diff --git a/Makefile b/Makefile index c884305..3765f6f 100644 --- a/Makefile +++ b/Makefile @@ -13,7 +13,7 @@ PYTEST_RUN = $(VENV_DIR)/bin/pytest # Default and Setup Targets # ============================================================================== -.PHONY: all setup sync develop build install clean test lint docs help +.PHONY: all setup sync develop build build-wheel install install-wheel reinstall clean test lint docs help all: develop @@ -35,16 +35,32 @@ develop: sync ## Install the Rust code as a Python module for development # Build, Clean, and Utility Targets # ============================================================================== -build: sync ## Build the release wheels for distribution - @echo "⚙️ Building release wheels..." +# Build a wheel (does NOT install it into the current environment) +build-wheel: sync ## Build the release wheel(s) for distribution (outputs to dist/) + @echo "⚙️ Building release wheel(s) into dist/ ..." # NOTE: This target assumes the virtual environment is manually activated uv run maturin build --release --out dist -install: build ## Install the project from the built wheel - @echo "📦 Installing built wheel into environment..." - # Find the latest built wheel and install it - uv pip uninstall eo_processor || true - uv pip install .[dask] +# Backwards-compatible alias (historically named "build") +build: build-wheel ## Alias for build-wheel + +# Install from the freshly built wheel in dist/ (the correct companion to build-wheel) +install-wheel: build-wheel ## Install the freshly built wheel from dist/ into the current environment + @echo "📦 Installing freshly built wheel into current environment..." + # Uninstall any previously installed distribution variants (name can differ across tools) + uv pip uninstall -y eo-processor eo_processor || true + # Install the wheel we just built. --force-reinstall ensures the native extension updates. + uv pip install --force-reinstall dist/*.whl + +# Backwards-compatible alias for "install" (historically misleading in this repo) +install: install-wheel ## Alias for install-wheel + +reinstall: sync ## Build and install the wheel into the current environment + @echo "⚙️ Building release wheel..." + uv run maturin build --release --out dist + @echo "📦 Installing freshly built wheel..." + uv pip uninstall eo-processor eo_processor || true + uv pip install --force-reinstall dist/*.whl clean: ## Clean up build artifacts @echo "🧹 Cleaning up..." diff --git a/examples/README.md b/examples/README.md index 8b7edbf..d828b20 100644 --- a/examples/README.md +++ b/examples/README.md @@ -5,6 +5,7 @@ This directory contains runnable Python scripts demonstrating usage of the high- ## Files - `basic_usage.py`: Core spectral indices (NDVI, NDWI, EVI, normalized difference) on 1D and 2D arrays plus performance snippet and temporal stats. +- `bfast_monitor_example.py`: Demonstrates `bfast_monitor` change detection on a synthetic seasonal time series (break vs stable) and includes optional PNG outputs (`--png`) for visualization. - `xarray_dask_usage.py`: Integration with XArray and Dask for chunked / lazy evaluation and time-series workflows. - `map_blocks.py`: Compares `xarray.apply_ufunc`, `xarray.map_blocks`, and `dask.array.map_blocks` for NDVI computation performance. - `temporal_operations.py`: Demonstrates `temporal_mean`, `temporal_std`, median compositing, and NaN handling. diff --git a/examples/_out_bfast_monitor/bfast_break_date_frac.png b/examples/_out_bfast_monitor/bfast_break_date_frac.png new file mode 100644 index 0000000..41073d4 Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_break_date_frac.png differ diff --git a/examples/_out_bfast_monitor/bfast_confidence.png b/examples/_out_bfast_monitor/bfast_confidence.png new file mode 100644 index 0000000..c9e4831 Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_confidence.png differ diff --git a/examples/_out_bfast_monitor/bfast_detected_mask.png b/examples/_out_bfast_monitor/bfast_detected_mask.png new file mode 100644 index 0000000..d3ff065 Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_detected_mask.png differ diff --git a/examples/_out_bfast_monitor/bfast_magnitude.png b/examples/_out_bfast_monitor/bfast_magnitude.png new file mode 100644 index 