5.78959650e-02\n",
-      "  6.00231865e-02  3.74316250e-02  1.08985646e-02  3.45612323e-02]\n",
-      "  cov noise:  [[ 2.19589442e-05  8.56190126e-07  1.92671871e-05 ... -1.39135165e-06\n",
-      "  -1.34812667e-04 -6.90306241e-06]\n",
-      " [ 8.56190126e-07  2.53822788e-05  7.38702068e-07 ...  2.38079278e-05\n",
-      "  -6.67669025e-06 -1.56052517e-04]\n",
-      " [ 1.92671871e-05  7.38702068e-07  1.69088044e-05 ... -1.23865090e-06\n",
-      "  -1.18465837e-04 -5.95382153e-06]\n",
-      " ...\n",
-      " [-1.39135165e-06  2.38079278e-05 -1.23865090e-06 ...  5.41606080e-05\n",
-      "   1.64169818e-05 -1.66014423e-04]\n",
-      " [-1.34812667e-04 -6.67669025e-06 -1.18465837e-04 ...  1.64169818e-05\n",
-      "   9.78502081e-04  4.82871258e-05]\n",
-      " [-6.90306241e-06 -1.56052517e-04 -5.95382153e-06 ... -1.66014423e-04\n",
-      "   4.82871258e-05  1.12939153e-03]]\n",
-      "  mean:  [ 1.00000707e+00 -1.19765369e-03  9.95733592e-01  9.74896017e-02\n",
-      "  9.81675775e-01  1.95269599e-01  9.57985240e-01  2.91097665e-01\n",
-      "  9.24937340e-01  3.83953180e-01  8.82898115e-01  4.72894485e-01\n",
-      "  8.32302316e-01  5.57078978e-01  7.73625370e-01  6.35741134e-01\n",
-      "  7.07415954e-01  7.08190264e-01  6.34316877e-01  7.73821388e-01\n",
-      "  5.55028952e-01  8.32088938e-01  4.70287011e-01  8.82497738e-01\n",
-      "  3.80868335e-01  9.24585247e-01  2.87609993e-01  9.57915836e-01\n",
-      "  1.91406744e-01  9.82107973e-01  9.32030273e-02  9.96880934e-01\n",
-      " -6.01880096e-03  1.00207537e+00 -1.05248637e-01  9.97638601e-01\n",
-      " -2.03433336e-01  9.83606910e-01 -2.99491193e-01  9.60094556e-01\n",
-      " -3.92381706e-01  9.27310300e-01 -4.81169434e-01  8.85557804e-01\n",
-      " -5.65043289e-01  8.35233088e-01 -6.43322688e-01  7.76808122e-01\n",
-      " -7.15425877e-01  7.10818686e-01 -7.80800272e-01  6.37874733e-01\n",
-      " -8.38885139e-01  5.58678595e-01 -8.89127692e-01  4.74039179e-01\n",
-      " -9.31019422e-01  3.84848310e-01 -9.64112502e-01  2.92031145e-01\n",
-      " -9.88019258e-01  1.96499017e-01 -1.00243029e+00  9.91557318e-02\n",
-      " -1.00714448e+00  9.06324709e-04 -1.00209055e+00 -9.73391635e-02\n",
-      " -9.87332537e-01 -1.94640118e-01 -9.63067000e-01 -2.90006890e-01\n",
-      " -9.29591352e-01 -3.82408172e-01 -8.87258796e-01 -4.70832717e-01\n",
-      " -8.36472749e-01 -5.54371134e-01 -7.77694302e-01 -6.32243823e-01\n",
-      " -7.11458935e-01 -7.03792456e-01 -6.38412530e-01 -7.68454918e-01\n",
-      " -5.59297696e-01 -8.25742856e-01 -4.74928494e-01 -8.75223445e-01\n",
-      " -3.86186666e-01 -9.16500700e-01 -2.93973233e-01 -9.49213257e-01\n",
-      " -1.99173593e-01 -9.73042739e-01 -1.02640973e-01 -9.87726573e-01\n",
-      " -5.19856934e-03 -9.93094167e-01  9.23443828e-02 -9.89086552e-01\n",
-      "  1.89166966e-01 -9.75749249e-01  2.84397160e-01 -9.53183420e-01\n",
-      "  3.77108424e-01 -9.21537209e-01  4.66337584e-01 -8.81000935e-01\n",
-      "  5.51118015e-01 -8.31824528e-01  6.30521292e-01 -7.74375509e-01\n",
-      "  7.03691495e-01 -7.09182030e-01  7.69884342e-01 -6.36909118e-01\n",
-      "  8.28509660e-01 -5.58306721e-01  8.79125831e-01 -4.74175796e-01\n",
-      "  9.21370576e-01 -3.85366309e-01  9.54899989e-01 -2.92766509e-01\n",
-      "  9.79383389e-01 -1.97273212e-01  9.94505554e-01 -9.97756307e-02]\n",
-      "  cov:  [[0.01312491 0.00038065 0.01304664 ... 0.00035021 0.01301173 0.00035641]\n",
-      " [0.00038065 0.01282547 0.0003652  ... 0.01242775 0.00040116 0.01272103]\n",
-      " [0.01304664 0.0003652  0.01317161 ... 0.00032059 0.01273806 0.00033529]\n",
-      " ...\n",
-      " [0.00035021 0.01242775 0.00032059 ... 0.01287691 0.00038655 0.01274527]\n",
-      " [0.01301173 0.00040116 0.01273806 ... 0.00038655 0.01310109 0.00038331]\n",
-      " [0.00035641 0.01272103 0.00033529 ... 0.01274527 0.00038331 0.01283048]]\n",
-      "  mean w/ noise:  [ 1.00142361 -0.00241625  0.99740532  0.09956     0.98412212  0.19607708\n",
-      "  0.95779004  0.2908187   0.92496211  0.38302144  0.88415029  0.471334\n",
-      "  0.83123628  0.5558799   0.77475209  0.63821465  0.70828218  0.70903967\n",
-      "  0.63520393  0.77415755  0.55451588  0.83377493  0.47028169  0.88314053\n",
-      "  0.37959355  0.92486088  0.28733974  0.95603882  0.19263373  0.98075142\n",
-      "  0.09452366  0.99859952 -0.00342695  1.00074159 -0.10392417  0.99893399\n",
-      " -0.20209678  0.98533418 -0.29932758  0.95927699 -0.39564783  0.93031935\n",
-      " -0.48017924  0.88657228 -0.56592292  0.83523844 -0.64234354  0.7760621\n",
-      " -0.71617362  0.71135364 -0.78155107  0.63712556 -0.83786039  0.55865488\n",
-      " -0.88814132  0.47675987 -0.93022022  0.38289425 -0.96480799  0.28964621\n",
-      " -0.98768377  0.19699432 -1.00289325  0.09651488 -1.00476315  0.00186693\n",
-      " -1.00353898 -0.09714408 -0.98778379 -0.19494678 -0.96319498 -0.28964158\n",
-      " -0.9303804  -0.3831968  -0.88751245 -0.47078856 -0.83450729 -0.55553203\n",
-      " -0.77617023 -0.63130748 -0.71420518 -0.70553555 -0.64157544 -0.76695094\n",
-      " -0.55758064 -0.82851737 -0.47410941 -0.87538367 -0.38447856 -0.91305422\n",
-      " -0.29496665 -0.94655262 -0.19753877 -0.97134782 -0.10125363 -0.98792042\n",
-      " -0.00821359 -0.99232848  0.09198588 -0.99053092  0.18864642 -0.97471586\n",
-      "  0.28425281 -0.95394655  0.37952112 -0.92310043  0.46856467 -0.88110203\n",
-      "  0.5490201  -0.82898441  0.63296693 -0.77093322  0.70130708 -0.71025975\n",
-      "  0.76985437 -0.63577615  0.82782073 -0.55584304  0.87603188 -0.47651938\n",
-      "  0.92326267 -0.38506522  0.95385589 -0.29416695  0.97713108 -0.19786484\n",
-      "  0.99314419 -0.10140965]\n",
-      "  cov w/ noise:  [[0.01362129 0.00058227 0.01265326 ... 0.00063959 0.01260571 0.00082827]\n",
-      " [0.00058227 0.01391241 0.0004533  ... 0.01241597 0.00077955 0.01274722]\n",
-      " [0.01265326 0.0004533  0.01374601 ... 0.00030262 0.01235099 0.00066597]\n",
-      " ...\n",
-      " [0.00063959 0.01241597 0.00030262 ... 0.0137802  0.00074776 0.01278541]\n",
-      " [0.01260571 0.00077955 0.01235099 ... 0.00074776 0.01366027 0.00093493]\n",
-      " [0.00082827 0.01274722 0.00066597 ... 0.01278541 0.00093493 0.01383613]]\n",
-      "  mean conditional:  [ 0.99410184 -0.0108298   1.00952198  0.09595913  1.01397253  0.19600157\n",
-      "  1.00589928  0.29015152  0.9840938   0.37935873  0.94791088  0.46438159\n",
-      "  0.89743176  0.54567615  0.83364147  0.62324211  0.7582566   0.69665658\n",
-      "  0.67356504  0.76499897  0.5819189   0.82718645  0.48548277  0.88192195\n",
-      "  0.38587521  0.92814894  0.28404633  0.96517181  0.18023433  0.99272975\n",
-      "  0.0744791   1.01103868 -0.0332329   1.02061328 -0.14244838  1.02206775\n",
-      " -0.25210303  1.01600751 -0.36052824  1.00271059 -0.46569474  0.98219053\n",
-      " -0.56562302  0.95422004 -0.65857593  0.91825841 -0.74342613  0.873691\n",
-      " -0.81952969  0.82000335 -0.88660975  0.75675865 -0.94459877  0.68375483\n",
-      " -0.99367226  0.60111872 -1.03387664  0.50950252 -1.06511514  0.40998878\n",
-      " -1.08713246  0.30411218 -1.09941093  0.19371611 -1.10133561  0.0808636\n",
-      " -1.09237217 -0.03255766 -1.0723682  -0.14483923 -1.04158009 -0.25484755\n",
-      " -1.00083663 -0.36174652 -0.95133054 -0.46518176 -0.89435523 -0.56490326\n",
-      " -0.83114468 -0.66084347 -0.76263172 -0.75286718 -0.68936962 -0.84078293\n",
-      " -0.61147169 -0.92416486 -0.52884798 -1.00212143 -0.44136439 -1.07337683\n",
-      " -0.34912846 -1.13627502 -0.25269688 -1.18909283 -0.15301721 -1.23036699\n",
-      " -0.05163534 -1.25902033  0.04973506 -1.27446107  0.14918073 -1.27648803\n",
-      "  0.24504913 -1.2648389   0.3357661  -1.23894778  0.42043974 -1.19799059\n",
-      "  0.49863447 -1.14101355  0.57038082 -1.06748617  0.63633508 -0.97775015\n",
-      "  0.69710377 -0.87322253  0.75349626 -0.75662954  0.80583992 -0.63149625\n",
-      "  0.85418265 -0.50206096  0.89813579 -0.37225024  0.93722289 -0.24557641\n",
-      "  0.96868283 -0.12272836] , sample:  [ 1.0192342  -0.03604606  0.9509144   0.1072186   0.95754532  0.19442316\n",
-      "  1.04083275  0.24707118  1.04528818  0.40647507  0.97541873  0.49449584\n",
-      "  0.8771037   0.53335075  0.87049806  0.59567504  0.74035617  0.67957596\n",
-      "  0.72009483  0.74346663  0.54332795  0.83819372  0.43497811  0.91252651\n",
-      "  0.39624056  0.91689251  0.22935505  0.99679345  0.21788752  0.97121565\n",
-      "  0.06036061  1.00308444  0.01422633  1.03993529 -0.14125681  1.01697782\n",
-      " -0.20549396  1.02346975 -0.36566397  0.98324652 -0.51359723  1.00502977\n",
-      " -0.61380006  0.92720536 -0.62063973  0.91795668 -0.78226446  0.86941987\n",
-      " -0.77969609  0.84257628 -0.87052027  0.71673661 -0.94622935  0.71139387\n",
-      " -1.03064392  0.59288862 -1.00493842  0.53842234 -1.07948605  0.40314216\n",
-      " -1.06549509  0.31533217 -1.06475275  0.20527693 -1.10169566  0.15153116\n",
-      " -1.16335566 -0.08761004 -1.08287579 -0.20044606 -1.05057028 -0.28110556\n",
-      " -0.96032973 -0.31072039 -0.96848523 -0.47623454 -0.88590949 -0.55912933\n",
-      " -0.811311   -0.68872153 -0.76516299 -0.72562078 -0.71604904 -0.79841176\n",
-      " -0.61523587 -0.95983997 -0.50640878 -0.97816544 -0.420605   -1.07341923\n",
-      " -0.38301093 -1.09704371 -0.22637062 -1.23241362 -0.19993069 -1.28274941\n",
-      " -0.01467164 -1.24032776  0.08208328 -1.2843254   0.09679406 -1.25884267\n",
-      "  0.22329818 -1.24262607  0.3404166  -1.20519474  0.46804229 -1.20511784\n",
-      "  0.49411744 -1.19369114  0.55271069 -1.0669716   0.61181699 -0.98940322\n",
-      "  0.697432   -0.87674376  0.74919818 -0.78975486  0.87396314 -0.6310661\n",
-      "  0.83694532 -0.46989448  0.91835601 -0.38343449  0.87089878 -0.19457923\n",
-      "  0.98456805 -0.10530792]\n",
-      "  cov conditional:  [[ 2.19609822e-05  8.63838375e-07  1.92610199e-05 ... -1.40280856e-06\n",
-      "  -1.33642859e-04 -3.21577160e-06]\n",
-      " [ 8.63838375e-07  2.53818462e-05  7.37315360e-07 ...  2.27806688e-05\n",
-      "  -2.08826153e-06 -1.56422687e-04]\n",
-      " [ 1.92610199e-05  7.37315360e-07  1.68964799e-05 ... -1.24210485e-06\n",
-      "  -1.17447536e-04 -2.69886247e-06]\n",
-      " ...\n",
-      " [-1.40280856e-06  2.27806688e-05 -1.24210485e-06 ...  5.24860859e-05\n",
-      "   1.03502825e-05 -1.58993239e-04]\n",
-      " [-1.33642859e-04 -2.08826153e-06 -1.17447536e-04 ...  1.03502825e-05\n",
-      "   9.73780861e-04 -6.82683983e-06]\n",
-      " [-3.21577160e-06 -1.56422687e-04 -2.69886247e-06 ... -1.58993239e-04\n",
-      "  -6.82683983e-06  1.12181470e-03]]\n",
-      "  mean noise:  [-2.51323560e-02  2.52162609e-02  5.86075796e-02 -1.12594699e-02\n",
-      "  5.64272104e-02  1.57840581e-03 -3.49334666e-02  4.30803440e-02\n",
-      " -6.11943758e-02 -2.71163377e-02 -2.75078542e-02 -3.01142541e-02\n",
-      "  2.03280608e-02  1.23254013e-02 -3.68565955e-02  2.75670756e-02\n",
-      "  1.79004316e-02  1.70806236e-02 -4.65297964e-02  2.15323438e-02\n",
-      "  3.85909506e-02 -1.10072680e-02  5.05046555e-02 -3.06045558e-02\n",
-      " -1.03653559e-02  1.12564268e-02  5.46912823e-02 -3.16216398e-02\n",
-      " -3.76531930e-02  2.15140929e-02  1.41184983e-02  7.95424195e-03\n",
-      " -4.74592356e-02 -1.93220056e-02 -1.19156843e-03  5.08992246e-03\n",
-      " -4.66090740e-02 -7.46223984e-03  5.13572271e-03  1.94640710e-02\n",
-      "  4.79024823e-02 -2.28392437e-02  4.81770435e-02  2.70146736e-02\n",
-      " -3.79362065e-02  3.01732996e-04  3.88383342e-02  4.27112723e-03\n",
-      " -3.98336035e-02 -2.25729374e-02 -1.60894761e-02  4.00220412e-02\n",
-      "  1.63058129e-03 -2.76390396e-02  3.69716600e-02  8.23010548e-03\n",
-      " -2.89382242e-02 -2.89198198e-02  1.43709091e-02  6.84662269e-03\n",
-      " -2.16373638e-02 -1.12199871e-02 -3.46581839e-02 -1.15608148e-02\n",
-      "  3.60052299e-04 -7.06675639e-02  7.09834972e-02  5.50523807e-02\n",
-      "  1.05075838e-02  5.56068302e-02  8.99018953e-03  2.62580168e-02\n",
-      " -4.05068985e-02 -5.10261285e-02  1.71546922e-02  1.10527840e-02\n",
-      " -8.44573800e-03 -5.77392463e-03 -1.98336822e-02  2.78780549e-02\n",
-      "  2.53126259e-03 -2.72464028e-02  2.66794210e-02 -4.23711680e-02\n",
-      "  3.76418297e-03  3.56751158e-02 -2.24391932e-02 -2.39559919e-02\n",
-      " -2.07593952e-02  4.23932651e-05  3.38824758e-02 -3.92313110e-02\n",
-      " -2.63262637e-02  4.33207908e-02  4.69134785e-02  5.23824230e-02\n",
-      " -3.69637011e-02 -1.86925631e-02 -3.23482162e-02  9.86433774e-03\n",
-      "  5.23866730e-02 -1.76453559e-02  2.17509509e-02 -2.22128251e-02\n",
-      " -4.65049929e-03 -3.37530358e-02 -4.76025427e-02  7.12725053e-03\n",
-      "  4.51703058e-03  5.26775896e-02  1.76701349e-02 -5.14574934e-04\n",
-      "  2.45180921e-02  1.16530680e-02 -3.28229208e-04  3.52123070e-03\n",
-      "  4.29807265e-03  3.31253115e-02 -6.81232160e-02 -4.30142936e-04\n",
-      "  1.72373310e-02 -3.21664822e-02 -2.02202261e-02  1.11842471e-02\n",
-      "  6.63241087e-02 -5.09971746e-02 -1.58852259e-02 -1.74204378e-02]\n",
-      "  cov noise:  [[ 2.19609822e-05  8.63838375e-07  1.92610199e-05 ... -1.40280856e-06\n",
-      "  -1.33642859e-04 -3.21577160e-06]\n",
-      " [ 8.63838375e-07  2.53818462e-05  7.37315360e-07 ...  2.27806688e-05\n",
-      "  -2.08826153e-06 -1.56422687e-04]\n",
-      " [ 1.92610199e-05  7.37315360e-07  1.68964799e-05 ... -1.24210485e-06\n",
-      "  -1.17447536e-04 -2.69886247e-06]\n",
-      " ...\n",
-      " [-1.40280856e-06  2.27806688e-05 -1.24210485e-06 ...  5.24860859e-05\n",
-      "   1.03502825e-05 -1.58993239e-04]\n",
-      " [-1.33642859e-04 -2.08826153e-06 -1.17447536e-04 ...  1.03502825e-05\n",
-      "   9.73780861e-04 -6.82683983e-06]\n",
-      " [-3.21577160e-06 -1.56422687e-04 -2.69886247e-06 ... -1.58993239e-04\n",
-      "  -6.82683983e-06  1.12181470e-03]]\n",
-      "  mean:  [ 9.97926016e-01  1.98639702e-03  9.92634006e-01  1.00276341e-01\n",
-      "  9.77826755e-01  1.97651695e-01  9.53653233e-01  2.93117735e-01\n",
-      "  9.20355251e-01  3.85717995e-01  8.78270496e-01  4.74544993e-01\n",
-      "  8.27828872e-01  5.58765074e-01  7.69532273e-01  6.37606532e-01\n",
-      "  7.03946918e-01  7.10355034e-01  6.31680495e-01  7.76352984e-01\n",
-      "  5.53371534e-01  8.34997312e-01  4.69716020e-01  8.85754020e-01\n",
-      "  3.81488085e-01  9.28153403e-01  2.89516866e-01  9.61757724e-01\n",
-      "  1.94649374e-01  9.86159707e-01  9.77365480e-02  1.00101640e+00\n",
-      " -3.59409682e-04  1.00607858e+00 -9.87427153e-02  1.00121816e+00\n",
-      " -1.96468469e-01  9.86442042e-01 -2.92550901e-01  9.61889767e-01\n",
-      " -3.85993568e-01  9.27849073e-01 -4.75813432e-01  8.84751152e-01\n",
-      " -5.61061095e-01  8.33156230e-01 -6.40864978e-01  7.73690514e-01\n",
-      " -7.14465217e-01  7.06979497e-01 -7.81176695e-01  6.33645260e-01\n",
-      " -8.40342454e-01  5.54363546e-01 -8.91328331e-01  4.69909892e-01\n",
-      " -9.33574659e-01  3.81133677e-01 -9.66611170e-01  2.88914365e-01\n",
-      " -9.90076400e-01  1.94128244e-01 -1.00375683e+00  9.76505971e-02\n",
-      " -1.00758686e+00  3.67326706e-04 -1.00164646e+00 -9.68157343e-02\n",
-      " -9.86134322e-01 -1.92957207e-01 -9.61328643e-01 -2.87085661e-01\n",
-      " -9.27563114e-01 -3.78241207e-01 -8.85197108e-01 -4.65552173e-01\n",
-      " -8.34611199e-01 -5.48266837e-01 -7.76211205e-01 -6.25709492e-01\n",
-      " -7.10432446e-01 -6.97242695e-01 -6.37762325e-01 -7.62262715e-01\n",
-      " -5.58773550e-01 -8.20194888e-01 -4.74187768e-01 -8.70502014e-01\n",
-      " -3.84888112e-01 -9.12669574e-01 -2.91858793e-01 -9.46219799e-01\n",
-      " -1.96117200e-01 -9.70736012e-01 -9.86537829e-02 -9.85888144e-01\n",
-      " -3.90412693e-04 -9.91460005e-01  9.77950772e-02 -9.87369732e-01\n",
-      "  1.95023464e-01 -9.73665335e-01  2.90393135e-01 -9.50492905e-01\n",
-      "  3.83007363e-01 -9.18083464e-01  4.71987941e-01 -8.76732931e-01\n",
-      "  5.56450627e-01 -8.26816300e-01  6.35517427e-01 -7.68809035e-01\n",
-      "  7.08348321e-01 -7.03295121e-01  7.74178419e-01 -6.30941946e-01\n",
-      "  8.32375257e-01 -5.52478342e-01  8.82437753e-01 -4.68676229e-01\n",
-      "  9.23938829e-01 -3.80327626e-01  9.56498750e-01 -2.88242071e-01\n",
-      "  9.79804827e-01 -1.93244659e-01  9.93646405e-01 -9.61903942e-02]\n",
-      "  cov:  [[1.55406967e-02 1.26403132e-04 1.55082435e-02 ... 1.91816345e-04\n",
-      "  1.53308939e-02 1.43813495e-04]\n",
-      " [1.26403132e-04 1.33365651e-02 2.53994201e-05 ... 1.29616129e-02\n",
-      "  2.19137740e-04 1.32389007e-02]\n",
-      " [1.55082435e-02 2.53994201e-05 1.57060574e-02 ... 7.86243465e-05\n",
-      "  1.50728276e-02 3.75568421e-05]\n",
-      " ...\n",
-      " [1.91816345e-04 1.29616129e-02 7.86243465e-05 ... 1.34027384e-02\n",
-      "  2.99043002e-04 1.32676313e-02]\n",
-      " [1.53308939e-02 2.19137740e-04 1.50728276e-02 ... 2.99043002e-04\n",
-      "  1.53568056e-02 2.43006931e-04]\n",
-      " [1.43813495e-04 1.32389007e-02 3.75568421e-05 ... 1.32676313e-02\n",
-      "  2.43006931e-04 1.33445049e-02]]\n",
-      "  mean w/ noise:  [ 0.99593432  0.00170922  0.99084169  0.10022108  0.97968184  0.19606917\n",
-      "  0.95514274  0.29251176  0.92042149  0.38354563  0.87850627  0.47376827\n",
-      "  0.82832238  0.5606367   0.77030876  0.63661756  0.70621352  0.71167673\n",
-      "  0.63263594  0.77692589  0.55273046  0.83247534  0.46982205  0.88409444\n",
-      "  0.38053043  0.92531616  0.29081535  0.96243752  0.19380836  0.98412672\n",
-      "  0.09736534  1.00099075 -0.00278279  1.00787092 -0.09882044  1.00302417\n",
-      " -0.19743501  0.98758446 -0.29561339  0.96214757 -0.38612278  0.92724975\n",
-      " -0.47771359  0.88131436 -0.5619379   0.83358937 -0.64124693  0.77454238\n",
-      " -0.71264303  0.7078407  -0.781303    0.63446765 -0.83939117  0.55496863\n",
-      " -0.8896447   0.47254556 -0.93339633  0.38105912 -0.97014391  0.28978088\n",
-      " -0.98730254  0.19430228 -1.00436873  0.09977523 -1.0076183   0.00199323\n",
-      " -1.0013212  -0.09757232 -0.9837092  -0.19408434 -0.95879284 -0.28815787\n",
-      " -0.92639579 -0.37763141 -0.88570436 -0.46282242 -0.83451906 -0.54837677\n",
-      " -0.77558265 -0.62661117 -0.71119081 -0.69670081 -0.63873236 -0.76184287\n",
-      " -0.55869278 -0.82099934 -0.47436079 -0.86734854 -0.38563748 -0.91310798\n",
-      " -0.2922913  -0.94603029 -0.19639886 -0.9718122  -0.0963383  -0.98546139\n",
-      " -0.00242114 -0.99203934  0.09759066 -0.98618994  0.19595386 -0.97575626\n",
-      "  0.29222957 -0.95107903  0.38329303 -0.91833352  0.47262375 -0.87787349\n",
-      "  0.55939146 -0.82844629  0.63577745 -0.76955604  0.70910177 -0.70333615\n",
-      "  0.7734415  -0.63066836  0.83375431 -0.5521164   0.88655287 -0.46744179\n",
-      "  0.92385107 -0.37974416  0.95634507 -0.28836612  0.97867369 -0.19403996\n",
-      "  0.9939596  -0.09574841]\n",
-      "  cov w/ noise:  [[ 1.72368515e-02  1.02110250e-04  1.57949565e-02 ...  1.40733225e-04\n",
-      "   1.54098433e-02  1.62886125e-06]\n",
-      " [ 1.02110250e-04  1.41316895e-02 -1.09746189e-04 ...  1.29813324e-02\n",
-      "   1.62061343e-04  1.30002853e-02]\n",
-      " [ 1.57949565e-02 -1.09746189e-04  1.68116710e-02 ...  4.98513689e-05\n",
-      "   1.46894435e-02 -2.43070532e-04]\n",
-      " ...\n",
-      " [ 1.40733225e-04  1.29813324e-02  4.98513689e-05 ...  1.47004707e-02\n",
-      "   3.10719724e-04  1.30767866e-02]\n",
-      " [ 1.54098433e-02  1.62061343e-04  1.46894435e-02 ...  3.10719724e-04\n",
-      "   1.57352407e-02  1.30655574e-04]\n",
-      " [ 1.62886125e-06  1.30002853e-02 -2.43070532e-04 ...  1.30767866e-02\n",
-      "   1.30655574e-04  1.39802254e-02]]\n",
-      "  mean conditional:  [ 0.98812185  0.0967769   0.98114261  0.20231449  0.96473158  0.30373309\n",
-      "  0.93896249  0.39997146  0.90411961  0.49003031  0.86066422  0.57301829\n",
-      "  0.80916804  0.64815722  0.75034943  0.71483025  0.6848568   0.77260636\n",
-      "  0.61325828  0.82119304  0.53577438  0.86056365  0.45238888  0.89092223\n",
-      "  0.36279563  0.91273139  0.26663593  0.92674458  0.16363516  0.93379954\n",
-      "  0.05404958  0.9346932  -0.06129391  0.93006369 -0.18075635  0.92014182\n",
-      " -0.30209213  0.90481637 -0.42249441  0.88355613 -0.53898668  0.85557779\n",
-      " -0.64879412  0.82007542 -0.74935645  0.77622617 -0.83872926  0.72349751\n",
-      " -0.91540731  0.66160122 -0.9784757   0.59059134 -1.02750202  0.51080763\n",
-      " -1.06281062  0.42302611 -1.08512552  0.32830337 -1.09555722  0.22808978\n",
-      " -1.09537905  0.12396125 -1.08574122  0.01763684 -1.06760171 -0.08931317\n",
-      " -1.04169954 -0.19542983 -1.00869886 -0.29944195 -0.96927856 -0.40036052\n",
-      " -0.92423788 -0.49718608 -0.87449707 -0.58890471 -0.82100992 -0.67463764\n",
-      " -0.76452632 -0.75351416 -0.70532138 -0.82494968 -0.64320338 -0.88864202\n",
-      " -0.57735978 -0.94440292 -0.50649817 -0.99205147 -0.4292441  -1.0312267\n",
-      " -0.3445099  -1.06125145 -0.25187807 -1.0813793  -0.1517046  -1.09100474\n",
-      " -0.04545564 -1.08988896  0.06477552 -1.07817783  0.17624477 -1.05651397\n",
-      "  0.28614985 -1.02550228  0.39157457 -0.98549367  0.49023537 -0.93648629\n",
-      "  0.58034002 -0.87813488  0.66092306 -0.81013552  0.7317973  -0.73241198\n",
-      "  0.79319741 -0.64548704  0.84576325 -0.55041245  0.8899972  -0.44873485\n",
-      "  0.92623072 -0.3421631   0.95442118 -0.23266939  0.97463954 -0.12162809\n",
-      "  0.98679127 -0.01114553] , sample:  [ 0.99406379  0.09655986  0.95411831  0.17812708  0.97070865  0.35687207\n",
-      "  0.94292031  0.40384358  0.94167864  0.50363566  0.8233782   0.56092178\n",
-      "  0.83547546  0.65019956  0.738833    0.69624213  0.70034281  0.78428243\n",
-      "  0.59542233  0.84236005  0.46517077  0.84323895  0.49765107  0.90143945\n",
-      "  0.34892695  0.91110791  0.3164758   0.94904184  0.16033541  0.89868831\n",
-      "  0.06355049  0.92412107 -0.04003047  0.91641308 -0.19163742  0.89460622\n",
-      " -0.29673283  0.93332643 -0.48361321  0.93524173 -0.56159123  0.83798673\n",
-      " -0.60008254  0.79395976 -0.71619975  0.78460324 -0.85949031  0.7250993\n",
-      " -0.90538605  0.635018   -1.02175038  0.60151163 -1.02254376  0.50349913\n",
-      " -1.10759915  0.44594609 -1.05656498  0.35231681 -1.13056836  0.211013\n",
-      " -1.045685    0.11575746 -1.08701701  0.01674377 -1.04413305 -0.07728921\n",
-      " -1.03318812 -0.23077297 -1.03852758 -0.28140525 -0.97306137 -0.39737957\n",
-      " -0.91212979 -0.46004496 -0.9032786  -0.61760849 -0.83818349 -0.67835817\n",
-      " -0.72992834 -0.767669   -0.70703082 -0.89114088 -0.6037324  -0.80642696\n",
-      " -0.60071409 -0.97235259 -0.45464185 -1.00677879 -0.45218141 -0.98043149\n",
-      " -0.3566793  -1.02741643 -0.32463905 -1.09842826 -0.12401994 -1.11857396\n",
-      " -0.01078592 -1.13220074  0.05033579 -1.07913812  0.14190373 -1.05759993\n",
-      "  0.28171772 -0.96469707  0.39363497 -0.96816537  0.50829662 -0.94300254\n",
-      "  0.59098961 -0.90965291  0.6603485  -0.82485589  0.72140023 -0.70440551\n",
-      "  0.83836945 -0.6777094   0.85871069 -0.49305032  0.8578826  -0.43035019\n",
-      "  0.88894236 -0.34377774  0.98625332 -0.27219933  1.03231999 -0.13168432\n",
-      "  0.92384361 -0.02063892]\n",
-      "  cov conditional:  [[ 2.19565920e-05  8.58689240e-07  1.92580489e-05 ...  1.27259411e-07\n",
-      "  -1.32713199e-04 -4.85541191e-06]\n",
-      " [ 8.58689240e-07  2.53902529e-05  7.52459601e-07 ...  2.40403674e-05\n",
-      "  -3.25127758e-06 -1.59342444e-04]\n",
-      " [ 1.92580489e-05  7.52459601e-07  1.68945063e-05 ...  1.23158435e-07\n",
-      "  -1.16636591e-04 -4.29462468e-06]\n",
-      " ...\n",
-      " [ 1.27259411e-07  2.40403674e-05  1.23158435e-07 ...  5.52551162e-05\n",
-      "   4.98589963e-08 -1.71855256e-04]\n",
-      " [-1.32713199e-04 -3.25127758e-06 -1.16636591e-04 ...  4.98589963e-08\n",
-      "   9.54672721e-04  1.95636821e-05]\n",
-      " [-4.85541191e-06 -1.59342444e-04 -4.29462468e-06 ... -1.71855256e-04\n",
-      "   1.95636821e-05  1.17502586e-03]]\n",
-      "  mean noise:  [-0.00594194  0.00021704  0.0270243   0.02418741 -0.00597707 -0.05313898\n",
-      " -0.00395782 -0.00387212 -0.03755903 -0.01360535  0.03728601  0.01209652\n",
-      " -0.02630742 -0.00204233  0.01151642  0.01858812 -0.01548601 -0.01167606\n",
-      "  0.01783595 -0.02116701  0.0706036   0.0173247  -0.04526219 -0.01051722\n",
-      "  0.01386867  0.00162348 -0.04983988 -0.02229726  0.00329975  0.03511124\n",
-      " -0.00950091  0.01057212 -0.02126344  0.01365061  0.01088107  0.0255356\n",
-      " -0.00535929 -0.02851007  0.0611188  -0.0516856   0.02260455  0.01759106\n",
-      " -0.04871158  0.02611566 -0.0331567  -0.00837707  0.02076105 -0.00160179\n",
-      " -0.01002126  0.02658322  0.04327467 -0.01092029 -0.00495825  0.0073085\n",
-      "  0.04478853 -0.02291998 -0.02856054 -0.02401344  0.03501114  0.01707678\n",
-      " -0.04969405  0.00820379  0.00127578  0.00089306 -0.02346866 -0.01202396\n",
-      " -0.00851142  0.03534315  0.02982873 -0.0180367   0.00378281 -0.00298095\n",
-      " -0.01210809 -0.03714112  0.02878153  0.02870377  0.01717357  0.00372053\n",
-      " -0.03459797  0.01415484  0.00170944  0.0661912  -0.03947097 -0.08221506\n",
-      "  0.02335432  0.02794967 -0.05185632  0.01472732  0.0229373  -0.05079522\n",
-      "  0.0121694  -0.03383501  0.07276097  0.01704897 -0.02768466  0.02756922\n",
-      " -0.03466972  0.04231178  0.01443973  0.00096029  0.03434104  0.00108595\n",
-      "  0.00443213 -0.06080521 -0.00206039 -0.0173283  -0.01806126  0.00651626\n",
-      " -0.01064959  0.03151803  0.00057456  0.01472037  0.01039707 -0.02800647\n",
-      " -0.04517204  0.03222236 -0.01294744 -0.05736213  0.0321146  -0.01838466\n",
-      "  0.03728837  0.00161464 -0.03183215  0.03952994 -0.05768045  0.01005623\n",
-      "  0.06294766  0.00949339]\n",
-      "  cov noise:  [[ 2.19565920e-05  8.58689240e-07  1.92580489e-05 ...  1.27259411e-07\n",
-      "  -1.32713199e-04 -4.85541191e-06]\n",
-      " [ 8.58689240e-07  2.53902529e-05  7.52459601e-07 ...  2.40403674e-05\n",
-      "  -3.25127758e-06 -1.59342444e-04]\n",
-      " [ 1.92580489e-05  7.52459601e-07  1.68945063e-05 ...  1.23158435e-07\n",
-      "  -1.16636591e-04 -4.29462468e-06]\n",
-      " ...\n",
-      " [ 1.27259411e-07  2.40403674e-05  1.23158435e-07 ...  5.52551162e-05\n",
-      "   4.98589963e-08 -1.71855256e-04]\n",
-      " [-1.32713199e-04 -3.25127758e-06 -1.16636591e-04 ...  4.98589963e-08\n",
-      "   9.54672721e-04  1.95636821e-05]\n",
-      " [-4.85541191e-06 -1.59342444e-04 -4.29462468e-06 ... -1.71855256e-04\n",
-      "   1.95636821e-05  1.17502586e-03]]\n",
-      "  mean:  [ 1.0121341   0.00374713  1.00713929  0.10170186  0.9924566   0.19893051\n",
-      "  0.96819156  0.29454665  0.93460111  0.38763343  0.89207508  0.47726334\n",
-      "  0.84106934  0.56253441  0.78207875  0.64258277  0.71567001  0.71657786\n",
-      "  0.64247995  0.78372273  0.56317983  0.84329834  0.47847444  0.89469567\n",
-      "  0.38915696  0.93741308  0.29614582  0.97105146  0.20044099  0.99529721\n",
-      "  0.10307476  1.00989232  0.00505507  1.01465154 -0.09267064  1.00950656\n",
-      " -0.18922486  0.99452293 -0.28379389  0.96989169 -0.37559826  0.93590205\n",
-      " -0.46387378  0.89292623 -0.54783807  0.84140853 -0.62667316  0.78185809\n",
-      " -0.69958904  0.7148613  -0.76587489  0.64106979 -0.82488657  0.5612258\n",
-      " -0.87604438  0.47616675 -0.9188227   0.38678688 -0.95276683  0.29400914\n",
-      " -0.97752316  0.19875115 -0.99285571  0.10189982 -0.99862615  0.00430995\n",
-      " -0.99478151 -0.09318627 -0.98136064 -0.18977515 -0.95852599 -0.28462782\n",
-      " -0.92654596 -0.37688123 -0.88573361 -0.46564485 -0.83643259 -0.55001907\n",
-      " -0.77902561 -0.62913379 -0.71397345 -0.70216823 -0.64184041 -0.76839106\n",
-      " -0.56327283 -0.82716758 -0.47898361 -0.87792493 -0.38976281 -0.92013925\n",
-      " -0.29649595 -0.95335475 -0.20012467 -0.97720107 -0.10160845 -0.99141089\n",
-      " -0.00192351 -0.99584485  0.09792804 -0.99051016  0.19691711 -0.97554987\n",
-      "  0.29401214 -0.95120914  0.38819976 -0.91779165  0.47852818 -0.87563813\n",
-      "  0.56413646 -0.82513065  0.64420371 -0.76671894  0.71794402 -0.70093736\n",
-      "  0.78464114 -0.62840123  0.84366136 -0.54982339  0.89444378 -0.46598321\n",
-      "  0.93651592 -0.37766239  0.96952082 -0.28566146  0.99321961 -0.19083216\n",
-      "  1.00746281 -0.09406083]\n",
-      "  cov:  [[ 0.01499772 -0.00081668  0.01494426 ... -0.00091136  0.01481048\n",
-      "  -0.00088036]\n",
-      " [-0.00081668  0.01424212 -0.00084161 ...  0.01416105 -0.0007773\n",
-      "   0.01430317]\n",
-      " [ 0.01494426 -0.00084161  0.01513099 ... -0.0009347   0.01452343\n",
-      "  -0.00090449]\n",
-      " ...\n",
-      " [-0.00091136  0.01416105 -0.0009347  ...  0.01491059 -0.00086907\n",
-      "   0.01463776]\n",
-      " [ 0.01481048 -0.0007773   0.01452343 ... -0.00086907  0.01486493\n",
-      "  -0.00083907]\n",
-      " [-0.00088036  0.01430317 -0.00090449 ...  0.01463776 -0.00083907\n",
-      "   0.01457588]]\n",
-      "  mean w/ noise:  [ 1.01108124  0.00474072  1.00643133  0.10335516  0.99355208  0.19699455\n",
-      "  0.96884504  0.29244844  0.93352005  0.38883831  0.89191447  0.47859289\n",
-      "  0.84475116  0.56165763  0.78189567  0.64154488  0.71508439  0.71748529\n",
-      "  0.64115977  0.78532539  0.56372479  0.84183548  0.47534645  0.89588408\n",
-      "  0.39012973  0.93611344  0.29680866  0.97101835  0.20112652  0.99555184\n",
-      "  0.10263429  1.00992431  0.00566443  1.01434211 -0.09023553  1.00787628\n",
-      " -0.19024599  0.9922394  -0.28679817  0.96970913 -0.37595785  0.9367961\n",
-      " -0.46591613  0.89418807 -0.54941152  0.83810731 -0.62769279  0.78202545\n",
-      " -0.70197819  0.71272722 -0.76242464  0.6402965  -0.82555566  0.56135212\n",
-      " -0.87546251  0.47583673 -0.91895167  0.38462014 -0.95065167  0.29426468\n",
-      " -0.97899826  0.19914458 -0.99302927  0.10240703 -0.99835721  0.00302733\n",
-      " -0.99308287 -0.09107248 -0.98152116 -0.19282861 -0.95992103 -0.28506316\n",
-      " -0.92564722 -0.37693652 -0.888081   -0.46719733 -0.8379396  -0.55232205\n",
-      " -0.77935015 -0.63025446 -0.71600685 -0.70293568 -0.64375555 -0.76952847\n",
-      " -0.56358675 -0.82715245 -0.47689426 -0.87496925 -0.38913834 -0.92261949\n",
-      " -0.29525913 -0.95089691 -0.20229176 -0.97631299 -0.10083527 -0.99181417\n",
-      " -0.0017217  -0.9974192   0.09784529 -0.98708088  0.19745789 -0.97439254\n",
-      "  0.29419752 -0.9486041   0.38968833 -0.92052189  0.47814857 -0.87599218\n",
-      "  0.5651786  -0.82525632  0.6434845  -0.76553218  0.71639453 -0.70120847\n",
-      "  0.78606236 -0.62944198  0.84331061 -0.55032802  0.89406439 -0.46419274\n",
-      "  0.936947   -0.37798901  0.96850329 -0.28769249  0.99338087 -0.19252558\n",
-      "  1.00871763 -0.09339793]\n",
-      "  cov w/ noise:  [[ 0.0160297  -0.00066702  0.01519663 ... -0.00081611  0.01500982\n",
-      "  -0.000526  ]\n",
-      " [-0.00066702  0.01495051 -0.00054233 ...  0.01417294 -0.00110898\n",
-      "   0.0142226 ]\n",
-      " [ 0.01519663 -0.00054233  0.01662543 ... -0.00092379  0.01504152\n",
-      "  -0.00060956]\n",
-      " ...\n",
-      " [-0.00081611  0.01417294 -0.00092379 ...  0.0162669  -0.00121951\n",
-      "   0.01479491]\n",
-      " [ 0.01500982 -0.00110898  0.01504152 ... -0.00121951  0.01620954\n",
-      "  -0.00101194]\n",
-      " [-0.000526    0.0142226  -0.00060956 ...  0.01479491 -0.00101194\n",
-      "   0.01563931]]\n",
-      "  mean conditional:  [ 0.92746684 -0.17523777  0.94933474 -0.08633279  0.95941066  0.00254154\n",
-      "  0.95674043  0.09084212  0.9407412   0.17799603  0.91125332  0.26328746\n",
-      "  0.86862457  0.34594532  0.81369539  0.42502849  0.74782152  0.49958719\n",
-      "  0.67281092  0.56851832  0.59077154  0.63085715  0.50383889  0.68565535\n",
-      "  0.4139013   0.73235147  0.32223122  0.77061237  0.22951507  0.80039465\n",
-      "  0.13561465  0.82182165  0.03982759  0.8349338  -0.05865554  0.83970865\n",
-      " -0.16057208  0.83606232 -0.26603517  0.82405285 -0.37436205  0.80377411\n",
-      " -0.48398533  0.77565817 -0.59238712  0.74016044 -0.69644497  0.69758905\n",
-      " -0.79287273  0.64819424 -0.87859992  0.59178555 -0.95129893  0.52806855\n",
-      " -1.0095521   0.45667333 -1.05287818  0.37742911 -1.08188153  0.29044883\n",
-      " -1.09760408  0.19625741 -1.10167117  0.09599096 -1.09569142 -0.00893872\n",
-      " -1.08119095 -0.1167234  -1.05940289 -0.22546239 -1.03120535 -0.3331571\n",
-      " -0.99698432 -0.43798382 -0.95676126 -0.53820397 -0.91015837 -0.63229569\n",
-      " -0.85664914 -0.71895989 -0.79578669 -0.79706925 -0.72729497 -0.86579947\n",
-      " -0.65140564 -0.92438752 -0.56877848 -0.97240537 -0.48073693 -1.00968611\n",
-      " -0.38880045 -1.03630208 -0.29493574 -1.05279381 -0.20102125 -1.0599267\n",
-      " -0.10874784 -1.05868658 -0.01938835 -1.04989109  0.0662165  -1.0341283\n",
-      "  0.14775079 -1.01165106  0.22534827 -0.98251164  0.2993453  -0.94665551\n",
-      "  0.37024021 -0.90402795  0.4385426  -0.8547258   0.50478886 -0.79894642\n",
-      "  0.56925118 -0.73694772  0.63206809 -0.6690736   0.69289225 -0.59579521\n",
-      "  0.75097831 -0.51768789  0.80509992 -0.43584101  0.85362955 -0.35048327\n",
-      "  0.89716122 -0.26759799] , sample:  [ 0.90807456 -0.18487558  0.97515232 -0.08462446  0.9194233  -0.02593547\n",
-      "  0.9768056   0.0859713   0.92620926  0.18646071  0.91548978  0.30010869\n",
-      "  0.89401654  0.31284173  0.8148711   0.41938832  0.73346184  0.52074727\n",
-      "  0.7053033   0.54131417  0.56614152  0.65192952  0.50413261  0.71703433\n",
-      "  0.37080163  0.7429443   0.31415818  0.75949325  0.27485436  0.75581063\n",
-      "  0.17861133  0.81864652  0.00331528  0.8494636  -0.03669282  0.83999873\n",
-      " -0.21367596  0.81939287 -0.26012532  0.85523023 -0.3495017   0.77523079\n",
-      " -0.45357514  0.78594814 -0.63394159  0.75703573 -0.70079069  0.64012765\n",
-      " -0.78160803  0.67938633 -0.88616269  0.61231162 -0.94820032  0.53246161\n",
-      " -1.02870308  0.47346465 -1.05037737  0.33844967 -1.06390525  0.26608915\n",
-      " -1.11534639  0.23327238 -1.12803202  0.07768512 -1.08485703  0.03698734\n",
-      " -1.07216343 -0.12254966 -1.00440964 -0.23563906 -1.07676826 -0.34037176\n",
-      " -1.01973115 -0.4144936  -0.95391514 -0.56968634 -0.8888753  -0.65900165\n",
-      " -0.81438319 -0.73681096 -0.80648887 -0.75836126 -0.72749229 -0.86747894\n",
-      " -0.66089029 -0.95721923 -0.58525275 -0.91255946 -0.5096778  -1.00066628\n",
-      " -0.41456047 -1.04592991 -0.2928491  -1.10281282 -0.14941638 -1.06593126\n",
-      " -0.1267908  -1.0426378  -0.00485636 -1.02552396  0.07753434 -1.01976284\n",
-      "  0.13564324 -1.02086911  0.25667357 -1.03264239  0.27699671 -0.92462174\n",
-      "  0.36461958 -0.92538258  0.44452168 -0.77591251  0.45932243 -0.80587611\n",
-      "  0.57954737 -0.74470437  0.61881937 -0.69950928  0.64602401 -0.6036196\n",
-      "  0.79327124 -0.51100672  0.79913182 -0.45255105  0.87916628 -0.34933657\n",
-      "  0.90929881 -0.2356838 ]\n",
-      "  cov conditional:  [[ 2.38364522e-05 -1.25232731e-06  2.09140363e-05 ...  7.04554441e-07\n",
-      "  -1.48042504e-04  3.47937360e-06]\n",
-      " [-1.25232731e-06  2.43532782e-05 -1.10829569e-06 ...  1.96061855e-05\n",
-      "   5.50244351e-06 -1.48744502e-04]\n",
-      " [ 2.09140363e-05 -1.10829569e-06  1.83529713e-05 ...  6.23642915e-07\n",
-      "  -1.30065090e-04  3.05531547e-06]\n",
-      " ...\n",
-      " [ 7.04554441e-07  1.96061855e-05  6.23642915e-07 ...  5.22021798e-05\n",
-      "  -4.30801107e-06 -1.36024670e-04]\n",
-      " [-1.48042504e-04  5.50244351e-06 -1.30065090e-04 ... -4.30801107e-06\n",
-      "   1.06925458e-03 -5.16148650e-06]\n",
-      " [ 3.47937360e-06 -1.48744502e-04  3.05531547e-06 ... -1.36024670e-04\n",
-      "  -5.16148650e-06  1.06747090e-03]]\n",
-      "  mean noise:  [ 0.01939228  0.00963781 -0.02581758 -0.00170833  0.03998736  0.02847701\n",
-      " -0.02006517  0.00487082  0.01453193 -0.00846468 -0.00423646 -0.03682124\n",
-      " -0.02539197  0.03310359 -0.00117571  0.00564017  0.01435969 -0.02116008\n",
-      " -0.03249237  0.02720415  0.02463002 -0.02107236 -0.00029373 -0.03137898\n",
-      "  0.04309967 -0.01059283  0.00807304  0.01111913 -0.04533929  0.04458402\n",
-      " -0.04299669  0.00317513  0.03651231 -0.0145298  -0.02196272 -0.00029008\n",
-      "  0.05310388  0.01666945 -0.00590985 -0.03117738 -0.02486035  0.02854332\n",
-      " -0.03041019 -0.01028997  0.04155447 -0.01687528  0.00434572  0.0574614\n",
-      " -0.01126471 -0.03119209  0.00756277 -0.02052607 -0.00309861 -0.00439306\n",
-      "  0.01915098 -0.01679132 -0.00250081  0.03897944 -0.01797628  0.02435968\n",
-      "  0.01774231 -0.03701497  0.02636085  0.01830584 -0.01083439 -0.04592606\n",
-      " -0.00902753  0.00582626 -0.05499325  0.01017668  0.04556292  0.00721466\n",
-      "  0.02274683 -0.02349022 -0.00284612  0.03148237 -0.02128306  0.02670596\n",
-      " -0.04226595  0.01785107  0.01070218 -0.03870799  0.00019732  0.00167946\n",
-      "  0.00948465  0.03283171  0.01647427 -0.0598459   0.02894086 -0.00901983\n",
-      "  0.02576002  0.00962783 -0.00208664  0.05001901 -0.05160487  0.00600456\n",
-      "  0.01804296 -0.01604878 -0.01453199 -0.02436713 -0.01131784 -0.01436546\n",
-      "  0.01210755  0.00921804 -0.0313253   0.05013075  0.02234859 -0.02203377\n",
-      "  0.00562063  0.02135463 -0.00597908 -0.07881328  0.04546644  0.00692969\n",
-      " -0.01029618  0.00775665  0.01324872  0.03043568  0.04686824  0.00782439\n",
-      " -0.04229293 -0.00668117  0.0059681   0.01671004 -0.02553673 -0.0011467\n",
-      " -0.01213758 -0.0319142 ]\n",
-      "  cov noise:  [[ 2.38364522e-05 -1.25232731e-06  2.09140363e-05 ...  7.04554441e-07\n",
-      "  -1.48042504e-04  3.47937360e-06]\n",
-      " [-1.25232731e-06  2.43532782e-05 -1.10829569e-06 ...  1.96061855e-05\n",
-      "   5.50244351e-06 -1.48744502e-04]\n",
-      " [ 2.09140363e-05 -1.10829569e-06  1.83529713e-05 ...  6.23642915e-07\n",
-      "  -1.30065090e-04  3.05531547e-06]\n",
-      " ...\n",
-      " [ 7.04554441e-07  1.96061855e-05  6.23642915e-07 ...  5.22021798e-05\n",
-      "  -4.30801107e-06 -1.36024670e-04]\n",
-      " [-1.48042504e-04  5.50244351e-06 -1.30065090e-04 ... -4.30801107e-06\n",
-      "   1.06925458e-03 -5.16148650e-06]\n",
-      " [ 3.47937360e-06 -1.48744502e-04  3.05531547e-06 ... -1.36024670e-04\n",
-      "  -5.16148650e-06  1.06747090e-03]]\n",
-      "  mean:  [ 1.01135215  0.00195151  1.00715011  0.09955348  0.99324177  0.19625128\n",
-      "  0.96969311  0.29117804  0.93675165  0.38344721  0.89481493  0.47217197\n",
-      "  0.8443556   0.55649152  0.7858853   0.63558344  0.71995406  0.70868301\n",
-      "  0.64714432  0.7750914   0.56807831  0.83417426  0.48345524  0.88536991\n",
-      "  0.39407049  0.9281815   0.30082921  0.96217384  0.20473875  0.98695643\n",
-      "  0.10686422  1.00218684  0.00825907  1.00760053 -0.09008863  1.00304354\n",
-      " -0.1872687   0.9885152  -0.28242004  0.96418138 -0.37472221  0.93036198\n",
-      " -0.46340263  0.88752299 -0.54772203  0.83622598 -0.62693147  0.77705714\n",
-      " -0.70029707  0.71062268 -0.76713136  0.63755668 -0.82680615  0.55851825\n",
-      " -0.87874889  0.47420757 -0.92242667  0.38541437 -0.95733549  0.2930319\n",
-      " -0.98303816  0.19802227 -0.99920159  0.10135079 -1.00560646  0.00394016\n",
-      " -1.00216049 -0.0933412  -0.98888735 -0.18967232 -0.96592515 -0.28423756\n",
-      " -0.93352638 -0.37619282 -0.89202398 -0.46465861 -0.84182183 -0.54873742\n",
-      " -0.78339949 -0.62756113 -0.71733477 -0.70031452 -0.64431289 -0.76626703\n",
-      " -0.56509246 -0.8247829  -0.48047262 -0.87530197 -0.39127326 -0.91733286\n",
-      " -0.29833739 -0.95047242 -0.20250166 -0.97440025 -0.10458627 -0.98888206\n",
-      " -0.00543178 -0.99376746  0.09405513 -0.98898878  0.19288107 -0.97458564\n",
-      "  0.2900014  -0.95071943  0.38436014 -0.91766812  0.4749375  -0.87580756\n",
-      "  0.56079024 -0.82558048  0.6410371  -0.76746998  0.7148565  -0.70200051\n",
-      "  0.78152472 -0.62975216  0.84045275 -0.55138956  0.89118636 -0.46765154\n",
-      "  0.93337163 -0.37933006  0.96671653 -0.28727647  0.99097024 -0.19240612\n",
-      "  1.00591214 -0.09567591]\n",
-      "  cov:  [[ 1.48865418e-02 -5.87742819e-05  1.47025514e-02 ...  1.72927118e-05\n",
-      "   1.48226457e-02 -2.84784595e-05]\n",
-      " [-5.87742819e-05  1.50462459e-02 -1.18463507e-04 ...  1.48186026e-02\n",
-      "  -1.49449251e-05  1.50419784e-02]\n",
-      " [ 1.47025514e-02 -1.18463507e-04  1.47508266e-02 ... -4.40970703e-05\n",
-      "   1.44170742e-02 -8.97255182e-05]\n",
-      " ...\n",
-      " [ 1.72927118e-05  1.48186026e-02 -4.40970703e-05 ...  1.54939736e-02\n",
-      "   6.14867107e-05  1.52671026e-02]\n",
-      " [ 1.48226457e-02 -1.49449251e-05  1.44170742e-02 ...  6.14867107e-05\n",
-      "   1.49841739e-02  1.66993973e-05]\n",
-      " [-2.84784595e-05  1.50419784e-02 -8.97255182e-05 ...  1.52671026e-02\n",
-      "   1.66993973e-05  1.52683706e-02]]\n",
-      "  mean w/ noise:  [ 1.01110649  0.00121646  1.00509828  0.10152077  0.99321446  0.19665811\n",
-      "  0.97447045  0.29045445  0.93893547  0.38312805  0.89556374  0.47216807\n",
-      "  0.84492526  0.55738456  0.78799376  0.63628744  0.7211134   0.71150484\n",
-      "  0.64859164  0.77577815  0.5674622   0.83563871  0.48386592  0.88571375\n",
-      "  0.396544    0.92849547  0.30065615  0.9641115   0.20633313  0.98571308\n",
-      "  0.1075281   1.00230456  0.00849069  1.0065872  -0.09131761  1.0040294\n",
-      " -0.18657699  0.98885398 -0.28182419  0.96330849 -0.37378485  0.92704547\n",
-      " -0.46538015  0.89012544 -0.54655294  0.83784083 -0.62814797  0.77617165\n",
-      " -0.69819257  0.7122496  -0.76346003  0.63629049 -0.82740647  0.55824297\n",
-      " -0.87695894  0.47624729 -0.92171366  0.38628808 -0.95923629  0.29647943\n",
-      " -0.98311639  0.19568364 -0.99849184  0.09973944 -1.00683766  0.0033591\n",
-      " -1.00304097 -0.09161418 -0.98913253 -0.19028873 -0.96749664 -0.28616476\n",
-      " -0.93272383 -0.37748621 -0.89206987 -0.46585177 -0.84378886 -0.54917134\n",
-      " -0.78253323 -0.62676882 -0.71392448 -0.69545118 -0.64521349 -0.76592367\n",
-      " -0.56484239 -0.82670206 -0.48087622 -0.87296814 -0.38913685 -0.91830476\n",
-      " -0.2982097  -0.95055674 -0.20462774 -0.97527398 -0.10360996 -0.98840453\n",
-      " -0.00308952 -0.99145402  0.09259622 -0.98903416  0.19466012 -0.97353885\n",
-      "  0.29043733 -0.9503273   0.38631024 -0.91782099  0.47595159 -0.87719344\n",
-      "  0.56179553 -0.8249444   0.64211697 -0.76821115  0.71415372 -0.69874023\n",
-      "  0.78138414 -0.62853758  0.84255556 -0.55062182  0.89169144 -0.46599263\n",
-      "  0.9304607  -0.37946905  0.96577003 -0.28555236  0.992091   -0.19397726\n",
-      "  1.00528181 -0.0964644 ]\n",
-      "  cov w/ noise:  [[ 1.56831437e-02 -1.56648960e-04  1.47893187e-02 ... -1.28091186e-04\n",
-      "   1.45721148e-02  3.08725335e-04]\n",
-      " [-1.56648960e-04  1.57101138e-02 -2.32594541e-04 ...  1.47347854e-02\n",
-      "  -2.40320069e-05  1.48188824e-02]\n",
-      " [ 1.47893187e-02 -2.32594541e-04  1.62095535e-02 ... -3.38996841e-04\n",
-      "   1.44585397e-02  1.17930224e-04]\n",
-      " ...\n",
-      " [-1.28091186e-04  1.47347854e-02 -3.38996841e-04 ...  1.70045279e-02\n",
-      "  -5.68573293e-05  1.53879250e-02]\n",
-      " [ 1.45721148e-02 -2.40320069e-05  1.44585397e-02 ... -5.68573293e-05\n",
-      "   1.57262580e-02  3.80061089e-04]\n",
-      " [ 3.08725335e-04  1.48188824e-02  1.17930224e-04 ...  1.53879250e-02\n",
-      "   3.80061089e-04  1.62371422e-02]]\n",
-      "  mean conditional:  [ 1.10602744 -0.15539594  1.10933352 -0.06686937  1.10235891  0.01899635\n",
-      "  1.08405889  0.10153878  1.05393395  0.18039967  1.01210334  0.25542506\n",
-      "  0.95928047  0.32677372  0.8966252   0.39464619  0.82567418  0.45932611\n",
-      "  0.74825483  0.52074249  0.66626566  0.5785129   0.58158614  0.6318498\n",
-      "  0.49580623  0.67963883  0.40990645  0.72068942  0.32425997  0.75387491\n",
-      "  0.23834144  0.77834574  0.15104009  0.79340459  0.06086439  0.79873195\n",
-      " -0.03337653  0.79439582 -0.1323389   0.78086731 -0.23559611  0.75900806\n",
-      " -0.34168709  0.7300223  -0.44798052  0.69503876 -0.55122343  0.65486725\n",
-      " -0.64789612  0.60970001 -0.73475664  0.55920443 -0.80931458  0.5025077\n",
-      " -0.87002272  0.43861786 -0.91640269  0.36679911 -0.94896612  0.28684976\n",
-      " -0.96877093  0.19915669 -0.97725532  0.104846   -0.97575149  0.00539693\n",
-      " -0.96538493 -0.0974444  -0.94684968 -0.20199214 -0.92025727 -0.3066926\n",
-      " -0.88528659 -0.41016998 -0.84108192 -0.51101637 -0.78667486 -0.60778619\n",
-      " -0.72118903 -0.69900475 -0.64424264 -0.78298858 -0.55606623 -0.85814572\n",
-      " -0.45777741 -0.92307958 -0.35109383 -0.97669436 -0.23837148 -1.01861219\n",
-      " -0.12207034 -1.04886802 -0.00487741 -1.06830481  0.11073725 -1.07809027\n",
-      "  0.22262614 -1.07950524  0.32901224 -1.07361683  0.4286516  -1.06099333\n",
-      "  0.52064223 -1.04154398  0.60461433 -1.0148335   0.68041254 -0.98022482\n",
-      "  0.74815683 -0.9371659   0.8082135  -0.88542898  0.86122725 -0.8252173\n",
-      "  0.90803963 -0.75710797  0.94961368 -0.68205505  0.9868068  -0.60124497\n",
-      "  1.02016207 -0.51574618  1.04959263 -0.42715084  1.07466349 -0.33637791\n",
-      "  1.09567924 -0.24793627] , sample:  [ 1.10521404 -0.15193718  1.12406568 -0.0572388   1.13605886  0.01270658\n",
-      "  1.08705916  0.08938239  1.07608411  0.2159024   1.00410386  0.25976545\n",
-      "  0.92048432  0.34731232  0.88515257  0.41714442  0.84963474  0.43844522\n",
-      "  0.75377487  0.46414919  0.63305672  0.578763    0.59342421  0.61993796\n",
-      "  0.53976029  0.69219693  0.40128709  0.75706574  0.27676452  0.7129484\n",
-      "  0.24876517  0.81204093  0.14737062  0.79427491  0.08469422  0.76716157\n",
-      " -0.06253866  0.81099502 -0.06944636  0.79385108 -0.23174028  0.78004975\n",
-      " -0.3703923   0.70939892 -0.45210903  0.68497578 -0.58305436  0.60779248\n",
-      " -0.59195887  0.60893754 -0.71518515  0.54272097 -0.90138479  0.53832823\n",
-      " -0.84461868  0.49122489 -0.92095176  0.34374063 -0.90431053  0.27760816\n",
-      " -0.98363984  0.22077524 -1.01366907  0.11590519 -0.96536903 -0.02312512\n",
-      " -0.96965782 -0.10927064 -0.92653002 -0.21468639 -0.878091   -0.30926651\n",
-      " -0.89031831 -0.36225945 -0.8455556  -0.52049205 -0.83273246 -0.65393617\n",
-      " -0.69746159 -0.65810129 -0.65222601 -0.74793115 -0.56751008 -0.92893229\n",
-      " -0.45785618 -0.89199363 -0.34872777 -0.96735044 -0.22356191 -1.0689352\n",
-      " -0.11961688 -1.02987078 -0.05213883 -1.06346902  0.11323419 -1.08574866\n",
-      "  0.27891662 -1.09385379  0.30938992 -1.02366164  0.44746905 -1.08049856\n",
-      "  0.55096979 -1.03979737  0.59025889 -0.97721868  0.65295907 -0.98567951\n",
-      "  0.77593028 -0.98926317  0.79628119 -0.89700131  0.86811479 -0.82182585\n",
-      "  0.92458719 -0.7512135   0.88819123 -0.64751804  1.0321624  -0.61712737\n",
-      "  1.05586548 -0.49196995  0.99789196 -0.44335961  1.03431604 -0.3613858\n",
-      "  1.11666096 -0.25979345]\n",
-      "  cov conditional:  [[ 2.38352007e-05 -1.25353651e-06  2.09209357e-05 ...  1.71240194e-07\n",
-      "  -1.42555292e-04  7.90487266e-06]\n",
-      " [-1.25353651e-06  2.43433830e-05 -1.10052285e-06 ...  2.21710040e-05\n",
-      "   3.84781984e-06 -1.41320887e-04]\n",
-      " [ 2.09209357e-05 -1.10052285e-06  1.83660036e-05 ...  1.61345191e-07\n",
-      "  -1.25296881e-04  6.99600746e-06]\n",
-      " ...\n",
-      " [ 1.71240194e-07  2.21710040e-05  1.61345191e-07 ...  5.47275712e-05\n",
-      "  -7.12733507e-06 -1.47044580e-04]\n",
-      " [-1.42555292e-04  3.84781984e-06 -1.25296881e-04 ... -7.12733507e-06\n",
-      "   1.01050727e-03 -2.75885028e-05]\n",
-      " [ 7.90487266e-06 -1.41320887e-04  6.99600746e-06 ... -1.47044580e-04\n",
-      "  -2.75885028e-05  9.78813070e-04]]\n",
-      "  mean noise:  [ 8.13393104e-04 -3.45875506e-03 -1.47321641e-02 -9.63056727e-03\n",
-      " -3.36999517e-02  6.28976546e-03 -3.00027605e-03  1.21563906e-02\n",
-      " -2.21501561e-02 -3.55027246e-02  7.99947758e-03 -4.34038780e-03\n",
-      "  3.87961525e-02 -2.05386047e-02  1.14726260e-02 -2.24982256e-02\n",
-      " -2.39605602e-02  2.08808867e-02 -5.52004744e-03  5.65932903e-02\n",
-      "  3.32089361e-02 -2.50098371e-04 -1.18380728e-02  1.19118366e-02\n",
-      " -4.39540619e-02 -1.25581022e-02  8.61935168e-03 -3.63763180e-02\n",
-      "  4.74954549e-02  4.09265037e-02 -1.04237262e-02 -3.36951892e-02\n",
-      "  3.66947177e-03 -8.70319077e-04 -2.38298285e-02  3.15703800e-02\n",
-      "  2.91621284e-02 -1.65992011e-02 -6.28925365e-02 -1.29837722e-02\n",
-      " -3.85582972e-03 -2.10416833e-02  2.87052108e-02  2.06233752e-02\n",
-      "  4.12851012e-03  1.00629821e-02  3.18309280e-02  4.70747689e-02\n",
-      " -5.59372512e-02  7.62464291e-04 -1.95714844e-02  1.64834606e-02\n",
-      "  9.20702010e-02 -3.58205382e-02 -2.54040401e-02 -5.26070328e-02\n",
-      "  4.54907453e-03  2.30584793e-02 -4.46555911e-02  9.24160170e-03\n",
-      "  1.48689114e-02 -2.16185560e-02  3.64137540e-02 -1.10591879e-02\n",
-      " -1.03824646e-02  2.85220521e-02  4.27288514e-03  1.18262451e-02\n",
-      " -2.03196619e-02  1.26942488e-02 -4.21662765e-02  2.57391234e-03\n",
-      "  5.03172361e-03 -4.79105302e-02  4.47367795e-03  9.47567987e-03\n",
-      "  4.60575929e-02  4.61499798e-02 -2.37274345e-02 -4.09034598e-02\n",
-      "  7.98337125e-03 -3.50574313e-02  1.14438479e-02  7.07865645e-02\n",
-      "  7.87689144e-05 -3.10859456e-02 -2.36605444e-03 -9.34391155e-03\n",
-      " -1.48095650e-02  5.03230084e-02 -2.45345971e-03 -1.89972448e-02\n",
-      "  4.72614122e-02 -4.83579572e-03 -2.49693637e-03  7.65838772e-03\n",
-      " -5.62904821e-02  1.43485522e-02  1.96223167e-02 -4.99551861e-02\n",
-      " -1.88174469e-02  1.95052235e-02 -3.03275574e-02 -1.74660635e-03\n",
-      "  1.43554428e-02 -3.76148251e-02  2.74534699e-02  5.45468921e-03\n",
-      " -2.77734437e-02  5.20972765e-02  1.19323057e-02  1.15723291e-02\n",
-      " -6.88753760e-03 -3.39144772e-03 -1.65475684e-02 -5.89446261e-03\n",
-      "  6.14224477e-02 -3.45370050e-02 -4.53555941e-02  1.58823925e-02\n",
-      " -3.57034080e-02 -2.37762225e-02  5.17006652e-02  1.62087628e-02\n",
-      "  4.03474473e-02  2.50078944e-02 -2.09817179e-02  1.18571826e-02]\n",
-      "  cov noise:  [[ 2.38352007e-05 -1.25353651e-06  2.09209357e-05 ...  1.71240194e-07\n",
-      "  -1.42555292e-04  7.90487266e-06]\n",
-      " [-1.25353651e-06  2.43433830e-05 -1.10052285e-06 ...  2.21710040e-05\n",
-      "   3.84781984e-06 -1.41320887e-04]\n",
-      " [ 2.09209357e-05 -1.10052285e-06  1.83660036e-05 ...  1.61345191e-07\n",
-      "  -1.25296881e-04  6.99600746e-06]\n",
-      " ...\n",
-      " [ 1.71240194e-07  2.21710040e-05  1.61345191e-07 ...  5.47275712e-05\n",
-      "  -7.12733507e-06 -1.47044580e-04]\n",
-      " [-1.42555292e-04  3.84781984e-06 -1.25296881e-04 ... -7.12733507e-06\n",
-      "   1.01050727e-03 -2.75885028e-05]\n",
-      " [ 7.90487266e-06 -1.41320887e-04  6.99600746e-06 ... -1.47044580e-04\n",
-      "  -2.75885028e-05  9.78813070e-04]]\n",
-      "  mean:  [ 1.00497964e+00  2.89958119e-04  1.00041338e+00  9.78247953e-02\n",
-      "  9.86298385e-01  1.94624908e-01  9.62702954e-01  2.89809534e-01\n",
-      "  9.29822720e-01  3.82456485e-01  8.87976679e-01  4.71629590e-01\n",
-      "  8.37551491e-01  5.56429836e-01  7.78995569e-01  6.36001414e-01\n",
-      "  7.12854105e-01  7.09524683e-01  6.39775440e-01  7.76236369e-01\n",
-      "  5.60481655e-01  8.35471236e-01  4.75768127e-01  8.86667864e-01\n",
-      "  3.86514820e-01  9.29348143e-01  2.93665979e-01  9.63110611e-01\n",
-      "  1.98191034e-01  9.87613907e-01  1.01065733e-01  1.00257279e+00\n",
-      "  3.25987362e-03  1.00775808e+00 -9.42822295e-02  1.00302519e+00\n",
-      " -1.90670455e-01  9.88351433e-01 -2.85057221e-01  9.63864289e-01\n",
-      " -3.76611619e-01  9.29839036e-01 -4.64531507e-01  8.86688855e-01\n",
-      " -5.48064469e-01  8.34941742e-01 -6.26484647e-01  7.75203137e-01\n",
-      " -6.99093503e-01  7.08149818e-01 -7.65222869e-01  6.34522632e-01\n",
-      " -8.24242293e-01  5.55117634e-01 -8.75572697e-01  4.70752649e-01\n",
-      " -9.18673971e-01  3.82259033e-01 -9.53038091e-01  2.90488829e-01\n",
-      " -9.78229422e-01  1.96286860e-01 -9.93923710e-01  1.00461312e-01\n",
-      " -9.99918621e-01  3.79979246e-03 -9.96145510e-01 -9.29119967e-02\n",
-      " -9.82661597e-01 -1.88879271e-01 -9.59635325e-01 -2.83275434e-01\n",
-      " -9.27324965e-01 -3.75222682e-01 -8.86046931e-01 -4.63798399e-01\n",
-      " -8.36152085e-01 -5.48052331e-01 -7.78061319e-01 -6.27061399e-01\n",
-      " -7.12312023e-01 -6.99989520e-01 -6.39557031e-01 -7.66135310e-01\n",
-      " -5.60529887e-01 -8.24898763e-01 -4.76023123e-01 -8.75729991e-01\n",
-      " -3.86869274e-01 -9.18104535e-01 -2.93949090e-01 -9.51543809e-01\n",
-      " -1.98182763e-01 -9.75665431e-01 -1.00496717e-01 -9.90219128e-01\n",
-      " -1.81505837e-03 -9.95081110e-01  9.69341772e-02 -9.90232339e-01\n",
-      "  1.94802439e-01 -9.75740710e-01  2.90816658e-01 -9.51775805e-01\n",
-      "  3.84006427e-01 -9.18605359e-01  4.73460310e-01 -8.76580773e-01\n",
-      "  5.58335396e-01 -8.26127863e-01  6.37798690e-01 -7.67753976e-01\n",
-      "  7.11035384e-01 -7.02051819e-01  7.77308982e-01 -6.29679232e-01\n",
-      "  8.36000849e-01 -5.51347491e-01  8.86592257e-01 -4.67784974e-01\n",
-      "  9.28628782e-01 -3.79741475e-01  9.61715736e-01 -2.88027027e-01\n",
-      "  9.85566176e-01 -1.93517837e-01  1.00001425e+00 -9.71155934e-02]\n",
-      "  cov:  [[0.01412564 0.00025578 0.01397774 ... 0.00036163 0.0140582  0.00031785]\n",
-      " [0.00025578 0.01516991 0.00034015 ... 0.01473789 0.00015889 0.01505477]\n",
-      " [0.01397774 0.00034015 0.01405842 ... 0.00045235 0.01368793 0.00040743]\n",
-      " ...\n",
-      " [0.00036163 0.01473789 0.00045235 ... 0.01517882 0.00025398 0.01506109]\n",
-      " [0.0140582  0.00015889 0.01368793 ... 0.00025398 0.0142206  0.00021353]\n",
-      " [0.00031785 0.01505477 0.00040743 ... 0.01506109 0.00021353 0.01516359]]\n",
-      "  mean w/ noise:  [ 1.0051459  -0.00254514  0.99990225  0.09863993  0.98342386  0.19423737\n",
-      "  0.95968603  0.28803984  0.9293259   0.38373691  0.88785071  0.47238756\n",
-      "  0.83961394  0.55653564  0.78061271  0.63300745  0.71258788  0.71271863\n",
-      "  0.6387806   0.77749049  0.56266742  0.83513409  0.47699011  0.88701073\n",
-      "  0.38827472  0.92643955  0.29438201  0.96308261  0.19835858  0.99040238\n",
-      "  0.10164456  0.99932227  0.00515984  1.00805423 -0.09451384  1.0027807\n",
-      " -0.19163526  0.98785623 -0.28687617  0.96108929 -0.37848583  0.92944226\n",
-      " -0.46364711  0.88654489 -0.54873155  0.83433569 -0.62587143  0.77718748\n",
-      " -0.69871846  0.70773142 -0.76578272  0.63421149 -0.82388141  0.55391344\n",
-      " -0.87376626  0.47089148 -0.91677463  0.38526245 -0.95492404  0.29103677\n",
-      " -0.97657549  0.19741057 -0.99571191  0.09980705 -0.99656935  0.00576191\n",
-      " -0.99455965 -0.0933401  -0.98340933 -0.190239   -0.95577372 -0.28191812\n",
-      " -0.92843348 -0.37783209 -0.88781268 -0.46502368 -0.8356788  -0.54692026\n",
-      " -0.77965614 -0.62653621 -0.71260813 -0.70036621 -0.6409021  -0.76538833\n",
-      " -0.55792385 -0.82462237 -0.47671168 -0.87340876 -0.38931054 -0.91908932\n",
-      " -0.29559408 -0.95260444 -0.19889747 -0.97567104 -0.10121525 -0.99155441\n",
-      " -0.00135731 -0.99412457  0.09413953 -0.99135462  0.19193172 -0.97467614\n",
-      "  0.28947532 -0.95193922  0.38504308 -0.91867317  0.47402987 -0.87814518\n",
-      "  0.55803025 -0.82624006  0.63946401 -0.76873515  0.7127405  -0.70140393\n",
-      "  0.77869111 -0.62840822  0.83495366 -0.55066401  0.8857475  -0.46784453\n",
-      "  0.93084308 -0.37894323  0.96183568 -0.28830623  0.98559755 -0.19366011\n",
-      "  1.000018   -0.09803498]\n",
-      "  cov w/ noise:  [[1.54158203e-02 3.45096327e-04 1.43553071e-02 ... 2.79917499e-04\n",
-      "  1.44554541e-02 2.33494614e-04]\n",
-      " [3.45096327e-04 1.63333054e-02 3.36419383e-04 ... 1.46854155e-02\n",
-      "  2.97388381e-04 1.50201565e-02]\n",
-      " [1.43553071e-02 3.36419383e-04 1.54636613e-02 ... 9.25592212e-05\n",
-      "  1.39119325e-02 1.55857221e-04]\n",
-      " ...\n",
-      " [2.79917499e-04 1.46854155e-02 9.25592212e-05 ... 1.61406262e-02\n",
-      "  2.19418979e-04 1.48534307e-02]\n",
-      " [1.44554541e-02 2.97388381e-04 1.39119325e-02 ... 2.19418979e-04\n",
-      "  1.56153547e-02 2.49446148e-04]\n",
-      " [2.33494614e-04 1.50201565e-02 1.55857221e-04 ... 1.48534307e-02\n",
-      "  2.49446148e-04 1.59617739e-02]]\n",
-      "  mean conditional:  [ 1.03554572 -0.10212608  1.0190344  -0.02663722  0.99255856  0.04952407\n",
-      "  0.95607503  0.12683876  0.90991038  0.2055631   0.85484693  0.28553122\n",
-      "  0.79207447  0.36620399  0.72311917  0.44660943  0.64980104  0.52557087\n",
-      "  0.57408683  0.60158852  0.49797075  0.67313927  0.42311522  0.73874887\n",
-      "  0.35078605  0.79711243  0.2813374   0.84718446  0.21437335  0.88807141\n",
-      "  0.14837527  0.91900965  0.08121734  0.93917291  0.01042924  0.94777925\n",
-      " -0.06624728  0.94421984 -0.1503039   0.92826564 -0.24216571  0.90034891\n",
-      " -0.34096322  0.86149194 -0.44454413  0.81333363 -0.54972127  0.75766034\n",
-      " -0.65278818  0.69629903 -0.7499439   0.63068076 -0.83788863  0.56187596\n",
-      " -0.91413302  0.49045386 -0.97711461  0.41660008 -1.02637759  0.34027225\n",
-      " -1.06211337  0.2611485  -1.08503766  0.17892192 -1.09600417  0.09328466\n",
-      " -1.09583147  0.00405387 -1.08510388 -0.0887883  -1.06400247 -0.18468742\n",
-      " -1.03241652 -0.28269395 -0.98981203 -0.38120496 -0.93548222 -0.47800649\n",
-      " -0.86892931 -0.57062343 -0.79011558 -0.65647132 -0.69953584 -0.73324123\n",
-      " -0.59852521 -0.7990921  -0.48880345 -0.85274347 -0.3725283  -0.89380086\n",
-      " -0.25181372 -0.92230752 -0.12878114 -0.93913379 -0.00542622 -0.94542059\n",
-      "  0.11644748 -0.94259313  0.23520387 -0.93184714  0.34922765 -0.91415755\n",
-      "  0.45696654 -0.88998982  0.55699176 -0.8595437   0.64809034 -0.82279738\n",
-      "  0.72929898 -0.77955535  0.8001353  -0.72990095  0.86063132 -0.67405613\n",
-      "  0.91128247 -0.61255739  0.9528702  -0.54615652  0.98621167 -0.47584115\n",
-      "  1.01186442 -0.40274853  1.03001892 -0.32819503  1.04014873 -0.25300091\n",
-      "  1.0442321  -0.17934093] , sample:  [ 1.0435645  -0.11306088  0.96943581 -0.02384177  1.03281224  0.04388265\n",
-      "  0.95160252  0.06720908  0.89466713  0.19916684  0.88022051  0.31638355\n",
-      "  0.80798599  0.38163069  0.6667935   0.4390582   0.65753778  0.51398457\n",
-      "  0.58023736  0.57917227  0.48952481  0.71103517  0.41608686  0.78008853\n",
-      "  0.3712484   0.77580984  0.24013699  0.83476223  0.19760989  0.88080104\n",
-      "  0.18849332  0.90064635  0.07760744  0.9166605   0.00211009  0.94282416\n",
-      " -0.05986973  0.98464226 -0.11529095  0.95208673 -0.28138816  0.91865499\n",
-      " -0.29516147  0.85545337 -0.47793078  0.7571912  -0.54436622  0.79315564\n",
-      " -0.59795364  0.7213785  -0.76160084  0.64075016 -0.84486996  0.49300758\n",
-      " -0.99108795  0.50924978 -0.953544    0.44673779 -0.99057014  0.2795359\n",
-      " -1.05084043  0.28011037 -1.1253339   0.17068629 -1.1003968   0.07454069\n",
-      " -1.12130442  0.01267685 -1.04169612 -0.06449883 -1.07437608 -0.15563438\n",
-      " -0.97607788 -0.2188221  -1.00702236 -0.41006177 -0.94700047 -0.51309193\n",
-      " -0.90959604 -0.56186443 -0.82575512 -0.67603084 -0.68965058 -0.77030325\n",
-      " -0.55613817 -0.78884516 -0.47894858 -0.82456963 -0.38217838 -0.89411626\n",
-      " -0.28194073 -0.93016149 -0.10267589 -0.90478972  0.00973542 -0.95924912\n",
-      "  0.1016836  -0.99770617  0.18788782 -0.90195993  0.3793607  -0.90388901\n",
-      "  0.43645361 -0.91506931  0.61709484 -0.77920041  0.62274323 -0.87567824\n",
-      "  0.75267515 -0.75834127  0.80784157 -0.71393313  0.89897275 -0.72342448\n",
-      "  0.86580064 -0.57013711  0.93055667 -0.58542116  0.98752753 -0.44254594\n",
-      "  0.99428949 -0.45679699  1.05989653 -0.33775069  1.03183933 -0.15524699\n",
-      "  1.07683733 -0.1911937 ]\n",
-      "  cov conditional:  [[ 2.38341975e-05 -1.25387671e-06  2.09252375e-05 ...  4.02360896e-07\n",
-      "  -1.44142605e-04  9.04770195e-06]\n",
-      " [-1.25387671e-06  2.43510793e-05 -1.10074051e-06 ...  2.13903534e-05\n",
-      "   7.58023742e-06 -1.53121244e-04]\n",
-      " [ 2.09252375e-05 -1.10074051e-06  1.83743749e-05 ...  3.53721529e-07\n",
-      "  -1.26724464e-04  7.92958194e-06]\n",
-      " ...\n",
-      " [ 4.02360896e-07  2.13903534e-05  3.53721529e-07 ...  5.24644189e-05\n",
-      "  -4.25711637e-06 -1.53123538e-04]\n",
-      " [-1.44142605e-04  7.58023742e-06 -1.26724464e-04 ... -4.25711637e-06\n",
-      "   1.02747656e-03 -5.03858012e-05]\n",
-      " [ 9.04770195e-06 -1.53121244e-04  7.92958194e-06 ... -1.53123538e-04\n",
-      "  -5.03858012e-05  1.13535490e-03]]\n",
-      "  mean noise:  [-0.00801878  0.0109348   0.04959859 -0.00279546 -0.04025368  0.00564142\n",
-      "  0.00447251  0.05962968  0.01524326  0.00639626 -0.02537358 -0.03085233\n",
-      " -0.01591152 -0.0154267   0.05632567  0.00755123 -0.00773674  0.0115863\n",
-      " -0.00615053  0.02241625  0.00844594 -0.0378959   0.00702837 -0.04133966\n",
-      " -0.02046236  0.0213026   0.04120041  0.01242223  0.01676345  0.00727037\n",
-      " -0.04011805  0.0183633   0.0036099   0.02251241  0.00831915  0.00495509\n",
-      " -0.00637755 -0.04042242 -0.03501296 -0.02382109  0.03922246 -0.01830608\n",
-      " -0.04580175  0.00603857  0.03338665  0.05614243 -0.00535505 -0.0354953\n",
-      " -0.05483455 -0.02507946  0.01165694 -0.01006941  0.00698133  0.06886838\n",
-      "  0.07695493 -0.01879592 -0.02357062 -0.03013771 -0.03580745  0.06073635\n",
-      " -0.01127294 -0.01896187  0.04029624  0.00823563  0.00439263  0.01874397\n",
-      "  0.02547295 -0.00862298 -0.04340776 -0.02428947  0.01037361 -0.02905304\n",
-      " -0.05633864 -0.06387184  0.01721034  0.02885681  0.01151825  0.03508545\n",
-      "  0.04066673 -0.00875901  0.03563953  0.01955951 -0.00988526  0.03706202\n",
-      " -0.04238704 -0.01024695 -0.00985487 -0.02817383  0.00965008  0.00031539\n",
-      "  0.03012701  0.00785397 -0.02610526 -0.03434407 -0.01516164  0.01382853\n",
-      "  0.01476388  0.05511304  0.04731604 -0.02988722 -0.03013305 -0.01026854\n",
-      "  0.02051293  0.02507949 -0.06010308 -0.08034329  0.02534711  0.05288087\n",
-      " -0.02337618 -0.02121407 -0.00770627 -0.01596782 -0.03834143  0.04936835\n",
-      "  0.04548184 -0.04242029  0.02231354  0.03926464 -0.00131585 -0.03329521\n",
-      "  0.01757493  0.05404846 -0.02987761  0.00955566  0.0083094  -0.09775392\n",
-      " -0.03260523  0.01185277]\n",
-      "  cov noise:  [[ 2.38341975e-05 -1.25387671e-06  2.09252375e-05 ...  4.02360896e-07\n",
-      "  -1.44142605e-04  9.04770195e-06]\n",
-      " [-1.25387671e-06  2.43510793e-05 -1.10074051e-06 ...  2.13903534e-05\n",
-      "   7.58023742e-06 -1.53121244e-04]\n",
-      " [ 2.09252375e-05 -1.10074051e-06  1.83743749e-05 ...  3.53721529e-07\n",
-      "  -1.26724464e-04  7.92958194e-06]\n",
-      " ...\n",
-      " [ 4.02360896e-07  2.13903534e-05  3.53721529e-07 ...  5.24644189e-05\n",
-      "  -4.25711637e-06 -1.53123538e-04]\n",
-      " [-1.44142605e-04  7.58023742e-06 -1.26724464e-04 ... -4.25711637e-06\n",
-      "   1.02747656e-03 -5.03858012e-05]\n",
-      " [ 9.04770195e-06 -1.53121244e-04  7.92958194e-06 ... -1.53123538e-04\n",
-      "  -5.03858012e-05  1.13535490e-03]]\n",
-      "  mean:  [ 1.0091237  -0.00463717  1.00376189  0.09346595  0.98868603  0.19081765\n",
-      "  0.96403188  0.28648472  0.930059    0.37954487  0.88715361  0.4690961\n",
-      "  0.83577027  0.55425007  0.77640018  0.63414176  0.70960551  0.70794983\n",
-      "  0.63603736  0.77491252  0.55641947  0.83434892  0.47151613  0.88566907\n",
-      "  0.38211651  0.92836646  0.28905795  0.96203066  0.19325109  0.98633531\n",
-      "  0.09567878  1.00103429 -0.00263354  1.00595596 -0.10067572  1.00101322\n",
-      " -0.19749409  0.98624053 -0.29219184  0.96179625 -0.38392253  0.92797021\n",
-      " -0.47188654  0.88520687 -0.5553278   0.83406114 -0.63351419  0.77512264\n",
-      " -0.70575548  0.70899564 -0.77141139  0.63633257 -0.82988971  0.55783467\n",
-      " -0.88064175  0.47422962 -0.92314885  0.38627715 -0.95693549  0.29478225\n",
-      " -0.98161887  0.20056615 -0.99693146  0.1044384  -1.00270146  0.00722208\n",
-      " -0.99884018 -0.09020592 -0.98534908 -0.18691586 -0.96234064 -0.28195418\n",
-      " -0.93003346 -0.37438092 -0.88871723 -0.46328597 -0.83875128 -0.54777695\n",
-      " -0.78056487 -0.62699809 -0.71468984 -0.70015334 -0.64179233 -0.76652553\n",
-      " -0.56264042 -0.82546683 -0.47803736 -0.87637694 -0.38879086 -0.91869718\n",
-      " -0.29573633 -0.95194827 -0.19974732 -0.97576213 -0.10172206 -0.98992368\n",
-      " -0.00259464 -0.9943804   0.09665486 -0.98921205  0.19501868 -0.97457583\n",
-      "  0.29149481 -0.95068237  0.38512848 -0.91780216  0.47503839 -0.87627448\n",
-      "  0.56040222 -0.82652301  0.64039641 -0.7690517   0.71420152 -0.70440916\n",
-      "  0.78106212 -0.63314128  0.84031106 -0.5558107   0.89136069 -0.47302775\n",
-      "  0.93369417 -0.38547493  0.96688488 -0.2939413   0.9906267  -0.19931595\n",
-      "  1.00473644 -0.10255251]\n",
-      "  cov:  [[0.01484009 0.00097828 0.0147249  ... 0.00114226 0.01473877 0.00105268]\n",
-      " [0.00097828 0.01452544 0.00085426 ... 0.01417317 0.00106591 0.01445553]\n",
-      " [0.0147249  0.00085426 0.01484424 ... 0.0010188  0.01439637 0.00092699]\n",
-      " ...\n",
-      " [0.00114226 0.01417317 0.0010188  ... 0.01470682 0.00123007 0.01454392]\n",
-      " [0.01473877 0.00106591 0.01439637 ... 0.00123007 0.01487083 0.00114229]\n",
-      " [0.00105268 0.01445553 0.00092699 ... 0.01454392 0.00114229 0.01460829]]\n",
-      "  mean w/ noise:  [ 1.00932577 -0.00639894  1.00366375  0.09339935  0.98891926  0.18997079\n",
-      "  0.96389109  0.28392624  0.93032391  0.38133623  0.88786747  0.46980615\n",
-      "  0.83534487  0.55413167  0.77646525  0.63307102  0.71038222  0.70882344\n",
-      "  0.63565876  0.77721345  0.55600561  0.83634547  0.47058943  0.88403654\n",
-      "  0.3800412   0.92804339  0.28946193  0.95970291  0.19227451  0.98623572\n",
-      "  0.09588239  1.00192407 -0.00283739  1.00495759 -0.1024983   1.00259582\n",
-      " -0.19630963  0.98794658 -0.2918364   0.96040971 -0.38467718  0.92654806\n",
-      " -0.47276209  0.88302175 -0.55531272  0.83327621 -0.63158112  0.77499042\n",
-      " -0.7046936   0.70776018 -0.76855872  0.63623929 -0.82951869  0.56064524\n",
-      " -0.88051385  0.47361446 -0.92328803  0.38898635 -0.95636675  0.29627337\n",
-      " -0.97913254  0.20069687 -0.99643164  0.1051546  -1.00097784  0.0079733\n",
-      " -0.99932056 -0.09051309 -0.9845457  -0.18790496 -0.96579706 -0.27949586\n",
-      " -0.93110393 -0.37228272 -0.88855944 -0.46496892 -0.83676247 -0.54740428\n",
-      " -0.77751221 -0.62800457 -0.71457252 -0.70203433 -0.64273289 -0.76827203\n",
-      " -0.56261678 -0.82733873 -0.47914432 -0.87576268 -0.38996801 -0.91785342\n",
-      " -0.29669508 -0.95201611 -0.19787611 -0.9754208  -0.10303825 -0.98883866\n",
-      " -0.00304805 -0.99308515  0.09554719 -0.99054869  0.19632475 -0.97390552\n",
-      "  0.29214266 -0.95026583  0.3861167  -0.91747552  0.47453274 -0.8758057\n",
-      "  0.55866942 -0.82768194  0.64134591 -0.76699174  0.71243113 -0.70290707\n",
-      "  0.77844797 -0.63269676  0.84114925 -0.55512404  0.89280399 -0.4752547\n",
-      "  0.93154648 -0.38517333  0.96724742 -0.29582134  0.99099773 -0.19813958\n",
-      "  1.00132651 -0.10481663]\n",
-      "  cov w/ noise:  [[0.01588915 0.0009189  0.01470888 ... 0.00100285 0.01457657 0.00087568]\n",
-      " [0.0009189  0.01555476 0.00096362 ... 0.0141297  0.00077551 0.01427771]\n",
-      " [0.01470888 0.00096362 0.01550131 ... 0.00090092 0.01420017 0.00080306]\n",
-      " ...\n",
-      " [0.00100285 0.0141297  0.00090092 ... 0.01560321 0.00084798 0.01415793]\n",
-      " [0.01457657 0.00077551 0.01420017 ... 0.00084798 0.0154464  0.00079546]\n",
-      " [0.00087568 0.01427771 0.00080306 ... 0.01415793 0.00079546 0.01508276]]\n",
-      "  mean conditional:  [ 0.90399944 -0.12484317  0.896377   -0.05383087  0.88262639  0.0150534\n",
-      "  0.86252745  0.08277655  0.83577207  0.15029795  0.80200211  0.21825398\n",
-      "  0.76087565  0.28685869  0.71211553  0.35570965  0.65575031  0.42387772\n",
-      "  0.59225787  0.48981353  0.52268889  0.55170176  0.44856724  0.6076236\n",
-      "  0.37172043  0.65589408  0.29391213  0.6953377   0.21645238  0.72535376\n",
-      "  0.13987805  0.74585115  0.0638959   0.75706152 -0.01241118  0.75929313\n",
-      " -0.09031926  0.75301058 -0.17090713  0.73875549 -0.25476476  0.71708464\n",
-      " -0.3416186   0.68876001 -0.43013705  0.65441039 -0.51820922  0.61447869\n",
-      " -0.60314492  0.56915771 -0.68213802  0.51821911 -0.75267023  0.46125519\n",
-      " -0.81282912  0.39785646 -0.86137958  0.3277978  -0.89783918  0.25127269\n",
-      " -0.92216976  0.1689366  -0.93461883  0.0818     -0.93556941 -0.00873761\n",
-      " -0.92537903 -0.10125023 -0.90451458 -0.19438065 -0.87330324 -0.2869258\n",
-      " -0.83220921 -0.37790273 -0.78162556 -0.46641399 -0.72190281 -0.5516203\n",
-      " -0.653699   -0.63254625 -0.57785644 -0.70818069 -0.49558607 -0.77744543\n",
-      " -0.40852665 -0.83925965 -0.31834549 -0.89265032 -0.22682194 -0.93698678\n",
-      " -0.13517519 -0.9719141  -0.04443954 -0.99756717  0.04494903 -1.01426647\n",
-      "  0.13294579 -1.02242174  0.21954524 -1.02215835  0.30469421 -1.01325745\n",
-      "  0.38798713 -0.99513028  0.46861078 -0.96712243  0.54523228 -0.9288037\n",
-      "  0.61632746 -0.88015579  0.68035293 -0.82179248  0.7361574  -0.75489277\n",
-      "  0.78317646 -0.68110506  0.82158397 -0.60237908  0.85188279 -0.52063786\n",
-      "  0.87488259 -0.43782119  0.89123967 -0.35576017  0.90116384 -0.2756256\n",
-      "  0.90662966 -0.20046194] , sample:  [ 0.88852902 -0.10979854  0.88587473 -0.07502411  0.89363127  0.02822456\n",
-      "  0.85942682  0.09332084  0.81765538  0.13257699  0.80490121  0.17370423\n",
-      "  0.70744383  0.29843356  0.72030142  0.39921836  0.6565708   0.37392377\n",
-      "  0.62301207  0.45053012  0.56712467  0.58345336  0.44805315  0.64108375\n",
-      "  0.35336588  0.65081402  0.28482634  0.7308576   0.2190235   0.7340133\n",
-      "  0.13214303  0.72763109  0.05685428  0.68267059 -0.04196111  0.79686625\n",
-      " -0.08252399  0.73849249 -0.16704161  0.76575712 -0.26441164  0.7191677\n",
-      " -0.30568918  0.66547883 -0.41435896  0.64796501 -0.53782278  0.59496294\n",
-      " -0.59778373  0.58570248 -0.66601913  0.51351448 -0.7407011   0.50027389\n",
-      " -0.87176268  0.40091384 -0.84854266  0.30005247 -0.89675376  0.22731271\n",
-      " -0.86554937  0.19066811 -0.95138063  0.11625784 -0.9485925  -0.01550975\n",
-      " -0.94313621 -0.14324799 -0.92771448 -0.20055439 -0.86896677 -0.24710178\n",
-      " -0.81819561 -0.40249495 -0.77000511 -0.46219014 -0.67028653 -0.56189843\n",
-      " -0.69276882 -0.61835567 -0.62614785 -0.71731726 -0.49238778 -0.77290758\n",
-      " -0.35946983 -0.80138603 -0.32012518 -0.90680438 -0.25354874 -0.94744963\n",
-      " -0.08548944 -0.98979003 -0.02018194 -1.01341114  0.03299362 -1.01583939\n",
-      "  0.08216737 -0.97327857  0.25228013 -0.99668212  0.31230216 -1.00268479\n",
-      "  0.34830769 -1.04724041  0.4502408  -1.00511526  0.52895151 -0.91632131\n",
-      "  0.63812851 -0.83904672  0.69597922 -0.86755465  0.76553736 -0.7477686\n",
-      "  0.84225205 -0.66609152  0.77942211 -0.60116739  0.81424642 -0.5129251\n",
-      "  0.84624365 -0.44754705  0.90822995 -0.3477946   0.92268284 -0.29491059\n",
-      "  0.93066107 -0.16763656]\n",
-      "  cov conditional:  [[ 2.38305967e-05 -1.25393047e-06  2.09104038e-05 ...  1.77525808e-07\n",
-      "  -1.48199538e-04  3.36023339e-06]\n",
-      " [-1.25393047e-06  2.43446488e-05 -1.10840237e-06 ...  2.19208734e-05\n",
-      "   7.40583386e-06 -1.48722632e-04]\n",
-      " [ 2.09104038e-05 -1.10840237e-06  1.83510138e-05 ...  1.74066640e-07\n",
-      "  -1.30239770e-04  2.93657305e-06]\n",
-      " ...\n",
-      " [ 1.77525808e-07  2.19208734e-05  1.74066640e-07 ...  5.18492565e-05\n",
-      "  -8.10222868e-07 -1.53049802e-04]\n",
-      " [-1.48199538e-04  7.40583386e-06 -1.30239770e-04 ... -8.10222868e-07\n",
-      "   1.07998774e-03 -1.14129610e-05]\n",
-      " [ 3.36023339e-06 -1.48722632e-04  2.93657305e-06 ... -1.53049802e-04\n",
-      "  -1.14129610e-05  1.07319616e-03]]\n",
-      "  mean noise:  [ 0.01547042 -0.01504463  0.01050227  0.02119324 -0.01100488 -0.01317116\n",
-      "  0.00310063 -0.01054428  0.01811669  0.01772096 -0.0028991   0.04454975\n",
-      "  0.05343181 -0.01157487 -0.00818589 -0.04350871 -0.00082049  0.04995394\n",
-      " -0.0307542   0.0392834  -0.04443579 -0.0317516   0.0005141  -0.03346015\n",
-      "  0.01835455  0.00508006  0.00908579 -0.0355199  -0.00257111 -0.00865954\n",
-      "  0.00773502  0.01822006  0.00704162  0.07439093  0.02954994 -0.03757312\n",
-      " -0.00779527  0.01451809 -0.00386551 -0.02700163  0.00964688 -0.00208306\n",
-      " -0.03592942  0.02328117 -0.01577809  0.00644538  0.01961356  0.01951575\n",
-      " -0.0053612  -0.01654477 -0.01611889  0.00470464 -0.01196913 -0.0390187\n",
-      "  0.05893356 -0.00305737 -0.01283692  0.02774533 -0.00108541  0.02395998\n",
-      " -0.05662039 -0.02173151  0.01676181 -0.03445784  0.01302309  0.00677214\n",
-      "  0.01775719  0.04199776  0.0231999   0.00617374 -0.00433647 -0.03982402\n",
-      " -0.0140136   0.02459222 -0.01162044 -0.00422385 -0.05161629  0.01027813\n",
-      "  0.03906982 -0.01419058  0.04829142  0.00913657 -0.0031983  -0.00453785\n",
-      " -0.04905682 -0.03787362  0.0017797   0.01415406  0.0267268   0.01046285\n",
-      " -0.04968575  0.01787593 -0.0242576   0.01584398  0.01195541  0.00157291\n",
-      "  0.05077842 -0.04914317 -0.03273489 -0.02547623 -0.00760795 -0.01057266\n",
-      "  0.03967944  0.05211014  0.01836998  0.03799283  0.01628076 -0.01248239\n",
-      " -0.02180105 -0.04110907 -0.01562629  0.04576218 -0.02937996 -0.00712417\n",
-      " -0.05907558 -0.01501354  0.04216186 -0.00121169  0.03763637 -0.00771276\n",
-      "  0.02863893  0.00972586 -0.01699027 -0.00796558 -0.021519    0.01928499\n",
-      " -0.02403141 -0.03282537]\n",
-      "  cov noise:  [[ 2.38305967e-05 -1.25393047e-06  2.09104038e-05 ...  1.77525808e-07\n",
-      "  -1.48199538e-04  3.36023339e-06]\n",
-      " [-1.25393047e-06  2.43446488e-05 -1.10840237e-06 ...  2.19208734e-05\n",
-      "   7.40583386e-06 -1.48722632e-04]\n",
-      " [ 2.09104038e-05 -1.10840237e-06  1.83510138e-05 ...  1.74066640e-07\n",
-      "  -1.30239770e-04  2.93657305e-06]\n",
-      " ...\n",
-      " [ 1.77525808e-07  2.19208734e-05  1.74066640e-07 ...  5.18492565e-05\n",
-      "  -8.10222868e-07 -1.53049802e-04]\n",
-      " [-1.48199538e-04  7.40583386e-06 -1.30239770e-04 ... -8.10222868e-07\n",
-      "   1.07998774e-03 -1.14129610e-05]\n",
-      " [ 3.36023339e-06 -1.48722632e-04  2.93657305e-06 ... -1.53049802e-04\n",
-      "  -1.14129610e-05  1.07319616e-03]]\n",
-      "  mean:  [ 1.00967638e+00  3.21023093e-04  1.00501982e+00  9.85650180e-02\n",
-      "  9.90492389e-01  1.95684378e-01  9.66165835e-01  2.90815192e-01\n",
-      "  9.32318239e-01  3.83135677e-01  8.89409770e-01  4.71862495e-01\n",
-      "  8.37969961e-01  5.56240752e-01  7.78532924e-01  6.35534329e-01\n",
-      "  7.11650222e-01  7.09007125e-01  6.37913186e-01  7.75928142e-01\n",
-      "  5.57990065e-01  8.35599267e-01  4.72664674e-01  8.87356295e-01\n",
-      "  3.82835224e-01  9.30563479e-01  2.89470513e-01  9.64664548e-01\n",
-      "  1.93567494e-01  9.89207581e-01  9.61240123e-02  1.00385827e+00\n",
-      " -1.89897633e-03  1.00841574e+00 -9.95968916e-02  1.00282346e+00\n",
-      " -1.96103580e-01  9.87177141e-01 -2.90574489e-01  9.61711039e-01\n",
-      " -3.82194410e-01  9.26777406e-01 -4.70188076e-01  8.82853523e-01\n",
-      " -5.53790676e-01  8.30520147e-01 -6.32213927e-01  7.70409402e-01\n",
-      " -7.04693225e-01  7.03174937e-01 -7.70522760e-01  6.29480140e-01\n",
-      " -8.29074095e-01  5.50033170e-01 -8.79801523e-01  4.65608678e-01\n",
-      " -9.22223514e-01  3.77036430e-01 -9.55911710e-01  2.85172083e-01\n",
-      " -9.80510908e-01  1.90864919e-01 -9.95750884e-01  9.49495940e-02\n",
-      " -1.00144292e+00 -1.75682199e-03 -9.97489230e-01 -9.84655289e-02\n",
-      " -9.83906165e-01 -1.94400896e-01 -9.60830627e-01 -2.88736068e-01\n",
-      " -9.28512900e-01 -3.80593452e-01 -8.87264352e-01 -4.69084894e-01\n",
-      " -8.37441122e-01 -5.53323144e-01 -7.79465819e-01 -6.32423949e-01\n",
-      " -7.13858604e-01 -7.05513914e-01 -6.41233664e-01 -7.71793547e-01\n",
-      " -5.62267611e-01 -8.30569395e-01 -4.77686698e-01 -8.81253778e-01\n",
-      " -3.88262654e-01 -9.23345647e-01 -2.94839223e-01 -9.56419929e-01\n",
-      " -1.98329766e-01 -9.80145460e-01 -9.96937655e-02 -9.94326121e-01\n",
-      "  5.98219700e-05 -9.98909333e-01  9.98870137e-02 -9.93953387e-01\n",
-      "  1.98739026e-01 -9.79587776e-01  2.95590716e-01 -9.55989833e-01\n",
-      "  3.89435361e-01 -9.23381567e-01  4.79311368e-01 -8.82021255e-01\n",
-      "  5.64336536e-01 -8.32219961e-01  6.43690278e-01 -7.74374847e-01\n",
-      "  7.16609758e-01 -7.08968488e-01  7.82434106e-01 -6.36554447e-01\n",
-      "  8.40649553e-01 -5.57795930e-01  8.90855588e-01 -4.73464554e-01\n",
-      "  9.32691698e-01 -3.84402896e-01  9.65799793e-01 -2.91510950e-01\n",
-      "  9.89851602e-01 -1.95745808e-01  1.00455794e+00 -9.81191488e-02]\n",
-      "  cov:  [[ 0.01455513 -0.0004824   0.01452549 ... -0.00032971  0.01433893\n",
-      "  -0.00041368]\n",
-      " [-0.0004824   0.01452797 -0.00052808 ...  0.01413029 -0.00043512\n",
-      "   0.01444214]\n",
-      " [ 0.01452549 -0.00052808  0.01471436 ... -0.00035396  0.01409647\n",
-      "  -0.00044935]\n",
-      " ...\n",
-      " [-0.00032971  0.01413029 -0.00035396 ...  0.01459118 -0.0003085\n",
-      "   0.01447495]\n",
-      " [ 0.01433893 -0.00043512  0.01409647 ... -0.0003085   0.01434512\n",
-      "  -0.00037849]\n",
-      " [-0.00041368  0.01444214 -0.00044935 ...  0.01447495 -0.00037849\n",
-      "   0.01457626]]\n",
-      "  mean w/ noise:  [ 1.00737903 -0.00253323  1.0017409   0.09852377  0.98891086  0.1974229\n",
-      "  0.96627701  0.29046861  0.9299734   0.38024417  0.88972805  0.47472409\n",
-      "  0.83951561  0.55626015  0.77791734  0.63503882  0.71038593  0.70818095\n",
-      "  0.6383689   0.77683177  0.55812123  0.8355363   0.47176809  0.89159367\n",
-      "  0.38289052  0.9320171   0.28950496  0.96628144  0.19298169  0.991404\n",
-      "  0.09445842  1.00236566 -0.00333615  1.00929681 -0.10152407  1.00249132\n",
-      " -0.19786171  0.98417296 -0.29053422  0.96115967 -0.38411297  0.93028325\n",
-      " -0.47031726  0.88486695 -0.5543549   0.83144771 -0.63450939  0.77068831\n",
-      " -0.70211345  0.70400145 -0.7711768   0.63069564 -0.82953793  0.54805753\n",
-      " -0.88187126  0.46657325 -0.92140138  0.37675555 -0.95520271  0.28653372\n",
-      " -0.98228564  0.18958962 -0.9956827   0.09437863 -0.9994715  -0.00262456\n",
-      " -0.99879445 -0.09728002 -0.98291603 -0.19334145 -0.96086471 -0.28880042\n",
-      " -0.92713657 -0.38187696 -0.88855227 -0.46870469 -0.83651296 -0.55079604\n",
-      " -0.77963461 -0.63155009 -0.71310723 -0.70540939 -0.64149875 -0.77368956\n",
-      " -0.56215278 -0.83001885 -0.47692241 -0.87963036 -0.38807482 -0.92253721\n",
-      " -0.29372145 -0.95778952 -0.19911053 -0.97826769 -0.09971864 -0.99416535\n",
-      " -0.00106732 -0.99719702  0.10105797 -0.993731    0.19932175 -0.97686661\n",
-      "  0.29760038 -0.9572758   0.39016439 -0.92392452  0.47915768 -0.88033527\n",
-      "  0.56338235 -0.83115117  0.64380394 -0.77284614  0.71617457 -0.70726203\n",
-      "  0.78200414 -0.63729576  0.84171087 -0.55723377  0.88971933 -0.47312936\n",
-      "  0.93206179 -0.38415419  0.96656705 -0.29049276  0.98833004 -0.19424625\n",
-      "  1.00422062 -0.09825287]\n",
-      "  cov w/ noise:  [[ 0.01482821 -0.00056178  0.01427278 ... -0.00050985  0.01389899\n",
-      "  -0.00068806]\n",
-      " [-0.00056178  0.01596483 -0.00034379 ...  0.01426362 -0.0006292\n",
-      "   0.01469567]\n",
-      " [ 0.01427278 -0.00034379  0.01593838 ... -0.00016686  0.01413885\n",
-      "  -0.00037321]\n",
-      " ...\n",
-      " [-0.00050985  0.01426362 -0.00016686 ...  0.01573741 -0.00047263\n",
-      "   0.01444803]\n",
-      " [ 0.01389899 -0.0006292   0.01413885 ... -0.00047263  0.01521806\n",
-      "  -0.00065079]\n",
-      " [-0.00068806  0.01469567 -0.00037321 ...  0.01444803 -0.00065079\n",
-      "   0.01578979]]\n",
-      "  mean conditional:  [ 0.95127645 -0.08667336  0.96878383 -0.00192544  0.97999805  0.0797439\n",
-      "  0.98300145  0.15881155  0.97568039  0.23604114  0.95609477  0.31230258\n",
-      "  0.9228899   0.38839992  0.87553637  0.46486804  0.81458884  0.54188931\n",
-      "  0.74166545  0.61898896  0.65928141  0.69512511  0.57039562  0.76869946\n",
-      "  0.47809781  0.83771696  0.38482646  0.90003887  0.29218286  0.95343555\n",
-      "  0.20059125  0.99600114  0.10946564  1.02601086  0.01766293  1.04214878\n",
-      " -0.07602062  1.04370407 -0.17226889  1.03063423 -0.27073365  1.00360447\n",
-      " -0.37012282  0.96396061 -0.46805569  0.91355394 -0.56167494  0.85416281\n",
-      " -0.64807046  0.78743668 -0.72483886  0.71444277 -0.7904632   0.63579051\n",
-      " -0.8444822   0.55163847 -0.8872536   0.46211299 -0.91982129  0.36753544\n",
-      " -0.94335197  0.26862686 -0.95884957  0.16666233 -0.96681928  0.06314585\n",
-      " -0.96730678 -0.04035267 -0.95994965 -0.1426142  -0.94415694 -0.24282611\n",
-      " -0.91937571 -0.34063678 -0.8850918  -0.43594249 -0.84113482 -0.52854294\n",
-      " -0.78774039 -0.61792836 -0.72562211 -0.70308616 -0.65591408 -0.78261957\n",
-      " -0.58019479 -0.85490723 -0.50009912 -0.91821022 -0.41724006 -0.97135207\n",
-      " -0.33265037 -1.01352992 -0.24705481 -1.04466591 -0.16063061 -1.06507576\n",
-      " -0.07319942 -1.07533642  0.0155379  -1.07578227  0.10572922 -1.06653703\n",
-      "  0.19705945 -1.04725001  0.28871423 -1.01749792  0.37917208 -0.97702839\n",
-      "  0.46641051 -0.92589695  0.54827268 -0.86459686  0.62285471 -0.79417105\n",
-      "  0.68889145 -0.71594987  0.74608121 -0.63145864  0.79487025 -0.54244779\n",
-      "  0.83632197 -0.45057264  0.87182461 -0.3576949   0.90217524 -0.2649957\n",
-      "  0.93071211 -0.17718235] , sample:  [ 0.91737465 -0.08106961  0.98894971 -0.01335362  0.97490325  0.09597956\n",
-      "  0.9320919   0.14861973  1.02261821  0.24731939  0.93477607  0.32734556\n",
-      "  0.97257047  0.36916758  0.91080658  0.47865995  0.79755259  0.48370781\n",
-      "  0.72815402  0.65982629  0.69391495  0.68913348  0.57743142  0.75931452\n",
-      "  0.4471689   0.81798563  0.38622171  0.87105036  0.25476358  0.99476026\n",
-      "  0.15848001  1.03436469  0.14718102  1.00288846  0.0573478   1.05149767\n",
-      " -0.08474076  1.02625417 -0.1924723   1.04265973 -0.208018    1.02689226\n",
-      " -0.34955907  0.97137817 -0.54841902  0.92462294 -0.54538579  0.84522684\n",
-      " -0.65063529  0.7769221  -0.76533576  0.71905605 -0.79628775  0.55519\n",
-      " -0.79826024  0.56957737 -0.92041121  0.48892801 -0.89332297  0.41771935\n",
-      " -0.93685594  0.31139748 -0.90717884  0.14114713 -0.95448151  0.04473124\n",
-      " -1.0193686  -0.06813756 -0.9533357  -0.19019786 -0.97340296 -0.24262998\n",
-      " -0.91854586 -0.3223296  -0.88042084 -0.43404009 -0.84123498 -0.51054715\n",
-      " -0.75389677 -0.59278707 -0.75715888 -0.6564395  -0.6901609  -0.82721594\n",
-      " -0.56232167 -0.82122245 -0.4479231  -0.96797695 -0.4344984  -0.98224457\n",
-      " -0.31151109 -1.02082558 -0.23803021 -1.07095957 -0.21423834 -1.04896451\n",
-      " -0.05591573 -1.00698332  0.03062422 -1.06795496  0.11576163 -1.09184863\n",
-      "  0.16791977 -1.07119859  0.24993105 -1.04236554  0.35229744 -0.97471144\n",
-      "  0.44759723 -0.90909469  0.57803778 -0.88298639  0.66414929 -0.77660415\n",
-      "  0.71381589 -0.71960313  0.7514363  -0.59947325  0.76955825 -0.55849665\n",
-      "  0.89394645 -0.48502593  0.84871042 -0.34064403  0.866366   -0.28225521\n",
-      "  0.9237716  -0.14821111]\n",
-      "  cov conditional:  [[ 2.38346727e-05 -1.25372444e-06  2.09139073e-05 ...  8.92629257e-07\n",
-      "  -1.42043409e-04  6.56698166e-06]\n",
-      " [-1.25372444e-06  2.43511796e-05 -1.10558826e-06 ...  2.06070322e-05\n",
-      "   6.14946732e-06 -1.48372053e-04]\n",
-      " [ 2.09139073e-05 -1.10558826e-06  1.83541851e-05 ...  7.84923889e-07\n",
-      "  -1.24794458e-04  5.80416444e-06]\n",
-      " ...\n",
-      " [ 8.92629257e-07  2.06070322e-05  7.84923889e-07 ...  5.26900057e-05\n",
-      "  -7.25311138e-06 -1.44365853e-04]\n",
-      " [-1.42043409e-04  6.14946732e-06 -1.24794458e-04 ... -7.25311138e-06\n",
-      "   9.97594376e-04 -2.30552536e-05]\n",
-      " [ 6.56698166e-06 -1.48372053e-04  5.80416444e-06 ... -1.44365853e-04\n",
-      "  -2.30552536e-05  1.05033141e-03]]\n",
-      "  mean noise:  [ 0.0339018  -0.00560375 -0.02016588  0.01142818  0.0050948  -0.01623567\n",
-      "  0.05090955  0.01019181 -0.04693781 -0.01127825  0.0213187  -0.01504298\n",
-      " -0.04968057  0.01923234 -0.03527021 -0.0137919   0.01703625  0.0581815\n",
-      "  0.01351142 -0.04083733 -0.03463354  0.00599163 -0.0070358   0.00938494\n",
-      "  0.03092891  0.01973133 -0.00139524  0.02898851  0.03741928 -0.04132471\n",
-      "  0.04211124 -0.03836355 -0.03771538  0.0231224  -0.03968487 -0.00934889\n",
-      "  0.00872014  0.0174499   0.02020341 -0.0120255  -0.06271565 -0.0232878\n",
-      " -0.02056375 -0.00741756  0.08036334 -0.011069   -0.01628915  0.00893598\n",
-      "  0.00256483  0.01051458  0.0404969  -0.00461328  0.00582456  0.08060051\n",
-      " -0.04622196 -0.01793889  0.03315761 -0.02681502 -0.02649833 -0.0501839\n",
-      " -0.00649603 -0.04277062 -0.05167072  0.0255152  -0.01233778  0.01841461\n",
-      "  0.05206182  0.02778489 -0.00661394  0.04758366  0.02924602 -0.00019613\n",
-      " -0.00082985 -0.01830718 -0.00467096 -0.0019024   0.00010015 -0.01799578\n",
-      " -0.03384362 -0.02514129  0.03153677 -0.04664665  0.03424682  0.04459636\n",
-      " -0.01787312 -0.03368478 -0.05217602  0.04976673  0.01725834  0.0108925\n",
-      " -0.02113928  0.00729567 -0.00902459  0.02629365  0.05360774 -0.01611124\n",
-      " -0.01728369 -0.0683531  -0.01508632 -0.00782731 -0.01003241  0.0253116\n",
-      "  0.02913968  0.02394858  0.03878318  0.02486762  0.02687463 -0.00231695\n",
-      "  0.01881328 -0.01680226 -0.02976509  0.01838953 -0.04129459 -0.0175669\n",
-      " -0.02492443  0.00365326 -0.0053551  -0.03198539  0.025312    0.01604886\n",
-      " -0.05762448  0.03445329  0.02311419 -0.01705088  0.03580924  0.01725951\n",
-      "  0.00694052 -0.02897124]\n",
-      "  cov noise:  [[ 2.38346727e-05 -1.25372444e-06  2.09139073e-05 ...  8.92629257e-07\n",
-      "  -1.42043409e-04  6.56698166e-06]\n",
-      " [-1.25372444e-06  2.43511796e-05 -1.10558826e-06 ...  2.06070322e-05\n",
-      "   6.14946732e-06 -1.48372053e-04]\n",
-      " [ 2.09139073e-05 -1.10558826e-06  1.83541851e-05 ...  7.84923889e-07\n",
-      "  -1.24794458e-04  5.80416444e-06]\n",
-      " ...\n",
-      " [ 8.92629257e-07  2.06070322e-05  7.84923889e-07 ...  5.26900057e-05\n",
-      "  -7.25311138e-06 -1.44365853e-04]\n",
-      " [-1.42043409e-04  6.14946732e-06 -1.24794458e-04 ... -7.25311138e-06\n",
-      "   9.97594376e-04 -2.30552536e-05]\n",
-      " [ 6.56698166e-06 -1.48372053e-04  5.80416444e-06 ... -1.44365853e-04\n",
-      "  -2.30552536e-05  1.05033141e-03]]\n",
-      "  mean:  [ 0.99918859  0.00212185  0.99340044  0.09888707  0.97782948  0.19446206\n",
-      "  0.95260032  0.28797138  0.91796559  0.37860906  0.87432926  0.46564461\n",
-      "  0.82224676  0.54838541  0.76241117  0.6261558   0.6955861   0.69828639\n",
-      "  0.62253699  0.76409823  0.54400477  0.82289699  0.46069621  0.87398577\n",
-      "  0.37329893  0.91672134  0.28250968  0.95056909  0.18905182  0.97511133\n",
-      "  0.09369785  0.99005415 -0.00272494  0.99523952 -0.0993578   0.99064997\n",
-      " -0.19533342  0.97636861 -0.28976607  0.95253838 -0.38173987  0.91937086\n",
-      " -0.4703091   0.87717121 -0.55451665  0.82634466 -0.63343183  0.76739462\n",
-      " -0.70619981  0.70091046 -0.77208655  0.62757564 -0.83048712  0.5481707\n",
-      " -0.8808999   0.46354747 -0.9228886   0.37458281 -0.95607467  0.28215245\n",
-      " -0.9801289   0.18714203 -0.99478864  0.09047317 -0.99988844 -0.00689713\n",
-      " -0.99536882 -0.10401266 -0.98128194 -0.19994151 -0.95778107 -0.29381134\n",
-      " -0.92510435 -0.38483584 -0.88358903 -0.47226138 -0.83367999 -0.55533195\n",
-      " -0.77589814 -0.63329203 -0.7107873  -0.70542512 -0.63889243 -0.77107898\n",
-      " -0.56077612 -0.82962811 -0.47706622 -0.88044384 -0.38849169 -0.92289945\n",
-      " -0.29587577 -0.95639507 -0.20013195 -0.98042875 -0.1022492  -0.99468526\n",
-      " -0.00328906 -0.99903733  0.0956557  -0.99351763  0.19351776 -0.97829068\n",
-      "  0.28932053 -0.95361104  0.38216992 -0.91980047  0.47122997 -0.8772494\n",
-      "  0.55571555 -0.82641085  0.63487452 -0.76777706  0.70795783 -0.70186794\n",
-      "  0.77421327 -0.6292442   0.83292424 -0.55054592  0.8834627  -0.46652227\n",
-      "  0.92530879 -0.37801725  0.95806207 -0.28594664  0.98142329 -0.19125665\n",
-      "  0.99517605 -0.09491079]\n",
-      "  cov:  [[ 0.01658325 -0.00157715  0.01624494 ... -0.00151208  0.01665139\n",
-      "  -0.00155751]\n",
-      " [-0.00157715  0.01594447 -0.00162748 ...  0.01518065 -0.001523\n",
-      "   0.01568385]\n",
-      " [ 0.01624494 -0.00162748  0.01611579 ... -0.00155565  0.01611242\n",
-      "  -0.00160303]\n",
-      " ...\n",
-      " [-0.00151208  0.01518065 -0.00155565 ...  0.01532681 -0.00146082\n",
-      "   0.01537168]\n",
-      " [ 0.01665139 -0.001523    0.01611242 ... -0.00146082  0.01692778\n",
-      "  -0.00150618]\n",
-      " [-0.00155751  0.01568385 -0.00160303 ...  0.01537168 -0.00150618\n",
-      "   0.01565163]]\n",
-      "  mean w/ noise:  [ 0.99967055  0.00259295  0.99294436  0.10021352  0.97675686  0.19532044\n",
-      "  0.95485362  0.28734449  0.9175186   0.37635168  0.87483235  0.46418787\n",
-      "  0.82132495  0.54912021  0.76175199  0.62791665  0.69390137  0.69786986\n",
-      "  0.62341213  0.76597699  0.54392053  0.82393524  0.46040757  0.87497834\n",
-      "  0.37122202  0.91917503  0.28137051  0.95237117  0.18940303  0.9763288\n",
-      "  0.09464724  0.99167301 -0.00189028  0.99547132 -0.09540268  0.99127697\n",
-      " -0.19633508  0.97695776 -0.2906423   0.95311489 -0.38058007  0.92014262\n",
-      " -0.470588    0.87720627 -0.55419847  0.82555136 -0.63567437  0.76712847\n",
-      " -0.70493634  0.69840469 -0.77275973  0.62652097 -0.82906142  0.54741707\n",
-      " -0.88122309  0.46481932 -0.92212309  0.37161524 -0.95728343  0.28341407\n",
-      " -0.97685213  0.18649573 -0.99502926  0.08933764 -0.99944874 -0.0049502\n",
-      " -0.99405699 -0.10544688 -0.98126538 -0.2010619  -0.95529892 -0.29553585\n",
-      " -0.92318028 -0.38712026 -0.88336374 -0.47065558 -0.83445541 -0.55580007\n",
-      " -0.77696106 -0.63333498 -0.71176889 -0.70303533 -0.63825245 -0.77258721\n",
-      " -0.56050904 -0.82814406 -0.47699153 -0.88273474 -0.38875113 -0.92173983\n",
-      " -0.29468702 -0.95912609 -0.20141874 -0.98078352 -0.10253522 -0.99246388\n",
-      " -0.00222052 -1.00241789  0.09845283 -0.99315317  0.1946891  -0.97819022\n",
-      "  0.28789816 -0.95268895  0.38273243 -0.91969827  0.47045172 -0.87615302\n",
-      "  0.55783019 -0.82712923  0.63442823 -0.76992515  0.70941002 -0.70340469\n",
-      "  0.77579796 -0.63071572  0.83157108 -0.55209392  0.88374536 -0.46597871\n",
-      "  0.92819344 -0.37719596  0.95747491 -0.28425866  0.9808669  -0.19171942\n",
-      "  0.9949765  -0.09741542]\n",
-      "  cov w/ noise:  [[ 0.01674694 -0.00164179  0.01581638 ... -0.00203214  0.01646799\n",
-      "  -0.0020569 ]\n",
-      " [-0.00164179  0.01670622 -0.00147604 ...  0.01512686 -0.00145086\n",
-      "   0.01592312]\n",
-      " [ 0.01581638 -0.00147604  0.01704837 ... -0.00173825  0.01642682\n",
-      "  -0.00182653]\n",
-      " ...\n",
-      " [-0.00203214  0.01512686 -0.00173825 ...  0.01635648 -0.00185139\n",
-      "   0.01569973]\n",
-      " [ 0.01646799 -0.00145086  0.01642682 ... -0.00185139  0.01837903\n",
-      "  -0.00177469]\n",
-      " [-0.0020569   0.01592312 -0.00182653 ...  0.01569973 -0.00177469\n",
-      "   0.01719906]]\n",
-      "  mean conditional:  [ 0.91404307  0.04526703  0.92298796  0.1560643   0.921488    0.2629705\n",
-      "  0.90933603  0.36537147  0.88653188  0.46309646  0.85334769  0.55612441\n",
-      "  0.81050139  0.6443442   0.75900128  0.72723384  0.70013914  0.80397886\n",
-      "  0.63533566  0.87360934  0.56578715  0.93513674  0.49235681  0.98787963\n",
-      "  0.41531719  1.03135897  0.33436532  1.06550676  0.24881237  1.09024073\n",
-      "  0.15783594  1.1057048   0.06105247  1.11191127 -0.04125675  1.1088287\n",
-      " -0.1481606   1.09647985 -0.25805732  1.07484085 -0.36901021  1.04396257\n",
-      " -0.47901789  1.00391821 -0.58639234  0.95467614 -0.68981351  0.89617702\n",
-      " -0.78832462  0.82824421 -0.88113797  0.75075346 -0.96734968  0.66377003\n",
-      " -1.0457535   0.56776139 -1.1146433   0.46379255 -1.17209237  0.35350164\n",
-      " -1.21615175  0.23907238 -1.24534455  0.12297743 -1.25885483  0.00757127\n",
-      " -1.25682516 -0.10484688 -1.24028166 -0.21253374 -1.21101448 -0.31418663\n",
-      " -1.17128246 -0.40904954 -1.12319764 -0.49689004 -1.06846168 -0.57787459\n",
-      " -1.00794582 -0.65239685 -0.94163253 -0.72103175 -0.86869979 -0.78422372\n",
-      " -0.78816752 -0.84231959 -0.69927118 -0.89530428 -0.60217882 -0.94296015\n",
-      " -0.49820302 -0.98464367 -0.38965697 -1.01940853 -0.27944794 -1.04600094\n",
-      " -0.17065463 -1.06304733 -0.06585732 -1.0693491   0.03341065 -1.06381965\n",
-      "  0.12669542 -1.0459688   0.21447386 -1.01562965  0.29776028 -0.97324905\n",
-      "  0.3775692  -0.91956464  0.45458225 -0.85544155  0.52879542 -0.78181167\n",
-      "  0.59963782 -0.69935986  0.66619886 -0.60868602  0.7272441  -0.51050126\n",
-      "  0.7816213  -0.40575008  0.82824174 -0.29580586  0.86601067 -0.18267581\n",
-      "  0.89475291 -0.06802378] , sample:  [ 9.22061616e-01  3.85910931e-03  9.09786372e-01  2.11744312e-01\n",
-      "  9.10325977e-01  3.04134402e-01  9.08640789e-01  3.42749336e-01\n",
-      "  8.75183059e-01  4.46111680e-01  8.44813973e-01  5.47835792e-01\n",
-      "  8.17640362e-01  5.97856596e-01  7.46688286e-01  7.78428813e-01\n",
-      "  7.37192972e-01  8.03604965e-01  6.23348198e-01  8.22488568e-01\n",
-      "  5.99743824e-01  1.00608629e+00  5.07502393e-01  9.70220076e-01\n",
-      "  3.59491176e-01  1.05330375e+00  3.05895832e-01  1.06827949e+00\n",
-      "  2.39704490e-01  1.04065290e+00  2.00532077e-01  1.11833371e+00\n",
-      "  1.00294823e-01  1.11679375e+00 -5.73362387e-02  1.14409218e+00\n",
-      " -1.67873222e-01  1.06081911e+00 -2.44006031e-01  1.07086318e+00\n",
-      " -3.68038083e-01  1.01841275e+00 -4.81538686e-01  1.01221046e+00\n",
-      " -6.21343855e-01  9.89472114e-01 -7.10630347e-01  8.58025351e-01\n",
-      " -7.74900767e-01  8.68804792e-01 -8.82873775e-01  7.47504307e-01\n",
-      " -9.22723638e-01  6.45451946e-01 -1.01389744e+00  5.93700111e-01\n",
-      " -1.12039677e+00  4.50547122e-01 -1.21681998e+00  3.68763142e-01\n",
-      " -1.19711417e+00  2.29079960e-01 -1.25167361e+00  1.22800321e-01\n",
-      " -1.29467110e+00  3.51449464e-05 -1.25838965e+00 -9.07411887e-02\n",
-      " -1.25828950e+00 -2.45893237e-01 -1.21529411e+00 -3.11123276e-01\n",
-      " -1.15960542e+00 -4.16611503e-01 -1.10975886e+00 -4.91043249e-01\n",
-      " -1.05838438e+00 -5.59386273e-01 -9.87877185e-01 -6.33213818e-01\n",
-      " -9.44895406e-01 -7.34045111e-01 -8.54917924e-01 -7.83117867e-01\n",
-      " -8.13329670e-01 -8.47278875e-01 -7.20381785e-01 -9.24862686e-01\n",
-      " -6.17588927e-01 -9.38497793e-01 -4.87822628e-01 -9.42188899e-01\n",
-      " -4.26179819e-01 -1.04694750e+00 -2.60187000e-01 -1.03546648e+00\n",
-      " -1.91359155e-01 -1.11320293e+00  2.03349280e-03 -1.00712186e+00\n",
-      "  5.44494967e-02 -1.06372647e+00  1.15132595e-01 -1.02230005e+00\n",
-      "  2.04359952e-01 -1.08167308e+00  2.42574548e-01 -9.48574084e-01\n",
-      "  4.29971317e-01 -9.60452293e-01  3.81768973e-01 -8.45958643e-01\n",
-      "  5.67720709e-01 -7.40502292e-01  6.04370197e-01 -6.69434506e-01\n",
-      "  6.56299061e-01 -6.41509152e-01  7.18472421e-01 -5.23698133e-01\n",
-      "  7.68807612e-01 -4.33187157e-01  8.67045130e-01 -2.64491696e-01\n",
-      "  8.57900156e-01 -2.00177791e-01  9.00396112e-01 -5.50727080e-02]\n",
-      "  cov conditional:  [[ 2.24927376e-05  7.73843178e-07  1.97344291e-05 ...  8.08653244e-07\n",
-      "  -1.37949997e-04 -2.32626783e-06]\n",
-      " [ 7.73843178e-07  2.20457935e-05  6.83847233e-07 ...  2.08739238e-05\n",
-      "  -8.30527467e-06 -1.38657594e-04]\n",
-      " [ 1.97344291e-05  6.83847233e-07  1.73179266e-05 ...  7.05241221e-07\n",
-      "  -1.21252383e-04 -2.05628536e-06]\n",
-      " ...\n",
-      " [ 8.08653244e-07  2.08739238e-05  7.05241221e-07 ...  5.26677596e-05\n",
-      "  -5.87225839e-06 -1.51155641e-04]\n",
-      " [-1.37949997e-04 -8.30527467e-06 -1.21252383e-04 ... -5.87225839e-06\n",
-      "   1.01110709e-03  3.32060811e-05]\n",
-      " [-2.32626783e-06 -1.38657594e-04 -2.05628536e-06 ... -1.51155641e-04\n",
-      "   3.32060811e-05  1.01963822e-03]]\n",
-      "  mean noise:  [-8.01854608e-03  4.14079178e-02  1.32015838e-02 -5.56800129e-02\n",
-      "  1.11620249e-02 -4.11639036e-02  6.95240510e-04  2.26221369e-02\n",
-      "  1.13488205e-02  1.69847842e-02  8.53371202e-03  8.28861708e-03\n",
-      " -7.13897227e-03  4.64876048e-02  1.23129909e-02 -5.11949730e-02\n",
-      " -3.70538350e-02  3.73892147e-04  1.19874658e-02  5.11207763e-02\n",
-      " -3.39566770e-02 -7.09495487e-02 -1.51455847e-02  1.76595503e-02\n",
-      "  5.58260138e-02 -2.19447724e-02  2.84694866e-02 -2.77273428e-03\n",
-      "  9.10788278e-03  4.95878343e-02 -4.26961349e-02 -1.26289158e-02\n",
-      " -3.92423487e-02 -4.88248030e-03  1.60794923e-02 -3.52634788e-02\n",
-      "  1.97126182e-02  3.56607363e-02 -1.40512925e-02  3.97767423e-03\n",
-      " -9.72131706e-04  2.55498142e-02  2.52080040e-03 -8.29225028e-03\n",
-      "  3.49515102e-02 -3.47959757e-02  2.08168412e-02  3.81516642e-02\n",
-      " -1.34238566e-02 -4.05605810e-02  1.73580760e-03  3.24915339e-03\n",
-      " -4.46260461e-02  1.83180851e-02 -3.18560641e-02 -2.59387246e-02\n",
-      "  5.75347060e-03  1.32454268e-02  4.47276079e-02 -1.52615039e-02\n",
-      " -1.90375842e-02  9.99241591e-03  6.32906609e-03  1.77105189e-04\n",
-      "  3.58162722e-02  7.53612126e-03  1.56449828e-03 -1.41056896e-02\n",
-      "  1.80078413e-02  3.33594934e-02  4.27963061e-03 -3.06335639e-03\n",
-      " -1.16770375e-02  7.56196528e-03 -1.34387876e-02 -5.84678996e-03\n",
-      " -1.00773001e-02 -1.84883186e-02 -2.00686314e-02 -1.91830313e-02\n",
-      "  3.26287321e-03  1.30133577e-02 -1.37818621e-02 -1.10585175e-03\n",
-      "  2.51621492e-02  4.95928832e-03  2.11106017e-02  2.95584064e-02\n",
-      "  1.54101115e-02 -4.46235227e-03 -1.03803915e-02 -4.24547689e-02\n",
-      "  3.65228501e-02  2.75389648e-02 -1.92609410e-02 -1.05344670e-02\n",
-      "  2.07045206e-02  5.01555965e-02 -6.78908081e-02 -6.22272425e-02\n",
-      " -2.10388469e-02 -9.31821579e-05  1.15628252e-02 -2.36687536e-02\n",
-      "  1.01139039e-02  6.60434374e-02  5.51857300e-02 -2.46749654e-02\n",
-      " -5.24021148e-02  4.08876487e-02  7.28132769e-02 -9.48290576e-03\n",
-      " -3.89252898e-02 -4.13093819e-02 -4.73238026e-03 -2.99253499e-02\n",
-      "  9.89980071e-03  3.28231344e-02  8.77167481e-03  1.31968723e-02\n",
-      "  1.28136858e-02  2.74370814e-02 -3.88033857e-02 -3.13141672e-02\n",
-      "  8.11051595e-03  1.75019792e-02 -5.64320597e-03 -1.29510725e-02]\n",
-      "  cov noise:  [[ 2.24927376e-05  7.73843178e-07  1.97344291e-05 ...  8.08653244e-07\n",
-      "  -1.37949997e-04 -2.32626783e-06]\n",
-      " [ 7.73843178e-07  2.20457935e-05  6.83847233e-07 ...  2.08739238e-05\n",
-      "  -8.30527467e-06 -1.38657594e-04]\n",
-      " [ 1.97344291e-05  6.83847233e-07  1.73179266e-05 ...  7.05241221e-07\n",
-      "  -1.21252383e-04 -2.05628536e-06]\n",
-      " ...\n",
-      " [ 8.08653244e-07  2.08739238e-05  7.05241221e-07 ...  5.26677596e-05\n",
-      "  -5.87225839e-06 -1.51155641e-04]\n",
-      " [-1.37949997e-04 -8.30527467e-06 -1.21252383e-04 ... -5.87225839e-06\n",
-      "   1.01110709e-03  3.32060811e-05]\n",
-      " [-2.32626783e-06 -1.38657594e-04 -2.05628536e-06 ... -1.51155641e-04\n",
-      "   3.32060811e-05  1.01963822e-03]]\n",
-      "  mean:  [ 1.00386575  0.01177826  0.99877992  0.10866037  0.98373041  0.20456378\n",
-      "  0.95892323  0.2985157   0.92469489  0.38959455  0.88149874  0.47694321\n",
-      "  0.82988627  0.5597483   0.77049571  0.63724911  0.70401833  0.70873955\n",
-      "  0.63117752  0.77355215  0.55270907  0.83107348  0.46932968  0.88077244\n",
-      "  0.38175304  0.92220375  0.29071274  0.95497851  0.19696965  0.97874889\n",
-      "  0.10132447  0.99321555  0.00463112  0.99815955 -0.09221811  0.99349397\n",
-      " -0.1883325   0.97926235 -0.28284833  0.95560775 -0.37491685  0.92274015\n",
-      " -0.46366778  0.88092258 -0.54820223  0.83046004 -0.62762015  0.77172924\n",
-      " -0.70106957  0.70523938 -0.76779755  0.63167521 -0.82715507  0.55188416\n",
-      " -0.87857667  0.46677926 -0.92158303  0.37725335 -0.95578201  0.28415404\n",
-      " -0.9808341   0.18830892 -0.99643471  0.09057165 -1.00233886 -0.00815121\n",
-      " -0.99841231 -0.10690812 -0.98466375 -0.20470814 -0.96124609 -0.3005498\n",
-      " -0.92844101 -0.39344925 -0.88664977 -0.48244396 -0.83635573 -0.5666308\n",
-      " -0.77810096 -0.6451978  -0.71246162 -0.71744413 -0.64004147 -0.78276683\n",
-      " -0.56149628 -0.84060568 -0.47753441 -0.89038826 -0.38892389 -0.9315123\n",
-      " -0.29650891 -0.96339346 -0.20120404 -0.98557405 -0.10395981 -0.99782417\n",
-      " -0.00574706 -1.00015412  0.09246402 -0.99273989  0.18972437 -0.97581945\n",
-      "  0.28512905 -0.94963652  0.37780132 -0.91445294  0.46688357 -0.87059735\n",
-      "  0.55156219 -0.81849382  0.63106494 -0.75866843  0.70462917 -0.69172768\n",
-      "  0.77150368 -0.61833717  0.83099    -0.53921818  0.88247761 -0.45514258\n",
-      "  0.92544316 -0.36689982  0.95943378 -0.27528152  0.98404263 -0.18108122\n",
-      "  0.99892692 -0.08510985]\n",
-      "  cov:  [[ 0.01507679 -0.00114682  0.01493327 ... -0.00130033  0.01495309\n",
-      "  -0.00122423]\n",
-      " [-0.00114682  0.01394548 -0.00120017 ...  0.01341774 -0.00107593\n",
-      "   0.01379322]\n",
-      " [ 0.01493327 -0.00120017  0.01502862 ... -0.00131168  0.01457647\n",
-      "  -0.00125725]\n",
-      " ...\n",
-      " [-0.00130033  0.01341774 -0.00131168 ...  0.01377754 -0.00126652\n",
-      "   0.01370801]\n",
-      " [ 0.01495309 -0.00107593  0.01457647 ... -0.00126652  0.01507188\n",
-      "  -0.00117088]\n",
-      " [-0.00122423  0.01379322 -0.00125725 ...  0.01370801 -0.00117088\n",
-      "   0.01386559]]\n",
-      "  mean w/ noise:  [ 1.00307199  0.01049259  0.99757298  0.10784324  0.98488817  0.20380928\n",
-      "  0.95873429  0.29829238  0.92466653  0.39043942  0.88169047  0.4758985\n",
-      "  0.82877482  0.5605057   0.76831369  0.63549613  0.7048568   0.70913837\n",
-      "  0.63120999  0.77434708  0.55340304  0.83132007  0.4689151   0.87919877\n",
-      "  0.38262373  0.92225105  0.29075618  0.95339764  0.19381673  0.97931576\n",
-      "  0.10018901  0.99349842  0.00581813  0.99915739 -0.09117676  0.994325\n",
-      " -0.18977775  0.98250667 -0.28288016  0.95551983 -0.37218962  0.92366224\n",
-      " -0.46267798  0.87820535 -0.5495708   0.83039639 -0.62934566  0.77054197\n",
-      " -0.70174765  0.70571232 -0.76826487  0.6335478  -0.82544972  0.55125422\n",
-      " -0.88004374  0.46537711 -0.92013475  0.37944568 -0.95734795  0.28413325\n",
-      " -0.9820323   0.1894838  -0.99541125  0.08976493 -1.0022719  -0.0088212\n",
-      " -0.99548209 -0.10844571 -0.98353497 -0.20335249 -0.95974829 -0.29928719\n",
-      " -0.92699566 -0.39420489 -0.8876288  -0.48342011 -0.83578203 -0.56849889\n",
-      " -0.77624584 -0.64364351 -0.71018448 -0.71753521 -0.64087812 -0.78340213\n",
-      " -0.56021346 -0.8386088  -0.47982678 -0.88857339 -0.39029073 -0.9295775\n",
-      " -0.29695742 -0.96376282 -0.20006737 -0.98529046 -0.10629133 -0.99577497\n",
-      " -0.00936641 -1.00188149  0.09231739 -0.99346308  0.18972172 -0.97430843\n",
-      "  0.2846047  -0.94969443  0.37956226 -0.91514372  0.46379911 -0.86996975\n",
-      "  0.55108392 -0.82040763  0.63058437 -0.75948148  0.7034994  -0.69234232\n",
-      "  0.76973608 -0.6203049   0.83063482 -0.54133501  0.88426242 -0.4543287\n",
-      "  0.92833902 -0.36666515  0.95873617 -0.27475576  0.98443821 -0.1817802\n",
-      "  1.00145799 -0.08524067]\n",
-      "  cov w/ noise:  [[ 0.01596823 -0.00121264  0.01477598 ... -0.0013903   0.0151345\n",
-      "  -0.00113037]\n",
-      " [-0.00121264  0.01539416 -0.00120899 ...  0.01360722 -0.00100235\n",
-      "   0.01422509]\n",
-      " [ 0.01477598 -0.00120899  0.01599022 ... -0.00136096  0.01486499\n",
-      "  -0.00113917]\n",
-      " ...\n",
-      " [-0.0013903   0.01360722 -0.00136096 ...  0.01468721 -0.00121611\n",
-      "   0.01390067]\n",
-      " [ 0.0151345  -0.00100235  0.01486499 ... -0.00121611  0.01649074\n",
-      "  -0.00108621]\n",
-      " [-0.00113037  0.01422509 -0.00113917 ...  0.01390067 -0.00108621\n",
-      "   0.01524056]]\n",
-      "  mean conditional:  [ 1.09408569  0.02180719  1.09708582  0.14077977  1.08619908  0.25598789\n",
-      "  1.06116681  0.36644633  1.02266032  0.47186292  0.97231764  0.57237073\n",
-      "  0.91272919  0.66814821  0.84692989  0.75912719  0.77812806  0.84486325\n",
-      "  0.70925252  0.9246399   0.64245949  0.99750906  0.57896508  1.06235764\n",
-      "  0.51875219  1.11795025  0.46080546  1.16308893  0.40332187  1.1965735\n",
-      "  0.34410629  1.21753008  0.28122496  1.22535755  0.21316883  1.22009335\n",
-      "  0.13919114  1.20244486  0.05924372  1.17370315 -0.02608273  1.13563227\n",
-      " -0.11576881  1.09008304 -0.208599    1.03862964 -0.303208    0.98234379\n",
-      " -0.39816002  0.92140061 -0.49203924  0.85516857 -0.58326976  0.78254796\n",
-      " -0.67016185  0.70228356 -0.75086434  0.6134618  -0.82345586  0.51595971\n",
-      " -0.88608371  0.41043317 -0.93717417  0.29833797 -0.97548554  0.1816761\n",
-      " -1.00033718  0.06289611 -1.01137693 -0.0555333  -1.0088314  -0.17123183\n",
-      " -0.99321825 -0.28197307 -0.96515719 -0.3860734  -0.92518654 -0.48218247\n",
-      " -0.87362186 -0.56950917 -0.81031024 -0.64769118 -0.73497267 -0.71671754\n",
-      " -0.64748162 -0.77700313 -0.54830146 -0.82894099 -0.43886345 -0.87318768\n",
-      " -0.32170012 -0.91029032 -0.20019752 -0.94050976 -0.0779928  -0.9638728\n",
-      "  0.04122692 -0.98003701  0.15448516 -0.98860435  0.25993447 -0.9888129\n",
-      "  0.35677626 -0.98020392  0.4453794  -0.96226839  0.52670837 -0.93460629\n",
-      "  0.60211412 -0.89695856  0.67285678 -0.84903892  0.73976137 -0.79056477\n",
-      "  0.80319183 -0.72112469  0.86311695 -0.6404505   0.9189413  -0.54873586\n",
-      "  0.96981484 -0.44683196  1.01440613 -0.33623767  1.05046904 -0.21926743\n",
-      "  1.0773626  -0.0980003 ] , sample:  [ 1.12522056 -0.0046028   1.11606676  0.21953333  1.10022978  0.27734103\n",
-      "  1.07240986  0.38007385  1.03326366  0.43719905  0.95846546  0.52423918\n",
-      "  0.91492337  0.66770242  0.82756945  0.78756968  0.7549522   0.87423603\n",
-      "  0.73869089  0.93878417  0.60578379  0.94378622  0.60247912  1.03098389\n",
-      "  0.51200802  1.1298084   0.45526849  1.19116805  0.37492613  1.18985882\n",
-      "  0.34950402  1.21600076  0.30805536  1.27242387  0.23382898  1.22540201\n",
-      "  0.11609005  1.20515028  0.04868802  1.16305205 -0.00214183  1.13264527\n",
-      " -0.11808355  1.09992843 -0.1776463   1.01143325 -0.28569213  0.97255864\n",
-      " -0.41163214  0.89726142 -0.51549346  0.82623685 -0.5839157   0.77622968\n",
-      " -0.64747244  0.75184632 -0.73330688  0.68343901 -0.84016465  0.48851017\n",
-      " -0.90412782  0.39616015 -0.91256461  0.29214496 -0.96486983  0.21135556\n",
-      " -0.97228224  0.05936904 -1.03416072 -0.07199449 -1.04359846 -0.14197601\n",
-      " -1.02262294 -0.31818895 -0.94684375 -0.38028075 -0.90209836 -0.45871382\n",
-      " -0.85691425 -0.57534983 -0.80378495 -0.64563255 -0.77269962 -0.72280194\n",
-      " -0.66997569 -0.79844228 -0.54219617 -0.85146924 -0.47267421 -0.87043666\n",
-      " -0.28800532 -0.91241333 -0.1821877  -0.90475021 -0.06510951 -0.9750458\n",
-      "  0.01541747 -0.97703475  0.15333935 -0.96554466  0.26853858 -0.99163834\n",
-      "  0.3722784  -0.96855116  0.46347829 -0.96941029  0.53615533 -0.92908267\n",
-      "  0.58853439 -0.93159701  0.65311291 -0.83177981  0.72674674 -0.76504662\n",
-      "  0.85290714 -0.73726741  0.8108732  -0.65740733  0.94468706 -0.52731447\n",
-      "  0.97191021 -0.4778903   0.96596478 -0.36247712  1.05531346 -0.21759093\n",
-      "  1.06747955 -0.09763575]\n",
-      "  cov conditional:  [[ 2.24983789e-05  7.72524262e-07  1.97454107e-05 ...  4.67634341e-07\n",
-      "  -1.38308871e-04 -3.49075849e-06]\n",
-      " [ 7.72524262e-07  2.20482109e-05  6.83621322e-07 ...  2.24782650e-05\n",
-      "  -2.81880728e-06 -1.42717268e-04]\n",
-      " [ 1.97454107e-05  6.83621322e-07  1.73327126e-05 ...  4.11664595e-07\n",
-      "  -1.21611547e-04 -3.12982666e-06]\n",
-      " ...\n",
-      " [ 4.67634341e-07  2.24782650e-05  4.11664595e-07 ...  5.43725068e-05\n",
-      "   2.38722319e-07 -1.63472607e-04]\n",
-      " [-1.38308871e-04 -2.81880728e-06 -1.21611547e-04 ...  2.38722319e-07\n",
-      "   9.93894064e-04  4.40060236e-06]\n",
-      " [-3.49075849e-06 -1.42717268e-04 -3.12982666e-06 ... -1.63472607e-04\n",
-      "   4.40060236e-06  1.07896200e-03]]\n",
-      "  mean noise:  [-0.03113488  0.02640999 -0.01898094 -0.07875356 -0.0140307  -0.02135314\n",
-      " -0.01124305 -0.01362752 -0.01060334  0.03466387  0.01385217  0.04813155\n",
-      " -0.00219418  0.00044579  0.01936044 -0.02844249  0.02317586 -0.02937278\n",
-      " -0.02943837 -0.01414427  0.0366757   0.05372285 -0.02351403  0.03137375\n",
-      "  0.00674417 -0.01185815  0.00553697 -0.02807912  0.02839575  0.00671467\n",
-      " -0.00539773  0.00152932 -0.0268304  -0.04706632 -0.02066015 -0.00530866\n",
-      "  0.0231011  -0.00270541  0.0105557   0.0106511  -0.0239409   0.00298701\n",
-      "  0.00231474 -0.00984539 -0.0309527   0.02719639 -0.01751587  0.00978515\n",
-      "  0.01347212  0.02413919  0.02345422  0.02893172  0.00064594  0.00631828\n",
-      " -0.0226894  -0.04956276 -0.01755746 -0.06997721  0.01670879  0.02744954\n",
-      "  0.01804411  0.01427302 -0.02460956  0.006193   -0.01061571 -0.02967946\n",
-      " -0.02805493  0.00352708  0.02278379  0.01646119  0.03476706 -0.02925581\n",
-      "  0.0294047   0.03621588 -0.01831344 -0.00579265 -0.02308818 -0.02346865\n",
-      " -0.01670761  0.00584066 -0.00652529 -0.00205863  0.03772695  0.0060844\n",
-      "  0.02249407  0.02143915 -0.00610529  0.02252826  0.03381075 -0.00275102\n",
-      " -0.0336948   0.00212301 -0.01800981 -0.03575956 -0.01288328  0.011173\n",
-      "  0.02580946 -0.00300226  0.00114581 -0.02305969 -0.00860411  0.00282543\n",
-      " -0.01550214 -0.01165276 -0.01809889  0.00714191 -0.00944697 -0.00552362\n",
-      "  0.01357973  0.03463845  0.01974387 -0.01725911  0.01301464 -0.02551815\n",
-      " -0.0497153   0.01614272  0.05224375  0.01695683 -0.02574576 -0.02142139\n",
-      " -0.00209538  0.03105834  0.04844135  0.02623946 -0.00484442 -0.0016765\n",
-      "  0.00988305 -0.00036456]\n",
-      "  cov noise:  [[ 2.24983789e-05  7.72524262e-07  1.97454107e-05 ...  4.67634341e-07\n",
-      "  -1.38308871e-04 -3.49075849e-06]\n",
-      " [ 7.72524262e-07  2.20482109e-05  6.83621322e-07 ...  2.24782650e-05\n",
-      "  -2.81880728e-06 -1.42717268e-04]\n",
-      " [ 1.97454107e-05  6.83621322e-07  1.73327126e-05 ...  4.11664595e-07\n",
-      "  -1.21611547e-04 -3.12982666e-06]\n",
-      " ...\n",
-      " [ 4.67634341e-07  2.24782650e-05  4.11664595e-07 ...  5.43725068e-05\n",
-      "   2.38722319e-07 -1.63472607e-04]\n",
-      " [-1.38308871e-04 -2.81880728e-06 -1.21611547e-04 ...  2.38722319e-07\n",
-      "   9.93894064e-04  4.40060236e-06]\n",
-      " [-3.49075849e-06 -1.42717268e-04 -3.12982666e-06 ... -1.63472607e-04\n",
-      "   4.40060236e-06  1.07896200e-03]]\n",
-      "  mean:  [ 1.00447911e+00  1.50604872e-02  9.99737617e-01  1.12405303e-01\n",
-      "  9.85264208e-01  2.08400326e-01  9.61151349e-01  3.02158803e-01\n",
-      "  9.27610988e-01  3.92871121e-01  8.85003126e-01  4.79810021e-01\n",
-      "  8.33836721e-01  5.62322083e-01  7.74733214e-01  6.39801574e-01\n",
-      "  7.08367280e-01  7.11640961e-01  6.35427548e-01  7.77187471e-01\n",
-      "  5.56620516e-01  8.35752911e-01  4.72683218e-01  8.86664113e-01\n",
-      "  3.84394107e-01  9.29314693e-01  2.92565435e-01  9.63178893e-01\n",
-      "  1.98033656e-01  9.87818465e-01  1.01660167e-01  1.00290281e+00\n",
-      "  4.31678332e-03  1.00825240e+00 -9.31389309e-02  1.00385691e+00\n",
-      " -1.89869887e-01  9.89828627e-01 -2.85032516e-01  9.66347132e-01\n",
-      " -3.77733985e-01  9.33641533e-01 -4.67014504e-01  8.91999141e-01\n",
-      " -5.51892180e-01  8.41763241e-01 -6.31426395e-01  7.83328969e-01\n",
-      " -7.04762062e-01  7.17164661e-01 -7.71155749e-01  6.43865302e-01\n",
-      " -8.29962772e-01  5.64183209e-01 -8.80617052e-01  4.78985330e-01\n",
-      " -9.22634747e-01  3.89174649e-01 -9.55628774e-01  2.95645021e-01\n",
-      " -9.79289882e-01  1.99299559e-01 -9.93371727e-01  1.01094273e-01\n",
-      " -9.97731638e-01  2.05734083e-03 -9.92354882e-01 -9.67656436e-02\n",
-      " -9.77359763e-01 -1.94358935e-01 -9.52976176e-01 -2.89744086e-01\n",
-      " -9.19518567e-01 -3.81990813e-01 -8.77379279e-01 -4.70243042e-01\n",
-      " -8.27008654e-01 -5.53740166e-01 -7.68902926e-01 -6.31797260e-01\n",
-      " -7.03600056e-01 -7.03793436e-01 -6.31671380e-01 -7.69164074e-01\n",
-      " -5.53719715e-01 -8.27340691e-01 -4.70385533e-01 -8.77714769e-01\n",
-      " -3.82362882e-01 -9.19663879e-01 -2.90419382e-01 -9.52608939e-01\n",
-      " -1.95421581e-01 -9.76085103e-01 -9.83136598e-02 -9.89797430e-01\n",
-      " -9.56534090e-05 -9.93631023e-01  9.82112375e-02 -9.87639135e-01\n",
-      "  1.95586490e-01 -9.72004739e-01  2.91039719e-01 -9.46999730e-01\n",
-      "  3.83636428e-01 -9.12945223e-01  4.72516884e-01 -8.70197091e-01\n",
-      "  5.56891515e-01 -8.19148629e-01  6.35997438e-01 -7.60230554e-01\n",
-      "  7.09067628e-01 -6.93937760e-01  7.75352754e-01 -6.20845320e-01\n",
-      "  8.34176345e-01 -5.41624192e-01  8.84968240e-01 -4.57042323e-01\n",
-      "  9.27255193e-01 -3.67932887e-01  9.60648065e-01 -2.75190257e-01\n",
-      "  9.84813212e-01 -1.79766757e-01  9.99480417e-01 -8.26704420e-02]\n",
-      "  cov:  [[ 0.0132744  -0.00049186  0.01320758 ... -0.00038261  0.01310672\n",
-      "  -0.00042637]\n",
-      " [-0.00049186  0.014532   -0.00060006 ...  0.01402019 -0.00039117\n",
-      "   0.01440565]\n",
-      " [ 0.01320758 -0.00060006  0.01338993 ... -0.00043773  0.01279574\n",
-      "  -0.00050782]\n",
-      " ...\n",
-      " [-0.00038261  0.01402019 -0.00043773 ...  0.01438886 -0.0003424\n",
-      "   0.01433226]\n",
-      " [ 0.01310672 -0.00039117  0.01279574 ... -0.0003424   0.01319423\n",
-      "  -0.00035601]\n",
-      " [-0.00042637  0.01440565 -0.00050782 ...  0.01433226 -0.00035601\n",
-      "   0.01450182]]\n",
-      "  mean w/ noise:  [ 1.00662865  0.01570459  0.99954191  0.1155815   0.9839201   0.20801381\n",
-      "  0.95859828  0.30070934  0.92899556  0.39451126  0.88459309  0.47784431\n",
-      "  0.83639021  0.56509953  0.77709147  0.64000408  0.70937326  0.7138181\n",
-      "  0.63530656  0.77533895  0.55545822  0.83699407  0.4711466   0.88580803\n",
-      "  0.38502365  0.93068799  0.29216351  0.96338107  0.19804715  0.98777777\n",
-      "  0.10182912  1.00341782  0.00481979  1.00716587 -0.09111707  1.00272001\n",
-      " -0.19003008  0.99068194 -0.28603367  0.96793789 -0.376493    0.93299855\n",
-      " -0.46671068  0.88971952 -0.55226933  0.84131379 -0.63225085  0.78136492\n",
-      " -0.70598283  0.71691131 -0.77176313  0.64421539 -0.83132712  0.56309553\n",
-      " -0.88203205  0.47972714 -0.92283359  0.39162222 -0.95304385  0.29546812\n",
-      " -0.98131938  0.19688835 -0.99333004  0.10206459 -0.99694704  0.00144063\n",
-      " -0.99224419 -0.09688696 -0.97719799 -0.19513173 -0.95280941 -0.28735053\n",
-      " -0.92133023 -0.38242095 -0.87788035 -0.47174212 -0.82705623 -0.55606613\n",
-      " -0.76990763 -0.63121809 -0.70342718 -0.70390734 -0.62984887 -0.7666945\n",
-      " -0.55410122 -0.82928555 -0.47109996 -0.87796421 -0.37822427 -0.91977326\n",
-      " -0.28855393 -0.95549461 -0.19567579 -0.97511275 -0.09711638 -0.98920501\n",
-      "  0.00184985 -0.99163874  0.09783359 -0.98682855  0.19822522 -0.97154757\n",
-      "  0.29051549 -0.94624363  0.38566607 -0.91124073  0.46938724 -0.87100458\n",
-      "  0.55727415 -0.82210678  0.63450572 -0.76001951  0.70971786 -0.69317404\n",
-      "  0.77581913 -0.62219707  0.83566324 -0.54233225  0.88390769 -0.45544118\n",
-      "  0.92754177 -0.36858272  0.95944878 -0.27496458  0.98556088 -0.18123245\n",
-      "  0.99842735 -0.08232662]\n",
-      "  cov w/ noise:  [[ 1.44590417e-02 -4.69604613e-04  1.31880322e-02 ... -5.72749096e-04\n",
-      "   1.31479128e-02 -4.37314447e-04]\n",
-      " [-4.69604613e-04  1.54063476e-02 -3.96571561e-04 ...  1.38925981e-02\n",
-      "  -9.71603165e-05  1.44635984e-02]\n",
-      " [ 1.31880322e-02 -3.96571561e-04  1.41260490e-02 ... -3.97666237e-04\n",
-      "   1.26295353e-02 -4.75968374e-04]\n",
-      " ...\n",
-      " [-5.72749096e-04  1.38925981e-02 -3.97666237e-04 ...  1.52259904e-02\n",
-      "  -1.82270813e-04  1.44834425e-02]\n",
-      " [ 1.31479128e-02 -9.71603165e-05  1.26295353e-02 ... -1.82270813e-04\n",
-      "   1.42333372e-02 -1.18458634e-04]\n",
-      " [-4.37314447e-04  1.44635984e-02 -4.75968374e-04 ...  1.44834425e-02\n",
-      "  -1.18458634e-04  1.58948622e-02]]\n",
-      "  mean conditional:  [ 1.06171645  0.08306266  1.06169354  0.17620788  1.05085921  0.26490598\n",
-      "  1.02873435  0.3497723   0.99502338  0.43176195  0.94987812  0.51183017\n",
-      "  0.89414106  0.59052942  0.82930895  0.66785095  0.75744366  0.74310662\n",
-      "  0.68085165  0.81526556  0.60169697  0.88308912  0.52162714  0.9454376\n",
-      "  0.44135798  1.00133267  0.36067359  1.04993415  0.27860627  1.09051731\n",
-      "  0.19378144  1.12231296  0.10506198  1.14447562  0.01184659  1.1561031\n",
-      " -0.08561913  1.15630233 -0.18616218  1.14444392 -0.28786934  1.12009061\n",
-      " -0.38848564  1.08319852 -0.48555641  1.0339927  -0.57690754  0.97288824\n",
-      " -0.66067323  0.90044904 -0.73553853  0.81732285 -0.80055289  0.72441746\n",
-      " -0.85509511  0.6229192  -0.89874378  0.51457874 -0.93124714  0.40162\n",
-      " -0.95251374  0.28667967 -0.96270992  0.17249358 -0.96218066  0.0617666\n",
-      " -0.95159453 -0.0432698  -0.93172513 -0.14100408 -0.90361159 -0.23065233\n",
-      " -0.86838515 -0.31224492 -0.82706688 -0.38664367 -0.78038151 -0.45525341\n",
-      " -0.72862993 -0.51983261 -0.6716249  -0.58213596 -0.60867801 -0.64339249\n",
-      " -0.5389992  -0.7043565  -0.4620019  -0.76470382 -0.37784786 -0.82340127\n",
-      " -0.28747338 -0.87856982 -0.19256207 -0.92779366 -0.0951456  -0.96834422\n",
-      "  0.00272554 -0.997541    0.09938034 -1.01321745  0.19396623 -1.01371467\n",
-      "  0.28615981 -0.99838135  0.37610484 -0.9672911   0.46394602 -0.92137434\n",
-      "  0.54961155 -0.8620744   0.63246033 -0.79121711  0.71144718 -0.71062552\n",
-      "  0.78509365 -0.62199152  0.85194181 -0.52689175  0.91075555 -0.42693181\n",
-      "  0.96049111 -0.32388342  1.0006984  -0.21940342  1.03072048 -0.11569767\n",
-      "  1.0512501  -0.01354115] , sample:  [ 1.03416114  0.12759165  1.0907562   0.15575627  0.99505383  0.28015233\n",
-      "  1.03065924  0.37132304  1.02028168  0.42377598  0.95866652  0.46741117\n",
-      "  0.90416884  0.58135015  0.840265    0.64205227  0.74260974  0.77150373\n",
-      "  0.71575725  0.81317863  0.62756241  0.89707137  0.47505274  0.95061113\n",
-      "  0.38288268  1.00295114  0.3618915   1.06008685  0.28605225  1.07884042\n",
-      "  0.21198086  1.11028616  0.11755206  1.11765476  0.00881263  1.16869304\n",
-      " -0.08780186  1.1547623  -0.16269421  1.18322942 -0.27941077  1.08670915\n",
-      " -0.39104025  1.09541252 -0.48519163  1.04025875 -0.61177965  1.00115089\n",
-      " -0.64806079  0.88209449 -0.73545729  0.82012122 -0.83813966  0.70883023\n",
-      " -0.84472405  0.62715229 -0.88093812  0.54951266 -0.95086692  0.38880983\n",
-      " -0.92210233  0.25684007 -0.97106352  0.20003832 -0.94528468  0.07614479\n",
-      " -0.93983546 -0.03483295 -0.9318144  -0.23220007 -0.90905281 -0.22408376\n",
-      " -0.89133136 -0.28673035 -0.80957909 -0.3619263  -0.81500272 -0.50523419\n",
-      " -0.71518592 -0.5148857  -0.63473584 -0.52494366 -0.62742811 -0.65798999\n",
-      " -0.47697788 -0.6550332  -0.50394322 -0.77551672 -0.4455126  -0.82967675\n",
-      " -0.27303103 -0.88321657 -0.15967195 -0.94477762 -0.07007813 -0.97840846\n",
-      "  0.00539692 -0.97457463  0.11272733 -1.03864303  0.16641709 -0.98145869\n",
-      "  0.24770724 -1.02212465  0.40056829 -0.98276483  0.45290278 -0.93393876\n",
-      "  0.52344256 -0.85883959  0.66479286 -0.76044974  0.69139804 -0.70518401\n",
-      "  0.82286086 -0.61542919  0.80979589 -0.57733227  0.97150834 -0.42199474\n",
-      "  0.91876219 -0.31331104  1.03839062 -0.18351842  1.03979948 -0.11512843\n",
-      "  1.03436212 -0.04135877]\n",
-      "  cov conditional:  [[ 2.24978905e-05  7.67737557e-07  1.97451810e-05 ...  2.91762761e-07\n",
-      "  -1.41458178e-04 -4.33345979e-06]\n",
-      " [ 7.67737557e-07  2.20447766e-05  6.81730699e-07 ...  2.10983497e-05\n",
-      "  -2.44183672e-06 -1.36866622e-04]\n",
-      " [ 1.97451810e-05  6.81730699e-07  1.73329236e-05 ...  2.57197797e-07\n",
-      "  -1.24352868e-04 -3.86403494e-06]\n",
-      " ...\n",
-      " [ 2.91762761e-07  2.10983497e-05  2.57197797e-07 ...  5.34285839e-05\n",
-      "   1.98801806e-06 -1.52477136e-04]\n",
-      " [-1.41458178e-04 -2.44183672e-06 -1.24352868e-04 ...  1.98801806e-06\n",
-      "   1.04397345e-03  1.71260748e-05]\n",
-      " [-4.33345979e-06 -1.36866622e-04 -3.86403494e-06 ... -1.52477136e-04\n",
-      "   1.71260748e-05  1.02183878e-03]]\n",
-      "  mean noise:  [ 2.75553110e-02 -4.45289883e-02 -2.90626530e-02  2.04516175e-02\n",
-      "  5.58053736e-02 -1.52463495e-02 -1.92488956e-03 -2.15507340e-02\n",
-      " -2.52582938e-02  7.98596736e-03 -8.78840648e-03  4.44190052e-02\n",
-      " -1.00277796e-02  9.17927065e-03 -1.09560521e-02  2.57986865e-02\n",
-      "  1.48339239e-02 -2.83971116e-02 -3.49055986e-02  2.08692476e-03\n",
-      " -2.58654384e-02 -1.39822433e-02  4.65744070e-02 -5.17352625e-03\n",
-      "  5.84752941e-02 -1.61847039e-03 -1.21790891e-03 -1.01527027e-02\n",
-      " -7.44597426e-03  1.16768973e-02 -1.81994163e-02  1.20267971e-02\n",
-      " -1.24900865e-02  2.68208592e-02  3.03395316e-03 -1.25899404e-02\n",
-      "  2.18272938e-03  1.54002760e-03 -2.34679758e-02 -3.87855013e-02\n",
-      " -8.45857351e-03  3.33814609e-02  2.55461234e-03 -1.22140012e-02\n",
-      " -3.64781806e-04 -6.26605473e-03  3.48721101e-02 -2.82626429e-02\n",
-      " -1.26124410e-02  1.83545443e-02 -8.12325169e-05 -2.79837030e-03\n",
-      "  3.75867692e-02  1.55872270e-02 -1.03710641e-02 -4.23308832e-03\n",
-      " -1.78056578e-02 -3.49339187e-02  1.96197746e-02  1.28101720e-02\n",
-      " -3.04114145e-02  2.98396002e-02  8.35359950e-03 -2.75447352e-02\n",
-      " -1.68959769e-02 -1.43781891e-02 -1.17590618e-02 -8.43685903e-03\n",
-      "  8.92663139e-05  9.11959900e-02  5.44122575e-03 -6.56857349e-03\n",
-      "  2.29462087e-02 -2.55145724e-02 -1.74877884e-02 -2.47173647e-02\n",
-      "  3.46212100e-02  4.99807786e-02 -1.34440067e-02 -4.94691777e-03\n",
-      " -3.68890605e-02 -5.71922989e-02  1.87500986e-02  1.45974923e-02\n",
-      " -6.20213126e-02 -4.93233033e-02  4.19413138e-02  1.08129006e-02\n",
-      "  6.76647375e-02  6.27548653e-03 -1.44423479e-02  4.64675564e-03\n",
-      " -3.28901252e-02  1.69839627e-02 -2.50674704e-02  1.00642441e-02\n",
-      " -2.67137276e-03 -2.29663731e-02 -1.33469891e-02  2.54255806e-02\n",
-      "  2.75491417e-02 -3.22559815e-02  3.84525674e-02  2.37432964e-02\n",
-      " -2.44634470e-02  1.54737277e-02  1.10432327e-02  1.25644175e-02\n",
-      "  2.61689887e-02 -3.23481418e-03 -3.23325273e-02 -3.07673742e-02\n",
-      "  2.00491389e-02 -5.44150861e-03 -3.77672076e-02 -6.56233251e-03\n",
-      "  4.21459123e-02  5.04405252e-02 -6.07527902e-02 -4.93707701e-03\n",
-      "  4.17289225e-02 -1.05723842e-02 -3.76922187e-02 -3.58849997e-02\n",
-      " -9.07899853e-03 -5.69249019e-04  1.68879866e-02  2.78176191e-02]\n",
-      "  cov noise:  [[ 2.24978905e-05  7.67737557e-07  1.97451810e-05 ...  2.91762761e-07\n",
-      "  -1.41458178e-04 -4.33345979e-06]\n",
-      " [ 7.67737557e-07  2.20447766e-05  6.81730699e-07 ...  2.10983497e-05\n",
-      "  -2.44183672e-06 -1.36866622e-04]\n",
-      " [ 1.97451810e-05  6.81730699e-07  1.73329236e-05 ...  2.57197797e-07\n",
-      "  -1.24352868e-04 -3.86403494e-06]\n",
-      " ...\n",
-      " [ 2.91762761e-07  2.10983497e-05  2.57197797e-07 ...  5.34285839e-05\n",
-      "   1.98801806e-06 -1.52477136e-04]\n",
-      " [-1.41458178e-04 -2.44183672e-06 -1.24352868e-04 ...  1.98801806e-06\n",
-      "   1.04397345e-03  1.71260748e-05]\n",
-      " [-4.33345979e-06 -1.36866622e-04 -3.86403494e-06 ... -1.52477136e-04\n",
-      "   1.71260748e-05  1.02183878e-03]]\n",
-      "  mean:  [ 1.00904393  0.00696514  1.00334382  0.10295072  0.98792696  0.19793306\n",
-      "  0.9629546   0.29103284  0.92870973  0.38140504  0.88561227  0.46826917\n",
-      "  0.83420186  0.55087942  0.77507687  0.62852154  0.70884122  0.70052349\n",
-      "  0.6361162   0.76623295  0.55755671  0.82500957  0.47385366  0.87623051\n",
-      "  0.38573391  0.91928841  0.29397058  0.95362481  0.19938864  0.97878203\n",
-      "  0.10288902  0.99443334  0.00544353  1.00039254 -0.09196196  0.9966037\n",
-      " -0.18838828  0.98311485 -0.2829407   0.96007456 -0.37475911  0.92771321\n",
-      " -0.46301876  0.88631996 -0.54692234  0.83624587 -0.62568907  0.77794156\n",
-      " -0.69853496  0.71196944 -0.76471166  0.63901178 -0.82356016  0.55986506\n",
-      " -0.87450895  0.47537285 -0.91706446  0.3863778  -0.95080962  0.29372492\n",
-      " -0.97538998  0.19828819 -0.99051177  0.10099673 -0.99597552  0.0028413\n",
-      " -0.99170652 -0.09517717 -0.97777078 -0.19210439 -0.95436317 -0.28704842\n",
-      " -0.92178145 -0.37917221 -0.8804092  -0.46765091 -0.83070196 -0.55165157\n",
-      " -0.77318044 -0.63037669 -0.7083988  -0.70309956 -0.63689902 -0.76916947\n",
-      " -0.55922286 -0.82794165 -0.47596605 -0.87875166 -0.38781877 -0.92095674\n",
-      " -0.29556841 -0.95400161 -0.2000859  -0.9774635  -0.10228953 -0.99108552\n",
-      " -0.00315115 -0.99478277  0.09631319 -0.98863732  0.19505695 -0.97284633\n",
-      "  0.29202405 -0.94766907  0.38618528 -0.91339451  0.47658128 -0.87035324\n",
-      "  0.56232586 -0.81895975  0.64258548 -0.75973432  0.71655464 -0.69328483\n",
-      "  0.78344713 -0.62029135  0.84253526 -0.54149495  0.89323746 -0.45768391\n",
-      "  0.93512312 -0.36968109  0.96786645 -0.27834272  0.99121559 -0.1845431\n",
-      "  1.00498254 -0.08915247]\n",
-      "  cov:  [[ 0.01451118 -0.00010498  0.01444718 ... -0.00014488  0.0143221\n",
-      "  -0.0001221 ]\n",
-      " [-0.00010498  0.01538485 -0.00010947 ...  0.01486826 -0.00011212\n",
-      "   0.01525638]\n",
-      " [ 0.01444718 -0.00010947  0.01461402 ... -0.00013991  0.01402838\n",
-      "  -0.00012382]\n",
-      " ...\n",
-      " [-0.00014488  0.01486826 -0.00013991 ...  0.01529555 -0.00016806\n",
-      "   0.01520974]\n",
-      " [ 0.0143221  -0.00011212  0.01402838 ... -0.00016806  0.01437655\n",
-      "  -0.00013493]\n",
-      " [-0.0001221   0.01525638 -0.00012382 ...  0.01520974 -0.00013493\n",
-      "   0.0153664 ]]\n",
-      "  mean w/ noise:  [ 1.00925262  0.00706924  1.00471328  0.10188715  0.98939895  0.1979807\n",
-      "  0.96425852  0.28941439  0.92942703  0.38250141  0.88690296  0.46595772\n",
-      "  0.83524455  0.55196336  0.77553511  0.62872234  0.70663902  0.70256377\n",
-      "  0.63636615  0.76859209  0.55997371  0.82327406  0.47471249  0.8748917\n",
-      "  0.38825141  0.92065244  0.29447478  0.95297336  0.19675052  0.97995443\n",
-      "  0.10448411  0.99619077  0.00396722  0.99775779 -0.09145094  0.99596939\n",
-      " -0.18725578  0.9830379  -0.28068135  0.96193622 -0.37521767  0.92861581\n",
-      " -0.46327176  0.88661397 -0.5451864   0.83652262 -0.62852871  0.778498\n",
-      " -0.69486656  0.71037161 -0.76589321  0.64056437 -0.82410629  0.55914445\n",
-      " -0.87596705  0.47427373 -0.91695109  0.38720254 -0.95067934  0.29468091\n",
-      " -0.97256757  0.19670872 -0.99310425  0.10040987 -0.99554074  0.00362802\n",
-      " -0.99342681 -0.09640561 -0.97956836 -0.19225758 -0.95341162 -0.28838369\n",
-      " -0.92340144 -0.37826844 -0.87840098 -0.46662718 -0.83172372 -0.55181258\n",
-      " -0.77305557 -0.63012687 -0.70905067 -0.7032688  -0.63545334 -0.7711346\n",
-      " -0.55998469 -0.82979213 -0.47547105 -0.87713821 -0.38641698 -0.92277186\n",
-      " -0.29402104 -0.95168746 -0.20094523 -0.97727173 -0.10238347 -0.98984911\n",
-      " -0.00419967 -0.9969559   0.09575615 -0.99013211  0.19317828 -0.97493959\n",
-      "  0.28954507 -0.94910481  0.38555618 -0.91360497  0.47349014 -0.87082761\n",
-      "  0.56277064 -0.8197413   0.64540486 -0.76057217  0.71579465 -0.69499635\n",
-      "  0.78591044 -0.62294124  0.84281241 -0.54192532  0.89429652 -0.46120279\n",
-      "  0.93593789 -0.36841218  0.96907451 -0.27939874  0.99177697 -0.18633875\n",
-      "  1.00479987 -0.08914438]\n",
-      "  cov w/ noise:  [[ 1.53772404e-02  2.21898634e-04  1.44224814e-02 ...  9.24718364e-05\n",
-      "   1.42674913e-02 -1.91025281e-04]\n",
-      " [ 2.21898634e-04  1.65794885e-02  2.89325631e-04 ...  1.49798177e-02\n",
-      "   2.71276362e-04  1.52769723e-02]\n",
-      " [ 1.44224814e-02  2.89325631e-04  1.55868849e-02 ...  2.74269554e-04\n",
-      "   1.40486046e-02 -1.24316070e-04]\n",
-      " ...\n",
-      " [ 9.24718364e-05  1.49798177e-02  2.74269554e-04 ...  1.65405875e-02\n",
-      "   1.42528987e-04  1.52362111e-02]\n",
-      " [ 1.42674913e-02  2.71276362e-04  1.40486046e-02 ...  1.42528987e-04\n",
-      "   1.54885677e-02 -2.02922710e-04]\n",
-      " [-1.91025281e-04  1.52769723e-02 -1.24316070e-04 ...  1.52362111e-02\n",
-      "  -2.02922710e-04  1.61781536e-02]]\n",
-      "  mean conditional:  [ 1.14619008 -0.00350821  1.15472732  0.10656659  1.15134255  0.2140562\n",
-      "  1.13521548  0.31834912  1.10606332  0.419114    1.0643095   0.51606668\n",
-      "  1.01119066  0.6087      0.94851551  0.69622216  0.87839898  0.77738857\n",
-      "  0.80289202  0.85083976  0.72347354  0.91524613  0.64083684  0.96959711\n",
-      "  0.55471583  1.01309392  0.46409583  1.04546557  0.36765163  1.06666512\n",
-      "  0.2643545   1.07700001  0.15398583  1.07682018  0.03747425  1.0664571\n",
-      " -0.08327439  1.0463005  -0.20551407  1.01669763 -0.32618119  0.97791335\n",
-      " -0.44241962  0.93022288 -0.55186701  0.87370364 -0.65277284  0.80825831\n",
-      " -0.74407077  0.73368025 -0.82503918  0.64970285 -0.89508746  0.55626982\n",
-      " -0.95353605  0.45387371 -0.9996093   0.34357009 -1.03255347  0.22713426\n",
-      " -1.05179089  0.10679645 -1.05726095 -0.01501689 -1.04936152 -0.13593169\n",
-      " -1.02917581 -0.25373826 -0.99820143 -0.36650905 -0.95822883 -0.472699\n",
-      " -0.91110532 -0.57083522 -0.85837759 -0.65997188 -0.80111388 -0.73942792\n",
-      " -0.73967537 -0.80915824 -0.67360855 -0.86965413 -0.60194527 -0.92185546\n",
-      " -0.5235735  -0.96710524 -0.43757141 -1.00657138 -0.34387854 -1.04117989\n",
-      " -0.24335979 -1.07121667 -0.13791976 -1.0961706  -0.03002638 -1.11477569\n",
-      "  0.07755853 -1.12534133  0.18227982 -1.12614617  0.2824392  -1.11567597\n",
-      "  0.3771238  -1.0931484   0.46627894 -1.05843729  0.55028026 -1.01210974\n",
-      "  0.62976896 -0.95517113  0.70517741 -0.88878317  0.77668127 -0.81402174\n",
-      "  0.84395536 -0.73161325  0.90665446 -0.64206831  0.96411608 -0.5458786\n",
-      "  1.01565261 -0.44386397  1.0605481  -0.33693271  1.09732267 -0.22663026\n",
-      "  1.12599627 -0.11364252] , sample:  [ 1.1402799   0.00210071  1.15253402  0.05261734  1.13024059  0.23672178\n",
-      "  1.15633753  0.33659676  1.13744946  0.397258    1.05487333  0.54705128\n",
-      "  1.0212571   0.56037367  0.93955245  0.69976572  0.90198071  0.78587554\n",
-      "  0.80976234  0.86989151  0.67801137  0.87962825  0.60929682  0.97530022\n",
-      "  0.5339791   1.0524903   0.47974169  1.05222217  0.43154318  1.09641843\n",
-      "  0.29183281  1.03829605  0.17438986  1.07489091 -0.01311564  1.02021425\n",
-      " -0.04209009  1.07876532 -0.21389645  1.03038225 -0.36677992  0.94782011\n",
-      " -0.47707207  0.964378   -0.56380089  0.83609481 -0.65722095  0.79663263\n",
-      " -0.69586921  0.73755249 -0.8041326   0.67013898 -0.92558542  0.5826288\n",
-      " -0.91485071  0.46693519 -1.03179945  0.35529511 -1.05420963  0.18681355\n",
-      " -1.05087774  0.11737556 -1.08218379 -0.04075548 -1.00414654 -0.20189421\n",
-      " -1.03939217 -0.18972613 -0.99220538 -0.37580915 -0.96362246 -0.4265109\n",
-      " -0.90840326 -0.57578974 -0.86876623 -0.63195444 -0.76350818 -0.79403363\n",
-      " -0.7418172  -0.83204408 -0.71243168 -0.89548551 -0.58734337 -0.90970203\n",
-      " -0.4859745  -1.00731497 -0.44026209 -0.98442827 -0.36609702 -0.99789887\n",
-      " -0.25464082 -1.04323766 -0.15340368 -1.10409699 -0.02286338 -1.09765508\n",
-      "  0.05632546 -1.14474284  0.22047575 -1.15756331  0.31569318 -1.10915958\n",
-      "  0.34301685 -1.09872631  0.47817279 -1.08707327  0.54559616 -0.98245032\n",
-      "  0.63544685 -0.97340267  0.69105906 -0.8638667   0.77010175 -0.7922148\n",
-      "  0.86042825 -0.73084038  0.9127951  -0.65911322  0.95902211 -0.57330135\n",
-      "  1.02251834 -0.41803073  1.04267948 -0.36107675  1.0844802  -0.24306131\n",
-      "  1.15028317 -0.05699072]\n",
-      "  cov conditional:  [[ 2.24965069e-05  7.69911484e-07  1.97394496e-05 ...  3.02247484e-07\n",
-      "  -1.36085716e-04 -5.97444289e-06]\n",
-      " [ 7.69911484e-07  2.20483231e-05  6.84921080e-07 ...  2.11316297e-05\n",
-      "  -1.20827356e-06 -1.39057849e-04]\n",
-      " [ 1.97394496e-05  6.84921080e-07  1.73238395e-05 ...  2.63666745e-07\n",
-      "  -1.19641999e-04 -5.32677270e-06]\n",
-      " ...\n",
-      " [ 3.02247484e-07  2.11316297e-05  2.63666745e-07 ...  4.95811600e-05\n",
-      "   9.79610950e-07 -1.53268163e-04]\n",
-      " [-1.36085716e-04 -1.20827356e-06 -1.19641999e-04 ...  9.79610950e-07\n",
-      "   9.82150336e-04  2.96017363e-05]\n",
-      " [-5.97444289e-06 -1.39057849e-04 -5.32677270e-06 ... -1.53268163e-04\n",
-      "   2.96017363e-05  1.03499521e-03]]\n",
-      "  mean noise:  [ 0.00591019 -0.00560892  0.00219329  0.05394925  0.02110196 -0.02266558\n",
-      " -0.02112206 -0.01824763 -0.03138614  0.021856    0.00943616 -0.0309846\n",
-      " -0.01006644  0.04832633  0.00896305 -0.00354356 -0.02358173 -0.00848697\n",
-      " -0.00687032 -0.01905175  0.04546216  0.03561788  0.03154003 -0.00570311\n",
-      "  0.02073672 -0.03939638 -0.01564587 -0.0067566  -0.06389155 -0.02975331\n",
-      " -0.0274783   0.03870396 -0.02040403  0.00192927  0.0505899   0.04624284\n",
-      " -0.0411843  -0.03246482  0.00838238 -0.01368463  0.04059873  0.03009324\n",
-      "  0.03465245 -0.03415513  0.01193388  0.03760883  0.00444811  0.01162568\n",
-      " -0.04820156 -0.00387223 -0.02090658 -0.02043613  0.03049796 -0.02635899\n",
-      " -0.03868535 -0.01306148  0.03219015 -0.01172502  0.02165617  0.04032071\n",
-      " -0.00091315 -0.0105791   0.02492284  0.0257386  -0.04521498  0.06596252\n",
-      "  0.01021636 -0.06401213 -0.00599605  0.0093001   0.00539363 -0.0461881\n",
-      " -0.00270205  0.00495452  0.01038864 -0.02801744 -0.0376057   0.05460571\n",
-      "  0.00214183  0.02288584  0.03882313  0.02583138 -0.0146019  -0.01215343\n",
-      " -0.037599    0.04020973  0.00269068 -0.02214311  0.02221848 -0.04328102\n",
-      "  0.01128103 -0.02797902  0.01548392  0.00792639 -0.00716299 -0.0171206\n",
-      "  0.02123307  0.01940152 -0.03819594  0.03141714 -0.03325397 -0.00651639\n",
-      "  0.03410695  0.00557792 -0.01189385  0.02863597  0.0046841  -0.02965942\n",
-      " -0.00567789  0.01823154  0.01411835 -0.02491647  0.00657951 -0.02180694\n",
-      " -0.01647289 -0.00077287 -0.00614064  0.01704491  0.00509397  0.02742275\n",
-      " -0.00686573 -0.02583324  0.01786862  0.02414404  0.01284247  0.01643106\n",
-      " -0.0242869  -0.05665181]\n",
-      "  cov noise:  [[ 2.24965069e-05  7.69911484e-07  1.97394496e-05 ...  3.02247484e-07\n",
-      "  -1.36085716e-04 -5.97444289e-06]\n",
-      " [ 7.69911484e-07  2.20483231e-05  6.84921080e-07 ...  2.11316297e-05\n",
-      "  -1.20827356e-06 -1.39057849e-04]\n",
-      " [ 1.97394496e-05  6.84921080e-07  1.73238395e-05 ...  2.63666745e-07\n",
-      "  -1.19641999e-04 -5.32677270e-06]\n",
-      " ...\n",
-      " [ 3.02247484e-07  2.11316297e-05  2.63666745e-07 ...  4.95811600e-05\n",
-      "   9.79610950e-07 -1.53268163e-04]\n",
-      " [-1.36085716e-04 -1.20827356e-06 -1.19641999e-04 ...  9.79610950e-07\n",
-      "   9.82150336e-04  2.96017363e-05]\n",
-      " [-5.97444289e-06 -1.39057849e-04 -5.32677270e-06 ... -1.53268163e-04\n",
-      "   2.96017363e-05  1.03499521e-03]]\n",
-      "  mean:  [ 1.01022405e+00  4.06329374e-03  1.00511911e+00  1.00675919e-01\n",
-      "  9.90172263e-01  1.96225914e-01  9.65526950e-01  2.89826042e-01\n",
-      "  9.31409446e-01  3.80658440e-01  8.88181225e-01  4.67988063e-01\n",
-      "  8.36349689e-01  5.51115771e-01  7.76533910e-01  6.29346715e-01\n",
-      "  7.09409929e-01  7.01984637e-01  6.35675280e-01  7.68317893e-01\n",
-      "  5.56056387e-01  8.27655094e-01  4.71315166e-01  8.79360539e-01\n",
-      "  3.82244312e-01  9.22846608e-01  2.89662493e-01  9.57563418e-01\n",
-      "  1.94398392e-01  9.83026469e-01  9.72995327e-02  9.98864912e-01\n",
-      " -7.51735538e-04  1.00486162e+00 -9.88445739e-02  1.00096206e+00\n",
-      " -1.96057997e-01  9.87248708e-01 -2.91471756e-01  9.63910569e-01\n",
-      " -3.84177057e-01  9.31215099e-01 -4.73267599e-01  8.89497798e-01\n",
-      " -5.57822131e-01  8.39159567e-01 -6.36927590e-01  7.80695215e-01\n",
-      " -7.09742116e-01  7.14714048e-01 -7.75560274e-01  6.41925765e-01\n",
-      " -8.33803218e-01  5.63104095e-01 -8.83985047e-01  4.79040727e-01\n",
-      " -9.25710755e-01  3.90514120e-01 -9.58651028e-01  2.98307924e-01\n",
-      " -9.82517933e-01  2.03240906e-01 -9.97060541e-01  1.06181000e-01\n",
-      " -1.00209970e+00  8.04531777e-03 -9.97573272e-01 -9.02272071e-02\n",
-      " -9.83544791e-01 -1.87693623e-01 -9.60186065e-01 -2.83402626e-01\n",
-      " -9.27751404e-01 -3.76387891e-01 -8.86577305e-01 -4.65686154e-01\n",
-      " -8.37092685e-01 -5.50389343e-01 -7.79785541e-01 -6.29686229e-01\n",
-      " -7.15163427e-01 -7.02876193e-01 -6.43738234e-01 -7.69353526e-01\n",
-      " -5.66058899e-01 -8.28534509e-01 -4.82759358e-01 -8.79805373e-01\n",
-      " -3.94583776e-01 -9.22534873e-01 -3.02375495e-01 -9.56121373e-01\n",
-      " -2.07048086e-01 -9.80079268e-01 -1.09540603e-01 -9.94116924e-01\n",
-      " -1.08085016e-02 -9.98150021e-01  8.81841428e-02 -9.92272525e-01\n",
-      "  1.86472419e-01 -9.76700941e-01  2.83101647e-01 -9.51713021e-01\n",
-      "  3.77130761e-01 -9.17618371e-01  4.67643704e-01 -8.74764008e-01\n",
-      "  5.53749641e-01 -8.23561584e-01  6.34577677e-01 -7.64507005e-01\n",
-      "  7.09284098e-01 -6.98182699e-01  7.77067640e-01 -6.25253874e-01\n",
-      "  8.37207073e-01 -5.46463141e-01  8.89095470e-01 -4.62599073e-01\n",
-      "  9.32233403e-01 -3.74458679e-01  9.66212374e-01 -2.82853928e-01\n",
-      "  9.90692063e-01 -1.88632048e-01  1.00541029e+00 -9.26849238e-02]\n",
-      "  cov:  [[ 0.01475178 -0.0012162   0.01467266 ... -0.00130382  0.01458315\n",
-      "  -0.0012599 ]\n",
-      " [-0.0012162   0.01542675 -0.0012411  ...  0.01507843 -0.0011858\n",
-      "   0.01536705]\n",
-      " [ 0.01467266 -0.0012411   0.01484698 ... -0.00133179  0.01425319\n",
-      "  -0.0012866 ]\n",
-      " ...\n",
-      " [-0.00130382  0.01507843 -0.00133179 ...  0.01574723 -0.00127239\n",
-      "   0.01552932]\n",
-      " [ 0.01458315 -0.0011858   0.01425319 ... -0.00127239  0.0146784\n",
-      "  -0.00122869]\n",
-      " [-0.0012599   0.01536705 -0.0012866  ...  0.01552932 -0.00122869\n",
-      "   0.01556756]]\n",
-      "  mean w/ noise:  [ 1.00783773  0.00758598  1.00043148  0.10089666  0.99002785  0.19527545\n",
-      "  0.96727589  0.28991423  0.93198013  0.37983367  0.88605282  0.46676791\n",
-      "  0.83553003  0.54897833  0.77614999  0.62706029  0.71111558  0.70237348\n",
-      "  0.63521297  0.76675901  0.55657042  0.82774185  0.47204052  0.87851483\n",
-      "  0.38303838  0.9228508   0.28858882  0.95960451  0.19493061  0.98415413\n",
-      "  0.09704501  1.00072451 -0.00114012  1.00604227 -0.09939316  0.99889539\n",
-      " -0.1940764   0.98861595 -0.29351564  0.96301773 -0.3832862   0.93206875\n",
-      " -0.47041638  0.88946375 -0.55742908  0.83933604 -0.63657196  0.77864913\n",
-      " -0.71152009  0.71724253 -0.77836832  0.64243602 -0.83186367  0.56224623\n",
-      " -0.88528142  0.47898616 -0.92538567  0.38839885 -0.95702736  0.29841844\n",
-      " -0.98586334  0.20598874 -0.99631283  0.10744719 -1.00024324  0.00678057\n",
-      " -0.99698022 -0.09052377 -0.98621762 -0.1872954  -0.96070075 -0.28286572\n",
-      " -0.92787994 -0.37644428 -0.88542894 -0.46891539 -0.83597803 -0.54848306\n",
-      " -0.77826697 -0.62773565 -0.71629099 -0.70120067 -0.64187171 -0.76883863\n",
-      " -0.56563164 -0.8309971  -0.48200662 -0.87958116 -0.39448142 -0.9251813\n",
-      " -0.30251546 -0.95715431 -0.20876999 -0.97813085 -0.10997311 -0.99239458\n",
-      " -0.01081099 -0.99843943  0.08836333 -0.99508783  0.18582201 -0.97712173\n",
-      "  0.28379804 -0.9525202   0.37705329 -0.9166331   0.46953143 -0.87456685\n",
-      "  0.55388194 -0.82677529  0.63244939 -0.76408871  0.71015518 -0.69929219\n",
-      "  0.77825725 -0.62604207  0.83730264 -0.54808588  0.89015802 -0.46346631\n",
-      "  0.93348484 -0.37190967  0.96576014 -0.28202031  0.98851561 -0.18855247\n",
-      "  1.00650978 -0.09230805]\n",
-      "  cov w/ noise:  [[ 0.01591152 -0.00113653  0.0146979  ... -0.00120276  0.01477119\n",
-      "  -0.00124194]\n",
-      " [-0.00113653  0.01643434 -0.00149188 ...  0.01510357 -0.00162558\n",
-      "   0.01499381]\n",
-      " [ 0.0146979  -0.00149188  0.0157749  ... -0.00155956  0.01428525\n",
-      "  -0.0014791 ]\n",
-      " ...\n",
-      " [-0.00120276  0.01510357 -0.00155956 ...  0.01690228 -0.00164915\n",
-      "   0.0153306 ]\n",
-      " [ 0.01477119 -0.00162558  0.01428525 ... -0.00164915  0.0158767\n",
-      "  -0.0016544 ]\n",
-      " [-0.00124194  0.01499381 -0.0014791  ...  0.0153306  -0.0016544\n",
-      "   0.0157722 ]]\n",
-      "  mean conditional:  [ 0.91004751 -0.03243944  0.90602804  0.07732927  0.89401268  0.18642145\n",
-      "  0.87370203  0.29323843  0.84498119  0.39628732  0.80813447  0.49423746\n",
-      "  0.76390943  0.58589758  0.71335764  0.67033477  0.65762295  0.74686939\n",
-      "  0.59777054  0.81528647  0.53434946  0.8756208   0.4675873   0.92837307\n",
-      "  0.39704538  0.97382785  0.32201704  1.01218541  0.24174239  1.04301995\n",
-      "  0.15563171  1.06546397  0.06375248  1.0781844  -0.03332921  1.0797022\n",
-      " -0.13453678  1.06882185 -0.23829168  1.04493443 -0.34285628  1.00817099\n",
-      " -0.44647203  0.95940347 -0.5473704   0.90011388 -0.64402541  0.83201867\n",
-      " -0.7348581   0.75679143 -0.8185269   0.67583126 -0.8934823   0.59020497\n",
-      " -0.95818023  0.50074048 -1.01092706  0.40810491 -1.05020157  0.31309266\n",
-      " -1.07478486  0.21646831 -1.08412274  0.11908909 -1.07838438  0.0217671\n",
-      " -1.0586667  -0.07451432 -1.02661537 -0.16886228 -0.98441247 -0.26049112\n",
-      " -0.93439209 -0.34866758 -0.87859113 -0.43305213 -0.81849047 -0.51350093\n",
-      " -0.75475474 -0.5902876  -0.68714866 -0.66374673 -0.61484008 -0.7340111\n",
-      " -0.53681142 -0.80110882 -0.45226914 -0.86431482 -0.3612438  -0.92254506\n",
-      " -0.26476387 -0.97416463 -0.16468329 -1.01715553 -0.06328286 -1.04947768\n",
-      "  0.03692973 -1.06928452  0.13379064 -1.07537835  0.22599485 -1.0672333\n",
-      "  0.31289419 -1.04527888  0.39447561 -1.01065606  0.47100709 -0.96504355\n",
-      "  0.54270596 -0.91023873  0.60953294 -0.84790927  0.67102868 -0.77924585\n",
-      "  0.7264956  -0.70482362  0.7752632  -0.62470304  0.81673144 -0.53882626\n",
-      "  0.85058092 -0.44709754  0.87682907 -0.34977952  0.89503455 -0.2474089\n",
-      "  0.90627543 -0.13916516] , sample:  [ 0.88733483 -0.04425711  0.90026582  0.04968832  0.87784777  0.1718588\n",
-      "  0.90413018  0.30669327  0.84977903  0.39431635  0.82701911  0.51924062\n",
-      "  0.75628892  0.59234461  0.68534941  0.69590474  0.63230462  0.70809269\n",
-      "  0.6059353   0.87801987  0.52260058  0.88284235  0.49250856  0.89363772\n",
-      "  0.39883166  0.94502499  0.35852957  1.00281343  0.23610686  1.02607813\n",
-      "  0.1946665   1.03278783  0.0072914   1.10459476 -0.05553548  1.11698846\n",
-      " -0.17100668  1.06233089 -0.21504796  1.04983581 -0.2969025   1.02772594\n",
-      " -0.4318502   1.01146211 -0.58744829  0.85861013 -0.63218614  0.79571891\n",
-      " -0.75233576  0.7546842  -0.80209374  0.69984152 -0.88834741  0.55289875\n",
-      " -0.95626457  0.5367107  -1.00876587  0.37495207 -1.03758536  0.29926851\n",
-      " -1.1367253   0.22483316 -1.06753884  0.10175609 -1.09860723  0.03034977\n",
-      " -1.01066336 -0.01996552 -1.05181191 -0.15601899 -1.00714332 -0.25571699\n",
-      " -0.93717645 -0.39897847 -0.81609101 -0.47779857 -0.82789642 -0.47209857\n",
-      " -0.73727968 -0.57955235 -0.71200797 -0.67244391 -0.60573465 -0.70218837\n",
-      " -0.55049775 -0.79594643 -0.47029863 -0.88352456 -0.32885523 -0.88883749\n",
-      " -0.27149603 -1.00784171 -0.18005806 -1.01569984 -0.09340603 -1.04042608\n",
-      "  0.10277361 -1.06011506  0.13487413 -1.14954404  0.21679457 -1.05181632\n",
-      "  0.296116   -1.03217767  0.38646349 -1.01612951  0.49188778 -0.97031284\n",
-      "  0.5144607  -0.84972106  0.60113289 -0.86301353  0.70307628 -0.78720684\n",
-      "  0.72095038 -0.70055479  0.7827558  -0.64170435  0.81004099 -0.53423347\n",
-      "  0.88074305 -0.43833023  0.8548127  -0.32391654  0.90643387 -0.2609306\n",
-      "  0.89179569 -0.1520655 ]\n",
-      "  cov conditional:  [[ 2.24998699e-05  7.71341952e-07  1.97478847e-05 ...  1.18182718e-06\n",
-      "  -1.38004289e-04 -4.62412906e-06]\n",
-      " [ 7.71341952e-07  2.20456752e-05  6.85718882e-07 ...  2.20299376e-05\n",
-      "  -4.54441781e-06 -1.41755968e-04]\n",
-      " [ 1.97478847e-05  6.85718882e-07  1.73361484e-05 ...  1.03806485e-06\n",
-      "  -1.21331694e-04 -4.11746841e-06]\n",
-      " ...\n",
-      " [ 1.18182718e-06  2.20299376e-05  1.03806485e-06 ...  5.14601889e-05\n",
-      "  -7.95996078e-06 -1.61563221e-04]\n",
-      " [-1.38004289e-04 -4.54441781e-06 -1.21331694e-04 ... -7.95996078e-06\n",
-      "   1.01113179e-03  2.83790665e-05]\n",
-      " [-4.62412906e-06 -1.41755968e-04 -4.11746841e-06 ... -1.61563221e-04\n",
-      "   2.83790665e-05  1.08050781e-03]]\n",
-      "  mean noise:  [ 0.02271269  0.01181767  0.00576221  0.02764095  0.01616491  0.01456265\n",
-      " -0.03042816 -0.01345484 -0.00479784  0.00197096 -0.01888464 -0.02500316\n",
-      "  0.00762051 -0.00644702  0.02800822 -0.02556997  0.02531833  0.03877671\n",
-      " -0.00816476 -0.0627334   0.01174887 -0.00722154 -0.02492125  0.03473536\n",
-      " -0.00178628  0.02880287 -0.03651252  0.00937198  0.00563552  0.01694182\n",
-      " -0.0390348   0.03267614  0.05646108 -0.02641036  0.02220627 -0.03728625\n",
-      "  0.03646989  0.00649097 -0.02324372 -0.00490138 -0.04595378 -0.01955495\n",
-      " -0.01462183 -0.05205865  0.04007789  0.04150375 -0.01183927  0.03629976\n",
-      "  0.01747766  0.00210723 -0.01643316 -0.02401026 -0.0051349   0.03730622\n",
-      " -0.00191566 -0.03597022 -0.00216119  0.03315284 -0.01261621  0.01382415\n",
-      "  0.06194044 -0.00836485 -0.0165839   0.017333    0.02022285 -0.00858267\n",
-      " -0.04800334 -0.0545488   0.02519655 -0.01284329  0.02273085 -0.00477413\n",
-      "  0.00278436  0.05031089 -0.06250012  0.04474644  0.00940595 -0.04140237\n",
-      " -0.01747506 -0.01073526  0.02485931  0.00869719 -0.00910543 -0.03182273\n",
-      "  0.01368633 -0.00516239  0.01802949  0.01920974 -0.03238857 -0.03370757\n",
-      "  0.00673215  0.03367708  0.01537477 -0.00145569  0.03012316 -0.00905159\n",
-      " -0.06584389 -0.00916946 -0.00108349  0.07416569  0.00920027 -0.01541699\n",
-      "  0.01677818 -0.01310121  0.00801212  0.00547345 -0.02088069  0.00526929\n",
-      "  0.02824525 -0.06051767  0.00840005  0.01510426 -0.0320476   0.00796099\n",
-      "  0.00554523 -0.00426883 -0.0074926   0.01700131  0.00669045 -0.00459279\n",
-      " -0.03016213 -0.00876731  0.02201637 -0.02586298 -0.01139932  0.0135217\n",
-      "  0.01447974  0.01290035]\n",
-      "  cov noise:  [[ 2.24998699e-05  7.71341952e-07  1.97478847e-05 ...  1.18182718e-06\n",
-      "  -1.38004289e-04 -4.62412906e-06]\n",
-      " [ 7.71341952e-07  2.20456752e-05  6.85718882e-07 ...  2.20299376e-05\n",
-      "  -4.54441781e-06 -1.41755968e-04]\n",
-      " [ 1.97478847e-05  6.85718882e-07  1.73361484e-05 ...  1.03806485e-06\n",
-      "  -1.21331694e-04 -4.11746841e-06]\n",
-      " ...\n",
-      " [ 1.18182718e-06  2.20299376e-05  1.03806485e-06 ...  5.14601889e-05\n",
-      "  -7.95996078e-06 -1.61563221e-04]\n",
-      " [-1.38004289e-04 -4.54441781e-06 -1.21331694e-04 ... -7.95996078e-06\n",
-      "   1.01113179e-03  2.83790665e-05]\n",
-      " [-4.62412906e-06 -1.41755968e-04 -4.11746841e-06 ... -1.61563221e-04\n",
-      "   2.83790665e-05  1.08050781e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# do statistics on the leaf values\n",
-    "F = tree.data['F_T'][0]; H = tree.data['H_T'][0]\n",
-    "print(\"root conditional mean vs. sample mean:\",jnp.linalg.solve(H,F),jnp.mean(leaf_values,0))\n",
-    "print(\"root conditional cov vs. sample cov:\",jnp.linalg.inv(H),jnp.cov(leaf_values.T))\n",
-    "\n",
-    "# sample statistics\n",
-    "K = 500 # number samples\n",
-    "samples = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_noise = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_conditional = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "\n",
-    "for i in tqdm(range(K)):\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "    values = tree.data['value'][tree.is_leaf]\n",
-    "    # collect values\n",
-    "    samples = samples.at[i].set(values)\n",
-    "    # add observation noise\n",
-    "    subkey,key = jax.random.split(key)\n",
-    "    samples_noise = samples_noise.at[i].set(values+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,values.shape))\n",
-    "    # conditional sampling\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_conditional.down(tree,params.values())\n",
-    "    samples_conditional = samples_conditional.at[i].set(tree.data['value'][tree.is_leaf])\n",
-    "# print sample statistics for all leaves\n",
-    "print(\"data parameters: \",params.values())\n",
-    "print(\"observation noise: \")\n",
-    "print(\" mean: \",jnp.mean(samples_conditional-leaf_values,axis=(0,1)))\n",
-    "print(\" cov: \",jnp.cov((samples_conditional-leaf_values).reshape(-1,n*d).T))\n",
-    "print(\"leaves: \")\n",
-    "for i in range(samples.shape[1]):\n",
-    "    print(\"  mean: \",jnp.mean(samples[:,i],0))\n",
-    "    print(\"  cov: \",jnp.cov(samples[:,i].T))\n",
-    "    print(\"  mean w/ noise: \",jnp.mean(samples_noise[:,i],0))\n",
-    "    print(\"  cov w/ noise: \",jnp.cov(samples_noise[:,i].T))\n",
-    "    print(\"  mean conditional: \",jnp.mean(samples_conditional[:,i],0), \", sample: \",leaf_values[i])\n",
-    "    print(\"  cov conditional: \",jnp.cov(samples_conditional[:,i].T))\n",
-    "    print(\"  mean noise: \",jnp.mean(samples_conditional[:,i]-leaf_values[i],0))\n",
-    "    print(\"  cov noise: \",jnp.cov((samples_conditional[:,i]-leaf_values[i]).T))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make two MCMC runs: First, we use that the model with constant node covariance is fully Gaussian and we can read the data likelihood directly from the results of upwards pass. Subsequently, we also sample the state of the tree to get a likelihood approximation from the conditional downwards pass. This version is not necessary in the current model, but it points towards how inference in non-Gaussian models (e.g. non-linear diffusion processes along the edges) will look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.25, 'k_sigma': 0.5, 'obs_var': 0.001}\n",
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1200/1200 [01:49<00:00, 10.97it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.3650\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# inference for Gaussian model, likelihood from backwards filtering\n",
-    "def log_likelihood(state):\n",
-    "    \"\"\"Log likelihood of the tree.\"\"\"\n",
-    "    params, = state\n",
-    "    v,c,F,H = tree.data['value'][0],tree.data['c_T'][0],tree.data['F_T'][0],tree.data['H_T'][0]\n",
-    "    #c = -jnp.real(jax.scipy.stats.multivariate_normal.logpdf(v,jnp.zeros(n*d),jnp.linalg.inv(H)))\n",
-    "    return -c+F@v-.5*v.T@H@v\n",
-    "\n",
-    "def log_posterior(data,state):\n",
-    "    \"\"\"Log posterior given the state and data.\"\"\"\n",
-    "    params, = state\n",
-    "    # backwards filtering with current parameters\n",
-    "    init_up(data,params); up.up(tree,params.values())\n",
-    "\n",
-    "    log_prior = params.log_prior()\n",
-    "    log_like = log_likelihood(state)\n",
-    "    return log_prior + log_like\n",
-    "\n",
-    "def proposal(data, state, key):\n",
-    "    parameters, = state\n",
-    "    new_parameters = parameters.propose(key)\n",
-    "\n",
-    "    return new_parameters,\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.25,keep_constant=False), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.5,keep_constant=False), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3,alpha=2,beta=.003,keep_constant=True) # observation noise variance. We keep it constant here because of lacking identifiability (which will not be the case with higher-dimensional correlated data)\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = init_params,\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods,samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 1000, burn_in=200, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,true_params=params.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.1, 'k_sigma': 0.25, 'obs_var': 0.001}\n",
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1200/1200 [02:56<00:00,  6.80it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.6250\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# inference for Gaussian model, likelihood from forward guiding\n",
-    "\n",
-    "# Crank-Nicolson update with possibly node-dependent lambd\n",
-    "lambd = .9\n",
-    "update_CN = lambda noise,key: noise*lambd+jnp.sqrt((1-lambd**2))*jax.random.normal(key,noise.shape)\n",
-    "\n",
-    "# downwards pass to compute likelihoods\n",
-    "tree.add_property('log_likelihood', shape=())\n",
-    "@jax.jit\n",
-    "def down_log_likelihood(noise,value,edge_length,parent_value,params,**args):\n",
-    "    var = edge_length # variance is edge length\n",
-    "    covar = a(params)/params['k_alpha']**2 # covariance without amplitude\n",
-    "    sqrt_covar = sigma(params)/params['k_alpha'] # square root covariance without amplitude\n",
-    "    return {'log_likelihood': jnp.mean(\n",
-    "        jax.vmap(lambda v,m,var: jax.scipy.stats.norm.logpdf(jnp.linalg.solve(sqrt_covar,v-m),0,jnp.sqrt(var)*params['k_alpha']))(value,parent_value,var)\n",
-    "        ,1)\n",
-    "        }\n",
-    "downmodel_log_likelihood = DownLambda(down_fn=down_log_likelihood)\n",
-    "down_log_likelihood = OrderedExecutor(downmodel_log_likelihood)\n",
-    "\n",
-    "# log likelihood of the tree\n",
-    "def log_likelihood(data,state):\n",
-    "    \"\"\"Log likelihood of the tree.\"\"\"\n",
-    "    params,noise = state\n",
-    "    # backwards filtering with current parameters\n",
-    "    init_up(data,params); up.up(tree,params.values())\n",
-    "    # forwards guiding with current noise\n",
-    "    tree.data['noise'] = noise; down_conditional.down(tree,params.values())\n",
-    "    # compute log likelihood\n",
-    "    down_log_likelihood.down(tree,params.values())\n",
-    "    tree_log_likelihood = jnp.mean(tree.data['log_likelihood'][1:]) # ignore root node\n",
-    "    residuals = tree.data['value'][tree.is_leaf]-data\n",
-    "    leaves_log_likelihood = jnp.mean(jax.scipy.stats.norm.logpdf(residuals,0,jnp.sqrt(params['obs_var'].value)))\n",
-    "    return tree_log_likelihood+leaves_log_likelihood\n",
-    "\n",
-    "def log_posterior(data,state):\n",
-    "    \"\"\"Log posterior given the state and data.\"\"\"\n",
-    "    parameters,_ = state\n",
-    "    log_prior = parameters.log_prior()\n",
-    "    log_like = log_likelihood(data,state)\n",
-    "    return log_prior + log_like\n",
-    "\n",
-    "def proposal(data, state, key):\n",
-    "    subkeys = jax.random.split(key,2)\n",
-    "    parameters,noise = state\n",
-    "\n",
-    "    # update parameters\n",
-    "    new_parameters = parameters.propose(subkeys[0])\n",
-    "    # update noise\n",
-    "    new_noise = update_CN(noise,subkeys[1])\n",
-    "\n",
-    "    return new_parameters,new_noise\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.1,alpha=3,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.25), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3,alpha=2,beta=.003) # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,jnp.zeros_like(tree.data['noise']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 1000, burn_in=200, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,true_params=params.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "jaxenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/notebooks/mean_estimation.ipynb b/docs/source/notebooks/mean_estimation.ipynb
deleted file mode 100644
index e818189..0000000
--- a/docs/source/notebooks/mean_estimation.ipynb
+++ /dev/null
@@ -1,688 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Execute basic operations on the tree\n",
-    "\n",
-    "Hyperiax is designed for fast and easy-to-use tree-level executions. In this notebook, you will know the general workflow, including defining your operations, executing them using Hyperiax's engine, visualize the results, and a simple way to integrate all the parts using Hyperiax's prebuilt modules.\n",
-    "\n",
-    "For better illustration, we shall use an concrete example in phylogenetic analysis: estimating inner nodes of a phylogenetic tree, given:\n",
-    "- observations of the leaf nodes\n",
-    "- knowledge of the tree topology, including number of nodes, the edges connecting them and the edge lengths\n",
-    "\n",
-    "This is a basic problem in the field of _phylogenetic comparative methods_. We may be particularly interested in the root node estimation, which can be interpreted as the phylogenetic mean of the observations, where we shall use a very simple estimator the inner nodes: **mean estimator**, that is, any inner node will be estimated as the mean of its children weighted by its edge length:\n",
-    "$$\n",
-    "\\hat{x} = \\sum^k_{i=1} \\frac{1}{w_i}\\cdot c_i\n",
-    "$$\n",
-    "where $\\hat{x}$ is the estimation of node $x$, $w_i$ is the edge length of the $i$-th child node, and $c_i$ is the child's value. We set all the node values are 2-dimensional vectors, which allows for nicer visualizations, but you can of course use Hyperiax to deal with other high-dimensional data.\n",
-    "\n",
-    "In practice, we create trees from real data. However, in this notebook, we are not going to study a real-life example. Instead, we will create a hypothetical tree, and of course, we can manually assign values to each node. But we will show a faster method: simulating the tree from the root node, based on the Brownian motion model. Specifically, an edge of length $T$ corresponds to a Brownian motion running for a duration of $T$. \n",
-    "\n",
-    "**Content of the notebook**\n",
-    "- Create the tree with a chosen topology and simulate its node values, based on the Brownian motion model\n",
-    "- Estimate inner nodes based on the simulated tree\n",
-    "- Compute the estimations more conveniently using `prebuilts` modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import hyperiax\n",
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter\n",
-    "\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulate the tree (downwards)\n",
-    "\n",
-    "Recall briefly last notebook, where we learn that a `topology` is needed for Hyperiax to create the tree, this `topology` contains no actual data, and only serves as a representation of the data structure we intend to work on.\n",
-    "\n",
-    "We first create a symmetric tree with `height=4` and `degree=3`, which means the tree will have `4+1=5` layers, and each node has `3` children."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                 None\n",
-      "                     ┌────────────────────────────────────────────┼────────────────────────────────────────────┐\n",
-      "                    None                                         None                                         None                    \n",
-      "      ┌──────────────┼──────────────┐              ┌──────────────┼──────────────┐              ┌──────────────┼──────────────┐       \n",
-      "     None           None           None           None           None           None           None           None           None     \n",
-      " ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐    ┌────┼────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None None None\n"
-     ]
-    }
-   ],
-   "source": [
-    "topology = symmetric_topology(height=3, degree=3)\n",
-    "plot_tree_text(topology)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The topology meets our expectation. Now we need to specify which kind of data we intend to store in the tree. Hyperiax allows for fast computation using any backend supported by _JAX_, and if the operations are suitable, _JAX_ will automatically remove all the computation to the GPUs (if available) and runs them at an extraordinary speed. Therefore, all the data store in the `HypTree` is stored in the format of `jax.numpy.ndarray`, which is the _Numpy_-like array with _JAX_ backend. To assign the data, we need to let Hyperiax know the shape of coming data first, which is for initializing buffer. But don't worry, all you need to do is just to pass the shape as tuples, and Hyperiax will take over the rest.\n",
-    "\n",
-    "In our case, we need to assign three properties to each node:\n",
-    "- `edge_length`: as we use in the last notebook, we will use it to determine the scale of Brownian motions along the edges, with shape of `(1,)`\n",
-    "- `value`: the data value stored in each node, in this case, is a 2-d vector with shape of `(2, )`\n",
-    "- `noise`: the noise (for example, from the observations) in each node, with the same shape as `value`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree = HypTree(topology)\n",
-    "tree.add_property('noise', shape=(2,))\n",
-    "tree.add_property('edge_length', shape=(1,))\n",
-    "tree.add_property('value', shape=(2,))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When calling `add_property()` method of `HypTree`, Hyperiax will initialize the a contiguous buffer in the memory with given shapes and number of nodes, with the BFS order in `tree.data`, and all the actual values are initialized to `0`.\n",
-    "\n",
-    "We first set the edge lengths to be exponentially decaying with the depth, like we did in the previous notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.data['edge_length'] = jnp.exp(-jnp.log(2) * tree.node_depths)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We might also need to deal with some randomness, for example, the `noise` of each node. With the help of contiguous buffer created by Hyperiax, we can directly sample values for the entire tree, instead of do it one node at a time. This should be the preferred method, except the underlying distribution is node-dependent.\n",
-    "\n",
-    "Now, we can do the same for the 2-dimensional values that we intend to store in `noise`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = PRNGKey(0)\n",
-    "key, subkey = split(key)\n",
-    "tree.data['noise'] = jax.random.normal(subkey, shape=tree.data['noise'].shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After we initialize the tree, we need to define a function that runs downwards to simulate the Brownian motions along the edges. We shall call it `down` function for short in the result of the notebook. Generally, a `down` function should have the following features:\n",
-    "- Defined on a edge $(u, v)$, where $u$ is the source node and $v$ is the target node.\n",
-    "- Used to compute the new value of $v$ based on the current states (can only depends on $u$ or $v$ or both)\n",
-    "\n",
-    "With this in mind, go back to our Brownian case. The goal of our Brownian simulation is to simulate all the node values except the root, and the value of every node is sample from a Brownian motion starts from its parent node and runs for the time of the distance from the root to the evaluated node. If we denote the edge length as $T$, the value of evaluated root $v$ should follow the distribution of $\\mathcal{N}(v; u, T\\cdot\\mathbf{I})$. In practice, $v = u + \\sqrt{T}\\cdot\\epsilon$, where $\\epsilon\\sim\\mathcal{N}(0,\\mathbf{I})$ and can be generated using `jax.random.normal()` for example, or we can just use our previously defined `noise`.\n",
-    "\n",
-    "Before we start to define the `down` function, we need to know a bit about how Hyperiax treats the `down` function: In Hyperiax, functions on a tree always consider the _current_ node. When referring to properties such as `noise`, it means the `noise` stored in the _current_ node. To define custom operations on a tree, we can query the necessary data within nodes by specifying parameter names. In this case, we need both the `noise` and the `edge_length` in the _current_ node. Additionally, we need the result of calculations from the parent node. In hyperiax, values stored in the _parent_ node are prefixed with `parent_`. For this computation, we need `parent_value` (the result of the computation in the parent node).\n",
-    "\n",
-    "Now we are ready to define the `down` function. The implementation of the `down` function involves the following steps:\n",
-    "\n",
-    "1. **Extract the necessary parameters**:\n",
-    "    - `noise`: the noise value in the _current_ node.\n",
-    "    - `edge_length`: the edge length stored in the _current_ node (this is by convention the length of the edge connecting the _current_ and _parent_ node)\n",
-    "    - `parent_value`: the result of the computation in the _parent_ node.\n",
-    "\n",
-    "2. **Compute the new value for the child node**:\n",
-    "    - Calculate the new value as `sqrt(edge_length) * noise + parent_value`.\n",
-    "\n",
-    "3. **Return the new value**:\n",
-    "    - Return a dictionary specifying the `value` parameter to overwrite in the child node $v$. Values that are not being changed, such as `edge_length`, can be omitted.\n",
-    "\n",
-    "Hyperiax will greedily distribute your workloads, using vectorization among other techniques. This means that any `down` function is expected to work on _batched_ data. In our example, the input shapes will be as follows;\n",
-    "\n",
-    "- `noise`: `(b, 2)`.\n",
-    "- `edge_length`: `(b, 1)`.\n",
-    "\n",
-    "Similarly, we need to return a _batched_ output, in this case `value`, should have the shape `(b, 2)`, where `b` is some batch size determined by hyperiax.\n",
-    "\n",
-    "If the concept of batching seems uncomfortable to you, we highly recommend using [`jax.vmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.vmap.html) which can do the batching for you automatically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@jax.jit\n",
-    "def down(noise, edge_length,parent_value, **args):\n",
-    "    return {'value': jnp.sqrt(edge_length) * noise + parent_value}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we set the root value to be the `noise` we sampled. All the data is stored in BFS order in the buffers, this means that the root is always the first element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.data['value'] = tree.data['value'].at[0].set(tree.data['noise'][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we create both an **executor** and a **model**. The **model** object contains all user code related to the operations we wish to run on a tree. The **executor** object schedules execution within the tree, and is in charge of setting the data in the correct format, as well as batching things together and making sure everything runs in the correct order.\n",
-    "\n",
-    "You are welcome to define your operations as an entire class, in cases where a constructor is needed. In this example we use the anonymous **lambda models** in `hyperiax.models.lambdamodels.py`, which enables you to pass tree operations as arguments, such that they are ready to be executed on a tree directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downmodel = DownLambda(down_fn=down)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have now created a `downmodel`, specified by the `down` function that will generate Brownian motion samples on a tree. The only thing left to do is give this **model** to an executor, which will run the code in the correct order on the tree. In this case we use the `OrderedExecutor`, since order is important for our operation. This executor will work on models going in either the up or down direction on a tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exe = OrderedExecutor(downmodel)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With these objects created, we can now run our `down` function on the tree! Be careful as these operations are **INPLACE**. Because of this, we reccomend [idempotent](https://en.wikipedia.org/wiki/Idempotence) operations like here, where the result is stored in `value`, instead of overwriting `noise`(notice how this would give a different result for each execution)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exe.down(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Besides topology visualizations, Hyperiax also provides avenues of displaying the underlying data in the tree. When dealing with $2$ dimensional vectors, we can perform a classic scatter plot, but with the tree topology overlaid.\n",
-    "\n",
-    "Note that passing in a pyplot axis object is optional, if you do not wish to use your own, then it can be omitted.\n",
-    "\n",
-    "Using these two plots, we can see how the `down` function infact seems to correlate our nodes!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-2, 4)\n",
-    "    a.set_ylim(-2, 2)\n",
-    "plot_tree_2d_scatter(tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(tree, 'noise', ax=ax[1])\n",
-    "ax[1].set_title('Noise');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The green node represents the root, and the light blue nodes represent the leafs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimate the inner nodes (upwards)\n",
-    "\n",
-    "So far, we assume we know the value of root, together with the model that runs from top to bottom. However, in most of the real cases, we know about the leaf nodes, and want to inference the root and inner nodes. In phylogenetics, the leaf nodes are the species we observe at current time, and the root and inner nodes represents their ancestors.\n",
-    "\n",
-    "Now we going to mask all the data on the inner nodes of our simulated tree, and only keeps the leaf nodes. To do this, `HypTree` has a boolean mask that indicates whether or not a node is a obtainable leaf via `tree.is_leaf`. Therefore, we can add another property `estimated_value` to the tree, with the same shape as `value`, and initialized as `0`. Then we index the leaf nodes by slicing through `is_leaf` and manually set them to be the simulated leaf `value`: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.add_property('estimated_value', shape=(2,))\n",
-    "leaf_data = tree.data['value'][tree.is_leaf]\n",
-    "tree.data['estimated_value'] = tree.data['estimated_value'].at[tree.is_leaf].set(leaf_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have all the observations on the leaf nodes, we want to estimate the nodes above. The direction is upward, from bottom to top. Thus, we need a `up` function just like the `down` function we defined before. But unlike the downward pass, where the message is passing through a one-to-one pair $(u, v)$, the upward pass will aggregate all the information from child nodes to the parent node, so it is a many-to-one correspondence. Therefore beside `up` function, we need to define another two addons to facilitate Hyperiax to handle this: `reductions` parameter and `transform` function:\n",
-    "* The `reductions` parameter of the `UpLambda` class, determines how each property should be reduced when multiple are incoming from the _child_ nodes. In this case we have `sum` for both arguments. This means that after a message is sent from each child toward its parent, each property is first summed before the `transform` operation in the _parent_.\n",
-    "* The `transform` function processes the reduced incoming messages of the _child_ nodes, and treats the receiving node of the messages as the _current_ node. This means that we need to use the prefix `child_` when getting the reduced values of the children.\n",
-    "* The `up` function then takes the value in a _current_ node, and forms a message that is passed toward its _parent_."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def up(estimated_value, edge_length, **kwargs):\n",
-    "    return {'weighted_value': estimated_value/edge_length, \n",
-    "            'inverse_edge_length':1/edge_length}\n",
-    "    \n",
-    "def transform(child_weighted_value,child_inverse_edge_length, **kwargs):\n",
-    "    return {'estimated_value': child_weighted_value/child_inverse_edge_length}\n",
-    "\n",
-    "upmodel = UpLambdaReducer(up, \n",
-    "                          transform, \n",
-    "                          reductions={\n",
-    "                            'weighted_value': 'sum', \n",
-    "                            'inverse_edge_length': 'sum'\n",
-    "                    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since upward pass is more complicated than the downward pass, it is worthy to explain it more here. We first summarize what we expect about `up`, `reduction` and `transform`:\n",
-    "- **Up Operation (`up` function)**:\n",
-    "   - **Purpose**: To generate a message at _each_ node that will be passed to its parent.\n",
-    "   - **Inputs**: Values specific to the current node.\n",
-    "   - **Outputs**: A message containing the processed values to be sent to the parent node.\n",
-    "\n",
-    "- **Reduction (`reductions` parameter)**:\n",
-    "   - **Purpose**: To aggregate messages from _multiple_ child nodes.\n",
-    "   - **Inputs**: Messages from the child nodes.\n",
-    "   - **Outputs**: A single aggregated message that combines the values from all child nodes using a specified reduction method (e.g., sum).\n",
-    "\n",
-    "- **Transform Operation (`transform` function)**:\n",
-    "   - **Purpose**: To process the aggregated message at the _parent_ node.\n",
-    "   - **Inputs**: Aggregated messages from the child nodes aswell as the values in the _current_ node.\n",
-    "   - **Outputs**: A transformed value for the parent node.\n",
-    "\n",
-    "Then we can explain the above code in details:\n",
-    "\n",
-    "**1. Up Operation (`up` function)**\n",
-    "\n",
-    "The `up` function takes the value at the current node and forms a message that will be passed to the parent node. \n",
-    "\n",
-    "When hyperiax executes functions on a tree, it always has a sense of the _current_ node. In hyperiax terminology, when referring to a specific property, such as `estimated_value`, it means the `estimated_value` stored in the _current_ node.\n",
-    "\n",
-    "- **Parameters**:\n",
-    "  - `estimated_value`: The value associated with the current node.\n",
-    "  - `edge_length`: The length of the edge connecting the current node to its parent.\n",
-    "  - `**kwargs`: Any additional arguments that might be needed.\n",
-    "- **Returns**: A dictionary with two properties:\n",
-    "  - `weighted_value`: Calculated as `estimated_value / edge_length`.\n",
-    "  - `inverse_edge_length`: Calculated as `1 / edge_length`.\n",
-    "\n",
-    "In this case, with $w_i=1/l_i$ with $l_i$ being the edge length in node $i$. We can write the inner node estimation $p$ as a sum over children $c_i$\n",
-    "\n",
-    "$$p=\\frac{1}{\\sum w_i}\\sum c_iw_i$$\n",
-    "\n",
-    "The key is to observe that we can compute $\\sum w_i$ and $\\sum c_iw_i$ independently, where we use the sum as a reduction and $w_i$ and $w_ic_i$ are the messages being passed up\n",
-    "\n",
-    "**2. Transform Operation (`transform` function)**\n",
-    "\n",
-    "The `transform` function processes the aggregated message at the parent node. It uses the reduced values from the child nodes to compute a new value for the current (parent) node.\n",
-    "\n",
-    "- **Parameters**:\n",
-    "  - `child_weighted_value`: The aggregated `weighted_value` from all child nodes.\n",
-    "  - `child_inverse_edge_length`: The aggregated `inverse_edge_length` from all child nodes.\n",
-    "  - `**kwargs`: Any additional arguments that might be needed.\n",
-    "- **Returns**: A dictionary with one property:\n",
-    "  - `estimated_value`: Calculated as `child_weighted_value / child_inverse_edge_length`.\n",
-    "\n",
-    "**3. Reduction (`reductions` parameter)**\n",
-    "\n",
-    "The reduction step determines how to combine multiple messages received from child nodes. In this example, we use the sum operation for both `weighted_value` and `inverse_edge_length` properties.\n",
-    "\n",
-    "- **`weighted_value`**: The messages' `weighted_value` properties from all child nodes are summed.\n",
-    "- **`inverse_edge_length`**: The messages' `inverse_edge_length` properties from all child nodes are summed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After understanding what all these are used for, we can put them all together and warp them by the **lambda model** as we did for the downward pass. To set up the upward pass, we define an `UpLambda` model with the `up` and `transform` functions, and the specified reductions.\n",
-    "\n",
-    "- **`UpLambda`**: Initializes the upward pass model with the `up` and `transform` functions and the reduction rules.\n",
-    "- **`OrderedExecutor`**: Executes the upward pass using the defined model.\n",
-    "\n",
-    "This setup ensures that values propagate correctly from the leaves to the root, are aggregated at each step, and transformed to provide the final values at each node. Then we can execute the computation and let Hyperiax take over all the rest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "13.3 μs ± 178 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "upward_exe = OrderedExecutor(upmodel)\n",
-    "upward_exe.up(tree)\n",
-    "%timeit upward_exe.up(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can of course visualize the estimated results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-2, 4)\n",
-    "    a.set_ylim(-2, 2)\n",
-    "plot_tree_2d_scatter(tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(tree, 'estimated_value', ax=ax[1])\n",
-    "ax[1].set_title('Estimated Value');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the estimated values follow the same distribution as the simulated ones, which is reasonable since we used the same model for downward and upward passes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Getting around with `reductions`**\n",
-    "\n",
-    "We also allow for a simpler interface in the up-direction at a cost. If your tree has a constant or zero number of children for all nodes, and you are willing to do some upfront work `(precompute_child_gathers=True)`, then you can get around the reduce operation by simply specifying an `up` function that can access data in child nodes and the current node. The returned values are to be set in the current node.\n",
-    "\n",
-    "The way it handles children is a bit different. In our example, our `value`s in the children are 2 dimensional vectors, and since each node in our topology has exactly `3` or `0` children, we say that the base of the tree is `3`. We can now query the child values by giving `child_estimated_value` as a parameter to our up function. This will have the shape `[batch, base, dimension]` where in our case `batch` is determined by hyperiax, `base=3` from the tree, and `dimension=2` because we are working in 2d. We now need to aggregate these such that we return something with the shape `[batch, dimension]` as the estimated value. In this example, we do it by a weighted mean, using the edge lengths as weights."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pre_tree = HypTree(topology, precompute_child_gathers=True)\n",
-    "pre_tree.data = tree.data.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use precomputed child gathers to accelerate the upward pass\n",
-    "def up(child_estimated_value, child_edge_length, **kwargs):\n",
-    "    inv_el = 1/child_edge_length\n",
-    "    normalizer = inv_el.sum(axis=1)\n",
-    "\n",
-    "    return {'estimated_value': jnp.einsum('bcd,bc1->bd', child_estimated_value, inv_el)/normalizer}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10.2 μs ± 298 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
-      "True\n"
-     ]
-    }
-   ],
-   "source": [
-    "upmodel = UpLambda(up_fn=up)\n",
-    "upmodelexe = OrderedExecutor(upmodel)\n",
-    "res = upmodelexe.up(pre_tree)\n",
-    "%timeit upmodelexe.up(pre_tree)\n",
-    "print((pre_tree.data['estimated_value'] == tree.data['estimated_value']).all())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-2, 4)\n",
-    "    a.set_ylim(-2, 2)\n",
-    "plot_tree_2d_scatter(tree, 'estimated_value', ax=ax[0])\n",
-    "ax[0].set_title('Estimated Value')\n",
-    "plot_tree_2d_scatter(pre_tree, 'estimated_value', ax=ax[1])\n",
-    "ax[1].set_title('Estimated Value (precomputed)');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see the precomputed tree gets exactly the same results as doing it in the normal way, but runs 25% faster (9.64μs compared to 12.9μs). However, such precomputation is only available when the parents have constant number of children, while in a more general case, the previous way of defining `up` is recommended."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using `prebuilts` to finish them all in once\n",
-    "\n",
-    "Alternatively, we can use the cool `hyperiax.models.prebuilts` to do the same computations as above, but more conveniently!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.models.prebuilts import PhyloMeanModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `PhyloMeanExecutor` expects leaf nodes to all contain a field called `estimated_value`. Since we already know the exact values here, we can simply set them directly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pme = PhyloMeanModel()\n",
