diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..a6c092f --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,56 @@ +name: CI + +on: + push: + branches: [main, feature, dev, develop] + pull_request: null + +jobs: + test-and-lint: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup uv + uses: astral-sh/setup-uv@v5 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version-file: .python-version + + - name: Sync dependencies (including dev) + run: uv sync --extra dev --frozen + + - name: Ruff lint + run: uv run --no-sync --frozen ruff check . + + - name: Ruff format check + run: uv run --no-sync --frozen ruff format --check . + + - name: Run tests + run: uv run --no-sync --frozen pytest -q + + build: + runs-on: ubuntu-latest + needs: test-and-lint + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup uv + uses: astral-sh/setup-uv@v5 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version-file: .python-version + + - name: Sync dependencies + run: uv sync --frozen + + - name: Build package + run: uv build \ No newline at end of file diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..8098ee4 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,46 @@ +name: Publish to PyPI + +on: + release: + types: [published] + workflow_dispatch: null + +jobs: + publish: + runs-on: ubuntu-latest + permissions: + id-token: write + contents: read + + environment: + name: pypi + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup uv + uses: astral-sh/setup-uv@v5 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version-file: .python-version + + - name: Sync dependencies (including dev) + run: uv sync --extra dev --frozen + + - name: Lint + run: uv run --no-sync --frozen ruff check . + + - name: Format check + run: uv run --no-sync --frozen ruff format --check . + + - name: Test + run: uv run --no-sync --frozen pytest -q + + - name: Build + run: uv build + + - name: Publish + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index 73b4b91..61d697e 100644 --- a/.gitignore +++ b/.gitignore @@ -85,7 +85,7 @@ profile_default/ ipython_config.py # pyenv -.python-version +# .python-version is tracked for CI reproducibility # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. @@ -152,6 +152,16 @@ dmypy.json *.xlsx output/** +# Keep package template scaffold files tracked +!src/patchsim/templates/project/data/ +!src/patchsim/templates/project/data/networks/ +!src/patchsim/templates/project/data/networks/*.csv +!src/patchsim/templates/project/data/patch/ +!src/patchsim/templates/project/data/patch/*.csv +!src/patchsim/templates/project/data/seeds/ +!src/patchsim/templates/project/data/seeds/*.csv +!src/patchsim/templates/project/output/.gitkeep + # Additions for GeoJSON, .zip files, and shapefiles *.geojson *.zip @@ -164,4 +174,15 @@ output/** .pdm-build/ # Exception for .png files in assets/ -!assets/*.png \ No newline at end of file +!assets/*.png + +# Allow tracked data CSVs +!data/ +!data/networks/ +!data/networks/*.csv + +!data/patch/ +!data/patch/*.csv + +!data/seeds/ +!data/seeds/*.csv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..dd34628 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,15 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-yaml + - id: check-toml + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.8 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format 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/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..e69de29 diff --git a/README.md b/README.md index 04da738..1c17dcd 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,27 @@ To develop a general-purpose, modular simulation framework for patch-based metap --- +## Unique Selling Point (USP) + +PatchSim combines metapopulation network dynamics with a lightweight, configuration-first workflow: + +- Network-aware compartment transitions across connected patches +- Arrow-map transition syntax in YAML for explicit model specification +- Fast iteration loop from config edits to reproducible outputs + +--- + +## Unique Value Proposition (UVP) + +Compared to many epidemiology tools that are either code-heavy or tightly bound to a specific disease model, PatchSim offers: + +- **Model flexibility**: define SIR/SIRS-style variants through config transitions +- **Research-friendly reproducibility**: deterministic inputs, logged runs, and versioned config files +- **Dual interface**: SDK import for programmatic workflows and CLI for operational runs +- **Extensibility path**: built-in structure for adding custom models and project templates + +--- + ## Core Features PatchSim aims to support a range of modelling features commonly used in metapopulation disease simulations: @@ -40,7 +61,13 @@ PatchSim aims to support a range of modelling features commonly used in metapopu ## Installation -To install PatchSim using [uv](https://github.com/astral-sh/uv): +Install from PyPI: + +```bash +pip install patchsim +``` + +Install from source using [uv](https://github.com/astral-sh/uv): ```bash # Clone the repository @@ -62,17 +89,36 @@ uv pip install -e .[dev] ### Command Line Interface -PatchSim provides a command-line interface for running simulations: +PatchSim provides a subcommand-based CLI. Always run using `uv run` to ensure correct dependency resolution: ```bash # Show help and available options -patchsim --help +uv run patchsim --help + +# Show package version +uv run patchsim --version + +# Initialize a new self-contained project +uv run patchsim init my-project + +# Validate config without running +uv run patchsim validate -c my-project/config.yaml + +# Run simulation +uv run patchsim run -c my-project/config.yaml + +# List built-in models +uv run patchsim list-models +``` + +### Python SDK -# Run simulation with sample SIR model -patchsim --config configs/sample-sir-ode.yaml +```python +import patchsim -# Run simulation with custom model -patchsim --config path/to/your/config.yaml +config = patchsim.load_config("config.yaml") +net, y0, patches, n = patchsim.setup_simulation(config) +patchsim.run_simulation(config, "my-model", net, y0, patches, n) ``` The simulation outputs are saved in the following structure: diff --git a/configs/sample-sir-ode.yaml b/configs/sample-sir-ode.yaml index 003178d..c2e0c07 100644 --- a/configs/sample-sir-ode.yaml +++ b/configs/sample-sir-ode.yaml @@ -15,6 +15,10 @@ StartDate: 2020-01-01 EndDate: 2022-12-31 OutputDir: output/sample-sir-ode compartments: ["S", "I", "R"] -Parameters: {"beta": 0.5, "gamma": 0.1} -Transitions: [{"from": "S", "to": "I", "rate": "beta * S * I / (S + I + R)"}, {"from": "I", "to": "R", "rate": "gamma * I"}] - +Parameters: + beta: 0.08 + gamma: 0.1 +#PatchParameters: [{"patch": "PatchA", "parameters": {"beta": 0.2, "gamma": 0.1}}, {"patch": "PatchB", "parameters": {"beta": 0.25, "gamma": 0.1}}] +Transitions: + "S -> I": "beta" + "I -> R": "gamma * I" diff --git a/data/networks/sample-network-static.csv b/data/networks/sample-network-static.csv new file mode 100644 index 0000000..4231aaf --- /dev/null +++ b/data/networks/sample-network-static.csv @@ -0,0 +1,5 @@ +day,source,target,weight +0,A,A,0.9 +0,A,B,0.1 +0,B,B,0.9 +0,B,A,0.1 \ No newline at end of file diff --git a/data/patch/sample-sir-ode-patch-population.csv b/data/patch/sample-sir-ode-patch-population.csv new file mode 100644 index 0000000..3d2fc7d --- /dev/null +++ b/data/patch/sample-sir-ode-patch-population.csv @@ -0,0 +1,3 @@ +patch,Population +A, 1000 +B, 500 \ No newline at end of file diff --git a/data/seeds/sample-sir-ode-patchA-2.csv b/data/seeds/sample-sir-ode-patchA-2.csv new file mode 100644 index 0000000..830bce8 --- /dev/null +++ b/data/seeds/sample-sir-ode-patchA-2.csv @@ -0,0 +1,3 @@ +patch,S,I,R +A,999,1,0 +B,500,0,0 \ No newline at end of file diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..99df4d1 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,25 @@ + +# Architecture + +PatchSim follows a layered architecture: + +## Core components + +### Patch +Represents a single subpopulation with: +- Fixed population size +- Compartment state vector +- Parameter dictionary +- Validation logic + +### Network +Defines interactions between patches via a weighted directed graph. + +### Model +Orchestrates: +- ODE construction +- Numerical integration +- Visualization of results + +This separation ensures that changes to network structure do not require +rewriting patch-level dynamics. diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..7370c3d --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,30 @@ +# Configuration + +PatchSim simulations are configured using YAML files. + +## Model definition + +```yaml +# Input files (required) +PatchFile: data/patch/patch-population.csv +NetworkFile: data/networks/network.csv +SeedFile: data/seeds/seed-initial.csv + +# Model configuration +ModelName: sample-sir-ode + +# Simulation parameters +TMax: 50 +Tolerance: 1e-8 +MaxIter: 10000 +StartDate: 2020-01-01 +EndDate: 2022-12-31 +OutputDir: output/sample-sir-ode +compartments: ["S", "I", "R"] +Parameters: + beta: 0.08 + gamma: 0.1 +Transitions: + "S -> I": "beta" + "I -> R": "gamma * I" +``` \ No newline at end of file diff --git a/docs/getting-started.md b/docs/getting-started.md new file mode 100644 index 0000000..a099e8f --- /dev/null +++ b/docs/getting-started.md @@ -0,0 +1,23 @@ + + +# Getting Started + +## Installation + +### From PyPI (Recommended) + +Install patchsim from PyPI: + +```bash +pip install patchsim +``` + +### For Contributors + +Clone the repository and install in editable mode for development: + +```bash +git clone https://github.com/dsih-artpark/patchsim.git +cd patchsim +pip install -e . +``` \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 8512686..b0c42d8 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,13 +1,25 @@ # PatchSim -PatchSim is a modular, general-purpose metapopulation epidemiological simulation framework designed for research and translation use cases. +PatchSim is a modular, general-purpose metapopulation epidemiological simulation framework designed for research and translation use cases. It is a flexible and modular system for simulating epidemiological compartment models across single or multiple interacting patches. +The framework generalizes classical compartmental models (e.g. SIR) by allowing multiple subpopulations ("patches") to interact through a weighted directed network. + +PatchSim emphasizes: +- Explicit separation between patch dynamics and network structure +- Continuous-time ODE-based simulation +- Configurable parameters via YAML +- Extensibility toward richer network and mobility models + ## Features -- ODE and discrete-time simulation modes -- Supports multiple diseases and problem types -- Integrated CLI for running models -- Built-in reproducibility and parameter management -- Extensible and user-friendly + +- **Flexible Compartment Models**: Define custom SIR, SEIR, or other compartmental models that support multiple diseases and problem types +- **Multi-Patch Networks**: Simulate disease spread across connected geographical regions +- **CLI Integration**: Integrated CLI for running models +- **Multiple Simulation Methods**: Support for both ODE and discrete-time simulation modes +- **YAML Configuration**: Structured, readable configuration files +- **Per-Patch Parameters**: Built-in reproducibility and management of different parameters for each patch +- **Network Connectivity**: Define custom mixing matrices between patches +- **Extensibility**: Extensible and user-friendly ## Documentation - [Core Module](reference/core.md) diff --git a/docs/mathematical-model.md b/docs/mathematical-model.md new file mode 100644 index 0000000..7774a7e --- /dev/null +++ b/docs/mathematical-model.md @@ -0,0 +1,25 @@ +# Mathematical Model + +PatchSim implements compartmental models using continuous-time ODEs. + +## Network-coupled SIR model + +Let: +- S_i, I_i, R_i be compartments of patch i +- N_i = S_i + I_i + R_i +- W_ij be the network weight from source patch j to target patch i (i.e., W_ij denotes the weight of transmission from j to i) + +The force of infection for patch i is: + +λ_i(t) = Σ_j W_ij * (I_j / N_j) + +where j is the source patch and i is the target patch, matching the CSV weight matrix ordering (target, source). + +The resulting dynamics are: + +dS_i/dt = -β S_i λ_i +dI_i/dt = β S_i λ_i - γ I_i +dR_i/dt = γ I_i + +This formulation reduces to the classical SIR model when the network +contains a single patch with W_ii = 1. diff --git a/docs/network-design.md b/docs/network-design.md new file mode 100644 index 0000000..639950b --- /dev/null +++ b/docs/network-design.md @@ -0,0 +1,19 @@ +# Network Design + +Networks are represented as weighted directed graphs. + +Example CSV: + +day,source,target,weight +0,A,A,0.99 +0,A,B,0.01 +0,B,B,0.995 +0,B,A,0.005 + +Each row specifies the contribution of infections in the source patch +to the force of infection in the target patch. + +## Normalization + +For each day, rows are normalized so outgoing weights from each source patch sum to 1 across all targets. +Normalization occurs exactly once to prevent scaling errors. diff --git a/docs/reference/models.md b/docs/reference/models.md index 4c2f6c2..9a2a65b 100644 --- a/docs/reference/models.md +++ b/docs/reference/models.md @@ -1,9 +1,57 @@ -# Models Module +# Model Implementations -This module contains standard epidemiological models implemented in PatchSim. +This section documents concrete epidemiological models implemented using the PatchSim framework. -## Sample SIR ODE Model +Unlike the core modules, models are **not reusable APIs** but +self-contained reference implementations that demonstrate how to +instantiate and run simulations using the framework. -A basic susceptible-infected-recovered (SIR) model implemented using ordinary differential equations (ODEs). +--- -::: patchsim.models.sample_sir_ode +## Design Philosophy + +PatchSim separates: + +- **Model definition** (compartments, transitions, parameters) +- **Simulation engine** (ODE solvers, network coupling) +- **Execution scripts** (models in this directory) + +Files in `patchsim.models` serve as: +- Validation cases +- Reference implementations +- Templates for new models + +--- + +## ka_fmd_sirsv_discrete + +**File:** +`patchsim/models/ka_fmd_sirsv_discrete.py` + +**Description:** +A discrete-time, multi-compartment SIRSV-style model designed for +foot-and-mouth disease (FMD) dynamics. + +**Key features:** +- Discrete-time update rules +- Multiple infectious states +- Demonstrates non-ODE simulation capability +- Designed for Karnataka (KA) regional modeling + +**Usage pattern:** +- Defines compartments and transitions explicitly +- Uses PatchSim core utilities for execution +- Can be adapted into YAML-driven workflows + +--- + +## Extending Models + +To create a new model: +1. Copy an existing model script +2. Modify compartments and transitions +3. Adjust parameters or network structure +4. Run via the `patchsim` CLI using `uv run`, for example `uv run patchsim run -c config.yaml` + +For most use cases, users are encouraged to prefer +**YAML-based model definitions** over Python scripts. diff --git a/docs/reference/utils.md b/docs/reference/utils.md index aa3b1cc..41b4c9f 100644 --- a/docs/reference/utils.md +++ b/docs/reference/utils.md @@ -20,3 +20,5 @@ Provides a logging setup for the simulation runs. Functions for visualising simulation outputs, including plotting and analysis tools. ::: patchsim.utils.viz + + diff --git a/docs/results.md b/docs/results.md new file mode 100644 index 0000000..e4cb225 --- /dev/null +++ b/docs/results.md @@ -0,0 +1,10 @@ +# Results + +Simulations of two network-coupled patches show: + +- Faster epidemic spread compared to isolated patches +- Synchronization between patches +- Conservation of total population within each patch + +These behaviors arise from network-mediated infection pressure and +continuous-time integration. diff --git a/docs/simulation-workflow.md b/docs/simulation-workflow.md new file mode 100644 index 0000000..d9bc5bd --- /dev/null +++ b/docs/simulation-workflow.md @@ -0,0 +1,12 @@ +# Simulation Workflow + +1. Load configuration +2. Initialize patches and initial conditions +3. Load network adjacency matrix +4. Flatten patch-compartment states into a single vector +5. Construct the ODE right-hand side +6. Solve using scipy.integrate.odeint +7. Reshape results per patch +8. Generate plots and outputs + +All simulation logic is deterministic and continuous-time. diff --git a/mkdocs.yml b/mkdocs.yml index db01694..0290719 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,6 +1,36 @@ -site_name: PatchSim + +site_name: Compartmental Multi-Patch Simulation Framework +site_description: Documentation for a modular metapopulation simulation framework for multi-disease epidemiological modelling. +site_author: Shreya Mukherjee +repo_url: https://github.com/dsih-artpark/patchsim +repo_name: GitHub + theme: name: material + palette: + # Light mode + - scheme: default + primary: indigo + accent: indigo + toggle: + icon: material/brightness-7 + name: Switch to dark mode + # Dark mode + - scheme: slate + primary: indigo + accent: indigo + toggle: + icon: material/brightness-4 + name: Switch to light mode + features: + - navigation.tabs + - navigation.sections + - navigation.expand + - navigation.top + - search.suggest + - search.highlight + - content.code.copy + - content.code.annotate plugins: - search @@ -8,11 +38,39 @@ plugins: default_handler: python handlers: python: - paths: ["src/patchsim"] + paths: ["src"] + options: + docstring_style: google + show_source: true + +markdown_extensions: + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences + - pymdownx.tabbed: + alternate_style: true + - admonition + - pymdownx.details + - tables + - attr_list + - md_in_html nav: - Home: index.md - - Reference: + - Getting Started: getting-started.md + - Configuration: configuration.md + - Architecture: architecture.md + - Mathematical Model: mathematical-model.md + - Simulation Workflow: simulation-workflow.md + - Network Design: network-design.md + - Results: results.md + - References: - Core: reference/core.md - Models: reference/models.md - Utils: reference/utils.md +extra: + social: + - icon: fontawesome/brands/github + link: https://github.com/dsih-artpark/patchsim \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index c72565b..77cffb5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,31 @@ +[build-system] +requires = ["hatchling>=1.25.0"] +build-backend = "hatchling.build" + [project] name = "patchsim" -version = "0.1.0" +version = "0.1.0b1" description = "A modular simulation framework for patch-based metapopulation epidemiology" readme = "README.md" requires-python = ">=3.11" -license = {text = "GPL-3.0"} +license = { text = "GPL-3.0" } +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Medical Science Apps.", +] + +authors = [ + { name = "Adish Assain", email = "adish@artpark.in" }, + { name = "Sneha S", email = "sneha@artpark.in" }, + { name = "Shreya Mukherjee", email = "shreyamukherjee@artpark.in" } +] dependencies = [ "numpy", @@ -15,27 +36,32 @@ dependencies = [ "tqdm", "networkx", "SALib", + "rich>=14.3.3", ] -authors = [ - {name = "Adish Assain", email = "adish@artpark.in"}, - {name = "Sneha S", email = "sneha@artpark.in"}, -] -[dependency-groups] +[project.urls] +Homepage = "https://github.com/dsih-artpark/patchsim" +Repository = "https://github.com/dsih-artpark/patchsim" +Documentation = "https://dsih-artpark.github.io/patchsim/" + +[project.optional-dependencies] dev = [ "pytest>=8.3.5", + "pytest-cov", "ruff", "mypy", - "pytest-cov", "mkdocs>=1.6.1", "mkdocs-material>=9.6.12", "mkdocstrings[python]>=0.29.1", + "rich>=13.0.0", + "pre-commit>=3.0.0" ] + notebook = [ "jupyter", "seaborn", "plotly", - "ipywidgets", + "ipywidgets" ] [project.scripts] @@ -43,15 +69,28 @@ patchsim = "patchsim.cli:main" [tool.ruff] line-length = 120 -target-version = "py39" -select = ["E", "F", "W", "I", "FLY", "RUF", "B", "ICN"] +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "W", "I", "B", "RUF"] fixable = ["ALL"] -unfixable = [] -[tool.ruff.isort] +[tool.ruff.lint.isort] known-first-party = ["patchsim"] +[tool.ruff.lint.per-file-ignores] +"src/patchsim/models/ka_fmd_sirsv_discrete.py" = ["E501", "E741"] -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +[tool.hatch.build.targets.wheel] +packages = ["src/patchsim"] +include = [ + { path = "src/patchsim/templates", from = "." }, +] + +[tool.hatch.build.targets.sdist] +include = [ + "/src/patchsim/**", + "/src/patchsim/templates/**", + "/README.md", + "/LICENSE", +] diff --git a/src/patchsim/__init__.py b/src/patchsim/__init__.py index e69de29..1c3ec1c 100644 --- a/src/patchsim/__init__.py +++ b/src/patchsim/__init__.py @@ -0,0 +1,24 @@ +"""PatchSim public SDK interface.""" + +from __future__ import annotations + +from importlib.metadata import PackageNotFoundError, version + +from patchsim.core.model import CompartmentalModel, NetworkModel +from patchsim.core.simulation import load_config, run_simulation, setup_simulation +from patchsim.utils.viz import plot_patch_subplots + +try: + __version__ = version("patchsim") +except PackageNotFoundError: # pragma: no cover - local editable fallback + __version__ = "0.1.0b1" + +__all__ = [ + "CompartmentalModel", + "NetworkModel", + "__version__", + "load_config", + "plot_patch_subplots", + "run_simulation", + "setup_simulation", +] diff --git a/src/patchsim/cli.py b/src/patchsim/cli.py index 71a2719..784802b 100644 --- a/src/patchsim/cli.py +++ b/src/patchsim/cli.py @@ -1,39 +1,155 @@ import argparse import logging +import shutil +import textwrap +from importlib import resources +from pathlib import Path +from patchsim import __version__ from patchsim.core.simulation import load_config, run_simulation, setup_simulation +def _configure_logging() -> None: + """Configure CLI logging with rich handler when available.""" + try: + from rich.logging import RichHandler + + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + datefmt="[%X]", + handlers=[RichHandler(rich_tracebacks=True)], + ) + except Exception: + logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(name)s:%(message)s") + + +def _cmd_run(config_path: str) -> None: + logging.info("Starting PatchSim simulation...") + config = load_config(config_path) + net, y0, patches, num_patches = setup_simulation(config) + run_simulation(config, config["ModelName"], net, y0, patches, num_patches) + logging.info("Simulation completed successfully.") + + +def _cmd_validate(config_path: str) -> None: + config = load_config(config_path) + setup_simulation(config) + logging.info("Configuration is valid: %s", config_path) + + +def _copy_template_tree(template_node, target_path: Path) -> None: + if template_node.is_dir(): + target_path.mkdir(parents=True, exist_ok=True) + for child in template_node.iterdir(): + _copy_template_tree(child, target_path / child.name) + return + + target_path.parent.mkdir(parents=True, exist_ok=True) + target_path.write_bytes(template_node.read_bytes()) + + +def _cmd_init(name: str, force: bool = False) -> None: + project_dir = Path(name) + + # Safety checks: prevent deleting cwd, parent dirs, root, or non-directories + resolved = project_dir.resolve() + cwd = Path.cwd().resolve() + + if project_dir.exists() and not project_dir.is_dir(): + raise NotADirectoryError(f"Target exists and is not a directory: {project_dir}") + + # Block deletion of root, cwd, or any ancestor of cwd + if force and ( + resolved == Path(resolved.anchor) # Filesystem root (/ or C:\) + or resolved == cwd # Current working directory + or cwd in resolved.parents # resolved is ancestor of cwd (e.g., ..) + ): + raise ValueError(f"Refusing to overwrite unsafe target: {resolved}") + + if project_dir.exists() and any(project_dir.iterdir()) and not force: + raise FileExistsError(f"Refusing to overwrite existing directory: {project_dir}. Use --force to overwrite.") + + if project_dir.exists() and force: + shutil.rmtree(project_dir) + + template_root = resources.files("patchsim").joinpath("templates", "project") + _copy_template_tree(template_root, project_dir) + + config_path = project_dir / "config.yaml" + rendered = config_path.read_text(encoding="utf-8").replace("{{PROJECT_NAME}}", project_dir.name) + config_path.write_text(rendered, encoding="utf-8") + + logging.info("Created project scaffold at: %s", project_dir) + + +def _list_builtin_models() -> list[str]: + models_dir = Path(__file__).resolve().parent / "models" + models = [] + for f in sorted(models_dir.glob("*.py")): + if f.name == "__init__.py": + continue + models.append(f.stem) + return models + + +def _cmd_list_models() -> None: + models = _list_builtin_models() + if not models: + logging.info("No built-in models found.") + return + logging.info("Built-in models:") + for model in models: + logging.info("- %s", model) + + def main(): """Command-line interface for running the PatchSim simulation.""" + _configure_logging() + parser = argparse.ArgumentParser( description=( - "PatchSim: A modular metapopulation simulation framework for multi-disease " - "epidemiological modelling." + "PatchSim: A modular metapopulation simulation framework for multi-disease epidemiological modelling." ), formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" + epilog=textwrap.dedent(""" Examples: - # Run simulation with sample SIR model - patchsim --config configs/sample-sir-ode.yaml - """ - ) - parser.add_argument( - "--config", - type=str, - required=True, - help="Path to the configuration file (YAML) containing model parameters, input files, and simulation settings", + uv run patchsim init my-project + uv run patchsim run -c my-project/config.yaml + uv run patchsim validate -c my-project/config.yaml + uv run patchsim list-models + """), ) + parser.add_argument("--version", action="version", version=f"patchsim {__version__}") + + subparsers = parser.add_subparsers(dest="command", required=True) + + init_p = subparsers.add_parser("init", help="Scaffold a new PatchSim project") + init_p.add_argument("name", help="Directory name for the new project") + init_p.add_argument("--force", action="store_true", help="Overwrite target directory if it already exists") + + run_p = subparsers.add_parser("run", help="Run a simulation") + run_p.add_argument("-c", "--config", required=True, help="Path to simulation config YAML") + + validate_p = subparsers.add_parser("validate", help="Validate config and inputs") + validate_p.add_argument("-c", "--config", required=True, help="Path to simulation config YAML") + + subparsers.add_parser("list-models", help="List available built-in models") + args = parser.parse_args() try: - logging.info("Starting PatchSim simulation...") - config = load_config(args.config) - - # Set up simulation - net, y0, patches, num_patches = setup_simulation(config) - run_simulation(config, config['ModelName'], net, y0, patches, num_patches) - logging.info("Simulation completed successfully.") + if args.command == "init": + _cmd_init(args.name, force=args.force) + elif args.command == "run": + _cmd_run(args.config) + elif args.command == "validate": + _cmd_validate(args.config) + elif args.command == "list-models": + _cmd_list_models() + else: + parser.print_help() + raise SystemExit(2) except Exception as e: logging.error(f"Simulation failed: {e}") raise diff --git a/src/patchsim/core/model.py b/src/patchsim/core/model.py index 91ab86e..665ee26 100644 --- a/src/patchsim/core/model.py +++ b/src/patchsim/core/model.py @@ -2,6 +2,7 @@ Core model implementation for compartmental models. """ +import re from typing import Any, Callable, Dict from scipy.integrate import odeint @@ -16,21 +17,39 @@ def __init__(self, compartments: list[str], parameters: dict[str, float], transi self.parameters = parameters self.transitions = transitions - def compute_rates(self, state: dict[str, float]) -> dict[str, float]: - """Compute transition rates for each compartment.""" + def compute_rates(self, state: dict[str, float], parameters: dict[str, float] | None = None) -> dict[str, float]: + """Compute transition rates for each compartment. + + Args: + state: Current compartment state + parameters: Optional parameter override (defaults to self.parameters) + """ + params = parameters if parameters is not None else self.parameters rates = {} for transition in self.transitions: - source = transition['from'] - target = transition['to'] - rate = transition['rate'] + transition_label = transition["transition"] + source, target = [p.strip() for p in transition_label.split("->")] + rate = transition["rate"] + rate_expr = rate # Handle rate