0000000..7a5bd5c Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_magnitude.png differ diff --git a/examples/_out_bfast_monitor/bfast_timeseries_y32_x96.png b/examples/_out_bfast_monitor/bfast_timeseries_y32_x96.png new file mode 100644 index 0000000..89d8114 Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_timeseries_y32_x96.png differ diff --git a/examples/_out_bfast_monitor/bfast_timeseries_y64_x64.png b/examples/_out_bfast_monitor/bfast_timeseries_y64_x64.png new file mode 100644 index 0000000..9893458 Binary files /dev/null and b/examples/_out_bfast_monitor/bfast_timeseries_y64_x64.png differ diff --git a/examples/bfast_monitor_example.py b/examples/bfast_monitor_example.py new file mode 100644 index 0000000..342d377 --- /dev/null +++ b/examples/bfast_monitor_example.py @@ -0,0 +1,437 @@ +""" +BFAST Monitor example (synthetic time series) + optional PNG outputs. + +This script demonstrates the `eo_processor.bfast_monitor` workflow using a +synthetic seasonal signal with (a) a known breakpoint and (b) a stable series. + +It is designed for: +- onboarding: show how to format `stack` and `dates` inputs +- smoke testing: exercise the Rust/PyO3 path end-to-end + +Run: + python examples/bfast_monitor_example.py + +Optional PNG outputs (written under examples/_out_bfast_monitor/): + python examples/bfast_monitor_example.py --png + +Optional parameters: + --height 128 --width 128 --seed 42 --order 1 --h 0.35 --alpha 0.001 + +Notes: +- `dates` are passed as integers in YYYYMMDD format (e.g., 20150101). +- `stack` is a NumPy array shaped (time, y, x) and dtype float. +- Output is a (2, y, x) float64 array: + channel 0: breakpoint date in fractional years (0.0 if none) + channel 1: magnitude at detected breakpoint (0.0 if none) +- PNG writing is optional and uses matplotlib. Images are not committed into + any distribution build artifacts because they are generated at runtime. + +Outputs (`--png`): +- `bfast_detected_mask.png`: boolean mask (white=detected) +- `bfast_confidence.png`: a magnitude-based "confidence-like" visualization for demos + +Goal: +- With the default parameters, the stable scenario should typically yield <5% + detections while the break scenario detects widespread change. +- The break scenario should show *spatial variation* in both magnitude and (usually) + break timing. If timing is uniform, increase delay range or tune `h`/`alpha`. +""" + +from __future__ import annotations + +import argparse +import os +from pathlib import Path +from typing import Tuple + +import numpy as np +import pandas as pd + +from eo_processor import bfast_monitor + + +def _date_range_16d_int(start_yyyymmdd: int, end_yyyymmdd: int) -> np.ndarray: + """ + Create a 16-day cadence list of dates as integers (YYYYMMDD). + + Uses pandas for correctness and simplicity. + """ + start_year = start_yyyymmdd // 10000 + start_month = (start_yyyymmdd // 100) % 100 + start_day = start_yyyymmdd % 100 + + end_year = end_yyyymmdd // 10000 + end_month = (end_yyyymmdd // 100) % 100 + end_day = end_yyyymmdd % 100 + + start = f"{start_year:04d}-{start_month:02d}-{start_day:02d}" + end = f"{end_year:04d}-{end_month:02d}-{end_day:02d}" + + dates = pd.to_datetime(pd.date_range(start=start, end=end, freq="16D")) + return (dates.year * 10000 + dates.month * 100 + dates.day).to_numpy(dtype=np.int64) + + +def _frac_years_from_yyyymmdd(dates_int: np.ndarray) -> np.ndarray: + """ + Convert YYYYMMDD int dates to fractional years (year + day_of_year/365.25). + + Uses pandas for correctness (avoids invalid date parsing edge cases). + """ + years = dates_int // 10000 + months = (dates_int // 100) % 100 + days = dates_int % 100 + + dates = pd.to_datetime( + { + "year": years.astype(int), + "month": months.astype(int), + "day": days.astype(int), + } + ) + # pandas returns a Series here; day-of-year is accessed via the `.dt` accessor. + doy = dates.dt.dayofyear.to_numpy(dtype=np.float64) + + return years.astype(np.float64) + doy / 365.25 + + +def _make_synthetic_stack( + *, + height: int, + width: int, + dates_int: np.ndarray, + history_start_date: int, + monitor_start_date: int, + seed: int, + break_drop: float, +) -> Tuple[np.ndarray, np.ndarray]: + """ + Build two (time, y, x) stacks: + - stack_break: monitoring period includes a spatially-varying break (break scenario) + - stack_stable: stable signal designed to yield <5% detections with defaults + + Goals: + - Make the spatial fields (magnitude + break timing) visually interesting (not uniform). + - Keep the *stable* scenario clean enough under default parameters. + - Avoid external data dependencies; everything is synthetic. + """ + rng = np.random.default_rng(seed) + t_frac = _frac_years_from_yyyymmdd(dates_int) + + # Spatial coordinate grids in [-1, 1] for smooth fields. + yy, xx = np.mgrid[0:height, 0:width] + yy = (yy / max(1, height - 1)) * 2.0 - 1.0 + xx = (xx / max(1, width - 1)) * 2.0 - 1.0 + + # Stable base signal (seasonal) with low noise. + # Add a *spatially varying* offset and phase so pixels are not perfectly correlated. + stable_noise = rng.normal(0.0, 0.005, size=t_frac.shape[0]) + spatial_offset = 0.03 * (0.6 * xx + 0.4 * yy) # gentle gradient + spatial_phase = 0.40 * np.sin(2.0 * np.pi * (0.35 * xx + 0.20 * yy)) # radians-ish + spatial_amp = 0.90 + 0.15 * (np.sin(2.0 * np.pi * xx) * np.cos(2.0 * np.pi * yy)) + + # Build time × space stable stack with per-pixel phase shifts. + # This keeps the seasonal shape but makes maps less uniform. + base_cos = np.cos(2.0 * np.pi * t_frac)[:, None, None] + base_sin = np.sin(4.0 * np.pi * t_frac)[:, None, None] + phase_term = np.cos(spatial_phase)[None, :, :] # couples spatial phase into seasonal term + + stack_stable = ( + (0.55 + stable_noise)[:, None, None] + + (0.18 * base_cos) * phase_term + + (0.10 * base_sin) + ) + stack_stable = (stack_stable * spatial_amp[None, :, :] + spatial_offset[None, :, :]).astype(np.float64) + + # --- Break scenario: spatially varying break timing + magnitude --- + stack_break = stack_stable.copy() + + # Spatially varying drop magnitude (kept near break_drop, but not uniform). + drop_field = break_drop * (0.75 + 0.35 * (np.sin(2.0 * np.pi * (0.22 * xx + 0.31 * yy)) + 1.0) / 2.0) + drop_field = drop_field.astype(np.float64) + + # Spatially varying delay (in number of *samples*) after monitor_start. + # This is intended to spread detected break dates across the map. + # + # NOTE: Depending on `h`/`alpha` and the MOSUM windowing, detections may cluster at a + # similar date even when the onset varies. Increasing delay range generally helps. + # + # Range: [0, max_delay] samples. + max_delay = 40 + delay_field = ( + max_delay * (0.5 + 0.5 * np.sin(2.0 * np.pi * (0.18 * xx - 0.27 * yy))) + ).round().astype(int) + + # Find the monitoring start index in the time axis. + monitor_start_idx = int(np.argmax(dates_int >= np.int64(monitor_start_date))) + + # Apply the break with per-pixel onset and magnitude. + for y in range(height): + for x in range(width): + onset = monitor_start_idx + int(delay_field[y, x]) + if onset < stack_break.shape[0]: + stack_break[onset:, y, x] = stack_break[onset:, y, x] - drop_field[y, x] + + # Add modest heteroscedastic noise to the break stack (varies over time) for texture. + break_noise_t = rng.normal(0.0, 0.010, size=stack_break.shape[0]).astype(np.float64) + stack_break = stack_break + break_noise_t[:, None, None] + + # Keep within a plausible range for demo stability. + stack_stable = np.clip(stack_stable, -0.2, 1.2) + stack_break = np.clip(stack_break, -0.2, 1.2) + + return stack_break, stack_stable + + +def _summarize_result(name: str, result: np.ndarray) -> None: + break_dates = result[0] + magnitudes = result[1] + + detected = break_dates > 0.0 + n = detected.size + n_det = int(detected.sum()) + + print(f"\n{name}:") + print(f" result shape: {result.shape} (channels, y, x)") + print(f" detected pixels: {n_det}/{n} ({(n_det / n * 100.0):.1f}%)") + + if n_det > 0: + bd = break_dates[detected] + mag = magnitudes[detected] + print(f" break_date_frac: min={bd.min():.3f}, p50={np.median(bd):.3f}, max={bd.max():.3f}") + print(f" magnitude: min={mag.min():.3f}, p50={np.median(mag):.3f}, max={mag.max():.3f}") + else: + print(" break_date_frac: (none detected)") + print(" magnitude: (none detected)") + + +def _write_pngs(out_dir: Path, *, stack_break: np.ndarray, dates_int: np.ndarray, result_break: np.ndarray) -> None: + """ + Write a few diagnostic PNGs for demonstration; requires matplotlib. + + Includes debug prints so you can confirm the numeric detection stats match + what is being written to the PNGs. + """ + try: + import matplotlib.pyplot as plt # type: ignore + except Exception as e: # pragma: no cover + raise SystemExit( + "PNG output requires matplotlib. Install it and re-run with --png.