-    "prebuilt_exe = OrderedExecutor(pme)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prebuilt_exe.up(tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GdQ15jGcmLPH000/LuFQMmVESQgghhBAPZPWeOIbPWm4SJAHcSM5k+KzlrN4T95jOTIiHJ4GSEEJUEocOHaJr1644Ojri7u7O0KFDuXnzJgD/+9//8PT0RKfTmTymZ8+ejBgxQv1+0aJFhIaGYmdnh5+fHzNmzCA3N9fs62VnZzNhwgQ8PT2xs7PD19eXt956q+IuUAhRreh0el6ZvRmDmX3Ktldnb5Y0vCqspo9LEigJIUQlkJycTL9+/WjevDm//fYbP//8M8nJyQwbNgyAiRMnkpGRwYYNG9THJCYmsnfvXsaPHw/AL7/8wksvvcTUqVM5fvw4n3/+OT/99FOxg8x7773Htm3bWLhwIadOneK7774jMDCw4i9WCFEt7Dl1tchMUkEGID45k73R1x7dSYlyI+OSrFESQohK4YMPPiA0NJQvv/xS3fbDDz8QHBzMyZMnadGiBV27duXHH39kyJAhACxcuBBXV1cGDhwIwL/+9S+mT5/On//8ZwBCQ0NJSEjgn//8Jx9//HGR14yPj6dBgwb07dsXrVZLkyZNHsGVCiGqMoPBwM2bN4mNjeXb9Qctesyt1DsVfFaiIsi4JIGSEEJUCtHR0Rw8eNBsw9qzZ8/SokULxowZw6uvvsr9+/dxcHBg+fLlDB48GCsrKwDOnDmj3rFT6PV6cnJyuHPnTpGu7M8//zyDBg0iMDCQyMhIBg0axNNPP12xFyqEqHIMBmMi3fbt2zly5Ah6vTGVzt7KssfXda9d+kGi0pFxSQIlIYSoFLKysujRowcfffRRkX3169cHYPTo0bzyyiv89NNPdO7cmaNHj5rckbt//z6vvfYaY8aMKfIc5ga6Ll26cOnSJVatWsX27dt59tlnmTdvHps3by7HKxNCVEV6vZ6rV68SGxvLsWPHaN68uUmQFBAQQP/QMDZd3k5yZo7Z59BgrH7XJbz+IzxzUV5kXJJASQghKoXmzZuzceNGGjdujK2trdljatWqRb9+/Vi6dCnnz58nICCALl26qPtDQkI4d+4czZo1s/h13dzcmDJlClOmTGHlypWMGDGCxMREvL29H/qahBBVi06n4/Lly8TGxnL8+HF1u1ZrXNIeGBhIs2bNaNKkCQ4ODly8eJFIvxyWxxZ9Ls3v//90Wj+srGRJfFUk45IESqKS0+kNRCdlkHo3S/3e5jGfkxAP686dO0RFRZls+9Of/sSSJUsYPHgwb731Fp6ensTFxbFs2TKWLVuGtbXx7XrcuHGMHj2ac+fOMXz4cJPneOeddxg9ejQzZsxgzJgxaLVajh49SnR0NF988UWR8/jnP/+Jr68v7dq1Q6vVsnz5cjw8PPDw8Ki4ixdCVCr5+flcvHiR2NhYTp06VWR/kyZNCAkJ4cqVK4waNQobG+MonJSUxI8//kioJ0zr5smi/clkFihk5ufpzKfSR6nKkHHJPAmURKW192oysw9fIDkrF1sMTHWByesP82LbxnRp4Pm4T0+IB3bo0CE6d+5ssm3EiBHs2rWL119/ncGDB5Obm4uvry+RkZHq3VyAwYMH4+zszJUrV5g0aZLJcwwfPpxly5bx3nvv8dVXX2FtbU3Dhg2ZMGGC2fOoXbs2n376KVevXkWr1RIeHs7atWvV3HIhRPWUm5vLhQsXiImJITa26HRQWFgYoaGhBAUFYWtrS15eHleuXFH337t3j6+//hoAX19ffAH3DnA1A+7mwvQpE+kSXl9mkqoQGZfMk0BJVEp7ryYza3fRN+/UrDxm7Y5lVrdQCZZElbR69eoS92/durXE/VZWViQnJxe7f/jw4UXu6BWkLMoGeP3113n99ddLfD0hRPWQk5PDuXPniImJ4ezZs0X2N2/enNDQUBo1aqTOFJiTl5fHt99+C4CNjQ0jRozgs88+Q6uBQFfjtu4RARV0FaIirFq1Co1GU+z+mjwuSaAkKh2d3sDswxdKPGb24Yt08vfASlv8P2whhBCiJrt//z5nz57l9OnTXLx4scj+li1bEhoaSmBgoEV37A0GA6tXr+b27dsAvPTSS1y+fBkAZ2dnMjMzJXVXVCsSKIlKJzopg+Qs8x2bFclZOUQnZRDh4/poTkoIIYSoAu7du8eZM2eIjo7m6tWrRfa3adOG0NBQGjRoYJI+ZYm9e/eqs1HPPfccderUYdmyZQA0bNiQEydO4Okp2R6i+pBASVQ6afdLDpLKepwQQghRnd25c4e4uDiio6O5fv26yT6tVkvbtm0JDQ3F39+/xBSr0iiL/YcNG4a/vz/37t0jKSkJAFdXV4AifXGEqMokUBKVjpuD+RKUD3qcEEIIUd2kp6cTFxfHyZMnSUxMNNlna2urzhz5+voWGxwplWXT7ufi5mBLuJeL2ZT2+Ph49etu3boRHh4OQFxcHGAs6JCba7x5aa43jhBVlQRKotIJ93LB09G2xPQ7T0c7wr1cHuFZCSGEEI9XamoqcXFxnDhxgtTUVJN9jo6OtG7dmtDQULy9vUudOSpYWVbh6WjLtLZBJsWS0tLSWLx4Mc2bN6dp06Z069ZN3ffbb78B0K5dO3UNlARKojqRQElUOlZaDdPaBjFrdwwYADNv9tPaNpJCDkIIIaq95ORkYmNjOXbsGJmZmSb7XFxc1IIMZVkbVFxl2eSsXJPKsvfv3+err75S9w8cOFANwO7cuUN6ejoATZs25cCBA4CxAakQ1YUESqJS8tPfJTQxhgvuQeRa26vb3R1tebFtsJQGF0IIUS0ZDAYSEhKIi4vj6NGjZGVlmez38PCgRYsWhIaG4ubmVubnt7SybHvfOixcuBCdTqcWfShYNlzpv9SgQQPs7OxISUkBZEZJVC8SKIlKR6fTsWTJEjzy8xncsimezVqQejeLuzGHmT+oDfZ2sjZJCCFE9WEwGLhx4wZxcXEcPnyYvLw8k/0+Pj40b96ckJAQtWjCg7K0suw3a38hLSEBgClTpnDw4EGTY/bu3QsYq+gB5OfnAxIoiepFAiVR6Rw+fFh9w+3WtcvvXcFrsSkGSbcTQghRLej1euLj49XgSK/Xm+z38/MjPDyckJCQcq0kZ2nF2DNXr+MFPPPMM7i7u5vsy8jI4N69ewA0btzYZJ+k3onqpEIDpT179vC///2Po0ePcuvWLdasWcPQoUOLPX7Xrl1ERkYW2X7r1i18fHwq8ExFZXH37l22bNkCGDs529rK7JGo2s6lniPxbmLpB5YTbydvGrs3Lv3AGkzGJvG46PV6rly5QlxcHEeOHCmyPyAggGbNmtG0adMKCzgsrRhrq8tl0KBBNGzYsMgMV0xMDABBQUHY2tqi0+nUfTY2NuV3sqLCyNhkmQoNlO7du0eLFi147rnnGDZsmMWPO3v2LM7Ozur3Xl5eFXF6ohJSgiQvLy9CQ0Mf89kI8XDOpZ4j/OtwcnWPrueXrZUt0S9Fl2lA2rRpE//73/84ffo0KSkpLFy4kGeeeaYCz/LxkrFJPEo6nY5Lly4RFxfH8ePHi+wPCgoiLCyMJk2a4ODgUOHno1aWvZdjtlgSBgN2uhz6RoTQqlUrs8+xe/duAHV/wXVUD9OnSTwaMjZZrkIDpf79+9O/f/8yP87Ly+uhc3BF1XP9+nVOnz4NwNNPPy1vtqLKS7yb+EgHIoBcXS6JdxPLNBjdvXuXZs2a8eyzz/Lss89W3MlVEjI2iYqWl5fHxYsXiYuL49SpU0X2N23alNDQUBo3boydnd0jPTcrrYZJzerxwcFLYDCYBksGAwCdrO/Qt8/TZh+flpam9kwKDg4GKFJwQlRuMjZZrlKuUYqIiCAnJ4dmzZoxa9YsOnfu/LhPSVQwg8HAsmXLAGM/BrlTK8SjM3LkSEaOHAlQIwKlByVjU81kaVPW3Nxczp8/T2xsrFoRrqCwsDBCQ0PVdLXH6f6ZY4Qm3eCKV1OyCnwUtNPlEJZ1gzdfGI9Go1GvPfWuMRDS6Q1q2l3Tpk3VKnjKeqUHqcInRHEqw9hUqQKlunXrMmfOHNq0aUNOTg7z58+ne/fuHDx4sNjp35ycHHJyctTvlR4DeXl5RXJqqxvl+qrDdZ48eZL79++j1Wrp0qVLkWuqTtdakppynVD9rlUpQALGwP9xe9hzsOTx+fn5xf5brU5kbCqb6vRve398CnOPXSI1649rcXe04YVWDWnjY2x6Hh0dzblz5zh//rx6jFJOOzw8nMaNGxMYGGhSWvtx/mzS0tKIjo7GC/hrr6Yk6m1Yv2MXd5MTcM7O4E/Tp6PRaNhz6ZZ67TYYeN4Fpq4/iOu107hrtTRv3ly9jrt376LVavH09Kzyv/fq9PcLNW9sKu9xSWN4RD81jUZT6oJZc7p160b9+vVZtGiR2f2zZs3i3XffLbJ9yZIlUqJSCPHIODs74+bmRlBQkPres+/aProu6PrIz2XPs3t4ov4TD/RYrVbLwoULGT9+fLHHZGVlceHCBdLS0oo0wMzKymLs2LFkZGSYrOeprGRsEkJUZzVlbKqocalSzSiZ065dO/bt21fs/pkzZzJjxgz1+8zMTPz9/YmMjCxSzrK6ycvLY9u2bfTu3btKV5nZsmULx48fp3bt2rz88stm1yZVl2stTU25Tqh+1xofH8+dO3ce92k8Ug0aNMDf399kW2pq6mM6m0dLxqbiVYd/2zq9gcnrD5vMJJkwGKilz+YZt1xOnz5NREQETZo0wd/fX51NqowSExP5/vvvAZg6dSoXL15k27ZtgLHSbFBQkNlrN84oZfJthjN5Bqil1fPjyC5qCuLevXv57bff6Nq1K506dXr0F1aOqsPfb0E1bWwq73Gp0gdKJ06coG7dusXut7OzM7sQ0sbGplr8gVuiKl9rYmIiR48eBYy5qKXlbVflay2LmnKdUH2utWBaTWUoRPKw52DJ462trYv87qrD79ISMjaVripfa0xCOrey8oFi/h1oNGDlAOTyxhtvPPY1R5bavHkzer2e1q1bk5GRoVaa7du3LyEhIUDJ156HhlyNhlyDljO3s4jwcQWMd+z1ej1OTk5V9ndeWFX++y2opo1N5T0uVWigdPfuXS5cuKB+f/nyZU6cOIGbmxv169dn5syZ3Lhxg4ULFwLw6aefEhgYSFhYGNnZ2cyfP5+dO3eydevWijxN8ZgYDAZWrlwJQIsWLfD19X3MZySEqAlkbBLFSU9PJzY2lvWnL4O9n0WPqQwfPi1x9epVbty4ARgLSyh/361bt6Z9+/bqcZY2pC14nHLHXtJKRXVToYHSkSNHTJr0KWkIEydOZMGCBdy6dYtr166p+3Nzc3nttde4ceMGjo6ONG/enO3bt5tt9CeqvpiYGFJSUgDo3bv3Yz4bIWou5cOh4uLFi0RFReHh4UHjxlWvQWBpZGwSBaWmphIbG8uJEydIS0sDINveFepaFihVBQaDgbVr1wLGtFElSPLz8+PJJ580CfYsbUhb8Ljk5GRAAiVRvirD2FShgVL37t1LrE6xYMECk+/ffPNN3nzzzYo8JVFJ5ObmsmrVKsDY06SiOpALIUq3b98+Bg0apH7/7rvv8u677/LUU0+xevXqx3hmFUPGpprNYDCQnJxMbGwsx48fL7Lw29XVlW4REVxPtCYtO7+YZwF3x6qRbgdw/vx50tPTAThz5gxgTA8dN25ckTVV4V4u1LG14nZOvvmGtICnox3hXi7q90p5cBnLRXmqDGNTpV+jJKonpau3ra0tbdq0ecxnI0TF8HbyxtbK9pF3P/d28i7TYwYOHFgpysYKUVEMBgMJCQnExsZy7NixIg1SPTw8iIiIICQkRO0FZHU1mVm7Y4s2Zf3dC60CuR2d/EjO/2EUTHN3d3dX0+SmTp2Kvb190eP1OurGn+S2V5j5hrQaDdPaNjLbS0pmlKoGGZssJ4GSeORSU1OJiooCYMyYMZW6QpAQD6Oxe2OiX4om8W7iI3tNbyfvMnU+F6K6MhgM3Lhxg9jYWI4cOVKkl0rdunVp3rw5ISEhuLi4FHl8lwaevN66Pp8fPEeu9R8BhaejHdPaNqKDryuboiv8Mh5adHS0eu1KkPT888/j6upq9vjffvsNj6wUQpNiSApsRUrWHx+mnawM/LlLKF0aeKrbCn6QdXBwqIArEOVNxibLSaAkHjklT7px48YEBAQ81nMRoqI1dm9MsFuw+n1VWfgtRFWk1+uJj48nNjaWw4cPF7kb7e/vT3h4OE2bNqV27dqlPl8DzX3axx/Axi+Ijj164+ZgS7iXC1ZaTZVoSKrT6VizZo3JtuHDh+PnZ379VVpaGrt27QLglaf6EtiwEacS0/l+6U/gUpf/dg6kYYEgCSA7O1v9Wt7fqg4ZmywjgZJ4pM6dO8f169cBGDBgwGM+GyGEEFWdXq/nypUrxMbGqu0mCgoMDKRZs2Y0adKkzGtoEhIS0ACdGvoSGehVTmf86Bw7dszk+8jISMLCwsweazAY1HUfQUFBBAUFAeCRfxfPe8lAXfzq1SvyOGV9khDVkQRK4pHJz89n6dKlAPTs2bPM3ZGFEEIIMM6UXLp0Sa1WV1hQUBDNmjWjcePGD5UOdvHiRQB8fHwe+Dkel7y8PDZt2qR+36xZM7p06VLs8XFxcWr58IIL6KOj/8gvNDfroKz3kvVJojqSQEmUSKc3EJ2UQdr9XJOUgwehrEsC6NChQ3mdohBCiBogLy+PixcvEhsba/LhXdG0aVPCwsIIDg422+z3Qdy8eROomoHSvn371K89PDwYMmRIselVOTk5rFixAoA+ffqoNzL1ej2HDx8ucS2xEih5enoWe4wQVZUESqJYe68mM/vwBZILLOT0dLRlWtsgk4WclsjIyODXX38FYOzYsSadooUQQghzcnNzOX/+PLGxsSb9VBRhYWGEhYURFBSEjY1Nub72/fv31a+LK3xQWd29e5c9e/ao30+aNKnEcVcZn+3s7EyazxbsJ1YcJfXO3d39QU9XiEpLPq0Ks/YqZVELSc7KZdbuWGZ1Cy1TsLRx40YAGjRoQHBwcClHCyGEqKmys7M5d+4cMTExnDt3rsj+Fi1aEBoaSsOGDSv0pltCQgIA9vb2VWqhu8Fg4IcfflC/nzZtWolpcYmJiRw8eBCgSF+lU6dOAdCxY8di1yIpM0rSQ0lURxIoiSJ0egOzD18o8ZjZhy/Syd/DojS8y5cvc/78eQAGDx5cLucohBCi+sjKyuLs2bOcPn2aS5cuFdnfqlUrQkNDCQgIwMrK6pGcU2KisXRyccUPKqsdO3aQkpICQL9+/fDw8Cj2WIPBwE8//QRA8+bN8ff3V/fp9XqOHz8OQEhICEeOHDH7HNJsVlRnEiiJIqKTMkzS7cxJzsohOimDCB9Xs/t1Oj17o69xIzmDnVt+xs8JunZ5Qm3kJ4QQoma7e/cuZ86cITo62myKV9u2bQkNDaV+/fqPpd+eUqG1bt26j/y1H1RMTAy//fYbAHXq1DFJozPn+PHj3L59G4C+ffua7Lt8+bL6dUnrjzIyMgAp5iCqJwmURBFp9y3r1HwoOpa6mkZ4eXmZpCWs3hPHK7M3cz05U93mbAthXateaVUhHta5y9dJTEl7ZK/n7eFG40DzPVKEeNwyMzOJi4vj1KlTaqEEhZWVlRoc+fn5PfZ0t5iYGKDqFHK4fv06K1euVL8fOXJkicdnZWWxfv16wJjtUTjQOXnyJADdunUr8XeRnJwMyIxSVSNjk2UkUBJFuDnYWnRc3LHD3IraBhin5Rs2bMjpRB3PfbQZQ6Fj7+TC6P9bjbW1NcO6hpTzGQtROZ27fJ3wvpPIzct/ZK9pa2NN9JbvLR6Q3nrrLTZs2MDly5exs7OjVatWfPjhh0RERFTsiYoaIz09ndjYWE6ePElSUpLJPjs7O9q0aUNoaCh169Z97MGRQqfTqV97eVX+m3zp6el8++236veNGzcuNcDbvHkzYJx5KvzvXafTqZUFmzVrVuLzpKamAjKjVJXI2GQ5CZREEeFeLng62paYfldba6CRkzWpvzfkjouLIyY2jk8PUCRIAuM2DfDq7M0M6dwEK6tHn0YhxKOWmJL2SAcigNy8fBJT0iwejH777TemTJlCp06dyM/PZ+bMmTz55JOcOXNGep2Jh7J//35OnjxJWprpXWsnJyd1zVHhjITKQlnjA5R7Nb3ylp2dzZw5c0y2FU6jK+zatWtqIDRq1KgivwOlf5RGo8HDw4O8vLxSz0MCpapDxibLSaAkirDSapjWNshs1TvFa13C6NKgO9nZ2Vy9epUrV66wfm80mbnFd+g2APHJmeyNvkb3iIDyP3EhRJnt3bvX5PvFixfj5+fHvn37ePLJJx/TWYmqxmAwkJycTGxsLMePH6dhw4bs3r0bvV4PGMtrt2zZktDQ0BKLC1QWSsW7oKCgx3wmJdPr9SxevJicnBx1W0RERInrgZXHALRv3x5vb+8ixyhFHCIjIy0+FwmURHmqLGOTBErCrC4NPJnVLdRMHyU7prVtpJYGt7e3p0mTJjRp0oQ0a19mR60u9blvpd6psPMWQjwcZWF3VfgwKx4vg8FAQkICsbGxHD16VO07pBRecHd3V0t516lT53GeapkpgVKDBg0e85kUz2AwsGnTJrXohKK04ObgwYPk5uYWe2x+fj5nzpwBSk+7U54HkP6IokI9rrFJ/qpFsbo08KSTvwfRSRmk3c/FzcGWcC+XYkuC13WvbdHzWnqcEOLR0ul0TJ8+nZYtW9KuXbvHfTqiEjIYDNy4cYPY2FiOHDlSJCWrbt26NGvWjOTkZKZMmVLp09aKowQKlbmQw6FDhzh69CgAtWvX5s6dO3To0KHEtKTMzEy2bt0KwIgRI7Czs1P36fQGopMyOHX+Eun2rnjqs0oNcJUeSkJUpMc5NkmgJEpkpdUUWwK8sC7h9fHzdDapdleYn6czXcLrl9PZCSHK04QJEzh37hx79ux53KciKhG9Xk98fDyxsbEcOnSoyH5/f3/Cw8MJCQnBycmJvLw8Nm3a9BjOtHwYDAbS09MBzKalVQbnzp1TizG0adNG7XHUtWvXEh/3888/A+Dn50dIyB+FlfZeTTbNIKkbQW2tgb1Xk0tsLq8ESo+qt5WomR7n2CSBkig3VlZa/jGmLS98vqPYY4aG2WG+3IMQ4nGaMGECO3bs4Ndff6306zJExdPr9Vy5ckVNqyssMDCQZs2a0bRp02q3NuXOnT/Sw52cnB7jmZiXkJDA0qVLAWjdujWnT58GjGl0Dg4OxT7uwoULapGGp556Si3gsPdqstk1yXf0MGt3LLO6hdLB19XscyrNZiVVV1SUxz02SaAkypU29SwjQ2HnNVtS7v6Ru1zXrRZdfO7hYUhmxYoVjBw58rE0EBRCmNLr9Tz77LNs3ryZ7du3m9xlFtWDTqdj7+FobiWlUtfLnS5tw83OAOTn53P58mViY2M5ceJEkf3BwcGEhYXRpEkT7O3tH8GZPx7K+iQ3N7cHrsinNF2/lXqHuu616RJev1yqvd65c4dvvvkGgPr169OgQQM1kO3QoUOxj8vLy1MLOHTv3l0t9qDTG5h9+EIxjzJe++zDF2k7qJXZI5QZpZIa0grxICrL2CSBkig3169f5/r164R6wtz3pnH8cprJIHH16hUWLVrE2bNnWbduHUOHDq2UZWGFqEkmTJjAunXrWLZsGS4uLly9ehUwLsSvjHfTRdms3ryHV979kusJyeo2Px9PPvvndIb160peXh4XL14kNjZWLRddUEhICKGhoQQHB5usZ6nOEhMTAWjSpEmRfco6nrT7ubjamA98zDVd9/N05rNp/R6qj2Bubi7z5s0DjIWURo8ezQcffABAv379sLUtvgfivn371K87d+6sfh2dlFFiKxCA5KwcYotJqVcCJWklIMpbZRmbJFAS5eaXX34BjOVGXVyc6R5h+sbZsGFDRo8ezbJlyzh16hT29vb069dPgiUhHiPlLvPAgQNNtn/66ae88sorj+OURDlZvXkPw1/+J4ZC2c43EpMZ/tI/+cszfXG2yinyuGbNmhEaGkpQUFCVLcbwMK5cuQIULeRQeB2PLQamusD++BS6NqwLGIOk4bOWF0kwv5GcyfBZy1k5a+QDBUsGg4Hly5eraYFTp04lLi5O3d+6detiH5uamqqu7XjmmWdMqtOl3S85SFLczjZ/nJJ6V93SL8XjV1nGJgmURLmIj4/n5s2bADzxxBPFHtekSROGDRvG6tWrOXToELa2tvTs2fNRnaYQj5S3hxu2NtaPvPu5t0fxPVQKMxT+FC2qBZ1OxyvvflkkSALUbfNX7eLV4R3QajVEREQQEhJCw4YNa3yZ50uXLgGmgVJx63gA/rPvLBorazr5ufPK7M0V0nR927Zt6vqiyZMn4+joyPr16wEYOnRosb8zg8HAypUrAWjcuDENGzY02e/mUPwsVEF17G25a2b73bvGrRIoVS0yNlmuZr8binKjzCZ16NCh1CnR8PBwcnNz2bBhA/v27cPe3t4kFUCI6qJxoB/RW74nMSXtkb2mt4ebxZ3PRfW193C0SbqdOZlZOTRu3pbRQ/tJ1bLfFewL5O7uDpS2jsdo9uGL5KXdLbHq64M2XT927Bj79+8HjCW969WrR1RUFGCsNhceHl7sY2NiYtQ1V4XvzAOEe7ng6WhbYvqdp6MdoZ7OxJvZl5xs/BurVauWpZcjKgEZmywngZJ4aPHx8dy6dQsoeTapoNatW5Obm8vWrVvZvn07tra2tG3btiJPU4jHonGgH8EB9dTvJdVUPAq3klItOk5rYy9BUgFJSUnq18rPxdJ1PMdv3bboNcrSdP3y5cvqzFHPnj0JDQ0lJyeHbdu2ATB8+PBiCyPl5OSwatUqwLiGqXbtoj0MrbQaprUNKna2DGBa20bF9k9UAiWZUap6ZGyyjJQdEw9t48aNAHTs2LFMd5U6duyo9nzYtGkTJ0+erJDzE0KImqaul3u5HldTKLMvBWdpLF3Hk5NfdL2XOZY2XU9JSWHhwoUANG/eXM28UGaTateubbbghGLHDmOrDgcHhxJvRHZp4MmsbqF4Opqm4Xk62jGrW2iJfZSUhsMyoySqK5lREg/l2rVraoUgS2eTCurevTs5OTkcPHiQtWvXYmtrK+WJhRDiIXVpG46fjyc3EpPNrlMCcHa0w16Tg8FgkLvJv1PW2tar98eddkvX8STGHsLZFjKLias0WN50PSsri9mzZwPGtVKDBw9Go9Fw7949tTBDwV5Ihd26dYvDhw8DMHbs2FLbcXRp4Eknfw+1op+bgy3hXi7FziQVJjNKorqSGSXxUJTZpM6dOz/QG6VGo6Fv3760bNkSgOXLl3PhQsm54EIIIUpmZWXFZ/+cDkBxMVC/9kFs2byZefPmkZb26NYqVGYxMTGAaSEHZR1P8QzY5WdTJyediZ290aB0ICrq02n9Si3kkJ+fz/fffw8Yf48TJkxQ0wB3796tnl9gYKD5szEYWLZsGQARERH4+Vm2LsRKqyHCx5UegV5E+LiWGiTpdDr165JKkwtRlUmgJB7Y1atX1XzuTp06PfDzaDQaBg4cqM4kLV68WK2XL4QQ4sEM69eVlV+9Sz1v09QpZ0c7RkaGMaSX8X371q1bfPHFFxw4cAC9Xv84TrVSMBgMajEHb29vdbuyjqf4B0Kj1Av07dOHz/72IitnjaSep2l7DGc7+HHm4FJLgxsMBtasWUNKSgoAL7/8Mg4ODgCkp6ers0SDBg0q9jmOHTtGZqaxqESfPn1KfL2Hcf/+ffVrmZEU1VWFBkp79uxh0KBB+Pr6otFoWLt2bamP2bVrF61atcLOzo6goCAWLFhQkacoHsKGDRsAY8rdw067a7Vann76afUO2YIFC9QUCCGEKE81aWwa1q8rV/Yt5deln7Dks7/x+vhevDq8A6ENPDl//jxDhw5Vg4ItW7YwZ84c9UN6TVNwVs3e3t5kX3HreACapMTx1zFD6NixIxqNhmFdQ7iy5BV+/Xgi/ze2JRNbwKvtoXb29VLPYffu3cTGGgsrPPvss7i5/VFOWVlz1KhRI3x9fc0+/t69e+rYPGTIEDXIqghKDyUhqrMKDZTu3btHixYt1Dzb0ly+fJkBAwYQGRnJiRMnePXVV5k8eTJbtmypyNMUD+DKlSvqYPows0kFWVlZMXbsWHUAmDdvHklJScZO6IkZAEQnZqDTV47a+kKIqqmmjU1WVlZ07xDBmME9eXXqs2gLpFStXbuWsWPH0q9fP8BYxWz27Nns27evxs0uKetti0tV69LAk3n9m9OXBJomxRKWeAqARra6Io+xstLSPSKAV8f0JNAVtBrjTE/BdLXCoqOj1dS6oUOH0qBBA3VfcnIyp0+fBlB/V+YorTrc3d1p0aJFKVf8cLKysgBwdXWt0NcR4nGq0GIO/fv3p3///hYfP2fOHAIDA/noo48ACAkJYd++fXzyySf07du3ok5TlJHBYFDvWHXp0qVc71hZW1szceJE5s6dS2pqKv/+YQVXvJqSr7Fiqgu8s/M0Lo52TGsbVGIlHiGEKE5NHpvq1auHr68vN2/exNbWltzcXBYuXMhLL71E48aNWbVqFTdu3GDHjh0cO3aMUaNGmaShVWdKxbvCTVkV165dU9cOeQGRkZHcvn2b9PR0UlNT1b5LBdWqVQt7e3uys7MBOHnyJK1atSpyXHx8PKtXrwaMWRqFg5zNmzcDxmp8Hh4eZs/v6tWr6hqrkSNHlikdTqc3lLmQgxIoeXrKWCyqr0pV9W7//v306tXLZFvfvn159dVXi31MTk4OOTl/lORU8nLz8vLUspXVlXJ9j/o6r169yu3bt9FqtbRt27bcX1+j0fDss8/yt28WcsHDmM9t83uvcxsMZGTl8P92xzDziSZ09Dc/YFRVj+t3+jhUt2vNz/+jw3nBjuLnb6SRmGaup33F8HZzIrie5d3PH0Z+fn6R3191+X0WVN3Gpj59+rBw4ULy8/PRarXcvn2bLVu20Lt3byZMmMDJkyfZvHkzGRkZzJ07ly5dutChQweL+y1V1X/bFy9eRKvV4unpaXLuBoOBAwcOsHv3brRaLVZWVjzzzDO4u7uzbds2tFotR44coUePHmaft1OnTuzatQuA7du3F2kQm56ezoIFC9BqtQQHB9OlSxeT17916xZXrlxBq9XSrVs3sz9XnU7HkiVL0Gq1tGvXjjp16lj8898fn8LcY5dIzfrjeHdHG15o1VAdY839Tu/cuYNWq8XV1bXK/a5LUlX/fotT08am8h6XNAZDcYVDy5dGo2HNmjUMHTq02GMaN27MpEmTmDlzprpt06ZNDBgwgKysLLMzF7NmzeLdd98tsn3JkiVSrlII8cg4Ozvj5uZGUFCQ+t5z/kYq4c/NITe/+HSb8mZrbUX0d1MJrmdZf5z//ve/fPfdd+qawKCgIN5++21GjBhR7GOysrK4cOECaWlpagBQcN/YsWPJyMjA2dm5mGeoPGRsEkJUZzVlbKqocalSzSg9iJkzZzJjxgz1+8zMTPz9/YmMjDQ7DV6d5OXlsW3bNnr37o2Njc0jec0rV66oZUf/8pe/YGdnVyGvE52YwTs7T6vf22DgeZdMvs1wJq9A4dX3ejQj3NulQs7hcXgcv9PHpbpda3x8PHfu3DHZlph275EORAC5+ToS0+5ZPBj5+/vz73//m5CQEAwGA/PmzWPcuHEEBgbSpk2bEh/boEED/P39TbalpqY+8LlXJ5V9bLp586bazLRDhw4cOHAAgMmTJ6upXQaDgdOnT6ttIMDYKLxz585YWxf/8aGy/9s2N4NSx8Ear2uncM9K5a9//SsajYb4+HgWL16sHtOnTx9atmypprQp13n69Gn0ej1Dhw6ladOmZl/z/fffV7/28/Nj/Pjx6HQ6fvzxR27dugXAn/70pyKNWwuOuX/+85/NBtmZmZl89dVXAAwbNozGjRtb9HPQ6Q1MXn/Y5OdQmLujLfMHtUGvyy/yO92yZQvHjx83afFRHVT2v9+yqmljU3mPS5UqUPLx8VEXUyoSExNxdnYudh2MnZ2d2Q/rNjY21eIP3BKP6loNBgMbN25Er9fTvXt3nJycKuy10vP05JrpRJGHxmR7ep6+Wv6e5e+36in4wbEylMq19BzGjRtn8v2XX37JokWL2LdvH23bti3xsdbW1kV+d9Xhd1lYdRybGjRogLe3N7du3cJgMODv78/Vq1f59ttveeONN9Rzb9WqFcHBwaxbt46LFy/y22+/cezYMcaOHVtqf57Kcq0F7b2azLv7zv3+3R//RhLv60j0CCUi/Tw2Njbs3buXX3/9FTBex3PPPWfSW6mgyMhItm3bxr59+4qk1Snat2/P/v37AeNap6ysLHbv3s2NGzcAeOmll4oURTAYDPz888/o9Xq6du2Ki4v5m4LKuFy/fn1CQ0Mt/rcfk5DOrax8iu/6BLey8jhzO4swd2MAV/B3mpaWhl6vx8nJqdL9nstDZfz7fRA1bWwq73GpUvVR6tixo1r+UrFt2zY6duz4mM5IFHTp0iUyMozV59q3b1+hr2VpJ3RLjxNClC4/P5+5c+dy//59unTp8rhPp9KormOTUtBi//79al8enU7H6tWrTdYy1K5dm3HjxjFs2DDA2D/n22+/ZevWrVVqHYdOb2D24ZIbml9wC+Lb779Xg6TQ0FBee+21YoMkQA2OkpKSuH37ttljCs80rVmzhuPHjwPGD4ReXl5FHnPmzBnu3jWuISmuuuy5c+e4fPkyYKyUV5YPwmn3cx/qOKXyraSSior2OMemCg2U7t69y4kTJzhx4gRgLLF64sQJrl27BhhTEyZMmKAeP3XqVC5dusSbb77JmTNn+Oqrr1i+fDl/+ctfKvI0hQWUO1tgvHtWuMdEeSu9Ezp4OtoR7lV90u6EeFwOHjyIo6Mj9vb2vPbaayxatKjUtLuqTMYmI39/fzUAOHbsGC+88AJg/PB98OBBk2M1Gg3h4eG89tprNGnSBDAGWB9++KH6c6vsopMySM4qITjQaLhr0BKTbExTGjhwIMOHDy81xdze3l79mRw7dszsMYVn35Sm6k8++SRBQUWb2er1elasWAEYU/7MnUNeXh5Lly4FoEePHtSpU6fE8yzsYW9IKulchdMFhSgvlWFsqtBA6ciRI7Rs2VLNXZ0xYwYtW7bkH//4B2Cs5FLwDTYwMJCNGzeybds2WrRowUcffcT8+fOrXPnV6ujixYvq4riKnk0CCzqhA9PaNiq1fKkQonQtWrTg0KFD7Ny5k2eeeYapU6dy5MiRx31aFUbGpj8os0pRUVG4uLgwePBgwLj+xFwA5OTkxOjRo9UF1bm5uXz//ff88ssv5OZaNkPxuFg6g6K3d2Tq1Km0bt3a4hkaZXZx3759ZnslabVamjVrZrItMDCw2BSiU6dOqbN6xR2j9FzSaDQP1M+wvG5IyoySqCiVYWyq0DVK3bt3p6SieuY6m3fv3l2djhaVg8FgYN26dYDxrlVFFXAoTOmEPvvwBTKy/iiz6+lox7S2jaSPkhDlxN7eXv0Q17VrV44fP85HH32k3q2ubmRs+kP9+vXx9vYmMTGRqKgoevXqxZUrVzh16hTff/89r732mtn1qKGhobzxxhts2rSJmJgYDh06xJEjRxg/fnypa5ceF0tnUJ4ZNhRv77KNL/Xr11e/Pn/+vNmiDg0bNlSbxoJxrZLBYCgSjOXn56tj7qBBg8wWzkhJSeG3334znu8zz1hcur0g5YbkrN2xYDCAmaBQuSGpLxT7Ffz3U569FIUoqDKMTZVqjZKonC5cuKDmSbdr1+6RvnaXBp4sHtaB93oY/6G816MZi4e1lyBJiApkMBhMegCJ6u3JJ58E4LfffiMrK4tBgwap6VSLFi1Cr9ebfZyjoyPDhw9n9OjRgDFdbOHChfzyyy+P5Lx1egMnEtLZeTmJEwnp6PQldzspdQbFYMDT0Y6W9cren0+j0dC7d28A9uzZU2R/bm4u27dvNz1/nU5NwSvo6NGj6tcRERFmTtOgpuU1bdqUwMDAMp+vQrkhaasz/ffu6WjHrG6hxY61SgNdMM6WCfEoPI6xSf66RYkKzib17Nnzkc0mFWSl1aglwMO9S+8WLoSw3Msvv8ymTZs4e/YsBw8e5OWXX+bQoUOMHz/+cZ+aeETq16+vFhOIiorC2tqa559/HjAWKCj8Ab+wJk2a8Ne//pUWLVoAcPLkScBYAKii7L2azLjVB3ht60ne2xvHa1tPMm71AfZeTS72MaYp3YWCKoMBNA+X0q0ENbdu3VILH4ExgFy2bBlZWVlFHrNz506T73Nycti8eTMAI0aMMBuEnD59mqSkJAAGDBjwQOdaUCsPR9rHH6D5rRO8/URTPurTotQbkuauRYjyVFnGJgmURInOnz/PvXv3gEc/mySEqHjJyclMnjyZ8PBw+vfvz/Hjx1m5cqVa4UzUDIVnlerUqcPYsWMBY9GGuLi4Eh9vb2/P0KFDTT7ELF++nDVr1nD//v1yPde9V5OZtTu2SGGG5KxcZu2OLTFYalHHjk4517HNN70rbafLYZBT9kNlKzg6OhIcHAyYFnXYunWrWpmua9euJo+Jj483qZSn9LNydHQkJCSkyGtkZ2ezevVqwLi+rDzadNy4cQMNEGAPPRt6E+HjWmqwqARKFV3YSdRclWVskkBJFMtgMLB27VoAevXqha2tlOIWoiy83ZywtS772oGHYWtthbeb5R+eVqxYwc2bN8nNzSUtLY39+/dLkFQDNWjQAE9PY5Cg9PsJDg5WS/EuX77coqaNjRo1Mmm0e+rUKT744APOnj1bLudpSYnv2Ycvmk3Du3DhAp988gnWNy/QPv4ALzeqxTtdQmiVfJp28QewuXXxoc9PKeqwZ88e9Ho9hw4fZsvJsyTV8qJl/6doZaZilxIcZWVlsWvXLsDYONZcIQlldq9WrVrlVv1L6eUUFhZm8WOUG6jK34yoWmRsslylajgrKpdz586pdwJLazwphCiqsZ870d+9RGLa3Uf2mt5uTjT2s6zzuRAFDRgwgAULFrBv3z46deqEg4MD3bt359KlS9y4cYN58+YxY8aMUm+aKfvHjh3L0qVL0el0LFu2jNDQUAYMGPBQVdJKLfENJGflEJ2UQYSPK2BMfdu5c6da/KBWrVpMnDgRT09PDAYDe+8a+wGlpKRw8+ZNfH19H/j8AgIC1K/nbtnLulvZ5NaNAODMmdssuXYPPzc/aqddV487dOgQPXr0YO/evQB4eHjQsGHDIs998+ZNdf3S2LFjy21t0JkzZ4CiJcxLoswoeXiUfT2XePxkbLKcBErCrIKzSb1795bZJCEeUGM/d4LruanfV4bO6EKY06BBAzw8PEhJSWH//v306NEDrVbL2LFj+d///kdOTg5r165lxIgRFv0d169fnzfffJOdO3dy8OBBYmNjiY2NZfjw4WWavSiorE1SMzIyWLJkibqmJyIigieffBIbGxvgj15Aiq1bt/Lss88+0LmB8d93z549+Wn/SfYkGcDKdF1vclYuyS5BhGZn09A6V227cfDgQXVmaciQIUV+vnq9Xq301apVq4cK5goyGAzqz6ZevXoWP04JlMoj9U88HjI2WUZS74RZZ8+eVavaVOfGk0IIIf6gFAfYu3evmlHg6OjI5MmTAYiLiytTHxNbW1v69evHpEmT1GJAK1euZOnSpWo11bIoS5PUc+fO8emnn6qBwLBhwxgyZIgaJAEkJCQA4OrqChgbwSrbHlRQcDAX3H8vGlHMh8+L7kFk/B4kAfz666+AMVg1N7Nz9OhR9eelVNcrD+np6erXtWvXtvhxSuqd9FAS1Z0ESqIIg8HAmjVrAGNHcJlNEkKImiEgIEBNp1LWKoFxtkEJojZt2sT169fNPr449evX57XXXlMbo547d46PPvrIpLGqJSxrkmpLwslD6gxM7dq1mT59OuHh4UWOVYKikJAQWrduDcC2bdssPp/C8vPz+WrVRnKt7YsNkgByrO3JsHctsl0pqlHQ3bt32bRpEwBPPfVUuRZQUNYn+fv7l+lxSlU/CZREdSeBkijizJkzaod1mU0SQoiaxdysEkDr1q3VlLlvv/1WnVWwlI2NDb1792by5MnqB+w1a9bw448/qilopSmxxPfvGqZe4OCB/eo5//nPf8bd3fzaCKWPkY+Pj1qR7tKlSyQmJlp6WSqDwcCqVatIzbKsz0uulS0NGjRQv7ezs1PLtBekBEleXl5mg72HoQS8SrU+SyUnGysLKv22hKiuJFASJgwGg1p6tG/fviYpCkIIIaq/gIAANbBQ1s2AcQ3DkCFDcHBwAGDx4sXFNqMtSb169fjLX/6iVtS7dOkSn3zyCcePH7dodklpkupiY/oRpo6tltDE09glGPs3DR8+nIEDB2JtXfxybKXXk4+PD87OzrRs2RKg1N5R5uzatYszZ85gq7NsHZWtLtek4WxOTo4agCiuXLmilmYfPnx4ua8jiY6OBsq2PgmMhS9AZpRE9SeBkjARFxdHfn4+gJqGIIQQomZRZpX27NljMqtkY2Ojrle6deuWuramrKytrenRowcvvvgizs7OAPz88898//33JutmitOlgScveebR/NYJhrrpGeWaQ7OzO/HISsHFxYU//elPpRaMUDIn4I/qbd26dQOMpcQLBy0lOXXqFHv27AFgYr/uFqQH2uGSnV5ke1RUlPq1Tqdj0aJFAHTu3LncS3Hr9Xq1KMODFoeQQElUdxIoCVXB2aR+/frJbJIQQtRQAQEBuLkZK2IVnFUCcHNzY9SoUQDs27fvoXok+fj48Oc//5nIyEjA2ID1s88+4/Dhw6XOLl26eAHX7HRyzhzl1vH9aDC2spg+fbp67iVRijwAaqltFxcXWrRoAcCOHTssuobr16+r63q7du1KRIsWf6QHFnMN09o2omFgYJHtJ06cUAPTqKgodcZOCeDKU8Hrf9B1T5J6J6o7CZSEKjY2Fp1OB8hskhBC1GQajYaBAwcCxlklpQqqomnTpmphhmXLlnH79u0Hfi0rKyu6du3KSy+9pAY4mzZtYt68eaSlpRX7OKUQg7JWasSIETz55JMlptqZe3zhdT9KUHL27Fk1xawkP/74I2AsCNG9e3fgj/RAR3Qmx3o62jGrWyhP1PdQ10FZWZk2/jx27Bjp6ens3LkTgNGjR1fIjcubN28Cxt9lWeTl5alfW/qzFqKqkr9wARin4JXZpP79+8ubnxDl5HrmPdLu55V+YDlxc7DBz1nu8oqHFxAQQJ06dbh9+zYHDhxQgwBFz549uXTpEgkJCcyfP59XX331oT7Qe3l5MW3aNA4cOMC2bdu4desWX3zxBX379qVdu3bqrE9+fr5a4ACMpb0nTJhAnTp1yvR6SqBQeH1OnTp1aNasGadPn2bnzp2MHDnS7OMLBo9ubm4MGzbMZA1RlwaeeHX0Z86aX8i1sqV98zDG9WqPlVbD2bNn1bQ3nU5H165d1dS97du3c+7cOcD4O2jSpEmZrstS165dU1+jLJTzFlWbjE2WkU/DAjDOJilT/K1atXrMZyNE9XA98x7P/3yUfL3l5Y8flrVWw7eDWz/wgDRz5kzef/99Jk2axHfffVfOZyeqEmVWadGiRezevZsOHTqYpGhptVrGjx/Phx9+SFZWFuvXr+epp556qNfUarV06tSJJk2asHz5cpKSktiyZQvHjh1j5MiRaLVaFi5cqJan1mg0TJ8+vcisjCViYmIAY/pfYZGRkZw+fZq4uDjS0tKKpPLpdDqWLl2Kt7c3AM8995zZG4zBQUG4/r4WySHDAyutRq2OB+Ds7ExmZmaR1DcliBkyZEiZr8tSJ0+eBMpeyEGZwZMGpVWXjE2Wk9Q7gV6vV9+0y5K2IIQoWdr9vEc6EAHk6w0PfJdw165dLFy4kMaNG5fzWYmqKjAwUG3GevDgwSL7a9WqxXPPPQcYK6gdO3asXF7X3d2dqVOn0q9fP8BYjnr27Nl88cUXapAExrS5BwmS9Hq9WsxBCXYKcnNzIyQkBDD+uyjIYDCwfv16NXVu8uTJxa7V0Wq16r+n+Ph4wPhzUtLXnnjiCQBOnz5NUFCQyWN79uyp/uzLW8H0OXOBYkmUGaXyLi4hHh0ZmywngZJQ76oBamlUIUTNkp6ezsSJE/nyyy9xcXF53KcjKgmNRsOgQYMA44eVwmuVwNisVAloNmzYoK79KY/Xbt++PS+//LJJdTWtVqsGFXXr1n2g5y64pqq4QgY9evQAjIFNweOjoqLU2Rj4o2JecZQeRXfu3CEzM1Mt/DBgwAA1GLt582aRvoVlTYkri1u3bgHGn3FZb44qgVJp1y1EeXjcY5MESjVcwbVJAwYMkNkkIWqo5557jl69ej106pSofgIDA9UPKOZmlQDatWunFgVYsGBBub12amoqixYtMlkXo9fruXDhAvDgH9aVYM7Pz6/YYzw8PNQ72Lt37waMLTSUHktKcFiags1uC66tatmyJU5OTmoQWLggRsFS4eXtxo0bAERERJT5sUrqndxQEY/C4x6bJFCq4ZRmcyCzSULUVHPnziU6OprPP//8cZ+KqIQsmVXSaDQ89dRTJsUcLGkeW5Lo6Gi+/PJL7ty5A8CYMWN49dVX8ff3V49Zu3atOjtSFkqg1KhRoxKP69mzJ2Bcz3Pu3DmWL18OQIcOHSwOMgrOWCml1IcNG6amDHbs2BGgSE+quLg47t69a9FrlNXly5cBqF+/fpkfqwSt0kNJVLTKMDZJoFSD6fV61q5dC8DAgQMfKM9bCFG1nT9/npkzZ7Jo0SLpiSKK1bBhQ7Ux7KFDh8weY2trywsvvKB+/9tvvz3Qa+Xl5fHzzz+r2Q5eXl68+uqrNG7cGBcXF4YNG6Yee+/ePebOncuOHTvUZumWuHjxImB+fVJBXl5eajC1dOlSwPiz6N27t8WvVTi1z8bGhmbNmqnfKzNxBRvgKg4fPmzx65TF+fPngbIXcgDU4E3eL0RFqixjkwRKNdipU6fUrx9k+l0IUfUdOHCAtLQ0nnjiCaytrbG2tubw4cMsWLAAa2vrMn34FNWXRqNh8ODBgHHmIycnx+xxHh4eDB06FDA2o1VS5CyVkpLCZ599xvHjxwHo3LkzL7zwgkmaV3JyMmAsC96wYUP1tT755BM1paw0yiyUJYUMCjZ7dXR0ZNSoUWqpcksUDpQ8PT1NKsYV/BDYsWNHk1mePXv2lPu/wYJpjAXTAi2l9JaSGSVRkSrL2CSBUg2l1+tZt24dAIMGDZLZJCFqqIEDB3Lw4EGioqLU/8LCwhg8eDBRUVGyblGoCs4qFbdWCUwbmC5evJj09HSLnv/kyZPMnj1bXQMzbtw4evXqVWR8UqrNNW3alPHjx6szTFlZWcyfP5+tW7eaVHUrrGCgUFpVOb1ez44dO9Tvg4KCsLW1teh6FHZ2dibf37x50+Qctm3bZvJ6nTt3Njn+9OnTZXq90ij9o5ydncsU8CmUQFUCJVGRKsvYJIFSNaLTGziRkM7Oy0mcSEhHV0Lpx4IVe1q0aPEoTk8IUQnVqVOHdu3amfzn6OiIm5sb7dq1e9ynJyqRgmuVCs4qFTf2KLMV3333XYl3f3Nzc1mzZo2aCu7j48Nf/vKXIuWyFUqZbW9vbzQaDeHh4bz22mtqY9b9+/fz4Ycfqr2IClMCLUdHx1J7AW3ZsoWrV6+q3586dUpdM2UpJfAD1Ka4yhh848YNdfYMjAFo4XVTv/zyy0Ov9ypImXUrmP5XFsrvXVLvREWqLGOTBErVxN6ryYxbfYDXtp7kvb1xvLb1JONWH2Dv1eQix+p0On7++WcABg8eLLNJQgghLNKoUSNq164NGNcqmRt7Jq83rqsZM2YMYCyLXbDaW0FJSUl8+umnaip4ly5dmDJlijpzZc65c+cA4/ohhZOTE6NHj2bEiBGAMfj6/vvv+eWXX4qs/VEKOYSGhpZ4rYcPH1bXY40ePVotIlHWtVcF+zAps0Vbt25Fp9OxZMkSAFq3bq0ek5aWRteuXdXvc3Nziw36HoRSUOJB1icVJDNKoiaQQKka2B+fwqzdsSRnmQ4GyVm5zNodWyRYKjib1Lx580dyjkLURG4ONlhrH233emutBjcHm9IPLMGhQ4ceaedzUXUUXKu0/MAps2NPapYx7S36djbPPvssAMePH+fEiRPqMQaDgePHj/P1119z//59AMaPH0+PHj1KTAfT6XTq1+YanoaGhvLGG28QFhYGGP+W//vf/6pV3gCuX78OlNyD6cKFC2pw16dPH5o0aULfvn0B46xPwVmikty+fdukCa+vr6/69bZt29QUvF69eqkzPGfPni1ShXbPnj0WvV5pDAaDuj7rQQIlvV6vfl04pVBUHTI2WU6Sz6uBucculbh/9uGLdPL3wEqrQafTsX79egCGDBkis0lCVCA/51p8O7j1A3cjfxBuDjb4OUtKjKg4jRo1opaTEwfqmE+NU8w9dpkfhnWkd+/ebNu2jXXr1uHj44Obmxvr169X1974+voyevRodaaqJKmpqerXBUuRF+To6Mjw4cMJDw9n2bJl6PV6Fi5cSKtWrejTpw+xsbFA8YUckpKSWLx4MQCtWrWiQ4cOgDGw8PX15ebNm/z2229ERkaWer5bt24FjE1y9Xo9eXl5dOrUiaioKHWd19NPP429vT1hYWGcPn2aqKgonnjiCerWrasGNZcuXSI9Pb3UNVWlycjIUL8uadauOAVLw5eWtigqLxmbLCeBUjVgvHtX/BtWclYO0UkZRPi4mtzRk9kkISqen3Mt6tX+Y32BfLgQVZ1Go6Fpt778Epta4nGpWblEJ2XQsWNHLl++zIULF/jmm2+ws7NT17l07dqVbt26WVxUQFlfFBgYWOqxTZo04a9//SubN2/m5MmTHDt2zGR2x9yM1N27d/n6668BY2D05JNPmvyb7devH9999x379+9XA6iSzvXMmTOAsRpgUlIS2dnZtG7dWm0m6+Xlpc5+KRX87t+/z927d+nSpYvatwmMM1nKrNaDUgo5+Pr6PtB7kaUzaaLyk7HJMo8k9W727NkEBARgb29P+/bti+3BAMaO3hqNxuS/wqU1Rdml3c9Fp9OxYcMGAIYOHVqmajdlKRQhhBCVnYxLD8fBrWiQYU5C5l00Gg1PP/20+kFMCZImTJhAZGRkmcYiJVBq0KCBRcfb29szdOhQxo8fX2Rf4QITeXl5fPvtt4CxJ9T48eOLZF34+/urM1ElVf4D1NS9iIgINSjLzs42qQLYpEkT9edia2uLn58fYFyH1bhxY5PnO3DggNleS2WhpB0Wfm5LKWmSDzIbJURVVOGB0k8//cSMGTP45z//ybFjx2jRogV9+/YlKSmp2Mc4Oztz69Yt9b+CFWfEg3FzsDWprBMeHm7xY8tSKEIIISo7GZcenrujZetTtqxZxZo1a1i+fLlJ5bYnnnjColmhwpRGsQULOViiUaNGvPXWWwQEBKjbPvjgA7WwgcFgYOXKlWoQM3Xq1GKD4f79+wPGwKU48fHxagGGyMhItULcvXv3WLRokXrcvn37TH4uytqko0ePYmVlpRZ/UAK2gmuMH4SS7vighRyUNVXmZuOEqI4qPFD6+OOPmTJlCpMmTSI0NJQ5c+bg6OhY4mIsjUaDj4+P+l9pnbNrOnfHEhbHGQzY5WeTFneCjRs3AvDUU09ZfAdv79XkMhWKEKKmK88yvpVVVb9GGZceXriXCx6OtlDC34KtLhuX7HROnTqlFlNQCijs27ePS5dKXl9rjlKx7kF+/nZ2durjlFmcZcuWsWLFCrZs2aJW03vuuefUMt7m1K9fHw8Pj2L3GwwGtU9hhw4dqOVUm0SNA0m1vNh7/hqGQscWbJKrzPTcvHmT3NxcWrVqBfxRxGLTpk0P/O9Pr9erpc0fNlByc3N7oMeLx6eqv2+XpqKur0IDpdzcXI4ePUqvXr3+eEGtll69erF///5iH3f37l0aNGiAv78/Q4YMISYmpiJPs8p7oVXD4ndqoFHqBfbu2a1usrR3gk5vYPbhkruqzz58UdLwhABcXFwwGAwmjSSrq6ysLAwGw0MvLH8cZFwqH1ZaDdPbBoFGU2ywNNS/dpHVs0pxAoBFixaRmZlp8WsWLCRQUiBTEmXN0MiRI2nfvj0AsbGxJoUVlDLgJRkwYID6tZJKqLh48aJadMK6YTPGrT7AoiQNZ7xC2YIPB/07ENL3KXWNU8EUPicnJ7Xs9sWLF3FzcysSlCmzamWVkpKifu3g4PBAz6Gk3kkPparD0dERg8Hw0GmblV1ubi4Gg6Hcy9ZXaDGHlJQUdDpdkTs/3t7e6ptVYU2aNOG7776jefPmZGRk8OGHH9KpUydiYmLU3N2CcnJyTN6klDfdvLy8EjtzVwfK9bXxceGfTzRm7rFLallWAHdHW6a0CsQhxZEtW7ao20+cOEGzZs1KXbgXnZhBRlYOJfUgz8jK5uTNVMK9XR7qWkqjXGtN+Z1W9+uE6netjo6O6PV69Y63Jc0sqxolEExISECv1+Pg4FDk91fZf5+PYlyCmjE2dfB15R9PNObj3afItfojFc/LwZgmdvPgLrRaLf7+/vTq1Yvz58+zb98+k+f44osv6N27N2FhYdjaljTaGIMsrVaLlZVViQ1si2MwGLhz5w5arRYfHx8aNWqEq6sr27ZtU4+Jjo7Gz8+v1EDA19dXDdYOHz7ME088ob7GmjVr0Gq1eLfuwof7zwOYjqPWtnx/Lo3pEQ3Rag8RGxtL37591XLb7du3Z/fu3cTExBAUFESXLl1Yt26dWjlv165dFq/RKig+Ph6tVkujRo3K/DeoHH/37l20Wi329vbV5u+4sOo4Nul0OpKSkrCxsSnTmsCqQq/Xk5SUhE6nw9HRsVzHJY2hAufibt68Sb169YiKiqJjx47q9jfffJPdu3eXuhASjBcXEhLCmDFj+Pe//11k/6xZs3j33XeLbF+yZIk0QxNCPHK+vr7Y2NhUuyBJYTAYyMvLU6tnFZaVlcXYsWPJyMiolAu+H8W4BDI2CSEqD0dHR9zd3at1SxidTkdqaqrZrI6HGZcqdEbJw8MDKysrtUqNIjExsdj+BYXZ2NjQsmVLLlwwnwI2c+ZMZsyYoX6fmZmJv78/kZGRuLu7P/jJVwF5eXls27aN3r17F9tPIj8/nw8//BCAQYMGcfv2bZM7eqNGjSp2QW10Ygbv7Dxd6nm816PZI5lRKu1aq4Oacp1Qva/1zp07ZGZmqs0ZDQYD165do379+lU6gNJqtTg7O1O7dm0iIiLMHlOwz01l9CjGJahZY5PBYOCzzz5TU+O0Wi3NmjUjLCysxJmPmJgYta9fQR4eHrRr146QkBCT94ZffvmFkydP0rNnT9q2bVvm87xw4QIrV67Ezc2NCRMm8Pnnn6PX6/Hw8ODZZ58lOTmZFStWqB+0AgICGDBgQLH9nXJzc9m+fTunT5/miSeeoH379vzvf/8DIKhzJAuvlz7rNaWhEzG//oK1tTWvvfaa+v7w/vvvAzBu3Dj8/f3Ztm0bR48excXFhYyMDFq0aKEWlbDUf//7XwwGA+PHjy92JrQ4yvv1lStXyMzMZOzYsdSvX79Mz1FVVNexKScnh9TUVJOmzdVlbLKyssLd3b3YJsgPMy5VaKBka2tL69at2bFjB0OHDgWM02M7duxg+vTpFj2HTqcjOjqaJ5980ux+Ozs7sz8YGxubavUHXpKSrvXYsWPo9Xq0Wi0tWrRAo9EQHBzMDz/8QH5+PkuXLqV9+/b06tULa2vTPwdvbS7k5xhTKor5B+TpaEcLX3esHlGH55rye60p1wnV81rd3NxMFjvn5eVx+vRp6tevX+2utbDKfn2PYlyCmjU2ZWdn4+rqqs4yKmt8GjRoUOK1RkREcOfOHXbu3AmgNnNNSkpiw4YNbNiwgXbt2tG6dWu8vLw4c+YMer2eunXrPtDPMDk5Gb1eT3BwMD/++KOavjdx4kQcHByoX78+r7zyCnv27GHv3r1cunSJL774gsGDBxMREVHsB0klHc7e3l69OeJRvxG518+Vek61feqh1+vJzc0lJSUFX19fANq1a8eBAwc4f/48DRs2pG3bthw+fJjbt28DcPz4cfr06WNxmfr8/Hz1A7Kfn98D/w0qN4Bq165d7f6OC6tu/1ZtbGxwcnIy2VZTxqaHubYKT1ScMWMG8+bN44cffiAuLo6XXnqJe/fuMWnSJMDYR2HmzJnq8f/617/YunUrly5d4tixY4wfP56rV68yefLkij7VaicvL4/NmzcDMGzYMPVN3s/PjzfeeEO9I3zw4EE++eQTdW0FGO+uzp83j6DUkos5TGvb6JEFSUIIUR5kXCo/N27c4IMPPjBJxWzUqJHFjy9YJjwhIYGpU6eq630ADh06xNdff82cOXPUQgJlLQ2uuHLlCmDsJaSUgp82bZpJKqS1tTU9evTghRdeUGeSfv75ZxYsWGDS/6ggZT2TMt4OHToUd8eS11spPJ0caNeuHYBJL6+QkBDAWILcYDDg6emJs7MzBuBebU+Sanmxat8Ri4spFRzfy+MDsaSPipqiwgOlUaNG8eGHH/KPf/yDiIgITpw4webNm9WFtNeuXTOpgnP79m2mTJlCSEgITz75JJmZmURFRREaGlrRp1rtHD16FDC+8Rf++dna2jJkyBBGjRoFGPM3v/nmG6KiokhKSmLOnDkANHEw8LcnmuBZ6E3f09GOWd1C6dJAeikIIaoWGZcensFgYP/+/cyfP18ty9uzZ08Adu3aZfHzaDQaRo4cCRhnZrZv306PHj3429/+xvDhw9XZqYKpknv37jWp4GYppUR5fHw8YAyIiyvzXbduXV555RUiIyMB49/EZ599xuHDh4uUIS6YAqfVagkODuboL2uxzc8usXy6p6Md4V4utGnTBjD2SFIKgBRMjVOu1TaoOQf9O3DUI4wzXqF8d1PHWAt7GiolyItLmS0rCZRETVGhqXeK6dOnF5vSUPgN9ZNPPuGTTz55BGdVveXl5amV7p566qliUwaaNm3KjBkzWLVqFVevXmXbtm1qBSBXV1cmT56Mvb09XQN9iE7KIO1+Lm4OtoR7uchMkhCiypJx6cHdv3+fVatWqWWqGzVqxNNPP429vT379u0rc4Upe3t7XnzxRb755hsuXLjA/v376dSpE2FhYYSFhZGamsqOHTuIi4sDjFkQBw8exNfXlw4dOhASElIkdbywwqWRBw0aVGrDWysrK7p27UrTpk356aefSEtLY9OmTZw4cYKnn35anXEqGNQ0bdqU2bNnk5WVRZBjNrFeYcZgycwYrGRkKLNFmZmZnD59mtatW6PVagkLCyMmJoazZ89yJgtW37aGQovxU37vaVjajUtlNs2S0ueWqI6V04QwR/7SqxGdTseuAydY+vMO5i5cjl5vwMbGRp3CL3q8nl0nrrDh0BUatOjGE126muzv0qUL9vb26HR69p66SlxMPNqMLMI9nSVIEkKIGig+Pp4PPvhADZL69u3LuHHjcHBwQKPRqOu+oGwleX18fBgyZAgA27Zt4+rVq+o+d3d3de1OUFCQ2iz15s2brF69mvfee4+tW7eWuGD79Ok/ChN16tRJbeRqCS8vL6ZNm6b23rp58yZffPEFhw8fBkxT5mJjY9ViEB5ZKYQmxeBiY/pRy8PBtkhg06NHDwC2b9+ublN6Hv4WFfVHT8NibnqW1tNQKX3/oI1mhaipHsmMkqh4qzfv4ZV3v+R6wh9T8M6Odvz7LxPMziat3hPHK7M3cz35j2Z/zrbQLwhCf3/vXr9+PUu3nWDp0XRupNxRj/PzdOazaf0Y1tV8ACaEEKJ6MRgMREVFqR/kNRoNzz//fJEP3k2aNFH7IR0/fpzOnTtb/BoRERFcu3aN48ePs2DBAmbMmKHO2ijpcmFhYURERJCSksLRo0c5cOAAAPv372f//v34+fnRvn17QkJC1FLI6enpanU9Jycnk2bDltJqtXTu3JkmTZqwYsUKkpKS2LFjB82bNycqKsrkWBsbG4KCgoiLiyPINp9PRj3B6eRM5v24FFtdLn9/cSJuhZo1h4aGsnbtWrKzs7l16xZ169alYUNjM/kEgx3JWSU3C03OyiE6KYMIH9ci+5S1XQCeng+fLl9cZTEhqiOZUaoG1m+PYvjL/zQJkgAys3J49f/NY/XmPSbbV++JY/is5SZBEkBmLiyPhYbtB9KpUydik+HDLfEmQRLAjeRMhs9azuo9cRVzQUIIIR6LgpkJuw6cQKfTkZWVxaJFi9QgqXHjxrz55ptmZyc0Gg0DBgwAYOfOnWVOw3vyySfV4GjhwoVqpbZz54wV5JRCDh4eHvTt25d33nmHp556irp16wLGQg2rVq3i//7v/9i2bRsJCQl888036vN37Njxocoge3h48OKLL9KvX79ij8nLy1PTBEeNGoW1lZYIH1e87iXhmp1O7u9l1AuysbGhdevWAOpMla2tLfXq1SPXyrLCEGn3zQdTSqENR0fHckmZK25dlxDVkQRK1cBf3/+mpPWivPLuF6Snp5Oenk5ycgp/+nwjJdXJeXPuDuq4e7Iz3vybs/LYV2dvRqfTP/B5CyGEqDxWb95DwBNjiBzzF8a+8n9EjvkL/h1HMGn6m2ohhP79+zN69OgSy1I3btxY/VopKmQpa2trnnvuOcBYxGDbtm0mfV8Kz4hYW1vTvHlzXnjhBV5++WXat2+v7ouKiuKbb75R+ztB+aSeabVa2rdvz5gxY4qcS8EgrF27dia9uZQAMNtMoASovaGOHz+urqlq1aoVtrqSZ5MUbg7mx2wlUAoPD7foeUojgZKoSSRQqgZuJBafl20wwPWEFF59axafffYZb/y/2dxMu1fi8yWk3+dvX68j5U7xb84GID45k//7chG7du0iJiaGxMTEMt89FEII8fit3rzHbGbCreTbLP81hjPxqUyZMoV27dqVOiNTcP+WLVvKPC64uroybtw44I/CDYqSSlt7enrSr18/3n77bYYMGWI2mDtz5kyxZb7LIi8vj59//tlkW35+vklFvK5dTdf91qlTByg+UPL29lZLjcfExADGoNMlO91YQa84BgPOVhDuZb7xuzIbV9Yms8UprgGvENWRrFGqIe7+PiV/17IbU2Tp7YCcUo+LPncF0q+YbLOxsaF+/fp4enri6emJh4cHHh4eUk5UCCEqIZ1OxyvvflliZsK+mAS1fLqlbGxsyMnJ4ejRo3To0KFMjw0KCqJbt27s3r1brcRaWpW6wq+rBCQNGjRQi0McOHCAAwcOEBAQQPv27WncuHGZ09Hu3bvHvHnzuHfPeNOxe/fuNG/enOXLl5v0kzp48KBapAGMAeC1a9eKDZTAWGL9559/ZseOHbRs2RInJyccHRwISr1ArHezYh/nd/M0GJ4ArIrsu379OlB+hRxkLBc1iQRKNcQLzz9LZMeW7Im+xqrXfyz1+P6R7dl7eU+pxzn9PtOv1WrVjuR5eXlcvHhRrYpUkK+vL97e3iYBlIuLi5QaFUKIx2Tv4egiM0mFXU9IZu/haLp3iLD4eQcMGMDq1avZsmULrVu3LnOj027dunHp0iW1kIOlMyLnz59Xm7/269cPJycnrl69Sp06dbCysiIlJYUrV66oJbO7du1Kq1atcHExPyNTUGpqKl999RV6vV4dtzp06ICNjU2RAGLv3r3odDoiIyOxtrZWZ4tKCpTCwsL4+eefuXfvHomJiXh7e9OpUyd27NhBX9sMjll7mhR28HS0w/PyUTyyUoiJiaF58+Ymz5eZ+cdaZNdCBSQelHIdQtQEEihVA/W83bkYf6vYu4F1PevQs3NrrKys6N4iED9PZ24kZxa7TsnZDrLO78HL2Y7kzByzx2kAT2c7AuvkgsGgBknOzs4EBwdTq1Yt0tPTuXnzpkljwJs3b5rccVO4uLhQr149kwDK3d29XDqICyGEKN6tpOLTtx/kOEWTJk2wtrYmPz+fY8eOmawfsoRGo2HMmDF88MEHAFy8eJHIyMgSU/8SExNZsmQJAG3atKF9+/bs2LEDgObNm9O9e3cSEhI4cuSIun5qz5497Nmzh4YNG9KuXTuCg4PN3ry7evUqCxYsMPu6Fy9e5MIFYwnviRMn8sMPPwDGdVInTpxg7NixaipgSYGSra0tLVu25Pjx4xw5coQBAwbQpEkTduzYwf2zx1n0zt+ISblj0tPw+DFrNm7cyObNmwkPDzf5+SiNZr28vB6qiEVBMqMkahIJlKqB/771IsOnzUKjMd8EvEszX65evUrDhg2xstLy2bR+DJ+1HA2YDYL6NQJrLXT3y2F5LEWOU95qv54xhKFPNOHixYscPHiQixcvkpmZqQ4+bdq0Yfjw4Xh5eXHnzh1SUlLU/5KSkkz6ZGRkZJCRkVHkXKysrAgICFAXj8bHx+Pt7Y2jo+NDvenr9AZpoCuEEEBdL/dyPU6h0Wh46qmnWLFiBZs3b6Z169alNoYtzMHBQf365s2bHDp0qNiA686dO8yZMwcwNlbt378/gBrAKKmDPj4+DBw4kD59+hAdHU1UVBRpaWlcunSJS5cuAcbZrFatWuHs7AwY+zCtWrUKMI5LOp2O1q1bk5eXR35+Pj/++KP6uICAAIYMGcK6desAyMrKYv78+QQEBACm5brNadu2rRoo9enTx6R4wq2bN4ioX9/k+BYtWrBx40bu37/P9evXTZrKKoFScf0UH4QESqImkUCpGhjUqxMrv3q3SB+let7uPB0ZgZu9jkWLFvHMM8/QsGFDhnUNYeWskUz/fCO3ChR28Pd0ZkInL2zSjYNKqCeMDIXNF4ylwxV+ns58WqCPUnBwMMHBwWRkZHDs2DH27DGm7B05coQjR47g7e1Nx44dCQ0NVftCKLLuZxN1MZ74lHQMWXewz0jixvV4tWGfTqfj4sWLXL58mebNm7N48WJ19qpu3br4+PioM1AeHh64urqWmsa392oysw9fKJS+YMu0tkEldjYXQojqqEvbcPx8PLmRmFxsZoKzox23E66QlxdWppl+pZ+RTqfj6NGjZZ5VKjz7snnzZnx9fU2CATCmfM+fPx8Ae3t7xo0bp44FCQkJACYV6MA4e9O6dWtat27NzZs3OXLkCMePHwdg9+7d7N69m0aNGuHk5MTJkycB47hz69YtADp37syuXbvUXk4ATzzxBGCsMKcESj4+PiQkJKipfklJSSVec926dbGzsyMnJ4fY2FhatGhBhw4dOHDgAHFxcdQvFCjZ2NjQqVMnoqKi2LNnj1oIA/4oCqE07C0PknonahIJlKqJYf26MqR3Z/YejmbBoqWgy+GtV18iODiIJUuWcOnSJRYtWsSECRMIDAxkWNcQmtW15v99tQSNXW0mjh5Gl/D6aLUatm7dqr7xdw/zoqlHElczjIUgBveNZNyAJ7CyKhqMuLi4EBkZSdeuXTl37hwHDx7k6tWrJCYmsnbtWtauXUv79u1p06YNHh4eZgIWKzwdGzBtZE86+blx+/Ztk1mowm7duqUOWAU5Ozvj5+dnEkC5u7tja2vLnqvJvLs7tshjkrNymbU7tki3dCGEqO6srKz47J/TGf7yP4vNTOjXPohTJ09y7uxZJk6cWCToKI4yq7Ry5coHmlVSggpra2tCQkKIjo7mu+++47XXXsPJyQkwNsNdvny5uh7nxRdfVJuiKjfdoOQ1Or6+vgwePJi+ffty6tQpfvvtNzIyMkzW2vr6+qoV87p27arOrOzbtw+A8ePHq9dmZWXFoEGDWL9+PQkJCQwfPpyVK1cCcOXKFX755Rd69uypNuctrHfv3mzYsIFff/2VFi1aEBISohai6NOnT5GMinbt2hEVFcWFCxfIyMjAxcUFg8Ggnm95FXIAmVESNYsEStWIlZUV3TtEkJ2ewMGDB0lNTaFp0yaMHTtWDZYWLlyoBkvZ9+8T6Aqhof50jwhQn6dPnz5otVqioqJISkqiTetWaI8dA+Dy8V9ZmnmNQYMGFbvw1crKipCQEEJCQkhLS+Po0aNq53Kl1GuKowexXmFQ6M2+cMCipBzk5eWxadMm/vrXv5KTk1MkjU+5UwfGxauxsUWDIY1Wy/567cDKrsjrKmYfvkgnfw9JwxNC1CjD+nU1m5ngUsuOvu2CeLJbW65cuUJ2djbffPMN/fv3p23btuoH9oLpzK42pjfSQkND1YI/x44do127dhaflxIohYaGMnDgQC5evEhWVhY//vgjL7zwAlqtlu3bt6vpdZMnTzYJiJTZJEvTte3s7Gjbti3h4eEsWLCAxMREdV/B9bU+Pj5qdgMYMysaNWpk8lwtWrRg/fr1AOj1esaMGcPSpUsBOHToEEePHmXcuHFmq/k1a9aMDRs2kJGRQXJyskkhi5SUlCL9pFxcXGjUqBEXL17k0KFD9O7dm9TUP9aUPewsUH5+vvq1rB0WNYmUGquGlM7lypu6lZUVY8eOVd+MFy5cyJUrV9TSpoUDHo1GQ69evdQUgmPHjtGjRw81ZeLixYt8+umnHDhwwGSgMMfNzY3evXvzzjvvMGzYMHx9fTEAF9yDSnzc7MMX0emL3tbUaDTUrl2bwMBA2rZtS//+/Zk4cSL//Oc/eeutt5gyZQpPPfUUXbp0oWnTpiaDw21bZ3Kt7YsNkgCSs3KITiq6VkoIIaq7Yf26cmXfUj6bOYmnu4bw0RvPEP3LfEIbeHLlyhWeeuopNe3rl19+4ccffyQrK4u9V5MZt/oAr209yXt743hn52kA9scbMwE0Gg3Dhg1TH1fwQ3dplKyBunXrYmNjw+TJkwFj0YYdO3Zw/Phx9Ubc8OHDi8ycKIFOaGioxa+ZmZnJl19+qT522LBh9OvXz+SY5cuX89FHH6nfDxw4sMjzWFlZMWDAAABWr16trndS6HQ6Fi5cyIYNG8jJMW3HYWdnp1awO3LkCFqtlrCwMADOnj1r9ryVvk1RUVHk5eWp65MKp7w/iNLWVQlRXUmgVA0pgVJcXJy6rXCw9MMPP6iBlLk7TRqNhh49eqhvvDt37qR27dq88MILatO8LVu28MUXX5itYleYtbU14eHhTJkyhe6jJlZIwGJnZ4evry/NmzenR48ejBo1itdff52///3vTJ8+ndZPdLPoedLuW9hsSgghqhkrKys6tgwhvKE3TQN88Pf3o1s343vnmjVrGDlyJH369AHg0qVL/HX298zaHWuy5lPxn31n2Xs1GZ3eQK6bL8lO3qTbu3L09wwFSyjjmFKIoU6dOowZMwYwBgRK09cePXqogURBSmnxunXrWvR6CQkJfPLJJ+qNxMmTJxMeHq6m8oFxtgeMgY5i8+bNXLp0yaThLEBERIT6tdLPCOCvf/0rLVq0AODo0aO8//776qyYQpl5O3ToEPn5+err7t+/3+y5+/v7q5X1Tp06pV574ZmuB1EwhVGImkQCpWpImZI3GAwmb9rW1taMHTtWrbxz+rTxrl9xU/IajYbIyEi6d+8OoKY3TJ8+Xb27lp6ezrx589i8ebN6R0ynN3AiIZ2dl5M4kZBeZGZIb2dZfnN5BSxarRZ3d3eaNQqw6Hg3B/M540IIURMoH7bv3r0LQJcuXdT1QOvWraNDhw5MmTIFK2trY3ZACZ1qPz5wjrGr9vP6tlPEeYZwqm4Es07fZtflxGIfozAYDOpMhnIDEKBx48a0atVK/T44OFjNgChMCbQsWVN14cIFvvnmG8B44+2VV16hXr165Ofnq4UZBg0axNNPP23y+srrLFq0iP/+97/89ttvaqBlbW2tVt/75Zdf1OPt7e0ZOnSoSeGFxYsXs2bNGvWafX191TVPcXFx6sxQVlaW+rspSKPRqGPzxo0bOXHiBFA+65OUQMndvWyVD4Wo6iRQqoYK3vlSFnIqrK2tGTduHA0aNFC3KW/oxenWrZvaXXznzp3s2bOH9u3b85e//IWgIGMK3cGDB3n//ff5euOvjFy+T03BeG3rScatPsDeq3/kvFsaiJR3wBLu5YKnY8nP6eloR7hX6U0HhRCiulICJaVlg5WVFRMmTACMzVxPnz6Nr68vfSdMKTU7IDMnn5T7eSbbcq3s+PfeMybjgtnHFmiWWvCGXlZWFscKzEpdu3bNbDpfwW2F1/QUdvToURYvXgwYZ59eeeUVda3TkSNH1OMiIiJISEgwef1x48apsz05OTls376dDz/8kBUrVnDlyhVatmwJYJKqrnwdFBTEW2+9RevWrQHjTNAHH3zA2bNn0Wg06uzdrl27sLW1VYOec+fOmb0OZVbNYDCoM16WzqaVRAneCpYqF6ImkECpmlIGheTkogORtbU148ePV7/fvn27SU8jc7p06UKvXr0AY9lUJRVv3LhxjBo1CoAURw9WpmhIz9GZPFYp0KAMio8rYLHSapjWtuS1UdPaNpJCDkKIGk252VbwRpunp6f6oX316tXcuXOHO5YvNTL1e2m92YcvmF2LqlAKObi5uanb8vPz1aavSnGGnJwc1q5dWyTtrWC11OIKEBgMBrZt28aGDRsAY1rd888/r/ZvysnJYcuWLQCMGDECjUbDsmXLAGMJcDCmvD399NO88cYb9OnTRw00Y2Nj+eGHH/jkk09o0qSJyesWXJNkZ2fHwIEDmTBhAlZWVgAsW7aMFStWqDcj09LSSE1NVWeyjhWTvmhtba2mSiqKq6xXFsoN1cLrrISo7iRQqqaUnOTi+jUULs+6YMECrl27VuJzdu7cWR0o9+7dy44dO8jX6cl29aHd6Oe56NXUeGAJFeV0esNjDVi6NPBkVrfQIoGap6OdlAYXQgj+mFEqvC6lQ4cO6k245cuXU8f+IaqfaTQkZ+WWuBY1MTERA2Af0JSdl5M4fus2q9esVW8ATps2jeeffx4wBiVKs3OFUvGucePGZp8/Pz+f5cuXq8UgunbtyrBhw9RgBVBbZTg6OhISEsLx48fVmTYl00Lh6OhIx44defPNN5k4caJaQOL+/ftFCjCYK44QGBjIm2++qa5Nio2N5fPPP1d7Rh09elS9lhs3bpCbaz49vU2bNurXwcHBZo8pK+V8pTS4qGmkPHg1VbjynUIp4ZqalUO6vSsu2en4+/lx/fp1vv/+eyZNmlSkmV1BHTt2RKvVsnnzZtYdP8vH1/TkWP+e6qcp+c9JKdAQ4eOqBixFG7/aMa1towoNWLo08KSTv4daytbNwZZwLxeZSRJCCP4IlArTaDSMGTOGzz//nOvXrxN+/QKejrZmCzlYKvnufcDV7L49V5M56N+B3Nu2sNe41sg2vzZBjh68NmKAul7mySefZNOmTWzcuJG6deuq6WlKoGRuTMvKymLBggVq0DVkyBCTwgs6vYEj1xJZfjgWW3tXxj41gPv376vlvgcPHqzOOhWm0WgICAggICCAe/fuceLECfbs2WMS2MydO5fIyEiaN29u8jy2trb079+fsLAwFi9eTG5urlqUYf/+/fTs2RMHBwfu37/PpUuXaNq0aZHXd3JywtnZmczMzCLV9B6UsiZKAiVR00igVE2Zq3xXpMFr3Qhs87PpHhkBOzeowdJzzz1XpOt5Qe3btyf2rp4918v+BlywQMPjDFistBoifFwr/HWEEKKqKS5QAmPVOaWR6i+bNjFx+AQ+PFpyNkJJ0m5cg6Cia2j2Xk1mR34dsDLdnmtlR6x3M+I1tQj4fVubNm24cuUKsbGxzJ8/nzfeeANHR8ciFfPU10xL46uvvlLX8Ci9BQu+tjpWehlnhf52IpnWR43P5+bmRkREhEVlzmvVqkXnzp3p1KkT586dU9P2cnJy2Lx5M5s3b6ZFixa0bt0aPz8/NZ2wfv36vP766/z6668mVe62bt1Kx44d2blzJ6dPnzYbKMEf67uuXbtGdnZ2ib9TS6SlpeHm5lZscChEdSWpd9VUwYWrBoOBvVeTzZZwVRbVNowcoDa0++6779Q7WObo9Aa23P79Gwsa+BVUuECDErD0CPQiwsdVZnWEEOIxK7iep/C6H4CWLVuqszQXf93IP7qGmF13aqXLK74insGAXX42cbs2m5TZBuMY8+Xh30tlFx5jfv++YK89jUbDkCFD1LVVS5YsQafTqcFCwYp38fHxfPHFF+prvvTSS0WCJHNjZUpWLluya5Pi6MHIkSMtal5retoamjRpoq63sra2VlP8Tp48yXfffcenn37KoUOHyM7OBoy/hz59+jB58mT1d3Lo0CG1kENMTIzZXoaFZ5GU6ncPQ2le+7CNa4WoaiRQqgJKK7dtTsHFmylpacw+fMH8gb+/2X9x+BKjx45XUxa+++47k54PBUUnZRgHkTIOFFJRTgghKr+CQYC5dTAajYbhw4cDxg/Q1jfOs3hYBz7q04J3uoTwXg9jBbjXu/7e16hIsGT8vlHqBTQU/SAfnZRBSiljTOFee7a2tkyZMgUwrt/ZunWruk8pbR4TE8N3330HGButv/baayZlx3V6Q/Fj5e/i64bh4elV4jElUW5I5ufnM2rUKMaNG6euO8rMzOSXX37hv//9L+vWrePGjRsYDAbq1avHn/70J/U5Co7N5m5qKin3ylrkLVu2lNocvjTKGiWZURI1jQRKlVzhjufmym0XR0k3OHDpRqk55Bk5eTzz81Ea9RiIr68vAN9++63ZYOlB+xtJRTkhhKhalNmNwmrXrq0GS9u3byc1JVnNDgj3Nt4Q61zfk392D8NJa/ohvbYWRvtY4ZFlrEq3YcMGk1klS8eYwse5u7szcuRIwDjzAsZy1gaDgd9++42VK1cC0LBhQ15++WU1gFKoNwGLo9FwR68pczP0gmrXrq1+vWHDBho1asSYMWOYMWMGkZGRaLXGj2UnTpxg/vz5fP755xw5cgRbW1s1za7gjdBVq1apxSUUN27cAEyb3Z4/f/6Bz7kgWaMkahoJlCqx4lIAlHLb++NTinmkkVL5Lj4l3aLXy8jJ4//2nSWo5yC174K5YKms/Y2kopwQQlRNxQVKYOzZo5S9/uGHH4qk0AF0beDJqrHdGe+ho2lSLM1vnSDi4m7unz1Ox44d1eOUIAYertdeSEiIyfP6+/uzYcMGtm/fDkDbtm0ZN26c2ZLZDxqglUXBtUKZmZlcvnwZMAZQXbt25Z133mHs2LFqWfD09HQ2btzI+++/r84K5ebm0qJFCwDu3LnDp59+ypEjR9Q0yQsXjLNi/v7+9O7dG0C9/gdRcDaqYJ9GIWoCCZQqKUtSAOYeu1zifmWdUvbtkgOqwr45doUJE59V87q//fZb9Q4VWNYHycXOhpmdm/JRnxYsHtZegiQhhKhClNmWkgIlMFaLA2MVud27d5s9xtpKy6Qne/DMEy1xzU5HA9y+fZv9+/er6d5nzpxh3759gHGMcdCXsL6JklO5e/Xqpa7pOXHihNpzqG/fvvTv31+dtSnM0gDt6L7dxMTEWFTMoTAlUFIyNzZu3GiyX6vVEhwczLhx43j11VdNeiIVbDJbuMXHxo0bmT9/Prdv31b7ItarV09tdpuSkkJiYmKZzxdMS5mXdW2WEFWdBEqVVKkpAEBqKfuV3OvM89GlBjYFJWflcOZ2FpMmTVKDpfnz56vBkiV9kP7SIZhejbylQIMQQlRBLi7GIKSk8tI6vYGzGTkE9xlKur0re/buNbmpVliHDh0YPHiwybaCx+/YsYOoqCistBoCk88WfriJklK5tVoteXl5wB/FKEaOHEmHDh1K/KAf7uWCg6H0AhT3LsWycuVKPvzwQ8C4Zshc0QtzlEBJSWFLS0vjypUrZo91cXGhe/fu/P3vf2f06NE0bNhQ3VewZ5RSpfbmzZt8/vnn6nalSp0SLCn9osqqcD8tIWoSCZQqqYeZ2ld4eHgAoAFeamNMwyvpDl3h17e1tWXSpEnqWqf58+eri0SlcasQQlRfrq6uQPEzSgXXz357Pp1TdSM46N+B/y5dpwYp5rRs2ZKnn3662P3btm1jz549eGSlEJoUg8cDjDHmChyYa/Ba5HHXrhKYVEKAptHwQkQD2hZo6Arw448/8p///Idff/2VtLS0El9DCZQyMzPp0qULAJs2bSrxMVqtliZNmvDMM8/wwgsvFD3v+Hjq1Kljsv7JyspKrVTXqVMn9AZYt+cUC345yq4TV9DpLC/uIIGSqMmkj1IlVdZ1QOYUzMFuVtuKvnaZ7MiyJ9+q9OdWXt/W1pbnnnuOb7/9lqSkJObNm8eUKVPw9fWVxq1CCFFNKWWgzQVKyvrZwnKt7IjxDGX+1r00KGEYaNasGba2tixdutTs/l9//RUAn9x0Ph3WoUxjzMWLF/nxxx/V77t06cLevXtZv349devWVdffFmYwGFi7di0eWRkMc9ezLUPLHd0fr2PaDL0p/fr148yZM+p6oLy8PPbs2cOePXvw8vKiTZs2NGvWrEiVOCVQSk9Pp2PHjuzdu5fk5GSuXbtWYrN3Rd26dQkODub8+fM0adKEs2eNgd3t27dNjtPpdMyePZtevXpxK9+VLw5ruX1fzw8nNwDg5+nMZ9P6MaxrSKmvee/evVKPEaK6eiQzSrNnzyYgIAB7e3vat2+vVqMpzooVK2jatCn29vaEh4eXerelOrJkHZC7Bel0yqBw48YN7p85RodrUTjbWpUp99vW1pbnn39eTeWbN28et27dAqQPkhCiapJxqWTKB/rCgVKJ62d/T2vbcbv0zIXGjRszceJE9Xulp1BBXl5eZRpjjh07ZhIkhYaGEhkZqZbfnjt3brEzS+fOnVOrxw1t2YSIC7tpfusEk5u4mV1ra2VlpT7vK6+8wpNPPqmOkUlJSWzatIkPPviAZcuWERcXp65nUn6uubm5ODg40LlzZwB++eWXUn5if1CKVZw9e1Zd66QUfyjs86XbGfHuSm7fN51BupGcyfBZy1m9J87s4wqSGSVRk1V4oPTTTz8xY8YM/vnPf3Ls2DFatGhB3759SUpKMnt8VFQUY8aM4fnnn+f48eMMHTqUoUOHcvr06Yo+1UrFknVAL7QKLHE/oOY0R0dHA+Dt6cmr7YONO4sJlszlfivBklIgYu7cuWqwJIQQVYmMS6VTPtAXXqNkSQntXCvzjy0sICCA559/HsBsxbybN2+WGsCCcTZox44drF+/HvgjbbB+/fpoNBqGDRumFj9YtmxZkfVEBoOBVatWAdCjRw82bNiABmjfwJsx7cNLDdAcHBxo27YtL730EtOnT6dr165qMYmzZ8+yfPly3nvvPTZu3FgkNa9Tp04AJCQkFNu7sLCAgAD1a6UYRnZ2Nu+8847JcXoDbC4mplV+Aq/O3lxqGp4ESqImq/BA6eOPP2bKlClMmjSJ0NBQ5syZg6Ojo9r0rbDPPvuMfv368cYbbxASEsK///1vWrVqxZdfflnRp1rplLYOqKO/R6nPoQQ2SnpA9+7dcUq7TmhSDLY600GstNxvCZaEENWBjEulUwKlwh+Sy7J+tmDT1+L4+fkxderUYvf/8ssvJQZL+fn5rFy5Uq2Y16VLFzVVTClGZGdnp67tuXbtGnv27DF5jujoaHVdVe3atUlISABgwIABpZ5/Ye7u7kRGRjJz5kwmTZpEq1at1H1HjhwxSTe8ffs2jo6OdOjQAYDNmzdb9BoajYZevXoBfzSfvX79Oikpf1S4/dOf/kRtvzAyS/h1GYD45Ez2Rl8r8fUk9U7UZBW6Rik3N5ejR48yc+ZMdZtWq6VXr17s37/f7GP279/PjBkzTLb17duXtWvXmj0+JyfH5K5VZmYmYMwXLmlBaVXRwdeVtoNa8/cv5pCjtWVw7x480TgAK61Gvb6SrtPd3d2kFGqDBg34+OOP8QI8bxxi4KSXuJ2dSx17W0I9nU2e1xytVsvEiRP54YcfSE1NZf78+SYFHyqKJddaHdSU6wS51uqqsl/joxiXoOqPTTY2Nmi1WjIyMkzO19VGiy0lp9bZ/L4/Li6O2NhYgoODSzzezc2NqVOnMnfuXLP7t2zZQlZWFh06dGR/7HUS0u7i4+ZEy4Ye/PTTMpKSktBqtTz55JM0a9aM3377Da1Wi7u7u3rurq6uDBs2jLVr17Jnzx7q1q1LYGAgOp2OdevWodVq6dGjB+vXr1f/Huzt7Uv8XZX271pZE9WrVy8uXLjAqVOnuHTpkrr/yy+/xMfHh5CQELRaLbdu3SI+Pl4N8EoSFhbGzp07SUxMxNHRkezsbI4fP45Wq8Xf35/atWtTLyAYB9vSU+u2/roPd6s71KtXz6QghOL27dvq54iq8Lf7sGri+3V1v9aHuT6NwdKalg/g5s2b1KtXj6ioKJMGcG+++Sa7d+/m4MGDRR5ja2vLDz/8wJgxY9RtX331Fe+++67ZHgCzZs3i3XffLbJ9yZIl0kFaCCEesaysLMaOHUtGRgbOzs6P+3SKeBTjEsjYJIQQlcXDjEtVvurdzJkzTe70ZWZm4u/vT2RkJO7u7o/xzMrXwoULuXnzJsOHD1cXbebl5bFt2zZ69+6t5kMXdu/ePb744gvA2Edi+fLl6j4nJyemT5/+wOeUm5vL999/r1bbqciZJUuutTqoKdcJcq3VlVKSuKar6mNTQkICCxYsQKvV8uabb5rs2x+fwn/2mSmjbTCARsNbHYNIjz3K5cuXuXPnDg0bNmTEiBEWNSuNjo42acL69NNP8+H361kWXXwO2eyXezC+fzvA2Lh27dq1+Pn5MX78+CLH6nQ6vv32W9LS0nBycuLu3bsAdO3aVU3JmzhxYrHV8Qp60H/X77//PmAck+Pj4zl48CB6vek6odatWxMeHo63t3exP7fLly/z008/Fdk+ZswYGjRogE6nJ/z5r7mZeqfYOUAvZ3v+N7Ih58+dNbtOTKHVamnWrBmRkZFFKvlVNzXp/bqmXOvDjEsVGih5eHhgZWVV5I5bYmJisVPLPj4+ZTrezs4OOzu7ItttbGyq1S+9du3a6PV68vPzi1xXSdcaHR2tvgGfPXsWvV6PRqPBYDDg5eX1UD8jGxsbpkyZwty5c0lLS+Pbb79l6tSpFZqGV91+r8WpKdcJcq3VTWW/vkcxLkHVH5tq1aqFXq9Hr9cXOd+uDeuisbJm9uELJoUd7HQ5DPK2pVOAN5tiYfTo0XzzzTdcuHCBM2fO0Lx581JfNysryyRo+Gn5Cn655Mj93OILDvxt4X4GdWpKrVqOJCYmotfrCQwMNPtztrGxYcKECXz88cdqOqSNjQ1Hjx5Fr9fTsmVLi8p0F37OsvxOraysyMvLw8nJiV69etGzZ0+uXr3Khg0b1A90hw8f5vDhw+oapubNm6tNgBXBwcHG35EBrmbA3VxwsoW6dX1/Pyf474t9GT7LeIPUXLD0wdR+jOgRDhiD+fj4eK5du8b58+eLlBwH43o9Dw8PGjVqRP369fH391dLyVc3VeXfanmo7tf6MNdWoYGSra0trVu3ZseOHQwdOhQAvV7Pjh07ip3J6NixIzt27ODVV19Vt23bts0kRaImUt6ISqsiVJBer2fnzp3q98ePHwegRYsWnDhxAjc3t4c+Lzs7O6ZMmcK8efNIS0tjzpw5FR4sCSHEg5JxyTJKMYfiFO6jdz8tmcMbVpIUD0kRxrLZ7u7u9O3bly1btrBmzRoCAgJKTXsp3Cz2agYk3C656trNtHu8/t6XBLr+sW337t0cOnQIZ2dnnJycqFWrFnZ2dtjb22Nvb0/btm05fPgwYCx6pDRT79GjR4mvVR5cXV1JTk5WS69rNBoCAgKYMGECn3zyCWCsZnfjxg2ysrLYuXMnO3fuxM/Pj1atWhEaGoqdnR0ajQYr72Z8vPY0mXng6u2KnYMtLV9fzP8b3Znh3UIZ1jWElbNG8srszVxPzlTPwdkO+jUCq9vnAGOg5OzsTFhYGGFhYfTv35+cnByuX79uUnIdjOXPk5KS1DV9Dg4ONGnShPr161O/fn3c3Nwsmj0Uoiqo8NS7GTNmMHHiRNq0aUO7du349NNPuXfvHpMmTQJgwoQJ1KtXj//85z+AsR9Bt27d+OijjxgwYADLli3jyJEjxS7yrCmUO5O5uZZXHLp48WKRbdbW1upizfK6C2Rvb8+UKVP45ptvSE9PZ86cObz00ktqTwkhhKhMZFwqnbnZsMKUHkcABHpx/3IcMTExLF++XG1N0b59e06cOEFiYiLLly/n+eefL/FD9Llz54A/GsXetXDIM3fc/fv3uX//frHryBRKkATw0UcfqV87ODjg4uJCrVq11ABL+c/Ozk5t6n7jxg2TY6ytrUu8xjp16pCcnFykp5OzszMtW7bk+PHjODg48NprrxEdHc2hQ4dIT0/n+vXrXL9+nZ9//pmwsDAS9W78Y/lpPOp70Kl9EPa1/ghuP4mN53JWDm/0b8mwriEM6dyEvdHXuJV6Bx83J/ZtXIxep+P06dM0bdqUsLCwIudpZ2eHn5+fybZnn32WmzdvcvXqVeLi4tSf84kTJzhx4oR6XMHAqW7dumb7ZAlRFVR4oDRq1CiSk5P5xz/+QUJCAhEREWzevFmdcbh27ZpJVbZOnTqxZMkS/va3v/H2228THBzM2rVradasWUWfaqWmDFplmVFS8q1bt27N0aNHARg2bJhaKrw8p8vt7e158cUX1WDp66+/lmBJCFEpybhUuoIfbPPz89U+RCUZMGAAMTEx6rofMM6WjB49ms8++4wbN25w5MgR2rZta/bxBdfIdOrUib179+JUel91ANpHhBIR4EpUVBRgDDqU1LoHpQRaxdFqtTRv3pxFixYVWWMExtlLZUbLwcFBDaSUn8+FCxewsbExCcA6dOjA8ePHuXDhAvfv36djx4507NiRpKQkTp48qV5f9OkYPj0AHvU9aBZpJshxtOOXpAzaXU6iW6AXVlZaukcEqPvraAaybt06AFauXImfn1+R1D4oWh7ex8cHf39/2rdvj8FgID09nWvXrhEfH8+5c+e4c+cOYEz1P3v2j3Vsfn5+BAQEqOl6pc1YClFZPJJiDtOnTy82pWHXrl1Fto0YMYIRI0ZU8FlVLcqdK0sDpYyMDLW/QsGFl8HBwRw4cAAo30AJzAdLL7/8stp3SQghKgsZlyyXk5NjUaDk4ODAiBEj1Oatt2/fxsvLC1dXVwYPHszPP//Mpk2baNSokdnU74ILru3t7Rk0aBDrfl6Psy0l9gNytoO7N2JJc24KGMe2v/zlL4Ax+MrMzCQzM5OMjAwyMjI4deoUKSkpaLVaswFOaezt7dFqtUUa1xaWm5tLSkqKSX+jgk6ePMnJkyeLffxXX32Fq6srtWvXxtHRUU0ZzM7O5reYG2TmpdGpvbG4U+EZLGUt8qcHzvFEA88iDXObNWumBkoAP/74Iy+99JLJDQL4o4eSuTUeGo2GOnXqUKdOHVq0aAEYg8v4+Hji4+O5dOmSOlunzIYpnJ2dady4Mf7+/tSvXx8XFxdJ1xOVUpWveldTKDNKSk5zaZQZpEaNGqmN+MA4cCnd5ytiAaa9vT0vvPAC33zzDRkZGXz11VcSLAkhRBWWnZ1t8XgRGhqqjj+rVq1i6tSpaDQaIiIiOHXqFFeuXGHx4sVMmzatyIdyJUUuMDAQMK6nXb9+Pf2CYEVs0WIEysfqaX0aos28xJkzZ0weD8aZMeXDPEBaWpq6dlf5YB4eHk50dDQAISEheHl5kZGRQXp6OomJiUVmlZRxuPD5K+zs7KhTpw4ODg7Y2tpibW2NlZWVGrzcunWL5ORkHBwcyM/PL7HHS3p6Ounp6UW2X00yrkkqmG5XmEajITNPxwc/LKVlXTd8fHzw8fHB09MTa2trNb0RICUlhb1799KtWzeT51BmlDw8Sm9wD8ZguXHjxjRu3JiePXuSn5/PrVu3uHbtGteuXVNTKzMzMzly5AhHjhxRHxsWFqbOOHl7exf78xXiUZJAqYpQAiVLUgl0Op365qdMpSt3zgouIK2oSjUODg68+OKLzJkzh8zMTAmWhBCiCrK2tiY/P9/iG3SKgQMHsmfPHlJSUjh69Cht2rRBo9Hw9NNP89FHH5GWlkZUVBRPPPGEyeOUQKlBgwaAMcgZOHAgbNjAiFCISqrN9ZQ76vEezrZ8PWMIT3cN5cyZM2qp7NOnT9OwYUMiIiKKzFJs27bN+FgPD3WmZ+DAgQQFBbFmzRri4uJo27atSbBlMBjIzs5WZ6SU2SllPLaysjKZmcrJySEhIaHUn5ONjQ2hoaG4uLjg7OyszhrZ2Niwbds2Ll26RL169WjRogXZ2dnqfzk5OeQ4pLEvu/hy3gVdT80g99p5k20uLi74+vqabNu1axcNGzbE399f3aYESg9a0t7a2hp/f3/8/f3p3LkzBoOB1NRUNXA6c+aMmikTExNDTEyM+tiAgAACAgLw9/fHz89PzawR4lGSQKmKUN4glPzfkhTMCz527BhgvEsWExOjziZBxQVKYAyWpk6dytdff82dO3f46quvmDZtmsV3pYQQQjxeLi4upKamljlQcnJyUr/euHEjwcHBuLi44OTkxIgRI1ixYgU7duwgODjYpEKqUoCo4LaIiAg2bNhAqCe8NbknGbiwe/9RLp87TQOXXBz+f3v3HR9lmS7+/zOT3gMpJCGFElroVYr0YgAFpdhQwYKKIKKcPbp7zllxf2e/7u7ZVRBRcVVsoFgoovQepSOhBWJCSUJ6h/SQmd8f4/Mwk0ySSTKTer1fL18mM8/M3E8S5p7rua/7ugquo9f3omfPniZj+OGHH4iLi2PGjBnqfpj09HR11UkJkh566CEcHR3p168fiYmJnD59ms8//5yXX35ZrdCn0WhwcXHBxcXFpCR8eXk527dv5w9/+AN2dnYUFhaaBFLK19nZ2SZzr+LmzZvq6lt1kpOT8fX1pUOHDgQFBeHl5YWXlxcznV34bum62n8ZwMgBfXG9lcn169fV4E4ZX2WffPIJI0aMIDg4mA4dOqipd+7u7lX2K9WHRqPB19cXX19fBg0aBEBBQYFalvzKlStkZmYCcP36da5fv64+1sfHh/DwcHXVSSlMJYQtSaDUQigrSpYESgcOHAAgPDyc+Ph4PDw86N69OxcvXiQxMVE9ztZXZ1xcXFi0aJEaLK1Zs0aCJSGEaCG8vb3Jzs6uUxEhY4GBgSQnJ7Np0yYWLFiARqMhIiKCnj17cvnyZT777DOWL1+uFo5QVmGMiwApq0o//vgjP2zdwv/8z/8wbkAnTp0K46effuLkyZOUlJQYVp5+p6SUXbp0icuXL/Pkk08SEhLC9u3bAdRGs2FhYSYBVmRkJFeuXCEvL4/PPvuMF154weJqbVqtFg8Pjxo/vCv7pS5fvszu3btxcXGhd+/e5Ofnk5mZaTbFDqh2H9MgBw1JhaE4uTpVu7/Hz9WJhyfcpe5Run37NpmZmaSlpZGens61a9eqBHFK2W9jSUlJ+Pj4kJKSQlBQkFV77ri7u9OrVy969eoFGALQ5ORkEhMTSUhI4OrVq4BhD1t2djbHjx8HDCtyvXr1UgMnPz8/2eckrE4CpRbC0qp32dnZ6pUypbrdzJkzcXd3R6fTs//IrxQUl+Hu4ohOp1MngQqdXu2H0d7Fkb7+XlU2f9aHEiy99957FBQUSLAkhBAthLKiUtcVJcX999/PmjVrSExM5Pz582rD2RkzZnD58mWKi4s5ePAgEydONHkNZT+RQllVAkN6Vt++fRkyZAhOTk5s2rSJ8+fPm6yOTJgwgfDwcNatW4der+eTTz5h0KBB6oVCpeqc0kdLYW9vz4IFC1i5ciU5OTns2rWLadOm1evczVH2SymphWVlZUyfPt3kmLKyMnVFKjk5Wb3w2bFjR3Jyckz2S/Xy0VOSEs/N8N7o9XrTIOH3QhNByef48otYvL298fT0VFekgoOD6d27N46Ojrz99tvcvHlTDWCVsRpXIUxOTsbHx4fPP/8cnU6Hi4sLXbp0oUOHDureJ3d3d6sEKg4ODmraHaBuG1DS9WJiYtDpdJSXl3Pu3DnOnTunPjY8PJywsDBCQ0MJCgqyqAiJEDWRv6AWwtLVH+MGepmZmXh7e9OlSxe+236Qld8d42bRnUBr392PsOr1Jfj16lWlw7qfqyOLh4YzOqzh+4qMg6XCwkLWrFnDkiVL6p3zLIQQwvZcXV2B+gdKXl5eREZGsnPnTjZv3kzXrl1xc3PDxcWFefPmsX79en7++Wd69Oih7vEx14PIzs6O6dOn89NPP7Fp0yZ69+6NVqulb9++ODg4sHHjRjUI6tatGwChoaH853/+J1u3biU2NlZNQ1dMmDABb29vs2N+/PHH+eKLLzh58iShoaFWLwOvpAIaByIKR0dHNTWta9eupKSkEBsbi6+vL88880yV/VLTbt7kWGo+23M0lGnufKRzqiila3Y8XkVZXM+reTxKZdzLly+rmSgVFRU8/PDD7Nq1i9zcXDVoURQXF1fZUwSGfUWBgYFq8OTj49PgHkparZYOHTrQoUMHhg4dyuzZs8nPz1fT9eLi4tTVuPj4ePUiMRjSOLt27aquOil/00JYSgKlFsKS5n/l5eXqkrSS4ztjxgw274rioRf/QuVKpsnpmTy/+mv6PvRQlefKLCpjxaEYVoyNsEqw5OrqygsvvKAGS++++64ES0II0YzVtdqqOcOGDePEiRPk5OTwww8/8MgjjwCGK/9Kc9VPPvmEKVOmAIaqeeYMHDiQn376CbizqgTQs2dPNbABQ/PY0tJSnJyccHFx4aGHHmLnzp2cOHFCfS6NRsPIkSOrHXOXLl0YP348Bw4c4Pvvv6dDhw5WLUZUlx5CEydOJDY2lrNnzzJu3Di8vb2r7JcaCrxglBVyI+4yV48eo2uXLgwePN5k35S5/VLGq1TGQcbXX3+tfp2bm4unpyeDBw/GwcGB27dvU1xcTF5eHklJSepxlfcVAbRv357Q0FA1eOrQoUOD+ygpK2NKEFtaWsqNGzdITEzk2rVr6pjS09NJT09X+0+5urqaNMNt166dpOuJGkmg1EJYsqIUExMD3Klw5+PjQ0hICGMefbVKkASgR0O3yMiqS/ZG1py8wsgQX6uk4SnB0po1aygqKpJgSQghmjHlw2xDAiWNRsMjjzzCmjVr+O2337h8+bK6LygyMpIzZ86g1+vVogaVK7Ep7OzsmDZtGtu3bzdZVQJDYOPq6kpRURGFhYV88MEHLFy4UF09ME7NAkMVu127djFlypRqU7NGjx7N1atXSUhIYO3atfzhD3+w6IKlJYyDhJrmXzBkhyirPIcPH2bGjBlmj7PTahgQ4A1ApruG944e4OrVq8ybN89sme3K/aXOnTvHlStXcHR0xNnZuUqFXSW18fTp02Z7T2k0Gtq3b4+9vb363GVlhiyVnJwccnJyTMdrZ0fXrl3V1acOHTrg7e1d56CloqKCqJPnSc3IJtDfhzFjxjB+/HgqKipIT09X9zkpKYVFRUWcOXOGM2fOqM/Ro0cPNV3PuFiHECCBUoth/EZXXZf0vXv3AqhvYvfeey8/n7rAjbRMs8/pHRaGs5lO3MYyi0o5n5GvvgE3lKurK4sXL+bdd9+luLiYd999lxdffNFs80HRNGy1X00I0bIoH+iVPT315evry4QJE9i/fz8bN27k1VdfxdnZGUdHR5588knWrVun7q01LuRQ2cCBA9WCDDExMepqgl6vN6nIlpeXx+rVq3nhhRdISEiosv8pNzeXkydPcunSJZ544gmzq0UajYaHHnqIf/zjH1RUVPD999/zyCOPWGX1ofJ8XlthhEmTJhEfH8+ZM2cYN26cunesOsZ7gG/cuEFoaGiVYyr3lwoPD+f//u//KCsr45lnniE/P5/169erx0+ZMoW0tDT69etHfn4+aWlpJitRStnvmih9pMAQ4Pz2229qXyVFx44d6dixo7r3yd/fv9pgdtPOw7z0xrsmn3GCA/xY9foSZkWOISgoiKCgIIYPH45eryc3N1dN14uNjVUr+sXGxppUCw4JCcHHx4erV68SFhbW4NUv0bJJoNQClZWVVXnjSEtLM5nM/P396dSpE0fP7av2eZyMSrjWJKe4hpbo9aAES2vWrKG4uJjVq1dLsNRMRCVk2nS/mhCi5VA+IJorI11XI0eO5OjRoxQXF7Njxw4eeOABwLCXaPjw4Rw7dgy40/vPHHt7e5NVpYiICLRarcnqhzK3lJSU8Pbbb6sfzBVPPfUUycnJfP311xQUFPDee+9x7733MmjQoCpBkNITcO3atcTFxXHs2DFGjBjR4J+FsZKSkloDpQ4dOtC5c2euXbtGVFRUlQIQlWk0Gu666y6OHz/O5cuXzQZKlbm6ujJgwACio6M5cuQIM2fOZOjQoeq+5549e5KWlsa0adPU8Rrvl6pcEj03N5fU1FSTfViVfxfmJCcnk5ycbHKbi4uL2otJWYHaFXWaOS+8bnZLwZwXXue7995gVuQYk59J+/btad++Pf379+e+++6jqKhITde7evUqqamp6hh8fHz45ptv0Ol0eHl50a1bNzVdr6a/UdH6SKDUApWWllbZkGicfw2ob6SB/tWntZVaeJWwvYv1y4i7ubmpK0slJSUSLDUDUQmZrDgUU+V2a+9XE0K0DEqqWXVlq+vCzs6Oxx9/nA8//JBz587Rv39/unTpAsDQoUPVQGn//v3MmTOn2udRVpX0er26qqSUFVeKICxdupTVq1dX+WA+bdo03N3d6dGjBy+//DIbN24kJSWFH3/8kbi4OGbOnKkWNlAEBAQwY8YMfvjhB3bv3k1QUJBatc4aSkpKLOoHNGXKFNauXcupU6cYM2ZMrY/p1asXx48f5+jRo+r+r9qMGDGC6OhooqOjmTJlCsOHD1cDpa1bt1YJEKrrL2VMr9dTUFBQpVFvdfulzCkuLjZZfdLp9Kz87pj5LQV60Ghg2V/eZebkUTUWknB1daV79+50794dMKzupaSkkJCQYJIqmJ+fz6lTpzh16pR63hEREWrg5O/vbza9UbQOEii1QErer/H3xvm2gYGB6hWk0UP7EuDXjrTM3CrPk5eQQEl+Pk6entWkE+hp52hPhI9lK0915ebmxpIlS0yCpaVLl1YpDStsr0KnZ83J+BqPseZ+NSFE86esKFmj0SgY5qaRI0dy5MgRvvjiC/74xz/i6Oiopt2BoVBDnz59qjSQVdjb2zN16lR27NjB5s2b6d27txooKR9427Vrx5IlS1i9erX6OBcXFwYPHqx+7+buwZDpszl69gJx585wOTaW2H/8gyeffLLKCszAgQNJSEjg7NmzfPrppyxfvtykqW59ODs7U1JSYvH+r4CAAEJDQ0lMTOTnn39m6tSpNR4fEhKifp2VlWVRSw5/f3/8/f3JyMjg9OnThIeHq/clJCSo5d3rQqPRWNxfqnIglZeXR3p6epX+kQnpeSYVfCvT6yEpNZOok+cZN3yAxWO1t7cnNDSUwMBAtm/fzquvvmpSXe/SpUuUlZWh1+urVPzr3Lmzus+pY8eONu9TKRqPhMAtiJubG1C1l1Ll8pzGfR9SUlIY08f85lgNeuJ27kSjgSqXZvR60ENg0lne/H9/5ccff+T69etmN3E2hBIsKVcu33nnHXJzqwZ1wrbOZ+SbpNuZo+xXq4sKnZ7otDz2X8sgOi2PCl3tqRdCiObBFnszxo8fr16Y27fPkBqurCoEBwcDsHHjRnX/iDmDBg0CDPtxY2Ji1CprxqsayuZ9RXFxsZrSFZWQybxNx/iPvef5PlPPucABHA8ZTparL+vWrePQoUNV5rp7771XDY4+//xzs6W960K5IFiXQhn33HMPYMggqW3fmFarVSsIGu+/qc3EiRMBw+9GeQ3jDBaloq41KfulQkNDCQsLo0OHDrRr1w43NzezAUeBhdsBUjNq3jNVG41Gg5+fH4MGDeL+++/nj3/8I8uXL2fu3LkMHz7cJPi8du0aBw8e5PPPP+fNN99kzZo17Nq1i5iYGIv3+Ml82TzJilIL4uHhQWFhYZVASZlswDDRKJNNWloan3zyCRFhfjw4vjc7j8ebXIUJDvBj5YsP075HD/5+IJoy+zuTYjsnO4Zp8iguMlzpO336tFqVaNiwYfTp04fg4GCrbGxVgqXVq1dTVlbGO++8IytLjczSfWh12a8m+52EaNlsESgpTV3XrVvHiRMn6Nu3r1rKedCgQdy6dYv8/Hw2bdrEY489ZnaOqbyqpAQtSqBUUlLCnj17TM6jpKSETz75hN5TZ/P+5aofoMvsnYnp0JuI9IscPHiQy5cv8/DDD6vpZvb29jz11FO88847ZGZmsnfvXiZMmFDvn0O7du1ITU2tU6AUFBREx44dSU5O5pdfflEDp+r06dOHmJgYjh49yqhRoyx6DaUPFUBcXBxgWJ1SfsZffvklr7zySoMauer1egoLC8nIyCAzM5OMjAxSUlLUlcHqaLVaOnfujJtPR74/fKnW16lp60F9ubu7ExERQUREBPfccw9lZWUkJyer1fWuXbsGGFbxsrKy1JRSR0dHevXqRUhICKGhofj6+pr8bct82XxJoNSCeHh4kJaWViX17vbt2+rXynJ8VlYWa9euVR8XEQY9Q3zxDgjjl2MnGXnXEF56bj52dnZkZGRwV9Ix8p29ue/BR0wqnZWXRxIXF8f58+fVK3QnTpzgxIkTaLVahg8fTu/evQkMDGxQ0OTu7s6LL75oEiy99NJLZhsCCuuzdB+apcdZst9peJB3XYYohGhkxkUGaitjXRehoaEMGTKEU6dO8dlnn6lzWEBAAI899hhr1qzh6tWr6l4mcwYNGsSOHTtMVnaUVhNKzxzFCy+8wLfffktiUhIfX0gG++oCQA0pwX3x+e0AaWlprFy5kgcffJBevXoBhuDm0UcfZcOGDRw7dqzaUuaWUFanjCvHWSIyMpKPP/6YY8eOcffdd6uZJuZ07doVgMLCQgoKCmpMFzSudtp9zGRiD+9Ry6p7e3szatQo9u/fT2lpKTt37SZ48EiLKqMWFxerwVBGRgapqancuHGj1vMMCwtTq975+/vj5+enZp5UVFSwav1uktMyMbfmotEYLgSPHtq31tdpKEdHRzp37kznzp0BwypnRkYGiYmJJCYmEhMTg16vp6ysjLNnz3L27Fn1sUqBiKu3HfkssQgw/RnK/uDmQQKlFkTJ8a28oqQICwsjKCiI3Nxc1qxZAxgqCCkVi2bPnoWdnR03MxJp56JRNzlmZ2ejAfr4ujOhs2lpVgcHB/XqSWlpKbGxsWq/BZ1Ox5EjRzhy5AhOTk7qSlNN5V1rogRL77zzDuXl5axatUqCpUbS198LP1fHGtPv/Fyd6Otfe7UfS/c7Db1vUJ3HKYRoPMaBUVlZmdX6CAFMnjyZU6dOmVzo8/X1xcHBgcjISHbu3MmWLVvo1KmT2Spj9vb26nFgWG3QarUUFhYSFRWlHvfAAw/g4eHBE088waqvt1Cmq3mVLK9cz/iHF3D6p++5desW33zzDYMHD+aee+7BwcGBbt26MWbMGA4fPsyWLVvqtW8H6t+jKjg4mICAANLS0jh69CiTJk2q9lhHR0eCgoJISUkhLi6OgQMHmj3O3GqGY8hwwrPj8aUENzc3dbzZrj6sSbejbPedD/x+ro48N7AT3Z11akCUnp6urq7UpGPHjgQGBpoERJWLVVVmZ2fHqteXMHvR61XuU/5kV/55SY2FHGxFq9WqjXWHDRsGGIpBKIFTXFyc+pksLi6O3+LiOB4yHOycKsdJKtkf3LQkUGpBlElKCZQqv8FGRkZy8+ZN3nnnHcCweXXIkCHs27cPjUZDnz591D4HiYmJ6uOUjbS1lRB1cnKiX79+9OvXj+LiYi5fvszZs2dJSEigtLSUqKgooqKicHd3Z+jQofTu3bvOzWSNg6Xbt2+rwVJNV81Ew9lpNSweGm52FUixeGhXi96oLd3vFJN5s8ZjhBDNR0lJiVUDJUdHRx577DG+/PJL9TZlBWvYsGFER0eTlpbGxo0bWbhwodnVrMGDB6uBklK84NChQ+r9/v7+9O1rWFWwt7en/4i72f5L7ft1KhxdWLp0KXv37uX48eOcPn2aS5cuMX/+fHx8/fDu0Z/b1zIozk4HDEFkbSW+K2tIM9+pU6eybt06fvnlF0aNGlWlUp+xQYMGkZKSwq+//mo2UKpu9b/M3okY/95EZFw0CVxifXtRVnnlo7CU//05loiMi/gWZVV+KsDwu+jYsaMaDPn7++Pu7l7vVcpZkWNYMG0Imw6er7ql4M9LTEqDNzUvLy/69u2r/i2WlJSoZcmPXU8z2fZgjrX7WYq6kUCpBVEmKSX1zrjbeJcuXfDw8FCDJDs7OxYuXKh+P3v2bLRarUn5baVxrdI7wJKqOAoXFxcGDhzIwIEDKSwsJCYmhujoaFJSUigoKODAgQMcOHCAdu3aMXjwYHr37m3xypCHhwdLly41CZaWLFli8dhE/YwO82PF2AjePRlPlkmetBOLh3a1eOnf0n1MuSXW7c8lhLCdkpISq/eP6dq1K8HBwdy4cQOtVotOp0Or1aLRaHj44YdZuXIlqampnDx5Ur06b8ze3l7ds5OUlKQ2klXMnTvX5IO4r5tle67auziqK1ZdunThq6++oqioiP/vs29J7tiX/Nt6sA/AsUMHRpPPmq17+MMjM+v0oV8JbupTUTA0NBQ/Pz8yMzM5evRojXullEqAN27coLy83CSgq3n1XwPoueITzqXLsfwWF4+ry+8/v8rnqdGA3nBsuFMFoSEhJgGRl5eX1dI2FeXl5XTyd2fZnOGMmjiN7LxbBPr7MHpo3yZZSaoLZ2dnwsPDDVUFr2WwL6r2/VbW7mcpLCdV71oQ4xUlvV7P/v371fvGjRvHBx98oAZRL774orqnSEmfA0zeQJSVJGXDZl0CJWNubm4MHTqUhQsX8vLLL3PPPfeonc5zc3PZu3cvq1at4v333+fYsWNVSn2aowRLynjfe++9eo1N1M3oMD82zBrOpIpkembEsKxXO9bPuqtO+dGW7mNq5yzlU4Vo7pTV/PqsfFiiY8eOgGFvx9GjR9Xbvby8mDlzJgA7duxQsyEqS09PVx+/adMm9fZRo0ZVmdOUFGOzDXh+VznFuHv37rzyyivoQ3oQ49+b/PKqlV8PlHuxbv+x2k7VhLKiVN8eVUp126ioqBp/Nx4eHuprXblyxeS+Wlf/NRpK7Z05fSOT00np6m01HTv2wSd44IEHGDVqFN27d8fb29vqQRLcyYpxcnJkyphhPDJjIuOGD2j2QVJl1t4fLKxPAqUWRCmTWVJSYpL726lTJ7Zu3aqWoHzxxRdxcXFh9+7dgCFH2/iNStngmZmZiV6vV3PE6xsoGfP09GT48OG88MILLF26lIkTJ6orSRkZGezatYu33nqLjz76iFOnTtVYArZysASYdGAXtmGn1RDioMO/MIOubvZ1zou29MNIhJ9nQ4cqhLAx5f27ur2xDZWQkKB+vXfvXpOAqH///uom+S+//LJKyW6dTmeyx8m4SMDYsWOrvJadVsOzAzsZvqny/mT43lyKsaubO5c8wwxBQjUf+r9LuEmCUUp7bZTgpb7tMDp16qRmiCiV1aozcuRIAKKjo4mLi+OXX35hy5YtfLVlm0WvVWbnSGj3CIuObayVj6tXrwKYXWlsSdT5sgaW7g8WtiGBUguirCgVFBRw8OBB9faCggJ1clm0aBHt27fnxIkTgGF5v3LjPiWXOyMjwyRQqSnPuT7atWvH3XffzUsvvcTixYsZN26cenUyOTmZn376iX/+85989tlnnDlzxuxVMU9PT5YuXap2vX7//fclWGoExtWF6spOq2FO6O/NBasJlizd7ySEaFpKoGSrFSWlJLSyR/brr79G//v7hkajYfbs2YBh5eWXX34xeWxOTo7Z53zkkUeq3TPknH6NiIyLOOvLTW53ul3KNNdCRoVU3Vd7PiPfJB25it9XU1Z+tbnGi38m4/g9UGrIfDZ9+nTAsC9LCWT1ej0FBQVcvXqVY8eOsW3bNjVNPzY2lg0bNrB3717Onj1LWb75n19ljhVlBLWz7MJWY618HD9+HDBsO2jJlP3BNZH5smnJHqUWRPnwmpeXR2Zmpho8KCl0CxcuxN/fn9LSUrW30v33319l2VtJi7t+/bq6umTtIKkyX19fxo4dy5gxY8jIyODChQucOHGCsrIyrl+/zvXr1/nhhx8IDw+nX79+9OjRQ11B8/T0ZNGiRURFRaHX69UCD56esiJhK8rPvj6BUnFxMRd3fE+Eqy+JAREU6O/8/RnvdyovL6/hWYQQzYEtU++Mn3POnDm89dZbZGVlcerUKYYOHaq+/oMPPsg333zD/v376datm9ovSQmy/P391aa1Go3GpBeQsYqKCvbs2YMv8MLwiZR6B5BTXEZOciIX9h2kAPi24hZz5swxyWSwdJWkzM6RL7/8koULF6rzc3Ws0aMqICAANzc3CgsL+eKLL9BoNHUqve3r50dSfDG5pdW8z+v1OFWU4lWSR/zRA/SqdNG1ssZa+SguLlbnJuXCb0um7A+u2kepbvuDhW1IoNSC2Nnbcy0PLmZl42oPnY36sS5YsEDt6aAsw3t4eJidMJTy3Tdu3FBXoqqbWKxNo9HQoUMHOnTowIQJE0hNTeXChQscO3YMvV5PfHw88fGGzaW9evWiT58+dOvWTS2NrtFoDD0UlGp47m5EJUaReiuVQI9ARoeOxk7bsnKUmyOlmWB9AqUffvgBgGBdAf986G4u5RRa1G9DCNH8NKQ6W22U4Mbe3h4PDw9mzpzJ1q1b2b59O927d1eLR/Tq1YuIiAhiYmL47LPPWL58Ofb29mqgpJRbBsOKSmxsbJVMCoDz58+rX/fs0ePORcTO/kS0d+Xbb7/l8uXLrF+/3mRVytJVEseKMtLS8ti/fz/jJ4yvcW6qS6BUWlpKZmam2o8oLS2N69evmxyTnJxc5XHGpbevX79OTEwMI0aMYMqUKeoxL/lmsuLQRUPmoZm0wq7Z8WiA8rLag8XGWvlQzt3b27vO1Qabq9FhfowM8VV7Wcl82XxIoNRCbDp8iSXv/ERqDoAhT9vfDT7sDw899BBhYWGA4UqLkpY3c6b5KjzGle+UjbAdOnSw6fjN0Wg0BAUFERQUxOTJk0lKSuLChQtq1aJLly5x6ZKhGkyfPn2wt7fn2Wef5f3330en0/Hcquc47HqYlIIU9TmDPYNZFbmKWb1mNfr5tCb1DZQuXLigFhFZsGABTo4OUtJUiBZM+UBviz1KyvyjFBvq378/p06dIjk5me+++46nnnpKncPuu+8+YmJiKCkpYe++/Th36M5X+89TUQxhXqVoNYY0rKtXr7J582Zee+01k/lPr9ezdetWAGbMmFFlboyIiGDevHmsX7+ea9eusW7dOhYsWICjo2PtfeaMVl4A/v3Lv3n818dJK05TD6k8NxmXWlea+d6+fZusrKwqvYhqex9WKgZ269aN4cOH4+/vj5ubm8k5+vv7ExMTw9GjR00CpdFhfjzcwY5NyUUmZap9nO3pkBCNb1EWd999t9rE94939+C9X6836cqHcjG1ur5QLZWdViPzZTMkgVILsOnwJeas+KZKB+pbv2cuXUgt4/cKoGrVoHbt2lWbu2ucEqBUwbFGIYeG0Gg0hIaGEhoaSmRkJAkJCVy4cIFff/0VgJiYGPr168fatWuJiIjgu5jv+Eb3DRSYPk/yzWTmfDOH7x78rsUHS8ad0hv76pKSdmK8Ubo2N2/e5PvvvwcMzSSbIvgWQliXEijVp4x1bZQVISUbQqPRMGfOHFatWsWNGzc4f/682tDV2dmZxx57jD+9/SVvvX2Um2V3KuR5OsK07hpefXUuf//73ykrK6uyqqRUdwXUfjaVhYeHs2DBAj799FNSU1NZu3YtzzzzDC4uLrX2meuWexUNEEMM3/ANFJvebzw3zew+02R/1VdffUVSUlKtq3bt27cnODjYpDmrl5cXcXFxfPXVV8TFxTFnzhw1ddqYcYpaVlaWyZxfHHuGu3JzGXDPDDw7dKS9iyNeJXl8fmmvWtVWCZQiPB1YP2t4k658KJ8LWvr+JNEySKDUzJWVV/DaVz/j39mf0uIy8tLzqBwxvfbhXmbeHUFpaYnakdzcFTNj3bp1Iy4uTn2zrmtjWFvSarV07tyZzp07M23aNK5evcrFixfV+y/EXGAnO80+Vo8eDRqW7VzGzB4zW2wa3tGkLDNX7RxZPDS8Ua7aKYGSpStKer2ejRs3AoY9cCNGjLDZ2IQQjccaRQeqo2QMKOngYEinmjp1Kjt27GDz5s106dIFd3d3AM4ml/GNmVjlZhlsvKBn7olrTJkyhd27d7NlyxZeffVVdR7csWMHAJMmTVJXzM0JCwtj4cKF/Pvf/yYnJ4c1a9bw/PPPV7uPBKBH1iXaFWTi5u7GzoLq5yaAp799mrP6s2iNamkZB3FgaLweFhZm0ouoXbt21e576tatGy4uLhQXF3Pq1Cm1yp0xrVarpi/GxsaqgVJZWRm5ublogHv691T3K1+6dKe/oqenp6HnD3Dq1CkiIyObbOXD+O9QCbCFsCWpeteMRSVk8uB3RwkZ0Z3eYyMYFDmAkXOG4xdquvpzI/sWUecT+fnnnwHDB9VOnTrV+NyBQR25lgfnM+BaHnh6Ns/Sk3Z2dnTr1k2t7jNr1iw0YRpuUv2krUdP0s0kDl0/VO0xzd2bP8dWmYwzi8pYcSiGqIRMm79+XQOlEydOkJJiSIF89NFHbdI3QwjR+IyLCFmTXq+nuNiw7FJ59Xno0KFqiriy57GiQsdLa8wHIYpla3YycOAgwJAq+NtvvwGQlJSkjn/w4MG1ji0oKIhFixYBUFhYyKpVq8jPz2d0mB/rZw3nX1P681+je/HXCX0AeG2eIXvhQsGFGucmgDx9HgkkmNw2ePBgZs+ezaJFi/jv//5vli9fzpw5cxgzZgy9evXCx8enxuIQGo2GGTNmALBnz55qC+X06WMYr3G/KqXViJubm0lRJ2UFUbmIqgRfJ0+eVPs1NgVlvMHBwbUWzBDCGmz6V5aTk8O8efPw9PTE29ubp59+Wu31U51x48ah0WhM/nv++edtOcxmKSohkxWHYrhVbvpB1cnViT7je1cJlq4lZ6pvfvfee2+Nz73p8CXmvvULn52F7y/BZ2ehy2Or2XS49u7QTa179+70GNzDomPf++I91qxZw7Zt2zh58iQ3btxo0jd4S1Toqu89pFhz8opFxzVEXQKlzMxMdu40fIC5//771XLCQjRHMi/VjbKiZFwwwRqMVwZcXV1N7tNoNDzyyCOAYbXl8uXLRJ1P5EZmTRfIICnzJscupzJ58mQAtmzZYtKcfcSIERYXUfD39+fFF18EDCnIK1euJCcnR91HMqGzP307GC4w+vj48Mwzz1BQORe8GsMmDONPf/qTulLWt29f+vTpg7+/f70bphpXij19+rTZY5Qqt4WFhWoZ89jYWMOYKvUjUu5Xxqis3uiBDfuPsP9aBtFpeTafiypTgt/evXs36uuKtsumgdK8efO4ePEie/bs4ccff+Tw4cM8++yztT5u4cKFpKamqv/94x//sOUwm50KnZ41J+PN3qdcqe82LByMLtpnJhuusgQGBqr9KMxR9jtlF5hecUrOvMmcFd+0iGAp0CPQouPccScrK4tff/2V7du38/HHH/Pmm2/yf//3f3z//ff88ssvXL161Sa59/UVU8MHAUVmUSnnM6z7oaUySwOliooKPvvsMwC1tLsQzZnMS3WjBBb1qYBZE6WQg3FxIWO+vr5MmDABgI0bN5KQmm32uMpSs28xZMgQwFCp79SpU2qVtLqmBLdv356XX35Zray2evVqtVJfZR07duSBSQ9Y9LwRIRE4ODhYtUeVRqNh5syZAOzatcvs/lJHR0cCAw3z52+//YZer+fMmTMAamqdQpkXKwexp4KH8kWGhr9GXWL57rPM23SsUbIcwLAKGRNjyL2U/Umisdhsj9KlS5fYuXMnJ0+eVN+0Vq9ezbRp0/jnP/9ZY26pq6ur2iehLTqfkV99dR0Mb4jO7s54+RmuZvl6OFGYcgmtxrDicuHCBezt7bGzs8Pe3l79WqPV8uLq7VWKQsDvlUExpC7MHNUDO7vmu6Q9OnQ0wZ7BJN9MVvO+jWnQ0NGzI3+Z8xcy0g2lVJOSktQJrqioiAsXLnDhwgWTx3Xr1o3AwEACAgIIDAzEy8ur0VPIckssW/GydfdzS6veHTx4UL3y+MADD0jKnWjWZF6qO2v0+zFHeT/urlQiMmPkyJEcO3aMoqIiEuIuVnucsUAfDxwdHZk8eTJ79uxRV7v79u2rtpmoC09PT1566SU++OADCgoKeP/993nmmWfo2LFjlWMfHvEwy39ZTnpxutnn0qAh2DOY0aGjAUPRpRs3blit9HqvXr2ws7OjoqKCX3/9tcoqERjS/H788UfOnDljUuBBCaAUSqqi0kfraJKhX2O51snkOCUlfMXYCJvvn1XamcCdfpBC2JrNAqWjR4/i7e2tTkZg2ESp1Wo5fvw4DzxQ/ZWX9evX8+WXXxIQEMB9993H//zP/1S5qtGaWfoh2On33g6jgwylUcHQobs61/IgpYaLckrqQtT5RMYN6GTZYJuAndaOVZGrmPPNHDRoTIIlze/LbKsiVxEaEkpoyJ3VNZ1OR3Z2NqmpqaSlpZGSkkJCwp1c8bi4uCqbakNCQujYsaMaPPn6+to0L7qds6NFyRu27n6urCjV1BQ2KSlJ3Rc3b968NvVvVLRMMi/VnXEZa2tKSkoCam5NYWdnx+OPP87atWupyLlGQDtX0nOLzF7s0wDBfp6M7mt4zx8yZAh79uxBpzO00xgzZky9x+rm5sbixYv56KOPyM7O5qOPPmL+/PlVgiU7rR3v3fces7+ZbWZ8hrlpZeRKtciQtZv5ajQa7r//fr7//nt27NjBoEGDqhSuUALTpKQkNe2uV69eVS5yZWYaVolcXV2p0On58NerzHXAbK8lMKSEjwzxtWn1O2V/Uvfu3eWinGg0NguU0tLSTCrZgOEqdfv27dWSoOY8+uijhIWFERQUxLlz53j11VeJjY1l06ZNZo8vLS016e+g5D2Xl5fX+CGvOfN20OJodiow5WlvmAAi/LXodHdKfJeWllJWVkZZWRl6/Z3nKa0ASz5fp2blN7ufnTIe5f/3hd/Hd7O/49W9r5J8606jvWCPYP426W/cF36f2XPw9vbG29ubXr16AYal/Fu3bpGenk56ejppaWlcvXpVnVyTk5OrNPLz8fEhODiYDh06GLqb+/qaLcdaH93buZAEOKCjusxYH1dHerZztenvSKPRoNVqq/13VFZWxqeffopWq6V///6EhYXVeTyVf6etWVs81+aoseYlaF1zk3JxqLi4uMaKcQpL/t7j4+PRarX4+vrWeJyPj4+6sjQhuIQthYaxGM+QykfmlS9MRqerQKerQKPREBYWRlJSElqtFk9Pzwb93O3s7HjyySf56quvSE1N5YsvvmDWrFlVzvO+8Pv4fvb3LPtpGVllWert5uYmJycntFotRUVFVvub6Natm/r7+vXXX6v0GnJ2dsbV1ZWSkhJOnjyJVqulR48eVV4/Ly8PrVaLo6MjZ1OyuVlUBl7gUM1nk/yiEs6mZKv7tmwhJiYGrVZLt27dbPpvqC2+X7f2c23I+Wn0xp+kLfDaa6/x97//vcZjLl26xKZNm/jss8/UKxYKf39/3njjDbWiTG3279/PxIkTiY+PVzciGluxYgVvvPFGlds3bNjQJq72CSFEc1JUVMSjjz5Kfn4+np6ejfKazW1eApmbhBCiuWjIvFTnQCkzM9MkT9ScLl268OWXX7J8+XJyc3PV22/fvo2zszPffvttjSkOxgoLC3F3d2fnzp3cc889Ve43d9UuJCSE1NTUZtUbqK6OJmXx5s+x1d7/x7t70M3FUJb5woULLFiwoMqV0soqKnT0ffp9UrJvVbte5eEILwwBrQbGjh3L4MGDrbZa0hDl5eXs2bOHyZMnqxtrm2IMWVlZpKWlkZ6ezsm0fKLtfSm3u5PD71BRQpecq/gUGf6NdO3alYCAAPz9/QkICMDT07PGlAHlPM9k3uJXO9Pn9nF15NlBnRkRYvvmwL/99hubNm0iJCSEefPmmdwXFxenNpadP39+ldx2SzWH32ljaUvnmp2dTWBgYKMGSs1tXoLWNTf97W9/A+DZZ5+ttviCsdr+3jMyMvjkk08AQ5BrieTkZL744gsAHp03j8Q8PWk5BQS0d2dERHCVfbXHjx/nwIEDODk5UVpaipubG0uWLLFKylZFRQU//PADcXFx9OnTh6CgIAYMGFDlOL1ez6ZNm9SU7ieffNIk1fDChQv8+OOPREREqOW9rUGn06nFRqZOnUr//v1N7s/MzOTjjz8GDAUeXnnllSrjVi48/OEPfyAmq4AV+8/ztNdNPs73pBzzP8O/TuhjsxWltLQ0Pv30U8Dyv5n6akvv123lXBsyL9U59c7Pz8+iTXQjRowgLy+P06dPq30L9u/fj06n46677rL49aKjo4GqGw0VTk5OZnOoHRwcWvQvfUyXQDR29lWa2/m5OrF4aFdGh/mxadMm7Ozs6Ny5s9mNpZU5OMDfn7uHOSu+AcynLvzlkSEUJZxGp9Nz4MABDhw4wJQpUxgyZEiz+Hk25e/VwcGB0NBQQkNDiUrI5Jfcqp0Py7TOnPeNICLjIr5FWfXe92SXeo1BuitEzn+WIr1do3c/d3R0RKfTUVZWZvLzLiws5NtvvwUMgXRNFRYt1dL/rdZFWzjXpji/5jYvQeuam7RaLbdv3+b27dt1Gnt155qTk4NOp6Nz584WP1+nTp0YPHgwJ0+e5KsNG3jttdeqTQOsqKhg3759AEyfPp3vvvuOW7dukZCQQLdu3Swef3UcHByYM2cO27ZtA2Dnzp3V/g3NnTuXTz/9lBs3bvDxxx+zZMkSNVB2c3NDp9ORm5tr9b+JGTNmsGXLFn766ScGDhxoUnY8MDBQTS/v2bNnldcuKytT73d2dqZ/kBOeroYLpuVoKDMTKPm5OtE/yMdmc1RiYiI6nY7+/fs32r+flvhvtb5a+7k25NxstkepV69eREZGsnDhQj744APKy8tZsmQJDz/8sFpZKDk5mYkTJ/L5558zbNgwrly5woYNG5g2bRo+Pj6cO3eOl19+mTFjxrTJssOjw/wYGeLL+Yx8corLTD4s5+TkcPHiRfr168f48eMtfs5ZY3rx3YoHeWnNTpOeFMF+nqxcHMmsMb24ffsezpw5w/bt2wHYvXs3u3fvZurUqVU2h1bo9GbH15rVVL5d2eia3mkgi/q0IyM9ndTUVOLj49WJJykpSd3IrPD19SU0NNRkVdDF2Znhneq+WlNRUUHUyfOkZmQT6O/D6KF9LerNYfy7LC24jR5Mrojr9Xp1JcnLy6tBm6OFaAoyL9WPl5cX2dnZJu8HDaGUBg8LC6vT4yZNmsTJkyepqKjg0KFDTJw40exx58+fV7+OiIhg4sSJ7Nu3j61bt7J8+XKrrCpptVqmTp3Kjh07AEOwVFpayujRo02eXylI8f7775OXl8e7777LK6+8goeHh1pR0NrNfMFQ5W/Lli2A4edhvOJlPD5zlU2VSqbqOWg1PDuoC7nns6ocq1g8tKtN5/6zZ88CVcuYC2FrNguUwFAlaMmSJUycOBGtVsvs2bN555131PvLy8uJjY1V6/U7Ojqyd+9eVq5cSWFhISEhIcyePZv//u//tuUwmzWluV1le/fuVb9WijhYataYXswc1YOo84mkZt8i0MeD0X1D1dQFe3t7hg4dysCBAzl16hS7du0CYMeOHezYsYPp06czcOBAjtzIMbPi5cjioeE2LxPalGor3w6QXVxOqVcHxv1eNAIMObLpvwdOaWlpXLt2TW10mZWVRVZWFlqtVv3wVVFRwffff6+uPAUEBNS6t2HTzsO89Ma73Ei709ciOMCPVa8vYVZk9YFNVEJmld+lY8hwehfeUL+Pjo5Wqw49/vjj0hVdtEgyL9Wdt7c32dnZVqvOduXKFaDminfmODo68thjj/Hll1/y888/07t37yol2/V6PT/88ANgWFXRaDQMHTqUffv2UVhYSHx8vFVWleBOwHH33Xdz+PBhDhw4QGlpKZMmTTIJRhwdHVm4cCErV66kvLyc9957j6VLl6qBUuXAxBq0Wi0zZszghx9+YOvWrfTr1099z87JyQEMWSVRcUl4X8swudCp/O0bX2AbEeLL9vPg4+pAatGdHk3GWS62cvv2bbUKX+fOnW32OkKYY9NAqX379mzYsKHa+zt16mRSlS0kJKTG8tbCIDMzk0uXLjXog6qdnbbWEuD29vYMHz5cTXfYs2cPAD/99BNfHDrJBb9eUGkJvjF7KjQVS8u3f7RhI2NDfQgPD6dLly54e3vTuXNnkzf68vJyMjIySE1NJTkllUPRhg8QCfnQ0b28xn5PSvCk9HvatPMwc154ncq7DpPTM5nzwut8994bZoOlqIRMVhwyk0Zo58QZz65EJWTSx9Ne/fAxffr0FrfHQgiFzEt1p+T0WytQUioM1rav1pyuXbvSp08fLly4wJdffskrr7xiMhfGxcWpv7++ffsChjTICRMmsH//fn744QdeeeUVq5aXvvvuu3FxcWHXrl0cOXKE0tJSpk+fbvIarq6uLF68mJUrV1JSUsLHH3/MI488YrUxmNOvXz/1ffvChQvqRbj4+HiyXH2J9wmnzN6Zc1GGRvPKhc6AMkOgZC6d9aP7hnI5t6hRs0iMK88qJdWFaCw2DZSEbSgBS+/evRvl9RwcHBg5ciSDBw/mxIkT7Nu/n9/adbnTpdaMxuip0FQs7WHkWFHGpUuXuHTJMAk5OzvTt29funTpQufOnXFycsLBwYGOHTty/MpNXlofT3Z+AWP7w4YL4OnizLMTQuniUVprv6eOHTvyH6u+rxIkAej1hozAZX95l5mTR5lcJaw1jVCvN/wuU08CEBwcrO7tEEK0DS4uLoB1AiXj52jXrl29nmPatGlcuHCBwsJCjhw5wt13363epzSYnThxokma+LBhw9i/fz8FBQVcuXLF6ilcw4cPx9HRkW3btnH69GlKSkqYNWuWSRDn5eXFCy+8wHvvvUd2drYaxNiKnZ0d9957Lz/++CObN2+mT58+aLVafjwXT4x/1c8PyoXO+WGGzAVzgVJ1WS62dPXqVYA67SMUwlokd6aFSU9PVz8kjx49ulFf28nJidGjRzPtyUWU2TtX23gOILOolPMZ+Y04usbT198LP9eagyVfF0eeuz+SYcOGqelySt+KjRs38re//Y0PP/yQQ4cO8eHmKOas+MZkzxhARn4J/7v5Nzy73MWf//xnli1bxoMPPsjIkSMJCwszuVr586kL5BdU/yFGr4ek1EyiTp43ub3WNEKNhsyiUq4UGvZXPfjgg9LoT4g2RkkRs0aglJGRARgyFur7XuLi4sKDDz4IwL59+9SKhzdu3FArGho3FYY7q0oAP/zwA3Us+GuRQYMGMXu2odnsxYsX2bBhA7dv3zY5xs/Pj6effhowFChQKHtYrc244l1MTAylZeWc1P6eEVDNz39LsqGpr4eHh03GVFenT58GDJUrhWhssqLUwij7hQYMGIC3t3eTjKGg6t5PsyxNUWtp7LQaFg8NN5uuplgyLJyeYX707NGDqVOnkp+fz9WrV7ly5QoXL14EUNPtVh7DbLl25baF/7eJ5HNe5FQqf6wH8p29KbNzJNdJp64A1eTz9V9TkHWDoKAgAgMDySqy7IPKD9cceXncyGYzcQohGo81AyWlkENDMyJ69epFeHg48fHxfPXVVyxevJj9+/cDhtUdZczGlFWlW7du2WRVCaBPnz44Ojry1VdfceXKFT777DMef/xxkzYbwcHBzJs3j/Xr16u3lZSU2KS/lr29PdOmTWP79u1s2bKFgVMfMFzorEH+bcPc0hz6fZWVlal7uOpa/EMIa5AVpRYkNTVV3Uw/bty4JhuHpalnlh7XEo0O82PF2IgqK0t+rk5m92d5eXkxcOBA5syZw5///GeeffZZJk2aRImTPzdriSdzCm9z+oppkJTl6svxkOGcCxzAZf8ICgZPYOTLy/AzKh5hjq68hNOnT7Nt2zY+/PBDfvpuo0Xnm3erjGX/PsKmw5csOl4I0XooQYdSfKYhlP1J9e2/ZmzmzJmAoUfKwYMH1flxxIgRZo93cnJSq8Ru27bNJqtKAN27d+eJJ54ADKtc//73v6sEmeHh4cyaNUv9/pdffrHJWAAGDhwIGAoEnY6pvj+jsTI7x2axH0hJO3dxcTFbbl8IW5NAqQVRypAOHjwYLy/bNHWzhCWpZ36uTvT1b7oxNobRYX6snzWcf03pz3+N7sW/pvRn/ay7ai1iodFoCAwMZNSoUQwYdneNxyoKygx7xcLDwwm4azwxHXpTZm86aTh5etLnoQfNBksaDQT5t+f5Jx9h4MCB6t4Ar5I8HG+XVLsSpdfrKSkoIS89D4Bla3ZSUWGbFBEhRPOkfEDNz294OrWyZ7M+hRwqc3d35/777wfg8OHDgGGlqqaGkso+l5s3b6p7X2yhc+fOaopdVlYW7733XpXqdkqxCYAjR47w66+/2mQs9vb2REZGApCbklTL0QaOFWXNYkVJqZBYOZVSiMYigVILkZycrPbeGTt2bJOORUk9q4mteyo0F8rG1gmd/RkQ4F3ncw70sSyVzd3RUCEvLj6eLSnFv+flmb6Wku/fbWpk1dxzPax+4yWG33UXM2bMYOnSpfzXf/0XC595hoc6eRmeqlKwpFxtjTsRD3rDSyZl3iTqfCJCiLbDWv1+9Ho9xcXFQN1Lg1enX79+JqtTtfV3a6xVJTCk2D3//PMA3Lp1i9WrV3PzpuleVOMU+m3btnH58mWbjGXQoEGA0cWxGjjdLsWrJK9ZrCgdP34ckP1JoulIoNRC/PTTT4Dhalhz2CdS19QzYd7ovqEE+3lWVzwQDRDi58k//2sJs2bNInDA8BoLaWg0Gpy9vPA2yuX2dHXiqRnDeeAe0+If9vb2dOzYkQUThrNibG/c7EzfDkoLS7lw4CKZiaZNBlOzb9X5PIUQLZcSKCn9derLOEiw1mqFRqOhY8eO6vepqam1PmbYsGEA6t5RW+rQoQNLliwBDA283377bbXgBNyp/Ke0jdi4cSPXr1+3+jgcHBzo2bMnGqBbzpUajw24lUKmmz9Xi3RU6GwXSNbGeAUuODi4ycYh2jYp5tBAFTo95zPybdpTIDExUX3zb+xKdzUZHebHyBBfm59/a2Znp2XV4kjmrPimSrCkfL9ycSS+vj74+vqQ6d4BomrfJ/T//XERPiW38PZw5djBXWi1Go4fP87w4cPNHj86zI/ynAJm/98POLk4UlpcZki3MzNHWroKJoRoHcwVRqgPpZBD+/btrfJ8YAg+Tp06pX6/ZcsWunbtiru7e7WPcXZ2Zty4cRw8eJBt27bx0ksv2bSap4+PD8uWLWP16tVUVFTwzjvv8MILL+Dn56em0ffp0wcHBwd+++03PvvsM5599lmr7OMypgSqPoWZPNvdm803ikyqnno62aPT6UloZwjaVhy9it/ZGyweGs7wIG+rjsUSSsDo6+trUupdiMYkK0oNEJWQybxNx1i++yx/jbrE8t1nmbfpGFEJmVZ9nR9//BEwbFBtDkvhxhqaeiZg1phefLfiQTr6mebVB/t58t2KB5k15s6eI0sLZNzdrzuPzJjI1PEjmDPHUK52165dakd2c8b2C8O9QkfGtQzy0vKqBEnK6tbovqEWjUEI0TpYK1BSSoN3797dKs8HqPt6PDw81ABs69attT5O2auUn5+vFoGwJS8vL5YtW6b2pHrvvfdITU1VV9ZKS0uZO3euGhx9+OGHNb5f19Xt27dJSUlRv0/6eQ9fPnCXusd2fv8wbpbepqDctKyt0lvpaFJW5ae0ufh4Q48/4xLnQjQ2CZTqKSohkxWHYqr0oFHeVKwVLF2/fp3MTMNzGTfVE63LrDG9uL7hJX76f48C8NP/e5RrG14yCZKgfoU0+vTpo6Z1fPXVV9Xm5CurW1C1j7Dx6padnbxtCNGWODg4qF83ZE+Pss/WWvuTKioq2L17N2BoQvvoo4b3z/j4eLVoRHWcnZ3V/b623qukcHd358UXX1T3JX344YdqNbySkhLs7e2ZP3++Woxi9erV3LplnVRn5WevKCws5Pq1qwwI8GZsmB/b42pOWfzwV9sHk5VFR0cDsj9JNC35xFMPFTo9a07G13jMmpNXGpzbq9fr1c7do0ePbhYVaITt2Nlpufv31Zq7+4aaDUjqU0hDo9GoZWizsrI4ceJEtY+ty+qWEKJtME5LKyurf3+83377DbBeoHThwgX16x49euDj48PEiRMB+Oabb9TCEdVRUpHz8vJssi/IHBcXF55//nkCAgKAOytiylidnJx49tlnsbOzA+CDDz6wSv8q5Wc/atSoKsUsam08DmTXcr+1GRcOUX5WQjQFCZTqwZI3lcyiUs5nNKyU6tWrV9VNn9X1hRBtT30Kabi7u/PAAw8AsHPnzhpTOpTVrQNvzWfDf83iwFvzza5uCSHanvp+aK+ouJPS5evr2+Bx6PV6tm3bBsB9992nBnMjR45UU9S3b99e43M0xaoSGIKhp556yqSBqnFanJubm1oAoqioiE8++YTy8vIGveaxY8cAQ/8m4xLp165da5bN4ZV0yLCwMLRa+agqmo789dWDpW8qDXnz0ev1ap71uHHj1LxmIaB+PZz69u1Lp06dgJpT8MCwujVuQCcemdiXcQM6SbqdEAKof6CUlXVnj4txKl99xcfHq8FXv3791Nu1Wi2PPfYYYFhxUvrwVEdZVcrNzW20VSUw/Awef/xxtWJfcnIy586dU+/39vZm0aJFAGRmZvLVV1+h09Wvh51x/6uQkBCcnJzUAPHHH3+knXPDfx/WFhtraIwbERHRxCMRbZ18+qkHSzfUW3qcOXFxcWpucnWVykTbVtdCGpVT8E6ePNkYwxRCtALKKk19AyWlkIO19pvs2rULgIkTJ1apiBYQEKDu6f3yyy9rTBd0dnZWey8phZMai52dHZMnT1a/37x5s8n7sr+/P0899RRgWGHZsmVLvVa9lKIIYWFhakqfcYDoVZJX695Xn1rutya9Xq8GSrI/STQ1CZTqoT4b6utCr9ezZcsWwDAJKF3RhWgoDw8PNQVvx44dJv08hBCiOkoBgtLS0no9XikNHhra8KqZycnJZGdnAzBkyBCzx4wdO1YNCvbu3Vvj8ylBQ05OTqOuKgFVKtlu376dX375Rf0+JCRELVJx/vx59uzZU+fXOH/+PGDIKlA4Ozur7UZ2bN9e697XZwd1rvPr1pdSwAoMpdWFaEoSKNVDjRvqf7/aU3lDfV3ExsaqGzuVxnhCWEvfvn3V3PjaUvCEEALuBEr1XVFSUuCsUchh//79gKHEd3Wly5UKcgAnT54kMTGx2udzcXFpslUl4/ErgcvevXvZt2+f+t7crVs39QLX0aNHTQKp2lRUVJCQkAAY9icZU/Y+Z2VlEUpRjXtfR4Q0fF+ZpZT9Sb169bJpfyshLCGBUj1Vt6HeqaKUp7t61rhXpCZ6vZ5NmzYBMHnyZBwdG2+5W7QNGo2G2bMNvZUyMzMlBU8IUauGpt6lpaUBhnSyhsjOzubq1auAoXBDTUJCQhg6dCgAn332Gbdv3672WGVVKTs7u1FXlYwDpQkTJjBp0iQAfv75Z3bs2KEGS/369eOee+4BDIGUUjq7Njdu3FC/VprbKlxcXBg1ahRgyDCoz95XW1CqGVqz35YQ9SWBUgNUflN5OtiBYUnHSPp5d72v0sfExKjVbZQ3eCGszcPDg/vvvx+QFDwhRO2UD/T1CZSMH9OuXbsGjePnn38GoHfv3mq/oZoogYdOp+PgwYPVHufi4qKu6Pz0008NGmNdGO+vqqioYNSoUUyfPh0wrIRt2bJFLeIwfPhwde/V1q1b1X08NYmLi1Mfa44SbKanp5OUlNTkTeR1Op0a3Mn+JNEcSKDUQMZvKnNHD0WDYVKordKOOTqdju+//x6AyMhIq1QGEqI6/fr1U/cLfP3115KCJ4SolhIo1WePklLIwd7evkGpVAUFBepKipIqVxtHR0cef/xxAH755Rd1Zcsc41Q0JV2tMSkB5ZAhQ9RUu3PnzrFx40a1wt+ECRMYMGAAYHjfrm2cSppet27dzN7v6uqqnvfOnTsbfA4NZVwm3ZJAWAhbk0DJihwcHJgwYQJQe/8Gcy5cuKB+WB08eLBVxyZEZRqNhjlz5gCGDzKnTp1q4hEJIZorJVAqKiqq82OVQg69e/du0BiOHz8OQHBwcJ1S+Lp06aIWMvjiiy9MejoZc3FxUVdsGnNVSWG88tavXz8eeughwNAs9vPPP6e8vByNRsN9992n7jf69NNPqw3+lMq5UHMRDSX9LiUlheTk5AafR0Mo+5MGDRrUpOMQQiGBkpUp6XK5ubl1uiKl0+nYvHkzANOnT69S7lQIW/Dw8GDmzJmAIbg37oYuhBAKJVC6efNmnR+rfJAPDAys9+uXlZWpaXdKOl1dTJs2DTAEekeOHKn2OGV1JTMzs86rShU6PdFpeey/lkF0Wh4VOstW6ZX5vnJaY8+ePdWeUImJiXz88ceUlpai1Wp56KGHCAgIAGDt2rVm06eVsuDBwcE1fqZwc3NTm9AqZdebypkzZwDo2rVrk45DCIUESlbm7OysXpGqyxuOcaO5gQMHWn1cQlSnf//+hISEAJKCJ4QwT2lTUZ+LKTExMUDDCjn8+uuvALi7u9erxLizs7O6QrN//36TBrjGXF1d1Tm8LpkhUQmZzNt0jOW7z/LXqEss332WeZuOEZWQWetja6oo2LVrV5588knAsDL3wQcfUFRUhL29PQsWLMDDwwOAd955h4KCApPHXrx4ETBtyFsdZVUpKSmJ1NTUWo+3hfLycjXg69y58cqRC1ETCZRsQNk0mZqaapJvW52Kigq2bt0KwH333af2fhCiMRin4KWnp3P69OkmHpEQorlRVpTy8/Pr9Di9Xq8GAPUtDV5RUaFeeJw2bVq99zn17NlT3atTU2sEZVUpIyOjxrLiiqNJWaw4FENmkWlj28yiMlYciqk1WFIKXFRXKCM0NJRnn30WMASq7777Lrdu3cLJyYnnnnsOrdbwUe6DDz5Qn0On06l7pSuXBTfHw8ND7Um1e/fuWo+3BaWIg1arxcXFpUnGIERlEijZgJubm5qCt2/fvlqPNy7z2b9/f1sNS4hqeXp6MmPGDMCQmy8peEIIY0qgVN3+nuoYp+q5urrW67WVlRGAHj161Os5FMr7XE5OTrWtEVxdXdUVFkv2Kn3469Ua719z8kqNaXhK0YKaKgoGBgbywgsvAFBcXMyqVavIy8vDzc2NF198EYDCwkLWrVvH7du3TfYaWVppUKn6d/36dXVfWWNSyr4raYBCNAcSKNmIsnR/9epVky7Tld2+fVttcHf//ffLapJoMgMGDCA4OBiAjRs3SgqeEEJVXWPX2igfuH18fOr1eL1ez7Zt2wC499571dWT+nJ3d1cryu3YsaPai0JK2WxLVpWyi8prvD+zqJTzGdWvxCmrJ7WVXvfz82Pp0qWAIWBdtWoVWVlZeHt78/zzz6vj/eqrr/jtt9+AujWt9/T0VIso7Nmzx+LHWYsSuEpZcNGcSKBkI56enmpecE29G5S8a0CtyiNEU9BoNMydOxcwbL42/tsUQrRtyh6lulJKg1dXnro2V65cURvFWivjom/fvupFoe+++87sRSHjVaX6VLGtLKe4rNr76tKjql27drz88svq72PNmjWkpaXRoUMHdS/T1atXay0LXh2l7PqVK1fU311jKCkpUUvP12cPmhC2IoGSDY0dOxYwbGQ1V5GmvLycHTt2ADB79uwGXykToqGMU/B+/PFHScETQgCYZDsogYslkpKSgPrvT1J6+0yYMMFq1WCN92UmJydz9uxZs8dVbsbaEO1dHKu9TwmUiouLLXouT09Pli5dqqbsrV27lhs3bhAaGsojjzwCoAZ/nTp1qtM4vby81IDUkq0D1qJUGPT09MTRsfqflRCNTT6Z21D79u3p3r07AFFRUVXuVzbN29vbN7i/hBDWYpyC980331icglff0rhCiJalLk1nlRSw+gRKycnJZGdnA6iFBqzFy8tLLRm+devWKhXjwLQZq7lVJSV4cqgogRreJ/1cnejr71Xt/fUplOHq6sqiRYvw8/MD4OOPP+batWt0797dpAdRdfuwajJu3DjA8LurrjqgtSmFJ6R/kmhubBYo/fWvf2XkyJG4urqqpS9ro9fr+fOf/0xgYCAuLi5MmjSJuLg4Ww2xUSgNaM+cOWPS/K2srEyt4jNr1qwGdSsXwpqMr7ampqZalILXkNK4QjQmmZsazpIUMTAt/KB8oK+LAwcOAIbN/baogjZkyBB8fX0B2LJli9ljlPS7tLQ0k1WlM2fOsH79egD6FKdCDXP44qFdsdNWf78SKJnLPKmJs7MzzzzzjHph6/PPPyc2NtYk6Nu9e3e1K2bV8fb2VrcC7N+/v06PrS8loJOy4KK5sVmgVFZWxty5c1m0aJHFj/nHP/7BO++8wwcffMDx48dxc3PjnnvusfhNuTnq0KGDmm+r5AzDnTcFJycnevbs2SRjE6I6Xl5e3HfffYAhBa+mK51RCZkNKo0rRGOSuan+qmuMWh1lNcj4sZbKyclRVxmUVR1r02g0PPzww4BhRUPp92TMzc1Nff0dO3ag0+nYtWsXP/zwg3rMfy94iBVjI/BzNU0Z83N1YsXYCEaH1Rwk1rf0OoCjoyPz589XS4B//fXX6iqesjqzZcsW9TZLKatKly5dIicnp87jqgvjwK5jx442fS0h6spmgdIbb7zByy+/bHGBAr1ez8qVK/nv//5vZs6cSb9+/fj8889JSUmp9kpPSzF58mQAjh47zs5jl/nkp5P8+7u96PTwwAMPyGqSaJYGDhxIUFAQUH0KXoVOz5qT8TU+T22lcYVoTDI31Z+XlyF9zNLUOyVtqz5VzH7++WcAevXqpb6uLfj4+DBp0iQAvv32W7P7hJRVpdTUVD766COOHTtmcruDgwOjw/xYP2s4/5rSn/8a3Yt/TenP+ll31RokwZ1AqS57v4zZ29vzyCOPVEnhv/fee9X9Rl999ZVFPaEU7du3JyIiArD9qtK1a9cACAgIkMq/otmxzs5IK7h27RppaWnqGxYY3pTvuusujh49ql71qay0tNTkTVvp2VBeXk55ec0lOxtLhw4dSL/txffRt/i/Y9/+fqsWD0foN76MLl3qN07l/JrLedpSWznX5naes2bN4r333iMtLY3Tp09XqTp1Pj2f/KJSatp6m19UwtmUbPp2MP2w09zO1Zba4rm2Fq15bqorb29vcnNzKSwsrPEclPvS09PRarWEhITU6ZyLioo4e/YsWq2WUaNG2fznNWTIEI4fP05hYSHbt29XC9ooHB0dGTBgAOfOnVPPaebMmYSHh7Nnzx6T8fX2cQPcANBV3EZnQdspBwcHtZhTQ851xowZ5ObmkpaWBsCRI0eYOnUqt27d4vr163z22Wc89dRT+Pv7W/R8o0eP5vLly1y6dEkNCm3xu4iLi0Or1dKnT59m8W+jLb5ft/Zzbcj5afQ2bpby6aefsmzZslqrZx05coRRo0aRkpJCYGCgevuDDz6IRqNh48aNZh+3YsUK3njjjSq3b9iwod7N7YQQQtRPUVERjz76KPn5+WpVruZI5iYhhGgbGjIv1WlF6bXXXuPvf/97jcdcunSpUffc/PGPf+SVV15Rv7958yYhISGMHz++3g3urKmiQkffp98nOfuW2fs1QEcfD859vAg7u7plQpaXl7Nnzx4mT56Mg4ODFUbbfLWVc22O56nX6/n8889JTU0lKCiIxx9/XE0XPZ+ez3/tv1Drc/x1Qh+zK0rN7VxtpS2dq/G+lMYic1PjOHDgAMePH2f8+PHcdddd1R6n/L1fuHABnU7H888/b3HhjLKyMt566y0AHn300UbtqXP48GGOHDkCwCuvvIKjoyMXLlxQm8I7ODhQXl5OUFAQTzzxhFX/Xf/tb38D4D/+4z/qXQa9uLiYVatWAYbVIKXa7tChQxk1ahT//ve/KSwsRKvVsmTJEosC9qysLD766CN1xcfa72G5ubmsXbsWgFdffbVZbEVoS+/XbeVcGzIv1elf4/Lly1mwYEGNx9S3o3JAQABgWKo3vmqXnp7OgAEDqn2ck5OT2UZ4Dg4OzeKX/svF68Sn1rxBMy41n2OXUxk3oFO9XqO5nGtjaCvn2tzOc+7cuaxcuZIbN25w8eJFBg4cCED/IB+8XJ2qFHIw5ufqRP8gn2qrPjW3c7WltnCuTXF+Mjc1DhcXF3Q6HeXl5Radg06nQ6fT4evra/EH4F9//RWdToebmxtdunRp1A/OY8eO5dixY9y+fZsDBw7g5OSkBk7h4eFMmzaNd955hxs3bpCRkaGmsFnjd6rT6QBDpcD6Vvi7fPkyOp0OPz8/xo0bh6urKzt27OD48eOUlZXxzDPPsHLlSm7fvs1HH33EkiVLam0kHBgYSJcuXbh69Spg/b/fpKQkdDodXbt2bXb9k1ryv9W6au3n2pBzq9MShp+fHz179qzxv/r+oXfu3JmAgACTBmc3b97k+PHjNqt40xhSq1lJqu9xQjQFLy8v7r33XgB++OEHdb+FnVbD4qHhNT62ttK4QjSUzE2NQyk6UJdqfw4ODhYHOxUVFWqD2WnTpjX66oK9vT3z588HDH0OlSBp1KhRPProo7Rr105dSVOaxVubpU1nzbl06RKAeiFr2LBhzJw5EzCUM9+5cycvvPACYKg09+mnn1pUQMJ4f57y3m8typil+q9ormxW9S4xMZHo6GgSExOpqKggOjqa6OhokzKQPXv2ZPPmzYChTOeyZcv43//9X3744QfOnz/PE088QVBQEPfff7+thmlzgT4eVj1OiKYyaNAg9er6t99+q1bBGx3mZ7Y0rqcdFpXGFaIxydxUf0qgZK45a3WUymmWuHjxovp1U31w9vDwMLn6PGPGDCZNmqQGbXfffTdgaIabmppqtddVVnbqW3Jer9erQUe3bt3U2wcMGMDcuXMBQ1Cyfft2nnnmGcDQG2rjxo3qalZ1OnToQFhYGIAaPFqDXq9XS8DXd8VXCFuzWaD05z//mYEDB/L6669TUFDAwIEDGThwIKdOnVKPiY2NNekb8J//+Z+8+OKLPPvsswwdOpSCggJ27typvjm3RKP7hhLgXf0yugYI8fNkdN/Gy8MWoj40Gg0PPfQQADdu3CA6Olq9z7g07vzOHvRLjeaewlgJkkSzI3NT/Skf5msrgGHMOF2xJnq9nm3btgGGstZKFbjGdOPGDVauXGlSIavy3gZ3d3d1VWnv3r1We+127doB9Q+UjIO2ynvgIiIiePTRRwFDFcft27fz2GOPARAfH8+2bdvMtn8wNmHCBACio6O5dcs6GTDp6enq18r5C9Hc2Oyd6NNPP0Wv11f5T2liBoY3RuO8co1Gw1/+8hfS0tIoKSlh7969dO/e3VZDtJkKnZ7otDz2X8vgyNVkRgeZX0pXkgpWLo6scyEHIZqCt7c306dPB0xT8MCQhjcgwJv7+4fjXZJHempqrZOvEI2tLc9NDaUEhnUJlCwtRX3lyhU1DaxyG4LGcP78eT7++GMAXF1deeCBBwBDo/jKK0fGq0rWohS7qG+gFB9v6Gc3cOBAsymL3bp1U/+mU1JS2L59O7NmzQIMwU9tvZI6dOigfq30uGoopX9Snz59mkURByHMkU/nVhaVkMm8TcdYvvssf426xIqjV7k1aDiPTgkl2Nc0vS7Yz5PvVjzIrDG9mmi0QtTd4MGDzabgKYxLbyoNJ4UQLZ8SKBUVFdV4nPF7gvEH7Jrs2rULgPHjx9e76lt96PV69u/fz6ZNmwBDCtjSpUvp168f/fr1A+CLL76gouJOQyR3d3eGDRtm1XF4eBg+H9Q3UDpx4gQAPXr0qPaYsLAwNe0uJyeH3bt3ExkZCRiCH6WRriWvVZf0y+qcO3cOME0VFKK5kUDJiqISMllxKKZKBbAyOyfSgrrw5d8f44+RgczuBeuWjuXahpckSBItTuUUvLNnz1Y5RrnaXpdO8EKI5s3SVEPj1CxLSlCnpKSoF1WGDh1av8HVQ3l5OV9//bVaRnvEiBHMmzdPTTGcOnUqYCiwUHlvzujRo9WvlQavDaFUuqtPoFRcXExhYSEAnTp1qvHYjh07smjRIsCw12zv3r3quezatUsNXqoTFBQENHxVqaKiQv25de7cuUHPJYQtSaBkJRU6PWtOxpu/8/cl5fdPX2VYjwD6+kO/UE9JtxMtlre3N9OmTQNg69atVSohhYcbKuEpJWWFEC1fbaWkFRkZGQC0b9/eouMPHDgAGKq01bc0dl3dvHmTNWvW8NtvvwGGog1Tpkwx2Rvl7OzMww8/DMD+/ftNVsjd3d0ZPHgwYJ29SvWpKKhQ3mfbtWtn0e/I39+fF198EYDbt28TFRWlVsrbvHkzcXFx1T5WqYB3/PhxNTirD+O0RWU1TYjmSD6pW8n5jPwae8kAZBaVkm3vBtR/eV2I5mLIkCFqWs33339vkm4TEhICQExMTJOMTQhhfcYl1mvaf5iZmQlA165da33OnJwcdX/NyJEjGzhCy6SkpPD222+rBTsWLFigBgqV9ejRQ10h37Bhg8l5K+Xhb9y40eAKeJamNZpz+fJlADVws0T79u1ZtmyZWuHvzJkzagrchg0bSEpKMvu4oKAgtUBHQyrgKfuThgwZUu/nEKIxSKBkJTnFNQdJijI7w5tSQ3olCNEcGKfgJSYmmqRsGG/gtkYuuxCi6RlvuC8rq37Ou3HjBmBZIYdffvkFgF69euHl5dXAEdbu4sWL/Pvf/wYMwcnSpUvV0tfVmTFjBgC5ubnqXiAwrCoplD1W9aUESsbVFi2h1+u5cOECUPe9Pl5eXrz00ku4uRku4MbFxampdZ988om6MliZkpJ45MiRegV2YGgsDJYF00I0JQmUrKS9i2XNDH1cDcviEiiJ1qBdu3ZqCt6WLVvUvQlarVYtUVvdlUkhRMtVU1aE0huntkCpsLBQ/cA8duxY6w3ODL1ez6FDh/juu+8AQ2GDZcuWWVSW2s3NTa2Ct3PnTrNV/xISEhq0qqQESrm5uXV6nHGJbT+/urdjcHNzY/HixWqaZEpKilqB7/333zd7riEhIerv1tICEMbKysrUdO3a9lQJ0dQkULKSvv5eVRpuVubn6kSv9oYrN9bubi1EUxkyZIg6aRqn4PXp0weQgg5CtEbVBUrG1eEq9/OpTFmdCQoKsrg6Xn2Ul5fz7bffcvDgQQDuuusunnjiCYv3XAH07dtXTSmuXO1TSdtryKpSfUqvw52y4P369at3iW0XFxeee+45dTUpLy9P/dm8++67ZvciKRfIoqKi6nzhV7l45ujo2OZ6kYmWRwIlK7HTalg8NLzGYxYP7Yrr7xtV63rVSIjGYNwDLDotjwpd7X2QNBqNuuE5ISGB8+fPAxAaamiifObMGdsNWAjRqJQ0reoCJeOCBzWV+S4rK+Pw4cMATJ482YojNHXr1i3ef/99Ll26BBia2UZGRta5oa1Go2H27NmAYdXFuNqnsrcqISGh3hXw6hswnDx5EoCePXvW6/EKR0dHnnzySbUCXWlpKWAIfD/88MMqqZZhYWFqIHz8+PE6vZZSfMLaJdaFsAUJlKxodJgfK8ZGVFlZ8nN1YsXYCEaH+dX7qpEQtla5B9jy3WeZt+kYUQmZtT62Xbt2at765s2buXXrFh07dgQME65xp3shRMulpGUpH6Qrq25fS2XKBRRXV9da9wjVV2pqKm+99ZZ6YfKJJ56oU8GDyry8vNSG21u3blX3X3p4eKjPW99VpfoESqWlpWp2ijVKbNvb2zNv3jx69TJtW3Lz5k02bNhQ5XjlZ3Ho0KE6FahSAispCy5aAgmUrGx0mB/rZw3nX1P681+je/GvKf1ZP+suRocZcoeV0qdKB3IhmoPqeoBlFpWx4lCMRcHS0KFD1Rz5TZs2mVTISklJse6AhRBNQgmUqvtgbLxnpjo6nY6dO3cChg/b9U0Zq8mlS5f48MMPAcNqyZIlS6zywXzw4MHq+9yPP/6o3j5mzBgArl+/Xq9VJeNAqaaKgsaUlRlPT0+rpbDZ2dkxZ84cBgwYYHK7ck46nU69rVOnTmoBDuMiFzUpLi5W0zOVVEYhmjMJlGzATqthQIA3Ezr7MyDAGzvtnUlA8nFFc1NjD7DfrTl5pdY0POMUvOvXr3P+/Hm19KsUdBCidagt9U7ZM1OTixcvql83NGWsMr1eT1RUFN988w0AwcHBvPzyy7Xul7KUcbXP69evq7d7enqqq0q7d++u1/MqLF2Bj42NBazfpFer1TJjxgyGDx9e5b5du3apgZxGo+G+++4DDL2wqltlNKb8zNq1a6eWJheiOZNAqZHV56qRELZkaQ+w8xm1l61t3749kZGRgCEFT9mgrfT5EEK0bLU1Rq1tRUmv16srMdOnT6/zXqGa3L59m++//579+/cDhkIzTz75pNUvUPr4+KiNV+HOz0JZVbp27ZpFK2vVsSSNTa/Xq/uklAbf1qTRaJgyZUqVaoRnz55VGwQDdOnSRS2TruyXqokSSFfXt0qI5kYCpUZmPClI+p1oDiztARZzLaHG3imKYcOG4evrC6D2VkpOTpYLA0K0AkrQYW71wJIP+FevXlXfR/r372+1cRUUFLB27Vp1tWratGlWD8SMjRgxQg0QlH1Jnp6eDBo0CKjfqpLCkp+j0tQXsFnFQI1Gw7hx45gyZYrJ7VFRUeo+I41Go/aZ2rdvX61zhFIOXvYniZZCAqUmVJfNj0LYiqU9wE4ePsibb77JJ598ws8//0xqaqrZ4Eej0fDII48AhpQ7nR6u5cGHW49yMPo6FRW6Ko8RQrQMSqBkrtGoUsihppQqJYAYN26c1VKv0tPT+de//qVW3Hv88cetno5WmVar5cEHHwQM+6GUlRJlVenq1at1XlVS0hotKbetvF7v3r1tssfL2IgRI9RMAcXOnTvVCqfh4eG4uroCcOrUqWqfx7gtilKKXIjmTgKlJiSBkmgOLOkB5kYFXiV5gCH42bdvHx9++CF/+ctf+P7774mOjlabzcKdFLyYTFh5DD47C8+/s4fxr3xGp0dXsenwJVuekhDCRpT+OuZ6ASqBQXX7jlJTU9VgylqloWNjY/nggw8AQyGCxYsX06VLF6s8d22MG+quX7+e0tJSvLy81LSyPXv21On5aiuUYez06dMAVSrU2Url4g5gKNoTHx9vsqq0Z8+eavdYXbt2DTDsG7PVSp8Q1iZ/qU3A2cWFPGdvDlzPtLhXjRC2YkkPsD+M7cuf/+d/eOaZZxg/fjyBgYHqfRcuXGDr1q289dZbvPXWW+zatYv4+HgSCl35JgZuVsrESM68yZwV37DtSKwtTkcIYUM1tbhITU0Fqk8FU/a2DB06VK0AW196vZ4jR47w9ddfAxAYGMjy5cvVtN/GpKyMKYGRsq/nypUrdVpVateuHVB7oFRWVkZOTg5AowWFijlz5ph8v379em7cuEH37t3VIFoJ4ir77bffgDvNyIVoCSRQamSHrmdw0Lc/5wIHsPZSZp161QhhK5b0ANNqtXTs2JExY8bw7LPP8tprr/HQQw8xZMgQdYK8desWx44d44sv17PwH5vMvpZyWeC1D/fa8pSEEDagBEr5+VWLuyhNXY1XWhS5ubnExcUBdxq01tft27fZsmWLGpgMGjSIp59+usHBV30pqcanT58mISHBZFVp717L3+dqqyioUFZmXF1dG/2cw8PDeeKJJ0xu+/jjj8nKymLmzJmAYc9W5T3Yer2emJgYQPYniZZFAqVGFJWQyV8OX6LMzsnk9rr0qhHCVmrrAVaZk5MTPXv2ZPr06bz22mssXbqU6dOn06NHDxLyq64kGdMDN7JvVX+AEKJZUgIlpReOQq/Xqx/wlT5Dxn755RcAevTooaaY1UdhYSH//ve/1UIxkZGR3HvvvdjZ2dX7ORsqKCiIu+66C4DPPvuM8vJydVUpPj7e4ia8tVUUVChlwZXXbGydO3fm6aefNrntvffeIzAwEHt7e+BO0QZFdna2+rW5vw8hmisJlBqJSa+aajZeWtKrRghbqqkHWG3atWvHkCFDePjhh5k8/QEbjlII0VSqK7VtvGep8ipHYWGhmo41bty4er92RkYG//rXv9TAY968edx11102L2ZgiYkTJwKGgPHgwYN4eXmp+3os3auk/NxqCpT0ej1nzpwBbFMW3FLBwcE899xzJretWbOGqVOnArBjxw6TVSVlFax79+7N4vclhKUkUGok1uxVI0Rz19HXs6mHIISwASXNtjJlL465xq4nTpwADPuIAgIC6vW6cXFxvP/+++j1ejQaDS+88EKTBgqVOTg4qClpR44cITU1tc6rSkoQWlhYWO0xxiszxntFm0JAQACLFy9Wv799+zYHDx5Uv4+Ojla/VtLurN1gWAhbk0CpkVjaq8bS44Rozkb3DSXYz5PqrhtqgGAfj8YckhDCCoxT3IxXDJRAqVu3bibHl5WVcfjwYQAmT55c59fT6/UcO3aMDRs2AIZCEf/xH//RLNO3OnfurK4iff7553h4eKi9oizZq6QESrm5udUeo5QF79mzZ7NYmfH19eWll15Sx3Lr1i08PQ0Xyn766ScqKirQ6XRcv34dkP1JouWRQKmRWNqrxtLjhGjO7Oy0rFps6LtReSpXvv/bs5MQQrRcxk1nk5KSAKqsGCmrCi4uLnTq1KlOz19RUcG2bdvUhq79+vVj4cKFas+e5uiee+4BDOlzv/zyi5pqGBcXZ9Ik1pyaKgoqlLS7iIiIhg/WSry9vXn55ZfNlo7/9Uw0m/b/yvkMQz89Dw/JNhAtiwRKjcSSXjV+rk709fdqpBEJYVuzxvTiuxUPEujjbnJ7sJ8n3614kPtG9miikQkhrMF4L41S0c644p1Op2PHjh0ATJ8+vU4rIEVFRXz88cdqYDB58mTuv//+Ji3aYAlnZ2cefvhhwFAOvby83OJVJSVQKigoMHt/eXm5msLXtWtXaw3ZKjw8PFi6dKlauQ8gJhMm/fePzP1/P/H9JUM/PemjJ1oaCZQaiSW9ahYP7VqnzfNCNHcz7+7Je3+YzJxx/swY5s2+fz3BtQ0vMWtM4zRJFEJYn1LZTAmUjCvgGafEKdXZoG6NUbOysnjrrbfUvkyPPPIII0eObBapZpbo0aMHPXoYLgRt2LCBMWPGAIY+QjWtKlVXKEOhpK85Ojo2y1U1V1dXXnzxRby8vIjJpMY+ehIsiZZCAqVGZEmvGiFai6iETOZtOsY7l3PJ6RRBQcQAPohP5ciN7NofLIRotry8DJkPSupdVlaWep8SRAHs3LkTgGnTpqHVWvZx48qVK6xZs0YNvp5//nm6d+9ulXE3pvvuuw8wpNHFxcXRr18/oOZVpdoCJaVh6/Dhw600SutzcnLi+ecXsfuq+aBWqeu7bM1OKip0jTcwIerJvvZDhDWNDvNjZIgv5zPyySkuo72LI339vWQlSbQqUQmZrDgUU+X27OJyVhyKYcXYCIYHeTf+wIQQDebt7U12dra6oqQUcujSpYvJcUogpRQ4qM2JEyfUVD1fX18WLFhgksrVkri5uTFr1iw2bdrEzp07mT9/PufOneO3334jKysLX1/fKo9xdLxzEVWp7mfs1KlTQNWCGc3Nscup5JVU3+pEDyRl3iTqfCLjBnRqtHEJUR+yotQEGtKrRojmzqRnWDWkZ5gQLZdS1UwJlJR9M6GhoVWOHTduHA4ODjU+n06n46efflKDpD59+vDcc8+12CBJ0adPH/VnsmvXLvr06QNUv6pkHBgZF8oAyMnJUb8OCgqy9lCtKtXCZuKWHidEU7JZoPTXv/6VkSNH4urqanEX7gULFqDRaEz+i4yMtNUQhRA2YGnPsJjMmzUeI4QtyNzUcJUboyolqzt06ADcWWECGDp0aI3PVVxczLp169TVkokTJzJr1iyTFL6WSqPRMHv2bADS0tLUioCxsbEm6YrmVG46q/yMu3XrZnEaY1MJtLD1g6XHCdGUbPavraysjLlz57Jo0aI6PS4yMpLU1FT1v6+++spGIxRC2IKlvcByS6RnmGh8Mjc1nLKXRln1UAIjJVCKiooCYODAgTUWHcjOzubtt9/mxo0bADz00EPcfffdLaZogyU8PT259957AcNKklLkYd++fTU+rnKgpJRZ7927t/UHaWWW9NEL8fNkdN+qK5BCNDc2u2TzxhtvAPDpp5/W6XFOTk717twthGh6lvYCa+fsiPkiuELYjsxNDacESiUlJSYf6L29vcnNzSU+Pp5+/frVWHTg2rVrfP755+r3zz33XKv9+Q4aNIgTJ06QkZFBUVERAJcvXyY7OxsfHx+TY728vMjPzzf5ud6+fVutANjcyoKbo/TRm7PiGzTcKeAAd/rorVwciZ1d814ZEwKa4R6lgwcP4u/vT48ePVi0aBHZ2VIhS4iWxNKeYRF+0nhQtBwyN91h3O9H2Z/k4OCARqPhyJEj6nFKdbzKTp06pQZJ7du3Z/ny5a02SAJDCt5DDz0EGBrzBgcHA+ZXlZR0UONAKSEhAQA7Ozvc3d2rPKY5Uvrodaz0Pq/00ZMWEaKlaFZJwJGRkcyaNYvOnTtz5coV/vSnPzF16lSOHj1abZO50tJSk02PSkfo8vJyysvLG2XcTUU5v9Z+ntB2zrW1nOcLgzrx5s+xNdwfhq7iNtDyz9USreX3aonWeI4yN5myt7dHq9WSn59PamoqWq2WiIgIbt68ya+//qruoal8njqdjn379nH69Gm0Wi09evTg3nvvxcHBoUX+TOry79rDw4OJEydy4MABUlJS0Gq1xMbGkp6eTvv27dXjvL29SUpKorCwUH3e3377Da1Wy/Dhw5vs51Sf97D7RoQzbdgLHI25QVpOAQHt3RkREYydnbZZ/77b4vt1az/XhpyfRq/XW1x66rXXXuPvf/97jcdcunSJnj17qt9/+umnLFu2jLy8vDoP7urVq3Tt2pW9e/cyceJEs8esWLFCTaUwtmHDhmbZkE0IIVqzoqIiHn30UfLz89XqaLYmc5MQQojqNGReqlOglJmZWWu6QZcuXUx6ATRkMgJDl+///d//5bnnnjN7v7mrdiEhIaSmplbJ/W1tysvL2bNnD5MnT661/GpL11bOtbWdZ4VOT0zmTXJLymjn7EiEn6daDr+1nWtN2tK5ZmdnExgY2KiBksxNjSszM5OPP/4YMKThlZSU8OCDD/LNN98AMHfuXGJjY9W/99zcXD799FP153H//febBK0tVX3+XRv/7BTPPvusuqp05MgRDh8+zKhRoxg9ejT5+fm8//77APznf/5nk1W8a0vvYXKurU9D5qU6pd75+fnh5+dXpxdoiBs3bqgnVx0nJyecnJyq3O7g4NCqf+nG5Fxbn9Zyng7AoOCqjRVNjmkl52qJtnCuTXF+Mjc1Lnd3d3Q6HYBanCArKwudToezszNdunQhNjYWBwcHUlJSTApnLFy4sNn3AaqruvxOg4KCGD16NIcOHVJvO3z4MHPnzgUMpdd1Oh2lpaU4ODhw/fp1dDodXbp0Mfv31Nhaw9+vpeRcW4+GnJvNLk0kJiYSHR1NYmIiFRUVREdHEx0dTUHBnTpXPXv2ZPPmzYBhU+gf/vAHjh07xvXr19m3bx8zZ84kPDyce+65x1bDFEII0YbI3NRw5j6wK01Up0+frpb3PnfunBokeXl58corr7S6IKk+Ro8ebbK6GRMTozaUVXpU3bplaMZ67tw5ALVZrRCicdksUPrzn//MwIEDef311ykoKGDgwIEMHDhQbSoHhqZr+fn5gKGay7lz55gxYwbdu3fn6aefZvDgwURFRTWLqyhCCCFaPpmbGs74Qz5gUoktIiJCXW3avn07AD169GDx4sV4eEiDUTD8TT3xxBMmt+3fvx+4U1EwLy+PiooKkpKSAAgPD2/cQQohABtWvfv0009r7VNhvD3KxcWFXbt22Wo4QgghhMxNVlC5IWxxcTEAU6dOpby8nI0bN6qlwceMGcO4ceNaVRNZa+jYsSN33XUXx48fB+DixYtMmDBBDZRyc3NJTExUj5cgU4im0ez6KAkhhBCi5aioqACgc+fOvPPOO2rfn5kzZzJ+/HgJkqpRuWLi/v37TZr5xsXFATBy5MhGH5sQwkACJSGEEEJYrKJCx7U8OJ8B1/JAp4cBAwbw3nvvqcUdAHr1kqaiNXFwcDBJwbt48aJJo9mjR48C0K1bt0YfmxDCoFk1nBVCCCFE87Xp8CWWvPMTqTl3bvN0hMisaCL8DCli8+fP5+eff266QbYgnTt3ZsCAAURHRwN3giNjISEhjTwqIYRCVpSEEEIIUatNhy8xZ8U3pOYUmtx+swy+iYFc+wCWLFnSaP2zWgvj6okXYy6ZrNYFB4dgZ2fXZGMToq2TFSUhhBBC1KiiQsdLa3ZSU4f6TeeK+JedPTpdRaONqzVwdnbmkUce4X/e+Yqd8YbAU7Hjeibtul1i1hhJYxSiKciKkhBCCCFqFHU+kRuZN2s8JinzJlHnE2s8Rph3Ia2Cb2JMgySAzPwS5qz4hk2HLzXNwIRo4yRQEkIIIUSNUrNvWfU4cYeyWmeOsoK3bM1OKip0jTcoIQQggZIQQgghahHoxp89lAAAC7tJREFUY1kfH0uPE3fUtlqnR1brhGgqEigJIYQQokaj+4YS7OdJdR2RNECInyej+4Y25rBaBVmtE6L5kkBJCCGEEDWys9OyanEkQJVgSfl+5eJI7OzkY0VdyWqdEM2XvKMJIYQQolazxvTiuxUP0tHPtPx3sJ8n3614UCqz1ZOs1gnRfEl5cCGEEEJYZNaYXswc1YOo84mkZt8i0MeD0X1DZSWpAZTVujkrvkEDJiXYZbVOiKYlgZIQQgghLGZnp2XcgE5NPYxWRVmte2nNTpPCDsF+nqxcHCmrdUI0EQmUhBBCCCGamKzWCdH8SKAkhBBCCNEMyGqdEM2LXKYQQgghhBBCiEokUBJCCCGEEEKISiRQEkIIIYQQQohKJFASQgghhBBCiEokUBJCCCGEEEKISiRQEkIIIYQQQohKJFASQgghhBBCiEokUBJCCCGEEEKISiRQEkIIIYQQQohKJFASQgghhBBCiEokUBJCCCGEEEKISiRQEkIIIYQQQohKJFASQgghhBBCiEpsFihdv36dp59+ms6dO+Pi4kLXrl15/fXXKSsrq/FxJSUlLF68GB8fH9zd3Zk9ezbp6em2GqYQQog2QuYlIYQQdWGzQOny5cvodDrWrl3LxYsXefvtt/nggw/405/+VOPjXn75ZbZt28a3337LoUOHSElJYdasWbYaphBCiDZC5iUhhBB1YW+rJ46MjCQyMlL9vkuXLsTGxvL+++/zz3/+0+xj8vPz+fjjj9mwYQMTJkwAYN26dfTq1Ytjx44xfPhwWw1XCCFEKyfzkhBCiLqwWaBkTn5+Pu3bt6/2/tOnT1NeXs6kSZPU23r27EloaChHjx41OyGVlpZSWlpq8hoAOTk5Vhx581ReXk5RURHZ2dk4ODg09XBsqq2ca1s5T5Bzba2U9169Xt/EI7GMLeYlkLmpLfy9t5XzBDnX1qqtnGtD5qVGC5Ti4+NZvXp1tVftANLS0nB0dMTb29vk9g4dOpCWlmb2MW+++SZvvPFGldu7d+/eoPEKIYSov+zsbLy8vJp6GDWy1bwEMjcJIURzU595qc6B0muvvcbf//73Go+5dOkSPXv2VL9PTk4mMjKSuXPnsnDhwrq+ZI3++Mc/8sorr6jf5+XlERYWRmJiYrOfpBvq5s2bhISEkJSUhKenZ1MPx6bayrm2lfMEOdfWKj8/n9DQ0BpXaaytuc1LIHNTW/h7byvnCXKurVVbOdeGzEt1DpSWL1/OggULajymS5cu6tcpKSmMHz+ekSNH8uGHH9b4uICAAMrKysjLyzO5epeenk5AQIDZxzg5OeHk5FTldi8vr1b9Szfm6ekp59rKtJXzBDnX1kqrbbzuE81tXgKZm6Dt/L23lfMEOdfWqq2ca33mpToHSn5+fvj5+Vl0bHJyMuPHj2fw4MGsW7eu1gEOHjwYBwcH9u3bx+zZswGIjY0lMTGRESNG1HWoQggh2gCZl4QQQtiCzS75JScnM27cOEJDQ/nnP/9JZmYmaWlpJjndycnJ9OzZkxMnTgCGK21PP/00r7zyCgcOHOD06dM8+eSTjBgxQioLCSGEaBCZl4QQQtSFzYo57Nmzh/j4eOLj4wkODja5T6k6UV5eTmxsLEVFRep9b7/9NlqtltmzZ1NaWso999zDe++9Z/HrOjk58frrr5tNeWht5Fxbn7ZyniDn2lo153NtqnkJmvfPxdrayrm2lfMEOdfWqq2ca0POU6NvKTVchRBCCCGEEKKRNN5uWyGEEEIIIYRoISRQEkIIIYQQQohKJFASQgghhBBCiEokUBJCCCGEEEKISlp1oHT9+nWefvppOnfujIuLC127duX111+nrKysqYdmdX/9618ZOXIkrq6uJk0RW4M1a9bQqVMnnJ2dueuuu9Syva3N4cOHue+++wgKCkKj0bBly5amHpJNvPnmmwwdOhQPDw/8/f25//77iY2Nbeph2cT7779Pv3791GZ+I0aMYMeOHU09LJv729/+hkajYdmyZU09lGanLc1LIHNTS9dW5iVoO3NTW52XoH5zU6sOlC5fvoxOp2Pt2rVcvHiRt99+mw8++IA//elPTT00qysrK2Pu3LksWrSoqYdiVRs3buSVV17h9ddf59dff6V///7cc889ZGRkNPXQrK6wsJD+/fuzZs2aph6KTR06dIjFixdz7Ngx9uzZQ3l5OVOmTKGwsLCph2Z1wcHB/O1vf+P06dOcOnWKCRMmMHPmTC5evNjUQ7OZkydPsnbtWvr169fUQ2mW2tK8BDI3tXRtZV6CtjM3tcV5CRowN+nbmH/84x/6zp07N/UwbGbdunV6Ly+vph6G1QwbNky/ePFi9fuKigp9UFCQ/s0332zCUdkeoN+8eXNTD6NRZGRk6AH9oUOHmnoojaJdu3b6jz76qKmHYRO3bt3Sd+vWTb9nzx792LFj9S+99FJTD6lFaO3zkl4vc1Nr0JbmJb2+bc1NrXle0usbNje16hUlc/Lz82nfvn1TD0NYoKysjNOnTzNp0iT1Nq1Wy6RJkzh69GgTjkxYU35+PkCr/3dZUVHB119/TWFhISNGjGjq4djE4sWLmT59usm/WVE7mZdaFpmb2oa2MDe1hXkJGjY32dtgPM1WfHw8q1ev5p///GdTD0VYICsri4qKCjp06GBye4cOHbh8+XITjUpYk06nY9myZYwaNYo+ffo09XBs4vz584wYMYKSkhLc3d3ZvHkzERERTT0sq/v666/59ddfOXnyZFMPpUWReanlkbmp9Wvtc1NbmZeg4XNTi1xReu2119BoNDX+V/nNKjk5mcjISObOncvChQubaOR1U5/zFKIlWbx4MRcuXODrr79u6qHYTI8ePYiOjub48eMsWrSI+fPnExMT09TDsqqkpCReeukl1q9fj7Ozc1MPp0m0lXkJZG4SrV9rn5vawrwE1pmbWuSK0vLly1mwYEGNx3Tp0kX9OiUlhfHjxzNy5Eg+/PBDG4/Oeup6nq2Nr68vdnZ2pKenm9yenp5OQEBAE41KWMuSJUv48ccfOXz4MMHBwU09HJtxdHQkPDwcgMGDB3Py5ElWrVrF2rVrm3hk1nP69GkyMjIYNGiQeltFRQWHDx/m3XffpbS0FDs7uyYcoe21lXkJZG6Sual1awtzU1uYl8A6c1OLDJT8/Pzw8/Oz6Njk5GTGjx/P4MGDWbduHVpty1lEq8t5tkaOjo4MHjyYffv2cf/99wOG5fB9+/axZMmSph2cqDe9Xs+LL77I5s2bOXjwIJ07d27qITUqnU5HaWlpUw/DqiZOnMj58+dNbnvyySfp2bMnr776aqsPkqDtzEsgc5PMTa1TW56bWuO8BNaZm1pkoGSp5ORkxo0bR1hYGP/85z/JzMxU72ttV30SExPJyckhMTGRiooKoqOjAQgPD8fd3b1pB9cAr7zyCvPnz2fIkCEMGzaMlStXUlhYyJNPPtnUQ7O6goIC4uPj1e+vXbtGdHQ07du3JzQ0tAlHZl2LFy9mw4YNbN26FQ8PD9LS0gDw8vLCxcWliUdnXX/84x+ZOnUqoaGh3Lp1iw0bNnDw4EF27drV1EOzKg8Pjyp5/G5ubvj4+LTK/P6GaEvzEsjc1NK1lXkJ2s7c1FbmJbDS3GSrUnzNwbp16/SA2f9am/nz55s9zwMHDjT10Bps9erV+tDQUL2jo6N+2LBh+mPHjjX1kGziwIEDZn+H8+fPb+qhWVV1/ybXrVvX1EOzuqeeekofFhamd3R01Pv5+eknTpyo3717d1MPq1FIeXDz2tK8pNfL3NTStZV5Sa9vO3NTW56X9Pq6z00avV6vtzAwE0IIIYQQQog2oWUlRgshhBBCCCFEI5BASQghhBBCCCEqkUBJCCGEEEIIISqRQEkIIYQQQgghKpFASQghhBBCCCEqkUBJCCGEEEIIISqRQEkIIYQQQgghKpFASQghhBBCCCEqkUBJCCGEEEIIISqRQEkIIYQQQgghKpFASQghhBBCCCEqkUBJCCGEEEIIISr5/wHGTG2MJTUvwQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-2, 4)\n",
-    "    a.set_ylim(-2, 2)\n",
-    "plot_tree_2d_scatter(tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(tree, 'noise', ax=ax[1])\n",
-    "ax[1].set_title('Noise');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case, we don't need to care about defining `up`, `reductions` or `transform` anymore, just run `prebuilt_exe.up()`, which is super easy and cool!\n",
-    "\n",
-    "## Summary\n",
-    "Now you know how to define a basic mean estimation operation in Hyperiax, and execute it downwards or upwards. In the next notebook, we will show a more complicated, but also very interesting task in biological morphometry: aligning two species' shapes on the phylogenetic tree, and how Hyperiax can handle this in an easy and efficient way."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/notebooks/tree_building.ipynb b/docs/source/notebooks/tree_building.ipynb
deleted file mode 100644
index 1dcf6ab..0000000
--- a/docs/source/notebooks/tree_building.ipynb
+++ /dev/null
@@ -1,250 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Build a tree in Hyperiax\n",
-    "\n",
-    "All the functions of Hyperiax are about trees. Thus, it is necessary to know how to actually build a tree in Hyperiax. This notebook will show you two main ways of building trees which should meet your needs in most of the cases:\n",
-    "- **Build a tree with a defined topology**\n",
-    "- **Build a tree from Newick strings**\n",
-    "\n",
-    "A tree in Hyperiax is instantiated from the `HypTree` class defined in `hyperiax.tree.tree.py`, which is essentially a linked list by tree nodes as `TopologyNode`. When initializing the `HypTree`, Hyperiax will automatically do all the work including assigning data according to the desired topological order, precomputing a series of indices that is required for the execution engine to run on the tree, or other functions for tree visualizations.\n",
-    "\n",
-    "The topology of the tree can be simply specified by some predefined functions in `hyperiax.tree.topology.py`, so that you don't need to arrange them by hands. Let's first see how we can create and plot trees with simple topologies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import hyperiax\n",
-    "from hyperiax.tree.topology import symmetric_topology, asymmetric_topology, read_topology, write_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.plotting import plot_tree\n",
-    "\n",
-    "import jax\n",
-    "from jax import numpy as jnp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a tree with a simple topology\n",
-    "\n",
-    "### Choose the topology\n",
-    "The shape of the tree is determined by the topology. Take an asymmetric tree as an example, to build an asymmetric tree, with `h` depth and branchings, use `hyperiax.tree.topology.asymmetric_topology(depth)`. Note that before the branch lengths have been specified explicitly, the lengths are set to be uniform by default"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "topology = asymmetric_topology(5)\n",
-    "tree = HypTree(topology)    \n",
-    "plot_tree(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Add node names\n",
-    "It is also possible to add names to the tree nodes to distinguish them. The names of nodes are recorded as the attributes of `TopologyNode`, therefore, can be accessed and changed through iterating over all the nodes. We use a symmetric tree to illustrate it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# build a symmetric tree of height 4 and degree 2\n",
-    "topology = symmetric_topology(height=4, degree=2)\n",
-    "tree = HypTree(topology)\n",
-    "\n",
-    "# iterate over all the nodes in the tree and assign them a name\n",
-    "for i,node in enumerate(tree.iter_topology_bfs()):\n",
-    "    node.name = 'node' + str(i)\n",
-    "    # alternatively the below will yield the same result but save an enumeration\n",
-    "    # node.name = node.id \n",
-    "\n",
-    "plot_tree(tree,inc_names=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Add edge lengths\n",
-    "Besides the node names, all the other properties including the edge lengths are stored in `TopologyNode`'s `data` attribute, which is a dictionary. It means you can assign and access these properties using their names as keys, and the value can be either constants read from files or expressions you want to compute, just like what you can do in a normal Python dict. Here, we use decaying exponential lengths based on the depth of nodes, such that nodes closer to the leaves have shorted edge lengths.\n",
-    "\n",
-    "Since each `HypTree` will calculate the depth of each node, we can use them here. The depth of nodes is returned as a `jax.numpy.ndarray` that holds a single integer, and it is stored in the same order as the rest of the data in the tree. Thus, we can use it freely to do the computations or assign it to any data slot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tree.add_property('edge_length', shape=(1,))\n",
-    "tree.data['edge_length'] = jnp.exp(-jnp.log(2)*tree.node_depths)\n",
-    "plot_tree(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a tree from a Newick string\n",
-    "\n",
-    "For some trees with complex topologies that can not be included into `symmetric_topology` or `asymmetric_topology`, Hyperiax also provides the function that can directly create the tree by read the Newick string which is a way of representing graph-theoretical trees with edge lengths using parentheses and commas. The Newick format is commonly used in phylogenetics, which means it can be easily downloaded from a lot of phylogenetic databases and read by Hyperiax.\n",
-    "\n",
-    "### A handwritten Newick tree\n",
-    "Let's start from a simple Newick string: `'(((A:1,B:1)H:0.5,(C:1,D:2)I:0.5)L:0.1,((E:1,F:1,G:1)J:0.5, K:0.5)M:0.1)N;'`. This string is totally enough to tell us all the information about this tree:\n",
-    "- The tree has height of 4\n",
-    "- The tree has 8 leaves named after A, B, C, D, E, F, G, K, and except node D and K, all the leaf nodes have same edge lengths of 1, and D has 2, K has 0.5\n",
-    "- The root of tree is called N\n",
-    "- The inner nodes are called H, I, J, L, M, individually\n",
-    "\n",
-    "Let's create this tree with `hyperiax.tree.topology.tree_from_newick_recursive(newick_string)` and verify it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "newick_string = '(((A:1,B:1)H:0.5,(C:1,D:2)I:0.5)L:0.1,((E:1,F:1,G:1)J:0.5, K:0.5)M:0.1)N;'\n",
-    "tree = read_topology(newick_string)\n",
-    "plot_tree(tree,inc_names=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is exactly what we are expecting!\n",
-    "\n",
-    "### A real phylogenetic tree\n",
-    "Now it's time to work on something real. Here is the Newick string of a smaller subset of the tree from Kawahara 2023 (DOI: 10.1038/s41559-023-02041-9), let's check if Hyperiax can make it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'(Baronia_brevicornis:0.1711,((Iphiclides_podalirius:0.0847,((Graphium_evemon:0.0688,(Graphium_sarpedon:0.0547,Graphium_agamemnon:0.0519)QS1811:0.0137)QS1810:0.0610,Protographium_marcellus:0.0923)QS1808:0.0132)QS1807:0.0653,(((Hypermnestra_helios:0.0444,(Parnassius_orleans:0.0179,Parnassius_honrathi:0.0312)QS1821:0.0276)QS1820:0.0604,((Archon_apollinus:0.0358,Luehdorfia_puziloi:0.0365)QS1826:0.0339,(Sericinus_montela:0.0897,(Zerynthia_polyxena:0.0241,Allancastria_cerisyi:0.0235)QS1831:0.0304)QS1828:0.0309)QS1825:0.0197)QS1819:0.0257,(Teinopalpus_imperialis:0.0958,(((Battus_polydamas:0.0359,Battus_belus:0.0434)QS1835:0.0877,(Pharmacophagus_antenor:0.0796,(((Trogonoptera_brookiana:0.0370,(Troides_rhadamantus:0.0207,Ornithoptera_richmondia:0.0196)QS1840:0.0224)QS1839:0.0081,((Euryades_corethrus:0.0483,(Parides_agavus:0.0307,(Parides_photinus:0.0265,Parides_eurimedes:0.0282)QS1844:0.0108)QS1843:0.0125)QS1842:0.0056,Cressida_cressida:0.0735)QS1841:0.0134)QS1838:0.0094,((Byasa_alcinous:0.0225,(Atrophaneura_semperi:0.0104,Atrophaneura_dixoni:0.0117)QS1847:0.0129)QS1846:0.0188,((Pachliopta_aristolochiae:0.0035,Pachliopta_kotzebuea:0.0050)QS1849:0.0278,Losaria_coon:0.0230)QS1848:0.0236)QS1845:0.0093)QS1837:0.0343)QS1836:0.0381)QS1834:0.0341,((((Papilio_aristodemus:0.0310,(Papilio_thoas:0.0091,Papilio_cresphontes:0.0088)QS1855:0.0266)QS1854:0.0368,(Papilio_slateri:0.0338,(Papilio_glaucus:0.0301,Papilio_troilus:0.0384)QS1858:0.0096)QS1856:0.0101)QS1853:0.0063,(Papilio_gigon:0.0646,((Papilio_xuthus:0.0353,(Papilio_polyxenes:0.0058,Papilio_zelicaon:0.0054)QS1866:0.0260)QS1863:0.0179,(Papilio_deiphobus:0.0214,(Papilio_protenor:0.0122,(Papilio_polytes:0.0113,(Papilio_phestus:0.0038,Papilio_ambrax:0.0033)QS1872:0.0089)QS1871:0.0099)QS1870:0.0070)QS1868:0.0398)QS1862:0.0075)QS1861:0.0459)QS1852:0.0595,Meandrusa_sciron:0.0910)QS1851:0.0272)QS1833:0.0141)QS1832:0.0172)QS1818:0.0204)QS1804:0.0453)QS1803:0.0000;'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "string_tree = \"(Baronia_brevicornis:0.1711,((Iphiclides_podalirius:0.08472,((Graphium_evemon:0.06883,(Graphium_sarpedon:0.05466,Graphium_agamemnon:0.05188)QS1811:0.01369)QS1810:0.06096,Protographium_marcellus:0.09227)QS1808:0.01315)QS1807:0.0653,(((Hypermnestra_helios:0.04444,(Parnassius_orleans:0.01792,Parnassius_honrathi:0.03123)QS1821:0.0276)QS1820:0.06041,((Archon_apollinus:0.03581,Luehdorfia_puziloi:0.03646)QS1826:0.03388,(Sericinus_montela:0.08969,(Zerynthia_polyxena:0.02408,Allancastria_cerisyi:0.02345)QS1831:0.03039)QS1828:0.03093)QS1825:0.01972)QS1819:0.02572,(Teinopalpus_imperialis:0.09583,(((Battus_polydamas:0.0359,Battus_belus:0.04337)QS1835:0.08774,(Pharmacophagus_antenor:0.07956,(((Trogonoptera_brookiana:0.03698,(Troides_rhadamantus:0.02074,Ornithoptera_richmondia:0.01965)QS1840:0.02241)QS1839:0.00805,((Euryades_corethrus:0.04833,(Parides_agavus:0.03072,(Parides_photinus:0.02647,Parides_eurimedes:0.02819)QS1844:0.0108)QS1843:0.01245)QS1842:0.00564,Cressida_cressida:0.07354)QS1841:0.01338)QS1838:0.00944,((Byasa_alcinous:0.02248,(Atrophaneura_semperi:0.01043,Atrophaneura_dixoni:0.01171)QS1847:0.0129)QS1846:0.01879,((Pachliopta_aristolochiae:0.00348,Pachliopta_kotzebuea:0.00499)QS1849:0.0278,Losaria_coon:0.02297)QS1848:0.02356)QS1845:0.00935)QS1837:0.03434)QS1836:0.03812)QS1834:0.03412,((((Papilio_aristodemus:0.03095,(Papilio_thoas:0.00907,Papilio_cresphontes:0.00884)QS1855:0.02664)QS1854:0.03683,(Papilio_slateri:0.03379,(Papilio_glaucus:0.03012,Papilio_troilus:0.03835)QS1858:0.0096)QS1856:0.01012)QS1853:0.00628,(Papilio_gigon:0.06457,((Papilio_xuthus:0.03532,(Papilio_polyxenes:0.00579,Papilio_zelicaon:0.00541)QS1866:0.02599)QS1863:0.01789,(Papilio_deiphobus:0.02135,(Papilio_protenor:0.01221,(Papilio_polytes:0.01127,(Papilio_phestus:0.00377,Papilio_ambrax:0.00325)QS1872:0.00893)QS1871:0.00991)QS1870:0.007)QS1868:0.03981)QS1862:0.00747)QS1861:0.04588)QS1852:0.05955,Meandrusa_sciron:0.09096)QS1851:0.02719)QS1833:0.01405)QS1832:0.01719)QS1818:0.02038)QS1804:0.04527)QS1803:0;\"\n",
-    "tree = read_topology(string_tree)\n",
-    "plot_tree(tree,inc_names=True)\n",
-    "write_topology(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It works! \n",
-    "\n",
-    "## Summary\n",
-    "In this notebook, we know how to create a tree in Hyperiax, from either predefined topology or Newick strings. In the next one, we will see how to define some basic executions on the tree, and appreciate how fast and easy you can do this with Hyperiax."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
deleted file mode 100644
index 981ee75..0000000
--- a/docs/source/usage.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-=====
-Usage
-=====
-
-.. toctree::
-   :maxdepth: 2
-
-   ./notebooks/tree_building
-   ./notebooks/mean_estimation
-   ./notebooks/Upwards_LDDMM
-   ./notebooks/mcmc_Gaussian_BFFG
\ No newline at end of file
diff --git a/examples/ABFFG.py b/examples/ABFFG.py
deleted file mode 100644
index 966f2f9..0000000
--- a/examples/ABFFG.py
+++ /dev/null
@@ -1,475 +0,0 @@
-# Automatic backward filtering and forward guiding
-import jax
-import jax.numpy as jnp
-from jax.experimental.ode import odeint
-import time
-
-from jax.random import split
-
-from examples.SDE import dts, dot, solve, forward
-from jax.scipy.linalg import cholesky
-
-from hyperiax.execution import OrderedExecutor
-from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda
-from hyperiax.mcmc import ParameterStore, VarianceParameter
-
-# functions for pullback and likelihood ratios, see https://arxiv.org/abs/2010.03509 section 6.1 for details
-quadratic = lambda x,H: jnp.dot(x,jnp.dot(H,x))
-logphi = lambda x,mu,Sigma: jax.scipy.stats.multivariate_normal.logpdf(x,mu,Sigma) # log Gaussian density given precision matrix H
-phi = lambda x,mu,Sigma: jax.scipy.stats.multivariate_normal.pdf(x,mu,Sigma) # Gaussian density in standard form
-omega = lambda Sigma: (jnp.linalg.det(Sigma)*(2*jnp.pi)**Sigma.shape[0])**(-.5) # normalization constant for Gaussian
-omega_H = lambda H: jnp.sqrt(jnp.linalg.det(H)/((2*jnp.pi)**H.shape[0])) # normalization constant for Gaussian
-logomega = lambda Sigma: .5*(-jnp.linalg.slogdet(Sigma)[1]-jnp.log(2*jnp.pi)*Sigma.shape[0]) # log normalization constant for Gaussian
-logomega_H = lambda H: .5*(jnp.linalg.slogdet(H)[1]-jnp.log(2*jnp.pi)*H.shape[0]) # log normalization constant for Gaussian
-logphi_H = lambda x,mu,H:  logomega_H(H)-.5*quadratic(x-mu,H) # log Gaussian density in standard form
-phi_H = lambda x,mu,H: jnp.exp(logphi_H(x,mu,H))
-#logphi_can = lambda y,F,H: logomega_H(H)-.5*jnp.dot(F,jnp.solve(H,F))-.5*jnp.einsum('i,ij,j->',y,H,y)+jnp.dot(F,y) # Gaussian density in canonical form with normalization
-logphi_can = lambda y,F,H: logphi_H(y,jnp.linalg.solve(H,F),H)
-phi_can = lambda y,F,H: jnp.exp(logphi_can(y,F,H))
-logU = lambda y,c,F,H: c-.5*quadratic(y,H)+jnp.dot(F,y) # unnormalized Gaussian density in canonical form
-U = lambda y,c,F,H: jnp.exp(logU(y,c,F,H))
-
-# sample new noise on tree
-def update_noise(tree,key): 
-    tree.data['noise'] = jax.random.normal(key, shape=tree.data['noise'].shape)
-
-# add leaf noise to tree
-def add_leaf_noise(tree,key,params):
-    if tree.data['value'][0].ndim == 1:
-        leaf_values = tree.data['value'][tree.is_leaf]+jnp.sqrt(params['obs_var'].value)*jax.random.normal(key,tree.data['value'][tree.is_leaf].shape)
-    else:
-        # Only add noise to the last timestep of the values
-        leaf_values = tree.data['value'][tree.is_leaf].at[:,-1].add(jnp.sqrt(params['obs_var'].value)*jax.random.normal(key,tree.data['value'][tree.is_leaf][:,-1].shape))
-    tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)
-
-# forward guided sampling, assumes already backward filtered (H,F parameters)
-def forward_guided(x0,dts,dWs,b,sigma,params,a=None,B=None,beta=None,tildea0=None,tildeaT=None,F_T=None,H_T=None,F_t=None,H_t=None):
-    # check inputs
-    assert(tildeaT is not None)
-    assert(F_T is not None or F_t is not None)
-    assert(H_T is not None or H_t is not None)
-
-    # dimension
-    n = tildeaT.shape[0]
-    d = x0.size//n # for tensor product diffusivity, e.g. for landmarks
-    # time
-    ts = jnp.cumsum(dts); T = ts[-1]
-
-    if tildea0 is None:
-        tildea = lambda t,T: tildeaT
-        Phi_inv = lambda t: jnp.eye(n)+H_T@tildea*(T-t)
-    else:
-        tildea = lambda t,T: tildeaT*(t/T)+tildea0*(1-t/T)
-        Phi_inv = lambda t:  jnp.eye(n)+H_T@((-(t**2-T**2)/(2*T))*tildeaT+((T-t)**2/(2*T))*tildea0)
-
-    if B is None and beta is None: # closed form solution
-        tildeb = lambda t,x,params: jnp.zeros((n*d,))
-        Ht = lambda i: solve(Phi_inv(ts[i]),H_T).reshape(H_T.shape) 
-        Ft = lambda i: solve(Phi_inv(ts[i]),F_T).reshape(F_T.shape) 
-    else:
-        tildeb = lambda t,x,params: beta(t,params)+jnp.dot(B(t,params),x)
-        Ht = lambda i: H_t[i]
-        Ft = lambda i: F_t[i]
-
-    def bridge_SFvdM(carry,val):
-        i, X, logpsi = carry
-        dt, dW = val
-        t = ts[i]
-        H = Ht(i); F = Ft(i)
-        tilderx =  F-dot(H,X)
-        if sigma is not None:
-            _sigma = sigma(X,params)
-            _a = jnp.einsum('ij,kj->ik',_sigma,_sigma)
-        else:
-            assert(a is not None)
-            _a = a(X,params)
-            _sigma = cholesky(_a,lower=True,check_finite=False)
-        
-        # SDE
-        Xtp1 = X + b(t,X,params)*dt + dot(_a,tilderx)*dt + dot(_sigma,dW)
-        tp1 = t + dt
-        
-        # logpsi
-        amtildea = _a-tildea(t,T)
-        logpsitp1 = logpsi+(
-                jnp.dot(b(t,X,params)-tildeb(t,X,params),tilderx)
-                -.5*d*jnp.einsum('ij,ji->',amtildea,H)
-                +.5*jnp.einsum('ij,jd,id->',
-                           amtildea,tilderx.reshape((n,d)),tilderx.reshape((n,d)))
-                    )*dt
-        return((i+1,Xtp1,logpsitp1),(t,X,logpsi))    
-
-    # sample
-    (T,X,logpsi), (ts,Xs,logpsis) = jax.lax.scan(bridge_SFvdM,(0,x0,0.),(dts,dWs))#,H,F))
-    Xscirc = jnp.vstack((Xs, X))
-    return Xscirc,logpsi
-
-def Gaussian_down_unconditional(sigma,params_fn=None):
-    @jax.jit
-    def down_unconditional(key,noise,edge_length,parent_value,params,**args):
-        def f(key,noise,edge_length,parent_value):
-            var = edge_length # variance is edge length
-            _params = params if params_fn is None else params_fn(key,params)
-            return {'value': parent_value+jnp.sqrt(var)*dot(sigma(parent_value,_params),noise)}
-
-        return jax.vmap(f)(key,noise,edge_length,parent_value)
-    downmodel_unconditional = DownLambda(down_fn=down_unconditional)
-    return OrderedExecutor(downmodel_unconditional)
-
-# construct down lambda reducers
-def SDE_down_unconditional(n_steps,b,sigma,a=None,params_fn=None):
-    # down_unconditional using vmap
-    @jax.jit
-    def down_unconditional(key,noise,edge_length,parent_value,params,**args):
-        def f(key,noise,edge_length,parent_value):
-            _params = params if params_fn is None else params_fn(key,params)
-            var = edge_length # variance is edge length
-            _dts = dts(T=var,n_steps=n_steps); _dWs = jnp.sqrt(_dts)[:,None]*noise
-            Xs = forward(parent_value.reshape((n_steps+1,-1))[-1],_dts,_dWs,b,sigma,_params,a=a)
-            return {'value': Xs}
-
-        return jax.vmap(f)(key,noise,edge_length,parent_value)
-    downmodel_unconditional = DownLambda(down_fn=down_unconditional)
-    return OrderedExecutor(downmodel_unconditional)
-
-def SDE_down_conditional(n_steps,b,sigma,a,B=None,beta=None,params_fn=None):
-    @jax.jit
-    def down_conditional_closed_form(key,noise,edge_length,v_0,v_T,F_T,H_T,parent_value,params,**args):
-        def f(key,noise,edge_length,v_0,v_T,F_T,H_T,parent_value):
-            _params = params if params_fn is None else params_fn(key,params)
-            var = edge_length # variance is edge length
-            _dts = dts(T=var,n_steps=n_steps)
-            _dWs = jnp.sqrt(_dts)[:,None]*noise
-            tildea0 = a(v_0,_params); tildeaT = a(v_T,_params)
-            Xs,logpsi = forward_guided(parent_value.reshape((n_steps+1,-1))[-1],_dts,_dWs,b,sigma,_params,a=a,beta=beta,B=B,tildea0=tildea0,tildeaT=tildeaT,F_T=F_T,H_T=H_T)
-            return {'value': Xs, 'logpsi': logpsi}
-
-        return jax.vmap(f)(key,noise,edge_length,v_0,v_T,F_T,H_T,parent_value)
-    def down_conditional(key,noise,edge_length,v_0,v_T,F_t,H_t,parent_value,params,**args):
-        def f(key,noise,edge_length,v_0,v_T,F_t,H_t,parent_value):
-            _params = params if params_fn is None else params_fn(key,params)
-            var = edge_length # variance is edge length
-            _dts = dts(T=var,n_steps=n_steps)
-            _dWs = jnp.sqrt(_dts)[:,None]*noise
-            tildea0 = a(v_0,_params); tildeaT = a(v_T,_params)
-            Xs,logpsi = forward_guided(parent_value.reshape((n_steps+1,-1))[-1],_dts,_dWs,b,sigma,_params,a=a,beta=beta,B=B,tildea0=tildea0,tildeaT=tildeaT,F_t=F_t,H_t=H_t)
-            return {'value': Xs, 'logpsi': logpsi}
-
-        return jax.vmap(f)(key,noise,edge_length,v_0,v_T,F_t,H_t,parent_value)
-    if B is None and beta is None:
-        downmodel_conditional = DownLambda(down_fn=down_conditional_closed_form)
-    else:
-        downmodel_conditional = DownLambda(down_fn=down_conditional)
-    return OrderedExecutor(downmodel_conditional)
-
-def Gaussian_down_conditional(n,a,d=1,params_fn=None):
-    @jax.jit
-    def down_conditional(key,noise,edge_length,c_0,F_0,H_0,F_T,H_T,parent_value,params,**args):
-        def f(key,noise,edge_length,c_0,F_0,H_0,F_T,H_T,parent_value):
-            _params = params if params_fn is None else params_fn(key,params)
-            x = parent_value
-            var = edge_length # variance is edge length
-            covar = var*a(parent_value,_params) # covariance matrix
-
-            #invSigma = jnp.linalg.inv(covar)
-            Sigma = covar
-            #H = H_T+invSigma
-            invH = jnp.linalg.solve(jnp.eye(n)+Sigma@H_T,Sigma)
-            #mu = solve(H,F_T+dot(invSigma,x))
-            mu = dot(invH,F_T+solve(Sigma,x))
-
-            # for likelihood ratio
-            #Sigma_T = jnp.linalg.inv(H_T)
-            Sigma_T = invH
-            v_T = dot(Sigma_T,F_T) # solve(H_T,F_T)
-
-            # test
-            #Sigma_0 = jnp.linalg.inv(H_0)
-            #v_0 = dot(Sigma_0,F_0)
-            v_0 = solve(H_0,F_0)
-
-            inv_covar_Sigma_T = jnp.linalg.solve(jnp.eye(n)+H_T@Sigma_T,H_T) # inv(covar+Sigma_T)
-
-            return {
-                #'value': mu+jax.scipy.linalg.solve_triangular(cholesky(H,lower=True),noise.reshape((n,d))).flatten(),
-                'value': mu+dot(cholesky(invH,lower=True),noise),
-                #'value': mu+dot(jax.scipy.linalg.sqrtm(invH),noise),
-                'logw': jnp.sum(jax.vmap(
-                    #lambda v_T,parent_value,c_0,F_0: logphi(v_T,parent_value,covar+Sigma_T)-logU(parent_value,c_0,F_0,H_0),
-                    lambda v_T,parent_value,c_0,F_0: logphi_H(v_T,parent_value,inv_covar_Sigma_T)-logU(parent_value,c_0,F_0,H_0),
-                    (1,1,0,1))(v_T.reshape((n,d)),parent_value.reshape((n,d)),c_0.reshape(d),F_0.reshape((n,d)))),
-                }
-
-        return jax.vmap(f)(key,noise,edge_length,c_0,F_0,H_0,F_T,H_T,parent_value)
-    downmodel_conditional = DownLambda(down_fn=down_conditional)
-    return OrderedExecutor(downmodel_conditional)
-
-#backwards filter
-def backward_filter(dts,params,c_T,v_T,F_T,H_T,B=None,beta=None,tildea0=None,tildeaT=None,params_fn=None):
-    # backward filter according to the following ODEs
-    # dH(u) = (-B(u)'H(u) - H(u)B(u) + H(u)\tilde a(u)H(u)) du
-    # dF(u) = (-B(u)'F(u) + H(u)\tilde a(u)F(u) + H(u)β(u)) du
-    # dc(u) = (β(u)'F(u) + 1/2 F(u)'\tilde a(u)F(u) - 1/2 tr(H(u)\tilde a(u))) du
-
-    # check inputs
-    assert(tildeaT is not None)
-    # make c_T an array if it is a scalar
-    if jnp.isscalar(c_T):
-        c_T = jnp.array([c_T])
-
-    # dimension
-    n = tildeaT.shape[0]
-    d = v_T.size//n # for tensor product diffusivity, e.g. for landmarks
-    # time
-    # time
-    T = dts.sum()
-    ts = jnp.cumsum(jnp.concatenate((jnp.array([0.]), dts)))[:-1] # time discretization
-
-
-    if tildea0 is None:
-        tildea = lambda t: tildeaT
-        Phi_inv = lambda t:  jnp.eye(n)+H_T@tildeaT*(T-t)
-    else:
-        tildea = lambda t: tildeaT*(t/T)+tildea0*(1-t/T)
-        Phi_inv = lambda t:  jnp.eye(n)+H_T@((-(t**2-T**2)/(2*T))*tildeaT+((T-t)**2/(2*T))*tildea0)
-
-    if B is None and beta is None: # closed form solution
-        H_0 = solve(Phi_inv(0),H_T).reshape(H_T.shape)
-        F_0 = solve(Phi_inv(0),F_T).reshape(F_T.shape)
-        # log determinant of Phi_inv(0)
-        log_det_phi_inv = jnp.linalg.slogdet(Phi_inv(0))[1]
-        c_0 = jax.vmap(lambda v_T,c_T: c_T+.5*v_T.T@(H_T-H_0)@v_T-.5*log_det_phi_inv,(1,0))(v_T.reshape((n,d)),c_T)
-        #c_0 = jax.vmap(lambda v_T: logphi_H(jnp.zeros(n),v_T,H_0),1)(v_T.reshape((n,d)))
-        #c_0 = jax.vmap(lambda v_T,c_T: c_T-logphi_H(jnp.zeros(n),v_T,H_T)+logphi_H(jnp.zeros(n),v_T,H_0),(1,0))(v_T.reshape((n,d)),c_T)
-        return {'c_0': c_0, 'F_0': F_0, 'H_0': H_0}
-    else:
-        # Define the ODE system
-        def ode_system(y, tau, params):
-            # Unpack state
-            Ht, Ft, ct = y[:n*n].reshape((n,n)), y[n*n:n*n+n], y[-1:]
-            t = T-tau
-            # Get coefficients at time t
-            Bt = B(t,params); betat = beta(t,params) if beta is not None else jnp.zeros(n)
-            # Compute derivatives
-            dH = -Bt.T@Ht - Ht@Bt + Ht@tildea(t)@Ht
-            dF = -Bt.T@Ft + Ht@tildea(t)@Ft + Ht@betat
-            dc = -(betat.T@Ft + 0.5*Ft.T@tildea(t)@Ft - 0.5*jnp.trace(Ht@tildea(t)))
-            # Flatten and combine
-            return -jnp.concatenate([dH.flatten(), dF, jnp.array([dc])])
-        
-        # Solve backwards in time from T to 0
-        # Initial conditions at time T
-        y0 = jnp.concatenate([H_T.flatten(), F_T, c_T])
-        # Solve ODE
-        solution = odeint(ode_system, y0, ts, params)
-        ## direct Euler method
-        #def scan_step(carry, dt):
-        #    y_curr, t_curr = carry
-        #    t_next = t_curr+dt
-        #    # Compute derivative and update using Euler step
-        #    dy = ode_system(y_curr, t_curr, params)
-        #    y_next = y_curr+dt*dy
-        #    return (y_next, t_next), y_next
-        ## Use scan to iterate through time steps
-        #(y,t), solution = jax.lax.scan( scan_step, (y0, 0.), dts)
-        
-        # Extract result and final values (at t=0)
-        H_t = solution[::-1,:n*n].reshape((-1,n,n))
-        F_t = solution[::-1,n*n:n*n+n]
-        c_t = solution[::-1,-1:]
-        H_0 = H_t[0]; F_0 = F_t[0]; c_0 = c_t[0]
-
-        return {'c_0': c_0, 'F_0': F_0, 'H_0': H_0, 'F_t': F_t, 'H_t': H_t}
-        
-# initialize tree for up pass
-def get_init_up(n,tree,d=1,root=None,n_steps=1,B=None,beta=None):
-    tree.add_property('c_0', shape=(d,)); tree.add_property('F_0', shape=(n*d,)); tree.add_property('H_0', shape=(n,n)); tree.add_property('v_0', shape=(n*d,)); tree.add_property('c_T', shape=(d,)); tree.add_property('F_T', shape=(n*d,)); tree.add_property('H_T', shape=(n,n)); tree.add_property('v_T', shape=(n*d,)); tree.add_property('logw'); tree.add_property('logpsi')
-    if B is not None or beta is not None:
-        tree.add_property('F_t', shape=(n_steps,n*d)); tree.add_property('H_t', shape=(n_steps,n,n));
-    if root is not None:
-        tree.data['v_0'] = tree.data['v_0'].at[:].set(root); tree.data['v_T'] = tree.data['v_T'].at[:].set(root)
-    def init_up(leaf_values,params,v_T=None):
-        #tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(leaf_values)
-        tree.data['H_T'] = tree.data['H_T'].at[tree.is_leaf].set((jnp.eye(n)/params['obs_var'].value)[None,:,:])
-        tree.data['F_T'] = tree.data['F_T'].at[tree.is_leaf].set(jax.vmap(lambda H,v: dot(H,v))(tree.data['H_T'][tree.is_leaf],leaf_values))
-        Sigma = params['obs_var'].value*jnp.eye(n)
-        tree.data['v_T'] = tree.data['v_T'].at[tree.is_leaf].set(leaf_values)
-        tree.data['c_T'] = tree.data['c_T'].at[tree.is_leaf].set(jax.vmap(lambda v: jax.vmap(lambda v: logphi(jnp.zeros(n),v,Sigma),1)(v.reshape((n,d))))(leaf_values))
-        # update v_0 from parent v_T
-        if v_T is not None and root is not None:
-            tree.data['v_0'] = v_T[tree.parents]
-            tree.data['v_0'] = tree.data['v_0'].at[tree.is_root].set(root)
-    return init_up
-
-# construct up lambda reducer
-def SDE_up(n_steps,a,B=None,beta=None,params_fn=None):
-    @jax.jit
-    def up(key,edge_length,v_0,c_T,v_T,F_T,H_T,params,**args):
-        def f(key,edge_length,v_0,c_T,v_T,F_T,H_T):
-            var = edge_length # variance is edge length
-            T = var # running time of Brownian motion
-            _params = params if params_fn is None else params_fn(key,params)
-            return backward_filter(dts(T=T,n_steps=n_steps),_params,c_T,v_T,F_T,H_T,B=B,beta=beta,tildea0=a(v_0,_params),tildeaT=a(v_T,_params))
-        return jax.vmap(f)(key,edge_length,v_0,c_T,v_T,F_T,H_T)
-    def transform(child_c_0,child_F_0,child_H_0,**args):
-        F_T = child_F_0
-        H_T = child_H_0
-        c_T = child_c_0
-        v_T = jax.vmap(lambda H,F: solve(H,F))(H_T,F_T)
-        return {'c_T': c_T, 'v_T': v_T, 'F_T': F_T, 'H_T': H_T}
-    up_preserves = ['c_0','F_t','H_t'] if B is not None or beta is not None else []
-    upmodel = UpLambdaReducer(up, transform,
-                       reductions={
-                           'c_0': 'sum',
-                           'F_0': 'sum',
-                           'H_0': 'sum',
-                        },
-                        up_preserves=up_preserves
-            )
-    return OrderedExecutor(upmodel)
-
-def Gaussian_up(n,a,d=1,params_fn=None):
-    @jax.jit
-    def up(key,edge_length,c_T,F_T,H_T,params,**args):
-        def f(key,edge_length,c_T,F_T,H_T):
-            var = edge_length # variance is edge length
-            _params = params if params_fn is None else params_fn(key,params)
-            #Sigma_T = jnp.linalg.inv(H_T) # alt. Q_T
-            #v_T = dot(Sigma_T,F_T)
-            v_T = solve(H_T,F_T)
-            covar = var*a(v_T,_params) # covariance matrix
-
-            invPhi_0 = (jnp.eye(n)+H_T@covar)
-            #Sigma_0 = Sigma_T@invPhi_0 # = Sigma_T+covar, alt. C_0
-            Sigma_0 = jnp.linalg.solve(H_T,invPhi_0) # = Sigma_T+covar, alt. C_0
-            #H_0 = jnp.linalg.inv(Sigma_0) # hat H
-            H_0 = jnp.linalg.solve(invPhi_0,H_T) # hat H
-            F_0 = solve(invPhi_0,F_T) # hat F
-            v_0 = dot(Sigma_0,F_0)
-            c_0 = jax.vmap(
-                lambda v_T,c_T,F_T: 
-                c_T-logphi_H(jnp.zeros(n),v_T,H_T)+logphi_H(jnp.zeros(n),v_T,H_0), 
-                # = (c_T-logphi_can(jnp.zeros(n),F_T,H_T))+logphi_H(jnp.zeros(n),v_T,H_0),
-                #jax.scipy.stats.multivariate_normal.logpdf(v_0,jnp.zeros(n*d),Sigma_0),
-                (1,0,1))(v_T.reshape((n,d)),c_T.reshape(d),F_T.reshape((n,d)))
-
-            return {'c_0': c_0, 'F_0': F_0, 'H_0': H_0}
-        return jax.vmap(f)(key,edge_length,c_T,F_T,H_T)
-    def transform(child_c_0,child_F_0,child_H_0,**args):
-        F_T = child_F_0
-        H_T = child_H_0
-        return {'c_T': jax.vmap(lambda F_T,H_T: jax.vmap(lambda F_T: logphi_can(jnp.zeros(n),F_T,H_T),1)(F_T.reshape((n,d))))(F_T,H_T), 'F_T': F_T, 'H_T': H_T}
-    upmodel = UpLambdaReducer(up, transform,
-                       reductions={
-                           'c_0': 'sum',
-                           'F_0': 'sum',
-                           'H_0': 'sum',
-                        }
-            )
-    return OrderedExecutor(upmodel)
-
-# Crank-Nicolson update with possibly node-dependent lambd
-lambd = .9
-update_CN = lambda noise,key: noise*lambd+jnp.sqrt((1-lambd**2))*jax.random.normal(key,noise.shape)
-
-# log posterior, SDE case
-def get_log_likelihood(tree,down_conditional,up,init_up,include_leaves=False):
-    def log_likelihood(data,state):
-        """Log likelihood of the tree."""
-        params,(noise,v_T) = state
-        # backwards filtering with current parameters
-        init_up(data,params,v_T); up.up(tree,params.values())
-        v = tree.data['value'][tree.is_root][:,0] if tree.data['value'].ndim==3 else tree.data['value'][tree.is_root]
-        c,F,H = tree.data['c_T'][tree.is_root],tree.data['F_T'][tree.is_root],tree.data['H_T'][tree.is_root]
-        v = jnp.squeeze(v); c = jnp.squeeze(c); F = jnp.squeeze(F); H = jnp.squeeze(H)
-        tree_log_likelihood = c.sum()+F@v-.5*v.T@dot(H,v)
-        # forwards guiding with current noise
-        tree.data['noise'] = noise; down_conditional.down(tree,params.values())
-        tree_logcorrection = jnp.sum(tree.data['logpsi']) if 'logpsi' in tree.data else jnp.sum(tree.data['logw'])
-        # residuals
-        # compute log likelihood
-        residuals = tree.data['value'][tree.is_leaf][:,-1]-data if tree.data['value'].ndim==3 else tree.data['value'][tree.is_leaf]-data
-        leaves_log_likelihood = jnp.sum(jax.scipy.stats.norm.logpdf(residuals,0,jnp.sqrt(params['obs_var'].value))) if include_leaves else 0.
-        return tree_log_likelihood+tree_logcorrection+leaves_log_likelihood
-    return log_likelihood
-
-# posterior
-def get_log_posterior(log_likelihood,skip_obs_var=False):
-    def log_posterior(data,state):
-        """Log posterior given the state and data."""
-        parameters,_ = state
-        log_prior = sum([v.log_prior() for k,v in parameters.params.items() if (not skip_obs_var or k!='obs_var')])
-        log_like = log_likelihood(data,state)
-        return log_prior + log_like
-
-    return log_posterior
-
-def get_proposal(tree,obs_var_sample_conditional=False):
-    def proposal(data, state, key):
-        subkeys = jax.random.split(key,2)
-        params,(noise,v_T) = state
-
-        # Use static variable to alternate between parameter and noise updates
-        if not hasattr(proposal, 'update_params'):
-            proposal.update_params = True
-        if not hasattr(proposal, 'update_obs_var'):
-            proposal.update_obs_var = False
-
-        if proposal.update_params:
-            if not obs_var_sample_conditional:
-                new_params,log_correction = params.propose(subkeys[1])
-            else:
-                if not proposal.update_obs_var:
-                    # update all parameters except obs_var
-                    subkeys = jax.random.split(subkeys[1],len(params.params))
-                    proposals_and_corrections = {k: v.propose(rngkey) for rngkey,(k,v) in zip(subkeys,params.params.items()) if k!='obs_var'}
-                    new_params = {k: v[0] for k,v in proposals_and_corrections.items()}
-                    new_params['obs_var'] = params['obs_var']
-                    log_correction = sum(v[1] for v in proposals_and_corrections.values())
-                else:
-                    # obs_var
-                    residuals = tree.data['value'][tree.is_leaf][:,-1]-data if tree.data['value'].ndim==3 else tree.data['value'][tree.is_leaf]-data
-                    alpha_post = params['obs_var'].alpha+.5*residuals.size # alpha from prior
-                    beta_post = params['obs_var'].beta+.5*jnp.sum(residuals**2) # beta from prior
-                    new_obs_var = beta_post/jax.random.gamma(subkeys[-1],alpha_post) # inverse gamma sample
-                    new_params = {k: v for k,v in params.params.items() if k!='obs_var'}
-                    new_params['obs_var'] = VarianceParameter(**{**params['obs_var'].__dict__,'value':new_obs_var}) if not params['obs_var'].keep_constant else params['obs_var']
-                    log_correction = 0.
-                new_params = ParameterStore(new_params)
-                proposal.update_obs_var = not proposal.update_obs_var
-            new_state = new_params,(noise,tree.data['v_T'])
-        else:
-            new_noise = update_CN(noise,subkeys[1])
-            new_state = params,(new_noise,v_T)
-            log_correction = 0.
-
-        proposal.update_params = not proposal.update_params
-        return new_state,log_correction
-    return proposal
-
-# test and time the operations
-def test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key):
-    init_up(leaf_values,params)
-    up.up(tree,params.values())
-    down_conditional.down(tree,params.values())
-
-    # time the operations
-    subkey, key = split(key)
-    update_noise(tree,subkey)
-    down_unconditional.down(tree,params.values())
-
-    start_time = time.time()
-    down_unconditional.down(tree,params.values())
-    jax.block_until_ready(tree.data['value'])
-    print(f"Time elapsed: {(time.time() - start_time)*1000:.4f} ms")
-
-    start_time = time.time()
-    down_conditional.down(tree,params.values())
-    jax.block_until_ready(tree.data['value'])
-    print(f"Time elapsed: {(time.time() - start_time)*1000:.4f} ms")
-    
-    start_time = time.time()
-    up.up(tree,params.values())
-    jax.block_until_ready(tree.data['H_0'])
-    print(f"Time elapsed: {(time.time() - start_time)*1000:.4f} ms")
-    None
\ No newline at end of file
diff --git a/examples/DICAROS.ipynb b/examples/DICAROS.ipynb
deleted file mode 100644
index 0c62cc2..0000000
--- a/examples/DICAROS.ipynb
+++ /dev/null
@@ -1,609 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes (DICAROS)\n",
-    "\n",
-    "In this notebook we estimate the inner nodes and mean of a phylogenetic tree, where only the leaf node values are known. This aim is the same as in the example notebook mean_estimation.ipynb. The current notebook adds another layer of complexity in that nodes values are no longer assumed to be vectors, but _shapes_; in our case discretized outline curves describing the wing morphology of certain butterfly species. The inner nodes thus represent wing shapes of ancestral butterfly species.\n",
-    "\n",
-    "As in mean_estimation.ipynb we use a simple, recursive estimator for the inner nodes; any inner node will be estimated as a weighted mean of its children. Due to the _non-linear_ nature of shapes, we cannot use the ordinary 'Euclidean' weighted mean. Luckily, the python library _jaxgeometry_ is built for handling such data, including computing weighted means of shape observations. Below, we construct a 'fuse' function which computes such weighted means of shapes using jaxgeometry. \n",
-    "\n",
-    "This is a simplified version of the code\n",
-    "\n",
-    "Preprint can be found at; https://doi.org/10.1101/2025.02.26.638985\n",
-    "\n",
-    "__Content of the notebook__\n",
-    "\n",
-    "1. We load a dataset of shapes of 4 butterfly species and an associated evolutionary tree (phylogeny) for these species \n",
-    "\n",
-    "2. We estimate and plot the inner nodes, i.e. the estimated shapes of ancestral butterfly species. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. load example data\n",
-    "\n",
-    "The example data is 4 observations of butterfly wings, also described in Baker et. al. 2024, https://doi.org/10.48550/arXiv.2402.01434 .\n",
-    "We represent each wing by 25 landmarks.  \n",
-    "\n",
-    "We also load a phylogeny for these 4 species, i.e. a tree with 4 leaf nodes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a new Hyperiax client\n",
-    "\n",
-    "import hyperiax\n",
-    "from jax.random import PRNGKey, split\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from jax import vmap, lax\n",
-    "\n",
-    "key = PRNGKey(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import requests \n",
-    "\n",
-    "from hyperiax.tree.topology import read_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "# Load all files\n",
-    "tree_string = requests.get(\"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/tree.txt\").text\n",
-    "tree = read_topology(tree_string,precompute_child_gathers=True)\n",
-    "landmark = pd.read_csv(\"https://raw.githubusercontent.com/MichaelSev/Hyperiax_data/main/landmarks.csv\", sep=\",\", header=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "((Graphium_evemon:0.06883,Graphium_sarpedon:0.06835)QS1810:0.13941,(Atrophaneura_dixoni:0.0434,Pachliopta_aristolochiae:0.05484)QS1845:0.16755)QS1804:0;\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      ") missing from font(s) DejaVu Sans.se/envs/hyperiax_dev/lib/python3.11/site-packages/IPython/core/events.py:82: UserWarning: Glyph 13 (\n",
-      "  func(*args, **kwargs)\n",
-      ") missing from font(s) DejaVu Sans.se/envs/hyperiax_dev/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 13 (\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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CAKBw1mjQqvr06ZP777//A+936dIlyy2n/wIAVDtPdLSq+++/P/X19R94f5dddsn8+fNbMREAAJ8GRYNWtcIKK5QdAQCAVmDqFK2uvr4+t956axYsWJAkeeutt3L00UfniCOOyCOPPFJyOgAAiqBo0KomTJiQTp06Zeutt07Xrl1z7733plevXjn33HNzwQUXZMiQIbn88svLjgkAwCekaNCqDj744PTu3TsTJ07MwIEDs+WWW2bllVfOnDlz8s4776Rfv3455phjyo4JAMAnpGjQqmbNmpWxY8dm+PDhmTx5curr63Pqqaemc+fO6dixY84555y88cYbZccEAOATUjRodR06dEjy3pkaSdK+ffvme+3atSslEwAAxVI0aFXdu3fPYYcdlvvuuy+77LJL2rVrl1NOOaX5/nHHHZfll1++xIQAABRB0aBVnXPOOXnqqafy+c9/PrfeemuuueaavPTSS6mrq0tdXV0eeeSRHH/88WXHBADgE6ppampqKjvE0pozZ066du2aJJk9e3a6dOlSciL+E6+++mpuuummbLfddvnMZz6TN998M0cddVTmzJmTAw44IDvuuGPZEfmEfFYByuHvL5VE0QAK57MKUA5/f6kkpk5RUV599dVccMEFZccAAOATUjSoKLfeemuOOOKIsmMAAPAJKRoAAEDhlis7AG1Lbe2Hd9sqWjIEAMCHUDRoVU1NTdl4442z4YYbLvH+jBkzcsstt7RyKgAAiqZo0KqWX375rL766rn44ouXeH/8+PGKBgDAMsAaDVrVpptumjfeeOMD76+xxhrp379/KyYCAODT4BwNoHA+qwDl8PeXSmJEAwAAKJyiQau68cYbc9lll7W4duaZZ6ZHjx7p2rVrtt9++5KSAQBQJEWDVrX//vu3KBq33357Ro8enbq6uvTv3z8333xzhg8fXmJCAACKoGjQql599dXsu+++za9PPfXUdOzYMTNnzsxDDz2UkSNH5oYbbigxIQAARVA0aFUNDQ3ZYIMNml8/8MADGTp0aPPr/fbbL/PmzSsjGgAABVI0aFW1tbV59NFHkyQLFizIG2+8kW233bb5/ty5c8uKBgBAgRQNWlXfvn0zZsyY3Hvvvdljjz2SJIccckjz/dtuuy0dO3YsKx4AAAVxMjit6sorr8w222yTTTfdNEmy1157pVevXi3uDxw4sKx4AAAURNGgVW2xxRZ56623cv3116d///7ZaKONWtw/77zzsuGGG5aUDgCAojgZnFI98cQT6dKlS/r06VN2FArkswpQDn9/qSTWaNDqnn322QwePDi1tbUZMGBA+vbtm7q6umy22WaZOXNm2fEAACiAqVO0qhkzZmTgwIGZP39+BgwYkAEDBiRJHn/88fz1r3/NGmuskRdffDHXXXdd/vjHP+baa68tOTEAAP8JRYNW9bWvfS21tbV5+OGHM2jQoBb3pk2blk022SSDBg3Kiy++mAMPPLCklAAAfFKmTtGqHn744ZxwwgmLlYwkGTJkSH70ox/lxRdfzFZbbZWLL764hIQAABRB0aBVNTQ0ZPvtt//A+zvssEOS5JZbbmmtSAAAfAoUDVpVbW1t/v73v3/g/XvvvTe1tf6zBACodp7oaFVrr712fvjDH2b27NmL3Xvrrbdy/PHHZ+211y4hGQAARbIYnFb161//Optuumm6d++e7bffPhtuuGGamprywAMPZNKkSWlsbMxf/vKXsmMCAPAJObCPVnf77bdnjz32WOzMjJVWWim//e1vs+2225aUjKL4rAKUw99fKokRDVrdlltumddeey3PPPNMbr311uZrpkwBACw7FA1Ks+aaa2bNNdcsOwYALDPq6+ubf7/wwgtz9NFHp3379iUmoi2zGBwAYBkwevTo9OzZs/n1mDFj0rlz54wePbrEVLRlRjQAAKrc6NGjc+aZZy52vaGhofn6GWec0dqxaOMsBgcK57MK1aGxsbF5Y47OnTunpqam5ET8J+rr69OzZ880NjZ+4Htqa2szc+ZM06iqVFNTU+bOnZsk6dmzZ9WcOWZEAwDaqJkzZ2aVVVYpOwatoLGxMSuuuGLZMSjAK6+8kl69epUdY6lURx0CAACqihENAGijOnfu3Pz7K6+8YppjlbrwwgszZsyYj3zf6aefnsMOO6wVElG0OXPmNI8+Lvq5rXTWaACF81mF6uCzumyor69P586d09DQ8IHvqaury9y5c63RqFLV+lk1dQoAoIq1b98+xxxzzIe+55hjjlEyaHWmTgEAVLmFW9eeffbZLUY26urqcswxx9jallKYOgUUzmcVqoPP6rKnvr4+P//5zzNjxoysvfbaOeSQQ4xkLAOq9bNqRAMAYBnRvn37HHXUUWXHgCTWaAAAAJ8CRQMAACicogEAABRO0QAAAAqnaAAAAIVTNAAAlhEPPPBAvvSlL6Vfv34ZOnRo/vznP5cdiTZM0QAAqFI1NTV57LHHkiTXX399Nt5449xzzz1paGjIww8/nJ133jkTJkwoOSVtlaIBAFDFGhsbkyQHHXRQVllllbz11lt5/vnnM2fOnKy++uo59NBDS05IW6VoAAAsA1577bUcf/zx6dixY5JkueWWy+mnn56ZM2eWnIy2StEAAKhitbX//3HuM5/5TIt7q622WvOIB7Q2RQMAoIqtv/76qa2tTUNDQyZPntzi3t13353llluupGS0df7LAwCoUgceeGCL1xtvvHGL1zfffHOGDBnSmpGgmaIBAFClLr744g+9//4RDmhNpk4BACxjDjzwwDz33HNlx6CNUzQAAJYxl156aaZNm1Z2DNo4U6cAAKrUojtOvd+uu+6ampqaJLHzFKVQNAAAqlRTU1N69eqV3XbbrcW1Sy+9NF/+8pezxhprlJiOtk7RAACoUpMmTcruu++eKVOm5K677mo+R+PSSy/NYYcd1qKAQGuzRgMAoEptu+22mTlzZnr27Jm+ffvmF7/4RdmRoJmiAQBQxTp27Jh77703p59+eg4//PB88YtfLDsSJFE0AACWCaNGjcpjjz2Wf/zjH6mpqUm7du3KjkQbZ40GAMAyYp111slLL71UdgxIYkQDAKCq1dfX59Zbb82CBQuSJG+99VaOPvroHHHEEXnkkUdKTkdbpmgAAFSpCRMmpFOnTtl6663TtWvX3HvvvenVq1fOPffcXHDBBRkyZEguv/zysmPSRikaAABV6uCDD07v3r0zceLEDBw4MFtuuWVWXnnlzJkzJ++880769euXY445puyYtFGKBgBAlZo1a1bGjh2b4cOHZ/Lkyamvr8+pp56azp07p2PHjjnnnHPyxhtvlB2TNkrRAACoYh06dEjy3ja3SdK+ffvme3aeokyKBgBAlerevXsOO+yw3Hfffdlll13Srl27nHLKKc33jzvuuCy//PIlJqQtUzQAAKrUOeeck6eeeiqf//znc+utt+aaa67JSy+9lLq6utTV1eWRRx7J8ccfX3ZM2ijnaAAAVKmRI0dmxx13zE033ZTtttsun/nMZ/LCCy/kqKOOypw5c3LAAQdkxx13LDsmbVRNU1NTU9khltacOXPStWvXJMns2bPTpUuXkhMBS+KzCtXBZxWqQ7V+Vk2dAgBYRr366qu54IILyo5BG6VoAAAso2699dYcccQRZcegjVI0AACAwlkMDgBQpWprP/w74ypaissySNEAAKhSTU1N2XjjjbPhhhsu8f6MGTNyyy23tHIqeI+iAQBQpZZffvmsvvrqufjii5d4f/z48YoGpbFGAwCgSm266aZ54403PvD+Gmuskf79+7diIvj/jGgAAFSpm2666UPvb7LJJnnqqadaKQ20ZEQDAAAonKIBAFClbrzxxlx22WUtrp155pnp0aNHunbtmu23376kZKBoAABUrf33379F0bj99tszevTo1NXVpX///rn55pszfPjwEhPSlikaAABV6tVXX82+++7b/PrUU09Nx44dM3PmzDz00EMZOXJkbrjhhhIT0pYpGgAAVaqhoSEbbLBB8+sHHnggQ4cObX693377Zd68eWVEA0UDAKBa1dbW5tFHH02SLFiwIG+88Ua23Xbb5vtz584tKxooGgAA1apv374ZM2ZM7r333uyxxx5JkkMOOaT5/m233ZaOHTuWFY82zjkaAABV6sorr8w222yTTTfdNEmy1157pVevXi3uDxw4sKx4tHGKBgBAldpiiy3y1ltv5frrr0///v2z0UYbtbh/3nnnZcMNNywpHW2dogEAUMU6duyYPffcM0nyxBNPpEuXLunTp0+SNF+HMlijAQBQxZ599tkMHjw4tbW1GTBgQPr27Zu6urpsttlmmTlzZtnxaMOMaAAAVKkZM2Zk4MCBmT9/fgYMGJABAwYkSR5//PH89a9/zRprrJEXX3wx1113Xf74xz/m2muvLTkxbYmiAQBQpb72ta+ltrY2Dz/8cAYNGtTi3rRp07LJJptk0KBBefHFF3PggQeWlJK2ytQpAIAq9fDDD+eEE05YrGQkyZAhQ/KjH/0oL774YrbaaqtcfPHFJSSkLVM0AACqVENDQ7bffvsPvL/DDjskSW655ZbWigTNFA0AgCpVW1ubv//97x94/957701trcc9yuG/PACAKrX22mvnhz/8YWbPnr3YvbfeeivHH3981l577RKSgcXgAABV69e//nU23XTTdO/ePdtvv3023HDDNDU15YEHHsikSZPS2NiYv/zlL2XHpI1SNAAAqtTnP//53HLLLdljjz3yl7/8pUWpWGmllfLb3/42m222WYkJacsUDQCAKrblllvmtddeyzPPPJNbb721+ZopU5RN0QAAWAasueaaWXPNNcuOAc0sBgcAAAqnaAAAAIVTNAAAgMIpGgAAQOEUDQAAoHCKBgAAUDhFAwAAKJyiAQAAFE7RAAAACqdoAAAAhVM0AACAwikaAABA4RQNAACgcIoGAABQOEUDAAAonKIBAAAUTtEAAAAKp2gAAACFUzQAAIDCKRoAAEDhFA0AAKBwigYAAFA4RQMAACicogEAABRO0QAAAAqnaAAAAIVTNAAAgMIpGgAAQOEUDQAAoHCKBlC4hoaG5t9vv/32Fq8BgLahqoqGhxeofBMnTszAgQObX++0007p169fJk6cWGIqAKC1VU3R8PAClW/ixIn52te+lpdeeqnF9RdffDFf+9rXfF4BoA2paWpqaio7xEdZ+PDy/qg1NTVJkgkTJmTEiBFlRAP+raGhIf369csLL7ywxPs1NTXp06dPnnnmmdTV1bVyOmBJ5syZk65duyZJZs+enS5dupScCFiSav2sVvyIRkNDQ4488sjFSkaS5mtHHXWUaVRQsjvuuOMDS0by3uf1+eefzx133NGKqQCAslR80fDwAtXh5ZdfLvR9AEB1q/ii4eEFqkPv3r0LfR8AUN0qvmh4eIHqMGzYsPTp06d57dT71dTUpG/fvhk2bFgrJwMAylDxRcPDC1SHurq6nHfeeUmy2Od14etzzz3XQnCoILaNBz5NFV80PLxA9RgxYkQmTJiQ1VZbrcX1Pn362B0OKoxt44FPW1Vsb5u89wfxyCOPbLEwvG/fvjn33HM9vECFaWhoyB133JGXX345vXv3zrBhw3wZABXEtvFQXap1e9uqKRqJhxcA+KSceQPVR9EAACrerbfemq233voj33fLLbdkq622+vQDAR+pWotGxa/RAACKY9t4oLUoGgDQhtg2Hmgtpk4BQBuycI3Giy++uNhi8MQaDahEpk4BABXPtvFAazGiARSioaEh48aNy+TJk/Pqq6+msbGxxf0pU6aUlAxYEtvGQ/Wo1hGNii8aHl6gOhx22GEZN25cdt555/Tu3Xuxb0rPOeeckpIBH8S28VAdqrVoLFd2gI9y5JFHNj+8DBo0aLGHF6AyXH311Rk/fnx22mmnsqMAS6murs4WtsCnpuKLhocXqA7t27dP//79y44BAFSIil8M7uEFqsOoUaNy3nnnLXEXGwCg7an4NRo//elP8/TTT+fCCy80bQoq2PDhw3PLLbdkxRVXzPrrr5927dq1uD9x4sSSkgFAdbNG41Ny55135pZbbslf/vIXDy9Qwbp3757hw4eXHQMAqBAVXzQ8vEB1uOyyy8qOAABUkIqfOgVUl9deey1PPPFEkmTdddfNyiuvXHIiYKONNsrkyZPTo0ePbLjhhh86FfmBBx5oxWTA0jB16lPm4QUq25w5c3L44YfniiuuaD7vpq6uLvvtt18uuOCCdO7cueSE0Hbtvvvu6dChQ5Lkq1/9arlhgDaj4kc0PLxAdTjooIMyadKkXHjhhdl8882TvLfG6ogjjsiXv/zl/OIXvyg5IQBUp2od0aj4ouHhBapDz549M2HChMUO/7rllluy55575rXXXisnGLBE9fX1efXVV5u/xFto9dVXLykR8EGqtWhU/NSpa6+9drGHl5122imdOnXKnnvuqWhAhZg7d25WWWWVxa736tUrc+fOLSERsCRPPvlkvv3tb+fuu+9ucb2pqSk1NTVpaGgoKRmwrKn4ouHhBarDZpttlpNOOilXXHFFOnbsmCR55513csopp2SzzTYrOR2w0P7775/lllsuf/zjH9O7d29nVAGfmoqfOrXttttmpZVWWuzhZeTIkZk1a1YmTZpUckIgSR555JHssMMOeffdd7PBBhskSaZOnZqOHTvmxhtvzPrrr19yQiBJunTpkvvvvz8DBgwoOwqwlEyd+pScd9552WGHHdKnT58lPrwAlWHQoEF56qmn8utf/zqPP/54kmTvvffOPvvsk06dOpWcDlhovfXWy8yZM8uOAbQBFT+ikbw3fWrRh5eBAwd6eAGA/8CUKVNy/PHH57TTTsvgwYPTrl27FvdXWGGFkpIBH6RaRzSqomgAlen6669f6vfutttun2ISYGnV1tYmyWJrMywGh8pVrUWj4qdOJclLL72UO++8c4nb8B1xxBElpQLef/BXTU1N3v/dxcKHGQ8vUBluueWWsiMAbUTFj2iMGzcuBx10UNq3b5+VVlqpxTcwNTU1efrpp0tMByw0adKkHHfccTnttNOad5m65557mqdofPnLXy45IQBUp2od0aj4otG3b98cfPDB+cEPftA83AtUnkGDBmXs2LHZYostWly/44478t3vfjePPfZYScmA93vzzTdz6aWXNn8u119//RxwwAHp1q1bycmAJXnrrbeaP59//vOfs/3226eurq7kVB+t4p/c586dm7322kvJgAo3Y8aMdO/efbHr3bp1yz/+8Y9WzwMs2X333Ze1114755xzTmbNmpVZs2bl7LPPztprr50HHnig7HjA+0ycODEDBw5sfr3TTjulX79+mThxYomplk7Fj2iMHj06K664YsaMGVN2FOBDbLnllunYsWP+93//t/mQzVdeeSX77bdf5s2bl9tuu63khECSDBs2LP3798/FF1+c5ZZ7b6nmggULcuCBB+bpp5/O7bffXnJCYKGJEyfma1/72geuf5wwYUJGjBhRRrSlUvFFo6GhIbvsskveeeedJW7Dd/bZZ5eUDFjU9OnTM3z48Dz55JPp27dvkuT555/POuusk9/97nfp379/yQmBJOnUqVMefPDBxQ7se/TRRzN06NDMnTu3pGTAohoaGtKvX7+88MILS7xfU1OTPn365JlnnqnYaVQVv+vU//zP/+TGG2/MuuuumySLLQYHKkP//v0zbdq03HzzzS3OvNluu+18VqGCrLDCCnnuuecWKxrPP/98ll9++ZJSAe93xx13fGDJSN7bkvr555/PHXfcka222qr1gn0MFV80fvrTn+ZXv/pVvvWtb5UdBfgINTU12X777bPlllumQ4cOCgZUoG984xv59re/nbPOOitf/OIXkyR33XVXjj322Oy9994lpwMWevnllwt9Xxkqvmh06NAhm2++edkxgI/Q2NiYn/zkJxk7dmxeeeWVPPnkk1lrrbVywgknpF+/fvn2t79ddkQgyVlnnZWamprst99+WbBgQZKkXbt2+d73vpfTTz+95HTAQr179y70fWWo+K2cjjzyyFxwwQVlxwA+wo9//OOMGzcuZ5xxRtq3b998fdCgQbnkkktKTAYsqn379jnvvPPyxhtv5KGHHspDDz2UWbNm5ZxzzkmHDh3Kjgf827Bhw9KnT58PnB1QU1OTvn37ZtiwYa2cbOlV/GLw4cOHZ8qUKVlppZWy/vrrL7YYvBq29oK2oH///vnlL3+ZbbfdNssvv3ymTp2atdZaK48//ng222yzvPHGG2VHBPLeQbhLmo68YMGCnHDCCfmf//mf1g8FLNHCXaeStNh5qlp2nar4EY3u3btnxIgR+dKXvpSePXumW7duLX6AyvDiiy8ucWepxsbGzJ8/v4REwJIcccQR+frXv96i/D/xxBPZZJNN8pvf/KbEZMD7jRgxIhMmTMhqq63W4nqfPn0qvmQkVbBG47LLLis7ArAU1ltvvdxxxx1ZY401WlyfMGFCNtxww5JSAe/34IMPZt99983gwYNz2WWX5cknn8zo0aPz1a9+NT//+c/Ljge8z4gRI7L77rvnjjvuyMsvv5zevXtn2LBhFbul7aIqvmgk7w3n3nrrrZkxY0a++c1vZvnll89LL72UFVZYIV27di07HpDkxBNPzMiRI/Piiy+msbExEydOzBNPPJErrrgif/zjH8uOB/zb2muvnbvuuitHHXVUvvKVr6Suri6XX365HaeggtXV1VXsFrYfpuLXaDz77LP5yle+kueeey7vvvtu8042Rx55ZN59992MHTu27IjAv91xxx059dRTM3Xq1MyePTsbbbRRTjzxxGy//fZlRwMW8Yc//CHf/va389nPfjZPPvlkhgwZkiuuuCKrrrpq2dGAZUjFr9E48sgjM3To0Lzxxhvp1KlT8/Xhw4dn8uTJJSYD3m/YsGG5+eab8+qrr2bu3Lm58847lQyoMAcddFC+/vWv57jjjssdd9yRadOmpX379hk8eHDGjx9fdjxgGVLxIxorrbRS7r777qy77rotdrL5xz/+kfXWWy9z584tOyKwiPvuuy+PPfZYkvfWbWy88cYlJwIWNWjQoPz617/OBhts0OL6z372sxx33HGZPXt2ScmAZU3Fr9FobGxMQ0PDYtdfeOGFLL/88iUkApbkhRdeyN5775277ror3bt3T5K8+eab+eIXv5irr746ffr0KTcgkCS5//77l3hexqGHHprtttuuhETAsqrip05tv/32Offcc5tf19TUZPbs2TnppJOy0047lRcMaOHAAw/M/Pnz89hjj2XWrFmZNWtWHnvssTQ2NubAAw8sOx7wbx92KN+6667bikmAj7JgwYJMmjQpv/zlL/P2228nSV566aWqGXms+KlTL7zwQnbYYYc0NTXlqaeeytChQ/PUU0+lZ8+euf3229OrV6+yIwJJOnXqlLvvvnuxrWzvv//+DBs2zDRHKNFGG22UyZMnp0ePHtlwww0/8KThJHnggQdaMRnwQZaFDZEqfupUnz59MnXq1Fx99dWZNm1aZs+enW9/+9vZZ599WiwOB8rVt2/fJR7M19DQYCcbKNnuu+/ePJLx1a9+tdwwwFJZuCHS1KlTs9JKKzVfHz58eL7zne+UmGzpVfyIxrx589KxY8eyYwAf4fe//31OO+20/OxnP8vQoUOTvLcw/PDDD89xxx3n4QYAPoZlYUOkih/R6NWrV4YPH55999032267bWprK35ZCbRJ3/rWtzJ37txssskmWW659/60LFiwIMstt1wOOOCAHHDAAc3vnTVrVlkxAaAqLAsbIlV80bj88stz1VVXZffdd0+3bt3yjW98I/vuu2/zN6ZAZVh00wagsvTo0eND12UsyhcBUBkWboh00UUXJanODZEqfurUQm+//XYmTJiQ3/zmN5kyZUrWWmut7LvvvjnxxBPLjgZt3oIFC3LVVVdlhx12yCqrrFJ2HOB9Lr/88ubfX3/99fz4xz/ODjvskM022yxJcs899+TGG2/MCSeckKOPPrqsmMAiloUNkaqmaCzq0UcfzT777JNp06YtcUgJaH2dO3fOY489ljXWWKPsKMCH2GOPPbL11lvnsMMOa3H9wgsvzKRJk/K73/2unGDAYhYsWNBiQ6SNNtqoqjZEqpqiMW/evFx//fW56qqrcsMNN2SVVVbJ3nvvndNPP73saECSrbbaKkcddZRF31Dhunbtmoceeij9+/dvcX369On53Oc+VzX78wOVr+LXaNx444256qqr8rvf/S7LLbdcvva1r+Wmm27KlltuWXY0YBGHHHJIRo0alRdeeCEbb7xxunTp0uL+kCFDSkoGLGqllVbK73//+4waNarF9d///vctttAEKsOjjz6a5557LvX19S2u77bbbiUlWnoVP6LRuXPn7LLLLtlnn32y0047pV27dmVHApZgSTvC1dTUpKmpKTU1NaY5QoUYN25cDjzwwOy4447ZZJNNkiT33ntvbrjhhlx88cX51re+VW5AIEny9NNPZ/jw4Xn44Yeb/z1N0ryxQzX8u1rxRePtt9+umi28oC179tlnP/S+tRtQOe69996cf/75eeyxx5IkAwcOzBFHHNFcPIDy7brrrqmrq8sll1ySNddcM3/729/y+uuvZ9SoUTnrrLMybNiwsiN+pIovGkkyY8aMXHbZZZkxY0bOO++89OrVK3/5y1+y+uqrZ/311y87HgAsc04//fQcfPDB6d69e9lRoE3q2bNnpkyZkiFDhqRbt27529/+lnXXXTdTpkzJqFGj8uCDD5Yd8SNV/Ol3t912WwYPHpx77703EydObF6kNnXq1Jx00kklpwPe79FHH80NN9yQ66+/vsUPUF1OO+00Z2pAiRoaGppn9fTs2TMvvfRSkvdmCDzxxBNlRltqFb8YfMyYMfnxj3+cY445psUUqm222SYXXnhhicmARS0Lc0mB/68KJjzAMm3QoEGZOnVq1lxzzWyyySY544wz0r59+1x00UVZa621yo63VCp+ROPhhx/O8OHDF7veq1evzJw5s4REwJIceeSRWXPNNfPqq6+mc+fO+b//+7/cfvvtGTp0aG699day4wFAVTn++OPT2NiYJDn11FPzzDPPZNiwYfnzn/+c888/v+R0S6fiRzS6d++el19+OWuuuWaL6w8++GBWW221klIB73fPPfdkypQp6dmzZ2pra1NbW5stttgi//M//5MjjjiiKuaSAkCl2GGHHZp/79+/fx5//PHMmjUrPXr0aJ4tUOkqfkRjr732ynHHHZd//vOfqampSWNjY+666658//vfz3777Vd2PODfloW5pABQyVZcccWqKRlJFYxonHbaaTn00EPTt2/fNDQ0ZL311ktDQ0O++c1v5vjjjy87HvBvy8JcUgAo04gRIzJu3LissMIKGTFixIe+d+LEia2U6j9X8UWjffv2ufjii3PiiSfm4YcfzuzZs7PhhhtmnXXWKTsasIjjjz8+c+bMSfLeXNJddtklw4YNy0orrZTf/va3JacDPq5hw4alU6dOZceANqVbt27NIxbdunUrOc0nVxXnaCyNFVZYIQ899JBvTqGCVNtcUmhr5s2bl/r6+hbXVlhhhZLSAMuail+jsbSWkb4Ey4y33nort99+u/UZUGHmzp2bww47LL169UqXLl3So0ePFj8ARVlmigZQrj333LP5bJt33nknQ4cOzZ577pnBgwfn2muvLTkdsNCxxx6bKVOm5Be/+EU6dOiQSy65JKecckpWXXXVXHHFFWXHA/7tlVdeyX/9139l1VVXzXLLLZe6uroWP9Wg4tdoANXh9ttvz49+9KMkyXXXXZempqa8+eabufzyy/PjH/84e+yxR8kJgST5wx/+kCuuuCJbbbVV9t9//wwbNiz9+/fPGmuskV//+tfZZ599yo4IJPnWt76V5557LieccEJ69+5dldOQFQ2gEP/617+y4oorJkluuOGG7LHHHuncuXN23nnnHHvssSWnAxaaNWtW83rGFVZYIbNmzUqSbLHFFvne975XZjRgEXfeeWfuuOOOfO5znys7yn9smZk6VY0tD5Ylffv2zT333JM5c+bkhhtuyPbbb58keeONN9KxY8eS0wELrbXWWnnmmWeSJAMGDMj48eOTvDfS0b179xKTAYvq27dv1a9BXmaKRrX/PwKq3VFHHZV99tknffr0yaqrrpqtttoqyXtTqgYPHlxuOKDZ/vvvn6lTpyZJxowZk5/97Gfp2LFjjj76aKOPUEHOPffcjBkzJv/4xz/KjvIfW2a2t73zzjvz+c9/Ph06dCg7CrRZ999/f5577rl8+ctfTteuXZMkf/rTn9K9e/dsvvnmJacDluTZZ5/N/fffn/79+2fIkCFlx4E27f1bws+ZMycLFixI586d065duxbvXTjtsZJVfNFoamrKhAkTcsstt+TVV19NY2Nji/vVcCoi8P858wbKM3/+/HzlK1/J2LFjHXwLFejyyy9f6veOHDnyU0xSjIpfDH7UUUfll7/8Zbbeeuusssoq1mJAlavw7zZgmdauXbtMmzat7BjAB6iG8vBxVPyIxoorrpgrr7wyO+20U9lRgAIsv/zymTp1qhENKMnRRx+dDh065PTTTy87CvAh/vznP6euri477LBDi+s33XRTGhoasuOOO5aUbOlV/IhGt27dPJAAQEEWLFiQX/3qV5k0aVI23njjdOnSpcX9s88+u6RkwKLGjBmzxC8EGhsbM2bMGEWjCCeffHJOOeWU/OpXv0qnTp3KjgMAVe2RRx7JRhttlCR58sknW9wzPRkqx1NPPZX11ltvsesDBgzI9OnTS0j08VV80dhzzz3zm9/8Jr169Uq/fv0WW3H/wAMPlJQM+E94kIFy3XLLLWVHAJZCt27d8vTTT6dfv34trk+fPn2xkchKVfFFY+TIkbn//vuz7777WgwOy4AKXxYGABVh9913z1FHHZXrrrsua6+9dpL3SsaoUaOy2267lZxu6VT8YvAuXbrkxhtvzBZbbFF2FKAAzryBcm299dYf+qXdlClTWjEN8EH+9a9/5Stf+Uruu+++9OnTJ0nywgsvZNiwYZk4cWK6d+9ebsClUPEjGn379s0KK6xQdgzgIyztmTe+NIByfe5zn2vxev78+XnooYfyyCOPLHNba0I169atW+6+++7cfPPNmTp1ajp16pQhQ4Zkyy23LDvaUqv4EY0//elPueCCCzJ27NjF5qgBlePII4/80DNvLrvsspKSAUvj5JNPzuzZs3PWWWeVHQVYRlR80ejRo0fmzp1b1cevQ1vgzBuobtOnT88XvvAF/65CBbntttty1lln5bHHHkuSrLfeejn22GMzbNiwkpMtnYqfOnXuueeWHQFYCs68gep2zz33pGPHjmXHAP7tyiuvzP77758RI0bkiCOOSJLcdddd2XbbbTNu3Lh885vfLDnhR6v4EQ2gOlx++eW54YYbnHkDFW7EiBEtXjc1NeXll1/OfffdlxNOOCEnnXRSScmARQ0cODDf/e53c/TRR7e4fvbZZ+fiiy9uHuWoZBVfNJ577rkPvb/66qu3UhLgw7zzzjsZPnx47rrrLmfeQAXbf//9W7yura3NyiuvnG222Sbbb799SamA9+vQoUP+7//+L/37929xffr06Rk0aFDmzZtXUrKlV/FTp/r16/eh2/A1NDS0YhrggzjzBqqDjRmgOvTt2zeTJ09erGhMmjQpffv2LSnVx1PxRePBBx9s8Xr+/Pl58MEHc/bZZ+cnP/lJSamA9/vTn/7kzBuoEm+++WYmTJiQGTNm5Nhjj82KK66YBx54IKusskpWW221suMBSUaNGpUjjjgiDz30UL74xS8meW+Nxrhx43LeeeeVnG7pVPzUqQ/ypz/9KWeeeWZuvfXWsqMASQYMGJDx48dnyJAhZUcBPsS0adOy7bbbpnv37vnHP/6RJ554ImuttVaOP/74PPfcc7niiivKjgj823XXXZef/vSnzesxBg4cmGOPPTa77757ycmWTtUWjenTp2eDDTbInDlzyo4CxJk3UC222267bLTRRjnjjDOy/PLLZ+rUqVlrrbVy991355vf/Gb+8Y9/lB0RWEZU/NSpt956q8XrhbtjnHzyyVlnnXVKSgW837777pu5c+dm7bXXduYNVLC///3v+eUvf7nY9dVWWy3//Oc/S0gELKsqvmh07959sUWlTU1N6du3b66++uqSUgHv58wbqA4dOnRY7Eu8JHnyySez8sorl5AIWKhHjx5LvZlKNXyBV/FTp2677bYWrxduw9e/f/8st1zF9yQAqCgHHnhgXn/99YwfPz4rrrhipk2blrq6unz1q1/Nlltu6UsDKNHll1++1O8dOXLkp5ikGBVfNIDq4MwbqA7/+te/8rWvfS333Xdf3n777ay66qr55z//mc022yx//vOf06VLl7IjAsuIiiwa119/fXbccce0a9cu119//Ye+d7fddmulVMCHqa2tdeYNVJE777wz06ZNy+zZs7PRRhtlu+22KzsS8D4NDQ353e9+17zr1Prrr5/ddtstdXV1JSdbOhVZNGpra/PPf/4zvXr1Sm1t7Qe+r6amxsMLVIipU6e2eP3+M29GjBhRUjIAqD7Tp0/PTjvtlBdffDHrrrtukuSJJ55I375986c//Slrr712yQk/WkUWDWDZ4cwbqDyTJ0/O5MmT8+qrr6axsbHFvV/96lclpQIWtdNOO6WpqSm//vWvs+KKKyZJXn/99ey7776pra3Nn/70p5ITfjRFA/hUOfMGKsspp5ySU089NUOHDk3v3r0Xm/J43XXXlZQMWFSXLl3y17/+NYMHD25xferUqdl8880ze/bskpItvarYtsk3L1D5nHkD1WHs2LEZN25c/uu//qvsKMCH6NChQ95+++3Frs+ePTvt27cvIdHHV/FF46O+eQEqgzNvoDrU19fni1/8YtkxgI+wyy675Lvf/W4uvfTSfOELX0iS3HvvvTn44IOrZjOkip861bt375xxxhm+eYEK58wbqA7HHXdcunbtmhNOOKHsKMCHePPNNzNy5Mj84Q9/SLt27ZIkCxYsyG677ZbLLrss3bt3LzfgUqj4orHSSivlb3/7W1WsrAeASnfkkUfmiiuuyJAhQzJkyJDmB5iFzj777JKSAUsyffr05u1tBw4cmP79+5ecaOlVfNHwzQtULmfeQPXZeuutP/BeTU1NpkyZ0oppgA9y6qmn5vvf/346d+7c4vo777yTM888MyeeeGJJyZZeRRaNY445pvn3xsbGXH755b55gQrkzBsA+HTU1dXl5ZdfTq9evVpcf/3119OrV6+q+He1IidOP/jggy1ef+5zn0uSPPLIIy2uWxgO5Vp0F7j37wgHAPznmpqalvisO3Xq1OZzNSpdRRaNW265pewIALDMuu+++zJ+/Pg899xzqa+vb3Fv4sSJJaUCkqRHjx6pqalJTU1NPvvZz7YoGw0NDZk9e3YOPvjgEhMuvYosGh/k+eefT5L07du35CTAkjjzBirf1Vdfnf322y877LBDbrrppmy//fZ58skn88orr2T48OFlx4M279xzz01TU1MOOOCAnHLKKenWrVvzvfbt26dfv37ZbLPNSky49CpyjcaiFixYkFNOOSXnn39+8wmIXbt2zeGHH56TTjppsTUbQDmcNgzVYciQITnooINy6KGHZvnll8/UqVOz5ppr5qCDDkrv3r1zyimnlB0RyHvbxm+++eZVvUV8xReN733ve5k4cWJOPfXU5vZ2zz335OSTT85Xv/rV/OIXvyg5IZA48waqRZcuXfJ///d/6devX1ZaaaXceuutGTx4cB577LFss802efnll8uOCCR54IEH0q5duwwePDhJ8vvf/z6XXXZZ1ltvvZx88slVcTr4B28TUyGuuuqqjBs3LgcddFDzzlMHHXRQLr300lx11VVlxwP+zWnDUB169OiRt99+O0my2mqrNW+08uabb2bu3LllRgMWcdBBB+XJJ59Mkjz99NP5xje+kc6dO+eaa67J6NGjS063dCq+aHTo0CH9+vVb7Pqaa65ZFU0O2ooDDzxQ+YcqsOWWW+bmm29Oknz961/PkUceme985zvZe++9s+2225acDljoySefbN559ZprrsmXvvSl5i/gr7322nLDLaWKn/R12GGH5b//+79z2WWXpUOHDkmSd999Nz/5yU9y2GGHlZwO2rb3n3lz0UUXZdKkSc68gQp24YUXZt68eUmSH/3oR2nXrl3uvvvu7LHHHjn++ONLTgcs1NTU1LyxyqRJk7LLLrskeW9TpJkzZ5YZbalV/BqN4cOHZ/LkyenQoUM22GCDJO/tH1xfX7/YNy+25IPW9WEnDC/KacMA8PFss8026du3b7bbbrt8+9vfzqOPPpr+/fvntttuy8iRI/OPf/yj7IgfqeJHNLp375499tijxTXb20JlcOYNVKfGxsZMnz59iVtRb7nlliWlAhZ17rnnZp999snvfve7/OhHP0r//v2TJBMmTKiaNZEVP6IBVB9n3kDl+utf/5pvfvObefbZZ/P+R4Campo0NDSUlAxYGvPmzUtdXV1VHPFQ8YvBgeqwYMGCnHDCCenWrVv69euXfv36pVu3bjn++OMzf/78suMB/3bwwQdn6NCheeSRRzJr1qy88cYbzT+zZs0qOx7wETp27FgVJSOpgqlTyXtDROPHj89zzz2X+vr6FvceeOCBklIBizr88MMzceLEnHHGGYudefP666878wYqxFNPPZUJEyY0T8MAKseKK66YJ598Mj179kyPHj0WO/x2UdXwxUDFF43zzz8/P/rRj/Ktb30rv//977P//vtnxowZ+fvf/55DDz207HjAv1111VW5+uqrs+OOOzZfGzJkSPr27Zu9995b0YAKsckmm2T69OmKBlSgc845J8svv3yS99ZoVLuKLxo///nPc9FFF2XvvffOuHHjMnr06Ky11lo58cQTq6LJQVvhzBuoXNOmTWv+/fDDD8+oUaPyz3/+M4MHD15sCsaQIUNaOx7wbyNHjkzy3nTkmpqa7LDDDllllVVKTvWfq/jF4J07d85jjz2WNdZYI7169crNN9+cDTbYIE899VQ23XTTvP7662VHBJKceuqpefzxxxc78+bb3/521llnnZx00kklJ4S2q7a2NjU1NYst/l5o4T2LwaFyLPoMXK0qfkTjM5/5TGbNmpU11lgjq6++ev76179mgw02yDPPPPOBfzCB1vfggw9m8uTJ6dOnzxLPvBkxYkTze515A63rmWeeKTsC8DF94QtfyIMPPqhofJq22WabXH/99dlwww2z//775+ijj86ECRNy3333tXhwAcrlzBuoXNX8oAJt1SGHHJJRo0blhRdeyMYbb5wuXbq0uF8N0xwrfupUY2NjGhsbs9xy73Wiq6++OnfffXfWWWedHHTQQeZ+A8DH9MQTT+SCCy7IY489liQZOHBgDj/88Ky77rolJwMWqq1d/BSKapvmWNFFY8GCBTnttNNywAEHpE+fPmXHAYCqd+2112avvfbK0KFDm7ei/utf/5q///3vufrqqxcbmQTK8eyzz37o/WoYqazoopEkXbt2zSOPPLLE3WyAyuLMG6h8a6+9dvbZZ5+ceuqpLa6fdNJJufLKKzNjxoySkgHLmoo/GXzbbbfNbbfdVnYM4COcf/752X///bPKKqvkwQcfzBe+8IWstNJKefrpp1ucrQGU6+WXX85+++232PV99903L7/8cgmJgA/z6KOP5oYbbsj111/f4qcaVPxi8B133DFjxozJww8/vMSFMLvttltJyYBFOfMGqsNWW22VO+64Y7ED++68884MGzaspFTA+z399NMZPnx4Hn744RbbUy88LdwajQIsaSHMQtWyEAbaAmfeQHUYO3ZsTjzxxOy5557ZdNNNk7y3RuOaa67JKaecklVXXbX5vb7Mg/LsuuuuqauryyWXXJI111wzf/vb3/L6669n1KhROeuss6rii4GKLxpAdVhrrbVy7bXXZsMNN8zQoUPzne98JwcddFBuuumm7LXXXkY1oEJ82Bd4i/JlHpSrZ8+emTJlSoYMGZJu3brlb3/7W9Zdd91MmTIlo0aNyoMPPlh2xI9U8Ws0gOqw8MybJM1n3nz5y1/ON77xjQwfPrzkdMBCC7eN/6gfJQPK1dDQkOWXXz7Je6XjpZdeSvLeblNPPPFEmdGWWsWu0XjnnXcyefLk7LLLLkmSH/zgB3n33Xeb79fV1eW///u/07Fjx7IiAou46KKL0tjYmCQ59NBDs9JKK+Xuu+/ObrvtloMOOqjkdABQXQYNGpSpU6dmzTXXzCabbJIzzjgj7du3z0UXXZS11lqr7HhLpWKnTo0dOzZ/+tOf8oc//CFJsvzyy2f99ddPp06dkiSPP/54Ro8enaOPPrrMmECceQPV5rbbbstZZ53VfGDfeuutl2OPPbYq5nxDW3HjjTdmzpw5GTFiRKZPn55ddtklTz75ZFZaaaX89re/zTbbbFN2xI9UsUVj2LBhGT16dHbdddck7xWNqVOnNje4K6+8Mj/72c9yzz33lBkT+Ddn3kB1uPLKK7P//vtnxIgR2XzzzZMkd911V6677rqMGzcu3/zmN0tOCHyQWbNmpUePHs07TyXJCy+8kFVXXXWp11+1pootGr17984999zT/NCy8sor5+9//3vz6yeffDKf//zn869//au8kECz3XffPSNGjMjIkSPLjgJ8iIEDB+a73/3uYjMCzj777Fx88cXNoxxAdVhhhRXy0EMPVeR0qopdo/Hmm2+2WJPx2muvtbjf2NjY4j5QLmfeQHV4+umnm2cLLGq33XbLD3/4wxISAZ9EhY4ZJKngotGnT5888sgjWXfddZd4f9q0aeaCQwU55JBDkrz3rej72SYTKkffvn0zefLkxQ7smzRpUvr27VtSKmBZVLFFY6eddsqJJ56YnXfeebGdpd55552ccsop2XnnnUtKB7zfwh2ngMo2atSoHHHEEXnooYfyxS9+Mcl7azTGjRuX8847r+R0wLKkYtdovPLKK/nc5z6X9u3b57DDDstnP/vZJMkTTzyRCy+8MAsWLMiDDz6YVVZZpeSkAFBdrrvuuvz0pz9tXo8xcODAHHvssdl9991LTgZ8XO/fMKmSVGzRSJJnnnkm3/ve93LzzTc3zz+rqanJl7/85fz85z+vyP9Boa1x5g1UD1tRw7KnkheDV3TRWGjWrFmZPn16kqR///5ZccUVS04ELOTMG6gutqKGZYsRDWCZ5cwbqC62ooZly/PPP59VV101dXV1ZUdZTMUuBgeqw/Tp0zN48ODm1x07dmxxaNAXvvCFHHrooWVEA5bAVtRQPe67776MHz8+zz33XOrr61vcmzhxYpJU9G5xigbwiTjzBqqLraihOlx99dXZb7/9ssMOO+Smm27K9ttvnyeffDKvvPJKhg8fXna8pVJ5Z5UDVWXhmTcfxJk3UFkaGxs/8EfJgMpx2mmn5Zxzzskf/vCHtG/fPuedd14ef/zx7Lnnnll99dXLjrdUFA3gE1l45s28efMWu+fMG6g8V1xxxRJHGevr63PFFVeUkAhYkhkzZjT/+9m+ffvMmTMnNTU1Ofroo3PRRReVnG7pWAwOfCLOvIHqUldXl5dffjm9evVqcf31119Pr169jGpAhejTp0/+8pe/ZPDgwRkyZEh+8IMfZO+9984999yTr3zlK/nXv/5VdsSPZI0G8Imsssoqufvuu/O9730vY8aMWeKZN0oGVI6mpqbU1NQsdv2FF15It27dSkgELMmWW26Zm2++OYMHD87Xv/71HHnkkZkyZUpuvvnmbLvttmXHWypGNIDCOPMGKteGG26YmpqaTJ06Neuvv36WW+7/f9fY0NCQZ555Jl/5ylcyfvz4ElMCC82aNSvz5s3LqquumsbGxpxxxhm5++67s8466+T4449Pjx49yo74kRQNAGgDTjnllOb/O2rUqHTt2rX5Xvv27dOvX7/ssccead++fVkRgWWMogEAbcjll1+eb3zjG+nYseNi9x555JEMGjSohFTA+y0L66nsOgUAbcjIkSNblIy33347F110Ub7whS9kgw02KDEZsKgPGgt49913q2bk0WJwAGiDbr/99lx66aW59tprs+qqq2bEiBH52c9+VnYsaPPOP//8JO9tqnLJJZe0mObY0NCQ22+/PQMGDCgr3seiaABAG/HPf/4z48aNy6WXXpq33nore+65Z95999387ne/y3rrrVd2PCDJOeeck+S9EY2xY8emrq6u+d7C9VRjx44tK97HYo0GALQBu+66a26//fbsvPPO2WefffKVr3wldXV1adeuXaZOnapoQIXZeuutM3HixKrYXeqDKBoA0AYst9xyOeKII/K9730v66yzTvN1RQMq36JnVFUTi8EBoA2488478/bbb2fjjTfOJptskgsvvDAzZ84sOxbwIS699NIMGjQoHTt2TMeOHTNo0KBccsklZcdaaooGALQBm266aS6++OK8/PLLOeigg3L11Vc3HwR288035+233y47IrCIE088MUceeWR23XXXXHPNNbnmmmuy66675uijj86JJ55YdrylYuoUALRRTzzxRC699NL87//+b9588818+ctfzvXXX192LCDJyiuvnPPPPz977713i+u/+c1vcvjhh1fFiKQRDQBoo9Zdd92cccYZeeGFF/Kb3/ym7DjAIubPn5+hQ4cudn3jjTfOggULSkj08RnRAACACnP44YenXbt2Ofvss1tc//73v5933nmnKs69UTQAAKDCHH744bniiivSt2/fbLrppkmSe++9N88991z222+/tGvXrvm97y8jlULRAACACrP11lsv1ftqamoyZcqUTznNf0bRAAAACmcxOAAAVLAXXnghL7zwQtkxPjZFAwAAKkxjY2NOPfXUdOvWLWussUbWWGONdO/ePf/93/+dxsbGsuMtleXKDgAAALT0ox/9KJdeemlOP/30bL755kmSO++8MyeffHLmzZuXn/zkJyUn/GjWaAAAQIVZddVVM3bs2Oy2224trv/+97/PIYcckhdffLGkZEvP1CkAAKgws2bNyoABAxa7PmDAgMyaNauERB+fogEAABVmgw02yIUXXrjY9QsvvDAbbLBBCYk+PlOnAACgwtx2223Zeeeds/rqq2ezzTZLktxzzz15/vnn8+c//znDhg0rOeFHUzQAAKACvfTSS/nZz36Wxx9/PEkycODAHHLIIVl11VVLTrZ0FA0AAKBwtrcFAIAKMG3atAwaNCi1tbWZNm3ah753yJAhrZTqP2dEAwAAKkBtbW3++c9/plevXqmtrU1NTU2W9KheU1OThoaGEhJ+PEY0AACgAjzzzDNZeeWVm3+vdkY0AACAwhnRAACACnD99dcv9Xvff2J4JTKiAQAAFaC2dunO0q6WNRqKBgAAULilq00AAAAfgzUaAABQgSZPnpzJkyfn1VdfTWNjY4t7v/rVr0pKtfQUDQAAqDCnnHJKTj311AwdOjS9e/dOTU1N2ZE+Nms0AACgwvTu3TtnnHFG/uu//qvsKP8xazQAAKDC1NfX54tf/GLZMT4RRQMAACrMgQcemKuuuqrsGJ+IqVMAAFABjjnmmObfGxsbc/nll2fIkCEZMmRI2rVr1+K9Z599dmvH+9gUDQAAqABbb731Ur2vpqYmU6ZM+ZTTfHKKBgAAUDhrNAAAoML861//yqxZsxa7PmvWrLz11lslJPr4FA0AAKgwe+21V66++urFro8fPz577bVXCYk+PlOnAACgwqy44oq56667MnDgwBbXH3/88Wy++eZ5/fXXS0q29IxoAABAhXn33XezYMGCxa7Pnz8/77zzTgmJPj5FAwAAKswXvvCFXHTRRYtdHzt2bDbeeOMSEn18y5UdAAAAaOnHP/5xtttuu0ydOjXbbrttkmTy5Mn5+9//nptuuqnkdEvHGg0AAKhADz30UM4888w89NBD6dSpU4YMGZIf/OAHWWeddcqOtlQUDQAAoHCmTgEAQAWbN29e6uvrW1xbYYUVSkqz9CwGBwCACjN37twcdthh6dWrV7p06ZIePXq0+KkGigYAAFSYY489NlOmTMkvfvGLdOjQIZdccklOOeWUrLrqqrniiivKjrdUrNEAAIAKs/rqq+eKK67IVlttlRVWWCEPPPBA+vfvn//93//Nb37zm/z5z38uO+JHMqIBAAAVZtasWVlrrbWSvLceY9asWUmSLbbYIrfffnuZ0ZaaogEAABVmrbXWyjPPPJMkGTBgQMaPH58k+cMf/pDu3buXmGzpmToFAAAV5pxzzkldXV2OOOKITJo0Kbvuumuampoyf/78nH322TnyyCPLjviRFA0AAKhwzz77bO6///70798/Q4YMKTvOUlE0AACgQjQ2NubMM8/M9ddfn/r6+my77bY56aST0qlTp7KjfWzWaAAAQIX4yU9+kh/+8Ifp2rVrVltttZx33nk59NBDy471HzGiAQAAFWKdddbJ97///Rx00EFJkkmTJmXnnXfOO++8k9ra6hojUDQAAKBCdOjQIdOnT0/fvn2br3Xs2DHTp09Pnz59Skz28VVXLQIAgGXYggUL0rFjxxbX2rVrl/nz55eU6D+3XNkBAACA9zQ1NeVb3/pWOnTo0Hxt3rx5Ofjgg9OlS5fmaxMnTiwj3seiaAAAQIUYOXLkYtf23XffEpJ8ctZoAAAAhbNGAwAAKJyiAQAAFE7RAAAACqdoAAAAhVM0AACAwikaAABA4RQNAACgcIoGAABQuP8HQFkYThcEoqkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from hyperiax.plotting import plot_tree\n",
-    "plot_tree(tree,inc_names=True)\n",
-    "print(tree_string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The name of the landmarks are placed in the tree, alongside with the edgelength \n",
-    "#landmark = landmark.iloc[:,0:10] # reduce landmark numbers \n",
-    "l_dim = (jnp.shape(landmark)[1])\n",
-    "tree.add_property('value', shape=(int(jnp.shape(landmark)[1]),))\n",
-    "# add landmarks\n",
-    "landmark_array = landmark.to_numpy()\n",
-    "reshaped_array = landmark_array.reshape(landmark_array.shape[0], landmark_array.shape[1])\n",
-    "tree.data['value'] = tree.data['value'].at[tree.is_leaf].set(reshaped_array)\n",
-    "\n",
-    "# Dimensions\n",
-    "d = 2\n",
-    "\n",
-    "# For phylomorphospace: \n",
-    "tree.add_property('p_adj', shape=(int(jnp.shape(landmark)[1]),))\n",
-    "tree.add_property('phi', shape=(int(jnp.shape(landmark)[1]/d),d,d))\n",
-    "\n",
-    "# How many compartments in the output pca\n",
-    "tree.add_property('pc', shape=(5,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6fTBWO9/3ohwn8basds3UIpPh75CAVJdfjLVsMXhx9DB11FHHPBBoz0K5NkI4YNmgAiV+MGuXkbo/KI8oC+oALQQuDiVsfNJomYoiAl7IWEpnOZc+IzxFaGGpVXZua0NwXHygW0Rdqwfo0E0wl7VhrlnxYu6zVv1r7V/I3VPLPbAQqoljrf01iYrvk75nB9bQIH5nF/ltJ4NlzSoWb5eF2t6QkEodirGchGWq6iw0y5f3kUgkY7qSoLwoZ6kNrSpS2px22vawZijl4zgJ4yKSmmQizejYIJZlB1qW6udpqJFJqmYqZFkW2ldIS5gU9SLQn0hzPagkF/F7jYcJx0lwddvNp+mpfieX+g7UCUoddbxYEfaTQkKYbCywAljSKncslDvX6EClKM/IjALfEBINQuB6Pq7r/q7u5LeCuWbO84ll44g6X0BrYRpRGJHhYmb34SAXjlk1hZc1B+DICYTJCqpnla0+rtasdi5dxFJI0lz7q/fFCdRir1MtkK11bC3XROaaq+n8zCdx+vujY92eHoY+9Neo1rYK0qKrMqlWD6Cz6qg1WumYS2e2LU1IAb5GK5/W1na0EhBzh2owMwNTGjMh0ICiubm5ZjsKpHnWKhCua2N9sSyB74cWFEw0jiivkaSUMuL24FI6eEer2zNEmP8kTkbi71It0XKtZw3MSn1fbXVZDOoEpY46XtQwmolsZpp0ywiW0AjLRokJ7GBWJYXEtsArFYxFRQhcS1HM5wy50QKvlMORE1gIhMyhxDToF1cUTzVmDaKxP43QsPqAoLP3fVL37MAeGsTr7KKw7WTQmvQ9O5BDg/gdnWZGH4Sglk8/2woR/zvq6M1Gag2Pcw36hiAElYzdWzWJqXW9Wued1wWygCVloQFurjILWVC01mSvuZred11azbix+/vpfdelFbFMbk8Pgx/6GFPnXzDnPcx1r9WEz/xt3C1am6SIe/buDSLogqRnvgq8KwKhK/OCaK3K4lht3gXlq4CKCpRWKF/heiUcx6loC8syuYsAhCXLYcVCzBKuxoWvtRAPfY4vTRB3+8TPE38G8WtorSvEs08XdYJSRx0vamik8Dlv605OP+4B0gkfITQ/uL7E449NBbN9ydjYBHv37o8id9pasmzYeBxCmul90ZtmfOzfmSjkED6oAghReq5v7hlhfkuJiP0OBqLgc2R3qjpOIMhcczUdn/4Edmz27re0oAF7fDzaFs7opy+4kMBXAJStVjoyqZRFhpUDZbiKbVg3GVlzENLUWuqo/loTZDYlOkctl9VitAK1rA3zlZ2L3MyHuchMLUGm9jwy9+7E6j9C5xWfiiwbFccx29JhDwyw7N2Xcugr/8D0+RdGdZ3r/ub6XP4pn9v3FRMTY3jKRwrwhES6JeypadJj46RHB5EI8o2N5JvbcFtaUCkLrUwocfjMLFkmBr7nIaQMNCiVUVrhs1PKR1oWSvmgqXDThPUOha5zWYvmembh9aKMsLF91RqouPuq1jNbLOoEpY46XqQII4eF0KTtaZqTPg3JIkJoElbaDG7adCjZbAat444giWXbCGlFSaCK7iiuO41WFkI5LMFS+4JB7U40tk0D8cEx1o9nrvkl3e/8y1nERcaISQh7YIDed1/G4S99Db+1FWtwALejnektJ0ZaCbOoWnkWW1lPIteQ4TIqqp+QOuYyCkkKwex+7rwisyw1VW6U+KAVn02X22J+98hiyM9cmGtwy17zK7qu+JsKd86c56j+rDVaCLqv+DTT55wHVVaB6t/x9XhqDeKxgw3J0D5YAjxIFCdoefgROp58mOaJcZOMz1dY0mYymWVkRR/jx51IrrMDpTyEkFiWFQTkBKHIUqKUH5BNGRENYx0rWyp0mDcnRhDCc9R6RtVEp9ryUcs9WK1HiW+LH79Yl95cqBOUOur4PYAAPGVSq1nBsCYkxp+uYWomZwrO6kO0GQ1Dv3XQzZu1NWaLDl9IWKjD1LqWpaEy/X3U8fo+HZ/55Jyz92oIbUjEsr96FyI2wy11d9P//g8yce55VLh/Kuoa2gNibqZYcq8wKsOIImcncYO5SUj1/tltMv9igvO5gp7OADUfstf8imXvnu3OWQqE1jj9R2jYuYOZk0+t2LeQdah6n9Y6CucVgKsVti+xp8fpvOtmevY+iZNpwHrpS9F9y9Gui79vN02PP0nDU/eTGTzM/peeTX7ZciwLtJIIK3DJKPOdlUJGj9qQgeC7LIzQ3fcV8cig6uiaarIRT08fkotwX9wNWO3iqbYmxc9rBQT76ZKSOOoEpY46XqQQujzEKQSetnGVRgmNtBxSCRVF67Q0NeB7pUC7J/B9F88tgTBRPJ7nYokGnGCxDylt0C9skWyIudwJNREIDePJrrTWpHfcXeHWWQwEQJV/3hkcpO+9l6P+9otMnHMukXspVkfT8YMOhZci5m6qcuGEg055sKlt9qo1kCzkBlqqtSSubZiP5Mz7HHyf9D13Yw0M0P25T9ckhE8H1uDgosvWuteoTQCEwNc+UmsoTtJ592307nkSZ9PxOK99JfSuwrWNqzBR8nAnh3F+fTtt11wDt9zE7nMuwO3pwlI+nlbRGjjmmSvjqpNGXBs+cGNZKWtgoLyGTi0rV/hsfd/Htu1Z1pLqFY6rI3VCVFtK4tepjup5Oq6eOkGpo44XIyom3IIjVgO7MklSabN+x4TlksuPowONw8CRfrCyKC1Mn+cXyeVzEAzEbtHmqBWX4fo2peI4bn6U/oe/+1zc2e8M8XEfQrtFmKjN7IxmjUOLH+DmvaY2ssjln/kU5PN4XV1MbdkaC5GNLyWoK1w9RH+Xc3FUnZ044ak2/9eytiykN6meTVdjPitE9cx7IaKYveZquq/4NM7A0ojgYpDYt7fmvdSyFlRvj36CB6ENk8DyNG1PPE7PU48hthyP82d/jso2UZLG7SqFxEtayPZueOXLEUKSvvpndN17D4fPOwfR0IgM2yMgxb7vodHBGj9xN8tsQbem8hlDmRxWrq5cuVCgECKyglQ/o7irKNxedjPNfneqSUncSrMY1AlKHXW8yCGEwE06TCUEfoNxtXu2xPPCheKh6Pp4vi5rUISFnUgFHYsgUYJUMmUWGix62Fa4jPsLF7UiWapKxOUmMV1HbDAI9jv79j579ULjjI2x+q8/AkCpq5uD730/E+ecGxCmgKSIcPkCAr1R/D7Ka8FE540bWmq4dGoRhGr9yUI6krn2V597qS6f7DVXs/zydz5td06Z1NXe13Hl18iv3xCJZeN1XIi0mMic0L0T5rMBUcrTtutRdEqit7+UPeNTJF2XifFxstksLS0tHDp4CCU0K1f2kTjvbJK7dtO1bx/DQyOoldlg7R1zHTdIrCaE0ScJYbQnoWBWShmsdA2+9qNXQOhyncNyQohgHR8xy6JVS+ga/57E9SlzPW/f97EsKwpbrj5+sXgRytzqqKOOCmhQvo/yw0yl4Csf0DiOmaNYQqBFOPTpmHWgWiCpUaqELT2StvPc3M+ziPnMzbMH0NmDqsAMnm3/8JXZ8p1nCc7gIGve/16ar78+eDrhIGjM/yoy65eTvYWDZHywrRZ5hr+rtQcLEZL5ytciNIshNXOW8X0a7ryD3o9/9Bm7c+Z6PsYgJuj5/GfQnjerreY9p9aRWyeEFBKNID02RmpyFFZtwFu2ksGRYfK5HJZlMT09zeTkJDO5GVCaibFJ8qlGnDNeipCK7MQEBKsUh9cIiYVl2UhpzSKb1SRASiO0rZVuPrSSxK0rYflqy0doJYlH/tQis/F9YfRQ3GUUD3leLOoWlDrqeDEi7DfMVBuwIUgYJYQgk0rRkkggpETYKdKWhdDCJIRCm1wKqmTKA0KX0Ci0sLGcDKokKHkv/Dwo83WWszt+Y1GJSBygfZ/2OcSx0TXC8z3NOoZpvFZ+9m9wsw1MbT0JbMusQh2VCUuVXQKhiT/uvjH3U07sVX2vC2E+srGQ9aRW2fmu2XjdNfR+7jNPy6WjgalUks9lbfqF5qS8y6XTc2umhNYk+vtp2LmD6W2n1D7nHKSrFiQ+Tj6P5SmSfaugtYVjmpuwLIuZmRmSySSHDx9m3bp1KKVIZzJYGkRHB77vkZieQPsa4Yhy5trw3FXRRmWyGBKPwF2DEdFaVS4crU1K/WpSWV0mfr3qHCjzCa/j54sTlmoStRjUCUoddbzIoRGM7Wtkv5vFSiocy+boUpqzTlyGxGSIPZjt5ebiCSASgIs7shtu+zwyCHP1rWbE8gtRJLHzObzpCZR84ROUuRC6QypdHxhzvtKRziO9c8eCIa4CmBLQGBvTlDDJuhbbVQvAnpjg6EsvodjVxYHL3xuIaAlIaJipS4AM3VGVupKwsFLhttk5VubDXFaVWlaZ6mOq/57v/ABN111D33vf/bRcOiH9+kRGcm3K3GOvsGAeghLCGhqqaQGKW5oq9hG8D1X1lEichIMILA/CtklaEu2bbLHFYpFCoUAiYayQnlvCtpNoKbAUuJ4JK9ZBMrfw2rWibkLRdKRViWlM4u0aEovq+wnPGycY8WywkfuIskjbtm08z6u4TvVyC3ECFOZOiROjxaBOUOqo40UOreHQ40kmdjXhKw9HpGhfIei0QGpAC4a9Zgq54/CUA8JDOdNkRv4TBxc05FJdsPECtAA1pSn5BXhhRxkDtc3jtcrM9dlepDj2081Jhi1Jl4IhW9IuJJ8bnEKxdD97YnCQdR98P09d8XnGzj5ndkRMGF6MnDW+l++XQMMw/+KC1ZjLHVNre61ZdvW5wjpVwPfp/cIVT9ulM2gJvtiW5sa0hSVMG/QvMuLMbe+Yk5xU/wR3EbnVgApxcikhUY5EDA4iPR/XV9yz426WL1/OeKBDeeDBB/E9j5aWVo5ZtwFvYBAJlBqbAuNnWcRaK7dI/P2tdp9UP4Pqdzx+Xq1N9ttqq0pIOqzYOkbVid+qiUg8Iig8Pk54loI6Qamjjhcj4mOC1nhuCU9rfOWjZZ6cbzGhS5H5eMabpODvw/dTKErkisMM53ySwnSI475LfnycqZLGEhZusfACl8g+PVQPtl5H16KOG3FsdiZtpBQIobEtyYdllncPzdDtL81KEMbi9H35S4y+7Ayw7UrdQCjiFUEq9RpExehpxJyagIUIy2L21XKFzOfuAcD3af+P75JYgltHAWMCPtecYjRp85ukhY/GtuxgnRrNfUmbcSloVrVJjxYCt6ub6RO3zklQZtfZaLWEphwGHGQDRkCusRE/00xp1+MkD+4lte4oujo66F3Wi+sWSSQSLFuxnCMHDyEBVZqhtONeSnaKfHu7sZYw20IFs90oYWbXOCEpExgIE/SFuqU48anWDcUJRS3LUYhaK2hDZaRQNUldinsH6gSljjp+L+CpAlr7SGGj8cnjM6pdtDLRBxMqj6uH8UkghY0viuRdi5I2g1zJkri5GYSTBUtSmp4Okke9OLCUjjM+G81vOxm3pwd7YABRa/AFxiXsTJiQCiklFhqv4HJtyuE32zfRt+8wjRPTfGTKpUUvzqIigOTgAF3/+X0GX/f6KAw5fheKcB2XcjRSeSAJ9DS6MudF/B5rtctC7pv5XDoLWWiar7+WZX97BYmBgXnLxRHO5f/tpGNoecOr6JSCh//lmxQnppGi7A45u+DSNMf7atxhmsPv+6Bpx3lISXx7hV6j+tRC4mWb6D96A+vuvJPCT35K+q1vY/maVSQTktWrVwMWwhI0t7aSKJbwb7we/8lHOLByLaWWFmwUFrOjbMK6xH9XCGF1uUylC0ZHzzxOHuL3EnchhZE+IaoX/wuvUctKs9gosYVQj+Kpo44XI0RZyCktixl3hCl9EC85TEEOcceh3XznwB6+e2gP3z24h+v23s/M+I0UJq9De7cxUzrENXsb+MljFj9+1OIXD+R59NrbePKX1/PYz67h/qtuBJ1YdHWuuOIKTj75ZBobG+nq6uI1r3kNjz/+eEWZQqHApZdeSnt7O9lslosvvpiBqsFq//79XHTRRTQ0NNDV1cX73ve+ik70aTWVELMG6FrQ5SYtf7Yshj/88UCUWhstCs4u+FGHX/Q8ilrTtXYVl17xKc688ovsv/BMPtpk2nMpyp7VX/sKJ/zRq2m58QZDNnQ5m6mpYxjt41eFhs52WcQH3GqNQvgT7YvlUwm3zxf5shhysup978FZAjkBGLAEn996FEd/9bNsOf8CjjpxG6l0IwCe5xmLoe/xvrE8MLdQ2WtuYfzMsxdsk4UgguzCCIkQDiPrNjGwei1y16Pkf/hftIxMYvmCpGWTsCwkkHFdnDvupviLn3Mk287AxuOwMlmEqhSYxjUiIcGY7X4sb4+TE/M5nvNER2XD2zJJ/ESwZpMMiEw5Cqg6BX6cjMTrFq9TXMOyVOsJ1C0oddTx4oYQnHvhH3LU8S9hbGQQ33NNiLGQNDRkUFrjuS4CKHluFL7oez6W7CMtBEKYNUFWWLbpxISFWL+VVLqJh++5blHVuPnmm7n00ks5+eST8TyPD3/4w1xwwQU88sgjZDIZAN7znvfw85//nB/84Ac0Nzdz2WWX8drXvpbbbrsNMLkVLrroInp6erj99ts5cuQIb37zm3Ech89+9rPPVoMROlFqayiCGaouf5w593xUS0vNNXcEhnC8fzzPTWkHadu4notEUMoVKJR8Wlev5rX/88/56q6nePdTh/jQdIneJbCUxNAgGz78QZ74zBWMn3Mu8Wy3phKB1gSF0VyGgwnB/dXWjMRnvRWiy3K+t5oi1qVaTrTnsWwJmhMFTAj4q7YG7ktZnHbaVjZhMfzUfm788U8ZHRouZ0cVghMLLj3zuNEE4EyMk71vJ5Nbt80iJHPrUIgN7gIJBI6WgMQB6Sz7T34JSEnn/Ttwd++BzZsQq1chpED2H6a0azfqwH4OdvRy8PiTSHd2VTyLkBgAkZg1bsWJu3vQZZFs6PYpt79x8YQw7q/wnOG1NFKKgMBYSKkr9COhoDZ6djGrSdgOYb3ilprwHpY6magTlDrqeCEhNjDW+BjbUNYjHL3pOFZusJiemcRXHp7nBjNiP5pdh+aBeGcmpYn0CIV0tu2YBQRFAqUd8jMzi6721VdfXfH5W9/6Fl1dXezcuZMzzjiDiYkJvvGNb/C9732Pc845B4BvfvObHHPMMdx5552cdtppXHPNNTzyyCNcd911dHd3s2XLFj71qU/xgQ98gE984hMkEou36ETNpasFouHAAEJoYi0b5cuoGHA1pO/ZgVWDnISQQI+vObHgck9K43smedXgwcN870tf4SUXXURTUzMN6QauzyS5Lumwrejy1akSzYtw+YR8YfVXv8x9Z5yBtu2KAaOSINQeqGuJhWtpEsIzVJSrygxa6xyzocH3ydy3k+xdd5IYXJzlJLzSJ1pS3JW00AruuOYmHr7vQSZHRtC5HEIEg29Qh85Fkj07FsFT7cqJ31t1O0XbY1YlKQVaC3wpES2dHDjlZUy3ddKxZw/pe+5A33kLPqAsh1wmw+GNxzC2+mgSbZ0kU0l07LohSakO1a38berhKx8Z6U6qiWacbIooiVp1Fti4G6c6a2y1Oyc8vtpFFG+X+N/V79KCz2TRJeuoo47nD3T5j3CmbIbTsJMMh1aBUuB6HkqDbTvG1eB74AuIJ08S5eMhXPZdYkmB4zggJZZ0AAvtWc8oMdnExAQAbW1tAOzcuRPXdTnvvPOiMhs3bmTlypXccccdnHbaadxxxx0cd9xxdHd3R2UuvPBCLrnkEh5++GFOPPHEZ1Ajg3AmSZDoDMphnLFM8RWQQ4sbXLuUmXJbUuJrhXJ9Hrvnfh6772GkbeG7RZPbxJbcYyf5uJR8ZbywqEgfo0kZZPm//StT205m6sQT0Xa5e6/WI8S3h5qUMjmr1ApUH2O2l3UXtQhJ9aBXbd5vuu5alv/t5xZNTEL0S8EXWtPckLYQWCQSNr4qMXrgAL7ysbRJA+/YtglpRbDKW9ybWmxvCwbPuIWktiUFQZQ51khjA3qiZeAWsfA83wiUhcLPNjN47IkMrzsKpiYQMwU85aMbGvAzGUS20SQ+FALX8yOXoWWVn3xEHBRISyKkQPkKT3kI4QMKGblcIP7ClkmGeQ4hgRLCrOMTCmjjliFfecGioGW3UZysVGeIDZ+7Ob+saLv4MUtx9dQJSh11vMCgAS0kxZLRFVi2g21bUX9kFhIDQzcEWDaWA5YrEZbAJF+zEFqhhTS+6LATDgZmKYL8DYH1BMuKZbC0kFrC01zNWCnFu9/9bk4//XSOPfZYAPr7+0kkErS0tFSU7e7upj/IM9Lf319BTsL94b5aKBaLFIvF6PPk5GTF/mrTtIGOEZXYVh3+F342f3sdnfPcbRl9XnkWjBKIgPz4vgvaZ93RG1DA6OAQM5NT3NKY5nJL8oGxPL2LFCT3fftb8O1vUezsYu/llzN21tmVosU5LBo6YmBlzNY3VIkfBRUDVnUI6+zzGzRffy2r33/5nHWZC59rSvHdBhuRSrBqwzq2X3gBvatXMtZ/hO9/5Upmcjlj6bMsECY09uxciUsmi8y+u1jdEJS6u5g8fguV2pza4t/qewxFtmYboAWu61IqlXBdN2obKQQ4DaiWFKLVnMOxLAQaJYCQ1GmTwt6QiLKLJbR4hNcNXThoje+D1grLsmPvbtmSEupLLMtCqzAzrF3pxhMyaiSBRAjNgYP7aWzMkkgkarqV4kR0rhT4IUI31VJCjesEpY46XkAIrSRTRbjm1/cxNTXFllNO45jjTiCYoCOkqOiQ01qQ0pom3U7YaanYYDXb8x/myRCBVUVEE2YRltdQOvD0uo9LL72Uhx56iFtvvfVpHb8UXHHFFXzyk5+ctb16sI3P9Gqbs81xOvZ/uC+3dRtudw/2YO1InvCISyaL7Ek6XJeSgWDRCnzymp7Vfbzl4x9AppLMjI7x65//ivuuu5mbbcmNKYvXTxb48NTiV49ODA1y1Ic+yBOfvYKxs88J6goiSmMWzqAriVmshWrfR+z+FhvhU0FqfJ/lf/u5RetNwLh1BizBdzMJCkJx8umn8sf/820km5vxpUSjcdFISyKRUVp4Synev4A4Nnye+9/9XnSQGK38TlRb0+L3OvukoaVNaYXnuRTyeRoaMsbtIiW2Zdrbssz3xmxzDBmxHaQ057CkZVLUW9Xp6AWWlFjSMhYUYQXnEZGtRAYTjur09uYcEkO+zXo+lYJWEVlfCEjWTG6CBx+6n0ymoeL7EHcLxX9CC08tHUpIrkzbLZ6Y1glKHXW84CAMwRCKU886i1XrN1FSopzVXhOkOTeuAS0wlhBtARqtTJCPikZdjfI8kgkz+xJoSq4fRCKYjitcuj0kLFJAU2bpQYCXXXYZV111FbfccgsrVqyItvf09FAqlRgfH6+wogwMDNDT0xOVufvuuyvOF0b5hGWq8aEPfYjLL788+jw5OUlfX9+scvNrJipUoeWBKRzALIuBD36U5e+5bM6ZeiiW/avRGW7ua0Kh8X2BJW0830VaEjuRRibTtHQmecWb3ojQcMtPfoYjLf5fU4q3zXh0K73oMGQNrP7Kl6NcKaBNdlI0BO9CKF6cff+1o5riA1Kc3M2teYm5SXyfzv/47pLcOqFa4YrGJK7yae3p4pVv+hMSrU1BGLXPxJF+tOsZWYvyCUZ6tuRLC+aYEcDBv/jfjJ51tnGr1HDnhC6t0CphrByVUVtlUmt+PLfE1NQUF1zwcuMOUorDhw6glaCvb1XggjHE59ChI+TzRQqFPI2NjbS1tpFuSFIo5EgkUjzx+OOsWbMet+Ri2cZ95LouQoDrlrAsh8amxqDe4ZOv9exMqHF4f2G9o+ccvtqxV11rNes5K6WiTLK1XD9xV061ZmWpqBOUOup4AcJ2bJatWEFDJosjNRk71qFiSISQyszogxmVFCGBic9uwbEkWts4toXrKwolTcEKfdPGNCyEmaUJIQgtwU3ZxS8WqLXmHe94Bz/60Y+46aabWLNmTcX+k046CcdxuP7667n44osBePzxx9m/fz/bt28HYPv27XzmM59hcHCQri6TIO3aa6+lqamJTZs21bxuMpkkmUwuup7Vda7l5qjV0U6ffyHDl76Tziu/Nuf5QrHslrzHzlQgeERj2zbDR/rZe9/9rDt1G0iBtGyO3b6Nu66+lmIuhxQWVzQm+MpEcdHZZ0NdSva++5jeti1W95AuVQ4e82lPqgWh1eGks8XGlWi+4TpWPE3NyWezDtclJZaQNDZksRIOvlLg+hx+Yjc//38/xHc9tDAuE61NVtS20uIiRvLL+2ZZf6q1J/F9QKA4qXYP6sC6WA5PtqSFQlIozHDrrbfR0JBlxYqVmCcYWjIUN970K1avWsPgoMVERw8DA0d48onH2HLcFh555BFSToJbb/01jY1NtLW1cf8D99PV3c7I8BCdXd388eveGIvGqU0s53o2FfVXoeC2MpEbMCubbFzwGicvcRdOqEUJjw3PuVjUCUoddbyAEIlgFZRc8DyYmZykrbsZWwYLxgUzISFstNBBbgaI/DTaJPEyHZJEWgKBCS/2fIGQFulkIIINLCYx2UGwDdKpxWtQLr30Ur73ve/xk5/8hMbGxkgz0tzcTDqdprm5mbe//e1cfvnltLW10dTUxDve8Q62b9/OaaedBsAFF1zApk2beNOb3sQXvvAF+vv7+ehHP8qll176tEnI4jruOcoFKUTDhHWllasXdc3WokdJGlO7DEzqXq7Af33jm1ykXNYedyzCsZnsH6atsZHhYhGF4OamFO9G86Epd9GaFIDmnTuY2rIFHa1sqyvcetqfvQBd9LyrrCXVs2IhYHConwMHDnDS1pMDQkvFuVpuvJ4177ucShfS/PjHbII7HIu7bRMeLTHrvwwcPMS//+3fs3z9Okb7h9hz/4Pkp8YDS59x7xhroWbVIoNFim1tsyJLakWaBN8Y4taJSpIWWhkFSkiTBNGsdkWpWEJKycz0FGNjYxw50s/mTZvQCHp7l7GybxUnnngi9963k127HqOhoYFcPoeTclixagWH+g/S0d2BZUkmpsdoaEwjpGTl6jVkMtkoPDi09tSKyoo/t+o1dKSU0fddIxA6ENRqqp63iIhINYGrFX4czyobd50tFnWCUkcdLyiYzsLzPPqPHCLV1EJhepwVnZuNaVurclRPfLYXuH20VmaGFywkp5Xx3YPJfeArY3FRESHR5HPT7Nn1JCXXZdXadTS3tJkQ5CX0NF//+tcBOOussyq2f/Ob3+Stb30rAF/+8peRUnLxxRdTLBa58MIL+cd//MeorGVZXHXVVVxyySVs376dTCbDW97yFv7mb/7mabRjbSwcHhuiLIrUAUlxOzoWdY2VftlVIKXx3bu+z8DBAb77d1+ntauDZGMDR/bsI2s7pBqy5As5lNbc3JzipkyCLXmP7SWfS2YW1qWs/Pa36f7FL9n97nczHuhRwvp7nsdTTz1FX18fDekMZatKuT2q3TcVbh4099yzIxicdDBIxwZ332fF336OWkqnWlAYq8k/tWQo+T7K93Esm+bmZmYKBQqFIo/t/A277n0QaUkcwKpyUUkpOXtmMeJYKHV1M3nCllnPOz7Ah5+DP6L2qT5vteC0TPQ0TU2NnHvOeaRSDaRTDaRSqcifIoTk/PMuREiLl73sTNDgOAle9rIzSaVS0UBfTQDKz0VGxHA+q1et7bM+xzUp0oiNq6Nvarnw4qSl2u1T6QIqu5kWgzpBqaOOFyC01rilIrZlsW7Dugrncdw7HhlOhDBml4h4iIi0yFjHEc6tQ0/2kUOHuP3mG2lpa8OSkscfeZjVGzZy8qmnMXfXX7u+CyGVSnHllVdy5ZVXzllm1apV/OIXv1j0deerz3x1msuyUjOaA4EWSxDLTpXYnbC5MWMGdSOcNNYNoVymhgbJT6XwlMfIZAGA5uYsFppiIY+0JPckLJ5oTXPx/gk6PLWgyycxNMjGj3yYxz7zWUbPPKuCdAwM9HPVVT/l1a/+I9auWRdZAioHGSq2l39gYmKSdevWobQORLjle8/et3PJOU6+0JrG9VwsKc17i2RscgoNNHe0seroDfSsWMbg3gM8fs+9ZpZO2QqkPY/3jy9GHAu73/lu9Bwuh0qdTbit0g5U6z0xWVhDC4OhSFJatLd3ETrnGoLkhMZCQSCclaRTIUkU2HbZKigoW0fDL3VIfqJ9c9QnVrPYpGIed15wn6FVSogw86yusLTVIiLVVsdqMiRFXM+1MJakcns+p6uuo47fF2g0tmOzvG85g/2HOLT/QFkfEvwzBMZF69CCoszfWqG0MpaUQLmvMf2Wp4LQSspdSDqdZuWadbS0tDA5McG5f/CHbDxmU1CPFzZqiUDn2j9vWWFcHcKxOPL+D88SUFYUDX6/bzyPUCqYTQbpx30fhOaVb/0TLvviZ/hfH38/Z7725WTbm5gYm6RY9EgmkqzpW0FvVyuNjWm+vqI1UpTMey/B7zVf/QrKDRP1mXdi+/aXcsq2U/jZT3+C55f3hT++79XY5gd/w7Gbj6NUKqHVbNK3lNT1g5bkI8uaub2lgfaWRjraWsg0pPEV6GSSU191AX/52Y/yZ+97J+e+6U848zWvREkLo4MtR61sCTLHzje4CWD/29/OyJlnBK03WxyrQ59HgIhuiPIqwOE1ozIalG8IhYVFmLk1jMIJfgXuE/NB68BxK8qWtfLVQseSMvUUpq4hOSQgJ7OjpkD5GrSILKqhe9gcVmldMdo1ES18CGGqASuoU+X91spnUr0sQFxvUiZ6CkvO/72LY0kEJUxXfeedd3Lttdfiui4XXHABM7GMku95z3v42c9+xg9+8ANuvvlmDh8+zGtf+9pof5iuulQqcfvtt/Ptb3+bb33rW3zsYx9bSlXqqOP3FqEINp1O0d3TFSj4RbRTo/F9j4ce+A333L3DDJjhbE7r2HQw6PqCgcWv9rtruOfO2xgdHuL+e+9lzdq1/Oqqn5BtbAr8Gr/Dm36WUe2XLw8MSz9HHFPnXcDgJZfNa1sqZ5b1os7c98vr2XQsW0ZTVyerjz2G89/6et7+kb/i5DNOpqOliZKvODIwSHdHK+vXr2VnTyufWNnKsLNwVy6A1OAgzb+5L7qWCgjr8ccfT0NDg0lupst1MdmGdYzQzF57Z/mK5YagVAzumuYbrqPvy3+3qLb8QlOSP17bxgMr2jhqXR+NjRkmZ/LkSx4iKXnV217PK9/6BpqXdaNtC4lgbHjEkLrYYCiEoKuGSLQW8stNFFmcBlQitEiKuQln8A6Ya5s2tm0by7JwHCdyg5VKJfbu3cvw8HAUymxIiSCXm+GRRx7hyJEjTE1NRrl6zLMwVx4dHeL+++/loYce4MiRIxSLRUqlEoVCHt/3KRaLTE9PRxP9/v7D/PTHP+T6a69mdHSQXG6K0dFhJsbHTfRNjXsoa1eoIKPxUOf48420K7qcq6WavIRlwh/x20zU9nxNV11HHb9vUEozMjzOus19tHW0Byb48gzJkpKpSTPr1rGZkxDSqCIFCBSaQDsQaFBMXHLZc79s+Upuuu5XjI+Nsfupp2gPkpJpYAmu5Oc1wtntfIhrEeIRK9UmciEEpVWrF3XdLgTJZJJSqYSvNJaw0L5mqL+fFeI4wEZKQddRaznnTa/jx1/7Z8TUOMKymZqZYXR8nEQyxW0tGe5syfAnh8Z468jCyw9s/Nhf88R738/wGWcAoJThm80tzRFZMvdS7QIQsW1hVlJBOpUOzhPmxoC2m25g3Qffx0IsVgGDluCHbQ1kGzII6TA0Msng8AQFT5FqSLHm+KPZcu5LkZaN9ny0UEyNDLPj2uuN5iqWl0NrzeAip92l9vbgLx0TlZTfhbk8JdXRXeVolrIYVAhwlW/aSECxmOPXt9xIqeRy1FEbmJqZ4dxzzieZSjM9PcWvfvkz2js6Wbt2Le0dnWit2LHjLv7gD15Bd/cyHNvmoQfvJ5FwSKczJBJJLCEY6O8n29hIvphnZGScN7zhT2lta8O2LIpugc5MB9ddezXTU1N09/TS1NzKhvVH09O7LG5EKZMPETx3wpwtVnTPxs1TSeyrFxAEZuU8mbUC8uIeD/AMVzNearpqYM501ZOTkzz88MM1r1MsFpmcnKz4qaOO30sE33Pf9xkeHCKfy0eWEBHToUhp0dnVyeTUhOlYdGDKDZJyhzM4pU08T6hHCWzY0bXWHbWRbaedxvFbTmBmaoqXnHVOYJpeigLl+Ymyj7xsRan1U6m5mDs/SHiexWaWHUBTLJXwopmywLZs7r7uJg4/+qTJRuprSp4m1d1L91EbyeVLzOQLTOWKCMsmkUjQ0dFBZ08vj7Q1Leq6ztQUmz/+17TffBPKVyhfUSqVaG5uDqxtxgU4eyXjSvdOWC4cyPzA2qI9j74vfYGFhLHhkPaljix2KkkileJQ/xBHBkaZmMqTy+VxSyXa2jopTruMDk+w57E93Pbja/m/n/kSTz34cDSCObYdkcT7kjbjcm6rhwYKXV1MHH9C2SIQWUsqjYwVqBrQK3YFgmchQ7eGjs4FYElByc1RLE7x1O7HeOCBe5iYHEajcByHVCpFY1MjxVKRRx99lCP9h9l/YB8HDhxAa022sYXVa9bRt2oVR204ipmZGXytWblmDQiwLOPoS6WNbqWjs5O+lavo6V1GU3MrbR1d9K1chR/kLqmuuxCiwu0T/2zIX6itKbuTQssKGKtR3JpULZitTpe/WDxtkezvMl31XNkg66jj9w/lzqGju5t0MgWxMOCwjFKKvU/txpJBboTIiB1X+xlCEiZs01oTJJsMPsP1V/+c/sOHGBwcZMvWrdx60w1ceNGrYjV5YaK6k44EhzGLwUK5PWad05yY3NZtlLq7cQYH5xTL+kCT5+P5RgdkWQLPMwP/wcd3881P/x19x2xk+bo1tC/vIZFIs2/3frSwyZVKNBRLCOXhFgu0t7djyQSjR61meN8QbSVvQf2FBjZc+Q/0n7odHWT6TKfTQYbhss4hnm021DyUTf0ACiHNAKWVQvkuvf/1fZKDgwu217gUfK6zkevSFrbnMzgwSL7oorVEC5CYtWbuuvlmDhzYRzLVQCGXZ2z/YWbGJ/DREEREecpHSpOF9czpAk1zmPfCrU+9452xpRpEXGM+u6HCv8M/5yCokRVNGmGp6xkXnkSQTKX4w1f+UZAhVlIqubS1dAKKpqZm3vinb8ZJJSiVikhhkXBsTt62jXQ6Yyw0Gk7dfjoCjdSClSvXYDtO8FAUDz90P6tW+SSTqaA+ktNO244QgtWr12L0ZkYDIoVdUeea77cwVljbcQKrEGjtR9ai0GUTDzUuW9ZMIrcoFT+V6fGXgqdNUH6X6aoXmw2yjjp+XyCkpKW1hXQmFYjhylZqDQgp2HD00Tz62OPG/Ko1UhLNikQQ3hiKZ2chnPnZDuuOOZZXXnwsj9y/E/fF4tepwlwhmUs6VoL2NcK26H//h+l777vnDHOVwBfHi7xXCq5NmSyvhqQohGXh5nIkExLbhqGD+xk+PMz02BgNmRTTUy7FUonWTCPKdxke7Ke5qRkQfOeoZbzrof1zXjeqM5AaGqL5gfsZO+EEtA4WkFOB5DZcyVmXF5UruwotU0QYt6DEDGCtN93I+r//KsmhhckJwFe7styYcSgVSrheOV9GqeQjlCKZTnHUyVs59eXn0rVyOYlUCoHFvp07+c+//2d0wWQ71oCSwWwdzQcnitE91oLb1MTI6S8N7GOhi5MgoiYUrJYbSiCizwqCbLwgqlx9OmZZlMHEAHzQNpaVpKtnudkfTA50YGKxbYuWVuOFyDRko+9eOp2N6izRQV3NBZLphop7Ou74kwILRZk0SBmm1A/eaSHMmUIR7pyveRAVJIP1eSBa2byWFiU8phw+LFEqcCdXXeR3QlB+1+mqn0k2yDrqeHGhrNovFXMU8jlKrgckKjtkrRkYGGByfJx9e3Zz15138UcXX0wqlQi6YxkwGhEoUOJuDiKm85IzzuKpXU/glgr4WnP6mWfP6Zt/oSI0JsHSO9A4yocKps6/gANf+BJ9H/grI/Koviam637fRJEbUjZ+oCGSwey7Z8VyXvHWN5LoaAVAeT75XIEHr7+d2/77p+Qmx5nJF2hIJpEIRkcmcRybuzvb+LdTN/KGnU/Q6C2cqSw5PBxdt5x8K/yhanwJtyl8ysJUrTXrfnMfm775jSW112GtcF3XLKUQaB38cHE9KenbdBR/eMlbyDQZ15VSCmXb9GxYQyKVpFQoGvdlzFx0YsGjZ5709gJITE7S/MADTJ54UrTVPI+Yzih2QBjhEh4f/o5HtYS6kyDfcsV7NFuQHW6v4UqqUfVq/VP1eQ1qrXNTLX4NrKg6fnzMTBRyUjQisJzZjh3cmyR0yoXvS5kImTxKYaK48gKCKiItccvLUrCk0lprLrvsMn70ox9xww03zJuuOkStdNUPPvgggzET4ELpquuoo44yBOB7HgP9/dxx84089tCDgfPG+PzDdXOaGptYvXYtvu+Rn8nhukbhH7kxqqJ6ZvWNWnPtL37Gjttv5Vv/+k8cOXSQ39y7s7z7t3yfv1townBTHYVhh+LAuQf6MJ+MCeeMDRpa4Le0IuY5VgK9vubEko/SEt+X+D64nsf09DSFYhGhBVIbIpDKNLDtgnN46asvwrIdRqfyTM4UsG2bRNLG83yGhsa4JZvk307ZuKi73vjPX6fz1782a6u4PioWUuz7Pkr7KOXhKx/lG64VRn2FbVPK5znjR/8VtMfCUEC/JXkwk0IggkghH9ctUSq5+MGaOy//8z8l2ZQ1ddC+sVi4iqFDR8jN5EwdlI/2ASXxPU1byV/o8gAkR0cMsRE6sBZVvs1VKqRouxRVzzlAqCEyg7KPWa/KWHTCayhtfsKyWpevZCJnZlsblPI4fPggu5/ahfI9437TQXRVGA2kFE89+SR333k3M1PTlIoFfM+lWCjguX5U//7+w1z1sx8xPjbO9OQkuZlc8O7KSFcWXZswqSPRYn+h9ihOlOIi2tlWyMqInkrysjgsyYLyfE1XXUcdv18QCClJJpMcu/VU1q5bb3iG0lE/IwDLtjhy6BBtrc1MjE9EwlhRMYzo8u/ARySEjqIPwu22ZZPNNlIqFMsXeFExlHI7RBwj2BSfLcYRhpASukCChdrCDtweHl7UlTs9hUyAFgItNZ7WjI+M8sSO+zj5D7tRQgSDhUCkHY479yweufseDjzyCDOihNaKxoYUjmPheT6TE5Pc5kj+zLFod/15Z6GJyUm2fvbT8OGP8JtkEtctYdsJojT+sXBSM+CWLXjhbLhh5z00jo8v6l7DFYD+ri3NVKEURXw4wsLzXOMasS1OeNnpdK5ejdblSBglNaLk8+jO36A8P9BBmARilgVKSYbtxS2/UBHBA4Tr4oQaGyhbTnRMm1QrGVlkUQgIhu+H69SUk6nl83l23HU3juPQ2trKoUOH6OvrY3BwkMbGRiYnJymVSmzdupXW1nLdRkZG2LFjB22tLRw8uJ/p6Wmy2QyDg4Occsp2+lauwvNdbr3terSSTE2PMjo6SlNTE4MDg3R3L+f8Cy5ASEFbWxvFYoF8foYf/OA/WLtmPa985auD77yg5JbKodG6bGmJW0zC+w8jc0IC4vu6gnyUNVyV7V6OeFoclmRB+frXv87ExARnnXUWvb290c/3v//9qMyXv/xlXvnKV3LxxRdzxhln0NPTw3//939H+8N01ZZlsX37dv7sz/6MN7/5zc9quuo66nixIrR1SGnR3tlNQ7bRCN9kvGMwkTldvd1sPv44mlta2XrSSViWNN2t9gnN+LNyP+jKPLSvePXFnPeKP+SP3/in9C5fzqmnv4zIC/ACRpxwxDUHlYNPeUCKD0zx6J9aA1b42+tcXDTPqCNxbIljgSU0jmXhCMGuu+8jPz4Z1C+4jlLIxiY2nHwSmcYmfK1xFeQLLq1tzTgJm1w+T6FU4p9WtARz+rkR1nzTv/wz6USCXC5HOZlf2Twftlk86iWytOzfv6j7BJOM7b0dDVxlaYquj8ICYVMs+WaQ0xo7kaD7qLW4QqKx8YWDry3cfImHb7mDx27bYbLMam1CpLWxPGkNOxMWRxYZwWPuX0QhtaaNITSKld+JmIZDxDZGepXybyEltm0RZmgOCaslJQP9hxjsP8S+PbsYHx9mz55dCHwee+xhpqYmmJ6ZJJVOsGvXE1Hb+76HbVskEhaHDu1DSkX/4YO0Njeyf//+iAg5TgLP9xkY6CeZdBifGKWlrZmx8RE8zyyHMDk5RT5fYnp6klQ6iUYxNNzP3n1PUXLz3HjjDYGwNbYwaExvIqVV4aLR2izKaAhHJcEPmymevC1+3GKxJAvKYk78u0xXXUcdv4/QsT+051MqFklYKeNW8F0OHzxMOpNBIHj80Uc5adu2aECJTYuIz/ZFYOaWkV/aXOC+Hbez64nHSaYbcByH5avW0bNs+e/6ln8HCCM5Al1OLDphntJRBBTEtQhmn4nm6cEZ6K/p+lDAgCXYYUu0ryDQYjSmUyQTCfLDQ9zy/77P9le9ikRbG54WCMtG49PU3E5jYwqrwcaSDlMz0wyNjNOQSVEq+Piu4p72Fj6L5LKDozS7c7s+BJAeHmbj8BD7GhvJNGYCw1lVSLXwg+YRgcZS0HPbrRz9/76zqBb+29YGvttg4wuBdl1jOXFsXNfF80y+nkTSQWjYde8DKA+wJJYHo0cO0v/k4xzatYvcTI5SFC4buCIQCC3Bsri6weGt06VZQuHwSQ2ecy5YVrBXVhTShAMz5aN14DaNkxKqiBtlJyGWNJFRujKz6sz0NGs3b6apuZnJmWnWrV3LU7t3c8ZLz6ChIYuvPXK5PIPDA6xdvx4QdHZ1cvxxx+J5PmvXbWBiYpK2tla0BttOoLUmkUhw/oWvQCtNS0szY2PjNDU1EgbRWLYZ5ltb23jtxReTSiVZ0bcKx0lgWZJ27eH7Pscdd6xx2YTaEnOT0X1KWbZ+WJZV4QqUUmJZ5aRt8aie0EU0X5j+XKivxVNHHc8lfJ/UPfdiDQ7hd3VS2LY16DxrQwRTO9/3GOw/RFN7J+2d7SZpWuCbvun669h47AmsWr2C/Xv30NTUxKpVKykWCzQ1Bur/aKApq/DDWWO5/xD0Lu/jN/fex8vOuRDPc2lqalqUzuCFgGq/OQSz5cUcHLadjm+qWvXXtpl4+UV0fOsbNQdLAfwyaaGEwHPNLNeyLKZzBWYKRQSC0ZvvYtf9j9LU1UFTdzeppmachMPQ3n24tiRX9HFnZrBsm+l8Ec/3SSeSlIou+fwEv86mSCxr5n37Rhe8pTV79vBQayu6bwVBxr452w2g5/bb2fLZTy943pCI/XvaMgnbfR9LSjxFlIFWCIHjODR1dnDMqaewfP06Mm1ZLMfGncmhDuU49NQuJien8FFlsWow4Nm2jfZ9LHxennNDGXgFQq9k1w3Xs///XAKWLO8RQNX7MOvdCO+/+rwxAWplOxFZZxLJJK957R/TFAh+w4yqJ7V1AFagATGi0lNPaQmqI5DSoa9vTXQ3nR3Loi+oiHRnkq7OZVHturvTRORLB7mPtMa2bRynGQDLittJLSwLli1rKJNsw9RibWCsJdVWxNmrYJeFs7NDkGe36UKoE5Q6nl0sccD9vUOsfey9+2n8/g+xB8qCca+nm9GPfoDchefNc5Lygn5Ka4TtlHtfAclkCuV7pNMNvOSlL6O1rYVHH3qEbaedRpg6K5wQSiHwg3Oig/OGJm0Eq9Zu4E///C/INjYTFXv2W+U5gw50N+XhJy6u0bFtlZ2rOU5HbVV2A8TOoBQtv/w58bMTO6MGXl7w+arWWJYdzEbNStOmkEQqTbLkcuqrX86KjcegrARKCBPtkivymxtu4v5rbmB6Yoxs0sFTinyxiIXAsmw8z2M85SyqLTb/93+z8vrreeR//2+ObD89uudaIdhCKTb9yz/VvLc4QvfS51sayHuemXkHUTauX3Y+OZZNU28Pr/4//4dlmzeghcLCuCqV77PumHWM9Q/ywG13IZUw7kohTJ4ZbZLRW7bFSXmXnnlC4QVBuv9YFI95/GWX3axj4qQl+NFzDLShy8W4iVx8pXC00e+0tnYE3x+NFqEaRxJ/9wyhsYLvoY6+r5pydt+IeokwCX/4pZTROXXQJqHFK1Sfxe4g6j/KawBV3W9kOTFr8mhduRBkuC/UlYRapbhb6JkIZKFOUOp4FtHwq+to+/TnsfvLC4R5Pd2Mfvh9qLbW33vSUqt9qrtSa2CQznf8FUN//8V5SYq0LHqWLWN8ZIjDBzKsbN8QaVrRmmxjlrHxUR568EG2bD2RsbEJLNuapW2VQgSr0BqIYIANzecAjU2GnIQGg7JY7pm0xnOLCiGkCEOtoXq4De8xHppZEeopA3dH3HUWNF3DvffgDNROPhleqVdpTvbhbscMLkqZxdSU1gitEErTsbKX1cdsRCSTJhwZgWU5iIYUL/mjPyTpJLn5+z9kKpcnk0khE9LkzQhI0w5LMbQIwSxAdmKCU77wBe5+/wc4sv0lNWfBAG0PPUR6ESLgUQGfbmvglw6gjIBUSisYrIIU6QLsxixnv/F1LNt8FEL7SO0jAu+LsCyULeg7ej0P3HpnpMMKI0zCteeklHQuMk9PcnQkckGEZrOFZvmztle/QwGpN24ShW1bsS+bMNFQvodt22adCFF5bq2hWJoBBKmUSbgWCddj9pt8fgbQuK4bWGQ00zPTJOw0iUQCIaBUKrJ3715SqRTLli0Pom1MVNDExCRDQ0OsW7fe5DnRRCSlGnFLSOj2iltJ4sQjLBtaVUI3EBAlbatltZwPdYJSx7OChl9dR+c7/irmMjCw+gfofOd7K159v62V6VddRP68s38/yIrv0/yP/0rL1/5x1q5ZM+vAn93+sU+TO/sMmHdtKklDQwOZhnTkI5fSone5WbwtDHUE6Otbhhu4ESLxHiKYrRt/fCS0pTxLFBhzr9KQm55ieHiQFSvXLHkm9HxFJHatud2gWoZSTVIIzyDKHboGnOGhRdWh09cIJ7yWmfUqbfRCWpikX05gOZEojIxRoX2FcmyOPfcMnnr8cXbtuIeJ6SJkoK25Ab9QMv5/LflKR4ZPH5lEMX9kREhLj/vGNzh88imIWAr5+P2nxhZ2GQFc0ZzmV0nL1DUYlIU2RCWkhE4iwYatJ7Lu1JPRwkcKE0obzvaFAJ0v0b/3oKlLGI0mRGCR8SJ35ZC9uPey1N5RMfBX2MpqjNW13D21XT2BZTK2iJ4QZoC+/Y5bGRkeYsuWEwFBQ7qB8YlJWppb6OzsQmufX13zc4S2WLVqNZZt09XZY5Z2EYLVq1djWYIHH7yP3Xt2I5D09a2kqamR3bt3kXBSnHnmOaTSaQqFPNdf9ysaG7M0NrUgpcWmTZu4994drFy5kltuuYVzzj6PrSdtI245iiYfUiJ0OVoHyhaRkHRUr2hcy90THhPm2VlKBA/UCUodzwZ8n7ZPfz5yEcRR64tsjY7R/K3v0Pyt7yzSpfHCRS2ryUIQgDU6St9Lz2fkU39ds21832d4aJjVR2+mvas7OlL5PpnGLMlUkunJadau30DfypV4novrliJ3RmjWltLCV/H+WJR/KU0+NxN0shZTk2NMTYwbDYElXzRaFJhbXxDtr7acxDt0hFlkTQfhwMH5vM6uRV17SJrjtFIoDVorY5kRAqnhyL5DjPb307xqGVqBkBIJuMUSUgjsxhQvufjVDO/ezdjQMAW3RNFNkLAdQFB0Xa7PJCl1Zvir0dy8iczCujeMDNP28IOMHHfCrPsXQtBw6PCi783zFEoLfBXMsg3tBYI1XGybjaedBlYCpVyEsNFKRuQapXjo13fy0G13BU4QsII09b7yyWQacAsm3PqBdJJxmaNZ1V4HSAOlrm6mTtiKiDK8ybKXT1d9F8Ts42uZDstuFHOcJsgRIox7xvVKAJx88inceuutrOzrY+/evTQ1NeGWfF716j9CC4W0LLraOtl5950MjQzR07uChnQDvoaurg6y2QzphjS53Aza19yz4y4cx+GEE7bg+z75fI6BwX7aWltB+Zx04onsvPdeDh46SEd7C7mZGTo7OkglbHIzU2hl1g+quI/AChvmWImTilArFBKX+DsRrnwc/wxUlIl/XgzqBKWOZ4zUPffOOwDPN5BZ/QN0XnY5k2/9sxePRSXQmaSvu5Gmby0uwqEW5NjYbHeP0JFLYXJyEtd1TWeB6TylJelZ1sPY2CS5mRlyuRmUr5ienKKjozOYk8bcEQA6XAwsVO4TdVI3/PIqDh08QJDVij+8+PU4CTPdf6EbUWab7MNfS+lIy46hKF9GYDIvnHwKbncP9hxRPOHZW5TCkk6Fpd1MWrURv05Ocd13/oMz/uR/0Na3EulLlGUhHBstzDotrR1d2E6SRCpJoVhEyzSHh4ZJpZL4vgcCrs8kubHB5n8OF/g/M8UF7yw1Ph5FyMRzYPTecTsb/+N7tQwNEaIIJUeilYpWSAZwXTeaVTuOQ9ErcvCRx+hctgynIU2hWMT3SpRyOYqT0wzs3s39N12PKhYiF4JlWWit8X2PXC5HykmiFLxsZuF1ePa86z3zC9EjDQaz3Djx88TL60i/VXYTOY4DwTuRSCTIZDI88sgjHHf88QwNDXHCiVuiBHgakMJm7dr1lIpFzjrvAvqPDNDc2mQErpZNNptFCMHyZSvpekUP0rIYHh6iIZOlra2Fw4cPkUqnEFLQkGng4te9gebmZrp7l6OUIpFIsnHjJjKZLH/x9ktQke6k/CQjl174HCPha2WYfTyLbty9EyIkKvHkbmE7LSWbbJ2g1PGMYQ0uzpRdC2EH92KxqDwdi8lcCElH22e+QO68s02nGvQBlm2zfEUvjU1ZtPKiDlIpxW03/5pjjj2eNWvXsGf37mh27is/OK+Z7UlhBpIwtDjWH5vtUnLRxa/jhqt/zj133kkylUK+yCwntRHP8vkMJMGWxcAHP8Ly97yj5mAenv294wWuT1r4KhTIiqgOyvdBCXb/5kGe2rWbZSvX0dbbS6KpETuVwPM9hBYM7N7H9MQknudhO0kOHhlAK0XB82nKpnCEwENR8DW3OoL/s4jqZw4fDtqh3Aba8zjuG/8W1b8WwtJfaGtESxFEnplQVBW4rTSadDKBpzS9q1dRyE3y6D13kUgmKU7nmJmaYnxkkJE9e5gaGQuinIzLUmuNixfM0qHke3ieT2M6zftG8/PWzWtqYvSlZ5RpZdUjjlsTIvIZsneIrCQBf6zSVJTLC8yz8z2TYE8Kiy1bthKSvaOjRL8ak+jPRE1tOuY4cxUlWLNmPYiAIATvo9YmXNjzSvhK09ERWOmE5qgNTQgEDQ0ZtNZ0dfUCmmQyXSbOoWw5mQ7XNQ/sR7NoV3iXMVfObOIe6kw8z4uIR1yTU4uQ1DUodfxOYe9bfKKmhbBYkejzDvPoTJ4JhNbYR/pJ3XMvhVNPjrZLIQJXTgLf9wKfPIDGsiz6jxzBsWBoYJCpqSkmJyfQSgXakoCkCA0qFoUSu24YZKqVz+5du+jo7KSxqYlioVARXfDiRLzDLv9dU3sSNEOUH4TyZ9Co1tZ5W0oAPb7m2OkidyWMS0D5gdUCbXRAQlDwPYTnM2rvp3f9CnrWL6e5rQvHSYAQbD79VHKTf8Cjt9/FAzfcQlrBdD6H8n2KrkcilSKfL6A1/KbB4chkkW6l59SjaGDzf/4nk32rOHjKKdG9dT76CA0jIwu23t83pfilLaNEXpaUELafEKSSSYq+ywkXnMuZb/gfOOk0wrJASiwNwldov8SBBx7kqiv/hfz0NGYhPDMrd70gk6mwTPZZqdk8nV9wHR5ncpLmB37D1EknE5kNRZW1rMYAqmvsmq1cMvcWEhrLtisGdiGscrnwr+i6oaUitEyE1k0R6Zvi5ScmJhgbG2fDhqOjcnPfdfBblN9THYRqi9ClGE5SohfaHBNPZW8SQsoKDUo8y2xYLp4OP251qXb1LAZ1glLHM4Pv0/j9/5rX3LsULE0k+vzAs2k1mQuhlSowtqKVYnJsjExLB+nYyqbhsmdTkxMs7zuZjs52pBQkU8koYVOo5It3fObclZ17KNDs6u1FAA/e9xtOP/uC6CovdBdPHOWBYrFWkzC9fXSG6t2gwRpanHWxW1gmykoIPB2kcbcsXM8NGtrGlnDsKSfyste9FldKbGkTrpniomno6eD0tasYHx5h346dNDZmmcnlmMmbNVmS6RTKd/F8n880JfnaeGHO723YClu+9f9xaNtJ6EDzkRwdW9T97JOWEcKGwko0yvNBClLJNKBJNWY47cILSWQyWHj4XhEnCLcWtg3SYsX69TQ2NVHKG8uI54XuyECjosGSEiEU7Z63qLo5AcESIvb+i/ln+WF7xEnJ7LekzPKNQD2WZyWwYmqIEptFGpsYQSoViziOUxZba1C+WQtJSgvP80glk7S3t9HW1hEd57kuCHBsJ05/iKxxaHIzMzz55BOsX7+e3Mw042PjNDU1MzY+xvoNG4L6SoQMw51NNJDv+5H7UmsdudeA8jIIwYKBoQarGvHIn6UIZesEpY5nhIX0J08HgoVFos8XzBW9tFgsltj5XSZtethB+r5ibHSMQ4dvZeWadRy98nyENrO2k04+mT2791IqlijkC6SSaQq5ArmZXGwmE4odwloQnL9cL/OHRnkutp3gT9/2drp6luH7Gst6EbGTCoRtA/PFuxgTf+gsm21dCcmf37U4oeyILbCCZ2HbEl+ZhdpSCRuBoKSNkjndlAVbYlsSdJDfXWhjdRAaP2nTsWYVu+64A8dJk0wnyecKuL5C54s0ZpJYCZubpcs/eJp3Ts+tRRFAw8gIHQ8/zNBxxwOQ7V+kONYyEWIaglBihWVLHCeBY1kUCnm6e1aTams3A7eUCCuBD0jbDtbskSihEJYApfCDWbwQAiuamesg35pkZJHaBrejA6h2z8TuO7bNWBvMxKm6aK3nbnLjhC4bia8w2VyUx9VX/5KBgX4SiQSbN29mw4aNjIyO0NjUyPTUFJa0ePTRR2htbWfjxmNIJJO4Xolf33wD4+MTrN+wAYHFuvXrePzxh1m/fiNNTc1orbn5putQSnH8CVvZu28PmUyG/v4jNDa2sPWkk7Ftwb4Du7nl5hvINKT4+S9+wolbtvLoY48AcNdddyCEZP26o3nJS04PDDIiyApr3nMpRWTBKutOgu9BzFISHhcXxYZEphwVtDgsbe3jOuqowjPRnywEOTZG52WX0/wP/0SUt/n5At8ndftdtH/kEzWjlxaL2amPKqExuWQK27ZGn8F0BMlUAyee8hJOPOVUk6Ip4BulUonm1lbyhTxr1q8jlU4yOTVJIuFQnZgsnv1RV7ElAQjL4pwL/oDpiTE+/zcfZ2pyDDOZDpNELR2f+9znEELw7ne/O9pWKBS49NJLaW9vJ5vNcvHFFzMwUEl89+/fz0UXXURDQwNdXV28733vw1vkrLkWFkpnv9A+s7/GxtjMPH/SybjdPXMm9tLAmIT7krIcmhrNTjWeq3BdHwtBQloo18OyEkhhI4WDEDZoC+ULpLCxseletRrpJMnnCwCBWNOskjw5U8BTkE4nOZRYXPe//ctfoueO2+m943Y2/+d/zvvUFXDEEjyQMWnUQ0Grbdskk0kcx6JYyoFQNHZ3kchksZwkwnIQloMlEwhspLARwiE/Os3U5BTKV1EK9cpw1fL726J0Bb2s1dbFri4mtmxBVS/gG5gEa+VCEYS6kvK/uDukQoMSemY0s7QXa9asorm5ia7uDp548lF23HMXV/3sJ/z8pz/mnh13cuutN5EvzLBv/y4eevh+BJDLT1EqFWhIJbDQHNi3m4HDB/n1TTdw9913RfVrbmmhpaWF++/bSW5qkh133s7hg/s4cnA/I4ODHD54iKZMlhNOOIHDhw7R1NjIU7t3o7WmVCqRy+WYmZlidGwQrf2YYaksblUqWFfIsmKLB5bvMcxtEwqDa4Ug13KTzoe6BaWOZ4RwZv/bQPh6t371H2n8jx8y+tcffF5YU56pSyf8ej605SiO+80T85YVwNTrL66IOtCYaJ2Orm4am5tiZzQdw4F9e+nvH2bDa19FS0sLSinWrVtPJpMNzhnrOISIxtPIbRMaVzT4rsv1V/+C7uV9/M3ffpHWttZZosClYMeOHfzzP/8zxx9/fMX297znPfz85z/nBz/4Ac3NzVx22WW89rWv5bbbbgPMrO2iiy6ip6eH22+/nSNHjvDmN78Zx3H47Gc/u+R6VKNyVici4rFQh1oz/DgeCWFZDHz4r1n+rkvntJa1KDin4HNtKhAWqiAMVwgTViyEyb5qSQ4+uZfi2BR2eyvGcSJRAixbojCjbkf3cppbmpgYHsR1PWzLRgiJ53soBLmCh0w7jDqLi5ZLzszwsq98mamA6Mz11MNW+PtlbSAF0pJoqbEtC6E1jmNTdEvYloMQPo4QTI+Okco2Ih2zjo4QFsrzDXkoFHjwtrso5vIQiG2j9zS4VyOVEKB83jdhXFa1aFdYt93veBfasqJ7CFbxie4qFLgGHyomBEZWVPtdiHQdQhApuIQElCHyUtDR2UPH8Bi9vd0sW9bL1NQ069atpqGhgZaWFjyl8FyPxqZGHCcFQpPNZli5ehWFmQLZxia6e3oQUvLSM84i29gaWYE2HLWRPbv3sH5DF+PjY2zd2s4999zJ5k2b0NpnenqKFSuW09tbZNny5Ww46miU1jRmm5icmow0JplMNuBZCiGCZwc1BbDmczlCx7JkRahxWL76mLqLp47fGQrbtuK3tmKNLeyb/nVTimOni7QuMttjHM+5ePZZCh0GGLIl/7ayg3OdxX39vFUryx+EMTcD+J6L73m4pRJmJhl22JLOnk4Egiceewwn4XBg316QkhUrlsUNKEC5z5VVs8aw8/c8l0cfvJ87bruVd3/wo2QyDYR+7aVgenqaP/3TP+Vf//Vf+fSny2u4TExM8I1vfIPvfe97nHPOOQB885vf5JhjjuHOO+/ktNNO45prruGRRx7huuuuo7u7my1btvCpT32KD3zgA3ziE58g8Qy0SrNNzrWz5FaHmoqqbWGZqIGDhp0+51z8lhas8fHZ18YMku8bL3Jdlwx89JViQ3NiQbHksffRJ7j9pz9n22suxm7IoNwi+C7FfJH89DTjg0M88ZsHEEojtIVWGl9qUqkklILIE1+TK7o82trMwHCOTl8tmLxNAU1Bor/5yv1rR4YbGhwouCbiJnBbJZNJikWjhZFa0tzXTefyFRx6+AESyQYKxRJuoUihUCA3MorvlRjtP8zA40/geT6+CtaHCd9VadrIliap2LaSt6BAFsBtaQksniJg4+HvsGAlwawWd0ZTgfi7EO7H2BUFMjiPyR4biso7Ojs4vf10Y2XQwXmjEN54+vvwImBLh82bjo+2r1qzFoDVrAPKidJaWtrYcmJrVF/f9zh60zEkEmkAepb1IqXFUUe3oLUmk2mM6l7OFF3OEG1WRDfnCaP/4hbHWmJxY9kqLwro+/4schIPUV4M6gSljmcGy2L61RfRvIhB+47mBr66qY+PvvG1LN/5MB3fvgpY3Dw8HAyeC/Hss2UxeeSCU/lFYYafHR7mjFyRU3Y8sqjjq61UGvPlHxrsp7mji8bGbGD90GgFqWSSgYEhXNejWCjiuT7LViwnkUxGVmgRdKWKmHsnBoExgVuWxSte9Rq0UvzoBz+gWCySyWSCMkuzoFx66aVcdNFFnHfeeRUEZefOnbiuy3nnlYnnxo0bWblyJXfccQennXYad9xxB8cddxzd3d1RmQsvvJBLLrmEhx9+mBNPPLHmNYvFIsViWWcxOTlZvsc5feHlNpmrTETgwiPig5iqjFZI77wHuwY5CSExKe+3eZq7HYnSZtVgY1LXketBSolXLHHXz3/Gw3fsIN3aQktPF2uPOppUSwuZzl5aV6xi+7o1FM8/m59d+U+MPvUUAh2IL20saaNcj5LnMp4r8KXuJq44PL5ghtnFDikD6WTQ3paZfStFMpHA9VwEAltaaKXYfM75bPqj/4GNxEIjUCitKc3MMH5gHzd99zscfvgRfK3x/ICoGblNYPFTCGlyrFBy6VzkpNwZHgJtBOEy9Mjoso4oZjqL3u74gBqPTpmdgCwsFDlQg3ehbJY0ieoCpZMQiMDHayYXQRoBEQqBBVpbQX0VSivCxQfN9aq/tGV9mbQkSTuD1hKBHyPTta2C8TwlcViWhSWt4F0sE+f4MdX5UeLkujrip57qvo7fOfLnnb0ogtI2U2BkfIYv/upGelf30rpxGZc82U+Lv7jeRfC7F88+UxEswLAt+fqKdm44fAihIZlyeOfewQWP00Lgx/QnEE7OzSxdBv5hx7HDPZQzPSoSjg1C0NbezuTYMMVCMdYJh7NBHXVq0Uwy6E8F4LpFrr7qKibGx2nr6KS5qfFptcF//Md/cO+997Jjx45Z+/r7jXCwpaWlYnt3dzf9/f1RmTg5CfeH++bCFVdcwSc/+clZ26s74sqOMzbSLBLVnbuOtetiI3laS5oSCk8ZcgI6+A1KClDGXWDlfdTwIFvPfinHvvKPSGabccN8GcFAkGrvZuP207hj71OgfSQS4Xo4aRs7k0blNEW3xC8TNnJ5Ox88Mkrz07BsVmPEsU0dLZPXRWJRLBUDQixBghKCVFMzAvBQYDkIbRb+SzWm6d3cyso1d3Dk0UextEDostUqfGeVUgitzKtsCXpzpUXVz2vviH3SNUl2JIw1H2q78cIzVG03+/yA+rtGe6QDq0p4RR3cTUSGQqsEJhWADKJjgmPD46S0jWVDK4SwDXlFo5RPyfVxEglKxRKWZaO1j2VLtPZwS3ka0hlERIAq77UWtA5ImS/wvRKQCESyuuInJG+hPiie/6T681LT3EOdoNTxLKCwbStedxfWwOC8/unXTBX49dY2ZkZG2D02yrgU3LShh+8/2U/TAmbmOELx7Pi7/pKJS/7it5d5dp4U/gsh7La+19vK/21LIxI2TYkEazav40w7QddDBxZxEs3oR95feX+RiVvS1t7O+MggWimOWtFBKNxLpdOsXruGqelpmpubcRyblatWUSi6gd/eJI8SQiCxEMKs7hJM66KBYHJ8jO9/59sM9A/Q1t7BxMR4hdVALPKBHThwgHe9611ce+215UXQfkf40Ic+xOWXXx59npycpK+vr6JMtVi2Vqc9lw5lztloTOzqdy5Op7XC9fAtC59gdo3ACgiHZaa+ZpAIkm0Viy4N2Sawk/iYJG/aTJURjqSxowutLQQKackgyZmH5/tkG5LM5DWuElybshnrbuafjowvWMe5LC0KGHYs7rAMoZBSYtsCt1TCSjq4ro/r+mgfZCpJe+8ybDtpZtaB8EkqjVQSlZ/iSCDgjLexCFaPDvVRUhi35B+4PpfNuPNGxIUC2fHjjw8icsLsr1UuuuB3SE4qNCg13oHK7SL6X2CsD/FQ43ByoGPEKEr8JmBoqJ9f//rXvOIVF3HjjTdy6imn0N7Wjud5DA0O4DgOuXyOgYF+jj9+K8mksSLv3r2bJ594nM7OTkbHRshmMuTyOVauXMXQ0DCTk+OccMJWli9fFbmVallh4vcRkhHjspHBOl2iQnMSt6SEx4dEJd4+UF4scKki2XoUTx3PHJbF1Ov/eN5BXAAdruJjvX2cduHpJBM2jQ0JhG3xxa7mWSbz+RB2AK1f/UdWnHkhDb+6Ltq3lJd/IYQh1EslJwBjaYfPrOnm/3ZmaWhK09LYwLpj1vGHf/rHHL3r4KLOMfnWP5vTSiSFJNvYSLaxiUxjKH7V5PM5Vq1Zw+ZjN9PS0sLLznwZqVQSy0kgLYv+/n5GR0Y5dPAQruuitI4WETTQUfs2NrXw8lf+IavWrOENb3oTbrGA57mBmXjxbbFz504GBwfZunUrtm1j2zY333wzX/va17Btm+7ubkqlEuNVbpCBgQF6enoA6OnpmRXVE34Oy9RCMpmkqamp4gfmcd1UmOuNcDDcPuuYQF9RqefRhEvbh51xboFInuAsvCvvcZEWJCyJLTRWMCBblowGN+UrtALf9Xn4ttuYHh/Gd8C2LGxpk7AcHCuBZaVoaGrBTthmlquNCxDtk7QlUns0NqRxHBtPKe5tSDBgSeaa4ypgNMiyWl0mVE58bVkbJS1NXhJAaEVLUyPNjVm0Vti2RFiaVHMjzT3Lg8gdGyktpLSRdgLLsRk9fIj+A/sCwXBM7yCCReyEQAiTM8ZG88GJQvlxzNG2AE+9451gWWhhQn99PTvqZ5YaKa7LquHSqHUlk5BNIoSN55ejzBQ6yqQ763xC0N7eQcI2C3e2trSRz+Uj68OB/fu4+pdXseepJxkc6Gd6agLfUxSLJTKZBqamJrAtQcpJIJH0dvcyNTlOoTADWpOfmWF0ZJBCYSayvNQmJ5WNpjALV/q+0ZfECUicmMxF7uP5T2q16UKoW1DqeFbgrV65cCFg7b//kq1/8Sp2NTfiFn2UhlsbU3w60cFfTRVpGJ1a0nXj4tmZC86t2jvfnGphpK+7cUnlNeC3ZDn4tb/kB0/s5qb/vp20Y5F0HBKOxcTgEE9+4ArOvWf3os6XP+/s2RsDJuf5PkcO9bNu4yZ6ly9HCHBLLl//+39gdGSc7t4ennz8UQoFl+0vPZ2br78OIW3OPf88DuzfTzKRpLO3mz970xsDv7g2qa8DX3/ok+/o7iWfy/Gf3/l3mtvaSSSSS2oTgHPPPZcHH3ywYtvb3vY2Nm7cyAc+8AH6+vpwHIfrr7+eiy++GIDHH3+c/fv3s337dgC2b9/OZz7zGQYHB+kKcotce+21NDU1sWnTpiXXqaJJY7qCMuKzwEAjEO/EA0tFOJuvRYujmaaUDHzooyx/92VzvpEhQf/QVJFftSVQQX8e5o4AGdUx1FYW8wVwFQ42rgBhgdI+vpAoIehct55sdy+F/kPYNiQdG8dykPhoofG1Rybp4HoerobPtzfyd4MTs6wkISH5SFOaRNLmg8PT9MbcsoO2xZe6GrmlIUnashDKx3FspBC4bolCsYht22QyGUbGxlh2zCYSnZ2owLURRudoCb6AhmwTiYY0pRmzUGWk6QmtDublRAjByZ6/qMUP97ztzxk548zKCB09DzlZYBCdz6JiWk+ArlwWQkMQd1XLegET41NMTU7Rf+Qw09PjDPZDa2srjz/+OMcefzyH+49w9DGb2LFjB4NDI2gBw0ODFItFWlpa6OldRi5fpGf5ckZGRii5Plu2bGXPnn2sWLmSX//6Vl6yfXttgW81sQgIjIbIEljrOKBmbpNa5eKRQItFnaDU8axgKeHGx33r53zt2OW42vguLVtwVzbLr//xg2y59k66vvJDYHHUIuzYw/VqdIVC/OmTk4ZfXbekaJ1wFvnoX72eoRXdjN5xP4mkhecpZgpFpJ2msSHB6x5ceFkAoz3pqtCeVEAYX/X01CSFQh4pyh2h1rBnzx6cpEPCSdHa2sk1v/wFDQ1Z/vx//QXXXn01Svlc88urueCii4IZuhWMtWJWhwqSDRs3sW/PU2QyWaQl49VYFBobGzn22GMrtmUyGdrb26Ptb3/727n88stpa2ujqamJd7zjHWzfvp3TTjsNgAsuuIBNmzbxpje9iS984Qv09/fz0Y9+lEsvvZRkcumkaS7oQIpTaUkJ/lYBIYnuvDahiXbpMkmZOv9Chi59J11Xfm3Oa0ug19ec6sHtdnnFX7PeSWXacCkFy9evJ9vWgalWmN0zzBekSXd2sfncC7jzu/8XXBepPRKZBNlMI0J55IslPKWwEhaTeZ9fJGzc9iwfHs9VEJABS/KZljQ3ZhoQEq7pczhhukSH6zOZTvJIYxpX+SS1RnglEqkkpVLRNIHSSCHR2qdYKiFTKY7a/lKwkhAJSE2DmfbVNHf3snLdUTwyNBIJleMiTiFEkJ9DL0hOQuSXLw9cOuFz1oEMpLaLJ/w7ql2NAXd29JaIndOcIVrR1xSKCGa1pk0ALS0tvP4Nf4KQNiv7ViEFCCk54YQTEJbkVa95LUIIXvHKZQhsBIr2NhOtd8LxCrSmu7cPDaxctdYIiYWgrb0bjeD0l55BOjX7u1It/A1DrUNiUs5sW47QCXPRWJZVsTBgmFU23q7xvDU1LZHzoE5Q6nhWUNi2Fa+nG2sBl4gAOl2fMzINPJC2mJkpmhxsQvCDf/sB959wNE2nb+QNdz1Bi7dI8aw269W419/ET6ZynHnm6XS2ty7NDxGH79P2qc8t6ZDxhiT/trKdvQ89TvP+gxzac5CkbSGkoLWzma5Vq/iD9g7af3jnwifTmtGPfGBebY20LNo62slmM1FnZ0mbttZWtp1yKslkgvUbjsItFMnlZhgcHKS/v5+m5kaTtKm5iaamRpQ2Dh2l/ApzdzguHz60n11PPEapVGJ6egbXdUk6iVmm8WeKL3/5y0gpufjiiykWi1x44YX84z+W1zWyLIurrrqKSy65hO3bt5PJZHjLW97C3/zN3zyLtQhRHjTDgcZ8FBWjVrVbsjyrNnviYkGA0qrVi7p6r9LY0sJT5QHUth08zwuEiQLbFqw/aiNOugUvtH8JjdQ2QulApyLZuG0bv/nxDyE/jZTguQUmp1w0GttxQILrKywE2YY0t6UTXJhNccxEnh4pmUjb3O1ofC0oFgqGLFmC2xyBtG0yDQkc7WNLgee6lJRP0XORUpJwHJIpB9d1kUoxUyyQyDbT3tKENTWGTKeN0BvjEsECK7CWpJKZsrg0aG+tfaQkyOsCLy8p3jeRX1Sbltrbo4E38soJOWuwFFV/x4Wg8WdcUzgbWByEDMNtfTzPL583ICbxcOU4LCnRwjFfZ2lFdZOyPHUwhM+8X4LQCmTaScfeS/O/seSELtt0KhUTrVbeTyUClUxQ6XIyxHLrhEQkTjjsYO2hiJRViWXDfXULSh2/e1gWox/9AJ2XXb5wWeDVrzifmaED7HnkKYaHJim5PqmxaXbd9yTDwubWlxzNP939FJlCadEz9bUf/jjnv+MSfnnDLfzxa/8Q2168eDZu6mz++r9iDywiygb4aW8bQ6dt5j5cXF8jPR/LSeP5PnbSRkhBIV+ksbWJ5TsfW1Rd5tOeBNIILCnp7Ggnk0kH4YeAAMu2yOdmKBbzjI6OIISksbkJYUlmZmbIzeSYnp7m2BNOoG9lXyToqczlYOD5Ph2dnfhuiRO2bCFXKJbX+hDPxD4FN910U8XnVCrFlVdeyZVXXjnnMatWreIXv/jFM7jq3ChbJ6CGEgEIUn2rWnvKJvD54HUszsr4gSmXohD8MmHhBxYEpYLVgIOFBKWUHD64B39yGJ1IoV2zZkpCg/JLIDR+wWX88EEymSz5Yo5EwsFJBkTHV+RzJVzXQwlpFt3TZsCRjsOOBg/h2CSTDlor3EIRVyuktPG1RkiJhSGOtgRbCqRwKBTy+J5vRhat8X0/SHNuQoy7160n1dKGdPM4XsHoalwP7cHkdA6vWODQ3sd48sHfzIr6sFCcUvLpUh6rfM1lUwtH7mig2NnF+HHHV7itQt3HQqg1258rJDfmsAm4bFnDVOvYuFWmbKmRkRA1pLqz3DGx9zMiHLH9IalSsags3/exbTsiDPl8nmQyWZFUrXy/Okbg4m4ZU6Nq0hFvi+oon1qRO3WCUsdzgtyF5zH+zr+kdREr+h751c2MLWskYUvSKQcETM/kmCmWyBU8xl3Fl7ub+ei+oUUrSeTEJMd/5gsU/updDA0P09vTvfBBVM6C01dfS8tXF7ci8U+7mvjG8jbc3QeYyhcpuD6JpEXbgSMkLE26IU0x71Lwitx/0x1ceucz0J7EIYzZdGx0lIamZppa2qI2amhIc+RIP6eeeipP7Xoc20ly+OBBfF8xMT7O8hUrSKZSWHaCU049NZjZaSzHRgRi2ZCkDB05zPDwMAjBb3buZMXqNYT5U9BP30D1fEd5dVeI5qOBaT7I5F3RIS/unJrcSdtwu3uwBweiZHu10KI0XxovopocRoSgU2mGpMUOx0KiOLWkOD1Xgmtv5eEn9pH/gwtZs/UkWnt7sJJpPCuLZ0mEkvR19LD83nt46pabyBeL5Eql4K5MpJGQNhKJtMD1CmjMejnN2TTTRRdpJfCVh7QVtq/BtpBKk7QtkpbAtizcUhFXaxJOkHXUspBC4PkKzw8iO4R5c7I9vaj2LkpOEi0FdpBt1dLQ7rnkh/q556oH8CZHEWjM+Ka5oOTzsekivSpOA+bvF8KST156WWSNDIbYeY8J91a7I+YKL472g1khXJt1eMqLARp30sjIINPTM6xcuQqEYHxilMmJCZIJk5+opdUkWiuVSiQSCUqui2M5ZluxgFsqsnffXtau3YAdrBTtB4Q1nU4yOTmJWyrx8CMPc8oppzEwNEBzcwtaacbHxjj66I2Ebqdbb72Z9euOorOznYGBYXp7ezmwfw9OwmHt2qMgJqQthxCXF/uLE5MwIVuIsEy1+ywkK9WkaD7UCUodzyom/vIvaPz+DxcMOd54x8MMbFtNyTOJmBA2JRdKBR8fn9zMDA9t6OGrrVne8vghWvMLz5ZCk+xJ/9+3+fm2k+jpNmLKRfk8BVAs0frRTy72VnlsfQ+rVi9DCZ8jBwfpPzJOYzZNoeCCY+F5RSxpojCOHp2heREuK7+tbW7tCQTmaYGvFCMjwwwMDrB311Osft1r0MDDDz7M5MQEd915J30rV9K3cjntnR1cd/UvefThR+jp6aa5tZXRkSEeeuhhunvOAVQkjI06Z2DZij6aW1q5587bKRbG6Vu1ChHqXcQsN/oLFrN1BfFWKCPSL9Q4ruqM0bkqQmUtiyMf/Ah9l79z3sHVDNnw5UmXclfuMgokgGy88EO7KOwf5Na/XsGeo481Icpa42mfovYoFfJMFfOUlMIOSZYwWToEkLBMQrVkykZpTaHk4iQTWFLgaYmvNEgLLTSpZBJtSbSCbCpBNmnhuoqSskgkbJoyaSyhyeeL5ApFip5CSAutA80CkJuZoKTzpg5a4GuJLaRx5dgJkj19HHvSS+jecR/HzxTRCDyleUd+dhbbhb7VpZYWHn/Xuxk544yIdMfoBujaZsDqZzOXFWW2aFSANuvRSGFWozYDudn11FOPc9NNN/P2t/8Fbe2d5HJT/OcPvocUFj09PTQ1NWHZJtJu9erVPPnELlpb2uju6Wbnjh2cffYZ3H33XRSLLoePHKBUckk4CfL5HN1d7dz3m3s5+eRTufXXN7Ost5eh0UEmJ8Zpa2vjkUcfYsOGDYFrRtCYzTAxPs59997Nvv37aGxspKerg3yhxNq1RwdirHIUlWVVClzj73Z1uLHneZH7J2yjuCWlbkGp47lDEHI8nxVFAB0ln+NKPrs6GpmcyJOfyuEr2PqSE2nqaGL340/yukveTKathUeUoPPK/2TDt38WHT/fue3RMf7gf13K0Cc/gvfKly9YZQGkr76O9o99CnticsHyGih2tXHG1z5BMuPg+0UOPrGLO6+/k7MuOoc7rv41Tz64FyfhkG3K0NDcxFmegEeuWvDc0696xbzak7CjlVLQkMngK5O4affe/azoW062sZHunh7a2lrxlc++vfuYmZnhdW/8Uw4eOIDyFSv6lnPvjrtZvdbkYgk70NBXHd6j1rDr8UdZu2EDJ249if/47nfZctLJkdn3xWpBWQghiYl0kbP6W7P6axhpEu/Mp867gANf/Aq9n/oEzjzLQ9TK/9A6R9nk5CTnvu9DXP/5z/HUOWeSd4sU8RFpC6E0fqkIVqCfUcY9o7VJBGZbkr7eDrKZFDOFAsNjk7i+oqEhS94T5D0fz3PRSmPZNp5WaN/HFpJsQwPTU9Noqelua6aroxntlvB8ODQ4xsDopNFlKLO+kJ0QFGcmsHBBusa6oqHoC6SSJKXNxptu4o2f/gLZQuUqy4u1osbxxP+5hKEzzjRtWWEJqTxrtVtvsULOagIar20YDu0rk6vZ93327tvD0Udv4Oabb+S447YiLc2y3h6SyRRjY2M0pFO4Jc3KFcuZHBsD5TE5MUZjtoGGhgY6O7vp7V2OUj4T4+Ns2XIiExOTKNVKS3MjPT0rSCXTHLNpE9PTM+DDyOAICTuBYztMTk6aNX88F8/3KJVKbDlxCw2ZNL3LlnHLjTewYuUq0y4iuK/IGgJCVBKyuCUl3Bb+DgWz4eeQpIQC2sWiTlDqeNax2JDjl597Jt8b3E8mW6JheBRhSc559dks27CSG396Hb7rkW1uwS0V2fe2V7FjeIBXXr2D5kUo9xOTkyx/zwcYtB3ycT1HVb+j0TT86jq63vneJZkEfn72CbSVisiUjUBy8Kk9vOwVZ7Ly6BU8fHeWmalphIRCscCazlaOChZaWwgLuXfCjlpKi7aOLqSUFEsuB8fytPVCT28vmcZmZqYmGRufoL2zk2w2S65QoFgqgZCMDI/gljyeeuJJPM9DaY3UBANqrBkELO/r48brruXRhx5izYaNUU6NGk35gsVcIZJzH6Cj37VS+EWz9aCfr3YNTJx7AeTzrPzIB5dWz3m2a+DUL36R35yyGT+VQCRsLOXjqRLSy5MUAoRtXBBCYAmNLQQNqSSN2RRKe1hC0dXSxOj4FFNTU2htI9FIpRBKoJSg6PqkEjZO2qGgXHx82toaAY+iWyKVtMmNTpussCkb31NoaSGESfrljo3iTs8gUg04UmAjERL8ks+Ka27g/L+pvfDj03nXSh0dkYYj+i1C8ShE8c0hO6/x9Z8r58l8ifsALGlhWwny+RnyuSJaa84950Ic26JYKhnXmpScd+7LDQEACsUiyWTSLNLpG2IopYnOWr9hI54Pxx57PLmZKXzPI5/P09XVhdbg+4rNm0/AsiyOOeY4AFKpNEoZvUlXRye7nnwc23GMVimRREgYHh2ltb2N0bFR2rq6cJIJrrvhl9iWhed7jI2N0tzchJHsVApga917LbdOnJwsJYIH6gSljt8CFhty3J0r4SRsZvwZfM+s5Dk9PoNlOWzacgI/+uZ3WLVhLZOjo+zfdYDJ4SkeXtfDZ584suC5w/6m7TOf59B5ZwVWiRpfDs+n/dNfMOr6Rd7fD1d38O2ndtP4yS+RaWog3eCQn5ph7abNjPaP4RVKZBvTTE1Ok0gmSSYydPz7L+edBWqYldZ+bpi6auVTcgsoJXAsI5w89rjj+PF//Yh0poHWthY2bdrIDddcS+6JJ2hr62BiYgzHtll31NGsXGVmSzLqhCvrk5uZ5q7bb2Prtm3cf999HHPsZrNzjs78RY9QTKzjHXT5qVZ02rriV+VphMDrnju53NOsGo2DQ6zZ9SSHTz4RT/kIBf7YCIWpMdq62khms0yPj1GcnsESNrYQZDMp0o1ZmpoyuJ7PyPA4qZJHqeCifEnJ94KssDZaaTzXo6g109N5CtKnrakJULS0tZHNNjE+NgEIutsaSeUFBdcj1dRCz+pVTE/PMDo5Sf6px2lraUInk9iWQLk+Epdzr/x6dC/PBEYY28n4ccfHxKfhM4JIHyvKWZNDIln9Ez+2ZrvXEMuKwL0jpeS/fvh9vGIJrTRK6CDpmSEfYWVc18OxLSwBvueRSCaxHTvQaoQrBZvzCSmC5HaSu+6+LUgRoLGDwT+RcFBKk0gmkLaF7xtikEyaCB7HTpBKpbADvVC5XRS9y7qjzMXgY9uCjRs34jgORhyrKu47rkEJdSWWZUVZY0OBtAmLL7t86haUOp5TLDb1febbP0X88UsQwqK5vRXfVzxw604OPPEUUlokZILxQ/vZ8+QhJicK+K7mN0nJkGPR7voLpkEWgNM/QNPX/5XJyy6p2fOF2WIXA41J5/2tlhT5vEtpYJSxoRFWrGgnmcxwy4+uRvuK0cExfM9HKZgaH0feeCupkfEF6zr1utcumLY/6DLxfJ+hgX5KxQIISXNbO1IKHn7wAfL5HLZj09razcH9+5mansZJONx5++184Stf5vprr+bwoQOkG5ImyZK0QUA8rZQA7vz1zWgEv/rlL3ndG9/IbTffyBv+7K2RS+hFI0KZE+XIhVovT3lwCn4i/5iO2icuyNToiATmtp5EqbsbZ3BwXsHsUrFiaAQLga80Xi5HamaaP/7j/0FnRydYFo/ccyf333YH2vXwXY/xsXGmciWclMOaNSsplFw8t8SWmTzZnEe/JWh55SvYtWcf9917P7YQnJov0VfymGiw2d3gks6k8Xx4+NFd5KZnaEhImjIpUimbxmySjaedwjEvOQulNfmZHEr5JCbGacg2Y6eMrqVh5700j8zt8loswpZ8/JJLzXepKlontASYv00+lfDAatdO3Bowl0B2NjkhWgJCCMEJJ5xAoZBnZmaGQjGPUspkcPZN3hJfBcsQyCCSSlpIyxARy7KxLBn8Fji2bcLDbRvHsbGlYzLwShkdI4UM9juANouF+j6WbdPS3IwUgsHBfqRl4TgOlrTwfdfchzAZqs17GrgEhYx0KDpGzOPELB6GXe32iZOSxRC+atQJSh3PPhapQ2mcnCG98wmeyKbJZBtIp9Mc2neYA3sOMzYxhZTQs6yZQgnyeRfbkijb4udnncBbrr130X7p1q99HW/DBmYuPI/we2fqoGm4/qZF3VJ4zI9fcgzuxBiFkov0oGd5B66ymR4aY3JkknQmTaFglofPNGbINGZYPrCwrgXAW71qUeXAfMllNItRSKGwhEmqVCgW6UylKBYLKKXJZhuZnJzgJS99KcODg6A1Pb3LgnNIY48JRLJxsezExDjNrW1kG7No5eO5RqisIu3Fi52ghFjgTRPlXzpGTqggcpUWOm1ZHHn/h1j53veYdXeepbbMdnfToRSer3CFINPehejsRQR5KzadcBKP7biH6VwezzWLzJVK06gpcBWcP1PkL3YdpLPkR+cc+pfv8KUVPfQUXT42Ns2ymOBx4OAo/7K+l5tyLlNTMzi2HSSAy5H1kzRbmrauXppaO0AImls8SkE+FdtyEJ4RaLcODD+t+61+MoXOTp645C8ZetnLou8HUKEHqoYIXDzV7omluiPCioSk1bIsXnL66WQymcA1EnMpaTOYh1qNyclJWluNysj3fUZGRuiMreEkQ7Et1Wnja+doCes+NjZGsWhC0JcvX47vexw+chChfWzHxlMe4QrJWgep+GMRO+hyqHQYnRN31cy1Hk8ty9Jc2+dDnaDU8VvBYnUoyxI2I32d5KdzaL+EXywwMVWgtbONi974KtqW9/DAXTu5+WfXsG7zUbz0ogvp7FvGVcu+wcu+cw0ti8wk2fbZz5M7/+wKC0X66sVni52wJXf/z9dw1P+8mDc/9gQ3//SXTE4Wee3b38D6Y1YxuG8/V3/3J8yMF8nnTAecTidp7+6le81muOeJBa+xWNeYRmNJSXt7B0r7aK2YGh/l0P59bDv5VFatWks2m6Wru5sjR44gLCtYA8ehVCxyzBvfzPTEBG3tbSSTKTxfx/KPlv84+pjNPPTgA/StWsVD9z/A0ZuORSkPKe0Xjf6kGrNT3sNSnA5zRS5UCwkBJs89n31/9yWWfeFzJGLrDOlACLSUNtZArq2FiRNM2n9p2zjSwkqkIq2FQpDMNgNQ8nwKJQ8tLCxpNBpbDw7xwSOzrRjtRZfPPHWgpmev01N85LFDTPa2cmM2ae5PmvV9ZgoudkLg+xopzcra0nKwbZPoz8Lk2VDFAo8ePsKJS7jfEAJwGxo4eMEFDG1/icl34jiVZCSwoswiH6J8knC14VrunUW7eIJfnlce9JPJJKlUisnJSUoll/b2DkBTKhkRcLjeVDJpFk4cHBxEKcXIyAjNzc0opUilUhQKRRrSGQrFggk4LJYYGx+hsbGR7u7uWWLd8LfjOBw6dIh169YF76XEtmwU5VD6kMhJKWNJ2Qzii/yFriYgKhsSljDdfXieMBdLrQieOMFaCHWCUsdvBYsdbFfKBA9os1S4EgJEiZ7eLs5/46s55rQTQFp09C2jIZsknXZYefQ6Du/Zy48nh/jaunb+c9cILWr+zlwA9pEBUvfcS+HUk01Zz6f9M19YsH4aGLckb9jQwbKhw7zi0BFWblzPht3rWbXpeDZu3UTCkazafBTn/NG5/PgbP8b3XNxiCQRMT07wxJqV5DtaSQ2P1axnZNEZGV24wWLq+mxTo7FqSEm6IYPd0ER72yq6124kDANe1bEMpcFXpuP85j/9A//jLf+LW+6+iXPOehlNvsllgR9ENFC2oEhpQlDXb9hMIpHg4L793H/vvWzZdsrviQxlPrdOrIzWRGk8F2iVaFAIOvXJcy9g4syzydx7L9bgIIkD+1j2z19fUi3DK3776BWsOHyYtr4+pDBZjGUwI/a1RguzaNzMdJ6C6+FpQ1p83ydhWbxvcMLUrer8MnaNWvsUcPngBDdnOs0MXCmEFCgfJiaLPPXgYxx78ktR6ZQR22LS3wupUK7L7gcf5trDezjflnR4asnk187lWP3jHzN+7PEI2wxpIiAlQaLkoP4y2Caj1aLDVb0NSRHRPcXf71rkMtyutQ5WidbRYoC2bVdEsCil2LVrF8uWLSdc2cjzPAYGBkgkzIrE+XyeQwfNIqLr1q0j4TiMjowwNjZGa2urCSlOOBQKBaSUjIyMsHnzZgYGBmhubiaRSNQkUplMhqOOOirSkQgEtp3A9YqRGyeuC6kmFaHVJMxrEiffcVIUz20SWWOqkrRVu38Wg/pqxnX8VhDqUObrrjWw/akjdC/roWvlcrLZDG3tjXhuno7uNmO+1BaWlWLbueczOT7D97/8Nf7pk19k/xNHUNLii92Ni65T+tobor+XslLxV3qbyGvJrod2808f+xL/8okvokUD6zZvQgobpST5GY/cdAnludi26dJLhQJTo0O4/gxPXPLa6J6rEdah/XN/B75fo0QMQWSI7ysGBoY4fKSfAwcOo4VFY2s7BV+S8wU5T5D3BUVfUPIFJSUo+jA2kWMiryh4ygxOyiRFD826YR8sgKM2HctLzzqHyYkJ+o/0s3nLiZxw0rbYxPPFT1GMOLBycKocsOLC2NqRDXN1yDJY9RfLZnrbyYydcy5d3/+P4KqLx4yET69u5xqK3Hnt1RSmJs0gbCqAlmZWa3ua3Y88znTORSkLjQVCIqXFllyRHl/NOSDUVuEE9wH0+ooTckUzKAnTZkoBToKulX24roulRaStEFKitKQwOcXj99/L9MQo/9BrVppe6lsV1mvT338NSqWK7RKBhSjrqyKtiQx+zJ2F5CQuHK22uiy6PqLyHfF9n/Xr15PLzVAsFsnn86RSKaanp7FtI4YtFAqk02mam5vxfZ9sNktbWxv/P3vvHS/XUd/9v+eU7bu3V/Uuq7jKluWCm2wZm2IwEIJDCMljAqHz/OidEBx4EgImlACJY4pD4CEQE8BgDDYGd9myLcmWVax2e9++e8r8/jhlz+7de+9e2RjLz/3qtdq958yZ+c6cc2a+8/m2pUuXuhm5k+i6Tnd3N0uXLuX0008nmUzS1NTkZyOfybOmVnjRdd0NI+AY7XrlPJWTYRi+IBLMteOVq/XWCQo49ex4PJoPcuJfM+8rAvT3f//3CCF417ve5R8rFou89a1vpa2tjUQiwbXXXjstTfrRo0e5+uqricVidHZ28t73vncatLRAJzm5dihzIRvR0Um6Dh7DKJUoFgqYpkV6IsN3v/BN/ufff8BvfvgT7rvtVzz067t5+qmjjA6MUC6UKZQcaPH3TRG+605sc1Hq5u8S+9kvQYI61Jhh7E8XNXFnMkLZsB0kwgLbsjj81NP89ze/xS2f/yr/8Y9f47uf/Qr/8+2fUCxIMukShiUpFQ1y6Tx773uYO+66Z9ZJPojyzE5OLVI60SEnJqbIptPkC0WyBYtM3iJXsCkZkmLZJle0nONZg6lMkXLZpGyYLhSrghQBoUQEdpsO9NvR1cNF26/ksiuvZsnS5ahViQlfWAKKt+urdqWsGLYGqXrh8nbh1fB6Pfdlb0Gk6hua7vgVm6++An2yPspWl19gQhW8cl0Hdyc0irks+3Y9zNF9T7q2MIq7yKooqgq6xqYXnUdLVw8C1Tc6EorkwkyxwVZnpouyRQzLRFEd/EFKGzUaofu0DZixkGvzhPORAk1oHH1qPwefeIJsNsevYjof6EoypZyAUACEpya56HV/Sufv7vaPK6KSi8b3JmH6e+jfGyF8wW5aG7MJK8JvxUWsbBe9EaTTafbu2eOrQcrlMkIIUqkUra2t6LpOb28vq1evZsmSJSRTKeKJBPF4nObmZmKxGD09PSxesoTmlhai0SixWMz1zgmzb9+THD9+zLcV8fmUUC6XKJfLFRsY3OSTpoWqKL73kqd6UV11sCdY1Uvy5wkknvBSe9xT/XjHvO/a3D2N0AmreB588EH+5V/+hVNPPbXq+Lvf/W5++tOf8oMf/ICmpibe9ra38cpXvpLf//73gKPTuvrqq+nu7uaee+5hYGCAP//zP0fXdT7zmfo+8At0clKjdijK4CRjWphS2cS2JfGWdoYG0/T9z+9oaUnSs3oZZ1ywlZf+xespF9M8cNsv+P2vHiSXKRANqfxsfQ8vy5RJZIuzStwC6Hj3+0g/+jjJ7//fhnh7tLeF0kQOhCAWD3PaeWdw6bUvIZ5IkptKc/zAQfY99DBPP3WUctEAKSiUTIShYtsWmYLJeLrE6nRjC4A6PNJAKYlQBKnmZmKmAQiiIRV76JcYpRJKJELByGGjOsnHRBSpRLBKZYoTh+jf+yui9jDRSAhNVyqGwzPpbaT0J+0T2FCeFFTPI6F6MCqTcK1w4v+awQCwVkXgIFWuDYQUNP36Vyx733vm5RXlgeefaY2QLpnobqTPkjHOQ3fezeI164k0t6AKFQuBiU1RWpRCjqBi2o6QKoELJ3O8Lt1Y0r3Z6Lp0kZ0Rjd+7ahYkGIUyY+OTRDu6QVEJayFUCZOjgxza8yS3/+iHjPYfd7zepOQXYY1fL23ltGyBV2XL7CiajrqxQR709BSbP/kJdn/iU4xffDFQvbg63wHvqkDCwCByMtedCKIIHhohXZWRR7aUSNeNf82aNTQ1N6O63jPes+CpaEzTJBQK+W7DAP0DA0jX6ycej2OYJsViEduy0HUd27ZJJpMsXboMRRE8/fTTxOMJUqkUUkqikRg7dz6ErodQFJWmpmYSiaSPlFimVYV++P2R0lfteMeDf/v9C6h56n3XCiLWXOhwHTohASWbzXLdddfxjW98g09/+tP+8ampKf71X/+VW265hUsvvRSAm266iVNOOYX77ruPc889l1/+8pfs3buXX/3qV3R1dXH66afzt3/7t7z//e/nE5/4hK+TW6CTnxq1QzleNpmaSDOVzmCaYKNQNCw2bz2bq697FT0rl6KFHJjRNItc8icpjHKJ++/cRS5XIhwL873TlnL97+c2RBW2Terfbp6znAQmIjp7mmNoU3lUTWX1xpXseN0raevucXZAbe0sWr2asy+7mAM7H+E/v/zvpMeyaApI20ZVhPPRFJaZ00N116NGxkwKxz6kt7cH0ygjgWQqyfjBr/GL//k9F20tkYyWEYqBbcHTxyP0dOkoimRDk+Qn/3IHV774UnTdMZAMTsYy+C3hztt/xr4nn2Dp8hWce+HFtLS0+ktyvURgJzvN5mVQmXztOrLE3MKFE1/W8ztxhRXbovdzN8zbKHZIEdyQ0rlTVxFlE8MVrGzDZNcjj5C49b/Ztv0yOhavoIxNyTIp2ib5comiYYCiYFsWqlB4/1huzrYrnm+z0wfGC1ydiLnB2RQMw2ZwYIS21atQDAUTSUTVaOtdRKypmXx2kh9/+7uULdNFr6Aobe4Jafy+WXB5UeWD6XJVDp7ZyBvZNV/+Zx540YtA0yqqGt/+x1PniPrGs8+AhHCzD/vGonD8+HEMw2ApzjOk6zrJZBLDMAiFQg4aOjnJkSNHWL9+PX19fQgh6Onu5uGHH6a3t5cDBw8Sj8cZGhoil82SSCRocdEUXdcYHR1FCIVDhw4Ri8UoFouccfqZJBJJTNOiUCgwOnqAjvZOhoeHCYUqtjr1VDKeeqeS7LE6dH2wbG2EWO+8hxgFj803m/EJqXje+ta3cvXVV7N9e3XG1Z07d2IYRtXx9evXs3TpUu69914A7r33XjZv3kxXVyWR244dO0in0+zZs6due6VSiXQ6XfVZoOc/Fc84DakoM07dErCAxzQnkZ1t2qiKSkhTWbVmJa9/99tYvHYNaiiENzWqik6ytYsN551He3uSSCREsWDwC6Fwa09Lw7zNNQ0J4OcdSRRNJRrVaG9NcMrZZ9Lc3hGIV6A58LkeZd2527jgqktwMpi6Ex+CRCrK0rUruCZnzmmPYzYSqM3beEub7NQk6ckJpibGyWYy2EozE1Mmv7kvxE9/l+JgXwsFo5knno6TzoOmFNA0yUTaRgiJqgVdFJ3KhceMy9Xk5CQvftk1LFm2AqNsYMuKEe3J7MpTz+CxNnZDUFipTOIeVO65cwbQ/UC5+moe4f/zKP7wTkJDjdlC2cCEgD9v0rm0Lcyvo2FsKQAVoeiUTZuSCetPPY3LX/UntC1ajiEFZatMwS6RwySbN7GLZUfFo6hsmMrSbc1POJqJBNBlWpxVNCiZEhOQls2RvU+RKVtkbZuiVaZolSlZNkpYRw2F/J11ZcduY1k2Uqj8IhLi0o4Yf94SYUJUkKO5+IiMDNP02GP+fRWKd69cY3DPMNY7H/jA7LZDMwoxbuhgKT2nckeQDYfDhEIh9uzZw9DQEI/vddQ9x48fJ5/LUS6VOHbsGNlMBtMwWLZ0KUuWLCEcDhONRmlpaaFYKKAqCkuXLKGrq4tYLMbY2BiFQgFFUYlEosTjcU455RQWLVrE8uXL0XSVaDTGmjVrOOuss9i2bRunbFhPc3MKyzJ99CQodNR63ASFkFoXZk9oqXZ7FlUB2oJU30Nudpo3gvK9732Phx9+mAcffHDaucHBQUKhEM3NzVXHu7q6GBwc9MsEhRPvvHeuHt1www188pOfnC+rC/RHpsgjjyJm2WU7UyucE4lxVz4PioZ0X4SW9hYisQRIBWwLKSo2AbZl0NrWRigSprurmfRUjkLJ4p5kgpfXcZOs124jdEzTGB1K09qcoFAqE47omOUCiiJQ0B0bDqGBtEmPjzM6OIJnV2BZDs+lssmV3YtpLvx+Tp4yf3LtnIHaPLItm9HRMcrFAhLJ0OAAS2ITnLqpE1XkSeciPP5khsjpKbo6LSxZRtEEh44VMQxAEb5rJRCAqYN/QzQW5/vf+Tad3T28+GWvcNqWjh2BbHBXe7LRvG1rhCfaySpkZfqO0y3soScCknf+ura2uuS9RR9N6jwQ0RFCwbSd9ixpYdgWtmUTUhUsaSJVgS1ctY4tKdqCEholW8W2bFRNpZQv0iUbQ8H+oy2JaVn8+WR+zrJXCbhXWGh6GKmAFgpj6lGKio4OCCkQVonC0Cj9B57GtqxpUUdN0wR3B24heCAS4mNNcONkseH4R+GJ8Rp0xPv2FtUKelhPIJkNTatHgTvrJgtUfMQkk8mwYvlyIrEore1t2LbNpk2b0DSNcCjEOWef7dt0eEaptm2zfv16wuEwmzZtouwaAHd0dDA4OEgkEqFUKtHZ2UksFvMRGe96KaVv4xKJRJww+kKQTCbJZCqbfM/eJIiCBL1wgl49s6EttQJNcPw8lGW+NK8rjh07xjvf+U6++93vEolE5t3YidIHP/hBpqam/M+xY8ees7YX6MSpMXsKaHWRk2g0hKY7O50j+/bzw699lcd+/3vGB4fIT01RzExSzE6Rz06SaG/lktdcQy6fJxoNk0pGeSyqMhrWnzXfknREo6M1SalYYtP557Bsw3pKpSL5zASZsSHGB49z5MnHue07/8EX3/dx7r3jfixboijSEVJsKOYNxh9+vKH2zGUN2Ow42zOEgHA4RDgaJhwOs27TqSxZtpijx4tIESUWMYlGddrbNPqHC0SiccKRKJduNUnFIR7VfNuTihGh24TXjIC29nZSTU3E43Ei0VglfYlf8oVL9RcnERA06tN0mwe3PvefZxubvP2XtH17bnUjOMjJO1IhfhXVEYqCZdmUTRvDlpQtm5JhYEpJyTQ48sRTPHnfAxRKRQq2QcEWlKWGiY4aiqCFI5iGjS1tzJZ4Q+3fnYxwZyzcUNmX9U1wjS0xTQNV0wlHwyhCR9qOgJS3FYoWTGayPHDv/ZRLBoYbedk0Lcw6tgq2bfOLiMbHV/WQCTeW18poa6uLkARRFUWIakNaqBFqqtUZs5OHjjnojJMk0aarq4t169axeMkS2ts76OrqIhQK0dbWBoBpWUSjUUe40CqYgWVZ9Pf3c/jwYYaHh7nnnns4cuSIj1IAvlBx3333cfToUdLpNH19faTTafr7+4lGo4RDIaTtBMQTCCKRKEJUxyqpRUPqIYFBDx5PgPE8fjy7llpBxrvGE5pqEZe5aF4Cys6dOxkeHubMM890Az9p3HXXXdx4441omkZXVxflcpnJycmq67xgNADd3d3TvHq8v70yteS4WaWqPgv0/KdGbVAix0YQwol0mWppIdnSCnqYXffv4qff+xGZdBY1EkOPN6HFmog19xJt6qRn5QpCYY2yUcaWNs1tCW5e/cxznNjAaFijr7eFUjFPOKzRuWQRrT2LSbT2EG3uItbaRbKjm44ly1AVycTwGIWiSdmCkiVRdQ2hOQaK4sCRhtptdLxAIBSVptY2Wts76OjqIplKYZQtimWbo/1Qlk1IEUGik4iH6BuU3P9olLa2MN2dkkTCCakNAYGEip2mBCzT4vHHHudVr3s9AIODA9gBoWT+EStOXqosUAF9Th2qJ5wEVWK+gGLbdP/9383dLpCNhLmsO8EdMR1bSsqGiWm790o62YKRzuJoGZLRkXEOHH2avICiVCgJDRMNUDENJ/S5bUsSUZ1VqXiFvxnaH42EeDSm82hUZ1BTG1KzvHNoiqhiY1sW2ckphGkhEZgo5FFI2xIlFkKPRjBNG8vCCRhoS0zT8ZiblrdFwn/mc1zYEsaag2epKGQ2nzqDcOKUE1S8fIJCSj2aSzVRWbwdIUBxVdulUonHHnuMxx57jHw+T6lYpFwsOcauts2xY8eQUmKYJmXDYHJykkwmQ6lUQtd1enp60HUd0zRJpVK+qmXRokW0t7f7Qo5t27S1tdHf34+iKDz11FM0NTVx7Ngxjh45whN799LfdxzblmiaU1/QViTYxyBiMlP/g4KK1371OFQbzc4XjfJoXgLKZZddxuOPP86uXbv8z5YtW7juuuv837quc8cdd/jX7Nu3j6NHj7Jt2zYAtm3bxuOPP87w8LBf5vbbbyeVSrFhw4Z5d2CBnr/UaCyUy0czRGIx1HCY0dFJBgaHGRkcpZTL84o3vJFl6zYTCqfQtDiqGkNVIwihEQrHiCcThMMRQMEsl9m/pouvn7uW2ezF5+JHAD88bSll0yLserpEolEUVUdRdBQlhFAjKGoELZrk/Je9jCXLuohoENLc4FiWha4pRBSFV47nnx37EyAIJKuqilBULAmKotHcEiUZh11P5JicMhFKmJKhk4xHsSzBfQ/nmMxFKRYlRrkiXkjfQNPd+bnMKopKd083//1/v8/g4CDhSMQRXKQT+CvosfD/EtVbrOaafL2J29s9xnY+hD402JBx6t93NVF0d6huUBZ3kfVgeNe90zCxLIlhQzyeID01RVGqGFJFKho2gtzkBMV0GinK9DbHed3efoe/GdoG+Pam9aDqKLrO/+lqbsh+q61ocKkqQBpM9g9TmEg7weKkwJYK2bFJvv1//pGRvn5wE+EF7RyktLFtWbUICgSaEJxpSmZI/em3L2yb1O7dVWNf+VRioNQuqFX9n8Ujq/Z38JhwoUkBpDMZ2traWL9+PU8++ST9/f089thjPPnkk5imydKlS+nv7+e3v/0t+/fv59ChQxw8eJC+vj6gYvsRibjJ/gIeQKlUyletLFmyhP7+fvr6+hgeHiYajZLL5QiFQpQNg1KpxOTkJFJKP/ZK8Jn03OuD3jpB9MgrWxsxVg9kafdURbWePMFAb/OledmgJJNJNm3aVHUsHo/T1tbmH/+rv/or3vOe99Da2koqleLtb38727Zt49xzzwXgiiuuYMOGDbz+9a/nc5/7HIODg3zkIx/hrW99K+FwYxDiAp0k1GBOnraSwfKhEXbHY5RLhmO3IiTSKqOqGorQXMdAC6TzsNsSos0tLNmwkT2/ux+hKEhF0LViKftbJ/mmLXjTA/v8NjyyqSzx9Sa4fEjjtqvOYXfYRhlJYwO2HqJn9SokOFEwcdQjSAdiVfUQEkkkpAIKluIk3do2leNdwzlSc9hqzNf+RAKWZdDf14dlmSiKSjzVTDKcoGSGeMVVizg+kCebLXDr7Rl03WZpb5izTm+ho3UCVdPQVBMvj4fAibipVGQfF7a1mJqYID05wSmbT6OjI2g7dnK7HM+2I57rXHDCrufJNFPEUX+HLQTaaGPqz5ujKj8oZp3orF49OIF/bbdOy4XvAVRNxbYlt37rFtbs2c2V7/7fKJEmx5bLKtH3xFPY5SJRXeF8VaMpN7P7uzcKRlOK0HiOkKbzG6Fw/5KVnHvfdBvEWloa0gmbFrnxIQ7cfRebX/oSjGjUCem+/yAD+w9iGTbSVYwEXWw94deWAiEkQjrpHRQkbQ16j4XGxgKxNzyhxOlZrS2Ff8caQEpqDaj9Ory/BS4iLIjHo6QnJhkcHMS2bZqbm537r4XI54sMDw8ihKC5uZlEwnERbkqlyGaz2LbN6Ogo6XSa1tZWmpub/Xw9ExMTPnqSy+Uol8ssXbqUZcuWIYRgdHSUVCpFZ2enw6ttk8vlQUh0XXVz7ghAwbZNNFe1FOxbEP2o9eQJuuUHhY9ag9na72CcoUboWQ91/0//9E8oisK1115LqVRix44dfOUrlQVKVVX+53/+h7e85S1s27aNeDzOG97wBj71qU8926ws0POAGo2FcoXQeEIFXVfIZEuAoLm9FT2qUjYLoAikNLHsEqZVwrYMTKvM4g1reeT3D2ObElUNITSdqUyO+3pa0C/azCsefIqWfMlvZyKe5LZUnCunsrTls/7xbEjjnk0r+emaHlqXLyaz7wAFSyGkqLQt6iHR1oxpWQ4crFggFGdalRI1HOGy17ya733hy5QKJkJKXj00xV9NzG1Q6I9TI/YnUJGuJFiGQbFUxLIsSqUyR8cER47n2Hp6jqdyGeIRwZLFbew7MEnZCJHOZDkyEKVsFCgZTrbTyoIAUjqCirdftW0b0zS54iUvY3h4mLJhoIVCXkSQKm+U/xeonldCo+WdsgJFcSZ4s6Mxdd7vW5IIq4xQVd+A3JYSWzrPIIBtO7E2vABbpm1iZ3I8vWs3k8cHaFmTxLJtJg4c4sgD96EIm9amOD1aYyEdloTCRKMRbNMkqSTpO+ssaEBAWWIJYmGdiWKekb2PcLitjeXnX4iIqmiasxaYZatKQIhEIpTLjuu8r44RnhAhsTRBZNM6+O2uOds3OtrrGL/OYAzLzJuWIM12z32hxWHej4HS29tLuVxm8eLFjntwc4t/7+LxGIoiME3TifarOXmuEokE5XKZlpYWX80TROAURWHfvn1+sNM1a9b4/KmqSjKZrEZ7FIV4Iu4jb8IfhwrKEfTOqde3YP+DArrX79ocPLUGt8Hw/43SMxZQ7rzzzqq/I5EIX/7yl/nyl7884zXLli3jZz/72TNteoFOAmrUruJFTxxjqruJ38XC7NIU1LDO1a97Lcs2bMTGxLJNJLabBTWKIuJYZolVq1excdMqyukcqgb5wX4ow/Bwlv9E8uDLzmT9WAE1HWU03sKDIY3s1Ai3Lt3EprFBmtMDiHZ4qjnJZLqMMZlFHxlm44Y12JZJqVimc+1K4uEwmlCwcQzfELYf7smyJavOOosVp6yl/c6H+ZvBKTobTGI433HyoGNFUWhrb8OyHWXW5NgoTdYAPR1h0lmJaYFFnOUrV7B8yTC2WWZ0tMTirjSKLFMsOnFZnN2V9E0rXA0C4BgsHjpwgNGREVrbOzjj7G3+LO4HGpsH9fX18f73v5+f//zn5PN5Vq9ezU033cSWLVucrknJxz/+cb7xjW8wOTnJ+eefz1e/+lV/8gUYHx/n7W9/Oz/5yU/8jdAXv/hFEonE/JiZJ9Uzlqw70QY8dZyfns6serC0yclZY4vYQK61ifjrryX6Xz8hN5nBQnqpmJwFwuVB9WKgBHamNpKCYSBRMCwY2vUIu+/6Fc1NMcxRlY5kgkX5xnaypZZmSA9i2hZNrZ1kz9xCrqWF2MTMUW8lcN7BIW4+YymmHuasl76MY4ef5qH/uJk1F16EHo6iajq6LjEMEy8Kqmk6u/lyuVy12Anh5NDpWbsKbfvFjD30JK354qyqKX1qyr1vgTgogbg//rGae9CIvclMKFmQCSEEmWyGvmPHMU3TR0l6exdhWQaKqjqxaFSddDqNEIKWlhZUVSWXzbrxSkJomkY+nycSiWAYBslkimgk6hq7Co4dO0ZTUxPHjx/HNC1OP/10J79WhbsKW/6z6G1OlKpnu7Yfwfw5wfPB72DdtZ5AtShKI+MbpIVkgQv0B6XiljMxu7tQ58h7I4CXDU7xMmBEgV3NcVbe+guiA6OM/8VrCYVjjirCNjHNEpY0MK0S4VSC7jUr2fnL36Ar0NzuhIZua1GxlBCmLdjbEifb0slkroxRypFsaidbKHJ/OEHb8mWo5gC5sSy6ImhO6JSyU+zfO4lEokUibH7xZdjSRtqmk4FVcRZ10zSxLBujkMe49VfsuPNRXtI3Oa/xkUJgNWx/At70qwiFVCpFqVTEBtq6mkmUuimWBXc9kGHZ4gT5YhnTNMlNFWlKadiWQUiHNStC5IuOKsBfPGUQnKm4y0ZiMS7avoPW9jZ0N4iic8n8BLCJiQnOP/98LrnkEn7+85/T0dHB/v37fcga4HOf+xw33ngjN998MytWrOCjH/0oO3bsYO/evb7X4HXXXcfAwAC33347hmHwxje+kTe96U3ccsst8+LH6UelD3NNmrXxUGohcLcW/393ig5A34GcPpZFxw2fDlxR05Z7/F/XL2X58hXIa67iV9/9EXa57GQ6RjoJ/GzppB5QHDVP1eIiQUhBaXCIQ7seYeTpg2x96UsoDo1w56GDJKIhzrxvz6yogQTyra2Mb16LNnAAbJtly5aiRyIcvPRSTv3hD2ccLwE054psKUt+ZhkYQuWcl76aR2/7Cbd96Qvoqk6pUMY0DMdtX3VUBeVy2UeC/B26dLxA4k1NnHvhi/jtrbeRjsI/5uujHt74L/viF5i6dDtCc3L/eKHnvb559zGIstTGBam+v43t/j3kRyBoampmxdLlHD16FMuynOziQtDf3097ezvpdBpFUWhqbmZoaIhSqcSi3l7Gx8dJJBJMTEwQi8XIZrMcPnyYQqHI5k2nkkwmCYfD6LrGkSNHsG2bUsnJeDw8NEJ7ezuhsBey3kZTNSepIbZvN+LZ+gSfm+A4BNUxnvotaItSq85RVbVuiPt649MoLSQLXKA/LKkq4x95/7wuabfh8vEcq37+G3o/9Q9sXH0OLZ+4gUIpR8EsYGAhFYmmKai6QlN3O+hRDCVBNq+g6GHUSAKJkxsqWzIA5yXVdY2mljhmMQfSpqW1HdMWpFJR4skooUgI0xLkioJiSUGPhUm0JFFUgS1NDGlQNIqUrDKWtGm9/bdsffFfcfnffoWX9o3N4eNRTd6rO/7h9zVsf+JdZ0uLqakp8vk8hVweTQvR0tZKe4vGeVs6GJsoEAlJMAbZf2iUySmDsQkw7RSDowq5bN4VUFxubSe+ie0jAAJdD3Pxpdt5/JGd3H3nbzANy/f28VydG6XPfvazLFmyhJtuuolzzjmHFStWcMUVV7Bq1SqnT1LyhS98gY985CO8/OUv59RTT+Vb3/oW/f39/PjHPwbgiSee4LbbbuOb3/wmW7du5YILLuBLX/oS3/ve9+jv72+cGZcanyirg3h51yqKMrPLZN2qnTsefehB9MGZDWS9Z+jRfU/xvRv/GatssPrMzW7cncriYUmJYVuO8SnVO2BbSkqlEj/40hd4eueDXHrdn9G5Zh2jxwcIq4LTyhaJqeycm4ZDl15KxLUzTCbjtHe0oiiCXG/vXIMGQKdQ0G2L4X37kLrGaS++mlWbNzA10I/tGlQahpMrxjPe9GwaPINLBzEU2JbN8NNHSQ+NMaWos3vdAOGhIZK7dtXs4gOICtULcpCmeRB59dZRD834HAkHyRK6ihoO0d7RwcaNGymVSrS2tjqCiZuJOBaNsnjxYt+jxjRNDMPwhYR0Ok1TUxM93d1YlgkCQqEQoVCIZcuWsXjxYtrb21m3bp2vEvL6MTgwUNUfR12kuM9RxXYkiIoE3Y6DXj5ePcHzwQzI3vlge0GBb76GsgsCygL9wSm/Yzvpv/izhstPe91tm85vfJvFn/tndE1BFSa2mccw8iAtupcuQ8S7yVpJbDVBrKmDWLyDVKqD1es3s2LNZjQ1AlgookxvbwuxmI6qlCmXxujq6UAIHaHFCaV60eMdGFIhV7JpXbSUSCyMpgpCuko0pBGPhIhGwnTf9SDr3/MZIqMTJzQudmsrI1/6R/I7ts9duGZ8LMtidHSU4aEhhoeGyGQygKBQKnP/I5OsXJbCtmwsS1AyNEplm+XL4vz3L9OEYylQQxXdv6zER0VW1Dz5bJp77v4tExOTxGNxZ2frczG/iebWW29ly5YtvPrVr6azs5MzzjiDb3zjG/75p59+msHBwaoo1E1NTWzdurUqCnVzc7OvEgLYvn07iqJw//331213tijUtbvn+dLMC1MgHsa0p1mijjSWqPKfxktsHZjgsXvvY/2mDaia6mSflsL5uIuX793j9slGIlQnKZ40Tc5/5TXEFy1Blkr0HdhLMqLS22BKkWxvL4l4Ak1zsh4XS2WEolJsaWvo+mVSoSkWZ2jfExi5KWRIZ8MllyHCYd8zBRRsC4yy5dthIJwkhgiJEBKkTTGT5ne/uJ1SscTFxcZSR4RGR6v+9u+K+5A3smDWU+fM+dwIUISKtEEoKp2dnbS2tRKLx1m+ciUrVq9h6YqVLFm6jJWrVtPe0UlnZyfNzc3kCwUWLVpEOBxm+fLlNDc3s3jxYlauXMnKlStRNYWhoQFaWprJZDLYtk0+nyefz/PAg/dz9NhhHt/9KGNjY/T19TExOY6UFcdsy6542tTainjCb5C8kPcw3T7FK1/vutoyXl3zoQUVzwI9J1TYfglN//6dE7rWg2zbvv4tjr7jTxCRMKoWQiOMbRiUs1MsX7GK9OgQazesIBwyOLx7N4YZJqJJFu8fYUn/OEetEverZfbueoLT8pJF2EylcyjbVlFOl4iGBWs3ryU9MYYS0ojE2tD1OPmpEroawlYtN88G2KbBok9+wVeNzIckYLe2cOzuX8IJ5p5ShCAajWK5QZjC4TBC1YmEw2xe2k1HK1imTamskElniYQSDAzmOHVDG3fdN065VHC8P/wxFiC9xGfOpFI2DKKxGKFwlMmJCYrFArF4wjcCnA8dOnSIr371q7znPe/hQx/6EA8++CDveMc7CIVCvOENb/CjSNeLMh2MQt3Z2Vl1XtM0Wltb5x2Fen47uUbMJ6sqx8l24On6q0/bnV11L6ullIQvTJZ496E+fjH8Q8xS2TXonJ7e3vtomoZtGCCcAGSJlma6V65EAab6B5gcHGBFaxyro3P2xl0qtbaSzmRQVQVVwPjIMEjJ2IYN5FtbiY6Pz6oiOmPvYZLnrqVvbIzsQB/NqSZiTSkUXadcdoTFaNSxpfDUO7btBG4MRxwh2naT2imKoH1ZL9vzZa5rUJVqdHS4tiYV86CgHU/QHqXeTr9R4XW6XYrz27Zsitkchw4dIplMunG8mjhwYD/t7e3s3buXZcuWsWzZUlRVJZ/Pc+zYMTra2wEYHh7Gtm2iUSeUva7rHD9+nFwuR3t7O6qq8sgjj7BixQp2797NyMgIBw4cIBwOE4/HOX78OC0tLdjSRnG9mBRF8VVqqlqxX/LsRzwVjvc7KFTURogFfJSlrvGxa7QbFH6eUyPZBVqgRsi3RRkadnYv8yQBYNv0/sftTPyvP8M2nTgJdllh8eI1xLen+PUPv0N2YoByOEahaHPW4BBX/+cvSKYr3jpTrjtdk2n6x9IPP8H3T1/Gzp528tkJclPDtDSl2PGnf44WCpGZHEOzUoR1HVuWQNpE73+Y8ODYvPvhGU6OfeqjJyicuJb2qkZndw+m6UwMqeYWhBJyEqHJIoPDBiNjRdpbDTatS7Fupca6pTb37JykmE+zstdCyMri6XkxOKiKq+IJOZPcwMAAm844m3Ak5qt4FOanS7Ztmy1btvgZy8844wx2797N1772Nd7whjecwDg0Rh/84Ad5z3ve4/+dTqdZsmTJrDvg+kaAtSqASllwx0LWlgJcd+xgucKWszG6u9GGhmZ9FxQcZOsDEyV+petYqkBYFdRH4HhnqO7CIqVE1TSiqkq+VEQRglAsghKNIqTkyJ49mPk8iaXNhIsVz5mZ7GAKbW2MbdhAaPQwsViM3MQkhWyacjFPLNnK09uvYOP3vzcj/wJoyhbYXChxzCxy7JFHaVm9Dj0SJZyIMTXoLL6lctlVFzjCl2E6RrPFYgndTfaHLYk2t/Di6/6Md3z8HxuKH2N0dZE986wqfjxZU+AusjM8A/UWW5jbi6fSluKYSUvJ8WPHSbsR0Ldt20ahUKBULPLYo4/S09NDc7OTzVhVFbLZLG2trYyNjdHS0oJhGFiWRVtbm2P3oarkcjkURWFsbAzDMDjjjDNINTWhqiptbW2Uy2UURWFycpIlS5agaRrDw8O0tbURDodRFa3iiRfwwAm+E166gYpRbfC9cHpo2xJVrXju1I6P6c6xmqb5KqC5xrCWFgSUBXpuyLVF6Xj7/0YKcUJCCkDs+BilUCeEFQeqjDkuc9FIio7eFYwMjmCLEqccLfAnd/xu2vWpgGDiUTJb4K9+9yTWeZt5uq0PpMGqTWfQ3rEYqaikmnqwpe3GCnCgznj60RPi325KMfZ3n5i3WqeanJ1ILue4GBtGmViqBWGbZHI2erjEwOA461a3M5UWHDk2xthEngu32KxappDLaSBLmK4HkC+dANKfuiAUjtDS1oFEsPm00/HipgT5aJR6enqmBWI85ZRT+KFraOlFkR4aGqKnp8cvMzQ0xOmnn+6XCQZ4BGcSHB8fnzUK9UzxlWqNIWenen0VM5eQvh9PpbQ3SasqIx/+GD3veOuc2IwC9FiSV2/cwPce34upOMZCElBctEHi2COYpkmxWHQWGhukKlFCGraQlAtZnt77GKqASDjKtlt/XacH1f14/I1/iaLpmIa761VVivk846NDRFNt5Bc1ZofSpejoSpHDex5n00uuRglHiLe0YMvDAFi27bjkSoWyaeJLwdIG2wQVdFVlzVkbaXv4ERLjkw21e+z/ez9SU91xFChebyt24ZU+zzAf1fNsmalcFbmGsra06OzqxJY2sXgcKSWFghOPpKW1maf278Mwy6xcudJHklJNTcQTCZLJJN3d3RQKBVKplBsB2GbN2rWE3M1NyHU/RghWr1njS85ezBVPPePlHjINi/HxCcrlMomE69ikqFV9COZEcuKkOIKFJ8QE++xk9q5+j7zrg8iJZ6sy3wzoCzYoC/ScUX7Hdka+9I/YqeQJ12GuWI2qRVHVMLoWRdOiCEUjHEnRuexU1PgShIhz7QM7gfoW/jMdu/7e3Wx8egwl2okWa8JGoKgaoUiUaDSJpkfQQnFUPQa9DcYtqSFl6pln4pY4k8DI8BDDAwNMjE/w2M6dPLn7Ec45VScZLXH6hiY6WwwSCQWwaGtRmUrnKZYUdj5exrKcyaf+rt+hUqmEaZoMD/Txn9+52Teo83iYj9rj/PPPZ9++fVXHnnrqKZYtWwbAihUr6O7uropCnU6nuf/++6uiUE9OTrJz506/zK9//Wts22br1q0N89IoNbIwTRNw3IdJuCqemUYod8WVDNz4ZaympoZ4OWPJEjqXL0bI6iRunjFpqVx2cqEEDEuFEGixKEKo5CbGGO87SlgTnJIrk5jKzGmkazQ1IxSVVLIFKR3PtVKpTDabRVUUyq3tDfHeUTDRQgrpoWHGjx9DKtDS5QiUjhzi9MdTKwQ1iLbrfixCOt3dvaR+8suG2hx63XVMXrZ9xofbaWN2O5L52iZVudW61xuGgaqqpFIpFi1a5BjExmIsW76cVatWcdZZZ9Hd041t22SzWVrbHOPZcDhMuVym7KJLmWyWfD7P5OQkhmGQzWbJZjKMj48zPDLC0PAQQ8PDDA0NMTA4yNDQEKOjowwODTE0NER/fz8DA/0MDAyQTqcJh8NVKp1gf6u9mipuxkEjWN9Pzf3bNE3Xq9HybaO8OizLwjTNuhmO56IFBGWBnnM6kUVaAigq5b9+C7qmI20bwzJRhYIiVPqHjnLkaB+20FjeP0BTZv5tKFLyZ7+4i++E4hxs6mfV4GEWLV4Ntu1A6aqGh16kN62lOR4lmivMu53Wv/sc+e2XzMtzJ0gOPG1jGgaGWQahsHbjZtrji7jt339JsSQJR0IIWSCRSjE0kuOScy10kWbPviSxiI5th9BUrWIY620pAwKLMzFJLrvq5SxbucZRH3kk5TSEYDZ697vfzXnnncdnPvMZXvOa1/DAAw/w9a9/na9//et+W+9617v49Kc/zZo1a3w3497eXq655hrAQVyuvPJKrr/+er72ta9hGAZve9vbeO1rX0tvg14l9Wg2t9LpNP1cRZDxtuYV4+NKIxVExRN6cldcyUAiyZI3vn5OHh/Yu4e1Z53BwKFjgOvlYpq+mse0LSeyLAJME6Eo6KaJommoqsbw/kOU0gWaEgoddmP70ujkJIqiEk02oSo6uWyBbN7CMA00RWVi8yYKbW1ExsZmtUM596mj/PtZi5hKZzh0/wOcvXYNTW3NKAoYlpPTKei1LqVEKE4kVk3XsWwLLRxh456n+JPR7AwtVdPURRfjZYuqBCVzyUUcnJ8nZiBdj4ICrW3bSFvSP9DP8b7jACgojquvqH4WKmiF87fqmlgjBKZrO+LZHQlAeCkAFKd3lm3VpAmQ/hzhPI+2444e4C8SiWEYjnrYNC3HM4jK0+17irlCoxk0xBYS7xlEVlASIYSfTdmzh/LGpZ4rcyO0IKAs0HNHlkXrpz97wpc/uf0CQplxEq2OgZ8iFEpGnpKVJdyiEoppjAxNsnz3ialfPHrpnXfz75ddSrRVI2eOElEThEkgsZC2zdN77uM3//Vt2pa38bY9zuTT6CsnAG1gkMhDD1PcevYJ86iqKosW9zp6XgGtrU10tbex9syrWNwTo2g309Mdo1hWOasgGel/kkg8Dm3D/K8P7uDQvgPk8yXibjJbx/tDCcSHgCOH9nP0yFEG+oc4dPAAV770lVTHj218ojn77LP50Y9+xAc/+EE+9alPsWLFCr7whS9w3XXX+WXe9773kcvleNOb3sTk5CQXXHABt912W1Xm9O9+97u87W1v47LLLvMDtd14440nNIa1O8d5XOnv8Kd7eFBXOKlHUkoKZ21BKoojBNcrg2OH8uvxMc5JJiGkIQIGid637bl21vRLURTMUp4Dj+xEswzCWoT2bKlOS9PJaG9HVQSEo8Sb2tFCT5NQVBKJuBMcTlU49uIXs/Y7Mxu/CyCZzrE5Z9CH5Pi+J9gwMoyq665NSACVkxXkSUpQFQXLMFAVFbNU4jW/fWhOnj3bk9yZZ/oohsOHp0yr/JqNatGEWrukWoPaWrTNM2ZuaW72+yhcQ18hFBCKi7RVXHV9112Eb8iK+9tzL3eSGrrtCpB2tReNRPoeN76AYVUMUz27EC9KNEhfOLEtqwol8VCRoGGsbdtYtoEtHcHF8RR0EBMvqaFpmui6XpXBuFY11CgtCCgL9JxR5KGH0QYbc7EMkgR+3N3KD40svTd8iNf/fx8imkoAOmWZwZQlbGHStbiDI/sGuGD/vrmqnJEE0JzJsLL/OEI5F0sWKVhlLJkHaVMu5vjVrbfwxO4DWKkmuruyvGpoct7tRH/1m2cgoDhiQiiko4c0TMuJsBtrW8eFr/2UM4E5WzBfnb/Cdiauex7/B+LLtnP490fYOJWm3Q25Xq2Pdz6RaIyx0VEWLVnK+NgYtpSOHl9QI6g0Ri95yUt4yUteMuN5IQSf+tSnZk170draekJB2U6Uqo0jPcGkjils7QLljZO76DoAy/Troo887OSemql9QAU25svoqoKmCNBULAtfz29LiWE5qImTYM+5zsYJtDfe38fwgUOUywVUO8TmnU/MGaCt2N7OxKZNDmqmhNhw6hYO7X2MkpEmkYy7iykUFy9pZBjZMprjtpjC1Mgwh3btwrAlXrh3D9AQQkEgCYXCGOUyAIqqUrZNtqUNWnKNCVbH3/s+hBvsDRc9qaCCDVXhlp2Oqk23wai/2ArX5mX9uvVEPGN4aVWeBeEhJn5tVN8R6T87tTSb7VSwPu93sKj3bHgt4iF/wuO7WoCo/e3bnXgonuUetyW2tBkdHWHXrkcwLVAtD3WmrpdPI7Rgg7JAzxmpw40lSPPIA81vWN7L13s7yKQnEXYGS0tTtnOU7HEsWURiI9Ao5k3Ov/MXxKzZchk3Rh133wPSNQzDxrAy2BgIvUyqNUFmcoKRsQlubG3iE0u6mJynuiZx60/hRPkUTiyDyfFJxkZGSU9Okc/lURQI6yrhkEIkpBANCaJhQTQkiEUUIiE3KZsiME0D2zarjQVl9Rq6dMVarnn162hpbeeyK1+K4u76/t8lOcPHoSohRVSumW3DqI0Mz3wyQJdkSgwcOIDiZi9WFIGqKliWo/cPqix8dEdAKBJmbHgQI59HCJWNOYNEOjfrbRTA8RdfhaLrKIqKKhQSqTYSre2UCiX3WVMQioLR3pgdymWHhrkoU8TKFTm8cxfSMBw1jqq4/VFQhGPMahlOjBPbdiLmhjWFngY33cOvu46py7a7Y+HaWTk+Z4E8UzPv4qvtL2Y2nJ2NHMNTB+nwUBwhHFWc9AyU3GdHCA8jc357n8q5mZ63+uemX29XXacg/H8KARd1nNAFQZd1RVFQVRXVzTbt/db1EKFQhHA4SiwaJR6LkUgmaWpqIp6IUioXKJXyGIYjZAbz8MxXSFkQUBboOSOrvbHgTh6N6BofW9TOL0OScj5LuVym/+hxBp4+QrlYxMbLJ6NgGmWeeORRrjp44uhJkDY+8hhj/cNO7ABFRQqBaZYYOdbP/sf3USo56h7TKHNbCF68rIO3L+3iR53NNGKnro5PEHno4XnzJf25TZLJZklnsoyPjZPP5VCFRFdBU5zMxJ6xoxDO36oCuq4SCSkgbUrFcmXxFJUYKN4cKIRgzSmbuPJlr6Kzq6f6XOD/k5FmW2JqI2HOVq5SW21Zy1EJYoEwAavucJkNxiN5XbpAy6/uplwuYbs2B16kUcsy/aoVBYSw8TIAlwtFxg8eppjLoChw9kRjNlPFxYt9tYOqqoSicV50+TVsPO0cUk0trk2WyuTmUyl2dDRkjfTW/jSKZTHy5AGO7NwJthPd2bNdqAoU5j1s0lFnJs84vSG+05dcQuXJr6AH1TYo9Q1ka9U6gjrX1qHgou41IKgYlXrHHERk5rqc/jNnubqn6j2CtbzPiPZU1GrSbUC4Oxc5U72++FVTEY5xfblsOEktA7l8PH7mI6QsCCgL9NxRg7ug36bivH1xO69e0sodIcXNe2ORzZfp65/gf275IYViBidZn0RKi+Hj/SR37iRpNBZhci5KFYuM3fJjDKOIjY0QCjY2j92/kz17j5Er2X4WWSFUTCl5UBP8Y3Ocny9tLBDXfBElIBBkSnGDN0VJppIM9h3jx//5n+x84EFHQJLT91tI0HQdRYGQrvpnfZsJTx1B4KIaVKWyV5th5jqJaD7czzyxBnewdmDX65pnCoG/g68j9HgxUebiRQIfSRuEQk4WaoSCqupomg6BHbFzD12DSATFkQmOP7YXyyhzWb7ElccnGuqv0d5RZRuhqSrtnYvYdulLWLRkjWtLIRBaiINvf9ec9Qmg07A5q1TCKhUYPXzcUQtYFrqqYpYNf8ETQqCrKkJIpAprtp1H65veiB1I9FdvfKSikD31jDoCg8eDqBRugBQhZlwgZ2yjyhi0OoEj4Bqk2o5XlPQEqQrSUxGO68sT1c+Qe71fT+XldQyDvXq9bs/QcVERtqWsrS/I4ywkPWFSR0pQFA1FVNyXg7FW5mODsiCgLNBzRupYY4HNDmxawe54iJJUEGoIKRRKpk3OsBnP26jhGNFk3F2tJZZhsefBXbQVGtsd2iG9oXKlffsY7zuObZWRQqLpIZasWoGNgqVolKWCYTlwu6bpSKEQS0Rp/uvr5q4csNrmhyg55E5+ikpzWzst7Z00tbazev1GLth+OWtOOQUpnRwgVeCuBIRwECGcCazacj+AoHjkzk81m89pQstJSTPYksDMi89MNBPa4lw/HTavsmdQVUY+9LE521CAbtPiTRdeTKKtE6RwDRktBAIV0Pww5O5CZUmOP3mA4eP9qALeOZyfuy9AsaOT9GmnTTPe1HSNtvZOotGYC/urqIrK+EWX0P/qP2lorC7IlLEsw4/fgmu/gPQi8Hi2NQq6qiF0jY1bz8L43g9Q7EqZWhKAsG2Sjz0aWNyDQmLwU2mn3r0O3k8PSZkJbanLi6i4RweDk/X3D3D//Q/w+OO7KRaLlEolMhkHGS6VSuTzeQ4fPsKxY8dJpzMMDAy6cY4MSqUShUKBYrHo12dZFsViCcuyKJfK2JaDqlmm6eY3khiGgZSScrnM448/XuUK7KRIgEKhwPDwMJZtMzo6ysjoqCtE4SJazoBOTU2RyWR8lCsofHnCjyKclAhObJXK2J5IFFlYMJJdoOeQGlXxyO4OlEyBzpYY46OjZMs2QtGdBIGKYKSvj/xUmnhLCiF1zGyOppYUx+xyQ/XnXvJikv9165zlsvEE4VAExXDjAGDw9JNPYZYNFAmqqiCEimXbxMIqi5YtY3RokEd+dx/nN8LIiWhIArYNxVIZy7RQNQ0tFKa1rR0pwTSdpH8V40PP+M3XVCOlTblkeFV5R/E0SBWjORFoU7jl5oarTxaq9bw4EZp5V+jtSmd2yBZCkL1iB5NveCMtN980Z1urW1t50atewc++/m+Om7EvdDqLiCIUx6MH6WTKtixUy2JL0aLTbCxI1sG3v93JABzsSaB/XtAvgUAojlXDxIsuZtEP/nPOul+bLvJgWOMX4ZAv5UopnWi4AqTtLr6miaZrRBNJFCkZnyHXUi1po6NVAqAvoAdUNvVif8xE9e7b3CiA8O+57Zazpc2+fU+yYcMGcrk8d999N8lkknzeERpLpRIgURUn3H1zSzOhUIhiIcfw8DD5QsHPcHzeeeeRTCbZtWsX+XyecDjM8NAw3V1d5As5AHK5HE3NLRw5fISly5aQTqcpl0pMjk8ipSQUCpFOp9ly9hby+Rz9/f3s2fsEyWSKcqnI44UiXV1dDI+MuHm4VI4ePUxnZye5XI7lK1aQyWQ49dRTfXdiByVRUVVnnvBRtsBYLwRqW6DnLzUoPB/cfYDW5jhSSEq2IBSOoKuCRFjBtk0OHzzGyMAwigyDYaNGVVA0DjSr2GIOGFgIcldf2RAf+fQUFiGEJpCGRJYkA0f6sW0ch1zTxLYtDMMkkyvx9MHDFAplOh94vKH61dETCJXvQsOWaTE2MsrIyAiDfQNk0hlnQrTBtMGwBCUDigaUylA2wbKd87YLIZfKhj8uiirQVMd+QUoLyzQwTQPDKGEYJcrlIuVywfnbLGJL04WoT2aqv4Nu+OqG1D6y7jW112UvbSyy8O2/vYv2jjbCqYQrIDsCpCUllnSMSoV0YmkobsbayzIF/s9wY/FDjr/q1YxddHEVehI0kHRiXKgoooKgqKpK9owzKHV2NqSq+vBYFsUVrhxMw1GPqYrjlaS4ri5OBFSV/OgEi4qNbT7M9na8uCeOkFCdb6fR++0JN7aUvpAxH1JU1UExpe0b5xpGmWPHjtLff5xYLIJplpHScc8tlUromkokEmLx4l6McolyqciBAwcoFAp0dnTQ29NDUyqFrutIKYnFooyNjjA8NIhtm+TyWdauXUsu5wgph58+xPLlS5kYHycWjfpJGIulAtlchuaWJoSAY8eOEY/HmRgfQ9omo6MjKKpgdGyEWCzC8MggY2OjqELh4P4DhEIhJiYm/FxAzo2tVuMIYaOolWzIvm0R89sILCAoC/ScUaMqntNHJnisvYVYMoEYHMU2DZA2XjCxcqnM/b++l86exYQTKigqRw4cYnM2izLLZOKg+pLIzl0N8bHx8FH6j/bR0tWEVEsceGwPTzy2D01VkZaJUAS2iyb0LO9lx7U7eOqR3Vx+808bqt/q7GioXH2ysYwSlmkACpZlOJOEZ5eDwEVmMSVgOl4RxaJFrgCFohN91HN/FYCmCFRfb+zAtpZdMVy03cBPEomiMMcu8uSj+YW+n5mm7bDdBXc2e4Xi2edgNjejTk7WBdYkMCHgjokRLhs8TqqlmeGptK+O8KH0QL1CSq4oGnx+cm7VjkeTL3rRtIy1tTwLFB9B89RWqCrH/vf7WPX+/2/W+hWgzYY7hzN8JB7iF6GKnUJwPKSigpQUpsY49shDvD9T9i176pEEjK5u8oHcO8xiJxL8rtdH7/ogylPV3mzPvmuZ7hnKgoM6nXf++UxOTNDmqnYtyyKbzVEoFEkkEqxbu9pv21PhZDIZUqmUHzbetm0ikQhSSlLJJBdeeCHFYhFN0wiHI1iWSTgcZtOmTZimSalUYtmyZWQyGbq7uzl+/Djd3d3+eDc1NbFmzRrS6TQbN26kra2NZDJJqVSio6ODUCjE4sWL6evro6ery7lHQtDS2kJ7e/v0sVUq/EvbtdNzhdwTeb8WBJQFes6o0QX5ynyJtk9/GD0a4hNveS+5bAlF0SiVDVRNIxaLMjoyjB5TUDWdzESOvoMHuaBh+LCxhfXCgTE+e899bNyyAU3TWbJ2Ge2dzZQLZYpFg2LZRFEVpKkQS8Q59/IXcVEoRMtNP5mzbqu1leKWMxvkN0CurqaSLNDEsmxi8YRr4AbSthgbGKSto5tS2dFf25aNUTZYsXIN44MDtHZ0UyiWeOKJPZSLRYYGBkmkEliWxDBMLNNzZ1VRhIKqayiq6u7aJe0dnbS3tc6f/+cROeBDfePVZ163t4hL/3ELmjZSZUwpKsdnIU2CYkL/UwcwS3lfFPXkiVrbCcW2+XC6XGN5UZ8kUO7sJHP6GbMsJtK1dhF+Flzh7ZgVweRl2xl+3XV03fLdOVqDVlvy5UyJv0l4QorAMCyC7qgCgWXYND/+BN1zvNoCGL/2WtBUBzNxba0cG09XtJH1hRNR51545PFTm14g+A2ewCJBSISsjLcjKDq/E/E4CS8yonsukUhhmiZLlixxXa6pGH1JSTLV5NqPVQtKQgh6Fy0mYPrrbChMkwsuuAC9KhGpoMf93tDUHDBKc+pqb2+no6PDtS0TtLV2+Gx4RXt7FrsKXq+f08cQIVCFY5xr2wIpvXupEgyZPx9aEFAW6Dmj4pYzsVpaUCcmZi2XKhk0P7qXJzqayedLSGkT0kOompth0zZoSoTQNOdl6Dt0lKnRYYzmRGN8nL0Fq+X/zslHk2Gh/+5+Jv/0FbT1tBKORojFo2QyeUzLRqgqRqmEjcLT+4/ywB33cNa3ftgQD9mXXfUMQt07E2c8GXN3KgLbMhnsO4ZpS0zD4Okn9xHeZCIlFPMFDNOku3cRF5x3Brl8gQsvPB/LNOle1Eu5WODgwWOc+6JtrgrJ2akVCnlMw0AREtMs+xEtDcNi35NP0L7tnBPi//lC0p+oZ4edZ1q86tmvTIsm6tn3BHfjAXsI75roQw+iTk7OyIMAUsBvhvN89t5d7BEmCu4C7Lal6xrFUkUV8raiRU8gf9KM/XO/D7/7PYhAePLpfZYI1yPJV5UozsLkQfvpSy5rSEDxbJ0+lje4XVeQQtQIJxBJxlCBC4caQ16NZcucjMjSswkXBLWQ8xVOGvm7WrB1nwFcQdG1Aar2uqkVbiSaG1TOMaTtp7293U/6mEy6ecsCz2qwvSBCIyWomoYiHQFzcnKSeDyOaVpICdFo1IksbJmMjY3Q3NRE2Q2IF4/HKRXLqKpGNpulubnZMXSVFWnLC3yH8MClmhQROIK586wHggZWXb+g4lmg5yupKtmXX03Tv88cGtujqa/+Oz9a3OlappsIBSzDpGRYqIkIw8f7KBfLhGMRDu87AFaZU/LTMxXX50NpmI8z+4Y4+tRB2no6yWdy5DNZNEXBNExU4XjKCNuiXCrxvS/fzJ/vO9YQC4XtlzTGax2SAizLZGRoGMu2QCiMjI5w+OlDjo2ADWapzC9+9guaWtpJJaOMDA2zfPUUhVyaqak0kWicTLbAuaEw8VgUITRUzYt46UwmY6NDPLF3D+WSQdkwGB+boLurg1w+R3dPD140zJOZqlCNGqoytpwBYfH07t7verXXeoh4i3PwuDbamMt5i5TcMDBBoTnKz8MKwrLdBbF6l39l2eJdxcbeBzPVxNMf/BCTl1ziCD21yE7Vgu56Zzh/IHxDSGdhyp91NuWuLvTh4TkzlitAry05x5Lc5y563liuPutMtl21g+V338effX9ug3aoxJQJ8hOQC71BD/Rlbs8c71jtPZ7dUNbJJQSOh5IniFRceSvXeSibAKQt+dUvb+ess85keHiEaCxCMtWEbVvouk42m2H58uUcP36cnp4eBgYGKZcNslnHWNWyLMbHx33hIT01RUtLC2XDRFM1li1fRjQSo1wu0d/Xz74nn6RQKCCEYN26dezevYdzt57Lnr17WdTby6rVqwMDVzV008bLU78J147IMiuqHaes4o+BF26/EVoQUBboOaXC9ksaEgzOOHiMQcvENEyQNpZpo0qbmCbQNYWJ0TGymTyKGuXg3ic5f2yKi/c0Jhyoo2MN83HxyBSfuudhTrvgXAaPD3L86AhCEcR11bHJUBXK0kIB/nRgjEQDO9YTVu/gI73YlkU2naZsGEgE6zedytbzLgABhWya+397N+ddeD4gmJyaZNHS5YTDEUqlDto6SqSam2lpbaGYzzveIKpW2S25jjuhUITOrl7Gx0bpTCSJRWNE43G0cNjPE3JSkycpeE6Ssv4iBfUXpFrIulYQqW6ofj3eNVZnYwHbPJY/UrAovfaV7N21i4EDB5FCwTRtkKBLm8/kjYbvzsEbbiBz9jlVHhPTdsdCIKl2kQ4KAp4BOppG/wc+zLL3vHNWm5Eg7TAlD0VUN+CcIN7ezrlXXw5miWt+eeec10shMLu6KGzZ4vKn+MJzUN3iq0kICKYzqB1m2+XPJpx4qFm10k4gsaDuiLgJ/YSz8ejs7uLQ4UOOQasisWyLcrnM2NgYS5cu5dChQwz091PI5+kfGGD58uXE4zEefOB+IpEI6XSapUuXMuGiwwcOHKC3txfLsujq6iQajhGNhBFCkkwmUVUVwzBobW0lGo2Qakqi6yqppqTDmz8OAT3lDEPjCd26rmLbJlLaNWcdUueBHC8IKAv0nFKjap4Wy+aapb38fHKC8dFJhHQyCmuKQNoWQipIqTA5OsxE/zH+8sBgwzxYnR0N89FkmMQfeozMZJZUcyuoGqZdJhbWKRTLCCSaqrL5rE38xU/vbqj9Z6Le8UhRFeKJBGHTxJKSsJdQT4KiqJgIjvYNYZoS03YTepkTPHj37UyMDhGJJdl20SUkEzFKxTyZqREeeej3IFR3plHQNR2kpLm5CaEIehZ1oSgKtpmgWC469hUvCPIQgeqjjULSMxn/+ZC410pduwWHilvOwejuRhsaagh56CyVuXrFSlZfdB63ffNbHH18L7Ztc6XhCCdtDdwax7C0i+xZW6bxP13wcj7VXjA1f7vXpC/fwdF/upHeT34MfY73C+AN+TJPtCT4sSKRlmTVhnUMP32Q8n/+F6nJBrKSS8ngBz4IquoLBvW67wlSTomZ1TUnKpxIKtf7cVBkQBzyjTrqdgGARYsWYRhlwuEQQgiGhoZQVZUVK1bQ2trK8PAwmqYRi8UIh8Pouu5HFO7q6qK5uZmuri6OHTtGd3e3bxAbi8UYHR11vPzMEgcOHOT0009neHgYVVXJZDKMjY3x+OOPMzQ0xNq1a/1eVXiUdcfO74D7HFiW5ScerB23oDdPI7QgoCzQc0vzUPO8YdkytJdczLdvvJmQrlIqmyiqwDYtCpaKHgrRv+8QVxw8Rltp7giyErB6uh30Yh58nHW8j6f3Pc26U1fR1tVOduoItpSEQjqWbaMKwbXLlxAtFBsZgWek3sGFrFVVp62j05kAhSSZSvpFQpE4F+24CtN20v3Y0nU/Lpf5yv/5GA/+7jckmjt5zV9cz4b1y7Gt6uiVjsdOJd26ZZl+SnbLsrEtm9a2VqRsHKp9XpK3bjzDauoJHJVzlYZkIOpdlRGlEEhVZewjH6Pr7W9tGHnY9x/f4+gVl7L1kos5smcvV5YtvpybXyTl4//f+1D0OoELpQLB5VzxePdy/bhCiQfhu531xiJzxZXsu/hS1l9yIerE+Kz9kcB7+8f55aIWsnaZwsgIjx3cz7kNZl0ee/0byFx+hcOHJ4QEbkVQ3vBG379n7jVKsKBrA+PFMvHtWeYQHAWOQCJcQURAIBii4hvSzlSXEIJNmzYFnieb9evX++cAli9fjmk6yUFP2bARTdMwTYuBgX56unvQdA3bttlx5YvRNZVyuYxl2UTCEcbGxojHYuh6E1e9+CVomkpvzyJUzVHFXLnjxSiqyqqVq4lGIy4P05G0mchTdyqKii1LaJriq5ukrLwfs3mJ1dLJr0ReoJOOGl2gW39yB6p0IGRd0xyjK5wYA4tWLmfRPTu56k0f4frDjSVcAxj/8Pt89KJRPl7aP0rvP38TXQvRu3QxYV1F1zVSyTimYSNtycSuPQ3VZcdiJ6zeAfyJT0qbUskklytRLllYprcIgiWdmCcewipwc/MolV0e2GiKQljTiYRCRMNhouEwsUiYRCxKMh4jlYjT3JSirbWVzs4Ourt7WLRoEYuXLCaeiM+4GzyZaD7CyXw9EII011AJAbkrr2Ton7+M3dLSUJ27jx3hnv/+EfmhPlTFMTh1FRwN0eBb3kJ6+3YfAalEjXWTOSlOHionkZ8SsOuojjCrKAqKm1CuClEJhej/xMyZqT1SgHYJvzo+wdtyZTbueZKVx/oZb3C4ZVOqRlUj8QCdenYTiqhEhw2iP/44BNVYNPaM1H02xNyByWoX/+ox9KKyKoHjCrquo6oqmqb7fy9duoxQOIwQTgbhUCiMoihMTEwwMT6Bqmp0dnYRiUSczOexGOFwhFg8TiQSIRQKkUwmicfiNDU3E46Eq/ibDSWsFba85IISG9t2VFsn6iW3gKAs0HNOjapXtLEJRm75MbqqYhgWtpToqooCnD84zMq3fXhei+TkO/+G/I5KQKxG+VCAS+9+AGPLVVx4xTaOtaSYnJokYxgoCpTLFjsPHuX1DfAglWfDbkNgmhajwyMYpoktbZLNrU4SORQsieuNU4GOhXCSBfqLV2CbXpnWpaMIF863B+VKx/rALyiF9/sktkFp8LmZj1Ay3csnYKAJrqnBTDtxZ7zzO17MiGnR9c63e5dMIxsnJsojuoYxmeXeO+7kr6eKDXnsgKva6exi+H+9qWZh9xZmsN37rcig+isQFTTocRNY0GvHI3PFlYz9+Rtp/9ZNc/LVCrwzZ4CLAjWS61sCzT/4PqN//WbQXDuqGrUaVNAQryse0uIJIZ5Q4itknK3/nMhJPfRMCIGqab49SuW40/pMSFvFq8yxNKoMp/NjZGQE27aZnEyjaRrHjh0jEomwaNEiurq6GBwc5MiRIzQ1NWGaBmOjI6RSKXq6e3nssV1MTaXp6upmcnKC5pYW1q5ZU3XPJEH1Y6Uvc/Xf81iadqxmjBqpr5YWEJQFeu7JVa80Quv3HyUa1igZBiEXMqRs8pq7HvDRhEbIamlh6i3X1+HjqobZ1qYyvOoHv+T040MIRaNYMgEnV4WeyTeWxTibO6EsxkGSOEGQSsUSpWIJyzDYs2sX3/7mN7n37t/5wollg2mBYUoMU1I2nABuQBUc75H/l/RChAf0z/7HhamfUQ/++NTozvjE46N4iIPijJaoJAys4qPqPihg2bTf8Bmfx/o1Q5uEXwwXuKxosXVgjHc1qNrxFt+BD3zADWfvZPJxPor/URFonlGs4oS093fyTspk/9j0nX/1J3PZZQ3xVtvfRhYnAehDQ8R3PuL8VYU8qL6tDELx74G3mHrJAGvb9Z91IZx3uo4AViX41EERPHGnVsiY/Xmy3Wsqb1vw6qnJSR5/7DH27H6MJ5/Yw9azz0ZaNkcPH8E0yjy17wnO3rIFyyzT29NNNBqlo6OD39z5G8pGAUUVHHr6AEPDgzQ3p5yNiAjyL10ebL/tWkFjToNiEYjia1fq8PLwzDdY24KAskB/FGpUvfKybBFpmtjSgZyLpsWfjGZImda8Fsnsy6+ua5g6H3sQr7139I2iqBqW7cCZV5TLfG50quF6TiSLcS0TqqbR3tVBZ3cXrR1dnH7OVv7kL/6Ss887D3DsTkzLCdOtCNAUJ1Ls1a94NS9+6St8GBbpToXuztOfFt1gEk5+HkFlwgrsNE948T756ET6GlQd+Mdqz9dQ5MEH0QYHGnq2u2zJjVMlbpgsNC6ot7Zy7PP/RObyK5wFZxbBwhE8pgsf1SqH6Z5MtccKrgGwnKeKbD6l1ZER3JSJVC9rNUJFQL1TdfwE2qziVVQMcKWUCHfcKiqe+ohC8NhMAoBHqqrS3d1NS0sLkUiEvr5+stksmqYxPDSElJKhoSFKpRKhUIhQKMTSpUvZtGkTTU1NhPQQW7eeW8NXpddzCQ51BTGP78AxP2sx1e/NgpHsAp001Kh6JSklH+vq5ZttSQ4/sR+jWOZPM41lLQ5SfgZBpLjlLMzuLtTBocZ0zUCLLXnN8SH+rSXFyg2n8LHfPgQ0Lu0/sxD3DimKQjQWx7ItFEUnFosTCjmpzk3bEUiSceEkXwNs22JkfJRX/dn1JBMp7r//QSd8NkGkxIVqHezbO4IHOzsItQwIKSc7jjKdTgTSn7W+wO+giqH2aq9edaRxeyrveYs3wIrEQRGfuuM3SF33eRAB1V09poWPSjjIhN+PoJ1GYMyCO2Qvp45UVYY+9DEWvbNxA+D5ktXZQb2u+DxWH/D7IPAdfSt9ogY5gMB4uchKPTUN7jshQaAi7dkXY2/cZhJYap+xxUuWgJSsWLkSRQiGhoY5c8uZhEIhMukpztm6laHBIdavW0cylWL9Kaegahrr169DUQSLFwvC4TCnnHKKH4vEcYkOpgmVdduf01BWCIQb46eSTNJJpuoNbTAlQ6O0gKAs0B+H5qHmedHjT/Gy17wMgFPzJZrnsZuVgOl57szAx/hH3t9wfR69ZiyDrgrONm3i45ONqQuEmJ2XeZBt2eSzWfLZHOmpCUolx4NICFCF401QNiS2dKDh3/7qdj73yU8xMjQSSDLnTsh+uE1ncvWCiSFdDyBnK+SAKraruyeYhu3ko7mEkLm8Feb06BCVssFdZj1UJUh2g/FQToQGPv4JCIWqbUbcx6CiCQkiKFSpceqhJ0qNLYp/zh8Hx+g0e8UO+r74ZawGDYAbJSkERncP+S1nE1REelSLTPjjHvgOoh+1VA9d8QSW6c+JozDz21QEtm35XixefbUB2xp5niSg6zp6KEQ8HicWj7N8xXLa2lpJJhP0LlpEU1MT69avo7m1FUVTSaUc4+FEIkE0GvMjya5cuZIlS5ZU93PGPtXhpQ7fwbHRNM2JQusePZEkgR4tCCgL9EejRtUr2mSa9Pf/G2nbXG407toq3Zl3/MPvQ8wSdyS/YzsjN/4Dch7ub83AX4xnKDz5ZGO8uN9BL6ITInfHY1km4+OjjI+NMDE2QiGf84s484Az0dgSsrkCre1dXP6Sl3Pk6YOoqla92xT+f34FtgwiK444Ykv+YDvg55pmmizr2RRIN/KWH8J8luuntzO9rI9NCW9BqAgIxbPPwTwBdchcNPK2t5O9YkcVY1XqHCpG0d4iW+tRElzkaxf9aVB/rcpCCLI7ruTAXfdgtrQ+K6KtN0ZDH/qIYyDrtTeDEBjkZZpqqqbuaahLsN2ZgrRRubfSthEISqUSuVyWbC5Lxv1k8zlyhTz5YoF8sUChVKRYKlIslygZZUrlMuVyGcMwME3T/1i2hWU78UUs28KWtp9t2ZaOjYclbTeLckCImKEPHjrquQB7KpnZBKb60XC90P7OMx2NRtA1DaEI3/W43nWN0LxVPH19fbz//e/n5z//Ofl8ntWrV3PTTTexZcsWn4GPf/zjfOMb32BycpLzzz+fr371q6xZs8avY3x8nLe//e385Cc/QVEUrr32Wr74xS+SSDSWS2WBXhhU3HImVlMKdWruYEzX3XoXG8Ia5xUaj/NgdXcx/uH3VXnuzET5F1/BCNDxDicbayPLw1+OTPGzUmNp4BGCkS/+n4Z4mYskTijtaDyKZVpYErRQJUy98y2xpbODUxTBwQP7eNH2q7BMm133Dbvnbbdotf1JReFTySXjj4jwoq5WeyjMRZZl8YlPfILvfOc7DA4O0tvby1/8xV/wkY98pEpt8nyaO3xjvwAM741EPQi+Gq6XTBPn/DpscFEq6UFVbkmpKYx97BN0vvUtTs6eZ2jnIwGzq4vxN7+lum9uH5ymFVdgtb0TPjTvCbpCiCqhSalZ/L3+ELiXdSkcZuhTn6b3Ha665xn0z+zqZujDHyWzY0fNGVGxraIibATVNH5JT83iHQ+ob4LXTOPSRxklXsQYT5kj3H6pqEyMjzI6Muw8R8JBE7w2VU1zMv5Kiaqojr2Pq3ZVFOGEyXcXeNV1N/aeRz+ho8uz1xcvdmI9wcAbB29MvGOqqlYJa8G/a4XQoNDqHVcUAcJJWimEwLJs9JCGk/vQaf9EhZR5CSgTExOcf/75XHLJJfz85z+no6OD/fv30xKA7T73uc9x4403cvPNN7NixQo++tGPsmPHDvbu3UvEjXZ53XXXMTAwwO23345hGLzxjW/kTW96E7fccst82Fmgk51UlfTrX0fLP39tzqJxW3JBg8KJHQ4z/PV/prh1y7zQivyLr2Dknz9P2/s/iprLzVleAFenG7OHEVJitz478LZAoCgqLU0tGJaJoqpkJsa5/+7foSgCy5JooRibNp/Kzvt+TySWoGfRUp7au4fTt5xLOBrFti2KuYxfo6yqX/qLoxTCFUbcHb9b0NmnNT7RfPazn+WrX/0qN998Mxs3buShhx7ijW98I01NTbzjHe8Anj9zxzSjT6fD7gGmTd71Jty6k7C3a61stakn5eWvvJKhL3+F9k99Em2w8QjJ05oLIgx+lNWZycsf4zFXhZyI6fYS01CkGpsFfwyC10hJ9oor6b/xy3T+3afQA/2rFR7q/Q0w/oY3krtsO/mztoBevYT519QRIHHPydrF1+/r7KoeP6ieNw4zoBMCJ1aTd10ikUAIgVEuY5qO87RlWdiGhZQ20rYxpIEQCpqmOuOlqm4QRlBsiVScNj1vGI9fqzZiq4eGzCI8e0KSd50nzNi2XZV80Et0GDxeTz0FEssyka5juK6HiURiaFrIv2nB9uej6pmXgPLZz36WJUuWcNNNN/nHVqxYUTUIX/jCF/jIRz7Cy1/+cgC+9a1v0dXVxY9//GNe+9rX8sQTT3Dbbbfx4IMP+qjLl770Ja666ir+4R/+gd7e3vmwtEAnOZXOPutZrzPzp6+meN7WE7o2v2M7+UtexNKtFyGyuWdVnfGMvHdqSFEEoUgIxXImslAkTDQWB2mDUFG1KIVcnsnJSS7behFCSH79i59SKBY45fRtXPril5OZHMeWNWiRM+9W9vRB3blfyJ2o5yGg3HPPPbz85S/n6qsdu6Ply5fzH//xHzzwwANOjc/ruaMimQWFktkm2upzlYh5jY5afseVHN1+OeEHHiB+x69ouunfHGRjHrtPs6ub4Q99iPyOK6clAfQXGPd/h1vXNRpnEfdsVLzsy0pg51zLxWzoiQiWd+vK7biSQ5ddTvShB9BHRtAPH6bp+99DHwoIZIrihED2+tPdw8iHP0rmiivdel1xpFZFGXhmg9++wjIgmAR5qo3nEeSZgHDinZPuOElXkHdQFOf+WrZ0XZRVVq9aQyqVcK51BQgbZ+H3BP5g1mMZFM1cAWHfgf2UjbKDwkiQto2mKlWoiiN0WK5wht+n2mfWR19q1Fy6G1HYE1jAEWY84cWyrCoExRN0LMtRN1mWCRI3gJw7LoEnxROu5kPzElBuvfVWduzYwatf/WruuusuFi1axN/8zd9w/fVOfImnn36awcFBtm+vwNhNTU1s3bqVe++9l9e+9rXce++9NDc3+xMMwPbt21EUhfvvv59XvOIV8+rAAp3cpI41lkp9PvSMQskDhEKM/v3f0vG29zw7DLn0bHjvgAvbmxbDbjZj25asWruBU08/wz9fNsAqljBKZQyziLRtTMtC1zSikSid3b2oqlaliKioeIK75OpNvnQPKvO0RjnvvPP4+te/zlNPPcXatWt59NFH+d3vfsfnP/954Pk5d9R6MfiQeoPXVgkpgbxFzsJRU29wYfT+1jRK27ZRPPdcimef3RCiIgGrqZmBL36RwjlbZ0QQK4t34JgL7VSMZ10nc+GJL7WCavDaGfpNjcASQB2EplHYuo2iKxWPv+XNRB56CG1kGKujk/zppxPbtQttZNT5e8vZoGnBpxM/e0BFmmjosQyqfYLHgiiJPzgz2WMEvn2bDvcSobgecrYzruPj4+SyOUqlkpMmwpauMamCaZrk83mam5sxDAMpJaVyCWlLWltbffTQcgUCL3aJbTu2Lh66YVmWw7ttIwDbltP6VGvgWg9Z8VAT77hXb1AY8sMU4BjF2rbt5O4KjKWHvHj1nYgXz7wElEOHDvHVr36V97znPXzoQx/iwQcf5B3veAehUIg3vOENDLovT1dXV9V1XpQ7gMHBQTprLNU1TaO1tdUvU0ulUolSqZKXIZ1uIIHUAp0U9Gwt2uBOzs+Sl0x+x3Ym3/E3tNz4lecVX67iG9uWFPJ5N1eOJJ/PVm9VJeihMFu2ncfDD9yLUARnnH0ueiiElDamaTnePPWYnWGTMw1un8dm6AMf+ADpdJr169c76dgti7/7u7/juuuuA/ijzR3+Yul916gwghN1rUqn1lCwnrBRtzG/RWdHXSuceG0F68/vuJIjl20n8uCDxO+4naabbpqGqHiqi+FPf5rieedP2/XX9m9afwNLtqfeCZYTtdcHhA1PXK1nm+MhFF6v/St8VYnzt1RVilu3+oKBkFA891ycvEB+VdPGcJr84AoX9QSlmYxfZ7IxqrrHgbL1VCi1pASiRqenprj33vsol8tOTBJNJ5fLkc3n0DQNXQ+BtInF40xNTiJtm1RTExdceCGtba0VFYstfUHZS8hXTwiRuIHoZrD98JAPUVMm2L/g+AXfA+93MJ5KUNhRVTWA6FRURsE6GqV5CSi2bbNlyxY+8xkn0uEZZ5zB7t27+drXvsYb3vCGeTU8H7rhhhv45Cc/+Qerf4H+eFTccqYTh2Ro+BkbBMKz4CUToKm/uZ7k93/YcIyU54Qvd2OnKAqppiYMw0Cozu8gCeFMEN29i+js6cVRfUsmxoZpaetASomm61XoybTVWgY2jzUDMM95hu9///t897vf5ZZbbmHjxo3s2rWLd73rXfT29v7R5g5Z89uH+V2az26vkXIVlMH5y0EjquNk1FsYfVJVB00591yKW86m/W8/VYWomN3djH74o+SuqCTOq/2ebjQZxBEC3i2eR1sduwElqNYICFQEflcJbFSeMadYoG+2a5ireAEBvbIuNDIHigFQbTmDr25xwYbg4E4TQGcjr15JJey+hyDWxjcRoiIsOaqc6hgoPb29vOhFFyGEQrFYJJVKMT4+jmVZLmpiY5kGXV1d9Pf3E41EUFSVeDzu5EGSEiw74NatYGFVCQHVwqOjKgramwTvSz1hwbadJIRefZ56J4iGeM9Hvee0ctyxkbMsc5pQP18BZV5uxj09PWzYsKHq2CmnnMLRo0cB6O7uBmBoaKiqzNDQkH+uu7ub4eHqYESmaTI+Pu6XqaUPfvCDTE1N+Z9jx47Nh+0Fej5TIA7JMxFPpKIwcuM/PCteMj6dYIyUIP1B+JICVdVo6+igvauT1rYO4omkI7zgzveKoGxDyQYDgdQEhm3wXz/4PnlLomkKuubsTyq6+MCXr4JwP8FyXvF5TDbvfe97+cAHPsBrX/taNm/ezOtf/3re/e53c8MNNwB/xLmjTheqYP6afs6lQ/fXaRFUmUG9fbtflVtopkm83jjndlzJkbvupu8732Xg8/9E33e+y+Ff30nO9WiZ6d4EhRXhCwAV1U7lmFPGszupe/9r6q23+FcjNDXn/Oum1+kfCqjCpndJVupwr/J4VhEo0hn34KJaj9d641PdSs3fdRCJGq79vz1X20I+z/DwMGNjY+i6zsjICE1NTbS2trJkyRLa29uxLIvJyUlWrlzJho0bOWXDBl+9Y7tqFbfWGYxVA30IsGJZln88mJpAVdUqtKMW8fDqDQpaQfsUr87a98U77tUfrHemMZ6J5oWgnH/++ezbt6/q2FNPPcWyZcsAx2C2u7ubO+64g9NPPx1wINX777+ft7zFcXPbtm0bk5OT7Ny5k7POcgwkf/3rX2PbNlu31jdsDIfDhMPh+bC6QCcR5XdsZ+RL/0jrpz+LNjg09wUB8l6JkS98jvyLr/jD8HbjP9Dxrvch5hGi+Q/Gl7PNx7YlmXQeW1oYhkWqOUshn0XTQuihEEJxc6fhLQQgbNA1BVVxM45q2rTdp99E9WasypjS2xPPZy+Uz+enpVkP7tae67ljRjFjxsXHoVntK6pLztSCs9DWGBA6V1RMaL0JP6j3n8aHplE8d5vPRz01VbCuen1wforAolYRUjx7k5mEDCllJfMvdcbUa1cEjFCD9YganmTVWVe4cI7IqtarGqnwLKcL27XjMNPfvnAYuNRTmc20AHvnFFFxTVYIut9XbC+y2SxHjhwhny9gWRaxaBRd00kmE4TCIVpbWzn89BF6e3oAhUg0RiwWQ0oXAbGqVSVBFUxQmPB5DYxBPdVkUF1TazxbW9Z7Dj1bk2Bwvtq/nXGyse1qo1hPEJoRHZyB5iWgvPvd7+a8887jM5/5DK95zWt44IEH+PrXv87Xv/51n8F3vetdfPrTn2bNmjW+q2Bvby/XXHMN4CAuV155Jddffz1f+9rXMAyDt73tbbz2ta9d8OD5f5jyO7aT334JkYceJvqr35C49aeo4xNzXmf1dDcc6+SEeTuBGCl/SL4kzq5oZHgYyzIQQjAxNsLuRx5m7cbNnHXO2agChOJ4E3iwtAAU1QmDrygqIT1cmcRkpe6gigfvb08tgYcQzC/Q/Utf+lL+7u/+jqVLl7Jx40YeeeQRPv/5z/OXf/mXThPP9dxRrdlwFjfXHkSK6TYGM1ZTtchVfJGltBGKqFq1qxeK6vGrZ99Qq5qZbedZe11t+XrXigB64vHtSbP1mppJPPDqCu6U/fFzVR+earKiMnTEsUpZp20h64H69fmpy5uv26ntZ+V3vbEM3pugsCWpHttp7YqKGsvnFcd+x3E1dsalra2N004/jXgsQblURg/pmKZJJBIKCBiOIawtJZprcOrXrwjKpbK/0Ne6/nr8BYUMG1Br+AwKM/XGZ6bxqjWg9dryhz2o0nOfH0URjgZPzKK2nIPmJaCcffbZ/OhHP+KDH/wgn/rUp1ixYgVf+MIXfEM3gPe9733kcjne9KY3MTk5yQUXXMBtt93mQ1UA3/3ud3nb297GZZdd5gdbuvHGG+fN/AK9wEhVKW49m+LWs5n4wP+eUVixWlvIvuxqCtsvcQxPnyWbk9nIi5EyG8rznPDlw9YORGuaBtK2OOW0MzjrnHNqYHdQFScCrH8MEMKZ8DQ3hoTwDVBElTpHiopsIgNrreItrsocq0aAvvSlL/HRj36Uv/mbv2F4eJje3l7++q//mo997GN+mT/a3BEQxCRzq3FmJk+gcBfiWaqp3dXOWfMMaqbZFtzZFuJKZVTQElERWoLIia8SmWWRqRKwqk54wocnpFSkX19VUUflUhFfqCtsBNECH32ZS4ipo3aqV6ZWSPF+10WJXHuT6VQJ5AZQNsrk83kmJ6Zob28HRZDJ5+jq7WJwYADTshCaSjlf4pR16wiF9EAT0lfNeIathmH4wspM/RACbNN2Vb7VwoSHblQJNAEbE69ccLw8GxVv7C3LQtO0aWogqHj+eNfV1t0oCXnib+MfjdLpNE1NTTz28D0kkwvRZ1/wZFlEHnoYdXgEq7PjORNK5uSlvQ2k4yr9nPHlvq3D+RAP7t3vG6ItX72GDRtOqSrmLLZuFA4J5XKJW/71m7zu+jfzjS98kdf96atYsXzp9CZkACuXtXv9ymSdmZpi/aIWpqamSKVSz35f/wDkzR27d+8llazmeaapc6ZJdfoutNqjJBjPQsxwnQ+l12GitlxQ4Kg9Vw95qTe1T+uLEHimiE5QtspzQz0BpV69AbTEPxfslyetzbLUVKErzIxa1ApqFRTIPe+hQASETZf/Wmxmpja8sba9692PxLEF8c/btn+u1p3WNA2GR49SLpXYuGEDiiJ5+OGHCYXCWJZNT08vTz31FBs2bOD48ePouu4ER7NtzjzjdFS1Wm2z8+GdlMpO1GrbtjFNE821IQuqTuoJF75hrGX7Nju1Y+ChX1JKX+gIIh+1tiaecFPrASRlxVbGGw/PBsYrl8/necub39XQvLGQzXiBnv/kIivPC3pe8OKEsQ9HYxjlAlI67rZ1t3i+rCH9YF0C0EM6oVDIMzuoFJc1i1iNukeKijpofnuhk5dqF/7gsWoS7kLr2TPMPUJ+nVJW2f1452a2HamvCqpb92zHahZ4j+taXvzFr84iGEQa/PI1bQg5Q9RVMeNVs/PtH5tZjSXcthE1dyKozqntSz2BBddzqY7nTkUAm86bJwwUi0WEsFm6dCmmadHc3IKUsHbtWlpaWohGo5imSalUclM2yCrD1nrPQBA9CdqQBPsQ9O7x1ERBryYvWmwQmQnatcykJgzaq9Tjod5zWWvr0igtCCgLNDfVYp3u76qJSVaKuntJf1vkTxS1dTXQlkQiatv1F113cplzHZihUXd35f/2i9RWKH0ovOqaOTs0HRh2/gocC0DTImhgJ2rrCB4WmJYkPZXBMksoikKpUJg2v4uqqwOTMk6gNU1xcrB4m866KLmsDIn0u1sflj+ZaH4mvg49W2Czvyi6/89V60zISO34z8bfdCEmsLj7/Ag/amyjaFK9RcxXvVQKTbPlmEt4ms1Govp44Bq3L9NFrspxD1mkpowMzAP1ECLbFXSmCSM1gpcnoDroAVjSYv+hg1i25QdVO9Y/4I/R0ePHnPdREdi2g4Yqbrh7VVGcj6pSNi0s6c1DivPU2F5vHOHJUckq7hTlXCulBNv2c/vUBmALjmVQlVTP9iQYyyRo81KrvvEEknr3cb62KAsCygLNSFKCUFQ3Bbt31HsIKy+6nLbdmv6nt64F7RrqUkCOqWmyUrWoHJbB48xRdz326iAO0/ipbeRE1ykXfZCBv+vVJ4PHAn844+xkKi0ZgomRYYxSGtOyaG1rolTM+fOnRPrzl3TrKJXKFAt58vkMxUIWhKyStYKyWRUP3nFfNvN2WSc4Ds8HCgqkz4Lc0cikO61M1eDXWfxoDAWZbdGfsR4ZEFi8c4EF33805+hXPaFCBPpS2ZzUCkjT+Z+t3no7er9q6ry2orofwXZqH+sqXoLf09RUM/M9TV3kVmyUyximhWWagON27MSRcWoOLvRCON55iuFsHJzorJqrelPcd9mumhv9Jm3plHHHPih8KIrA8xCrtUUJ2o8E+a9NCgiOS391hutKAsGgx06tfVCtCnJBQFmgZ42kqnPgcD8trS0UCgWyuTyqohCPx4lEIhimSSaTQVMUItEokWiEbDZHqVhC1TSSqSQhPUShUGByaopwKEQ0GkPTNbKZjGPJHg4TjycolUoUigWkLZ26IhFKpRLZXBGkSTKZQFE1pjJZivk88XiMaDyOUTZIp9NIKUmlUghNpVgqMzmVRg+FSCQShDWNTDqHYRSJxmJE4zFyuTzZfAEFaGpKoWoak1NTlIpFEokE0WgUKaVTt23TlEoRjkTJpNMUSyX0kE4sFkMIQWZqCtu2iUajxKJRSuUy6UwGXddpamrCNAxyuRyWZdGUSqHpOoVCgUwmQygSJRqLAZBOT2GaJslkknA0SnoqQ7FQACEYGxljcmKSUtEgnc1TKuXwnFXvv+dedt7/gKt3t1G8ZGMIVEXFm3pzuTTf+devY0ibUCyC7c1XsnriltJDwbwjwkVRHARGAPbJC6DMDBE8A6q3gDbGyHQBY9bFr7aG+Qgm1Sd9JGkmnywfWZtJUAraH9TUXbWo+zua+nYSfh2BY3MtatN5CqIpsuZoYIdf51rhtRHkO6DSmmtRrYf42LbN6tWriUXigX6LqgizjgrK48E579m9FEslIuGwD95Wj6djD1Oxg3G/pA04go6maYyPj3PkyGEURZuObAj8UPt+jJWaMffug+dO7AWWi0QiVc97UBDxhMmg7Umt0NIoLQgoCzQjCQF7ntzHVMGkLATZbIbxsXFsy6KlpYVYIkGpXGZoaAhVVWlqaqK5pYXh4REy6Sl0Tae9qxM9FGJ0eJipqTSJeJzmlhZCoRAjQ0Pkcllamlvo6Ggnn8s70RVNk7a2NhLJJOlMhuGRETRNpa29g0g8zuDAEOnJCVrbWmnv6CCXyzE2MophmHT1dCNUnXwhx0D/ELF4jLbOTnRVYaivD8Mo09nVSTKVIpNOMzoyiq5pdHQ71w0NDpPNTNHS2kIy1YS0bYYGBrClTVd3D9FYjKHBYaamJmlqStHU3IKiwGBfH7oWIh6PEk8lyeUKjAwNE40l6OjswDZNhoeGMS2D7p5uQuEwmUyWkaEhovE4qZZWJIKxkRHKpQI9Pd3Ek00MDo0wNT5OKByho7uHRLtOtq+PsbFB3vrhDxOPxQFvjhIBQUPg77W8r4C8UTZMInHFSTzm7eZwJxNX1SQlWLZzzPMEciZLibQk+bzFyU1ymrrwD9LKDOoKGThWr8yM1zWAONQeA28BdO0UfFAjAEdWlcVXXzQkAEmJKkSVCsV7njzhRIpKtNjafgcXuFreG1nQKhiN88NDautAK9Woiv8jIGjVuR+16IB/3D1WGyFJ4NiJJBNJmptavKanAzG1zAQKSU99JKuRTm9MbSzuvvtuoqEw0WiUYrFILpejKdVEJpNh6ZJlJJIJTMtECPygaUI4cVu8zY0Xj8i2bd9Yt7b/nnASTCIIFRWPz5c3HnUyJVfx3yAtCCgLNCsZRplTNm0mGo1RNgwK+RxIOHzkCM2trTQ3t7Bo8VIUBSLhMCWjzOjYOKefcSaqphJPxBFCob2jg3t/93s2nnYq4UgERSi0tLdjmSbHjxyhqa2Nzt5eOnN5pC1JJOIUiwWmslk2n34aQlGJxWJoIZ3mlhZ+f9fdGJYTM8C0bGwclawtFCd9uSUxLRPTshHCsY633P2/ExtEYEknhLUC2CiUC4Y7SSpI4aTDMy0bw8voKSWGZSGFwDKdeqWUfubSQrlEPJnAcgUC03KnAEVFCgsppH/OwjGKtGyJYVoOP5aNZQNCwQbKpqOT9o4pqgaGiURB03U0VXPRkWrBwiMvaJSsE41NVXUG0iaKYlfUb+CneK/srIPaEMcVVQHKeZOxQfMP9NQ9F1Q/6d20Ug3snoNUb1cZrAvqa5Qa3VXOZZsha8pWGySKql9VfPjPR+N9FcFvT8Co5SOoPpJVZ/zjs/V8mjqL6jW9nl2Lj56IWs+dgOBXp96ZqBE0yxFWKjCkEALhLfSB92mm21wxrK6jgnLgk2nqEgEM9w+ALQlHwixbuoyByUl0RaWnq4tCoUA8lcALSeAhIX4fXETUS11Qmwyw1g7FCWtg+uWCajfTrA5r750zDAOoNrpdQFAW6FkhKWHFypUsXbaM39x5F5s2b2LXzp3k8gW2XXAe7a1tNDdVu4llcjlCoRCrV62q1IMTgMgolVi/4ZTq3RGQSMTpWdRLKpHwjwFk0hkisRhr1qyuqsuUknt/93tUxdG5OgnzbBTV1dO6iz2iEtbZMp2kekKAZVoorgCiiOCL47Rh2xLLMF0BxAbcWCKKQFFVBAJb2s7kKpzfjlW96U9S0oUcnNgBCqbhuua5E5ftBlDzxkbVNGyrjCIElnCEJwkuBOzGJ3F5VVU3IddMCvTgLCcqkzXSMYwzyiXyeQPDVgiFw6i6kx69cp2zDAR3+MH75XRRwZYn9/QxbZGuQ/MRTmY35py/sDOTvUa9Sd7byXsL2owIhLcrp6Lecf6qEW68gzPw4u3uRZ3FXtS5pp76q3oo5laPeTxJHOG7ljfbF1Sc8wIHSanRjMxcf4P3pt59dI55LQQS+km7apwbVQPW42WacCoFmqYyePw4K1euZu+e3axbt57Hdz9OqVQkEovT2dMZkBFFtS2PqKAdnm2Jd65ekkxPSAlSraeQd6x2jGqRp0bp5J5hFugPTJKuzi4UIRgdG6OQLyAVhbJpsWzpUkKqiqyZ2pLxOPFVqzAtk+PHjrN39x6OHzvO+Pg4+XyBe39/L20dbaxbv55Va9cQjcVYvW4t4MTrCG72k6kkiVQywE3lo6gqp5x6KvFEgny+QLHkeLMM9fcTjiVYsmol5WIRTdOcMrksg/19nHnueUQiEWKxKF2lIoVcnkcf2sni5SvQwyGKhQKmaaJrOlMTE4yOGpy1bRtSQiweIxTS6epZzG9++Qs2nXkmkUgE27IolYpI2+bowQO0drTT0tbGilyBcEgnFo+TzWQZHhxi64suIBGPI4TAMC1y6ws8ct99LF2xglgsRrlcxrYtQuEIuUyaXCbPRVdcTkgPEY5EHHfEYpEffvvmGnRYuDu16YtuuVjiwBN7ePTBBxkfHcGyDJ549DFWnrIZRVUIh6OsWL+BzVvOpaWtY5o7ok9e5d6OUJy8VrKeWkc0IqXUoUYWmpkWPAGgnJhdSTCsuN+O05gjuMxxvfeM1BiMuP/XuDoHyszYlxkEoWmtuwuWRPixRCrlq1U/QZpmeOqpi2raqO5vjUWNj2DUCFJ1VGkztT2bcOmfk951VIRE/5jENE2Gh4fp6emZEUmoVXnNTgJd0wlHIixZvooNm06juSWFGtKZmJggFNIRSsXFNygk+EiM54FDtYABFVfmYMLAeqrEYL2esBO0Oak2BG5cOIEFAWWBZiUHtpQCpiYdI1BNVd0Q3t5OrBqaNCyLh3fu5O67fktLWzubTzuV07dsYaCvj/seeIhXvvpahodHeGLPXm772W0sXbaUy6+6kuaWFlfVgl+fRzLw7XwkliVZsXI5eijkn7eB0SHH7mT9+tWViwRkMxkevFdhw6kb/XkOnMnksZ2P0NnVRXNLstKOhCd372VifJwNGzfgKjgc3a9l89s7bmflqlXompf11eG57/DTxJNJVq5cUdWJzFQGoSisO2V9AIVw23/gAdra2mhvb6tSt/QdPYIQgpUBNArANExs05o+CUvpT/USME2Dnffew713/polK1ex7dLL6OpdhKrqfOWGv+U1b34P0ViU7NQk+/c+zn/d9DVaO7q47GWvormtDUW4AEw9lN5Fdk56OgHhBPAXmGmLSM2DW7uQVoZu9sVxVtbEdPVUPZRrRr5q6qptLJgTaDonswghVWUr/au3KNULuV6fvSCMU2mhtr/euPrI0PTLGmonyO9swkI9hMD79sbOCggG4Cze2Wy2jurN61dQIpz2o5ZjoBIROpdPk85McPsde2lpbubiiy9maGjYyZUkqoUDT5D17UMs2/XUrI96eJ9g3JR66FhwvGqzJ3v9ny8tCCgLNCMFd2Ot7W1Ytk04EqGjvX3aC2sDE5OTfP9736ejs5O//Ou/JpVK+pNHPpslFA7T2t5Ga3sba09ZT7ls8PiuR/nGP3+NCy+5iHPOOxdVUao1FO63rPoEXfWqafmqleiR6LROCIRjpOKuGEFk+vSzzyYSDfvtVDUmghy4L7giOO/CFzmxBdx6PMTBmXhEpR73qnAkyjnnb6ve9blFbFwju0A9gCPgnXmWv8uXgQttabs7tQCveCKlYHJinP/893+jrauLN77zPaRSTQHBxZmQdF0lEgkTiXTR3tXF2RdexBOPPMR/fO3zXPqSa9h85hbXRTEwFC4fZYXGZv6TgmRFmGhA6Jq7jAvz16AzQRUFgYVrXjRH2/7ZwO7d30RULUDV9Ug3t5Jwn2cPEJgJSKuz92+IfY8PVwSYU+VSJYhItw9IH6UAWRlTn4/Z7YvmUsXND8UIVgBSAWl777REisoojoyMEAqFqjIIz0y1KFPNWZevcDhMT083999/Dz3di1izZhVP7dnNT279EZs2nY6qqKiKow62pV35DtqQmJi+tAAA6jNJREFUqI59HdL10PPQLlkdwr4eeuJFkw0azc4U/G1BQFmgPwA59heFQgFFUUilkiQTKQdJcUvYwPG+4/zwB/+Xq176MlatWulf7b+eQmCWjYrgISAU1jnrnC2s33gK3//2fzDQP8BLX3kNmqZWTVyy5hvpCAm2rNhxeOcWLVtSA027AoQi/IlMBM4BrN+4flqvPXRDBl9KKi/wptM2V7Xh7+6k9IWH4OZUD2msXb/WKesJG2770rYdmxXvoHBULLF4nCWel05gsXOqdZRhQeHBg2lHBwe4+V++wuXXXMvGU09zdlGBa92hIKQKwmqF95Cmc9a521izbh3/+a9fo5jNcO7Fl/gqHxEYP1U5+QGU2ufgmVLw2fbq9O4L4HqWVIcP9699tpjwF//qoH8eauO34/ElgrYbNUa97n+zb+bng4B4nOEuhoFGvNpmWJRrVUm+aOMiBHUfxaBE3wAF0Y6qtho4FmyughwogWdL0NzcjGVZM6uU3BqUOQSsIFmWxejwMKds2MiSJStQVcmdt/+SdRs2kMtlUcT0CLNBtU2tGst7ejzhRdO0auSl5t4EBZJaz56gkW1t/JVG6eRVIi/QH5a8xROJaVmsWrWKbCbDwOAQOx9+mFK57AsGwyMjfO+7t/CaP/1TVq9aWbVwOQgBoAgs08AV1P2FGiAej/P6//VGiqUy//OjW/0MvN61ss7Hsm0s6YEi3s4nwLesLBJSgrQl5XK57nwaVPdUQd2eLYbwJsjKxDGtmmB71gwTkKwpR2VSsgOZcGsFmOkNOskCg/x7Q56ZmuJbX/8qL/+zP2fzaaehCi/kdvBqibQtFFU6H8UJuqYqTiqhlvZW/vyt7+SxB+9nzyMPu2OBy6dEuAHelHnsmp/X5KmrREW14QH1Elc49Mt4Qqj0BWQfUqizDlbV4TdXJzmeqA5+5VVbr7y/0Pi8VZfxF0pFuNC9Am6EUYQCQvEXotpnTHjfHi8I3+BU+N9VnDtnRO2Z4MtUeXOF93HH1DNOFy7SIFwGhGD6eAR+K8L94CIr/rvuvDyVvwOIyDTepd/pRhbNqjGuV15Wl3VQENv/HY/HnThNgc2M/wlw6M151NzzioBRaaypqQVF1dn92GM89MC97HtyH4uXr0QPRxGqilAqQd+kdFE9Nw5LME6J0zelCuUTiuc2XtltzSRABtU804QeUV8ob4QWEJQFmoWc+Bfjk5McOnyYc87ZSkjXKeTz/sNdMgy+/e83c821r6arq8uZ5D2dK5WpSQjHk8ZDIWpfb1VTecWfvJqbvvovPPzgTs46Z0tlc+UKCUGBwzQNfzGpFRemTx3u4hNM6+tBAYHygUPOVa6tDVJUYjjUFqpp03kR7YqQUVPIX9ACpCiK4w0RLFNbdwBBAYllTBe2LMvi/37nW1xy9UtZvWq1owbD4796wVNUxZnc8VQ4lY5JIJaI8drr38S//tM/smTFcprb2hACNLUCz88jmfHJQVVjHDhGRZAM4mn+pO0VnWnyPYFxclCCmucxoNP3znvvxbQmxLQf1UiR29egR4ezVtf0CbdMDR9Qu0gHXtTa91F6183UW1n3/GyLWe2Z6bfNGZh6HE6rSMoq1Kuq3jp2JjP9HazdX5QDf3sNOqiChSPAOEdt21G7mpaFrmoYhlGFfJTLZTeJn002m0HVVJCCRYuXsHbdevL5LJZlEQ6HyeVyxGIxFEVB1/WKMIq3wai2DfEjwSIrz5V7L4XilXUO1cu14/EftK0JojNBdVD9cZuZFgSUBZqTOtrbaWlpYXhwENtyslN6u8d7f/d7li5fwcpVK6rU3rLmI1wfeukulhJnl+PtLIV0Et695vV/xlc+/wXWnrLeyVQ9I2Sh+NB08Hmv/+w77r2KmyF02sxfI7d4fzsLvO1f4L3AwbYEVNQvAhRVc4Kb1fLsnvdC3Vez6UwKtpyRrWn9UtTqiJRCwJ5dj6CFQpx+5lm+8ODFLvGWHc+I1luUPK8ppWZhAkFLWxsXXXkVv/jxD3nNX17vqIoIAAYvNAFFVn01dkkD9gonsnOcbnwbvNeV+yBlxQOm2sDTr8m9dC7hSbgoRhUXM3EXQAG9DcfMZSvfYtpRn4mqXXdtKVHzuz6PQXzHi4Jc8U6Z+d7MILrMipbUN5Ku1CMQ6Lpeddhb+G1psWfvYySTScrlMrqmMzo2SjKZYmpqilPWrWP//v1ICRs2bmZ0dJRMJoMQgubmJrK5SUZGRtH1CJ0dXQwMDDA5OcHy5cs4frwPRREkEgmGh4dpaWkN7EwCfQ4IRoqoRIsNChne8xQM0Ab1Ewl65WvVSPXGez7vwoKKZ4HqUwBdsHEClqkhnZVrVnHeBecTiUYplQ1+d/fdXL7jCr9s8Jrgd23sD/8j3Y/7d7K5ibPP28Zvbr+jOpeM+21LV2Uk4am9+zjy9FGnTff8TM++BCzLpv/4cUxrlgio3m7P/Z6aSvPIQzspF0vTyiCqhRNw8l7kCwXu//3vGBsd84tLMe1Sn0zLIpvNMTw0zO7HnmDngzspGcasO++gbhicPBm/+cVt7Hj5NaiqwN/yMF2gkDjp4CuISoUpgQubu5/TzzmH/mPHSU9MVPh2T77gBBRgmhKgdtfM/ASYYD0zfeqVqVNBXU4VOX0SF/69twF7hjolUgR95oR/3Hn4nTfXeaeC5z1WHKRQAraoj4AE25rpaFC8qOYlOEvY1SWnNeSojXCRnuDVIGd+j8SMf0wvGtADi5rS9cdXgFQQ0yL3OqQIleGhEfLZPKViCaNkMDoyTjgUZWJyCltCIpmiWCz6hqgdHR3YtiQ9lScRb0LXQgwNDTkRvJtbKJdNmpqaicXilMsmiUQKRVHRNd3lSKC4saF8QzRFIBWB5SKCQbsRcFBZP9IsjrrfDgghQQF9ppw83m+v3HyMZRcQlAWalaSEdDrD8aPHaG1pYf9T+zm4/yCrVq+i79gxehYvJuXGKnEEjRr7EV/4CECeNTtVr4w3B2y94Hz++XP/wOVXX0U4pFd4CXxQFB59+FGWrVjGkuVLq/itqlxUvsxyma/84xd53V+9kY2bN0yDdf0lPbChevqpA1i2YNW6dYTCoSo+fVREVPNWKpbY/8STLFq+ahqIUo8URWGgb4ABy2T/vv2kWppZv2kjIU33x2smeNrrw2B/H7FEgs6urgqkO203HQhGJ71dYPU4eQKXdzgUCrHpjDPZ/fBDXLj9Cl+I0RSqc4qcxDQNtRIBYbfeTQsgEo16hJwQX0EIvZ6apVaFMUebVbC7f9D7y3tjqdtnp7maE3W6PpOqZEYSTJfyZ0A0GqkKAu9w8Hgd+WHWumZBxPzdUKBsxeYEf54Tbp6bUrnsXlaJVbNkyRIn3lJIR1U1unt7ETgqmXw+x4YN7ei6jm1DMpmkra0NXdexLIvm5mYfnbFtG03T6O3trVL/VVithJp3vi0ICAnBfiqK4jxTATWQh5YEo8nWug4Ho8jWM8gNPpfzRVAWBJQFmpUkEI3HEMLZpadaWojE46h6iEd3PcqpZ5xRhZxUPm5QJvdvRdWQiqiah2rff09lEovHaWltYaCvn+UrllULJi5Zpklre5JSocDI4GBFN+oKO6oifMRGAEapRKlUYtMZp9Ha0sTY8HCAgYrw5E00ti1JT0wQj0cp5TJMjY9hFAs+nONFoHVeYqcty7YZGx4hEi+CtJkYG0ETNrbt6XbdbKXuy+9EthVMjo/T0pEjEglz2Y7t2LZkdGgI27Jd41nhpGu3nAEyyyUK2axjHOj2YO+ju9h02ukOXBu4H1AjULnjoaq6Hybf3xzaTvh+zzJY4uzQN552Orf9939x4fbLCcYffWEiKM8OVdmKzLFiz2l4iIeAuUJRoEyVuqfmekeOqr75njIm+F1XGKiS0Gbi3zs3Wx+DAeSqy1QbDleELZvpKIVzdSXGSLCPgeXYb6NeoMGqGEvOLqeCcEjvv4phqIvL+Ko0z7tG+u9RpeT0G+BEfD5y5DAHD+wHKkapiqJU7OEUt24hUFXNHU5ZSd7netsowjF2VRQVVa1YjzmqWtfQ2C2vuMdUL2mf7aAg3hPgoR0eT35gNQ+FCtioeIiId01Q4PGe71qVz0yRZGvRmUZoQUBZoNnJ3WmXiiUKhRKKAvlcjmKpRH9fHxdt3+6/+BVBRWJTcXP0dhNBT1q/+loEw51tVq5dy6H9B1i+YlmVrYpXoVEuYpTLlEMa99/3IALp5MdR3DgJTgIbx8fftinm8/z4lm+TmZrk8x93LC88/XNwd6jrIUzDcGxsbJvLr3kV2y66mMcefgTLnaScHbaNIlTnt235fXl050Pc+r1bKhMFlXDXEom0pZ+jw7YshKIQjsR4zyc/g0wl2bd3N0hv+VAQqurULxQUoWBJCyybfKEQmJklx58+zItfeW3FcLXemoO30IGmqY73ThUaFIBPZOV+tHV0kplyMjoLVXGhYghp849rcLJQ0HPkRGk+6Mls9is+T9UnKj/rna8pV1l6g4+NJ4RMb1O656vde2fg1RWivXPTBakaocLbbU8TN6YjmNXnHH5rbW2qpoaaHXuVZ5PAF0J8vbHHj6hBRaSHBNdsuQRObBMpfSHPL1PFt+ep5KbXsCxUtWJgqmqau/hbeEiYaTp5a6QQlC2zfj8CPCpCdVJ6AEKRAZSkIlQI4XgiKoqTjyxYX623jfddGzG21pPKEzoh4B0UQEfqefME261nmzITnZQCitfxbDb3R+bkhU4SCBPWwwz299OzaDG6rjE+Ns7k5CRT6QzFcpnJqbRTWko3Eyi+2sZT86QnM+TSWSYnplx42t8sVBAV8HcCiXicp/btIz2VrnrvvdwaeijM+RdfRCIRr1xLtcDjHwOMYoGbbvwnDg70zaP/gnUbN3HRZZchqNi+AL5Bob9Llc7Ozyjm+dZXvoSUjS/eLR3dnHn2Vto72yuMB/rs/3YPGKbJA7+9i1wmjW0YSFsyNTmBbUmmJqYc5Mh2hCJTWs5uTTgToIKgVDKYHBun71gfIU31J5Oym9jLtmwMw0QRAsOwyOcLTI6PkZ6aIKKHACjkJdKYcnh7BmqM55o8XjOZTP3zMCf8/8eiWu8Lj6QraMzEtnTL1ENe6pFdZ+EKth9UE3m/fG+wugJKnX44jPv1VNXnygu119t1BBRktQ3ObAKKdCcI4Qso3qJL1SJbYcoNViZdzzwE0o1x5H1sKbHcwGdeRmBn82GSzWbJZnNIyzM+dZDWKtHNdUMOCgWWbYGi+LYfXl8q0I0rBHjRHZFIaU0TYnyBQFRUN6ZtTRsf3z7EHRtb2m6uMh9zcYQKT7D0dNpQdb0/ftLDnxzk1ysHjkBTdO35Gpk3hDyZZheXDh06xKqa8N8LtEAL9MehY8eOsXjx4j82Gw3RwtyxQAv0/KBG5o2TEkFpbW0F4OjRozQ1Nf2RuXnmlE6nWbJkCceOHSOVSs19wUlAL7Q+LfRnOkkpyWQy9Pb2Psvc/eHohTZ3PFf0Qnv+nytaGLfpNJ9546QUUDwdVlNT0wvqpqdSqRdUf+CF16eF/lTTybbIv1DnjueKXmjP/3NFC+NWTY3OGwtxUBZogRZogRZogRboeUcLAsoCLdACLdACLdACPe/opBRQwuEwH//4xwmHw39sVp4VeqH1B154fVrozwuD/l/t9zOlhXE7MVoYt2dGJ6UXzwIt0AIt0AIt0AK9sOmkRFAWaIEWaIEWaIEW6IVNCwLKAi3QAi3QAi3QAj3vaEFAWaAFWqAFWqAFWqDnHZ2UAsqXv/xlli9fTiQSYevWrTzwwAN/bJam0Q033MDZZ59NMpmks7OTa665hn379lWVufjiiyv5EtzPm9/85qoyR48e5eqrryYWi9HZ2cl73/teTNN8Lrvi0yc+8Ylp/K5fv94/XywWeetb30pbWxuJRIJrr72WoaGhqjqeT/1Zvnz5tP4IIXjrW98KPP/vz29/+1te+tKX0tvbixCCH//4x1XnpZR87GMfo6enh2g0yvbt29m/f39VmfHxca677jpSqRTNzc381V/9FdlstqrMY489xoUXXkgkEmHJkiV87nOf+0N37Q9CJ8O88VzSC+19/kPRwnv2RyR5ktH3vvc9GQqF5L/927/JPXv2yOuvv142NzfLoaGhPzZrVbRjxw550003yd27d8tdu3bJq666Si5dulRms1m/zEUXXSSvv/56OTAw4H+mpqb886Zpyk2bNsnt27fLRx55RP7sZz+T7e3t8oMf/OAfo0vy4x//uNy4cWMVvyMjI/75N7/5zXLJkiXyjjvukA899JA899xz5Xnnnff/s/ffcXZd1d0//t6n3n7v9KYZFatZxVXu2NhYRrRQvwkBEgx5Qn6PY1NinoTwBJIQCKakkIJTCIHwEGJKaDHGxr3gKlfJ6m3UpmjK7ffUvX9/nJmRZKuMZJWROW+/Rp4599x79znnnn3XXuuz1pp6fKYdz/Dw8EHHcvfddytA3X///UqpmX997rjjDvUnf/In6oc//KEC1I9+9KODHv/CF76g8vm8+vGPf6yef/559da3vlXNnTtXNRqNqX3e8IY3qHPPPVc9/vjj6uGHH1bz589X73nPe6YeL5VKqqOjQ73vfe9Ta9euVf/1X/+lksmk+pd/+ZdTcownijNl3jiVvNru55NFfJ+dPs44A+Xiiy9WN95449TfYRiq7u5udcstt5zGUR2d4eFhBagHH3xwattrX/ta9dGPfvSwz7njjjuUpmlqcHBwats//dM/qVwup1zXPZnDPSR/9md/ps4999xDPlYsFpVpmur73//+1Lb169crQD322GNKqZl3PC/lox/9qDrrrLOUlFIpdWZdn5dOnFJK1dnZqb785S9PbSsWi8q2bfVf//VfSiml1q1bpwD11FNPTe3z85//XAkh1J49e5RSSt16662qqanpoOP5xCc+oRYtWnSSj+jEcqbOGyeTV/v9fDKI77NTyxkV4vE8j6effpqVK1dObdM0jZUrV/LYY4+dxpEdnVIp6vw62Qtkkv/8z/+ktbWVZcuW8clPfpJ6vT712GOPPcby5cvp6OiY2rZq1SrK5TIvvvjiqRn4S9i8eTPd3d3MmzeP973vfezcuROAp59+Gt/3D7o2ixcvpq+vb+razMTjmcTzPL797W/zO7/zOwd1cD3Trs8k27dvZ3Bw8KDrkc/nueSSSw66HoVCgRUrVkzts3LlSjRN44knnpja56qrrsKyrKl9Vq1axcaNGxkfHz9FR/PKOJPnjZPNq/V+PlXE99nJ5YzqxTMyMkIYhgfdEAAdHR1s2LDhNI3q6Egp+djHPsYVV1zBsmXLpra/973vZfbs2XR3d/PCCy/wiU98go0bN/LDH/4QgMHBwUMe6+Rjp5pLLrmEb37zmyxatIiBgQE+85nPcOWVV7J27VoGBwexLItCofCy8U6OdaYdz4H8+Mc/plgs8oEPfGBq25l2fQ5k8v0PNb4Dr0d7e/tBjxuGQXNz80H7zJ0792WvMflYU1PTSRn/ieRMnTdONq/m+/lUEd9nJ5czykA5U7nxxhtZu3YtjzzyyEHbf+/3fm/q9+XLl9PV1cW1117L1q1bZ2RL+De+8Y1Tv59zzjlccsklzJ49m+9973skk8nTOLJXzte//nXe+MY3HtRh80y7PjExx8Kr+X6OeXVwRoV4Wltb0XX9ZUryoaEhOjs7T9OojsxNN93E7bffzv3338+sWbOOuO8ll1wCwJYtWwDo7Ow85LFOPna6KRQKLFy4kC1bttDZ2YnneRSLxYP2OfDazNTj6e/v55577uF3f/d3j7jfmXR9Jt//SPdKZ2cnw8PDBz0eBAFjY2Mz/podC2fivHE6eLXcz6eS+D47uZxRBoplWVx44YXce++9U9uklNx7771cdtllp3FkL0cpxU033cSPfvQj7rvvvpe57w7Fc889B0BXVxcAl112GWvWrDnow3333XeTy+VYsmTJSRn3sVCtVtm6dStdXV1ceOGFmKZ50LXZuHEjO3funLo2M/V4vvGNb9De3s6b3/zmI+53Jl2fuXPn0tnZedD1KJfLPPHEEwddj2KxyNNPPz21z3333YeUcsoYu+yyy3jooYfwfX9qn7vvvptFixadMW7nM2neOJ28Wu7nU0l8n51kTrdK91i57bbblG3b6pvf/KZat26d+r3f+z1VKBQOUpLPBG644QaVz+fVAw88cFAaX71eV0optWXLFvUXf/EXavXq1Wr79u3qJz/5iZo3b5666qqrpl5jMo3v9a9/vXruuefUnXfeqdra2k5bGt/HP/5x9cADD6jt27erX/7yl2rlypWqtbVVDQ8PK6WitMS+vj513333qdWrV6vLLrtMXXbZZTP2eJSKsjn6+vrUJz7xiYO2nwnXp1KpqGeffVY9++yzClB/8zd/o5599lnV39+vlIrSHwuFgvrJT36iXnjhBfW2t73tkOmP559/vnriiSfUI488ohYsWHBQ+mOxWFQdHR3qt3/7t9XatWvVbbfdplKp1BmX/nimzBunklfj/XwyiO+z08cZZ6AopdQ//MM/qL6+PmVZlrr44ovV448/frqH9DKAQ/584xvfUEoptXPnTnXVVVep5uZmZdu2mj9/vvrDP/zDg+psKKXUjh071Bvf+EaVTCZVa2ur+vjHP6583z8NR6TUu9/9btXV1aUsy1I9PT3q3e9+t9qyZcvU441GQ/3+7/++ampqUqlUSr3jHe9QAwMDB73GTDoepZS66667FKA2btx40PYz4frcf//9h/yMXX/99UqpKAXy05/+tOro6FC2batrr732Zcc5Ojqq3vOe96hMJqNyuZz64Ac/qCqVykH7PP/88+o1r3mNsm1b9fT0qC984Qun5PhONGfCvHEqeTXezyeD+D47fcTdjGNiYmJiYmJmHGeUBiUmJiYmJibmV4PYQImJiYmJiYmZccQGSkxMTExMTMyMIzZQYmJiYmJiYmYcsYESExMTExMTM+OIDZSYmJiYmJiYGUdsoMTExMTExMTMOGIDJSYmJiYmJmbGERsoMTExMTExMTOO2ECJiYmJiYmJmXHEBkpMTExMTEzMjCM2UGJiYmJiYmJmHLGBEhMTExMTEzPjiA2UmJiYmJiYmBlHbKDExMTExMTEzDhiAyUmJiYmJiZmxhEbKDExMTExMTEzjthAiYmJiYmJiZlxxAZKTExMTExMzIwjNlBiYmJiYmJiZhyxgRITExMTExMz44gNlJiYmJiYmJgZR2ygxMTExMTExMw4YgMlJiYmJiYmZsYRGygxMTExMTExM47YQImJiYmJiYmZcZxWA+WrX/0qc+bMIZFIcMkll/Dkk0+ezuHExMTExMTEzBBOm4Hy3e9+l5tvvpk/+7M/45lnnuHcc89l1apVDA8Pn64hxcTExMTExMwQhFJKnY43vuSSS7jooov4x3/8RwCklPT29vLhD3+YP/7jPz4dQ4qJiYmJiYmZIRin4009z+Ppp5/mk5/85NQ2TdNYuXIljz322Mv2d10X13Wn/pZSMjY2RktLC0KIUzLmmJiYg1FKUalU6O7uRtNiOVtMTMyJ5bQYKCMjI4RhSEdHx0HbOzo62LBhw8v2v+WWW/jMZz5zqoYXExNzDOzatYtZs2ad7mHExMS8yjgtBsqx8slPfpKbb7556u9SqURfXx+PPnQ3mUz6NI4sJuZXl2q1xuVXXUc2mz3dQ4mJiXkVcloMlNbWVnRdZ2ho6KDtQ0NDdHZ2vmx/27axbftl2zOZNNls5qSNMyYm5ujEYdaYmJiTwWkJHFuWxYUXXsi99947tU1Kyb333stll112OoYUExMTExMTM4M4bSGem2++meuvv54VK1Zw8cUX85WvfIVarcYHP/jB0zWkmJiYmJiYmBnCaTNQ3v3ud7Nv3z7+9E//lMHBQc477zzuvPPOlwlnY2JiYmJiYn71OG11UF4J5XKZfD7PC888GmtQYmJOE5VKlXMuuJxSqUQulzvdw4mJiXmVERcviImJOaOIW2TExPxqEBsoMTExZwxxi4yYmF8dYgMlJibmjOFv/uZv+NCHPsQHP/hBlixZwj//8z+TSqX493//99M9tJiYmBNMbKDExMScEUy2yFi5cuXUtiO1yIiJiTmzOSMqycbExMQca4sMiPt4xcTMNI6lh1dsoMTExLxqift4xcTMTKbTwys2UGJiYs4IjrVFBsR9vGJiZhrH0sMrNlBiYmLOCA5skfH2t78d2N8i46abbjrkc+I+XjExM5PphFhjAyUmJuaMIW6RERPzq0NsoMTExJwxxC0yYmJ+dYgNlJiYmDOKm2666bAhnZiYmFcPcR2UmJiYmJiYmBlHbKDExMTExMTEzDhiAyUmJiYmJiZmxhEbKDExMTExMTEzjlgkOxMIQxKrn0Ef3kfY3oaz4gLQ9cNvj5m5nAnXTKmXbIhLvsfExMw8YgPlNJO66x6aP/dFjMH91TGDzg5qb3kj6dt//rLtY5/6BPVVKw/1UjGnmcNdy7FPfYL6ymtOu+GiJgwTcQIMEiUUipcaOjExMTEnjthAOY2k7rqHtg9//GUrWn1wiNy/ffNl++tDw7R9+OPs+7svI5ubZvYq/dXOSzwl2tg4bR/9w5dfy6Fh2m66GVnIoxdLU9tPp7Gp4OBxHs5eUVP/oJgwbCb3lQJU7HmJiYk5ecQGyilGoRBKQBjS/LkvglIv+34QTH4hvGS7itasbR/7I4SUU9uDzg7G/uQT1N8Qe1ZOGgd8n6d+cTfNn/vSQZ4SpWmHvpYT10w7wDiBA4zNv//rCSNFTVzwk/ClryaGL0AoWLdxGz/86R0IKTE0HSUCdEOi6xqhVISuicBAyhBPgI8GKIQMMJSLUiAEyNiBEhMTcxKJDZTTRGL1Mwd9wb2Uw31NCYADjBOY+LL7SOxZOSkc6Clpm/CUfOzlnhLxkmty0GOH2qYUSgiaP/9F6iuvia7ToazSE8nEkJ99fgO7n36WC1M2Qvk0LbE46zUhuq6QXpYdD16KX8oBig3dSbbkcmhKkXRKNG+8Hc13qErJpj2VkzjYmJiYX3ViA+UUIxSIMMB+7PET+JpH8KzEmpXj5lCaksN5So4HoRTGwBDZ//efVH77fafEmIzGrZhjWpwlNFSo05KWLJ4XIESI57po+S6CoB0pJMPNNlZTAaUkVnWAhGEhhCSja/Qb1ZM+3piYmF9d4jTjU0zqrnvoueaNNN36tRP6uoKXr+InwwipO+8m1jNOj0nZRerOSB+kv8TLJaQ84U6Ols//FbOueQOpu+45wa98CIRA0wQIgZQSHYmhFIYu0A0dXddIW2lydpacnSOlp9CtJIaVRDOSaIbAD0KCE2SkxcTExByO2INyCknddQ9tH/k/h0jzfDknwts/FUb4yy8hczn00dE49DMdwpDmvzy0PuhkMRWm+4e/PokeL4Ga0KOUZxns7E2gS5e9aYu195gIqaEwWNCXJDUnBUoyv0WjryMLShFWXEojc/EadZRQrN8bh3hiYmJOHrGBcqo4gij2pagD/n8ijBRjcIjO6z80tS0O/RyGMCS5+mkSjz5xRH3QsTB5LY92HQ80Jqc0KScFgRCC4R6BukRDSybRdiQZfVBDSkEyYbHwd5tINLcAkiVJnd7ObhDQKKXZOrKE0Heo1Bs8sGb7SRpjTExMTBziOWVMimKnY3A0mrNse9PlOM25g7ZLIU5IpGYq9HMqQgpnCKm77mHW1W+g87d+l8Jxht8U0TU6EDedmHrsaESalEESq585rvefDgJQGoQqRGmgEKALQEcJDQUoGaJElFWkCQ1NEwihIYRA0zQMNFQQIuKwYUzMrwRK7f+Z/GX/35M/kXv2wH1e+jPpwZ0usQflFKEP75vWfg9fPI8HLlxAoAm0OVfSuX2AdNXBb2mizTB58w8eeMWelVO3Wj8zOFw9mmNh8pkPvv91jCExxiuUEwb93U0s7R/l2rtfIFd1pvVa0/2sHNc4J3LYhZtEr7eCVJihhWV5CAWmZRB4IaHXAASuHuL7HlKBHwT4SiAxkGYCIWIVSkzMrxICiRIglIYQEqVEtMgRaiI6oBBoKCEQSiJU9PsBVgzHssyODZRTRNjeNq397DdcyaXnzAcUhm7heQF+EFAuldlbrvMDz+O6e5+hUNv/ZSeFQByjXmJytZ7/+1txL7/0V1aXooLph96mngMoIdAOMGhKaZvbL19Af1sGy7Yw5nSSsE2aZMhAWwv/cc5cFj+0huvuX3fU15/uZ2XaTM4fTNThQVDcOJf6rmvQtXZSmZ2Udv4HSoXops7Y2AiGnQABCT2HVFHNE0fpPFDsoFYPCbwy4zJ7YscZExMzYwklbNy8nVq1gYZAaFo0nwiJENqEdySqmRQhkGLyt2h+FUCjMb2FGsQGyinDWXEBYSGPViwd8otQEYV2xs/uxTZNhDDRdQs7pWPaFt19JoYukJdfwLoPvYPwkWeobN7NTsehScFv/Pyp4/KsNN36Nbj1a79aupQDaptoI6PHpDeZvPV+9JYLGEXRqdlYs3vRXnMecwsZFlkGUkbWQOC5BIGD22jgeSH1OX3Unu0nVawd9jMQdnVGxuJJRRG4NtLLotNMUqsiQ4WUQWTMiIlwj1KAhtD0qIqsbtEgRVmFhDJAM5MneZwxMTEzhdGxEl/8q6/RVq9SAKQh6LrQoKkrQNMETi3F4PNnEToZJAF7mm322HkQAssvk9r7NCpwCI5QM+qlxAbKKSJ1z/0vqyY6yeSX3sOvX87Q4B4yuTy6ZuF5HkpooJnoQsNz6xiGIJfPk7j8AjrecA3NvsPWzTv4gW6w6sHnyFUaxzW+qcqmJzWL5PRzqNomx0Ipk+Bnr1mGeOsbWTKnj1TSxmtUqdXKjI/sw3UdlPRp1Br4nkcY+mi6jtNwUUrx4qJOLnpi68uMycnPQO3NbzjpniyBhiBA1ySCEKGB0EBDQ4jJ1Y4iiuBMumVB10DTBJoGARqOF57UccbExMwMhFCoUGKHIefqJrbvIgLFwrMUncsagKReSrF913LCaopAk8ieFCO5VgJNYDmj5IvPo7kaUkxf+hobKKeCyQweDu/h8DIJGq+/jDntHSSSWWQosSyLRLoZ3Uzi+zVC36NRLVGrlunfsRVTN8gV8ixYuAjr3HO56y1rqd/9KN2aTqKvjQv/7eckS/VpeVV+FXQpr0Rr8tCli9nS00b+Ta9lVkcbvh/Qv2UDnufgOnWSySTppE02lyWdbcMwLJQMCUOJ6zZwGg3chsvSDbcDh2hjQGQKpH92J+P/56Mn9fwLFIt793DR0vswdZuq2+BHe+u4AdgYFIujSKGhCQ0z9Ci3taIQNOo1CJ4l9EsoAjRGT9oYY2JiZg5RWDtayARaiKF5aEIg0QikjhAQKo2EnUKEORSQsARWIoEBWKGJrgmkDF6WSHAkjslAueWWW/jhD3/Ihg0bSCaTXH755Xzxi19k0aJFU/s4jsPHP/5xbrvtNlzXZdWqVdx66610dHRM7bNz505uuOEG7r//fjKZDNdffz233HILhvHqtJemU9berjpU7vgl6+Z2IYSBDEL8ICSTLZBMZ0hmU7S0tJEv5Glq76NrzmJC36NeHqFc3kc44jJvwRz0xYt4ce0adu7Yif6br+Oyf7l92qGfA7NInEsuOlGHPzM4Spr34c6RIvKa7L3+LSyY28fY0ACb1j2HYSZpbWuhubWPTK4ZoRm4bo16uUSt7tFoFAkDl0ppnFBJVChp37yXdKl22CEKODXnX8Cspn7efMEOEpbL9qEUP/ipi+frgEmj3kDoUZXYQi6NaSdQSuH4DqPlRxmvDiNDMIzj89bFnCG8pCGmc/65JJ59Pm6l8StJ1CjUSWrsWJEHK4EQsH6rTe0pH00IMpkmrrm8B8tIIZXk8hadlR0dCKVwKkn2BucSNBqUnAY/ve++ab3rMVkEDz74IDfeeCMXXXQRQRDwf//v/+X1r38969atI51OA/AHf/AH/OxnP+P73/8++Xyem266iXe+85388pe/BCAMQ9785jfT2dnJo48+ysDAAO9///sxTZPPf/7zx3jSzgymm5UxO5kis2Q+CTtJ4Ifohg0CPN+nWmvQv2UTjuNQq1ZIJFJkm5tobe1iVncHTbPyGAJct8SFl1zK3LPm8+gjD9N4/7VccfsTJMamX5Y8defdAKdtAlITHo4TkiUyMckmHn38mHsfTfpZBj/2Xvrm9+G7Ia2d3fTOW0Cj7jI2XmLLll2Mjj5DrVKhVq+iC4Fp6mi6haGbKOXj+SEqCEhsmF7dkJOZxQORu1YTEqlAVwEmMhK8KRASAj8kDCOTTYYgpZpKHRSETIZ8TNM6qeOMOcUcYJAYO3aS/e4PMIaGpx5Wmha30pgOr0bDbrJBqKEYbQWRNxBCEe61GB0EITTa2hOIjgTCTmIA7QWbzq4CmlI0ygK9oxvlugyMT9/zekwGyp133nnQ39/85jdpb2/n6aef5qqrrqJUKvH1r3+d73znO7zuda8D4Bvf+AZnn302jz/+OJdeeim/+MUvWLduHffccw8dHR2cd955fPazn+UTn/gEf/7nf45lvfomvelmZZh9naTSaQzDoKWtBcO0sZIJDCsFKkQTAl3Xcd0GtUqVkeEh9u4d5qFN6zE0HcNI0tnVRVdvO81NBVa94Y2sXv04a3pzvC6RI/f8Nub84OGjjiP37dvIffu2M34CeqV6k1ohy+DH30/pyguojZcYGy2yZ/dOKqUiQSBJpJK0tbcwZ+48WlpbSGabsC0blI+uSVynjuvUcBwPGUo004YHNxz1fU94Fs9BTBge0sBVkfjV1VKYlkNCKZIpmzDwaNQiT4/jNPBcD0VkuBg0YWogVIChHd4bFHNmcci+Uy/d6VCtNG66mfIHfovGymvOzC/eE8FxGHajn/ojGquuOx2jPW4EAqEEwrcQvkJoAt0wMU2JJjQMXUeFEmQAKJTUQYaECkIlkUJEGYGnSoNSKkWiz+bmZgCefvppfN9n5cr9X2iLFy+mr6+Pxx57jEsvvZTHHnuM5cuXHxTyWbVqFTfccAMvvvgi559//isZ0ozEWXEBQWcH+tAw4jD6BylgZMNmhnI6diJJMl1G0zU0zUApnUa9hAwlUimy2RxNLa20drQze958dM0gDBXDQ4Ps7N/JU489itsIMS2bTCbJwGCNH9h1XvPO19B5//PYo+VphTnOZOHs8epNnnznFbywdwTR3UHjwsWMjI7gfvcnCBHQ3tnB3LPOor2zk0wujxCCwHOpVIvUqhUqpTK16gie46NEiO+F0Q0qAxSCakJnRSZBtuoc+vwLQdjZcdKyeCZ1LgLBiGGzvpAnkQypemmqjQ3U65KG4zOwdxDDrgGKXDZF3XGRgOfBgp4P4fghTm2YyuafAQ+clLHGnDoOd68cSid10N8T++e/+W3y3/w2QWcHlXe/i2B235nrKZgORzNIXrr/Sw27wSHab/o4xY/+PqUbPnRGnKNo7lBIJ8XQg5ejVCe6liEhHmR85IkozTj0KI7vw05kEEKRTrTieCESqLmCp8qt1Oo+tdopMFCklHzsYx/jiiuuYNmyZQAMDg5iWRaFQuGgfTs6OhgcHJza50DjZPLxyccOheu6uK479Xe5XD7eYZ8edJ2xT32Ctg9//LBaB6HgLf/9FM/PmY3zhotJpZowDAPdtDHtBKGSIMGpjVMrVxkbHWDfwE4q5TJhCJlcC62ts1i8aDnnnbMctJBqvUy5WCLfZLNz5y7uf+BxguvO5Y23PYzk4DLChxrX6RLOvuLQzjG0FZhEAeVskv9KmWQuWkx7VytNhTzzFsylqakJdJ1qpUStUmHjuudxGw6OU8e2DQQa2VyOppYm2jtnYRgaGoIw9PA9B9/3CALBoLaTFxd3cenql4d6Jie1sT/5o5N2nve3UFAEGngCsCSeKVFSRBOpAiXVVJhNCQ1hRJ4WLQBNz6JcF6RJKhmnGZ/xHMe9cjj0wSGa/u7Wqb/PdA/soZiOp+moht3E/5v+7layt/2AsU//8RlwjqKj1LEIR5rxa91oZgatYOE2HBSQSiYAHSkFQoDSDIRhoaHAsBiReYpS4vjTz/47bgPlxhtvZO3atTzyyCPH+xLT5pZbbuEzn/nMSX+fk0l91Ur2/d2XafvYH73Moob9q9u5//ozvp60kZoJCEzDJJXOkM5mMA0b04Kuntn0LjgXy7QxDAOnVmJ0cA8De3azfs1TaIZGc1sblm3Q2prlgguXc8nl51MuVVmzZj23FausevBFmhveQe9/KCaFs7lvfYfy+987s639MCRxHL10JieYp3/jOn79N99EJp3CDwKKxTEG9g6wZeOLjI2MoAlFJtdE16xZzJ4zm+bWLqxEEhX6hGGAH/i4jovr1fHdEN93CDyPMAyRocdZ6wcOaZxMUv6d60/SRDXpN4EoyVhHhQIpo4qQmq5h6RqhMKLU4gNKUqvAB6KKkIgQpQKUlAhhThRlijmTOZqA/1h46RyiDw7RdtPNZ5Sn4LCEIflbv0bh72992UOvxLA707zUigDTrpDQK2iaQtc9NC2aL3RdgJIoFRkgMgwIfR8lFDIM0DUfTQTo2kku1HbTTTdx++2389BDDzFr1qyp7Z2dnXieR7FYPMiLMjQ0RGdn59Q+Tz755EGvNzQ0NPXYofjkJz/JzTffPPV3uVymt7f3eIZ+WpHNTQfFIl+KAPKVBsvLNUYWL4xcamGIplsEbp3SyAiVSpl1z79Aw3XxfIlpZmhpaaK7q5PO3kUsXn4hvl/HqVcYHtpL/9atVIsj9MzupdDSztXXvZ7+sxbzR8nv07F1J1dXKly3/eiipebPf5ncv39rxq6IXonepJrPsu+Pb6D7DZfSqI2zu38bIyPDNOpVpBR0dXZxzjnnYCVSSAzGx0r09w/xwgubGR8bo1apEMqQMPCwLRM/CJFSIZAYho7v+mgo/uyHTwGHn9DSd9zJ+B+erBTj/T4yhaI2nmX3Mx1Ytk9WZvk/57WA4+NrOuvMhez0O9CQOPsGCJ/9DzTA1yxU9lz8VBLhB7iad8R3PJA4A3BmcjIF2Wemp+DlvFIt25GI1gOK5s99cUaXd1BCIBCk7SrXv/Eu2nMKXVfc8UiZ7VsrKKHRqKfYvasfTbcRAtJJg7a2ThSKhluhWv4+tVoVv+FP+32P6a5WSvHhD3+YH/3oRzzwwAPMnTv3oMcvvPBCTNPk3nvv5V3vehcAGzduZOfOnVx22WUAXHbZZfzlX/4lw8PDtLe3A3D33XeTy+VYsmTJId/Xtm1s2z6Woc5IpjsZdACFhbOxEikMw8I0TIQmMAwbzTARQuA5DcqVcUZH9rF75162b9vI88+uQWKRTGfo6u4gnUzSM+ccWprSWJaGDDVq9Sprnl+LpjmEl/axbsvYtAwUmLnW/vHqTX62pI81TRm8Sy/lmhXLKQQOujBoaWsnW2hnbLTI8L5xNm0Z4qmnN1AcL6HrGumUTTafoau7i9l9yzAMjWy+gKmDboBSGqHfIPAcPD+k0aiTeXYjzUe4MU9ZijFRCGffDotn9uUgVPQWLN7bHmBpHl6osz6YS7WxHCV8wvJ9JLf9BJ2Q0E5jXDQfTVgox0empm+gxBmAMxOjf+cpeZ9JQe0Z402Z0Jkk77mf3De/fVLfSgDG4BD5W79G6cP/+6S+1ytCgKn7tGZG6cjV0Q2fpJkCqVBIdE0nDBVSySjEg0DoBhoCTYDr7cXxioTeSdKg3HjjjXznO9/hJz/5Cdlsdkozks/nSSaT5PN5/tf/+l/cfPPNNDc3k8vl+PCHP8xll13GpZdeCsDrX/96lixZwm//9m/zpS99icHBQT71qU9x4403viqMkCMx3eyMZ3bupPTwL0mkookbKUkmEwhhoBsGhmGgaRrZXJ5UJsPSpWdz4YoUlp3Dc0PGi6Ps6t/CvoEBhvbqFIvj6JpJU2sLO/sHSBkO1117HoXmAvLKgMrqraTLjaM67PdrUr5I9XWvRZjmlFBOif1BhJOJgv3xciFQQXBcepNaPo37sbfT6wSsX7+Z7/2/7zD7rDmYuqBWHsdxGygZks0X6Oxq4eyl80gkbLLZDIlUBk238DwX13WolMcYGx0i9BwaDQfDNECFBJ6LLyWu63DW7uGjjgtO4IpWqUPGxtXEL0EQEAYaUnrUApdR18cOQhzp4hmjuOFulADXrzDuBlhCUZE+TqlGDUHCsqZ0KtMhzgCcgYQh2e/+97TrJL2SJqVnkjflZHpMjkTh72/FXzh/hp4XFXn0UXhCp6YsbKUjNAPL0lFo2LaO7zmghWhCI/ADfNcDofB9D1NrwTaSyGO4TY/JQPmnf/onAK6++uqDtn/jG9/gAx/4AAB/+7d/i6ZpvOtd7zrITTuJruvcfvvt3HDDDVx22WWk02muv/56/uIv/uJYhnJGMr1sHsF5c+fiXHYxdsJGCAMrmUbXLaQM0XUDGUqcaol6rYJbL1LaV6fRcPE8ieP4pNJpMpkUbcvOJpdJk85kGB8f4eln1rBpw4v81nuvZfacbtBNQqnYfePbWHzLbS8Tzh6KSJMyxPNf/Rqzr38fzYUsIF7Z7HUMCCJjSEpo1Bs0r1lzXHqT/t9/O92z5xKGAR2dBX78owfZsmEL1167glkXL0ETgkq1gueH1Ko1auUiA7vGcRoNpAzRNLDsBJoWvWoqlUapkCAIEdgoBRKFoVl4yiO5d3peqhOSYjxxkNrURZnQ4IvoCguhcIMqDmPYVoqG7/BMqYIZhvgKxrR9KJlA02wCTVLyUmgqxLNt/GoNLWnjjFYpDx9/JdlTlQF4xgvsTyLHqj+RwInwe8xUT+yRdCavhGOZGmd0JW8FnhBsNXOM5cEwYdT2aTRGUGgUx4sMDexFES2g586bE9WAQiLDFAt6PkbN96iO7wJ+OK23POYQz9FIJBJ89atf5atf/eph95k9ezZ33HHHsbz1q4NpZfMozv3b/+ZppTN85YXouoFWbmAlEoBA0w1M08awMqSTGTICuiwLTdMwDQu36lArj7N393Z2b+2n0SjRO7ePbKEZXU+i6yk2bh4kU2ilkLewk2lqq65mbagx959/TKZcn9ahXNC/k5//1d/xuk//IZadQjD9BlCvBKUU1WqN7/33j1k4bx6/tvrpY3p+oyXPzht/g+qV5+L7Po2aw+BQGV9a5PImuYzF4J7NbNm4hfHxMomETaG5ic7OLuYvnk+u0EwykUQ3THQzhVISlESGIWHoIX0P160idJ1GrUoYSNL3PMGlj2464kR1QlKM1eQ/AqGbJJIZDkyIUhOPLVt+Dr/xHkEYhCilCGXI4L5R+nfvxmu4nNNZ4+oLUnhugFdr5/E9lzA0MECxXKH87P24bsjCeb2cM2sWjx7HME9lBuCrQWB/spiut+7ns5p5qquJbW1NnF1xOW9gnNdv2U3USvLYmdRdtPzp56hfcxXMAM/XifSavPQ+n65hdyrDvMdH1Avd0XUcU2GbCglIpQAZRdhl9E2gAKXpCN0EJOghmpnDkA6amP71jpVlp5jpZvOc9bWf8F9jIySyKQQKJaHheEgJgVR4vsTQLYQGiUSaZDLJvLln0dUzi96+Ds69pIfQc/AbUYPBxx59nMcfeZzZfSn27hlg8+bttLTkWLBgFt3dHaRWzGHo3z5B8lt3cMXtTxz1OGbffgf/G6jdcy/Vv/gUjddfd1K9KEqpSHvjeXzr/32HN3k+V/7Jn017Qrnvwvmo116MunwZflCnsWsT/TsG2LhpL2PjFUJfsWfQJZVWXHjBYnpnd7P0/PNpamlF1y1cL6RYrLFpYz+NWoNiqULD8cnlUsgwoFQqIaUk9EN8r45t21imTuj5/J9v3Q9HODUnKsU4+sKILoJm2OTbutG0yU/U/sXF61Z2cvV1r41Si0W0/+69Q2zdM4xlm5yzcAGpVBKBol6rs/qJJ9m5Yzv5QhPnXHA+mWyOsDpKpTjCP//TPx/zOE9lBuCrRWB/Mpiut+6prhY2dhQwdJv1LUk2d7SyvaOZdz+7iab6/oyMY7n9BaCPjdH7musY/eynT6sn5ZX06IKXH/dY0uahOV0MpROUbINNzWlev24Hv7F1aFrn52RXkj5eBIAUhI0kflmiJaDTznBpWwNN07FzeQQFAnRkKFG1EcTY+ui5rkRaLYSE6Onpmx2xgXIamE42T1PN5bcXnAVXrQAUuh6JZIVuoCT4foBUUK83KJaKjI+VGRoYYNOm9Uip0dHexuzZ3cyZNxdD19i0eZDzl89l8aJO9uwdot7w2NY/xs9uf4reWc2sWLGItrZm3F+/mspDa8iUD91k8KU3Y2psnNSHP87w3/8NjZM2yUSddaWU/M/td3LV2DhXf+s705pQJnvpjP7e2wllDWfnZtZv2MmWbUMoCXNmt9DX1U4qlWDt5r2MFyXtXXOQQcBoscYzT29kx7ZdjI2O4XkBLa0FCvkMpm1RyKdpasqjC0E2beO6dQLfB5WOWhS4Dq079lCouUccowDGP3LDK5yk1YRxO5mpM+ExEdE5CsMA13EjLZMm0NCQQk3sIzEsi1QyRSJloetatDJWgjAMOfeC83jNVa/FdRyKlTJNhSaK7vGFSk51BuCrRWB/MnDOPxelaSDlYe91CezsaiKXtkmnswih4weSNckkz/X2cNbwCLl6g8LoONft2kezM33hNIA2Pn56xbOeR8unP/uK6sCMJSzu7uki6OumaGk8n0nhS4UMXcLQQwWK/57XRU+lwWuGj37fnNxK0sePUuA1TFb/uAVUiKmbXFEw+b15LQgFFSvD96urqHh5lFEi3PBdEvv+DQlUUx1w7ocIQh05jXMwSWygnAamayEnylVKXp1kIoPQdBA6mqaDCLCTaUw7TYduojQNXTdRSsNxHMbHhtm5dTub12/mqcdXE4SCvYO7eesbzqOrtxclBGMjw3R2zWf+/E6ee24X/3nbA6y4aCHnLe3j+d+4giv+7e6XGSOHLOZG9MFt+fyX2H3dyYudKgXPPruG8tg4b7jz7mlNKJPmy7O/fiW+X2LDxp08/Ww/5ZrDxRfMZ05fAUKXIAyYNXce6Da/uHcN3//uXbiNOihBa0cLZy9ZSFd3F02tLaQyWQzDQCCj2KqMzkHoNyLvVuDhuzV8t0G9XqNp/MjGySTB7L5XcHaig1UIxqo+/bsH6OmdRVvPwWdi06ZNzJ8/n3QmPRGulZEBoxSappFMZzFNHaHpU9d6dN8It//0pxQKTfT0zuKqa66Z7Bv28oqZRxreacoAjDk8iWefP+pCSQcWFSvsyKbw/AAZetiJBIEMMUzJzlktgMCw5vIUGlc9voZfW7tt6vlH43SKZ1N33UPLn34WfXz8mJ87+dn/77PP4mdLF5JMpSfqgkjSSAxNJww8At8lDH3cBpxdqh85zAsntZL0K0chA0W14oMUaPiUzZAx5SAU1BBU5S6qXgUROFTqZfYZ44DGuGdRHR2mJg2UN/0WGbGBchqYroX8y83b6HfGaG3rwDBNarUKhCG+62CZNkI3SadsNEMnmyuQzeUpNLeTL7Rx3kWXctEVr6Fer7Jrxw5+8oMfs2nbHhYvX0zfvPm0dXQxMjyEocP8d1zKC2u28fCjm9jZP8JFF87Fed8VXPTj1eQPWP0ftpgb+2OnjYtXnJgmfwegFAwPj/D4E09yw9mLMYemlxFTTFn88i0Xsr67iRd+8Tzb+weY09fBr71xBQlL4vk+djZPc0snmUITa9dsxjBMerq7WX7BUlra2kgkMgSBQ+B71GtVBnePE/getVqJwA9AhYRSYegChUYoJagAz3WRoaTqOVwyjbGeiFWTIKoA29rRyZyzzjrAlxJV5zUMA6GJl2nJlBCYlk1raya6lpo+sR16enu58qorWbpsOUKIqWt7rM7wOANw5jHdhVKu4eN6HsIPCYOoHUIoBYaukUonsSwT3w+pNer8YEEXewopfvOZzTTVp2ecT43nJItnFVMOxSis85HjD+uMp5L8+PIVrJndjVktE7oVfBVimhq6riEMAwHYdhJFgkUjNVrc4IivKYDKu981IwWyQglAA11Ql6Ok7SakDNhS8ygGdUDh6lXqiQ2EKokhLEY9yYsjKZSCWsJgZNM2PMOmfAw6n9hAOQ1MtzdPu6ERzuok31TAtiyEKmBoOpoQSDRqVQcZ+LieS600xtDuHYQKHNelrXMWvXPm09U7hwVLz+EdBvzbV/+DO372AHPnzWJWXx9dvbOojI9RrVe5YMVCZvd18sBDL/DDnz7BxsV9rP3w68lt2E3X6u28cXfxqMdlP/oEzorzJ26wV2akTJ4WIcB1fe699wF+/Z1vo/m/vjut599zXh/Pr1rOxq0jrL/9MZqbM7zj1y6hp6eJRsPFtiya21pBMxgZK7Jm7VZWP7udt7x1Fa957eVUKuOsX/McoyP7KI6OEjguxVKJIAyjL3qhYVompqEjNLAsi4brRl2ApaJeq+H7IedsHybk8CK5E9d/JzrfQeCD0jFNY2o2VkoR+D6joyP09fYikqAmpWwqEjpmkyZJpeP5AbrY7xkbHx1l69atbN26jaXLlrFg8dnsDyRNnzgDcOYxXaO4p+GyLmGh60YkkxQ6juPjeC6NusRpaOimTjqTIJ1JsjZb4E96O/m1jf284ZnNwPS9KSdXPDshE/d8Wv702MI6cmJ89y6ew+aFc9nYXKDu+pi+h2nb6LpOEIT4vkfDi5qFQoguove1i5Vpvc8r9qSeJBSKdDbLb77/g4yOFClXylFFaQ3sZBLf90lJyQWhIGqEHqCpJQzKhSgFmm7Sriz00MDNprnzqO8YIdSxFDOYIZTLZfL5PC888yjZbOZ0D+e4OFCYdbj4L8CLf/wewrdcg65pCAGGaaGbFrpugibQEAhhIJE4jSpOvU5pdIRapYrjeNTqdfrOWkhv3xwefvBhXlj9LJdecjZbtvSjhMGSpfPJZ5Mo5eO5DYQweeqJtfzyyU0EMuDSFfOZt3uED/70+WkdV9DZweif/BH1VStfkSdl8lOpUNx//0NcsmeAxf/69WmLYv/97Rfw47E64+MVVqyYx0UXLMFO6CgpMQybsfESmzZup1ytMWd2DwMDo0ijwPXXv42d2zexZeNGatUaumVHKdtt7eQKzZi2RSKRxDQNDNNAA2ToE4YSJSX1RoXA86lWahQefoGV/+9B4NATdFSTRJzQFeOu4SKeMpi3YB7drc1RwSQlEVKxbt16Zs3qIZ1Jg3bwiNSEQ98LFLZhRjVmgAfuvZe+vl4KhSYeevghfu3t70QIQWlwB6P7Bjj3gisolUrkcrkTMv6Tzath7jhhhCGzXrsqWigdZhcFFFM2n//NaxCGga7r6JpAaAKhaSgJ1VqdUqUaaRS8gISdIJWwGBwa4+zNu7l55xCF8Niy/MKmphMonlVT80nyrnto/dPPYowXj+kV9lkmP7n8fJ6d1UbDcbGtJJpmInSBkiG+76LCAClDgiBACIFmCELfx3cbvG3zHt699ehz1+C3v356MnjUoQL6MJkGqBT4wqZIC7VqFc9zCWVAEIZIKZEyWvAoJCgNTegoESU26JqOZuiYhommW1QrAe+86qxpzRuxB+U0UV+1kuGvfJn2PzhyNs+8f72dH/X1kExncBo1ZBBi2UmEppHJJNA0i2w+izBMMtkcqVyGQksvmm6gaQLfadBwawQy4LIrL+P51c/ScBze+KZreObp57jvFw/TcGVUjGzJbHJZjUuvWM68+d3cfscTPPDwRurnzaGUtsnW3KOmFepDQ7R/5P8w/Pd/ReMNr6SdeHSD7Njez+L1G1n6d1+dtih2PGXxHztGyeds3vHWFfT2dBIowdBwmb0D42zevA3XcVi8eA5vevM1+K7D8y9s573vfyNSeiRTGS6+4ipSmTSmYaObBlIGKM1EKYMwDEBJfLeB43kEvkQqiZIhYWjhhwFhGHD5/6yeupaHRNPY95UvnVB3thACJSOPyeQZEQIajTpDQ0PkcjkGBiONSiqZYrKc20SllOgci+h1pFKcd/753HPXnYRByKVXXHGQ0XkqCvPFnER0ncq7/z+ajlD3QwBNdZezBvexrbsVN/CRMvJqen6AmOgX1lTIk0gmo8aYnsfw8BjjpRIPN6V5LNfH91/cSS6Q005LnhTPlj/wWzRWXvPKOiOHIcnVT5O8+0Fy/zH9qrCTs813etv43qx2SsUS89uaKeQzDOwZolqrY5oa2VyW5pYWUil7qkBZGIZIpQgCH69WZeXuNTNafxIp0jRGRsdQUtHW2Yama/t9pUqAMNGkhmaZmJqGkAFChoRBOPEVJpETpQ6EACF0dE1D0zQ0w8A0LDSho+knuRdPzIlBthw9myc1VmHkh3fR39eN7/oEgY9pJxDoSBniegGaEBiWga6b2HaCZCpNU3MLnd09tLY1kc8nSaUNDAMuvuIS7v35Xcxf0MelV1zMuRecy7oXt3DfvU/x1FMbaO9o4YILljB/QTe//q5refjB1Ty3dhffPaePDz22+ajF3ISKPsstn/8yu6973XFOKtFtUXcctm3ZwvW3ff+YRLF/05HnwvN6WbqkF4TGs2v72bx1gLGxMqYuWb5sDhddcg69s2bh+w1+8N1H6J03n66ebhCCdF6nUimzd3AvpeI4IyNFQt+nWmuglKBeq2Ink5TGixNGk8K2LYSm8AOJCkMWDhdJF48sBhNSIpubjuP8HB5d15FegK7rTOXyKIVhGmQyUWVi3w/QhDYhdo3OaignUpEP2CaA7du3YugGe/cOsHfvXnrnzmPSVf7yWrUxZxrBnOmFFBZtG2RXXztJM0EYSlqbCySSNkKPqohKDHzPY2R4H6VineJ4lUUL+8hkEmzftocvjlf5y50j005Fntwn/81vk//mt4+7M/IrqW8yrml8ZXYHj7U2YaBIpG227NjBvLm9NLU2U2hrIgxCHNdl38g4SoYkExa6oWMnEhi6DjrMHxmjxT1y/5nTrT8RQENPsXZ4F61tLWTa5yJMYzIKjJLRvG8LgalyIBVSRSUvAqlASWwRkrIik0JJxcjAIEMDg6TzOTpnd2AmbKQCd8/0RcmxgXIama5I7arFZ1G89kIsU8eyEtiJLAgNTdcj11vgIiWEoaRaa1AuVRndN8qeHevZst4nVBK3UcO2dTzXo1T2+OlPH+Itb7mCdDbLeRcu4ezFc1i7ZisPPLiaO25/iDnzZrN8WS9XX30RZ82fzZ33vUBw8Xzes3YnTfUjpxJOdkA+7oJDEwv5F55bw1XomNOcXMYSFrf2NZF6y/nM72lj2/Z9rFnXT63m0d6a4Kor5rNwwWxm9fYgdB3PrfDgA0+xeds+Fp6d44ff+zGNhk8QhDQaDUzTJJ/PkM6kyOVSdHS0oGlg2yZoJkoJVOgS+M5EKEVQq9WoV6r07Bub1phPZM0DBeiagZQe5VKZno6WqccMw6C1tQXT1BkcHGDh4oUHPTca/0QdFbFfm1IpVzAsk1k9sygVSwjFVEm+2INy5jNdHcrF/ft4LJVCt5KAoFavUxmuE4YBDcfFn/DsmrbJwqXzyTXn6d/aTz6XwtQFm+d38znL5Pe2D9F5jOEeOD4B7fHWN1HAmKbxxtld2NkkfR15WlpyZLMZXnhxG3v3jtDZ0UKuUKClrUAqaWMZBq7XoFGv47oOrutTb9SpVctcu2nXtN739OpPBKGUaLpJz4KFpG0LY9KNL0FqkUEiNKbE8goIJZHXRCkMTce0ouXr3h072be7n3QmTVgrM7B9G2efuwwlBG05c9qjig2U04ic5uRQTggEAYlEBjuZRjdMDMOM/m9aCE1DMxNouoFSGrpuoQkDJSVhGBCELvXqGEHgEfo+e/bs5cF7H+X2/3mIN735SnL5DIalccGKxSw+ey4b1m3m8SfWceedTzGrr4fLLlrMqpUXcve9z3H/xQv47b1jvHXTwFHHrR/LquUAzYlA0L9zF4V8jvbHj140DuC/57bz3XntXHnFAuoNl/vuX8PeoVHmzunktVf2MHt2F4VCAXuiuqrXqPHQQ0+zfuMQb3n761mwsI9kwsK0ovNomAkMw8QwDHRDB6VQQkPJEJRPKBVSClTQwHcrhEGA47g4jkNpdARz3yiw4ajjPpE1D4QCzdDJNRXo7e1BQGSQFovkslnCMCQMQ1pbW9C0g/1gh0wbVlFq78DevRSamrFsO4orH3CtYs5snBUXEDY1HTXVNt3wSDy7jR293Wi6jmmamGaSRFqnpSuFadmEXoNyucyuHf3kC3m6Z3Uyum8flmFQazj8IpngFwt6eN9Qkf/feGWqCcN02N8HbJql4MOQ5s9+4Zjrm0yaTp9vb6KlI09zSw7TNCiVawwOj6PrJueft5RKqUxxvMjY8DBSBlimjq7ppNJpUimLdEIjZSe4Yt8ob9k9vcXK6ax/ooAgCHF8j2KpSkfKJpu0MYgE/uoAr6o64DmTSASuEoQTNcWrpTL9m7cgDAPTTvCmd78TTURzxvTNk9hAOa0cLZtHAfWEyV3lKs2rN5BKpaJ4nq5h2za6piHDACV9dDOJpgsEimSmCctKYZkWpmVEdVNsg0whRzJls2TZcrq6u/jpD+7g+9+7jze/5XKaW5pQKkA3YMnyhcybM4916zZy3yPP8t8/HuCcJfN47RWLeeaFrTydtHnrNI6v6S+/hLIT1N8wfbdspJdwMP7nDlZ+7wfTds2uaU5zxaVnMV4OeGr1Zlpb07ztTRfR2dVMJpfDMHVCqWjUa4yPjfHwg89SqoS86z1vZtHiRQReA6fRwKlXCbyAUAoC38P3nYnMBUEimSQMA2TgI6VE6CKKZ4U+DcfB932q1RphIGnaO4oUoB3mO/zEZe8cePJASYnvh4BAU+C7LqMjIyRsm2KxiO/79PTMOvhpE3qTA/+G6PP32KOPseb55+no6WbxkqUsWbp08q1iXg3oOtW3vZn8NDr2njcwzOCCDkzTjDxuMqBWdaiU6zj1Bq5TRzN00tk0vuuTSqTY0wjQktDaZBH6DkNln39IJdhsGHxqtEiznL6Reyye2fw/fQ1jmuUIDmRME3ypq5WnmrMox2d8+yAJ2yCRskEZnH/BEoaH9qCUoKurg0QqgWGZCE0n8AOcRoNGvYpXd3HrNd700AtHfc+TMhccD0oRSjWhHXm5ARgqxcjYOM1NBQxNY9JcmTQ0dbG/4UkQKBaeex5ds7p45rEn0dDQiOaZMJj+NY8NlNPJZG+em24+bGw25fi0rd7AurntJBIGmXQaoUEukwUZYlkmiijdK5lI4DgO2XyNIFSYukUY+iB8QKJpkE6naGptYVbfXN72/63ih9+7ne98+x7OO3c+48VR/CCkvb2VWX2zWbp8Gd1d7Tz9/GZWr95Md1eB88+dxzMqZGzjXgqOd0Q9ij5epO0jH2ff3/9VZKSoiSM81IEKNSnnoPyt/+SKf/jqtIptKGA0YZK++mzWrB+kVHG54vIFzJ3djqFruJ7PC8+vZ2RfmUD6pBMWmzYPkyq08q7ffCPdHTl2bnqO3bt2U6vUqTeiVGHf8/Fcl1CCaRjU6g3sZALPDwj9qJ6BbpnIEDzPwXWjim0yVJy3u8h7nju8W3fysF5pafuXva6KwnxhEKImUyqFYM/evfT19ZHNZkinMuzdO0BhQvsSGSNqSkvzUi674grmL1hA96xZyMk40CH3jDlTaay8ZloGyjUb9rC9o4mNC/tIplLotkWhtRVNNxEqRDcMhGbiOXWq1RIyhK7uVvbuGcRtOKgwJJc2CaTJL6oOv2hv4qHhcQpSHVNPn+Q99x/RQEnddQ+Fvzu2hn+TYZ23zO2hFkpksU46ZdHTXcA0dYZHKnR1NVEcH8dzAxoNj1K5gYZC18C0TJLpFJZtIkMPXQ95+5adtEynsq5SJ3wuOGYEIAQmku0b17O48/KX7RIEAes2buKyiy7E1DQmS3dOzgiTJosGNLc08eQjj/LEgw+y+NxzQUQBYU2AnZj+8iY2UE4z9ZXXIAt5tGLpZY9NutM+tHWU//mdXyPX1EQ2kyWZTJJI2Gi6hmmn0HUdhULXEyghUAh8L4j0CCok8B00IfHdWtQcLvRRQpLJNvGO3/g1vvvtH/HU6g287spFlKoN1jz/Ik89+SILFy5m6dkLuOTC8+np6ODe+5/mmTU7Oeec+XxnqMKNq7ccUTQ7Wdeg+fNforbyGoR++I+bUpH3ZHhwH+d+89uTes0jMvklee81S3hm7S5MQ+Oa1yygvb3A8EiR9Ru3s2vnKLoesmBhDwvnzuaJ1VvIt7Tx67/xBjo689TKY8jApbWlQHd3Nwodx3URCISuRzetrhOEIZoWFToLAx/f84Co9kG5XJ7omKuoFiv8zn0bp47/kJyE7J2p95MSz3NRUqKIKsQWCgVM06RcriCEhmFE4reXp4G/NNAjsCybnlm9TIjzmSgHN7Fqiv0orwamPLmDR+8V884nN/GZWR2EoUAIl7GxGo16A98LkCpESYXruui6hmXZZLM2ixfPY+fuIYb2jGKYBo1KA9PQUabJp3NZ/qFYnlYn9Uly3/w27ooLDn3/hCHNn/viMR3/5Kr/M01ZxhyXlG2Rti1MU1Au1VBozOrppK2lgG0nsFoTmJaNaVpR4UMUgR9MLAgU5dIYi9bv4E3Pb5vW+5c/8Funv6vzxALFME0WL1+GZUWBmAOXLWHg47nelHB26qkc+Hc0O+za0U8qk6FQaGJo7yBeGCL0yITRtdhAOWNIrH4G/RDGySQCyJTqzBuqoBYswLLsqECYaaHpBpadmfoiFVrUpwci3YTvhwgl0ZWNlB6aZRMEPpoIkcqgUvFxHJcVly5ldLzMcxuHeMuqC7hwxTJWP7OWZ59dx8b1W5k3bz7nLl/Kqusu4sFfPsdza/Zw1iXL+Fsv5Hc37CLnHb5CYlRldmjKLRt9Jx7ShUIQhHj33kdiZHRa527UNrjjioX82PHpak+zbEk3QRjwy8fXs2nzDixDY9nSXi64YBkoyZ2/eIqRfQHv+o2ryReyeL7ASrbSNbst0vEYdpSeLQQILfoCnxSEqYk8f6lAhcjAIfR9HKeB7zVwGw7VShl7cPzoIuKTkL0DkYMqCHyGBgdZu/ZFrrj4AnzfJ5lK4XkeqVSK5uZmRsdGealtsl9PcujJQ4Yh23dsp2/OHPSJUvixBuVVwgGe3CMhgKaGx9u37+Op112KZSVRmoGSIaZlYFomUgZRuMfzKBfHqVUb+FKwZPkiEnY/e3YPkcsmaDgB1YbDQ/k0HxEa/7dUpvsIGY0vpflzXzykFiXx5OpjztgZ0jU+l8twXzpBWjdR0kdi4oYGtm6zeMFsNA32jYxTbzSQUmHoE3WQtKiirtAgYdsIAb7X4O2Pr5n2+zdWXnNM4z0piP13vmVZUZowk4bHRChHM0hns7hBgOd5ZDNpxMRiZfLKhQgCoNDeRrlS4Zp3/BqJZBLNNPCZdNTEBsoZw3SzONwdQ/S39ROGIZ7vTohgo74pEoXTcDF0k3KljqbbBKFOrRbVTXHdOoYusW2dwPcwLQ3T1LAMnWTSQjN85vblGdhb4ue/WM1rrljOtVdfzcKzFvDIL59mw6ZNjBYrXHLhYi6+cB6PP7mJbf1DpBf38m+Ox82b9hx1/GIyHnxALGtyVS4mNvbv2MlZh4h9HorbZhV48NKz2L13nAVndbJoYRdbtw+xYeNeFJJzlvVyzrKz6O7pYGBwjLvveRbXhUWLu9jTv42d2zZF3aFDSRAqgkCiaQaGaYOmRXF2BLqhY5oWuXyGRNLGMAxSyQRCqKhYmy6QoU+jUaNerdEzNr2KkSerY6luGHT39LBs+TmAwLIt8rkcUilkqDAMA8uyJq7BAbqTqatwINHjY6MjFEvFaCI+YCaLPSivHuqrVlL+wG9NK9Tz2kdf4DmnxtM97fihhjHhGTWM6AvbNEDXFJahY1kmUkoG+ncjQ49EIkEtaGBZAbZvUnN9fpFMcJdlcYHj8HrP53cajSN+sgRgDA6Ru/VfKX/4hqntybvuofVPPjOt41XAN9Mp7rEtnjR1bNskbRr4YUAqaWEYBul0ioULe6lVytRqLrl8np5Z3eSamkgkEhiGjhAaQeijVFSoUYYh3dt20eIcOa14cgxhV+fp157AlFckkJJQRfVMJAp9f4UkXN/DNA127dlLS1OBdCY9pT+RSkUh4An29O8iCCVP3v8wqeY8K66+Ct2IClsey7ImNlBOM9NVbj+2uZ/1xTICDd3QSaUSaELDNHWaWzMoFelLEmZIJp3AMAXpuR2kM0nSmRSGaZDOZDAtE8vSJ8S2Ucl2UHi+x+jwLh5/9AXuufd5hpaOcNEFl7Pyda3M6l7P409u4vY7H+KcpXM475w5PPPCDoaGG3SkE9Ma/94wpOmQuSLR3+VKhfZfPkbvrf8yrdfb2JFlx64R5s1tI5UyeOSxTQwOl+nqTHPuOX3MP6ubTCbHmrWbeOLJbcyb18tlrzmfzq5ONKERyhDfC/EDie/5EwLXKpVKlUqpiueFuK7H6Mgo1ZpLEEg8LyDwA2QoozRvQ0RGjW4ghEQzYMFgkddPY/wnS7Gv6TqGpU140gSNRp19I/vI5/PU6zU8z6NSqaKJ/e0IJucVMVGgTbBfLhR4Hnf87Hbmz5/P3Hnz0PRjUQvEnElMV4sC8Dub96K/43WgR7oTIXSUFCihIQDXreHWawgUtXodIQJMU6BrYFmKWkXDdWrYpk7DU4Sa4KmEzROWjRCC36nXjzqGpr//J4KFC6ivWnnMKcX/mM/yz4UcYSjRlMQPJUJE2ThywrDPZmyK40VM06SltQkFFMfG2Tc4TCglruNFBRonag0pJQkDxQc2Ty+tGE68Du34EaAiIWyt4eCGOXRdRMkbEynFpmliGwaGELiuOylbgQNKD0wyf9nZmMkk6Wwm2lfXiIr/H5vAPjZQTjPT6csDcMPafh58Uxujly0nk0nRlM+QSmUwE0ls28ZKJDEsG03TMK0U6DqakQRCkCFhEIAM8b0avlvFaTgoFU4YOVFabVtHJ9dca9Hd3c69v3iC/h23c+Xlr2HBonPRhMHq59bxyJOb6etrY25vK5u3DvJ8Os0+U6fFD48YQ1765a+wB4F45/78HzEh5QylQP73Tzjnli8fdYKRwKhl8KgU9HYW0ITgqWd3EQQBC+YVWLa0l96eDmQgeeDBpxneV+M1V57DBSvOJzlR6bLWqBEGAaEkKmWfTWLZBXqMXnTDmtDxKJQE321ExonboFapUKtWKZcquI5LtVaj0XCoVOo4NQdfSgpheIp67xzqxcE0LXQZRk0LAV3To6JtSqHpGp7nRcJqJdHQ988Whwq9KcUTjz/O4OAglm3R1t5OG7FA9tXKdLUoAshVGnR883Z+OK8TgY5tG5i2hWGb2FaCaiUyhsPQx/d8lFK4ToAg8uRpAtrbCwwNF3H9YKL0jkDTBL+wrWkZKEDUt+eqK6adUiyBfabBtzraEJ6DUlGZdt2Iqp4mEza5TIJs2iCdtrDsSGtiJW0SydREuXZjYr418dx6FBJGoaRP2Giw8okXpzX24kd///RrTyYRE7Vkw5D+zVvpa2kiYVmRMeL5bN+1i57uLjRdR040Ho3CvGJiHuegH6TkO7f+M3MWzCeZSnPFqusQCfslepWjExsop5vJ+O+HP44S4rBGSrbq8JbvPcLqjmYqrz2XTCZFwk5gJVNohoHQFJomkIGkVB1EKROn4VCrlXBqFSqlMTy3iue7mIaBpoFhaDQ1FUilM9hJk1Qm6iy7aHEPpr6c++9bz+13PcC5SxfS1T6L88+uY2g76N85xsiwRW9PMzv6R/jHnnb+fMfAEYVu1ugYcz/xKQYSNt4bV01tF2gM7d3Lin/7xlEnmMmGXX/f1US2kKRSd9iyo0QyqViyqMCiBV20NuUYHBpj9TP9ZAsZrl15Pr2ze1GqQnFklEajTqVYplyuUK85+KFC0/SJyVEnk8mSyuRIprM0t7Zh2gkMM0ozTKVsWlrzkUjW9/AcB8dxqVbrjO4bpu2Jzbz90S2HHf/Jyt6ZREz8pxRTvXbshD0lkpVSoqSiXq9P6GsOfu5LT74QgtbWVnKZDE2FJsqV6YWvYs5QpqlFmeTXtw7Qm8uxY+kCRhbNw0gksZNJNA3q1TFCL+pRVSmXcJ061UoVPxDUag2Ko2P4gUd3dztWscbwvnHkxB3yhG6yV9PolEcujS8AfWyM3otfi+4cvXz65Cr/c7kcNcdBStB0AwhJWAYdLVkMQ+B7DkP7fMRIBcMwsW0LwzSwbDsqYKhrIKIwcOA5EwkKAikDlo2WyUwjvBM2NVG64UNH3e+UoaKSCZpusHj5MkzTnFor1ht1KtWo11K90SBrmWjG/momB9ZFmfy9o6+Xd/zWe+no640aKU54YeIQzxlIfdVKhv/hr2n57BcOm7s/GRxZ9M1f8O1mi3Quj51IYlkWQeDTaDjohkG94VAdL2IYFkqCnTCxbBPT1klYFqm0RXNrC5l8K7qhkUpn0Y0EEIKSKKVIJCy6Z83nwhUOj/xyCw8+spZF8/fS2Zph0bx2bHOE/sEGm7aN0pRP8GA+zad7O/jI4Ajtfnjo8SuFEpG4beD1K8HQEUrgeh6pZ5/HmoYmo6hrfKmziTXdOXB8SlWf5oLG7O40vd1N6MLguee3s32PSyplMm9uN7lcPnJFajapXIpMoYWmVh/XaVCvVaiUK5SKFarlKk69Rrk4TighlBpSKoSmYdl2lJGQz1Io5KMS1oQEgUfgh9TqDUqjZd5994tT1+qQaBrDf/slGidx1SRlQLk4xt7du2lddFbkBfJ8PN+jqVDAskyE0KKfA8YauV7FlDgOFcWVNU0jm81SrVQ4e+myo0hpY8506qtWUvzI7x+xP88kArjs2Y1c9uxGiukUP7r0HF6c24MMAjyvOpF2H+I26ugiyuiTUuE5LrahoZRJsVTFNBL0dPcyVixSqVVQQvCZTJp/Lk/PINamYZwAlDXBnzY3caedIPQCNAGWptHSlKKtKUnoNwh8RTJh0dKSIZ1Ok0pnMS0Ly7QwTAvEhEeS6L4SmsBxqgS+y8IN/fzWM4dfoBxI9W1vniGhnYORSuH7Ej2UJNDRRCT+NTUDx3XxPB+n0cBPZyY0hPvNDW3iJwQ0IeiZNxeJIpQSFSq0Ca/Lob8hDk1soMwQGqtWMpLN0nn94a3qyLXq0Lh/Hc+1JDFNgWlFTQPT6TSdXV20tTXR3pInlcmSyzeRSqfQLYtMrgkhJIaeRGmSUPqEvsR1XHwnpF6t0ajVqVXqFMdr1Kou+0ZcOtsNcjmNHXtK7B0cpavNpq3ZQuiKzTsq7Nzj0dyU4b4ASsA/7Dq8gl4oSOwbwbv/AcLLr0DoGvv2jTA/nN5H9m9b8zzanMIKoOb4tOcs+roSNDelqVQ8NmwZJpFIs2xJCwkbnHqd9Wu3YJoWmUwGO5UgncuRyaTJZpvIFDrpmm0gRJRCF3guvufRqNdxGg6NeoOxsXEqlSrFYomRffuo1x1Ax3FcdDPKmlII5u0tkq+5Rxy/kBLVcuKzdw4kCEKUkqRS6Ymidw32Duylp7ubltZWDEMnl8u8xMIQU8bJwSEf6OjspLOzi30jIxMdSw+lI4p5NVH6/Q+R/d5/TyvteJJcrc4H7n2cb61cQf+580kkmzFNCxlIapUK1UqF0dFRxkpjlIoOrqdw/Sgl1Q8chG6SSqXJZrJUq2XuFfA3YcjHa0cP9Ux3jB8uFPilaaGCAMtUNOXSpBI2KvCplqvohk7CNjENCL2AaliNikaaFqZpYZgmhmlHomAh0A0DO2GTSiR43Yvbee3jR68cPcmMyNx5CQpFIMNILCsEgQBzYpGCANdxKeTyGJY+NQuIqecePHVMzhRaqNizbQc112HR0qVIERsoZyz66PTSa3ssUIt7aGtrob29hVw+TzKZIpUpYNgJTN1EsxIgiWKsSuF7AY1amVJxK5XxMpVKFdM28DwPXdNA+CQSCSzLoqNLYFlpzlaL8LzZVEqjjOyrsG7DCDsHaiQSAkvzsfQGQkr2DtVJJRJkvel99Co/uYOvP/YQyszy1l97G+bo9EpBjycTBBIalQbN+QRNLUnsZIKdAz4ImwXzOpkzt4nm5jzpTBLLjETAru9TKVep1RyGd+9ge6WG60msRJJsLktTWxNNLa2kM2mS2RSpXGFCOKqDFPi+Q71epVGrUS6XqFUbjI4WGRwaoTheozheJxw8fKr4gZys7J2IqOKtbSdonxDh6oZOT88spJJs2bKF9vY2KtXqAXVQJhyzExqUAycZgcC0TIZHhlmwcBHZbJaXVjyIeRVyjKEe2O+6f+sv1/DHhSS+lFGXdgUq9KP6PK6H8qLMENPQQCm8QBEqie84OJ6PZRjkcylyuSz/YY/z3vouOtSxFXJ7KRIY1DQeNXUMXdFcSJNJWTiOQ3G8hKZp6BpoWoDjBiQSJqmURiGbIpfPkUpnsOwoGSBqDujhNOoUixWWbxvk+nU7aT2M5/ilzKjMnUNgCI3+zVuZlT8HiEotuK43VXLB9T1sK4kmxNQ1j0I34mX6kr39O3nmkUfZvWsXb37Pu6PK2sQi2TOW6WZ2JGZ1MmtWN+0draTSKWzbxk5kEJpO6Ac4tTr1xj7q1TJjo+OMjVao12qgJJZtk8+laWpKkStkyOSzpDKt6KaOkjUCr4H0o4Z5Tr1B6PkIoZPN2Vx4XjejYwEbN1cZHBgkmbDIZBz8kqTmOIzayWmNf8Vdd9Oja9yeSnHF939MvlI+4v4SGDZ0nrB0CASZlEkuk8BthAwGAfPntjF7dgvZXArbskBKpB+gdIFmCtLZNE2treiGhRA6vutTLo0xPlZmfGScHes3sLbeQDctTDtBtlCgublAc3MrqUwaXReYVhLDsEmls9TrdXK5Aq0tzYyNlxgbLZPxFWwdOeqxn+x+G7oexcYn8VyPXTv7aSrkEULQ0dGJ67kTk4SaMknEAT/RIxGaEAwODoLQaO/oJNd0cj1AMTODYwn1TBLVSXG5Yf0uds3vpZKy2d7ZRN11KI+PUy67CDfEtDX8RgACdF1gMVEAMVQ4roOzr0EyaZPJZfj7vi7+sn/vIVLgj41bmnK0NufIZSw812e8VMcPAoyJekeWZUS1jhS4jocKAnzXp9Fw0bRxQqXQdQPDMEmm02SzGa6quPzG89unPYaozqGYQZk7+xFEITghFJ1d7RiG2L9c0TTcIETXNfxQUnddsgXtoLnipT+CqPT90MAAs2f3TYTFo7lcTjPTCmIDZUYx3Yyea3/4GGsSSYr5PK5fQQZF6s4eXKdOrVyjXKrhuhJd18nm8hQKBWbPnkVrRyvpbAo7mUBooMIQp1HBdSv4VY/y2CCuU44+rFJGJXiUQkmJadioMKA5b3Lu0ixtLc1s2bqTWn0ftlnFd3x+qQSDhk57cOSMHoCOUPK7lerLtr90IpoUtn2xpUDNC+loz5E0NWSo6OzKcta8ZrLZNKYpMCasegDP9/FDHwijpokyKsacSGZIpbNk8820dPSgawauU6c4NsLYvn0MDQ6xb3APu7fuoFpzyDcXSExU7k3YFul0Ctu2JsamQEmW7djHtY9vPeLxnop+GwrQdR3Tsia8G5FgttDUjB+ECD2qmeMFwUT+1H7zRCg1IaxVL3vNpqZmurt7SKXTU1tj78mrn+MJ9QCc9+IOzntxBxB1GP/mwk42taTxXJdG3cfzJVJGIV9NKAwEXigRaBi6hkTRcFxcz+VnpoU1r4c/2DlIITiW4MB+vtXVyubeTrRKlZHRGoFSGEKhayLynugaYSgRIkRoGoYBiZRGOm2QK5hkc3l03ULXTQQGnicp7RvhDT//JTB9w0kW8ox+7s9mTubOAUSzRVTTJpPPTc0NAoWpa2TTUR84U9ejGjATiyDBhPdMMJUkEeUDQTafp6evl+HBQWqVCh0T7xUXajtTmWZGT6bc4LJ//hnf3z3Ei2d14NUbUUGubAbb0umb3UNza45CPk+uqR07kUY3Ekjp4zllyiMD1KrjuE6DIAgQWjBhgPjYpoFh55GSSBBVq+O6Hq4beWB8V0NTeTqaLdJ2Czt2wbZdOnKsTENTfL5Q4Csjo0ctXb2/k8PBvPTvIUPniy1N3J00SVg6CV2nrSCY05ukpSVNwtajNDcpowydSoCmCzLZLImkFaU/GgZBEBAEIb5TZXB8BEPX0A2bdDZPttBMS3sH7V3dLFwW4jp1SuNFRveNMDpWZGDvKDv3DtFwfDw/BDSErpOwDS7cO867Htx4xMuqJm7IU7NyEgRhSBAEjI+VGR7aR1OhCcu0mDtnDvVanfa2DuoTxbBSyeREiO/QVR6HBgc5/7zzeP6FFzjnvHOm3iOWyf4KcOB8BEdcNB2OJsfjD17YSdvVS3hyTjuNmk/DCSJDxQsJfImvNBJSIwwkQRgiEdTqdZSS1L06/ykFd8/t5I5tA+TCI2f2HIgCRm2Lf2tuoj5exXE9hIiWPMGEPS7DEKU0TE0gNJAahCF4DZ+l4w5tu0rUs2Ps7m3nnLqkJQhpLde49IUtZKYZ1plk39//Fc5llxzbCTxVCBUtWkVUfE6qSP+qAMdx8P2AZDJJb3cnmzZvprWldUpnIqKno0/oSybnftPQSScTZNJRk9XJfbVjWNzEBsoMo75qJfv+4a9pnkZGz6oH1rBzSRfZ9m5aWnK0tBTIZDNk823ohjlRhE3Hq7vUysPUnRIQTsRY0+QKBTQjAcoD5eL7DqEfEIYKGWoYWhJLT2EaWcZGh6hWxqlUx3CcQXQtRRCEtBQMknYb/XtNBkZq3OMHfKSlwKeKFTqPIn492lfcl1ub+Y+UTaAkhXSStoLJ7FkaTTmdhK3jNAJct4xhRO3fk6kEiUSKZCJBKpkikTSxE2aky0imUUJELQKEFlXkdVxqtRp7+7cgZeRhyOcLJNIZck3N5Jpb6HMbnL04qn0yNDTC8PA4IyMVGk6A5zR4+xPbjnosYUc7Y5/6xElfOU2OoVapsHXzFlSjyI9++BN6+3pZs/ZFLrnkIoaGhghCxYoVF9K/YwdnzZvL6669FnGICr6u6/DM6qdBSfr6ZtO/o5+l5xQ4wPkb8ypnaj763BePuYQ87P9M/tYD6+iY3853z+5CVwGGUEihoXQNIVJo0sYTIYg6SoWkk0mEEGhINEvD9UP+vLXA3wyNvWzxc6jMsskvz88356j4wUR4W8M0bAxdoIuoyZ8mQEy0sdB1gaELrq40+N1No7S4+1t4hE9tPai20bGYalPe04tXHMOzTi1ioqy3VGHUcDRU6EJFHdF9H9dx8RyH8fEiuXwWQ480RAf2zDgwTBx4Pnt39FMeKyJDSSadmbpmsQblDKe+aiVhNkvXUTJ68lWXFc9sYe9bLsa2DRQG9UZArbYHXTPwPZcwcMk3zSKdaSbX3IGuSRAeYejgOCV8pwQyROiKMJRIqZAyIJQhjlOnUWvg1F1UKLGNDEa2QBmXhlPFqY9Ta9Txg5BcRqLpaXTd4GHT5PXpDDeVS/xe8fhrZ+zTQDdNmtMJejosmnMSywhxPAiUQ9K2JkIvSUzTQNc0DB10Q+D5dQKlEZKOCjHp2kStEw0pJZoelbXPt7bSZkat4Bu1CpVykV07NhOEgkQqTyabJ/TrCKGRy2UR6Fi6Tu7F3XRuGZxWSet9X/oc7ilaOXmeh2kYLFq8mE0vPEl7RztC01l53bVs3bKVsbFx3vmOd/DY448xMDDI3j17uXbldYf0iQhNo1KrYGgG1UqFnt5eogWomm7BzphXAfVVK6mvvIbE6mdI3nM/uW9++5gNVAFct2WYuuvxrz0ZPE8SBIIgkPiyTMOFhicJpUQX2kSfG41kIoltJUjYPmvzNn/ZlON/b9tDm7f/vitq0Vdf0wG9fIZNk690tfNkc5a2hE2jXsdzXTQBgR/gK4lpaNiWjpoIJGsKLhur80dbXy5kf6n5Pt3jP9m1j04UUTgfCEPWPfscLZddhMAEFKYRza1jo2PUGi6WKWi4LrmJUvf7X0GgExmHpmnQ09PDhnUbWHr2YoYGBumbO2fCgxKHeM54jGlm9Fx9z3rKT2znnmsW8/TiWbS2NpPO2HR399HZ04dp2qBMZKDhOg00Q2IYEs3QyeTyKBmgVICUYdSsz2ugBaCFGr5vYCVMgjCk4VZp+OPUqwHVakijAXUnBJIIBQIPIWu05jWUSlBtBDwZeK/IQCklLPKpBK3NJppwcDwNqdu4NQ+hPHJpC8vW8IM6lmWSTFkEMkGAT1KkSBg2SoN0NoehGei6hqZFuoyo/qHA90Nq1SKB7yJQNLU009LeTrVcZd/QCFs3b2B8rEip4qCU4PzddT7w2DYKtWm0UZ/AGBnlyAnIJw6FIghDBArbsmlpaSGUiqSdpKurk3Q6Ra1WI5FIkk6nmd0356Dp4sAIj2VavOtdv47jNNi1azdz58ydepc41fhXDF3HueQinEsuQuZyxySePZBf213k3kuW4EmJ64c4rqTRCPACjbqjaDgOQeCAkBOtOCTVWgVbVwgVcKet87P57axKJ2nxFfusDD+f6IZ8BYrZiQx7QsnaTBYpQPgOji9xfUXgg/J8VBgSIDGEQIaKhC0wDR1LE9ywK8ooPFEewrCz45R4T18xE/aJYZgsXnEByYSNPnGPyzAkl01jGgZWIoGmC5ITOrxJFGIqQ0ebCA2Zpolfb7BpzRqWXrTiAFFtHOI54zmWbI9sxeEdP32O+/JpUq+5OPIWCCiVhjCMBLZtYdlZElYGqRyU9PAcFykbeF6DMHRRQkOGEiFMAikxzQLppj4SvoadctCNIpZVxk6W0O0ianyEUHnsG6kyWnSo1UNcL4ofK6UwdYt1+TyDw6OHFc0eTpkflaM2eTGXQimHvcNlNBGg6zpJW6OpkKA5nyCVitIA09lslM1kmWSzSbK5DKYddSaWKOoNDyXrUbM7EY1PE5E71zB0EkkbLZPB9zxqlRKu00CGilw+Ra5wFtW6w+5dQ6Tue4H3Pdx/Uq/lK0UqiaZFTf0ajsu+kREWzF/Aho0byGQyEz2HKmQzGQzD4NzzzonOyUteZ9IEyWSy1Go1TNM4KAx0LEK3mFcXxyueFYCu4DXrdvDTrjzluke55uO6IaEUIAyUECQTFpZloGmKIPCiBp2pFDIMqHsOdSm52/NIJUwQRN2RPcXTlmKtrfBdiXTH0DWdhusjQ4VlGqTTGdx6DRWCaZiYlsCyIZnQSSUszik5tE2zVMKRmLyXih/5fUq//6EZ7Tl5KVIGhKHED0JCDLZv2cLzL6ylpb2VsbExZvX2Ui6XyRWaKBTyZDMZmpuaJgT2IjIoBKhQsqd/Z1TqwdJJJOyXlliaFrGBMkOZbkYP7NekXPLTZ7nnynNIpFIU8rmJ7rs6KgwIvBqeW0ZKB0WAElFvFqWbWHYKw0hiaElAw3MltWqd6vg45XKdaqVO4LrR8lqmyCYS2M1ZKsYoKatGc75Osdxg73CFwdE6papLMhUJMP9l0QL+9MUNL4sbH1jy61Cx439fuohidRyCANuElkKKno4ErU0GLS150pkE6WQSO2Gj6wa6oeF5PpVSQLVax7KiIkrpTI5UJoOdtJEEBEGADCcae4Uh0pO4Xh1UldD30A2DTDaHHyhqtTqVYolSqYL0HH7j2cGp8z0dTkXmzsHvB5ZpY1o+Qgj6+3cyODiIYRgsXrQIz3d56ql+hNAwDJ2W5iaCwEcJNdUdcMo3MvFLuVLi+9+7jdm9s2lt76C1vX0iXh3zK8uB4tlp9L95KflaQGjkSGQUVjKkVnVwXY9QCfwwRNcEMog6BGuGwNAEruuQSGWwpEEi71NI6wwOlak2ilwaBDR5PiOJJC/mk1iGgWEbeG6dtK0RhtHM47seCB1MDTf0cWsS4UiqDUE+I0kUp9f/52jI5mZG/+JTM99rciAqSjMOlcI2BLqmECj27Rtl4+ZtpAaGkEqxafNWXD9g0dmL2NPfz5bNm/mzz32WQi7LZFNBBaBr9M6bwwP33kdPVydqolfbZM2U6RIbKDOVaWb0TCKATKnO4p8+xfiy2Ywv6kJq0QrYENCydRhjdJywLU/9/AUYdgLlB2Sf3Yr12Hp8z2XfonbGz+5BaRpCKCzTJJPRSCSiLr5BEKKkwncChNbAD328sI7tuxQyGoEjsLQEw7ZOuR5SrLs8tWghf5XL8sFn19B6QEnqQV3nZ+kUb204dPj748mjiQR/N6eXe02DbL4JW/NIiBrNBZ1sRsO2dZx6FU1TSCkJCWhpbSKVtkincySTCRAGmtDxXJdavcHg7n1RKwBdnyhqlyaRzoBpooRCyhAZBviOj+t7KOnQqNUJQ4luCJIJk9YNJXLV6QdqTm3mDkzGgDUETsNhV38/nZ0drFhxEZVKhaHhYcIw4E1vegPPPPMcS5cu49lnn6GlrX3i+ZOdfCZebcJNW61U0RCMF8cpFYu0tnUQh3diJsWzLZ/+LPr4+DE9d4s0GBpzSSRSmGaSdD5PMgjwAocw8CfCzT5B4GMZNugaSiqceoPzah6tgUMoEyyu1Llh5yjt/n4x67Bl8E9zunm8PUcymSb0JaEWdfkJpcDzAoRSmJaJqWuYto6mgURjSH9l3hMFyOYmdj38C7Cso+4/81CEfsjQ4ACtc+eigJaWFrpmzSaTSWBbFoMDewmknJhnGjQ1NWNoGpqYTEuezNQRWJbFnDmzGRkeol6voTFZNiIO8bwqOB4F/eLbV8Ptq6kXUmz+4NVYdoJ5/3Y39sh+LYjbkmXoNWfTee8LWNX9RsM8wM8m2Pbh6xi9fCkhNn4o0A2BYStCt47jVEDzsRIaKdJIJdB1l4bl0nA9SrUySVuj0NZBU+dsmprb2Obr/Pnyi5i1fTvpahW3YPO9/t2U6h4/mn8eV+kGnbJGKaHzXL4ZdJ/lmqJcGcerjxNUXExdRLqJbBpdhOTzzSSSFsmUja5b+J7EMXx8BWgaugBN10lmbNJZGxlIwjCqZlkcHaSxx4taAGRzU712TCuJ0CD0PQhDSqUqo8NjpJ7fQcuLx5bBEHZ2MPYnf3RKV1EKhW6Z5JoKdPf2kLahWCzjug66bpBK5XEaDpZlUSwWsUwLp+FOVpAiCELQjYOmj7b2dpYuW8a2rduYNWvWVHv1V8IXvvAFPvnJT/LRj36Ur3zlK0CUyvjxj3+c2267Ddd1WbVqFbfeeisdHR1Tz9u5cyc33HAD999/P5lMhuuvv55bbrkFw4insdNBfdVK7KdWk/+P70xr/8n6GP+qBP7gCLpuoIiadRqGEaWjygDXcRACNF3g+i5CCK6t1/no7qGDjJFDfQxbvYA/3bSTT/s9PJTPRgXglEDTdbLZFJ5TJ5WMuhB7vsR1FJ4v8UOPewLBH2oazQeIbafL5IJk9C8+fYYaJ5GD3DaNqD7NhPB4eGgvWzduZvl5S9nV348f+IyMjqDrGi2trTS1qKhdgARJiKHrU4LiPdv7Wb9mDedfeAEb16/nvAkdyikL8cQTzclnUkGf+9Z3aP78l6f9vGSxzjl/e8chH7NGK/T+5MlDPmZUHBZ+/n8Y/btzqb1+JZqm0NEIPR+3PoZTHaJaHaZSHmN0tEitFJBKmDh1Hykhm09jp0HZKVynyr7BBlII2rIa27otZs06h2te28cjX/whzu4qw6N7eHb+QtpbmjEMF8Yq6NRwGj6modPR10NxT52MraOLJAZRCCOVNMlkkyRSFgnbAiHRLR3D1NHNqFFiGCqkkoAkkD4SjXTCYv69/Rh7xikXEqy9eBY110MzTCzTpmdXiXzNx29K01ys8aZvP0q61Jj2eS/+/u/hXH5JFNY5xbFnoSDwfWQQkjBNHlnzPOvXbSCbS9FUaOGFNWuYP28eZ5+9mGqlgq7r+J4X+V6EQNM1JpJ0otcDhoeH2bp1K6GUbNm6mWXLz3tFY3zqqaf4l3/5F84555yDtv/BH/wBP/vZz/j+979PPp/npptu4p3vfCe//GVUDCsMQ9785jfT2dnJo48+ysDAAO9///sxTZPPf/7zr2hMMceJ55H54U+nteukMfGNbIbQMBBKoekC3bAxdB3btiiXS1MFBk1jQrWpaVw5XuZzew69QHjpl93kKv0juwa5P2WjDBshFEoqhkeLeK6LLiBhGTRcHy8Ioy7fCmSo+HQ2wz+Wyod87SNxOhYkJxIBU+e7Z3YP1mS/HaWj6TA6OkahpQUVetSrJaQMaWoqMLB3AH2iw6guNKLCFhGGaTB77lzWPPc885cuPa51zXFbBPFEcwrRdcrvfy+5f//WtDQpcHBLt0MVQzucQHXysexn/47dSxLYCROlfMJA0qgH+I5E+hqWkaClqYXRoTqO66AJA8tMguZjK59G6BIGGo5TQ9c8Bmo2iWwbjz6xlg0btjM8WiWVEAREwtpSrU5pbA+tOZ+s4eBaXSilsGiQL6TAr2EYikQ6qmPg+XUcR6FUgOd5WLbARkegoeFFxgmCIJRoE2r9ed96kL7bn0fI6Mz0AItvW83W6xYx3Jvj3O+/QLq036N0qLN8uPM2qTcpfuSG0ySKi0YVhgGh76EJQb1WY2x0jNaWPKlUEqRi0+Yt7Nq1m3nz5rF3z15aW1v3B3eEOOCgo09CIZ8HYPbs2XR1dr8i/Um1WuV973sfX/va1/jc5z43tb1UKvH1r3+d73znO7zuda8D4Bvf+AZnn302jz/+OJdeeim/+MUvWLduHffccw8dHR2cd955fPazn+UTn/gEf/7nf451hq5az1RSd91Dy59+Fv0Q1aAPhQS+09rC17u7yYmoE7AfBHh+gBCCUrk8JV7XDYEQUYzRFPBH+6KMxqMVdZxEA9qDkA+OjvPPzU0oKTAMI0oC0A08z8cPHbKZFHqgJoq1ScJA8oCZ49/CgN+tTl+PEqaS7L7rJ5CcXquPmYhiIqwbBuhERoaGoFDIsWjBPPbs2UtrS45aw2Ph4rPxAp9CLkv/1u0EfoBtGJGhMoEAemb1UC6X0U2LkeHhqCq5EByLf+q4DJR4ojkNHKMmBY68AjjaY/ZIhW3fuZ3BuU1oQmDZCUzDJGGlEAIcp0HDDdg1NECp5KIbimq5jgLshIYlxtHNkKGyj/BGkCJJxTOo1zzG9tUol0rk01kMO6RSGSWT6cDWygQNh4GyQUuXIKGGyBgSMyUojwcUy2XGSxWkEuSyNqmURS6bwU5aWLaODMYnQk5RDxnTskCApsG5P36BvvsP0QpdKs66awNnTfM8Hcq4O/V6k8OjaVpUIVPBFa+5miVLzqOtvYWmQhPz5p2FphtUq1WSyQTNLS20t7cz0QdsigM/WUEQYNs24+PjlEqlSLNynEbKjTfeyJvf/GZWrlx50Lzx9NNP4/s+K1fuX30uXryYvr4+HnvsMS699FIee+wxli9ffpAndtWqVdxwww28+OKLnH/++Yd8T9d1cd392qFy+ch9n2KOTuque2j78MeZbjGcZ9JJburro+4H+OVy1DFXKoIgxLJMsE1cz0MIhaHrCBlVNs6kLT40VqTNC47+Jofgfw2Oszub4g7DxvP9qKS95xOGEoSgWKljaBqO66MbOrZpYpomX5szi42j4/zfodFphXv0eoPe176B0c9++oz1oADRJGDoCBG1DTGB4niRsdFxemb1kUun6d+6lXqjwdIlZ7N+3YvkCvkJ4SsHaEwixseLXHnN1dTKFayEjTExyRjH4Js6riaRB040B3K0iQY47ERTLpd58cUXj2c4vzJMalLCjvaj73wCaA5M6m7IWLnB4PA4uwaG2LprNxu27mDTtu1s2LiJSm0c04YwdLEsSFoCU9fQQoVoVMgkQoo1k2KlgesGpFI2uYxNNp3C8RykDBjZN4BhQs/cZQyPOJh2C0FxG8qtEPgBInRJZ6J06UzWopBLIAT4XkixWKFU8QlIU/dC6nWfSsWnWpcMDBbp3z7M+ud3suSByDg50ipsuiu0l24POzvY9w9/PTMmJ6XwPI89AwO0dfaweNkSmtva0UyTs5cvZ9GSs7ngogs5e9lSXnvtNZy9fCmBlBNNvCZfY+ofpJSYpkk6k6HRaBy3/OS2227jmWee4ZZbbnnZY4ODg1iWRaFQOGh7R0dH1KhwYp8D54zJxycfOxy33HIL+Xx+6qe3t/c4jyAGgDCk+XNfhGPI3vnrZJqxWg3TkmSzJum0QToVlQRIppKUKxVCKdE0HX1CcNnSnOfNoeS9/cdevfZAbuwfoq05TSIRaauEEKCikvquF6JpUfq8F4SUa3WK5Tpj4xX+xzS5ek4vd6VT03ofbXyctg9/nNRd97yi8Z4uoqpQQBDy1C8fo1oqoyPJppI05zLkUxajQ0MUshkWzpvLyNAACdMgaRoYmkCbMD4OFMo++vDDBK6HJuDsRYswARs4FhfEMXtQJieap5566mWPnayJJl4F7efAqo6JRx+ncOvXTtp7NbIJMuk8ZARB6IMKcV0HoRQq1EmnEiQtSRgIpGnjuT5BENUTVJqGZkDClzRlkwyNCdyxURLtBRQBOlE3U1PXsdM2YyNDjPsBbe09pOw6hjSwzRSa0BGaj6YpsC0SdgJNKBASVIBUIbbpE7rjyMCNeuQkkhiGSS6XjFJsH9iIdoRv1uOt6FH+rd+k/obrTove5FAoBKZhEkrYsX0Hszo7MI2ocaBSk+WRJnxAaqJ65GRit5osoCSiSM/E+dJ1nbb2dmb19jF7zpxjFrkB7Nq1i49+9KPcfffdJBKJE3a80+GTn/wkN99889Tf5XI5NlJeAYnVz0xbsC+BQU3nuUSC11y1lKuvuQTXdRjcO0QymaZr1izuvOMhHn90LZousEydbCZNc1MOUwV84JlXtmAVQJsX8L7dI3zvrFmMlWoUXRch1ERxyaiMe2dHnjBw0DRBtdpAqigPpVp3+YZls6p29HDPpGe1+S+/RH3lNTNiPjgeTF1HScXu4X20NmdZcdGFXLTiQiSKEEUoFXsHBrFME0PXMQwNTUrMCbdy1DQg+sfUjchI8X3Gxsa44qorSdo2xzKDHJOBcrommltuuYXPfOYzp+z9ZjyTVR1XXEDmhz89rC7lcBqUo6EAtyVPduVbOFvpNBoefuAQBnUcp0jg1nAcB6feoNFwcJwGTsNDt0xcfFzXxQ8DlGai6zaoAC3wuCis0zPgMiAVj/k+6UwThWyejjlzGC+Ok8+nCas7CTwPqTxKFYesTGLqOppuoaOTSaRI2Ek03QY9RAkP04jqeiipEQoJmk4QSoJQEjZcrIFjS4OcLvU3XIdzyUUn5bWPFzXRHyOUkoHhUXq6u6fqE0RCOMX4+DjlUplKpcKCRQvZumUzSxafHRX4U2qiCFs05Zqmwe7du9m9ew92IkFPb9/Ue023I8/TTz/N8PAwF1ywvx5MGIY89NBD/OM//iN33XUXnudRLBYPWtwMDQ3R2dkJQGdnJ08+ebCwe2hoaOqxw2HbNrZtT2ucMUcnec/909pv0tX/F7ksmbyJpkI2btjM0qVn89Z3XE6+KUutUmLrlh1kMs1sXr+RSrlK6EvKpQof2Dtw3KGdl/Ke/gF2ZRM83t6CpgLqTkjD8QikTyJh8I53XcPSZfPZumU7u3ftpJAv0NzczIMPPMZTj29hb0mjUx69SaFQCmNgkMTqZ2bcvHB0ontZKolUkqGxcapCYE8IYD0EDakIBdDWztZtOyiV6+jKpT2T5cLlS6ZqoETZWopFZy9GFwJd13EaDTZt3DihWT1Jacana6KJV0GH4Qi6FHXAMvhwws5DMfkKG69fhR/WkdL8/7f33nGS1HX+//NTodP09OS8u7M5w7KZFVA4VhYxi57e4Z3p9ETAhGf4ciryvVNP73vBhF/93on3M3BBwYQgYcnLspENwOa8k1PnSp/P74/q7pnZ2IOwO7PUk0czs1XV1VU1XZ/Pu97h9cYwwphmGMcxMQ2NnJLY+TzhUBQ776HhYIb9PpbRyhiuF8ayFamUQ0+fy8qeHv76QAeNjgR6AejUNb5Wa/O059Hd30c8HsIetIjpGnpM0tyQIBwBgQcKQiGNkBlD02KYoSoi4RBmGMwQKOGi64JEogalgaYXoqGFRlfe5C5YP3YF2NNeo3MswDYWFH6vEaUk2WzGl/suPN7JwquiqoZYZRUbfv0r2qZNY+3aR5k7Z05JQKn4rVFSsfaRR2hvb2fDs8/iuu4Jn1XeQHP11Vezffv2Ucs+8IEPMHfuXD73uc8xefJkTNPk4Ycf5vrrrwdg165dHD58mFWrVgGwatUq/v7v/57u7m4aG/0Q54MPPkgikWD+/Pkv9XIFjAXPI/7r35W1ab8QfLG6hidrK5k1qY0VV1zGqstWkEhUo5RL5/EjdBzr4s1veyv5nMUj9/+W/XsPs2vXMV43mOR9Hb0v66F/cNdhnq6O+vlWSBob48yZN5NJU9qoqooxbcZMZs29iMH+QZ57biub1m/EzuWJxUN8JVHJnYNDZY+jevfJvXzGPwqUxPE8HClxlIsH2AU/q4Ug7QgsR6KkQX/WpXfIJSQcGiqHlahLHhQhmDpzBi2tLaRTKSoqK4nGYn4oeQxHNSYD5XwNNMFT0Ok5nVaK19SIyOfRBofG5EFxwibPvPe1HJ1eheg+6JffSV8CLJfN41gWrm2haRJ0HS3kIZwcEd1AaCbS1cnlsuh6DMvJsLK3g0/vPnbS5zR6kn/u6eNTKLZUV1DVGMNUFlOaqxhMuQwMpWiNVWEaIUxdYBgCoWVxZIZ0LonrVRNyQ5B1MUNGoW5fYYRDeLLYkdNDSsWWxVNZ9IsNJbfuWBjPCbEno7Bti1ShIkK5LoYoHL/wxWIVAg+BkgJN+eJuhq77GfgKUKJUzJPP53BclxkzZzJp0iQOHzxI+7TpozNqy6CyspKFCxeOWlZRUUFdXV1p+Yc+9CE+/elPU1tbSyKR4JZbbmHVqlVceumlAFxzzTXMnz+fv/iLv+Ab3/gGnZ2d/O3f/i033XRTMDacIyIbN6P3n90bmTY0rp4yBT0c5vVXLeEvPvBe6pvqEEgsK8cL27ejgIuXrEQIgz27nuDqa66gtu4Fjh3q5uadx8s+pqyhE3W9syb9Nzge7zrcw3+0NlJXEeFNb3sdkUiI1151DYOD/WzdvIVZc+ZSU9/IlauvYdGSFTz92NOkcg/xlK1zkyb4u4Eh6spIDPbq6so+/vGEFH7OmUBg5x36evuJxyt88UsFSno4jsKT4FqSnGXhejmSjkteSaLC9zGV1GQBMxymxgwVpPCHtXDKZUwGSjDQjE9G5qXo3T1+7xfHpfkDf132PhRgVUZ48F/+DKnrxJQCPFzP92DYeQtUDinzOJ5DRSQKwsa28ggh8DwPPIVuCBI1lXR1JamMuHxwbzdwer2Cz/cP8s3qKuanJQO6w1HlsGBeNT3dLlZOEqsRGEYIIcCy88Qqopimi6SHnKWjCJEfsolFI4TCGrrrGysKreQJIATbr5jNxY/vfknepJGMb72DwpkpX6bak6N1G4sDhwCE5jf30jW/w/NwRcbw1QmHI3iux4sv7qK/r5dFl1zykvN1zsY///M/o2ka119//Sj9pCK6rvPb3/6WG2+8kVWrVlFRUcH73vc+7rjjjlfoiAJOpFzPwG9rqnCERiwkeOe73059cxMoSXdnJ+ueeoK5c+cyZ/5CNGHy4s7tNDU1EI3XMDSY5C86ekZ1Kj4dCugLm/xs0TxueXZbWcd1w+Ee9kUjHF46n2UrlpHLZTl4YA+zFiyiqraW7Vu3UBnvYcac+VTX1fGGt7+BS5Yv5t++/yPWbXiRWw2Nu7r7z/5Br9RN8gqjAaaGL7qWtzi4dx+TJk+moq0FIcDr2ITo2omOR/b5o8xvUQhlEx+Iku7ViDYsOGmfjz/2BP3dveStHNe/+11EIpExJdq/7MpowUBznijkpUBBo+C28nN2il+YnR+8EjNWgZQS6fmtzzUhcF0H6Uqk6xsr1dUVKCSOLdGEIhILIRBIqeMp6Dg+SFWVxpTDQ9RkTz/YaECLJ/mnPQdgj79sYHMXj187g8TSFnJZg6FBB7NKoBsaERFBSYHnga5rgIuhe0TCCk2TaMLvfqoUGGEd0wgVmgN67LnhNYRMnTmPvFC2lkcx8c2rraX/f31mOKwz7jwnRQrmhyYQQuOFF19k7779XPHaK6iuqS5toaB0DYQQhMKhkgaKKkjmF1Zy0UUXkc1mmD1rFmbIJDk4SNUJSfAvhUcffXTUvyORCN/97nf57ne/e9r3tLe3c999pxYfDHjl8erL8wz8VmrkMzlMXdJx7Cjxykq2P7edDevX8ZZ3vJnZc+cj0OnuOo5tW0yaNo/B7m5WrV3H5Qc7yj6euxdP58Vpkzi+cBFt//7/lfWemw508PmLZjCUzDO5vZ1dz29noKeXusYWFi+7lE0bnuUPv7+f11xxGYlqX+b9T9/zFrZvfYEGyrvvo488Rn7VyrLPY9ygfE+5qzxqquIsv3Q5hhB+kj0CwxlAS22go2eQbGcv8do41QkN3XXxhhajNcwf3S4DqK+pYaCnl4po7CXZbX+0gRIMNOOLsWoUAORqKtj556voWzEVDYkSGpquITRQOHg5ByufRUqXSMxEaAKlNAxTAFFcx0Phkc9Y5HIec+c10PDsXtb8Yf+Yj7865/KWe3axtiJC99JWGuoTHD4ySCiiEY2YIASi0CtICYlhCExDw3YsbMsjFI0WclA0f9LVAOmHLba9YzFbr5nLO778G0J5t6wbRgB6f79vnIzjxLeCWj3hkN8gEQFT2tuZPn06mq7hef5Wxe2EAlkI1cQi0ULYZlioTSlfS6YiHueFF1/ghRd20dbWyvKVl45y4Qa8iijzjy49iTIU6ZTDP379TmqqqzHDOjd/8q+YM3c+CINsNsML259n5RVXUfnIE0z63FeoGEydfecFtr/tOp5w+miOVNDzoQ/SdN8D6J3dZw311Nsun7TzPLlrF+3TZzJ3/sXs2L6NyuoaDDPC8hWXsWXTRn7wvf/HsuXLmD1vNvv37iedtjjmlpe0m7jrJ1jLloxTL+vpUUriujajZT6HEZpAEx4dfRaRygR7j1jMmR7BdSRhL1/KPxnpi62trSFsGHR1dyMLc9JYtE0CbfkLiTFoFCjAqQjz7F//CelL2lC6hhAaQg+jKek3BXRcXMchl80gCp4SwzQRmoYuNDLpDJrw80P6B/IYBsyZ20LN07u59lcvvqRTKN4aK379Ar9Z2kok5jF9Ri0HDnThupLa2ioUfiMqoUmkclFSEtHBdRWObVNRaaJrWkGATKCbpu/90QRe2OCJ6y/h6p9uHFO4Z2Ikvvny4YnqBLOnTaW7pxcDRV9PN/UNjX4op3CBHdfhkksWk8tmaWluRinfmFOFQcT3PClaWtt481vaRnzCsM5BwKuL6NrHytquXsrCl0SRzLgoMrz9+tXMmr8QJUyk4/HI/X9g4cWLqF67jtqPfqZs40fhh1mfXf06eOh3qHAUM1ZB399+jsabby1rHwt+/QyDk1rJXpGmur6Jye3T2Pzs06xYdRXoYRYtW4ntePy/7/8Y2/IYGhjEsiXPGDrHtfIqeiZcuXHhptZ00w/Xj6rR88cC15M4bo5YWDLY65ATCsuDPQfzVE3NlsTaYEQ1j1TYjo3QDCzLIhKNjGnseElCbQHjk6JGQTnGCcD297+GjlnVZHI5pNJQSmDnLaysRT6bI5/JkhroRxeSaCxMKBIqNNbztTUi0SjhSJihZIbq2jhTZzSRz2e5/Pe7gJc+iQkgnrSZ/+BBlDCIRhVz57YQiYRIJnPouoGUfpWOoRvouo7QBbqp8JwsqcFBPNfG81yk52+nGzpmKEw0FqNr+VQeee8SMonyc568xoaXeDbnFl9sTSOdStE/0MOvf/drHn/iMZ7fuROUoqerGzuf58jhw+zZu4uO48epjMfRRrZKh9JvyaFB8rks0it0ehWjfgS8WhhDBU8yFsY0DCJR/6dtZWlpbcJzLHq7uvj1Pfdyz//cx70/+U9in/5fMMbk9b7bPsuU9snUV0WZXF9PKGSQXXO132aiTFb+7A8c3LUT0wjR3DqJ/p5edmzdisKXw1+yfCXz588gncpjucJvOig0vlIZP+uxCiiVG08YCq5VgUICQ4ODPPX4kzz+6BM88eiTPLH2Cba/cIgDR/PEq6L0DNhMmdqG0MNkcy552/ZbFVCUyQdNQTqVIpPJUlNTRSgUKjQjKZ/Ag3IBUa5GgRU1WfvmeeyrN6jMOITDURR5NOHLpOsm6LrCExZK2UQqKkAr9NpRCtt10HUD17Hp6RmgdXIz8YRJaihD3e5uKpPW2Q+iDC6+7wXy0+rpvKQJU5dMnd5IT2+eVMqmtrYacNCEJBwN4bgu+byNGYJs1iKfzaIZJppm+B4lzQ9ZGLqOEBoHFzaxa2qcSUeHeP3dO4jkT10JUJywtf4ykuPGAUp6KOnR0NzCc89v56KLL0EHJk9uBzTq6xsAQTqTIWflaZs0ic4jh/2wD5yQn+NXBW3evInOzk4WLryIBRddHIR4XoWUW8GTiYTYXVtFhRRIpbDyFijFz378P/z2nt+TTGVIpiyWH+vmfz21lagc2zdp8BMfI3vt66npOM6Uhlbqq2vQNQ0UDH3sw1T+1y/Rz/KQJgBzMM2aj9zB8zft4z5TsWH9c4QjG/lz12XmrFlEoxVUV1Xjug5mKAxoKGmxNh5lsyFZ2nf2HkQTw+vqU/SMRkwT01Akaqq56OKFhQcXRV7BwEHF7x7/BUsWNZOIhxka6ieiSVJDaRwrV8hU8RFAX18f6556mlAoTGNzM6LQIVmNIf0gMFAuEGIPPETirp+Ute1/XDmTnrZm6s0qEhVxjDCEQiZGyA/zeEqRz1tIFGY4ghn28zqUBE/65buO7ZBKppk0tYVoxCCXy2A5LpH0yyOuVGThzzbSvextOJ5NyBC0tdVheVG6j3cTCQs0PYTrOYRCYTRhks/nMQwXw9QJx2IYpol0Xay8RT5v4+QdPM8/RtszebEhgbt6Om//7Z5ThnyKIae6L/0dsrqK/Irl49pt60lfyyASi/Gm695WWl5sn+4/2/hN2Xbv2s3K5ZcSjvjqjmqksonwn6fq6urpOHYMoQls2wo8J69Syp1sn5s1CWVEEZZDNpn2PW8C9h/sxtA1IhUxXtvXzz/3jE3npBjaGbrxwwilMEMRjg+mmdLu58QJoYZ1oW7+9Fn3BxDJ5FnyjX/jJ8317InGMIw+/ukbd1JZEaayspLjx7rIZl10QxGOhP2Ecmkzpzd/9p0zcbyuUIzwqFIZsG6YxBNx9IJGQRi/yjMaDhOLQHNDmJbmOHWxHIvm1xINuyiGQzIaUF9XyxvfeB333/cAe3fvZdHiRYgIJVn8cghCPBMdzyPy9Hrqbrv9rJtKYKAiTH7xDGbPbGH27AZmTK+npbmOaGUctBBoOggNTRNomq8CaJghXFfiSQ/Hsslncji2zaSpbVRUhMjnUuTyNkrTqRs6e4kgwKBhnrUeXgAVAzma9qeIRCr9yVNahESKlqYYmXQK28kjNAPbcdEMnUjU7+/hVyL5XpFwNEqippr6xhqa22ppaKymIm7ieS4DA3m6XHHGvAoB6INDNP/lR5h05bXjst+GKPxfExqGYYDyy4eLLxjOm1bKbx9RX1+PAkzTHC26JoavhRAapmEyZ+5c2tunnrPzCRhflDvZPt+bxspbZFKZQpdg5YdwBERjEXBcPt/rdycud/IpCn/1/+3nCg8HglgszGuXLKa+3m+TopT/jc2uWc3gxz9W1n6LDx+f7e73c+0sm6Ehi2PHh9i2/QCdXWlcV+LYHrlsDt3QWYUibp39IcyrrR2XQo6nRyCl39VZKIVeqC8oZqLogBBhcpZLJmXR25sil3HYfShHMi2Rnl0aYEpjqYDBoSSJ6moi4Qh7du8t6KyUT2CgTGBiDzzEpCuvpfl9H0YfSpYVG338mnnEqysQmo6UAtMMo4TAsW2EVpAdLcgToxSxWASlFFIq8rk8lpUnGtVpmdSAYWh4to3jAHqYmc/3s/T+nWd0/yugJxLh27Nnl/1FTTy5G0cppBA4rovCxYwKJrXXkcvmyaSzaMLAcz0kknDYQNfFsNdAKf/G03XCkQhNTdVMmVRPc1MFLc0R6p3yQ1J6VzcNt9xK9IGHxl+YQ/mZ9rquc8ogTGHkEMI3UKqrq9E036A5ndtVaIIFCxewZdNmDh8+/IoefsD4ResfQGlnni4U8Kaefqxs1p+INPyYoVAYhS658wcGafHOnmQ6khObcSoBZiTKzAWLmTR7zognC/+XoY99GLe5qaz7UwNapOTGoRSep8jlbTTDBE0rvd/3GLtYWYuqfHke4vRbrhvXntZToQQ4UmJLie36LUvcEX8rV5jUVFXRPiVOOutx4IjDwWMOhztsXNdDKv+Rs/jnSCdTeK7LQH8fA339dHZ0ksvlxnRMgYEyQSmWE+tlNu4CeGLhZDa1ttDZmaKzsw/pOBhCETZB1zyUtJDSxXNdXNtBSYEQOrZlYeezVMbDNDXVEKuMIqWHlUvjOC5CD2FoIVb+cgtwek9E8Ya/c+Z0Hkok+PHUaWUd95y1u2lYfwhNCyG0kN+q3XLRTYPmlnpqauK4joWVt5Ce8mOdSpVexf4SUkokgrAZxpWKvC3J5z0yFeX31yy2E6j7+29AMXF0nOA/tehougmMVmxUI18CqqqriEaiVFbEUWij/mj+fkRJMHb3nj1cdvkVdHZ2jCl+HHAB4HlUffv7NHz8MyDP7PMUQLPrstLKo6R/b2iawDQNotEIecviT7LlTVDF7+rAxz/G0UfvH1WyKwqJ4FUtLUQrK/xlI8MGhVBPsQdVOXwqlebabB7XlWSzeULhEKGwWdi3nzfhOi7HvfL2mHvd5WV+8jhC+u0t8CQD/YM8+fjTHD/eifJHCOqqYlx2xXzCIZNoNEpldYzqKo3KuCCXyzHyagsgEgqzY/sONF2nvqGOOfPmEq+oCDwoFzwvoeU5wDPVjfT2WuStHLEKnUQijBkS6JpAaOA5Drl0BiubQ7oOZkgghIuhebS2NRCPh1G4gMKzLTzX8aXf9RCth4aoGMycNTntwF/8GesaahF4/H7hQnrCobIGkYt+9iyukggjgh6OIz0LTSkMQ8MwdSoTMSoqI9hWFuWBYRhY+QyubaFcBx0wDYPKijChkEB5EtdWpNMuW6Jh+qNG2YPZyKZg4wklwHNtXCtHMpVCKsm+A/vYvmMbBw8dRKF4cdeLbNm6lfUbNlBVXY0ZCqMbOsNBrhOHGZjSPpXtO3b4a4MklFcNRQ9tzbe+N6bS8jfMrGPhjHo0/PswGg6TiBrMaanhequ8/A1ZW0vPd/6JoVs+erInQkBRUvB092yxBYisqSnr8xTwxcFBdKV8UUpPYhoGuiEQwq9uUUKhq/KE2hs+9flxGQo+HcXraOgahq7R0NjAVa+/iimTWyn+5fO5FNt3HOLJDcdJZSwS5gAGDmHNJZ/LDyt3FwhFQlx11etQSpHP54nHYn7YaAxjSGCgTEDKLScuooCBWJjdjTE8L0/I0KiujBGLGFi2RTqTJZ3MYeUcQoZJNKITCmtUxGOEQjo1tZVomsL1PDwp0QV4nosQAhcDzYgw7fnyFCCzrc1EoyHCpk7eznPn9KlnfY8AYv1Zand2knbjCLMBTQthWzaaVvCOoDBMnXhlBZZtoRk6hqmjkFi2hZXP+T2EBJimTiyqI8jjSQtlwE/mNZauVbnEyqyaOmcocB2bfM5vD688D93QOd7ZSUtLCyCYNWs28+bNY+rUqcyZPRdPDpcPF3VjRk5GAn+SmT1rFm1tbUH5zquEl+KhLTLrWC/vrangXZ7N5Y7D8myW660cX+3ro9o7+wTvVVZy5Ik/nFHoTAgx6nUqsmtWc+TJB/Fqa876tdWAVqW4TVpcNLWB6Q0JrvAc3u44XOpYhA2NjzfE+M7Q0FmPH0AbHKTh5k9T9Z3vjztP66kQKJTnguehKYlQ0s87Ka2Hgf4Mm3ccpa1tElUJjXzewTBhRnuMXM4GRckAEWI4fyUUMpk3fy6RSLhQKVi+hRJU8UxAxlK+Vrwxf7NiDhqSRFynuTlBPG6QzmRwBxWOhEjYRDN1pOdiWx6RWMT/VmoGesjEzttIT6EJDaVcf0LTDCJmNS0bDzHt92V6E5qbkc9tQeHHex+rrWHxtHbefODQWd+64juPs+WDHn2rZqCbGp6jhsViC8qxhgmRmIsnFeFoFMd1iYQieK6Llc/T36cwRBWRSJTGhlp6elP09KR4vMrEWtDEB/f2UVdGEhyMP8VIAbieQjfDVFUmcByHnTu209jQRHd3N22TJqNpGv19vRw5fBjbdli6ZOmowUTTRg4eClnw0j38yCMsXbokEEB5NeB51P7vr4/ZQ1vkkgO9XHKgl3eOXHi0/Penr38rhMoPu56RUIi+O75YdmXP+7v6uCweoq17iIpUtrR8SNdIHCu/zV3xutX86/eovPt/6P/i58fNOHEqFAKJwBMCz5MoTUONCJEpoKZSMWuSjp3rQ3nQM+hQU6kYHIQDKauwl+FvjAbU1dagKdiwfhM19Q3U11Sdwe91MoEHZQIylvK1bFUFP7/yYp6tr6S+1mT6jFpCIUFqKIttS6pr4jQ3VhGtCPtxVtcjHDYBhfQkIcPEtV10XUc3fHVWPwHOBD2Gptdwyc/PrjCphMBtaUJdfimNzS1opoHnuZhmhK2TJ5V1LmbGYsW3H6F14z5c188vcRwPQzcKGiei4CEJoWsgpYeGQnouumkSikWQUtHfO0Bff4pwJExLax31dVFMzWNtzOCvl0zin1ZNJxPSy+q6WTuOclFUwfVtaBpKgGVbhCMxWlvbCh4Un5qaGqRUpNJpYhUVCG1YAn/E3kBAV+dxXMdm/ry5fofks+QhBEx8qu78IUbXmWXjX0lyq696Wfc31sqe2fs6iI0wTgAS3ugE0LFQTKwfzyEfgQLl4boOtuPQ19vLli3Psbnw2rBpK0cPHUB6gva2Cqa1VzB3eiUaUbbvzSCFPuraFPWUBgcHSWUzSBS9PT2IQj5LuQQGygQkv2wJXnXVae1QBeQMjZ+8Yzk/+uBV7J/TyPw5dTQ0mLh2HqU0qmsTNNbXUFkRwwybmKaG9DxCpuFLnkvpt7UsRHoFfnwS5bfbllJDCEH9zv1E+1Jnv3GVov+2zxKKxsjlLGzbQjcVjvQ4MGkSTlOjn89yBopr5/x4PZpSeK5EKQ3Xk0jllxTrul6KHQMITcOy8th2Hk3TaWyqpW1SI7FoiHzeQUOnrbWB9imVNNbqgMvmKPx0ceuEU4wUyjfOorGY727VBN3dXRw+fBhd6P6gIT127txBe3s7TY2N7N+3d4Quwcln7Doumzdv5vjx4wwNDo2pPUDAxCP2wENU/+v3zr7hK4AC3JbmV6Q8dyyVPXBqPaSX+r0vvm88PcycClVIPjZ0g1gsRlNTI81NTTQ1NdLS3EQ0FiGZdUmnHY51ZXFti1Quy0Wz49Qlihq0PgK/NLm5sYFrrv4TZrRPZkpbC8YoH8vZCQyUCxRX0+if1cyChVOZNrWanJvGk4p4ZSXhSJhc3iGXz+PYFiBRro2hgW5qKCH9HjaaHzwRQiGEwnNdpJR4UmGYISZvPsSK7/yyrONJvv+9ZNe8HseyiYYN4jGTWCRELGwQikTo/rzfR+NsA4gAon0Zal7sBqHhSekfv1J4ChAaQugo6ZcWK/zmgp5tIaSLYSgMHaoSMWqqKvzmiAoaGmqY3JZgcmuU6e2V7JlZya9b4mWd23hSjBRCoOl+wq/reixdsox8Po9t2wggOTTEnt27mT17DrNmzMI0TKRUhQt/8tWf3D6FufPmceVVVyGVJJ/Pj9ZMCbhwKCbfn0f6b/vsK1OeW6zsOU+M18T6kQgliJomEdOkKl7JpNYW2lqbaWttoa2tmbrqKG0NUTRdR0enPwNzZtSiCUV9jY7GyXpSYdPkovlzuf7tb2Zm++QxG3mBgTIBiWzcjD44dEZhsUrbZYmdpy/VxfH+ASxPRwiDZDLLkSM9HD7cw1Ayg23bZFJplCdprE8QNg3CoRC6JjAMv6Oxpglcx/OTZD0PQw8zafNBln/7Qcx0eVn5RbetRCKlBdJBKBfT9JtTpV9/NT3f+j/IqkRZ+2veeoxQ2A9L2Y6DACQKJcAwNaqrqomYYaLhKPF4gsbGBpRrY+dy5LMZhgYH6e7uI5O3kErieYqKyhh1jXFmzWpk8pQaMqHybo/xpBipCYFpGP53IJ6gMl7J4aNHyVt5QKHrBtdd92Zam1upb2xk+sxZhSRDVajKViUvSVH8bd78+WzcuAnLsvy+R+fv9AJeQcaafP9yM/iJj72ieRpjCfW8UpTbjuRcowR+HyLhtwUpes2LCAS25RCLCY53DNE7kKe9pYKDh/uxHEFdzPB1tArblpPEXA5BkuwEpNwn9uT+Lp5NZRDCJBI2GMzlcR2FbgiaG2Mk0zlCIYHnuTQ21hKLRQlHYgylU2S9HJrueyiE0HBd26+RRyekwUV3PQ2c3e2phMBrbiq5bQ0jhBIaUgl0BI7jEo3FMEyD7LWrkYlKmt/34bOeW/vvn2dgThNHL2ktKVYKT6JpGkoqPCRCSHRDUFVZQTyiEdZdUkNDyFiEXNaiq3uA7t40tuVimFqhg6/g0PE+4rEQb+jKnDGkUZTfHk+Kka7roml+g0SlYDA5RHNLM/0DA8Qrq4jFE1DwgdiWRSgU8pPilJ/gphS4jix4n/xzjFdV89br3wGA0HRcFXTjuRA5X57AkTL2rzR+v55fnLVfzytF/Ne/Y+Dzt447ETehKPRi8x/KhNCQ0h9PfT86COkbLblsDtOQZFMWNYkwTj6P5YGuXn5/R2CgTEDKfWLfnbaxEr6EcU+Phet4RKMGiYSBQJK3XFLpPA11FZi6AOU3i/IiIZSSWI4L0jcilATHkbRuPsLF/7GecOrs6qvFKWyk29Y0TcLhMFJKcrk8rtT9p3bPT8rMr1yG29xU1gCy4P89Tf5jl9MxvQYpJMp1EZqvIOtP1BrxqEF1ZRShPCrjUbKZDANDGaTjYlkeyaRNb38ahSAcMgmFDCJhndrePmryZ44XCyD17uvH1WDjjWgFb9l5duzcTlNTM01NTSX9AaV8b1NyaIj6ujqkp1BKlDwpUio8NdyZRylV0gVX0s9JGmOPt4AJwPnwBBbzzoZl7F9hiv16brm1YJSfW/T+ASIbN5Nfufwcf/JZECALYXJPKY53dND3YA8XX3IJTU0NaEBt40zmLbueWMxkz/5jxOtq6NyzmyVLW4jXzEXTTv/3K3lRxjhuBCGeCUh+8SK/DOw06xXgCTjSmCCdcujutbEdCIVC2I6FlB66IfC8wlOzVHiuB8orvpt4vIKQGQIlkJ6vxjpp61GWffdxQmUYJwCyumqURDWArms01DcQi8dB04lWRAlFIpRK48uMFQsglLZY9Y2HufYLv6N10zGkVLiuXyIsNIESAg2FqYGu+SXU0bCJ59ik0nks28GyXfqHbHr7XTp78vT0WQwOWuh95Z2j2z6lrO3OCaKQuFvoraOkRBc6mXSGSCTi3+zSI51Kks2kCUfCDA0l/RwU4ZsjxRAPQpTS2fzBpRBfVqNLCQMuHPLLlviJpGNwyY9UKC53+5GcKGN/LiiKuHlNjefsM0cynnLWRqKUwvNcPClpndTGmmtW09TkG60CiDUtYMalf07Txe/CaLiU2PQ1ZM15hJrfxPR5r/+jQjmnIzBQJiCRLc8hztB0SQC6goUDLtLWyOU8cnmXnOUQjYYJhwThkIHjeAwks3T2DJFM5fzESs/CMMLEY3GqKxOETRNNAFIy/6cbSvsvh55v/eNJA48QgukzZiKlJBwJY5ghcjkL13VKg1d2zWqS739v2dcjOpBjxfefYtLWDv8J39BJVFbQ0lRPNBrCcW3Aw7VdclkLx3bJ5mx0I4QRitLaXI/teGQtweCQQ/+gQ3eZJ2kcHD/9aUqGhYCuri6y2SyLFy9mxowZ/nqlyOVyvPDiixw8cICuzk5cz098LlgnpYaCwOg/tBpOjQ3k7i9QRjwclPsXlrW1JP/q/XjNTWVt7zU3MfDxj9HzT1+n8yf/xtG1vz8v+iDZNas5+tgDDHziY6c0sM52/n9MkHM85ayVUCCUREOgC4UmQBd+iEVDlBoHCqGVqv4MURo2Cg8thaakpXw2hvPaiq8xXrUgxDMBKTfRak5VmBeNEK6nyDguIDENQXUihqbp9PQk8aRHVXUYTTeJVEQwdIgnatEQVITDkIiTz2apfbGbaH/2rJ8JI/JOViw7aZ0Q0D51GoZpksumMUSYTCaLJkY/l+dWX0XVXT8p6/MK0Qcu+ekmOi5pJVERo6G2npAZwrYzJAf6iIRCpLIWBw530z+QQTd0NM2gob6Knu40Qui4nsTQNcLCYHBqLcld/VRm7DPmoFT+1y8Y+tiHx02YRys0fLRtm1w+B0rR3NBYkqnXNB1NE0ilyGazeJ6kpiYBiEJH2GG390g7xM/PUf5TUmCgXLAUvQu1//vrGF3dp91OAbK2hiNP/AFCIQY+8wk/eb+7B6++DhTofX2jf29s8PO1xsm9gq4zdPNHcWbNpPbv/gFjhGqu19xEfsklVNz3AHCCrV6YoDNvuIZ4YX05nJiPN96Q+IaJkoUeZgXhNTHCrCimwRZF/JTr+jmAhW1H5+yNHideikRBYKBMMGIPPESizIm7enYrF4dM1Iud9KdzhKIQi5lEYxF6epOk0jaWZaFpVQxWZqkciJCIh4jFXJAKXWhEQiE0IYgMlt/kC85cLlhXV088XoWT97ClIhqNYpomI7/CRXez3tVdatB3JgQQSVtc9ze/oevWa3GuaUQoQciMoCQkk2m6+wYZSOXp7k2haYLKyhhhM4xh5KmrCZFMO2gK2tvqWLJkBl2uTuLXpy8LFIDR2TWuYsqaphMOh5ncNoljx4+SSqY4fvw4cxJ+dVQkEmHJ4iXk8jn6+vqY1DaZ/v7ugvHh72OEr2TYKCkgpXxFXLkB44fsmtVkV19F1Z0/LGminGqC7rvji8OKr7o+bu6BsVI835KBNcKQyl635pTGS/9tn/Xfs3lrWflypVybV6qM+mVAFFLNDF0jl0xyYO9+3wOCQBY0TpTy89z2793L0NAQLz7/AldcuuokjceRxolC4RW2GKvBERgoE4kydQoUMFQR4oUqk9mtTeQdh6PdfQzlclTGQ/T2JUmlHdIZB/BIJnPU1MRIZSx0Q8fu6SMWCVNVWYmUipCpU9GdKusQZW0tfXf87RnctoJoNEKiqoa+3n5S6QyNTfUnW98jk9mEKMtIAT8vZfJXfkVPOETyijnYnoMUGv1DKTIZi0zaIpl0kErSP5ChvjZBVSJGOuNi6qBjsGDeVObMmsSxLXuYVcZnjp+YssKy8sRcz/fuxBMMDQ2RzuQKtp/vIdGExqOPPkrOsmiob0QTxa4bvuN6ZNOvwBh5lXIW70L/bZ8d19LtY+Y0BtaZjBdgeIyCUWPUid6C8X7NFAJPeli2A5rAkQrHkyAEOanIKYGU0H3sOB2Hj/L8th38z799j4ZJU/j0Zz8/ShLfUwoPcABb+q+8VFToinpjbFklgYEygSjqFJTDTxZMRvZnmD5VsWzpVNLrktjYeK5iYMhmaMjCdSWxqIbnSHKZHJ5XRTpjIzSB7UjC4Si6UDRvPMTkX2w9a8ntSJfv6VDK1+KYNn0mh4910RgLsfSSi/2W5ifsveRuPmGAPBPFabbmX+5n35w6BrM5pPTIZPPkMjlSyRyWrcjkXFAS1x1iclsdFTGTREWExRfNYtqkBrY9t43Og51cWcZnjpc8FIHAc12y2QzZTBpNE3R1d7F4se9SVkrhODbZbI5JkybR1d1NKBQik5YneUpO+xkiSJF9NXG2CfpVwRm8Q6cbo7zmJlJ/ej3u1CkT4poV0+B1XcOVitraGubPmeU/7HrQn1dYjkRoUbIW1NZ3Mql9GtVNbRw+3kkuk0IJ6B8cIhStQGmgawZSKpTQMEyDeEinsr5uTFkogYEygSj3SX1LUw1P1iRoO9rL3Gn1NDRW4UmXSCRCb2+SZFIylHTQdA1d98jbDrquI12PoXwGoWkFz0mGGIo533oUOLNxghCjXb6nozAJzp49i42bt0IuS7VW2Ls42QQqDZDPbqThls+gDZ1eoK70EYDZnUI9s5eBKdU4to2mPFzHI593EZqB7VjYtkQKje7eDA0NCRKxCLNnTqKz8xgbdh7ikBL0mDp1jnfabHIFVH/rezizZ573pyOF734VmuD48WOkUynmzZ1Hf08vNYlqAPL5PENDgyxaeDEHDx3E0PRCfomklDNfsPJGRp/VCU+HAa8iJnD45lxwoRhxvmI4hEwd09ApZqSFXJtm5WIYMKmpgosaFqKuuIRbPvY+slYeQ9OoDIcJh8JsWL+eN7zhOhzLQtM08DxfY0UXDKXSDKZSxIzyzY6gimcCUW729+KuAZYe7yeZzjGQSdM3OIDtKnJ5CxA4joenhK9t4voNAg3ToKtrgEOHuhgcSGHZDpG1LzDrz3+IMZQ/o1EggMGP3zimCXrmjOksnDebeKKK2hGN7E6JrpNftZK+v/8yiPJ7YYqeNMnBJB3HuhgcTCIK+ijZTB7H8SfkXNYllXHIZiwkkt6+Ho5193K8J4MnBN+ZVF3yypzu3BFiXPTZ8MWWfCVZzTCZPnMmicpKqmtroJA8WxGP09PXx6atWxgcHCzmuyGlKqk+6vgv/9+F/H3hC9nxRyhDHjt2jPe+973U1dURjUa56KKL2LhxY2m9UoovfelLtLS0EI1GWb16NXv27Bm1j/7+fm644QYSiQTV1dV86EMfIp1O/xFXLSDgZaBgxGXefJ1vzE0w4wSUf49rOprwReuFKjT2E4J77/01P/vP/+ahtY/y87v/kxd27mT39u088dBa7v/N79i4cTNGOEQ4FAaluPfeX/HMsxt4ZuNGnt28kd/dfz8HDx3CGINxAoEHZUJRShw9S1KWAt6/+zhfnFVDNmfRP5QiZzvEwzqNiVqsbB/JlIfCw9A1YjED11Xs399DznKY4glm7+tl4b89W/axlasHUjxuw9B505veyLHjnVTW1Z6w9tQU3al1X/zf6AMDZ/2sdNwgnc5x/PgQpqGYPKmeysoomZyNbgmyeZtYWCceC+O6DoNDaQ4f68aVfh8bT/rNETOaIH4GZbKRfTbO25Nm4fB0w2DXnr24UjFv3nza29upjFbgyKIvRGP+RZeQz+fYvGkjyXye3/7mN9zw3hsKVT5aqSSwWFqsipU7avj3YWWU8hgYGOCyyy7jqquu4ve//z0NDQ3s2bOHmpqa0jbf+MY3+Na3vsWPf/xjpk2bxhe/+EXWrFnD888/TyQSAeCGG26go6ODBx98EMdx+MAHPsBHPvIRfvazn71cVzIg4FWJhsLUdZAeRw4coOPYMZYuX0ZlZZw1V1+J67pY+Txy4QKqa2sYGBykpa2N2toav9u9pqELjVg0ynVvWINl2WiaRiwcAqUYymT9/m6y/Ae5wECZSBQTR2/+9Bk304AGy2FhKkvedukbSNPaEGf+7HZC4Rih0CFs6yBCgB7yiMejdHYN0J+yUZ4iOZBl8X9vA8ovCxtLbX8xXBCLxZg1czpKlD/hZdesJnvVa5l8xevR+gdO+Y6iKeF29tNfbTA4lEcIgRlOYoZ0XM8vHa4ImbQ1JVg4vxXHSjOQzHK8s59IRYiQabC4I8nth5Jln9d5TZYtaBII3SAUiWIqxdEjhzl+7CiqEPYpKrlJT+K5Lvf99jese+oJdj3/PH/+3huKuyl5SYQa7s+DECUxN3+7sXlR/uEf/oHJkyfzox/9qLRs2rRppd+VUvzLv/wLf/u3f8tb3/pWAP7jP/6DpqYm7r33Xt7znvfwwgsvcP/997NhwwaWLfNL2L/97W9z3XXX8Y//+I+0tra+xIsXEPDqxg/T677OiaExfeYs5s2ZAUIjZzscS+cIaYJwOIyu6/zmvt9x3z33MnnWHD7x6U9SG43g2jaXLFmMMAwampqQnocuBLbtgJKE43EikQh2NlP2cQUhnglGds1qku8rT8RsSVeKVCpLNGwwpa2eWCyKYQoWL5rCa5ZMYm57FbUJk5Cpk05bCASW7VG3t4fKlFXWFKSEGHOL9FITKQpNqcaa1BAK0Xf7bf7nn2r/hZ+X3reHvp4MuaxDOuvS158hFDJIVEaYNbmWi6c1ctnyWSxbPJOVy+cxdUoDnvTIZByiYZOPH0+P2t/ZON8CTEKBkB64LkJ6tE+ZxLJlS1i+YhnLli0t/ayIRdCRDPb28PU7bqeyspJwOHzy/gqib5o2ovFX4WqMVXDp17/+NcuWLeNd73oXjY2NLF68mB/+8Iel9QcOHKCzs5PVq4fDhFVVVaxcuZJ169YBsG7dOqqrq0vGCcDq1avRNI3169ef8nMtyyKZTI56BQQEjKbQ5AIlFJ7yQBR686DQQyaVbVMINUxGr5kEVU1kpc727dt46un1xKtriFdUEI1GmDJlMkITCNPAiEQQ4TDhyjjRRILqqioMQ+f0EqMnExgoE5Dc668qa7tV2zpo33mccMRk5+4OfvGbZ3jyqR0I5XLpyjmsWD6ThoYKhJLUVkaZXFdJZcwkmrbL2n85midnpKgeVpBSHwuytoYzvUsANVmH5oNJspZHWNfQXD+2WhkzmNpWzVWrFzNzdgu9Xb10dg8Sq4hRV5vAtl0uSudpdMq7lV6KkfZKIT2J6zq4rkM4HCZWUVF6RaNRYrEY7VOn0tLa5odNlMIwzeG8khEnLKXEk9LvYi1dvEI366IHbCxGyv79+7nzzjuZNWsWDzzwADfeeCMf//jH+fGPfwxAZ2cnAE1NoxVJm5qaSus6OztpbBwtT24YBrW1taVtTuRrX/saVVVVpdfkyZPLPuaAgFcTylPYtkfednF9PVgkAhOYFtVp0S1U3xHc7iO8+11v5xOf+TSaXmqIAQXFWR0KeWzDw4lk7G0RIAjxTEjKzUUBuPaJg/zj5FoOd2awLUXvgIVru1x5xQIQDrGIgWPbLLh4Kpoy2Lh1L1qqPAPl7JonrxzlhlOqbY+m1mpa6iuoTMTIu1nqaytoa62nvrmKF57fzfpndzOQzRGLh6mvjaMkrCizF88fbaS9bAwbDYIR/XKKoklq+FjrGxqpr6/HNMOYZhih+c8pUnoINOQoaeoiomRLuq5b6n5cLlJKli1bxle/+lUAFi9ezI4dO/j+97/P+973vj/25E/LF77wBT796eGQaDKZDIyUgIATUELgobA8B03A4OAQ+w8eLjQRhMnNTXR1dTKlrQ3bcXn+wEGiVdVI10O6Lorw6LGi8K/iiFisz1RQkHwrj8CDMhEZQ0O9qrRNdFsnmYyH60kGkjYvHOhhz6EOLDtPJKLT1lZPbX010UqDxQvbmRw2z2jpKsAraJ6cr9LacsMpC3SDeTMaeO2VS7jy6iU01sUJhzWE4THQ28uWrfvYeyRJ34BHR0eWfQf6mX9gkD/Z01/W/mVt7TlvdnZmFNLzSFRWUlVZiY7yn2aEwBACQ/g9NAxN41O3fpoH1z7Kpz51K0oqnl2/3nftahqaphUS33Q0oSPQCvkoAitv+f17xkBLSwvz588ftWzevHkcPuxryDQ3NwN+D6GRdHV1ldY1NzfT3T1aft11Xfr7+0vbnEg4HCaRSIx6BQQEjEaoopaj34Q2b9ukMhnSmSyZfB7peeTyFg4CRylcKbn89dey4JKLyeayRVnHk7Ss/KUUevkU/1W+DyXwoExQig31yulXc8vGw/zr9CbWN8YxDI1kWrH/UDdaexVGxEDqJhu37cYQioaqSq59Yh9w6vBJ8avVd/ttZ9c8eQXJL1uC29ToS+GfZhsFrDo4RHdTDR3dHeRopnnKZI4eOkwynWLfkU6OdqXI5SWe8hACnKzkXTtOHS44cd/lCNOdO4pKvALTDCF0nX27d+M4Dq7r+uXDusGU9nZs2+LgwYMMDQ3R2trGpEmTURIG+geQsiRVw8YNG0glk9i2w9DgIPX19RimQSqdZuWSBWM6ussuu4xdu3aNWrZ7927a29sBP2G2ubmZhx9+mEsuuQTwvR3r16/nxhtvBGDVqlUMDg6yadMmli5dCsAjjzyClJKVK1e+9EsXEBAAym8M6Em/HcmC+XPQ8BVhTaVoaW3h4cceZVL7NObMnY3UDapra9AKnuPRD7WiYIqM1k8aq0dkzB6UQMtg/JBbXV4uStyV3La7gw/t72FubwbP8nBsh6xloxsmu/d18OLuLg4dHcR+5gAVZ9A9KeZ9yNqa02xxjtB1Uu9+51n1WSrTFmzZz44XjrLu6W30DWQIRyOkcnmGUnk8pWGGdXRd47KBPD9/roOqMnNPyhKmO5cIMMwQkVgUy7ZZu/YRnnrqKQ4cPEhfXx/PPbeV7q4u9u3dS29PDwP9/fT29SClL42vaXpBE8WvKh4cGGCwv5/urk76+3rp6e3BylvE4xUF5d/yn4Q+9alP8cwzz/DVr36VvXv38rOf/Ywf/OAH3HTTTf6hC8EnP/lJ/u7v/o5f//rXbN++nb/8y7+ktbWVt73tbYDvcbn22mv58Ic/zLPPPstTTz3FzTffzHve856ggicg4I9CFTSRlO85NQwk4LeYBduTdPb20dk7yGAqTTZnISVIT2GYRqlJhhJ+k8Fi3+JiSGfk72NJkh2TByXQMhhflNtQr/h1eNvxQd52fJDesMHaCkV/SxTXg46jA0w6mqRZQVOuvBr18dB/xp1anvZK87YOBhY0YFtppJTMmdWM5WSwbIl0FAsHclw6mOdP+8rr1gyQfP97x1FYh9Kji0Rhe5KKijhvfstbEGgkUyniiUoWXLyI6poaahrq0XUDz3aw8jmSySS1tbXU1dcXvCd+/snipUsxTRNd13FcF03X0QDPdfHyg2M6vOXLl3PPPffwhS98gTvuuINp06bxL//yL9xwww2lbT772c+SyWT4yEc+wuDgIJdffjn3339/adwA+OlPf8rNN9/M1VdfjaZpXH/99XzrW9/6oy9fQMCrHU0zCIVNpALd0PGURAgND4VuaEybPo2ugSHmz59LOBIm70rMcATDMIGCgVLw5Pre3JH+lOGfY3mwEUqdYWY7gc9//vM89dRTPPHEE6dcr5SitbWVW2+9lc985jMADA0N0dTUxF133VXSMpg/f/4oLYP777+f6667jqNHj5b1JJRMJqmqqmLb5qeprIyXe/gXJLEHHqLhlltL4lnlIPG/KNsunUSv57F4ew+1eXdMn9v5k3877/LXkfUbaH7vh866nQL+a2oVv2hPEImHWTi/DZRN06bjvPGpo9TbY1eAHQ/nP4rCXXy4a4iBjIXnSSLRKDU1NX4VTyxKKBwiZIYQmu8x+vhff5j7f/cb3vdXH+GrX/3qcMa98pNaSy5bpUqDilCgC8FQ71H6ejpYtOQyhoaGJkxuRzB2BAScAqU43JfmQFc/EoVpmMTCIZatupRYLIJeqNCxXQ9d18m7LrZSfP9fvsOnPnkT8VCo4B0ZDvOUEmPVcNhYA9LJJG3V1WWNG2MK8QRaBuOPorqqrCp/gigmLC165ihXb+ig5gTj5EwWqwLc5qZxUVJb9CCdzcIWwLsPDvG9dcdYcjRNbMthrnxoP3+59hB1YzROxlNJ8amw8jnSySEyySHa26cwb/482tvbaWhopCpRTTQaIxIOo2sa2UyGwYEBLNsq6Z1oQmBoAtPQMXUdU9MxNP+nqekYul6KOQcEBFw4KMCVgNKYNW8el73utcSiERRgo8ihcA2dHPDw2iexXI9QRRyhaSNKiH2/ieVJbCmxlcITlIwXCYxlxB2TgRJoGYxPsmtW0/Ot//OS33+i5+VseR2pd18/PnpNlFnNVKTOlnziuU5ufugAK7Z2nVFH5VSMn5LiU1M8Pik9hAamoWFoYGjKf4nhlykK7eGFQCuKr5Uuhi/IpgmBpgl0TaAXKns0TSs9DQUEBFw4KKlwXRfHdUATaIaOEn5ljiy8AKRQdPf04AI1jY0IoZU8J8WfviSBP4aMrNsZ6WEphzHloARaBuOX/MplZeWjvByU23fnXDCWaqaxFbidjKytoe+OL46v3JMiRYNDSbRCH50N69YhlSrpnBTVe5VUWFYe1/N4x/Xvor+3F6REaPpoi00V4siFVjyjPiwg4DxQzEjwO3CP/W4+sdHlS9nH2fZ7tn2Wu+1Lbco5cp9n20fpeuLnhkjNfzyRYvRYOTKHpOiBFwCFJqMwYnvhP9CMWnbijspkTAbK6bQMfvGLXwCjtQxaRnSo7erqKpUOvlQtg1NJcQeMoNin55ZbUYU+Kq8U51vS/URyq68qy0CBlza1DpcUPzi+qnZOQCGIVsSJWQqEYtHiS2hqbBplYCilOHTwIEcPH+Etb30b17/rXdzx5S8VhNeK+ylGj4cRwyv/KCMvIOClUJQh1DQdoRtIKdFGKFCLwv+E0Apf9lK9YenL6+dDqIKM4eifI6XEhodOv/9U8XdF8Z/FmpWRxyaGEy4KC4UQw1uOuGnkCZsW38qoZScbYCNzwor7Lh3pCbesKh3nyDMrnt+wQeE6Dq7rB10ylksum0cqRcfR43i5vD8uKJCqkJMmFVJJOo8e48VtOxjo7cfFF2QrfVrpEpQy14YNnDEOHmMyUAItg/FNMR+l9u/+AaOz6+xvGCNKCLxxkn8yknKrmV4KqjC4jbuS4tMgNEE4FsF1bH9w0Qr9cwqN/pSCvoFBWtom0dXZAVpBN0UbLXXvh3+KA93ISaCoVBsQcI5RkMrkSFqKXC7nl7lKia7puFJh6AaIMELEkDKEbmSQ0iFkKjQUKA/XddFNE891QQik6yKFQAgFSiCl8nOxNB2haehGGMc2EJqJENJv9eBmEMLFky5SeiAEhq7jKjANw1dilsqvlHNddF3Hc12UBq7neyoNhR9GEZp/b0q/csZxPXRNQyJxHRshNFzPQyFwpUIIDcdz0HUDTTcQhfsR6ZsDpqHjur5OgCslUklkIVwrJXiawNAMHNfBU4JkcohMKkMunycS1lm4cAFS8428oVTaN240gdD8QLCm++1Crr7WzxtdveZPiAowKeSYKHBHPOAohX/cWsH7IsBxyxd5HJOB8qlPfYrXvOY1fPWrX+VP//RPefbZZ/nBD37AD37wA2C0lsGsWbNKZcan0zL4/ve/j+M4gZbBy0h2zWqyq68isnEz0YfWkrjrJ/ASPConPkMXJ+pxmX8x0nvEyxuA8Jqb6L/ts+MzrHMSBXerUoRDYULmcPlfUSlSKUEkVoHQdDzPAwWhUKj0lHnit6RYdlz6BPXHBsoCAl4Cvr3MYCpJJF5HbU0tAshbNmg6hw4fZeqUSUilIUQUKTQOHU7R2lxLyBS+gYJHf/8AjufR2NiIVArXcUDT2H/wADOnTUNJPySqaxpdPX1UhMJUV8UBA00ILDvH/v3d6LoFSiJQeNLPo3SUIKRrOK6Lrmu+cWE7GIY/zUrpotBAGOgFHSHT8LWHPE9imCau62AaOo7nS8j7/bA8JAJPKdAMbNdGaH6yuigktuvK3y5kGDiOL8zoeR6ulCj8PDKJjqPhG2JCw/UkZkUFiVAILZmksaGOZav8ysRh/8zocWF02EfhSOj3FJoGXsEYkSXJA19TyXYVpul7UXQBxwdeIQMl0DKYIOg6+ZXLya9cjrVsyZg9Kqeafsb7RF1MFK79u69jdHWf/Q2noXjuyfe/l9zqq3xv0XgzyE6Jb5pJT4ICx3GRCjxP4SnfNVt0Ec+cMRNNwVbDRADRWHREd+kT8k1G2CPF5LcR2bQBAeeE4jeuraWZxtap/OHRJ1m0cAGPPbORRQvmccWqpVRVxv3wdiGM0VwfozIeRqDIZnOkUklcpwKpoK62hkRlBeFQCIWgpbGBhrq6EeEWRWtrC5GQSSQULnzlFdlsnkOHj5CIh3EcB4QENCzbIWIYhEwT3XV9g8W2wZBEImEMXSvJOwylc1RUVKKU9Lv7SkU+bxEOhwlFQuiaRlQoHNsmnc5QEYv7xonQ8DyFk7aJVlT4YmpKEgqZONk8QnpUVFTguQ6apuG5HkPJNJVVlWi67o8HQpBKpohUVvpJrKaBY7u+fEBBz2Q4HDQ6b0+hcF2P/t5+ujo66e/vZ2hwkIrqWuLVVTS2tFJdV4OuG35pMQpNh7Cu+QaPApTA88ofT8csdf+mN72JN73pTaddL4Tgjjvu4I477jjtNrW1tYEo2zmiHI/KiV4Hr6WZ/i98Bllbg97dg9fYMCEm6uy1q8m+/iqq7vwh1f/6PeCknM+zele8luZxbYidGYXr2DhWHs/z2L9nD9osQW19HUKAlbfQNA0zFPJdyQUPSzQaZYR5MqpKZ0SEZ8zx44CAlxdBKBQiHArheB6elFRUJtB0jepEAlCl77FAoQnJM+u309vbQzhiUFkZY/2zG5g3fx5dPb0MDiXRhWL2jBnMnj1r1PdeCEH1CJ0crbR3QThscvmq5Sglixuz9rEnWL58KRXRaCmH69jxDjqOd7B82WL/nQKkUjzwyONctmpZqdJFScWmrdtobmpiUmszRZMgm83x1DMbuPp1l4NSSAGOgt/9/mFes2oFsUgIrbDf3XsPkM9bLF44178CQmA7Dvf/4RGuet1l6EIg8bsTP7T2cS5atICa6kqk8s/r+R076egdGjZICp7YYg6NlbPYtnkbBw4foba+lpbWFpomT2LLc9t544oVpAYG2bFpMwP9/UybPZOFl1yMGQmPzJz1c2oUaFr5A0nQi+fVwFk8Kl5zE6k/vR536pQJY4ycFl1n6OaP4syaeUrP0Umhq8LPiecxORUCXdP9MIymE62IY4RMhFQcOnwIwzDJ5S3i8Ti1NXVomoZCUVlZOSKUc7IJV8zw9wmslIDxgD8xN9XXUpOoHPXNtR2H9Zv20dc/xOKLp7Fq5UKMwj3d29PN4osW0trciASSmSw7n3+R//nlvaxcsYxp7e2FCiExKnkVoOAWQNMEIVPHTw31t7ho/jwSFTHMQjhHAaauIYQgEjIp3kVSwZJFFxWWlXaMrgkMwyBkDk/JWoVg8aKLCBceJHxfjQShCJk6ZmG5DkyZ1IqUCtMwhrVDpIfC99LoCLzCUSxdfAkViTiGpg+X/ZbU1ApXsvAAq5Ri3559PLtxMwsWXcRbV7yVSOEYh1IpqqprmTy5FTG5lQUXz8fJW+x8bhu/vfu/eN2a1TS2tpQuXdEro41B3CQwUF5ljPSoTCTvyFg51Xlq/QPUfvWbo42zCe0xGUlBowA/aU4XgtbWFirjlTiOjed6tE9px/MkO3Y+T11NXUGjQKOmoam0h5NyUEb8HkR2As4XJQNE+ZNmY30dSilsx8L1vNL3NJvLc98Dm5k7ZzKXrZhTmAxHl/X6ng9fGTVREWPV8qUsWjifRx5ZS3dXDyuXL0MUcjVO7XL1OHFFa2tLaYk/6Q8npY8s0tUETG4p3G8nJKHrIxPVlZ/X0to8rBnm35ua30lcDFfGKCAer0ArfHZxHx5QiPiWlgugvq6mIJrmdzovhp780uAROSdKsXHDFo51dPHmd7yVikh4lHCakgpN19Dxk2QBQpEwy1cuZ8bMGfzunt9wxeqraJs6BU0Uj0ONKUkwMFBejRQ8Khc8pzjP7DVXX7DGmUBhaBq6oZUeiAR+E8B83sLzPPL5PBUV0dI6BNTU1AwPoSfFcUTxwXHUgBgQcE4pTOZKQDafp6e3l+aGelzXIZXJgALX8/jdH55h6eJ5TGtv9AUIxcnGiZT+dO1PmP76imiUN157LQ+vXcuGzZtZsXTpybW7/k6Qnj360EpiZCOTyf1wTikMVNjuVOflWykjQqtqeNvSHpW/f0cpPClHV9KNEEMTSpUMDqkKBb4nnIagqNAqSqtcz8XQxYjNFM8/v4ujnV1c98ZrCBl66d2lBxkFhqZjUAyBDX9MfV0t73jX2/mvn/0X77jhPSQScd/eE8I3/spkzN2MAwL+eHytAVXM2iy+TtrqFNsUXsPvL+zrpDeM2MfIj9B1ciuXk3nzdeRWLkdp+uh9MLzv4lNEKV3nNMdZ3nGP/ABO+F2dvIMT9nOmazEyzmuGwoQift8dXdcR+E9i7e1T2Lx5C719/UybOrU0CColsa388IB3UrWXKu27+BIjrZWAgHNE8SsXjkapSCRwXAfTMIjH/LyP9Ru3MWVSPdPaG/xp9BTfUU3zk1JhWHrdwfc26LrGn1z1Ovbu20dH96lVzX1DSWLZVkmnZORYVvod3ztZNFRgtAFTorBISr8E2nXd0mI16pfCxxcmeTtvk8/nGXUAajgRXqqCB0VoWK5HOm+TymRRSpbuZI0RomtKEQmbhXWKbDLDhvUbWXPN1UQMfYQ423A7QCmlryw9Yn86qvCCqqoEr3vdFTz5yKNoyl+mA8YYxo7AQAk4t5TmO1G4eYtPGv7vSonCTwoTc/F2ECiBX+4sCreDECP+7f+uRs2eBQ2QUdqHIyZaik8qYoTLVPjLSsv9csHiNsOz8+lffkKcNrx96fdT7Edow8sZPpaT9lM6Lm3E/oZf/vXRkUriOA6eJ7HyFlJ6KKVIpdOkc3lcT2JZNp50SafT9PX2ks1mSo5df7AqPDwW/w6FBaLwCgg4HxS/innbYWBwsBDisdF1jZyVZ+++PSxdNLcQvDk1YoS3VOFP4iN/GrrOVVdeyeNPPnVKg0IAyWSKdRu34LguwwGUYjZHUSdIIYVi19793P/wE74nRRW9OMOv4oDoeZJnNm3haGe3v0xJ/1UKxkiKknJCwJbnd7Nx+67SspHnVDxqqRSugg1bt/Po01t5ZsuLuN7ocxJIP69FSkKFbuU6sGXDFpYtX0pFODTCABn+LAEo10PXtdHKsiO21VDMmjOTvs5urFy+sAxMvXwLJQjxBJxjfN2AnK04drwDTdeQroth6BiGiRAa2VwOoRko5RILRxCaryYsNAPbtrAtB89zkZ5ENwShUBRD1/CUX5onEZi6Rsj024crDCwrj+d4eJ4LAkzTQDd0XBccKweaTtj0J/5IOAJCw3JdkB55y0bXfAMmHAkjlULXdBxXIj0XITQMHYQmCJkhlBIoDTzHI5fLoQkNgYumGeimiW4YeJ5E1wXSA9dzCuOch6ZpGIaBpheUG6T/xOZ5EiU9vIL1Zpgm0vMQCPr6+snmsjiuxHIcohUVoBTPrFvHay67jIb6BirjccKhEInKSgzTJJ/LYpommzdvZsqU9pIpd/Jfa4TnZ0SQJ3CgBJwPFAqh6wwMJnFdj2QyiWXZHD5yhKlTJpU0R04n8a4XnvhPNCsEvpFiIGhqaCCXz5PN5qiIxUbtTwG5fJZ0JsP+Q4fQNR1P+t6Eosqqp/yHqaNHj3Hs+FGisTBPP70OqaTvV9H8PBJd15HSDznteP4FolW1HD52nGwuU7jf/U/0pB/WkUrheB7Pb99B/PAxorEoQ52HUa5ECN/IAYVUCs/1cFyHFw92AJKWhgaUcHl2wyYoqMJI6YeglJK8sH0ni1cuQwc8qTh08BArLltZ8nYMO3z9q+W/XxIOGaUrWHys0kZsr2k6k9ta6e7oZMaMaf41DpJkA8Yn/tc2m7M51p1ED0V961uYaLoglbP8AUE3kSKErptYjoNmGujoaMLE9mySORs7nyMSCqMpDSkkwvXI512kcnEkmLoiHo1heb5AkuM42JaF6zooJEIpzJCJVAa2lcPxPCoiIVAeFfFKFBqu9L0QQ+kkIV3H0DXMiImmhdCFwHIknmOjaTqG5t/osYoKlPIbbeXyNul0GulKNOEihEGsIkwsniCbyWMYGihB3sojpd+OSykImSaGqaMVBj/b9j9DCMjl8ijpoesmUvrGUTQcIlFdTf/gEHOmT2P5yqLYkj/YCASV8QoWzJ1FVXU1mqYRDiV415++0zeCPIkrKSWylZ7uip4pNSxaXYwAFfc70Si65dPpzHk+koCxUDQiDMdFplPkLYtUOk0un+d4RwdWLsPktskkk8nTGidKKXK5LEPJJIlkEhsoZpNo+JNhuPA50WiUAwcOMmXyJKAYDlXk8nnMkMnsGdN9dValCp3AtdLkrSHwkCjP41v/5x/IplOlcE3J0FGKUCiE67pIJamtbeC/f/lLmhvrUUphlLTjFZ7QcAv3Y1gp9u1+kV/8/GeUgkajnCIj40EaN/7N51m5fLFfbyQABLr/w797hYZEMdTfh5XLkkkmyebzuJ6NY+VI2b70PQrcQu6O63l4StFx7DiDff0cOXQU6fqeWsdxUNL34iqlcPIex491UFlVSXNDHRLIZZKla3A2hHq5uiWdQ5LJJFVVVWzb/DSVI2rVA8Y7/hc97yiEWUEkGsUMR+jp6SZsmCSTKRYtumhU8te+ffuoramhpqa6dDNmszl27HyeZcsvoVhaIoDNW3eycMFsDN0oJZz19PaTt2wmtbUU8kn8p50/PPgQ4UgYhIHj2NiOQyxk4EmXeLwSKUEYBvlcnqHUEAY6Qiii0RBmKApC4TjgOhZSaAjpoqGIx+NIJTBCBq6nyGYz5LJ5lHQQGFRETfRQCITuu0c1Ayuf82PGro2U/sBl6KKQUKbjev7AoOuCfN7BcywUvjy3QhAxfcGmdCbH3LlzWXTJxaUr7ilKSXNCDT/9CPxyPwW+CNSIEp2RA8Jw/s2wM1kpxVDnQQb7urh4yWsYGhoikUi8bN+SV5L9+/czY8aM830YAQGveo4cOcKkSZPOuE3gQQk4h/gTXDwWoXnSVNZt3EpVdQ0HDh4lFo2w+srLT3gmV8yZNRNQ5C2Lo8eO09XdR09PJ5rQ2LxlO61NDbS0NBMKhVi5bBGaNjqD0xc+Gu3ylVJi6BptkyYjC+5Zz/Po7+2hKl5DdU2NP4kLjXQ6Qz5v0drsCyiFwga60Ojs6aaltQXleX4ejecyMDBAKBKlMpHwk8c0DSuf58CBw7RNmYTywDAEoUiIjo4OGhubMU0Tz3PxpKK/rx/P82hubKDwuIUQGh2dnbS0tqJrAldKhJTsP3CItrZWTNPwn96k5MiRI6UM+VKmvRhOhivm/WQzGfbu3cvRI0fp7x8gmUzS3NpCoqqKObNn09zailbqgFzMQxnhL1EnPLRNIGprawE4fPgwVVVV5/loLjyKneaPHDkyYYzWicSFcH2VUqRSqbJa2wQGSsA5ZWQ6g+P6+SAV8ThGoeLE79KpSrNrKpXkqfUbGBhMMmVyG62tLQwNDhGORqivb+DgkWM8te5ZmhobWHXp8uGYcTHlVTuVu1ehpMuihfMK/1SAxtrHHmf6jBm0jNAeSA0lGejv59JLl/peBuF7YboeeoSLFszDLHg6ULBp8xZisRhz586l6H51HIeO411cumJZKXVPCeg8fpx5c2YRKiShoWDXnr3kclkuvnhByR1sOzZdXR0smD+X4Q6scPTocebNm0s0OtxCIpVKFhLrilkiqpTABpDNZnnyyafp7+tn9pzZXLrqUgb6B3jhhRd57VVX0tvby7bnnuORRx7h8iuuYOrUqae+fhMvslOiaHhVVVVN2AF+IpBIJILr+woy0a9vuQ8HgYEScN7QdR0lPaqrK4mEwv7CggWjlGLb9h3sfGEPl1++iraWJrRCZ97uzk7C0Sizp09h1vQpeJ5i/4ED/PKe37Ji+RLmzJ5ZCmOcajb1y/BG5FAULIT58+ZQVVVVMCT8AxEChKEVNBUKIRKhuPiihZi6Tmn+LngahKYXcjn8vYcMg2VLLyl0HR2uHpKuhyb8eHUhNExba4tfulcKEANKIV0H/YQ0tRXLlxIudFcuxsf9MLVe2q54agI4ePAQjz/xJJdd9hqmvv7qQqt6yGWzRKNhKhNxKhNxpk+bSjqV4qEHH+bgvv1cdfWVGJr/6cUoUPmtvgICAgJeOkGZccB5ozKRIF5ZSXIoWVAx9KdfpRRPPv0Mxzq7eef1b2VKW0sh+96fIaX0Sh4WgcDQNWbPnMGfvuvt7Ni5ky1bt5U0AU6JOjFBy5/Gm5uaiEZ8Q6lUfgzoYoQVgp8G19rczInOBaX8duTDe/Sf2FubG9FG+TLAsmxKfp7C/hPxuN9TRAwvU1LiOl6x6Lj0X11dLYaulbwvxZJkTTtZdG7Xrj08++wG3vnO65k+fVrJOAGQnsQ0jJJGgS6gKlHJ29/+VkKhEA898BAoWQoTlcJFEzXGExAQMGEIDJSA84DCcVyGkkOk0hl03aC7p6e0dvuOnQwlh7hm9VW+gmFhwhYjJu2igeFPzP4rHArx1je/iT2797B3335/25LewGhcxyu8vyglIka9KBlDclRc6sTtfBXJ4QlfLxoop9pOiFLPG096BcmTYT2WkfsbafvIUxx/UQm2UGeDwC8t1E9Qxe3t6WXz5s289a1vKYWDSiqygOu66LoxwrfjvzRN43WvuxwpYcf2ncPXCjjBLptQhMNhvvzlLxMOh8/3oVyQBNf3leXVdn0DAyXg3CN8ddNYRQWO45AvdNlVKJKpNBs3beb1V1+NcZquUvI0Rgf4+33zm9/Io489Tj5vnXIbpfy24QV/zVmPtWh0lINWpo6zkmfREikclixmuo4+pFO+XNfBHNFszPMkDz30MGuuXUMoHOLkPRUSho3h94zWfxOsXn0VG57dgm1bJe+JKmgtTETC4TC33377q2aAP9cE1/eV5dV2fQMDJeC8IASETANdg4aGOqZMmQwonnrqaV7zmlWEQ+bwTDnCoyDwjYCTelUUdyoE0WiUpUsWs+6ZZ0qr1AnbSinp6Or2BZHUiA1GafAXPDBA3rJP/LRR+6ZwXKl0lnQmO6wWWfqvGL7yz0LoGoNDSfr6+0ufoUbssGg4SSkRmk4qnaGnr5/u3v7CdRjOsCl5NZQiHAqXDIm9e/bQ3NJMTU114fIUr+fwZ1mWPcJAGSHrXyAcCbHwogXs2PHCqBOeoPZJQEDABCIwUALOOUVxsCOHj+DaDkcOH+HI4cM4tsux4x3MmDF91PYnPvUr1EmhjBNZsGA+u3fvwfW8U67P5CzWbXyOrGWV9jlsSAxPwEoqujq7uO8Pj+CdIFN9olw1ArZu30nf4NBI6cURhg8UtSt1TWP/wUN0dHaPOLFTeIaUQnqS7c8/z669B+juGxh1fCNxXRdzRN+M7c89x7IlS4azX0Y1GfJxHIdQqfX7qX068xfM5cUXd5VOZ2TeTEBAQMArRVDFE3CO8WdsTfdVVKVSoAnCkSidXd00NDRgFIyP002BQoiC3skJy0d8QigUoqoqQX//AI0N9Sdtm8tlUQr27D1I2DSRo7qODifR9vf3093VxfTpMTY+u6HUnVQIMUqu2pMeu/ccQAtFOHDgIKlkElVQXkSI0u++UJzHnr17yebyxKJhero68DyvVI3jn4NCSkU+l+fI0U4qqyqpqa3BzqbYuuU5bMcBFI7r+TLYUvLc9u0sumQRALZt4zguicqKUdLTMNp2cj2PRKziNNfZt2fi8Ri25eJ5Hpqun9qQCggICHiZmZAGSiBXPXFRgG7YxIaS2FYe27bp7x/AzlukhgZIxCtIJZN+AumpTBSlSKczaIZJMpnET1w91Wa+quuBAweIhEOjhNocx0EzTObMmIqm+Z+j5MhyXeXrfyiF41h87Wt/R3JwsLTfkXLVsViMfD6PlIrq2jp+fvfdNNTVolAFzQ1fj0SYJqrQJh10tm7ezBdv+1/DE/0pq440NCH4xj/+EzOmTS0k0I7eqlRMLGDfnj1ks1mSySR9ff1Eo1FSqVTpOhbLhEc6dAYHB9FQhWvJ6Os5QuJeCOjt7SUSjeK6kmQyRa5w/00UY+W73/0u3/zmN+ns7GTRokV8+9vfZsWKFef7sMY9t99+O1/5yldGLZszZw4vvvgiAPl8nltvvZW7774by7JYs2YN3/ve92hqaiptf/jwYW688UbWrl1LPB7nfe97H1/72tdG5T+9Wnj88cf55je/yaZNm+jo6OCee+7hbW97W2m9Uoovf/nL/PCHP2RwcJDLLruMO++8k1mzZpW26e/v55ZbbuE3v/kNmqZx/fXX86//+q/E48PK6tu2beOmm25iw4YNNDQ0cMstt/DZz372XJ7qH4+agOzbt29U1kDwCl7B6/y9jhw5cr6HhLNy9913q1AopP793/9d7dy5U334wx9W1dXVqqur63wf2rjny1/+slqwYIHq6OgovXp6ekrrP/rRj6rJkyerhx9+WG3cuFFdeuml6jWveU1pveu6auHChWr16tVqy5Yt6r777lP19fXqC1/4wvk4nfPOfffdp2677Tb1y1/+UgHqnnvuGbX+61//uqqqqlL33nuveu6559Rb3vIWNW3aNJXL5UrbXHvttWrRokXqmWeeUU888YSaOXOm+rM/+7PS+qGhIdXU1KRuuOEGtWPHDvXzn/9cRaNR9X//7/89V6f5sjAhe/EMDg5SU1NzwchVXwjyxSdyoZ1TcD4no0ZIVmunqbgaL6xcuZLly5fzne98B/CTjydPnswtt9zC5z//+fN8dOOb22+/nXvvvZetW7eetG5oaIiGhgZ+9rOf8c53vhOAF198kXnz5rFu3TouvfRSfv/73/OmN72J48ePl7wq3//+9/nc5z5HT08PoYLg4KsRIcQoD4pSitbWVm699VY+85nPAP41bmpq4q677uI973kPL7zwAvPnz2fDhg0sW7YMgPvvv5/rrruOo0eP0trayp133sltt91GZ2dn6fp+/vOf59577y15viYCE9K/dqHKVU90+eJTcaGdU3A+o5kIDwi2bbNp0ya+8IUvlJZpmsbq1atZt27deTyyicOePXtobW0lEomwatUqvva1rzFlyhQ2bdqE4zisXr26tO3cuXOZMmVKyUBZt24dF1100aiQz5o1a7jxxhvZuXMnixcvPh+nNC45cOAAnZ2do65nVVUVK1euZN26dbznPe9h3bp1VFdXl4wTgNWrV6NpGuvXr+ftb38769at47Wvfe0o42/NmjX8wz/8AwMDA9TU1JzT83qpjO/HnoCAgIA/kt7eXjzPGzVBAjQ1NdHZ2XmejmrisHLlSu666y7uv/9+7rzzTg4cOMAVV1xBKpUqPaFXV1ePes/Ia9vZ2XnKa19cFzBM8Xqc6bva2dlJY2PjqPWGYVBbW3vBXfMJ6UEJCAgICDg3vOENbyj9fvHFF7Ny5Ura29v5r//6L6LR6Hk8soALnQnpQbnQ5H4vtPOBC++cgvOZuNTX16PrOl1dXaOWd3V10dzcfJ6OauJSXV3N7Nmz2bt3L83Nzdi2zWChyq3IyGvb3Nx8ymtfXBcwTPF6nOm72tzcTHd396j1ruvS399/wV3zCWugXEhyvxfa+cCFd07B+UxcQqEQS5cu5eGHHy4tk1Ly8MMPs2rVqvN4ZBOTdDrNvn37aGlpYenSpZimOera7tq1i8OHD5eu7apVq9i+ffuoSfXBBx8kkUgwf/78c37845lp06bR3Nw86nomk0nWr18/6noODg6yadOm0jaPPPIIUkpWrlxZ2ubxxx/HcZzSNg8++CBz5syZMPknABOyzDggICBgLNx9990qHA6ru+66Sz3//PPqIx/5iKqurladnZ3n+9DGPbfeeqt69NFH1YEDB9RTTz2lVq9ererr61V3d7dSyi8znjJlinrkkUfUxo0b1apVq9SqVatK7y+WGV9zzTVq69at6v7771cNDQ2v2jLjVCqltmzZorZs2aIA9U//9E9qy5Yt6tChQ0opv8y4urpa/epXv1Lbtm1Tb33rW09ZZrx48WK1fv169eSTT6pZs2aNKjMeHBxUTU1N6i/+4i/Ujh071N13361isdiEKzMODJSAgIBXBd/+9rfVlClTVCgUUitWrFDPPPPM+T6kCcG73/1u1dLSokKhkGpra1Pvfve71d69e0vrc7mc+tjHPqZqampULBZTb3/721VHR8eofRw8eFC94Q1vUNFoVNXX16tbb71VOY5zrk9lXLB27dpT6gm9733vU0opJaVUX/ziF1VTU5MKh8Pq6quvVrt27Rq1j76+PvVnf/ZnKh6Pq0QioT7wgQ+oVCo1apvnnntOXX755SocDqu2tjb19a9//Vyd4svGhNRBCQgICAgICLiwmZA5KAEBAQEBAQEXNhPSQPnud7/L1KlTiUQirFy5kmefffZ8H9JJfO1rX2P58uVUVlbS2NjI2972Nnbt2jVqmyuvvNLvrzLi9dGPfnTUNocPH+aNb3wjsViMxsZG/uZv/gbXdc/lqZS4/fbbTzreuXPnltbn83luuukm6urqiMfjXH/99Sdlko+n85k6depJ5yOE4KabbgLG/9/n8ccf581vfjOtra0IIbj33ntHrVdK8aUvfYmWlhai0SirV69mz549o7bp7+/nhhtuIJFIUF1dzYc+9CHS6fSobbZt28YVV1xBJBJh8uTJfOMb33ilTy0gICBg4hko//mf/8mnP/1pvvzlL7N582YWLVrEmjVrTiq7Ot889thj3HTTTTzzzDM8+OCDOI7DNddcQyYzusHhhz/8YTo6OkqvkYO/53m88Y1vxLZtnn76aX784x9z11138aUvfelcn06JBQsWjDreJ598srTuU5/6FL/5zW/47//+bx577DGOHz/OO97xjtL68XY+GzZsGHUuDz74IADvete7StuM579PJpNh0aJFfPe73z3l+m984xt861vf4vvf/z7r16+noqKCNWvWkM/nS9vccMMN7Ny5kwcffJDf/va3PP7443zkIx8prU8mk1xzzTW0t7ezadMmvvnNb3L77bfzgx/84BU/v4CAgFc55zkHZsysWLFC3XTTTaV/e56nWltb1de+9rXzeFRnp7u7WwHqscceKy173etepz7xiU+c9j333Xef0jRtVKXBnXfeqRKJhLIs65U83FPy5S9/WS1atOiU6wYHB5Vpmuq///u/S8teeOEFBah169Yppcbf+ZzIJz7xCTVjxgwlpVRKTay/Dyc0HZNSqubmZvXNb36ztGxwcFCFw2H185//XCml1PPPP68AtWHDhtI2v//975UQQh07dkwppdT3vvc9VVNTM+p8Pve5z6k5c+a8wmcUEBDwamdCeVCKPTVG9imYKD01hoaGAKitrR21/Kc//Sn19fUsXLiQL3zhC2Sz2dK60/WwSCaT7Ny589wc+AkUe3JMnz6dG264gcOHDwOctScHjM/zKWLbNj/5yU/44Ac/iBCitHyi/X2KnK2nB3DWnh7FbU7V02PXrl0MDAyco7MJCAh4NTKhpO7P1FNjPHdolFLyyU9+kssuu4yFCxeWlv/5n/857e3ttLa2sm3bNj73uc+xa9cufvnLXwLjr59CsSfHnDlz6Ojo4Ctf+QpXXHEFO3bsmPA9Oe69914GBwd5//vfX1o20f4+I3k5e3pMmzbtpH0U100o0aeAgIAJxYQyUCYqN910Ezt27BiVrwGMivVfdNFFtLS0cPXVV7Nv3z5mzJhxrg/zrFzIPTn+7d/+jTe84Q20traWlk20v09AQEDAhcSECvFMxJ4aN998M7/97W9Zu3YtkyZNOuO2RZnivXv3AuO/n8KF0pPj0KFDPPTQQ/zVX/3VGbebSH+foKdHQEDARGdCGSgTqaeGUoqbb76Ze+65h0ceeeQkN/mp2Lp1KwAtLS3A+O9hcaH05PjRj35EY2Mjb3zjG8+43UT6+wQ9PQICAiY85ztLd6xMlJ4aN954o6qqqlKPPvqo6ujoKL2y2axSSqm9e/eqO+64Q23cuFEdOHBA/epXv1LTp09Xr33ta0v7GG89LC7Enhye56kpU6aoz33uc6OWT4S/T9DTIyAg4EJmwhkoSk2MnhqcotcCoH70ox8ppZQ6fPiweu1rX6tqa2tVOBxWM2fOVH/zN3+jhoaGRu1nPPWwuBB7cjzwwAMKOKnXxUT4+wQ9PQICAi5kgl48AQEBAQEBAeOOCZWDEhAQEBAQEPDqIDBQAgICAgICAsYdgYESEBAQEBAQMO4IDJSAgICAgICAcUdgoAQEBAQEBASMOwIDJSAgICAgIGDcERgoAQEBAQEBAeOOwEAJCAgICAgIGHcEBkpAQEBAQEDAuCMwUAICAgICAgLGHYGBEhAQEBAQEDDuCAyUgICAgICAgHHH/w+oOUat6wWljQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Example of landmarks \n",
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "\n",
-    "main_path = \"https://github.com/MichaelSev/Hyperiax_data/raw/main/image/\"\n",
-    "\n",
-    "list_of_names =[\"img0.jpg\",\"img1.jpg\",\"img2.jpg\",\"img3.jpg\"]\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_leaf]):\n",
-    "    plt.subplot(2,2,i+1)\n",
-    "    im = Image.open(requests.get(main_path+list_of_names[i], stream=True).raw)\n",
-    "    implot = plt.imshow(im)\n",
-    "    plt.scatter(data[::2],data[1::2],c=\"r\")\n",
-    "    #plt.title(str(leaf.name))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prepare tree for computing\n",
-    "\n",
-    "We load in the packages and reload the tree with precumted children for faster compution, and fix the space we expect to use for each parameter.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "\n",
-    "from functools import partial\n",
-    "\n",
-    "# Requires Jaxdifferentalgeometry package\n",
-    "from jaxgeometry.manifolds.landmarks import *   \n",
-    "from jaxgeometry.Riemannian import metric\n",
-    "from jaxgeometry.dynamics import Hamiltonian\n",
-    "from jaxgeometry.Riemannian import Log\n",
-    "from jaxgeometry.dynamics import flow_differential\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Saved parameters within the tree \n",
-    "tree.add_property('sigma', shape=(1,))\n",
-    "\n",
-    "# Fixed sigma for simplification\n",
-    "tree.data['sigma'] =  tree.data['sigma'].at[:].set(0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. compute inner nodes (i.e. ancestral shapes)\n",
-    "\n",
-    "We compute the inner nodes of the tree, i.e. the wing shapes of ancestral species. For this we need to define a 'fuse' function which computes a non-linear version of a weighted mean: each inner node is a non-linear weighted mean of its children nodes. The particular non-linear weighted mean we compute is based on the LDDMM framework, as implemented in the jaxgeometry library.\n",
-    "\n",
-    "Note this is a quicker version of the code, since we exclude the contrast and lift function for the tangent space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fuse_DICAROS(value,child_value, child_sigma, child_edge_length,**kwargs):\n",
-    "  \n",
-    "    def DICAROS(child_coords1, child_coords2, kernel_sigma, parent_index):\n",
-    "      \n",
-    "        # Estimate the average distance between each landmark,\n",
-    "        # predefined kernel size\n",
-    "       \n",
-    "        M = landmarks(jnp.shape(child_coords1)[0]//2, k_sigma=jnp.array(kernel_sigma) * jnp.eye(2),m=2)\n",
-    "        # Riemannian structure\n",
-    "    \n",
-    "        metric.initialize(M)\n",
-    "        q = M.coords(jnp.array(child_coords1))\n",
-    "        v = (jnp.array(child_coords2), [0])\n",
-    "      \n",
-    "        Hamiltonian.initialize(M)\n",
-    "        # Logarithm map\n",
-    "        Log.initialize(M, f=M.Exp_Hamiltonian)\n",
-    "   \n",
-    "\n",
-    "        # Estimate momentum\n",
-    "        p = M.Log(q, v)[0]\n",
-    "   \n",
-    "        # Hamiltonian\n",
-    "        (_, qps, charts_qp) = M.Hamiltonian_dynamics(q, p, dts(n_steps=100))\n",
-    " \n",
-    "        #lift\n",
-    "        flow_differential.initialize(M)\n",
-    "        _,dphis,_ = M.flow_differential(qps,dts())  \n",
-    "\n",
-    "        # Return landmarks, contrast and phi\n",
-    "        return qps[:,0][parent_index],p,dphis[parent_index]\n",
-    "\n",
-    "    # Map function, to determine the shortest branch from child to parent\n",
-    "    def map_function(child_coords_pair, child_edge_length_pair, kernel_sigma_pair):\n",
-    "        edge_length_sum = child_edge_length_pair.sum()\n",
-    " \n",
-    "        def true_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[0] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return DICAROS(child_coords_pair[0, :], child_coords_pair[1, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        def false_fun(_):\n",
-    "            parent_index = jnp.floor(child_edge_length_pair[1] / edge_length_sum * 100 - 1).astype(int)[0]\n",
-    "            return DICAROS(child_coords_pair[1, :], child_coords_pair[0, :], kernel_sigma_pair[:, 0], parent_index)\n",
-    "\n",
-    "        # Run Upwards LDDMM; from either left or right\n",
-    "\n",
-    "        return jax.lax.cond((child_edge_length_pair[0] < child_edge_length_pair[1])[0], true_fun, false_fun, operand=None)\n",
-    "\n",
-    "    # vmap to iterate over set of children\n",
-    "    qps,p,dphis = jax.vmap(map_function, in_axes=(0, 0, 0))(child_value, child_edge_length, child_sigma)\n",
-    "\n",
-    "    # P should be divided with the sum of p\n",
-    "\n",
-    "    p_adj= p/jnp.sum(child_edge_length,axis=1)\n",
-    "\n",
-    "    return {'value': qps,'p_adj':p_adj,'phi':dphis}\n",
-    "\n",
-    "#### Now we use UpLambda() to wrap the fuse function and throw it to OrderedExecutor, then simply just #### call .up().\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "upmodel = UpLambda(up_fn=fuse_DICAROS)\n",
-    "upmodelexe = OrderedExecutor(upmodel)\n",
-    "res = upmodelexe.up(tree) \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2.1 Plots\n",
-    "We can now plot one of the estimated inner nodes, computed as the weighted mean of two neighbouring leaf-node shapes. The two leaf shapes are represented by the red and green points, the estimated inner node is represented by the blue points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "\n",
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img0.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][3][::2], tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][4][::2], tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][1][::2], tree.data['value'][1][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img1.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][3][::2],tree.data['value'][3][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][4][::2], tree.data['value'][4][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][1][::2],tree.data['value'][1][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from PIL import Image\n",
-    "import requests\n",
-    "\n",
-    "# Illustrate the results\n",
-    "plt.subplot(1, 2, 1)\n",
-    "# Load image \n",
-    "im = Image.open(requests.get(main_path + \"img2.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][5][::2], tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][6][::2], tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "plt.scatter(tree.data['value'][2][::2], tree.data['value'][2][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "# Load image\n",
-    "im = Image.open(requests.get(main_path + \"img3.jpg\", stream=True).raw)\n",
-    "implot = plt.imshow(im)\n",
-    "\n",
-    "# Plot points\n",
-    "plt.scatter(tree.data['value'][5][::2], tree.data['value'][5][1::2], color='red')    # child1 of root\n",
-    "plt.scatter(tree.data['value'][6][::2], tree.data['value'][6][1::2], color='green')  # child2 of root\n",
-    "\n",
-    "\n",
-    "plt.scatter(tree.data['value'][2][::2], tree.data['value'][2][1::2], color='blue')   # root\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The points on the left and right figures are identical, the only difference is the superimposed image.\n",
-    "\n",
-    "Next, we plot all leaf nodes (points in light colors) along with the estimated root node shape (blue points), i.e. the common ancestor of all 4 species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x16c6fc250>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Cnam5VEEA5Ozhsxo9VvnGEsBFD4A4yeWksTFpfFxaXpZaWqTBQWloKOJdqn3ETVogeLs2NjY2/Prm3/zmN/XGN75Rr3vd6/R7v/d7ShR1Zfmt3/otvec979ETTzyhZz3rWZKkd7/73frUpz6lxx9/XJL0kz/5k/rOd76jP/qjP9r8usOHD+s1r3mNPvzhD1c1jjt37mjPnj26ffu2du/e7dFPBwDwXE+PqWm2dblmImGKEc/OhjeuIulsWl0TXdu2zw/MM5kFAEAmaHb0qLSwIK2v393e1CQdOiRdvUrwDEB4aokT+Vbj7Jvf/Kbe8IY36KUvfal+7dd+TX/3d3+nW7duFdQm6+3t1b333quBgQF99atf1cc//nF98IMf1Nvf/vbNz3nb296m2dlZ/fqv/7oef/xxDQ8P6/Of/7x+8Rd/0a+hAwDC4kgHL4q8A0C40tm0pq5P0ZXWYmNj24Nmknm8sGD2A4ALfAuczc3N6caNG/rMZz6j1tZWveQlL9n8l7dnzx59+tOf1te//nW97nWv06OPPqr3vve9Ghwc3Pycw4cP62Mf+5jGx8d18OBBffKTn9TMzIxe9apX+TV0AEBYHOng5XeR91xOGhmRkkmTcJdMmse5nCffHgCclppLqWuiS/0z/eqa6FJqLhX2kFDC+Pj2oFne+rrZDwAu8HWppi1YqgkA8FpxjbPUkZQnnVRZ2gIA5bFU3h2JRPnAmWTOa8WNtIFNmYxpHNXWRkdXC0SxIVYtcaKamwMAAAD/irxXs7Tl3DlPngoAnFNpqXxULuaioqVFymYr768bQZXoWlmRenuly5fvbjt+3JTtsGwFQlzQEMvHpZoAAERdZ2un+g72eXqxxtIWACjP76Xy8M7goMkqK6Wpyeyv2cqKaSS0f7908qTU0WEer642NFZYpLfXNIra6soV6cyZcMYTc+lsuiBoJkkXrl2IXX1JAmcAAFhkebmx/QAQZZ2tnTp7+GzBttSRFNlmFhoaMiUGioNn+dIDQ0N1fFOCKtGWyZhMs+I1vGtrZvviYjjjijEaYhks1QQAwCK+Lm0BgAjwa6k8vNXcbOpyjo2ZbOnlZXMOGxw0QbOa63XmgyrFtgZVQly2GcUaUIG7ebPy/hs3WJobMLJ8DTLOAACwiC9LWwAgYvxYKg/vNTebupxLSya+tbRkHtfV5KaaoEpI6PTqkX37Ku9vawtmHNhElq9BV00gZNydchRFaeETumrCC5xbAEROJmNqm1XaH8KcjE6vHuvpMctvty7XTCSk7m5pdja8ccVcFOcVtcSJyDgDQsTdKQdRlBY+yy9tGR6WWltNwKy11TyOfNAsk5EuXaKGSYM4twARFfdjZEeH6a6YSBRuTyTM9pBuZFIDqjbpbFpT16fKF5efnjZBsq26u812hCbuWb5knAEh4e6Uo7gLFi9kFgaD1vOe4dwCRBDHyLtWV00jAIt+Fxx3q5eaSxV0aDx7+KxGj42W/uTFRbP8ljkYfELGGeAA7k45iE4/8UFmYbDokuYZzi3w0o6ZIQgGx8i79u41NyozGeniRfNxdjbUACI1oKqTzqYLgmaSdOHahfLHl/Z26cQJgmawAl01gZDQocRi5bKM6PQTH5UuUsgs9JblXdJcw7kFXqkpMwT+4RhZWnu7VT83nV53VunGDr8v2I6MMyAk3J2y0E5ZRnT6iQcyC4NlcZc0F3FugRdqzgyBfzhGOiOoGlCuZoL6eWMnl5NGRqRk0lRQSSbN41yu4W8NSCLjDAgVd6csUyHLKPfJWY19vEPj9/1vLT/1fLVoWYMa15DG1Jz4J1PjzKI7n2gAmYXBIiDtOc4taBSZIRbhGIktXM4Ezd/Y2Tp+L27slOpGns2apkozMzForIRA0BwAAKSKLc5zeq6OvvLvtfCX92+ekCWpSWs6pC/q6r/6T2r+xO/Er0BvVFna7j7SaLoBWIVi55bhGAlF532ZzqY9vbEzMmKCZFvn6HlNTWbfuXMNPw0iiOYAAFCrCllGYxrSwv+6b9sJeV0JLTS9TmNHZwiaRYml7e4jjdbzgFVY8msZjpFQdJq/eL2kdXy8dNBMMtvHxz15GsQcGWcAIFXMMkrqG8oqWfZLW1ulpSW/BoZQWNjuPhZoPQ9YxevMEDSIY2SsRSXjzGuJRPnAmWSyzorL1gJSbXEiAmcAkFdmKURi7SmtK1H2yzghRxgXKQAAwBLFNc5SR1I6330+xBGFL5k0Nc3K8eQGdyZjVqcwH4yUWuJENAcAgLzp6e1ZRt3davnyLmWXy39ZS4v/Q0NILGt3DwAA4ovmL9sNDlaucTY42MA3X1kxzcNYgRB7ZJyhKqTqI1aKsowoOho/HPMAAADsV6qrpmTm6IcONdhVk8YckcZSzSIEzhrjcttjwAu+npBDQFCoMo55AIAoyuWksTFTLH152WTMDw5KQ0NuzWOAYr68tumyHnkEzooQOKsfRSgBIyqTTYJClXHMAwBEUdRuAgK+u3RJOnmy/P6LF6UTJ4IbDzxXS5yoKaAxwVFRaXsMNKq52SzHXFoy2dpLS+axS5PMdDZdEDSTpAvXLiidTYc0IvtwzAMARNHY2PagmWQeLyyY/QC22Lev8v62tmDGASsQOENFHQ901LQdgL0ICu0szse8XE4aGTHdqRIJ83FkxGwHALhtfLx0rVbJbB8fr/MbZzImM2dxse6x+S2dTWvq+hQ3ClGbjg7TCCCRKNyeSJjtLNOMFQJnqKiztVNnD58t2JY6kmLJEuCgOAeFquXKMc/ri4D8Ep7hYdPSfX3dfBweNtsJngEoRjDCLcsVuoNXs3+blRVTOH3/frOcraPDPF5drXuMfkjNpdQ10aX+mX51TXQpNZcKe0hwyfS0aQSwVXe32Y5YocYZqkIxcSAaimucpY6kdL77fIgjspPNxzw/6tTRORa1svk9Av9RL9M9yaS5IVJOa6spQ1E1B7oNUrcUnllclG7cMMszyTSLDJoDFCFwBgB3ccHrLr8uAjy/oEKkETSJN4IRbvL0Bokj3Qanrk+pf6Z/2/bJU5PqO9gXwogA2ITmAACAsjpbO9V3sI8LHAf5VafO8yU8iCyajIB6mW4aGjLdM5uKrv7yXTWHhmr4ZjdvVt5/40aNo/MHJSoAeIXAGQAAjvDrIqClpbH9iA+CJiAY4abmZunqVZNZ1tpqAmatrebx1as1dgl3pNugK3VLAdiPwBkAADYq0anMr4uAwcHtWQh5TU1mPyARNAHBCJc1N5vlmEtLpjTZ0pJ5XFPQTHKq2+DosVHND8xr8tSk5gfmqesKoC7UOAMAwCYrK1Jvr3T58t1tx4+bDk5790ryvk5dvqvmwkJh/Zv8Ep6asxEQaTQZgUS9TGUyZsliXIuFr65KZ85UPFcBcEccj+k0ByhC4AwA4IyQOpXlctLYmDQ+bmqatbSYTLOhoQgHzeJ+4duAOE6wAUlV3dyIFboNAs6La9MfAmdFCJwBAJzgSKcy53HhC6BeId3cAAA/xLlTMl01AQBwkSOdypzX22sufLe6csUsOwKAcjIZE3DfGjSTzOPLlwtqUgKAC2j6Ux0CZwAA2MKRTmVO48IXQL24uQEgYmj6Ux0CZwAA2MKhTmXO4sIXQL24uQE0pkTHcISLTsnVuSfsAQAAgC2mp7d3KuvuNtvROC58AdQrf3OjXI0zbm4ApVFb1Gqjx0Z1+sBpmv5UQHMAAABsRKcy/1DcG0C9Vle339wgAABUxnkXFqKrZhECZwAAYBMXvgAaxc0NoDp0DIelaokTsVQTAADEy9695g43F74A6tXeznEDqEY1tUV5L8FyBM4AAEA8ceELAIC/qC2KCKCrJgAAAAAgFOlsWlPXp5TOpsMeCvxAx3BEABlnAOC3TMakqbMcDACqls6m6fAFRFxqLqUL1y5sPj57+KxGj42GOCJUq6ZjNB3D4TiaAwAO4mLCEjsFxGi9DQB14WIaUZXLSWNj0vi4tLwstbRIg4PS0JDU3Bz26IKVzqbVNdG1bfv8wDzzW8vVfYymtigsQlfNIgTOECVcTFig2oAYrbcBoGZxvpjmxli05XLS0aPSwoK0vn53e1OTdOiQdPVqvIJnU9en1D/Tv2375KlJ9R3sC2FEqEacj9GIllriRNQ4AxySzqYLgmaSdOHaBWpCBK231wTEtrpyxaSg52UyJrC2NWgmmceXL5s7bgCAbTJPZmraHhWpuZS6JrrUP9OvrokupeZSYQ8JHhsb2x40k8zjhQWzP046HuioaTvsENQxOpeTRkakZNLcd04mzeNcztOnAapC4AxwSFwvJqxSRUBs80Svbyihf1ZS39CIflk5Pffu59+4Eey40TCKFwPBiOPFNDfG4mF8fHvQLG993eyPk87WTp09fLZgW+pIiqwlywVxjM5nZw4PS9mseX9ks+bx0aMEzxA8AmeAQ+J4MWGdmzcr7s59+evmRP+xdmWV1LoSyiqpYQ3rqD53N3hG622nkAniH+4oo1gcL6a5MRYPy8uN7Y+i0WOjmh+Y1+SpSc0PzOt89/mwh4QdBHGM9jU7M5ORLl1i9QdqQo0zwDHFNc5SR1JMMoKUyUj795fdPfK2v9fwbz5Q8o5yk9Y0vOtXdO6RNDXOHEItD/9Q7weVxKneF8eZeEgmTdZMOa2t0tJSg09CJ28ExM9jtC/vFZp2oQg1zoAI485cyDo6zEk2kSjcnkhIx49r/A9LB80kaV1NGr/3F2m97RgyQfxDvR9U0tnaqb6DfbEIHMUxyy6OBgfNjYFSmprM/rqtrJimRPv3SydPmvlKT4+0utrANwXK8/MY7Ut2ZjU1ioEyyDgDgFqtrpqTbIk7VokX7C0bOJPMxLi4PBrsRiaIfwLJvgAcEqcsuzjyNcuWTt6IEM/nBzusGFEmQ4ZmDJFxBgB+2rvXTEIzGeniRfNxdlbau1ctLZW/dKf9sA+ZIP6h3g9QKE5ZdnHU3GyCY8PD5sK/qcl8HB5uMGhGJ29EjOfZmTvUKKZpF3ZCxhkAeGhkxEyAS9Y4azL7zp0LelTwApkg3iPjDGgcxybo0iWzPLOcixelEyeCGw+24X1aG8+zM8k4QwlknAFASIaGzAm9+C5Z/kQ/NBTGqOCFyGeC7NBlKp1Na+r6lNLZtGdP6Wu9HyAG6PgLSdK+fZX308k7VLxPa+d5duYONYoJmmEnZJwBgMdyOVPUfHzcLDVraTEBgKEhOgTCQlV0mSru5nv28FmNHhtt+KnpqgnUj/qLKECNMyvxPrVIhRrFdNWMJzLOACBEzc1mOebSkpm/Li2ZxwQAYKUdukyls+mCoJkkXbh2wZPMM9/q/QAxQMffEnbInI206WkTJNuqu5tO3iHjfWqRCjWKgZ3cE/YAAADxQp0Pi+QLShfbUlA6853yk34v/n75QHOka/9lMqYwcVsby0HgmY4HOmraHmlVZM5GXj4osLhoCp1zvLEC71MLtbfz3kDNyDgDAASGOh+WqaLLFJP+BqysmOVT+/ebwt0dHebx6mrYI0ME0PF3ix0yZ2Olvd00AiAwYAXepwiTH/Vp44oaZwCAQFDnw0JVdpkqrnGWOpLS+e7zAQzQcdQcQgBin8VLtzw4IPbvUwTOr/q0UVJLnIjAGULDCQSIl6nrU+qf6d+2ffLUpPoO9oUwIkiqOrjDMbtGXMwDwbh0yWR0lnPxosnAAoCY4GZ1dWgOAOuxXAuIH5b8WarKgtKdrZ3qO9jHhKtaVSyDBeCBffsq729rC2YcAGAJmlJ4j8AZAudnhzYA9qLOh6XoMuUPLuaBYHR0mEYAiUTh9kTCbCezsyHUSALcw81q79FVE4GrFAHnAhqIttFjozp94DRL/mxElylv5S/myy2D5XcdGpYdR9D0tGkEsLWrZonMWdSGGkmAm/I3q4vr03LOqx81zhA41lwDAGJhdXX7xfzx4+Zinoy+UBAIiLjFRbMMuq2N4HSDmK/DNdwU2Y7fSWW1xInIOEPgiIADAGIhvwyWi3krlCsVcfrAaeYgUUHmrGdYIQKXcFOktM7WTt6vHiFwhlCwXAsAEBtczFuBQABQPWokwRXcFEEQaA6A0NChDQAABIVAAFA9GvrAFXSQRBDIOAMAAEDkUSoCqA0rROACboogCDQHAAAAEEV044K/M0rJ5aSxMWl8XFpellpapMFBaWhIam4Oe3SAhTIZ6eZNK+p3Ftc4Sx1J6Xz3+RBHBBfUEicicAYAAGKPwsJAfOVy0tGj0sKCtL5+d3tTk3TokHT1KsEzYNPKitTbW3XH6KBuVnBTBLUicFaEwBkAACgnnU2ra6Jr2/b5gXkm30AMjIxIw8OFQbO8piaz79y5Kr+ZRVk4gC96eqQrV6S1tbvbEgmpu9t0kt6Cm1KwWS1xIpoDAADgsXQ2ranrU0pn02EPBVvkcuYCOZk0c/xk0jz+0tJflfx8CgsD8TA+XjpoJpnt4+NVfJOVFRNQ2L9fOnlS6ugwj1dXPR0rEKpMxmSabQ2aSebx5cvS4uLmpnLdLpkbwUUEzgAA8FBqLqWuiS71z/Sra6JLqblU2EOC7i7FGh6WsllzMZzNmse//rP/Wnrqudu+hsLCQDwsLze2X5JZunblSuG2K1ekM2fqHhdgnZs3K++/cWPzv3S7RJQQOAMAwCPcXbXX2Nj2+kWSebz41Wb9y29+qmA73RaB+GhpaWx/LVk4gNP27au8v61t8790u0SUEDgDgKBkMtKlSztPoKv9PFiHu6v22mkp1l9/9hHND8xr8tSk5gfm6cYFxMjgoKllVkpTk9lfUQ1ZOIDTOjpMI4BEonB7ImG2b6nr19naqbOHzxZ8Gjel4CqaAwCA36rtPlRjlyLYhyLz9kokygfOJHNxXJws4jc6gAF2aLirZiZjaptV2k+jAETF6qpZgmxZV02gVjQHAACbVFv3hPoozuPuqr0aXorlMWrhAfZobjbBseFhqbXVBMxaW83jHYNmUk1ZOIDz9u413TMzGeniRfNxdrbsTd5Xfk+n/vq/9+nHH+4saMyTywU8bqABZJwBgJ+qvAud++Kixl47qXENalktatGyBjWuIY2pWd/mbrVjuLtqn5ERcxFcKuusqcnsO3cumLGQmQhEUI1ZOEAcNJzNCfiIjDMA26SzaU1dn6JIedCqqHuSy0lH/88XaVjDyiqpdSWUVVLDGtZRfU45PZf6KI7pbO1U38E+giDVCqCu39CQmaQX1zHKT96Hhnx76m2ohQdEUI1ZOI1iXgcv+fV6qtSYZ2HB7AdcQOAMiAGWBIWoiu5DY2PSwl/t1roKl3isK6EF/YDGNFTQpQiIjJUVqafHZGWePGmWO/X0mMwNjzW8FMtDdBpDrMSt4U17u3TihK9Z4szr4CU/X087NeYZH/fsqQBfsVQTiDiWBFmgp8fUKttaeTyRkLq7pdlZJZNSNlvuizfUet/fa+mfXhjESIFg7fDeiLLUXEoXrl24+/hIik6elvByqXWsl23T8MYXzOvgpYm/mNBP/9FPb9vu1espkMY8mYxZ4dHWRlkT1KSWONE9AY0JQEgqLQlighWQ6entdU+6u812ScvLlb54l5a/+wJfhweEIpMpfE/kra2Z7YuLkZ4Ajx4b1ekDp70PqnAB0ZDigObZw2c1emy0pu+RD5bN3ZzT1JenGvpeTqvU8CbigXE/Ma+DV4qPd1t59Xpqaal0c7jBxjwE5xEglmoCEceSIAvsUPdk525/uwIYJBCwKur/RZ2ntfACXPYaVelsettF5IVrF2qq+bN1ydPWoFk938tp+cB4cSrJ1sA46sK8Dl4odbzbyqvX0+Dg9tqieU1NZn/dfOhGT+1AlEPgDIi4ztZOnT18tmBb6kiKu5JhKFP3xNdJBWCrHer/zXz3K0xca1HiAmLjypy++aNH+T1WqdGmDTtdiNbyvZxHYNw3zOvghUrHIi9fT7415vEhOO9K7UCCe+EgcAbEwOixUc0PzGvy1KTmB+apo2MZm7r9AYHp6DBLKhKFTTHWmnZpdp/0b7541uqJq1XKXEDsWlvX9177sn7qAr/HajSayVNNUCw2WUFVNMZB/ZjXoVHljkX/7V//N09fT7415vE4OO9FxnEQXAnuRRGBMyAmPF0SBE/Z1O0PCNT0tKn3t8Xcyzd05sfvPrZx4mqdHS4g2lb4PVaj0UyenYJiscoKKhMYVyJhtlN/r2HM69CIcse7gdcOeP5czc3SuXPS0pK5v7O0ZB43NL/1ODjfaMZxEFwJ7kUVzQEAwAL5ScW5c2GPBAhQvv7f4qJ044ZmvvsV/Zsvnt32aRS93sEOFxA3nm8+8nvcWSNNG/IXolsvbPpe06djrzgWz66aOzTGARAu35rUBCEfnC/XmbvG4LwLtQNpDBIuAmcAACBc7e1Se7tekn2+9MXtu22auFqpzAXEP++SrrxCuvHAM5/G77Eqna2ddV+EOH0h6rWiwDidXgH7NHK8C52HwflSNz5syxJ2IbgXZbs2NjY2wh6E3+7cuaM9e/bo9u3b2r17d9jDAQAAZaTmUtsmrtTvqcLq6rYLiNl90pkfl771bH6PAICI8jA4n86mrb7xwRzJW7XEiQicAQAAq9g+cbXalguI9LNX+D0CABAhzJG8Q+CsCIEzAADiiQkmAAAAitUSJ6LGGQAAiKTiJQ1nD5/V6LHREEcEAAAA1zSFPQAAAIKQzqY1dX2Ktt0xQdt2AEAUMH8BwkfGGQAg8sg8ih/atgMAXMf8BbADGWcAgEgj8yieaNsOAHAZ8xfAHgTOAACRVinzCNHV2dqps4fPFmxLHUmRbeYRlg4hKLmcNDIiJZNSImE+joyY7UCUMX8B7MFSTQBApLmUeUQHSG+NHhvV6QOn+Z16jKVDCEouJx09Ki0sSOvrZls2Kw0PSzMz0tWrUnNziAME5N+526X5CxB1ZJwBACLNlcyj1FxKXRNd6p/pV9dEl1JzqbCHFAmdrZ3qO9hn3d/bVSwdQpDGxgqDZnnr62b72FgYowLu8vPc7cr8BYiDXRsbGxthD8Jvd+7c0Z49e3T79m3t3r077OEAAEJgczZXOptW10TXtu3zA/PWjRWV5XLmYn58XFpellpapMFBaWgoGpkxU9en1D/Tv2375KlJ9R3sC2FEiLJk0mSYldPaKi0tBTceYKugzt02z18Al9USJ2KpJgAgFjpbO62dcNIBMhrisKyMpUMI0vJyY/u3yWSkmzeltjapvb3ucQFScOdu3+YvvB+AqrFUEwCAkBGMiIY4LCtj6RCC1NLS2P5NKytST4+0f7908qTU0WEer642PEbEl7Pnbt4PQM0InAEAEDKCEdEwPr49aJa3vm72R8HosVHND8xr8tSk5gfmdb77fNhDQkQNDkpNZa5WmprM/qr09kpXrhRuu3JFOnOmofHVi6600eDsuduy9wPgAmqcwRms7wcQdRzn3JZIlA+cSeZCf20tuPEAriu1/Fky76VDh6pc/pzJmMyaSvsDXKZGV9rocercbdn7AQhTLXEiMs7gBLrNAYgDOkD6LJORLl2SFhd9+faeLSsDisQ1Q6m52QTHhodNI4CmJvNxeLiGmoE3b1bef+OGByOtDl1po8mpc7dF7wfAJQTOYD0mGQCAhgRUz8WzZWXAFnG/edjcLJ07Z7pnrq2Zj+fO1dBoY9++yvvb2hoeY7UqFZMHAmHR+wFwCYEzWI9JBlAjn7NqAOcEVM9laMgsHysOnuWXlQ0Nefp0iAFuHnqgo0M6ftyspd4qkTDbA1yW5mwxeUSHRe8HwCUEzmA9JhmIpXqCX3RJArbLZKTLl7cXF1tbM9s9DDB7sqwM2IKbhx6Znpa6uwu3dXeb7QFytpg8quLMkmpL3g9Bc+bvAyvRHABOKC6kmjqSoosXomllxWTHXL58d9vx42Yys3dv5a/t6TFZNFsDBImEmQzNzvozXsB2ly6ZQHI5Fy9KJ04ENx6gBulsWl0TXdu2zw/M+xNsyWRMDaS2tmhmniwumhpOIf98ThWTR1WcbPpgyfshCE7+feC7WuJEBM7gDCYZiIV6g190SQJK470BxwVy87CRmzZAzAUe4EZN+PugHLpqIpKc6lgD1KPKJWW5nDQyIiWTJqaWTJrHOT23/PemSxLiinoujQuwbiJLabYbPTaq+YF5TZ6a1PzAvD8Z9wHVAQSiKI5LqsvORXNhj2w7G/8+nOvcc0/YAwAAPKOKFuG5l7Tr6FFpYUFaXzebs1lp+GPtmtHndFVvULO+vf1r6ZKEOJueNgGArdk0Majn0rCAs5BYSlNeZ2unfzcO8zdtim29aUOAGSgrbvWYczmVnosOSzMz9tX0tO3vw7nOTWScAYAtqmgRPjZWOFHJW1/fpQW9VmO7Hi3cQVYNYII8s7MmQHDxovk4O8sStJ0EmIVE98gQVXHTBkB5cWv6UH4uaraPjTXwzX3IcLbp78O5zl3UOAMAm+xQ4yyZNHf1SttQ631/r6WnXnR3EzVqgLKonVlBwLXhpq5PqX+mf9v2yVOT6jvY59nzoATqAAKeiMs5pfJc1HSTXlqq8ZsGkOFsw9+Hc51daokTsVQTAGyyw5Ky5eVKX7xLy999obnIiUmXJKBeLJXYQTVZSB4eX2xbShMr+TqA5W7acB4BquLrkmqLVJ6L7ry/pEoZzh51hrfh78O5zl0s1QQAm+ywpKylpfKXt7TIXOScOMHFDlAGSyWqUMXScS8FvZSGwsxFpqdNkGwr6gACKKGquWgtqmyOFQU2LRtFbcg4A7DJhhRmPKO9vWTga3DQFF8trishSU1NZj+Ayi4tXiq5PfNkhmNfXghZSKPHRnX6wGnfz0NkG5aQv2mzuEjGMhARfs3rPZ+LBpzhHLagznXwFjXOAEjiQsIVpToZSWaicuiQfZ2MANsUH+u2mh+YZwK71erq9qXjjtdNTGfT6pro2radvz2AKPFzXu/5XJQ6iwhJLXEiX5dq/tiP/Zhe+tKX6v7779dLXvIS9fX1ablo0fOXvvQlvf71r9f999+vZDKpCxe2T2Y/8YlP6KGHHtL999+vV7/61bp48aKfwwZih2VL7mhuNhOS4WFTfLWpyXwcHiZoBuyk1LEuj6USJVjQjdTrJZWZJzM1bQcA1/g9r/d8LprPcE4kCrfTGR4W8TVw9sY3vlF/8Ad/oK997Wv6wz/8Q928eVM//uM/vrn/zp07euSRR/Syl71MX/jCF/SBD3xAw8PDGh8f3/yca9eu6cyZMxoYGNAXv/hFnTp1SqdOndJXvvIVP4cOxAoXEm5pbpbOnTMdi9bWzMdz5wiaATspd0x73w+/T+e7zwc8GoeEVDcxNZdS10SX+mf61TXRpdRcquHvSWFmAFEXxLze87kodRZhuUCXav73//7fderUKT311FN61rOepd/6rd/Se97zHt26dUv33nuvJOmd73ynZmZm9Pjjj0uSfvInf1Lf/va39T/+x//Y/D5dXV06dOiQPvzhD5d8nqeeekpPPfXU5uM7d+4omUyyVBMog6UrAOKAY507/PxbFS9hSh1JETgFEBlOn+uos4gAWbNUc6uVlRV99KMf1eHDh/WsZz1LkvTYY4/ph3/4hzeDZpJ0/Phxfe1rX9Pq6urm53QXRZ+PHz+uxx57rOxzvf/979eePXs2/yWTSR9+IiA66PACuIvugNXjWOcOPzMmRo+Nan5gXpOnJjU/ME/QDECkOH2uozM8LOV7V81UKqUPfehD+s53vqOurq6CzLFbt27p5S9/ecHnv/jFL97ct3fvXt26dWtz29bPuXXrVtnnfNe73qW3v/3tm4/zGWcAyqPDC+AemnrUjmOdG/xeUtnZ2snf3iO5nDQ2Jo2PS8vLUkuL6ao3NEQJASAsnOsAb9WccfbOd75Tu3btqvgvv8xSkt7xjnfoi1/8oj796U8rkUiov79ffq8Ove+++7R79+6CfwB21tnaqb6DfZxcAQfQ1KN+HOvs52XGBFmZ/sl31xselrJZ02EvmzWPjx41+wHbxOWYwLkO8E7NGWePPvqo3vKWt1T8nFe84hWb/3/BC16gF7zgBero6NCBAweUTCY1Pz+vhx9+WA8++KCeeOKJgq/NP37wwQc3P5b6nPx+AADiqNJSNibJiAIvMibIyvTX2Ji0sGACZlutr5vtY2OmYDhgC44JAOpRc8bZC1/4Qj300EMV/22tWbbV+jNn1Xzh/ocfflh/+qd/qu9+97ubnzM3N6f9+/dr7zOtzh9++GF95jOfKfg+c3Nzevjhh2sdOgAAkUF3wPjK5aSRESmZlBIJ83FkJJrZPY1kTJCV6b/x8e1Bs7z1dbMfsAXHBAD18q05QDqd1oc+9CEtLCzob/7mb/TZz35WZ86c0b59+zaDXr29vbr33ns1MDCgr371q/r4xz+uD37wgwX1yd72trdpdnZWv/7rv67HH39cw8PD+vznP69f/MVf9GvoAABYz+niv6gbS+Oq52eDARjLy43td0FclvXFAccEAPXyrTnAc57zHH3qU5/S+973Pn3729/WS17yEvX09OiXf/mXdd9990mS9uzZo09/+tP6hV/4Bb3uda/TC17wAr33ve/V4ODg5vc5fPiwPvaxj+mXf/mX9e53v1vt7e2amZnRq171Kr+GDgCAEyj+Gz8sjaseWZl3pbNpX44TLS0mcFtpv8tY1hctHBMA1GvXht+V+i1w584d7dmzR7dv36ZRAAAAcFYyWTlQ0doqLS0FNx7bFQc+UkdSOt99PsQRBc/P4M/IiMl2LLVcs6nJ7KspkJvJSDdvSm1tUnu7J2OsVzqbVtdE17bt8wPz3KRwGMcEAHm1xIkInAEAADgikShfU0oywYq1teDG4wK/sq1c4HfwJ790uDgLsqlJOnRIunpVam6u4hutrEi9vdLly3e3HT8uTU9Lz9Q9DtrU9Sn1z/Rv2z55alJ9B/tCGBG8EudjAoC7aokT+bZUEwAAMEGHt6K+NM4Pna2dsX3v+d19t7nZBMfGxkwjgOVl8xocHJSGhqoMmkkmaHblSuG2K1ekM2ek2dmGx1kPlvVFV5yPCVHEPAtB8K05AAAAcZeaS6lrokv9M/3qmuhSai4V9pDguMFBk81TSlOT2Q/kBRH8aW42yzGXlky249KSeVx10CyTMZlmxamSa2tm++KiZ2OtBQ1YAPvZMs+iiUj0sVQTAAAfUB8HfvBsaRxiw/qaTpcuSSdPlt9/8aJ04kRw4ylCNgtgJ1vmWTQRcRdLNQEACJnfS6RgoQAKm3u2NA6xYX333X37Ku9vawtmHGWwrA+wkw3zrHQ2XRA0k6QL1y7o9IHTHDcihsAZAAA+oD5OjARc2Dy/NK6mboWINauDPx0d5v1y5Urhcs1EQuruDr27JuAci7rT+smGeZYNwTsEgxpnAAD4gPo40bFj7ZJKhc0B7Gx62gTJturuNtsBVGdlRerpkfbvN8ufOzrM49XVsEfmCxvmWTYE7xAMapwBAOAj6uO4bcfaJZmMuUgpJ5OJ9B1/hCiKWSWLi9KNG9H6mVATzpkN6Okpn7kZUnfaIIT9mrG+jiTKqiVOROAMAKImihdTQAiqKjxseWFzRFDAS4OBoFBkvQHcxAlV2ME71KeWOBFLNQEgKmKWog/4rVLtkk2WFzZHBLE0GBFUrsh62SXyKHTzZuX9N24EM46Y6mztVN/BPoJmEUbgDACiop6LqUzGZMwsLvo7NsBBVdUuyRc2TyQKPymRMNu5w38Xx5vGZTIm02zrUizJPL58md8tnFXVjQqUx00cwFcEzgAgCmq9mCI7DdhR1YWHKWxeGccb75BVgoiiyHqDuIkD+IrAGQBEQa0XUyz1AaoyemxU8wPzmjw1qfmB+dIFf/fuNYWXMxlT0yyTMY+pN2VE6HizY4dVv5FVgoiyoUOi82y7iUOWMSKE5gAAEAW1FIWlgCyAoEToeGNN4fKYds5DPFBk3QNhd6elgQkcQXMAAIibWlL0WeoDICgROd5YVbjctqwSwEMUWfdAe7vp5hzWTYkIZRkDeQTOACAqqr2YYqkPgKBE5HhjVeFylgYDCEguJ42MSMmkuRebTJrHuVyZL6CBCSKKwBkARETuWXs1cmRWyQefVqJpXckHn9bIkVnlnlV0MUUBWRQJvW4ToisixxsrC5eHnVUCQFJ0z6G5nHT0qDQ8LGWz0vq6+Tg8bLaXDJ5FJMsYKEbgDAAioGByc+tZWl/fpeytZ5Wf3LDUB89IzaXUNdGl/pl+dU10KTWXCntIiJoGjje2XJBSuBxAKVE+h46NSQsLJmC21fq62T42VuKLIpJlDBSjOQAARMDIiAmaFU9uJKmpyew7d67EF4ZdQBahSmfT6pro2rZ9fmCegAC8V+Pxxppi/FtQuBxAXtTPocmkyTArp7VVWloqsYMGJnAEzQEAIGbGx0sHzSSzfXy8zBey1CfWrKrbhOir4XhjVTH+LShcDiAv6ufQ5eU697OqARF0T9gDAAA0ru7JDWLNyrpNgCpfkBK0wk7IDEQQon4ObWmpnHHW0lJmR76BCasaECFknAFABJSdvFS5H/FE3SZv2FKHK0qifkHqMttf71GuOQW7RP0cOjhoyn2U0tRk9lfEqgZECDXOAHiGO7zhqbvGGSDeu42wsQ5XVBT/blNHUjrffT7EEUVDLmeKeo+Pm2zklhZzATw0JDU3V/5a21/vUa85BTv5eQ4N8/ycbzxV3CCgqUk6dEi6enXnY0Y9mJMgKLXEiQicwSkcSO1l+2Q66sKa3ABxxkW6/zjve6uRc4ULr/ep61Pqn+nftn3y1KT6DvaFMCKgfjbMrRsJtNfDhp8Z8UFzAEQSqff2srWIc5w0N5sLnuFh0+Woqcl8HB4maAb4JeqFoW1AMX5vjY1tD5pJ5vHCgtlfjguvd5b4IipsmVs3N5sVC0tLpknm0pJ57FemmQ0/M1AKgTM4gQOp3VyYTMdBkJMbANG6SM/lzJLvZFJKJMzHkRGzHdFRdwdmufF6j3rNKcRHHOfWcfyZ4Q4CZ3ACB1K7uTCZBgCvReUiPb98b3jYdFBbXzcfh4fNdoJn0dFIB2ZXXu+jx0Y1PzCvyVOTmh+Ypy4enBTHuXXQP7PtjU5gl3vCHgBQjTiePFySn0wXF3G2bTINAF4bPTaq0wdOF9Thcq0uVzXL94qbi7j2M9oq6N9jS4sJilbaX0mp17uNOls7rR0bUI04zq2D/JmppYZa0RwAzqC7lv24kAIQdy5OxpPJysGU1laz9DvPxZ/RRmH8Hj3pwJzJSDdvSm1tUnu7D6MEkBfHubXfP7MLjU4QDLpqFiFwFh1xPHkAANzg6mQ8kShf90oyAZW1NfN/V39G24T1e2yoA/PKitTbK12+fHfb8ePS9LS0d69vYwYAL9F9F3l01URk0V0LANwX1boirtbj3Gl53tb9rv6Mtgnr99hQB+beXunKlcJtV65IZ874OGIA8BYlgFAPAmcAACAwqbmUuia61D/Tr66JLqXmUmEPyTOuTsYHB00ApZSmJrM/z9Wf0TZh/h7r6sCcyZhMs3zqYd7amtm+uOjrmOGeqN4ggftcaXQCuxA4AwAAgUhn0wU1nSTpwrULkbmw8n0ynslIly55HqQYGjLL9IqDZ/nle0NDd7dxweEN536PN29W3n/jRjDjgBOifIME0UD3XdSKGmcAACAQcakr4nk9zgBqS+Vypnvm+Li0vGyWZw4OmqBZqUwkao56w5nfYyYj7d9feT+NAiDqIAJwB80BihA4AwAgfFxQ1amnx9SS2rpMLpGQurul2dnwxoV44XWIKsTlBgkA99EcAAAAWMe55Wk2oLZUNPi0zDZQ09MmSLZVd7fZDjyDOogAouiesAcAAADiY/TYqE4fOO3G8jQbVFNbiiVy9gpgmW1g9u41mWWLi+Z119bGaw/b5G+QbK1nyQ0SAK5jqSYAoLRMxly0c3EEhIfaUjuz+VjF8kbElDP1+wDEFjXOihA4A4AaRClDIoa4WIkggi+l2X6sIugJAIC1qHEGAKhfb6+5SN/qyhXpzJlwxoOqpeZS6proUv9Mv7omupSaS4U9JHiB2lKl2X6sqmaZLQAAsB4ZZwCAu8iQcBYdK2OA2lJ3uXCscmGMAADEFBlnAID6kCHhrMyTmZq2w0Ht7dKJEwRbJDeOVR0dZuloIlG4PZEw2/k7AvBaox18o9ABGPABgTMAwF379lXe39YWzDhQs44HOmraDjjNlWMVy2wBBGFlxdTD3L9fOnnSBO57eqTV1WC+3mPpbFpT16eUzqZDeX6gGIEzAMBdZEg4q7O1U2cPny3YljqSYpkmosmVY9XevaaBQyYjXbxoPs7O2tG8AEB0NFrz0aKakdRrhY2ocQYAKLS6aiZKtnaqQ0V01URscKwCgMbrKVpUj5F6rQhSLXGiewIaEwDAFfkMCQqRO6mztZPJJeKBYxUAVFfzsdKxsdGvr8FON/cq1WtlboMwETgDAJTW3s5FKAD7cawCEGeN1nwMqGZkai6lC9cubD4+e/isRo+NFnwO9VphK2qcAQDoogQAjqOYNhBTjdZ8DKBmZDqbLgiaSdKFaxe2Ha+o1wpbkXEGAHG2smIKwlIjCACcVU0mB4AIm57eXvOxlg6+jX79DmpZgjl6bFSnD5ymXiusQnMAAIiZXE4aG5PGx6Xl7Jpa9Lca1H/VkMbUrG+bO4zd3aZ2EADAahTTBrCpxpqPBXPCZanlRd/VYPfXNfRok5oPebNEU+I4BTvVEidiqSYAxEguJx09Kg0PS9mstK6EsmrVsIZ1VJ9TTs+V1tbMHUeWbQK+iNqSuqj9PK6plMkBIGba26UTJ6oOmhXMCdel7K1nafhjHTo60KZczrthsQQTrmOpJgDEyNiYtLBgJkdbrSuhBf2AxjSkc/pPZqOHXZQAGFFbUhe1n8dFFNOGH3bqfgj3lZ0TrpvtY2PSuXPePR9LMOEylmoCQIwkk+auYmkbalVWS3qpeZjJEDiD9Vy6uKtnqYrNPx9Lb4KzbTlVizQ4KA0NSc3N2wOYqSMpne8+H+KI4TIC4vFQeU4otbZKS0vBjQcIWi1xIjLOACBGlpcr7d2lZbXcrXFG0AyWc+3irpbiyJL9P1+tPw/qk19OtTUzJJs1y6tmZqSrV8nkgHfKdT88feA0r6uIqTwn3Hk/ECfUOAOAGGlpqbR3Qy1a9rSLEuCXalvb26SWJXUu/HwsEQxGNcupJFNDqO9gH8ENNISaefFReU64834gTgicAbAWBae9NzgoNZU58jftkgbf9hzTTXPv3mAHBtTIxYu7Wooju/Dzlfp5hl57Tp/+nU4lkyZ5NZmURkbkaZHpuBkf3x40y1tfN/sBrxAQ94YLc9iKc8Imsx+AQY0zAFayfYmSq0ot+ZHMBOnQIbPkp7k5rNEB1XO5vlY1dctc+vnyP0/r/Q/pP/T+EMcXjyUS5QNnkvn9rq0FNx5EHzXzGuPKHJY5IeKuljgRgTMA1nHpgtFFOxWZBlwR5sVdEEX7Xbt4HRkxdbdKBXmamsw+Lzu0xQUFvBEGmxuT2My1OSxzQsQZgbMiBM4At0xdn1L/TP+27ZOnJtV3sC+EESFQmYx086bU1kaDAuwojIu7ILMJXLp4JcDjD08CkhxXgUAwhwXcUUuciBpnAKxDfY2YWlmRenqk/fulkyeljg7zeHU17JHBYkEXRA+6aL9LBd/p0OaPoSGzbKq4FlF+OdXQUIUv5rgKBIo5LBBNBM4AWKeWAtqIkN5e6cqVwm1XrkhnzoQzHqAEF4r2h4UObf5obja1hoaHTdZeU5P5ODxcRQ0ijqtAoJjDAtHEUk0A1nJpiRIalMmYjIhK+1leBAu4Vr+mKh4t46PGmWU4rgKhYQ4L2I+lmgAiwaUlSmjQzZuV99+4Ecw4gB1EKpvA42V8DS0phPc4rgKeSmfTmro+VdXSfOawQLSQcQYACB+ZEXBMJLIJenrMsr21tbvbEgmpu1uana3rW9KhzSIcVwHPBNkUZhNNPQBf0VWzCIEzAHCADxfxAMogqBIPHFeBhgW+RH9lxdQnvHz57rbjx6XpaWnvXu+fD4gplmoCANwzPW0u5rbq7jbbAXiLZXzxwHEVaFjgTWEcaepRy9JVwHX3hD0AAAAkmbuos7PS4qK5aGdpAuCfffsq729rC2Yc8FeNx9VILEEGPNbxQEdN2xuSyRRmmuWtrZnti4tWzI1CWboKhIiMMwCAXdrbpRMnrJgYAn4L4o59yefo6DBLfxKJwk9OJMx23n/RUsVxNTWXUtdEl/pn+tU10aXUXCrAAQL2CrQpjAPZwOlsuiBoJkkXrl0g8wyRRsYZAABACIK4Y1/xOaanzdKfrdkNLOOLpXIXwqcPnCbzDJA0emxUpw+c9j8j04Fs4EpLVzleIKrIOEMksMYeAOCSIO7Y7/gc+WV8mYx08aL5ODtL8ekYCryGE+CgztZO9R3s8zc45EA2cKBLVwFLEDiD81haAABwTRCBiqqfg+XRsceFcLRwQ9lxNTT1CONvXevSVV6PiAKWasJpLC0AALjo6bWna9peD4IhqFb+QnjrnMq3Gk7wFUXbI6DKph5h/q2rXbrK6xFRQcYZnMbSAgCAi+5N3FvT9noEWtAazhs9Nqr5gXlNnprU/MC8znefD3tIqBFF2yOmQjawDX/rnZau2jBGwCtknMFp3E0HALgoqPNXYAWtEQmdrZ28RhxG0fb4cOFv7cIYgWqRcQancTcdAOCiUuevvtf4U3Q6kILWAELHDeVwBVnLy4W/tQtjBKpF4AzOY2kBAMBFo8dG9VOv/qnNx1NfmqLBDYC6cUM5PEE3K3Phb+3CGIFq7drY2NgIexB+u3Pnjvbs2aPbt29r9+7dYQ8HAABA6WxaXRNd27bPD8xzYQGgbulsmuXZASp3LH/fD79PJ9pP+Po3cOFv7cIYEU+1xIkInAEAAIRg6vqU+mf6t22fPDWpvoN9IYwIAFCrcsfyPDpJAnaqJU7EUk0AAIAQUP8l2nI5aWRESialRMJ8HBkx2wFEx07HbDpJAu4jcAYAAHYUZNHjuKD+S3TlctLRo9LwsJTNSuvr5uPwsNlO8AyIjlLH8mLlOkx6JpORLl2SFhf9fR4gpgicAQB2FMnMCSaZVQu66HGc0OAmmsbGpIUFEzDban3dbB8bC2NUAPySP5a/74ffV3K/b5nEKytST4+0f7908qRyHT+gkfZJJb93PTrzNcAC1DgDAFSUz5wovghsapIOHZKuXpWam8MaXR1WVqTeXuny5bvbjh+XpqelvXvDG5elKGCPsOVyJtA0Pi4tL0stLdLgoDQ0ZO+xJ5k0GWbltLZKS0vBjQeIk7CL0afmUrpw7cLdx0dS/t0U6emRrlyR1taU03N1VJ/Tgn5A60psfoqz8zXAZ9Q4AwB4I5PR2M9ntLCwEZ3Mid5eM8nc6soV6cyZcMZjuXLLS3xfdgLI3SWPy8uN7QdQHxsypAPLJM5kzE3AtTVJ0piGtgXNJIfna4BFCJwBQBFqOakg9X/8956t9fVdJT9tfd1kgTijaJK5aW3NbA972aaFy0fjXMCeY0H4XF3y2NLS2H4AtUtn0wWZXlJ4hfk7WzvVd7DP34y3mzcLHo5rcFvQLM+5+RpgGQJnALCFDXcqrbAlK2tZla/wnMqcKJpkbnPjRjDjKFZUo0QdHebx6mo449kirgXsORbYYXx8e9Asz+YLwcFBszyqlKYmsx+At2KXIb1vX8HDSM3XAMtQ4wwAnkEtp2dkMiaA84ykvqGskmU/3alaPUU/W8n97e3BjSdvS42STYmE1N0tzc4GP54Swq4ZEySOBfZIJMoHziQThCpOILVB5GpDAg6I5bF7y/whUvM1IADUOAOAOsTuTmU5RVlZgxpXk0pfmTqXOdHRYRoBJIqWMiQSZnsYQTPbl48+I5BlJ5bgWGAPV5c8Njeb4NjwsLlYbWoyH4eHoxs0Y2kzwhbLDOnpaXOTTRGbrwGWIXAGAM+Icy2nAkWp/0Ma0yF98ZnJ2N0k5XzmxNBQsMNr2JZJ5qbubrM9DLYuH40xjgX2cHnJY3OzdO6cyfBYWzMfz52LZtCMpc2wRWCF+W2xd6/JTM9kNPSHr9ehV3532zHT2fkaYBECZwDwjFjeqSylKCurWd/WVb1Bw7t+Ra33/b37mRNbJpm6eNF8nJ0128NQFKjcpq0tmHFgE8cCewwNmQs+LgTtZVNBdkCKV4b0pvZ2NZ9+RFfn749VpisQFGqcAUCRONVyKmt1VTpzxiwVzDt+3GRlhRVgijIHapzFEccCO+Rypnvm+Lgpbt3SYjLNhoZiciGYyZjM1La2cJaT72Dq+pT6Z/q3bZ88Nam+g30hjAjwieXvRQC1qSVOROAMAFDe4qJZKsgk0V8EKgEUW1kxHY4tPy7EsiC7CwjyeMeR9yKA2hA4K0LgDADgBAKVcBUX6d5zKBM1NZcqWK6ZOpKKfm2pANWU/UqQx3uWvhfJigYaQ+CsCIEzAAAAH3CR7o9MRtq/v/J+ywKUXMT7ozgoefbwWY0eGy3/BZYGeZxl6Xux5tcFgG1qiRPRHAAAgJ1kMtKlSyYjDMBdvb3mIn2rK1fM0mPUz8Fuu7EsyO6zmhsvZDImiL01aCaZx5cvcw6rh4XvRRpyAMEjcAYAQDkrK+bu/f790smTpuNoT4+pSQbEHRfp/qHbLiRlnszUtN3GII/zLHwv1vy6ANAwAmcAAJQT12waMuxQDS7S/dPRYZa8JhKF2xMJs92yZZrwR8cDHTVttzHI4zwL34s1vy4ANIzAGQAApcQxm4YMO9SCi3R/TU+bulRbdXeb7YiFztZOnT18tmBb6kiq/HJYC4M8DbPhRo5l78WaXxcAGkZzAAAASrl0yQSPyrl4UTpxIrjxBIGi0qgVrxn/0W039mpqvLC6arKiXW/YYWPjEcveizTkABpDV80iBM4AADWztJOWb+L28zrAiYuiqFykA1FjWZCnZgTlI8WJ8xlip5Y40T0BjQkAALfkl7yUm7i7eCFSSTX1qqL2M1ssNZcq6Jp29vBZjR4bDXFEZezday5iXb9IB6Kmvd3d92K+VEKxraUSXP3ZYsiZ8xlQATXOAAAox7K6Jr6iXpU10tl0wUWGJF24dkHpbDqkEVWhvd0sXeZiFkCjaDwSGU6ez4ASCJwBAFBOPpsmkzE1zTIZ8ziKS9CiWFTaUZknMzVtB4BI4UZOZHA+Q1QQOAMAYCdxyaaJU4adxToe6KhpOwBECjdyIoPzGaKCwBkAADDilGFnsc7WTp09fLZgW+pIioLKAOKDGzmRwPkMUUFXTQCwEN2HAHAcQJzx+ockGo9EBO9n2KiWOBGBMwCwjEvdh1ybCKWzaV1avCRJOtF+wokxl5XJmALKXEwAiBiXzoNwl2tzGADeInBWhMAZAFeks2l1TXRt2z4/MG/dpM61C5vi8Ur2j7mklRWpt1e6fPnutuPHzfIVllRWzdYLJlvHBfih1OvdpfMg3OXaHAaA92qJE1HjDAAs4kr3Idfai5car2T3mMvq7ZWuXCncduWKdOZMOONxUGoupa6JLvXP9KtrokupuVTYQ5Jk77gAP5R7vbtyHoS7XJvDAAgfgTMAsIgr3Ydcu7CpNC5bx1xSJmMyzdbWCrevrZnti4vhjMshtl4w2TouwA+VXu+unAfhLpvmMLmcNDIiJZOmaWgyaR7ncoEPBUAFBM4AwCKudB9q9MIm6IlipXE5dTF282bl/TduBDMOh9l0wVTN84c9LsAPlV7vrpwHUVk6m9bU9Skrg/+2BGdzOenoUWl4WMpmpfV183F42GwneAbYg8AZAFhm9Nio5gfmNXlqUvMD8zrffT7sIW3TyIVNGBPFUuOtZczW2Lev8v62tmDG4TBbLpiqff6wxwX4YafXuwvnwVjKZKRLl3bMbrZ92bktwdmxMWlhwcyDtlpfN9vHxgIdDoAKaA6A2KMQM1C/et4/IyMmSFY8UZSkpiaz79w5T4e5KRJdNXt6TE2zrcs1Ewmpu1uanQ1vXA4pLgqdOpKy4sLc1nEBfuD17pAamtK41Nwh7GuAZNLcOCyntVVaWgpuPEDc0FWzCIEzlENHHSB4TBQbtLpqGgHQVbMhYV8wlWPruAA/8Hp3RA03bKauT6l/pn/bt5g8Nam+g31+j9QpiUTpm4h5TU1bfuWZjCnX0NYmtbcHMj4g6gicFSFwhlJcuiMGRElNE0WUt7hoapoxiQbsZfnFLoEr7CiTkfbvr7x/y2ub+XX1qrqReL36bD8AtaklThRIjbOnnnpKhw4d0q5du7SwsFCw70tf+pJe//rX6/7771cymdSFCxe2ff0nPvEJPfTQQ7r//vv16le/WhcvXgxi2Ig4CjED4WhpaWw/ntHeLp04YeXFeCNsLigNVG1lxWTp7N8vnTwpdXSYx6urYY9sk+11qKLOmWNdjU1pbKkf5oLBQXOzsJSmJrNfvb0m22+rK1dM5jmAwAQSODt79qxaSlwJ3blzR4888ohe9rKX6Qtf+II+8IEPaHh4WOPj45ufc+3aNZ05c0YDAwP64he/qFOnTunUqVP6yle+EsTQEWEUYgbCUdVEEbHEhTwiw/KL3XQ2XVCqQpIuXLtgfxAnIpw61tXRlIbmDtUZGpIOHdo+J2pqMtuHfnTRZJoVp+GvrZntOzRpyHMmSAtYzPfA2aVLl/TpT39av/Zrv7Zt30c/+lE9/fTT+u3f/m298pWv1Jvf/Ga99a1v1W/8xm9sfs4HP/hB9fT06B3veIcOHDigkZERvfa1r9WHPvQhv4eOiOOOGOCBKjtsbbXjRHHI0xHCEVzIIzIyGU8udv1E1n14nDvWdXSYpYGJROH2RMJsL5P13Nnaqb6Dfdvn1XXMG6KquVm6etU0RWptNfOg1lbz+OpVqfnWjcrf4MYO++VYkBawmK+BsyeeeEI/8zM/o6mpKT3nOc/Ztv+xxx7TD//wD+vee+/d3Hb8+HF97Wtf0+ozqeyPPfaYuru7C77u+PHjeuyxx8o+71NPPaU7d+4U/ANK4Y4YUKcGliHtOFFs9n30sBAX8tjG1QvsGpe2hYGs+/A4eaybnjaNALbq7jbbq+XA8uUwNDebTuJLSya2vrRkHjc3q65sv62cC9ICFvMtcLaxsaG3vOUt+rmf+zn94A/+YMnPuXXrll784hcXbMs/vnXrVsXPye8v5f3vf7/27Nmz+S+ZTDbyoyDiyt4RA1Beg8uQKk4UEUtcyGOT6xfYDV7sBoGs+/A4eazbu9d0z8xkpIsXzcfZ2dqK01u+fNlKdWb75TkZpAUsVXPg7J3vfKd27dpV8d/jjz+u3/zN39Q//MM/6F3vepcf467oXe96l27fvr35b2lpKfAxAEBkObAMCe7hQh6bXL/AbvBiNyhk3Zfmdz0or491gdavqrcpDfOG+jWQ7edkkBaw1D21fsGjjz6qt7zlLRU/5xWveIU++9nP6rHHHtN9991XsO8Hf/AH9X/9X/+XPvKRj+jBBx/UE088UbA///jBBx/c/Fjqc/L7S7nvvvu2PS8AwCPVLEOy5MIQbhk9NqrTB04r82RGHQ90EDSLo/wFdrGtF9guHF+mp02gb+vPUuvStgB0tnbyPtsiNZcqWNr2U6/+KU2dnvL8ebw61hWP9+zhsxo9NurVML3DvKF++Wy/xUXze2prq/p3lQ/Sbn2NcEMKqM+ujY2NDT++8Te+8Y2C2mLLy8s6fvy4PvnJT6qzs1Otra36rd/6Lb3nPe/RE088oWc961mSpHe/+9361Kc+pccff1yS9JM/+ZP6zne+oz/6oz/a/F6HDx/Wa17zGn34wx+uaix37tzRnj17dPv2be3evdvDnxIAYiiTMUuoKu1nAgygHpcumeWZ5Vy8aDJeXFHHxS7Ckc6m1TXRtW1736v7NHl6MoQRVVZuvPMD8/YFRpg3hCqdTXNDCiihljiRbzXOXvrSl+pVr3rV5r+ODpMSum/fPrW2tkqSent7de+992pgYEBf/epX9fGPf1wf/OAH9fa3v33z+7ztbW/T7Oysfv3Xf12PP/64hoeH9fnPf16/+Iu/6NfQAQCVOLIMCYCDHKgPVpN6l7YhcOXqPk19OaBlkDVyqn4V84ZQhVHPOdAlxEAAfO2quZM9e/bo05/+tL7+9a/rda97nR599FG9973v1eDg4ObnHD58WB/72Mc0Pj6ugwcP6pOf/KRmZmb0qle9KsSRA0DMedFhC8COYnfxwQU2quT1e6NS3adKwaiw3qPO1a9i3hAbqbmUuia61D/Tr66JLqXmUmEPCWiYb0s1bcJSTQDwSQDLkFhigLhypn6R11ZXt9cHO37cXGDX0sUPkeXXe6PvU336vS//3rbt5ZY/hv0eLX7+1JGU/U0eWL4caU4tIUbs1RInInAGALBW2BclnstkTJFkLhiwAy4+xAU2Sqr2vVHvTZf+T/Vr6st3GwKUC0bZ8h7l5hJsMnV9Sv0z/du2T56aVN/BvhBGBJRXS5yo5q6aAAAEIZ1NFwTNJOnCtQs6feC0excHKytSby8ZNKhapfpFzr3+69XeTsAM21Tz3mjkpsvk6Un9wr/4hR2DUba8R+mMCps4t4QYqFKoNc4AACjHqcLHO+ntla5cKdx25YpZjoYdxa7Ol7j4AMrZ6b1R7qZLLcePaoqp8x4Ftuts7dTZw2cLtqWOpAjuwnkEzgAAVorMRUkmYzLN1tYKt6+tme2Li+GMyxFxLTLMxQdQ2k7vjaBuuvAeBUobPTaq+YF5TZ6a1PzAvP1194AqUOMMAGAtJwsfF7t0STp5svz+ixelEyeCG49DbKkhFCbqFwGllXtvBH3c4D0KAG6ixhkAIBJGj43q9IHTbl+U7NtXeX9bWzDjcJAtNYTCRP0ioLRy7418JljxTRe/3ke8RwEg+gicAQCs5vxFSUeHaQRw5Urhcs1EQurupvh5BZFZrgsgUJG46QIAsAY1zgAA8Nv0tAmSbdXdbbajLGoIAahXNQX+AQCoBjXOAAAIyuKidOOGWZ5JplnVqCEUc5mMdPMm7xsAAOCZWuJEBM4AAABgn5UVqbfXdJ/NO37cZGru3RveuCxDYBlA0DjuIApqiROxVBMAECm5nDQyIiWTpoxYMmke53JhjwxATXp7TW3Ara5ckc6cCWc8FkrNpdQ10aX+mX51TXQpNZcKe0gAIo7jDuKIjDMAQGTkctLRo9LCgrS+fnd7U5N06JB09arU3BzW6ABULZOR9u+vvN+2ZZsBLylNZ9Pqmujatn1+YJ4MEMAjuZw0NiaNj0vLy1JLizQ4KA0NxXM+wXEHUULGGQAglsbGtgfNJPN4YcHsB+CAmzcr779xI5hxVGNlRerpMYG+kydNJ92eHml11denzTyZqWk7gNrkb8YND0vZrJlLZLPm8dGj8cxk57iDuCJwBgCIjPHx7UGzvPV1sx+AA/btq7y/rS2YcVQjpCWlHQ901LQd4Utn05q6PqV0Nh32UNyXyUiXLpmmOz7hZtx2HHcQVwTOAACRsbzc2H4AlujoMI0AEonC7YmE2W7LMs1MxjQvWFsr3L62Zrb7eFHf2dqps4fPFmxLHUmxXMpS1IXySIAZntyM247jDuKKGmcAgLrY2FEpmTTLKMppbZWWloIbD4AGrK6arC2bu2peumQu3su5eFE6ccLXIdh4LEYh6kJ5qKfHZHRuDVYnElJ3tzQ76+lTJRLlA2eSqZ9aHDOPC447iIJa4kT3BDQmAECEpOZSunDtwubjs4fPavTYaIgjMgYHTe2RUhPdpiazH4Aj9u41F8KLi6amWUBF92tiwZLSztZOLlwtV6kuFH+7GuQzPIttzfD08BjR0lL5ZlxLi2dP5RyOO4gblmoCAGqSzqYLgmaSdOHaBStqtgwNme6ZTUVnt3xXzaGhMEYFoCHt7SZry7agmeTOklKEirpQHgm4acjg4Pb5RB4344B4IXAGAKiJzR2Vmpulq1dN1llrq5nYtraax1evhtM6PpeTRkbMMtJEwnwcGYlnNy4gkqanzTKxrbq7zXZA1IXyTMAZntyMA5BHjTMAQE2o1VK9fCv74q5c+Ul3WME8AD6weUkprEBdKA8EWONMMufxsTHTCGB52SzPHBw0QbM4n795LSMKaokTETgDANSsuMZZ6khK57vPhzgiO42MVK65NjwsnTsX9KgAAHCUC01DqpHJmKWnDgbaba1zC9SKwFkRAmcA4L3Y3G1sYHJLl08AAHzgaobnyorU2+ts4I9VB4iSWuJE1DgDANSls7VTfQf7ojtRWlkxS0L275dOnjRFwHt6zN3uKi0vN7YfqCSdTWvq+pQVjTkA2CE2dTWfaRqSfvaKW8fB3l6z1HSrK1dMFp0DbK5zC/iJwBkAAKV4MLndqVV9nFvZl0IgqHqpuZS6JrrUP9OvrokupeZSYQ8JiASXj0P5uprDwybbeX3dfBweNttdCZ5VG/xz7jiYyZhMs6312STz+PJlk0VnOTrEIq4InAEAUMyjyS2t7Kvn3AVQiNLZdEF9GUm6cO2Ckxf6gE1cPw6NjW1vRiOZxwsLZr/tqg3+OXkcvHmz8v4bN4IZRwPoEIu4InAGAAhOJiNdumT/XVWPJre0sq+OkxdAIWKpDOC9KByHxsdLN6ORzPbx8WDHU49qg39OHgf37au8v60tmHE0aPTYqOYH5jV5alLzA/M0h0IsEDgDAPjPg3phgfJoctvcLF29au6Ut7aagFlrq3l89Wq8W9lv5eQFkI92WirGUhnAe1E4DkWhrma1wT8nj4MdHaYRQCJRuD2RMNsdanIQ+Tq3QBECZwAA/7lWDNfDyW1zs3TunOmeubZmPp47Vxg0i00x5zKcvADySTVLxVgqA3gvCsehKNTVrDb45+xxcHpa6u4u3NbdbbYDsNaujY2NjbAH4bda2owCADyWyZhMs0r7bbzLurpqAns+t4zP13MpXpqSX87pZ2ZaLmeWvYyPm4uRlhZTd21oKPhsuNRcqmCZVOpIKnbLP9LZtLomurZtnx+YL3kxmM6mlXkyo44HOuy/WAQc4PpxaGTEZDSXythqajL7zp0LelS1SSZNTbNyWlvNDag8Z4+Di4um7ENbm51zICAGaokTETgDGuTsCRsIyqVLZnlmORcvSidOBDeeWvk8uQ3rQifMgF05cT+eTl2fUv9M/7btk6cm1XewL4QRAfHj8nHIxuN6raIQ/APgBgJnRQicwS/FdybPHj6r0WOjIY4IsJCrGWcBqfXuule4OLFPrRlnAFDMpkziekQh+AfADQTOihA4gx+4wAFq0NNjapqtrd3dlkiYuh6zs+GNywKJRPlCyJK5WNj6a/NKWAE7VOb6UrGyMhnTrZZlSQB24HrwD4AbaokT3RPQmIDIqdR9icAZUGR6enu9MIrhSjIXBJUCWH4Vc45C97UoGj02qtMHTju7VGyblRXTHMTnWoGuc3l5IOwQpddQvqkOWc8AbEHgDKhTFLovAYHZu9dkllEMd5vBwcpLJgcH/XnesAJ22Flna6fzF76bKnXUjXm2ad4Hfudn9NnP/DfdeL504wHKPqB2lA4BAH81hT0AwFXOtsEGwtTebhoBEDTbNDRk6rY0FZ2R8/Vchob8ed7Bwe3PufW5/QrYIUYyGZNpVrzWeG3NbF9cDGdctlhZ0bfe8LDe8e/+my59VFr8TenSlDT+mQtKZ9Nhjw6OSGfTBUEzSbpwjdcQ/JfOpjV1fYrXGmKBwBnQgNFjo5ofmNfkqUnND8xHow4NgEA1N5tix8PDpq5YU5P5ODzsbxHksAJ2iJGbNyvvv3EjmHHYqrdXu//nnxVs6v4rafqT5ctBAMUqlQ5BCZmM6fYd98B9g1JzKXVNdKl/pl9dE11KzaXCHhLgK5oDAAAQUxRghq/oqFveDr+bhT/9Ax16/U8EOCC4imZVVaLeomd4zSEqaokTkXEGAEBM5QswLy2Z1XNLS+YxQTN4oqPDXJgmEoXbEwmzPa5BM2nHbLxDOd6EqA6lQ6pUqd4iakKWI+KI5gAAEDNR6rwFIHwVjyl01C1t377K+9vaghkHIiFy3Xi9lq+3WGxrvcU4B/JrRIM0xBGBMwCIETpvAfDSjscUOuqWls/Gu3KlsHlCImECi/yOUKNIdeP1WjX1FnnPVS2f5bj12E+WI6KOGmcAEBPUpADgJY4pDVpd3Z6NR80lwHvUW/QFKxjgulriRGScAUBMVKpJwYQHQK04pjSIbDwgGGR4ltRo4IssR8QJzQEAICaoSeGuXE4aGZGSSTPPTybN41wu7JHBRelsWlPXp5TOphv6PhxTPNLeLp04EduLdyAQ09MmSLZVjOstpuZS6proUv9Mv7omupSaS4U9JMBqBM4AICbovOWmXE46elQaHpayWWl93XwcHjbbCZ6hFl5eLHFMAeLD+Rs4+QzPTEa6eNF8nJ2N5bLodDZdUJ9Mki5cu9DwzRQgyqhxBgAxQ00Kt4yMmCDZ+vr2fU1NZt+5c0GPCi7yqyYZxxQg2vI3cBYWCs9FTU3SoUPS1atSc3NYo0Otpq5PqX+mf9v2yVOT6jvYF8KIgHDUEici4wwAYqaztVN9B/u4wHXE+HjpoJlkto+PBzseuKtSTbJGcEwBom1sbHvQTDKPFxbMfriDZfZA7QicAQDiLZORLl0yBbottLzc2H7beFVfC7XjYglAPbiBEy0sswdqR1dNAEA8raxIvb3S5ct3tx0/bgoFW1TzpKXF1DSrtN8VqblUQV2Vs4fPavTYaIgjipf8xdLWvwEXS4C3orh0OWo3cCCNHhvV6QOnI/daBfxCjTMAQDz19JRvTT87G964ikSlxplf9bVQuyhe2AM2iOrNgWSy8g2c1lZpaam675XLmaWd4+Mm4NbSIg0OSkND1EkDECxqnAEAUEkmYzLNtgbNJPP48mWrlm0ODZniy01FZ+x8UeahoTBGVTu/6mvZyuYlqdQkA7wX5U6Fg4Pbz0F5TU1mfzXoEg3AVQTOAADxc/Nm5f03bgQzjio0N5uOZcPD5q5+U5P5ODzsViezONXXSs2l1DXRpf6ZfnVNdCk1lwp7SAB8FuWbA17dwKHJAABXETgDAMTPvn2V97e1BTOOKjU3m+WYS0smKW5pyTx2JWgmxacYcZSzTgCUF+WbA17dwHG2yYDlTYQA+I/mAACA+OnoMI0AytU4a28Pb2wRFodixJWyTqL48wIwot58I38Dp5Gams41GQixiRC1KAG70BwAABBPq6vSmTPWd9WEW2iCEA9c1KIcXhvledlkIBAhNRGKapMJwDY0BwAAxEMjyyf27jUT30xGunjRfJydJWiGhsRlSWqcUcMOldB8ozyvmgwEIqQmQiz3B+zEUk0AgHu8XD7R3s7STHgq8ktSMxnTYKOtbdt7J+rZNuUuak8fOB3Jnxfw0tCQNDOzvUGAlV2iq2ki5MPcgeX+gJ3IOAMAuKe31yyf2OrKFbP0ErBAJLNOVlbM0qX9+6WTJ02twJ4es+xZ8cjEinLnRMBvTnWJDqmJUJSbTAAuI3AGAHBLSMsngNirELCOy/Ii6y5q6faHGqWzaU1dnwrtvelMl+h8E6FSa0sfeEB6wQt8eVqW+wN2InAGAHBLNcsnAHhrh4D13/7Fn5b+sohlYllzUbtD9h9QShyyQj1VrvzDt77la4b76LFRzQ/Ma/LUpOYH5nW++7xvzxWUsAO2QKOocQYAcEtIyyeAWNshYP3Q7dJTyiguL7Kihl2l5eo+dvuDu6jPV4e/+zvpySe3b9+a4e5TjdTO1s7I/F3oEoooIOMMAOCW/PKJRKJweyJhtodY6D+Xk0ZGpGTSDCeZNI9zudCGBHhjh4D1Q10/akcmVkBCrWHHcnXUgfp8dSDDvWFxWcaP6CPjDADgnulpk1mxtatmd7fZHpJcTjp6tLBbWDZrih7PzFhY+BioRT5gfeVKYcAmkTDvvfZ2jbZXl4lVqfNmLieNjUnj49LystTSIg0Omm57W98/+e/x9NrTujdxb2S7eJYUUrc/uM26+nwuIMO9YXQJRVQQOAMAuGfvXrMcaXHRXCS2tYV+oTg2Vhg0y1tfN9vHxkwBZMBZVQSsd1peVGnJTrXB5+LvUep7RRoX86hDvj7f1veOn1mhlQLkzqjihoEUkZ/VJwRsERW7NjY2NsIehN/u3LmjPXv26Pbt29q9e3fYwwEARFAyaS7yy2ltNd3DAOfVGbBOZ9Pqmujatn1+YF6drZ0aGTFBsuLgs2Qa2w0PS4/8f0t/j+LvFbRqM+U809NT/mKeGmeoIIggT6RqWq2ubr9hcPz4ZuOASP2sPin+HaWOpCLR8ADuqyVOROAMAAAPJBKlL/jzmpq2lyQC4mTq+pT6Z/q3bZ88Nam+g31VBZ//8/8o/T2Kv1eQSmXKSeY9f+iQT8u0d7iYB8KyU4DcWSVuGET2Z/UBWXmwUS1xIpoDAADggZaWxvYDUbfTkp3l5cpfv7y88/KeMJb/VLNM23P55eqZjHTxovk4O0vQDKGLbBOC9nbpxImCLNvI/qw+CLWhCuABAmcAECHpbFpT16foVhSCwUGTYVJKU5PZD8RZvsbSVltrLFUTfC71PUp9ryCNj5fPNl1fN/t9U+JiHgiTZzWtMhnp0qWqusSG1dGa+l1AfLBUEwAiIpZ1NjIZ02HOguYAoSzXAhxUbslONTXO8g02bOqqyTJtoFBDNa1WVqTe3qqXIYd97i33s7I0EbAfNc6KEDhDVHASRjmxq7NR48Q6EJmMcl/5a4396es0/ocPBFMgHIiQsC+A60VjEFgp5BtLdc9Za2x8UUvA3S/FP2ssb2QCDiJwVoTAGaKAkzAq2anoduTY1FHOxiAe4KjAu1N6wIYLd2CTy+ekTEbav7/y/qIgoG2B69jdyAQcRnMAIGLS2XRB0EySLly7QB0rbIpVnY1MxlwQFK99Wlsz26uoh+Kp3l4TxNvqyhXT8S7GqLeHejQ3myDT0pJ5Sy8tmceNBM38fi0ODZmMuOIah/lMuaEhX54WEVf369blc9LNm5X337ixbVM1TUWCRMMAIJoInAEO4CSMnexUdDtS6phY+8a2IJ4lUnMpdU10qX+mX10TXUrNpcIeEmIqiNdic7NZRjo8bLJbmprMx+Fhe5eXwm51v25dPyft21d5f1vbtk22dbSO1Y1MIEYInAEO4CSMaoweG9X8wLwmT01qfmC++kK8rqljYu0bm4J4liBDFvXyOjMsyNeiH5lyiKeGXreun5M6Osyy0kSicHsiYbaXqNVmW0frWN3IBGKEwBngAE7CqFZna6f6DvZF+7VRx8TaNzYF8SxBhizq4UdmGK9FuKih120UzknT06Ze6Vbd3WZ7CTYulY7NjUwgRu4JewAAqjN6bFSnD5ymqyYgmQn0mTOFxY8rTKx9kw/ilWtUEEIns7CRIYtalcuwOX3gdEPnOl6LcFFDr9sonJP27jVNfhYXTYbcDl1B80ulbWsq0tnayVwdiBAyzgCHxCKbCKhGfmKdyUgXL5qPs7PhdAyr8e54KDIZ6dKlwvo2pbZ5IKoZsjQ78I9fmWFRfS0i2hp+3YZ4TvL0ONneLp04UVWwj6XSAPy2a2NjYyPsQfitljajAADUpcq744FaWTEd1rZm5r3xjdKuXdJnP3t32/Hj5qLKw8BjOpuOTIZsai5VkBF19vBZjR4bDXFE0ZLOptU10bVt+/zAvCevnSi9FhEfDb9uAz4ncZz0FsctwH+1xIkInAEAEFU9PduX7EgmcLb19J9fxjM7G+z4HOB3UAdG8UV36kiKukCAIzhOeosgJBCMWuJELNUEACCKMhmTaVYcNJMKg2aS+ZzLlz1fthkFkSow79PyXC9QTBtwV6SOkyGjMzVgJwJnAIDYyuWkkREpmTRJV8mkeZzLhT0yD9y8WfvX3Ljh/TgcF4kC8ysrJvtw/37p5ElTQLynR1pdDXtkBajjCbgpEsdJSxCEBOxE4AwAEEu5nHT0qDQ8LGWz0vq6+Tg8bLY7Hzzbt6/2r2lr834cjotEgfneXrNkd6srV0xnWsSXxRmIcEskjpOWIAgJ2IkaZwCAWBoZMUGy9fXt+5qazL5z54IelceoceYZZws1ZzIm06zSfluaWSAYpZqG+NAgxGu2vAfDGIctP/tOXBlnoDIZkwFeQ5MGaj4CwaA5QBECZ0D4mEzBNsmkyTArp7XVtLR32uqqySraeoH8Iz9iPvrcVROWuHTJLM8s5+JF6cSJ4MaD8JUKqFsePLelWHoY47DlZ0eNGgxQR2HeHIWfAdFG4KwIgTMgXEz6YKNEonS2WV5TU+m6+k5aXDT1y7be8S61DdETUMYZF0j1yeWksTFpfFxaXpZaWqTBQWloSGpu9uEJHcxAtKVjYxjjsOVnRx0cDFB7ibk/XEBXTQDWoDsQbNXS0th+p7S3m6yirRfEpbYhejo6TJZDIlG4PZEw2z34+6fmUuqa6FL/TL+6JrqUmktJinjzDQ+EUmdxp6YhFjYIsaVYehjjsOVnR43KdbWOSQdr5v6IIgJnAHzFpM9IZ9Oauj7FpMEig4Mmq6yUpiazH4iE6WmT5bBVd7fZ3qByF0h/8rU/9yUoFKVj6diYtLCwPfN1fd1sHxvz4Ul3ahpiYYMQW4qlhzEOW3521MjBALWXmPsjigicAfAVk77y2RgI19CQdOjQ9uBZU5PZPjQUxqgAH+zda5YGZTKmplkmYx57UNOu3IXQh/7vZ3keFIrasXR8vPxy8fV1s99zAWQges2Wjo1hjMPv54xSINoqDgaovcTcH1FEjTMAvotzdyDqk9gt8PpCQMSUO8a98MP/pL+7dV/Zr6u1+UYUj6Wh1Vks1TTEgQYhttTRi0pXTWpQ+YwaZ7Gd+8MdNAcoQuAMCJ8tE96gTV2fUv9M/7btk6cm1XewL4QRAYC3Sl0gfeD4eU+DQlE8lobe2ZcGIbEVxUB0I3y5ieZogNpLcZ37wx21xInuCWhMAGKus7UzlidN0tUBRN3osVGdPnC64ALpoy2Vg0I7Nd8ovuCK4rF0cNDUfCsVYAykzmJ7OwGzmKpUgypuc7V8k46tS8vz9RhnZqSrV+sMnuWXyIcQoLYlYBXXuT+iiRpnAFCDSvVASu2zpTYLAPips7VTfQf7No9tjTTfKFXLLIrHUuosIiyeBKIzGenSJec7RPrepCPgDtZRqwUJ2IKlmgBQpUr1QHaqFWLL3T8ACEKpLA7pblCoXBbHTkvIonYspc4iwlJ3DaqVFam3NzJLEENfMu0hluACtaHGWRECZwAaVWkyIomJSlAyGdPmPW41eSL8c0ctEIK76gkKRbGWGewSleL+XqhrXBEreh9akw4fcPwEakONMwDwWKV6IJW+xqYJstMidoe7ahH/uenqFm3NzdK5c+ZftaJYywz2COOYY/NxruYaVJlM4fkob23NbF9cdO7mTkuD9RhtwvET8A81zgCgCpUmI0xUAtDba+5wb3XliulYFWUR/rnT2XTBxaQkXbh2oWT9QMRHFGuZwQ5hHHMid5y7ebPy/hs3av+eIddKa6Qeo204fgL+IXAGAFWoNBlhouKz/B3u4rUSW+9wR1HEf+56sjgRjEpNUIIwemxU8wPzmjw1qfmB+erqLgE7KHdsGbk6EvhzOnuc27ev8v62tuq/18qKWfa5f7908qTU0WEer642NsYaRa1JB8dPwB8s1QSAKo0eG9XpA6dL1gOptA8NquYOt2NLQ6oS8Z+bTE072bKsrOYlZHCe33XAyh1b/vjGHyudTQf6nM4e5zo6TLmAcjXOajknVcqoDrBWWnOzaVYSpSYdHD8B79EcAABgt0zG3JGutN/hAFJZMfi56+7qBl/QkQ1hCSpg+6Mf/VH98Y0/3rbdz+LpkTvOra6a4FYjtTdjcH4DYD+aAwAAosPLO9wuicHPTaamXSotK+NvA7+UqwN2+sBpz193546eKxk48zMDLHLHub17TUbY4qLJfK6n23PEM6oBRA81zgAA9pueNsGirbq7zfYoi8HP3dnaqb6Dfe5fTPolwMLZkVtWBicEWQcsrJqkkTzOtbdLJ07UF+DyslYaAASAjDMAgP28uMPtorj+3D7wu36S51ZWTA2gRpZD1SgfVCheVubE7wvOCjpgG7kMMBdVyKj+1r/8If3Rd+bVkV3hbwPAGtQ4AwAAkWZLwfua9PSUX6brc+Fs54KMcF7k6oBhZyVqpX3tB1+urn/1dX3r2eaxE8dqAM6qJU5E4AwAAESWkwXvKZyNGCJgG1PPZFQvNOf0A5/9P7fttvpYDcBptcSJqHEGAAAiK8j6SZ6ppnA2YAMPa/BFsg4YdvZMrbQv7/6nkrutPlYDiA0CZwCASMrlpJERKZk0K9ySSfM4lwt7ZAiSkwXv41w4O8BmCGjAyopZTrx/v3TypKlZ1dNjlt8BdXDyWO2wdDatqetTSmfTYQ8FcAKBMwBA5ORy0tGj0vCwlM1K6+vm4/Cw2U7wLD7C6qLXkHzh7ESicHsiYbZHcZlmVAIxcQn89faaGnxbXblialYBdXDyWO2o1FxKXRNd6p/pV9dEl1JzqYa/J4E4RB01zgAAkTMyYoJk6+vb9zU1mX3nzgU9KoTJufpJJQpn+91VM1QhNkPwRAhdUENDDT74yLljtWRe8zdvOtH52o+6n0424AFEc4BtCJwBQLwkkybDrJzWVmlpKbjxAHV7pnC2CxdkdQsoEOPrBbnrgb9aXLpksgLLuXhROnEiuPEgFE4GuLzmYMB86vqU+mf6t22fPDWpvoN9NX8/JxvwAM+gOQAAINaWlxvbD1jjmcLZkQ2aSYE0Q/BjadKmTMZcOG8Nmknm8eXLni3btKZuY5xr8EGSz+8nlzi4ZNnrWnJONuAB6kDgDAAQOS0tje2vSVxqGgF+8TkQk86mC5YRSdKFaxe8q8UTQODPqrqNcazBh02+v59cEVDA3Gte15KjqQPigsAZACByBgdNLbNSmprM/oZFpZh5lSj8C8mnrCefAzG+Z0QEkIE1NiYtLGyv27i+braPjTX8FLWZnjbLULfq7jbbsSOXj6dOZRj5eWMrgIC5X0aPjWp+YF6TpyY1PzCv893n6/5eNHVAXFDjDAB8RA2QcOSzM4ovNJuapEOHpKtXpebmBp8kRjWNKPwLyef3lY/NEAKpwePz8cDauo1xqMHnMdePp07UtAqi9hhNMgow34WLqHEGABagBkh4mpvNRfzwsLmgbGoyH4eHPQqaObpEox4sy4mPnbJgGs16qvj99+41AaZMxhSXz2TMYw8ucgPJiPA5A8vauo1xqMHnoSgcT53IMPK49ljJTNuPdyj3r/4Pliw/o7O1U30H++x6HQAeIuMMAHzgxB1Z1C9GXeW87sAFO1WTBdNI1pMNWTaBZET4lIFlbcYZahKl46m1GUYeZ4JVzLR99T/r6gt+XM2f+f/d3WF5V00Ad5FxBgDPCKuOiFM1QFC7GHWVo/Bv9FWbBVNv1pMtWTaBZET4lIEVSN3GALhc28sLT689XdN2m1mbYeRx7bGKmbZfvkdjR2d8yZQFYBdfA2ff933fp127dhX8O3++sPjgl770Jb3+9a/X/fffr2QyqQsXLmz7Pp/4xCf00EMP6f7779erX/1qXbx40c9hA4iIMJdKEmyIuBh1lXNiWQ4aUm2gv95utdxIaNzQkKkjVxw8y9eXGxoKY1S1oXyBdG/i3pq2ow4e39gaH98eNMtbXzf7WbIMRJ/vGWe/8iu/or/927/d/Pfv//2/39x3584dPfLII3rZy16mL3zhC/rABz6g4eFhjY+Pb37OtWvXdObMGQ0MDOiLX/yiTp06pVOnTukrX/mK30MH4LCwMxwINsRAjLrKedmBC/apNtBfb9YTNxIa53vdRp+FfU62Be+FAHh8Y8va+oIAAuV74Ox5z3ueHnzwwc1/z33uczf3ffSjH9XTTz+t3/7t39YrX/lKvfnNb9Zb3/pW/cZv/Mbm53zwgx9UT0+P3vGOd+jAgQMaGRnRa1/7Wn3oQx/ye+gAHGZDhgPBhoD42W6+Eh+LmdvI2mU5aFi1gf56s564keCN5mbp3DlTy2xtzXw8d87+oJlkxznZBrwXAuLhja16M20BRIuvzQG+7/u+T//0T/+k7373u3rpS1+q3t5eDQ0N6Z577pEk9ff3686dO5qZmdn8mj/5kz/Rj/zIj2hlZUV79+7VS1/6Ur397W/XL/3SL21+zvve9z7NzMzo+vXrJZ/3qaee0lNPPbX5+M6dO0omkzQHAGKE4vwxEES7eSBGqin2ncuZmj/j4ybToqXFZJoNDe0cwLG2mDh8xzm5EO+FgHjQrGNkxGR2llqu2dRk9p0719AoAYSkluYA9/g5kLe+9a167Wtfq+c///m6du2a3vWud+lv//ZvNzPKbt26pZe//OUFX/PiF794c9/evXt169atzW1bP+fWrVtln/f973+//uN//I8e/zQAXJK/q7t1aQh3dSOmUrv52dlwxgQ4rLO1c8djZD7rqZ4LxWq+P6KJc3Ih3gsBaW9vuO7Y0JA0M1Omq+YhN+oLAmhczYGzd77znRodrdw+/C//8i/10EMP6e1vf/vmtte85jW699579bM/+7N6//vfr/vuu6/20VbpXe96V8Fz5zPOAMTL6LFRnT5wmru6UZTJFGaa5a2tme2LixTpRXgyGdPZrYEsByBq6j4n835CiPL1BevNtAUQDTUHzh599FG95S1vqfg5r3jFK0pu7+zs1D//8z/rr//6r7V//349+OCDeuKJJwo+J//4wQcf3PxY6nPy+0u57777fA3MAXAHd3Ujqpp28zG7wGLpjwVYPgxUVNM5mfcTLNFIpi28wzwHYao5cPbCF75QL3zhC+t6soWFBTU1NelFL3qRJOnhhx/We97zHn33u9/Vs571LEnS3Nyc9u/fr73PnBAffvhhfeYznymocTY3N6eHH364rjEAACLA43bzrkvNpQqWQJ09fFajxypnh8MHLB8GvMP7CcAzmOcgbL41B3jssceUTqf1xje+Uc973vP02GOPaWhoSCdOnNBHPvIRSdLt27e1f/9+PfLII0qlUvrKV76if/fv/p3GxsY0+ExP82vXruno0aM6f/683vSmN+n3f//39Z//83/WX/zFX+hVr3pVVWOppegbAMARPT3mImpt7e62RMJ0zorRRRVFty2RyUj791feH7MsSKBuvJ8A37mSwcU8B36pJU7UVHFvA+677z79/u//vo4ePapXvvKV+tVf/VUNDQ1pfHx883P27NmjT3/60/r617+u173udXr00Uf13ve+dzNoJkmHDx/Wxz72MY2Pj+vgwYP65Cc/qZmZmaqDZgCAiPKw3bzLMk9matruoom/mNDP/dHPaeIvJsIeSnnVLB8GKslkpEuXTI3GuOP9BPgqNZdS10SX+mf61TXRpdRcKuwhlRWHeQ7s51vGmU3IOAOACPOg3bzLon4ntvP/6dSfLf/Z5uN/0fIvlP6ZdIgjKoMMGdSLWl7b8X4CfOPavMG18cIdVmScAQAQiPZ26cSJ2F5EdbZ26uzhswXbUkdSkZhMTvzFREHQTJL+bPnP7Mw86+gwwY5EonB7ImG2x/T1iSpUquUVV7yfIiedTWvq+pTSWQtvfMSMaxlcUZ7nwB01NwcAAMBJmYxZ/hPBzLTRY6M6feC0E7VKavHn3/zzstsHXjsQ8GiqMD1tgh1bM4diuHwYNchkCl8veWtrZvviYuSOV1Xj/RQZFHa3S8cDHTVtt0FU5zlwBxlnAADn5HLSyIiUTJoEhGTSPM7lSnzyyoppJLB/v3TypMlk6OmRVlcDH7efOls71Xewz8nJZLlMhB/63h8q+fnltodu717TmCKTkS5eNB9nZ+O73E6ibtdOqOVVXozeT1HOxkpn0wVBM0m6cO1CJH9WV7iaweXyPAfuo8YZAMApuZx09Ki0sCCtr9/d3tQkHTokXb0qNTdv+QK6b1ptp0yE4hpnnd/bqfmfng90jKgDdbuqQy2v2It6NtbU9Sn1z/Rv2z55alJ9B/tCGBHyXOmqCfilljgRgTMAgFNGRqTh4cKgWV5Tk9l37twzG7gotVq1BX8n/mJCf/7NP9cPfe8P2blEswG2XLh4Pg4C1tXjdxVbcSh6HoefcVOES0IAUURzAABAZI2Plw6aSWb7+PiWDSyDslq1BYoHXjugD//rD0cuaJaaS6lrokv9M/3qmuhSai4VjXHk63ZtDQRJhXW7HOXLkrrpaRMk24paXrHgWpH2epRaFtj3mogtt4tJSQggzgicAQCcsrxcw/59+yp/cltbw+NB/VwsUOwVW+r++DKOiAasfQt0+lTLq6ZakAhFXI6Bo8dG1ffqu8syp740FdqNgrrsVKuRzrhA5BE4AwA4paWlhv0dHaauUiJR+EmJhNnOUopQuVqg2Au2ZJr4Mo4IBqwDCXS2t0snTnhyXMrXghwelrJZk42bzZrHR48SPLNFGMfAMBoRpLNpTX15qmCbEw0Cqskki3CGLYC7CJwBAJwyOGhqmZXS1GT2F2AZlNVGj41qfmBek6cmNT8wr/Pd58MeUiBsyTSpZhw1X2hHMGBtS6CzWmNj2xuoSObxwoLZH7Yod5KsRZDHwLCWh7v2/tlUTSZZRDJseT8CldEcAIBvbCl6jWipuatm3uKimcBStBcBqnQcLO6mlzqSCiVwWGkcdXf8W101F5cR6arpWoHzZNJkmJXT2iotLQU3nmJR7yRpozBfw669fyRV31woAk2IeD8iruiqWYTAGRA8TsLwUy5nMibGx01Ns5YWk2k2NFQmaAaEoJrjoC03GNLZtL609Ff6/B/+S138eFLLy9IDL3pKf3fgP0ldY9J939783JoudiMUsLYl0FmNRKJ8ExXJ3GgoXlkWFCeDKBEwdX1K/TP927ZPnppU38G+El/hLZfeP5JMTbOTJ8vvv3hR6Vc/X5knM/rXb/sv+p7/+edOdsbl/XiXLedjBIfAWRECZ0CwOAkDiDvXjoPlMjm1a0168IvSW96wGTwL6kLbRq5cWNmccRZ2ACeubDgmufL+kVQxkyyn5+r0v/k9zX3mddI/tGhX87L+/fM+oV9dfq+a9cxNBkcybHk/Gtzwj6da4kTUOAPgOWdrWfiAmhGIA17n2wV1HPTqd1+uJpY2EtKtH5DmhzY3Ra3jXy06WzvVd7DP+ov+mmtBBsiW+n5xY0MzFlfeP5LK1mrMNe3Wv2z+vOZm/rV0JyltJLTxD0n933/7Nv2Ljm8o94eXPeuMGwTej/Z0uYbdCJwB8BwnYSOsIrxO2anFO6zH67y0II6DXv7ux8crLO3baJK+YCItcel66rqhIVPzsTh4lq8FOTRU6quCYUMAJ67i2oylbiWaC4294jf1pW93mJsKW20k9PiNvRr76iOhL0uv5YYK70du+KM6LNUE4Avnall4zIYlEVZbWTHdqiJSODyuXH6dB7FkyM/joNe/+51qYu1qWtdjf/Pn1v9dcZfttSCdWraHeNtSqzH5I+0VlkFvqLV1l5ONN+L8fnR5LoPGUOOsCIEzIBxxPglTM2IHPT2mpbuDhXRxl6uv8yBrmfh1HPT6d29zTSwAsAWNN6Ip7jf846qWONE9AY0JQAx1tnbG9kTNctUKMpnCTLO8tTWzfXEx9GUOqI6Lr/NytUxOHzjty/HKr+Og17/7wUFpeLj0BWHYNbGAumUy0s2bkejqCju0tFS+ydDSEtxYilVachjX+Xi1Ro+N6vSB07G94Y+dUeMMAHxAzYgKbt6svP/GjWDGgYa5+DqPSi0Tr3/3NtfEAmq2smIym/fvl06eNIXee3qk1dWwRwbH0XgjupxqXoHAsVQTAHwU5+WqZVVo8b65n8wAp7j0Oo/aUhYvf/e218QCqkY5APgkl5OOHt3ehTh/k+Hq1XCPlyw5BKpHjbMiBM4AwDJc1CBENV1YsNQLcEtEbs64dEMibmy/yWD7a8f28SE+CJwVIXAGAJZZXZXOnKGrZhmuTypdGP+OY6TzK+CmS5fM8sxyLl6UTpwIbjx1CLKBCRAkXtuwCYGzIgTOAMBSW1q8u5ABEATXJ5Wuj38TWZGAmxzPOIvacnIgj9c2bFNLnIjmAACA8LS3mzv/Fl/EBKlcx8d0Nh3SiGrj+vg35Tu/bg2aSYWdX2MmnU1r6vqUe39LxE9Hh8kOTSQKtycSZrvl55uoNDAJC8cqe/HahssInAEAYAnXJ5Wuj38TnV8LpOZS6proUv9Mv7omupSaS4U9pMgre/GfyZiliDEM3tZketpkh27V3W22W47OiPXjWGU3XttwGYEzAAAs4fqk0vXxb9q3r/L+trZgxmGByGQROqTkxf/Kilk+vH+/qd/V0WEer66GPVw77d1rllRnMqamWSZjHjtQn7CztVNnD58t2JY6kmIp2w6ifKyKShYdr224jMAZAACWcH1S6fr4Nzm+1MtLkckidES5i/9vnX6Tqbm31ZUrpskKynO0HMDosVHND8xr8tSk5gfmy3f9jQgvAkNRPVZFLYsubq9tRAfNAQAA7stkzPK6iDQZcKErZSWuj18SnV+fQTHnYE1dn1L/TH/Btva/lzIfqvBFlhe7ByrxqqFMZI5VW+Yz6WevRONnAixFcwAAQDxEdPlSZ2un+g72OTsxdn38kpxe6uWlyGQROqLUsuZ9Ox3OHKq5F5UlZ/CGl8srnT9WlZjPtP7kz+h7/nH7p7qeRQe46J6wBwAAQN16ewuWL+X0XI19ulPjL/lnLX9XammRBgeloSGpuTnEccJd7e2xz+YZPTaq0wdOb88ijFimpw3yF/9bgwn/qvtnpI/+P+W/yJGae15lFiE6Ki2vrCfgVfZY5YKi+YwktaS/quknpBN9hZ/qXN1QIAJYqgkAcFMmY+7MPiOn5+qoPqcF/YDWdbc2VVOTdOiQdPUqwTPAEysr5iIv5stY/bRtuXNPj7moXlu7+0mJhOkUOTsb3kCrFJlldPAUr4tnFM1nirX/e+nGA+b/qSMp6oIBHmGpJgAg+m7eLHg4pqFtQTNJWl+XFhaksbEAxwZEWYnMCArVe2vbcufpaRMk26q722z3SC4njYxIyaSJySWT5nEu1/j3jmrhdjTG+eWVXimazxT7ox/6DYrpAyEj4wwA4KaiO7RJfUNZJct+emurtLQUxMDsFonC/dgU+N9zh8wICtX7bHHR1DTzeHlsLicdPWpuMqyv393uVcYumUWoJPbnpQaPq7H//QF1IuMMAGCfTEa6dMlc+Hmho8MsD0uYDLNltVT89OVlb57WZVFrax93ofw9d8iMcKlQvZPa26UTJzwPTo6NbQ+aSd5l7JJZhEqcaSjj9Twmr2g+symRMNsrvN85rwPBIOMMiAHuRCFUftZDWl01y8MuXybjbAdkfERLaH9PMs4iKZmUstny+706fjIfgZOCqOu4ZT5T7XNwXgcaQ8YZgE3ciULo/KyHtHevKYydyWjwp/5RTU2l7wU1NZnumnHmd42hdDatqetTSmfTnnw/VFbN39OXmlUNZEbAXjtl5HqVsetMZhEaFqlzQhB1HbfMZ3Txovk4O1sxMEftwGBF6jWNmhE4AyIsnU0XtH6XpAvXLnDAR3AyGXP3dGsnOMk8vnzZu+UO7e0a+q0OHTq0S01FZ7Z8jZ6hIW+eylXl2td70daeAH3wdvp75mtWDQ+bTKL1dfNxeNhsbyh4FkChegSrpfJK9x33A1tF6pwQ1Dwmr4bl2H6e11EoUq9p1IXAGRBh3IlC6AKsh9TcbApYDw+bZUVNTebj8HDjha2jwK8aQwTow7HT39PXmlV1ZEbAboOD2nbTIY+MXdQiCueEgmzdA+1K6hsa0S8rp+du/+QQ6zpSOzAYUXhNo3H3hD0AAP7hTpT3qM9So337Ku9va/P06ZqbpXPnzD9sN3psVKcPnPb0NVwpQM97xF+V/p7j49uDZnnr62Z/w++T9naWZkbE0JA0M1O+q2bcM3Zt4Mr8w/VzwvYOs7uUVVLDGtaM/g9d1RvUrG/f/QKP5zG18uO8jkKuv6bhDQJnQITl70RtvUvCnaj6peZSBb/Ls4fPavTYaIgjckC+HtKVK4XLHBIJs7SLi+7AdbZ2enoMIEAfrnJ/z6BqVuW5clGP0vIZu2NjJqi6vGyWZw4OmqBZ3DN2w+bS/MP1c0LZbF0ltKAf0JiGdE7/yap5jNfndRRy/TUNb9BVE4gBLmgaR+eiBtTRKQpuKb6oSx1J6Xz3+RBHhKC6JEpuXdQDrnFx/uHyOaHysXNDrcpqSS9lHhMzLr+mUV4tcSIyzoAY4E5U40jTbkC+HtLioqkF0tZmxR1aeIelIvYZHDT1/Uot1/SyZlW52i+nD5zmdQB4wMX5h8vnhMrZuLu03NQqPZ5hHhMzLr+m4Q0CZwBQBdK0PUA9pEizLkCfyZjmFDEN1AZVs8rFi3qgIsuOHa7OP6w7J1SppaVytm5Lyy4rXhcwglxV4+prGt6gqyYAVIHORYAjVlaknh5p/37p5ElTZ6+nxywZjpGgusy6elEPbGPpsYP5R7DoMOuO1FxKXRNd6p/pV9dEl1JzqbCHhAijxhkA1IB6cYDlenrKN6OYnQ1vXBFG7RfkOX2OtPzY4fTv1iHbu2oa+WxdL288oH4u1v6DfWqJExE4AwAA0ZDJmGyRSvtZYuMLLurtEdbfwukmERw7sEUuF2KHWcuWCttq6vqU+mf6t22fPDWpvoN9IYwILqI5AAAAiJ+bNyvvv3GDCxGfUPvFDmEFr5xvEsGxA1s0N0vnzpl/gVlZkXp76UBeJcoEIGjUOAMAACWls2lNXZ9SOpsOeyjV2bev8v62tmDGAYSgXPAqiPdvpSYRTuDYgbD19pqlwltduSKdORPOeCxH7T8EjYwzAACwjZPLrjo6zB36cnWKyBhBhIXZ4dT57A+OHQhTJlOYaZa3tma2Ly7yGixh9NioTh84TZkABIKMMwAAUCDMzJWGTU+bC92turvNdiDCwgxeRSL7g2MHwlLFUmHnMsAD0tnaqb6DfW4da+AkMs4AAECBMDNXGrZ3r+mAt7ho6hJRYBkxkQ9e/b///YL2rUo3ni/9f34suOCV89kfHDsQlh2WCl944lNK/dl/23zsRAY4EDF01QQAAAVo8w44iOLigLt6ekouFf7Wv/wh7X3j/LZP53wMNK6WOBFLNQEAQIFILLsC4obi4oC7yiwVvjzyb0t+ujONN4CIYKkmAADYxvllV0CcWFBcPJeTxsak8XFpeVlqaZEGB6WhIam52denRoyks+lonpfKLBX+vjI1zZxpvAFEBEs1AQDuymRMUV1q0QCIs0uXpJMny++/eFE6ccK3p8/lpKNHpYUFaX397vamJunQIenqVYJnaJyT3Z49UPxzp46kdL77fIgjAqKBpZoAgGhbWTH1QPbvNxeLHR3m8erqtk/N5aSRESmZlBIJ83FkxGwHgEjYobi42tp8ffqxse1BM8k8Xlgw++GPuHRbdLrbc4NGj41qfmBek6cmNT8wT9AMCAGBMwBA8DIZkyGxuFjf11dZyyefBTE8LGWz5iIumzWPjx4leAYgIjo6TCOARKJweyJhtvuckTs+vj1olre+bvbDe6m5lLomutQ/06+uiS6l5lJhD8k3lbo9x0Fna6f6DvZFa3kq4BACZwCA4NSQKVZWvpbP1s5TUmEtn2eQBQF4bIegd1yyX6xUpri4pqd9f+rl5cb2o3Zxy8AqV9OLWl8AgkDgDAAQHC+6vt28WXn/jRub/yULwmGNZiXCW1UEveOU/WKlfHHxTMbUNMtkzOO9e31/6paWxvajdnHLwAqt2zPnIgAicAYACEoNmWIV1VDLhywIB3mRlRhBodfq2yHoHbfsF6u1t5tGAAE2TBkcNI0ASmlqMvvhrThmYAVa64tzEYAtCJwBAIJRQ6ZYRTXU8iELwkFeZCVGTOi1+qoIesct+wWFhoZM98zi4Fm+q+bQUBijirbQMrBCFlitL85FALYgcAYACIaXXd+qrOVDFoRjvMpKjJjQa/VVEfSOY/YL7mpulq5eNcHc1lZzfG1tNY+vXjX74T26LfqEcxGAIgTOAADB8LLrW5W1fMiCcIxXWYkRE3qtviqC3nHNfomjcg0gmpulc+ekpSUTX1haMo8JmvmLbouNKbsMXs8t/0UxPRcBcbZrY2NjI+xB+O3OnTvas2ePbt++rd27d4c9HAAxkM6mlXkyo44HOpjMbrW6apY5XL58d9vx4yZTzKcC1rmcycgZHzc1zVpaTKbZ0BAXdNbJZEw9mUr7A6zbZItEonzgTDKB4OLECM/19JhlSlufKJEwmZ6zs5ubOPZFW2ouVVDL7uzhsxo9NhriiCyTyZgbAG1tsTxWuSa/DL44o7epaUOH1r+gq3qDmvXt7V8Y03MREDW1xIkInAGAx7iwqMLiorljy8UFilUZoImTZNLUNCuntdVk99Si5gBXCEFv2CWdTatromvb9vmBeYKkKyumJhbvD6eMjJjlxKVuTDRpXcO7/qPObfzK3Y0xPxcBUVNLnIilmgDgITrLVSmErm9wRJX16+LE61p9qbmUuia61D/Tr66JLqXmUjt/UZXLoxFdNICogELyTqq4DF67NH7vLxZujPm5CIgzAmcA4CEuLIAGEaDZxstafQ0H9wl6xxYNIMqgkLyzlpcr7d2l5e++kHMRAEkEzgAnlCvEC/twYQF4hADNJi87FhLcR71oAFEGTU2c1dJSxX7LzkVcEwDhuCfsAQCojHpZbslfWGz9m3FhAaBR+Y6F58419n0I7qMRo8dGdfrAaRpAbFVF11nb0MTDGBysUOOsjmXwfuOaAAgPzQEAi1GI111MShE2XoMop/jiK3UkpfPd50McEeA4h5qahBV8sfGcVL6rplkGX2tGb12q7MTKNQHgPZoDABHBkhp3dbZ2qu9gH5MZhKKu4u+IjdFjo5ofmNfkqUnND8wTNAMa5UhTk7AaGNl6TvJyGXzNVlZMwHX/funkSamjwzxeXS356VwTAOEicAZYjCU1AGrl5YURtVSii+B+vPBe9pkjTU3CCL7Y3m08vwx+ackkDC4tmce+Z5rV2ImVawIgXATOAItRiBewSCYjXbpkfYc0ry6MbM0QAFCbSu9lAmoes6yQfLEwgi9kSpVQRydWrgmAcNEcALAchXiBkK2smDvDly/f3Xb8uFmCY1k2geTNhVG5DIHTB05Xfwyqsm4LAP9Uei9/6i8/RaFxG/l47AyjgRGZUiVU04m1xN+eawIgPATOAAd0tnZycgTK8TtAU2k5hWVFnyVvLowqZQjs+H0cCzQiODYWB4+6cu/lS4uXGg+Ow1sBHTu9DL5U857+1F9+atu22GdKNdCJlWsCIBwEzgAAbgriIiO/nKLY1uUUXgXrPAwANnph1FCGgGOBRgQjrE5+cVfqPdv+91JbelFtT0o3HijcV1VwHP4I8NjpRfClmvd0qYxHSfo3D/2bhp7beR0dZr5SrhMrWdqAdahxBgBwU42FdetSzXKKRtXYWatajRR/r7uWSh11WxB9thcHj7Kt7+W935EuTUmZD0k/9a6PafE3zePv+ce7nx/r5XNhcuzYWe17mvpmFTjSiRWAQeAMAOCeoC4yGlhOUbUgAoB1GD02qvmBeU2emtT8wLzOd5/f+YuCCDTCOVw8hyv/Xv7y/3y1jv914dS/+6+k6U+a/we5fC6Xk0ZGpGTSJNkkk+ZxLhfI09vHsWNnte9p6ptV4EgnVgAGgTMAgHuqvMho+OIsv5wikSjcnkiY7Y0upwg6y6DGzqA1Z60FEWiEc7h4Dl/nd/bqe699WbvW1gu237Mh9dyUvvgjf1BdcNwDuZx09Kg0PCxls9L6uvk4PGy2xzJ45tixs9r3NJ0gq2B5J1YABoEzAIB7qrjIKH9xtqGjP3BbuYUq7+D7uZwiqCwDn5aDbuN3oBFO4uLZAjscaw7lmgMaiDQ2Ji0smGPyVuvrZvvYWGBDsYdjx85a3tN1ZS/Xo8YbQwBQi10bGxsbYQ/Cb3fu3NGePXt0+/Zt7d69O+zhAAC80NNTvrDu7KxGRkzQrPjiTJKatKZhDevc8T+vvpnA4qIJZHnZvTOTMcGsSvu9eK4dfleeWl01y0zpqokidNUMUVDHmiokk+YmRjmtrdLSUiBDsYuDx84d39N+d72W6OQMoG61xIkInAEA3LTDRUbli7MNtSqrpcTL/Qke1cLvoFZYF8x+BBoB1C/IAHoFiUTpGxp5TU3bV6/HShSOnUEGsyx5XdeKGwlA+AicFSFwBgARVuYiY8eLM61pTfeYBwFmW2zjd5bBpUtmeWY5Fy+a+ioAos2SjCYyzmIgqGBWUDeGPM6cS82lCrqSnj18VqPHRhv+vgBqU0uciBpnAAC3lSms29JS6Ys21KLluw/D7Fjmd2ctx4pOA/CJJV38BgdNVlkpTU1mPxwWZNObRuuE7lQXzYf6oOlsuiBoJkkXrl1QOpuu+3sC8B+BM+D/397dR0dZ3nn8/ySDRFycmALhwUQKJEGpiA/0TBJ07dZgeHArS3+tgoJWWI4tdoV6zGCVmpaflcTTw3HP76iblaOFdml1d0stD4mJKFsLGR8qKO7KhAeFCIglkBC1CMn1++NuhkwymWSSmbnvmXm/zsmJc91DcsXcuWfmM9f1/QJISmFfnKldS1R1fsAJ4VGsOmslWNHpVDDgbq9AJ75Gn9bvXt/3F942d/Fbvly6+uru1+f0dGt8+XI7ZoWoiVfTG6n/bwz1NRCbP99aOddZXZ21crOf/Cf8EY0DcAaCMwBAUgr94swoXW26Wu9oudakTngUy86giEjP3V6tcUeFZ3SpczxvrVeFawu1cONCFa4tlLfWa/eUejV0qLR9u3XO5+RY1+icHOv29u3WcSSweK5y7u8bQ30JxGK0cq5gWEFE4wCcgeAMABwo4hUE6Kb7izOjnIy/qFzl2q5vaKg+S53wyCFbtCCtWSPt2tW9/l57uzW+Zo0ds+oiBtuTEH2JvOVr6FBp5Uqrlllbm/V55UpCs7iJZSge71XOkb4x1MdArHXPh1qlR5SrQ3LpnHJ1SKv0iFr1d9b9+7lyzpPjUVlxWdCYd5qXBgGAw9EcAAAchqKxMZYMHcuQsBKiMHqCdqlLNet3r9fCjQu7ja+bs04LpiywYUZwvHh1u7SjEUVfH9v70DCn9YaZurHwr9r1/gVq1/kAsGPF+nZ9Q0P97wzoOQRdNQH70VWzC4IzAInC1+hT4drCbuP1i+p5YgUkgV67vaZ3XwgRV/HqUocB4/ECEYt3KO7EN6r6cI1b9Zt8lZeHvlanq03leb/WyobuoTWAxEJXTQBIUBSNBZJb+G6vvR+PuXgW9g6Bbep9x5YvRCSe3S472NyIIqQ+bCWtqur5DY52pavq8ztjP08AjjLI7gkAAM6jaCyQ3JYsUc8rGdKt47aKZ2HvLtimHrmK6RWae8Vctnyhd30JxZ0UcMXShg3dt5J2qot25Ei4f5ymI8fSYjo9AM7DVk0AcJiuLx6907xaXbLaxhkBiJaOrppdGwSkp1tdYB3RVdCGGmdsOwRiLM7bsBOihlcPW0kTohYlgAFjqyYAJLCK6RWqX1SvdXPWqX5RPaEZkES6d3u1PpeXOyQ0kyLvUhcFbFMHYiyO3S69tV4Vri3Uwo0LVbi2UN5ab9S+dlT1sJV0yRLr2hyKI1YGA4g7VpwBAACguzgW9mbFGRAHceh2mQx/ywmxMhjAgLHiDAAAAAMTx8LeFLoH4iAry9pu7fdLW7ZYn6uroxaaScmxejQhVgYDiCtWnAEAAMAREqIuEoAeJcOKMwCpgRVnAAAkIV+jT+t3r5ev0Wf3VICY8OR4tGDKAl5gAwmK1aMAkhErzgAASABdu62WFZepYnqFjTNCKmJFWGrh943OIjkfOHcAOF0kORHBGQAADsfWFzgB4W1q4feNzjgfACQbtmoCAJBEkqHYMhKbr9EX9KJZkip3VLJtOEnx+0ZnnA8AUh3BGQAADlcwrCCicSDaCG9TC79vdMb5EDvULgUSA8EZAAAOR7Fl2I3w1h5xf1Ht90tbt2pyy4UhD9v9+25tlVatknJzJZfL+rxqlTWO2OHvPza8tV4Vri3Uwo0LVbi2UN5ar91TAtADapwBAJJOa6u0Zo1UVSUdOSKNGSMtWSItXy4NHWr37PqPYsuwU9caR95pXq0uWW3jjJJbXGtKNTVJ8+dLNTWBob1Tx6nwpoM6NeRv87H5993aKt14o7Rrl9Tefn48PV26+mpp+/bEvr47HX//0UXtUsB+NAfoguAMiYYXx0D/8eIKiB0en+Ij7i+qZ8yQ6uqktrbzYy6XTl3/df3hyR844ve9apVUXh58Xe+Qnm4dW7ky3rNKLdH6++c6Iq3fvV4LNy7sNr5uzjotmLLAhhkBqYfmAEACY9k2MDBr1nQPzSTr9q5d1nEgEmwPO8+T49GCKQtS9sVuvMS1ppTfb6006xyaSVJbmy7ZXq8FFxU64vddVRU6NJOs8aqq+M4nFUXj75/nuRa2vwKJheAMcBC6FgEDx4srRFPHCsbycqmx0TqHGhut2zfemJrhGWIvri+q9+8Pf3zfvuh/z344cmRgx2E/nueeR+1SILEQnAEOQtciYOAG9OLqb4Wx1dAQ1Tk5Fd28escKRtghri+qJ0wIfzwvL/rfsx/GjBnYcdiP57nBKqZXqH5RvdbNWaf6RfXUjAMcjOAMcBCWbQMD168XV01NVo2fiROlWbOkggLr9smTMZmjE7Bdpm9YwWgPQt04vqguKJBKS619yJ25XNZ4fn5svm+EliyxapmFkp5uHYez9fR89su2L+M8k2B2bsdn+zuQGAjOAAdh2TYwcP16cTV/vlUYu7O6OmnevKjPzwnYLtN3bA+LP0Ld8+L2onrDBqmkJHispMQad4jly60GL12v7x2NX5Yvt2NWiESo57mStPgPi237O2c7PoC+oKsm4EB0GwL6L+Kumn6/tdKsJ36/Y1ZcRAvdvPouN9d6EdWTnBzp8OH4zSfZxb2bJII1NFg1zfLyHHnda221tkdXVVmh9Zgx1pshy5fTLTmRrP3zWi3+w+Ju41H7O/f7rdp9fTiP6dYKpC66agIJjmXbGIhU3+I0dKgVjpWXW6FGerr1ubw8RGgmJUxh7GhiW3jfsT0svqiBZLP8fGnmTEeGZpJ1/V650gqr29qszytXEpolmsGuwSHHB/x33o+yC2zHB9AXBGcAkETY4mSJ6MVVghTGjia2hfdd0m4Pc2gjDEJdIPnF7O+8H2UXorUdP9XftASSHcEZACQJ6lb1U4IUxo42unn1TcQrGJ3O4Y0wCHWB5BeTv3O/X6qpsd4t66ytzRrv4U2CaHRr5U1LIPlR4wwAkgR1qwbg5EnrHemamvNjpaVWYeysLPvmBUTbjBnWCozOLy5dLqsQfHW1ffPqglqfQPKL6t/51q3WmwE92bLF2obcxUBrnFGXEUhckeREg+I0JwBAjLHFaQCysqzQwOGFsYEB6ViR0VXnFRkOOe89OR5edAJJLqp/5/0su7B8ubRxY88NhXrbjh+uLiPXMCB5sFUTAJIEW5yiwOGFsYEBSbFGGE6sOeTEOQFJoZ9lFwa6HZ83LYHUENPgbPPmzfJ4PBoyZIiysrI0Z86coOOHDh3S7NmzddFFFyk7O1sPPvigzp07F3Sf1157Tddee60yMjKUl5en559/PpZTBoCERt0qAD1KoUYYTqw55MQ5IfmlVFi7YYO17byzkhJrPIyBdGvlTUsgNcSsxtl//dd/6Z//+Z/185//XN/85jd17tw57dmzR9/97nclSW1tbbr66qs1atQoPfHEEzp69KgWLlwY+DeSdPDgQV155ZW69957tXjxYr3yyitatmyZNm/erNLS0j7PhRpnAAAASpgaZwPhxJpDTpwTkp+31hvUNKisuEwV0ytsnFH0hK2PZkPZBeoyAoknkpwoJsHZuXPn9NWvflU//elPtWjRopD32bp1q2655RYdOXJEI0eOlCQ988wz8nq9+vTTTzV48GB5vV5t3rxZe/bsCfy722+/XadOnVJ1BE/uCM4AAACUEo0wnNgoxYlzgtTaKq1ZI1VVSUeOWB0Ulyyx6lolXMfcLpI5rE3mQBBA/ESSE8Vkq+af//xnffzxx0pPT9c111yj0aNHa+bMmUEB2M6dOzV58uRAaCZJpaWlamlp0fvvvx+4T0mX5balpaXauXNn2O9/5swZtbS0BH0AAACkvI5GGH6/1WXO77duJ0loJjmz5pAT55TqWlulG2+0alk1NlqF4Rsbrds33mgdT2ThitYnMl+jLyg0k6TKHZWpsRUVgG1iEpwdOHBAklReXq5HHnlEmzZtUlZWlr7xjW+oqalJknTs2LGg0ExS4PaxY8fC3qelpUVffPFFj9//8ccfV2ZmZuAjNzc3aj8bACC0lKqjAiS6JG6EEe2aQ9G4tlEHyXnWrOneSVGybu/aZR1PZMka1sYqEOQ5DIBwIgrOVqxYobS0tLAfH3zwgdr/9gj08MMP69vf/rauu+46Pffcc0pLS9OLL74Ykx+ks4ceekjNzc2Bj8OHD8f8ewJAKqPoNQAniVajlGhe22je4ixVVd1Dsw7t7dbxRJasYW0sAkGewwDozaBI7vzAAw/o7rvvDnuf8ePH6+jRo5KkSZMmBcYzMjI0fvx4HTp0SJI0atQovfHGG0H/9pNPPgkc6/jcMdb5Pm63W0OGDOlxDhkZGcrIyOjbDwUAGJCetk3MvWJuwj9BB5C4PDmeAV2DYnFtG+icED1HjgzseCKomF6huVfMTaqi9R2BYOe/zYGuKOU5DIDeRBScjRgxQiNGjOj1ftddd50yMjK0d+9eXX/99ZKks2fP6sMPP9TYsWMlSUVFRXrsscd0/PhxZWdnS5Jqa2vldrsDgVtRUZG2bNkS9LVra2tVVFQUybQBADEUbtsETzojQ1cuwDm4tvUuka9ZY8ZYNc3CHU8GyRjWRjMQ5O8cQF/EpMaZ2+3Wvffeq0cffVQvv/yy9u7dq+9///uSpO985zuSpJtvvlmTJk3SggULtHv3btXU1OiRRx7R0qVLA6vF7r33Xh04cEBlZWX64IMP9NRTT+mFF17Q8uXLYzFtAEA/JGsdlXhjq0hstbZKq1ZJubmSy2V9XrUq8QuAI3a4toWX6NesJUuk9B5eCaWnW8fhXJ4cjxZMWTDgcIu/cwB9EZPgTJKeeOIJ3X777VqwYIG+/vWv66OPPtK2bduU9beuTS6XS5s2bZLL5VJRUZHuvPNOLVy4UD/72c8CX2PcuHHavHmzamtrNWXKFP3iF7/Qs88+q9LS0lhNGwAQoWSoo2J3qEKXsNhK9u55iI1kuLbFSjJcs5Yvl66+unt4lp5ujfM+fWrg7xxAX6QZY4zdk4i1lpYWZWZmqrm5WW632+7pAEBSStQtOx2hStfuah0vnrZvl4YOje0c1u9er4UbF3YbXzdnnRZMWRDbb54CVq2yQrJQhcDT061jK1fGe1ZIFIl6bYsJv1/av18bz+7RP71T1u1wol2zWlut7plVVVZNszFjrJVmy5fH/roPZ+HvHEg9keREBGcAgJTmhFDF1+hT4drCbuP1i+p5Ah8Fubnhaxnl5Eg04AbCaGqS5s+XamoCQ9UTpHn/j3SqU78urll9R1ADAPaKJCeK2VZNAAASQVVV6NBMssarqmI/B7aKxFYqdM8DYmr+fKmuLmho+sE0bfjP87e5ZvVdoteHA4BUw4ozAEBKc7l6Ds4ka9VZW1t85sIKhNhgxRkwAH6/NHFij4c3/r5So6/9e65ZfcQKYwBwBlacAQDQR2PGhDtqNCz7TLymErUuYQhG9zx7+Bp9Wr97fUIVjEcI+/eHPTzngiu5ZkXAf8If0TgAwH4EZwCAlBYuVFFauz694v8NvY3G75e2bpUaGmI6Pwwc3fPij61oSWTChPDH8/LiM48kUTCsIKJxRIfd3bMBJDaCMwBASgsdqhgprU0a9Y5UuEaVOyrPr5ppapJmzLC2Ls2aJRUUWLdPnrRh9uiLoUOt7qjl5da2zPR063N5eXy6pqYaX6NPlTsqg8aC/oaQWAoKpNJSK23ozOWyxvPz7ZlXgqKmZfx1dM8uL7e27be3W5/Ly61xwjMAvSE4AwCktK6hSlp6u+RulL5RLt39DSnjM0mdttGEKJKtujpp3rx4ThsRGjrU6o56+LBVs+7wYes2oVn0sRUtCW3YIJWUBI+VlFjjiFjF9ArVL6rXujnrVL+oXqtLVts9paS2Zo20a1f3eqbt7db4mjV2zApAIqE5AAAAnYQt3Px5Vtgi2fL7WX2BlEfx8yTW0CDt22dtz+RaB6fw+61afD2clzSIARAKzQEAAOinsNtoeimSrX37YjgzIDGwFS2J5edLM2fGLTSjwYSz2f776WPphCNHwn+Z3o4DACvOgCTna/TJf8KvgmEFvGhBwrLjPA75Pf1+VpzBeXpZbWEXHn8wEN5ab1CtvLLiMlVMr7BxRpbWVmtrX1WVFbiMGWM1mVm+PLW2fjvi9zNjhlUqoa3t/JjLZW0jrq4ODLHiDEAokeREBGdAEnPEkxpggBx3HvfxiToQc01NVs29mprzY6WlVt2prCz75gUMkF3bfXsLezuKzHetl9XRoTdVmo04Yjt2BG9krVpl1THtWuNMsn535eVWzUsAqYWtmgDoaoak4MjzmCLZcAoaVSBJ2dFgwlvrVeHaQi3cuFCFawvlrfV2uw9F5i2OaAASQemE0N2zzweey5dHfXYAkgzBGZCkHPGkBhggR57HWVnWyjK/X9qyxfpcXc0KH8SX32+tNOu88lGybtfUWEXcgQRVMKwgovGB6uubNFVVoVctSdZ4VVVMpuc48f79hDRhQvjjeXmB/+zaPTs93fpcXp46qwQBDAzBGZCkHPGkBhggR5/HcS6SDQShUQWSWLwbTPT1TRqKzFsc0QCkoMDamu5yBY+7XNZ4l8fmoUOt7ZiHD1vvLxw+bN0mNAPQFwRnQJJyxJMaYIA4j9GhtdWqU5Oba70uys21bre22j0zm0Sw2gJIRBXTK1S/qF7r5qxT/aJ6rS5ZHbPv1dc3acaMCf91ejueTOL5++kRpRMAxAnNAYAkR1czJAPO4/5Jlv9vFOTuAY0qgKjp2ojGO83bLQyiyLxDNTRYq2wd1lkYgLPRVbMLgjMAQKpxXDfSAeDFag9OnrQaAdBVE4gKumoCQOogOOuC4AwAkEp8jT4Vri3sNl6/qD4hV57l5kqNjT0fz8mx6tWkLFZbAHHT2mp1z6yqsmqajRkjLVlidWYkNAOAxBFJTjQoTnMCAABxEq7QdSIGZxTk7kV+PoEZECcdReZTcpUrAKQomgMAAJBkHN2NtB8oyA0AAAC7EJwBAJBkkq0b6ZIlVg2hUNLTreMAAABALFDjDACAMBK5ng1dNQEAAIDuaA7QBcEZAKA/CGycI5EDTACS/H5p/36aWAAAHCGSnIitmgAA9GDNmu6hmWTd3rXLOo746CjIffiw1NZmfV65ktAMcLymJmnGDGniRGnWLKmgwLp98qTdMwMAoE8IzgAA6EFVVffQrEN7u3UcABDG/PlSXV3wWF2dNG+ePfNBUmhtlVatknJzJZfL+rxqlTUOANHGVk0AAHrgcvUcnEnWls22tvjNBwASit9vrTQLd5xtm4gQZRQARANbNQEAiIIxYwZ2vK+S6Z3zZPpZAAzQ/v3hj+/bF595JACunX1HGQUA8caKMwAAerBqlVReHnrVWXq6dWzlyoF9j2R65zyZfhYAUcCKsz7h2hmZ3FypsbHn4zk5Vh1MAAiHFWcAAETB8uXWi5b0Lo+WHS9mli8f+PdIpnfOk+lnARAFBQVSaam1hKozl8saJzSTlJzXzliuoDtyZGDHASBSrDgDACCM1lbrRUtVlfVkfMwYackSKzSLxgqAZHrnPJl+lpjx+63ta3l5hAZIDSdPWo0AamrOj5WWShs2SFlZ9s3LQeJ57Yz1Y1rH94jlCjoeawBEQyQ5EcEZAAA2SqYGBMn0s0RdU5PVXZDwAKmqocGqaUZo3E28rp3x2hIa1TIHId5siEcZBQDJj62aAAAkiHg1IIiHZPpZom7+fKmuLnisrs5aiQOkgvx8aeZMQrMQ4nXtjNeW0KqqnoPA9nbreK+amqQZM6waebNmWdt+Z8yQTp6MSxkFAOiM4AwAgP7y+6WtW62VFP20ZEn3J/8d0tOt44kimX6WqPL7rZVmXZeMtLVZ4wM4fwAkvnhdO6MSaPVBVGqQhXmzYehQa3Vcebm1LTM93fpcXk4jBQCxwVZNAAAiFcVtd/Hsphbr2jZ0huvB1q3WiomebNlircQBkJLide2M15bQAdcgoxsrgDhgqyYAALEUxW138XrnvOOFWXm59YKmvd36XF5ujUej0xmrAHowYUL443l58ZkHAEeK17UzXltCB7yCbv/+8Mf37evXvACgv1hxBgBAJBL0nXCKKdtsxgwrXO28nMPlkkpKpOpq++YFIGXE63FgwCvoEvRxFkBiYcUZAACxkqDvhMertg16sGGDFZJ1VlJijQMYuCjUnEx28SqqP+AVdAUFVvkDlyt43OWyxgnNAMQZK84AAIhEgr4THq/aNuhFQ4MVrublOfI8iTdfo0/+E34VDCuQJ8dj93SQiKJYczIVxLrWZdScPGmVP+D3CiBGIsmJCM4AAIhUAm67G3CxZiDKvLVeVe6oDNwuKy5TxfQKG2eEhJSA12NEgDcbAMQIWzUBAIilBNx2N+BizUAU+Rp9QaGZJFXuqJSv0WfTjJCQ/H5rRVLX5bJtbdY42zYTX36+1XWY0AyAjQjOAACIVFaWtZLB75e2bLE+V1c7evtIvGrbAH3hP+GPaBwIKUFrTgIAEgvBGQAA/ZVA74QPuFgzEEUFwwoiGgdCmjAh/PG8vPjMI8H5Gn1av3s9Kz4BoAcEZwAApIihQ6WVK61aZm1t1ueVKwnNEH+eHI/KisuCxrzTvDQIQGTovjhg3lqvCtcWauHGhSpcWyhvrdfuKQGA49AcAAAAALagqyYGjO6L/eZr9KlwbWG38fpF9fw9Akh6keREg+I0JwAAACCIJ8fDC3QMTEfNSbovRixcrUH+LgHgPIIzAAAAAIktP5/ALELUGgSAvqHGGZCCKAILAACQ2qg1CAB9Q40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-      "text/plain": [
-       "<Figure size 1500x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_leaf]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"green\",s=10)\n",
-    "\n",
-    "for i,data in enumerate(tree.data['value'][tree.is_inner]):   \n",
-    "    plt.scatter(data[::2],-data[1::2],color=\"red\",s=20)\n",
-    "\n",
-    "plt.scatter(tree.data['value'][0][::2], -tree.data['value'][0][1::2], color='blue',s=30)    # root\n",
-    "   \n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phylomorphospace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_all = []\n",
-    "p_all.append(tree.data[\"p_adj\"][0])\n",
-    "\n",
-    "\n",
-    "n = int(l_dim/2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Lift all contrast to the root\n",
-    "for node in tree.iter_topology_bfs():\n",
-    "    if node.parent is None: \n",
-    "        continue\n",
-    "    org_node = node\n",
-    "    node = node.parent\n",
-    "    \n",
-    "    p_lifted = jax.vmap(lambda A,v: jnp.dot(A.T,v))(tree.data[\"phi\"][org_node.id],tree.data[\"p_adj\"][org_node.id].reshape((n,2)))\n",
-    "    while node.parent is not None: \n",
-    "        p_lifted = jax.vmap(lambda A,v: jnp.dot(A.T,v))(tree.data[\"phi\"][node.parent.id],p_lifted)\n",
-    "        node = node.parent\n",
-    "\n",
-    "    \n",
-    "    p_all.append(p_lifted.flatten())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pca_and_update_tree(tree, p_all, n, n_components=5):\n",
-    "    # Initialize Riemannian metrics for estimating the covariance matrix\n",
-    "    from jaxgeometry.manifolds.landmarks import landmarks   \n",
-    "    M = landmarks(n, k_sigma=1*jnp.eye(2))\n",
-    "    x = M.coords(tree.data[\"value\"][0])\n",
-    "    \n",
-    "    # Riemannian structure\n",
-    "    from jaxgeometry.Riemannian import metric\n",
-    "    metric.initialize(M)\n",
-    "\n",
-    "    # Compute covariance matrix\n",
-    "    cov_uplddmm = jnp.zeros((n*2,n*2))\n",
-    "    for i in range(len(p_all)):\n",
-    "        cov_uplddmm += np.dot(np.dot(p_all[i].reshape(n*2,-1), \n",
-    "                                    p_all[i].reshape(-1,n*2)), M.g(x))\n",
-    "    cov_uplddmm = cov_uplddmm/(len(p_all)/2)\n",
-    "\n",
-    "    # Compute PCA\n",
-    "    eigenvalues, eigenvectors = np.linalg.eig(cov_uplddmm)\n",
-    "    \n",
-    "    # Sort eigenvectors by eigenvalues in descending order\n",
-    "    idx = np.argsort(eigenvalues)[::-1]\n",
-    "    eigenvalues = eigenvalues[idx]\n",
-    "    eigenvectors = eigenvectors[:, idx]\n",
-    "    \n",
-    "    # Select top components and get landmarks\n",
-    "    components = eigenvectors[:, :n_components]\n",
-    "    landmarks = [tree.data[\"value\"][leaf.id] for leaf in tree.iter_topology_bfs()]\n",
-    "    \n",
-    "    # Project landmarks onto components\n",
-    "    X_projected = np.dot(landmarks, np.real(components))\n",
-    "    \n",
-    "    # Calculate variance explained\n",
-    "    varexp = eigenvalues[:n_components]/sum(eigenvalues)\n",
-    "\n",
-    "    # Update tree with projected values\n",
-    "    tree.data[\"pc\"] = tree.data[\"pc\"].at[:].set(X_projected)\n",
-    "        \n",
-    "    return varexp,X_projected"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "varexp,x_projected = compute_pca_and_update_tree(tree, p_all, n, n_components=5)\n",
-    "\n",
-    "# Here just for plotting\n",
-    "tree.add_property('pc_plot', shape=(2,))\n",
-    "tree.data[\"pc_plot\"] = tree.data[\"pc_plot\"].at[:].set(x_projected[:,0:2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_tree_2d_scatter(tree, 'pc_plot')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/SDE.py b/examples/SDE.py
deleted file mode 100644
index 1de1132..0000000
--- a/examples/SDE.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# SDE utilities and guiding
-
-import jax
-import jax.numpy as jnp
-from jax.scipy.linalg import cholesky
-
-# for 2d + 3d cases with factorizable matrices
-# multiply on the factorized matrix, e.g. covariance matrix
-dot = lambda A,v: jnp.einsum('ij,jd->id',A,v.reshape((A.shape[0],-1))).flatten()
-# multiple on inverse factorized matrix, e.g. inverse covariance matrix
-solve = lambda A,v: jnp.linalg.solve(A,v.reshape((A.shape[0],-1))).flatten()
-
-# time increments
-def dts(T=1.,n_steps=100):
-    return jnp.array([T/n_steps]*n_steps)
-
-# Euler-Maruyama SDE integration
-def forward(x0,dts,dWs,b,sigma,params,a=None):
-    def SDE(carry, val):
-        t,X = carry
-        dt,dW = val
-        
-        # SDE
-        if sigma is not None:
-            Xtp1 = X + b(t,X,params)*dt + dot(sigma(X,params),dW)
-        else:
-            assert(a is not None)
-            Xtp1 = X + b(t,X,params)*dt + dot(cholesky(a(X,params),lower=True,check_finite=False),dW)
-        tp1 = t + dt
-        
-        return((tp1,Xtp1),(t,X))    
-
-    # sample
-    (T,X), (ts,Xs) = jax.lax.scan(SDE,(0.,x0),(dts,dWs))
-    Xs = jnp.vstack((Xs,X))
-    return Xs
diff --git a/examples/Tree building.ipynb b/examples/Tree building.ipynb
deleted file mode 100644
index f302b6e..0000000
--- a/examples/Tree building.ipynb	
+++ /dev/null
@@ -1,402 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1a0c8e27",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9697a314",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import hyperiax\n",
-    "from hyperiax.tree.topology import symmetric_topology, asymmetric_topology, read_topology, write_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.plotting import plot_tree\n",
-    "import jax\n",
-    "from jax import numpy as jnp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "805cfcb8",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f134b9a3",
-   "metadata": {},
-   "source": [
-    "# Creating the Tree\n",
-    "\n",
-    "First, we initialize the tree. Importantly there are 2 concepts related to trees in hyperiax that you should familiarize yourself with. We call the two concepts a `topology` and a `HypTree`. The topology is simply a connection of nodes that save the relations among eacother. At each node, you can inspect its `node.parent` or `node.children`. However, these nodes never contain any data themselves, except for some plotting utility like names etc. \n",
-    "\n",
-    "The `HypTree` class takes a topology, and does all the work in both storing data that fits the topology, and precomputing a series of indices that is needed for the execution engine to run on the tree. It stores the values from each\n",
-    "\n",
-    "Lets first see how we can create and plot a simple topology\n",
-    "\n",
-    "``hyperiax.tree.topology.asymmetric_topology(depth)``\n",
-    "\n",
-    "``hyperiax.tree.topology.symmetric_topology(h,branching)``\n",
-    "\n",
-    "\n",
-    "builds a symmetric tree, with h depth, and branching\n",
-    "note the tree does not include a length of the branches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "776f5a8e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "topology = hyperiax.tree.topology.asymmetric_topology(5)\n",
-    "tree = HypTree(topology)    \n",
-    "\n",
-    "plot_tree(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1382efc",
-   "metadata": {},
-   "source": [
-    "## Add names \n",
-    "\n",
-    "It is also possible to add names to the tree. \n",
-    "\n",
-    "The names can be stores in the attribut \"node.name\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "75b4b214",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "topology = hyperiax.tree.topology.symmetric_topology(4,2)\n",
-    "tree = HypTree(topology)\n",
-    "\n",
-    "\n",
-    "\n",
-    "for i,node in enumerate(tree.iter_topology_bfs()):\n",
-    "    node.name = str(i)\n",
-    "    # alternatively the below will yield the same result but save an enumeration\n",
-    "    # node.name = node.id \n",
-    "\n",
-    "\n",
-    "plot_tree(tree,inc_names=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "919da8bf",
-   "metadata": {},
-   "source": [
-    "# Add branch length \n",
-    "Lets say we now want to set the `edge_length` to be a function of the node depth. Lets for the sake of example some decaying exponential such that nodes closer to the leaves have shorter edge lengths. \n",
-    "\n",
-    "Each tree calculates the depth of each node, so we can use that here. It is simply a jax array that holds a single integer for each corresponding node. This array is stored in the same order as the rest of the data in the tree, so we are free to do computation on this and assign it to any data slot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8153c641",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "topology = hyperiax.tree.topology.symmetric_topology(4,2)\n",
-    "tree = HypTree(topology)\n",
-    "\n",
-    "tree.add_property('edge_length', shape=(1,))\n",
-    "tree.data['edge_length'] = jnp.exp(-jnp.log(2)*tree.node_depths)\n",
-    "\n",
-    "plot_tree(tree)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a85c6d8",
-   "metadata": {},
-   "source": [
-    "# Tree reader and writer\n",
-    "It is possible to both read a tree file, or write your tree to a tree file. The tree file is a newick file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "34f89aa0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "my_Tree = write_topology(tree)\n",
-    "tree = read_topology(my_Tree)\n",
-    "plot_tree(tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4765e905",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'(Baronia_brevicornis:0.1711,((Iphiclides_podalirius:0.0847,((Graphium_evemon:0.0688,(Graphium_sarpedon:0.0547,Graphium_agamemnon:0.0519)QS1811:0.0137)QS1810:0.0610,Protographium_marcellus:0.0923)QS1808:0.0132)QS1807:0.0653,(((Hypermnestra_helios:0.0444,(Parnassius_orleans:0.0179,Parnassius_honrathi:0.0312)QS1821:0.0276)QS1820:0.0604,((Archon_apollinus:0.0358,Luehdorfia_puziloi:0.0365)QS1826:0.0339,(Sericinus_montela:0.0897,(Zerynthia_polyxena:0.0241,Allancastria_cerisyi:0.0235)QS1831:0.0304)QS1828:0.0309)QS1825:0.0197)QS1819:0.0257,(Teinopalpus_imperialis:0.0958,(((Battus_polydamas:0.0359,Battus_belus:0.0434)QS1835:0.0877,(Pharmacophagus_antenor:0.0796,(((Trogonoptera_brookiana:0.0370,(Troides_rhadamantus:0.0207,Ornithoptera_richmondia:0.0196)QS1840:0.0224)QS1839:0.0081,((Euryades_corethrus:0.0483,(Parides_agavus:0.0307,(Parides_photinus:0.0265,Parides_eurimedes:0.0282)QS1844:0.0108)QS1843:0.0125)QS1842:0.0056,Cressida_cressida:0.0735)QS1841:0.0134)QS1838:0.0094,((Byasa_alcinous:0.0225,(Atrophaneura_semperi:0.0104,Atrophaneura_dixoni:0.0117)QS1847:0.0129)QS1846:0.0188,((Pachliopta_aristolochiae:0.0035,Pachliopta_kotzebuea:0.0050)QS1849:0.0278,Losaria_coon:0.0230)QS1848:0.0236)QS1845:0.0093)QS1837:0.0343)QS1836:0.0381)QS1834:0.0341,((((Papilio_aristodemus:0.0310,(Papilio_thoas:0.0091,Papilio_cresphontes:0.0088)QS1855:0.0266)QS1854:0.0368,(Papilio_slateri:0.0338,(Papilio_glaucus:0.0301,Papilio_troilus:0.0384)QS1858:0.0096)QS1856:0.0101)QS1853:0.0063,(Papilio_gigon:0.0646,((Papilio_xuthus:0.0353,(Papilio_polyxenes:0.0058,Papilio_zelicaon:0.0054)QS1866:0.0260)QS1863:0.0179,(Papilio_deiphobus:0.0214,(Papilio_protenor:0.0122,(Papilio_polytes:0.0113,(Papilio_phestus:0.0038,Papilio_ambrax:0.0033)QS1872:0.0089)QS1871:0.0099)QS1870:0.0070)QS1868:0.0398)QS1862:0.0075)QS1861:0.0459)QS1852:0.0595,Meandrusa_sciron:0.0910)QS1851:0.0272)QS1833:0.0141)QS1832:0.0172)QS1818:0.0204)QS1804:0.0453)QS1803:0.0000;'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Smaller subset of the tree from Kawahara 2023 (DOI: 10.1038/s41559-023-02041-9),\n",
-    "string_tree = \"(Baronia_brevicornis:0.1711,((Iphiclides_podalirius:0.08472,((Graphium_evemon:0.06883,(Graphium_sarpedon:0.05466,Graphium_agamemnon:0.05188)QS1811:0.01369)QS1810:0.06096,Protographium_marcellus:0.09227)QS1808:0.01315)QS1807:0.0653,(((Hypermnestra_helios:0.04444,(Parnassius_orleans:0.01792,Parnassius_honrathi:0.03123)QS1821:0.0276)QS1820:0.06041,((Archon_apollinus:0.03581,Luehdorfia_puziloi:0.03646)QS1826:0.03388,(Sericinus_montela:0.08969,(Zerynthia_polyxena:0.02408,Allancastria_cerisyi:0.02345)QS1831:0.03039)QS1828:0.03093)QS1825:0.01972)QS1819:0.02572,(Teinopalpus_imperialis:0.09583,(((Battus_polydamas:0.0359,Battus_belus:0.04337)QS1835:0.08774,(Pharmacophagus_antenor:0.07956,(((Trogonoptera_brookiana:0.03698,(Troides_rhadamantus:0.02074,Ornithoptera_richmondia:0.01965)QS1840:0.02241)QS1839:0.00805,((Euryades_corethrus:0.04833,(Parides_agavus:0.03072,(Parides_photinus:0.02647,Parides_eurimedes:0.02819)QS1844:0.0108)QS1843:0.01245)QS1842:0.00564,Cressida_cressida:0.07354)QS1841:0.01338)QS1838:0.00944,((Byasa_alcinous:0.02248,(Atrophaneura_semperi:0.01043,Atrophaneura_dixoni:0.01171)QS1847:0.0129)QS1846:0.01879,((Pachliopta_aristolochiae:0.00348,Pachliopta_kotzebuea:0.00499)QS1849:0.0278,Losaria_coon:0.02297)QS1848:0.02356)QS1845:0.00935)QS1837:0.03434)QS1836:0.03812)QS1834:0.03412,((((Papilio_aristodemus:0.03095,(Papilio_thoas:0.00907,Papilio_cresphontes:0.00884)QS1855:0.02664)QS1854:0.03683,(Papilio_slateri:0.03379,(Papilio_glaucus:0.03012,Papilio_troilus:0.03835)QS1858:0.0096)QS1856:0.01012)QS1853:0.00628,(Papilio_gigon:0.06457,((Papilio_xuthus:0.03532,(Papilio_polyxenes:0.00579,Papilio_zelicaon:0.00541)QS1866:0.02599)QS1863:0.01789,(Papilio_deiphobus:0.02135,(Papilio_protenor:0.01221,(Papilio_polytes:0.01127,(Papilio_phestus:0.00377,Papilio_ambrax:0.00325)QS1872:0.00893)QS1871:0.00991)QS1870:0.007)QS1868:0.03981)QS1862:0.00747)QS1861:0.04588)QS1852:0.05955,Meandrusa_sciron:0.09096)QS1851:0.02719)QS1833:0.01405)QS1832:0.01719)QS1818:0.02038)QS1804:0.04527)QS1803:0;\"\n",
-    "tree = read_topology(string_tree)\n",
-    "plot_tree(tree,inc_names=True)\n",
-    "write_topology(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "289e0c2f",
-   "metadata": {},
-   "source": [
-    "# # Get Node Information \n",
-    "# \n",
-    "All edge lengths are stored in tree.data, specifically in tree.data[\"edge_length\"].\n",
-    " \n",
-    "The Hyperiax HypTree is built such that all information which should be part of the upwards or downwards pass is stored in tree.data.\n",
-    "See more about this in the mean estiamtion note book. \n",
-    "This jax-compiled array dictionary cannot store strings, hence the node names are stored inside each individual node and must be accessed by iterating through the tree.\n",
-    "#\n",
-    "Below you can see how to obtain both edge lengths and node names:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "6de4e0b4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "QS1803 Papilio_ambrax\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_nodenames = []\n",
-    "for node in tree.iter_topology_bfs():\n",
-    "    my_nodenames.append(node.name)\n",
-    "\n",
-    "print(my_nodenames[0],my_nodenames[-1])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0c27967",
-   "metadata": {},
-   "source": [
-    "# Tree Iterators in Hyperiax\n",
-    "#\n",
-    "Hyperiax implements several different ways to iterate through a tree structure:\n",
-    "\n",
-    "1. tree.iter_topology_bfs - Breadth-first search traversal\n",
-    "   - Visits nodes level by level, starting from root\n",
-    "   - Visits all nodes at current depth before going deeper\n",
-    "\n",
-    "2. tree.iter_topology_dfs - Depth-first search traversal  \n",
-    "   - Explores branches completely before backtracking\n",
-    "   - Visits nodes by going as deep as possible first\n",
-    "\n",
-    "3. tree.iter_topology_leaves_bfs - Breadth-first search for leaves only\n",
-    "   - Like BFS but only yields leaf nodes\n",
-    "   - Visits leaf nodes level by level\n",
-    "\n",
-    "4. tree.iter_topology_leaves_dfs - Depth-first search for leaves only\n",
-    "   - Like DFS but only yields leaf nodes\n",
-    "   - Visits leaf nodes in depth-first order\n",
-    "\n",
-    "5. tree.iter_topology_levels - Level-order traversal\n",
-    "   - Returns nodes grouped by their depth level\n",
-    "   - Useful for processing nodes at same depth together\n",
-    "\n",
-    "\n",
-    "# These iterators can be used depending on the purpose, and work as Python generators.\n",
-    "This means they can be used with:\n",
-    " - Direct iteration (for node in tree.iter_...)\n",
-    " - List conversion (list(tree.iter_...))\n",
-    " - Reversed iteration (reversed(list(tree.iter_...)))\n",
-    " \n",
-    " The example below demonstrates using reversed BFS iteration to get node names in reverse order."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e79905a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "QS1803 Papilio_ambrax\n",
-      "Papilio_ambrax QS1803\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_nodenames_rev = []\n",
-    "for node in reversed(list(tree.iter_topology_bfs())):\n",
-    "    my_nodenames_rev.append(node.name)\n",
-    "\n",
-    "print(my_nodenames[0],my_nodenames[-1])\n",
-    "print(my_nodenames_rev[0],my_nodenames_rev[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd245412",
-   "metadata": {},
-   "source": [
-    "The order inside of tree.data is by default tree.iter_topology_bfs, going from root and downwards.\n",
-    "\n",
-    "This means that when accessing tree.data values like edge_length, node_ids etc, they will be ordered in the same way as if you iterate through the tree using iter_topology_bfs(). \n",
-    "\n",
-    "This makes it easy to match node properties with their corresponding data values, as shown in the example below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d54c718a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Node edge length: [0.], Data value: 0.0000\n",
-      "Node edge length: [0.1711], Data value: 0.1711\n",
-      "Node edge length: [0.04527], Data value: 0.0453\n",
-      "Node edge length: [0.0653], Data value: 0.0653\n",
-      "Node edge length: [0.02038], Data value: 0.0204\n"
-     ]
-    }
-   ],
-   "source": [
-    "counter = 0 \n",
-    "for node, value in zip(tree.iter_topology_bfs(), tree.data[\"edge_length\"]):\n",
-    "    print(f\"Node edge length: {node['edge_length']}, Data value: {value[0]:.4f}\")\n",
-    "    counter += 1\n",
-    "    if counter == 5: break "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31145638",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/fast.ipynb b/examples/fast.ipynb
deleted file mode 100644
index a745845..0000000
--- a/examples/fast.ipynb
+++ /dev/null
@@ -1,285 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'FastOrderedExecutor' from 'hyperiax.execution' (/Users/vbd402/Documents/Projects/hyperiax/hyperiax/execution/__init__.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mhyperiax\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrandom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PRNGKey, split\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhyperiax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexecution\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastOrderedExecutor\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhyperiax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfasttree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastTree, FastBiTree\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhyperiax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UpLambda, DownLambda\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'FastOrderedExecutor' from 'hyperiax.execution' (/Users/vbd402/Documents/Projects/hyperiax/hyperiax/execution/__init__.py)"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "import jax\n",
-    "from jax.random import PRNGKey, split\n",
-    "from jax import numpy as jnp\n",
-    "from tqdm import tqdm\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from hyperiax.tree.fasttree import FastTree, FastBiTree\n",
-    "from hyperiax.tree.builders import symmetric_topology\n",
-    "from hyperiax.execution import FastOrderedExecutor\n",
-    "from hyperiax.execution.collate import dict_collate\n",
-    "from hyperiax.models import UpLambda, DownLambda\n",
-    "from hyperiax.models import UpLambda, DownLambda, FuseLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "from hyperiax.plotting import plot_tree_2d_scatter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of nodes: 2047\n"
-     ]
-    }
-   ],
-   "source": [
-    "key = PRNGKey(1337)\n",
-    "depth = 10\n",
-    "print(\"Number of nodes:\", (2<<depth)-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topology = symmetric_topology(depth, 2)\n",
-    "tree = FastTree(topology)\n",
-    "#tree = FastBiTree(depth)\n",
-    "tree.add_property('noise', (2,))\n",
-    "tree.add_property('edge_length', (1,))\n",
-    "tree.add_property('value', (2,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "tree.data['noise'] = jax.random.normal(key, tree.data['noise'].shape)\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(tree.data['noise'][0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def down(noise, edge_length,parent_value, **args):\n",
-    "    return {'value': jnp.sqrt(edge_length)*noise+parent_value}\n",
-    "downmodel = DownLambda(down_fn=down)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downmodel = DownLambda(down_fn=down)\n",
-    "exe = FastOrderedExecutor(downmodel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "down 0.15671300888061523\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "exe.down(tree);tree.data['value'].block_until_ready()\n",
-    "print('down', time.time()-start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_tree_2d_scatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/repos/hyperiax/hyperiax/plotting/scatter.py:23\u001b[0m, in \u001b[0;36mplot_tree_2d_scatter\u001b[0;34m(tree, property, ax)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m nodeidx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(l_start, l_end):\n\u001b[1;32m     22\u001b[0m     cdat \u001b[38;5;241m=\u001b[39m tree\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;28mproperty\u001b[39m][nodeidx]\n\u001b[0;32m---> 23\u001b[0m     \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcdat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m handles \u001b[38;5;241m=\u001b[39m [mpatch\u001b[38;5;241m.\u001b[39mPatch(color\u001b[38;5;241m=\u001b[39mcmap(i\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(tree\u001b[38;5;241m.\u001b[39mlevels)), label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(tree\u001b[38;5;241m.\u001b[39mlevels))]\n\u001b[1;32m     26\u001b[0m legend \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mlegend(handles\u001b[38;5;241m=\u001b[39mhandles, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLevels\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/miniconda3/envs/jaxenv/lib/python3.11/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
-      "File \u001b[0;32m~/miniconda3/envs/jaxenv/lib/python3.11/site-packages/matplotlib/axes/_axes.py:4779\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4776\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ymargin \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.05\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   4777\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_ymargin(\u001b[38;5;241m0.05\u001b[39m)\n\u001b[0;32m-> 4779\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcollection\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4780\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m   4782\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collection\n",
-      "File \u001b[0;32m~/miniconda3/envs/jaxenv/lib/python3.11/site-packages/matplotlib/axes/_base.py:2268\u001b[0m, in \u001b[0;36m_AxesBase.add_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m   2263\u001b[0m     collection\u001b[38;5;241m.\u001b[39mset_clip_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch)\n\u001b[1;32m   2265\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m autolim:\n\u001b[1;32m   2266\u001b[0m     \u001b[38;5;66;03m# Make sure viewLim is not stale (mostly to match\u001b[39;00m\n\u001b[1;32m   2267\u001b[0m     \u001b[38;5;66;03m# pre-lazy-autoscale behavior, which is not really better).\u001b[39;00m\n\u001b[0;32m-> 2268\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_unstale_viewLim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2269\u001b[0m     datalim \u001b[38;5;241m=\u001b[39m collection\u001b[38;5;241m.\u001b[39mget_datalim(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransData)\n\u001b[1;32m   2270\u001b[0m     points \u001b[38;5;241m=\u001b[39m datalim\u001b[38;5;241m.\u001b[39mget_points()\n",
-      "File \u001b[0;32m~/miniconda3/envs/jaxenv/lib/python3.11/site-packages/matplotlib/axes/_base.py:844\u001b[0m, in \u001b[0;36m_AxesBase._unstale_viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    842\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[name]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    843\u001b[0m         ax\u001b[38;5;241m.\u001b[39m_stale_viewlims[name] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 844\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautoscale_view\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscale\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[43m                       \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mneed_scale\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/jaxenv/lib/python3.11/site-packages/matplotlib/axes/_base.py:2860\u001b[0m, in \u001b[0;36m_AxesBase.autoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m   2858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_sticky_edges:\n\u001b[1;32m   2859\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xmargin \u001b[38;5;129;01mand\u001b[39;00m scalex \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscalex_on():\n\u001b[0;32m-> 2860\u001b[0m         x_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m   2861\u001b[0m \u001b[43m            \u001b[49m\u001b[43martist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msticky_edges\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\n\u001b[1;32m   2862\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_shared_axes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_siblings\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2863\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43martist\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_children\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2864\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ymargin \u001b[38;5;129;01mand\u001b[39;00m scaley \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_autoscaley_on():\n\u001b[1;32m   2865\u001b[0m         y_stickies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(np\u001b[38;5;241m.\u001b[39mconcatenate([\n\u001b[1;32m   2866\u001b[0m             artist\u001b[38;5;241m.\u001b[39msticky_edges\u001b[38;5;241m.\u001b[39my\n\u001b[1;32m   2867\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shared_axes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget_siblings(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2868\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m artist \u001b[38;5;129;01min\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_children()]))\n",