expressions - if isinstance(rate, str): - for param, value in self.parameters.items(): - rate = rate.replace(param, str(value)) - for comp, value in state.items(): - rate = rate.replace(comp, str(value)) - rate = eval(rate) - rates[f"{source}_to_{target}"] = rate * state[source] + if isinstance(rate_expr, str): + # Safe evaluation: build scope from parameters and state, disable builtins + scope = {**params, **state} + try: + rate_val = eval(rate_expr, {"__builtins__": {}}, scope) + except (KeyError, NameError, ValueError, SyntaxError, TypeError, ZeroDivisionError) as e: + msg = f"Invalid rate expression '{rate_expr}' in transition '{transition_label}': {e}" + raise ValueError(msg) from e + else: + rate_val = rate_expr + + # If expression already includes the source compartment, don't multiply again. + if isinstance(rate, str) and re.search(rf"\b{re.escape(source)}\b", rate): + flow = rate_val + else: + flow = rate_val * state[source] + + rates[transition_label] = flow return rates @@ -53,53 +72,138 @@ def get_patch_population(self, state: Dict[str, float]) -> float: return sum(state[c] for c in self.base_model.compartments) def compute_force_of_infection(self, full_state: dict[str, float], infected_compartment: str = "I") -> list[float]: - """Compute force of infection for each patch. + """Compute force of infection for each patch (per-capita rate, before beta scaling). Args: full_state: Current state of all compartments infected_compartment: Name of the compartment representing infected individuals + + Returns: + List of per-capita forces of infection (model_runner applies beta * FOI * S) """ lambdas = [] for i in range(self.num_patches): if self.num_patches == 1: - # Single patch case: local force of infection + # Single patch case: infected proportion patch_state = self.get_patch_state(full_state, 0) infected = patch_state[infected_compartment] total_pop = self.get_patch_population(patch_state) - force = self.base_model.parameters['beta'] * infected / total_pop + force = infected / total_pop if total_pop > 0 else 0 else: - # Multi-patch case: network-based force of infection + # Multi-patch case: network-weighted infected proportion force = 0 for j in range(self.num_patches): patch_state_j = self.get_patch_state(full_state, j) infected_j = patch_state_j[infected_compartment] pop_j = self.get_patch_population(patch_state_j) - force += self.network[i][j] * infected_j / pop_j + force += self.network[i][j] * (infected_j / pop_j if pop_j > 0 else 0) lambdas.append(force) return lambdas + def _adjust_infection_rate( + self, + patch_params: dict[str, float], + original_rate_expr: Any, + rate: float, + patch_state: dict[str, float], + lambdas: list[float], + patch_idx: int, + is_infection_transition: bool, + has_network: bool, + ) -> float: + """Adjust infection rate for network-mediated FOI. + + Args: + patch_params: Parameters for the current patch + original_rate_expr: Original rate expression from transition definition + rate: Computed rate from base model + patch_state: Current state for the patch + lambdas: Force of infection for each patch + patch_idx: Current patch index + is_infection_transition: Whether this is an infection transition + has_network: Whether multi-patch network exists + + Returns: + Adjusted rate incorporating network FOI if applicable + """ + if is_infection_transition and has_network: + # Network case: Apply network FOI (lambdas already computed) + # Check if original expression includes beta term + beta = patch_params.get("beta", 1.0) + if isinstance(original_rate_expr, str) and re.search(r"\bbeta\b", original_rate_expr): + # Rate expression includes beta; apply network FOI correction + adjusted_rate = beta * patch_state["S"] * lambdas[patch_idx] + else: + # Rate is already computed; apply FOI scaling + adjusted_rate = rate * lambdas[patch_idx] if patch_state["S"] > 0 else 0 + else: + # Single patch or non-infection transition: use rate as-is + adjusted_rate = rate + return adjusted_rate + def compute_derivatives(self, state: dict[str, float]) -> dict[str, float]: - """Compute derivatives for all compartments based on transitions.""" + """Compute derivatives for all compartments based on transitions, incorporating network-mediated FOI.""" derivatives = {c: 0.0 for c in self.all_compartments} + # Compute network-mediated force of infection for each patch + lambdas = self.compute_force_of_infection(state) + # Process each patch for i in range(self.num_patches): # Get state for this patch patch_state = self.get_patch_state(state, i) - # Get rates for this patch - rates = self.base_model.compute_rates(patch_state) + # Resolve patch parameters using canonical patch ordering when available. + if hasattr(self, "patch_parameters"): + patch_name = None + if hasattr(self, "patch_names") and i < len(self.patch_names): + patch_name = self.patch_names[i] + elif self.patch_parameters: + import logging + + logger = logging.getLogger(__name__) + logger.warning( + "patch_parameters defined but patch_names not set; " + "patch-specific parameters will be ignored for patch %d", + i, + ) + patch_params = {**self.base_model.parameters, **self.patch_parameters.get(patch_name, {})} + else: + patch_params = self.base_model.parameters + + # Compute rates with patch-specific parameters without mutating shared state + rates = self.base_model.compute_rates(patch_state, parameters=patch_params) - # Update derivatives based on rates + # Update derivatives based on rates, applying network-mediated FOI to infection transitions for transition in self.base_model.transitions: - source = transition['from'] - target = transition['to'] - rate = rates[f"{source}_to_{target}"] + transition_label = transition["transition"] + source, target = [p.strip() for p in transition_label.split("->")] + rate = rates[transition_label] + original_rate_expr = transition.get("rate", "") + + # Apply network-mediated FOI to susceptible-to-infection transitions. + # Allow model-level override via `infection_compartments` attribute. + infection_compartments = set(getattr(self, "infection_compartments", {"I", "E"})) + is_infection_transition = source == "S" and target in infection_compartments + + has_network = self.num_patches > 1 and self.network is not None + + # Use shared helper to adjust infection rate for network FOI + adjusted_rate = self._adjust_infection_rate( + patch_params, + original_rate_expr, + rate, + patch_state, + lambdas, + i, + is_infection_transition, + has_network, + ) # Decrease source compartment - derivatives[f"{source}_{i}"] -= rate + derivatives[f"{source}_{i}"] -= adjusted_rate # Increase target compartment - derivatives[f"{target}_{i}"] += rate + derivatives[f"{target}_{i}"] += adjusted_rate return derivatives @@ -110,10 +214,7 @@ def simulate_discrete(self, y0_dict: dict[str, float], t_range: list[float]) -> for _ in t_range[1:]: derivatives = self.compute_derivatives(state) - new_state = { - c: state[c] + derivatives[c] - for c in self.all_compartments - } + new_state = {c: state[c] + derivatives[c] for c in self.all_compartments} state = new_state for c in self.all_compartments: history[c].append(state[c]) diff --git a/src/patchsim/core/model_runner.py b/src/patchsim/core/model_runner.py new file mode 100644 index 0000000..e66febd --- /dev/null +++ b/src/patchsim/core/model_runner.py @@ -0,0 +1,62 @@ +from scipy.integrate import odeint + + +class Model: + """ + High-level simulation model. + Owns the Network and builds/solves the ODE. + """ + + def __init__(self, network_model, compartments): + self.network = network_model + self.compartments = compartments + self.all_vars = self.network.all_compartments + + def construct_ode(self): + def rhs(y, t): + state = {v: y[i] for i, v in enumerate(self.all_vars)} + dydt = {v: 0.0 for v in self.all_vars} + + # Compute network-based force of infection (per-capita, without beta) + lambdas = self.network.compute_force_of_infection(state) + + # Apply transitions for all patches + for i in range(self.network.num_patches): + patch_state = {c: state[f"{c}_{i}"] for c in self.compartments} + rates = self.network.base_model.compute_rates(patch_state) + patch_params = self.network.base_model.parameters + + for key, rate in rates.items(): + src, tgt = [p.strip() for p in key.split("->")] + + # Determine if this is an infection transition + infection_compartments = set(getattr(self.network, "infection_compartments", {"I", "E"})) + is_infection_transition = src == "S" and tgt in infection_compartments + + # Use network helper to apply FOI adjustment consistently + has_network = self.network.num_patches > 1 and self.network.network is not None + adjusted_rate = self.network._adjust_infection_rate( + patch_params, key, rate, patch_state, lambdas, i, is_infection_transition, has_network + ) + + dydt[f"{src}_{i}"] -= adjusted_rate + dydt[f"{tgt}_{i}"] += adjusted_rate + + return [dydt[v] for v in self.all_vars] + + return rhs + + def solve(self, y0, t_range): + rhs = self.construct_ode() + # Validate all required variables are present in y0 + missing = [v for v in self.all_vars if v not in y0] + if missing: + raise ValueError(f"Missing initial values for: {missing}") + y0_vec = [y0[v] for v in self.all_vars] + sol = odeint(rhs, y0_vec, t_range) + return {v: sol[:, i] for i, v in enumerate(self.all_vars)} + + def visualize(self, t, results, patches, outdir, model_name): + from patchsim.utils.viz import plot_patch_subplots + + plot_patch_subplots(t, results, patches, outdir, model_name) diff --git a/src/patchsim/core/simulation.py b/src/patchsim/core/simulation.py