\n" + f"Import error: {e}" + ) from e + + out_dir.mkdir(parents=True, exist_ok=True) + + break_dates = result_break[0] + magnitudes = result_break[1] + detected = break_dates > 0.0 + + # Debug: confirm the exact arrays used to generate PNGs match the printed summary. + # This helps catch cases where you are viewing stale PNGs or results are different + # from what you think is being plotted. + det_ratio = float(detected.mean()) + bd_min = float(break_dates.min()) + bd_max = float(break_dates.max()) + mag_min = float(magnitudes.min()) + mag_max = float(magnitudes.max()) + print( + "\nPNG debug (from result_break used for images):\n" + f" detected_ratio={det_ratio:.6f} (detected pixels={int(detected.sum())}/{detected.size})\n" + f" break_date_frac min/max={bd_min:.6f}/{bd_max:.6f}\n" + f" magnitude min/max={mag_min:.6f}/{mag_max:.6f}\n" + f" output_dir={out_dir}" + ) + + # 1) Detected mask + # + # NOTE: Some viewers (and some matplotlib backends) can make a 0/1 uint8 image + # appear “all black” due to autoscaling / interpolation / display heuristics. + # Scale to 0/255 and force vmin/vmax so white pixels are unmistakably white. + mask_img = (detected.astype(np.uint8) * 255) + plt.figure(figsize=(6, 5)) + plt.imshow(mask_img, cmap="gray", vmin=0, vmax=255, interpolation="nearest") + plt.title("BFAST Monitor: detected break mask (white=detected)") + plt.axis("off") + plt.tight_layout() + plt.savefig(out_dir / "bfast_detected_mask.png", dpi=150) + plt.close() + + # 1b) Magnitude-based "confidence-like" visualization + # + # This is intended purely for demonstrations: higher magnitudes are rendered as + # higher confidence of change. It does not represent a calibrated probability. + # + # We normalize magnitudes robustly using percentiles among detected pixels so + # outliers don't dominate the colormap. + conf = np.zeros_like(magnitudes, dtype=np.float64) + if detected.any(): + mags = magnitudes[detected] + lo = float(np.percentile(mags, 5)) + hi = float(np.percentile(mags, 95)) + if hi <= lo: + hi = lo + 1e-12 + conf_detected = (mags - lo) / (hi - lo) + conf_detected = np.clip(conf_detected, 0.0, 1.0) + conf[detected] = conf_detected + + plt.figure(figsize=(6, 5)) + plt.imshow(conf, cmap="inferno", vmin=0.0, vmax=1.0, interpolation="nearest") + plt.title("BFAST Monitor: magnitude-based confidence (normalized)") + plt.colorbar(fraction=0.046, pad=0.04) + plt.axis("off") + plt.tight_layout() + plt.savefig(out_dir / "bfast_confidence.png", dpi=150) + plt.close() + + # 2) Magnitude heatmap + plt.figure(figsize=(6, 5)) + plt.imshow(magnitudes, cmap="magma") + plt.title("BFAST Monitor: break magnitude") + plt.colorbar(fraction=0.046, pad=0.04) + plt.axis("off") + plt.tight_layout() + plt.savefig(out_dir / "bfast_magnitude.png", dpi=150) + plt.close() + + # 3) Break date map (fractional years; zeros where none) + plt.figure(figsize=(6, 5)) + plt.imshow(break_dates, cmap="viridis") + plt.title("BFAST Monitor: break date (fractional years; 0=none)") + plt.colorbar(fraction=0.046, pad=0.04) + plt.axis("off") + plt.tight_layout() + plt.savefig(out_dir / "bfast_break_date_frac.png", dpi=150) + plt.close() + + # 4) A couple of pixel time series plots (one expected break, one stable-ish) + # Pick a pixel near center and a pixel with a strong amplitude modulation. + h, w = break_dates.shape + pixels = [(h // 2, w // 2), (h // 4, 3 * w // 4)] + t_frac = _frac_years_from_yyyymmdd(dates_int) + + for (yy, xx) in pixels: + ts = stack_break[:, yy, xx] + bd = break_dates[yy, xx] + mag = magnitudes[yy, xx] + + plt.figure(figsize=(9, 3)) + plt.plot(t_frac, ts, linewidth=1.5) + plt.axvline(_frac_years_from_yyyymmdd(np.array([dates_int[0]], dtype=np.int64))[0], color="none") # noop (keeps style) + if bd > 0.0: + plt.axvline(bd, color="red", linestyle="--", linewidth=1.25, label=f"break ~ {bd:.3f}") + plt.title(f"Pixel ({yy}, {xx}) time series | mag={mag:.3f}") + plt.xlabel("Year (fractional)") + plt.ylabel("Signal") + plt.grid(True, alpha=0.25) + if bd > 0.0: + plt.legend(loc="best") + plt.tight_layout() + plt.savefig(out_dir / f"bfast_timeseries_y{yy}_x{xx}.png", dpi=150) + plt.close() + + print(f"\nWrote PNG outputs to: {out_dir}") + + +def main() -> int: + parser = argparse.ArgumentParser(description="Run a synthetic BFAST Monitor demo.") + parser.add_argument("--height", type=int, default=128, help="Raster height (y).") + parser.add_argument("--width", type=int, default=128, help="Raster width (x).") + parser.