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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_tree_2d_scatter(tree, 'value')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ll1, ll2 = tree.levels[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.add_property('value_est', (2,))\n",
-    "tree.data['value_est'] = tree.data['value_est'].at[ll1:ll2].set(tree.data['value'][ll1:ll2]) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def transform(weighted_value,inverse_edge_length, **kwargs):\n",
-    "    return {'value_est': weighted_value/inverse_edge_length}\n",
-    "\n",
-    "def up(value_est, edge_length, **kwargs):\n",
-    "    return {'weighted_value': value_est/edge_length, 'inverse_edge_length':1/edge_length}\n",
-    "upmodel = UpLambda(up, transform, \n",
-    "                   reductions={\n",
-    "                       'weighted_value': 'sum', \n",
-    "                       'inverse_edge_length': 'sum'\n",
-    "                    }\n",
-    "        )\n",
-    "root_exe = FastOrderedExecutor(upmodel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "up 0.0006308555603027344\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "root_exe.up(tree);tree.data['value_est'].block_until_ready()\n",
-    "print('up', time.time()-start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fuse(child_value_est, child_edge_length, **kwargs):\n",
-    "    childrent_inv = 1 / child_edge_length\n",
-    "\n",
-    "    result = jnp.einsum('c1,cd->d',childrent_inv, child_value_est)/childrent_inv.sum()\n",
-    "    return {'value': result}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fusemodel = FuseLambda(fuse)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_exe_fuse = FastOrderedExecutor(fusemodel)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/localfast.ipynb b/examples/localfast.ipynb
deleted file mode 100644
index b00f8a9..0000000
--- a/examples/localfast.ipynb
+++ /dev/null
@@ -1,189 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:38.177646Z",
-     "iopub.status.busy": "2025-03-04T16:58:38.177530Z",
-     "iopub.status.idle": "2025-03-04T16:58:38.187869Z",
-     "shell.execute_reply": "2025-03-04T16:58:38.187591Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:38.189404Z",
-     "iopub.status.busy": "2025-03-04T16:58:38.189300Z",
-     "iopub.status.idle": "2025-03-04T16:58:38.573088Z",
-     "shell.execute_reply": "2025-03-04T16:58:38.572767Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from jax.random import PRNGKey\n",
-    "from hyperiax.execution import UnorderedExecutor\n",
-    "from hyperiax.tree.tree import HypTree\n",
-    "from hyperiax.models import UpdateLambda\n",
-    "import jax\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from jax import numpy as jnp\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:38.574750Z",
-     "iopub.status.busy": "2025-03-04T16:58:38.574660Z",
-     "iopub.status.idle": "2025-03-04T16:58:38.605497Z",
-     "shell.execute_reply": "2025-03-04T16:58:38.605241Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of nodes: 32767\n"
-     ]
-    }
-   ],
-   "source": [
-    "key = PRNGKey(1337)\n",
-    "depth = 14\n",
-    "print(\"Number of nodes:\", (2<<depth)-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:38.620633Z",
-     "iopub.status.busy": "2025-03-04T16:58:38.620538Z",
-     "iopub.status.idle": "2025-03-04T16:58:41.944141Z",
-     "shell.execute_reply": "2025-03-04T16:58:41.943778Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "topology = symmetric_topology(depth, 2)\n",
-    "tree = HypTree(topology, precompute_child_gathers=True)\n",
-    "#tree = FastBiTree(depth)\n",
-    "tree.add_property('edge_length', (1,))\n",
-    "tree.add_property('value', (1,))\n",
-    "tree.add_property('test_value', (1,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:41.945785Z",
-     "iopub.status.busy": "2025-03-04T16:58:41.945712Z",
-     "iopub.status.idle": "2025-03-04T16:58:41.960049Z",
-     "shell.execute_reply": "2025-03-04T16:58:41.959789Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "tree.data['value'] = jnp.arange(len(tree))[:, jnp.newaxis]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:41.961421Z",
-     "iopub.status.busy": "2025-03-04T16:58:41.961356Z",
-     "iopub.status.idle": "2025-03-04T16:58:41.968181Z",
-     "shell.execute_reply": "2025-03-04T16:58:41.967925Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def update(child_value, parent_value, value, leaf_mask, **kwargs):\n",
-    "    child_sums = child_value.sum(axis=1)\n",
-    "    child_sums = jnp.where(leaf_mask[:, jnp.newaxis], jnp.zeros_like(child_sums), child_sums)\n",
-    "\n",
-    "    return {'test_value': (child_sums+parent_value+value)}\n",
-    "\n",
-    "model = UpdateLambda(update)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:41.969427Z",
-     "iopub.status.busy": "2025-03-04T16:58:41.969361Z",
-     "iopub.status.idle": "2025-03-04T16:58:41.975308Z",
-     "shell.execute_reply": "2025-03-04T16:58:41.975089Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "exe = UnorderedExecutor(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-03-04T16:58:41.976551Z",
-     "iopub.status.busy": "2025-03-04T16:58:41.976453Z",
-     "iopub.status.idle": "2025-03-04T16:58:42.122665Z",
-     "shell.execute_reply": "2025-03-04T16:58:42.122296Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "exe.update(tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mcmc_Gaussian_BFFG_shapes.ipynb b/examples/mcmc_Gaussian_BFFG_shapes.ipynb
deleted file mode 100644
index 23abeea..0000000
--- a/examples/mcmc_Gaussian_BFFG_shapes.ipynb
+++ /dev/null
@@ -1,2673 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for shapes\n",
-    "\n",
-    "Parameter inference for trees with Gaussian transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebook [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb) for a simpler version with $\\mathbb R^2$ data. In the present version, the node covariance is constant throughout the tree similarly to [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb). Shape dependent node covariance will follow in a later version.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter, plot_tree_2d_shape, plot_shape_2d\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.ABFFG import *\n",
-    "from examples.shape import k_K2\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed:  513714401784\n"
-     ]
-    }
-   ],
-   "source": [
-    "# seed,\n",
-    "#seed = 42\n",
-    "import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "print(\"seed: \",seed)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 2; n = 16 # d = dimension of embedding space (usually 2), n = number of landmarkds\n",
-    "phis = jnp.linspace(0,2*jnp.pi,n,endpoint=False) # circular shape\n",
-    "root = jnp.vstack((jnp.cos(phis),jnp.sin(phis))).T.flatten()\n",
-    "\n",
-    "# plot\n",
-    "plot_shape_2d(root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# diffusion and covariance specification for shape processes\n",
-    "kQ12 = lambda x,params: (1/n)*k_K2(x,params)\n",
-    "\n",
-    "# evaluate k on two pairs of landmark configurations\n",
-    "kQ12_q = lambda q1,q2,params: kQ12(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# evaluate k on one landmark configurations against itself with each landmark pair resulting in a dxd matric\n",
-    "# i,jth entry of result is kQ12(x_i,x_j)*eye(d)\n",
-    "def Q12(q,params): \n",
-    "    A = jnp.einsum('ij,kl->ikjl',kQ12_q(q,q,params),jnp.eye(2))\n",
-    "    return A.reshape((A.shape[0]*A.shape[1],A.shape[2]*A.shape[3]))\n",
-    "\n",
-    "# diffusion matrix\n",
-    "sigma = lambda _,params: Q12(root,params)\n",
-    "# covariance matrix\n",
-    "def a(_,params): _sigma = sigma(_,params); return jnp.einsum('ij,kj->ik',_sigma,_sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gaussian tree, constant node covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                            None\n",
-      "           ┌────────────────────────┬────────────────────────┼────────────────────────┬────────────────────────┐\n",
-      "          None                     None                     None                     None                     None          \n",
-      " ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "topology = symmetric_topology(height=2, degree=5)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "# data dimension\n",
-    "d = 2\n",
-    "tree.add_property('value', shape=(n*d,))\n",
-    "tree.add_property('noise', shape=(n*d,))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.1), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.5), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions. At first, we define the unconditional transitions, which are just Gaussian samples. The covariance is sqare of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unconditional down\n",
-    "down_unconditional = Gaussian_down_unconditional(sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "add_leaf_noise(tree,key,params)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a generated tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots(figsize=(10,8))\n",
-    "plot_tree_2d_scatter(tree,'value',selector=lambda z: z.reshape((n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "\n",
-    "plot_tree_2d_shape(tree,'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "up = Gaussian_up(n*d,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree\n",
-    "init_up = get_init_up(n*d,tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# conditional down\n",
-    "down_conditional = Gaussian_down_conditional(n*d,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time elapsed: 0.0892 ms\n",
-      "Time elapsed: 1.9119 ms\n",
-      "Time elapsed: 1.0769 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding, time the operations\n",
-    "test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(31, 32)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tree.data['value'].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We test the setup by sampling a number of trees and computing mean and covariance of the leaf data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "root conditional mean vs. sample mean: [ 0.9212389  -0.06188519  0.85476349  0.33498547  0.64485078  0.70677158\n",
-      "  0.30292327  0.97737707 -0.08423276  1.06192903 -0.47922945  0.97972511\n",
-      " -0.77600708  0.73296924 -0.97367059  0.39121674 -1.03230681 -0.02754362\n",
-      " -0.92007822 -0.42001239 -0.70295622 -0.76796927 -0.38171084 -0.9878419\n",
-      " -0.00230393 -1.07235641  0.33111386 -0.99761781  0.64766172 -0.78868532\n",
-      "  0.8515469  -0.45247469] [ 0.9212389  -0.06188519  0.85476349  0.33498547  0.64485078  0.70677158\n",
-      "  0.30292327  0.97737707 -0.08423276  1.06192903 -0.47922945  0.97972511\n",
-      " -0.77600708  0.73296924 -0.97367059  0.39121674 -1.03230681 -0.02754362\n",
-      " -0.92007822 -0.42001239 -0.70295622 -0.76796927 -0.38171084 -0.9878419\n",
-      " -0.00230393 -1.07235641  0.33111386 -0.99761781  0.64766172 -0.78868532\n",
-      "  0.8515469  -0.45247469]\n",
-      "root conditional cov vs. sample cov: [[ 0.00667753  0.          0.00648189 ...  0.          0.00648189\n",
-      "   0.        ]\n",
-      " [ 0.          0.00667753  0.         ...  0.00606088  0.\n",
-      "   0.00648189]\n",
-      " [ 0.00648189  0.          0.00667753 ...  0.          0.00606088\n",
-      "   0.        ]\n",
-      " ...\n",
-      " [-0.          0.00606088 -0.         ...  0.00667753 -0.\n",
-      "   0.00648189]\n",
-      " [ 0.00648189  0.          0.00606088 ...  0.          0.00667753\n",
-      "   0.        ]\n",
-      " [ 0.          0.00648189  0.         ...  0.00648189  0.\n",
-      "   0.00667753]] [[ 0.05632817 -0.00621062  0.0534062  ... -0.00147347  0.05723851\n",
-      "  -0.00355136]\n",
-      " [-0.00621062  0.10139527 -0.01032717 ...  0.09236201 -0.00770252\n",
-      "   0.09612715]\n",
-      " [ 0.0534062  -0.01032717  0.05837232 ... -0.0021168   0.05217555\n",
-      "  -0.00507989]\n",
-      " ...\n",
-      " [-0.00147347  0.09236201 -0.0021168  ...  0.09116495 -0.00420191\n",
-      "   0.09225992]\n",
-      " [ 0.05723851 -0.00770252  0.05217555 ... -0.00420191  0.06144539\n",
-      "  -0.00550382]\n",
-      " [-0.00355136  0.09612715 -0.00507989 ...  0.09225992 -0.00550382\n",
-      "   0.09576185]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 500/500 [00:01<00:00, 368.34it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data parameters:  {'k_alpha': 0.1, 'k_sigma': 0.5, 'obs_var': 0.001}\n",
-      "observation noise: \n",
-      " mean:  [ 4.75152878e-03 -2.91349767e-03 -2.59504499e-05  9.68868659e-03\n",
-      " -6.70143022e-03  3.26492109e-03  2.89982805e-03 -1.11120536e-02\n",
-      " -2.32140626e-03  1.01949374e-05  8.95370351e-03 -2.31079422e-04\n",
-      " -6.03708430e-03  5.83156256e-03 -2.06207382e-03 -5.66696767e-03\n",
-      "  6.71542112e-03  4.85050411e-03 -7.44416064e-03 -5.10807513e-03\n",
-      "  3.37244713e-03  4.36799081e-03  2.88724997e-03 -1.76245182e-03\n",
-      " -1.20316806e-02  9.17336093e-04  1.12129343e-02 -1.33483901e-03\n",
-      " -3.63096072e-03  4.45582222e-03 -2.02644606e-03 -5.81733019e-03]\n",
-      " cov:  [[ 1.01103784e-03  1.48122091e-04 -5.72032686e-05 ...  3.11242243e-04\n",
-      "  -5.73853964e-05 -1.65371163e-04]\n",
-      " [ 1.48122091e-04  1.09232350e-03  9.58052885e-06 ...  2.34334582e-04\n",
-      "  -8.31240808e-05 -1.79346058e-04]\n",
-      " [-5.72032686e-05  9.58052885e-06  1.15705343e-03 ...  5.98315613e-07\n",
-      "   3.97093437e-05 -1.74750801e-05]\n",
-      " ...\n",
-      " [ 3.11242243e-04  2.34334582e-04  5.98315613e-07 ...  8.96097951e-04\n",
-      "  -1.52805075e-04 -1.41057120e-04]\n",
-      " [-5.73853964e-05 -8.31240808e-05  3.97093437e-05 ... -1.52805075e-04\n",
-      "   1.03415612e-03  5.83244851e-05]\n",
-      " [-1.65371163e-04 -1.79346058e-04 -1.74750801e-05 ... -1.41057120e-04\n",
-      "   5.83244851e-05  9.80815882e-04]]\n",
-      "leaves: \n",
-      "  mean:  [ 1.01273345e+00 -4.57689583e-03  9.33719700e-01  3.79065981e-01\n",
-      "  7.16048292e-01  7.03535254e-01  3.91127039e-01  9.18923320e-01\n",
-      "  8.14995827e-03  9.93866123e-01 -3.74257020e-01  9.18736864e-01\n",
-      " -6.97933054e-01  7.04622544e-01 -9.13566768e-01  3.82455872e-01\n",
-      " -9.88026856e-01  2.85002810e-04 -9.08697152e-01 -3.83178349e-01\n",
-      " -6.86794901e-01 -7.09328691e-01 -3.57083881e-01 -9.28424656e-01\n",
-      "  2.86220259e-02 -1.00655641e+00  4.10873553e-01 -9.31091136e-01\n",
-      "  7.31442890e-01 -7.13634678e-01  9.42124173e-01 -3.88228464e-01]\n",
-      "  cov:  [[0.05350862 0.0009976  0.05158183 ... 0.00130519 0.0526602  0.00134079]\n",
-      " [0.0009976  0.05778123 0.0013252  ... 0.05380561 0.00074227 0.0569305 ]\n",
-      " [0.05158183 0.0013252  0.05205817 ... 0.00154622 0.04873422 0.00164316]\n",
-      " ...\n",
-      " [0.00130519 0.05380561 0.00154622 ... 0.05930352 0.000871   0.05773529]\n",
-      " [0.0526602  0.00074227 0.04873422 ... 0.000871   0.05419653 0.00096926]\n",
-      " [0.00134079 0.0569305  0.00164316 ... 0.05773529 0.00096926 0.05867782]]\n",
-      "  mean w/ noise:  [ 1.01336650e+00 -4.31412519e-03  9.31420419e-01  3.79517318e-01\n",
-      "  7.18096318e-01  7.05513280e-01  3.90459838e-01  9.19065377e-01\n",
-      "  7.67202110e-03  9.93851136e-01 -3.74437579e-01  9.17611013e-01\n",
-      " -6.97172982e-01  7.04391568e-01 -9.11028848e-01  3.83213449e-01\n",
-      " -9.87501175e-01 -1.44611836e-04 -9.05924197e-01 -3.81656583e-01\n",
-      " -6.85359921e-01 -7.07876968e-01 -3.58958248e-01 -9.26758485e-01\n",
-      "  2.58048351e-02 -1.00575512e+00  4.08243425e-01 -9.32933594e-01\n",
-      "  7.32324381e-01 -7.14054053e-01  9.41769898e-01 -3.86539571e-01]\n",
-      "  cov w/ noise:  [[5.46440395e-02 5.21542847e-05 5.18982899e-02 ... 2.49293243e-04\n",
-      "  5.28449827e-02 2.06740644e-04]\n",
-      " [5.21542847e-05 5.91670935e-02 1.27830100e-03 ... 5.39087576e-02\n",
-      "  3.48833566e-04 5.77939752e-02]\n",
-      " [5.18982899e-02 1.27830100e-03 5.35122043e-02 ... 1.33334600e-03\n",
-      "  4.90650356e-02 1.39390873e-03]\n",
-      " ...\n",
-      " [2.49293243e-04 5.39087576e-02 1.33334600e-03 ... 6.01289638e-02\n",
-      "  4.12515342e-04 5.82158084e-02]\n",
-      " [5.28449827e-02 3.48833566e-04 4.90650356e-02 ... 4.12515342e-04\n",
-      "  5.50849141e-02 4.37459122e-04]\n",
-      " [2.06740644e-04 5.77939752e-02 1.39390873e-03 ... 5.82158084e-02\n",
-      "  4.37459122e-04 6.09326816e-02]]\n",
-      "  mean conditional:  [ 1.06905171  0.33901115  0.96304569  0.88579754  0.72313011  1.3464421\n",
-      "  0.3814916   1.62575511 -0.01234437  1.67686248 -0.40219729  1.5128399\n",
-      " -0.72865194  1.18294396 -0.92844896  0.74526789 -0.95091514  0.25793917\n",
-      " -0.78845571 -0.21897171 -0.48316813 -0.62218715 -0.103687   -0.89234104\n",
-      "  0.28047247 -0.98863009  0.61737204 -0.89516421  0.8761807  -0.61930936\n",
-      "  1.03464339 -0.19009674] , sample:  [ 1.11344998  0.30954709  0.95566519  0.86152183  0.71859744  1.39113144\n",
-      "  0.37500487  1.64584902  0.00732533  1.66705678 -0.41489995  1.50180772\n",
-      " -0.67482479  1.1664989  -0.97142044  0.74389612 -0.98892135  0.26072092\n",
-      " -0.75917782 -0.1853881  -0.47201133 -0.64702487 -0.11391999 -0.88927468\n",
-      "  0.30459864 -1.00405438  0.63521199 -0.9089661   0.8339099  -0.59249757\n",
-      "  1.01462933 -0.18453318]\n",
-      "  cov conditional:  [[ 3.23424699e-04  6.04773351e-06  2.44759121e-04 ... -3.80443591e-06\n",
-      "   2.61057547e-04  8.23096981e-06]\n",
-      " [ 6.04773351e-06  3.38611162e-04 -3.55625045e-06 ...  9.92537514e-05\n",
-      "   9.86032917e-06  2.56124197e-04]\n",
-      " [ 2.44759121e-04 -3.55625045e-06  3.12807509e-04 ... -7.77787334e-06\n",
-      "   1.03301669e-04 -1.54080740e-06]\n",
-      " ...\n",
-      " [-3.80443591e-06  9.92537514e-05 -7.77787334e-06 ...  3.24877902e-04\n",
-      "   6.49210076e-06  2.48456668e-04]\n",
-      " [ 2.61057547e-04  9.86032917e-06  1.03301669e-04 ...  6.49210076e-06\n",
-      "   3.37324172e-04  1.43268693e-05]\n",
-      " [ 8.23096981e-06  2.56124197e-04 -1.54080740e-06 ...  2.48456668e-04\n",
-      "   1.43268693e-05  3.19973002e-04]]\n",
-      "  mean noise:  [-0.04439827  0.02946406  0.0073805   0.02427571  0.00453267 -0.04468934\n",
-      "  0.00648673 -0.02009391 -0.0196697   0.0098057   0.01270266  0.01103218\n",
-      " -0.05382715  0.01644506  0.04297147  0.00137177  0.03800621 -0.00278175\n",
-      " -0.02927789 -0.03358361 -0.0111568   0.02483772  0.01023298 -0.00306636\n",
-      " -0.02412618  0.01542429 -0.01783995  0.01380189  0.0422708  -0.02681178\n",
-      "  0.02001406 -0.00556356]\n",
-      "  cov noise:  [[ 3.23424699e-04  6.04773351e-06  2.44759121e-04 ... -3.80443591e-06\n",
-      "   2.61057547e-04  8.23096981e-06]\n",
-      " [ 6.04773351e-06  3.38611162e-04 -3.55625045e-06 ...  9.92537514e-05\n",
-      "   9.86032917e-06  2.56124197e-04]\n",
-      " [ 2.44759121e-04 -3.55625045e-06  3.12807509e-04 ... -7.77787334e-06\n",
-      "   1.03301669e-04 -1.54080740e-06]\n",
-      " ...\n",
-      " [-3.80443591e-06  9.92537514e-05 -7.77787334e-06 ...  3.24877902e-04\n",
-      "   6.49210076e-06  2.48456668e-04]\n",
-      " [ 2.61057547e-04  9.86032917e-06  1.03301669e-04 ...  6.49210076e-06\n",
-      "   3.37324172e-04  1.43268693e-05]\n",
-      " [ 8.23096981e-06  2.56124197e-04 -1.54080740e-06 ...  2.48456668e-04\n",
-      "   1.43268693e-05  3.19973002e-04]]\n",
-      "  mean:  [ 1.00666714  0.00808337  0.92653877  0.38976773  0.70694232  0.71349505\n",
-      "  0.38078643  0.9292579  -0.00225834  1.00436322 -0.38321148  0.92797965\n",
-      " -0.70379044  0.71155901 -0.91581897  0.38739199 -0.98784354  0.00450325\n",
-      " -0.90848925 -0.37880162 -0.68915711 -0.70357171 -0.36334118 -0.91961845\n",
-      "  0.01930873 -0.99373332  0.40083345 -0.91517827  0.72277787 -0.69705442\n",
-      "  0.93537022 -0.37314555]\n",
-      "  cov:  [[ 4.78553279e-02 -5.48612437e-04  4.64680942e-02 ... -1.44216391e-03\n",
-      "   4.67152628e-02 -9.77077805e-04]\n",
-      " [-5.48612437e-04  5.33791934e-02 -1.05896603e-03 ...  4.83496415e-02\n",
-      "   3.33587776e-05  5.18310842e-02]\n",
-      " [ 4.64680942e-02 -1.05896603e-03  4.75036494e-02 ... -1.97414626e-03\n",
-      "   4.31739259e-02 -1.45597652e-03]\n",
-      " ...\n",
-      " [-1.44216391e-03  4.83496415e-02 -1.97414626e-03 ...  5.32412254e-02\n",
-      "  -7.94923012e-04  5.16948666e-02]\n",
-      " [ 4.67152628e-02  3.33587776e-05  4.31739259e-02 ... -7.94923012e-04\n",
-      "   4.82311047e-02 -4.19537248e-04]\n",
-      " [-9.77077805e-04  5.18310842e-02 -1.45597652e-03 ...  5.16948666e-02\n",
-      "  -4.19537248e-04  5.28768642e-02]]\n",
-      "  mean w/ noise:  [ 1.00480652  0.00897009  0.92704611  0.38886067  0.70718506  0.71244471\n",
-      "  0.38213838  0.92923288 -0.00143136  1.00522664 -0.38343955  0.92829168\n",
-      " -0.70411682  0.71284671 -0.91392738  0.38797882 -0.98803347  0.00605989\n",
-      " -0.90889544 -0.37877356 -0.69130893 -0.70419195 -0.36250731 -0.91988553\n",
-      "  0.02029914 -0.99456265  0.40045416 -0.91386133  0.72624546 -0.69768229\n",
-      "  0.93662357 -0.37141758]\n",
-      "  cov w/ noise:  [[ 0.04834533  0.0001517   0.04648331 ... -0.00057877  0.04666182\n",
-      "   0.00014262]\n",
-      " [ 0.0001517   0.05537631 -0.00085785 ...  0.04898661 -0.00050928\n",
-      "   0.05257113]\n",
-      " [ 0.04648331 -0.00085785  0.04915006 ... -0.0021333   0.04386891\n",
-      "  -0.00103542]\n",
-      " ...\n",
-      " [-0.00057877  0.04898661 -0.0021333  ...  0.05364833 -0.00137588\n",
-      "   0.05143564]\n",
-      " [ 0.04666182 -0.00050928  0.04386891 ... -0.00137588  0.04990703\n",
-      "  -0.00047162]\n",
-      " [ 0.00014262  0.05257113 -0.00103542 ...  0.05143564 -0.00047162\n",
-      "   0.05390704]]\n",
-      "  mean conditional:  [ 0.84864435  0.3116889   0.72810217  0.7652058   0.49672681  1.14877666\n",
-      "  0.1865466   1.38797238 -0.16255098  1.44405804 -0.50492829  1.32441483\n",
-      " -0.79441976  1.0645401  -0.98676465  0.70774546 -1.04541798  0.3004603\n",
-      " -0.95485715 -0.10556966 -0.72447317 -0.45373575 -0.38569071 -0.69102887\n",
-      "  0.00660986 -0.78150157  0.38182287 -0.71123366  0.67267375 -0.48550205\n",
-      "  0.83354448 -0.12862912] , sample:  [ 0.8279477   0.32443317  0.70200777  0.76600388  0.4945541   1.11110325\n",
-      "  0.21571646  1.44318921 -0.16177803  1.43780221 -0.52727268  1.2896007\n",
-      " -0.76346841  1.06716026 -1.01392745  0.71783381 -1.03413507  0.27191891\n",
-      " -0.91826187 -0.04915385 -0.75902038 -0.47201991 -0.41227736 -0.69199832\n",
-      "  0.00665081 -0.82474415  0.3835449  -0.64067294  0.70295675 -0.53788435\n",
-      "  0.84186128 -0.10993564]\n",
-      "  cov conditional:  [[ 3.05955812e-04  6.20843493e-06  2.32006437e-04 ...  1.59333672e-05\n",
-      "   2.49747169e-04  1.46838872e-05]\n",
-      " [ 6.20843493e-06  3.30654113e-04 -4.10817541e-06 ...  9.92609341e-05\n",
-      "   1.75380007e-05  2.55193351e-04]\n",
-      " [ 2.32006437e-04 -4.10817541e-06  2.93456061e-04 ...  1.59145166e-05\n",
-      "   1.03360339e-04  6.31722549e-06]\n",
-      " ...\n",
-      " [ 1.59333672e-05  9.92609341e-05  1.59145166e-05 ...  3.54755345e-04\n",
-      "   1.89291655e-05  2.71094875e-04]\n",
-      " [ 2.49747169e-04  1.75380007e-05  1.03360339e-04 ...  1.89291655e-05\n",
-      "   3.26262645e-04  2.04658298e-05]\n",
-      " [ 1.46838872e-05  2.55193351e-04  6.31722549e-06 ...  2.71094875e-04\n",
-      "   2.04658298e-05  3.37471968e-04]]\n",
-      "  mean noise:  [ 2.06966548e-02 -1.27442678e-02  2.60944067e-02 -7.98081368e-04\n",
-      "  2.17270123e-03  3.76734114e-02 -2.91698604e-02 -5.52168272e-02\n",
-      " -7.72957675e-04  6.25582636e-03  2.23443878e-02  3.48141297e-02\n",
-      " -3.09513447e-02 -2.62016595e-03  2.71627994e-02 -1.00883454e-02\n",
-      " -1.12829167e-02  2.85413913e-02 -3.65952790e-02 -5.64158127e-02\n",
-      "  3.45472132e-02  1.82841661e-02  2.65866489e-02  9.69456081e-04\n",
-      " -4.09491725e-05  4.32425802e-02 -1.72202916e-03 -7.05607161e-02\n",
-      " -3.02829965e-02  5.23822997e-02 -8.31680702e-03 -1.86934754e-02]\n",
-      "  cov noise:  [[ 3.05955812e-04  6.20843493e-06  2.32006437e-04 ...  1.59333672e-05\n",
-      "   2.49747169e-04  1.46838872e-05]\n",
-      " [ 6.20843493e-06  3.30654113e-04 -4.10817541e-06 ...  9.92609341e-05\n",
-      "   1.75380007e-05  2.55193351e-04]\n",
-      " [ 2.32006437e-04 -4.10817541e-06  2.93456061e-04 ...  1.59145166e-05\n",
-      "   1.03360339e-04  6.31722549e-06]\n",
-      " ...\n",
-      " [ 1.59333672e-05  9.92609341e-05  1.59145166e-05 ...  3.54755345e-04\n",
-      "   1.89291655e-05  2.71094875e-04]\n",
-      " [ 2.49747169e-04  1.75380007e-05  1.03360339e-04 ...  1.89291655e-05\n",
-      "   3.26262645e-04  2.04658298e-05]\n",
-      " [ 1.46838872e-05  2.55193351e-04  6.31722549e-06 ...  2.71094875e-04\n",
-      "   2.04658298e-05  3.37471968e-04]]\n",
-      "  mean:  [ 1.00996156e+00 -7.28661541e-03  9.32595279e-01  3.76106165e-01\n",
-      "  7.16670124e-01  7.03092488e-01  3.94133332e-01  9.22583685e-01\n",
-      "  1.32002605e-02  1.00024307e+00 -3.69208886e-01  9.24811294e-01\n",
-      " -6.95532879e-01  7.08208633e-01 -9.15135569e-01  3.83018860e-01\n",
-      " -9.93737780e-01 -9.07622764e-04 -9.18397720e-01 -3.84373104e-01\n",
-      " -7.00186610e-01 -7.09365570e-01 -3.73071186e-01 -9.26886186e-01\n",
-      "  1.24222395e-02 -1.00368368e+00  3.97216304e-01 -9.27833547e-01\n",
-      "  7.21978378e-01 -7.11656947e-01  9.36664096e-01 -3.88786590e-01]\n",
-      "  cov:  [[ 0.04803295 -0.00149937  0.04723545 ... -0.00293515  0.04619247\n",
-      "  -0.00211455]\n",
-      " [-0.00149937  0.05520964 -0.00132241 ...  0.05025727 -0.00163387\n",
-      "   0.05397861]\n",
-      " [ 0.04723545 -0.00132241  0.04879495 ... -0.00308886  0.04336187\n",
-      "  -0.00213879]\n",
-      " ...\n",
-      " [-0.00293515  0.05025727 -0.00308886 ...  0.05519936 -0.0026651\n",
-      "   0.05403076]\n",
-      " [ 0.04619247 -0.00163387  0.04336187 ... -0.0026651   0.04684597\n",
-      "  -0.00201556]\n",
-      " [-0.00211455  0.05397861 -0.00213879 ...  0.05403076 -0.00201556\n",
-      "   0.05549247]]\n",
-      "  mean w/ noise:  [ 1.01072629e+00 -7.96330092e-03  9.34095047e-01  3.76200951e-01\n",
-      "  7.16623188e-01  7.02584807e-01  3.94645570e-01  9.23833043e-01\n",
-      "  1.16202912e-02  1.00230999e+00 -3.68741636e-01  9.23101679e-01\n",
-      " -6.95753673e-01  7.09445898e-01 -9.16285311e-01  3.83107330e-01\n",
-      " -9.93297830e-01  7.77659550e-04 -9.17352503e-01 -3.81408268e-01\n",
-      " -7.00442943e-01 -7.10698724e-01 -3.72968649e-01 -9.26532223e-01\n",
-      "  9.60123934e-03 -1.00414010e+00  3.94967555e-01 -9.29279874e-01\n",
-      "  7.22652752e-01 -7.12796085e-01  9.37874708e-01 -3.89754048e-01]\n",
-      "  cov w/ noise:  [[ 0.0493233  -0.00186856  0.04743607 ... -0.00264501  0.04604974\n",
-      "  -0.0020286 ]\n",
-      " [-0.00186856  0.05643519 -0.00201451 ...  0.05065012 -0.00223984\n",
-      "   0.05397237]\n",
-      " [ 0.04743607 -0.00201451  0.04983388 ... -0.00340767  0.04309525\n",
-      "  -0.00243971]\n",
-      " ...\n",
-      " [-0.00264501  0.05065012 -0.00340767 ...  0.05684534 -0.00297865\n",
-      "   0.05434082]\n",
-      " [ 0.04604974 -0.00223984  0.04309525 ... -0.00297865  0.04746783\n",
-      "  -0.00234476]\n",
-      " [-0.0020286   0.05397237 -0.00243971 ...  0.05434082 -0.00234476\n",
-      "   0.05624632]]\n",
-      "  mean conditional:  [ 1.05249444  0.46642902  0.91982897  0.92417031  0.65133571  1.34075244\n",
-      "  0.28862678  1.62602349 -0.10209252  1.71665545 -0.45270052  1.59670872\n",
-      " -0.71053211  1.29278818 -0.83774458  0.86377259 -0.81100227  0.39050943\n",
-      " -0.63408305 -0.04214557 -0.34391189 -0.36746084  0.00359808 -0.55125128\n",
-      "  0.3532786  -0.59070713  0.66238003 -0.49733709  0.90062019 -0.28117196\n",
-      "  1.04023893  0.04784313] , sample:  [ 1.05641549  0.43105311  0.88899393  0.90023844  0.72916632  1.35915362\n",
-      "  0.24716268  1.631929   -0.11073534  1.71574013 -0.46602235  1.60864831\n",
-      " -0.70133534  1.2896376  -0.79914957  0.88464417 -0.85886614  0.36580133\n",
-      " -0.62087266 -0.05411069 -0.34487707 -0.37384388  0.03726187 -0.54197395\n",
-      "  0.3635736  -0.57901007  0.63865476 -0.48457766  0.89549215 -0.29335748\n",
-      "  1.04580072  0.08867978]\n",
-      "  cov conditional:  [[ 3.49987073e-04  1.59869846e-05  2.75464932e-04 ... -3.04734449e-06\n",
-      "   2.75900932e-04  1.63123850e-05]\n",
-      " [ 1.59869846e-05  3.52949456e-04  1.96022196e-05 ...  9.87966702e-05\n",
-      "   1.97600163e-05  2.60939895e-04]\n",
-      " [ 2.75464932e-04  1.96022196e-05  3.31192440e-04 ...  7.34038228e-06\n",
-      "   1.33766872e-04  2.08485865e-05]\n",
-      " ...\n",
-      " [-3.04734449e-06  9.87966702e-05  7.34038228e-06 ...  3.09680209e-04\n",
-      "  -1.27474936e-05  2.31241465e-04]\n",
-      " [ 2.75900932e-04  1.97600163e-05  1.33766872e-04 ... -1.27474936e-05\n",
-      "   3.41172064e-04  1.33404555e-05]\n",
-      " [ 1.63123850e-05  2.60939895e-04  2.08485865e-05 ...  2.31241465e-04\n",
-      "   1.33404555e-05  3.05787978e-04]]\n",
-      "  mean noise:  [-0.00392105  0.03537591  0.03083504  0.02393186 -0.07783061 -0.01840117\n",
-      "  0.0414641  -0.00590551  0.00864282  0.00091533  0.01332183 -0.01193958\n",
-      " -0.00919677  0.00315058 -0.03859502 -0.02087158  0.04786387  0.0247081\n",
-      " -0.01321039  0.01196512  0.00096518  0.00638304 -0.03366379 -0.00927733\n",
-      " -0.01029499 -0.01169706  0.02372527 -0.01275943  0.00512804  0.01218552\n",
-      " -0.00556179 -0.04083665]\n",
-      "  cov noise:  [[ 3.49987073e-04  1.59869846e-05  2.75464932e-04 ... -3.04734449e-06\n",
-      "   2.75900932e-04  1.63123850e-05]\n",
-      " [ 1.59869846e-05  3.52949456e-04  1.96022196e-05 ...  9.87966702e-05\n",
-      "   1.97600163e-05  2.60939895e-04]\n",
-      " [ 2.75464932e-04  1.96022196e-05  3.31192440e-04 ...  7.34038228e-06\n",
-      "   1.33766872e-04  2.08485865e-05]\n",
-      " ...\n",
-      " [-3.04734449e-06  9.87966702e-05  7.34038228e-06 ...  3.09680209e-04\n",
-      "  -1.27474936e-05  2.31241465e-04]\n",
-      " [ 2.75900932e-04  1.97600163e-05  1.33766872e-04 ... -1.27474936e-05\n",
-      "   3.41172064e-04  1.33404555e-05]\n",
-      " [ 1.63123850e-05  2.60939895e-04  2.08485865e-05 ...  2.31241465e-04\n",
-      "   1.33404555e-05  3.05787978e-04]]\n",
-      "  mean:  [ 1.01077615  0.00390931  0.93078338  0.38711835  0.71168147  0.71176571\n",
-      "  0.38596122  0.92853654  0.00221893  1.00479372 -0.38201365  0.92949381\n",
-      " -0.70813174  0.71424238 -0.9252412   0.39101956 -0.99906198  0.00870293\n",
-      " -0.91806828 -0.37405006 -0.69515493 -0.69935055 -0.36539153 -0.91819669\n",
-      "  0.01999924 -0.99668226  0.40302137 -0.9217942   0.72600328 -0.70484577\n",
-      "  0.93924721 -0.3795924 ]\n",
-      "  cov:  [[ 5.20756828e-02  7.47971103e-05  5.06135450e-02 ...  2.45637220e-04\n",
-      "   5.08473972e-02  2.72878231e-04]\n",
-      " [ 7.47971103e-05  6.06358742e-02 -6.73183825e-04 ...  5.60922150e-02\n",
-      "   4.92617383e-04  5.94850981e-02]\n",
-      " [ 5.06135450e-02 -6.73183825e-04  5.14837453e-02 ... -4.84385949e-04\n",
-      "   4.73607749e-02 -4.64773618e-04]\n",
-      " ...\n",
-      " [ 2.45637220e-04  5.60922150e-02 -4.84385949e-04 ...  6.12323420e-02\n",
-      "   6.49189349e-04  5.97506354e-02]\n",
-      " [ 5.08473972e-02  4.92617383e-04  4.73607749e-02 ...  6.49189349e-04\n",
-      "   5.21073598e-02  6.67585274e-04]\n",
-      " [ 2.72878231e-04  5.94850981e-02 -4.64773618e-04 ...  5.97506354e-02\n",
-      "   6.67585274e-04  6.09115205e-02]]\n",
-      "  mean w/ noise:  [ 1.01155955  0.00599227  0.931586    0.38483579  0.71144344  0.71378706\n",
-      "  0.38654052  0.92937387  0.00118126  1.0032753  -0.38264082  0.92970371\n",
-      " -0.71107583  0.71516192 -0.92453478  0.39143125 -1.00017883  0.00853424\n",
-      " -0.91785887 -0.37499845 -0.69396738 -0.69902701 -0.36521734 -0.91544277\n",
-      "  0.02283974 -0.996349    0.40168004 -0.92042392  0.72720661 -0.70575208\n",
-      "  0.93755962 -0.37800385]\n",
-      "  cov w/ noise:  [[ 5.18429133e-02 -1.74143393e-04  4.93046262e-02 ...  5.90625196e-04\n",
-      "   5.00793273e-02 -1.63255132e-04]\n",
-      " [-1.74143393e-04  6.13299725e-02 -7.68957133e-04 ...  5.61449039e-02\n",
-      "   1.43122150e-04  5.93877337e-02]\n",
-      " [ 4.93046262e-02 -7.68957133e-04  5.10136624e-02 ... -4.19232193e-06\n",
-      "   4.68609323e-02 -8.91045761e-04]\n",
-      " ...\n",
-      " [ 5.90625196e-04  5.61449039e-02 -4.19232193e-06 ...  6.23985855e-02\n",
-      "   7.35357530e-04  5.95705336e-02]\n",
-      " [ 5.00793273e-02  1.43122150e-04  4.68609323e-02 ...  7.35357530e-04\n",
-      "   5.26485726e-02  2.24991033e-04]\n",
-      " [-1.63255132e-04  5.93877337e-02 -8.91045761e-04 ...  5.95705336e-02\n",
-      "   2.24991033e-04  6.14639194e-02]]\n",
-      "  mean conditional:  [ 0.84295587 -0.12826814  0.74440899  0.41183885  0.50983522  0.91659341\n",
-      "  0.17076957  1.29081624 -0.2216525   1.47097148 -0.60624385  1.43531111\n",
-      " -0.92431528  1.19702198 -1.12546777  0.79757372 -1.17543984  0.30497597\n",
-      " -1.06785918 -0.19673602 -0.82339909 -0.62913986 -0.47992047 -0.9397089\n",
-      " -0.08883275 -1.10448422  0.29020458 -1.11034583  0.5992461  -0.94371862\n",
-      "  0.79251615 -0.60482097] , sample:  [ 0.80961312 -0.1305682   0.71120373  0.41704839  0.5523899   0.91344069\n",
-      "  0.17206013  1.3080673  -0.22828866  1.44129375 -0.61230529  1.43662388\n",
-      " -0.92501492  1.23792784 -1.12098508  0.78593457 -1.21203188  0.31667896\n",
-      " -1.00704998 -0.19614172 -0.84384206 -0.67520337 -0.5196058  -0.93307378\n",
-      " -0.07749586 -1.04749419  0.30110042 -1.12379352  0.58626905 -0.97924193\n",
-      "  0.83097993 -0.60832731]\n",
-      "  cov conditional:  [[ 3.05120123e-04 -8.21883245e-06  2.21374606e-04 ... -5.59482813e-06\n",
-      "   2.56418753e-04 -1.01104891e-05]\n",
-      " [-8.21883245e-06  3.09682762e-04  6.63525503e-07 ...  8.81215895e-05\n",
-      "  -5.46354040e-06  2.37914770e-04]\n",
-      " [ 2.21374606e-04  6.63525503e-07  2.78280406e-04 ...  1.67270348e-05\n",
-      "   9.73435336e-05  4.10385936e-06]\n",
-      " ...\n",
-      " [-5.59482813e-06  8.81215895e-05  1.67270348e-05 ...  3.16885571e-04\n",
-      "  -2.08603729e-05  2.37096116e-04]\n",
-      " [ 2.56418753e-04 -5.46354040e-06  9.73435336e-05 ... -2.08603729e-05\n",
-      "   3.33582006e-04 -1.16307171e-05]\n",
-      " [-1.01104891e-05  2.37914770e-04  4.10385936e-06 ...  2.37096116e-04\n",
-      "  -1.16307171e-05  3.05215621e-04]]\n",
-      "  mean noise:  [ 0.03334275  0.00230006  0.03320526 -0.00520954 -0.04255467  0.00315272\n",
-      " -0.00129056 -0.01725106  0.00663617  0.02967772  0.00606144 -0.00131277\n",
-      "  0.00069964 -0.04090586 -0.00448269  0.01163915  0.03659204 -0.01170299\n",
-      " -0.06080919 -0.00059431  0.02044297  0.04606351  0.03968532 -0.00663512\n",
-      " -0.01133689 -0.05699003 -0.01089584  0.01344769  0.01297705  0.03552332\n",
-      " -0.03846378  0.00350634]\n",
-      "  cov noise:  [[ 3.05120123e-04 -8.21883245e-06  2.21374606e-04 ... -5.59482813e-06\n",
-      "   2.56418753e-04 -1.01104891e-05]\n",
-      " [-8.21883245e-06  3.09682762e-04  6.63525503e-07 ...  8.81215895e-05\n",
-      "  -5.46354040e-06  2.37914770e-04]\n",
-      " [ 2.21374606e-04  6.63525503e-07  2.78280406e-04 ...  1.67270348e-05\n",
-      "   9.73435336e-05  4.10385936e-06]\n",
-      " ...\n",
-      " [-5.59482813e-06  8.81215895e-05  1.67270348e-05 ...  3.16885571e-04\n",
-      "  -2.08603729e-05  2.37096116e-04]\n",
-      " [ 2.56418753e-04 -5.46354040e-06  9.73435336e-05 ... -2.08603729e-05\n",
-      "   3.33582006e-04 -1.16307171e-05]\n",
-      " [-1.01104891e-05  2.37914770e-04  4.10385936e-06 ...  2.37096116e-04\n",
-      "  -1.16307171e-05  3.05215621e-04]]\n",
-      "  mean:  [ 1.00411923e+00 -2.02704411e-03  9.28558696e-01  3.78640820e-01\n",
-      "  7.13589436e-01  7.01651775e-01  3.90326937e-01  9.18079807e-01\n",
-      "  6.76730424e-03  9.95008061e-01 -3.78080430e-01  9.20699788e-01\n",
-      " -7.04044210e-01  7.06216913e-01 -9.20876467e-01  3.83193357e-01\n",
-      " -9.96103049e-01  1.23444224e-04 -9.18314114e-01 -3.83787783e-01\n",
-      " -6.99316959e-01 -7.09113775e-01 -3.73207400e-01 -9.26265396e-01\n",
-      "  9.67737308e-03 -1.00234314e+00  3.91403205e-01 -9.25465435e-01\n",
-      "  7.14310990e-01 -7.07626332e-01  9.29215126e-01 -3.83204995e-01]\n",
-      "  cov:  [[0.0473438  0.00541524 0.04619261 ... 0.0051368  0.04571516 0.00535865]\n",
-      " [0.00541524 0.05257355 0.00444833 ... 0.04933878 0.00549234 0.05203095]\n",
-      " [0.04619261 0.00444833 0.0471977  ... 0.00404672 0.04275265 0.0043314 ]\n",
-      " ...\n",
-      " [0.0051368  0.04933878 0.00404672 ... 0.05437616 0.00525061 0.05292934]\n",
-      " [0.04571516 0.00549234 0.04275265 ... 0.00525061 0.04636909 0.00543268]\n",
-      " [0.00535865 0.05203095 0.0043314  ... 0.05292934 0.00543268 0.0537391 ]]\n",
-      "  mean w/ noise:  [ 1.00474420e+00 -2.62858286e-03  9.29440143e-01  3.79894752e-01\n",
-      "  7.13419705e-01  6.99326390e-01  3.91733210e-01  9.17425760e-01\n",
-      "  6.77162592e-03  9.95898009e-01 -3.76650821e-01  9.19938889e-01\n",
-      " -7.04637863e-01  7.07825644e-01 -9.20347811e-01  3.83425729e-01\n",
-      " -9.96867854e-01 -1.99014507e-04 -9.19234210e-01 -3.84165531e-01\n",
-      " -6.97083662e-01 -7.08752085e-01 -3.73612038e-01 -9.26559891e-01\n",
-      "  9.83351628e-03 -1.00316051e+00  3.91728582e-01 -9.28257658e-01\n",
-      "  7.15598761e-01 -7.08935019e-01  9.27726058e-01 -3.84332964e-01]\n",
-      "  cov w/ noise:  [[0.04815421 0.00527888 0.04568504 ... 0.00478069 0.04524112 0.00479523]\n",
-      " [0.00527888 0.05460242 0.00482998 ... 0.04944344 0.00530551 0.05271481]\n",
-      " [0.04568504 0.00482998 0.04767379 ... 0.00449468 0.04204266 0.00425525]\n",
-      " ...\n",
-      " [0.00478069 0.04944344 0.00449468 ... 0.05426111 0.00470396 0.05243303]\n",
-      " [0.04524112 0.00530551 0.04204266 ... 0.00470396 0.04655572 0.00488518]\n",
-      " [0.00479523 0.05271481 0.00425525 ... 0.05243303 0.00488518 0.05472247]]\n",
-      "  mean conditional:  [ 0.94435748  0.57121788  0.73167546  1.05000321  0.38086597  1.45739433\n",
-      " -0.04069213  1.72062647 -0.45934655  1.79808899 -0.81607853  1.68605649\n",
-      " -1.06927403  1.40981556 -1.18506771  1.01565359 -1.13917587  0.56595496\n",
-      " -0.93251441  0.128834   -0.59871249 -0.23433708 -0.19439198 -0.4774478\n",
-      "  0.21779461 -0.57226853  0.58212588 -0.50389246  0.85131643 -0.27055521\n",
-      "  0.98278419  0.10543161] , sample:  [ 0.9926964   0.5794277   0.73673777  1.07634214  0.36807622  1.43620698\n",
-      " -0.07389713  1.72693799 -0.47098081  1.77076593 -0.79428754  1.71161847\n",
-      " -1.07223069  1.42390469 -1.1959478   0.99425379 -1.130075    0.5683664\n",
-      " -0.93734222  0.14689716 -0.61919883 -0.26486406 -0.15962892 -0.44544166\n",
-      "  0.22221613 -0.55477665  0.57170644 -0.55434421  0.84507058 -0.24857825\n",
-      "  0.96886148  0.09656836]\n",
-      "  cov conditional:  [[ 3.72074478e-04 -5.86283272e-06  2.87346952e-04 ... -6.07681501e-06\n",
-      "   3.02439681e-04 -7.70071976e-06]\n",
-      " [-5.86283272e-06  3.27593239e-04 -1.12312277e-05 ...  8.84539486e-05\n",
-      "  -9.65007494e-06  2.44203821e-04]\n",
-      " [ 2.87346952e-04 -1.12312277e-05  3.41653421e-04 ... -8.68762571e-06\n",
-      "   1.36740081e-04 -1.44080914e-05]\n",
-      " ...\n",
-      " [-6.07681501e-06  8.84539486e-05 -8.68762571e-06 ...  2.98021369e-04\n",
-      "  -2.90482889e-06  2.32114156e-04]\n",
-      " [ 3.02439681e-04 -9.65007494e-06  1.36740081e-04 ... -2.90482889e-06\n",
-      "   3.83318557e-04 -3.89846428e-06]\n",
-      " [-7.70071976e-06  2.44203821e-04 -1.44080914e-05 ...  2.32114156e-04\n",
-      "  -3.89846428e-06  3.03355081e-04]]\n",
-      "  mean noise:  [-0.04833892 -0.00820982 -0.00506231 -0.02633894  0.01278975  0.02118735\n",
-      "  0.033205   -0.00631151  0.01163425  0.02732306 -0.02179099 -0.02556198\n",
-      "  0.00295666 -0.01408913  0.01088008  0.0213998  -0.00910087 -0.00241144\n",
-      "  0.00482781 -0.01806315  0.02048635  0.03052698 -0.03476307 -0.03200614\n",
-      " -0.00442152 -0.01749188  0.01041944  0.05045175  0.00624585 -0.02197696\n",
-      "  0.01392272  0.00886325]\n",
-      "  cov noise:  [[ 3.72074478e-04 -5.86283272e-06  2.87346952e-04 ... -6.07681501e-06\n",
-      "   3.02439681e-04 -7.70071976e-06]\n",
-      " [-5.86283272e-06  3.27593239e-04 -1.12312277e-05 ...  8.84539486e-05\n",
-      "  -9.65007494e-06  2.44203821e-04]\n",
-      " [ 2.87346952e-04 -1.12312277e-05  3.41653421e-04 ... -8.68762571e-06\n",
-      "   1.36740081e-04 -1.44080914e-05]\n",
-      " ...\n",
-      " [-6.07681501e-06  8.84539486e-05 -8.68762571e-06 ...  2.98021369e-04\n",
-      "  -2.90482889e-06  2.32114156e-04]\n",
-      " [ 3.02439681e-04 -9.65007494e-06  1.36740081e-04 ... -2.90482889e-06\n",
-      "   3.83318557e-04 -3.89846428e-06]\n",
-      " [-7.70071976e-06  2.44203821e-04 -1.44080914e-05 ...  2.32114156e-04\n",
-      "  -3.89846428e-06  3.03355081e-04]]\n",
-      "  mean:  [ 1.00356712e+00  6.73610141e-03  9.28270224e-01  3.89089071e-01\n",
-      "  7.10969988e-01  7.13064257e-01  3.84667959e-01  9.28468012e-01\n",
-      " -8.74715795e-04  1.00251638e+00 -3.86481056e-01  9.24895655e-01\n",
-      " -7.12181379e-01  7.07513440e-01 -9.28248797e-01  3.83062265e-01\n",
-      " -1.00283713e+00  1.12945054e-03 -9.25379793e-01 -3.79343836e-01\n",
-      " -7.07953022e-01 -7.00256166e-01 -3.83628129e-01 -9.13691066e-01\n",
-      " -1.43455100e-03 -9.88561788e-01  3.81253192e-01 -9.13548412e-01\n",
-      "  7.06788967e-01 -6.98611411e-01  9.25575792e-01 -3.75422055e-01]\n",
-      "  cov:  [[ 0.05750444  0.0007868   0.05580096 ... -0.00195367  0.05654142\n",
-      "  -0.00051022]\n",
-      " [ 0.0007868   0.0537825   0.00049031 ...  0.04906228  0.00123456\n",
-      "   0.05237674]\n",
-      " [ 0.05580096  0.00049031  0.05706454 ... -0.00190303  0.05240387\n",
-      "  -0.00062689]\n",
-      " ...\n",
-      " [-0.00195367  0.04906228 -0.00190303 ...  0.05467919 -0.00175208\n",
-      "   0.05289486]\n",
-      " [ 0.05654142  0.00123456  0.05240387 ... -0.00175208  0.05830953\n",
-      "  -0.00020663]\n",
-      " [-0.00051022  0.05237674 -0.00062689 ...  0.05289486 -0.00020663\n",
-      "   0.05379787]]\n",
-      "  mean w/ noise:  [ 1.00512032e+00  6.26965080e-03  9.29137270e-01  3.89355053e-01\n",
-      "  7.07993557e-01  7.12423813e-01  3.87492361e-01  9.28468187e-01\n",
-      "  9.20628852e-04  1.00299687e+00 -3.87240657e-01  9.26271129e-01\n",
-      " -7.14627484e-01  7.08188270e-01 -9.31127910e-01  3.82216859e-01\n",
-      " -1.00348362e+00  1.72009106e-03 -9.25587555e-01 -3.80712427e-01\n",
-      " -7.08600171e-01 -6.99812246e-01 -3.81486927e-01 -9.12576491e-01\n",
-      "  2.60621130e-04 -9.89664327e-01  3.83704775e-01 -9.13007176e-01\n",
-      "  7.05452976e-01 -6.98698044e-01  9.25541952e-01 -3.74632917e-01]\n",
-      "  cov w/ noise:  [[ 5.84533782e-02  1.41997116e-03  5.63367069e-02 ... -1.92823708e-03\n",
-      "   5.67681273e-02 -1.53420392e-04]\n",
-      " [ 1.41997116e-03  5.55036419e-02  4.06603202e-04 ...  4.90219988e-02\n",
-      "   1.71926594e-03  5.26435752e-02]\n",
-      " [ 5.63367069e-02  4.06603202e-04  5.90509196e-02 ... -2.51591498e-03\n",
-      "   5.30282695e-02 -8.62488497e-04]\n",
-      " ...\n",
-      " [-1.92823708e-03  4.90219988e-02 -2.51591498e-03 ...  5.52175982e-02\n",
-      "  -1.78497575e-03  5.25789884e-02]\n",
-      " [ 5.67681273e-02  1.71926594e-03  5.30282695e-02 ... -1.78497575e-03\n",
-      "   5.99422381e-02  8.01452825e-05]\n",
-      " [-1.53420392e-04  5.26435752e-02 -8.62488497e-04 ...  5.25789884e-02\n",
-      "   8.01452825e-05  5.44758448e-02]]\n",
-      "  mean conditional:  [ 1.19485966 -0.22872685  1.18300445  0.19584513  0.99670227  0.58222359\n",
-      "  0.65153826  0.85097729  0.20339443  0.93963238 -0.26086418  0.83145767\n",
-      " -0.64639572  0.55916885 -0.87890218  0.1879927  -0.92757739 -0.21021763\n",
-      " -0.80819726 -0.57986731 -0.5647003  -0.88437594 -0.24533853 -1.09345238\n",
-      "  0.10989194 -1.17911753  0.46769349 -1.12492444  0.79473246 -0.93285875\n",
-      "  1.05190455 -0.62223852] , sample:  [ 1.20879513 -0.22238289  1.15317798  0.17831176  1.03083425  0.58122103\n",
-      "  0.65294813  0.87658987  0.19886774  0.9559592  -0.25450183  0.8084737\n",
-      " -0.66567994  0.58131276 -0.87674901  0.16554621 -0.95683995 -0.2420491\n",
-      " -0.78393809 -0.55427907 -0.53018523 -0.85637132 -0.26243819 -1.11893914\n",
-      "  0.08690089 -1.18313385  0.46746888 -1.11198357  0.79905002 -0.93252919\n",
-      "  1.05612504 -0.6397874 ]\n",
-      "  cov conditional:  [[ 3.55061860e-04 -1.88720011e-05  2.71229942e-04 ...  9.97082188e-06\n",
-      "   2.86631834e-04 -1.32614173e-06]\n",
-      " [-1.88720011e-05  3.58278532e-04 -6.53174694e-06 ...  1.20756054e-04\n",
-      "  -1.47112661e-05  2.80085234e-04]\n",
-      " [ 2.71229942e-04 -6.53174694e-06  3.34158370e-04 ...  1.34376041e-05\n",
-      "   1.27283125e-04  4.72738052e-06]\n",
-      " ...\n",
-      " [ 9.97082188e-06  1.20756054e-04  1.34376041e-05 ...  3.22718147e-04\n",
-      "   2.50620273e-07  2.56114924e-04]\n",
-      " [ 2.86631834e-04 -1.47112661e-05  1.27283125e-04 ...  2.50620273e-07\n",
-      "   3.54290657e-04 -2.04238247e-06]\n",
-      " [-1.32614173e-06  2.80085234e-04  4.72738052e-06 ...  2.56114924e-04\n",
-      "  -2.04238247e-06  3.32256404e-04]]\n",
-      "  mean noise:  [-0.01393548 -0.00634395  0.02982647  0.01753338 -0.03413198  0.00100256\n",
-      " -0.00140987 -0.02561258  0.00452669 -0.01632682 -0.00636235  0.02298396\n",
-      "  0.01928422 -0.02214391 -0.00215318  0.02244649  0.02926256  0.03183147\n",
-      " -0.02425917 -0.02558824 -0.03451507 -0.02800462  0.01709966  0.02548677\n",
-      "  0.02299105  0.00401632  0.00022461 -0.01294086 -0.00431756 -0.00032956\n",
-      " -0.00422048  0.01754887]\n",
-      "  cov noise:  [[ 3.55061860e-04 -1.88720011e-05  2.71229942e-04 ...  9.97082188e-06\n",
-      "   2.86631834e-04 -1.32614173e-06]\n",
-      " [-1.88720011e-05  3.58278532e-04 -6.53174694e-06 ...  1.20756054e-04\n",
-      "  -1.47112661e-05  2.80085234e-04]\n",
-      " [ 2.71229942e-04 -6.53174694e-06  3.34158370e-04 ...  1.34376041e-05\n",
-      "   1.27283125e-04  4.72738052e-06]\n",
-      " ...\n",
-      " [ 9.97082188e-06  1.20756054e-04  1.34376041e-05 ...  3.22718147e-04\n",
-      "   2.50620273e-07  2.56114924e-04]\n",
-      " [ 2.86631834e-04 -1.47112661e-05  1.27283125e-04 ...  2.50620273e-07\n",
-      "   3.54290657e-04 -2.04238247e-06]\n",
-      " [-1.32614173e-06  2.80085234e-04  4.72738052e-06 ...  2.56114924e-04\n",
-      "  -2.04238247e-06  3.32256404e-04]]\n",
-      "  mean:  [ 0.99739447  0.00339163  0.92139332  0.38188804  0.70370195  0.70190768\n",
-      "  0.3773763   0.91460317 -0.00739168  0.98763914 -0.39139422  0.91023134\n",
-      " -0.71573478  0.69429247 -0.9314312   0.37243804 -1.00636307 -0.00662936\n",
-      " -0.9298836  -0.38474159 -0.71336192 -0.70399649 -0.38925071 -0.91632163\n",
-      " -0.00656899 -0.9898847   0.37675601 -0.91360041  0.70226439 -0.69829927\n",
-      "  0.92033849 -0.37615088]\n",
-      "  cov:  [[0.05757731 0.00532233 0.05613815 ... 0.00232014 0.05601485 0.00390535]\n",
-      " [0.00532233 0.05091947 0.00615158 ... 0.04840821 0.0041421  0.05083338]\n",
-      " [0.05613815 0.00615158 0.0573856  ... 0.00333779 0.0523115  0.00481141]\n",
-      " ...\n",
-      " [0.00232014 0.04840821 0.00333779 ... 0.05496356 0.00097475 0.05292163]\n",
-      " [0.05601485 0.0041421  0.0523115  ... 0.00097475 0.05710638 0.0026339 ]\n",
-      " [0.00390535 0.05083338 0.00481141 ... 0.05292163 0.0026339  0.05327544]]\n",
-      "  mean w/ noise:  [ 0.99630469  0.00376075  0.92169061  0.38388119  0.7009872   0.70146464\n",
-      "  0.37697357  0.91181522 -0.00958729  0.99074424 -0.39106868  0.9109527\n",
-      " -0.71724373  0.69483169 -0.93156933  0.3707826  -1.00579191 -0.00817713\n",
-      " -0.92952881 -0.38478601 -0.71114532 -0.70446938 -0.38594899 -0.91426714\n",
-      " -0.01020731 -0.98725582  0.37484071 -0.91378064  0.7039707  -0.70051562\n",
-      "  0.91664527 -0.37697268]\n",
-      "  cov w/ noise:  [[0.05772059 0.0051635  0.05599862 ... 0.00234786 0.05560836 0.00337449]\n",
-      " [0.0051635  0.05173115 0.00584865 ... 0.04873186 0.00400255 0.05028014]\n",
-      " [0.05599862 0.00584865 0.05925928 ... 0.00297139 0.05288428 0.0039805 ]\n",
-      " ...\n",
-      " [0.00234786 0.04873186 0.00297139 ... 0.0567622  0.00115197 0.05294153]\n",
-      " [0.05560836 0.00400255 0.05288428 ... 0.00115197 0.05824165 0.00241559]\n",
-      " [0.00337449 0.05028014 0.0039805  ... 0.05294153 0.00241559 0.05333009]]\n",
-      "  mean conditional:  [ 0.76109512 -0.33962276  0.7521285   0.01565878  0.60936005  0.37011234\n",
-      "  0.33509094  0.66612922 -0.03185218  0.8408407  -0.41881131  0.85025817\n",
-      " -0.73796939  0.68832279 -0.91840551  0.39241987 -0.93291932  0.02861186\n",
-      " -0.80014491 -0.3362976  -0.56613582 -0.65239572 -0.28333596 -0.88546059\n",
-      "  0.00346762 -1.01054033  0.26520475 -1.01174051  0.48841666 -0.88692075\n",
-      "  0.66166765 -0.6525627 ] , sample:  [ 0.77184192 -0.3464785   0.74246172  0.00604918  0.61765032  0.33981011\n",
-      "  0.32514396  0.68674583 -0.01008643  0.8176425  -0.40659676  0.87776951\n",
-      " -0.75952308  0.68608178 -0.97711306  0.41036742 -0.8743369   0.01250913\n",
-      " -0.79309766 -0.34857743 -0.58303579 -0.65793346 -0.27132112 -0.86191334\n",
-      "  0.01582387 -0.96962359  0.26422133 -1.05208099  0.4856642  -0.92169865\n",
-      "  0.6329182  -0.59832515]\n",
-      "  cov conditional:  [[ 3.64879440e-04 -2.10946290e-05  2.97116029e-04 ... -1.43272137e-05\n",
-      "   2.82932376e-04 -1.83490857e-05]\n",
-      " [-2.10946290e-05  3.80696068e-04 -1.23292703e-05 ...  1.25526832e-04\n",
-      "  -2.32532508e-05  2.93733018e-04]\n",
-      " [ 2.97116029e-04 -1.23292703e-05  3.61830931e-04 ... -1.38590363e-05\n",
-      "   1.51696161e-04 -1.43002938e-05]\n",
-      " ...\n",
-      " [-1.43272137e-05  1.25526832e-04 -1.38590363e-05 ...  3.42929821e-04\n",
-      "  -8.02533474e-06  2.70731937e-04]\n",
-      " [ 2.82932376e-04 -2.32532508e-05  1.51696161e-04 ... -8.02533474e-06\n",
-      "   3.22871723e-04 -1.67395430e-05]\n",
-      " [-1.83490857e-05  2.93733018e-04 -1.43002938e-05 ...  2.70731937e-04\n",
-      "  -1.67395430e-05  3.49388528e-04]]\n",
-      "  mean noise:  [-0.0107468   0.00685575  0.00966679  0.00960961 -0.00829027  0.03030223\n",
-      "  0.00994698 -0.02061662 -0.02176575  0.0231982  -0.01221455 -0.02751134\n",
-      "  0.0215537   0.00224101  0.05870755 -0.01794755 -0.05858242  0.01610274\n",
-      " -0.00704725  0.01227983  0.01689997  0.00553773 -0.01201484 -0.02354725\n",
-      " -0.01235625 -0.04091674  0.00098342  0.04034049  0.00275246  0.0347779\n",
-      "  0.02874944 -0.05423755]\n",
-      "  cov noise:  [[ 3.64879440e-04 -2.10946290e-05  2.97116029e-04 ... -1.43272137e-05\n",
-      "   2.82932376e-04 -1.83490857e-05]\n",
-      " [-2.10946290e-05  3.80696068e-04 -1.23292703e-05 ...  1.25526832e-04\n",
-      "  -2.32532508e-05  2.93733018e-04]\n",
-      " [ 2.97116029e-04 -1.23292703e-05  3.61830931e-04 ... -1.38590363e-05\n",
-      "   1.51696161e-04 -1.43002938e-05]\n",
-      " ...\n",
-      " [-1.43272137e-05  1.25526832e-04 -1.38590363e-05 ...  3.42929821e-04\n",
-      "  -8.02533474e-06  2.70731937e-04]\n",
-      " [ 2.82932376e-04 -2.32532508e-05  1.51696161e-04 ... -8.02533474e-06\n",
-      "   3.22871723e-04 -1.67395430e-05]\n",
-      " [-1.83490857e-05  2.93733018e-04 -1.43002938e-05 ...  2.70731937e-04\n",
-      "  -1.67395430e-05  3.49388528e-04]]\n",
-      "  mean:  [ 1.01780672 -0.01821114  0.94092284  0.36361569  0.72189358  0.68725784\n",
-      "  0.39346887  0.90387627  0.00606027  0.98047411 -0.38000544  0.90518641\n",
-      " -0.70479608  0.68865331 -0.91946458  0.36375256 -0.99309701 -0.01941323\n",
-      " -0.91560545 -0.40152485 -0.69801288 -0.72437826 -0.37223439 -0.93971419\n",
-      "  0.01236676 -1.01543808  0.39706589 -0.93984465  0.72330086 -0.72393593\n",
-      "  0.94135168 -0.40025776]\n",
-      "  cov:  [[ 5.69839264e-02  8.62103746e-04  5.59667197e-02 ... -1.28271193e-03\n",
-      "   5.51999872e-02 -1.45115297e-04]\n",
-      " [ 8.62103746e-04  5.96427041e-02  1.08879065e-03 ...  5.48793440e-02\n",
-      "   6.64780637e-05  5.85551597e-02]\n",
-      " [ 5.59667197e-02  1.08879065e-03  5.74243733e-02 ... -8.46577729e-04\n",
-      "   5.19408283e-02  1.69424032e-04]\n",
-      " ...\n",
-      " [-1.28271193e-03  5.48793440e-02 -8.46577729e-04 ...  5.96322918e-02\n",
-      "  -1.89409838e-03  5.86191617e-02]\n",
-      " [ 5.51999872e-02  6.64780637e-05  5.19408283e-02 ... -1.89409838e-03\n",
-      "   5.62773617e-02 -8.75213090e-04]\n",
-      " [-1.45115297e-04  5.85551597e-02  1.69424032e-04 ...  5.86191617e-02\n",
-      "  -8.75213090e-04  6.01114300e-02]]\n",
-      "  mean w/ noise:  [ 1.01734399 -0.01823274  0.94155838  0.36347327  0.72158118  0.68641547\n",
-      "  0.39529005  0.90286322  0.00556895  0.9801254  -0.37923648  0.90517293\n",
-      " -0.70661894  0.68486853 -0.92029748  0.36392076 -0.99180287 -0.02110001\n",
-      " -0.91585374 -0.40032018 -0.69909069 -0.72439335 -0.37188953 -0.93787323\n",
-      "  0.00974048 -1.01395434  0.3994374  -0.94053377  0.72353818 -0.7243203\n",
-      "  0.9419473  -0.3991395 ]\n",
-      "  cov w/ noise:  [[ 0.05760067  0.00220771  0.05593254 ... -0.00034033  0.05495298\n",
-      "   0.00103935]\n",
-      " [ 0.00220771  0.06129923  0.00123448 ...  0.05546291  0.00098029\n",
-      "   0.05911186]\n",
-      " [ 0.05593254  0.00123448  0.05861809 ... -0.00102861  0.05211664\n",
-      "   0.00012233]\n",
-      " ...\n",
-      " [-0.00034033  0.05546291 -0.00102861 ...  0.06149439 -0.00145283\n",
-      "   0.05916081]\n",
-      " [ 0.05495298  0.00098029  0.05211664 ... -0.00145283  0.05702774\n",
-      "  -0.00014058]\n",
-      " [ 0.00103935  0.05911186  0.00012233 ...  0.05916081 -0.00014058\n",
-      "   0.06152976]]\n",
-      "  mean conditional:  [ 0.97803878 -0.1579801   0.91510643  0.22800069  0.70524014  0.60087133\n",
-      "  0.37236804  0.89337258 -0.03364401  1.04194809 -0.44529003  1.01126484\n",
-      " -0.78881574  0.80669583 -1.0009808   0.47222927 -1.04584435  0.07407407\n",
-      " -0.92179466 -0.32229651 -0.65928694 -0.66184818 -0.31119485 -0.89809051\n",
-      "  0.062893   -0.99826117  0.41235156 -0.95414502  0.70068321 -0.78118508\n",
-      "  0.89864441 -0.50607743] , sample:  [ 0.99276216 -0.14809162  0.9329294   0.23036869  0.69867289  0.5665511\n",
-      "  0.33751734  0.93482893 -0.00154647  1.00730098 -0.46439534  1.02773098\n",
-      " -0.78508135  0.83321186 -0.98715106  0.46661897 -1.06454843  0.04234972\n",
-      " -0.91830307 -0.30053615 -0.65676244 -0.63077159 -0.31644792 -0.93588731\n",
-      "  0.07389925 -1.01762325  0.40139606 -0.93730294  0.72488809 -0.74572327\n",
-      "  0.86972251 -0.54745104]\n",
-      "  cov conditional:  [[ 3.46953616e-04 -1.71809208e-05  2.67102551e-04 ...  1.13903846e-06\n",
-      "   2.79764692e-04 -5.35505616e-06]\n",
-      " [-1.71809208e-05  3.26339184e-04 -7.18854913e-06 ...  1.27738597e-04\n",
-      "  -3.34748259e-05  2.63808435e-04]\n",
-      " [ 2.67102551e-04 -7.18854913e-06  3.22490574e-04 ...  8.41136364e-06\n",
-      "   1.23794833e-04  2.83538077e-06]\n",
-      " ...\n",
-      " [ 1.13903846e-06  1.27738597e-04  8.41136364e-06 ...  3.39918961e-04\n",
-      "  -4.26832493e-06  2.78775627e-04]\n",
-      " [ 2.79764692e-04 -3.34748259e-05  1.23794833e-04 ... -4.26832493e-06\n",
-      "   3.56396058e-04 -1.62273585e-05]\n",
-      " [-5.35505616e-06  2.63808435e-04  2.83538077e-06 ...  2.78775627e-04\n",
-      "  -1.62273585e-05  3.44746003e-04]]\n",
-      "  mean noise:  [-0.01472338 -0.00988849 -0.01782296 -0.002368    0.00656725  0.03432023\n",
-      "  0.03485071 -0.04145635 -0.03209754  0.03464711  0.01910531 -0.01646614\n",
-      " -0.0037344  -0.02651604 -0.01382973  0.00561031  0.01870408  0.03172435\n",
-      " -0.00349159 -0.02176036 -0.0025245  -0.03107658  0.00525307  0.0377968\n",
-      " -0.01100625  0.01936208  0.0109555  -0.01684208 -0.02420488 -0.03546182\n",
-      "  0.0289219   0.0413736 ]\n",
-      "  cov noise:  [[ 3.46953616e-04 -1.71809208e-05  2.67102551e-04 ...  1.13903846e-06\n",
-      "   2.79764692e-04 -5.35505616e-06]\n",
-      " [-1.71809208e-05  3.26339184e-04 -7.18854913e-06 ...  1.27738597e-04\n",
-      "  -3.34748259e-05  2.63808435e-04]\n",
-      " [ 2.67102551e-04 -7.18854913e-06  3.22490574e-04 ...  8.41136364e-06\n",
-      "   1.23794833e-04  2.83538077e-06]\n",
-      " ...\n",
-      " [ 1.13903846e-06  1.27738597e-04  8.41136364e-06 ...  3.39918961e-04\n",
-      "  -4.26832493e-06  2.78775627e-04]\n",
-      " [ 2.79764692e-04 -3.34748259e-05  1.23794833e-04 ... -4.26832493e-06\n",
-      "   3.56396058e-04 -1.62273585e-05]\n",
-      " [-5.35505616e-06  2.63808435e-04  2.83538077e-06 ...  2.78775627e-04\n",
-      "  -1.62273585e-05  3.44746003e-04]]\n",
-      "  mean:  [ 1.00507971e+00  9.69795650e-03  9.30961370e-01  3.90416168e-01\n",
-      "  7.15904242e-01  7.13542583e-01  3.92217514e-01  9.28829216e-01\n",
-      "  8.53331212e-03  1.00268125e+00 -3.77082621e-01  9.24035628e-01\n",
-      " -7.05048782e-01  7.05193178e-01 -9.24360325e-01  3.79682732e-01\n",
-      " -1.00129430e+00 -2.88675332e-03 -9.24699267e-01 -3.83671510e-01\n",
-      " -7.07303496e-01 -7.04110035e-01 -3.82487808e-01 -9.15832660e-01\n",
-      "  6.65912494e-04 -9.88009891e-01  3.84068488e-01 -9.10248212e-01\n",
-      "  7.09310371e-01 -6.93706812e-01  9.27308182e-01 -3.70811717e-01]\n",
-      "  cov:  [[ 0.059656   -0.00095582  0.05837205 ... -0.0014117   0.0581248\n",
-      "  -0.00132124]\n",
-      " [-0.00095582  0.0540336  -0.00049975 ...  0.0493772  -0.00114948\n",
-      "   0.05262635]\n",
-      " [ 0.05837205 -0.00049975  0.05957283 ... -0.00137451  0.05483255\n",
-      "  -0.00111446]\n",
-      " ...\n",
-      " [-0.0014117   0.0493772  -0.00137451 ...  0.05394112 -0.00120931\n",
-      "   0.05261731]\n",
-      " [ 0.0581248  -0.00114948  0.05483255 ... -0.00120931  0.05892405\n",
-      "  -0.00129261]\n",
-      " [-0.00132124  0.05262635 -0.00111446 ...  0.05261731 -0.00129261\n",
-      "   0.05375262]]\n",
-      "  mean w/ noise:  [ 1.00373022e+00  9.75304230e-03  9.30941187e-01  3.91055041e-01\n",
-      "  7.18978836e-01  7.12866578e-01  3.89718344e-01  9.29055974e-01\n",
-      "  9.22272331e-03  1.00162510e+00 -3.79454462e-01  9.24321636e-01\n",
-      " -7.05418964e-01  7.04437480e-01 -9.24258393e-01  3.80478221e-01\n",
-      " -1.00146678e+00 -2.31848133e-03 -9.27456967e-01 -3.81828134e-01\n",
-      " -7.06887223e-01 -7.04253907e-01 -3.82308682e-01 -9.16410397e-01\n",
-      "  4.84509465e-04 -9.86467494e-01  3.82596857e-01 -9.09437370e-01\n",
-      "  7.09338635e-01 -6.94379391e-01  9.26059702e-01 -3.70787617e-01]\n",
-      "  cov w/ noise:  [[ 0.06050996 -0.00088421  0.05780848 ... -0.00093403  0.05812282\n",
-      "  -0.00126009]\n",
-      " [-0.00088421  0.0550448  -0.00053737 ...  0.04948548 -0.0013164\n",
-      "   0.0526519 ]\n",
-      " [ 0.05780848 -0.00053737  0.05953006 ... -0.00078083  0.05447775\n",
-      "  -0.00117374]\n",
-      " ...\n",
-      " [-0.00093403  0.04948548 -0.00078083 ...  0.05523179 -0.00121682\n",
-      "   0.05296874]\n",
-      " [ 0.05812282 -0.0013164   0.05447775 ... -0.00121682  0.06001201\n",
-      "  -0.00171229]\n",
-      " [-0.00126009  0.0526519  -0.00117374 ...  0.05296874 -0.00171229\n",
-      "   0.05496676]]\n",
-      "  mean conditional:  [ 1.18584301  0.24708597  1.17747753  0.6240801   0.99216288  0.948757\n",
-      "  0.66120641  1.16621306  0.24078223  1.23360264 -0.19945932  1.13565311\n",
-      " -0.58789211  0.89066439 -0.8642203   0.54551352 -0.99043326  0.16071306\n",
-      " -0.95504793 -0.20658781 -0.77016576 -0.50811075 -0.46553137 -0.70252662\n",
-      " -0.0822826  -0.75993314  0.33047306 -0.67075323  0.71760832 -0.44890612\n",
-      "  1.02065233 -0.12770183] , sample:  [ 1.18173373  0.24456941  1.18628024  0.63180802  0.98778299  0.96041372\n",
-      "  0.65178653  1.15162911  0.25288895  1.23740752 -0.21794733  1.14715428\n",
-      " -0.5625461   0.87228883 -0.85889597  0.56107147 -1.00806459  0.14266784\n",
-      " -0.94340937 -0.18906319 -0.772188   -0.50668303 -0.45923613 -0.72556428\n",
-      " -0.11642578 -0.7406796   0.33467863 -0.66808617  0.70116424 -0.47465131\n",
-      "  1.05807243 -0.10904602]\n",
-      "  cov conditional:  [[ 3.05969953e-04  1.00080431e-05  2.47517746e-04 ...  1.33703182e-05\n",
-      "   2.28335496e-04  1.42889910e-05]\n",
-      " [ 1.00080431e-05  3.36174686e-04  2.22962422e-05 ...  1.27672548e-04\n",
-      "  -9.32583050e-06  2.76181218e-04]\n",
-      " [ 2.47517746e-04  2.22962422e-05  3.14260998e-04 ...  9.32073080e-06\n",
-      "   9.75548383e-05  1.96709420e-05]\n",
-      " ...\n",
-      " [ 1.33703182e-05  1.27672548e-04  9.32073080e-06 ...  3.48656736e-04\n",
-      "   8.00714359e-06  2.82148653e-04]\n",
-      " [ 2.28335496e-04 -9.32583050e-06  9.75548383e-05 ...  8.00714359e-06\n",
-      "   2.88248835e-04  1.22176969e-06]\n",
-      " [ 1.42889910e-05  2.76181218e-04  1.96709420e-05 ...  2.82148653e-04\n",
-      "   1.22176969e-06  3.55580666e-04]]\n",
-      "  mean noise:  [ 0.00410928  0.00251656 -0.00880271 -0.00772791  0.00437988 -0.01165672\n",
-      "  0.00941988  0.01458395 -0.01210672 -0.00380488  0.01848801 -0.01150117\n",
-      " -0.02534602  0.01837556 -0.00532432 -0.01555794  0.01763133  0.01804522\n",
-      " -0.01163855 -0.01752462  0.00202224 -0.00142772 -0.00629524  0.02303766\n",
-      "  0.03414318 -0.01925354 -0.00420558 -0.00266706  0.01644407  0.02574519\n",
-      " -0.0374201  -0.01865581]\n",
-      "  cov noise:  [[ 3.05969953e-04  1.00080431e-05  2.47517746e-04 ...  1.33703182e-05\n",
-      "   2.28335496e-04  1.42889910e-05]\n",
-      " [ 1.00080431e-05  3.36174686e-04  2.22962422e-05 ...  1.27672548e-04\n",
-      "  -9.32583050e-06  2.76181218e-04]\n",
-      " [ 2.47517746e-04  2.22962422e-05  3.14260998e-04 ...  9.32073080e-06\n",
-      "   9.75548383e-05  1.96709420e-05]\n",
-      " ...\n",
-      " [ 1.33703182e-05  1.27672548e-04  9.32073080e-06 ...  3.48656736e-04\n",
-      "   8.00714359e-06  2.82148653e-04]\n",
-      " [ 2.28335496e-04 -9.32583050e-06  9.75548383e-05 ...  8.00714359e-06\n",
-      "   2.88248835e-04  1.22176969e-06]\n",
-      " [ 1.42889910e-05  2.76181218e-04  1.96709420e-05 ...  2.82148653e-04\n",
-      "   1.22176969e-06  3.55580666e-04]]\n",
-      "  mean:  [ 1.00504493 -0.00734631  0.93034374  0.37225332  0.71450535  0.69409735\n",
-      "  0.38914145  0.90850354  0.00365973  0.98187018 -0.38151101  0.90305244\n",
-      " -0.7059613   0.68420743 -0.92040334  0.35928507 -0.99301619 -0.0211064\n",
-      " -0.9137785  -0.3985799  -0.69556164 -0.7164115  -0.37161716 -0.92765607\n",
-      "  0.00948123 -1.00070395  0.38998445 -0.9239027   0.71234382 -0.70852249\n",
-      "  0.9283083  -0.3868969 ]\n",
-      "  cov:  [[ 0.05983969 -0.00325821  0.05851766 ... -0.00528548  0.05799611\n",
-      "  -0.00447399]\n",
-      " [-0.00325821  0.05291165 -0.00340249 ...  0.04708164 -0.00301485\n",
-      "   0.05114529]\n",
-      " [ 0.05851766 -0.00340249  0.05979085 ... -0.00493847  0.05445659\n",
-      "  -0.00437775]\n",
-      " ...\n",
-      " [-0.00528548  0.04708164 -0.00493847 ...  0.05110632 -0.00530169\n",
-      "   0.05024168]\n",
-      " [ 0.05799611 -0.00301485  0.05445659 ... -0.00530169  0.05876008\n",
-      "  -0.00432958]\n",
-      " [-0.00447399  0.05114529 -0.00437775 ...  0.05024168 -0.00432958\n",
-      "   0.05204245]]\n",
-      "  mean w/ noise:  [ 1.00302802 -0.00576479  0.92922764  0.37379764  0.71362937  0.69432625\n",
-      "  0.38827545  0.9091693   0.00234369  0.98274985 -0.38061517  0.90268248\n",
-      " -0.70364569  0.68610484 -0.91992239  0.35899116 -0.99240414 -0.02221531\n",
-      " -0.91412929 -0.39707423 -0.69454239 -0.71420995 -0.36766303 -0.92941098\n",
-      "  0.00877374 -1.00022092  0.39094125 -0.92555276  0.71390233 -0.70895528\n",
-      "  0.92894414 -0.38707452]\n",
-      "  cov w/ noise:  [[ 0.06097921 -0.00272952  0.05819426 ... -0.00580848  0.05773064\n",
-      "  -0.00436966]\n",
-      " [-0.00272952  0.05412557 -0.00297037 ...  0.04713779 -0.00272118\n",
-      "   0.05141899]\n",
-      " [ 0.05819426 -0.00297037  0.06002565 ... -0.0059094   0.05350661\n",
-      "  -0.00462116]\n",
-      " ...\n",
-      " [-0.00580848  0.04713779 -0.0059094  ...  0.05257446 -0.00597153\n",
-      "   0.05074578]\n",
-      " [ 0.05773064 -0.00272118  0.05350661 ... -0.00597153  0.05943428\n",
-      "  -0.00437151]\n",
-      " [-0.00436966  0.05141899 -0.00462116 ...  0.05074578 -0.00437151\n",
-      "   0.05376625]]\n",
-      "  mean conditional:  [ 0.72159125  0.33373734  0.69055523  0.69164694  0.54307778  1.00973443\n",
-      "  0.28440333  1.23018583 -0.05264961  1.30550027 -0.41270625  1.22039475\n",
-      " -0.73039844  0.99325469 -0.94545734  0.66603459 -1.014957    0.29320965\n",
-      " -0.92090349 -0.0687188  -0.68084588 -0.36767979 -0.35199595 -0.55892229\n",
-      " -0.01044919 -0.61287383  0.28351303 -0.5247274   0.50538684 -0.31421038\n",
-      "  0.6533516  -0.01517396] , sample:  [ 0.70673831  0.35843182  0.73536196  0.65656528  0.51398114  1.0153027\n",
-      "  0.27764602  1.2644404  -0.02779559  1.30484822 -0.43834109  1.19445193\n",
-      " -0.7255514   0.97841495 -0.92331237  0.69043382 -0.98116554  0.29807552\n",
-      " -0.98232598 -0.05792174 -0.70910896 -0.38811967 -0.32826595 -0.54763648\n",
-      "  0.04859237 -0.63355425  0.25723196 -0.50312317  0.53349495 -0.31109388\n",
-      "  0.59496885 -0.01478149]\n",
-      "  cov conditional:  [[3.41922684e-04 9.23428614e-06 2.70324714e-04 ... 2.35498768e-05\n",
-      "  2.70162332e-04 2.71979745e-05]\n",
-      " [9.23428614e-06 3.27887519e-04 1.68474464e-05 ... 1.05376110e-04\n",
-      "  1.30557956e-05 2.55576250e-04]\n",
-      " [2.70324714e-04 1.68474464e-05 3.20968647e-04 ... 1.50519566e-05\n",
-      "  1.32663423e-04 2.55107181e-05]\n",
-      " ...\n",
-      " [2.35498768e-05 1.05376110e-04 1.50519566e-05 ... 3.09251639e-04\n",
-      "  2.67509001e-05 2.46839463e-04]\n",
-      " [2.70162332e-04 1.30557956e-05 1.32663423e-04 ... 2.67509001e-05\n",
-      "  3.25616942e-04 3.03032394e-05]\n",
-      " [2.71979745e-05 2.55576250e-04 2.55107181e-05 ... 2.46839463e-04\n",
-      "  3.03032394e-05 3.22137666e-04]]\n",
-      "  mean noise:  [ 0.01485295 -0.02469448 -0.04480673  0.03508166  0.02909664 -0.00556827\n",
-      "  0.00675731 -0.03425458 -0.02485402  0.00065205  0.02563484  0.02594281\n",
-      " -0.00484704  0.01483973 -0.02214498 -0.02439924 -0.03379146 -0.00486588\n",
-      "  0.06142249 -0.01079706  0.02826308  0.02043988 -0.02373    -0.01128581\n",
-      " -0.05904156  0.02068042  0.02628107 -0.02160424 -0.02810811 -0.00311651\n",
-      "  0.05838275 -0.00039247]\n",
-      "  cov noise:  [[3.41922684e-04 9.23428614e-06 2.70324714e-04 ... 2.35498768e-05\n",
-      "  2.70162332e-04 2.71979745e-05]\n",
-      " [9.23428614e-06 3.27887519e-04 1.68474464e-05 ... 1.05376110e-04\n",
-      "  1.30557956e-05 2.55576250e-04]\n",
-      " [2.70324714e-04 1.68474464e-05 3.20968647e-04 ... 1.50519566e-05\n",
-      "  1.32663423e-04 2.55107181e-05]\n",
-      " ...\n",
-      " [2.35498768e-05 1.05376110e-04 1.50519566e-05 ... 3.09251639e-04\n",
-      "  2.67509001e-05 2.46839463e-04]\n",
-      " [2.70162332e-04 1.30557956e-05 1.32663423e-04 ... 2.67509001e-05\n",
-      "  3.25616942e-04 3.03032394e-05]\n",
-      " [2.71979745e-05 2.55576250e-04 2.55107181e-05 ... 2.46839463e-04\n",
-      "  3.03032394e-05 3.22137666e-04]]\n",
-      "  mean:  [ 1.00340159  0.02700665  0.92869143  0.41029484  0.71262667  0.73567915\n",
-      "  0.38852677  0.95354376  0.00613674  1.03021742 -0.37609214  0.95357092\n",
-      " -0.70066827  0.73499188 -0.91904283  0.40820726 -0.99815477  0.02388552\n",
-      " -0.9256704  -0.35934608 -0.71193643 -0.68364764 -0.38891281 -0.89988108\n",
-      " -0.00585852 -0.97537828  0.37847847 -0.89860638  0.70540364 -0.68105948\n",
-      "  0.92505127 -0.3560887 ]\n",
-      "  cov:  [[ 0.05060184 -0.0040792   0.04927505 ... -0.00327205  0.04947234\n",
-      "  -0.00357957]\n",
-      " [-0.0040792   0.0574965  -0.00372401 ...  0.05323689 -0.0043757\n",
-      "   0.05640307]\n",
-      " [ 0.04927505 -0.00372401  0.05043289 ... -0.00270273  0.0461384\n",
-      "  -0.00312449]\n",
-      " ...\n",
-      " [-0.00327205  0.05323689 -0.00270273 ...  0.05897754 -0.00351981\n",
-      "   0.0570732 ]\n",
-      " [ 0.04947234 -0.0043757   0.0461384  ... -0.00351981  0.05070442\n",
-      "  -0.00385609]\n",
-      " [-0.00357957  0.05640307 -0.00312449 ...  0.0570732  -0.00385609\n",
-      "   0.05791763]]\n",
-      "  mean w/ noise:  [ 1.00389051  0.02661978  0.92988388  0.41253436  0.71376494  0.73396956\n",
-      "  0.38738457  0.95314189  0.00705863  1.03039577 -0.37252268  0.95553851\n",
-      " -0.70214325  0.73516541 -0.91806151  0.40768521 -0.99658606  0.02451784\n",
-      " -0.92512845 -0.35797746 -0.71316122 -0.68271582 -0.39092268 -0.90144556\n",
-      " -0.00553076 -0.97422307  0.37590993 -0.89799016  0.70516224 -0.68182688\n",
-      "  0.92502608 -0.35546239]\n",
-      "  cov w/ noise:  [[ 0.05195256 -0.00306015  0.04894159 ... -0.00232524  0.04970905\n",
-      "  -0.00325828]\n",
-      " [-0.00306015  0.05920121 -0.00269309 ...  0.05352936 -0.00368608\n",
-      "   0.05689939]\n",
-      " [ 0.04894159 -0.00269309  0.05026782 ... -0.00191701  0.04573402\n",
-      "  -0.00303552]\n",
-      " ...\n",
-      " [-0.00232524  0.05352936 -0.00191701 ...  0.05972699 -0.0031347\n",
-      "   0.0572418 ]\n",
-      " [ 0.04970905 -0.00368608  0.04573402 ... -0.0031347   0.05165855\n",
-      "  -0.00374035]\n",
-      " [-0.00325828  0.05689939 -0.00303552 ...  0.0572418  -0.00374035\n",
-      "   0.05926792]]\n",
-      "  mean conditional:  [ 1.30972627  0.01105297  1.24448304  0.37622069  0.96798164  0.73576391\n",
-      "  0.53115237  1.01884082  0.0098015   1.1517057  -0.50965671  1.10370547\n",
-      " -0.94500921  0.9042475  -1.23339413  0.61200885 -1.3406667   0.27520642\n",
-      " -1.26218322 -0.07244951 -1.01789733 -0.3930554  -0.64152764 -0.63710961\n",
-      " -0.17386057 -0.75800532  0.33164123 -0.73386042  0.80169764 -0.57585364\n",
-      "  1.151322   -0.3162142 ] , sample:  [ 1.28851186e+00  1.26839658e-03  1.26311697e+00  3.46195639e-01\n",
-      "  9.62501286e-01  7.16281049e-01  5.14259600e-01  1.06027807e+00\n",
-      "  4.96395608e-02  1.14390167e+00 -5.17087274e-01  1.11940516e+00\n",
-      " -9.40609164e-01  8.65883104e-01 -1.25415042e+00  6.05824140e-01\n",
-      " -1.34139741e+00  2.87811057e-01 -1.27199490e+00 -5.61324311e-02\n",
-      " -9.72774412e-01 -3.70001591e-01 -6.78823729e-01 -6.62530203e-01\n",
-      " -1.49657618e-01 -7.64045029e-01  2.73259912e-01 -7.11564897e-01\n",
-      "  8.31239285e-01 -6.11744502e-01  1.17877427e+00 -2.58512110e-01]\n",
-      "  cov conditional:  [[ 3.17222239e-04  9.52482972e-06  2.50894164e-04 ... -2.43384958e-06\n",
-      "   2.43484004e-04  6.08356900e-06]\n",
-      " [ 9.52482972e-06  3.52753429e-04  1.56317224e-05 ...  1.04081917e-04\n",
-      "   2.76203005e-06  2.72819529e-04]\n",
-      " [ 2.50894164e-04  1.56317224e-05  3.14754019e-04 ... -1.31790193e-05\n",
-      "   1.05394516e-04  2.36737929e-06]\n",
-      " ...\n",
-      " [-2.43384958e-06  1.04081917e-04 -1.31790193e-05 ...  3.08979431e-04\n",
-      "   3.79223429e-06  2.46271872e-04]\n",
-      " [ 2.43484004e-04  2.76203005e-06  1.05394516e-04 ...  3.79223429e-06\n",
-      "   3.08516986e-04  5.78733358e-06]\n",
-      " [ 6.08356900e-06  2.72819529e-04  2.36737929e-06 ...  2.46271872e-04\n",
-      "   5.78733358e-06  3.34809508e-04]]\n",
-      "  mean noise:  [ 0.02121441  0.00978457 -0.01863393  0.03002505  0.00548036  0.01948287\n",
-      "  0.01689277 -0.04143726 -0.03983806  0.00780403  0.00743057 -0.01569968\n",
-      " -0.00440005  0.0383644   0.02075629  0.00618471  0.00073071 -0.01260464\n",
-      "  0.00981168 -0.01631708 -0.04512292 -0.02305381  0.03729609  0.0254206\n",
-      " -0.02420296  0.00603971  0.05838131 -0.02229553 -0.02954165  0.03589086\n",
-      " -0.02745227 -0.05770209]\n",
-      "  cov noise:  [[ 3.17222239e-04  9.52482972e-06  2.50894164e-04 ... -2.43384958e-06\n",
-      "   2.43484004e-04  6.08356900e-06]\n",
-      " [ 9.52482972e-06  3.52753429e-04  1.56317224e-05 ...  1.04081917e-04\n",
-      "   2.76203005e-06  2.72819529e-04]\n",
-      " [ 2.50894164e-04  1.56317224e-05  3.14754019e-04 ... -1.31790193e-05\n",
-      "   1.05394516e-04  2.36737929e-06]\n",
-      " ...\n",
-      " [-2.43384958e-06  1.04081917e-04 -1.31790193e-05 ...  3.08979431e-04\n",
-      "   3.79223429e-06  2.46271872e-04]\n",
-      " [ 2.43484004e-04  2.76203005e-06  1.05394516e-04 ...  3.79223429e-06\n",
-      "   3.08516986e-04  5.78733358e-06]\n",
-      " [ 6.08356900e-06  2.72819529e-04  2.36737929e-06 ...  2.46271872e-04\n",
-      "   5.78733358e-06  3.34809508e-04]]\n",
-      "  mean:  [ 1.00387137  0.02320496  0.92886021  0.40700992  0.71171537  0.73205201\n",
-      "  0.38587213  0.94881133  0.00161805  1.0239352  -0.38130468  0.94599916\n",
-      " -0.70457507  0.72732087 -0.92002567  0.40067259 -0.99535744  0.01519775\n",
-      " -0.9196196  -0.370098   -0.70441616 -0.69561546 -0.38220721 -0.9113396\n",
-      " -0.00149698 -0.98525399  0.38059616 -0.90654487  0.70634447 -0.68711753\n",
-      "  0.9255946  -0.36079623]\n",
-      "  cov:  [[0.05221335 0.00041254 0.05075362 ... 0.00217964 0.05075834 0.00136499]\n",
-      " [0.00041254 0.05274953 0.00025681 ... 0.04813841 0.00046822 0.05161513]\n",
-      " [0.05075362 0.00025681 0.05195328 ... 0.00231335 0.04709434 0.00135137]\n",
-      " ...\n",
-      " [0.00217964 0.04813841 0.00231335 ... 0.05356796 0.00192585 0.05192705]\n",
-      " [0.05075834 0.00046822 0.04709434 ... 0.00192585 0.05189808 0.00127226]\n",
-      " [0.00136499 0.05161513 0.00135137 ... 0.05192705 0.00127226 0.05308362]]\n",
-      "  mean w/ noise:  [ 1.00299781  0.02317465  0.92965358  0.40801187  0.71488236  0.7303372\n",
-      "  0.38689665  0.94820563  0.00329183  1.02474176 -0.3800727   0.94474928\n",
-      " -0.70800114  0.72841639 -0.92148258  0.40031684 -0.99465256  0.01472433\n",
-      " -0.9200294  -0.36970203 -0.70444366 -0.69470662 -0.38384888 -0.91254581\n",
-      " -0.0024061  -0.98406777  0.3794958  -0.90366449  0.70740759 -0.68864431\n",
-      "  0.92554057 -0.36232994]\n",
-      "  cov w/ noise:  [[ 5.31970951e-02  5.52617723e-04  5.03401896e-02 ...  2.34087033e-03\n",
-      "   5.09514019e-02  7.46389144e-04]\n",
-      " [ 5.52617723e-04  5.36703186e-02 -9.36933452e-05 ...  4.84946936e-02\n",
-      "   2.21661155e-04  5.19561418e-02]\n",
-      " [ 5.03401896e-02 -9.36933452e-05  5.21085833e-02 ...  2.05499857e-03\n",
-      "   4.70247848e-02  3.05759280e-04]\n",
-      " ...\n",
-      " [ 2.34087033e-03  4.84946936e-02  2.05499857e-03 ...  5.52568617e-02\n",
-      "   2.01316714e-03  5.26345992e-02]\n",
-      " [ 5.09514019e-02  2.21661155e-04  4.70247848e-02 ...  2.01316714e-03\n",
-      "   5.31089938e-02  5.41388815e-04]\n",
-      " [ 7.46389144e-04  5.19561418e-02  3.05759280e-04 ...  5.26345992e-02\n",
-      "   5.41388815e-04  5.48389426e-02]]\n",
-      "  mean conditional:  [ 1.24517671 -0.4788796   1.08483439 -0.07123898  0.74531242  0.32230551\n",
-      "  0.28388049  0.62232778 -0.22180582  0.74729937 -0.69000652  0.66288719\n",
-      " -1.05237889  0.40560608 -1.26337908  0.05252419 -1.29772608 -0.32659359\n",
-      " -1.14881996 -0.68775158 -0.83584826 -1.00042559 -0.40331876 -1.23156787\n",
-      "  0.08833603 -1.34359778  0.56793923 -1.3110212   0.96067018 -1.13642644\n",
-      "  1.20061699 -0.84689953] , sample:  [ 1.2169601  -0.51112853  1.09356615 -0.05465988  0.73476549  0.30537531\n",
-      "  0.28676771  0.60254948 -0.21535312  0.79291311 -0.69512282  0.67618133\n",
-      " -1.08688381  0.37882539 -1.19170158  0.05916942 -1.3451729  -0.36252602\n",
-      " -1.15848775 -0.69710507 -0.82398046 -0.95007939 -0.39782016 -1.22436112\n",
-      "  0.0800011  -1.38491787  0.58680444 -1.29791038  0.95805052 -1.16205394\n",
-      "  1.22761524 -0.80404276]\n",
-      "  cov conditional:  [[ 3.27836357e-04  5.15538007e-06  2.53431013e-04 ... -2.28841589e-06\n",
-      "   2.77170639e-04  2.88376466e-06]\n",
-      " [ 5.15538007e-06  3.19326224e-04  7.68701265e-06 ...  1.07879308e-04\n",
-      "   3.16809055e-06  2.48142431e-04]\n",
-      " [ 2.53431013e-04  7.68701265e-06  3.09047415e-04 ...  5.46746803e-06\n",
-      "   1.31966428e-04  8.05987037e-06]\n",
-      " ...\n",
-      " [-2.28841589e-06  1.07879308e-04  5.46746803e-06 ...  3.35256045e-04\n",
-      "  -4.94043219e-06  2.67162099e-04]\n",
-      " [ 2.77170639e-04  3.16809055e-06  1.31966428e-04 ... -4.94043219e-06\n",
-      "   3.49285661e-04  4.70675864e-07]\n",
-      " [ 2.88376466e-06  2.48142431e-04  8.05987037e-06 ...  2.67162099e-04\n",
-      "   4.70675864e-07  3.28894717e-04]]\n",
-      "  mean noise:  [ 0.0282166   0.03224893 -0.00873176 -0.0165791   0.01054694  0.0169302\n",
-      " -0.00288722  0.01977831 -0.0064527  -0.04561374  0.0051163  -0.01329414\n",
-      "  0.03450492  0.02678069 -0.0716775  -0.00664522  0.04744682  0.03593243\n",
-      "  0.0096678   0.00935348 -0.0118678  -0.0503462  -0.00549859 -0.00720675\n",
-      "  0.00833493  0.04132008 -0.01886521 -0.01311081  0.00261966  0.0256275\n",
-      " -0.02699825 -0.04285677]\n",
-      "  cov noise:  [[ 3.27836357e-04  5.15538007e-06  2.53431013e-04 ... -2.28841589e-06\n",
-      "   2.77170639e-04  2.88376466e-06]\n",
-      " [ 5.15538007e-06  3.19326224e-04  7.68701265e-06 ...  1.07879308e-04\n",
-      "   3.16809055e-06  2.48142431e-04]\n",
-      " [ 2.53431013e-04  7.68701265e-06  3.09047415e-04 ...  5.46746803e-06\n",
-      "   1.31966428e-04  8.05987037e-06]\n",
-      " ...\n",
-      " [-2.28841589e-06  1.07879308e-04  5.46746803e-06 ...  3.35256045e-04\n",
-      "  -4.94043219e-06  2.67162099e-04]\n",
-      " [ 2.77170639e-04  3.16809055e-06  1.31966428e-04 ... -4.94043219e-06\n",
-      "   3.49285661e-04  4.70675864e-07]\n",
-      " [ 2.88376466e-06  2.48142431e-04  8.05987037e-06 ...  2.67162099e-04\n",
-      "   4.70675864e-07  3.28894717e-04]]\n",
-      "  mean:  [ 0.9843451   0.01262626  0.91047606  0.39391212  0.69568832  0.71846873\n",
-      "  0.37384724  0.93625489 -0.00563216  1.01305505 -0.38579965  0.93680285\n",
-      " -0.70964913  0.71969236 -0.92789431  0.39494857 -1.00712674  0.0118696\n",
-      " -0.93538312 -0.37046599 -0.72302022 -0.69322443 -0.40146357 -0.90769086\n",
-      " -0.01999136 -0.98165991  0.3625286  -0.90482978  0.68765657 -0.68931679\n",
-      "  0.90603878 -0.3674387 ]\n",
-      "  cov:  [[ 0.05204045 -0.00255548  0.05037076 ... -0.003008    0.05078422\n",
-      "  -0.0027249 ]\n",
-      " [-0.00255548  0.05370519 -0.00232037 ...  0.04905735 -0.00206231\n",
-      "   0.05238949]\n",
-      " [ 0.05037076 -0.00232037  0.05132415 ... -0.00226226  0.04689558\n",
-      "  -0.00222751]\n",
-      " ...\n",
-      " [-0.003008    0.04905735 -0.00226226 ...  0.05358691 -0.00294032\n",
-      "   0.05231118]\n",
-      " [ 0.05078422 -0.00206231  0.04689558 ... -0.00294032  0.05217286\n",
-      "  -0.00246375]\n",
-      " [-0.0027249   0.05238949 -0.00222751 ...  0.05231118 -0.00246375\n",
-      "   0.05354647]]\n",
-      "  mean w/ noise:  [ 0.98558469  0.0122717   0.90883893  0.39459526  0.69226315  0.71779684\n",
-      "  0.37449425  0.93696375 -0.0064925   1.01272295 -0.38448978  0.93659562\n",
-      " -0.70829055  0.72082507 -0.92658555  0.39615397 -1.00629359  0.01170939\n",
-      " -0.93567746 -0.36926729 -0.72152152 -0.69246635 -0.40322155 -0.9050261\n",
-      " -0.01992442 -0.97966647  0.36209566 -0.90327569  0.68864864 -0.68997408\n",
-      "  0.90782572 -0.36521054]\n",
-      "  cov w/ noise:  [[ 0.0519593  -0.0026388   0.05050468 ... -0.00304328  0.05030206\n",
-      "  -0.00285326]\n",
-      " [-0.0026388   0.05545636 -0.00231407 ...  0.04944775 -0.00184196\n",
-      "   0.05357388]\n",
-      " [ 0.05050468 -0.00231407  0.05353577 ... -0.00214735  0.04743652\n",
-      "  -0.00204685]\n",
-      " ...\n",
-      " [-0.00304328  0.04944775 -0.00214735 ...  0.05503805 -0.00266529\n",
-      "   0.05348929]\n",
-      " [ 0.05030206 -0.00184196  0.04743652 ... -0.00266529  0.05326283\n",
-      "  -0.00234725]\n",
-      " [-0.00285326  0.05357388 -0.00204685 ...  0.05348929 -0.00234725\n",
-      "   0.05648367]]\n",
-      "  mean conditional:  [ 0.84858229  0.09518983  0.77345947  0.51909364  0.49154077  0.91303355\n",
-      "  0.04565584  1.19104653 -0.48554307  1.28227684 -1.00313819  1.17125076\n",
-      " -1.41431941  0.90363921 -1.65677586  0.55031658 -1.70670297  0.16939618\n",
-      " -1.57404806 -0.19810413 -1.29329886 -0.51433429 -0.90893335 -0.73888608\n",
-      " -0.46423234 -0.83547654 -0.0053313  -0.7860215   0.41021235 -0.59672079\n",
-      "  0.71537103 -0.29167981] , sample:  [ 0.86354482  0.10519547  0.75301324  0.48610285  0.50502428  0.92165441\n",
-      "  0.040786    1.24823178 -0.45110522  1.25281275 -1.02428742  1.18109396\n",
-      " -1.44154361  0.86593332 -1.64921292  0.57580937 -1.72867131  0.15405804\n",
-      " -1.56470279 -0.19075645 -1.26916872 -0.51004361 -0.92562508 -0.73496116\n",
-      " -0.46256545 -0.84012077 -0.01188591 -0.778324    0.40015073 -0.5942951\n",
-      "  0.71559338 -0.30138325]\n",
-      "  cov conditional:  [[ 2.88642040e-04 -3.21308872e-05  2.38367575e-04 ...  5.05949548e-06\n",
-      "   2.32878257e-04 -1.23336209e-05]\n",
-      " [-3.21308872e-05  3.34796358e-04 -2.67168875e-05 ...  9.08324813e-05\n",
-      "  -1.91209052e-05  2.48632306e-04]\n",
-      " [ 2.38367575e-04 -2.67168875e-05  3.27171503e-04 ...  1.16506221e-05\n",
-      "   9.07169083e-05 -5.61521671e-06]\n",
-      " ...\n",
-      " [ 5.05949548e-06  9.08324813e-05  1.16506221e-05 ...  3.22955902e-04\n",
-      "  -5.72176933e-06  2.42734406e-04]\n",
-      " [ 2.32878257e-04 -1.91209052e-05  9.07169083e-05 ... -5.72176933e-06\n",
-      "   3.20825996e-04 -1.09077518e-05]\n",
-      " [-1.23336209e-05  2.48632306e-04 -5.61521671e-06 ...  2.42734406e-04\n",
-      "  -1.09077518e-05  3.10099550e-04]]\n",
-      "  mean noise:  [-0.01496253 -0.01000563  0.02044623  0.03299079 -0.01348351 -0.00862086\n",
-      "  0.00486984 -0.05718525 -0.03443785  0.02946409  0.02114923 -0.0098432\n",
-      "  0.0272242   0.0377059  -0.00756294 -0.02549279  0.02196834  0.01533813\n",
-      " -0.00934527 -0.00734768 -0.02413014 -0.00429069  0.01669173 -0.00392493\n",
-      " -0.00166689  0.00464423  0.0065546  -0.0076975   0.01006162 -0.00242568\n",
-      " -0.00022235  0.00970345]\n",
-      "  cov noise:  [[ 2.88642040e-04 -3.21308872e-05  2.38367575e-04 ...  5.05949548e-06\n",
-      "   2.32878257e-04 -1.23336209e-05]\n",
-      " [-3.21308872e-05  3.34796358e-04 -2.67168875e-05 ...  9.08324813e-05\n",
-      "  -1.91209052e-05  2.48632306e-04]\n",
-      " [ 2.38367575e-04 -2.67168875e-05  3.27171503e-04 ...  1.16506221e-05\n",
-      "   9.07169083e-05 -5.61521671e-06]\n",
-      " ...\n",
-      " [ 5.05949548e-06  9.08324813e-05  1.16506221e-05 ...  3.22955902e-04\n",
-      "  -5.72176933e-06  2.42734406e-04]\n",
-      " [ 2.32878257e-04 -1.91209052e-05  9.07169083e-05 ... -5.72176933e-06\n",
-      "   3.20825996e-04 -1.09077518e-05]\n",
-      " [-1.23336209e-05  2.48632306e-04 -5.61521671e-06 ...  2.42734406e-04\n",
-      "  -1.09077518e-05  3.10099550e-04]]\n",
-      "  mean:  [ 0.99590802  0.01302387  0.92220041  0.39473603  0.70742057  0.72036766\n",
-      "  0.3844117   0.93972349  0.00196313  1.01814236 -0.38115698  0.94325552\n",
-      " -0.70638918  0.72708284 -0.92491996  0.40327705 -1.00409099  0.02190942\n",
-      " -0.9319753  -0.3587385  -0.71875737 -0.68115026 -0.39620698 -0.89710227\n",
-      " -0.01339855 -0.97425153  0.37092048 -0.90073672  0.69776312 -0.68756137\n",
-      "  0.91719325 -0.36688951]\n",
-      "  cov:  [[ 0.0504904  -0.00170855  0.04877921 ... -0.00061098  0.04977519\n",
-      "  -0.00119277]\n",
-      " [-0.00170855  0.0559685  -0.00192534 ...  0.05137628 -0.00138766\n",
-      "   0.05486771]\n",
-      " [ 0.04877921 -0.00192534  0.04946755 ... -0.00058182  0.04597377\n",
-      "  -0.00127668]\n",
-      " ...\n",
-      " [-0.00061098  0.05137628 -0.00058182 ...  0.05725949 -0.00048839\n",
-      "   0.0554911 ]\n",
-      " [ 0.04977519 -0.00138766  0.04597377 ... -0.00048839  0.0515448\n",
-      "  -0.00098156]\n",
-      " [-0.00119277  0.05486771 -0.00127668 ...  0.0554911  -0.00098156\n",
-      "   0.05657458]]\n",
-      "  mean w/ noise:  [ 9.96404092e-01  1.15995360e-02  9.20949835e-01  3.96697972e-01\n",
-      "  7.09285452e-01  7.21335670e-01  3.82308197e-01  9.41092406e-01\n",
-      "  8.96891029e-04  1.01820377e+00 -3.83385216e-01  9.41913145e-01\n",
-      " -7.05164816e-01  7.27798845e-01 -9.22871291e-01  4.02876021e-01\n",
-      " -1.00221080e+00  2.32765592e-02 -9.32687437e-01 -3.59473323e-01\n",
-      " -7.21454776e-01 -6.80349083e-01 -3.94613503e-01 -8.94616735e-01\n",
-      " -1.21140076e-02 -9.74575980e-01  3.69152882e-01 -9.00860976e-01\n",
-      "  6.99184394e-01 -6.87979848e-01  9.17291109e-01 -3.67831328e-01]\n",
-      "  cov w/ noise:  [[ 0.05240428 -0.00194722  0.0487552  ... -0.00105657  0.05023331\n",
-      "  -0.00166156]\n",
-      " [-0.00194722  0.05740064 -0.00158393 ...  0.0518396  -0.001223\n",
-      "   0.05517309]\n",
-      " [ 0.0487552  -0.00158393  0.04948974 ... -0.00056152  0.04546508\n",
-      "  -0.00125413]\n",
-      " ...\n",
-      " [-0.00105657  0.0518396  -0.00056152 ...  0.05827026 -0.00058976\n",
-      "   0.05558345]\n",
-      " [ 0.05023331 -0.001223    0.04546508 ... -0.00058976  0.05233293\n",
-      "  -0.00084797]\n",
-      " [-0.00166156  0.05517309 -0.00125413 ...  0.05558345 -0.00084797\n",
-      "   0.05757611]]\n",
-      "  mean conditional:  [ 1.40644382e+00 -4.41069766e-01  1.30727269e+00 -7.71038631e-02\n",
-      "  9.90655083e-01  2.77539986e-01  5.22789279e-01  5.71626383e-01\n",
-      "  5.64293163e-05  7.42813871e-01 -4.82511260e-01  7.49104054e-01\n",
-      " -8.55175889e-01  5.89302616e-01 -1.08184646e+00  2.92020690e-01\n",
-      " -1.15054587e+00 -1.00296825e-01 -1.06068350e+00 -5.29122018e-01\n",
-      " -8.20931252e-01 -9.18443091e-01 -4.52967255e-01 -1.19424203e+00\n",
-      "  5.21304262e-03 -1.31000210e+00  4.96573492e-01 -1.25865331e+00\n",
-      "  9.46887976e-01 -1.06789102e+00  1.27401231e+00 -7.81602583e-01] , sample:  [ 1.36935143 -0.39695144  1.37296166 -0.08164699  0.96392606  0.25778551\n",
-      "  0.52691816  0.54205624 -0.01604114  0.76907719 -0.48916347  0.72879808\n",
-      " -0.84557436  0.57717187 -1.09238592  0.32191344 -1.12795755 -0.05680395\n",
-      " -1.06161282 -0.5535415  -0.8083479  -0.94611138 -0.49827944 -1.18699863\n",
-      "  0.04674148 -1.33491101  0.4755689  -1.22389689  0.93688694 -1.08060224\n",
-      "  1.30447536 -0.79969639]\n",
-      "  cov conditional:  [[ 3.31139625e-04 -4.70213185e-07  2.74865122e-04 ...  1.08734494e-05\n",
-      "   2.65330780e-04  7.83634506e-07]\n",
-      " [-4.70213185e-07  3.15673787e-04 -1.00269925e-05 ...  1.03923605e-04\n",
-      "  -6.99630516e-06  2.44536429e-04]\n",
-      " [ 2.74865122e-04 -1.00269925e-05  3.56609955e-04 ... -2.97562127e-06\n",
-      "   1.30408742e-04 -1.07365010e-05]\n",
-      " ...\n",
-      " [ 1.08734494e-05  1.03923605e-04 -2.97562127e-06 ...  3.17183170e-04\n",
-      "   6.15070662e-06  2.47404508e-04]\n",
-      " [ 2.65330780e-04 -6.99630516e-06  1.30408742e-04 ...  6.15070662e-06\n",
-      "   3.32781772e-04 -7.41520256e-06]\n",
-      " [ 7.83634506e-07  2.44536429e-04 -1.07365010e-05 ...  2.47404508e-04\n",
-      "  -7.41520256e-06  3.09300608e-04]]\n",
-      "  mean noise:  [ 0.03709239 -0.04411832 -0.06568897  0.00454313  0.02672902  0.01975447\n",
-      " -0.00412888  0.02957015  0.01609757 -0.02626332  0.00665221  0.02030597\n",
-      " -0.00960153  0.01213075  0.01053946 -0.02989275 -0.02258832 -0.04349288\n",
-      "  0.00092932  0.02441948 -0.01258335  0.02766829  0.04531219 -0.0072434\n",
-      " -0.04152844  0.02490892  0.02100459 -0.03475641  0.01000104  0.01271122\n",
-      " -0.03046305  0.01809381]\n",
-      "  cov noise:  [[ 3.31139625e-04 -4.70213185e-07  2.74865122e-04 ...  1.08734494e-05\n",
-      "   2.65330780e-04  7.83634506e-07]\n",
-      " [-4.70213185e-07  3.15673787e-04 -1.00269925e-05 ...  1.03923605e-04\n",
-      "  -6.99630516e-06  2.44536429e-04]\n",
-      " [ 2.74865122e-04 -1.00269925e-05  3.56609955e-04 ... -2.97562127e-06\n",
-      "   1.30408742e-04 -1.07365010e-05]\n",
-      " ...\n",
-      " [ 1.08734494e-05  1.03923605e-04 -2.97562127e-06 ...  3.17183170e-04\n",
-      "   6.15070662e-06  2.47404508e-04]\n",
-      " [ 2.65330780e-04 -6.99630516e-06  1.30408742e-04 ...  6.15070662e-06\n",
-      "   3.32781772e-04 -7.41520256e-06]\n",
-      " [ 7.83634506e-07  2.44536429e-04 -1.07365010e-05 ...  2.47404508e-04\n",
-      "  -7.41520256e-06  3.09300608e-04]]\n",
-      "  mean:  [ 0.9986523   0.01347626  0.92579454  0.39699973  0.71187902  0.7234538\n",
-      "  0.38980874  0.94307709  0.00826928  1.02243643 -0.37418744  0.94907466\n",
-      " -0.6991184   0.73375364 -0.91750986  0.40899984 -0.99638391  0.02425514\n",
-      " -0.92375737 -0.36089068 -0.71013137 -0.68702009 -0.38794211 -0.9045338\n",
-      " -0.00663085 -0.9812521   0.37535783 -0.90654428  0.70036447 -0.69159651\n",
-      "  0.91932489 -0.36866493]\n",
-      "  cov:  [[0.05818029 0.00298504 0.05610307 ... 0.00222629 0.05766043 0.00277538]\n",
-      " [0.00298504 0.0598062  0.00283048 ... 0.05621999 0.00305739 0.05922154]\n",
-      " [0.05610307 0.00283048 0.05669695 ... 0.00222276 0.05341802 0.00268351]\n",
-      " ...\n",
-      " [0.00222629 0.05621999 0.00222276 ... 0.06178772 0.00228507 0.06026208]\n",
-      " [0.05766043 0.00305739 0.05341802 ... 0.00228507 0.0594973  0.00284381]\n",
-      " [0.00277538 0.05922154 0.00268351 ... 0.06026208 0.00284381 0.06118455]]\n",
-      "  mean w/ noise:  [ 0.99879273  0.01093405  0.92775052  0.39871308  0.71000797  0.72265367\n",
-      "  0.38798916  0.94133065  0.00685578  1.02271549 -0.37315786  0.94948334\n",
-      " -0.69863017  0.73005004 -0.91803193  0.40837967 -0.99603221  0.02312004\n",
-      " -0.92445824 -0.36112442 -0.70912309 -0.68718927 -0.38745374 -0.9043035\n",
-      " -0.00937179 -0.98199277  0.37430785 -0.9050295   0.6994587  -0.69080046\n",
-      "  0.9196724  -0.36829163]\n",
-      "  cov w/ noise:  [[0.05925036 0.00276244 0.05652786 ... 0.00257288 0.05769395 0.00311268]\n",
-      " [0.00276244 0.0610199  0.00265804 ... 0.05576201 0.0031793  0.0588592 ]\n",
-      " [0.05652786 0.00265804 0.05822111 ... 0.00238124 0.05372149 0.0029245 ]\n",
-      " ...\n",
-      " [0.00257288 0.05576201 0.00238124 ... 0.06222119 0.00272246 0.05930177]\n",
-      " [0.05769395 0.0031793  0.05372149 ... 0.00272246 0.06056483 0.00337343]\n",
-      " [0.00311268 0.0588592  0.0029245  ... 0.05930177 0.00337343 0.06116174]]\n",
-      "  mean conditional:  [ 1.08863768 -0.23963754  1.00234129  0.18966542  0.74997771  0.60754237\n",
-      "  0.3791009   0.9276113  -0.03546599  1.06311001 -0.41902242  0.97627344\n",
-      " -0.71997093  0.70207572 -0.91166053  0.31826348 -0.97696314 -0.10144252\n",
-      " -0.90222099 -0.50232237 -0.69022963 -0.83861461 -0.36788265 -1.06940131\n",
-      "  0.01924874 -1.16444773  0.41520405 -1.11241416  0.76083428 -0.92373915\n",
-      "  1.0003061  -0.62284856] , sample:  [ 1.05911542 -0.23370156  1.03795979  0.17570713  0.74397667  0.5951812\n",
-      "  0.38676428  0.92130767 -0.06715713  1.10858908 -0.43269558  0.99737832\n",
-      " -0.68283792  0.65360698 -0.89281054  0.32571993 -0.96889217 -0.1052509\n",
-      " -0.94085758 -0.48364355 -0.67228635 -0.88053816 -0.37941686 -1.04212049\n",
-      "  0.03936064 -1.15499717  0.41060878 -1.12127719  0.77245043 -0.92745098\n",
-      "  0.98157718 -0.62192945]\n",
-      "  cov conditional:  [[ 3.42124279e-04 -1.32547941e-05  2.81694453e-04 ... -1.07026158e-05\n",
-      "   2.63310143e-04 -1.40824027e-05]\n",
-      " [-1.32547941e-05  3.14280107e-04 -1.69437708e-05 ...  8.63729554e-05\n",
-      "  -1.29234222e-05  2.38305548e-04]\n",
-      " [ 2.81694453e-04 -1.69437708e-05  3.52358571e-04 ... -1.74278655e-05\n",
-      "   1.21775066e-04 -1.80380747e-05]\n",
-      " ...\n",
-      " [-1.07026158e-05  8.63729554e-05 -1.74278655e-05 ...  3.23825846e-04\n",
-      "  -2.02293949e-06  2.41895043e-04]\n",
-      " [ 2.63310143e-04 -1.29234222e-05  1.21775066e-04 ... -2.02293949e-06\n",
-      "   3.31236981e-04 -1.06679120e-05]\n",
-      " [-1.40824027e-05  2.38305548e-04 -1.80380747e-05 ...  2.41895043e-04\n",
-      "  -1.06679120e-05  3.06678793e-04]]\n",
-      "  mean noise:  [ 0.02952226 -0.00593598 -0.0356185   0.01395828  0.00600104  0.01236118\n",
-      " -0.00766338  0.00630363  0.03169114 -0.04547906  0.01367316 -0.02110487\n",
-      " -0.03713301  0.04846875 -0.01884999 -0.00745645 -0.00807097  0.00380838\n",
-      "  0.03863659 -0.01867882 -0.01794328  0.04192354  0.01153422 -0.02728081\n",
-      " -0.0201119  -0.00945056  0.00459527  0.00886303 -0.01161615  0.00371183\n",
-      "  0.01872892 -0.00091911]\n",
-      "  cov noise:  [[ 3.42124279e-04 -1.32547941e-05  2.81694453e-04 ... -1.07026158e-05\n",
-      "   2.63310143e-04 -1.40824027e-05]\n",
-      " [-1.32547941e-05  3.14280107e-04 -1.69437708e-05 ...  8.63729554e-05\n",
-      "  -1.29234222e-05  2.38305548e-04]\n",
-      " [ 2.81694453e-04 -1.69437708e-05  3.52358571e-04 ... -1.74278655e-05\n",
-      "   1.21775066e-04 -1.80380747e-05]\n",
-      " ...\n",
-      " [-1.07026158e-05  8.63729554e-05 -1.74278655e-05 ...  3.23825846e-04\n",
-      "  -2.02293949e-06  2.41895043e-04]\n",
-      " [ 2.63310143e-04 -1.29234222e-05  1.21775066e-04 ... -2.02293949e-06\n",
-      "   3.31236981e-04 -1.06679120e-05]\n",
-      " [-1.40824027e-05  2.38305548e-04 -1.80380747e-05 ...  2.41895043e-04\n",
-      "  -1.06679120e-05  3.06678793e-04]]\n",
-      "  mean:  [ 0.98437707  0.00421871  0.90954835  0.38675885  0.6948441   0.71157928\n",
-      "  0.37280887  0.92844345 -0.00756612  1.00398784 -0.388357    0.92707177\n",
-      " -0.71122045  0.7098081  -0.92670088  0.3860168  -1.00157349  0.00508565\n",
-      " -0.92463293 -0.37568424 -0.70856184 -0.69835146 -0.38694735 -0.91396355\n",
-      " -0.00851904 -0.99012311  0.37015914 -0.9148583   0.69215463 -0.69964714\n",
-      "  0.90800775 -0.37716243]\n",
-      "  cov:  [[ 0.05682197 -0.00018807  0.05466839 ...  0.00215037  0.05598708\n",
-      "   0.00100782]\n",
-      " [-0.00018807  0.05121032  0.00054568 ...  0.04729477 -0.00085245\n",
-      "   0.05042205]\n",
-      " [ 0.05466839  0.00054568  0.05492659 ...  0.00259767  0.05181341\n",
-      "   0.00161588]\n",
-      " ...\n",
-      " [ 0.00215037  0.04729477  0.00259767 ...  0.05246258  0.00172047\n",
-      "   0.05104461]\n",
-      " [ 0.05598708 -0.00085245  0.05181341 ...  0.00172047  0.05759265\n",
-      "   0.00043077]\n",
-      " [ 0.00100782  0.05042205  0.00161588 ...  0.05104461  0.00043077\n",
-      "   0.05208867]]\n",
-      "  mean w/ noise:  [ 0.985693    0.00133714  0.90880712  0.38613036  0.69476597  0.71127202\n",
-      "  0.37481616  0.92942778 -0.00789514  1.0052408  -0.38660291  0.92754664\n",
-      " -0.71030134  0.70977215 -0.92839533  0.38970747 -1.00336095  0.0021592\n",
-      " -0.92384821 -0.37628078 -0.70974012 -0.70028221 -0.38951482 -0.91443844\n",
-      " -0.01036383 -0.98928266  0.37139937 -0.91656342  0.69272478 -0.70034852\n",
-      "  0.9072581  -0.3749853 ]\n",
-      "  cov w/ noise:  [[ 5.78265261e-02 -1.09211465e-05  5.44319827e-02 ...  2.17849638e-03\n",
-      "   5.63857585e-02  1.87082604e-03]\n",
-      " [-1.09211465e-05  5.25577382e-02  1.85421982e-05 ...  4.72546627e-02\n",
-      "  -1.37017055e-03  5.07660580e-02]\n",
-      " [ 5.44319827e-02  1.85421982e-05  5.51637364e-02 ...  1.94691294e-03\n",
-      "   5.17649963e-02  1.79688750e-03]\n",
-      " ...\n",
-      " [ 2.17849638e-03  4.72546627e-02  1.94691294e-03 ...  5.34503437e-02\n",
-      "   1.31178012e-03  5.16110862e-02]\n",
-      " [ 5.63857585e-02 -1.37017055e-03  5.17649963e-02 ...  1.31178012e-03\n",
-      "   5.92122934e-02  8.50963516e-04]\n",
-      " [ 1.87082604e-03  5.07660580e-02  1.79688750e-03 ...  5.16110862e-02\n",
-      "   8.50963516e-04  5.40177617e-02]]\n",
-      "  mean conditional:  [ 0.81232727 -0.08796147  0.67615559  0.27586006  0.41686395  0.56494941\n",
-      "  0.07888454  0.73994972 -0.27783634  0.77524748 -0.5909185   0.66354959\n",
-      " -0.81853312  0.41771208 -0.9444314   0.06777338 -0.96123772 -0.34022929\n",
-      " -0.85905585 -0.74218691 -0.63754401 -1.06539185 -0.32428386 -1.25037174\n",
-      "  0.02977167 -1.26780717  0.36950572 -1.12114059  0.64252411 -0.8415086\n",
-      "  0.80045154 -0.47835378] , sample:  [ 0.81204212 -0.09071977  0.65289497  0.28141369  0.42600697  0.5876714\n",
-      "  0.12985756  0.71892715 -0.33981461  0.77384274 -0.58581084  0.65530243\n",
-      " -0.81750737  0.42511612 -0.92868641  0.11303925 -0.97609031 -0.39548287\n",
-      " -0.86088319 -0.71794535 -0.65924505 -1.06553651 -0.28055467 -1.27695751\n",
-      "  0.00971312 -1.2510516   0.36717969 -1.13165411  0.65368914 -0.81334936\n",
-      "  0.80373455 -0.49406696]\n",
-      "  cov conditional:  [[ 3.19790646e-04  1.92776264e-05  2.42897989e-04 ... -1.04081157e-05\n",
-      "   2.70518020e-04  3.72363663e-06]\n",
-      " [ 1.92776264e-05  3.58779883e-04  2.79480354e-05 ...  1.32968490e-04\n",
-      "   1.51978509e-05  2.87331931e-04]\n",
-      " [ 2.42897989e-04  2.79480354e-05  3.12744085e-04 ... -2.22795825e-05\n",
-      "   1.15027421e-04  1.87176993e-06]\n",
-      " ...\n",
-      " [-1.04081157e-05  1.32968490e-04 -2.22795825e-05 ...  3.50858052e-04\n",
-      "  -1.10094216e-05  2.85064896e-04]\n",
-      " [ 2.70518020e-04  1.51978509e-05  1.15027421e-04 ... -1.10094216e-05\n",
-      "   3.50483590e-04  1.87126853e-06]\n",
-      " [ 3.72363663e-06  2.87331931e-04  1.87176993e-06 ...  2.85064896e-04\n",
-      "   1.87126853e-06  3.58816962e-04]]\n",
-      "  mean noise:  [ 0.00028515  0.0027583   0.02326062 -0.00555363 -0.00914302 -0.02272199\n",
-      " -0.05097303  0.02102256  0.06197828  0.00140475 -0.00510766  0.00824716\n",
-      " -0.00102575 -0.00740404 -0.01574499 -0.04526587  0.01485259  0.05525357\n",
-      "  0.00182734 -0.02424156  0.02170104  0.00014466 -0.0437292   0.02658577\n",
-      "  0.02005855 -0.01675557  0.00232603  0.01051352 -0.01116503 -0.02815924\n",
-      " -0.00328301  0.01571318]\n",
-      "  cov noise:  [[ 3.19790646e-04  1.92776264e-05  2.42897989e-04 ... -1.04081157e-05\n",
-      "   2.70518020e-04  3.72363663e-06]\n",
-      " [ 1.92776264e-05  3.58779883e-04  2.79480354e-05 ...  1.32968490e-04\n",
-      "   1.51978509e-05  2.87331931e-04]\n",
-      " [ 2.42897989e-04  2.79480354e-05  3.12744085e-04 ... -2.22795825e-05\n",
-      "   1.15027421e-04  1.87176993e-06]\n",
-      " ...\n",
-      " [-1.04081157e-05  1.32968490e-04 -2.22795825e-05 ...  3.50858052e-04\n",
-      "  -1.10094216e-05  2.85064896e-04]\n",
-      " [ 2.70518020e-04  1.51978509e-05  1.15027421e-04 ... -1.10094216e-05\n",
-      "   3.50483590e-04  1.87126853e-06]\n",
-      " [ 3.72363663e-06  2.87331931e-04  1.87176993e-06 ...  2.85064896e-04\n",
-      "   1.87126853e-06  3.58816962e-04]]\n",
-      "  mean:  [ 0.9901696   0.01758502  0.91396273  0.39906961  0.70011526  0.72210857\n",
-      "  0.38040047  0.93715271  0.00225716  1.01143346 -0.37724282  0.93408175\n",
-      " -0.69971175  0.71693285 -0.91556116  0.39310209 -0.99156391  0.01245234\n",
-      " -0.91574861 -0.36748521 -0.70025092 -0.68934184 -0.37828972 -0.90461782\n",
-      "  0.00132235 -0.9802477   0.38092485 -0.90372556  0.70242711 -0.68707319\n",
-      "  0.9163425  -0.36372028]\n",
-      "  cov:  [[0.05532542 0.00019487 0.05343204 ... 0.00186115 0.0543129  0.00104921]\n",
-      " [0.00019487 0.05189168 0.0003114  ... 0.04794079 0.00014932 0.05108393]\n",
-      " [0.05343204 0.0003114  0.05404312 ... 0.00179575 0.05034561 0.00104399]\n",
-      " ...\n",
-      " [0.00186115 0.04794079 0.00179575 ... 0.05329338 0.00189158 0.0517223 ]\n",
-      " [0.0543129  0.00014932 0.05034561 ... 0.00189158 0.05579744 0.0010469 ]\n",
-      " [0.00104921 0.05108393 0.00104399 ... 0.0517223  0.0010469  0.05272429]]\n",
-      "  mean w/ noise:  [ 9.91896610e-01  1.81493894e-02  9.16060820e-01  3.99297553e-01\n",
-      "  6.98343944e-01  7.23037880e-01  3.79768222e-01  9.35190114e-01\n",
-      "  1.93921095e-03  1.01112544e+00 -3.77422211e-01  9.33573572e-01\n",
-      " -7.01147862e-01  7.17303195e-01 -9.15711827e-01  3.93826619e-01\n",
-      " -9.91756851e-01  1.49449847e-02 -9.16359432e-01 -3.67781812e-01\n",
-      " -7.00776503e-01 -6.87583294e-01 -3.75067655e-01 -9.06108069e-01\n",
-      "  5.69981299e-04 -9.82043913e-01  3.80614905e-01 -9.05144544e-01\n",
-      "  7.02533681e-01 -6.87044405e-01  9.18291119e-01 -3.62218189e-01]\n",
-      "  cov w/ noise:  [[0.05578725 0.00036032 0.05361885 ... 0.00195046 0.05435045 0.00114467]\n",
-      " [0.00036032 0.05186901 0.00049419 ... 0.04823773 0.00013502 0.04980583]\n",
-      " [0.05361885 0.00049419 0.05588269 ... 0.00190153 0.05129588 0.00114908]\n",
-      " ...\n",
-      " [0.00195046 0.04823773 0.00190153 ... 0.05594056 0.00157211 0.0517343 ]\n",
-      " [0.05435045 0.00013502 0.05129588 ... 0.00157211 0.05720194 0.00091428]\n",
-      " [0.00114467 0.04980583 0.00114908 ... 0.0517343  0.00091428 0.05238527]]\n",
-      "  mean conditional:  [ 0.98932331 -0.41990883  0.872073   -0.06569647  0.63428379  0.23179654\n",
-      "  0.33603924  0.42366186  0.03657218  0.47091013 -0.22287759  0.36313878\n",
-      " -0.42821794  0.12279798 -0.58083207 -0.21107901 -0.67117191 -0.59435391\n",
-      " -0.66813098 -0.96976788 -0.53985912 -1.27108055 -0.28619485 -1.44717788\n",
-      "  0.05434391 -1.47500102  0.42054754 -1.35587303  0.74104485 -1.11204463\n",
-      "  0.94564127 -0.78407779] , sample:  [ 1.00078257 -0.40260388  0.85819445 -0.07512738  0.63817482  0.1934968\n",
-      "  0.32724661  0.43472603  0.02015065  0.51505536 -0.22917737  0.34657221\n",
-      " -0.39081579  0.0687414  -0.58305225 -0.18263958 -0.65092006 -0.53911631\n",
-      " -0.68423334 -1.03081098 -0.557441   -1.30499707 -0.28575846 -1.3880553\n",
-      "  0.07171874 -1.48872166  0.38914579 -1.3579913   0.74914265 -1.11244986\n",
-      "  0.9717711  -0.78082069]\n",
-      "  cov conditional:  [[ 3.55872490e-04  1.52274362e-05  2.86974478e-04 ... -6.89747366e-06\n",
-      "   2.79217709e-04  3.91183777e-06]\n",
-      " [ 1.52274362e-05  3.13915508e-04  7.19385084e-06 ...  1.03829363e-04\n",
-      "   1.64634112e-05  2.44712717e-04]\n",
-      " [ 2.86974478e-04  7.19385084e-06  3.43173702e-04 ... -1.41396396e-05\n",
-      "   1.40103702e-04 -4.08859288e-06]\n",
-      " ...\n",
-      " [-6.89747366e-06  1.03829363e-04 -1.41396396e-05 ...  3.07481188e-04\n",
-      "  -6.31578657e-06  2.43212360e-04]\n",
-      " [ 2.79217709e-04  1.64634112e-05  1.40103702e-04 ... -6.31578657e-06\n",
-      "   3.42368329e-04  4.32135002e-06]\n",
-      " [ 3.91183777e-06  2.44712717e-04 -4.08859288e-06 ...  2.43212360e-04\n",
-      "   4.32135002e-06  3.11714422e-04]]\n",
-      "  mean noise:  [-0.01145926 -0.01730496  0.01387855  0.00943091 -0.00389103  0.03829974\n",
-      "  0.00879263 -0.01106417  0.01642153 -0.04414524  0.00629978  0.01656657\n",
-      " -0.03740216  0.05405658  0.00222018 -0.02843943 -0.02025186 -0.0552376\n",
-      "  0.01610235  0.06104309  0.01758187  0.03391653 -0.00043639 -0.05912258\n",
-      " -0.01737482  0.01372064  0.03140174  0.00211827 -0.00809781  0.00040523\n",
-      " -0.02612982 -0.0032571 ]\n",
-      "  cov noise:  [[ 3.55872490e-04  1.52274362e-05  2.86974478e-04 ... -6.89747366e-06\n",
-      "   2.79217709e-04  3.91183777e-06]\n",
-      " [ 1.52274362e-05  3.13915508e-04  7.19385084e-06 ...  1.03829363e-04\n",
-      "   1.64634112e-05  2.44712717e-04]\n",
-      " [ 2.86974478e-04  7.19385084e-06  3.43173702e-04 ... -1.41396396e-05\n",
-      "   1.40103702e-04 -4.08859288e-06]\n",
-      " ...\n",
-      " [-6.89747366e-06  1.03829363e-04 -1.41396396e-05 ...  3.07481188e-04\n",
-      "  -6.31578657e-06  2.43212360e-04]\n",
-      " [ 2.79217709e-04  1.64634112e-05  1.40103702e-04 ... -6.31578657e-06\n",
-      "   3.42368329e-04  4.32135002e-06]\n",
-      " [ 3.91183777e-06  2.44712717e-04 -4.08859288e-06 ...  2.43212360e-04\n",
-      "   4.32135002e-06  3.11714422e-04]]\n",
-      "  mean:  [ 9.86880701e-01  9.32264201e-04  9.10727064e-01  3.84942745e-01\n",
-      "  6.96368681e-01  7.10210137e-01  3.75542228e-01  9.26813585e-01\n",
-      " -3.59825561e-03  1.00193357e+00 -3.83585465e-01  9.24624323e-01\n",
-      " -7.06294545e-01  7.06986869e-01 -9.21866532e-01  3.82156854e-01\n",
-      " -9.96454423e-01 -5.14554634e-04 -9.19151765e-01 -3.83263364e-01\n",
-      " -7.03015663e-01 -7.08253354e-01 -3.80980808e-01 -9.25727295e-01\n",
-      " -1.31129518e-03 -1.00208250e+00  3.78158777e-01 -9.25440016e-01\n",
-      "  6.99302498e-01 -7.07729533e-01  9.13032614e-01 -3.82606258e-01]\n",
-      "  cov:  [[ 0.06161044 -0.00210413  0.05954335 ... -0.00099509  0.06055644\n",
-      "  -0.00156739]\n",
-      " [-0.00210413  0.05651886 -0.00147608 ...  0.05214224 -0.0024104\n",
-      "   0.05543716]\n",
-      " [ 0.05954335 -0.00147608  0.05994975 ... -0.00037171  0.05649736\n",
-      "  -0.00096356]\n",
-      " ...\n",
-      " [-0.00099509  0.05214224 -0.00037171 ...  0.05768247 -0.00139888\n",
-      "   0.05605203]\n",
-      " [ 0.06055644 -0.0024104   0.05649736 ... -0.00139888  0.06189581\n",
-      "  -0.00190134]\n",
-      " [-0.00156739  0.05543716 -0.00096356 ...  0.05605203 -0.00190134\n",
-      "   0.05704657]]\n",
-      "  mean w/ noise:  [ 9.85197147e-01  3.66145547e-03  9.12443559e-01  3.85111321e-01\n",
-      "  6.95639460e-01  7.10481028e-01  3.76658741e-01  9.25893397e-01\n",
-      "  1.15597246e-03  1.00073998e+00 -3.84225671e-01  9.23444143e-01\n",
-      " -7.06388513e-01  7.07496235e-01 -9.22868678e-01  3.81691784e-01\n",
-      " -9.95412900e-01  3.35793359e-04 -9.19183214e-01 -3.81482383e-01\n",
-      " -7.04007328e-01 -7.09511292e-01 -3.80827160e-01 -9.25757686e-01\n",
-      "  5.52401779e-04 -1.00297241e+00  3.77631119e-01 -9.27009065e-01\n",
-      "  6.99132005e-01 -7.06068850e-01  9.13366330e-01 -3.82413919e-01]\n",
-      "  cov w/ noise:  [[ 0.06252554 -0.00135893  0.05912695 ... -0.00113942  0.06008682\n",
-      "  -0.00182542]\n",
-      " [-0.00135893  0.05602838 -0.00016967 ...  0.05100662 -0.00161825\n",
-      "   0.05511939]\n",
-      " [ 0.05912695 -0.00016967  0.06019676 ...  0.00052338  0.05584334\n",
-      "  -0.00033772]\n",
-      " ...\n",
-      " [-0.00113942  0.05100662  0.00052338 ...  0.05826831 -0.00117081\n",
-      "   0.05611202]\n",
-      " [ 0.06008682 -0.00161825  0.05584334 ... -0.00117081  0.06176075\n",
-      "  -0.00201436]\n",
-      " [-0.00182542  0.05511939 -0.00033772 ...  0.05611202 -0.00201436\n",
-      "   0.05870946]]\n",
-      "  mean conditional:  [ 6.14717447e-01 -3.27148693e-01  5.14177323e-01  5.18640566e-02\n",
-      "  3.17656524e-01  3.64253949e-01  6.65520510e-02  5.59536214e-01\n",
-      " -1.86340602e-01  6.07124694e-01 -3.94389657e-01  5.03176868e-01\n",
-      " -5.37104666e-01  2.68653951e-01 -6.18892990e-01 -6.05075982e-02\n",
-      " -6.43870692e-01 -4.40771935e-01 -5.98914817e-01 -8.18047799e-01\n",
-      " -4.67625383e-01 -1.13158600e+00 -2.56804733e-01 -1.33080317e+00\n",
-      " -8.07873856e-04 -1.38552957e+00  2.55795921e-01 -1.28731154e+00\n",
-      "  4.69097301e-01 -1.05114694e+00  5.98188896e-01 -7.13987525e-01] , sample:  [ 0.62639001 -0.35233312  0.49824373  0.043141    0.33842798  0.41117007\n",
-      "  0.06114567  0.53287656 -0.19013768  0.5906701  -0.41522848  0.53844123\n",
-      " -0.54397105  0.24179592 -0.5633229  -0.04489772 -0.65047936 -0.44066804\n",
-      " -0.62090181 -0.83362773 -0.45848328 -1.13089595 -0.2613829  -1.31856515\n",
-      "  0.01324939 -1.38120711  0.24897835 -1.30258374  0.47757624 -1.04543416\n",
-      "  0.56867871 -0.69691769]\n",
-      "  cov conditional:  [[ 3.25887833e-04  1.45672500e-05  2.51439643e-04 ...  9.48202736e-07\n",
-      "   2.58243478e-04  7.50777291e-06]\n",
-      " [ 1.45672500e-05  3.15410484e-04  9.71436497e-06 ...  1.15283614e-04\n",
-      "   2.26167001e-05  2.49715927e-04]\n",
-      " [ 2.51439643e-04  9.71436497e-06  3.24190823e-04 ... -1.45909907e-05\n",
-      "   9.46140784e-05 -2.06616183e-06]\n",
-      " ...\n",
-      " [ 9.48202736e-07  1.15283614e-04 -1.45909907e-05 ...  3.25913705e-04\n",
-      "   1.96489384e-05  2.56595976e-04]\n",
-      " [ 2.58243478e-04  2.26167001e-05  9.46140784e-05 ...  1.96489384e-05\n",
-      "   3.38124926e-04  2.28424107e-05]\n",
-      " [ 7.50777291e-06  2.49715927e-04 -2.06616183e-06 ...  2.56595976e-04\n",
-      "   2.28424107e-05  3.18823241e-04]]\n",
-      "  mean noise:  [-0.01167256  0.02518443  0.01593359  0.00872306 -0.02077146 -0.04691613\n",
-      "  0.00540638  0.02665966  0.00379708  0.01645459  0.02083883 -0.03526436\n",
-      "  0.00686638  0.02685804 -0.05557009 -0.01560988  0.00660867 -0.0001039\n",
-      "  0.02198699  0.01557993 -0.0091421  -0.00069005  0.00457817 -0.01223802\n",
-      " -0.01405727 -0.00432246  0.00681757  0.01527219 -0.00847893 -0.00571278\n",
-      "  0.02951019 -0.01706983]\n",
-      "  cov noise:  [[ 3.25887833e-04  1.45672500e-05  2.51439643e-04 ...  9.48202736e-07\n",
-      "   2.58243478e-04  7.50777291e-06]\n",
-      " [ 1.45672500e-05  3.15410484e-04  9.71436497e-06 ...  1.15283614e-04\n",
-      "   2.26167001e-05  2.49715927e-04]\n",
-      " [ 2.51439643e-04  9.71436497e-06  3.24190823e-04 ... -1.45909907e-05\n",
-      "   9.46140784e-05 -2.06616183e-06]\n",
-      " ...\n",
-      " [ 9.48202736e-07  1.15283614e-04 -1.45909907e-05 ...  3.25913705e-04\n",
-      "   1.96489384e-05  2.56595976e-04]\n",
-      " [ 2.58243478e-04  2.26167001e-05  9.46140784e-05 ...  1.96489384e-05\n",
-      "   3.38124926e-04  2.28424107e-05]\n",
-      " [ 7.50777291e-06  2.49715927e-04 -2.06616183e-06 ...  2.56595976e-04\n",
-      "   2.28424107e-05  3.18823241e-04]]\n",
-      "  mean:  [ 0.99078048  0.01112108  0.9180149   0.39307391  0.70670363  0.71573225\n",
-      "  0.38797041  0.93044309  0.00872331  1.00465525 -0.37381839  0.92656945\n",
-      " -0.7001608   0.70778667 -0.91957554  0.38169886 -0.99862413 -0.00194027\n",
-      " -0.92591234 -0.38461207 -0.71276203 -0.70754429 -0.3908127  -0.92162611\n",
-      " -0.00932922 -0.99475642  0.37253746 -0.91597251  0.69649057 -0.69724665\n",
-      "  0.91343114 -0.37172408]\n",
-      "  cov:  [[ 5.80271590e-02 -1.49523992e-03  5.59849969e-02 ...  4.36257505e-04\n",
-      "   5.69644886e-02 -6.28290781e-04]\n",
-      " [-1.49523992e-03  5.81028575e-02 -4.86068315e-04 ...  5.32944200e-02\n",
-      "  -2.46568555e-03  5.70439370e-02]\n",
-      " [ 5.59849969e-02 -4.86068315e-04  5.64597992e-02 ...  8.01401886e-04\n",
-      "   5.28416496e-02  8.66884686e-05]\n",
-      " ...\n",
-      " [ 4.36257505e-04  5.32944200e-02  8.01401886e-04 ...  5.80616901e-02\n",
-      "  -2.47126060e-04  5.69491974e-02]\n",
-      " [ 5.69644886e-02 -2.46568555e-03  5.28416496e-02 ... -2.47126060e-04\n",
-      "   5.83664224e-02 -1.45546944e-03]\n",
-      " [-6.28290781e-04  5.70439370e-02  8.66884686e-05 ...  5.69491974e-02\n",
-      "  -1.45546944e-03  5.85040087e-02]]\n",
-      "  mean w/ noise:  [ 9.90254391e-01  1.00721013e-02  9.19354255e-01  3.90170136e-01\n",
-      "  7.07804210e-01  7.18641421e-01  3.85756388e-01  9.30946353e-01\n",
-      "  8.51498159e-03  1.00421165e+00 -3.73799768e-01  9.25785813e-01\n",
-      " -7.01486442e-01  7.07202717e-01 -9.20360104e-01  3.81967560e-01\n",
-      " -9.99626172e-01  1.03245801e-04 -9.23920232e-01 -3.84182924e-01\n",
-      " -7.14515193e-01 -7.08650900e-01 -3.91175304e-01 -9.23871612e-01\n",
-      " -9.57352003e-03 -9.95826628e-01  3.72016038e-01 -9.14610372e-01\n",
-      "  6.96261538e-01 -6.98506144e-01  9.12348923e-01 -3.74813217e-01]\n",
-      "  cov w/ noise:  [[ 0.05924249 -0.00146711  0.05588576 ...  0.00025372  0.05731878\n",
-      "  -0.00057983]\n",
-      " [-0.00146711  0.05921582 -0.00016304 ...  0.05296781 -0.0025458\n",
-      "   0.05648177]\n",
-      " [ 0.05588576 -0.00016304  0.05749636 ...  0.00082448  0.05279732\n",
-      "   0.00036902]\n",
-      " ...\n",
-      " [ 0.00025372  0.05296781  0.00082448 ...  0.05857112 -0.00075511\n",
-      "   0.05610195]\n",
-      " [ 0.05731878 -0.0025458   0.05279732 ... -0.00075511  0.05982513\n",
-      "  -0.00160721]\n",
-      " [-0.00057983  0.05648177  0.00036902 ...  0.05610195 -0.00160721\n",
-      "   0.05825356]]\n",
-      "  mean conditional:  [ 0.89438434 -0.54682997  0.80247235 -0.1431621   0.60455813  0.18040193\n",
-      "  0.33653364  0.3743859   0.04076188  0.40850663 -0.23762528  0.28131235\n",
-      " -0.46203761  0.02095501 -0.61110573 -0.32859511 -0.67049974 -0.7222355\n",
-      " -0.6240952  -1.11161226 -0.46362364 -1.43995303 -0.20642306 -1.65338049\n",
-      "  0.10538319 -1.71375039  0.42083525 -1.60585203  0.68754278 -1.34338396\n",
-      "  0.85590821 -0.96946446] , sample:  [ 0.89624632 -0.49667494  0.78519383 -0.18100859  0.60816418  0.21219064\n",
-      "  0.32444274  0.33926725  0.0713706   0.43014263 -0.23373638  0.30078198\n",
-      " -0.48622894  0.01042504 -0.60037522 -0.37752512 -0.6632077  -0.67487272\n",
-      " -0.62580226 -1.11967846 -0.47561137 -1.4500701  -0.18579386 -1.62706994\n",
-      "  0.09285592 -1.74439164  0.41307007 -1.59312718  0.70110172 -1.34998391\n",
-      "  0.85591527 -0.99973376]\n",
-      "  cov conditional:  [[ 3.07366075e-04  5.88484438e-08  2.42696250e-04 ...  4.57810660e-06\n",
-      "   2.38551207e-04  1.51840023e-06]\n",
-      " [ 5.88484438e-08  3.44341314e-04 -2.98026734e-07 ...  1.21974456e-04\n",
-      "  -6.39047436e-06  2.71432125e-04]\n",
-      " [ 2.42696250e-04 -2.98026734e-07  3.14565550e-04 ...  2.53279273e-05\n",
-      "   9.57508535e-05  1.17444554e-05]\n",
-      " ...\n",
-      " [ 4.57810660e-06  1.21974456e-04  2.53279273e-05 ...  3.08403661e-04\n",
-      "  -1.32523253e-05  2.59036787e-04]\n",
-      " [ 2.38551207e-04 -6.39047436e-06  9.57508535e-05 ... -1.32523253e-05\n",
-      "   3.11702256e-04 -1.05836546e-05]\n",
-      " [ 1.51840023e-06  2.71432125e-04  1.17444554e-05 ...  2.59036787e-04\n",
-      "  -1.05836546e-05  3.38825111e-04]]\n",
-      "  mean noise:  [-1.86198006e-03 -5.01550271e-02  1.72785159e-02  3.78464867e-02\n",
-      " -3.60605128e-03 -3.17887068e-02  1.20908970e-02  3.51186524e-02\n",
-      " -3.06087194e-02 -2.16359921e-02 -3.88890356e-03 -1.94696274e-02\n",
-      "  2.41913215e-02  1.05299726e-02 -1.07305159e-02  4.89300089e-02\n",
-      " -7.29203399e-03 -4.73627818e-02  1.70706368e-03  8.06620267e-03\n",
-      "  1.19877264e-02  1.01170677e-02 -2.06292041e-02 -2.63105475e-02\n",
-      "  1.25272679e-02  3.06412491e-02  7.76518618e-03 -1.27248544e-02\n",
-      " -1.35589362e-02  6.59994537e-03 -7.05254090e-06  3.02692990e-02]\n",
-      "  cov noise:  [[ 3.07366075e-04  5.88484438e-08  2.42696250e-04 ...  4.57810660e-06\n",
-      "   2.38551207e-04  1.51840023e-06]\n",
-      " [ 5.88484438e-08  3.44341314e-04 -2.98026734e-07 ...  1.21974456e-04\n",
-      "  -6.39047436e-06  2.71432125e-04]\n",
-      " [ 2.42696250e-04 -2.98026734e-07  3.14565550e-04 ...  2.53279273e-05\n",
-      "   9.57508535e-05  1.17444554e-05]\n",
-      " ...\n",
-      " [ 4.57810660e-06  1.21974456e-04  2.53279273e-05 ...  3.08403661e-04\n",
-      "  -1.32523253e-05  2.59036787e-04]\n",
-      " [ 2.38551207e-04 -6.39047436e-06  9.57508535e-05 ... -1.32523253e-05\n",
-      "   3.11702256e-04 -1.05836546e-05]\n",
-      " [ 1.51840023e-06  2.71432125e-04  1.17444554e-05 ...  2.59036787e-04\n",
-      "  -1.05836546e-05  3.38825111e-04]]\n",
-      "  mean:  [ 0.99117053  0.00977772  0.91354377  0.39192281  0.69738552  0.71568522\n",
-      "  0.37506719  0.93132016 -0.00522885  1.00584835 -0.38594534  0.9283336\n",
-      " -0.70850037  0.71092313 -0.92359863  0.38667065 -0.99804533  0.00503366\n",
-      " -0.92008665 -0.37587922 -0.70251384 -0.69825577 -0.37898568 -0.9133569\n",
-      "  0.00161814 -0.98856163  0.38175346 -0.91209841  0.70368681 -0.69561725\n",
-      "  0.91781651 -0.37208197]\n",
-      "  cov:  [[ 0.05641131 -0.00121561  0.05495331 ...  0.00042543  0.0552383\n",
-      "  -0.0003401 ]\n",
-      " [-0.00121561  0.05665185 -0.00037606 ...  0.05142217 -0.00206265\n",
-      "   0.055308  ]\n",
-      " [ 0.05495331 -0.00037606  0.05606699 ...  0.00078373  0.05154013\n",
-      "   0.0002743 ]\n",
-      " ...\n",
-      " [ 0.00042543  0.05142217  0.00078373 ...  0.05547076 -0.00020924\n",
-      "   0.05468337]\n",
-      " [ 0.0552383  -0.00206265  0.05154013 ... -0.00020924  0.05674503\n",
-      "  -0.00112732]\n",
-      " [-0.0003401   0.055308    0.0002743  ...  0.05468337 -0.00112732\n",
-      "   0.05645074]]\n",
-      "  mean w/ noise:  [ 9.90661656e-01  7.65105121e-03  9.12518864e-01  3.93297714e-01\n",
-      "  6.97385095e-01  7.12527173e-01  3.72091876e-01  9.29757073e-01\n",
-      " -5.62046706e-03  1.00612577e+00 -3.85155711e-01  9.28536400e-01\n",
-      " -7.08566617e-01  7.10857894e-01 -9.21825049e-01  3.85678916e-01\n",
-      " -1.00171675e+00  6.38678174e-03 -9.21287016e-01 -3.75958117e-01\n",
-      " -7.05196330e-01 -6.98482650e-01 -3.79217397e-01 -9.12986694e-01\n",
-      "  5.88557869e-04 -9.90920111e-01  3.83513446e-01 -9.11634981e-01\n",
-      "  7.04893542e-01 -6.96537561e-01  9.18344153e-01 -3.70184048e-01]\n",
-      "  cov w/ noise:  [[ 5.67658286e-02 -1.15648655e-03  5.44762165e-02 ...  9.02439510e-04\n",
-      "   5.50749525e-02 -8.51465100e-06]\n",
-      " [-1.15648655e-03  5.71626432e-02 -1.32547868e-03 ...  5.12898594e-02\n",
-      "  -2.37687758e-03  5.54724454e-02]\n",
-      " [ 5.44762165e-02 -1.32547868e-03  5.67191336e-02 ...  4.64756861e-04\n",
-      "   5.18183499e-02 -5.35355421e-04]\n",
-      " ...\n",
-      " [ 9.02439510e-04  5.12898594e-02  4.64756861e-04 ...  5.60728143e-02\n",
-      "  -1.19419753e-04  5.45827056e-02]\n",
-      " [ 5.50749525e-02 -2.37687758e-03  5.18183499e-02 ... -1.19419753e-04\n",
-      "   5.83600909e-02 -1.14821773e-03]\n",
-      " [-8.51465100e-06  5.54724454e-02 -5.35355421e-04 ...  5.45827056e-02\n",
-      "  -1.14821773e-03  5.78888826e-02]]\n",
-      "  mean conditional:  [ 0.7094184  -0.36905811  0.60493854  0.01875764  0.37269479  0.32687655\n",
-      "  0.05569826  0.51001457 -0.28622014  0.53854567 -0.58815225  0.4127331\n",
-      " -0.80283928  0.16042188 -0.91160049 -0.1814376  -0.91371022 -0.57639562\n",
-      " -0.81320254 -0.97673322 -0.6201289  -1.3180061  -0.35853378 -1.53524369\n",
-      " -0.06277644 -1.58414344  0.23376979 -1.45337227  0.4936649  -1.16854972\n",
-      "  0.66720384 -0.78504093] , sample:  [ 7.26247860e-01 -3.37867780e-01  5.76190571e-01  3.21450640e-04\n",
-      "  3.80191119e-01  3.05887718e-01  6.86957681e-02  5.48164006e-01\n",
-      " -2.75338563e-01  5.35248081e-01 -6.13149187e-01  4.08484628e-01\n",
-      " -7.91514074e-01  1.26996054e-01 -9.48808351e-01 -1.65321975e-01\n",
-      " -8.66135118e-01 -5.65228883e-01 -8.28806054e-01 -9.72730917e-01\n",
-      " -6.29671325e-01 -1.31509159e+00 -3.23923595e-01 -1.55425480e+00\n",
-      " -4.23554850e-02 -1.56556071e+00  1.80977321e-01 -1.49909816e+00\n",
-      "  4.86248501e-01 -1.12562939e+00  6.91186893e-01 -8.03334661e-01]\n",
-      "  cov conditional:  [[ 3.04265209e-04  2.78507954e-06  2.30293012e-04 ...  9.18704235e-06\n",
-      "   2.56489125e-04  2.24538139e-06]\n",
-      " [ 2.78507954e-06  3.55824855e-04  2.96933448e-06 ...  1.43836695e-04\n",
-      "  -2.21426425e-07  2.95821734e-04]\n",
-      " [ 2.30293012e-04  2.96933448e-06  2.93527270e-04 ...  5.97880975e-06\n",
-      "   1.07786417e-04  2.00285521e-06]\n",
-      " ...\n",
-      " [ 9.18704235e-06  1.43836695e-04  5.97880975e-06 ...  3.55967234e-04\n",
-      "   6.05866416e-07  2.93614434e-04]\n",
-      " [ 2.56489125e-04 -2.21426425e-07  1.07786417e-04 ...  6.05866416e-07\n",
-      "   3.37593570e-04 -4.17780117e-06]\n",
-      " [ 2.24538139e-06  2.95821734e-04  2.00285521e-06 ...  2.93614434e-04\n",
-      "  -4.17780117e-06  3.69891279e-04]]\n",
-      "  mean noise:  [-0.01682946 -0.03119033  0.02874796  0.01843619 -0.00749633  0.02098883\n",
-      " -0.01299751 -0.03814944 -0.01088157  0.00329759  0.02499694  0.00424847\n",
-      " -0.01132521  0.03342583  0.03720786 -0.01611562 -0.0475751  -0.01116674\n",
-      "  0.01560352 -0.0040023   0.00954243 -0.0029145  -0.03461018  0.01901111\n",
-      " -0.02042095 -0.01858273  0.05279247  0.04572589  0.00741639 -0.04292033\n",
-      " -0.02398305  0.01829373]\n",
-      "  cov noise:  [[ 3.04265209e-04  2.78507954e-06  2.30293012e-04 ...  9.18704235e-06\n",
-      "   2.56489125e-04  2.24538139e-06]\n",
-      " [ 2.78507954e-06  3.55824855e-04  2.96933448e-06 ...  1.43836695e-04\n",
-      "  -2.21426425e-07  2.95821734e-04]\n",
-      " [ 2.30293012e-04  2.96933448e-06  2.93527270e-04 ...  5.97880975e-06\n",
-      "   1.07786417e-04  2.00285521e-06]\n",
-      " ...\n",
-      " [ 9.18704235e-06  1.43836695e-04  5.97880975e-06 ...  3.55967234e-04\n",
-      "   6.05866416e-07  2.93614434e-04]\n",
-      " [ 2.56489125e-04 -2.21426425e-07  1.07786417e-04 ...  6.05866416e-07\n",
-      "   3.37593570e-04 -4.17780117e-06]\n",
-      " [ 2.24538139e-06  2.95821734e-04  2.00285521e-06 ...  2.93614434e-04\n",
-      "  -4.17780117e-06  3.69891279e-04]]\n",
-      "  mean:  [ 0.99079086  0.00170188  0.91728763  0.38741537  0.70165495  0.71347016\n",
-      "  0.37748172  0.93050114 -0.00483126  1.00671603 -0.38639526  0.93093937\n",
-      " -0.70895334  0.71485211 -0.92397653  0.3917713  -1.00006098  0.01048476\n",
-      " -0.92655069 -0.37181303 -0.71428673 -0.69718659 -0.39463364 -0.91649967\n",
-      " -0.01590042 -0.99621538  0.36469354 -0.92359641  0.69035121 -0.70878073\n",
-      "  0.91079653 -0.3837105 ]\n",
-      "  cov:  [[ 0.05301149  0.0025259   0.05234491 ... -0.00019124  0.05134465\n",
-      "   0.00126755]\n",
-      " [ 0.0025259   0.05507437  0.00286362 ...  0.04852386  0.00216656\n",
-      "   0.05297115]\n",
-      " [ 0.05234491  0.00286362  0.05435508 ...  0.00034371  0.0483152\n",
-      "   0.00169379]\n",
-      " ...\n",
-      " [-0.00019124  0.04852386  0.00034371 ...  0.05162863 -0.00067206\n",
-      "   0.05128298]\n",
-      " [ 0.05134465  0.00216656  0.0483152  ... -0.00067206  0.052471\n",
-      "   0.00081647]\n",
-      " [ 0.00126755  0.05297115  0.00169379 ...  0.05128298  0.00081647\n",
-      "   0.0535186 ]]\n",
-      "  mean w/ noise:  [ 0.99285443 -0.00111251  0.9160468   0.38810862  0.70145048  0.71242884\n",
-      "  0.37576129  0.9309328  -0.00841642  1.00864184 -0.38669365  0.93365193\n",
-      " -0.70851718  0.71322497 -0.9236416   0.39056182 -0.99693065  0.00811312\n",
-      " -0.92757982 -0.37255495 -0.71436575 -0.69648999 -0.3954256  -0.91757579\n",
-      " -0.01577801 -0.9941432   0.36540307 -0.92113269  0.69061864 -0.70683632\n",
-      "  0.90990006 -0.3824933 ]\n",
-      "  cov w/ noise:  [[ 0.05391955  0.00117856  0.05241268 ... -0.00088371  0.05115013\n",
-      "   0.00069016]\n",
-      " [ 0.00117856  0.05554597  0.0021928  ...  0.04862865  0.00122132\n",
-      "   0.05260014]\n",
-      " [ 0.05241268  0.0021928   0.05579588 ...  0.00021374  0.04833576\n",
-      "   0.0018123 ]\n",
-      " ...\n",
-      " [-0.00088371  0.04862865  0.00021374 ...  0.05340205 -0.0010937\n",
-      "   0.05141295]\n",
-      " [ 0.05115013  0.00122132  0.04833576 ... -0.0010937   0.05389088\n",
-      "   0.00050716]\n",
-      " [ 0.00069016  0.05260014  0.0018123  ...  0.05141295  0.00050716\n",
-      "   0.05404515]]\n",
-      "  mean conditional:  [ 0.98016646 -0.32033713  0.98893723  0.09670304  0.84933247  0.48439972\n",
-      "  0.54252773  0.7750346   0.10435094  0.91773414 -0.37844323  0.88768701\n",
-      " -0.80151144  0.68708247 -1.0790822   0.35007893 -1.16455342 -0.05896354\n",
-      " -1.0556641  -0.46660542 -0.7866781  -0.81375874 -0.41541662 -1.06016811\n",
-      " -0.01249714 -1.17935153  0.35655053 -1.15681141  0.65178336 -0.99152119\n",
-      "  0.86097372 -0.70066224] , sample:  [ 9.06545379e-01 -3.97652891e-01  1.01158312e+00  1.14411164e-01\n",
-      "  8.67258504e-01  5.27036194e-01  5.47502576e-01  7.59741221e-01\n",
-      "  1.43435562e-01  8.89117527e-01 -4.16757020e-01  8.90795532e-01\n",
-      " -7.98592487e-01  7.39744455e-01 -1.08473009e+00  3.26114043e-01\n",
-      " -1.17116159e+00 -9.19988623e-02 -1.05822479e+00 -4.52966694e-01\n",
-      " -7.99629326e-01 -8.03240039e-01 -4.04310271e-01 -1.05853676e+00\n",
-      "  2.15853036e-04 -1.16812108e+00  3.57089329e-01 -1.16151485e+00\n",
-      "  6.52249663e-01 -1.00752145e+00  9.00489970e-01 -6.63451591e-01]\n",
-      "  cov conditional:  [[ 3.29812560e-04  1.10593653e-05  2.68316490e-04 ... -4.95797215e-06\n",
-      "   2.61661702e-04 -1.93624545e-08]\n",
-      " [ 1.10593653e-05  3.20327978e-04  2.58931474e-06 ...  1.11465458e-04\n",
-      "   1.08856841e-05  2.51545725e-04]\n",
-      " [ 2.68316490e-04  2.58931474e-06  3.41676149e-04 ... -3.70392478e-06\n",
-      "   1.15999640e-04 -1.45115537e-06]\n",
-      " ...\n",
-      " [-4.95797215e-06  1.11465458e-04 -3.70392478e-06 ...  3.48177570e-04\n",
-      "  -1.53221843e-05  2.67627772e-04]\n",
-      " [ 2.61661702e-04  1.08856841e-05  1.15999640e-04 ... -1.53221843e-05\n",
-      "   3.37735343e-04 -6.70414340e-06]\n",
-      " [-1.93624545e-08  2.51545725e-04 -1.45115537e-06 ...  2.67627772e-04\n",
-      "  -6.70414340e-06  3.25110189e-04]]\n",
-      "  mean noise:  [ 0.07362108  0.07731576 -0.02264589 -0.01770812 -0.01792603 -0.04263648\n",
-      " -0.00497485  0.01529338 -0.03908462  0.02861662  0.03831379 -0.00310852\n",
-      " -0.00291895 -0.05266198  0.00564789  0.02396489  0.00660818  0.03303532\n",
-      "  0.00256068 -0.01363873  0.01295122 -0.0105187  -0.01110634 -0.00163136\n",
-      " -0.01271299 -0.01123046 -0.0005388   0.00470344 -0.0004663   0.01600026\n",
-      " -0.03951625 -0.03721065]\n",
-      "  cov noise:  [[ 3.29812560e-04  1.10593653e-05  2.68316490e-04 ... -4.95797215e-06\n",
-      "   2.61661702e-04 -1.93624545e-08]\n",
-      " [ 1.10593653e-05  3.20327978e-04  2.58931474e-06 ...  1.11465458e-04\n",
-      "   1.08856841e-05  2.51545725e-04]\n",
-      " [ 2.68316490e-04  2.58931474e-06  3.41676149e-04 ... -3.70392478e-06\n",
-      "   1.15999640e-04 -1.45115537e-06]\n",
-      " ...\n",
-      " [-4.95797215e-06  1.11465458e-04 -3.70392478e-06 ...  3.48177570e-04\n",
-      "  -1.53221843e-05  2.67627772e-04]\n",
-      " [ 2.61661702e-04  1.08856841e-05  1.15999640e-04 ... -1.53221843e-05\n",
-      "   3.37735343e-04 -6.70414340e-06]\n",
-      " [-1.93624545e-08  2.51545725e-04 -1.45115537e-06 ...  2.67627772e-04\n",
-      "  -6.70414340e-06  3.25110189e-04]]\n",
-      "  mean:  [ 0.99780063 -0.00224607  0.92278823  0.38355768  0.70523673  0.71010305\n",
-      "  0.37797975  0.92739244 -0.00797123  1.00327213 -0.39291797  0.9269887\n",
-      " -0.71822475  0.71076078 -0.93445158  0.38813209 -1.0097225   0.0075067\n",
-      " -0.93293992 -0.37473685 -0.71564984 -0.70045878 -0.39098467 -0.91966468\n",
-      " -0.00816836 -0.99899309  0.37496104 -0.92629063  0.70082845 -0.71191886\n",
-      "  0.9196905  -0.38738184]\n",
-      "  cov:  [[ 5.31820311e-02  6.94285205e-04  5.10987178e-02 ... -6.39698427e-04\n",
-      "   5.29492834e-02  9.82822148e-05]\n",
-      " [ 6.94285205e-04  5.20333360e-02 -7.82989967e-05 ...  4.74834262e-02\n",
-      "   1.12594824e-03  5.09302940e-02]\n",
-      " [ 5.10987178e-02 -7.82989967e-05  5.17449681e-02 ... -1.22403919e-03\n",
-      "   4.84984026e-02 -5.96948815e-04]\n",
-      " ...\n",
-      " [-6.39698427e-04  4.74834262e-02 -1.22403919e-03 ...  5.31638847e-02\n",
-      "  -4.41262430e-04  5.14904954e-02]\n",
-      " [ 5.29492834e-02  1.12594824e-03  4.84984026e-02 ... -4.41262430e-04\n",
-      "   5.54526021e-02  4.14389977e-04]\n",
-      " [ 9.82822148e-05  5.09302940e-02 -5.96948815e-04 ...  5.14904954e-02\n",
-      "   4.14389977e-04  5.25761937e-02]]\n",
-      "  mean w/ noise:  [ 0.99827008 -0.00385693  0.91950954  0.38350503  0.70540067  0.71071671\n",
-      "  0.3775629   0.92846689 -0.00914028  1.00164637 -0.39308559  0.92522436\n",
-      " -0.71876461  0.71040394 -0.93305863  0.38757242 -1.00770892  0.00689542\n",
-      " -0.93361764 -0.37479468 -0.71512738 -0.70188488 -0.39127389 -0.91986727\n",
-      " -0.00862378 -1.00201803  0.37390908 -0.92651562  0.69902089 -0.71132639\n",
-      "  0.9192711  -0.38777704]\n",
-      "  cov w/ noise:  [[ 5.36390042e-02  8.85074813e-04  5.06084522e-02 ...  4.98491510e-05\n",
-      "   5.27244305e-02  4.78784133e-04]\n",
-      " [ 8.85074813e-04  5.38097240e-02 -1.22645135e-04 ...  4.77643042e-02\n",
-      "   1.00564783e-03  5.06721704e-02]\n",
-      " [ 5.06084522e-02 -1.22645135e-04  5.21072522e-02 ... -8.09733750e-04\n",
-      "   4.85360847e-02 -4.47148057e-04]\n",
-      " ...\n",
-      " [ 4.98491510e-05  4.77643042e-02 -8.09733750e-04 ...  5.42962662e-02\n",
-      "  -2.51005311e-04  5.09596173e-02]\n",
-      " [ 5.27244305e-02  1.00564783e-03  4.85360847e-02 ... -2.51005311e-04\n",
-      "   5.65949884e-02  5.60689570e-04]\n",
-      " [ 4.78784133e-04  5.06721704e-02 -4.47148057e-04 ...  5.09596173e-02\n",
-      "   5.60689570e-04  5.24495958e-02]]\n",
-      "  mean conditional:  [ 0.79468253 -0.25218863  0.79481671  0.1174841   0.63743392  0.42386902\n",
-      "  0.32558546  0.63252318 -0.09039098  0.71212885 -0.52638371  0.64205061\n",
-      " -0.89629559  0.42087844 -1.1367886   0.0741475  -1.21749954 -0.34483203\n",
-      " -1.14027012 -0.76664529 -0.92823322 -1.12186424 -0.61590384 -1.35004339\n",
-      " -0.25078593 -1.40879213  0.11225239 -1.28730115  0.42737691 -1.01385813\n",
-      "  0.66294011 -0.64695062] , sample:  [ 0.79622075 -0.22136996  0.78605837  0.11126164  0.66843756  0.41170974\n",
-      "  0.30949947  0.60293661 -0.09056219  0.73285612 -0.52769614  0.63696295\n",
-      " -0.89250759  0.44377942 -1.15672687  0.02429921 -1.21251275 -0.30312172\n",
-      " -1.11013821 -0.7784176  -0.94518878 -1.1177513  -0.65404884 -1.34752978\n",
-      " -0.18393504 -1.43023455  0.04961168 -1.30018543  0.46199525 -1.01278347\n",
-      "  0.65631223 -0.64012848]\n",
-      "  cov conditional:  [[ 3.18512665e-04 -1.42028810e-05  2.48861559e-04 ...  3.85495008e-06\n",
-      "   2.52441295e-04 -1.35238660e-05]\n",
-      " [-1.42028810e-05  3.11820662e-04 -1.56660476e-05 ...  1.17612024e-04\n",
-      "   2.28121454e-06  2.48161852e-04]\n",
-      " [ 2.48861559e-04 -1.56660476e-05  3.17843964e-04 ... -6.64760501e-06\n",
-      "   1.04966280e-04 -1.56997418e-05]\n",
-      " ...\n",
-      " [ 3.85495008e-06  1.17612024e-04 -6.64760501e-06 ...  3.48278250e-04\n",
-      "   1.60196313e-05  2.75910656e-04]\n",
-      " [ 2.52441295e-04  2.28121454e-06  1.04966280e-04 ...  1.60196313e-05\n",
-      "   3.17023433e-04  2.89567300e-06]\n",
-      " [-1.35238660e-05  2.48161852e-04 -1.56997418e-05 ...  2.75910656e-04\n",
-      "   2.89567300e-06  3.30665056e-04]]\n",
-      "  mean noise:  [-0.00153822 -0.03081867  0.00875834  0.00622245 -0.03100364  0.01215929\n",
-      "  0.01608599  0.02958657  0.00017121 -0.02072728  0.00131243  0.00508766\n",
-      " -0.00378799 -0.02290098  0.01993828  0.04984829 -0.00498679 -0.04171031\n",
-      " -0.03013192  0.0117723   0.01695556 -0.00411294  0.038145   -0.00251361\n",
-      " -0.06685089  0.02144242  0.06264071  0.01288428 -0.03461834 -0.00107466\n",
-      "  0.00662788 -0.00682214]\n",
-      "  cov noise:  [[ 3.18512665e-04 -1.42028810e-05  2.48861559e-04 ...  3.85495008e-06\n",
-      "   2.52441295e-04 -1.35238660e-05]\n",
-      " [-1.42028810e-05  3.11820662e-04 -1.56660476e-05 ...  1.17612024e-04\n",
-      "   2.28121454e-06  2.48161852e-04]\n",
-      " [ 2.48861559e-04 -1.56660476e-05  3.17843964e-04 ... -6.64760501e-06\n",
-      "   1.04966280e-04 -1.56997418e-05]\n",
-      " ...\n",
-      " [ 3.85495008e-06  1.17612024e-04 -6.64760501e-06 ...  3.48278250e-04\n",
-      "   1.60196313e-05  2.75910656e-04]\n",
-      " [ 2.52441295e-04  2.28121454e-06  1.04966280e-04 ...  1.60196313e-05\n",
-      "   3.17023433e-04  2.89567300e-06]\n",
-      " [-1.35238660e-05  2.48161852e-04 -1.56997418e-05 ...  2.75910656e-04\n",
-      "   2.89567300e-06  3.30665056e-04]]\n",
-      "  mean:  [ 0.98896347 -0.0056945   0.91556178  0.3799089   0.69988237  0.70669439\n",
-      "  0.37389149  0.92448932 -0.01191939  1.00045573 -0.39702721  0.92317839\n",
-      " -0.72157796  0.70475602 -0.93619253  0.37881984 -1.00963826 -0.00524186\n",
-      " -0.93184587 -0.38909185 -0.71517419 -0.71384179 -0.39257092 -0.9305241\n",
-      " -0.01253461 -1.00681584  0.36746394 -0.93140711  0.69124045 -0.71543229\n",
-      "  0.90986813 -0.39054041]\n",
-      "  cov:  [[ 5.43406522e-02  6.60338374e-04  5.30692601e-02 ... -5.06954972e-04\n",
-      "   5.31701011e-02 -1.06265706e-04]\n",
-      " [ 6.60338374e-04  5.95633418e-02 -8.55474750e-05 ...  5.46777600e-02\n",
-      "   1.52632611e-03  5.83376062e-02]\n",
-      " [ 5.30692601e-02 -8.55474750e-05  5.45745488e-02 ... -9.33088196e-04\n",
-      "   4.93612882e-02 -7.23936098e-04]\n",
-      " ...\n",
-      " [-5.06954972e-04  5.46777600e-02 -9.33088196e-04 ...  5.91954697e-02\n",
-      "  -1.41515070e-04  5.82222539e-02]\n",
-      " [ 5.31701011e-02  1.52632611e-03  4.93612882e-02 ... -1.41515070e-04\n",
-      "   5.50635576e-02  5.46089233e-04]\n",
-      " [-1.06265706e-04  5.83376062e-02 -7.23936098e-04 ...  5.82222539e-02\n",
-      "   5.46089233e-04  5.97248855e-02]]\n",
-      "  mean w/ noise:  [ 0.99050972 -0.00662536  0.91454979  0.37997015  0.7009573   0.7059872\n",
-      "  0.37577146  0.92423012 -0.01474306  1.00264895 -0.39716664  0.92236119\n",
-      " -0.72273871  0.70254451 -0.9357225   0.37857446 -1.0093949  -0.00540419\n",
-      " -0.93351675 -0.38948449 -0.71568001 -0.71424519 -0.39084505 -0.92945536\n",
-      " -0.01200721 -1.00779535  0.3686627  -0.93384302  0.69114486 -0.71474152\n",
-      "  0.90920616 -0.39058859]\n",
-      "  cov w/ noise:  [[ 0.0562852   0.00107396  0.05380272 ... -0.00023857  0.05385476\n",
-      "  -0.00031785]\n",
-      " [ 0.00107396  0.05919294  0.00077734 ...  0.05387516  0.0025222\n",
-      "   0.05802203]\n",
-      " [ 0.05380272  0.00077734  0.05602763 ... -0.00045571  0.04982152\n",
-      "  -0.00063496]\n",
-      " ...\n",
-      " [-0.00023857  0.05387516 -0.00045571 ...  0.06020868  0.00045024\n",
-      "   0.05866794]\n",
-      " [ 0.05385476  0.0025222   0.04982152 ...  0.00045024  0.05675654\n",
-      "   0.00053457]\n",
-      " [-0.00031785  0.05802203 -0.00063496 ...  0.05866794  0.00053457\n",
-      "   0.06158726]]\n",
-      "  mean conditional:  [ 0.93523367  0.1866929   0.92278943  0.55326277  0.77331321  0.89299704\n",
-      "  0.49593759  1.15172083  0.13820039  1.27184061 -0.23144393  1.21810613\n",
-      " -0.55115578  0.99280784 -0.77808766  0.63469289 -0.88513553  0.21038855\n",
-      " -0.85799349 -0.20206685 -0.69537749 -0.53347282 -0.41424966 -0.73604087\n",
-      " -0.05996985 -0.78756279  0.30079448 -0.69159783  0.60793248 -0.47255674\n",
-      "  0.82520717 -0.16702262] , sample:  [ 0.90050981  0.2137109   0.94648992  0.57006034  0.75613735  0.79752697\n",
-      "  0.52167918  1.19034143  0.11265677  1.31221344 -0.25626682  1.21232762\n",
-      " -0.52116409  0.95232007 -0.7444471   0.67670068 -0.90931015  0.18078152\n",
-      " -0.83153859 -0.19050249 -0.71504872 -0.54252524 -0.44851045 -0.74098456\n",
-      " -0.03784745 -0.78910332  0.30857237 -0.68252448  0.60177758 -0.45319287\n",
-      "  0.85934949 -0.18938113]\n",
-      "  cov conditional:  [[ 3.16886549e-04  1.42684260e-05  2.48254467e-04 ...  9.60475830e-06\n",
-      "   2.58239861e-04  1.71981501e-05]\n",
-      " [ 1.42684260e-05  3.40626030e-04  2.15400429e-05 ...  1.41011766e-04\n",
-      "  -6.69024299e-06  2.77869245e-04]\n",
-      " [ 2.48254467e-04  2.15400429e-05  3.35000513e-04 ...  1.93818183e-05\n",
-      "   9.76431783e-05  2.89672214e-05]\n",
-      " ...\n",
-      " [ 9.60475830e-06  1.41011766e-04  1.93818183e-05 ...  3.38091216e-04\n",
-      "  -1.29484301e-05  2.82741219e-04]\n",
-      " [ 2.58239861e-04 -6.69024299e-06  9.76431783e-05 ... -1.29484301e-05\n",
-      "   3.48437339e-04 -1.02041811e-05]\n",
-      " [ 1.71981501e-05  2.77869245e-04  2.89672214e-05 ...  2.82741219e-04\n",
-      "  -1.02041811e-05  3.50677917e-04]]\n",
-      "  mean noise:  [ 0.03472386 -0.02701799 -0.02370048 -0.01679757  0.01717586  0.09547008\n",
-      " -0.02574159 -0.03862061  0.02554362 -0.04037283  0.02482289  0.0057785\n",
-      " -0.02999169  0.04048777 -0.03364056 -0.04200779  0.02417461  0.02960703\n",
-      " -0.0264549  -0.01156436  0.01967123  0.00905242  0.03426079  0.00494369\n",
-      " -0.0221224   0.00154053 -0.00777788 -0.00907335  0.0061549  -0.01936387\n",
-      " -0.03414232  0.02235851]\n",
-      "  cov noise:  [[ 3.16886549e-04  1.42684260e-05  2.48254467e-04 ...  9.60475830e-06\n",
-      "   2.58239861e-04  1.71981501e-05]\n",
-      " [ 1.42684260e-05  3.40626030e-04  2.15400429e-05 ...  1.41011766e-04\n",
-      "  -6.69024299e-06  2.77869245e-04]\n",
-      " [ 2.48254467e-04  2.15400429e-05  3.35000513e-04 ...  1.93818183e-05\n",
-      "   9.76431783e-05  2.89672214e-05]\n",
-      " ...\n",
-      " [ 9.60475830e-06  1.41011766e-04  1.93818183e-05 ...  3.38091216e-04\n",
-      "  -1.29484301e-05  2.82741219e-04]\n",
-      " [ 2.58239861e-04 -6.69024299e-06  9.76431783e-05 ... -1.29484301e-05\n",
-      "   3.48437339e-04 -1.02041811e-05]\n",
-      " [ 1.71981501e-05  2.77869245e-04  2.89672214e-05 ...  2.82741219e-04\n",
-      "  -1.02041811e-05  3.50677917e-04]]\n",
-      "  mean:  [ 0.97892917 -0.01356462  0.90694937  0.37410984  0.69523775  0.70369703\n",
-      "  0.37552314  0.92451479 -0.00309438  1.00307848 -0.38212764  0.92792665\n",
-      " -0.70363399  0.71133489 -0.91916389  0.38723141 -0.9969785   0.00491725\n",
-      " -0.92526044 -0.37820172 -0.71393436 -0.70389503 -0.39486948 -0.92306017\n",
-      " -0.01715914 -1.00321503  0.36128288 -0.93265732  0.68351243 -0.72129247\n",
-      "  0.90047871 -0.3987932 ]\n",
-      "  cov:  [[ 5.81918720e-02  5.51824232e-04  5.63634338e-02 ...  8.12218240e-04\n",
-      "   5.78717669e-02  6.85987352e-04]\n",
-      " [ 5.51824232e-04  5.61554276e-02 -1.57155548e-04 ...  4.91451326e-02\n",
-      "   1.13695570e-03  5.38918663e-02]\n",
-      " [ 5.63634338e-02 -1.57155548e-04  5.70461408e-02 ...  3.13882857e-04\n",
-      "   5.38276105e-02  8.87717297e-05]\n",
-      " ...\n",
-      " [ 8.12218240e-04  4.91451326e-02  3.13882857e-04 ...  5.20039090e-02\n",
-      "   9.13559433e-04  5.17777854e-02]\n",
-      " [ 5.78717669e-02  1.13695570e-03  5.38276105e-02 ...  9.13559433e-04\n",
-      "   6.02057020e-02  1.00360377e-03]\n",
-      " [ 6.85987352e-04  5.38918663e-02  8.87717297e-05 ...  5.17777854e-02\n",
-      "   1.00360377e-03  5.42461192e-02]]\n",
-      "  mean w/ noise:  [ 9.77700205e-01 -1.47186814e-02  9.04984575e-01  3.73209515e-01\n",
-      "  6.96559462e-01  7.01926671e-01  3.75373244e-01  9.25722834e-01\n",
-      " -2.39146611e-04  1.00141925e+00 -3.82478689e-01  9.26169969e-01\n",
-      " -7.08834900e-01  7.11814478e-01 -9.19844915e-01  3.83763990e-01\n",
-      " -9.95949413e-01  3.88106895e-03 -9.28605706e-01 -3.78910463e-01\n",
-      " -7.16400204e-01 -7.04177385e-01 -3.94293096e-01 -9.23513246e-01\n",
-      " -1.67077571e-02 -1.00221974e+00  3.61674557e-01 -9.31190625e-01\n",
-      "  6.83830870e-01 -7.22526167e-01  9.00488837e-01 -3.97340323e-01]\n",
-      "  cov w/ noise:  [[ 5.85649003e-02  5.36216747e-04  5.65571469e-02 ...  1.64012768e-03\n",
-      "   5.76983697e-02  1.07221900e-03]\n",
-      " [ 5.36216747e-04  5.73707500e-02 -8.66115602e-06 ...  4.90410880e-02\n",
-      "   1.09803579e-03  5.45918459e-02]\n",
-      " [ 5.65571469e-02 -8.66115602e-06  5.90520764e-02 ...  1.39242178e-03\n",
-      "   5.42694072e-02  5.94995686e-04]\n",
-      " ...\n",
-      " [ 1.64012768e-03  4.90410880e-02  1.39242178e-03 ...  5.23850076e-02\n",
-      "   1.99828250e-03  5.18862585e-02]\n",
-      " [ 5.76983697e-02  1.09803579e-03  5.42694072e-02 ...  1.99828250e-03\n",
-      "   6.19805637e-02  1.74192564e-03]\n",
-      " [ 1.07221900e-03  5.45918459e-02  5.94995686e-04 ...  5.18862585e-02\n",
-      "   1.74192564e-03  5.61042418e-02]]\n",
-      "  mean conditional:  [ 0.5026066   0.12741267  0.52676353  0.54625179  0.40827978  0.91395709\n",
-      "  0.14570967  1.16445071 -0.20655353  1.24961236 -0.5627328   1.15395239\n",
-      " -0.84256929  0.89019721 -0.99502332  0.49649941 -1.00769031  0.03795282\n",
-      " -0.90256405 -0.40432826 -0.71762734 -0.75505224 -0.48877106 -0.96170876\n",
-      " -0.2463475  -1.00228033 -0.01333249 -0.88377713  0.19817646 -0.63196387\n",
-      "  0.37826859 -0.28156631] , sample:  [ 0.4905844   0.11956006  0.58326013  0.50410111  0.41153542  0.95482645\n",
-      "  0.11932617  1.1993849  -0.21474467  1.22658057 -0.55800679  1.1285177\n",
-      " -0.85403604  0.91813663 -0.97865472  0.50438724 -1.05659438  0.02415664\n",
-      " -0.81936305 -0.40461639 -0.74649313 -0.7523544  -0.48050982 -0.99663855\n",
-      " -0.23550716 -0.99700484 -0.02121034 -0.8437796   0.1814504  -0.64775654\n",
-      "  0.34854503 -0.27029199]\n",
-      "  cov conditional:  [[ 3.20130953e-04 -9.86250230e-06  2.53323443e-04 ... -2.29649106e-05\n",
-      "   2.48558147e-04 -1.53199200e-05]\n",
-      " [-9.86250230e-06  3.24348955e-04 -1.14211556e-05 ...  1.22374146e-04\n",
-      "  -5.89386830e-06  2.63268889e-04]\n",
-      " [ 2.53323443e-04 -1.14211556e-05  3.25479944e-04 ... -1.74931056e-05\n",
-      "   1.01738641e-04 -1.29046873e-05]\n",
-      " ...\n",
-      " [-2.29649106e-05  1.22374146e-04 -1.74931056e-05 ...  3.40741187e-04\n",
-      "  -1.39672113e-05  2.65871022e-04]\n",
-      " [ 2.48558147e-04 -5.89386830e-06  1.01738641e-04 ... -1.39672113e-05\n",
-      "   3.16993474e-04 -8.89950628e-06]\n",
-      " [-1.53199200e-05  2.63268889e-04 -1.29046873e-05 ...  2.65871022e-04\n",
-      "  -8.89950628e-06  3.31945780e-04]]\n",
-      "  mean noise:  [ 0.0120222   0.00785261 -0.0564966   0.04215068 -0.00325564 -0.04086936\n",
-      "  0.0263835  -0.03493418  0.00819114  0.02303178 -0.00472602  0.02543469\n",
-      "  0.01146674 -0.02793942 -0.01636859 -0.00788782  0.04890407  0.01379617\n",
-      " -0.083201    0.00028812  0.02886579 -0.00269785 -0.00826124  0.03492979\n",
-      " -0.01084034 -0.00527549  0.00787785 -0.03999754  0.01672606  0.01579267\n",
-      "  0.02972356 -0.01127431]\n",
-      "  cov noise:  [[ 3.20130953e-04 -9.86250230e-06  2.53323443e-04 ... -2.29649106e-05\n",
-      "   2.48558147e-04 -1.53199200e-05]\n",
-      " [-9.86250230e-06  3.24348955e-04 -1.14211556e-05 ...  1.22374146e-04\n",
-      "  -5.89386830e-06  2.63268889e-04]\n",
-      " [ 2.53323443e-04 -1.14211556e-05  3.25479944e-04 ... -1.74931056e-05\n",
-      "   1.01738641e-04 -1.29046873e-05]\n",
-      " ...\n",
-      " [-2.29649106e-05  1.22374146e-04 -1.74931056e-05 ...  3.40741187e-04\n",
-      "  -1.39672113e-05  2.65871022e-04]\n",
-      " [ 2.48558147e-04 -5.89386830e-06  1.01738641e-04 ... -1.39672113e-05\n",
-      "   3.16993474e-04 -8.89950628e-06]\n",
-      " [-1.53199200e-05  2.63268889e-04 -1.29046873e-05 ...  2.65871022e-04\n",
-      "  -8.89950628e-06  3.31945780e-04]]\n",
-      "  mean:  [ 0.99253128 -0.00229089  0.91856391  0.38567731  0.7037049   0.71427612\n",
-      "  0.37988232  0.93279885 -0.00339087  1.00911258 -0.38657922  0.93321085\n",
-      " -0.71071373  0.71818002 -0.92695647  0.39705802 -1.0034127   0.01707938\n",
-      " -0.9286204  -0.36589414 -0.71335628 -0.69356341 -0.39025924 -0.9150791\n",
-      " -0.008589   -0.99632045  0.37317002 -0.92534281  0.69727406 -0.71252563\n",
-      "  0.91472196 -0.38845801]\n",
-      "  cov:  [[0.05675966 0.00031587 0.05573938 ... 0.00040537 0.05522157 0.00046096]\n",
-      " [0.00031587 0.05402636 0.00010652 ... 0.04930163 0.00072218 0.05270989]\n",
-      " [0.05573938 0.00010652 0.05736109 ... 0.0004094  0.05188249 0.00036387]\n",
-      " ...\n",
-      " [0.00040537 0.04930163 0.0004094  ... 0.05474951 0.0005658  0.05316802]\n",
-      " [0.05522157 0.00072218 0.05188249 ... 0.0005658  0.0564198  0.00072245]\n",
-      " [0.00046096 0.05270989 0.00036387 ... 0.05316802 0.00072245 0.05421843]]\n",
-      "  mean w/ noise:  [ 0.99344754 -0.00310642  0.92042609  0.38610585  0.70493227  0.71371978\n",
-      "  0.3805353   0.93342401 -0.00457899  1.00882204 -0.387478    0.93259384\n",
-      " -0.71061013  0.71727098 -0.92755324  0.3962937  -1.00397908  0.01398107\n",
-      " -0.92889348 -0.36669332 -0.71505751 -0.69448853 -0.39220039 -0.91374048\n",
-      " -0.008661   -0.99602891  0.3753555  -0.92453234  0.69794726 -0.71495687\n",
-      "  0.91359764 -0.38642382]\n",
-      "  cov w/ noise:  [[ 5.75614205e-02 -2.18994178e-04  5.58121985e-02 ... -1.61981521e-04\n",
-      "   5.55037138e-02  6.44042930e-05]\n",
-      " [-2.18994178e-04  5.55295642e-02  1.19115638e-04 ...  4.96920376e-02\n",
-      "   6.26638131e-04  5.28064299e-02]\n",
-      " [ 5.58121985e-02  1.19115638e-04  5.87241325e-02 ...  5.28825081e-04\n",
-      "   5.23327096e-02  4.30466656e-04]\n",
-      " ...\n",
-      " [-1.61981521e-04  4.96920376e-02  5.28825081e-04 ...  5.63612392e-02\n",
-      "   6.11767602e-04  5.32314551e-02]\n",
-      " [ 5.55037138e-02  6.26638131e-04  5.23327096e-02 ...  6.11767602e-04\n",
-      "   5.81635439e-02  7.96702127e-04]\n",
-      " [ 6.44042930e-05  5.28064299e-02  4.30466656e-04 ...  5.32314551e-02\n",
-      "   7.96702127e-04  5.50402112e-02]]\n",
-      "  mean conditional:  [ 0.41940224  0.02813178  0.44759043  0.43664465  0.38541259  0.78956828\n",
-      "  0.20818095  1.03582342 -0.07148487  1.1414638  -0.40031208  1.09406356\n",
-      " -0.70532063  0.89842581 -0.91895626  0.5778346  -0.99812838  0.17961194\n",
-      " -0.93635488 -0.23191107 -0.75989237 -0.59372233 -0.51188989 -0.85373356\n",
-      " -0.24225269 -0.97191152  0.00228271 -0.9245448   0.19445881 -0.71423438\n",
-      "  0.33165175 -0.37690306] , sample:  [ 0.41592568  0.05419824  0.40554061  0.41010584  0.40503621  0.79716137\n",
-      "  0.22710124  1.06343177 -0.09068844  1.11938821 -0.38998055  1.06820526\n",
-      " -0.67113481  0.91931585 -0.94804764  0.59722593 -1.03018258  0.16263276\n",
-      " -0.90062962 -0.22955932 -0.75030564 -0.58716022 -0.53213336 -0.84478065\n",
-      " -0.22792013 -0.95982796 -0.00513929 -0.95008177  0.21957407 -0.73562939\n",
-      "  0.31071394 -0.36123729]\n",
-      "  cov conditional:  [[ 2.93911273e-04  7.56907154e-06  2.35479261e-04 ... -2.82681071e-05\n",
-      "   2.30395483e-04 -8.40465570e-06]\n",
-      " [ 7.56907154e-06  3.22007451e-04 -1.33968097e-06 ...  1.28103115e-04\n",
-      "   1.09683154e-05  2.58485440e-04]\n",
-      " [ 2.35479261e-04 -1.33968097e-06  3.11629932e-04 ... -3.12020656e-05\n",
-      "   9.51468852e-05 -1.86942874e-05]\n",
-      " ...\n",
-      " [-2.82681071e-05  1.28103115e-04 -3.12020656e-05 ...  3.40960069e-04\n",
-      "  -2.32495047e-05  2.77413479e-04]\n",
-      " [ 2.30395483e-04  1.09683154e-05  9.51468852e-05 ... -2.32495047e-05\n",
-      "   3.01353657e-04 -3.35669751e-06]\n",
-      " [-8.40465570e-06  2.58485440e-04 -1.86942874e-05 ...  2.77413479e-04\n",
-      "  -3.35669751e-06  3.38248611e-04]]\n",
-      "  mean noise:  [ 0.00347656 -0.02606647  0.04204982  0.02653881 -0.01962363 -0.00759309\n",
-      " -0.01892029 -0.02760834  0.01920356  0.02207558 -0.01033153  0.0258583\n",
-      " -0.03418582 -0.02089004  0.02909139 -0.01939134  0.0320542   0.01697918\n",
-      " -0.03572526 -0.00235175 -0.00958672 -0.00656212  0.02024347 -0.0089529\n",
-      " -0.01433256 -0.01208356  0.007422    0.02553696 -0.02511526  0.02139501\n",
-      "  0.02093781 -0.01566577]\n",
-      "  cov noise:  [[ 2.93911273e-04  7.56907154e-06  2.35479261e-04 ... -2.82681071e-05\n",
-      "   2.30395483e-04 -8.40465570e-06]\n",
-      " [ 7.56907154e-06  3.22007451e-04 -1.33968097e-06 ...  1.28103115e-04\n",
-      "   1.09683154e-05  2.58485440e-04]\n",
-      " [ 2.35479261e-04 -1.33968097e-06  3.11629932e-04 ... -3.12020656e-05\n",
-      "   9.51468852e-05 -1.86942874e-05]\n",
-      " ...\n",
-      " [-2.82681071e-05  1.28103115e-04 -3.12020656e-05 ...  3.40960069e-04\n",
-      "  -2.32495047e-05  2.77413479e-04]\n",
-      " [ 2.30395483e-04  1.09683154e-05  9.51468852e-05 ... -2.32495047e-05\n",
-      "   3.01353657e-04 -3.35669751e-06]\n",
-      " [-8.40465570e-06  2.58485440e-04 -1.86942874e-05 ...  2.77413479e-04\n",
-      "  -3.35669751e-06  3.38248611e-04]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# do statistics on the leaf values\n",
-    "F = tree.data['F_T'][0]; H = tree.data['H_T'][0]\n",
-    "print(\"root conditional mean vs. sample mean:\",jnp.linalg.solve(H,F),jnp.mean(leaf_values,0))\n",
-    "print(\"root conditional cov vs. sample cov:\",jnp.linalg.inv(H),jnp.cov(leaf_values.T))\n",
-    "\n",
-    "# sample statistics\n",
-    "K = 500 # number samples\n",
-    "samples = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_noise = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_conditional = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "\n",
-    "for i in tqdm(range(K)):\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "    values = tree.data['value'][tree.is_leaf]\n",
-    "    # collect values\n",
-    "    samples = samples.at[i].set(values)\n",
-    "    # add observation noise\n",
-    "    subkey,key = jax.random.split(key)\n",
-    "    samples_noise = samples_noise.at[i].set(values+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,values.shape))\n",
-    "    # conditional sampling\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_conditional.down(tree,params.values())\n",
-    "    samples_conditional = samples_conditional.at[i].set(tree.data['value'][tree.is_leaf])\n",
-    "# print sample statistics for all leaves\n",
-    "print(\"data parameters: \",params.values())\n",
-    "print(\"observation noise: \")\n",
-    "print(\" mean: \",jnp.mean(samples_conditional-leaf_values,axis=(0,1)))\n",
-    "print(\" cov: \",jnp.cov((samples_conditional-leaf_values).reshape(-1,n*d).T))\n",
-    "print(\"leaves: \")\n",
-    "for i in range(samples.shape[1]):\n",
-    "    print(\"  mean: \",jnp.mean(samples[:,i],0))\n",
-    "    print(\"  cov: \",jnp.cov(samples[:,i].T))\n",
-    "    print(\"  mean w/ noise: \",jnp.mean(samples_noise[:,i],0))\n",
-    "    print(\"  cov w/ noise: \",jnp.cov(samples_noise[:,i].T))\n",
-    "    print(\"  mean conditional: \",jnp.mean(samples_conditional[:,i],0), \", sample: \",leaf_values[i])\n",
-    "    print(\"  cov conditional: \",jnp.cov(samples_conditional[:,i].T))\n",
-    "    print(\"  mean noise: \",jnp.mean(samples_conditional[:,i]-leaf_values[i],0))\n",
-    "    print(\"  cov noise: \",jnp.cov((samples_conditional[:,i]-leaf_values[i]).T))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MCMC for parameter estimation. In this case, with constant node covariance the model is fully Gaussian and we can use the result of upwards pass without correction (logw is zero)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.1, 'k_sigma': 0.1, 'obs_var': 0.001}\n",
-      "data parameters:  {'k_alpha': 0.1, 'k_sigma': 0.5, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2000/2000 [00:25<00:00, 78.92it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.8370\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "log_likelihood = get_log_likelihood(tree,down_conditional,up,init_up)\n",
-    "log_posterior = get_log_posterior(log_likelihood)\n",
-    "proposal = get_proposal(tree)\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.1,alpha=3,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.1), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3,min=1e-4,max=.1,prior='uniform') # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 2000, burn_in=000, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,true_params=params.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mcmc_Gaussian_BFFG_shapes_SDEs.ipynb b/examples/mcmc_Gaussian_BFFG_shapes_SDEs.ipynb
deleted file mode 100644
index 32a3d14..0000000
--- a/examples/mcmc_Gaussian_BFFG_shapes_SDEs.ipynb
+++ /dev/null
@@ -1,1453 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for shapes with SDE transitions\n",
-    "\n",
-    "Parameter inference for trees with Kunita-like SDEs transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebooks [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb), [mcmc_Gaussian_BFFG_shapes.ipynb](mcmc_Gaussian_BFFG_shapes.ipynb), [mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb](mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb) for simplified versions. The covariance of each stochastic step is shape dependent and the processes nonlinear, thus requiring computation of likelihood ratios in the downwards passes for use in the MCMC iterations.\n",
-    "\n",
-    "The covariance is assumed rotationally invariant, allowing factorization into a kernel product the identity matrix of the ambient space.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_shape_2d, plot_tree_2d_scatter, plot_tree_2d_shape\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.SDE import dot, solve\n",
-    "from examples.ABFFG import *\n",
-    "from examples.shape import k_K2\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed:  554162341406\n"
-     ]
-    }
-   ],
-   "source": [
-    "# seed\n",
-    "#seed = 216298397259\n",
-    "import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "print(\"seed: \",seed)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 2; n = 32 # d = dimension of embedding space (usually 2), n = number of landmarkds\n",
-    "phis = jnp.linspace(0,2*jnp.pi,n,endpoint=False) # circular shape\n",
-    "root = jnp.vstack((jnp.cos(phis),jnp.sin(phis))).T.flatten()\n",
-    "\n",
-    "# plot\n",
-    "plot_shape = plot_shape_2d\n",
-    "plot_shape(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SDE setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 500 # number of time steps in the discretization\n",
-    "\n",
-    " # drift\n",
-    "def b(t,x,params): return jnp.zeros(n*d)\n",
-    "\n",
-    "# diffusion and covariance specification for shape processes\n",
-    "# assumes same covariance in all directions (x,y or x,y,z), thus allowing some factorization of the covariance matrix\n",
-    "kQ12 = lambda x,params: (.25/max(16,n))*k_K2(x,params)\n",
-    "\n",
-    "# evaluate k on two pairs of landmark configurations\n",
-    "kQ12_q = lambda q1,q2,params: kQ12(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# diffusion field and covariance matrix factored in dimensions\n",
-    "sigma = lambda q,theta: kQ12_q(q,q,theta) # diffusion field, kernel evaluated on all landmark pairs\n",
-    "def a(q,theta): sigmaq = sigma(q,theta); return jnp.einsum('ij,kj->ik',sigmaq,sigmaq) # covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tree with SDE transitions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                            None\n",
-      "           ┌────────────────────────┬────────────────────────┼────────────────────────┬────────────────────────┐\n",
-      "          None                     None                     None                     None                     None          \n",
-      " ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "degree = 5; topology = symmetric_topology(height=2, degree=degree)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "n_leaves = tree.is_leaf.sum()\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "n_edges = tree.data['edge_length'].shape[0]\n",
-    "tree.add_property('value', shape=(n_steps+1,n*d,))\n",
-    "tree.add_property('noise', shape=(n_steps,n*d))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.3), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.25), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions using the SDE functions above. The step covariance is square of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unconditional down\n",
-    "down_unconditional = SDE_down_unconditional(n_steps,b,sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sample new noise\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "add_leaf_noise(tree,key,params)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf][:,-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots(figsize=(10,8))\n",
-    "plot_tree_2d_scatter(tree,'value',selector=lambda z: z.reshape((-1,n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "\n",
-    "plot_tree_2d_shape(tree,'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "up = SDE_up(n_steps,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree\n",
-    "init_up = get_init_up(n,tree,d=d,root=root,n_steps=n_steps)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# conditional down\n",
-    "down_conditional = SDE_down_conditional(n_steps,b,sigma,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time elapsed: 20.4148 ms\n",
-      "Time elapsed: 342.7048 ms\n",
-      "Time elapsed: 1.4248 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding, time the operations\n",
-    "test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We test the setup by sampling a number of trees and computing mean and covariance of the leaf data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "root conditional mean vs. sample mean: [ 0.99405117 -0.03243309  0.9754613   0.16226373  0.89992143  0.35290806\n",
-      "  0.80147825  0.53522285  0.65600004  0.69076377  0.50043207  0.80196746\n",
-      "  0.32264719  0.89328509  0.13580591  0.93640074 -0.04120073  0.96215393\n",
-      " -0.22048102  0.92175122 -0.40289001  0.88581968 -0.55520195  0.78022565\n",
-      " -0.71481688  0.66375997 -0.83050676  0.52504455 -0.9280369   0.37561325\n",
-      " -0.99587267  0.19752576 -1.00858105  0.02442058 -1.00040215 -0.15688369\n",
-      " -0.92685407 -0.34120184 -0.85173227 -0.51389761 -0.73933808 -0.69259064\n",
-      " -0.59038408 -0.82503648 -0.40855213 -0.93896263 -0.21024649 -1.00155637\n",
-      " -0.01945543 -1.00731609  0.15684732 -0.991991    0.35667581 -0.92766944\n",
-      "  0.5508466  -0.82655973  0.71234805 -0.71665558  0.82087696 -0.55550884\n",
-      "  0.92188412 -0.39521286  0.98763189 -0.2196976 ] [ 1.01485612 -0.03404368  0.99533558  0.16476686  0.91817055  0.35978668\n",
-      "  0.81709451  0.54704732  0.66716023  0.70658295  0.50829717  0.82083739\n",
-      "  0.32589605  0.91368073  0.13359475  0.95874448 -0.04840527  0.98459485\n",
-      " -0.2313408   0.94449089 -0.41767553  0.90715328 -0.5730582   0.79949598\n",
-      " -0.73424646  0.68009497 -0.85035449  0.53779171 -0.94740134  0.384535\n",
-      " -1.01561134  0.20169208 -1.02724671  0.02344421 -1.01659853 -0.16249006\n",
-      " -0.94189119 -0.35261389 -0.86505513 -0.52809745 -0.74928521 -0.70842982\n",
-      " -0.59860817 -0.84090599 -0.41274121 -0.95390503 -0.20969056 -1.01572627\n",
-      " -0.01446987 -1.02166753  0.16515478 -1.0061973   0.36820862 -0.94159204\n",
-      "  0.56479216 -0.84016701  0.72753586 -0.72940668  0.83817409 -0.56642664\n",
-      "  0.94010726 -0.40379461  1.00732711 -0.2253367 ]\n",
-      "root conditional cov vs. sample cov: [[0.00089395 0.00082209 0.00074355 ... 0.00066128 0.00076159 0.0008293 ]\n",
-      " [0.00082209 0.0008842  0.00081303 ... 0.00054402 0.00065506 0.00075196]\n",
-      " [0.00074355 0.00081303 0.00087855 ... 0.00042959 0.00053598 0.00064353]\n",
-      " ...\n",
-      " [0.00066128 0.00054402 0.00042959 ... 0.00089863 0.00083321 0.00076135]\n",
-      " [0.00076159 0.00065506 0.00053598 ... 0.00083321 0.00089994 0.00083272]\n",
-      " [0.0008293  0.00075196 0.00064353 ... 0.00076135 0.00083272 0.00089625]] [[ 0.00718469 -0.00132511  0.00823342 ... -0.00315738  0.00396857\n",
-      "  -0.00245465]\n",
-      " [-0.00132511  0.00493895 -0.00210864 ...  0.00450425 -0.00060389\n",
-      "   0.00468356]\n",
-      " [ 0.00823342 -0.00210864  0.01141844 ... -0.00423675  0.00438641\n",
-      "  -0.00377032]\n",
-      " ...\n",
-      " [-0.00315738  0.00450425 -0.00423675 ...  0.00698123 -0.00201888\n",
-      "   0.00643921]\n",
-      " [ 0.00396857 -0.00060389  0.00438641 ... -0.00201888  0.00350913\n",
-      "  -0.00128571]\n",
-      " [-0.00245465  0.00468356 -0.00377032 ...  0.00643921 -0.00128571\n",
-      "   0.00750901]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 500/500 [03:14<00:00,  2.57it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n",
-      "observation noise: \n",
-      " mean:  [ 9.36730811e-04  2.99828619e-03 -6.26267043e-03  1.81388087e-03\n",
-      "  1.43045573e-03  1.43352625e-03 -6.07054643e-03 -4.74436206e-03\n",
-      "  3.02770213e-03 -7.60898658e-03 -2.43827366e-03  2.36868009e-03\n",
-      "  6.58640388e-04 -2.49464353e-03  5.95693444e-03  3.30769220e-03\n",
-      " -6.85528632e-04 -8.66313367e-03 -3.14081245e-03  8.45353949e-03\n",
-      "  5.12614589e-03 -1.32042975e-02 -5.75878858e-03  2.31241331e-03\n",
-      "  6.59474091e-03  1.41539353e-03 -2.26453228e-03  9.53589467e-04\n",
-      "  3.01956285e-05 -5.80532036e-03  8.95219402e-03  4.13806376e-03\n",
-      " -4.85930919e-05  3.99253970e-04  7.62945712e-03 -1.03809784e-03\n",
-      " -1.18828471e-02  9.57280312e-04  8.43688512e-04 -5.84320869e-03\n",
-      "  7.33980754e-03  7.24807112e-03  9.15502776e-03 -1.33230291e-03\n",
-      "  1.76262960e-04  6.96513544e-03 -1.04247724e-02  6.74295382e-03\n",
-      " -6.28713632e-03 -5.10394529e-03  1.39669081e-02  3.87888796e-03\n",
-      "  5.64685189e-03  1.91870644e-03 -9.02005702e-03 -3.62308818e-03\n",
-      " -1.18565260e-02  9.73802341e-03  7.81428444e-03 -5.59921635e-03\n",
-      "  1.62798356e-03 -1.46127282e-03 -7.97430059e-03  1.81877562e-03]\n",
-      " diag cov:  [0.00077908 0.00114603 0.00088311 0.00095977 0.00161111 0.00107903\n",
-      " 0.00107368 0.00114795 0.00101499 0.00068285 0.00092533 0.00093827\n",
-      " 0.00115722 0.001059   0.00098173 0.00089501 0.0008969  0.00079056\n",
-      " 0.0012274  0.00088466 0.00084465 0.00101962 0.00080439 0.00113365\n",
-      " 0.00094479 0.00086737 0.00093142 0.0006345  0.00093057 0.0009375\n",
-      " 0.00082688 0.00106647 0.00075135 0.00108181 0.00081262 0.00118119\n",
-      " 0.00075244 0.00071425 0.00112575 0.00102279 0.00134583 0.00099533\n",
-      " 0.00090139 0.00083697 0.00069581 0.00099567 0.00109866 0.00080135\n",
-      " 0.00077245 0.00096917 0.00072503 0.00082007 0.00073304 0.00113678\n",
-      " 0.00100026 0.00102884 0.00122644 0.00112274 0.00082902 0.00127047\n",
-      " 0.00077075 0.00084303 0.00073916 0.00098163]\n",
-      "leaves: \n",
-      "  mean noise:  [ 0.01633013 -0.02245652 -0.00488444 -0.02112328 -0.03654929 -0.01227854\n",
-      "  0.01327332  0.05010722  0.02104832 -0.01467501 -0.03618786  0.01837276\n",
-      " -0.00037638 -0.00019737 -0.00739577  0.00106791  0.00680291 -0.01888449\n",
-      "  0.01212073  0.01738732 -0.01337789 -0.03334231  0.0353508   0.01697592\n",
-      "  0.01178999  0.06007845 -0.02824022 -0.03513946 -0.02288478 -0.06814743\n",
-      " -0.00447729  0.0312917   0.02790009  0.01867183  0.0044646  -0.011469\n",
-      " -0.01901866 -0.02495249  0.0300503   0.01082063  0.02425391  0.02302454\n",
-      " -0.0273576  -0.03679473 -0.00112481  0.05694378 -0.00593123  0.02521101\n",
-      "  0.03310361 -0.05643722 -0.01639573 -0.00379289 -0.0006502  -0.0150917\n",
-      " -0.00150163  0.02318688  0.0095843   0.01474917 -0.01339328  0.00479204\n",
-      "  0.00267343 -0.01828779 -0.01349433  0.00497975]\n",
-      "  diag cov noise:  [0.00024521 0.00020145 0.0002301  0.00019193 0.00022452 0.0001931\n",
-      " 0.00022748 0.00019451 0.00025192 0.0001897  0.00027174 0.00019364\n",
-      " 0.00027485 0.00021389 0.00026884 0.00023945 0.00024696 0.00024449\n",
-      " 0.00021905 0.00022941 0.0002092  0.00022323 0.00022384 0.00023305\n",
-      " 0.00025426 0.00024344 0.00026692 0.00024017 0.00024375 0.00022126\n",
-      " 0.00022256 0.00021096 0.00023084 0.00023163 0.00024258 0.00025404\n",
-      " 0.00023764 0.00026322 0.00023347 0.00027029 0.0002447  0.00026755\n",
-      " 0.00024654 0.00024875 0.0002428  0.00023859 0.00024053 0.00023604\n",
-      " 0.00023764 0.00023711 0.00023919 0.00024955 0.00024257 0.00026375\n",
-      " 0.00024643 0.0002625  0.00026233 0.00024501 0.00028237 0.0002336\n",
-      " 0.00028793 0.00023295 0.00027204 0.00022105]\n",
-      "  mean noise:  [ 0.01051979 -0.04521713  0.00743378  0.01753257 -0.00966338  0.0032835\n",
-      " -0.01655674  0.01055637 -0.00430776 -0.02024569  0.02077841 -0.02028499\n",
-      "  0.04166856  0.04500146 -0.03136938 -0.03867458  0.01868528 -0.02883435\n",
-      " -0.07857688 -0.02048301 -0.00076164  0.04294665  0.02239415 -0.01988734\n",
-      "  0.03949219  0.01266434 -0.02891237  0.0208409  -0.01658537  0.02119059\n",
-      "  0.02187985  0.01505097  0.00609379 -0.02210642  0.00109664 -0.06619762\n",
-      "  0.00824549  0.01428061 -0.02675814  0.0351162   0.01787829 -0.03300029\n",
-      "  0.03261785  0.00116669 -0.0166345  -0.01396284  0.00107924  0.05749286\n",
-      "  0.00618399 -0.00925875 -0.00436295 -0.0085665  -0.02534987 -0.01517493\n",
-      "  0.03349864 -0.0218368  -0.03895928  0.03112347 -0.00465149  0.04194315\n",
-      " -0.00192103  0.00406682 -0.00300877 -0.00179588]\n",
-      "  diag cov noise:  [0.00025872 0.00024506 0.00026316 0.00025677 0.0002547  0.00025657\n",
-      " 0.00023687 0.00024548 0.00022045 0.00024275 0.0002203  0.00024583\n",
-      " 0.00022677 0.00024902 0.0002297  0.0002465  0.00022323 0.00023309\n",
-      " 0.00020789 0.00022085 0.00019806 0.00022632 0.00021371 0.00024701\n",
-      " 0.00024446 0.00025586 0.00024357 0.00025251 0.00022044 0.00024114\n",
-      " 0.00021971 0.00021984 0.00022406 0.0002013  0.00022893 0.00019226\n",
-      " 0.00024102 0.00020012 0.00025378 0.0002346  0.00025912 0.00027568\n",
-      " 0.00026517 0.0002677  0.00026702 0.00023901 0.00025631 0.00023225\n",
-      " 0.0002538  0.00022836 0.00025688 0.00022694 0.0002542  0.00023493\n",
-      " 0.00025652 0.000241   0.00025587 0.00023955 0.00023925 0.00024998\n",
-      " 0.00023447 0.00025953 0.00024777 0.00024778]\n",
-      "  mean noise:  [-0.00411108 -0.05115619 -0.01012536  0.00314428  0.0086699   0.03533013\n",
-      " -0.01447614 -0.03450566  0.02455476 -0.01143464 -0.03244577  0.01557407\n",
-      "  0.01859346 -0.03854592 -0.02549666 -0.00191078  0.03518424 -0.01535855\n",
-      " -0.02151438  0.05681422 -0.01416771 -0.007475   -0.03099301  0.01645156\n",
-      "  0.0582958  -0.033161    0.01596629 -0.01382824 -0.02296372  0.00722686\n",
-      "  0.01656818 -0.01280434 -0.00794758  0.04425712  0.02291572 -0.04196463\n",
-      " -0.00265742 -0.00017694 -0.00107966 -0.01355235  0.01082782  0.05479361\n",
-      " -0.01912397 -0.00197914 -0.02239667  0.01201483 -0.0276213  -0.01624611\n",
-      "  0.03779598  0.01194506 -0.00980731  0.00749156 -0.01089142  0.04755276\n",
-      "  0.01803969 -0.04706814  0.0221878  -0.00310328  0.00726623 -0.03134114\n",
-      "  0.00355201  0.03603191 -0.01942733  0.05576356]\n",
-      "  diag cov noise:  [0.00022263 0.00022435 0.00022709 0.00023931 0.00022633 0.00023776\n",
-      " 0.000219   0.00024185 0.00022547 0.0002573  0.00024187 0.00025773\n",
-      " 0.00023894 0.00022952 0.0002313  0.00021521 0.00023388 0.00022766\n",
-      " 0.00023838 0.0002406  0.00023933 0.00024065 0.00023744 0.00023195\n",
-      " 0.00024683 0.00022423 0.00026492 0.00022931 0.00026487 0.00024041\n",
-      " 0.00025066 0.00024981 0.00024613 0.00025343 0.00025589 0.0002416\n",
-      " 0.00025962 0.0002197  0.00024932 0.00022345 0.00024    0.00025049\n",
-      " 0.00022628 0.00024759 0.00020894 0.00023177 0.00019844 0.00023008\n",
-      " 0.00019245 0.00022861 0.00019187 0.00021751 0.0001975  0.00021527\n",
-      " 0.00021551 0.00023671 0.00023595 0.00025523 0.00024209 0.00025188\n",
-      " 0.00024068 0.0002343  0.00023104 0.00021691]\n",
-      "  mean noise:  [ 7.45837728e-03 -5.13556192e-03 -2.16172211e-03  2.08781104e-02\n",
-      "  1.41954139e-02  4.84071200e-02 -7.05295830e-02 -7.18452784e-03\n",
-      "  3.01493103e-02 -3.99273243e-02  2.07777946e-02  3.94997019e-02\n",
-      " -3.93304058e-02 -2.52479294e-02  3.68800625e-02 -1.20355539e-02\n",
-      "  1.53624965e-02  1.45797179e-02 -1.16316320e-02  2.13243878e-02\n",
-      "  1.24295358e-02 -2.70293562e-02 -1.08867020e-02 -1.65133841e-02\n",
-      " -2.12278337e-02 -9.71110832e-03  1.93242078e-03  1.07170658e-02\n",
-      "  4.02640227e-02 -3.77846527e-03 -5.89632593e-03  1.34498224e-02\n",
-      " -5.54647137e-03  3.38495337e-02  2.80948663e-02 -2.08174911e-02\n",
-      " -5.72207584e-02 -2.27637080e-02  5.10430158e-02 -6.51085336e-03\n",
-      " -1.54498721e-02  5.90458761e-02  2.58024768e-02 -2.29509397e-02\n",
-      " -1.80663468e-02 -3.27140464e-02 -1.20740865e-02 -1.53520039e-02\n",
-      " -6.30154760e-03  4.40307605e-02  6.41550780e-02  5.24839609e-03\n",
-      " -2.36491735e-03  1.74619822e-02 -7.30880549e-02 -6.80754612e-03\n",
-      " -1.24538622e-02 -3.76663700e-02  2.20549721e-02  1.11021802e-02\n",
-      "  3.05095096e-03 -2.05185158e-02  1.08781161e-02  3.97051777e-05]\n",
-      "  diag cov noise:  [0.00022404 0.0002148  0.00024127 0.00021929 0.00024701 0.00022612\n",
-      " 0.00024043 0.00023073 0.00023701 0.00023912 0.00023302 0.0002344\n",
-      " 0.00022165 0.00023036 0.0002058  0.00023367 0.000199   0.00023295\n",
-      " 0.00021417 0.00023777 0.0002343  0.00024312 0.00023965 0.00023953\n",
-      " 0.00023872 0.00023383 0.00023513 0.00023534 0.00023163 0.00023684\n",
-      " 0.00022875 0.00024275 0.00022901 0.00025102 0.00022536 0.00024797\n",
-      " 0.00022013 0.00022791 0.00022946 0.00020333 0.00023959 0.00020319\n",
-      " 0.00023534 0.00022171 0.00024611 0.00023497 0.00026594 0.00022899\n",
-      " 0.00026309 0.00021486 0.0002439  0.00020852 0.00023259 0.00021891\n",
-      " 0.00024854 0.00023345 0.00026794 0.00023823 0.00026036 0.00023983\n",
-      " 0.00023711 0.00023477 0.00022013 0.00022196]\n",
-      "  mean noise:  [ 0.01247405  0.018625   -0.00669375  0.01945473  0.00037882 -0.01143009\n",
-      " -0.02171583 -0.02344809  0.02385663 -0.01044096 -0.03739867  0.01800077\n",
-      "  0.00706681  0.00701948  0.0355597  -0.0207235  -0.01833462  0.0175573\n",
-      "  0.03483168 -0.00174628  0.00986957 -0.01405255 -0.04973731 -0.02383967\n",
-      " -0.01120332 -0.01811965  0.02468696 -0.00505864 -0.00849523 -0.01782629\n",
-      "  0.0040833   0.04635458 -0.02040545  0.03133101 -0.00406892 -0.01161809\n",
-      "  0.02521558 -0.00341984 -0.00826899 -0.01452985  0.0473363  -0.01106505\n",
-      " -0.01262668 -0.00829038 -0.02313427  0.03130745 -0.02864094 -0.02325201\n",
-      " -0.01350709 -0.01423154  0.02591089  0.01794938  0.07036879 -0.02703432\n",
-      " -0.02633032  0.039175    0.01430918  0.01857843 -0.0068387  -0.00690642\n",
-      " -0.03468527 -0.01534196  0.00835365 -0.00911861]\n",
-      "  diag cov noise:  [0.00025238 0.00023355 0.00025472 0.00022618 0.00025386 0.0002369\n",
-      " 0.0002505  0.00024491 0.00025453 0.00024072 0.00025453 0.00023917\n",
-      " 0.00024524 0.00023703 0.00023254 0.00022473 0.00021737 0.00021915\n",
-      " 0.0002047  0.00022397 0.00019779 0.00023636 0.00020105 0.00024821\n",
-      " 0.00021137 0.0002446  0.0002202  0.00024059 0.00022319 0.00023741\n",
-      " 0.0002137  0.00023192 0.00021619 0.00022948 0.00023566 0.00023014\n",
-      " 0.00025356 0.00022868 0.00026299 0.00022023 0.0002554  0.00020937\n",
-      " 0.00023113 0.00021033 0.00021943 0.00021658 0.00022612 0.00022534\n",
-      " 0.00023276 0.00023572 0.00022968 0.00023652 0.00022406 0.00022616\n",
-      " 0.00022761 0.00020888 0.00024369 0.00020733 0.0002496  0.00023883\n",
-      " 0.00024556 0.00026469 0.00025091 0.00025505]\n",
-      "  mean noise:  [ 2.89581590e-02  6.11859417e-03 -2.07445658e-03  3.53622056e-02\n",
-      " -8.35716702e-02 -4.69353622e-02  6.44717542e-03 -3.09709956e-02\n",
-      "  2.48414877e-02  1.03017617e-02  4.19276368e-03  2.48863323e-02\n",
-      " -4.94420123e-06 -2.27026858e-02 -6.59935905e-03  9.43368045e-03\n",
-      "  1.49033719e-02 -9.13514235e-03  3.77588385e-02 -8.56184055e-03\n",
-      "  3.86392821e-03  5.11153480e-03 -3.55939179e-02  2.31469491e-03\n",
-      " -3.29989476e-02  2.01784392e-02  1.31268536e-02  1.65875613e-02\n",
-      "  3.58450492e-02 -2.48824157e-02  8.62405444e-04  2.93961017e-02\n",
-      "  1.64836133e-02 -2.76613249e-02  1.31950218e-02  5.03744807e-03\n",
-      " -5.22918155e-02  3.57764434e-03 -5.19219433e-02 -1.05966302e-02\n",
-      "  6.54951854e-02 -1.80129847e-02 -7.48096029e-04  3.63019657e-02\n",
-      " -1.97288731e-02 -1.35741716e-02  8.20387606e-03  2.37283217e-02\n",
-      "  1.50517234e-03 -3.03299740e-03  3.16177578e-02 -1.35787398e-03\n",
-      "  1.98007457e-02  2.02308438e-02 -2.63841464e-02 -2.48153875e-02\n",
-      " -2.40956657e-02  1.58228803e-02  1.51633174e-02 -1.77013688e-02\n",
-      "  2.03066593e-02  1.16776705e-02 -1.98620857e-03 -8.28389194e-03]\n",
-      "  diag cov noise:  [0.00023215 0.00028061 0.00023722 0.00027201 0.00023885 0.00025844\n",
-      " 0.00025231 0.00025985 0.000297   0.00026111 0.00031629 0.00024757\n",
-      " 0.00028722 0.00023127 0.00024038 0.00022115 0.00020642 0.00021334\n",
-      " 0.00019874 0.0002086  0.00019852 0.00021081 0.00019342 0.00020903\n",
-      " 0.00018491 0.00020565 0.00018797 0.00021054 0.0002052  0.00021478\n",
-      " 0.00021353 0.00021194 0.00020399 0.00020686 0.0001948  0.00020357\n",
-      " 0.0001995  0.00020464 0.00020928 0.00021196 0.0002055  0.00021922\n",
-      " 0.0001995  0.00021669 0.00020909 0.00021763 0.00023333 0.00022826\n",
-      " 0.00025911 0.000247   0.00027581 0.00027065 0.00026493 0.00027123\n",
-      " 0.00025386 0.00025841 0.00027185 0.00025916 0.00028377 0.0002628\n",
-      " 0.00026507 0.00026614 0.00024399 0.00027503]\n",
-      "  mean noise:  [ 1.58570263e-02  9.63981525e-03 -2.60750331e-02  2.51489490e-02\n",
-      "  3.04197857e-02 -5.59921256e-02  8.00323734e-03  3.01241518e-02\n",
-      " -1.42529338e-02 -7.48420383e-03 -1.75025239e-02  2.14779021e-02\n",
-      " -1.36637447e-03 -4.00160020e-02  1.62692723e-02 -2.84329646e-03\n",
-      " -1.32651156e-02  1.51468419e-02 -3.17521418e-02  3.29550189e-02\n",
-      "  1.30198720e-03 -3.56497857e-02 -1.48964454e-02 -2.24637987e-02\n",
-      "  5.45201031e-02  2.92834971e-02  1.47108848e-02 -2.18814243e-02\n",
-      " -1.36391938e-02  2.55131956e-02  2.81608607e-02 -2.09202838e-02\n",
-      "  4.74195870e-03 -7.33217652e-05 -1.01588255e-02 -2.20754203e-02\n",
-      "  5.01928053e-03  9.97543624e-03 -2.86903358e-02  4.54697781e-02\n",
-      "  1.66740760e-02 -3.17977867e-02 -1.36387721e-02 -2.92718114e-02\n",
-      " -1.80739154e-02 -7.99519944e-03  6.10739098e-02  4.18374887e-03\n",
-      " -2.82337274e-02  2.26685983e-02  1.80466702e-02 -3.00550178e-02\n",
-      " -1.13658452e-02  6.19968151e-03  3.56283971e-02  7.93296841e-03\n",
-      "  4.87222071e-03  1.07799827e-01 -4.17435759e-02 -8.27921064e-02\n",
-      "  1.47344390e-02 -3.70651934e-02 -2.62237214e-02  7.31996682e-03]\n",
-      "  diag cov noise:  [0.00024274 0.00024639 0.00024333 0.0002516  0.00024939 0.00025498\n",
-      " 0.00024153 0.00025976 0.00022587 0.0002699  0.00022882 0.0002815\n",
-      " 0.00024304 0.00027894 0.00024607 0.00026287 0.00023609 0.00024431\n",
-      " 0.0002236  0.00022451 0.00021765 0.00020794 0.00022509 0.00019408\n",
-      " 0.00023683 0.00018639 0.00024054 0.00019048 0.00023838 0.000199\n",
-      " 0.00023359 0.00020486 0.00023068 0.00020536 0.00023313 0.00020275\n",
-      " 0.00023716 0.00020007 0.00023983 0.00020337 0.00024198 0.00021777\n",
-      " 0.00023647 0.00022684 0.00022023 0.0002291  0.00020733 0.00023475\n",
-      " 0.00021691 0.00024173 0.00024532 0.00024132 0.00026606 0.00024156\n",
-      " 0.00025408 0.00024283 0.00023587 0.0002268  0.00023031 0.00021362\n",
-      " 0.00023593 0.00022009 0.00024192 0.00023419]\n",
-      "  mean noise:  [ 0.0345539   0.01554579 -0.00556704 -0.01947675  0.0301666  -0.0042572\n",
-      " -0.03650646  0.02134853 -0.00810962 -0.02664298  0.04029669  0.00882941\n",
-      " -0.06200466 -0.03702412 -0.00062503  0.01964067  0.03414985 -0.02310414\n",
-      "  0.03834544  0.02144812  0.03725877  0.01916173 -0.04899117 -0.06722207\n",
-      "  0.01512564 -0.00908969  0.00054302  0.03390081 -0.03827347  0.05465561\n",
-      "  0.01456529 -0.05845108  0.00282878 -0.01767365  0.01875425  0.04202592\n",
-      " -0.0178136   0.02741501 -0.00136472 -0.06287154  0.00776958  0.01699477\n",
-      "  0.03786304  0.03922086  0.03585589  0.03830326 -0.02674749 -0.03523151\n",
-      " -0.02541258 -0.01249647 -0.03042651 -0.00297855 -0.00176512  0.03341531\n",
-      "  0.00284522 -0.03494226  0.01197836  0.0184515  -0.00726339  0.01416029\n",
-      "  0.02989352  0.03700125 -0.05997326 -0.04740215]\n",
-      "  diag cov noise:  [0.00024793 0.00023885 0.00027358 0.00026084 0.00029081 0.00025626\n",
-      " 0.00027949 0.00023764 0.00025929 0.00022008 0.00025186 0.00021248\n",
-      " 0.0002425  0.00022174 0.00022714 0.00022045 0.00022365 0.00021147\n",
-      " 0.00023259 0.00021606 0.00023316 0.00023242 0.00022095 0.00022822\n",
-      " 0.00020756 0.00020879 0.00020061 0.00021418 0.00020394 0.00023411\n",
-      " 0.00020976 0.00024083 0.00020795 0.00023783 0.00020632 0.00023985\n",
-      " 0.00022448 0.00024704 0.00024818 0.00023934 0.00024955 0.0002229\n",
-      " 0.00023583 0.00022109 0.000232   0.00022787 0.00024292 0.00023513\n",
-      " 0.00025183 0.00025484 0.0002554  0.00025826 0.00026337 0.00024238\n",
-      " 0.00026281 0.00024011 0.00023894 0.00024639 0.00021053 0.00024654\n",
-      " 0.00020418 0.00023342 0.00022088 0.00022257]\n",
-      "  mean noise:  [ 0.00348428  0.02559519  0.02161701  0.00618716  0.00576275 -0.03991352\n",
-      "  0.00706095  0.00109034  0.02228323  0.01932252 -0.01952918  0.00291337\n",
-      "  0.00657564 -0.03182185  0.02123835  0.00730574 -0.05933982  0.00287672\n",
-      "  0.01400236  0.01780813  0.03208539 -0.01841064 -0.01685391 -0.02099734\n",
-      "  0.02463739  0.00216881 -0.02482101  0.0099807   0.00238606  0.01994352\n",
-      " -0.00164061 -0.0103986   0.00495783  0.00706261  0.04256578  0.00237539\n",
-      " -0.03876027 -0.02036404  0.00291863  0.01684004 -0.0064177  -0.02899365\n",
-      "  0.0165927  -0.01401408  0.00018823  0.01258761 -0.00645748  0.02674983\n",
-      "  0.0002121   0.04143202  0.0088867  -0.01646347  0.00743507 -0.03181022\n",
-      " -0.00295118  0.00425661 -0.0411928   0.01094037  0.02682396 -0.05203212\n",
-      " -0.02384165  0.03905987 -0.02283825  0.0031172 ]\n",
-      "  diag cov noise:  [0.00023679 0.00022712 0.00025537 0.00023781 0.00026335 0.00026023\n",
-      " 0.00025595 0.00027841 0.0002397  0.00026179 0.00022353 0.00023548\n",
-      " 0.00022499 0.00022818 0.00023108 0.00023156 0.000228   0.00022677\n",
-      " 0.00021809 0.00021582 0.00020934 0.00021773 0.0002056  0.00022344\n",
-      " 0.00020491 0.00022293 0.00021147 0.00022562 0.00021701 0.00023481\n",
-      " 0.00021326 0.00023777 0.00021656 0.00023277 0.00023325 0.00023397\n",
-      " 0.00024945 0.00024954 0.00024566 0.00026369 0.00022949 0.0002581\n",
-      " 0.00022313 0.00024329 0.0002266  0.00025026 0.00023368 0.00027267\n",
-      " 0.00024018 0.00029385 0.00024921 0.00030346 0.00025684 0.00027776\n",
-      " 0.00024361 0.00024468 0.00022309 0.0002243  0.0002182  0.00022062\n",
-      " 0.00023102 0.000221   0.00023427 0.00022176]\n",
-      "  mean noise:  [-0.00588116 -0.00259427 -0.00749823  0.01293583 -0.05906817  0.03700089\n",
-      "  0.02787894 -0.00321757  0.049784   -0.03691972 -0.04063885  0.02052558\n",
-      "  0.0363304  -0.02082563 -0.02355543  0.03519153  0.0281576   0.00213084\n",
-      " -0.00752156  0.00657211 -0.02140813  0.02291093  0.00980654 -0.05812021\n",
-      "  0.00470553 -0.0386558  -0.01109089  0.03958291  0.03080159 -0.00793911\n",
-      " -0.01455832  0.00671307  0.04950602 -0.01969657 -0.01443258  0.05430254\n",
-      " -0.02706423 -0.03419334  0.00053393  0.01777285 -0.02423919 -0.0038128\n",
-      "  0.02039222 -0.04177164  0.00224966  0.05544278 -0.01194101  0.00808152\n",
-      "  0.00738476 -0.01194936  0.00551451  0.03376102  0.01254878 -0.04994344\n",
-      " -0.00560298  0.00798478 -0.03453314  0.02175427  0.01075054 -0.00894163\n",
-      " -0.00020011 -0.00208506  0.00463189 -0.01943058]\n",
-      "  diag cov noise:  [0.0002409  0.00022343 0.00024852 0.00023238 0.00024193 0.00023134\n",
-      " 0.00023065 0.00022378 0.00022822 0.00022511 0.00023121 0.00023625\n",
-      " 0.00023136 0.00023312 0.00023984 0.00022434 0.00025159 0.00022076\n",
-      " 0.00024343 0.00022091 0.00021774 0.00022573 0.00019606 0.00022029\n",
-      " 0.00019263 0.00020582 0.00020593 0.00020415 0.00022087 0.00021606\n",
-      " 0.00022577 0.00022557 0.00023051 0.00023025 0.00023578 0.00023388\n",
-      " 0.00024593 0.00024014 0.00025893 0.00024392 0.00024811 0.00023354\n",
-      " 0.00023213 0.00021562 0.00023592 0.00021076 0.00024488 0.0002211\n",
-      " 0.00024621 0.00023467 0.00025322 0.00023694 0.00026921 0.00023933\n",
-      " 0.00026398 0.00023528 0.00024018 0.00022427 0.00022424 0.00021578\n",
-      " 0.00022423 0.00020777 0.00023104 0.0002081 ]\n",
-      "  mean noise:  [-0.02129658 -0.00097161  0.05213797  0.0122742  -0.02686858 -0.00807209\n",
-      " -0.03026215  0.01933478  0.04532411 -0.02210304 -0.02535528  0.03162553\n",
-      " -0.00191132  0.01186932  0.02987087 -0.03599764  0.00585533  0.00525599\n",
-      " -0.04579814 -0.00634042 -0.0177801  -0.00584557  0.02392646 -0.00131957\n",
-      "  0.0469661  -0.01292472 -0.02683261  0.01757277 -0.04628151 -0.01573331\n",
-      "  0.06480147  0.00442931  0.00347927 -0.01877115 -0.002774    0.04804107\n",
-      " -0.03020713 -0.01076346  0.02288694  0.0066261  -0.03273772 -0.02009098\n",
-      "  0.0347618  -0.00864246  0.03119627  0.02746215 -0.00797597  0.01961432\n",
-      " -0.03121867 -0.0209825   0.01586789  0.03839329  0.01610444 -0.01300938\n",
-      " -0.00405325  0.00039266  0.00021338 -0.02931202  0.00761413  0.07395837\n",
-      " -0.02495343 -0.03163923 -0.00891884  0.00251657]\n",
-      "  diag cov noise:  [0.00024389 0.00023328 0.00022941 0.00024294 0.0002386  0.00025942\n",
-      " 0.00024501 0.00027861 0.00024415 0.00027578 0.00024459 0.00025129\n",
-      " 0.00024352 0.00023821 0.00025688 0.00024882 0.00027919 0.00026567\n",
-      " 0.00027869 0.000267   0.00026255 0.00025332 0.00026165 0.00023564\n",
-      " 0.000265   0.00022449 0.0002588  0.00021923 0.00024781 0.00022391\n",
-      " 0.00024284 0.0002348  0.00023935 0.00024669 0.00023419 0.00026961\n",
-      " 0.00023498 0.00029677 0.00023478 0.00029843 0.00023196 0.00028223\n",
-      " 0.00023284 0.00026282 0.00023866 0.00023845 0.00025882 0.00023444\n",
-      " 0.00027287 0.00024139 0.00026275 0.00024295 0.00024572 0.0002411\n",
-      " 0.0002512  0.00024862 0.00026019 0.0002574  0.00025589 0.00024958\n",
-      " 0.00025747 0.00023279 0.00026083 0.00022779]\n",
-      "  mean noise:  [-3.42983477e-02  2.48905153e-02 -3.35262232e-02 -6.06465472e-02\n",
-      "  4.34458532e-02  3.47959537e-02  2.10511063e-02  3.19348995e-02\n",
-      " -1.72505492e-02 -2.38150855e-02  1.64956476e-03  6.99565647e-03\n",
-      "  1.65014498e-02  1.15135740e-02 -8.80397163e-03  6.19568020e-03\n",
-      "  6.54305392e-05 -2.16366010e-03 -8.60231295e-03  9.44308886e-04\n",
-      "  3.67408279e-02 -2.07495023e-02 -1.45581981e-02  2.79432565e-02\n",
-      " -3.15595148e-02 -1.20945964e-02  2.78931765e-02  1.87794972e-02\n",
-      "  1.58718353e-02 -6.38632824e-03 -1.62096811e-02 -4.09871004e-03\n",
-      " -7.73296612e-03 -3.05645817e-02  2.32611444e-02  6.70263137e-03\n",
-      "  4.97175758e-03  2.55144990e-02 -1.96408730e-02 -1.04683429e-02\n",
-      "  9.82906193e-03  2.13287794e-02  6.17626003e-02 -1.62565023e-02\n",
-      " -3.82746936e-02 -3.14676778e-03 -1.20347595e-02  7.15881556e-03\n",
-      " -2.04908003e-03 -2.96846286e-02  7.29827537e-03 -1.53121613e-02\n",
-      "  2.53340648e-02  5.12168598e-02 -6.74214087e-02 -1.18632334e-02\n",
-      "  1.70901785e-02  1.14963928e-02  3.23113127e-02 -3.51321035e-03\n",
-      "  4.39128567e-03 -1.17340892e-02  7.30470533e-04 -3.50849554e-02]\n",
-      "  diag cov noise:  [0.00025597 0.00020596 0.00025832 0.00021014 0.00025581 0.0002392\n",
-      " 0.00025612 0.00024918 0.00025227 0.00024483 0.00024626 0.00024603\n",
-      " 0.00024855 0.00024729 0.00025359 0.0002415  0.00026055 0.00023436\n",
-      " 0.00026689 0.00023846 0.00026387 0.00025229 0.00026356 0.00025673\n",
-      " 0.00026295 0.00024898 0.00026077 0.00024824 0.00025658 0.00024238\n",
-      " 0.00024693 0.00022728 0.00024024 0.00022081 0.00023848 0.00023524\n",
-      " 0.00024211 0.00025398 0.00024107 0.00025993 0.00024084 0.00026161\n",
-      " 0.00024953 0.00026041 0.00025056 0.00025025 0.00025677 0.00024619\n",
-      " 0.00027779 0.00025479 0.0002856  0.0002512  0.00027117 0.00023982\n",
-      " 0.00026084 0.00023921 0.00025402 0.00024614 0.00023933 0.00025861\n",
-      " 0.00023435 0.00026616 0.00024386 0.00024042]\n",
-      "  mean noise:  [ 0.02050978 -0.02061017 -0.0161727  -0.02999804 -0.02846215  0.01513992\n",
-      "  0.03083922 -0.0173896  -0.02124656  0.0173692  -0.03056451 -0.02839345\n",
-      "  0.00507488  0.03440696  0.05218795 -0.04641979 -0.00569095  0.0077128\n",
-      " -0.04969518  0.02894353  0.02098518 -0.02337725  0.00632489  0.01169347\n",
-      " -0.01811252 -0.01700644  0.00965476  0.01667516  0.02082638  0.0084712\n",
-      "  0.01852116  0.02265359 -0.01370483 -0.06606904 -0.02157253  0.01985097\n",
-      "  0.00737105 -0.00427935 -0.02341525  0.00667465  0.04066027 -0.0138147\n",
-      " -0.0214939  -0.01256477  0.01310889  0.01762434 -0.02946889  0.01890629\n",
-      "  0.03151548  0.01259821 -0.00217976 -0.00698177 -0.01111221  0.02872809\n",
-      " -0.01925109 -0.06343295  0.02695059 -0.03898163  0.0247402   0.04894943\n",
-      "  0.00858309  0.03883926 -0.04274641  0.03043902]\n",
-      "  diag cov noise:  [0.0002566  0.00026421 0.00026591 0.00029578 0.00027539 0.00027361\n",
-      " 0.00028421 0.00024522 0.00027785 0.00024078 0.00025532 0.00023884\n",
-      " 0.00024367 0.00023461 0.00024086 0.00024153 0.00023622 0.00024321\n",
-      " 0.00024162 0.00024533 0.00025117 0.00024642 0.00026268 0.00023355\n",
-      " 0.00026971 0.0002162  0.00026662 0.0002062  0.00025765 0.00020517\n",
-      " 0.00024784 0.00021255 0.00023943 0.00022553 0.00022813 0.00024102\n",
-      " 0.0002224  0.00025709 0.00023825 0.00026188 0.00025287 0.00025061\n",
-      " 0.00023732 0.00023685 0.00021343 0.00023367 0.00020768 0.00024325\n",
-      " 0.00021224 0.00024918 0.00022125 0.00024003 0.00023816 0.00023775\n",
-      " 0.00025715 0.00024271 0.00025902 0.00024808 0.00025031 0.00025371\n",
-      " 0.00024401 0.0002467  0.00024623 0.00023572]\n",
-      "  mean noise:  [ 0.02339346  0.03600837  0.02217096 -0.05275951 -0.01721641  0.02686777\n",
-      " -0.00954375  0.00089093 -0.05058923 -0.00258542  0.01392005 -0.0170874\n",
-      "  0.00306415  0.00095704 -0.01420761 -0.02176237  0.04192442  0.01627662\n",
-      " -0.01178359  0.07104028  0.04356223 -0.08659666  0.00339851 -0.02432932\n",
-      " -0.01512381  0.04698375 -0.04454228  0.01513626 -0.00267865  0.01194251\n",
-      "  0.01156088 -0.03618223  0.04146328 -0.02694892  0.02152067  0.03030576\n",
-      " -0.01296929 -0.00538978  0.01803673 -0.01986609 -0.01415264  0.04865212\n",
-      " -0.00244521  0.01869152 -0.01373111 -0.02351512  0.02582534 -0.01474764\n",
-      "  0.01005979 -0.00902676  0.0048545   0.01565597 -0.02258766  0.00649795\n",
-      " -0.01276787  0.02223569  0.03118388 -0.01279152 -0.0440074  -0.00755427\n",
-      " -0.0152923  -0.00248656  0.00671575 -0.00254293]\n",
-      "  diag cov noise:  [0.00022099 0.00025016 0.00025059 0.00024799 0.00026828 0.00026884\n",
-      " 0.00027211 0.00028348 0.00027203 0.00028576 0.00027064 0.00026751\n",
-      " 0.00029115 0.00024315 0.0003054  0.00024136 0.00028346 0.0002612\n",
-      " 0.0002663  0.00027974 0.00026376 0.00026033 0.00025058 0.0002252\n",
-      " 0.00023088 0.00022648 0.0002207  0.00023604 0.00020693 0.00022424\n",
-      " 0.00019863 0.00021397 0.00020583 0.00022042 0.00021954 0.00023746\n",
-      " 0.0002355  0.00025146 0.00024165 0.00025838 0.00024057 0.00026226\n",
-      " 0.00024496 0.00027437 0.00025994 0.00028456 0.00027003 0.00028024\n",
-      " 0.00024693 0.00027199 0.00022071 0.00027476 0.00023428 0.00027428\n",
-      " 0.00025325 0.00026224 0.00025133 0.00025269 0.0002365  0.00025611\n",
-      " 0.00021794 0.00026518 0.00020641 0.00026494]\n",
-      "  mean noise:  [-0.01529846  0.06002777 -0.06640053 -0.00433472  0.00527458  0.03107179\n",
-      "  0.03315165 -0.06734392 -0.01544805 -0.00115798  0.04761165 -0.01693737\n",
-      " -0.00875608  0.0153419  -0.05272842  0.04719708  0.00018622 -0.06021481\n",
-      "  0.02274404  0.02786886 -0.01134306 -0.03718119 -0.00106021  0.02813486\n",
-      "  0.00843609 -0.00427731  0.00787459 -0.01669891  0.00022842 -0.00365594\n",
-      "  0.00160522  0.02184706 -0.02570588 -0.01192234  0.00166301  0.0425183\n",
-      "  0.01837612 -0.00498478 -0.02295669 -0.06590567  0.03439977  0.02013382\n",
-      " -0.00209488  0.05829868  0.03020342 -0.02535645 -0.08672391  0.02088233\n",
-      "  0.03476358 -0.03570171  0.01683067  0.00118652  0.02126345 -0.00156922\n",
-      "  0.01205944  0.05481886 -0.07727098  0.01849879 -0.00809333 -0.04840984\n",
-      "  0.06053483 -0.00278653  0.01905328 -0.01828226]\n",
-      "  diag cov noise:  [0.00024135 0.00023567 0.00023287 0.00023051 0.00022328 0.00024165\n",
-      " 0.0002283  0.00025323 0.00023492 0.00025453 0.00024092 0.00025566\n",
-      " 0.00023496 0.00025435 0.00021759 0.00023489 0.0002218  0.00023225\n",
-      " 0.00024177 0.00024358 0.00025455 0.00024738 0.00025779 0.00025616\n",
-      " 0.00024656 0.00025621 0.00023423 0.00024111 0.00022851 0.00023052\n",
-      " 0.00023214 0.00022483 0.00023298 0.000215   0.00023529 0.00022332\n",
-      " 0.00024629 0.0002402  0.00026655 0.00022843 0.00027782 0.0002054\n",
-      " 0.00025563 0.00020912 0.00023982 0.00022631 0.00025883 0.00023586\n",
-      " 0.00028091 0.00023776 0.00027798 0.00023081 0.00024761 0.00023357\n",
-      " 0.00023233 0.00023857 0.00024373 0.00024295 0.000251   0.00025296\n",
-      " 0.00024118 0.00025077 0.00023667 0.00024433]\n",
-      "  mean noise:  [-0.02245135 -0.00452537 -0.01077479  0.00673444 -0.00038752 -0.01038993\n",
-      " -0.05412756  0.02185405  0.06671986  0.00975888 -0.01095195  0.00559596\n",
-      "  0.01562505 -0.0144969   0.0230091   0.04602056 -0.01559102 -0.04353303\n",
-      "  0.01297287 -0.02132465 -0.03046721 -0.00448603  0.00675784  0.0324342\n",
-      "  0.0038405  -0.01919207 -0.02684918 -0.00244782  0.01725678 -0.01887909\n",
-      "  0.00471185  0.02562885  0.03505872  0.04348422 -0.06390434 -0.00735093\n",
-      " -0.02409311 -0.04079185  0.06996879  0.03391653  0.01826215 -0.00181453\n",
-      " -0.00668265 -0.0274392   0.00862171 -0.01779854 -0.05095676 -0.03125492\n",
-      " -0.02247253  0.06596658  0.05276786  0.04160017  0.01900055  0.00678368\n",
-      " -0.01704783 -0.03001733 -0.0213873   0.02323564  0.03502274 -0.00830429\n",
-      " -0.01674323 -0.02915674  0.01825692  0.00812061]\n",
-      "  diag cov noise:  [0.00027017 0.00024166 0.00026731 0.00025847 0.00026128 0.00027637\n",
-      " 0.00024707 0.00026299 0.00022319 0.00024242 0.00021864 0.00023529\n",
-      " 0.00021825 0.0002273  0.00020805 0.00021518 0.00019616 0.0002063\n",
-      " 0.00018771 0.00020019 0.00018219 0.0002028  0.00018369 0.00022313\n",
-      " 0.00019538 0.00024943 0.00021082 0.00025095 0.000224   0.00021853\n",
-      " 0.00023857 0.00019055 0.00024318 0.00018949 0.00023339 0.00019733\n",
-      " 0.00022783 0.00020872 0.00022778 0.00023228 0.00023432 0.00024325\n",
-      " 0.00024399 0.00023378 0.00023957 0.00024093 0.00023619 0.00025509\n",
-      " 0.00023879 0.00025542 0.00023455 0.00023862 0.00022675 0.000222\n",
-      " 0.00024197 0.00023572 0.00027853 0.00026403 0.00029863 0.00026128\n",
-      " 0.00029114 0.00024556 0.00027538 0.00024049]\n",
-      "  mean noise:  [-0.00725069 -0.01909018 -0.02199684  0.00981533 -0.02001286 -0.02647901\n",
-      " -0.03593245  0.00153388 -0.01412588  0.03591332  0.03044537 -0.02462607\n",
-      "  0.06430897 -0.01101307 -0.01163139  0.00815927 -0.06304935 -0.04209193\n",
-      " -0.00797198  0.01627083  0.03248457  0.00539301  0.01939398  0.01157683\n",
-      " -0.01339412  0.03672857  0.03300409 -0.03652956 -0.00092012  0.00280256\n",
-      " -0.04763465  0.0210497   0.02431714 -0.02878942 -0.0084758   0.0097824\n",
-      "  0.00526267  0.0136908   0.0202584  -0.03902342  0.00140589  0.03308982\n",
-      " -0.00470516  0.01579249  0.01391644  0.00128286  0.02443031 -0.01519005\n",
-      " -0.05626328 -0.02290621  0.04824791  0.01592938  0.01415526  0.01166974\n",
-      " -0.03108356 -0.00987697 -0.06147118  0.00292432  0.06003112  0.00233907\n",
-      "  0.00884125 -0.02227556  0.01423455  0.03595204]\n",
-      "  diag cov noise:  [0.00022802 0.0002314  0.00024367 0.00024763 0.00025314 0.00027494\n",
-      " 0.00025134 0.00026727 0.00024667 0.00023751 0.00024755 0.0002265\n",