index 494e3cf..7c3552a 100644 --- a/src/patchsim/core/simulation.py +++ b/src/patchsim/core/simulation.py @@ -9,11 +9,10 @@ import numpy as np import pandas as pd import yaml -from scipy.integrate import odeint from patchsim.core.model import CompartmentalModel, NetworkModel +from patchsim.core.model_runner import Model from patchsim.utils.logger import setup_logger -from patchsim.utils.viz import plot_patch_subplots EPSILON = 1e-6 @@ -24,7 +23,7 @@ def load_config(config_path: str) -> dict[str, Any]: config = yaml.safe_load(f) # Validate required fields - required_fields = ['PatchFile', 'SeedFile', 'OutputDir'] + required_fields = ["PatchFile", "SeedFile", "OutputDir", "Transitions", "TMax"] for field in required_fields: if field not in config: raise ValueError(f"Missing required field '{field}' in config") @@ -35,20 +34,22 @@ def load_config(config_path: str) -> dict[str, Any]: def setup_simulation(config: dict[str, Any]) -> tuple[NetworkModel, dict[str, float], list, int]: """Set up the simulation model and initial conditions.""" # Load patch data - patch_df = pd.read_csv(config['PatchFile']) - patches = patch_df['patch'].tolist() - populations = patch_df.set_index('patch')['Population'].to_dict() + patch_df = pd.read_csv(config["PatchFile"]) + patches = patch_df["patch"].tolist() + populations = patch_df.set_index("patch")["Population"].to_dict() for p, pop in populations.items(): if pop <= 0: raise ValueError(f"Population for patch {p} must be positive") # Load seed data - seed_df = pd.read_csv(config['SeedFile']) - compartments = [col for col in seed_df.columns if col != 'patch'] + seed_df = pd.read_csv(config["SeedFile"]) + compartments = [col for col in seed_df.columns if col != "patch"] for _, row in seed_df.iterrows(): - patch = row['patch'] + patch = row["patch"] + if patch not in populations: + raise ValueError(f"SeedFile contains unknown patch '{patch}' not present in PatchFile") total = sum(row[c] for c in compartments) if not all(row[c] >= 0 for c in compartments): raise ValueError(f"Seed values must be non-negative for patch {patch}") @@ -57,35 +58,62 @@ def setup_simulation(config: dict[str, Any]) -> tuple[NetworkModel, dict[str, fl # Set up network num_patches = len(patches) - if 'NetworkFile' not in config or config['NetworkFile'] is None: - # Single patch model - network_matrix = [[0]] - num_patches = 1 + if "NetworkFile" not in config or config["NetworkFile"] is None: + # Multi-patch model with no network: use zero matrix + network_matrix = np.zeros((num_patches, num_patches)) else: # Multi-patch model - net_df = pd.read_csv(config['NetworkFile']) - net_df = net_df[net_df['day'] == 0] + net_df = pd.read_csv(config["NetworkFile"]) + net_df = net_df[net_df["day"] == 0] patch_idx = {p: i for i, p in enumerate(patches)} network_matrix = np.zeros((num_patches, num_patches)) for _, row in net_df.iterrows(): - i = patch_idx[row['source'].strip('"')] - j = patch_idx[row['target'].strip('"')] - if row['weight'] < 0: + source = row["source"].strip('"') + target = row["target"].strip('"') + if source not in patch_idx: + raise ValueError(f"NetworkFile contains unknown source patch '{source}'") + if target not in patch_idx: + raise ValueError(f"NetworkFile contains unknown target patch '{target}'") + i = patch_idx[source] + j = patch_idx[target] + if row["weight"] < 0: raise ValueError(f"Network weight must be non-negative between {row['source']} and {row['target']}") - network_matrix[i, j] = row['weight'] + network_matrix[i, j] = row["weight"] # Set up model - parameters = config.get('Parameters', {}) - transitions = config.get('Transitions', []) - if not transitions: - raise ValueError("No transitions defined in config") - - base_model = CompartmentalModel( - compartments=compartments, - parameters=parameters, - transitions=transitions - ) + global_params = config.get("Parameters", {}) + transitions_cfg = config.get("Transitions", {}) + # Transitions must be provided as arrow-map syntax in config, e.g.: + # Transitions: {S -> I: beta, I -> R: gamma * I} + if not isinstance(transitions_cfg, dict) or not transitions_cfg: + raise ValueError("'Transitions' must be a non-empty mapping in arrow syntax, e.g. {S -> I: 'beta'}.") + + transitions: list[dict[str, Any]] = [] + for k, v in transitions_cfg.items(): + parts = [p.strip() for p in str(k).split("->")] + if len(parts) != 2 or not all(parts): + raise ValueError(f"Invalid transition key '{k}'. Use 'S -> I' format.") + transitions.append({"transition": f"{parts[0]}->{parts[1]}", "rate": v}) + + # Collect per-patch parameters if provided + patch_params: dict[str, dict[str, Any]] = {} + if "PatchParameters" in config: + for entry in config["PatchParameters"]: + patch_name = entry["patch"] + patch_params[patch_name] = entry.get("parameters", {}) + + # Validate that all configured patches exist in PatchFile + unknown_patches = set(patch_params) - set(patches) + if unknown_patches: + raise ValueError(f"PatchParameters contains unknown patches: {sorted(unknown_patches)}") + + # Ensure every patch has a full parameter set: global + per-patch override + for p in patches: + patch_params[p] = {**global_params, **patch_params.get(p, {})} + + # Initialize the base model (will hold default/global transitions) + base_model = CompartmentalModel(compartments=compartments, parameters=global_params, transitions=transitions) # Prepare initial conditions patch_idx = {p: i for i, p in enumerate(patches)} @@ -97,42 +125,48 @@ def setup_simulation(config: dict[str, Any]) -> tuple[NetworkModel, dict[str, fl # Create network model net = NetworkModel(base_model=base_model, num_patches=num_patches, network_matrix=network_matrix) + # Attach per-patch parameters to the network model + net.patch_parameters = patch_params + net.patch_names = patches + return net, y0, patches, num_patches def run_simulation( - config: dict[str, Any], - model_name: str, - net: NetworkModel, - y0: dict[str, float], - patches: list, - num_patches: int + config: dict[str, Any], model_name: str, net: NetworkModel, y0: dict[str, float], patches: list, num_patches: int ) -> None: """Run the simulation and save results.""" # Create output directories - for subdir in ['plots', 'runs']: - dir_path = os.path.join(config['OutputDir'], subdir) + for subdir in ["plots", "runs"]: + dir_path = os.path.join(config["OutputDir"], subdir) os.makedirs(dir_path, exist_ok=True) - plots_dir = os.path.join(config['OutputDir'], 'plots') - runs_dir = os.path.join(config['OutputDir'], 'runs') + plots_dir = os.path.join(config["OutputDir"], "plots") + runs_dir = os.path.join(config["OutputDir"], "runs") # Set up logger logger = setup_logger(model_name, config, num_patches, patches, net.base_model) + # Validate and construct time range + t_max = config.get("TMax") + if not isinstance(t_max, int) or t_max <= 0: + raise ValueError("'TMax' must be a positive integer.") + t_range = np.arange(t_max, dtype=float) + # Run simulation - t_range = np.linspace(0, config['TMax']-1, int(config['TMax'])) - _, out_ode = net.simulate_ode(y0, t_range, odeint) + model = Model(net, compartments=list(net.base_model.compartments)) + out_ode = model.solve(y0, t_range) # Save results out_df = pd.DataFrame(out_ode) - out_df['time'] = t_range - cols = ['time'] + [c for c in out_df.columns if c != 'time'] + out_df["time"] = t_range + cols = ["time"] + [c for c in out_df.columns if c != "time"] out_df = out_df[cols] csv_path = os.path.join(runs_dir, f"all_patches_{model_name}_ode.csv") out_df.to_csv(csv_path, index=False) logger.info(f"Saved simulation output to {csv_path}") - plot_patch_subplots(t_range, out_ode, patches, plots_dir, model_name) + model.visualize(t_range, out_ode, patches, plots_dir, model_name) + logger.info(f"Saved all patch subplots to {plots_dir}/patch_timeseries_{model_name}_ode.png") diff --git a/src/patchsim/models/ka-fmd-sirsv-discrete.py b/src/patchsim/models/ka_fmd_sirsv_discrete.py similarity index 62% rename from src/patchsim/models/ka-fmd-sirsv-discrete.py rename to src/patchsim/models/ka_fmd_sirsv_discrete.py index a889324..341cc6b 100644 --- a/src/patchsim/models/ka-fmd-sirsv-discrete.py +++ b/src/patchsim/models/ka_fmd_sirsv_discrete.py @@ -5,7 +5,7 @@ import numpy as np from tqdm import tqdm -warnings.filterwarnings('ignore') +warnings.filterwarnings("ignore") def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42, diagnosis=None): @@ -13,21 +13,21 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 random.seed(random_seed) # Extract parameters - beta = params['beta'] - gamma = params['gamma'] - vax_rate = params['vax_rate'] - weibull_shape_vax = params['weibull_shape_vax'] - weibull_scale_vax = params['weibull_scale_vax'] - weibull_shape_rec = params['weibull_shape_rec'] - weibull_scale_rec = params['weibull_scale_rec'] - days = params['days'] - seed_rate = params['seed_rate'] - vax_period = params['vax_period'] - vax_duration = params['vax_duration'] - start_vax_day = params['start_vax_day'] + beta = params["beta"] + gamma = params["gamma"] + vax_rate = params["vax_rate"] + weibull_shape_vax = params["weibull_shape_vax"] + weibull_scale_vax = params["weibull_scale_vax"] + weibull_shape_rec = params["weibull_shape_rec"] + weibull_scale_rec = params["weibull_scale_rec"] + days = params["days"] + seed_rate = params["seed_rate"] + vax_period = params["vax_period"] + vax_duration = params["vax_duration"] + start_vax_day = params["start_vax_day"] # Initial conditions - S0, I0, R0, V0 = params['S0'], params['I0'], params['R0'], params['V0'] + S0, I0, R0, V0 = params["S0"], params["I0"], params["R0"], params["V0"] N = S0 + I0 + R0 + V0 S, I, R, V = [np.zeros(days) for _ in range(4)] @@ -47,16 +47,16 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 logging.info(f"Starting simulation for scenario: {scenario}") for t in tqdm(range(1, days), desc=f"Running {scenario} simulation", unit="day"): - new_seeds = min(seed_rate, S[t-1]) + new_seeds = min(seed_rate, S[t - 1]) # VACCINATION ROUND if t == start_vax_day or (t > start_vax_day and (t - start_vax_day) % vax_period == 0): round_counter += 1 - to_vaccinate = min(vax_rate * S[t-1], S[t-1]) + to_vaccinate = min(vax_rate * S[t - 1], S[t - 1]) logging.info(f"Round {round_counter} start day: {t}") # Calculate the number of vaccinations to reset, considering the vaccination period - num_vax_to_reset = int(min(vax_rate * vax_period * V[t-1], V[t-1])) + num_vax_to_reset = int(min(vax_rate * vax_period * V[t - 1], V[t - 1])) if num_vax_to_reset > 0 and len(decay_times_vax) > 0: num_vax_to_reset = min(num_vax_to_reset, len(decay_times_vax)) @@ -72,7 +72,7 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 logging.info(f"Day {t}: Re-vaccination reset: {num_vax_to_reset} decay times reset") # if diagnosis: - # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=True) + # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=True) # Check if it's within a vaccination period is_vax_period = (t >= start_vax_day) and ((t - start_vax_day) % vax_period < vax_duration) @@ -82,35 +82,43 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 # Update compartments for new vaccinations if new_vaccinations > 0: - new_susceptible_decay_times = (weibull_scale_vax * np.random.weibull(weibull_shape_vax, new_vaccinations)).astype(int).tolist() + new_susceptible_decay_times = ( + (weibull_scale_vax * np.random.weibull(weibull_shape_vax, new_vaccinations)).astype(int).tolist() + ) initial_len = len(decay_times_vax) decay_times_vax.extend(new_susceptible_decay_times) updated_len = len(decay_times_vax) - logging.info(f"Day {t}: New vaccinations: {new_vaccinations}, Length of decay_times_vax: {initial_len}, Length of decay_times_vax: {updated_len}") + logging.info( + f"Day {t}: New vaccinations: {new_vaccinations}, Length of decay_times_vax: {initial_len}, Length of decay_times_vax: {updated_len}" + ) else: - S[t] = S[t-1] - R[t] = R[t-1] - V[t] = V[t-1] + S[t] = S[t - 1] + R[t] = R[t - 1] + V[t] = V[t - 1] else: new_vaccinations = 0 # Calculate transitions - new_infections = beta * S[t-1] * I[t-1] / N + new_seeds - new_recoveries = gamma * I[t-1] + new_infections = beta * S[t - 1] * I[t - 1] / N + new_seeds + new_recoveries = gamma * I[t - 1] # Update compartments - S[t] = S[t-1] - new_infections - new_vaccinations - I[t] = I[t-1] + new_infections - new_recoveries - R[t] = R[t-1] + new_recoveries - V[t] = V[t-1] + new_vaccinations + S[t] = S[t - 1] - new_infections - new_vaccinations + I[t] = I[t - 1] + new_infections - new_recoveries + R[t] = R[t - 1] + new_recoveries + V[t] = V[t - 1] + new_vaccinations if new_recoveries > 0: - new_recovered_decay_times = (weibull_scale_rec * np.random.weibull(weibull_shape_rec, int(new_recoveries))).astype(int).tolist() + new_recovered_decay_times = ( + (weibull_scale_rec * np.random.weibull(weibull_shape_rec, int(new_recoveries))).astype(int).tolist() + ) decay_times_rec.extend(new_recovered_decay_times) # IMMUNITY WANING - logging.info(f"Day {t}: Before waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}") + logging.info( + f"Day {t}: Before waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}" + ) decay_times_vax_before = len(decay_times_vax) decay_times_vax = [x - 1 for x in decay_times_vax if x > 0] @@ -121,7 +129,9 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 num_waned_rec = decay_times_rec_before - len(decay_times_rec) logging.info(f"Day {t}: Waned vaccinated: {num_waned_vax}, Waned recovered: {num_waned_rec}") - logging.info(f"Day {t}: After waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}") + logging.info( + f"Day {t}: After waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}" + ) # Move waned individuals back to susceptible compartment S[t] += num_waned_vax + num_waned_rec @@ -129,11 +139,15 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 R[t] -= num_waned_rec # DIAGNOSIS AND LOG - logging.info(f"Day {t}: Length of decay_times_vax={len(decay_times_vax)}, V[{t}]={V[t]}, Difference={V[t] - len(decay_times_vax)}") + logging.info( + f"Day {t}: Length of decay_times_vax={len(decay_times_vax)}, V[{t}]={V[t]}, Difference={V[t] - len(decay_times_vax)}" + ) logging.info(f"Day {t}: S[t]={S[t]}, I[t]={I[t]}, R[t]={R[t]}, V[t]={V[t]}, Waned_vax={num_waned_vax}") if len(decay_times_vax) != V[t]: - logging.warning(f"Day {t}: Length discrepancy: Length of decay_times_vax={len(decay_times_vax)}, V[t]={V[t]}") + logging.warning( + f"Day {t}: Length discrepancy: Length of decay_times_vax={len(decay_times_vax)}, V[t]={V[t]}" + ) total_population = S[t] + I[t] + R[t] + V[t] if not np.isclose(total_population, N): @@ -143,10 +157,12 @@ def sirsv_model_with_weibull_random_vaccination(params, scenario, random_seed=42 logging.error(f"Negative compartment values on day {t}: S={S[t]}, I={I[t]}, R={R[t]}, V={V[t]}") # if is_vax_period and ((t - start_vax_day) % vax_period == vax_duration - 1) and diagnosis: - # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=False) + # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=False) if t % 30 == 0 or is_vax_period: - logging.info(f"Day {t}: S={S[t]:.2f}, I={I[t]:.2f}, R={R[t]:.2f}, V={V[t]:.2f}, New Vaccinations={new_vaccinations if is_vax_period else 0}") + logging.info( + f"Day {t}: S={S[t]:.2f}, I={I[t]:.2f}, R={R[t]:.2f}, V={V[t]:.2f}, New Vaccinations={new_vaccinations if is_vax_period else 0}" + ) logging.info(f"Simulation of the {scenario.capitalize()} model completed.") @@ -158,21 +174,21 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed random.seed(random_seed) # Extract parameters - beta = params['beta'] - gamma = params['gamma'] - vax_rate = params['vax_rate'] - weibull_shape_vax = params['weibull_shape_vax'] - weibull_scale_vax = params['weibull_scale_vax'] - weibull_shape_rec = params['weibull_shape_rec'] - weibull_scale_rec = params['weibull_scale_rec'] - days = params['days'] - seed_rate = params['seed_rate'] - vax_period = params['vax_period'] - vax_duration = params['vax_duration'] - start_vax_day = params['start_vax_day'] + beta = params["beta"] + gamma = params["gamma"] + vax_rate = params["vax_rate"] + weibull_shape_vax = params["weibull_shape_vax"] + weibull_scale_vax = params["weibull_scale_vax"] + weibull_shape_rec = params["weibull_shape_rec"] + weibull_scale_rec = params["weibull_scale_rec"] + days = params["days"] + seed_rate = params["seed_rate"] + vax_period = params["vax_period"] + vax_duration = params["vax_duration"] + start_vax_day = params["start_vax_day"] # Initial conditions - S0, I0, R0, V0 = params['S0'], params['I0'], params['R0'], params['V0'] + S0, I0, R0, V0 = params["S0"], params["I0"], params["R0"], params["V0"] N = S0 + I0 + R0 + V0 S, I, R, V = [np.zeros(days) for _ in range(4)] @@ -192,16 +208,16 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed logging.info(f"Starting simulation for scenario: {scenario}") for t in tqdm(range(1, days), desc=f"Running {scenario} simulation", unit="day"): - new_seeds = min(seed_rate, S[t-1]) + new_seeds = min(seed_rate, S[t - 1]) # VACCINATION ROUND if t == start_vax_day or (t > start_vax_day and (t - start_vax_day) % vax_period == 0): round_counter += 1 - to_vaccinate = min(vax_rate * S[t-1], S[t-1]) + to_vaccinate = min(vax_rate * S[t - 1], S[t - 1]) logging.info(f"Round {round_counter} start day: {t}") # Calculate the number of vaccinations to reset, considering the vaccination period - num_vax_to_reset = int(min(vax_rate * vax_period * V[t-1], V[t-1])) + num_vax_to_reset = int(min(vax_rate * vax_period * V[t - 1], V[t - 1])) if num_vax_to_reset > 0 and len(decay_times_vax) > 0: num_vax_to_reset = min(num_vax_to_reset, len(decay_times_vax)) @@ -217,7 +233,7 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed logging.info(f"Day {t}: Re-vaccination reset: {num_vax_to_reset} decay times reset") # if diagnosis: - # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=True) + # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=True) # Check if it's within a vaccination period is_vax_period = (t >= start_vax_day) and ((t - start_vax_day) % vax_period < vax_duration) @@ -227,35 +243,43 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed # Update compartments for new vaccinations if new_vaccinations > 0: - new_susceptible_decay_times = (weibull_scale_vax * np.random.weibull(weibull_shape_vax, new_vaccinations)).astype(int).tolist() + new_susceptible_decay_times = ( + (weibull_scale_vax * np.random.weibull(weibull_shape_vax, new_vaccinations)).astype(int).tolist() + ) initial_len = len(decay_times_vax) decay_times_vax.extend(new_susceptible_decay_times) updated_len = len(decay_times_vax) - logging.info(f"Day {t}: New vaccinations: {new_vaccinations}, Length of decay_times_vax: {initial_len}, Length of decay_times_vax: {updated_len}") + logging.info( + f"Day {t}: New vaccinations: {new_vaccinations}, Length of decay_times_vax: {initial_len}, Length of decay_times_vax: {updated_len}" + ) else: - S[t] = S[t-1] - R[t] = R[t-1] - V[t] = V[t-1] + S[t] = S[t - 1] + R[t] = R[t - 1] + V[t] = V[t - 1] else: new_vaccinations = 0 # Calculate transitions - new_infections = beta * S[t-1] * I[t-1] / N + new_seeds - new_recoveries = gamma * I[t-1] + new_infections = beta * S[t - 1] * I[t - 1] / N + new_seeds + new_recoveries = gamma * I[t - 1] # Update compartments - S[t] = S[t-1] - new_infections - new_vaccinations - I[t] = I[t-1] + new_infections - new_recoveries - R[t] = R[t-1] + new_recoveries - V[t] = V[t-1] + new_vaccinations + S[t] = S[t - 1] - new_infections - new_vaccinations + I[t] = I[t - 1] + new_infections - new_recoveries + R[t] = R[t - 1] + new_recoveries + V[t] = V[t - 1] + new_vaccinations if new_recoveries > 0: - new_recovered_decay_times = (weibull_scale_rec * np.random.weibull(weibull_shape_rec, int(new_recoveries))).astype(int).tolist() + new_recovered_decay_times = ( + (weibull_scale_rec * np.random.weibull(weibull_shape_rec, int(new_recoveries))).astype(int).tolist() + ) decay_times_rec.extend(new_recovered_decay_times) # IMMUNITY WANING - logging.info(f"Day {t}: Before waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}") + logging.info( + f"Day {t}: Before waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}" + ) decay_times_vax_before = len(decay_times_vax) decay_times_vax = [x - 1 for x in decay_times_vax if x > 0] @@ -266,7 +290,9 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed num_waned_rec = decay_times_rec_before - len(decay_times_rec) logging.info(f"Day {t}: Waned vaccinated: {num_waned_vax}, Waned recovered: {num_waned_rec}") - logging.info(f"Day {t}: After waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}") + logging.info( + f"Day {t}: After waning: Length of decay_times_vax={len(decay_times_vax)}, Length of decay_times_rec={len(decay_times_rec)}" + ) # Move waned individuals back to susceptible compartment S[t] += num_waned_vax + num_waned_rec @@ -274,11 +300,15 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed R[t] -= num_waned_rec # DIAGNOSIS AND LOG - logging.info(f"Day {t}: Length of decay_times_vax={len(decay_times_vax)}, V[{t}]={V[t]}, Difference={V[t] - len(decay_times_vax)}") + logging.info( + f"Day {t}: Length of decay_times_vax={len(decay_times_vax)}, V[{t}]={V[t]}, Difference={V[t] - len(decay_times_vax)}" + ) logging.info(f"Day {t}: S[t]={S[t]}, I[t]={I[t]}, R[t]={R[t]}, V[t]={V[t]}, Waned_vax={num_waned_vax}") if len(decay_times_vax) != V[t]: - logging.warning(f"Day {t}: Length discrepancy: Length of decay_times_vax={len(decay_times_vax)}, V[t]={V[t]}") + logging.warning( + f"Day {t}: Length discrepancy: Length of decay_times_vax={len(decay_times_vax)}, V[t]={V[t]}" + ) total_population = S[t] + I[t] + R[t] + V[t] if not np.isclose(total_population, N): @@ -288,10 +318,12 @@ def sirsv_model_with_weibull_targetted_vaccination(params, scenario, random_seed logging.error(f"Negative compartment values on day {t}: S={S[t]}, I={I[t]}, R={R[t]}, V={V[t]}") # if is_vax_period and ((t - start_vax_day) % vax_period == vax_duration - 1) and diagnosis: - # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=False) + # plot_histogram(decay_times_vax, decay_times_rec, scenario, round_counter, start=False) if t % 30 == 0 or is_vax_period: - logging.info(f"Day {t}: S={S[t]:.2f}, I={I[t]:.2f}, R={R[t]:.2f}, V={V[t]:.2f}, New Vaccinations={new_vaccinations if is_vax_period else 0}") + logging.info( + f"Day {t}: S={S[t]:.2f}, I={I[t]:.2f}, R={R[t]:.2f}, V={V[t]:.2f}, New Vaccinations={new_vaccinations if is_vax_period else 0}" + ) logging.info(f"Simulation of the {scenario.capitalize()} model completed.") diff --git a/src/patchsim/templates/project/config.yaml b/src/patchsim/templates/project/config.yaml new file mode 100644 index 0000000..21e4bdf --- /dev/null +++ b/src/patchsim/templates/project/config.yaml @@ -0,0 +1,31 @@ +# ----------------------------------------------------------------------------- +# PatchSim project configuration +# ----------------------------------------------------------------------------- +# This project was created by: patchsim init {{PROJECT_NAME}} + +# Input files (relative to this config file) +PatchFile: data/patch/patch-population.csv +NetworkFile: data/networks/network-static.csv +SeedFile: data/seeds/seed-initial.csv +Logging: False + +# Model configuration +ModelName: {{PROJECT_NAME}} + +# Simulation parameters +TMax: 60 +Tolerance: 1e-8 +MaxIter: 10000 +StartDate: 2020-01-01 +EndDate: 2022-12-31 +OutputDir: output/{{PROJECT_NAME}} +compartments: ["S", "I", "R"] + +# Global model parameters +Parameters: + beta: 0.08 + gamma: 0.10 + +# Transition map (required format) +# Use arrow notation only: X -> Y +Transitions: {S -> I: "beta", I -> R: "gamma * I"} diff --git a/src/patchsim/templates/project/data/networks/network-static.csv b/src/patchsim/templates/project/data/networks/network-static.csv new file mode 100644 index 0000000..71a4a85 --- /dev/null +++ b/src/patchsim/templates/project/data/networks/network-static.csv @@ -0,0 +1,5 @@ +day,source,target,weight +0,A,A,0.9 +0,A,B,0.1 +0,B,A,0.1 +0,B,B,0.9 diff --git a/src/patchsim/templates/project/data/patch/patch-population.csv b/src/patchsim/templates/project/data/patch/patch-population.csv new file mode 100644 index 0000000..0d20842 --- /dev/null +++ b/src/patchsim/templates/project/data/patch/patch-population.csv @@ -0,0 +1,3 @@ +patch,Population +A,1000 +B,500 diff --git a/src/patchsim/templates/project/data/seeds/seed-initial.csv b/src/patchsim/templates/project/data/seeds/seed-initial.csv new file mode 100644 index 0000000..146f345 --- /dev/null +++ b/src/patchsim/templates/project/data/seeds/seed-initial.csv @@ -0,0 +1,3 @@ +patch,S,I,R +A,999,1,0 +B,500,0,0 diff --git a/src/patchsim/templates/project/output/.gitkeep b/src/patchsim/templates/project/output/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/patchsim/utils/loader.py b/src/patchsim/utils/loader.py index 72a8ab8..7842151 100644 --- a/src/patchsim/utils/loader.py +++ b/src/patchsim/utils/loader.py @@ -2,6 +2,7 @@ Utility functions for loading and parsing data files. """ +import logging import random from datetime import datetime @@ -9,9 +10,7 @@ import pandas as pd import yaml -from patchsim.utils.logger import setup_logger - -logger = setup_logger() +logger = logging.getLogger("PatchSimLogger") def read_config(config_path: str) -> dict[str, str]: diff --git a/src/patchsim/utils/logger.py b/src/patchsim/utils/logger.py index d6e0d0a..0928164 100644 --- a/src/patchsim/utils/logger.py +++ b/src/patchsim/utils/logger.py @@ -17,12 +17,12 @@ def setup_logger(model_name, config, num_patches, patches, base_model): Returns: logging.Logger: Configured logger. """ - log_dir = os.path.join(config['OutputDir'], "logs") + log_dir = os.path.join(config["OutputDir"], "logs") os.makedirs(log_dir, exist_ok=True) log_file = os.path.join(log_dir, f"{model_name}_run_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log") logger = logging.getLogger("PatchSimLogger") logger.setLevel(logging.INFO) - formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s') + formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s") fh = logging.FileHandler(log_file) fh.setFormatter(formatter) logger.handlers = [] @@ -32,6 +32,18 @@ def setup_logger(model_name, config, num_patches, patches, base_model): logger.info(f"Python version: {sys.version}") logger.info(f"Platform: {platform.platform()}") logger.info(f"Parameters: {base_model.parameters}") + # log per-patch parameters if defined + if "PatchParameters" in config: + logger.info("Per-patch parameter overrides detected:") + for idx, entry in enumerate(config["PatchParameters"]): + if not isinstance(entry, dict): + logger.warning(f" PatchParameters[{idx}] is not a mapping: {entry!r}") + continue + patch = entry.get("patch", f"") + params = entry.get("parameters", {}) + logger.info(f" {patch}: {params}") + else: + logger.info("No per-patch parameter overrides provided.") # Parameter agnostic positivity check for param, value in base_model.parameters.items(): try: diff --git a/src/patchsim/utils/viz.py b/src/patchsim/utils/viz.py index 8c8a2de..cea71e5 100644 --- a/src/patchsim/utils/viz.py +++ b/src/patchsim/utils/viz.py @@ -4,29 +4,34 @@ import matplotlib.pyplot as plt -def plot_patch_subplots(t_range, out_ode, patches, output_dir, model_name): +def plot_patch_subplots(t_range, out_ode, patches, output_dir, model_name, patch_parameters=None): """ Plots all patches as subplots in a single figure and saves the figure. """ n = len(patches) + if n == 0: + raise ValueError("`patches` must contain at least one patch") ncols = math.ceil(math.sqrt(n)) nrows = math.ceil(n / ncols) - fig, axes = plt.subplots(nrows, ncols, figsize=(5*ncols, 4*nrows)) + fig, axes = plt.subplots(nrows, ncols, figsize=(5 * ncols, 4 * nrows)) axes = axes.flatten() if n > 1 else [axes] for i, patch in enumerate(patches): ax = axes[i] for c in ["S", "I", "R"]: ax.plot(t_range, out_ode[f"{c}_{i}"], label=c) - ax.set_title(f"Patch {patch} (ODE)") + title = f"Patch {patch} (ODE)" + if patch_parameters and patch in patch_parameters: + params = patch_parameters[patch] + param_str = ", ".join(f"{k}={v}" for k, v in params.items()) + title += f"\n({param_str})" + ax.set_title(title) ax.set_xlabel("Time") ax.set_ylabel("Count") ax.legend() - # Hide unused subplots - for j in range(i+1, len(axes)): + # Hide unused subplots (use n instead of loop index i) + for j in range(n, len(axes)): fig.delaxes(axes[j]) plt.tight_layout() os.makedirs(output_dir, exist_ok=True) plt.savefig(os.path.join(output_dir, f"all_patches_{model_name}_ode.png")) plt.close() - - diff --git a/test.py b/test.py new file mode 100644 index 0000000..4265bfd --- /dev/null +++ b/test.py @@ -0,0 +1,7 @@ +"""Deprecated compatibility placeholder. + +This file exists for backward compatibility with older test runners that expect a test.py at the repository root. +It is no longer used by the current pytest setup (see tests/ directory for all tests). +Scheduled for removal: when Python < 3.10 support is dropped. +Refer to: pyproject.toml [project.optional-dependencies] for test dependencies. +""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..bab4d86 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,56 @@ +import pandas as pd +import pytest +import yaml + + +@pytest.fixture +def tmp_data_dir(tmp_path): + """Create small patch, seed, network CSVs and a config YAML in a temp folder.""" + folder = tmp_path / "data" + patch_folder = folder / "patch" + net_folder = folder / "networks" + seed_folder = folder / "seeds" + patch_folder.mkdir(parents=True) + net_folder.mkdir() + seed_folder.mkdir() + + # sample patch CSV + patch_df = pd.DataFrame({"patch": ["A", "B"], "Population": [1000, 800]}) + patch_csv = patch_folder / "sample-patch.csv" + patch_df.to_csv(patch_csv, index=False) + + # sample seed CSV (S, I, R columns) + seed_df = pd.DataFrame({"patch": ["A", "B"], "S": [990, 795], "I": [10, 5], "R": [0, 0]}) + seed_csv = seed_folder / "sample-seed.csv" + seed_df.to_csv(seed_csv, index=False) + + # sample network CSV (day, source, target, weight) + net_df = pd.DataFrame({"day": [0, 0], "source": ["A", "B"], "target": ["B", "A"], "weight": [0.1, 0.1]}) + net_csv = net_folder / "sample-net.csv" + net_df.to_csv(net_csv, index=False) + + # sample yaml config with inline dicts (matching new format) + config = { + "PatchFile": str(patch_csv), + "NetworkFile": str(net_csv), + "SeedFile": str(seed_csv), + "OutputDir": str(tmp_path / "output"), + "TMax": 5, + "compartments": ["S", "I", "R"], + "PatchParameters": [ + {"patch": "A", "parameters": {"beta": 0.5, "gamma": 0.1}}, + {"patch": "B", "parameters": {"beta": 0.3, "gamma": 0.08}}, + ], + "Transitions": {"S -> I": "beta * S * I / (S + I + R)", "I -> R": "gamma * I"}, + } + config_path = tmp_path / "config.yaml" + with open(config_path, "w") as f: + yaml.safe_dump(config, f) + + return { + "root": tmp_path, + "patch_csv": str(patch_csv), + "seed_csv": str(seed_csv), + "net_csv": str(net_csv), + "config": str(config_path), + } diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..c8e1574 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,18 @@ +import pytest + +from patchsim.core.simulation import load_config + + +def test_load_config_success(tmp_path): + cfg_file = tmp_path / "c.yaml" + cfg_file.write_text('PatchFile: foo\nSeedFile: bar\nOutputDir: out\nTMax: 10\nTransitions:\n "S -> I": "beta"\n') + cfg = load_config(str(cfg_file)) + assert "PatchFile" in cfg + assert cfg["OutputDir"] == "out" + + +def test_load_config_missing_field(tmp_path): + cfg_file = tmp_path / "bad.yaml" + cfg_file.write_text("PatchFile: foo\n") # missing SeedFile and OutputDir + with pytest.raises(ValueError): + load_config(str(cfg_file)) diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..b445729 --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,42 @@ +import pytest + +from patchsim.core.model import CompartmentalModel, NetworkModel + + +def test_compute_rates_with_parameters(): + compartments = ["S", "I", "R"] + transitions = [ + {"transition": "S->I", "rate": "beta * S * I / (S + I + R)"}, + {"transition": "I->R", "rate": "gamma * I"}, + ] + params = {"beta": 0.5, "gamma": 0.1} + model = CompartmentalModel(compartments=compartments, parameters=params, transitions=transitions) + + state = {"S": 1000.0, "I": 10.0, "R": 0.0} + rates = model.compute_rates(state) + # expected infection rate per susceptible: beta * S * I / N => numeric value + # check keys present + assert "S->I" in rates + assert "I->R" in rates + # numeric sanity: rates positive + assert rates["S->I"] > 0 + assert rates["I->R"] == pytest.approx(0.1 * state["I"]) + + +def test_networkmodel_derivatives_conserve_population(): + compartments = ["S", "I", "R"] + transitions = [ + {"transition": "S->I", "rate": "beta * S * I / (S + I + R)"}, + {"transition": "I->R", "rate": "gamma * I"}, + ] + params = {"beta": 0.5, "gamma": 0.1} + base = CompartmentalModel(compartments=compartments, parameters=params, transitions=transitions) + net_matrix = [[0, 0], [0, 0]] + nm = NetworkModel(base_model=base, num_patches=2, network_matrix=net_matrix) + # attach patch params to keep code path consistent (optional) + nm.patch_parameters = {"PatchA": params, "PatchB": params} + state = {"S_0": 990.0, "I_0": 10.0, "R_0": 0.0, "S_1": 995.0, "I_1": 5.0, "R_1": 0.0} + deriv = nm.compute_derivatives(state) + # population conservation: sum deriv across all compartments ~ 0 + total_change = sum(deriv.values()) + assert total_change == pytest.approx(0.0) diff --git a/tests/test_simulation_setup.py b/tests/test_simulation_setup.py new file mode 100644 index 0000000..8bfa1f4 --- /dev/null +++ b/tests/test_simulation_setup.py @@ -0,0 +1,43 @@ +import pandas as pd +import pytest +import yaml + +from patchsim.core.simulation import load_config, setup_simulation + + +def test_setup_simulation_returns_objects(tmp_data_dir): + cfg = load_config(tmp_data_dir["config"]) + net, y0, patches, num_patches = setup_simulation(cfg) + # basic assertions: + assert num_patches == 2 + assert isinstance(y0, dict) + # check that y0 keys include S_0 and I_1 etc. + assert "S_0" in y0 and "I_1" in y0 + assert patches == ["A", "B"] + + +def test_setup_simulation_population_check(tmp_data_dir): + # mutate seed so it doesn't sum to population to trigger error + seed_df = pd.read_csv(tmp_data_dir["seed_csv"]) + seed_df.loc[0, "S"] = 500 # break conservation for PatchA + seed_df.to_csv(tmp_data_dir["seed_csv"], index=False) + cfg = load_config(tmp_data_dir["config"]) + with pytest.raises(ValueError): + setup_simulation(cfg) + + +def test_setup_simulation_rejects_list_transition_format(tmp_data_dir): + with open(tmp_data_dir["config"], "r") as f: + cfg = yaml.safe_load(f) + + cfg["Transitions"] = [ + {"from": "S", "to": "I", "rate": "beta"}, + {"from": "I", "to": "R", "rate": "gamma * I"}, + ] + + with open(tmp_data_dir["config"], "w") as f: + yaml.safe_dump(cfg, f) + + cfg = load_config(tmp_data_dir["config"]) + with pytest.raises(ValueError): + setup_simulation(cfg) diff --git a/tests/test_smoke.py b/tests/test_smoke.py new file mode 100644 index 0000000..16995c3 --- /dev/null +++ b/tests/test_smoke.py @@ -0,0 +1,110 @@ +import subprocess + + +def test_patchsim_import(): + import patchsim + + assert hasattr(patchsim, "__version__") + + +def test_patchsim_sdk_exports(): + import patchsim + + assert hasattr(patchsim, "CompartmentalModel") + assert hasattr(patchsim, "NetworkModel") + assert hasattr(patchsim, "load_config") + assert hasattr(patchsim, "setup_simulation") + assert hasattr(patchsim, "run_simulation") + assert hasattr(patchsim, "plot_patch_subplots") + + +def test_core_imports(): + pass + + +def test_cli_help(): + result = subprocess.run( + ["patchsim", "--help"], + capture_output=True, + text=True, + ) + assert result.returncode == 0 + assert "patchsim" in result.stdout.lower() + + +def test_cli_version(): + result = subprocess.run( + ["patchsim", "--version"], + capture_output=True, + text=True, + ) + assert result.returncode == 0 + assert "patchsim" in result.stdout.lower() + + +def test_cli_list_models(): + result = subprocess.run( + ["patchsim", "list-models"], + capture_output=True, + text=True, + ) + assert result.returncode == 0 + output = (result.stdout + result.stderr).lower() + assert "ka_fmd_sirsv_discrete" in output + + +def test_cli_init_scaffold(tmp_path): + project_dir = tmp_path / "demo-project" + result = subprocess.run( + ["patchsim", "init", str(project_dir)], + capture_output=True, + text=True, + ) + assert result.returncode == 0 + + assert (project_dir / "config.yaml").exists() + assert (project_dir / "data" / "patch" / "patch-population.csv").exists() + assert (project_dir / "data" / "networks" / "network-static.csv").exists() + assert (project_dir / "data" / "seeds" / "seed-initial.csv").exists() + assert (project_dir / "output").exists() + + +def test_yaml_model_loading(): + from pathlib import Path + + from patchsim.core.simulation import load_config + + # Resolve config path relative to test file (not CWD) for cross-directory execution + config_path = str(Path(__file__).resolve().parents[1] / "configs" / "sample-sir-ode.yaml") + config = load_config(config_path) + assert "compartments" in config + assert "Parameters" in config + assert "Transitions" in config + + +def test_minimal_sir_simulation(): + import numpy as np + + from patchsim.core.model import CompartmentalModel, NetworkModel + from patchsim.core.model_runner import Model + + base_model = CompartmentalModel( + compartments=["S", "I", "R"], + parameters={"beta": 0.3, "gamma": 0.1}, + transitions=[{"transition": "S->I", "rate": "beta"}, {"transition": "I->R", "rate": "gamma * I"}], + ) + # Create single-patch network model + network_model = NetworkModel(base_model=base_model, num_patches=1, network_matrix=[[1.0]]) + # Initial conditions + y0 = {"S_0": 999.0, "I_0": 1.0, "R_0": 0.0} + t_range = np.linspace(0, 10, 11) + + # Use Model runner + model = Model(network_model, compartments=["S", "I", "R"]) + results = model.solve(y0, t_range) + + # Smoke-level assertions + assert results is not None + assert "S_0" in results + assert "I_0" in results + assert "R_0" in results diff --git a/uv.lock b/uv.lock index 1dac5ff..90dbb32 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ 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