-      " 0.00024124 0.00023326 0.00022358 0.00024493 0.00020669 0.0002513\n",
-      " 0.00020261 0.00024654 0.00021522 0.00023933 0.0002286  0.00023939\n",
-      " 0.00023984 0.00024008 0.00024607 0.000236   0.00023742 0.00022994\n",
-      " 0.00022671 0.00022609 0.00022982 0.00022913 0.0002446  0.00023095\n",
-      " 0.00024695 0.00022206 0.00023107 0.0002217  0.00023019 0.00024885\n",
-      " 0.00025335 0.00026817 0.00026731 0.00025716 0.00026153 0.000251\n",
-      " 0.00026107 0.00025488 0.00024853 0.00024853 0.00023029 0.00023578\n",
-      " 0.00024122 0.00023525 0.00025276 0.00023211 0.00023389 0.00021411\n",
-      " 0.00020722 0.00021197 0.00020813 0.00022538]\n",
-      "  mean noise:  [-1.85510509e-02  4.82770702e-02 -1.11191900e-02  1.60372666e-02\n",
-      "  4.76636491e-02  2.79203847e-02 -1.78943019e-03 -5.26632952e-02\n",
-      " -3.34524218e-02 -2.15880406e-02  3.21644876e-02 -5.20434291e-02\n",
-      " -1.02245410e-02  6.91034138e-02 -1.20387833e-02  1.62218462e-02\n",
-      " -1.79114542e-02 -3.72314169e-02  1.43328522e-03 -3.70158770e-04\n",
-      "  2.81259245e-02 -1.88430536e-03 -3.99691577e-03  4.77764356e-02\n",
-      "  1.57427560e-02 -1.35327238e-02 -4.26509672e-02 -1.08573676e-02\n",
-      "  1.66367797e-02 -4.57997489e-02 -4.06602615e-04  2.10394498e-02\n",
-      " -9.94032176e-05  2.39481617e-02  4.04094409e-02 -1.19085399e-02\n",
-      " -3.24286740e-02  1.58033666e-03  4.86185528e-04 -2.57632063e-02\n",
-      " -2.81012610e-02  2.36212259e-02  4.39611607e-03 -9.52673551e-03\n",
-      "  1.88653773e-02  4.12011215e-02 -2.48517676e-02  3.59581239e-03\n",
-      " -2.08823227e-02  1.31473602e-03  3.32055633e-02 -3.46105083e-03\n",
-      " -9.16850492e-03  5.16142118e-03  4.51974201e-03 -1.23439760e-02\n",
-      "  2.36394098e-02 -1.68910295e-02  1.54649056e-03  1.07730981e-02\n",
-      " -1.80660282e-03 -5.06228015e-03 -3.32875220e-02 -1.30245081e-02]\n",
-      "  diag cov noise:  [0.00025222 0.00025538 0.0002516  0.0002459  0.00026098 0.00023891\n",
-      " 0.00025255 0.00025261 0.00023613 0.00025512 0.00022844 0.00023647\n",
-      " 0.00021537 0.00021714 0.00019885 0.00020299 0.00018926 0.00019296\n",
-      " 0.00019073 0.00018959 0.00020643 0.00020065 0.00023293 0.00022437\n",
-      " 0.00025134 0.00024339 0.0002539  0.00024347 0.00024512 0.0002331\n",
-      " 0.00023292 0.00023481 0.00022791 0.00025257 0.00022586 0.00026909\n",
-      " 0.00023003 0.00027716 0.0002427  0.00027473 0.00024216 0.00026477\n",
-      " 0.00023137 0.00024609 0.00023251 0.00022535 0.00023035 0.00022224\n",
-      " 0.00021648 0.00023236 0.00021124 0.00024435 0.00021197 0.00025046\n",
-      " 0.00022074 0.00024947 0.00023923 0.00024696 0.00023501 0.00024713\n",
-      " 0.00023274 0.00025087 0.00024945 0.00025505]\n",
-      "  mean noise:  [-0.02859005  0.01130377  0.01940123 -0.03445291  0.05457368  0.01878388\n",
-      " -0.03620215  0.0375147  -0.0130358  -0.04434209  0.01730173  0.01880489\n",
-      "  0.00188385 -0.04787226  0.02576922  0.01773429  0.00923771  0.03162664\n",
-      " -0.01432147 -0.03444983 -0.04687776 -0.00220591  0.02571513 -0.01224627\n",
-      "  0.01098938  0.03217867 -0.03816538  0.00452616  0.06421604 -0.03635421\n",
-      " -0.02687328  0.01447339 -0.01554144  0.02482591  0.01244334  0.00220314\n",
-      "  0.01049186 -0.0390369  -0.03170313  0.022682    0.08000375  0.02055998\n",
-      " -0.03176262 -0.00825715 -0.0077019   0.01378741  0.02048993  0.00485025\n",
-      " -0.02107248 -0.00211649  0.01782664 -0.02655483  0.01201512  0.0160146\n",
-      " -0.02077398 -0.01969287 -0.04038399  0.01064838  0.00182012  0.00019557\n",
-      "  0.04207472  0.02533759 -0.04149112 -0.02881486]\n",
-      "  diag cov noise:  [0.00023632 0.00024642 0.00025614 0.00023406 0.0002765  0.00024837\n",
-      " 0.00026979 0.00025021 0.00024371 0.0002226  0.00022542 0.00019754\n",
-      " 0.0002108  0.00018245 0.00020335 0.00017959 0.00021049 0.00020284\n",
-      " 0.00023093 0.00023245 0.00024271 0.00023883 0.00022625 0.0002238\n",
-      " 0.0002063  0.0002122  0.00021067 0.00022287 0.00022816 0.00023601\n",
-      " 0.00024618 0.00023723 0.00026158 0.00024212 0.00026545 0.00024929\n",
-      " 0.00025139 0.00025071 0.00023101 0.00025038 0.0002199  0.00024243\n",
-      " 0.000232   0.0002304  0.00025839 0.00023131 0.00027939 0.00025097\n",
-      " 0.00027724 0.00025566 0.00026142 0.00024814 0.00025513 0.00025663\n",
-      " 0.00025623 0.00026148 0.00025307 0.00025871 0.00024491 0.00025763\n",
-      " 0.00023641 0.00026341 0.00023101 0.00026549]\n",
-      "  mean noise:  [ 0.05915836  0.02547097  0.01920348 -0.01744495 -0.01103246  0.03718085\n",
-      "  0.00342362 -0.02349886 -0.0018793  -0.02425236  0.02331588 -0.01432547\n",
-      " -0.08283056  0.01822848  0.0467134   0.03311526  0.0083925  -0.02983033\n",
-      "  0.02463787 -0.0036136   0.00716054 -0.0326751  -0.00809343  0.0294832\n",
-      "  0.00749199 -0.02664517  0.00515972  0.01418537 -0.00365932  0.01685267\n",
-      "  0.04267716 -0.03972999 -0.03116063  0.01048519  0.01453928  0.02036737\n",
-      " -0.02948848  0.01227222  0.02873855 -0.00672199 -0.05579924 -0.03508577\n",
-      " -0.00077729  0.01174389  0.03338283  0.02250414  0.02253479  0.01129991\n",
-      " -0.03035791 -0.02146216  0.00591489  0.02302261  0.01809878 -0.04447574\n",
-      " -0.03823715  0.05040453  0.00837134 -0.02420847 -0.03641089  0.02266117\n",
-      "  0.01769896 -0.0538146  -0.04169196  0.02559553]\n",
-      "  diag cov noise:  [0.00024588 0.0002494  0.00023481 0.00024231 0.00023917 0.00025086\n",
-      " 0.00026558 0.00026752 0.00027351 0.00026945 0.00025704 0.00024946\n",
-      " 0.00023905 0.00022047 0.00023531 0.00019945 0.0002323  0.00019209\n",
-      " 0.00022253 0.00018875 0.00021593 0.00018729 0.00021382 0.00019859\n",
-      " 0.00021257 0.00022434 0.00022542 0.00024729 0.00024154 0.00025222\n",
-      " 0.00024377 0.00024332 0.00023836 0.00024257 0.00022668 0.00025366\n",
-      " 0.000223   0.00026087 0.00023498 0.00026613 0.00024632 0.00027348\n",
-      " 0.00024982 0.00026992 0.00023071 0.00025463 0.00020636 0.0002404\n",
-      " 0.00021622 0.00022555 0.00024401 0.00021148 0.0002437  0.00020774\n",
-      " 0.0002214  0.00021341 0.00020576 0.00021179 0.00021068 0.00021131\n",
-      " 0.00022715 0.00023098 0.00024247 0.00025149]\n",
-      "  mean noise:  [-0.02320634 -0.02493246 -0.03818111  0.02427287 -0.01148533  0.0044722\n",
-      "  0.05382787  0.00181664 -0.00043017  0.01401701 -0.027586   -0.05858361\n",
-      " -0.03419829  0.005928    0.02767727  0.03358422 -0.00788205 -0.02184828\n",
-      "  0.04586549 -0.0252776  -0.02889628  0.01394794 -0.02979272  0.01064052\n",
-      "  0.02763014 -0.01990708 -0.02152365 -0.02185292 -0.03481892 -0.02814367\n",
-      "  0.06089798  0.04113004 -0.02208886  0.04521001 -0.01394499 -0.06357727\n",
-      "  0.00340667  0.04020091 -0.03422418 -0.03065216  0.04745617  0.03079308\n",
-      "  0.02397186  0.01529465 -0.02774895 -0.05577656 -0.01648601  0.03451457\n",
-      " -0.00254909  0.00064553  0.01942711 -0.01614372 -0.00620891  0.03890499\n",
-      "  0.00207007  0.01715016 -0.05105184 -0.01889257  0.02840956 -0.00413718\n",
-      "  0.00388219  0.01533851  0.03160616 -0.0012098 ]\n",
-      "  diag cov noise:  [0.00025167 0.00024009 0.00026064 0.00023269 0.00024593 0.00023858\n",
-      " 0.00022534 0.0002528  0.00021702 0.00024282 0.00021346 0.00022286\n",
-      " 0.00020219 0.00021345 0.00019321 0.00022627 0.00021447 0.00024934\n",
-      " 0.00024008 0.00025038 0.00024584 0.00024018 0.0002438  0.00024924\n",
-      " 0.0002397  0.00025525 0.00023961 0.00024677 0.00024079 0.00023907\n",
-      " 0.00025173 0.00023936 0.00027534 0.00023627 0.00029044 0.00023926\n",
-      " 0.0002853  0.00025126 0.00027296 0.00025742 0.00026233 0.00026538\n",
-      " 0.00026499 0.00026928 0.00027397 0.00026633 0.00025941 0.00024971\n",
-      " 0.00023278 0.00023039 0.00022032 0.00021446 0.00021859 0.000206\n",
-      " 0.00021972 0.00021455 0.00022029 0.00022883 0.00022126 0.00022752\n",
-      " 0.00022728 0.00022643 0.00023601 0.00023476]\n",
-      "  mean noise:  [-0.02429936 -0.05522877 -0.04127632  0.0621038   0.09519437 -0.04336305\n",
-      "  0.01347007 -0.04239974 -0.01928662 -0.01150608  0.00113149  0.05276744\n",
-      " -0.00846252 -0.01600447 -0.00394096 -0.00697703  0.0040236  -0.03439591\n",
-      "  0.00597637  0.04430485  0.00197929 -0.01458005  0.02255565  0.00389792\n",
-      " -0.02798922  0.02197474 -0.02085634  0.01465566 -0.00347157  0.00038724\n",
-      "  0.023576   -0.06189777 -0.04794475  0.02717247  0.01495842 -0.02986174\n",
-      "  0.017465    0.01151836 -0.01246129  0.01549448 -0.02430829  0.02047028\n",
-      "  0.05486151  0.02618555 -0.02069152  0.0003137  -0.00564991 -0.01124996\n",
-      " -0.02063844 -0.03124743  0.00079839  0.01283569 -0.0105633  -0.00903495\n",
-      " -0.01609528 -0.00024265  0.03537515  0.01719007  0.01260749 -0.02782713\n",
-      " -0.0470024   0.01013521 -0.00387264  0.06400589]\n",
-      "  diag cov noise:  [0.00021409 0.0002356  0.00021001 0.00024065 0.00023093 0.0002474\n",
-      " 0.00024955 0.00024912 0.00025324 0.00024914 0.00024322 0.00024569\n",
-      " 0.00022957 0.00023887 0.00021789 0.00023603 0.00022452 0.00023839\n",
-      " 0.00024809 0.0002363  0.00026186 0.00022941 0.00025106 0.00021722\n",
-      " 0.00023233 0.00021046 0.00022047 0.00021816 0.00021902 0.00023167\n",
-      " 0.00023545 0.00023491 0.00025453 0.00022663 0.00025857 0.00022891\n",
-      " 0.00024491 0.00024123 0.00023056 0.00024383 0.00023931 0.00024824\n",
-      " 0.00025054 0.00025107 0.00024879 0.00025098 0.00023193 0.00025593\n",
-      " 0.00022518 0.00026023 0.00023813 0.00025414 0.00024852 0.00025003\n",
-      " 0.000249   0.00024914 0.00024421 0.00023499 0.00023687 0.00020951\n",
-      " 0.00023297 0.00020391 0.00023007 0.00022298]\n",
-      "  mean noise:  [ 0.00767855 -0.01983698  0.01587891  0.00167992 -0.02217685 -0.00465968\n",
-      " -0.0177704  -0.04254976  0.01931936  0.0342412  -0.00383175 -0.01184964\n",
-      "  0.00386951  0.02210747 -0.02857091  0.0043044  -0.00015131  0.01218135\n",
-      "  0.02600922 -0.01825599 -0.01632707  0.02085737 -0.03405592 -0.01192649\n",
-      "  0.03300045 -0.00566105  0.0139269  -0.02097051 -0.01730338  0.02852397\n",
-      "  0.00579664 -0.01765698 -0.00433784  0.00897489  0.05719551 -0.0192949\n",
-      " -0.00457935  0.02388359 -0.00946698 -0.04005469 -0.03295421  0.02448461\n",
-      "  0.0535462  -0.01639415  0.02644282  0.01525897 -0.05623083  0.05498511\n",
-      " -0.02486331 -0.03579452  0.00817028 -0.0394658   0.05174238  0.02758128\n",
-      " -0.03154966 -0.02499144 -0.01984968  0.03117018  0.02852085 -0.01839999\n",
-      " -0.0270472   0.01845961  0.00666351  0.03694749]\n",
-      "  diag cov noise:  [0.00021467 0.00021726 0.00021782 0.0002347  0.00022331 0.00025327\n",
-      " 0.00022169 0.00024682 0.0002236  0.00022859 0.00022822 0.00022915\n",
-      " 0.00022348 0.00023613 0.00022718 0.00023792 0.0002409  0.00025114\n",
-      " 0.00025166 0.00025081 0.00026524 0.00023625 0.00024853 0.00022588\n",
-      " 0.00022863 0.00022658 0.00024456 0.00022867 0.00026841 0.00022484\n",
-      " 0.00027331 0.00023768 0.00026041 0.00026808 0.00025144 0.00027596\n",
-      " 0.00026237 0.0002596  0.00029037 0.00026731 0.00030486 0.00028109\n",
-      " 0.00028514 0.00027113 0.00025593 0.00025505 0.0002391  0.00024861\n",
-      " 0.00024776 0.00025064 0.00026022 0.00024569 0.00025562 0.00023238\n",
-      " 0.00024322 0.00021408 0.00022795 0.00020378 0.00021321 0.00021005\n",
-      " 0.00021151 0.0002236  0.00021573 0.00022302]\n",
-      "  mean noise:  [-0.03107086  0.01303556  0.02263711 -0.00059738 -0.00257099 -0.01105345\n",
-      "  0.00298471  0.02402663 -0.02448644 -0.00444599 -0.0037818   0.02206825\n",
-      "  0.01931219 -0.02642847  0.03937867  0.02144613 -0.0439967   0.01319596\n",
-      " -0.00562819 -0.01318199  0.01916906 -0.07265014  0.01032329  0.0363182\n",
-      " -0.03428645  0.02085943  0.05121114 -0.00759552  0.02031782 -0.02671066\n",
-      " -0.00578956  0.02748153  0.00360347 -0.00153728 -0.00846459 -0.00533824\n",
-      " -0.00850949  0.02708665 -0.00897334  0.00123202  0.02339899 -0.00420381\n",
-      " -0.00845937 -0.01735074  0.01992885  0.03269571  0.0013304  -0.00742713\n",
-      " -0.00247171 -0.0141279  -0.01285482  0.06782776 -0.01058497 -0.07175844\n",
-      "  0.02807173  0.01162068 -0.04653051  0.03189501  0.01899388 -0.02702251\n",
-      "  0.00652522 -0.009125    0.00618884 -0.04153153]\n",
-      "  diag cov noise:  [0.00021486 0.00022048 0.00025084 0.00021921 0.00025734 0.00022049\n",
-      " 0.00023615 0.00021476 0.00022393 0.00021151 0.0002429  0.00021351\n",
-      " 0.00027279 0.00022005 0.00028672 0.00023519 0.00028985 0.00024534\n",
-      " 0.0002975  0.000245   0.0002857  0.00023502 0.00024549 0.00022344\n",
-      " 0.00023013 0.00023168 0.00024434 0.00024004 0.00024876 0.00024406\n",
-      " 0.00022705 0.00025401 0.00021411 0.00025466 0.00024453 0.00025556\n",
-      " 0.00027076 0.00026534 0.0002704  0.00025879 0.00025803 0.00023482\n",
-      " 0.00022592 0.00022762 0.00022033 0.00024439 0.00024698 0.00025412\n",
-      " 0.00026486 0.00024939 0.0002493  0.00023696 0.00021931 0.00024738\n",
-      " 0.00020335 0.00027413 0.00020846 0.00026697 0.00020948 0.00024648\n",
-      " 0.00019627 0.00024136 0.0001914  0.00023233]\n",
-      "  mean noise:  [ 0.01934775  0.05217391 -0.03251946  0.01261944  0.02908165 -0.00959219\n",
-      " -0.02776289 -0.02557016 -0.01428718 -0.00758193 -0.02876858 -0.00458919\n",
-      "  0.02605717  0.02835352  0.02133316 -0.03658145  0.00514321  0.01150693\n",
-      " -0.06042102  0.0012619   0.0425437  -0.02224525 -0.03040709  0.06103475\n",
-      "  0.00810022 -0.00773543  0.03818079 -0.01644074 -0.03192064 -0.03840626\n",
-      "  0.02702292  0.02360244 -0.0194327  -0.03747759  0.02145532  0.00200849\n",
-      " -0.04579439  0.0240524   0.05709625 -0.01220871 -0.02799589 -0.01409837\n",
-      "  0.01422351  0.01550056 -0.00224626 -0.01076202 -0.01579475  0.01727048\n",
-      " -0.0114091   0.00225652  0.03985821 -0.01279593 -0.02408321  0.00945082\n",
-      "  0.03190502 -0.02180448 -0.0329887   0.03901877  0.02408227 -0.02597157\n",
-      "  0.00745025 -0.01110041 -0.00771031 -0.00280598]\n",
-      "  diag cov noise:  [0.00024549 0.00025465 0.00023774 0.00024014 0.00023523 0.00023907\n",
-      " 0.00024238 0.00024499 0.000255   0.00024409 0.00026778 0.00023887\n",
-      " 0.00026688 0.00024203 0.00025593 0.00026133 0.00024835 0.00026455\n",
-      " 0.00024196 0.00024076 0.00024732 0.00022398 0.00025934 0.00022859\n",
-      " 0.0002648  0.00022803 0.00025736 0.00020963 0.00024147 0.00020484\n",
-      " 0.0002269  0.00022705 0.00022276 0.00026077 0.00023511 0.00028019\n",
-      " 0.00025674 0.00026939 0.00025875 0.00025297 0.00024118 0.00025988\n",
-      " 0.00022027 0.00026588 0.00021277 0.00026684 0.00021744 0.00027099\n",
-      " 0.00021957 0.00026891 0.000222   0.00025568 0.00023572 0.00022907\n",
-      " 0.0002508  0.00021128 0.00024927 0.00020913 0.00023926 0.00021678\n",
-      " 0.00024078 0.00023369 0.0002481  0.00025336]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# do statistics on the leaf values\n",
-    "F = tree.data['F_T'][0]; H = tree.data['H_T'][0]\n",
-    "print(\"root conditional mean vs. sample mean:\",solve(H,F),jnp.mean(leaf_values,0))\n",
-    "print(\"root conditional cov vs. sample cov:\",jnp.linalg.inv(H),jnp.cov(leaf_values.T))\n",
-    "\n",
-    "# sample statistics\n",
-    "K = 500 # number samples\n",
-    "samples = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_noise = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_conditional = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "\n",
-    "from tqdm import tqdm\n",
-    "for i in tqdm(range(K)):\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "    values = tree.data['value'][tree.is_leaf][:,-1]\n",
-    "    # collect values\n",
-    "    samples = samples.at[i].set(values)\n",
-    "    # add observation noise\n",
-    "    subkey,key = jax.random.split(key)\n",
-    "    samples_noise = samples_noise.at[i].set(values+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,values.shape))\n",
-    "    # conditional sampling\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_conditional.down(tree,params.values())\n",
-    "    samples_conditional = samples_conditional.at[i].set(tree.data['value'][tree.is_leaf][:,-1])\n",
-    "# print sample statistics for all leaves\n",
-    "print(\"data parameters: \",params.values())\n",
-    "print(\"observation noise: \")\n",
-    "print(\" mean: \",jnp.mean(samples_conditional-leaf_values,axis=(0,1)))\n",
-    "print(\" diag cov: \",jnp.diag(jnp.cov((samples_conditional-leaf_values).reshape(-1,n*d).T)))\n",
-    "print(\"leaves: \")\n",
-    "for i in range(samples.shape[1]):\n",
-    "    #print(\"  mean: \",jnp.mean(samples[:,i],0))\n",
-    "    #print(\"  cov: \",jnp.cov(samples[:,i].T))\n",
-    "    #print(\"  mean w/ noise: \",jnp.mean(samples_noise[:,i],0))\n",
-    "    #print(\"  diag cov w/ noise: \",jnp.cov(jnp.cov(samples_noise[:,i].T)))\n",
-    "    #print(\"  mean conditional: \",jnp.mean(samples_conditional[:,i],0), \", sample: \",leaf_values[i])\n",
-    "    #print(\"  diag cov conditional: \",jnp.diag(jnp.cov(samples_conditional[:,i].T)))\n",
-    "    print(\"  mean noise: \",jnp.mean(samples_conditional[:,i]-leaf_values[i],0))\n",
-    "    print(\"  diag cov noise: \",jnp.diag(jnp.cov((samples_conditional[:,i]-leaf_values[i]).T)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MCMC run incorporating the likelihood ratios arising from the Gaussian approximation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.5, 'k_sigma': 0.5, 'obs_var': 0.005}\n",
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2000/2000 [13:11<00:00,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overall acceptance rate: 0.4145\n",
-      "Acceptance rate (update_params=True): 0.6060\n",
-      "Acceptance rate (update_params=False): 0.2230\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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+f30mbTQ/63P75rw95ucezSvJf7ceFz9fH9Ncp9/6uzeLltIlvDNyJb01B85Iz4kbclR27IO3mFqo/247bpo0W9W4SdrULm8Cnr6V4hNTpNfnf25L35fbf7sgyameP81+1be5qbH83wV7XZrwtHmoe7NK8krXuub9ZAUEGAJMkaadD6esPWLarvVDU/sV6IfOnhPxkpicajpP9rq9svlg0W/sJQL95MzFJDMkMD86RSJvtPai+j/mm58jQorLN8+0MH05tC1/36mMNSZZ0T4ut1e/STYdOWfa/x9uVkk61c+6067WJMzbcUIOxl6Sb7f8bpZpLbvWpDxyWyVHOe1D8NC/1uV4PzRUzH/uTgnwu/4JZMvR8zJ7y+9SNzLEnOTtASs72iQwee0RuXQlRR5rVcV0rrXTE9ivZxNMaPPkt289lehz9/v5ROndorI0fP3HbE/cN1cIkfcfbmT2zU5rlLJ7nrT/zmvf7TI1ErUjgsXXx0dS0mzSuFJpua9RBQn0K2Y+A7R2Sc9w+jTqa6DHr02Tc7edMJ2TtWnn4X+tl0tJKY5tP926mrSsXlbqVwgxgXH+jhPy4tfbpH3dcJn4aNNsnwftxKx9jd56oIEJi/pe7jv1J1m271q/K30fac2Uvq8qhZWQTb+eN01BzjVjF532x+xTTDVTO6afTcHF/aXnxPWO51a399SdVaVauVLm99j4K6bmJiL0z75PVkOAIcDkmVYP67cM/UeoE3HtOdS3i1az6j+/fjiUDPSVu+qEmw8Mba+/qVSA+QAqW8q7IWDoN9tk1qbfHL83qVRafj6asRkhPf12qN9eazp9oOLG0pE42ryio1z0g377ax0cnSG1I+yLs7aZE1iF0kGmX8fXm/98nZ1tefVutzv7quMXEk3Q1er9zE78zjUO9zeqYE7COupGOxnrPBmd3l9pTkTdGlWQN//SwIzwKEomrjoky/bFms+CXcfjHTVLmdFamrUHzsiRs5fN782qlDEh4t6GFUzw0MeevZQkgf6+8snS/Y65SnKrQmjxLEfjfNKjsXSuH5lpvxBtUtP3grs1GBow9TkoXcI/wwizlNQ0Gbf8oCRcTZFnW9eQuTuOy//M2yMNo0qbcFc+ODBD51p936fZbKapK7MRa1ZHgCHAZEtrJxbsPCk3Vwg1H65aZa3vhn+vOmTCiNZg2Gn1ttZYaJ8E5+VZ0fZb/eanVeXPt8/5MMrJaw6bURHVy5UyTQT6rdmdD4vekzZkGBGh9HNAOzXavw1l5ZHbos2oDv224wnazHEuIcnUCGT3bTMz+m+b02/nVpKcmmbCi30Crb/eGiX//FvD6z5Ga1f+OsG1NqRp5TLyzTMtxdO0v4R2DNamhfRNMhqy9Hg89R6yGn0udCj2zt/jTF+fnKpfMUR2/v5n34/0BrWvaWp9tC+TjrRJT2sttI/L9dxWNUxmPd0ix/sEzyLAEGCua/fxeOny0apM/5G1ejwz2plMvwloDYu9av9sQlK21cT67eqWiqHSpHIZR1WxVrHq8E/9YPnLuLXi7+uT6Xa041mVm0rKqPtulmkbfpUGFUNNKMrqpK/fSLp+tMoxeuO5u2rIR3/MSaHmPXeHCWxvzN0tn68+LAPb1jAjWjRs6RwdI7/b5bK9F+6uZfZf+1yo3IaGzIKGNmN1/nBVtpNALd8Xa0YKtK1d3nFs2gym3/y106bWCug8JjpkdvHuU2Ykgo6K0CYzfc40rOl8DQVlaOT1Aqfuo97rJFn2zq1aczG8S11TDZ/dc/zFul/Nt/SHm0XL4j2nTIdUb80Oiuxpzcznqw47Oh+3qV3OvGf1Pa6vm/5/f7byUIbH6RcaDYZaK6zhtlujii61bLrdWT8dM/8T+kXlmdbVzXtLO+hqCDoZlyj//PEXU/bffZqa/lAaqOpEhhS5GrKCjABDgMnS2oNnpMe/s++0puc9DQtNq5SRd3/8xfQdsdv7RidzAtW3jw7t23PiommL1g+X05eSzDeiEXN2umxPz+NfPdlcLiQmy7N/dMZ01/S+zaV8SHHp98UmM1JEhwxqtb2+m3WIp9Je/iuHtpWSAX6m+lZP7tq3QOl+67DYQL8/mwZ0mX6g9v78WrNAZnSbTSuHyZTHm2U7x8NnKw/KW/P3OjrzafOHBjYdQeJMq8l18qhp63411dtVbirhqEpXOqS2QVSoCX3/2Zj7zqN6cpjYp6lH56TQE8SHi/ebENovppo5kSzZc8o0A2nVu4ZVHb6pfTN2/H5B2tUNl/cW/SLTNxw1j9dOkM7DlOtFhsj85+/02P6iYNDRWdqBWmt30/tu6+/y/Iytjt9/eqV9vvRPO59w1dQG6ecHCiYCDAHGtOHrSWTrsbgsh4t+3b+F6eA6ac0R+WjJfseID503Qk8i9pPe3e+tcEwhrRMg7crB0Ej9oHhuxhbTHKB9A65Hv1np39TQpHNr6Leify78xWXYYG5oh7cvnrjNrfZhbWvXJqaXvr42Z0dm3n6ggRnOqqNH0l9fRD+UtUZn/PKDciPdUaPsdYfTaojQuSLG92pi2tdzUjOjH/TarJeQlCp/axolUWWuPZ86r8TbP+yVpXtjPTLkVK+3ot+QUXTpl6FGo380Q8V1BJr2B0HREE+AKToBZt3Bs6bWo3P9CHNiCQ8ubk5OOkoiq+YgNb5nE+ncINJxotKTn058lFnzzHs/7nNpijnydtdc7ePYBXtNR7X0tEbkvwNbZVk7oD3431m4T9YdPGOuqxIZGmSGi2bXpj31idtMFXJeaxW0w2irGtdm39SOhR8uOWCagJyn4dap3/U50wnP0teu2Ol06vW0lmX90euGDG2uqlW+lIz677UhsZnVTvzfMy3NRGgt317qWLb8pTYZwlRWz7lddicF7Uw7YcVB+eqPWhJ1V53ypqZK+0LpqI/saEbSTpi/nMq6A+c7f73FdP5+Ysomx2O09sXecRxFl15sUL94xdQq5zKyCoUbAaaQBBgdBbT513OmTffJVlXN3AmDZ22V0/FJUr18KdOBMLNvwJn1ZdETYbu65c0IGx1u2LpmuRz3j0jf7JTbAONcHaxtzfc2jDQddR9vVdWt2SC1hkPnV9Dhkzoaqnm1MHliyk+y/tA56dm8kplgzBNNJtr59l8rD5r5Fq5Xm6Rt9NqW/2iLKuYE7jykUb9Z6mgGfX10Pkzd/0DfYtKyRllHGR0CevTcZbm9Wph8tuqQjF2wzyzXpiudd8IeXGf+dFQeahrt8lhnOmS079RN5vnSkJo+SIQU9zN9TLR5UGutPure2HRGfXfRPvnXiox9ENK7rUqY1K8YKoH+xaR0kL9Elg4yQ3ljapV11Nakb05ztmlEe8eINe1bpZ08dTbT7Pq9ACi8CDAWDzD6smjnVufrlOTF4hdipEb5vA0N1lkidXIs7ZPR3WlOjJzQk9N/Nh41367dHXlTkByIvWg6AW87FmdqR5yHiB4e0yVfRwhpTcidY5eZOWq+G9AqT9vSZr0PFv9iphfPKW1i1LCp17rxLVbMcR0h9f3AVnJLVOkcbUcvDqf9YfSbtNbeWGVSLQA3FgHG4gFGO7Y1f2vJdcvot2d98e65pYL8/Ot5M920fYSQ8+yhL3Ws7XJBN+S/t+bvkZk/HTOTSr3cuU6+b1/7EGkNR37Nnqr/9jrkVPu2aI3QkG+2ZZiWXOesWPvyXRlmf9V5Kz5YvN8EEb2KMgDkJwKMRQOMTlCkU0FrnxZtrtBmiJ9fvds0l3z78++mU2vVm0rKmAcaZNr8M3jmVjOTp4642TzibsvXdODG0Kamv01YZ5qt6lcINVdk1sn/GKkB4EYjwFgwwJyMuyIjv9spPzpd+C27C9elp1Nia98IveZJYZyhEQBQuOX0/M3MPQWATlPd7dM1jtlHnd3fuGKutqUdZHUIKgAAhRkBpgBYs/9MhvCiI3N0xMmdNfM2FBgAgMKIDhIFQPpL3qs3u9UnvAAAkAUCjJeNmb9H1h68dtHBPi0qm6nqlc4dAgAAMkcTkpeGSOvkYumvnKpT6evFA/VChPZr9gAAgIwIMF7w+n93ZQgvOiW9fa4WnW0XAABkjSYkL0g/YZj65pmWXtkXAACsiBoYL9CZTFWnmyOkU/0IueeWSI9ctwcAgMKKAOMFemVj9bemUdKuLnO2AACQW3zt94Lk1GsBhloXAADcwxnUC1LSrjUh+WdyLSMAAJA9AowXpFADAwBAnnAG9YLkP2pg/HypgQEAwB0EGC/WwPgX4+kHAMAduTqDjhkzRpo1aybBwcFSvnx56datm+zbt8+x/siRI+Lj45Pp7euvv3aUO3r0qHTt2lVKlChhtjNkyBBJSUlx+VvLly+XJk2aSGBgoNSoUUOmTJkihYHNZpMzl5LMz9TAAABwAwLMihUrZMCAAbJ+/XpZtGiRJCcnS4cOHSQh4dqVlKOjo+XEiRMut9dff11KlSolnTt3NmVSU1NNeLl69aqsXbtWpk6dasLJyJEjHX/n8OHDpkzbtm1l69atMmjQIOnbt68sXLhQrO7jpQcco5D8CTAAALjFx6ZVAm46ffq0qUHRYBMTE5NpmcaNG5ualM8//9z8/sMPP8g999wjx48fl/Dwa3OgTJgwQYYNG2a2FxAQYH6eN2+e7Ny507Gd7t27y4ULF2TBggU52rf4+HgJDQ2VuLg4CQkpONcVqvLyPMfPy19qI1XKlvTq/gAAUJDk9Pydp04YunEVFhaW6frNmzebGpQnn3zSsWzdunXSoEEDR3hRHTt2NDu8a9cuR5n27du7bEvL6PKsJCUlmW043wq60iX8vb0LAABYktsBJi0tzTTttGrVSurXr59pGa11qVu3rrRs+ed1fk6ePOkSXpT9d113vTIaShITE7Psn6OJzX7T5qyCyMep1YiLNgIAcIMDjPaF0SaeGTNmZLpeg8b06dNdal88afjw4aZGyH47duyYFETt6pQ396/eU8/buwIAQNG6FtLAgQNl7ty5snLlSomKisq0zDfffCOXL1+WPn36uCyPiIiQjRs3uiw7deqUY5393r7MuYy2hQUFBWX693S0kt4KOnuPo+BALkMFAMANqYHR/r4aXmbPni1Lly6VqlWrZllWm4/uu+8+KVeunMvyFi1ayI4dOyQ2NtaxTEc0aTipV6+eo8ySJUtcHqdldLnVpfxxIUdfLiMAAMCNCTDabDRt2jTTNKRzwWhfFb2l75dy4MABUzujQ5/T02HXGlR69+4t27ZtM0OjR4wYYbZtr0Hp37+/HDp0SIYOHSp79+6VcePGyaxZs2Tw4MFSWK5ETYABAOAGBZjx48eb/iVt2rSRyMhIx23mzJku5SZNmmSaljSspOfr62uan/Rea1R69eplmplGjx7tKKM1OzqMWmtdGjZsKO+++65MnDjRjESyOgIMAABengemICuo88A8NGGdbDxyTsb1bCJdGkR6e3cAACh688Ag91L+uJAjNTAAALiPAHOD/XEVAfEjwAAA4DYCzA2W+kcNTDECDAAAbiPA3GCp1/ILNTAAAOQBAcZLNTC+ztcUAAAAuUKAucGYyA4AgLwjwNxgzAMDAEDeEWBuoKV7T8mvZy+bn28qVfCv2wQAQEFFgLlBNv96Tp6Yssn8fFPJAKkcVsLbuwQAgGVxSWQP2XbsgizYdVK6NaooR89dlqe+uBZe1Ix+tzOMGgCAPCDAeMj9n64x9+OXH3RZPumxplIzPNhLewUAQOFAE9IN9I8udeSuOuHe3g0AACyPAHMD/aVxlLd3AQCAQoEmJA9If4HvcsGB8nirKuYeAADkHQHGg3O9qA+7N5L7G1X06v4AAFDY0ITkwdl2Vbu69HkBACC/EWA84Kr9io0i4u/LcGkAAPIbAcYDUlL/rIHxL8ZTDABAfuPs6gHJf9TA6PWOmLAOAID8R4DxYIDxI7wAAOARBBgPNiEF+PL0AgDgCZxhPUCvfaT86MALAIBHEGA84NDpS+b+/OVkb+8KAACFEgHGA5L/aEK6ux5zwAAA4AkEGA9ITE4192VLcekAAAA8gQDjwQAT5O/r7V0BAKBQIsB4wOHTCea+uD9PLwAAnsAZ1gMW7Dpp7rceu+DtXQEAoFAiwHhQo+jS3t4FAAAKJQKMB9xUMsDcd70l0tu7AgBAoUSA8YCUtGvDqAP96MQLAIAn+Hlkq0VU3OVkmfHTUYlLvDaBXaAf+RAAAE8gwOSTK8mp0nD0jy7LAggwAAB4BGfYfJCWZpOvN/+WYbk/F3MEAMAjqIHJg/k7TohvMR/5YccJmbP1eIb11MAAAOAZBBg3/X4hUZ796ufrlgmgBgYAAI/gDOum8wlXMyxrGF1aRt5Tz/xcPjhQ/H19vLBnAAAUftTA5HGotLNxPZtIxdJBckfNshJWMkB8fAgwAAB4AgHGTQlJKRmW+Re7FlhqhQd7YY8AACg6aEJyQ0pqmvScuCHDckYdAQBwY3DGdUP8lYy1L8qfUUcAANwQnHHdkGbL2P9F+f3RhAQAADyLAJOPAYYmJAAAbgzOuG7IIr+YSe0AAIDnEWDysQYGAADcGAQYN2QyBYxEhhb3xq4AAFAkMQ+MmxdvdPZh90Zya+UyXtsfAACKmlzVwIwZM0aaNWsmwcHBUr58eenWrZvs27cvQ7l169bJXXfdJSVLlpSQkBCJiYmRxMREx/pz585Jz549zbrSpUvLk08+KZcuXXLZxvbt2+XOO++U4sWLS3R0tIwdO1YKYhPSkI615f5GFSWqTAmv7hMAAEVJrgLMihUrZMCAAbJ+/XpZtGiRJCcnS4cOHSQhIcElvHTq1Mks37hxo/z0008ycOBAKVbszz+l4WXXrl1mG3PnzpWVK1dKv379HOvj4+PN4ytXriybN2+Wd955R0aNGiWfffaZFATOFTDPtqnuzV0BAKBI8rHZ3O+Revr0aVMTo8FGa1nU7bffLnfffbe88cYbmT5mz549Uq9ePRNsmjZtapYtWLBAunTpIr/99ptUqFBBxo8fL6+88oqcPHlSAgICTJmXX35Z5syZI3v37s3RvmkICg0Nlbi4OFPTk58Onr4k7d5dISHF/WT7qI75um0AAIqy+Byev/PUiVc3rsLCwsx9bGysbNiwwYSali1bSnh4uLRu3VpWr17tUkOjzUb28KLat29vamj0sfYyGojs4UV17NjRNFedP39evM2e+YoxbBoAAK9wO8CkpaXJoEGDpFWrVlK/fn2z7NChQ+Zem3ueeuopU7PSpEkTadeunezfv9+s01oVDTjO/Pz8TAjSdfYyGn6c2X+3l0kvKSnJpDbnm6ebkIpxtWkAAKwVYLQvzM6dO2XGjBkuoUY9/fTT8vjjj0vjxo3l/fffl9q1a8ukSZPEk7SDsVY52W/a8dfTnXipgAEAwEIBRjvlaufbZcuWSVRUlGN5ZGSkudc+Ls7q1q0rR48eNT9HRESYpiZnKSkpZmSSrrOXOXXqlEsZ++/2MukNHz7cNGnZb8eOHRNP+SOnUQMDAIAVAoz2/dDwMnv2bFm6dKlUrVrVZX2VKlVMJ9z0Q6t/+eUXM6JItWjRQi5cuGBGF9nptrT2pnnz5o4yOjJJRznZ6YglrckpUybz+VYCAwNNZx/nm6f8WQNDgAEAoMAHGG02mjZtmkyfPt3MBaP9UfRmn+PFx8dHhgwZIh999JF88803cuDAAXn11VfNyCGd68VeG6PDrLWPjA6zXrNmjQlF3bt3N+FH9ejRw3Tg1cfocOuZM2fKhx9+KC+88IIUBPZxWzQhAQBggZl4dXizatOmjcvyyZMny2OPPWZ+1o69V65ckcGDB5tmoYYNG5rak+rV/5wv5auvvjKhRTv36uijBx980IQeO+3D8uOPP5rAdOutt0rZsmVl5MiRLnPFeJO9BkYDGwAAsNg8MAWZJ+eB+fnoeXlg3FqJDguSVUPvytdtAwBQlMXfiHlgiirHPDDUwAAA4BUEGDcs3HVtRFThrLsCAKDgI8C4cRmBz1Zem7Dv6LnL3t4dAACKJAJMLh058+eFKwEAgHcQYHIp9mKSt3cBAIAijwCTS0nJqd7eBQAAijwCDAAAsBwCDAAAsBwCTC4xchoAAO8jwORBtbIlvb0LAAAUSQSYPBjXq4m3dwEAgCKJAOOmextWkDoR+XuNJQAAkDMEmFzi8gEAAHgfAcZNXMYRAADvIcAAAADLIcAAAADLIcDkEl1gAADwPgKMm3zoBAMAgNcQYAAAgOUQYAAAgOUQYHLJxkQwAAB4HQHGTXSBAQDAewgwAADAcggwAADAcggwAADAcggwbvJhIhgAALyGAAMAACyHAAMAACyHAJNLTAMDAID3EWDcRA8YAAC8hwADAAAshwADAAAshwCTSzahEwwAAN5GgHEXnWAAAPAaAgwAALAcAgwAALAcAkwuMQ8MAADeR4Bxkw+dYAAA8BoCDAAAsBwCDAAAsBwCTC7RBQYAAO8jwLjJhy4wAAB4DQEGAABYDgEGAABYDgEml5gHBgAA7yPAuIkuMAAAeA8BBgAAWA4BBgAAFO4AM2bMGGnWrJkEBwdL+fLlpVu3brJv3z6XMm3atBEfHx+XW//+/V3KHD16VLp27SolSpQw2xkyZIikpKS4lFm+fLk0adJEAgMDpUaNGjJlyhQpCGzMBAMAgLUCzIoVK2TAgAGyfv16WbRokSQnJ0uHDh0kISHBpdxTTz0lJ06ccNzGjh3rWJeammrCy9WrV2Xt2rUydepUE05GjhzpKHP48GFTpm3btrJ161YZNGiQ9O3bVxYuXCgFBfPAAADgPX65KbxgwQKX3zV4aA3K5s2bJSYmxrFca1YiIiIy3caPP/4ou3fvlsWLF0t4eLg0atRI3njjDRk2bJiMGjVKAgICZMKECVK1alV59913zWPq1q0rq1evlvfff186duzo3pECAIBCI099YOLi4sx9WFiYy/KvvvpKypYtK/Xr15fhw4fL5cuXHevWrVsnDRo0MOHFTkNJfHy87Nq1y1Gmffv2LtvUMro8K0lJSWYbzjcAAFA45aoGxllaWppp2mnVqpUJKnY9evSQypUrS4UKFWT79u2mZkX7yXz77bdm/cmTJ13Ci7L/ruuuV0ZDSWJiogQFBWXaP+f1118XT2MeGAAALBxgtC/Mzp07TdOOs379+jl+1pqWyMhIadeunRw8eFCqV68unqI1PS+88ILjdw070dHRHvt7PswEAwCAtZqQBg4cKHPnzpVly5ZJVFTUdcs2b97c3B84cMDca9+YU6dOuZSx/27vN5NVmZCQkExrX5SOVtL1zjcAAFA45SrA2Gw2E15mz54tS5cuNR1ts6OjiJTWxKgWLVrIjh07JDY21lFGRzRp4KhXr56jzJIlS1y2o2V0OQAAQLHcNhtNmzZNpk+fbuaC0b4qetN+KUqbiXREkY5KOnLkiHz//ffSp08fM0LplltuMWV02LUGld69e8u2bdvM0OgRI0aYbWstitJ5Yw4dOiRDhw6VvXv3yrhx42TWrFkyePBgTzwHAACgMAeY8ePHm5FHOlmd1qjYbzNnzjTrdQi0Do/WkFKnTh158cUX5cEHH5T//ve/jm34+vqa5ie91xqVXr16mZAzevRoRxmt2Zk3b56pdWnYsKEZTj1x4sQCNYSaeWAAALBIJ15tQroe7TSrk91lR0cpzZ8//7plNCRt2bIlN7sHAACKCK6FBAAALIcAk0vZ1UIBAADPI8C4iT4wAAB4DwEGAABYDgEGAABYDgEml+gCAwCA9xFg3EYnGAAAvIUAAwAALIcAAwAALIcAk0t0gQEAwPsIMG5iHhgAALyHAAMAACyHAAMAACyHAJNLzAMDAID3EWDcRBcYAAC8hwADAAAshwADAAAshwCTSzZmggEAwOsIMG5iHhgAALyHAAMAACyHAAMAACyHAJNLzAMDAID3EWDc5MNMMAAAeA0BBgAAWA4BBgAAWA4BJpfoAgMAgPcRYNzEPDAAAHgPAQYAAFgOAQYAAFgOASa3mAgGAACvI8C4iS4wAAB4DwEGAABYDgEGAABYDgEml+gBAwCA9xFg3OTDRDAAAHgNAQYAAFgOAQYAAFgOASaXmAYGAADvI8AAAADLIcAAAADLIcAAAADLIcDkko2ZYAAA8DoCjJuYBgYAAO8hwAAAAMshwAAAAMshwOQS88AAAOB9BBg3+QidYAAA8BYCDAAAKNwBZsyYMdKsWTMJDg6W8uXLS7du3WTfvn2ZlrXZbNK5c2dz1eY5c+a4rDt69Kh07dpVSpQoYbYzZMgQSUlJcSmzfPlyadKkiQQGBkqNGjVkypQp7hwfAAAo6gFmxYoVMmDAAFm/fr0sWrRIkpOTpUOHDpKQkJCh7AcffGDCS3qpqakmvFy9elXWrl0rU6dONeFk5MiRjjKHDx82Zdq2bStbt26VQYMGSd++fWXhwoXibXSBAQDA+/xyU3jBggUuv2vw0BqUzZs3S0xMjGO5ho53331XNm3aJJGRkS6P+fHHH2X37t2yePFiCQ8Pl0aNGskbb7whw4YNk1GjRklAQIBMmDBBqlatarah6tatK6tXr5b3339fOnbsKAUB88AAAGDRPjBxcXHmPiwszLHs8uXL0qNHD/n0008lIiIiw2PWrVsnDRo0MOHFTkNJfHy87Nq1y1Gmffv2Lo/TMro8K0lJSWYbzjcAAFA4uR1g0tLSTNNOq1atpH79+o7lgwcPlpYtW8r999+f6eNOnjzpEl6U/Xddd70yGkoSExOz7J8TGhrquEVHR7t7aAAAoDA1ITnTvjA7d+40TTt233//vSxdulS2bNkiN9rw4cPlhRdecPyuYccTIYZ5YAAAsGgNzMCBA2Xu3LmybNkyiYqKcizX8HLw4EEpXbq0+Pn5mZt68MEHpU2bNuZnbVY6deqUy/bsv9ubnLIqExISIkFBQZnuk45W0vXON0+iCwwAABYJMDo0WsPL7NmzTVjRjrbOXn75Zdm+fbvpxGu/Ke18O3nyZPNzixYtZMeOHRIbG+t4nI5o0sBRr149R5klS5a4bFvL6HIAAAC/3DYbTZ8+Xb777jszF4y9z4r2OdGaEa05yazjbqVKlRxhR4dda1Dp3bu3jB071mxjxIgRZttai6L69+8vn3zyiQwdOlSeeOIJE5ZmzZol8+bNy5+jBgAARacGZvz48WbkkTYH6fBo+23mzJk53oavr69pftJ7rVHp1auX9OnTR0aPHu0oo2FHw4rWujRs2NAMp544cWKBGEJtYyYYAACsVQOjTUi5ldljKleuLPPnz7/u4zQkeaMzcE4xDwwAAN7DtZAAAIDlEGAAAIDlEGByiy4wAAB4HQHGTZldqBIAANwYBBgAAGA5BBgAAGA5BJhcogsMAADeR4BxEz1gAADwHgIMAACwHAIMAACwHAJMLrlzOQUAAJC/CDDuohMMAABeQ4ABAACWQ4ABAACWQ4DJJbrAAADgfQQYN/nQCQYAAK8hwAAAAMshwAAAAMshwOQSXWAAAPA+AoybfOgCAwCA1xBgAACA5RBgAACA5RBgcol5YAAA8D4CjJvoAgMAgPcQYAAAgOUQYAAAgOUQYHLJxkwwAAB4HQHGTcwDAwCA9xBgAACA5RBgAACA5RBgcol5YAAA8D4CjJt8mAkGAACvIcAAAADLIcAAAADLIcAAAADLIcC4iXlgAADwHgIMAACwHAIMAACwHAJMLtmYCAYAAK8jwLiJLjAAAHgPAQYAAFgOAQYAAFgOASaX6AEDAID3EWDcxUQwAAB4DQEGAABYDgEGAABYDgEml5gGBgAA7yPAuIkeMAAAWCTAjBkzRpo1aybBwcFSvnx56datm+zbt8+lzNNPPy3Vq1eXoKAgKVeunNx///2yd+9elzJHjx6Vrl27SokSJcx2hgwZIikpKS5lli9fLk2aNJHAwECpUaOGTJkyJS/HCQAAimqAWbFihQwYMEDWr18vixYtkuTkZOnQoYMkJCQ4ytx6660yefJk2bNnjyxcuNBMva9lUlNTzXq91/By9epVWbt2rUydOtWEk5EjRzq2cfjwYVOmbdu2snXrVhk0aJD07dvXbA8AAMDHloeL+5w+fdrUoGiwiYmJybTM9u3bpWHDhnLgwAFTM/PDDz/IPffcI8ePH5fw8HBTZsKECTJs2DCzvYCAAPPzvHnzZOfOnY7tdO/eXS5cuCALFizI0b7Fx8dLaGioxMXFSUhIiOSXEXN2yLT1R+X5djVl8N218m27AABAcnz+zlMfGN24CgsLy3S91sxobUzVqlUlOjraLFu3bp00aNDAEV5Ux44dzQ7v2rXLUaZ9+/Yu29IyujwrSUlJZhvON09iGhgAALzH7QCTlpZmmnZatWol9evXd1k3btw4KVWqlLlpjYs2N2nNijp58qRLeFH233Xd9cpoKElMTMyyf44mNvvNHpgAAEDh43aA0b4w2sQzY8aMDOt69uwpW7ZsMU1LtWrVkoceekiuXLkinjR8+HBTI2S/HTt2zKN/DwAAeI+fOw8aOHCgzJ07V1auXClRUVEZ1ttrQWrWrCm33367lClTRmbPni2PPPKIREREyMaNG13Knzp1ytzrOvu9fZlzGW0L09FNmdHRSnrzNOaBAQDAYjUw2t9Xw4uGkaVLl5q+LTl5jN60j4pq0aKF7NixQ2JjYx1ltIlJw0m9evUcZZYsWeKyHS2jywsKH2aCAQDAGgFGm42mTZsm06dPN3PBaF8Vvdn7pRw6dMj0Rdm8ebOZ60WHSf/tb38ztSZdunQxZXRItQaV3r17y7Zt28zQ6BEjRpht22tQ+vfvb7Y1dOhQM4eM9qmZNWuWDB482BPPAQAAKMwBZvz48aZ/SZs2bSQyMtJxmzlzpllfvHhxWbVqlQkrOvncww8/bIKOBhkdbq18fX1N85Pea41Kr169pE+fPjJ69GjH39GaHR1GrbUuOgT73XfflYkTJ5qRSAAAALnqA5PdlDEVKlSQ+fPnZ7udypUrZ1tOQ5J2BC5o6AIDAID3cS0kNzEPDAAA3kOAAQAAlkOAAQAAlkOAySXmgQEAwPsIMG6iCwwAAN5DgAEAAJZDgAEAAJZDgMk1OsEAAOBtBBg3MQ8MAADeQ4ABAACWQ4ABAACWQ4DJJeaBAQDA+wgwbvKhEwwAAF5DgAEAAJZDgAEAAJZDgMkl+sAAAOB9BBgAAGA5BBgAAGA5BBgAAGA5BJhcsnEtJAAAvI4A4yamgQEAwHsIMAAAwHIIMAAAwHIIMLnEPDAAAHgfAcZNPkInGAAAvIUAAwAALIcAAwAALIcAk0t0gQEAwPsIMG5iHhgAALyHAAMAACyHAAMAACyHAJNLzAMDAID3EWDcRBcYAAC8hwADAAAshwADAAAshwCTSzZmggEAwOsIMG5iHhgAALyHAAMAACyHAAMAACyHAJNbdIEBAMDrCDBu8mEmGAAAvIYAAwAALIcAAwAALIcAk0t0gQEAwPsIMG5iHhgAALyHAAMAACyHAAMAACyHAJNLNhu9YAAAsFSAGTNmjDRr1kyCg4OlfPny0q1bN9m3b59j/blz5+Tvf/+71K5dW4KCgqRSpUry3HPPSVxcnMt2jh49Kl27dpUSJUqY7QwZMkRSUlJcyixfvlyaNGkigYGBUqNGDZkyZUpejxUAABTFALNixQoZMGCArF+/XhYtWiTJycnSoUMHSUhIMOuPHz9ubv/85z9l586dJnQsWLBAnnzyScc2UlNTTXi5evWqrF27VqZOnWrKjRw50lHm8OHDpkzbtm1l69atMmjQIOnbt68sXLgwP48dAABYlI8tD20ip0+fNjUoGmxiYmIyLfP1119Lr169TMjx8/OTH374Qe655x4TdMLDw02ZCRMmyLBhw8z2AgICzM/z5s0zIciue/fucuHCBROIciI+Pl5CQ0NN7U9ISIjkl+dnbJHvth6XEV3rSt87q+XbdgEAgOT4/J2nPjD2pqGwsLDrltEd0PCi1q1bJw0aNHCEF9WxY0ezw7t27XKUad++vct2tIwuz0pSUpLZhvPNk3wYRw0AgNe4HWDS0tJM006rVq2kfv36mZY5c+aMvPHGG9KvXz/HspMnT7qEF2X/Xdddr4yGksTExCz752his9+io6PdPTQAAFBYA4z2hdEmnhkzZmS6XsOG9mOpV6+ejBo1Ki/7mCPDhw83tT3227Fjxzz+NwEAgHdca9fJpYEDB8rcuXNl5cqVEhUVlWH9xYsXpVOnTma00uzZs8Xf39+xLiIiQjZu3OhS/tSpU4519nv7Mucy2hSlo5syo6OV9OZpjKIGAMBiNTDa31fDi4aSpUuXStWqVTOtedGRSdoZ9/vvv5fixYu7rG/RooXs2LFDYmNjHct0RJOGE62tsZdZsmSJy+O0jC4vKOgBAwCARQKMNhtNmzZNpk+fbmpXtK+K3uz9UuzhRUccff755+Z3exkdPq10vQaV3r17y7Zt28zQ6BEjRpht22tQ+vfvL4cOHZKhQ4fK3r17Zdy4cTJr1iwZPHiwJ54DAABQmJuQxo8fb+7btGnjsnzy5Mny2GOPyc8//ywbNmwwy3TyOWc6t0uVKlXE19fXND8988wzpkalZMmS8uijj8ro0aMdZbVmR4dRa2D58MMPTTPVxIkTzUgkAACAXAWY7KaM0WCTk2llKleuLPPnz892W1u2bJGCxt+3mBT3LyZ+vjQiAQBgyYnsCjJPTWQHAAAsPpEdAACANxBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5fhJIWWz2RyX5QYAANZgP2/bz+NFLsBcvHjR3EdHR3t7VwAAgBvn8dDQ0CzX+9iyizgWlZaWJsePH5fg4GDx8fHJ12SooejYsWMSEhIihVFhP8bCfnxF4Rg5Pusr7MdY2I/Pk8eosUTDS4UKFaRYsWJFrwZGDzoqKspj29cXq7C+KYvKMRb24ysKx8jxWV9hP8bCfnyeOsbr1bzY0YkXAABYDgEGAABYDgEmlwIDA+W1114z94VVYT/Gwn58ReEYOT7rK+zHWNiPryAcY6HtxAsAAAovamAAAIDlEGAAAIDlEGAAAIDlEGAAAIDlEGBy6dNPP5UqVapI8eLFpXnz5rJx40axgjFjxkizZs3MzMTly5eXbt26yb59+1zKtGnTxsxa7Hzr37+/S5mjR49K165dpUSJEmY7Q4YMkZSUFPG2UaNGZdj3OnXqONZfuXJFBgwYIDfddJOUKlVKHnzwQTl16pQljs1O33fpj1FvelxWfP1Wrlwp9957r5ltU/d1zpw5Lut1fMHIkSMlMjJSgoKCpH379rJ//36XMufOnZOePXuaSbRKly4tTz75pFy6dMmlzPbt2+XOO+80/7M6a+jYsWO9fnzJyckybNgwadCggZQsWdKU6dOnj5k9PLvX/O233y4Qx5fdMarHHnssw/536tSpULyGKrP/R7298847lngNx+TgvJBfn53Lly+XJk2amBFLNWrUkClTpuT9AHQUEnJmxowZtoCAANukSZNsu3btsj311FO20qVL206dOmUr6Dp27GibPHmybefOnbatW7faunTpYqtUqZLt0qVLjjKtW7c2x3TixAnHLS4uzrE+JSXFVr9+fVv79u1tW7Zssc2fP99WtmxZ2/Dhw23e9tprr9luvvlml30/ffq0Y33//v1t0dHRtiVLltg2bdpku/32220tW7a0xLHZxcbGuhzfokWLdAShbdmyZZZ8/fTvv/LKK7Zvv/3WHMfs2bNd1r/99tu20NBQ25w5c2zbtm2z3XfffbaqVavaEhMTHWU6depka9iwoW39+vW2VatW2WrUqGF75JFHHOv1+MPDw209e/Y07/3//Oc/tqCgINu//vUvrx7fhQsXzOswc+ZM2969e23r1q2z3XbbbbZbb73VZRuVK1e2jR492uU1df6f9ebxZXeM6tFHHzWvkfP+nzt3zqWMVV9D5XxcetNzg4+Pj+3gwYOWeA075uC8kB+fnYcOHbKVKFHC9sILL9h2795t+/jjj22+vr62BQsW5Gn/CTC5oB8wAwYMcPyemppqq1Chgm3MmDE2q9GTof5DrlixwrFMT4DPP/98lo/RN2axYsVsJ0+edCwbP368LSQkxJaUlGTzdoDRD8HM6MnC39/f9vXXXzuW7dmzxxy/njgK+rFlRV+r6tWr29LS0iz/+qU/OegxRURE2N555x2X1zEwMNB8wCv9INTH/fTTT44yP/zwgzmB/P777+b3cePG2cqUKeNyfMOGDbPVrl3bdiNldvJLb+PGjabcr7/+6nLye//997N8TEE5PpVVgLn//vuzfExhew31WO+66y6XZVZ6DWPTnRfy67Nz6NCh5gums4cfftgEqLygCSmHrl69Kps3bzbV2M7XW9Lf161bJ1YTFxdn7sPCwlyWf/XVV1K2bFmpX7++DB8+XC5fvuxYp8epVd7h4eGOZR07djQX9Nq1a5d4mzYvaFVvtWrVTJW0Vmsqfd20yt75tdPmpUqVKjleu4J+bJm9H6dNmyZPPPGEy8VKrfz6OTt8+LCcPHnS5TXTa6Nos63za6ZNDk2bNnWU0fL6f7lhwwZHmZiYGAkICHA5Zq0mP3/+vBS0/0l9LfWYnGlzg1bfN27c2DRNOFfNW+H4tOlAmxVq164tzzzzjJw9e9axrjC9htqsMm/ePNMElp5VXsO4dOeF/Prs1DLO27CXyeu5s9BezDG/nTlzRlJTU11eJKW/7927V6x2pe5BgwZJq1atzInOrkePHlK5cmUTArRNVtvo9Z/o22+/Nev1hJLZ8dvXeZOe2LRNVT8kT5w4Ia+//rppU965c6fZN/1wSH9i0H2373dBPrbMaFv8hQsXTB+DwvD6pWffn8z21/k10xOjMz8/P/Ph61ymatWqGbZhX1emTBkpCLSfgb5ejzzyiMtF8Z577jnTb0CPae3atSaU6vv7vffes8TxaX+XBx54wOzjwYMH5R//+Id07tzZnLh8fX0L1Ws4depU05dEj9eZVV7DtEzOC/n12ZlVGQ05iYmJpo+bOwgwRZB2yNIT++rVq12W9+vXz/GzJmrtPNmuXTvzwVO9enUpyPRD0e6WW24xgUZP5rNmzXL7n6Mg+/zzz80xa1gpDK9fUabfcB966CHTaXn8+PEu61544QWX97WeTJ5++mnT+dIKU9R3797d5T2px6DvRa2V0fdmYTJp0iRT86sdca34Gg7I4rxQkNGElENaLa/fGNL3vtbfIyIixCoGDhwoc+fOlWXLlklUVNR1y2oIUAcOHDD3epyZHb99XUGi3xhq1apl9l33TZtctMYiq9fOSsf266+/yuLFi6Vv376F9vWz78/1/t/0PjY21mW9Vs3rqBarvK728KKv6aJFi1xqX7J6TfUYjxw5YonjS0+bd/Wz1Pk9afXXUK1atcrUdmb3P1lQX8OBWZwX8uuzM6sy+n7PyxdMAkwOaWq+9dZbZcmSJS5Vbvp7ixYtpKDTb3f6Jp09e7YsXbo0Q5VlZrZu3Wru9Zu80uPcsWOHyweO/UO3Xr16UpDoMEytedB919fN39/f5bXTDxvtI2N/7ax0bJMnTzbV7jpssbC+fvr+1A8959dMq5u1X4Tza6YfrNpOb6fvbf2/tIc3LaNDYTUoOB+zNjV6u+nBHl6075YGUu0jkR19TbV/iL3ZpSAfX2Z+++030wfG+T1p5dfQuUZUP2caNmxoqdfQls15Ib8+O7WM8zbsZfJ87sxTF+AiOIxaR0FMmTLF9J7v16+fGUbt3Pu6oHrmmWfMkNTly5e7DOe7fPmyWX/gwAEz1E+HyR0+fNj23Xff2apVq2aLiYnJMFyuQ4cOZsidDoErV65cgRhq/OKLL5pj031fs2aNGdKnQ/m0V719KKAOD1y6dKk5xhYtWpibFY7NmY580+PQUQrOrPj6Xbx40Qy71Jt+FL333nvmZ/soHB1Grf9feizbt283IzwyG0bduHFj24YNG2yrV6+21axZ02UIro6i0CGqvXv3NkNF9X9Yh3PeiCGq1zu+q1evmmHhUVFR5rVw/p+0j9xYu3atGb2i63VY7rRp08zr1adPnwJxfNkdo6576aWXzGgVfU8uXrzY1qRJE/MaXblyxfKvofMwaN0fHXmTXkF/DZ/J5ryQX5+d9mHUQ4YMMaOYPv30U4ZRe4OOX9cXU+eD0WHVOneBFeg/X2Y3nQNAHT161JzswsLCTEjTuRj0zeY8j4g6cuSIrXPnzmaeAg0IGhySk5Nt3qZD8iIjI83rUrFiRfO7ntTt9KT37LPPmuGK+o/0l7/8xfyjWuHYnC1cuNC8bvv27XNZbsXXT+evyew9qUNv7UOpX331VfPhrsfUrl27DMd99uxZc7IrVaqUGbb5+OOPm5OOM51D5o477jDb0PeGBiNvH5+e0LP6n7TP67N582Zb8+bNzQmmePHitrp169reeustl5O/N48vu2PUk6Ce1PRkpkNxdTixzlOU/gufVV9DOw0a+v+kQSS9gv4aSjbnhfz87NTnslGjRuYzWr9cOf8Nd/n8cRAAAACWQR8YAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAIVGlSpV5IMPPvD2bgC4AQgwANzy2GOPSbdu3czPbdq0kUGDBt2wvz1lyhRzwc70fvrpJ5ercgMovPy8vQMAYKdXvtULp7qrXLly+bo/AAouamAA5LkmZsWKFfLhhx+Kj4+PuR05csSs27lzp3Tu3FlKlSol4eHh0rt3bzlz5ozjsVpzo1fD1dqbsmXLSseOHc3y9957Txo0aCAlS5aU6OhoefbZZ80VxtXy5cvl8ccfl7i4OMffGzVqVKZNSHrV3Pvvv9/8fb06rl79+dSpU471+rhGjRrJl19+aR4bGhoq3bt3l4sXLzrKfPPNN2ZfgoKCzBWj27dvLwkJCTfgmQVwPQQYAHmiwaVFixby1FNPyYkTJ8xNQ8eFCxfkrrvuksaNG8umTZtkwYIFJjxoiHA2depUU+uyZs0amTBhgllWrFgx+eijj2TXrl1m/dKlS2Xo0KFmXcuWLU1I0UBi/3svvfRShv1KS0sz4eXcuXMmYC1atEgOHTokDz/8sEu5gwcPypw5c2Tu3LnmpmXffvtts063/cgjj8gTTzwhe/bsMeHpgQce0IvgevAZBZATNCEByBOttdAAUqJECYmIiHAs/+STT0x4eeuttxzLJk2aZMLNL7/8IrVq1TLLatasKWPHjnXZpnN/Gq0ZefPNN6V///4ybtw487f0b2rNi/PfS2/JkiWyY8cOOXz4sPmb6osvvpCbb77Z9JVp1qyZI+hon5rg4GDzu9YS6WP/53/+xwSYlJQUE1oqV65s1mttDADvowYGgEds27ZNli1bZppv7Lc6deo4aj3sbr311gyPXbx4sbRr104qVqxogoWGirNnz8rly5dz/Pe1xkSDiz28qHr16pnOv7rOOSDZw4uKjIyU2NhY83PDhg3Nfmho+dvf/ib//ve/5fz58248GwDyGwEGgEdon5V7771Xtm7d6nLbv3+/xMTEOMppPxdn2n/mnnvukVtuuUX+7//+TzZv3iyffvqpo5NvfvP393f5XWt2tFZG+fr6mqanH374wYSfjz/+WGrXrm1qdQB4FwEGQJ5ps05qaqrLsiZNmpg+LFrDUaNGDZdb+tDiTAOLBoh3331Xbr/9dtPUdPz48Wz/Xnp169aVY8eOmZvd7t27Td8cDSM5pYGmVatW8vrrr8uWLVvM3549e3aOHw/AMwgwAPJMQ8qGDRtM7YmOMtIAMmDAANOBVjvBap8TbTZauHChGUF0vfChASc5OdnUdminWx0hZO/c6/z3tIZH+6ro38usaUlHC2nTT8+ePeXnn3+WjRs3Sp8+faR169bStGnTHB2XHpP24dFOyDqi6dtvv5XTp0+bcATAuwgwAPJMRwFpc4vWbOhcLHqyr1ChghlZpGGlQ4cOJkxo51ztg6KjjLKi/U50GPX//u//Sv369eWrr76SMWPGuJTRkUjaqVdHFOnfS98J2F5z8t1330mZMmVMk5UGmmrVqsnMmTNzfFw60mnlypXSpUsXUxM0YsQIUzOkQ8MBeJePjfGAAADAYqiBAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAYjX/DzBI4RP/qPtLAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "log_likelihood = get_log_likelihood(tree,down_conditional,up,init_up)\n",
-    "obs_var_sample_conditional = True\n",
-    "log_posterior = get_log_posterior(log_likelihood,skip_obs_var=obs_var_sample_conditional)\n",
-    "proposal = get_proposal(tree,obs_var_sample_conditional=obs_var_sample_conditional)\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.5,alpha=2.,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.5), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    #'obs_var': VarianceParameter(.5*1e-3,min=1e-4,max=.1,prior='uniform'), # observation noise variance\n",
-    "    'obs_var': VarianceParameter(5*1e-3,alpha=2,beta=.005,min=1e-4,keep_constant=False), # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 2000, burn_in=000, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "burnin = 500\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,params.values(),burnin=burnin)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## plot v_0, v_T by level from leaves up (excluding root)\n",
-    "#for level_num, level in enumerate(tree.levels[1:]):  # Skip root level\n",
-    "#    print(f\"Level {level_num+1}\")\n",
-    "#    for i in range(*level):\n",
-    "#        print(tree.data['logpsi'][i])\n",
-    "#        ax = plot_shape(tree.data['v_0'][i],color='blue',label='v_0')\n",
-    "#        plot_shape(tree.data['v_T'][i],color='red',label='v_T',ax=ax)\n",
-    "#        ax.set_aspect('auto')\n",
-    "#        ax.axis([-1.5,1.5,-1.5,1.5])\n",
-    "#        plt.legend()\n",
-    "#        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of workers: 10\n",
-      "Cores per worker: {'tcp://127.0.0.1:57443': 2, 'tcp://127.0.0.1:57444': 2, 'tcp://127.0.0.1:57449': 2, 'tcp://127.0.0.1:57452': 2, 'tcp://127.0.0.1:57455': 2, 'tcp://127.0.0.1:57458': 2, 'tcp://127.0.0.1:57461': 2, 'tcp://127.0.0.1:57464': 2, 'tcp://127.0.0.1:57467': 2, 'tcp://127.0.0.1:57470': 2}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/stefan/projects/hyperiax/lib/python3.13/site-packages/distributed/client.py:3371: UserWarning: Sending large graph of size 15.74 MiB.\n",
-      "This may cause some slowdown.\n",
-      "Consider loading the data with Dask directly\n",
-      " or using futures or delayed objects to embed the data into the graph without repetition.\n",
-      "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k_alpha - mean: 0.3002, median: 0.2991\n",
-      "k_sigma - mean: 0.2503, median: 0.2512\n",
-      "obs_var - mean: 0.0010, median: 0.0010\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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LNxOzM5Esj2e6qyrTpUuXYhcHWo4VK0Lo0eN/5//1rxA+9KFilwjI07Jly9KBa1XsyxoxG0qIdgaUdNwWs6EEid2Qd8zORLK86pKwGMAFcaihdev/zMfvhkAIJSOrl0OL2VBCtDOgpOO2mA0lSOyGvGN2tgZVAwAAAACAJpCJnuVAHdq1C+HnP//PPABAoWhnAEC2iN2QN8lyyLK2bUM4/fRilwIAKEXaGQCQLWI35K1kkuWVlZVhzZo1xS4GTaBt27ahdc1xtwDIvPXr14e1a9cWuxgUmJgNAEBL51ikNLUt0LFISSTLY5J83rx5KWFOadpyyy1D9+7dM3njnCa1fn0If/7z/85/4hO1b+YB0ALlcrmwePHi8O677xa7KDQRMbuEaGcAQLaI3ZvkWKT0bVmAY5E2pfBBX7RoUTpz0Lt379CqlXuWlpK4f99///3wxhtvpMfbb799sYvUsqxaFcLBB//v/PLl7nQNtHhVjdPtttsudOrUSUK1hIjZJUg7AwCyRezeJMcipStXwGORzCfL161blzZGjx490ged0tOxY8f0f/zAxx80l3cDZPdyx6rG6Yc//OFiF4cmIGYDANASORYpfR0LdCzSqhQ+7FE7d/ktaVUnQowpBZBdVb/hTm6XNjEbAICWxrFIeehUgGORzCfLq7h0orTZvwClw296abN/AQBoqbRVS1tFAfZvySTLAQAAAACgsSTLKchZm6lTpxa7GADAZojZAABAMVRk5FhEsrxIH45NTRdffHGxiwgAiNkAAECROBYpjjZFet+ytmjRour52267LVx44YXhpZdeql62xRZbVM/ncrl0E9M2bewqNqJt2xB+8pP/zANQUGI2ZU07AwCyRewuKY5FMtiz/LLLLktnMs4666xNrnfHHXeEXXbZJXTo0CHstttu4d577w3lrHv37tVT165d0zasevziiy+Gzp07hz/84Q9h8ODBoX379uHRRx8Nxx9/fDj66KNrvU7c7sOGDat+XFlZGcaPHx/69OkTOnbsGAYNGhTuvPPOOstx/vnnh/322+8Dy+PzLrnkkjT/l7/8JRx66KFhm222SWU96KCDwlNPPVXna86YMSPV5913361e9vTTT6dlr732WvWyWKdPfOITqZy9e/cO3/rWt8KKFSsasBVJ2rUL4Zxz/neK8wD/Z9KkSWH33XcPXbp0SdOQIUNSbNkU8fqDxGwxu6xpZ0CzEbeBghC7S4pjkVCUY5FGJ8vjRrjuuutSQN+UmTNnhuOOOy6cdNJJYe7cuWmHxenZZ58NTSpuuLqmVavqv+7KlfVbt8DOO++8dDLihRde2Ow2rhI/6DfffHOYPHlyeO6558LZZ58dvvrVr4aHH354o+t/5StfCU8++WR45ZVXqpfF5/3tb38LX/7yl9Pj9957L4waNSp9OB9//PGw8847h09/+tNpeWPF9zv88MPD5z//+fRe8exYfP0zzjij0a8JQG29evVKcWTOnDlh9uzZ4ZOf/GQ46qij0u98i4rXkZgtZgOUuUzFbYBS4ljEsciGco3w3nvv5Xbeeefc9OnTcwcddFDuzDPPrHPdL37xi7kRI0bUWrbffvvlTj311Hq/39KlS3OxqPH/Da1cuTL3/PPPp/9riVWra/r0p2uv26lT3esedFDtdbfZZuPrNdJNN92U69q1a/Xjhx56KNV16tSptdYbNWpU7qijjqq1LG73uP2jVatW5Tp16pSbOXNmrXVOOumk3HHHHVfn+w8aNCh3ySWXVD8eO3Zs2j91Wb9+fa5z5865//mf/6leFst711131Sr/O++8U/33uXPnpmXz5s2rLtMpp5xS63X//Oc/51q1avXB/bi5/Vzu1q3L5Z588n+nOA9k3qZiXr622mqr3A033NBk8ToSs8VsMbuEaGdAScftpiw7UCRid50cizgWWVrPuNeonuWnn356GDFiRDjkkEM2u+6sWbM+sN7w4cPTcuq29957N2j9l19+Obz//vvpkoc4ZlHVFM8U1Tzzs7GzQ7fcckuaj5/b3/3ud2lZlSVLloTRo0enM0LxMop4SeDy5cvDggULGl23v/71r2HKlCm1yhk/E/EykHnz5jX6dctSPMu5777/O214xhPg/8Sx62699dZ0uVq8rHtjxOvGE7MpWdoZUBTiNtBoYnfZcSxSeA0e9T0G7TjmTByGpT4WL14cunXrVmtZfByX12X16tVpqrJs2bKGFjOE5cvr/lvr1rUfv/FG3eu22uB8Qo1xc5rShz70oQ2K0Sp9GGtau3Zt9Xz8AEb33HNP6NmzZ6314rhFdYmX7n33u99N+3TlypVh4cKF4dhjj63+e7yE4t///ne46qqrwo477pheKzbY1qxZs9HXi+WMapa1ZjmrynrqqaemcYY2tMMOO9RZVgAa5plnnkm/2atWrUoNi7vuuisMGDCgYPE6ErPFbACyEbcLErMBSo1jkWqORRqRLI8b4swzzwzTp09PNxFpKnHsnHHjxuX3Iht8WIqybgFtu+22Hxh/Lg583/b/7m4cG1HxgxjP2MRB9BsyNl5c/7e//W36sMczS9ttt1313x977LFw7bXXpnGGqj4Db7311ibLWXXH3q222qq6nDXttdde4fnnnw99+/YN5W6n8+7J6/kd16wKL/zffP/v3xde+OnnC1IuWtZ+3tBrl40o6OtRuvr165d+g5cuXZpu2BIbMHEcuroOvBtDzP4gMZtixIamiA+pbVFjfmW7Di0ufomxpa+c9nFTx+2CxGxKRlPEoabSkr+3LW1fbZgjyDd2l8V+cixSkGORPz/6WDj/h5eHXrsPDetDCK/+6/V0LPKvd1eGv73+n5t4vvbWivT47fX/m5ifMfel8NGP7ZLm735oZvr/hUXLwrI274aP7DKwKMciDRqGJd5s5I033kgHTm3atElTDN4/+9nP0ny8XGxD8Q6tsSt+TfFxXF6XsWPHpgZC1RQP9spdvMFLvNFLvCziH//4R7joootqffjjHXC/853vpEH5f/WrX6VLJ+LZnquvvjo93pR42US8YiDeTb3mJRRRvHzi17/+dbpRwBNPPJH+Hu9AW5f4AY53p7344otTOeOZqgkTJtRaJ56JijekiQPyxy9sXG/atGluFgZQYO3atUu/y/Hu6PEAOd6tPJ7p35jGxOtIzP4gMRuAlhi3xWyA0lesY5Ed+nwk3P3/bg+v/uOl8Le5s8PYb54SOnSo+1ik904fCd179AyTJ/44zJ/3SnjkgfvDzddfU2udE75xZlGORRqULP/Upz6VLg2LBaya4tg4cQPF+dYbXp4QQupy/8ADD9RaFnum1zX2WhTPcMSxbWpO5S6OyfP9738/nHvuuWGfffZJd5MdOXJkrXUuvfTStE5sWPXv3z/dMTYe+Pbp02eTr33MMcekSyXimEXxDuo1/fKXvwzvvPNOOkHyta99LV36UPPM0Ybimao4btGLL76Y7sL74x//OPzgBz+otU5cHk+y/P3vfw+f+MQnwp577hkuvPDC0KNHj0ZtGwDqJ47tVvPy63zjdSRmf5CYDUBLjNtiNkDpK9axyMWXXx3eW/pu+NIRw8L3zjwtfPnEU8PW22yzyWORy35+Q5j38t/DFw79eLjp2qvCGed8r9Y6H+s/sCjHIhX/dzfSRhs2bFjYY489wpVXXpkexx0Qx7yJGzyKZwBiN/3LLrss3RQ0noH40Y9+lM5aDBw4sF7vEcdSi4PDx7PfGwb0OJ5bHNQ97tCmHBqG4irV/VyQYVgmHpPm+599p2FYWqhyunyY/G0q5jVE7D12xBFHpLHcYgMp3owlJkPvv//+dMlcU8TrzZW/VH/Lqc1+LqFhWMb8v1rtDMOwUAwtfR9nOW4Xquxkk2FYSngYlgLG7lLaT9qoTetvNYZaKaTde21ZsP1c37jX4Bt8bk4c86ZqkPZo6NChKdBfcMEF4fzzz0+XCE+dOrVBB94AQMPEYdPigXUcjzo2CGIP4aoD7ki8BoCWQ9wGgJYh72T5jBkzNvk4+sIXvpAmoLDWtW4drjzguOp5gJpDcmyKeA1sjnYGNB9xGygEsRvyV/Ce5UDzWdu6bbjy47VvqgAAUAjaGQCQLWI3NPMNPgEAAAAAoBTpWQ4ZVpGrDH3fWpjmX96md7GLAwCUcDsjV6GfDQC0ZGI35K9kkuW5XK7YRaAJVVZWFrsILVKHtWvC9BtPr77TNUAW+E0vbfZv6bYzVrbrUOwiAQCbIHZvnrZqaasswP7NfLK8bdu2oaKiIrz55pth2223TfOUjngSZM2aNWn/xru/t2vXrthFAqCR4m94/C3/17/+lWJ2fCxulw4xGwCAlsqxSNPKrVvTJK+7atWqZj8WyXyyvHXr1qFXr17h9ddfD6+99lqxi0MT6dSpU9hhhx3SBx6AbIq/4X369AmLFi1KjVRKk5gNAEBL41ikab3xzsomed12Kzs2+7FI5pPl0RZbbBF23nnnsHbt2mIXhSY6IdKmTRtn/ABKQDzDHxsv69atC+vXry92cSgwMRsAgJbKsUjTOfn3M5rkdR/49rBmPxYpiWR51QaJEwDQssXGSxxGLU4AAADNxbFI0/jne01z8qFDh+Yfd9/1sQAAAAAAlD3JcgAAAAAAyl7JDMMC5Whd69bhun0/Vz0PAFAo2hkAkC1iN+RPshwybG3rtmH8wScWuxgAQAnSzgCAbBG7IX+GYQEAAAAAoOzpWQ4ZVpGrDD2XvZnm/9ll22IXBwAo4XZGrkI/GwBoycRuyJ9kOWRYh7VrwqOTT0rz/c++s9jFAQBKuJ2xsl2HYhcJANgEsRvy5xQTAAAAAABlT7IcAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7bYpdAKDx1rdqHW7ec0T1PABAoWhnAEC2iN2QP8lyyLA1bdqGCw/7erGLAQCUIO0MAMgWsRvyZxgWAAAAAADKnp7lkGW5XNh65bI0+3bHLsUuDQBQyu2MiopilwgA2BSxG/ImWQ4Z1nHt6vDU1V9J8/3PvrPYxQEASridsbJdh2IXCQDYBLEb8mcYFgAAAAAAyp5kOQAAAAAAZU+yHAAAAACAsidZDgAAAABA2ZMsBwAAAACg7EmWAwAAAABQ9toUuwBA461v1TrcOfBT1fMAAIWinQEA2SJ2Q/4kyyHD1rRpG74z4uxiFwMAKEHaGQCQLWI35M8wLAAAAAAAlD09yyHLcrnQce3qNLuybftilwYAKOV2RkVFsUsEAGyK2A15kyyHDItB8IWJx6T5/mffWeziAAAl3M5Y2a5DsYsEAGyC2A35MwwLAAAAAABlT7IcAAAAAICy16Bk+aRJk8Luu+8eunTpkqYhQ4aEP/zhD3WuP2XKlFBRUVFr6tDBJSAAAAAAAGR4zPJevXqFyy67LOy8884hl8uFX/3qV+Goo44Kc+fODbvuuutGnxOT6i+99FL145gwBwAAAACAzCbLjzzyyFqPf/jDH6be5o8//nidyfKYHO/evXt+pQQAAAAAgJY4Zvn69evDrbfeGlasWJGGY6nL8uXLw4477hh69+6deqE/99xzjX1LAAAAAAAofs/y6JlnnknJ8VWrVoUtttgi3HXXXWHAgAEbXbdfv37hxhtvTOOcL126NFxxxRVh6NChKWEeh3Spy+rVq9NUZdmyZQ0tJpSFylatwj39DqieBwAoFO0MAMgWsRuKkCyPCfCnn346Jb/vvPPOMGrUqPDwww9vNGEek+o1e53HRHn//v3DddddFy699NI632P8+PFh3LhxDS0alJ3VbdqF048eW+xiAAAlSDsDALJF7Ib8Nfg0U7t27ULfvn3D4MGDU1J70KBB4aqrrqrXc9u2bRv23HPP8PLLL29yvbFjx6ZkfNW0cOHChhYTAAAAAADqLe9rMiorK2sNmbK5cc7jMC7bb7/9Jtdr37596NKlS60JAAAAAABaxDAsscf3EUccEXbYYYfw3nvvhVtuuSXMmDEj3H///envI0eODD179kw9zqNLLrkk7L///qkn+rvvvhsuv/zyMH/+/HDyySc3TW2gzHRcsyq8MPGYNN//7DuLXRwAoITbGSvbdSh2kQCATRC7oZmT5W+88UZKiC9atCh07do13bgzJsoPPfTQ9PcFCxaEVjVuIPDOO++E0aNHh8WLF4etttoqDd0yc+bMOm8ICgAAAAAALT5Z/stf/nKTf4+9zGuaOHFimgAAAAAAoKTHLAcAAAAAgKyTLAcAAAAAoOxJlgMAAAAAUPYkywEAAAAAKHsNusEn0LJUtmoVHvzI3tXzAACFop0BANkidkP+JMshw1a3aRdO/MLFxS4GAFCCtDMAIFvEbsif00wAUILGjx8f9tlnn9C5c+ew3XbbhaOPPjq89NJLm3zOlClTQkVFRa2pQ4cOzVZmAChHYjYAtByS5QBQgh5++OFw+umnh8cffzxMnz49rF27Nhx22GFhxYoVm3xely5dwqJFi6qn+fPnN1uZAaAcidkA0HIYhgUyrOOaVWHOz7+S5gef8dtiFwdoQe67774P9ECLvdXmzJkTDjzwwDqfF3umde/evRlKCGStnbGynV6r0BTEbKBQxG7In57lkHGd1q5OE8CmLF26NP2/9dZbb3K95cuXhx133DH07t07HHXUUeG5555rphICLZF2BjQ/MRvIh9gN+ZEsB4ASV1lZGc4666xwwAEHhIEDB9a5Xr9+/cKNN94Ypk2bFn7zm9+k5w0dOjS8/vrrdT5n9erVYdmyZbUmAKBxxGwAKC7DsABAiYvjoD777LPh0Ucf3eR6Q4YMSVOVeNDdv3//cN1114VLL720zpuSjRs3ruBlBoByJGYDQHHpWQ4AJeyMM84Id999d3jooYdCr169GvTctm3bhj333DO8/PLLda4zduzYdLl41bRw4cIClBoAyo+YDQDFp2c5AJSgXC4XvvnNb4a77rorzJgxI/Tp06fBr7F+/frwzDPPhE9/+tN1rtO+ffs0AQCNI2YDQMshWQ4AJXoZ9y233JLGMu3cuXNYvHhxWt61a9fQsWPHND9y5MjQs2fPdFl2dMkll4T9998/9O3bN7z77rvh8ssvD/Pnzw8nn3xyUesCAKVMzAaAlkOyHDKssqIiPN57YPU8QJVJkyal/4cNG1Zr+U033RSOP/74NL9gwYLQqtV/RmR75513wujRo9NB+lZbbRUGDx4cZs6cGQYMGNDMpQdaAu0MaB5iNlAoYjfkT7IcMmx12/bhS1++rNjFAFroJd2bEy/1rmnixIlpAoi0M6B5iNlAoYjdkD83+AQAAAAAoOxJlgMAAAAAUPYkyyHDOq5ZFeb87MtpivMAAIWinQEA2SJ2Q/6MWQ4Z9+GVy4pdBACgRGlnAEC2iN2QHz3LAQAAAAAoe5LlAAAAAACUPclyAAAAAADKnmQ5AAAAAABlT7IcAAAAAICy16bYBQAar7KiIvy1+87V8wAAhaKdAQDZInZD/iTLIcNWt20fjho1sdjFAABKkHYGAGSL2A35MwwLAAAAAABlT7IcAAAAAICyJ1kOGdZh7arw6KQT0xTnAQAKRTsDALJF7Ib8GbMcMqwiF0KvZW9UzwMAFIp2BgBki9gN+dOzHAAAAACAsidZDgAAAABA2ZMsBwAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe22KXQCg8XIVIfz9wztUzwMAFIp2BgBki9gN+ZMshwxb1bZDOOzka4tdDACgBGlnAEC2iN2QP8OwAAAAAABQ9hqULJ80aVLYfffdQ5cuXdI0ZMiQ8Ic//GGTz7njjjvCLrvsEjp06BB22223cO+99+ZbZgAAAAAAKF6yvFevXuGyyy4Lc+bMCbNnzw6f/OQnw1FHHRWee+65ja4/c+bMcNxxx4WTTjopzJ07Nxx99NFpevbZZwtVfihrHdauCn+84RtpivMAAIWinQEA2SJ2QzOPWX7kkUfWevzDH/4w9TZ//PHHw6677vqB9a+66qpw+OGHh3POOSc9vvTSS8P06dPDz3/+8zB58uR8yw5lryIXwsf+vaB6HgCgULQzACBbxG4o4pjl69evD7feemtYsWJFGo5lY2bNmhUOOeSQWsuGDx+elm/K6tWrw7Jly2pNAAAAAADQInqWR88880xKjq9atSpsscUW4a677goDBgzY6LqLFy8O3bp1q7UsPo7LN2X8+PFh3LhxDS0aNLmdzrun2EUAAAAAAFpCz/J+/fqFp59+OjzxxBPh61//ehg1alR4/vnnC1qosWPHhqVLl1ZPCxcuLOjrAwAAAABAXj3L27VrF/r27ZvmBw8eHP7yl7+kscmvu+66D6zbvXv3sGTJklrL4uO4fFPat2+fJgAAAAAAaNFjlleprKxMY4xvTByu5YEHHqi1LN7gs64xzgEAAAAAoMX3LI/DoxxxxBFhhx12CO+991645ZZbwowZM8L999+f/j5y5MjQs2fPNOZ4dOaZZ4aDDjooTJgwIYwYMSLdEHT27Nnh+uuvb5raQJnJVYTwepftqucBAApFOwMAskXshmZOlr/xxhspIb5o0aLQtWvXsPvuu6dE+aGHHpr+vmDBgtCq1X86qw8dOjQl1C+44IJw/vnnh5133jlMnTo1DBw4sABFB1a17RA+/vUbi10MAKAEaWcAQLaI3dDMyfJf/vKXm/x77GW+oS984QtpAgAAAACAkh2zHAAAAAAAsk6yHDKs/drVYdqvzk5TnAcAKBTtDADIFrEbmnkYFqBlaZXLhUGL/1E9DwBQKNoZAJAtYjfkT89yAAAAAADKnmQ5AAAAAABlT7IcAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDstSl2AYD8/Ltjl2IXAQAoUdoZAJAtYjfkR7IcMmxluw5h8LduKXYxAIASpJ0BANkidkP+DMMCAAAAAEDZkywHAAAAAKDsSZZDhrVfuzrcest5aYrzAACFop0BANkidkP+jFkOGdYqlwv7L3y2eh4AoFC0MwAgW8RuyJ+e5QAAAAAAlD3JcgAAAAAAyp5kOQAAAAAAZU+yHAAAAACAsidZDgAAAABA2WtT7AIA+Xm/bftiFwEAKFHaGQCQLWI35EeyHDJsZbsOYcCY/1fsYgAAJUg7AwCyReyG/BmGBQAAAACAsidZDgAAAABA2ZMshwxrv25NuPGOi9MU5wGqjB8/Puyzzz6hc+fOYbvttgtHH310eOmllzb7vDvuuCPssssuoUOHDmG33XYL9957b7OUF2h5tDOgeYjZQKGI3ZA/yXLIsFaVleGTr85OU5wHqPLwww+H008/PTz++ONh+vTpYe3ateGwww4LK1asqPM5M2fODMcdd1w46aSTwty5c9PBepyeffbZZi070DJoZ0DzELOBQhG7IX9u8AkAJei+++6r9XjKlCmpt9qcOXPCgQceuNHnXHXVVeHwww8P55xzTnp86aWXpoP2n//852Hy5MnNUm4AKDdiNgC0HHqWA0AZWLp0afp/6623rnOdWbNmhUMOOaTWsuHDh6fldVm9enVYtmxZrQkAaDwxGwCKR89yAChxlZWV4ayzzgoHHHBAGDhwYJ3rLV68OHTr1q3Wsvg4Lt/UOKvjxo0LTWmn8+4p6Ou9dtmIUG7KcRuWY52hMXxXWpasx2ygfBU6nmQtrpR7/UuJnuUAUOLiOKhxDNNbb7214K89duzY1AOualq4cGHB3wMAyoWYDQDFpWc5AJSwM844I9x9993hkUceCb169drkut27dw9LliyptSw+jsvr0r59+zQBAPkRswGg+PQsB4ASlMvl0kH3XXfdFR588MHQp0+fzT5nyJAh4YEHHqi1LN4sLC4HAJqGmA0ALYee5ZBhK9t1CDt99+5iFwNooZdx33LLLWHatGmhc+fO1WOYdu3aNXTs2DHNjxw5MvTs2TONYRqdeeaZ4aCDDgoTJkwII0aMSJeAz549O1x//fVFrQtQHNoZ0DzEbKBQxG7In57lAFCCJk2alMYjHTZsWNh+++2rp9tuu616nQULFoRFixZVPx46dGg6WI8H2oMGDQp33nlnmDp16iZvMAYA5EfMBoCWQ89yACjRS7o3Z8aMGR9Y9oUvfCFNAEDzELMBoOWQLIcMa79uTfjp3RPS/Jj/+naxiwMAlHA7Y3WbdsUuEgCwCWI35M8wLJBhrSorw4iXHktTnAcAKBTtDADIFrEb8idZDgAAAABA2ZMsBwAAAACg7EmWAwAAAABQ9hqULB8/fnzYZ599QufOncN2220Xjj766PDSSy9t8jlTpkwJFRUVtaYOHTrkW24AAAAAAChOsvzhhx8Op59+enj88cfD9OnTw9q1a8Nhhx0WVqxYscnndenSJSxatKh6mj9/fr7lBgAAAACAgmnTkJXvu+++D/Qajz3M58yZEw488MA6nxd7k3fv3r3xpQQAAAAAgJaSLN/Q0qVL0/9bb731Jtdbvnx52HHHHUNlZWXYa6+9wo9+9KOw66675vPWQAhhZdv2of/Zd1bPAwAUinYGAGSL2A1FTJbHxPdZZ50VDjjggDBw4MA61+vXr1+48cYbw+67756S61dccUUYOnRoeO6550KvXr02+pzVq1enqcqyZcsaW0wobRUVYWU79wAAAJqAdgYAZIvYDcVLlsexy5999tnw6KOPbnK9IUOGpKlKTJT3798/XHfddeHSSy+t80ai48aNa2zRAAAAAACg6W7wWeWMM84Id999d3jooYfq7B1el7Zt24Y999wzvPzyy3WuM3bs2NQLvWpauHBhY4oJJa/durXhinsmpinOAwAUinYGAGSL2A3NnCzP5XIpUX7XXXeFBx98MPTp06fBb7h+/frwzDPPhO23377Oddq3bx+6dOlSawI+qHXl+nDMsw+kKc4DABSKdgYAZIvYDc08DEsceuWWW24J06ZNC507dw6LFy9Oy7t27Ro6duyY5keOHBl69uyZhlKJLrnkkrD//vuHvn37hnfffTdcfvnlYf78+eHkk08uQPEBAAAAAKCZk+WTJk1K/w8bNqzW8ptuuikcf/zxaX7BggWhVav/dFh/5513wujRo1NifauttgqDBw8OM2fODAMGDChA8QEAAAAAoJmT5XEYls2ZMWNGrccTJ05MEwAAAAAAlNQNPgEAAAAAoJRIlgMAAAAAUPYkywEAAAAAKHsNGrMcaFlWtm0f9vrmb6vnAQAKRTsDALJF7Ib8SZZDllVUhLc7dS12KQCAUqSdAQDZInZD3gzDAgAAAABA2dOzHDKs3bq14YIHb0jzP/jkycUuDgBQwu2MNW3aFrtIAMAmiN2QPz3LIcNaV64PI+fek6Y4DwBQKNoZAJAtYjfkT7IcAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJS9NsUuANB4q9q2Cx8/7ZfV8wAAhaKdAQDZInZD/iTLIcNyFa3C6127FbsYAEAJ0s4AgGwRuyF/hmEBAAAAAKDs6VkOGdZ2/drwnUd+neavOPBrxS4OAFDC7Yy1rdsWu0gAwCaI3ZA/Pcshw9qsXx9OffL3aYrzAACFop0BANkidkP+JMsBAAAAACh7kuUAAAAAAJQ9yXIAAAAAAMqeZDkAAAAAAGVPshwAAAAAgLInWQ4AAAAAQNlrU+wCAI23qm27cOiJ11TPAwAUinYGAGSL2A35kyyHDMtVtAr/2HbHYhcDAChB2hkAkC1iN+TPMCwAAAAAAJQ9Pcshw9quXxtOn3V7mr9myBeLXRwAoITbGWtbty12kQCATRC7IX+S5ZBhbdavD2c99rs0f92+ny92cQCAEm5nOOAGgJZN7Ib8GYYFAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJS9NsUuANB4q9u0DZ8Z+dPqeQCAQtHOAIBsEbshf5LlkGGVrVqHv23/sWIXAwAoQdoZAJAtYjfkzzAsAAAAAACUPT3LIcParl8bTpj932n+pr0/U+ziAAAl3M5Y29rl3ADQkond0Mw9y8ePHx/22Wef0Llz57DddtuFo48+Orz00kubfd4dd9wRdtlll9ChQ4ew2267hXvvvTefMgP/p8369eH8GTelKc4D1PTII4+EI488MvTo0SNUVFSEqVOnbnL9GTNmpPU2nBYvXtxsZQZaDu0MaD5iNlAIYjc0c7L84YcfDqeffnp4/PHHw/Tp08PatWvDYYcdFlasWFHnc2bOnBmOO+64cNJJJ4W5c+emBHucnn322QIUHwCoS4zPgwYNCtdcc02DnhdPhC9atKh6iifIAYCmI2YDQAaHYbnvvvtqPZ4yZUoKxnPmzAkHHnjgRp9z1VVXhcMPPzycc8456fGll16aEu0///nPw+TJk/MpOwCwCUcccUSaGirG9i233LJJygQAfJCYDQAlcIPPpUuXpv+33nrrOteZNWtWOOSQQ2otGz58eFpel9WrV4dly5bVmgCA5rHHHnuE7bffPhx66KHhscce2+S6YjYAFI+YDQAt5AaflZWV4ayzzgoHHHBAGDhwYJ3rxTHTunXrVmtZfLypsdTi2Ojjxo0L5Wyn8+4p+Gu+dtmIUG6aYju2ZFn43BS6jOX4uYamEg+241Vfe++9dzqgvuGGG8KwYcPCE088Efbaa6+NPkfMBoDmJ2YDQAtLlsexy+O4448++mhhSxRCGDt2bBgzZkz143jGu3fv3gV/HwDgP/r165emKkOHDg2vvPJKmDhxYvj1r3+90eeI2QDQ/MRsAGhByfIzzjgj3H333emO3b169drkut27dw9LliyptSw+jsvr0r59+zQBAMW17777bvLEuJgNAC2DmA0AzZwsz+Vy4Zvf/Ga46667wowZM0KfPn02+5whQ4aEBx54IA3ZUiXe4DMuB/Kzuk3b8KXjflQ9D1BoTz/9dLrUGyg/2hmQLWI2IHZDMyfL49Art9xyS5g2bVro3Llz9bjjXbt2DR07dkzzI0eODD179kzjoUVnnnlmOOigg8KECRPCiBEjwq233hpmz54drr/++gIUH8pbZavW4fEddi92MYAWavny5eHll1+ufjxv3rx0IB1vzL3DDjuky7H/+c9/hptvvjn9/corr0wnwnfdddewatWqNP7pgw8+GP74xz8WsRZAsWhnQPMRs4FCELuhmZPlkyZNSv/HG4fUdNNNN4Xjjz8+zS9YsCC0atWq1thpMcF+wQUXhPPPPz/svPPOYerUqZu8KSgAkL94cvrggw+uflw1TumoUaPClClTwqJFi1LcrrJmzZrw7W9/Ox2Md+rUKey+++7hT3/6U63XAAAKT8wGgIwOw7I5cXiWDX3hC19IE1BYbdavC8f99b40/7tBh4d1rRt9z16gBMWT25uK3fHgu6Zzzz03TQCRdgY0HzEbKASxG/LnWwMZ1nb9unDp9Mlp/s6BhwiEAEDBaGcAQLaI3ZC//4yXAgAAAAAAZUqyHAAAAACAsidZDgAAAABA2ZMsBwAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe22KXQCg8da0aRtOOOai6nkAgELRzgCAbBG7IX+S5ZBh61u1Dg99dJ9iFwMAKEHaGQCQLWI35M8wLAAAAAAAlD09yyHD2qxfF45+fkaanzpgWFjX2lcaACgM7QwAyBaxG/LnWwMZ1nb9unDFvVem+Xv6fVwgBAAKRjsDALJF7Ib8GYYFAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJS9NsUuANB4a9q0Dd846rzqeQCAQtHOAIBsEbshf5LlkGHrW7UO9+7y8WIXAwAoQdoZAJAtYjfkzzAsAAAAAACUPT3LIcNaV64Pw/8+K83f/7Eh6SwyAEAhaGcAQLaI3ZA/yXLIsHbr1oZrp12W5vuffWdY2U4gBAAKQzsDALJF7Ib8GYYFAAAAAICyJ1kOAAAAAEDZkywHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJS9NsUuANB4a1u3Cd/59FnV8wAAhaKdAQDZInZD/nxzIMPWtW4T7tztkGIXAwAoQdoZAJAtYjfkzzAsAAAAAACUPT3LIcNaV64PB857Ks0/0mevsL5V62IXCQAoEdoZAJAtYjfkT7IcMqzdurXhpjvHpfn+Z98ZVrYTCAGAwtDOAIBsEbshf4ZhAQAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe5LlAAAAAACUPclyAAAAAADKXoOT5Y888kg48sgjQ48ePUJFRUWYOnXqJtefMWNGWm/DafHixfmUGwAAAAAACqZNQ5+wYsWKMGjQoHDiiSeGz33uc/V+3ksvvRS6dOlS/Xi77bZr6FsDG1jbuk34/qGnVc8DABSKdgYAZIvYDflr8DfniCOOSFNDxeT4lltu2eDnAXVb17pN+PVe/1XsYgAAJUg7AwCyReyGDI1Zvscee4Ttt98+HHrooeGxxx5rrrcFAAAAAIDNavJrMmKCfPLkyWHvvfcOq1evDjfccEMYNmxYeOKJJ8Jee+210efE9eJUZdmyZU1dTMikVpXrw76vP5fmn+y1a6hs1brYRQIASoR2BgBki9gNGUiW9+vXL01Vhg4dGl555ZUwceLE8Otf/3qjzxk/fnwYN25cUxcNMq/9urXh1t+dn+b7n31nWNlOIAQACkM7AwCyReyGDA3DUtO+++4bXn755Tr/Pnbs2LB06dLqaeHChc1aPgAAAAAAyktRbo379NNPp+FZ6tK+ffs0AQAAAABAi0yWL1++vFav8Hnz5qXk99Zbbx122GGH1Cv8n//8Z7j55pvT36+88srQp0+fsOuuu4ZVq1alMcsffPDB8Mc//rGwNQEAAAAAgOZKls+ePTscfPDB1Y/HjBmT/h81alSYMmVKWLRoUViwYEH139esWRO+/e1vpwR6p06dwu677x7+9Kc/1XoNAAAAAADIVLJ82LBhIZfL1fn3mDCv6dxzz00TAAAAAAC0VEW5wScAAAAAAIRyv8EnUBjrWrcOPxp2QvU8AEChaGcAQLaI3ZA/yXLIsLWt24br9/t8sYsBAJQg7QwAyBaxG/JnGBYAAAAAAMqenuWQYa0q14eBS15J8892+2iobOUyKwCgMLQzACBbxG7In2Q5ZFj7dWvDf988Js33P/vOsLKdQAgAFIZ2BgBki9gN+TMMCwCUqEceeSQceeSRoUePHqGioiJMnTp1s8+ZMWNG2GuvvUL79u1D3759w5QpU5qlrABQzsRsAGgZJMsBoEStWLEiDBo0KFxzzTX1Wn/evHlhxIgR4eCDDw5PP/10OOuss8LJJ58c7r///iYvKwCUMzEbAFoGw7AAQIk64ogj0lRfkydPDn369AkTJkxIj/v37x8effTRMHHixDB8+PAmLCkAlDcxGwBaBj3LAYBk1qxZ4ZBDDqm1LB5wx+UAQMshZgNA09CzHABIFi9eHLp161ZrWXy8bNmysHLlytCxY8cPPGf16tVpqhLXBQCalpgNAE1DshwAaLTx48eHcePGhSzZ6bx7Cv6ar102ouCvCaWmKb575VhGaMkxO0vfoaaI3U1V/3JvZ2i7lbcs/a5QGiTLIcPWtW4drjzguOp5gHx07949LFmypNay+LhLly4b7aEWjR07NowZM6ZWL7XevXs3eVmBpqedAS2XmA1sjNgN+ZMshwxb27ptuPLjXyl2MYASMWTIkHDvvffWWjZ9+vS0vC7t27dPE1B6tDOg5RKzgY0RuyF/bvAJACVq+fLl4emnn05TNG/evDS/YMGC6h5mI0eOrF7/tNNOC6+++mo499xzw4svvhiuvfbacPvtt4ezzz67aHUAgHIgZgNAyyBZDhlWkasMO785P01xHqCm2bNnhz333DNNUbz0Os5feOGF6fGiRYuqD8KjPn36hHvuuSf1TBs0aFCYMGFCuOGGG8Lw4cOLVgegeLQzoPmI2UAhiN2QP8OwQIZ1WLsmTL/x9DTf/+w7w8p2HYpdJKAFGTZsWMjlcnX+fcqUKRt9zty5c5u4ZEAWaGdA8xGzgUIQuyF/epYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJQ9yXIAAAAAAMqeZDkAAAAAAGVPshwAAAAAgLLXptgFABpvXevW4bp9P1c9DwBQKNoZAJAtYjfkT7IcMmxt67Zh/MEnFrsYAEAJ0s4AgGwRuyF/hmEBAAAAAKDs6VkOGVaRqww9l72Z5v/ZZduQq3D+CwAoDO0MAMgWsRvyJ1kOGdZh7Zrw6OST0nz/s+8MK9t1KHaRAIASoZ0BANkidkP+nGICAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJQ9yXIAAAAAAMqeZDkAAAAAAGWvTbELADTe+latw817jqieBwAoFO0MAMgWsRvyJ1kOGbamTdtw4WFfL3YxAIASpJ0BANkidkP+DMMCAAAAAEDZ07McsiyXC1uvXJZm3+7YJYSKimKXCAAoFdoZAJAtYjfkTbIcMqzj2tXhqau/kub7n31nWNmuQ7GLBACUCO0MAMgWsRvyZxgWAAAAAADKXoOT5Y888kg48sgjQ48ePUJFRUWYOnXqZp8zY8aMsNdee4X27duHvn37hilTpjS2vAAAAAAAUPxk+YoVK8KgQYPCNddcU6/1582bF0aMGBEOPvjg8PTTT4ezzjornHzyyeH+++9vTHkBAAAAAKD4Y5YfccQRaaqvyZMnhz59+oQJEyakx/379w+PPvpomDhxYhg+fHhD3x4AAAAAALI3ZvmsWbPCIYccUmtZTJLH5XVZvXp1WLZsWa0JAAAAAABaTM/yhlq8eHHo1q1brWXxcUyAr1y5MnTs2PEDzxk/fnwYN25ck5Zrp/PuadLXp+Hsk5appe+XQpfvtctGFPT1aJl8bgAAAIBmT5Y3xtixY8OYMWOqH8fEeu/evYtaJmiJ1rdqHe4c+KnqeQCAQtHOAIBsEbshA8ny7t27hyVLltRaFh936dJlo73Ko/bt26cJ2LQ1bdqG74w4u9jFAABKkHYGAGSL2A0ZGLN8yJAh4YEHHqi1bPr06Wk5AAAAAABkMlm+fPny8PTTT6cpmjdvXppfsGBB9RAqI0eOrF7/tNNOC6+++mo499xzw4svvhiuvfbacPvtt4ezz3amC/KWy4WOa1alKc4DABSMdgYAZIvYDc2fLJ89e3bYc8890xTFscXj/IUXXpgeL1q0qDpxHvXp0yfcc889qTf5oEGDwoQJE8INN9wQhg8fnn/pocx1XLs6vDDxmDTFeQCAQtHOAIBsEbuhCGOWDxs2LOQ2cXZqypQpG33O3LlzG146AAAAAAAohTHLAQAAAACgpZMsBwAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe5LlAAAAAACUvTbFLgDQeJWtWoV7+h1QPQ8AUCjaGQCQLWI35E+yHDJsdZt24fSjxxa7GABACdLOAIBsEbshf04zAQAAAABQ9iTLAQAAAAAoe5LlkGEd16wKr/34v9IU5wEACkU7AwCyReyG/EmWAwAAAABQ9iTLAQAAAAAoe5LlAAAAAACUPclyAAAAAADKnmQ5AAAAAABlT7IcAAAAAICy16bYBQAar7JVq/DgR/aungcAKBTtDADIFrEb8idZDhm2uk27cOIXLi52MQCAEqSdAQDZInZD/pxmAgAAAACg7EmWAwAAAABQ9iTLIcM6rlkVnv/p59MU5wEACkU7AwCyReyG/BmzHDKu09rVxS4CAFCitDMAIFvEbsiPnuUAUMKuueaasNNOO4UOHTqE/fbbLzz55JN1rjtlypRQUVFRa4rPAwCanpgNAMUnWQ4AJeq2224LY8aMCRdddFF46qmnwqBBg8Lw4cPDG2+8UedzunTpEhYtWlQ9zZ8/v1nLDADlSMwGgJZBshwAStRPf/rTMHr06HDCCSeEAQMGhMmTJ4dOnTqFG2+8sc7nxJ5p3bt3r566devWrGUGgHIkZgNAyyBZDgAlaM2aNWHOnDnhkEMOqV7WqlWr9HjWrFl1Pm/58uVhxx13DL179w5HHXVUeO6555qpxABQnsRsAGg5JMsBoAS99dZbYf369R/oZRYfL168eKPP6devX+rBNm3atPCb3/wmVFZWhqFDh4bXX3+9zvdZvXp1WLZsWa0JAKg/MRsAWo42xS4A0HiVFRXh8d4Dq+cB8jFkyJA0VYkH3f379w/XXXdduPTSSzf6nPHjx4dx48Y1YymB5qKdAS2XmA1sjNgN+ZMshwxb3bZ9+NKXLyt2MYAWaJtttgmtW7cOS5YsqbU8Po7jmtZH27Ztw5577hlefvnlOtcZO3ZsuiFZldhLLV4ODmSfdgY0DzEbKBSxG/JnGBYAKEHt2rULgwcPDg888ED1sniJdnxcsyfapsRLwp955pmw/fbb17lO+/btQ5cuXWpNAED9idkA0HLoWQ4AJSr2Hhs1alTYe++9w7777huuvPLKsGLFinDCCSekv48cOTL07NkzXZYdXXLJJWH//fcPffv2De+++264/PLLw/z588PJJ59c5JoAQGkTswGgZZAshwzruGZVeHTyiWn+46fdGFa261DsIgEtyLHHHhvefPPNcOGFF6YbhO2xxx7hvvvuq76B2IIFC0KrVv+5yOydd94Jo0ePTututdVWqZfbzJkzw4ABA4pYC6BYtDOg+YjZQCGI3ZA/yXLIuA+vdBd7oG5nnHFGmjZmxowZtR5PnDgxTQBVtDOg+YjZQCGI3ZAfY5YDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJQ9yXIAAAAAAMqeZDkAAAAAAGWvTbELADReZUVF+Gv3navnAQAKRTsDALJF7IYiJcuvueaacPnll4fFixeHQYMGhauvvjrsu+++G113ypQp4YQTTqi1rH379mHVqlWNKzFQbXXb9uGoUROLXQwAoARpZwBAtojdUIRhWG677bYwZsyYcNFFF4WnnnoqJcuHDx8e3njjjTqf06VLl7Bo0aLqaf78+fmWGwAAAAAAipcs/+lPfxpGjx6deosPGDAgTJ48OXTq1CnceOONdT6noqIidO/evXrq1q1bvuUGAAAAAIDiJMvXrFkT5syZEw455JD/vECrVunxrFmz6nze8uXLw4477hh69+4djjrqqPDcc8/lV2og6bB2VXh00olpivMAAIWinQEA2SJ2QzOPWf7WW2+F9evXf6BneHz84osvbvQ5/fr1S73Od99997B06dJwxRVXhKFDh6aEea9evTb6nNWrV6epyrJlyxpSTCgbFbkQei17o3oeAKBQtDMAIFvEbijCMCwNNWTIkDBy5Miwxx57hIMOOij8/ve/D9tuu2247rrr6nzO+PHjQ9euXaun2CMdAAAAAABaRLJ8m222Ca1btw5LliyptTw+jmOR10fbtm3DnnvuGV5++eU61xk7dmzqhV41LVy4sCHFBAAAAACApkuWt2vXLgwePDg88MAD1csqKyvT49iDvD7iMC7PPPNM2H777etcp3379qFLly61JgAAAAAAaBFjlkdjxowJo0aNCnvvvXfYd999w5VXXhlWrFgRTjjhhPT3OORKz54901Aq0SWXXBL233//0Ldv3/Duu++Gyy+/PMyfPz+cfPLJha8NAAAAAAA0R7L82GOPDW+++Wa48MILw+LFi9NY5Pfdd1/1TT8XLFgQWrX6T4f1d955J4wePTqtu9VWW6We6TNnzgwDBgxoTHkBAAAAAKD4yfLojDPOSNPGzJgxo9bjiRMnpgkovFxFCH//8A7V8wAAhaKdAQDZInZDkZLlQMuwqm2HcNjJ1xa7GABACdLOAIBsEbuhmW/wCQAAAAAApUiyHAAAAACAsidZDhnWYe2q8McbvpGmOA8AUCjaGQCQLWI35M+Y5ZBhFbkQPvbvBdXzAACFop0BANkidkP+9CwHAAAAAKDsSZYDAAAAAFD2JMsBAAAAACh7kuUAAAAAAJQ9yXIAAAAAAMpem2IXAGi8XEUIr3fZrnoeAKBQtDMAIFvEbsifZDlk2Kq2HcLHv35jsYsBAJQg7QwAyBaxG/JnGBYAAAAAAMqeZDkAAAAAAGVPshwyrP3a1WHar85OU5wHACgU7QwAyBaxG/JnzHLIsFa5XBi0+B/V8wAAhaKdAQDZInZD/vQsBwAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe5LlAAAAAACUPclyAAAAAADKXptiFwDIz787dil2EQCAEqWdAQDZInZDfiTLIcNWtusQBn/rlmIXAwAoQdoZAJAtYjfkzzAsAAAAAACUPclyAAAAAADKnmQ5ZFj7tavDrbecl6Y4DwBQKNoZAJAtYjfkz5jlkGGtcrmw/8Jnq+cBAApFOwMAskXshvzpWQ4AAAAAQNmTLAcAAAAAoOxJlgMAAAAAUPYkywEAAAAAKHuS5QAAAAAAlL02xS4AkJ/327YvdhEAgBKlnQEA2SJ2Q34kyyHDVrbrEAaM+X/FLgYAUIK0MwAgW8RuyJ9hWAAAAAAAKHuS5QAAAAAAlD3DsECGtV+3Jky660dp/uufPT+sbtOu2EUCAEqEdgYAZIvYDfmTLIcMa1VZGT756uzqeQCAQtHOAIBsEbshf4ZhAQAAAACg7EmWAwAAAABQ9hqVLL/mmmvCTjvtFDp06BD222+/8OSTT25y/TvuuCPssssuaf3ddtst3HvvvY0tLwDQAGI2AGSDmA0AGUyW33bbbWHMmDHhoosuCk899VQYNGhQGD58eHjjjTc2uv7MmTPDcccdF0466aQwd+7ccPTRR6fp2WefLUT5AYA6iNkAkA1iNgBkNFn+05/+NIwePTqccMIJYcCAAWHy5MmhU6dO4cYbb9zo+ldddVU4/PDDwznnnBP69+8fLr300rDXXnuFn//854UoPwBQBzEbALJBzAaAlqFNQ1Zes2ZNmDNnThg7dmz1slatWoVDDjkkzJo1a6PPicvjGfKa4hnyqVOn1vk+q1evTlOVpUuXpv+XLVsWCqVy9fuh3BRy+zWFctwn+Vq/ZlWo2qvrV78fKnPudt0SvyeF/my39O9yFrTkfVL1WrlcLq/XEbObV0v/Xrbkz3y51rkpPteFLmNsW2hnUGrflUKXrxBxW8wujiy0+7NY1qzIQlumMftKjqD0ZOWzWpmB37/6xuwGJcvfeuutsH79+tCtW7day+PjF198caPPWbx48UbXj8vrMn78+DBu3LgPLO/du3dDissGul5Z7BLQFLpWzVw7srgFKRFZ+J5koYzlpin2yXvvvRe6dq3+hjeYmN28yu17WW71zUqdm6KM2hmU2nelqcqXT9wWs4ujpX9Ws1rWrCjlbSp2l5ZS/qy21GPtBiXLm0s8o17zLHllZWV4++23w4c//OFQUVERsiievYiNkIULF4YuXboUuzgtnu1Vf7ZV/dlWDWN7FWdbxbPcMXj36NEjZEE+MbtcPmPlUs9yqmu51DNS19JTLvVsrrpmKW43x3F2qX++Sr1+kTpmX6nXrxzqWOr1K1Yd6xuzG5Qs32abbULr1q3DkiVLai2Pj7t3777R58TlDVk/at++fZpq2nLLLUMpiB+AUv2gNwXbq/5sq/qzrRrG9mr+bZVPj/Isxuxy+YyVSz3Lqa7lUs9IXUtPudSzOeqab9zOUsyur1L/fJV6/SJ1zL5Sr1851LHU61eMOtYnZjfoBp/t2rULgwcPDg888ECts9Hx8ZAhQzb6nLi85vrR9OnT61wfAMifmA0A2SBmA0DL0eBhWOJlW6NGjQp777132HfffcOVV14ZVqxYke7aHY0cOTL07NkzjYcWnXnmmeGggw4KEyZMCCNGjAi33nprmD17drj++usLXxsAoJqYDQDZIGYDQEaT5ccee2x48803w4UXXphuHrLHHnuE++67r/rmIgsWLEh37q4ydOjQcMstt4QLLrggnH/++WHnnXdOd+geOHBgKCfxcreLLrroA5e9sXG2V/3ZVvVnWzWM7ZX9bdXSY3ZL3W6FVi71LKe6lks9I3UtPeVSz6zVtaXH7FLc5o1R6vWL1DH7Sr1+5VDHUq9fS69jRS6Obg4AAAAAAGWsQWOWAwAAAABAKZIsBwAAAACg7EmWAwAAAABQ9iTLAQAAAAAoe5LlebjmmmvCTjvtFDp06BD222+/8OSTT9a57i9+8YvwiU98Imy11VZpOuSQQz6w/vLly8MZZ5wRevXqFTp27BgGDBgQJk+eHEpBobfVkiVLwvHHHx969OgROnXqFA4//PDwj3/8I5SKhmyv3//+92HvvfcOW265ZfjQhz4U9thjj/DrX/+61jrxPr4XXnhh2H777dNnK27TUtlehd5WcZ3DDjssfPjDHw4VFRXh6aefDqWikNtq7dq14bvf/W7Ybbfd0t/jd3HkyJHhX//6VygVhf5sXXzxxWGXXXZJf6/6bXviiSdClhX6t70+2+jtt98OX/nKV0KXLl3S9j7ppJNS/Cy1esb3i79BNafLLrssNLVC17Wm0047LdXjyiuvLPo+LVZdi7FfC13P2P7asA6xHVaK+7Q+dS2FfRq98MIL4TOf+Uzo2rVr+m3aZ599woIFC6r/vmrVqnD66aen9tEWW2wRPv/5z6f2eFMrRl2HDRv2gX0av9OloCHbM7rjjjtSvIrrxzbfvffe2+BjjPr8Htx+++2p7RSP63bcccdw+eWXf6Cddeihh4Ztt902vc6QIUPC/fffX1J1rOmxxx4Lbdq0SeuXUv1Wr14dvve976W/t2/fPtXjxhtvLKk6/va3vw2DBg1K68T3O/HEE8O///3votfvhz/8YRg6dGgqV6zfxsTfwREjRqR1tttuu3DOOeeEdevW1VpnxowZYa+99kr7r2/fvmHKlCkbfa2s1rGl/9YUaj+21N+aHxaofvX9rdmkHI1y66235tq1a5e78cYbc88991xu9OjRuS233DK3ZMmSja7/5S9/OXfNNdfk5s6dm3vhhRdyxx9/fK5r1665119/vXqd+Bof/ehHcw899FBu3rx5ueuuuy7XunXr3LRp03JZVuhtVVlZmdt///1zn/jEJ3JPPvlk7sUXX8ydcsopuR122CG3fPnyXNY1dHvFz8vvf//73PPPP597+eWXc1deeWX63Nx3333V61x22WVpG06dOjX317/+NfeZz3wm16dPn9zKlStzWdYU2+rmm2/OjRs3LveLX/wiF38i4+ewFBR6W7377ru5Qw45JHfbbbel7+CsWbNy++67b27w4MG5UtAUn63f/va3uenTp+deeeWV3LPPPps76aSTcl26dMm98cYbuSxqijhYn210+OGH5wYNGpR7/PHHc3/+859zffv2zR133HElV88dd9wxd8kll+QWLVpUPTV1jGuKulaJ34+433r06JGbOHFirb819z4tZl2be782RT1HjRqV9lnNOrz99tsluU/rU9dS2Kcxbm299da5c845J/fUU0+lx/H4o+ZrnnbaabnevXvnHnjggdzs2bNTW3zo0KFNVs9i1vWggw5K71Vzny5dujSXdQ3dno899lhqy/zkJz9J7ZsLLrgg17Zt29wzzzzToGOMzf0e3Hvvvbk2bdrkJk2alOLi3Xffndt+++1zV199dfU6Z555Zu7HP/5xOvb7+9//nhs7dmwqS9yHpVLHKu+8807uIx/5SO6www5Lr1lK9Yuvvd9++6U2UMx3zJw5M/foo4+WTB1jXVq1apW76qqrcq+++mp6nV133TX32c9+tuj1u/DCC3M//elPc2PGjEnrbmjdunW5gQMHpuO7+Bsa67vNNtuk71qVWKdOnTql14hliXXf8Hgn63Vs6b81hahjS/6tubBA9avPb83mSJY3UkwKnX766dWP169fnw6Kxo8fX6/nx53cuXPn3K9+9avqZfGHNDa2a9prr71y3/ve93JZVuht9dJLL6UkZkww1HzNbbfdNiU4sy7f7RXtueee6Qeq6uRC9+7dc5dffnn132Ois3379rnf/e53uSwr9LaqKf6ollKyvCm3VZXYqIjbbP78+bmsa47tFQ+84/b605/+lMuipoiDm9tGsfEVH//lL3+pXucPf/hDrqKiIvfPf/4zVyr1rErAbZhobWpNVdeYqOrZs2eK2xvWqxj7tFh1LcZ+bYp6xgTyUUcdVedzSmmfbq6upbJPjz322NxXv/rVOp8T243xoPeOO+6oXhaT0XE/x5PlpVTXqmR5TJiUmoZuzy9+8Yu5ESNG1FoWkw+nnnpqvY8x6vN7EBOSxxxzTK33+dnPfpbr1atXeo+6DBgwIHVwKbU6xs9obD9edNFFH0hgZbl+8Tkx+fXvf/97o2UthTrG94jJxw3Xie2CYtavpptuummjSciYdIyJ/sWLF1cviycGYmeO1atXp8fnnntuyllt+HkdPnx4rWVZrmNL/q0pdB1b2m9NoepX39+azTEMSyOsWbMmzJkzJ102UKVVq1bp8axZs+r1Gu+//34axmDrrbeuXhYvN/jv//7v8M9//jNdovDQQw+Fv//972lIiKxqim0VL6mI4uUdNV8zXl7x6KOPhizLd3vFz80DDzwQXnrppXDggQemZfPmzQuLFy+u9ZrxstN4KU1990G5bKtS1VzbaunSpelS5boumcqK5the8T2uv/769F2Ml2pmTVPFwc1to/ja8fMVh7ypEt8zvndTDGlTrHpWiUM5xCEP9txzz3Spb12XULbkulZWVoavfe1r6RLJXXfd9QPPae59Wsy6Nvd+bcrPb7wMO1762q9fv/D1r3+91iXmpbRPN1fXUtin8XN7zz33hI997GNh+PDhqa6xjTh16tTq58T3jM+p+b7xUusddtihydqSxaprzaEUttlmmzBw4MAwduzY9FpZ1pjtGZfXXD+K261q/focY9Tn9yAe29U8roviJfqvv/56mD9//kbLFvfle++9V+v7Wgp1vOmmm8Krr74aLrroog+UNev1i3mO+Bo/+clPQs+ePdP38Dvf+U5YuXJlydQxDtmxcOHCNPxEPB6IQ1Xdeeed4dOf/nRR61cfcd04dEa3bt1qvc+yZcvCc889V6+ylEIdW/JvTSHr2BJ/awpVv/r81tSHZHkjvPXWW2H9+vW1dlAUH8cPQH3EsX7jGL81PyxXX311Gqc8jlnerl27NCZiHCcoy4m8pthWVY3z2HB955130pf5xz/+cQpUixYtClnW2O0Vk5Rx/Mj4uYnjN8XPUhxrK6p6Xj77oFy2Valqjm0VxzKN39Xjjjsuje+WZU25ve6+++60TmxsT5w4MUyfPj0djGdNU8XBzW2j+NoxuVFTHGcvNmCb4vesWPWMvvWtb4Vbb701nTg/9dRTw49+9KNw7rnnhqbSVHWN8Tnuo1ifjWnufVrMujb3fm2qesb26c0335xOCsY6P/zww+GII45I71Vq+3RzdS2FffrGG2+kcXljwj/W949//GP47Gc/Gz73uc+l+kbxtWNs2/BkeFO2JYtV1+jLX/5y+M1vfpP2aTzeiPcg+epXvxqyrDHbMy7f1Pr1Ocaoz+9BTHTEcYLj9ywmpmJnsQkTJqS/1XVsd8UVV6R9+cUvfrFk6hjH7j3vvPPSZy8+f0NZr19MzMWObc8++2y466670j09YiL5G9/4RsnU8YADDkgn2o499tj0m9m9e/eUEIw5nWLWrz7qep+a71HXOjFRWZWIzHodW/JvTaHq2FJ/awpVv/r81tTHB7cMTS420GKjOvZUqXl2MiZWHn/88XQmJA5E/8gjj6Qb6WzsILuct1Xbtm1ToIo33YgBrHXr1mn7xIOXeAa3HHXu3DndiDL+kMcAPmbMmPCRj3wk3aCI2myrwm+r2GsrNiDi92/SpEmhXNVnex188MFpndgIiTcii9st9ljZsIFernGw1LZRPvWMn58qu+++ezroiom48ePHpyupslDX2CvlqquuCk899VS66qRU5FPXLO3Xuj6/X/rSl6rnY++eWI+PfvSjab1PfepTIYvyqWvW92lM+kRHHXVUOPvss9N8vNHXzJkzw+TJk8NBBx0Usiifup5yyim19nu8GVnc36+88kra/xTW6NGj07b9r//6r9SmjJ0uzjzzzHQz7NibcUO33HJLGDduXJg2bVpm2gabq2NMTMWTNLFesRdkKe7D+P2L8TEmk2MCOfrpT38ajjnmmHDttdemXtpZr+Pzzz+flsWbGMbkekyix6vN4g2Cf/nLXxa7CjRQFn9rNifrvzX1UajfGj3LGyH2/IoJ2g3vAB8fx7OHmxLPTMXGW+zJEBvUVeKZuPPPPz/txCOPPDL97YwzzkhnJeNzsqoptlU0ePDglGR49913UxC677770mWxMTGVZY3dXjFAx7tRxwb/t7/97fRDEA/SoqrnNWYflNu2KlVNua2qEuXx8sPYMzbrvcqbent96EMfSuvsv//+qdEcz+ZnsfHcVL/tm9tG8bVjz8Ca4nAHb7/9dpP8nhWrnhsTL1OMdX3ttddCU2iKuv75z39O+yteDRbrF6f4WxG/H/Gu9MXYp8Wsa3Pv16b8/NYU217xvV5++eWS2qf1qWsp7NP4mvHzGq9ural///5hwYIFaT6+drySM7a7G/q+WatrXfs02tR+b+kasz3j8k2tX59jjPr8HsSkRrxyI3Y2iL+bsXfgvvvum/624bFdPAFy8sknh9tvv/0DncmyXMc4zMPs2bPT8X9VDLnkkkvCX//61zT/4IMPZrp+UTzpFIdEqEpeVX33YmebeIV4lPU6xnZ/7F0eE+TxtycmzGNy7sYbb0w5i2LVrz7qep+a71HXOvH4ryoBmfU6tuTfmkLUsSX/1hRqH9bnt6Y+JMsbIfYYicna2HOw5tmL+DiOU1WXOGbOpZdemhK7NcfDqko4xWnDs+fxQ1rVCyKLmmJb1RS/ANtuu226lCR+6WNPkSxr7PbaUHxO1djuffr0ST8cNV8zXioVezA25DXLYVuVqqbaVlWJ8vj9+9Of/pTGay0FzfnZyurnr6l/2+vaRvG1Y7Im9uKtEht1cZ2qZEYp1HNj4gni2EZoqp4tTVHXOH733/72t1T2qileLRcPIu+///6i7NNi1rW592tzfX7jgUfssBAPTkppn9anrqWwT+Nr7rPPPuk+GzXFIQbila5RfM94ZWfN943rxwRzU7Uli1XXuvZptKn93tI1ZnvG5TXXj2LHiKr163OM0ZDfg3jcGxMcsay/+93v0nPjcV6VuOyEE05I/8fh7kqpjjHZ+Mwzz9SKIbE3crxXQpyPr5Pl+kUxifyvf/0rJZtrfvfi72UchjbKeh3jvQ02ls+JYqKuWPWrj7hu/AzWPGlQ1RGq6gTj5soSZb2OLfm3phB1bMm/NYXah/X5ramXvG4PWsZuvfXWdOfWKVOmpLsnn3LKKbktt9yy+q6sX/va13LnnXde9fqXXXZZrl27drk777wzt2jRourpvffeq3Xn9Xh34Yceeij36quvpjvAdujQIXfttdfmsqwpttXtt9+ettMrr7ySmzp1am7HHXfMfe5zn8uVgoZurx/96Ee5P/7xj2lbxPWvuOKKXJs2bXK/+MUvam3T+BrTpk3L/e1vf8sdddRRuT59+uRWrlyZy7Km2Fbxrslz587N3XPPPemu6PE94uP4GcyyQm+rNWvW5D7zmc+kO8A//fTTtb6rm7qbeLlur+XLl+fGjh2bmzVrVu61117LzZ49O3fCCSek93j22WdzWVTo3/b6bqPDDz88t+eee+aeeOKJ3KOPPprbeeedc8cdd1xJ1XPmzJm5iRMnpu9W/Ez95je/yW277ba5kSNHNlk9m6KuGxPjdaxbTc29T4tV12Ls10LXM/7/ne98J31+582bl/vTn/6U22uvvdI+W7VqVUnt0/rUtRT2afT73/8+17Zt29z111+f+8c//pG7+uqrc61bt879+c9/rl7ntNNOy+2www65Bx98MP1uDRkyJE1NqRh1ffnll3OXXHJJqmPc77Ht/JGPfCR34IEH5rKuodvzscceS22Z2KZ54YUXchdddFHads8880yDjjE293vw5ptv5iZNmpTeI7a5v/Wtb6Vj4Lh+ld/+9repLNdcc02tffvuu++WTB03FMsyaNCgkqlf/B7G44Zjjjkm99xzz+Uefvjh9Donn3xyydQx5m9iWWL+JsaE+Dp77713bt999y16/ebPn5/KPW7cuNwWW2yR5uNU9fu4bt263MCBA3OHHXZYimn33XdfimexzVol5qg6deqUO+ecc1JZ4vcx/n7GdVvCPixEHVv6b00h6tiSf2vmF6B+9f2t2RzJ8jzExlVsNMZGWfwBfPzxx2slvkeNGlXrgCkm3jac4oeoSvwSHn/88bkePXqkH95+/frlJkyYkKusrMxlXaG31VVXXZW+APELGF/3ggsuKIkEXWO21/e+971c375902dmq622Sgcu8cetpvgZ+v73v5/r1q1b+tH71Kc+lXvppZdypaDQ2yo2cjb3+cuqQm6reAC5se0Up3giqxQUcnvFRsBnP/vZ9PseX2/77bdPJxuefPLJXJYV8re9vtsontCKBy+xAdWlS5eUaN5UwjKL9ZwzZ05uv/32y3Xt2jV9pvr3759OyNRMRmahrvVNlhdjnxajrsXar4Ws5/vvv58OUOKBSWyDxfVHjx5dffBUSvu0PnUthX1a5Ze//GV1HIsHzbEzSk3xt+sb3/hGinExWRJ/x5qjI0Fz13XBggUpMb711lunNnNcLyaGli5dmisFDdmeVR2UPvaxj6X1Y6eu2JmkoccYm/s9iEnI/fffP/ehD30ofbbia9QsV1XZNrZvNyxvlutYnwRW1usXk2iHHHJIrmPHjulYfsyYMem3tpTq+LOf/Sw3YMCAVMfYxvvKV76Se/3114tev/iamztui504jjjiiFT2bbbZJvftb387t3bt2lqvE9ffY489UlniicR47LwxWa1jS/+tKdR+bKm/NaMKVL/6/tZsSkX8p/790AEAAAAAoPQYsxwAAAAAgLInWQ4AAAAAQNmTLAcAAAAAoOxJlgMAAAAAUPYkywEAAAAAKHuS5QAAAAAAlD3JcgAAAAAAyp5kOQAAAAAAZU+yHAAAAACAsidZDgAAAABA2ZMsBwAAAACg7EmWAwAAAAAQyt3/B+vz69wbTlfEAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import dask\n",
-    "from dask.distributed import Client, LocalCluster\n",
-    "\n",
-    "# Set up dask client for parallel processing\n",
-    "# Using 10 workers as a balanced choice for M3\n",
-    "cluster = LocalCluster(n_workers=10)  # Use 10 workers to mainly utilize P cores\n",
-    "client = Client(cluster)\n",
-    "# Get number of workers and threads\n",
-    "print(f\"Number of workers: {len(client.ncores())}\")\n",
-    "print(f\"Cores per worker: {client.ncores()}\")\n",
-    "\n",
-    "# Number of parallel chains\n",
-    "K = 25\n",
-    "\n",
-    "# Function to run single MCMC chain\n",
-    "def run_mcmc_chain(seed):\n",
-    "    key = jax.random.PRNGKey(seed)\n",
-    "\n",
-    "    # sample leaf values\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "\n",
-    "    # add noise to leaf nodes\n",
-    "    key, subkey = split(key)\n",
-    "    leaf_values = tree.data['value'][tree.is_leaf,-1]+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,tree.data['value'][tree.is_leaf,-1].shape)\n",
-    "\n",
-    "    # mcmc\n",
-    "    subkey, key = split(key)\n",
-    "    init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "    log_likes, chain_samples = metropolis_hastings(log_posterior, proposal, leaf_values, \n",
-    "                                                 init_state, 500, burn_in=2000, \n",
-    "                                                 rng_key=key, savef=lambda state: state[0],\n",
-    "                                                 verbose=False)\n",
-    "    # Get mean of samples for each parameter\n",
-    "    means = {k: jnp.mean(jnp.array([s[k].value for s in chain_samples])) for k in chain_samples[0].values().keys()}\n",
-    "    return means\n",
-    "\n",
-    "# Run chains in parallel\n",
-    "futures = [dask.delayed(run_mcmc_chain)(i) for i in range(K)]\n",
-    "results = dask.compute(*futures)\n",
-    "\n",
-    "# Convert results to parameter arrays\n",
-    "k_alpha_means = jnp.array([r['k_alpha'] for r in results])\n",
-    "k_sigma_means = jnp.array([r['k_sigma'] for r in results])\n",
-    "obs_var_means = jnp.array([r['obs_var'] for r in results])\n",
-    "\n",
-    "print(f\"k_alpha - mean: {jnp.mean(k_alpha_means):.4f}, median: {jnp.median(k_alpha_means):.4f}\")\n",
-    "print(f\"k_sigma - mean: {jnp.mean(k_sigma_means):.4f}, median: {jnp.median(k_sigma_means):.4f}\")\n",
-    "print(f\"obs_var - mean: {jnp.mean(obs_var_means):.4f}, median: {jnp.median(obs_var_means):.4f}\")\n",
-    "\n",
-    "# Plot histograms\n",
-    "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5))\n",
-    "\n",
-    "ax1.hist(k_alpha_means,bins=20)\n",
-    "ax1.axvline(params['k_alpha'].value,color='r',linestyle='--',label='True value')\n",
-    "ax1.set_title('k_alpha')\n",
-    "ax1.legend()\n",
-    "\n",
-    "ax2.hist(k_sigma_means,bins=20)\n",
-    "ax2.axvline(params['k_sigma'].value,color='r',linestyle='--',label='True value')\n",
-    "ax2.set_title('k_sigma')\n",
-    "ax2.legend()\n",
-    "\n",
-    "ax3.hist(obs_var_means,bins=jnp.linspace(min(obs_var_means),max(obs_var_means),20))\n",
-    "ax3.axvline(params['obs_var'].value,color='r',linestyle='--',label='True value')\n",
-    "ax3.set_title('obs_var')\n",
-    "ax3.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Clean up dask client\n",
-    "client.close()\n",
-    "cluster.close()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prior is maximized at x = 0.16245454545454546\n",
-      "Prior is maximized at x = 0.4954545454545455\n",
-      "Prior is maximized at x = 0.001673737373737374\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Bjh07cO+996qp4GPGjNHop7DPVYnXHztr8Y0yq+PRftHq9u99iThx9vJLCRARkb5pGm4+//xzNXbWr18/1fNiPObMmWN6TFxcnFrLxigtLQ0PPfSQqrMZPHiwGoPbuHEjWrZsqdFPYX9ky4P8ohLU8XRBs2AfWANZ96Zfs3qQCrKv18do3RwiItKQ1RQUW2NBElXuh80n8cr8fbimaT18/2BXWItNx89hxFeb4ebsiI0vXIu63m5aN4mIiOy1oJhsy5748+q2bS3tJVVV3RsFoG2En+pV+n4TV60mIrJXDDdUbXvLZiRJkLAmsnL12GsaqfPvN51AbkGx1k0iIiINMNxQteQUFOFIUqZpd25rc0OrEEQGeCAtpxC/bI/XujlERKQBhhuqlv1nMlBiAIJ93RDsa327sTs7OeKhPqW9N1+vi0WxNJaIiOwKww1Vy+6yeps24dbXa2N0Z6dINZMrLjUHi/clat0cIiKqZQw3VKN6m3ZWVm9TnoerE+7r0UCdf7n2uFoskoiI7AfDDVXLnlNlxcRWWG9T3qge9dWU8N2n0rHtZJrWzSEiolrEcENVlp5biNiy1X+tbRr4hWSNm1s7hKvz7zbEat0cIiKqRQw3VGV7y3ptZDZSHS9XWLv7e5UOTS3Zn4TT5//d0oOIiPSN4YaqbM/pssX7Iqx7SMqoeYgvejauq2ZMybo3RERkHxhuqMr2xFt/MfGFHuhVutP8z1vi1Ro9RESkfww3VGV7TtlWz424tnkQogI8Vb3Q7ztPa90cIiKqBQw3VCUpmfk4k54HBwegtZUXE5fn5OiAUT1La29mbDjBaeFERHaA4YaqZG9ZvU3jet7wdnOGLbmzcwS8XJ1wNDkL64+d1bo5RERkYQw3VCW7461zs8yq8HV3wR2dItT5dxtYWExEpHcMN1Stept2NlRvU55xaGrloWTTWj1ERKRPDDd0RVKnYlyZuI0N9tyIRvW80b9ZPXU+cyN7b4iI9Izhhq5IConPZRfA2dEBLUN9YauM08J/234KWfmcFk5EpFcMN3RFe8p2Am8W4gN3FyfYqt7RgWgU6IXM/CJOCyci0jGGG6ryTuC2WExcnqOjA+7tXl+d/7CJ08KJiPSK4YauSKZQG7czsHW3d4qAp6sTjiRlYXNMqtbNISIiC2C4oSuKSSkNN43qecHW+Xm4mHYL535TRET6xHBDl1VUXIK41BzTjCM9GNmjdFr40gNJOMPdwomIdIfhhi4rPi0XhcUGuLs4ItTXHXoghdHdGgao3cJ//CdO6+YQEZGZMdzQZR0vq7dpGOitCnL1wrio389b45BfVKx1c4iIyIwYbuiyYs7qp96mvOtaBiPE1x1nswrw995ErZtDRERmxHBDlxWTUrpVQeNAfYUbFydH3NMtSp3PZGExEZGuMNxQlcKNXoqJy/tP1yi4ODlgZ9x57C3bXoKIiGwfww3Z5bCUqOfjhsFtQtX5D5vZe0NEpBcMN3RJ6bmFqiZFNNTZsJTRfWUrFv+x6wzO55T+rEREZNsYbuiKi/cF+bjBx90FetSpfh20CPVFflEJftl2SuvmEBGRGTDcUBXqbfTZayMcHBwwskdp782sf06ipIT7TRER2TqGG7pivU1jHRYTlze0fRh83J1x8lwO1h5N0bo5RER0lRhuyC5nSpXn6eqMOzpFqPMfNp3UujlERHSVGG7IroelLiwsXnk4GfFle2kREZFtYrihSsm+S7HnjAv46bvnxtg71adJIAwGYDb3myIismkMN1Qp2S27oKgErs6OCK/jAXtwb1nvzZytccgr5H5TRES2iuGGKnW8bBp4g7qecNLRhpmXM6B5EML83JGWU4iFexK0bg4REdUQww1dvt7GDoakjJxlv6my3pvvN7OwmIjIVjHc0GV7buyhmLi84V0i4erkiN3x57Hn1Hmtm0NERDXAcEN2PQ38QoHest9UiDr/ntPCiYhsEsMN2d2GmVdyX48G6vav3WeQls39poiIbA3DDV0kK78ISRn5djMN/EIdo/zRKqx0v6m52+K1bg4REVUTww1dJLZsSKqulyv8PPW5YeaV9psaVdZ7I/tNyZo/RERkOxhu6CL2PCRlNKRdGPw8XBCfmovVh5O1bg4REVUDww1d5LgdTgO/kIerE+7qXLrfFAuLiYhsC8MNXSTGTqeBV7ZisYMDsOZICk6cLQ18RERk/Rhu6CL2Og38QvXreqFv03rqfBYX9SMishkMN1RBiWyYWdZLYe89N8JYWCyzpnILuN8UEZEtYLihCpIy85BbWKz2k4oK8IS9k54beR8y8orwx67TWjeHiIiqgOGGKjiVlqtuw/zd4eLEfx6Ojg64r2y/qZmbTsJg4LRwIiJrp+mn1+TJk9GlSxf4+PggKCgIw4YNw+HDh6/4vF9++QXNmzeHu7s72rRpg0WLFtVKe+3BaWO48fPQuilW467OkXB3ccTBhAxsPZGmdXOIiMiaw82aNWvw+OOPY/PmzVi2bBkKCwtx/fXXIzv70jNTNm7ciBEjRmD06NHYuXOnCkRy7Nu3r1bbrlenz5eGm/A6DDdGspDhrR3C1fnMjSe0bg4REV2Bg8GK+tlTUlJUD46EnmuuuabSxwwfPlyFnwULFpiude/eHe3bt8f06dOv+D0yMjLg5+eH9PR0+Pr6mrX9ejBh3l78tCUOT10bjfHXN9O6OVZDem1u/HidqkXa8Py1CPFz17pJRER2JaMan99WVVQhDRYBAQGXfMymTZswcODACtcGDRqkrtPVY89N5VqE+qJrwwC1FcPsfzgtnIjImllNuCkpKcEzzzyDXr16oXXr1pd8XGJiIoKDgytck/tyvTL5+fkq7ZU/6NJOp+Wo23B/zpS60P09S6eFS89WfhGnhRMRWSurCTdSeyN1Mz///LPZi5alG8t4REZGmvX19URGKNlzc2nXtQxGiK87zmYVYNHeBK2bQ0RE1hxunnjiCVVDs2rVKkRElO7ncykhISFISkqqcE3uy/XKTJgwQQ13GY/4+Hiztl1PUrMLkFdYos5DWVNyEZkaf2/3KHU+YyOHpoiIrJWj1j0FEmx+//13rFy5Eg0bNrzic3r06IEVK1ZUuCYzreR6Zdzc3FThUfmDKmfstann4wZ3Fyetm2OV/tM1Cq5Ojtgdfx674s9r3RwiIrK2cCNDUbNmzcKPP/6o1rqRuhk5cnNLP2TFyJEjVe+L0dNPP43Fixfjgw8+wKFDh/Daa69h27ZtKiSReda4CffnkNSlBHq74ea2oeqc08KJiKyTpuHm888/V0NF/fr1Q2hoqOmYM2eO6TFxcXFISPi3vqFnz54qDH355Zdo164dfv31V8yfP/+yRchUNay3qZpRZYXFC/acQXJmntbNISKiCzhDQ1VZYmf16tUXXbvzzjvVQZbZeiGCPTeX1S7SHx2i/LEz7jxmb47DuOuaat0kIiKytoJisg7suam6B3uV1ofJmjecFk5EZF0YbsiENTdVd0PrENO08AW7OS2ciMiaMNyQCXtuqjct/L4epbuFf7cxlruFExFZEYYbUrLyi5CeW6jO2XNTNXd3jYKbsyP2nc7AtpPcLZyIyFow3FCFISlfd2f4uLto3RybUMfL1bRb+LfrY7VuDhERlWG4IeX0+bI9pepwT6nquL9X6bTwJfsTcapsXy4iItIWww0pLCaumeYhvujZuC5KDMAPm7glAxGRNWC4IeVUWTFxBIuJazwtXHYLzyko0ro5RER2j+GGlDPnS1faZc9N9V3bPAj163oiI68Iv+04rXVziIjsHsMNKafL6kU4Dbz6HB0dcH/ZlgzfrY9FiYxRERGRZhhuqOIaN+y5qZG7OkfCx90ZMWezsfJQstbNISKyaww3hIKiEiRn5qtz9tzUjJebM+7uFqXOv1oXo3VziIjsGsMNISE9F7LArruLI+p6uWrdHJslQ1POjg74JzYVe0+la90cIiK7xXBDpmngYf4ecHBw0Lo5NivUzwM3tw1V59+sZ+8NEZFWGG7INA2c9TZXb0yfRup2wZ4E1SNGRES1j+GGTD03XOPm6rUO90P3RgEoKjFgxsYTWjeHiMguMdwQZ0qZ2Zjepb03P/4Th+x8LupHRFTbGG7o360X2HNjtkX9GgV6ITOvCHO3xWvdHCIiu8NwQ+V6brhpprkW9Xuwd+mWDN9uiEVRcYnWTSIisisMN3ZOVtM1Fr6y58Z8bu8YgQAvV8Sn5uLvfYlaN4eIyK4w3Ng5WbyvsNgAJ0cHBPu4ad0c3fBwdcKoHqVbMnyx9jgMspAQERHVCoYbO3f6fOmeUiG+7nB24j8HcxrZoz48XJyw73QGNh4/p3VziIjsBj/N7NwpFhNbTB0vVwzvEqnOp685rnVziIjsBsONnTMWE0dwGrhFjO7dUA35rTt6FvtOc0sGIqLawHBj5zgN3LIiAzxNWzJ8uZZbMhAR1QaGGzuXkJ5n2leKLGPsNcYtGc4gPrW0xomIiCyH4cbOJWXkmQqKyTJahfmhT5NAlBiAr9ex94aIyNIYbuycMdwE+XIauCU90rexup2zLR6p2QVaN4eISNcYbuxYYXEJzmaVftCy58ayejauizbhfsgrLOGGmkREFsZwY8dSMvPVrYuTA+p4umrdHF1zcHAw9d7M2BCLzLxCrZtERKRbDDd2LNE4JOXjrvZDIsu6oXUIGtXzQkZeEWZtjtO6OUREusVwY8eSWW9Tq2S9m8f6Ravzb9bHIK+wWOsmERHpEsONHUvKKB2WYr1N7RnaPgzh/h6q1mnO1nitm0NEpEsMN3bMOCwVzHBTa1ycHPFI39J1b75YcxwFRSVaN4mISHeqHW5GjRqFtWvXWqY1VKs4DVwbd3aORD0fN5xJz8P8nae1bg4Rke5UO9ykp6dj4MCBaNKkCd5++22cPs1fzrYqmcNSmnB3ccJDfRqq88/XHEexrO5HRETahZv58+erQPPoo49izpw5aNCgAW688Ub8+uuvKCzk9FZbwmEp7dzTrT78PV0QezYbi/YmaN0cIiJdqVHNTb169TB+/Hjs3r0b//zzD6Kjo3HfffchLCwM48aNw9GjR83fUrLYsFQwh6VqnZebMx7oWdp7M3XVMZSw94aIyDoKihMSErBs2TJ1ODk5YfDgwdi7dy9atmyJjz76yHytJLPLKShCZl6ROmfPjTbu79kA3m7OOJSYiWUHk7RuDhGR/YYbGXr67bffcPPNN6N+/fr45Zdf8Mwzz+DMmTOYOXMmli9fjrlz52LSpEmWaTGZdRq4p6uT+oCl2ufn6aICjvh4+VEYDOy9ISIyh2p/qoWGhqKkpAQjRozAli1b0L59+4se079/f/j7+5ulgWTpISl3tTUAaWN074Zqr6kDCRlYeiAJg1qFaN0kIiL767mR4SbppZk6dWqlwUZIsImNjTVH+8hCWG9jHep4ubL3hohI63CzatWqSmdFZWdn48EHHzRXu6gWe25I+94bGRo09t4QEVEthxupq8nNzb3oulz7/vvvr7I5VNs1Nww32mPvDRGRRuEmIyNDLeAnv3gzMzPVfeORlpaGRYsWISgoyMzNI0thz411Ye8NEZEGBcVSRyOFp3I0bdr0oq/L9ddff92MTSNLYs2NdfbefLbqmOq9ub5lMAu9iYgsHW6k1kZ6ba699lo1FTwgIMD0NVdXVzUtXBbxI9vAYSnrnjm1ZH8SbmjNmVNERBYNN3379lW3MgsqKiqKf1XaMAmpxp4b7itlnb03U5YfUb03jo78/xkRkUXCzZ49e9C6dWs4OjqquhtZhfhS2rZtW+1GUO1Kzy1EflGJOpfdqcl6PNSnEWZuOqFWLf5rzxkMbR+udZOIiPQZbmQ9m8TERFUwLOfSa1PZjA65XlxcbIl2kgWGpGTjRtmhmqxr1eKHr2mE95cewUfLjmBwm1C4OF3VLilERHanSuFGhqJks0zjOdk2DklZtwd6NcR3G07gxLkc/Lr9FEZ0jdK6SURE+gs3Uixc2TnZpsSycBPEcGO1O4Y/1j8abyw4gE9WHMWtHcLZw0ZEZOlF/BYuXGi6/9xzz6lp4j179sTJkyer9Vpr167FkCFD1CwrGdKaP3/+ZR+/evVq03T08ocMmVHVJRungbPexmrd0y0KoX7uSEjPw+x/4rRuDhGRvsPN22+/DQ8PD3W+adMmfPbZZ3j33XcRGBiIcePGVeu1ZMuGdu3aqX2qquPw4cNISEgwHVw8sGY1NyF+7LmxVtJT8/SAJup82qpjyMov0rpJRET63RU8Pj4e0dHR6lx6Wu644w6MHTsWvXr1Qr9+/ar1WjfeeKM6qkvCDHcdrzkOS9mG2ztFYPqa46r25rv1sXiyLOwQEZGZe268vb1x7tw5db506VJcd9116tzd3b3SPacsQWZshYaGqu+9YcOGyz42Pz+/wlYRctg7DkvZBpklNe660tXAv1wXg/M5BVo3iYhIn+FGAsWYMWPUceTIEQwePFhd379/Pxo0KN38z1Ik0EyfPl2tkCxHZGSk6i3asWPHJZ8zefJk+Pn5mQ55jr3jsJTtGNI2DM1DfJCZV4Spq45p3RwiIn2GG6mP6dGjB1JSUlTAqFu3rrq+fft2jBgxApbUrFkzPPzww+jUqZMqYP7222/V7UcffXTJ50yYMEEtPGg8ZFjNnhWXGJCSxa0XbIWsUPzCjc3V+cyNJxGfmqN1k4iI9FdzI7UuUkR8Ia02zezatSvWr19/ya+7ubmpg0qdy8pXAUdW9a/r5ap1c6gK+jath17RdbHh2Dl8sPQwpvyng9ZNIiLSV7gR58+fx5YtW5CcnIySktJl/IVMy77vvvtQm3bt2qWGq6h6Q1Ky7YIzV761CfL/qwk3tsDNn67H/F1nMKZPI7QO99O6WURE+gk3f/31F+655x5kZWXB19e3wgaa1Q038hrHjv1bRyCrH0tYkR3HZXNOGVI6ffo0vv/+e/X1KVOmoGHDhmjVqhXy8vLw9ddfY+XKlaqwmao3U4pDUrZFwsyw9mEq3Ez++yBmje7GzWuJiC6h2n+6/9///R8efPBBFUykByctLc10pKamVuu1tm3bhg4dOqhDjB8/Xp2/+uqr6r6sYRMX9+8CZgUFBer7t2nTRu1Svnv3bixfvhwDBgyo7o8Be996IciH4cbW/N/1zeDq5KiGp9YePat1c4iIrJaDobIdMC/Dy8tL7QreqFEj2CKZCi6zpqS4WHqe7M2HSw/jk5XHcG/3KLw5rI3WzaFqemvhAXy1LlbNoFr4VB84SfEUEZEdyKjG53e1e24GDRqkelzIxoel2HNjkx7vHw1fd2ccSszEvB2ntG4OEZE+am5uuukmPPvsszhw4IAaHnJxcanw9VtuucWc7SMLFRSz5sY2+Xu64olro/H2okP4YOkR3NQ2FJ6uNZoXQESkW9X+rfjQQw+p20mTJl30NSlwLC4uNk/LyKI1N8FcwM9mjezRAD9sljVvcjF99XGMv76Z1k0iIrIq1R6WkqnflzoYbGwo3Phy7R9b3lTzpcEt1PkXa2Nw+nztbHtCRGQrrmqhE5mOTbYjv6gYaTmF6pw1N7ZtUKsQdGsYgPyiErzz9yGtm0NEZNvhRnpn3njjDYSHh6tNNGNiYtT1V155Bd98840l2khmklxWb+Pq7Ah/z4q1UmRbZAj41SEtIUvd/LX7DLafrN4yDEREelbtcPPWW29hxowZePfdd+Hq+u/y/a1bt1aL6pEtrHHjxgXgdKBVmB+Gdy7dCPb1vw6gpKRaqzoQEelWtcONrBb85ZdfqlWKnZycTNfbtWuHQ4fYPW7NUjI5U0qPC/t5uzljz6l0zNt5WuvmEBHZZriR7RCio6Mvui4FxYWFpfUcZJ2Mu4HX82YxsV7IHmEyNVy8u/gQsvOLtG4SEZHthZuWLVti3bp1F13/9ddfTdsokHX33MgHIunHA70aoH5dTyRn5uPTlf/u1UZEZK+qvc6N7Ps0atQo1YMjvTXz5s3D4cOH1XDVggULLNNKMguGG31yc3bCqze3xOiZ2/D1uhjc0Skc0UE+WjeLiMh2em6GDh2qdgaXDStlnykJOwcPHlTXrrvuOsu0ksxC/rI3FhSTvgxoEYyBLYJQVGLAxD/3o5pbxhER6UqN1m3v06cPli1bZv7WkEWx50bfJg5phXVHz6pdwxfuTcDNbcO0bhIRke0t4ke2heFG3yIDPPFYv9Li4jcWHEAWi4uJyE5VqeemTp06VV4XJTWVi4lZI1kD5axxthTDjW493LcRfttxCnGpOfh0xVFMKNumgYjInlQp3EyZMsV0fu7cObz55psYNGgQevTooa5t2rQJS5YsUasUk3VKyylQ9RiirhfDjZ73nXrtlpZ4cMY2fLM+Fnd0ikCTYBYXE5F9cTBUs/Lw9ttvR//+/fHEE09UuP7ZZ5+pIuP58+fDmmVkZMDPzw/p6enw9fWFvTiUmIEbpqxDgJcrdrzCwm+9e+j7bVh2IAndGwXgp4e6c0VqIrJ51fn8rnbNjfTQ3HDDDRddl2sSbsjK6224gJ9dkKnh7i6O2ByTil+3n9K6OUREtara4aZu3br4448/Lrou1+RrZJ1YTGx/xcXjBjZV528tOohzZfVWRET2oNpTwV9//XWMGTMGq1evRrdu3dS1f/75B4sXL8ZXX31liTaSGTDc2J/RvRvij11ncCAhA28uPIiPhrfXuklERNbZc3P//fdjw4YNarxLVieWQ87Xr1+vvkbWiQv42R9nJ0dMvq0NHB2A33eextojKVo3iYjIehfxkx6b2bNnm781ZDHsubFP7SL9MapnA3y34QRemr8XS5/pCw9XJ62bRURkUVzEz04w3Niv/7u+GUL93BGfmospK45o3RwiIotjuLETKcYF/Dhbyu54uznjjaGt1fnX62Kx/0y61k0iIrIohhs7wZ4b+zawZTAGtwlBcYkB//1lDwqLS7RuEhGRxTDc2IG8wmKk5xaq8yAfd62bQxp5/ZbW8Pd0wcGEDExbdVzr5hARWUe4KSwshLOzM/bt22e5FpHZGfeUcnVyhK9HjWrISQek1+71W1qp809XHsWBMxlaN4mISPtw4+LigqioKBQXF1uuRWTRISkuw2/fbmkXhutbBqt9xp79dTeHp4hIl6o9LPXSSy/hxRdf5O7fNhhuAllvY/ck3L55a2v4ebhg/5kMTF/N4Ski0p9qj1HIBpnHjh1DWFgY6tevDy8vrwpf37FjhznbR2bABfyoPKm7kuGpZ+bswicrj+K6VsFoHmI/m8gSkf5VO9wMGzbMMi0hi+FMKbrQ0PZhWLAnAcsPJuG/v+zGvEd7wdWZ8wuIyE7DzcSJEy3TErIYrnFDlQ1PvX1ra2w7mYp9pzPwyYqj+O+gZlo3i4jILGr8p9r27dsxa9YsdezcudM8rSGLYM8NVSbI1x1vDWujzqetPobtJ1lHR0R2Gm6Sk5Nx7bXXokuXLnjqqafU0alTJwwYMAApKdyYzxox3NCl3NQ2FLd1CEeJARg3Zzey8ou0bhIRUe2HmyeffBKZmZnYv3+/mjElh6x7k5GRoYIOWW+4YUExVea1oa0Q7u+BuNQcvPHXAa2bQ0RU++Fm8eLFmDZtGlq0aGG61rJlS0ydOhV///331beIzMpgMLDnhi7L190FH9zVDrIE0pxt8ViyP1HrJhER1W64KSkpUYv5XUiuydfIumTkFqGgbKG2QBYU0yV0b1QXY/s0UucT5u1Fcmae1k0iIqq9cCP1Nk8//TTOnDljunb69GmMGzdO1d2QdUnJKv2Q8nV3hruLk9bNISs2/vqmaBHqi9TsArW5ZokU4hAR2UO4kUX8pL6mQYMGaNy4sToaNmyorn366aeWaSXVWHJGWb2NLzfMpMtzc3bCx/9pD3cXR6w9koIv18Vo3SQiotpZ5yYyMlKtQrx8+XIcOnRIXZP6m4EDB9asBWRRXOOGqqNpsA9eG9IKL8zbi/eXHEbXhgHoGFVH62YREVWLc00XALvuuuvUQdaNxcRUXcO7RGL9sbNqBeOnftqJhU/1UXtRERHpKtx88sknGDt2LNzd3dX55XA6uHVhuKEarV58WxvsOZWupodPmLcHU+/uyB3liUhf4eajjz7CPffco8KNnF+K/PJjuLEuDDdU0+nhn47ogNs/34hFexPx45Y43NOtvtbNIiIyX7iJjY2t9JysH3cEp5pqF+mP529ojrcWHcTrfx1Auwh/tA7307pZRETmnS1VWFioZkcdPHiwOk8jDbHnhq7G6N4NcW3zIBQUleCx2TuQnlOodZOIiMwbbmShvrw8Lu5lk7OlGG6oBhwdHfDRXe0RUad0e4b/+2UX178hIv2tc/P444/jf//7H4qKuMGetSssLlELsglOBaea8vN0wfR7O8HV2RHLDybj8zXHtW4SEZF5p4Jv3boVK1aswNKlS9GmTRt4eXlV+Pq8efOq+5JkIWfLem2cHR1Qx9NV6+aQDZNamzeGtsLzv+3FB0sPq/qb3k0CtW4WEZF5wo2/vz9uv/326j6NNKy3kT2lZHiB6GoM7xKF7SfTMHfbKTz1804seLI3wvw9tG4WEdHVh5vvvvuuuk8hjbCYmMxt0tDW2Hc6AwcSMvDorO2Y83AP7llGRLZbcyM7fkutTa9evdClSxe88MILyM3NtWzr6Kow3JC5SZCR+ht/TxfsPpWudhA3GFhgTEQ2Gm7eeustvPjii/D29kZ4eDg+/vhjVVxMNhBuWExMZhRV1xPT7u4IJ0cH/L7zNL7iBptEZKvh5vvvv8e0adOwZMkSzJ8/H3/99Rdmz56tenRqau3atRgyZAjCwsLU6sbyuleyevVqdOzYEW5uboiOjsaMGTNq/P3tZgE/X4YbMq+e0YF45aYW6vydvw9h9eFkrZtERFT9cBMXF4fBgweb7ssu4BJIzpw5g5rKzs5Gu3btMHXq1Co9XlZHvummm9C/f3/s2rULzzzzDMaMGaMCF12Mw1JkSaN6NsDwzpGQZW+e/GknYlKytG4SEVH1CoplXRvZW+rCRf1k1eKauvHGG9VRVdOnT0fDhg3xwQcfqPstWrTA+vXr1X5XgwYNqnE79L6An8yWIjI3+eNm0rBWOJaSpWZRjfl+G35/rBd3ECci2wk3UjR4//33q+EgI1mt+JFHHqmw1o0l17nZtGmT6jEqT0KN9OBcSn5+vjqMMjIyYC+SM0tXk+a+UmQpbs6lBca3fLYeMSnZagbVjAe6qgX/iIi0UuXfQKNGjUJQUBD8/PxMx7333qvqZcpfs6TExEQEBwdXuCb3JbBcaubW5MmTK7QvMjIS9kDCaHKGcdPMij1uROYkw57fjOoCL1cnbDx+Di/P5wwqIrKRnhtbXd9mwoQJGD9+vOm+BCF7CDgZeUXILyot9mZBMVlayzBffHZ3R4yeuVUt8le/rhce7x+tdbOIyE7ZVN9xSEgIkpKSKlyT+76+vvDwqHylVBlGk6+XP+ypmNjH3ZmLrFGt6N88CK/d0kqdv7fkMBbsqflkAyIiuwk3PXr0UPtalbds2TJ1nSpivQ1pYWSPBniwV0N1Pn7ublVoTERkV+EmKytLTemWwzjVW85l2rlxSGnkyJGmx0vxckxMDJ577jkcOnRIrbszd+5cjBs3TrOfwVpxGjhp5aWbWmBgi2AUFJWoYapjyZwiTkR2FG62bduGDh06qENIbYycv/rqq+p+QkKCKegImQa+cOFC1Vsj6+PIlPCvv/6a08ArwWJi0oqsXPzJiPZoF+mP8zmFGPXtFiSml/YkEhHVBgeDnU1rkIJimTWVnp6u6/qbtxYewFfrYjGmd0O8fHNLrZtDdig1uwB3fL4RMWez0SzYB3Mf7gE/T66BQ0SW//y2qZobqv6wFGdKkVYCvFwx88Guqu7rcFImHvp+G/IKi7VuFhHZAYYbne8rxZob0lJkgKcKODJrb8uJVDz1004UFdd8PzoioqpguNH7ppmsuSGNtQj1xVcjO6tVi5ceSMLzv+1FiWxIRURkIQw3OpWcwangZD26N6qLT0d0UMXGv+04hYl/7ucqxkRkMQw3OiR1DbJCsWDPDVmLQa1C8MGd7eDgAPyw+STeWXyIAYeILILhRsfFxDIM4OtR5R02iCxuWIdwvDWsjTr/Yk0MPlt5TOsmEZEOMdzouZjY2w0O8mcykRW5u1sUXr6phTr/YNkRfL0uRusmEZHOMNzoUIpx6wVOAycrNaZPI4wb2FSdv7nwIL5ZH6t1k4hIRxhudD1TiuGGrNdTA6LxRNnO4W8sOMAeHCIyG4YbPS/gx2JismIyZPp/1zfFk9dGm3pwGHCIyBwYbnS8rxQX8CNbCDjjr2PAISLzYrjRoWRjzQ3DDdlQwHmqXMD5fPVxrZtFRDaM4UbPNTcsKCYbCjjjJOAMaKLu/2/xIby3hOvgEFHNMNzoELdeIFvuwXnhxubq/tRVx/H6Xwe4VQMRVRvDjc4UlxhwLos1N2S7HunbGG8Ma63OZ2w8ged/26P+XRMRVRXDjc6cy86HfA7I2n11vVy1bg5RjdzXvT4+vKsdHB2AX7afwpM/7UB+UbHWzSIiG8Fwo9OZUnW93ODsxP+8ZLtu6xiBafd0hIuTAxbtTcSob7cgI69Q62YRkQ3gp59u17jhkBTZvhtah2LGA13h7eaMzTGpGP7FZtOO90REl8Jwo9dp4JwpRTrRKzoQP4/tjkBvNxxMyMBtn29ETEqW1s0iIivGcKPTnhvZNJNIL1qH+2Heoz3RoK4nTqXl4o7pm7AjLk3rZhGRlWK40RmucUN6FVXXE78+2hNtI/yQml2AEV9uxsI9CVo3i4isEMONTguKucYN6ZEMTf30UHcMbBGE/KISPP7jDkxddYyL/RFRBQw3OsOtF0jvvNyc8cV9nfFArwbq/ntLDqu1cAqKSrRuGhFZCYYbnUnhAn5kB5wcHTBxSCtMGtpKrYUzd9spNVU8LbtA66YRkRVguNER6ZrnsBTZk5E9GuCbUV3g5eqETTHncMvU9TiUmKF1s4hIYww3OpKRV6TqEAQLisle9G8ehN8e64nIAA/Ep+bitmkbsXhfotbNIiINMdzoSEpZvY2PuzPcXZy0bg5RrWke4os/H++NXtF1kVNQjEdmbceU5Ue46SaRnWK40ZF/h6TYa0P2p46XK2Y+0NVUaDxl+VGM/WEb0nO5ZQORvWG40REWE5O9k/3UpND43TvawtXZEcsPJmPIp+ux/0y61k0jolrEcKMjLCYmKnVX50j8+kgPhPt7IC41R9Xh/LItXutmEVEtYbjREa5xQ/SvthH+WPhUb/RrVk8V2j/76x5MmLcHeYXFWjeNiCyM4UZHuPUCUUX+nq74dlQXjBvYFA4OwE9b4jH0sw04mpSpddOIyIIYbnQ4LMWaG6J/OTo64OmBTfD9g10R6O2Kw0mZGPLZeszdGs9tG4h0iuFGhwXFrLkhulifJvWw6Ok+6B0diLzCEjz32x48/fMuZOZxNhWR3jDc6EhyBmtuiC5Hgr/04Dx3QzO1hcOfu8/gxo/XYeuJVK2bRkRmxHCjE1IkKSsUC/bcEF1+mOqxftGY+3B3NZvqVFouhn+xCe8tOcTNN4l0guFGJ1LKiollbQ9fD2etm0Nk9TrVD8Dfz/TBbR3DIQsZT111HLd/vhHHkrO0bhoRXSWGG51NA6/n7QYHmRZCRFfk6+6CD+9qj2n3dIS/pwv2nk7HTZ+sw9frYlDMrRuIbBbDjd4W8OM0cKJqG9wmFEueuQZ9mgSqNXHeXHhQDVXFns3WumlEVAMMNzqRkF7acxPqx3obopoI9i0tNn7r1tbwcnXCtpNpuGHKWvbiENkghhudOHM+V92G+Xlo3RQimyVDuvd0q48l465RU8aNvTh3Tt+II1z4j8hmMNzorefGn+GG6GpF1PHED6O74u1b26henB1x51UtzodLD3P7BiIbwHCjE6fLem7C/TksRWSuXpy7u0Vh2fi+GNgiGIXFBnyy8hgGf7IO/8Sc07p5RHQZDDc6kZBeGm5COSxFZFZh/h74amQnNaNKtjaJScnG8C834//m7sbZslXBici6MNzogCw8Ztw0U34RE5H5e3FkRtXy8X0xomuUuvbbjlO49v3V+GHzSRYcE1kZhhsdSMrIg+z/Jwv41fVy1bo5RLrl5+GCybe1wbzHeqJVmK9aFfyV+ftw67QN2BV/XuvmEVEZhhsdzZSSaeCytDwRWVbHqDr484neeP2WVvBxd8aeU+kYNnWDGqqSPzaISFsMNzpwpqzehtPAiWqPbLw5qmcDrPy/fmoLB+NQVf/3V2PqqmOcVUWkIYYbHThz3jgNnDOliGqbFBnLFg6/P9YTHaL8kVNQjPeWHMZ1H63Bgj1nYJAxYyKqVQw3OhqWkh2OiUgbHaLq4LdHemLK8PYI9nVDfGounvhxJ4ZN24gtsalaN4/IrjDc6GrrBYYbIi1JzduwDuFY9d9+eGZgE3i6OmF3/Hnc9cUmPPT9Nu44TmRP4Wbq1Klo0KAB3N3d0a1bN2zZsuWSj50xY4aalln+kOfZM9PWCxyWIrIKnq7OeGZgU6x+tp9aCFDqc5YdSML1H63Bc7/uxqm0HK2bSKRrmoebOXPmYPz48Zg4cSJ27NiBdu3aYdCgQUhOTr7kc3x9fZGQkGA6Tp48CXvGYSki6xTk4662cFjyTB9c1zIYshzO3G2yPs4avPbnfqSUrU9FRDoLNx9++CEeeughPPDAA2jZsiWmT58OT09PfPvtt5d8jvTWhISEmI7g4GDYq6z8IrXWhuC+UkTWKTrIB1+N7KzWx+nRqC4KikswY+MJXPPuKrzz9yGkZhdo3UQiXdE03BQUFGD79u0YOHDgvw1ydFT3N23adMnnZWVloX79+oiMjMTQoUOxf//+Sz42Pz8fGRkZFQ49SSjrtfF1d4a3m7PWzSGiK6yP8+ND3TBrdDe0i/BDbmExpq85jt7/W4n/LWbIIdJFuDl79iyKi4sv6nmR+4mJiZU+p1mzZqpX548//sCsWbNQUlKCnj174tSpU5U+fvLkyfDz8zMdEoj0uGEmt10gsg3S89y7SSDmP95L9ebISscyffzz1aUhR3pyuGcVkY0PS1VXjx49MHLkSLRv3x59+/bFvHnzUK9ePXzxxReVPn7ChAlIT083HfHx8dDjGjcMN0S2F3KkDmfBk70rhBzpyen1zkpVk2P844WIqkfTcYzAwEA4OTkhKSmpwnW5L7U0VeHi4oIOHTrg2LFjlX7dzc1NHfrfDZwzpYhsOeQMbBGEFQeT8emqY2r6uNTkzNp8Uq1+/HDfxmhcz1vrphLZDE17blxdXdGpUyesWLHCdE2GmeS+9NBUhQxr7d27F6GhobBHHJYi0k/IGdgyGPMf64nZY7qhZ+O6KCoxqNlVAz5YgzEzt2HriVSueExUBZpXoMo08FGjRqFz587o2rUrpkyZguzsbDV7SsgQVHh4uKqdEZMmTUL37t0RHR2N8+fP47333lNTwceMGQN7lFA2LMVp4ET6CTm9ogPVsSMuTdXiyBo5yw+WHrLFw9g+jXB9qxC1fg4RWWG4GT58OFJSUvDqq6+qImKppVm8eLGpyDguLk7NoDJKS0tTU8flsXXq1FE9Pxs3blTTyO1500wOSxHpc3aV1OMcT8nC1+ti8NuO09gZdx6Pzt6BiDoeGNWjAe7qEgk/Dxetm0pkVRwMdtbHKVPBZdaUFBfLYoC2rKTEgOavLkZBUQnWPdcfkQGeWjeJiCxIFv2bufEEZv9zEmk5heqabPFwR6cItUM563JIzzKq8fnNcGPjv+i6vLUcDg7AkTdvhIuTzU1+I6IayCssxvydp/HdhhM4nJRput47OhD3dq+vipOd+fuA7PjzW/NhKbr6mVJBPm4MNkR2xN3FCf/pGoXhXSKx8fg5FXJWHkrC+mNn1SHD1Hd3jVJDVsG+HLIm+8Nwo4sNM1lMTGTvxcfxqTn4cUsc5myNR0J6Hj5YdgRTVhzFgOZBGNEtCtc0qccCZLIbDDc27LRxAT8/hhsieyc1d8/f0BzPDGyCRXsT8OM/cdh6Ig1LDySpQ2ZU3tk5QtXnRNRhfR7pG8ONDvaVCvNntzMRlXJzdsKtHSLUcTQpEz9ticdvO06pNbGmLD+Kj1ccVWvo3NkpEje0DlFDXER6w3Cji2ng7Lkhoos1CfbBq0Na4rkbmmHxvkTM3RavanQ2HCs9fP5wxk1tQnFbxwh0rl8Hjhy2Ip1guLFh3FeKiKpCemeGdQhXh9Tm/Lr9lDqkN+fnrfHqkHVzbit7TCNOKScbx6ngNqzrW8uRnJmPP5/ohbYR/lo3h4hsbJ2szbHn8PuO0/h7XyKy8otMX2sd7otb2oVhSLsw9gyT1eA6N3YQbmThvmav/A35r7ft5YEI9Nbv5qBEZFm5BcVYdjAJ83acwrqjZ1FcUvqxIGtodWkQgCFtQzGodQiCfFjfR9rhOjd2ICkjTwUbV2dH1PVy1bo5RGTDPFydVE+NHOey8rFoXyL+2nUGW06kYkts6fHqn/vRrWGAqtFh0CFrx3Bj67uB+7mrtS6IiMyhrrcb7uteXx2yltaCPWewcG8idsefx+aYVHVI0JEC5EGtQtTBrV/I2jDc2PjqxCwmJiJLkd8vY69prA4pRP57X4Ip6MgaOnK8ufCgqtG5vmUIBrYIRotQH/7BRZpjuLHxmVIs9iOi2iC9M8agIz06S/cnYvH+RDVkte90hjo+XHZELRZ4XctgFXS6NgxQQ+dEtY3hxsa3XgjnAn5EpEGPzv29GqpDanSWH0zCsgPJWH8sRQ2Zz9h4Qh1erk7o06Qerm0ehH7N67FOh2oNw42Nh5tQDksRkcY1OsO7yCaeUWrWlWzcuexAIlYeSsHZrHzVuyOHkOGrvk3roV+zIHSI9OfO5WQxDDc2SjbGE6y5ISJrmnUlQ1JyyDo6+86kY8XBZKw6nIw9p9JNw1dTVx2Hj7szekcHoneTQPSJroeouixKJvNhuLFBsgbFyXM56jyyDsMNEVkf2cpBFheVY9x1TZGcmYd1R85izZEUrDuagrScQrV4oBwiKsCzLOgEonujuqjDJS7oKjDc2KC41BzkFhbDzdkR9et6ad0cIqIrknqb2ztFqEP+QNt7Oh1rj6Rg/dGz2BGXpn6vyU7mcshkq5ahvmqDz57RgWohQW83flxR1fFfiw06nJihbpsEe8OJG90RkY2R31vtI/3V8dSAJmrrh83Hz6l6nY3Hz+JIUhb2n8lQx1frYtXjW4f7oXujAHRvWBedG9SBj7uL1j8GWTGGGxt0KDFT3TYLtt3tI4iIjKRXZqBMH28ZrO7LENam4+ew8dg5bIw5i/jUXLW2jhxfrImB/E3XItRX9ejIdHO5refDLWjoXww3NuhwWbhpHuKjdVOIiCwyhDW0fbg6hEwv/yfmHP6RFZJjz6maQ2PPjkw5Fw3qeqJT/QDVqyOrJzeu563qfsg+MdzYcLhpxnBDRHZAFga8rWOEOkRiep7a92prbCq2nkhVvdknzuWo47cdp9Rj/Dxc1LBXx6g66FjfH+0i/eHLoSy7wXBjY/IKi3HiXLY6Z88NEdmjED9300afIj2nUBUlbzuZiu0n07Ar/jzScwvVzCw5hBQpS29Ouwh/tI/yR/sIf/UHIldQ1ieGGxtzLDkLJQagjqcLx5iJiKSXxtMF/ZsHqUMUFpfgUEKmCjw749KwI+68mo0lvz/lMPbuSLCRWVltI/zQJtxP9e5IAOJEDdvHcGOrxcQh3JyOiKgyLk6OaCOBJcIPo3o2UNdktWRjUfKuU+nqVnp3pJdHDiMPFye1+aeEnVbhfmgd5qdmpsprku1guLHRaeDNQzhTioioqgK93TCgRbA6hMFQuhjqntPp2BN/Xt3uO52OnIJi1dMjh5Grk6MKOK3CfFVPj8zUah7qq+p6yDox3Nhwzw0REdWM9Hw3CPRSh7F2RxYXjD2bjf1nSoOO2i7iTDoy84pMs7MuLHSWXh4JO/I7WeogG9T14p5ZVoDhxsZwphQRkWVIrU10kLc6jNPQpYfnVFquCjYHEjJw4Ew6DiZkqunpxmP5weQKvTyNg7xV0JHenqZBPur3tQQhTk2vPQw3NiQtuwDJmfnqvGkwww0RUW308EQGeKrjhtYhputSr3MoIQMHEzJUj7ocR5Iy1bCWXJOjPKnlMQYn49EkyFvtqcWeHvNjuLHBIanIAA/us0JEpCGpt+nWqK46jGQndOnlOZSYoYKObCMhtzEp2Wo/QNlPS47yXJwc1B6B0fW80TjIC40CvdGwnhcaB3qrWWBUM/yEtMFiYm67QERkfWTYKaqupzqub/VvL09RcQlOpubgaFIWjqdk4WhSJo4mZ5lCj3GKOvZXfL26Xq5oGOilDqkNMp7Xr+sJT1d+fF8O3x0bcjiJ2y4QEdkaGXaS9XPkKE96ehIy8nC8LNxI8JHAE3M2C0kZ+TiXXaCObSfTLnrNYF831eMj207IrQxvSeipH+DFHh+GG9vCmVJERPrq6ZFCYzmuaVqvwtdkp/QTZ7NV4DlxVraWyFYzueSQeh8JP3JsiU2tdMgsKsBTHREBHoisU3oudUNh/u5wc3aC3jHc2AhJ+Ee4YSYRkV2QusrWsohguN9FXzufU6D20Tp5LlsFH7mVYS9ZhTklM1+Fn8rqe4Ss/Rrs446IOh5lhyfC65QGLLkf5u8BdxfbDz8MNzZCphtmFxSraYYy9kpERPbJ39MV7eWI9L/oazkFRSrkxKfmlt2WHnIuxc5S45OYkaeOyoa7RKC3qwo5YX4eCPV3V8En1E+Cj7u6LgsiWvsWFQw3NjYkJesncBlwIiKqjBQaNw/xrXQVe1mzJzW7APFpuSrwqHV60nJxKu3fc/kj+mxWgTr2nLq450c4Ozog2NddbWAqR2i58xBfd/U1ObTclJThxua2XeCQFBER1WzNnrrebuqorNdHwk9GbhFOnc/BmfN5OJ2Wg4T0PJyR43wuEs7nqh6fohKDaQHDS2kW7IMl466BVhhubASLiYmIyNLhx8/TBX6efmgVdnGtj3Fau/TqJKTnquAjR2K6hJ58JMn9jFwkpecj2M8dWmK4sRHcdoGIiKxhWntI2RBUh0s8RnqApLZHSyzesAH5RcWIOZutzjksRURE1t4D5KnxIoMMNzbgeHK22q3W191ZFWsRERHRpTHc2ADZp0RI9bskYiIiIro0hhsbsGhvorrtEHVxdTsRERFVxHBj5aQifeWhJHV+Z+dIrZtDRERk9RhurNycrfEoMQBdGwYgOqjipmtERER0MYYbKyZFxBJuxD3dorRuDhERkU1guLFiqw8nqwWS6ni64IbWIVo3h4iIyCYw3FixH/+JU7d3dIqwiy3qiYiIzIHhxkrJPh6rDier8xFdOSRFRERUVQw3VurnskLi7o0C0KgeC4mJiIiqiuHGCsnGZHO2lg5J3d2tvtbNISIisikMN1Zo1eEUJGXkI8DLFYNaBWvdHCIiIptiFeFm6tSpaNCgAdzd3dGtWzds2bLlso//5Zdf0Lx5c/X4Nm3aYNGiRdCL3IJifLs+Vp3fyUJiIiIi2ws3c+bMwfjx4zFx4kTs2LED7dq1w6BBg5CcXFpMe6GNGzdixIgRGD16NHbu3Ilhw4apY9++fbD1nb9nbjyBa95bhU0x5+DoAPyHhcRERETV5mAwGAzQkPTUdOnSBZ999pm6X1JSgsjISDz55JN44YUXLnr88OHDkZ2djQULFpiude/eHe3bt8f06dOv+P0yMjLg5+eH9PR0+Pr6mjWcpGTmX/Fx8m7L4nyFxSXILypRt4cTM/HpymM4fT5XPSaijgdevqkFbmgdarb2ERER2bLqfH47Q0MFBQXYvn07JkyYYLrm6OiIgQMHYtOmTZU+R65LT0950tMzf/78Sh+fn5+vjvJvjiXsP5OB26ZtvKrXCPJxw5MDmmB450i4OmveqUZERGSTNA03Z8+eRXFxMYKDKxbNyv1Dhw5V+pzExMRKHy/XKzN58mS8/vrrsDQHAG5VDCTOjg5wcXaEq5OjCjHebs64rWM47uveAB6urLEhIiKy2XBTG6RXqHxPj/TcyLCXuXWIqoPDb95o9tclIiIiGwo3gYGBcHJyQlJSUoXrcj8kpPK9lOR6dR7v5uamDiIiIrIPmhZ2uLq6olOnTlixYoXpmhQUy/0ePXpU+hy5Xv7xYtmyZZd8PBEREdkXzYelZMho1KhR6Ny5M7p27YopU6ao2VAPPPCA+vrIkSMRHh6uamfE008/jb59++KDDz7ATTfdhJ9//hnbtm3Dl19+qfFPQkRERNZA83AjU7tTUlLw6quvqqJgmdK9ePFiU9FwXFycmkFl1LNnT/z44494+eWX8eKLL6JJkyZqplTr1q01/CmIiIjIWmi+zk1ts9Q6N0RERGQdn99cTIWIiIh0heGGiIiIdIXhhoiIiHSF4YaIiIh0heGGiIiIdIXhhoiIiHSF4YaIiIh0heGGiIiIdIXhhoiIiHRF8+0XaptxQWZZ6ZCIiIhsg/FzuyobK9hduMnMzFS3kZGRWjeFiIiIavA5LtswXI7d7S1VUlKCM2fOwMfHBw4ODmZNlBKY4uPjuWeVhfG9rh18n2sH3+fawffZ9t9riSsSbMLCwipsqF0Zu+u5kTckIiLCYq8v/yH5f5zawfe6dvB9rh18n2sH32fbfq+v1GNjxIJiIiIi0hWGGyIiItIVhhszcXNzw8SJE9UtWRbf69rB97l28H2uHXyf7eu9truCYiIiItI39twQERGRrjDcEBERka4w3BAREZGuMNwQERGRrjDcVMPUqVPRoEEDuLu7o1u3btiyZctlH//LL7+gefPm6vFt2rTBokWLaq2t9vRef/XVV+jTpw/q1KmjjoEDB17xvw3V7N+00c8//6xW+B42bJjF22iP7/P58+fx+OOPIzQ0VM04adq0KX9/WOB9njJlCpo1awYPDw+1ou64ceOQl5dXa+21RWvXrsWQIUPUKsHyO2D+/PlXfM7q1avRsWNH9W85OjoaM2bMsHxDZbYUXdnPP/9scHV1NXz77beG/fv3Gx566CGDv7+/ISkpqdLHb9iwweDk5GR49913DQcOHDC8/PLLBhcXF8PevXtrve16f6/vvvtuw9SpUw07d+40HDx40HD//fcb/Pz8DKdOnar1tuv5fTaKjY01hIeHG/r06WMYOnRorbXXXt7n/Px8Q+fOnQ2DBw82rF+/Xr3fq1evNuzatavW267n93n27NkGNzc3dSvv8ZIlSwyhoaGGcePG1XrbbcmiRYsML730kmHevHky09rw+++/X/bxMTExBk9PT8P48ePVZ+Gnn36qPhsXL15s0XYy3FRR165dDY8//rjpfnFxsSEsLMwwefLkSh9/1113GW666aYK17p162Z4+OGHLd5We3uvL1RUVGTw8fExzJw504KttM/3Wd7bnj17Gr7++mvDqFGjGG4s8D5//vnnhkaNGhkKCgpqsZX29z7LY6+99toK1+QDuFevXhZvq16gCuHmueeeM7Rq1arCteHDhxsGDRpk0bZxWKoKCgoKsH37djXcUX6PKrm/adOmSp8j18s/XgwaNOiSj6eav9cXysnJQWFhIQICAizYUvt8nydNmoSgoCCMHj26llpqf+/zn3/+iR49eqhhqeDgYLRu3Rpvv/02iouLa7Hl+n+fe/bsqZ5jHLqKiYlRQ3+DBw+utXbbg00afRba3caZNXH27Fn1i0V+0ZQn9w8dOlTpcxITEyt9vFwn877XF3r++efVePCF/4eiq3uf169fj2+++Qa7du2qpVba5/ssH7IrV67EPffcoz5sjx07hscee0wFdln1lczzPt99993qeb1791a7TRcVFeGRRx7Biy++WEuttg+Jl/gslJ3Dc3NzVb2TJbDnhnTlnXfeUcWuv//+uyoqJPPIzMzEfffdp4q3AwMDtW6OrpWUlKjesS+//BKdOnXC8OHD8dJLL2H69OlaN01XpMhVesSmTZuGHTt2YN68eVi4cCHeeOMNrZtGZsCemyqQX+ZOTk5ISkqqcF3uh4SEVPocuV6dx1PN32uj999/X4Wb5cuXo23bthZuqX29z8ePH8eJEyfULInyH8LC2dkZhw8fRuPGjWuh5fr/9ywzpFxcXNTzjFq0aKH+ApbhF1dXV4u32x7e51deeUUF9jFjxqj7MqM1OzsbY8eOVWFShrXo6l3qs9DX19divTaC//WqQH6ZyF9QK1asqPCLXe7L2Hhl5Hr5x4tly5Zd8vFU8/davPvuu+ovrsWLF6Nz58611Fr7eZ9lSYO9e/eqISnjccstt6B///7qXKbRknn+Pffq1UsNRRnDozhy5IgKPQw25nufpTbvwgBjDJTcctF8NPsstGi5ss6mGcq0wRkzZqjpbGPHjlXTDBMTE9XX77vvPsMLL7xQYSq4s7Oz4f3331fTkydOnMip4BZ6r9955x01BfTXX381JCQkmI7MzEwNfwr9vc8X4mwpy7zPcXFxarbfE088YTh8+LBhwYIFhqCgIMObb76p4U+hv/dZfifL+/zTTz+p6cpLly41NG7cWM10pUuT36uy7IYcEiE+/PBDdX7y5En1dXmP5b2+cCr4s88+qz4LZdkOTgW3MjI/PyoqSn2QyrTDzZs3m77Wt29f9cu+vLlz5xqaNm2qHi9T4RYuXKhBq/X/XtevX1/9n+zCQ355kXn/TZfHcGO593njxo1q6Qj5sJZp4W+99Zaahk/me58LCwsNr732mgo07u7uhsjISMNjjz1mSEtL06j1tmHVqlWV/r41vrdyK+/1hc9p3769+u8i/56/++47i7fTQf7Hsn1DRERERLWHNTdERESkKww3REREpCsMN0RERKQrDDdERESkKww3REREpCsMN0RERKQrDDdERESkKww3RES1wMHBAfPnz9e6GUR2geGGyM7cf//96oNWDtmTJzo6GpMmTUJRURFsFYMDEZXHXcGJ7NANN9yA7777Dvn5+Vi0aBEef/xxtRP1hAkTqv1axcXFKlzoYRflwsJC9T4QkW2z/d9GRFRtbm5uCAkJQf369fHoo49i4MCB+PPPP9XXPvzwQ7Rp0wZeXl5qt+/HHnsMWVlZpufOmDED/v7+6vEtW7ZUrxUXF4etW7fiuuuuQ2BgIPz8/NC3b1/s2LGjwveVEPTFF1/g5ptvhqenJ1q0aIFNmzapXbD79eunvmfPnj1x/PjxCs/7448/0LFjR7i7u6NRo0Z4/fXXTT1NDRo0ULe33nqren3j/Ss9z9iezz//XO1wLt/7rbfeuui9evHFF9GtW7eLrrdr1071eImq/OzlrV69Wn3v8+fPm67J7upy7cSJE6Zr69evR58+feDh4aH+Wzz11FPIzs6+5OsSUSmGGyJSH54FBQXqXHpgPvnkE+zfvx8zZ87EypUr8dxzz1V4fE5ODv73v//h66+/Vo8LCgpCZmYmRo0apT6QN2/ejCZNmmDw4MHqenlvvPEGRo4cqT7MmzdvjrvvvhsPP/yw6jXatm2bbOaLJ554wvT4devWqcc//fTTOHDggApHErCMQUSChZCeqISEBNP9Kz3P6LXXXlPBaO/evXjwwQcvem/uuecebNmypULgkp95z549qu2iqj97dcj3kx6222+/XX2vOXPmqNcv/94Q0SVYfGtOIrIq5XfzLikpMSxbtkztPv3f//630sf/8ssvhrp165ruy46+8qtj165dl/0+xcXFBh8fH8Nff/1luibPe/nll033N23apK598803pms//fST2qXZaMCAAYa33367wmv/8MMPhtDQ0Aqv+/vvv1d4TFWf98wzzxiupF27doZJkyaZ7k+YMEHt2l3dn93YRuPOyuV3oN65c6e6Fhsbq+6PHj3aMHbs2Aqvu27dOoOjo6MhNzf3im0msmesuSGyQwsWLIC3t7eqMSkpKVE9ENKDIZYvX47Jkyfj0KFDyMjIUMM4eXl5qrdGhpKEFCK3bdu2wmsmJSXh5ZdfVkMuycnJqhZHniNDVuWVf15wcLC6lWGw8tfk+8n39vX1xe7du7Fhw4YKPS7y2he26UJVfV7nzp2v+H5J7823336LV155RfUs/fTTTxg/fny1f/bqkPZLj83s2bNN1+R7y3+v2NhYNaRHRJVjuCGyQ/3791e1JhJSwsLC4Oxc+qtA6j2kHkbqcCQUBAQEqKGQ0aNHq2ErYyCQYSypDylPhmXOnTuHjz/+WNXySC1Ojx49TMNdRuULdo2vUdk1+RAXUu8jtTK33XbbRT+H1NJcSlWfJ7U2VzJixAg8//zzqo4mNzcX8fHxGD58eLV/diNj8XVph04pCZoXtl+G66TO5kJRUVFXbDORPWO4IbJD8oEuU8AvtH37dhUqPvjgA9MH8Ny5c6v0mtJLMm3aNFVrIiQAnD179qrbKgXBhw8frrS9RhKOpLekus+rqoiICFUkLL0oEm6keFjqjGr6s9erV0/dSo1QnTp11LnUIF3YfqkVMkf7iewNww0RmcgHqfQgfPrppxgyZIj60J4+fXqVnitFtD/88IMa5pEhpWeffVb18FytV199VfUmSW/FHXfcoUKXDNns27cPb775pnqMzJBasWIFevXqpXpNJDBU5XnVIUNTEydOVL0xH3300VX97PI+y+wnGQqUHrIjR46oQFme9BR1795dFRCPGTNGBVIJO8uWLcNnn31W7fYT2RPOliKiCtObZSq4zIRq3bq16qmQ+puq+Oabb5CWlqZ6HO677z41nFK+d6OmBg0apGqEli5dii5duqgPfAkXMvxjJMFAPvQlMHTo0KHKz6sOCUgy9CS1NMOGDbuqn116mqRuR+qapAZJ3u8LA5dcX7NmjQo+Mh1cfi4JbDKMSESX5yBVxVd4DBEREZHNYM8NERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERFBT/4f26ap9ZoPNS8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot priors for all initial parameters\n",
-    "init_params['k_alpha'].plot_prior()\n",
-    "plt.title('k_alpha prior')\n",
-    "\n",
-    "init_params['k_sigma'].plot_prior()\n",
-    "plt.title('k_sigma prior')\n",
-    "\n",
-    "init_params['obs_var'].plot_prior(x_min=1e-4,x_max=2*1e-3)\n",
-    "plt.title('obs_var prior')\n",
-    "\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mcmc_Gaussian_BFFG_shapes_SDEs_3D.ipynb b/examples/mcmc_Gaussian_BFFG_shapes_SDEs_3D.ipynb
deleted file mode 100644
index 164f86c..0000000
--- a/examples/mcmc_Gaussian_BFFG_shapes_SDEs_3D.ipynb
+++ /dev/null
@@ -1,485177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for 3D shapes, SDE transitions\n",
-    "\n",
-    "Parameter inference for trees with Gaussian transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebook [mcmc_Gaussian_BFFG_shapes_SDES.ipynb](mcmc_Gaussian_BFFG_shapes_SDEs.ipynb) and [mcmc_Gaussian_BFFG_shapes_state-dependent_covariance_3D.ipynb](mcmc_Gaussian_BFFG_shapes_state-dependent_covariance_3D.ipynb) simpler versions. \n",
-    "\n",
-    "The covariance is assumed rotationally invariant, allowing factorization into a kernel product the identity matrix of the ambient space.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example. The notation, likelihood ratio calculation etc. follow https://arxiv.org/abs/2203.04155 section 6.1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_shape_3d, plot_tree_3d_scatter, plot_tree_3d_shape, plot_mesh\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.SDE import dot, solve\n",
-    "from examples.ABFFG import *\n",
-    "from examples.shape import k_K2\n",
-    "from examples.shape import mesh_sphere\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed:  773648267371\n"
-     ]
-    }
-   ],
-   "source": [
-    "# seed,\n",
-    "#seed = 42\n",
-    "import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "print(\"seed: \",seed)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Minimum pairwise distance between the 42 points: 0.5465330578253433\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 3; n = 42 # d = dimension of embedding space, n = number of landmarks\n",
-    "\n",
-    "# Create and plot meshed sphere\n",
-    "meshed_sphere = mesh_sphere(n)\n",
-    "def plot_mesh_points(points):\n",
-    "    meshed_sphere.vertices = points.reshape((-1,3))\n",
-    "    plot_mesh(meshed_sphere)\n",
-    "root = meshed_sphere.vertices.flatten()\n",
-    "# Compute minimum pairwise distance between points in root\n",
-    "vertices = root.reshape((-1,d)); n = vertices.shape[0]\n",
-    "diffs = vertices[:,jnp.newaxis,:] - vertices[jnp.newaxis,:,:]\n",
-    "# Compute Euclidean distances and mask the diagonal (zero distances)\n",
-    "distances = jnp.sqrt(jnp.sum(diffs**2, axis=2))\n",
-    "min_dist = jnp.min(distances+ jnp.eye(distances.shape[0])*10)\n",
-    "print(f\"Minimum pairwise distance between the {n} points: {min_dist}\")\n",
-    "\n",
-    "ax = plot_mesh_points(root)\n",
-    "plot_shape = plot_shape_3d\n",
-    "plot_shape(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SDE setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 100 # number of time steps in the discretization\n",
-    "\n",
-    " # drift\n",
-    "def b(t,x,params): return jnp.zeros(n*d)\n",
-    "\n",
-    "# diffusion and covariance specification for shape processes\n",
-    "# assumes same covariance in all directions (x,y or x,y,z), thus allowing some factorization of the covariance matrix\n",
-    "\n",
-    "kQ12 = lambda x,params: (.25/max(42,n))*k_K2(x,params)\n",
-    "\n",
-    "# evaluate k on two pairs of landmark configurations\n",
-    "kQ12_q = lambda q1,q2,params: kQ12(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# diffusion field and covariance matrix factored in dimensions\n",
-    "sigma = lambda q,theta: kQ12_q(q,q,theta) # diffusion field, kernel evaluated on all landmark pairs\n",
-    "def a(q,theta): sigmaq = sigma(q,theta); return jnp.einsum('ij,kj->ik',sigmaq,sigmaq) # covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tree with SDE transitions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                            None\n",
-      "           ┌────────────────────────┬────────────────────────┼────────────────────────┬────────────────────────┐\n",
-      "          None                     None                     None                     None                     None          \n",
-      " ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n",
-      "(31, 101, 126) (126,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "#degree = 2; topology = symmetric_topology(height=3, degree=degree)\n",
-    "degree = 5; topology = symmetric_topology(height=2, degree=degree)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "n_leaves = tree.is_leaf.sum()\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "n_edges = tree.data['edge_length'].shape[0]\n",
-    "# data dimension\n",
-    "tree.add_property('value', shape=(n_steps+1,n*d,))\n",
-    "tree.add_property('noise', shape=(n_steps,n*d))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "print(tree.data['value'].shape,root.shape)\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.3), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.25), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions using the SDE functions above. The step covariance is square of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unconditional down\n",
-    "down_unconditional = SDE_down_unconditional(n_steps,b,sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sample new noise\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "add_leaf_noise(tree,key,params)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf][:,-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a generated tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
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-          "title": {
-           "x": 0.05
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-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
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-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(10,8)); ax = fig.add_subplot(projection='3d')\n",
-    "plot_tree_3d_scatter(tree,'value',selector=lambda z: z.reshape((-1,n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "plt.show()\n",
-    "\n",
-    "fig = plot_tree_3d_shape(tree,'value',scale=.5,mesh=meshed_sphere)\n",
-    "#fig.write_html('dtree.html')\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "up = SDE_up(n_steps,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree\n",
-    "init_up = get_init_up(n,tree,d=d,root=root,n_steps=n_steps)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# conditional down\n",
-    "down_conditional = SDE_down_conditional(n_steps,b,sigma,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time elapsed: 233.5360 ms\n",
-      "Time elapsed: 298.4889 ms\n",
-      "Time elapsed: 3.1250 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding, time the operations\n",
-    "test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MCMC run incorporating the likelihood ratios arising from the Gaussian approximation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.5, 'k_sigma': 0.1, 'obs_var': 0.0005}\n",
-      "data parameters:  {'k_alpha': 0.3, 'k_sigma': 0.25, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1000/1000 [05:14<00:00,  3.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.2240\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Qyx5++GHx/1tvvaVbrz/96U9e77t160YrVqygzz//HOJ5EMhL+aCOefTGtcPqLf+nt1cLwR3SRBD42LY4Q7aV8Ijn4Uae+yKj0eNtwDdSWFWR53dN7E2frz1Y73nN31mx+zgVlLvuo6Dh4DAwOBUrIJ6bRFNL3AAAAAAA0NRYuqOIbnxvDVXU1lGzDAu9f/2p1Lddjk85aaUSKPI8N93X8uK6N1f5fIfLcZQvJ1z03obDr7ioeJ6H+WAqrU9Oap0l/s9KTabLT+kU0roen7tFCK/SjuSzNQfpu81Hlc9z0ix0+9huqsjzyIcdqZsuXPH8mtdXifPiSGk1zbx4YFjr+u7XI/TAV5vF32lukVpayoRr26JE8RkUv7UWQHIKvVF7IbGtECwMFM9ziOf1wtHfHOE9ffp0ZRknLxw3bpwQqvXg5RyproajylkYZ/bs2SPsX3gdktzcXBHVzt+V4nkolJaWUvPmxpPHAo+YmpqUSC2yUustz+XU3wP1o20pv57nyl/+r00yF0nI4rncgs4m5LKmcmxlG/J95HeD2olXfRyvqKWhj80XInu85TMBDTdZOMRzk5DPdfA8BwAAAABonCzZWSgEUoa9spfvKtIVzxXblgACsBSk1fx6qEy37JxNR+jGM7sbFhflsnAThnoivvxHihslLTmJbPY6RZB/6H+bqdzdlpKuLdJJSg3RjzwPb13yvNh+rCLcatGP24uUv8f0bmVKJKPPMTUofsvjIPs4yqyKIPzhLVJIMuBDL7G7Z2Eg8rx+2EvcbreLqHA1/H7r1q2632FhXK88L5efy2X+yoQC27awxQxHxfujtrZWvCRlZa7ros1mE69Qkd8NZx2xwlYnr5VOg/WXg111Ie1vQ26rUHFobgL+zre6OtcAMDkdfttJXh5rrKG1vxTGeVs+33d/ZrPrfBYGnOB06c7jIc1Y4+v06T1aBBzYCfWcqq51lefbiNHvOuzuY8THoNaqJBptCDTF354ah/vez8cwUBuY2U5G1wHx3CSa2vQZAAAAAICmhjZGorLW00HTFc8D2LZwJNTQzs1ozb5i8f6uib3opNbeyRQ//mU/zd9SQOU1tqBsLTye5+ZEnssounBItSQJsZwjz7nuUji//7w+tGBLgZhiXWmtC9vzPFTxPFyLG886w19HYbnL2/jRC/vRFcM7eYnVYQ+IGPDj1z2X3NuV51RwkefBR83L7UE8bzz8+uuvwgKGk5BOmDDBbzn2YZd2MGp++OEHysjwnoETCmxP09BYW8S/gyQqPnGc5s6dW2/54hP8+0ykNWvXkXO/s0m1VahUVvIgsed6s+qX1VS507ftNh1zHYtjx47RvHlHdNvp2BFX+2/a/BvNLXHNIgoGq9VVl59++pG2pXt/dsS97s287uLg1+2P2VsSaUtJ6Pf6fs0cdENvh+nn1PrDrvYuPHbU0LnPVNd5pM65334nhPd4e5b8tTiBSqyeZW0znNQjp2n+9tSUlrnO/V9+WUVl2+u/dpnRTlVVxnL4QDw3iaaWuAEAAAAAoKmhFVgr3BFR/qJ76xMY1dG/E/u1oR6tvKPR2bOWxfOKGu8I7fpETI/VRrhRyvag/a39IQV4Fs+rrJ79Ye9t9iVl8ZzbLZqe5+ptmPUMX99U412FFfUmW91TVCn+b5Ob7usbHmbkuYz+NnpM1QlD+fy3hyCehxI1LyPdYdtSP/n5+SIBJ4t5avh9mzb6ySJ5eaDy8n9e1ratJ4Esvx88eHDQdeQko2PHjhXJQtlHPRBsP6O2lOHIc042yoJ7To7vTJ9gogtZaBk/fjxZLL62WfGMfeMRoh2bqGV+Pk2aVL/n+UfHVtOOshPiWE0a6Dl+TaGtQuWZrUuIaquV90OHDaMxvVr6lCtZdYBo9xbxGxk/vp9uO63432+0svAgdetxEk0623vWmBHuX7eQyF5HZ515JnXN904KvvDTTbSm6Aj16dOHJp3Whczild3L+amGBnbIoWYZKUHk57DSpkNllJCeR5MmnWrqOVVYXkv3zVrKvwDq0qkDTZrkSmZsZCbYvb8sFH9PmDgxYJ6VWLBufwnd/uoqn+eRZXedQTmpiU3ut6fmhZ3LiKoradSpI2hE1+ZRuUbJ2U31AfHcJOSDMrRzAAAAAIDGidaeT1p1aDEanasWMPXE4sxU16N6hU6Euz1AZK5cVm2z0yerD/h83j4vnUZ2b1GvD6iZkedplkRlnZw0VNaT1614Ytc5PJHnQdiChIq66cyKPA8k+rMf+vhnfzQ8U7V1jmcKvJx2bl7kubH2lQlD+Wtq8TsY2xZ5nluDqLv8DSHyvH5SUlJo6NChtGDBArrooouUqe/83l9SzpEjR4rPb7/9dmUZCxG8nOnatasQB7mMFMtZYFi5ciXddNNNQdVv8+bNNGbMGLr22mvp8ccfr7d8amqqeGlhgcQMMcms9USTxESX+JeUlGio7jLvRWJSUlj72hDbKmQSfNtcb9+5TWUby8+17ZRmcd27HZQQWvs5/bc/nwOiuqrtm4G8ut97Th8a1SPf8Pd+3F5I176xStzXjNQnmHPq9eU7qNL9vNAsM9Xw99KcnmeWxKRksriPR7xwwhUaL3Ln8LPYoq2F4nmtsNKuWN80qd+eCvmUYElONv18CrQOI8TXWdQoEoZCPQcAAAAAaMyeqJw4k4VzPVFbm+AqEGpxXUb4qslKcz2qV+qI9IE8z9NTXJ37GpuD7vp0o+62v/m/0dS/fW7A+llNFc9lnezK/mSkJIlnaLl+FnYdzoSoiaa8bW52fnwPRztXC++BIs/3Ha8S2+H91Vr0aOnZKov6tcsNy/ok4LkZdMJQh0Y8Dz7yXJ5PwbRpNBLHNgY4UpvF6WHDhtHw4cNp1qxZVFlZSVOmTBGfT548mdq3by8sUZjbbruNzjzzTPrnP/9J5513Hn300Ue0evVq+s9//qP8NlhYf+yxx6hnz55CTH/ggQeoXbt2ikDP7N+/n06cOCH+Z9/19evXi+U9evSgrKwsYdXCwjknI+U6Sr90jpRv2dI3qhfoIzUGo4kPZTFoE8aRTcX3Hr7++Gs7OUsp0KGQg4vWcBOGRtEuONQBS/XsJLMprrIqdbr+9K6Gv6e+bcTjb4CTpzO92mTTy1cNpXOfW0JbjpTRCbG/4VtTNWScTu+B+3gC4rnpCUNjXRMAAAAAABAJ5HNeTppLPC+rtnkJgkqErXtZfdHTalFaTyPMSk3yG+EeKKFi29x0+uuYHrTxUKnPZ2v2Fgu/8QLhq51rMPI8yUTbFk/kOYvn6nartakiz6MkmrLYbXf6F0qMoBaVA3X45KABd5j/d+vooLYh28MWZmcj2IShnshzp9Lhj6RtC5/rt3+0njYfLo3bDnQ8cvnll1NhYSHNmDFDCNQcLf7dd98pCT9Z3E5U/aZGjRpFH3zwgbBQue+++4RA/uWXX1L//h5bhLvvvlsI8Gy1UlJSQqNHjxbrTEtLU8rw9t5++23l/ZAhQ8T/ixYtorPOOos+/fRTUa/33ntPvCSdO3emvXv3RrxdGgvyZ2/05yAH8cJ07mqaol0Cm4T4t/LylEuo95r37x930/g+rWlYF//2E8EK9PIcMFsTVgYsg5z1JS8rZs3e0rtfcW4Ufq4JKRl4HP4G6tyVkucJR6AzxZUqE/QmisN9YsfjrR/iuUkkqHyZAAAAAABA44NFViYn3UKHS2vEdOWT7v9W+Zy9SS8Y1I7WupOAhmvbkpVqUbzPL/v3Ci8hWiYa9dfRnTahl+7y37+8TPhtGgmIk1FzKSZGntfWeSLPM1NcXRF15Hk0Pc/VQkk44rkcZGAC6Q5yECTTvd/BII/zz7uP08TnltGE/ASaFEJdpXWK4YShbhGCoxLr3OcDH5tgjo/RqHk+z//vw3VenvAdmhkXTJo6bNHiz6Zl8eLFPssuvfRS8fIHRzk/8sgj4uWPt956S7z88dBDD4kXMEtQQuR5pHB6tTEPqAYupxsW7kZ9fb3704208M6zgquLJspcd2DE5GMrxW+j55j2HiGfj8xESb4exGCtuk6Rqle4yIFoObOrWabLY/7TNQfpaGkVtYxDwT9ayN+d0Vk20QTiuUnIB/2Pf9lPN50VfFIIAAAAAACgD1t9sFjMnUXuFHE0arJbwOvTNidqyaBkp3lIpzw6UlpDpdU2n0SPzy/YobzPdtuu+EPtG60nRnZu4Zq+y5Hiq/acCOhtG3RH10A4Vq3Nbppti1zHG0v3UKr7eGW4I+vlexahPZHn0RLPAwslRlDPPgjU4ZMR99LLPhi6t8wSghhva3dRJS2xJ9AdUUgYKo8D//bkYEqwx8bjeR74nLv1w7V04IQrYd+A9rk0fVJvOiXIiE0AGrewa6w88rEFj2wqvr7VBmi7QJYqEvX1la/Xodv0+H4ml5mV5Dpcqyz57CJt7SIhMgc7gK+esRSJiHizBgVkLpN2ua7ZPEt3FonXn05KoN9R08QR5EBhNIF4bhInd8qjn7YXhvQwDAAAAAAA/HPfF5vo87WHdD8b1rkZfXrTKEPrOVhcRe1y00O2gpCdsA7NMuiXv48TCZ4kLG7zs6DT3bVOtyTRdacF9uiUHSd/nufsi/3lLafRoWKXoMgs31VE76/c71lHsP6kqgSQ9SETdRm1+AhEG3fncMNBj5WMnIatThiaGPXI8/A7/uqI6kAddSXi3j1oEAzsT7/0njG0bGeRiGTcXJxI/R6er1uWz6Vbx/SgW87uESBhaLC2LSohI8jzwajnuRTOmY7N02lUd+NJ6wBozAQbjalYeyhSLwjWa9m/53n9x0It/IUyc8uzDd/PEiI0MBKq57lyj4jASE2wNmNe9Upw/W7MHmQwA5n4W+7X1DO6UXpKMn2x7qC4D1brp9NpEjiUGRAUd0DpNYnTe+bTrPk7dD0pAQAAAABA6Bx0i8ft89KFTzZ30tgf+1BJNe0qrDC0jpcX76SnvttGFw5uR89d4fLlDd13NkF0iNWd4vF9W4tXMKiFb38d1sEd88RLYrXbvcTzoJN7GezocoeTpxCbZdty18TeNLBDntIZ5nrI9kq1uD3P6xyUGuXIcyVqLowOtloUDrQexbYlxGAbPv9/N6gdzZq3XdgGBRKjv9l4RFc8l0K/0dkE6pkKyhT65NDE86pau25fKdOdONZ7GbqpAITqAyx/T3EYdBu3yIGG+u4JRiLP+7XLUf7mHCnB18W9DR31PFK59hzhJgyNgNVIONZxvB8OuzNObVu88+K0yk6jaeNPot8OlwnxPFK/2yprndeMSbYFapWdGle5RRwGcgrECjyVmIT0pCyvgXgOAAAAABCJTt0D5/elc/q3USxSzn5msRItVR9vLHUlp/tq/eEwxHNzI2LUnVSjnRdt8s7QI88dhn28h3RqRuHSPDOFrhzeSfczJfLc7lA6J8Ha0YSK7J+FM7VbbUciI8rUHDhRJaLFtx8rF++zwpipyhZFP9w+mj79+jsaM2YMJScn+/iG3/T+WuEtr+XXQ6W00R35b9jzPEkdeS6j1kOzbfl49QHx0jKhb2v6z+RhXsswmxeA0AUlj8Aaf8JhvKJNBOrXtiWApYrkjJNa0kc3nEpX/OdnkSQ7+Mq4/kuIop99qJHnnmeKyEWeB+t5rrZki0fbljo/FjkemzTzt8nPIRNn/aTYx0nG9m5Fr193CsULDgO/r1iBpxKTyHKPKFZAPAcAAAAAMBU90Vp2Mox2jKw6YmLQ9Qixc+kPtVWLnm2LHtrpy2rrFzNtW9SC8KCOuRRJpOc525okuTfrLxGq2XiiDENfhzoCXO98/OG3Y7Ri93HlffeWmaFvzB013iyVbW/SyGJxBfBICstr3X71vgf4ujd/8em71Ic8L19ctINe+XFnSELGiK7NxYwRbadd8tOOQp9loVjbANA0klnWj0w0GYe6YdziNJiQUyuy+6Ob+zpfaa0Txy+YBIgyCl7vK4q4TxQhz/MgbVuikDA0lMjzePb9l7lHtAPRZjyP+GPToVLlHszPkXyOsRXb4u2FtOVImagLB2d0bO7KtRMrHFG27gsGiOcmIRNCcUeDIz20UUEAAAAAAMC8h2n5t9HIc6PljNQjmE6wkahe8bfRyHO3xUnIkecGE4aqxVczPM8DIS1EdhRwYrXE2Hieh9jDPlFppc2HPT7udTrtKqPA2eaRp2arbXjMxmOB4y1U8/uiCpew/qfTutLA9sYGRAZ0cJXjTrb0PB/oXmaUYV2a04YHJ/gMLLDQf/pTi8QsB60vLSLPAfAduDV665EBrfHo9xyvyKaStzv/CUPdx6Ke9WW4rad4PXyNCyaxueJ5rrMVjygcmYShoUaeRyZhaGizndT1isfIc5u7TtqBaKUtQxjAn/rOajEjk+3weJamFmnXMq5PK3rt2lPE+TPssfl0vNJK5z63RCk34/y+9KfRgfP1xNNAYTTBU4lJZKp8+XYcqxBJfQAAAAAAgJmR5wkhR56bIZ7LyCqzgqItqim7RjsK2gCN0KdYG/caNWuwwB992+VQ1/xM2ne8UnSc8jJSaGS3FhQNwrE3YP/Qs55eRGWqmad656O0cunQLN0UCxwj54c28lx2nPlQ3n9eH8M2QZcN60hnndRSSY7LYg7vR7CwSKDVjnLSXVHz3PQszLOdjfREz3V/BgAIfuA2UkklGzfy/m4s8rw+9ZyThkvKamzBiedyEwG24Ygb8ZziNmEoE4+e53Uaz/NwbVt+O1JGP253zeB6fekeumtiL5/zTT4DyPsuXyNYJH9j6R5xLvE9ni2GeF2xxGGyPaKZQDw3CfVF5vvNRyGeAwAAAADUA3swc8JPfm7q3jIrKA9EdVSRkSnRsrNiSkSMSU/1HCH09oq9dHKnZoYjq7TTl8P1POdO/Y/bCkXEdKfmGTS0c3OvTmtqhKPOmZw0Cy268yyy2Ww0d+5cmjTpbB87kkihTDkPYXDlUHG1EM65SeXX9QZp5LkXim9rsKS5I89rNJHnpVU2RZQO9vxtlZNGkUCdtNQ1czeRODh+VPcWdP7AdhHZJgANkWAFpXBn1DRFlGjv+jzPyX9UuPZey/d1Hhj8ZPVB3QTO/uviP7o9UnYkctZUyLYtEYk8d4Zu2xLBiPhwkc8J2mcCpc5BVlk7k/BoaQ11yc/UFc/VA9N8Tsrz8rUlu+mxOVtMeVaOpxmeZgLx3ERG98inpTuL4nJqCAAAAABAPMEWEhe8uFR0APlhfvX94/yKi7Jf4B157inLz171eWSb8Xgmn/HMmk46qGMebZgxISgxUy04hhIlJrcl9+XRr3+jT9YcVD5nEZujwJWIrxA6rQ2JcIQItmxhOrfIpMcu6k9XvbZStx9g85McLJKR5yw6cF3k+aHXcY413uK5Q5mi/+hF/eOqngDEC8EnDI1sfRoTTh/f6cCR50YOBc+mKa6yUZn7+htsXfTUc0/C0KBWGXh7TqcnKW2UbFvY7owHoC3JiXRq1xaUnpLkN3F5KAPPnhkEFHd4EqGaE3muLf/8gh3UJtd70HuJO7eIv3ur3LZ8XomnmabxAsRzk6ecsnhuxrRgAAAAAIDGzPEKq9IJZWGPO5ctslIDPkx7eZ6rOh387BWNdDNK59LEh/pgO6q+kefBdSoVuxv3vhwtq/GJWGpK4nk4vqgsijDNMizKORE48jzynUFtNPcTc7fQF2sPKfXKiyNRmiPL+Pzic41FklAT1gHQ2NGbfRUIWQye58bRPmc4wvQ8Z645tTM9v3CnYntljue5+cdWff8LNfI8mFvo9mPldN7zS5X3fxzRiZ74/QBzE4bGsee5J8pfPxhCPp8ZRbuPn6875Ldsq2z9mWQy+bxMZhornMpzNsUdEM9NRBmtifFUBwAAAACAeEcb1VVZa6cWWcaFA3UHL1qdIxlZFQX3jYhFnmsThkqRXCKfY2XCycYunnui+HzPoYLyGtpZUOH3u+v2F4v/m2WkKDMfdCPP3T3h+mZHmC6e2xz06ZqDwsdUcnLnyHquh1JfIZ7b7IrALzvxAIDQBm5h2xJOwlD3bCRP/LduOSOHIs0dTV1tDU48l+htQ5ktReah9gUPeTZbEOfaweIqr/dzNx2hGp02qnTnwAjH8zwefwMyD4p2QF0595zBHQP1M/LU07v51SP5WeV3g/Ut0WRdzA4E/u1wGd3/5SbxHMRe7Gec1DKqMzzNBOK5icgHPfljAAAAAAAA+mg7NOW1tqCEA3UHL1qz/jwdlNg91GsThgYryGoThmo7WTIiKpxEXQ0Jf1GGPHgw6bklVFThsmYJRLPMlIAR7LKNo2Hbwv0RHlji34RMAMZ8ectplJ+VQu3zgk/2GenzuZzqROS5Ip7HY8gZAA3I8xwJQ4NHRnLXZ/eh5D4x8Bwgk4YGE3mujijX3YYUhU187lHft0IdkA+mPnJAuWerLNp3oopKqmx+o6X5fiCTXIZUrzj8EXgG1P1Enge5Pmlt2Kt1Nt03qU9IdZLWOGYHAn+z8TCt3V8i/v74lwP1iufBzrKJJhDPTcTiPtllpwMAAABorLz00kv09NNP09GjR2nQoEH0wgsv0PDhw3XLvvrqq/TOO+/Qr7/+Kt4PHTqUnnjiCa/y1113Hb399tte35s4cSJ99913Ed4TECu0/ZmKGleEUeCIb5V4rnqyHjlzgYio0ZKfnUr/uWYotTYp4aGMj1BvO9rIhJCSYMVtbcJQ7cCDtc71vjaM6dINCXWEJkeZf7XeZXFSUmUVwjm3b5f8DL/fT7Mk0WXDOnrscHRtW0JPehZqNHed1U47VFHzJ7XOooyU+Ov6yUj5Gpsdti0A1GtlAM/zSKEkAq3HFiWYyHMpnvP1zSjqYxYoYaiZx1b9HBB85DkFHXku74k88Pz47wfQhgMucVWPfu1yQsqBEa+2Lfxs8fWGw7pe7qF7nocf2BGpQOA61c4Y0UmDvdZFk/h7gmrAKD5BiDwHAADQiPn4449p2rRpNHv2bBoxYgTNmjVLCN3btm2jVq1a+ZRfvHgxXXnllTRq1ChKS0ujf/zjHzRhwgTavHkztW/fXil3zjnn0Jtvvqm8T03V978GjQNtNFCFe3puoLLqPp3aK7zKaqcqa7XP9w6VVNOP2wrpslM6mlJnJeIshnpydpqFbj6rOy3fdVzk2+ncwr+wG7hDSbq2LdrIc61NTKO1bXE46bE5v9Hiba6kWhKOknrt2mGGkp/56xzaFH/T6HQGZWf1v78cUPYxLRpJAUIg1T0YpLY1iEaEPgANCTmAbFQcg21LCGhsW/xFUnuWGog8Twkv8lzftkXWw7xjq97XYK+/MpiAq811N3KOejy/E2h41+biZTbx+hu485MNZHU/gGmDIRLDFM/DmSgYqUBgu2pnjBwKvRxH8QLEcxNRfILgeQ4AAKAR8+yzz9LUqVNpypQp4j2L6HPmzKE33niD7r33Xp/y77//vtf71157jT777DNasGABTZ482Ussb9OmTRT2AMQD2s7B52sP0ebDZcr7Ts0z6MLB7URHTJYN1Cn76pbTvN4/t2AHLdxaQMfKaui855eYUud48WK8+5zeIX/XEyHt8E7IlZQoOnTyfZNJGCo94J1OKq50WbSM79uaOjbLIEtyAl1xSidD65GCQ6DI82h5eZ/esyXN33KMCitqlejHYBPTRtuG6GCJZ/ArGt7wADQkPNZlxsrXFz3dGNh3vJJ+3F5IrbJTaULfNmFf42RL1ZswNBjPc0vwnufqzeolDJXLzDy06ujgYJtRLXLy/c/I9dufbYmZaC3q4gWZR6Vldqp41jAz8jycZ9NIBQLbVTvjb78OnKiiIvfzipzBEIeB5xDPI5IwNM6mhgAAAABmYbVaac2aNTR9+nRlWWJiIo0bN45WrFhhaB1VVVVks9moefPmPhHqHLnerFkzGjNmDD322GPUokWLoOpXaa2kJKtvhGVSYhKlJad5lfOH3Z0o0UjZxIRESrd4PISrbFV+O6ss/GZYMkIqW22rJofTfw8gMyUzpLI1dTVkd9hDKsvHsMZeI9rH4rSI+kpxu7auluoc/iPJuax82HeSjZxkp6837aGvN3mX69B8FJ3cqbXy8G132LyOh4NqlL97tLaIY8HHRHy3mUV8vnj7Adp02JXYUcLr4POBzwvGareSze7fc12W5To7qY6s9mq/50VqciolJ7oesXmdYt2attIry+3F7eaPlKQUsiRZgi7Lx4yPnaTOUS3apbquUtSnts6qRMjVVtvowf+tpX/N30TlNXXkICslJFqVfeV18roZPsf4XPNHMGW5DbgtGP5NaNspUFn+HfnDyO+e24Jf3EbSH/y6UV1ocKd0v9/T/u5FO9qrxHoKKmqotLpc6YhyWTkgwYE2Zlwj+HyqdXgff/XvfmSPTPphSw0dLDkh6pRq8W7LaF0jtOhdI5KSakUd7/x0lWo9VZSRkm34eqL+3Wt/y9rfXqCyWrTXCABiRbDimPztNGZZ4q8frqMNB10zfj64fgSN6pEf1vq0Xub+mk5GfBs5Eh7Pc+MKrtclP0DkuZkR1fIZiwXnYK0/1IMWLHwaERhlsKmMdo4E8Zow9IR7kP7DqSOoVba3pWCSexA+aPHcfXqFJ55HJvLc6dX+vju28WAJ/e7FZT7LY2mP6A+I5yaS5H5ItsO2BQAAQCOlqKiI7HY7tW7tHS3B77du3WpoHffccw+1a9dOCO5qy5aLL76YunbtSrt27aL77ruPzj33XCHIJyX5iuG1tbXiJSkrc0Ust/tnOyIde+tzu59LX13+lfK+1TOt/Ipup3c8ne5ocYcQXZgus7pQUXWRbtmhbYfSiimeQYO+L/WlfaX7dMv2ye9DG27YoLwf9p9htKVoi27ZzrmdacctOzx1evN0WnNkjW7Z/PR8Ovw3l38ic85759BP+3/yK16V3OXxlrz444vp213fkj+s93kEo6s+v4o+3/q5byG34F18Z7EipE39eiq9u+ldv+s9dNshstlcgt4Jy2tUkTxHt9wpbxFtv3m70vl5YfUj9O7mlzwFVLkPs2YSrZu6jvq17Cferyp6jQ6kv0QHCrzLybLLr1tOw9q57Die/flZmr7QMyCkZd5V8+jMzmeKDl9F0nd02TcXEX2jX/bLy76kST0mib/f2fgOXf/N9T5tJfng9x/QJX0uEX9/uuVT+uMXf/Rbh9fOf40mD3TN1Ji7cy5d9N+L/JZ9bsJzdNOwm8TfP+77kca/P967QDrR/b+4Xp2SplICXUjplkQqqNlFm2gakfxppBN9dIjoo5mut/ePvp9mnDFD/L25cDMNeXWI3zpMGzGNnhz7pPh7b8leOunlk/yWvfHkG+n5c54Xv7kyexk1e6aZ37LXDLiGXr/gdfE3C6KByl7c+2L66OKPlPdZM7P0C6YT3ffjWEq1/V28TSJHwGvEGZ3OoPlXz1feK9cI93mW95T3NWJQysvi7wSn07RrREtLSzp/4vn+rxHpRAd4zCSd6Igjl2y2wthdI6iea4Tm95n/jOsa0TLTlVjs9u9up9lrZ/tdL18juuR1EX9PXzCdnl35rG8h929PfY149KdH6bGlj/ldr/oa8eLKF/2WAyDSeITdhi0cmok6mXORW5A0M/LcFM9zt23LzoJyGvPPxT6fd8vPoleuPtnL+1ptx6KbLzQCyWDV4nmwqEVOo7qrDDaN5Cyj+ux3YgEPpJe58/s0z/S1pqwvWa0/ZLR2OGMRFrdwb3PnvTELu+pE1TtndxdWKhaBrXJcbXJK5+bUPNM3j1GsgXhuIkgYCgAAAATmySefpI8++khEmbP/ueSKK65Q/h4wYAANHDiQunfvLsqNHTvWZz0zZ86khx9+2PB2CwoLaO7cucp7HgDwx4kTJ4haEM2bN0+JtvdHaUmp13o5qt4fFRUVXmX5vT94PeqyvB1/cP3UZY8fP+63LO+3uiy3SyDUZY8eORqw7Pfff09pSa5jevDgwYBl5y+YT0VVueJRND3JSf5bgmjRokVUVd1OhGAdPnQo4HqX/LSE9qW7hMmyYv/twCxbvowKMlz7v7Ug8MDPzz//TJWbK+n4ifp9o1f/sppou+vvDcc9Qqge69ato4w9rkjjdSXrApbdsGEDzT3oOh6rS1cHLMv5BOYWuMpuKtco9hpqrXVivMlh9USn+2PHjh00t8K13v3V+wOW3b17N82tdZU9VnssYNl9+/d5nWuB4HNLluWI4kDwOWt0vSUlJZRWwZ24BFq9ckXAawT/xtTrre8acdjB+59IWzZvMu0awchrlNyOf5wN8hqRm8zXCKJ9B/UHG9TXiNaprgHd3Yd2G75G7DjiGaCs7xqxfZ/7Rw1ADDBiXaZGiZ52uiy4vt98lMpqbDSuT2vTEmjHGrXmYoY9jSdRYeCBByWxqIHY887NM8SMI7bCkCKhGl7GdnWDO+b51MO1jQB5OiIReR5CtK+XbYvBOtndkeeRtG2Rv4FgEpmGw/GKWnph4U4qrfY/m0l6nXPV9JKgysEER5izJkJBblvmaDELh6r59c5Zee6N6NaC3vnTcIpnIJ6bCBKGAgAAaOzk5+eLSPBjx7zFMH5fn1/5M888I8Tz+fPnC3E8EN26dRPb2rlzp654zrYxnLRUHXnesWNH2jd1C+VkZ/uUT0rwtm84OmpXQNuWFT+tENYxFouF9o36zW9ZYd+Q7Amb3DFqvd8kTtzRUlsynDXqZ8Nlzz7tp8A2CxaPdcLZp80zXHbMaSPI7rSHVJajhH/88Uc688wzRTtlJHssGcbWnUp1zgC2LckZtGZ/Mb26bh31yr6BPr7hP16fT35zNW07WkH/umwgje3Vnt7bvIys1lp6atwz1K2VJwJ0xExPJNfK6WeJYyEtGdp260uT3zpfdETvP68XndatOZ37gmuWwE93nUG5qRmKJcM4+0ia5Xjcb31Z8OOyH+76hVoeP4veOPdGOrOX/hTx1CSPFct4+yh6xHGPT1vplZ3gOI3us9/mtw4piR4rFi57h/1GQ2UnOkbTrXZXfgLm5cW76e0V++mKU9rT38b1pAmzVlC5tYY6tsimsrL2lFP7nii34p4zfTxkLYneVizXjL3Ybx20ZS87+zy/ZZMTXFYs3E6cC6Fg6h6vdtIrKzuMxaf7F2G1v/viUb5lr3trDW05Uk7/mjCYHpmzi2qtNho7fAQdnbDL8O9efY2Y8NwyKq2y0fvXD6MeLbNE2ds+2kJ7C4rplN796J6Lwr9GuM6nn2j8qFFKO6mvEbuKKumPr/6ilB/cMYcmnXl61K8RWvxdI15dupdeW7JXscFcds+ZQV1P1L97/i3bHJ5rhPa3py072/HPen/3zPCS/vThbR/6LQtAPNm2KEklnU769tcjdNtH68X7RVsL6LVrT6HGgNpLWS/XRLDIa60n0aSfckFE+bbKSaMf7zpbeDprmfbfDSKpeU2AZKJ6gyX11S8cz/NQklqrz0mjx0FuL7K2LdG1Lvpq/WF6a7nrPlYfHZql60b51+e3X29OhDDa05O/0dwGc6gThup8Ls+ZaCVUDweI5yainHCIPAcAANBISUlJoaFDhwqB66KLXLYRDodDvL/11lv9fu+pp56ixx9/XEQeDhvmmgYfCI5K5OjItm3b6n7OyUX5pSWvex/KMbAfnhgffc4Poqya3AiVtcRp2UvCWO9IIlIMKe73/ux/8o9/uf77SfNesk39RvMZx69s1Xy2zU9Zo+3wkZ/v+4PXm1FPW6nLatwrTCurjjO8z/2SeJymNRjYR99fYHhlua4XUHAEM7FX77f8pfzjX0Tnqv6mENe7Smcdb4a43kDXCO35pD6Hh2p/G8y0+L2e3OV+Kfwzfq5TEs8QAQANIWGoR4QrLPdY3BWqrE4aOuokl2YIpHIdHgGzPtsWYwejXV66eGnhyGM98by+yHPPOWCeyLl67wnxf1IINipqEdioRUo0E4ZGy7alstY1wMuzCM4fqN93kYzumV9PAvNQk9lTyEjrIOlHH62EoXYTouajBcRzE0mWPkGIPAcAANCI4Yjva6+9Vojgw4cPp1mzZlFlZSVNmeKKbp08eTK1b99eWKsw//jHP2jGjBn0wQcfUJcuXejoUdfU/qysLPFiawK2YPnDH/4gotfZ8/zuu++mHj160MSJE2O6rwAAAAAAsUSJdjaojqmtPbzEqzjyfw4XdZ45U/ZLI577c/vwN3MoWNIsLu2ots5brDTqeR5uvGZ5jY0+X3uIUpITafrnLnu3lBDEbPUpadQiRUkYGkHPc1kvM2YlBOPjPqB9Ll1/ereQ1lHfuecPOdATime9j5Zpcns5VKvTs1fy+O1T3APx3ESUDLUmj9YAAAAA8cTll19OhYWFQhBnIXzw4MH03XffKUlE9+/fT4nuhzDmlVdeEZ7Al1ziHf/34IMP0kMPPSRsYDZu3Ehvv/228B3mZKITJkygRx99VDe6PCCHDxPlGIk99w9P8+cIeRbu/VlHgPDbafnOIvrz26vppNZZ9NWto70+u/q1n2nNvhJ67vLBNKF/Gzp15gJhhfHN/42m7q08SR/7PPCd8veWR88JuD1+Phvw0A/i75+nj6XcjODrfPHLy4TFx7+vOZnOOKlVgzynXl60U/hyXjqsAz38u37Ud8b3YvnEfq3p+80uO6bWOam0+K6zo163WLTTVa/+TGv3l9Czlw0S0+jDOT+Y69/+hZbtPC7as3cbl4XUR78coGNltfTyVUPo7N7eyZZDIZ7OJzNYsOUY3fqBy/Of253b3yxMaytOSt2Ocy8AEH2kOGY0OFNtWaGO0I6WkBgN1EKtGf7fWtsWMxKGBiI1OUlfPPeKPE+ImOf5q0v20PMLvPM+TBvvP7G3P1jMZ82WTy3DkeeKVUcEPc/rmUFgNlIDDCcJquJ5HqJ4Hk70tse2xWzPc6fyt96h8Ni2xL96DvE8IrYtjeemBAAAAOjBFi3+bFo4yaeavXsDewCmp6cLccMUMjNdr3Cw2cjOyUx5PY1AmIoYYbZTXXoVVaekkTUtw+eY1aVnUHVKDVnT08VnlcmpVJ2SRInZWV5l+fuM6CPVc9yTnE6lfJ1Yb5ADM2J76VSdYiPK9K5HQzqnnJmZoh1qUtLILtrZ1SZJ2dlUneJOOpnpe0yiQgzayZbmOtfKxTnmaovUvGx+sA9pfTn5eVS9v5K+3FFKxC9BAlFKGmU0yzWnXePofDKDYX07Us9uh+lwSTX9flhHc889s9oqQAJZAKJn2xKk5zlpIs+jJCQ2RNsW2TT1+U4HkzDUSOS5j22L6m+9w62I+2FtnWjfce8EpuP6tKIrhncKaV3cZg67M+jI83CE5nrrlBBl8Vz6uIcRQu1Jchrc9+QsBKNWQnpICx2zPc/tXp7n/iPPw/FrjxYQz00Eti0AAAAAAMFGyvh+Jjuv8qFa9n38PVsbmaqqjo4y2sHzqbMyvTT+H/L94Wlb72fWjBSPWJwWonDcEJHBTmpf4FCmrktuPbsHZacmk1UTvdU+L52GdWkeekUbMRxt/j/N7BMAgLH7pR5SROOvqUXmxhTk55Uw1JTIc23Ufmwiz9Xb1bVt0SkXCtr7XHZa6IOLrjbzHqiJtNBsrE6uZ51oYJMDAmFZp4Q2MCLP/3DGIuS2q2x2xcbHKGf0zKdzB+j7vKvPUz2rIcVypgE8VkM8NxF5wsG2BQAAAAAgMB5B3PeJOcmtaMqOWH1TUo2K2Up0VIgCQrBT5+P5eXXz4VJ6fuEOXfE8XfV3Y0eeU09/v00RFMKJgOrWMosevrC/afUDAIBA90s9FGsPB9/vHI3O85wtVbwiWs0QzxXfaAoceS6fA8zyPA+YMNR3Kx5bmfC2r92/7LTQpcFg62SG0FxvnRLNG1j5esNhKqqopWtO7ew3yWmdCUlQleAGZ4g5EcJ4OM1Jt4jt8+/qw1X7g/ruF+sO0jn92+hGvnvZtugMC8iBFPncH89APDcR+UOpstppy5EyZXnX/MwmFcEDAAAAgNjw6ZqDtGrPccPlM1OT6bwBbaltXjqlW5KoeWYKxUXkeYL3Q7XsJPvrF7QwaMHCHQNbGOK5J7qn4arnWamux/+tR8vFS3bi1YJ5mjsirikwvm9rWr2vWAl+OX+gfvQUAADECmXgloL3PFfH9ZkhJMYD2nu4GV7uTu1gvL/Ic/f/4dpM+Is8V+uL+glDzYk8r6mz6z4bhIJ2tmB9mCE0G63TJ6sP0O8GhZ6voqC8hv7vw3XK4PiZJ7UMOCBgCeO8kHXeV55AV7/xi64YnZmSTPee25t6tnblVPGydQpj27npFnrt2mH060FpN1c/tXUOenHRTqqxOcTzul4CWG/bKN91IGFoE0WeLIdKqunc55Yoy/u3z6Fv/u/0GNYMAAAAAI2daqud7vlsY9CdyDeXuTzp+Rn9+SuG0AVhdDKCQVZTr3OgeI5qbFu0EebTz+1Nry7ZTfec29vQNqXoHWpHW4n+a8C2LecNbCueVYurrMqy03u2pGYZKfTakj1UbbPT2D7Gk6E2dKac1lW8AAAgXvHMegrO85y/p448b0gJQz9YuZ+2Hyunv40/SQh7arT2M2Z6nss2jlbkua/nucq2pR5LnnDQRryHZ9vi+n/csz+KgZustGR65aqTaUS3Frrl6xzhC81GnonNOOdPVHqelSpq6vyWs5kwINA2N138X2VPoJV7iv2W65KfSQ+c39fXNzzM5jy7VyvxMkplbZ0Qz+WAiF68sFfzOxu2HSLEcxPp0zaHhndtTrsLK5WbFf/YtrmjegAAAAAAIkVtnV15gL5zwkn1drK5Y/Htr0foQHG1iLrlr67bXxJF8bx+z/O6emxb/nJmd/GKVHSUFk8HJf4f8v3BHeQ7JvTS/WzjgxNE36YhdGIAAKCpEKxti7qcV2LNBiKesyh33xcu3+VebbLpSk0iS+093AzbFqOJJpXFYd4mpTPBNxuPiEECvWOn9xynHhgJB44WVtOzVVbI6xrauRkt2lboPtdcGtiSHUW64nlFbR1V1NojHnnOg+Jr968Le5CBXSUkgdpcGRAIw7z71G7N6f0/D6MfflpJQ4YMoaQkbzV64dYC+mLdIa/gB2/Loeg+uyWr9tXmcFA6+arn6muOXvt5bFvi/7kzLsTzl156iZ5++mk6evQoDRo0iF544QUaPnx4vd/76KOP6Morr6QLL7yQvvzyS4o1fAH8719GKu+PV9TS0Mfmi1EoPqHDyX4LAAAAABAIdQf5lrN7GHruuHOiS0SdNX87zZq/w2cabyQJ5NGoRJ67y8ip5uE+SoUrngebtK2h0ZAj6gEAoLGi2DIYTRiqfM/pFe3ZUGxbWGCVWLW2JjqR5+FGF6vFd/mcwEKljDpOTU4UYixH/Hq08/Dul61z0sT/e4oqxUsPvS2Y5XkuI97/PLorXTqsA/VukxPyul6/9hQ6Vl4j/n5u/g766JcDuufar8UJdMcTi1QJQyPoeV7PIIhRqtxCP+M0YEUTThJUfm4f3qU5Ff3mpEkD2pDF4j0boLymTojnZdXeEfAea8PoPsNZVD7lcv8De577/7wh2CHGXDz/+OOPadq0aTR79mwaMWIEzZo1iyZOnEjbtm2jVq38TxnYu3cv3XnnnXT66fFrh6IeSWMBPSU5/k8IAAAAADRMZHQH9/uCfYBmv3O96cPREQP8Jwzlh3Hu1Cq2LWE+XMv1rjtQ4uUXabjOjSDyHAAAQMNCGWw2qJ57rEecXrYJ9gYUeR4oqly7H+Hulvr7eRkWn7wgjM3hpCd+P0Bl7xLeNv84ohO1yEoRgqjk418O0KZDpYY8z/WSLwaDDJYY3TM/LOFcnpfScoQTT/qb5bC3PEERzptlWOhUP7YuZmBWhH6VNfC56JMENYIDAtK+qKzGZvh5OpIkJiaIdubty7wxWtSaeqDfckMI3oi5eP7ss8/S1KlTacqUKeI9i+hz5syhN954g+69917d79jtdrrqqqvo4YcfpiVLllBJSQnFI+qRNJ7GkUINwAUfAAAAAA0SJZllCA+gaTEQzwN1QJNUnR71s3a4HYPjlbXi/3X7i+myYR0NJ4sqKKv1SuzVEKaXAgAAaGye5yEkDFWpVw1FPFdbZegFtPqK5+ZFnk8Z1ZU6Nc9QBHxOKL14W6HioS1F6/A9z5PowsHtvZb9sveERjxP8D8woq9VBm3bwlH1ZqK13VMjF3G0u9qzOxJIMTbcU159LgY6zZRoelU0ttnkpLvk27Jqm5/obYo6yYmJZLW7Eobq4W3b4v+3nNwAnqtjKp5brVZas2YNTZ8+XVmWmJhI48aNoxUrVvj93iOPPCKi0v/85z8L8TxeUU/ZsNU5iVJiWh0AAAAANGLC8eP2JK4KszcWBP58zL0izx1Or6m/4Yrnt489if41f7vit1kfB05U0dnPLPbpFCDyHAAAQLQINrJU6lAsCqvvX4028jxc2xZNdK86ifTrS/cI8VxuM1j/+WAwMjBvRkQ1R1PvLKjwCp4wi0CJ2eWiaAilptm2GPQ8j2bkOc+IYIvoFlmpXvWKxbNpclICcROFatuCyHODFBUViSjy1q1bey3n91u3btX9ztKlS+n111+n9evXG9pGbW2teEnKysrE/zabTbzCQX7f33rUF/rq2lpyzwBqctTXTsAD2soYaCfjoK2i205oZxBLwoneiEXkufKwrxOkI+MPeJ/UD94JYQb0dGjmmlZcokm25I/Nh0uF8JCSlCimVzM9WmVRz9ahJ9YCAAAAIplvQ4m6dfB91DMo3kC0c6pSPYvoJxn0HugP18vd6+sJgcVqqfFEQqdUP7/5W7/0Wg9nj9fu8zg3dGyWQWYSKLeMXBSN2XvKcQvjpN9/vIp2FHisewKdZh7xPHKR513zM5W/1+0voXF9W3sPrsVAgE52b5MThuqhPg90B8LgeR4ZysvL6ZprrqFXX32V8vPzDX1n5syZwt5Fyw8//EAZGeZcKObNm+f3s6SEJLI7E+j7eQsozzUw1GQJ1E7AG7SVMdBOxkFbRaedqqqqTKsLAMESTvSGFM+r6xHP5/12TERjM9mpiZQYRqB6oOgtGXnO+6R+1g734Vrxi9RMefXHMbddy5jerWj2NUPD2jYAAAAQCqFGO7PFSEOMPFcnaQxk9RCoTDCo/cO1TSyFXnkMPAlDzUctKicYmFUQKla7q33zs1KoZXZq1G1boht5Htr31+w7QX94xdsNI1DkuYy8Tolg5Hl2moX6tcuhzYfLdH/XsQjetrgHCwxFngf4LTcEO8SYiucsgCclJdGxY8e8lvP7Nm3a+JTftWuXSBR6wQUXKMsc7hGO5ORkkWS0e/fuXt9hSxhOSKqOPO/YsSNNmDCBcnJywo4wZKFl/PjxPplwJfeunk/VNgedftZZpo/qNRSMtBNwgbYyBtrJOGir6LaTnN0EQCxQPA/Dijz3r4b/driMpr6z2mvZVT0S6HcUroernnjueahWd5LDnZIqE4HtP1FFLy7cUW/55buOi/9b5zTxCAgAAAAxI9D9sl7P8zgXz7lOzy3YQQeLq7wiftWfa9EKs+EIya7ve/7WtrBsc61tSyRCz9XPb/6ed+Tivcer6LUlu30+P7lzMzq5U7OA27G5hc6OzTMitg96Ed92TYBEdDzPQzs3thxxRZxnpCQp1i2B1sQJZaUHeCTJTE32G9EdK9sWdeS9FvVpoHcsIJ4bJCUlhYYOHUoLFiygiy66SBHD+f2tt97qU7537960adMmr2X333+/iEh/7rnnhCiuJTU1Vby0sDhilpAUaF08EsPiuTMhqckLV2a2eWMHbWUMtJNx0FbRaSe0MYglsjMZSnR2uls8rw0Qec6JM2X0dnZaMh0srqYKmxkerr6fyc6Hj21LmM/WrbLTxP/FVTZ65ofthr/X3m33AgAAAESbQPdLPdRWI17ieZgicyRYuec4Pb/A/2C2Ec9zMwcFtAMUPrYtJiUM1UMtvPp73pHBDuxZ/ticLT6fZ6Um07oZ473y72mRUcKRSG4ZKPJcHspI+oJrj1uo50ape4bieQPa0olKKy3YWqB7LvKyPUWVik9/pPfN4l6/2rpI6taxsW1JdNdHv529RX7fzz3JTiGe1wtHhV977bU0bNgwGj58OM2aNYsqKytpypQp4vPJkydT+/bthf1KWloa9e/f3+v7eXl54n/t8nihvmkMAAAAAABmEE70hkwYuruokm5+fw299MeTfTqQ1jrX03m3lpnUo2UWfbLmIIXzeBMowZFcxh199fN4uJEpnVpk0MO/60dbjxqfJZKTZqFLh/oGaAAAAADRINiEgLKcswFEnksruJ6tsujSYR2U5U/MdeXA06uyVlsJ27YlQOS5FPU8nufuchHQ+tTHV3qbaxnbpzVdP7orFVV48vox3CRfbzhMFbV1VFvnCCyeS/eGCAi90pJFN8o4gslWA/0GQkHa++WkW6jYnSdHb13P/LCNXlq0S3kfqN3NQG1rGGpOBDNJdp9D6twKatTngW7+AvdJgYShBrj88supsLCQZsyYQUePHqXBgwfTd999pyQR3b9/PyVGYVpHpJA/Hn/TGAAAAAAAzEA+t4YiMHdukSmiyctr6mjupqPC67tNritKW8KdMYaTZ8qESOF0WD3TTOtJGGqibQtz7aguYa8DAAAAiBZSyDN6e5e3ShartBGhfE+NJ6Fq48FS8f+wLs3ohjM8Frxsr/bez/t1BX9fz3NnxDzPtd7ZHs/zCAjPajHbz+o5svz+8/vqtgmL50xdPdqTtG2JRHJL2V7x43ke2rlRVmNTZlvKYBK9NW1127vwceGE8oM6uoJ7I4VsO3X7OmMYvZ0sE4Ya8DzXQ86GicY50eDFc4YtWvRsWpjFixcH/O5bb71F8Yy8AFohngMAAAAggsgH0FAEZu4crJg+lk5+dJ6IMJ/94y566Hf9dCPPUy1JykMuJ0UPFfncr+95rm/b0gCerQEAAADT4Pvgkh1FrjcG7+8JfiLPxfqcTkqMiOlIaLy/cr/4v6XbVs03cljP89xbW9Hz1w498jzB70CEuj6ReB4xkjA00He5rlw9f0KmRIrrlgjshNS/9I6JXBQNf2ut3Y5XPRxO+mTNATpU4rIj1GPNvmKPeE7+1yW9znlW4x+GemZORArFFkd1jO1B5kSIptOGXfVT9XcsGHieAxGdxcC2BQAAAACRxB7mNFyOmunXLofW7S+hzYddkWB6keepyRx5LsXz0OvrMBp5rpoiHYuOAQAAABArKq0uL2VmVPcWQQuHWqGZ76tu2+yYoxbGx/RuFTDiW80Pvx0z1bYlUG4VKeppE4ZG4nFEHTkcyvrZw5yDNutzPVCSW0bAtsUTee7wH3keDc9zxT7G97O1+4vpns+8cyn6o2V2akALmHCfvYNFCV5Rta8nJ0IME4Y6HPX+xvXaT0bQx6LuwQLxPMbZZwEAAAAAzEA+aoQzbfP60d3olg/W6kYt1da5kommsHgeoFNilEAP++rIc0+UV/w/WAMAAABmoo7g7dw8IyzP83jzPVdXpUuLDMO2G0XltSbbtlC9ti1yEx7xPCHCkecJIWlPVnv9gZsy8jwSti0ecVcnypiiGXnu//yRyUBbZKbQpAFtAwrnPKgj7XB0Z0FIC5woWU3LY6a2bfFEb0elCvoJQ/1GnqvEc53P5fGJ1uBDOEA8jzDwPAcAAABANJAPqOF4mWakJPnt9Fi9Is/d4rYpnuc64rl7WUm1TfieusqFvi0AAACgIaK+HRtPGOoZ9JazxrQWD/GAd8R3gu4+6NVXLuNk5zU2R/jieQDblkRt5LmuBGgOat/nULR5xX/aTxSwRAqdkbBt0baXrm1LVBKGym36r0fH5hn06EX9DazLv+d5XZRtR/QGJ4JNKGwmFrfo7c9nX/371U0ii8hzIJGdy/p8pwAAAAAAwkE+lIbTKdFOT9a3bUkyJ/Lc/WW96soIFI72kRE/sGwBAADQ1FDfj40Ojsv75fwtBT6fhesPHrF900Z8uxfo6eJyHzhQkcXzsKPpnYEiz7We5/rlzEB9fENZPc8MZOqLPJfieiQjz/UShspqRTXy3BGcbaAu8hzQ2Se5n9FKeKl4nnuJ567/Y5EIONkdec59BG3AcIL296trewPPc+Amxd35u/n9NX5PiL5tc+jjv4xUotQBAAAAAILFjOiXQOK5V+S5tFWJkG3LGT1b0vs/71Om1jK/G9wu9I0BAAAADXlgPIh7+9DOzUSiQ3kPbZWdSgVuq5N4sm1RC2va/VMSdQYQYaV+Er52roqAp3psW5RyCRGOPA/BtiXRmOuBEnkeAasMeRwDRXxHw6IjUD2CtQMMFHkuvceToux5ro709kRvR6UKXshjefvH68VLewz0IuS57h+s2k9HS2toR0GFUjbegXgeYYZ0aka/7C0Wkef+os/X7i8RU5K7t8zyWs4n1R9mr6CjpdX06Y2jxLQSAAAAAAA9HGaK507/nufqhKHheZ77f9gf0CGXlk8fG/rKAQAAgEaAEpkZhJjap20OrXtgvHIv5+/2+Ptccc+OJ/FcLWxqhUy5v46AkecyuthE2xYf+xjv5yJZ54gkDA0z8txovj3F8zwCPt1KZLQ9gG1LFPzBlcGXACK+4Zkcmu/pW+AkRj3yfOvRMtpTWEl7iipjZn0yqns+LdlRpPuZ9loj3y3fdZxmfLXZ67Ps1PiXpuO/hg2c6ef2pimndfE7deb8F5aKEWG95ANHSmtow4ES8fcve09APAcAAABARD3Pjdm2eBKGhud5Tg3G5xAAAECMqazk8M7Qv2+zUVJNjWs9Fgs1FJwVVZRurRH3XlF3g7CUp5bzsupqRTCfo6KCKMk1GB7rtnLU2MS+MQlVlaz+Kp+l1laLz5KqK332O7G6UnyWbScqE2WqgmobLc5Kq6cevB7Vc5Sl2tX+lhrXNlKqXfVKcb83s53kPjMZiclB71O2vVZ8XxzjyhT/Batc7Zduqw6r3fRIran2ai8Fm41Sarl+CaKM2dvVwueEqx4On23xMRb7bzVWjzR5LvI5qimfrDk/wqae3x7XmbdXcOQE/eHbTYqgny4+c38vitx0ShuaPDDfx6bn10MldP3ba7yWpfDkl8pKKj9RKuraJjeVJvRtQy2yUujsjpnB1d3Ma5TB7UI8jzA8ctk2l09lfeRoqd7goHqUrLymLjIVBAAAAECjwBNhRhERz99ZsU/x1PRMhw19W57oLYjnAAAA6qFdeNZdLK+cTw2P9kS0Rb55OvT1bJR//Ct+2ipbvW+aet3mfunxgt7CyaHXo3mAeoxVfzaN6ElyvWQ5M9vpGvdLYWZw3//W4DGe5n4ZOReCZby6vW73PqfmGKyfGfRW1+NR78/Odb8Ef61/XU+5X3r1/sbkfarvt3e/+8U8of0wCu2qR6bOstPU7a/mAaLzyPVqaNdzmGzHGNlh1OukqpeV13g8PwEAAAAA/D03hDMNV06T9plqqRrQb5eXrviMmuN5Hvo6AAAAuHjppZeoS5culJaWRiNGjKBVq1YFLP/JJ59Q7969RfkBAwbQ3Llzfa77M2bMoLZt21J6ejqNGzeOduzY4VXm8ccfp1GjRlFGRgbl5eXpbmf//v103nnniTKtWrWiu+66i+rqEBgGAACg4YDI8xjj8fLy78XEIPIcAAAAAMZsW8yPPFfnbRnbpzV9veGwiZ7nUM8BACAcPv74Y5o2bRrNnj1bCOezZs2iiRMn0rZt24RgrWX58uV05ZVX0syZM+n888+nDz74gC666CJau3Yt9e/fX5R56qmn6Pnnn6e3336bunbtSg888IBY52+//SYEd8ZqtdKll15KI0eOpNdff91nO3a7XQjnbdq0Eds8cuQITZ48mSwWCz3xhE/cZGAOHybKyQm1ichms9H3338v9oG331DYVVAhrF45AejP94WeC6TPA9+J/68/vSvdMaFXXLTViUornfbkQvH3b49M9JqJ9vKinfTCwp102Skd6OHfuc5JyfXvrKZlO4qoT9ts2nKknM4f2JaevnRQyPUoLK+lM55aJP7e8ug5Xp/9tL2A/vLuWrGtz28+je76ZAN9s/EI3XNOL7pqeAdT2+nTNQfogS9dXtB5GRZaEWTul8tmL6dNh8ro5auG0Nm9W/st9/DXv9JHqw7STWd1p7+O7Ulmom0v9Tl17j/n06HKBJp9zcl05km+1yUz2V1YQec9v5Ry0pJp5d/HeX32vw2H6J5PN9FpPVrQa9eeUu+6pn+2ib5cf4jumHASXX96N6/Pzn5mER0traVPbhxJ/dvnhl3v+n57/5q3jf7z0x7q0DyNDp6ooYEdcunjv4ykeORYWQ1tPVJGFVY73fnfDdQ2N5UW3nk2ffzLAXrof5tpTJ9W9NIfTw5p3aZeo8rKDM1sgngeY2S0lb547llWBvEcAAAAAAGQzw1mJAzVehfWORxelnPmeJ6HL/YDAAAgevbZZ2nq1Kk0ZcoU8Z5F9Dlz5tAbb7xB9957r0/55557js455xwRBc48+uijNG/ePHrxxRfFd/n6zAL8/fffTxdeeKEo884771Dr1q3pyy+/pCuuuEIse/jhh8X/b731lm69fvjhByG2z58/X3x38ODBYlv33HMPPfTQQ5SSEsCXWUtmpusVKjYb2Vn053U0IPHcnuGg6pQ0Sku1hLX/w/t3pB+3F5I9PaP+9USpreyOZLFvTEJWltdnjowM8VltSrpPfWssaeIzR0YmVafYXOsIo22c9iSxDqHda9bD9eLPqiyubdSmpov39gzX+WhqO2W6tsWkp6YEvU9cp+oUK9WmBj7G1RbXPiSE+5vSITEr26u9FGw2saw6JYES+VibvF0tCdUk6pFsSfbZli3VdW7Z0gz8FvgZOD3db/mKZJP3qZ7fnlMc4zQqclioOoWi0pah0jozk1q3bUG/HirV/IZcv19nhrH2j/g1yl5PDgg3EM9jjEzqpZtFWiWeL9hyjG5+30oju+dTh2bplJmSTEM7Nwurgxwpauvs9OuhMhrQPlf4ogIAAAAg8sho8aRwbFuU5xL/kedcJtlt2xJe5Lnrf3ieAwBA6HD095o1a2j69OnKssTERGGzsmLFCt3v8HKOVFfDEXwsjDN79uyho0ePinVIcnNzRVQ7f1eK5/XBZdkShoVz9XZuuukm2rx5Mw0ZMsTwflZaKynJ6pswNCkxidKS07zK+YtUrHXU+qzTH4kJiZRu8eQuq7JVeVmYqeH7WIYlI6Sy1bZqcjh1EqDpDIzXVzYzxSNE1dTVkN3hEYU6t0gkB9VQdV2l2O9AZbmtauw1opzFaRH1lffq2rpaqnP4D+wLpqzdkaDsm9VuJZvdY1Vrc1aL+tbYq0Q9+FjwMWG4LH+WkFgj/rfaq72OJZ8PfF7Isur1auGy8kglJNh9zolae5XYhs2RLPZFlrU7bKKsup3UpCanUjIn/RQBCHWiLfyRkpRCliSLCExwkp2cZCOH07cu6rKuOtjFsZMkJLjao7y2wlWnJIsoL9qhro4enbOeDhRX05bDpeSgWrKTq+6MuiyfY3yu+SNQ2VpHpfuYJIl1cxtwW4j6Op1iu1aH65hqUZfl3w//jvxR3+++ts513OqcSaKN1GWrbK462p3e543e754/l+ei/O2oy9a5kxdyYIkZ1wi965T6d1/ndB3j0ppacpCTLMlWv2X1CPS7D6dsRoDfPbeb61g4RPtZ6xyKzWR91wjt717+lrXXqEBl9dBeI4wA8TzGJBr0PC8or6W5m46Kl+TBC/rSlNO6Urxx72eb6It1h+jyYR3pH5cMjHV1AAAAgCaBjBY3I2Go7Awo61a9tyQmmhJ57rFtCX0dAADQ1CkqKhL2KGqBmuH3W7du1f0OC+N65Xm5/Fwu81fGCP62o96GltraWvGSlPGUes638c92RB79S+Hc7ufSV5d/pbxv9Uwrv6Jbv8x+dL7Nk2auy6wuVFRdpFt2aNuhtGKKZ/Ch70t9aV+pK3G2lj75fWjDDRuU98P+M4y2FOmmy6POuZ1pxy0e7/jT3zyd1hxZo1s2Pz2f5l+xVdENznnvHPpp/09+xauSu0qU9xd/fDF9u0tJIekineihNa6X9T6PYHTV51fR51s/913pJtd/xXcWK0La1K+n0rub3iV/HLrtELXMbCn+vv2722n22tl+yy6bvEl5Dpg+bzo9u/JZn/o+t8X1Wjd1HfVr2U8s/rX8LTqQ/gYdKHCVeXU30auq5JrLr1tOw9oNE38/+/OzNH2hZ2BJy7yr5lGvZiPE3+WJ31HWTNdMC209DpQTzdn2JdntnMKVaMWRL+gvz9zr1U5qPvj9B3RJn0vE359u+ZT++MUf/dbhtfNfo8kDJ5PTYafqxLVUmPowHXASZekkDH1uwnN007CbxN8/7vuRxr8/3qeul3EWy2+IZo6ZSXeceoerPusW0WPrJ3qV+9tS14u5f/T9NOOMGeLvzYWbacir/ge2po2YRk+OFWlTaW/JXjrp5ZN826vCVf8bT76Rnj/neSF2Wp1ldCD9ahr3kf56rxlwDb1+gcv+iUXRZs8081uHi3tfTB9d7FlR1kzvmQuyHqLsR97XiKnzh1BtejW9f5DofU0bn9HpDJp/9Xzfa0Q60R3LXC/1NaLO8bj4m49d35cGmHKNaGlpSedPPN//NcKj7dMnh/LodRv/EFyEfY1QYegaQQavEXxO1LnOiSdPdV1XkxKcdPu3ga8R22/eTl3yuoi/py/QuUaofnvqa8SjPz1Kjy19zO961deIF1e+SEaAeB5jZCdVb9RJvWjmxQPop+2FdLC4WngHsZi+77j/kbhYwsI58/HqAxDPAQAAgCjhcJhg26LkYvHjp57gmjWX7FboHc4EExKGQj0HAADggn3YpR2MEQoKC7ySnfJAQiDYnkYdte+P0pJSr/VWVfnve1dUVHiV5ff+4PWoy/J2/MH1W7KE1c1kstbWUNXx437L8n6r18vtEgh12aNHAg+IsLdwWpJr5OLgwYMBy85fMJ9yk13ez/sO6guJkuXLlhNRe3I6HLR79+6AZZf8tIT2pbvWV1XlPyqaWbZ8GRVkuPZ/a4H+AJLk559/pkMpHE2cTE4lrlyf1b+spsPHOLI1kY6wB38A1q1bRxl7XJHG60rWBSy7YcMGmntwLm08Xv/zEM/YmFvgOnabynVUexXzVm+ljsfmUpaFaG3BzoBlORnw3ArXevdX7w9Ylo/V3FpX2WO1xwKW3b5nH3305Vzx/CgCLgLsIp9b8rzkqOJA8DmrTXJs9BrhUFkRajl+/LhX2fquEQlWjm5OoCU//WjaNUJ7nQp0jeDfjnq9XP94v0Zs286Dh83pyOFDVJYa+BqxaNEiap3qGnDdfcj4NWLHEe/k1oGuEdv3bScjQDyPMbK/GCjyvH1eOl05vJN4MS8t2klPf7+NqqzwQQcAAAAAKdNhwxWjPZ7n3h0Lm/uZRNq1mOp5DvEcAABCJj8/n5KSkujYMW8Ri99zok49eHmg8vJ/Xta2bVuvMuxbbhRez6pVq3y2o96GFrafUVvKcOR5x44dad9f91GOTsJQrX3D0XH6Ig9HvrIQM378eCXB3L5x/oUbrX3DjvE7DFsynDX+LMNlzx5/dkCbhZ0FdUSbVlJmRjp9e/Nyw5YMY+rGeNksPL9wJ722bB9dPbwD3T2xV8Cy3FYLFy6kMWPGiLZSWzKMrRtr2LalvrInKole/HUZJScn0Xt/es/LZuGdn/fRM/N20nn929DM3/fzsmR4bX8OOQ79gc7smU8/7iiik1pn0SdThyvbVVsyjLOPo1n2WX7rwGULym304NqfKM95Lu280zsUee2BYrrurbXUuXk63Xf52fS3/24mOn6M/jD0Jnp34P1e7eTPtmWCYwLdV3ef3zpIKxbLbwWUvp2oY/WnlJ9poYXTzvBblpnomEi31t2qfHbnp5vohy0eQXRndRItyW9PL1w4iDK2H6Mv3+1CvbitbnBF2geyYrnGdo3f+mrLXnbeZcpnmw6V0lVvrPaqwwOHXe3gpFzqSJ/S+38aJix+67NtKZ5Y7LcO2t998TjvssfKa2j8rGXCTmXRjeO9yr7cfA09MmcrjenVkmZdNjDg756vEY/M3UqfrjlEN5/ZlW48o5tX2WFPLGM1nsaPHUOXXBD+NULvOqW+Rry7cj89/YNHGL7ilPY0adJQ5f3ZtsDXk0C/+3DKZgT43e8oKKc//HsVNc+w0OI7zqDXlx6hhYd2U5fOnej+Sf81bNvCv2W1bYv2t6ctO9vuP6JdfY0YXjycPrztQ6oPiOcxxl+El2uZa6G2T5lucR3kKqtdEdmX7yqiLi0yqWNzzw8SAAAAAE2HHcdcUSwyKjwsz3PNc7fi6ej+nH0KmRo7d+wryMIJmYKEv8dAOwcAgNDhpJtDhw6lBQsW0EUXXaREVvL7W2/1CGtqRo4cKT6//fbbvSIdeTnTtWtXIW5zGSmWs4i9cuVK4VduFF7f448/TgUFBdSqVStlOyyC9+3bV/c7qamp4qUlLzOPcjJ9xXOfcpY83eUstqQmpgqhRYot/srqkWvJjUhZreiqJTHJJQryrK+cjJyQ15uRkk2JlEYpyVmiLQOV5bbiKFIup/2svvoGU7a8tkoZRM9M804cmJVaLOprScrwqW9CgkV8lpWaRYlUQTuP1dGjc/fRs5f5DuwYqW9StRzMt/hsKzfdKbaVQOmUnpruEegtrvbx105edSCL+G59dG2VTQmUJF5d85v51EVvvWmpHlH41jEDKDlxF9nsDiqqqKUNB0vpWHmtqFtyUorYj3RLZr3rZVJTfH+DRsqe0jWbBnc4IJ7x2E6QZ0VKW8EESqCOeXk0pHN7ykqt/7kxmITC2t+y1V4j9pd15Oz0bK/P0ix83qRRWnL9bcHrTU/OdJW3+P525PNxWoqFcjN0fKWCvEboXafU51ZOWomoi6RZRq7X52b+Ps0qm5uRJOqcmJDiar8E1wBqanISZaVnhbTeQNeoYOurvfb4A+J5jJHRVtrEXOpl2unXGSku8bzaLZ5/tvYg3f3pRspNt9D6GeOReAsAAABopKzac4KmvrOaymtckRc9WmXRV7eMpvSUJPp+syvazloXejh4sr/Ic7sm8twt0B+pSqBzX+Bp16GDyHMAAAgPjtS+9tpradiwYTR8+HCaNWsWVVZW0pQpU8TnkydPpvbt2wtLFOa2226jM888k/75z3/SeeedRx999BGtXr2a/vOf/4jPuT/Jwvpjjz1GPXv2FGL6Aw88QO3atVMEemb//v104sQJ8T9bAqxfv14s79GjB2VlZdGECROESH7NNdfQU089JXzO77//frrlllt0BXIQwJItzHsli+9ifX4iY2OBJ/eJ775JCUSvvnXuZ5JR3VvQt7+6nn3W7vMfpVwfMgJYr4W19VCqE4Fnlz5tc2jhHWfS0bIaGtTB+MCOZHDHPJp9jSsKmS1/J7+ximps7kSTJlj7GSHNkkT/u3W0z/LaWit9M/dbOn/S6ZRqQDgPl4QAQarKzMdEo+uS3/P9bcr1R7pdJWf3akVfdTpExVU2ykxNogsGtqN4J1FpP6dun6KhAPE8xsibmJ5tiz8vUO4gqyPPfz3k8kAqrbaJEzElGZ1QAAAAoDGydEehuN9Lth+roO3HymlQxzxlZtr4vq7ovvA6166HXNn5kGI6T39lhnRsRn3bZtPewjJKsaQE9LDUo6KmTunIxVNHHgAAGiKXX345FRYW0owZM4RAzdHi3333nZKck8XtRJVSNGrUKPrggw+EkH3fffcJgfzLL7+k/v37K2XuvvtuIcDfcMMNVFJSQqNHjxbrTEvzRD3y9t5++23l/ZAhriSDbDtw1llnCTuZb775RkSrcxR6ZmamEPkfeeSRKLVMw0fJORKmOBdIjI71vunp0IHEfrmsa34WzfnraDrv+aVU6dZGQkFuQrce7oWyjPRFj5Ti0q1llniZIWIztTbpVuDwCpKINiJfTmL457FR1GK2+nlWrbMZDTpVzgE/donqGZmRplOLDPr85tOoYZHg1e4yWj+cmbKxAOJ5jJGDLXo3BXVyLjUZKa7DVuW+EFbUejyCeIpOCl+VAAAAANDoqK1zPXBefWon+ml7Ee0/UUVW90OofB7o1874dHEt6k4VP4fIB1sZ5SU7B7kZFvrq5pEimdCkSWcHNT1Scsd/N9D8LcdEFA0AAIDwYIsWfzYtixcv9ll26aWXipc/WFhikTuQ0P3WW2+JVyA6d+5sOLEf8EUKdGFHnru/735kiAsCJQ5XZug7Ag0osL2LSxupVGkiocK2Iv5EWLnNQEJ7PJHq1oRq3JpRtCLP4wX1bqqfZ+ub8aBHgkqEZ/cHOYAio/obohAcy8jzOve5aInSgINZQDxvALYtPpHn7lHEanfC0EqNeA4AAACAxi2eN8tIUWzcat0P7/J5wIiPpD/UEUHcYU/WPF+Y2Tn452WDTFsXAAAA0BiR4nG4FmdaASsekHXRE3QD6SRSyOYBBSWw0GoXNhqhRDYHEsTlMlmPQIJ/PKFEnrufG2WbRStCOtboRZrXF6Ra37peWLhTvPRoKoMSoSDbT/7O5GzWhjbg0DR+OXFMoBFVuUz7Q5S2LQXltfTqT7vFlG2J1X1xBAAAAEDjo7bOFUGUkpRIqUrHyNVhlFOWM8MQz7WR5xIlSqSB+RMCAAAADRkloC6xMXqeu/7X2zWPzYz/aHwW39QBA9XuKOvg6+HfiiXJp90ia9tiFmkWTeS5ewZhUxF51fupPeflW8OR5/UUG9G1uRLpDwIM3Lnfe2azNqxzEZHnMUaeL2q/JJ+LuObXmpfhmhpdUmWjx+du8fpMTt0GAAAAQONDRpmnWhKVB3WOKlJ3GMOKPE/QF8+VyPMG9qALAAAANArbljC1OU8kN8UN/vSO+sR+T+RwghCJuSgv4hl4oQQQyC0EtI+RnucNxLZFRp7X+ESex3nFTUK9mz6JPpXzzui6vAt+dctp1LN1lpczhFH/9KZIguJ5joShwIQRMb3pU/5ulN3yM+mec3rTjmPlyrLP1x3yOhEBAAAA0PiQ029Tk5NU4rldsWzhxwoZbWRq5HkDfdAFAAAAGjI8s8wcz3Pv9cUDHpvaIO1tVf7dLFpmpiRTeW0dHSypplY5aca27XDSmv3FIoH50bIa18KAEfBuz/MA/ujxhHxG5Gc5TtDY9DzPvW0I1cifgNHflLYUD9BIuyBQP7KZfWxbGti5iCMeY+QIlV7AuBTUtSNd/J2bzurutezH7YV0vNIK2xYAAACgCdi2cKeIBXTmbx9vUDpJmSnJYUW/JPm1bWmYD7oAAABAQ0bei8ONbA0kRseKQPYZgWxbtEKw/Pqnaw7SyZ2aGdr2f1cfoHs/3+S1TO8Zx5NoVSYMlaHn1CAiz2X0ub2B+kyHivqU0p7zwSYM1VomNZUBCLNI0AxAyYCchmYFCfE8xsjRLv3pSGT4Ry1PPCQMBQAAAJpA5DnbtqgizOXykzsb6zT6gzvncvqzfuQ5OgwAAABAtJA6QbiCXTzatqjtV7TIZXoz9LVtMrRzM1q0rZCq3LPwjLCjwJU3rmV2KrVxR6tfOLhdgHqQJvI8vlF7cH+x9iDtKqwUfyc1kYSh6qhyp8NPkKrBptAe63BngTQ1EtztxX2V819YQnvc52JD61NAPI8x8gerOx0pwDQmLSnui2NT9zzniLz5vxVQn7bZ1K2lx4cKAAAAaFSe5yrbFubVycOod5tsap+XHvY2uDPqsDu9prkqCUObSKcLAAAAiAeCtZgIJddarAjkPS2jfdUD+RK5TLbJuQPaCvG8pNpmeNucP47502ldfWb1B0oY6vE8j2/hz2VnkySSyT/w1WZleVOZQWjEtsXoMdSWw6NwcKhPuV8PlSl/d22RSQ0JiOcxRjsNyJ+XV31Y3KM2Td225Z3l+0QS1WYZFlo3Y0KsqwMAAABE3LaFYeG8Y/MMU7bBzx2cQ+Xp77dRtjvx1u6ihhklAgAAADRklOjscBOGBsi1FvOBAV27FFnGt75yQF/uU166Rfxf7BbEjVDqFtpz3d/1h9RN5XEIJsAx1vz9vL40Z9NhWrbzeJOzHDFm2xL8uppSG5pFgiZ2/y9ndqNLh3akHq0aVrArxPMYE2j6VDAjYrBtcbH5cGnQN04AAACgISYMVXeA8zICd/6CIS89hY7aaujzta5k5GqaZaSYth0AAAAABCZYf+Z6dYc4kgv85XirVydxL5RR1M0yXc8mGw6UiHUa0U9Kq62Gnp+0ti2SOA88F/xxRCf6/ZD21GfGd00u8lxtQ3j5v1dQekoS3T72JBrXt7VyThn2PNeK5w3h4McRiZrmGtG1eYMTzhmI5zFGmQakNx0piBExOXW7qYvnchQaAAAAaAxUW+10+X9WKP6AFdY6xa5NbdWW5Y4QN4OXrhpCP24r9FmenJQoOmEAAAAAiA6KRYlJnufxZdtC/m1bAniee3QSV5kWbvGc6fn3bynPwEB/cZVbPK8n8ly2u9ymJ19owxBQ1RZ/TS1qulPzDNp7vErxe39/5T4hngcatNFDe6y1CURBPWiaq01O+BaTsQDieYwJNB1J/qiN2ba4Pc+buG2L2v6GByRwYQMAANCQ+e1IGW086JpVJWEPy24tM+m8AW3p6w2H6cyTWprqvTm0c3PxAgAAAEBsCTZK1h+yWxxfti2BIs9d/6/eV0y97v9Wdxae1Em65mfSxUPa0+frDolguqKKWkPbT7MkUo/WWYZtW6b9dz1tO1butTzeYT2EBXTZZk0l8pz58pbTaPPhMlq2s4heXrxLCToJ5LWvByLPw0P7+2aryYYIxPMYE2gEOFD2aX/ieVO3K1GL59ymiQ1kRBgAAADQQ84o69g8nd790wjxd352qog0H9unNW16aKJPVBEAAAAAGgfB5EELhAwqi6eJ2oG8p3u1yRbido3NoQi/ajhBen5WqvibAwievXww3Xtubzrhjig3QpuctHqj1DNTkiklyTXbT21nZyS6PV5IsySpBhyazjMjH6PTeuQrsww4n09IA1I+CUOhMQWDurXO6tWywbYfxPMG4Hlu5EctE3i9vnQPXTm8E8ULenY0URPPHU6yeHKpAQAAAA2OOveDfoYlmbrkZ+p2iAAAAADQOAnGytWY7hA/6rn0X9fTOzq3yKTV94+nEj9ieMvsVGFhp6ZVTpp4mUlmajK98+fhtPFgiWc72Wk0ukc+OewuK714hwchSqupySZ+l4GmdZrI88RQI88bqPgbKxJVv++05Ibbb4F4Hsee53KZkd9mr9bZtGRHUcyjz7TTwHjaVEKMPM85Wg+iAgAAgIZMnbtn2RQ7OwAAAEBTJ5jZ6IGQmoI62CzWeOwz9PeNZ9mZmdMlVE7t1kK8tDjs1CBQayJNUfi1uJ+hpVYkJatQPc9h2xIc6ubixK0NldhfiZo48kTSGwGWy4xc4M4b2JZeW7qHSmJs26K9F/PNOTkhdpHnAAAAQGOIPG9KHpUAAABAU2f2j7vop+2FdLS0xtSEoXEUeB50BDAIDXW0b1N8nkx2W9Uoti1unchoviBtkzUh5xtTSFBHnlsabuNBPI8xSvZmvchzJft0/T9q6blVWh1b8Vy7HxwxF82ZGdIb1rXtOHoyAAAAAMKKPG+4D5sAAAAAMI61zkH/+G6rl9DdOkw7Eo/nefz0kWVVmmI0dDRRC5ZNsa3l7E2PbYtrudGm0MpxUowHwbdfQ3aGgHgeY+SUD72bmPQ3MzItJC/dIv6vqK0TvmDcyebpKalR9hTSiufjnv1RJNgY1zKBJkVh+zKDsl5dAAAAgIaGjJJpipFCAAAAQFOE+7FSHnj6koGUnWahM07Kb3K2LcAcUlWCZVN8nlQ8z93nvmfGg0HbFm3C0KbXhOZ5nkM8B6Eif4h69zDpH25kYCvHLZ4zgx+Zp4wwvjVluK4/V6SQgr/kWFmt+P+XKDmf19oculHoAAAAQEOOPJcP/gAAAABo3KgD684f2M4Un+D4tG1x/Q8xMrIM69yMVu05IaLO+7fPpaaGHDDgGR2h2AWptXP+DgZ7gkPdWukQz0GoyL6w3ghwMMlB+EI4rk9rmr/lmLKsxuagNfuKoyueq/bj85tH0fKdRfTMD9tJpWmbCg8wvLhwJ+09XiXeHyiu0q0LAAAA0KA9z5EwFAAAAGgSqAPSzHKIkJpCPNm2BBsBDELj7nN603WjulBKcqJi99s0I8+9xfOEEBKGNkXbm3BJSU4Ugw4sz3VukUENFYQxxRjPCLB/z3OjN5PXrh1GWx89R7wuHdrBtY4oC8hqwXpQhzzq1jJL/F3niMxFZsPBUvrnvO302dqD4lVeU6d8Bs9zAACIHC+99BJ16dKF0tLSaMSIEbRq1Sq/ZV999VU6/fTTqVmzZuI1btw4n/J8H5wxYwa1bduW0tPTRZkdO3ZQU0fey+CvCAAAADQNnKrAM7OEZan5xZN4rsy0hx4ZcVrlpDVJ4dzb81zatgT321KfnxjoCW3w4pv/O51mX30ynTegLTVU0BOLMTJxh57DiBS+gxndYg8hflmSE3VtVKIpnnO1U931qItQNY6WVov/OzZPp+nn9hYvvboAEA41Njtd8/pKmvzGKvE3AE2djz/+mKZNm0YPPvggrV27lgYNGkQTJ06kgoIC3fKLFy+mK6+8khYtWkQrVqygjh070oQJE+jQoUNKmaeeeoqef/55mj17Nq1cuZIyMzPFOmtqaqgpI5MbcR4TAAAAADR+1H14I/nPjCBFP7d+GBdIDQQ2GCAakefS1jfYQRv16YnI89Do2y6HzunfVuRmbKjAtiXGBBoB9kwnCScRKUUMvvis3VesJDNjiqusru0nJoibIE/REGUjZNtSWOHaXp82OfSXM7uLv19fuocKymsbnef5V+sPUW2dQ8wqwAOGNweLq+iHzcfEb6ZHqyw6q1crU9fP5/mSHUXi718PldKwLs1NXT8ADY1nn32Wpk6dSlOmTBHvWfCeM2cOvfHGG3Tvvff6lH///fe93r/22mv02Wef0YIFC2jy5MniIXbWrFl0//3304UXXijKvPPOO9S6dWv68ssv6YorrqCmirzH4mEdAAAAaBqotQGzun1yApvejPdYEaz3NAChYEnUJAx1eAey1oc62tyswSzQ8IB4HmPkj2/JjkKfiNalO4tCnhoiO9mRtG157Jvf6O0V+wImZUhNdiUEcOdmCAtO8PDNxsNUXGVTli3dUSj+z89O9dl2Y4o831NUSbd9tF783at1Ng3qmEfxBp/D246W0x9HdKKMlOheWh748ldatM11LjArpo+htrnppq2/qNI1SMOoB4tC5WhpDbXKTjV8wwYgnrBarbRmzRqaPn26siwxMVHYrHBUuRGqqqrIZrNR8+augag9e/bQ0aNHxTokubm5wg6G19mUxXMkDAUAAACaFuogOrOCpuLZ8xwBAiC6ti2he+2j/950gXgeY7LTLOL/tftLxEuPUEa35Fciaduy74QrOWfb3DTKTXfth2SS28soxUTblrmbjtC0/27Q/axNTpryd5K8ODYi8fx4Ra3yd2m1Z/AgXuAo/2ted/kXs23Q1ad2jur2i9wzECTHK6ymiucnVO0f7qDMa0t202NzttB5A9vSS3882YTaARBdioqKyG63i6hwNfx+69athtZxzz33ULt27RSxnIVzuQ7tOuVnWmpra8VLUlZWJv5nUZ5f4SC/H+56zKDW6srlkZjgjIv6xGs7xTtoK2OgnYyBdop+W6GtQTSRkbFmRrkqti1xNDlbShXwkQbREM9tSsJQ13KjOrhX5DnE8yYLxPMYc+2oLuLHXGX1jjr/Yt0hKix3iQKh5Ajz2LZETkCWI3d3n9OLfj/ElaBUS6qJti1FbgGzfV46DevSTFmenZZMV5zS0WdaTmOKPK9WzUqIto+9EapqPfU7oYrSjhZai57auvp9yXlWxs+7j3tFlftj1d4TPlGgofK/DYfF/3M2HqGX/hjWqgBokDz55JP00UcfCR90TjYaKjNnzqSHH37YZ/kPP/xAGRnmZHKfN28exZotB/l+nkRHDh6kuXP3UzwSD+3UUEBbGQPtZAy0U/TaimdMARAtwomM9YdcV6xtWxZtLaAXFu4QgW6y3whLUhBJpD7Epz5rRB7Pc2PnnboYBnqaLhDPY0zL7FS6fdxJPsuPlNbQ126RTVqfxJttixQskwOo+0rkucO8h4gRXZvTs5cPrnffG5PnebVqcCXWDzx6VNlckZGxivj3Ec8NjNb8uL2Qprz1S9DbCndQppkqyznX4Yye+XhgBA2K/Px8SkpKomPHjnkt5/dt2rQJ+N1nnnlGiOfz58+ngQMHKsvl93gdbdt6srDz+8GD9a/3bBvDSUvVkecyEWlOTg6FG2HIQsv48ePJYvGeWRVtti/YSXRgN3Xr2pkmTepD8UQ8tVO8g7YyBtrJGGin6LeVnN0EQDSQ/Y1Qguj8IdcVDdsW7q/yZvQsLt5Ytsdnxn37vNCDKQAwGnkudYNgcwt6R56bXz/QMIB4HqfceGY3stU5xE2OPaSDRYpxkdSPpUhqUV2M/EWemyGey32pz2dKiueNKfK8RtWAYQY+R1zc13r3RwOtYM+JVeuDB6iY5pkpwke+PlbsPq67rWBJt3gGw659YxV9cfMoGtLJM5MCgHgnJSWFhg4dKpJ9XnTRRWKZw+EQ72+99Va/33vqqafo8ccfp++//56GDRvm9VnXrl2FgM7rkGI5CxUrV66km266SXd9qamp4qWFxRGzxCQz1xUKBWU19M7PrmjzlOTkuBXJYt1ODQm0lTHQTsZAO0WvrdDOoKHbmXhsWyLbR+a+4PkvLBVWo9/832hqrbJXZeSM+1vP7kEnd84TgXjDu7py4AAQCdR5g5buKKJjZbUhR54jYWjTBeJ5nNKvXS7NvmZoyN+X14fI2rYYjzy3ORPCjpi2u1Xj+i5YSkKIxiSeW+PctiXW4rnbQogHa1g4N2LbYlfNZHjl6vp/a5f9ewWt2nMi7AdOre3L4ZIaGhL8+BgAMYUjvq+99lohgg8fPpxmzZpFlZWVNGXKFPH55MmTqX379sJahfnHP/5BM2bMoA8++IC6dOmi+JhnZWWJFw/43n777fTYY49Rz549hZj+wAMPCF90KdA3RR7832Yqr6mrd6AaAAAAAI0H2U+JhOd5pLuS+45X0c6CCvH3hgMlNKGf96xE2Vcc2qUZnd2rVWQrA4BbPOfTn8/9699ZrRuRHgj1LHEkDG26QDxvpNTnef7Cgh300uKdSrKE+uBrBI8O3zqmp7JMitOBLjpqy5mvNhyhFEtop1z/9rlK5LlMCOoPKebb3YJqY/M8j0fbFnX9Ii2eP/vDNvru16NUXp5E/967gqaN70VW98mRlZpMtXVWqjFg22J3f8do0o/kRHMGZbTfb0z2QqDpcPnll1NhYaEQxFkI52jx7777Tkn4uX//fkpUDay+8sorZLVa6ZJLLvFaz4MPPkgPPfSQ+Pvuu+8WAvwNN9xAJSUlNHr0aLHOcHzRGzoLtxYofyNBEQAAANA0CNZWwgjyMSLSti3qvo1ev0n2FdNCsKYFIBT4Gfrms7rTgi0FXlaqY3obG7xJ0NEEQNMD4nkjRY6I+YuS/XrjYUMCo5pvNh7xFs/d4rR6GoyWNItnlO+uz36lUOGkoFef2tlY5Ll732/9cK2SHII5qU02fTj1VCUaviGhFqTjUWv1jjyPXAV54OClxbvc53UCHakup/dX7lNmQWSmJtPxSquhyHP5MGdUkPLYAYW3f/J3I5HCPwANDbZo8WfTwslA1ezdu9dQVMcjjzwiXsBFflYqHSqpFn83oslUAAAAAAiAzFtm5sC51Aci/Tyhts/UCxKSfUXWCQCIFndN7C1eoaC2d0HkedMF4nkjRf7A/Y0sS7Hz1cnDqF+7wInV1u4vpls/WOcjxNukjUpi4Mjzuyb0pC9WbBNJ5oJNjMjVZ69pnrZeXmOrd3vMgA65tHpfsbgx15Dnhr1mXzHtKCgXljgNDXVkdzSSvARLtbUuKpHnNrtTOQ/PauugxUcSRdvwchl5btTzXLZjsJHncltm2bZoxfRgOVxSTUfLaqhv2xxKU/mpN7UOxu6iSjG4wpeYLi0yKRnZXEAjQD24dsEgTyJVAAAAADReHBH0PGdB+3iFy/NZwn0IDkIyA7VgrtdvkkFOTbXfAhoe8DwHDMTzRooUBP0FyUrxvFPzDGqXlx5wXTLqTTvtyhN5HvgCMnV0V2pftoUmTRoWdLIdFsO6Tp/rNUpd30PEjPP70p9O6+ol9l/12kqxH+rElg2Jz9YejGvxfL5qCpRa6I+kkNQly6mcF/IhTRHPdaLf+Vx6dt522nCwlIZ2akaW5ISgboBJ0g4oXNsW9++Gf6O8rnBsW3YVVtDYf/4o/h7auRl9dtMoaor87b/r6av1h5X3Z57Ukt7+0/CY1gkAM+BkW8zSe86mDs0yYl0dAAAAAEQB2d8wM8pVrupIaQ0NfWy+12cpSYn05pRT6LQe+WFvx6oKYpKzg/UjzyGeg4aB+mcIG8WmC8TzRoqSTdtv5LkrUjgjJSkIr2dH0AlDw4Uj1Vmc51FrGU1cX2IH/k7H5t4iQ066RYjnlQ1UPC9wZ4SOV/F8x7Fy5e+jpTXuCGDzbyy1KmE+LdkT6S4HdjJTXeeznm0LRya/sHCn+Pun7YU05bQuQSUKMdvzPN2SRBW1dWGJ53sKK72E9GjC7b7+QIkyrZRpkZVKvdpkU7TZfLhMGTzhNl2x67ho10CWUgDEI5xg6y/vrqaSKhs5VR3Q3PTgBp4BAAAA0HCR/T0zdboerbKoW8tM2q3qP6gDlPi53gzx3DvyXE88tyt9IQAaAiO75VP3lpl0otJKvx/SPtbVATEC4nkjRWpGamFLPZItR3zTDYnnibr2EjYDCUPNgAUwm92uCKehTF/LdO+n2l6kIaHe5TAtt0OGBfHtxypEJCR70Pduk60rkO8oqKAb3l0jLIHMRg6g8IBKSqKMPLcr0RlZaS6B6fN1h2jjwVKv7xZppifKBzfDnufu81wmGg0VWdc0RTwPXYxXD2hFO0HuHZ9soDkbj/gsf/fPw+n0ni2jWhc5kPfGdafQlDdXiUGynn//VvFTnHX5EDqnf5uo1gmAUFi49Rjt0nRq+WFdzqoBAAAAQONHsZc0MRgpIyWZFt5xls/y6Z9vog9X7Q97dq1e5Lm2n8PP7DKQCJ7noKHQqUUGLdD57YCmRdPtjVVWshoW3jpsNkqqqXGtK0g7kkiTUlNN6dYaSq6uctVPRXVtnfiMybTVElUGFpQttVWifFK1w2tdlhpebhPbosrkiLVTdl0tOa115KisFPVIs/ruU33kkU18t6aknKgysMd7TPHTVpbqakqQDzRVlUHvvxnM+/Uo3fbxeuX9U5cMpAsGtRN/J1W7zhHJhi2HiCr7mF4HW6nrHOAI8zRrLaVbk6mu3C7OQ6ZbulN8fuQQv477fF9tUOSscJ9PtdWG2jPTWuPax8rw2j+xyrXdZnwZstaQs6Ii5PU53PvAJDsT9dcToevUkUNFYtvt8tLE4NTR0loqr62jgwcKidpF114iWVyLaii1toou6J5LX23wWLiQlWjFpn10Ttd6IuLNaqcY/DZB4+FEpetadvHJ7enGM7sr9mqRmMkDAAAAgPhEdvuicf+XcURmzW5W22weLK6mbzZ6nsvV1pqwbQEANCSarnjeziX6hQPLK+dTfHKd+yWY4v1ZFhFtkW/+Vf+6eqvLP+pZvtzgOsJtp5XaBQbqrOW1ML4bTfy11Sb1mxjtwwT1eaCpx+d6X3jS/Dp01tThZm2BfxH9LYj1BVPFp9yvcNv/K+2CMNY3yf1SEBWMznVK95jH6PxUXBv/RTQoxFMvnq/noHHCUz+/Wn9IzKhpmZUqBPPiSqv4rFt+Jp3UOvoWSAAAAACIo8jzKPgre3KlmR95/sayPfTGMv1tpiYj8hwA0HBouuI5AAAAAECMeGHhDnpz2V7lPXdZj5S5ZpLkZaTEsGYAAAAAiCVSyI5GbsL6cqUFi54l5ZBOeV5i+djerTGrDgDQoGi64vnhw0Q54dl32Gw2+v7772nixIlkiTPblg9X7qNHvtlCE/q2pueuHOL12YP/+5X++8tB4Vu96u/j6l3XwRNVNP5fP4mkHmtnjFeWD3zoe3FzXHjHmdQ2T22IYW47jXt2MR0qrqGTWmcJz+3bxvagG8/qEdQ6Hv1mM32w8gBlpybTqvv97/OoJxdQsXvafCiM7pkflte3XltV1tbRMFVG9BkX9KUrh3cKar13/ncDzdl0RDy0SN/weX87gzpoEqsG4rn522n2j7vFQxw/z03s15pmXeE6tyb860c6cKKa3rxuGE15a7VY9sv940z36V277wRd9doq6tQ8nSZ3KqfH1nuv/7dHJtb7IDbi8flUVlNHZ5yUTz9tL6LrT+9Kd0zoVe+25e/m/8b0oJvPDu78UzP+X4vp4IkaOqVLM/plbzFdO6oz3XtuaBY3H/9ygB7632bl/aaHJlCyJklmoN/f1a/9TGv2lVC3/Ayac9sZQW1bHvMPpo6gIZ2a0eNzfqP3ft5PN57ZjW4bdxJFkucXbKdXFu+m/OwUWnL3GDr1iQXCi/+b/zuNurfyROu+tWwP/eO7bXTBwLb01KUckx6F63lZmSkzm0DTiDxn5HX5zk82KJ81z4R4DgAAADRVZBB4YhQjz8NM66RgrXPllVLD/eP8rFRzNgAAADGg6YrnmZmuVzjYbGRPS3OtJ87Ec0dmJlWnpImXdj8PWZPE8nMHtTfUBkl1iaJ8HSdMdJfn5JFlSalESURJOVlEmWkRayd7eiZVV5LYXnVKndi3YI9dVvM8qk4ppLR0S8DvFjktZNUkUX3i9wPqFTLWHSimf/+4m4rZ/CGc80qnreoSbK7jKIukpQe9jb01JNbxzB9PpumfbxTicW2Q66myuM6nltmpVFheS4XOZOX7FeLYOCmtWa5S12pLGmVlmvuQVJ1SLdbvzMikhAxuF88lLCUpkRKy2JQoMNa0dKp22KgyOU1Zl5F24HJcviY1+PZXU5HE2yVKzM4S+8PtGur6alPdv3E3dekZlKz1Dwzw+1t6pIYoJY02lzmCroM85knZ/PvPdO9PGlUkp4Z/ba2Hdcddv4kDnAM2M1NsszoliZKys7237b4OVulcByN2Pbf7dhgA0EMm5vr9kPY0f0sBlVa7xPQ2uWk0rAtnRQAAAABAU35GkFHhUbFtiWDkeYamfw0AAA2NpiueN3JkZm55E9xZUE5PfruNamx22nCgRCy7cEj7oG6oMjO2a72ezy2JkfUrS3Zvn+su6hPCQ8RFQ9rTcwt26N7MJbV1di+PNsm5/dtQs3rEc84WzuJ5ldV84czm8K5TsH50h0uqafW+YvE3J3dMcU+ZqwvQFrr1cJdvkZkixPNlO4/TR6v20xXDOynnhiUpQbRFjc2hHC8z4WPE8D5oE7Qn8+BOEOezPNZGIzo8URnhhWXItpIPkYHOyfrwyWCvOTe2HS2nuRsP0fYDCbRr0S5KSjTvwdXq3nay+/efmuxaNx/7SKPtSNjcoTLacyApKbRzHYBodoz7tcuhJ/8wMNbVAQAAAECcwIFqofZ7Q7ZtMcvzXCeEPc3dTwAAgIYKxPNGihQE+SbIth/jnv3JRwjsZTAZmRTH+R7O6+PvSrEqGNEyVCxuAUzajYSSOEUKxnriuKSyVl/szTRgPcKWNkwkBGN1WzP+nmvmbDxCS3cW+SyvstYpf3MCOtme2vUarUePVlm09Wi5+PuhrzdrxPNEkTndTPG8vMYm7FXqHA7acKBUsTngQ9oyK4UKK1zRmu0CWAfpPSDWSsHV4Pkky/G+st0CW4JUqM6ZsX1a0Wk98utdj3wwlRnmgz0Oauo037VrROK7P91AGw5ymyXRtwd3+V1PKBYRfDyYlGRXu/CgSaR+A1rU1jzcnvL8k0K+3jEDIG6jyqJhaAoAAACABoP0H4+GLbh0fJTPJSzcc/9hW0kC5e46TslJwUlGuwoqfPrJeNYBADR0IJ43UqRAyPfANe6oY+a2sT2pa34mdWuZKaaGGyFJJY6zYMbRqxW1HkFWirGRgqOZmVoZeR6KeO6uI4+E8wOBni92RU2d7vek8B6IDLd9SCQiz7VRs3pT6vhh52//XR9wcOCCQe3EQIAc7NCLCjAilvKgy6Sr2tLN768VIjlvW4n8TUwQD0glZDMtAvnRb36j/64+6LUsPSVJeK9/+pcR9NvRSrFsaOdmQf02bEEOxiS5hVkWqN9ZsZeeX7jT6/PP1h6kdQ+Mr/fhULZVuhniuUYU1s5SOFHlGlgY2NxB/bp3okSNuLzpUCltPFhKaSFku6/zE3kuB7kiifqSw7MR5MO+T+S5SbMFAIgEynmLDiUAAAAAVMhH/FD6veHOWP9w1QG674tNIvjm5S1rwl4/99sAAKChA/G8kSLFJb4JykjQtrlp9LfxwSfyU9uysGDGgdjfbDgcNfFcJkCsMSHyXFpdyGhZCQvqelHbmalJQT0UVEcg6lYrcuuJ52rLmb+O6SGOCZf61/ztYsaAtGxRH69QbVssyYl05kktPfWrc3gJqTKiukYnWUwoHC6pUUR79ltngfRPozpR8dZjItq8c8vgEv8qti324M4ndRTz3iKXYD+6Rz4N7JBLry/dIxJWXvHqz8pAjRZ+Lr1kaAdFMJPi+br9Je4HVBf8fR7c4umNZ/duJfbZH1rhXTvd0lbnej+xg4Nu+F1fn0SYW46U0bnPLSFbCJHZWquUaEaeq21bvAbyNIMDcuANkecgnqPKouFnCgAAAICGg7TpjMYzgnrGOrPvhKufk5nspA4tsnWDzupDzlJW93kAAKAhA/G8kaL2LpORoF1ahJbETy0uShGqoJwz9RG1z0uP+Ii4FMDCSZyiFjRZNNVGk6/YddxLwAzGssVLPI9C5LleoLIUSZn/G9tTEcj/u/oAHSyuFn+3yk7zEhhDtW1hEZltU9TCvYxKZyFVfnbp7BX0w9/OEFYxZvic3zauJ00a0NZVF5uN5m4NbX1SX1XvjxHkeb5ufzEVV9nE35ed0pF+N6idiOBesqOIVu05EXAdW46UK8ezVY5LFN9/ooo+WLlft/zEfq3p39cMM3xuaI+pfO9vF+VvK5Tod7VVjzryvKiiljYJqxiiTs0zKDfD/GTKajFcbbfkG3nuni0A8RzEIf5mTAAAAACgaSODpaIxOU0beS5tIE9t5aTZN43yCb4xwmtLdtNjc7aIv5EsFADQGIB43khRZ82W4nmqNsOiQdTiovRYZs9n5vJTOlKk0Ua2hzLFXS2WiwhtTTAvC5iSDs3SFcHZ6M0+wz2izsI8t5GMlmc4Kvnn3ce9yg9on0t/HdszRM9znchzu0tA5Gcfdfv86bSu9OqS3ZSbbqHxfVqLZZbkEG1bZOR5UqLYPz7H5OCM4jmdlCDsU2S0weJtBSaI5+7zNwRrkUAPiNK2xehgTHaa63Lp8hB30dU9IDXr8sG0bNdxv8lc+Zjd9elGISxLzu3fVtj9yN+SOsHrjoIKYbd0qMR1HvpDa9OiFYnlMdZMtFBQ/O+DtFrh/dRaTsiokrX7S+iCF5eKv/MyLLT83jGKrZFZqH8Tarsl7UCeMlsACUNBHBLOgDAAAAAAGi/RzIuijTyX/bpwxvZlv0n2BwAAoKED8byxe547PJG7oYqPfEPleyrfR+XN9Lhb8Asl0WCwaMXyUB4iWFSTYq9elK0UaM8b2Jb6ts2hp7/fJt73apMTtJfbBS8uE+3FgvX0c/sIz24t8347Rhef3J46NMsIXjzXEWilZQtH2Kun1v1pdFfxUhOqbYsUYj2RxonC451fUs/nqPbHLupPJVU2mrPpCBW5k3mGg9w3GdkcLvL8UYRlg0+GF5/cQexPWY0r6rxbfib1b+86P1pkpYoI9EB8tOoArdrrikznc4PtWK4+tbNuWY5gv+zfK/wmsfUfea4fiZ5Un3geZGS2WrSXA0WndmtOQzrl0bFSl83O0bIacR7wYECPVuENoAQUzwPkX5BiupwZAUA84S/RLQAAAACaNvLRPBoD7J4cQdrB/dDXee6AtrS7sFLYWl46LPLBdgAAEGkgnjd22xaOPLeFLz6yQCa8rR1OeuTr34T4GzXxXCuIhfgQwRYV/DCgl1RT+jSzIKweZPjj8E6G1s3faZebRodLa4SPtOSp77cq7XTXxF7i7ye/3SoeJFhY7GAgx6XWr1lP51TEcwMDJFrbFhYf1+8vIadwSfeGI4aHdMwTgnOdRmyW4nmlSrzk5LIs3g/okOsSz932PuEQ7swJLfL8kW1m1HaIj+G95/YOebuvXzdM2LtwM/donaV4w+shvfbVbauHPCYSH89zxYte//vyWNoCJNLV367TxxKJBxC+uPk0ZfmZTy+ifcerxHluNmqbItlGXHV/keewbQHxiBwIjXDaEAAAAAA0MPi5PJx+bzi2LbLvmZgQ+vNzTpqFpk/qY1INAQAg9kA8b0q2LWHYXrAIZXULju/+vFdZ3r9drgm1rX/bakL1WGeRr8bm0LUr8bRRklc7tcl1+YTXB4uOX906mjYfdll6/Li9kN5ctlf4YDM9WmXRlW4hnj3gWDxXR8yGa9si98nIMZa2LXK9f3rrl4Be3Q//rh9dO6qLyuNaiucs8HrvhxTmW2a5fHFYQNda1hiB2/3N64YLv+xa1cCGGfgkDI2SZUJ2moVGdc83VDbL7bVfn3iujRhXnytqaxV/kedS+OZTissajcJXi+f+vpOXbqF9RIo/vJnUqvaz0lqnmyzUVTf3LAuI5yAOUaZFI/IcAAAAADpJxaPRTdHatniSlUZ+2wAA0FCIix7bSy+9RF26dKG0tDQaMWIErVq1ym/Zzz//nIYNG0Z5eXmUmZlJgwcPpnfffTeq9W0IyJsd3/yUqOowInelgM0WDDKadfX946hTi/ptR8Klf/tcc8Rzd+S9XuS52tpGWrWwrQYnRDUKlz+rVyvxunZkFy+x94yeHtE0K83i49UcCK0Vh654rrJtqQ9pESDXe8Dt996lRQb1bpOtvHh/mG3Hyr22odi2uM8ntcArhVSOPOeHPR6U4Gj8YF/sm73CLbqrBzbMnJURbOR5NJGJaiutdr8+6nqR52qR2MtapR7bFu1368Nr3X7aLy/DNSvleEWtaGsjL064+/T3W2nax+t1Xy8t2ikicdQe7TxI5U/Eh+c5iGfktRyR5wAAAKLdp2Y++eQT6t27tyg/YMAAmjt3rtfn/Mw1Y8YMatu2LaWnp9O4ceNox44dXmVOnDhBV111FeXk5Ij++Z///GeqqKjwKvP999/TqaeeStnZ2dSyZUv6wx/+QHv3eoKxgC/ysTwa/RRFN9BGnkd8ywAA0HCIeeT5xx9/TNOmTaPZs2eLm/ysWbNo4sSJtG3bNmrVqpVP+ebNm9Pf//53caNPSUmhb775hqZMmSLK8veAZgTZK/I8PNsW5s9v/yL+79g8nfLd0cWRZkLf1ooHeTgPEVLM1hXP3dY2bKUxvGtzWjF9jIj+NWKDokeX/EwxuMCWFSxQqiPYs9yWHDJie+bcLfTez/sU05S6uiSavmZBANuWAOK5kchz6XPtFl7l+l++aij1befxeH97+V568H+bqdjtb6/155XtyQKvVqzkJKEr7h1LhSHYtjzyzWb6ZW+x4i1udsJQGeQZzYfSYJGR50xhRa3fxLXV7vNWYleJ2upBF3+7qBacORI/kJWMGnnu8PH2Z/UikwPd+/km8TKL8X1be0XYy3NM7zjC8xzEM3JQB5HnAAAAot2nXr58OV155ZU0c+ZMOv/88+mDDz6giy66iNauXUv9+/cXZZ566il6/vnn6e2336auXbvSAw88INb522+/CcGdYeH8yJEjNG/ePLLZbKJffsMNN4j1MXv27KELL7xQ1O3999+n0tJS+tvf/kYXX3yx2BbQxxP9HT3Pc9nHlP2JOOwiAQBA0xXPn332WZo6daq40TJ8w58zZw698cYbdO+99/qUP+uss7ze33bbbeKGvnTp0qDE80prJSVZfYWipMQkSktO8yrnD7s7WtlI2cSEREq3eKKYq2xVipeZFhajMiwZIZWttlWTw8nWJFXkoBqy2i1UXlsu/k5IcCXy05b1R2ZKpvJ333Zp9NOOMpIODEO7tPDaX3XZmroasjs8bcMPUjX2GlHe4rSI+krBrbauluoc/iOwuayMYHWSjZxkJ6u9WretuX25nRmr3Uo2u7ddRGJijWiHsppycjhzvMqWuduIElz1zBGHykqVVqs4H/i88LdeNeqyHGDePIuPm92rvqkWm9hWSXWNeDB69+d9VGmtJSep2sHqLTonkIUSSNahzmf/S3mfqIaSkpJE/SxJLuGS25bb2Gtd7naorK0QZT1+0A6v9aan1olyBeWlYrnVvR6Xd7ydkpKs4vMTlWWubScmiHPVVcYiBgz4xecYn2v+4LIpSa5jzGVb5nBNaqiwgrfbnGrq+Fx2kJ2qqbYumVKTXYM2/JuQ55QeyYneZWXdnOTaf4nNUS3OWaO/+2CuEdrfvdGyPFCQkFgrjs0pT8zRL0+eOsj9KaupoEqra59LqlzHR7aF3u+ezz9ZpqS6nJISU71+y/6uEWU1rutLclKG39/9KV0z6X8bXfugra+TrMRb9seEPp1oRNcW4m+bo5YcTjv956fdVFJtE+dFdV2lUu+DJU5K4F9IUqLP9cSmXAcTRNsHukZor1P1XU/UaK8RAAQVeR4l6ygAAACNl2D71M899xydc845dNddd4n3jz76qBDAX3zxRfFdfnZmAf7+++8X4jfzzjvvUOvWrenLL7+kK664grZs2ULfffcd/fLLL2JmOPPCCy/QpEmT6JlnnqF27drRmjVryG6302OPPUaJ7sHiO++8U6yTn70sFv3n+KaOfEaQAXFRyZXmfmZXbOXweAIAAPEhnlutVnFDnT59urKMb6o8JWzFihX1fp9v6gsXLhQj6v/4xz90y9TW1oqXpKzMlcyx3T/bkUrLUTi3+7n01eVfKe9bPdNKEd20nN7xdLqjxR3ixs90mdWFiqpdHtdahrYdSiumePap70t9aV8pOwL70ie/D224YYPyfth/htGWoi26ZTvndqYdt3imz53+5um05sga15t0ogOlRPN4VelET65vRvdMcCX6ZM557xz6af9PfkXrkrtKlPfHU5+gA+nfKe+f2+J6Saz3eQSjqz6/ij7f+rnvSt3Bp8V3FisC3dSvp9K7m/zb7hy67RDlprpEtBOW16gieQ79ng+P5xApbL95O3XJ6yL+nr5gOj278lnfQulEZ3xAdHHbD6lZSjexaG3Jq7Su9DXx2fSVrpea5dctp2HtXA+Ez/78LE1f6Dlftcy7ah6d2flM8fcrq1+h2364Tb9gOtEj8x6h15ecJpJu1lgWU0HyLL/rfWnCW1RUOJTeWL6PNh3/gbJm3qG7zgPlRO+sf40mD5wsFs3dOZcu+u9FumWnLiSqSn6O7A5XItPVh5dSv1cv8F1nAdHnM4naJ1xPyXQRJZCDVh1YRd8VTxCfT5nnKsdkzXT9f//o+2nGGTPE35sLN9OQV4f43bdpI6bRk2OfFH/vLdlLr+w4Tazv1h9dL3LnpO3xEtGNJ99Iz5/zvPjNldnLqNkz/jOuXjPgGnr9gtfF3yyIepVVufFcMYfo4l0X00cXf6Qsy5qZ5Xe9wVwjzuh0Bs2/er7yPphrRFHGrVRhP6Jb1uLoRO1qX3bVNTWZttM0siXupzGeXVD2M9nZihIT/qNcp7yuEe4yTOfnifLT8+nw3w6L92z31P2506jQuk63DglpqdSPvlLWe/HHF9O3u771LqSanHJ8mmfgYMrXV9P/dnxB/njmEs814s9f/9lzjUgnGv2ed707VL9PSZQrouBv//Z2mr12tk8bHKh2nZuGrhHu69S6qeuoX8t+4u9Hf3qUHlv6mN/6qq8RL6580W85APQ9z9E7BQAAEN0+NS/naHA1HIjGwriMGD969KhYhyQ3N1dEtfN3WTzn/9mqRQrnDJfnba9cuZJ+//vf09ChQ8X7N998k6677jph6cKWq1zOn3Dur//Oz5zyuTMU5HfDWUe0sNpcwSAJbFcY6fq6A2XYDlK0sTtAkDX1htBWsaQhnVOxBm1lDLRT9NvJ6DpiKp4XFRWJkWgewVbD77du3er3ezzdq3379uKmypG2L7/8Mo0fP163LE9Fe/jhhw3XqaCwwMvvjevnD/Z4oxYkRunlg4vfOpeUeq23qkpfbGP4oUJdVusbp4bXoy7L2/EH74u67PHjxw2XLSwspECoyx49cjRgWfa9S0tyjVwcPHgwYNn5C+ZTbnIupbqjOgOxaNEiap3qOpd2H9odsOyKPScoxZkt/i5JriQKEPSwbPkyKsgoEH9vLfB/XjI///wzVW6uVETjQFTU2ulAtSsiu2Wqkwr8n2q0d/tmKiptLuJ3jx71DIDosWHDBpp70HU8VpeuDlj2182bqcbKgwgJtGnj+oBl2Z6F3ec3rVlFtiRvv0Mt7Ic4t8JVh/3V+wOW3b17N82tdZU9Vht43/bt3+fjx+gPPrdkWY4oDgSfs0bXG8w1gn9j6rLBXCPSE+uows+qW6c76ZnBrofqxIQ6unoDz8sIjLxOBbpGcP1kHXaUJlBZtY3cEx50aZFsU8pzuwRiyULX9pmigmOmXSOYBHJSr4xqcX6YdY1Y8tMS2pfuWt+OI4HPd/U1Yvu+7fXWFwD1lGyI5wAAAKLdp2ZhXK88L5efy2WBymgtYZKTk4XNqizDdi8//PADXXbZZfSXv/xF1HPkyJEBn7v99d95PRkZ4efbks/EsWTFsQTaVppAF3RyUAudgL71x/jZIImKirz7HZFgc4FrW0eOHhPbOnyEZwgkisjzeGirhgDayThoK2OgnaLXToG02biybQkFTjayfv16ISovWLBAjJp369bNx9KF4RF49ag6j1x37NiR9v11n0hsUp8lw9FxRwPatixdvFQI9zxyvm/cPsP2DTvG7zBsxXLW+LMMlz17/NnCZuFoWQ2d9+JyL9/jF68cROf27ewpa3OV9YfavmFM3RgvS4ZgyvJIDs8QGDNmjGgntW3L2Lqx9dq2cNnVzq30ydobqE3mLfTmdUOpWbo7HFmF2mZhnH2cj81CQXkNLdlxXHiLWxLTlLKbDk2jj9b8Qfz9wHm96NKTO/i1ZOD1zrL7jxBXlx1vH0//sPvOiOBjufVoOdkdycJOg/evR8szKCnpIZ+2krD9yKz5e2jh4T10Rs8r6Mvx3tHv320+Rnd//iud0jmPnvzjSMW2ZYJjAt1R5x2l/vCcrfTZ2kPi7/5detOSfft5nh7dMOlm+nv2rV5lP159gHYVuQYDkshCnVvk0JSRnclJE+nyymvp593FyhS/wR1zqU1Omq4VyzW2a/y2mbZsdYsR9M95O33KrZ5+NmWkpIi24HbiB+iC2wr8Rq1obVuKJxaLv6e8s4bW7HPNqvj8LyOoR6ssn9998ThXWT2CuUZof/eRukbMzOpKry7dI3IcsI8y+8R3y8+g3UVVise+vE7Ja4TktKd/pPKaOurcPJ3OOimfJk1yzRKYu+kotfrtYerTNpNmTOqtW49B7VtTqtsnPZxrhBa9a8QVr62i346Ue9bRqyX985IBlJ2aqbKBOtvresLJbi/9zypqkWmhRdPOCHiN0P72tGVn2zUR7X5+98OLh9OHt33otywAEkSeAwAAaOywiM52Mtdee63wWC8vLxdJSC+55BIheujlzvHXf58wYYJu/90o/KzH25TPxLHktgd+EP8P6NmFrtF5zn7uuWUcukStW7VWns0jRc26Q/Thrs2U37IlTZo0lL48sZaouEh4nsdDW8Uz8XROxTtoK2OgnaLfTnJ2U1yL5/n5+SJy/Ngx7yhEft+mTRu/3+OpXz169BB/Dx48WPit8Qi1nniempoqXlryMvMoJ7P+m2+eJa/e8H4+WPwKVFZLriU3ImXliZOXSfTzvefR0dIaJXlfx+beI/XBnGThlOV24ihSbnPtZ0bX++hFA8Qr1DrINjlJ57xatz+P/rvGdQ42y8gT9QxmvYHKZpB+dMTIHvp2I4HaKtmdMPOdnw/TF+v+v707AY+iyh4+fLKHLSyGXXZQQHYQBEGcYVVUUIYBZAQz/OETRUFmREEEURAERESQuAyjjiDo6KAyDoIsOiqyo4KgqCAMCmHfIln7e85NqunudEIl6XQn3b/3ecrurrpdXTmG3K5Tt851H+Wrkz1qTekyMeWkdOylz4ySKCkV41KjREQGtmsk/9qeddfBoVPpmjc3ysSWkgpl3Nv+v665x6JWTKzUqlRZ7IiJtj+57M3N6su/tp+UU1aBfU1eNqkqVcrryPtL9Mu2tzjlRicYVrERZSQ8u152fLmKUqHMpaSuJT//louqbX7+3Y/p3sIsavXuIzLyH9sk6WyY+Z2IMRdSfnP+nfKMV4P4K+Tr/52RQye1/v4xmXBzmJSOjpSzKRkSLjFSr1K8XN/o0kW33BT135PSUfr/7dLvxOJhXXLUgfTcb6UyEVm11h1ROf5dF8XfKVUmNufvE+CNdeGR5DkAwN/n1Lo+r/bWo66rXr26Wxs9/7baJCW5n5Okp6ebu7Ot9y9cuNCUe9HJRy1vvPGGSYZraZfrrrvO9vm7t++xBeGr/Xiz/eApmbv6e/lD2yulX+ual21/+rd0r8cSVyprXdnYojtWS3RUVkpIZxHSz8p0ZH0v0a8nRRmrYEKc7CNW9hAn/8XJ7vsDmjzXZJbWQdPR4zq7t8rMzDSvR492HwGbF32Pa100ZKlcLsYsyFuDKmXNJJg6Sr+2xwWG4sR1tvVzKd5H67eoefmE63X1r5BHb24i0z/cY0Y+6mjl4pTAaVS1nGx46HdFtn/XnzM6+4JEMLH+zWstfaUTaeZlyf91kG/+d0b+9LdNorm88xfTJSYyQv67L6s2e8UyOe/yCIRIl59D/73amUDJ+n/9W1qGvLcz626L3KSnZ5g5AwB/J8+1Xj8AAP48p9bSKbp97NixznU6ik/XW+VWNAGubaxkuY7O04T3qFGjnPs4ffq0qbeun6/0Lj79bK2Nbt0Ob00UatFEv3WMwWb6v/fItp9PyWc/HLeVPLcmBs3tO8LtNvbh6wlDrcfgO0sCgIILeNkWvSVLb+PSiUbat29vZvW+cOGCc6bwoUOHmvrmOrJc6aO2bdCggUmYa10unXRk0aJFAf5JUFLFxUbJ2nE3ytmLaXJNjYLfCujP5PmMO5pLx/pZE6laoiLDpWYF95HjuYnMnj49LSMz5EY/ut4dGszJc9dEc17KxUZJp4bxZuLRsxfTzfLChh9l9bdZo5GuKCbJ82iX5Lnr87zEZpeUuZiWKWOW5V3TX11bOVzuK8QxAvlhXbh0/dsOAIA/zqnHjBkjXbt2lWeeeUb69Okjy5Ytk61bt8pLL73kvMNTE+vTpk2TRo0amWT6Y489JjVq1HAm6Js0aSK9e/c2ZVkSExPNXXyarNfJRLWd0n0/++yz8sQTTzjLtkycOFHq1KkjrVsXbTmSQDh40l7t3Mslz/1Z2s36DOucMD37osZlTiEAIKQEPHk+cOBAMxml1j7Tmmh6ZXvVqlXOyUkOHjzodrVavwTce++9ZhK5UqVKSePGjc2tX7ofoKBqX1F8R5xbXBMs9ePLSN34gpeHsEY6pqZfGvERKqMfrS+GwZo8r1G+lNx4dWXZeuCU6P/Rvi1riKRefhJLTaJr4vx8SrrsPJRVE17d3PzSrbqB5HoRwO7/txoVSsmoGxuYkfWXo3Xg49PynhgZKJKR55ydAkDI0XltDh06ZCbcjI31MmNkEZ9Td+rUSZYuXSqTJk0yyWxNkK9YsUKaNWvmbDN+/Hhz7j1y5Egzwrxz585mn67Hu2TJEpMw79atm9l///79Zf78+c7tOpeMfo6WbdFFJ/zUEeu6Hz2XDzbpVj3MPLjOaZTb4PuM7A3+OD+LyD7HtBL51vcTru0DQDFKnivtcHO7pWzDhg1ur/Xqty5AqHEdbFs2NtInJTAuuiTP7ZTBCAbWSI78jGAuSfT/46sJ7Z2vdRTQhx/aSZ5n/U6du5gmx85llcF6995O0rSY3I3hWrYlPxc9Hu7tfbJTT1lx+lCC3T//+U956623zEl0amqq27bt27cH7LhCkfO2aM5OASDkaAJV5/DavXu3SVz7+5xaDRgwwCy50dHnOmJcl9xUqlTJJMfzoiPRdQkF6RneR5LnNpDHugstt/34Y+S5dQ7oWbaFa/sAcEmBM3A68vv999/3egI+d+7cgu4WQC5cZ6PXUjOFYX0RS0nLqosdsiPPgzB5XlBW8nz7z6fl2Pms5HnlssVnzgS3si1BeMeAP+hIsEcffVTuvvtuee+998yt3D/++KNs2bJF7ruPgjX+lknNcwAIWTpKW5PmJ06c8FnyHIGXZqOOu2vC3PoukNtgH9fBI0U98tzK+1+6uF/kHw0AwZ0814lDbrvtNqlfv77s3bvX3N514MABcwW9TZs2vj9KAG63+FmJzsKWwEhxGXkeEYIjz0NltL0dWrZFPfvxpVHqxWnCYdeyLTqhKfLvhRdeMLVMteboq6++am7H1n5cb/E+efJkoA8v5PiznikAoPiZOXOmPPTQQ2buLtdyKSi50myMPHfNr19uwlC/lG3J/gwrkW99P+HrCQBcUqAM3IQJE+Svf/2rTJ06VcqVKyfvvPOOqdc2ZMgQM2kIAN9zTRiWiSlk2ZbsmoduyfMQKR1g1RCEuz9dV1uOnLloJpFVv29SxTnhZnEQVYAJQ+FO7xTTGqdK64zqpF3qrrvukuuuu04WLFgQ4CMMLdbIM5LnABCadBLP5ORkadmypURHR+eoAc6F7ZJ9h2uubVwS5rnl2p2TdgawbAtfTwDgkgJl4Pbs2SNvvvlm1g4iI+W3336TsmXLmnpoffv2lVGjRhVktwDy8Ie2V8ovZ36TFleWd0skFoQ1iiEl/VLZllBJ4NipRRiKft+4qlmKq4LWPMcl1apVMyfiderUkdq1a8uXX35pTtj379/vdmcL/MOasDlU/vYCANzNmzcv0IeAACfYc/v+5c9JxT0nDHWOPC/yTwaAIE+elylTxlnnvHr16qZm6jXXXGNeHz9+3LdHCMCoWCZaptya9e/MV4nIlLSs5I3mblxrqof6iBAUP9EuJw8kzwvm97//vZmrpHXr1qbe+YMPPmgmEN26davccccdgT68kPLVodPO50wYCgChadiwYYE+BASAa53zjMvVPPfHyPPsj9h75Jw5Nuv4wsM4ZwKAQiXP9fbuzz77TJo0aSI333yz/OUvf5FvvvlG3n33XbMNQPFmjWKwRp5bZVxCreY5Sg7Xuy1iSJ4XiNY7z8y+DVgnCL3iiivkiy++MHOY/L//9/8CfXghZcuBS7fix5eNDuixAAAC7+LFi87BaZa4uLiAHQ+KjlvZllzOSzKy75SN8MM5WrzLHEdbfz51aU4Wru0DQOGS53PnzpXz58+b51r3XJ8vX77czBSu2wAUb86yLWmhVzbghkbxsv/4BWlanROSkiTKJWFOzfOCCQ8PN4tl0KBBZoH/6fwCakSXeiFz1w8AwN2FCxfk4YcflrfeektOnDiRY3tGxqXyiggemTaS52nZgx38MfL8qqrlpGxMpJxPSZd/7Tgs5y6mmfUhdHoIAEWTPK9fv75bCZfExMSC7AZAgHhOGBpKyfPxvRtLu7qV5Nq6lQJ9KMiH6xvEy+tfHJDUjEwzmSkKPrLt66+/lqSkJOcodIuOQEfRW/9dkrzy2X7zvFp598nhAAChY/z48bJ+/XpZtGiRmbx74cKFcvjwYXnxxRdl5syZgT485NOuw2dstXP9+pWW4f5dLBA1z9WQDrXlxU9/kjc3H3Suiwyd00MAKJrkuUVvLfN2Aq4TkQEovqKyv4hpIjLUkudlYiLl1pY1An0YyKfOjeJl19ReooN1wkPo99WXVq1aJUOHDvU6N4mOfmaEm39MW/mt83mDymUCeiwAgMD54IMP5PXXX5cbb7zRzEXSpUsXadiwoZnYe8mSJTJkyJBAHyJs0tHadyz6It9lW9Kyy7N4cpZO8dN33js71JYDJy7IhZSs74L14ktLNfnJL58NAEGbPP/+++9l+PDhplaqK50tmhNwoPjz/CIWSslzlFzav1DhouDuv/9+GTBggEyePFmqVq0a6MMJWcmpWd+R7unaQG5oVDnQhwMACJCTJ0867+jW+ub6WnXu3FlGjRoV4KNDfpxOTpPU7Dt6VWxUuK0JQ72NPNftVn7dX/NS1bmijLx4V7tLx5WWJh9+SPIcAAqVPNcr45GRkbJy5UqpXr069TqBEjz5oiJ5DgS/o0ePyrhx40icB5h1otyvdQ3uogCAEKaJ8/3795u7ths3bmxqn7dv396MSK9QoUKgDw/5YI0Ut+RSyjxHnfN9SeedAxC97YtzNAAowcnznTt3yrZt20wnD6Dk8ayf54/JaAAE1h/+8AfZsGGDNGjQINCHEtKskWmeFzEBAKFFB6R99dVX0rVrV3nkkUfk1ltvlQULFphRv3Pnzg304SEf0j1GkLuOLs+rbIvacei0tKld8dJ2l/dyjgYAJTh53rRpU681UwGUDJ5fxMK5ewQIenpCrmVb/vvf/0rz5s0lKirKbfsDDzwQsGMLJdaIsmiS5wAQ0h588EHn8+7du8vevXvNADWte96iRYuAHhsKO/I89+S5Z2L9yJmLHvu6lIj314ShAAAfJc/Pnj3rfP7000+b2cGfeuopryfgWrMNQPHlWT+PL2ZA8HvzzTdl9erVEhsba0agu94irM9Jnvu3bAt/dwEgtB06dEhq1arlfK0TheqCkic949KF8dSMTFO2xbMcS24jz08lp7pvdxt5zoV2AChRyXOtu+b6x187g27durm1YcJQoGTwTNpQTw8Ifo8++qhMnTrV3BoezslYQOj3pLTsE2zKtgBAaKtbt66ZHPRPf/qTKa1WseKl0h0oWdKyR4tHR2Ylz5XmyL3d3OuaHLcmG3XbV/b3BMUpGgCUsOT5+vXri/ZIAPhNxdLRUjo6QpJTsy501axQKtCHBKCIpaamysCBA0mcB5DrCTHJcwAIbVu3bpWlS5fKE088Iffff7/07t3bJNK19nlMTEygDw/5YCXENXkuKZdKt4RLzuy3S1UWY8WOw/JD0nnz/MarK0uHelc4y2x6G7kOACjGyXOdyARAcCgTEykfjb1BvjtyTjSP1q5upUAfEoAiNmzYMFm+fLlMnDgx0IcSslzrmEZRtgUAQlrr1q3NMmvWLFNOTRPpI0eOlMzMTLnjjjtk8eLFgT5E5LMkm+t8JlqexVuyxbNsy76k82ZRK7/+Rd4ccZ15zp3BAFDCJwxVp06dkr/97W+yZ88e5ySiOmN4pUok4YCSoFal0mYBEBq0pJqeoH/00UdmIjLP+Urmzp0bsGMLFWnpjDwHALjT0cW/+93vzDJq1CgZPny4vPbaayTPS+rI82y5zRlqta1cLkYe6nW1nP0tq2xL4ic/yfHzKfKHxI3OkecAgBKcPP/000/N7WTly5eXdu3amXXz5883t5x98MEHcsMNN/j6OAEAQCF88803ZoSb2rVrl9s2bgv2D6sOquKkGACg/ve//5lR57po/9yxY0dZuHBhoA8LBZkw1CV57lnb3HX+E6UlNP/Y7tKEsSnpmTLv4+8lPft9vZtVL+KjBgAUafL8vvvuM3VTFy1aJBEREc4Rbffee6/ZpifoAACg+GDukuJzW7eWbOGCBQCEthdffNEkzD///HNp3LixDBkyRN577z2pU6dOoA8N+WQlvF3LtmjNc2+spHqEx/eA+37X0CwAgCBJnv/www/yz3/+05k4V/p83Lhx8vrrr/vy+AAAAIJqZBolWwAA06ZNk8GDB5s7uFu2bBnow0EhpFs1z11GnntODOpZ8zycO9AAILiT523atDG1zq+++mq39bqOjh8AgOLn9ttv9zraWdfFxsZKw4YN5c4778zRt8P3ZVtIngMADh48aOsuJL27W8ujxsfH++W4kH9pXmqe5zby3Eqqe448BwAUXwU6e3vggQdkzJgxMmfOHPnss8/Mos8ffPBBs3z99dfOBQAABJ7OU7Ju3TrZvn27OVnXZceOHWZdenq6LF++3FwA19vHUfRlWwAAoc1u+a433nhDzp49W+THg4LLyM6Ix7jWPM+tbAsjzwEgNEae6+1lavz48V636RcBnQhDH7UWOgAACKxq1aqZkeULFiyQ8PCsk7vMzExzMbxcuXKybNkyueeee+Thhx82F8Xhe5RtAQDklzXBJIqvtOz+XScD12si+r8s95Hn2TXP+SoAAMGdPN+/f7/vjwQAABSZv/3tb2ZUuZU4V/r8/vvvl06dOslTTz0lo0ePli5dugT0OIPFCxt+kEUbfjQn0J6ThJE8BwAgeDgnAQ0PN+VY0h2O3Gue5zJhKAAgyJLnzAAOAEDJoqVZ9u7dK1dddZXbel1n3SWmtc/t3kaOvK3YcVjOXUz3uq35leX9fjwAAKBoJwzVsmzh5nuUI8fI8+PnU0z5thMXUsxryrYAQBAmz99//33bO73tttsKejwAAKAI3HXXXTJ8+HCZOHGiXHvttWbdli1bzIjzoUOHmteffPKJXHPNNbb2t3DhQpk9e7YcOXLE1Ep//vnnpX379l7b7t69WyZPnizbtm2Tn3/+WZ599lkZO3asW5vHH39cpk6d6rZOJy/V5H5JlJKedSK98M420qxmnHN9mITJlRVLBfDIAACAL6U7R56HibnBL+PSCHOV+MmPMvM/7t9nGHkOAEGYPO/Xr5+tdtQ5BwCg+NGEddWqVWXWrFly9OhRs05f60TfWudc9ezZU3r37n3ZfenkouPGjZPExETp0KGDzJs3T3r16iXfffedVKlSJUf75ORkqV+/vgwYMMB8Xm40cf/xxx87X0dGFugGuWIhJS0reV7nitJS54oygT4cAADghzlNskaeZ9U9t2w9cMqZXNekuT72blYtMAcLAMg322elOqkYAAAomSIiIuTRRx81y9mzZ826uLhLI6JV7dq1be1r7ty5MmLECElISDCvNYn+73//WxYvXiyPPPJIjvY60t0a7e5tu2uyXCc2DQYp6VkDCWIiqW8OACi4P/3pTzn6awTOlz+dkJ2HTudY55ocVxku2XPrO8Gs/i2kf9sr/Xq8AIDCK7lDugAAQIEU5iQ8NTXVlF+ZMGGC28Sj3bt3l40bNxbquPbt2yc1atQwtdc7duwoM2bMyDWhn5KSYhaLdUEgLS3NLIVhvb8w+7HKtoRLZqGPp7jyRZxCBbGyhzjZQ5z8H6uiiPWqVaukbNmy0rlzZ2c5tJdfflmaNm1qnlesWNGsX7Rokc8/GwWTnJouwxZvdvbxnsrGRIpVjcW15nlqdvuYKC6oA0BIJc8vXLhgaqMePHjQnEi7euCBB3xxbAAAoBDatGkja9euNSfgrVu3znMy0O3bt9va5/Hjx015Ni354kpfF6Y+uZZ/efXVV02d819//dXUP+/SpYvs2rVLypUrl6O9JtY9a6Sr1atXS+nSpcUX1qxZU+D3XkyNMBXOP/90g+yKlqBWmDiFGmJlD3Gyhzj5L1ZafszXHnroIXn66afN82+++Ub+8pe/mJJo69evN49///vfff6ZKJwT51NN4jwyPEz6ta7ptq1UVIQkXF9X3tt52LzOdKl5npo9oWh0BMlzAAiZ5PmOHTvk5ptvNl8iNIleqVIlczKtJ6ta65TkOQAAgde3b1+JiYnJ19wlgXLTTTc5n7do0cIk0+vUqSNvvfWWmejUk4581+SC68jzWrVqmbrthb29XUcYaqKlR48eEhUVle/3p2dkSubGrNrtN/XsIRVK538fJUFh4xRKiJU9xMke4uT/WFl3N/nS/v37zShz9c4778gtt9xiJvHWi9l6ro3i58xvWXcgVCoTLXMGtPTaxqp57pI7d86DEk0pNwAIneS5TvZ16623mhqn5cuXly+//NJ8GdF6bGPGjPH9UQIAgHybMmWK1+eFER8fb+qnW5OOWvS1L+uVV6hQQa666ir54YcfvG7XiwLWhQFX+n3EV8mkgu4rNTPd+bxsqRiJitJR6MHLlzEPdsTKHuJkD3HyX6yKIs7R0dHOEe06WfbQoUPNcx2YVhTJehTe2YtZyfO4Urn/PoSHZ9c89zLyPCYyuL8PAECwKtClz507d5rbyrTGqZ5Aa81RHe01a9YsmThxou+PEgAAFMqhQ4fkf//7n/P15s2bZezYsfLSSy/l+2S/bdu2phyM66Ti+lrrlPvK+fPn5ccff5Tq1atLSeNaC5VRZgAAb7TWud5B9eSTT5o+uU+fPmb9999/L1deyaSSxdHZ7JHn5fNKnmdXyNt+8JQknb3oVvOc7wQAEEIjz/XKuybOlZZp0brnTZo0MaPQ9eQcAAAUL3feeaeMHDlS7rrrLjly5IiZ4LNZs2ayZMkS83ry5Mm296Un+8OGDZN27dpJ+/btZd68eaaMW0JCgtmuo+dq1qxp6pIrnRvl22+/dT4/fPiwuRCvE6U1bNjQrP/rX/9q7mrTUi2//PKLGSmvF+gHDx4sJU1KeoZ5jIoIkwjrLBoAABcLFiyQe++9V/75z3+aSUG131T/+c9/pHfv3oE+vJCUlpEpU97fLYdOeq9xfzQ7GZ5X8jwyO08yacUueWb1d/LlxG7O7wUxJM8BIHSS5zrp2JYtW6RRo0bStWtXc8KtNc//8Y9/mBNxAABQvOjEm5roVlpHvHnz5vL555+bCTbvueeefCXPBw4cKMeOHTPv0cR7q1atZNWqVc5JRPWiunWRXWkyXL87WObMmWMW/Q6xYcMGs05HxWui/MSJE1K5cmUzIk/Lwunz4kprm3uTnGqdJHN7NgDAu9q1a8vKlStzrH/22WcDcjwQ+erQaVm66eBl29WulPvE5CNvqC9vbj4o3x09J6eS0+TYuRTnyHOS5wAQQslzncjk3Llz5vn06dPNCLNRo0aZZPrixYt9fYwAAMAHk6ZZNcK1tuptt91mnjdu3Fh+/fXXfO9v9OjRZvHGSohb6tatKw6Hy8xZXixbtkxKkuGvbpG1e5PybMNJMgAgLxkZGfKvf/1L9uzZY17r3dw6wXdkZIFO01FIVpK7WlysPHzT1V7b6IXxrlflfmF/WKe6Zmk//WNJOpcip5PTKNsCACVcgXplvU3bomVbdLSZNzqiTdt6m9ALAAD4zzXXXGMm+taaqmvWrDE1Vq1R4VdccUWgD69E0UnALpc4V50axvvleAAAJc/u3btNuTKdcPvqq7MStU8//bS54+qDDz7gju4AsOb4rFA6Sm5vXbi68xVLR5vk+ankVOdcKNyRBgAlU5Fe0r7ppptMTdP69esX5ccAAIDL0BPy22+/XWbPnm3qlbds2dKsf//9953lXGC/Jqrls4d/J+VivNQ+DROJi2XkIADAu//7v/8zCfJt27ZJxYoVzbpTp07J3XffbeYo+eKLLwJ9iCEnM/suubCwws9Xogl4te3nU5KenZVn5DkAlExFelZ3uVu0AQCAf9x4441mfpKzZ886T9KVnqCXLn2pdid3jeUveR5fNkZioxhJBgDIHx1ktnXrVrc+WZ9rWdRrr702oMcWqqzshS+m+q5UJto8zvt4n3Md5dwAoGTirzcAACEiIiLC7STdqkeuJdhc7xo7fPhwAI6u5EjLuDQ4ICqCr1IAgPy76qqrTMkWT0lJSdKwYcOAHFOos0aeu8x5XmCD2teWxtXKSf34MmYZ1rGOlInhjjQAKIn46w0AAJy4a8z+yPOI8DCzAABgh979ZZkxY4Y88MAD8vjjj8t1111n1n355ZfyxBNPmFJrCNx3oHAflG3RSUXzmlgUAFBykDwHAADIh9Tsib+iIkicAwDsq1Chgls9bU3W/vGPf3Sus5K3OpFoRkZGwI4zVGVmV2XzRc1zAEDwKNLkOZ0OAAAI1pHnlGwBAOTH+vXrA30IsFO2hTQGAKCwyfM333xTBg8e7HXbQw89JLNnzzbPufUbAAAEm/TMrO830STPAQD50LVrV7fXp0+flr/97W+yZ88e87pp06YyfPhwKV++fICOMLRld+8+KdsCAAgeBTrrGzVqlPznP//Jsf7BBx+UN954w/n63LlzUr9+/cIdIQAA8BvuGrNftiWSsi0AgALaunWrmRj02WeflZMnT5pFnzdo0EC2b98e6MML8ZrngT4SAECJT54vWbLEjDz/7LPPnOvuv/9+eeutt7gVDQCAYkjvGsuN3jVm4a6xy6NsCwCgsHTgmdY2P3DggLz77rtm2b9/v9xyyy0yduzYQB9eSI88ZyABAMBVgc76+vTpIy+88ILcdtttsm3bNrn33ntNZ6+J88aNGxdklwAAoAhx15jvpGVQtgUAUPiR5w8//LBERl6qpKrPx48fb7bl18KFC6Vu3boSGxsrHTp0kM2bN+fZ/u233zbn7tq+efPm8uGHH7pt14vpkydPlurVq0upUqWke/fusm/fPrc2Olp+yJAhEhcXZyZD1ZIz58+fz7GfOXPmyFVXXSUxMTFSs2ZNmT59uhRH1DwHAHhT4LO+O++8U6ZNmybXX3+9fPDBB/LJJ5+YDhEAABQ/3DXmO4w8BwAUliacDx48mGP9oUOHpFy5cvna1/Lly2XcuHEyZcoUU/KlZcuW0qtXL0lKSvLa/osvvjDfCTTZvWPHDunXr59Zdu3a5Wwza9YsmT9/viQmJsqmTZukTJkyZp8XL150ttHE+e7du2XNmjWycuVK+fTTT2XkyJFunzVmzBh55ZVXTAJ979698v7770v79u2leCfPyZ4DAAowYah2xt5UrlxZ2rRpY0aiW+bOnWt3twAAwA9c7xrTk1ydoOy9994ziXMufudPqpU8j+TkGgBQMAMHDjTJa00qd+rUyaz7/PPPTSk1TWznh55/jxgxQhISEsxrTXj/+9//lsWLF8sjjzySo/1zzz0nvXv3dpZte/LJJ813gwULFpj36mjxefPmyaRJk6Rv376mzeuvvy5Vq1aVFStWyKBBg8wkp6tWrZItW7ZIu3btTJvnn39ebr75ZvMz1ahRw7RZtGiRScpfffXVpk29evWkuCN5DgAoUPJcr0h7o5OcnD171rmd+mAAABRPetfY6dOnzV1jevFb7xrTfhz5k5Y9YSgjzwEABaUJZj13Hjp0qKSnp5t1UVFRpszazJkzbe8nNTXVlFKdMGGCc114eLgps7Jx40av79H1noPjdFS5JsaV1l4/cuSI2YelfPnyphyMvleT5/qopVqsxLnS9vrZOlL99ttvN3eoayk4HZWuyXpNymsbHdVeqVIlr8eWkpJiFovmGlRaWppZCsp6b177SEvL+v8g4ijUZ5V0dmIF4pQfxMoe4uT/ONndh+3kObd0AwBQsnDXWNFIz55RjOQ5AKCgoqOjzQjwGTNmyI8//mjWNWjQQEqXLp2v/Rw/flwyMjLMqHBX+lrLpHijiXFv7XW9td1al1ebKlWquG3Xmu2aFLfa/PTTT/Lzzz+b+uo6cl2PU+da+cMf/iDr1q3zemwaj6lTp+ZYv3r16nzHxhsdYZ+bHcd0IGCEHD92LEcN+FCUV6xwCXGyj1jZQ5z8F6fk5GTfJs8BAEDJwl1jvnHoZLJMWrFLzvyWNTLhVHKqeWTCUABAYWlCWCfsDEaZmZlmFLkmzq0ScVo2rm3btvLdd985S7m40hH0rhf/9ftKrVq1pGfPnqZOfGFGF2qipUePHmaEvze/bT8s8sNuqVqlitx8cxsJVXZiBeKUH8TKHuLk/zhZdzddDslzAACCFHeN+cbKr3+VT74/lmP9lRVLBeR4AACwxMfHS0REhBw9etRtvb6uVq2a1/fo+rzaW4+6rnr16m5tWrVq5WzjOSGplp85efKk8/36Xh2N7jq3SpMmTcyjTpbqLXkeExNjFk+aIPFFMimv/YSHR5jHiIhwElc+jHmwI072ESt7iJP/4mT3/QyZAgAAyMPZi1kjzrs3qSovD21nlr8nXCtTbr0m0IcGAAhxWv5FR3KvXbvWbcS3vu7YsaPX9+h61/ZKR/FZ7XVST02Au7bR0Xlay9xqo486j4rWW7doKRb9bK2NrnSOFU2oW2Vp1Pfff28e69SpI8VNpiOrLFs4N+QBAFww8hwAACAP5y9mTSDWtEac9GjqXv8VAIBA0zInw4YNM5N3tm/fXubNmycXLlyQhIQEs10nJa1Zs6apJ67GjBkjXbt2lWeeeUb69Okjy5Ytk61bt8pLL73kLOc2duxYmTZtmjRq1Mgk0x977DGpUaOG9OvXzzmCXCcBHTFihCQmJprb6EePHm0mE9V2SicH1TlW/vznP5tj0sT6fffdZ261dx2NXlxkT2lCOTsAgBuS5wAAAHk4n5KVPC8Xw9cmAEDxM3DgQDl27JhMnjzZTNappVVWrVrlnPBTS6SEh1+66bxTp06ydOlSmTRpkkycONEkyFesWCHNmjVzthk/frxJwI8cOdKMMO/cubPZZ2xsrLPNkiVLTMK8W7duZv/9+/eX+fPnO7frug8++EDuv/9+ueGGG6RMmTJy0003maR9ccTIcwCAN5wFAgAA5OFc9sjzsrF8bQIAFE+axNbFmw0bNuRYN2DAALPkRkdfP/HEE2bJTaVKlUwSPi86Cv2dd96RksDhTJ6TPQcAXELNcwAAgDxcyB55XpaR5wAABC2rbAvJcwCAK5LnAAAAubiYliEbfzphnpM8BwAgeFkjz8mdAwBckTwHAADIxZYDJ53PG1YpG9BjAQAARYeR5wAAb0ieAwAA5OL1jT+bxza1K0itSqUDfTgAAKCIMGEoAMAbkucAAAC5+O++Y+axbZ2KgT4UAABQhLJz54w8BwC4IXkOAACQS+3T1PRM83zgtbUCfTgAAMAPI8+F3DkAwAXJcwAAAC9SMxzO+qdV4mIDfTgAAKAIUfMcAOANyXMAAAAvLqZlOJ+XiooI6LEAAICiRc1zAECxTZ4vXLhQ6tatK7GxsdKhQwfZvHlzrm1ffvll6dKli1SsWNEs3bt3z7M9AABAQfyWnTyPigiTqIhi8ZUJAAAUYbk2xchzAICrgJ8JLl++XMaNGydTpkyR7du3S8uWLaVXr16SlJTktf2GDRtk8ODBsn79etm4caPUqlVLevbsKYcPH/b7sQMAgOAfeR7LqHMAAEKmbEsYyXMAQHFKns+dO1dGjBghCQkJ0rRpU0lMTJTSpUvL4sWLvbZfsmSJ3HvvvdKqVStp3LixvPLKK5KZmSlr1671+7EDAIDglZyalTynZAsAAMHPmi+Usi0AgGKTPE9NTZVt27aZ0ivOAwoPN691VLkdycnJkpaWJpUqVSrCIwUAAKHmYlqmeSwVTfIcAIDQqXlO9hwAcEmkBNDx48clIyNDqlat6rZeX+/du9fWPh5++GGpUaOGWwLeVUpKilksZ8+eNY+acNelMKz3F3Y/wY442Ues7CFO9hEr/8aJOAeXo2cvmkdGngMAEEo1zwN9JACA4iSgyfPCmjlzpixbtszUQdfJRr2ZMWOGTJ06Ncf61atXm/IwvrBmzRqf7CfYESf7iJU9xMk+YuWfOOndUAgO+86EyYLlX5vnMSTPAQAIetQ8BwAUu+R5fHy8REREyNGjR93W6+tq1arl+d45c+aY5PnHH38sLVq0yLXdhAkTzISkriPPrUlG4+LiCj3CUBMtPXr0kKioqELtK5gRJ/uIlT3EyT5i5d84WXc3oeQ7cP7S81tbVA/koQAAAB87cPyCZDgc0qByWec6yrYAAIpd8jw6Olratm1rJvvs16+fWWdN/jl69Ohc3zdr1iyZPn26fPTRR9KuXbs8PyMmJsYsnjQ54qtEki/3FcyIk33Eyh7iZB+x8k+ciHHwuJiRdeJ8d6e68n9d6gf6cAAAgI/8cvo3+d0zG8wEoWv/0tWZQLdGnlO2BQBQbCYMVToq/OWXX5bXXntN9uzZI6NGjZILFy5IQkKC2T506FAzetzy9NNPy2OPPSaLFy+WunXrypEjR8xy/rzLEDEAAIBCSMnIeiwbU6Ir3AEAAA+7fzlrEudq39HzOWuekz0HALgI+BnhwIED5dixYzJ58mSTBG/VqpWsWrXKOYnowYMHJTz8Uo5/0aJFkpqaKn/4wx/c9jNlyhR5/PHH/X78AAAg+FjJ8zIkzwEACBonLqTK7I/2Ol+f+S01R9kWUucAAFfF4oxQS7TkVqZFJwN1deDAAT8dFQAACFUXrZHnscXiqxIAAPCBZ9bsk+9dRpufSk5zPmfCUABAsSzbAgAAUFzoLdu6OJPnMRGBPiQAAOAjx8+nuL0+di7Fy4Shfj8sAEAxRvIcAAAg2/rvkuSqyWvk+zNZX5HKRDPyHACAYJGZmfVY54rS5vFvn+131jq36qCHM/IcAOCC5DkAAIAX5WIjpfmV5QN9GAAAwEcysjPknRrEO9ddTMt0nzCU3DkAwAXJcwAAgGydG1aWTY/cKNPbpcvGh2+U6uVLBfqQAACAj2RmFza/rn4lsQaYn0vJqntOzXMAgDckzwEAALJFR4ZLpTLRUjZKJCaSr0kAAAST9OwMeUR4mJSNySrNdu5iukfNc5LnAIBLOCsEAAAAAABBz0qQR4SFSVxslEfyPKsNZVsAAK5IngMAAAAAgKCXkZ0hDw8PM3ObqPPZyXNnzXOy5wAAF1m9BQAAAAAAQAhMGKojz62yLfPX7ZO3tx2SHQdPm9dUbQEAuCJ5DgAAAAAAQmbkeUREmNSoUErk51Oyef9JtzaVSkcH6OgAAMURyXMAAAAAABD0MjLFOfJ8Up8m0q5uRUnLyC52LiLlS0XJLS2qB+4AAQDFDslzAAAAAAAQ9DKtkefhYVIlLlaGdqwb6EMCABRzTBgKAAAAAABCpuZ5OIXNAQA2kTwHAAAAAAAhU/M8MoLkOQDAHpLnAAAAAAAgZJLnjDwHANhF8hwAAOTbwoULpW7duhIbGysdOnSQzZs359p29+7d0r9/f9M+LCxM5s2bV+h9AgAA5Fem41LNcwAA7CB5DgAA8mX58uUybtw4mTJlimzfvl1atmwpvXr1kqSkJK/tk5OTpX79+jJz5kypVq2aT/YJAABQ0JHnEYw8BwDYRPIcAADky9y5c2X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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "log_likelihood = get_log_likelihood(tree,down_conditional,up,init_up)\n",
-    "log_posterior = get_log_posterior(log_likelihood)\n",
-    "proposal = get_proposal(tree)\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.5,alpha=2.,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.1), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(.5*1e-3,min=1e-4,max=.1,prior='uniform',keep_constant=False) # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 1000, burn_in=000, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,params.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prior is maximized at x = 0.16245454545454546\n",
-      "Prior is maximized at x = 0.4954545454545455\n",
-      "Prior is maximized at x = 0.0001\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot priors for all initial parameters\n",
-    "init_params['k_alpha'].plot_prior()\n",
-    "plt.title('k_alpha prior')\n",
-    "\n",
-    "init_params['k_sigma'].plot_prior()\n",
-    "plt.title('k_sigma prior')\n",
-    "\n",
-    "init_params['obs_var'].plot_prior(x_min=1e-4,x_max=2*1e-3)\n",
-    "plt.title('obs_var prior')\n",
-    "\n",
-    "plt.tight_layout()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mcmc_Gaussian_BFFG_shapes_SDEs_Kunita.ipynb b/examples/mcmc_Gaussian_BFFG_shapes_SDEs_Kunita.ipynb
deleted file mode 100644
index e55e4db..0000000
--- a/examples/mcmc_Gaussian_BFFG_shapes_SDEs_Kunita.ipynb
+++ /dev/null
@@ -1,1371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for shapes with SDE transitions\n",
-    "\n",
-    "Parameter inference for trees with Kunita SDEs transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebooks [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb), [mcmc_Gaussian_BFFG_shapes.ipynb](mcmc_Gaussian_BFFG_shapes.ipynb), [mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb](mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb) for simplified versions. The covariance of each stochastic step is shape dependent and the processes nonlinear, thus requiring computation of likelihood ratios in the downwards passes for use in the MCMC iterations.\n",
-    "\n",
-    "The covariance is assumed rotationally invariant, allowing factorization into a kernel product the identity matrix of the ambient space. In this notebook, the SDE is given in terms of its step covariance, and the diffusion field is found from this covariance.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_shape_2d, plot_tree_2d_scatter, plot_tree_2d_shape\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.SDE import dot, solve\n",
-    "from examples.ABFFG import *\n",
-    "from examples.shape import g_K0, g_K1\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed:  778163868103\n"
-     ]
-    }
-   ],
-   "source": [
-    "# seed\n",
-    "#seed = 216298397259\n",
-    "import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "print(\"seed: \",seed)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 2; n = 32 # d = dimension of embedding space (usually 2), n = number of landmarkds\n",
-    "phis = jnp.linspace(0,2*jnp.pi,n,endpoint=False) # circular shape\n",
-    "root = jnp.vstack((jnp.cos(phis),jnp.sin(phis))).T.flatten()\n",
-    "\n",
-    "# plot\n",
-    "plot_shape = plot_shape_2d\n",
-    "plot_shape(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SDE setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_steps = 100 # number of time steps in the discretization\n",
-    "\n",
-    " # drift\n",
-    "def b(t,x,params): return jnp.zeros(n*d)\n",
-    "\n",
-    "# covariance specification for shape processes\n",
-    "# assumes same covariance in all directions (x,y or x,y,z), thus allowing some factorization of the covariance matrix\n",
-    "gQ = lambda x,params: g_K1(x,params)\n",
-    "\n",
-    "# evaluate g on two pairs of landmark configurations\n",
-    "gQ_q = lambda q1,q2,params: gQ(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# diffusion field and covariance matrix factored in dimensions\n",
-    "sigma = None # diffusion field, given implicitly from square root of covariance matrix a\n",
-    "a = lambda q,theta: gQ_q(q,q,theta) # covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tree with SDE transitions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                        o\n",
-      "    ┌─────────┬─────────┼─────────┬─────────┐\n",
-      "    o         o         o         o         o    \n",
-      "┌─┬─┼─┬─┐ ┌─┬─┼─┬─┐ ┌─┬─┼─┬─┐ ┌─┬─┼─┬─┐ ┌─┬─┼─┬─┐\n",
-      "o o o o o o o o o o o o o o o o o o o o o o o o o\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "degree = 5; topology = symmetric_topology(height=2, degree=degree)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "n_leaves = tree.is_leaf.sum()\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "n_edges = tree.data['edge_length'].shape[0]\n",
-    "tree.add_property('value', shape=(n_steps+1,n*d,))\n",
-    "tree.add_property('noise', shape=(n_steps,n*d))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(1e-1), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.25), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions using the SDE functions above. The step covariance is square of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unconditional down\n",
-    "down_unconditional = SDE_down_unconditional(n_steps,b,sigma,a=a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sample new noise\n",
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "add_leaf_noise(tree,key,params)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf][:,-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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hcDgcDofzysOFEYfD4XA4HA6Hw3nl4TVGHA6Hw+FwOBwO55WHR4w4HA6Hw+FwOBzOKw8XRhwOh8PhcDgcDueV56Vr8FpQUMC6v1NjNz09vee9ORwOh8PhcDgcDuc5QZ2JUlNT4ebmBn19/VdLGJEo8vT0fN6bweFwOBwOh8PhcKoIT58+hYeHx6sljChSJO68tbX1894cTgWSm5uLI0eOoFevXjAyMuLH+hWGnwscfi5w+HWBw38jOKpISUlhQRNRI7xSwkhMnyNRxIXRyz8YNjc3Z58zF0avNvxc4PBzgcOvCxz+G8HRhDYlNtx8gcPhcDgcDofD4bzycGHE4XA4HA6Hw+FwXnm4MOJwOBwOh8PhcDivPC9djRGHw+FwOBwOh8MpJisrCwkJCS/tIbG3t4epqWmZX4cLIw6Hw+FwOBwO5yUlLi4OT548gYGBAV5WoqKiUL16dTg6Opbpdbgw4nA4HA6Hw+FwXkLy8vKYKCIXX3d39xIbnL6IFBQUICIigu2nra0tDA1LL2+4MOJwOBwOh8PhcF5CUlNTmRhydnbWqo/PiwrtHwkj2l87O7tSv87LJxs5HA6Hw+FwOBwOMjMzWf8eY2Pjl/poGBsbs/2k/S0LXBhxOBwOh8PhcDiveHPTF5ny2j8ujDgcDofD4XA4HM4rDxdGHA6Hw+FwOBwO57lFe9avX4+qABdGHA6Hw+FwOBzOK8qwYcPQo0eP570ZVQIujDgcDofD4XA4HM4rDxdGHA6Hw+FwOBwOR4krV66gU6dOrA+Sg4MDhgwZgsjISPbYr7/+CicnJ+Tn58s9p3v37hg5cqTsPqXJ+fr6wsTEBB4eHvjoo4+Qk5Oj/GYAsrKyMGHCBPa6tL6bmxs+//xzVBZcGHE4HA6Hw+FwOBw5YmNj0adPHzRq1Ajnz5/Hnj172DJKvSPeeOMNJCcnY9++fbLnREdH4+zZs3j99dfZ/YMHD2LatGmYOnUqbt68iSVLlmDz5s1qxc68efNw9OhRrFu3Dv7+/lizZg1q1KiByoI3eOVwOBwOh8PhcDhy/PLLLyzSs2zZMtmyf/75B3Xq1MHt27fRuHFjFk36999/MXjwYPY4CRpbW1sMGDCA3f/2228xY8YMvP/+++w+vV5UVBTmzp2LBQsWyL8hgKdPn8LLywu9e/dmjWnr1auHyoRHjDgcDofD4XA4HI4cAQEBuHz5MkujE28UPSIePHjA/n3ttddw6NAhWWPVLVu2YNCgQTAwMGD3AwMDsWjRIrnXoFS6uLg4pKamyr8hgMmTJ7PnUJRo4sSJ2L59OyoTHjHicDgcDofD4XA4cmRkZKBbt2747bff5B8AUL16dfbvmDFj8MEHH7D0uPbt2+P69etykSASTB9//DETUIqQSFKkY8eOCA4OZoLo2LFjTBytXr2aia/KgAsjDofD4XA4HA6HIwdFh/bv34+6devC2NgYqrCwsGB1SP/99x8ePXoEb29vJm5EfHx88PDhQ/j5+UFb7O3tMWXKFHbbtm0bM3Kg2iUXFxdUNFwYcTgcDofD4XA4rzCU1nbhwgW5Ze+99x42btzIUuPILIGc4u7fv49Nmzaxm6GhICPGjRvHIkckgEaMGCH3Gv/73//YY5Q+R1EjqhuiqBKl6S1dulRpO6j2iJzoWrVqxdal1DxHR0d2qwy4MOJwOBwOh8PhcF5xW+727dvLLaNIzalTp/DJJ58wcUQW2yRaunbtykSLCD1mbW2N0NBQTJo0Se41SCiRiCK3ueXLlzMxVbNmTWbJrQorKytWkxQWFsbeo2HDhti1a5esZqmi4cKIw+FwOBwOh8N5RdmxY4fGx48cOaLxcRItZOOtDhJHipEkKYWFhbK/SYTR7XnBhRGHw+G8ZOQXFODsoyg8S85ANRtzdKzjCgPJ7B6Hw+FwOBxluDDicDicl4gdN0LwwZYLCE9Mly3zsLPA4lHtMKxZ5TXJ43A4HA7nRYNPIXI4HM5LJIpGrDwqJ4qIiMR0tpwe10R2bj6+2Xsd264H40Xl2P0I/HA9CQfuPH3em8LhcDicFwweMeJwOJyXJH2OIkXFmdrF0DI9ADO3XMDgJl5KaXWZOXkIS0jD9hsh+HrfdbasbU0XnJ81CHp69EztoVxxXZ9TVug9zzx6hh8O3sKRe+Fs2ZAVx1C48u1K3Q4Oh8PhvNhwYcThcDgvAVRTpBgpUhRHTxPT2Xpd6rnJPfban8ex+3aY3LKLwdFoNm8HbvxvmEqh8/3+Gzh49ynSsnMRmZSBuLQsucfpeU2rO1a4IDp45yl+OHgT5x9HKz0em5oJJyuzCt0GDofD4bw88FQ6DofD0ZHrYbEYv+YENl0NYgYH4Ylpz/0Y0nbost7j2BQEx6awvxt5OKhc99bTeHy77wb7Oze/AFuuPcb5oCjcjUzA3L3XcOFxNPzDE5REEXHo7lMltyEpkUnpOPPwmdrHtWHO7qvov+wQE0XGhvqY2skHD74eAS8rYc7vXFBUqV+bw+FwOK8ePGLE4XA4OjJ1wzlcC4vFv5eDZMuSFk2EjZnqzuCVAbnPaUNYfCp6LtrPanGsTI3w+Psx+HpAcybyHsUIQknKyQeRmDuwOfbcDsXo1ceVHh/WxBtBscnwj0hk9ye1q4sWXk4Y3aIW3lp3GpuvBWNUi5r4vHcT1HGxkT1v/JqTOPEgkv39w5CW+KJvU533+XpYHPt3VPOaWDiqLbtf7+ttssd/PeKPvIJCuNuao7GHAyxMjHR+Dw6Hw+G8OnBhxOFwODpwLTSWiSJFLE2e7+WULLnJfY6MFtTFYAz0gC92XZXdT83Kxbsbz+FqaCyrMVLF6UfPoPfOKrXvu+NWKLrXd4OJYQqy8/IxpmVtVrPU8NttsujUmvMP2I2Eo5utOdxsLGSiiJi96ypeb10HnvaWOu2zuJ8DGlWHm60FVp8NVEoHvLiqOMXu2If90L2+h07vweFwOJxXBy6MOBzOK0VZe/zs8ZevxRHZcDkIE9rWlVuWnJnDanGcrEzxaa/GFWpKQPtAltzkPkfvokoc5atYuK0EpzpteJKQjnc7+2Lh8QCMXHkUKVm5bHldFxv8r29TbLkejP0BT9jxoNv9Z0lKr1H9i43Y/k5PDGrsBUMDzZ9HSmYOqy2idEBCv+i4UjRME/2WHMJ/b3Uv0bZ81Zn7WHHmHvu8DPT1YFD0r5GBPqZ09MHYVrVLPCYcDofDefHgwojD4bwylEePnyFNvPHdfqHuRsoba09hTMtaMDY0kC27FByN+Uf92d+f7bjCXN7a1XJV+9pf7r6KG0/i8FrL2hja1JulfqVl5WKvfxgTGdXtLVnURfoeUmgftr3TEzM2nde65qg8eBSTjMshMTA3NmSiiEQECcGv+jeDmbEhE4ypWTnMpCEiKZ39G5mcjuC4VKw8c1/2OsNXHoWXgyXWTOiMWk7W7LORilbap8XHA/Dv5UeISCrev+C4FNSc/R9CShBGOfkFTDjSMdL0eS87dRcBEQkqH3sQncyFEYfDeeF5GP8Q0WnKpjUVhYulC+o6yE8eVkW4MOJwOK9Ujx/FoInY46ekwbJIs+qOTABtuvpY6TGT6X+xf5MXTYS1mTG613eXe7z9L3vw24g2+KhnI7nlNOCnGh+KuKRn57EePBYbDTG0ibdcHZOIqZEBsnLz2d+tvJ1Q39UW3w1uyYRTTGpmhYiiRu728FcjFogLwcIPbJ8GHvh+cEs093KSe9zK1Bj1XOlmK7d8+WsdEJuWif/tuoq/zj9AWHwaui/cL4sEFUjMGShiQyYQhLGBPhM6xFd7BItxbVFnWy7iZGnK/v2we0N0q+/GjvXma49ZdI2ObXRKBlystavp4nA4nKooihr+0RA5+TmV9p7GBsYImBagkzg6cOAAfv31V9y5cwdxcXFYt24dxo8fX6HbyV3pOBzOS09IbDIm/nNSbY8fcbBMaXaaKCgoRFZuHl5rUYtFRXwUBvkiNjPXwuWT9Uyk5P8xhdXViCg6pcWnZaHLb3vx0dZLTBSJ0N+qRBEhiiLiSmgs1l16xAwVJvx9Eh9uvYiy0sRTcKlzsS62utYkiqR81ruJkijShL6+HhMZf07ojGVj2qOppwMM9YXUOKkoIkRRRIiiSFektuWq6sfWX3woq38ioTrw98MYueqYLOXQ3sIEJmoidhwOh/MiQJGiyhRFBL2frhGqtLQ0+Pn5Yf78+agseMSIw+G8lNyJiMc3+27g0N1w1mtH28Fy57rVWDqYosMc2UqTocC9Z4L7GnE/SrlWRoREkftnG5SWZ+bms747zlZmsDA2xNi/TmjctpqOVix1LjAqCW1qOiMqOQOOlmZKBhAPo5PZrTwgm24iOiWzxHW71XND6xrOzDihc51q8HWzk9UBUYqdbzU7lk6nDVTzRe+tq4H3T0NbsUjVkpN3mckD6aqCEl5EGlUjwUufV8sfd2p8zhd9muD9bn6wNTdBZUJpiGSL7mpjzlIMORwO51Vg1KhR7EZMnDixUt6TCyMOh/NS8TA6CV/uvsYK/nWFBstD/zjCmp1emz1UFvnIyy/A+5svyImi0kL9fcQeP1J6+LjDzcacRX+kbJ/aE008HVk0y3Dan2xZaPzz75skEhqfit9GtmHbKPI0IY2ZKYhc+nwIE0+aoP2j+i9NeobsxclJjyABe/rhM/Y3GWg09nTEynEdsc8/DDGpyn2VFLEsEmszN1/A4hN3oA1ta7owcVKZUI8op4/Xye4//Ha0nO05h8PhcMoPnkrH4XBeeMhMYeShaBjP+Bs+c7eWShQRX+y8wkQRQaYAefn56LFwP4ze/RN/nL5X4vOppmjfjD6leu8FI9vin0ldUbBiCjN4IO7MHcEEB0WrRFFU1SADhabf78DsnVdwLzIRH265ICeKiMhY9ZE1EYrWSU0xVCGKIkIURWLtVqsfd7LUu8GNqmu13ScfPWO1QtqKImLTNeW6soqCPvOt14PhoRB1JEMKDofD4VQMPGLE4XBe+AhRva+2yO7T4LiBmx3uRuoe3ZH28ll9LhBRKRk4Hhih1XOpHofSsSjipApK7+rrVx0ZOXmsaaqUye3rwb4oPYssondM7Qkqr6H6G4L+tjM3QWJGNsoTa1MjVsuUr1DLUxp+PHSL3ZR4FIo3hs3BjamD8NH0IbCzU92rqKyGEdSLicwvBjT01Gp9qmNytDRln5s2KYPEntthrEeTmBpI4oXQ1ob9SUIatt8IhqedJUY0r6l2vZC4FLy+5iQuPJbPx7/8+RCt0xI5HA6Hozv8CsvhcF5oxv4pX6NjZmSA5IzyKSrd6/9E63VpcC0OsElwNHS3x3nJwJZqXgY18sLbnXzYgLrxt9sQUCTeyI2NbhQp2jmtFxtoS8faJJBufzkMW6+H4ONtl8q0T35udmhXywUjm9dEl7puzNpbapld7qRnIDU1E9//uhlLVu7Fh+8Oxsxpg2BsbAgzMxOZqKD6ovJgX4BymqIiXepWY855lHKprSgiqFbtm33Xmfi+HCI0+qXzrWs9N3brVt8d9VxsVAolEsRekkha9u+T1dquLzt5l4kisj+f2d0PPx26zd7T2JAneXA4HE5FwoURh8N5oRndohauP4mTMzcIT9KcklXRNPJwwIbJ3dD0++1ISC+O8ryz4Szrj0M9iURRJOXm0zgmmlQNrGfvuqrWpU4X3mxfDx/2KLYLn9i2LjM8oD5E5QWJnPa1XHBw4UakR8Vj/vdv4p//TiDgbii++ek/diPGjeqCf1d/LKsTojQxsk8ve/xKM6cePpNZq2tFXCLgYEuhIfx8+LaS4CHHOtG1jsSLq7U5i0SRvbipoQHbrxYKTn1JGTmsHxb1TCJISM8f0Qa9fD1kxh+vtayFgY288MNBIRJHKYuqWDGuA97p5FthTY05HA7nVYELIw6H80Lzae/GmNnNFzNXbMPyO5obfFYWZMktRgdI4tBAn+y98wsKZQNhRag3EjWaVZeWFauFoYA2rL/0CFM7+cpSstrUdMEPQ1oy0wJy2SMXP52gdDKFbb75SX8EBT/D3oRk9viwgW3x4fTB+OLrdfhl8XbZehu2nML6VR+xfaaBOu0/9ZQSj5ku1Ha2xrlPB+FKcBRGrTqGrGJH87Jz4jxgbwvrXh3g4WSDjuTE5+3MDCWSMrNxIjCSpUdShDA7L5+lzNFNiqKpxqR/TrF+VSIkmHsvPoC3O9ZH8+qCkQUJGRJVnnYWzDVRHVM3nMP4NnVZhKkimhpzOBzO8yApKQn37hXX9z5+/BgXLlyAo6Mj6tatmGaxXBhxOJwXmoT0LBy+8wTVLQ3w4+Dm+GK3bs0+KxpxgL93em82+P1sxxW5x5eOaYe3O/qoTauSRsJKgl6LIhrSQTANqheNaoeW3k5o/sMO3Hwaz/odjW1Vm72niaE+ei46oPuOJacC1/yB7u3llx84CddNe+UW1Ww8BRYWpkhPVxZ3CYmpcLAXLKhpoL54dDt8tfcai6goUsPRCiFxqsVvUEwK+i45yPav3KnhCTx+gpSNe/HAQB+txnTFG6+9D/2iqEu7Wq6Y078ZcvLymV34tI3nSnxJqSiSsupsoKx/FLnwedhZIuzHsSy6NHfvdTlxQzVnVCO2cXI3taKoPJoaczgczvPg3LlzGDhwoOz+N998w25Dhw7Fjh2qI+hlhQsjDofzQvPB5guSFLOqJYqkvPPvWaVZ/+0aBqYFBQV4Gh6H6p5OLKLyNLFki+6Dd54y4wYaLKtKm9oypQd6LNovl/pVmogQIzNLXhRRc9wt+9W+jCpRNOPtAbC3s5LdT8vKZbbo6lAnikSkoogidGSwkJ1XukawMnLzAGMj2d38/AKs3XAcJ87449qphXBytJF7z3kHb2r1slSbtGNqL2bTfuReOPovOyR7jOqeaNvnDmjO7tPnT/bkUkj8tvJ2wtlPB6kU1ZQ+9/7m82qbGusVNTUe3MSLp9VxOK8YLpYuMDYwrtQmr8YGxux9dWHAgAEyk5vKggsjDofzQnMuSLtO2jSjTvUg6vioR0M2i0+NVEVo0HrsvnaudCWhKhWqsaeD7O/s3Hw2QKYBsLWZMb78/l/88NtWDOjTEkt+fpulaJUEbb8YiVg+tgNea1Vb7vEu9dyw9e0e+P3UPWTm5iErNx83JPVZKlHnuGZjLS+Sbt5D87YNMKpPS7RtVZ8Ju58XbcfBo6rF6p9L38Ob43vKpQ7azFyrtN5X/Zvh2/035JZRXZS3gxW+3qf82vbmxkjIyGFpi9XtLTGtky/MjQ2w9Jg/IuNSkKprl4rQcOC+sk33k6exaNvjUxzYNhd1a7trbTku8r9+TdHHT3DQ69PAE339PJmwFZncvj58qtmxHlrfH7iB7/YrC64robH4/dRduZoxcs37aOtF9m9EUoZWTY3pvOBwOK8OdR3qImBaAKLTtPv9LA9IFNH7VnW4MOJwOC80zao7sCajhLuNKSKSs+Rm8Ce1q4s2NVzQr2F1FkGhZpnUNJPqOBaNboc2NZxxJyIBvx3zlxNFxIY3u+FKaAwG/n5Yt2iKFvwzsQtqORWLi7UXH7BaEbF/UegTwQxh36GrOH76Nt7+YCQWq2gHNLxZDWxXEf15d+M5TOusXJA/tGkNVtD/z8WHLGVNLXceCFZ6jeorP/Y4TIgQOdgBl28JaXUUr3sSgesXlWuoOrVvgO/njEdMbBJGTPgJri52eH10VzlR9Dg2hTmvKaIoioi1Fx/K3fetZovHsanY/W4v1HO2widr9uFkVD6LMM3acRnmRgYoeBKJrGq6zVYy3F2A6wFAMz/AyR64eReIESJTj0Oe4bU3f8X1M4t0thz//sBNtn3URLixhz0TclI2X3uMAQ2rY+Dy4nOPzuWlY9qzGjY6V5ytTGU9r0QuhcRghQ4ug2W1SedwOC8mJFJeBKFS2XBhxOFwXmjISGDHzVD2t1QUETTY/OvcA/RtIIgiYlTzmlh++h4zH6CakL3+Yax+g9KwhPQrfVl0hlzFaPZdLaUURbQNE9rK/yBZmQpOZITfN9sAA2tgzEAmQDLPXMHinzagnq8XHjQSogMN3e1wffYwTP/vvOx5NR2t4OVghVMPIzGpXT2V7x0cm8KE3r1ngiuevYWJnHMeg4SApxvgXBTRouMhTdcKfIyRQzvg/MmbiCwSRapo0bQ25n05Hj27NWUiKDc3Dx7ujgiPiGPGCxQxktZCkWPeqQeRrGmsKg681xcT/j6BuLTi7e3fsDq2vd0DaTl5rC9Rbm4uxte3wpoZvbDlZhgWHLmN+9HJQGlEEWFuBoweUPxZ+9UFTgjnhJGRIQb1ay1bVVvLcUqBo2iPaNOuiqTMHDlRtH5SV7zepg77u3cDT4T88JrSc77cfRXzDmiXyqfrNnM4HM6rABdGHA6n1Hy77zorFNdkFVzR7LhZcq2MtJbi8z5N8Of5QDbr3n1hcU0MDaq3vN2DpS4NWn4YFsaG+PfSQzyKSSm3bTXUA1weBeHMqfMYcOw8mjepjW6dGqFzh4bMDIGMJN7bpFBjQ/VBXdrAJCoGDyTpawERiczym1KyKPJEfXTIYEFTs9Hc/AJ0nL8HkZIUK6o7ol5GtL9Bp68j2b0a0Lyh8GB+AZCRAVhZChEi0eK5b1dsfRgBqIg2fPXZGLz7Vj8ExqTA08UWNZ2Ka3BISFAPo0/mrMH8pTsxcVx3mYEB1cn8NaGzbF3KK6fUx0cxyUjJzEWrGk4srVEqioj9AU+YMQV9flJI+L7VoT72LN+G+/cigdZNUGokx9Q49CnadvDDgN4t8cbY7nI1RtpajpMoWvZae0QmpcusuEvCwVJoAKz4eVIPYEqJ/PXIbRaF0nqXigwcaJsrAopEOVmasskGbRvgqiMmJRPpObmo4ShJ3+RwOJwKgAsjDoejM1SXsvCYv8z0gGokKFJR0kCJ+rOocs8qDTSoPHz3aYnNSRVrKTztLTFvcEshamRkyJqxktvZj0NbyfYh6Lsx2HkzBO9pMAIoDXmFQMQ1YXv3RyVg/+Fr+PbnTTh3+Gd4uDmimbkBVnapg/lXQ/EoPVfuudmuzsV3ikRKenYumlV3xGd9Sh70XwuNxeT1p+VEEUFmDDfnDGNmAPUV641ycopEUSFw7iqMqjnBp5Uv/BOzAG8PwMsdCAoDbtyBm6sdjuz8Fg18vHDzSRy6LC2Odszq1RijWtRkA3mj+jVhVtcL9x+Esfqj/r1bqtxeGkxbmBihiaejTCipSxEb+9cJHHy/r/zxys1Hy5/24I6pHeCWD72UNBRaW6KsmDvb49D2uTCVRPhEpJbjJTHjv/P4fnALlY+RU6CiaQSZM1Dd0cpxHZiYJBFd/6stLKpJwpAc6gi6T+KfIqHUn0oVokwht0JN/YzomJO9O6WZvtmuHt7v5qeVyFlzPhCT152R3Y/85fUyRaaazduBiKLeZBQhpP5O1PeJ+oVxOBxOecKFEYfDKRGymaYibyropoJ2xZnp3bfD2KBJHTSrP2DZIXSqUw3zhrTExcfR6NfQE3cjE5mwoSatb3fy0eqTeBidhBErj7FtEqGhWk1rQzxOUW+uMP2/c9g+tRfqu9rik16N2U0dVLM0U1MKXSnRi47Fr9+/iTYt6+HG7cf4aeE2RD5LQIfenymvbGkBDOim+oWyslmKFwk7Ql1TWJF1Fx/izXWnWWqhg4UJ+wyo5w4Neulp+np6CE9UUWtiVhSFobBEp9YgqbbwjW4IvBuKv26E4kZ8BlDHG/M/GY6P+wkOaoSXg7wA+eXIbXaT0awRkJDCokbqhJEUcqtr9dNO3H+mosgKwKG7ytbXBvrAk7gU5BsZAZ7Vyq1pbJKBMZb/eQAfzRii8nFyGSQbbMXeQaqYs1u+xosif/e+Hokas/9DZFE0jg496VIqv/rzXCAOBDzBO518cPx+BGLTlJ3+Nk7uir5+1bHwWAC7T5MAf5y5p9THiESRJqvulMwcVqe24Yow+TFzy0UmtFaM6wgTI83W8oqOh56fb2D1Up/2aoyNV4JYmiAZaNRztWUplCWJLTdbc5kwomsJ3YgZXRpg6WsKdvEcDodTBrgw4nA4KqHB9uG74VhwzB9HS3BmS87M0TjAIlFEnHn0jP1N61NRvAilUWkSRiTIgmJTWGrO7lthcqKIbSsVwmsQRcS9Z0n4+/wD/Dy8uCZEHVPWn1Eqhi8VlIpGI/Qits8ehaGthDqR9m18sW33eSaMVNKzg+a6F3bc9IXozG97WSrebyPbKkXklp28I0vPI6OGP8Z2gJOVkP64blJX2Xr/FQ2ASyI6NQvvjujAbguO+uPjbZfw9SF/jGhdVxZxs7cwRf4fUzBy1VFZ/RdB6W60faz5abd2OLXrCG75B6NJo5qydZaeuMMsu2lbF41qCxNDA3T5bZ9aUSQSm5rJ9kvk8+/+ReK9UKBmdZQXPjamuH/kEebde4A3X+8BW1vVESgSHJS6SVHKrgv2af36ZEBB5iApWblydVV9lhyU3SfBJO1nJGVmdz9mrrHp6mOkZefCw9YcX/Rtgs/6NGbbosrCXRX3nyWy7ynVelEq3IhmNbD1eggzvXgYk4ydU3vBuajXkqrvqtRdj4R4fHo2mzyhmwgJJJHjH/ZHt/qCs58q2tZ0wdXQWKXl1Cx54ai2MJR8xzgcDqcscGHE4XDkoBqbT7dfwqLjd7Q+Mp0kdQp/nL4H//B4ZhdNM8FUvyJS28maCRxFpnfxZQOloU28WW2IIu1+2a02LUgbDPT0kF9YiLAEzX1wiCCqaZEMTEvNk0jgwnVWn2I8sBvcPJzRp4n8DL2RobCva/+Yia4dG2LL+UD8vOcK4mISAZOiVK3EZODwGcC3NtDIR2lmnowjCEoz2xfwBE9/GicTtlSI/+Wea7JB828j2kKfQhAK3I1MYPVi2iBNifqgux+r8aLoE31GNINPIpfOIao92+svzOyLkBugDBqYD+uDpr8fw4pxHTC5fT1mvS72MSKnvX3+T7SyKZc2wKX3+O9hGvYno9xE0aa3ujPHvJHNaqDx6Uu4F/gUvyzegR/mTlDqHaQoQHRF8dwbs/o4evt64Oj9cBY50oS9uQk7H8RoUXhSBvtsOtR21dqSm84bSoMjUUSRP2oeSw1sJ7V7itGrj+PC42i0/HEn9rzbW85unqD9dpv1r+z+mU8GomOdamgxbweua7CFp1o/er2Bjb1UpoAuOSFci8R1KFpGKaBEp/l7sH9GX9hZKNdgcTgcjq5wYcThvILQ7DoNOmkg83HPRiw1huo/KKJiamSgkygiXKyFwTINWCj9hpjepQEO3HnC0rVEolNUWwOLdtivt66N9W8K6WMUVfhwywWWfhdWZMddWkgUEeqMFCiqRSl9ZJGsaQCnEqrDOXwWqOYMVK8GpGUIA3IPV8DCDMO7N8XKJZNhZWUu14gzKSkNiUlC09ak5HSs3HsFP1BzUEc74fnpmUBSCnDuClunRm4WFG0mRFEkQulS5OpGYkIaZZg7oBlrFqouZYlStALJuU3HYn2KOlDUqeei/WwgPXvXVZQGsp7+fOcVJGXIRx5ViaJevh6s39OENnXQuoazzJVv6B9HMLpFTXy2QzhehJOVKWJTldPNVCJahSscoy/6NMHolrVk93+c+wYGv/Y9Fv2xGzPfHQRnJ1u2fMeNEKX0OTpe73VtwPpGiVbk41rVxtYbwcjRsvEspZ0dvheu1bpfqYgkLT4ewISROhFEQowEnZ25CTs/9Keulj2+bmJXJopEJ7zLnw9h31UyxCAh/O+bXVmEiiDxQs2Wpfx06BbWXXoEf4UIr+r9VHBGLELss0XikETR1dAYfChJc70YHIOfD9/CT8NKjgRXNKqEsabIHIfDqXpwYcThvCLQIIgGwGSYQCltIvQDPql9Pdx6God3Npxly5p4OugUoWnxww5E/Pw6Bv4upMwRDb/dprReanYeTA0N8EnPRvieRIACtG2iMDr9MJKlYdGNCsqJozP7saLrmNQsJrLoX6pRIIvm7ouL31uE0oCkKXEPopKQmpUjZ41NjFx1jA22xeeQw9uFx1HIyNEiWmFsDAzsLlha7z0G75puiDcxQmp2Lub/M4cJTylZWTl4672lzK5aZObnxYNRUE2MlxuQmweERQCmJixSlNu0Lkbr5WBzkuZtotQtqhkSGdTYC18PVF3kLxIaLwi0SW3rynoEFWpZrF/TyRp3vx7JBv/XwmJZGldpUBRF6hA/p1m9G6OBmz261HVD59/2sgG0YrNaURS5sxoVZVFe38VGJgiN9IDvhrTEf9dCcDs8nqXy/TqsNWpIek0RpiZG7N/MzBwkJKYyYUSiiAwXFAM65E637ORdVnPUtLoDS00lm3ZK/3L+ZD0qA4q8rTxzD409HJh7HTn7/X76rlbHm75rUqgmiMTRqNXH2OsMW3EU3w1qwZwRrYqOixSx2XBJjG1VC53rurFrFAlcSr3zc7NjInjjVSHl7mF0Mn49fBtnHz1TSqXdej2Y7d/zFCPqhPHiEmq5OJznxcOgCETHak5RLk9cnGxlzbCrMlwYcTivCDRofPtfQfhIEfvZ1JRY4eqatpaenYc+iw/I0pk0QVGAeQdvYkhjb+y6XVx/IvLNvuvM4OFGWHFNAUWzCLLY7uHjwSJU0kEbCaPFHR2wL9YEJx8+Q16RGFKsE0rPyYP1B2uxY2pP1hiTZsivh8Wywbahvh4Wj26Hkc1rsloVGugM18JdTIahARq29UNsndpITc1kgm140Wy6lD0HLsuJIgalt1GfHW93wM0FMDAAUtMAS3Ogfm3KuUN4ciZGTuiILT9vRyFFpzQgbZT6VnsVDVoV3P3OP45if49oXhMDGnmpHOBpKtY3NTKUCUBVwsjWzBh+7vbs8ysPyOiDRBFB6V4kZGlwrI6W3s6IuKV8rpEo6tvAAwfvhqNuNXt80rsJWtd0ZTP+SRnZaPjdNia8yLHvm4Et4GZuhMnvLWXPnT6lP+rX9WRRAjpeqrLcCotEJdnF07lFjohiDRilqJGbXkVD2yA2DtYVVZEYSlk7+F5fVltGUSJK1bz7LJFZrVPU56Otl+TW717fDZdDYpCWrb4GcOOVx7geGocniWmyawidK1IXwpB4oVmvFG8HSybqKVopHsvnIUY0CWNaTsKYiyNOVRNFDdvOQE6O5trc8sTY2BABF5dpLY4+//xz7Nu3DyEhITAxMUGzZs0wf/58NGlShtYLWsCFEYfzCkB9QMjJTRXzj/rj1xFt4GBpCt9qdjKhpCuXQmK0Wk8cPJwNilT5+Ncq0oEoAEJj/WcqZv1FvKwMsX90bxwNfMbSfaTiQBGa6a7tbI0gSWodCaJ3uzQoXqdZDZYGtfTkXaXnu1qboWl1R1ZoTo1UKcJG0QkSUsmpmWjgZodD7/eFh51ycf7Avq0w59PR+P7XzdB3skdBdXegultxTRGRny84wjWUFzU5RkZ4s7oN/iLDBkf52Xx1FGrwY8vKzWPHgradPnsSGFTjJRoHlCYliKzbFV3ijs3shz+X7cS5g6cF44jOZUt7ogE53ag2acGxABZNIN7t7Isp7evg2Kmz+O1OJqJSMtG+lgumdfbFLhXCiKBUMRJG5EToPfs/JSc50QHt9MNnaJeewJrT1qpRDT9/M5Etp+OkyX1OtIun40zQNn+w+aJSmiCJL20mFnRBLycHhRTRVAM1BFbXTJf4+fBtfDuohVwKKEFmByT0GlSzY26PJIYfRSdj97u9mVFC2593y9Y9Hqj6e67IgxjNqZyaIp3qxMjAhh4oD8gWn74j3o7KLQm0FcZiHzUOpypAkaLKFEUEvR+9r7bC6Pz585gyZQratWuHvLw8fPHFF+jXrx8CAwNhbV1xPc24MOJwXnIS07Ph8qnmtB0aZNLA0bgS3Z3i07VLnSJEjbPqXCDmDmwON1sLlevRIIWsxDWJIhGpKKII1fwRbZTWIWcvgvqm0MCYHMF2v9sLgxp7y61Hx46K5GmwSwPxvdP7qC0Gj0zLhmFjH9R+f5zcNshBESMVUL2IoZs7kFGcClkSoWoGvpS2VPfLzWzQTnStVw3/XQ2CrZkJbM2N4WptztKT6G+xNomiKA+ik9FKQyPZA+/1Ycdh87VgTFwrRMamfLgcN08IgrfZqJ64kVoO5haSSIi7rQXWTuzMookUPdyTV4D49Cw09XRg507vxQeUnkv79d/k7mhby4VFPSiaSDd1kDX27nxT5qY3f0YfWFgIVuYkHkuzzYqIoogil2LEs6yoE0UUwaPaJU2iiDj8QT8lUSSFnCTrudpg+IqjrDav7lebkKkm/ZRS7fKLmvZWJFIx0q/BiHJ5Tdo/qvOi+svXW9fBh90bwtfNTidhLPZR43A42nH2rHyGy4YNG+Dh4YFz584xgVRRcGHE4bzknA1SHkQrzk4rFk3TwKK8+r6UN+6fbcCnvRrhl+HKQubkg2e4GByt82tOaldXpdgiMURQROi/ohQxKviXsurMfUzbeI6JMarnIQczRWc9asZJQuHfy4+Yq5eIhYkhhjWtwWos3lp3hjVf1XTcL4uWxVSHpCUUCVQFRcJEUURQjZAqyC6bokbkMvckPhXPUjLxVf9m+GaQ6rolEiJ60MOwpt5Yf8YJx4NjcTM9H5aWZvh31UcYssdftu4JsmleKLiLacOUDvWZRbl0W+l9Vo/vxCzCiZy8fMy9IuTN3yxKCaXzmfaB0qwolY1qbUhEz951BbkUndMWmjhwdsDQLVcxOSIFf51/gK8HFPdvKg9KFEUUadIgVrSBRFFJnP54IDrVrVbieo3c7fHNwOb4dNtljTV5f7/RmX2Pan+5GRWNKEYuPtYuil0SovkFnTdUp0m3fn6erBfas6L+SiWhq4DmcDjyJCYK2SyOjkLT74qCCyMO5yVnQEMvlvYiip8ePu5YOa4jUrJy0PT7HbLUMEo7EhGHZmJzyaoADWjF2eZfj/izQen6SV3RyMMezhbCzLhYgK3rIOTYvXCWRvY0IR11XWzQqoYzniYItQuEWOvQ388TJwIj2Xt0qO2CHw7ekvWUeatDfdYjSOypkp2bz9Kw1l9+xP4V66TIGIE+g/Gt67A6J0tTQeQsGd2epQCpY2SzmqwpLjnRyWb6aVBfUKBRKFE0RREqYFcUw9QHiSJCNGhOSM9GVArV2eSw6A+lmdFN5Nv9N9h+kS13Dt3yCth6JEpUDuy9PXD0l4lo06QWegUlsJouEpBxaaqdyBTRLyxEgZ4eetdwwMIL8v2W3uvqJxNFxFIVAo+2iKJ9YtNUraFjS+lPeXlAkbW6CJ1/xNf7rlf8RAJ9znGJgnV7veKeTxXJ1sNX0dqrL0xUmCqINYvN5wnXD20gJ7kjMytullcVdA4Xm8uXDzT5sdc/jBlL0K2Os7XONvccDkc38vPzMWPGDDRt2hStWrVCRcKFEYfzkkN9a6hYvXOdaghPSoO5kSErhhYG966syFld6tnV2UPRY+EBJGaUPIAl5y8nSzNm5UuD5PJKBxJRTMGhwXv/osaxLbwcMdtXn9kzU2E49TZR7AdD7llUT6WqGe354BjWRJSgdJmY+ePlhENIXCpI7uy/85TdCDNjA1na0Ifd/VhtzZITASz16E5kIrZcC5Y7biTaxrepw9LyVEWnqKaJ6iIUjQ9EDtx9gi8HNGOpPCRMZCl3atLuCGrwSfVBlDZHiOlvUrcw+tzICZCEoSJUQ3U3MhGtftwpG/iLIpqOidbo6WHTnXAmjEgQxaZlopaTNavp0QYSRcSIX3fCPSYWdZv6IMnYBDGZuawvFqUokSi7HhaHL3YJfZukUOrf1E6+zN2QboYGerK/fzhwE+ckUTw5G/bwKME6nUVp1P9cVvjcQVIqOYwA9YttwzXCwnaqUx21fe6yO1FYM2wehnvYwL2GK6p7uqBPe1/U8HaV1fLoAkVw1l8stu7v7eOOwyU0ji4rlA6aonsAWQmKDonfGYpIk405/fv3hQdqWwBIoe9YaXpacTgcgQkTJuDhw4c4c+YMKhoujDicV4THsSlKg277ojoYsr1WRZufdqOAZs21gOyQVVkiV+ggrghq6plfKMzIUtNJ6q0z5I8jcuuIjU5LgtJlyLlOEcWjIIoiah678PgddlPEzdac9a0Z36Yuc9ET+5xQip4qUwMSR4rGByRaZ22/zGo4pm04i+/7NsG3Wu2J0ODTcNqf7O86zja4OWcYLEyMWENd6jFD9G9YXaUoIiiqSM1CpQN/MbJ4ftYglmZHQpBq0yjFzdiA7utj7b9HMet/f6GWlwvSOrZGdHY+Fh+6iV9GtWO1V3SjfkuyczE+CXAQ+gEpIhe1dHVGRHIacOUu4FuHGVTM/e8M/j53HzfCE9hnp4pW3s5wtDTB7ZM3sHLVXqxdPhNdOjXE6rOB6k1DKMJXw1P4myzTywk6PuNa1WGDaq1hx0b18VFJWb5PkudmeLhh/baDQN8uQFAS9H7dhqCdX6FmDVdmpe3jaov7Udrb/W6/WdyJ6583u8L10+JmsOWJ2HOrbS1nHC7WYqXmzfb1ZMKITBioVur3sR3Qvb67nHOlushhQ3cHbrzA4ZRBFB0/fhwnT55E7dq1UdFwYcThvAKos5OlqIsmxPSvCkVxECemL2kJpYpd/HQgLp85ITObYI1SKwmxeawIFc+Pa12HNaslwSEKH237nND6ikXaO6f1gs/XW3D+cTTGLN4LGBgDCcmAnbXWg2CK5In9jaiWSWTV2UDWW6qJpyMTQhTtuxYWx6KK1GRXFTO6NJA1/lQ6Hvn5WLFiLxMWH707GBeyCrHhTiRgZoYpH6/EP0tnsPVk6Y5RMUzwqEMURSS+KG1PMZUsMg+IDCm2dlfFslN32Y29mE89fLbnGjJPPcC9Z0nqhTq5ApZTTY/0tSntUJ0ocrYyVTtJ8TyY2aw6Ys17YUOGsP+FrZvg9yuPMd/bBZm5eTJRRFFQsQZPE/VcbJl5B9UakcV/RSCKE7JzP/NIe3t4iqrSR08RdhFy2qMUQNGZUITSbI/cfYqPtgmNZt/p5INePh5K328S45QuSi6Nx+6HM3MQReh6RRFMmmSgCKY6UxMO51WjoKAAEydOxKFDh3Ds2DH4+PhUyvtyYcThvORospOVYm9ugve7NcDX+4rStJ4XCUlaW1ETEUnp+PbATdwNSsaQA3/jeeBlb4mwhDQWPbr95QiZY1V59Tmh/jcfdqyP74/dQTSJIkIHUWRnbszMNszfW4N1k7ogO09e8C5RYUmuDuq/81or9bN2u/dfRnCoMCB9EBSBA2fvAQ19WehnXWAU/i4oZINPWc2FoihSE0Gc0cUXC1RE5RjZOawZrqGtFfKcHeXSF9t4O2HvaX/mBggbK3ZuXU/PB9KT2CB6VPOaLNXu4+1FPXIU37u8RJHktdVFFqqSKCIW3SBBIL//C07cRXdfT/RrWB3NqjuyWiOKHh2+9xQJGpwmjQ30WP3eRz0aMgOTlj9oX5+kCwYF+cwxUBQjnza1RkmVTVQrR+miolkH9SCzNjNmkwkkZEmweDtYsfuqos80UUD7NqhxdZwLipZFeym1t9WPu1gacGBUkpIwWnjMX6nvE2FnboK5A5rhg+4Ny3w8OJwXOVK0e/dubNq0CTY2NggLC2PLHRwcYGmp3AqjvODCiMN5ySnJTlakW303GGmoV6k0dBBFIn+cCcTzRGzaSTPHiqJI1z4n958l4v3NF1iPmM/6NGEDrMPHbmD5rGWAq4vQ24gG2DrMLCdmFA9Yv9h5FcO1bH65b3pvDPj9sOw+9X4a2tSbza4rzmyLaYIL1xanMC6hyFEtr6KdLWT1Oo4f/8OMJsa0rAnjgnzk6Cucc2r2S60oIqgHVN0ayJM4zFmGPYVTUjRWLrgG2NvCMDUNjjVcEdWksdzxfxiTjHAtncXKC21qkkwN9JBFNWrS/lZVBCdLU8zafgkPo4WI0dw91zSKIiInv5DV+KnCQI8ir+WzbXn6BkjOFOoLqc7x15vJaN86Av0VLPalFBbZsYswt8aiaybVDi4a1RYLjwfIhJEiDb/dJvv73tcjWcQ3OSMbrX8SRBHVcr7dUXm229pU9WdLtYm6NtnmcF42NmzYwP4dMGCA3PJFixbhgw8+qLD35cKIw3nJ0dahjXrqNPdyYtEFGlBYGBvK6knEBqtVxZVOr6AAhRrS7cho4vTO00BcAtClyNb7eoAwSLdV7SJVlgablB5EfNhDeYb3+P0IrfucWJoYoc+SA4hPz8ax+xFYefY+vAyFyAsaNYCltTlcHK0Qk5aN1KIeS7qyd3pvtc1OFfnn0kOlc8Rsxhpmm71qfCfZcrk0wTxDQe1BD81b+6J691bY+ThOJnhIpI3/+yTmH7qpLIrKiijsc/OQZmSMYzbOQKdWgKsT8nJyEEXph0WQk2FMeg4TRpTWRWeTUuKoprTO9EzAwgwVgYWxodBTqQqKIqLVT7vk7j/RYuLF294SoWpSM0kUlbuzX0YmrAz0kGpiipGrj2P/e33V1tJRypynnaXsezqhTR1MaFMXNmbGaO7lyCYBpnXyxcoid0pNkBvl/CO38c2+G6wPGqX6bnunh8p+UJM71MeoFjWVahrJaXPByLal3nUOpyRcnGxhbGxYqU1ejY0N2ftqi2gaVNlwYcThvORoaxN7Kzye3URSJa5ulXV9kg6O9LKyUGhqqtKVTp0oWtW/Icb3bYlLwTHouuUYEBULXLgupOelZQDBT4H2zQE3F6XnioX71Oj1y91XtRJJekUubdTbh4rqazrKiy5y/Ouz9KBW+77u0kNsvR7CBlOUomRiqI+LwTF4QB+Du1DPQ8PKtHjVg8uS8DLVx88DmqGhmx2z5f5Wi/ZBO26qFlAXJL2ilNIEqzkDfbsCRoa4YWaKwuRMlULjtqr6nlJCZSGDGnkxx0BWF2dkWPwZuzoJ/1KzU/HvnFzEpGcAFuayWpfCs1cBc1OgsU+xA52mWrcKEkWE2GiWhLLYZPhFgdJJFevuCHWiSKQ8LjFyEzjmZiDfRCcTPcRm56PvkoMsDVQxZZUGX+1/2c1s6ill7t3OvvhxaCulXmRk6qIJmlDq61sdo/88jodFEyVi/ZWLtfprMLUIIAz09eBsZcYmstrUcFbbIJrDKQ/q1nZHwMVliI4tv+twSZAoovet6nBhxOG85JDzGRX5Uz2LNoMPKgTO0yKvJe+Pt5jdMs2yUmqNNBVFFXp5eSjUYHlMIxqDq7eQ7+yEQm8POVFkqgcYZWQg1UyDyAsKxdHtCWjh5QyjrByYoxAsVkY9fnp2BM5dA2LjgTNXgM6tYV/LE9O7+uJqaCwO3Q2XHZtbT+NYf6GSisnFhK93OvmyXjY1HKzkCreJjVeCtBaVf18ojs6MbF4TzUyA3n/ugqGjHcaP7ARLN2csPR6gkzEF21+qv3F3RVhWAcZsvQrD5YdQOz8bSM0BqjkBlhZq09fy1Viuk4W3x2cb0LqGE+tJpLSWtSXrvVN41R83KGpHDnIkish9rrlfmV0IFaHN3HU7DL7VbFUbKohkZLIBM4yNhJsEWzMjJD4KBWLiode9HQpJSClQ2Y2PXzRR5GRpgroutswk5Hmg6rvWysUU+rZO2Ov/BCNWHcXS0e0xvWsD2eP+4Qmyhrf/69sUcwc2R0FBIY7eC2f9zChqRJx8EKnxvSkSuvGafI8tYo9/mFwKIaXbUr0ROVOuGNdBloZL6XbfDmyBzr/txepzgawvWktv9aYkHE5ZIZHyIgiVykaHX1gOh/MiQnUr5HxGaDMcpT5H5Kymie3v9GSvSwMJMZpja2as8fU1iiK2cXrIa95YiAbdDxIG0oR/ILK2HEDq3pNAstA75422QnNUcreq52gJXL4FXAvA1l3n0azTTHToNQsZD4qsgak5Z8hTQSSInL6MFWPb49tBLXHw/X6Y3L6e7KHN14IREJFQwlESHOXINMHdzkKWTregaABETlNvrz/DBlel4YudV/DvqQA2SB/p64bVMwdj7YrdwPaDOoXvrM1NYOBRrViI0DF2dkRgNXfA3gY4cg44cgaIiVP5/DfqOcPs+DmABEMRDhYmbHabTC8oopSmyl2MnNy2HgAehwmf2cUbwK17QIuGZRZF1kUNcVWhURQRGnprJbZogppvj0S1UX2LRVGOvDCxVU624xRhZqSP2LTs5yaK1PFWAytsntyVCRL66szYdB6fbLuI0LgUvL/pPJp8v1227qMY4fw5ePcpei0+AO/ZG5FWFDkvSRipQxo9Iigdj0QRMXXDOWbuQFiZGKFT3WoswkTb+d4m+QbMHA6ncuARIw7nFUBd81ByX6KhonSZYmNUag47tlUtnHzwDKceRrIfdbFYfVK7unh/83lZ6k9J6JElrnRgnJoOZGez4ngWCTHQB6q7AZExwoDazgZITGbRh1FDO2DBDxPh7iaf0nL3fhj8lqluFFqnvhcS69RA3N5Tcst7DWyPkS2LndVWjOuIC4+jmf0wpWJRg9aS+HZQC3ZcP9oiWPYSH2+7xESitj2T1EH9j8KuC/UMndr5sfqj1NBIobcOiZS62pknpFhYyhpUtvR2Yr2DKM0wm16HTC6G9ATuPGBCEQN7CP16iowVbMIjsH7LfqGPVWwiE4ARjk7oXLca6xNFzVRXnLmnOrKWKp821aVPawzs0RRf3opERl7ZYi6K56dOWGpOKw1OEaKetsYGKAgIREpAEOwHdkGCiZA2l8jnEtWSmVv1RGO/Bh7Q08uFoYE+vh7QjKW9Uu3Pb0cD2E2Rw/ciUHvOJtbzjUjKyMHkdaexaUp3lmJHkwJkxf/zoVtqjSR0ZflpwRGS0vioHlHsl0TbQJErxSg0h8OpWLgw4nBeEVQ1D23t7QTnT9ezx9/p6MOK/RVZcuIOdt4MEZyailh0PADvdW3ACop7+Xoq9flQm/8vjSklpQAPg4W/4xKFZpqU3kQrUo0IFbeTMLIww8HtX6NPj+YqX7eBjxemvNELq/+Rb+hK2JgZ4b2OdfG+RBjlJe5WarZIAyey21XVrJIGLNTIcf+MPjCYtlq2nGZ7/dzs4VNNvpi0rKKIOPfxAPg23Mn+7tzBDzcS0wQxRJGx5BStej3Rke5Upxo+6dUIAxoVOcMNFGoqyPVuGVl0k1lBY1+gbk25JqaF+44jiWqyALRv44Pzl+6jIDIacHTC8cBI1nOFZrep+axKYUSfW+9OwjZaW2La2z1YQ9myiqLKIoma99JA39UJCdn5QNGh0YuNx4LBLVCvWT2MWHVMru6NU4Uo+n7M6dcEzwKuoPH3O9l3m1JU/xzfCW+tP6O2UTTdpGy5Hsyc6Sa1ryezzz4eGFFum3rjiRDJ7unrgc3XHiM5Mwf1XW2xeUp3Loo4nOcAF0YczkvOzM0X2Ozjlrd7sIJiafNQ6kEipkKpEkUiJIrsLUwwvnUdrDhzHyFxqazOxM/dHvtm9GHrhMalosb/VEduCKpDkuvTQu5wrZoIfyclCw5cVA9D/1qYwtjIAJT5//qb/dWKouTkdHz1wwas3ywIn+lT+qFXt2b4e8Mx7Dt0FdduBrGbSP/eLdR2oO/r54l9AU9YXY2pkQE+7N4Qn/ZqDFtzYxZBUXTIoUaoI1cdw9sd6+PvNzpjz+1QXAiOYWIhVkM/GhcrU2YnnEXpZipY+0YX3AoIRRaZGNhY46d/T+DwlhNAVFF63+MnwJNIYFAPoX5KDYVFDSb7+VWXW0770rWumyCMRMRmpsIKQN8uQHQccOchE0XEs/shsG3djNVjUF1W21ousvo1la57djZMnFH065fDt2U9Yl4Y/IrTK0UKnRzwxNwSe4/6c1FUlSn6jndZcEBoClxESFwKvBwsUcfJGo+KokLauG5S2qiUye3rs5Q8XaAatqQiG3FFjs3sh+4+HrJU3AZudmjkodnsgcPhVAxcGHE4LzE0+7j4xB1ZbvvMIjvp+LQs1ifj5lPVtSUilDpCUQfqe0M3UyNDBMWmsAjRvIM3WTpVdEoGOtaphk51XFkTxNB4oQ5oepcGGNbUm0Wcdt8O09y80tZG+Fe0JzY2Rk6R+UKIhv4o3/2ySeiVU8Tvqw8gODQa29d/gdPn72DI2HnIlhSwHzt1G6Fh0fD2KnalS83KYQJx160wmdkAOdT9eOgWuwV9Pwa1nKxl+yWFls3edVVuGdnz7n63N8IT01h63tmgKDyROHJFazgOVOg9Z88VhCdmAK2F+q11/uFMFOkZG6GQnNZCw5kdNSKiAW/5hpFieqS+nh5rOLv9Zije/e8cqzEzMTKQ66ukEYok0XvRjVLt7jxkSivpQShLdfzjRAATRmL92vCVR1W+TGGRs9r1J3HMZU+xsewLgyRCt/CYcgoWp/xRdb7op6WjgMxCtEQqiohrYXHoueiA0nqaRBGZI5ANPzk5krMdWenrlcKCQ50oou+qm60Fm3ih67XY4LU8oFS8uXuvsWt9TSdr9GngwaJfij3IOBxOMVwYcTgvMDT4PhEYidda1WYpX4q0kfQb+XDrRSw9eYfV0EjT4hTxcbXFNwObw6eaHXyr2eFYYAQ+3nqROWTRQFhMm9skSaGiZYrpdJeCozF/RGsMXi6kuPm52cHB0lRWeKwRiVHDxeBonAt6hva1XJV+0Du09cVvy+R7qhw8eh3mriOgr6/P6mNsbSzwy7eTsHHbaZw6G4DZ367Dxr8+ZTP+NENbUurbPxcfsnoist4WoX1RV4dEs8tPE9KY89W7XQT3q8ikdPRadAB3n2muXaKBkTg4EmliAsz66xNk2dnhzbfnCwspUkR1WSSQKO1QAqVJTuvsi6VFESESxFRbRHUSTTwdNTf8pREijfmkdQ0UOYmJF/51FJrXrr8WjA96xbK+V718PeBmY4ZIqS23BDKlMDHQUyuK1Nk7Vyl0cQLklAt0vswd0IzVBIm82bsZ1px/UKkWGN4OlvjfrqvsOkhR8vKGIsy+X29ltZxRRT3nKJWuPPhqzzU2gSVCk1SNPRzwWe/GLK2QUog5HI48XBhxOC8YNKC/8DiKCZzX/jzBBtJvrD2F11vXZn106PF7zxJlRbxSguOKox6UZhWXlq20DuXi/3n+Afa825vluP921J+JAKqpKQlKE8stKERCejaLElDhMgkqihrc+nI4W8fri42ISNKu6SxBQZyOv+7Fd4NaYE7/ZnKPDRnQFh9MG4TFf+xRfl5BAcaO7IwFP0yGi7MdGjasgbZfb8Z/T5Ix4OgNjNtWci3Q4tHtMK6VYNLwIEpwlxraxBs7pvViYoeasJJd9dH7EYhJLRYGlF4mhWaESxJFROc6rmzdEw8iEZ2SiXfa18OKCW+zlME+w+cKYqhODaE+i5zkansrvQY15xXTIsWmuCROmn6/A+93bSCzJlYJCU9RE5FYEYVoN8HVUMq0jWcxb3BL9FqsuU9TPRcbdj6os3Ov8qLoZUdMX62C/KmQ3rvzVmil+wKS02GJboflAF1Ptt0IKTdhRO0ZpKJI5HZ4PMb+dQL/230Vn/RshEnt6in1bOJwXmX4t4HDecGYuuEs1l96pLT838vKPTRU4WZjjsjkDJWiSIQG+10X7MWuab3Z39qimCb2KEbI4+9arxqLNtGMqC6iSMrlkBiWBqZYI7Topyn45L0h+G/zLji51GD1SBQ1qlOrGjq0Le5Xcj4hS9b0c+qaE4C1fDNWVXyw+YLMyrtfQ0820Nh1OxRXQmJYj5MJbeuyG6WsvLnuNIsuEVTHoAil44luV+o4/ShKll5DNPZywtjVx7D/zB2kOLkBtYpe10tz7wnRAljRHGCJtK6IIOc/GyvVL1JCus3V0Di1oohSMMnRi0QPibLSQk0zqT8MpwKpoqKIiFCIQlIamyq61naB/7NExGtIu61oytrjShRFxJzdV9G/oXxtoK5k5ubBzMiANaqmmiXFaBfViU7/7zy23wjB8Y8GlOm9OC8mD6OT2ARcZeFibcb6nFV1uDDicF4wKHVOHW1rOONiSIzG55MoIkwNDdQaANCY+GJwDFyKHOsUoRz4xAz1woogs4ZrYbHs79lF/Y7WFgmH0kDGCNRQlmpaxO71lJdPP/wuzrZwq+6Ies0bwtXFDpNe7yH3XKoP+EEye5oaT4N1wTFNEbL0jZL8WFSb9S+MDfRlIoMCHK1/2sUaMv41oRO70FNkbemYdvAPj2cD+XoqLv4bJneTS21Uh6WJocwQ45u91xFNkShzzTbTpUYURZoEUinoWs8N/0zqytIPj90Px4JjAWqbxWqCi6JXDGmUUgdOBpVv76Rmng64oaNZSHnGPSn6XBqrbkrhpe9cs+qOzGUzadFE1nPs+/03cTfyumy9K18MwZv/nGaZADxe++qKoobfbFOqw6tI6Hc0YO4IrcXRTz/9hDVr1iAyUhjz1K5dG7Nnz8aoUaMqdDu5MOJwXiBiUzOVHJKklCSKpJAoWjK6HTNXCI5LYWKA6ouaezni/rMkNvhXB4kiarQ5p18zzNpxWeU6lD4lYm1qjBOBEczUgAqAb4cnsFqY0qSHjFh5lPVkInE04e+TLFK29o1OWHIrGTeO7mZ1CV8PbCH3PHJFo+1xtDQV7Hgp4qKmbkQqiohUNX1zaACy+Pgd/D62A7tvZWqMK18MZeM6xagWRbpuPdFsdCGmIlqbGbOURxISTBSRy7aTGfzc7dCnY0OM+Pu02rS0Us9kl6MoIsj4gdL4qP6oS91qKnvGvNJQbdjV20A71W6L5Q6lXhZZpyMjU7DCp3oxBRFC4/BNb3XHr0f9mfOgrogRilJTRUwBdBVFZYX2emLbuvjn0iNWc3Tkg37sUKw8c481USYjiq/6N0cLbyeNr0N1pBQBEid4yIGUjF/IBIYY0awG69lG0XcSRSSaFoxsUyn7yKlaUKSoMkURQe9H76utMPL09MR3330HHx9qzlyI1atXY9y4cahRowZatmyJioILIw6nCkPucUfvC6lsbjYWzCJaV6hGZsMVIc2OehdRtIdqkIiv9l7Do+/GoLGnvDUspYnVdrJmDnTq+HFoK4SocGpT94OtS0qeOgqLBhEzt1xgPZk2XxP6IE38p7gvCRVrkzPclI4+7H5EYhoWHg+Q1bvEkdgooZi+obs9Pujmx3L9Kf/+UXQyxvx5XGm95afvwdnKDO91awB7C1O5YubMnDwmnsjenEShxtoeSSqiKte68UPaYWSLWuxvGtioc4FTRWXOCIs1TeJ5SoLw/ONoNth74bj3iPUxYnVdVIcTEQXUrC5YpdOo1d2FnUeUJkrNj3UiJQW9fNzRY1hrDGnihTY/75ZNJJB1M7kJ/nkukNURDm3ogV0HrwqOhKUxgaBjT42Ub98XRFFtL6CZn0orNgrqLd18GldTdO/PRN/LLW93x8DflfuJlYb/9W2KTVeD8FhSF1lekFg/pY0JjIQ6zjZ4FFP6tFBNFN64i+HDmuJ/342Gs5Upm2TZfiNYrq6TIubUa27ekJbsWqMKauQsCiOa4BENcqxMjbBsTHuMb1OHfT/JqZKY2b0hM2ThcKoi48aNk7u/bNkyrF+/HufOnePCiMN5VaHBOBX4i4i1K6Oa18SaCZ3h/Mk6ZJQwQ0s1MSIUpaFwNkEDekrZ+HL3VfwxrqPcc3bcCFEayFuYGDIraKoRoigK5adri6IoauHlxH6gqUeQOJupLTScI1c9SkujgaM6NyaKal0KjmHvTZEqggbp2sxKB0Qk4JPtl/DdwBZ4b7O8tfWBGX3Qb9kh2f2v911nN3FGlty0kjOzcSU0Vva+ZWXy+jPMzKKeqy0bgFZV62sx3bAgLx/Nv96MRwkZSC2yS38h3Oek9ty0qSSK6O/z1wB3V+GxtHTAPxCoVxNo2kBZFOXnC3bnmnCwB8mHIzsus/RLURRRFPZcUDT23Q5FHRszHJ/ZF69PWYDCc7eBBnWBhsq9lUqEznfPakKvsLBIQRSJy1Wkrp0thSgiyAa6pbczyovFJwJkKaXlzUc9GuokjIwM9LQSRXRlXT6uA97bdEHttUklzRrA0sGG1SESNDv+8+Hb7O8JbeqwiQWKjNMkC9Ui/TysFSa2raeUatfAzZ71YKPrTk1HKzYJRtcNul5T64G/zj/AmUfCftPH/vWA4oglTWJIm39TjzJ1Pd84nMomLy+PpdVlZmaiY0f58Up5wyNGHE4VhgaTUsLi02Td2OkmpVl1B9ZFvVs9N+ZqJpIuGVxQ6gSlOVER5OLRbTFm9QmsOhuIqZ18ZVEjEkWUrqY4hM3IzkNgkTObrpAYI7c0+oHvWNsVpz4eiEXHAzB71xWUFupJog6aLf1sh/xr00CB6quyaPBegjjysrdkx0lRFBEkisgaXdXAR1pAXZ5igAY24kDpuSHuhxbC8o7kPCFBTUKYZrspQhkUkyIbvFVZIqOBBnWEv6/fEezKi4w7ZCQms+8TpTxSXRgZXrDUlJJEkQJ3nyUUB5KycmUim6g2+mfg5j25dEdVhfQlEp8IPAoVUvgqKHXNytSQXTdKTN2ksJQWtTMVJYqIQUUtBLQlN1/Yoynt6+P97g1Yzc5mhesvQVeEaRvOYfX4TrgbkYCFRT3ktOFmUja6vLNKVp9JYplSE+ePaAMnKzO81aE+m4yiz37yujMsMkR1i7aSnkdUN0TfK7q+U4sBacQpMCqJ3UQaVLODpamR7JpPvc2kNv7krCmt5+RwngeXL19G165dkZOTAzMzMxYxatFCPlW+vKnQ6YAzZ85g4MCBcHNzY/1Hdu0qufD41KlTaNasGUxMTFih1dq1aytyEzmcKg3V/2gLiSJxUB/w1QiV69AgjmYRj87sj9EtamN0i5pMrIi2rmLzT1UDG1XLTAwNSnQVI2jAKKZTUcPTA3ee4ONtl+SiHlM61MeZTwZqvb8tvTTn2yvSw8cdWRS9kA4G1YiWtzvVl/1Ng3hFSpoNJiFI+/7CREi0gVl566lNoZMjJxe4chs4eAqr+vjBzsyYNa8VezRVaVFEuBZFPh6GAI/D5B+jY0ApdmamyL98CwgKQ1p4DHISU0p0g1SFohV0+1oSAVavllAXZGzMGusSpeqlc/Ki8Jk4OwBRutcOacOW6yEs2lXiGa+joUB5Q2JD3RYYG2oeEq0+H4hPt19WKYpEaP/fWn+mWBRpeQ2gdGMRMYL4dkcfJoqIznXdcHPOcCaU6JpE7RioDlQqdpIyhedtf6cnc7akVgnEp70aKb3fiSInOnEiTLG3mVjPSY9zOM+Lxo0b48qVKzhx4gTGjx+PqVOn4tq1klttVFlhlJ6eznbq999/12r9kJAQ9O/fn6nDW7duYebMmXjrrbdw+PDhitxMDqfK8nHPRtg5rRdLm/t1eGutnvP3xYd4qMEi+aehrVgNjVR4ZRXNJGts/qkARQG2vi24v7Wu4YzMZW/iz/Gd5NahH+dnv7zOZlCl/HLktspaqI51qmHNBPl11Q0yt0+Vd54TBzb/TOoCWzNlC2KKjCnVaKgZ6F8OjpUNZM2NDFDP2Qa6QEKwPB3VyFDi6Mx+7DyoisjZgl+6gQH1XFGrbUOgTVOM20UW52Hov+xQiUK6ykDbSVGimwr25oS3B9ClDdC2GdCysVCz42Sv0uFwdp8maORhL+cGqYg4OUATAw+/HQ2HmwFAuCTNq19XYHDPsu2Pbx3AxRGoX0sQdSootVwRB/5SAVCKWsjKgswhCkuwuZdOcNhbyF9LDt3VXCtJUcSyROUowrp+UlccfK8vfh4mf82nSDX9JpyfNZilNdN1vvWPO7HPPww3nsQhMimDXQN9qtlieLMa6OvnyZ736xF/2WsTJKzIYEebiTCq56T1OJzngampKfz8/NCpUycsX74c9evXx2+//fbiCqO+ffvi+++/x9ChQ7Vaf8WKFcxtgnaaXChmzJiBESNGYOHChRW5mRxOlcXEyABDmnhjUvt6aKJgkKAJ+jG7/81IlY9JDRxEhztKvftu33VmMqAtN5/GYeuNYNkAwtTIEJM71Ef+H29h0ci2bIAwvUsDuNqYs15ATYu2n/LoSYApsubCA0SnZOCznSWn15ErW8f5e1UObPzDE3B19lDsmNoTvX09YG5sAH0dLQj2+IfJBrIJGTl4oKK+wM/NDpUFGUqkZOaWKfWw0mjdFPtC4vE4Xx+ws5EbGGoruqsEF64BRkZCbVHTBoCXh7DczFSIvlDk5VmM8K9iiho16dTPxw+HbrHzsZplcbqTOlIyc1DLyQqnz98BLhSn0zGoLrAs0UcSRl3bAtXU1wCV+tXFz1dPDwY5RZMBUTGC6x6ZVqh9QxXvWMUirDTBkaBjbyRFW/pq1mayuk5teLuDD8a1ro0+fp7s+q8KsuO+NnsYS0um9MuBvx9G83k7hG3OK2DXYmNDA+beKeXy50MwoGF1FrEd8Psh5tap6Tsp1nOqul5zOM8Dqr/LztbcKuSlqjG6ePEievSQnwXu3bs3ixypgw6Q9CClpAjpDLm5uezGeXkRP99X5XN2txFSKkTop9ZIH6D0d/otFuf0TA2AuNQMPEtIQ9C3I1H7q61yz3t/8wW806EuS28d3Kg6/r38CE8S0vHVXoXBmAro/Qz19eBqY4GQ+DRZo1lKkWr87TbEp2exxrGi+Fpx5h561q/GIkrTOtXH2xvOq21yuu7SI3aTYmygh5yi/H7FVDaqtyK3pZMf9kOLH3fLHvvtqD+uh8YwW3C9rCxkkN02RYpo1pP+lRScGzJrbYAmiultrI0NMKGWA9buvijUOFiYATU8Vc76kt1tZaKLE91zRTxWZFJw5yHQ3E8QGC8aPTsCFirS37KygQfBMKxeDQUWZigwMlZ5fgQWFA9q6bsIYyONvXo2XwmC8a27aNWsDo5eU2jgTKmbpy8B3doVLyMxdvcR0ERwX9SaAyeB2t4AiZjUNKBtOViGx8YD1wIwd844fHU9QhCTt+7D0aAQaisBVUVStImukLOeufy1UBcooNPEwwF+1Wyx7org2laRPNOxgSY5aNJ177tBmj8XOzNDHJzRCxPXncG2G8UGO8SENSfw3+Su2HxVvum3o7kxNkzqjIHLj+JMUBRm77rKjgf9luSJJYSsx5385/AsMfWF/5191cYLisYFUmHxvCnUchumT5+OAQMGoFatWkhKSsI///zD0uq2bdum8TVofxU/Z10+9yoljKKiouDiIl/gSvdJ7JATBRVeKfLjjz/im2++UVp+5MgRmFdUU0ROleLo0codMNJA3T8+B1ejs3EtNhu1bYwwxdcKjmZlTxNKzi5AbGY+TAz02I+jiQEJHX3ZD9X6Hk44+ywLq++mMiFE66TmFl8g7Ez08Xd3IVXm7s0r6HZGdT+Oef/swsOkXGwK0n4Gv56tEX5sa8fS405HZGKh5KX9I4oLyEVoi2NSs9Dxt/2g1H0DLTNKaB8SswWZp0oUyb1HQR52HT2Nfl5mOBBWPAA59SiK3Rhi+py+PkwN9PB9a3vUtpUfqG8NSsOGh+kwz0rHsjMxQHI6TCMikUWiioruTUue8VeHq5k+ojILQGdH1U0wKhk6ijol1FhaAC0aAoa6/8wM9jZHUEQS7uYqp0RWGqIooh5ANPCnOh/qf0XnQmMf6GQNIIoijYP/QqzffFJIQ6P0PCI6Dib3H8HF1hRPKLXv1j2gia/wmJGhdqKIBFToU6CWl/BdaN0EeqcvYfKoFjiHGggsD48DaysmGL/ac73Yvc/SHOO8DHE6yxC34svRSIE+jzIII5pEoj5Fld2rSBd+PuKPB0FBmFhfc38xGhzuuaXcu47s3o1n/C23bHZzGxw/dhQWRvqYXqsQ9vnm2BOawY6HeEo2dzLGQG8zWBkbwNvKsDgtMDoQBw4E4mWgsscLVQFLS0s4Or54tuyxsbGsnCYuLo7tQ7169ZgoKikL7dGjR7h5s7ihO5GRoX3fRL3CSpKPNDu9c+dODBkyRO06devWxaRJk/DFF1/Ilh04cIDVHdFOqRJGqiJG1BSKDqS1tWB9yXk5oRkAusj17NkTRpUwI334Xjj+PP8Ax+5HIl1aU0EDucbVsXVKd61fi/LBp2w4hy/7NcWQxl4sh5uKsJtLIh8i9KNVx8kaD2NSMH9YS6y7FAR/hWiFOGilf00MBaesHA2jWHWuapoGvxSJIlFE+e/2libos/Qwq4+gpqTzBreAq7U5HC1N4GBpCgcLQUi88c8Z7PV/ovVx8bAzR3pWLhIz5Wd3Nr7ZBdM3XShT3Q79xP/zWls8CgzDxidJiEzOlM06OViYIiKluH+QpT4QNf91XAyMQM9VJ2GIQtjFxyPW4cX7cSkvSJuLs8ovHXQeJCQBDgrpkSmpgpsb3UyMYTisNztn8p/FCsKJ6oqkESA10SCdHeSMkwErb8EWnPopiVhZAP27ocycucKc9z6dNwW/BpRTmlRmlpBmSMKOwrCKx4GWl6HGjOR1NVtTPE1S6PNVHse8ivJ6q1oaay7pGu4ya0OJDn5kA08pd0T/hh5YMLw1zI2NEBidiP7LjqidgDLWJzMfC/h/NeKFt+6u7PFCVeLZs2dITExkhmZiwIDs5xt+s61Sm7waG+gjYO4I1hOsIiCdEBQUBDs7O1SrJm9cRdqAxGFycnKJ2qBKRYxcXV0RHS3f04Tu006oEkUEudfRTRE68V+1k/9VpTI+69jUTJZ+oI7hzWrqtA1LT91HQEQiRq0+gamdfFh/CnXQ7z6JIuKTHVdVrlMg+TdTi9GrOlc1TZdIJ2sLLBrVjqXJ9Vl6hIkiqnvaO703POyUC8+JPdP7sGNHPYX+OH0Pi45rtq8NT1Q9qzN5/VlWNF0WajtbY9efe7Fjr+D+dPzSH8x6m/Zj/7ReaPLTbtkAy9vFDoFx6bhbNAjrWM8dGyePR7VZ/+JVpbJEkUNeNuINSx+hKxV6erB2skNKgYpISPOGgmlBbn7RMdADbK0FEUBQHY0YURQH6JS6ohgpE1M5SxrMkzgrLIpWURNZeh69H0VJqGarDNC7699/hLxnwu+sY14uvujThPW4KbOAEY9HUKhwvBT3kcTS/SDAp7Z2r6fw/LzsHCwa3ZM5WlI/HhmqjiNtg6sjTZXjRWZvwFMYGhqyiWXil8O3mN3972M7sMkt+skhd7lWPyo7/n7Vvxk7VtTWQBRFxP6AcHbTBppcq+Fsh6/23URPHw/09C2qs3uBeRXHhnQOiYjnUl0XWyZSonVM9SwLZCNP71sZ+6v4GevymVcpYdS2bVsWIZJCCp+WczjPk8MlOBEtPBaAHj4erDGeNoxsXhMbrgj536pEEUVcqMGqYiFvaSD734vB0bL2IfVcbHFfYvGqDXWcrVmkiNzkdtwU8tkHNqqOjZO7I68ExyKym3X+ZH2Z9kGdKKIoRiG06xXkYm2OQFsHwMqSDdKGD52D7M5CzUaTAV8CHYp7I9x5logWP+yU3T/5ILJKiCIbU0MkZ6meHaaaKnsTQ2w8dksYSD+PGd5ymL2PDwwF/ErRyFTHJqvNPOxxI1zSQ0jTaaxYaySKAEIURdJ9f/pMSJ+jNExxGX0eNCHhf1/ok0S1Sh1aCm5xiuiZCK9HIomazJZTRIR28bOZI7Ds/d+QmpqJz+auhZOnM/Q9PVBQ3R0IDQcc7YQ0yNJCooiIjpN3wKN90FYUieuLFBbCqTCP1d9QA1KNNUdxCUIN1QtOl7rV8F43P9lAliazxN5sZsaGWDxauHZRU93c5W/B6N0/5Z6/+MQdZrZQf+6WMm0HNcimG/XNC/1hbJlei1O1IJFSGULlRaNCfznT0tKY7TbdRDtu+vvJEyG1hlLmJkyYIFuf/MmDg4Mxa9YsBAYGMmu+LVu24MMPP6zIzeRwSsTP3Q71XGzQvb67zH5Xys2n8Wj7s3xPCU0MbFRUQ6AGJ0vTchFFBDnDie83rbOvTu52Io9iUtDm591MFFHe+f/6NmU24v9dDYLdh/+gx8L9uBqqnOtOUOqR6EhX3tAMvra9gs4FReFerh7QvyvQpzOS2kqaxFEzT3Egllp1ndPUiSLRRngjiW0aSIsmEyKVZberaQBPQoAG3uqgdLU9RwEPFb27wsKBa4LlsEoRpAiJIvr+SNPQFJCKonLfdzLsoFobxeNBbnMPgtk5ZkCfCR2Tkl6vrKIovVhIeDlYok+9akg1MRNqlcYMRGz71oIoIkiklUUUSSHnvtKieL7q6SHW1Jx9h5XMWxTrjRyF63PvOq4y2/7KSLSzUOzlVUZOPXyGdjWLa64XHC0+/5ecuIOCot8HMr5ZdfY+WteQt2Gn5TtuhiD798lwty17vXU1a16zzXk1qNCIETVhop5EIh999BH794033mCNWynvURRJBFl179+/nwmhxYsXw8PDA3/++SdzpuNwnidNPB0R+O1o9vfCY/74aOslpXXIJa3dz7sxsW1dOFmZwtFSuJEjm5ut/GBDX1+PpXZRWoQqHqqwhy4t264HY3L7+th9OwwbLgexSFRpoaalpz4eiEYegtC5VVTAfDwwAq1+jMDPw1rh3c4NcDUsFpeCo1mk6lJIDGJTFeoCqgJUTC9CYkJMISpDYfdzR5q+JR1QV4X6AIqsaBICdbyLDAIUhrEk8Mh9rbvEkU1KnproEL0O2VQ/R+yM9JGYWwB7MyOsfq09Bi57E29/sAxrNxzHZzOH41hgBK4kJpc5RU4jFuYwzM3FOz0bIy+/AJ2XHAJ6tFe9rjQSpiOm+kCWVM+QwC0pcifWJYmfMwlZFychaiVG4KQpiNpAguGaP/q388bhR8K1TpcpJjKJKcHzRSWKdadlhay4qdWButfvtXg/kjJyWNsEdXNo5DpH12ZLk7KnjtF1nMQY/XZxOC8zFSqMunTpotFSj8SRqucouklwOFWJD7o1RF+/6qwfT1JGNob8cQQXHgs5+1RLQ+keUsiwgNKcSJz09fPA5ZBYlg7ybidffLRNXmBRNGd6Z18sOXmH1SCVBztuhbIbIYoiaTFuSSl/WyVd3vs3rC4TRUSBwvebUj3EdI8qgS6DKrGuQoeeIzLI+jg+SWOfmCrH8yhaF9OsFGF2snqqa1toG6nJqTpMSnCue47F+W928WM9Z3r5erBJEiI6Rogqp6VnYfTQjrj51xHkVqQwKihAnpERfj91r+LegwKCikFJNoA2UP95X7kNVHcrjhDSZ0STEicuwGNod4QbFQkmNd9fuv6qtP4PfsJuH87bCHRVI6Y1UBpRVF64GuohqqiY72xQFI7dD0e7Wq5Yd/Ehvtt/Q27d44GRcoe6kZs9/CMTlESSdD0Rc2ND+abMWkC1sFwUvdhUBavuF2H/qlSNEYfzIkA/DvVdbWX1M5TuQMLo7Y71WWQpJC4FcWlZiE/PZrnZ1EzvwJ2n7EY9gPIkv1yK9ykCM3PLxXKffZRioKfHRBENW0q6jIiiSBRS226EwNbsPD7r01it4YIilH5Is5bPhVJESlp6O+JqqNruK+qjTy+SKBIHopTuJLWSrii2HQBG9NP8OQUGA7461KBoy3N0LCPnp/kj2sgtMzcTapKWrKAGxXuBNk2L65LoHBLF4aMQQE+/2Lq7tJMCFR0tpEkBafS1JOh6t+2gIIIo5VAK3Q+PQjiJR9H2WxWPQvEYauqIilIu82MSYPs4FGP6+SDEwB6H72l3DSKRUZYs5jfa1GH92ErzEqIoEum5SL7mWh20vbeKWiaQ81dJTmO6iqKFI9tiZo+GOj2HU3Ug8zISDTliA+aXlJycHLaf6szatIULIw6njFBKHPE4NhWf9GyMC8GGsDIxYnVJe26Hya0rFUGq7hMVKYoIsSZH2x9uimL980YXvL3hDIt2LTt1F7tuh+LpT+MwoU1d1uiUBB7lxLvZmsPWzAT3nhVHu56bKCol1KBWa150q+DKEEUPQ4R0t6AwoKan6oE6LasIUVTBaJpc2PRWdwxsrCxqVi6eDmNjQ/y37YywgJqsEs4OxaLo3DUg/Jnwd0Ag0KwB4KWjI5iq40w9gMiYozSoO9d1EUVsu/SATq0EYwpVdGyp/L603WSaIh6fJxGAg62QAithcAMPhDWsifp1PXD9VhAeXQ3AlexEHNn/K6p9vknmxqnpcytraec/5wOF9EExTbCSrxEVYb/84daLeBybjKWvdSj31+ZUPFZWVigoKEBMTAxzZ9OvCqnV5Yy4f/Qv7W9Z4MKIwykjtZ1sZAKg7lebX/jj6WxpghiJOJjcvh4aeTpgeZ+GaP72UqBpA2QWpeS1reWCs58OUvk64YlpOHY/gjm6UY+ma2GxTGTdfhpfqtnUyoAEXkhcquaV0jMBi6IZKXUDnhddMJUndWsIKVMUwaC+PGQ+MKJvia5xVYXqduaY3MEHc/deZ/epV9fq1zuhU51qOBoYjjGrTyilzv4xtgNGt1SdNmhoYIDeI7vhv6hM4NxVID2rOCVQPG/IWU2ERKX/A+D6HWBYH/UbShEnTxXGFcTV20BmNnPD2/nfHDw1tcD7my/odiDK83zWJroqHosrt4DoeGBQj+LHuquukcr3D8SwQW3xOOSZkFajp4cb5k5w/HSjXORb8fpD5jB25iY48UA57UxrxO0Vz2uxduoluQ4cvf9iTXBx5O2rq1evzmr6yeDsZSU/P5/tp9SevDRwYcThlJE2NZ3ZDytZYFPa3IsKuc2RE152rvyM41dbL2BymzqwsjIHjIRLRkJkLI6f9kf3zo3Uvh6l2k1sV4/diJiUTLT8cScblPi42iI5M5s1WS2RwMfqa1PKGVURPCWKjoEmRIvdqioAn4vxAg3wyWjAzfmFEEViEX5UahbSsnNRzcYMz5IzWUQxOC4VQ5rWgJ+bPRv3SlPbb385DH7u6l0Yu/SfjVsBwcUDZmeJy6W4jKImJy4ChQXAgG7amSKQYYE6WjYW/t1xCFuP38Zvs0Zi0uJJsP7g7+dzjmojFsR1mjTQXEdGhg1kspGTi307TmKf4nfVuzjSplhXSUY5E9rWRac6rsxYwPL9v0sfcSknAWRqZIAxLWph/50nMtOaA+/1YWnabs+xZcCnvYrOIc4LCTU3tbS0REJCObtxViHs7e1halp6AxkRLow4nDJiYWKEG3OGy+6nZ+eyvkabrj3G3cjyMVCoFIpGSMlZOexH3jAhEXn2dkjMB/wGfIlNv7wFPIsFfOqg0N4W/b79D5cWWKBp45JFS05ePoavPIonCWms6/X5WYNhbWaEDUdv4eNftiCudk31T64kUVQiFPGgZp5aDFKrpCB63lEscigj1zlqWvoCIBbh5+QV4Ncj8lbhH2+7xArYp3b2xc05w9Hku+1s+YQ2dTSKIoLS6BiimroeANSsLr8S9S9q1ww4f12w29ZGGEnWUZsqlpOLjX/uxX9r9uHZg38wpmUt/Hf1Mao0oiiKTxSOiyJFzoNdazqh1czhiI5NQu0a1WDm4gBDG0sEPA7Cnw8y1F6XWno5wUBfn3muHP+wPzrOp9qvysfMyIDVo41rXYdFrh32muK3o/6sXxyZ/ZDRz/OEzC7Ki9C4VKy79BDbb4agfS1XLB/LU/QqAxINbm5ulfJeLzJcGHE4FSCU5vRvxm7En+cCMWV9UT1BKXC2MkNMaiaMDPSQW4GWSbJ+QEWDZxJFIsFeXhg9fSls8nKQfPMO0KIRcpyd0HvYXGxZ+xm6dFRfmEspLdP/O896kFAqy57pvWFnIRSg58cmMKOKF0JA0HLqQ6MmjafKU1ZRRFbbNEhXNTjVdoBLKXUvCe/+dw6GBvpo6F4c8enhU9QPSAMjBrfHlesPoWdshEIyv6AIxd2HQIO68iuS+cCo/tDPzYNefj7ydYiyqb1KjBkoPH7mCo4+fIalY9pj161QtQ2UK4Ubd4HEpJK/V5rOu8JCDG5bH4fvR+CpvgV2R2YgMajIOMZY/Xm/8epjNjj3cbVDbFoWolNUCyhteb9rAyw5ebdUzzXU18d7m86za6WUvf4UOcpEZFLZtq0sjp6edhboWEeDGYYWPIhKwqfbL7H9kZKmhTsqh1OZvHwVWBxOFaO6fdkaJlJTxooWRSWSk4eQ0CgUNvYBmvkJy/LyERufgq4DZuO3pTvVPnXZybtMHBJf9m8mc/Qj3hjbDZaNhFS7SkXS9FJrAUED0xdVFJUHVABfxtztl4G903vjva4NmE6mCY82P+2Sc3Fcf/ouHj5LVGsdO2KwYCHNRJFIwAMgIkrl+gVGhtqLIoqqUNE/ocmBqlMrjF9/Dh9tuSgzj3luWJoLxhNlQC83F6vOBeLgnafMDIbaJoik5Gi+bmbnFeBWeDwiktK1S6VVw+BG1bFwVFvm4FYaUrNz1Ro/rL3wkDkcViqSJtGLRrVjUbXSQpNi9eduURJFxKzePEWPU7Xgv3IcTgXTy9eTpUOo+lHQhquhsXiukGsWWSn37YIUqYuZvQ3QoQVw9irmzd+Cj2YMKa6tKSxkf1MfDmmR97+Xg/Bhj4Y4+yiK9XJysDDByTnD0fKH4sFlpWDBu7jrjKsTngvU86YKCbKBvx9Gb1/VkSH6jovfc0MqBDY3QrcGnpg1pA3quAgmLTW8XbHxr09wL/Ap0kxMsfFhNGIK9QXhTamaognD5VtARDRgZQH06ljidvW3N8b+TeeEPDp9A2FgS4KjXXO1vbnWXX6E5w7ZkZfRJavQ2JgZvJBV9fwRrTF37w05cVQZ7L5wH0PPXYNR59bl/tq0L9EhWtRjSvCtZsuOSZnIyMQ3fZtgWLMaZXoZdZMEFz8bjDY1X4zUWs6rg17hS9bxKSUlBTY2NkhOToa19XOeCeNUKLm5uThw4AD69evHLCirMv9cfICJa0/jhUSxSarE8tcwOxtvOBghzt0NLet7sG7tk9edYelFPw9rreTSV8/Fhrm+VYSlrCJmhvrQyytAJSegVDy5eZoNICq6lqiKCZUqRVSsYDRBYkYS5fG2t0DIj+NUPiW/oAC15mxCWHya/OucuiR8jmS+UIKQt7h5B1NGd8Kiq2HC+98PEs4DivCSOQE1UaXPzNy04vsavYRQNl4OhXPou6fJ4p5s1ju0KHMvJCmGBnq49b/hmHfwZqnrwchxb2SzGvj74gOdMg/cz19G2O1VMCgHsxQaapKzo9io9tLnQ9C6RuX1fnuRxguc56sN+K8bh1MB0GCHoiIkAv67GlTxVqcVORhWGEj1auiFI08FU4k8ExPs1TNDzIMo7H5QnAoUFJuCnbdClV7qQXTlpYNk5lW8+Kp06HO+/who5KN+HdEmraLOB3WiKDcXeMkHHP3rOGP/oxj0qO+Okw8jmYujlIHNa2Hd+wNwJyQKHVcU23ibZKmuo4uIjMfXi7YjTOFrMaqzH6p3qIP5QdrN+Kc3qIdFD+IBm6L+HU18ix/0qS002KVN7dVJiPSSHbi1VeX0sdIWEh1Ux0aisorBsvHo+1TS8aIIOoDxrWtDT08fzao7wt7CBB1qu+LGvTAM+7coek77SQJWC/LyC+H37bYybT85gDb0sMfh9/uj20I53z6NLJ73ZrmIIoIyCLwdipuC+4fHV6ow4nC0hQsjDkfHXOk7EQkY2MgLWXn5uPkkjjVkfb11bVkO9o4bIfhgywWEJ6ZX+LElM4Nfh7dmTm/dFu5HZXAnRT5FhYwhtMHC2LDCm9eWSGo6YGby4kY8aHCmSRRJ16tsXnJRRJAoIk7fDcNkJxNsS85HgsS3YG9ECmxtLbHoSojc8+JPX0N65uuISstmttD0H2mqX1fsw5pV+4AGdeDq6QL7Wh64F5eGvY9jSxT2LQ3zcDWv6DwWB+whTwV3OrLuFs9xGtgO7iU/EHeUWIRXFR6HAU8jgZ4lpw1WdWzCn2Hxl/IRwgsmEmdBNaKoe303/DK8Db7ff0NpYqlZdQd0ql0N/1x6pHOaIJ1rPx28hXc6aXHtKMLaAPBtJRHYZYQcAKURL4oejW1Vm5kVcThViRd0dMDhVD5kNd1r0X7m4DRt4zm5x6iJ6d9vdMbOm6EYsfJohdg1923ggYN3w+WW1XO1hZGBPg7ceYrKIjI5Q2PneEVm923CRNH/dl8r922xNzdGQoaGInNFKAXtRRVFVSFNrxKZ2skHK87cR1UkNy4Jq7ZeFOzHW8r38gqOTWERpe03isVRXHY+LGf+o/rFqGfRmSuIuvMQUUaGsBzYHWnQ0LeHiE3AVXKy69JGvt9XTDwzVlBCy+jEc4UiRc6OeBlYEp6OJe+swpx+TVm0qF0tFzZ5VRLu1uZwtTZj13RFbocn4MaT+FJvU0RyBr7ae53VYWmTypySD/h+vRV/TeiEN9vXR1mIT8ti7RpOP3wmW0Y1pt0X7mdp1xRZm9a5/EQYh1MWqsYvIIfzAjBr+yW1trb/XHzICrJn7bhSYT1sFEWRaMxQ6eYMhYWY0skH6y4+RJYW6Wo/HLxVYZuikygSB4g0fUpFAOVVc/UyQSlfRsXpLlLeaFsHx/zDEJGu4zEvBZYmRpg3pCUT4Xtuh6Gq8UF3Pyw+eVGIckREwWZMfyQX2Q5bmRqx/kaXQmLYdUGaYqUSqtkjD4Ka1fAkPBZpB08DA7sXn2Nb9gvRuKG95M9B0R1S2u/refX8otRNMmmh9LzSQs1/qSbqJeL7Azd1Wn/dlSBsvRnCfmeo4fb7Xf0wsHF1/HToNo7cU77+lwYSRdQzSVuL9odlSH++9TSO9T+a8PdJJorou/FuZ1/8fPg2e/xySAy7Ef0bVkd1e0u5miSacFx++h6ikjPwZvt6GN+mrkrRyOGUJ1wYcTgqoIsypYhZmRqzRo5nHz3D5mvB0NfTQ4Eav5Kxf518NY6lnh5WnRXstyuTMhc0i3U3ZRFFbEOeww8zDYRp2ys6Rc5KIooo+lBko2xraoT/9W2KpwnpiHgQWbHbQL1NsnPRd8lBnPxoAJw+WYeMnOfYZ0cFP03tB/eCPMz66m8sn/cmQu0c8MuR23C3tcDhu+EsRaiRuz1szIxZVDMvLQMZ2bmIR1G9RkoacO6qkNpZdD2JeBaPqd+/gyU3FNwrh/QSzBjCIqBnoIdCDzfApYpFVmj7qpWxXkRVFEN06SMqsm6uvF6bLlAXrwNtmql1AiwJEiyedpbYM70XmngKn7Me9MpNGInvoS0kYj7q0QjO1mY6vcfmq48x5s/j7DtBVujEynEdmQCi9g3x6UI6oCjSYlIymTDKzMnD0D+O4LDC/p5/HI1v99/A572bYFK7ejAxKp/aJw5HES69ORwVUKM910//hcV7a6D3zip0KuqGPqVjfXYhl0KpD5UNzbxVGSrJ2LLMLk/lMfChfSWRUtmQGKusuiFynSMehsiiGTbGBqj71RacqARRJHIlNBbun29AbhUw0ahmXewKd232EJgaGeLj94Yg/P5aTHurH0Y2rwk3W3M2ABz/90k0m7cDDdzskLjwDQT/MBazh7eFp4cz6hdZdsPaUqjzKfruGBoaIDMzB2sDVAx+qX6IGuN6ucOxnje61q2GKgV9H6iuqazXCy93IF8yYL9xB9hzFDh9Ga4XrsLzdgAQ/KRivn/03aK+UmW+lhUCDSntrGyv09fPQyaKxLSz8qBPAw+dn0Npbo6Wqj/f3PwCrL/0EN6zN2LhMX/Zcqqj++6A4D4niiIiOC4F1mbGWDKmvZJIe1bUXHfqhrNKokiEnBspjd1r9kbWCoLDqQi4MOJwVDjK/X7qntJxMTbUx5f9minNtkWl6NZfojxILUrbOf3xQLTyfk79ZZ5nof/zgkWc9IH0os+8qJlmXx932DytYOfByqaGh2DLXliIsBTVrmrlST8/T6VlSRk5yC0v3+MyQIM2p6LB4a5bQmqfvr4+3N2EiFoLbyc8+m4MfhzaikWJbofHo8+Sg+i2YD/e+Pskpm08z5qIBkrTklo1Bjq3Arq1Y41Ffep5Ys/0PmjhJf99pmay9JqEl6MNjn80AOlL30SVQWwEGh0nRI6kjWu1vV5QZIjONQMD6FNK3ZGzgjCnKNKzGIReW46wS0ux5d0+8LwVwPrrlMj5a4JTogrqWpuoFqBlvZbRsSDRW0YnN4rIU02rSDUb3fquuduaY93ELkrLD6lIx1ZJ0XXNxNAA6yd1hb6+HrJz83E+KAp7boeyjIrbT+Nh/O6fmPD3KSZYPtp6CWNWH0NEYjoO3HmCu5GCc6mUObuvYeTKoxj3V7Fjo8imImMGN9tiV8JPejZCXXEyQUJ0SqbK32hVPIpORl4ltIfgvDxwYcThKECpMKpmzeYPb6MxUuNsZcqsWTVhYVy+2audf9vL6jFE6O9THw8o1/d4FTDUNr0uLlHoL3O76EeZHO6o/ut+BJI9VTf9LAs2JgZwMZcvxN85tScqFNGcwt1V+FfDYJHSxSgyQpiWMrWFel8Rh+4qG4jUdy25YF0dHWqVb+PI2LQsWWpRaFyq0uOUcvt5nyb4Y2wH2bJTDyOx7pKGJqrVXFi6Yt3BXbBl7Weo4WKLiW3ryK2y9ORdJGfmwMLEEBsmd2W2x/ReVQo7GyG9j5oAl8IC3OLKTVjQ+VNQiIKr/nBAPlxdhPOKMDExYvs9cmgHXDj4E2CuRZSeej9dVFHjk5KGhwrOmpUGCY6YOK1W7b5wH4u8ELGpWTDQQrTRKjum9mQpqD8eLmVt54NgOQOWr/deR9ff9sL2w7Xo8OseDF5+BL0XH0CT77crPZXSzT0+38CaIBMfdPODl8Sim9gmMSWRsvFKELbfCGbfGZH5R/3V1jhN6VC/xAlOyvagXnpzdl8tYac5nGK4MOJwFCCXHBdJelz3+u64/PkQvNfND6HxygMikZjULLhYaf7Brgi76lMSpx+KatEgqlRuZK8wNGOvFWSD3L450LpJ0YKKjZb9MLQNHCSzxVTnM6RpDeT98Ra6VIGUqh4+7pjdtyn7O0uHugUp6yd1YTPc0o+AJhmM9PUQGFW6wu/ZzW1w95lkxroc068ofei1P4/j1INI/HcliP1LgzCC0onGqpgNV0Qv/BlwqLjh80MTC3x77hFqzP4PMzYV9bqRWPLXdLTCuoldUdfFVracHM8qFC3Syqhhc/OkOGGy4OIN4PqdUr3Vj99Nwr1vR+PXTrUxfXh7+F9YinuXl+P9qQMReO0PuXU9HK1YJEGJ2ATh35yc4l5OlJonTc8jKKJTGdD7Ug2ZFEo5dHaEwd5jMFATzRIJiknB+suPWPuHUauPIV+Lz4OVSgFo98se3H+mXQ8sOe48ANLSZZMj2Xn52MrEyjP2/bYtmqQR+/IpppUrQrVA0jRAewvNbosjVh7DpWDBjEERc2MD9vtG9PXzRL+G1dW+TmRSOgyn/Sm771TC7zKHI4ULIw5HAU97S0T9Oh535o5gs7THAyMwe9cV2Y8VQY3pPurRUOnY3Y/S7ceoYMUUTGpXt0yfgdQMgkTRsBVHdX8RcYaQ0ljKAhWV7zqisIGqB6UmRT9ylVWjVG6QQ1g5NT0sidm7r+KeZIBDUZWQuBRmArJ3eh+tZpF1xcvOHBve6IQBzub4d2w7jeuSBTEZDeyb0afU7+f9v00wVDC08HawkhOr9D1URDp5Qfw8rBUmt6/H/j4YloFEqWNhORtmkONc1wX7mAiif71n/4et1x6zdCJ1NPawx8S2wne90KMaBnzyutzjW68HqzR2ScnKxZSOPhjWrIbc8m8HtYBDUYR6pMJj5YLYKFgDSZk5SCcnPHLdI5c839ql+j6/f+Qeav7vP3wwtiuWzZ8Kt2oOsLOzxOKf30a9OkJdTEZOHrZdD8bYP4+zSIIiE3s2Ft7bWDL47tpW6+9quX+T6H3VNKud/cenyFw9TW4Z2WgrMnHtKby1/rT2b6kHDF1xFHFF0U2dISFHpivUTyo+SfZZ2pkbs9/D+N/ewNsd67Mo765pvTC0aY0STVR+GdZadj+hDI6WUzv5IievgEX3F4xsq3Hd+nO3yP72qWaLj1UJaQ5HDVwYcThqaOBmL0uLoR/ipIxsPIoRhENtJ2v8Mrw1/hzfqVTHj/KmKXebfndcJYXdqpjRpQEKV76NW3OGy5bRzNmiUW1ZWhXlgYvkFxSyW6kJjyrb7DqluNSsLivax1V/GO86DPP9xwXb4WfFs4HZYlH9i1Cj9JzqXEjoWpoYwohGPACuP4lDzf9tgv7U1Sy9RZtZZE3M7O7HzsOg70fDoyi3PyI5E55ONtjz7Tg0r+cBBwv1hfVinyFymipLrVuYpJ5CjERI9yw9Wz6iSbU852cNllv22Y4rstnpuwkl1Lloi5bHl5o5j1p9XOM61IeGJkHImYu+v/t06D1GEzMUOZBCqWVi/cVWNelJZaaE7ybVerCoHkUYyD2OIiL0HLqGbD0gmCWo4kmk0nWGrlv/akg7nLbhLEauOibXJFTK2ltPS76WaIjSVOY3/LsDN5mV+7XZQ5ktN0UFfx7Wmn3XFZET+CVQ5n1o2wzo2wXwdBOiR3uOse8AbQOl81Gt0crXO+HMp4PQtqYLS38jaB9UQe5yvx4RrLm1QdOnJ/YFm9G1Aeq7FkdOFVl99r6sBpdITM/G4OWHWY0Uh6MNVSxRmcOpWtAPP0GWo1RgGhRb1HPE2RoBEQn4ZLv6GWJN7Hu3N+q42mL3rVD8eEh1Ljj1a6DBgrudMGBt7OnABJJiYWm/hp7sB2XHTflO6aUaBNKMb1lm10mkNaov5NLHJTCBlJOTx24m3dsh20koVn/hKKvFdxlIUxAFIteeaFeroInXWtZm5/Syk3cRXuQeRZEacmGkwRqNM5Mzc1l0TyZkJVBKi8i2d3qyniOrzt5HQpEVb2lZf1kYcKnizCcD0bFONaRmKQ8YxUbHOQXlZMtczqK982/7WA3I8Zn90WfpQTnBR2JwYCMvuWjI3AHN2DWIBOi4NSdw3KY/2tVyZbUnZHnsH16UPvac8HOzQ5uazth2PYRFj2TceySkkl0LECZLqPZIZP9JoecRudA5O6BGTgaaje2LpMxc9lqqUhcvPo7WXKslhd5XGiWSfuZGRrChdOMTl4R1/OoCNpLeS7RdFDVxK9/6NFVMWX8GbWo4oaePO4vwfLTtYpmD5+U6f9O5NbBpr3A8rC2VnPHsLEzwWstaTKiqm4wT6/K0pbCEyRNygP2qf3O55ZTGevZRFNu+2NRMfLDlopI5EvVCkzs/ORwNcGHE4aiBBh+LT9yRzQhLi00phei1P08w1yxVULfzGo5WLD3izKNneFSUgifSfdF++H85AouOBzBRI/1BoFQBGpzSoHPn1F5slk4RGhTOO3BT1iivVOTkwNnMGDHiRBoNHsorRYxmjunWswOQkAQP/UKE2xYXU3O0Y97gFqyHz7yDmhtFjm5RkxU+a8s7HeszO+B3N55jUShVUAoX0aaGM77o0wSD/zii3M/n0C3s9Q9jUaem1R1ZJOduZAL2+j8pe98pFVCtTW1nGwTHpqDWnE26v4CuQofqLaiIvxwFEqW6UgqkYrTPztwExwIjmIEKpSAR3+y7geVjO2BAw+rYF/AEUzecw6H3+2LM6uM4GxQl93xNPdbKDL1uVrYgeMg9rpYXW3wnMpHdZGYQogihiBDh5SaIIlp+PwgICATq1AAcbYGQcNabieIAaRHrEPP4X5Vv/fXea7o1iVa8hil8dhc/7g/9mX3QqtvHSD1yBtXfHomwxAwgKBS4HgDQ5A0ZSWh7LczOJncIlIZLIbFKKaTkRldhn2NpoPRqa0slZ7yrITFqjRQqAjcbM7zbpQGWn76LyKQMJoQoov4gOgkRScp25r19PWS239RUVjH1lsNRBxdGHI4aClEoF5KX8ttRf8Snq58Ne7+bH+ttQlC/hZ6LDjDXrsGNvdgA9mliOuw++ke2PhWx1nSyZhanFCnKK8hn9sUkinLy8jFr+2Xm/vVWh/rsx+j9zecRlVxGm3BjiSiqSOxt8UJ0nKD6KGlhNg0EqfaqkuqJEBQG1BYGnCJ/nLmPznVKNlnQRRQRhgYGmL3rKhIzNEd23u5QH4tGt4OZsSF6+XqobDJJ5yyJfxJGhNi48d83u6KajQUTX7rW3vm42rIIaY/67nhr/RnZ8uC4VDT+dpvOM9FqCXgAmBoLg3VFaHBqqbpGpKyoSoGkuiVVhMWnsusJCaOkzGy0+WkXu35IoVSmklJo9fPyUCA6DuoKiQua6GjekNloGyUlI9e22DGQ6n9k65H1Nn2X2H394uUOtoC9HXtMLz5RmAyyMANqe0PfxQ7f7rvO+uW80bYuLCROm6oGvWXB9+e9qJWdjswG9VBoaCiIIrIY9w8UZqioxoZSAFWdE6oopShShSZzn7LgYGqEeDW/ZRqhDAJnB3jaWaBjHVcmREioLjpeOpONshCZnMnsvrVFKuSWjGmHAkUTDg5HDVwYcV4aaKaN0hNEe+0v+zcrk62tgb4+Ar4agf0BT1hTOSk3n8bj4meD2Qwu9SxRLAIXRZHoard5Snf4VrNj4oaiT9TFWwr1RhL7Poh9kk4+iGSv4/DROtl6W64H41iRI5AU2k/Z4KQioVQTKsqlNBjpTGxFdqWvDB6HyWbBZZialG8zSXL+k9jgykGDMQVRRFBPkA1XgmRRxPLi91N3tVrv4N2nWF5kkrH73V4wm7FG7nGyrydXOprJlUZ1LjyOxti/TpZ6+yhaSjU0H24tTouhAnAaMNP3p9QUFsKgKEWV0POphcJHocCBk0D7FvJpVZV4Pn8/uAUzoKBrGKUjElRDOKpFLeZ0uaCoeebTBNX7TrbIybGJ+OvmE7WpsIqiyF0vHz2M8xEPfey7FyEIF4nYUUvdGtA4xC4sRK/BHTB6dDes3H4OV2RuGU5AW3Pg4CkUFvWVaTiiBwKyAboazt17nS17HJuC3yTF9SU5n5WGxyYWgJdE9D54LN9/KS+/OFpCVuQK+1fVr3Xd67vheGCx7XWpRBHRyIdlNCwa1Y6lxzp/sh7PCy97S/i62THBs+b8gxLXX3vxIfuXhDb9lnNhxNEWLow4LzQ0wKHB279XgpmrkwjNbHet58ZmucvqUDe1sy9rWEfpQWJe/dsdfRASl4oFI9vgv6tB+POccKFeOqYdZnT1k3sNGuDRAEeMNJGoklLd3hIedha4FhbLXHekUQDFSIAqUUToLIqoESNFRLx1PD6nLwFpmYJgqOFZPEAoz4ECzexd9QdChehErSHd8Ni0Ymbu5UwnpMJIHPyIg8zyGAxJRRGlGlV3K77vrLr2SpRC5SmKdIEiE5S/36WeG0yNhD49lD5HNK/uiIPv92VWuJTa2e7n/YhNy5T1XtEG32q2cq57NRysEBKfis3XHitFCnZN682iJxPWnirdOXXgFJCegYzYHQhNTMeg3w/jAfVI8akt3Mrhc6a6LEqJUzST0AS5fNH1QzFaQEYv4qy34uSLIguOBQB5ecDjJ3DPz0GEr4oeLyRGJM5n5+e9Di8HQQhu3n4Wr+24VvbifTp+V2/jSEQ0juw+B7i7AB1bFU8MUARuZH/h/oNgpLGIUgGLjpPopWs4DWjlhVHFDlMompIQG49CMh6giDG52lEki1AURUQVF0U0ibJuUle4f7aheGEZzmuqHSRHxJiUTNaHT2w5QRMgFMGtLCzpc0rPxkEdTEuI594AnfPCwV3pOC8k958l4vNdV/HWyTgM/uOYnCgiajlZsx8DxT4jpYU60pO19vZ3erL0o/c3X2BWvd0X7meDGvrJWfl6R7zbuXjmXBFKiftk2yUlEUNpQ+SwlbJoEissd9Oiyzk1t3v2y+vM1ahdaRpZ0gAgNl63iAgNvCwsgKG9Bee5ihggiClGZAFMtthkgGFkXDHvIaWBxDKd3JgUB/fltK90wW1BKWdSUVTFkRZdkzASob5eYn8Q6nFCg3cyKFFM81KFk6XgdCcVRQSJInXpU9TMuFSiiKB0yIHd0fPNgTA2NmL9gO59PQp73u2NBvbm8g18ywDVZZEo+m1EG2aUMraVMCFCkBNdpzrV2ETIZ70bY0YXX3Sr54ZVZwOVRJGTlSl+Hd5Gdp8mYUqEIkL1aqoWRYSCHbQ0akiNUz08lY0PdCbkKcwSEtG5gx+LzLEJGKotOn0ZOK+QBlWvJkKyhOtPRnae7BruLOk5U1hYKEtZJse20nwLrY30YKyv4lom/plfgMJu7YUoOAkhURS9oJCNtputBYY19Wb3e5lQPyV5oW4cGw8/F2uVz18wog3e7yr8jg1v5o0u9YRUXmdrM8TMHw9bM+F6/E4nH1QmlFFxWU26qSaotQaHowtcGHFeOM48fAbfr7diwbE7SMxWPbCnGezeSw7K9RlRtLuV/vjSY9Ql23bmWrUz3luuBWP4yqMqB230jHf+PQvPLzYouWWREPpu/w2YTP9L5etSgejUDWdZxIjS7eI01C6JkEW3q405mns54dyng0pcv3hDi/aNnKJaNtbNgY5S/Br7CLa8FYVoAEHCjRzu/FyQZmBU/u8hPR4kDqlxq0hBISb4ubE0sXIjXhhwF5STm1xlIi1atjEzxoVZg1m66Outa8s1UPxuUIvi55TQUJFSSp8L1YoHSVS/N7CxF+78+Doufz4Yk5t44peBzcpsQEjGClQrQ9cUumaIUDQ4OC6F1W2RacqyU/dw4kFxupMUskb2mr0R0zeeY9e7md0bwstBt8akE9rUUWsMo9gYmo7Fg29HI/KX17Hn3V4oFVRXdC0AmZk5yMrKRWjAX+jY2ge4fV+w6SeRpKbO49Sj4m0Z16q27LpMqdGiG93EtkKPKl0/nuyCQmUTEElaYXJl2ziXU3qulaQOS1prRm5xxNqJXXB+1iAcWjwVrRvIp+nmZGbh6dpdGFpPOBekfLTtEpacFETz9huhLJXbbda/6LloP3N9fL21cF5dD4tjmQ6aqIg0SF2g2ihKq+dwdIELI84LR1hCybOnMalZSrUaI1YelRNHJFguBUezHgckeAgqLg0sKhSn1AGKOPmHx+PQ3SfMLrckyC1HbFZJAmvN+UBYvLcGX+3RXDS68sx9dPh1D3OaOvR+vxLfZ9mpu3jn3zOyVD1yrdIKyUypyF8TOqGugRaDAzMTZqSgCSN1o0rFwYC0kazoYMXWKxrB0GCKxJFfURpORSFNlxNpVB/r7kcpG29I9yE2oVhY0YAvMEhITVQXlUooRRf6MqTSlCfWZvIDsLa1XDCrdxOWty/lk16N0LmuMLssDs4UoUjukx/H4sRHA1Bp0LEnYXrNHxve7qlylVY1XPDntL6YNLITrMiMoYzGCtSjhq43iimQVB81p4RrgVQcUb0RRco+2noRmVqmy1JNY8BXw5V6yxx8ry/791yRm92NJ3FykzhksEGpe93qu2t+A3WOaRRpGdUfGDMQufVqwcPdEUd3fYctaz/D4R3fID5kI+YMbMEGq5qgnli0jTefxOOvoloSmvVfOKotNk/pIWtfICI2uVVHdj6QV4VM3sqr2XBqkXMhQbWoJCgptbzbD9vxJCqRncdk7U6/D58rWFyjujuSk9NxYIEk3a6EqDGlcY9efRwtilLTdt0ORVNPze0X/n2zGxPblGKqCoqeU63u/a9HoiKg6DXtP4ejC7zGiPPC0aWuG/sBp9QKX4s81K5VE78d0+ySI/4ukpubu505Zm2/wpy0VHEnMgENvtla6u0zf0++QF0RW3NjZvNN6TTk1kXRn6jkDCaKqDZqTr+mWr0PpeDsvhWGhh72amuPlChKT5MyeR0JrPKZ2ctVV1+iOBi4E0iFM0LqEv1uubsKqT40mKNlFDVqoHrGu1SUR42QuA8hTwEq2O/VUXhNsiOmG4k90cVO8b3I4So9Q7B+LiOK9u7S2Vky7pAOxt1tzBGh0H9E14Lnxh7azbjqQY/V14hpo/VdbYTmnxKosL7bwn1KPVEqFFHMO9jhbHAMq5dQBW3z8BVH2eSIrlCE6IchLVkNIdUtkQV6Wcfiv7/WHnN2k3Ngjiw98ZuBzdmMff25m5Gbr/wO7iYGWD6wCVacDcTfF4Tic5F7z+TTBGkQfT4oGn0kvagIqvWhwSxFCVSi7nskcW/s1VMYiJuYGLE0PYLSoL4/cLPEKAj10CK7copOUlsEqrWi5zb6dhsCo5NgbGDAHqPeWjT4p9Qu0QmxSiHWdFWQWYMpCpBVNLftGhKC0PA4QN8S2aYmaP/1JgQtfQsXg2PZd41+L6kpudiHj7Ab0AWJjzX7hTbxcMCe6b0RmZyBDzZfYJ/DjP/Oo5ajFR7HpcrqbqHmekSpsX9feIChTWuwlMXwhDScltjMU/S87c+7UVGUxXyJ8+qiV0jx6peIlJQU2NjYIDk5GdbWqnNoOS8Hubm5OHDgANwbtUGH+fuQLToJvSDQbNuNOcPZ31uuPWazcXWdbdiPl7SPhZutOYtElQf1oiLxwLUS61s0DQoowrL3uCAqOrYs//em84HS8aTbQpbY/oFwdneESXM/PDUSal20hm3zMWHQ06qxUGulGFUqpxlhdbjbmsulc1K9zopxHZnoWHn2PhPa4iCY6ljI8luTlbOViSFS1TSRpS7zC0e2haFCfYoqlp64w2rviI96NGTvSX3AyKaeapCmd/FlTYgrWhQZ6glRsywVwoGgQv/xbepgUGNv2cCJfgYnrzstExOT2tVVEhaaoNorqstwkxa8l5EOtV1wLkjevTJrySSEhEWh0/JjiM1U4TQWEwecuMic49BM3gRGFYc/6KfWoGb8mhP4V0OjXQY5uRnLT7ZQSuX/+jWVm6mn40sRcXIrpHRhRZH2UlJR7nW37glmMWSlLV5/wp8JphESExe97BwUStKexSbNJD6l0aaSIBE6s7sfJrWthzfXnVaZ/vlpr0bo51cdE9ee0mg8Qq0oqGFveUF9AkVTBlVQOvTTn8axfRDHC/369YORiglCzstNig7agAsjzguL9EKXllPAUuIUmx5WZaiRI/UlMjIQehUtPqFsoUxmD0tO3sFpST2AJhRtu1e/3hE+1eyw6ux9fD2wOWo4WrP0PiFKJKShPNfZ1lOXhLolVe5P5TU4oegO5eN7ecj2mYwyfFztShcZpD4tlD6XkVnsZqYOctajfSMb6Nxc+YidlgMn6mP0ae/GGLDsEFs9dv54lmY04PdDbKBz/5uRqO8q1OykZOZg161QZvFNUURtGkWSu9S41rXx3f6bamuMwn8aV6I4+mLnFZljnSJd6lZjKaJLT97Bp9svywQemY4ExSSjx6IDKC/MDPXQ08MMe0I1CzALE0M2mCPhtr6ojqUsXP1iCAb+fhhRKWXsL6YG26QkpJ65JvRAcrIH2imkR4k8iRAmBRRFu4rrT+qSSWofp/NoqIqmvtpcg6jvEgnQtjVd2Hmz7XowRq46xh57+O1oJKRnodF3xQ2zqxQ0iVDO6ajlxt1HrEmuu5sDIugy362d6vUo1ZfEU1NfpYfa1XTGhWDtTAykbQLokNDvFWUqqEPwGKx8PunZiNUSUWSM+i1Rs2O/b7YxAf7T0Fb4rE8TLoxecVK4MOIRo1cBxRmg4/fDy22ART/eNGguz9mt0s6I5ZRyG6Z3aYBlr7VX+dg+/zA2iCsRcpSiGWEdXOicLU0RQw04q0K/j5t3mS2wsGEOQoNKGyuaEUJPaliqJgWRcuLJYUwXVEVelnSvj/0Po3D4qW41RpRO6WFrgakbz7FZ1uB5Y9i/lFZFNRrU1+arPdeZg1fIvDE4FhiBDZeDsMc/jEVnRNrXcmHuUVT3NmvHZbU9gP4Y24E9j0wRqPbk851X5I7Fs1/Hl5iWQjV1lJ566O5Tth33FRzn5g1uia/2XmORJKqJoAawxoYGzDGSzFGoDrC06Qs9fNwR+CwJ4UnpTBit6+6EkYd1d7CSUs/FBt4OViytS9p3jL6TNBMtnVAY0awGtr7TE9EpGWj14y7Wj6g8oMFoeTq1k4EDTQicehjJ7NbPzRqscf3wxDR4fr6x1O9HacPzh7fBvIM3mbPe3AHN8PXAFgiISMDOm8HYeSsMtxTaF0gR7dtLfB8zY1a/RimMUuzNjJCgKrKmDVXh+iWFUnEpwi5Sx1u4nimSlSUIKDK6ILdNhfRdB3MTxJfQ2Lkq88uwVixdVaw/I+j7mLJYXuRTnR9FsMjhMWTea8yZkEeMXl1SdBBGPAGT89JAVrjlBdUZPS9RJK0h0UYU9fRxx3td/ZCanYOPt16SzViTHS81yqRZP7JvreNsjTsRiRi64ghiUjNx5IO+GL36BLMfVwsVors6qx8gJKUIpgyS7u8jmtfAoTvhCK6gLu460bQBEBEFpGUITVQPnwYGdEehuZlaUUSQKOpUxxVnHgkRyFpOVuhR3x2rzz1QG4WRiiKqObkzdwRcrM3h7R+Gw9qIUAmUbvRaq9pYf/kRG5S3+GEns9Glgf9nO4pFC32OFu//rTSgH9e6Dl5rWQu1nYsjcaNa1GQ9iSiVjdLvei4unkSg2oG/J3Zhf9PMqyiM5o9ow9zntMnVJ2cz6ndEt5+GtWaW+ttvhODLIrOB/+2+KktTWz2+k8y8gf5dPKodMysoLdM6+cLewoQ5ULJt0SMBQ98f1euL6X2aoMEX63OkAH0nSRSR6CSDg61v94CztTD4pM97zRud0GNh+UzQOFmaITq1/CJQZItNVv97p/dWMmdQJDIpvdSiiKLgVANFtZRf77vOBDnN5n/aqzEztaF6IW3QRhQRdJ2jVFIRqnX7uGdDfNjVB68v2Y6dwRnaRUvS0oVom6118TWP7lOKrELKoBx0nSYhQjWGFSGmzl4VrmPUX7e2Owb3a43jT+JxQ9W6pqaqBVMRJIrIGXHVOfWRn6pMIw8HLC1yzROh+iVFxraqjW/3XWe9lihjYkbnyrUX57y48FQ6zksTMaI89mqz/kV0BaWyVCUWjWrDOpurKn6l2dMkFQXklF7gbGUql+qj2GCzXGp5yoqamdrhNCv/dg/oT11dOmeyzCwhD5/SkDTkvk/pWB+ri9JFSOAkpmez9CVKByMBSiKitr0Ffj1+B48kgzExpYOiOTTL38jdnm0zNRqmBpyUmqQtJz8awATGvchE5pgoOiVq4oNufqx2hoRNSU5MNJNKM6oi3g6WCPlhrOz++aAoloZCXeMpqlMW6HtJNvgkNkmkU/NOGpyTC5oUcoyc8u8ZtfUCmqAoWtD3o1FrzmYkpGZgYy9nvHYkBplq7MiaeDpg29s92DaQUKTbwmMBrH6CJhAauNnLlkelZMg1Xlbk1pzhaCxx5yJHtR4Lqebx+UabGQrpm3oKTTs1MXvnFfyoJjWyJOhaQxMIJL4oQkg9nHZP682MHi48jkL7X/aU+BpDmniz4v+j98NlETsStBRNnbNbO2c/k0s3YJ6ciFrDeuFaer4w6aTq+lJetYH3HgK+kp5oZYVcREVr8ZRUjG1dB7NHdcCD8HgMX31c2ObnGNmiSI2Se+dzgvqGKfLnuUBm+U6i/MHXw3Hi6BFeY/SKksJT6Xgq3auAqmJKsp9dcvwOFhwP0DjAohnwsB/HYu2FB7KaB3GARakzlLJUWkqThqWqMLWJhz0zYkhTUxivzur23c4+GPPnCfaD5W5rATtzY+ZqVRq3LY1QjQ31Q1LYd8pJV2xiW14sGNmGiQx1KWFaQQMJcpBTYT1O6UVXvhjKZrmp95S2GOgBpz8ZhEYe9rD+YK3cY/QZUNpYXFom3vn3HBI0ROhoeEO9QUJ+eE0WUcnOzYfpDNU9sKSMal4Tm9/uodX2tv5xJ66Exsoty1j6ppJYKS9IhFENkmi8QMfp6hdDlUTX8cDwUkdbSEzSd378X8eYMBp9OJpZNatjdt8mmDeklWzG2f2zDSwCt39GH/ST2N/TQJqiqtfC4vAoOhlz916TpdHRwDD61/FKx42stf86H4j3N10osztdmQbwKgbNqs4xRagGiPrXlAdUy0WiqLtPsQ04pWvam5uwKN+HWy9ijSQtikhc+AZsixwOCXLIo3OnbXUHXHyiPvVOyRjixAUhqi1Gj+vVRIUS8ABoKPRbqsjPW5ZVEBYBhIUDnVorP5fqIKkekhrXvgKoEkZUu1vny80stXXBiNaomRHKhdErSooOwoj3MeK8VJB96//6N8OzX8Zh4cg2TCTQrD8hTR0Z3NiLFeFTiF0KCYiyiCKiNKKIUEzduxWeoJMoEtOohq04JpvFowEo2ZsnLJiAZ7+8zgaPVE9C0Y8yQSERBVFEJC2aiPWTuqp+TjkYYH609VLpRZH4/jRQVCGK6PSgpojUhPPfy7oV45MBGglPSnlTJCIpnaV4jVtzskRRRCwa1U5uwGpiZIDNU7qXuA3bbgSzSI82UB+hIx/0w/wRxQOqihKzBEWeqE5q+dgOzCHKPzwBR1WkMtK5SoP20sx/08CZoiB0/pGY0SSKiNda1mbrUa8ystkmUUTXCqo9k0LRt4tk873iCGZsOi9XW1Tf1ValmKRlM7r6MUHcrqYLi8iVGWqgqmtUQ0UkobDoOkdpleooay8naUrb8Q8HyIkigqKa3o5WsDYzlmsMLCIVRaLrGaG1KAJQOzoSu5bPwKwZ3dGrWxPo3boL7DshHMeKQtpigKJ1EvRL08tMzedNnyH7OSPRo0oUEf6BQusDLa67pqyYrQpEOLXk3KcDUbBiCuIXTGC/5ate76hyPZp4oQkQ4ss91xGRVnHXOM7LAxdGnJeO7deD4TX7P3y49RKWn76PuLQsFo2RWhYfuhvOUpsexcinQr3oUDSFbMvJSYw4eOcpawZLtRKuNuYsPWtqZ19M7VTGfGs19QmUz02uQCp53oXMJbz/m+3qw8/dntXEUKE4pf+IDUu1aWLbf9khPFRRkyIipmP196uOLVN6KHWNp/vqUpxGtajF6n007Rqd3vsD1PcVUexVQwLg456NmcEAcTey2EKZvjOrztxn/bXKC1MjQ0zr7MtEEkGCRBGx3ojQ9WyhdBliYGMv9u/Ipt7sXxI7ZFv+Vf9mcuvTZ33zaTzG/nVCVlM1ukVNdq2Q8vvJuxj0+2Gkq5ik2Di5m8ZtotSn858NZoJQyqJRbbFmQmfWJ0prrDQ3Ri2NkCTDDIpIKqJ4DEoDRYROfTyANWfVBNU/1nAUzkF1DK/nCpy+LESBtGTImwPRr1cLtGtVA/u2fIWa3q5CDZG0EXN5Q0JGFBhiClwRBfa2+H5AM9z9bCB+6NkALuZlE58lmnJQn7XmfsLFgeqfyIhGDVm5eRXeZqA8ycotYBMW9ham2PVub0zpqP73jJz0aCIwPScPn1xIwJ/nHqg85zkcEW6+wHmpoDqFEauOqY3GtPZ2xoOYJDhYmmDYitIXe1cE5Wl1umtaLwz544is3ooax1I608c9G2HT1ceyfjPlTe05m9iA6EXk8L2nzCGtt68HO0Y0aPx1eGvEp2WzuqJD957iUnCM+ia2WuIfEY9hzbzZTTRDEG1m1aU2EfTZ3QiLxcarj5Uea+zhwNy9qB5GV/zc7BAan4q31p9hM/MUWSWhEJuaxdzEjs7sh7ouyhG20kJF0UtO3MHWG8H4eVhruCsIRBKGJBA/2HJBq+igmBomFeQ0aNowuSu2SOprKL3tW0l6pN47q5ReSxRtBJ0Ln267jIXHA9h9qhXzcbXF8tP3ZOuQnfWs3o0xpmVttWIiKSNb6Vozc8vFEvdLeUfVSMWcHKF2Tkd+OXwLs7ZfRlZePs5+OlBm+U77rc52XRfMjA1wPDCCNc11tTbDmU8Hqa19e6ejj5wTopTs7FyMfONn4FkMcOQM0KezkuhQxfyj/ujtI/RsC45MwONq7kBDP5WR7nJF/A6r2Nc5+27g2MNnuBAcrbFuTQYJGknj3DEtamJaJx/YWZqxBueXLt3D2Im/CGKsjhfQpIH63kcaDBmk7/EiQN8pbaHG0zQhc+rhM1Zz+O6mC7CzNGXfWQ5HFdx8gfNC1xjt278fx9IdcONpPCtQz8zN19jolZrbxS0YDxTqwUSLuo0qQSmKa9U10jMx0kd2bkHp3rOq2ddqSZ8GHkzYaJMi+e+krqxJqtgPi9zb7n09Eo2/315uTXal5gq6suL0PUzbeE5p+budfdmAnSJcB97ry7rOU2ocRYZKgo7NwN8PMSGkCrK7PfheXzT3ckJ50enXPewYU+PIhUURIkVogC4Kx0cxyfh673W2XCpLVZkJaGrkOGb1MWy+VmTfrgAZZdDriPxx+h7eVXGsiQ+7N8Tqc4GsHlGsTfyoRyNm3KF4zElgT/znFBOuVPNA6bGUXlkqUtOVI0cKg+fSQuKc3BepeTBFTMsbqjX6vHcT1kbAzqJ48iQkLgW15mxil5e6LjZYOa6j3Hdjxicr8Pvq/exvT08nZHZqgzgtJ/z/eaMTHtzxx4EoPdyKqNpNZaU9g2QkpwKm5PopCF/Xu/cRFRCENXt+Qg1XO7jqF8CnxTSgUX3AV5LGJ8G4IB+1Ax/gXmoOqnVvjQaejrgVGoM4TTbmj8MAayuVRjVVAc8SauREdt4MYddLRUOm7VqYj3BeLniNEeelhQZLpx5EsjScc4+i8DApF8vP3MelkBjmxKZJFBHU8fvC4xhcCS1bj5NKRQcxQk0bPW3N1VqNayWKiN1HgacKTWUrURTRIKE0kCOflNpO1jj4fj8sG6O6n5Mi1NmdBuzi28emZbH6ILLPVkeDarpHU0QTAl15vXUdZhChiPjRUCNgq/f/ZoXzlu//jeqfb0Cz77cz0wB1UKpT0Hdj0KG2fAokRY+oFoQEEx2Dkyo63peWOUVpbSvP3Gf2zaqgQQ8NkMm2/KsBzZlocdch/VAVo5rXUrm8trO1nCgSI2lutvI9YEQ2Xg2SiSIU1eyQiQBF3VTZmK+b1BX+X41AnwaepRdFZCSw/wRw7LzgBFmEWVwCygM6J88FRVeIKCIoFZHSVJeflk/pmn/wJhNFngaFmFndCgbxiUhIELZh267zMlG08a9P8OTOGlz77jX2eZXEpre6o5ajFX64nlTlRBGlUirug5IoIqgxdJEoIqLq1WV1TG8O/R+6zvkXDYd8AwzqoVYUETn6BrDwrYWBPm7onBgD+1t30CUhGjb5aupt6LfD0R6wtxHMG6ogJdXIiewLeKLSpban76thSMEpHTyVjvNCpclJ02tYh3tP3dMiJvx9Urs0hioH/XBqFgw0WPtpaEvsuBnKrIdLzZBewMMQ4Gkk4Kl7ZKOsqBwklICnpQGephX/kJsY6rNBP4lpqmPQBupR062eG/6c0AkfbrmI3bfDcCcykRXxjiS78Bsh6F7fjVmli9wthd25WA+jK5m5eQhT0TyUJgooRY1Sl2ggIPamogEE3ep+tVnWhFQVVARPUSHqfUWNE6nWhnrOzOnXjKVkkijqs+QAOtRyxdCm3sxYoCxQ762W3k64GhrLUtV+HCq4w2mCxM/gJl46pR8q0tyLrMzl69Fp9nnDm8q1Qh3rVEP4T+PY9earPdewVmJvrjjYIiFP9Q6UqqsOOg/XXdLN1EPGg8dCHy6ChNC2AzC0ssDZM4vhbGmCWl9uwfOAelL9ezlIp55vzmmpcrUtYqPOpyeu4N0NQh8qwsXZFtExxd+tNeuP4bURneHlYMUcDckWvaePB+t15DN3i1Ij2zouNvhunxBlrGpQY2aabAmS1riSNTedy5rOZ3JxbFgfqO3N7LvzOrTU6v2uwgTIMQTO3gBSi64fbs6qjRsoJZQEGftbIRJ5+ZYgwsq53q00aDO59NPQVrgWFsvMXqSQc2jCgjdYevl/V4PY7wO1V6CU5N4NPFRG2tOyctlr0W9KRbl3cqoGPJWO8+LUDq08KpdGQ8LIxcwAoalld5ohZ5u/3+jMrD2lrlOvPAp9UMoFSg+JjRcclcr7tVWkpbiZGyMyI0dteqEISc7fx7bH1E6+rBZCTKUyNtBngqmk5xP04+ofkVBiPcyj70ZDX19f5yJ3MkPw+XoLa5ypyJMfx8LT3hIen21QG5WggeyaN4RGrtqSnp2L+nO3yNX7bH+nBxMCpRUoxJ7boRi8/AiLcm54sysrji7ta0nRlEon1gWJPaWmd/FldU7apByqqkmSpuGRc2BJ201pY5TKR2KMROyz5Ewcuvu05PemaZHHYdC/fgcFRcX9TRvVxI2zi9nfptP/KjFaXhGMb10Hv41sg7Y/75ZrsKqWmDjg5KX/s3cW0FFcbRh+4+5uJIQQnBAkBHeX4g6lRWpAW9pS2lKlRqEttNC/FAqFosXd3d0hEAgEIhDi7vnPd2dnM7s7a8kmBJjnnIXsZmWS3cy9n70vSlI5H6O5ey7jfYFhsWVREQoKC1F0+AwnqS/gvbf74tcfJqg85cKjN/HmSvGWRwWOngXaag/AK4OD7/fCgk0nseFBCkDCB2QMe/AkOjWqjs792+LQwxQcfpiEfJK7NDSR99HF3hT9eocjr8QI57OLsOqmUnfAs4YEMqiF0ADtyEmZueg6byeTiNfVLJjakXnO3k/A1HWn5F5aVEn/aaB6IRyJqonkYyT5GL1QUKY14NPVKoPYFBh18LHErugcw3qFVDWEsz3PYs6Hhrtpg6nldXlTR0NXwcoiEcxXTPSBKky5C8arbLia+rsyDxtdIRPKXA2qRyTdHJeWDUtTE1z/cjALZvSBPDn8P1kl+r2/x7RFx1o+6DJvh2I2WgC1dDXwEZ8deJCYwdpNqY2M/t6GLjqAU1HchkATFOyRmpw+ffsklR342Wo8SMos93PpExgdvROPdj9vY18/+nEEfJ10+/0rB0Zd6/oyURPeF43k8KnyptexFhUj9NsNCoqAYlAgRTLuVsnJGPjpP8jx84GRkwOOfNyPfdYpwBTiamvBqn3aRB6GNA2EjZkplgqqYfpALae3vh6Cn/ZckVd+eH7oF4af911BokiiaVojH4zr2RS1vt8i+rybJnSEj1ExLCzMkJScgcPHr2HauwNhY2OpMn92LTZZwYiWFDlV2qf2HQeSUoDG9YDgCvYy0jYTRjLvWWmIOHsT6NACxjm5KN5xCLUCPRHfMozZPaTPG8sk0zdcjMKQvw7odF4l6Xjy8SLZea2s2YZ//pkO39oB7LkpOdHyJ/H3ojxQUok+YyvP3DXoGk3vceyskTonUEisYc7eK7hx+w42R5VWmkixkgyDmc/R/mvywGd690ZM9e9RciYaf7dR4bla1fBg86p0jlwzoZNWM22J5y8wkuqBElUeWgDF1KlIHWxiPXscis1V63D/QiA88YqchJkFRUX++DoqXukfFBGGX1TKEhQReUrtlRYy81FdgiJvB2sW7BCkykeD5uqk4PlAgNo5Fx27hS/7NNGrQlLN2RanPn6FZemVGbf8KP4c2RqRM4chJSsPzlOXiVYtxAIjamtqOHN9mZzsY1OyWEVXn3mfTZceqARFZX0ufaCK1OutajFJcjLf1QWxOSg+KKJN5c7J3fUOivhAVF1QRGbJX/Zuwua86JjpfWn+41nkhHLqYvTXRjLiYv5TXev6obaHg8rtfPui8D14r5MGtTIt0Hmn5++7WZDLB3CdavtgbMtgtAv2Ro8GfmzGTfn89NPlWPx08ZFo2xht8F9pUkNhw9mhLedjJNZSTTham8urqMpBUU0HU/Qf3QE/zd3I+fqUCg9WLCQNfv4qC36UibBxgJGPB3sPi60sceLAbNQP9IbDRyvY999dexILR7XF0pN3FM+rVEVztOcSZEq/OxIfquftpBAYXf9iEDYcuYYvSfGPZoZovcjKRnH/bhi7+wb5Vsjv24iULWN094lSR5CbHe4+5dolWwV5wMPOSm1QRNYHCiqfOib+6D0ev/wok3nv1ygADX1dNN6ffLG+7BWKnUbxmDWmJ0YsOYyWNTzwdZ+m7Fzdbg6XKKHzAT23JnNjvnJ0Mz4Fc4e2LHNbtETV5fkRrpd4adF1UJ2ytsTsAc1Z9v9loUKDIn6xoqoR7yCvL2u4Red5Q8zDSB27pvRAxrzX8G7H+pjapQELiqidb/nY9gp+RcOb1mDGwjwzd15i1VDa7OlDeKAHc3rnzXR71veTf4+yn9RyJxYUEX0acj4/Yj5DpMInpI6XI6uMaIP/CL7330mWzdcG3Yc2t4Z4Ln2hDfffY9phy9vddM72XnokHhzT3Ba1MNI8UlnQlEygysEH608zwQIyoCVPMmWfLBKcoTZPIfSZ6N2gGvooVZEIYVDEV6xm772C8kAtdORfRtA828jmQUzIg7L0HWdvUTw/UdsYj0hQVNPdHmemv6L2feFbqpUTZWkaFCcj0wpxw9UDQZNGAC2boLKwzMkBnqhPqpTIKlf0o/r4eeC8IECmgKj9z9uYLxlVXZCVA8Q+BvYcBdZuBzbtETVu5YPspk6WWN65Nnp0/QBfvjVHHhQRxTbWTNBB+TdMQdHq8R2ZDHh54IMi4tDtePwsq8QQlJDp3ygAbWt6skpNzKyRsDQxhnGuTBFT6X03M1H/90kzf19uu4DPNp/T6/gomDr/2QD8NqwVU0f8df81Jt5EUAuyujlA3hSdkgs8p+49qZBzlMSzRaoYSVR51GVkTJXOmZQ5pZkNN3srlez/C01xMdxzslHwIAY5D+PQuFcbnCw24OwOLVZUNaINWGY2YKtnhqxecGlLCe2Syqg4Vxl4ffQvCwZIUpnaKHQlZOYGJj0985WmmLyG2/BTRnLl2bsKm7jV5+8ZtEIyKrwmuwj76amVkCTHxaDWMU3BwLZ3uqPe1+vk12/FpzL55M97NUab2dxciDpKBGpR2nr/1VWBy/JclUHH2j7Mx0iozDe5Qz3MG9qyXK00gW72CK/ujnPRTxUMqIWQyABdNFWHyV9p5biObHh8xuaz+GHPlfLJRGuhmpM1HqZkI9DVTh4U8XSZu5Nl3o3S0pFcYlI6Q0Pw/6uBEgoO7y3DlI712e9WLJgWO1JtR7/jOlfdq0y6NquJrTpUXym+oeSIMiepMlFSgvxDp4E4pXZW+p1G3ufOyanpTMmvyNsDqBUI3LyL8/eiMYa/r7ugmlJUjD5ulujeJAhj+zSHzZSl8m9FfDUEHg5W+G9iZ/x3PgoXop/impYWT31YOqYdXm0ZrPL3snpiZwxffIBrP6YqG6nwmZnBtSgfgQE+OKsUzFMSaOf10rk8qpKVh0KlwIZEMagapAz5ICknFwYu3Ffu1l+JqocUGElUeaiNhE4+yhsp/gRL/7raWLBedsqAjv3nMF4q4hOQcKw0a3Yy39hwf9lFRXirtjv+F/EUsFKsJjBoUdHWBtagVunXVTgoIh6n57CLJtRtTuceuM4uPEdlcrL0MSUZcXqImGgCP2VFFRJSXSuP8ICLrOKjzn9H2zyNsLrFM2PLeRz5sA8mtqmNv45FGKTCq2sVuKyy5oaGEi7rJnZGnwV7YG5qzOSWG/m5GuR5T03vx74uLCpmqoM5+UXsf1LAGrRwv+bZGRm3HqeqzEKogzyuyO+NoKDs2Ed90Wf+buyWtQaKQRV4PtlEM3RZsscrB0U8nPiHTNFMT4ER/vX0DaarGltJWMHPS2chGyqOFN2+j1Y13NGtTyucPHUTu1fvU1+pv1jaBsfeDVIRpYuAvj2b446nNyKyZZU6E2PsuBqNGiiA1cBWaBnowcxmifa/bNN67lN4+c8GIHzWZp0UXslnTTkoovbL+YdusDk4qpz2aVAN2z7/Hxc8e7ii3ZAO2KAUFBHCoIi3MCgPn/YIZe3JFAyODq/JJPW9P17B5OV1oaJbfyUqHykwkqjy0Ebx424h8ky8GLMHhuPXg9dUZDmrOgaZD8rOZVnBmrWr4fGVO8iQzcYYhMQU/G/mTsDHE2gjIg1bjk28oWHKXQZ6Lh9Ha6yd0Bk13R3YrIfV5CUKrWgk460rlBHWZi5rqApJXkER4tLKvnmkORna+PObHTtLM/w2tCW+2nZeZbheHbr03Oval1+V+vcp6Dz58SsV9vymJsawMzGHnSz/QLLUfDDap2E11vpHgcHdp+lc+9rTdFYhoqrNnH1Xse5ClE6ffwqKyOOHRDYGhAaw1905pQem/ncKcw+WBvZChBV4yuxrEhcxxDmC5oyqapBcZqgdriCDk8I2Vw2QRtqWYPnF6zhxEbiw7Thyc1XPGUZWFijJUZyhbNa9BaJdXJFw4hLwoDS4Hdi3JfN++mDDWUQcLg2iin29MPdJATZ/uhrVXEoTJXxQRJ8natUlWeropAy5KMGn3Rvhhz2X2fns7Cf9sOJMpE5BkYOVOeYPb8Ue1/9/e9jnjVqOZ+64iH23Ytl9JrSujbYWxdhGiba8fHw8oAVmxWhv3abqkb7iNcpQsDY6PJhdeKgCJOZJJoZyYosoj6WAxLNHCowkqjzUQrH63D2VzKWwp35A0yAMaBzA2jCeJ1hQJJQm1UGmVHlQ1SjIHyU1A8A6o4sMdAImrxRXZ65t4dAprsWhPPBDtWQYSG11NLNErR9CGV93pQy82CBufoHopoLdHYYjNjWbBQnu9pxPFi1uvKGgPkGRvpR180dtf9XUKNXxkM8RmSNrW6jrezvLpW1JAnrsssO4/ChJpdqgToqcnl/XKjBlW0vK+VwvEtQy9/vB6+wzJpTf3nb1IcJ+2IwRYTXwfueGrK1PyNqJnTEt+imafr9Jp9ehDP341rUVNoe/Dm2JHwaEYeD/9mLnDW6DHV7dDafvq2btNUEeYCt2X8DhhLKfM0jhsioHyXqRk8vNGnm5A47qBTosBdV0saCIcLQwQ4GpMTIFAgvnKBno6AqEh3LB19MkBAV6Yc3SaTA1NcHcoS3Y+/3HkZulT2RmigfJmWjkpyhaQLM/h6b2YabEvFQ/Hxh9L1P+C/VzYW1+/O3aSMvJx8QVx3A0Ml4+I7dBNlNJ1cfv+zVDUEEOBoz6gd329vie+OGrVzHrzUUan/faF4NQXyYiI1QpNEQwMk9NgkBbYuu7nZew6HiEQmVTarV7/pDCWIkqDw0e04mYstc3vxrM/AtWjeuIHe90Z+X4IA8HdmK8VsXczcUUzkQRBkKyr40pc6ZN5Y2iqtw8lAg3EYYwYz12FnDkhnXf6CEbVs4WtFhQoEQBij7wx0j94zSjFB2r6KpubQWkKGUIxWY3ijRkqsukiqf4cp3rcI7obnaWqOdV2rse7F46vBzgIjM/pCFnfzf2mSRlMnXQQk2qdbpQ1s2f8lC+UFqWf85VZ++iwy/btYo9/DO21OeIMsIUFJFgBPn0rHi9IwtalN8Z/jpJSuuyIaH7UFYWBniuFwXyS6E5rv8uRIl6ElFrHckeq4MqRrqi7nNGAhw7pvRkcsWEvkERQWph8qCIZhIjVOfqtNEiUJA0UQqmdW3GpTYxSxMD6V7SOZHkvvXA06iEmwWiFuQAX+7cp+G8uemeQKiB1ouGtYHqfjARnFtSUjNZUGQibE8Uzm21asK6B2Z9PZYFRQT9DalTHuSTjDSnQ0kPav+dd7A04CF/r8FNFOXNSQZ+wWEuyKKq0pIx7ZgFAInB/DpYVYGPWHw8QuUcRTOYl2cMRCsnSwx+dRYKC4swamh7/D77DRaoJ8wZzdr11J3XeBVIOpfROY3ObSP+PqhwjqN9ASWDyAD7uCyxpQuT2tdDWSAxCOV2T7pOs0jr9fj7lHi2vDyrjsRzyfXYZKbKRFBff6CbA8sy3U/MwOOMHAzbm4CQbzex4CmsujuClSRqXWx1rL5UMKLmi7TJpwyf8oY+PRPFl2+KBwbChZUWR12rS7pCJoi0mJuakPszrO1kGyi6jX9tWojVVG10go6Zspv0fPcfygQdbGgKnMuuakI45yQM1kpK0N5EfN5BF+g3vX5iFzSWzY1QnzmfNaX2NFKH4vmyd2PE/DgSW97qyjardb9ah8w8xQ0PbTJ4KIu5fVI3jZs6I5nUcVkrJB1rezMJ7x2TurPhaVJe4qVllatQfE+8uuCIlKMoG8urPJKK1I2vBmNI0xqsh5566X2UZpHoZ9O3x96Qz/Ui4O1orfAnT4kgmisSEh7orpCJJ6PajNx8Jj0+X9YuRUFseT9nP/QPw/xhrXQ6bkomkHGwv6AtSw4lQWrXgL6QoTD5Rs3aTa1bJSrBtC6cecApjblaad7mmGtQPpPTJgxw0W/I38nWAotGtwWu3eYSQacuAaRUp4ant7lKdO1gX7i3DwPq1gSaN0JRr05A4/pcC56MImMTrr2ZzlGW5orn1o4tMXDBbnQZOhM7957Ht7PXovYMVXEH4lFKplzNjmThiembzuKqQLZ7ZFiQwmNokx+ZkMY8gOhc81qrWnL5/zfa1hENukm+fdnY9vh3cBgWt6uB7xr7YLijCXZtPII+Q2ciT3b+dHdzxKUrUSgoKISbnRVCq7kqKG7y0HmNvNz+OXFbVKWQznF0nB4f/isPmHot2M2+t02Hiv/EtnWY4bshJ2Jp7lNSsHs+MCrhzzovoYmTRNUmv7AI4T9uxqVHSTAxNkK7ml5syFis3YjahEiVafLqE/INgrbWn8qgW11f7BEONd99wA3TksobBQJuLtyCK8iMO1qYIlVs8HPfMcDOlgsqKorrd7hgpWtbluGko6prWozrB85xUrcUFFFAZ2LAOSZqraPnFQR51K5B77uu0HF+3swR82/mIEnEUFLbLFKLQHdMaF0Hry8/wq7/PCgcU7s0lHvY0DAurxpGcx9utlb4btclta9Bqlq/CdoxKKj3c7JliziUXp9ffA0ZDKTn5MHn45VMXQ8aWtXufz9cbVXmVnwKG/Yn2XJlJSlDtq4Yug1Gm8Hr8wSZUn604YzCbcOb1cDvw1rhy23n5Zl7yr5TVZ02tzQLMviv/Qb5nE3bcBqz915V+33+72TPjUcY8td+JjFeEVDrYC0PB7QP9mY/B210i6r61uXCNbilpOBpsRHQtY3Kt7sEuLBz3r6IUqVDHD6NyQNbYlFKMXLVje+QbLezI5ckIp8kXy/A0w24eJ0LoISQvDetN0N7l962ZR9MXBwROqIHPuwagmGLD6i8BKkKRn03DOamJiwwnXfgOt5fp2gWvOXtrugbEqDy2AWHbmDSmhMKt73Tri5S0rKwbs5KFJCXlBi0ttEc69Mk9PR3xvJ577C5vs+3nMO3O9Wfa3WFDOFXd3XHiL0J+HdcZ61/A+cfPEXr2VtYVY2SRAVFRSgoKt9njrpdqoLK5stIuh6xgRQYSVQpqN2HhopJMlmoyKQNvt/Yb/rKKqVcNCosCCvOytpfklOBwkKu37xBaX+/EPKskPuSPHkKnL1CESKXGaTWMxsroE/n0gekZwA2NqrKTyTvKpzh0ZWMTK5qRAtvo7qqSnQ6GvDpA2Wwh1oXYc7OS7BsEIxOIdXZovREFwf3ctLU31XUwLVNkCdWje8IbwcbVjlac+4uhi8+qFFdjDKitGETI+mXMXC2sRQ1p6Sfn9rGDFkhofYRypS+jAv1ixQYkUnvmvP31KoMKrP5ra54pVGAwT5ndxPSUPPztfLrYQFuiE7OVFDHC3K3ZxV8Shy0remFjW92Ye2XP+y+jEtR8Uj+bzfQrCFX4TBAUoVat/gERoVA52g6xnKe50xyc1FkZMy10IkQ8vA+zm79BhaTSiWzGVnZAHkNEWkysQZduHwdaKQUGFGwRF0JvHAOzYpu585RS//3LsaO6IxjkfGsBexabDKrPArfVzKKJv44fAPvrFYMdvL/GM/Oe2KJjoF/7hOfxUxOQ2BkJOoE+yDXwhIm5mawy89DUkoGDjt5cC3VMuwszPBZz1Cm0Pj19osoL9RWuaabBwuMXOysNSaFCBIj+fWAbnNUukIjAMOVKnASVS8wksQXJKoUI9VsLLVBPdJ/Kw09lhcKUl5vGYw/dZAoVscNoR8CBRuEssiAIOBQMGv0cOMqSSTzKlSgE3L5VqnHRadWgBvX0qB3UMTLblPWrldH9fczcFDEt/58OrUP3hnbDbeSs3E7MVOhda0s0II9olkQlp2+o/F+E9vUgbFRhIpXxrG7j+E3fZW82vNupwZMpUhMwrVjLW/U8XLCpxqMBslJ/fdhLTGpQ32mXFTRqkXPmxy2hDhkQEkVIk2BEbXgkWAGBT59QzhVLAp+yvs5o4r9hH+PKlRY+b8TytPYmJsiI68QdxO42cBXWwTjr1FtWJWhUx0fdln0zx5MXJFfeu6j5I5g86vcvte3ob+Cj5bynOZb7eow1URD4W5niYQMpXOqqalie/P120CdIL0DuiJLEXsDOq1bmuLJsYu4cv8RajSaCDxOAfp2KW1P5oMiQtegiOCVL6ntmWaiKAhNSlUwd2UtyzJee2se+zyEtW/MgiMycia/rg/Xn2bfp/eV2hlJga6hTP1QKM8uFhQR9Jyrxndi1fH0nHw2q7n7chTzvaJjaT9pCJacE8zbmNrio34dcVjJaJhUE6mtj7pFDEFhiW4KoE8zcuD+4b8qtzvbWOCjLiFsBksoa07VJPJQ1AVPpdZYiaqJFBhJVAnoxEJtI2WBBtuNtSjYlAUKUjrU9mGBEZ2c+VYqfSS2KXtKohB3n6Zh6jpuwRGFnpB63e/HcFUh3pSvWztg/c7S+9GCffwcJ47wMJbNIylkB9s151rSdPEXElLWjfmjeOB2FJCSBptWjZHlrf+MzOWYZDT4Zp1cDa6sWJsaIVu2+pGf1dKx7TCyeRDLXtMcBBn3URWKNoqRT9JgY2GKHvX9mDoXGbRSxlQM8tmgmaMbXw5WMWKkzdrB23Hsog2Sm6fnCXJ3qPAqzfMohy0hjr2lOWq42TOJbiFkukub1UGNA2FraSa6QS3P54xejze1dLezQoivM5MHX3ryNteuamSEA+/3Ym2WlESidj7llks2p0eVIqo80/wgtVFV4zaHdOwDG1eXt+rRI7dejWa5F2FcQnLP5IND7ameDtasGlpe6DXGt6qF7/qFsfND13k7Ve9A1XiafSlDUKSJJ7mFXAWtYW3EPE4ATCyAI6eBzq3Ll3jiAwg6Vmqf42dCk9OAAyeBtmGsYlSzhhci73Hva3JKJvbejGEGv+QPpOwRRJAaYvGfE3Dvu+EYveQgVpy5i/VvCLoWRKBgYd3rHbDnwEUcPHoFDxfv5Pzs6gUrBkUyZous/XRupdlcft11sjbXanugCeVRMnVJIT7QV04+7H+vF0uAfdStIXZff8Tkytecj9I5KCI6/rqDtTDf/noIrDUI9kg8WyTxBYlnCvUvk2JM7S/WsnJ+WYhTOsGR1KuynG1ZGbqI678WutJrC4o+7NwQcT+NYio995MymfQnKZmdntAO2KGmIibLvjXxdYL7dYGsqpiaXcxjLospDIqIlDRg815g424u0BGqvukAiS3oTVoaJ+1taV6moIiHAiJ1QRH1p9PCpG3RW9LRFW1qlFbKyEy1S11ffNQthA3xWpiZMCM/UlIaFV4T/UOrMxUu2sztebenxuc/ff8JE/2gqo9QMYsWblrASaCABpGzfn+dteEJoY0db6bpaF05YiDaFLzKK/YgUXlQcBE5c6jCbbRR/aZvU4xtWUs0KDIEvNIi7dVpuJ6CIzpDTO3Mzd5l5Bag0687mGJjqyBPlaCIeH10F1TrIGvjuhcNnL6I0bYl7G+Gqg8DQquzihhB1YFPNp2VB0Uk6EDiBY9+HIHv+4exoIjI16RMqSP0GmTo+dOeK2gd5Ik1r5UqMTJIgY6q51SBN+Q8JS8aQ0ELtdj5+wItGgNd2gAPOE+fMkNCF7QGUIBE3QNBXPWQ8TSJWxf2HUfUgyfIebIBmXHrMOXNPjIzXs3wFfzFo9vhyezRCPd0wJzfNiI9XX0ia/zk3zFw9A9YsEgWdEZEwVqPap+yYJGpsTGTgjeUBLy6pFCLGh44/2l/1inAM6xpDRYUEbuuP8K4f4+yoEio5EleY7pAXS1LT2nuZJB4tkiBkcQzgzIttLDSXAaVtrVBGzk+G0XGm22CxNvFyLPhQrT+MrO6QBv09zop9XErMWf/VeQVFuK9tSfR8ZftOBARi+WnIxHWJBgt61aD0/VbLMMqxgUzG/y7+XvYWXDF3AaZqZg6qR9unv0DPbo0ga2tansGr54mJ9CP/a7qBZQqWMkrShpg+xFq28svgJOVGcwEVSReoUyFG8w9iVvcywhlAtXBZ5EvzxCXbeWxMDGCpakxtr3dhW10aEaNVL10hRZJN5HfrbDaQ2IG9P+pqCdske1Sx4cpFz2ZMxob3+rKZG3p9/RhV27jyJM6dywLkk9N78eUnCoDSQ77xYKCjv+NaC2/HvE4tcJfkwIhFgyVgJ3DVp69y9Q/P9ui2DLqO30lgmasweCF+/Dj7stsPpDmTIqLS/DPyTt4mF/CbTTuPWRZJWczYwyTBUNk8sl71NHfILVz8Sw9eQd1POnvmDs/kALkxkv3cSE6Ue35Ux/ScguYMe4rf+zBUIG5J4MEcdRUb/hby9ziRa2EYqqe1Ut/dgYFOfpQLxiOlKjiW/CaKp6H2BtZXIyiomJs3XkGNjaW7HOlXIkUg35PBCWXyNvt8+9W4KPPl6Jdz0/UPiZLue27oADVcnVvdadgXKiI9zQzF3n5ReVKwqbmFbHfg7akUBN/N9Y+Ta2hBAk63Yzj2uL/PHpLYcaO+Hr7BeY1pguUxCMhKYmqi9RKJ/HMeJKejcN3dG+LGNeqNt7r3AD2lmbshJ6fn48/1m7FykfG8HWyZfK1h2RtFhWhjkQb4d1TerL18nRUAtJy8/H36HZ4f91JnFHy+6j+6RqVkzwd87HdP+JE5GNQ8X3S6hO4KZxBkhnY0lxBxDdDWVaVVHl4dq7/CsXFxdh/+DK69f+S3WZra4XMTMWTdI2ifNz+fjhGLD6IG/GCDVStQLlxqyj0g8mG1VNyClSCWPp2kKs9awss4bcHfJFJ3XPqgKb2CL4f/Ktt6odvhdsTak849lFftgiKZbA1sWZCJxaoi0HZcYKqTaSAOKRJoDyDrcwfMqUwnpz8QlipCywrEF4OW3kI37cCxB4kKp4329VlSR8KEHg59oqENsHk0XXpUSIep+XgcXo2m614nMb9T4ad1LJK0OaaLusv3scnsnkMCnRIBpoYGV4TjUM88N2c/9CpXQi86lTHslN3sO9WjLxFilqblNubWs/eiqufD8L3uy4xEQoeQ0467rsVC5vJf+t8f/6UJ+wiMBRN7M1xOTETRTl5XMu0UGhHG0ZGSA1QrFq0GdQBx9YfYl/XrumLiEhOIXXH3vMYMoDb9POBESnRUWujUE2TJzImCV1e+RytwuvA0sIMS1dwwkiXr0Xhzt1YBAdx/m9CAqopJeb4gN7XSlVkQgRKVN1W8j76aKOiQqM+5BYBYw9wQjvUOfDv6UgWiHs7KtoFCKE2a5LEpxbDWXsuY9lrHZhXk/IMLImP0HpNnSo96vvCxdoSX++4gEiRtrwZvRrLjWklqiaSKp3EM4XUk95adZx5YJxTGoDneSXEX65w88vgcJibmKCwuBhrz9/DqSjOq6KiIanYUx/3Y0ELwW+8KTva6dftWgOxGT1DMbRpDZZ5pZkV3hyUNjliValNb3ZFVCK32aCWE1LqowDhzbZ1mMRqj4FfYvf+i+jZtSmiHybgRoTiiXrSvPdx9MFTRCVlIZN6oIVqcjQX5OXGVniPrAws/WAAGtf2YbKkNHBNKkCkCEiGfI5W5nirXV0W2NGG+uLDRPnxIyYeOM55TDF5b+GQr4FxtDZnrXFig65MbWtQc5jE3iiXEhkFMNaTl2hV/OKD+jP3E9jmr5mgMkcbRaf3/5GLNHSq7YO97/ZUrepVIoaWw67qvEiqdFUJOucdv/sYV2KSmFw9eXeRIAkxa0AY+0zRED8lp/jzIc3xkSHo+50asEBLmLBo/sMmJuYgPL+LQWILZDRcFbC1MGWJF5pDoaDvelwyCwbLQ01Sf+M30GRd8CCGk9/mhXTKARPOIGXTKMX1Ydq7AzHrm7Hs635/7NH4+2fQMZ0Wl8weNrAtVi/5SH49LS0L2/ecw6gJPyu2hFMFi4xuKZg+LDMJt7KEo5MdUkNUDVVruNojKildJ99uel/UWRNog9Y2FiA5WMPUxJglXp2sLdjFz9mGJZXaztnG7nv0wz5oU9MLMzafxXe7LitIl9O5ngxx1cnsK3+mz07vjxA/2SyxRIUjyXVLPkbPJf87clNnWVoe2t5R9jTY05GdSP88epMNkhI0GPzGymOiw5T60MjXGYc/6AMHwXxIXGoWG9YnDwjasJNMLW3et8oWGAqkUnPyVUruYoR4O+HKo0SdetnpJH76w94YMX4OtuxQPPG2a1UfR05w2T7jvp1RrEb9iXDIzMTrQS748bMRMFdq6+DfB/p5yHl806UHWHQ8gmX7yHGczPXkkADE+WtAhxaAk4PcHLYi1OsoADo+7RVm7Cdk8ei2KCgshO3TSAzt17vMm+HopAzUmLFGbSaYgndaLFOy8xCdVDrftUHJF4aCZerbp2F0MimUqFykwKhioIHzHr/vUvt9Umcc17o2+jUKwOVHiex80a2un4qBLxnS7r8Vi3kHruFI5GOdXpuqGaQWpqDa+Ywgc2f+ZzpwKxad54pXmXXl1fCaWH7qDkp0PWfSbKm9LZfsovOtSFuerbkplxDjoXM0eejJiL+zHJ4eTnID621XozFm6SG1nn/hJoU4vVL9e//Dl2Pw4GECTp6NwPWb0XJTXgatCy0bczNbdPuZy1ygxdO4HhAcqPFH1iR41MjXBZcFhrS68GmPRth4NhK3k7JUPO000dDHGclZeYiRzWU18HHCW23rsmouBfzUPlpcUozBfx1glSZtJP/yqjzZKlGxSIGRFBg91+y7GaOqEqSGgYHWWD11qHwzvOJ0JEYv5VoHDIWrjQW+7dcMb7Sty67vvPYQveZzLto81A5CqmeURaX+YbrvhotR+GLbBXY7zbuQJ4jQfPTnweHspB7u7wob32FMpQjB3AabslbU31/f2wmN/d1w8t5j7L4RwwIwj2s3sG6zoqfEgD4tEBjgiTm/b+JuCK3Htc5pYcHwVqyVgBYeem4KKoM/X8s2/+R8T/M9pFqkCRtzU9impOCJkalaKd7yoKsKYHsfS+z9ZHiZA6PTUU/QYtaWMj2WAmFqf5R49kiBUcXwMDlTJSkhBlWHfhEMrgv5budFzNgiqzILIMGSFeM6shan2JQslpz578I90VYkfajuYsuOu5y+nHK8M9PRpCgHwTW8WfvYspgMnIxJgUVRIfKo/U0gh10WLLOyUJiVi0JeldSQnL0MRD2Ck6MtkqNXs008tSeSuTV1B9CsFXUNMP+8o+eAwYqCNNUtTdCqKAv3Ih7iRsQjpGdkc2qDJCJBKnhiBPoBYY1Kr1NgtPsI58/EQ/NQtFZRAEUXDThYmrHZMHcbCyTQWlpSAsuiIuSSxLpSC1yQmz0T9BDD9eoNJN6M4ozWPV1hE+CDJk1rwdbZnlU8y2IKT7LkCvYcOkDdIbGzRun9WhL6I/kYSTzXPEpRUlvTQA0HxU3woCbVWcsQzS5RqxMfiNDGlfqAy5JxTCSTxXP35IHRo2TV46Meeb6SciUmGb8dugFTWftUbkEhPu4Wgu71fOHx0Qr5Yz5Yd5oNYo5oXhPu7ZshwbXU34haUehCAdUHXUPwxdbzckWmUy6eQJsw4MR5ILQuULM6NtI3nxYCw/pwBoVKC4U6lE37Xm9ViwVFlBmj4WJhUKRgPisgK78QWTZ2zPxR2Q/IEOjayt/aq3zCBuGBHlj+WnuMWXpY4XbyhpnRI5T9nF3m7pBXJIVQLzy9R590b/RM5okkJCoaClpIRIQqDNRCRm21/PyFkI611SuCKrc+U5WJpO7JE4een6BqDCWiEjJyyh0YkSqooWjtYIbja45AYSrW3wcIC0GeiSlgW/6/+1wy675xt9SuoayIGXFTgFJUjJRoTv2u1pdrWTcFVeJpzWTnNVI8PXmBs3ugNunWTeUPv59bhPtpBcDNh9z60rwR9/NTW+65q5zqoIz33u6LX74fD69pKxS7JuiYyFJi7zEgNw9oUp+tX7pgUVyEenFPcNLZgwuKZM+Xm5iCBa91RIvGNdH4u41MGKeZvxsaV3NF9A8jFIJ5k+IiFBmbINHbCygo5vy17txH1vlrOHrrLsy7tEa+jlsE8txKzcnDUVnVU5+gyIcCotRsxKVm451Vx5nqIq9eKvHskWaMJCodKrNTdopMNHvU82MDn70aVGPSycTZ+wmsZYNK1toYXtMGfdvSIprHNuWzBjSXtznQ69B8Ds2D3H6cVi639OtfDkI9b2fcT0xHm9nb5BKnOyZ1Z7NCdJ2GlC89TGL/U2lfxQyUfDwow2YgbMxNkFUOlR5lyFeEgkfa9tNGhR+sFprTCY3t+Nve69QA3+26JBcoEINmldJz8zUGOUKvKF3ba/j34cYXAxB5/oRB5krWX4jC4L+44WIhuhj5zezblA3XSjw7pIpR5fHtjov4XJa04SG/IZI6FhM/IcNPh/f+YV9Ty928oS1R/dPVTFSCFB7JqJuEdDzsrFD9s1V4mJzFkkPCc0uonwt2Tu6B9/47ibUCyWRlSBlaeTSJ1Cvzylk+cklJxshAZ9y9F8+EB8ydHVDYsA7uZBfKZyHLRVYWQAFSRXHiPM7v+A5Nv5d1F/BQe9vZy+jSLgSuLva4aeOAK9qX4NJA7PBp4AknbuBfzR33ry5mPlgfbziDc8qztBQUUXAlZkehjgvXOGXDIb0Ubv6ohiN+mjaEfU0qsLwAk8ZjrYBW7/Kwe0oPdKvn96wP44UmPT0dunaTSYGRRKVDbWGtftqqcFu3ur7YLfCSoTI/qSDRRp38JpadvsNU57RBcshUmRnSpAa61fNlC+qFh0/RbZ76/mhtvNmmDvqHBiDYw4EZtNEx8Uzv3og5tosNvF+LSWaqSlRp0BfT3FzYOtohtQLU9Yh5Q1tgSscGKCwqZgEJbWKUh3Bp6RgeFsQyb3Q/EorYdOm+Tm0p9D4kZirJtYpgYmSEIjXTtdVd7Ljf7+7LeJCUoXB/OqYz0/uhyXcbcTU2GWPDa6Kfc7rBBu4zcwvw64Fr8kqdGFSFJEEMYbBEgXLPBrr5WUhUDFJgVHnQOXn71WiWxJqy9qTC90jquJaHI+LSspiUPYkzTN94BrP2XIGHvRUzTCbVzf7/26syj6GcmCn4Yzx+2HUJ95MyMH94a7l9wIYLURgkksQgRTMKUpQrTs63biO5Tq1yb46ndQ3BrIHN5dcpCUeJOaq+vLbsiNb2Y1FoXoiSg2SCSxLlfFteGRNqFJzKhXI0EGBthgfbDsM4KRX//vspUmxsmd3B/EM3sEmHORkFrkUAqRkY3Ls5po9ojx/O3NdPnEKdUh1VsHYfBpo1UhClmNS+Ln4fXipjT4lLOm7q2iChI1HiEwAvVcW8ZwlZOkgVo4pFCoykGaMqzai/DzJPDKoS2Zib4r8LUWyW5tqXg0VV6wYt3KfXgGRlQ+0lqdl5XA++ix3234phZrU/9AuDh4MVmv+wGVWNXwaF4/0upT4Xq85EYuQSxdmsZtQa90l/fLP9QpnNd3WleYAbzii14ZGHxWc9Q5la0OS1J5m8Km14yM+C1K6OfdiXtU7wG6ma9iaIyzVirW+UZaY+8Q7B3szMtawkpOdg1JKDTNJXGzQTRp5GEs8WKTCqfMjDSHkDTEpffIBAipZf9GqMN1ceZ9UhocIjzV5SAoKq+6QAps5wlMR0Otb2kQciP+y6rOKppIn3w6rj16m/AeGhnDpaOdj4ZhdmEF0h6xW1ulGLWjm49dVg7L0Vi3eVglVlLE2Mkfs0mWXB3Dyc8VTXPrJnCM3fjg6viQUyW4RV4zpiQIg/Dh+7xtTw5v+1HUGBXujx0Wj8fuQWu08Tk0JkGhmjmqMJjqw6jPzmjbnZpipAQ19nXPl80LM+jBeedD0qRlIzvESlQlWEdRe59oevejfBiXuPWWBE1Rixqgt5sJSURaIUlUe9r9bh1uMUloR0sTFHQVEJmw/q+8ceVFVyCgvZ5oLMSpecuI2lJ2+r3Gdwk+pYdOyWgmcDDUlTC6Q2aL5KbBZHHcpBESk1UTBCATRlm/+b2BkuNhaYe4BT3aM2RT4oIii7fDOFW9TJTJdn9dm7GNk8SG9PI4J+PyRdPiY8WKfAyNJMj7YQCYkXhOSsXCbSooywakIBz8QVx+QJDz4oIoLcHbBqfCf5dWp99hTMYvKQx9gbbeuwBIm2llYxmtjLKsmR97lqkS7BR1IKfAvyMP2t3mhay5f5mKmjrOuVKkrPoMfMKCO/ACcOXYKrG6c6p4ncItmcDY2oigRFJO9Pv2ut3RqkTlqtfMGcrlBFfu7gFijKzcefp+9i9OIDGHfkNHIec218xN1CIyzafhaw44Kf0OoeGNqsBi5H38UhXy+DBkVibefqsDE3lXeQ0ONGNQ9i7f8SVQspMJKoVI7ciWOeFCQ73TTADf+c4jbk1V3t5V4yP+25gq+2l71CUdnVJaFJa1JWOfvLdWBim9rwtrfCVzvEfSV04bPN5/Hzvmsa57hmbD6vIragS1BE6BMUKdPYzwXLBMENaVjQZmnfzVidAmJLUxNWNaKAm1zayxIU8S0/+swZ8S7pEhIvEzRErs5niAbhqa2Wqs+UYKG5wN+GtVRIPpDHFs2F1nDj1gAPe2vc/244qn+2WuX5Fh7lKgCaCKEMfEyyyu0ropIBGyugi5a/08Ki0tkXFydkWJkjrLavgl+ZGPRzCI2Uy4y/UjVLn6AoOZW14I1fewrIyy9X5YlvE3/yNA0df9+Nm0pmqwqcvgycvYLm74/GmUeK0tltrI1wIrsEakMH8jOiFkIdz9MkhLTp7+3Iu3UX6N4eRQ52yAmuAXtvd6RHRrP3jKqCfCO3Y3Eh3nglHN/tv4bNV5KBusFaX2PzW13w4+4rOH1fu0+irkEREeBiBxMTI1yNSWY/7ld9mkqWDlUQKTCSqFTO3ucqA+SHQ/DO1j/vu8q8LW7EJZdrU/0yQDNXk5v4Y3SwG/69U3YVOGFQRMFELm0IBPBBUZc6Pqxas0Y26CwmwkAEONvgQXL5NwYXlRZWZxtLeQa5mb8re22a7VGuUDV1M8fpJ3ns5yAPKYKk0vWFNmtLT95Ruf3IB33QNtgLfx+PYG2Tng7Wct8t8jKShmclXkYoyXX4g954kJSJb3delPvGda/nh52Tu8sTE3eepDIfMPp7Jh6nZWPCv0ex/dpD1mpHwRAZbBK6eMDw0NOvn9iZmc2SOihtOtdM6MRa7ciMlmd3VCIca1dHqrYnVBIEoGpJmKAd2pnOheM7oUtdxQCG97CrMHSZi5JVf5hBbBmhRFTsj6NgZEw/0wNMmzwPt20dAL9StcHGpkW4WCj4Pb3SBUjPQAs/J1yOT0WeYC3J93DD5OrumHeQq/arIOLDpI28+rW4oI/8nAgKiuAOIz8flCjNY6UsepsZl2++orvf1PvrThsmyFVCqFxHyYSZOy5i0ei2Bn8difIhiS9IVCodft7GlGqWjGmHsS2DYfzmogp/zd4NquFIZLxG1TS9oJM+rR4kU/osod5wA7ij6wr9tPp0oFO2mOYJDIlyi96SMW2RmJmHUWGB8J4ukmEe2QYT29bR+3UeJGZg/cUouXs5CT2EKbXR0GJLsxHtgr3Qo74kuFBVkGaMni0UAJFaHM2QkkiKWMsZ+RR9vPGsQvV1+6Tu7DFEn/m7WcBUVmjG8OiHfRD6bWm7rcowP3npaPHN0cTnvRpjRs9QmMsCKb+PV8qNP5932tX01Gy+u2E3MLC7Ts9la2HGhCAocDXU2qKPmM+RD3oz7yxdux0qE2m+qGrOGD3jnZ3EywQtiOejuT7gsOpueJqhXbXMEGTlFbBNLA+psPF82j2EtW7pBS2EzzooIioxKILIwqUudzmnuT8ivh7Ceqj1gapS2hAGRW+1q4uxLWvho24hKCwuRoCdqUome0Kb2igLAa52eL9zA1ZJI6KeprPPrxDaEP04oLkUFElICAj2cGRBg1hQRKw6exeT15yUB0WUuCIG/rkPb608Buf3l4kGRZScID84XaAkmLqgyM3SDG6Xr5UqvWWqD2aEa4UylO2/FV9af/rntXYa7/88oTYoinsCbNyNsIbVsWlAY9Y9IER4zud/FWR6rikoIgwl+SCmcNru5+3PNCgiry7quhDDy94ah2/HqawtylyLTcb45UdwRambQqJikCpGEpXG9dhkNPhmPaskvN2uLuYfvqHx/k2queLCw9KBSkMwvlVtDA+rwQZ56cRd39uZyT2/DIxuXpO5yTN384ogJp4zBfR0g0PnlkjT8XVo/aRWmpmvNMXYf3T3miJ5UwtTE5iaGCE5Mxe5Sq93YlpftKzhqfePwatL0SC1sJ2CjnHekJYY0Fg3Q0KJZ4NUMaraxKRkovOvO+Rt1FRxJVNpaoFTx4rXO2Bkc05dkqwDtl2NZiIrZDTLt+7pypa3uzKJ8UXHb8POzAT13e1xKlbW4hT1EPD2ACy5JM23rzTFr/uvyY3CeUgsiPzb3mxbR2GGcdWZuxi55CBeVNp72GLVpJ7wcneUiyn5TV+J3IIihQrIuQcJaDdnG1MGreoeQmJYmZmoHnsZIQn3yJlD8cXWC/hpr+rcqra1hawj7N5dKr8+rFkNrBaIlUjohiTXLcl1V0mWnIjAuOWaM0dCwzOa4+CVjAyBv7MtxreuzbwOlojMkDwXlHFhCXCxZTMAlf26ukDP+nWfJvjCQJLgtBBtfLMrE/coC+okd/mffv0bXaTgqAojBUZVn9yCQny74xJm7bnMqsBioia0OZ0zKJwFTmSurQ6aU1p8PKJMx0EBDvkprdp1Hq+OnAnUCgRC6paWO5Qq0NR9QDMhHWp5qxV1ofPH26uP44nIHOaLAPlDkVVCfR/uPZm5/YL83E1zZbum9GBfLzt1B2P/Ocw9KCUNeBgHVPMGHO1V1hIjmdgDiewYYm3uVscHR+8+lgc39HY+y9HlQ1N7o30tb6w+cxcjRAJn/rexclxHVHe1w4XoROYbSJfrcaVzSUKLEAn9kOS6JaokNBirCTpXrpvYGdFJGXj1n8MGX1iikzMVXNptzE3wWc/GmLPvqkZ1tipFGYOTcgVF5XhdrU9LktrjO6CoxEi++ehc24eZq+qLs4Ux3u8airfa12PGkWVBk+Qu3UZH+d5/J/FKI39mWCkhIaE/lmam+LZfMybFT21WtAEkPujSEN/0bSo3cNWFP0a0hrmJMf44wvna6MOdJ2lM8GF0r2bYM7gdVuVQm7T4uW7e0JYsINIGZf3p/DDvwDV8sJ6bUXyRSM3OZ50fVJEn+fLj957Ivze2Raln3KstgjH/0HWufZ6U56wsuJmu1HQuOBJAAfBUma/e36+2Z/9/vOGM2gqLJqhtbXjzIOyRWSyQWAa1/N0UtD1WNvFp2WxtmbZJ/PPArzcj/tat2kg2FCSl3qamp7QOVQCSKp1EpUEbXuX2iFECU1HqT67l4YBBC1WdzCuCrPwinLz3hGUBhVnK8OruOHQnvlKO4UWmkYMFLqdpDjhpQRi2+JBCLzUN0OrLuJbB6GaXin7dGsLMTH+VI10ld+l4SRGP7kcZQAkJibIT4ueCUx+/grXn7yHQ1R4tZGql+kDrxoIRrfHjgDDM2XuVJbp09TnaNZmrblD153+/vI39/b9BQlCg6H37/bEH697owjbe2iwAKGni5WCDF5nrsZx3n9CvKi4tu/Tr+CQ0NyvG+aIiwMO1VEFOKSgihPNoVE0ka4av+jRh7ykZB9N8DXkD6gIZBf+4+7L8OiU96UKfEyuTEqTnV37p6PKjJLjbWeqsdNezvh+a+Luhqb8rO+6ev+9W+D4fQEnt3RWDlPKUqFBI+pj8Kz7ddBbDFh9Q+N7Fh4m4880Q+XXmb/TNBvn1tRMqvo+WhnyFMzdUeq/yQVHe81Hd0hYUqX2cQGJXV7ZefWiQVgnK7BnyfhISEpqhig3ND5UlKBJiZ2mOr/s2xeUZA3W6P/mOda/vJ79ub2+NlT++jmY24kbNtDHvNm8nzujgbUNQRl8XqrvYYlq3EAxp8oxmF9N0m9GiULCJlyPmNPPDRGcTLFuwEa1nb1W4z54bMTh28gbqhr0Nn9pjseDbZcBtzuZBLnYh0vpMlQ8etw/+Zebof5+IkAegNEuqzR+IKnqU2Lz3NB0Rj9NE/YaMUQIPu7J1E4iJBa14rQObddUGVb4+2XROp+c1kh3rhein+Gb7RfT73161941NyWJt39S+KWE4pIqRRIVC3g7q/rA71daceRu6SDGQkpBhoV29rcyQOs4zahGztTBFZp7+jvbE08xclKD8bua6bmZ0vZ+EhETlQkaxpFh5nCmRleAaL6wggJJuQ5rWYF/HpWYxhTmydBCqzJHkt5jFQ5aO5yja7FNGnzavmnI295Myman55RkDmNLlijN3Uanndgd7rHm9AxYcvaVRvY1+hgvxqbhwNYpLzvl6qdxn/60YnFiwFplPkmFsbIxiap+zMNe4roxrVVveDvbh+tNMxY4g5cLtVx9ij6AipY47M4eyysqY8Jr4cP0p/H1CfE4plbr583MNZrT+2ZZzzOdKk+H4sKY1sO5iFM7J2kW1QY/ZJ2sD5KFWUWWzdf6+Unu34ZECI4kKJTzQHY38XFgpmSA5U/J++LhbI5x9kIBaX/yndSF6LiHXcZIu1ce13MD+DWXiGSoG8UFRQx9n/DK4Bas00lB1hqDVkbKLpAb1wfrTKo9PyCm/2p62zQyvoCfMcEpISFQdjI2N2NwR8fPey/hww1mV+3Spw5mzko1D4+82qsyzUps3qX/Rhp1au0yNuRkmmoVqHaTb3z49lpTGKKPPb5I1QQbaf41sa/jAiBfO0ZDwGrbkENxsLVHPyxEJmTl4mpFXJpuIohIg09oG/t1qw6FeEK6KCAco89X2C1h97q5cpVCILkER0eDrdWydiEutvEr+D7tL55/od5eem6/QfULrxFyZ0txHD0MwbtkRrd0QtI5f/WIg9t6MZfshMhFPSM/B68vVq7VK7d2GRwqMJCoUD3trXPh0ANZdiMIX286zYdevt1/EouMR8LCzUjAAfWGCIoKyZJWIQYIiAwVGdTwdWatZqppMmjbOftIfFmYm6FTHB7MHNWfzPPR8NEDrZGOBKWtOij4uJrNs1SZdNzP8b4YWO0l4QUKi6iMWFC0f256dRwiqMrSq4YGNlx7Iv09VB14anBeKIKZ0rK/xtSiA4u/LQ5tiUrGcsvYEYrVs2mkuppDmcQwBPY+JiV7ndKq606XcKqXNGyGa/tcQFHV1s0K/Lo3x9qoT7LpYUKQP5X18eRH+3tzsLLFmfCempsivEzRDdfbT/njtn8NYefauxnV8/uGb8sCeF1rQBam923BIM0YSlZLBG9qsBpNF/XtMW1RztmWZnUuSWRmHoYKaCmht00a/RgFwFRizkpTrX6Pb4soXg+Bup2j+J8TFRjxwJFVCMms9eiceu64/hBGMYGNhyrym3lh5nJk2qmv3SMwxzKaC38z4OCkOT1MGUJLqlpB4PshRI8Bw/N5jvLf2JN5edZydev99vSPCZNL+ga52mD0wXK/XIfPnWbsvw27KUoz6+6BczCclKw9vrzyGd9eehKc913qrLUSZs09/NU5RUtKBHP1axjR60yqvUWVNoJ2/xoK2vU9z8P2uy/B3kQkyPMfQ741EEshyguaNyLi+qLiEqeBdFVSIKAhfMa6jbC5JUSDI0qx0hmrX9UcK35PauysfqWIkUXkfNhNjVmqmDWawuz32R8RJv31DVGkqaC5I07wPDZ4e+6gv6ng5ITU7Dy1mbUFNd3uW6fJ14ha72FkjYfb2YtHHb5vUHX0X7GUGgUJI1ch72gq1CkQ0hNu1rg+2XX2o8r1Ah7Kr0amT3OWrVZI0qoTE88WSE7dFb//rWKnnEbXrkpH42QdPmawzefC426tP6CjzxdbzbD6Jh6oBlLh5mpGjYBAak8qpkVVaCqyQ5LGd9HpIScQ9oBY3d4WMLJiYGKPI2qp0jaLgiC4xjzkhhcwsIO4J0Kqp2uckM3dhVwiaNmD/kUgCr9BG1RQSYuL5bWhL+DjawMPeis0bUaDxICmDBVFzD1xXuG9FQ10KtOaQ1YcYJEm+ZEw7lvwlqCWTWi5JNY7WNgqGUueOVZCfHxleE70bVkPv+bvlUudUtfxzZBvU/Hwt+1lP3nssNyfn27s1KdrReiy1dxsOKTCSqFQoS/ei4GZpisTsfJQ8az+bZzAXdOSDPiwoIhytLXDr61J1QWEg/N/Ezhjyl6r8emFRCWp7OsoGpEuh7KoYtGn5Y3hr9Gzgx9SnSO2QMmu95pfKmDZwMWz7IrVBSJLcEhLPJ62CPBBe3Q03YhKRUSAekpCJOF3MTY2x5e1uCPZw1Om58wqK8NaqY1gqYkZKz/fMsdRfoKeED4oIOxsUibXOUfB05ZbC43zOn0FCagEKOrQobd+ToRAUCdg7vgPeX34IF7KKFAKdrnV98Wa7uiygEDOIF9532dj2GNI0kPkjnrr3hBnKbr3KmvgMRt+QACwc1YYF0GfvJ+DDrg3RaOYGJgnOB358UEQMDwtigRGf8CN1uWIRuVQHawsc/agvFh2LYL54ByLi0PzHzfLvt/ppK3Lmv84k0ZOycvFjvzCMWlpqa6GMvZW51N5tQIxKaIfxkrrbSlQ+wxbtx9rzMvlOAXYWZgpD9lURO3MTZOQXcRkzHw/MGxiGH//YhnhPT/37sMtDQQFw7BxsWoQiy0r37GZF8PTnMXDVYqZKbuaeH61gX5saG7ETeMTXQ2BuaoRZu6/gt0M3dHotZbfvAf/bi02XudmA8a2C0dshDT179iyXj5HE809BQQF27twpfRYkVD4LpCB24u5jloEnxbMZW8+x0zTNhFC7tzaoEvT3idv4df9VJGQYRt2sykLrDH8uPX0ZSEqmjBZQwx9ISgHiFWXLbRrXQlZwsG7P/eQpcOg0ao3qjduFimtkXS8n3PhqsMpD7N9dKp9DblvTC4c/6K2gatty1haciio1mzUUJLbxfqcG6NWgGlvOkzJzUWPGGnlFkAKYndceMQEJsv/IFVQKqdV801tdNT5/xONUjFh8QONogTpVOp4dk7qjZ4NqZfr5XhbS9YgNpIqRRIVALs9ibUhrJnTG2vN/Kdy3fbAXXGwsseHSfZ1OApVFj3p+GNk8SG5Cm5GWxVoIPO0s8W3HYKz930bEP0gC1AVGwqCosBDYfpBT9AmtD1jr6KWQnQNEPoClpwtyPdyBgkLgzGWgdVNkmcsqJNTO4K3oAeJoZc4GOQ0paBHgYosHSYrZ0A0Xo/BG27oaHydsASgsLsGq8R1Q/bPVer/+Z5vP4rt+YfLr1I5AQ6+vtQzGqGaB2LVrl97PKSEh8fJAMyD8BrKhrws61vZm/nltg1Wlp4UcuBWLhcduYfPlB6wK8MJzNYJba5rIBCdoBiZDdh6/fru0IkUePmGXANcMZBWfxXDXd2CcbI+IW9G4llOM/Eb1xJ/fww3w91EJivhlk6oswkoM8UWvxvhowxn2db9G/gpBEakLVkRQRFBXg3JngxCXqcuQTQlTGcEeDhjerAaGNQtiXRHaoPucnt4Ps/dewYwt51U6JWwtzNRWIf2dbdljSb1OwnBIgZGEwSGzsXf/O6mwIaZe3ZaBHth0pVT9h+ewkqFqVQiKiF03HuH9DoJN//2HQN1gPHZwxPh9t4DMEt3V53jZbi933YMigp6/Tg3k8kEQqR61bqZ4HwqKTl2EcbOGKJa9Dq8IR/LoNAiqCepPTsrSbsaqHBTRwOnocO0ZQr7tgEestU6ZAaEBWDWuE9r/vA2nZYaKpBwlDIxeb1WbXfjMsISEhIQ+hAd6aE3uURXg531XFSoagxpXx7HIeL3MwKs52+Bhsvo5kSpFQ+68KoefNeLJla0XLmnAEVojuSBlNZRMTNUFRgA69GuDQ0+438e5T/qjaYAbC3CojU7M3/DDriGsSkNzXcKWuozcfFa96xDsVW5z9jfa1MZCwQwar7Ia8SRVrUYSBUVkIzFMFgyF+rlo9GcUg/yrPuvZmLWJk0FtM383NK/uztroaF5JjF8Ht8A7HeqJth1KlA8pMJIweFBEUsfK55DH6TnYKGt7ep7oOe5noH4trqWgriAIoACkhp6l634iJfVzV4DgQMDBTtED6VE84O4M2Nup9G2LkpGF4vW70GZCfxzLKBVN4IMiUr0hj4QsgVITBU3kGUT90BQYNa7mgosPNfssCE0Up3ZuqDBUqg5aNKiFjqpFukBBNBkw3nuaIQ+K+JaDLnN34KveTdBKRy8RCQkJCUMk93huxqfgmx2cFLU+57XnJigSkpUD2FipT+bFaGnlPnAC6NRK5eamVkaYOqg1Di3Yw12XqQJSgKAJEmoghOtU2znbmE9iWQOEZgFu6N8ogIk7DGsahEFNAtFl7k759289TpWvSwNCq7O19Jf9peqB77Svh58HhTOLifKiLAk/+sdDuK+UWCR2TO4OKzNTJhFvZlK51iAvA1JgJGEwKMNGi8mLNLRWSG1vBK/IUx6hBXo8VTb46g/RLES8ShTkz32dkgY4Oah/zuhYbiCW7gfg+4HN8euVWAVfDqKulyNbmIWBEQVNfF8z5bcycsVV6P4Y3gpvr+b8JlaP78ikZ/kWSXrPtXn6UFb2+peDUfvLUjNfTVAQffzuE5apVWb/rVjWJigFRhISEpWZ3BND16DouSEtA3gYy7VwR8exWVqmOqdcMdKVp8nA2m2AsxMXYLVswm4e0bs5etavhm3vdGMG8LrCB0aUJCNZdFKM483jqcWRghcS/dGk4CZk69vdWEu2sMLTuY4vNrzRBQMX7pPftnh0W4xqXpMFP65Tlyk8x4LDN1hL+Sc9QvF2u7rs9Q0FL/2uTK/fOdGhCa1rM3sMCcMiBUYSBoPaDnQ9IT1X8CIK5RVSoMcLgyJd0BQUkU/FqVKpWCI/vxCN/FzlgdH3/Zrh083n5Bk23rTU3c4Sb7ati29kUrN0W2SCuEkeHxQRwxcrlvVpiJkMUUneWhPKstzaoMDrfmK66PdmvqLUSighISHxsib3qLvAT/OMlE6Q2twtJTPRbNl5uxwiP21b1sfKRR8gpNUUJB8+Dds6gWjuaMlmiHo3lCUAdYSScRT8UPLsakwyanqUro/nP+2PWh4OTPJaITA7f5UL+DzdgGregKMdPH098FanBugTIv76ZM7Kk/X76/LOCAqAxNrO6XhIUZUu5NdY11s/qXR1UGudJlKytbfAS+iP1JwoYTCeB+fldjXLsIDoGhA9SQQSk/V77thYlJms0t+3l6cz+/9uVDymdQ3B3nd7MrUcoYN7Q19ndpKPnDkUkTOH4as+TWBmUr5gLzYli2VXKcuqCVKD0ncB/H1YK7YI8nSp44OkX8boNNAqISEhUdHJPfLj+aBLA5z9pB8GhmpODlUIN+4AJ84D+eWcsTx0Coi4q7rW8Sax5FtURo6euA6/uq9h76ZvEGRtisxDZ9Cq0wcY9tpPSCNBIz0pbadLRLpsXaHDJu+fQQv3syBFDokd+crW/MdPgbNX0N+8EPG/jsUXvbnqlRi8bxF5KfFB0d2ENPb82hBWmsoLtbuLQT5PhJsGE3WJsiMFRhIGQ1eH5mcFBQFUQbEwNVaVDr1wDbh4nVtkzlzivtY3bxj1EMhUCg4jNcxVURuGkzOQxPUw6829h2gVXgcladvQr1dzdtOeAxcRGRmDznV8YGlmyuZ7eCjDZmVuiiB3B+Z7QO0Duvp2qKNEdiEvBsq2qqNbPV8mifu/Ea2Z94M66Dt0zKRi6O1og9hZo7D01XaYO6QFdk7uAWebsi/QEhISEoZM7pHyZ3UXe/a4nddLW3/tLM1QTWZ0XWHQenPnPmrW8AI27+WulxXyIBraBxjaW1FQiAIjWqeoPUwPbyQTEyP88fObCuf6C5fv4n+/vC2/vnbjMaz877Deh8q33q2/GIWop+noUMuLNXVMWXsKe27GqD6gQS1gWB/5HO9fv72j9TWikzLkqm9EZm6BYiVKA9Tmd8kAJrQXop+qVcOLlRkGU+eHJtT5KEloRmqlkzAYvEMzVRGq2p8iLwF+8VEiXmtZi5nF5RfJjvLEBZZxe31UFyxZIcj2+HgCHlx2SidaNBa5UfCbsN0OFHoC+a5AkT+5w3G922Xo37aNfoTM+4/w9mcfsOs1a3iz/zduO8UuxN/zp+D10V2Y/wIZ1BH8TBAZ4n21/QJuxHFDxOXlUUoWy7aqM0Sl1+R9QpysLTBs8QG1zzV3SEv53BK1W4xtWcsgxyghISFhqOQeJXDovDdpzQlWWeB9bQiySSA1s4cpFWj2GlgNCPDFa/2a4Y0Wweg/+nscTU0HHMvp30iBxHmZuABFHFlZ3DJGiqi8Gp0WimCMkqAARN1bifs3HyDiTgzqBPuife/P5PcZNbQ9Rg3toNehUWfCwqO35Gq2dCEBDJ3o0R64FgG3T//Dm23rsI4EdfNAvDx2NVlgtP2aonHsTwOaY3R4TblM9qzdlzF901n59xt/t1HFd09XyPdv06UH+GD9aXbd1sIUmXni87+1NCQ2rzxKQviszcxXiZQUqWNkSof6aFFDvRKjBIcUGEkYDNrM0ryJIUvJhoKXAJ+85qRq+5usDUEYFBm5u6BEn6AoP198figooPTrzN6lX6s5l9f3dsJ1HYKVzGyuhaCwsBjvf7II23YpyaTSyXz3ORYY0YCp1eS/mV/HzfhUNu/T8ZftWl+Dn0cydLaVAiRSEJq05jji00rbHpyszbF4dDut80oSEhISzyK5R+dEH0drXJwxAK62Vph34Do+2nAaT4TtWzLOPnhaMW9SUREZ/XBHY2aKz7ZdQCN/N+zb9A36Tv0Le8rrXEBr1rXbnDoqsYPz8YNdafeBVuoE4Z3VJzBt4xlk/vY62rdpgN8XbkNxcTEa1AvA2qXTUKeWn0FEMbQJYBjdjUYJL2bUgJMh//PoLaw4EYHZQ1thbMtgmBobs7Y1XoQhOkkxMOpW1w+f9Qhls0dd6vqqvAYZzirTaOZ6VjGr4+XE1vX63s6o5+2EABc7FY+m7PxC1omUyZ0AAQAASURBVBo4Y/M5HIlUlBwXC4rGtghm6nnktUhQVYgqVU8zc1grKO+9KFRSpMuac/dYN8myse1ZR4aEOFJgJGFQzE2NWXn3uXEFp+CHWgf4RYAwNoZxp5bgC0o6oU5UQcf5pO51fXEi6gkLikhWm1o0iJaB7lg0uh07kdJsjdEbMnPcOjWAKzfx6pu/KjwPVY6aNw1G3VrVMGliL9mPY8QyS9dik9Hku406GxSWVGC2lYKfVxr5syrTwYhY5nX0y+BwuNtX7XZMCQmJFz+5Rxtw5cQQfyaf0KYOvtx2Ed/0bYr3OjdAeKA782aj6lHlHKSJgoUDLRUkikNzThl+PkBUQqlgUFnp3w1Ys630Om3keYNXXXDjxAey8gpRWFgEU1MT3LvPtYV16xiqEhQlPE3FpStR6NopVNQDSG9RDGoBpHXO1hol0TGssgYlKfDMohK8teo4u8gP284S3er6Yuf1R+w6tZ0TTjYW+LafquhPSUkJ5h64hmky41m+/Y5mlK7EcPPGvPIrD80skUiEi60l7CzM2Np3PS5Zq98gD1WQXG0t8NvB67gRl8z8jj7ZdE6jwe3Oyd2x9nwUlp26w5RdFx+P0Dhj9bJTKYHRggULMHv2bDx+/BghISH4/fffERZWatQo5J9//sFrr72mcJuFhQVyc5+TjfZLCJ201py9h8+2nJMPLQoDJapUGAplw9KZzZ3wxNId84/cLt8ikJWNGT1CYe9ki713HrPy/O4bIv3KBEluGxmrnGjLQ7d6flg8ph0G/LlXIdNIPj71vl4He0sz1PfhBBZ4ug9sj90buB7tT6YOxpQ3+8DTQ1wN5/dhLTF00QGVzCY5wYsJI/w1qg0+XH8a6bkFLOChdTEuNVvtwkQO3ZRt1XcTQq136trvJCQkJCobStqsf6OLio8RVZKozfeX/Vdx4t4T/O/ITRT/OYHZEVyaMRAtf9qCO0/ElT0rCkqikQ8PrRPd5u7EA379VQgu6KytIUgqLsaIag5Yu/s8isizj1CelaU1l1rp6Hl1EXrILV1TLNwGoFunUOw7dJldr1Fdtboy9dO/2bwRzUsNeqU1Rg5ph3p1/MsmipGXx4lJ8K3tNBtFM8TNG2l96NOMXKw4U6rM91iDOAStm68vOyxXgB3SJBCLRrdlNhMjZaasW97uimsxyTh09QHuJmYgLjufVYeUgyWChIZofdYWHlEFac4+rtVxr9hMlRK8ee5mgY9kE3/ON0riGQVGa9euxdSpU/Hnn3+iefPmmDt3Lrp164bbt2/D3d1d9DH29vbs+zz6ughLVC6df93Ben15qH+XStWEoYIiKmGPoZ5ee2v0ms9p+LvbmqOBizm+21OOoIjHxhrfHhV/nuoudrgvG8Zk3L7PqQHRoGoZmdimNt5oW5eVzyf8e5TJZlNWKlVJfpOPASlAOXlPMSN0ws4ZHSf0g9G9aHwydRDs7NRXW0J8XUTbPejk3rtBNXSu440n6bnILyrCe50awNfJFu2DvdFw5nrWIkf+DLQRUNdeN39oKyw/FcnkUw/cisUbbevI+68lJCQknieEFW3es40SP5TMoXM2BUbEH4dv4u32dZFXWIRZ/cPQ/8/KbSP3dLBCkLs9zkcnlgZFAD7q2hCXHyZhXwSpnmrZPxkbY9X9FM60XAhVpahtj5TdmjUE7Gzh62CNkt1HEPtQvDpB3Qls2J9Xs6M1bEgv7KIvBnkDR86gaWiQyuN4EYbIe/H44Zd17DL9/UHIys7F08R0XIuKB6Iec0EZzf42rqfeU/DASc4Ynbe6CK1XZh+m6w8TUVhUxHz1hJ8DEjKiqiJVe6gtnLodyOiV9qqtgzzZbdSZcf1SJP6ctRIxcbJAyMgIHoE+eO2tV1C/YQ22rlOHDVV9hvx1QFFRT8D6iV0w6K+yfbaGLNqvYhI78M99+K5fM/bJIDGLjrV9yvTcLypGJVQLrEAoGGrWrBnmz5/PrlOPqZ+fHyZPnozp06eLVozee+89pKaWTakrPT0dDg4OSEtLYwGWRMXj/uFylmnhoaFT3i3a0JjJenMLiktgbgys6OKOIXsSUFm826Qa5n0ma2drXJ8r0ZdBCGJEWBBz0KaM3x9HbpZLrIJK6xQ4LhjeGv4unPKOGCRnSkFlqmw+SYyjH/ZBG0G/9Jy9V/DRhjOssvRux/r48+hNlTbJRr4u6FTHBz/vuyq/bXizGlg1vhMqi4KCAuzcuRM9e/aEmZlZpb2uRNVD+ixIVORngQbz/T9ZVaV/ybYWZshUYw6qQnomYC+ioHctAv0DXTHl87EKQcFb7/2BRcv2ICjQC3fjkgFvd8DNBUhIBO7LKhj+PqJiRH2C3LD1o/4Kt61YewijJ/6i3w/o6sQFOxS01QjgWv0qCUdrc2TmFKCwpAQeNhbYOrkHwqqXJvk7T5iLA/kmXKWKJMIPcyIKDGdHoG4Qa9+f3LkhZozqgAMRcaya89+FKI2y8EJxD0ND1a7fhrWExwvcyp6uR2xQoXLd+fn5uHDhAjp37lz6gsbG7PqpU5xylhiZmZnw9/dnAdQrr7yCGzduVORhSpSTsADFyh8FRbTpPz29H2q6GzY4pYCILkR+MTBybxmDopISGJGZnQaJaTGWnYsCBnTnLmUIinghiH9O3WFVtQWCoIgCprJApfUd1x4h4NPV+H7nJdb3LEbLGp5M/pp8jAoWjMeqcR1V7rPvlqKvElWPqFWDKktU1RKbHbsck6QQFBGrz91j6joSEhISLxIkElPpqDmnhwW4YVyrWiw5xkMhgs5BEUHrIF1iH8O9qACm1KFDfnxPkhAc5MNanYeHBbH/qWJW3Z9TNcuidbhXByCsEVDdDwitX/qcD1X9+UKLcrDt+6XYJFNNJWJiEzF+8m9qD40qUD7eLghrUpNr42sbBrQIBRJTgIdxwIXrwMETQGY55rvuP4LNgWPArkOa9wPUXvgghiUWKSiigDLxeiRLbO6WzSRlZubggLF1qbQ5mcpSkEjGsj07AF3bcL5Kbi74/UosPD5agRF/H1QbFH3RqzGzFylLUNSnYTWd70uv3/zHzbgVbxiV2uedCm2lS0xMRFFRETw8FOUB6XpERIToY2rVqoUlS5agYcOGLLKbM2cOWrZsyYIjX1/VjWheXh67CKNCPlNEF4mKJ1vkJPx1r8ao7W6HV5sHsZLykpMRSMrMQ3ahYQuUBWV9OiMjlNRRLelrI9W4/H8y37/SFMUlJUxGc+mpO4hNzVZQzlPG1caCuW0Lf1TyZaVzM+8jxENzXh52FmzBDHS1g7nSHJSZEeDvZI2SkiIMCvXHN54OiHjM9cXPHhCGSe3rqPzdLBndBhNXHmdVwZyCQuTkFyG7oJAdvyY8P1qB1eM6oH+Iv4oKj6Hhj1n6m5eQPgsSFXVeKCwqxuebzyvc5mxtgWSlFmgxGvk647JsIF9f6PxJ9nuUOssVnHYntKqFT7ecZ8kxbwcrxKXlsFkooRAEJdxo4D9VZJbUJjYerkV5iL7Cbexb920BOzMrLNt/gl2f9et6zPxshMJj/Hw5tdZ4qhaFCdYXczP4+roiNy8fFqamiE1O5SokMi7tOMF6w3fsOYfe3ZsiIyMH3Qd+iTwl1TU7Wyu0bF4b5y/dRVJyBmLjktgF7i6At2wveelmqXQ4BUm7jwCdWpW2z+kKBXdnr8DI2gLIymXP4+TmiJSYBG72WIitNXfhsbdFkb0tEzSgy7TWNdE50E1HGw/doGRkWWgb5ImJrWth21XN3la0j3CztcDjjDymxBcycwOufT6A7R1eNPQ5B1RoK11cXBx8fHxw8uRJtGjRQn77tGnTcOTIEZw5U6rkoemHqVOnDoYPH46ZM2eqfP+rr77C119/rXL7qlWrYG394pYFqxKr72Ri7V3FjM3XYY448yQPO6NzYGVqhB/CnRBgb4b/7mZi1R3uvoH2pugdYI26zmaIzyrCjgfZuJSYr1YNzs3KGE9z9KjwlFeZx4A4JTxBm7AADKhhC0eL0srQiL0JLFh0sTRmlbD0fNUfPtTVHDUcTLH+XmkFhp6CYkxNynl2Zkb4va2rwusRaXnFOBafi1OPc3EjufRksblnaQKDTguaZvtIAGPY3gQUKL0dPjYmiM1SDJjG1bFDn+rS36KEhMTzCZ0P19/LwkrZ2mUo7M2MMKymLbY8yMaTbP2rAlYmQE4Rt5ZOqGuHGWdS9FNTJTXRtETE33oEJKWIKs+NGdIMA3qHyK8/TczEm/OPoygsVO1zTm5gjyMPM3E1TbBAHD0LxHGzSa8Nb47TFx7g1h3uup2tBdq2DEJRYTGG9guFk6M1Vq4/j3VbOcEGRsPaQN2a3Nf7j7OAKMDPGQ8eyYLNmgFAkwZqj8kEJSgiGXKZZLccqpZd4WaiiRkfdMO3669rD2iouhSfwM09UcWssBD+9+8i2jcAsNJuRN7M3RznElSD1Y6+ljgYUz6xMfo81HEyw45oxZklWtGr25virfr2qOlY2lr66+U0HInjXnNaqANaer14RurZ2dkYMWKETq10FRoYUSsdBSfr169Hv3795Le/+uqrbIZoy5YtOj3P4MGDYWpqitWrV+tUMaIWPKpWSTNGlcP3uy/jq+2XFG4jVTdlf4EPO9fHuvN38Sgtl8UslqZG8HW0xo/9m6NPCKdA03neLhyNfCya2TAz5gIBI1kb3fNGkIst9r7XE96k8iaroNAgL1WDOpA/wm+7cT76KfIMqOL35KcRzFA1r6AIO64/woqzd5nanvJ7Y2ZihP8Nb4UgN3sMWHgAIb7O2DOlu+hz3ohPwRfbLmDbVS7LOLhxAFrX8MS76wS91AJ2vNMVXepU7HAnJVD27duHLl26SDNGLznSZ0HC0J+F5Kw8eE1fJe9omzu4OZr5u2Hs8qPMF45mesoj163JxFMdtCbSekiWDpvf7AxHawt8tuU8ZssUy8rEkTPcZl+JzLi1MDfnfn+5BYWwf/9fjU9DHQvN/F2x4EhpwMGCIgqORPju89FoUM8fptQ2lpOP+X/twKFjSj9Ht7alFSGa23n8FCYmxiiiTgsSiujXlVPO08SdKCA4kPs6JY17PvIyJBU7IdTW7u3JzU9R0KPM02Tg+LlSm4/OrQBXRdVYbZhduYmgjs1wK0nxc7N+YkcM+otTtTMkrjbmyJSpBSonPSnRye+p2gd7Yu+UHnjRoNjA1dVVp8CoQlvpzM3N0aRJExw4cEAeGJH4Al2fNGmSTs9BrXjXrl1jw5NikJQ3XZShk6A0hF05lIio3oiZrs3Zf53Jbf8yuAUTHfhq+wXYWVkiPiMPmfnFTJWN1NPEAiNaAEggp6Jwt7VEoJs9kz2tKO4mZSLw8//Y1+SB8Xmvxmheo1To4L+JneHz8UqDvqa1hTk+2XKBeS0IZc6b+ruxlgtewrOgqAQTVh6XL/yH7sSr/P3cfpyKr7dfwJrz9xRa3tddfMAuPDRfxrfZ/dA/DD0bCkxuKxjp715C+ixIGPq84OFohj9HtMEbK4+x6zN3XmbSzHdmDmPXKb/sPW2FgqpYp9o+TIjo7xMRTJ2V96YTQ9+giF8Te9b3Y+uGjQX3s33Vt1n5AqN2zYGDJ4GEJJW9nJks6DgUqd4vh4csJ4Y0raF4I7XB0VzugxjAwY613iEplVVePpupOdBiXoOCNjmP9s3wZM1Orjrm4wG0Ebd/UYEPiggbWReDsLpDAVHDOoCtDdeqV6jmfeGV+o5zbZVhRTm4Y1SCPGNj5BaVaBdTirgHj4x0NK7hiVtJ99hNbYI8se6NzrC3NGfKdsfviiWIjeDjZIP0nHw296v8Ostf64AxSxXNXXkSs4TVKfVHePjO4xdy76zPz1Thct0k1U0VoqZNmzLvIpLrzsrKknsVjRkzhrXb/fDDD+z6N998g/DwcAQFBbGqEvkfRUdHY/z48RV9qBJl5NMeoXiakYM/hNkhNdDm3N7KHA+TMlmlhC4X15zAB+tPY2Dj6rgao6rvXxlQ//W56ApyKhdhweEbLDDiA46lJ2+rHcAsqxfU/GEtYffuPyq3zxkUjvc61mf+SEKE63arGh74dsdFFuDQXBFlQzdcvM9mozRBfkZR3w1nGVA6sZPgg4SEhMTzzsS2ddiGdeSSg7j8KAkD/tzHhA8mdaiHD9adVgiKMn97TR6s/D68FR6nZTNvm4O34wx2PGT4ve2dbkzQiodmieYOaYH3/lMvbqUVCjKoguLnBc/aAYj5800muiBcGyixRt0NxNTODfD7oRtslpjWcFoniA61vNHAx5kZi8shmW0KhmoHAWcUu0w04s7NNZmjBPkwwhOYAAN7aK8QaUJWAYO1pfpWPL4ilJ/PeRdSpwf9Lqji4uuFguTNMJWZ7VJw/M7qE8zWQiuRDzDvz3fRrVszfNg1BHU9nbDuYhSmrOFMbMWCIoKCa1JFVMfo8Jrwc7ZhHoQXohNRFpysLXA3IQ013OxfWqucCpfrJkiqmzd4bdSoEX777Tcm4020b98eAQEBTKabeP/997Fx40Z2XycnJ1Zx+vbbbxEaqr6fVYgk1135xKRkouWsLZXn/P0cQ4sKeWA4W5sj6dex7LbBC/dhvWwxMRQ96vlh1w2u1U2MQWRiWIbX7N3AD9uvqX/edRM7Y1ATQVaukpAkmiWkz4JEZZwX8guL8DlrWbsiKhZHCS/qCBCDPH4iHqfi2N14/Hc+Su9AycXGgj33iGZBcLA2V7txHb/8CHZce4gPuzTEhxu0z3Jr4r8JnZiM85OMHHSs5Q0XW0tWrXB8TzXppgwFR0fuxIsn1CLuAZdLgwj6WerU8sW7b/bFa6M6swrV9ZvR6DLwKzxuWzqjXhHzxUbZOSgpg9dR5zo++KhLQ5bYvR6nu6Lbh439MPuN0na1X/ZdZc9RHt7rVB+nohJwxkBdLy4y0SdiWLMaWF2J9hsVgT6xQaUERpWJFBjprq5DJ6yd1x8yOeZhzfRXaOO58igJjb7dIPo9sVkjOl9Zm5kiK1//9oHnjqwcwMZK9PfyUdcQtKzhgVf/Ocx62CsaD3srpGXnI7dQt55EPycb9GsUACszU5iaGLMAePnpSI2PyVswTkUJrzKQAiMJ6bMgUZnnhcO341jbEiUEaYP858g2TM1Lnyz7jbhk1P96vfw6zZ/GpYnbHHzSvRFmvtJUoXqjyzF2+GU7DMWrLYLxz9j2WHPuLoYv1j4HY2xkxGSjt1yJVv3m3mMAKdcRHVtg8ZS+GNdBIPkt4/iVKLT5Y7/a16CqXd8Qf7z5x07EUxNUcipGtayFFRHaW/4MQbC7A+4kcMquPCSxrW1WuHE1VwwIDUCvBtUQl5otN67XF37WTBneZJb/2tzECFn56td+e0sz5j94JSYZFx8lqnSpUHC0eHRbeSX0eaPK+BhJVE3oj4UCmc5zd+CX/dcwY4ui/Ki+hPi54OJnA/B2u7qsdC6EjEGVoVCcD4osTE2wYHgrvLAIgiLh74KCxR92X0afBXs0BkW0xDavLiIBWgaepOfoHBQRtOD/OqQFRjYPYrNI2oIiOsE/i6BIQkJCorIhX5+bXw3BuU/6Y++7PcvUelTP25mZcxO0ST77ST+RiV2OL3o31isoIsiQlWZJDUW2bN3WJSgipncPYe19otTgBJfY3JG7K2p4i4sXtA4JxK2vh6CtwHicp+h/EzBrQHNWHWNBEeHsqDEosjYz7BqlHBQRuggokfAS7b1Cv92IHvX9WFtkWaCgiBKt1I1CVR6iez0/JP/yKt5qV1e+57Oz1Oy/lZ5bgIXHIvAgKYP5J52Z3g8fdytVI1xz7h6bo4tLffE7gyp8xkii6nHodhxuCMq+956mY8PFKOy/FcuyBY38XJiqzKjmNVmlQBdCq7liwYjW7GvqT/37xG2Wgfh08zmNj8srLGJ9ucouzMEeDvh25yW2SLwoJU1qP9CV8Oru6FLHFzN3XsSZ+5U3+6SM6VuLWfBK75OnvRVb5KISM0Tv+1kP3dpdJSQkJF4EbC3N0DSgfImrtRM64dLDJHSt64v8oiK0DfZiQ/Zkrk0zm9SGFurnCssyzNNQINUvJADzD9+AIfCws0JmbgGqOdtqnHUh3u1YH7/uv6benLSaF9dO5+vJZlKvxCSxYFPdPNWRD/uwwOzH3Zcxc8dFFojO2nNZ6x6DGN+6Nhp4OeLX3efwIKNIXs3SNjOrDXoOCjwb+bqw+aoVZyJVTNJ1gfZg69TMGOtayVt59i6bCabfC1V26LP5x4jWeCXEH+P/PYoY2agDreF8gMtXt1xtrZCcRT6FRWxWjgK2rVeiMblDfST+PAauHyyXB0/krUTV0RcZqZXuJeSNFUfx1zFxg12xjAxprByLfIz4tGx4OVizLBSdcIuKi1VuJxPQTzafxd4bMSx7cS8xHdkayrfqSuPUf52hj3v3c04tDwckZOQgJTsfw5rWYAIJbnaWsHjnb1QFejeohiWvtmMnSzrJKjNvaEtM6ajaBlFZSK10EtJnQUI6Lyhy4u5jtJ69VW1bu75QsEMBW/XPSq1Tfhvakim69tanFYwsVkhNODOLU4eTVdpKFk7U6eEkGf7D1nP4Zq+iAh+pAFJlbc7eq2w9pcCHN1An3YRy/vgKhPg44ebjVKboamVmgqzfX2cVQxpT8J2+knVoiJEx7zVsvRrNxDj4ljqqHhmKuJ9Gsf2YEFKx+3TzWSw4rCgMsfy19hgRFsT2czRVQ/u3jzeewapzd+WtdGKfG13fp+e1lU6qGL2E7FAzPE+BDM288G7J1V3tWBbjww2n5dkGgjIkw5vWwOrz9xRu93a0ZuUdvkdaXa+0NqjaJEYdL0e0qO6OZaciNUqfilJQWD4FG03wx6JHG0VNd3tEJqSzr38aEIaPujXC0L/2M2U6ksOmS1WCJEIpozeudW2WkTr34CkCXO3YMHH/0ACphU5CQkKiirHxEiewQ1WDNRM6sURmnwW7y+yVl5qTx877tNTxy96UtSfx8IcRbL708J14JvygVYjp4vVSWWzBDKxYslWsfTArIxvfLz8IeCpV64yAYA9HXFAKNISbezdbSzzNLJ+BKnEltrTrpn9odXkbJXXZkFBBR8FsF/3++TmrI5Hx2Hql1N6CgiJ6KLW/kd9fu5peaPL9pjIfF1UblSEl4PnDW2N4syCW2CQBEGp9Hx0eLL8PHb+7vRWWjm3P2hMXHb/FEujKlcF32nPteS8yUmD0kkGyobFqekTPfdofi49HsMBocJNADG0SiMF/7VdpZaNgaPa+qyqPpwFCXeHbs3SB+lz/OHwTq8d3hJe9DZacvAO9IFM3+iFqVZBaWhkkLfmgiJi28Sy7VGUWHr3FLtRPT+0O6loeJCQkJCSePanZefjn1B15qxW14nWp64uBjQOx6uxdvZ5rUvu6mH/4JrZffciqNcvHdsBogV8OtWfRfCm1A0bOHMZa3GbtuaLQssUzonEAVv+3HSX2dkC90o15n1pe8BzzKxJjE4GMTCAtE9aONlj2v/cxSMkT6Y/FO1EoC6pM8vJQJPOyvBWfihazNqu8prDiYYigSAjNDv8yOJx9zQd2NIfjaGXObEAo4CNLEz4wup+YjrXnS9vmJrevxypuN+NT8OfRW/h441k0qeaqEtzpArUMutiq+nrytAryxOUZA7HvVgzaiMxs8bjbW+Gzno3ZcVNgREp3JOBAQl0+joabWauqSIHRSwZl/dVB1SFqYeO/poFGQ873LBzZhpW26WStq6QktZP9OKA5u/CeP3pDpm7qjNoqCDpZis0U1XCzQ3RcMgo1KCTZGwPpZUvoVTg/7L6EDW92fdaHISEhISGhAZrFIWGfet5OTLWNZ+W4jsyEe4mazgwh69/ozNrS/pB585B8c5vZ21iXg7L3DY+FmQm+6N0EM3o2xoXop1h47BbzfSKRCX8XO3St4Y5Vnxormq0WF2PxJ38A+Yrt89kJiRg8ZzM2TBuAAY2rIyMjmwVFX/y0lvMxomDk1j2gUWkVg1rA/F1sEZ2UCTtLM2TkVmxL/vf9mjE5840X7+Pd/04qdNEQtuam7LalY9ph+7VoTF5zUv49msP+XWT+qyxBUcqvr8JR8D6og96f3g1LPw+aMDIyYu8ZXV4mpMDoJePSI/WBEWUqhIhle8rDn0dvIiY1i/Wx6gpVKIT4ULuegCAHU0SRT4KHmgFY6mGmzJKpaZUQWri3fBuqO9vifu1anPu3MheuI72wAGguEDKgxSLqERAcwJnLGRB9xS2O360cCVQJCQkJibLxKDkT8w5eZ1//2D9MpR3tmz5NdQqMxiw9rLIPIGPXjr/uULiNDEXJXDTE14VVGC49SuRa4WZvlVdrbCxM8fPgFvj39B1gUM/SBx85AxQVlQZF/LEWy7KDFuaYsuoojq0/gL+X70VGhpIFhiAo4nG3s8JXvZswrz5K8PK4WRrDwtISMXp0t2iDxKPodzRtwxnRtfReYgYGLtyncBuJTWTmFTIxA8LV1hI25qZslGH1Oa6NPsDFDuc/7c8C3F8PXEORhgEpCgR1CYokdEMKjF4yPtmkW8tWRajBaQrKxAjxdVbIVCRm5iL487UK93mYUYiA6AeIcnRgJ1AFczc62VorBlLPHFtrVkqHzC1b7q5Nx56bB0Te547dyBhjhnXA8qhk1PZ2wtpP+qLRz7sM/p44WpkhJUf3jBqpJklISEhIVB7UoqUc3Cw/dYfNpNLMR9TTDKYWRvLMdb0cWdszKZSRhDN5I9HmmuZXaNCfqgDU3qULykERLwt9JPKxwu20caeLcXExitn6xbWXm5HJjoyjkY9h9MZfis9XWIjCeFn3CCUKA6tx8t1XbwH3HgI92wOWFog9cBZzox4q+gMePwe0biZ63DQD++HT03KDUoKO5KNQB1j61sKxqCfYdzOWtboZgo+0mOjyM04ksjR/eCv2/ggN1mlvQzWiaME8D814k6UKVdsIEnig9T+voEhlH9BQySZFonxIgdFLAslBDvlLvUlaQ19nXI1Jll+vChLZCem5TCmFH2rcff0hUrIVT3RTQ+zRrPurGDj2J6BFY8BPMPsiDD6qCnR8JI9jKwjY+GAjnTsp2jvY4LNXO8I6pBaWR51ELR8XnIjPqJD3JFWPoIjdPzsfWXkFz63Jm4SEhMTzAq1/k1afYK1spAw6pGkgqySQAM7a8/ew8/ojhYrI19svKDz+xL0naP7jZrYp/2STYuvb4MbVsU6wOdcFSlRqOvcXKwVvpNimifqejrhcNwjw9gRcnUq/4eXBBUZGxsDDWCDqIdsHrFr8ITq2a4iJ787HsTxjlO5YVOGDIt7olI5k2ikSTDiNyoZ+/9ve7oZeDaux6powKCLIIiUnvxC3n6QpBMPUDsijVvacBUYuFXTkLydSYPSSsP5ClKgkJJ1kd0zqjrreTirZnIqmQy1v5qmkjvj0bAR8ugoPk8XFIuhE9+OldPzX1BP2o15Ber6sHG9ehTft7i5cVaiaT+lt1rLsnbsLjIb1gYOTDT6Y0h8rznADsrtvxGg8KZYHftlS7sWm3nLqi37vv1Mqj3n/v1P4a3TbCjkeCQkJCQmO7r/twt6bMezr7dcesou+8K1sNFdEVSSCEozCoKhFoDtuP05DsiDxqC7BShdt+JsbIaaghJmPauJyYianTse3zsU+ZkEQqFIVWo9boc5yQk8jx3bHsEHcurN51QwMW7RfQcRAHRQUqYM8fMqq0KcvZD9Cwd2Y8GCm9pYpsCPhq0KQGbqbkv9UowCseL0jXlt2BAci1HsjNfF3xeutalX48b9MGHZgQaLK8k77enhb5oIshFRGKCgipRllaro7VOgx/TG8FUyoeqIBdUGRkCGLDnJBEWFuBluzKvyxpiAoRL3cJa0jJHVKyjbDm9VgvdKk3scvjhWFMCgiozoyeBMLiohFxyNYoC0hISEhUTHcik8p83mfVOKUsTRV30EREZ+qNSjSh+hzN1G06whrjtAKBUT3HwE7DwMnqOJlBLRrzqnIXr/DhJNMPVyw+KcJCg9bOKotzn7SD12fxgKb96KHl/4CAZUVFBG8t1CInwuifxiOb/o2Fb3fD/3C2H6NTH3JAmX/+71wYlpftYHdgmGtmKqdhOGowjtICUNCLsgLRrRG1HfDmHQnD6nPNfh6HawmLVG4/2c9QvHdK+J/uIbi7IMEjQOFZSWzoIpKuukB+ThQZo5M6ohhzUrlSqnXWBc+6tKQ9ZXryqc9GrH/N1y8z3wONKmQf7lNsWVDQkJCQsJwlEcJrL63oC1NhqZ5mhQDzdrICakDtG7KGapSFUgT1H5X3Q/o1QHo2obrqoh7zLmxFnAJO/Nq3jBVCuxI0c3B0gzHY1OB4Oro3ThQY/BnIfgWVc+61/Mt5w8JLBjeirXBacPPyYZ5MtHM1lsrj8Hn45X4Yut50fuSsMWIvw+iwy/b4fvxSvx55CZO3nuiNrDbIPOqkjAcUivdS0Z1V3v8M7Y9EzaYuo7rtb0eV2pURtB++PNeoXD5YHmFHsur/xypNOGHMpGaDjg+m0zM++tOMS8DnjUypRoiV0f/JzGvKU2QtCvPhje6oH0tL9iYm6H3gt2stN+xljdTFSQfrF71q+n13BISEhIS+lV9SEzhfzKpbDGqOduqGHAS5x88ffa/at681cdTt/tTJs7ZkbsQt+4CcZwwQ3ZSGuuiINW7eQevsa9JzInNHDeiljvgw73XMbZlMPMCEkPYUWdnYYbtk7qzGZ5mP2xSWPv0VaTThQ+7hLA2xt7zdzMTXILmxOi909Tq9zg9B2+tOl5lql4vC1Jg9BJCw5qz916RX29e3V3BV4iCEkulClJlUu6giFelKy+GDIoomImNB/x1y1I9Sc9hg7XqfjxbCzO425HsaBbzbTAE/IJCC3KfEH82tErse6+XQZ5fQkJCQkJ34YUvejXGuFa1MG3DaRy8zW2oecgX6GK0uN9NcUWtiRUFzd3eiAQ8XEsvCbK5GxIq8vNiXRQkFf719ouKwkUp6XBysUdKAbDyTKTalyiUbSyo055MXim4Onb3MQuKSEqc5L1J4lzoQ0RqcrQ/IhELWhfLamFC/kaU7CQfRx7qCPF0sMLjNK4rpDz7uXlDW5brOSQUkQKjl5DIJ2mIF/wx9m3or7Ph6nMBLQDUt2xgz59yEfeEO/GT6IKWBYr6sjV1GJqbGOPyjAFsFonK7Yakjqcj/h7TTh4USUhISEhUPh9vPIPZe6/C19Fa1HfniKzyoBNVOShiGHF2FdGx3EV4zJYWrIp0O+ks/HMFxqRUadm8FygoxGc/TsCq5EJRgSkho4JtsCEqGwXFJSzQImW+L7eeR1ZeIUs2Pvh+ONacvYf1F6PQvZ4fxrepLZdJf5qRA5+PV2hV2lOHMCjieVLOoIgPsEg4q2ThxHI/lwSHtPt5CVFWMPlsyzm8KHR3MIVxcqrhgyId29fUcvE6J8f9KJ7L3qnpQ6YWtqI/JyLp5zGYO6QFqov0mecXFWPgwv2IS9UuTKEvn/dqjBY1PAz+vBISEhISmtlwMYptculCQRGhzoyU/IpsREQWnkeMHgsSsxQQtWkGdGgBmJkCiSnAtn34etwSvD72R2DXYc77j5J3zRoC1X3x4Zz12PxWV2aQqomUvGJWiePFEEiVl1+NqWXN9K3FiE3Lwq9DWsLJxgJGrLGfw83OCh90CTHoz23IkYH3/ztpwGd7uZECo5cQeytzvNayVIBBDDdby8o5GEGQ0NFH1ltcDiYMb48BDf1gcKhkv2YbV/XRl4MnuWxY43pcW4BI9u7XweH4qGsIvtp+gZnwknEfSZnfT8oQfUoyiuNVbgxJRTynhISEhIRmNl68j3HLxeduiZaBHqxbgOdmfCqyytjaVdUoaRMmk+cmqdyG3FxSYWFp5EDt4llWMDIvxLsjO2BsiB+3d6AOjOahQOfWqPfVf2pFCnh2ROcgr4hTc/t531U4TV2mcp+PN55F9c9WY+iiA9h9Q7Gd/eNupYFRAy2mqjTHXZnMPXBdVF1YQn9ejHSDhF6QysnSk3c03od6cCsFQZBwMDYVb7SpjYXHIsr8dAP/VG9iWy5I7WZQD8BUzz8ZcvXme6WpkhUcqPBt+ul9nWyYbHqb2dvYOnAtVtG2jpzLSUqV96AgKHCii6Hgj4OUcyQkJCQkKjcoGrRwn8YKwsmo0k2/iZERitR0Hjy3BPgCKWlAoEzYx9uD2x9U9+W+fpqCkugYzDsfDSefHMDCUmF+KiNPt6CgRCZYoIsnFHVnqJNBvxmvKFqlzJUYTfaz+kOmvL8MDkdaTj5cbS2ZeBap1tKviH4FdJvdlKVYOKoNXm9V26Cv/bIhVYxeQrSpnPDQQCLhYmuBysKxsABdausuMV1pmJjoHxQRXu5A387AsD5AeGOFb/Eh4dwhLZnwgbpl7r1ODRSCIrHnMAR0HHw/tYSEhIRExVNUXMyG83UNc2hdFptXee6xMAfCQ0uvZ2UD/bpyFSE/b67jomMLFjil8EFRBc9Pfbj+ND5YdwopMtU6c1MTTO/eiHVWVITViCY2vdUFY1vWwrudGmBk85q4/PlAdKjlLW+6SczMZWa+c7So0V6Ifopf9l1FQnr555teVKRd0Et4EtaEkWzWJX/BOIyXZR287K2xa3IPrBrXETsmdUd97/K3vKlAf93pmcjJzMY+dS7Pd/TU668qZWUydRWBKjTr3+iCAY2rI7y6Ym+00Bjvx92XRR9P/dQxs0bivwmd2XOVFT/BcUhISEhIVB6kjiZUQtMGCQW8gGERhzDgs7Hm5ojINiNRVn1xMLx9Bq2d73duoFbY4Jf91zBrz2UWHOUXFuGH/mGInTUSH6h5jCFxsSlNSiuo8cna3ve911PBl5J4JSQAsSlZ8lkqnjtPUuHx4b9o+v0mfLD+NGsllBBHaqV7SSAZy3HLjyJPi4gA/SmR2hnJU1JmZM35e8znaPW5uxjfujYWHY/A3acZKkZr6bmcEVuZoaxPURGMrSxEZTEDoqIQd/028oP12LyTsZy/dhU4BdSo2dX1ctJaOtcHm7xcbH+1GxrW4VR2yDNIyMGpvdH+Z05xjnck5/2dKEv07StNEehqj3Y/b2N95/e/G4bjd5+wE+LCY7eQmVfAfBLo5Hk/MUNt20D7YC/mrC1ViiQkJCQqH1JHk5AhtlaTbQbJebsKZnaiHpa23OmBtSmn+JpbBOx9tydaBXkyU1hjYyNcepgo9xhSZtaeK+xCexNKEv959CbWX7wPH0drxKoRxygvo5vXxNKx7WD5zt+sEkSm68rQuq08F0yJVLo09HXGvCEt2SzUq/8cZrLeQia2rVMhx/0iIAVGLwGkXjZqySG9T9bta3ljzfhO6PTrDiw/HckuPPTH1qu+H347dEMlKCLtf/pD1hsnB8w9+0D0Ww8CA2FeIxATGvpi0ZUY3XuWDYRCUKTGE8LYyAhTOtbD0dtxuKilvzjL3ALNft2Fj3s1wZkHCTgWqTg0OuDPfQrXA11s8V7nhpiy9iQTZWj101aF79Pvm94vYmR4TZXXe5yWjdazt7IMmIOVOetTJih4koIiCQkJiWdDeQVvOtfxwf5barosqir6+irVVEqIliEoIrILAXNjrkuiY21vtvYdvh3HWuYuaJH6Zo/PL8RHG07jrMxAV12LuyEg6XE6vsRfXmXH16k2t74r80GXhswklpLfNd0d8DAlk1Ugr8Yki9p5tKrhwZKhlqT4J+gkosol7fu8HKzZrPHLvC+QAqOXAPqgf9m7sUopVttjCNps/9g/DNM2nmHZkuHNamBCmzoIC3CDkZERM0ij6pJyYETKd9SDW1hUjJz8QlXDuTKQXwLdg6KyIjwZZGZzKnLKqDmhf94rFO2DvfH0UQIu5hcA5mbqX8fICPkwwsydl0S/rezEPbJ5MCZ1qMdOlv+cUhXOEJ7kxPB0sGbDmRQYZeUVwNbCFJl5hdh1/RGuxiShoa+LxsdLSEhISBge2oRSOxdrf1JzHzNjI+a9IwZtah2tzZGazSW7ngueoa9SQTEQ6ueKv4/fhru9FRYdu6VTUERYmhixoIjv3khSWqc10bamJ45GPtb5/jfiU1jAQ4nMRaPbqr0fretzBoWzC0EtdPMOXGeGssoc/bAP2tT0UhH+oBk3YTunr5MNqza9rO31L29I+BJBAcxXfZri2heDtN9XNnMiVCf7qFsIrn4xCPE/jcLiMe2YEzQlfLrN26kSFBG5hcVsY09ViaxyBkX/TeyMaurmZ54maXZCLSv0w9HzigVFGqDAkzI0K+8kaA6KdGRyh7osgCFm7ryIDj9vQ7HIz1vP20nrc92KT5Gb+FJ1iYIiIi4tGyEzN2DmjossiJWQkJCQqDwoM0+bUEI5XDCSXVaO64QAF1vRxx+6Hf98BUX8/K+ygAS1xxkSNQIVdOvWq9F4Y+Ux9P/fXuy9qXuyNVdm7lqWXYcuQVFNd3uMF/hMkjcRV82Jx8cbzmDF6Uh5t4cytDegoIg6SsSCIuKvY7dE1RCVZ9xiU7LY7fT9lxEpMHqJqO/jjDvfDFX7faFKmnIZlVrnqArEQ/46whOKt6NqEPHTwOY4Ma1vmY+X1HdeXXoID1Oy4OdgBew4COSUyoibergqqhQYMpul5/NSlcxE22M0KAmJPXLR8dvyAIY4EvkYy8+o+ijdiEtBTEqmyMuVsPeo3ZxtqPvVOo2H9sXW83ht2WHNxy8hISEhYXAoM08COD5KSUBeoGdw00DsfbcX/J3Fg6PnDupwkFWNLCl1Sj5/urbHCQWk9h6Vf3n6g14wEq6xIlWpzr6WMDUCmysii4zqrnZla/tXUxG6+dVg3P9ueJmfIzIhHYtP3JZfX3D4JmwmL0HbOdvw094rGL30EBzf+wedftnOZoYoGNp/K4YlqU3eWgTjNxdhmUhHCY+w9U+TGmKJ7P/3ZIHZy4bUSveSQeVZTQoo/xvZRm35lDbaD5Iy4O1gg0BXO/wztj0WHLoOfxc7NoioTItAD/xx+GaZj5XUd3gy0jI5yU6S9OQOhnm+VRV0OrlqaB+gBdDMxBhRiaXCFtr6l38b2pLNHBFU9Vn6ajv0DQlgJ8tvd17El9su6PMjVLr8qISEhIQEB627rzTyVzvr8b8jNxGdrJoAq5JQ90F+PmBlCUQ9AtydAVvxzo9cys9TwtNSR1sQYdK2bXP5lx1/34MSDWusuQkwqaEDTjzOQ05hEX4a0Bztgr3wxb8H8e2JeygvVBHSloBs5OeCy49kvoYynG0sFFrn3ewskZlbgBzZ+k+eS8ocvB3HKkNiK7ZwFlyZb19pprMaYolMiIvux88vvyxIgdFLRF5BEcYs5UQY6PwxsXVtZuS6/epDZmSWmJWHr7dfYN+jdjlykab+VWcbS5y69wRvr+b8jz7s0hCzB4WjgbcT6809F63an2tpZoLzD54yZZSVZ8t/7KkwARoKTMt4VzOa5eGDpWcM33dcFugEpG/xiwYtqQI4bPEBNnv0yh97Ma5VLRyIiGMBrL6seL2j3o+RkJCQkDAMFASp24TW1aFlukIpLNTdy48UYf1ksyxXb3FCSI3qqr+/k4N+xxKXAFhZKDxOWclWGTLFFULBJyUfDREU6YpyUEQkZ+ZyexljY7aH6BDshfTcQuy+8Ujjc+m716C9XC1PR73VEE/ce/zSiTFIgdFLhLmpMfo3CmAZBfo7XHgsApvf6ooBodXx8caziE3NYtLcgxbu1/g8v+y/imuxSdhzU70SDp1wqM/VoI1usY9h5OOBEv5Z6URXwUFRU39XnI9OFJUQV6a89RZdCzYUEEUmpGH7tWjMH94aJ6e9gk82ncWvB67hb0EZXhuhfi7s57oam4x32tdjkqUSEhISElUPsstYfzEKe26ozsTU9XTETRE5Z4PCB0W6KMpVkwV3j+K4NrnoWM2BkTKaxItu3QXcXTC8UwiScgqwV0dFPuqkSEjMQAnNNxmZsPldsh+xNDVmc9HPDPpdyn6ftAX474Jq9w1919bCDBl5ZbNFcbI2x8KRbRRui0pM1+mxM7acx+qzd7FrSg/4OdvhZUAKjF5w6GSQkp0HF1tLJsKw7LUOCqXWfv/bq/9zlkBjUCSE5m7K3cP7IAY4fQnWVhZwqteP+QZQ29/YFjVx9NItRKaXIJWkZgwcEPk52TKfHzr8yR3qsZ5esZZBQ0EVvOvxKaxSpw4bc1PmnUCB0bkHT9Hz913MR2pIkxqsLeCr7RdEs1JCRofXxPxhrRRmxiQkJCQkqiYknkMCOeqEAlrU8Kj4wEhfRTkSRzoha+cWzAZrwjo1FdnUQu+gZgOemcW10rk4YfVFcWsPdeRdj8TEVRGAryfQuhn6/rG3fBLiMqxOn0dOeFNUBNReT2JY1GIvFhTZ6RgspWTnY+DCffh1cAumLEyzRWvPR+l8HDfiU1Htk9X4uk8TfNG7CV50pMDoBYZmgkYtOcj+AOYMao6kzDzc0zFLwENFhPLENQYZbCST1msRyM4vQLbMTI1kMn/ef519bWNuilA/J1ziAwI6wVGWijJOJiZlekmqEr3Rti7eWnmM/QyvLTsi/x4ZvPX4fRcMCZ38/hjZGg+TsxA0Y7X8d65snksqf7wJHe+lQNB7/Ha7urg0YyAGL9wnGsDRDBK5ZFOALCEhISFR9SEPm5/3XZVfp1YrmhVOyCgNNqhTwNrcBNn5FeerozduLiyAQVIKYKebaES2Y2mrlyg0p1QrsGzHQ0axRP1S1TcFyrguTvnidcxZdxxFDrLn10RBIcwtzZAvU7fTeveiYoW5Y2XMTI2x/+2e6PzrTnbd1dYCiZnqJcSpi0edYp0ufLntAvNHWvZae9hYlF95t6ry8jQNviTQHxJ51VCliKQZV5+7h+KSEkxddxrf7bqENef066etyHl88l7g8Xe0Rm03NVkiIyMY92jP6sxWpqofWQoW5EGR7P5s6LOMQRHPhH+PigZ2ykGRg2X5TxCkBEj0mb9b4XeubJ6rrTpHCjJiQVG/Rv4Y27KWFBRJSEhIVEGuxyazGeBf9l3F3YQ05BYUIiM3H6vP3VUY3j/4QR88mTMGW9/uxgb3efQJitoEldpxlAsNSquMLq2BwT3h07cDKoUiDb8Dbw8gyB94XJpQZPNP1yI4+fAy/Jz2FqZo4GCJNpnJwJptMCbBCSJO1vVRqHQ8ZqY6B0UKx6kGEm3ggyJCU1BkKDZcug/XqcvxzfYLSMnKY7PkJHse8OkqNov+IiBVjF4wvtt5UcXIVV9RgPKICOiKt4M1rFCC1IvXWf9xdF4+UKMa0CwEVkYlyEnJKM3wUIBG/c1NGyBHQy8wyXsLlewqizQ9ghd1TNtwGn8cuaGgGmhjboqikhKd3bV/P3SDXcjv4kFSporXBXkSvKyGbRISEhJVmQ0X7+Pf05H4F5H4YP1phXWNp3E1V/nXfUL8WYfA8MUHNLZfK3P184HYeyuGmbOXi3jOFw9e7prvZ2KCWD0LWbQHMTPiTN1FoSoUyUhTVYo4dhZ4mszNJvXvpn72uGlDhauWPh74NCwAdeoHYsiWy2r3PV6FeYgnryhhVai4GM4nzmHUsk3yg2b7FOLabcDVWXxOSteWPU+3StmLlYXcwiJWPVJWvh255CCufD4QdpbPd5u+VDF6wWjkV3ri5LGz0q+iURl/iGQsei8tB7C2AigoImoEsP9y7sdyvcTKkLKNGmiA8lkERfqg6VR4LzGDqckpV8LUBUWd6/jgtZbB6FbPV8XbQjkoIsgU7mU2bJOQkJCoykxoUxsOInOftK7RPvqH/mEqA/TVnG1x+IM++LhbiE6v8UqIP37Zfw0frj9T/gOmgIi8BLVVjfTEx8Ga7UEoKKKf++26HjDm9whE5H1g33Hg+HkgQ7ZPCArggiKiQPdEZXiLeugzvDOmHb+rcd9j7+UKxCtVcIyN8SBGcJuZeamUeLvmwOME7sJXkfRt2UvPrDJB0Wc9QjV6aI0Jrwl/F1vcT8zAmyuPI1+5UvacIQVGLxj9GgVgwfBWCrel55S/olFRTJ3UD5eP/4ZvZ4wC7G1KAyBfmdQnL/2ZncN9XViImo5W7EuhiNqzUpWZ3r0RRobV0Ho/Rytzg57k9t+KxdKTd5hCkT7eFi+rYZuEhIREVcbb0YYNx4tBscfRyHiYvb0YLWdtwRVB6zgN6P84oDmOfdQH9lqSoFuuROMfEQPQ3g38mDeh3tAmX1sbmhIN3e0xLKQaPu/GVW/MjI2wemw75M5/Hcm/vIpNb3eV35epWLu7IuevN/Brx9pMhAnUZUJQsHRU5gXi6Ybvf5iIR7eWwtLOulRePClZ47HQvG7otxtxXySZKOR2YiZQW2Sd5ytTtGdp0wy4F81VssiTqZoPzLzc8Xq7+uJPKgwoRYJLr4g7uP/tUKx/ozPufTcM974dhsrg5LRXUNerVBr+3Y718W2/Zrj77VCE+Dor3Jc+e5dnDGCiXsvGtmcfh1Vn76L5j5txLVbz774qIwVGLyBvt6/H/oiSfhmDwx/0rrDX8XUUN2zThwWHb8DT3wPTpw4W90iIeggcO1daVTI1RWQqFySpjP/QibACIE8mddR0t4exDvr+1BuuDVog5g0Jh2kFyWYLDdskJCQkJKoWY1sGq53/2XWd87U5FfUETb7fiOkbzyhYSBQWlZQ5CRrk7qCSUCWa+rshLMBN/QNpJ6zcLkbmrkfOqDUyvzJzGAaH18Lfp7nZqYLiEgz/5wiCPl/LTEvpNUk5lWf+4RvIKihGMbXKkUKtcN3PyAQSk9lxpHm648+z95FbIls/aT/horiRNygR9zhRCQqYwkMBN2eghr+CAW1BCbBEJBBVqRwJv75+Gzh8CvO/HoMANwcMbByIQFd7BLrZI/O319BRg9lq97rqu2p0peVPW3BT0NI/7yAXiJqamODy54PQt2E1dn16txCkzR2LEFmXUn1vZ3l8R8q4Db9Zj//OV55HlCGRAqMXFPojImPWdsHeWDy6bYW8RkyqetdkXSFX54BPVmHQX2q8k5wcYDKgm0YDOF9HWYZIV/M5HTHLyESAvaVCO1sdL0cmZPDnyNb4qncTDG8WxIZmiTfb1IGZieAER5Hbk0RW4r+lRkrVydpCnqmjBeLd/06XWcmPAitd0NXYTUJCQkKi8iDF0PkiAYoyRcUlmLXnCtt83ohLLvd5/XpsCsb/e1Tl9vreTritTQacKiTCioeJMddKpoSHvRWOfNCHtXNTWze10wuJTclit2+69AB/j2nHXpvn90PX4e/nJp6EfMSptNLvgwSmDEpqOv7tXhfN8jK4IEz4c1JA1Lqpfv5MStB2wThPIJiQkwvflGRsmD0BA/pygkxCyHeQWi7V4WxrgWpO5U9Ya2LLO91RsnAifhjQHFbmpXuumBTV/WBF2ptUJJL4wnMiu00KaSTJ2TrIkzkYUzsUnRxNTYzRubYPhjQNhIe9LEBQorcswq8o2tb0xNFyVCGoDW7zZc6TgE57xWu2Ad3acsGQkwO0davGyCS8DU2BnS0ebDuMCW/3x/4nmSxztmZCZ4X70KJEinhUUqZys4utBb7bdZn7JpX9H8YCzRoiQZZVszIzQY4s0KLKEHlM0cUgx6tjQOXlIP45kZCQkJB4tjT0dWEt8bQm0rlaOeCZNSAMtT0d8c7qE0yBtu2cbcxCQtfzOu0fEjMVfYX2R4j7Eoq13anABys0F5yeyXkQWZIqrDEXSGTnoFOjQPw6vDWbQ2n38zbRtnK6zUjW7k17CjKb56Ehf9qMnzv0M5q0e1/xgVRFCqzGtbVRC5sYuggeUICXlQPYlQYW/+sbipH9WuHc6Zs49+c2wNwcsLcFAv0ADze42VigVf1qaBTgwTwE9YUJ1FmUHvNPvUIxdek7MFGjqPvbwet47z/1cturzpZWaGh/8XG3RuUOFrvU8dHpfg19ndGhljer+hHudlb4e0zFJOUrGikweg6gigVvxnVcRElm65Vo9sdCH+Bp3UJQ090BdpZmsDA1YbMom6/clw9qetpbKfjfqIPOIdpmKqnSQTM2g5sEwun9ZTAEbPqlfTiQmqGxSmRQNJ006wRh+5LteHThD9GTFakIET3r+zH58YXHIkq/2bAW1+OcnYOS1HROxaa6F4I9ndjJoygtgzsZa/A/eKNtHfSqXw39/9zDOhTKC1XX2tQ0kFSrhISEhITB2fhmF6w9f48FQDTQPuDPffLvNfBxRo/61ViStOfvu3HmfgLCZ21GgLNuM0LKQZEutA7yQPd6fujT0B9Nvtso3tVAHkN0EWJkhCbFudj9Xi+Ymprg8O040cqCcru3mEk5BYi375YGcOuWfYw9By+xxPGD6ARcOx+NBIo0SA0uuLrisegieHDrLqcmVy8Y5o3qIL+oGIeySvAGqa0NaY/fKDAiIQVq3UtMRsytf+Dj7YKMjGy8N30xcOomEBwIuGjxYtLAhD5haoMiQjkomtq5AeYfusGOlWdQ4+qsUkPvkaagiAQVFh67pfJ5aBfshSMyr8TNb3VlQk9ikCXMnpuP4O9sxzppdl5/JA+KiISMHNg+p15HUmD0HEDlyp2Tu7PMkBhUyaBgZ8/NGHZRx8PkTHbRBW1BEZV07303nH295IQgGDAEnhp6misCTSdNd1fEu7qgzXcbsf2DPohLzWbvRw03e2TlFWDlWa5PmnqiaXYnMSOnNKqkE3Ofzpzrd3IqkJSK2MgHsKpfHbCyQ4kW07uRzQKZ8lz4j1sUbremqlNhUZnEgKhateVytCTbLSEhIVGFW+qGNQuSK83mLRiHt1Ydx424FLQJ4oSJHK0smKUDQWvB/ST1RqDlpZ6XM9rW9EJ1Vzv1rd5UMYqOBczMgAAfdKwfgIN34uDTOpQFRfq0+4nd73TUE/z463r29TefjcSgfq3ZhYcClJ6Dv8ZxClDuPwKq+3HHYm0JWFkB3lpkxanaRF5H9YPlgcZ/F6Jw50kqdkzugX//morRE3+R3z09IxvGj43Qpvt03Lsfz96zjwe0wJR3B+CL7Zew+HjpvqhloAdORpVKqs8f1gobL93HQUEgQdjrKXNNCoNC+ob4w9Gaq0CF+rko+jsqcTkmiVUfxy1XbKG8EpOE+98NZ9XIjrW92c9FQVBcWharSpoYG+NC9FNMWn0Cp+8nsL1gLQ8H0dd6Xs3kpcDoOaFNTS/kzh8Hy0l/K9xOiiFzh7Zkxlo0NKcPJBxA2YIU0ufXExr6JNWRdReiMHOHom+SNl5vEax+ILG8lJQg3N8Vpx+qPyHoRUoaq1ydik1hVbspa06wheGbvk0x98A1ZrBGlaJeDaqx3mgWBJEEOX9CoBWLzGZ9PLkLgFK7Ps0cuvoAK89xlUIh2Tr6GolBx0t93Ovf6CIFRxISEhLPAeamJmzuJjU7Dz/uvqxQCSBxIG1ed35ONsxEvH0tL/Sev4cJOOgDVRboQssajbKKxka07tnbwa9hEHstqiLU/Woddlx7iLjULKa6p2u73xdK/jjED5vP4ur1B7C0NMekCaqiUnZ21ti1/iv0GvI1jp64AdwRzLfUC9YeGJFwAl2UuByTjG7zduLqF4MQF5+Mj7/8h93esOVkFApkqQ/v+B5tW3EKdPOGtsSKM5Hy9+X9zg3QO6EaPt18jl2noOPA1N5YeSYSo5YcYrdteKMLjLXMCdNcs6aWPar0kDUH8W6nBnCyNscrf+xVuM/Bqb2RkJ6D8EB3+DnZolmAOxsJWHPuHvbdisWQJoGs5ZH8s1afu4fdNx4x9VuqANF72szfDf+eiZQnZmkvSEERBUjvtK/L9kL9/7ePWYk8r0iB0XMEzaIYU/Qu+0SSrwG1shEtanjgy96NVcxd63k7sSyTGCTzTPKgdTwd2dckB3ruQQKux3HDlqRbr0kKmgY/eWhQUtgTrIkKC4oIIyPDBUWkcidTw/O1s0TXur7ybNkXW8+z/4Pc7bFgeGtYmplyJ/07UdxjcvO4kj6dkMtIXJ7hZbWFfdyvNPJn2R8JCQkJiarN04wcuH/4r8rt84a0RJe6Pgj8bI3o497v1ACzBzWXn+t5u4ZNb3aBjYUZus7bqfMx0NZDbaOCsTF+nToAkzvWY6+VJwsKaBZ62ak7+KRHKGvjJmU6Te10tD49EKl+nXucBnRtjdy9x9FtwBdo37oB3hrXA5euRsHRwQYxcUmIuBODqAdc0Ofu4wqvVqG4YmyJ8kJ7m+1XH+L9yf0xpH9rvDt9EbbuLFXe++7z0fKgiH6/Z+8noLaHI6vKVHO2wWAlcanlp+9gRq9QTFpzgl13s7VUm6gkT6C9N2NYkEJVJiHBHg5oHuDOghOqPvHzyjTf06OeH9ztrdhs1mv/HJbPi/17+g6WvNpeoTWTLoMaB+LsgwTsvhHD5LbPRz9V6Uq5FZ/KLsSo5kH4uk9TJoxBLXNTOtaHmx1npRI7a6RGNd+qjhQYPSdQ6bLPgj0sKKL5oX9eba/yh/RVn6asJ3n44oPy29QFRUR+YTEb4JwzKBwfdGmIsS1rweTNRfLvU5mVTgjf7lTfp0pzS78MDkf/RtVxICIWfx27xTJEvMBAeXGzMUfxo8dIcq4A2U1tA5mkckfu00VFGBZeE0EzFBee3g2qYfPbXeULDp30TeOfopBmhwjKJpUjMCoLTaq5shPSCQ1O6ELZ7vYapD8lJCQkJKoOLjYWSMoqFeshY1dSKaOWJbLmoPatFWcUexKo3Skjt0DeYsX/n55bwISZaAU0hMceJW0pQUv//7TnMj7eKPMYAlilZGjTGkwtlwK5gQtLZ6aUodkpCijomFVwdmItcucuRrLL7N82qt6HWudaNkFiNS8kKNmq25kCGTKF87bmxahpa4b3JvZCXE4hqwopI/zd9P1jD6uihAW4IyekPtxdPZFwIwqIjsGbr/dg9yHFvXf/O6kQ+AnbAucNbYENFx9gbItgNi+UKuvWGdasBqv07L7+CNuvRbORCEpY02v9fSJCtKtn2zvd0LthaYWLAjKaQY9MSGfiHSSywUNz5zyhMnltIiYlk1WDqCpEAhz88fCE+Lqgez1fdKvnh3peTvhl/1VciUnGpz0asS4m4tchqup5QrW655Hn++hfEugPpt//9rI/MKoAUcm1lqf4gB/1JZMYgulbi3V+fspCUGBEGSm+GsWfODUFRQSVaklnn+hS15edzPSWaNQQoHg52WLPx2/Aq+EEoG1zwNmAggw69r++28Qfx++qBhokN5qZVyh3K6cFoXuXJti+8SgnqkC+C1v2AaH1AD8v3R2vy8GFh4mooaNRnyTbLSEhIfF8QNn4hDlj2DLiO30lm3elYIOfASHJ7c2Xo9l9zU2N8UabOqxKQG1zrWdvZcp1fs62zGyc31dQ0GSIoIhavF5vVYs9/85rDxWCIp4aM9bgzbZ1tLbTkcjDqy2CReXDiWkfDsXPU35BkTo1IpLRrubNCTnxxCcA1yKQ0bVUJW3LrNfkQSLVeg683wtvrzqO20/SFPwHqU2MhKxI5S0rr1BBYAA1A9jltxO3sft6DM48SFA5nAImPccxpWMDdiG+3126t9p2NRq/H7qh8LinGY9V1H7pOPKLuPlimvcSQglasmehizKfbeFa+IhFxyNYEpsCHOXKnLONBRPxoveAOmSo/VEIGQm/DEiB0XPA9qvR8gzEmPCa7MMrRmFRMZv5oYrSmvGdMGzxAZ2e/+S9J8yLhxTseFa83oGVvO0tzViApA764xq++ACrYJmaGGHSmuN6/3yaAgbyNjAxNYFJ51YoUla8qSR+u/QQxUaqLWcXHyZi+OyNCMpMw7Wb0az/OTlFdqKhXmE/b6BBLVWlngomVkf5ckm2W0JCQuL5gZ9BoQ0y8fO+q6yCdOHhUzzN4NTFgtzs0aqGByZ1qIdxrWqj5/xdrHOk5udrWRUiMYu7X1JWLubsvVruY9rzbk+2ieZpXM1VLjeuzJ9Hb2l9PuEmXhkSLbi375T6oAjAnHd648N9NxXnhK9GAB1ayG9q6u8q/x3ydKztg4hvhqrMa9P98gqLoGyqQRUZ+l5saha+2XFRqxiSj6M1q+rwHSZLxrSTC2o9SOJGFqglLTOvQLRSWFBUrLAX00fxjfaNy2UKujQbThc+mUviXRQIda/vx34vJlJ7vRQYVXVI+WziimPy65SJoQsNusX8OBJOsiBpz41HTNKTek3JUyfx5zEsw0EzMfz/mrj9JBVLTt5mX5MD9sjmnPM0ZaiozDrv4DXciEtlA3jK0NAeBW4UsMWnqX6/PNBJv853m55ZUESIBUU8u85FAkdLs2PGlhZwCauPTC9P5Gh4nCbMjY2QX0aTV8LHyYadyCmoFHsWWlop6JVkuyUkJCSeP6zMuJw2r4pKUEL0h35hmLHlHJadjmSXjrW8MaNnY8zee4XJflOLGg+JJpVjmZHzw65LeJKezTpHaF/i6WCNTW91Rde5O9gwvzr6NwrAr0NaIODT1Tq/1qw9l1G8XdzHp04tP6xdOg316/pj9e0E1j0h9xMM8AVkXoLE+ehE9J6/G1vf6cbmrIRQVw6prFHlqFmAm1plN6HMtS4KsZSw5NvXT9x9jOmbzjL7DKEPIx8U0XspbCWk+fF32tfHnYQ0Nr9Eey1/F906Q4hlr3Vgl+SsXGy4eJ/tE6lNjipifNVMohSpYvQcqNHQh1gZus156jL2B0JRv/CPlP6o7K3M0bNBNeZxREERZXHIbIvap8j7QJnBC/ezTbSpsTGGy2RCCQtmSErlY07XXh1i/kqGQthTrZbcXNgeOY3Mpo0AWysF07QKxdsDfQZ3QKvwOriQXYwdkU/wVPZ+kdP3k3T9A8Wfh7TARxvOaFUaIpr5u+JaXIrCfUc0C2InPFKfU+4f52tzpBokZYYkJCQknj/IW+ZmvOL8MJmHzzt4HakyVTKCBvKVJaF5DBEUEYfvxLPLmKWH5cfh62SLyITSlrRpXUMwa2BzlsDt/tsudtumyw/YRR9oNhY1g+Dg643J/Vpg0oAW8Kk9llWQdq77EgH+Hux+X3etjz5/7ENJ5H2AZn55b6Drd4AnT2HaqSX7vby58hj+fb0j+9Y/J2+z7pmoxHQWFFHy8L1ODfCX0JtQBvlBPi7D2r7g8A0WGP164Bp7LR5KZner68v2Wgci4lTmqyiIpcCIZsjpUlacbSwxoU2dMj/+ZUEKjKo4ZN6mTX6Z/8OiUivxdZ8m7H+SZI54nMq6uuiPyQhGasuv/DmysLiYtcb1qO/HnpN6lX0dNfvtVAksLZHZsRVw4CTQvV3FvQ6tJvQLFcxFbTOxxbZzj+R3oaCEDPEW6tA2IMbkNSfZ714Xbj1OY73j1H7An2jJoK1THR/2/isPgtLJnoIidQo4EhISEhJVm6uCyg9PQkYuuwghIR6aKRLbxC8Z05aJ9Px9gusU0caA0AA2nK8caCl3pJDwkjAoIr7uy+1JhC13ZcbdBWlwwXeXYlC7/mN5W52ZrIpG/PvHZpRsPMaU8lzbNkGKtzcodVgzwAWR12/D1agEj0tI4ZdmgIrR6/ddCtUtapGj9ZPkxsUoS1BE0Pz1omO3WMVICB3DtkndsfXKAxYYKfM0Mw9DF+3H6vGdYKrj3kCi7EiBURXmJpm5zd6q9vu0yd37bk8mh0nVCcoq0ADfdzsvsaFMGtYkGUbil31X8cH60zq9rjaj2CoLqci5lyqulIuk1FIH67gnQEYWcP02FxB1bg042pMMDE08KqjUkYgFBSYkT1pQJOhz1hM+yNUGld47/LJd4TZqZyAo+CFJbirfU6WQZoqofU6qFElISEg8vywe005FJVWMO98MZa1ttOGmfQHfFkY5vdda1eb8kXQIjBr6OuPW41KpZiEUFJFPEqvmiEDrET9Lc+7BUxgKCsWmC/yOpn/1D/796wMcO3kDazceg5GHKwIGdML9FK5VrVMtb9hcjkNkNW88LjFmQeO3rzTFkTtxKi1/1Ioe/uNmVATC0QghU/87xfYPayd0woi/D7J9nXJQZbLkINZM6FwhxyVRihQYVUEuP0pE6LciMpRKkHoItWqRqhz10/aq78fkJCmbQT3FPw0Ml9/3xD31rW4UPPHDeOWBTjQDQqtjlaDvudKg4OV+DNCKy0yVG3tb4NxVVnZHfgEQFgL06cz1KfMNxUqZGzqp8fLXpKpDFRxSgKlsjt2NZ7Kp3CEaS5LcEhISEi8QYu31YtBKRd0HNP9Dl+93XmLdBeRZQ6aery8/ovHxI8OCWEJtfOvazIpDLDAilr/WngkI7Lz+iAlAKR+rzgFRchpgb8MlOXWA5nN+XzwN706cgxVrD6NX12b4c+UBtl6XBFZjQRF5+swd0gIDG1XD4NfPcCqxZDTfshbroiF1v8pgVFgQnG0t8dvB6+w6jTeQgBMPtdfRxRDCShLlQ6rJVTFItUSTRPb2d7rJ5ZiXnrzDqgVUJaJN+GdbzjM5SRNjI7SVaczzLH+tAzOEbR/sxWaSeD7q2pA5OpNCCs+P/cNw6uNXmCKdPtCcCynRzBvSAs7WnCSoNs+d09Nfwf73emH2wHLKQDrYGy4oItIzgXvRQFYO0C4c8PUqHd5Uo6InlL8mCdU/RrRGz/p+qGzm7LuK3ALdFk4JCQkJieeLul6OzFxcE5vf6soqOUI+7RmKnPmvs6oEbbLJy1AOGZOTgpuAsWE10MnRDG9Onofty3ch1MeJ3U7r9YddONlpYtaeK3ht2RGVoIgY2jQQ2yd1Zxc6pkntucAEV28Bu5UCM7Lj0DEoQmo6sGYbrkc8woyPhrCbhr//J47YuwKBnD8TyYNHfD0Ew8OC2Jp839wesOL8ff44chPVP1uNV//hZqN4qF2tIjh+7wnqCuaDSNjhzPR+eK2l7l6HNMtNnUESFYtUMapCAVHIzA0aDVkp89OroT8bzBv+d6mJqzKf92ysYPxFkPLK9O6N2CXqaTrGLT/CBiZn773KyuukdEKQ2evUzg3YSeSd9vXww+7LrEdZONCpCcoOUZUk+ocR+GX/NTYMys9B8bQIdMeqcZ1YtmTQwv0aXbB1xoZzXDYINKB5g2svqN+pKa7zLXVa+PPoTXbicrQ2h7+zHfxdbFlP9a7rjwziFaErJNv6/a7L+KZv00p8VQkJCQmJyoA6AT7p3gjjlot7/YwOr4lXGgWIfs9SNotz+zIn3yzHwpy7COgy4AsgvDHg6MHmdexPXsWPb/fB9msPceROqSDTo2T1a3jc/tP4ft1e3Lr9CCmpnCw1mjUEAvyAhuUQAqB29iYNsHXtATy8/Bcu3YjGtiILwNKCBU193a3wv5FtFB5S3dkS1xLzWbeHhYU58pRa1n8eFM4CubjULByJjMeMnqH4fMt5+WgBzWr3beiPn/Ze0dg5IyacRJ5BwhGF6i52CKvuzi5vt6uHZj9s0unHJs+jz3s11um+EmVDCoyqCGQUqikoIg5O7c0CqDdXifeo8ny78yJCfJ3RL1R1wP7e03SV3mTevyjYw4G1g/HYWnAfD8pM0bHRUKUuXI9LwdR1p/HX6Lb4rGco/jh8E1PWnmSaBdQ2eyoqARNXHGWvW5kBg87UD2YGcdh7FNfzueqQuYkx8rXM/ZAZm7IhW0WizvOA+HH3ZYxuXhM1PQxoiCshISEhUSVoHeQpevtnPULxbb9mGh9bVFSE72etAsI13I/Wu/YtObEhGen+fpi+47KK8MINUsi7chO4dQ+gwCsogCm24v4jHIt6qPrcnm6AjbV4FYgPenShZgDiawYwM1jHzi2A05FwNDfBoGr2mDSmi8JdMzNzsGfZQcDIGN4dmsErMJglhXlTe0okTpXtf+h//uvd7/ZknTDLT91hEtdf77ig0F1DstvEvKEtMaVjfcw7cA3v/cdJio9tEYzQai4suCIPInqNGm72LECi9kSepgFu2DOlB7rJFPs0kafjPkyi7EiBURXBwcocsbNG4nRUAj5cfxr3lRyJiYMRsUzuMi1HveEqQSeq/n/uw7Z3uqlUjpSbwHjzMmJ6t0YsYNl06YGC/Ddp/utCNWdbPEzmMkJUNaITwMfdG2Fyx/rsxEJqNu52lkw552BEXNUMioQzRoN6yq9qC4rKS496vth1Q7vgBUmh0ntGFTxhUER94NQiSX4S9P6TeAMtVlJgJCEhIfHiEeTmoOJ3Qy3w4YHcfKkmjp28ice5WjbYOqifMTU6XqGVb9+mVrgQrhLk6uOGnz7oDwd7azjY28DExBh37sXindUnUSgWGOkaECmx9kKUvErzTf8wNAhMws2M00h5EI021drAxNgEzgEjuTs3ro84R2fECeZ7iLkHrqmtxDhZm2NYsxpsLujf03dwL5Hbn9F+hjpDyFS3oY8z9t+KkYtceTta4+8x7Zgp76T29ZniL4ldzB4UjtlltSUBWJJaaBQrYXikwKgK4e1ow5TEBi7cJ/r9LwUKLLrwzurjOPsgAeHVPVDXywnu9lZMyW7hyDbYfOUBk948H80NRTb1d2OzSYtENPt1CerScvLlQREPZVI+6hrCTgw/Dghj81C8nGiRLo5o5aGwUPde5SrAOS3BZ68G1XA66gk7eYpV7p5m5KDgfxOQX1jEJNpTsvPQJkhxzkxCQkJC4sWA1lVqm5+28Qy7PqhxdZ2CIiL+SXL518e9x7h5HRcHoG4wEBwI3I0GOpSKPiXmF2HE0A4sbiKlVrIBade6Pgr8/TBpzQkYCgqKeM++KWtPASkPAdO9QK1z8LHyg/uB0iSnOjdW5ZZ/nuLiEvRZsEfFW4h4c+VxljSNTspkMttC30ASdQj6fA061/ZhvlMda/vA1VbmpyQCJTd1gfZOkQnp5fIzktDM87NzfIkIcLFj/ajKNK/uzgb8lQMQdTxMzsLMHeqFHISMah4kOjipCxQU9Qvxx+Yr0ey6q60l67OlUn8JO00YoVmAO+7NHMb6aNXJehoUXU76icmAKydnXlHw7YPq4FsR+Aod5YCUa1O3vh7CnLipHP/dLvH3c/Pb3WTPZ4yGvi6G+wEkJCQkJKokH3ULYZ0ZlExVXlfTZV0FlHBVxsvDGYiJ55RXad6nLHQVzO8UFXFdFkN7q4gTUWv/mvP3cDdB1iZXAVibmyD74WMgNw+o5gM4VQMwHrjcGbFnziI2q1SO27GGL8S09dSJIFAAqiBSIUC5k4Rf6oPc7BCdnIX7iRmse4YutCc68kEfNlMkBllpUOI6NiVLazcNJUAlKg6pFlcFufbFICaTqUwzfzf8NDDMIK9B7VjONhby619sLR0wLAt8UETQJp9Ub0jdhS/35uYXYsii/ZUTFOnCk0Rg/wlg3Q5gwy5g2wFKDen00Lfb1RW9neaQjPR0Fxca47H7i9xn5N8H2VyYuqCIRDlI4Y8k170/XoE+83dLfcgSEhISLwGBbpxKLclvP0zKwKl7T/DaP4fh+dG/qPbJKny38yJrvcrJL0SxbI1r07IuvH3dAFdOZa7cmJioVWwllV2dgqIydJEYUUB2NQLZV25zKrKnLwG7DrHZJoZ7AFDgAJgUwmvkI2xePh5hDbjZaxKiWjy6LTOxJ6i7RV3FiFQA9eHu0wy0CfLExjc7s6QzX9XK0iCzTnuleUNacj+XludPyMhhvkfUKSJheKSKURXE1tIMK8Z1ZBfn95extihi/uEb7GIIaK6Iny0iaDCQr2CYmBghr6B8MzVkKHf2wVNEJaTjamwS81kigYkqg4cr4OMBxD7hhkypP3rHQc6zqFs7wFZ9WZtkPkUx4jJGpES3t5wGueQynplbgJNRTxS8DsSgeSKvaSvk10kxqNf83djzbg+pD1lCQkKiikIBS3kNuCm3RtUIWl/9P12t8v0ZW87jq20X4GRjwRRLqQXrxLS+CBrSDXFxivLcauHniHRF3/sTdH8K3JR//qhHQKCf6POXPIgBbiqp65EZ++WbQHXZYwoKgBJTPLlqjOIuJXCx5RLCLjYWGNe6Nrto4p9Tt+XGuPpAM9UUwJDCL0GBEtml8JDyHb33y07fYcq13/drhk96hGL9G13w7n8nFdR6veytWFDF79O6zN3J/vdxslEQzJIwDFJgVMWhTMWJe08q7fWogqFcxSgLs/Zc1fh948JCFFO7GwkI5OYCpibiKjUVBQVD1BNdvRrXBsBfrKw0BkWaoHK7v7NtuYMiYuOlB+V6/IGIWDakKbXVSUhISFQ9Nl68r7IBplYqqhrQrLEYVAn659QdvV+L1nQKigiaQXWZuly/JyhLkFMWhEFRTi7X7kdKd8qBEf/8Nfy59jlSwTMzA5JSADdn0UpU8TUfvPvZBvT9bAK7zv8+tLHrmoiqno6QQi8PVfTCZ21GmL8bNlx6oOB7SNC+gWa9v991CQ6WpqDmPzryjW92Qd8QfzazRBVAIa1kRu6aKCkpkQdnErohBUZVHF0lsp83WFBEWJgBxUXAlQigUR3Og6AyIMUdqhoZmGiR+S9l5aCwADdWTatIBjcJRH3vip2fkpCQkJAoW1A0aOE+lVkSmi+h26lqQMFRVl4BC5wepWQyr6CyBEWVBlVqzlwCHOwAT3fAT4P4DyVfBTLgopCwg5sLVzHSRD3BbJByUKTUHv+oyAwL9lxmz33qYiRWFGYjuGkdvPLnPnzRuwneUmqT37LjNDZ88zeCBnTE3YLyBRfJ2fk4e/8pu4hBvpJ0EUJKsxQUURWRrFaU0UVso/tvu1jQNbBxdayb2FkKknRACoyqODR0r62VSh3Dm9VAz/rVMHrpIYXbaX6pU21vvK5kDuftYI04pSxGRWKal49CMpSjE2BCInDhOtCwFmBnW74nJi8E6nm2s1Et6VOrnLmZ/Krx4wS4FRXgSaLsMeS87ewIIxNjlND1ckpi0vt354liu0J5gyJyM1/6ant0nrtD431oaFRCQkJComq1z1GlSKwvg79t2OIDzKeOb6PXFV7MJ8DFFg+SMhVUYzVBinbrL95HuXiaBCSmcGusoxb/PF2XJpLv7ta2bMcTHcPmj1hLXdOGivLjGVk4tv0gjmVmAcP6sJveXnUcFqbGGNW8JpPVzsrKxTsfLUSJrTXuZhaomN8aCjMTIxQUiXfpkJAV31oZHugutzvhWXTsFvND4itCX207jzn7ruKHfmHwcrDCxYdJ8g6WDRfvw/7df3Djq8HMWkVCPVJgVMWZNaA5Vp+7V6bHpmTno1s9X9z4cjB6zt/FJCUJku5uLSLlXNFBkVDKkk6ehZduAOGhMMrMQp+uTbDV3FF7FkkXKGOVLRtKzM0HrARVKEFQRBST98+xiwq3jX+1K7bkGuHpnmNcoNQspMytAbeVgiJDQAIWXedxQREpEm16qwu6zt2Jx+mlg5gkDyohISEhUbWguRJh+5y6uVE+KKKOAz8nW9ZmRwkv+trP2Qbf7LgoX9N5hjQNxKqz9+DA1jzue9qCIqLcQRERWA220THIDKquvloUn8C10ft5o8KhYzDOA3wCVBOclDTt1YEdD2vFk3WqjFt+lF3ktGlR4YepHBSRH9JPA5uzSo+ztTmy8wux7Uo0/jl5W0UyfOKKY1h99h52TunOAqivt3N7mSlrT4q+FqkU+n+yCpve6opudX1hZS6FAGIYlVAD4gtEeno6HBwckJaWBnv7spmFVTVIeYSGJ2mz+/O+qzrNHJFSGZ1c6URKGYKsvEL5gP7ENrVxMz4Fx+9W3uwSj725CdLzi+D15DESMvNQRD3CyWnAvQdcAJKXD6RlAO4GkpymuaFLN7kZJlLgcXYCrC1Lv0+DmaROR69JBFeHfbMGSOdPVmUZIq1kaMH8qX9zjFhykF1fOa4jRoioGr7IFBQUYOfOnejZsyfMqNdc4qVF+ixIVOXPwuqzdzHib+5crYmfBoRhYtu6rOIjBonz/HXsFpaevM1mWWiV2jWlB/r/by9rwacWrK0CtVieuUPC8d5/nAlpebA0NYajtYVCQk6UoiI09XTA+adcoDa5bW2kFxRh2alIkfuSEFI+YClYo8sMZxVCuFoDidryvilpgJOWSlcl0TrIQ74/o+2Htl06zRqRGrA+iViqSB54v5da+fAXDX1iA0mu+znAzc4KC0a0Rv/Q6vj2lWY6PWbhyNasP5WqC0/Sc+DpYI3M317D3CEtsO9mbIUGRVSOTv5lDFPKEeJmZ8mCIirjrp8xFMVJqdxfPFVlQv/P3lmAVXX/YfyluxFQwARRURS7u7trbnOb67lwvbnpNufqv043t+nCnt3d3Y0tKiIh3fV/3t+553Lv5RYloOfzPOcBbnG53Ht+v2+9b6jqzrZlFxQRtsOFNZDK6LEJgL3OImNjA8s+nWHVqgnQrysQGlwYFJFKHhTJfekP/bFV9A+f/2C0aKFUUFBQUKh8mGvkSe8/Q0GRrF47pVeYaIMjXLX6f79ePZd89Hos3PXcvyyCIrL5lYGI/PQh0ze0shJBkdwM8vPuCLjqrsMXrgCxd6V1uqRBEdv45E4RQeHazaDo0oejsKB3NTjq7Etk3uhYHz8PDtd7nU1k6QWVioPm/oxbJO7ljMFkuW5Q5OvqgLf6NBUKtyfeG4GT748sUj1q8+lynLtdKBChIKEERpWArJw8LDh0CRHR+mzH9JuAGcPP1QFjW9VDvirNIJdfqWrCitPV+BRRUSqPgIg08feCh5M9Mn54AvvfGordrw/GDx1qIy5ByhgN8HPCI5O+RMGVSGDTboABUmlduI3B9rkmDYDwRloldZ4gm9TwQL6lJfLq1ZZa8OzKXvzBw7F8epNl5DBuyuJ9CPJxVYYrFRQUFCop8hpuaKvLy9npwduZIjcvHz/vPKc2hpfXfHIzMR2JZrTRGXyeQcZ/f3xapmjvIvREXDe5n9Hby2K39e0sEX9XR0iACrG6wgn64N+3YSf8Ii5KYg+a4grsCDEi3nTmdgKOx2UjXUfQysrSAv0aB2LG84PwzIBWOD51hFhHNfn3lSGSpQehSl4Zw//dB4NaGPRH1Py/mgsT4p9uOCEUbtefuYEm/p56VewOlbMQVFVEaTCsBPx78KJWX+voFnXRrq4vGtXwENklDtx5OdnD2spSbQJGp2tD0MjtcmwK+oYGig/EmN+2oK63i5aBK9vsyposlTv04eux2Hs5WvwNber4IDYuCR3nbAOC6wB3E/Hz+6u0+4C9zDRPK+O2Np4gI1XBWkl4sXsovttq3FeqaYAXHmpdD28sPYjSwP/flThVu58eeNpkdZD9611D7kH/toKCgoJCsZHXcKrPac3datQ4vhnd3iw/I67v3ACzG+PCR2Nw4U4iftp+1rDXXjHYdSla6+cvR7bBq0sOqH8e8tNG9fcMjJ6Zt0v/A+l4E53NyEPs4UtATALg72daSEkWaCJc//t0hvYz04C/hzNDTLSyfV6DIT9vLnLz9wY0F0p0mlW8poFeuPjRWGHsSnnsW4lp+HDzacDGWuxfkGSGWW0JxiU+XH1E73XZZbBXG9NS6iLZ8spAHL8ZL0x/V52MxPsDmuPhtsGlfvz7DSUwqgQMaFJT6+dFR66IQ5dhzWpj/qQeQsbTwcaqiJQ3VeVy8/OFakmrmcuw8vk+OHkzHhdjksRR3miq33T4fKX4OiisJoY0rSX5DRAar8kwu9OwGLMwt2OltrsylPROytAeZjQXZnGqOesv+TMDxWWARnrfjm6Lrl8ZVo8zhaxCw4ySscBIRtcbQUFBQUGhcsE1XJ+RJytJ3xjxMdLtNHljqRSoTGgdLLpAbiWmo0Utbxx7dzjCP15aps/5TQPJPaqqXYqRggVbFCD7yk2gVg2pjZ3IQRFV62jI2rwxYm3spKCIqnCsxLBbQ9fDiHO/nPkphhpcj5qe2PLjYmBQD9M3vn4LnZxbGWxtpKrrM50b4r2Vh3GBqnSE3S3l0G1D89bygt1DtbxcxPd2NlYiWb3y+b7l9vvuBxTxhUok4fn5hhN4Z/mhEt2fJ8Uu9atj7+U7QsWEMHgKquaKUxomY8TJzhqONtaITTXP4MxcOFOUacx36U48sE1HLYWBUdOG2pfl5gEpqdJJkdmmjCzAyUG/K7Y+zl+WAjFmeCoASnTvfG2QMJBjD29ZnPQ4KMmeYFNsmzLwgawYVcYha4WKQXkvKFSV9wLXfVb5mdDiBp3tc+ZUiggrDNNWHRGzJKfeG4l3VxzCb7vPi+vqeLtg+sAWeHTO9nL+C6R9RnigN/Izs7B/7mqge3vTwQNFlnQDHs2OEO4BdCo++uBvscjNRR7b8tmOZ6yrhJWkyCghsKRm4y4EeThiyKB26NivHYa0qa/Vis7OmpD3ForxA+l55QIJyea1/VUg9DGcMaQlftt1Hm/2bSZkv029B+mTRPPffo0DhVl9eE0v+LqWzOy+qosvKIFRJYOeRS00Mj2c0WFvqNPkP/Te3srCQogZqD+4ZuDlZIe76VkmlU7KhQUabXQyHVoAmvKdHKCMiZdOPk7GP5juthZIzK4cwooejnbF9p0o6xmv5G8nwrY857UqKZV9A6Rw71DeCwr3+3uBG9imHy0RG9gFk3ogOTNbSDdzT8+2eyqUlcQ3p6zQbREswq5DUgWpdoAkvFQWLfLmtNrfiAJ8vLWDMoo+qAKdeU90xziVomt2dg6WHbqEsX8ZaBGsxFz/ZLxZXkU0GtatWmoS/cWE+yY4UlTpqig3E1Lx7L+71EZta17oK8qejrbWmDaweZHb8xSQV1CAmUNbYe7EriYfv7NqmDM+rWhQ9MWI1nijd1OMbVlPtImVBLby6TstedprbNQ7tZI8BGSY5dH1NHB0kE6YmkGRgT7bex0U0TfIEOYERc521uIoDzjjVVaKQwoKCgoKlQ/Ovjz1904RFPVvHCi8i2QfomYBXjDlwFLeQZGcfDXI6QggKhqdHhsk2urKbG7YnMfhXkO3UqVR/Tl+8RZ++HU1+o2YBjuf4Rj7+zZUZijsRKEOUsO9cL8UY0pCXRUUcc7NmKeW3+v/wO/1v9Hg/YXCAJeJ+weBBy+1XAnhiezT9cfVbXSsPMyf1B19QgPVt5k+qKUwbP1u6+nC+6m+jtPwROCHhEP4+rI2Oy8WHVmkxPPIFnWFug09Dz7feKLEf0dmbp5op9OdfbqbqdFKxr5iUwOX+iiHvt6SwHJzcWlVqxo6BvsJyc2Ph7QS5fkBP6zHzou3TWfWzIAyqLLiz887zooKIwdKFRQUFBTuL/7YGyGEEZgwfbVXGMb8uhkbVcJKx27EGw1WmBQ1BNfugU1qokcDf7GmzN1/UbTml4Q4I78HjUPEsSupZPO9ZsFWPP4RDJbYwu/nbdbdPt8ZIc09WbtILYEV1I5vLn8+2gW9GgVi18XbYpTC4QWps8iYzLvcPsdKkTl7jzvJGeKgHDj3F80CvfBEhxA80aHBfWsQWzl2mw84lNDWnC36enQ79G4UUOR2345pL/pGjaEZ/Rt709f3lYzM/tp/Qf3VmNKdKbp42OFuWpY6KPJ1scev4zvAvrjJIFl+8+oN4NoNVHYsVIpxAxoXBrGkb2iAEE4gj3cIwVej2uF/I9uK4UcKZNSr5qL+H/EkwxL+1P76PRRMIQdFMszs2D0/W6gTKigoKCjcH9xJTsfr/0ldAS/3aIwXF+7FYlW1SB8LnywUIaBqHWdfn+6sM9MLYGTz2oj78hEsfroXnunSSBjLcj1i54o5UN2sXOB+QJbJTk4putgZgvNJnNW6fktb9dac+QFnJ8DdFahWhn6K5UD7ur5oXcdXBMhMotvbWOPuV4+K8Ytg1f7OEJwpMlYpknmtV5iQL1/1fB8h4mBrbYnjN+IxecFeYQ8iw7l2zrz1/mZNiZLHlQ0lMKoEnLipneWZOGc7LJ/5DX/vv4ArscmiopSdm4c2nyzD1BWHjT6WoY/93491FfLZ8hAeMwBk3ekbQiryxM274ufwQC+81bcZRjevW6y/Yccd7Rmn5MRUPDNnGzLNLYew95fzR5TnJBySPCV5JFRm+OdRLW7N6cIg7tNhrdAtpIZaTY6Bkcz1+BQ0mr4Yf+6VAtJH29UXQRP7mpmpKw30r5Jhm4Xj5D/E+0ZBQUFBoepz4GoMEtMl5deZ647jjI6wEg1dKf7Dtf7yjLFaKqW/745Ap+DqqOkhzZ40qu6Ox9tLa9OSo9cwR7UmMaE2ef4eYRabayIQkT2X3tfT6q9Lt/rVUcfL8NyLs601mvh76PwCC6lqw4DG1UXTs9U82JLP+yckAWlp2u12OeVYsSpH+Hr/93Qv7HlzSBFFPQ8nOzF+oUlmTq6o9Hy+4bjYS6Zl5aj9p0zRvKa3kC8fGFZLKCIffme4UN0lDKzupmVi7r4LotWOQiCbzt3C28tKZ01SGbg/62BVjLZ1fPHPgUtFLn/kT0lRhsGMoWFKfs5NJUHGtKyLCW3ri8239bOzxWWU1W5Zq5rwHJq8YI+6ZE5H6kkdGyBo6oLi/RG22qXbDCtrwLSoTCGsbnRqLSnRkWaNJAnPKoaVBStEDRDy/iLx8/RBLWBrbSVaJZcfvyYM1rhYsa2B2TkGULrGf7cS0vQGuBaq4Keml4vIFvVq5I/n5+/BVZWMd3RyBvJ/eVK06THgJWydMNdpXUFBQUGhclt7fD68DZYdv4Z9V7Tb3A69PQwta1cr0h4nw/s83y0UX285JX5+p184xrcOgquDDb7ZchovLNgjjuLAdYry4rKHoTG2XbiNz4a1xtQVh5CjJ+BKzc7FqVvagZ46kNH9ag5UoaO1x5mLwKVrQGgwEKQx30wxDiYOY+Kk6lAZts2VRYu8LlQa/OORLmYpF3IO7UZCqghaGLDI7LhwG2s1krim8NVItr60cK/WKMfqU5HwfvUv9f6TexcGS0uOXsHFO0kmq1aVGSUwqkA4a0IFulO3pGqNPli6NKYwYyooalfXBwue7Cm+/2qTdEIk/IBZW1mI4Gvh4ULPpB0XbyN0urSpL1OYnaFTtWdRM9dJbYPxu+6JRA6K8vIK/RAqETxh+Lg4FPnf0dD1my2nhBBDQz93kd35c08EvttyGreT00XGT5ZX1wyKTBn/yfwwrqOWx8W2KR6o/c588f33Y9sLqdG1k/uJTA6DXyUoUlBQULg/4Brxep+m4vh+62kxJ9I9xB+/TuiEujrCQKmZOVpiQWzhnr37vNhPMOvPpNrH647hzLRRwg5ixtpj6tsyAfdKzybCu4hy3JxbPXgtVuvxZS/F4rTgv1lG1QRnSyCVwZixAIFBEYOjq5HSHqKmf6GQkzyzzJa7Gr4oa1zsbZCcWbYVKVqhyFYcey9H4/X/DiA+NROpWbnCyoMqdDSsHRFeB1bP/qb3MYoTFMn7Rxl9+1TuPxtWd8dDrYMwpWcYRv+6WQRMry7Zh9kPd4GPRmBVlVACowrgxt1U4ahsCg5WclifTsXdv1ot+ji9neyMDzbqsO9KDF5fsh//23RS6/Jz0Qlwd7BDz4b+2HzultZ15mR/CNXrzHZlZnZGI8BZN7kvHn3rd8RYWMMl2lWY3T39z87Cv40+Ri7O0n3MkeG8x7zXPxwBHs4Y+nOh+zc5eiNeHIQ+UT2+1m/ueidFamVkAHMlNgVX4pJxPT4VsakZ6BDkh906ruM0k/3loU5FjP/srK3EoCUD6Be6NVZf7ulkLw4FBQUFhfuPyd0bi0NXxpsB094rd3Dy5l3ka2ROmaz7QiWu9FyXRvh+2xmRPPt47TF8NKQV/N2d8Oy83RjXqp6YZ55/UOpi4WPsfmMIar89D1EarXm6Ikv3kia1fcTexiQMjgb2KNxDJKdKc0dNQso18VraoGhyt1DRucO2Sf7fUjJzhHGvzFtLDxYRxmDgwsCESdey4NF29eFgWyhxv/qFvlh54hp+3XUe2yKiRPXyt4c7q5Ovb/y3XwRFZNXJSPi+/rcIzJc/2xuN/Su375Muio/RPeT0rbto8uESg9dTEUZ+Y/HNnfrdY6INiySmZ6HxB4uFs3V5o6lyVh442Vrjr8e6If3iNTz81Ffw9nLFtVO/49e9FzBliUpuOupOYSbndgxQvTBzoRsc1nBzQFSSaXnKyoaccTOHx9vXx++P6pdkl+VZNY3pHjTuV78SheKjvBcUHtT3QrcvV2H7hdvqn9mxwE0pjd6ZWP19T4TYyF79eBzsX2CfhgSFF7x0TEAtnv5V/X3ad48JBTK2gTORyvZ7fZt/13KolJQl3ZyzsS/DFpmayy6DIwtLaeNTDFytLZFy7RYKAqoL49dW1d1xKDZVfT2TlZz1LS4XPxqDIJ+ibWjJGdmiTe2v/RdFS5zMkXeHi73E4iNX8MXGk2YZwZvC3dFWCDkUZ08R8Oa/uJWoX9Ah6ZuJcDWhlFfeKD5GlRRG2YbMuApmPYVVL/RFzk+T8MtDHbHvzSHqoIi4O9phxpBWZv2edZP7iRNd3s9P4tNhrYtcz7In+0ENUSZBkb6hf9UGPi07V5TfvztzB6heDXF16+CR2Zvx+65zhbeTAyE+GY2giGTIKjUAQtxt0Eanr7pMMNGj6GnMpwFAkEp1zhhyUERjVi5cDIxf6dFErwdEoxqGMy48eT3IQZGCgoKCgtTKTTg/fPPTh3B15jjhT9imtg82nLmpVhqjOuqlGWPVLxlnRZYcKWypvxyTpPVyUrWWa0wNdyc80q4+pg6QFFRpQSFDBbuG1T3EJr2yIavrbUvVCYoIK0bmBkUa+4Lk3HwpKMrPx4InuuHLR7shLKBwne7bSFup1lxY9ZNFMLacu4Wpyw+hw+cr4DVlLp74a6dWUMSqEgUS+Lq/P7AFrnw8VnQaFYcbn47HtikD8ePYDsIXiTB4Lu6e4osRbfRe3qdRgOhsqUoorXT3EMpk0oRr9clIzNl3QZxUdr8+WMuh2NrKEk931u9BY+6bi6VXOfvzZt9m8HNzxBN/7UBefoFQPvvnie548u+dot+YbH91oPgQ9vt+fZn8nao/pOhl/KDJA5EcFr2TDHRpK75felYjaNT8QOo5YaVlF57Z2Mq38Zz+gLNU6Dkp2FhaiKFRmuq1ql0NH6w+avDul2K1VfqMwdbFS7HJ4vjv6Z74anQ7kd1rOG0RIu+mivdJsI9hY1kFBQUFBYXH2ofg262ncfJWPDafu4mpKw9ryTJzOZUH6tnmNKxZbSHKQD7bcAJDmtXGN5tP4b2VhfYhhMbh4TWrCcUzdijsV7WxDQuvjW3no3A3PUso2MkztLJCnpOd9T3pcjGFKXU9vehr4Y+Jl+S8nQpnZywsLfH8koNFPKJWnrwuRpnMnTaQYQDL0QeKa+hWnDjP80jb+mLPyDZ8Xb/Cai4OwhaECfGP1hzFh2sM71FkjkXGY1DTWmJ+6bluoThyPVYk4s0dC3n6311ibk3f/pRiUFUxaasERvcQVoBGNK+Loc1qCx8hSjm3q2f+4J+5g/S6t2OvKIcp+WGTq04X7hRmhKhE16V+DXwzuh1eXrRPXZ7lbNJWA1Uu4mJnLQb/inXKUQVFJcXNxgojWgUJkztyNaUc+5zpoRATDys3Z+Q5OIigiH5FfD0f/WwJ4O5mdPjT3tpK9GebPYfFocorMRjevK7wJmAlUZ8xGz0IqDjDRYgZo2aB5pnXKSgoKCjcv1BamZYbNHqdOHdHkesZHzz8xzbYWlmhmos9YlSzriQxPRO+r/0ljOR1ycrNw5CfNmD7q4NwNDIOS49dE1UYbyd7ERTpIzEjW2zgKzQwSs+QKkJ2ttJ6bkLNTQt9G3pfbylgopBUfAJQyx8FFhYGjXOLGxSRH7afUX/Pua9uIdWFUBOPOt7mJUiZYP9gcEsxEyYnwA3x594I9G8SqFa6a1HLvA6ctacihQKuMapiUESUwKgC4Bvw7X7FN/M0R86Z1/N2utAAjAf5Z/9F7LxYWI7dfiEK4TW98VKPJuLDx5MZMwYsmRsLjFKycuHlbIf4VANiEGUsmjB3YlfxnMiHg1vi991nMW11oZKOOTBjxnYDYw7hhTe2BPyqQTP0ol/RmiOXgd1HgJo1JFlxDnS6Ohc58WZyMUmLw+QnByDO0RljZ28x+KucVUHm/it3kJWTCzs90qFLj14VKkSaGcAft58Vnga6ogwKCgoKCg8eTLoaW9+4dxj722bkG+lyqOHmKCoPHer54XRUvFCnOx2VgGYfLVG3zzEBO0tufzdATKyO/Pa9xtFBCmAYGHFtzs+XRKPyS7E34f1cnKSjHKBCIPcpfJ6zH+6Evo1rlvixqFZoKjBixZCeh7c/n1BEsCkhLQsbz97EujM3sPX8LdGJNKZlPVGt4jyTTPeQGsIOhl+ZdB/443r0ahiAqooivlDF4OaYcs5EMziSP+JLTGyS+ebu+906IRUuw+pE8rcTi2jj5+blo86783AzIb3kEt1lNPC6dnJf9NM5QWRkZsHxpblalzGLpa9sLstf00eIairvLNduFSg2GZnAtZtAwyDg/GUgPhGIigZaNAHqajzP0xGwOncJ3372FF44rK3+ZwieFBc+2VNUhWq4OWFMq3ri/25IFtXCyP/9QRFmeNCGrBUMo7wXFB7k94LnK3P0Vn2KC1cMGysLZOcVILSGB3Jy83AhRmXAbia+dpa4czkK8PctH2VZeeanAte3pjU8kJCZI9ZtClScuy3NCJWVqFVp29HYZSL7VxpjVIu6WPhkDxy/ES98ENeejhTKf5rKhrq80DVUBNCcWdNtsWMQxb1lZaE44guV51krmAU3v9wE61YOWCmi0ZqxoIiqeMN+3iiCIlaFOL9COM/CysOLOtKfLMde+GAsPKbMEXMwHKpkSd3sD3cZLkRLjl4tEhjx+fUOdMDGG4XtAAyKqIxDmXP6NrDnlpmX0b9J1Zo9l++IQ0ARh5Kaut28XdgWyLmpG6rK2qGTwOkLwGDJO8rSxQl5efl44c1f4TCqPzJgAXtLINNIiZ2v76hfN6t/bl/PB5MXGjbe47/j8bnbMSisJmw0+nwZFHX9crX4n+15Y7BJUzgFBQUFhaoNfWxm75FazUsD1xUGReRMVEKJrDruZOUDAUU7WMqMCk74zRzaSnT/HLwaI2a7+oYGICsnD0/+s0t9G67L9BfinHcRA1szYGBiVYq/c9zsrerv50/qjpnrjgtp738e7yb+r6z63U3LEqp2uy7eFkbxmjSq7oF+jQOFiML1u6lCxp3z0BRbGN2ynt7fGagxN18VUQKjKgiDnyHNaolZE8pnsqSt6YZM1+NVJ6+LNrg3+zQVSjKEHgaU0mRvMT8gnb5Ypc4G0NX4h21nxAaavjjvLj+k9j56rH19/Ln3gjAYK0cVb5P9rPqY1NBZKzAiDPBe0Anyvrl6Gx/MXoeEjBwpoGFQVK9myRX3jp0FWjYBrkQCCUlSyb5jS8DVBa8380fcxUhsvZGAz14Zgsh+zfDGwj0iKGLPc+bBE0BXSXTCHGq+LRm4GiMpM0cMbQ5sWktt7rfs+FV1y+SNu2mo7W1aKU9BQUFBoeryUJvgMgmMjNG3cSBWnriOBx0GEa8u3odvtpwWe6l5By+hU5Cf8CGiTxT3WZzfnr7qcJGgqGv96kKJlkHEGFXiVheKKJQmockkOAMeGSbAGcwxqX45Nln4JdLLSvPvcbS1Fv6W/UIDRUBUy0t738C55vsdJTCqZMjD9foCHk14meyCrElmTq7IECxXKc1QzUw2/ny4bbD44MamZOLv/Rfx47gO+HbLKZxXCTFcjEmCz2t/C+W8KI2BSWYTiBwUUX2EVYh7CT+8bO1jlYhcjUtGfl4e1usERWTuvgvCsE7m5OmreHnSZ4U3YGDUKqzk5rHZ2cCIvtL9j52RHq9eLcDTXVzdtHVDPDR5sPrmGwJ84HstGXfY3pCaDgTXAZJSALeyDVQG/bTB4HX0F1ACIwUFBYWqzS87zorNLpVRh4XXQcta3lp7BFOzyGVBfGpmmT6elQXthIopjlAJ+G7rafX3vRsFiEBj16VoYbA7vlU9EYiM/31rEfePwWE1MbZVkKi60cT1nX7NMO/gZbSv5yv2aIQBClWFSwsDMHpbCRXkS9FFzONJAz93EQhRhKFTUPUirXEPGsqMUSVC33A9T3DfmmiRk2Hg0OfbtVqCCec+GC3e9DILDl3C+NlbTZ4wqWKnW1KVodfOsRtxWkZy94rpjX2x/sB57Hd0FwGNm60FkrILivTwdq7lBcf8fGSmZyAmJhFnz1wTRm5WttawahSMbAtL8BRcAtGYQowEVfSK+GJkW6RkZsP1pTmoaOhtxSzf/cqDOEugoB/lvaBQld8LtM5gW9aP28+gbR0f0R7OKsTzXUPFhrXL/1ZpiSfRjJND776ujth07qbwz+lSvzpGq9qxSxsccYPOdntdmLiNTkovk+CL/jllMRdVEXDEgDNYFC54pWcT/LrznFoC3ZABrqHXVBMGMqkqY92ygIHyh2uOYPWpSKF219DPXcivsypkrtpdVUaZMarCogoFet7MvNyUqAI5ezuhiIrczHXHMOfRrrC0tMDKE9ew5VyU0RPZxLb10Ss0AK1rV8PUFYew8HBhGVbm6y2nzJMOZwZIVeEpK6bP2Sy1sKmQgyKWfqmGIs9N7byuocpjZQ+ESeVf1rnkWlctDydc1QhCi42RShPbEJ/p0giPzdle8scHsOK53vjv6FXhdl1cqDBIs1gGxlwoFRQUFBQqvxH828sOiu+XJFwVX9kB8uqS/Xpvz4oD5bNlfog5Izbei5/qiUn/7BTXlwbdDby/uyPGtw4SprGchRXCRuyAcHE2yyhVFkLSpNyDotxcwLp8GqRo1H74epz4nvumpc/2xrrTkWIuOszfE8PD66Dvd2tx9naiCIrk19Texgpd69cQBu+0cuHcFgNffrWytEC7ur5lFhQRfw8nzJrQucwe735GaaWrJO1zrBTpC1gKVCeSlxftFXNFxvpNm/h7Ys7Erpi+6giuxUvSm2yZ48GskjknyDn7L4jDFGz1M0h0LJCSKslktgkvmwFJihtExQCtmxa5qk8jf6x/aQCyc/Pw84LteHnGPMDJERZODnBwsoeHpytu3UkodLhmG2At/8KgqIxlxWVazVxa6hP+z9vPIjlLOpkWh4nt6uPPiV1L9bsVFBQUFO4tvRoFYETzOiIhZgqas9vbWGPT2ZvYcv6Wuovj840ntG4X6OEkZJa5/H2uIbNcXNi+t+WVAXCxtxU/M2E7acY8JBw+A/TrKq2tW/dKJqieboC/n1rWukeDGiJxeZHKdrzd3UTAxwtlyeu9w/DtltOSMER6OnD1FhAaXG5BkQwDmbz8AiH2RCgUJYtFUdCAQZEuDIrWvdjP6CgFf1ZEk+49SmBUCeAHQbN9Tl9wdCMhTdxO31yRDCUdaT76SNtghLy/SMwMyZQ2a1Tfxw0XNB7PKDRB8/HGyEHtseT8bdhYWWrJg5PBtT2w8loxFFoCa8CxbiDSc/JEv5xfRhpefqgr3lp+GBvO3sKdxDQ88thn2Lj1mNbrxvAtS9N+mn4GvTpJQRJdrM9eBAKrSzNCZUxZZMHWn71ZYi8EBQUFBYWqBddLBhznbifgk/XHRWLT2Owth/vZEjV1QHNx2cy1x/DuikI7ii9HtsVLPRqLDfaFO4n4ctNJqMTmik1iWhaem7cbLWpWQ8va1ZB78zaStx8EavpLN4i7KwU8PCJvAcfPAmMHiau2nJe6WSwyM1Gw4yDQq2PJngSACW2C0Lymt3itKArFmeeO9fxE+3+HIF9si7gNODpKXoMXrgL1y9fnj0ERCXB3EsEMu1eOXI/DihPXsPRY0QD3+a6N8GqvsDIdpVAoO5TAqBJgtPpSgtsxQGJ5ltSr5irUR0qL2UGR9AREmcvT243PWh0UMVtFY9apKw5jW1QKcPUGUMf8uRcRFJG4u/j24Y4Y0LURZqw5gtScAoQ++T3iNYIiTcRQp/wEOrQEnB0l5+pzl4qlDleZkXuXZZoFlm0mTkFBQUHh3sA1s/Uny5FqoltgyE8bcW3mOC3lsLf7NcPsPedxNS5FzApPUW3AOYPc5dNlhUFRYpJQUTVL8EDVVXExNlkc/xy4VHh5r05wc3eC2CF4ewBd2gB3k4DEZCnpqPtQl66jdV1fJPm5I8JMX6RJHUMwe7ektDd9YAtMG9RC6/plx66iz3fr1KJQ7g62SKTamjBiLdugwpgfEdV9H5mzDWlZxueH/jlwUQR1LWtVQ5/QQOy8cLvUoxQKZUfVkgC5TzFrXkfndtTKX33yepFKDGWaObwpBzIMio5NHY5T74+Ep6NU/r5X/Lr7PLqFVFe3HfNkQvds9t2mZOfCp0lwyR7YxwtjNpyD88t/iaCIxHt6AvVrG79fWEOpdJ+dIy0KPIHfJ8hBEXu/D749FE88AJKaCgoKCvcj3DTTE8cUz3ZpJKoKuonRCx+OwayHOmHFc33Ul8/9bxei0zUCLXc381XgYuOF1QQJD/Qq3IswCeruiiSoVMzYslbdR2pf69BCqtjo0jAI839/XaiymYN7UhJOzVsHm/w8WOTlYfqE6bBwG4TW3abg7t0UtTqcplKuWoLaiDkpdh1Cu1vX8e2I4u0DjPk43kxME0ER54SMjVslZeQIie8Jf2xDw/cXYuLcbQZHKaAapWAlSuHeoFSMKhiacFJJxhj8fPHkRxlO3p59uv2/Xy/EFmT1M7L3cjQG/LC+SNtc+IylqChY0v5oSEvhBk0ZygWHLmP2w50x6e+diC1jyU80C5UyVXEGWvQ8VMortjaiNU8LnnSMLBLWlhZCHUifIg2HLysD26YMNNpqqaCgoKBQNZg3qQfs5mzHvyr5Zn0eN4bknGlr8VTnhuqf8xhQTPsT6NiuqEACAwoNY3C9+Hirvz12Q0PYqDgUFMDDxhJPdWqEyxm5+GDNUdP3ycxC4qFTOMA13eKslmzDoaMXsXbTYfTo2wY7NFT6xK+SvzE0O5yXB0RFY9+taISHBCDAw7XM5M3rervgSpwUsJmiupsDbifpV/8t7iiFQtmhVIwqEFZ9mDGgE7Eh5I/1N6PbC/nGjl+sRO135ougSFY/S87Ixp5L0ejz7bpSzxI936URPhve2uD1NjQc0JHZNMV7Kw6LFjo+fzKgSU0Ma1Zb6yTkYl1K8QM5sOnYSggvFIHX+VbTvmzHAWDBKmDpeslfyAj6giJy8O1hJp+a3NZY3lCJSEFBQUHh/qga/fVYN2EWqg9dgQVj7Np7Fjej4oEtu4teqaoElTsWFkjILcDic1EYNWuTefext4Nlr44Ieno0Qsf0VosoOLs5YdyEXmjcOhTNPvrPaGFIE/X2hSp1qvv4+riLOR7xFA3cb2q/cLFvMUejydygiNRwczL4O3WhwvCHq4/gTrJ5IxUKJUfZSVUA9LZ5Yf4etQQzqxGzJnSCu4Od3uE7BkXsL2WP8B093kJuL8/RMvOq7+uGX3edL9Fz+3EHszKGydGZ2jRHYIAn+A9WHRHO0B6OduLv09X5T8ktZa7mv/XAqP6S2WrbcGDLnsLr6tUsGhSRkLpAUjLQqTXg6lzsX9mnUYB4rU2RlVu+JfDGNTyEj4Kvq6SIo6CgoKBQNdFVJvtqVFthqs4kKM/zFELi+ikpoWmrliWmZ2Hd6RtC2W7LuVtifQqv6Y3bd+5KN4hPBHJyARuNrV8J1j7icicGKZ4eQDH9oYoTOBDmJC+JqootMKIfPCyBpAILzM8twPxPlxu971OdGmDNqUjcSkyHg7UF5vSohud3xMLx0GlEqm7zzOP94FPNXczx6O6/+Br/NbEbxreR2v4m/L7VYAWvJByJlGS+zWHTuVvi4D5q+qCWpXpPsftIUbur4MDoxx9/xBdffIHo6Gg0bdoU33//PVq3NlyVWLx4Md577z1cu3YNwcHB+Oyzz4RB2/0AZTV7f7tW67K1k/uJExmhJDffwOejE3E3LQsv92gMR5XCGMvjB94aKhRnft99Xm8Vg3Kd8WlZZfJceRJmCxw/iCVleHhtdA6qjpcX7xM/s43uo7VmlNCLy9Behd8zGyTjVw1oVVTiW33dYI37mQG7EKwtLYUc6OMdQoT/wN43hqD95ytQEXQJ9hODtwyy6Tl16O1hQi1IQUFBQaFqYUyZrGDWU+JnzhX7vPYXYlMysftStFClI5vP3cRjc3foVbit5+EINA8FagVoB0UlwLKgAE92aYj5B20ADcGfe0WCyDOa3pMwoHylZxgWn/kPzy37BWnpgJ3V60iK/AJxl0PUt/MNehjT3x6PaW+NQ6irLV6b8S+6j+iKz3ZGiEQ0zexHt6yLh37fikVHtH0dX+gair8OXEByRuleB3aVmJtA7RtaPKN2Re2u+JR7j8/ChQsxZcoUTJs2DUePHhWBUZ8+fRATE6P39nv37sW4cePwxBNP4NixYxg6dKg4Tp8+jaoMhwGf/HtnkaCI1PQszNgwim9fzxdfbT4lAiCnF//EH3vOi2oR8XK2xy8PdUL2T5OQ9v3jen8Xq0bBPm7wdrbH/Ce6443eYWji74F61QqVa8zhTFSCWUERKxaGuBSTjFdUQVGnIP5dJ4V+f5kjZ63on7T7cOHlVKCToQrerTv673/jNvyRq1ftTfd/xaCIvhDyCYqyoaWBhnl8rDreLvhpXIci17sYkd7ecTFay/xVU6ZVQUFBQaFqmbzrBjayMhmvlzswhjSVhIYe+XO7kPV+ccEe9Ppmrbgvr9flckI6UL+uZFdRSvItLDBr53ktFdR7hXt2FlY92xvYfUgSUTLAoPp+eLFbKJaeW4oxS0YhLm8P4KBKyDoW7XL58vtlOHz0IvqPmI7Vy3Zh9a8r8PsjXcR1rNTZPDe7SFD02fA2GNmiDq58NA6ejnYl+nvGt64nPKtmDGll9n2oZFfW7ykFbSwKOM1fjrRp0watWrXCDz/8IH7Oz89HYGAgJk+ejLfeeqvI7ceMGYO0tDSsXr1afVnbtm3RrFkz/PLLLyZ/X3JyMtzc3JCUlARXV9WwfQXD8nazGf/henyq1uW21pbIzs0XnjN/PNoFo1rUFZdTXKHW2/P0PhYrAmnZOYhJyUTLWt6o++4CretreTnj0odj8emG4/hh+xm9rXf3DRygpB+RJqcvAKclWU/I8qE9O0p91Cs3CwUd9JVOeHpNZHVFGfTAAGbrKwNRw91RBI7/HriISX/vKtWf8vWodsKEl6VyilRwXkjXcdwc5CzZg0ZOTg7Wrl0rKss2xWztULi/UN4LClXtvcBWJ84OG/IzlAWYrs4cJ5KnnDPp8r9ViLiTJOZe5F0cVeo+HtIKP24/g/dWaiQIzSUmTgo4uF7ejgWC6wA1fFBZsMrIgEVMPHJZ+TK0Fzh5XvgTVvfzQKL3ZWQk5wFpdkCSEwJ9vXAj7g6QVRjI8PXUp/h24cI/qP/+QoP/D77kfO2/HdYa306bjcuNGhW7GidXAcmSI5cx5tctMFY38nayQ/T/HjarDa6476n7neRixAbl2kqXnZ2NI0eO4O2331ZfZmlpiZ49e2LfPqmKoAsvZ4VJE1aYli/X30ualZUlDs0/Xj4h8qgMQdGvuyOKBEWEQRGhV8HoXzfj7b5N8cHA5rAy8tFo9ckyrZ871PXBniuF1TdrCws8M28nft9zAVWO6Fipvc1cdIMi8Rg6lciEZKlJ2dFBmj9iRSk1Tfpe1w2bLt3k6Bmp7UAPIb5uWD+5D2JT0hD6weISBS/6kKtqMoYelxoVxsaxktKzKsX7/l4j/80P4t+uoI3yXlCoau+F3RejEZ8izcGQDNVJ3s6K7dvSZXEp6dh5/hY6BvvB08FGrEM9v1mHy3EpqO7qgF8ndBRzr+TN3k0woHEAms80PoOjxfnLkiGrJrdj0Oatx3HgWiwqlJvRQIAf8hwcpHZADV7q2hCPtq6HLSevYcXx6/DJ9sPmG7dwOzoBiPbUuu2NW4l8VeWwRlwW0tgXTesHY/6SnVq3Hf3tKoNPR16CGZC+uPQgUKeuCIp8nexwR88ow/v9m+GN3k3g/PLf6sv4f9V8X3o52MJO9f/PyS+A3FlH7SYblZJgWla2+j1QnPcUDWizqU9lAdhaSpLuuu+p+52cYpwDyrViFBUVBX9/f9Ee165dO/Xlb7zxBnbs2IEDBw4UuY+trS3mzp0r2ulkfvrpJ3zwwQe4c6doG9T06dPFdbrMmzcPjnQ+rkCOx2Zh+iF+EM2DJ8GFfXyRkJWHx7aYP5SnD775+UG413jYWSLc2xae1gVYcuwO4GAnBSYO9qY9EzKzpIClpOTno1XqHRzachLI0iiXs0LEStHOg0DUHSmrw+cSVAtoouP3k5bBFJLB5/F9Jy8EulgjIiEbb+4zIAteAmwsgSZetohOy0VUuuF/nL+TFW6lGZYHb1nNFlNbGW5tVFBQUFCo3Dy3Iw5RaXn4uK0HQj0Nt78lZuXhUEwW2vjaw5WLvgbsZhixLsZ8CerVW4qoszbs0AjnAusV67n7OFgiJqNiPHemtnRHSx87ZGfn4re/92HTDo3uER3s7KyQlVW4lvbt3hDrt54rvIGXB9CrY7F+v6H1+bEGzhhS1wl/nE3BymvSa1zbxRrTWrvDgxs/PWyITMf6yAx81MYDztwglJC9tzPx1fEkkVBt5WOL18PdYaujLvwgkJ6ejvHjx1d8xehewGqUZoWJFSO26vXu3bvCW+ncLt8BDhWdKSIrn+2Fk7fuYurKI+rLeofWRJcenTFm9jaDj9mhni/cHGyx9vQNo7+7IoIi0qquH5Y/00sIRbT8YTnemv6XuLz/I/2wNtvEh7s0QRGxtMQh1+rAsOoQk5ZRMVJ1KCFJCoxq+UtS3k4Oqq+ORf2LeJ0OjjZWSFd5FV21q46n+7dCv4ICrLizFns1qnWlIScfiMmxRoIJTyRDQdE3o9rg4LU4jAivjf5hNfGgwWzQpk2b0KtXr0rdMqNQ/ijvBYWq9l5gdn/Aj+vVP2eqKkYfHEwQymgya57vWyS7P17P4yWkZ+Ht5YdRANPrUy1PZ1y/m4pBr4yDb1oKLkTdhVVyKvLr1sKOuOJLQzMosrcEujbwx+HrcWjm74Etx6+igF0bJYX5e12t7LR0uLg4IkVjrzPjcCIeaeCDMyv34MjRwtlbNXwtLSxF6506KKKvYXaOdlBE4hMwqFENrDobZfbTNLQ+W3rWQP/+HdC1Zy4enrMDq05G4lpKLl4/kIK9rw9CbS+XIu8B1iz4Zz+5TTtJru89oA8+Xp/v14m9BWEsdCo+G49uiS3R41V15G4ycyjXwMjb2xtWVlZFKj382c9P/z+Clxfn9nZ2duLQhSfBij4Rdm0QYFCprVdoIJ74R3suZUzLenh92WFsPHdLGIf+MK6DkLce/ssmLXO1/o31q5Jw6JKKNU90CBGKcpxXOXI9Dt1CquO9Ac1FG15GdvmakW48F4XXlh4Sz/31l0Zgx54zWLfpCLYfugA0bYR7BoOeYGlAFXEqqVIGRjyMnXQpcpGahie7huKP4zeQV1CAUH9PHFK1Emw4cwtfjpI8D97qG47BP23QeiiWxzUFK6o52xs1sqWIA83byM3EkvsT/LjjPC58NAZlRVWV96wMn3uFyoHyXlCoKu+Fzg384eXiWMRkVCQ48wvU8yC8naHzMM/ZV+NShJLsLzvPFkmedgzyxYAmtXDwaoyWXQaDIrLqohxEOQC0fihBUCTubWOFOY92xehWUqXp82/+w+YbtyV7DJKRKXWQFAeuz0xyOjtJP/P7w6cw7uVRSLSywY5zN3BHWIdY4K/zMUBQfaBmHSAmHribCDSoJwVA8mt38hxw9pKUMO3dCUhOBfYdBZIKpcT/WPIhHl9SNmJG+6/FYvKi/ajj5YJH29XHtAEtMP6PrbhwJwmLj17HO/3DDb4HRMeNihpujkbfA5qcjk5SB0WETiscxZIbAc15T91PFOfzX66BEdviWrRogS1btghlOVl8gT+/8MILeu/Dljte//LLL6svY8ZHsxWvKnF2+ig0mr5Y67Klx66h/nsLhdymJjR7lcnIycNnG06ID47u7MkSA0oiDIrI73si0LOhPz4f3kZ4GMSmZOD9lYfLPSiSKMBPO87i0slL2PDpRMz5+WWEdXkFd2LvAks3cPBM8hvSNx+kW70pK1gST+SsUb5USeKJOSVN8nNo3lg6Ycqwjc7NBb8dk10OoA6KyKXYJHT73yq80C20yFyQ+BNUQZGTnTXSsnJFUORka400AzNDclBUUujTQM+qQWVYJVLkPRUUFBTuHdyYUpKbSmHyYL8mBaqDyarOwdVx5nYCzkYl4Fx0ggiEzkUnir1CVq7hNX73pTuY9VBnRNwxv72/OFCd9pnOjfB05wZYvGw3xv+yFK4ujpj153rAxRkOQTWRYWXNjSFw7aaUvKymPQOkF7bF7zksrd2DekqXMUDq2ha/HtcM/jQqStxHsAOlZg3p0IVVI/lxeFs5QDp7UQqmEpORH6+99yrNCAAVfnnIdA+pod7bUUn2QkwSrsWnID4102jrY3RyhtiPtK3ra/T3fbr+ON5edtDg9fIrRY/MByEoqnSqdJTrfvTRRzFr1izhXfTNN99g0aJFOH/+PHx9ffHII4+IOaRPPvlE3J7zSF26dMGnn36KAQMGYMGCBZg5c6aQ+m7cuHGVVKWTiUvNxP82nsD3286UeGj/+7HtMXnBXvXPU3o2EdLehuAH8FTU3SJBWLmjr/R94SpsTp3DZz++gilb9fT+UhjBiSeqcuh/zcwEjp0FPN2AEMM90/zNrMilZuXi8PVYdUAzsElNbIuIMhjg1PFyhrezAw5dl4Koqf3DhccEfaXKA01FGQ5W0k/JFJR8Z4ujOfKeuicF+T9CEzyaDVc2qor6lEL5o7wXFKrqe0FfUkoTP1cHkeykias+7G2sEOLrjgZ+7rhxNxV7rxiwpyimx46nkx3e7dccY1rVE4m+Jh8uweXYwtak/41sgxY1qyEiMgb//rgUu7YdK/pAnO1t3VS/8iuV8JiwdNNjKcKKyeLVQJe2JsWZqjvZ4uuxHXAoMh5fbjqp/0Ysm1Do6dpN4cmU39GAVDZFvfR0I5WEng1r4HZShjo4ksx5S7b13vBSf/RWiWzowu38u8sP4ZP1x8XPL3VvjE5BfsJHUvM9xU4VBkWVcS2/71XpZPnt2NhYvP/++8LglbLb69evF0ERiYyMFEp1Mu3btxfCCVOnTsU777wjDF6pSGdOUFTZoa/Qp8PbYErPMDz1z06sOHFdXK4vQ2QIfpguzRiLoKmSTPdRDefk2l7OeK13U+GJM23lYeHQvDUiSn1CZbbhnqEbFPHNlpCI3Rs+R+sW9fH7gSs4k6ajEuJSAgduZpGYeTKFvT3QrrnJm/H/sEbPosMFxlBQxEzZsakjkJiRjWqvSjNVzAbtuhSN8oLPc1LHBlh0+IpZrW4sz9OElqX4Zc/2hp+bo95WDC7K+t6LsobPy4v2ChNiJcukoKCgUHZJU9o+UKFW9izUB9dwOShqFuiF8EAvNPTzQKMaHmjo5y7sOnhuPnkzHk0/+q/Yz4NzKGy50oTGo2/1aYbnuxUqtXIPcvxGHMJnLBU/f7jmKJJjk4DNu6UKiqUFBo3oilWLVV0wrMhwfTp4Ap61auCu6k+0t7JAJn8huzY0Oze0/ugYTRE5iVMRQJNCk1aZ22nZePrf3Zg+qKWeB1I9CLtVqEDr6Y78A1IAIeaQL1yFbb2asPJ0Q4alVZkFRWTzucI5pYnt6mPOvpKrBhvyTfxp+xl8vvGElgIyO1YGhNXE0PDaVbI1/r6tGN1rKnPFSJPVJ69j0I/a8ynmcOPT8QjwcMZHa46K9jhNsn58Ql05yM8vgMtLf6orU/V93ES5tjLwUvdQ/LjlNHL1BE/F5sgpqR1Oz2BmkYCJGSCW8cvi9+pU5VrX8RGB6dbzUaJ/e2zLumJB0TWFKy7WqsxSgc5lLvY2SBA91YUw+KX/0SNt64P34MzZkx0biva+GWuP4sftZ7Xm3CjiwcpYSmaOWJCjk9JxSSMLaIhtUwaia0g5mPQ+QJlhhfJDeS8oVKX3AgOhGm/+Y7Sro2F1d7zWKwxP/7MLuapKw5lpo0RApElqZg6mLNmHfqGB2HHhNr7derrMnqc+j7y5ey9g4tzt0g9UfKXyK+nVERZeHpjepjYW/r0RZ4ODtdZdVrY4P82NuoDbUM73uDoXbaenUt7+Y/xnAqH1JZ/Cpg2l4CYhEbCx1TZy10dmFrz2HkST8d2xPVqVkL0TC1jbAF7u0n5h1RZp9qlrW/2Vq1LCv56VNy9n+yIjEsbgOj2kaS1h5M5K4LkPRhe5zfnoRDSctsjgY9Sr5opG1d3RqLoHBjSpiY5BfmrJ7geF5MpUMVLQD01djcEh/mAfVyx5ppcIcj5edwzv9gsXQRF5vXcY/twbIYYt29fzxS8PddJqp7K0tMDbfZupTd4qJijSTfNIrDl1A65WFuqsUalo0UT7Zw5aerpLGSBm3niileFJ8MAJoG0z0a1nVQCUhbsFq3JyZU7m7wOXxFd3B1tRSTKFZkWPz43BTW5+vvi/spLz2pID+GNvhAiSuDDqBkVEvv/01YVKh1NX6Df545xbSVEvZgoKCgoKpYJtVT0b+GP+octFrvv9kc54vEOhpcStxHR1QpQbZl0WHLqE33adF0dpsbayQK5G+ejzDSfEprq+r7v4mevS1JUa4gS+1QAfL6ktLjkVBZ7u+PFMNOZ+/yL6fV+otmZnZSHEoLyc7PDMv7ulC7lJZzCib7POoKdnB+lxT52XhBKqeUnXeUjPBbl5aOvrjAuZ+UhKzxaiSVrY2yG+fjC238goNGHl89W4XvpdTuUSFBE+o/i0LHEUh5Pvj0RNT2f8/kgXva3w2bl56PTFSqOPwbZHHlTD4+w6q42Tu4ViXKsgONgqYYAuyitSQQR66m8be7JjA9EK16Ohv+gX9Xhlrvq6SzHJOPLucPG9vY01vh7VDkN/3ihmYagEI/P77vOil5VVAw9HW72b6HuD/owEsx6rzahMlAgGRSSguv7e4urSybBlrWrwQR5WX1Mp1pWCJv6eIhPkbGeNy7EpInsjY05Q5GZvg8PvDEODaYtEBYfn9C9GthWtlzK/PtxZZHxeWby/xM+TCykXoytxKVozawzSWYHiifNNmtWZgKV4BQUFBYXSw8z9vEk98O8T3UVyiy1q/b9fL6r6mkERYbKT6wuFlmq4S+fh6/EpYn6EidTJCwvnj4tLNRd7raqVZlBE+NxC3l+ETS/3R8+GAaI1S2sWipv27pJiq0xMaqZWUESy8qQ5GD0vhPEnyFY7dodQYEJuu6Og0pVIIZiwv30L9U1HhdfGkl1nUCCr2JEAI5LUbK9ju19sPEAFvUCd/QPFmwwFbmZib22JxU/3QnZuvkhUT1t1WHzfNMBLiDpRrEmmbR0f7L8ao64CEn1BUUZ2Lkb9ulm0YhriuzHt0djfU4h1cAZ60eHLOH4jHk/8tVMkzo+9OwI+VCFUUKMERhUES5u6PNelEX4c3xFRiWmiX/QdnZMH54mOXI8VPaY8mQ5uWksM4W08exOvLNqHVS/0Faomk/7WdnA2BVuz3u4Xju+3njZrI18aXuzeGEfOFSq+lUrMoTjIUp8que6DZejkfeqWFFyx1eG1Xk2xLeIWPlyjZ/jUAHS5HvbLJhEUcdCVLXjbI6IwsoVK3lRFWIAqS2aCT4e1xlt6FGmSMrKx8rk+eHPpAXHSHdG8Dl7oVji7xwwghUGKyIXqCD6wP1lBQUFBoezgms6kU3W3miiY9ZTe23BzzADqTnI6vt58CgsPXy7VWqY536yvle+viV0RmZCq1Xkgq9/q7RzgOs3Aheqz/L6gQHgN5WVlIz03X7S3s0LWunY1ONraiP3KiZvx5pvQMkDRtNygYAOFFHQEFBazI4LrvabEtykoDMHnzmBLFwZNpVSma1eXcuk11S1sb/Vtpr4uJjlDKMmdvZ2A78a2F22GPq/9La6jAiFbJhkQf735JF7q3kT8nJKZjYE/bMDOi8YFnvo3qSn2m91U7e9sifx9dwQ+3XAcUYnpQiTqQRJhMAclMKpAqFw2Y23hBpoy1/QU4HClpheOJi1nLsMznRvi54c6iQ/Yt2Pao8kHi7H6VCTWnooUH4KHWgcJ4QUysnkd8SHjrEsdb2dMH9hSnJRazFymPrHxQzZjzVHzT04moELN9JWHkapHHvy7kvY8l7YflifPOvb6ZcLLiP9tOikOBg9ORiS69WV9KMHJ+/i6OoiKjq4qERfCFRreE8Zg2X3fm0OEFPiI8DrCa2nNKSkY7f71aiEUwcqRZkXKlGSsIu+poKCgUDlo99kK0UYvt93zXB6TUjxxJQo1aA7q6yMmNQPv9m8uDsK2frbpG+wc4DrNVjUqzG7dKwKNFM3renVCnqcbbkTGoLaTLY4nGU7E2uXm4P0R7bHncnShJ5OHW6GvD2eGOUdsSFWOmBsUaXablCRhqwqKrDIykOegXX3534g2QuyIbWuG5npYsflqtH5LGgdrK7SauUx0BhHacxRH1Y5rPxPSMp5O9ni9T1OhVvz3/otaHS4yK45fw53kDDzZqcEDN4tElMCoAmHFRzMwIvIGtkM9X+y5XCi1+WafpqI3lCw8fEUERnJbGt/0lOxmW10db+3+WCc7G9GGxUMTtuSN/nWzyBacvFn6djLC8+XxqSOQmZuHdvX8hMFsiaGfwI0ooE+XMnluCFKZvd4DDFVcDCHflrNifM3Y0kBZVJKYnqXVTmkOXLDoc9BW9TMN5eT3FU9xVhYWqOGuf8Fg5oiS3LqSsQEPoLyngoKCQmUktLqHCIwotsM1l2tGcdcJBkXsFon4cDR2XIgWXQR/7DkHFnZk2OqliRwUEXYOcF3Qu95R5IhJSNlXiRUXttldvQF4uOJmrgVuGgmKSJa1jfD4KQKfAytSuqpxd5MkOw5zYdscg6HSJEs1ggZbKwtkawRFHG/o37gmXu3d1KR5OrtFqEqYmZMnqoL8v66d3FcIIznYWauDosLHKLrDWPxUT9FWpwu7jzQDo2txKZi165wIishf+y/g1V5hsLOxQlpWDl6Yv0etmufqYIOxrYLwoKEERhVIq9o++GRYay0jrg8GtcDolvVEwNP9q9XCO+efx7thTMt6Ym6IlaGE9Cx0/mKlUAZjACUHNixxa6qd1PZywTjVBlsXfiC3ThmISX/tEGonZUF+WibCPlhcNiatHLjsJQV/9xIPB1sklLKdUJac4IkxPcd8U105kKRrOP8/bJtkhbA4cKZMt9WtTR0f2FpbwtfFAYue6llEyUgXBj+U5FbkPRUUFBQqH3882gUtPl4qugLeWnZAJLPeH9BcyGZrQv89zrMYUkGjkA/nStnWNaFtMOLTMvGfhoF8opH5ZIMdBqyi7NgvGbPK9OooBSAhdTHAzRprjl2RDNY5K1QnUGpFi0sA7G2ldjlWgqr7GH4BGHjpQrW6hkGmTWPjEyTTdwo4UPHOkKLdys3AoB5md6tka8xk+bg44PbnE7QCSWPm6W1q++C/Y4Wvuww7O8wRa6Bfkb6giGx6eYD6+0sxSQh+b6HW9RF3kuAw+Xd1BTAqsbBFki2Uw8PrmOWTeD+hyHVXMOwTdX1pjvpnzd5iDtTRpI2GbnLg89TfO/Vq4NtaWSI7L1/IYP7+cBfUreaC1rV9tD6YzFQcuR4nWq1Y2j18LRatPinextsoPNGxjM4e3RIER9bHzggRgyQPd2S5uRn2NdBgRF1HzHtlNMJmLBMf8LLAzd4WSZkVI1jxSo8m+HBwS4z/fYtQkCkuHLScrJEdkqEUt7ujrRDtuB+pCrK8CvcG5b2g8CC8Fw5cjUHHz1eo5bvLkgltgrD4yFUxj/Jc10L/In0U2eynZYiOD7ucbGRl5QBxd4FR0uac/4GcBauk2zFQGtW/8IFOnJMU58IalEgZbngtD1zZcgDHXT0BH/1eP7h1R2rBq1vT+IOlpKKauzNizcxrsvKm+X8Y1aIu+oYGoHENT7So5S2CSEPm6ZpQdIH7MwZD7ObJkitupSDt+8dFBYpwP0k/Q0MGwrJC7uxHuuCJv3aIdrofx3Uw+R6oCihy3VUIF3tbkdVhJWhMS+1he/YNa86B0Jfmz4ld8U6/cOy4eBs7LkQJr4JqLg74bUInoUiz9/IdrD51Xajc6PLn3gt40ogwg4XqdzDAKgl29rbIytMJijgcycyRqeHFfUeRe/0WYniyrFkDaG1aaGDji32RGHEU9d5bhJsaWY7SUlFBkau9DWYMaYle364V/0dN5J7irvWriwUxQ08lqlXtanqDIqLP0FVBQUFBofLCmR7ODsnn78i7qWLDzI4SFztrMcwvG8XLvNA1FD9sP1Os36M7U0o1vDkTu5plAip3GOy8cFtULeJ5Yasw6Ktz5ERqtNfrVnboTaQ7u6MScJD3FPV9XNGshjsWbTxaJPhZej0BCKqvXRUi3H/YqSpM9CwyIZIgcHYyOygiusHp4iNXxCHz92Nd8eJC/ebpmkwf1AJ9QgPF9/Sg/GbzKf3thAZY8VxvdKjnJ4IfuUpIlWJ5X0A15OszxyM2NQM37qYhKilNJNC5v7gWn4KYlEysPHFNdDExKCLPz98j2i7ZbvegqNfdn+njKgbbm/7YE4Fh4ebNwQT7uoljUkdtKc/vx3ZAy5lLhR8C26dYzmV5lDKe9Nlh76g+mKF4pG0wPhjUEtfvpqDuuwu0rqdM9JReYfhiwwmjVRkRFOlCw7SjZ4AOhVKaWsjVpXbNRZldZIrM7Pnt/Z22DGhlJsjHVcitk9PTRoqTHtUENdV+kjNzsOLEtSJBEdVsBoXVFCqF2y9ICjSdg/2ErxUzS8zwsBrUxN881ToFBQUFhcrP6//tF/PDrEC8068ZwmcsNXr7Ol4uoi1udMu6ImDq1dAfry7eL5TNjKG7YR/xyyYcmzoctbzMq9wwgKrp5WK67Usz6RhrZLaZQRGrO/QtKiiAfb/OyIQlLsQkS6IThipCMjSa9fWW2vJYhaLfYXBtqauFyVqKOOiDlh7cf5Sx4MDDf6pMcE2gKbrFKs87/cORnZeHD1Zrt0jqg++RwU2lPWTEh2Pw8dqjQtRJVqOTYReRr6ujOIBCLycm2MmHq4+olXZlPt94Qhz9GwdizeR+uN8pg2EQhdJCg63nu4UaHIg3F8p492ogSVm+vGgfxv++Fd2+Wo2At+bhkT+3GyzLcgjw5K27uJOSIYb9NOH5gSajj7cPgZeTtoqZOOHI0Dn6pn7ZSJ8ebeDAYUl9aGak5EFIA4p8ppjzaBcxS6OPwWG1UBHQo+jhtkHqoOjc9FHYeTEaT/2zS6/56pTF+4WPlQzFNPa8MRj9GhdexsVu2bO90Ts0EONaB6FbA3+E16ym1+dAQUFBQaFqwhkiwupD209XmLz91fgUBLz1r6gysU1/wA/rTQZFmlDkiRtyzjGzM6E4sGJkktuqpB+rNs1DtdZ6qusR+gs+7G2LXtbZIrAZ1qkxrnw2QcziyPYWMvrCF3uGeTSDjYwq3KOcU81RuzhJ80yG9hhlpFrb2MQcryH0zXRREZBKwqZg657u/ZY/10d4GJkDfTMpzPCajliEJhQEyy1hR1FVQqkY3Uewh3VjMZXgZFO3lSeu4+DVGCx8UrsFj743vb5Zqz+octUwqeWw5NptQO1AoEmIdOJjNcjCAjEZOfqHJQ1RwmxNZEKaeL59v1tX5Lr/nukFm+dm416z9NnemDinMFvU8pNlaiM3O2sr0X+sWSGiid7DbYKw/owkT9opyE/IZTYL9Mafj3aBn6sj+oQGPJASmgoKCgoPEk0DPNUtWcWZNxk5q3AQn90j5gY5P+84K7oZ2NZNRbXioOun42pjJZTOYjU3+23DgcQUqY1Op02P1RImNx9pV1+sbx98Oh+bth2Ht5crqrs7Y/frg1Hnnfla1S194U0mwyVKeFPIwd4ecLDT/l3GRB3KiNNRCWoBrBBfN2w4e9Os++mTQOd4w9+PdxPmusZ4uK2qjbCE+L3+j0HJ97TvH8ceVQvng5CAvf//wgcECitwALK4bJ8yCCfeG4HQ6u5iU97ly9Va1w/6cYPhEzLLzprwZNQqrLCHlycjbuBNbeI37wY4kHknDqWBTtJcBNh3rYumCZ6LnY1Qcdn88gAhVcoPPWd3yoMeX69RZ/0IgyKeXL4e1Q63PntInOw/H95G6z4P/bFN/f1nGtdNbB+Cvo0DlaBIQUFB4QGgUXXjlYfuITUQ/9UjmD+pu97rf3u4k+g4YLXF0CrMy2u4OaCBn5to5yaPtQ+Bs71NiQKjUSpLh+ScPEzq3EjbL48S22xxMzC7RPUzOekXUENqDY+8Ka3dbOubXN9bapOjAp1u1Yc/xydKCVl/X2nGyMmh8Hfp3p7mr6welTEUYqCX5Jcj24gWeHOCIv7FgUbM0+v7umPTyxpCFXp4TCMBW1z2Xo7WCop8VG11MlGJaejVKEDMKD0IKBWj+wRKKxtTGjEEN+nsOeV8Co1F9Q31G0Q34JEzMQyYGEzJA4/GYMmbTtadWhfevqAA7nY2SDRgkFrby1mcuD9Zf1zo/st8MaItftx+Ru3ZI8PWgIZ+bniqUwME+7jhhW6hWupsLCEzoJLnd8qTna8NQkdVFUiGZms13B0xQSMgkqn59jzxvH8Y17Hcn5uCgoKCQuWBbdVjW9UTth3yMLwm61/qLyoKtPN45t/dSNKxmnj6n91Izcw1adz9/diOaFfPFz2/XiPmjA2J+BiCCrrcP5DFGnLf3249hXQ9Ru/mVEzq1paChMtXpXU5LS0Ti2avAtiBEtCg6P7j8vVCv0Iaul+6BqRnApk8sqWAiRWr2gHFN38tBg62Vvj34CVxmIO55uk9GwaIJK7T5D/0Xs89hCmoSrf8+DURxHJ0o1mAF7yc7cSohfQ7/LH6+b6i0kfhj0l/78DxG/GlHvOoaiiB0X0C54SKC0u8MgsOX9Z7Gw721/d1w+24ZFw8dQUI0Mho6PsQc9jx5Dmgs3YVxCCU5A4uahqqGxS5O9iK58HKz7X4VLg52GLJkz0w9JeNyC+wEJkxKun0/maN+j4cHuWJlmpuHk72mDVB2+SWREQnosE0nRI1M0vsTWY2idmtMuKV7o3RKVh/ZeqhNsF4a9nBIsEtJdqXHb+mBEYKCgoKD+D88fxJPcQwfNiHS8Rl9DWUk2i0YfB3d8LgHzcUCYrk9rRXFu/DlyPbmmXczTUzNSsHnrrzxGb453GNTtR5DsUJitjJ0aq2tzpZOfPLxeL7S1du48PP5mPV+kOIblAf8PEybeJ+Kxpo1kio3QpVunbhQA1fkzNEuoFjSUjJ1J/QNURxzNPtrC3FvJFmB8zgprWw4rk+em/P1/Hs7QQRDC07dg1HIg135TBhy8exs5FeIybM/3i0Kx5ElMDoPuFijPkePjQeXfhkT/RuFFDEgEyXfx7vLh772Xm7paBIltE0BGW5NU9QJUHP438+og2e7NQQ01YeFiZ2FJf4ZXwHfNfJC+06dkKzmlK1ir24XER6NPA3q+WMSnt6fz/V8UrgpWCMr7eeFgcrQH0aBQrPpqYBXvBwsjMa3A6qIOEIBQUFBYWK593lB9XKY+NbB6kDI3YUzHqoI9acjjSpbnfxozG4NnOcUeNutrJ5mmnmeTQyDr/sOCs6Mnik0LNIBdc2rr7mmJPK8P59vl0nBAPOnbyMzbeTpQrP+cuYtvII0DBYf2Cjb09CFToq2rG1LjQYCNRWZjNE2TtCFeLjbI+uIdWx+tQNMcdFwgO9cPDtobA2U/ThxQV71UERA+Qu9aujmrN22xsrPZwpW3b8KpYfv661N+TLRDnvfqGBosp37EYcTty8K8QiVr3QV+139KCjvAr3iejC9FVHzMo2pH73mNkfQtLzmzV4tWeTwgvMGfpnZqYMCQvwxOMdQtQ6/2nZufhy00k8M28PJjVywSSNPuxCGUrz6NekJn7fGyH9kJsLWJf/R+LXXefFoQkrYKxs6WN8qyCD2aCTN+8itIbHAzEQqaCgoPCgwaF3mn0zh8mqTPvPtNXpnv53t8nH4NLCdqndbwxBVx355pLy/dbTRczmm9f0wtHIeNzVCYi44ZaDAXcHGySyHc6A6lno9EWw4R8brjIVlVvfDKG5J9EMkpo0kIQe+NVM2GrvaGOFEzpy1WVBTGomFh0pbDMkVAN+/K+domWSAa8x7qZl4qcdZ9U/T/p7J/5+rBtGtqiL7Nw8bD0fheUnrmHF8WtiXlyGSr29GgZgaLPaQp33QfEiKg1KYHSfiC6Yk+mY90QPg0HRmhf6CmlP2X159Qt98ebSA/h9TwS+3HwKFYFc1v5qZDt1VotVoC9GtBFtA7N3n8fssyl46mY8WtXRP7RoCpahO7pYY3dcunkzUeWEvjYImbZ19avo8O+n7HcTf0/8+0R3kQH0YqZOUaxTUFBQqPJExqdg/Owt6uDmt93aCbXiwKCjLHm4bbA6MOLGnuINHw1uhakrDuG7bafFZezeYOueHBQRQ0GRDE1GS4zu2texZbHu3rKWt/CBvFcwGfr3/oviuBqXLCS2DUE5c1qy0Li1Qz1fIepAQ1224rECqJlYpaogRaiGhdcRMt4uVOhTMBslMHpARBc+GNTCaA8rS7Iytbyc4eVsj98e7iwCo7LG09EWd/Xo9evCj/mQprXQo6HkzSTDjf9nw1uLwICkq+SvSwIHV3d8PhEHj1/G9sh4vL3upNb1TFwZKOTcM1advI4RzesWufzjdcfEV83+c19XB6F0F+RjwMBOQUFBQaHSM3X5IXyy/ph6/WHSa0iz2ugcXB1Nanhi1q6z2HL+Fi7HSqIH9xIGOjSG79MoQGzQaS9hb2OFHRdv45sx7TFzWGt1lciQWIAmLvY26BzkhzWnJZuKEsG5YE0LEWIsSainBe9eBkWfDmstOkXEmAL/3ysOGw2M3B3tcOTd4eqEOI17v916Wr3/4zw43x/DmtUWpq5si1QoGUpg9ICILrBEbAwnu0JpTs7nyAEIXbQ/XX/c8B0Tkgy7SBvAnKCIsAIybWALvdf9p1K+qelsLXpqeaJgVYlfH/p9KxYevqIO8thPS/8flpCDfVzFSUOzr9rS0hJtmweL462hbcVlyRnZ4newP5c+EjcTUpGZWzHGZhRg0Mcfj3QRcuCaUHJTMzunoKCgoFD1YLAhB0XM/i99phesLC3w5N87tbzxOM9ja2UpVOvuVQ7v2X934a/9F9FKw3iUA/58LosOX5aCpRf74fn5e8x6PBrLMyiysgDySvJH5OQWr+PD1Kx0GQsyEHdHW7zXvzmc7Kzh4Wgn5sW4x/rv2FVsPncL0w3sdfTB/QsD0Cc6NhDrfYC7k9gvmZoZVzAPJTCq4vADZg4ULKCb9dOdGxqUhKQize5L0XiiQ2FP7pSeTfDX3ghE6ZEKlZ6AW7kGfUN/3ohdrw/Grou3xcApJSk/GdoakxdIJ9xONewx4Mf18HJxFJKkA8NqioFDmevxqfhl5zmtx2Ww9EznRniiQwiq6ej1y7g62GJC63qwtrYS2S/O88zddwGPzd2Be42DhrS4Jt0b+OPjIa3w7opD6ssmtquPsAADqj068G9itulB8SZQUFBQqCowQSmriN1OTEOtt+dpzY6Qt/s2w8s9moh125Act/yzt7N5ewVziLybKr4e0lBHI091aoi2ny4X34fPWFrsxy1RUEQMrJFqOD/MMQI5GDIQFGl2iHC2mcEK5aq5fzIGA0LduSpdQqt7CKsQVnJy8/LF/4wekbLBu25njDmwjV6h7FECoyrOgWvmOVqfj04U2RtmnJ7u3EjvbZoFeouDsPLCDNDHa44aDorKGZbmeQKmFOm4VvXEZVGJ6XhUI1vWqYYdll5Jw62ENLEwUJL0/AejxXzUIpVjuC4Mlt5edlAc5PdHOgtfJAYJHGjkgGvrGu5494eVcKnmgTjHitXwf3/lIXECjE3NFP+XH7efxbX4FKyb3E94XGgGRn/uvYB2dX1F/7emV5MurLKN+W0zlhy9iuXP9hYleAUFBQWFykGX+n74YuMJEdgcvRFf5PqrH49DbW9JOZVt8obkuKk+Rn9CmouXFf880R0Bb/5b5HJdYYhKg5miSgyKuNaystOiljeuxqWIShzFIHKM9NTLQRGrNoa6eDjj5fbyHIQHck4osYhi35IjV5CYniVaJZmYVag4LAqYNr6PSE5OhpubG5KSkuDq6or7DW6M311+CGeiEsRJj7KMx/ScNDV5vksj/KhSM9nyygBRaTDF8/N2aymglAUs92cbaAszhFxap6TlY3N24MztBK3rJ4e54rczycjMk7JjfE2uzhynrooxM3PwWgzWnb4hlH1O3DT+WlUlpvYPF//L7l+t1nv9quf7YKABqe/Xl+zH/zZJ81SsOr3TPxxVmZycHKxduxb9+/eHjU3xHNsV7i+U94LC/fJemHfwIh76vajxd/QXE/Sqr3J/oCvH3f/79dh49qa6/foxlcJrcdly7hY+23Acp6MSiuWbSDXcrApqQ7/XMPFsSF2W80SaIktUi8s28Lrc/PQh+Hs8WKaqlSk2UCpGVUyW+/kFuxGdVLwKjhwUUTOf8zXm4O1cPIM3c6SuixsU9W/kj43nbonvX3/3D9w5eAZwdQdaFsqHf38yWf09T0c3EtLEwiBLklLGun09P7SuVQ1dXG3w1fxIrLMogxMOzV/t7UrUs1xWfL7xhNG2OVaD+jeuWaTveNbOs+qgiCgzSQoKCgqVj7/3X9L6mcHO3jeGGLSkYEJQV46bsz6Wz/wmvn/8rx1CiZXiSsXh6PFLGPf9esRaF1Yy6ni7CE/Es7cTjd63KgdF1pYWyC2G+pK+oKhhdXecu50oBLD6hAbi8LVYMRLw+YYTYhZLHxTd+GFcx1I9d4WSo5ifVKGgiK1ixQ2KNPloSCu9Us7sdb2h6hmW+WBwS5yeNhJtXKwl52h9aBYby8H/Z+2es8jlr8jIxI7VexAXkwhcugZs22f0fr/vOa/ugY64eBPDH5qJGiGPotfQ97Buqx4hCX1F03z9J3MvWyt42FkjhN5Jmvcr56DIR2cWKsTXTWSb2A5nCM5EWT37GzI0xBg2nLmhHohldoucv5OImApql1RQUFBQ0A9nfDXpFOQnWtzXnY4U8s7m8IKOAIL3q3+J+VJzuHotGg9N+h9adHkFsbdU80SRUcCStQi6fAWbX+hb/v+6CmxqKk5QZAgGRYSzSg383DGhbTBuJaYZDIqIqyKvXaEoFaP7zKvIGAN/WI+vRrXF5G6NRSUlPjVTlNjH/75VXJ//y5PqwCkrJw97Lt3B3NeGIfNuElZcjsO01ToDiLwtA4i4BGn4sayFGFSPV83OCu98MgkX7yThp28WA0kpRis0/xy4JNycT7w3An2HT8O1yMI5LOcAH2iHgIDP1l2IiU0CPN2B3p2kC2WBCv59GmIV8dl54msC+7X1/X75eencr7RQKOITDXXAba8Owow1R81qd/R4ZQ4GN62N13qFoe9364pkt6i+xyobA2FDYhQKCgoKCveWXo0CMGNIK0xfdVhs0jk3qzk7+96A5vhwcEu9ZqDfbT0tBBwO6ggkkE/WHccvO8+KWRauLQ+3rV+kS+TmrTg06/QSkpOltrnAam4QYtpUf8vLx6Z1+xEYdgJe3dsi3kkS8Olazxfby9gv6V53YpQX/F/JNK7hKRT9KJ5FUYZGNTzUX+lBpHgRVixKYHQfeRWREc3riArBWgN+AFMW7xfD+9Wc7bH/qrZwA8vtsx7qhCc7NcD437dg6bFrqOPlgpd7NhF9yR+tPVY0g8LNPwOVerXKTNZSl1hLG6QG+kPtC80AxsTJkt4+fN2emzQAb3zwF9C4PlDdB6nu2r2lFgeOw16W+UxMBi5eA2r4AE6qVgVDwc3Zi0B8IhCfAAztre2l4OZiflBkZgueZlBEfFzs8cO4DvBzc8T7Kw+bbGWg5PiqE9cM3oYy368t2Y+5j3Uz73krKCgoKJQ5J2/GC/nq8Jrewgvo3f7hmNQxBBvO3ETEnURE3EnCjgu3hajClVipasQK0B97IkSLFqsN42ZvEW3lH6w+KlredPl6yylxfySkiT3BW8sOCgGin8d3VG/KN28/LoKienWq45m3JuD1bSpzWV9voFs7YO8R5KVlIH7VNsDfFz16t8bWy9EqLTwFXepWK9x78H978O1hyotUSVECoypAcQYdZX8fY1yOTRaHPp7+d5c4ZK7Gp+ClhXvFYZBgSdGsPAve77FadU7Vbx11B8jKEv2/PSySsC3XGdn2RSsdP24/g5YN6gGj+mtfcTcROBXByVwUxCUgUr6cVZ6ERKBuzaJPIDUdcC7s6/bMzUZ1F1v4hoVjz7mLyGoYLF1hW8wB3xu3gZrmzX1pIotLMAv1554I8X8yRafg6kI9R5aA1YW+FI+2q2+WOIeCgoKCgvkwiGk5c5mwzZBh4OPv7oT/jWyDzJw8kdCbsVYy7pbnglvX8UGb2j6iolPH2xVDmtYW3R7bL9xGWnYudl6IQpcv9QvwECqr6RJ39grgV029XrEte9bOc3h/QHPUcJdmcE+flWwv+vZsjm+P6yRafbyAAd2B6FggL0/M225Jyin++veAsG3KwIp+CgrFQAmMqgAcuCwvFj/VE6N+NTynYhY8MdIjoLwJqQtEXBHCBxZrtyE3KwcbmJ9ytIdXh+b4ZspwRCam4+N1x4SgwH9HtdsORIVnx34gO6foY/NE37194c93YqXZqpqqICEnB1i7DegvVVR6PTYIA8Jqikpe/bupWH0qUqrqqZSPnu3cENsu3BYy6UYpQVDEat/3W09jTMt6wrTWnKCIsPrHFkN9sHyfnJmDZ/7djZPvjzAq9a2goKCgUDwycnK1giLCdYom4kN+2qj3PlSc5cGgRRNWd3ZcvC3mjXgUm8DqejsVQt6ZB19XByx/rg8WLJUSpB41/XDzjB5bEK4RfJwHnGAfNwwKqykyw0dvxImAdUjTWqJS17NhALrWrw4nOyVgrEoo4gtVAEpuUoa6PArU8w5ewnNdGuq97uUejc17kHsRFLHljFWSzm0Afz8UBNYAmoQArZuioGUY4p1d8PDcHTh4ORrfdg6CfVQ08q5Jw43WvO/uw8DWvfqDIqIZFJ06D2zbDxw5DWRLwhM96vnAMTSI+t/i54VHruCRP7fjneWHhIFsYaujVDebf/gyDr09DK/2ChM/O9lao5532cjH08/oxYV70fjDxcLjyFx+3X1etMzpex+F+LmLAJyLNPvPFRQUFBTKjtAanrj40Ri82L0x3B1N+9QMbVYbS57uidd7h6FL/epoWauamEshf+6NKKJJUMPNESPCa6OxtzTv42xrZF020L6dmleAywnpaPHCL7jVJBQO7ZsjoLG6id0o/b3sgQ07USfx/rHEMAeumQwmH24XrDbgndg+BF+Pbo8BTWoqQVEVRPExqgREJ6ULTXtPJ3uTqnTQ42xNOMR3VycbVVpoqjr/0GVUCsyVw+a80+Y9UiYrqJY0j5SeAaw0URUb3BNwVLXjLd0gBUStwsTsFLHMy0O+KgAM8/cQcqkct/JnT7eVBfJS0xFx4iK2Lt8lBVmuzvh+bHthpstWyJqezkhIy8KoXzdhy/koVAS6Pgoyfq4O+Gl8RyHGwOqhjZUlTr4/UijoVBWqul+JQtmhvBcUKvt74dC1GCw6fAU/7zxr0Hj1xqfjEeAhBTmaxtxUGpXhhnxy11A82r4+Du46ieenzUV082b0qQAysgAHDUsJU+trskrUyF1bRCnQw0nMK5nTLlbb3gpeni7ItbSC55S5eFC5NnMcankVne1SqDgUH6Mqwpmou/h0/XERfDDbc23m+CKeMzLGnK2/Gd1eOGTriimUltUnI/Fo22AhK+nlZIczJvwKyhXNoCglVZr5oWgC537ok+CoCiopfDBCR0JUVeUxCoMpBkdkeJ8iV4ugKDEZIekpmNKlHk6cvorjJ69i1akrSEjU0bm7cFV4LX279TQGNK6J3/dEiAD2xK34CguK6DeRrBMU2VlbiaogVY+oUsgBXma41pyKxNP/7ML2VwfqVcfRZyIozzwpKCgoKBiGiq+dvliFLBPrUuBb81DbywVzJ3ZF5/pSyxr3B6w6Bb+3UPw8bWALPNulEZau3IuRj3wiuidEUCRO+mYGRYTneTftjgY/5AEuziaDItlYXXMdoKH6g4SdlSVy8vPRNMALb/cLV4KiKo4ySFAB0OBr5rpjWKbRG8yTD7XtAz21M0S6wdGQZrX0bkpZdl94+LIotSemZwkRhk83nCjV80zJysHc/RfF93KJuMLJzkZwneq4qDlQWmDAZ0km1US2y8VZasszdn+KNVy/hQiWyXdqy5ZbW1uhUYNANGtSF9t3n0LktRtAWANciknGiFkbceyG/rmee0lGTtFFODs3T5jMta7tI95bDIJ+HNcB2yKisPPibczZe6GISzorl/qC829HtxePoaCgoKBgGDsbK7Fub1aZlxvjWnwKXl60F0feHa5OUgX5uCHl28ew98odMb+Sl5eHF9/8FQXd2kuzsmVENKyw/7neeHPZQaGCpw85bcbkLPchj8/djj/3XjDr8c1SsWUHyN0k6e9yqrxWEll5+Zg5tJUIiuTK3odrjogZ418e6gR3x8IglZ0j7N4wlARXqHiUwOgeQz8CSmjqg5vUb8e0N/qB0edsTXifca0Le4GTMnLMDozq+7jiQoxhszhWi+LTssScDFVwKhRbW+2giMhy24ao4Stl0fRl6DzdgB4dpDkpthFcvyVN3tUMkK4/cxE4HaE2mePa1Kl9KMLD6qFZkzoiGGoYEgg7jeHKNdtP4IMdETgUlVgpgiJDyIvS5AV7RMDN9xbL//TFoHT3a//tR7/GATgfnSQCcfZST1t1pMjj3EpIE22erGgqwZGCgoKCcWY/3BmNP1iC1KwcsabozgvR2HVQWC0M/HG9EF9Yf+YG+jUuVEt1treBvbUVnpu3GyF2lrh1JwHoXHZBkczluBRsfnkA+n23DnsvR4ugLiE9W6uV78dxHcV5PycvX1h8mAPvd8dUspUvCjtAqHRnX4zqVwWx5OhVbI2IEmvlmagE9eXsEon+YoJYX7ecu4W+360VXpJfjW6nTjaejrqL5jW9xRwZLTgUKhYlMLqHnI1KMBgUkR+2n0FcWibmPNpVnIBKQ9eQ6qjmYo/YlEyhkLLyxHX1RrhdXR/suyK13bWp44PH2tXHs/N2683eUE6UQRExFhTZWFkgJ6/iHKpL5J7N+LN3Z+n7mHjgyCkpQ0WOnxeS4DSy0+Tutflwdy9a1WMb2qqT1zF793nRRlAWjtn3iqikdFg/O1vIwVKu9YWujfD3/os4cTMedd9doLfapEmB6qVkZlMOsBQUFBQU9MMEFGdQH/9rh96lafelaHw5qh2e6dwIX246iY/XHkPf0EB11YgbbAZNlPgW9OxY6pfaytJCbfrN3Oymlwegc3B10X2y+bxU3Woa6KXeO5DEjGzcSEgVFZJVJ67rnWGV4WPKy6IcFHk42moFWlrIbdwOhmevKxofFweRMKYy7FEDNhj0i+L6yrndnt+sUftIyYHRo3O2iwBZhvLtY1vVE2sxDXgV7j3KDuYezhO1mLm06D9AZ4ZjwaHL6Pf9OqMnGHPg5jTmf4+gYNZTuBiTrBX0yMo25ODVGDxjICi6PGOMGCLkHIop3YMyC4r0rRKlhf5E9BlydRaKdkJQoWtbYMygwtvsP1YYFBEKNuTlo3+vFuqLPD2c9QZFnLkRVZefNmLVyUgRFDUL9BIZvapEnErtLuCteUJUgpgKimQKVO2gbPNUUFBQUDAOlcvOTBslLDModERfvpHN66B7SA0hxy1XjijMtOfyHby8aJ8IQHZciMIgVVDEmRZ7K0vAQ1swoSTQYuKrkW3F9wxgGLzQKHbs7C3q22h64FHwic+Bz6v95ysw4Y+tRh9fX67QYFBURYhNzTDbLiPswyXq7+01Et+Na3iov+c+iyMVDIYbTl8kqkkK9x6lYlSOyEPqv+w8i4WHNfx0VCcV2dNAt6zMGQ/3l+cg7stH4OVc+myJrpnriuPX0a1+dSGmQPlmqpJxyFNTvCH7p0kiw0PlnJ0Xo8slXtHLpt2SLxIV5YLrSEENhRYY1JQEPnE+RjVPoHF9w7djIKTDqoXvoV+vFugxaCp27DmNQf1aF71bdi7Gz96CFSckMzwGkZM6NICns70QO2gwbZHBX9m/cSDWVsIhVQZIJX1exTEjVlBQUHiQaVjdQxwjW9TFvEk9ilxPs1XOb7Kj47utp4VJ7LYLUSJhxQrS8md7Iyk9E3VHfoy0WoGlss5wc7TDujOF5/3xv28tksSlEayMo60VRjZvgD/2RuBAaYSfuD6zY4OGs1WMku6L6HFEGOhyjoxserk/2tbxxdaIW3h1yX4xozzy1004+d5INPb3LMunrWACJTAqJ/QNqWsiB0X5BQWY9VAnIcZw8Fqs1m28X/1LDF2y97Q07HxtENp8ulz98/W7qeKQMxRHp44QQg4xyRnYd+UO+jepKcriLyzYc+83uj07SH5FMvy+pEGR/AdaWwO+Rl7DxGSpl1mzYkTX8bB6sLKywva1nyAuPglentqqPbEpGRj84wYRUFLh7e/Hu2FUi7r4dec5PP3vLjQP9MK3Y9rhpYX79P5as4MPLhz3qD1t7eS+ou2S3kyVzYxYQUFB4UHjmS6N4GRnjYlztgsjcdKzoT+WPtNLtNz7uDmhcUE2DjCByHWshHy27hiquzsZvJ57FU2iEtPx2+7zsLWyRJ4xGYXYu0B8AtCgnv7rubZVwaDIXJr4e+LULe1Z44xsqRPj5K14IWzF/2+noOri/zm4aW30bhQglAsPX48VLXpKYHRvUVrpygHZc8hQUKTJx0NaYUiz2tj52mBseKl/keuP39Dft1ocWtfxEf2tr/UKw7kPRuOVHk3U14UHeKs3sz6uDuK5MCji87+nQVFODpxoSHcv51NuqJR23F2BhkFF4qmAhhNh4TYI85fsgLeXm5Z0NefFfF77WwRFrP4x28OgSDPoPXojHv8cuIS+jVRCDoTVsOJy8961p3HAl33lxcVC5XdBtSUFBQUFhbKBVYUTN++KeV/SNMATK57rAwfVz8SzX+fiBUUMctLS4WdRWAHKLZDaoc1+iORUYNUWZB8wIfLEbo1SBGxVHd2giLBKZPH0rwifIY1X0MtKc67c3sZa3WLHGS6Fe4tSMSqH9jlWisytsL674hBy8/Px/sAWIkugCU1BWVotLQxw2Cvs6WgnSvYzh7US5VqebI/eiBMf0LTvHxcn3uzcXFHtuOfSATY2SFNlUfRhUVCAAnMMXosDTWAJT/BnLwK1A4C24YUnfVFtssLjWy/g+8uJok1ueHgdcaIK/WCx+mEYCLEVkka9VJQZGFYLby07KK47dC1WO/tQglYHx7oBSNdoYShPKCJR3KqPrmSrgoKCgkLZ+L5pGrqK862FhfCl0yQyNat4QdHiNWLoZ+uJ2fj14BXMP3gJd1KKYclBhdeT50Rwhbt6/A2jYgBvD0lRLicHcHHSTg7qroN8Tjzu8/WDLXRXdVV1VeMUusjWLcUJVhXKBiUwKmN4AjSnUvTf072E79CiI1ew/sxNERgRzvrIPaeRd1MR9tESPN81FNMHtoCHU8kkK19csEctH9n20+UiAOJsjCZOk//A0Ka1sevibcSrKh4VBk+QOkFQQVwC4OVe9ifOy9eBo2ekk3W75oWXa7TvZeYViBZDHvTs0TVKJaN+3Sy+Vnd1RGp2ocIMKW1IU1ZBEYd7KQdKYQhDrXIc/OTCzb+TMtzmBMiyyXBJpLoVs1gFBYX7ldL6vq1Vtc5pcvxGPPZfiUG7eoVJUyrZtvpkmVlrKTIyRVBkZWWJC4kZ+Gv/BdxVKc+aDYWFOrYCMrP022XU8Cn83sZGOuTnoy85qC/puXWv9NgdWqKqk//LkyKg3XDmBvp+t07vflBTkfDrzSfV0uezdp4T0u39QgOFLQvXzM/WnxDy6O8PbK7XhF2hdNzf4XkFYG77GV2vx6t8h5ill5nUUVKjYVsSZbYpn8mhy+D3FuCXHWdFWd1ceNsvNpwQ+vpWKsUbohsUySw/ca3igyKi74POcnxJgyLN3uiUVGmWaOdBYO024NDJwva2PYel643ABS45Uzvw0eR2cjpSjFxvDutf7CeqhWUJ2zRzfn4S+94aKuS5dU/aMjZWliKbyYWb6P4nLFTHB4NaYN4T3bFtykBcnTmuREERNw2135mPbl+tFoO+/MqfFSUeBQWF+7WlXvZ9M+c8N+CH9Xov/3BNoe0H9w8c0jd7LZWFhlo3xdCfNxY/KGJgxfbu3FzJX8jExpydKiPCa8Pf3tr4bXWvS0nTrjTdQ/WEbiE18OHgQkXa0nJCNRLRJzRQKAVn/vAEnurUQH19xy9WIj41Ez/vOItOX6ws4gc18If1aPzhYvy++zzGz94qOo2mrz6Cv/ZfLLPnqFCIUjEqY8xtQ+LtkjOlyoPmR3PqisPq8umayf2E9POaU5HCS4jKNDx3PN25kfr2FEz4cfsZxKZmIi0rR3gNZefmCS18ynRvUfkPvNG7KWYOa40lR66oqxuVieYBnjh604QZanQM4OkuTF6LhcYJ1+JKJJpY5eNk1J2it+Pw6ppt4ts6LRpg+HMj8N/RK7gWn2pWNebg28PQ//t1YpiypPBxhv60AZll2DoX5OOq1aap6WLOoU9mnM5OHyVaAn1dpfcvAx0aturLdpa0OqRv06C7LClmsQoKCvdzS725vm8R0Xpa1FTQ8JXiP9VcHIS1x3WdNWpEeB1hy3HwWkyRTbaFgz0sQoORV0u7dV+Xh1oH4d+Dl/QHRrsPAU4OwKCeRh8DJ85hdLtg/PR0L7HOfP7rWrzJNvMAP4OJzgB3J9z8b6P0e1xVs0nZ2SKLn1/ctd8UBgI1KgPzKCvoX7nsuT7qnzlPNGtCZ9Eh9M7yQ+Kypceuws666GtCuXZaf5y7nYhJf+/Uuu71//ZjUFhNeDpVXq+nqogSGJUxptqQ+DHk9bzd2lOSKllGTq7I+pzXORFq6t6TGu6OCPPXdreesy9CK3ukC3uRJaO4huLn8JrecHOwLbVPEnm3XzPM3h1RvN5kPUzytEbWzUgc/W830KIxUMtf/wnLT6M8X0IsGgbh5NINBq//6atn0bFtI9QP8kdydq7wEzCH3x7uLF7b0la1GQiX1Bw29buJ8H9zXpH/LWU/5UVUDsrl21BNj1AyVhcGP1y4S9MfX16bBgUFBYWq2lKv6fvWNaSG3m6PJ/7aIb7v0ygA343tgJD3F2rdxu/1f7DuxX5o4OeudfmSp3uiXV1fvLSQSa2iHSwFTo4oaFJYrdAHH9OgairnhkhaBhAZBdQs+vzV2Fjjlz/W4amnBuJUYib25VrB2t8XuUbO653rV0erKcPxytuzpdlfzvtevo78rGxJKKlvF9xLmKzkmkwT+1a1fLD3ip6kqgmWn7gOrylzMaxZbXw9uh1c7G3Fnu/tfuGiOrX53C0MC68jTNbpYaXbHsf1mubx32w5hcT0bPz7RDe8vewQzt5OEIHVLw91KsO/WEEJjMoYuQ2J2XC+tQuMDKnX96XSGXDy5l3xhpe9cPTRvp4vfh7fEWEBhYFRZk6uOsMv88PYDrC2ssCNu2lIy87BU50aqje97F2lMZzmxpmVhGYBXvh8owllGT3MGNpaHJxLem3JfhHYeTjY4vr1aNF7/NKoTvh2h2nZ59l32drnDAztBTsUIKuMematLCyQp1Mqz98tVeQMYW9niyahtXE0MlbIZZoLXz++vvHFGYItY77dckb8bxlAT+0fjufm7VFfV/2Nf4Rx4JiW9eClMatWzYRPFt+n+hbuitw0KCgoKNwPLfWGbscuEJq6OtvZYNaETpi26ohe+ewhP23Ahhe11Ww5k/LUP7uK3yKngZykZdXp7b7NMPyXTcLHSEh2OzsBIXWBiCtSO52OuEDbOj7YtHwn4q7eRpi/JyyeH4tW320QYwECfTNGx8/izF+volFIoPqipx/rC3t7W0TdvotDRy/g4JGL2LLjBA7+tx4Y0RfliZOtNT4c1AKvLz0ogiKumUwoawZFuvs7U/D/8fueCNH+xjlvim79/Vg3ERC1rVs4L6ZvZoi/+9VeYUIAivuMiXN2qGfR+f/mOv5Ov3AtpUKFkqO8iuWAuW1IIX7u+N+ItsLMa8ri/VqPwdY3zWBl7+U7aPrRf/hqVFu80jMMh67FYNCPG7SMYcnolnXVlQFdft11TmQbmA2iMdyn649jzr4L2Hj2ZrH/xpufPqT+niax/DBz9kbM37D87eqCff9tQ/V6dXA7ycyKkpUVyjKs0AyK2tSuhrC7sfiNRnL6cHYUVapTZ6/h9SX78T8zK0UyLHPzMPxk9CjxlBGcHaMD+furpKCPctvuDoXBD+fLuChtOndLHJrl+m/HSLNEVWnToKCgoHC/tNTrwlb4t5dLqqafDW+NWl4u2H6hsK2Licxtrw5Ey5nLhHn7w39K7d8yPMfrg35D2Xnmt2hzf855V7ZpZf34hHheh6/HqboHBuLCvpN4/o3fkJucik6tQvDXtPH4bOa/WPP1RsTdlGZqagxuj/Xxqj1QTq6oIOnDOz4e9evVwOUrt1Gnti8sLS3hoFrD/Gt4wb9GOwwd2A7rNh1G/7Ez9D9hVq+okseqEgM2JyfA0w3FIbS6B85FJ4iRhFf/O6C+nKMMPDQpqXIvRRPk5LTuY5qCiUrOMMtBkcyMtcfEsfeNIVqiHAolQwmMyglz25Be6dkEr/93oIh52phW9RCdnF5kuI4BFPuJ6TbNDwhlHns28BcGYCG+bgaDIkKn7L/3X8T1+BShdqfpYl1cWBFb/UJfEbCNnKV/ZumghR1rwNoXUvjg3CUg7i4wsKjTd3nArBv7e+8kpOK3xTuBhCTtG9QJgEWbcHGi+zo+HyhmUGQW5RQUMch5tWcYano5o8mHS0RWasGhy+KQuf35BHT/ajVOq5QJ+X+nQeB7/ZuLtoWqtGlQUFBQqCot9YYq45ot9TJZOXk4dD0W605HCl8bInvK1fN2Vc8RhdbwgLujnZgvltVrjeHuYIvEjOxiBUWEWxKu77SfsLW2EodmBZ/fV/Nyw/AJM3E+Lg4zkIPf/9YWgVj/63KgSxugmpfBoIjEdWkHu+GfID8yCs6xcejfqwWuXr+D7WtmwtGxsKuhWZO68K7pB7W7481o2ETeQg6DIl3aNNMKjCwOncCid7tjzMZ4g0qxZ25La+S9oEkNDzzarn6x7sP2uwFNauLZLo2EUIMuey5HK4FRGaAERuWIOW1IP20/WyQoIl9tOomrOlkBmW+3nhZfu9avjpXP9xH9qubA/lZPJ7tSldjl6gONTVvMXIofxhqpOHh5qE8A73RthHFPfQmwYtO9PRBUC7h+E+AA6K5DQKMg9e1N8XCbIPx9QM9QqAFSs3JQ441/RK8w+nSWjF2v3pAUb8JDxW3uuW9TGZGVm48e36xB6neP4cBbQ4V6TcQd7cCPgbkcFJEJbYPx12PdUFGYIwfu7WSnmMUqKChU2bWfRursBtFFbpT6ZGhrMVvC5OnOi7dx8FqsUKstfAxV6xqVqzWEAOhvk5uXj5Qs0+qnbNmyp+eRkaYNDvZf/2ScCLZo63EkMg5HrseK7g+20hljQJ+WcHZ2QGxcUpGgqG5tP2RkZuM2W+4YGBnD2hr51X2A6j5ITUjCopuJQHo+lq/ZjxbNgpCbm4fc3Hx4ebpg6hfP4WW5w+bAMeSwEqWPA8clsSaVuaxV66ZYciWzSFDk5+qgJZg0c2grnL51F/M0kovE0cYK6Tl5atXgYB83WFtZonOwHy7dSRJJbH0BF+e9xrSsK4zg+f98bO4O0Zb458SuQgW2OIJFI2ZJr3G9aq5a1+15Y7D42qp26eewFZTAqML5ZuspvZfrU4PJ+WmSaH2bsnifmBeZN6mH2g3bHNh/uuWVAcLYlUN+3ESbwtXeRkueWt0nzDmQu6mY8Id2GV8fp6ISMHHhXqBuoFSfp/EbM1mRN9HbzwmLWD1yNFzp0qU4QZEmalEDGrvK5q7lCU/Yh08BzRoBGq1tZQ3l15/8e6c42bIXWZdGOsIKlNqurHN4MrW9XRThBQUFhSqLrAhLAaQM1Yaa+Ls7iva4R+ZsL5IU9XFxEAmhTkF+Yv5Xng+mjyHlmQlb4NnWZu7aYMieQyYzN094I/UODRQCQjwmdTQuziBja2uD1FTtqCu4Xg1sWv4RatX0wdHIOLT4eKnhB6D5q+xxJOPhJh1Uxvt6JXBG1TWjWize+GGK+qar/30HUbfj4e7mjJ9mr8W5q7dxp1kYcPEqcOGqVqdGbkEBFl4qWsGTg6LPh7fBuNb1EODhLNrdCCt4VPclclAkjw9seWUggn0LK1KzHu4kEt2nbt3Fn3svqNc1+h+evBWP1O8ex8oT19SBTYtaxoNOXXZcLFSTZQulJlQu/nho62I9noJhlIpRBfPlyLaYd/ASNpy9KeZ/NMUWRreoK3pJv9kiVYiYneAJa2K7+uL7ktAs0FscCWlZWmay3Dyz4uDy0p/q29KjJq8gHx+sOopdl6KLPBbjjKQM/VmrF7uFwjY3Fz8s34dMZ2dk0aitpr90qEj09MSe1DxgWKGMZXlCL4UeDfzx3PxCUYJyge2CzFZRTad7u3INimQ41GmID1QLKnGxt0EdbynbdC0uRVT9GOzSk6h/k5q413N4Ly7cg1uJRWeJzhmRqlVQUFCozOy5FC1U3dilcGzqcHGO4zl68ZErGNKsNn7cflYtVsAgiC1zDIhYhdA3fD9tUAvRPi+3zX21WX9CtaSsPhUpAqPikpRUNND47bsXRFBE2szUYzqriW5QpEuTBrCNu4v88MbIdXIEjp6Czc3b+GZ0O/SimtvqPfj251W4ck1jfxKSIXWFsFLE2WFdyVMDUESqaaAXvt1yGhvP3RSiWIbIzSvQqtixxS0uNUvsrR5tF4LBTWtjzG9b1BVAtkb+uO00pq48rE44G4PCWgxWWWWi9QptNY5FSg2EFLf4ZFhrMV/OriLe9vEO5gWyCuahBEYVDD9APHZciELXL1eLy8a2qof5k6T5GwojyIGRTEmDIk08nOxw6v2RmLH2qJCk5nxS72/Xqq/ndRSHoFPziuf6wHPKXLMfm5WLnr5OuHk5Cr2sc7Bq2QZR0rZu3RS5HIyUqR0ArTFRnkQ4g8WWt3Lgv2PX4OZYxj4I+uAJuWE94PQFID0TcMmVsleJyUCHe1+t4VCmDNsvmdH6bP1xraqkqT718gqOmAWjgbEuVOtRUFBQqIrM3XdB3aUw/JfNIsMvb5LloIiiOYuf7mX2Y17/ZHyJhIHMgQnDkjD5jVlFLus64B2MGd5JVLVyrV0L5b1LSHY3jXb9NuH4JyIGV99qjPc//hczvpAkzD3cnZGVnYP09Cy4JCQihVuIrm21H8jCAlYxcSjw8dbb8rbqVKQ4NGle0xvv9gvHwLCaSErPxqIjV/DCgj0ixqJv5I/bzmDJ0Ss4eeuuekSBIwucHdJsiyQvLNirDmyYFNSFwRUrbH/ujcC8g5eFr6BmQpMz5WyLXPx0T1HVUig/lMCoktAxyA91vV1wJS4FLnY2wuuFLUe9GvoLuUxKe5c1zvY2+HR4G7zbP1wMWrq9PEdczvkPijn8tkuS/aT8M12af911XlJ7sTb+tslPS8fgoe9pX3g3ER43biLWvdCcVgtWV7bvl1y5mUUqJznOP/ZIC1a5IbdGBNaQjuhYYNdBabZKULaBUXGVhladjBSHJm3q+OBplc/VvaZdXR98t1X7MvZjU8JUQUFBoapBD6Lfdp9X/0yvGX1MaBNcrMc9cDUGy1WtWGUJ06z9Ghs3e9VHfn4+Fi3brfe6hUt3SW1sw4t2gzhYABm6/dPZOcDpCKBRMD0zNH+JlCy9dhMWVlYoCKyO63aOaPjaXLS1zJb2Crm52LH2E4S1nyzuUtfRGicG6w8483y8pbXYx/DM08NtgtG/SaAIFjXFrKq5OuD5bqE4cC1GiFh1/p9+O48CVQVOH6Na1MU/j3cTQhYy9Bj858BF0X7HpKUMJcLZ0sdRBgZFslqxEhSVP0pgVAngUJ2mtDdPquvO3BBzGMyqzxxmunf0UkyS6HvlJre4ppgUb7iTXNjOdP6D0eLkLpubRiWmS0ERMREUCTgvREUYtpPxROhgi8YNa+FyWnZR2eqYOGDvUeF7JJ6LswPCWjdEOTe7lR8WFkIpjqIIAr9q0kFzOopMHD0NNG9ceMIvJXW9XXH+TmHb2SMUVth/UVQd+f3Df2wzKgn6/dj2eKFbY9xrmB0TA8dXY/U8pw73/PkoKCgolAWahuec5Y1LzTSoEmvuuXLmumOYusK4B19J4Uq193JMsT3jKKn981fP4ez5SLRr3QDhYXUxaOxHOHNOFRRwrb9+C6hbU0oYqloEdYMiR0vg1TA/fLT7kDooYtMIW/WbB3jh5fZB+PLrazjhWyhDHZGajQjOJzGBml+AsE9XAD06CLnuE44mqilGgiLi4Wgr/P70tTSySsR2SN1xCHZi0GuKAVEtT2e1QJYuvC8tV/xcHbHu9A1RHVp18rp6/pmG68PCa+Px9iHo3qCGSp47W7RiJmdko2Ux55IUSoYSGFWCoIhD6LoJFCp28XKWXGXfI30f0iVHr+L33efVM0Cs9vBDRWNXfw8ns58HjWLpTeTqYCMGRZ1fLJw10ktsLFDNyIe0TqDkKXD6AjIysnHoqDRA6Rl7F4+M7YZHx/eElacbatjbwLvuePXd3NycsG3JNNg+NxtVEZa6B4fVxKIjV7Wv4IwV543kilIpg6LJ3ULx5ah2okVj+fFrIiCmgzb549Eu6uD42NQRGP3rZqEiqMvYlvUqJCgiv+w8h+fmFWYb3R1tseu1wUKKVt+CpKCgoFAVoGKcjG5QxA00uzR4vjYF26r+O3oFsSmZWhWo8uBWomHDbWM8NqGn1s+n9/+IS5ej8OUPy3EzKg4nnT0gwiQj5/T0fGBIz3B8pJq/IYPCagk/pqNRCVh1JQ5Xg4M5eKOdUJTnkxhFMRnLo5qndFkpEo/fbTsjjsVP9cTw8Dqw1Gjt/2LjCeEZKNO/cSCm9ArDgkOX1FUiQ0GRTJf/rRKCGJpKeAx4HmtfH+NaBYkxB93EdQO/ezACoKBGCYwqELbLsVKkT5VLnhN8edFe4YekWQViNYcD9Z9rfEg518M+VFadPlxzFD/tOCv8a4ozjyQHUluPm1GuNxYUyTQOAULrA+cvS2XyvHzcjpgrlGxkzpy7rnWXbp2aiIHVH8Z2QGxqBlIzc/BlGQ+aliezHuqEmp7ORQMjonuipmR4Lf8SncBZBaLUJ8163+rbTOs6zfcK+7wb+LrrDYwWHL6MF7s3rhDfA0rEyvz2cGfRYqBPUU9BQUGhKmEoiOGMENcGU9xMkNRed1woVCErd8rQryKoXg38/PVz4vu3lh7AZxsKjeoN0fKHzZJqrQr6J8lqeouPaqyl+tbKrXul+WQnB4ACDc5OUpWqlIz6dbMYIxjerA5mDG0l1icGbNsv3MbNu6l4rmsoXuwu2X2Mm63TD24EjkuQai72mNA6GI91CEETf1VAp1ApUAKjCoT+BYYM4ORz1Y2ENFFC5ywJZTSZvHh72SGtvmXKgV74aAyqOTvg9z3n8fz8PUhMzzKWpDHK4Ka1sG5yPyEx+eZSyYFbL8yMmQq8+CQaBknH4ZN4aNKX8PP1gJ2ttcgqqeEfVq8WVtu5iwoHS8vc+P9vRJsqExjVcHMUC1+3ryQRDc32gSIwo8WqWgnRHMw0FHSP+W0z/jtaNMi98el48Z7igHD7z1eIy5jB3PX6YPFvKG4rJjl+I0787XRJNwcaGE9euEeo9bBVRAmKFBQU7gee79pIqL7m5OdjfKsgMR9MI3ZzK+E8L5c2KPr38e546A/zNus0gB3Tqi7KAnaxUJ66Qz1f/HPgklDSMxuNNv1YA+2HRWA7na83EHUHyMgCGteXNk5lJODEMYIftp9BoxoewlSVEtusIr20cK/wqHpn+cHCtnkdqCwc5u+JlrWr4dl/d4t5IXaUUIb9sfYhotqkOWukUHlQAqMKhMab5kDfAmOw9Y3KY9MGtlCX7kvjA8MTeN/GgfBx5eChkcCIQRFlMc9elIxTTVGvFpas2Kn/Og93oEUTJGTlivZAzZN2efHx0FbYfO4mtkWUTWbuq1HtRL+wGmMLYSlb6TiYaQhWFHt8tUbL90CTwLfmiUqT7mAv2xdZddz4Un+0rWu6ihSfmin+xB+2nRH91T0b+mPTywPMev6vLt4ngiL+PrkFUEFBQaGqw7b078eVfE5yYrsQcU7VbLUqDgMa10R4TS/YWlsiOze/iIGpLk92bABrzbnfYsLEFhO1z8/bjUPX40z6JpUpbKdjVwqPMmZq/3Dxdy09dk2sdTKHrxfOxRoKiv54pAty8/OFHcraU5HqoOjyjLGo4W7+iINCxaAERhVIdTcNjf1SMmvnOXHIPNK29CeKZgGFQ4oWicko0JTalvFyB2ysTVdICE3batYAIgtdvMXt6wbCrnF96KuBGOutNmQOai5cfDKohqMBT17NA71xKuquWgnGXNgOxsFJVrv0SVCXBvYgZ+Tk4tWeYUJCVNNYTpfo5HR1UMS2RLWxrQbnDfgE8W8e9vNGRH0+wWiGMzopHQ2nL9Ly3toeESUWRVOmw7/sOCtmjPjwlKUP8il7xUUFBQWFqghb2pc+01tdzTcE2+etrSzwSo8maBrgJfYTbP2iPyG9eBgUEU1zWX2wpfqT4a1LlEh9fO52LDh02eTvqIpo2lxEaSSxz0bpVxnU5PG/dhS5bEhYLVE5VKj8lF4WS6HE0NCNYgmlLfpyoFNXztvKwsJsd2xDcOgw+4fHMTQ1DgUpRoYzGTBRaMGMVoHA0Lo4suMbdOnYWFSQMGYg0Kopshwc7nlbNCt2iToGtZzZ2nvlTrGDIjJnX4Qojc/o3wzn3xgEXNKenzILHSd0Qvn2na8NxOlpo0Q/Mk3ojAUfTnaFM1w3P3uo2E+B2UVWc4wRn5apFRQRBmBOk/9As4/+Q9DUBbB4+ldxaAaJ605HYvICSXNw5tDWGHAPTWUVFBQUKhNUk2VwseqE9lphaO6Tpu/k8Q4hYob46sfjhJjDuNZBQlWuvq+7WIOe6BgiWrZIUob2eVoXtutP+msn/tp3QZic3ribKlqxzYEKqKaCIibyKhxj+xcz+HnHWbGWRSWmoUOQHwI9nODrYl7buMzCI1dQ+535QnBLoXKjVIwqEGZoKMlN9Tnd6kdxqiFd69fA+wObi9kWuTf53RWHxLH82d7CadsULPVyTmTflRjsvRyN20kZGN+qLv757G/sPnQBGNBd/x2pBEP5aTO54eaBvn9sR2ymJVCvcmyKPZ3s1OZsmjjbWYvsGxcatvSxhE5PAX1wzqtRdQ/k5eWhesijSEtTld7jEyTpcnPRE1zSYfvA1Viz5VSpZCTDv+viR2OEEo5m1ssUNBY2pIZIQmt4igWPykm6nLgpezZJbD1/Cy90DcWh67EYOUuaH5vQJghv9mlq9vNRUFBQuJ/YcylarNlce+mLM2diV7y2ZL9ITHFNYvJLty2NBqMkMydX1equH856rn+xH576eydm7zE95zNn3wVxyHC+t5aXM+p4uaCOt4tYB/mVc8x8vvRXtLKyFAnYPBM7FX1rxD1lx34h6W3ZuTXyS9EySPh/+nZMe3wyrDWqvWq+6X1x1IYVKh4lMKpg+OHgh0TTx4iwkkTpRirPOdvZCG19ZnZ0oSksVevY9rT91UHYfSkanb5Yqb5+6M8bcXzqCFFlMDSP8vz83eKkqClDSegzg+q1gOGqwcyMTMmbgKov8uCjLJlZDGKz8oCgWqgs6AZFU3o2wezd50UQdJplc5ra3okD/P2MPg4XkxOnrhYGReGhQEjhUKvDzSi4e7ritimfhRLOo3Gx5JCn5gL39eZT+Gl8xyJBkbUlKzz6H8eQGqIufJ2onmSKFSeuw+rZ37T8O/54tKsiy62goPDAsv1ClAgyiEgWaZxL9SXqNKEYkym4J3ioTbBZgdGAxoHIzM3H1bhkRN5NFc/rUkyyOAxBvx76NBUn4VYhdGkrvuQbEEigAh6Fg2js+vG6Y3Cys4aXk714HXRnjkJ83dH2k2VCFTY9u/gdObpqw7l5BVh/5oZIeioCRJUHJTCqJMERPyRUqeMGmL3CbLN7e5kkfMDZEpd8KQDxd3fCwbeHou67C5CVm4d3+oUj0MMZPb5ajV6NAoR0c/xXj2DUrM3YGiHN8iw6ctlgYLTw8GUx70GYpWpbxwft6vri9K27ovQLTdUUB3vtihZVYBJTCr0DSoDmDMz0gS0wffURlBcMLhN02r/0BTdt6/jiK00lPKrl6AuK7sQCpy4AocHIqO6Dbp8tR8qyTYXXawRFSEhCRlwiMtgyqDFaJhvZlcU8mverfxVpgeOM1pbzt8T/VVOy21BQpKmGyPejsSpV4xqG/+9PdWogvLV44ucAqrwBIPSH4Ot8v8I2FN3PckmFUBQUFO5PXu/dFNaWlnhLtc4Xh/Ca1YrVrs9Khb5lhus4r1/xfB/1OYo+TPQ14nn7apzqUH1/8ma8umviuk7gUBVhUETY/cCumzWnI3H8RjzSslL1Bpqv/bdfeAeWBnl93R5xGz2/WaNukVz4lLYnlELFoQRGlQSelHQ3oTxxXoyRDDy5yeJm8peHOooBPrZ5MTCa9PdOcRAGQixbcxjz+a6h6sBo5rrjCA/0xkhVf7JMVk4e3ll+SHz//oDmmD6ohTqLzz5YERhRnICbWJ0StDjJujpLstOl4LVeYfh0wwmhTvZSj8Z4tF0w6ry7AOWBqaCIcAM/+rfNxm90Iwrwrw74VoOdrQ1sj51GioM9Ujhr1bk1cOwsYK+hpnfpGuDnAzRrpP04B44jPzML6NLG6K/zd5c216YwNBdE34TuITX0ehmVpkoV5OMq/m+cx+LbRh6PCg/0wqwJhSqFXGhZxeJ80Zcj28HZvvhVxqoCPzf6qr9smVVaJxQUFGTYov1m32bCjP3Zebuw8HBhC7Qhgqq54oPBLYU5d1m065NvRrfXStzQ+7CWl4s4uuhoON1JSoffG/+U/z/RlJBTKekY5IehzWoLeXGu+ezOWXv6hli7GBjp46M1Rw0+nr21JbZMGYhtEVFYfuwaDpvRPigHRWTZ8Wu4Hp8iXnOFiseigFqL9xHJyclwc3NDUlISXF31qKhVQdafvoHZe87j6U4NxSZUd+NVHBY91RMjVG7OX206KbT4qWRz8aOxWgP9524noNH0xdIP129haP/WCK3lI04aDL7MlRo3l9d7h+HzEVLJm0OOZUJWNmBnXO67mrO9+Z4JGtgeO4XsJg1FNckN+Uhasx3o3l6rqmaQ1DTg7CXgSqRk8NquudGbt6zljbAAL1yLS8F3Y9uL+R598ARPyW19mFuZ0mTblIEm55rYbsD2TVal/tgTIVT99r851GCFsrzIycnB2rVr0b9/f9iUoL2zrIIibkB0X2Z5eVf6yh+c94JC5aAqvRe4pg75cYOYxdTHS90b438j2xbLtN1YwoYCAt+UIGFT661/EVmM/YedlQWy8irHNtPGEjjw1lCE1/JBTHIG7qZnobanswhS9qiqR4aSk7cSi+55mPQa1bwuHm1XX73mrT5xHYN+2lD852ZliTPTRhlVnFW4N7GBUjGqAtBTiIehjVdxoHkqVey2vTpI3UI3bUCLIipnrASoqeWP5Wdu4UpiBna8NggvzN+Dfw9eQlnyco8mokT9265CyXEXe2ukZJbCE8FEUERKEhSR7Ib1JdnxujWRRHHHVk2BnQeBnh2KVNe0OHQSuHwdU14Yiry8Zjhh74Ltt6XSvK7fhKu9jWhbOHw9Thyk3WcrxMkzUI+DOk+se94YjA6fF86YkZqeToi8a/5CJrdXmKpSUfVw8ZErQvacbRfkp3Ed73lQVFna57jxKDCjr1xpq1NQUNCFbbefDmuNHqpKAu0fmLj7acdZdbsXgyKKNjw3fzdO3ryLhtXdcfCtYSar8Iba9Yt7LmIeXTcoohDDpnO3DN6HQVFJEnNlCX3/OBfUobo91p25gRcW7hNGtCwLyOsu11sq+bHTRnPGq1Xtajh0LVbvGMCd5AzRicG1lxYY3MMcvGa6M0Pz9WAHEB9H9jpSqHiUwOg+2HhBtfHi8F6iCWlOsv3CbXT9chXcHaXAQd/Qn4PsTSRao7xx7EYcTt66C68pc8vlBNfn27Vic52q0Q5WqqBIBy4wnKHiMGleWRRJ7e1EUCTjFeiDY99PQs3hMyTRBRk6ctfwhUVMPAoiLgO37qCatxu+/PgJcfVP289g+/w9QvGH6nG6C9fQnzZi9alI9cPlFxSIFkpDtK/nh1Pvj0STD5eoL6Osduva1XBQz8ldF0PtFbpERCdi/O9btRSHuHBTTvxBnN/hczJWxTV3bktBQeHBhEJIi49eVv/MpBMrFbJgzfjWQULhs8fXhS1Y524nwuWlP5H5wxOwM7Gp1teuX1xkqwVNjAVFMvcyKOJqoNvgz6CIbL+Vie23jmgFKHIyckKbYKRl52oFRVwP9QVFRJqNLhABFo/iwP8nW/cIgyIZJTCqHCiBURXBnI2XOUGRDGW9wwKkliyW7tlve/JWPGp5uggZUC9ne2x/daCoJA34YX25n+CE+ls5YZmegVhVdYil9IJy+DviN+1F0PzVAFsGgmoDLk6SVHdCkgiMHh7YBn/R68nbE0s+eUR9v8fahwhFnF4NA4osXK4v/an2U6IU+Cs9m2BwWC29Mq1stxzy8wZh6vdsl0Z4qmMD/KoyxzUkMa6PABPtFZxL+2NvhJCVpZQsg82E9Cz0aRSIr0a1FS2YDfzcy0Vxztj8zqAmkmdHRWFua2lZt6AqKChUbWiP8e+BS0K9k6IHmsjtW6/2aoJH52zHPAOdGv2/X4dlz/aGq54kZ3FJSUnH8jX7EXElGs1bhsC7lh/2X40VM8cVLr1tBsamni1UFa6h4XUwKKwmWny8DDEpGepAlHsEGud+N6a9CALN2SZQNbhNnWqipdzD0U7MKa07c9PofeSgSJNnOjfE5nO3KlXC70FFCYyqCOZuqFgKpgcNy/BXYpNxRHUiG9G8jlADS83KQa9v1qJeNVeRfWc5nlWLLzedFLdj5eLyjLFiY9s5uLr4vbEpJWs3qxQUFCCfXksqckqnFVEURlhxd4GoGKjD0p0HgJ4dAS8P6QBwmOX1OoHi+84dCn2fHGytMTCsqHT5zYRULZPZsa3qYVLHBgafxp7L0Wqnc5rR6WKoWsQ+c00Z+CsfjxWVuv9tPCGkTFmBYvsE1Xv+PnARiw5fEYEQ6dHAH3891lUMpbItk7KjmlLeDPoa+5dcsVATQ22kal+IJ7ujIpsQzFENLM7tFBQU7m9oFjpl8T6zRBe4ZuvSpo4PQnzdhMkq27/cXp6DvJ+fFPPDxSUnJxfrNx/B90v2YOv1eORV95E8Cq+Xn0psRfBZOw94BdXFnZRMfLr+uLC40G2rp38T1zpzgiKKYVyKTcaW85LQFatPpoIiffBfxjVUHm9QBHsqFiUkrSKYu6Ea16oeCmY9JUQW5KCI/Dy+owh2mN34cHBLEfDIJ2RN52pulF//74CQ//Z4ZS783/wXVZryUrbJzpbmhWLiAB8vYOwgYGhvwNZGuGw/30y7gnHWgB8Eg9PtEVGYf/CS+Co7jlOWfcaQlqL6QmasPSqCAENM7d9cBMPFgZkxtthp8t3WM/CcMle8B6Ys3i8u40xZxy9WYtbOcyIo4nP7elQ7bHypvwi0f90lVaY0odw52/ncX54jVOl0+Wf/Rby8cK+YUyrt/A55a9kBVCSyLK4hLFSfLXPUBRUUFO5vNp+7iQbTFok1mOfhR9oGo0cDqVuAsy7k78e6Gn2MA1djRFCke1lxyc/PR6tuUzD4y5XYZO2MvHq1pKBIh2aBXmJ/UZV5Y18Cnvh7l1Dj/XH7Wb3dFJQl5+tI81pj8Fqq/2pCq5N+jQPFPkuTz4a3Fobmid9MFElqmfo+ktCCbgeLnPBjQlDh3qNUjKoIpvwIZNjrOrF90TmP0A8WY+VzffDZhhNC/lsf3ITvvBgtjrKEZnTZejbHFU5pJEFtbYHG9bVV6Dh3NLAHsGITCjwZ0OhfpD5Zdwxv9ws3Ke38bv/msIAF3l1xSCyeciYwLjVTVPgYLLP8TtlX9pezosRSvFzRMQXnlf47pv1eoEqhDBeHbedvib520rOhP97q0wxdQ6qry/ysFvFxDJGUkY26787H1ikDEaRaBF6Yv1ssSvLveLF7Y9HbnZyRjeTMbCRn5EhfM3PEZXx9TLWR3hQtJ8U3zi0r+Hr8b0RbjJ29pcRzWwoKCg8GbLuSOwL6hAbg46GtEPL+IvHzsmd6o32QL+xtrBHs6y4G+j/bcFx0d7Bz4KHWQVhzKlJUiYSSmUYbeohf8RXNLl6OEsbkFu2bq/cWlAP/ZUIn9PxkKQ7fkZJ6VKQ1JGVtENpRJKUAvt6o7FCdrmXtamLNik/NxLSBLdApuLpYbwf+sB4XYpJgZ22JLFVnBl+rVxbvK7L/Gty0llgrNfdZ3UJqoGWtaiI5TXVSkpuXZ9CaRBHsqVgUue4qBDfSI2ZpGIjqgRvoeU90x5hW9XDxThLqv79Qr5oKMx1UNuOsCOdXmAl6b+Vh9RD9jYRUdQsdq0+sRnC+pP1ny3H0RrzYwJsrGa7rn1Cp4KZ+yx4gwA9oEGT4dqzk5ORIAVEZVKHa1fXBvitFAydNaWd5vouBx+yHO6NtXR9sOHNTK3hpXMMDF2OSEB7ghQPXYov1Oo8OroZcZ2dsOndTq23PEFaWFmhf11eo7siLg7l8ObItpvQKw6WYJAS/V/Q9WVocrC0wv7dPhcnyTlt5GB8a8LkoqSyuwv0v0azwYL4XMrJz8cy/u9QVH/oSUniIycmz00eJDTSTQlyTqfrJNYBKsQyaejT013osu+dnq9uo2S1SXBb+twtjn/gcIR3DYdWyCc7elgKtBZN6wNbaEsN/2SQFOEz8FZf/1gIj+kvf5+Wx/C91VVRCgn1chW+kPvh/oW/f6pOFQkglgbPcnAUj7BDp9tXqMrHNUDCNItd9n8KN1QtdQ/HD9jNFFNee6BAihiNZBn7oj62wtrLAiOZ1sfeNIVh7OlKIG8gZDOrkfzOmvZgd6dnAX6jBiZOfMJ2zFBtyOSj67eHO6hYtViWmD2qJwT9tKJaP0j0Liq7dBGoXcwifQQ79h0xl8ml0a2hh4IzRifNA1zZaUt0tA73gkpSEbcm5QHKqdH/VoqAvKNLMFE36awcycvPEghhczRXTVh3W66Mgi1bsN0NxTpdFF2NhbRknKk/mBEZ5+QXYdalk1UQGc1Q/pO+RPjgI62pvK4aH2UoifS995X3koL0ywc8PM7/vrzwslB5lOL/3y/iOomddGaRVUFDQhbOlcyZ2FXNCz8/fo1ZjZcDENi+eV3jIjGlZF1+Naoca7kXbdeWgiOed4nDwyAVMXHwQ5xLSgTGDEMELVUERobhO38Z1JGlprl3RsYCfduu1SeSgiHBtNGZlcQ9pFuCFd/qFa5m5GwqKmGymFDcPUzQN8BJJ5QFNaoruHlb1NOEejIquIX7uimBPJUZppasiLDx0Gc/M2yWkl2UebhMshgS5Aft0wwmtDezY37Zg8dOWIkPRrp6v2MRZPvObWn6bMCtFz5n1oS64AAEAAElEQVRfdp5Vt0PxJCufkD8f3kZr4J+zHr/tLvQZMofOwX7Fbs3zdrYX5etiY465qj50gyI2/OoOsBrLlnl7Aj3aa1926CQOL7gufW9tDeSqhjw5i6QPvv6sSllZieAoQUNh8GJs0RM2pVvb1fHB5IV7URooOaopvlAeUKmH7X2T/t6plojXxFSWk++7WbvOGWwjFb5LKlnbe8mwnzcKJSldOMPXs1HFquQpKChUfigAoMn1u6lFLuMsZy8zzidv921m9u+dv2QHJrwzB/nd2um9nqbvD7cNFr5JrWv7CK+eoU0CsXzJDqBLG5Q5rEjRd7C8ZoJ1OBedgNz8fKx/sR/6frfO6G1j/vcwnp+/26RIRvr3j4uAV6bVzGVFbsPOnNpeLuJ7RbCn8qIERlWERUcuawVFZHL3UBEYGdrwcuMmb0xZJv54SCvhR0T3bE0+Hd4GXepXFwZjnGnhuamut6sIqDT5bdd5rNIpJTOTFVrDU2TN9WFuUPRarzD8T6WMV6KgiDCYOXwSaBmGEnP1BuDsCFQzYFLKoCk1jX1lgJORzbhmYMWTpZOD1Gu95zDQoaX+ipSGd5QpZgxuiaD39PcnVzb4HmE2k7NSuu9hc+BcDueuOIyq25Ypv8qfDmsD3NKupJYnDNY0/aVk2EfO/nwFBQUFU3CTzMQU19yRVI49oj1sn/ztRLjYG5fgptosO0Ue1/CQ27T1GBb8twtJyWlITklHckqG+vuo23elG4XWN/iYL3VvLJJZTFK6O0pJweWbj0vKqkzgaSYTdX8uCfcwKCJsBacPn26ijmMGkj9RIfRvXPBkT3w8NFnsqU7dUr1+Ouh6EEXcSVSPJtC4Vze4NTU3bq7RukLZowRGVQRu/JbqDMqztYpBDueECNXC+OHksKYmPMFRrpnH7tcHF8lUsMWJPbQZ2XlFgiFdVRqeuzRn7Zcfv14kk0In6Z/Gd8T1+FR8uuG4loGZIb7aLAVFpYLBjKGAxlwuXAESkiWlObbY6WJpAee8HKTamqhQNG0oKfvUrFEYQDH4SdBTjk9MBjbvAUKDgYZG5pw0qDu1agRF8uDp6Jb1RHsBWwlYqYxOShcZuzf6NBW91qYMW9lGyrkrXbEKD0db/PZwF+FjtPYeBkYM3lmZ1aSutwtWPd+nRHK5CgoKDxY8D258uT9eXrRPKH7qBkVbXxmgNyhi98eP289g8oK9mDawOd4b0LzIOXPyG7MQcVHDeJUtcP5+QJTGpt6IIMKbSw+KwzI/H/lc9HkE1wYcdboyyiIoIvcwKCIeDraiK0M3URcW4FXEq4kCF4TJvZPvjxTfU02V/7MXVR0bFFzQ9e5bN7mfEHKgmau+NcGchJ8i2FMxKIFRFeG7raeLXCZXhMij7epj1kOdkJmbh4lztutVnqOUZLBKGUwTblLbfbYCmTl5IuvED6uzSjJUk7Z1ffFqT6myQ2NPzn9wPklzwJ4ms+/2C1e7cE9sH4zAt+ape6j1Uc3ZDk62NrhmYP6kTNXkTNG9A7D3CHA7Bjh7EWgUXOQmqR6SN5FR2D6nG+RwvshXp0ebv2fXIaB6NSCoqJ8RcczLQbqVTYW1HZSGjkF+GBYuiQ40rO4hDk0xkQ6frzSoyqcLLxvSrJYwO5aHVu+mZ8PPrai0bHk71OubEfvvmV7wU3yKFBQUdGA3hrzB1oTKc7881Amta1XDU//u0kq2PPzndpz/YLRYi3nOScnKwdx9F/CSRvv0B6uP4qM1x/DFiDZC3IbQvzAnpB7g5SOSci7VvZGimkOya1gXI0MD4JGajB92Rpg+12kGPQyKdAMh3aDoRhRdwA2uZZUFuVVdNyDRDYr411FwSBcqwU7u3lgcDFT1GZp3CDJd6TGU8DNltK5QviiBURWBAgi6ogua/PloF/HhZECy9JlecJz8hwh0ZKq52KNvaIBQFtPl113n1Lf9Y08Edl68jX8f747WdXyK3PajIS2x7swNIRF6Ny2ryIA9e2gvxSaJ9jri7miPuRO7iawI0Vcyjk3NQiyyih8Use3N3RVwcymbrBVhOxsV6hiwUIHOHJJTAFepb1gLLnK8js/R0N9CwYiubaUKlT6u3kA6zfYYZ96MBvYckl5EFyegTTNpvqkCeadfM7zcowl8Xvtb7/Xsj9e3ITBp2Pp0L72LArNsVOihEW1dldQpg6sz7w9HeXM3LVNkd9eeikS8znufXIlNQTPV/J6CgoICYUeHbKBOvJzshHn60RtxsLWyEkGTZoJR5lZiGr7YeEIo1+m7XobzwUfORSKveyjO3E5E969WI97ZDeABqIMiuYXs3xOqFuD0DODoaTTp0w43UrNEUlMTB0sL2FhZIJmu6PGJVF8CnA17tYmgKbDqqKd9P6Y95u6/gMPXtYMhTfjKcS/1VKcG+Gx4G60ZIhl9QVFx0Ez4meqcULg3KHLdVQhuGp/+d5fwMNAl44fHRfZJhuaZ3+qpMsnQO2ZI01oiQ0LFLwZGr/RogiVHr4ieZ6rTbZ8ySG9rHVVVmLWiZr8+gzRZneXY1OHqk4Y+zx5TUJ6UztQ5eQZ07Q6eAK6oXgvO8PTrBlhblU1V6fhZSZ67VdOS3f90BHDmgghinnp1DKyDaot+Zl9XR/i6OKCOtwuO34gTikSip5mLikr5zhIFImAoUBfUJbY/1gn1AqvBxdkB/Wcuwd5EM4PJcmRShxB8NKSVUM3TZ/Sa/dMkke1kX3Z4TS9xsud8Tu135ht8L8i91VdnjjO6OLBiKldGPxvWCiFZkeUqy7vg0CWMm71V/d7sElxdtAKyXYKfo1d7hcFJx9hP4d5TWSWaFR7M98I7yw7iEx1BhdImSQuSUrBu23GkBaqku1duhmdQIO42KuphKGSyZTU43YqPap2s4+WMq/GmOzbsLYGQ6p44cesurAvykZuWYTxYuo+gNcaXo9oJ773nuzaCp1MJxZ4UKr1ctxKSViH8PZzEDMPciV3RPaQGtrwyQL335+ZMky9HtcWaF/oabc3r8fUavP7fAREUcf7jsxFtcOK9kcK5mep0lLKMTZHmgyLjUzBjzVHM3RshZpLWv9QfCV9PxGiVlLcud5LTtdoCmBW5NnOc0OSnz1IDX9NGdJSQNhgUkcDq0ldWjFhBkYMitprpCYpa1fLGbxM6wS/aDEGIZo1KHhSRyCh1eWz9oq04MXclxof4ioCUHlMcaqWSIIMiFztrIDNbrXz3zxP0jyiamfrnYgxWXbwD9zf+rRRBEZm9JwIbz94U7SC6MMvGatHbyw6i1SfLYP3sbDSctgg/7zhr0rCVwTkzaMY4pyEtO6G1ebNZpaF7iL9IGBB+tla90FcYAA4Mq4X3B7ZQgiIFBYUizBzWGoffGSY207TCMATPlzJsVddH70b+8MzKxJKtJ5Dmr1r/SNe2WkGRlhCAHBQlJAHXNeaOiGqdlIMil9g43PhkHGY/XPR8TjLzIYIikmthCdcsM4WSjJiAV3Zoh+Lr6iCsMfp8u1YITU1ZXOglqHD/obTSVTFYgXmkXX1xEBc7G1G1YWDEagT7XfnBZcadanSGYHAjnzxnDm2Fx9qHiMd+YcEe4RvD6gY3r3Tjru3ljGM6jtdzJnZBdTcnLDpSKLwQ/cUE+Lg4iLJ/NWcHIfWprw0qN09ShDFKRqZ0MqWAgSHYYta7E+Dprn15ShpsHeyQrXMuPnQ9Doeu7wJ8fcumqkT/IkOtbG2bAxt3im8jb8SKY+vOkwhtWAtnou6i1zdrRdncyc4aKZy/Yv92Wjomt6yDca2DRFsir9dk9m72hJvuC7+XcG5tTMt64r2T+/MkzFhzDNNXH0GIrxt+GNdR3KZz/er4essp8T29IDg0bA66f78mfA9F3ElS/+xoa9wfg58LtoxSKOSrUW1L1PKWnJktZuGycyUxEwZFCgoKCqZoUauaOBrX8MQfeyNwSI/vHCvubIlnaxzXyimL96nb1WW1tI1nVYENRX00cXXW+lGzjV7rNlRtpdefgXWvY1gdVHNxLNqVom+tLChAsqOTdkWqLCjHGWJPOwvczSpekMZ90qF3huHRP7djw9mb4jLOek0f2AK1vfW00CtUeZTAqIrDAEcOjAg3bDPWHjN4e1Ysvh0jqa3pGxrkCVGz+kRFOx66TJyzQ+tnBkUMzEgdb+Nlypi4RO5sCys8hjyJ9h2V5mgsLWGTnAKrO7HIDNapUDEo4omZj0dBAlLNs0hQpIX8N5fm5JuVDew7BgzqoX35sg3AwO6Ap5s0q8S5IFI3EPPvZGLqS39qtR+mZeWKsm1BxBUUnDqP71dtwbX9JzGmc3N8c8JwYFAR6HpSfTa8Nd7oU+idwcX8td5haFHLGz0b+qtni+ilFfX5BDSf8R+izVAo1FQ3NISu0uH+q/oNbq/FpeCbLaew5fwttRkuPb6OTR2ht1/cEEwWsMLKz0ITf0883l5Py4qCgoKCEWivoS8oknls7g6RlGTC6dG2wWKt2HzulvA3Iu521kiMjOYiCgTVLp53H4MXtr1RBdVDf8dGTEYOBv24HpvO6a8sFbnM0O/XbdkztdbyOd2JA0Lqlquo0IBajvj7QvF8+x5pGyz2Nmsn98PqU9dF+zuTxp9tOI6f9XRKKFR9lMCoihPo6SzajjjcSYlsDl7qg8Oe/xvZRvisGBsabODnhgMGNpnGkIMicyjIK4Dz7gNI5WSjPklsFR9/PAmbT17Dtsi7yHF1EUcRjp6RBAxyc9D+scHYe+VOUeW3YmLPdikLC/1ZN8JZIAZhFE3QJD5BCpguXJU8IhqHSIERF6TWzbDvVmHrl5rYu8g/ckpaGFSsWncQzrtOYdqPr+GDdWXXm24ubBeT3dRlmvh7YMdrg8XMl521lcGBU87YsLVMEw4XLzlyBdl52o9pCgb4bB+t76tdETx0LQbxqdrB+j8HL2GUd9GgiF5PurLarDR1/XIVDrw9zKznQdXGnl+vEcFRfV83bHq5PzwMtLooKCgoGIIBz97Ld4y+QJSQXnf6hjjIk50a4PkuocJ0dfOGgxj/xDIpeGBgVFyi7kit5/oCo5Q0HOE6lKjdHtfQzx3nKPMtBzqs6KSlS88hJU1KTsrzzfK6cCpCsqwwFwoUGRIpKkNMBUX1fdyECqCmOh2VVQkltwc3lV7zIT9txC87zyHIx03MlircXygzRlUcGocxM88WIa8pfyE9u6gsNjX2VzzXGxHRSULhJo1ymgbIpAKNqq/2+ifjEOBuerBysM5G2BT+NbwQc3wWYvZ9C1uNdjsHK+3N9rurj4qgyCiXrwPZ2UIBLjDhLrBtv3TSLgWZuflCVIDtYHqRFwHONXGe6cRZSX0uKRVoXB+4Ey8JN/BEH1C9aKufJtU84dm9DYaP6aZ1cWpOnlEVwvJENygiAR5SqwYFPsxV4WFFkhXIsA+XCL8HXRVDY/B9sTUiCk0+XIKhP21A+Iz/sPrkdfyy4yxaf7IcE+duFwqIMv8evIyPDycIMz7170dBkaBIhpXVMb9uxm+7zmH6qsMY8ctGbIuIKnI7tv71+mYNLsYkoZaXMza/PKBYSQAFBQUFmYnt6iPQw/iauueNwfh5fEc82bGBiDNorM6EXzUXB1T3VbVuM+goTrVIpllDoIGBeUyuZ3qCk87+blJAtWCVZC3BJ0XF1tuxUjv5oZPA4rXAwtXSekgjc1Z/WDXSBwOrXMP2HRXJ3Me6FpHs3nI+qkiXjqbiINc3rnUK9w+KKt19wK87zwm1OkNM7hYqAiK5RY6zR093bijcrTV9V1gNkIOrmp7O4th9Kdq8eZDPJ5TIw+VmQiomfbIIG67EAl5m+APpsnYbkKyjptO+OVBTpdZTCmytLJBtTPzBUC80N+OyLDolUY3NSWko3jSp4YH9e07j6tXbkpleWfZtlwKKcVBsoDiypJS2poIbhRkIxSY+GtwSjf090emLleKyt/o0xY87zgqRDZlAlX8DzYTZsrD+jJQ11UdYgCdO3pQCIUsLC9Gbz6f4waCWwvSQLDt2FcN/kaTiTcFqUMSHY9RVrs83HMeHa46KQJFtfbteHywyhAqVm8qgRKZQOaiM7wVuotnl0eD9hcjQ6UoID/TC0akj1D9/uv64EK8h7w9ojqn9msGv+5v61edKAbsAstiOrkNjT0ec/mlh4QVcAgb2kEzL2R0ht6+zM4LzS2x9Z0WKnn16BITuJbQmMZQY08frvcLw3bbTQtJcl5e7N0af0ED0auQv1hpaRWhKqFN9VZ8thULVVKVTAqMqzvaIKLXZpSno3Ewux0ptW0E+rrj40Vj19fsu30H7z1cU+zlwQ7n79cEio1USnv13J37ZWVTq2SyYxdKg/bAu2GvnWjmGP8vTiPYewNiO60qj6u7C8bs4vgocTqXRMNvyGIC/27+5VqZNhtLdz8/bg1m7zgkhkQsfjYafm5N6A/G/jSfxxtIDRn/XrxM6oV2danj0l9U4GpstgirOEBFWhJ76x3DSQBcGQLpzUHQunzWhk0gUKFR+KuNmWKFiqMzvhV0Xb4uKw+cj2ohWd1m0SNNcnedA3uarzZJ4TZ9GAdgREYVMtiVHxwJ+pWsbN4aXvTXi/1pRtPLDFnF2RhAuEFznGBQwUOJrrMcrsbLD8z7nVgtUAdXDbYIxZ9+FIrdj8NOhnq9QYf1h2xl1V8eshzrhqc7FaB1UuOcoct0PECNUxqnGcLK1xvjWQSLrH/HhaGEASy7FJCNRQ1jBQUPZiz47vRqaV3XpWr86Vp64LgIrzccjGRmFP8ckZ+DrzSfx9/4LamWxCb9v1Q6KkpIx2l27UvJe/3BhemYO112MtK2ZS0mDGc4elfBx3ujdVLiXP981FD0b1MDnA5qJLFTBrKew/dWB6tu10pgRK0/4zOVk29nbicJ3iF5U5uKqWtxb1KyGz0e01RsUEQZbP4zrIHyv6Ow+ffXRwudgYYG7Gu+n0Br6K4r0leDC1tJHmvvRDGB2Gah4ssKZ+M1E0WaqiWZQRCPGvx/rhrWT+ypBkYKCQplCVUvOOXapX0Oc6yhapBkUEV5O7xxadLCqQ1U0BkUW9A8qx6CIxLMiIgdFdQKBji2B+nWkQ4ZBEIMittCxeiQHRbprYSWH5325tkSPOsqi64OdBNsv3EaDaYswZ1+hQuzz83cjWccyRaHqoogvVHHe7NNUGLS+0DVUnVXSJS07F/MOXsKiw5fxQrdQlRGltVBEuxKXguY1pQ2lPJ9Et+vLM8aKk7LF07+qA6XzH4wWJ4Z3lx/SMo+lzKimuSeDmMfbBmHn7OU4dvwy3nl1FNq1bojvz9zG0mOSIaervS3WHLuCfw9eUt/PPj0dmVv2YhHL8f7SwCP5aO0xfNCvKaatOyFdcOJc4WBnr47A7sNS+d7LHbeyizfgXyYwcxdxGejYusQP8fnGE+I1p1z65vNR4mjfqCY6BPmJhZMD/5T4PhediHtBgR5z4ZGzNmHJ072EJ5Up5OphjMoHyxDrTkfiod+3qZUPZ+08h63no4TX1Z2UDNFKQhjMDwuvg/+OXsHIWZu1HuP3PRFCdc7TUlqY6mpIqFrpCU7p/yW3fa54ro+6chWXmokNZ27i0TnbhT8XA7aSVkEVFBQUygrac+TcTcKkeXuAgnwUuJRz9ToqBjiqsZ9ghYjtc7TI0NfirfLgU8NWuipKswAv4e9oilTabKgY26oe/jt6VYwrMMGXkpktFAU9He0wdUBzo/5VCpUPpZXuPoEl96E/bxSVG3NhWfjSjLHwcbHHJ+uO49MNx8U8BQObW589pBUYid8x6yn195qXUwKcZpusLNxK1FB9OX0BuHlbGtSMiYNrmzAk1zEs1OBlbYH4XCM9wcfOABFXpBO0rkx2RcLAiDCDR/+lI6eBDi3KpI3uj0e64LEOIeL/G/rBYpy7nSjaKWQ/hXsNZ4Cuzhxnsq3u993nMenvnSL79n/2zgI6qquLwjtC3IgQEgiBoMHd3d2hWKlBvaX2t9S9lLorNUqB4sXd3d0SXEMS4m7/2vfNm7yZjCYT5X2sWSSTZDIZee/ue87ZO/Hrhwx+D01APJ79w+htPNolXGQPkVn3d8UjnRvgZnwKen+1SjwOxqAdPV+T8u+o/cZ8rb33r/d3xZTO+UGKKhWPstw+pVKylPXXgqHIDGOkpqajdY8XcKZ5fkSCWa7dksyCAnyFqxx/l0VTN3RYjb4rndsoetq3qFBt4raAHQV9G1bH3kt3cCkmf95In20vDhFZfiqli9pKdw/S5qOlZkUR38RKWP2Zs/c8Gr+7SDtkznkKzgvJB2u2d8kD+Eq7ZGWWDReia54dgEENq6Gpqz0cjp/J32Xq3w3o2QHo3QmJ1U235sVqHPGM0qIR0LEV7E2IItfDJ9CsupHQ1eKCgkhua6BTEFsObHTSkINR+Xw82a2h+PhCdH6wqS3hUKk5ODS8/fwtk9/z39HLQhQRGivQ1MMQ3EX7bLSe5bkCWRQR3l7oq3NR7ZV/DIoi5Y5c3Spe2h5+Ci9ZFLFdURVFKioqpUlubh6+33IKVf/3N9p9vEychy3Bzc0FJ/d8Z/kvYr7fnkPA1r2Sa5yloojQCKlBbSmSQhZFSic5Q+cKttMROrTeiMq/D5Fswa54rm2xKRmYd+CCEEWcpeVIAS9KeM6W7b5Vyg9qfa+CcEjPYtIQskOYktf/Oyj+D/RyxXfjOmFUy1o6O1gv9W0meqEZailDu2SZ//VtJv5/bekB/LJbM6xoyJ7aX/Pzycw/4OCTgZkh5cGW7XJ3YgEvd1StE4LbXpqshBrByGVVxohVaVrLJjimcSorr8gp5+TEjbt4ceEe/HfsCgY0koRtZLTx3anC0qt+EM7fSRTCxxz9vl4t2gPeGtyqwNd+3n4aj/+zU+e6vRfvoHt9vaR2jdhjWycvnD/bfO6GCBb8fVd+7zZxdrQXTkHMETJGLT8PDKqaB4fAmnB2cECvL1YKu28ZzsvNGFH4VkcVFRUVW7Do8EXRMk24aUPnzLGt86MHjHE+Kh6PzJY2nIiHkwOSM41k7cnn0+aNgLORAGd99UNXjXH0tGSkwCpTWI3862WXOdmaWl8csbLEqIzVW8V5GsGatjvRJVI2qku+zna4m2EbkcaW7Z4NqmFwkxro1aCasPke+6vU5s1RBXY5jGtjxBpdpUyjttJVANjP6jXtzyLdxn2twzB/am+z30fDhEpPzhIfV/NxQ1UvN7HoZcCsSQyU3u3z8tDCxQ6H9ONt7sTAYds+dO/cGJu2KW537OD84c4TZwFvL+DsBaDvvZE+7Z6TjRSHsrOXQbvsDdMGooom18dUa5yyDdMUNO8ImT4XyZqsrXa1qggDBLZoMnC3Y+1Asfc4e895kY2kZEpDD2R7B+HPPRHaVlFWOrmL92S3RnCuVDbsz1Xu7fYplXv7tXDsWixafLhYqy8IM9JeH9BCVLQNtdYduRqD9jOXaTPmfN2d8eP4zrhv1ibz51tWpC5dAyIuA/26mBVHAxwzsSYqRRJT1QtR7WC1yM+n4NyRKW7dye+6KAMterIjqyF476rrtZRzRpXdDBS6jN5Y+FgfNKhqAyMolVJppSs7qyyVQuPhXPQD/rKjptvw0jKzRWvTwkMXtdfdiE8VF0uqVdqDnVyOd3RErp0BUbTvKHDlhjjQ6Igi7jwpbUCbaOZE/CtLlaUqfqgIuDg6oHWwN3ayXU3vxJJyKhJoWnbmY5ghRLe6J7s3xMXoJJyLihfBrJkGWkM8nv0dWdm5YmftqR4NMahJwVmzg5ej0WbGUu1uHOff5EVCXb2w3Wd6NhYzSC5P/6a97q+zycjKjRAtgUyLf7V/c4T65RsxqKioqJQ2zUL8RIvV91tPa6+7EpssYgWYJfR0D2k+UoZuZ9sjbukEbzMs25AoYnWdrpwRd6RIDgFd46r6A+cvSm5xivPKGwNb4IEO9UR7363EVIRU9hAijcf164Vt2a4WaP3PcKOT7XtlxLTBlCgizNpTztnSuEpu2d7+0lBUdrdCFKqUOdREqgoAF481DSwAOVNhKTwgrz5x1eiA6LhZm/Dcgj3YdUHqHe4QFojPR7fHe0Nbw9WanXiW4+MSjA98cmcrNxeuLk5o1kThfsbAUyVyfgLDU0tDFBWTHWl6dg52Xr1reLetGEURqyvyDJM1MKDw8w0nsPToZWG+YUgUETog8mtrTl3D4O/WYb7CjZDQvEMWRYRuie+sOCRa7Ayx9uQ1tP04//sJR9Ro9kBbeuZMqKJIRUWlLMKsNUM8M383jl+P1X7+9Lyd8H7uT3HulaHTWaARt8xX+jUXi/YCeLgDA3sUOK8836uJCK0OC/ASTrHbzt9Cry9XoUGgj80CWu1TU/Pb74zRroUkiuTv089OKgUYAVHVW/dxZqXIkDMrN4hlfNwMR1OolB+KtWJ09+5dPPPMM1ixYgXs7e0xatQofP311/DwMG412b17d2zbtk3nusceeww//fRTcd7Vcg13lJQpzITDgGEB1u2WD/purfh/ePOa+F/fpkL8UHT9e/CCMHbg4plmC6NbhiFEkRXTuoY/Bmp+1iICDJ8UhndrgmUbpPmUIQPbod+UoXh++q9ITE4DmisW7Tx4rtyEgd2bokvf+nANqSd6rXl/T9yIxZZzt8RMTrFSRna2BJb2jpuAA8A/bMvfwTQG2ydZoWRIsDwHVRjG/7ZZzAAxOJWvMYogfWgIMnPdMTzQoS4e69oQ+y/dwU/bTuPcnQTRVmeIbS8MQovQKoW+XyoqKirFzW7NBqMhmr2/GAdeHSHE0x+auV0u0sODKotK+sR2dfHPvggRU2DomDmxbR2xgL9uwbxojy9Xis0nWk/L7cu2JtdNMU+cnQM4GthIZdyGsv2viOczW9C2VhX0Da+GulW8hdEC3Xq71K2KnNw8PL9gN3rUD8bQZjWRnJ6F8ZrqnbxmUinfFKswmjhxIm7duoUNGzaIXt+HHnoIjz76KObOnWvy56ZOnYr33ntP+7mb8o2lUgAO+inhQZQLTu4AFYZlRy+Ly6NdGuDD4W3FLpZcdn++d9MCFqIPfroYcHUv0jNjn5GJryd2xbuDW2H+om1YFJeNBRw0bVhf9xv3HAZi4oDUNORlZiHc1wkDuzXU9o9zGHJar6aiymX/+K+W3wEz9qMUmspWhjJFMZxEmI/FCk8VDxe0rOEvTghsHXBydEBmdg4qP/8XsjW5V4Xl151nxfzPnaR0LDmSHx7L5PFpPRtjZ+Rt7L8cXSAnyxQBHoZNOVQKCuFdkbfFAoo7xioqKiUDg7LXnjIdt8Dq+WNdwkWLFqvgN2ZOgr2ilXya3nylkoNXotGuZhVcj7tkUTu00tmT5012Adgal7txSOc52lT+UgkIilFhbvjvUiqUqSBsP2STHLtmlHAzmJcfJnTGE4puim83n8BXm06Ky58PdhfZekevxaKKpyvmTelZ7H+DSjkWRmfOnMHatWtx4MABtG7dWlz37bffiiHIzz77DMHBBV2qlEKoalXV4tBSuNvj7eokwsU4w7H48T7ic87pcOHDcM7C7O0vOHRRHJgZfMmBwun9dfMTMjOzMOr+GbjDKnKNogmjXGcnYcdsjvZhgdh75Yb4mH+fMcxV7q09KFsrirhAj05OR3nkwQ718PFIyabdEDwJyGHARYUGCtyNk6nl54lLsUnipMO0d/a7f7bhuDhByV8zBXdJq/uZHqy8l6F1+kdrjuLXHWdE2jvXWrTxZ9stszbM5VOpqKgUTRQxKNuS09PPmrgCts0l0mDJxUmII573rscZd+g8F5UgLoZwq+SI2gFeOHGzoHOrfEynEGPMgi1J960MHDoBtGxc4uYK42gsdfAiHO2A++p6YPXVNGRn52nnheY+0ku0xr28eK9oCY9UzmdpHE2VNAyurP34wT+3aj9mCLnavl0xKLaz4J49e+Dj46MVRaR3796ipW7fPtOpwv/88w/8/f3RuHFjvPrqq0hlj6qKUZiJQFHUMKgy1k8bKEQR4SLna02/cWEORTNHtMOcfdIsCCtQrBYo+fL7/7B242HgxDnhDue2fjt+7d8I9RPjDNxa0S0yJ7Wtg71bDms/58CoMbKLsUdZ6QGhj50mBHVQE8nmtHJJ9RvTXvX0eSlgtoicuR0vWuWMQWt3H81rrKjIoohtCiue6odz79+HhzrWQ25eHib/uUW00sWnZuLNgS1w5t0x+GWSaQdCLiBUjPPPvki8v+qwEEWEbyHuXvf8cpUYuObCTUVFpWhQvGw9dxPz9keK//k5L9MW7Lb6TBgZnSgq9MGvzBEOtO+tPIy4VNPHOVZ/3hrUUswBs/r/XM/GYqMp8ZsHcfzt0Yj9YrLRn7W1KNLSqkmJiKK2NSV3O+YHcd503tTeWPxYH9T0c4eTg53OeWzmyHbaeaFPRrVHxPvjhINqfY3Zz7ppAwtU1PWzimT+t3ifqLiplH+KrWJ0+/ZtVKmi2+vv6OgIX19f8TVjTJgwAaGhoaKidPz4cbzyyis4d+4clixZYvD7MzIyxEVpyUfYusdLRYe7PF9uktzb/tenCXJyskWmmsyQJtWxaGpPvLJ0n3ZAkG/ePDspG8YQ6d88qBUWry3bLxavccnp4vHkjvP1+FRERCXgRp49EBIk9Q0fPQ3e+tQHP86fwRnZX/pY4xpnl5uLvCLsSE9pHYo5mo9dXZ0wcmgHSiCDzzMPf3tfHoKP1x3DsmOGTSUMEp8o3XeaOigyci7FSjt0r/dvhpYhfrj/jy1i8Z6eU1A0Pd0tHJGaJGxzJzCbsWIT0LaZ0Xwna9h36Q7C316Ax7rUx2v9m8Nfrz0t2MsFL/ZqhA/XHCnw98tUspdGn0w1ZfAcVcXLFbcS0nArIRVDvl8nZph6aE48PMes0hiC0JXpbko6tkcYP3ZMaeiJdqF+98T7vrDsjLylfex5Cpf3FpwcgLtJqbj/t41Abg8MaVbQNbC8ID//6utApTReCyuOXdE53xIe1x5oXw+xSalwcQBy8iSzGGug65nPc3+J8445Ggf7oE+DIBF27sL8Pw25OTni4vfCbO11PBbweE24JFC2melw+TpQ2UuKySijjG1VC3Me6q5zHZ97roP61h+OxavWYWzLWth3JRZnoxLw8pJ9GNioGurpuZ42Cq4sqm7HrkajR91AsQmblpWNXRfuYPAP6w3+7j0Xo7Ar4qaImFApe1hzDLA6x2j69OmYOXOm2TY6Cpm//vpLiBolFEvvvvsunnjiCYt+3+bNm9GrVy9ERkaidu2CIWjvvPOOuD19OMd0L8wmrbiUit/OJCHQ1QE/dPMT8xnWkpOXh0e3xCA2XTpSLxso2W3ypfH41lhEpeWgiZ8Twn0csfhiqjioGwyFu3EbqB4E+HpLuQSXb8Ap0Bd52VnI6tW1yH/riFquyDlzAbWreqJbR8uC0/g3HI/NxNv74w1+vV2gE/ZHZYpFYlNPIHrDXtyqHw5U9gaysiWrU3t7BOdlwMPDGY5OlXA6Lgu1vBxxKdE27WQ24VQE0KhukW7Cz8UeTzfxworLqTgcLQk6N0c7jAxzx5BabnBW7LYdi8nAF0cTkJBZuB2yqQ09Maimm3h+1l1Nw57bGYhMyEKK0bNyQfxd7MVrlj9xXx13jK9non9dRfDs9hhcTc7B8FpuWHZJtxK/uH+VQh0/VFRULDvPbr2RjvnnkxGtOdeWBPL5XOZqUjae3ZHvfOflZIcQd0dcSspCahk6pRWGobXc8HB4QdOpvbfTsepKGk7FZkL/kfd2sseLzb3RxK+SME6gucJ3JxKx5UY6elZzwbPNvPHt8QRsum6+G6OqmwN+6u5vw79IxVaw84yFF0tyjKwWRtHR0YiNzX9TGSIsLAxz5szBiy++iLi4/Laq7OxsuLi4YOHChRgxYoRFvy8lJUW42HFeqV+/fhZVjEJCQhATE2P2jy+vyDtSnKeQd+wD3J3w04TOhd7prfHafG17jbOIDLITO01xaaZVtqeDHZKolAyYFwS6VsKTPRrhg5WHkGVvu3DNi++PFbNT3AGgsUefPn3MhvexjYE7RDsibuP5RfvEwe+HcR0xpXN9hLw6D1FJ6TgwfRienLcLB64ocpkSkiTHHBtCu9W7qdJrdlCj6tgacRsppmZ2uK2fliZVsvT/zpPngcb1gOQU4OxFjBnfE0vWHUIOc59CdXujLeX7cR0Q5u+FV5cdwFHNcC53PN8Z1BKT2tXGrsgo9P7GChdCA/CV8tHQlmjkZIehiyRHuuWP98LQn4wEFhrg/LujRUsf547qB3hi6+ZN4rXAauiiI5fRpU6gyOVQyW+R8X9pjtFWHlc24WtY9VR/dK5bPuc8rTkuqFRsivO1YKgy5MCFNTsy8vJE5YXvKBZj+J5jhag0Gq0yv3tI5/PPNpzAa/8dLNyNcbNQUYGyOZmZgJN1bdpTOtWDSyUH0Z3xct+mCNcLVqX7af9v12k/bxPqr3uO19AhrAq61qmK33afQ0yydH6e0Ka2uBirEunz/pBWeKWfrkGVStmA2oAjOsUS8BoQECAu5ujQoQPi4+Nx6NAhtGrVSlv9yc3NRbt2xge79Tl69Kj4PyjIcF+ns7OzuOjDg2BFPCmK4c1fNxc4wEanZIrrDXnsW3KbsigiGUJs5SEt23zpUYgiQlGUl4dqLo4Y0aYO/j12RYiNt1cfBWwoikjYmwtwY+ZEIQYtfa751eY1qojLM72kAxdPXnTwSdW48GTk5BY8YPIkwKFR5j+wihRciPA6PWRRNKxZqMjvqertJmZ6eBI1eOLkLr67W75IimFrorQr1aBLc5yLS0Uecyrs7LB85V7k1C9YWeXcWUZWNtKzc4XoNdWO8c+Bi9j18jD0bVQD8w5E4rVlB3D1bjKm/rMT32w9jYGNQ4r8GPC3v7r8sCToeN9p4f3mHKBakBh2Hd+mDh6evU3c11A/d1yKKThsXO/tRYj+fDKq+njCTZPPkZEL+L4otYlMbl8Xfz3UA/c6609fF4s4LhBMLczSFNW628kZ5f74WVHPASql/1owdh4uHelj2khH/++urojasAh505MZdQZE0dQOdbF/1jIcu5sCdGlbtDtspSgis3adF8YH3esFixGA2NQs8b88a/3Y3F3i/zEta6GXVyJGDOyLwJcLGj3tuXhHXAj/3PGta4tWb2OiKMDTRbSZn7kldaM81jUcrw9qqdp1l1Gsef8Xm/QPDw9H//79hfU2M4i4c/P0009j3LhxWke6GzduiDa52bNno23btrhw4YJogaNznZ+fn5gxev7559G1a1c0baqqcEuGN59bsBvDmoda7C4l32ZRGN+mthgQPXA5GjE5QO8moWhQ3Q8frjkqZkdsARfI7k6OSNJkLdR5Yz7OvjO6SLfJnuBOnyzXfs5qUoHAWaZxc2jUDDRbkOdhLIGhuGtPXdOZ82IiOh3fTCJEUr4D4Nk4xePbshEyaMJggK/HdoCvuzOG/rDeqCh6d0grNKjqI5z/xK+ytxOZGaNa1hIp7R+sPowTN+6Ki81QBMKmVJM2P/xzsrD53A2tQ1L9QB9cjk026DQYoBFBNLz4pI0bBn6XvzPIn7vX4bxYv69XW/1zNMNQUVEpSGFNFEqDUzfvosFb/2Lv9OHiHHr8xl24GMoRMoXcCcK2cgP8uicCNbu2g2N0ArItiL4oDkb+tKHAdfx7g33cRGdNTT9P/DyxE7Zv2oC/9kTofN8XY9pjXJs6+GLDcZHZeI0uvnnA3AMXTP7OGpU9cPpWnDb78fvxnVRRVEEo1hwjustRDFH8yAGv33zzjfbrFEucQZJd55ycnLBx40Z89dVXooWOLXH8mTfeeKM472a5gQt3U6FtPFDzTc3v614/2Ca3acr1Rrb3PHQlBltfHIwn5u4UwarDf7Ss7GwpDFLbcu6mVhQRZi30/noNXm1ceHe0enoL55Un9EJhKYqU0ESCc1QtGhUQhhRGFDps0bMEZVaEo70dvFydEHnHsMVqAdwNp56Lk5FesrnMg39tw5CmklOeMX7cdhq3Pr2/wPVsqXyxT1PhFvfB6iP4cuMJ2ASefVwL3t9557lrJ+3c0W3RXOYHNK/78etTCrxu7nWCvd20FcKPhrcRz2Hrj5bqtP8o4XKGbarMrVJRUbHdOVPGyYF5cPYiZkMfxmJsf2momPVbfPgSDly+gx+3S7bdhUHugJj422bsvXQHdxXRCILrt4H0DOBuHFCrBuBfuVCihhtX2s4QW4kiWWBZKbT4rWxpZAC5/Dz9/kA3EUxOlG3rdPKVsxk/Hd1eHCs/WX/Mot9z6Kr02HarF4S5j/RUow4qEMUqjOhAZyrMtWbNmjr2hhRC27ZtK867VK6xtPpiTZWmsBUdZY7N+TsJCH7lH2x5YbBo1zFk98mD0oKpvXDgSjTeXiHNlFgCw2rXPDMAX246gZ+2n8awZjXFAn/K39sRcScRXx6rhEkjdX/m2LVYbDx7Q5TSn563SwS3nXhrNBpX89X5PpbBaWH6gCaLgI4zRuHr9GwkcPMOnP0rIyMkf9E978AFcSksPIAXOGGZuh8GThJMRKe4OnrhtjSLZIAVx01XtNY+O9Dk133dXTC0aajFwoiCj3+bUfh3FKJ1wlI6f7ocOT9NRXndlbZFplCIr4d4v3DD4kZ8Cqp6u+Ob+zoZzFKRX1Vfje2onuRVVIxQlC4Ib9dKiHx/HI5ci8X0pftw+Gp+hwAr82yFlzl2PbaAKHJ3csSOl4fiG4aL7jlv8e9dffKa4S9UlzdAQoHdh4AA3XNkqSKf58yIImdHB9zXOgyz90qVoFf6NceMEW1F+zet0hm8yk0y2ZXslb5NUaeKD/7aex5jW4Xp3NYbg1qgRQ0/EXw7Y600xiHj5+6sk7n3VPeGeLhTAzSv7qcTvqtS/lHT/MoRlra3WNMGY23LDFvAlj3RF+feG1vgaz2+WCkOHoZIzsjCwO/WWiWKCGefBn67Bv/r2xSXP5ogQmY3nrmBKxr77EwDftCPztmOlxbtxdS/t2vTrJu8twizdp4t8L2TO9Sz7I7w4NyxFR797n/47+OH8L+mVQuRImsD1u8AtuwB7sQUqED9PLELGp09A6wt3ObCN1tOItZMKK01iwKToqgEYJVk5I/rxa5reSE+NQMjflwv7HTXnLyK/45eRosPFsPusV/wzz7dFhBL4AybbHEuv1xHahZgrAwp4eeFmVFUUbmXKEqb6e+Tu4uZP7a3KkWRPBOpJFPRZkwe7dIA0Z/fjxYh/vjjwe5CJJmD7dMW01GaBS9v8BwviyKy9IiUxVbD10Oc3/vrzcQ6OthjUvu62PDcIEztEq7zNU8XJ9FWx44YfZSiiIxsUQsta/iroqgCUqwVIxXbwvYWLl5usAfWRm0w5m6TeLlUQqKmCsQFOFvlpvVsLCoybHVia4B8EBcldQuQ3XssgVWoZ+btEv3Ruy5E6XztXHyWWEwGeFcyekKRoVDadv4mJrevh9uJqYhJTsdfVuy6wdERv+w8Ky6CEu6jFgT6izBdRMUAdWtq558oWNp9vAzgLF59zZwRWySMtNYZ4vdd57D/0h0cen1kgTBfGbr/WIuHs6PBtpGSgEnmvPQOr4ZX+zcXO4e0ZLWWi9GJYkEzqV1deLgUz0B/VGIquny6AhGalsqB3+o6/036fQt2Rt7GjxNNh9zK8D3NBVhcaobI1vhkVL7pDcUPZxHZFsTXDhd7PBbYokqlolKRseScaQi6pTk72mH0z1sM/ty4XzehQ1igOD4xpPTbcR3F+/KdFYfEJg/Pg3Rec9V0BCx+vA8m/7EVd5LyjZP0Md+JwHtS/qodld2cRbshz+FK6lbxxsbnBxX59qf1aiwMHH7bdU6nbZLHUc5tkof+2oYrMyYU+XeplD2stusuD5Z83t7eFlnylUeEG87P0qCh8omTD22FdaUzdZs82LBdjkxqVwdz9kVqv4dlai68soyIkZLi5NujxdzE8euxIoG6yNB4ISEZCCu6A5tNWb5BMizIyES3x0ZiW4IVoWUGPJMoat8f2hqvLN2vvY5p6S/00TU72RFxC90+X1GoIlnWD4/AkfbhADo9+T12ZzuWiKhksnn7WlWw8sRV7ewXT2wUSEOahprc6eNhcc3Ja0I4Lzh0UWeTgJXLytbsxFrInL0RIjjYHD3rB2PTC4NNfg83C7p8uhwnb8aJ8EK6DOoH9VZU2DKzevVqYeKjutLd2xTXa8HYOVOGIeBsN2dIaJ5e21dmdo5Fguq5no3h5+GCzzYcR2pGNrJyc7Hg0d4Yo2j/ysjKEbOh76w8JDYpCxBzF/AvQ+1xNoBH7YzvH8Hui1F4efE+3ExIFdUbdpVU83EXbYqc1ZI399iWvPXcLRFDcnDfHnTr2hWNq/ujkhEzCSUvLNgj2vjJxyPa4pX+zcXmFI+tfC7jv3pAJ0BXpWJoA1UYlUN4UKYrjnIng65cnA0obBuMqdtkr+7zC/eI67ijRTe38sjMkW3xypJ8AWCUvDzUS0nADU8fpJThbYOGzkC7VvXw994Im7athQf54PQ7uq2SY37egEWHpRYFS1suZYOJZZO74MKRc7h5Kxaff7cM6N4BqFp8IXicZ+PJkL//4U718ebAlvhswzGx+5euuU9MNp/erznGtaktWiuUHL4ajVYfLjV6+7UDvBD5wTib328uch77Z4dFVUyelLe9NMRgyvq1u8miurQ94paY0dvzynDU9LdtFldZRhVGKiXxWjB0zpS9AtiKRbezfg1DMPmPzdgRmX/O9HR2RJe6QaICJB+PLOXgayPQKrRgXMqJ8zfQ7rFvULOaPy54eEPkbl+9KTbQUCe0dLobipErH40XDqucwzLFc70a405SOubuz9/MJZy7XP5Uf7O/572Vh7Tt/zk/ThWbadwErvXaPDGzSUObVwe0KOJfo1ISqMKoAleMZLjws3UbjKnb5EmAJgWcFVLCipGylF/JwQ7fj++MmWuP4kJMEkqavuHVsP6MZPWsFHj9GoWI3f4viuqolpYOrNqMx76Yhp93Wz/zYVNi4+C0bS86DemCLY5SjpEt6FQ7UCy66dBDccGTPNtGuGi3xpJcxvXAUaSlZQANamvzl4qTOlW8EHknUXwc6uchKjxyq9rXm07i+62ntK2hFBjsUaeQY086d3gt4dNR7fBS32Y2v+/fbD6Jaf9abp9/85NJqOLpAjvYYcOZ62L3mCYbbL3h650OV7SBv5dQhVH5PxeVl9eC/t9e088Dj8zeLkSPOfw9nEWQqDLw2xwjW9QUXSH67cDvzJiLdz+eJ33i6wP07WJ5YOvhE0C78rW4pwnQrgu3C8z9GIIdEC8u2lvg+qSvHzLbFs1WPc55jm0dJuaPZDjvyc0nd2dHcX65V6rx5RlVGN0Dwqg0OHs7XgyG8399+jcKEQsztixxl3pq5wZYd/o69l+OLtTvYvgpnbSIs6O9Tt6PKWaOaItXl+0XOahKdzSW2K/ctWz+yeyJJDsblVyckGVXiif/+ETJhCEjE3b2dsgb3s+oG52ltA71x0FFwC0X1mwHYQaGvEs5Z+95TPl7h9bUQoly1kyHyMtwqBWCHE07XUkyf0ov3NdGN/CWLSc/bD2FmeuOIiHNslZELkMWPtobp27FaXcQaXbxaFfd4d3Cwta981HxaPjOogI5U82q+wlxaqpSy7yOmwoLblrIfjKyHdoaqChVdFRhVHIYqppw/ubrInQvlPfXQm5uHhye+NXo15kjxPe4sVlYS9j43CD0Cq+mPXaEt3kC5yIUG4L9ukqB5JacR2SzHraEjRqAiggrbS8t2oOt5/OzCtdNG4i+DasX6vbEY/72ArGRtuKpfhjcNNSG91altIWR2hypYjEM/zzw6gh4TvtD5/oADxfRtiPPcdBJ7v3VRyy+XS765jzcQwTE0l64kr0dps7Zof06ZyS4NLUkWFQ5KyPDNjObiCL55FHJEZZP9tgQVqv2H5PaI+7mi9M8Cg4b9DkrRRGRqypsQ6Aw+mDVYby5/KDBn/3nkZ4iz2nI92ux6oSeNWztUOSYqWZZY8ZhTeq7n4ezqBQFeuU7SdE0hO0P03o1wYBvVmtd28zBdtJLH41HZnYuPlxzRNi52koYjfllo8gtUcLNhV/u76qzCH149jaxS+nn7iIqtVGJUrWWoohC9oEO9fB414ZoGKyXwaWiYmPkORv9dy2ry7z+XnU4zMwpuGnE1i3m53BwP8UGRjQP/bUVVz+eKD4+duKSEEUuLk6Iivgbzs6V4BJ2P1AtsEDmXgF8vIDwOsCZSMC94oY6M7tNH1p5F1YYsWLXtLqfEEayWY5KxUEVRipWwUWZPtFGLJ453GjOlCHI2xXXPp4oWi9GtKilPWDRdYamDuTEDSld2mq40E5MBpJTgNR0DtQhr4ov4GPBTpo1UKg4m8jjyc0F4hKkFoeitLu5ukiBqLf07Ke5E1qMPeRsP/x8TAcxa2aMbzdLVt8FRBHJzpbuo4kcJmtEkZdrJSRaUOlhzoec9fHb5K4ic0LmZnwK3lp+EDsVvf+mkMOTHZ+Ypb3ucmwSDl+NEZatMmmZ2eJxopi31P2Owm2FpjqqzDRRiiLCRab+QvOdFQex5dwtYYoyoW0duGtCDFVUihO2kLFSlGfC5+y5BbuF82FZaasrKdie+924TsL0hQP7ylZWzhHSNGD+gUgsPHRRuMwVBs76yvz2t2QCMahva3hpNoAqpacj68oN88KINAuX2pxXbgJuRwNVA3Q7JCqouQAzFbk+4fG3emUPq3++ToBUdWCeokrFomK+4lWKDS72XujdxKJZHR50WA1ShsFqb0fz/3fjOhc4cXavH4zYLyYLq1EOOLJNg5f5ByPFIpDQzpQHNpPrabZ7RVwCGtYFqlW1ylrVKq7ekKo5TY1UD/j3+dloB796EHBRV3zUqlsdltsiFA6ePGaMaCOyoAw9jkxV56UAfIKULSwWiIXmIX44es34UK0lokgf+TWWnZOLD1Yfxqfrj2tfl3R56hhWBc8vLNiHbo5WHy4R/7fh61HRNvrHA93wYMf6Zn/+elwyQqbPLbCh8PdDPSz6/e8MaY13hlh9t1VUigTnapTtc8Y2Evh9PJ7fa+fIp3o0Ehd9nCs5iHPiLzsKZupZA0NIr8Qm4dyJi/j+11XiuikP9BX/x8cnI4vnPi8rFvvc2DPURieLIt6ekQiH8grzEJ2elDa6FheiutlEExjPSj9NGHzcnHXiHR6ds0NEVTzfu4mYP2OVny6DKmUfVRipWASHEJlx0yGsilUGBvLi09OlEpI0rVkkwNMFoX6eRi0zeXLhQYQXlqzJAx3qwuf5v4STz6WYJJFg/fG648Z/OQ/qjetbleVj0O6UosbUgr5uLWDZeilfaFBPqbKjv0sqV0r4PytI5uZtFJUVGgM806MxPll/DA5BVZBDm2lN26K/vzeG9W2Jr64VPo3dEqr+b07hftDKStYTXcMxc2Q7eD33Z4HXjSWzZjUqu+Oq3oKNVu6NgqWTGK233115WLvr+tnoduhYu6qoUhYFpSgi4UHmhTBtezt9stzghsIn647h7SHlM3BRpeJjadCzNYHQ9wKcTWnPvLki8urSA+IiGN4XlZ0d8cHh6xiz/DjGNg0BQqsBHVrq/IyPmxP6hlfXiR+wmHImijgPet+vmwrMaxpj5YkrBoWRKWMRVvXrr/QW7XS135gv5qL5NXYQPDl3p/Y25Flpsm/68Hty7rO8oQojFYuYMnub9g3u7uQo+qUtsWomzBRoHRog2pqGN6+JrnWq4oVFe4WN5tAf1uHX+7uKChBnmJYdvYw5+yIQXrUyZoxoq5M141zJETc+nogOn/yH81EJxkUR2+fk3TJDooj91AxHdXSURIosYpJSgPMXtaGpzhevIGP/cZYbgFohQGvdbB8lLg3rIPhuLC6u2CSdREYPzP+isn2Mw64UTuaEkR19xvLw3tDWeLJ7I/i4OovHTzgANmkg9Ya7uyHG3dUiUWSnOTEWtnXDUhzs2BZn/c9RKHs4VcKSo5fx4/Yz2uspih7qUA9/7DkvRPQr/ZrjvVWSsFHSp0EwHgjJgmONxhj3m24WkFJ8y68mvt52vTxU2+5maXgyZ4x48ou8kyDChh/8c6vO93HOiq9ncy1t3Gj4ZN1RbXsizUEo/D9ac1R8fim25B0dVVQshYtEW37fvQTdL021JZuDM5Jezo5irlCcZZ2dwGZzOfz89z0RBUQRiU/NFE5uhvLswLkoS81xjLRDW4v+/fB1c8JdG52fmD3EfL7X/9OIRxM82a0hXh3Q3CpjkREtauLxf3ZoXUzZ3fLHbvMxCxwRUCn73FvNvyqF5r7W+c5epkQRUYoiwpDJ9aevi48pfCiKlEz9ezuavb8Yzk/9JnZ5aDfM6sgDf24Ru+pKfD1chAuMkgPTh+H0O2PE8LlAFkVGdosoYLB5jyRSKIo4A3PyPHDjtlYUkYzrmqF8zklxPkgBf5NdVn4FLD3yCi5evi2dXPQFlPIk4uUpiTFTaO73B93q441BreDr7iIEYs8GmpYUDssGVZH+TgtPZrxFiqK3BhXOlpWD1AzQM0dhRBGJTkoXYkA2E1BCUUQoqhnMSrMPJSOa18Sqp/vBo5K9doea9qkDG4dgyeN9UC/QR0eAya9h5QwQxQ5PeET/lC9/zkwvfh9bWJq8t6iAKOLvowmFJXM+Ly/eK9r5ZNgyqqyoWhI+qKJSWsgbCaaWx4xJ4Pep5MNjDqsZpmCrnZLKbk5iw4URAT3qB+Pih+MwyS0XOfNXAEvWAmu2Sg6lN6PMVndu3Iw13FJujWOojeZZlfeD1ZYvxnaArdh9IQpDmpl3iov5fDK+n9C5wIyRbCyi3y4qG4swVsESIaSE7dF1hZGUSllHPfuqWAQXpEWB6dTWMmdfJPp/swbxehkPXOhuf2kIBjSqji86+6JZdV/RuvTHA93FidrOzAG8U9dmkqvbuu3A4ZOSIOJi1k2v/zdacsFz8HADwmrofIlL2Dzl7AwFFmeZOOxa04TTDRe8rBgp4XySTGaWdL8vXcNj/Vvi2LVYISYpEFmJKyrvrbLcLVAmzN8Tn647qrOQtwgrXeZo990oyActjOTudAwLRPcvVgizD84hNa8utcfRcZAWuWyb+EFTbXpncCusemaA1tBDJsDDVfyvzN6SYSsFBWC1yu4613MBqHTYYqK6oWBGJq5vOntDtMuYY9t5aVZOybdbTmk/NmSJX1GRkulvYt7+SPE/P1cp25jaSJB5b0jre854wRIYEK1ohDDYfj6mVS083b0h3h/aChc+uA9zp/QSuWmbXxgsNspi70oD/w3DqgIJSWji74Fv7+9m+hfzfbXDfAWlNOheLwiT2tXFmwNtl6dEI5xpPRujvZHWNeYYGZr5MWcswsub/xl2Z9XHwd4Ofu7O+OvB7pjUvq7Vf4NK6aC20qlYxPSl+wzOCllDyxp+OHzVdFK1PlvO3USHmf9hzTMDUNPfU3s9k8Pb1/QXGRV345KwduMRfP3TCuSdvAT4+0o91npiRmZhRiUgvDYQ4A8Em+j35TDqknVw7toGZmVd364webZToi/YlEKJWUQUR0dOo2Hbp5A7qAdi0rONtwxGxwI1QyTBVUxcjEkSF6vIyACcrWsbYK7HqVv5gkAxSiVCZ2mOwPyk3uHVxLBsYnomwt9eKDnDzVgGP6Qj4k66eI1O7lDwJJSYlql1OoxNzhBiStmqSSh+6KRlKrCS1/VqUE2IICX8/j5frUbXukGijaNrvSCDfyfd+8w9no3uEbvtsp6Do2IceSNB//mTeWj2NoQFeBl9H9yrnL4Vp5Ozp4QZgIy7WHgo307nzeWH0KqGP74b3wntNW506zcfERt5129K59NLV6Lw+MhOiHGohHfXG5kB5jGsiXlDmNKAx3ZeXh/QQgRWs82+qPy99zxGtQzD52PaS5X+6AQM+mIJTt3NEtUbY+Gu5oxFSJJe0D2rfPUDvVG/qo9ok6a4fahjfdynFwyrUj5QhZGKRciW2dx92XBGd0Goz6m3x4gd76fm78TthPyd+YvRSWIn/52VUkAmCQ+S2pzOKBbE+vC2ar0+Dzc/maTtWV++eh+27z6JVo29MHLSDOy+kSBVa+rUM28vylaDZg0t+8NH9jMtiuTebEtFkSW927ytBmG4Q9tUY6Jo+UYgNU363tqWV/O+HNseH689ZrBlzWLYQmguLFH/67QYp31se8t3BOXFAytINDegcBraLBQLpvYW7k5erk74dlxHTPl7O04rXj88IRk6GQ37YR22aio1rC6lZWUbbHvjSdSckxb70mVh1LF2IOZN6YXP1h/DT9vPiEyvbp+vELugnB2iYx3NM9jfTxFnrzDV0G87lfF3r/juRaZycEb9vEFYHreuGYB26rBymRZHfRpWw4erj2DmumMFvl7FS6rQqkgkp2eh62crjD4cFEWGOHQ1BgO+WSOc0AK9XHG5eVPAzRWsG9nvPIDk67fx2+z1+OHDf4A8O6BPF8O/IDjf5lsHucpdjLEPlsB8OLkl3sfVCfFphZ85+m7raXGRN1qGNKmOD9v7mg37tdQwhDl5E9vVQf1AH3Gc199kUym/qMJIxSLkPKJaiqqNMV5ctBfrTl0rsODhQY6iyMO5EpI1Oy6c26A5A08WF6JN5wGcux0Pu/QMPPXij1iyYo+4bvDApth9KRroKbV1WEVsHODtZZnjDo0ZPHVbrIz2ZnNm6coNVKoTiixLu8k470RDBTkTiW15phjaG5i/AktmT8eY1ae04brGev0pQNefvoEftp7GpLZ18flGK9vilFiSIK/fQsOZKF547mWeFKtd3sZfSzzFMJw1JjlDWHfzr2N/PXeolfM3DDSt7uOOnZG3sOvYGTj5BGB6/4KDtD9uO60VRTLTl+zHt+M7obALQqbPc9eRu7nLj13B0qOXdXK75N/3z/5IIYjYGqp0SWKbhakFQtPqvhirmO2rSJhrVyFPz98l/udsBduIVMrmQp/zoXQJ1YfvCxrqqOTj6GCHaj5uuBFvfWs5z587IjVzr275gjO3cxvgTiwe/2AuEJckiR9LNq+UlLIg0g8WpwMp/97XB7bA8euxYu64sGgDh6f2hIMNDUN47rnXrOjvFdQGYBWLYAsTCfY2IA70WGtAFOnellQFYTtSn/Bqwi0n1Nd45gLXj1M71cfmNXtRu/lUrSjiF1bZ+wNd2hie1zHHnVjDooi92AePA3di8q8zJIpIQhJw4pzuPA3d7oKqwNGa9jaKIlaf0tOlUD3ahO87KgkyIzz89EiMGNIRHwxtbfKmW9fwF6JIDqMrkihSYs1jrSCPJ2ETokh8j3Buy4CjPb35JHZG3saLC/eIjIik9Ez8vP00Wn64BH2+Xo33Vh/BgFA3LHu8t8ETm9I+Vea7radEyGJh6RVeTVQzKIoMDeoqmb03ooB1bHKGaRMTGpHQIr8iYkm7iswrS/Zjs17bokrZgJlghkSRXOU4cLlivn4LQ1xKBv47egVh/lIwqM3geaOKn1QlGj0A6NzaOlFUBuleVxIcZ2/FFUkUkTy9cYCiGovwetVYpGKjVoxUzHr3n7hxV+x4y18rKlk5ecLM4YcJnUQ/bt+vVwtXLkNwCJX91r/uOidd0aE1cOU64MSqSh3kUYTIXLsJhFixg5Oiu2vXolIujsxZJR1J2fJVxV/n65Xs7ZGl//fTQahxvYI7bm6uSFNk7nw5pr2opMmFHXdanqek67b9scri6SH9zzkpntyMCbLDJ7G2U3OcvnkX03o2EbNY6+UWR1YtFKJs+bHLJkakiwBno1jhYn4T09NtANvMZAHBlgqG4tGtjk50NF2gQcH3W0/D1ckBKQphwR/55EgC+vVMQN2qlYVBghInR3tkGshAorNcLT9P/HfssnBO7NmgmhjYrWrhrqGpyoeSYG83xKdlIDVTt3XOpZIDutcNwmNdw5GenSPmkwI8XdHv69XiOaW7UkXMvbC0XYXGH5zHGjdrEy5/NKGAY5dK6VIv0Fu8hg2ZkRC+htvUrHivX2OkZ2Xj1x1ncTEmES1C/MX509XJUbTNMpqCFts2hxtpcucCzx02stMuTTafl3LlFh/huasgbFPW7wAwBY/P18Vj72GxsQg3u/QtxfUdSlUqJupZRsXsMLTMi32aGjwYsDUuMyfH4MLTGFz88qTBvmmKIp5gWQnI1msJq1vFG8+3qoEvVx+ShEqAr3TRJz1DEkvmYLVKFlMaW21WJR7r2hCPdw2H/f9GYcu+M3h6U8Fk8gKiiLi6Wpw9ofzTGjjl4VCm3tuPJzPlCc1UVaVlY9xMy0ajdxcV/JpepSrHkChS5jcVBbb92UgUyQvhSE1LJVsqeOG9f6RzfVFh/HzDCVGRVIoimajUHDR+f4n2820vDkbjar7CxSnj+yniuq6fLhc72Qzao1ClGGozY6n2Z/ZfjsbHa49icJMaYlZpcNMaWpHFVs/5ByKFEJUHdy2tfBhzZczIysHa09eFffj4tnXE72KrYOtQf7GorKh5Rpa2q8gmFbRzz1a0KaqUDR7u1EC8T2jIw7lFZtApc8bovti9XnCFFPeGmLXzLJ79d3fJ/lJm9dGwh+dARjiUc1FElC3JMq/0ayY2ypqF+KFXg2DUfHUerhvZUDVEZk4efjiRiOXxu8RjRPMdivppvZoUaPk0ZizCShJFkWoMU7Gxy7PEW7YckZiYCG9vbyQkJMDLy8Yl63t0GFpm0aO9EZWUhqfm7RJi4uOR7fB8rybaoUO+lFYcu4JhP643+7toX3k5NglvrzgkdtMOvDZCLHa5gByh9/Nd61ZFUFwc/v1jNdCxBeBnQBhZwnGKnTygcX0dUdAwyEdneL84GN2ylmgHYoAdf3MujRMUfeIVEcoJw/vIhePEW6Pxv8V7sfaUlIk1qEmIEOX/HjTeDsc1wk8TuuDRruHamQg6CnFhztaWxu8tNLmL2yEsELtfGSY+7v3lKmG40K9hdaydJgX40mJ6wm+bbfY3cu6oT3h1ccKmTTvNJv57Uje3qyLASlvN1+YZDdQlnDPI0Nts+fPB7qK33xRZWVnCrdLckLVK8bDpzA1MX7ofB69Ea6+jac+0Xo2FS1hJZnSV9GshIioB9d76FyUGl2+nzkutdHodDkXCmsDXEmBKp/r4dbKuHfnRazFo8UH+RlhhYT7fJ6PaW9Q9o+9QqlIxtYEqjFR0Fiqmdr/ZV8twOTrU0YaVVZDC3s6v93fFgG/XiOM6RdJkzWKHNpfVX/kHKZlZeLZHY3y16USBKpKKooXCnANfScMn9Ha01mihLDBKs/tniFM376KxoaqbAtkN0e6xX3SCAZmB8eHqw3jDwkwLcxhqSaIL1fG3RqMib8QQQ+/wV/s3x4y1RwtcH//VgwaPPTKqMCobHLoSja83n8T8Axe0FQC6d9HRkRsVDGEubkrztcB4AO/n/rTNjZlqj1N2QVgLZ4eTU6RKk7zgL0ZRpIxgsBRuXPI4yPwnbpSwyk5nvhY1/PHo39stzkj0dHZEXm4OkhWOSNyw/H58Z9U98R4g0QphpEpfFYtbgq7FpWBnZJQ4IBlbmMj9uTpBq3qwFP3m8oPiWD+uTW2tKCKcIbg+cyJSvnkYn45uj0sfjS/8M8QQV01IqyFeG1DQvcwYp98Zg0e7NLD9jMP2/XApbOdDWdy5otOfCVH08+i2WPV0fyx4tLdohygulI5vl01kBjUK9sV/T/Y1udAOfnkOVp+4KqpTMt9vPSXm7mwliki3ukE4995YvDuklba1Q/k7Kxpyu0qwT8G2uqmdG4g2R32+H9/J5HOlUnZoFRqA2Q/1wNUZE/D24Jao4ukq2qZf/+8AQqb/gymzt4mNiYoK89Q8C/P+ZVucPrIoMtTgU1hRJH7WAWCAufJcUoyVosLscbKbg10BH605is83HBemOVw/DP5urY4oalrNdCcJO1tkUcSIhd0vD8PCx/qookilAGVwZaVSloehLfk+ecFTrbJh44DhzWuK3R+y79IdpGRk4cjVGLy2dD/eXXFIfC47unERxNYlD+dCHPx9faR5JAMnkw5hVdC3YXWLb6rhOwvxy46zoqJlLdW83YyfILu2RXphC2LKlhTu8hU3tBQ/cFzaYVQKIXlmibvCNI0wwWOL9mPQd2sx9peN2HRWGrC1Ne5Ojjr25ZwpUkJHu5lrj+KffRGitU62FjYF77NsMU/+3H0erT4sehuHEran1gv0wVuDWwkhTpG0+pn+qMjwWHFlxgQhCkn/RiFI/fYhPNuzsc7zxqDE7B+n4MnujUrx3qoUBhqZvDOktRBI7A5oWcNfVEZ/23VOVGuvxyWXuwf2t51nRWdD43cXCnMiQ/BvZNaa1cHYkfnhrgUojvmhogirEkD/L2b7vqEA7ONGngfmIZGEtCwEuTng3yk9sPN/Q9GhtpFMJ5V7nrL9jlApc8PQln4fFzzDmodq+3O5wyPnFHFAl4GdhFavbDdQLmRXL9+FgAY1seq8JrNBQ8/qPth83cAskLnSv4GTyZ6Ld9D985UoCW5YKDotxcXRHun6RhfHzghDBovhoC6Hdq2BLRtR0UAbybRC4Kc5QR06Ke081q1p9ETbrLqvcCS8lZCCuNTCB/eZ4/PR7fG4wp5bvzI1d3+kmIEoCvqmCO8PbY0ft58ukuvUqpPXtB/b2UkiqaxDO2YeE6pXNu/2ZAxWmV/q2xTbIm6J26MBBc1YmL0lBz8/3aOx2ttfTtl+/pYIPT5/J0EYp9BIQ8mx63eL9PopDehiyeoXLy0/WIwbMyfB3dlRGxjNedvXlx0QX+fpx+JJbhoIVS07bchlge/GS+61zEDk48j1g6ENyie6NRRVZnmd0SS4Mr4Z1wnLjl3G15tOYkzLWhhbJQXDmtcUx1cVFWOoFSOVYvPu54KHAWh021quGCB/4M+tOmGu+uGk+zcdKCCKSGRiRoHrnqnvAixcDWwomFNT5knPQJtqPmgdGoAvx3RA5g9TxP+mcLKDJIo0WVBaYWiNKCIURdb6rrAaNLiX4a9dvAr4ehcURYrfwQXQ6VtxxSqKyJTODdBJsRvIap3SZp4udeYqN9YytnWY2BGnYUBR5xLKA9zsGPPzBrSdsQz13vwXn6w7WsBJitbFlsBF5LwDF8THzpUcRLsNxdHCR3vDx81JZEUpn0+V8sOZW3Ho9vkKsTH2996IAqKIfLflFMob343vrP2YM7CB//sbtV6fJzog+Hpmm9eXm06IrysPs5ytNUim5n3PBTs7HQy1092jsDrEKjoDtdmeyNgGungq+W1yVzzcsT7iUzO0P0ODHK4/5GMqN+YqFeLYrnLvoQojFZ3ZIKJ/6Ciqdz8XpezvZRuFyRmdcxdFqKlX51YGv3w1Ma3AdT9FpCOwaT3R0uWcV/p2vqYW1aF+eruiLs5wdnYSrRjZubnCrYmhoabIlE+ySgFS2J5wW+6a1Q8D9hwBLuvNhZTCzpzjk7NQy98TQ5rWEEnzy45dwXP/7hELFlJH08bp6264YlYYsw+2Bt5JSrc4/8gQ0/s1Eyf+sg5t9Ru+swCLDkstP2lZOSKE1enJWcIOnTNZNKpwffp3PD1vp1mbbWZTsYrHubD5U3ppXcs4/0Wxuf2lIeoObzmF7zFzhwBWkcpbEGwNXw9c/3iiznUUfT2/WIkftp7GakX1V8nUv7eL/9vWDMCaZwagXTVNxV0/asKp7B8HSopu9aSKPzPm9mgcQvX5aM0RdPt8OWJTMoTL7TM9GiHYx11s1hy8IrXk+rpZ2SGhcs+ittKpFJt3P61LOaQ+/+AFkXFhCid7O2SyDcveHvm1JON0csrFtaw8XM11QFyTcNgnpyODC9pSdhg1tKgWyzw7O1yJLdhLv/OitCBYcuQyztyOx+9ykK0pbBjgF5idgVoJd7HXT5rxKDTMMqoRbH17XjExZ1+kjlDlwC7bWjafuykG+8ndlIIVyMLCalj9t/5FjSK0BA1qGmpzAVAcdrOctZJnsyhmlBXfHZG6lV6G8fI6DjrLbUZKdkbexosL94iPPxvVHl00s0Yyni6q0UJ5JtDLTZgumDv+/7MvstwFwXKG9hYdK1+eo72OLV48f45vUxs34lNFC6EhmJd28dIt7Pt8NhAcKGXqubkYPdbzqGq7o1X5YXq/5uj/9WqsM2DEIsPN1gvR+a3N3Kh5/J+dIhx7e8RtEQtS2c0Zg5uG4PBOqTKtomIKVRipmJwNKuxi6tzteLSfuQzxmrYpHrzotEUnL30rTp40udNDUWSfkIRcZbgpB/6VVqIadmXmf57JHem2zcrsMyn+HjNta4evRmPPxSjLbjAt3WYZSFGOzoiKjAKKKozkFpAyBoXqU90a4vttp7H0qJSi/tmG48XyuxhyeepWnGg5Hdi4Bs7ejhdzM4ZgZeStFQdxPipBe92qE1fFTrIcKFscYc3c5Pi6iAGFTav74dDrI8Wc1hojO+NKjl+/C49n/xDVnxDffOEYeSdBtOPxOaK5AnNuVCoeE9vWwRcbpbYyJX7uzqjp5ymqLw92NJ1NVVZhhbhPeDVsOHNDex03Ctga6uVqouqTlo6nJr0vfRwabFgUycfVhCRkpKRKAuoe4YcJnVA/0Ae9vlxl8vu4MWOsZXf23gjtx0/3aISqXoWv5qvcW6jCSMXobFBhYXDm4O/XClHEGQHu+gxoHILH5uwo8L0zRrTF4Cahop3Mw6US0mPi0Pn5WYinMLpyA9hzGHB1ARrUBsJqFFtuD/fpGRz5157zBTJV/n6oBz5cc0QsdPWpW8ULEXcsqXHpQWe3G1FSMF9l7wJBlqbvrKaqwD70yMtAw7omv52LYWNW7HZ5eWju7YzTp88jo0GdsmkBXgRmP9gNIb6e2HPpTgFRLsNH01ZJWcxmoa39zzvOmPw+7iz/ML4zen+Vf+L/eO1RMdT966Su6FTH8lk+QzzwxxadhYEMA1WZHcTKcFHEEXNFmlf3s0gYydR4dS72Th8ugo5ZIZUDQBsGVcas+7up7XIVFAaB01aZeUZkxVP90LlOVfhUgNYmtol2ql1VK4y+vq8Drt1NEZsviWkm5oR4TuOFm1zJpqtpYmNQ3ii8R3hy7i6Lvk+uVnetUxWNq/nih22nDX7fyuNX8OaAsrt5qlK2qFirIJUyAXvGIzVigeKIO8vN3l+MvZfuCPtt7pbTgpsM+2E93l91WBvg1ig8FGf+egGDspOEKKoaWBk929TDW32bYN7IVhjWtIbN7y/nOk68NRrvDm2tzY9Rcv8fWwyKIlIoUSSLm9MRwLrtcJAHb7nzde2WlL9kjBNngaOag39WlmSTrYccnMhch1n3d8WZd8aKvmtD5NnZ4UiDcGQ0rFesooitkhPa1rboe98YaHm+lClGt6iJ1/47iB5frNSKIs48TOupW5lgpoXFMLzWCHysz793H57q3tDszby94iAWH75Y4Hq6sFEs0bLeWnJz87Dl3E08/NdWg6IICgH43ILdOoYU1sKWT/3w1TGtwsz+XPuPl+G1ZQeEKOJboEf9YCx/qp/YFFGpmLCdlR0EcvWIldWj12KL9PorSb7aeAL+L/wlNhv2XIjSziryf24y0C2NVPVyxbR/92gFoFkGdAN6dgQamd7YKr45zbIVnM6WS9mG217zN7Nj5bPR7QxmnSm5EJ2ATWfzq3b6qDb/KtagVoxUbM6QpqH4YFhr0UJD97kLMYlCIHWrFyQMGEL9PDGiRU28sHCPmEHgieXwtRj883BPqWrk4IgGAzqhTt8OQizRgYY2nDNWnyjgfGULRrWoJdyEOAvFOZ/CwFEW5jOxr5y3VYBb0UBamlT1Iu5SqN7oYZ3w3VcP491vluLHz+drz1UzZkxFi24txIKeC0npNu4ApyLyA1SZWn7yvEjNC+rSAr0bVEf7sCp49t/d4stccPKxJmxhUrZtFRru0BXC2SczOwf7T1yx6Hs/WK274C4si45IrXNKOFf0zeaT6F4vCFvPS21uuy5EYcf/hqLLp8vN32jVAKNf4nP1SOcGGN0yTLyuTcGq0o/b86tKzUP80CY0AL/uPCtyN1yMCFl9uDjjRsTSI5ex8sRVi3LG8jS/n+2yhakM8z04RTNELsOMIVaaf991Fo/M3q5dKCZlZCElI7/VRRiMNKiGkS1qYmizUDGDolKxWXH8Co5cixWV2X/2R4qLrdo6ixuaQry0eK+oTHCzwdiGA7mtmaO6Hm86KF0LDRfYMVBq2JW59jmasJy6GYfcvDysfmYAWtfwF+3J5mIQbiSkMajIqDCnM50saFVUzKEKIxWbQ3Hz+sCWBVqHlI50nKOg5WmrGgF4Yu5OLD92BS8u2iPyStrOWCrC8YxRP9Ab52yxyNfw557z4lJUvcD2IKMEBeS3v9FxiCInLR1vTx+PQG83fD39PgQ4AL/8uQ63o+KQGB2Pfo1C0LNeEF6b/L5ot0NUjGRu0LqJdFsJmmrV6Qi0aRyCN7v1wnOrj2nbC45dj0VIZQ/hCBjk5WpeGCWlAJ6GQ3m1FNbu1N4ekWmW2Tdz7oBCYZlmJsgaqvu4i4WJqfY4Xq8/z/X2nK0YU90TsVl52BxVuMDJv/dF4JtxHbHgoHUDvtwwcLS3F6KIvNC7qUUzfRRiz8zfJYaMZdi62iLEX1SOzEHbfG5gsOWHQ+IHrkSLXLFHuzTAz5O6GnSjG/PLxgItiZc+HK+9vw93aiAuMlyMUERxYcKNg0FNalSIFioVy+Dz//wCyVwjr5jaOm0NW8FdnSTb+Pt/3yKOp3zdBni4WHWe4OaGqfNYaRPi4QQHp0q4fNdCIVeMsKMh1NdT5xw68Ns1Ihvq05HtinTbnGF8cdFejGoeaoN7qnIvoAojlRLBmE33Q53qI8TXHX2+Wo1fdpwVLXXmNnaUosjZHsjINX/QpVNNSeBgZ4dJ7ergbFRCwcrRrTt4ZUBLzFy4EzguLYLvxCZiwGt/oXn9EDz8yCAcOXEJK1bvQ3Z2DuYv3o6HnvxKElMURZyv6tZOqjYlJgMJyfBs3gBJR89i+YLN4oJxQ7S/jm2Kq5/pjwGNa4h2sW0R+Y5hfDZ0ZMrKTQBnjMwJo2LGycFe2GnHpqQL9zgHO+CvvREWP3/ybq25vUH9ma7NtwrZEqmALULdPlshKlCWQJMGDp5fu5uMizFJcBabBZ1EDpMpopPS8MZ/B4SQUr5XHuxQDz9N7CJEnyXC6OrdZEz+Y2uB65cdvYIfJ+QJQa2E5hX6oogzQzX9jc8/0GXvt8ndzN4XlYrJyZt3xWvbEPJL95HZ23AxJlG8Viik+JrO4788bjjpfs6fkb/nbFS8tm2N7cPMualf1VtsMnAovzAOj68u3S9m/ZSwjWv2Q91F/hk3Ph76axsWa6zqTWGRKLoZVbymCiYcTK8ls4W7bOSmHXtztHiu3xjYQmySHr9xV1yfmZ2LJ+dZNm9kinFtaovn0Ta9CCoVHVUYqZQ6vcOr45FO9fHbrnP4cdsZrHq6P/49eEG7g26KN9tUxhXHKvjVhM11UUVRoJcrhjaVdro/33Bc7CTKyIGe8kI7Jy9PLOQLcPYCcOwMZm7bh8YNQ3EyKwstmtdBr1lbRRVo7e5IfLw7EqjkBXRti7igQNz/v1nI1tgig05lQ3pL1SYO7G7bB6Sk4rMlH2Lr0YuI3nsMmzceRu6mXUCvTtpfO+qnDXikhyu+38gFbb7zkY4o2roHGNCDrhsoSQxVdegwuFHTK85WL1LJwU44PyVnZFvu3GcBgSdOI+rsZSAkCD6hQXAJ9ENcnj0yCpsLpWnLs+TvZhvRpY/Go8l7i8TCkQJp8WN90Lqm8VY9Vl041P3lxhPCMlsfpsMzIJUuktzdZhCiJXBRyZ+V4SJQXxSRJ7s1FLNFDOlVOssxgFVFxRBRRtqblMSnZeJ/i/cV6QHk67f5B4u1n4cH+eDgayNRyUptxDBofX6e2EUbCk37eFa4OM/n8MSvKCruScmo4RKEM+kGdvcOnQBaaboDCEO99QO0zVEKOXKFod5b/xq83tLWebbLcVOVLZtKWHH6d2pvUfHL4kyuiooFqMJIpUzwyah2WHH8qlh0yU5dnEfiwW7av7sL5KPIuDna4dv7OghLbAorW/D6gBZoFeovWpIGfLtGGC/whPjJqPbCTpkn8cuaTCJTbnJf92qAj35bhyjqshPntJbdJ09fAapVFaYHxtyKZh27DvTpLCWiR8cB1RS7ihpR5NqmCR5bsFdcVbl6KJ7+rCNWH4hAZJ6uKPxuPe+rATtY+UTbvYPu9bRI33dUsgRv3RhwtY01uD6Wdnxn5eQJ1ydnGwu3Lk1rYdGpCyKUtn2dKvjwqX6Y9OgXOHMnAY79uyG7CC3pNf08tK8RfXizzAVjCxvNFuTKCweNTd7fz5aLuT1jfDi8jfg/NjlDJ1vIHDSk4MLSy6USfp7URbRwGsLVyRGLH++D8LcXiOrWsTdHob4BsxIVFRlLxXmn2oEilJlVHi7l+T+1ufJzMsuCzTLC99WXG4+Lauyhs3H4/PxqzBzZHh3MGK28NrCF2JyggyrzbwjPPzwPyfOahBsHNAu4k2Re+JkipX5tw6KIKEUR0d+wiYrmwRGo5g3Y5R+j+T5OlDfUygHVfNxFxpyHcyWEBXiKjDRjVUZT7XL6oogceX0U6gZ62/DeqtwLqMJIpUzAHTkuykb8uF5nBuKVfs2w9UWpPeyrTSdEr7CSmPRccZKaNbkbXu7XTBgVWNLmYAr+nuszJ4oKUasa/kIYyXMRo1vVFpfY5HSsPnkV/+w6K4XP8cStd+KatuksUDM0f36HVSOe311cgCYW5HZQuHBAVymKSEIi/AZ3R6xH/ok6LjUT3+y/ZN1b2tjuY0IS4OEG1KxebKKoMGTY2Hjj8edGYWj3Jnjsue+xduNhcZFpkZGMA06FD2s1JorIl2M6iJmKrZp2N1aPzIkiLhZMiaL10waKfCE6fU34bTPupmaIeatc5JkdXObsGU0RLn44Hn4aR0NjVPGUvp6RnaOTSaSiYghzr2uZD4a1scgIhKYdA79da9FtvvHfwfxPoqPEDKA5YUT6NqyOyA/uw997I/D6fwfEIp2dAt+My6/E831WVFFkNfrVH28vaQPLrxngCtilcAa1MhIt06JlhiszxotNIs5HUgBvOH0dfb9ebbAqxO4NGhwZoll1P7FxyeMp51RHtaxlMFRaRcUcql23SpmBw9lc4HEHW2bmumPo+tlyYelNh7q3B+uaOnx0KF6bil0v0Ee0ORx4dYTIyeBBsjDQBa/75yvwv0V7tA5K7nozUlxA3t++HoZ4OQBL1gFL1gILVqGvr5HB8uYNgUE9gdGDgGF9AB9v2Juzq6VwoYU3BRK/Vx4oGT1QRxSJ373/GJBehGx0tm2s2y79jtBqQKfWoqpVopSwa9CZm/G4f1xPPDC+F1AjGBg9AIHdWosFiE0c/IwwuUNdUaF5VJPr1d5MKxp3vkNfm2vyeyLuSPf3vZWHhW0tZ/rWTRsoQlW3vDAYcx/pKf7f/lL+DJp+y8qHq4/gthlXOybIh/pJgugPG1VoVSoubOvkQtXOzKwdv88S+Pq2tD10WLNQTO/XFM38ncR1v+06i7RMywxgaCbyYMf6IlOMfLvllMgsUs4PcaFOHu8aXmhPGquPjdxgk6ERD2dO2WUQn4i8dYeQV4Q24NKi9hvzxfl90HdrRUWwd3g1hBmYWwwL8MLlj8aLx9sQNBtqW6sK3hrcCpM71FNFkUqhUYWRSpmiT8PquP3p/cj9aSr+eaSnaNnh3AYtvYf/uB7vGjgxchdJCVshaL/M3uLCcuz6XXy2IT+tnQdrQzw0sZckWnjSzM3FuhkP4JuWQUCEgaoVjQ0U7WC5luQG0XCBAonfK+8Y6p/8RvbHe2/ej/3v3Ffw5xkAu3SdlL/DShBzkNiKF5M/J0IcWR2KSwAiNK5ATFqnNfjWvVqjiGIXLiXcD5+qSUx/4pEB8GneQDzOUUFBwJBeSAgx/HwbwsPZEf4KMS9TzUj2hreLk6iMUsxwtujr+zoavW063L2wcK+wGa9TxUvMOxx+fYT265zNmzmyLe5vX1dY2r+/Wnp//DKpCxoGV9aGNY9vW0f836VukJhlMvRQf7npBGq9Pg/P/bsbN41YDnNH95V+Us7Ux+uOIqMMu26VdWjO0vS9RWJRWFHh64+W3MTYu3vmiHYWuTDyNcksPHLkjZEie47ih0YLPE/IMDCYTonLnuyH94a0QoLGnYeD/JZkDHHTYvmxy3hl8T6d54Zt1TKsRBx9cxSuzJggNuGs6FwtHPIb1pg5zoHjgLeHWWMgRBuvOpcWV2KTxbGHYdGcYeQxhh0gMh3CApH23cM49fYYODo44M1BupujShjFYA2s/LFyP29/pPi/vGRrqRQvaiudSpmDbT1kQts6aB3qj9UnruHo9VgRCnjq5l3RTyzzfVc/NK9uOAtCtoktCtwJ7MkQymNXhJVxzwbBoheaznjclefi98bZP1GtwYPi+89H3kC1sGpALRsH0R49BTRvZPTLb607Li5aKHBOngMyNK5DFDgytWsA/pV1XIuy/SpLlaLDJ4HzFwEO94/qD6BuwZOzCaej8kTfhpL4adQwFLm+PtwGlr7A2SoroCkEL/oYavmgiQRn1HZG3havo7XPDhCLujl7I0QrCecs2AbChRcHvO/7dZP2Zzc9Nwg1/Dyx7Xy+4xzn6iiWEtIyMfG3zeKp4etyQGPDc0KEbXwcTP9773nxXvtweFtsPntDbDrQ3OLrzSfx0/YzIqj2s9HtEZvCmaVcbebQwx3ri+rS9bgU/LH7rKicyi0rvM8LD10Ufwd3b1V04eOz6sRVYXvOdkRy4sZdMddoyPCiIsDXGyv50xbsFq8ZfR7+exv+3HtetLDx0ji4skFHOVYEZOHTPMRffEzxw8eUj92OiFvo9eUqMadKI4Y/H+yOdqF+yMzJP1+wxdQQ1+OShQjaHnHbaJj3xjM3kJSeKeZNSaNgX63d/n2tw/DvwYKBzSUG51HNQGMglPHWMj93Z6w8fkUcP1qHBogg6C51qsKFG4Qagn3cRW5a5ef/Ek6gSrrVDbL4dy05fKnAa7I8ZGupFD92eRUs9SoxMRHe3t5ISEiAl5dXad8dFRvDHer7/9giFl/9GlbDEzWzMXDgQFSqVPCAX/eN+YiMLpwNs5OjvdhhNAcXlpWdHHDn0k2plS02DvYtGyHXzvAOqKl8HZPwbUqRw/YJMzhev4Xs/cfg5eWGRGcXKQiWQujCVcnq29RJdM9h4MoN6fcM76v7NbmdjxUrVpeu3wYyMoDWTVHaFt90s7MWr4gLcLh4DUGNwnC6eg1h/R7KvKf4kpkdoHPctpeGiBYSim4lHOx+tmcj3TkJjbCi8PnvWH5YLteQTYJ9tRa35H99mwqzEGvgqYCLv7eWH9TuzFOsMZxZSY/6wWIhGXEnUbz+2Ya34NHeaBjkg8bvLhLfw0oYd9NLA7pPrV692uhxobRgyHGLD5bouPqRPx7ohgc61MPP28/A06USJrZTbEZUICiu6TTJEGK6eTI+gFlltI3Xn0sa0bymMORRzojMXHsU05fuF9bL86b0Mvg7Np25gQf/2ioWu8wRinxvDLZu3oRJG6K138Pqg3KhTV5cuAdfbMzvEDAGW7zYydA+TJpVYnsdW68XHbqAKX9LrbEqtmXZE30xrHnNAtfXem2e1iBDZkjTGlj+FDf0TB8XVpy4LjK09M/Fshwva9laKiWrDdSKkUqZQN71MwftiOU5iMFNagBJxnfpilLUoCjijNL+y/knVENwUXgnLReoqplnqlldOOQZo9C7EPxjLBBFJLt6EFA9CAUkIXvRvfR6t9le5624rm4toE1T4G48cDcB8FU4+nCOqW5NwK8ynO3y0K5rU5y8m4LSbs4ojCgiiXVrA3VrQ16mZmRm4/yJC0CI+SFwWzl2NXxnofiYGRverk5alzoOdhtaqNGdzxBKUUQ+XX/camHEXXq2srJttP83a0Rrnr4oIsqMJNlOd+wvG3W+p3s9y3du7xUYoqsvilgpmty+nognYNA1Gdw0VLwWLBEYFBGcz7GkFa20kds6ZUa2DMNXYzuIx4SvtfWnb4hqKP+uH7adxs2EVNH2yQwjtlhti7glfo5tpcZgW5uPq5MQRpyH48Wjkj36NAjGhrM3ta6N1SrrLn1kI4Wudapiz6U7OjbRFHEPdqiPNaeuCSOGzp8ux5sDW+L1gS0w9Pt14voyQ05OwVbrolDC3QGsEvH1fSE6UTjrBXi6iIqcIZSi6Isx7UXL8aoT10TLJatKxuDts1Jk6FzM6/jXPrdgN4Y1Dy0X7ysV26MKI5VS5b+jl8XsEJnYto6YtzDkjBWVmIpdkVFoFFwZuzVZNtw9P7vfuDB6qGN94VJXWMyJIu0sTmoaEGC4na9MUcW/YI+6UhQRucWO36tP+xbaDzN8fLA9LgOwK0eHEM5ZyQLWEJzlKiFRpM/+6SPw8OxtWmFkbgGohBWkFAOtfIVl6ZHLBRbwllZQyUfD2+DVAfmvlYoEh/efnLtTBCYziNdQy5csXG7EpSA6OQ0Bnq7Ckpgh1vp8uOaIuCg5ei0G3eoF3xPtP3z82JLGy/O9myI9K1u0GtJZkdWk6Uv3IdTXE0/Pzw/5pIji80D7eH2uxSXj5E3ptUuB1fbj/9DGOxvP9uygFUbMttFHtrfebiAWgpEMP+84I6qnAxqFCCHEmdf1Z65bnLNTYtjafKGEW6YPvDZCW73m8Y/PlX51T5mrxkBvunzSmGHOvkgRQM025bGtaxv9HXsu3DHY0qkUR9fiUsR72BKnRJWKRzla1ahURJYfz28JogMcL9yx7lEvGH0aVoNrJUcRgqkPdwJr+XnAVKrFU90b4bstp8TOY7HB1jReuLO29whQ2RtoYPygXOzk5YmFcqqhoXgvj4KtcTeiRAUIbpIYbVKtMk7c0F0U63w/w2X595ZHTIkiM0PLoj+/mOgbXk0MkNsXchFiS1HEhbehFhNLRRExtSgp7zAK4M8958WFi+InuzcyK1yshYs7Q8LI2HNDAcbrK0L7DxfBo1qG4fesHEz6fYuofCqNFWQMXUfqVPHG1hcHixk5CvxTt+Jx6hbw9/n8iiatt98fJmV+yXCTjXOkpmD1VFkd2n0hSrSP3kvQiU8542sLKOz5fpFd/mTBbC464PsJui3hnEfme4d5RqaOQbcTLVsPUFir3JuodUKVUoW7y/pwzoH5EW1nLDMoikhcaoZZwePl6lS8okgJ3X7q1ATCjA+9lwh2doZFkSHYJhASpDOQa1QUyd9fhmY2CkVuLmpVdodr5CXg4lV45JgPQnR0kEImi4v1Z24g+OU5uHrXulBD2toHe7uhQdWCAYYtaxio+JnBVIuJNdR5Yz7sHvsFPb9YKYbVKxKbz+W3Mr636jBSFfbPsnApiigidOHUH/011/4DTftPRXHV4pzVp6PaiY9lkwo5foGD+abarikqOYN0+9NJ+H5cB9T3qaQTePzB6iM4rakqyTTWGClYy2lFhbdUkJ/v9AwOjBf7r6MoqlvFGy1DbNMhwVnEQ6+NEO6aK4zMBlkKA9kJxZEpqmpMZGyVwaVS8VCFkUqpwmBSmQc71MMHw1pjxoi2YvjWHP9bst/k119dul+nl7+FjQ7mBqniBwT4SoGslqA52VuCayXr2iNqeLmYzcYx2H7BapA5nGwjjB7qWA83ZkoZO/883ANuVv6NheWjvo1x8eOJ2DTjAbgcP4PkhWuBZeuFaYYx2EpT3EuOPDOLLEOvAbaaUPifva2budSgqg/2Th9u9X1g60hRF/X6s0iG7PXLM1vOSXMuJCoxDb/uOGMTUcmWIbYrylWprp+twHGNC5slz42y/aeiwF1/5et+cNMa2PPKMGx8bpBFP89Q7qmdG2BmR1+ceHMkpveXbOYJXRzLTXWA4seY4JVnYHJykMf5oiLgWcmy5SBjBg5fy39tWgtz0Jgz5eVSSTgHVvFyE/bc/U04aVpC+zDpnMccN7ajGqND7Soms7XsrMzWUql4qK10KqWKvKPMnaM/Huyu8zW2qiw+fFHYdDN/pXeDalh3+hqG/SDNJN1KSANMHLs+XntU+/EHw9vgvja1RW4IYVgsBzwLAwv87IUH2zkK24N97SZQSzoR2J04h7y4BDQa1h1nE9J0djdJmpVZMa72dvD3cBbtJvJuq2U/aLp1wZb0Da+OYB8PcSE/7zgrhtOtyQ4yZJFtCnfk4qURkiFB29b1MPnZ0fhlwzFhVAF36yy6CwsHwzlQTEc3c7ASRNHz2oDmaF8rEEN/WGfR76Bzlmx5bw3FsTjk31sRjGFYoeZimgPfbPmZObIdXly0VwRQP9Y1HHsvmp5bMMfsB3uIhf+XG0+IShTnJFp/tBTH3hyF8KDKFj83ZXqBbwX8O3p8vkIc+5hTRxH61vJD4n0gO8JZQ/1Ab7Hhxo03tp6ym6DcVAcofmRHUGPnGxu0N6crbM2Lg9XP9EdaZg4GNgkRLZN0aXQy0hJZGGjhPqplLbGx0P7j//BinyYY2aKW1F0Sn4r0zCz45uZps7VG/byhwG3Ij+5XYzuqxgv3MKowUilVEtOkViba1OrDxd24NnXERWZos5pI/+4R/LXnPJoG+yDqlPGqEUNev918EiNaSNWnRkGVMfuh7qIVwM/dBfXe+rdQ9zl9/or8ExaTxwOtb1sSomjJWuHylufvCzSqh1Nx0qKmTWgALsQkmh++T0sDXAsu6M/Fp+FcvAVOSTzZlpLrzvjfNouTojwTMaFtbauEkbKFSUnlvGzEGTGEmP1YP1yKTcb7yw9i8aELSMuzA+qHoSSJT8sUF5lWof44dMXw7mbvBtXh5eIkFoV7L0VZbPW++uQ1uD39G6I+u1+buWIJxbE4fHP5QeG4duLtMSivTPx9swgGlSs67WpVwYMd6+GlxXvFAv7I1dgiC5JvtpwU74VX+jdHbEq6mK1RztJY+tyU6QW+hcSnZqD/N6uFAxwND5RZNVZt9BiAi2J9UcRjydnbcYWPUigJTB2nGRNhoWOpKbIsnB0y9zhxUy4mWffclfH9IwVEkC1Fkcx34zoJYcTXyUdrjoqLksE1XTFssJSt9WiXBvhlh+6UMitJFEXlfVZPpWiorXQqpQqHzglzXEyVv/Utux/tGi4Wlaagdeu/j/bWCiv2pTOMkjuOdQO9xU5voQiuIrWfFVYUaSoeHvcNApo0AIKqiCRZn8x0/Da5K57p0UiIokoOJqpR3D00IIqKerJle0NJMfXvbdqZiMe6NsSF98bC6ZJl1rfyOZwZUkqMiSI5IHLYZ/9hzsGLkijKyERIZhpmDmiK4c1CURpQFLG/npkr+ny39ZRwRmSIK0/w1izauNNuTHAZg60jplpMTOHrZnxhJruElVdkES4bUAxtForvt54Wb8GqXq5IzsgSQZSFgZUMHgO3nb+FT9Ydxew957Wi6PvxnYTblqXPDSsr5b39h9W5Ad+swfHrd7WGB3z8OV/EjS5amdsKznF9uPowQqb/gyfm7iq7osgcloiijEy839Y2oePmHielKGLgau5PU4tFBBnCkGEDz2lyfMDKy2nYEXkbdxLTCoiiBoHe2P7SEFUUqajCSKV0oXjpWT9YnPwGfru2QNhfcVLomaOu7YChvQstigjbwJKzckQvc68GwRjTvCau//A4Hu7UQLjqkFY1CueiZhFRMcJF75muDdCgipeObS0XYCXB3dRMfLg636o4LNAH/5uqGMDNzs5vITFAw6o+yGVLo4U7nWxROrtyh5T1wcimqpXx2/9G4ZU1x7HMgCOV0iVJiaFrGxowQLCUHyd2Ee6LRH82jCf1dwa3wrBmoVa1pdX290Sz6ta9vuUWE2KtOLqbarq6+efucyivDG8mVZz5vtj24hARxvrZ+mPiutuJaej79WqLwkH1Gd+mNo69ORr3awJdX1myHw/8uVV8/FT3hpjaJdyq54aighk95RnabcvhwspA4U3PDxLnCkvhJpuxjba7d5Mw9oGPMWTKFyJAWVmZ79+wOvzdi1590YHHsdLG2Qlv7r9a4r+W2VP2j/+Kx//Zgd0Xin/+jV0mCV89iNPvjBH/Z/4wBQlfP4QtLw7BQx2k91mvr9Yg8H9/F/jZs1EJuJNkwZytSoVHrRiplCqODvZY/HgfkU/EdpSB364RrRQlQde6QYWaxRA4F312grbaFCKbzt7EwqOX4fHcnzh7O167+6y/QFBix/aJwkCRcew0EHMXaN4Q324/i7OaeRe27dAGPcDJAb5ZGUB0rJTRZGDwl9bSsnBQ2kyz9eXYW6OEaLGErzef1HHSyrBz0M0VYlXLSF/96dvxSGDlR9yPPK3gMcqarcDNKEyu4YXKbk6IiEsRi1pjGLOl/XJsBzStputidVrPAMFSAj1dcfLGXdEHT1twPuf8cye0laqctfy98PaQVlj2ZD/EfvEAHu5Y3yIzDoYchr8+F41fn4sVxy5bfH/YQkLb52oWiGNrxNOjc3bgcox1rntlBVaIWNXhDBGDeL/YcEKbeyNXagY0DpECpy2AToJk3oELop2XC39WhxhuSXo1qIYvNSLIkueGt8fKFQODmQmXmJYpjC9YgXpv5SExa8mPH/5rK8bP2iSc815ZvK9MOgaG+nmKijnhLB7h39JmxjK8v+owFh26iFM37xZw7ZM5di0W3T9fgRYfLEGrD5dixXFdMXD9Rgy6DpyOhct2YdXirQjUeyutPX0dMRbmh1kMj2NliJJKJlKaXfy8/Qw6fbJcVESLG7ZKci6P/yvP75+MbGvy536Y0BltrTEtUqmw2OUZO8KUUxITE+Ht7Y2EhAR4eeXvhKuUbVgpav/xMiGOWEFa8+wAs+X3rKwsrF69GgMHDkSlQtpIp2Rkou6bC8rU0HKPekHYdzkaob4eOHO7GOxgFUO8dmnp6FjVC6+N74p5W09gzlkpPFcfD0d7JF+5KdzbXpnUCw7Vqoj2LgZX3ojPHzrf/fIwdKgdiA9WHRbzJZZAZzoG6TG08dzteLT8cInZdg0hhRKTC2YzJaXkh9hmZUn24vx79x0FLl+XMqaaN4SlUHM93b2RaN3s8cVKRCelw93JASmZRZt1kPF0dkSuIouIcyy/T+4mRNmDf24Vu+RsIZLhY8SFn0XhwxpcYmLxzwPdMHJowcW2MeSQ0jUnr+ETTXVEnxBNP352bi7u+3WT6ftQyQER79+H6pX1nq9iQHlcyMyF2GxgG1zbWgGFGqiOSU5H2OvzxKwL3S1/3n5au3imdTSd0gI1FsCmcozkx4tC64/d50RIKIfCyeYXBguBdD0uWdgJc8PI3HPDYxZnitg+98PW03j2391W/21jW4WJv6FhUGXc17q2aFMuTbgcGfTdWvG6o1FCx9pVMen3zdrHSeb+9nUx+6EeOtdF3klAh5n/iedLhpsI77XxxrQJw3Dh0m106vsK7sYpBHpoNaBDS9xrdKsXJNo3ixtWvJWbCP892VfMCZcGPC78smA5tiR5YvER3c2iax9PKJFjk0r50AZlaytD5Z6FrnSrnu4vbGo3n7uJKX9vx18PdjeYLG9L3J2dxMCmIYcafYprOJeLxnSF89yW87fE3IlNRBGrMTsOSNWfEf2k2Sg+phQLN24jb+8R7MrOwYj565DZvYPWmc4u4pJk/1otSAiN5OtRwMETcMjMRPPQcZiy/qT4PuW+yobnBgpRRMKDLGgt48+mpuH4+eu4EBWPKf/stOhP8nRyhPOWXYhpbEDgUBRdvAqE1cjPXOLf274F0K65WRfBmn4euByb387JohGfj09GtReLx21Jt6Bj3lREA4skhbMe2+W+G99JnKA3n5XycuhO9t/Ryximsa+f+vd2q0QRSb8RhdGTZ2DR7FctFkcUEPUCvTHmFykY08/dGbEpGehUO1AEJ8sLclloeDhXwkN/bTXaivLb5G4luvCIz8hF+5nLdWyFWe2Z0KYOJrWri2ZWtNGO+XmD1gDgwzX5rZ9k7bMDtKJIruoMax4qhAuDV6OT0xDg6So2EJSPF9vkeD94nJu7P1JUdiiMLHmMeBvcSJChW15hRBFZcOii9mP+jU9pqjUlRe8vV4nX+Gej24kK/hv/HRCiiNCdk5blm58fLMJXV5+8iq2axfzfeyNQ298Lz/VuAm9XJ9FlwFZsiqJWNfxFZe2JuTux9tQ1fHs8Ad2aXkbb7i/k/2IeB9o20zqD6uPkYI/MnHKaCWXBMYkGP5y55AzXyBY1xcbkQStnEi1BKYqOvjHKqvddcVDdwxGf9W2rFUaXPxovqpQqKkrUipFKmWLtyWsY/P1acVJ8c1BLvD24ZYHdUXlxYYuKkQx3eqf8vQ1xsYlAQpLI67H39RG7+cod3zcHtsQrS/fp5C/pkJAIJCUD1Qsm15vi2R6N8M2WU7A5mZnAknVAy0ZAPY0DG0/4mh1pX0c7uJ06j+uVfaUsJg2v9W2Kc5E3sfii6ZPlqBa1sPjIJdSKi8WlddLizM3LDf2HdsGSbCfTffdsMTl+RqrydGptXcJ6bBwcPd2RbWlulIXI1SDZDlze8aRlNu+O0gLe1tz6ZBKqatqs2s1YqhVAbwxsgfeHtUF2Ti58nv9TW12ymMws2C1di+rB/rh0YhYc5NwqA+y9GIVVJ64KQ4H5+y/gdlKaaJXMzctDFU9XYR8t30d9ktOz8L/Fe/HT9jOiTVQ2LegYFoidLw81uclBEfHm8gMIr1oZLy/ZJ6478OoItNaEeloDjwuzFi7Hk9tizW7GUNC9O6SVqOIwHsBdEXZMKFjeXnHI6G0sfqxPkYa1r91NRu035ovfzcqgNXM0ytmiaf/uFmYd+nkxVzQin5VI2TxCbpultTGDgOm4x/ZdvsY/HG663cjW2D/+i7ERQoPvjZXHr4g5LHkuiAKmY+1A9KgfhLdXHNZeR5MGVpyavbco323txDkgLxeIT5I2TqpXlXY+TITFlltMWHvz/fzfE30xc/0xIUr5vNOxdeLvW4r1LsndBKWFvF7o338Avt12RrjTyhtOKhWfRCsqRqowUilzzNp5VuyME86CKEUIB6A5hMzFiC2FUU5ODp568Sf8/Mda7XUjhnTAU69Nxp3kdNHClJKZjY5+bhj5xNe4WreOQTeg6jv34usZU9C7T2v4vfCX6cV9MRtLTO/XHE/N34mYuOT86onM8bPwad0I8YqWMA8nRyRrFrN9wqthwxmpamEOtke88cTnOHHqMlCrOtCuhfkfOnYGaBauI9KUcK1SXA/d0Fp+eLhbI1xIzsSMtUd1Wm/0Ca/qU6By1zjYBydvFk/iPRdzrw9siU6f/IfdF6LEzNfFD8eJsEoyZ28EHvxra4GsK5MkJgGrpaH+LSs/QvcuTQx+246IW+j++UohggzB6ki/RiE6lQpuZHCujCKDF87iBLz4txBW+plMj3RqIFqglBUWmXdXHBKtZUqe6BqOb8d3srr9TT4uhLbogFm7zmPjmRs4F2V+Boxi7qeJnYVzJeEMju/zpt/DnO/hHEPPBsFiRoH5LNby6N/b8evOs2gcXBmfjW6Pvg2rF6pSbvfYLzqfP9uzsZjPOXFDcngjnOVk6CmfB193F20QNgX/c70aG5xtsiX6bYD7L98RphPmuDFzIoJ93LVCcNHhi3hp0V4RamsM+9xc6bVsYiPA+j8gx+TcY3mBd5+i2FrnyqLAYN5e4dVQWthyvaBS/lCFkTpjVO4Z/dMGUYnQRz4dsVViSJPqFh3o4lIy8M/+CGEJTnHDnW3+zwUNsww47Lxo2S6MfXCm9mccqvojp2E9nSqK+P2JychbvQWB9UKQ2r41kuQ8nbw81IuJxsD6gQisEYQ3jt3UbbkqYVjJuRiTqHW406dxVW/snD4CryzZJwZjycJHe4tF2T/7I/H6sv35gnT/MVF1QGfdqo6Mr7szdr84GF16vYhoLx+ghQUzPKbCCq3E2Q5oVasKTlyLRlKWBQ/67Wg47j2Ms4d+wUsrDmHZ0YLmBNxRz8zJgaezbo98SXDzk0lCiLeZsVTsjLPFbnybOqICwLaPXZG3MOYX0zM9BaAN+pFTmPvjNIwf3c1gxfS+XzcaFQHVfNww5+GeOhXbFxfuKeDG5u7kKN5bxmDlgq1RnfQqIwcvR4u/Vx93Z0c837MJ+jcOEcn2+iKJrZz6IkJ/AcTv4fug1YdLYCn9GlYXjlrKFldzXJkxQYhDa7kUk4jm7y/Wvs7YsjmuTW3xXmwd6m+xMNQXRjX9PPHHA92E4Kzl54mpXRqgQ1igzuPF1xkFxg/bTov23VmTC742bIWh+Su+rlgtYwsmDUcS0zOxUmOYMH9qL4zTzK59OqodXurbTOf20jKz4PbMHzrXhblXwsWkDNvns7HyzgwlVrmdLF9UOyAPOSVmd2CZPTxbFinES5plT/Qt1QqNKozubRLVipFqvlCe4a5i6KtzcUNv4FbGTlM5Ov/uaKxbu9aoMGLfNE0A5uyLELkuxuCJ4qn2tTH9lZ9xiWN3NasDHiZcuS5dBWoZcKBKSwcOn5JOoj06oKwgt0Lpc/69+0SeExelSRlZYsZB5ul5O0VWCxkU4IpVV+O080fk/aGt8WKfpujy6XIculpyu442E1UmbqMLDQ8ib5eYsDNE8xA/Ua1acPAicsz0GnEJ6OJgh1QLlPg/w1pgwsA2Ojv3EXcSTLaLKeFClu5p3L2PSkzFkiOXhSsbBQQrSIXdOX5nxUG8u1JqhTIFrcvZYstMMlpm/2/xPu2QPTc6ZHHRoaoz2jesgw51gsQMxaTft4iWseJiWs/G+Oq+wldb6Nj39eYTIrNKGWjKimHv8GpCJNH1zlgbI3ninx2ijVEJqzJH3hhpsEpH+2Q6hSlh5owsnFIysrD+9HX0bxQCVyfHIosiuuEZe4WyTfmj4W2FiLsQLblkPtypvnB/fG7BHvE5K113ktLwn8ZaP8gBuGWFBwr/gkIZZ0ffBfx8Ci+2SjFIW35vyOe/pU/0wfDmtcRc24N/bLE41LUw0LSGjpoBL84Wn9NU5qFO9VFaqMLo3iZRFUaqMCrPbD13UziAmWPztAFIjDhsUBjRArnJe4u0nzet7isWmv8ezB82NtR7r4X5OGzxKsUTWnEz95GeGK+xhTbElxuP44WFe7Wf2+flIXfpOrg62KNxeA1cvhqF6LQsoE8X4yGDV24Ii2xTzk+dawZgp5WGAjqzSrHx8M3NQljjEByMNbH0iUsAfLxMChpmy/x2fzc8OHurECUljXI2RzlvxQFzig9T1RhLeLJbuLAI/3H7GZHBUxS4aKfNOE1SuADne+6FRfmvF0MYmmM5HxWP+m8tsPj3skWMQ/nyIL4looW28MXJiqf6YWDjGkKwFQWKywUHL2Dd6euiBVApNjnvtv2loeJYZqjVbuOZ6+jzVUH7ebbP/TKpi3B4U/LPvgghGJUcfn0kWtTwF22EDFrddSFKWJHTGKewRjgU4TVfm2fQqc8cpW6CUAFmkLydHZGgmUvkJsEbA1uK53j/5Sh0+XSF4fOfldBYoYqni5gPnLMvUhwPrn88UbwfKIr/3HMOfz3YA5VtnRFlBaowurdJVIWRKozKM/P2R2LCb5vNft/ch7rBLeqsQWHEkzodiWQYdnkuKl6nNeab+zpiRIuamLHmqGgt4NwGZ2sG1a+KdfM3YpWzZVk8AraaOVWCp6M9UnLyjM5plCVoUWqfnoHMzGyE1qhSYOFz9Ox1dPhmDdI1lQgfl0poFnEe23bpLTIpIKv4A93aFfwl/22QWlAGdtepOBWau/HA0dPSLi6fc1pyax7rB14dhb8uZRoPtI28DDSuB3gbH7x0drQXJgel2QYp06Sar8gjohucwcfB14rXZzEysW0d0X5paSvPyBa1EB7kI4wWuGjmXFM6XyPFhP68HFsVuXAjtMd+ZPZ2Mcs4qmUYutUNwqfrj+GzDcfF1zk/lZCaaZEbJZ8vztsxe8oW8HV44HK0qNr8e+gCztyK17pY0mabTn9KW29j7YjKSiirbX0aVtf+7SHT5+p8z1djO2BYs5oY8v1anLwZp71+1v1d8UjnBlbd/8zsHBy+GoO/9pwvUMkqFlixt8UxpgLCSq+yA2NQkxp4uW8zXIhJFAYjSifOwpD+3SNaq3e2Z/L8p29kUtqowujeJlEVRqowutcrRnQvYqo5LUn12+bYAsRh7Ptah2H+1N7ietq9Uhj5eUgn1jUnrwr7V4u4cRuoZr2bVGlz451RqNV0ihBGPt7uaN4kDM0a14KTkyOWrtyDyIu3AHdXoGoAUNkHrklJSDtXcO5LwL77kf0Nf+1MBBATD3RpU/g7q2xdowg9ckqam9FQqZIDPnl7KLoNHYx2M5eLuQUd1m4D4hOBAd0kYVSGdoJZceFrb/7BC2IRLPPng90xqV0dMSD947bT+LMEwhHLMj9N6IznFu7Rbm5wDizDCkE1vHlN/PNIT1GVMwcrJsev3xWthmM1luWWiCMGre58eRhqBxQUR5x1ogvYk3N3Chc8axzg7qako93HyxCpCWMmNFD484Hu2irVI7O34fdd58Sszicj24mKOSt6hqpbdG0j4W8vEDlPj3cNFy18nG16ePY2kdfFv4UzSUuPXhZttuunDUTrj5aKYyozqepU0bXk5zF0z8Uo8TfujIwSxgrWzGgVGc3mlIpxAjxchNmMrfd98n5+tMw/7KowurdJVHOMVMozHPDmDBEtfPNMzBh1qF0F6yIM3wZP/LxwMXI5NgnHrt8Vu8IURlzwNP9gMRYeuoSpnW+IeQfZ9UuGhgwc+OWQOAeAOa9klLIqinJy4OFgh2QxhaIL3a8OHokUoojEJ6Rg684T4oIA3/xvTEkDLnAY+irSPNyALm2B3Bzg5h2pasHFCNsNK5vILQqvK1V2CjuvQ/tzVnzqaAZ3ufhhJlFIkJTRlJeHrKwcnI2IwlPBlZH5wxR8N38rntlyPt9JihbsymoR/4ZcO8BMiHBJ8Nuus9geoTvTFODpImZKOHjPNHbOXegIIyNufqWNftYXndaGNA3F2VtxWKqZDalXxQupWTlWt1a9tHgfejeohgc61BXmE+ZEEdsPla1oNNlwf+Z3xH4xWevIZgxPFyetQQSNXoyFturD9sRfd5zBxyMLVk9/3XEWj/2zQ3zMasz0/s3F77EE3t8VT/XHyJ/WC7HG/Blm+dAchNlXbL9kpZ080a0hqlV2x90vHxBVp4CXZovvl2EVigGf/N18PCmMWC2e80hPvLX8oBBFhNfJbWx0t2PlR95oqvvmv3ihdxM0ruYrbo9i6OTNuwWst5XzLSqlT3RyuhDlzCVjlZemHGNahWFs69qoZA80fX9JwU0lM7A1V0WlIqEGvKqUObgYpCU3h3X1F1ry8pkJ8pa4NfHkztYWZXsLQ+bo9MUh3t5frcIzPRoJK2G20NBViq0+rUL9hQsS2wJWPt1fzEGM/lnaObak/52LMlr/jp9lviXQHJwx4WNglUUzsbdHsgFHpCda1sDGnxbjqJ8BMeNXGejVSfqYYiY1XWpRYXCqu2KAO8S6nKYCduFKQWRIFJ2/BNStKX3N00O66BMcKImkDGnRF1hFCupbu/EQnnnsc6B6kOSkR1HkXxloJNkwC+guZQW0ZeaC1JY7ra/0a4bnezdB9Vf+0bZIcWCZw/Zd6gbpVDbk1i8tsiiSH0NWwzg/VcrkGficVYcgHzetMDqvqHpYAy3AV564KuYUuBiLSjI8I+Xv4YxP2npi4ojBSM7KFa2yzPiRqxdDvl+HXS8Ps/j3KkNb+RqgSH1z+UGj3z9znXQc4YV/O6vQFCgvLJJMBGSCX/4Hz/ZsZHHlqEFVH5x+Z6z4mLlF32w+KdzkBjetgXO3E4QAoYkBQ3hl2Gp3f7u6+FaRkfbeqsPCpp6tdXILI63hCdureF/ZWsy/lZlWMvrtkvqOhKROFS90rl1ViEoK4G6fm6/82wweC4rZFKUiwLBcitr5j/REk+p+2sofOzWsFUWE78MXFuwRxytXJwet4G4YXLkY7r2KSvGjCiOVMgkXI4Z2alkp+kqRY1RY/n64h3C0olU1Fw3KhQMXDZZgaiiYO9WDm4SK+8sFBhdItOU15rRnikJnIRlZIGy5HIuzG/UcwCg8+nbRrexQzHjzokkGt/Wiw9Rt1atl3t3p2k2tKCJVA7xE5WvAqHd0f46Crlt7i+87Az/fWXFIZ5F/M8H6580cXEBTcIU42eNSeo5YuPO1TmGkH2pstIrKv4kuiPJzZAQO7vP5S7Q2HNYIns6OwjpcOYdiiFM34/D8wj3IKuQMkf7GCGGlxBRPdWsI37ybYlOElZZPR7fHtF6N0XbGMvGYUgTsu3RHWGEbC4/Wh9d3V7g2cp5o+I/rxcfMg1n0WG+EvT4//z7M2yX+p1DZO304Rv20oUAwL4UeZ5neGtRKO59hDlZ3aK8tCxbu/LNaJIfivtC7qc6sIGe4xEZPDX9hic25ojWnrgsnws3nbmq/j4HG4n+XSnh1QHNsPHujQL4XX3v68HEb17q2CKZl0Kq+ax7NTOZZ4AZIR0EuyrnZ1KKGH/ZdKqQZSwUQRXxvcT6nqOYopqCo5cXTpRLiv3xQtGPyvVpYvtxUUCS3qRmABzvUE+2Zpiq0+tlWpt6HKiolgRrwqlKmMXXQtEXPMGc6JszaZHjAvQgwi+TUO2NExSk7N1fsoqWkZ8Fjmm7uRmlQyd4OWQtXA/6+QKC/NEOkrDaU5q6ryAqxsL0t4jJwSDohV3J0wN8/TsK4qX/lf51/X6+OVv0tNXzdEfnBeHT/bAV2X5R20fUDWDnDMWHWZuy9dKfQi3ujcGj5+Gmk1KutHST393DBzxO7iI9ZRYU1t1eMnHl3rKiKTF+yT2djQRZipvKf5MeE7a1seaUoPHotBrcS0nTCYSks6KL1zL+7hHsWF1t8b7GtyxRtqzjjqwf6oEMd3crmqJ/WC4tx7m5TDFAsGAqPtoQjV2PQfuYycb9oYc6/6cCVaJ2FPq/nsetGfEqB1j7C6g5nkiyBc2bPzt8lNkpYRX6mR2M81b0hen65SrT60nGMm0myMDKUG8S/m8+NvOgO8/cU9+ndIa3xVI9G4rpDV6LFLJElHHxtBFqFBhj8GtuYVxy7gmEaAWkO0R4dVgWLDl/SackbRFe8k/nzhEZ+WYUQRaReFW+ceHs0mr63yKJg4qIyf0ovkRPm89yfhb6NKZ3qC0t3Vi35Wt9w+rp2Q4/Or0ObhuLBjvVFPpjSMMTQa9Ta96GlqDNG9zaJqvmCar5wL2CLAx0PzKM0i01b8vGItqLX38tVmiHIyMqB57Q/dFoV2teqIrKAxmiGu0uMW1FAYIBtrcgtzOrgTAQzk2wGrcCZOXUrCnXd7RCx85Q0IxVeR2q1sxDOrysLc/qfy1CkXJ0xQSwCzt2OR4O3LbOZXjdtgGh/Ons7wbyb1tJ1klEFhZ1MXh6e7t4Qld1d8dOO09IcSCnnoyhd31hhsMaIUfa94GPMORSaIoxoUcvgRsjT83YJVzPOxWx5YbBYLNI0wBBcRK8+dU17X7gQmzGirbAnJgx3bjtjKc7clhzejIVHm1uURSelof83a4Trmj7yXA2rH9yJp+hjGxxbcik67tOElhIKHM4O9WlYzeyuee8vV2HT2RuiBW7W5K6oW8Ubjk/M0jpgMqCVor0nA6sPXbTouMJq16HXR+pcl56VjSfn7sIfu88VWhj9tvOsqGIxoNhS+NcWqzE3zVpqhaA8MK1HI3ytt9lQnMx5uEcB63ZLoQlK3JcP6GRd8f3BrCTORR5VhIwz84ytnQ90qIfzUQkGs62seR9agyqM7m0SVWGkCqN7gaIe6AqTr8HsH+5ac/jYHBRGP0zoLBzups7Zjhtxuu1YHHw99c5ohL+9EFdMmTsUFzRO4GxKFT+T39a3fhDWH7kEuBlvhxAZR9bs2DJTaM9hoH/3gu5w/JopMwdTJCYBXpq2stw8VHYE4nLtRGXDpIGGFWx7cYhoOeIC2MO5EnZduI0fflqBHUnZ4n4zZ4fP7ecbj4vXFher3esF4dE50uC9WZJTJLHHFrkbd4DQ4DIhgAqDu5Oj2Dk2Z5TAXeV1zw4U4kfZCkYhQSFDATW5fV0hitgGZwwOlr/cpwnenLcR229JTpNype/RLuHi/tR961/cNBMefemj8VphQtvpEzfuYtPZm9hy7obIGLJGBFKc/ftob1Ex4m1N+Xu7qNRE3EkUi0daxHMDRTY9kBeQX4zugAnt6gi3t192nMGn64+L9jY5C4pOdX4vSOGZ+uJdvw3OGMyaoVGDkibvLjTbIilz+p0xCA/SnSXhjFKNV+cKkWcttEd/vlcTLD5yCUOa1MCHa/Lbmh3s7MyGHRuFJi18Xw3oXrifr+C4OzsWaPW0ZJNr1TP9xTykKVgJZoWXM2rK17hcVbX0fVhUVGF0b5OoCiNVGN0LFPVAZ6ktuAwXwsz+8fNwxZTZ20RKvS2Y0rkBZu08W6TbWPRobxH++eic7ciwJrBPv+qQnmE8rNUAob4ehkWdpbeTkgo4OABXb0pzRVxAUygV9WQYeRlhuVk4tPIDvLvqELxdnLAt4haOXIst0M4kQ8FLhy1TFSPyyci2eHnJfu3ntC6e+f5szFq8A+89NxJPTx2EVbvPwN7fBxvP3MTlu8miUiG3WVkMBeK67ZJleu1QoGFdq37c1N9QUjRiS+kt62YXWBWS53naf7zMpBCSoYPf+jPXtWGVLf2dkOPsgWM37hbqfsv34cl/dogw3MLCKt+XYzvqtA/JUCS1m7EMR/UiBZRQUB+8EqNtL2Q1acnjfdA+LFAbS8DFJWePXlt2wOr7R0vu6zMnisVnbm4eZu89j4f+2mbxz1PDdqkThG/HdUTT6tIGy9qT1zDg2zVi/unih+Pw2JwdWHjoosXZYO5OjpjauQGe6BaOJu8vtkkAaUVrtytNaNwwumUYOihMPiyBr1NuEv65+zyWH79ikZmQ8lhQVFRhdG+TqNp1q6iYx9odTbbHBL40B+3Dqgj7b33YFlPF09XqQX324usH8Bmilr8nUjOyDbpx0QGItuTchTMljBiwqdO3ri9ArBBFxGilS/928vLgFB2LTAbBcsHPagjb+WiMsGlXvhOereyz69TERc6wvKCYOTKDLIqIqXP2gkO85Xyk6qEzMKQ33lpxCHNO38J5Z91d+EJxJxagRXpyKnD8LHDyPDC4J+BmmT1uaYsiYq0oItZsVsh5PnsuROksoA/HUPwWThSRj9YcEe/zwtx/JQyNNSSKiIO9He4kmx6w33r+ltb2nC6ZNDpQGjXIeUScP5IZ3zoM8w7qvkaNwVmjpUcuIyzAS7QsMofIWq2xPeKWMFiQhdHPOyQh6epgj1l/rMW/x6NMVtgc7YAgH3ckpGciMS1LbPB8tfmkuHSuHYjRrcJw6GoMnNLT8dvMf6T3RKfWsJoKIoo4lxensF/XJ9DTGVFJtp2ZVQppRj3oh4FbAs9NQ5vVFJeft53G43N3mv2ZwlQdVVSKSvnsz1BRsQEFLJAtgK0cuy5E6QyIy7BlSBZFDNJrWs1XuI6Z4/WBLfDNfZ0MGGvrcikmyahFcbP3Fwtr8GQz7RB3E1LgTYeykoCrIS7mmXfETBQ3zWNBYRQdl1+xYjaSDD8vBeoYCOQ0Bnfw2f7EKkUBklJw/pSREFwFzTWLSKPwcagfJrX+NKgtLepoR2yhKCorWBKmWlToUhcZXTgLcGNsOHND7GxX9bL+GGFoYcc2TgZLK+FMkbGWPn3YCsrXHI8/NHRhJtOZW3GITU4Tt7srMj8HK85IRdQYY3/diFYfLhGiiPMi1hLs4yYiD+TWR943cj0xDS++OgseaabFX3YecC0uRYgifXZeiBL5T9/c1xETm4ZIcQLtW6DcwWNhIZ0Z9cnIMn2Mt7crvmXdp6PyRRFn0bipp+S+XzbC7rFf8L9Fe0VLpTHqV/UptnO0ikpRUe26Ve5ZzAXJGoK9/3cS07Tfz3OEfG5gtgqHyGnlW8UrfwFb5435IvvE2K4xM5bYZsB0eaWFrty+99v93XD4WoyoWH2/teBALk9T/HljLWJKotMLZ9f85qCWQujRcei5BVIeSx1PJ0RyR53zMKb6uTjLRLjTC+DJB/vhB7k9iRUrJ8WJvJRmabiwblDV27A5ggGaVfcXmToF4P3n3NaWPVIOE7OUDGCqfUp7OyLE1QFo3lAKt023bG6kLEEr6fLKExbsaFuysNt/6Q7afbxMfB4e5IMdLw3BiRtxWHzYsqoOWX3ymrhwps3QpoySQ1diTGas6SMfvzgLN7xZKMYqzCHMwSr5gqm9EezjLgJmJ/ymyG07EwkHB3sk7T8Bhw4t4OHpJo5hlt4vGVbt/F+cLS3CWzcx/8eUxcoQ75NGdDLfqbBZXiQ1y/Tjd8tGNt9KN00GntP4IzI6QQjxlxfvw+y9EWI2qWWIv4gYYGCxXE2nDf2SI5eEuRDd6PQ3SCwNcef3qaiUNKpdtwrudVc6ayyQ29YMwO8PdNMOjVIEvbJkPz7fcByT2tXB3w/3LPAzPPgP/G4Njhtov/vrwe6iFaj69H+0u8cNg3zQO7w6hjatIfqr5eFTLgz6fb1a7GaXNE91byRaONg2sz0if3faFjDrJDQlCR+ftsz+uizw4bA2eP2/A6YXaHdi4ePlhnjXgruetIo2V91T0cUuKwv1q3jjib7N8f6qQ4hJLp52IUvhpoapOQlajR9+fSRaz1iKK7GFN/7gMYK5Q3P2RYp2WVM2/Kxa8x7RBY9zR5bCkFi649GQxu2Z343O9XDBShOJeL1NGLZY6eTuHD2Npwa3RnJAAGYfuIA8vUBlb9dKYti/UBltZVX8lEGsigowAYVNYTc5OGv2WNdwPNmtkY7Rh7Fzr+pKp1LaM0ZqK53KPY0cJKvvzGSM/Zej0f7j/8SMUbPqfkK0yNUgWt8agrfNbAolL/VpipufTMLkDvVEawKzWQj/P/bmaHx9X0f0Cq+u48jD7yut1gJWqj5YfaRwouh2NHDgmDQro2fzfOSNkZg7pRdmTBuO4sCSVkajiy8TgxFjWtYSC2ODJCZLC7dAf4OiiEzr3gg4dBLIVsWRxU9JpUo4G5cqbM/NiaK+IS7FfnKTbbINwVfGl2M6CAe6wooiO424mtiuDt4f1kYYGfD9sun5QQW+l7ONWRpRRKwRReJ35QGP/7NDhOB2M+QyxtdpTo64/Zf7NYOPJoZARiuKomOB/zYA1ari++up+OvIlQKiiCSkZdk8uJr3D5evSxXbMgodBUuSIB/bnC+sFUUMjqUhB+dimRH40ZqjqPnaXEz8bTMOXL5j8tzLSpGtrbpVVKxBrRiplFts6TIj56f8d+yysBY1Ndwqc1/rMPw4oQtaf7QEF2OSTDrocB6g4TsLxcfDm9fE0if66nz9SmySmBNiO9z34zvhSS6cDZCYlgnvIgTxlQqcl9l7BA6tmiDH2QljW4Vh/tReBQZ4PZ74BSlGukQ8r11HUkIKEBUDNKgDVDPviOTq6IA0ZV+/DXeaH+lcH7/tNOBKmJQMbNoNVK+Kuj3bIjIxXVqs0mzCycm4c196JuCi93UVg9hlZxtcbJd6To4GGqlwZvDK3SS8sHCvdqbMbPukFbvmnOMoENpcFtw2yK07QFAV2x07+J631e2VJBSThXid2pITb40SLaE7I60z1VDSq0GwEN50FqSYZXXwq7Edxdzb6F82GJyTo3Pd52M6iPPq8mNX8NWmk6LbQIabiAwnZp4RMZfhZQtUV7p7m0S1YqSiYh08EFPU0Fo3+vPJIoHdHP8evAjfF/4SokjpxmQIZn0w/4brcg4nd5z5Hy7F5O9shlT2ECcf4mjipMC8k8gPxulc514CA+6Fhtkh/Hvat0SeZuHPPnT9RPe4pDSkmljYJYVUlwJPo+8Cuw4CN/JPssbQEUXEhu03BkVRRqY0EzS8L9C6KSJkUcS2S31RpO/cJ1eO9KoQbgoHsqLCjB+d3fXt+6Td/VTbzCQYw2YPO4NwF6xE3pqtcKOVmZXYUhSJ9qAuDXQctwgdu67MmIAQX3cxhyFjjSgytmvOkGgaL3CxqU9RRRHv/VSK/cld4VGpiItSW4qY0xHSe0ofvk8Sym5lSNy/y9fhVMIdfw56v6/Je4sNiiK6HFrKxucHi6o+Z4iIu3MlXItLFvEHjGswROfaVbXnVQY3b3tpiAgSZts4YTDyI7O345vNJ7Xn3vHMe1O0jquolBbqK1BFRQ8emJktZC3z2Utvor2ma70g9A2vLj6mA1TY6/Px+66zeG/lITg88auYI5CHoE1RO8ALjTQnto+Gt8HJt8fY9Dn0cqkkHNc4SyDz7X0dkfvTVOtvLDUdLnfjhAmDcu3W9bPlmLM3Qvv5A6/+jjxzvnxVAwAfL6BlI8DFBUgwPm9RgMIGQ1qDs5PkGqf/u7ho5mKWbnwXr0k23PrOUhpjCh03DzFobRsnq651q2JMy7D8K27HAOF1gWF9rHa6oyEIh+5L/KF3dQHGDAKahqOet+n7XNzrUbYHzVKIY84culRywNPdG4njh7y7bi29GwSLFrzNLwwWcz8yj83ZDpenfxPzP45PzIKtodS6diMWR67GIFke7mflRxb81pBkeWC2WRrXNxxAzfeTp+FFeZmA7+M6NZFZwkU8S7OiaDZz9I1RwlTHFDNHtkXT9xahxQeLMax5Tfi6O4vW8f8t3odXl+7HnouG50LH/bYJF6MTRFaXDKum+m6cnTQCSkWlLKG20qmUW4qzNE6BY//4r1b/3NYXB6NbPeOBdAyrZGilKW5/OgmBZmyCr99NEun2DYN9xSIt+OU54vo/HuiGnZG3hbvUkB/Ww5bUC/TGeb1Kjzle6VAbLr5eeHfVEYNfz/5xilhIvjprHT4+cKVAu1tTXzccv5uav0Cj+ChpLGjBo933iGahaFLFE24pqZi1NwJrbyXlu+wdOyNcunRo0RCoF1buBsm/GdYSm+ZtwFpnb2Q46FYr+zWsLrXeeLmJSubpW3H4c895YTVfUoPlJdUyp0+YvycufDhefBydlCZm8rgjXlTGtamNDaevi/d5SeIQG4ecHQeAnGygSQPptapStMfUzk5Yrpc0nF3le5HQ9p1Zd2yJ2/XyMOGYyHOJEor8g6+OEG14OxRW8O8PbS3avY9djxUZdjwfFDDe0FCb74eYJDg52iPU11NcJ2/+DWwcgl/v7yrcDEsKtZXu3iZRDXhVUSkanH/hoHOLD5aIzye2rYO3BrfEtH/3YO2pazrf+9PELsLGmjtpdKkzBXM+zKHfSnDsWixeWrwXUzo1QN/6VfHtj8sx85ulyA0ORHrLJvBwcsD598aiqreb2PHlScszNQUOaenI4S67jbBWFJGZey6Y/PrDf23DXw/1wAcP90Gs8078uvOs9mveTg44fuyCZHPL9pzSEEXEAuFy6cQFfPbzAuQ5OwNd20ozRTSdqFZVqnLVCS0ojM5eBGqH5s8hpKRKVRE+/xGX4OFfGcmVLcv7METr0ACMbFFTLNBtaZv97H+HgZPXAc84ILyOjnhcd/o6DlyJxmNdwvF0j0YinPOtwa1w9FqM9r1kK4wtL20uiiycTbuTlIa5+yPx+rIDeLZnI2FvbAtYiS4NcvwqI+nKXCQnpKBKgDc2nb2Jvl+vRpmEFSReSnmmxxylIYqILIoIRRHNMyhmGr+7UFio65OelYOm7y8qEBL95vKDBb7XkCgiFEWEDoeyICI0ZGhXq4rI9ipJYaSiYilqxUil3FISO0DykDPb25hRRJLSM8VMwdhfNsLfwwW/Te5mURJ4cnoWar0+r8DunD5sWZNvj8GNg75bq5tRdOUGEOCr0wI1NjcJM9+YhPAZ/yFdPpklJgFe0k5dWYDdV4a6i/J+flT8z1yX0T9v1PbKW9oWUiSUBgjWwuBa3kcfT2kOgq19fN68jLT4LFwtzfbIdG8vtQeyxe7wSTE/VaVFA9ypL/Xh49AJuLg6oXKHFkVKgPf3cLbO2jotA3A185jwPlf2zp/9kQW4QkTwPcL3DbNMTt+Mw7hZlufjlHfcnByQmql4rncfAlo2LvxrzdKoHJowFPGN45ibCyeXSuL+s93K1clBLGJbB/ngoZ/WYNml/EW2VSzfIBmn1FPdxjycHJFcTjK+mK/n7uSII9diEODpKl4LfG/zwnlY/q8UPta05KZ+9whKCrVidG+TqFaMVFRsAwepP157FM/1aqy9zlNjIrD8qf5WW16bE0UdwgK1omj96esY8eP6grv9odUK/NyCZbuwYPspabEtY6Uo6mkgYNZWmAqc5GNy/HosJsySwiFb1fBHq+q+WLDpKOLtHGy/mKQY4kI1Jg7w9gT6dS2ci5SvD5C5HnCIAFIflm5LhrbFAX66QoJVr+sa04geHYSdN4VEPS8XnI++Cx9vd1xc9T6m/LIe88/eBlo1AV8t1ooidydHpCheM1aJoptRwN6jQFAA0EGaP/hkZFu8uuyAbmaPLIoIRREFEYUi51KCqwK+3mLuhg6PvPRtWM2oMK6I6IgiOhVevSkF9RpZIL7avzneWnGoSL+TT4E1ooibDxMdMjD7RiIQGKC9PtveHlXcnJGamYoOM5dpKwqu6WnSx8rZnpi7kimKJfTuLL0PyJ0Y4FY00CwcZQJWm+zthc10UrrpAF1bUNqiiOYhhlozAzxdMLBxDaw+eRXRSdK56t0hrdD1sxXi473Th6N65YIbP7N2nsX0JfuEiGKMBQNeuZnn4uiAlqH+2H2hoAEEs41UVMoiasVIpdxSnnaAUjKy4PHsH9rPWWnSF0nP9Ggk3H6iEtPFInKXgZNJcSDPajDA1RKbclszqkUt0YKVnFHMC5IrN4HQYCnrZK9i5mn0ACGInFJSkOleDK0dchWFqmDFRklAKQRskH0ubs1dJT4+e/BHNO8yDenD+mm/ri8oLK0AsV2FrZXGBKnB+8mqFu9r7RpiiP6DxwZgfmIeTt4sWCWwz8pCLoUkv1+eoerTGfCz3PGqTEODjErF15pFrzXbWGuUAPrthNZWWvV/vgxYWesQGydet5wBsrPLg5F82wpHx9qBYjOufa0q6FSnqrDL/n7LKTw9f5fZbgZTXI5JEpWltrUChFU3jRr085w+HN62QFxGcVp2l6f1gortUStGKiplDGUfNqtP7wxpjWfn78LsvRHwcXMS80P/7I8sUstUYXm5b1PMP3RRBFH2qBeELefNW2HLTGpbG5HRSdh7ybA7kSns7ehUl4fFRy6Jz2v6eeByIcMw5R1fo2RlSbvUFEacWVJC5yRHR2Ryp9i9GBbM8kKCCqd/twL22LdO5s+QNGj9hFRJkrlyXfqcLnwUUd5u+GNyN1yPTxYZOYkmdrdp9GEVvJ9jBwEXrwBhkrX3GxtPi0wmLXl5CPd0wuDmtRB34BRm7T0NtGgkDefzflYUUUSKURTB1qIoIxMPNq+B48nZOHwtRmyysCLAmSeboL8YtraKy5+neQod6zhzZ0wU2TBrzCr4us3Lk54T/bmaAS3w+87TuBudgDTN+7AiMKRpDYNdD19tPmH0ZywRRaSmv6e4kEnt6uDdlYfE3JKMs+IYvOTwJUxbsBvX41J07Oq/HttRDXlVKRXUipFKuaW87QBxoRro6ao9YdyMT8GCgxfhXMkeT87N36FjK9mhqzEoD3A+aOu5m+jxxcoi3U6/RtWx7tT1Atc3reaLEzfuSmsVU4sm5deMfSwTFQ1skYI3BSP7A06VLBdDF65ICyku8Az9fmvZeQDeyclIiNeIwro1RRsd8ThwBMltWqAsMqltHURtPYANtxIlEwmV0iMvD86HjuOx/03AN1tOlb1nwtL37j0C53Ke79UEn6w/Vqr3485n94u5ISUbz1zHp+uPi1ZufeK/elAnxsEa2s5YigOXJfMhhrs+1LG+cKvkrG6elQHH98J6QcW2qAGvKiplELY2yaKI0JHnud5NEF5Vd5e9tEURWyuah+jmTRjKranlJ/Was+2BO3yFWdoEeEg7sIZEETkuiyJiavGk/Jqxj2U4T9GzIzBuiHRhNckcyioTRYC+KFq8RnKhsxb+TGVvfPrbdGTGLEXq7UU48vdL6OVuB+9NO5B84ToQcRllkTn7I7HBrbLU5ycH1KpI2NJ9zJLbsrNDRutmZVMUGXofKv+me0wUETcnRwxoHGKz22tQ1TL3SrYKKqny0t9i9lVJ7/DqWDdtoBBNP07orPO1R//eXuj7SItume+3nkbrj5bivl83GXSXlK97bsFug4HGKirFiRrwqqJSyjDtO+27h7HhuYF4qU9TNAzysSpA09Zcj0vGk910B8XlsMqOYYGiI4xM6RwuTlo8wXm7OImTmf69ttNcFj/WBzc/mYTxbWqjT3g1bXhltBkzimJDGRrpbjozyiJRRjMFOszpc+q8NHtD5zZ96C7I61PS8OioN/D4c9+jc79X0KL909j070Yk+PtLA/u3SmbWzNiQtllY4SpL8yI2gM+2u7NDEW7AgvdvnoWCspQsnouVe1AMkRd6N9EKI3lDyRacvR0vQoGHN69p8OstNBtdhuzCn563SwQS5+o5o7CS9Hi3hmKjTKZ3eEHjH0th+/j1jyeK/KIRzWsK0xG2UhuDX7kWlyJmj1RUSpKKdTZTUSmnuFRyFDt1vDCP6KVFe+Hh7IjkjPyFU+PgyuLElJKRLWaRmKdEwfL56PZ4cZGiNayI8GTETCZWjY5ei9VeH+rrgZ0vDxW94FvO3UTv8GA4PjFL+/UZw9vi+22ndHrFg33c8M19nbTtEHOn9NJ+7b2Vh/B2EZ24yswwvbHZmkb1pMFueSYjPhG4egOIvArk5kj3h/bYAH5fugsICQb6dhWubloa1C61dqOSDhW1FM6jfX1fRwyzYYixW24OUu0dtIuyHPHeK8bHnM8nZ26UDn+GsPEQerFyD7XFOdjb6To1WkCUZtaU7Wg0F3hncCtMKUIVRklPI+3Mld2c8cWYDnB3dsSb/x0URjf6sJ0tvKoP7mtdG3svRYlzTGV3ZxF0zgwywly/5iGK+cdCUK2yO6Z0biAus/ecxwN/bjX7M6Uxd6tyb6MKIxWVMgazX15btl9HFBE6g/FCs4bu9YK1VRwmj7NnnfbItmLmOqn3/YfxnfDCor1icJZJ5z0+X4k1zw5AiK8Hqr0yV6dNcPqA5vhfv6Zih4824/Fpmfh3am/hdiTDE218agbWnryOHZEWmDww94cZQcagFTIfBmPZQUo0oqhBbgYuJGUiS2mvXZzIoonPD1vweGkaLpk+8M5z4cuPef+MLSoLs9hkRYrZSk0b6FztZAdkluMiBB2t3hjYEq5Otj19yaJIJr04RZGMOVFU3uDrtJjd/EoUVnTpYikHGSuwVhQRGuwQBqySSe3qFlkYseqk3IzSJy41Q8yAVvNxF5lUxkTemdvxeGel4Y0q2pjXCyx82LQhavhacMzWGM6oqJQkFeTopaJSceCJgCfM33edM/j1+NRMLDuaP3cy8fctxXZfnpyna9u6LeIWar02D1FJ+c5qT3dvhG/HdxIfcxeUrYFtagZgw5kbInH9cmwSnpy706SDmlFMiSKSmi7lCSmhAHEwvsueDTtkRVwCWjdFiaJ/n5RzS07FUBUwkHdFil0Unb8kLY4baYJqbbxp8OaglnBycBCByc6O9sjIzkWfBsHYcNbKDC5lMK2hakdJVT+Y/0RDDy7A+dod3geo5GTyNVymqSiiiCzfCNQ1P/zPar4hS3tDMz5sZ+NGErmbWnIV2RvxxsWTKZH3VLeGeLRruGj/syVyK+GNuBSDc0Z85/Hr/D4VlZKknB55VVQqNgzVo53q3w91t6oPnYF6xY1SFJHvtp7CH3oirkk1KfSRjnJ7L94pnCiyADtaROsvxAwtKGkVrCHS3qlkRRGrQTRQYPVGRlndO3oaOGLF0HyK5vHnfAqttcV1qUBiIa3ODeQTWUxyCnA6QhJCMvVqSTlIlmDl/Aztp12f/h0OT/wKz2l/CFFErBZFhLbpytkvfRFUEqKIg+UbdwIXrua/JvYdA46WUROFe42R/YCWjcx+G0XR24OlQGRTyDM+sumM/nHTGpiFx8gDQ9WiulWkSqSHc9Hc1/gOWH78ChoF296Gn5totOSWf4/+7yVfje1o8zwjFRVzVKCtHRWVigPTxeWMifpVfTDxty2IuJOQb31aMwD7NdanStJFe5bt4UmYw7nGdjgfnr1NzCS1qOGv0yZBYdQm1IApgYJK7L6xsopRJ8ALcanpiE2xMJA2MxNITgX8rGgHsVVLEMVq5GXg0AmpdapmCFBfswt9/Axw7mL+Ipy/k/e1cxvjt+eusdel4YEmb0jMLu08CAzpBbi55rfp6ZsiUITVCQU8jIjtg8eRa41oXL9TMrK4eRsIrS5dl5llec5Nac6jsMXx0jWglu3cwayGi76xg4Gte4G4BKBNU6B6kG1u+x6a9yk2lLbOfDzli14l+8XeTfDuysMmbyrI2xW3EqRNjeua6s3r/x0wexfYOs0uAX0YEL7osd6iehqbko7729fFyuNXxbHaz8MFrT5cUuTQbKUBAjsBbA1nT2nJbSjHiKLIVlbdKirWoOYYqZRb7qVcgvSsbHy4+oiY/TE1S9Syhj8i7yQUW4VGWRG6k5SmHSZW8nSPRqINMDUzG96ulZCQVrz3pdhghccWc0isCpy7AM/rt5AUm5BvrsD5KcIWqgPHdX+mGp2g7IAuugKJboXybJkON24DO6RF1rNvTEbcrVj8HZcNOCkyRyiWzl8EGuq1uNE1TzND0TUxEttPxEsLwotXpQUghRRDXPXh7UXHSsGuXOCnZwBRDNG13LnK181ZtI1yhoHCf8GhiwW+hy6IhRjn0FIvwAvnoxNRYaG5B0UeRe+la3Br3wxjujXBVbtKwiRFpfhZP20g+n692uz3cRb0vaGt8WKfpuJjZtu1/3iZ+BqrPw93qo9ZO8/q/AxbR6/EJomNsCqergj0chV5eKzi8PudjHQJ/L7rLB6ZbXh+iY5wHw1vK1zhLDHumftIT4xvW3DOylbQ3ZTii0YLbCVn+5ytK0X30npBpWg5RqowUim33IsHutM34/DS4r0ifM+S4V8mjGcUUxVJiZdLpWIXYyVGbBwq+fuiV51ArLXUKtbS3Xm2u3GmpEZwvtsYP9+8W1Pl0YMCikJKw9UPx6Hei38i3cVF99scgPR1O5AaEy9dQYOHfl1179PNKKlC0q65biWJ1wdrLHkXrS54P2hDzuymEBOVDL4Wi2AxP6VjPfw4qQsqPfmbwa/XOnMWl0JDpWqYhXSuHYj5kzpj0IOf4lhIqO5Ml4Iwf09cjFG0OZZT7M9E4I3+zbF89T4cPXFJyuhSKXZoXECRQ4Mac3w2ur1wHOUM5tYXh4i5HZrRPDpnh7DMlulRPxgfDmsj3r7c7DImfkxBo5tun63AjkjdY5iToz0ufjBeOMRZGs695YXBxVIxKknuxfWCSj5qwKuKSgWlYXBlrH5mAG5/ej9m3d8V3eqZa7vJK9aMG+ZRkETNIHy5RZ6rSUwSu+9ZeXmWiyJiacsS3fNqVte1YKZ5RKsm+XlK/LqnptXtbn77JKnx+nyk37lb4Gbv5gCpvbtIi2EKIlkUKWd42G5XrWpBQwtZFBF394L3t1s7oLqJAeg8C0SR/iwR2+0UzNp9Hk6NH8P8oc3QOrSgJXDj4d2tEkVk54UohLy1EMfcPE0+P+VaFHFOKjkVbnk5WDDjYbz72kRJFCnt48t5DlI1H+l9EeDpghAf2+T+2BJuUFkiighFETlwORpxGit8Hzdn/Du1F94b0ko4x300vI3ItOtQOxDtwwILJYqInZ0dJrbTrfI81b0hoj+bLESRpeHcIaoBgso9hloxUim3qDtA0q7gvAMXcPByNMa2DkOHmf8V++P+xZj26NcwRKSt0+moxqv5tt2EAontfkVpfyoxWMFhqxvnf7q2084U1fB1R5C3Oy5FJyIpIxNpWSWQvs5qDkWRnR1c0tPhunUP4uKTgQBfoJfk+ic4fFJyUzNgIVwAGjTI1SFlVev6bSl8tmdHw3NUmta8YW9PxX/nrAxYtKR6xqqU/oLv1h2EN6qFM3elWQNbW9DbDHnOpKwMhS9YheaNQsVdOnbyknTd8L6Wz3mVE4Y3C8WyYxqzkTKCu5MjUjItCOk1whsDW4jsoCbvLRKf//lgdzzQoZ7N7t9ttqa9PEd8HPP5ZDF7pM+Sw5cw+ucN4mNDh2yGc1eEWR91vXBvk2hFK10ZObKrqKgUdleQFsZfjO2AVqEBeHdIS5unr3DHUOmM99qyAxj6wzo0/2Cxjih6vGu4+J9OYbIoYpChzv2hCClLsCJC8ZCRiYAz57SFj6bV/DBzZFvcSU7PF0XFrfRoAqARFA3rVcfv3z8rPnZNTsbVN0cg+MBhYN12gFbjlwqGNBpE2TInMmayJAEWFQ00DzduLsHKUuP6WlHUqKqFphV0/7OkemZoFzyoilYUEYoizhuwjef3yd3M3qRdzF0pu+nEOTifvwD74qqW8u+zhSiyVSWnUV1RJRKiyMMNqOJvM1HkwMeQzoOmSEiUKq0mmNy+LhoF6mU2WXksKGuiiBRFFJEPVh/RiqLicBVlWHjOj1PFxZAoUhogyFUkZcvdnId6VAhRpKJiDcUmjD788EN07NgRbm5u8PHxsXj3+6233kJQUBBcXV3Ru3dvREREFNddVFGpMBy6Eo3WHy3B2ysOi+Vg7/BqIn+Csz+WIosCDsKfens0Tr8zRnzOgdjvNTlFhG0jF6ITheOcksNXY9BFEeZKEtIyMbZVmOKXlMG9GH9fYGAP1BvRE14uTtph4Or6bTtFmKGxlg/6N8MLr0nzNs8/OQwh1QPw7rQRknMZn+BaGgc4S6ExwolzwPFzwJ1YoFlDIDBAqtwYs4ZunL9zHblpH8LOn0fXOlVhzwX97Wgpe0cfZ4XZgyVwcUwxwyoYzQMoUjV0rl0F6x/vJWYbHupUHyenD4VTrvGWpbzdh4E9h4ErZ5ERm4BcAy2HZQpbOcbJ4pePff/uQI/2trldvg+27jPuYCjj7QV4mg7rHN+mDj7rUkd63ZTlY4EVBHoaFhpF4Z99ETh6LUY4gNoKe3s7cTEFxc/lj8aLTQgaLfD/1G8fxsT2ts8iU1G5Z1vp3n77bSGIrl+/jt9++w3x8ZqhYBPMnDkTM2bMwF9//YVatWrhzTffxIkTJ3D69Gm46A0b26JcplK+UUvjEhQoLT9YLNzKOPfz9X0dxc7jmF82mtwzn96vGT5ed8zg1/o3qi5mmXp+sRJbz9+yumXE7PcnpeTP0RQDvu7O+GJMO1yOTYa/uyuenq8bVGsOd2dHpGQUbTfYovYxA7hkZCBj2QaxUVQjJAAndn8HLy838fmRYxdw6UoUYr198Jhe+K4Ml0AFnndaeXdurXsdHeQOHJNCcmnzrQw71WffUanSJFfZBvaAzVizFWDeCm3CvfJdAAO37UJsdAJ+/eZpPDixN7qMeAc7I24CnfT+DpmYOMDNxepZpHLP3iPoEOCBfc4eyLWV1bet0Fje0wVt8eN9MOylX5EVWor26OWEhzrWw+8PdC/tu1GhUNcL9zaJZaGV7t1338Xzzz+PJk2aWPT9POl/9dVXeOONNzBs2DA0bdoUs2fPxs2bN7FsmWRnqaKiUpClRy5pLZyXPN4XY1rVwuNzd5ptJDp4NQafjGpr8GunbsaJNr0VT/VHrwbBBUSOnUZ8cCNSTkSv6SftGjs52OPZno1F+x2rTwYT04tRFJG7KRl4+K/teGfFYYtEEW2wldhcFBEL96DSnZ2RF15bfHz1WjQGjX0Xt27fxW+7zmHSssOoVLMazpmwn87Tr8hs3wdcv6UbAMtMpyOngXphwOgBpkURYb4ODSJo7mBLUUQGdAd6dtIRRawsRN26i+zsHDz05NcY8MAn2Ln5EBB9V/exVP7vX/neE0V0FLx8HRGZuQVFEZ975iMxQ6u0qOSIZtX9kJaVg4HfrgVCbOts1r5mFbPf4+5YNrOcutatio3PDTL4tT92n8e1u7YJbFZRUSmnAa+XLl3C7du3RfucDNVdu3btsGfPHowbN87gz2VwdzUjQ0cVyrsDvKhUXOTn915+njmH8faKQ9rPu32+Ap7ODkjOMO+StPHMDXEx5rTExzU3JxvHrsUW+HqeRnwQ5hURVmeIn4czZqw9itLGmm4Ug9lARYEZRfrub9aExTbl/E8lEQC7c89pHD17FVPn7RdfGvbDestvh+1KXdvBPiUVuWxXY3AsW7g4i9KnU8H7aOp26koOhCUCs5GYwcT3th2wdtmO/JZA2VCC1Qi5Ard5D1DVH6hXSzeUs6hcvSnNTZXk324pFIPMkbIHYqppsqPOX5Iek7Aa0nPWvb1kpFGyd0xsnTjYAZXsgY+HtcLsfZFYcPgSsmD+9Rbs7YqbCWni59kS3Ce8GkIqu+G+WVvF1xsEegsXzJsJqdh7+Y7Z20vJziuT4bisog3+fq3Rr9d5Yz4OvTYca09dxw/bTuNqXAr+16eJqPRvPHsDncICDc4NsQ14z4U7uJ2YiqpebuhQu4rNM4HKI+p64d4my4p1YrG70v3555947rnnzLbS7d69G506dRIVIs4YyYwdO1bsXP/7778Gf+6dd94R1Sl95s6dK+abVFQqGvEZufg3IhlXkrLRNtAZf561/c6ii4Md/ukTgFFrzS88jMGFzZttfHA3PRffHC+ZgM26Xg6ISCz+3KZCYeliiwtxeVYnLV362FbD/pYu9ijs6AjnVHSRYRefiDzmKhWFBSslpcuK0ND8zTMB51ZYGZEJqgI0C5eynGT2HpGqYrzeUlhh27RLej4oMGhqYO2cWXEssFkJ4m3Ktyufwvn5uYtAfcVMXyngZA9k5gIs1Pg52yEXdnBysMONFNPvy2A3B3G8cHW0wzv743E5KRsjw9wwuYFUSXxtz12cjsuCZyU7JGUVXLbM6e2PSRtjiuVv4t9SGH1VFNwc7ZBq4pc2qFwJH3fwLdH7pKJSXklNTcWECRMsaqWzqmI0ffp0MQdkijNnzqBBgwYoKV599VW88MILOhWjkJAQ9O3bV50xugd2ADZs2IA+ffrcM4Ft/x27gmf/3qENU+VCoThIz8nD+A1FW2Tk5EEscIoC5YA13lVxuTyklVFhZOkCWWlgYKjFjTtfhXm9W7NAZyXJ0mqSGYosiki/bsD+Y0BQQMGvHT+r+/mdGMlogiG6oZpKSusmwOqtkqGEub+L2UAUYJyl6tNZiA2Hk+fw3Xvt8MRXyyWbdFOthxSz8teLo+qgL5KVv6O4RBGd6fiYWCDQKYoI1/RR6VzYW6Yobqbm4I5nDbSpEYCrm6Wq6N5YYP7AgeLjRu2S0e6T5dpKtT6H86pi+RMtMPRHyXralpS0KCIURXSGy87JQ66B/esBLeph4MD8VugVx67g/j+2FHi05VfH3w/1wJBmobhXuRfXCyoo0E1mCVYJoxdffBEPPvigye8JCyvcgblqVcnNKioqSqdixM+bN29u9OecnZ3FRR++8NUX/73BvfRc/7LznBBFnN+5HmfGRtcEbk4OyMzONdlClmnDDBnO8BSmXc3aexCTbHjRVKEo56/1xsE+OHnTvGC2S0lDnrtmZsjbE+jdSVcEyNWYBrWl6g7b72jiQBc15QJeFpKsNDEw11fPNlof5ZwSb6tVE+Tm5OBIrhPaB3mjSm4ymg3siI/XHTWcs2RuXquwbZny35ueDtBxjyKxpF4L5pzprMTezs7gYv+T9Sd05hnZLvft1jN4oU9T/LE3ooAoquXviQlt6uDDNUfw5eaT+GLTSZQ3/D1cEJOcrvPYMOT40S7hIpuO5jqdPlle4Oce7dZIe95j+9y0RfuMVpj4rnlu0T4Maxl2z7fV3UvrBZV8rHnOrerPCAgIENUgUxcn9oUXArrQURxt2rRJR+Ht27cPHTp0KNRtqqhUNORe8ToB0i58g6remNi2Dka2sHwGgifJkS1qmRUqg5rUMGn33SLED5tfGCwuJT7Do4f8eBhadChpX8v8sHaZpaxlQBUSiqJQXw9U9TJtlJBHhzklSlHEOaNjZ6SPWRliJYhBuDRwUIoiLr6VJ0QfhcGDFeQ5OOCXnWdxoV5dfDjzcbzatwmeq+oMHDkFRFw2nfVT1G51ucIl//10aOXfXI4Xd4ZEETHkZPnior1YfOgipvVsgqmdG8DXLX8j9FJMkhBFtniYaSZj6DXJY6C+OYulMMctwMPFsAENPWicHXH7k0no3yhE57G5FpeCJtV84enihI61q+LTUe0K/Gx0UhruJKbh3O14/LTtjMmNMj40vM0dESU9b6aiUv4otom8q1ev4ujRo+L/nJwc8TEvycn58xAUUkuXLhUfc46Is0gffPABli9fLmy6J0+ejODgYAwfPry47qaKSpknKjEV9/++WQzjbjl3U1yXQetnAKNbhmHOIz3xTI/GFt0WT9ILH+2NIwYMFWQ6hFXByqf7YdWJq9qWPUPMfqgHGlT1wf2/b7H4b6lUTFlAkUZc2pQ7seSfR2zsqFaSFHHOyMPI4qyoUCBby5W7yRjRvKbIS/lwWGsE+xiYBzXVhkbzheYNzf8i/dvgY2hqBZ2UDF83aXOvX8PqqO2vK6Sik9JFIKfbc3/h02spwiLcJTXFdEXFXDsdK0KlBatp+oK7NO+PEUb/shEvLtqDD4a1wWej22tdMFuHGmitLCTxqZm4nZhW4Pqk9CyxsfP12A7oXCfQqtt8oXcT3Pl8MlK+fRgrnupX4OsDm9SAg4M9Vj3dH13r5nfKMDuu3cfLtHlGT3ZvhKbVdeeJun62AoH/+xsN3l5gcRwBb1dFRaWUhBGDWlu0aCHyjCiG+DEvBw8e1H7PuXPnxCCUzMsvv4xnnnkGjz76KNq0aSN+bu3atRZnGKmoVEQ+XX8cc/ZFilBVWRDtuyQFJf6zL1L836VuVdFeZ2oJFuDpguszJ6B2FS9hx20I7oyObVUbI3/K79P/aWJnRLx/n2hbkaEFL3dYg1+egxvxlrf0eVgROGsJ3FW1hmo+Hjjz7ljMn9IL5RouZhmwek0SypaQbEUOlTU80qkBxrepbZUvQZuaAXiudxMR3urv4Yo4I3MjliB+7anzkgHD8TPmXdhMCRVPD9xNzRQfrjt9Hf8+2lsEXhrE3h51x/TBnO+nFfq+y7ejA4WbIui2OHHPy8V7A5rBgXNVygpVGaxO8hgY/vYC7LogPb9Dm9XErPu7YkBDK8OOjbQWG6tiSR57PA4fw7nb+esVS6Bj6MKDF8THbF12dtR9rredu4Ulhy+JANbPRhesCv2r+VlWnA6/PtJgRcvHzQnB3pYZTQVZ+H0qKvcyxe5KV9KoAa/3DvdKYBtbJTp+8p/RoWPyyci2cHeuhKc1oZ+GBnAXPdZHJJy/t/KQjsU36VY3SCw4lC1v3eoF4fUBLdBHs/BISMuEz3N/Sh9/9aCwg2363mKtWAsP8sGZW8ZnR9iSYqoCJcMFtqWdd7Ty5Y5ufGoGHLNS0b9lfXy28aTBwFO2b0V+MA6ODvY4HxWP+m8tsOyXqFgEw4VjUzK0/+sTXtUHZ25Lr4/YLybD113a8Or6wULsuGZYqNvUva0QnH9nDBZvP4lXN2va9sidWKCKH6p4uOC/p/oh+cot/Lx4J3Y7eeBmiiSqbIa+qx0/T0mVDCLokmcFld2cEKcRfSpFw5pjlNS6XBOLjxjPk+peLwi7L0YJ8aTP0TdGoZmmKns3JR2RdxJR2c1ZbExRFLG9mjNGNV+bhxtxKQatLngfuHF26aPx9+yM0b2yXlApujYoMzlGKioqhqlf1QexXzwgPqYAOHw1Br/sOIN/D17Ufs/LS6SMmxq+HohLzRBiQbkgmnV/NyGKZMHTISxQnIxZaeodXh2VHOzxw9ZTeGreLjg7OuCLMe3xRLeGosVV2S+f+u3DcKnkIK73cnUSC1yu1eRKkFI8yTSv7ocTN+9aJIqIpQuONwe1xIt9miKZZhTT/xHXtUnJQKOgyjh1S1poD2wUgu8ndBaPi7RGlv4eJwcH0TIlVwf4WGw9fwtlFrY38YFmC5lywRwTB2RmAsHWtfgUB7IYMiSKyKpn+qPzp8txMz5VVD+37ziBkW/OQZ6LE9Cwru3vkA0WgPXeWVjguu3/G4ouHRthzYaD6NBiivb61/83FqfqhGLZsSuwGfqijp+zMpacis9+fgkzd0ci2oLMMmKNKDJnFV0R4FwiW3Cr+7iJ1yxDaC1lYrs6WHvyGqItMHvho2hKFBFTx57mHywWQdlXPpogNhPa1irYQUOx8/XYjhj98wbtRpCM/Ar6amzHe1YUqahYg/ouUVEpR/i4OaNng2qYN6UXQioXnGu4ejdZK4ooduQFEZ2N5OJwt3rB2P3KMHw0oi0GNK6h/T4KIRopnHpnjOhpV4oiGVcnR53rWaVStsdRPJ17b6zOzxy9Hou6Vbzg42qZMcv34zuJ+Q5TvDe0Nd4d0grpWdno9eVK7fXxaZk68yoHr8Zgz8UoIZzYj//n7nNi15WtK7IoImVaFMntTbIoUuJfWWodOxMphX3KxBZDBaaI/LXnPPydJCOBV75chhFfLkdey0aSKCpHjQsxOXlYvf4gBo7Wzc/7cN4O24oiYwQFIKh2NTRpEoZsB+k1wWqALSlPoshSQxUGqnLTR38uke2ST3VvZNXv/HtvpEWiyFbwmL7vkulMOW58sSugmt5rga8NuVtARUXFPGornUq55V4vjR+9FoMWHywx+vXmIf9v7zzAo6ibMD7phDRKSEIgEEIJJUDoVXrvINJUiqjApwgWVMCGgg1UVEQRpamAdJQWOkjvhBp674EQQkJL7nveuexl77LXkhwJZH7Ps7m2d9nb3bv7v/+Zeacg1xIplsI/96pPAxvaULCeBdy594B8hxhHjmwBs7dnvuhFo5fupU+WGKf7mdI4PNhgRmGJPK4udC813S8z1uEOBwKniL5tgU0oUSOkjK3bSnT9pt6QAPbVORCfM+foDgbyIcH6bc6hs9cuKSmUbG3bFqwgeqCKgHrlJSpfiqikA/vE3L+P/hSaD41oHUmfL99HTysft61Kx67dplk79TU3SgNp9EpDNEUdIddiUINyPGm09OB5o/sL+3nSpa9fpFWHL1CL75dRTgZGJajNq/HFQk65Q10RzG9gSBGgqj1CWh3c52C0gJoiZAVIpEjGC7mdeDtS6XLmL5MgCFaBAYK3R1oUAe5e6iDPvvOxRn1WJm44/Nj2KmxmdZNepR971LXreUjPA8idt4YtogioRRHIrChqUb6oZrQu08SkpUZag7+4lYPt7Ew+DapT909edawoykxUR6ejO6HF9KII5FBRBKb3a0y1TiMCd9P8Sp1bErVsQNSuCVHXNkTtmxIVDtA3d3UUZy+RkxlThqdZFMFwoFrxQkaiqH+9cHq+lj790pooAreT7rMoMg2Cw1kTkfTPUy2/gS0VaQ1KB7FL3cz+jck3j+WKhMwacQ5oUI4vX/lzI3m/MZXrOJGK+t+JKzR501GOhv+49iA9Sv2uhwiCqUnPmqX4UkSRINhHzv11EgQhHZgNXB9ziWbtOEFvztlKCff1AyW4rI1oU5VSfnmVLn71PN/+oE0Vo+d2zIau5683jqC48X2padnUAbEVDl+Oo00nrtDZ2DuUU1l5+AL3BMlqGvZrr3m/u4mTFTAt0b7j5kF/H82idMCHZhzRMmFikKnnPkawlb3qlKEVcz6i8IQ4orh48+8nv5/epltJz0JjWDR3hQU2DBKU2jA45p0yjlRkiNKhpNNKp3zKgYV2h4lRhtvlgvw41XfnGb0zpy3M3KmfdPjzpSZGkzXj1xykoXO2ciotnN8m9qyfLhVNiwrBBeiNphWpZ83SNLWP5RYAts7DmPZcA6ihXBJ9jq/DdEHLjROTX2/8vYWqjllAY6P200FV2rQgCPYjqXTCE0tuS6WDreuQOVvSNfLrVzecpvRpmG59GDXkf3O64fa9Cf3Jwy0tx/5xgRSWtj8up4OXbqUrDPb39qDIov7ssATb780nrxqKop9GUMf1c2rkrk6JANpx5hqnAyn74sZjrFuwCCITaF568hyRmytRoD9R6TCiQvmNoj1I4HG+fZvcC+SjW8obeQpAL6+xbSpT61bv0R28r7CiRBX1M/dWQYod9hnE05bdRPfuE928rY8u5dFOhXsqweD86nWiwEIOFcamqXRaroioo/y8U3V6p0Uk1yV2mriS7dhNGdigHNfCWTJieK5aGKclF1QJGXPfzai5hCFNVnPs0+5Ub+xi7qvVtGwRNuSB6Y4CItqtI0K4hvSZUkH8/apVM5qbyG3jBSHjqXQijIQnltz0RYcfXjgOmRt6ztcorkVUqdfva/k6DBHKBOajxw1y97ENaLSKfHeknpy9eZc2HrtMU7bEcFTrs441eN3TN+IpbORsyi1c+voF8nBxpnKfzKFrd7RTsNxdnHlGWGernXMWkvduAiXGJxIFpR/Yhhb0obYVQ2je6Cl09cJ1olqRRCVC9A/GJxD5elv/BxcvExVJa2qZ03BJSabkHdH6eqjQIkTBdtR/KaGCf1bphRGoVNYx7nupDGkSwQYno5buMbsOxPjhK3EOGayn4+oNvaC2ES8PV7qbGgE3xSk1onI9tWEznDHvWRAvEEtlAvxo97kbRrWFaIS9/tgliw2urXFydA8KK+SrGc0f8vcW+mn9YapctABH85Hy5uXuSnet9BBzcXai5NTQUpPwYEp88Ig/8+YMF9BUGe8hPNCPoj/qyo6fM3ecoOUHz/P709o3EH0/P/8M5VZy03hBSI/YdQvCUwT/4M7ZYnZwjEHD0DlbqGNkcaN88mblilDv2qWpd+0yj10UoWP7mOV76eN/d/HYHT/kCwe1oOIF9U1if9t0lC/LFc5vJAIyQ1FvF7qQYN1y18fDjUIKeNPhVEvv7GLo31s4Jc+cKAIPVDVimjhAFBXI60GDm1SgAG9Pem325jSxA9c79PC5fpPiPVzJf0A7ir2SWoejGBGgxubcRaKIcBv+kX3NeR83yc4uRLWN01Gt9kpShOr9B0TL1ukFIgwxCuSzTSxmAgySYbdvCQgFq+dUVpHf8qysKeZEEcB3nyKKIHBeqFmaftus/w4xpVX5ovRr7wY0bN42I2GE2sLv1hygjACny6Wvt+LJnUBf7Sap+O6NCNaf02H+vjS+e11q/M2/dOqG5bTgea824/S9xt8uYUGD7URLBfW2m6IIu5irt6nthBUcEYPzaLdqYfRi7dL0/ZoDtMMk1XDD8RzuvCkIOYTcl7AsCE8YcBgyTdEwHTRggI31UGyrUMjHkwvJHzexCffoxanrePYSvFy/LOf150F6USpHUxt97jpzna25kZaCtJOMoLjMFfRwtiqM3m8VSUObRvDg5pcNh2nQzE3p1sFs75nYO3Q7yba+S7aCFB+kuyg1B3N2nzI0vu1fvyx9tzpjgzYtPFyd6b5Gs0hrRLrp6Nw/q2jJmdO0P7AI31fN5RHtxiBfxc1EolFfzjLc/unl5vT60n2k8/UxL4ogGmLjiHy8iDzciTw99NEULPk0BtHxd4gS7+kjVjkVtYkEarOUc/zsRb04qlE5w4IIQieiSAGav+e0TesjKmstmfGxiSLgbvvnuXNkKF26nWjRkvrF2qWof72yVMLfl+bvMW9UsvLIRQodPtOotie/pzt93qUmDfprk9H3xpvNKlKnyFAWPPi+wsTNK39sZEc3NRAckSHWo1+KHTiaXqN32vq321Oz8Uvp2NXbZpvDPlstjC9n9m9Cz05aRRuPX+ZFK11Qq0n26iMXDde/NfMdgveI9D9BEKwjwkgQcjimP9KZXc/RdPp5JRsoKD1G3mgSYdQ/BJTw9+GBHGZwsSDN49uudSy+rpeZlBTFZW5/rGUhUzrAj77oXNMQ0TI3iNh/4SbVCi1E2+0o7rYF07qHqsUKckE1rHYT7metCMuIKAL7/tD3hLrplIeFjPPla3R0d7TF53h6utP/XmhKPTvXo/APZtN1c41EEUlB3yUFGBOg5gYiSQuILCyZ4OV6ZenPHcctpl3ZRWKS3mRBC5XwpzIluN8Q+Xin9ZUqqHrvNoBoACYLMGi/cjvRYsQYvWq00rueFBbus9wAFVy/c58alC5MC/eeobfmbjO7XoqG8cCtpAf04aKd3CNNiarhc4eaPwgtAEMH8FWXmtR76nqj56Oxqi0gxQ8o5xsi0z/3rE9Nxy9VbV/a+uqea20iitFfLzWhEYt28sSMwh/9GlOvmqW495p+PyRxG4akh484Xa/br6s1t2V8tzpUJcSfqhQryC6hgiDYhggjQcjhYGCUles5GpgnKMJo2+lrVOnTeXy9bslAWj64Nc+mDmpQnnae2WB4zi8bj3AKCIqGzTm+wW0PBcR+Q+3vjwQ6VCpGd+8/JE83V/prx3E6fk0/8NSqt8hqUaTFnnP6dBjMAKsdp7KLcFcdxeCKE5Gzf352vkvZc5DuJiRZfF5S0gP631sTaU30GbpespR9TWsdlA5Y2s+Vjt9+RBtPXM6UKArwcKFr91Ofv20vUfVKtj9ZEUXATlGkYJoOZY7x3epygX1mQR0PJixyIkjHdR44OcPPv3H3PouiAJ88PFGD75l2E6K42bU6Wg1zhf4zNhpaHXSpEkojW1tJqdSIGCmoRZE6AoWIMXqsdZoYxcY0p2/cMRJ1sOmGS54iiNSZAI3C08T5t8/VptHL9lKzskV4XRgyvN+qMjUrZ7lJtiAI2ogwEoQcDhr0YUb44q27mrPGyowx1ssJTO3biD5uV41KjExLtQJbTl41K2qQaoe+TJZssJEygrSTaX0aUt/paaJKCy93F7r7wHhA/M3qA7yogVCyRIG87nTTJAKS192FEk1eO6soUdCHzt28Y3Cqexw9YmCHDPIEFaT6g3vQ5TOX6aSTEw/aNiz+jGbOXU9Hj12gt17rSC27fKz5Oj//vpyoeBEijTZKSJXUcgCzCXV6mjWQenfyHL3SvhZ5FnCm49HxRilMNoM0OLwXojRRhGl+RLhMIp+PG9M0LNwuXzg//Rt9lkIKeJGvpxvFm0kBxfdEXitGABBFJQv5smlATqBMoB+Ne7Y2NzLt8duadBMwrSuE0OTNR+0Sv0pNH2rpUGfYY/Ia+ve1luSaWuOIlN/r3/SmfKnfVah9MhUn5lBcP5XtuW9mu5Q0WqQPqmuQvD3cOIWyS5USNLx1pE1Ocm82q8SLIAhZg/QxEoQcDop6v++m771h+jPppJoxzkmN/EL9bUuB+qF7XUr55RXudxR9wUJDTQy+Nx6m0BEz6Z/os1Zf11QUmcNas1dTUQQyIoraRoRQ1WL+VoMjp2PvkPNjPI7vtqjEtRZKCuGm63fppJevoTHukfs6eu3NbvTthKHUvEkVGv3BC5qv4+TiRO5Iibufli5YOsCX7v7Qj8L8fTLW5HLzLvN9hLRAPVLMKaoanI+KeLkYekDVKhFgtBqaByN6iVl7A8pM/cUrxul+CrfirBsxPAaU07VvnTIU6OvJtxFtmLb1GH22dK9FUaTulRMZUpAvn61Sgta/3c5o3ZwiisCekV2ofeXiVDrQjxb/rwU9X7MUR1Liv+9Lx0f3oLdbVDIcOpzHywa3sul1nVKd4MCKQ+fT1fQggoR6RKCVoqhunK0GkwkAtYTzdp+izj+vtGguAwGFY7LmzbbsUon3tWtkFxrRpkqut9cWhOxCIkaC8AQAK+55A5qn65WBSBFEkalVd3aTYEM3egxMulUP4wHAg0fJtPus5bShNUcv8eXZ2ATKThqVKUwDG5bnmWZbWXrwPFuqo7fIp0t2008bDhv1XrFl0OUI3pq33eLjfaen1Vq0r1SMpg3qQH/8vZ5ijl/AAeQUsYZdm9COOw/T9X5B/6rfN8fQzxuP2L1dLYO8KerCFaKa+sGpTaQ2Vb0Zd4fKl/SgRQOb0dU792iAquD+lfpluc4M4ih0hCqiuWaT3joctUFI8btzlyjutr4Hj7tbhlLh+tcL50jHsPmW97EtUT0Uz0/bcozFKpjYqz6L7B2nr9OBS7G0++wNrptD9GHP+RvpHN7wPfFFp5rUe5reRGNfqqvZ/L2nafuZa9SifFFuXOxo3Jydadkbraj5+GV8W+lrZtrfDHSoXJy8PNJslYvm96Y/+zcxWgdmCZuGdaBuk1dzKlrHiWlCxBJqlzsAExYIZeyH2mGB7PKGvmog2CRFefCszTR1awztHtGFwoPS3D63nLxCrX/Qvy8ch+dMan/MGV981qE6NSmrj1AKgpD9SB8j4YklN/YlgHU33OdgtICaIqTP5ZRIEZzmkO43d88p+mv7CZsMBZqXK0J1wgLpl42HDSkuzk5OmgXUWYW6rmjn8M4Ul3SfZmw7Tn9sO27T85GOhD4j9oIB1rkve7ExQnJyCrX/KeqJsNBFf5kHj1JYsJXw96aGYQG0bOdxuq5zSZ/amZxM/i5ENyhjKWde7q7UoTIctOqRp6szDf91BU07GasZuTMCgmH5eoM4WjTjZf5e+GplNH34z26+b3zTshRewItaNKlCG49fYXtko+craXKw4IbJgrMLjX+hAf17+IJBlLs4OZFPHleKs8GxcN1b7Wjnmev07oKMCyMnnY50qjAjavAweP+td0Pae+4G/bjuIM3ccZJrWrCa1sdmyWstqVVECA35eyv9tP5Q+v+hIUocCSJ4ivscolWNvlmS7jsB0RpE9dROlpZAM+uXpm9IZ+Lg4UzUunheWnTadmMaOMChl9DNu/fpvxNXqHv1MJr9SjN+LPpCLFX+bD5fn9K7IfWrp3dgfPaXlbRgr3UDCS1eb1SBvutWx5DKJziG3DheENKQPkaC8JQCEaS25M4pIGVkkcmgxMWJzNbKKCYLq45c5EVNkJ8njX+uDtsU/737FI3pWIPCCvlQz9/0zWot4eZM9NBKwEURRWguWz1UbwWNFCRLwqhh6cK8DXDF6melvskcqCeYuuUYvTV3K+l0OnaUysm4uzjRl11qUb+64XT86m1qPzGKTt9I4IXMCZ+jJ6lojXJ0IzE5XT2MLaD+ZdbOE7x0rFyc/ny5FW39fhltPXXV8hMPHjWIIvC/YXMo5Z15dPXmHSJvL6Lb8TR09r/82Jxp79GjIib1eOraIUw04DnoNbVgh+FuxSrZFlGE8xsR0U+X6kWZp5sLvVCrNE1O7d9ljeo+brRr8UZyalLbSLTgMwNh/szYfwwGJwpaogiip2ZoIY7g/vqfduROidigPimrLeq1UFtya9lz+3q6m42gHLx4kyqmmrncm9DfUNOTL68HzR/YnN8jahk7Vg5l62vUBvUt50PLziWSVgYsRM/INlU5Wo2IGb6LUGe1eH9aui5eD6CB6vOpDbNBqYC0FDtL7RQUEKVE6uLmk1cN7QrAhPWHeDk9pqfNKciCIDgOmaIQBCHTqEVR/VL6QaclAwEM8DAzrMXluETqPnkNHbikrzmKOnzeJlE0p2WAoV7GEl7urvT3K03ps441jBromgOveOpGPE1+sQH1rRvOtQCoc8gI78zbxil0OUkUoQaoQnB+ox5ISNUqks+bbZHzvzmdan65iK6mmjSoCfQxsTGuUIb2JepHoPaKIlMwOPUZMtW6KAL5jRsYX7oaT1euxZEOkZTbd6hQQT/DYwUL+Njs4IhomcK7LW1P63uneWVuvKkcZziJwSDBVnqUyE/klYdS0lUVEv247hCLIlvKtnAIAob9SfnenG4xRRPrQRQV8jbvbOflnrWZ90pEzRRMWGiBz6kiisAuk9RbpOQOaFCee7eh2TXSB5V9hEif0bqp4vWv/k2oYpEC/Lme+XJTujr2Rdo1ojNPxqDJKr5PlHNla6pAUmgw7l9KevCIUxg9TfaN1nfbG40jONIHG20tYFaDlGJBELIXqTESBCFDIOqx/thlbpSqMOn5+vTZsr02Pd80UmR43dTLw5f1s6pIe1JqNrpWK0FNwouQq7Mzfb1yP7s+YQBTpUg+mvXPcsqf14OS4i3bDVcr7k/B+fQRAXsa6CKiBPtxkCG3M1W0Kifgl8eNbt97aOSKBZDqBwFnS7oganiyk57VS/Io9/Ldm6TuPFM0OB/N+2MkJbp50PebjpGnUwrNHj6RPDzcqG6tcuTm7soDZ3PHXXF6PP15T6o2ZgEbU8AuGlEK2Lz3/G0Nmx5o0aZCCPl56htyquut7OEWInLBgYbbHCUqFWTk7ueI9DfU8VxPMO63pfTnqVbMnzaaRKkyA0TntL6N+DOLSJoSYfRCTZcGZT+eY3QbNUX1UidhzBnWvPh7+h4/lgxrEGGqVrwQLzBAuPfwEX/XgIOpEzVq8g6ewgYfSDW19t326dI99GzVEpwKGVrQx6hXEb+/oHwGQwhBELIPEUaCINgtiCZtPMKNWdUCAcX5aJZoS1pJRmA775t36fPle/l/XIxL5ALpH9Yd5HoAW4HQQioS0uK+6Vrb5sa4787fblSwndUoqVrF8nvROQftQzUQReaAKIItOYaRGamnelzM2nWSL/39A9k0wSklhVq3qEZhNUNpwPKDLGgMFA6g6sULUp7UZpffPVcnXYG86cA55sptQ3oZBuJI3YooUoCm9GlINb9YpLlNyw6dp13nbnD6JayXcZ6h0ScK+rH8+t9hmryJO0alAxbUJ67H0+mkR0Q3bpFnUCHqXK8cDWkSwbV3GbY9t5Fi+b1pb6oxgxpvd1dyT01byyqw/6oX96d6JYOMUgF3n7vOLnSmEwqmvb4QxbUEG9KkNCa6aFxXZY9hjVLjBIGkTNCYYiqKLIH6pNVD23KfoYF/baKCXh504avnba6lEgTB8cinURAEm0E6C37Qf0utlUDfjRdq6S10I0P8adaOEw7bm2/8bT7dDXi4ECltZ0xBU8f3WkZyp3k0b8RgHxa99cf+Q3+91Nim/28qijC7m5yBfDHUm5g6uAGIIvA4RJE5kHKUnDpCdVSvJlvx9nDl2fq4pAec3odIljluJD0k/76d6cPWkfT71uO07DgEkYlYrlSWHsampUONXblf03wgwNeTRrSKpG2nr1L3yavZ0h3HDJFGhRqhATSsRSUauzLacB/sswt65WG762t3kniBucaCfWfoytgXDNGJfHkjudZMyyoeogjMPH6dqGRxouRH5OPhRrW+XEQl/H0orKAPnYq9k+Fzz5r73b6LsWZ7XaGPWFaz6+wNXoDybjafuEo9ahg3CsY+wGf3UlzaJIap854WsPpedvEQLX2tFV1JuJ9hwxoIl4m96tHrszZrHreI4Pw0d0BzKmcS1dKi2fil7I74c6/6vC0iigQhZyHCSBAEm8CsKYqP4b6E2esvOtegQQ3Lk0/qDDywtXYDqTn2NGVUQDoPDBOK5POiIvny8iVmgAO83GnzutVUuW4jGrv6IP2sSu8DcLx7e962dK8H56sWFYpabKBrDtOBKQar5YLymU2xUoAoglBDI02w9VT6AnRzoB7io7ZVuZD7zTlbrf4ve4Eoyp/XnW5Zc4HLInD+XLiVQP9Gn2MhpK69KlbAh3YM70SF3/3TrLW5mht379MQS/bj+f3o3KVLhqL/Han1LQW8PLjHFOp2cGxQSzVkzlbD02ACgQiDaWE8aunUwmhCj3rUtVoYf05gnz139yn6fu1BLubvOmkVRz2m9mlEpQL8aEaLcvT2R9Pocp0aKI7R3l5fb0JV16RU0wRErBSyWhQBbF9SfHK6CNaBj7vSxPWHNT8/maWQdx5OhUVKLvYT0HKzRIpb07JFDAYpP/WsR92QRmkF1AAdvfWAhpYMJA/02coEqF+KCC7AUUbTKDM+hxDElYoWsNqPDSB1ELWKtn5fCoLw+BBhJAiCVe7ce0CdJq6ktTGXOKd+Vv+mmqkomAFVZpgt1W6guLn3NHVViHXwXMzCY1BkOuMLK1YUX0MsIT3HGq81Kk996pThmX+AegQMXjMDBquKUEFPlHaVilFswn16c27aIFst1BR7ckt0iSxOyw5eMPSvOXDxJvegeat5RVo6uDUVHz4zw9vbqnxR7hOz6sgFQ7QKPC5RBFA30716SSr5wSw6Y9Kf6vDlW1T5s3lcR2aJfnXLcATGlDB/bzrFDnppXC1ShJqPX2oU/Yi9e99QE4J6F6S7PUhOpqL5vNiVr23FYpr/t0ygseEDogkYNOO8+qB1FVp/TC/CwKJ9euMF9E7qVy6Ips5aS/QgxbwoygYgCuG0pk5Z+7BtVY5ovNW8ErsTdv9tNae8IXV1zzl9pCczYIJk9itN+XqJEbO4lg+THeZElMLABuVZLJly5PItWhdziYJ887KhSPufVtDxa/F03GkL/danUaa3FzVNu0d2oe/XHOD98G7LyjRqyW42xOg3fT2tebMdVVIZRFgi+N0/aUa/RvRi7TKZ3i5BELIOEUaCIFjlvQU7WBShCPzf11qatdOFYClewEdTGCkpS7CsVdvV2opiggCzBHOW5XN2n+ZUOUuo+4+YNtB95Y+NdDPR9nolc8zZfYqXzLIgdUCtZszyvVxX9XK9cI6gYYY9xkYzCHXaGEwXjl1L/zwIg8fVZDbAx5OiL8amE0UKJ68bF6ib8kaTCPq+e12qWzKIe9lgwHohNd3KVBRhJKvL56uZEvZ8zVI88G8cHqw54DYF7mGbTYwIrt7Rn/PTtx7jxRxTj1whiixPjgDvAT1x8BawT6uOnq+Z+mUO0zqePtPWsxAf3KQCRzcGNShPs3aeTGcVnlGUzzPSJXEdNDXjVqm29oaDW+/apem1RhUoyC8v1z32n7FBUyCD37cco3qlCqf73GcE7AcIZoWvn63F9Uf7L8TSazPTmgkrmKZcqpm357QII0HIYYgwEgTBIqeuxxtS01CbY6lx64qD52n7mWucalfIJ4+RxXNhP0+6dDuJxq89mKk9bsksAXUealYOacNOcn9uP85GEUibgjOUFhBHbSuG0LhV0VwrggFQp59XUk4C0bAHySmcrvPdmrT9GBlSkH7oVodem72ZDlw0n16nHiLfStSOWGXKGAvT6HZEQX5ad5CWHDyX4X+HCBp69Lxcvywt+GcLxR06SVSkcLr1vFyc6G7MGaISIWzSYMpfO07wolAmwI8FRqsKIUZCCdGryZuO0NdR+w0D+ZzEG00qcAPj2IQkavX9MhZFOKdhQmEtVbRmqD+5OLvQo5QUIxttmKxg8XB14UayCjCWQIoj+nNlBkQGP/xnF19/u3klFqdaYDIGPYAUl7/Ry/ayMyX6Q0E0HbKSVvq/WZuoSrGCXAuZlSCihqhXxKi53BDWtJnt551qUtKDZGoUXpi6VClBHy7exZMboH6q8YQgCDkHEUaCIFhtuqmm48SVtGlYh3RWuRg0Dv57M18f2jSCZ1IxGwwhA5EBu+PI0QvS2dTai6W8/EZlCnMKjTJIQtof0oHQGwViAoNGNJA0h4ebKzd8VNBNepWOXYmjrr+u5vShhPsPMtwEE/1L0CwyM5EkOPGNaB3JgyvMsitgVr/BN0vSrQ/XK6SKaXFDw5YZWDI5yEpRBBaqGmlmBKRNlf9kDhXxcKFtG/YRPXqUXhjpdHR38VqihLtEoSE2vS4iaehDBNDzSqlneXveVvppvX6SACmjGJRDSOcUuvy8kooX9OGUTqVmBxHafnXC+Rw2R92wQNr8XkfD7YB3ZtB1k1RPiCKkufWsUZJ61SzFYhy1QU2+TX/e2cPhS7cMrpJo6hxa0JsjX0jzNE3nVdJgnylVmKOm+DxN2azt8Kfg6oRawiC2Gu86aTX3KUJD2KwEVttadV8ft6tKri7O9GPPeob7WkWEGIQRrguCkLMQYSQIgiZIT1l79BJN25p+4DFlS0w6YTRu1X5OxYFw+bhdNU6rM015Q18YONuhvgZF9/N2n+ZZX1tQ6pNQx2SJAx915T48sPSuVqyQ/rlOTlQ5pKDV/3Hr7n0aNn8b1Q4L5CgEKBOUj6I/6srX4brX63frzWa1aFGuKA/qMiOMIDIHz7bszqeAqN3yN9pQ2wnL0w1yQZaX7zuwXgZmFeZqshC5OY8r4WHmN61GRcrvpKNbzk52v2/YtCvCCC6MoHhBbzo6qhtHUR6HMCrsm5dqhQXQlduJhl5aWpyOTeBFzUszNnLPIJwPKYontglopgzzAMUQJPrDrjRi0Q7uawRhhfeLz1P5wvmNImiI7qDm77VZ+gkRNaZuf+aYufMEDWxQlubtOcOTJji/0fMHrm3PVk07ptg+UCG4AHWvUZKXLSev0LiV0bRQ1WDaFGzDby/WpzIfz+PXeG/Bdpr0QgPKalBXqHy2BzYox9+BSPPTivrCpAFRabjZCYKQs3DSYfTzFBEfH09+fn50+/Zt8vXVf8kLTycouF+2bBm1adOG3Ny0mwIKGefrqH1cW6RFzKfdjIrPz8beYataFHCjmzxmlG0Fxcu/bjxiMSVHGYqhDkjL9CErzoVDl25SxKh5hiL8m9/1SbfO+phL1DgTM+TqwVNGKV84Hw9Yd56+RresNIytXSLA4kDaUbzeuDxNWGfsDGgLcPU7Yqb+DLqrbKD5xzVJSCTyzpzz15o32xpq6i7F3aWwkbM5erLurXZccwcrbUsojUvtpUygn1GfsLNf9KLSH8zmVEpH9Qnb8l5Hfk/2cjPhHvX/YwMtiT5nU02TIpqQsojoHATn4v+14Fq5cSv3G5oOv1i7NP3QvS5fD3l/Jqfxbni7PTUoYxwVdBrwq9n/5e3mRAkPjbdp0vPPUIfKxTWFS0bBUOpsbAL55HGjgiqjCCFnIOOF3E28HdpAIkaCIGj+yP+yUW8TDJGD9JmZO05w4TW6tmNRA+toiCJY72Jde8DMKpbeU9cZ7HhNsacpY0b4Y9sx6j01zSUPkR0tEK3KiLW3giKK4KlnbXi74o3WnP639dQVWqgyYTh8OY5n/19vEsHpi0kPH7EF9BCNPk9aogjbn5EmvEgpw6C5/U9RRvdH+PvQA2cnOnszwZCGp4iiGqGFjOpVrNEpMpSOrNin+Rim8OwSRSCTogg0/W4pXyKCgXoY1OzAsh7F/rY0B86IKMLgWi2KwGdLdxt6TNmCrREbBZgHvPrHRvrjpcYcYbWHTSevGJz3bEH5PLcoX5Q6TIzilEj0KUO0t3+9cPp0yR76YsU+/j7AZESdsEAWRYhYaUWMVw1tQ83HL9P8X6aiCAz46z9elM96IW9P3rexCfdYnH3Ytgo1LKNd62QO7DNTS3dBEJ48RBgJgpCOI5fjuG8KUoUwu+qdx43aVSpO0/s15iag6nQaGC4glQV9fNDLxd5BlcJzVcMMwggDw7HP1iLfPO4ZbspoC+g5A0Hx63/6hrVgcOMK9FnHGprrYxsUa297B55qbJnzx74f1rIyrTnibySMAGpIFGtwOLuF5Pdi0QJzjM6RoRZTiyDqMsJz1cKo5hfpoyMHU2f3sQ2KIQGEG44ZapzsEUZwe8upDDJxHFOiGraC6Ahs12EcYA2tvk2/bYqhvG4ulJiSto+C/fJy9Eqrjiwj5yYMKF5vXIFTSe2hdUQxNjVBjZAlygb6ce1hm4rFDJ/nBQObczQIdYFRhy5w3c3oTjVYVA/4a6M+VTJ1QuGd5pU0v1+alStKM/s34TQ5e00xlJoxNWuOXuT6QkEQch8ijARBSMeSA2cNNQQQRWorZ3OGC0OaRFBEkQIZ3psYEKGuh22Xu9elYDP9TLSA/ffJ2/aZIqSk6Kj1D8u5gFwN+upUGT2fahQvxCk+iQ8fUafKodSkbDC5u7oYrL2HzNmSociLrWBwBnFx6LKx29bNb/vQ4v1nuIEo+u+gtxMWgEhe9eKFaOPxyzxYRlH9llNXWbTivaCJpy6DltzOAydbfFw9IEUtywaT/WoKBvWIHCiNVoHiOvYkoAhRSyDSsfXUVe7Xs/6d9izyVx6+QC2/145uKLg6O2mmpCWa9HTCcc9I6pspOD9gHoDtQwqfveB8wmcibOQso0a04I9+jei5aiXJwy29GyCAEcLL9cNp/JqD9M3qaP4ewLn5/sIddDX+HpUN8qOjV26z2YWlFN2eNUtRjxolqePEKG4YnFm835hCc15pxt9pxQp4Z/r1BEF4MhBhJAhCOpRCd6RpIa3OXBTI1HAhM2BwNflF+4uiD168SdW/XEyPklOofv1Yqhlm3QIXjl3X7ySlE0Wg/th/DE5vCpM2HiHf1KgZIjJIGTw5ujsVfW8mXU91/zJH58ji9M/+s5Rs5xQ+7IinbT1GdUroZ+/RcHT+wOaU38uD+tYN5yU+6QEXlJ+/lUBXbidxH5+Ri3caXgOiCMDla+aOtDTFx9WnyBKoKTOtK7OlHqpOWABtPeXYuilPNxca0KAcD4j71gnnyYF35m6lH9YdMqyjJYrgqIb+QX6ebrRgYAsq4e9Ddb5azLb19b5eTFFvtOH0MUtNkIGtvYewnro5rzXcXZzZ0h7n7L2HySzc5r7ajC7G3eVoT9+6ZaiAl/31MTifYITQtWoJFiXqPmUNygSbFUUKQ5pU5Cap6DG17/wN2nziquE1YHENS2xM0lh7HXxPzejXmD75dzebOmiZjtjK3fuPDM6EUUP0x00QhKcfEUaCIKTj1WfK0jeronnWH7PSgb7pazVguDBmmd52dlzX2hZtsB0JehQ9SK1tQVpMjRKBPPsNm1xTMJh9f8F2mrD+ENes2ArS1bAfUGeFBSgDTGsoaXD1SgbSp+2r0ydLdqfrd2Ip7W3uLX0aUbtKxahmiQC+Dmc/tRV6m4gQ+nvXKTqsUYMDq3LYBq8+csFsI1VLRg+oaVKAO9ms/k2pTkm9WEPUqluqDXRmUgttdaZbPbQt+ft4UvC7fzrwPxE9X6s0fdetriG9D8YiiiuaGvTFalW+KLWoUJQalCpMK49coIF/baIAn7zUoHRhPgc3v9uRI0R4fr2xi2nZ661p3dvtWcQqdXyPCzgzbnuvE7s2ojcR3NEgNpDCeun2XX4/toKJhRWHzrPhQtThCxyN1BKYEPTWyJfXnSdGklOSqcroBeSvMi+AocGf/ZvYvF2IQI3vXpeXC7Hx1GX8AqpeNoxefqY8jVi4g7cVfNahOo1sU4XFFD5P+C77+N/dmq8Z6GPcH00QhKcXEUaCIBiBVLbwj+YYbmPAoyWMMmO4kFVEX4hlQaDw9+7TdPLGHdp59jr3OmlRvgi1LB/CA8Btp65Rn2nr6fg1fVE7BoFIQ8JSvIA31z6gzxFcx0xRUtXUqJvXWqurmNCzPqfifb58r82iiEyEBoTQ8IU7uOHu5pNXuKeROhKguJXhvdy8e4/u3H9EYzrWoBFtqrANubm+RZZArYlaGJ38rAe5qAQn6o7ixvel5QfPUc3QAIq5Gsdi+qso2yzY7Y1iVvpsPvfP0cLN2Yke2uF0AKtkpU7LFET4Rra+w8X0iBxqiSKweVgHKls4zXK5p3cpGrloJ59j07ceo/71y7IF9uZ3O3Da5l70mxr3D7WOCOEIyH/vtOe6mC0ZiIAtGtSCXpq+nm4mWnYmVOPl7sr1gbF37xlc9gq9PcPwOEwl/hvWgeqbWPGb8tWKfTRi0U4j+2/Uk6F2qG1EMY4gvTh1HTtXqusRzdH555UcwVJHdJV0T7jWZRRMXoysnp+qPxNJXX9dyxFJ9Dab2qeRUVoe6p3ebxVJFYsU4MkTmJlAiC/ad4ZTa+1J6xUE4clGhJEgCEaYps0pImlK74Y8EEZa0daTVw0F/j9mwnAhIyC1D0XaqEdA6g3In9ed8jol08W7yYY6FdToYIHlOCI+GGxhIIcmlb/3bkAtK6Rvrnj3vvW0pNEdq9PrjSNoyqaj9Na8bVbXn9irPjUuW4TO30wwRNi6Vw8zEnS2gFoj03ojBUUUlSrky1Gef6LPsYPXuy0r67dhwyF29cLAD65fb83dZranjRp188xCmMXXOMwQkz1q6AeZYYV8uRD/i841rdYkWat3UWhWNpiP1bD529OlOKoH5RAflT9bwGYEtgBRpFUnhHogCGGkUcG+eruZ9D7sD7UoAoiaDm9dhSMyiAw+X6sUp4FhYmH92+3p2Umr+JyFAMGSGTr9vNLu5yB19OjlW2yuArRqcZ4Z+w+dGN3D0NPIlDHL9tAHi3cZ3Rfok4faVirOkRWk5e0+d4PvD7exXql5uaJGaa29a5fm+yCuIDItpfOaAw1hd52+SkvOJNJbO5ayWQZs+CEoTe2+AeoHO1cxdr18s1klu/6nIAhPPllv8yQIwhMNBrpo2mjKSzM2kM+QqTRy0Q5Ov8EAFkxYd5AHLo8DREww4G7943IeYGIbIDLWDG1DHUoYR7Vg5NC2YjEe/GKgCyGAvigHP+6qKYqAl4cbhZmx3MX9j35+mUa2qcr76I2mEWweYGm4Bqc2ZRCGhpmIsEGcfKKqxxrWPOsGXyeux7MoAt91q8OpXBj4f7/2IN+HWXGkAmJfoKjdGmhGqYC0weELd1KCDTUtGMQuHNQ8Q+9BLYoA7JP71Cljdn28p2WDWxO0ISIx9qBVJ9Quohhfwpp78KzN9FWU3j68gkkzziA/7fSq/zUsz+ccjDk8X59i2F8QTagx2vJuR47k5cum1NNyn8zliKolII7Q10urns9UFIGrd+6xiIbF9tvzthnSTcOD0nqdWWJ460ij271rl2GXOxCX+IAnNWwBExuIopX+cDb5DplGTcYvp98O32FRhM/v1vc6aooiQRAEBRFGgiCko2vVMKpWLG1QrObz5fs4hQ3iCbbMsLpGsbOjQR3Al6oeN0ObRtCJz3rQbDhHBeenxEfGA+rfNh3ldW5805ubce4c3pkLs1GDYIlt73di23EMpCCAAARVzKfdjSzDFetuoCWOcB96tSjPUWbpUVTe+ZdVhvWqmNnPGaVqMX8a3irSUCyONDcUoSNShgaz77TQR5FgmkFWRN2us/qZf4WxK/dT2Aez6NtV0ZSkISou3EpgoVz3q0XUVfUeMwLqTpDuCLe77pPXUNQbrTXXG9aiErvwVRg1l1OfbCWPqzPVLxVoVA8D5u1Ns5z+Y/txQ8oi7KTV+Hlqn0ee7q7cLFUBkwmoa8O+2Xv+Br9m6QBfPqcygz1NlO0FtWsNx/1rZC1+4tptNpDQYmCDcvRph+r0RpMI3q7m5Yrw+dfXgqA1FcPKRAu/3sz/+JgiegeU9Fdr/LT+EE3dcsxwbhfJl5eqB7jTR22r0PbhnW0WaoIg5F4klU4QhHQE+HrSrpFdOBKE2VqkLyGVCU0gQb/pGzhXX0nH2mRH3UxGcSInToW5lXif6pYMNBTHK9QO9KDpR43NBdD0sWZoIRY7tqbiFPLxpNcaV+BFEWTmeiiZs+4OMWlIiwJ+vC5A3QTEEQb+rSuE0AtT1lFWsufcDV5gu4zBKQQRuJ6QxHVRcA5THM1Qg/VK/XKcAoeC93OxdzjNDhEF034w2PcQKqi3QVQAIgnF6/3rleW6KQxKUYhvS4qeLSAtUBFmaAC6w0xK23vzt6ezsbaFe49SaNOJNHtwRPPsYfOJKzxBUEklggCOLSy61aDm6uuo/YaasV82HM6UUUWT8GB6pX5ZQ2TGFrw9XOndlpH00T/pIz5awO79n/1nqHaJQJq355TFurEOlYtzCqU9oP/Zb5uP0uedalDpAD/qUb0k91ECEDatflieut1uFOznRk6LGwAALbZJREFUZfgcodZu2cFzFH3xJg14phzX2yF99ofudemHtYcM6a6vPlOO8uVxpWXLllGb1pHk5pbWdkAQBMEcIowEQTALxAQG9M3LF6V95YuyS9rvm4/S7F0nDREQUCS/Y4uTUdME8QFRBJ7XmC0v7OVKST/0ocpjFtGxq2kzzHBiy0wNlLXGshA/HSOLG7nEqRvSTv7vCL3653/pnlc1pCDN2nmSHAXEqzoagoH/2Kj99G23Oob7MejsWi3MIG47TFxp2Mdq2lUsRr88/wwL5hlbj9Fny/awW9jg2VvonXnbjep64L6H/kljV0VnavtNo1V3zfQMKhngR++2qEwjWkfSS9M3GPVFciQQNs//vpbdzVYeuUhfdq7J6XKf/JteeKib3yrPhdD0cHW2aNut5QwIAQBTBzwXqZz4TNoismAAgpREHw83enPuVpveI46vadNh/P9+9cLJ/X+/Ge7vOmk1JfzQz+rnDL3DYCevjvxiQgETF4jmztl9yshKHuLvk/bV+Px69peVtPLwRa6VU1BqDEHDb/5ld0rUm73TvDKn+z58aF9vM0EQBBFGgiDYDATQR+2qUbfqJdnCWGFQg/IO24sYzL+7YLthIA/7afSY0QJi5K+XmlDNLxca7LhhVe1o8H8bhQdrDgS1RBFYG5O+hxLqTuI0bI8zixINmbDhEH3dtRYXt8/Ypu9rdOV2Ig8wUehuCorpl73RmtPzFDAoRxrY75tj2EwCxfbo8YS+SkipgpsXxAnquubvPWM0kHUEMVfi2PmsQnABrt96XMJIMXBQ0iLhoobGxB+21deg1QgNYLMSXEeq6aglxumm8Un3Db2tIHBeqIV9ejRd7x3Ux6hBE1+lqStSObtOMp+yiGgprMePXY0zOEcOblKB09awr+AoWf3zhRxhtAUYF6AB6xETExDUaiF19ZVntD+XliYIlM/nwn2n6VFKmihC2uekF57hKHXnn1fRmVjj/aDg5uJED5N1Bst+RLq83phC8wc0p9bl038mBUEQLCHCSBAEu0B6nVoULX29laGvTVaDVCFFFKFeAW5nQX7prcPVVA8txEJt4obD7MyGlBpHgtoL9HNpV7E42zurwYAdA1lzgzpTMiuK4JSm9FZCfYbaAlmp5fhg8U56r2UkCyM4o604dEHThABcvZOk2QwWDl6DGpanfnXL0P4LN6lC4fzsVogIij3pXbaCQESwb166aNIQFizYe5p+feEZcnN1MXKQg1izp/lpZvlh7UFOjUzW6dgqHqDeavJ/R+msqlZHAaIIxwhmDaPaVycvD1f6N/psOmGkPoao3WlTMcTwOYSwQcrmsAXbDMJADYQZ6n5MRfxg1X27R3bhKNeoJXusvkeYnkx6/hmDI6UaiJ6Q/N4snMztHy07baTI4rF352/nyQycV6hfRPRp4vpD9NqszRa3CaLIFJznimvfjGaFrL4vQRAEBRFGgiDYhXqwOfnFBty7xBFsOHaJ+k1fz9ffalaRvnkuLQXMGmO71qYS/j7UKTJUs9FrVoIBGArz/9p+gu2dkRqFqImSSufoiIkaDK6vp47BTUVRsQLeXEw/dmU0nbx+h4rl96Jzt+6yKCoblI+bxKJ2q17JIK43cktNlUp88JBm7TjBaU51wgJoSJMI8nDT/3TAirpWatNZkFlRZGrVrU5FO/Zpdwoa9mc68YjzscqYBfRN19oskNYevUjHYmIowbMgLTt4nmuJMguOJ/oo2SIcbAVicu8HXVjQAYg6U4MH06a5R67E0eJ9Z6lzlVBqMO5fq7V9QRr9x7T4pH11Gtw4gs8ZnM+7LLjWDfhLOwKqvH/dpFc1HxvStCK98sdGo/tQ86beZ+0rFeNUPSUlT93o1VaqFy9ktP3XEu2vPxMEIfciwkgQBLtAahB6xmDQggaOjgCpOhigYRYctr1jn61t1/ORaqS4rzkaZTCL5pHfrT7AxgSoH2lWDs1li3KDXAWM97LS2dzV2Yln3RXnNNRTmUPtMIYoi7r+CDUeigMfUA+4m3y71HAdrm+w7IapAxz/cIlBLBzqLKV0WQNNaRFVqVSkAFUJKUhTthwz2fa7FDl6vpEocnVxokep0QIcAxTrI0KI6Jw+AmZ+X9gLXP7QsHXfhfQW1ubA8d915rphm4N8PQ31RHBzRE+qLaeuckrbN6ui6df/jhjVgwHTUwX1ROiF9E7zSuxwZ43v1x7gXkq21NgVTBUhUUNa06dL9hgs3tUg+olmyYjG+eRx50ukt0LULTlwjt62YD0Psw4t8P4hfJGuCet19UQGUnbhkLl4/xnqYqPL4fq32/HnHyl4Dx89okvR+oizIAiCLYgwEgTBbuqWDHLoXhvw53/cvwRRnz/6NeaUtJwG3NnGmQz2IIoADAzm7j7Fi5qsbvcEZzmOUHnnoWs29HpBRMbfKw+nyAEvd1da/WZbI1F06+597mNjDrjOIXUQi5LehUiG2pXPlMiiBWlfqqOhFnFJ91kQ7D0fSx+2qZJOGIGjV4wtmxVRBDCwhnsieg+BAE9n6l2/AvWsWZoH3n9tP25kRmErOOv+fKkJ9apVim3VG32zhNxdnA0NdS2x4dhl3jeIWgG4A9YrFcQRjd3nrtO83afp29UHWFSuUpkIWHLHU97xOJWxBUQWHA5v3r2vaWAB0RhRpIDN7/lSXCJFHb5gdN+d7/txqmRGuffwkZEhipLmCb1Wu0QAje5Yg409tMBn3zT6aQ5Y7KMXGYgM8WfzhUuZ8wARBCGXIcJIEIQcBWpalFnj0zfu0DvzttH47nW5uD0nAJvmCesP0Zxdp7LMmho9dexJ+UJqF9zk9p6LZWtoW0SRMuO/5s227J4Ha23UoFQOMbabtiRgtFAP6s0RfTGW8ud1p1uJ2jVUt5PS0g3HrbY+kkWUolfNkizQYeHcsXIo6UhHyw+ep/AAHzq1bzv5lw3miM2cXSdZxFoSZ6Ypa1qW666pqZHFCnrT8c968PVLcXfZEl4RZKbnsSKKwO5zN1gAzt+TFq37Z/9ZGtk60qZ9aEr36iXp710n+RzUEkWKaNJKTTQnXmZsPU5D52xhcRacLy/9/Uozql/K/kkQiGtEfNGLCILw5frhhsdQK9igdGEW1kjPnLzpKC+Nw4M5jQ6NdCH2z9y4Q+FBflTAKw87PdrCeJXjoiAIQkYQYSQIQo5i/OoD3LdGASYKGEBd/voFh9cLWQLpWV6Dpxjdh7ocpP+gj8rwhTsy/NoQRXANs2TfjYH68FZV6KuV+9gqG31gJvSsR4X9vKjK6Pk2/R8IhGpjFtDVcS9S7TBtw4xGZQrTV51r0ns2vB9YVGMAvT7mMs3ZfZIS7qc3cVCiA2pRBCHyZrOK9FnHGlxgjx5ZSkPOBqWCaM/ZWLqnsgBXg/0Ne+78XukbrKKm7M05W2jm9uv0YFWUxW1vHF6YJj3fgEoH+lFc4n2q9Ok8ttRuW7EYp6qpLdeBcu6pI1UQP1qiyByo7TLl5w36FDp76Vi5OAsjLSB4Z/RrxEYOIQW8zb4GBPLSA+fY9AHOhIoJB1JA/3ipsaH3lr2gDxZEEYBrodrYYdrWY7yMbF2FZr3chHr+tpbvx2e+4qfzjF4H6XUwh8CxsGRNjvMJjzuq3lEQhNyDCCNBEHIUGGibgjSpmKtxbMmcXaCZp5p9HzxriLagh5EpSLnC4B0F5raAwSjEwg9rDtKfqSYGbzerSNWKFzLqjQRXsibfLmEb5zY/ruA0KnuAnXGFT+bS9uGdjVLoFFCPYmvqIgwd0Ntm6pYYs710IIpQB6XeD6uGtqGm5YryddSCtatUnJZEn6Xxaw7Ql1HRmr180EcJ+0KxqtZiyuYYmrZVb0Ne2NeTIov5U0l/XypZSL+EFfKhEQt30j/RZ8nZyZlKBfjyuvnyenA0DcLohVqlNK3XXVLrdBRL6dM34ils5GyjdVBHhJoiRDcRAUFEyBo3NfpG2QLOieiPurKgMwW9lWCMYSqK4GSn1BtBWLf7aYVRRAniYnDjCtwHKDPpq0ghhNseJjXMMWb5Xhb7loCNeKeJUbRwUAuDNblpdE/ZSkT3rPUcEwRBsIYII0EQcgRIifo6aj8PmNRg8IpBDwrrsxNEWCAC0GwSbnzqFDQIGjSuVIM6lIJeedhAQqtpqilF83vT3fuPeNb7fnIKu8G92zIyXe1F8YI+tOndjjRm2R6at+e0pvBSrKqx705ci0/3OEwakDqlJYxANVXfIkvAAABOdNaStdTbCPGgiCKFw5du6e2azTwfBfjPlNZbYJtjwZ7T9FVUWuPQ2LtJdODiTXq5XllDOhz4rlsdrqFZc/Qim0l0rqJ/TGnACstpLWD2ACAkftlwmN6Zr68nU9j6XkeqGRrAdTM45tO2xrAwUpsuKCiDezdnJ3pokurm7eHK54G5faFERxShPK1PQ+qrqp9C1Cw8KJ+RGEKqHnpO7Txzjda93Z6P75QtMfxeygb5UZ2wQCod4Mf1Pg3KFM50TR/E10+96vNnAP2NzKFuemuOxfvPcqogjuG8Ac250bO6nq2oScqjIAhCZhBhJAhCjmDEop3szgUwk6weNCG9KTt5lJzCA3eIIoDogxpPd1e6+W0fKvPR3xzdUhjVvhqF+nuzY1psgnlxhFqQF6espcuqATQGfHCH0xrwIVowoWd9bij6+qzN9MvGI0Z9ixRL9fikh3Txq+epyHt/GZ6LaMaCgc0p0IKVMwbHllKXFGD9bS8QDkP+3kKfdqjOAu7OvQf0+uzNFv8PIlIftatqNiIAUYRogvo1sCswgMb9GFBjP0J8H7922xD1eWvuNj63UD+ElC9gLvVMqTHC8R00cxNfR63M1D4NKczkfPBwczHUF73euAK93jiCSn8w29BjqlfNUpzCptW3ypooMo2OdIwMpXJR+6mAlwcbRah7aW08dpkNQdT21Wg2u+30VTY3ARBtU1VmFzju32eR0IBLnSVhZCs+qcYP2KaOkcU5Qos0QHUkVRAEISsQYSQIQraD6MXvm/UDqAk96tH/GpXnKMvmE1epTKBftm5bSoqOuk9ezc1QwUdtq3IdjilIm0MDWqVXC4rMYZAAfn2+QbqBu9H/0OmMRBG4aDKoNyeo/ku11oaYPH4tnm3UUZux/tglqh0WQMH5vDi16sN/drEr3eyXm1qtxcBA01LqEm63jQih9ccu010zzWHNcfl2Ejf0xGKrfTlEMgbDWiluySkpHEUwfRmd6hKGAvH3HtDQOVuN7NNh7Y3UQqfUeiHsT61Cf5yfP67T21crDW+xL1cMbk2eHul/Rq/cTqS1R/V1cq0jihECMFhf4S8L/Z7U7yPAx5OupToImouOIA3w8Khumq/13K+rjZ4PFDdBBUUg2XPe2Qr6Y3WoXNyQUojo2f4Pu1LgsD/seh00FFafm1rngSAIQlYgwkgQhGwHAycM0DC4H9iwHKfiwI2qfeXi2b1pnJ4FUYQ0OhSko7DdHC/VDaeoQ+c5MvBDj7qG+82mAeXLy4YFWpEDDJCdUgf1mCXXmhXfdz6W7ZixHkQRUuMW/68Fu7VhAK/UxbzTohI3mkWEw9YCdXPbjH/m5e5KS1WOaxnFHlM/RAi0gGCyZBWuCCsYW6hFkYI+KqbfkCA/T1q49zT38PmwTVVqFRHCznMNv/nXkJKopL8hDc3rjSkU+21vTlWDeQCiYKiL6ThxJUcXUS8HN7yfNxxOl05nCzP6NuLoU0aiI4iO9a5d2sja2xZsOe/sAVHT9TGXqF/dcHaXhAGIvfz23xEqFeAn0SFBEByOCCNBELIdNM8EL9Yu7bC0GEQW7E3BwWD3g8W7+PpPPetZFEUAtRlzBzTXfEwrDShZl0LNvltmcZBqLloSn/SA+s/YYFgPogjNLdG/BajtzVGI/2WXWha33do2I9LwVdR+FjNI94IJwjOlgmjVkQtkoyM0U7ygNzUvW5TebVmZfD3daM/ZG9RmwgqrzzNn2QxhYwvlgvLRSpP+POCDxTtpQINyfB3HpfvkNXx9xrZjLIzQeBWiyMvdlaNjLqq6IPwt8NYMw2shpRFRJ0QAEbWb2Ks+nxM7zlyjjABjhp41S9n1nGvxSXZHZOw57+wF5+Pt7/sZbiMNdWb/JtTrd70bnTlg862kN77y539ZnuYnCIKghSTmCoKQraA4XKnHQLG8I0ANSuiIWdT42yU8IMMlbuN+c2CQ3PO3NTzIRVrcS/XSerFkFCUNCINdXF6Lv5ehaAlqnrr9upobojqprLMVUZSVKNuMNEE12C8QG/aIIgCr8d82H+UoDeqcWlQoygNec+X+uB+RRAhZLc7dSkh3n6szXOSM70MUSAtE9pR6NnUEC/VaqH9SrKaVlEFr/aaU3la/925AtUoEGOyrM4Kt/XvUXLptodFuSEH67rk6mY7SZRac/7pJr9KpMT2ov5nPlSKKtNL8LH1uBUEQMoMII0EQshV1k9RjV29n+esrhfmm6VbWBln/m7mJ3dTg7AaHLcXmOCuxdeBruh6a3sJZDaly2HtwE3v1GX3Uw1Hg/U/v28hIOIw3IzZsAQ08FeHVs3pJi+YLlqyYIVrVR8bDmWheq0ByT1VGTqnW3aVTrbnVLHm9FZUJzEc+Hvri/opFCtCQJhF8/ciVOPIdMs0ug4AGpYOoRmgh3qZKRfSuhRBXR6/Ekb1YEoOWgDg+/MlzNPfVZrRzeGe2lT/4cVc6Mqob7RjemWt+HCnM7KGEvy/91rsh96ayBeUcQZofIsCCIAhZjaTSCYKQraiLv2Ou3ianAb/S9vc7UU1ltv3MNfron90soOC85unmwqlh+kv9dQyCz5y8S6fXHyavPO6p67mSm6szDfjrP81Bt7Vairv39c5uSKMauWgHfd6pJrvPZSW2Nq40HSDP36sXc8mporJzlVA2MnAka49epL7T12fZ68Hmu3n5orRw7xmLdTBoZPrqnxvp542Hadng1kYpgqBwPi9qUjaY1qSaHcCX79dD8XQ/tREr/r5UrywNbVaRjSlQe4VGvbiMCNZbwPetW4aqFfen8MB8bLMN7BUzd+49pFHtq6dLPdt99oZdtVSUBX15yhXOz4sWcNCDUGr0zRKKTbhn13nnKBCNhCCFffrV+ET6RNUQ1pFpfoIgCKaIMBIEIVvRalBa68tFfPn3K03p4KVb6Zy0zDEjZrtd/9vSIGv+wOb08oyNNGf3KRq/5iCtOHSBjQ0QYcgqrLm/mRsgI23uhSnrDLffW7CDhcH4bnXMDogzU38FEcGW2iajaERH4MAGA4mE+w/oYaoYUSjo5cHGEuomogrTtx2n6wlJFHX4osVoEZzv8Bqrj1ykRt/8SxvfaW/YNgjKubtPsTObAjLdlp01NjpAbywsJfx9OP0P78fbw40b1MIS/Zmx/1BWoJV6Fm0lPRR9i7D/1JEiR/flQaPkSc8/k6Mapgb55aWBDcvTLAuOfY8jzU8QhNyNCCNBELIdGAdoOYahEL5p2SKGHj0QUbDLxsA26UEy3XuUzFbKd+89pBNnzlLBwCB6kKzjdZMePOI6hVOpKVv2DLLWHLlIfaatN6pzQAQBAgkF9VlJRhpXPl+rNAsG9IjJn9edbiU+YGOBqmMWcBoVUpRsBamEWv9bXeQ+Yd1BOnI5jiMuSMnCrD6spAv5eBq5oO04c533HWb+D1y6yVbYllh15JKmaFKjduzbcvIqvTR9A524Hs/XTQkt6E3lg/KR171bFFi0GIUF+LFLHLYLaZqnb9zhRUmbQ3rg6KXmoxP2opV6Vq9kYLr70GB1waAWLELgXJcdfXlyasPUjKaXCoIgZAUijARByFYwCMRgHkYC9x8mc/+il1N7AYE1Ry8apd2FFvShzlWMB20PHz6kZcviqE2bxuTmpq8XAbAJhtGCvYOsfRdiDaIItSOo4akQnJ+GNq1IjiAjjSu/6lKT5u05xaJIAU0+kUJoK1qNUU172VQqWoAboSopTxBHWLR6zdQvFcTLx+2rcSoi3k/rH5cb3Mhi794zSp1UegJZ4/NONbgBMJix7Xi6x5uEB1OdsEC2eg/09qBly5ZRmza1jc6FuMT73Oh055nrLHJfrl+W74eF9Kt/bGQ7bez3Qj55uEcT+j5djLMtKqFOPYNQf3POVnaoU46JKRBDiFYpYNvdXZ0dUsdmiZzYMDWj6aWCIAhZgQgjQRCyHTQhxaLQP3XQuuHYJR4Qq6MD9swUZ3SQhQiWunakbslAalQmmNd1FPY2ruwzdX265pzfPlebU5JswVxjVNP6KyWiU75wfu7TZCteHm7UvHwRw20IpVvf9aV9529QldELyB5gmW2J1xpVMEQ4IJK1QLpds3JFeTFtQrpxWAej+yCo7RFF6tQzpOYpogjc1IiaPVs1zfYdQqrkB7PZ5hvpbQ3LFOZ997jIaQ1TM5peKgiCkBWIMBIEIcfSsEwwD1zVwkgtoBwxyPp0yW76+N/dRq8Dy+ZRtCdH9VHRqrHoMXkt3XuYQq0rhHBD17TlESXcU99+SAcv3rLYGFWpv1IY17UW9+SxxptztnDKoSmIyCDFDtEUezkTm2bJ7eXhSjGjulOR/F50/OptOnT5FrWvZFvTWluxp37FNPUMzXURYdx2On3vIkTTIJyQDvjP/jP0ZrNKHEm7lCrC2qb2czr/ZS8qmj8topTbyKlpfoIgPP2IMBIEIccy6t/dNGWz3iUM7BrR2SgFKasHWajTMRVF5lLMsntw9s1ztTlN0NQQoe+0rHOOU4BpQesI6+Ij8cEjTVGk0PnnlRnehnkDmlGT8CLk4ebCzWVB6UA/XrIaW6OS3z1XmwY3iTAS1nDN2/JeR+o4MYr+jT5nuH9Qw/L0Tdfa9MKUtbRg7xnDUiVEb+utBvVQuVkY5dQ0P0EQnn5EGAmCkGNZciBtYHnp6xcyXHBt6yDrqxX7DFGJuyqnMFstvh8XMVfiqPPPqwyiyNXZiba934mqf77QaL0gX0/yzuPGDmxpiytfwuxi4b4zNv0/rcG7FhAs6J3zweKd3GdJoXLRghRzNY5NMcoE+tHywa3p5PV4On7tNr02a7PV10UqY5cqJR5bDY6Sgmkpohbo45lOFClgO6f0aUQVR83lSBminuO61ma79/kDW9ChSzdZQMI8A7V1pnSYGEUJP7xEuZ2cluYnCMLTjwgjQRByLJhh/+/EZRrSpCIP8B05yIJ72dqYS+Ti7KQpinJSHxU0eIVJRfGC3uzaB9FQrXghmv1yU+rx2xrDepNfbEDtKhU3W2MUOmKWxforT3cXSnyQTFVC/G3etuqhhWjFkDbsXIcePugxhF5T8/ecoqUHztGYTjVZmKKfDh77YsU+s9ug8OdLjR+rMYGSgvnspFVm18HmIEVRXY+mBgYVCwa2oLEr99PojjUMUS6lXkur4awCzr9/95+l9nY0YxUEQRAyj8SkBUHIsTQoU5hGtqmaaVFkC9XG6A0Bwvx9cnQflVWHL3AkDVGiqDfa0O+9G1Lbivo0t+41StL8Ac0N67b/KYp+2XDY4uAfmEoOpR5LMV6oHaZvtmsPgb55qU3FYiyKFMMBRFHUUT9L26DQs0ZJu+zHswpEGS2ZbSAS1Or7ZaSz0L21TslAtuUun9pIFqDWy3ngZO49ZQlEjSy9tiAIgpD1iDASBCHXox6AHr8WnyP7qGBAjWaqb83danBiCw/KpzmgX/p6K8PtQTM3mR1gK/VXMDJQg35REF73H6WwlXRjB0bGzG2Dwl/9m1B2sWpo23T3DW8VabgOgwX02rKHpt9p28crBhJqMQZHREEQBOHxIal0giDkeuxJ08quPiq1v1pEhy7dMtz+qF1VsylySNv6vlsdrmNBLZSl92daf+Xu4kzPTV7N9UvPVi1B0/s2cngtlek2XIq7S+/M325owppdoDYIDW3LfTzHcN/nnWtSz5qlqNKn8/j2jjPp3ecsgX5PprSOCKHBjSPYrAFiaOM77TmdztdMmp4gCILgGEQYCYKQ6zl/M80O2hayo4+KWhSBr6P205ddaqVr2Grqvjdvz2l6plRhiy566vqrR8kpVDrAj53RvNxdjWpjHIl6G5Yf1JtuPHiUTNkNxNHEXvXpp/WHaGqfhnwfBNujn1+mWTtOcp2XPax7qx2/1uaTV/m8qxxSkD5pV40CfDz5tc7GJtCKg+cpokgB7qckTmyCIAiPDxFGgiDkes7aKIx887jR1D6NssWqu6CXB8WqmoV+FbWfF/TB6VIllArkzUPDF+3ItMW4q4szNxpt+t1SmrHtOL/+R+2q0eMAKX9/7zrJ6X+gVEDWW3FnBFhtYzEVci/ULm33a2H/DmlakRdT6pQIZGH0eao7IshJvbMEQRCedkQYCYKQ66lXMpCWDW7FNTUBPnm4H8+KQxfom1XRRvsm/t5Dto5+3CA9Li7pgeZjsL0eu9J4OzNrMY7IzYSe9eh/MzdxXydETbpVL0mOBv/vl41H+HqRfF40qv3jEWQ5AUT7Zu86maN7ZwmCIDztiPmCIAi5HtSxoIFpp8hQqlsyiJqVK8p9Z3STXqW9H3Qx2j+nbthmzpCVQPwoDnEZQW0xbivqCAkMBmAf7WiUFMDIkIIU82k3tiDPDUD4IgVSC+WoQ9hiPUEQBMFxSMRIEATBQm8jxcYbdK8exi5tjxvU/Hz7XG2OZFUt5s/1KGiOOnPHCS7Yt5XMWIzDPhqk/PKKwwwRhjatyFbk+87HkvcbU2nTsA50NT4pW5vpPg4gWC01k80JvbMEQRByAyKMBEEQzNS7qEXR2Gdr0TstKmfLvoIQebNZJaP7EE0pmt/bLmFkr8V4i/JFaeXhC0b3oQfPmc97UvGCtvV7soem5YpQ83JFaNWRi3y7/th/DI85UpBlN7YK1uzqnSUIgpBbeHqn4ARBEDJJg9KF+RL1R9kliswBR7NnVMIBvYfMyQbcH5IBi/GoIW3o4Mdd6fo3vY3uDx0xiyasO+iQBqSmTnsKjb/V7v/zNGCrYH3cvbMEQRByGyKMBEEQNEB0Yv3b7bjOCPVHOY29528YriPl7PcX9VbSpuLIKZMW4xWCC5C/dx5K/PElo/sHz97CvXzOqRz9UAMDi+lZO07wZUZqYiKC82vev+HYZSr94WyHiLHsBoIV7nNZLWwFQRAE+5BUOkEQBDPk5NSt9pWK056RXdgxzjO11xCcy0z7GGHADVGUWUcz/A+Io0LvzODmo+DgpVtU4ZO5NK5rLfL3ykND525N97/ttZpGA9R7E/rT58v3UpOywVxvNHTOVn7sxLV4TuVLmvAS5XF7en6+IFixn+A+hzNOl4XCVhAEQbCdp+eXRRAEIZeJtirF/I3ugwCBUQGK9FGPgtSrrGwQCnF05/t+LFo+WLyL70u4/5AG/qXvO5RVVtMebi40qkN1vl6/VBD9+t9ROnw5rcHtkuhz1LVaGD1NYP84UtgKgiAI1hFhJAiC8BQBEeRI5zIIspFtqvKCVLnv1xykd+ZtM4pyZKaHkil4zphONajzzyv59pAmEWwK8TTiaGErCIIgWEaEkSAIgpAhMGCHfbilqp+ssJruWLk41SoRQNtPX2MxBhv1p1U4OFrYCoIgCOYRYSQIgiDkaKtpRKk+71SDmn63lCasP8xLZuqYBEEQBEGLp2eaTRAEQXhqrabjEh9YrGNasOd0pl5fEARBEEQYCYIgCDnaahrpczAl0EJJ40MdU0bswQVBEARBQYSRIAiCkGmraUf0UFJAfZLaqc1SHZMgCIIgZBQRRoIgCEKWWE0Xye9ldD8iSfZadWdXHZMgCIIgiPmCIAiCkKOtph9XHZMgCIKQuxFhJAiCIORoq2mljglGC1rW4EjZw+NYLyU5Ocv/vyAIgpA7kFQ6QRAEgXJ7HZMgCIIgyK+IIAiCQLm9jkkQBEEQJJVOEARBoNxexyQIgiAIIowEQRAEyu11TIIgCIIg02yCIAiCIAiCIOR6RBgJgiAIgiAIgpDrEWEkCIIgCIIgCEKuR4SRIAiCIAiCIAi5HocJozFjxlDdunUpb968lC9fPpue07dvX3JycjJaWrVqlesPkiAIgiAIgiAIT6gr3YMHD+i5556jOnXq0O+//27z8yCEpk6darjt4eHhoC0UBEEQBEEQBEFwsDAaNWoUX06bNs2u50EIBQUFOWirBEEQBEEQBEEQnoAao/Xr11NAQACFh4fToEGDKDY2Nrs3SRAEQRAEQRCEp5wc1eAVaXRdunShEiVK0MmTJ2nEiBHUunVr2rp1K7m4uGg+5/79+7woxMfH8+XDhw95EZ5elOMrx1mQc0GQ7wVBfiMEGS8IWtgzTnTS6XQ6W1d+//336auvvrK4zpEjR6hs2bKG20ilGzp0KMXFxZG9nDp1ikqWLEmrV6+mpk2baq7zySefGNL21MycOZONHwRBEARBEARByJ0kJiZSr1696Pbt2+Tr65t1wuj69etWU9vCwsLI3d09S4QRKFSoEI0ePZoGDBhgc8QoJCSEbty4YfXNC0/+DMCqVauoefPm5Obmlt2bI2Qjci4Ici4I8r0gyG+EoAW0gb+/v03CyNVekYLlcXHhwgUWYoULF7Zo1qDlXIeBsgyWcwdyrAU5FwT5XhDkN0KQ8YKghT16wGHmC+fOnaN9+/bxZXJyMl/HkpCQYFgHKXcLFy7k67h/2LBhtG3bNjpz5gytWbOGOnbsSKVKlaKWLVs6ajMFQRAEQRAEQRAcZ77w0Ucf0fTp0w23q1Spwpfr1q2jRo0a8fWYmBgOawGYK0RHR/NzkHYXHBxMLVq0oM8++0x6GQmCIAiCIAiC8GQKI9QWWethpC5v8vT0pKioKEdtjiAIgiAIgiAIwpPTx0gQBEEQBEEQBOFxI8JIEARBEARBEIRcjwgjQRAEQRAEQRByPSKMBEEQBEEQBEHI9YgwEgRBEARBEAQh1+MwV7rsQnG6Q5db4enm4cOHlJiYyMdamvnmbuRcEORcEOR7QZDfCEELRROo3bBzjTC6c+cOX4aEhGT3pgiCIAiCIAiCkEM0gp+fn8V1nHS2yKcniJSUFLp06RL5+PiQk5NTdm+O4OAZAAjg8+fPk6+vr+zrXIycC4KcC4J8LwjyGyFoAakDURQcHEzOzs65K2KEN1y0aNHs3gzhMQJRJMJIkHNBkO8FQX4jBBkvCFpYixQpiPmCIAiCIAiCIAi5HhFGgiAIgiAIgiDkekQYCU8sHh4e9PHHH/OlkLuRc0GQc0GQ7wVBfiOEzPLUmS8IgiAIgiAIgiDYi0SMBEEQBEEQBEHI9YgwEgRBEARBEAQh1yPCSBAEQRAEQRCEXI8II0EQBEEQBEEQcj0ijIQnhjFjxlDdunUpb968lC9fPpueA2+Rjz76iAoXLkyenp7UrFkzOn78uMO3VXAsN2/epOeff54b++Jc6N+/PyUkJFh8TqNGjcjJycloGThwoByqJ5CffvqJQkNDKU+ePFSrVi3asWOHxfXnzp1LZcuW5fUrVqxIy5Yte2zbKuScc2HatGnpvgPwPOHJZ+PGjdS+fXsKDg7m47po0SKrz1m/fj1VrVqVXU1LlSrF54cgiDASnhgePHhAzz33HA0aNMjm53z99df0ww8/0C+//ELbt28nLy8vatmyJd27d8+h2yo4FoiiQ4cO0apVq2jJkiX8o/jqq69afd4rr7xCly9fNiw4P4Qni7///pveeustturfs2cPVa5cmT/T165d01x/y5Yt1LNnTxbPe/fupU6dOvFy8ODBx77tQvaeCwCTKervgLNnz8pheQq4e/cuH38IZVs4ffo0tW3blho3bkz79u2joUOH0ssvv0xRUVEO31YhhwO7bkF4kpg6darOz8/P6nopKSm6oKAg3dixYw33xcXF6Tw8PHSzZs1y8FYKjuLw4cNoMaDbuXOn4b7ly5frnJycdBcvXjT7vIYNG+qGDBkiB+YJp2bNmrrXXnvNcDs5OVkXHBys++KLLzTX79atm65t27ZG99WqVUs3YMAAh2+rkLPOBVt/O4QnG/w+LFy40OI67777rq5ChQpG93Xv3l3XsmVLB2+dkNORiJHw1IIZoStXrnD6nIKfnx+nW2zdujVbt03IODh2SJ+rXr264T4cY2dnZ44KWuKvv/4if39/ioiIoOHDh1NiYqIciicsarx7926jzzSOO26b+0zjfvX6AFEF+Q7IfecCQMpt8eLFKSQkhDp27MiRZyH3Id8LgjlczT4iCE84EEUgMDDQ6H7cVh4Tnjxw7AICAozuc3V1pQIFClg8rr169eIBEXLQo6Oj6b333qOYmBhasGDBY9hqISu4ceMGJScna36mjx49qvkcnBPyHfD0kZFzITw8nKZMmUKVKlWi27dv07hx47huFeKoaNGij2nLhZyAue+F+Ph4SkpK4ppkIXciESMhW3n//ffTFcOaLuZ+5ISnC0efC6hBQqQAxfeoUZoxYwYtXLiQTp48maXvQxCEnEmdOnWod+/eFBkZSQ0bNuRJkUKFCtGkSZOye9MEQcghSMRIyFbefvtt6tu3r8V1wsLCMvTaQUFBfHn16lV2pVPAbfwwCk/muYDjalpc/ejRI3aqU465LSClEpw4cYJKliyZwa0WHidIg3RxceHPsBrcNnfscb896wtP77lgipubG1WpUoW/A4TchbnvBZhzSLQodyPCSMhWMFuHxRGUKFGCv/zWrFljEEIIk6MOxR5nOyFnnQuY9Y2Li+P6gmrVqvF9a9eupZSUFIPYsQU4EQG1aBZyNu7u7nzM8ZmGsxzAccft119/3ez5gsfhOqUAN0PcL+Suc8EUpOIdOHCA2rRp4+CtFXIa+Pyb2vbL94LAZLf7gyDYytmzZ3V79+7VjRo1Suft7c3Xsdy5c8ewTnh4uG7BggWG219++aUuX758usWLF+uio6N1HTt21JUoUUKXlJQkO/4JplWrVroqVarotm/frtu0aZOudOnSup49exoev3DhAp8LeBycOHFC9+mnn+p27dqlO336NJ8PYWFhugYNGmTjuxAywuzZs9lZctq0aexQ+Oqrr/Jn/MqVK/z4iy++qHv//fcN62/evFnn6uqqGzdunO7IkSO6jz/+WOfm5qY7cOCAHIBcdi7gtyMqKkp38uRJ3e7du3U9evTQ5cmTR3fo0KFsfBdCVoBxgDImwND222+/5esYNwCcBzgfFE6dOqXLmzevbtiwYfy98NNPP+lcXFx0K1askAOSyxFhJDwx9OnTh7/wTJd169YZ1sFtWLKqLbs//PBDXWBgIP+ANm3aVBcTE5NN70DIKmJjY1kIQSD7+vrq+vXrZySQIX7U58a5c+dYBBUoUIDPg1KlSvEP4u3bt+WgPIH8+OOPumLFiunc3d3Zsnnbtm1Gtuz4rlAzZ84cXZkyZXh9WPQuXbo0G7ZayO5zYejQoYZ18ZvQpk0b3Z49e+TAPAXgu15rfKAcf1zifDB9TmRkJJ8PmChTjx2E3IsT/kjwTBAEQRAEQRCE3Iy40gmCIAiCIAiCkOsRYSQIgiAIgiAIQq5HhJEgCIIgCIIgCLkeEUaCIAiCIAiCIOR6RBgJgiAIgiAIgpDrEWEkCIIgCIIgCEKuR4SRIAiCIAiCIAi5HhFGgiAIgiAIgiDkekQYCYIgCIIgCIKQ6xFhJAiCIAiCIAhCrkeEkSAIgiAIgiAIuR4RRoIgCIIgCIIgUG7n/2mLND7SKx8MAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots(figsize=(10,8))\n",
-    "plot_tree_2d_scatter(tree,'value',selector=lambda z: z.reshape((-1,n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "\n",
-    "plot_tree_2d_shape(tree,'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "up = SDE_up(n_steps,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree\n",
-    "init_up = get_init_up(n,tree,d=d,root=root,n_steps=n_steps)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# conditional down\n",
-    "down_conditional = SDE_down_conditional(n_steps,b,sigma,a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time elapsed: 20.4871 ms\n",
-      "Time elapsed: 74.1739 ms\n",
-      "Time elapsed: 0.8793 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding, time the operations\n",
-    "test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We test the setup by sampling a number of trees and computing mean and covariance of the leaf data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "root conditional mean vs. sample mean: [ 0.92955649 -0.0366928   0.93689898  0.1634001   0.89029691  0.37442937\n",
-      "  0.77290405  0.58255468  0.65001594  0.75578528  0.48967722  0.86942823\n",
-      "  0.31263484  0.91767715  0.12262827  0.9401263  -0.0845554   0.95561648\n",
-      " -0.27104564  0.94516923 -0.43917731  0.90250876 -0.60991941  0.80037213\n",
-      " -0.73130528  0.6645788  -0.83041545  0.52082525 -0.91309216  0.34911724\n",
-      " -0.95582289  0.1735423  -0.95127859 -0.00156949 -0.92139703 -0.19948972\n",
-      " -0.86137817 -0.38306225 -0.76979638 -0.54785133 -0.66273721 -0.67375978\n",
-      " -0.52564887 -0.79889683 -0.3946284  -0.88999443 -0.23383665 -0.96214368\n",
-      " -0.0716679  -1.00516872  0.1219796  -0.99907132  0.31475203 -0.96574018\n",
-      "  0.49966125 -0.86976287  0.645375   -0.75731636  0.76788429 -0.61513331\n",
-      "  0.84585652 -0.43393099  0.8908073  -0.24569898] [ 0.97425427 -0.02529025  0.98175503  0.1876719   0.93167369  0.40854542\n",
-      "  0.80483638  0.62188084  0.6782108   0.79963373  0.51654778  0.9178771\n",
-      "  0.33252655  0.96801552  0.13039789  0.98714953 -0.09076247  0.99735321\n",
-      " -0.28973501  0.98593557 -0.46247088  0.94124864 -0.64023777  0.83049901\n",
-      " -0.76792103  0.68344127 -0.87265168  0.53217205 -0.96072612  0.35616263\n",
-      " -1.00622847  0.17844077 -1.00005872  0.00153222 -0.97058828 -0.19993569\n",
-      " -0.90912637 -0.39039964 -0.81282747 -0.56405535 -0.69833007 -0.69615444\n",
-      " -0.55219944 -0.82630984 -0.41460218 -0.92351073 -0.24490333 -1.00138443\n",
-      " -0.07344635 -1.0506946   0.13202795 -1.04763626  0.33693402 -1.01644796\n",
-      "  0.52998933 -0.91462518  0.68223669 -0.7935538   0.80692475 -0.63913488\n",
-      "  0.8831495  -0.44469332  0.92907948 -0.24532347]\n",
-      "root conditional cov vs. sample cov: [[0.00238569 0.00224009 0.00195526 ... 0.00160552 0.00196016 0.00223301]\n",
-      " [0.00224009 0.00238485 0.00223016 ... 0.00124474 0.0016025  0.00195558]\n",
-      " [0.00195526 0.00223016 0.00238386 ... 0.00091972 0.0012312  0.00158617]\n",
-      " ...\n",
-      " [0.00160552 0.00124474 0.00091972 ... 0.00238949 0.00224398 0.00197557]\n",
-      " [0.00196016 0.0016025  0.0012312  ... 0.00224398 0.00238833 0.00224487]\n",
-      " [0.00223301 0.00195558 0.00158617 ... 0.00197557 0.00224487 0.00238638]] [[2.48553607e-02 3.23048175e-03 2.26846627e-02 ... 3.52562808e-03\n",
-      "  1.87285953e-02 2.07934302e-03]\n",
-      " [3.23048175e-03 2.00200027e-02 3.65289767e-03 ... 1.61595476e-02\n",
-      "  4.38849582e-04 1.90717770e-02]\n",
-      " [2.26846627e-02 3.65289767e-03 2.28619649e-02 ... 4.26655451e-03\n",
-      "  1.69290027e-02 3.08757371e-03]\n",
-      " ...\n",
-      " [3.52562808e-03 1.61595476e-02 4.26655451e-03 ... 1.90693253e-02\n",
-      "  1.14209834e-03 1.80836252e-02]\n",
-      " [1.87285953e-02 4.38849582e-04 1.69290027e-02 ... 1.14209834e-03\n",
-      "  1.64963925e-02 7.32605859e-05]\n",
-      " [2.07934302e-03 1.90717770e-02 3.08757371e-03 ... 1.80836252e-02\n",
-      "  7.32605859e-05 2.07115285e-02]]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 500/500 [00:43<00:00, 11.46it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data parameters:  {'k_alpha': 0.1, 'k_sigma': 0.25, 'obs_var': 0.001}\n",
-      "observation noise: \n",
-      " mean:  [ 1.17970933e-03 -4.72659689e-03 -5.78153726e-03  3.12095721e-03\n",
-      " -1.01028875e-02  4.74798000e-03  9.04842130e-03  7.07338573e-04\n",
-      " -3.47657525e-03 -6.26198320e-03 -1.78226158e-03 -9.26423038e-03\n",
-      " -7.17642906e-04  1.15861097e-03 -3.92735003e-03  5.35653763e-03\n",
-      "  3.80033150e-03  7.09693830e-04  4.07296440e-04 -3.16099194e-03\n",
-      " -8.56725949e-03 -1.11073256e-02  9.82826856e-03 -3.01745232e-04\n",
-      "  1.33784614e-03  7.61139329e-03 -4.19999088e-03 -1.93007674e-03\n",
-      "  4.05938754e-03  2.32540095e-03  6.71778614e-03  1.85573414e-03\n",
-      " -2.71895801e-03 -6.98732830e-03 -1.42713188e-04  1.43594649e-03\n",
-      "  1.98848353e-03  2.35166444e-03 -4.62345511e-04  5.21449254e-03\n",
-      "  3.74826803e-03 -7.38651092e-03 -6.64684655e-03  1.82227973e-03\n",
-      "  4.15643349e-03 -9.93289059e-04 -2.96332747e-03  8.58779557e-04\n",
-      "  6.51701305e-03  5.49501468e-03 -5.21189549e-04 -2.18964151e-03\n",
-      " -1.38359020e-03  8.80978090e-03 -4.16650432e-03 -5.14891853e-03\n",
-      "  1.44880141e-03 -8.46149886e-05 -7.11728324e-03  5.27583612e-03\n",
-      " -3.13988737e-03 -2.37362886e-03  9.00983268e-03  1.66519760e-03]\n",
-      " diag cov:  [0.00088645 0.00078072 0.0009161  0.0010908  0.00113312 0.00077768\n",
-      " 0.00109532 0.00094785 0.001065   0.00097508 0.00078818 0.00111722\n",
-      " 0.00062423 0.00107423 0.00079934 0.00175444 0.0009749  0.00103843\n",
-      " 0.00101823 0.001018   0.00113707 0.00117366 0.00099562 0.00105697\n",
-      " 0.00073818 0.00079505 0.0008609  0.00103298 0.0010149  0.0008568\n",
-      " 0.00079344 0.0006753  0.00095927 0.00088541 0.00101078 0.00080336\n",
-      " 0.00086409 0.00080531 0.00096419 0.00096064 0.00094135 0.00119754\n",
-      " 0.00101752 0.00120833 0.00095945 0.00143014 0.00109728 0.00089438\n",
-      " 0.00086551 0.00122172 0.00077206 0.00092152 0.00081154 0.00118438\n",
-      " 0.00089199 0.00153208 0.00104708 0.00107771 0.00072621 0.00106099\n",
-      " 0.00103754 0.00076373 0.00090098 0.00100948]\n",
-      "leaves: \n",
-      "  mean noise:  [-0.01906751  0.00747546  0.00891291 -0.01910824  0.00228511  0.00167275\n",
-      "  0.00883347 -0.00327777 -0.06208116  0.00504487  0.03620039 -0.00557723\n",
-      "  0.01573278 -0.0197953  -0.03703574  0.04032439  0.03901748 -0.02947521\n",
-      " -0.04341947 -0.01033242  0.05049566  0.05673397 -0.0210868  -0.02159338\n",
-      "  0.00737379 -0.01934315 -0.02203302  0.02942607  0.04264273 -0.0271659\n",
-      " -0.02235348  0.00731012 -0.01075208 -0.03367236  0.00632921  0.0423619\n",
-      "  0.02539693  0.00031207 -0.02417795 -0.03123758 -0.02101745  0.03858999\n",
-      "  0.02667609 -0.00862652 -0.02631382 -0.02785168 -0.00137117  0.0172185\n",
-      "  0.00515511  0.04225887  0.01511405 -0.01642249 -0.00024311 -0.0204212\n",
-      "  0.00869394 -0.00389785 -0.00732167  0.02564965 -0.0009665  -0.03515114\n",
-      "  0.01198223 -0.0080797  -0.01196311  0.03345745]\n",
-      "  diag cov noise:  [0.00041893 0.00045949 0.00037563 0.00047064 0.00042013 0.00042954\n",
-      " 0.00043752 0.00037978 0.00035363 0.00033308 0.000283   0.00028672\n",
-      " 0.00028287 0.00026386 0.00028225 0.00025317 0.00026779 0.0002533\n",
-      " 0.00025607 0.00025421 0.00026572 0.000242   0.00027697 0.0002425\n",
-      " 0.00027091 0.00025394 0.00026658 0.00026843 0.00027199 0.00029834\n",
-      " 0.00028949 0.00037957 0.00034957 0.00043189 0.0004523  0.00052944\n",
-      " 0.00045365 0.00058409 0.00040359 0.00040523 0.00033399 0.00032987\n",
-      " 0.00025094 0.00029412 0.00021908 0.00026572 0.00020841 0.00024468\n",
-      " 0.00021886 0.00025041 0.00024607 0.00029284 0.00027358 0.00034584\n",
-      " 0.00033905 0.00038825 0.00039047 0.00036763 0.00037468 0.00036184\n",
-      " 0.00040303 0.00041886 0.00042427 0.0004458 ]\n",
-      "  mean noise:  [-0.00394036 -0.01355496 -0.00311646 -0.01640656 -0.01377947  0.05875164\n",
-      "  0.03582234 -0.02320903 -0.01639707 -0.03058899 -0.01991576  0.00508597\n",
-      " -0.01458998 -0.0175486   0.00775548  0.05436404  0.03613243 -0.01682685\n",
-      " -0.01102678 -0.01052093  0.00054078 -0.00590029 -0.02250673  0.000417\n",
-      "  0.0144262  -0.01438571  0.0018238   0.00510652 -0.02528679  0.03117869\n",
-      "  0.02711469 -0.00604043 -0.00081355 -0.00613785 -0.00387305 -0.00978948\n",
-      "  0.00871583 -0.00392753 -0.02138222  0.00525657  0.0203557   0.00120047\n",
-      " -0.00384761 -0.03588188 -0.04045438  0.0268548   0.0332826   0.00054761\n",
-      "  0.01001893  0.02373131  0.03370501  0.0071894  -0.01813282 -0.0008623\n",
-      " -0.02410567 -0.05547938 -0.01692933  0.05321709 -0.0213338  -0.00596837\n",
-      "  0.04430032 -0.02079695 -0.00719202  0.01851161]\n",
-      "  diag cov noise:  [0.00036302 0.000376   0.0003787  0.00036672 0.00037855 0.00032182\n",
-      " 0.00032714 0.00030913 0.00033216 0.00031973 0.00031646 0.00028888\n",
-      " 0.00028062 0.00025477 0.00026599 0.00025738 0.00028105 0.00027891\n",
-      " 0.00029269 0.00029279 0.00029302 0.00030159 0.00029689 0.00033038\n",
-      " 0.00030779 0.0003441  0.00029693 0.00032129 0.00027905 0.00031338\n",
-      " 0.00030441 0.00032091 0.00031175 0.00033791 0.00034867 0.00035744\n",
-      " 0.00037571 0.00035623 0.00033955 0.00035226 0.00029231 0.00034655\n",
-      " 0.00024858 0.00028219 0.00025367 0.00027387 0.00028608 0.00032632\n",
-      " 0.00032224 0.0003483  0.00035736 0.00035689 0.00039879 0.00039046\n",
-      " 0.00048469 0.00041747 0.00045398 0.00041414 0.00041511 0.0003586\n",
-      " 0.00040066 0.00040606 0.00038901 0.00039881]\n",
-      "  mean noise:  [-0.01220231  0.04444665  0.01239054 -0.04064957 -0.02512477 -0.00314314\n",
-      "  0.00972514  0.00388267  0.0032311  -0.01091363 -0.03590786 -0.00341921\n",
-      "  0.01285792  0.04398628  0.01135287 -0.04580553  0.03478021  0.00694719\n",
-      " -0.03941731 -0.01688659  0.01926218 -0.00019731 -0.004511    0.02716972\n",
-      " -0.01607309 -0.00617724  0.0423393  -0.03037572 -0.02974433  0.02783728\n",
-      "  0.04115181  0.02601817 -0.03963751 -0.0170813  -0.00975088 -0.02468087\n",
-      "  0.01794218  0.00792444 -0.01171658  0.00915052  0.0266233   0.02416221\n",
-      " -0.0266756  -0.01831577  0.01986242  0.00391124 -0.01903157 -0.00505839\n",
-      "  0.00380249 -0.0016908   0.01227137  0.02696145  0.01763908 -0.0193745\n",
-      "  0.00618862  0.01520542 -0.05615583 -0.01686711  0.02186801  0.00491713\n",
-      " -0.00845573 -0.01055269  0.01361746  0.0092269 ]\n",
-      "  diag cov noise:  [0.00041772 0.00043995 0.00042351 0.00041239 0.00046521 0.00035672\n",
-      " 0.00043466 0.00034579 0.0004247  0.00036807 0.0003726  0.0003367\n",
-      " 0.00032189 0.00029433 0.00031091 0.00028748 0.00031808 0.00027279\n",
-      " 0.00029987 0.00026579 0.0003002  0.00028488 0.00031504 0.00031878\n",
-      " 0.00031791 0.00032411 0.00030834 0.00030025 0.00030063 0.00031699\n",
-      " 0.00030302 0.00036502 0.00033616 0.00039603 0.00036658 0.00043678\n",
-      " 0.00038037 0.00044042 0.00035347 0.00036676 0.00032603 0.00033166\n",
-      " 0.00029261 0.00030045 0.00026658 0.00026411 0.00027883 0.00026198\n",
-      " 0.00031287 0.00027124 0.00036968 0.00028852 0.00040244 0.00032169\n",
-      " 0.0003679  0.00035311 0.00043101 0.00033135 0.00037443 0.00034394\n",
-      " 0.00034057 0.00034771 0.00035223 0.00038982]\n",
-      "  mean noise:  [-0.00881753 -0.04225761  0.00518459  0.0197172  -0.01551483  0.021304\n",
-      "  0.01645614 -0.00983374 -0.01352438 -0.00108234  0.01816166 -0.0304455\n",
-      "  0.01790229  0.02538588 -0.00717397  0.04743    -0.03729251 -0.04021594\n",
-      "  0.02280846 -0.02331597 -0.01378895 -0.01348326  0.01230968  0.04229772\n",
-      "  0.00901272  0.00415426 -0.01978084 -0.03238772  0.02057356 -0.01626199\n",
-      "  0.01162704  0.05037805  0.00261484  0.00105361 -0.03033471  0.01564167\n",
-      "  0.00651309 -0.01741987  0.03708047 -0.02289977  0.01111446  0.00893351\n",
-      " -0.05625892 -0.01042622  0.03812997  0.04191221  0.02165944 -0.02895761\n",
-      " -0.0493289  -0.01221288  0.00807419 -0.03113756  0.01904547  0.04357108\n",
-      " -0.01848665  0.01852428 -0.00887016 -0.00680214  0.01219915 -0.02259937\n",
-      " -0.00619955  0.03097464  0.00229116  0.005238  ]\n",
-      "  diag cov noise:  [0.00034611 0.00040093 0.00038417 0.00040999 0.00035152 0.00037624\n",
-      " 0.00035264 0.00034253 0.00030448 0.00031575 0.00026476 0.00028628\n",
-      " 0.00026071 0.00025619 0.00025585 0.00026015 0.00025744 0.00029726\n",
-      " 0.00027106 0.00031749 0.00028169 0.00030651 0.00031606 0.0003466\n",
-      " 0.00035957 0.00040306 0.0003581  0.00036302 0.00034243 0.00028728\n",
-      " 0.00032263 0.00030084 0.00032999 0.00031205 0.00036592 0.00026778\n",
-      " 0.00036158 0.00027435 0.00034199 0.00031934 0.00030974 0.00029634\n",
-      " 0.00031286 0.00029516 0.0002954  0.00031803 0.00027503 0.00031273\n",
-      " 0.00027936 0.00030347 0.00030034 0.00031068 0.00031194 0.00033561\n",
-      " 0.00032649 0.00034176 0.00038426 0.00034616 0.00039248 0.00038702\n",
-      " 0.00042653 0.00044754 0.00041201 0.00041395]\n",
-      "  mean noise:  [-0.00454457  0.01248756 -0.06007926 -0.00489263  0.01745424 -0.00502887\n",
-      "  0.00725663  0.06003947  0.00565335 -0.03932011  0.02791934 -0.03445481\n",
-      " -0.01035124  0.02963143 -0.02173431 -0.05506432  0.0042439   0.02673485\n",
-      " -0.00658098 -0.00609323 -0.00417625  0.0129157   0.01287039  0.02129212\n",
-      " -0.00712088 -0.00404622  0.01268805  0.00717785 -0.0086025  -0.01755964\n",
-      "  0.00712977 -0.01403349 -0.00421751  0.00738765  0.00815004 -0.00945658\n",
-      " -0.00740841  0.02246237  0.00849028 -0.02764531  0.00012176  0.01904459\n",
-      "  0.0116009  -0.00190922  0.01347179 -0.00168184 -0.02710974  0.01388456\n",
-      "  0.02903998 -0.05183868 -0.02600541  0.02404931 -0.018442    0.04470813\n",
-      " -0.0070151  -0.04098967  0.04389628  0.00997484 -0.03264636  0.00812135\n",
-      " -0.00469045 -0.02109802  0.03729211  0.00169878]\n",
-      "  diag cov noise:  [0.00038664 0.00034462 0.00036242 0.00035868 0.00037323 0.00040577\n",
-      " 0.00040366 0.00041781 0.00033624 0.00037466 0.00029531 0.00036801\n",
-      " 0.00027426 0.00033979 0.00026009 0.00031171 0.00026264 0.00030582\n",
-      " 0.00028145 0.00031442 0.0003233  0.00032545 0.00035166 0.0003241\n",
-      " 0.00035108 0.0002965  0.00035222 0.00031159 0.00034344 0.00031405\n",
-      " 0.00034063 0.00030056 0.00032736 0.00031754 0.00036621 0.00037466\n",
-      " 0.00036834 0.00041131 0.00035442 0.00036216 0.00037237 0.00034163\n",
-      " 0.00034484 0.00028942 0.00032204 0.00026202 0.00033696 0.00026234\n",
-      " 0.00033014 0.00028238 0.00032652 0.00031415 0.00033749 0.00031818\n",
-      " 0.00030438 0.00032572 0.00031694 0.00035334 0.00035234 0.00033699\n",
-      " 0.00043207 0.00040646 0.00042163 0.00041089]\n",
-      "  mean noise:  [ 3.36956437e-02 -3.74380456e-03 -3.43398162e-02  1.43210262e-02\n",
-      "  3.12841581e-02 -5.54992230e-03 -1.69664364e-02 -1.92508253e-02\n",
-      " -3.13563165e-03  2.34857355e-02 -5.37743205e-05  2.53193744e-03\n",
-      "  1.25449910e-02 -1.25067405e-02 -1.55712332e-02 -1.30672147e-02\n",
-      "  1.08941936e-02  4.97790943e-02 -1.17470674e-02 -6.07362047e-02\n",
-      "  1.31335700e-02 -4.89992974e-02 -1.38923690e-02  1.26690979e-02\n",
-      "  7.69410809e-04  7.64824453e-02  3.36791918e-02 -1.82052144e-02\n",
-      " -6.76126738e-03  4.87676740e-03 -3.31127120e-02  1.80543581e-03\n",
-      "  2.63502919e-02 -1.35456488e-02 -2.34673707e-02  4.26679735e-03\n",
-      "  1.88156781e-02 -8.74210035e-03 -1.49869992e-02 -1.75382991e-02\n",
-      " -1.71808773e-02  3.71583720e-02  1.68487178e-02  2.96170634e-02\n",
-      "  2.31698249e-02 -5.78461275e-02  2.14947719e-02  9.49538348e-03\n",
-      " -3.15672210e-02  1.30260309e-02  3.38275704e-03 -1.78608902e-03\n",
-      " -2.22593709e-02 -2.56510834e-02 -4.00308160e-02  5.22201490e-02\n",
-      "  4.73686118e-02 -4.01231829e-02  1.92331856e-02  3.03249892e-03\n",
-      "  5.11941133e-03  1.48463557e-02 -3.90509994e-02 -5.52038724e-03]\n",
-      "  diag cov noise:  [0.00035006 0.0003329  0.00037923 0.00039734 0.00037451 0.00042729\n",
-      " 0.00042001 0.0004934  0.00038561 0.00042969 0.00032789 0.00035987\n",
-      " 0.00029627 0.00036188 0.00027638 0.00035698 0.00027945 0.00038364\n",
-      " 0.00031459 0.00043667 0.00030519 0.00037303 0.00028305 0.0002803\n",
-      " 0.00027372 0.00030439 0.00027652 0.00031905 0.00026925 0.000284\n",
-      " 0.00027232 0.00026861 0.00028816 0.00028407 0.00029213 0.00030043\n",
-      " 0.0002808  0.00031735 0.00028067 0.00028697 0.00029787 0.00026877\n",
-      " 0.0003266  0.00029661 0.00036765 0.0003178  0.00037037 0.00031545\n",
-      " 0.00032389 0.00032026 0.00030466 0.00030584 0.00027197 0.00028284\n",
-      " 0.0002477  0.0002579  0.00023421 0.00024149 0.00022026 0.00025336\n",
-      " 0.00023592 0.00028282 0.0002903  0.00030134]\n",
-      "  mean noise:  [ 1.52541893e-02  1.35905627e-02 -2.64549175e-04 -6.07937406e-04\n",
-      " -5.63914033e-03 -2.46037037e-02 -3.79417477e-02 -2.51763616e-03\n",
-      "  5.06257169e-02  1.76692457e-02 -1.70153855e-02 -3.65225961e-02\n",
-      "  9.92102280e-04 -6.27643962e-03 -1.10249235e-02  4.96879256e-02\n",
-      "  7.13268279e-05  4.15351933e-03  1.50177120e-02 -4.66235238e-02\n",
-      "  1.36612509e-03 -1.55756813e-03  1.91874122e-02  2.29452895e-02\n",
-      " -8.27374733e-03  4.93163090e-03 -6.43345002e-03 -8.05054902e-03\n",
-      " -3.75457276e-02  1.25253261e-02  1.42442773e-02 -1.50211674e-02\n",
-      "  1.19090465e-02  1.84187359e-02  4.99020962e-03 -1.67238857e-02\n",
-      "  4.09203592e-02 -1.80314458e-02 -3.83298391e-02  2.05200648e-02\n",
-      " -1.48441911e-02 -2.05141296e-02  3.12616857e-02  2.06299803e-02\n",
-      " -1.43220710e-02  2.84176862e-02  3.17195430e-02 -1.95757810e-02\n",
-      " -6.28816335e-03 -1.33419248e-02 -2.30366690e-02  1.91172614e-02\n",
-      "  2.70074413e-02 -1.61503763e-03 -4.72490989e-02  2.04670127e-02\n",
-      "  1.51884200e-02 -5.14878672e-02 -1.31960744e-02  1.80903844e-02\n",
-      "  3.29820149e-02 -2.08524118e-03 -2.77791215e-02  1.48229353e-02]\n",
-      "  diag cov noise:  [0.00035166 0.0003787  0.00040214 0.00040235 0.00043997 0.00038614\n",
-      " 0.00039351 0.00036572 0.00035007 0.00033874 0.00039272 0.00032506\n",
-      " 0.00037746 0.00031799 0.00031708 0.00030756 0.00026657 0.00031933\n",
-      " 0.00029471 0.00031971 0.00033097 0.00031385 0.00030435 0.00032292\n",
-      " 0.00029443 0.00031285 0.00030552 0.00028068 0.00028329 0.00026575\n",
-      " 0.00025769 0.00027041 0.00028335 0.00027588 0.00032223 0.00028759\n",
-      " 0.00030741 0.00029696 0.00029654 0.00032318 0.00032025 0.00034319\n",
-      " 0.00032457 0.00032676 0.00032403 0.00032055 0.00034887 0.00032538\n",
-      " 0.00036776 0.00032891 0.00034664 0.00032146 0.00031552 0.0002899\n",
-      " 0.00027249 0.00024992 0.00024623 0.00025056 0.00025414 0.0002641\n",
-      " 0.00026986 0.00028    0.00029141 0.00033124]\n",
-      "  mean noise:  [ 0.0532872  -0.01115184 -0.00233544 -0.01228309 -0.04075291 -0.0198503\n",
-      "  0.01729622  0.01779817 -0.00605841  0.02440251  0.01566263  0.02131773\n",
-      " -0.01667324 -0.06409273 -0.01810972 -0.03601964  0.03373711  0.03283974\n",
-      " -0.01668665  0.04051246 -0.05357796 -0.03886141  0.04814429  0.02306798\n",
-      "  0.01875588 -0.00035046  0.02068637  0.01061822 -0.05415387 -0.0425092\n",
-      "  0.02057295  0.01358677  0.05303808 -0.0018858  -0.04314544  0.01585718\n",
-      " -0.01871279  0.05012267  0.03617759 -0.02430133 -0.00922806 -0.03022042\n",
-      " -0.01300466  0.01369727  0.01001633 -0.02024367 -0.0367838  -0.01803022\n",
-      "  0.0587912   0.03427936 -0.02512752  0.00914492  0.03250188 -0.01344233\n",
-      " -0.01837606  0.00218475  0.03042563 -0.01077178 -0.04018748  0.02347071\n",
-      " -0.04668115  0.00043801  0.01283514  0.01102715]\n",
-      "  diag cov noise:  [0.00038259 0.00035732 0.00038488 0.0003751  0.00041799 0.00041024\n",
-      " 0.00042445 0.00044936 0.00041039 0.00044019 0.00042299 0.00038336\n",
-      " 0.00038691 0.00040987 0.00034363 0.00036007 0.00033735 0.00033811\n",
-      " 0.00032849 0.0003377  0.00029776 0.00030928 0.00030183 0.00028798\n",
-      " 0.00030368 0.00032331 0.00028713 0.00032238 0.00028182 0.00028851\n",
-      " 0.00029673 0.00027546 0.00031198 0.00027347 0.00031944 0.00027534\n",
-      " 0.00034808 0.00030475 0.00034512 0.00031604 0.00029536 0.00030113\n",
-      " 0.00029408 0.00031884 0.00031188 0.00035514 0.00033057 0.00036942\n",
-      " 0.00031869 0.00038001 0.00030201 0.00034874 0.00029016 0.00028233\n",
-      " 0.00027848 0.00025446 0.00028503 0.00025983 0.00031052 0.00029808\n",
-      " 0.00033429 0.00035504 0.00034251 0.00039819]\n",
-      "  mean noise:  [-0.00863687 -0.02628191 -0.01153585 -0.01930786  0.00467643  0.05130424\n",
-      "  0.01879259 -0.01875872 -0.03518109 -0.02988969  0.00865603  0.05243525\n",
-      "  0.01419484 -0.0447773  -0.0056629   0.02973628 -0.03339555 -0.02243944\n",
-      "  0.02452322  0.01825385  0.01802517 -0.02573608  0.00113524 -0.04774369\n",
-      "  0.00149427 -0.00309718 -0.03291966  0.04332929  0.00869201  0.01929493\n",
-      "  0.02990367  0.01746746 -0.00700121 -0.00997685 -0.01465056 -0.03387073\n",
-      "  0.03671297  0.02428119 -0.04885595 -0.00130506 -0.01743255 -0.02087352\n",
-      "  0.02760875  0.02201056  0.0460357   0.01232506 -0.01691054 -0.03605527\n",
-      " -0.00896501  0.01309711 -0.01804003  0.00110406  0.02709669  0.01373691\n",
-      " -0.0036999   0.01501774 -0.03078446 -0.01526895  0.00835174 -0.03267338\n",
-      "  0.02201851  0.01943987 -0.00588711  0.0303356 ]\n",
-      "  diag cov noise:  [0.00031518 0.0003418  0.00039392 0.0004402  0.00041579 0.00039049\n",
-      " 0.00046476 0.00037838 0.00042217 0.00046507 0.00038403 0.00041654\n",
-      " 0.00039642 0.00035972 0.00036728 0.00035111 0.00031326 0.00033907\n",
-      " 0.00033443 0.00033065 0.00037458 0.00036676 0.00033299 0.00037478\n",
-      " 0.00028099 0.00038103 0.0002723  0.00036691 0.00028741 0.00032188\n",
-      " 0.00029542 0.00029566 0.0003021  0.00028029 0.00031472 0.00026796\n",
-      " 0.00030446 0.00029838 0.00030988 0.00032689 0.00033752 0.00030586\n",
-      " 0.00033981 0.00030423 0.0003245  0.00031137 0.00035029 0.00033449\n",
-      " 0.00037562 0.00035109 0.00039065 0.0003266  0.0003453  0.00029302\n",
-      " 0.00026851 0.00027453 0.00026091 0.00026219 0.00028888 0.00026053\n",
-      " 0.000279   0.00025799 0.00026372 0.00026333]\n",
-      "  mean noise:  [ 0.00926052  0.00518424 -0.01758637  0.00256472 -0.0542419   0.0023103\n",
-      "  0.06416923  0.00479378  0.01320768 -0.02245347 -0.0202196   0.01097204\n",
-      " -0.01523177  0.00082972  0.00409094  0.02024687 -0.00591889 -0.02312062\n",
-      "  0.0127791  -0.02332129 -0.04572608 -0.01132422  0.01602798  0.03683175\n",
-      "  0.03043111  0.02178065 -0.02384888 -0.04616346  0.00529118  0.02387845\n",
-      "  0.02807086 -0.0127111  -0.02177011  0.02346964 -0.02298205  0.00654726\n",
-      "  0.02757473 -0.04108616  0.02674361  0.05247826 -0.00682213 -0.03064063\n",
-      " -0.02627525 -0.00746263 -0.01874458 -0.01243512 -0.00157431  0.02776033\n",
-      "  0.01554058  0.03077557  0.02322379 -0.01767657  0.00277213 -0.03732511\n",
-      " -0.03208551  0.02588988  0.02338033  0.02240652 -0.0068878  -0.01093102\n",
-      " -0.01008758  0.01379     0.00723863 -0.02873826]\n",
-      "  diag cov noise:  [0.00035906 0.00038021 0.00047332 0.00044604 0.000515   0.00044217\n",
-      " 0.00046863 0.00042368 0.00041706 0.00034353 0.00036043 0.00028405\n",
-      " 0.00034809 0.00029784 0.00030934 0.00032562 0.00032751 0.0003383\n",
-      " 0.0003743  0.00034268 0.00034355 0.0002972  0.00030315 0.00026747\n",
-      " 0.00031071 0.00030011 0.00030218 0.00029387 0.00029709 0.00025368\n",
-      " 0.00028043 0.00023916 0.00027044 0.00026826 0.00029786 0.00031301\n",
-      " 0.00034356 0.00029925 0.0003833  0.00035844 0.00038903 0.00043995\n",
-      " 0.00037602 0.00043163 0.00033133 0.000397   0.00030237 0.00039766\n",
-      " 0.00027575 0.00036989 0.00025544 0.00029432 0.00025623 0.00027075\n",
-      " 0.00026807 0.00028267 0.0002677  0.00029413 0.00028489 0.00029966\n",
-      " 0.00030895 0.00030405 0.00032554 0.00031919]\n",
-      "  mean noise:  [-0.03174132 -0.02093147 -0.0059892   0.02317391 -0.01206077  0.00571797\n",
-      "  0.00844363 -0.03676094  0.00510614  0.00251283  0.01951221 -0.01768625\n",
-      " -0.02861551 -0.00466629 -0.00610028  0.05305214  0.0099068  -0.03439557\n",
-      "  0.02465044 -0.00902617 -0.02800973 -0.02650067  0.0095138   0.00618584\n",
-      " -0.01417975  0.00369776  0.02498255  0.03181499 -0.00385499 -0.01302405\n",
-      "  0.03061644 -0.01344403 -0.00551846 -0.00297229 -0.02988321  0.00281041\n",
-      " -0.02665257 -0.00054131  0.05203643 -0.01779201 -0.04389703  0.02105801\n",
-      "  0.01915109  0.03646292  0.00990787 -0.02939093  0.0348496  -0.00818589\n",
-      " -0.01001676  0.0004561  -0.0248512   0.02456621 -0.02101852 -0.03311215\n",
-      "  0.0207603   0.02892308  0.02555668 -0.01901877 -0.00672372  0.00225513\n",
-      " -0.0072047  -0.03742555  0.02116395  0.0694771 ]\n",
-      "  diag cov noise:  [0.00032752 0.00030624 0.00029697 0.00031398 0.00031423 0.00033177\n",
-      " 0.00034618 0.00037053 0.00033452 0.00036368 0.00033795 0.00034571\n",
-      " 0.00037365 0.00035348 0.00036339 0.00042401 0.00041304 0.00042062\n",
-      " 0.00037691 0.00042835 0.0003359  0.00044055 0.00033609 0.00042357\n",
-      " 0.00035823 0.00038519 0.00033813 0.00034559 0.00033077 0.00030622\n",
-      " 0.00033059 0.00028553 0.00030491 0.00027457 0.00031862 0.00031529\n",
-      " 0.0003015  0.00031919 0.00029862 0.00027819 0.00028085 0.0003065\n",
-      " 0.00028812 0.00032293 0.00031317 0.00029434 0.00031055 0.00027789\n",
-      " 0.0003199  0.00031217 0.00039519 0.00037532 0.00038421 0.0003899\n",
-      " 0.00038508 0.00036277 0.00033498 0.0002965  0.00037133 0.00032084\n",
-      " 0.00040032 0.00032843 0.00037174 0.00031611]\n",
-      "  mean noise:  [ 0.00039685 -0.02477581  0.0207122   0.06314205 -0.05890669  0.00354929\n",
-      " -0.01094035 -0.04183801  0.02418643 -0.00403226  0.02728322 -0.00618365\n",
-      " -0.04647294 -0.02097635  0.03094061  0.04254992 -0.01744272 -0.01379257\n",
-      "  0.03356359 -0.01411978 -0.0106056  -0.0043596  -0.05077754  0.00398714\n",
-      "  0.0350641   0.03915979 -0.01514224 -0.04330035  0.01627402  0.00386165\n",
-      "  0.03675194 -0.02036956 -0.04814385  0.01004154  0.02625088  0.0173492\n",
-      " -0.00941778 -0.00294323  0.01879858  0.03269518  0.00613306 -0.04748411\n",
-      " -0.00659149 -0.02020618 -0.01325746  0.02815838 -0.04028004 -0.03986236\n",
-      "  0.02498592  0.05104567  0.00287927 -0.03346018  0.02109037  0.06134099\n",
-      " -0.01551407 -0.02863201  0.0212699  -0.02632154 -0.02322077  0.01810778\n",
-      " -0.05115101  0.00697576  0.0708032  -0.00471549]\n",
-      "  diag cov noise:  [0.00029212 0.00038025 0.00030843 0.00034215 0.00034668 0.00027714\n",
-      " 0.00031091 0.00032321 0.00032338 0.00037341 0.00041653 0.00036602\n",
-      " 0.00043384 0.00035515 0.00038936 0.00040475 0.00036553 0.00036867\n",
-      " 0.00035765 0.00032365 0.00038412 0.00033364 0.0003818  0.00031025\n",
-      " 0.00035817 0.00034475 0.0003242  0.00032025 0.0003121  0.00029895\n",
-      " 0.00030793 0.00030594 0.00031285 0.00031351 0.00036718 0.00031355\n",
-      " 0.00039454 0.00031394 0.00041757 0.00031912 0.00037484 0.00031858\n",
-      " 0.00029488 0.00030714 0.00029436 0.00029705 0.00029623 0.00029975\n",
-      " 0.00028244 0.00033145 0.00031515 0.00036631 0.00034392 0.00036409\n",
-      " 0.00032662 0.00034907 0.00033216 0.00042665 0.00034846 0.00042692\n",
-      " 0.00035209 0.00035336 0.00033276 0.00033442]\n",
-      "  mean noise:  [-0.00895339  0.01298875  0.03509622  0.01172669 -0.00920709 -0.00256857\n",
-      "  0.01701296  0.02064162 -0.05321388 -0.00489512  0.01132052 -0.03882842\n",
-      " -0.01105641  0.01250216  0.0141922   0.01363688  0.01371646  0.01533686\n",
-      "  0.00299434 -0.02010973 -0.02686554 -0.03375349  0.02742062 -0.00919338\n",
-      " -0.00791418  0.01431649 -0.02850037  0.04631669  0.02809413 -0.055\n",
-      "  0.00694284  0.0023602   0.00274082  0.00335004 -0.02962154  0.00867595\n",
-      "  0.00840114 -0.02197221  0.02365393  0.03526944 -0.00191566 -0.00930534\n",
-      " -0.05329092 -0.02170723  0.04660574  0.01593465 -0.00914289  0.05221543\n",
-      " -0.00659171 -0.05870648  0.01970633  0.01100504  0.00556989 -0.00878305\n",
-      " -0.01440769 -0.002188    0.00200243  0.00793248 -0.02510845  0.01393178\n",
-      " -0.0160129  -0.02630413  0.04067293 -0.00411255]\n",
-      "  diag cov noise:  [0.00028574 0.00030367 0.00027173 0.00029151 0.00030026 0.0003011\n",
-      " 0.00034003 0.00035727 0.00034883 0.00039353 0.00035814 0.00039328\n",
-      " 0.00040722 0.00038614 0.00036658 0.00041995 0.00034457 0.00036459\n",
-      " 0.00036232 0.00035187 0.00033337 0.0003783  0.00031623 0.0003841\n",
-      " 0.00035962 0.0004321  0.00035807 0.00042222 0.00034056 0.00035931\n",
-      " 0.00032073 0.00032188 0.00031829 0.00031473 0.00032161 0.00032843\n",
-      " 0.00032023 0.00034593 0.00032708 0.00034761 0.00030412 0.00031227\n",
-      " 0.00030252 0.00029209 0.00029555 0.00030024 0.00027167 0.0003211\n",
-      " 0.00028454 0.00034199 0.00033062 0.00037181 0.00037274 0.00036621\n",
-      " 0.00037918 0.00036178 0.00032023 0.0003514  0.00031302 0.00028713\n",
-      " 0.00028374 0.000277   0.00028668 0.00030803]\n",
-      "  mean noise:  [-0.02082319 -0.02429968  0.02877396  0.01975562  0.01348117  0.00648355\n",
-      " -0.03420291  0.00212737 -0.01864758  0.01679925 -0.0109303  -0.05310282\n",
-      "  0.0291054   0.02190459  0.00531037 -0.00164587 -0.00129845  0.0257785\n",
-      " -0.0281028   0.01111128  0.00611972 -0.01096444  0.02969657 -0.04789397\n",
-      " -0.0166793   0.02110558  0.00112986 -0.01226323  0.01665062  0.02277735\n",
-      " -0.0106432  -0.00314489 -0.02220748  0.04212745  0.06847114 -0.05085731\n",
-      " -0.03191306 -0.00830074 -0.03085453 -0.00567238  0.03855344  0.00885549\n",
-      " -0.02458624  0.02825819  0.02357578 -0.01349473 -0.04352803  0.01000246\n",
-      "  0.03254879  0.00735634  0.00613036 -0.00705094 -0.04334837 -0.02486651\n",
-      "  0.01940506  0.00867938 -0.00575806  0.01924872  0.00734356  0.00143189\n",
-      "  0.01089352  0.00831076 -0.01055787 -0.01971469]\n",
-      "  diag cov noise:  [0.00030714 0.00030788 0.00027956 0.00028861 0.00028194 0.00028193\n",
-      " 0.00031875 0.00030505 0.00034365 0.00033962 0.0003676  0.00038909\n",
-      " 0.00050625 0.00038037 0.00051103 0.00041424 0.00043695 0.00039281\n",
-      " 0.00041021 0.00033424 0.00037272 0.00041931 0.00038912 0.00040399\n",
-      " 0.00039662 0.00037746 0.00033444 0.00030549 0.00031095 0.00026487\n",
-      " 0.0003236  0.00028385 0.00032452 0.00029532 0.00032205 0.00030247\n",
-      " 0.00029778 0.00030538 0.00032558 0.00032866 0.00037109 0.00033512\n",
-      " 0.0003342  0.00031073 0.00032084 0.00030173 0.00037229 0.00032427\n",
-      " 0.00039194 0.00035617 0.00036148 0.00033559 0.00037465 0.00031127\n",
-      " 0.00035286 0.00033322 0.00034603 0.00037968 0.00033771 0.00038553\n",
-      " 0.00034113 0.00035974 0.00033465 0.00032564]\n",
-      "  mean noise:  [-2.29573887e-03  1.66257454e-02 -1.68791062e-02 -1.07084443e-02\n",
-      " -2.20137170e-03 -2.62536376e-02  6.31713297e-03  5.62493835e-02\n",
-      "  1.04623222e-03 -2.14516980e-02 -5.42129258e-03  1.72750231e-02\n",
-      " -1.98795485e-05 -1.76640995e-02 -3.47587091e-02 -2.99430235e-02\n",
-      "  5.23525615e-02  5.45016932e-03 -2.18467039e-02  5.26320046e-02\n",
-      " -6.67039056e-03 -2.32914387e-02  1.10193492e-02 -4.16897096e-03\n",
-      " -6.67782167e-03  1.23357815e-02 -7.30665289e-03 -2.44142256e-02\n",
-      "  1.48963635e-02  2.65962564e-02  1.43267196e-02 -4.55533218e-03\n",
-      " -1.67394148e-02 -4.68729153e-02  1.14437206e-02  4.81539097e-03\n",
-      " -2.68074888e-02  2.00013617e-02 -1.11380799e-03  1.63747944e-02\n",
-      "  3.60044956e-02 -3.59543473e-02  1.47528307e-02  3.71590527e-02\n",
-      " -3.69669235e-03 -3.95178671e-02 -1.90059770e-02  1.57430042e-02\n",
-      " -5.21031218e-03 -1.25319824e-02  7.14483530e-03  3.53519796e-02\n",
-      " -4.03321427e-03  3.36786104e-02 -5.83909665e-03 -7.04363285e-02\n",
-      " -2.18147785e-02  3.42347027e-02  7.59117131e-03  1.52499126e-02\n",
-      "  2.92130460e-02  1.39258460e-02 -4.01203559e-03 -4.04018097e-02]\n",
-      "  diag cov noise:  [0.00032569 0.00032456 0.00029542 0.00028259 0.00029389 0.00028928\n",
-      " 0.00030764 0.00029115 0.00030399 0.00029782 0.00031574 0.00032979\n",
-      " 0.00039282 0.00037502 0.00045827 0.00037226 0.00042557 0.00035774\n",
-      " 0.00037093 0.0003429  0.00033348 0.00034951 0.00032352 0.00034281\n",
-      " 0.00033687 0.00030367 0.00031784 0.00028022 0.00030281 0.00029418\n",
-      " 0.00030323 0.00032893 0.00032524 0.00034439 0.00035621 0.00036454\n",
-      " 0.00032897 0.00038377 0.00032945 0.00038011 0.00035746 0.00031819\n",
-      " 0.00036198 0.00030694 0.00037516 0.00033979 0.00040828 0.0003167\n",
-      " 0.00040536 0.0003523  0.00040051 0.0003962  0.00034516 0.00034065\n",
-      " 0.00035106 0.00035691 0.00036474 0.00049364 0.00032884 0.00047811\n",
-      " 0.00033807 0.00037878 0.00034188 0.00038749]\n",
-      "  mean noise:  [ 6.19653832e-03 -2.61620242e-02 -1.60522676e-02  7.87607529e-02\n",
-      " -5.15555946e-02  5.33641091e-03  6.48095148e-02 -1.98769653e-02\n",
-      " -5.03428946e-04  1.15039722e-02  4.12815310e-03 -1.81092964e-02\n",
-      "  7.46231066e-03  1.06401446e-03 -2.90260705e-02 -2.43357729e-02\n",
-      " -2.63725367e-02  1.94216561e-02  3.24971767e-02  3.65302199e-02\n",
-      " -8.31579354e-03 -2.26552788e-02 -6.87066325e-03 -4.43553573e-02\n",
-      "  2.69393998e-02  3.34121749e-02  2.69313227e-03 -1.20699009e-02\n",
-      "  1.79681306e-02  2.95232795e-03 -3.72015039e-02  2.52982786e-02\n",
-      "  1.14203890e-02 -1.72117232e-02  1.78462931e-02 -5.61847447e-03\n",
-      " -1.05216447e-02  1.84592941e-02  1.65599648e-02 -2.13503228e-02\n",
-      " -7.54714168e-03  2.68194620e-02 -4.01137181e-02 -3.81706382e-02\n",
-      " -2.67865792e-05  3.42284472e-02  2.33172843e-02 -2.76766047e-02\n",
-      "  2.59810407e-02  6.91557583e-03 -5.87005227e-03 -3.31891350e-02\n",
-      " -3.28468457e-02  3.80271920e-02  1.98396699e-02  4.89425591e-02\n",
-      "  7.47426178e-03 -2.13312322e-02 -4.02152676e-02  1.39795407e-02\n",
-      "  2.63260637e-02 -1.25825401e-02 -3.99674046e-03 -4.74611796e-02]\n",
-      "  diag cov noise:  [0.00029415 0.00029767 0.00032021 0.0002805  0.00029906 0.0002556\n",
-      " 0.00030388 0.0002707  0.00033344 0.00029744 0.00032727 0.00028361\n",
-      " 0.00030956 0.00026125 0.00030189 0.0002785  0.00030989 0.00030967\n",
-      " 0.00037646 0.00033323 0.00040204 0.00037357 0.00037695 0.00036941\n",
-      " 0.0003553  0.00033796 0.00036369 0.00037132 0.00035006 0.00035013\n",
-      " 0.00033666 0.00029999 0.00034247 0.00028115 0.00032614 0.00026002\n",
-      " 0.00029229 0.00024238 0.00026751 0.00024094 0.0002534  0.00025755\n",
-      " 0.00025991 0.00027292 0.000308   0.00028877 0.00034383 0.00032614\n",
-      " 0.00033568 0.0003522  0.00035885 0.00037716 0.0004304  0.00044254\n",
-      " 0.00040542 0.00044887 0.00034726 0.00045386 0.00031688 0.00042222\n",
-      " 0.00030833 0.00034115 0.00029659 0.00031608]\n",
-      "  mean noise:  [-1.73766037e-02 -2.60967106e-02 -1.13278626e-02  1.72425789e-02\n",
-      " -8.20112909e-03 -2.19267332e-02  2.18262668e-02  2.82450782e-02\n",
-      " -1.10806542e-02 -7.40090645e-02  6.76478012e-04  1.40834655e-02\n",
-      "  4.67468326e-03 -3.03584582e-03 -2.36480447e-02  2.92788634e-02\n",
-      " -3.71904406e-02  1.47302581e-02  6.37803539e-02 -1.86639021e-03\n",
-      " -6.83249945e-02 -8.97827048e-02  5.24447557e-02  5.45295792e-02\n",
-      " -9.88305851e-03  1.61417098e-02  3.11379461e-02 -6.23709400e-03\n",
-      " -1.09470438e-02  1.81858052e-04  5.27453022e-03 -3.34987167e-02\n",
-      "  1.73753238e-02  4.57115025e-02 -1.17043500e-02 -1.14566771e-03\n",
-      " -7.97004567e-03 -3.87303647e-02 -3.96075861e-03  4.18130008e-02\n",
-      "  2.03159609e-02  3.26686696e-02 -1.05539238e-02 -7.51609850e-02\n",
-      " -4.23130944e-02 -2.57748531e-02  5.93519593e-02  5.91397550e-02\n",
-      " -2.41075787e-02  1.91832492e-02  2.20034863e-06  2.57094644e-04\n",
-      "  1.28288722e-02 -1.15981843e-02 -1.80882913e-02 -1.51763734e-02\n",
-      "  9.02017487e-03 -1.99961876e-02 -6.21133748e-03 -7.99674049e-03\n",
-      " -8.73754680e-03  2.07121625e-02  2.78764976e-02  3.96140702e-02]\n",
-      "  diag cov noise:  [0.00039326 0.00036034 0.00039616 0.00042176 0.00032708 0.0003926\n",
-      " 0.00027627 0.00034953 0.0003145  0.00034073 0.00033577 0.00031375\n",
-      " 0.00033073 0.00030383 0.00032178 0.00034146 0.00029852 0.00032213\n",
-      " 0.0003341  0.00031608 0.00037729 0.00034619 0.00034476 0.00032551\n",
-      " 0.00031419 0.00035763 0.00032665 0.00043319 0.00031987 0.00040398\n",
-      " 0.00030665 0.0003367  0.00030848 0.00030306 0.00028504 0.0003155\n",
-      " 0.00028086 0.0003273  0.00027832 0.00031989 0.00027858 0.00030059\n",
-      " 0.00027447 0.0002953  0.00028359 0.0003019  0.00029452 0.00034918\n",
-      " 0.00030651 0.00037227 0.00032762 0.00038191 0.0003472  0.00035909\n",
-      " 0.00035926 0.00031352 0.00032799 0.00030907 0.00033757 0.00032424\n",
-      " 0.00037435 0.00032401 0.00038363 0.0003059 ]\n",
-      "  mean noise:  [ 0.03536373  0.00540732 -0.01937333 -0.01082937 -0.03633167  0.0394226\n",
-      " -0.01454736 -0.0054771   0.03722967 -0.03821915 -0.05276     0.02428324\n",
-      "  0.0100332  -0.01168532  0.05322892  0.03186727 -0.00667988 -0.03906304\n",
-      "  0.00087505  0.01310968 -0.01106454  0.01003631  0.02192453 -0.0230106\n",
-      " -0.02454952  0.00515581 -0.02673985 -0.00649338 -0.01279027  0.00995111\n",
-      "  0.0263113   0.00468717  0.02788088 -0.01305829  0.02213555 -0.00194736\n",
-      " -0.05270175  0.01623126  0.00274684 -0.01585188 -0.00576588 -0.00951834\n",
-      " -0.00493095  0.05103552  0.01522741 -0.0276805  -0.0028999  -0.00326529\n",
-      "  0.02802974  0.00404062 -0.03852725 -0.01866473  0.00746784  0.03806233\n",
-      "  0.05988878 -0.02605038 -0.05204406  0.00880949 -0.03191432  0.01915399\n",
-      " -0.01324751 -0.0183774   0.03350413 -0.00522581]\n",
-      "  diag cov noise:  [0.00030534 0.00032054 0.00034161 0.00037891 0.00036215 0.00038699\n",
-      " 0.00036898 0.00037938 0.00042727 0.0004015  0.00040863 0.00036395\n",
-      " 0.00030891 0.00033869 0.00029618 0.00032073 0.00027214 0.00027949\n",
-      " 0.00023513 0.00027848 0.00023837 0.00031183 0.00024757 0.00034021\n",
-      " 0.00026764 0.00035966 0.00033588 0.00036924 0.00035456 0.00034903\n",
-      " 0.00036955 0.0003736  0.00035864 0.00034675 0.00029539 0.0003052\n",
-      " 0.00027096 0.00029241 0.00028194 0.0002797  0.0002877  0.00027036\n",
-      " 0.0002962  0.0002734  0.00031681 0.00030668 0.00031775 0.00034012\n",
-      " 0.00030727 0.00034038 0.00035037 0.00037423 0.0004004  0.00036551\n",
-      " 0.00032078 0.00032508 0.00034021 0.00031165 0.00036254 0.00031879\n",
-      " 0.00032815 0.00030746 0.00030713 0.00030382]\n",
-      "  mean noise:  [-0.0053444  -0.02092185  0.02298647  0.01350447 -0.02347786  0.00976199\n",
-      "  0.01099501  0.01427894 -0.0113865  -0.02300617 -0.004061   -0.03835074\n",
-      "  0.00625761  0.06153485  0.01580335 -0.02972533  0.026809    0.01790845\n",
-      " -0.03865074  0.03089398 -0.05399958 -0.05223672  0.02361975  0.01561394\n",
-      "  0.02540605 -0.02562297 -0.00664716  0.00011149  0.01944909  0.01312295\n",
-      "  0.00648158  0.01910197  0.03930452 -0.01473126 -0.05079134 -0.00558441\n",
-      " -0.00812988  0.00736855 -0.0135613  -0.00184057  0.05001985  0.00097853\n",
-      "  0.02584342 -0.01528018 -0.04537048  0.01378954 -0.01649896  0.01584675\n",
-      "  0.00758315 -0.00810461  0.02618083 -0.03106503 -0.01098871  0.03967426\n",
-      " -0.01791306 -0.02355958  0.02327691  0.01183095 -0.00991353  0.04019052\n",
-      " -0.02296039 -0.00922363  0.02079833 -0.01186556]\n",
-      "  diag cov noise:  [0.00035231 0.00033616 0.00035176 0.00037713 0.00031964 0.00035146\n",
-      " 0.00030933 0.00028698 0.00030073 0.00028846 0.00026459 0.00028677\n",
-      " 0.0002472  0.00029081 0.00025791 0.00029257 0.00027822 0.00030623\n",
-      " 0.00032541 0.00031522 0.00033589 0.0003031  0.0003266  0.00030621\n",
-      " 0.00039535 0.00034525 0.00038911 0.00036421 0.00030999 0.00035198\n",
-      " 0.00034538 0.00036225 0.00032101 0.00035515 0.00027228 0.0003161\n",
-      " 0.00027025 0.00031047 0.00026824 0.00031304 0.00025447 0.00029555\n",
-      " 0.00024386 0.00029523 0.00026824 0.00031223 0.00032439 0.0003113\n",
-      " 0.00034419 0.00030857 0.00032936 0.00030868 0.00030718 0.00031693\n",
-      " 0.0003173  0.00029829 0.00032956 0.00032154 0.00032621 0.00032635\n",
-      " 0.00036107 0.00036214 0.00035341 0.00037525]\n",
-      "  mean noise:  [ 0.0243603   0.03194264 -0.04334327 -0.05076507  0.00861399  0.00443284\n",
-      "  0.00411736 -0.00851634  0.01444154  0.01476712 -0.00460799 -0.01248714\n",
-      " -0.0128126   0.02459881 -0.03219549 -0.01475125  0.01827261  0.00665274\n",
-      " -0.01896736 -0.0386538   0.03233465  0.01957684  0.01567396  0.01117926\n",
-      "  0.00631349 -0.02185523 -0.04363254  0.04587557  0.00842795 -0.02704711\n",
-      "  0.02467798  0.00607433 -0.01632918 -0.05887616  0.00454818  0.05043802\n",
-      "  0.02125398  0.01441866 -0.01678747  0.0160368   0.05917822 -0.07147765\n",
-      " -0.06504858  0.03713742  0.03688584 -0.00403815 -0.02962734  0.00189445\n",
-      "  0.00852849  0.02473226  0.01804355 -0.00303284 -0.04742126 -0.01314595\n",
-      " -0.00184244  0.04136301 -0.00579051 -0.04796579  0.02003716  0.05289024\n",
-      "  0.00038179 -0.03578482  0.00625838  0.0093285 ]\n",
-      "  diag cov noise:  [0.0002948  0.00034016 0.00030317 0.00037267 0.00033179 0.00034359\n",
-      " 0.00031383 0.00028852 0.00029124 0.00028012 0.00029327 0.0002825\n",
-      " 0.00032158 0.00030668 0.00034748 0.00032575 0.00033639 0.0003286\n",
-      " 0.00031667 0.00034933 0.0003198  0.00033518 0.00036515 0.00031766\n",
-      " 0.00035136 0.00033494 0.00034282 0.0003367  0.00031025 0.00031223\n",
-      " 0.00027188 0.00027602 0.00026742 0.00025311 0.00027703 0.0002544\n",
-      " 0.00029421 0.00026858 0.00029035 0.00027394 0.00027754 0.00027881\n",
-      " 0.00030347 0.00029775 0.00035925 0.00031382 0.00042977 0.00032856\n",
-      " 0.00045774 0.00037721 0.00039858 0.00036151 0.00036599 0.00035298\n",
-      " 0.0003505  0.0003589  0.00034712 0.00035959 0.00031879 0.00038534\n",
-      " 0.00034265 0.00039534 0.00034325 0.00038004]\n",
-      "  mean noise:  [-0.04498895  0.01777121  0.01113924  0.00610733 -0.00221592 -0.00179543\n",
-      " -0.01212929 -0.02689169  0.00859429 -0.02304235 -0.00535331  0.03310675\n",
-      "  0.01342572  0.03377518  0.00277711 -0.07322621 -0.01948656  0.05634533\n",
-      "  0.00711894 -0.00322757 -0.01330458 -0.01912416 -0.00293954 -0.02157478\n",
-      "  0.03812588  0.01011745 -0.00975444  0.00736481 -0.01369792  0.01147867\n",
-      " -0.02504081 -0.00409895  0.01942269 -0.02005671  0.02018522  0.01545688\n",
-      "  0.009231   -0.00135993 -0.02712331  0.02648284  0.02176075 -0.05376488\n",
-      "  0.00285201  0.00140444  0.00113461  0.03728538 -0.03530033  0.01715681\n",
-      "  0.03095086  0.00466818 -0.00286026 -0.04196635 -0.00180027 -0.00436717\n",
-      "  0.0098204   0.02227381 -0.00512738  0.0056653  -0.00659646  0.01945991\n",
-      " -0.00517872 -0.0403389   0.03342286  0.00991347]\n",
-      "  diag cov noise:  [0.00036948 0.00038339 0.00040175 0.0004439  0.00041817 0.00045603\n",
-      " 0.00036004 0.00039995 0.00033382 0.00035618 0.00032535 0.00030687\n",
-      " 0.0003313  0.00029314 0.00036084 0.00032856 0.00034905 0.00037218\n",
-      " 0.00037102 0.00035125 0.0003896  0.00032488 0.00036708 0.00034422\n",
-      " 0.00035979 0.00035525 0.00034256 0.00037592 0.00037007 0.00039989\n",
-      " 0.00038484 0.00037104 0.0003635  0.00034403 0.00036467 0.00031205\n",
-      " 0.00032464 0.0002849  0.00032141 0.00030825 0.00032116 0.00031761\n",
-      " 0.00029544 0.00028929 0.00031051 0.00031002 0.0003632  0.00034471\n",
-      " 0.00034041 0.0003135  0.00031474 0.00030799 0.00040196 0.00033636\n",
-      " 0.00042522 0.00032476 0.00033758 0.00028208 0.00031166 0.00027512\n",
-      " 0.00031188 0.00027971 0.00032585 0.00031622]\n",
-      "  mean noise:  [-0.01763774 -0.01370223 -0.01525789 -0.00293017  0.04209477  0.0154102\n",
-      "  0.01887854  0.00657461 -0.01689176  0.03121447 -0.02596657 -0.05219685\n",
-      " -0.02168393  0.00517525  0.02515683  0.02650465  0.02843267 -0.03090259\n",
-      " -0.03137055  0.01049939  0.00045651  0.00181923  0.0148274  -0.01390385\n",
-      " -0.00385443  0.02844952 -0.03820449 -0.00457769  0.07232857 -0.00184016\n",
-      " -0.01507848 -0.03381748 -0.05548248  0.00459541  0.01667751  0.00902408\n",
-      "  0.0261955   0.0228527  -0.01664762 -0.00291956 -0.00126403 -0.03446194\n",
-      "  0.01088931  0.02527642  0.01440125  0.05140798 -0.03754982 -0.01734137\n",
-      "  0.0045875  -0.04767312 -0.00301356  0.04565949  0.01474043  0.00649797\n",
-      "  0.0236842  -0.05119016  0.01209432 -0.00438194 -0.00456178  0.04847764\n",
-      " -0.04557641  0.00051803  0.01314831 -0.02506563]\n",
-      "  diag cov noise:  [0.00044304 0.00028736 0.00040219 0.00032515 0.00036926 0.00033705\n",
-      " 0.00036269 0.00034827 0.00039678 0.0003397  0.00043811 0.00037041\n",
-      " 0.00039428 0.00036937 0.00041356 0.00035654 0.00039002 0.00033468\n",
-      " 0.00034165 0.0003341  0.00029579 0.0003342  0.00029711 0.00030759\n",
-      " 0.00029329 0.00028658 0.00030076 0.00032049 0.00030585 0.00035656\n",
-      " 0.0003248  0.00036639 0.00032383 0.00037822 0.00029657 0.00038731\n",
-      " 0.00030556 0.00039503 0.00028844 0.00034562 0.00029138 0.00034751\n",
-      " 0.00030251 0.00034445 0.00027588 0.0003282  0.00027178 0.00034889\n",
-      " 0.00028968 0.00035283 0.0003156  0.00036036 0.00033569 0.00032151\n",
-      " 0.00038047 0.00033944 0.00042991 0.00041571 0.00040719 0.00036222\n",
-      " 0.00036392 0.00026584 0.00040702 0.00025079]\n",
-      "  mean noise:  [ 0.04740654 -0.00448065 -0.04812868  0.00040789  0.0097898   0.00395759\n",
-      "  0.05664764 -0.00619594 -0.04010748 -0.01065212 -0.00756697  0.01277322\n",
-      "  0.00319721  0.02365327 -0.01064665 -0.04160779  0.00276792  0.0109967\n",
-      "  0.01123361 -0.00876307  0.01098026  0.01960903  0.016045   -0.01526363\n",
-      " -0.04530034  0.00111304 -0.0030338   0.00933962  0.02218726 -0.00863508\n",
-      " -0.00396825  0.02642255  0.00538222 -0.02596422  0.01180812 -0.01312899\n",
-      " -0.01144595 -0.00600123  0.01973356  0.05965476 -0.03862818 -0.03932824\n",
-      " -0.01020316 -0.00245741  0.00648206  0.00427151  0.02554381 -0.01746363\n",
-      " -0.00590083  0.01092254  0.02161189 -0.00713801 -0.01659686  0.04175853\n",
-      "  0.00288609 -0.06898477 -0.03234073  0.0192265   0.01610143  0.02741188\n",
-      "  0.00739418  0.03864476 -0.02231132 -0.03208449]\n",
-      "  diag cov noise:  [0.00037939 0.00041712 0.00038368 0.00042647 0.0003781  0.0003912\n",
-      " 0.00034974 0.00036243 0.00031106 0.00032708 0.00030176 0.00031089\n",
-      " 0.00031877 0.00030025 0.00035929 0.00033782 0.00041353 0.00040309\n",
-      " 0.000364   0.00043046 0.0003222  0.00038127 0.00033215 0.00033279\n",
-      " 0.00034334 0.00031338 0.00033453 0.00028557 0.00032621 0.00027134\n",
-      " 0.00034825 0.00026281 0.00035248 0.00025075 0.00032426 0.00023066\n",
-      " 0.0003249  0.00023909 0.00032676 0.00023945 0.00030949 0.00023963\n",
-      " 0.00029862 0.0002612  0.00029744 0.00028202 0.00029472 0.00032168\n",
-      " 0.00028473 0.00033131 0.0003048  0.00030637 0.00034242 0.00030834\n",
-      " 0.00036964 0.00033085 0.00034595 0.00033533 0.00031687 0.00033331\n",
-      " 0.00029022 0.00031323 0.00031376 0.00034746]\n",
-      "  mean noise:  [-2.38871049e-03 -7.56242343e-03 -7.05538279e-03  4.17193394e-03\n",
-      "  3.31791833e-02 -9.47787923e-04 -4.01495993e-02  1.85683773e-02\n",
-      " -8.46084846e-03  2.67259457e-04  2.16031006e-02 -4.69948233e-02\n",
-      " -1.40388257e-03 -1.00001491e-02 -1.92066356e-02  3.52367787e-02\n",
-      " -1.95449397e-03 -1.24267390e-02  2.00685267e-02  5.82118179e-03\n",
-      " -4.53806580e-02  5.56890370e-03  6.86668040e-02 -1.99959596e-02\n",
-      " -2.89771286e-02 -1.03404482e-02  2.52263073e-02  3.91962355e-03\n",
-      " -1.69158731e-02  1.36772583e-02 -1.65335019e-02  6.84620063e-03\n",
-      " -2.19551752e-02 -1.32463748e-02  2.56656968e-02 -1.85843298e-02\n",
-      "  2.72272547e-02  4.17126608e-02 -6.37660373e-03 -1.03306330e-02\n",
-      " -3.50778207e-03 -6.66581775e-03  3.96593846e-03 -3.50470976e-02\n",
-      " -1.10879480e-02  2.90313452e-02  1.30828573e-04 -1.06464138e-03\n",
-      "  3.51253992e-02 -1.47556143e-02 -5.01456354e-02  2.10600010e-02\n",
-      "  1.86160243e-02  3.23280862e-02  1.93082800e-02 -3.11299346e-02\n",
-      " -1.33036249e-02  6.28435028e-03 -3.96294136e-02 -9.01629079e-03\n",
-      "  3.24063044e-02  1.52499006e-03  1.02599641e-05 -1.22589241e-03]\n",
-      "  diag cov noise:  [0.00036435 0.00042172 0.00037604 0.00042351 0.00035612 0.00046222\n",
-      " 0.00037736 0.00038718 0.00034185 0.00034286 0.00032567 0.00036261\n",
-      " 0.00034016 0.00037416 0.00039751 0.00042651 0.00045911 0.00046988\n",
-      " 0.00044226 0.00042146 0.00036567 0.0003468  0.00032025 0.00033856\n",
-      " 0.00028275 0.0003199  0.0002765  0.00028291 0.00031675 0.00028246\n",
-      " 0.00033257 0.00030838 0.00030682 0.00026656 0.00028561 0.00026515\n",
-      " 0.00031408 0.00030699 0.00035477 0.00030095 0.00040159 0.00032023\n",
-      " 0.00038443 0.0003557  0.00034004 0.00031912 0.00032039 0.00031741\n",
-      " 0.00028055 0.00031421 0.00029861 0.00033204 0.00033837 0.00033976\n",
-      " 0.00034517 0.00037761 0.000352   0.00038402 0.00034073 0.00034208\n",
-      " 0.0003276  0.00034586 0.00033661 0.00039245]\n",
-      "  mean noise:  [ 0.01303043 -0.02016209  0.02293017 -0.00808328 -0.05621992  0.00095223\n",
-      "  0.00568843  0.0068887   0.04643336  0.02933932 -0.02590046 -0.03111105\n",
-      "  0.01258925 -0.022051    0.00310225  0.02518939 -0.02909435 -0.01267415\n",
-      "  0.0060883  -0.00479218  0.02291455  0.02478481 -0.03223616 -0.01703249\n",
-      "  0.00881709  0.00314935 -0.0174089  -0.04411412  0.02830965  0.04298727\n",
-      "  0.00067819 -0.0002282  -0.01484504 -0.03554904  0.02213491  0.03404201\n",
-      " -0.01350718 -0.0182995   0.02229504  0.01531478 -0.00742333  0.00607729\n",
-      " -0.01624089  0.03352011  0.01459155 -0.09240498  0.0111814   0.00310152\n",
-      " -0.00976736  0.07174268  0.0069774  -0.03761733 -0.00383452  0.041425\n",
-      " -0.02998451 -0.01069961  0.0315067   0.05374052  0.01865558 -0.07394007\n",
-      " -0.05533094  0.01320768  0.0170628   0.01511014]\n",
-      "  diag cov noise:  [0.00029454 0.00036883 0.00033489 0.00037427 0.00034671 0.000387\n",
-      " 0.00032348 0.000372   0.00029664 0.00033187 0.00029968 0.00032288\n",
-      " 0.00031883 0.00033088 0.00033918 0.00033583 0.00033618 0.00033385\n",
-      " 0.00032194 0.00033617 0.00035987 0.00032609 0.00034108 0.00032532\n",
-      " 0.00030342 0.00031457 0.00031366 0.00030475 0.00032687 0.00034447\n",
-      " 0.00033611 0.00037822 0.00031781 0.00030614 0.00031035 0.00029301\n",
-      " 0.00034161 0.0003275  0.00033188 0.00031084 0.00032873 0.00033664\n",
-      " 0.00035716 0.00038889 0.0003415  0.00037191 0.00033789 0.00032112\n",
-      " 0.00035586 0.00036844 0.00034749 0.00037197 0.00035327 0.00037365\n",
-      " 0.00036188 0.00033052 0.00029606 0.00030244 0.00026331 0.00030734\n",
-      " 0.00028812 0.00031159 0.00028791 0.00034411]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# do statistics on the leaf values\n",
-    "F = tree.data['F_T'][0]; H = tree.data['H_T'][0]\n",
-    "print(\"root conditional mean vs. sample mean:\",solve(H,F),jnp.mean(leaf_values,0))\n",
-    "print(\"root conditional cov vs. sample cov:\",jnp.linalg.inv(H),jnp.cov(leaf_values.T))\n",
-    "\n",
-    "# sample statistics\n",
-    "K = 500 # number samples\n",
-    "samples = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_noise = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "samples_conditional = jnp.zeros((K,sum(tree.is_leaf),n*d))\n",
-    "\n",
-    "from tqdm import tqdm\n",
-    "for i in tqdm(range(K)):\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "    values = tree.data['value'][tree.is_leaf][:,-1]\n",
-    "    # collect values\n",
-    "    samples = samples.at[i].set(values)\n",
-    "    # add observation noise\n",
-    "    subkey,key = jax.random.split(key)\n",
-    "    samples_noise = samples_noise.at[i].set(values+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,values.shape))\n",
-    "    # conditional sampling\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_conditional.down(tree,params.values())\n",
-    "    samples_conditional = samples_conditional.at[i].set(tree.data['value'][tree.is_leaf][:,-1])\n",
-    "# print sample statistics for all leaves\n",
-    "print(\"data parameters: \",params.values())\n",
-    "print(\"observation noise: \")\n",
-    "print(\" mean: \",jnp.mean(samples_conditional-leaf_values,axis=(0,1)))\n",
-    "print(\" diag cov: \",jnp.diag(jnp.cov((samples_conditional-leaf_values).reshape(-1,n*d).T)))\n",
-    "print(\"leaves: \")\n",
-    "for i in range(samples.shape[1]):\n",
-    "    #print(\"  mean: \",jnp.mean(samples[:,i],0))\n",
-    "    #print(\"  cov: \",jnp.cov(samples[:,i].T))\n",
-    "    #print(\"  mean w/ noise: \",jnp.mean(samples_noise[:,i],0))\n",
-    "    #print(\"  diag cov w/ noise: \",jnp.cov(jnp.cov(samples_noise[:,i].T)))\n",
-    "    #print(\"  mean conditional: \",jnp.mean(samples_conditional[:,i],0), \", sample: \",leaf_values[i])\n",
-    "    #print(\"  diag cov conditional: \",jnp.diag(jnp.cov(samples_conditional[:,i].T)))\n",
-    "    print(\"  mean noise: \",jnp.mean(samples_conditional[:,i]-leaf_values[i],0))\n",
-    "    print(\"  diag cov noise: \",jnp.diag(jnp.cov((samples_conditional[:,i]-leaf_values[i]).T)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MCMC run incorporating the likelihood ratios arising from the Gaussian approximation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.2, 'k_sigma': 0.5, 'obs_var': 0.005}\n",
-      "data parameters:  {'k_alpha': 0.1, 'k_sigma': 0.25, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2000/2000 [02:47<00:00, 11.93it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overall acceptance rate: 0.3190\n",
-      "Acceptance rate (update_params=True): 0.5760\n",
-      "Acceptance rate (update_params=False): 0.0620\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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jIuSH3cdkZO9E6dqkpqk3aVk30mxr8Y4U06qk+9OoVrgkxkaY7jUNVvqZT1uVJL8knzb7Wqt6iCx+oqcEBPjJxG92yJc//WZaqux9ufE3W3gsiVl/6ma6/tSAtnXNa+jPh4YXpa+7Zu8Jc3tvZZJpvbE3vn8LmTh/p0P3U7PY6uIpZQowo0aNkrlz58qKFSskISHB9ryGlz179kh0tGMau/nmm+Waa64xI4u0W2ndunUOy1NSzqVia5eT3lufs19H+8Kctb4oHa2kNwDwNv9ZsUfeXrJbejaPlb/9oY38knxK6kSGysaDafLzwTRzstydetq0TjSsGW5OLjXCg+SGy+NNN412s2gXiXaVtKsfbVpFXl24S/YcPW1rRVEafh7/3WUOr60nqb6tL3Tn16sRJoH+fiZ8RIQGyss3tTUnZHuv3dbe6XFocLFqUy/K3I/o2VTeWrLbfH1F4wvlBM5ol9H4AS1NwNBj0n37563tpEODGg7r6Tbnb02WHs1qS5t6kebkXpIAaE+LVb//9ZipA7Lu64b9J02dTt9WdRzqd9RVTWvJqj3HTJjTFiPtdtLQoe9V4tPzzTpvLz13nMXRLqvlv5xrLbK2blzM79vFy7bD6bLnaNGBMBosev9zmS1c2HvjtvYyeuYmh+e+efQaqRYSYFpu/vHtLvMzdXVibfM+vLtst6l5apdw7n2wev73rUx3ngYwdVe3hrIr+ZSsTTrhEF5G92kmt1/RwPzM9m9TV37Yc0y6NakpjWpVE58p4tVVH3nkEVOUq2GkWbNmDsu16+fYMcd+Nq13efPNN+XGG2+Uxo0b24p4jxw5YgqA1X/+8x8zlDo1NdWEEC3i/eabb2TLlguJWOtqTpw4QREvAJ9jbaJ3Fw05sZEhcnPHBLn7qkaXXF9P7PuOZ5oTeWxkqEu6PbYezjBdVNpNUdloAJi0wHHKEG216dyohgljWhOi76fWCx08cUZ2Jl9o/SkNbR25pXOCrPzlmCzYdq6kwt5Lg9uY7h0NDo/+d6Oty+vaFrHy4T1dynh0YuqCVFhwgPk51Z9XqxsurytvV3CRr1uKeLXb6NNPP5WvvvrKzAVjrVnRF9KWEW05cVa426BBAxNelA67btWqlQwbNkwmTZpktvHMM8+YbVtbUEaMGCFvv/22jB07Vu677z7TsjNr1iwzMgkAfI12bRQXYLQVonPDGrJu3wnbOtVDAuVMTp5DUay2vuhNu1L0hHMqO8+0xsx8sKs5cZbG1c1qmZuraJeOtWuiMnq4V6L8oUM983Xt6iGmy0xP9sWZvmqfPPf1NofntCXnz/1bmICpX3eduNjW8qGSJg6wtTBp60bhAPN/D11lCmZt+9S7qdlOszoRMqJnk3Idn/2xxFQLlkevayb/WvyrrcXOW5WqBaa45rupU6fKPffcU+z32A+jVvv37zejjrQVp1q1anL33XfLK6+8IoGBF/KULtOC4O3bt5tuqmeffbbY13CGYdQAvEWPSUtN8WzhGoxNB9NMzco93c/9g6fN/SfP5JhaFW3VaPHsAtPV06VRDfl8xFUO36/rlja4oOJ8tz1Fftx/Qq5JrC0nzuRIn5axDl11GlbH/d9mOX46W+67urHpLrT3/sq98tK8HeZrrWmacGOrCv345mz8TY6kn5VbOycU6W5zt5Kev8s1D4w3I8AAVZv+adNiUR1tof976TDhzOx804px6myeeU4LURvEhMv+45lmhInWlGh3gNZM9LyslgkIGVl55r9RrYlQ+n2t46Pk6YHnajkK09qB1XuOm6+1DkELL7W5X025s6N5TW2tKMmIFq2P0AJcXTferv4EqMwIMExkB1RpC7Ymy4gZG9y2fS1Y7XlZbUk6lmnqHvRfwXyLxRSKFmfFmN7SoCYTuAEen8gOALxRasZZW3jRYbeP92lmRsro0FdtXdGRFDriZ/HOFBM8dJiwNpdfzMO9msrlCVEyYsa50SXrkk6YW0loYavOy6KTjwFwDQIMAJ+gc53oZF1aRGkdvql1ItpNU7g+7y+zL4xg1KGhWmNirTOxuuPKBmbuEf1W/f7dqadk08F0Mx/Kf9cdlLeW/Goboqz1Kjphl673+YhucsuU1Q7baVKrmhlirMt1f3S+ktiIUDmUdkaa14kwz6nSDAMGcHHUwABeSCe/2nggTUKD/M2IFJ0DRM99/VrHmZEHL87dLvuOZcqbt3cwc2uUhRaBaguCFhPqVOkLtyVLfFSYRIadez2dI+S2Lg1kWNeGF92OfQi4WD2KjqjRe50FVuep6N08Vron1jKTkWmhqs5foSNvosODzaRgEaFB8rd5O+Tz9QelV4tY+d9FZknV79Wp6XWmUq0/sR/G+t0TPSWxDJNt6UifLzceMtvWESj2x6eTl+1JPS392sSV+f0H4Bw1MNTAwEtMWb7HzD6aEBMuD/ZobK49sv1whhkCq5N4FZ5ETP3uteXya+qF2VXt6WRlOoRWaQHqe3d1NrOS6sX6Nh1IM2Hi/muamKCg833ozKk61LKwl+Zul/e/T7rk/sdGhJjQlJNvkaua1pTk9LNmxk4tfD155sJkVxp4tDhWu0u04FVbL7Rgtrjr6FyKdbK18vh5Ql/bjLQAfAMBhgADD9Npw/cfP2NaS4qj/9kPv7qxubjcrB8PmpO/jnixXtdEuyZ0qviL0RqPzOy8Iif7JrWrmenP7ecbWTD6GjOPiM5G+pcvt5htN61d7fysm35mKvj4qFATOlbvPTeSxpvoHBXWuVK0W+b5G1tJp4YxMuBfKx3W0+nNtXXn+d+39tCeAigrAgwBBh6eA+L+j9aXezt7Xx4gRzLOmuul6HBevRKt5hRt4Rjzxc9yNtfx+i5lUXiCLCttJXpx7jbTkqJdMtapxSfe1NbMQqoBYstv6fLB90mm5cU6YVuLuEjTJVXY1Hu6SHR4kJmmXLuvtItMrxvzeJ/LTAuSBibtAtLXjakeXGTmU51H48VBbcysqBrGHr22mbk+j9Lurz99fK5o99U/Xi63dOY6aICvIsAQYKoUvXianvh0lIhe/fbgiSwztbrWJ2jrhI4w0dYMPdFl5xXINc1qyeKdqaYLpmVcpKn7cGWB5T+/3WW7PswtnRJk0Y4USbPrbimJjg2i5cuHu1+09uSKl78zVxwujl5H5q+D2sid76+VTCddOTr6ZvmY3pec/l1rV7TlQ+dIsYYG+/3QgFMrItgUruq6L/xvu7nwn16Xp018lDbumJai0tCLBGoL1htD2pvX1pqZaiHFjzvQ1z16OtvsAwDfRYAhwFQqOXkFZmptPVl2aBBtTlLbj6Sb2o5lu47aJgqzp//tT/xDW3moBBdV0+CjF9Ab0qW+KSItr+e/3ibTVu0zQ2/HXt/CjHB5f2WSuVrtA9c0lt/Sssx6q/YcN+tqqLquRay5Doq2eGhrx+/bxzutj7Gn849oQay2yHRpFCNNaleXKxvHmNaNhBphpmtK/bjvhMPImbmPXC3Zeflmf7Q1wxvphfT0KsMhgcw2C1QlGcwDg8pAuxr0RDbzx4MXrSVxRls8ShJe1OZD6eaWdDRT/v7Hyx0Cgm6nfkyYCQU6U6tO9d7zslgzOkXrTGZv/E22HEqXutGh5popWnyqha5KR9KoxNgIeeXmC9vV4GC9/2OnBDM/SVlO1BpQ3hzSocjz3ZrWLFITUi04wLTCjO/fwnZ1Xm+mXVQB/oQXAM4xjBpe67mvtsr01ftLvP7g9vHmomRDujSQV+bvLLYIVQtBtXBVh9ZqaNCWEnsrx/Y2weXTdQdk7uYj5TqGvw5uc8lhyBXlZGaOHM/Mlqa1qzMfCQCvRQsMfN53O1KLPDfpj5fL2C822x4/e0MriQ4Lkt4tYk0wsV/vmklLHULJ7tTT0rVJzSJXkf3sxwMOxbD231ce2uKhVxn2FjWqBZsbAFQGzMSLCpNxNtdcJ0aLZuOiLl1omZmTZ6vX0C4W7UrSi+DpSJepP+wz16HRIcjOWGs/rOzrQQp75Npm8urCcyNeggP8zRwnhWl9r76etqYMn35udJHOsNoqPtIUp44f0EIOp52V3v9YZpbpKJ3V46+7ZHEsAKBsCDBwi0XbUyQ546zp1rHWgVz7j2W2ETPWScraJUTJW7d3NDPOzlizXxJqhEvtyBBZsCXZNmqnZvVgqRt14Uq817aoY24lGcXz0/li1ouNMHqwRxNpXKuamVhOJ217d+keef27X8yylwa3kTu7NjRFsnrl4bV23VKXxUXI67e1tz3WbTzUq6kZ6XT/1U0ILwDgRgQYuJxOkvbA+TlQnp2zVZY82dNMjGY/3Nc66drPh9Klx6sX77K52NDZi/n3sM6yas8xp3Oc2NNgoiOQrAZeHme6lXTCt6sTa9nWUfYjlGo4meF13PUtzA0A4F4EGLicXiPG3rX/XG4uhGf19ajuMuidH2wXyrvUSJRqlxhKXBydVXZQ+3ql/j4dMaTdP85cVqe6jOqdaKbtv6tbozLtFwCg/AgwcAm97o5Ona+jd77aVPSie9YakwFt4+TyhGjZ9kI/+XZbioyeucnp9no1ry27kk/Jvd0b2a7k6w20K+opuzAGAPAMAgzKdJVencZeJ5DTocgaSv677qAkFbpmz/1XNzbFulpwa524rWODc905OkHboPbxZgbcER//5FA4+/SAlvJAjyZ8MgCAYhFgUGpf/HTINj/KpoNp5uZMfHSY3Hd1Y3Nl5CPpWZKXb3EYCaStGVqMO7RrAxNy1Nt3dJAbLo/nUwEAXBQBBqXy7bZkU5hrpa0ov53MMsOc60aHman4h32wzizr1ybOtp79KKLCqtsV6YYXmqMFAABnCDC4JL0Y4s8H02TprlR5b2WS7flXbmorQ65oUGT96fddIRGhgVIvuvjQYq97Yi358PskqR4aKG3rRfOJAAAuiQCDS3rw4/Xyw+6i0/LrhQOd0QnfSkNnx93yfD/zdeErHQMA4AwBBpe088gph8fv3dVZNGZ0aeS6afIJLgCA0iDAoFgWi8Vcafl45rkJ6BrWDDeXAejTMpaLAQIAPIoAgyL2HD0tC7Ymm9qXd5ftMc+FBQXI4id6SuD5GWkBAPAkAgyKuO3fa+TY6WyH5z4afgXhBQDgNfh3GkUUDi+fj+gmXRrF8E4BALwGAQaSl19g6l2cuf2K+rbZcwEA8BZ0IVVx6Wdy5XevL5cujWPkstgIc5FCq7mPXC1t6kV5dP8AAHCGAFPFPTZzo6SeypZ5m4/IPDl3eQCrOpGhHtsvAAAuhi6kKk5n2HUmJNBfalYLrvD9AQCgJGiBqaJ2JmfI9FX75eSZXIfnvxjRTaat2id9WtZhcjkAgNciwFQxaWdy5M4P1srW3zKcLu/cKMbcAADwZnQhVTE6QV1x4eW+7o0rfH8AACgLWmCqWLfRn7/cYr6OjQiRIV3qy+6jp83FF2+4PF6qhfDjAADwDZyxqpAfk07Yvn7/7s5yeUK0R/cHAICyogupCjmVnWfub2wXT3gBAPg0AkwVcvrsuQDD8GgAgK8jwFQh1itLR4TScwgA8G0EmCpid+pp29eJsdU9ui8AAJQXAaaK2Hv0QoD5fbt4j+4LAADlRYCphE5n55n5XuxDS3Zegbnv2iRG/Pz8PLh3AACUH8UQldBf/7ddZq4/KMGB/rLl+b4SEhggZ3PzzbLQoABP7x4AAOVGgKmE9h3PNPc5eQVy6myeLNqbIpMW7rJdpBEAAF9HgKmEMnPODZdWR09ly6hPN9oea2sMAAC+jn/HKxmLxeJwraMTmTkOy0OD+MgBAL6Ps1kl8/7KJIfHp87mOjwOowYGAFAJEGAq8XwvKuP87LtWt3SuX8F7BACA61EDU8nkWywOj7WIVwUH+MvGCb/jitMAgKrXAjNx4kTp0qWLRERESGxsrAwePFh27To3usXqT3/6kzRt2lTCwsKkdu3aMmjQINm5c6fDOgcOHJCBAwdKeHi42c6YMWMkL8+xpWDZsmXSsWNHCQkJkcTERJk2bVp5jrPKKChwDDAfr95n7utGhxJeAABVM8AsX75cRo4cKWvWrJFFixZJbm6u9O3bVzIzzw3bVZ06dZKpU6fKjh07ZOHChaaoVNfJzz83D4nea3jJycmRVatWyfTp0004mTBhgm0bSUlJZp3evXvLpk2bZPTo0XL//feb7aF0LTD7jp+xtcAAAFBZ+Fk0YZTR0aNHTQuKBpsePXo4XWfz5s3Srl072b17t2mZmT9/vtxwww1y+PBhqVOnjllnypQpMm7cOLO94OBg8/W8efNk69attu0MGTJE0tLSZMGCBSXat4yMDImKipL09HSJjIyUqmLkpz/JvM1H5NbOCTJr/SHb863jI2Xeo9d4dN8AAHDV+btc/5brxlVMTIzT5doyo60xjRs3lvr1zxWPrl69Wtq2bWsLL6pfv35mh7dt22Zbp0+fPg7b0nX0+eJkZ2ebbdjfqpJ9xzLld68tN+FFtY6Pkj4tL7zHOisvAACVRZnPagUFBaZrp3v37tKmTRuHZe+++65Ur17d3LTFRbubtGVFJScnO4QXZX2syy62joaSrKysYutzNLFZb9bAVFWs3H1MfrUbgRQY4Cfv391Znh7QUq5pVksevbaZR/cPAACvCDBaC6NdPJ999lmRZUOHDpWNGzearqXLLrtMbr31Vjl79qy40/jx402LkPV28OBBqUry889drNEq4PwFGx/o0UQ+Hn6l9G4R66E9AwDAS4ZRjxo1SubOnSsrVqyQhISEIsutrSDNmjWTrl27So0aNWT27Nly++23S1xcnKxbt85h/ZSUFHOvy6z31ufs19G+MB3d5IyOVtJbVZVfqJLJ358rTgMAKq9StcBova+GFw0jS5YsMbUtJfkevWmNiurWrZts2bJFUlNTbetoF5OGk1atWtnWWbx4scN2dB19Hs7lFzhvgQEAQKp6gNFuoxkzZsinn35q5oLRWhW9WetS9u7da2pRNmzYYOZ60WHSt9xyi2k1GTBggFlHh1RrUBk2bJj8/PPPZmj0M888Y7ZtbUEZMWKE2dbYsWPNHDJaUzNr1ix5/PHH3fEeVAqFepAkgBYYAEAlVqoAM3nyZFNf0qtXL6lbt67tNnPmTLM8NDRUVq5cacKKTj532223maCjQUaHW6uAgADT/aT32qJy5513yl133SUvvvii7XW0ZUeHUWuriw7B/uc//ynvv/++GYmEkrXA0IUEAKjMSlUDc6kpY+Lj4+Wbb7655HYaNmx4yfU0JGkhMEomr9AMvIG0wAAAKjEmB6mklxDwpwYGAFCJEWAqaQsMNTAAgMqMAFNJ5BcJMB7bFQAAvHMeGFS8tDM58snaA1KrerCEBgXIx6v3y4C2deW+qxs7DTB0IQEAKjMCjI+YtmqfvPHdrw7Prd9/0hZg6EICAFQldDT4iFV7jhe77NO1B0zAsVcj/Ny1pwAAqIwIMD5iXdIJp8/n5hfIX2ZvKfJ86/jiL0EOAICvI8D4AJ1/p7hR0SkZzi+S6ccwagBAJUaA8WJZOfmyavcxycrNl+LmEEw9de4aUwAAVCUU8Xqx577eKrPWH5I7rmxQ7DqnzuYVea5RzXA37xkAAJ5FgPFiGl6sRbrFOX0+wIQG+cvkOzvJ1kPp8sfOCRW2jwAAeAIBxsedzs419+3rR0vv5rHmBgBAZUcNjJe61IUzrXYcOWXuw4PJogCAqoMA46UmLdxVovWs879UCyHAAACqDgKMF/pue4pMXranVN8z9CKFvgAAVDYEGC80Y+3+Uq1fLThAujap6bb9AQDA2xBgvFBpL8To71+69QEA8HUEGC8UEuj8Y4mp5vz6RgEEGABAFUOA8ULBTgLM1Ym1pGuTGKfrB3DZAABAFUOA8ZEWGO0mKq5riS4kAEBVQ4DxQqFBAUWeC/T3k53J5+Z8Ue0SohyWAQBQlRBgPCi/wCJzNv4mX2w4JDl5Bbbn8wqKTmKnrS+RoRfmenn5prYOywAAqEqY/cyDVvxyVEbP3GS+Ts/KtbWkfLPliO2ijPuOnzFfB/iLPNW3ufx9wU55uHeiBPpfyJ4U8QIAqhoCjAclZ5y1ff3XuduLLA/U1GL92t9frkqsJV+Nuto83p162raMAAMAqGroQvKgMzn5F10eZBdgChfqBgVceEyAAQBUNQQYD8rKySvy3NKnejkNKYULde1DC8OoAQBVDV1IHnQ6+0ILTI/Lasu/hrR3aHXRIt/iCnXta2AYRg0AqGoIMB706flrHo3o2VT+3L+F+brALrTk5l8YmWSXa4xAhy4k9+8rAADehFOfB8VHh5n76PAgp60pAQ4jjRw/KuZ+AQBUZQQYD7J2EbVLiHZ4/k89mkjr+EgZ3D6+2FYW+xoYS9FpYwAAqNToQvIg64R19t1BavyAluZ+7ubDxRbq2tfKEGAAAFUNLTAelFdQcNHuID9x3p107jHDqAEAVRcBxoPy8s+3wBQKJ1b2uaZwF5K2wAzr2lDqx4TJPVc1cut+AgDgbehC8qDcfOddSFb2vUZ2A5Js/jq4jdv2DQAAb0YLjAflX6oLyS7BJNQ4N2IJAAAQYLyjC6mYiVzsY01ibPUK2isAALwfLTDeMAqpmBYY+9l3mfcFAIALCDDeMAqpBDUwXLARAIALKOL1gDM5ebLil2MXiniLHYVk1wJTTMgBAKAqIsB4wGvf/iLvf5904UMopgvJvgim8DwwAABUZZwVPWDmjwcdHocGBThdjxoYAACcI8B4QPXQCw1f2ksUFuw8wNi3y1ADAwDABQQYD4iLCrV9Pbx742LXowUGAADnCDAeULNasLlvXz9aHrmuWbHrMQoJAADnCDAeYB19pNcyigoLKlGAKW6kEgAAVRFnRQ/IPX9ho0sNjXa4GjXDqAEAsCHAeDDABBdzCQEr+9HVzMQLAMAFBBgPdiEFXSLA2F/MkVFIAABcwER2HpCacdbcBwVePMA0qhUuoUH+UiM8+KK1MgAAVDUEmAq25VC6HE4/H2AuUdcSGxEqa//SR0IC/S/ZWgMAQFVSqrPixIkTpUuXLhIRESGxsbEyePBg2bVrl235iRMn5JFHHpHmzZtLWFiYNGjQQB599FFJT0932M6BAwdk4MCBEh4ebrYzZswYycvLc1hn2bJl0rFjRwkJCZHExESZNm2aVAbfbk+2fa3DqC9FW16Km6kXAICqqlQBZvny5TJy5EhZs2aNLFq0SHJzc6Vv376SmZlplh8+fNjc/vGPf8jWrVtN6FiwYIEMHz7cto38/HwTXnJycmTVqlUyffp0s96ECRNs6yQlJZl1evfuLZs2bZLRo0fL/fffLwsXLpTKUv8y9MoGEh5MAxgAAGXhZ7FYzp1Ry+Do0aOmBUWDTY8ePZyu8/nnn8udd95pQk5gYKDMnz9fbrjhBhN06tSpY9aZMmWKjBs3zmwvODjYfD1v3jwTgqyGDBkiaWlpJhCVREZGhkRFRZnWn8jISPEWf5u3Xd5bmSR/6tFExg9o6endAQDAq5T0/F2uwgpr11BMTMxF19Ed0PCiVq9eLW3btrWFF9WvXz+zw9u2bbOt06dPH4ft6Dr6fHGys7PNNuxv3uj8CGpGFQEAUA5lDjAFBQWma6d79+7Spk0bp+scO3ZM/vrXv8qDDz5oey45OdkhvCjrY112sXU0lGRlZRVbn6OJzXqrX7++eKP8gvOT2NlP8gIAAComwGgtjHbxfPbZZ06Xa9jQOpZWrVrJ888/L+42fvx409pjvR08eFC8UV7BuR67AC4NAABAmZWpinTUqFEyd+5cWbFihSQkJBRZfurUKbn++uvNaKXZs2dLUNCFOUzi4uJk3bp1DuunpKTYllnvrc/Zr6NdUTq6yRkdraQ3b5d/PsBc6jICAADARS0wWu+r4UVDyZIlS6Rx48ZOW150ZJIW43799dcSGhrqsLxbt26yZcsWSU1NtT2nI5o0nGhrjXWdxYsXO3yfrqPP+zprC4y//ZUaAQCA+wKMdhvNmDFDPv30U9O6orUqerPWpVjDi444+uCDD8xj6zo6fFrpcg0qw4YNk59//tkMjX7mmWfMtq0tKCNGjJC9e/fK2LFjZefOnfLuu+/KrFmz5PHHHxdfZ2uBoQYGAICK6UKaPHmyue/Vq5fD81OnTpV77rlHfvrpJ1m7dq15Tiefs6dzuzRq1EgCAgJM99NDDz1kWlSqVasmd999t7z44ou2dbVlR4dRa2B58803TTfV+++/b0YiVZYAw7WNAACooABzqSljNNiUZFqZhg0byjfffHPJbW3cuFEqG2pgAAAoPy6wU4F2HMmQeVuOmK9pgQEAoOyYy74C5OUXiPYcPfDR+gtvPDUwAACUGQHGzXLyCuR3ry+XgyfOmBBj5ccoJAAAyowA42YpGWdl//EzRZ7Pyjk3KgsAAJQeNTAekhhb3VMvDQCAz6MFxs0KCo3KmjH8SgkN8pdODWu4+6UBAKi0CDBuZl/3Eh8VKl0a15CQwAB3vywAAJUaXUgV2AKz5KlehBcAAFyAAONm1vwSHR4koUG0vAAA4AoEGDezzkzMxRsBAHAdAkwF1cBw7WkAAFyHAONmFjmXYJi4DgAA1yHAuFlBwfk3miYYAABchgBTQaOQqIEBAMB1CDAVNAqJSx8BAOA6BJgKqoGhBQYAANchwFTUKCRqYAAAcBkCTAXVwBBgAABwHQKMmzGRHQAArkeAqaAiXmpgAABwHQKMm1EDAwCA6xFgKqoGxt0vBABAFUKAcTO6kAAAcD0CjJtRxAsAgOsRYNyMGhgAAFyPAFNh88BQBQMAgKsQYNzs/ChqrkYNAIALEWDcjKtRAwDgegSYCiripQcJAADXIcC4WUHBuXtqYAAAcB0CjJtRAwMAgOsRYNws//w4aq6FBACA6wS6cFsoZOwXP8us9YfM1wyiBgDAdWiBcSNreDFvNFW8AAC4DAGmgpBfAABwHQJMBaEFBgAA1yHAVJAzOXkV9VIAAFR6BJgK0qR29Yp6KQAAKj0CTAVpWy+qol4KAIBKjwBTQUKDAirqpQAAqPQIMBUkLJi3GgAAV+GsWkHCaIEBAMBlCDAVJKFGeEW9FAAAlR4BpgK8d1dnaUMRLwAALkOAcROLxXodapGODaLd9TIAAFRJBBg3scsvzMILAICLEWDcpMAuwXAZAQAAXIsA4yYFdi0wfrzLAAC4FKfWCmiB8XPXiwAAUEWVKsBMnDhRunTpIhERERIbGyuDBw+WXbt2Oazzn//8R3r16iWRkZHi5+cnaWlpRbZz4sQJGTp0qFknOjpahg8fLqdPn3ZYZ/PmzXLNNddIaGio1K9fXyZNmiS+ii4kAAA8GGCWL18uI0eOlDVr1siiRYskNzdX+vbtK5mZmbZ1zpw5I9dff7385S9/KXY7Gl62bdtmtjF37lxZsWKFPPjgg7blGRkZZrsNGzaUDRs2yKuvvirPP/+8CUe+ghoYAADcx89iP963lI4ePWpaYjTY9OjRw2HZsmXLpHfv3nLy5EnTymK1Y8cOadWqlfz444/SuXNn89yCBQtkwIABcujQIYmPj5fJkyfL008/LcnJyRIcHGzW+fOf/yxz5syRnTt3lmjfNARFRUVJenq6aempaKez86TNcwvN1zv/ej3XQgIAwIXn73LVwOjGVUxMTIm/Z/Xq1SbQWMOL6tOnj/j7+8vatWtt62ggsoYX1a9fP9NdpYHImezsbHPQ9jevqYGhCAYAAJcqc4ApKCiQ0aNHS/fu3aVNmzYl/j5tVdFWG3uBgYEmBOky6zp16tRxWMf62LqOs/ocTWzWm9bNeBLzwAAA4IUBRmthtm7dKp999pl4g/Hjx5sWIevt4MGDHtmP3PwCc2/fM0cRLwAAXhBgRo0aZYpvly5dKgkJCaX63ri4OElNTXV4Li8vz4xM0mXWdVJSUhzWsT62rlNYSEiI6Suzv1W0xTtSpPVzC+XVhTsd5oHxpwsJAADPBRhtVdDwMnv2bFmyZIk0bty41C/YrVs3M7RaRxdZ6ba0S+rKK6+0raMjk3SUk5WOWGrevLnUqFFDvNWbi3+VnLwCeWfpnkI1MCQYAAA8FmC022jGjBny6aefmrlgtB5Fb1lZWbZ19PGmTZtk9+7d5vGWLVvMY21hUS1btjTDrB944AFZt26d/PDDDyYUDRkyxIxAUnfccYcp4NX5YXS49cyZM+XNN9+UJ554QrxZVk6+7eunZ28x92QXAAA8HGB0eLPWl+hEdXXr1rXdNGBYTZkyRTp06GACitLRRPr466+/tq3zySefSIsWLeS6664zw6evvvpqhzletAj322+/laSkJOnUqZM8+eSTMmHCBIe5YryRfVhZuO1clxf1LwAAeNk8MN7ME/PA9Ht9hexKOeXwXFCAn/z6twEV8voAAPi6CpkHBo6cdRf5cSUkAABcjgDjZjnnh1UDAADXIcC48s2kYhcAgApBgHEh8gsAABWDAONCBBgAACoGAcaFKNgFAKBiEGBc+WY6GYXUOr7iL2kAAEBlF+jpHaisfUjtEqLkjSEdpE5kiEd3CQCAyogA46YWmAB/P2lcq5orNw8AAM6jC8mF7HuQAgN4awEAcBfOsi5kf9XpQGcFMQAAwCUIMC5kH1m0CwkAALgHAcZN88DQAgMAgPsQYNw0D0yAP28tAADuwlnWhcJDAmxf16wW7MpNAwAAOwQYFwoLOhdgWsRFyNjrm7ty0wAAwA4BxoUKLBZzf2fXhlKzOhPYAQDgLgQYFyo4l1/En6s6AgDgVgQYF7Kcb4FhBDUAAO5FgHEhWmAAAKgYBBg31MDQgwQAgHsRYFzofH5xuKQAAABwPQKMG1pgqIEBAMC9CDBuaIFhFBIAAO5FgHEhamAAAKgYBBi3dCFRAwMAgDsRYFxo08E0c09+AQDAvQgwLnQ2t+Dcm0qCAQDArQgwLp6FV7WvH+2qzQIAACcIMC6ehVeFnr8qNQAAcA8CjIvk2yWYALqQAABwKwKMi0cgmTeVdxUAALfiVOuOFhim4gUAwK0IMC6Sb98CQxcSAABuRYBxkQK7FphAWmAAAHArAkw5hk2fzc23PaYLCQCAikOAKaN7p/0oHV5cJDuTMxy6kLT3yI8uJAAA3IoAU0bLdh2VrNx8WbA12TwuODcJL0OoAQCoAAQYF9W+WFtg/Kl/AQDA7Qgw5WSt3bUGGSaxAwDA/QgwLprALs8aYGiBAQDA7Qgw5aS5JS+/QD5Zs//cG+rnio8FAABcDAHGBcOpv9uRIu9/n2Qe0wIDAID7BVbAa1Rq01btk62Ha9geE2AAAHA/WmDKKTuvQH7Yfdz22O6KAgAAwE0IMG68JhIAAHAPAoyLpZ3JdfUmAQBAIQQYF+ueWNPVmwQAAIUQYFwsIiTI1ZsEAADlCTATJ06ULl26SEREhMTGxsrgwYNl165dDuucPXtWRo4cKTVr1pTq1avLzTffLCkpKQ7rHDhwQAYOHCjh4eFmO2PGjJG8vDyHdZYtWyYdO3aUkJAQSUxMlGnTpok3DZ0uTmAAE8EAAOBVAWb58uUmnKxZs0YWLVokubm50rdvX8nMzLSt8/jjj8v//vc/+fzzz836hw8flptuusm2PD8/34SXnJwcWbVqlUyfPt2EkwkTJtjWSUpKMuv07t1bNm3aJKNHj5b7779fFi5cKN7gYnW6gcxkBwCA2/lZLtaccAlHjx41LSgaVHr06CHp6elSu3Zt+fTTT+WPf/yjWWfnzp3SsmVLWb16tXTt2lXmz58vN9xwgwk2derUMetMmTJFxo0bZ7YXHBxsvp43b55s3brV9lpDhgyRtLQ0WbBgQYn2LSMjQ6Kiosw+RUZGiivpzLuJT893uuyubg3lxUFtXPp6AABUFRklPH+XqwZGN65iYmLM/YYNG0yrTJ8+fWzrtGjRQho0aGACjNL7tm3b2sKL6tevn9nhbdu22dax34Z1Hes2PO1iiU8DDAAA8NKZeAsKCkzXTvfu3aVNm3MtDsnJyaYFJTo62mFdDSu6zLqOfXixLrcuu9g6GnKysrIkLCysyP5kZ2ebm5Wu6y7FtVndfkUDSYyNcNvrAgCAcrbAaC2MdvF89tln4g20wFibnKy3+vXru+21LMW0wRw9dSFAAQAALwswo0aNkrlz58rSpUslISHB9nxcXJwpztVaFXs6CkmXWdcpPCrJ+vhS62hfmLPWFzV+/HjTpWW9HTx4UCq6BSbjLJPYAQDgdQFG6301vMyePVuWLFkijRs3dljeqVMnCQoKksWLF9ue02HWOmy6W7du5rHeb9myRVJTU23r6IgmDSetWrWyrWO/Des61m04o8OtdRv2t4rSICbc3P+hQ70Ke00AAKqywNJ2G+kIo6+++srMBWOtWdEuG20Z0fvhw4fLE088YQp7NUQ88sgjJnjoCCSlw641qAwbNkwmTZpktvHMM8+YbWsIUSNGjJC3335bxo4dK/fdd58JS7NmzTIjk7yBfQvMu0M7ynUtY2XfsTNyWZ3qntwtAACqDkspnB+AU+Q2depU2zpZWVmWhx9+2FKjRg1LeHi45Q9/+IPlyJEjDtvZt2+fpX///pawsDBLrVq1LE8++aQlNzfXYZ2lS5da2rdvbwkODrY0adLE4TVKIj093eyb3rva6bO5lobj5ppbZrbjfgMAgLIr6fm7XPPAeDN3zgNzOjtP2jx3blK9HS9eL2HBAS7dPgAAVVVGRcwDU1XZZz4/rhwAAECFI8CUgX2TFQEGAICKR4ApA/tONz+hCQYAgIpGgCkL+wBDfgEAoMIRYMo5Ey/5BQCAikeAKYMChxYYIgwAABWNAFPeUUiu/DQAAECJEGDKgFFIAAB4FgGmvKOQ6EICAKDCEWDKUcRLdgEAwDMIMGVxvgWG+hcAADyDAFMG1h4kuo8AAPAMAkwZFJwvgqEFBgAAzyDAlKOIlxoYAAA8gwBTni4k2mAAAPAIAkx5JrKjDwkAAI8gwJSBNb/4E2AAAPAIAkw50IUEAIBnEGDKgCJeAAA8iwBTBgyjBgDAswgwZcBEdgAAeBYBphyjkKjhBQDAMwgwZWC7GDUJBgAAjyDAlAHTwAAA4FkEmDI534XEtQQAAPAIAkwZMJEdAACeRYApg7wCWmAAAPAkAkwZrEs64TAfDAAAqFgEmDLIzMkz90EBvH0AAHgCZ+BSyi+wyBvf/Wq+/kOHeu74TAAAwCUQYMrQfZSTV2C+jo8KLe23AwAAFyDAlNLp7HPdR+rmTgmu+AwAAEApEWDKeBmBDg2iJSI0qLTfDgAAXIAAU9YLObri3QcAAGVCgCnrZQSYhRcAAI8hwJQaV6IGAMDTCDBlboFxw6cBAABKhABT5hoYEgwAAJ5CgCkl29UDyC8AAHgMAaaULNTAAADgcQSYUqIGBgAAzyPAlBI1MAAAeB4Bpowz8TIKCQAAzyHAlBEBBgAAzyHAlLUGhmFIAAB4DAGmrKOQGEYNAIDHEGDKOg8MAADwGAJMKXExRwAAPI8AU0pMxAsAgOcRYEqp4HwTjD81MAAA+E6AWbFihdx4440SHx8vfn5+MmfOHIflKSkpcs8995jl4eHhcv3118uvv/7qsM7Zs2dl5MiRUrNmTalevbrcfPPN5vvsHThwQAYOHGi2ERsbK2PGjJG8vDzxONvVqEkwAAD4TIDJzMyUdu3ayTvvvON0krfBgwfL3r175auvvpKNGzdKw4YNpU+fPub7rB5//HH53//+J59//rksX75cDh8+LDfddJNteX5+vgkvOTk5smrVKpk+fbpMmzZNJkyYIJ7GtZAAAPAClnLQb589e7bt8a5du8xzW7dutT2Xn59vqV27tuW9994zj9PS0ixBQUGWzz//3LbOjh07zPetXr3aPP7mm28s/v7+luTkZNs6kydPtkRGRlqys7NLtG/p6elmm3rvSv9du9/ScNxcy/Bp61y6XQAAYCnx+dulNTDZ2dnmPjQ01Pacv7+/hISEyPfff28eb9iwQXJzc02rjFWLFi2kQYMGsnr1avNY79u2bSt16tSxrdOvXz/JyMiQbdu2Ffvautz+5g4XRlHThQQAgKe4NMBYg8j48ePl5MmTpgvo73//uxw6dEiOHDli1klOTpbg4GCJjo52+F4NK7rMuo59eLEuty5zZuLEiRIVFWW71a9fX9yBq1EDAFDJAkxQUJB8+eWX8ssvv0hMTIwpwF26dKn079/ftMS4k4am9PR02+3gwYNueR1qYAAA8LxAV2+wU6dOsmnTJhMitAWmdu3acuWVV0rnzp3N8ri4OPN8WlqaQyuMjkLSZdZ11q1b57Bd6ygl6zqFaTeV3tyNFhgAADzPbc0i2o2j4UWHUK9fv14GDRpkCzjaUrN48WLburt27TLDprt162Ye6/2WLVskNTXVts6iRYskMjJSWrVqJd4xkR01MAAA+EwLzOnTp2X37t22x0lJSabFRbuMtP5Fh0ZrcNGvNYQ89thjZmh13759bcFm+PDh8sQTT5jv0VDyyCOPmNDStWtXs46uq0Fl2LBhMmnSJFP38swzz5i5YyqilaUkTTBMAwMAgA8FGG1N6d27t+2xBhF19913m7latFhXn9Mun7p168pdd90lzz77rMM2Xn/9dVMToxPY6eghHWH07rvv2pYHBATI3Llz5aGHHjLBplq1amb7L774oniarQWGBhgAADzGT8dSSyWkw6i1tUdrcbSVx1Wmr9onz329TQa2rSvvDO3osu0CAAAp8fmbayGVki3v0QIDAIDHEGBKiatRAwDgeQSYMg+jpgkGAABPIcCUEi0wAAB4HgGmjDUwNMAAAOA5BJgyogMJAADPIcCUEjUwAAB4HgGmlLiYIwAAnkeAKSXbtH/0IQEA4DEEmFLiYo4AAHgeAabMNTBu+DQAAECJEGBKiRoYAAA8jwBTSrTAAADgeQSYMvKjihcAAI8hwJQSM/ECAOB5BJhSogsJAADPI8CUcRg1E8EAAOA5BJhSogUGAADPI8CUEsOoAQDwPAJMKdECAwCA5xFgSolLCQAA4HkEmDI2wXApAQAAPIcAU0pcjBoAAM8jwJS5BoarOQIA4CkEmDKOQgIAAJ5DgCljC4w/LTAAAHgMAaaUAv39JCTQX4IC6EICAMBT/CzWqxNWMhkZGRIVFSXp6ekSGRnp6d0BAAAuPH/TAgMAAHwOAQYAAPgcAgwAAPA5BBgAAOBzCDAAAMDnEGAAAIDPIcAAAACfQ4ABAAA+hwADAAB8DgEGAAD4HAIMAADwOQQYAADgcwgwAADA5xBgAACAzwmUSspisdguyw0AAHyD9bxtPY9XuQBz6tQpc1+/fn1P7woAACjDeTwqKqrY5X6WS0UcH1VQUCCHDx+WiIgI8fPzc3k61GB08OBBiYyMlMqG4/N9fIa+rbJ/flXhGCv78bnzGDWWaHiJj48Xf3//qtcCowedkJDg1tfQD6yy/mAqjs/38Rn6tsr++VWFY6zsx+euY7xYy4sVRbwAAMDnEGAAAIDPIcCUQUhIiDz33HPmvjLi+Hwfn6Fvq+yfX1U4xsp+fN5wjJW2iBcAAFRetMAAAACfQ4ABAAA+hwADAAB8DgEGAAD4HAJMKb3zzjvSqFEjCQ0NlSuvvFLWrVsnvmDixInSpUsXMzNxbGysDB48WHbt2uWwTq9evcysxfa3ESNGOKxz4MABGThwoISHh5vtjBkzRvLy8sTTnn/++SL73qJFC9vys2fPysiRI6VmzZpSvXp1ufnmmyUlJcUnjs1Kf+4KH6Pe9Lh88fNbsWKF3HjjjWa2Td3XOXPmOCzX8QUTJkyQunXrSlhYmPTp00d+/fVXh3VOnDghQ4cONZNoRUdHy/Dhw+X06dMO62zevFmuueYa8zurs4ZOmjTJ48eXm5sr48aNk7Zt20q1atXMOnfddZeZPfxSn/krr7ziFcd3qWNU99xzT5H9v/766yvFZ6ic/T7q7dVXX/WJz3BiCc4LrvrbuWzZMunYsaMZsZSYmCjTpk0r/wHoKCSUzGeffWYJDg62fPjhh5Zt27ZZHnjgAUt0dLQlJSXF69/Cfv36WaZOnWrZunWrZdOmTZYBAwZYGjRoYDl9+rRtnZ49e5pjOnLkiO2Wnp5uW56Xl2dp06aNpU+fPpaNGzdavvnmG0utWrUs48ePt3jac889Z2ndurXDvh89etS2fMSIEZb69etbFi9ebFm/fr2la9eulquuusonjs0qNTXV4fgWLVqkIwgtS5cu9cnPT1//6aeftnz55ZfmOGbPnu2w/JVXXrFERUVZ5syZY/n5558tv//97y2NGze2ZGVl2da5/vrrLe3atbOsWbPGsnLlSktiYqLl9ttvty3X469Tp45l6NCh5mf/v//9ryUsLMzy73//26PHl5aWZj6HmTNnWnbu3GlZvXq15YorrrB06tTJYRsNGza0vPjiiw6fqf3vrCeP71LHqO6++27zGdnv/4kTJxzW8dXPUNkfl9703ODn52fZs2ePT3yG/UpwXnDF3869e/dawsPDLU888YRl+/btlrfeessSEBBgWbBgQbn2nwBTCvoHZuTIkbbH+fn5lvj4eMvEiRMtvkZPhvoLuXz5cttzegJ87LHHiv0e/cH09/e3JCcn256bPHmyJTIy0pKdnW3xdIDRP4LO6MkiKCjI8vnnn9ue27Fjhzl+PXF4+7EVRz+rpk2bWgoKCnz+8yt8ctBjiouLs7z66qsOn2NISIj5A6/0D6F+348//mhbZ/78+eYE8ttvv5nH7777rqVGjRoOxzdu3DhL8+bNLRXJ2cmvsHXr1pn19u/f73Dye/3114v9Hm85PlVcgBk0aFCx31PZPkM91muvvdbhOV/6DFMLnRdc9bdz7Nix5h9Me7fddpsJUOVBF1IJ5eTkyIYNG0wztv31lvTx6tWrxdekp6eb+5iYGIfnP/nkE6lVq5a0adNGxo8fL2fOnLEt0+PUJu86derYnuvXr5+5oNe2bdvE07R7QZt6mzRpYpqktVlT6eemTfb2n512LzVo0MD22Xn7sTn7eZwxY4bcd999Dhcr9eXPz15SUpIkJyc7fGZ6bRTttrX/zLTLoXPnzrZ1dH39vVy7dq1tnR49ekhwcLDDMWsz+cmTJ8Xbfif1s9RjsqfdDdp836FDB9M1Yd807wvHp10H2q3QvHlzeeihh+T48eO2ZZXpM9RulXnz5pkusMJ85TNML3RecNXfTl3HfhvWdcp77qy0F3N0tWPHjkl+fr7Dh6T08c6dO8XXrtQ9evRo6d69uznRWd1xxx3SsGFDEwK0T1b76PWX6MsvvzTL9YTi7PityzxJT2zap6p/JI8cOSIvvPCC6VPeunWr2Tf941D4xKD7bt1vbz42Z7QvPi0tzdQYVIbPrzDr/jjbX/vPTE+M9gIDA80fX/t1GjduXGQb1mU1atQQb6B1Bvp53X777Q4XxXv00UdN3YAe06pVq0wo1Z/v1157zSeOT+tdbrrpJrOPe/bskb/85S/Sv39/c+IKCAioVJ/h9OnTTS2JHq89X/kMC5ycF1z1t7O4dTTkZGVlmRq3siDAVEFakKUn9u+//97h+QcffND2tSZqLZ687rrrzB+epk2bijfTP4pWl19+uQk0ejKfNWtWmX85vNkHH3xgjlnDSmX4/Koy/Q/31ltvNUXLkydPdlj2xBNPOPxc68nkT3/6kym+9IUp6ocMGeLwM6nHoD+L2iqjP5uVyYcffmhafrUQ1xc/w5HFnBe8GV1IJaTN8vofQ+Hqa30cFxcnvmLUqFEyd+5cWbp0qSQkJFx0XQ0Bavfu3eZej9PZ8VuXeRP9j+Gyyy4z+677pl0u2mJR3GfnS8e2f/9++e677+T++++vtJ+fdX8u9vum96mpqQ7LtWleR7X4yudqDS/6mS5atMih9aW4z1SPcd++fT5xfIVp967+LbX/mfT1z1CtXLnStHZe6nfSWz/DUcWcF1z1t7O4dfTnvTz/YBJgSkhTc6dOnWTx4sUOTW76uFu3buLt9L87/SGdPXu2LFmypEiTpTObNm0y9/qfvNLj3LJli8MfHOsf3VatWok30WGY2vKg+66fW1BQkMNnp39stEbG+tn50rFNnTrVNLvrsMXK+vnpz6f+0bP/zLS5Wesi7D8z/cOq/fRW+rOtv5fW8Kbr6FBYDQr2x6xdjZ7uerCGF63d0kCqNRKXop+p1odYu128+ficOXTokKmBsf+Z9OXP0L5FVP/OtGvXzqc+Q8slzguu+tup69hvw7pOuc+d5SoBroLDqHUUxLRp00z1/IMPPmiGUdtXX3urhx56yAxJXbZsmcNwvjNnzpjlu3fvNkP9dJhcUlKS5auvvrI0adLE0qNHjyLD5fr27WuG3OkQuNq1a3vFUOMnn3zSHJvu+w8//GCG9OlQPq2qtw4F1OGBS5YsMcfYrVs3c/OFY7OnI9/0OHSUgj1f/PxOnTplhl3qTf8Uvfbaa+Zr6ygcHUatv196LJs3bzYjPJwNo+7QoYNl7dq1lu+//97SrFkzhyG4OopCh6gOGzbMDBXV32EdzlkRQ1Qvdnw5OTlmWHhCQoL5LOx/J60jN1atWmVGr+hyHZY7Y8YM83ndddddXnF8lzpGXfbUU0+Z0Sr6M/ndd99ZOnbsaD6js2fP+vxnaD8MWvdHR94U5u2f4UOXOC+46m+ndRj1mDFjzCimd955h2HUnqDj1/XD1PlgdFi1zl3gC/SXz9lN5wBQBw4cMCe7mJgYE9J0Lgb9YbOfR0Tt27fP0r9/fzNPgQYEDQ65ubkWT9MheXXr1jWfS7169cxjPalb6Unv4YcfNsMV9RfpD3/4g/lF9YVjs7dw4ULzue3atcvheV/8/HT+Gmc/kzr01jqU+tlnnzV/3PWYrrvuuiLHffz4cXOyq169uhm2ee+995qTjj2dQ+bqq68229CfDQ1Gnj4+PaEX9ztpnddnw4YNliuvvNKcYEJDQy0tW7a0vPzyyw4nf08e36WOUU+CelLTk5kOxdXhxDpPUeF/+Hz1M7TSoKG/TxpECvP2z1AucV5w5d9OfS/bt29v/kbrP1f2r1FWfucPAgAAwGdQAwMAAHwOAQYAAPgcAgwAAPA5BBgAAOBzCDAAAMDnEGAAAIDPIcAAAACfQ4ABUGk0atRI3njjDU/vBoAKQIABUCb33HOPDB482Hzdq1cvGT16dIW9k9OmTTMX7Czsxx9/dLgqN4DKK9DTOwAAVnrlW71walnVrl2bNxOoImiBAVDulpjly5fLm2++KX5+fua2b98+s2zr1q3Sv39/qV69utSpU0eGDRsmx44ds32vttzo1XC19aZWrVrSr18/8/xrr70mbdu2lWrVqkn9+vXl4YcfNlcYV8uWLZN7771X0tPTba/3/PPPO+1C0qvmDho0yLy+Xh1Xr/6ckpJiW67f1759e/n444/N90ZFRcmQIUPk1KlTtnW++OILsy9hYWHmitF9+vSRzMxMfmoADyPAACgXDS7dunWTBx54QI4cOWJuGjrS0tLk2muvlQ4dOsj69etlwYIFJjxoiLA3ffp00+ryww8/yJQpU879YfL3l3/961+ybds2s3zJkiUyduxYs+yqq64yIUUDifX1nnrqqSL7VVBQYMLLiRMnTMBatGiR7N27V2677TaH9fbs2SNz5syRuXPnmpuu+8orr5hluu3bb79d7rvvPtmxY4cJTzfddJNwCTnA8+hCAlAu2mqhASQ8PFzi4uJsz7/99tsmvLz88su25z788EMTbn755Re57LLLzHPNmjWTSZMmOWzTvp5GW0ZeeuklGTFihLz77rvmtfQ1teXF/vUKW7x4sWzZskWSkpLMa6qPPvpIWrdubWplunTpYgs6WlMTERFhHmsrkX7v3/72NxNg8vLyTGhp2LChWa6tMQA8jxYYAG7x888/y9KlS033jfXWokULW6uHVadOnYp873fffSfXXXed1KtXzwQLDRXHjx+XM2fOlPj1tcVEg4s1vKhWrVqZ4l9dZh+QrOFF1a1bV1JTU83X7dq1M/uhoeWWW26R9957T06ePFmGdwOAqxFgALiF1qzceOONsmnTJofbr7/+Kj169LCtp3Uu9rR+5oYbbpDLL79c/u///k82bNgg77zzjq3I19WCgoIcHmvLjrbKqICAANP1NH/+fBN+3nrrLWnevLlp1QHgWQQYAOWm3Tr5+fkOz3Xs2NHUsGgLR2JiosOtcGixp4FFA8Q///lP6dq1q+lqOnz48CVfr7CWLVvKwYMHzc1q+/btpjZHw0hJaaDp3r27vPDCC7Jx40bz2rNnzy7x9wNwDwIMgHLTkLJ27VrTeqKjjDSAjBw50hTQahGs1pxot9HChQvNCKKLhQ8NOLm5uaa1Q4tudYSQtbjX/vW0hUdrVfT1nHUt6Wgh7foZOnSo/PTTT7Ju3Tq56667pGfPntK5c+cSHZcek9bwaBGyjmj68ssv5ejRoyYcAfAsAgyActNRQNrdoi0bOheLnuzj4+PNyCINK3379jVhQotztQZFRxkVR+tOdBj13//+d2nTpo188sknMnHiRId1dCSSFvXqiCJ9vcJFwNaWk6+++kpq1Khhuqw00DRp0kRmzpxZ4uPSkU4rVqyQAQMGmJagZ555xrQM6dBwAJ7lZ2E8IAAA8DG0wAAAAJ9DgAEAAD6HAAMAAHwOAQYAAPgcAgwAAPA5BBgAAOBzCDAAAMDnEGAAAIDPIcAAAACfQ4ABAAA+hwADAAB8DgEGAACIr/l/yMRMZfs6cwoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "log_likelihood = get_log_likelihood(tree,down_conditional,up,init_up)\n",
-    "obs_var_sample_conditional = True\n",
-    "log_posterior = get_log_posterior(log_likelihood,skip_obs_var=obs_var_sample_conditional)\n",
-    "proposal = get_proposal(tree,obs_var_sample_conditional=obs_var_sample_conditional)\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.2,alpha=2.,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.5), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    #'obs_var': VarianceParameter(.5*1e-3,min=1e-4,max=.1,prior='uniform'), # observation noise variance\n",
-    "    'obs_var': VarianceParameter(5*1e-3,alpha=2,beta=.005,min=1e-4,keep_constant=False), # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "\n",
-    "# Run Metropolis-Hastings\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 2000, burn_in=000, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "burnin = 500\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,params.values(),burnin=burnin)\n",
-    "None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## plot v_0, v_T by level from leaves up (excluding root)\n",
-    "#for level_num, level in enumerate(tree.levels[1:]):  # Skip root level\n",
-    "#    print(f\"Level {level_num+1}\")\n",
-    "#    for i in range(*level):\n",
-    "#        print(tree.data['logpsi'][i])\n",
-    "#        ax = plot_shape(tree.data['v_0'][i],color='blue',label='v_0')\n",
-    "#        plot_shape(tree.data['v_T'][i],color='red',label='v_T',ax=ax)\n",
-    "#        ax.set_aspect('auto')\n",
-    "#        ax.axis([-1.5,1.5,-1.5,1.5])\n",
-    "#        plt.legend()\n",
-    "#        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import dask\n",
-    "from dask.distributed import Client, LocalCluster\n",
-    "\n",
-    "# Set up dask client for parallel processing\n",
-    "# Using 10 workers as a balanced choice for M3\n",
-    "cluster = LocalCluster(n_workers=10)  # Use 10 workers to mainly utilize P cores\n",
-    "client = Client(cluster)\n",
-    "# Get number of workers and threads\n",
-    "print(f\"Number of workers: {len(client.ncores())}\")\n",
-    "print(f\"Cores per worker: {client.ncores()}\")\n",
-    "\n",
-    "# Number of parallel chains\n",
-    "K = 25\n",
-    "\n",
-    "# Function to run single MCMC chain\n",
-    "def run_mcmc_chain(seed):\n",
-    "    key = jax.random.PRNGKey(seed)\n",
-    "\n",
-    "    # sample leaf values\n",
-    "    subkey, key = split(key)\n",
-    "    update_noise(tree,subkey)\n",
-    "    down_unconditional.down(tree,params.values())\n",
-    "\n",
-    "    # add noise to leaf nodes\n",
-    "    key, subkey = split(key)\n",
-    "    leaf_values = tree.data['value'][tree.is_leaf,-1]+jnp.sqrt(params['obs_var'].value)*jax.random.normal(subkey,tree.data['value'][tree.is_leaf,-1].shape)\n",
-    "\n",
-    "    # mcmc\n",
-    "    subkey, key = split(key)\n",
-    "    init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "    log_likes, chain_samples = metropolis_hastings(log_posterior, proposal, leaf_values, \n",
-    "                                                 init_state, 500, burn_in=2000, \n",
-    "                                                 rng_key=key, savef=lambda state: state[0],\n",
-    "                                                 verbose=False)\n",
-    "    # Get mean of samples for each parameter\n",
-    "    means = {k: jnp.mean(jnp.array([s[k].value for s in chain_samples])) for k in chain_samples[0].values().keys()}\n",
-    "    return means\n",
-    "\n",
-    "# Run chains in parallel\n",
-    "futures = [dask.delayed(run_mcmc_chain)(i) for i in range(K)]\n",
-    "results = dask.compute(*futures)\n",
-    "\n",
-    "# Convert results to parameter arrays\n",
-    "k_alpha_means = jnp.array([r['k_alpha'] for r in results])\n",
-    "k_sigma_means = jnp.array([r['k_sigma'] for r in results])\n",
-    "obs_var_means = jnp.array([r['obs_var'] for r in results])\n",
-    "\n",
-    "print(f\"k_alpha - mean: {jnp.mean(k_alpha_means):.4f}, median: {jnp.median(k_alpha_means):.4f}\")\n",
-    "print(f\"k_sigma - mean: {jnp.mean(k_sigma_means):.4f}, median: {jnp.median(k_sigma_means):.4f}\")\n",
-    "print(f\"obs_var - mean: {jnp.mean(obs_var_means):.4f}, median: {jnp.median(obs_var_means):.4f}\")\n",
-    "\n",
-    "# Plot histograms\n",
-    "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5))\n",
-    "\n",
-    "ax1.hist(k_alpha_means,bins=20)\n",
-    "ax1.axvline(params['k_alpha'].value,color='r',linestyle='--',label='True value')\n",
-    "ax1.set_title('k_alpha')\n",
-    "ax1.legend()\n",
-    "\n",
-    "ax2.hist(k_sigma_means,bins=20)\n",
-    "ax2.axvline(params['k_sigma'].value,color='r',linestyle='--',label='True value')\n",
-    "ax2.set_title('k_sigma')\n",
-    "ax2.legend()\n",
-    "\n",
-    "ax3.hist(obs_var_means,bins=jnp.linspace(min(obs_var_means),max(obs_var_means),20))\n",
-    "ax3.axvline(params['obs_var'].value,color='r',linestyle='--',label='True value')\n",
-    "ax3.set_title('obs_var')\n",
-    "ax3.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Clean up dask client\n",
-    "client.close()\n",
-    "cluster.close()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot priors for all initial parameters\n",
-    "init_params['k_alpha'].plot_prior()\n",
-    "plt.title('k_alpha prior')\n",
-    "\n",
-    "init_params['k_sigma'].plot_prior()\n",
-    "plt.title('k_sigma prior')\n",
-    "\n",
-    "init_params['obs_var'].plot_prior(x_min=1e-4,x_max=2*1e-3)\n",
-    "plt.title('obs_var prior')\n",
-    "\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb b/examples/mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb
deleted file mode 100644
index 12a95d0..0000000
--- a/examples/mcmc_Gaussian_BFFG_shapes_state-dependent_covariance.ipynb
+++ /dev/null
@@ -1,505 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC using backward filtering, forward guiding for shapes\n",
-    "\n",
-    "Parameter inference for trees with Gaussian transitions along edges and observations at the leaf nodes for landmark represented shapes. Please refer to the notebook [mcmc_Gaussian_BFFG.ipynb](mcmc_Gaussian_BFFG.ipynb) for a simpler version with $\\mathbb R^2$ data, and [mcmc_Gaussian_BFFG_shapes.ipynb](mcmc_Gaussian_BFFG_shapes.ipynb) for a version with constant covariance and thus a fully Gaussian model. In this notebook, the node covariance is shape depedent, thus requiring computation of likelihood ratios in the downwards passes for use in the MCMC iterations.\n",
-    "\n",
-    "The covariance is assumed rotationally invariant, allowing factorization into a kernel product the identity matrix of the ambient space.\n",
-    "\n",
-    "The conditioning and upwards/downwards message passing and fusing operations follow the backward filtering, forward guiding approach of Frank van der Meulen, Moritz Schauer et al., see https://arxiv.org/abs/2010.03509 and https://arxiv.org/abs/2203.04155 . The latter reference provides an accesible introduction to the scheme and the notation used in this example. The notation, likelihood ratio calculation etc. follow https://arxiv.org/abs/2203.04155 section 6.1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import jax\n",
-    "jax.config.update(\"jax_enable_x64\", True)\n",
-    "from jax import numpy as jnp\n",
-    "from jax.random import PRNGKey, split\n",
-    "\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "\n",
-    "from hyperiax.plotting import plot_tree_text, plot_shape_2d, plot_tree_2d_scatter, plot_tree_2d_shape\n",
-    "\n",
-    "from hyperiax.mcmc import ParameterStore, VarianceParameter\n",
-    "from hyperiax.mcmc.metropolis_hastings import metropolis_hastings\n",
-    "from hyperiax.mcmc.plotting import trace_plots\n",
-    "\n",
-    "from examples.ABFFG import *\n",
-    "from examples.shape import k_K2\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tqdm import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed:  858959100264\n"
-     ]
-    }
-   ],
-   "source": [
-    "# seed,\n",
-    "#seed = 42\n",
-    "import os; seed = int(os.urandom(5).hex(), 16)\n",
-    "print(\"seed: \",seed)\n",
-    "key = PRNGKey(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Shape related setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# define shape and plot\n",
-    "d = 2; n = 16 # d = dimension of embedding space (usually 2), n = number of landmarkds\n",
-    "phis = jnp.linspace(0,2*jnp.pi,n,endpoint=False) # circular shape\n",
-    "root = jnp.vstack((jnp.cos(phis),jnp.sin(phis))).T.flatten()\n",
-    "\n",
-    "# plot\n",
-    "plot_shape_2d(root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# diffusion and covariance specification for shape processes\n",
-    "# This version assumes same covariance in all directions (x,y or x,y,z), thus allowing some factorization of the covariance matrix\n",
-    "\n",
-    "kQ12 = lambda x,params: (1/n)*k_K2(x,params)\n",
-    "\n",
-    "# evaluate k on two pairs of landmark configurations\n",
-    "kQ12_q = lambda q1,q2,params: kQ12(q1.reshape((-1,d))[:,jnp.newaxis,:]-q2.reshape((-1,d))[jnp.newaxis,:,:],params)\n",
-    "\n",
-    "# diffusion field and covariance matrix factored in dimensions\n",
-    "sigma = lambda q,theta: kQ12_q(q,q,theta) # diffusion field, kernel evaluated on all landmark pairs\n",
-    "def a(q,theta): sigmaq = sigma(q,theta); return jnp.einsum('ij,kj->ik',sigmaq,sigmaq) # covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gaussian tree, node-dependent covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we initialize the tree. We set the root to the shape defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                            None\n",
-      "           ┌────────────────────────┬────────────────────────┼────────────────────────┬────────────────────────┐\n",
-      "          None                     None                     None                     None                     None          \n",
-      " ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐    ┌────┬────┼────┬────┐  \n",
-      "None None None None None None None None None None None None None None None None None None None None None None None None None\n",
-      "HypTree(size=31, levels=3, leaves=25, inner nodes=5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create topology and tree\n",
-    "topology = symmetric_topology(height=2, degree=5)\n",
-    "plot_tree_text(topology)\n",
-    "tree = HypTree(topology)\n",
-    "print(tree)\n",
-    "\n",
-    "# add properties to tree\n",
-    "tree.add_property('edge_length', shape=())\n",
-    "# data dimension\n",
-    "d = 2\n",
-    "tree.add_property('value', shape=(n*d,))\n",
-    "tree.add_property('noise', shape=(n*d,))\n",
-    "\n",
-    "# set edge lengths on all nodes\n",
-    "tree.data['edge_length'] = jnp.ones_like(tree.data['edge_length'])\n",
-    "\n",
-    "# root value\n",
-    "tree.data['value'] = tree.data['value'].at[0].set(root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then define parameters for the Gaussian transition kernel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters, variance and observation noise\n",
-    "params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.1), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.5), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3) # observation noise variance\n",
-    "    })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now follows the down transitions. At first, we define the unconditional transitions, which are just Gaussian samples. The covariance is square of the pairwise kernel evaluations in `sigma(params)` times the variance parameter times edge lengths."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unconditional down\n",
-    "down_unconditional = Gaussian_down_unconditional(sigma)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now draw noise and perform a downwards pass. This gives values at all nodes of the tree. Note that observation noise is not added to the leaves yet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subkey, key = split(key)\n",
-    "update_noise(tree,subkey)\n",
-    "down_unconditional.down(tree,params.values())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add uncorrelated observation noise to leaves.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add noise to leaf nodes\n",
-    "key, subkey = split(key)\n",
-    "add_leaf_noise(tree,key,params)\n",
-    "leaf_values = tree.data['value'][tree.is_leaf]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot a generated tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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f+9h0zz33jB5/3OMe1+tVDXbYAQAAAECJ6667Lu3cuTO95CUv2Xcrk/znL//yL1Of7LADAAAAYHBbthyRDjnkoPTQQ/892GsecshBo9eta2lpKS2CwA4AAACAwZ1wwhPT7bdfmbZvv3+w19yy5YjR60YnsAMAAABgIXJ4thoCtKG5hx0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgkIMWXQAAAAAADkx3/uA/0/ZdDw72els2HZpOOPIxtf/+5Zdfnt773vemf//3fx99f/zxx6e3vOUt6ayzzuqxlAI7AAAAABYU1j3j//5DeuiRPYO95iEb1qfbX/tLtUO7Jz/5yemyyy5LJ554YlpaWkrvec970ite8Yp07LHHpuc+97m9lVNgBwAAAMDg8s66IcO6LL9eft26gV0O58b92Z/9WfrABz6QPv/5zwvsAAAAAGCR/vu//zu9//3vT7t3704vetGLen0tgR0AAAAAVLj11lvTaaedlh566KF02GGHpQ996EPpOc95Tq+vKbADAAAAgAonnXRS+uIXv5h+9KMfpY985CPp/PPPT095ylN6De0EdgAAAABQ4Sd+4ifS05/+9NH/n3rqqenLX/5yuuKKK9L111+f+rK+t2cGAAAAgDVmz549o8tj+2SHHQAAAACUeN3rXpde+tKXpmOPPTb9+Mc/Th/84AdH97S76KKLUp8EdgAAAABQ4r777kvnnXfe6Ovhhx+enva0p6WPf/zj6cwzz0x9EtgBAAAAMLgtmw5Nh2xYnx56ZM9gr3nIhvWj163rox/9aFoEgR0AAAAAgzvhyMek21/7S2n7rgcHe80tmw4dvW50AjsAAAAAFiKHZ6shQBuaT4kFAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAADQu6WlpbTWLXX0HgV2AAAAAPTmsMMOGwVZu3btWnRRepffY36v+T234VNiAQAAAOjNIYccMgqw7r333tH3mzZtSuvWrUtrydLeQDK/x/xe83tuQ2AHAAAAQK+OP/74dNddd6Xvfe97ay6sKxQ76/J7bUtgBwAAAECv1q9fn5761Kemhx56KO3evTutRYd1sLOuILADAAAAYBA50Ooq1FrLfOgEAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAOWnQBOPBs27Yt7dixo9Vz7N69e9//f+UrX0mHHXZY63Jt3rw5bd26tfXzwGqhL0IM+iLEoC9CDPoiLFu3tLS0tPf/YZDB98QTT0wPPPBAJ893dErphJTSnSml79Z4fJqNGzemO+64wyDMAaHLvthlP8z0RQ4k+iKs/r44rY/N2//G6YscSJr0xbL+1fVcOE5fZGh22DGofKYkD77XXXfdaCBu4uB7702HbtuWHty6NT181FHpyE98Im297LK0bs+etLR+fdp26aXpB2eeWfn45L8flwfec845Z1Q+AzAHgnn6YlkfatoPZ9EXOdAssi+aF6F5X6y7Hs3mWZNO0hc50NTti2X9K+tyXTpOX2Qh8g47GMptt92Wd3SOvjby3vcuLa1fn7eDLn995zsf/b74s2HD0tKtt5Y/fvnl+//7/HxdlAtWqcZtfrIP5u/vuadZP8x/P//53OeWv3ZRLljlFtYXy56nTblglavV5uuuR4v5bo416VzlgjWkVpuv6l/r1jWbC4vnmrI2bVQu6JgPnSCm73wnpRtvXP6a/5x/fkp79iz/LH9985sf/b7wyCMp3XRT+ePjfz9/veCC5ecF5uuDuQ/dfHOzfnjNNSkdc0xKp5++/PV97xvu/cBq1UdfvOWW8ucxL0K1JuvRu+5K6c47m61Jx/s6UK7oJ1Xz3uTdvqbNhbmf5rWotSmBCeyIZ3LgzAf5k4Ns/n7duv0f27AhpVNPTWn9RLPO35cN3h/7mEURzNsHcx/KfXCyv1X1w/z4lVcKCCBCX8xzYtnzmBehWUBQtR49/viUTjih/prUCS1oNie+4hXlfa/u8WF+fNMmJ68IT2BH/LOXV19dPvi+4x3LX4vv3/WulJ7//JTe/e79H89/b3KQzt74RosiaNMHTz55ZX+r6odveEP5QUre6WNHAQzXF/Pjp5xiXoSuAoKy9eiTnrT8p86a1AktmG9OHD9ZVfS997yn/ly4c2f1LnRrU4LwoRPEUnX5wIUXLh+o5P8vBtnzzltePOXtzPlMZl4YZfnxM87Y//Gf+qnlxU/+9+P2LooO/uQnh3uPEFVemNxwQ/0+mPtWWX/LJh/Prrpq/+fOi6yXvWw5yMv/nxdTz372gG8YDsC+WDye+5t5EZoFBHmuyiFB/v9Z69E6a9LihNYVV5SHBmefPex7htU0J+bv8+OPf/z+fa/uXJifN/fpWWvT/G9hQQR2xJAHzBzWHX74yoEzL2Ze//rlP5ODb3EGc9Lk48UgnS/3yTsIxj3ySDr0nnv6emewenYRjB+YjJvWB5v0w/GAID9nfq1i152QAIbri5l5EZqfRK4KCKr6XtnP6pzQynJocP/9TmRxYJs1J+Yd5pN9r+5cWOyCnbE2HfXXqv4NPXNJLLEuN3jhC1N65SurLy047bT5B8z87/KZysnLGdavTw8++cnt3weslV0E47rsg/kg5e67ly8zuP760ktkhQQc0Ibqi5PzYskleuZFDnj5JHLVJeht+994H568dLaQ58h8Iuvee+d/HVjFRm2/zpzYRo216ShchwWxw454lxtcd93yZQC7dq3cPdCHyYEZDjRluwiyfNndWWd12weLg5OyyxCEBBzohuyLhfyc+UTZBz7w6GPnnJMePuqo7l8LVtuunvE1YlcBQVVo8JjHLO+qG+dEFgewQ7dtG2ZOnLY2zd/nD6eABRHYdWjPnj1px44do//fuHFjWjd5Vi6oIcv9wx/+sN4963JYl888di2/3mRAt7S0bzGUy7d9+/buX3ffSy2lBx54YPT/mzdvTuvLbvodzGoss77YsC/mT7LLrzV5YNJXQFAjJOizL67GNr1ay60vroK+mA9Qrr12/8euuy4dvDc40BfXRrn1xfp9sXRXT/4d55PJ+eb1fSk+DKbiRJa+uDbKvRr74tBlLvrig1u3lt8qqc8TWJP3d82vna8AG7vPsr64dsq9GgjsOpQHsqOckZ7vcoPJg5Pinh5dqzgYKhZDL37xi/t53TXi3nvvTVu2bEnR6YsNfeYz+3+f+0hfuwhqhgT64nT64hq1iL5YceKsOJGlL06nLx4gu3ry9/lkcp9mnMjSF6fTF9eeUdsv6RO9zol5t+tJJ6X0ghdU3mdZX1wbfXG1EH2y2MsN8hmLoS43mHIw5NIfDlT7dhKM98N8VizfYLdPM0ICONAsrC/edtvKx1yezgFs366ecX2eTJ51Iss97DhAjdp+SZ8Y9ZU+7dzpPsuEYYddh/IW4fFkedMqud59586d6QlPeMLo/7/5zW/2Wu6vfvWro7MSC7ncoLhfXtnB0N5tzZ/97GfTSfmsSk927dqVjjvuuNH/f//730+H5x2GweUyF2cCx9t4ZPpi/b5YupOguMFun2cwZ+x27bMvrsZ+mOmLwzlg+mKeF9/85pWPv+1t+05k6Ysr6Ytruy8uZFdPdvPNU09k6Ysr6Ytrrx+GWaNOuTxdX1wbfXG1ENh1aPx6/jyQrYYBeFLevtpnuR/3uMct7nKDqvvl5UH/iCP2la/PLbx5MCvkAXi1tZHVcJ+NTF+s3xdL7w+SfelL/dxHsuZu1z774mrvh5m+OJw13RerPuRi7MSZvjidvrj2+mLlrp4/+ZP+QoLiQy4mjYUE+uJ0+uLa6Ich1qgzLk/XF9dGX1wtXBLLQowG4MnOnL/v43KDvIMgf1R3PkOxiEscILDR4uPtb1/5g4su6u+Sg2m7XeEANXhfzM95333mRZhQuavnYx/rry9OXnWS5b7pti0cwBayRs1cnk4gAjvi6CONz2csjzkmpdNPX75fXj5bkg9GhrhfHqwWz3veysf6ODgpwvOKy35Gu13hQDZUXyzmxvwhLzk4L0I78yKU38Mue+Mbl/tN7j9dzYf5a9VO1498ZPkG+HAgq5oX822Uij7Upfx8N9zgPsuEIbBjcWcvJ2/mmQfGtgcl4wugyTOW+Wu+pKEY4O++20IIxu/V0efByXh4/vKXrwzo7eqB/vritLmxmIvzAYp5EZZ39bz73Y+e4B2399MiW61Vx+fD/DVf3le20/Xkk+d/DVjL82JeQ+a1ZNGHulinjvfNN71p5c98GBMLIrAj9tnL8YOMcWWPTy6ArrmmfBdPvjdAvu+BHQSwLPeFOgcnZf2uTh+tCgjsdoXu+mKbuTF///jH64NQyMF1DrCvumrlz4od4XXnxFknky++OKV3vMOcCHXmxeL4cbwPjYfoTebH8ce/+MXyS9PH+qTL01kEgR1xz15eccX+BxlFiDd58JG/L1sA5UWWe/NANwcn+SB/st+V9cW6AUEO7T78YbtdoYu+mJkbofug4Oyzy/tL3hVXZ06sezI5X/aX+705Earnxdw/8tpx8iqtIkSvuy4te/wFLygP666+Wp9koQR2xD0oefObV549mTz7UTxedk+s/H3eseeMJbQ7OMnf53463u9yPyzri2V9tCogyJf72O0K7fqiuRGG292Tv77tbSvXqGVzYtlj0wLz/FrmRChX9I9TTinvQ/nTVOuuS8senwwBi+c96yx9koUS2BH3oKTsDORNN5U/nu9lUDZ4v/71zlhC24OTfHBfdtBft48KCKC/vmhuhOF29+SveTdcnTmx6jHzIXQ7N+Y+tHNns/mx7PHMhzARzEGLLgDsG3jz2Y48gBZnL/NHdo8PpPnxU09dGeYVO3Umn2N8kDXYQrODkzPOWL68oLhMbnxXT9k9RGb10RwQ5D/Fc+qT0E1fNDdC/3JfGe8vk/2tbE6smifNh9Dt3Jj7UL4FRNk8WDU/Vj2eP5ww3+9c3yQIgR0Lcccdd+z/wLOfnQ7+5CdHH5edP4En3+PuyEsvTVsvuyyt27MnLa1fn7Zdckn6wYYN5Y9v3176HOnLX56vPHCAmNr2jzgipdy3UlrZ7y69dPR4oz46/pzF903KA2vY3H2xp7lRX+RAVaft15oTq+bJmvNhk/LAWjSz7U/0oUbz45THq/qmvsgirFtaKrtgm3ns2rUrHX744aP/37lzZ9qUr6VfBYYs97Zt29KJJ56YHnjggVp//+iUUt5TcFdK6bs1Hm9j48aNo4F4a/4E256sxjaizMOJ3Ber+l0ffbTvvqh9DGc1lnm19sVpj89LX1w75V6NZY7eF+vOiV30S31x7ZRbmfuZF+edH5v2T31xbZV7NbDDjkHlwS0Pcjt27Gj1PLt3706n5q3MKd+C4KZ02GGHtS7b5s2bew3rIBJ9EdZmX8wHHPoiNGdehLXbFz+qL7JKCewYXB7k2g50OcUvPOtZz5Liwxz0RYhBX4QY9EWIQV+EZT4lFgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABHLQogsQxbZt29KOHTtaPcfu3bv3/f9XvvKVdNhhh7Uu1+bNm9PWrVvXVLlXY5lXa7mVWftYa3UdtcyrtdxrrcyrtdzKrH2stbruosyrtdzKvExfLKfMy7SPcuq6frkPGEssffvb317auHHjUq6OLv4cndLSaXu/Tnuszp9crly+PstdVbZ5yzyt3F2UebJcXdX3EHXdRzvps667bs9DlbmPsvfdPtrWaaT20fb9dF3mpmXsuo30MX501b6HHKv7bh9ty92kPiOO1V3U7dBl7qPOrZvqlSfq+NFH3S66rrv4E6Wum7zPPvtiX/NLX3XddxsZusxdvJehjru6rveh+uIQY9485e67rHXLfSCxwy6lUaL8wAMPpOuuuy6deOKJrZ7ryE98Im297LK0bs+etLR+fdp26aWjxycf+8GZZ6aD7703HbptW3pw69b08FFHrXiuO+64I51zzjmj8pWly/OWe/x1j7jlltKylb2P/Hgd08rdtq4ny/XDl7wkPe5Tn6pV35PvfbzO+6rrqtes207uP/nkudtI0zK3KWfdttF1mVdjX6xbz03qtMoi2ke08aNpGectex/telq77KJvdl3mOnNLVdlnjdF1yjxvuavKlDVpI9PKvshxr27dTuqqzFWv20WdNylz03Kv5nFvWjmrfjb0+NFl3TZp20PXddPy9VXmyTLMU9dN2nif66auyti03H2UuW3b6LLMk/oY6+qUed5yN11fz1P3Q417WVfHBV3WdZfHVm3LfUBZdGIYwW233TZKcfPX2u65Z2npc59b/jr+2Pr1S0u5Wos/GzYsLa1bt/Kxyy9/9O/mr+99b+NyzVXu/Drjr1tWtltvLX8f+f2Vve8G5ZqrzNPqd/JPVX3nfzv53sfqvJe6zspes247Gf/9zNFGGpW5TTmL+s2Gbh9Vrxe1Lzap5wZ1WmUh7aNq/MiP13gfrcs8Xl/zlHHa76LPck+aMl7N1Tf7LnOduWXa72TGGF23XI3bSNP6rGojM8aRzttHkzVIVd3OGFs6KXPV77SLOi8pd6dzeRfj3pDrpjpj37SfDTl+dFW3Vc81xaB1Pa18Deb21mWeLMM73zlfO24wt/e2bqoqe512P8QapEmZa6w/6+i8TRffz9MfO6jnRuWed33dcNwYfNxreazVpFyVP2+zVprWtjtqIwcSHzoxj/e9L6Vjjknp9NOXv+bvszvvTGnPnv3/7iOPLDffycfe/OZH/27+esEFKd1wQ0rf+U63Zc3Pd+ONy1/zn/PP3/91y8p2003l7+Oaa8rf91DK6ndSVX3fcsvK957rvOv6HldW3/k1b765XjsZ//0U//aLX3z099lVGXO7+53fmb+c+bG77qruF32Z9npt+2KX7aLog/l316Q9LKJO644b08pdNX684AX9v4/J+spjVpMyVj2efxdD1Xfx/bTxqkn7zmNfl2NGm7kl12NV2Ycao8v6VJP6rGojfY4jk+2j6RqkrG7z9/mMdV99ctrckn/WRZ1/7GP9jNVdjntDrZvqjn3T+uBQ40dXdZvfS9Vz9bFWKp6vSV1Xle+d7xxubi8rw/h4Vbeuq9pNn3N7k7JPK+MQ/XDaWq+qzH2vP9vMKVV1Oa2NDLlmbTNe11lvTNbRoo8Jh67jtmulqra9qOOaVU5g19VBd/75CSektH6iSjdsSGnduv0fy3+nrHG/7GXdNt46i4pJubynnlr+Pq68cvjAa3ywLKvfSWX1nR/L9T30gXjVwJbLV6edTOp6YVS0j9zuytpj3XLmxzZt6v9gu87kPK2tNOmLXR0IjvfBF76wWXuoqtOuD0SmlXnawUhVucvGj2wyfO4jQJqsr6uvLm+vVWNc1ePHH//oa3Rd900WRcV41aR95/7d9cJo3rkl12NV2YcYo6vGjcMPrz/WlbWRqnEkHxR0XddXXNF8DVJWt2UnhLpq17Pmlvw7rduGp40pb3xjd+26j3FvqHVTk7FvWh8cYvwogtwu6ja/lyFCpMm2MRlCT6vrqvINEdQU81VZIJC/bzI3VrWbrOtxZHyeLau/qrJPa9t998NZa72yMlfNG0XQ29c6r+6cUjUvVrWRIY4DuhqvZ603ugyVpvXDiMdaXayVpq2Jht48s0YI7Lo66M6d/ElPSund715uvFn++q53pfSe9+z/2DveUR08dTnh1VnA5XIUZSnK+/znr3wfb3jDsLs3ygbLsvo999zZ9Z0fO+WU8oOsPPh1aXySrVo4nHxyvXZSFuBNLIzy/QE6aR+TmpQzP7Zz5/QzzG3bSJMz2lnbvtjFgWBZH2xSz1V1OvTZ7KqDkapyT44fZfXbZduYtRh605tml3HW47k99XFWsOmiqAgO67bv4gKFKHNLLndV2cvG6GKB2tUcU3XwvGtX/bGurI1UjSM57Oh6DJm2u6RJ3U7q6kRFnbklt+O6bbiszsd10a7zSZC6IUzdcW/WuqlLVe26bOyb1gf7Hj+KMTSXa1LTui3eS98hUt3dw1V1XVa+IYKa8fnqFa8ob8t53GpS15Ptpmpub9O+J+fZ224rnxfKyl7Vtvs+fqm71pssc9m8kR/P41FfO5CazClV82JVG5l2HBBtnVq13shz06wNAV33wy6Otbo+Fm+7Vqpq24vYPLNWLPqa3AimXiNd975pk/eRufHG6Y/la7nzv6t6zhtvbHd9f742vOx5L7zw0dfNX4vr5yfLO1nmsnqouMdaJ/egmnV/hPHy1qnvqjrfW+5O7qVQdm+C8dcs6ntaGccfG/+3FW3w3971rmZ1Xdw34KMfnd6em5Rz2u+s5B4GjdtHm/vttO2Le59zrvZR1QeL9zKrnucZe+qUa9rPqtpF2bgxrY7HH294z69GZW5y37RZY9y0x6vGv/ze5q3raW0kv/a0sWNa+YvHqn6XV1217+/P1RevvLL93FJV9sn3fO65jcaPqe8pv06uk1n3hakzlzQZR2qMIY3bx6yxr07dTj7HxJw4tVxlP5tnbpmnznP7reoz89yrZ3wM6XLcq1o3Fb+/Lu5xWNT5rPuONemDfY0f0+azeep2XI210tzlruqDTep6su/NcT+z1nWdvy9bezSt6/GflbW7sfbduC9OW0eW9cO6bbvB8Uvjus6q+kqd+q7bNlrc62u/n80zp7RZJ025f+Dcc3mT49uqspbVffH3pz1/k7pu0g+rylh3LdrifuelP899u+1aqax+q9pI7kMlbdw97B4lsJs32KjqcE3k17jhhvIPG7j11vkGs7YLuGlmLfjbHJiMD+bTDmLbqhiEvvqpT7UL7GZNVPPUd43QY1q55wo3cnucp5wNFh6Nytw0IJjXjAPBuRaeVeFA/l3WbQ91DkSKftFwQVT7wLWLdtxgUdrbYqiNaYvceU9UzBsg1TXtAHme8HxasNFFG+lgzCst92TZc30Xdd5lG8mq1gszxpDKn1UtmqsOYGcZ//20PFGx38/6nlsm38OUBX+jsXpWiNRFm54R5JaWa1qZx593vM5zuN3VfNjH+FE1hl59dXftYlqING+5y/rgPPNMy6Cmk7rOfbDt+NygfTcaq6et9Yoyd9UPZ5zw7WzdVHetN/7eph3/lKzxKss1rZ67nFNm1XOxcaHhydm5101N20lV8FT2O82PNZkX++qHDdry1Lqs+nlZm563TcwKSmeEjQK7Rx206B1+IeUtrMV22LylM29hzc1pfFtn3n6at4YWl3bMI/+7s89O6f77l7fb5m2hWX7dF74wHbn3o5LnLvcrX5nSddctP+/4lvHitZs677yUzjhjeevq9u3Ll/uUbWs94oh25S620o5vmx2/HKyNvJ14/He5t9yH3nPPfM9X3GPjvvuqt/medlq7NlL827wNuWgne3+ftT8mu2yLd9G28/8X7SO3x3mNt48p926pXdfj7WJSLu/rX7/8p3i9eet4vC9eeGF5u8vtva7xcuf6LcaP8csJ5qnTfGng5CX5Rfkm+1BuK/nftr2MLT9X23Fj8n1Mu69Pk/Gj6t42+f5Ij398+zYxrrisafL18u8198n83pqY1UbG67vN2DE+Zozbe4nHwZ/8ZPvLHLuYWybLnf/kyzvajB9VZS/q+cMfXr4Upas2khWX2bQdQ8bbyOTaI9d17k/58pqmY994eyr6ZL4MNt8CYM6+OLotQ99zy7R2XdR3Xo+sX99s3VT1QVZdjXtFPT/mMeXrpVz3uV6aPn/ZfJ7Xel2sS/sYP7J8mX/ZWvqss7rpg1VrpTblntYHx+ezOuWfHMvrzon5d9q07+T5qqyuux7vZrTvumP1kZ/4REqXXVY9vxR13LYfzjp+aVrXs9ZNddd6k++tbA7Jl8n+z/85/xqv8JnP7P99bidt5pRJk+06y5eEjo9V86yZmq6b6iprV/n7PCfme/uNy6/bZI2aL+ee1EU/rHssPsdrrJjPs9zect9ocuwyrX6L8ufnHL/P7Xjb6HqcWgPcw67OQmj8hpDjB91tgpjJxpsb7vi17Xv2pK2XXZaOblLuyXuwFAu4fOBz993zDe6T8vvN77vqnjh5Umlb3xddlNLb315+f4q2Ku4r9+CTn9zPvQm6CBkLeRC7/vrlQKLp77Mq3PjIR/ppH8W9W0rua1WnrksnjcLk/Uq66odV9z5pYEW580SU29s8v7PxcuX3WHXfkKzt/TaqDlxz++iiXZS1jWn3aKujrH0Vi6Gu2sRk25h2P742AVLbNlIlP1d+zquuKi137fCrqn3k+8Z0XeZCi/FjP1UHwjnU7XpRWNZO8mtPHiTNcyBYLJqLuu5i7CtOVLSYyw/dtm2YuaWsXec+U+wBmGfdVNHG0he+0G2Zq9ZL894rddr9hboc+7oaP/L7yyebyoKvPg7MOij31APX/Pxd9b9pc+K898GsCmX6OgiuuB9YnbE699XcZ2udDOr7+KVpXfexbipbh+bjoXxc1GaNN96mx/throciPOtqPT3+PLM+QCviuinLmwFaHNON6jrfG3DS297W3fFKVVtuce/fyvk8zy1dyuXfvLl0A4372ZUT2C0q2Jj0rW+taLjr9uxJtQ9f8034yxp+1wu4Qn6+vBNu0sUXN/sghKrBPIcTua67rvO8kJmcrJrsVGsa7HZV7+Ofuvfylzc/AJz2ARh9tI8qk220yaTRd0AwvtAvdtrlM2zHHLN8BnjecncZDhTlG+8XXSyIpp0J7EMXwcbQByW5rvOBfNtgfsgAaTyUaRN+VYWjXe2O6Xj8qFX2Lk+mjMsHP+Pl3LsLs9G8WPWpjl0vmjuYyx/curX8pvp9zy0VC/5G66ayMSSPS/PsJGhyEF6266tJ+yh2qw3RpqtC3ZqBzMzgqy8tyz3YgeusNtJ0/CjWplWhzIDz+RE1PnzihL19dtC1Xld1Pe2kYZfrvOc+t5PQq3J92mdAUvWhK00/8G8R66b8QQpzrlErj2H6nlvy13POWT5BMseHlozm86Hmlqrj0rxr0KfGriCwa3Lg2tfiM3emvENrwtL69anOMDqaXMrOJOaO0NdBSZYnkUlNL1matvDscvfU+C7EyYO+eRYyQwa7Zbsnm55dy3WYL5Eelwf1Pg+2cx1N1vXSUq32UTpp5O+HCghyf5pj18bGO+5Y+WDXk91kv2i5W633M4E9BBszzxT3JS+2xj8NK79mPvvdpJ6GDpC6CL+GDkdbjh8LLXtZuZvuRipZD/TaRlrM5aOTXfmAYbxN5/ff9KTSPErWD43WTUOOIat1t9qU9UOdE52DB18dnaAtDaL7HqdzG8lXUXS9G3qIXSsl83mdddOde/vs4CeDuqjrPueWWbsv5wi9FtKmy8LR4rZPNU+ETz0277PsLdaolSeymgaVTQPeHJJfe+2K48W6IfSKkL3P9dJk2xi/vUXXn468BgjsFn3gOuUeCNsuvTR9t02Sny+36HPCqxhAa+/aGHrhWbYLsbgnQdPfWb5n3VCX41Xtnmx6GV4exMflS6b7PItREUzMdflxNvlcfSlZ8NbZtZEXpkf/+Z8PH3y13K1WOX78zM+kXrUINhZypnh8cZR/p8U9uvKc0WRhscrCr4WFo13cvmARu00qTkK1uhXA5D3Voo3VuT4nA7s5LtlqpGL90Grd1PcYkn9/k/dVWw271SrWD+EOXGf1/axm3x+FekOf5MzyJW597Ibu+4RQyRxTZ92U+2rus/sdtDe8Fcki6nrQeXFK6NVk7bGwNt3ytk8LO6ncYo2670RWy99Z44A336d9znv/7rs8fcj10pTbWzTeeb7GCewWsYW15j0QfnDmmbWeonRBlDW9zLPpYqhiAK1z9nLwhWcubwe7EEdnhIpLU7NiAuoraOyo3As561oRTNQ+K99FuDrgro3KSzv6HD/6OhOY5Uuv+zzD1SIkqDwIHGKXWu6T+X4ycywsShf5uQ76Dr9ahEiVwUa+YX6fgUyLXT1TLy3tc9ybchJq7t1IXd9LsuOxetD5Jbe3vIsg31+vYv3Qat3Ud7DRct20sN1qLT5AavAD11lXQdRsk6OxeuiTnKtxN3TRJ/McM9Gf6u52HfXZ4lYk2d5bkSxkh02b27YUH1wxx73D5g29mpwYKW3T+fum9x4f+LZPCzk2b7lu2u93Nnkyvc+TWWXr6nXrapW59BimaNN9mnI/u7k/EHINEtgt8iCw2KlVdW+xmkYLonxZ1qR8UNnXoFB2INRgAB104ZnrIKf3LXchrrg5bp83PS0WQVX13HT35JBnXYv6bnH2cSEHUy13bZRe2jHUToLi7FSbA6rJsve9sGgREiz0MrwWB6+V417eRduXliFSZaA77w3zB9jVs+/kypCXltb5oIh51yB97nbtYhfiEJcrjX/I0wte0Hr9MPhuk2nrkJrrpoXNi6vtwHXaVRA162rwHZjja77Vsht6vE/mOSb3p7F7adXd7bpPPjnd8oMVBr1ty2RfzO0tn+ic495hteTdUy2utqlcf/Qdnre87dNCTtC2XDftU7HjrfcQbI6rk0qPYYY4ed/HFVlrkMBuUQeB4x8iUAQ/bXZqPe95Kx/ra3GRy5478KQGi6HKe5R1PQAX9fymN5WXN38SUE2VZx+yLhf544ugXM9li84G5R70rOt4u26x2F3I1v0pB9x1dm2suLRjiMXQjPbd6IDqwx9e+XhfO6m6CAnKLsPLz5kPivtc5Lc4eC0d94obbPd14NoyRCrdIVPo86CqYndco0s7hrq0tCqMmSdEGno3UsvdcYNcrlT2CYGTGgZXg+6gmrUOqVnuhcyLq+3AdXwN0uIqiMqQoI8boo+v+XKw0fVu6D52fZV98FruP8Vr3X137d2uU8ehvoKNKaFunbpeMVYXbaXPwLHqk4S/9KVa/7zy5Fvf4XnL2z4Nemw+vuGgiyvAqn5nfYRgVSfv9+yptW7adwwz9Mn7Lnb5HwAEdnUPArvcMly1+GyzU6vqpqRdLy6q7gvSxSWhXd+jrGqimLO8g5x9qGob458A1LDcg511nVbf83wQwlAHU9N2MzY84B4tUMt2EnQ9htRs340mu7KPh+9rJ1UXlypWXfLU5w1rWx68jv5O2UF71wcmHbbpOjfM7zzUnXJSaO5LO/q6tLSjMGYhu5Gm7fJvsxMpy7sCuwoJqm4d0uJE52A7qDpchww2L9a49DjkgWvZ+mnOqyBWBDJ5fZqfr6v5paqO89f8WhNte+7d0H3t+qoK2PLcMs99nIcMNmaEurXXTeM3+88nPMt2v3U5N+Y6rbiaqs7O88qrKfoo66xdxQ1u+zT1Pqm5/3Q1z0zbcDDvFWBl95gunq/L8nd08n70O6k6eZ/nxmLc6rKdFGPg0PdJXmUEdnUPAtue4R5v5FWv0ebjqSdvStrV4mKyc1YtnHMHb7AYqrxHWZuD1rplnfPj4qeefWgTyMxqG7mecv3O+emzvZ11rVvfcxyYDHamuM5uxqYH3GU7CfrYJVN1ufSc7bv0xsZ9BAYd7NCdusCfLG8XC4wuz7rm3bF9Hpj00aaL9nH22f2HujNOCtU5mKr85MEuQqTx9tTxSaFau5G6GrM72uXfa0gw5f5Yo3J+4Qtzz4tTLy9tO2bMmtPnHKcHuZ9kx5ce937gOquuc/3Mu7YevyF6saYeL/O8dV6njvPJhYZtu/aurzYhwaw+Oe9VMrPaR1ftu8NQd1+5c0A51AnP/FyTmtzrK7+/PG72XdZZJ7Ia3PZppKpv53V1F2F0hxsOGl3B0kX5hzh5n58n7+Qsxq0u20nZGDjUh8itIgK7ugeBk5NGkwXd+OScv+ZG38d9SGYtLppO0pPlzt9XfHJf08G30Y3u69R1k7K2+Lj4yrMP44FMH22jxafPtj6gKns/det7zoVQ7TI3PbCadrDdwW7GTsaQuu29KvRq0b5n7qSaJzAoq/MuduhOCxiL8uZ7w5UtMObpo12dde3zjGtfbXqeUHfevlkVRDc4KbTi8vT8NV86WLY4bjNe5/bVZWg+bQxpG4KNlz2Pr7/zO53s8u/k0rBZc0zJ/bFG7Tjf+23OeXFFuYvnzJfmtDkoqTunzzFON7qf5DyhYw+XHrc6cJ31HoZYWxc3RG8TlNZZd3S15quz66tOSDBvn2xzlc209tFk7VFW9mnzS5tQd94TnnX75+RJlhY7z/fJ42adss47hpSd3Bwra+fr6uJr01ui1Dmx0qbM46oC3bJ5smnb6Pvkff6ab3GRb33RdfhfdjxQvOYQHyK3miyxdNttt+XWMvo68t73Li2tX198wPD+f268cf+f56/5+8I99ywtfe5zy1+L7yefa8OGpaV3vnP5a/H9+HNUlavuz/Prl5W9qsxNyp0fz/92StmnlXu/n1XVc/E6k7+L8XKPl7dFWeeq67LXK/6sWzdI25irrovnzK+Zy1lWX5NlLKv/lvXdWZmb1HXZe7nwwvLf4Q03LPfzsX/buC/OO4bM297b9sVJZa8xrb67qPN5y5xfL//7yfLm76v6V933Ma2vT4xVjcv90Y92M06Pf1819pe06bnKPF6Oq67qZo7Mxv9+bmdV49M8Y3Uuz623Nm8LdcbrqjY2Uc9z1fW8Y/Y87Xj8dzdvmYu6rmrXxXO3mWPy77GkHdct89RyT1tL5Nctq+M69dzBeq9yXqwaj+rMI2Xfz1o7TpR9rvGjTluc1jeLMk/r013VdZ0yzxo36s6BFXU8SF1Ptoeyup+jT851DNN07VFnPGkwv8xd13XnxqoyznovZeUeay9zt4+qsuZ22ma9V/bn6qvbr6vH58Sq16lT3snfweWXl8/vee3U1bqpTvmn1fv4e+iwTVf+fHxubJor1FmXTHvOvc81s78dQAR2VQ2iajFQ9XjVIqmqQRadYMric+7Arukk3bTcxWvMMUmv+Nm0xf20xW+dBVCNss5d17Mmpp7bRi8HVJMTRdkElt9PnQOyOdt14zLXreseD7Y7G0Pqhgo1Qq+56npS3cCg+Lsd1Xkni6H8ddaBUZ33UfUcXRy8th2nzz23WZheovWBSRdzZNUBWsVB91x9cdZis814ndtIy5NCU+fyJmN2VfA/rR1PaSedhQRV9TrvHDPF3OumrOlBSd16brneK50Xqw60y05elM0jTcaPilCm1wPXWe+hLLzoo67LylzVd+qGAHOE0b3W9eRJlrL3N0efnLsv1l17zFvfU+aXVnVdZwyse1xT9l6q+nvXJ2dnrdPqzuMt5pfKn087CVe3vFXzTs3NEq3XTVXln1bvswK6Fm26drnrrlfrbkio6q+5buqW6wDikti6W4aLLbHT7i0zeePYvF102n0einsfdP0JX5PbWatuMFrcQLJpuYvX6KLsVdfK59epumfB5LbcfPneEGWtex+IqG1j2r028ve5HsfLeNFF5fU/fkPkyXKPv04XZZ91f5C6dV118/18KVGXl3S0GUNuuqlde296T5AuLq2ZNoYMXefj5c1fy+4Vl7+vuldG2fsoq+s297vpcpz+wAdW1nlW1ub6+BTJsssm5pkjy9pJy/t2rlB1uX4Xc3luZ+PtrssPtWg6ZheXqNRpx3kc76OdVLWLbLJc884xfalz2dX4ZUt16rmPOb3qfpL5tXLfqTOPNBk/Wlx6PHOsLltHVfXN8fcwOR/1vX6qc7uIOu27ag7suo7r1nV+/U2bpt+/a+g+WWft0aS++55f6o6B+fG6xzVl72VSF+u+srLm9tlknVQ1j4+Xs8v5JfeTaZf1zipv1bzzvOf1N5fPKv+0ep9s60W8tcg2XbVerVo/la1LLr44pXe8Y/86yK+R64YVBHZNDgDz91X3RataJOX7Gw11ANV0kp4Wig1V7mkTW92DrL6DlyqTA24uW9kN3qO1jboTdP6+Khgautxlk0WTuq5abPZ5sN10DDn11HjtfVpgMGsMWUSdjx+olbXz/AlrTd5HWV3n58wHy12HjE3H6aoFalmbizxHVrWTFvftXKFJW5hnvO7r5Nu08lct7suC/7J2/J739NdOytpFVf+KMsc0DdHrjhdDB+ZV4W608WPagWtZ36x6Dy0+Kbj3oLSqffe97mhS11UnWRbdJ2etPerWd/6+7/mlTFU/anJcM9RJljonO+dZ73V1cnNWufNrNF3XDb2Rpk29l7X1SUO36ar1atX6qWpDwhAh6Rpx0KILEMkdd9xR/oMjjkhp+/blPymlIy+9NG297LK0bs+e0SfRbbvkknT/Yx6TnrF+/eixQv7Z7bt3p4ef/ex08Cc/OfoUn3xj0NHNjr/85fnL0+Tv5bJXlPkH27eng//rvxZS7v3+zpTXmSz3d3/v99LRf/7nK8ubb4J7+umNy9qkzKV/b6LsR9xyyyBto0m5S//OxOtmz7jqqhVlHK/v8XYz7Xc2VJkb1fXhh6cjKvpAWR+fp8xtx5AfbNjQeXufu65LNB5D5qzzLstc1k4bv48h6nqecTqv38eeYt+YUpRtRptuXeau5shp7aSk7F2N1VVtoa/xunVd1xyzv755c3pa3Xac63dKO2ld5rHnrupfXc8xrcfqsdd95LDD0tNe/erStpC1GS/mKXedNp3rutY8Emn8qNE3q+bCr7///WnD7t391/WYJuNGZfuuWcddlXlaXR98772V80ubPtnJMUzb+s4fPpRS7fml8zVIye+4btsuey/3n3xyaf13UuairF2t9y69NP3gKU9pNb/U+ntPeUrj8lb2yyH7Ys16L23rOSBdt67TNl37701Zr1b1y8p1SZ5zpqxDGtXjWrfoa3Ij+Pa3v720cePG0XXSdf8cndLSL+z9Wjz2v1JaenjvZtWH936fWv7J5crl66LcZWUeutxd1HUf5e2yrodqG13XdVUZq9pNhDI3res276XLvjitLF239z77Yl913mWZu3ofQ5W7btn+sqMxpa+67rNv9j0vDj3HzFvXVeXssvxdlnmoOabL9jGtLrtuJ0Oum6KPH3XewyLbdZNxo6v23WddT5b9TT2XuWm529Z309/B0GuQLtpO12UeYr23yGPcPo4H+hj3qt7DIuu6aVlntZt5y30gWZf/s+jQMIJt27alHTt2tH6efKZqv7MfLW3evDlt3bp1TZW7izJ3Xd4h6rqPMndd132VcZL2Mdz76buuuyjjosq82up6smxd1PnQdd13mVfrGDJvmavK2VX5uy7zEHNM1+1jWplX07hn/Oi/3H22b+um1bmuHuq9DFnmIfrhgXyMO3T7GOp4YJ73MKvcBwqBHQAAAAAE4kMnAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAAAEIrADAAAAgEAEdgAAAAAQiMAOAAAAAAIR2AEAAABAIAI7AAAAAAhEYAcAAAAAgQjsAAAAACAQgR0AAAAABCKwAwAAAIBABHYAAAAAEIjADgAAAAACEdgBAAAAQCACOwAAAAAIRGAHAAAAAIEI7AAAAAAgEIEdAAAAAAQisAMAAACAQAR2AAAAABCIwA4AAAAAAhHYAQAAAEAgAjsAAAAACERgBwAAAACBCOwAAAAAIBCBHQAAAACkOP5/WN64ACw2vPUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots(figsize=(10,8))\n",
-    "plot_tree_2d_scatter(tree,'value',selector=lambda z: z.reshape((n,d)),ax=ax)\n",
-    "plt.gca().set_title('Sampled tree without leaf noise')\n",
-    "\n",
-    "plot_tree_2d_shape(tree,'value')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backwards filtering, forward guiding\n",
-    "\n",
-    "We now define the backwards filter through the up function. The Gaussian are parametrized in the $(c,F,H)$ format make the fuse just a sum of the results of the up operation. See https://arxiv.org/abs/2203.04155 for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# backwards filter\n",
-    "up = Gaussian_up(n,a,d=d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize the tree for up by computing the $c,F,H$-values at the leaves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# initialize tree\n",
-    "init_up = get_init_up(n,tree,d=d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now define the conditional downwards pass, i.e. the forwards guiding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# conditional down\n",
-    "down_conditional = Gaussian_down_conditional(n,a,d=d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We make an upwards pass and a downwards conditional sampling to test. Subsequently, we time the three operations (uncondtional down, conditional down, and up)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time elapsed: 0.1428 ms\n",
-      "Time elapsed: 0.7710 ms\n",
-      "Time elapsed: 0.5863 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "# backwards filter and fowards guiding\n",
-    "test_up_down(down_unconditional,down_conditional,up,init_up,tree,params,leaf_values,key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MCMC run incorporating the likelihood ratios arising from the Gaussian approximation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial parameters:  {'k_alpha': 0.1, 'k_sigma': 0.1, 'obs_var': 0.001}\n",
-      "data parameters:  {'k_alpha': 0.1, 'k_sigma': 0.5, 'obs_var': 0.001}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 2000/2000 [00:24<00:00, 81.74it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Acceptance rate: 0.8460\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "log_likelihood = get_log_likelihood(tree,down_conditional,up,init_up)\n",
-    "log_posterior = get_log_posterior(log_likelihood)\n",
-    "proposal = get_proposal(tree)\n",
-    "\n",
-    "# tree values and parameters\n",
-    "init_params = ParameterStore({\n",
-    "    'k_alpha': VarianceParameter(.1,alpha=3,beta=.5), # kernel amplitude, governs global tree variance\n",
-    "    'k_sigma': VarianceParameter(.1), # kernel width, for Gaussian kernels this is proportional to the variance\n",
-    "    'obs_var': VarianceParameter(1e-3,min=1e-4,max=.1,prior='uniform') # observation noise variance\n",
-    "    })\n",
-    "print(\"Initial parameters: \",init_params.values())\n",
-    "print(\"data parameters: \",params.values())\n",
-    "\n",
-    "# initial state\n",
-    "init_state = (init_params,(jnp.zeros_like(tree.data['noise']),tree.data['v_T']))\n",
-    "\n",
-    "# Run Metropolis-Hasting2\n",
-    "subkey, key = split(key)\n",
-    "log_likelihoods, samples = metropolis_hastings(log_posterior, proposal, leaf_values, init_state, 2000, burn_in=000, rng_key=key, savef=lambda state: state[0])\n",
-    "\n",
-    "# plot\n",
-    "plt.plot(log_likelihoods)\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.title('Log likelihood')\n",
-    "trace_plots(samples,params.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/mean_estimation.ipynb b/examples/mean_estimation.ipynb
deleted file mode 100644
index 59df3a0..0000000
--- a/examples/mean_estimation.ipynb
+++ /dev/null
@@ -1,667 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import hyperiax\n",
-    "from jax.random import PRNGKey, split\n",
-    "import jax\n",
-    "from jax import numpy as jnp\n",
-    "from hyperiax.execution import OrderedExecutor\n",
-    "from hyperiax.models import UpLambdaReducer, DownLambda, UpLambda\n",
-    "from hyperiax.models.functional import pass_up\n",
-    "from hyperiax.tree.topology import symmetric_topology\n",
-    "from hyperiax.tree import HypTree\n",
-    "from hyperiax.plotting import plot_tree_text, plot_tree_2d_scatter\n",
-    "from matplotlib import pyplot as plt\n",
-    "import jax"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = PRNGKey(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Estimation of inner nodes of an evolutionary tree\n",
-    "\n",
-    "In this notebook we illustrate how to use Hyperiax for estimating inner nodes of a phylogenetic tree, given \n",
-    "\n",
-    "- observations of the leaf-nodes and\n",
-    "- knowledge of the tree topology (number of nodes, the edges connecting them and edge lengths).\n",
-    "\n",
-    "This is a basic problem in the field of Phylogenetic Comparative Methods.\n",
-    "We will be particularly interested in the root-node estimate, which can be interpreted as the phylogenetic mean of the observations. \n",
-    "We will use a very simple estimator of the inner nodes; any inner node will be estimated as the weighted mean of its children. I.e. $x = \\sum_{i=1}^k c_i \\cdot 1/w_i$, where $w_i$ is the length of the edge between the inner node $x$ and its child $c_i$. We assume that all nodes are 2-dimensional vectors, which allows for nice visualizations.\n",
-    "\n",
-    "Furthermore we show how to use Hyperiax for simulating trees (i.e. inner nodes and leaf nodes) based on the Brownian motion model; an edge of length T corresponds to a Brownian motion run for a duration of T. This allows us to evaluate our estimates of inner nodes since we know the true values in this case.\n",
-    "\n",
-    "**Content of the notebook**\n",
-    "\n",
-    "1. we create the tree with a chosen topology\n",
-    "2. we simulate node values, based on the Brownian motion model\n",
-    "3. we estimate inner nodes based on a simulated tree\n",
-    "4. we show how to compute the estimations more conveniently using 'prebuilt' functions\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Creating the Tree\n",
-    "\n",
-    "First, we initialize a tree. This creates a tree with a chosen topology. This topology is a \"stupid\" class, in the sense that it contains no data - and only serves as a representation of the data we intend to work on.\n",
-    "\n",
-    "Setting `height=3` and `degree=2`, gives us a tree with 4 layers, where each node has `2` children"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       o\n",
-      "   ┌───┴───┐\n",
-      "   o       o   \n",
-      " ┌─┴─┐   ┌─┴─┐ \n",
-      " o   o   o   o \n",
-      "┌┴┐ ┌┴┐ ┌┴┐ ┌┴┐\n",
-      "o o o o o o o o\n"
-     ]
-    }
-   ],
-   "source": [
-    "topology = symmetric_topology(height=3, degree=2)\n",
-    "plot_tree_text(topology)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hyperiax allows for fast computation using any backend supported by `JAX`. That means that if your operations are suitable, hyperiax will execute your code on the GPU automatically. To do this, hyperiax need to calcuate a series of offsets, and put your data on the correct format. All of this functionality is done with the `HypTree` class, we simply construct a tree from the topology.\n",
-    "\n",
-    "Next, we need to specify which kind of data we intend to store in the tree. In this example we store 3 different values in each node, where the `noise` and `value` each are $2$ dimensional vectors, and `edge_length` is some scalar that represents the phylogenetic time between a node and its parent.\n",
-    "\n",
-    "Under the hood, hyperiax will initialize buffers for all 3 values, stored in `tree.data` in a BFS order. All values are initialized to $0$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree = HypTree(topology)\n",
-    "\n",
-    "tree.add_property('noise', shape=(2,))\n",
-    "tree.add_property('edge_length', shape=(1,))\n",
-    "tree.add_property('value', shape=(2,))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets say we now want to set the `edge_length` to be a decaying value as a function of the depth in the tree. Lets for the sake of example pick $l=exp(-ln(2)*d)$ where `d` is the depth of the node and `l` is the resulting `edge_length`. Each tree calculated the depth of each node, so we can use that here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key, subkey = split(key)\n",
-    "tree.data['edge_length'] = jnp.exp(-jnp.log(2)*tree.node_depths)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is an example of how we might use the underlying buffers to directly sample values for the entire tree. This is vastly more efficient than sample for one node at a time, and should be the preferred method.\n",
-    "\n",
-    "Now, we can do the same for the 2 dimensional values we intent to store in `noise`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key, subkey = split(key)\n",
-    "tree.data['noise'] = jax.random.normal(subkey, shape=tree.data['noise'].shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Simulating node values\n",
-    "\n",
-    "Now we define a down function to correlate the nodes. Notice that since the `noise` in each node was sampled independently, they are currently just all iid. In this example, we define a down function which ensures that each child node of an inner node $x$ is a sample from a (standard) Brownian motion starting at $x$ and stopped at time $T$, where $T$ equals the length of the edge connecting $x$ and the child $c$. Such a sample can be obtained by computing $\\sqrt{T} \\cdot N + x$, where $N$ is a sample from a standard 2D Normal distribution. \n",
-    "\n",
-    "The `down` function operates on an edge $(u,v)$, where we let $u$ be the source node, and $v$ be the target node. If we are going downwards in the tree (as in this case), $u$ is the parent and $v$ is the child. The goal of the `down` function is to calculate the new value of $v$, using the data store in $u$ and $v$.\n",
-    "\n",
-    "When hyperiax executes functions on a tree, it always has a sense of the _current_ node. In hyperiax terminology, when refering to a concrete property, for example the `noise` in an operation, we mean the `noise` stored in the _current_ node. When defining our own operations on a tree, we can query the data we need within nodes by specifying parameter names. In this case we need both the noise, and the edge length in the _current_ node. We do however, also need the result of the calculations done in the parent of each node. In hyperiax, all values stored in the _parent_ of the _current_ node, will be prefixed by `parent_`. In this case, we need the `parent_value`, since the result of our computation is named `value`, and we need the result stored in the _parent_.\n",
-    "\n",
-    "Now, we want to set the value parameter in the node we just calculated. We do this by simply returning a dictionary specifying the values to overwrite. Notice, you do not need to return values you do not intend to change. In this example, we are not changing the edge lengths, and thus we just ignore them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Simulating Node Values\n",
-    "\n",
-    "To correlate the nodes, we define a `down` function. Initially, the `noise` in each node is sampled independently, making them independent and identically distributed (iid). We will create a `down` function to ensure that each child node of an inner node $x$ is a sample from a Brownian motion starting at $x$ and stopped at time $T$, where $T$ is the length of the edge connecting $x$ and the child $c$. This sample is calculated as $\\sqrt{T} \\cdot N + x$, with $N$ being a sample from a standard 2D Normal distribution.\n",
-    "\n",
-    "In hyperiax, functions on a tree always consider the _current_ node. When referring to properties such as `noise`, it means the `noise` stored in the _current_ node. To define custom operations on a tree, we can query the necessary data within nodes by specifying parameter names. In this case, we need both the `noise` and the `edge_length` in the _current_ node. Additionally, we need the result of calculations from the parent node. In hyperiax, values stored in the _parent_ node are prefixed with `parent_`. For this computation, we need `parent_value` (the result of the computation in the parent node).\n",
-    "\n",
-    "The implementation of the `down` function involves the following steps:\n",
-    "\n",
-    "1. **Extract the necessary parameters**:\n",
-    "    - `noise`: the noise value in the _current_ node.\n",
-    "    - `edge_length`: the edge length stored in the _current_ node (this is by convention the length of the edge connecting the _current_ and _parent_ node)\n",
-    "    - `parent_value`: the result of the computation in the _parent_ node.\n",
-    "\n",
-    "2. **Compute the new value for the child node**:\n",
-    "    - Calculate the new value as `sqrt(edge_length) * noise + parent_value`.\n",
-    "\n",
-    "3. **Return the new value**:\n",
-    "    - Return a dictionary specifying the `value` parameter to overwrite in the child node $v$. Values that are not being changed, such as `edge_length`, can be omitted.\n",
-    "\n",
-    "Hyperiax will greedily distribute you workloads, using vectorization among other techniques. This means that any down function is expected to work on batched data. In our example, the input shapes will be as follows;\n",
-    "\n",
-    "- `noise`: `(b, 2)`.\n",
-    "- `edge_length`: `(b, 1)`.\n",
-    "\n",
-    "Similarly, we need to return a batched output, in this case `value`, should have the shape `(b, 2)`, where `b` is some batch size determined by hyperiax.\n",
-    "\n",
-    "If the concept of batching seems uncomfortable to you, we highly recommend using [`jax.vmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.vmap.html) which can do the batching for you automatically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@jax.jit\n",
-    "def down(noise, edge_length,parent_value, **args):\n",
-    "    return {'value': jnp.sqrt(edge_length)*noise+parent_value}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we set the root value to be the noise we sampled. All data is stored in BFS order in the buffers, this means that the root is always the first element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.data['value'] = tree.data['value'].at[0].set(tree.data['noise'][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we create both an executor and a model. The model object contains all user code related to the operations we wish to run on a tree. The executor object schedules execution within the tree, and is in charge of setting the data in the correct format, as well as batching things together and making sure everything runs in the correct order.\n",
-    "\n",
-    "You are welcome to define your operations as an entire class, in cases where a constructor is needed. In this example we use the anonymous lambda models from hyperiax, that lets you pass tree operations as arguments, such that they are ready to be executed on a tree directly.\n",
-    "\n",
-    "We have now created a model that will generate brownian motion samples on a tree. The only thing left to do is give this model to an executor, which will run the code in the correct order on the tree. In this case we use the `OrderedExecutor`, since order is important for our operation. This executor will work on models going in either the up or down direction on a tree."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downmodel = DownLambda(down_fn=down)\n",
-    "exe = OrderedExecutor(downmodel)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With these objects created, we can now run our down function on the tree! Be careful as these operations are **INPLACE**. Because of this, we reccomend [idempotent](https://en.wikipedia.org/wiki/Idempotence) operations like here, where the result is stored in `value`, instead of overwriting `noise`(notice how this would give a different result for each execution)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exe.down(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Besides topology visualizations, hyperiax also provides avenues of displaying the underlying data in the tree. When dealing with $2$ dimensional vectors, we can perform a classic scatter plot, but with the tree topology overlaid.\n",
-    "\n",
-    "Note that passing in a pyplot axis object is optional, if you do not wish to use your own, then it can be omitted.\n",
-    "\n",
-    "Using these two plots, we can see how the `down` function infact seems to correlate our nodes!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-3,2)\n",
-    "    a.set_ylim(-1,2)\n",
-    "plot_tree_2d_scatter(tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(tree, 'noise', ax=ax[1])\n",
-    "ax[1].set_title('Noise');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The green node represents the root, and the light blue nodes represent the leafs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Estimating the inner nodes\n",
-    "\n",
-    "Now we are going to mask out all of the data on the internal nodes, since we would often only know the `value` in the leaf nodes. The tree class has a boolean mask that indicates whether or not a node is a leaf obtainable via `tree.is_leaf`.\n",
-    "\n",
-    "First, we get the leaf data by simply indexing with the mask. Next, we overwrite the leaf values in `estimated_value` with the concrete observations from `leaf_data`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree.add_property('estimated_value', shape=(2,))\n",
-    "leaf_data = tree.data['value'][tree.is_leaf]\n",
-    "tree.data['estimated_value'] = tree.data['estimated_value'].at[tree.is_leaf].set(leaf_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now define 2 operations and a reduction.\n",
-    "\n",
-    "The `up` function takes the value in a _current_ node, and forms a message that is passed toward its _parent_.\n",
-    "\n",
-    "The `reductions` parameter of the `UpLambda` class, determines how each property should be reduced when multiple are incoming from the _child_ nodes. In this case we have `sum` for both arguments. This means that after a message is sent from each child toward its parent, each property is first summed before the `transform` operation in the _parent_.\n",
-    "\n",
-    "The `transform` function processes the reduced incoming messages of the _child_ nodes, and treats the recieving node of the messages as the _current_ node. This means that we need to use the prefix `child_` when getting the reduced values of the children.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Upward Pass Computation\n",
-    "\n",
-    "In this section, we explore the upward pass computation in hyperiax, where values propagate from the leaves of a tree toward the root. This process consists of two primary operations (`up` and `transform`) and a reduction step.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "1. **Up Operation (`up` function)**:\n",
-    "   - **Purpose**: To generate a message at each node that will be passed to its parent.\n",
-    "   - **Inputs**: Values specific to the current node.\n",
-    "   - **Outputs**: A message containing the processed values to be sent to the parent node.\n",
-    "\n",
-    "2. **Reduction (`reductions` parameter)**:\n",
-    "   - **Purpose**: To aggregate messages from multiple child nodes.\n",
-    "   - **Inputs**: Messages from the child nodes.\n",
-    "   - **Outputs**: A single aggregated message that combines the values from all child nodes using a specified reduction method (e.g., sum).\n",
-    "\n",
-    "3. **Transform Operation (`transform` function)**:\n",
-    "   - **Purpose**: To process the aggregated message at the parent node.\n",
-    "   - **Inputs**: Aggregated messages from the child nodes aswell as the values in the _current_ node.\n",
-    "   - **Outputs**: A transformed value for the parent node.\n",
-    "\n",
-    "## Explanation of the example\n",
-    "\n",
-    "### 1. Up Operation (`up` function)\n",
-    "\n",
-    "The `up` function takes the value at the current node and forms a message that will be passed to the parent node. \n",
-    "\n",
-    "When hyperiax executes functions on a tree, it always has a sense of the _current_ node. In hyperiax terminology, when referring to a specific property, such as `estimated_value`, it means the `estimated_value` stored in the _current_ node.\n",
-    "\n",
-    "- **Parameters**:\n",
-    "  - `estimated_value`: The value associated with the current node.\n",
-    "  - `edge_length`: The length of the edge connecting the current node to its parent.\n",
-    "  - `**kwargs`: Any additional arguments that might be needed.\n",
-    "- **Returns**: A dictionary with two properties:\n",
-    "  - `weighted_value`: Calculated as `estimated_value / edge_length`.\n",
-    "  - `inverse_edge_length`: Calculated as `1 / edge_length`.\n",
-    "\n",
-    "In this case, with $w_i=1/l_i$ with $l_i$ being the edge length in node $i$. We can write the inner node estimation $p$ as a sum over children $c_i$\n",
-    "\n",
-    "$$p=\\frac{1}{\\sum w_i}\\sum c_iw_i$$\n",
-    "\n",
-    "The key is to observe that we can compute $\\sum w_i$ and $\\sum c_iw_i$ independently, where we use the sum as a reduction and $w_i$ and $w_ic_i$ are the messages being passed up\n",
-    "\n",
-    "### 2. Reduction (`reductions` parameter)\n",
-    "\n",
-    "The reduction step determines how to combine multiple messages received from child nodes. In this example, we use the sum operation for both `weighted_value` and `inverse_edge_length` properties.\n",
-    "\n",
-    "- **`weighted_value`**: The messages' `weighted_value` properties from all child nodes are summed.\n",
-    "- **`inverse_edge_length`**: The messages' `inverse_edge_length` properties from all child nodes are summed.\n",
-    "\n",
-    "### 3. Transform Operation (`transform` function)\n",
-    "\n",
-    "The `transform` function processes the aggregated message at the parent node. It uses the reduced values from the child nodes to compute a new value for the current (parent) node.\n",
-    "\n",
-    "- **Parameters**:\n",
-    "  - `child_weighted_value`: The aggregated `weighted_value` from all child nodes.\n",
-    "  - `child_inverse_edge_length`: The aggregated `inverse_edge_length` from all child nodes.\n",
-    "  - `**kwargs`: Any additional arguments that might be needed.\n",
-    "- **Returns**: A dictionary with one property:\n",
-    "  - `estimated_value`: Calculated as `child_weighted_value / child_inverse_edge_length`.\n",
-    "\n",
-    "## Putting It All Together\n",
-    "\n",
-    "To set up the upward pass, we define an `UpLambda` model with the `up` and `transform` functions, and the specified reductions.\n",
-    "\n",
-    "- **`UpLambda`**: Initializes the upward pass model with the `up` and `transform` functions and the reduction rules.\n",
-    "- **`OrderedExecutor`**: Executes the upward pass using the defined model.\n",
-    "\n",
-    "This setup ensures that values propagate correctly from the leaves to the root, are aggregated at each step, and transformed to provide the final values at each node.\n",
-    "\n",
-    "By following this process, you can effectively implement and understand the upward pass computation in hyperiax.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def transform(child_weighted_value,child_inverse_edge_length, **kwargs):\n",
-    "    return {'estimated_value': child_weighted_value/child_inverse_edge_length}\n",
-    "\n",
-    "def up(estimated_value, edge_length, **kwargs):\n",
-    "    return {'weighted_value': estimated_value/edge_length, 'inverse_edge_length':1/edge_length}\n",
-    "upmodel = UpLambdaReducer(up, transform, \n",
-    "                   reductions={\n",
-    "                       'weighted_value': 'sum', \n",
-    "                       'inverse_edge_length': 'sum'\n",
-    "                    }\n",
-    "        )\n",
-    "upward_exe = OrderedExecutor(upmodel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upward_exe.up(tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can plot the estimated tree as follows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-3,2)\n",
-    "    a.set_ylim(-1,2)\n",
-    "plot_tree_2d_scatter(tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(tree, 'estimated_value', ax=ax[1])\n",
-    "ax[1].set_title('Estimated Value');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting around reductions\n",
-    "We also allow for a simpler interface in the up-direction at a cost. If your tree has a constant number of children for all nodes or zero, and you are willing to do some upfront work `(precompute_child_gathers=True)`, then you can get around the reduce operation by simply specifying an `up` function that can access data in child nodes and the current node. The returned values are to be set in the current node.\n",
-    "\n",
-    "The way it handles children is a bit different. In our example our `value`s in the children are 2 dimensional vectors, and since each node in our topology has exactly `2` or `0` children, we say that the base of the tree is `2`. We can now query the child values by giving `child_estimated_value` as a parameter to our up function. This will have the shape `[batch, base, dimension]` where in our case `batch` is determined by hyperiax, `base=2` from the tree, and `dimension=2` because we are working in 2d. We now need to aggregate these such that we return something with the shape `[batch, dimension]` as the estimated value. In this example we do it by a weighted mean, using the edge lengths as weights."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pre_tree = HypTree(topology, precompute_child_gathers=True)\n",
-    "pre_tree.data = tree.data.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def up(child_estimated_value, child_edge_length, **kwargs):\n",
-    "    inv_el = 1/child_edge_length\n",
-    "    normalizer = inv_el.sum(axis=1)\n",
-    "\n",
-    "    return {'estimated_value': jnp.einsum('bcd,bc1->bd', child_estimated_value, inv_el)/normalizer}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "upmodel = UpLambda(up_fn=up)\n",
-    "upmodelexe = OrderedExecutor(upmodel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = upmodelexe.up(pre_tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Array(True, dtype=bool)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(pre_tree.data['estimated_value'] == tree.data['estimated_value']).all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2, figsize = (10,4))\n",
-    "for a in ax:\n",
-    "    a.set_xlim(-3,2)\n",
-    "    a.set_ylim(-1,2)\n",
-    "plot_tree_2d_scatter(pre_tree, 'value', ax=ax[0])\n",
-    "ax[0].set_title('Value')\n",
-    "plot_tree_2d_scatter(pre_tree, 'estimated_value', ax=ax[1])\n",
-    "ax[1].set_title('Estimated Value');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4. Using prebuilts\n",
-    "\n",
-    "Alternatively, we can use the cool prebuilts to do the same computations as above, but more conveniently!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hyperiax.models.prebuilts import PhyloMeanModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `PhyloMeanExecutor` expects leaf nodes to all contain a field called `estimated_value`. Since we already know the exact values here, we can simply set them directly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pme = PhyloMeanModel()\n",
-    "exe = OrderedExecutor(pme)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exe.up(tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "hyperiax_dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/shape.py b/examples/shape.py
deleted file mode 100644
index c197dc2..0000000
--- a/examples/shape.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Shape related utiliti functions
-
-import numpy as np
-import jax.numpy as jnp
-import trimesh
-
-# kernels
-k_Gaussian = lambda x,params: params['k_alpha']/2*jnp.exp(-.5*jnp.sum(jnp.square(x)/params['k_sigma'],2))
-r = lambda x,params: jnp.sqrt(1e-7+jnp.sum(jnp.square(x/params['k_sigma']),2))
-
-def k_K0(x,params): 
-    """ Laplace K0 kernel"""
-    r_ = r(x,params)
-    return params['k_alpha']*jnp.exp(-r_)
-def g_K0(x, params):
-    """ g corresponding to Laplace K0 kernel (c=(d+1)/2), d=3. """
-    r_ = r(x, params)
-    return params['k_alpha']**2*(1+r_+(1/3)*r_**2)*jnp.exp(-r_)
-
-def k_K1(x,params): 
-    """ Laplace K1 kernel"""
-    r_ = r(x,params)
-    return params['k_alpha']*(1+r_)*jnp.exp(-r_) # params['k_alpha']*2*(1+r_)*jnp.exp(-r_)
-def g_K1(x, params):
-    """ g corresponding to Laplace K1 kernel (c=(d+3)/2), d=3. """
-    r_ = r(x, params)
-    return params['k_alpha']**2*(1+r_+(45/105)*r_**2+(10/105)*r_**3+(1/105)*r_**4)*jnp.exp(-r_)
-    
-def k_K2(x,params): 
-    """ Laplace K2 kernel"""
-    r_ = r(x,params)
-    return params['k_alpha']*(1+r_+(1/3)*r_**2)*jnp.exp(-r_) # params['k_alpha']*4*(3+3*r_+r_**2)*jnp.exp(-r_)
-
-def k_K3(x,params): 
-    """ Laplace K3 kernel"""
-    r_ = r(x,params)
-    return params['k_alpha']*(1+r_+(2/5)*r_**2+(1/15)*r_**3)*jnp.exp(-r_) # params['k_alpha']*8*(15+15*r+6*r**2+r**3)*jnp.exp(-r)
-
-def k_K4(x,params): 
-    """ Laplace K4 kernel"""
-    r_ = r(x,params)
-    return params['k_alpha']*(1+r_+(45/105)*r_**2+(10/105)*r_**3+(1/105)*r_**4)*jnp.exp(-r_) # params['k_alpha']*16*(105+105*r_+45*r_**2+10*r_**3+r_**4)*jnp.exp(-r_)
-
-
-# 3d
-
-# place points approximately uniformly on a sphere
-def fibonacci_sphere(samples=1):
-    points = []
-    phi = np.pi * (3. - np.sqrt(5.))  # Golden angle in radians
-
-    for i in range(samples):
-        y = 1 - (i / float(samples - 1)) * 2  # y goes from 1 to -1
-        radius = np.sqrt(1 - y * y)  # radius at y
-
-        theta = phi * i  # golden angle increment
-
-        x = np.cos(theta) * radius
-        z = np.sin(theta) * radius
-
-        points.append((x, y, z))
-
-    return np.array(points)
-
-def mesh_sphere(target_vertices=100):
-    # Start with a low number of subdivisions
-    subdivisions = 0
-    sphere = trimesh.creation.icosphere(subdivisions=subdivisions, radius=1)
-    # Increase subdivisions until the number of vertices is at least the target
-    while len(sphere.vertices) < target_vertices:
-        subdivisions += 1
-        sphere = trimesh.creation.icosphere(subdivisions=subdivisions, radius=1)
-    # If we overshoot the target, reduce subdivisions
-    while len(sphere.vertices) > target_vertices and subdivisions > 0:
-        subdivisions -= 1
-        sphere = trimesh.creation.icosphere(subdivisions=subdivisions, radius=1)
-    return sphere
diff --git a/hyperiax/__init__.py b/hyperiax/__init__.py
index 8e998fd..deed51d 100644
--- a/hyperiax/__init__.py
+++ b/hyperiax/__init__.py
@@ -1 +1,41 @@
-from . import tree, execution
\ No newline at end of file
+"""Hyperiax — JAX tree traversals."""
+
+from hyperiax.core import (
+    Children,
+    ChildrenAxis,
+    FieldSpec,
+    HyperiaxError,
+    MissingField,
+    Node,
+    Schema,
+    SchemaMismatch,
+    SweepFn,
+    Topology,
+    Tree,
+    down,
+    from_newick,
+    from_parents,
+    symmetric_topology,
+    to_newick,
+    up,
+)
+
+__all__ = [
+    "Children",
+    "ChildrenAxis",
+    "FieldSpec",
+    "HyperiaxError",
+    "MissingField",
+    "Node",
+    "Schema",
+    "SchemaMismatch",
+    "SweepFn",
+    "Topology",
+    "Tree",
+    "down",
+    "from_newick",
+    "from_parents",
+    "symmetric_topology",
+    "to_newick",
+    "up",
+]
diff --git a/hyperiax/core/__init__.py b/hyperiax/core/__init__.py
new file mode 100644
index 0000000..73e2db7
--- /dev/null
+++ b/hyperiax/core/__init__.py
@@ -0,0 +1,34 @@
+"""Hyperiax core: pure-JAX tree primitives.
+
+Strictly L1: only ``jax``, ``numpy``, and the standard library may be
+imported from this package. Everything else (Newick I/O, prebuilt
+models, plotting, MCMC) lives outside ``hyperiax.core``.
+"""
+
+from .builders import from_newick, from_parents, symmetric_topology, to_newick
+from .errors import HyperiaxError, MissingField, SchemaMismatch
+from .schema import FieldSpec, Schema
+from .sweep import SweepFn, down, up
+from .topology import Topology
+from .tree import Tree
+from .views import Children, ChildrenAxis, Node
+
+__all__ = [
+    "Children",
+    "ChildrenAxis",
+    "FieldSpec",
+    "HyperiaxError",
+    "MissingField",
+    "Node",
+    "Schema",
+    "SchemaMismatch",
+    "SweepFn",
+    "Topology",
+    "Tree",
+    "down",
+    "from_newick",
+    "from_parents",
+    "symmetric_topology",
+    "to_newick",
+    "up",
+]
diff --git a/hyperiax/core/builders.py b/hyperiax/core/builders.py
new file mode 100644
index 0000000..76ff355
--- /dev/null
+++ b/hyperiax/core/builders.py
@@ -0,0 +1,268 @@
+"""Topology and tree builders.
+
+Constructors that produce a fully-derived :class:`Topology` (or a
+:class:`Tree` carrying per-node data) from a compact description:
+
+- :func:`symmetric_topology` / :func:`from_parents` build a bare topology.
+- :func:`from_newick` / :func:`to_newick` (de)serialize Newick strings,
+  carrying branch lengths on ``tree.data['edge_length']``.
+
+The Newick reader/writer is a small pure-Python implementation — no
+external parser — so it stays inside the L1 ``jax + numpy + stdlib``
+boundary that ``hyperiax.core`` is allowed to import from.
+"""
+
+from __future__ import annotations
+
+from collections import deque
+from collections.abc import Mapping
+from pathlib import Path
+
+import numpy as np
+
+from .schema import FieldSpec, Schema
+from .topology import Topology
+from .tree import Tree
+
+
+def symmetric_topology(depth: int, degree: int) -> Topology:
+    """A regular tree where every internal node has exactly ``degree`` children.
+
+    A tree of ``depth=0`` contains just the root; ``depth=1`` has the root
+    plus one level of ``degree`` leaves; and so on. Total node count is
+    :math:`\\sum_{k=0}^{h} d^k = (d^{h+1} - 1) / (d - 1)` for ``d > 1``, and
+    ``h + 1`` for ``d == 1``.
+    """
+    if depth < 0:
+        raise ValueError(f"depth must be non-negative, got {depth}")
+    if degree < 1:
+        raise ValueError(f"degree must be >= 1, got {degree}")
+
+    if degree == 1:
+        size = depth + 1
+        # Chain: parents[i] = i - 1, root self-parents.
+        parents = np.arange(-1, depth, dtype=np.int32)
+        parents[0] = 0
+    else:
+        size = (degree ** (depth + 1) - 1) // (degree - 1)
+        parents = np.zeros(size, dtype=np.int32)
+        prev_level_start = 0
+        cursor = 1
+        for level in range(1, depth + 1):
+            n_at_level = degree**level
+            local = np.arange(n_at_level, dtype=np.int32)
+            parents[cursor : cursor + n_at_level] = prev_level_start + local // degree
+            prev_level_start = cursor
+            cursor += n_at_level
+
+    return Topology.from_parents(parents)
+
+
+def from_parents(parents, *, names=None) -> Topology:
+    """Thin re-export of :meth:`Topology.from_parents`."""
+    return Topology.from_parents(parents, names=names)
+
+
+# ── Newick I/O ─────────────────────────────────────────────────────
+def from_newick(
+    source: str | Path,
+    *,
+    schema: Schema | Mapping[str, tuple | FieldSpec | None] | None = None,
+) -> Tree:
+    """Read a Newick tree (literal or file path) into a hyperiax :class:`Tree`.
+
+    Branch lengths land on ``tree.data['edge_length']`` (shape ``()``) with
+    the current default floating dtype unless ``schema`` overrides it; the
+    root edge length is whatever the Newick string reports (usually 0). Node
+    names ride on :attr:`Topology.names` (static metadata), not in
+    ``tree.data``.
+
+    Args:
+        source: a Newick literal (string ending in ``;``) or a path to a
+            Newick file.
+        schema: optional extra fields beyond ``edge_length``; allocated as
+            zeros for the caller to fill in afterwards.
+
+    Returns:
+        A Tree whose schema always includes ``edge_length`` (``()``) plus any
+        extras requested.
+    """
+    root = _parse_newick(_read_newick_source(source))
+    parents, names, edge_lengths = _bfs_arrays(root)
+    topo = Topology.from_parents(parents, names=names)
+
+    merged: dict = {"edge_length": ()}
+    if schema is not None:
+        if isinstance(schema, Schema):
+            for name, spec in schema.fields:
+                merged[name] = spec
+        else:
+            merged.update(schema)
+    full_schema = Schema.from_dict(merged)
+
+    return Tree.empty(topo, full_schema).set(edge_length=edge_lengths)
+
+
+def to_newick(tree: Tree) -> str:
+    """Serialize a Tree back to a Newick string.
+
+    Requires an ``edge_length`` field. Uses :attr:`Topology.names` for node
+    labels (empty string for unnamed nodes). The root carries a label but no
+    branch length, so a name on the root survives a write -> read round trip.
+
+    Args:
+        tree: the Tree to serialize.
+    """
+    if "edge_length" not in tree.schema:
+        raise ValueError(
+            "Tree must have an 'edge_length' field to be written to Newick. "
+            "Add it via Tree.update(edge_length=jnp.ones(tree.size)) first."
+        )
+
+    topo = tree.topology
+    parents = np.asarray(topo.parents)
+    edge_lengths = np.asarray(tree["edge_length"])
+    names: tuple[str, ...] = topo.names if topo.names is not None else ("",) * topo.size
+
+    # BFS layout guarantees parents[i] < i, so a single pass builds the
+    # children adjacency in left-to-right insertion order.
+    children: list[list[int]] = [[] for _ in range(topo.size)]
+    for i in range(1, topo.size):
+        children[int(parents[i])].append(i)
+
+    def serialize(i: int, is_root: bool) -> str:
+        if children[i]:
+            inner = ",".join(serialize(c, False) for c in children[i])
+            label = f"({inner}){names[i]}"
+        else:
+            label = names[i]
+        if not is_root:
+            label += f":{_format_length(edge_lengths[i])}"
+        return label
+
+    return serialize(0, True) + ";"
+
+
+# ── Newick helpers ─────────────────────────────────────────────────
+class _NewickNode:
+    """Mutable parse-tree node: a name, a branch length, and children."""
+
+    __slots__ = ("name", "length", "children")
+
+    def __init__(self) -> None:
+        self.name: str = ""
+        self.length: float = 0.0
+        self.children: list[_NewickNode] = []
+
+
+def _read_newick_source(source: str | Path) -> str:
+    """Return the Newick text for a literal or a file path."""
+    if isinstance(source, Path):
+        return source.read_text(encoding="utf-8")
+    text = str(source)
+    # A literal ends in ';' or contains a '(' group; everything else (a bare
+    # name with no markup) is treated as a file path. Parsing a malformed
+    # literal then surfaces a clear error rather than a FileNotFoundError.
+    stripped = text.strip()
+    if stripped.endswith(";") or "(" in stripped:
+        return text
+    return Path(text).read_text(encoding="utf-8")
+
+
+def _skip_ws(s: str, i: int) -> int:
+    while i < len(s) and s[i] in " \t\n\r":
+        i += 1
+    return i
+
+
+def _parse_newick(text: str) -> _NewickNode:
+    """Parse a Newick string into a nested :class:`_NewickNode` tree.
+
+    Supports the common grammar (matching ete3's flexible format): nested
+    ``(...)`` groups, optional internal/root labels, and ``:length`` branch
+    lengths. Quoted labels are not handled.
+    """
+    text = text.strip()
+    if not text.endswith(";"):
+        raise ValueError(f"Newick string must end with ';', got ...{text[-20:]!r}")
+
+    node, pos = _parse_subtree(text, 0)
+    pos = _skip_ws(text, pos)
+    if pos >= len(text) or text[pos] != ";":
+        raise ValueError(f"malformed Newick: expected ';' terminator at position {pos}")
+    return node
+
+
+def _parse_subtree(s: str, i: int) -> tuple[_NewickNode, int]:
+    i = _skip_ws(s, i)
+    node = _NewickNode()
+    if i < len(s) and s[i] == "(":
+        i += 1
+        while True:
+            child, i = _parse_subtree(s, i)
+            node.children.append(child)
+            i = _skip_ws(s, i)
+            if i >= len(s):
+                raise ValueError("malformed Newick: unterminated '(' group")
+            if s[i] == ",":
+                i += 1
+                continue
+            if s[i] == ")":
+                i += 1
+                break
+            raise ValueError(f"malformed Newick: unexpected {s[i]!r} at position {i}")
+    return _parse_label(s, i, node)
+
+
+def _parse_label(s: str, i: int, node: _NewickNode) -> tuple[_NewickNode, int]:
+    i = _skip_ws(s, i)
+    start = i
+    while i < len(s) and s[i] not in ":,();":
+        i += 1
+    node.name = s[start:i].strip()
+
+    i = _skip_ws(s, i)
+    if i < len(s) and s[i] == ":":
+        i += 1
+        i = _skip_ws(s, i)
+        start = i
+        while i < len(s) and s[i] not in ",();":
+            i += 1
+        node.length = float(s[start:i].strip())
+    return node, i
+
+
+def _bfs_arrays(root: _NewickNode) -> tuple[np.ndarray, tuple[str, ...], np.ndarray]:
+    """BFS-traverse a parse tree into ``(parents, names, edge_lengths)``.
+
+    Root receives ``parents[0] == 0`` per the hyperiax convention; BFS order
+    guarantees ``parents[i] < i`` for all other nodes.
+    """
+    parents: list[int] = []
+    names: list[str] = []
+    edge_lengths: list[float] = []
+
+    queue: deque[tuple[_NewickNode, int | None]] = deque([(root, None)])
+    while queue:
+        node, parent_id = queue.popleft()
+        my_id = len(parents)
+        parents.append(my_id if parent_id is None else parent_id)
+        names.append(node.name)
+        edge_lengths.append(node.length)
+        for child in node.children:
+            queue.append((child, my_id))
+
+    return (
+        np.asarray(parents, dtype=np.int32),
+        tuple(names),
+        np.asarray(edge_lengths),
+    )
+
+
+def _format_length(x) -> str:
+    """Format a branch length minimally (``1.0`` -> ``1``, ``0.5`` -> ``0.5``).
+
+    Matches the 6-significant-figure ``%g`` style, which also keeps common
+    round-off artifacts compact.
+    """
+    return format(float(x), "g")
diff --git a/hyperiax/core/dispatch.py b/hyperiax/core/dispatch.py
new file mode 100644
index 0000000..734bb8a
--- /dev/null
+++ b/hyperiax/core/dispatch.py
@@ -0,0 +1,207 @@
+"""Internal dispatch engine.
+
+The dispatcher walks a Tree's levels in the right order, builds per-level
+:class:`Node`/:class:`Children` views over sliced/gathered JAX arrays,
+calls the user's sweep function, and scatters the writes into a new data
+dict. The whole thing is a pure ``Tree -> Tree`` function so it composes
+with ``jax.jit`` and ``jax.lax.scan`` without leaking state.
+
+Up-sweeps run on a single segment-based path that handles any topology
+(equal- or unequal-degree). Down-sweeps vmap over nodes at each level.
+"""
+
+from __future__ import annotations
+
+from functools import partial
+from typing import TYPE_CHECKING
+
+import jax
+import jax.numpy as jnp
+
+from .errors import MissingField, SchemaMismatch
+from .tree import Tree
+from .views import Children, ChildrenAxis, Node, Parent
+
+if TYPE_CHECKING:
+    from .sweep import SweepFn
+
+
+# ── public entrypoints ──────────────────────────────────────────────
+def up_dispatch(sweep: SweepFn, tree: Tree, params, key) -> Tree:
+    """Run an up-sweep on a Tree. Returns a new Tree.
+
+    One segment-based path handles any topology. At each parent level the
+    children one level deeper are exposed via :class:`ChildrenAxis` proxies
+    over a flat ``(M_total, *trailing)`` block: pure reductions
+    (``children.X.sum/.mean/.max/.min/.prod(0)``) dispatch to
+    ``jax.ops.segment_*``; per-child transforms go through
+    :meth:`Children.map` (a segment-preserving vmap). ``writes`` outputs
+    scatter to the parent node ids; ``writes_children`` outputs scatter
+    contiguously to the flat child block.
+    """
+    _validate_schema(sweep, tree)
+    return _up_dispatch(sweep, tree, params, key)
+
+
+def down_dispatch(sweep: SweepFn, tree: Tree, params, key) -> Tree:
+    """Run a down-sweep on a Tree. Returns a new Tree.
+
+    Works for arbitrary topologies — every non-root node has exactly one
+    parent, so the dispatcher slices each level contiguously and gathers
+    the parent record via ``topo.parents``.
+    """
+    _validate_schema(sweep, tree)
+    return _down_dispatch(sweep, tree, params, key)
+
+
+# ── shared helpers ─────────────────────────────────────────────────
+def _resolve_reads(
+    sweep_reads: tuple | None,
+    sweep_other_reads: tuple | None,
+    schema_names: tuple,
+) -> tuple[tuple, tuple, frozenset]:
+    """Resolve ``reads_self``, ``reads_other``, and ``expected_writes`` for a sweep."""
+    reads_self = sweep_reads if sweep_reads is not None else schema_names
+    reads_other = sweep_other_reads if sweep_other_reads is not None else schema_names
+    return reads_self, reads_other
+
+
+def _check_writes(out, expected: frozenset, declared: tuple, *, direction: str) -> None:
+    got = frozenset(out)
+    if got != expected:
+        extra = sorted(got - expected)
+        missing = sorted(expected - got)
+        raise SchemaMismatch(
+            f"{direction} returned keys {sorted(got)} but writes={sorted(declared)}. "
+            f"Extra: {extra}; missing: {missing}."
+        )
+
+
+# ── up dispatch (single segment-based path) ────────────────────────
+@partial(jax.jit, static_argnums=(0,))
+def _up_dispatch(sweep: SweepFn, tree: Tree, params, key) -> Tree:
+    """Up sweep over any tree (equal- or unequal-degree).
+
+    At each parent level, the children one level deeper are concatenated
+    into a flat ``(M_total, *trailing)`` view and assigned segment ids via
+    ``topo.pbuckets``. The user's ``children.X.sum(0)`` (or .max/.mean/...)
+    dispatches to the corresponding ``jax.ops.segment_*`` through a
+    :class:`ChildrenAxis` proxy; a per-child transform goes through
+    :meth:`Children.map`, which vmaps over the flat children and re-wraps the
+    outputs as segment-aware proxies. No outer vmap over parents is needed —
+    the body is called once per level with batched ``node`` data.
+
+    Per-parent (``writes``) outputs are scattered to the parent node ids; per-
+    child (``writes_children``) outputs are segment-aware proxies whose flat
+    block is the children in node order, so they scatter contiguously to
+    ``[child_ls, child_le)``.
+    """
+    topo = tree.topology
+    data = dict(tree.data)
+    reads_self, reads_children = _resolve_reads(
+        sweep.reads,
+        sweep.reads_children,
+        tree.schema.names,
+    )
+    parent_writes = sweep.writes
+    child_writes = sweep.writes_children
+    expected_writes = frozenset(parent_writes) | frozenset(child_writes)
+    all_writes_decl = parent_writes + child_writes
+    schema = tree.schema
+
+    for level in range(topo.depth - 1, -1, -1):
+        child_ls = int(topo.level_starts[level + 1])
+        child_le = int(topo.level_starts[level + 2])
+        parent_ids = topo.pbuckets_ref[level + 1]
+        num_segments = int(parent_ids.size)
+        if num_segments == 0:
+            continue
+
+        parent_idx_jax = jnp.asarray(parent_ids)
+        seg_ids = jnp.asarray(topo.pbuckets[child_ls:child_le])
+
+        node_data = {k: data[k][parent_idx_jax] for k in reads_self}
+        children_data = {
+            k: ChildrenAxis(
+                flat=data[k][child_ls:child_le],
+                segments=seg_ids,
+                num_segments=num_segments,
+                trailing=schema[k].shape,
+            )
+            for k in reads_children
+        }
+
+        out = sweep.fn(Node(node_data), Children(children_data), params)
+        _check_writes(out, expected_writes, all_writes_decl, direction="Up-sweep")
+
+        for k in parent_writes:
+            data[k] = data[k].at[parent_idx_jax].set(out[k])
+
+        for k in child_writes:
+            # out[k] is a ChildrenAxis whose flat block is the children at this
+            # level in node order, i.e. exactly data[k][child_ls:child_le].
+            data[k] = data[k].at[child_ls:child_le].set(out[k].flat)
+
+    return Tree(topology=topo, schema=tree.schema, data=data)
+
+
+# ── down dispatch ──────────────────────────────────────────────────
+@partial(jax.jit, static_argnums=(0,))
+def _down_dispatch(sweep: SweepFn, tree: Tree, params, key) -> Tree:
+    topo = tree.topology
+    data = dict(tree.data)
+    reads_self, reads_parent = _resolve_reads(
+        sweep.reads,
+        sweep.reads_parent,
+        tree.schema.names,
+    )
+    expected_writes = frozenset(sweep.writes)
+
+    per_level = jax.vmap(
+        lambda nd, pd, p: sweep.fn(Node(nd), Parent(pd), p),
+        in_axes=(0, 0, None),
+    )
+
+    # Walk root → leaves; level 1 is first (the root has no parent).
+    for level in range(1, topo.depth + 1):
+        ls = int(topo.level_starts[level])
+        le = int(topo.level_starts[level + 1])
+
+        parent_indices = jnp.asarray(topo.parents[ls:le])
+
+        node_data = {k: data[k][ls:le] for k in reads_self}
+        parent_data = {k: data[k][parent_indices] for k in reads_parent}
+
+        out = per_level(node_data, parent_data, params)
+        _check_writes(out, expected_writes, sweep.writes, direction="Down-sweep")
+
+        for k, v in out.items():
+            data[k] = data[k].at[ls:le].set(v)
+
+    return Tree(topology=topo, schema=tree.schema, data=data)
+
+
+# ── validation (runs eagerly, outside the jit-cached body) ─────────
+def _validate_schema(sweep: SweepFn, tree: Tree) -> None:
+    """Check that every field referenced by the sweep exists in the schema.
+
+    Runs eagerly so the error message has a clean Python traceback to the
+    decorator site — much friendlier than a JAX KeyError several frames deep.
+    """
+    needed: set[str] = set()
+    if sweep.reads is not None:
+        needed.update(sweep.reads)
+    if sweep.reads_children is not None:
+        needed.update(sweep.reads_children)
+    if sweep.reads_parent is not None:
+        needed.update(sweep.reads_parent)
+    needed.update(sweep.writes)
+    needed.update(sweep.writes_children)
+
+    missing = needed - set(tree.schema.names)
+    if missing:
+        raise MissingField(
+            f"Sweep references field(s) {sorted(missing)} that are not in the tree's schema. "
+            f"Schema has: {tree.schema.names}. "
+            f"Add them via tree.update(...) or include them in Tree.empty(topo, schema)."
+        )
diff --git a/hyperiax/core/errors.py b/hyperiax/core/errors.py
new file mode 100644
index 0000000..653f510
--- /dev/null
+++ b/hyperiax/core/errors.py
@@ -0,0 +1,13 @@
+"""Hyperiax exception hierarchy."""
+
+
+class HyperiaxError(Exception):
+    """Base class for all hyperiax-raised exceptions."""
+
+
+class SchemaMismatch(HyperiaxError):
+    """A value's shape or dtype does not match the field's spec."""
+
+
+class MissingField(HyperiaxError):
+    """A sweep or tree access references a name not in the tree's schema."""
diff --git a/hyperiax/core/schema.py b/hyperiax/core/schema.py
new file mode 100644
index 0000000..e1c0a0f
--- /dev/null
+++ b/hyperiax/core/schema.py
@@ -0,0 +1,106 @@
+"""Field-level schema for hyperiax Trees.
+
+A :class:`Schema` is a hashable, ordered collection of named
+:class:`FieldSpec` entries. It is the static contract for which arrays live
+on a Tree and what their per-node shape/dtype must be. The Schema sits in
+the JAX pytree aux_data of :class:`hyperiax.Tree`, so changing it
+invalidates the JIT cache by design — fields are meant to be declared
+up front via :meth:`Tree.empty`.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterator, Mapping
+from dataclasses import dataclass
+
+import jax.numpy as jnp
+import numpy as np
+
+
+def _default_float_dtype() -> np.dtype:
+    """Return JAX's current default floating dtype."""
+    return np.dtype(jnp.asarray(0.0).dtype)
+
+
+@dataclass(frozen=True)
+class FieldSpec:
+    """Per-node trailing shape and dtype of a single Tree field."""
+
+    shape: tuple[int, ...]
+    dtype: object | None = None
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "shape", tuple(self.shape))
+        dtype = _default_float_dtype() if self.dtype is None else np.dtype(self.dtype)
+        object.__setattr__(self, "dtype", dtype)
+
+    @staticmethod
+    def _coerce(spec) -> FieldSpec:
+        """Coerce a ``FieldSpec``, a shape tuple, or ``None`` into a ``FieldSpec``."""
+        if isinstance(spec, FieldSpec):
+            return spec
+        if spec is None:
+            return FieldSpec(shape=())
+        if isinstance(spec, tuple):
+            return FieldSpec(shape=spec)
+        raise TypeError(f"Cannot interpret {spec!r} as a FieldSpec")
+
+
+@dataclass(frozen=True, eq=False)
+class Schema:
+    """Ordered, hashable collection of named FieldSpecs.
+
+    Fields are stored as a ``tuple[(name, spec), ...]`` sorted by name so
+    that equal sets of fields always hash identically regardless of insert
+    order.
+    """
+
+    fields: tuple[tuple[str, FieldSpec], ...]
+
+    # ── construction ──────────────────────────────────────────────────
+    @classmethod
+    def from_dict(cls, d: Mapping[str, tuple | FieldSpec | None]) -> Schema:
+        items = tuple(sorted((name, FieldSpec._coerce(spec)) for name, spec in d.items()))
+        return cls(fields=items)
+
+    # ── mapping-like surface ─────────────────────────────────────────
+    @property
+    def names(self) -> tuple[str, ...]:
+        return tuple(name for name, _ in self.fields)
+
+    def __contains__(self, name: object) -> bool:
+        return any(n == name for n, _ in self.fields)
+
+    def __getitem__(self, name: str) -> FieldSpec:
+        for n, spec in self.fields:
+            if n == name:
+                return spec
+        raise KeyError(name)
+
+    def __len__(self) -> int:
+        return len(self.fields)
+
+    def __iter__(self) -> Iterator[tuple[str, FieldSpec]]:
+        return iter(self.fields)
+
+    # ── identity ──────────────────────────────────────────────────────
+    def __eq__(self, other) -> bool:
+        return isinstance(other, Schema) and self.fields == other.fields
+
+    def __hash__(self) -> int:
+        return hash(self.fields)
+
+    # ── functional updates ───────────────────────────────────────────
+    def with_added(self, **extra) -> Schema:
+        """Return a new Schema with extra fields appended. Raises if a name collides."""
+        merged = dict(self.fields)
+        for name, spec in extra.items():
+            if name in merged:
+                raise ValueError(f"Field {name!r} already in schema")
+            merged[name] = FieldSpec._coerce(spec)
+        return Schema.from_dict(merged)
+
+    def without(self, *names: str) -> Schema:
+        """Return a new Schema with the named fields removed."""
+        kept = tuple((n, s) for n, s in self.fields if n not in names)
+        return Schema(fields=kept)
diff --git a/hyperiax/core/sweep.py b/hyperiax/core/sweep.py
new file mode 100644
index 0000000..9168091
--- /dev/null
+++ b/hyperiax/core/sweep.py
@@ -0,0 +1,221 @@
+"""Sweep decorators — turn a user function into a Tree → Tree transform.
+
+A :class:`SweepFn` carries a direction (``up`` / ``down``), the user
+function, and the explicit ``reads`` / ``writes`` declarations. It is
+``frozen`` and hashable, so the dispatcher can use it as a JIT
+``static_argnums`` and reuse compilations across calls.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Literal
+
+import jax
+
+from .tree import Tree
+
+
+def _normalize(seq) -> tuple[str, ...] | None:
+    if seq is None:
+        return None
+    return tuple(seq)
+
+
+@dataclass(frozen=True, eq=False)
+class SweepFn:
+    """A direction-tagged, pure ``Tree -> Tree`` transform.
+
+    Hashable so the dispatcher can pin it as JIT static. Equality is
+    value-based on ``(direction, fn identity, read/write spec tuples)``.
+
+    ``writes_children`` (up sweeps only) declares fields whose per-parent
+    output has shape ``(k, *trailing)`` and gets scattered to the
+    children's slots rather than the parent's slot — useful for caching
+    per-edge quantities computed during the up-sweep (e.g. the per-step
+    ``F_t``/``H_t`` from a per-edge ODE filter).
+    """
+
+    direction: Literal["up", "down"]
+    fn: Callable
+    reads: tuple[str, ...] | None
+    reads_children: tuple[str, ...] | None
+    reads_parent: tuple[str, ...] | None
+    writes: tuple[str, ...]
+    writes_children: tuple[str, ...] = ()
+
+    def __post_init__(self):
+        if not self.writes and not self.writes_children:
+            raise ValueError(
+                "@up / @down requires writes=(...) (or writes_children=(...) on @up) "
+                "with at least one field"
+            )
+        if self.direction == "up" and self.reads_parent is not None:
+            raise ValueError("up sweeps cannot reference parent fields; use @down for that.")
+        if self.direction == "down" and self.reads_children is not None:
+            raise ValueError("down sweeps cannot reference children fields; use @up for that.")
+        if self.direction == "down" and self.writes_children:
+            raise ValueError(
+                "writes_children is only valid on up sweeps; down sweeps write per-node only."
+            )
+        overlap = set(self.writes) & set(self.writes_children)
+        if overlap:
+            raise ValueError(
+                f"writes and writes_children share fields {sorted(overlap)}; "
+                "each field must be one or the other."
+            )
+
+    # ── identity ──────────────────────────────────────────────────────
+    def __hash__(self) -> int:
+        # `id(self.fn)` is sufficient: two functions with different identity
+        # mean different traced code, so a different jit cache entry is correct.
+        return hash(
+            (
+                self.direction,
+                id(self.fn),
+                self.reads,
+                self.reads_children,
+                self.reads_parent,
+                self.writes,
+                self.writes_children,
+            )
+        )
+
+    def __eq__(self, other: object) -> bool:
+        return (
+            isinstance(other, SweepFn)
+            and self.direction == other.direction
+            and self.fn is other.fn
+            and self.reads == other.reads
+            and self.reads_children == other.reads_children
+            and self.reads_parent == other.reads_parent
+            and self.writes == other.writes
+            and self.writes_children == other.writes_children
+        )
+
+    def __repr__(self) -> str:
+        extra = f", writes_children={self.writes_children}" if self.writes_children else ""
+        return (
+            f"SweepFn({self.direction}, "
+            f"fn={getattr(self.fn, '__name__', repr(self.fn))}, "
+            f"writes={self.writes}{extra})"
+        )
+
+    # ── invocation ────────────────────────────────────────────────────
+    def __call__(
+        self,
+        tree: Tree,
+        *,
+        params: Mapping | None = None,
+        key: jax.Array | None = None,
+    ) -> Tree:
+        # Local import avoids a circular at module import time.
+        from .dispatch import down_dispatch, up_dispatch
+
+        if params is None:
+            params = {}
+        if self.direction == "up":
+            return up_dispatch(self, tree, params, key)
+        if self.direction == "down":
+            return down_dispatch(self, tree, params, key)
+        raise NotImplementedError(f"{self.direction!r} dispatch not yet wired up")
+
+
+# ── decorators ──────────────────────────────────────────────────────
+def up(
+    *,
+    reads: Sequence[str] | None = None,
+    reads_children: Sequence[str] | None = None,
+    writes: Sequence[str] = (),
+    writes_children: Sequence[str] = (),
+) -> Callable[[Callable], SweepFn]:
+    """Decorator: mark a function as an up-sweep.
+
+    The decorated function has signature ``(node, children, params)`` and
+    must return a ``dict[str, Array]`` whose key set equals
+    ``writes ∪ writes_children`` exactly. Values for keys in ``writes``
+    have shape ``(*trailing,)`` per parent (under :func:`jax.vmap`);
+    values for keys in ``writes_children`` have shape ``(k, *trailing)``
+    per parent and are scattered to the children's slots.
+
+    At least one of ``writes`` or ``writes_children`` must be non-empty.
+
+    Args:
+        reads: Fields the function reads from the current node. ``None``
+            (the default) means "all schema fields at call time."
+        reads_children: Fields the function reads from each child.
+            ``None`` defaults to all schema fields at call time.
+        writes: Fields written back per parent (canonical case).
+        writes_children: Fields written per child (one row per outgoing
+            edge). Use when an up sweep also produces per-edge byproducts
+            you want to cache on each child (e.g. per-edge ODE-filter
+            trajectories).
+
+    Example::
+
+        @hx.up(reads_children=('value',), writes=('value',))
+        def avg(node, children, params):
+            return {'value': children.value.mean(0)}
+
+        new_tree = avg(tree)
+    """
+
+    def _wrap(fn: Callable) -> SweepFn:
+        return SweepFn(
+            direction="up",
+            fn=fn,
+            reads=_normalize(reads),
+            reads_children=_normalize(reads_children),
+            reads_parent=None,
+            writes=tuple(writes),
+            writes_children=tuple(writes_children),
+        )
+
+    return _wrap
+
+
+def down(
+    *,
+    reads: Sequence[str] | None = None,
+    reads_parent: Sequence[str] | None = None,
+    writes: Sequence[str],
+) -> Callable[[Callable], SweepFn]:
+    """Decorator: mark a function as a down-sweep.
+
+    The decorated function has signature ``(node, parent, params)`` and
+    must return a ``dict[str, Array]`` whose key set equals ``writes``
+    exactly. Each returned value must have shape ``(*trailing,)`` matching
+    the schema for that field (per-node under :func:`jax.vmap`).
+
+    The root is never visited — it has no parent. Seed its data with
+    ``tree.set(...)`` before calling the sweep.
+
+    Args:
+        reads: Fields the function reads from the current node. ``None``
+            defaults to all schema fields at call time.
+        reads_parent: Fields the function reads from each node's parent.
+            ``None`` defaults to all schema fields at call time.
+        writes: Required. The fields the function writes back into the
+            tree. Must be non-empty.
+
+    Example::
+
+        @hx.down(reads=('delta',), reads_parent=('value',), writes=('value',))
+        def propagate(node, parent, params):
+            return {'value': parent.value + node.delta}
+
+        new_tree = propagate(tree)
+    """
+
+    def _wrap(fn: Callable) -> SweepFn:
+        return SweepFn(
+            direction="down",
+            fn=fn,
+            reads=_normalize(reads),
+            reads_children=None,
+            reads_parent=_normalize(reads_parent),
+            writes=tuple(writes),
+        )
+
+    return _wrap
diff --git a/hyperiax/core/topology.py b/hyperiax/core/topology.py
new file mode 100644
index 0000000..740c1c2
--- /dev/null
+++ b/hyperiax/core/topology.py
@@ -0,0 +1,197 @@
+"""Tree topology — static structure, hashable, JAX-pytree-aware.
+
+A :class:`Topology` carries everything the dispatcher needs to know about
+the shape of a tree (parent pointers, level boundaries, per-level segment
+layouts, etc.) but no per-node data. Every field is a :mod:`numpy` array,
+so the object is Python-hashable and rides through ``jax.jit`` as static
+``aux_data``. The dispatcher promotes these to ``jnp`` arrays at its
+boundary; under JIT they become traced constants.
+
+Convention
+----------
+- Nodes are laid out in **BFS order**: ``parents[i] < i`` for ``i > 0``.
+- The root is node ``0`` and is its own parent: ``parents[0] == 0``.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+
+import jax
+import numpy as np
+
+
+@dataclass(frozen=True, eq=False)
+class Topology:
+    """Immutable tree topology with precomputed dispatch layouts."""
+
+    # ── raw ──
+    parents: np.ndarray  # (N,) int32; parents[0] == 0
+
+    # ── derived: shape ──
+    size: int
+    depth: int  # = len(level_starts) - 2; a depth-0 tree has only the root
+    level_starts: np.ndarray  # (depth+2,) int32; level i is [level_starts[i], level_starts[i+1])
+    node_depths: np.ndarray  # (N,) int32
+
+    # ── derived: masks ──
+    child_counts: np.ndarray  # (N,) int32
+    is_root: np.ndarray  # (N,) bool
+    is_leaf: np.ndarray  # (N,) bool
+    is_inner: np.ndarray  # (N,) bool
+
+    # ── degree info ──
+    max_degree: int
+    equal_degree: bool
+
+    # ── segment-reduction layout (used by the up-sweep dispatcher) ──
+    # For each node, its *local* segment id within its level. Used as
+    # ``segment_ids`` for ``jax.ops.segment_*`` during up-sweeps.
+    pbuckets: np.ndarray  # (N,) int32
+    # For each level, the parent node ids (in level-1) that the segments at
+    # this level reduce into. ``pbuckets_ref[L]`` has shape ``(n_parents_at_L-1,)``.
+    pbuckets_ref: tuple  # tuple[np.ndarray, ...]
+
+    # ── informational ──
+    names: tuple | None  # node names (e.g. from Newick); not used by dispatch
+
+    # ── lifecycle ─────────────────────────────────────────────────────
+    def __post_init__(self):
+        # Frozen dataclass; bypass the immutability guard to cache the hash.
+        object.__setattr__(self, "_struct_hash", hash(self.parents.tobytes()))
+
+    # ── identity ──────────────────────────────────────────────────────
+    def __hash__(self) -> int:  # type: ignore[override]
+        return self._struct_hash  # type: ignore[attr-defined]
+
+    def __eq__(self, other: object) -> bool:
+        if self is other:
+            return True
+        if not isinstance(other, Topology):
+            return False
+        return (
+            self.parents.shape == other.parents.shape
+            and self._struct_hash == other._struct_hash  # type: ignore[attr-defined]
+            and np.array_equal(self.parents, other.parents)
+        )
+
+    def __repr__(self) -> str:
+        return (
+            f"Topology(size={self.size}, depth={self.depth}, "
+            f"equal_degree={self.equal_degree}, max_degree={self.max_degree})"
+        )
+
+    # ── construction ──────────────────────────────────────────────────
+    @classmethod
+    def from_parents(
+        cls,
+        parents,
+        *,
+        names: Sequence[str] | None = None,
+    ) -> Topology:
+        """Build a Topology from a BFS-ordered parents array.
+
+        Parameters
+        ----------
+        parents :
+            1-D array of int32; ``parents[i]`` is the index of node ``i``'s
+            parent. ``parents[0]`` must be ``0`` (root self-parent).
+            Must be BFS-ordered: ``parents[i] < i`` for ``i > 0``.
+        names :
+            Optional per-node names (informational only; used by Newick I/O).
+        """
+        return _build_topology(parents, names)
+
+
+# ── builder ───────────────────────────────────────────────────────────
+def _build_topology(parents_in, names) -> Topology:
+    parents = np.asarray(parents_in, dtype=np.int32)
+    if parents.ndim != 1:
+        raise ValueError(f"parents must be 1-D, got shape {parents.shape}")
+    if parents.size == 0:
+        raise ValueError("parents must be non-empty")
+    if int(parents[0]) != 0:
+        raise ValueError("Convention: parents[0] must be 0 (root is its own parent).")
+    n = int(parents.shape[0])
+
+    # ── child counts: subtract the root's self-reference ──
+    child_counts = np.bincount(parents, minlength=n).astype(np.int32)
+    child_counts[0] -= 1
+
+    # ── masks ──
+    is_leaf = child_counts == 0
+    is_root = np.zeros(n, dtype=bool)
+    is_root[0] = True
+    is_inner = ~is_leaf & ~is_root
+
+    # ── node depths (BFS ordering guarantees parents[i] < i for i > 0) ──
+    node_depths = np.zeros(n, dtype=np.int32)
+    for i in range(1, n):
+        p = int(parents[i])
+        if p >= i:
+            raise ValueError(
+                f"parents array is not BFS-ordered at index {i} (parents[{i}]={p} >= {i})"
+            )
+        node_depths[i] = node_depths[p] + 1
+    depth = int(node_depths.max()) if n > 0 else 0
+
+    if n > 1 and not np.all(node_depths[1:] >= node_depths[:-1]):
+        raise ValueError("parents array is not in BFS order (node_depths not monotone)")
+
+    # ── level_starts: contiguous index range per level ──
+    level_starts = np.zeros(depth + 2, dtype=np.int32)
+    for d in range(depth + 1):
+        level_starts[d + 1] = level_starts[d] + int((node_depths == d).sum())
+
+    # ── degree info ──
+    non_leaf_counts = child_counts[~is_leaf]
+    equal_degree = bool(
+        non_leaf_counts.size > 0 and bool(np.all(non_leaf_counts == non_leaf_counts[0]))
+    )
+    max_degree = int(child_counts.max())
+
+    # ── segment-reduction layout: pbuckets / pbuckets_ref ──
+    # For each level (taken bottom-up by the up-sweep), pvals = parent ids of the
+    # nodes in this level. ``unique`` produces (a) the destination parent ids and
+    # (b) the local segment id of each node within this level's reduction.
+    pbuckets = parents.copy()
+    pbuckets_ref: list = []
+    for d in range(depth + 1):
+        lo, hi = int(level_starts[d]), int(level_starts[d + 1])
+        pvals = pbuckets[lo:hi]
+        uniq, inv = np.unique(pvals, return_inverse=True)
+        pbuckets[lo:hi] = inv.astype(np.int32)
+        pbuckets_ref.append(uniq.astype(np.int32))
+
+    return Topology(
+        parents=parents,
+        size=n,
+        depth=depth,
+        level_starts=level_starts,
+        node_depths=node_depths,
+        child_counts=child_counts,
+        is_root=is_root,
+        is_leaf=is_leaf,
+        is_inner=is_inner,
+        max_degree=max_degree,
+        equal_degree=equal_degree,
+        pbuckets=pbuckets,
+        pbuckets_ref=tuple(pbuckets_ref),
+        names=tuple(names) if names is not None else None,
+    )
+
+
+# ── JAX pytree registration ───────────────────────────────────────────
+# Topology has no dynamic children; the whole object rides as static
+# aux_data through jit. Two Topologies built from the same parents array
+# compare equal and hit the same compile cache.
+def _topo_flatten(t: Topology):
+    return (), t
+
+
+def _topo_unflatten(aux, _children):
+    return aux
+
+
+jax.tree_util.register_pytree_node(Topology, _topo_flatten, _topo_unflatten)
diff --git a/hyperiax/core/tree.py b/hyperiax/core/tree.py
new file mode 100644
index 0000000..a480a10
--- /dev/null
+++ b/hyperiax/core/tree.py
@@ -0,0 +1,283 @@
+"""Tree — Topology + Schema + a dict of per-node arrays.
+
+Immutable, JAX-pytree-registered: every mutator returns a new Tree, so
+the Tree composes cleanly with ``@jax.jit`` and ``jax.lax.scan``.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass
+
+import jax
+import jax.numpy as jnp
+
+from .errors import MissingField, SchemaMismatch
+from .schema import FieldSpec, Schema
+from .topology import Topology
+
+# Field names that would shadow ``Tree`` attributes / methods under
+# attribute access (``tree.value``). Schema field names are checked against
+# this set at construction time so the shadowing fails loudly instead of
+# silently masking the data.
+_RESERVED_FIELD_NAMES = frozenset(
+    {
+        "topology",
+        "schema",
+        "data",
+        "size",  # dataclass fields + property
+        "set",
+        "at",
+        "update",
+        "drop",  # mutators
+        "empty",
+        "from_data",  # classmethods
+    }
+)
+
+
+@dataclass(frozen=True, eq=False)
+class Tree:
+    """A topology + a typed dict of ``(N, *trailing)`` arrays.
+
+    Construction is always functional: every "mutator" returns a new Tree
+    sharing the same Topology and (usually) Schema, with replaced data.
+
+    Data fields are reachable two ways: ``tree['value']`` (dict-style, the
+    safe form when the field name is dynamic or collides with a reserved
+    name) and ``tree.value`` (attribute-style, the canonical form mirroring
+    ``node.value`` / ``children.value`` inside a sweep).
+    """
+
+    topology: Topology
+    schema: Schema
+    data: dict  # dict[str, jax.Array]; keys match schema.names exactly
+
+    # ── constructors ──────────────────────────────────────────────────
+    @classmethod
+    def empty(
+        cls,
+        topology: Topology,
+        schema: Schema | Mapping[str, tuple | FieldSpec | None],
+    ) -> Tree:
+        """Allocate a Tree of zeros with the declared schema."""
+        if not isinstance(schema, Schema):
+            schema = Schema.from_dict(schema)
+        cls._check_reserved(schema)
+        n = topology.size
+        data = {name: jnp.zeros((n, *spec.shape), dtype=spec.dtype) for name, spec in schema.fields}
+        return cls(topology=topology, schema=schema, data=data)
+
+    @classmethod
+    def from_data(
+        cls,
+        topology: Topology,
+        data: Mapping[str, jax.Array],
+    ) -> Tree:
+        """Build a Tree from an existing data dict; schema is inferred from arrays."""
+        schema_dict = {}
+        for name, arr in data.items():
+            if arr.shape[0] != topology.size:
+                raise SchemaMismatch(
+                    f"Field {name!r}: leading axis {arr.shape[0]} != topology size {topology.size}"
+                )
+            schema_dict[name] = FieldSpec(shape=tuple(arr.shape[1:]), dtype=arr.dtype)
+        schema = Schema.from_dict(schema_dict)
+        cls._check_reserved(schema)
+        return cls(topology=topology, schema=schema, data=dict(data))
+
+    @classmethod
+    def _check_reserved(cls, schema: Schema) -> None:
+        conflicts = sorted(set(schema.names) & _RESERVED_FIELD_NAMES)
+        if conflicts:
+            raise SchemaMismatch(
+                f"Schema field name(s) {conflicts} would shadow Tree attributes "
+                f"under attribute access. Reserved names: {sorted(_RESERVED_FIELD_NAMES)}. "
+                f"Rename your fields, or access via tree['name'] only."
+            )
+
+    # ── access ────────────────────────────────────────────────────────
+    def __getitem__(self, name: str) -> jax.Array:
+        try:
+            return self.data[name]
+        except KeyError:
+            raise MissingField(
+                f"Field {name!r} is not in this tree. Known fields: {self.schema.names}"
+            ) from None
+
+    def __getattr__(self, name: str) -> jax.Array:
+        # Underscore names bail early to avoid pickle/repr/pytree recursion.
+        if name.startswith("_"):
+            raise AttributeError(name)
+        try:
+            return self.data[name]
+        except KeyError:
+            raise AttributeError(
+                f"Tree has no field {name!r}. Schema: {self.schema.names}. "
+                f"(Did you forget to add it via Tree.empty(topo, schema=...) or tree.update(...)?)"
+            ) from None
+
+    @property
+    def size(self) -> int:
+        return self.topology.size
+
+    def __len__(self) -> int:
+        return self.topology.size
+
+    def __repr__(self) -> str:
+        fields = ", ".join(f"{n}: {tuple(s.shape)}" for n, s in self.schema.fields)
+        return f"Tree(size={self.size}, fields={{{fields}}})"
+
+    # ── functional mutators ──────────────────────────────────────────
+    def set(self, **fields: jax.Array) -> Tree:
+        """Replace whole fields. Each value must already match ``(N, *spec.shape)``."""
+        new_data = dict(self.data)
+        for name, value in fields.items():
+            if name not in self.schema:
+                raise MissingField(
+                    f"Cannot .set({name}=...); field is not in the schema. "
+                    f"Use .update({name}=...) to add a brand-new field."
+                )
+            spec = self.schema[name]
+            arr = jnp.asarray(value, dtype=spec.dtype)
+            expected = (self.size, *spec.shape)
+            if arr.shape != expected:
+                raise SchemaMismatch(
+                    f"Field {name!r}: expected shape {expected}, got {tuple(arr.shape)}"
+                )
+            new_data[name] = arr
+        return Tree(topology=self.topology, schema=self.schema, data=new_data)
+
+    @property
+    def at(self) -> _AtAccessor:
+        """JAX-style scatter accessor: ``tree.at[indices].set(field=values)``.
+
+        Mirrors :attr:`jax.numpy.ndarray.at`. The returned indexer supports
+        ``set``, ``add``, ``multiply``, ``min``, ``max`` (all take fields as
+        keyword arguments and return a new :class:`Tree`) and ``get``
+        (returns a ``dict`` of every field evaluated at ``indices``).
+        """
+        return _AtAccessor(self)
+
+    def update(self, **fields) -> Tree:
+        """Add brand-new fields. Values can be a FieldSpec, a shape tuple, or an array.
+
+        Note: this changes the pytree structure and will invalidate any JIT
+        caches keyed on the previous structure. Prefer ``Tree.empty(topo, full_schema)``
+        when you know all the fields up front (the typical case in a sweep
+        pipeline).
+        """
+        merged = dict(self.schema.fields)
+        new_data = dict(self.data)
+        for name, spec_or_value in fields.items():
+            if name in self.schema:
+                raise ValueError(f"Field {name!r} already in schema; use .set() to overwrite")
+            if spec_or_value is None or isinstance(spec_or_value, (FieldSpec, tuple)):
+                spec = FieldSpec._coerce(spec_or_value)
+                merged[name] = spec
+                new_data[name] = jnp.zeros((self.size, *spec.shape), dtype=spec.dtype)
+            else:
+                arr = jnp.asarray(spec_or_value)
+                if arr.shape[0] != self.size:
+                    raise SchemaMismatch(
+                        f"Field {name!r}: leading axis {arr.shape[0]} != tree size {self.size}"
+                    )
+                merged[name] = FieldSpec(shape=tuple(arr.shape[1:]), dtype=arr.dtype)
+                new_data[name] = arr
+        new_schema = Schema.from_dict(merged)
+        self._check_reserved(new_schema)
+        return Tree(topology=self.topology, schema=new_schema, data=new_data)
+
+    def drop(self, *names: str) -> Tree:
+        """Drop fields; returns a Tree with reduced schema and data."""
+        new_data = {k: v for k, v in self.data.items() if k not in names}
+        return Tree(topology=self.topology, schema=self.schema.without(*names), data=new_data)
+
+    # ── identity ──────────────────────────────────────────────────────
+    def __eq__(self, other: object) -> bool:
+        if self is other:
+            return True
+        if not isinstance(other, Tree):
+            return False
+        if self.topology != other.topology or self.schema != other.schema:
+            return False
+        if set(self.data) != set(other.data):
+            return False
+        return all(jnp.array_equal(self.data[k], other.data[k]) for k in self.data)
+
+    def __hash__(self):  # type: ignore[override]
+        # Tree is never hashable: its dynamic data dict has no stable identity.
+        # If you find yourself wanting to hash a Tree, you are probably trying
+        # to pass it as a ``static_argnames`` — let it ride as a pytree instead.
+        raise TypeError("Tree is not hashable; pass it as a JAX pytree, not as a static arg.")
+
+
+# ── .at[...] accessor (JAX-style scatter for the multi-field Tree) ───
+class _TreeIndexer:
+    """Bound result of ``tree.at[idx]``; carries the indexer and the tree."""
+
+    __slots__ = ("_tree", "_idx")
+
+    def __init__(self, tree: Tree, idx) -> None:
+        self._tree = tree
+        self._idx = idx
+
+    def _apply(self, op: str, fields: Mapping[str, jax.Array]) -> Tree:
+        tree = self._tree
+        new_data = dict(tree.data)
+        for name, value in fields.items():
+            if name not in tree.schema:
+                raise MissingField(f"Cannot {op} unknown field {name!r}")
+            spec = tree.schema[name]
+            arr = jnp.asarray(value, dtype=spec.dtype)
+            new_data[name] = getattr(new_data[name].at[self._idx], op)(arr)
+        return Tree(topology=tree.topology, schema=tree.schema, data=new_data)
+
+    def set(self, **fields: jax.Array) -> Tree:
+        return self._apply("set", fields)
+
+    def add(self, **fields: jax.Array) -> Tree:
+        return self._apply("add", fields)
+
+    def multiply(self, **fields: jax.Array) -> Tree:
+        return self._apply("multiply", fields)
+
+    def min(self, **fields: jax.Array) -> Tree:
+        return self._apply("min", fields)
+
+    def max(self, **fields: jax.Array) -> Tree:
+        return self._apply("max", fields)
+
+    def get(self) -> dict:
+        """Return ``{field: tree.data[field][indices]}`` for every field."""
+        return {n: self._tree.data[n][self._idx] for n in self._tree.schema.names}
+
+
+class _AtAccessor:
+    """Descriptor returned by :attr:`Tree.at`; indexes into a :class:`_TreeIndexer`."""
+
+    __slots__ = ("_tree",)
+
+    def __init__(self, tree: Tree) -> None:
+        self._tree = tree
+
+    def __getitem__(self, idx) -> _TreeIndexer:
+        return _TreeIndexer(self._tree, idx)
+
+
+# ── JAX pytree registration ───────────────────────────────────────────
+# Leaves = data values sorted by key (deterministic for cache stability).
+# aux_data = (topology, schema, key tuple).
+def _tree_flatten(t: Tree):
+    keys = tuple(sorted(t.data))
+    leaves = tuple(t.data[k] for k in keys)
+    aux = (t.topology, t.schema, keys)
+    return leaves, aux
+
+
+def _tree_unflatten(aux, leaves):
+    topology, schema, keys = aux
+    return Tree(topology=topology, schema=schema, data=dict(zip(keys, leaves)))
+
+
+jax.tree_util.register_pytree_node(Tree, _tree_flatten, _tree_unflatten)
diff --git a/hyperiax/core/views.py b/hyperiax/core/views.py
new file mode 100644
index 0000000..45341c4
--- /dev/null
+++ b/hyperiax/core/views.py
@@ -0,0 +1,245 @@
+"""Per-call views handed to user sweep functions.
+
+Views are transient: the dispatcher constructs them anew at each level
+(holding sliced/gathered JAX arrays), passes them to the user function,
+and discards them when the function returns. They are *not* pytrees and
+*not* hashable — they live entirely inside a trace.
+
+Attribute access (``node.value``) is preferred over item access
+(``node['value']``), but both are supported.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+
+import jax
+import jax.numpy as jnp
+
+from .errors import HyperiaxError
+
+
+class _FieldsView:
+    """Shared base for per-call field-dict views.
+
+    Provides attribute / item / iteration / containment access over a fixed
+    dict of arrays. Subclasses set ``_kind`` (used in error messages and
+    repr) and a tag for which decorator argument adds missing fields.
+    """
+
+    __slots__ = ("_fields",)
+    _kind: str = "Fields"
+    _reads_arg: str = "reads"
+
+    def __init__(self, fields: Mapping[str, jax.Array]):
+        self._fields = dict(fields)
+
+    def __getattr__(self, name: str):
+        try:
+            return self._fields[name]
+        except KeyError:
+            raise AttributeError(
+                f"{self._kind} field {name!r} is not available in this scope. "
+                f"Add it to `{self._reads_arg}=...` on the sweep. "
+                f"Available: {sorted(self._fields)}"
+            ) from None
+
+    def __getitem__(self, name: str):
+        return self._fields[name]
+
+    def __iter__(self):
+        return iter(self._fields)
+
+    def __contains__(self, name) -> bool:
+        return name in self._fields
+
+    def __repr__(self) -> str:
+        return f"{self._kind}(fields={sorted(self._fields)})"
+
+
+class Node(_FieldsView):
+    """Sliced per-node fields for one level (or one selected subset).
+
+    Each attribute is a JAX array of shape ``(scope_size, *trailing)`` where
+    ``scope_size`` is the number of nodes in this dispatch scope (typically
+    a level's non-leaves).
+    """
+
+    __slots__ = ()
+    _kind = "Node"
+    _reads_arg = "reads"
+
+
+class Parent(_FieldsView):
+    """Per-node view of the parent's fields. Under :func:`jax.vmap`, each
+    attribute is a JAX array of shape ``(*trailing,)`` — one parent record
+    per node at the current level."""
+
+    __slots__ = ()
+    _kind = "Parent"
+    _reads_arg = "reads_parent"
+
+
+class Children(_FieldsView):
+    """View of the children of all parents at the current up-sweep level.
+
+    Each attribute is a :class:`ChildrenAxis` proxy backed by a flat
+    ``(M_total, *trailing)`` block (every child at the level concatenated)
+    plus segment ids. The proxy exposes:
+
+    - **reductions** ``.sum / .max / .min / .prod / .mean (axis=0)`` —
+      dispatched to ``jax.ops.segment_*``, yielding per-parent results;
+    - :meth:`map` — a segment-preserving per-child ``jax.vmap``, so a
+      non-linear per-child transform feeds the same reduction surface.
+
+    Direct array ops (indexing, arithmetic, ``__array__``) are deliberately
+    rejected: use ``children.map(fn)`` for any non-reduction per-child work.
+    """
+
+    __slots__ = ()
+    _kind = "Children"
+    _reads_arg = "reads_children"
+
+    def map(self, fn):
+        """Apply ``fn`` to each child, preserving the children axis.
+
+        ``fn`` receives a per-child :class:`Node` view (the children fields,
+        one child at a time) and returns a ``dict[str, jax.Array]``. The result
+        is a new :class:`Children` view of the per-child outputs whose fields
+        are :class:`ChildrenAxis` proxies sharing the input segments, so
+        trailing reductions (``msgs.field.sum(0)`` / ``.mean(0)`` / ...)
+        dispatch to ``jax.ops.segment_*``.
+
+        This lets a sweep apply a non-linear per-child transform and then fuse
+        with the same body regardless of degree (equal trees are just the
+        special case where all segments have the same size).
+        """
+        fields = self._fields
+        if not fields:
+            return Children({})
+        per_child = jax.vmap(lambda rec: fn(Node(rec)))
+        sample = next(iter(fields.values()))
+        out = per_child({k: v.flat for k, v in fields.items()})
+        seg, nseg = sample._segments, sample._num_segments
+        return Children(
+            {
+                k: ChildrenAxis(
+                    flat=v, segments=seg, num_segments=nseg, trailing=v.shape[1:]
+                )
+                for k, v in out.items()
+            }
+        )
+
+
+class ChildrenAxis:
+    """Virtual children-axis proxy for unequal-degree trees.
+
+    Backs a flat ``(M_total, *trailing)`` JAX array (every child at the
+    current level concatenated) plus a ``segments`` array assigning each
+    row to a parent. ``num_segments`` is a *static* Python int (derived
+    from the topology) — required for ``jax.ops.segment_*`` to produce a
+    statically-shaped output.
+
+    The user calls one of ``.sum / .prod / .max / .min / .mean (axis=0)``
+    to reduce over the children axis. Each dispatches to the matching
+    segment-reduction. Output shape is ``(num_segments, *trailing)``,
+    matching the corresponding ``Node`` view at the same level.
+
+    Non-reduction ops (indexing, broadcasted arithmetic, NumPy coercion)
+    are deliberately rejected: they would either silently produce wrong
+    results on the flat layout or imply a padded dense form the user
+    should request explicitly via :meth:`Children.gather` (TODO).
+    """
+
+    __slots__ = ("_flat", "_segments", "_num_segments", "_trailing")
+
+    def __init__(
+        self,
+        flat: jax.Array,
+        segments: jax.Array,
+        num_segments: int,
+        trailing: tuple,
+    ):
+        self._flat = flat
+        self._segments = segments
+        self._num_segments = num_segments
+        self._trailing = tuple(trailing)
+
+    @property
+    def flat(self) -> jax.Array:
+        """The backing flat ``(M_total, *trailing)`` array (all children of the
+        level concatenated, in node order). Used by :meth:`Children.map` and by
+        the dispatcher's per-child (``writes_children``) scatter."""
+        return self._flat
+
+    # ── reductions ────────────────────────────────────────────────────
+    def sum(self, axis: int = 0) -> jax.Array:
+        self._check_axis(axis)
+        return jax.ops.segment_sum(
+            self._flat,
+            self._segments,
+            num_segments=self._num_segments,
+            indices_are_sorted=True,
+        )
+
+    def prod(self, axis: int = 0) -> jax.Array:
+        self._check_axis(axis)
+        return jax.ops.segment_prod(
+            self._flat,
+            self._segments,
+            num_segments=self._num_segments,
+            indices_are_sorted=True,
+        )
+
+    def max(self, axis: int = 0) -> jax.Array:
+        self._check_axis(axis)
+        return jax.ops.segment_max(
+            self._flat,
+            self._segments,
+            num_segments=self._num_segments,
+            indices_are_sorted=True,
+        )
+
+    def min(self, axis: int = 0) -> jax.Array:
+        self._check_axis(axis)
+        return jax.ops.segment_min(
+            self._flat,
+            self._segments,
+            num_segments=self._num_segments,
+            indices_are_sorted=True,
+        )
+
+    def mean(self, axis: int = 0) -> jax.Array:
+        self._check_axis(axis)
+        sums = self.sum(0)
+        counts = jax.ops.segment_sum(
+            jnp.ones_like(self._segments, dtype=self._flat.dtype),
+            self._segments,
+            num_segments=self._num_segments,
+            indices_are_sorted=True,
+        )
+        # Reshape counts to broadcast against the trailing dims.
+        counts = counts.reshape((self._num_segments,) + (1,) * len(self._trailing))
+        return sums / counts
+
+    # ── guards ────────────────────────────────────────────────────────
+    @staticmethod
+    def _check_axis(axis: int) -> None:
+        if axis != 0:
+            raise ValueError(
+                f"ChildrenAxis reductions must use axis=0 (the children axis); got axis={axis}."
+            )
+
+    def __array__(self, dtype=None):
+        raise HyperiaxError(
+            "ChildrenAxis is a virtual proxy on an unequal-degree tree; it "
+            "cannot be coerced to a dense array. Reduce first via "
+            ".sum/.prod/.max/.min/.mean(axis=0), or fall back to "
+            "children.gather() for a padded view."
+        )
+
+    def __repr__(self) -> str:
+        return (
+            f"ChildrenAxis(num_segments={self._num_segments}, "
+            f"trailing={self._trailing}, flat_size={self._flat.shape[0]})"
+        )
diff --git a/hyperiax/execution/__init__.py b/hyperiax/execution/__init__.py
deleted file mode 100644
index 696daea..0000000
--- a/hyperiax/execution/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fastexecutor import OrderedExecutor
-from .unorderedexecutor import UnorderedExecutor
\ No newline at end of file
diff --git a/hyperiax/execution/fastexecutor.py b/hyperiax/execution/fastexecutor.py
deleted file mode 100644
index 4b1ab3d..0000000
--- a/hyperiax/execution/fastexecutor.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from __future__ import annotations
-from abc import ABC
-from ..tree import HypTree
-import jax
-from ..models import UpReducer, DownModel, UpModel
-from inspect import getfullargspec
-from functools import partial
-import jax.numpy as jnp
-
-
-class OrderedExecutor(ABC):
-    """Abstract for the ordered executor.
-
-    Ordered executors performs operations in a certain direction (eg. up/down).
-
-    It contains on abstract method `_determine_execution_order``, which gives the order in which nodes
-    are to be executed in the tree. It is assumed that all operations are batched.
-    """
-    def __init__(self, 
-                 model : UpReducer | DownModel,
-                 key = None) -> None:
-        self.key = key if key else jax.random.PRNGKey(0)
-        self.model = model
-
-    def up(self, tree : HypTree, params = {}):
-        if not issubclass(type(self.model), (UpReducer, UpModel)):
-            raise ValueError('Model needs to be of type UpReducer or UpModel')
-        # out here we fetch the data stored in the tree
-
-        #for k,val_shape in self.model.up_produces.items():
-        #    if k not in tree.data:
-        #        tree.add_property(k, val_shape)
-
-        if issubclass(type(self.model), UpReducer):
-            new_data = self._reduce_inner(tree.data, tree, params)
-
-            tree.data = {**tree.data, **new_data}
-
-            return
-        elif issubclass(type(self.model), UpModel):
-            new_data = self._up_inner(tree.data, tree, params = params)
-
-            tree.data = {**tree.data, **new_data}
-
-            return
-
-    @partial(jax.jit, static_argnames=['tree', 'self']) 
-    def _reduce_inner(self, data, tree, params):
-        iterator = zip(
-            reversed(tree.levels[1:]), # indices for levels
-            #reversed(tree.psizes[:-1]), # indices for grouping calculations
-            reversed(tree.pbuckets_ref) # indices for upwards propagation
-        )
-        for (level_start, level_end), up_ref in iterator:
-            node_data = {
-                k: jax.lax.slice_in_dim(data[k], level_start, level_end)
-                for k in self.model.up_keys
-            } | {'key': jax.numpy.arange(level_start, level_end)}
-
-            up_result = self.model.up(**node_data, params = params)
-            # save specified values
-            for k in self.model.up_preserves:
-                data[k] = data[k].at[level_start:level_end].set(up_result[k])
-
-            segments = tree.pbuckets[level_start:level_end]
-
-            fuse_scatter = {
-                f'child_{k}': self.model.reductions[k](v, segments, num_segments=len(up_ref), indices_are_sorted=True)
-                for k,v in up_result.items() if k in self.model.reductions
-            }
-
-            parent_data = {
-                k: data[k][up_ref]
-                for k in self.model.transform_parent_keys
-            }
-
-            result = self.model.transform(**parent_data, **fuse_scatter, params = params)
-            for k, val in result.items():
-                data[k] = data[k].at[up_ref].set(val)
-
-
-        return data
-    @partial(jax.jit, static_argnames=['tree', 'self']) 
-    def _up_inner(self, data, tree, params):
-        iterator = zip(
-            reversed(tree.levels), # indices for levels
-            reversed(tree.pbuckets_ref), # indices for upwards propagation
-            reversed(tree.level_non_leaf_indices) # precomputed non-leaf indices
-        )
-        # Process all levels, using precomputed non-leaf indices
-        for (level_start, level_end), up_ref, non_leaf_indices in iterator:
-            if len(non_leaf_indices) == 0:
-                continue  # Skip if all nodes in this level are leaves
-                
-            # Only gather data for non-leaf nodes
-            node_data = {
-                k: data[k][non_leaf_indices]
-                for k in self.model.up_current_keys
-            } | {'key': jax.numpy.arange(level_start, level_end)}
-
-
-            # Only gather child data for non-leaf nodes
-            child_data = {
-                f'child_{k}': data[k][tree.gather_child_idx[non_leaf_indices]]
-                for k in self.model.up_child_keys
-            }
-
-            result = self.model.up(
-                **node_data,
-                **child_data, 
-                root_mask=tree.is_root[non_leaf_indices], 
-                leaf_mask=jnp.zeros_like(non_leaf_indices, dtype=bool),  # All nodes we process are non-leaves
-                params = params)
-            
-            # Update data only for non-leaf nodes
-            for k, val in result.items():
-                data[k] = data[k].at[non_leaf_indices].set(val)
-
-        return data
-
-
-    @partial(jax.jit, static_argnames=['tree', 'self']) 
-    def _down_inner(self, data, tree, params): #inner jitted runner for MAXIMUM PERFORMANCE
-        #print(data, tree.levels)
-        for level_start, level_end in tree.levels[1:]:
-            node_data = {
-                k: jax.lax.slice_in_dim(data[k], level_start, level_end)
-                for k in self.model.down_child_keys
-            } | {'key': jax.numpy.arange(level_start, level_end)}
-            parent_indices = tree.parents[level_start:level_end] # no need to lax since tree is untraced
-            parent_data = {
-                f'parent_{k}': data[k][parent_indices]
-                for k in self.model.down_parent_keys
-            }
-
-            if params:
-                result = self.model.down(**node_data, **parent_data, params=params)
-            else:
-                result = self.model.down(**node_data, **parent_data)
-
-            for k, val in result.items():
-                data[k] = data[k].at[level_start:level_end].set(val)
-        return data
-    
-    def down(self, tree : HypTree, params = {}):
-        """Runs the down pass on the tree using the `down` function from the model.
-
-        The `down` function in the model takes the following special parameters;
-
-        `up_msg`: The message that was sent UP the edge during an upward pass
-        `key`: A PRNGKey
-        `params`: The same params passed to this function
-        `parent_*`: Data from the parent node will be passed as arguments prefixed by `parent`.\
-              If you have `value` stored in the parent node, `parent_value` will be an argument passed.
-        `*`: Data from the child node will be passed as arguments without prefix. \
-            If you have `value` stored in the child node, `value` will be an argument passed.
-
-        The executor expects the `down` function to return a dictionary, \
-            where each key returned overwrites the data in the lower node (with values not prefixed by `parent_`).
-        Because of this, all values returned from `down` functions must have a leading axis of size `b`.
-
-        Args:
-            tree (HypTree): The tree to execute on
-            params (dict, optional): optional parameters for mcmc. Defaults to {}.
-
-        Returns:
-            HypTree: The tree after running one pass
-        """
-        if not issubclass(type(self.model), DownModel):
-            raise ValueError('Model needs to be of type DownModel')
-        # out here we fetch the data stored in the tree
-
-        new_data = self._down_inner(tree.data, tree, params = params)
-
-        tree.data = {**tree.data, **new_data}
-
-        return 
\ No newline at end of file
diff --git a/hyperiax/execution/unorderedexecutor.py b/hyperiax/execution/unorderedexecutor.py
deleted file mode 100644
index b9632cf..0000000
--- a/hyperiax/execution/unorderedexecutor.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from ..models import UpdateReducer, UpdateModel
-import jax
-from inspect import getfullargspec
-from functools import partial
-from jax import numpy as jnp
-
-class UnorderedExecutor:
-    "Executes nodes in an unstructured way; giving full context from both parents and children"
-
-    def __init__(self, model, key = None) -> None:
-        self.model = model
-
-        self.key = key if key else jax.random.PRNGKey(0)
-        self.model = model
-
-    def update(self, tree, params = {}):
-        if issubclass(type(self.model), UpdateReducer):
-            new_data = self._update_reduce_inner(tree.data, tree, params)
-        elif issubclass(type(self.model), UpdateModel):
-            coloring = tree.coloring
-            red_indx = jnp.arange(tree.size)[coloring]
-            new_data = self._update_inner(tree.data, tree, red_indx, params)
-            black_indx = jnp.arange(tree.size)[~coloring]
-            new_data = self._update_inner(new_data, tree, black_indx, params)
-        else:
-            raise ValueError('Model needs to be of type UpdateReducer or UpdateModel')
-        tree.data = {**tree.data, **new_data}
-
-    @partial(jax.jit, static_argnames=['tree', 'self']) 
-    def _update_inner(self, data, tree, coloring, params):
-        node_data = {
-            k: data[k][coloring]
-            for k in self.model.update_current_keys
-        }
-        parent_data = {
-            f'parent_{k}': data[k][tree.parents[coloring]]
-            for k in self.model.update_parent_keys
-        }
-        child_data = {
-            f'child_{k}': data[k][tree.gather_child_idx[coloring]]
-            for k in self.model.update_child_keys
-        }
-
-        result = self.model.update(**parent_data, **node_data, **child_data, 
-                        root_mask=tree.is_root[coloring], 
-                        leaf_mask=tree.is_leaf[coloring], params = params)
-
-        for k, val in result.items():
-            data[k] = data[k].at[coloring].set(val)
-
-        return data
-
-
-    @partial(jax.jit, static_argnames=['tree', 'self']) 
-    def _update_reduce_inner(self, data, tree, params):
-        iterator = zip(
-            reversed(tree.levels[1:]), # indices for levels
-            reversed(tree.pbuckets_ref) # indices for upwards propagation
-        )
-        unrolled = list(iterator)
-        i1 = unrolled[::2]
-        i2 = unrolled[1::2]
-        # we can make this much faster by actually merging the underlying structures... #TODO
-        for it in (i1, i2):
-            for (level_start, level_end), up_ref in it:
-                child_node_data = {
-                    k: jax.lax.slice_in_dim(data[k], level_start, level_end)
-                    for k in self.up_keys
-                }
-                #print(child_node_data)
-                up_result = self.model.up(**child_node_data, params = params)
-
-                segments = tree.pbuckets[level_start:level_end]
-
-                fuse_scatter = {
-                    f'child_{k}': self.model.reductions[k](v, segments, num_segments=len(up_ref), indices_are_sorted=True)
-                    for k,v in up_result.items()
-                }
-
-                node_data = {
-                    k: data[k][up_ref]
-                    for k in self.update_node_keys
-                }
-
-                parent_data = {
-                    f'parent_{k}': data[k][tree.parents[up_ref]]
-                    for k in self.update_parent_keys
-                }
-
-                result = self.model.update(**parent_data, **node_data, **fuse_scatter, params = params)
-                for k, val in result.items():
-                    data[k] = data[k].at[up_ref].set(val)
-
-
-        return data
\ No newline at end of file
diff --git a/hyperiax/mcmc/__init__.py b/hyperiax/mcmc/__init__.py
deleted file mode 100644
index c56efc8..0000000
--- a/hyperiax/mcmc/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .parameterstore import ParameterStore
-from .parameter import Parameter
-from .fixedparameter import FixedParameter
-from .varianceParameter import VarianceParameter
-from .flatParameter import FlatParameter
-from .statistics import gelman_rubin
diff --git a/hyperiax/mcmc/fixedparameter.py b/hyperiax/mcmc/fixedparameter.py
deleted file mode 100644
index f9aa48d..0000000
--- a/hyperiax/mcmc/fixedparameter.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from .parameter import Parameter
-from copy import copy
-class FixedParameter(Parameter):
-    """ A parameter that is constant regardless of updates.  The parameter always have the value passed in the constructor
-    """
-    def __init__(self, value) -> None:
-        super().__init__(value)
-
-    def propose(self, key):
-        """ Propose a new value for the parameter
-
-        :param key: A key to sample with
-        :return: Copy of the parameter
-        """
-        return copy(self)
-    
-    def update(self, value, accepted): 
-        """ Update the parameter. Does nothing as the parameter is fixed, i.e. the new value is discarded
-
-        :param value: The new value to update the parameter with
-        :param accepted: Whether the new value was accepted
-        """
-        ...
-
-    def log_prior(self):
-        """ Logarithm of the prior probability of the parameter
-
-        :return: 0. as the parameter is fixed
-        """
-        return 0.
-
diff --git a/hyperiax/mcmc/flatParameter.py b/hyperiax/mcmc/flatParameter.py
deleted file mode 100644
index 213ade1..0000000
--- a/hyperiax/mcmc/flatParameter.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from .parameter import Parameter
-
-import jax
-import jax.numpy as jnp
-from jax.scipy.special import gammaln
-from scipy.stats import uniform
-import matplotlib.pyplot as plt
-
-def uniform_logpdf(x, a, b):
-    """ Log PDF of the uniform distribution
-    
-    :param x: The value to evaluate the PDF at
-    :param a: the left boundary of the uniform distribution
-    :param b: the right boundary of the uniform distribution
-    :return: The logarithm of the PDF of the uniform distribution at x
-    """
-    return uniform.logpdf(x, loc=a, scale=b-a)
-
-
-class FlatParameter(Parameter):
-    def __init__(self, value, min=None, max=None, proposal_var=.01, keep_constant=False) -> None:
-        """ Initialize a parameter with flat prior
-        
-        :param value: The initial value of the parameter
-        :param min: the left boundary of the interval
-        :param max: the right boundary of the interval
-        :param proposal_var: The variance of the proposal distribution
-        :param keep_constant: Whether the parameter should be kept constant in new proposals
-        """
-        super().__init__(value)
-        self.min = min
-        self.max = max
-        self.proposal_var = proposal_var
-        self.keep_constant = keep_constant
-
-    def propose(self, key): 
-        '''
-        Proposal distribution: 'mirorred_gaussian' 
-        '''
-
-        if self.keep_constant:
-            return self,0.
-        
-        shape = self.value.shape if hasattr(self.value,'shape') else ()
-        new_value = self.value + jnp.sqrt(self.proposal_var)*jax.random.normal(key,shape=shape)
-        # Mirror values outside bounds component-wise
-        if self.min is not None or self.max is not None:
-            def mirror_bounds(val):
-                if self.min is not None:
-                    below_min = val<self.min
-                    val = jnp.where(below_min,2*self.min-val,val)
-                if self.max is not None:
-                    above_max = val>self.max
-                    val = jnp.where(above_max,2*self.max-val,val)
-                return val, jnp.any(below_min) if self.min is not None else False, jnp.any(above_max) if self.max is not None else False
-            
-            new_value, below, above = mirror_bounds(new_value)
-            while below or above:
-                new_value, below, above = mirror_bounds(new_value)
-        log_correction = 0.
-        return FlatParameter(**{**self.__dict__,'value':new_value}),log_correction
-
-    def update(self, value, accepted): 
-        """ Update the parameter with a new value
-        
-        :param value: The new value to update the parameter with
-        :param accepted: Whether the new value was accepted.
-        """
-        if accepted:
-            if self.max and self.min is not None:
-                value = jnp.clip(value, self.min, self.max)
-            elif self.max is not None:
-                value = jnp.clip(value, None, self.max)
-            elif self.min is not None:
-                value = jnp.clip(value, self.min, None)
-            self.value = value
-        
-    def log_prior(self):
-        """ Logarithm of the prior probability of the parameter
-        
-        :return: The logarithm of the prior probability of the parameter
-        """
-        return uniform_logpdf(self.value, self.min, self.max).sum()
-
-    def plot_prior(self, x_min=1e-3, x_max=1, n_points=100):
-            """Plot the prior distribution of the parameter
-            
-            :param x_min: Minimum x value to plot
-            :param n_points: Number of points to plot
-            :param x_max: Maximum x value to plot
-            """
-            # Create array of parameter values
-            x = jnp.linspace(x_min,x_max,n_points)
-
-            # Calculate prior for each value
-            prior_vals = [FlatParameter(**{**self.__dict__,'value':val}).log_prior() for val in x]
-
-            # Plot exp(log_prior)
-            plt.figure()
-            plt.plot(x,jnp.exp(jnp.array(prior_vals)))
-            plt.xlabel('Parameter value')
-            plt.ylabel('Prior density')
-            plt.title('Prior distribution')
-
-            # Find x value where prior is maximized
-            max_idx = jnp.argmax(jnp.exp(jnp.array(prior_vals)))
-            max_x = x[max_idx]
-            print(f"Prior is maximized at x = {max_x}")
diff --git a/hyperiax/mcmc/metropolis_hastings.py b/hyperiax/mcmc/metropolis_hastings.py
deleted file mode 100644
index 0c9063d..0000000
--- a/hyperiax/mcmc/metropolis_hastings.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import jax
-import jax.numpy as jnp
-import numpy as np
-from tqdm import tqdm
-
-def metropolis_hastings(log_target, proposal, data, init, num_samples, burn_in=0, thin=1, rng_key=None, savef=lambda x: x, verbose=True):
-    """ MCMC using Metropolis-Hastings algorithm
-
-    :param log_target: Function returning the logarithm of the target distribution
-    :param proposal: Function returning a sample from the proposal distribution
-    :param data: Observed data
-    :param init: Initial state
-    :param num_samples: Number of samples to draw
-    :param burn_in: Number of samples to discard at the beginning
-    :param thin: Thinning rate
-    :param rng_key: Random number generator key
-    :param savef: Function to save the state
-    :param verbose: Whether to show progress bar and acceptance rate
-    :return: List of log likelihoods and samples
-    """
-    if rng_key is None:
-        rng_key = jax.random.PRNGKey(0)
-
-    rng_key, subkey = jax.random.split(rng_key)
-    current_state,_ = proposal(data, init, subkey)
-    log_likelihood_current = log_target(data,current_state)
-    log_likelihoods = []
-    samples = []
-    accepted_count = 0  # Initialize counter for accepted proposals
-    accepted_count_params = 0  # Counter for accepted proposals when update_params is True
-    accepted_count_no_params = 0  # Counter for accepted proposals when update_params is False
-    total_count_params = 0  # Total number of proposals when update_params is True
-    total_count_no_params = 0  # Total number of proposals when update_params is False
-
-    iterator=range(num_samples+burn_in)
-    if verbose:
-        iterator=tqdm(iterator)
-
-    for i in iterator:
-        rng_key, subkey = jax.random.split(rng_key)
-
-        # Propose a new state
-        update_params = proposal.update_params if hasattr(proposal,'update_params') else False
-        update_obs_var = proposal.update_obs_var if hasattr(proposal,'update_obs_var') else False
-        proposed_state,log_correction = proposal(data, current_state, subkey)
-
-        # Compute acceptance probability
-        log_likelihood_proposed = log_target(data,proposed_state)
-        alpha = jnp.minimum(1.0, jnp.exp(log_likelihood_proposed-log_likelihood_current+log_correction))
-        if update_params and update_obs_var:
-            alpha = 1.
-
-        # Update counters based on update_params
-        if update_params:
-            total_count_params += 1
-        else:
-            total_count_no_params += 1
-
-        # Decide whether to accept the proposed state
-        if jax.random.uniform(subkey)<alpha or (hasattr(proposal,'accept_next_state') and proposal.accept_next_state):
-            current_state = proposed_state
-            log_likelihood_current = log_likelihood_proposed
-            accepted_count += 1  # Increment overall count
-            if update_params:
-                accepted_count_params += 1  # Increment params count
-            else:
-                accepted_count_no_params += 1  # Increment no-params count
-
-        # Save the sample after burn-in and according to thinning rate
-        if i >= burn_in and (i - burn_in) % thin == 0:
-            log_likelihoods.append(log_likelihood_current)
-            samples.append(savef(current_state))
-
-    # acceptance rates
-    acceptance_rate = accepted_count/(num_samples+burn_in)
-    acceptance_rate_params = accepted_count_params/total_count_params if total_count_params > 0 else 0
-    acceptance_rate_no_params = accepted_count_no_params/total_count_no_params if total_count_no_params > 0 else 0
-    
-    if verbose:
-        print(f"Overall acceptance rate: {acceptance_rate:.4f}")
-        if hasattr(proposal,'update_params'):
-            print(f"Acceptance rate (update_params=True): {acceptance_rate_params:.4f}")
-            print(f"Acceptance rate (update_params=False): {acceptance_rate_no_params:.4f}")
-
-    return log_likelihoods,samples
\ No newline at end of file
diff --git a/hyperiax/mcmc/parameter.py b/hyperiax/mcmc/parameter.py
deleted file mode 100644
index 3416d05..0000000
--- a/hyperiax/mcmc/parameter.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from __future__ import annotations
-from abc import ABC, abstractmethod
-
-class Parameter(ABC):
-    """ Base class for a parameter in a Markov Chain Monte Carlo simulation.
-    """
-
-    def __init__(self, value) -> None:
-        """ Initialize the Parameter.
-
-        :param value: The initial value of the parameter.
-        """
-        self.value = value
-
-    @abstractmethod
-    def update(self, value, accepted=True): 
-        """ Update the value of the parameter, if accepted.
-
-        value: The new value of the parameter.
-        accepted: Whether the new value is accepted. Default true.
-        """
-        ...
-
-    @abstractmethod
-    def propose(self, key) -> Parameter: 
-        """ Propose a new parameter given a key
-
-        :param key: the key to generate the parameter with
-        :return: A new parameter proposal
-        """
-        ...
-
-    @abstractmethod
-    def log_prior(self):
-        """ Logarithm of the prior probability of the parameter.
-
-        :return: The logarithm of the prior probability of the parameter.
-        """
-        ...
-
diff --git a/hyperiax/mcmc/parameterstore.py b/hyperiax/mcmc/parameterstore.py
deleted file mode 100644
index b931ffa..0000000
--- a/hyperiax/mcmc/parameterstore.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from __future__ import annotations
-from typing import Dict
-from .parameter import Parameter
-from jax.random import split
-
-class ParameterStore:
-    """ The parameter store hosts a set of parameters. 
-    """
-
-    def __init__(self, params : Dict[str, Parameter]) -> None:
-        """ Initialize the parameter store.
-
-        :param params: Dict with the parameters to store and their associated keys (names).
-        """
-        self.params = params
-
-    def __len__(self):
-        """ Get the number of parameters in the store.
-
-        :return: The number of parameters in the store
-        """
-        return len(self.params)
-
-    def __getitem__(self, key):
-        return self.params[key]
-
-    def values(self):
-        """ Gets the values of the parameters in the store
-
-        :return: The parameters in a dictionary.
-        """
-        return {k: v.value for k,v in self.params.items()}
-
-
-    def propose(self, key) -> ParameterStore:
-        """ Propose a new parameter store, containing newly sampled parameters.
-
-        :param key: A key to sample with.
-        :return: The newly sampled parameter store.
-        """
-        pcount = len(self)
-        subkeys = split(key, pcount)
-        
-        proposals_and_corrections = {k: v.propose(rngkey) for rngkey,(k,v) in zip(subkeys,self.params.items())}
-        new_params = {k: v[0] for k,v in proposals_and_corrections.items()}
-        log_correction = sum(v[1] for v in proposals_and_corrections.values())
-        return ParameterStore(new_params),log_correction
-
-    def log_prior(self):
-        """ Logarithm of the prior probability of the parameters.
-
-        :return: The logarithm of the prior probability of the parameters.
-        """
-        return sum([v.log_prior() for v in self.params.values()])
-
diff --git a/hyperiax/mcmc/plotting.py b/hyperiax/mcmc/plotting.py
deleted file mode 100644
index 2b5c17f..0000000
--- a/hyperiax/mcmc/plotting.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import numpy as np
-from matplotlib import pyplot as plt
-
-# trace plots for MCMC
-def trace_plots(samples,true_params=None,rewrite_names=None,y_bottom=None,burnin=0):
-    """ Trace plots 
-    
-    :param samples: A list of ParameterStore objects.
-    """
-
-    # Determine grid layout
-    num_params = len(samples[0])
-    num_rows = int(np.sqrt(num_params))
-    num_cols = num_params // num_rows + (num_params % num_rows > 0)
-
-    # Create subplots
-    fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, figsize=(15, 5))
-    axes = np.atleast_2d(axes)  # Ensure axes is always 2D array
-
-    # Plot traces
-    for ax, (i, param) in zip(axes.ravel(), enumerate(samples[0].values().keys())):
-        name = param if rewrite_names is None else rewrite_names[param]
-        ax.plot(np.array([sample[param].value for sample in samples]))
-        ax.set_title(f"Trace for {name}")
-        ax.set_xlabel('Iteration')
-        # Add mean line
-        values = np.array([sample[param].value for sample in samples[burnin:]])
-        if values.ndim > 1:
-            # For multidimensional values, compute mean for each dimension
-            mean_vals = np.mean(values, axis=0)
-            for i, mean_val in enumerate(mean_vals):
-                ax.axhline(y=mean_val, color='r', linestyle='-', alpha=0.7, label=f'Mean dim {i}' if i==0 else None)
-        else:
-            # For scalar values, just one mean line
-            mean_val = np.mean(values)
-            ax.axhline(y=mean_val, color='r', linestyle='-')
-        ax.set_ylabel(name)
-        ax.grid(True)
-        if true_params is not None:
-            # Handle both scalar and array true parameters
-            true_param_values = np.atleast_1d(true_params[param])
-            for i, true_val in enumerate(true_param_values):
-                ax.axhline(y=true_val, color='g', linestyle='--', label=f'True value dim {i}' if i==0 and len(true_param_values)>1 else None)
-        ax.set_ylim(bottom=y_bottom if y_bottom is not None else min(np.min(values),0.))
-
-    # Remove any unused subplots
-    for ax in axes.ravel()[num_params:]:
-        ax.axis('off')
-
-    plt.tight_layout()
-    return axes
\ No newline at end of file
diff --git a/hyperiax/mcmc/statistics.py b/hyperiax/mcmc/statistics.py
deleted file mode 100644
index 48cb918..0000000
--- a/hyperiax/mcmc/statistics.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-
-# Calculate Gelman-Rubin statistics for each parameter
-def gelman_rubin(chains):
-    """Compute Gelman-Rubin statistic for each parameter
-    
-    :param chains: List of chains, each containing parameter samples
-    """
-    n = len(chains[0]) # length of each chain
-    m = len(chains) # number of chains
-    
-    # Get parameter names from first sample of first chain
-    param_names = list(chains[0][0].values().keys())
-    
-    # Convert chains to arrays for each parameter
-    chain_arrays = {}
-    for param in param_names:
-        chain_arrays[param] = np.array([[chain[i][param].value for i in range(n)] for chain in chains])
-    
-    # Calculate statistics for each parameter
-    r_hats = {}
-    for param in param_names:
-        # Chain means
-        chain_means = chain_arrays[param].mean(axis=1)
-        # Overall mean
-        overall_mean = chain_means.mean()
-        
-        # Between-chain variance
-        B = n*np.var(chain_means,ddof=1)
-        
-        # Within-chain variance
-        W = np.mean([np.var(chain,ddof=1) for chain in chain_arrays[param]])
-        
-        # Estimated variance
-        V = (n-1)/n*W + B/n
-        
-        # R-hat
-        r_hats[param] = np.sqrt(V/W)
-    
-    return r_hats
diff --git a/hyperiax/mcmc/varianceParameter.py b/hyperiax/mcmc/varianceParameter.py
deleted file mode 100644
index 8f122ef..0000000
--- a/hyperiax/mcmc/varianceParameter.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from .parameter import Parameter
-
-import jax
-import jax.numpy as jnp
-from jax.scipy.special import gammaln
-from scipy.stats import uniform
-import matplotlib.pyplot as plt
-
-def inverse_gamma_logpdf(x, alpha, beta):
-    """ Log PDF of the inverse gamma distribution
-    
-    :param x: The value to evaluate the PDF at
-    :param alpha: The alpha parameter of the inverse gamma distribution
-    :param beta: The beta parameter of the inverse gamma distribution
-    :return: The logarithm of the PDF of the inverse gamma distribution at x
-    """
-    return alpha * jnp.log(beta) - gammaln(alpha) - (alpha + 1) * jnp.log(x) - beta / x
-
-def uniform_logpdf(x, a, b):
-    """ Log PDF of the uniform distribution
-    
-    :param x: The value to evaluate the PDF at
-    :param a: the left boundary of the uniform distribution
-    :param b: the right boundary of the uniform distribution
-    :return: The logarithm of the PDF of the uniform distribution at x
-    """
-    return uniform.logpdf(x, loc=a, scale=b-a)
-
-
-class VarianceParameter(Parameter):
-    def __init__(self, value, alpha=3., beta=2., min=None, max=None, proposal_var=.01, keep_constant=False, prior='inv_gamma', proposal='log_normal') -> None:
-        """ Initialize a variance parameter
-        
-        :param value: The initial value of the parameter
-        :param alpha: The alpha parameter of the inverse gamma distribution #sofia/morten change: and uniform and mirrored gaussian interval endpoints
-        :param beta: The beta parameter of the inverse gamma distribution #sofia/morten change: and uniform and mirrored gaussian interval endpoints
-        :param a: the left boundary of the uniform distribution and the mirrored gaussian 
-        :param b: the right boundary of the uniform distribution and the mirrored gaussian
-        :param proposal_var: The variance of the proposal distribution
-        :param keep_constant: Whether the parameter should be kept constant in new proposals
-        :param prior: 'inv_gamma' or 'uniform'
-        :param proposal: 'log_normal' or 'mirrored_gaussian'
-        """
-        super().__init__(value)
-        self.alpha = alpha
-        self.beta = beta
-        self.min = min
-        self.max = max
-        self.proposal_var = proposal_var
-        self.keep_constant = keep_constant
-        self.prior = prior
-        self.proposal = proposal
-
-    def propose(self, key): 
-        '''
-        Proposal distribution: 'lognormal' or 'mirorred_gaussian' 
-        '''
-
-        if self.keep_constant:
-            return self,0.
-        
-        if self.proposal == 'log_normal':
-            shape = self.value.shape if hasattr(self.value,'shape') else ()
-            noise = jnp.sqrt(self.proposal_var)*jax.random.normal(key,shape=shape)
-            new_value = jnp.exp(jnp.log(self.value)+noise)
-            if self.min is not None:
-                new_value = jnp.clip(new_value,self.min,jnp.inf)
-            if self.max is not None:
-                new_value = jnp.clip(new_value,0,self.max)
-            # Add correction term for log-normal proposal asymmetry
-            log_correction = -noise  # Since q(x'|x)/q(x|x') = exp(-eps) where eps is the noise
-            return VarianceParameter(**{**self.__dict__,'value':new_value}),log_correction
-        
-        elif self.proposal == 'mirrored_gaussian':
-            shape = self.value.shape if hasattr(self.value,'shape') else ()
-            new_value = self.value + jnp.sqrt(self.proposal_var)*jax.random.normal(key,shape=shape)
-            # Mirror values outside bounds component-wise
-            if self.min is not None:
-                below_min = new_value<self.min
-                new_value = jnp.where(below_min,2*self.min-new_value,new_value)
-            if self.max is not None:
-                above_max = new_value>self.max
-                new_value = jnp.where(above_max,2*self.max-new_value,new_value)
-            log_correction = 0.
-            return VarianceParameter(**{**self.__dict__,'value':new_value}),log_correction
-        else:
-            raise ValueError(f"Unknown proposal type: {self.proposal}")
-
-
-    def update(self, value, accepted): 
-        """ Update the parameter with a new value
-        
-        :param value: The new value to update the parameter with
-        :param accepted: Whether the new value was accepted.
-        """
-        if accepted:
-            if self.max is not None:
-                self.value = jnp.clip(value, 0, self.max)
-            else:
-                self.value = value
-    
-        
-    def log_prior(self):
-        """ Logarithm of the prior probability of the parameter
-        
-        :return: The logarithm of the prior probability of the parameter
-        """
-        if self.prior == 'inv_gamma':
-            return inverse_gamma_logpdf(self.value, self.alpha, self.beta).sum()
-        elif self.prior == 'uniform':
-            return uniform_logpdf(self.value, self.min, self.max).sum()
-        else:
-            raise ValueError(f"Unknown prior type: {self.prior}")
-
-    def plot_prior(self, x_min=1e-3, x_max=1, n_points=100):
-            """Plot the prior distribution of the parameter
-            
-            :param x_min: Minimum x value to plot
-            :param n_points: Number of points to plot
-            :param x_max: Maximum x value to plot
-            """
-            # Create array of parameter values
-            x = jnp.linspace(x_min,x_max,n_points)
-
-            # Calculate prior for each value
-            prior_vals = [VarianceParameter(**{**self.__dict__,'value':val}).log_prior() for val in x]
-
-            # Plot exp(log_prior)
-            plt.figure()
-            plt.plot(x,jnp.exp(jnp.array(prior_vals)))
-            plt.xlabel('Parameter value')
-            plt.ylabel('Prior density')
-            plt.title('Prior distribution')
-
-            # Find x value where prior is maximized
-            max_idx = jnp.argmax(jnp.exp(jnp.array(prior_vals)))
-            max_x = x[max_idx]
-            print(f"Prior is maximized at x = {max_x}")
diff --git a/hyperiax/models/__init__.py b/hyperiax/models/__init__.py
deleted file mode 100644
index f2e53cd..0000000
--- a/hyperiax/models/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .updownmodel import UpReducer,DownModel,UpModel
-from .lambdamodels import UpLambdaReducer, DownLambda, UpdateLambda, UpLambda
-from .updatemodel import UpdateReducer, UpdateModel
-from . import functional    
\ No newline at end of file
diff --git a/hyperiax/models/basemodel.py b/hyperiax/models/basemodel.py
deleted file mode 100644
index fa39b39..0000000
--- a/hyperiax/models/basemodel.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from abc import ABC, abstractmethod
-from .utils import map_reduction
-
-class BaseModel(ABC):
-    def __init__(self) -> None:
-        super().__init__()
-        self._set_keys()
-    
-    @abstractmethod
-    def _set_keys(self): ...
-
-class ReducerModel(BaseModel):
-    def __init__(self, reductions, up_preserves = []) -> None:
-        super().__init__()
-        self.reductions = {k: map_reduction(v) for k,v in reductions.items()}
-        self.up_preserves = up_preserves
diff --git a/hyperiax/models/functional/__init__.py b/hyperiax/models/functional/__init__.py
deleted file mode 100644
index 77564d4..0000000
--- a/hyperiax/models/functional/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .reducers import sum_fuse_children
-from .shortup import pass_up
\ No newline at end of file
diff --git a/hyperiax/models/functional/reducers.py b/hyperiax/models/functional/reducers.py
deleted file mode 100644
index 526b339..0000000
--- a/hyperiax/models/functional/reducers.py
+++ /dev/null
@@ -1,14 +0,0 @@
-
-
-def sum_fuse_children(axis=-1):
-    """ Fuse across children 
-
-    :param axis:  axis (int, optional): the axis to sum over. Defaults to -1.
-    """
-    def _fuse(**kwargs):
-        """ _fuse
-
-        :return: returns a function that sums an axis of keyed values prefixed with `child_`
-        """
-        return {k[6:]:v.sum(axis=axis) for k,v in kwargs.items() if k.startswith('child_')}
-    return _fuse
diff --git a/hyperiax/models/functional/shortup.py b/hyperiax/models/functional/shortup.py
deleted file mode 100644
index 0858c7b..0000000
--- a/hyperiax/models/functional/shortup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-def pass_up(*args):
-    """ Returns a function that passes a set of keys on.
-
-    """
-    def _f(**kwargs):
-        """ _F to pass_up
-
-        :return: `pass_up('value', 'noise')` will give a function that given a dictionary, returns a new one containing only `value` and `noise`
-        """
-        return {k: kwargs[k] for k in args}
-    return _f
\ No newline at end of file
diff --git a/hyperiax/models/lambdamodels.py b/hyperiax/models/lambdamodels.py
deleted file mode 100644
index 77b4ed8..0000000
--- a/hyperiax/models/lambdamodels.py
+++ /dev/null
@@ -1,121 +0,0 @@
-from .updownmodel import UpReducer, DownModel, UpModel
-from .updatemodel import UpdateReducer, UpdateModel
-
-class UpLambdaReducer(UpReducer):
-    """ Lambda model that only contains an up interface
-
-    :param UpModel: Requires an up and fuse function.
-    """
-    def __init__(self, up_fn, transform_fn, reductions, up_preserves = []) -> None:
-        """Lambda model that only contains an up interface
-
-        :param up_fn: param intputs from node to transform_fn
-        :param transform_fn: function to transform or do calculations form up parameters
-        """
-
-        self.up = up_fn
-        self.transform = transform_fn
-
-        super().__init__(reductions=reductions, up_preserves=up_preserves)
-
-    def up(self, *args, **kwargs):
-        """ Up function to define values to fuse function
-        :return: input arguments to fuse function
-        """
-        raise ValueError('Model does not have a valid up function')
-    
-    def transform(self, *args, **kwargs):
-        """ Fuse function to define calculations from up parameters
-        :return: calculated values from up parameters to parent node
-        """
-        raise ValueError('Model does not have a valid fuse function')
-    
-class DownLambda(DownModel):
-    """ Lambda model that only contains a down interface
-
-    :param DownModel  Requires a down function.
-    """
-
-    def __init__(self, down_fn) -> None:
-        """ Initialize DownLambda model
-
-        :param down_fn: function to define values to fuse function
-        """
-        self.down = down_fn # need to do this to carry the argspec
-    
-        super().__init__()
-
-    def down(self, *args, **kwargs):
-        """ Down function to define values to fuse function
-        :return: input arguments to fuse function
-        """
-        raise ValueError('Model does not have a valid down function')
-    
-class UpdateLambdaReducer(UpdateReducer):
-    """Lambda model that only contains a local update interface
-
-    :param UpdateModel: Requires an update function.
-    """
-    def __init__(self, up_fn, update_fn, reductions) -> None:
-        """Lambda model that only contains an up interface
-
-        :param up_fn: param intputs from node to update_fn
-        :param update_fn: function to update or do calculations form up parameters
-        """
-        self.up = up_fn
-        self.update = update_fn
-
-        super().__init__(reductions=reductions)
-
-    def up(self, *args, **kwargs):
-        """ Up function to define values to fuse function
-        :return: input arguments to fuse function
-        """
-        raise ValueError('Model does not have a valid up function')
-    
-    def update(self, *args, **kwargs):
-        """ Fuse function to define calculations from up parameters
-        :return: calculated values from up parameters to parent node
-        """
-        raise ValueError('Model does not have a valid fuse function')
-    
-
-class UpdateLambda(UpdateModel):
-    """Lambda model that only contains a local update interface
-
-    :param UpdateModel: Requires an update function.
-    """
-    def __init__(self, update_fn) -> None:
-        """Lambda model that only contains an up interface
-
-        :param update_fn: function to update or do calculations from both parent and child nodes
-        """
-        self.update = update_fn
-
-        super().__init__()
-    
-
-    def update(self, *args, **kwargs):
-        """ update function to define calculations from up parameters
-        :return: calculated values from child and parent parameters
-        """
-        raise ValueError('Model does not have a valid update function')
-    
-class UpLambda(UpModel):
-    """Lambda model that only contains an upward interface
-    """
-    def __init__(self, up_fn) -> None:
-        """Lambda model that only contains an up interface
-
-        :param up_fn: function to take current node and children node values and proive values to se in current
-        """
-        self.up = up_fn
-
-        super().__init__()
-    
-
-    def up(self, *args, **kwargs):
-        """ Up function to calculate the upward action on the tree
-        :return: calculated values from child and current values
-        """
-        raise ValueError('Model does not have a valid up function')
\ No newline at end of file
diff --git a/hyperiax/models/prebuilts/__init__.py b/hyperiax/models/prebuilts/__init__.py
deleted file mode 100644
index 55a80f2..0000000
--- a/hyperiax/models/prebuilts/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .mean_estimation import PhyloMeanModel
\ No newline at end of file
diff --git a/hyperiax/models/prebuilts/mean_estimation.py b/hyperiax/models/prebuilts/mean_estimation.py
deleted file mode 100644
index b46aff2..0000000
--- a/hyperiax/models/prebuilts/mean_estimation.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from ..updownmodel import UpReducer
-import jax
-from jax import numpy as jnp
-from functools import partial
-from ..functional import pass_up
-
-class PhyloMeanModel(UpReducer):
-    """Prebuilt model that calculates phylogenetic means in each node
-
-    Assumes leaves all contain `estimated_value` before running it. This corresponds to actual observations.
-    """
-    def __init__(self, **kwargs) -> None:
-        super().__init__(reductions={'weighted_value': 'sum', 'inverse_edge_length': 'sum'}, **kwargs)
-
-    def up(self, estimated_value, edge_length, **kwargs):
-        return {'weighted_value': estimated_value/edge_length, 'inverse_edge_length':1/edge_length}
-
-
-    def transform(self, child_weighted_value,child_inverse_edge_length, **kwargs):
-        return {'estimated_value': child_weighted_value/child_inverse_edge_length}
\ No newline at end of file
diff --git a/hyperiax/models/updatemodel.py b/hyperiax/models/updatemodel.py
deleted file mode 100644
index e1ad6f6..0000000
--- a/hyperiax/models/updatemodel.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from .basemodel import ReducerModel, BaseModel
-from abc import abstractmethod
-from ..mcmc import ParameterStore
-from inspect import getfullargspec
-from .utils import filter_keywords
-
-class UpdateReducer(ReducerModel):
-    def __init__(self, reductions) -> None:
-        super().__init__(reductions=reductions)
-
-    @abstractmethod
-    def update(self, **kwargs): ...
-
-    @abstractmethod
-    def up(self, **kwargs): ...
-
-    def _set_keys(self):
-        up_arg_spec = getfullargspec(self.model.up)
-        upkeys = up_arg_spec.args
-        filter_keywords(upkeys)
-        self.up_keys = upkeys
-
-
-        update_arg_spec = getfullargspec(self.model.update)
-        keys = filter_keywords(update_arg_spec.args)
-
-        update_parent_keys = [k.removeprefix('parent_') for k in keys if k.startswith('parent_')]
-        update_node_keys = [k for k in keys if not k.startswith('parent_') and not k.startswith('child_')]
-
-        self.update_parent_keys = update_parent_keys
-        self.update_node_keys = update_node_keys
-
-class UpdateModel(BaseModel):
-    def __init__(self) -> None:
-        super().__init__()
-
-    @abstractmethod
-    def update(self, **kwargs): ...
-
-    def _set_keys(self):
-        update_arg_spec = getfullargspec(self.update)
-        keys = filter_keywords(update_arg_spec.args)
-        update_child_keys = [k.removeprefix('child_') for k in keys if k.startswith('child_')]
-        update_parent_keys = [k.removeprefix('parent_') for k in keys if k.startswith('parent_')]
-        update_current_keys = [k for k in keys if not k.startswith('parent_') and not k.startswith('child_')]
-
-        self.update_child_keys = update_child_keys
-        self.update_parent_keys = update_parent_keys
-        self.update_current_keys = update_current_keys
\ No newline at end of file
diff --git a/hyperiax/models/updownmodel.py b/hyperiax/models/updownmodel.py
deleted file mode 100644
index 80ff747..0000000
--- a/hyperiax/models/updownmodel.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from .basemodel import BaseModel, ReducerModel
-from abc import abstractmethod
-from inspect import getfullargspec
-from .utils import filter_keywords
-
-class UpReducer(ReducerModel):
-    def __init__(self, reductions, up_preserves = []) -> None:
-        super().__init__(reductions=reductions, up_preserves=up_preserves)
-
-    @abstractmethod
-    def transform(self, **kwargs): ...
-
-    @abstractmethod
-    def up(self, **kwargs): ...
-
-    def _set_keys(self):
-        up_arg_spec = getfullargspec(self.up)
-        upkeys = filter_keywords(up_arg_spec.args)
-        self.up_keys = [k for k in upkeys if k != 'self']
-        self.up_keys
-
-        transform_arg_spec = getfullargspec(self.transform)
-        keys = filter_keywords(transform_arg_spec.args)
-
-        transform_child_keys = [k.removeprefix('child_') for k in keys if k.startswith('child_')]
-        transform_parent_keys = [k for k in keys if not k.startswith('child_')]
-
-        self.transform_child_keys = transform_child_keys
-        self.transform_parent_keys = transform_parent_keys
-
-class DownModel(BaseModel):
-    def __init__(self) -> None:
-        super().__init__()
-
-    @abstractmethod
-    def down(self, **kwargs): ...
-
-    def _set_keys(self):
-        arg_spec = getfullargspec(self.down)
-
-        keys = filter_keywords(arg_spec.args)
-
-        down_parent_keys = [k.removeprefix('parent_') for k in keys if k.startswith('parent_')]
-        down_child_keys = [k for k in keys if not k.startswith('parent_')]
-
-        self.down_parent_keys = down_parent_keys
-        self.down_child_keys = down_child_keys
-
-class UpModel(BaseModel):
-    def __init__(self) -> None:
-        super().__init__()
-
-    @abstractmethod
-    def up(self, **kwargs): ...
-
-    def _set_keys(self):
-        arg_spec = getfullargspec(self.up)
-
-        keys = filter_keywords(arg_spec.args)
-
-        up_child_keys = [k.removeprefix('child_') for k in keys if k.startswith('child_')]
-        up_current_keys = [k for k in keys if not k.startswith('child_')]
-
-        self.up_child_keys = up_child_keys
-        self.up_current_keys = up_current_keys
\ No newline at end of file
diff --git a/hyperiax/models/utils.py b/hyperiax/models/utils.py
deleted file mode 100644
index 21b6598..0000000
--- a/hyperiax/models/utils.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from jax import ops
-
-def map_reduction(reduction_string):
-    map = {
-        'sum': ops.segment_sum,
-        'prod': ops.segment_prod,
-        'max': ops.segment_max,
-        'min': ops.segment_min
-    }
-    if reduction_string not in map.keys():
-        raise ValueError(f"Reduction '{reduction_string}' not supported")
-    return map[reduction_string]
-
-
-def filter_keywords(keys):
-    reserved_keywords = ['self', 'params', 'key', 'root_mask', 'leaf_mask']
-
-    return [k for k in keys if k not in reserved_keywords]
\ No newline at end of file
diff --git a/hyperiax/plotting/__init__.py b/hyperiax/plotting/__init__.py
deleted file mode 100644
index ba9b60c..0000000
--- a/hyperiax/plotting/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .scatter import plot_tree_2d_scatter, plot_tree_3d_scatter
-from .ascii import plot_tree_text
-from .tree_plots import plot_tree, plot_tree_2d_shape, plot_tree_3d_shape
-from .shape import plot_shape_2d, plot_shape_3d, plot_mesh
\ No newline at end of file
diff --git a/hyperiax/plotting/ascii.py b/hyperiax/plotting/ascii.py
deleted file mode 100644
index 193d7d2..0000000
--- a/hyperiax/plotting/ascii.py
+++ /dev/null
@@ -1,352 +0,0 @@
-from typing import Any, List, Tuple, Generator
-from hyperiax.tree.tree import HypTree, TopologyNode
-
-def plot_tree_text(tree: HypTree):
-    formatter = TreeFormatter(tree)
-    formatter.print_tree()
-
-
-# Functions for nicer tree printing in the terminal, modified from https://github.com/AharonSambol/PrettyPrintTree
-
-def zip_longest(*iterables: List, default: Any) -> Generator:
-    """
-    Returns an iterator that aggregates elements from each of the input iterables.
-    If the iterables are of unequal length, missing values are filled with the specified default value.
-
-    Args:
-        *iterables: Variable number of iterables to be zipped together.
-        default: The value to be used as a default when an iterable is exhausted.
-
-    Yields:
-        A tuple containing elements from each of the input iterables.
-
-    Raises:
-        None.
-
-    Examples:
-        >>> list(zip_longest([1, 2, 3], [4, 5], [6, 7, 8, 9], default=None))
-        [(1, 4, 6), (2, 5, 7), (3, None, 8), (None, None, 9)]
-    """
-    lens = [len(x) for x in iterables]
-    for line in range(max(lens)):
-        yield tuple(
-            iterable[line] if line < lens[i] else default
-            for i, iterable in enumerate(iterables)
-        )
-
-def left_adjust(text: str, amount: int, padding: str = ' ') -> str:
-    """
-    Adjusts the given text to the left by adding padding characters.
-
-    Args:
-        text (str): The text to be adjusted.
-        amount (int): The desired width of the adjusted text.
-        padding (str, optional): The character used for padding. Defaults to ' '.
-
-    Returns:
-        str: The adjusted text.
-
-    """
-    return text + padding * (amount - len(text))
-
-def right_adjust(text: str, amount: int, padding: str =' ') -> str:
-    """
-    Adjusts the given text to the right by adding padding characters.
-
-    Args:
-        text (str): The text to be right adjusted.
-        amount (int): The total width of the resulting string.
-        padding (str, optional): The character used for padding. Defaults to ' '.
-
-    Returns:
-        str: The right adjusted text.
-
-    """
-    return padding * (amount - len(text)) + text
-
-class TreeNodeFormatter:
-    """
-    A class that represents a formatter for tree nodes.
-    """
-
-    @classmethod
-    def from_string(cls: Any, string: str) -> Any:
-        """
-        Create a TreeNodeFormatter object from a string representation.
-
-        Args:
-            string (str): The string representation of the tree node.
-
-        Returns:
-            TreeNodeFormatter: The TreeNodeFormatter object.
-        """
-        lines = string.split('\n')
-        height = len(lines)
-        width = max(len(line) for line in lines)
-        return cls(lines, height=height, width=width)
-    
-    def __init__(self, lines: List[str], *, height: int, width = int, middle_height: int = None, middle_width: int = None) -> None:
-        """
-        Initialize a TreeNodeFormatter object.
-
-        Args:
-            lines (list[str]): The content of the tree node.
-            height (int): The height of the tree node text.
-            width (int): The width of the tree node text.
-            middle_height (int, optional): The middle height position of the tree node text. Defaults to None.
-            middle_width (int, optional): The middle width position of the tree node text. Defaults to None.
-        """
-        self.lines = lines
-        self.height = height
-        self.width = width
-        self._middle_height = middle_height
-        self._middle_width = middle_width
-
-    def to_string(self) -> str:
-        """
-        Convert the TreeNodeFormatter object to a string representation.
-
-        Returns:
-            str: The string representation of the tree node.
-        """
-        return '\n'.join(self.lines)
-    
-    @property
-    def middle_width(self) -> int:
-        """
-        Get the middle width of the tree node text.
-
-        Returns:
-            int: The middle width position of the tree node text.
-        """
-        if self._middle_width is None:
-            return sum(divmod(self.width, 2)) - 1
-        return self._middle_width
-    
-    @property
-    def middle_height(self) -> int:
-        """
-        Get the middle height of the tree node text.
-
-        Returns:
-            int: The middle height position of the tree node text.
-        """
-        if self._middle_height is None:
-            return sum(divmod(self.height, 2)) - 1
-        return self._middle_height
-
-def add_parent(parent_formatter: TreeNodeFormatter, children_formatter: TreeNodeFormatter) -> TreeNodeFormatter:
-    """
-    Combines the formatting information of a parent node and its children nodes to create a new TreeNodeFormatter object.
-
-    Args:
-        parent_formatter (TreeNodeFormatter): The formatting information of the parent node.
-        children_formatter (TreeNodeFormatter): The formatting information of the children nodes.
-
-    Returns:
-        TreeNodeFormatter: A new TreeNodeFormatter object with the combined formatting information.
-
-    """
-    parent_middle, children_middle = parent_formatter.middle_width, children_formatter.middle_width
-    parent_width, children_width = parent_formatter.width, children_formatter.width
-    
-    if parent_middle == children_middle:      # Only one child
-        lines = parent_formatter.lines + children_formatter.lines
-        middle = parent_middle
-    elif parent_middle < children_middle:     # Have several children
-        padding = ' ' * (children_middle - parent_middle)
-        lines = [padding + line for line in parent_formatter.lines] + children_formatter.lines
-        parent_width += children_middle - parent_middle
-        middle = children_middle
-    else:                                    # Have no child
-        padding = ' ' * (parent_middle - children_middle)
-        lines = parent_formatter.lines + [padding + line for line in children_formatter.lines]
-        children_width += parent_middle - children_middle
-        middle = parent_middle
-    
-    return TreeNodeFormatter(
-        lines,
-        height=parent_formatter.height + children_formatter.height,
-        width=max(parent_width, children_width),
-        middle_width=middle
-    )
-
-class TreeFormatter:
-    """
-    A class that formats a HypTree object into a string representation.
-
-    Attributes:
-        root (TreeNode): The root node of the HypTree object.
-
-    Methods:
-        get_children(node: TreeNode) -> list[TreeNode]:
-            Returns the children of a given node.
-        
-        get_name(node: TreeNode) -> str:
-            Returns the name of a given node. If the name is None, returns '*'.
-        
-        add_name(name: str, node_formatter: TreeNodeFormatter, parent_adder: callable=add_parent, seperator: str = ' ') -> TreeNodeFormatter:
-            Adds a name to the node formatter and returns the updated formatter.
-        
-        format() -> str:
-            Formats the HypTree object into a string representation and returns it.
-        
-        tree_join_formatter(node: TreeNode, depth: int = 0) -> TreeNodeFormatter:
-            Recursively formats the tree structure and returns the root node formatter.
-        
-        node_to_formatter(node: TreeNode) -> TreeNodeFormatter:
-            Converts a node into a TreeNodeFormatter object and returns it.
-        
-        print_tree() -> None:
-            Prints the formatted tree representation to the console.
-    """
-
-    def __init__(self, tree) -> None:
-        if issubclass(type(tree), HypTree):
-            self.root = tree.topology_root
-        elif issubclass(type(tree), TopologyNode):
-            self.root = tree
-        else:
-            raise ValueError('Tree must be of type HypTree or TopologyNode')
-
-    def get_children(self, node: TopologyNode) -> List[TopologyNode]:
-        return node.children
-    
-    def get_name(self, node: TopologyNode) -> str:
-        if "name" in node.__dict__ and node.name != None:
-            return node.name
-        else:
-            return 'o'
-    
-    def add_name(self, name: str, node_formatter: TreeNodeFormatter, parent_adder: callable=add_parent, seperator: str = ' ') -> TreeNodeFormatter:
-        if name:
-            name_formatter = TreeNodeFormatter.from_string(str(name))
-            node_formatter = parent_adder(TreeNodeFormatter.from_string(seperator), node_formatter)
-            node_formatter = parent_adder(name_formatter, node_formatter)
-        return node_formatter
-    
-    def format(self) -> str:
-        """
-        Formats the tree structure into a string representation.
-
-        Returns:
-            str: The formatted tree structure as a string.
-        """
-        res = self.tree_join_formatter(self.root)
-        res = res.to_string().rstrip()
-        return res
-    
-    def tree_join_formatter(self, node: TopologyNode, depth: int = 0) -> TreeNodeFormatter:
-        """
-        Recursively formats the tree structure and returns the root node formatter.
-
-        Args:
-            node (TreeNode): The current node being formatted.
-            depth (int): The depth of the current node in the tree structure.
-
-        Returns:
-            TreeNodeFormatter: The root node formatter.
-        """
-        name = self.get_name(node)
-        children = self.get_children(node)
-        node_formatter = self.node_to_formatter(node)
-
-        if children:
-            children_formatters = [
-                self.tree_join_formatter(child, depth=depth+1)
-                for child in children
-            ]
-            if len(children) == 1:
-                children_node_formatter = children_formatters[0]
-                children_node_formatter.lines.insert(0, ' ' * children_node_formatter.middle_width + '│')
-            else:
-                children_node_formatter = join_horizontally(children_formatters)
-            
-            node_formatter = add_parent(node_formatter, children_node_formatter)
-            
-        # node_formatter = self.add_name(name, node_formatter)
-            
-        return node_formatter
-    
-    def node_to_formatter(self, node: TopologyNode) -> TreeNodeFormatter:
-        """
-        Converts a node into a TreeNodeFormatter object and returns it.
-
-        Args:
-            node (TreeNode): The node to convert.
-
-        Returns:
-            TreeNodeFormatter: The converted node formatter.
-        """
-        name = self.get_name(node)
-        return TreeNodeFormatter.from_string(str(name))
-    
-    def print_tree(self) -> None:
-        """
-        Prints the formatted tree representation to the console.
-        """
-        print(self.format())
-
-def join_horizontally(boxes: List[TreeNodeFormatter]) -> TreeNodeFormatter:
-    """
-    Joins multiple TreeNodeFormatter boxes horizontally to form a line to print.
-
-    Args:
-        boxes (list[TreeNodeFormatter]): A list of TreeNodeFormatter objects to gather.
-
-    Returns:
-        TreeNodeFormatter: The joined TreeNodeFormatter box.
-
-    """
-    lines, width, height = join_boxes(boxes)
-    middle = add_pipes(boxes, lines)
-    height += 1
-    return TreeNodeFormatter(lines, height=height, width=width, middle_width=middle)
-
-def join_boxes(boxes: List[TreeNodeFormatter]) -> Tuple[List[str], int, int]:
-    """
-    Joins multiple TreeNodeFormatter together horizontally to get the text, width and height of this line.
-
-    Args:
-        boxes (List[TreeNodeFormatter]): A list of TreeNodeFormatter objects to gather.
-
-    Returns:
-        Tuple[List[str], int, int]: A tuple containing the joined lines, width, and height of the joined boxes.
-    """
-    lines = [
-        ' '.join(
-            left_adjust(text=line, amount=boxes[i].width)
-            for i, line in enumerate(lines)
-        )
-        for lines in zip_longest(*(box.lines for box in boxes), default='')
-    ]
-    width = sum(box.width for box in boxes) + len(boxes) - 1
-    height = max(box.height for box in boxes)
-    return lines, width, height
-
-def add_pipes(boxes: List[TreeNodeFormatter], lines: List[str]) -> int:
-    """
-    Adds pipes to the given lines to create a tree-like structure.
-
-    Args:
-        boxes (List[TreeNodeFormatter]): A list of TreeNodeFormatter objects to gather.
-        lines (List[str]): A list of strings representing the lines of the tree.
-
-    Returns:
-        int: The number of characters added as padding to the lines.
-
-    """
-    padding = ' ' * boxes[0].middle_width
-    pipes = '┌'
-    for prev, box in zip(boxes, boxes[1:]):
-        pipes += '─' * (prev.width - prev.middle_width + box.middle_width) + '┬'
-    middle_of_pipes = sum(divmod(len(pipes), 2)) - 1
-    pipes = (
-        padding 
-        + pipes[:middle_of_pipes] 
-        + {"─": "┴", "┬": "┼", "┌": "├", "┐": "┤"}[pipes[middle_of_pipes]]
-        + pipes[middle_of_pipes + 1:-1]
-        + '┐'
-    )
-    lines.insert(0, pipes)
-    return len(padding) + middle_of_pipes
\ No newline at end of file
diff --git a/hyperiax/plotting/scatter.py b/hyperiax/plotting/scatter.py
deleted file mode 100644
index 44e2e14..0000000
--- a/hyperiax/plotting/scatter.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from ..tree import HypTree
-
-from matplotlib import pyplot as plt
-from matplotlib import patches as mpatch
-
-cmap = lambda x: plt.cm.ocean(x*.9) # use a slightly darker color for the levels
-
-def plot_tree_2d_scatter(tree : HypTree, property : str, ax=None, selector=lambda z: z):
-
-    if ax == None:
-        fig,ax = plt.subplots(figsize=(10,8))
-
-    for i, (l_start, l_end) in enumerate(tree.levels):
-        for nodeidx in range(l_start, l_end):
-            cdat = selector(tree.data[property][nodeidx])
-            cdat = cdat if len(cdat.shape) <= 2 else cdat[-1] # possibly to last value if dat is a trajectory of values
-            if len(cdat.shape) == 1:
-                ax.scatter(*cdat, color=cmap(i/len(tree.levels)))
-            else:
-                ax.scatter(cdat[:,0], cdat[:,1], color=cmap(i/len(tree.levels)))
-
-            # plot transition from parent to child
-            if nodeidx == 0: continue
-            parentidx = tree.parents[nodeidx]
-            dat = selector(tree.data[property][parentidx])
-            dat = dat if len(dat.shape) <= 2 else dat[-1] # possibly to last value if dat is a trajectory of values
-            cdat = selector(tree.data[property][nodeidx])
-            if len(cdat.shape) == 1:
-                ax.arrow(*dat, *(cdat-dat), width=0.01, length_includes_head=True, color=cmap(i/len(tree.levels)))
-            elif len(cdat.shape) == 2:
-                for j in range(cdat.shape[0]):
-                    ax.plot([dat[j,0],cdat[j,0]], [dat[j,1],cdat[j,1]], color=cmap(i/len(tree.levels)))
-            elif len(cdat.shape) == 3:
-                for j in range(cdat.shape[1]):
-                    ax.plot(cdat[:,j,0], cdat[:,j,1], color=cmap(i/len(tree.levels)))
-
-        handles = [mpatch.Patch(color=cmap(i/len(tree.levels)), label = f'{i+1}') for i in range(len(tree.levels))]
-        legend = ax.legend(handles=handles, title="Levels")
-        ax.add_artist(legend)
-        ax.grid(True)
-
-def plot_tree_3d_scatter(tree : HypTree, property : str, ax=None, selector=lambda z: z):
-
-    if ax == None:
-        fig = plt.figure(figsize=(10,8))
-        ax = fig.add_subplot(111, projection='3d')
-    levels = list(tree.iter_topology_levels())
-
-    for i, level in enumerate(levels):
-        for node in level:
-            dat = selector(tree.data[property][node.id])
-            dat = dat if len(dat.shape) == 1 else dat[-1] # possibly to last value if dat is a trajectory of values
-            if len(dat.shape) == 1:
-                ax.scatter(*dat, color=cmap(i/len(tree.levels)))
-            else:
-                ax.scatter(dat[:,0], dat[:,1], dat[:,2], color=cmap(i/len(tree.levels)))
-            if 'name' in tree.data.keys():
-                ax.text(*dat, tree.data['name'][node.id], color='black')
-
-            # plot transition from parent to child
-            if node.children:
-                for child in node.children:
-                    cdat = selector(tree.data[property][child.id])
-                    if len(cdat.shape) == 1:
-                        ax.quiver(*dat, *(cdat-dat), length=1.0, arrow_length_ratio=0.1, color=cmap(i/len(tree.levels)))
-                    elif len(cdat.shape) == 2:
-                        ax.plot(cdat[:,0], cdat[:,1], cdat[:,2], color=cmap(i/len(tree.levels)))
-                    elif len(cdat.shape) == 3:
-                        for j in range(cdat.shape[1]):
-                            ax.plot(cdat[:,j,0], cdat[:,j,1], cdat[:,j,2], color=cmap(i/len(tree.levels)))
-
-    handles = [mpatch.Patch(color=cmap(i/len(levels)), label=f'{i+1}') for i in range(len(levels))]
-    legend = ax.legend(handles=handles, title="Levels")
-    ax.add_artist(legend)
-    ax.grid(True)
\ No newline at end of file
diff --git a/hyperiax/plotting/shape.py b/hyperiax/plotting/shape.py
deleted file mode 100644
index c99710d..0000000
--- a/hyperiax/plotting/shape.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Shape plots
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-# 2d
-def plot_shape_2d(q,color=None,ax=None,label=None,marker='.',alpha=1.0):
-    d = 2
-    if ax is None:
-        fig = plt.figure()
-        ax = fig.add_subplot(111)
-    q = q.reshape((-1,d))
-    if marker == '-':
-        # Connect first and last dots
-        q_closed = np.vstack([q, q[0]])  # Add first point to the end
-        ax.plot(q_closed[:,0], q_closed[:,1], marker, color=color, label=label, alpha=alpha)
-    else:
-        ax.plot(q[:,0], q[:,1], marker, color=color, label=label, alpha=alpha)
-    ax.axis('equal')
-    return ax
-
-# 3d
-def plot_shape_3d(points,ax=None,alpha=1.0):
-    d = 3
-    points = points.reshape((-1,3))
-    if ax is None:
-        fig = plt.figure()
-        ax = fig.add_subplot(111, projection='3d')
-    xs = [p[0] for p in points]
-    ys = [p[1] for p in points]
-    zs = [p[2] for p in points]
-    ax.scatter(xs, ys, zs,alpha=alpha)
-    ax.set_xlabel('X')
-    ax.set_ylabel('Y')
-    ax.set_zlabel('Z')
-    return ax
-
-def plot_mesh(mesh,ax=None):
-    # Plot the mesh
-    if ax is None:
-        fig = plt.figure()
-        ax = fig.add_subplot(111, projection='3d')
-    ax.plot_trisurf(mesh.vertices[:, 0], mesh.vertices[:, 1], mesh.vertices[:, 2], triangles=mesh.faces, color='b', edgecolor='k')
-    ax.set_xlabel('X')
-    ax.set_ylabel('Y')
-    ax.set_zlabel('Z')
-    return ax
diff --git a/hyperiax/plotting/tree_plots.py b/hyperiax/plotting/tree_plots.py
deleted file mode 100644
index 74a312c..0000000
--- a/hyperiax/plotting/tree_plots.py
+++ /dev/null
@@ -1,380 +0,0 @@
-from ..tree import HypTree
-from jax import numpy as jnp
-import copy
-
-import numpy as np
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import pyplot as plt
-from matplotlib import patches as mpatch
-import plotly.graph_objects as go
-        
-def estimate_position(self,normalize=True):
-    """ 
-    Estimate the x and y position in the plotting coordinates of each node in the tree, and add the x and y position to the data dictionary of each node.
-
-    :param tree: the tree to estimate the position in
-    :return: the tree with the x and y position added to the data dictionary of each node
-    """
-    
-    # Determine Y coordinates, with distance from.topology_root 
-    depth = jnp.max(self.node_depths)
-    self.data['y_temp'] = self.data['y_temp'].at[self.topology_root.id].set(0)
-    for leaf in self.iter_topology_dfs():
-        # initlize an empty x coordinate for later 
-        self.data['x_temp'] = self.data['x_temp'].at[leaf.id].set(0)
-
-        if leaf.parent is not None: # Skip.topology_root
-            if 'edge_length' in self.data.keys():
-                new_value = self.data['y_temp'][leaf.parent.id]-self.data['edge_length'][leaf.id]
-                self.data['y_temp'] = self.data['y_temp'].at[leaf.id].set(new_value)
-            else: 
-                self.data['y_temp'] = self.data['y_temp'].at[leaf.id].set(-self.node_depths[leaf.id])
-
-    # Define x coordinate for each leaf 
-    for i,leaf in enumerate(self.iter_topology_leaves_dfs()):
-        self.data['x_temp'] = self.data['x_temp'].at[leaf.id].set(i)
-
-    # Determine X coordinates from bottom and up 
-    for level in reversed(list(self.iter_topology_levels())):
-        for leaf in level:
-                while leaf.parent is not None:
-                    x_coordinate = 0
-                    leaf = leaf.parent
-                    for i,node in enumerate(leaf.children):
-                        x_coordinate += self.data['x_temp'][node.id]
-                    self.data['x_temp'] = self.data['x_temp'].at[leaf.id].set(x_coordinate/(i+1))
-    
-    if normalize:
-        # get the last leaf to see coordinates
-        min_depth = 0 ;  min_width = 0
-        # Get last leaf in leaves
-        leaf = list(self.iter_topology_leaves_bfs())[-1]
-        max_depth = self.data["y_temp"][leaf.id]
-        max_width = self.data["x_temp"][leaf.id]
-    
-        # Normalize everthing
-        try:
-            for leaf in self.iter_topology_dfs():
-                self.data['y_temp'] = self.data['y_temp'].at[leaf.id].set(-(self.data["y_temp"][leaf.id] - min_depth)/(max_depth - min_depth))
-                self.data['x_temp'] = self.data['x_temp'].at[leaf.id].set((self.data["x_temp"][leaf.id] - min_width)/(max_width - min_width))
-        except ZeroDivisionError:
-            pass
-
-     
-def plot_tree(self_input,ax=None,inc_names=False): 
-    from matplotlib import pyplot as plt
-    """
-    Visualize the hierarchical structure of the tree
-
-    :param tree: the tree to plot
-    :param ax: the axis to plot the tree on, if None, a new figure is created, defaults to None
-    :param inc_names: whether to include the names of the nodes in the plot, defaults to False
-    """
-
-    self = copy.deepcopy(self_input)
-    self.add_property('x_temp', shape=(1,))
-    self.add_property('y_temp', shape=(1,))
-    estimate_position(self)
-   
-    if ax == None:
-        fig,ax = plt.subplots(figsize=(10,8))
-
-    ax.plot(self.data["x_temp"], self.data["y_temp"], 'ko') # plot all nodes
-    ax.axis('off')
-     
-    # Plot the connection for each node 
-    for leaf in self.iter_topology_bfs():
-        if len(leaf.children) != 0:
-           # 1+1
-            plot_node(self,leaf,ax,inc_names)
-
-def plot_node(self,parent,ax,inc_names):
-    """
-    Plot a single node and its children
-
-    :param parent: the parent node to plot
-    :param ax: the axis to plot the node on
-    :param inc_names: whether to include the names of the nodes in the plot
-    """
-    from matplotlib import pyplot as plt
-    """Plot a single node and its children"""
-    ax.plot(self.data["x_temp"][parent.id],self.data["y_temp"][parent.id], 'ko')  # Plot the current node
-
-    if inc_names and parent.name is not None and not parent.name.startswith('xx_'):
-        ax.text(self.data["x_temp"][parent.id].item(),self.data["y_temp"][parent.id].item(),  parent.name+"  ", fontdict=None,rotation="vertical",va="top",ha="center")
- 
-
-    for child in parent.children:
-        if inc_names and child.name is not None and not child.name.startswith('xx_'):
-            ax.text(self.data["x_temp"][child.id].item(),self.data["y_temp"][child.id].item(), child.name+"  ", fontdict=None,rotation="vertical",va="top",ha="center")
-        
-        
-        # Draw vertical line to parent
-        ax.plot([self.data["x_temp"][child.id],self.data["x_temp"][parent.id]], 
-                [self.data["y_temp"][parent.id],self.data["y_temp"][parent.id]], 'k-')
-        # Draw horizontal line to child
-        ax.plot([self.data["x_temp"][child.id], self.data["x_temp"][child.id]], 
-                [self.data["y_temp"][parent.id], self.data["y_temp"][child.id]], 'k-') 
-
-
-
-#####################################################################################################
-# plot shape tree
-
-def plot_tree_2d_shape(self_input : HypTree, property : str,ax=None,inc_names=False): 
-    from matplotlib import pyplot as plt
-    """Plot the tree using matplotlib"""
-
-    self = copy.deepcopy(self_input)
-    self.add_property('x_temp', shape=(1,))
-    self.add_property('y_temp', shape=(1,))
-    estimate_position(self,normalize=True)
- 
-    if ax == None:
-        fig,ax = plt.subplots(figsize=(16,10))
-        ax.axis('off')
-
-    n_leafs = len(list(self.iter_topology_leaves_bfs()))
-    scale = 7/8
-    dis = 1/n_leafs*1/2*scale
-
-    ####### DO all for root 
-    leaf = self.topology_root
-
-    x = self.data["x_temp"][leaf.id]
-    y = self.data["y_temp"][leaf.id]
-    
-    # Include text
-    if inc_names and leaf.name is not None:
-        rotation = "horizontal" if len(leaf.name) < 3 else "vertical"
-        ax.text(x, y-dis, leaf.name, fontdict=None, rotation=rotation, va="top", ha="center")
-
-    plot_trajectory = len(self.data[property][self.topology_root.id].shape) > 1
-    if not plot_trajectory:
-        points = scale_points(self.data[property][leaf.id].reshape((-1,2)),[(x-dis,y-dis),(x+dis,y+dis)])
-    else:
-        points = scale_points(self.data[property][leaf.id][-1].reshape((-1,2)),[(x-dis,y-dis),(x+dis,y+dis)])
-    for point in points:
-        ax.plot(*point, 'r.')
-    self.add_property('temp_plotted_point', shape=np.array(points).shape)
-    self.data['temp_plotted_point'] = self.data['temp_plotted_point'].at[self.topology_root.id].set(points)
-        
-    plot_trajectory = len(self.data[property][leaf.id].shape) > 1
-    if not plot_trajectory:
-        draw_box(ax, x, y, dis) # regular case (no trajectory)
-
-    # ax.axis('off')
-    n_levels = len(self.levels)
-    for i, level in enumerate(self.iter_topology_levels()):
-        for node in level:
-            if len(node.children) != 0:
-                plot_node_shape(self,node,ax,inc_names,dis,property,i/n_levels)
-
-    cmap = plt.cm.ocean
-    handles = [mpatch.Patch(color=cmap(i/n_levels), label = f'{i+1}') for i in range(n_levels)]
-    legend = ax.legend(handles=handles, title="Levels")
-    ax.add_artist(legend)
-
-def plot_tree_3d_shape(self_input : HypTree,property : str,fig=None,inc_names=False,scale=1.,mesh=None): 
-    import plotly.graph_objects as go
-    """Plot the tree using plotly"""
-
-    self = copy.deepcopy(self_input)
-    self.add_property('p_temp', shape=(3,))
- 
-    if fig == None:
-        fig = go.Figure()
-        fig.update_layout(
-            scene=dict(
-                xaxis=dict(visible=False), 
-                yaxis=dict(visible=False), 
-                zaxis=dict(visible=False)
-            ),
-            showlegend=False,  # Disable legends
-            #width = 1000,
-            height = 800,
-        )
-
-    # simple placement in xz plane, does not take edge lengths into account
-    x_span = 1.5*len(self.is_leaf)
-    n_levels = len(self.levels)
-    level_z = 2.
-    def set_pos(node,childi,nchildren,x_span):
-        if node.parent is not None:
-            x = self.data['p_temp'][node.parent.id][0] + (childi-(nchildren-1)/2)*x_span
-            y = 0.
-            z = self.data['p_temp'][node.parent.id][2] - level_z
-        else:
-            x = 0.; y = 0.; z = 0.
-        self.data['p_temp'] = self.data['p_temp'].at[node.id].set(np.array([x,y,z]))
-        for i,child in enumerate(node.children):
-            set_pos(child,i,len(node.children),x_span/len(node.children))
-    set_pos(self.topology_root,0,len(self.topology_root.children),x_span)
-
-    ####### DO all for root 
-    plot_trajectory = len(self.data[property][self.topology_root.id].shape) > 1
-    if not plot_trajectory:
-        points = scale*self.data[property][self.topology_root.id].reshape((-1,3))
-    else:
-        points = scale*self.data[property][self.topology_root.id][-1].reshape((-1,3))
-    if mesh is None:
-        fig.add_trace(go.Scatter3d(x=points[:,0], y=points[:,1], z=points[:,2], mode='markers', marker=dict(color='blue')))
-    else:
-        plot_mesh_plotly(mesh,points,fig)
-    self.add_property('temp_plotted_point', shape=points.shape)
-    self.data['temp_plotted_point'] = self.data['temp_plotted_point'].at[self.topology_root.id].set(points)
-
-    n_levels = len(self.levels)
-    for i, level in enumerate(self.iter_topology_levels()):
-        for node in level:
-            if len(node.children) != 0:
-                plot_node_shape_3d(self,node,fig,property,scale,i/n_levels,mesh=mesh)
-    fig.update_layout(scene_aspectmode='data')
-    return fig
-
-def scale_points(points, bounding_box, padding=0.1):
-    # Unpack bounding box coordinates
-    box_min_x, box_min_y = bounding_box[0]
-    box_max_x, box_max_y = bounding_box[1]
-
-    # Calculate the range of the bounding box
-    box_range_x = box_max_x - box_min_x
-    box_range_y = box_max_y - box_min_y
-
-    # Add padding to the bounding box
-    box_min_x += box_range_x * padding
-    box_max_x -= box_range_x * padding
-    box_min_y += box_range_y * padding
-    box_max_y -= box_range_y * padding
-    box_range_x = box_max_x - box_min_x
-    box_range_y = box_max_y - box_min_y
-
-    # Find the min and max x, y in the points
-    points=np.array(points)
-    min_x,min_y=np.min(points,axis=0)
-    max_x,max_y=np.max(points,axis=0)
-
-    # Calculate the range of the points
-    range_x = max_x - min_x
-    range_y = max_y - min_y
-
-    # Scale the points to fit inside the bounding box
-    min_x,min_y = np.min(points,axis=0)
-    max_x,max_y = np.max(points,axis=0)
-    range_x = max_x-min_x
-    range_y = max_y-min_y
-
-    # Avoid division by zero
-    scaled_x = box_min_x+((points[:,0]-min_x)/range_x)*box_range_x
-    scaled_y = box_min_y+((points[:,1]-min_y)/range_y)*box_range_y
-
-    return np.column_stack((scaled_x,scaled_y))
-
-
-def draw_box(ax, x, y, dis, zorder=1):
-    #ax.plot([x-dis,x+dis],[y+dis,y+dis], 'k-')  # Upper Horizontal
-    #ax.plot([x-dis,x+dis],[y-dis,y-dis], 'k-')  # Lower horizontal
-    #ax.plot([x-dis,x-dis],[y-dis,y+dis], 'k-')  # Vertical lines
-    #ax.plot([x+dis,x+dis],[y-dis,y+dis], 'k-')  # Vertical lines opposite
-    ax.fill([x-dis, x+dis, x+dis, x-dis], 
-            [y-dis, y-dis, y+dis, y+dis], color='white', edgecolor='black', zorder=zorder)
-    
-
-
-def plot_node_shape(self, parent, ax, inc_names, dis, property, level, zorder_traj=1, zorder_box=2, zorder_points=3):
-    from matplotlib import pyplot as plt
-
-    x0 = self.data["x_temp"][parent.id]
-    y0 = self.data["y_temp"][parent.id]
-
-    cmap = plt.cm.ocean
-
-    for child in parent.children:
-        x = self.data["x_temp"][child.id]
-        y = self.data["y_temp"][child.id]
-
-        # plot just point configuration of entire trajectory
-        plot_trajectory = len(self.data[property][child.id].shape) > 1
-
-        if not plot_trajectory:
-            # Draw horizontal and vertical lines with lower zorder
-            if len(parent.children) > 1:
-                if x<x0-.5*dis or x>x0+.5*dis:
-                    ax.plot([x,x0-dis if x<x0 else x0+dis], [y0,y0],'k-', zorder=zorder_traj)
-                ax.plot([x,x],[y0 if x<x0-.5*dis or x>x0+.5*dis else y0-dis,y+dis],'k', zorder=zorder_traj)      
-            else:
-                ax.plot([x,x],[y0-dis,y+dis],'k', zorder=zorder_traj)
-
-            # Plot points with lower zorder
-            points = scale_points(self.data[property][child.id].reshape((-1,2)),[(x-dis,y-dis),(x+dis,y+dis)])
-            ax.scatter(points[:,0], points[:,1],color='r',marker='.', zorder=zorder_points)
-            
-            # Draw box with higher zorder
-            draw_box(ax, x, y, dis, zorder=zorder_box)
-
-            self.data['temp_plotted_point'] = self.data['temp_plotted_point'].at[child.id].set(points)
-        else:
-            # Plot trajectory with lower zorder
-            points = scale_points(self.data[property][child.id].reshape((-1,2)),[(x-dis,y-dis),(x+dis,y+dis)]).reshape((self.data[property][child.id].shape[0],-1,2))
-            if 'temp_plotted_point' in self.data.keys():
-                # linearly interpolate
-                child_first_point = points[0]
-                child_last_point = points[-1]
-                parent_last_point = self.data['temp_plotted_point'][parent.id]
-                num_points = self.data[property][child.id].shape[0]
-                interpolated_array = np.linspace(parent_last_point-child_first_point, np.zeros_like(child_last_point), num_points)
-                points = points+interpolated_array
-            for i in range(points.shape[1]):
-                ax.plot(points[:,i,0], points[:,i,1],color=cmap(level), zorder=zorder_traj)
-                ax.plot(*points[-1,i], 'r.', zorder=zorder_points)
-            self.data['temp_plotted_point'] = self.data['temp_plotted_point'].at[child.id].set(points[-1])
-
-        # Include text
-        if inc_names and child.name is not None:
-            rotation = "horizontal" if len(child.name) < 3 else "vertical"
-            ax.text(x, y-dis, child.name, fontdict=None, rotation=rotation, va="top", ha="center")
-
-
-def plot_node_shape_3d(self : HypTree, parent,fig,shape,scale,level,mesh=None):
-    cmap = plt.cm.ocean
-
-    for child in parent.children:
-        p = self.data['p_temp'][child.id]
-
-        # plot just point configuration of entire trajectory
-        plot_trajectory = len(self.data[shape][child.id].shape) > 1
-
-        if not plot_trajectory:
-            # no trajectory
-            point = scale*self.data[shape][child.id].reshape((-1,3)) + p[None,:]
-            if 'temp_plotted_point' in self.data.keys():
-                points = np.vstack((self.data['temp_plotted_point'][parent.id],point)).reshape([2,-1,3])
-            else:
-                points = point.reshape([1,-1,3])
-        else:
-            # trajectory
-            points = scale*self.data[shape][child.id].reshape((self.data[shape][child.id].shape[0],-1,3)) + p[None,None,:]
-            if 'temp_plotted_point' in self.data.keys():
-                # linearly interpolate
-                child_first_point = points[0]
-                child_last_point = points[-1]
-                parent_last_point = self.data['temp_plotted_point'][parent.id]
-                num_points = self.data[shape][child.id].shape[0]
-                interpolated_array = np.linspace(parent_last_point-child_first_point, np.zeros_like(child_last_point), num_points)
-                points = points+interpolated_array
-
-        for i in range(points.shape[1]):
-            fig.add_trace(go.Scatter3d(x=points[:,i,0], y=points[:,i,1], z=points[:,i,2], mode='lines', line=dict(color=cmap(level))))
-            if mesh is None:
-                fig.add_trace(go.Scatter3d(x=[points[-1,i,0]], y=[points[-1,i,1]], z=[points[-1,i,2]], mode='markers', marker=dict(color='red', size=6)))
-        if mesh is not None:
-            plot_mesh_plotly(mesh,points[-1],fig,edgecolor='red')
-        self.data['temp_plotted_point'] = self.data['temp_plotted_point'].at[child.id].set(points[-1])
-
-def plot_mesh_plotly(mesh,points,fig,color='lightblue',edgecolor='blue'):
-    fig.add_trace(go.Mesh3d(x=points[:,0],y=points[:,1],z=points[:,2],i=mesh.faces[:,0],j=mesh.faces[:,1],k=mesh.faces[:,2],color=color,opacity=0.95))
-    edges = np.vstack([mesh.faces[:,[0,1]],mesh.faces[:,[1,2]],mesh.faces[:,[2,0]]])
-    x_edges = np.hstack([points[edges[:,0],0],points[edges[:,1],0],np.full(edges.shape[0],None)])
-    y_edges = np.hstack([points[edges[:,0],1],points[edges[:,1],1],np.full(edges.shape[0],None)])
-    z_edges = np.hstack([points[edges[:,0],2],points[edges[:,1],2],np.full(edges.shape[0],None)])
-    fig.add_trace(go.Scatter3d(x=x_edges,y=y_edges,z=z_edges,mode='lines',line=dict(color=edgecolor,width=2)))
diff --git a/hyperiax/prebuilt/__init__.py b/hyperiax/prebuilt/__init__.py
new file mode 100644
index 0000000..c3f4cff
--- /dev/null
+++ b/hyperiax/prebuilt/__init__.py
@@ -0,0 +1,28 @@
+"""hyperiax.prebuilt — recipes built on the core API.
+
+This is an L2 submodule: each prebuilt is a small wrapper around
+:mod:`hyperiax.core` that ships ready-made sweeps for common tree
+inference tasks (phylogenetic mean estimation, BFFG filters).
+Importing :mod:`hyperiax.prebuilt` itself does not pull in any optional
+dependencies; individual prebuilts may, but they import lazily.
+
+The BFFG public API is still in flux (the ``discrete_*`` / ``continuous_*``
+rename is WIP), so the BFFG sweeps are not re-exported here — pull them
+directly from :mod:`hyperiax.prebuilt.bffg` (e.g. ``from
+hyperiax.prebuilt.bffg import discrete_bf_sweep``) until the surface
+stabilises.
+
+For Markov-chain Monte Carlo, use NumPyro: the BFFG-guided forward map
+composes cleanly with ``numpyro.factor`` inside a NumPyro ``model``, and
+the built-in samplers (``NUTS``, ``HMC``) or any custom ``MCMCKernel``
+drive the chain. See ``docs/source/notebooks/05_gaussian_bffg.ipynb`` and
+``06_gaussian_nuts.ipynb`` for worked examples.
+"""
+
+from . import bffg
+from .phylo_mean import phylo_mean
+
+__all__ = [
+    "bffg",
+    "phylo_mean",
+]
diff --git a/hyperiax/prebuilt/bffg.py b/hyperiax/prebuilt/bffg.py
new file mode 100644
index 0000000..9b32a6a
--- /dev/null
+++ b/hyperiax/prebuilt/bffg.py
@@ -0,0 +1,1019 @@
+"""Backward Filtering Forward Guiding (BFFG) on rooted directed trees.
+
+Implements the framework of van der Meulen & Sommer (2025), *Backward Filtering
+Forward Guiding*, JMLR 26(281), 1–51 (`arXiv:2505.18239
+<https://arxiv.org/abs/2505.18239>`_). Each tree edge is either:
+
+- **discrete** (§6.1, Theorem 14): a 1-step nonlinear-Gaussian Markov kernel
+  :math:`X_t \\mid X_{pa(t)}=x \\sim \\mathcal N(\\mu(x), Q(x))`, approximated
+  by a tractable linear-Gaussian auxiliary :math:`\\tilde X_t \\sim
+  \\mathcal N(\\Phi x + \\beta, Q_{\\text{aux}})`; or
+- **continuous** (§7.1, Theorem 23): an SDE
+  :math:`dX_u = b(u, X_u)\\,du + \\sigma(u, X_u)\\,dW_u` with a linear
+  auxiliary :math:`d\\tilde X_u = (B(u)\\tilde X_u + \\beta(u))\\,du +
+  \\tilde\\sigma(u)\\,dW_u`.
+
+The auxiliary is evaluated at a per-edge **linearisation anchor** (Algorithm 3
+§7.1) that the user iteratively refines toward the BFFG posterior mean.
+
+Typical workflow::
+
+    bf = continuous_bf_sweep(n_steps, B, beta, sigma_tilde)
+    refine = continuous_refine_anchor()
+    fg = continuous_fg_sweep(n_steps, b, sigma, B, beta, sigma_tilde)
+
+    tree = init_continuous_tree(empty, leaf_obs, obs_var=tau_sq, d=d,
+                                n_steps=n_steps, root_val=x_root)
+    for _ in range(n_lin_iters):
+        tree = bf(tree, params=theta)
+        tree = refine(tree, params=theta)
+    tree = tree.set(zs=z.reshape((N, n_steps, d)))
+    tree = fg(tree, params=theta)
+    # tree.vals — guided bridge trajectories; tree.log_corr — per-edge
+    # importance log-weights; tree.log_norm[0] — root marginal log-evidence.
+
+Schemas: see :func:`discrete_schema` and :func:`continuous_schema`.
+
+References:
+    van der Meulen, F. H. & Sommer, S. (2025). Backward Filtering Forward
+    Guiding. *JMLR* 26(281), 1–51. https://arxiv.org/abs/2505.18239
+"""
+
+from __future__ import annotations
+
+import jax
+import jax.numpy as jnp
+from jax.scipy.linalg import cho_solve, cholesky, solve_triangular
+
+from ..core.sweep import SweepFn, down, up
+from ..core.tree import Tree
+from ..utils.ode import RK4, solve_ode
+from ..utils.sde import EulerMaruyama, dot, solve, solve_sde
+
+
+# ── schema helpers ─────────────────────────────────────────────────
+def discrete_schema(d: int) -> dict[str, tuple[int, ...]]:
+    """Field layout for a discrete-edge BFFG tree.
+
+    Args:
+        d: State dimension.
+
+    Returns:
+        Mapping of field name to per-node trailing shape, suitable for
+        :class:`hyperiax.Schema.from_dict`. Fields:
+
+        - ``val: (d,)`` — node state (filled by the forward sweep).
+        - ``z: (d,)`` — i.i.d. standard-normal noise driving the forward sample.
+        - ``ptnl: (d,)`` — canonical-form potential ``F`` at the node (BF).
+        - ``prec: (d, d)`` — canonical-form precision ``H`` at the node (BF).
+        - ``anchor: (d,)`` — per-edge linearisation point for the auxiliary
+          kernel (Algorithm 3 §7.1); leaves are seeded at the observation,
+          internal nodes refined via :func:`discrete_refine_anchor`.
+        - ``log_norm: ()`` — canonical-message log-norm; sums up the tree so
+          ``log_norm[root]`` is the marginal log-evidence.
+        - ``log_corr: ()`` — per-edge importance log-weight from forward
+          guiding (Theorem 14.3).
+    """
+    return {
+        "val": (d,),
+        "z": (d,),
+        "ptnl": (d,),
+        "prec": (d, d),
+        "anchor": (d,),
+        "log_norm": (),
+        "log_corr": (),
+    }
+
+
+def continuous_schema(d: int, n_steps: int) -> dict[str, tuple[int, ...]]:
+    """Field layout for a continuous-edge (SDE) BFFG tree.
+
+    Args:
+        d: State dimension.
+        n_steps: Number of Euler-Maruyama / RK4 substeps per edge.
+
+    Returns:
+        Mapping of field name to per-node trailing shape. Each non-root node's
+        edge is discretised into ``n_steps`` substeps (``n_steps + 1`` time
+        points). Fields:
+
+        - ``edge_len: ()`` — Δt for this node's incoming edge.
+        - ``vals: (n_steps + 1, d)`` — forward-sampled trajectory along the edge.
+        - ``zs: (n_steps, d)`` — i.i.d. standard-normal increments driving the SDE.
+        - ``ptnls: (n_steps + 1, d)`` / ``precs: (n_steps + 1, d, d)`` —
+          per-step canonical ``(F, H)`` trajectory along the edge (BF).
+        - ``ptnl_v: (d,)`` / ``prec_v: (d, d)`` — vertex (fused) canonical
+          message at this node; written by the up-sweep, used as the terminal
+          condition for the next-level edge.
+        - ``anchor: (d,)`` — child-end linearisation point of this node's edge
+          (t = ``edge_len``); refined toward the BFFG posterior mean.
+        - ``anchor_pa: (d,)`` — parent-end linearisation point of this node's
+          edge (t = 0); cached on the child so the up-sweep can read both
+          endpoints inside ``children.map(...)``.
+        - ``log_norm: ()`` — canonical-message log-norm at the vertex; sums
+          up the tree so ``log_norm[root]`` is the marginal log-evidence.
+        - ``log_corr: ()`` — per-edge importance log-weight from forward
+          guiding (Theorem 23 eq 32 / Remark 24).
+    """
+    return {
+        "edge_len": (),
+        "vals": (n_steps + 1, d),
+        "zs": (n_steps, d),
+        "ptnls": (n_steps + 1, d),
+        "precs": (n_steps + 1, d, d),
+        "ptnl_v": (d,),
+        "prec_v": (d, d),
+        "anchor": (d,),
+        "anchor_pa": (d,),
+        "log_norm": (),
+        "log_corr": (),
+    }
+
+
+# ── shared leaf-message helper ──────────────────────────────────────
+def _canonical_leaf_messages(
+    leaf_obs: jax.Array,
+    obs_var: float | jax.Array,
+    *,
+    d: int,
+) -> dict[str, jax.Array]:
+    """Canonical-form Gaussian leaf message.
+
+    Returns ``(prec, ptnl, log_norm)`` for ``log p(y | x) = c + F·x - ½x'Hx``
+    with ``H = I/obs_var``, ``F = y/obs_var``, and the constant
+    ``c = -½d log(2π·obs_var) - ½ y'y / obs_var`` that turns the canonical
+    message into an actual log-density at ``x``.
+    """
+    n_leaves = leaf_obs.shape[0]
+    obs_var = jnp.asarray(obs_var, dtype=leaf_obs.dtype)
+    prec = jnp.eye(d, dtype=leaf_obs.dtype) / obs_var
+    prec = jnp.broadcast_to(prec, (n_leaves, d, d))
+    ptnl = leaf_obs / obs_var
+    log_norm = (
+        -0.5 * d * jnp.log(2.0 * jnp.pi * obs_var) - 0.5 * jnp.sum(leaf_obs**2, axis=-1) / obs_var
+    )  # (n_leaves,)
+    return {"prec": prec, "ptnl": ptnl, "log_norm": log_norm}
+
+
+# ── leaf initialization helpers ─────────────────────────────────────
+def init_discrete_tree(
+    tree: Tree,
+    leaf_obs: jax.Array,
+    obs_var: float | jax.Array,
+    *,
+    d: int,
+    root_val: jax.Array | None = None,
+    anchor_init: jax.Array | None = None,
+) -> Tree:
+    """Seed a discrete-edge BFFG tree with leaf observations and initial anchors.
+
+    Writes leaves' canonical-form message ``(prec, ptnl, log_norm)`` from the
+    isotropic-Gaussian likelihood :math:`y \\sim \\mathcal N(x, \\tau^2 I)` and
+    seeds every node's ``anchor`` (leaves at their observation, others at
+    ``anchor_init``). Optionally pins the root state.
+
+    Args:
+        tree: Empty tree with the schema returned by :func:`discrete_schema`.
+        leaf_obs: ``(n_leaves, d)`` leaf observations in BFS leaf order.
+        obs_var: Scalar observation variance :math:`\\tau^2`.
+        d: State dimension.
+        root_val: Optional ``(d,)`` value to pin at the root (writes the
+            ``val`` field). Required by samplers that condition on a fixed
+            root.
+        anchor_init: Optional ``(d,)`` initial anchor for non-leaf nodes
+            (defaults to ``root_val`` if given, else zeros).
+
+    Returns:
+        Tree with leaf canonical messages set, anchors seeded, and (optionally)
+        the root pinned.
+    """
+    msgs = _canonical_leaf_messages(leaf_obs, obs_var, d=d)
+    tree = tree.at[tree.topology.is_leaf].set(
+        prec=msgs["prec"], ptnl=msgs["ptnl"], log_norm=msgs["log_norm"]
+    )
+    if root_val is not None:
+        root_val = jnp.asarray(root_val)
+        if "val" in tree.schema:
+            tree = tree.at[tree.topology.is_root].set(val=root_val)
+        else:
+            raise ValueError("Schema must declare 'val' field to set root_val.")
+    # Seed every node's anchor (per-edge linearisation point).
+    # - Leaves: anchor = leaf_obs (the observed terminal state; this is the
+    #   posterior mean at the leaf since prec_leaf=I/τ², ptnl_leaf=y/τ²).
+    # - Inner/root: anchor = anchor_init (defaults to root_val or zeros).
+    if "anchor" in tree.schema:
+        if anchor_init is None:
+            anchor_init = (
+                jnp.zeros(d, dtype=leaf_obs.dtype) if root_val is None else jnp.asarray(root_val)
+            )
+        anchor_init = jnp.asarray(anchor_init)
+        tree = tree.set(anchor=jnp.broadcast_to(anchor_init, (tree.topology.size, d)))
+        tree = tree.at[tree.topology.is_leaf].set(anchor=leaf_obs)
+    return tree
+
+
+def init_continuous_tree(
+    tree: Tree,
+    leaf_obs: jax.Array,
+    obs_var: float | jax.Array,
+    *,
+    d: int,
+    n_steps: int,
+    root_val: jax.Array | None = None,
+    anchor_init: jax.Array | None = None,
+) -> Tree:
+    """Seed a continuous-edge BFFG tree with leaf observations and initial anchors.
+
+    Writes leaves' **vertex** canonical message ``(prec_v, ptnl_v, log_norm)``
+    from the isotropic-Gaussian likelihood :math:`y \\sim \\mathcal N(x,
+    \\tau^2 I)` and seeds both ``anchor`` (leaves at observation, others at
+    ``anchor_init``) and ``anchor_pa``. The per-edge ``precs`` / ``ptnls``
+    trajectories are filled by :func:`continuous_bf_sweep`. Optionally pins
+    the root trajectory.
+
+    Args:
+        tree: Empty tree with the schema returned by :func:`continuous_schema`.
+            ``edge_len`` should already be set on each non-root node.
+        leaf_obs: ``(n_leaves, d)`` leaf observations in BFS leaf order.
+        obs_var: Scalar observation variance :math:`\\tau^2`.
+        d: State dimension.
+        n_steps: Number of substeps per edge (must match the schema).
+        root_val: Optional ``(d,)`` value to pin at the root (broadcast to the
+            root's full ``(n_steps + 1, d)`` ``vals`` trajectory).
+        anchor_init: Optional ``(d,)`` initial anchor for non-leaf nodes (and
+            for every node's ``anchor_pa`` before refinement). Defaults to
+            ``root_val`` if given, else zeros.
+
+    Returns:
+        Tree with leaf vertex messages set, both anchors seeded, and
+        (optionally) the root trajectory pinned.
+    """
+    msgs = _canonical_leaf_messages(leaf_obs, obs_var, d=d)
+    # Leaf observation = vertex message (terminal/initial condition for the
+    # edge backward filter). The per-edge trajectory is filled by the up-sweep.
+    tree = tree.at[tree.topology.is_leaf].set(
+        prec_v=msgs["prec"], ptnl_v=msgs["ptnl"], log_norm=msgs["log_norm"]
+    )
+    if root_val is not None:
+        root_val = jnp.asarray(root_val)
+        if "vals" in tree.schema:
+            tree = tree.at[tree.topology.is_root].set(
+                vals=jnp.broadcast_to(root_val, (n_steps + 1, d))
+            )
+        else:
+            raise ValueError("Schema must declare 'vals' field to set root_val.")
+    # Seed every node's two anchors (Algorithm 3 §7.1 linearisation points).
+    # - anchor (child end of incoming edge):
+    #     leaves -> leaf_obs (= posterior mean at leaf vertex)
+    #     inner/root -> anchor_init (default = root_val)
+    # - anchor_pa (parent end of incoming edge): anchor_init everywhere
+    #   for the first BF; subsequent refine_anchor sweeps overwrite it with
+    #   the parent's just-refined anchor.
+    if "anchor" in tree.schema:
+        if anchor_init is None:
+            anchor_init = (
+                jnp.zeros(d, dtype=leaf_obs.dtype) if root_val is None else jnp.asarray(root_val)
+            )
+        anchor_init = jnp.asarray(anchor_init)
+        tree = tree.set(anchor=jnp.broadcast_to(anchor_init, (tree.topology.size, d)))
+        tree = tree.at[tree.topology.is_leaf].set(anchor=leaf_obs)
+        if "anchor_pa" in tree.schema:
+            tree = tree.set(anchor_pa=jnp.broadcast_to(anchor_init, (tree.topology.size, d)))
+    return tree
+
+
+def _discrete_backward_filtering(
+    prec,
+    ptnl,
+    log_norm,
+    anchor,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    prxy_covar_fn,
+    params,
+) -> tuple[jax.Array, jax.Array, jax.Array]:
+    """One BF edge step. Returns ``(prec_msg, ptnl_msg, log_norm_msg)`` such
+    that the parent's canonical message is the sum of these contributions
+    over its children.
+
+    The auxiliary transition ``x_v | x_pa ~ N(B x_pa + β, Q)`` is evaluated
+    at the per-edge linearisation point ``anchor`` (Algorithm 3 §7.1).
+    For linear-Gaussian models the user simply ignores ``anchor``.
+
+    With child canonical message ``exp(c_v + F_v·x - ½x'H_v x)``, the
+    integral ``∫ exp(...) N(x_v; Bx_pa+β, Q) dx_v`` gives a Gaussian
+    canonical message in ``x_pa`` with:
+
+    - ``prec_msg = Bᵀ (I + H_v Q)⁻¹ H_v B``
+    - ``ptnl_msg = Bᵀ (I + H_v Q)⁻¹ (F_v - H_v β)``
+    - ``log_norm_msg = c_v - ½ log det(I + H_v Q) + ½ F_v' Q (I+H_v Q)⁻¹ F_v
+                       + F_v' Q (I+H_v Q)⁻¹ Q⁻¹ β - ½ β' (I+H_v Q)⁻¹ H_v β``
+    """
+    B, beta = prxy_scale_fn(anchor, params), prxy_shift_fn(anchor, params)
+    Q = prxy_covar_fn(anchor, params)
+    d = prec.shape[-1]
+    phi_inv = jnp.eye(d, dtype=prec.dtype) + prec @ Q  # I + HQ
+    Cinv = jnp.linalg.solve(phi_inv, prec)  # phi_inv⁻¹ H = C⁻¹
+    prec_msg = B.T @ Cinv @ B
+    ptnl_msg = B.T @ solve(phi_inv, ptnl - prec @ beta)
+
+    # c-update:  Δc = -½ log det(phi_inv) + ½ F' Q phi_inv⁻¹ F
+    #                 + F' Q phi_inv⁻¹ Q⁻¹ β - ½ β' phi_inv⁻¹ H β.
+    sign, logdet_phi = jnp.linalg.slogdet(phi_inv)
+    Q_phi_inv = jnp.linalg.solve(phi_inv.T, Q.T).T  # Q · phi_inv⁻¹
+    term_F = 0.5 * ptnl @ Q_phi_inv @ ptnl
+    term_F_beta = ptnl @ Q_phi_inv @ jnp.linalg.solve(Q, beta)
+    term_beta = -0.5 * beta @ jnp.linalg.solve(phi_inv, prec @ beta)
+    log_norm_msg = log_norm - 0.5 * logdet_phi + term_F + term_F_beta + term_beta
+    return prec_msg, ptnl_msg, log_norm_msg
+
+
+def _discrete_forward_guiding(
+    x_pa,
+    z,
+    prec,
+    ptnl,
+    anchor,
+    *,
+    mean_fn,
+    covar_fn,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    prxy_covar_fn,
+    params,
+) -> tuple[jax.Array, jax.Array]:
+    d = prec.shape[-1]
+    mu = mean_fn(x_pa, params)
+    Q_true = covar_fn(x_pa, params)
+
+    # Sample under P°: Nᶜᵃⁿ(F + Q⁻¹μ, H + Q⁻¹)  (Theorem 14).
+    inv = jnp.linalg.solve(jnp.eye(d, dtype=prec.dtype) + Q_true @ prec, Q_true)  # (H + Q⁻¹)⁻¹
+    m_ch = dot(inv, ptnl + solve(Q_true, mu))
+    x_ch = m_ch + dot(cholesky(inv, lower=True, check_finite=False), z)
+
+    # Weight w = (Pg)/(P̃g) = φ(H⁻¹F; μ, Q+H⁻¹) / φ(H⁻¹F; Φx+β, Q̃+H⁻¹).
+    # Auxiliary is evaluated at the per-edge linearisation point `anchor`.
+    H_inv = cho_solve(
+        (cholesky(prec, lower=True, check_finite=False), True),
+        jnp.eye(d, dtype=prec.dtype),
+    )
+    m_star = solve(prec, ptnl)  # H⁻¹F
+    B, beta = prxy_scale_fn(anchor, params), prxy_shift_fn(anchor, params)
+    mu_prxy = B @ x_pa + beta
+
+    C_true = Q_true + H_inv
+    C_prxy = prxy_covar_fn(anchor, params) + H_inv
+    L_true = cholesky(C_true, lower=True, check_finite=False)
+    L_prxy = cholesky(C_prxy, lower=True, check_finite=False)
+    y_true = solve_triangular(L_true, m_star - mu, lower=True)
+    y_prxy = solve_triangular(L_prxy, m_star - mu_prxy, lower=True)
+    logdet_true = 2.0 * jnp.sum(jnp.log(jnp.diag(L_true)))
+    logdet_prxy = 2.0 * jnp.sum(jnp.log(jnp.diag(L_prxy)))
+    log_corr = -0.5 * (logdet_true - logdet_prxy) - 0.5 * (jnp.sum(y_true**2) - jnp.sum(y_prxy**2))
+    return x_ch, log_corr
+
+
+def discrete_bf_sweep(prxy_scale_fn, prxy_shift_fn, prxy_covar_fn) -> SweepFn:
+    """Build the discrete-edge backward-filtering up-sweep (Theorem 14 §6.1).
+
+    At each non-leaf parent, pulls each child's canonical message back through
+    the linear-Gaussian auxiliary :math:`\\tilde P(x, dy) = \\mathcal N(y;
+    \\Phi x + \\beta, Q)\\,dy` and fuses (sums) the contributions. The
+    auxiliary is evaluated per-child at the child's ``anchor``.
+
+    Args:
+        prxy_scale_fn: Callable ``(anchor, params) -> (d, d) array`` returning
+            the auxiliary scale matrix :math:`\\Phi`.
+        prxy_shift_fn: Callable ``(anchor, params) -> (d,) array`` returning
+            the auxiliary shift vector :math:`\\beta`.
+        prxy_covar_fn: Callable ``(anchor, params) -> (d, d) SPD array``
+            returning the auxiliary covariance :math:`Q`.
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that, when applied to a tree, writes
+        ``(prec, ptnl, log_norm)`` at every non-leaf node.
+
+    Notes:
+        Linear-Gaussian models (auxiliary equal to truth) ignore ``anchor``;
+        for nonlinear models, iterate this sweep with
+        :func:`discrete_refine_anchor` (Algorithm 3 §7.1).
+    """
+
+    @up(
+        reads_children=("prec", "ptnl", "log_norm", "anchor"),
+        writes=("prec", "ptnl", "log_norm"),
+    )
+    def _sweep(node, children, params):
+
+        def per_child(child):
+            prec_msg, ptnl_msg, log_norm_msg = _discrete_backward_filtering(
+                child.prec,
+                child.ptnl,
+                child.log_norm,
+                child.anchor,
+                prxy_scale_fn,
+                prxy_shift_fn,
+                prxy_covar_fn,
+                params,
+            )
+            return {"prec": prec_msg, "ptnl": ptnl_msg, "log_norm": log_norm_msg}
+
+        msgs = children.map(per_child)
+        # Canonical messages multiply at a parent: (prec, ptnl, log_norm) all sum.
+        return {
+            "prec": msgs.prec.sum(0),
+            "ptnl": msgs.ptnl.sum(0),
+            "log_norm": msgs.log_norm.sum(0),
+        }
+
+    return _sweep
+
+
+def discrete_forward_sweep(mean_fn, covar_fn) -> SweepFn:
+    """Build the unconditional forward-sampling down-sweep.
+
+    Draws each non-root state from the true 1-step Gaussian kernel
+    :math:`X_t \\mid X_{pa} = x \\sim \\mathcal N(\\mu(x), Q(x))` using the
+    pre-stored noise :math:`z \\sim \\mathcal N(0, I)` in ``node.z``::
+
+        val = mean_fn(parent.val, params)
+              + chol(covar_fn(parent.val, params)) @ node.z
+
+    Args:
+        mean_fn: Callable ``(x_parent, params) -> (d,) array`` returning the
+            true conditional mean :math:`\\mu(x)`.
+        covar_fn: Callable ``(x_parent, params) -> (d, d) SPD array``
+            returning the true conditional covariance :math:`Q(x)`.
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``val`` at every non-root node.
+        The root's ``val`` must be set by the caller (typically via
+        :func:`init_discrete_tree`'s ``root_val``).
+    """
+
+    @down(
+        reads_parent=("val",),
+        reads=("z",),
+        writes=("val",),
+    )
+    def _sweep(node, parent, params):
+        covar = covar_fn(parent.val, params)
+        return {
+            "val": mean_fn(parent.val, params)
+            + dot(cholesky(covar, lower=True, check_finite=False), node.z)
+        }
+
+    return _sweep
+
+
+def discrete_fg_sweep(mean_fn, covar_fn, prxy_scale_fn, prxy_shift_fn, prxy_covar_fn) -> SweepFn:
+    """Build the discrete-edge forward-guided down-sweep (Theorem 14 §6.1).
+
+    For each non-root node, samples the guided proposal :math:`X_t^\\circ
+    \\sim \\mathcal N^{\\mathrm{can}}(F + Q(x)^{-1}\\mu(x),\\ H + Q(x)^{-1})`
+    using the parent state ``parent.val``, the node's canonical message
+    ``(prec, ptnl)``, the true kernel ``(mean_fn, covar_fn)``, and the noise
+    ``node.z``. Writes the importance log-weight
+
+    .. math::
+
+       \\log w = \\log\\varphi(H^{-1}F;\\ \\mu(x),\\ Q(x)+H^{-1})
+                - \\log\\varphi(H^{-1}F;\\ \\Phi x+\\beta,\\ Q_{\\mathrm{aux}}+H^{-1})
+
+    to ``log_corr`` (Theorem 14 step 3). Sum ``log_corr`` over non-root nodes
+    to get the path's importance correction.
+
+    Args:
+        mean_fn / covar_fn: True kernel ``(x_parent, params) -> array``.
+        prxy_scale_fn / prxy_shift_fn / prxy_covar_fn: Auxiliary kernel
+            ``(anchor, params) -> array`` (same callables fed to
+            :func:`discrete_bf_sweep`).
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``(val, log_corr)`` at every
+        non-root node. Run :func:`discrete_bf_sweep` first to populate the
+        canonical messages.
+    """
+
+    @down(
+        reads_parent=("val",),
+        reads=("z", "prec", "ptnl", "anchor"),
+        writes=("val", "log_corr"),
+    )
+    def _sweep(node, parent, params):
+        x_ch, log_corr = _discrete_forward_guiding(
+            parent.val,
+            node.z,
+            node.prec,
+            node.ptnl,
+            node.anchor,
+            mean_fn=mean_fn,
+            covar_fn=covar_fn,
+            prxy_scale_fn=prxy_scale_fn,
+            prxy_shift_fn=prxy_shift_fn,
+            prxy_covar_fn=prxy_covar_fn,
+            params=params,
+        )
+        return {"val": x_ch, "log_corr": log_corr}
+
+    return _sweep
+
+
+def discrete_refine_anchor() -> SweepFn:
+    """Build the discrete anchor-refinement down-sweep (Algorithm 3 §7.1).
+
+    Overwrites each non-root node's ``anchor`` with the BFFG-implied posterior
+    mean :math:`H^{-1} F` derived from the canonical message
+    ``(prec, ptnl)`` left by :func:`discrete_bf_sweep`. The root keeps its
+    initial anchor (it is pinned at ``root_val``).
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``anchor`` at every non-root
+        node. Typical usage interleaves with the backward filter for several
+        iterations until the anchor — and thus the auxiliary linearisation —
+        converges::
+
+            for _ in range(n_lin_iters):
+                tree = bf(tree, params=theta)
+                tree = refine(tree, params=theta)
+    """
+
+    @down(reads=("prec", "ptnl"), writes=("anchor",))
+    def _sweep(node, parent, params):
+        anchor = jnp.linalg.solve(node.prec, node.ptnl)
+        return {"anchor": anchor}
+
+    return _sweep
+
+
+def _continuous_backward_filtering(
+    ts,
+    prec,
+    ptnl,
+    log_norm,
+    anchor_pa,
+    anchor_ch,
+    *,
+    prxy_diffusion_fn,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    params,
+) -> tuple[jax.Array, jax.Array, jax.Array]:
+    """Backward-filter one edge. Returns ``(precs, ptnls, log_norm_at_pa)``
+    where ``precs`` and ``ptnls`` are the per-step canonical trajectories
+    and ``log_norm_at_pa`` is the canonical-message log-norm propagated to
+    the parent end of the edge.
+
+    Two anchor points define the auxiliary diffusion's linearisation along
+    the edge (Algorithm 3 §7.1):
+
+    - ``anchor_pa``: state at the parent end of the edge (t = ts[0]).
+    - ``anchor_ch``: state at the child end of the edge (t = ts[-1]).
+
+    The auxiliary diffusion ``ã(t)`` is linearly interpolated between
+    ``σ̃(anchor_pa)σ̃ᵀ`` and ``σ̃(anchor_ch)σ̃ᵀ``.
+    """
+    if prxy_scale_fn is None and prxy_shift_fn is None:
+        return _continuous_bf_anlt(
+            ts,
+            prec,
+            ptnl,
+            log_norm,
+            anchor_pa,
+            anchor_ch,
+            prxy_scale_fn=None,
+            prxy_shift_fn=None,
+            prxy_diffusion_fn=prxy_diffusion_fn,
+            params=params,
+        )
+    else:
+        return _continuous_bf_ode(
+            ts,
+            prec,
+            ptnl,
+            log_norm,
+            anchor_pa,
+            anchor_ch,
+            prxy_scale_fn=prxy_scale_fn,
+            prxy_shift_fn=prxy_shift_fn,
+            prxy_diffusion_fn=prxy_diffusion_fn,
+            params=params,
+        )
+
+
+def _continuous_bf_anlt(
+    ts,
+    prec,
+    ptnl,
+    log_norm,
+    anchor_pa,
+    anchor_ch,
+    *,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    prxy_diffusion_fn,
+    params,
+):
+    # B = β = 0: driftless linear auxiliary with linearly-interpolated σ̃.
+    # ã(t) = (t/T) ã_ch + (1 - t/T) ã_pa, where ã_x = σ̃(x) σ̃(x)ᵀ.
+    # Then Φ̃⁻¹(t) = I + H_T · ∫_t^T ã(s) ds is computed analytically.
+    sigma_pa = prxy_diffusion_fn(ts[0], anchor_pa, params)
+    sigma_ch = prxy_diffusion_fn(ts[-1], anchor_ch, params)
+    a_pa = sigma_pa @ sigma_pa.T
+    a_ch = sigma_ch @ sigma_ch.T
+    t1 = ts[-1]
+    d = prec.shape[-1]
+
+    # Hoist H_T · ã_pa and H_T · ã_ch once so per_t reduces to elementwise
+    # weighting + one (d, d) solve per step.
+    H_a_pa = prec @ a_pa
+    H_a_ch = prec @ a_ch
+
+    def per_t(t):
+        # ∫_t^T ã(s) ds = c_ch · ã_ch + c_pa · ã_pa where
+        #   c_ch = ∫_t^T (s/T) ds = (T² - t²) / (2T)
+        #   c_pa = ∫_t^T (1 - s/T) ds = (T - t)² / (2T)
+        c_ch = -(t**2 - t1**2) / (2.0 * t1)
+        c_pa = ((t1 - t) ** 2) / (2.0 * t1)
+        phi_inv = jnp.eye(d, dtype=prec.dtype) + c_ch * H_a_ch + c_pa * H_a_pa
+        prec_t = jnp.linalg.solve(phi_inv, prec)
+        ptnl_t = solve(phi_inv, ptnl)
+        return prec_t, ptnl_t
+
+    precs, ptnls = jax.vmap(per_t)(ts)
+
+    # log-norm at the parent end (t = ts[0]).  Effective covariance over the
+    # whole edge under linear ã interpolation is (T/2)(ã_pa + ã_ch).
+    covar_eff = 0.5 * t1 * (a_pa + a_ch)
+    phi_inv_full = jnp.eye(d, dtype=prec.dtype) + prec @ covar_eff  # I + HQ
+    _sign, logdet_full = jnp.linalg.slogdet(phi_inv_full)
+    # Quadratic term ½ Fᵀ M⁻¹ F with M = H + Q⁻¹, and M⁻¹ = Q (I + HQ)⁻¹.
+    # The factor ordering matters: (I+HQ)⁻¹Q is *not* symmetric and only
+    # equals Q(I+HQ)⁻¹ when H and Q commute (e.g. an isotropic leaf prec).
+    # At internal edges the fused precision does not commute with ã, so the
+    # message normaliser must use Q(I+HQ)⁻¹ — otherwise log h(x_root) grows
+    # spuriously with the diffusion scale.
+    quad = 0.5 * ptnl @ (covar_eff @ jnp.linalg.solve(phi_inv_full, ptnl))
+    log_norm_at_pa = log_norm - 0.5 * logdet_full + quad
+    return precs, ptnls, log_norm_at_pa
+
+
+def _continuous_bf_ode(
+    ts,
+    prec,
+    ptnl,
+    log_norm,
+    anchor_pa,
+    anchor_ch,
+    *,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    prxy_diffusion_fn,
+    params,
+):
+    t1 = ts[-1]
+    d = prec.shape[-1]
+
+    def anchor_at(t):
+        # Linear interpolation between parent end (t=0) and child end (t=T).
+        return (t / t1) * anchor_ch + (1.0 - t / t1) * anchor_pa
+
+    def vector_field(tau, y, args):
+        # Recover (H, F, c) from the packed state.
+        Ht = y[: d * d].reshape((d, d))
+        Ft = y[d * d : d * d + d]
+        # ct = y[d*d+d]  # not used in the RHS, just propagated
+        t = t1 - tau  # integrate backward in time
+        anchor_t = anchor_at(t)
+        Bt = prxy_scale_fn(t, anchor_t, args)
+        betat = prxy_shift_fn(t, anchor_t, args)
+        sigmat = prxy_diffusion_fn(t, anchor_t, args)
+        at = sigmat @ sigmat.T
+
+        # Forward-in-t derivatives (Theorem 23, eq 29; ã = σ̃ σ̃ᵀ).
+        dH = -Bt.T @ Ht - Ht @ Bt + Ht @ at @ Ht
+        dF = -Bt.T @ Ft + Ht @ at @ Ft + Ht @ betat
+        # Matching c-ODE (Kolmogorov backward, constant-term collection):
+        #   dc/dt = -β'F + ½ tr(aH) - ½ F'aF.
+        dc = -betat @ Ft + 0.5 * jnp.trace(at @ Ht) - 0.5 * Ft @ at @ Ft
+        # Integrate in τ = T - t, so dy/dτ = -dy/dt.
+        return -jnp.concatenate([dH.flatten(), dF.flatten(), jnp.asarray(dc).reshape(1)])
+
+    y0 = jnp.concatenate([prec.flatten(), ptnl.flatten(), jnp.asarray(log_norm).reshape(1)])
+    sol = solve_ode(vector_field, y0, ts, solver=RK4(), args=params)
+    sol = sol[::-1]
+    precs = sol[:, : d * d].reshape((-1, d, d))
+    ptnls = sol[:, d * d : d * d + d].reshape((-1, d))
+    log_norms = sol[:, d * d + d]
+    return precs, ptnls, log_norms[0]  # parent end (after reversal)
+
+
+def _continuous_forward_guiding(
+    x_pa,
+    ts,
+    dws,
+    precs,
+    ptnls,
+    anchor_pa,
+    anchor_ch,
+    drift_fn,
+    diffusion_fn,
+    prxy_scale_fn,
+    prxy_shift_fn,
+    prxy_diffusion_fn,
+    params,
+) -> tuple[jax.Array, jax.Array]:
+    # Auxiliary's σ̃ varies linearly between anchor_pa (t=0) and anchor_ch (t=T)
+    # to match the BF's interpolated ã. We pre-compute the endpoint ã's once;
+    # each step reads ã(t_i) by linear lerp — same machinery the BF used.
+    t1 = ts[-1]
+    sigma_pa = prxy_diffusion_fn(ts[0], anchor_pa, params)
+    sigma_ch = prxy_diffusion_fn(t1, anchor_ch, params)
+    a_pa_endpoint = sigma_pa @ sigma_pa.T
+    a_ch_endpoint = sigma_ch @ sigma_ch.T
+
+    def anchor_at(t):
+        return (t / t1) * anchor_ch + (1.0 - t / t1) * anchor_pa
+
+    # Build the auxiliary drift closure. Both B(t) and β(t) see the
+    # interpolated anchor — matches the BF's `anchor_at(t)` view.
+    if prxy_scale_fn is None and prxy_shift_fn is None:
+        prxy_drift_fn = lambda t, x, params: jnp.zeros_like(x)
+    else:
+        prxy_drift_fn = lambda t, x, params: (
+            prxy_shift_fn(t, anchor_at(t), params) + prxy_scale_fn(t, anchor_at(t), params) @ x
+        )
+
+    def bridge_step_body(carry, val):
+        i, x, log_corr = carry
+        dt, dw = val
+        t = ts[i]
+        Ht, Ft = precs[i], ptnls[i]
+        r = Ft - dot(Ht, x)
+        f_true, g_true = drift_fn(t, x, params), diffusion_fn(t, x, params)
+        f_prxy = prxy_drift_fn(t, x, params)
+        a_true = g_true @ g_true.T
+        # Auxiliary ã(t) — linearly interpolated between endpoint covariances.
+        a_prxy_t = (t / t1) * a_ch_endpoint + (1.0 - t / t1) * a_pa_endpoint
+        x_next = x + (f_true + dot(a_true, r)) * dt + dot(g_true, dw)
+
+        # log w increment = (L - L̃)g / g   (Theorem 23 eq 32, Remark 24):
+        #   (b - b̃)·r - ½ tr((a - ã) H) + ½ r'(a - ã) r.
+        covar_diff = a_true - a_prxy_t  # a - ã (interpolated)
+        drift_diff = f_true - f_prxy  # b - b̃
+        log_corr_next = (
+            log_corr
+            + (jnp.dot(drift_diff, r) - 0.5 * jnp.sum(covar_diff * Ht) + 0.5 * (r @ covar_diff @ r))
+            * dt
+        )
+        return (i + 1, x_next, log_corr_next), x
+
+    dts = jnp.diff(ts)
+    log_corr0 = jnp.zeros((), dtype=x_pa.dtype)
+    (_, x_ch, log_corr), xs = jax.lax.scan(bridge_step_body, (0, x_pa, log_corr0), (dts, dws))
+    return jnp.vstack((xs, x_ch)), log_corr
+
+
+def continuous_bf_sweep(n_steps, prxy_scale_fn, prxy_shift_fn, prxy_diffusion_fn) -> SweepFn:
+    """Build the continuous-edge backward-filtering up-sweep (Theorem 23 §7.1).
+
+    For each parent, integrates the auxiliary-SDE backward equation (eq 29)
+    over every child's edge — either analytically (closed form when
+    :math:`B = \\beta = 0`) or via :class:`hyperiax.utils.ode.RK4` — to
+    produce the per-step canonical ``(H, F)`` trajectory. Each edge is
+    linearised between two endpoint anchors (``anchor_pa`` at :math:`t = 0`
+    and ``anchor`` at :math:`t = \\tau_e`), with :math:`\\tilde a(t) =
+    \\tilde\\sigma\\tilde\\sigma^\\top` linearly interpolated. The trajectory
+    is scattered to the child (``writes_children``); the time-0 messages are
+    fused into the parent's vertex message ``(prec_v, ptnl_v, log_norm)``.
+
+    Args:
+        n_steps: Number of integration substeps per edge.
+        prxy_scale_fn: ``(t, anchor, params) -> (d, d)`` returning :math:`B(t)`,
+            or ``None`` to take the driftless analytic path (requires
+            ``prxy_shift_fn`` also ``None``).
+        prxy_shift_fn: ``(t, anchor, params) -> (d,)`` returning
+            :math:`\\beta(t)`, or ``None`` for the analytic path.
+        prxy_diffusion_fn: ``(t, anchor, params) -> (d, d)`` returning
+            :math:`\\tilde\\sigma(t)`; the auxiliary covariance is
+            :math:`\\tilde a = \\tilde\\sigma \\tilde\\sigma^\\top`.
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``(prec_v, ptnl_v, log_norm)``
+        at every non-leaf node and the full ``(precs, ptnls)`` trajectories
+        at every non-root node.
+    """
+
+    @up(
+        reads_children=("edge_len", "prec_v", "ptnl_v", "log_norm", "anchor", "anchor_pa"),
+        writes=("prec_v", "ptnl_v", "log_norm"),
+        writes_children=("precs", "ptnls"),
+    )
+    def _sweep(node, children, params):
+
+        def per_child(child):
+            ts = jnp.linspace(0.0, child.edge_len, n_steps + 1, dtype=child.edge_len.dtype)
+            # Two-anchor linearisation along each edge:
+            #   anchor_pa = child.anchor_pa (parent end, t = 0)
+            #   anchor_ch = child.anchor    (child end,  t = T)
+            precs, ptnls, log_norm_at_pa = _continuous_backward_filtering(
+                ts,
+                child.prec_v,
+                child.ptnl_v,
+                child.log_norm,
+                child.anchor_pa,
+                child.anchor,
+                prxy_diffusion_fn=prxy_diffusion_fn,
+                prxy_scale_fn=prxy_scale_fn,
+                prxy_shift_fn=prxy_shift_fn,
+                params=params,
+            )
+            # Return the full trajectory (cached on the child edge for forward
+            # guiding), its parent-end (time-0) message for the fusion, and the
+            # canonical-message log-norm at the parent end. Explicit fields here
+            # avoid indexing a segment-axis object after the map.
+            return {
+                "precs": precs,
+                "ptnls": ptnls,
+                "m_prec": precs[0],
+                "m_ptnl": ptnls[0],
+                "m_log_norm": log_norm_at_pa,
+            }
+
+        # Per child: backward-filter its edge from the child's vertex message
+        # (terminal) into a full (n_steps+1) (H, F) trajectory.
+        msgs = children.map(per_child)
+        # Fusion: canonical messages multiply at the parent, so (prec_v, ptnl_v,
+        # log_norm) all sum over children. The result is the terminal/initial
+        # condition for this node's own edge, one level up.
+        return {
+            "precs": msgs.precs,  # (n_steps+1, d, d) per child -> children
+            "ptnls": msgs.ptnls,  # (n_steps+1, d)    per child -> children
+            "prec_v": msgs.m_prec.sum(0),  # (d, d)                      -> this node
+            "ptnl_v": msgs.m_ptnl.sum(0),  # (d,)
+            "log_norm": msgs.m_log_norm.sum(0),  # ()
+        }
+
+    return _sweep
+
+
+def continuous_forward_sweep(n_steps, drift_fn, diffusion_fn) -> SweepFn:
+    """Build the unconditional SDE forward-sampling down-sweep.
+
+    For each non-root node, integrates the true SDE
+    :math:`dX_u = b(u, X_u)\\,du + \\sigma(u, X_u)\\,dW_u` from the parent's
+    terminal value ``parent.vals[-1]`` over the edge using Euler-Maruyama
+    and the pre-stored increments :math:`dW = \\sqrt{\\Delta t}\\, z` derived
+    from ``node.zs``.
+
+    Args:
+        n_steps: Number of Euler-Maruyama substeps per edge.
+        drift_fn: ``(t, x, params) -> (d,)`` returning the true drift.
+        diffusion_fn: ``(t, x, params) -> (d, noise)`` returning the true
+            diffusion matrix :math:`\\sigma(t, x)`.
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes the full per-edge trajectory
+        to ``vals`` at every non-root node. The root's ``vals`` must be set by
+        the caller (typically via :func:`init_continuous_tree`'s ``root_val``).
+    """
+
+    @down(
+        reads_parent=("vals",),
+        reads=("zs", "edge_len"),
+        writes=("vals",),
+    )
+    def _sweep(node, parent, params):
+        ts = jnp.linspace(0, node.edge_len, n_steps + 1, dtype=node.edge_len.dtype)
+        dws = jnp.sqrt(jnp.diff(ts))[:, None] * node.zs
+        x0 = parent.vals[-1]
+        ys = solve_sde(drift_fn, diffusion_fn, x0, ts, dws, solver=EulerMaruyama(), args=params)
+        return {"vals": ys}
+
+    return _sweep
+
+
+def continuous_fg_sweep(
+    n_steps, drift_fn, diffusion_fn, prxy_scale_fn, prxy_shift_fn, prxy_diffusion_fn
+) -> SweepFn:
+    """Build the continuous-edge forward-guided down-sweep (Theorem 23 §7.1).
+
+    For each non-root node, integrates the guided SDE (eq 31)
+
+    .. math::
+
+       dX_u^\\circ = \\big(b(u, X_u^\\circ) + a(u, X_u^\\circ)(F(u) - H(u)X_u^\\circ)\\big)du
+                     + \\sigma(u, X_u^\\circ)\\,dW_u
+
+    from ``parent.vals[-1]`` over the edge, using the BF-cached
+    ``(precs, ptnls)`` trajectory as the guiding term and ``node.zs`` for the
+    noise. Writes the per-step Theorem-23 importance log-weight increment
+
+    .. math::
+
+       \\frac{(\\mathcal L - \\tilde{\\mathcal L})g}{g}
+         = (b - \\tilde b)\\cdot r - \\tfrac12 \\mathrm{tr}((a - \\tilde a) H)
+           + \\tfrac12 r^\\top(a - \\tilde a) r,
+         \\qquad r = F - H X^\\circ,
+
+    integrated over the edge into ``log_corr`` (Remark 24). Auxiliary
+    :math:`\\tilde a(t)` is linearly interpolated between the two anchors —
+    same convention as :func:`continuous_bf_sweep`.
+
+    Args:
+        n_steps: Number of substeps per edge.
+        drift_fn / diffusion_fn: True SDE ``(t, x, params) -> array``.
+        prxy_scale_fn / prxy_shift_fn / prxy_diffusion_fn: Auxiliary linear-SDE
+            ``(t, anchor, params) -> array`` (same callables fed to
+            :func:`continuous_bf_sweep`; pass ``None`` to ``prxy_scale_fn``
+            and ``prxy_shift_fn`` for the driftless analytic case).
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``(vals, log_corr)`` at every
+        non-root node. Run :func:`continuous_bf_sweep` first.
+    """
+
+    @down(
+        reads_parent=("vals",),
+        reads=("zs", "edge_len", "precs", "ptnls", "anchor", "anchor_pa"),
+        writes=(
+            "vals",
+            "log_corr",
+        ),
+    )
+    def _sweep(node, parent, params):
+        ts = jnp.linspace(0, node.edge_len, n_steps + 1, dtype=node.edge_len.dtype)
+        dws = jnp.sqrt(jnp.diff(ts))[:, None] * node.zs
+        # Two-anchor linearisation: node.anchor_pa at edge start (t=0) and
+        # node.anchor at edge end (t=T) — matches what the BF sweep used.
+        xs, log_corr = _continuous_forward_guiding(
+            parent.vals[-1],
+            ts,
+            dws,
+            node.precs,
+            node.ptnls,
+            node.anchor_pa,
+            node.anchor,
+            drift_fn,
+            diffusion_fn,
+            prxy_scale_fn,
+            prxy_shift_fn,
+            prxy_diffusion_fn,
+            params,
+        )
+        return {"vals": xs, "log_corr": log_corr}
+
+    return _sweep
+
+
+def continuous_refine_anchor() -> SweepFn:
+    """Build the continuous anchor-refinement down-sweep (Algorithm 3 §7.1).
+
+    Walks root → leaves, writing two fields per non-root node:
+
+    - ``anchor = prec_v⁻¹ ptnl_v`` — the BFFG-implied posterior mean at THIS
+      vertex (child end of the incoming edge).
+    - ``anchor_pa = parent.anchor`` — the parent's just-refined anchor,
+      cached on the child so the next up-sweep can read both endpoints inside
+      ``children.map(...)`` without a segment-aware gather.
+
+    Because ``@down`` proceeds top-down, ``parent.anchor`` reads the parent's
+    UPDATED value (refined at the previous level of this sweep). The root
+    keeps its initial anchor (it is pinned at ``root_val``).
+
+    Returns:
+        A :class:`hyperiax.SweepFn` that writes ``(anchor, anchor_pa)`` at
+        every non-root node. Typical usage iterates with the BF up-sweep::
+
+            for _ in range(n_lin_iters):
+                tree = bf(tree, params=theta)
+                tree = refine(tree, params=theta)
+    """
+
+    @down(
+        reads=("prec_v", "ptnl_v"),
+        reads_parent=("anchor",),
+        writes=("anchor", "anchor_pa"),
+    )
+    def _sweep(node, parent, params):
+        anchor = jnp.linalg.solve(node.prec_v, node.ptnl_v)
+        return {"anchor": anchor, "anchor_pa": parent.anchor}
+
+    return _sweep
+
+
+def propagate_linearization(tree: Tree, params=None) -> Tree:
+    """Propagate continuous BFFG linearisation anchors top-down.
+
+    This is the public one-shot form of :func:`continuous_refine_anchor`.
+    It overwrites each non-root node's ``anchor`` with ``prec_v^-1 ptnl_v``
+    and caches the parent's just-refined anchor in ``anchor_pa``.
+
+    Args:
+        tree: Continuous BFFG tree after :func:`continuous_bf_sweep`.
+        params: Optional params object passed through to the underlying sweep.
+
+    Returns:
+        Tree with updated ``anchor`` and ``anchor_pa`` fields.
+    """
+    return continuous_refine_anchor()(tree, params=params)
diff --git a/hyperiax/prebuilt/phylo_mean.py b/hyperiax/prebuilt/phylo_mean.py
new file mode 100644
index 0000000..4dc6374
--- /dev/null
+++ b/hyperiax/prebuilt/phylo_mean.py
@@ -0,0 +1,58 @@
+"""Phylogenetic mean: edge-length-weighted average up-sweep.
+
+The classical estimator for inner-node states given leaf observations on
+a phylogenetic tree::
+
+    \\hat{x}_p = \\frac{\\sum_c v_c / l_c}{\\sum_c 1 / l_c}
+
+where the sum is over a parent ``p``'s children and ``l_c`` is the
+length of the edge from ``p`` to child ``c``. Children with shorter
+edges contribute more — the same prior you get under a Brownian-motion
+diffusion model along edges.
+
+Usage::
+
+    tree = hx.Tree.empty(topo, {"estimated_value": (d,), "edge_length": ()})
+    tree = tree.set(edge_length=...)
+    tree = tree.at[topo.is_leaf].set(estimated_value=observed_leaves)
+
+    sweep = hyperiax.prebuilt.phylo_mean()
+    inferred = sweep(tree)
+    root_estimate = inferred["estimated_value"][0]
+
+The body expresses its per-child work via :meth:`Children.map`, so it
+runs unchanged on both equal- and unequal-degree topologies.
+"""
+
+from __future__ import annotations
+
+from ..core.sweep import SweepFn, up
+
+
+def phylo_mean() -> SweepFn:
+    """Return a SweepFn that fills each non-leaf node's ``estimated_value``
+    with the edge-length-weighted average of its children's
+    ``estimated_value``.
+
+    The sweep reads ``estimated_value`` and ``edge_length`` from each
+    child and writes back ``estimated_value`` on the parent. Leaves are
+    untouched.
+    """
+
+    def _per_child(c):
+        edge = c.edge_length      # () typically (scalar per child)
+        val = c.estimated_value   # (*value_trailing)
+        # Broadcast edge to match the value's trailing rank so the fields
+        # produced here share trailing shape and combine cleanly after fusion.
+        edge_b = edge.reshape(edge.shape + (1,) * (val.ndim - edge.ndim))
+        return {"weighted": val / edge_b, "inv_edge": 1.0 / edge_b}
+
+    @up(
+        reads_children=("estimated_value", "edge_length"),
+        writes=("estimated_value",),
+    )
+    def _sweep(node, children, params):
+        msgs = children.map(_per_child)
+        return {"estimated_value": msgs.weighted.sum(0) / msgs.inv_edge.sum(0)}
+
+    return _sweep
diff --git a/hyperiax/tree/__init__.py b/hyperiax/tree/__init__.py
deleted file mode 100644
index 869cc85..0000000
--- a/hyperiax/tree/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .tree import HypTree, TopologyNode
\ No newline at end of file
diff --git a/hyperiax/tree/topology.py b/hyperiax/tree/topology.py
deleted file mode 100644
index f64fce7..0000000
--- a/hyperiax/tree/topology.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from typing import Type
-
-from .tree import TopologyNode, HypTree
-
-def symmetric_topology(height : int, degree : int, fake_root : bool = False) -> TopologyNode:
-    """ Generate tree of given height and degree
-
-    A tree of height zero contains just the root;
-    a tree of height one contains the root and one level of leaves below it,
-    and so forth.
-
-    :param height: The height of the tree
-    :param degree: The degree of each node in the tree
-    :param new_node: The node used to construct the tree, defaults to TreeNode
-    :param fake_root: The fake root node, defaults to None
-    :raises ValueError: If height is negative
-    :return: The constructed tree
-    """
-    assert height >= 0, f'Height expected to be nonngeative, received {height=}.'
-
-    def _builder(h: int, degree: int, parent):
-        node = TopologyNode(); node.parent = parent; node.children = []
-        if h > 1:
-            node.children = [_builder(h - 1, degree, node) for _ in range(degree)]
-        return node
-
-    return _builder(height + 1, degree, None)
-
-def asymmetric_topology(h: int)  -> TopologyNode:
-    """ 
-    Generate an asymmetric binary tree of given height.
-    A tree of height zero contains just the root;
-    a tree of height one contains the root and one level of leaves below it, and so forth.
-
-    :param h: The height of the tree
-    :raises ValueError: If height is negative
-    :return: The constructed tree
-    """
-    if h < 0:
-        raise ValueError(f'Height shall be nonnegative integer, received {h=}.')
-    elif h == 0:
-        return ChildList([TreeNode()])
-
-    # Fake root 
-    root = TopologyNode(); root.parent = None; root.children = []
-    root.children = [TopologyNode(children= [],parent=root), TopologyNode(children=[],parent=root)]
-   
-    node = root.children[0]
-
-    for _ in range(h - 1):
-        node.children=  [TopologyNode(children=[],parent=node), TopologyNode(children=[],parent=node)]
-
-        node = node.children[0]
-
-    return root 
-
-### Alternative Newick tree generation
-def read_topology(newick_str: str,return_topology=False,precompute_child_gathers=False) -> HypTree:
-    """ 
-    Generate a tree from a Newick string recursively.
-
-    :param newick_str: newick string representation
-    :return: The constructed tree
-    """
-
-    def parse_newick(newick_str):
-        edge_lengths_collected = []
-        k = -1
-        root = current_node = TopologyNode(name=None, parent=None, children=[])  # Start with a root node
-        for i, char in enumerate(newick_str):
-            if i < k:
-                continue
-            elif char == '(':
-                # Create a new node and make it a child of the current node
-                new_node = TopologyNode(name=None, parent=current_node, children=[])
-                current_node.children += [new_node]
-                current_node = new_node  # Move down to the new node
-
-            elif char == ',':
-                # Go up to the parent, and then create a sibling node
-                current_node = current_node.parent
-                new_node = TopologyNode(name=None, parent=current_node, children=[])
-                current_node.children += [new_node]
-                current_node = new_node  # Move to the new sibling node
-
-            elif char == ')':
-                # End of a subtree, move up to the parent node
-                current_node = current_node.parent
-            elif char == ':':
-                # Branch length follows
-                start = i + 1
-                while i + 1 < len(newick_str) and newick_str[i + 1] not in [',', ')', ';', ' ']:
-                    i += 1
-                edge_lengths_collected.append(float(newick_str[start:i + 1]))
-                k = i+1
-
-            elif char not in [';', ' ',":"] and newick_str[i-1] not in [':']:
-                # Reading a node name, collect all characters till we hit a control character
-                start = i
-                while i + 1 < len(newick_str) and newick_str[i + 1] not in ['(', ')', ',', ';', ':', ' ']:
-                    i += 1
-                current_node.name = newick_str[start:i + 1]
-                k = i+1
-                
-        return root, edge_lengths_collected
-
-    root, edge_lengths_collected = parse_newick(newick_str)
-
-
-    if return_topology:
-        return root
-
-    else:
-        tree = HypTree(root,precompute_child_gathers=precompute_child_gathers)
-
-        if len(edge_lengths_collected) > 0:
-            tree.add_property('edge_length', (1,), dtype=float)
-
-            # Assign edge lengths to nodes in post transversal order
-            for i, node in enumerate(list(tree.iter_topology_post())[:-1]):
-                tree.data['edge_length'] = tree.data['edge_length'].at[node.id].set(edge_lengths_collected[i])
-     
-        return tree
-
-def write_topology(tree:HypTree) -> str:
-    """
-    Convert a tree to a Newick string representation.
-
-    :param tree: The tree to convert
-    :return: Newick string representation of the tree
-    """
-    # Check if edge_length exists, otherwise fill it with ones
-    if "edge_length" not in tree.data:
-        tree.add_property('edge_length', shape=(1,))
-        tree.data['edge_length'] =  tree.data['edge_length'].at[:].set(1)
-
-    def recursive_to_newick(node) -> str:
-        edge_length = tree.data["edge_length"].at[node.id].get().item()
-
-        if not node.children:
-            return f"{node.name}:{edge_length:.4f}" if node.name else f":{edge_length:.4f}"
-        
-        children_str = ",".join(recursive_to_newick(child) for child in node.children)
-        node_str = f"({children_str}){node.name}:{edge_length:.4f}" if node.name else f"({children_str}):{edge_length:.4f}"
-        return node_str
-
-    return recursive_to_newick(tree.topology_root) + ";"
diff --git a/hyperiax/tree/tree.py b/hyperiax/tree/tree.py
deleted file mode 100644
index e51cf0f..0000000
--- a/hyperiax/tree/tree.py
+++ /dev/null
@@ -1,238 +0,0 @@
-from __future__ import annotations
-from collections import deque
-from dataclasses import dataclass, field
-from jax import numpy as jnp
-from typing import Iterator, List
-from functools import partial
-import time
-import jax
-
-@dataclass
-class TopologyNode:
-    parent : TopologyNode = None
-    children : List[TopologyNode] = field(default_factory=list)
-    _backref : HypTree = None
-    name : str = None
-    def __getitem__(self, arg):
-        if not self._backref:
-            raise ValueError("This node is not part of a tree")
-        return self._backref.data[arg][self.id]
-    
-class HypTree:
-    def __init__(self, topology, precompute_child_gathers = False) -> None:
-        self.topology_root = topology
-
-        nodes = 0
-        parents = [0]
-        for i,node in enumerate(self.iter_topology_bfs()):
-            nodes += 1
-            node.id = i
-            node._backref = self
-            if node.parent: 
-                parents.append(node.parent.id)
-            #child_counts.append(len(node.children)) 
-
-        self.size = nodes
-        self.data = {}
-        self.masks = {}
-
-        self.levels = list(self._calculate_levels())
-        self.node_depths = jnp.concatenate([i*jnp.ones((lend-lstart, 1), dtype=int) for i,(lstart,lend) in enumerate(self.levels)])
-
-        self.parents = jnp.array(parents)
-        self.child_counts = jax.ops.segment_sum(jnp.ones((self.size,), dtype=int), self.parents, num_segments=self.size, indices_are_sorted=True)
-        self.child_counts = self.child_counts.at[0].add(-1)
-        self.is_leaf = ~self.child_counts.astype(bool)
-        self.is_root = jnp.zeros((self.size,), dtype=bool)
-        self.is_root = self.is_root.at[0].set(True)
-        self.is_inner = ~self.is_leaf & ~self.is_root
-
-        pbuckets = jnp.copy(self.parents)
-        pbuckets_ref = []
-        for lmin,lmax in self.levels:
-            pvals = pbuckets[lmin:lmax]
-            pbuckets_refi, pbucket = jnp.unique(pvals, return_inverse=True)
-            pbuckets = pbuckets.at[lmin:lmax].set(pbucket)
-            pbuckets_ref.append(pbuckets_refi)
-        self.pbuckets = pbuckets
-        self.pbuckets_ref = pbuckets_ref
-
-        #self.child_counts = jnp.array(child_counts)
-        self.depth = len(self.levels)-1
-        self.leaf_limits = self.levels[-1]
-
-        self.coloring = jnp.zeros_like(self.parents, dtype=bool)
-        for lmin, lmax in self.levels[::2]:
-            self.coloring = self.coloring.at[lmin:lmax].set(True)
-
-        if precompute_child_gathers:
-            uniq = jnp.unique(self.child_counts)
-            if len(uniq) == 2:
-                null, base = uniq
-                assert null == 0
-                self.base = base
-                #self.gather_child_idx = jnp.zeros((self.size, self.base), dtype=int)
-                children = []
-                for i,node in enumerate(self.iter_topology_bfs()):
-                    if not node.children: children.append(int(base)*[0])
-                    else: children.append([c.id for c in node.children])
-                self.gather_child_idx = jnp.array(children) 
-    
-
-                # Precompute non-leaf indices for each level
-                self.level_non_leaf_indices = []
-                for level_start, level_end in self.levels:
-                    level_leaf_mask = self.is_leaf[level_start:level_end]
-                    non_leaf_indices = jnp.where(~level_leaf_mask)[0] + level_start
-                    self.level_non_leaf_indices.append(non_leaf_indices)
-
-
-
-            else:
-                raise ValueError("Only trees with the same number of children are supported")
-            # 2 unique values implies that the tree has a "nice" structure
-
-
-
-    def _calculate_levels(self):
-        queue = deque()
-        buffer_queue = deque([self.topology_root])
-        while queue or buffer_queue:
-            if not queue: # if queue is empty, flush the buffer and yield a level
-                queue = buffer_queue
-                yield (buffer_queue[0].id, buffer_queue[-1].id+1) # to not pass the reference
-                buffer_queue = deque()
-
-            if children := queue.popleft().children:
-                buffer_queue.extend(children)
-
-
-
-    def __len__(self) -> int:
-        return self.size
-
-    def add_property(self, name, shape = (), dtype = None, initializer = None, key = None):
-        self.data[name] = jnp.empty((self.size, *shape), dtype=dtype)
-        self.masks[name] = jnp.zeros((self.size,), dtype=bool)
-
-    def __repr__(self):
-        """
-        Return a string representation of the tree
-        """
-        return f"HypTree(size={self.size}, levels={len(self.levels)}, leaves={jnp.sum(self.is_leaf)}, inner nodes={jnp.sum(self.is_inner)})"
-
-    def __str__(self):
-        return self.__repr__()
-
-
-    ################################
-    # travelsal methods for the tree
-    ################################
-    # dfs
-    def iter_topology_dfs(self) -> Iterator[TopologyNode]:
-        """
-        Iterate over all of the nodes in a depth-first manner.
-
-        """
-        stack = deque([self.topology_root])
-        
-        while stack:
-            current = stack.pop()
-            if current.children:
-                stack.extend(reversed(current.children))
-            yield current
-       
-    def iter_topology_leaves_dfs(self) -> Iterator[TopologyNode]:
-        """
-        Iterate over all of the leaves in the tree, in a depth-first manner.
-        """
-        stack = deque([self.topology_root])
-
-        while stack:
-            current = stack.pop()
-            if current.children:
-                stack.extend(reversed(current.children))
-            else:
-                yield current
-
-    # bfs
-    def iter_topology_bfs(self) -> Iterator[TopologyNode]:
-        """
-        Iterate over all of the nodes in a breadth first manner
-
-        """
-        queue = deque([self.topology_root])
-
-        while queue:
-            current = queue.popleft()
-            if current.children:
-                queue.extend(current.children)
-            yield current
-    
-    def iter_topology_leaves_bfs(self) -> Iterator[TopologyNode]:
-        """
-        Iterate over all of the leaves in the tree, in a breadth-first manner.
-        """
-        queue = deque([self.topology_root])
-
-        while queue:
-            current = queue.popleft()
-            if current.children:
-                queue.extend(current.children)
-            else:
-                yield current
-    
-
-    def iter_topology_levels(self) -> Iterator[List[TopologyNode]]:
-        """
-        Iterate over each level in the tree
-
-        """
-        queue = deque()
-        buffer_queue = deque([self.topology_root])
-        while queue or buffer_queue:
-            if not queue: # if queue is empty, flush the buffer and yield a level
-                queue = buffer_queue
-                yield list(buffer_queue) # to not pass the reference
-                buffer_queue = deque()
-
-            if children := queue.popleft().children:
-                buffer_queue.extend(children)   
-
-    def iter_topology_post(self) -> Iterator[TopologyNode]:
-        """
-        Iterate over all nodes in post-order traversal (children before parents),
-        matching the Newick format traversal order.
-        """
-        def post_order(node):
-            for child in node.children:
-                yield from post_order(child)
-            yield node
-                
-        yield from post_order(self.topology_root)
-
-class FastBiTree(HypTree):
-    """
-        Mostly for simulation purposes, creates a bifurcating tree
-        without the topology, making instantiation fast
-    """
-    def __init__(self, depth) -> None:
-        self.size = (2<<depth)-1
-        self.data = {}
-        self.masks = {}
-
-        self.levels = [(0,1)]+[((2<<i)-1, (2<<(i+1))-1) for i in range(depth)]
-        self.parents = jnp.concatenate([jnp.array([0]),jnp.arange(self.size-1)//2])
-        self.depth = len(self.levels)-1
-        self.leaves = self.levels[-1]
-
-## would love to get this work but https://github.com/google/jax/issues/4269 makes 
-## me believe this is probably a non JAX approach
-class TreeField:
-    def __init__(self, size, shape) -> None:
-        raise NotImplementedError()
-        self.shape = shape
-        self.mask = jnp.zeros((size,), dtype=bool)
-        self.data = jnp.empty((size, *shape))
-    def __repr__(self) -> str:
-        return str(self.data)
diff --git a/hyperiax/tree/updaters.py b/hyperiax/tree/updaters.py
deleted file mode 100644
index bb6fba9..0000000
--- a/hyperiax/tree/updaters.py
+++ /dev/null
@@ -1,18 +0,0 @@
-
-from . import HypTree, TreeNode
-from jax.random import PRNGKey, split
-import jax
-import jax.numpy as jnp
-import copy
-from typing import Callable
-from functools import partial
-
-def update_noise_inplace(update_f: Callable[[TreeNode,float],float], tree : HypTree, key=False, save_noise=False) -> HypTree:
-    raise DeprecationWarning("This function is deprecated")
-    for node in tree.iter_bfs():
-        subkey, key = split(key)
-        if save_noise:
-            node.data['old_noise'] = node.data['noise']
-        node.data['noise'] = update_f(node,jax.random.normal(subkey, node.data['noise'].shape))
-
-    return tree
\ No newline at end of file
diff --git a/hyperiax/utils/__init__.py b/hyperiax/utils/__init__.py
new file mode 100644
index 0000000..00ee25e
--- /dev/null
+++ b/hyperiax/utils/__init__.py
@@ -0,0 +1,21 @@
+"""hyperiax.utils — pure-JAX numerical utilities (L1).
+
+Standalone numerics with no dependency on the tree primitives in
+:mod:`hyperiax.core`: ODE and SDE solvers usable as the integration
+backbone of :mod:`hyperiax.prebuilt` sweeps. Imports only ``jax`` /
+``numpy`` / stdlib. The Kronecker helpers ``dot`` / ``solve`` and the
+``dts`` grid helper live in :mod:`hyperiax.utils.sde`.
+"""
+
+from .ode import RK4, Euler, Heun, solve_ode
+from .sde import EulerMaruyama, Milstein, solve_sde
+
+__all__ = [
+    "RK4",
+    "Euler",
+    "EulerMaruyama",
+    "Heun",
+    "Milstein",
+    "solve_ode",
+    "solve_sde",
+]
diff --git a/hyperiax/utils/ode.py b/hyperiax/utils/ode.py
new file mode 100644
index 0000000..13be725
--- /dev/null
+++ b/hyperiax/utils/ode.py
@@ -0,0 +1,73 @@
+"""ODE solvers: fixed-step explicit Runge-Kutta (Euler, Heun, RK4).
+
+Pure-JAX, tree-agnostic — part of the L1 ``hyperiax.utils`` layer
+(imports only ``jax`` / ``numpy`` / stdlib). Fixed-step (no adaptive
+control): the step count is static, so the integrator composes cleanly
+under ``jit`` / ``vmap`` / ``scan`` and the solution lands exactly on the
+requested grid ``ts`` — no interpolation needed.
+
+``vector_field(t, y, args) -> dy/dt``. Solvers are frozen, hashable
+dataclasses exposing a single ``step``; :func:`solve_ode` drives them
+over the grid with ``jax.lax.scan``.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import jax
+import jax.numpy as jnp
+
+
+@dataclass(frozen=True)
+class Euler:
+    """Explicit (forward) Euler. Order 1."""
+
+    def step(self, f, t, dt, y, args):
+        return y + dt * f(t, y, args)
+
+
+@dataclass(frozen=True)
+class Heun:
+    """Heun's method (explicit trapezoidal / RK2). Order 2."""
+
+    def step(self, f, t, dt, y, args):
+        k1 = f(t, y, args)
+        k2 = f(t + dt, y + dt * k1, args)
+        return y + 0.5 * dt * (k1 + k2)
+
+
+@dataclass(frozen=True)
+class RK4:
+    """Classic 4-stage Runge-Kutta. Order 4."""
+
+    def step(self, f, t, dt, y, args):
+        k1 = f(t, y, args)
+        k2 = f(t + 0.5 * dt, y + 0.5 * dt * k1, args)
+        k3 = f(t + 0.5 * dt, y + 0.5 * dt * k2, args)
+        k4 = f(t + dt, y + dt * k3, args)
+        return y + (dt / 6.0) * (k1 + 2.0 * k2 + 2.0 * k3 + k4)
+
+
+def solve_ode(vector_field, y0, ts, *, solver=RK4(), args=None):
+    """Integrate ``dy/dt = vector_field(t, y, args)`` over the grid ``ts``.
+
+    Args:
+        vector_field: ``(t, y, args) -> dy/dt``.
+        y0: initial state, the value at ``ts[0]``.
+        ts: time points ``(n_steps + 1,)`` (need not be uniform).
+        solver: a solver instance (default :class:`RK4`).
+        args: passed through to ``vector_field``.
+
+    Returns:
+        ``ys`` of shape ``(n_steps + 1, *y0.shape)`` with ``ys[0] == y0``.
+    """
+    step_dts = jnp.diff(ts)
+
+    def body(carry, dt):
+        t, y = carry
+        y_next = solver.step(vector_field, t, dt, y, args)
+        return (t + dt, y_next), y
+
+    (_, y_last), ys = jax.lax.scan(body, (ts[0], y0), step_dts)
+    return jnp.concatenate([ys, y_last[None]])
diff --git a/hyperiax/utils/sde.py b/hyperiax/utils/sde.py
new file mode 100644
index 0000000..f1f2d2c
--- /dev/null
+++ b/hyperiax/utils/sde.py
@@ -0,0 +1,106 @@
+"""SDE solvers: Euler-Maruyama (strong 0.5) and Milstein (strong 1.0).
+
+Pure-JAX, tree-agnostic numerics — part of the L1 ``hyperiax.utils``
+layer (imports only ``jax`` / ``numpy`` / stdlib). Solvers are frozen,
+hashable dataclasses exposing a single ``step``; :func:`solve_sde` drives
+them over a time grid with ``jax.lax.scan`` so the whole integration
+composes under ``jit`` / ``vmap`` / ``scan``.
+
+Conventions (mirroring standard SDE-solver libraries):
+
+- ``drift(t, y, args) -> (state,)`` is the deterministic term.
+- ``diffusion(t, y, args) -> (state, noise)`` is the matrix ``G``; the
+  stochastic term is ``G @ dW``.
+- ``ts`` are cumulative time points (shape ``(n_steps + 1,)``); ``dWs``
+  are the Brownian increments (shape ``(n_steps, noise)``).
+
+:class:`Milstein` assumes **commutative noise**, so the iterated Itô
+integrals collapse to ``½(dW_i dW_j − δ_ij dt)`` and no Lévy areas are
+needed.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import jax
+import jax.numpy as jnp
+
+
+# ── Kronecker-factored linear algebra (landmark-style state) ───────
+def dot(A: jax.Array, v: jax.Array) -> jax.Array:
+    """Compute ``(A ⊗ I_d) @ v`` on a flattened state vector.
+
+    ``v`` is interpreted as shape ``(n, d)`` where ``n = A.shape[0]``;
+    the result is flattened back to ``(n·d,)``.
+    """
+    return jnp.einsum("ij,jd->id", A, v.reshape((A.shape[0], -1))).flatten()
+
+
+def solve(A: jax.Array, v: jax.Array) -> jax.Array:
+    """Solve ``(A ⊗ I_d) x = v`` for ``x``, flattened (``dot`` with ``A⁻¹``)."""
+    return jnp.linalg.solve(A, v.reshape((A.shape[0], -1))).flatten()
+
+
+def dts(T: float = 1.0, n_steps: int = 100) -> jax.Array:
+    """Uniform time increments over ``[0, T]`` — shape ``(n_steps,)``.
+
+    Cumulative time points for :func:`solve_sde` / :func:`solve_ode` are
+    then ``jnp.concatenate([jnp.zeros(1), jnp.cumsum(dts(...))])``.
+    """
+    return jnp.full((n_steps,), T / n_steps)
+
+
+# ── solvers ────────────────────────────────────────────────────────
+@dataclass(frozen=True)
+class EulerMaruyama:
+    """Euler-Maruyama scheme. Strong order 0.5, weak order 1.0."""
+
+    def step(self, drift, diffusion, t, dt, y, dW, args):
+        return y + drift(t, y, args) * dt + diffusion(t, y, args) @ dW
+
+
+@dataclass(frozen=True)
+class Milstein:
+    """Milstein scheme for commutative noise. Strong order 1.0.
+
+    Adds the correction ``½ Σ_{j,k} (G·∇)G · (dW_j dW_k − δ_jk dt)``; the
+    diffusion Jacobian ``∂G/∂y`` is obtained with :func:`jax.jacfwd`.
+    """
+
+    def step(self, drift, diffusion, t, dt, y, dW, args):
+        G = diffusion(t, y, args)  # (state, noise)
+        J = jax.jacfwd(lambda yy: diffusion(t, yy, args))(y)  # (state, noise, state)
+        # M[j,k] = dW_j dW_k − δ_jk dt
+        M = jnp.outer(dW, dW) - dt * jnp.eye(dW.shape[0], dtype=dW.dtype)
+        # mil_i = ½ Σ_{j,k,m} G[m,j] J[i,k,m] M[j,k]
+        mil = 0.5 * jnp.einsum("mj,ikm,jk->i", G, J, M)
+        return y + drift(t, y, args) * dt + G @ dW + mil
+
+
+# ── driver ─────────────────────────────────────────────────────────
+def solve_sde(drift, diffusion, y0, ts, dWs, *, solver=EulerMaruyama(), args=None):
+    """Integrate ``dy = drift(t,y) dt + diffusion(t,y) dW`` over the grid ``ts``.
+
+    Args:
+        drift: ``(t, y, args) -> (state,)``.
+        diffusion: ``(t, y, args) -> (state, noise)``.
+        y0: initial state ``(state,)``.
+        ts: cumulative time points ``(n_steps + 1,)``.
+        dWs: Brownian increments ``(n_steps, noise)``.
+        solver: a solver instance (default :class:`EulerMaruyama`).
+        args: passed through to ``drift`` / ``diffusion``.
+
+    Returns:
+        Trajectory ``ys`` of shape ``(n_steps + 1, state)`` including ``y0``.
+    """
+    step_dts = jnp.diff(ts)
+
+    def body(carry, inp):
+        t, y = carry
+        dt, dW = inp
+        y_next = solver.step(drift, diffusion, t, dt, y, dW, args)
+        return (t + dt, y_next), y
+
+    (_, y_last), ys = jax.lax.scan(body, (ts[0], y0), (step_dts, dWs))
+    return jnp.concatenate([ys, y_last[None]])
diff --git a/makefile b/makefile
index dd03c82..4d9e529 100644
--- a/makefile
+++ b/makefile
@@ -1,9 +1,9 @@
-.PHONY: test local-notebook-test
-
-test:
-	python -m pytest -s
-
-local-notebook-test:
-	@echo "Running all notebooks in examples directory..."
-	@find examples -name "*.ipynb" -type f -print -exec sh -c "echo 'Testing {}' && jupyter nbconvert --execute --inplace {} || echo 'Error in {}'" \;
-	@echo "Notebook testing complete."
\ No newline at end of file
+.PHONY: test sync
+
+# Install the core (CPU) runtime + dev group into the project venv (uv-managed).
+# For a CUDA venv instead:  uv sync --group dev --extra gpu
+sync:
+	uv sync --group dev
+
+test:
+	uv run pytest
diff --git a/pyproject.toml b/pyproject.toml
index b229445..af9c726 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,54 +1,41 @@
 [project]
 name = "hyperiax"
 authors = [
-  { name="Stefan Sommer", email="sommer@di.ku.dk" },
-  { name="Marcus Holte Teller", email="marcus@di.ku.dk" },
-  { name="Gefan Yang", email="***@di.ku.dk" },
-  { name="Michael Baand Severinsen", email="***@di.ku.dk" },
-  { name="Morten Akhøj Pedersen", email="***@di.ku.dk" },
-  { name="Nicklas Boserup", email="***@di.ku.dk" }
+  { name = "Stefan Sommer", email = "sommer@di.ku.dk" },
+  { name = "Marcus Holte Teller", email = "marcus@di.ku.dk" },
+  { name = "Gefan Yang", email = "gy@di.ku.dk" },
+  { name = "Michael Baand Severinsen", email = "michael.baand@sund.ku.dk" },
+  { name = "Morten Akhøj Pedersen", email = "morten.akhoej@di.ku.dk" },
+  { name = "Nicklas Boserup", email = "nicklas.boserup@sund.ku.dk" },
 ]
 description = "Tree traversals using JAX"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 classifiers = [
     "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "License :: OSI Approved :: Apache Software License",
     "Operating System :: OS Independent",
 ]
 dependencies = [
-    "jax",
-    "jaxlib",
-    "tqdm",
+    "jax>=0.4.30",
+    "jaxlib>=0.4.30",
+    "numpy>=1.26",
 ]
 dynamic = ["version"]
 
 [project.optional-dependencies]
-examples = [
-    "jaxdifferentialgeometry",
-    "notebook",
-    "ipykernel",
-    "matplotlib",
-    "pandas",
-    "plotly",
-    "trimesh",
+gpu = [
+    "jax[cuda12]>=0.4.30",
 ]
-dev = [
-    "black",
-    "flake8",
-    "notebook",
-    "ipykernel",
-    "matplotlib",
-    "plotly",
-    "pytest",
+notebook = [
+    "matplotlib>=3.8",
+    "ipykernel>=6.29",
+    "numpyro>=0.15",
 ]
-doc = [
-    "hyperiax",
-    "sphinx-rtd-theme",
-    "sphinx-autodoc-annotation",
-    "nbsphinx",
-    "markupsafe==2.0.1",
-    "ipython",
+numpyro = [
+    "numpyro>=0.15",
 ]
 
 [project.urls]
@@ -57,10 +44,69 @@ Issues = "https://github.com/computationalevolutionarymorphometry/hyperiax/issue
 
 [build-system]
 requires = [
-    "setuptools>=60",
+    "setuptools>=68",
     "setuptools-scm>=8.0",
 ]
 build-backend = "setuptools.build_meta"
 
+[tool.setuptools.packages.find]
+include = ["hyperiax*"]
+
 [tool.setuptools_scm]
 version_file = "hyperiax/_version.py"
+# Match v-prefixed tags (e.g. v3.0.0). Used by the PyPI publish workflow.
+tag_regex = "^v(?P<version>[0-9]+\\.[0-9]+\\.[0-9]+(?:[a-z0-9.+-]*)?)$"
+# Reported when scm lookup fails entirely (tarball install w/o .git).
+fallback_version = "3.0.0"
+
+# ── uv-specific configuration ─────────────────────────────────────────────
+[dependency-groups]
+dev = [
+    # test + lint
+    "pytest>=8",
+    "pytest-xdist>=3",
+    "ruff>=0.6",
+    "pre-commit>=3.7",
+    # docs (sphinx)
+    "sphinx>=7.3",
+    "furo>=2024.5",
+    "myst-nb>=1.1",
+    "sphinx-autodoc-typehints>=2.1",
+    "sphinx-copybutton>=0.5",
+    # notebooks / examples
+    "ipykernel>=6.29",
+    "matplotlib>=3.8",
+]
+
+[tool.uv]
+required-version = ">=0.4"
+
+# ── pytest ────────────────────────────────────────────────────────────────
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = ["-ra", "--strict-markers"]
+
+# ── ruff ──────────────────────────────────────────────────────────────────
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+extend-exclude = ["hyperiax/_version.py", "docs/build", "docs/source/notebooks"]
+
+[tool.ruff.lint]
+select = [
+    "E",    # pycodestyle errors
+    "W",    # pycodestyle warnings
+    "F",    # pyflakes
+    "I",    # isort
+    "B",    # flake8-bugbear
+    "UP",   # pyupgrade
+]
+ignore = [
+    "E501",  # line length — formatter handles wrapping; long math/string lines are fine
+    "E731",  # lambda assignment — common shorthand for state-dependent JAX functions
+    "B008",  # function call in default arg — common JAX idiom
+    "B905",  # zip without strict — keep as-is for now (jax pytree code passes equal-length iterables)
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["B", "E402"]  # tests use bugbear-flagged asserts and pytest.importorskip imports
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 4f901b2..0000000
--- a/setup.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from setuptools import setup, find_packages
-
-
-setup(
-    packages=find_packages(),
-)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/builders_test.py b/tests/builders_test.py
deleted file mode 100644
index 6ef9863..0000000
--- a/tests/builders_test.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from jax.random import PRNGKey
-from hyperiax.tree.topology import symmetric_topology
-
-def test_symmetric_tree():
-    root = symmetric_topology(3, 100)
-
-    assert len(root.children) == 100
-    for c in root.children:
-        assert len(c.children) == 100
\ No newline at end of file
diff --git a/tests/executors_test.py b/tests/executors_test.py
deleted file mode 100644
index 616bcd5..0000000
--- a/tests/executors_test.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from jax import numpy as jnp
-
-from hyperiax.models.functional import pass_up, sum_fuse_children
-from hyperiax.tree import TopologyNode, HypTree
-from hyperiax.models.lambdamodels import UpLambdaReducer
-from hyperiax.execution import OrderedExecutor
-from hyperiax.plotting import plot_tree_text
-from hyperiax.models.lambdamodels import UpLambda, UpLambdaReducer
-
-from test_fixtures import noise_tree
-
-
-def test_asym():
-    root = TopologyNode()
-    root.children = [TopologyNode(root) for _ in range(4)]
-    root.children[1].children = [TopologyNode(root.children[1]) for _ in range(2)]
-    root.children[3].children = [TopologyNode(root.children[3])]
-    tree = HypTree(root)
-    tree.add_property('count', (1,))
-    tree.add_property('sum_count', (1,))
-    tree.data['count'] = jnp.ones_like(tree.data['count'])
-    tree.data['sum_count'] = tree.data['sum_count'].at[tree.is_leaf].set(tree.data['count'][tree.is_leaf])
-    def _up(sum_count, **kwargs):
-        return {'sub_count': sum_count}
-    def _transform(child_sub_count, count, **kwargs):
-        return {'sum_count': child_sub_count+count}
-    
-    print()
-    plot_tree_text(tree)
-    
-    upmodel = UpLambdaReducer(_up, _transform, {'sub_count': 'sum'})
-    exe = OrderedExecutor(upmodel)
-    exe.up(tree)
-
-    assert (tree.data['sum_count'].squeeze() == jnp.array([8,1,3,1,2,1,1,1])).all()
-    assert tree.data['sum_count'][0] == len(tree)
-
-def test_lwte_sum(noise_tree):
-    assert len(noise_tree.topology_root.children) == 2
-    up = lambda noise, **kwargs: {'noise':noise}
-    def transform(child_noise, noise,**kwargs): 
-        return {'noise': noise+child_noise}
-
-    model = UpLambdaReducer(up, transform_fn=transform, reductions={'noise': 'sum'})
-
-    exe = OrderedExecutor(model)
-
-    manual_sum = noise_tree.data['noise'].sum(axis=0)
-    exe.up(noise_tree)
-
-    assert jnp.isclose(noise_tree.data['noise'][0], manual_sum).all()
-
-    #print
-
diff --git a/tests/test_builders.py b/tests/test_builders.py
new file mode 100644
index 0000000..e45bf40
--- /dev/null
+++ b/tests/test_builders.py
@@ -0,0 +1,51 @@
+"""Builder sanity tests."""
+
+import numpy as np
+import pytest
+
+from hyperiax import symmetric_topology
+
+
+def test_symmetric_height0():
+    topo = symmetric_topology(depth=0, degree=2)
+    assert topo.size == 1
+    assert topo.depth == 0
+    assert topo.is_root[0] and topo.is_leaf[0]
+
+
+def test_symmetric_binary_height3_has_15_nodes():
+    topo = symmetric_topology(depth=3, degree=2)
+    assert topo.size == 15
+    assert topo.depth == 3
+    assert topo.equal_degree is True
+    assert topo.max_degree == 2
+    np.testing.assert_array_equal(
+        topo.parents,
+        [0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6],
+    )
+
+
+def test_symmetric_ternary_height2_has_13_nodes():
+    topo = symmetric_topology(depth=2, degree=3)
+    # 1 + 3 + 9 = 13
+    assert topo.size == 13
+    assert topo.depth == 2
+    assert topo.equal_degree is True
+    assert topo.max_degree == 3
+
+
+def test_symmetric_degree1_is_a_chain():
+    topo = symmetric_topology(depth=3, degree=1)
+    assert topo.size == 4
+    assert topo.depth == 3
+    np.testing.assert_array_equal(topo.parents, [0, 0, 1, 2])
+
+
+def test_symmetric_rejects_negative_height():
+    with pytest.raises(ValueError):
+        symmetric_topology(depth=-1, degree=2)
+
+
+def test_symmetric_rejects_zero_degree():
+    with pytest.raises(ValueError):
+        symmetric_topology(depth=2, degree=0)
diff --git a/tests/test_core_dependencies.py b/tests/test_core_dependencies.py
new file mode 100644
index 0000000..0d508ec
--- /dev/null
+++ b/tests/test_core_dependencies.py
@@ -0,0 +1,174 @@
+"""Architectural lint: enforce the L1/L2 layering between subpackages.
+
+- L1 (``hyperiax.core``): no optional / heavy deps. Newick I/O lives here
+  too (pure-Python parser in ``core/builders.py``), so ``ete3`` must never
+  appear.
+- L2 (``hyperiax.prebuilt``): may import L1; optional external deps must be
+  lazy (function-local import) so ``import hyperiax`` works with just JAX
+  installed.
+
+Covers T-16 plus the cross-package layering rules.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+# Heavy / optional dependencies that must not appear in core.
+FORBIDDEN_IMPORTS = (
+    "matplotlib",
+    "scipy",
+    "tqdm",
+    "ete3",
+    "ete4",
+    "flax",
+    "trimesh",
+    "plotly",
+    "diffrax",
+    "equinox",
+)
+
+
+def _l1_files() -> list[Path]:
+    """All L1 source files: ``hyperiax.core`` and ``hyperiax.utils``."""
+    here = Path(__file__).resolve().parent.parent
+    paths: list[Path] = []
+    for pkg in ("core", "utils"):
+        pkg_dir = here / "hyperiax" / pkg
+        assert pkg_dir.is_dir(), f"expected {pkg}/ at {pkg_dir}"
+        paths.extend(pkg_dir.rglob("*.py"))
+    return sorted(paths)
+
+
+def _imports_in(path: Path) -> list[tuple[int, str, bool]]:
+    """Return ``[(line_no, top_level_module, is_relative)]`` for every import."""
+    tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+    out: list[tuple[int, str, bool]] = []
+    for node in ast.walk(tree):
+        if isinstance(node, ast.Import):
+            for alias in node.names:
+                top = alias.name.split(".", 1)[0]
+                out.append((node.lineno, top, False))
+        elif isinstance(node, ast.ImportFrom):
+            is_relative = node.level > 0
+            top = (node.module or "").split(".", 1)[0]
+            out.append((node.lineno, top, is_relative))
+    return out
+
+
+@pytest.mark.parametrize("forbidden", FORBIDDEN_IMPORTS)
+def test_l1_does_not_import(forbidden: str) -> None:
+    """No L1 file (core/ or utils/) may import the forbidden package."""
+    offenders: list[str] = []
+    for path in _l1_files():
+        for line_no, top, is_relative in _imports_in(path):
+            if is_relative:
+                continue
+            if top == forbidden:
+                offenders.append(f"{path.relative_to(path.parents[2])}:{line_no}: {top}")
+    assert not offenders, f"L1 must not import {forbidden!r}; found:\n" + "\n".join(offenders)
+
+
+def test_l1_only_imports_allowed_top_level_packages() -> None:
+    """Whitelist: every absolute import in L1 (core/ + utils/) resolves to
+    ``jax``, ``numpy``, ``hyperiax``, or a stdlib module.
+    """
+    import importlib.util
+    import sys
+
+    allowed_external = {"jax", "numpy", "hyperiax"}
+    base = Path(sys.base_prefix).resolve()
+    offenders: list[str] = []
+    for path in _l1_files():
+        for line_no, top, is_relative in _imports_in(path):
+            if is_relative or not top:
+                continue
+            if top in allowed_external:
+                continue
+            spec = importlib.util.find_spec(top)
+            if spec is None:
+                offenders.append(f"{path.name}:{line_no}: unresolved import {top!r}")
+                continue
+            origin = getattr(spec, "origin", None)
+            if origin in (None, "built-in", "frozen"):
+                continue  # stdlib
+            try:
+                origin_path = Path(origin).resolve()
+                if base in origin_path.parents:
+                    continue  # stdlib (lives under sys.base_prefix)
+            except (OSError, ValueError):
+                pass
+            offenders.append(f"{path.name}:{line_no}: imports non-stdlib non-allowed {top!r}")
+    assert not offenders, (
+        "L1 (core/ + utils/) may only import jax, numpy, hyperiax, or stdlib; found:\n"
+        + "\n".join(offenders)
+    )
+
+
+def test_utils_does_not_import_core_or_prebuilt() -> None:
+    """``hyperiax.utils`` is a standalone L1 numerics layer: it must not
+    depend on the tree primitives in ``hyperiax.core`` nor on the L2
+    ``hyperiax.prebuilt`` package.
+    """
+    offenders: list[str] = []
+    for path in _subpkg_files("utils"):
+        src = path.read_text(encoding="utf-8")
+        tree = ast.parse(src, filename=str(path))
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ImportFrom):
+                module = node.module or ""
+                # Relative import escaping utils (level>=2 climbs to hyperiax.*).
+                if node.level >= 2 or module.startswith(("hyperiax.core", "hyperiax.prebuilt")):
+                    offenders.append(f"{path.name}:{node.lineno}: {'.' * node.level}{module}")
+    assert not offenders, (
+        "utils/ must not import from hyperiax.core or hyperiax.prebuilt:\n" + "\n".join(offenders)
+    )
+
+
+# ── L2 layering: prebuilt isolation ────────────────────────────────
+def _subpkg_files(subpkg: str) -> list[Path]:
+    here = Path(__file__).resolve().parent.parent
+    pkg_dir = here / "hyperiax" / subpkg
+    return sorted(pkg_dir.rglob("*.py")) if pkg_dir.is_dir() else []
+
+
+# Optional deps that must stay function-local (lazy) in L2 modules.
+LAZY_REQUIRED = ("jax_tqdm",)
+
+
+def _bare_top_level_imports(tree: ast.Module) -> list[ast.AST]:
+    """Module-level import statements *not* wrapped in try/except."""
+    return [n for n in tree.body if isinstance(n, (ast.Import, ast.ImportFrom))]
+
+
+def _imported_top_names(node: ast.AST) -> list[str]:
+    if isinstance(node, ast.Import):
+        return [alias.name.split(".", 1)[0] for alias in node.names]
+    if isinstance(node, ast.ImportFrom):
+        return [(node.module or "").split(".", 1)[0]]
+    return []
+
+
+@pytest.mark.parametrize("forbidden", LAZY_REQUIRED)
+def test_l2_does_not_import_optional_deps_at_module_top(forbidden: str) -> None:
+    """Optional external deps must not be bare module-level imports — that
+    would break ``import hyperiax`` for users without the relevant extra.
+
+    Acceptable forms: (a) function-local import; (b) module-level import
+    inside a ``try / except ImportError`` block.
+    """
+    paths = _subpkg_files("prebuilt")
+    offenders: list[str] = []
+    for path in paths:
+        src = path.read_text(encoding="utf-8")
+        tree = ast.parse(src, filename=str(path))
+        for node in _bare_top_level_imports(tree):
+            if forbidden in _imported_top_names(node):
+                offenders.append(f"{path.relative_to(path.parents[2])}:{node.lineno}")
+    assert not offenders, (
+        f"L2 modules must lazy-import {forbidden!r} (function-local or "
+        f"try/except-guarded). Found unguarded top-level imports at:\n" + "\n".join(offenders)
+    )
diff --git a/tests/test_fixtures.py b/tests/test_fixtures.py
deleted file mode 100644
index 3740310..0000000
--- a/tests/test_fixtures.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import jax
-from jax import numpy as jnp
-from jax.random import PRNGKey
-from pytest import fixture
-from hyperiax.tree.topology import symmetric_topology
-from hyperiax.tree import HypTree
-
-@fixture
-def small_tree():
-    return HypTree(symmetric_topology(5,2))
-
-@fixture
-def noise_tree():
-    key = PRNGKey(0)
-    t = HypTree(symmetric_topology(5,2))
-    t.add_property('noise', (2,))
-    t.data['noise'] = jax.random.normal(key, shape=t.data['noise'].shape)
-    return t
-
-
diff --git a/tests/test_newick.py b/tests/test_newick.py
new file mode 100644
index 0000000..a227d98
--- /dev/null
+++ b/tests/test_newick.py
@@ -0,0 +1,149 @@
+"""Newick (de)serialization in core.builders — pure-Python, no ete3."""
+
+from pathlib import Path
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax import Topology, Tree, from_newick, to_newick
+
+
+@pytest.fixture
+def restore_x64_config():
+    original = jax.config.jax_enable_x64
+    yield
+    jax.config.update("jax_enable_x64", original)
+
+
+# ── basic round-trips ───────────────────────────────────────────────
+def test_simple_newick_round_trip_bit_for_bit():
+    """write(read(s)) == s for a simple unnamed-internal Newick."""
+    src = "(A:1,(B:1,C:1):1);"
+    tree = from_newick(src)
+    assert to_newick(tree) == src
+
+
+def test_complex_newick_round_trip_with_internal_names():
+    """A more involved tree with named internal nodes and the root name 'N'.
+    The root name is preserved (it carries a label but no branch length)."""
+    src = "(((A:1,B:1)H:0.5,(C:1,D:2)I:0.5)L:0.1,((E:1,F:1,G:1)J:0.5,K:0.5)M:0.1)N;"
+    tree = from_newick(src)
+    assert to_newick(tree) == src
+
+
+def test_topology_equality_after_round_trip():
+    """Even on a write -> read -> write chain, topology must compare equal."""
+    src = "(((A:1,B:1):0.5,(C:1,D:2):0.5):0.1,((E:1,F:1,G:1):0.5,K:0.5):0.1)N;"
+    t1 = from_newick(src)
+    t2 = from_newick(to_newick(t1))
+    assert t1.topology == t2.topology
+    assert jnp.allclose(t1["edge_length"], t2["edge_length"], atol=1e-6)
+    assert t1.topology.names == t2.topology.names
+
+
+# ── parsing details ────────────────────────────────────────────────
+def test_read_extracts_edge_lengths():
+    tree = from_newick("(A:0.5,B:1.5);")
+    # BFS: root, A, B → edge_lengths in same order, root edge=0
+    np.testing.assert_allclose(tree["edge_length"], [0.0, 0.5, 1.5])
+
+
+def test_read_edge_length_dtype_tracks_jax_float_config(restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    tree = from_newick("(A:0.1,B:0.2);")
+    assert tree["edge_length"].dtype == np.dtype(jnp.float64)
+
+    jax.config.update("jax_enable_x64", False)
+    tree = from_newick("(A:0.1,B:0.2);")
+    assert tree["edge_length"].dtype == np.dtype(jnp.float32)
+
+
+def test_read_extracts_node_names_into_topology():
+    tree = from_newick("(A:1,B:1)R;")
+    assert tree.topology.names == ("R", "A", "B")
+
+
+def test_read_unnamed_internal_nodes_use_empty_string():
+    tree = from_newick("(A:1,(B:1,C:1):1);")
+    # Root and the inner node have no names → empty strings.
+    assert tree.topology.names == ("", "A", "", "B", "C")
+
+
+def test_read_produces_bfs_ordered_parents():
+    """The parents array hyperiax expects is BFS-ordered: parents[i] < i."""
+    tree = from_newick("(((A:1,B:1):1,C:1):1,D:1);")
+    parents = np.asarray(tree.topology.parents)
+    assert parents[0] == 0
+    for i in range(1, len(parents)):
+        assert parents[i] < i, f"non-BFS at index {i}"
+
+
+def test_read_handles_whitespace_between_tokens():
+    tree = from_newick("( A:1 , ( B:1 , C:1 ):1 );")
+    assert tree.topology.names == ("", "A", "", "B", "C")
+    np.testing.assert_allclose(tree["edge_length"], [0.0, 1.0, 1.0, 1.0, 1.0])
+
+
+def test_read_rejects_string_without_terminator():
+    with pytest.raises(ValueError, match="';'"):
+        from_newick("(A:1,B:1)")
+
+
+# ── extra schema fields ────────────────────────────────────────────
+def test_read_accepts_extra_schema_fields():
+    tree = from_newick("(A:1,B:1);", schema={"value": (2,), "noise": (2,)})
+    assert "edge_length" in tree.schema
+    assert "value" in tree.schema
+    assert "noise" in tree.schema
+    # Extra fields allocated as zeros
+    assert jnp.all(tree["value"] == 0)
+    assert jnp.all(tree["noise"] == 0)
+
+
+def test_read_extra_schema_does_not_disturb_edge_length():
+    tree = from_newick("(A:0.5,B:1.5);", schema={"value": (2,)})
+    np.testing.assert_allclose(tree["edge_length"], [0.0, 0.5, 1.5])
+
+
+# ── file path reading ──────────────────────────────────────────────
+def test_read_from_file_path(tmp_path: Path):
+    src = "(A:1,(B:1,C:1):1);"
+    nwk_file = tmp_path / "tree.nwk"
+    nwk_file.write_text(src)
+    tree = from_newick(nwk_file)
+    assert to_newick(tree) == src
+
+
+def test_read_from_file_path_as_string(tmp_path: Path):
+    src = "(A:1,B:1);"
+    nwk_file = tmp_path / "tree.nwk"
+    nwk_file.write_text(src)
+    tree = from_newick(str(nwk_file))
+    assert to_newick(tree) == src
+
+
+# ── write guards ───────────────────────────────────────────────────
+def test_write_rejects_tree_without_edge_length():
+    topo = Topology.from_parents([0, 0, 0])
+    tree = Tree.empty(topo, {"value": ()})
+    with pytest.raises(ValueError, match="edge_length"):
+        to_newick(tree)
+
+
+# ── interop with sweeps ────────────────────────────────────────────
+def test_newick_tree_can_run_a_sweep():
+    """Sanity: a Newick-parsed tree feeds into an up-sweep normally."""
+    import hyperiax as hx
+
+    tree = from_newick("(A:1,B:1,C:1)R;", schema={"value": ()})
+    tree = tree.at[tree.topology.is_leaf].set(value=jnp.array([10.0, 20.0, 30.0]))
+
+    @hx.up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    out = sum_up(tree)
+    # Root has 3 children with values 10/20/30 → sum = 60
+    assert jnp.allclose(out["value"][0], 60.0)
diff --git a/tests/test_prebuilt_bffg_precision.py b/tests/test_prebuilt_bffg_precision.py
new file mode 100644
index 0000000..5a19331
--- /dev/null
+++ b/tests/test_prebuilt_bffg_precision.py
@@ -0,0 +1,38 @@
+"""BFFG precision policy tests."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax import Topology, Tree
+from hyperiax.prebuilt.bffg import continuous_schema, init_continuous_tree
+
+
+@pytest.fixture
+def restore_x64_config():
+    original = jax.config.jax_enable_x64
+    yield
+    jax.config.update("jax_enable_x64", original)
+
+
+def test_continuous_bffg_default_dtype_tracks_jax_float_config(restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    topo = Topology.from_parents([0, 0])
+    tree = Tree.empty(topo, continuous_schema(d=1, n_steps=2))
+    tree = tree.set(edge_len=jnp.ones(topo.size))
+
+    tree = init_continuous_tree(
+        tree,
+        leaf_obs=jnp.array([[1.0]]),
+        obs_var=0.1,
+        d=1,
+        n_steps=2,
+        root_val=jnp.array([0.0]),
+    )
+
+    assert tree["vals"].dtype == np.dtype(jnp.float64)
+    assert tree["prec_v"].dtype == np.dtype(jnp.float64)
+    assert tree["ptnl_v"].dtype == np.dtype(jnp.float64)
+    assert tree["anchor"].dtype == np.dtype(jnp.float64)
+    assert tree["anchor_pa"].dtype == np.dtype(jnp.float64)
diff --git a/tests/test_prebuilt_phylo_mean.py b/tests/test_prebuilt_phylo_mean.py
new file mode 100644
index 0000000..bde3e9b
--- /dev/null
+++ b/tests/test_prebuilt_phylo_mean.py
@@ -0,0 +1,183 @@
+"""phylo_mean prebuilt. Covers T-13 (cross-check against numpy reference)."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+from hyperiax import Topology, Tree, symmetric_topology
+from hyperiax.prebuilt import phylo_mean
+
+
+# ── basic correctness (T-13) ────────────────────────────────────────
+def test_phylo_mean_matches_hand_computation_on_7_node_tree():
+    """7-node binary tree with non-uniform edges; the inner-node weighted
+    means are hand-verifiable."""
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    tree = Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+    tree = tree.set(edge_length=jnp.array([0.0, 0.5, 0.5, 1.0, 1.0, 2.0, 2.0]))
+    tree = tree.at[topo.is_leaf].set(estimated_value=jnp.array([10.0, 20.0, 30.0, 40.0]))
+
+    out = phylo_mean()(tree)
+
+    # Hand-computed weighted means (see module-level docstring example)
+    assert jnp.allclose(out["estimated_value"][1], 15.0)
+    assert jnp.allclose(out["estimated_value"][2], 35.0)
+    assert jnp.allclose(out["estimated_value"][0], 25.0)
+
+
+def test_phylo_mean_leaves_untouched():
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    leaves = jnp.array([10.0, 20.0, 30.0, 40.0])
+    tree = (
+        Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+        .set(edge_length=jnp.ones(7))
+        .at[topo.is_leaf]
+        .set(estimated_value=leaves)
+    )
+
+    out = phylo_mean()(tree)
+    assert jnp.array_equal(out["estimated_value"][topo.is_leaf], leaves)
+
+
+def test_phylo_mean_uniform_edges_recovers_simple_mean():
+    """When every edge has the same length, the weighted mean reduces to
+    a simple recursive average — root equals the leaf mean."""
+    topo = symmetric_topology(depth=3, degree=2)
+    leaves = jnp.arange(8, dtype=jnp.float32)
+    tree = (
+        Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+        .set(edge_length=jnp.ones(topo.size))
+        .at[topo.is_leaf]
+        .set(estimated_value=leaves)
+    )
+
+    out = phylo_mean()(tree)
+    assert jnp.allclose(out["estimated_value"][0], leaves.mean())
+
+
+def test_phylo_mean_with_vector_values():
+    """Multi-dimensional ``estimated_value``: broadcasting must handle the
+    trailing dims."""
+    topo = symmetric_topology(depth=2, degree=2)
+    leaves = jnp.arange(8, dtype=jnp.float32).reshape(4, 2)
+    tree = (
+        Tree.empty(topo, {"estimated_value": (2,), "edge_length": ()})
+        .set(edge_length=jnp.ones(topo.size))
+        .at[topo.is_leaf]
+        .set(estimated_value=leaves)
+    )
+
+    out = phylo_mean()(tree)
+    assert jnp.allclose(out["estimated_value"][0], leaves.mean(0))
+
+
+# ── numpy reference cross-check (T-13 core) ─────────────────────────
+def _numpy_phylo_mean(parents, edge_lengths, leaf_mask, leaf_values, depth, level_starts):
+    """Reference implementation in plain numpy. Walks levels bottom-up,
+    computing the edge-length-weighted mean at each parent."""
+    n = parents.shape[0]
+    vals = np.zeros(n, dtype=np.float32)
+    vals[leaf_mask] = leaf_values
+    for level in range(depth - 1, -1, -1):
+        ls = int(level_starts[level])
+        le = int(level_starts[level + 1])
+        for parent_id in range(ls, le):
+            child_ids = np.where(parents == parent_id)[0]
+            child_ids = child_ids[child_ids != parent_id]  # drop self-ref on root
+            if child_ids.size == 0:
+                continue
+            child_vals = vals[child_ids]
+            child_edges = edge_lengths[child_ids]
+            vals[parent_id] = (child_vals / child_edges).sum() / (1.0 / child_edges).sum()
+    return vals
+
+
+def test_phylo_mean_matches_numpy_reference_random():
+    """Random edges and leaf values on a depth-3 binary tree must agree
+    with a plain-numpy reference to floating-point tolerance."""
+    topo = symmetric_topology(depth=3, degree=2)
+    key = jax.random.PRNGKey(7)
+    k_e, k_v = jax.random.split(key)
+    edge_lengths = jax.random.uniform(k_e, (topo.size,), minval=0.1, maxval=2.0)
+    leaf_vals = jax.random.normal(k_v, (int(topo.is_leaf.sum()),))
+
+    tree = (
+        Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+        .set(edge_length=edge_lengths)
+        .at[topo.is_leaf]
+        .set(estimated_value=leaf_vals)
+    )
+    hx_result = np.asarray(phylo_mean()(tree)["estimated_value"])
+
+    np_result = _numpy_phylo_mean(
+        parents=np.asarray(topo.parents),
+        edge_lengths=np.asarray(edge_lengths),
+        leaf_mask=np.asarray(topo.is_leaf),
+        leaf_values=np.asarray(leaf_vals),
+        depth=topo.depth,
+        level_starts=np.asarray(topo.level_starts),
+    )
+    np.testing.assert_allclose(hx_result, np_result, atol=1e-5, rtol=1e-5)
+
+
+# ── pipeline composition ───────────────────────────────────────────
+def test_phylo_mean_composes_under_outer_jit():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = (
+        Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+        .set(edge_length=jnp.ones(topo.size))
+        .at[topo.is_leaf]
+        .set(estimated_value=jnp.array([1.0, 2.0, 3.0, 4.0]))
+    )
+    sweep = phylo_mean()
+
+    eager = sweep(tree)
+    jitted = jax.jit(sweep)(tree)
+    assert jnp.allclose(eager["estimated_value"], jitted["estimated_value"])
+
+
+def test_phylo_mean_runs_on_newick_tree():
+    """End-to-end: read a tree from Newick, run phylo_mean."""
+    from hyperiax import from_newick
+
+    src = "((A:1,B:1):2,(C:0.5,D:1.5):0.5);"
+    tree = from_newick(src, schema={"estimated_value": ()})
+    # Leaf indices in BFS order: identify leaves and seed.
+    leaf_vals = jnp.array([1.0, 2.0, 3.0, 4.0])
+    tree = tree.at[tree.topology.is_leaf].set(estimated_value=leaf_vals)
+
+    out = phylo_mean()(tree)
+    # Sanity: root estimate is some finite weighted mean of leaves.
+    root_est = float(out["estimated_value"][0])
+    assert min(leaf_vals.tolist()) <= root_est <= max(leaf_vals.tolist())
+
+
+# ── unequal-degree works via children.map ──────────────────────────
+def test_phylo_mean_on_unequal_degree_tree():
+    """Ragged-arity trees are supported: per-child work goes through
+    children.map, the fusion through segment reductions."""
+    # node 1 has 3 children (3,4,5); node 2 has 2 children (6,7); root has 2 (1,2).
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1, 2, 2])
+    assert not topo.equal_degree
+    leaves = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0])  # nodes 3..7
+    tree = (
+        Tree.empty(topo, {"estimated_value": (), "edge_length": ()})
+        .set(edge_length=jnp.array([0.0, 0.5, 0.5, 1.0, 1.0, 1.0, 2.0, 2.0]))
+        .at[topo.is_leaf]
+        .set(estimated_value=leaves)
+    )
+
+    out = phylo_mean()(tree)
+
+    # Hand reference per parent:  sum_c (v_c / l_c) / sum_c (1 / l_c).
+    def wmean(vs, ls):
+        return float((vs / ls).sum() / (1.0 / ls).sum())
+
+    n1 = wmean(leaves[:3], jnp.array([1.0, 1.0, 1.0]))  # children 3,4,5
+    n2 = wmean(leaves[3:], jnp.array([2.0, 2.0]))       # children 6,7
+    root = wmean(jnp.array([n1, n2]), jnp.array([0.5, 0.5]))
+    assert jnp.allclose(out["estimated_value"][1], n1)
+    assert jnp.allclose(out["estimated_value"][2], n2)
+    assert jnp.allclose(out["estimated_value"][0], root)
+    # Leaves untouched.
+    assert jnp.array_equal(out["estimated_value"][topo.is_leaf], leaves)
diff --git a/tests/test_regression.py b/tests/test_regression.py
new file mode 100644
index 0000000..f7c67cf
--- /dev/null
+++ b/tests/test_regression.py
@@ -0,0 +1,347 @@
+"""Regression tests for the v3 functional-purity story.
+
+These are the tests that justify the whole refactor. The legacy v1/v2
+``OrderedExecutor`` mutated ``tree.data = {...}`` in place; under an
+outer ``@jax.jit`` (e.g. a Flax + optax ``train_step``) this leaked
+tracers into Python state. The immutable Tree pytree fixes it.
+
+Coverage:
+- T-10: outer-jit composition (repeated calls, multi-sweep chains)
+- T-11: lax.scan composition (body traced once regardless of length)
+- jax.grad through up + down sweeps (no in-place state to break autodiff)
+- Tree.update + sweep + jit (schema growth interacts cleanly with jit cache)
+"""
+
+import jax
+import jax.numpy as jnp
+
+from hyperiax import Topology, Tree, down, symmetric_topology, up
+
+
+# ── T-10: outer-jit composition ─────────────────────────────────────
+def test_outer_jit_repeated_calls_no_leaked_tracer():
+    """The historic failure mode: a ``step`` function wrapping a sweep,
+    called repeatedly under ``@jax.jit``. The old in-place
+    ``tree.data = {...}`` would leak tracers between calls. The immutable
+    pytree Tree fixes it."""
+    topo = symmetric_topology(depth=3, degree=2)
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    @jax.jit
+    def step(tree):
+        return avg(tree)
+
+    tree = (
+        Tree.empty(topo, {"value": (2,)})
+        .at[topo.is_leaf]
+        .set(value=jnp.arange(16, dtype=jnp.float32).reshape(8, 2))
+    )
+
+    # 20 successive calls; old hyperiax would raise LeakedTracer within a few.
+    for _ in range(20):
+        tree = step(tree)
+        assert jnp.all(jnp.isfinite(tree["value"]))
+
+
+def test_outer_jit_composes_multi_sweep_pipeline():
+    """A train-step-style function chaining up + down + up inside one jit.
+    Each sweep returns a new Tree pytree; the chain composes without
+    intermediate Python-state mutation."""
+    topo = symmetric_topology(depth=3, degree=2)
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    @down(reads_parent=("value",), writes=("value",))
+    def copy_down(node, parent, params):
+        return {"value": parent.value}
+
+    @jax.jit
+    def step(tree):
+        t = avg(tree)  # root <- mean of leaves
+        t = copy_down(t)  # broadcast back down
+        t = avg(t)  # converged
+        return t
+
+    tree = (
+        Tree.empty(topo, {"value": (2,)})
+        .at[topo.is_leaf]
+        .set(value=jnp.arange(16, dtype=jnp.float32).reshape(8, 2))
+    )
+    leaf_mean = jnp.arange(16, dtype=jnp.float32).reshape(8, 2).mean(0)
+
+    out = step(tree)
+    # After up→down, every node holds the leaf mean. The second up averages
+    # k identical values per parent — still the leaf mean.
+    assert jnp.allclose(out["value"][0], leaf_mean)
+    assert jnp.all(jnp.abs(out["value"] - leaf_mean) < 1e-5)
+
+
+def test_outer_jit_yields_same_result_as_eager():
+    """`@jax.jit` is purely an optimization; numerical results must match
+    the eager path bit-for-bit."""
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": (), "delta": ()}).set(delta=jnp.ones(7))
+
+    @down(reads=("delta",), reads_parent=("value",), writes=("value",))
+    def propagate(node, parent, params):
+        return {"value": parent.value + node.delta}
+
+    eager = propagate(tree)
+    jit_fn = jax.jit(lambda t: propagate(t))
+    jitted = jit_fn(tree)
+
+    assert jnp.array_equal(eager["value"], jitted["value"])
+
+
+# ── T-11: lax.scan composition ──────────────────────────────────────
+def test_lax_scan_body_traces_user_fn_same_count_as_single_call():
+    """``lax.scan(body, ..., length=N)`` traces the body **once** — not N
+    times. We verify by comparing trace counts: a single direct call to
+    the sweep equals a 100-iteration scan."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)}).at[topo.is_leaf].set(value=jnp.ones((8, 2)))
+
+    # Direct call: trace count = number of levels touched (= depth)
+    direct_counter = [0]
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg_direct(node, children, params):
+        direct_counter[0] += 1
+        return {"value": children.value.mean(0)}
+
+    avg_direct(tree)["value"].block_until_ready()
+    direct_count = direct_counter[0]
+
+    # Long scan: same depth, traced once. (Fresh SweepFn ⇒ fresh compile.)
+    scan_counter = [0]
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg_scan(node, children, params):
+        scan_counter[0] += 1
+        return {"value": children.value.mean(0)}
+
+    def body(carry, _):
+        return avg_scan(carry), None
+
+    final, _ = jax.lax.scan(body, tree, xs=None, length=100)
+    final["value"].block_until_ready()
+
+    assert scan_counter[0] == direct_count, (
+        f"Direct call traced user fn {direct_count} times; "
+        f"100-iteration lax.scan traced it {scan_counter[0]} times. "
+        f"They must match — lax.scan compiles the body once, not per iter."
+    )
+
+
+def test_lax_scan_top_level_jaxpr_has_one_scan_primitive():
+    """Structural check: the jaxpr of ``lax.scan(body, length=N)(tree)`` has
+    a single top-level ``scan`` equation. If the body were re-traced or
+    unrolled per iteration, we'd see many more equations."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)}).at[topo.is_leaf].set(value=jnp.ones((8, 2)))
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    def body(carry, _):
+        return avg(carry), None
+
+    def run_scan(t):
+        final, _ = jax.lax.scan(body, t, xs=None, length=50)
+        return final
+
+    j = jax.make_jaxpr(run_scan)(tree)
+    eqn_names = [str(eq.primitive) for eq in j.jaxpr.eqns]
+    scan_count = sum(1 for name in eqn_names if name == "scan")
+    assert scan_count == 1, (
+        f"Expected exactly one top-level scan primitive; got {scan_count}. Equations: {eqn_names}"
+    )
+
+
+def test_lax_scan_yields_numerically_correct_repeated_application():
+    """Running an up-sweep K times via scan equals K direct applications."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = (
+        Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=jnp.arange(8, dtype=jnp.float32))
+    )
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    # Eager loop
+    eager = tree
+    for _ in range(5):
+        eager = avg(eager)
+
+    # Scan
+    def body(carry, _):
+        return avg(carry), None
+
+    scanned, _ = jax.lax.scan(body, tree, xs=None, length=5)
+
+    assert jnp.array_equal(eager["value"], scanned["value"])
+
+
+def test_lax_scan_works_on_unequal_degree_tree():
+    """The segment-reduction path also composes with lax.scan."""
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1, 2, 2])
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=jnp.ones(5))
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    def body(carry, _):
+        return sum_up(carry), None
+
+    final, _ = jax.lax.scan(body, tree, xs=None, length=3)
+    assert jnp.all(jnp.isfinite(final["value"]))
+
+
+# ── jax.grad through sweeps ─────────────────────────────────────────
+def test_grad_through_up_sweep_matches_hand_derivative():
+    """``avg(leaves)`` averaged up to root: ``loss = root**2``.
+    ``d_loss/d_leaf[i] = 2 * root / N_leaves`` for a symmetric binary tree."""
+    topo = symmetric_topology(depth=2, degree=2)  # 4 leaves
+    n_leaves = int(topo.is_leaf.sum())
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    def loss_fn(leaf_vals):
+        tree = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=leaf_vals)
+        return avg(tree)["value"][0] ** 2
+
+    leaf_vals = jnp.ones(n_leaves)
+    grads = jax.grad(loss_fn)(leaf_vals)
+    # root = mean(leaves) = 1; d_loss/d_leaf = 2 * 1 / 4 = 0.5
+    assert jnp.allclose(grads, 0.5)
+
+
+def test_grad_through_down_sweep_matches_hand_derivative():
+    """``propagate``: each node = parent + node.delta. Loss = sum of leaf
+    values squared. Differentiating w.r.t. root.value should give
+    ``2 * sum_leaf_value = 2 * (n_leaves * (root + sum_path_deltas))``."""
+    topo = symmetric_topology(depth=2, degree=2)  # depth 2, 4 leaves
+    # All deltas zero; leaves = root.
+    tree = Tree.empty(topo, {"value": (), "delta": ()}).set(delta=jnp.zeros(7))
+
+    @down(reads=("delta",), reads_parent=("value",), writes=("value",))
+    def propagate(node, parent, params):
+        return {"value": parent.value + node.delta}
+
+    def loss_fn(root_val):
+        t = tree.at[topo.is_root].set(value=jnp.array([root_val]))
+        out = propagate(t)
+        return (out["value"][topo.is_leaf] ** 2).sum()
+
+    g = jax.grad(loss_fn)(3.0)
+    # Each leaf = root = 3; loss = 4 * 9 = 36; d/d_root = 2 * (n_leaves * root) = 24
+    assert jnp.allclose(g, 24.0)
+
+
+def test_value_and_grad_through_unequal_degree_sweep():
+    """Same end-to-end smoothness on the ragged segment_sum path."""
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1, 2, 2])
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    def loss_fn(leaf_vals):
+        t = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=leaf_vals)
+        return sum_up(t)["value"][0] ** 2  # root squared
+
+    leaf_vals = jnp.ones(5)
+    v, g = jax.value_and_grad(loss_fn)(leaf_vals)
+    # Root accumulates ALL 5 leaves via two-level sum: root = 5
+    # d_root/d_leaf[i] = 1 for each leaf; d_loss/d_leaf[i] = 2 * root * 1 = 10
+    assert jnp.allclose(v, 25.0)
+    assert jnp.allclose(g, 10.0)
+
+
+# ── Tree.update + sweep + jit ───────────────────────────────────────
+def test_tree_update_then_sweep_uses_new_field():
+    """Common pattern: build a Tree, add a brand-new field via .update,
+    run a sweep that reads it. update returns a new Tree with extended
+    schema; the sweep dispatches on the new pytree structure cleanly."""
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=jnp.ones(4))
+    tree = tree.update(weights=jnp.arange(7, dtype=jnp.float32))
+
+    @up(reads=("weights",), reads_children=("value",), writes=("value",))
+    def weighted_sum(node, children, params):
+        return {"value": children.value.sum(0) * node.weights}
+
+    out = weighted_sum(tree)
+    # Node 1: sum(1, 1) * weights[1]=1 → 2
+    # Node 2: sum(1, 1) * weights[2]=2 → 4
+    # Node 0: sum(node1=2, node2=4) * weights[0]=0 → 0
+    assert jnp.allclose(out["value"][1], 2.0)
+    assert jnp.allclose(out["value"][2], 4.0)
+    assert jnp.allclose(out["value"][0], 0.0)
+
+
+def test_tree_update_then_sweep_under_outer_jit():
+    """Same as above, wrapped in @jax.jit."""
+    topo = symmetric_topology(depth=2, degree=2)
+
+    @up(reads=("weights",), reads_children=("value",), writes=("value",))
+    def weighted_sum(node, children, params):
+        return {"value": children.value.sum(0) * node.weights}
+
+    @jax.jit
+    def step(tree):
+        return weighted_sum(tree)
+
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=jnp.ones(4))
+    tree = tree.update(weights=jnp.arange(7, dtype=jnp.float32))
+
+    out = step(tree)
+    assert jnp.allclose(out["value"][1], 2.0)
+    assert jnp.allclose(out["value"][2], 4.0)
+    assert jnp.allclose(out["value"][0], 0.0)
+
+
+# ── 'train_step' style integration ──────────────────────────────────
+def test_flax_style_train_step_pattern():
+    """The exact pattern that broke the legacy code: an outer jit wrapping
+    a sweep PLUS some external state (here, a simple loss + gradient).
+    Old hyperiax leaked tracers; new hyperiax handles this naturally."""
+    topo = symmetric_topology(depth=2, degree=2)
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    def loss_and_grad(params, leaf_vals):
+        def loss(p, lv):
+            t = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=lv)
+            t = avg(t)
+            return (t["value"][0] - p["target"]) ** 2
+
+        return jax.value_and_grad(loss)(params, leaf_vals)
+
+    @jax.jit
+    def train_step(params, leaf_vals):
+        loss, g = loss_and_grad(params, leaf_vals)
+        new_params = {"target": params["target"] - 0.1 * g["target"]}
+        return new_params, loss
+
+    params = {"target": jnp.array(0.0)}
+    leaf_vals = jnp.array([1.0, 2.0, 3.0, 4.0])
+
+    for _ in range(10):
+        params, _loss = train_step(params, leaf_vals)
+
+    # Without leaked tracers and with correct grads, ``target`` should drift
+    # toward the leaf mean (2.5).
+    assert 1.0 < float(params["target"]) < 3.0
diff --git a/tests/test_schema.py b/tests/test_schema.py
new file mode 100644
index 0000000..334818a
--- /dev/null
+++ b/tests/test_schema.py
@@ -0,0 +1,84 @@
+"""Schema / FieldSpec sanity tests."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax import FieldSpec, Schema
+
+
+@pytest.fixture
+def restore_x64_config():
+    original = jax.config.jax_enable_x64
+    yield
+    jax.config.update("jax_enable_x64", original)
+
+
+def test_fieldspec_coerce_from_tuple():
+    spec = FieldSpec._coerce((2, 3))
+    assert spec.shape == (2, 3)
+
+
+def test_fieldspec_coerce_from_none_is_scalar():
+    assert FieldSpec._coerce(None).shape == ()
+
+
+def test_fieldspec_default_dtype_tracks_jax_float_config(restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    assert FieldSpec._coerce((2,)).dtype == np.dtype(jnp.float64)
+
+    jax.config.update("jax_enable_x64", False)
+    assert FieldSpec._coerce((2,)).dtype == np.dtype(jnp.float32)
+
+
+def test_fieldspec_explicit_dtype_overrides_jax_float_config(restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    spec = FieldSpec(shape=(2,), dtype=jnp.float32)
+    assert spec.dtype == np.dtype(jnp.float32)
+
+
+def test_fieldspec_coerce_rejects_garbage():
+    with pytest.raises(TypeError):
+        FieldSpec._coerce(object())
+
+
+def test_schema_from_dict_orders_fields_by_name():
+    s = Schema.from_dict({"z": (2,), "a": (3,), "m": ()})
+    assert s.names == ("a", "m", "z")
+
+
+def test_schema_is_value_equal_and_hashable():
+    a = Schema.from_dict({"x": (2,), "y": (3,)})
+    b = Schema.from_dict({"y": (3,), "x": (2,)})  # different insert order
+    assert a == b
+    assert hash(a) == hash(b)
+
+
+def test_schema_contains_and_getitem():
+    s = Schema.from_dict({"x": (2,)})
+    assert "x" in s
+    assert "y" not in s
+    assert s["x"].shape == (2,)
+    with pytest.raises(KeyError):
+        _ = s["y"]
+
+
+def test_schema_with_added_returns_new_and_keeps_original():
+    s = Schema.from_dict({"x": (2,)})
+    s2 = s.with_added(y=(3,))
+    assert s2.names == ("x", "y")
+    assert s.names == ("x",)  # original immutable
+    with pytest.raises(ValueError):
+        s.with_added(x=(4,))
+
+
+def test_schema_without_drops_fields():
+    s = Schema.from_dict({"x": (2,), "y": (3,), "z": ()})
+    assert s.without("y").names == ("x", "z")
+
+
+def test_schema_empty():
+    s = Schema.from_dict({})
+    assert s.names == ()
+    assert len(s) == 0
diff --git a/tests/test_sweep_down.py b/tests/test_sweep_down.py
new file mode 100644
index 0000000..2a315dd
--- /dev/null
+++ b/tests/test_sweep_down.py
@@ -0,0 +1,201 @@
+"""Down-sweep dispatch. Covers T-6."""
+
+import jax
+import jax.numpy as jnp
+import pytest
+
+import hyperiax as hx
+from hyperiax import (
+    MissingField,
+    SchemaMismatch,
+    Topology,
+    Tree,
+    down,
+    symmetric_topology,
+)
+
+
+# ── T-6: propagation correctness ────────────────────────────────────
+def test_down_sweep_propagates_root_value_via_delta():
+    """``value <- parent.value + node.delta`` accumulates along path-to-root."""
+    topo = symmetric_topology(depth=2, degree=2)  # 7 nodes
+    tree = Tree.empty(topo, {"value": (), "delta": ()}).set(delta=jnp.ones(7))
+
+    @down(reads=("delta",), reads_parent=("value",), writes=("value",))
+    def propagate(node, parent, params):
+        return {"value": parent.value + node.delta}
+
+    out = propagate(tree)
+    # root keeps its zero default; level 1 inherits +1; level 2 inherits +2.
+    expected = jnp.array([0, 1, 1, 2, 2, 2, 2], dtype=jnp.float32)
+    assert jnp.allclose(out["value"], expected)
+
+
+def test_down_sweep_copies_root_to_all_descendants():
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)}).at[topo.is_root].set(value=jnp.array([[3.0, 4.0]]))
+
+    @down(reads_parent=("value",), writes=("value",))
+    def copy_down(node, parent, params):
+        return {"value": parent.value}
+
+    out = copy_down(tree)
+    assert jnp.all(out["value"] == jnp.array([3.0, 4.0]))
+
+
+def test_down_sweep_threads_params():
+    topo = symmetric_topology(depth=1, degree=3)
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_root].set(value=jnp.array([2.0]))
+
+    @down(reads_parent=("value",), writes=("value",))
+    def scale(node, parent, params):
+        return {"value": parent.value * params["gain"]}
+
+    out = scale(tree, params={"gain": jnp.array(5.0)})
+    # root=2 unchanged; 3 leaves each = 2*5 = 10
+    assert jnp.allclose(out["value"][0], 2.0)
+    assert jnp.allclose(out["value"][topo.is_leaf], 10.0)
+
+
+def test_down_sweep_handles_unequal_degree_topology():
+    """Down direction doesn't need the equal-degree fast path — it should
+    work on any topology because every non-root node has exactly one
+    parent. This is the asymmetric tree where node 1 has 3 children and
+    node 2 has 0."""
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1])
+    assert not topo.equal_degree
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_root].set(value=jnp.array([7.0]))
+
+    @down(reads_parent=("value",), writes=("value",))
+    def copy_down(node, parent, params):
+        return {"value": parent.value}
+
+    out = copy_down(tree)
+    assert jnp.all(out["value"] == 7.0)
+
+
+def test_down_sweep_root_remains_unchanged():
+    """Root has no parent; the sweep starts at level 1, so root data is
+    never touched by the user function."""
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_root].set(value=jnp.array([99.0]))
+
+    @down(reads_parent=("value",), writes=("value",))
+    def reset_to_zero(node, parent, params):
+        return {"value": jnp.zeros(())}
+
+    out = reset_to_zero(tree)
+    assert jnp.allclose(out["value"][0], 99.0)
+    assert jnp.allclose(out["value"][1:], 0.0)
+
+
+# ── JIT cache + outer jit ──────────────────────────────────────────
+def test_down_sweep_outer_jit_does_not_leak_tracer():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": (), "delta": ()}).set(delta=jnp.ones(7))
+
+    @down(reads=("delta",), reads_parent=("value",), writes=("value",))
+    def propagate(node, parent, params):
+        return {"value": parent.value + node.delta}
+
+    @jax.jit
+    def step(t):
+        return propagate(t)
+
+    out_eager = propagate(tree)
+    out_jit = step(tree)
+    assert jnp.allclose(out_eager["value"], out_jit["value"])
+
+
+def test_down_sweep_jit_cache_hits_on_identical_topology():
+    trace_count = 0
+
+    @down(reads_parent=("value",), writes=("value",))
+    def copy_down(node, parent, params):
+        nonlocal trace_count
+        trace_count += 1
+        return {"value": parent.value}
+
+    topo1 = symmetric_topology(depth=3, degree=2)
+    tree1 = Tree.empty(topo1, {"value": (2,)}).at[topo1.is_root].set(value=jnp.array([[1.0, 2.0]]))
+    copy_down(tree1)["value"].block_until_ready()
+    initial = trace_count
+    assert initial > 0
+
+    topo2 = symmetric_topology(depth=3, degree=2)
+    tree2 = Tree.empty(topo2, {"value": (2,)}).at[topo2.is_root].set(value=jnp.array([[5.0, 6.0]]))
+    for _ in range(5):
+        copy_down(tree2)["value"].block_until_ready()
+    assert trace_count == initial
+
+
+# ── up + down round-trip ───────────────────────────────────────────
+def test_up_then_down_broadcasts_leaf_mean_to_all_nodes():
+    """A canonical pipeline: up-sweep computes the leaf mean at the root,
+    down-sweep broadcasts it to every node. After both, every node holds
+    the leaf mean."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)})
+    leaf_vals = jnp.arange(16, dtype=jnp.float32).reshape(8, 2)
+    tree = tree.at[topo.is_leaf].set(value=leaf_vals)
+
+    @hx.up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    @hx.down(reads_parent=("value",), writes=("value",))
+    def copy_down(node, parent, params):
+        return {"value": parent.value}
+
+    after_up = avg(tree)
+    after_down = copy_down(after_up)
+
+    expected_root = leaf_vals.mean(0)
+    assert jnp.allclose(after_up["value"][0], expected_root)
+    # All nodes (including leaves, since copy_down overwrites them) hold the mean.
+    assert jnp.all(after_down["value"] == expected_root)
+
+
+# ── validation paths ───────────────────────────────────────────────
+def test_down_sweep_raises_on_missing_field():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": ()})
+
+    @down(reads_parent=("does_not_exist",), writes=("value",))
+    def broken(node, parent, params):
+        return {"value": parent.does_not_exist}
+
+    with pytest.raises(MissingField):
+        broken(tree)
+
+
+def test_down_sweep_raises_on_extra_writes():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": ()})
+
+    @down(reads_parent=("value",), writes=("value",))
+    def extra(node, parent, params):
+        return {"value": parent.value, "phantom": jnp.zeros(())}
+
+    with pytest.raises(SchemaMismatch):
+        extra(tree)
+
+
+def test_down_decorator_requires_nonempty_writes():
+    with pytest.raises(ValueError):
+
+        @down(writes=())
+        def empty(node, parent, params):
+            return {}
+
+
+def test_sweepfn_rejects_children_reads_on_down_direction():
+    with pytest.raises(ValueError):
+        hx.SweepFn(
+            direction="down",
+            fn=lambda *a: {"value": None},
+            reads=None,
+            reads_children=("value",),
+            reads_parent=None,
+            writes=("value",),
+        )
diff --git a/tests/test_sweep_up.py b/tests/test_sweep_up.py
new file mode 100644
index 0000000..a588437
--- /dev/null
+++ b/tests/test_sweep_up.py
@@ -0,0 +1,541 @@
+"""Up-sweep dispatch.
+
+A single segment-based path handles any topology (equal- or unequal-degree).
+Tests are organised by capability, not by topology — most tests run the same
+body on both an equal-degree symmetric tree and a ragged tree and assert
+each matches its hand-derived expectation.
+"""
+
+import jax
+import jax.numpy as jnp
+import pytest
+
+import hyperiax as hx
+from hyperiax import (
+    HyperiaxError,
+    MissingField,
+    SchemaMismatch,
+    Topology,
+    Tree,
+    symmetric_topology,
+    up,
+)
+
+# Canonical ragged tree used throughout the unequal-degree cases:
+#       0
+#      / \
+#     1   2
+#    /|\  /\
+#   3 4 5 6 7         (node 1 has 3 children; node 2 has 2)
+RAGGED_PARENTS = [0, 0, 0, 1, 1, 1, 2, 2]
+
+
+def _ragged_tree(values, schema=("value",)):
+    topo = Topology.from_parents(RAGGED_PARENTS)
+    fields = {k: () for k in schema}
+    tree = Tree.empty(topo, fields).at[topo.is_leaf].set(value=jnp.asarray(values))
+    return topo, tree
+
+
+# ── reduction correctness on equal-degree trees ────────────────────
+def test_up_sweep_root_is_mean_of_all_leaves():
+    """Recursive child-averaging on a 2^h binary tree collapses to leaf mean."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)})
+    n_leaves = int(topo.is_leaf.sum())
+    leaf_vals = jnp.arange(2 * n_leaves, dtype=jnp.float32).reshape(n_leaves, 2)
+    tree = tree.at[topo.is_leaf].set(value=leaf_vals)
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    out = avg(tree)
+    assert jnp.allclose(out["value"][0], leaf_vals.mean(0))
+
+
+def test_up_sweep_per_level_values_match_hand_computed():
+    """Level-2 nodes (3,4,5,6) should each be the mean of their 2 leaf children."""
+    topo = symmetric_topology(depth=3, degree=2)
+    tree = Tree.empty(topo, {"value": (1,)})
+    leaf_vals = jnp.arange(8, dtype=jnp.float32).reshape(8, 1)
+    tree = tree.at[topo.is_leaf].set(value=leaf_vals)
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    out = avg(tree)
+    assert jnp.allclose(out["value"][3], 0.5)
+    assert jnp.allclose(out["value"][4], 2.5)
+    assert jnp.allclose(out["value"][5], 4.5)
+    assert jnp.allclose(out["value"][6], 6.5)
+    assert jnp.allclose(out["value"][1], 1.5)
+    assert jnp.allclose(out["value"][2], 5.5)
+    assert jnp.allclose(out["value"][0], 3.5)
+
+
+# ── reductions on ragged trees (segment_*) ─────────────────────────
+def test_sum_on_ragged_tree():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    out = sum_up(tree)
+    # node 1 = 10+20+30 = 60; node 2 = 100+200 = 300; root = 60+300 = 360
+    expected = jnp.array([360, 60, 300, 10, 20, 30, 100, 200], dtype=jnp.float32)
+    assert jnp.allclose(out["value"], expected)
+
+
+def test_mean_on_ragged_tree():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def mean_up(node, children, params):
+        return {"value": children.value.mean(0)}
+
+    out = mean_up(tree)
+    # node 1 = mean(10,20,30) = 20; node 2 = mean(100,200) = 150; root = mean(20,150) = 85
+    assert jnp.allclose(out["value"][1], 20.0)
+    assert jnp.allclose(out["value"][2], 150.0)
+    assert jnp.allclose(out["value"][0], 85.0)
+
+
+def test_max_on_ragged_tree():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def max_up(node, children, params):
+        return {"value": children.value.max(0)}
+
+    out = max_up(tree)
+    assert jnp.allclose(out["value"][1], 30.0)
+    assert jnp.allclose(out["value"][2], 200.0)
+    assert jnp.allclose(out["value"][0], 200.0)
+
+
+def test_min_on_ragged_tree():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def min_up(node, children, params):
+        return {"value": children.value.min(0)}
+
+    out = min_up(tree)
+    assert jnp.allclose(out["value"][1], 10.0)
+    assert jnp.allclose(out["value"][2], 100.0)
+    assert jnp.allclose(out["value"][0], 10.0)
+
+
+def test_prod_on_ragged_tree():
+    topo, tree = _ragged_tree([2.0, 3.0, 5.0, 7.0, 11.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def prod_up(node, children, params):
+        return {"value": children.value.prod(0)}
+
+    out = prod_up(tree)
+    # node 1 = 2*3*5 = 30; node 2 = 7*11 = 77; root = 30*77 = 2310
+    assert jnp.allclose(out["value"][1], 30.0)
+    assert jnp.allclose(out["value"][2], 77.0)
+    assert jnp.allclose(out["value"][0], 2310.0)
+
+
+def test_ragged_tree_with_mixed_depth_leaves():
+    """A tree where some level-1 nodes are leaves (depth varies) AND the
+    internal arities differ — the genuinely-ragged case."""
+    # root 0 → {1, 2}; node 1 → {3, 4, 5} (leaves); node 2 itself a leaf at level 1.
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1])
+    assert not topo.equal_degree
+    tree = Tree.empty(topo, {"value": ()})
+    # is_leaf = [F, F, T, T, T, T] → leaves are at indices 2, 3, 4, 5.
+    tree = tree.at[topo.is_leaf].set(value=jnp.array([7.0, 100.0, 200.0, 300.0]))
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    out = sum_up(tree)
+    # node 1 reduces children {3,4,5} → 600;  node 2 is a leaf at level 1 → 7;
+    # root reduces children {1, 2} → 607.
+    assert jnp.allclose(out["value"][1], 600.0)
+    assert jnp.allclose(out["value"][2], 7.0)
+    assert jnp.allclose(out["value"][0], 607.0)
+
+
+# ── node + children combination ────────────────────────────────────
+def test_up_sweep_passes_node_data_alongside_children():
+    """The user fn gets both node and children data."""
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": (), "bias": ()})
+    tree = tree.at[topo.is_leaf].set(value=jnp.ones(4))
+    tree = tree.set(bias=jnp.arange(7, dtype=jnp.float32))
+
+    @up(reads=("bias",), reads_children=("value",), writes=("value",))
+    def sum_plus_bias(node, children, params):
+        return {"value": children.value.sum(0) + node.bias}
+
+    out = sum_plus_bias(tree)
+    assert jnp.allclose(out["value"][1], 3.0)  # bias=1, 2 leaves → 1+2
+    assert jnp.allclose(out["value"][2], 4.0)
+    assert jnp.allclose(out["value"][0], 7.0)
+
+
+def test_node_plus_children_on_ragged_tree():
+    topo = Topology.from_parents(RAGGED_PARENTS)
+    tree = Tree.empty(topo, {"value": (), "bias": ()})
+    tree = tree.at[topo.is_leaf].set(value=jnp.ones(5))
+    tree = tree.set(bias=jnp.arange(8, dtype=jnp.float32))
+
+    @up(reads=("bias",), reads_children=("value",), writes=("value",))
+    def with_bias(node, children, params):
+        return {"value": children.value.sum(0) + node.bias}
+
+    out = with_bias(tree)
+    # node 1 (bias=1): sum(1,1,1) + 1 = 4 ;  node 2 (bias=2): sum(1,1) + 2 = 4
+    # root  (bias=0): sum(4, 4)   + 0 = 8
+    assert jnp.allclose(out["value"][1], 4.0)
+    assert jnp.allclose(out["value"][2], 4.0)
+    assert jnp.allclose(out["value"][0], 8.0)
+
+
+def test_ragged_tree_with_multidim_trailing():
+    topo = Topology.from_parents(RAGGED_PARENTS)
+    tree = Tree.empty(topo, {"value": (2,)})
+    leaf_vals = jnp.array([[1, 10], [2, 20], [3, 30], [4, 40], [5, 50]], dtype=jnp.float32)
+    tree = tree.at[topo.is_leaf].set(value=leaf_vals)
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        return {"value": children.value.sum(0)}
+
+    out = sum_up(tree)
+    assert jnp.allclose(out["value"][1], jnp.array([6, 60]))
+    assert jnp.allclose(out["value"][2], jnp.array([9, 90]))
+    assert jnp.allclose(out["value"][0], jnp.array([15, 150]))
+
+
+# ── params threading ───────────────────────────────────────────────
+def test_up_sweep_threads_params():
+    topo = symmetric_topology(depth=1, degree=2)
+    tree = Tree.empty(topo, {"value": ()}).at[topo.is_leaf].set(value=jnp.ones(2))
+
+    @up(reads_children=("value",), writes=("value",))
+    def scaled_sum(node, children, params):
+        return {"value": children.value.sum(0) * params["gain"]}
+
+    out = scaled_sum(tree, params={"gain": jnp.array(3.0)})
+    assert jnp.allclose(out["value"][0], 6.0)
+
+
+# ── children.map (per-child transform) ─────────────────────────────
+def _sq1(c):
+    """Non-linear per-child map: v -> v**2 + 1 (cannot be expressed as a plain
+    segment reduction of the raw field)."""
+    return {"value": c.value**2 + 1.0}
+
+
+def test_map_then_sum_on_ragged_tree():
+    topo, tree = _ragged_tree([2.0, 3.0, 4.0, 5.0, 6.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def sweep(node, children, params):
+        return {"value": children.map(_sq1).value.sum(0)}
+
+    out = sweep(tree)
+    f = lambda v: v**2 + 1.0
+    # node 1 = f(2)+f(3)+f(4); node 2 = f(5)+f(6); root = f(node1) + f(node2)
+    assert jnp.allclose(out["value"][1], f(2.0) + f(3.0) + f(4.0))
+    assert jnp.allclose(out["value"][2], f(5.0) + f(6.0))
+    assert jnp.allclose(out["value"][0], f(32.0) + f(63.0))
+
+
+def test_same_map_sweep_runs_on_equal_and_unequal():
+    """One user fn, both topologies — exactly the same body."""
+
+    @up(reads_children=("value",), writes=("value",))
+    def sweep(node, children, params):
+        return {"value": children.map(_sq1).value.sum(0)}
+
+    f = lambda v: v**2 + 1.0
+
+    sym = symmetric_topology(depth=2, degree=2)  # root>1,2 ; 1>3,4 ; 2>5,6
+    sym_tree = Tree.empty(sym, {"value": ()}).at[sym.is_leaf].set(
+        value=jnp.array([2.0, 3.0, 4.0, 5.0])
+    )
+    out_sym = sweep(sym_tree)
+    assert jnp.allclose(out_sym["value"][1], f(2.0) + f(3.0))
+    assert jnp.allclose(out_sym["value"][2], f(4.0) + f(5.0))
+    assert jnp.allclose(out_sym["value"][0], f(15.0) + f(43.0))
+
+    _, rag_tree = _ragged_tree([2.0, 3.0, 4.0, 5.0, 6.0])
+    out_rag = sweep(rag_tree)
+    assert jnp.allclose(out_rag["value"][0], f(32.0) + f(63.0))
+
+
+def test_map_then_mean_and_max_stay_exact_on_ragged_tree():
+    """After a per-child transform, segment-aware ``.mean``/``.max`` are
+    still exact (no padding-identity assumption)."""
+    topo, tree = _ragged_tree([2.0, 3.0, 4.0, 5.0, 6.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def mean_sweep(node, children, params):
+        return {"value": children.map(lambda c: {"value": c.value * 10.0}).value.mean(0)}
+
+    @up(reads_children=("value",), writes=("value",))
+    def max_sweep(node, children, params):
+        return {"value": children.map(lambda c: {"value": c.value * 10.0}).value.max(0)}
+
+    out_mean = mean_sweep(tree)
+    assert jnp.allclose(out_mean["value"][1], 30.0)  # mean([2,3,4]*10)
+    assert jnp.allclose(out_mean["value"][2], 55.0)  # mean([5,6]*10)
+
+    out_max = max_sweep(tree)
+    assert jnp.allclose(out_max["value"][1], 40.0)
+    assert jnp.allclose(out_max["value"][2], 60.0)
+
+
+# ── writes_children (per-edge scatter) ─────────────────────────────
+def test_writes_children_scatters_per_child_equal_degree():
+    topo = symmetric_topology(depth=2, degree=2)  # nodes 0..6, leaves 3..6
+    tree = Tree.empty(topo, {"value": (), "doubled": ()}).at[topo.is_leaf].set(
+        value=jnp.array([2.0, 3.0, 4.0, 5.0])
+    )
+
+    @up(reads_children=("value",), writes=("value",), writes_children=("doubled",))
+    def sweep(node, children, params):
+        msgs = children.map(lambda c: {"value": c.value, "doubled": 2.0 * c.value})
+        return {"value": msgs.value.sum(0), "doubled": msgs.doubled}
+
+    out = sweep(tree)
+    # node 1 = 5; node 2 = 9; root = 14.
+    assert jnp.allclose(out["value"][0], 14.0)
+    assert jnp.allclose(out["value"][1], 5.0)
+    assert jnp.allclose(out["value"][2], 9.0)
+    # leaves get 2*value; nodes 1,2 get 2*(fused value); root never written.
+    assert jnp.allclose(out["doubled"][3:7], jnp.array([4.0, 6.0, 8.0, 10.0]))
+    assert jnp.allclose(out["doubled"][1], 10.0)
+    assert jnp.allclose(out["doubled"][2], 18.0)
+    assert jnp.allclose(out["doubled"][0], 0.0)
+
+
+def test_writes_children_on_ragged_tree():
+    topo, tree = _ragged_tree([2.0, 3.0, 4.0, 5.0, 6.0], schema=("value", "doubled"))
+
+    @up(reads_children=("value",), writes=("value",), writes_children=("doubled",))
+    def sweep(node, children, params):
+        msgs = children.map(lambda c: {"value": c.value, "doubled": 2.0 * c.value})
+        return {"value": msgs.value.sum(0), "doubled": msgs.doubled}
+
+    out = sweep(tree)
+    # node 1 = 9; node 2 = 11; root = 20.
+    assert jnp.allclose(out["value"][1], 9.0)
+    assert jnp.allclose(out["value"][2], 11.0)
+    assert jnp.allclose(out["value"][0], 20.0)
+    # per-child scatter
+    expected = jnp.array([0.0, 2 * 9.0, 2 * 11.0, 4.0, 6.0, 8.0, 10.0, 12.0])
+    assert jnp.allclose(out["doubled"], expected)
+
+
+def test_writes_children_on_unbalanced_ragged_tree():
+    """Single-child internal node: writes_children scatter still lands correctly."""
+    topo = Topology.from_parents([0, 0, 0, 0, 1])  # root has 3 children, node 1 has 1
+    assert not topo.equal_degree
+    tree = Tree.empty(topo, {"value": (), "doubled": ()}).at[topo.is_leaf].set(
+        value=jnp.array([2.0, 3.0, 4.0])  # leaves 2, 3, 4
+    )
+
+    @up(reads_children=("value",), writes=("value",), writes_children=("doubled",))
+    def sweep(node, children, params):
+        msgs = children.map(lambda c: {"value": c.value, "doubled": 2.0 * c.value})
+        return {"value": msgs.value.sum(0), "doubled": msgs.doubled}
+
+    out = sweep(tree)
+    # node 1's only child is leaf 4 → node1.value = 4;  doubled[4] = 8
+    # root's children are 1 (=4), 2 (=2), 3 (=3) → root.value = 9
+    assert jnp.allclose(out["value"][1], 4.0)
+    assert jnp.allclose(out["value"][0], 9.0)
+    assert jnp.allclose(out["doubled"][2], 4.0)
+    assert jnp.allclose(out["doubled"][3], 6.0)
+    assert jnp.allclose(out["doubled"][4], 8.0)
+    assert jnp.allclose(out["doubled"][1], 8.0)
+
+
+# ── ChildrenAxis proxy guards ──────────────────────────────────────
+def test_children_axis_rejects_non_zero_axis():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def bad_axis(node, children, params):
+        return {"value": children.value.sum(1)}  # only axis=0 is allowed
+
+    with pytest.raises(ValueError, match="axis=0"):
+        bad_axis(tree)
+
+
+def test_children_axis_rejects_array_coercion():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0])
+
+    @up(reads_children=("value",), writes=("value",))
+    def coerce(node, children, params):
+        # Pulling the proxy into a dense jnp array should hit __array__ and fail.
+        return {"value": jnp.asarray(children.value)}
+
+    with pytest.raises((HyperiaxError, TypeError, ValueError)):
+        coerce(tree)
+
+
+# ── JIT cache + outer JIT ──────────────────────────────────────────
+def test_up_sweep_jit_cache_hits_on_identical_equal_degree_topology():
+    trace_count = 0
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        nonlocal trace_count
+        trace_count += 1
+        return {"value": children.value.mean(0)}
+
+    topo1 = symmetric_topology(depth=3, degree=2)
+    tree1 = Tree.empty(topo1, {"value": (2,)}).at[topo1.is_leaf].set(value=jnp.ones((8, 2)))
+    avg(tree1)["value"].block_until_ready()
+    initial = trace_count
+    assert initial > 0
+
+    topo2 = symmetric_topology(depth=3, degree=2)
+    assert topo2 is not topo1 and topo2 == topo1
+    tree2 = Tree.empty(topo2, {"value": (2,)}).at[topo2.is_leaf].set(value=jnp.ones((8, 2)))
+    for _ in range(5):
+        avg(tree2)["value"].block_until_ready()
+    assert trace_count == initial
+
+
+def test_up_sweep_jit_cache_hits_on_identical_ragged_topology():
+    trace_count = 0
+
+    @up(reads_children=("value",), writes=("value",))
+    def sum_up(node, children, params):
+        nonlocal trace_count
+        trace_count += 1
+        return {"value": children.value.sum(0)}
+
+    topo1 = Topology.from_parents(RAGGED_PARENTS)
+    tree1 = Tree.empty(topo1, {"value": ()}).at[topo1.is_leaf].set(value=jnp.ones(5))
+    sum_up(tree1)["value"].block_until_ready()
+    initial = trace_count
+    assert initial > 0
+
+    topo2 = Topology.from_parents(RAGGED_PARENTS)
+    tree2 = Tree.empty(topo2, {"value": ()}).at[topo2.is_leaf].set(value=jnp.ones(5))
+    for _ in range(5):
+        sum_up(tree2)["value"].block_until_ready()
+    assert trace_count == initial
+
+
+def test_up_sweep_does_recompile_for_different_schema():
+    """Different schema (different pytree structure) → separate compile."""
+    trace_count = 0
+
+    @up(reads_children=("value",), writes=("value",))
+    def avg(node, children, params):
+        nonlocal trace_count
+        trace_count += 1
+        return {"value": children.value.mean(0)}
+
+    topo = symmetric_topology(depth=2, degree=2)
+    t_2d = Tree.empty(topo, {"value": (2,)}).at[topo.is_leaf].set(value=jnp.ones((4, 2)))
+    t_3d = Tree.empty(topo, {"value": (3,)}).at[topo.is_leaf].set(value=jnp.ones((4, 3)))
+    avg(t_2d)["value"].block_until_ready()
+    count_after_first = trace_count
+    avg(t_3d)["value"].block_until_ready()
+    assert trace_count > count_after_first
+
+
+def test_up_sweep_outer_jit_does_not_leak_on_ragged_tree():
+    topo, tree = _ragged_tree([10.0, 20.0, 30.0, 100.0, 200.0], schema=("value", "doubled"))
+
+    @up(reads_children=("value",), writes=("value",), writes_children=("doubled",))
+    def sweep(node, children, params):
+        msgs = children.map(lambda c: {"value": c.value**2, "doubled": 2.0 * c.value})
+        return {"value": msgs.value.sum(0), "doubled": msgs.doubled}
+
+    @jax.jit
+    def step(t):
+        return sweep(t)
+
+    out_eager = sweep(tree)
+    out_jit = step(tree)
+    assert jnp.allclose(out_eager["value"], out_jit["value"])
+    assert jnp.allclose(out_eager["doubled"], out_jit["doubled"])
+
+
+# ── validation paths ───────────────────────────────────────────────
+def test_up_sweep_raises_on_missing_field():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)})
+
+    @up(reads_children=("not_in_schema",), writes=("value",))
+    def broken(node, children, params):
+        return {"value": children.not_in_schema.mean(0)}
+
+    with pytest.raises(MissingField):
+        broken(tree)
+
+
+def test_up_sweep_raises_on_extra_writes():
+    topo = symmetric_topology(depth=2, degree=2)
+    tree = Tree.empty(topo, {"value": (2,)}).at[topo.is_leaf].set(value=jnp.ones((4, 2)))
+
+    @up(reads_children=("value",), writes=("value",))
+    def extra_key(node, children, params):
+        return {"value": children.value.mean(0), "phantom": jnp.zeros((2,))}
+
+    with pytest.raises(SchemaMismatch):
+        extra_key(tree)
+
+
+def test_up_decorator_requires_nonempty_writes():
+    with pytest.raises(ValueError):
+
+        @up(writes=())
+        def empty(node, children, params):
+            return {}
+
+
+def test_sweepfn_rejects_parent_reads_on_up_direction():
+    with pytest.raises(ValueError):
+        hx.SweepFn(
+            direction="up",
+            fn=lambda *a: {"value": None},
+            reads=None,
+            reads_children=None,
+            reads_parent=("value",),
+            writes=("value",),
+        )
+
+
+def test_writes_children_rejected_on_down_direction():
+    with pytest.raises(ValueError, match="writes_children"):
+        hx.SweepFn(
+            direction="down",
+            fn=lambda *a: {"value": None},
+            reads=None,
+            reads_children=None,
+            reads_parent=None,
+            writes=("value",),
+            writes_children=("edge_id",),
+        )
+
+
+def test_writes_children_overlap_with_writes_rejected():
+    with pytest.raises(ValueError, match="share fields"):
+        hx.SweepFn(
+            direction="up",
+            fn=lambda *a: {"value": None},
+            reads=None,
+            reads_children=None,
+            reads_parent=None,
+            writes=("value",),
+            writes_children=("value",),
+        )
diff --git a/tests/test_topology.py b/tests/test_topology.py
new file mode 100644
index 0000000..5e57f17
--- /dev/null
+++ b/tests/test_topology.py
@@ -0,0 +1,112 @@
+"""Topology construction + pytree behavior. Covers T-1."""
+
+import jax
+import numpy as np
+import pytest
+
+from hyperiax import Topology
+
+
+def test_from_parents_basic_structure():
+    # 3-level binary tree:
+    #          0
+    #         / \
+    #        1   2
+    #       / \ / \
+    #      3  4 5  6
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    assert topo.size == 7
+    assert topo.depth == 2
+    np.testing.assert_array_equal(topo.level_starts, [0, 1, 3, 7])
+    np.testing.assert_array_equal(topo.child_counts, [2, 2, 2, 0, 0, 0, 0])
+    np.testing.assert_array_equal(topo.is_leaf, [False, False, False, True, True, True, True])
+    np.testing.assert_array_equal(topo.is_root, [True, False, False, False, False, False, False])
+    np.testing.assert_array_equal(topo.is_inner, [False, True, True, False, False, False, False])
+    np.testing.assert_array_equal(topo.node_depths, [0, 1, 1, 2, 2, 2, 2])
+
+
+def test_from_parents_detects_equal_degree():
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    assert topo.equal_degree is True
+    assert topo.max_degree == 2
+
+
+def test_from_parents_unequal_degree():
+    # Node 0 has 2 children; node 1 has 3 children; node 2 is a leaf with 0.
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1])
+    assert topo.equal_degree is False
+    assert topo.max_degree == 3
+
+
+def test_from_parents_segment_layout():
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    # Level 2 (leaves) reduces into parents [1, 2]; their local seg ids are [0,0,1,1].
+    np.testing.assert_array_equal(topo.pbuckets[3:7], [0, 0, 1, 1])
+    np.testing.assert_array_equal(topo.pbuckets_ref[2], [1, 2])
+    # Level 1 reduces into the root [0]; seg ids [0, 0].
+    np.testing.assert_array_equal(topo.pbuckets[1:3], [0, 0])
+    np.testing.assert_array_equal(topo.pbuckets_ref[1], [0])
+
+
+def test_from_parents_segment_layout_on_ragged_tree():
+    # node 1 has 3 children {3,4,5}; node 2 has 2 children {6,7}.
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 1, 2, 2])
+    assert not topo.equal_degree
+    # Level 2 children [3..7] reduce into parents [1, 2] with seg ids [0,0,0,1,1].
+    np.testing.assert_array_equal(topo.pbuckets[3:8], [0, 0, 0, 1, 1])
+    np.testing.assert_array_equal(topo.pbuckets_ref[2], [1, 2])
+
+
+def test_from_parents_rejects_non_bfs_layout():
+    # parents[2] = 3 points forward → not BFS.
+    with pytest.raises(ValueError):
+        Topology.from_parents([0, 0, 3, 0])
+
+
+def test_from_parents_rejects_non_zero_root():
+    with pytest.raises(ValueError):
+        Topology.from_parents([1, 0])
+
+
+def test_topology_single_node():
+    topo = Topology.from_parents([0])
+    assert topo.size == 1
+    assert topo.depth == 0
+    assert topo.is_root[0] and topo.is_leaf[0]
+    assert topo.equal_degree is False
+
+
+def test_topology_is_hashable_and_structurally_equal():
+    a = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    b = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    assert hash(a) == hash(b)
+    assert a == b
+    c = Topology.from_parents([0, 0, 0])  # different shape
+    assert a != c
+    assert hash(a) != hash(c)
+
+
+def test_topology_is_pytree_with_no_dynamic_leaves():
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    leaves, treedef = jax.tree_util.tree_flatten(topo)
+    assert leaves == []
+    rebuilt = jax.tree_util.tree_unflatten(treedef, leaves)
+    assert rebuilt == topo
+
+
+def test_topology_all_derived_arrays_are_numpy_not_jax():
+    """Every derived field must be ``np.ndarray`` so the Topology is hashable
+    and rides through ``jax.jit`` as static aux_data."""
+    topo = Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+    for field_name in [
+        "parents",
+        "level_starts",
+        "node_depths",
+        "child_counts",
+        "is_root",
+        "is_leaf",
+        "is_inner",
+        "pbuckets",
+    ]:
+        val = getattr(topo, field_name)
+        assert isinstance(val, np.ndarray), f"{field_name} should be np.ndarray, got {type(val)}"
diff --git a/tests/test_tree.py b/tests/test_tree.py
new file mode 100644
index 0000000..be2e5bf
--- /dev/null
+++ b/tests/test_tree.py
@@ -0,0 +1,207 @@
+"""Tree construction, mutation, and pytree behavior. Covers T-2, T-3."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax import FieldSpec, MissingField, SchemaMismatch, Topology, Tree
+
+
+@pytest.fixture
+def topo():
+    return Topology.from_parents([0, 0, 0, 1, 1, 2, 2])
+
+
+@pytest.fixture
+def restore_x64_config():
+    original = jax.config.jax_enable_x64
+    yield
+    jax.config.update("jax_enable_x64", original)
+
+
+# ── construction ──────────────────────────────────────────────────────
+def test_empty_allocates_zeros(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    assert tree["value"].shape == (7, 2)
+    assert jnp.all(tree["value"] == 0)
+
+
+def test_empty_default_dtype_tracks_jax_float_config(topo, restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    tree = Tree.empty(topo, {"value": (2,)})
+    assert tree["value"].dtype == np.dtype(jnp.float64)
+
+    jax.config.update("jax_enable_x64", False)
+    tree = Tree.empty(topo, {"value": (2,)})
+    assert tree["value"].dtype == np.dtype(jnp.float32)
+
+
+def test_empty_explicit_dtype_overrides_jax_float_config(topo, restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    tree = Tree.empty(topo, {"value": FieldSpec((2,), dtype=jnp.float32)})
+    assert tree["value"].dtype == np.dtype(jnp.float32)
+
+
+def test_from_data_infers_schema(topo):
+    tree = Tree.from_data(topo, {"value": jnp.ones((7, 3))})
+    assert tree.schema["value"].shape == (3,)
+    assert jnp.all(tree["value"] == 1)
+
+
+def test_from_data_rejects_wrong_leading_axis(topo):
+    with pytest.raises(SchemaMismatch):
+        Tree.from_data(topo, {"value": jnp.ones((5, 3))})
+
+
+# ── set / at[].set / shape validation (T-3) ─────────────────────────
+def test_set_validates_shape(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(SchemaMismatch):
+        tree.set(value=jnp.zeros((7, 3)))  # wrong trailing shape
+
+
+def test_set_rejects_wrong_size(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(SchemaMismatch):
+        tree.set(value=jnp.zeros((5, 2)))  # wrong leading axis
+
+
+def test_set_rejects_unknown_field(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(MissingField):
+        tree.set(other=jnp.zeros((7, 2)))
+
+
+def test_set_returns_new_tree_original_untouched(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    new = tree.set(value=jnp.ones((7, 2)))
+    assert tree is not new
+    assert jnp.all(tree["value"] == 0)
+    assert jnp.all(new["value"] == 1)
+
+
+def test_at_set_with_boolean_mask(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    leaf_vals = jnp.ones((int(topo.is_leaf.sum()), 2))
+    new = tree.at[topo.is_leaf].set(value=leaf_vals)
+    assert jnp.all(new["value"][topo.is_leaf] == 1)
+    assert jnp.all(new["value"][~topo.is_leaf] == 0)
+
+
+# ── update / drop ───────────────────────────────────────────────────
+def test_update_extends_schema_with_spec(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    extended = tree.update(noise=(2,))
+    assert "noise" in extended.schema
+    assert extended["noise"].shape == (7, 2)
+    assert "noise" not in tree.schema  # original immutable
+
+
+def test_update_extends_schema_with_array(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    extended = tree.update(edges=jnp.arange(7, dtype=jnp.float32))
+    assert extended.schema["edges"].shape == ()
+    assert jnp.array_equal(extended["edges"], jnp.arange(7, dtype=jnp.float32))
+
+
+def test_update_rejects_existing_field(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(ValueError):
+        tree.update(value=(3,))
+
+
+def test_drop_removes_field(topo):
+    tree = Tree.empty(topo, {"value": (2,), "noise": (2,)})
+    smaller = tree.drop("noise")
+    assert "noise" not in smaller.schema
+    assert "value" in smaller.schema
+
+
+# ── pytree round-trip (T-2) ──────────────────────────────────────────
+def test_pytree_flatten_round_trips(topo):
+    tree = Tree.empty(topo, {"value": (2,), "edge": ()}).set(
+        value=jnp.ones((7, 2)), edge=jnp.arange(7, dtype=jnp.float32)
+    )
+    leaves, treedef = jax.tree_util.tree_flatten(tree)
+    assert len(leaves) == 2
+    rebuilt = jax.tree_util.tree_unflatten(treedef, leaves)
+    assert isinstance(rebuilt, Tree)
+    assert jnp.array_equal(rebuilt["value"], tree["value"])
+    assert jnp.array_equal(rebuilt["edge"], tree["edge"])
+
+
+def test_pytree_leaf_order_is_alphabetical_by_key(topo):
+    tree = Tree.empty(topo, {"value": (2,), "edge": (), "a_thing": (1,)}).set(
+        value=jnp.full((7, 2), 2.0),
+        edge=jnp.full((7,), 3.0),
+        a_thing=jnp.full((7, 1), 1.0),
+    )
+    leaves, _ = jax.tree_util.tree_flatten(tree)
+    assert jnp.array_equal(leaves[0], jnp.full((7, 1), 1.0))
+    assert jnp.array_equal(leaves[1], jnp.full((7,), 3.0))
+    assert jnp.array_equal(leaves[2], jnp.full((7, 2), 2.0))
+
+
+def test_pytree_structure_stable_across_topologically_identical_trees(topo):
+    """Building 'the same' Tree twice must produce identical pytree structures —
+    this is what lets JAX hit the JIT cache."""
+    t1 = Tree.empty(topo, {"value": (2,)})
+    t2 = Tree.empty(Topology.from_parents([0, 0, 0, 1, 1, 2, 2]), {"value": (2,)})
+    _, d1 = jax.tree_util.tree_flatten(t1)
+    _, d2 = jax.tree_util.tree_flatten(t2)
+    assert d1 == d2
+
+
+# ── identity ────────────────────────────────────────────────────────
+def test_tree_is_not_hashable(topo):
+    """Tree must not be hashable so users can't accidentally pass it as
+    ``static_argnames`` — it must ride through JIT as a pytree."""
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(TypeError):
+        hash(tree)
+
+
+def test_tree_equality_compares_data(topo):
+    a = Tree.empty(topo, {"value": (2,)}).set(value=jnp.ones((7, 2)))
+    b = Tree.empty(topo, {"value": (2,)}).set(value=jnp.ones((7, 2)))
+    c = Tree.empty(topo, {"value": (2,)}).set(value=jnp.zeros((7, 2)))
+    assert a == b
+    assert a != c
+
+
+# ── attribute-style access ───────────────────────────────────────────
+def test_attribute_access_returns_field(topo):
+    tree = Tree.empty(topo, {"value": (2,)}).set(value=jnp.ones((7, 2)))
+    assert jnp.array_equal(tree.value, tree["value"])
+
+
+def test_attribute_access_unknown_field_raises_attribute_error(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    with pytest.raises(AttributeError, match="missing"):
+        _ = tree.missing
+
+
+def test_attribute_access_does_not_shadow_dataclass_fields(topo):
+    tree = Tree.empty(topo, {"value": (2,)})
+    # topology, schema, data, size are real dataclass attrs / properties,
+    # not data fields — must keep returning the dataclass values.
+    assert tree.topology is topo
+    assert "value" in tree.schema
+    assert isinstance(tree.data, dict)
+    assert tree.size == topo.size
+
+
+@pytest.mark.parametrize(
+    "reserved",
+    ["topology", "schema", "data", "size", "set", "update", "drop", "empty"],
+)
+def test_reserved_field_name_rejected(topo, reserved):
+    with pytest.raises(SchemaMismatch, match="shadow"):
+        Tree.empty(topo, {reserved: ()})
+
+
+def test_reserved_field_name_rejected_in_update(topo):
+    tree = Tree.empty(topo, {"value": ()})
+    with pytest.raises(SchemaMismatch, match="shadow"):
+        tree.update(size=())
diff --git a/tests/test_utils_ode.py b/tests/test_utils_ode.py
new file mode 100644
index 0000000..52bc127
--- /dev/null
+++ b/tests/test_utils_ode.py
@@ -0,0 +1,77 @@
+"""Fixed-step ODE solvers in hyperiax.utils.ode."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax.utils.ode import RK4, Euler, Heun, solve_ode
+
+
+def test_solve_ode_returns_grid_with_initial_value():
+    ts = jnp.linspace(0.0, 1.0, 11)
+    ys = solve_ode(lambda t, y, a: y, jnp.array([1.0]), ts, solver=RK4())
+    assert ys.shape == (11, 1)
+    np.testing.assert_allclose(np.asarray(ys[0]), [1.0])
+
+
+def test_rk4_matches_exponential_to_high_accuracy():
+    """dy/dt = y, y(0)=1 -> e^t; RK4 should be ~float32-exact."""
+    ts = jnp.linspace(0.0, 1.0, 51)
+    ys = solve_ode(lambda t, y, a: y, jnp.array([1.0]), ts, solver=RK4())
+    np.testing.assert_allclose(float(ys[-1, 0]), float(jnp.exp(1.0)), rtol=1e-5)
+
+
+def test_rk4_solves_harmonic_oscillator():
+    """y'' = -y as a first-order system: (x, v)' = (v, -x); x(0)=1,v(0)=0
+    has exact solution x(t)=cos t, v(t)=-sin t."""
+    ts = jnp.linspace(0.0, 2.0 * jnp.pi, 400)
+    ys = solve_ode(
+        lambda t, y, a: jnp.array([y[1], -y[0]]),
+        jnp.array([1.0, 0.0]),
+        ts,
+        solver=RK4(),
+    )
+    np.testing.assert_allclose(float(ys[-1, 0]), 1.0, atol=1e-4)  # cos(2π)
+    np.testing.assert_allclose(float(ys[-1, 1]), 0.0, atol=1e-4)  # -sin(2π)
+
+
+def test_solver_order_euler_worse_than_heun_worse_than_rk4():
+    """At a fixed (coarse) step size, terminal error must strictly improve
+    with solver order on the smooth exponential test."""
+    ts = jnp.linspace(0.0, 1.0, 9)  # coarse: 8 steps
+    truth = float(jnp.exp(1.0))
+    err = {}
+    for name, solver in (("euler", Euler()), ("heun", Heun()), ("rk4", RK4())):
+        ys = solve_ode(lambda t, y, a: y, jnp.array([1.0]), ts, solver=solver)
+        err[name] = abs(float(ys[-1, 0]) - truth)
+    assert err["euler"] > err["heun"] > err["rk4"]
+
+
+def test_solve_ode_args_threaded_through():
+    """`args` reaches the vector field (here a decay rate)."""
+    ts = jnp.linspace(0.0, 1.0, 101)
+    ys = solve_ode(lambda t, y, k: -k * y, jnp.array([1.0]), ts, solver=RK4(), args=2.0)
+    np.testing.assert_allclose(float(ys[-1, 0]), float(jnp.exp(-2.0)), rtol=1e-5)
+
+
+def test_solve_ode_jit_and_vmap_compose():
+    ts = jnp.linspace(0.0, 1.0, 51)
+
+    @jax.jit
+    def run(y0):
+        return solve_ode(lambda t, y, a: y, y0, ts, solver=RK4())
+
+    out = run(jnp.array([1.0]))
+    assert out.shape == (51, 1)
+
+    batched = jax.vmap(run)(jnp.array([[1.0], [2.0], [3.0]]))
+    assert batched.shape == (3, 51, 1)
+    np.testing.assert_allclose(np.asarray(batched[:, 0, 0]), [1.0, 2.0, 3.0])
+
+
+@pytest.mark.parametrize("solver", [Euler(), Heun(), RK4()])
+def test_all_solvers_run_and_preserve_shape(solver):
+    ts = jnp.linspace(0.0, 0.5, 6)
+    ys = solve_ode(lambda t, y, a: -y, jnp.array([1.0, 2.0]), ts, solver=solver)
+    assert ys.shape == (6, 2)
diff --git a/tests/test_utils_sde.py b/tests/test_utils_sde.py
new file mode 100644
index 0000000..650d06d
--- /dev/null
+++ b/tests/test_utils_sde.py
@@ -0,0 +1,152 @@
+"""SDE solvers in hyperiax.utils.sde (Euler-Maruyama + Milstein)."""
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+from hyperiax.utils.sde import EulerMaruyama, Milstein, dot, dts, solve, solve_sde
+
+
+@pytest.fixture
+def restore_x64_config():
+    original = jax.config.jax_enable_x64
+    yield
+    jax.config.update("jax_enable_x64", original)
+
+
+def _ts(T, n_steps):
+    return jnp.concatenate([jnp.zeros(1), jnp.cumsum(dts(T=T, n_steps=n_steps))])
+
+
+# ── Kronecker helpers ──────────────────────────────────────────────
+def test_dot_solve_round_trip_scalar():
+    A = jnp.array([[2.0]])
+    v = jnp.array([3.0])
+    assert jnp.allclose(solve(A, dot(A, v)), v)
+
+
+def test_dot_solve_round_trip_multi_dim():
+    A = jnp.array([[2.0, 0.5], [0.5, 3.0]])
+    v = jnp.array([1.0, 2.0, 3.0, 4.0])  # (n=2, d=2)
+    assert jnp.allclose(solve(A, dot(A, v)), v, atol=1e-6)
+
+
+def test_dts_uniform_sum_equals_T():
+    d = dts(T=2.5, n_steps=50)
+    assert d.shape == (50,)
+    assert jnp.allclose(d.sum(), 2.5)
+
+
+# ── Euler-Maruyama ─────────────────────────────────────────────────
+def test_em_zero_noise_matches_deterministic_euler_on_ou():
+    """Zero noise -> the SDE reduces to explicit Euler; for dx=-x dt we get
+    x_{k+1} = x_k(1-dt)."""
+    n_steps, T = 20, 1.0
+    ts = _ts(T, n_steps)
+    dWs = jnp.zeros((n_steps, 1))
+    ys = solve_sde(
+        lambda t, y, a: -y,
+        lambda t, y, a: jnp.eye(1),
+        jnp.array([1.0]),
+        ts,
+        dWs,
+        solver=EulerMaruyama(),
+    )
+    dt = T / n_steps
+    expected = jnp.array([(1.0 - dt) ** k for k in range(n_steps + 1)])
+    np.testing.assert_allclose(np.asarray(ys).flatten(), np.asarray(expected), atol=1e-5)
+
+
+def test_em_brownian_terminal_variance_scales_with_T():
+    n_steps, T = 1000, 2.0
+    ts = _ts(T, n_steps)
+    step_dt = jnp.diff(ts)
+
+    def one(key):
+        dWs = jnp.sqrt(step_dt)[:, None] * jax.random.normal(key, (n_steps, 1))
+        ys = solve_sde(
+            lambda t, y, a: jnp.zeros_like(y),
+            lambda t, y, a: jnp.eye(1),
+            jnp.array([0.0]),
+            ts,
+            dWs,
+            solver=EulerMaruyama(),
+        )
+        return ys[-1, 0]
+
+    terminals = jax.vmap(one)(jax.random.split(jax.random.PRNGKey(0), 500))
+    assert abs(float(jnp.var(terminals)) - T) < 0.3
+
+
+# ── Milstein ───────────────────────────────────────────────────────
+def test_milstein_equals_em_for_additive_noise():
+    """State-independent diffusion -> ∂σ/∂y = 0 -> the Milstein correction
+    vanishes and the two schemes coincide exactly."""
+    n_steps, T = 15, 1.0
+    ts = _ts(T, n_steps)
+    dWs = 0.1 * jnp.ones((n_steps, 2))
+    drift = lambda t, y, a: jnp.array([0.3, -0.2])
+    diffusion = lambda t, y, a: jnp.array([[1.0, 0.0], [0.5, 2.0]])  # constant
+    y0 = jnp.array([0.0, 0.0])
+    em = solve_sde(drift, diffusion, y0, ts, dWs, solver=EulerMaruyama())
+    mil = solve_sde(drift, diffusion, y0, ts, dWs, solver=Milstein())
+    np.testing.assert_allclose(np.asarray(em), np.asarray(mil), atol=1e-6)
+
+
+def test_milstein_single_step_beats_em_on_geometric_bm():
+    """1D GBM dX = σX dW has exact X(dt) = X0·exp(-½σ²dt + σ dW). The
+    Milstein step includes the −½σ²dt correction that EM misses, so a
+    single Milstein step lands closer to the exact solution."""
+    sigma, x0, dt, dW = 0.8, 1.0, 0.5, 0.3
+    ts = jnp.array([0.0, dt])
+    dWs = jnp.array([[dW]])
+    drift = lambda t, y, a: jnp.zeros_like(y)
+    diffusion = lambda t, y, a: (sigma * y).reshape(1, 1)  # σX, shape (state, noise)
+
+    exact = x0 * np.exp(-0.5 * sigma**2 * dt + sigma * dW)
+    em = float(solve_sde(drift, diffusion, jnp.array([x0]), ts, dWs, solver=EulerMaruyama())[-1, 0])
+    mil = float(solve_sde(drift, diffusion, jnp.array([x0]), ts, dWs, solver=Milstein())[-1, 0])
+
+    # Closed-form Milstein value for this scalar case.
+    expected_mil = x0 + sigma * x0 * dW + 0.5 * sigma**2 * x0 * (dW**2 - dt)
+    np.testing.assert_allclose(mil, expected_mil, atol=1e-6)
+    assert abs(mil - exact) < abs(em - exact)
+
+
+def test_milstein_preserves_explicit_float32_when_x64_enabled(restore_x64_config):
+    jax.config.update("jax_enable_x64", True)
+    ts = jnp.array([0.0, 0.1], dtype=jnp.float32)
+    dWs = jnp.array([[0.0]], dtype=jnp.float32)
+    drift = lambda t, y, a: jnp.zeros_like(y)
+    diffusion = lambda t, y, a: jnp.eye(1, dtype=y.dtype)
+
+    ys = solve_sde(
+        drift,
+        diffusion,
+        jnp.array([1.0], dtype=jnp.float32),
+        ts,
+        dWs,
+        solver=Milstein(),
+    )
+
+    assert ys.dtype == np.dtype(jnp.float32)
+
+
+# ── jit / vmap composition ─────────────────────────────────────────
+@pytest.mark.parametrize("solver", [EulerMaruyama(), Milstein()])
+def test_solvers_jit_and_vmap_compose(solver):
+    n_steps = 30
+    ts = _ts(1.0, n_steps)
+    drift = lambda t, y, a: -y
+    diffusion = lambda t, y, a: 0.5 * jnp.eye(1) * jnp.exp(y)  # state-dependent
+
+    @jax.jit
+    def run(y0, dWs):
+        return solve_sde(drift, diffusion, y0, ts, dWs, solver=solver)
+
+    out = run(jnp.array([0.1]), jnp.zeros((n_steps, 1)))
+    assert out.shape == (n_steps + 1, 1)
+
+    batched = jax.vmap(lambda y0: run(y0, jnp.zeros((n_steps, 1))))(jnp.array([[0.1], [0.2]]))
+    assert batched.shape == (2, n_steps + 1, 1)
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new file mode 100644
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