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility.") + parser.add_argument("--order", type=int, default=1, help="Harmonic order for BFAST Monitor.") + parser.add_argument("--h", type=float, default=0.35, help="MOSUM window fraction (h).") + parser.add_argument("--alpha", type=float, default=0.001, help="Significance level (alpha).") + parser.add_argument( + "--break-drop", + type=float, + default=0.4, + help="Drop applied to monitoring-period values for the break scenario.", + ) + parser.add_argument( + "--png", + action="store_true", + help="Write demonstration PNG(s) under examples/_out_bfast_monitor/ (requires matplotlib).", + ) + args = parser.parse_args() + + # Date configuration mirrors the unit test pattern: history then monitoring. + history_start_date = 20100101 + monitor_start_date = 20150101 + end_date = 20171231 + + # 16-day cadence date list across full period + dates_int = _date_range_16d_int(history_start_date, end_date) + + print("BFAST Monitor example") + print("--------------------") + print(f"dates: {int(dates_int[0])} .. {int(dates_int[-1])} (n={dates_int.size})") + print(f"stack shape: (time={dates_int.size}, y={args.height}, x={args.width})") + + # Build two synthetic stacks: with a break and without. + stack_break, stack_stable = _make_synthetic_stack( + height=args.height, + width=args.width, + dates_int=dates_int, + history_start_date=history_start_date, + monitor_start_date=monitor_start_date, + seed=args.seed, + break_drop=args.break_drop, + ) + + # Run BFAST Monitor (Rust-backed workflow) + result_break = bfast_monitor( + stack_break, + dates_int.tolist(), + history_start_date=history_start_date, + monitor_start_date=monitor_start_date, + order=args.order, + h=args.h, + alpha=args.alpha, + ) + + result_stable = bfast_monitor( + stack_stable, + dates_int.tolist(), + history_start_date=history_start_date, + monitor_start_date=monitor_start_date, + order=args.order, + h=args.h, + alpha=args.alpha, + ) + + # Basic invariants (smoke-test style) + assert isinstance(result_break, np.ndarray), "Expected numpy ndarray output" + assert result_break.shape == (2, args.height, args.width) + assert result_stable.shape == (2, args.height, args.width) + + _summarize_result("Break scenario", result_break) + _summarize_result("Stable scenario", result_stable) + + # We want a demonstration that is both: + # - a smoke run (exercises the compiled workflow end-to-end) + # - a reasonably interpretable demo (stable scenario should be mostly "no break") + break_detected_ratio = float((result_break[0] > 0.0).mean()) + stable_detected_ratio = float((result_stable[0] > 0.0).mean()) + print("\nSanity checks:") + print(f" break detected ratio: {break_detected_ratio:.3f}") + print(f" stable detected ratio: {stable_detected_ratio:.3f}") + + # The break scenario should generally detect widespread change for this synthetic drop. + # If it doesn't, something is likely wrong with the install / wiring / parameters. + assert break_detected_ratio > 0.05, "Expected some break detections in the break scenario" + + # Stable scenario target: <5% detections with default parameters. + # This is still a demo (not a unit test), but we keep this threshold to catch regressions + # where the algorithm becomes overly sensitive for a clean seasonal signal. + assert stable_detected_ratio < 0.05, ( + "Expected <5% detections in the stable scenario. If this fails, consider tuning " + "`alpha` (smaller is stricter), `h`, or reducing synthetic noise/complexity." + ) + + if args.png: + out_dir = Path("examples") / "_out_bfast_monitor" + _write_pngs(out_dir, stack_break=stack_break, dates_int=dates_int, result_break=result_break) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index dbc4765..1c1a1e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "structlog>=23.1.0", "xarray>=2023.0.0", "dask[array]>=2023.0.0", + "matplotlib>=3.9.4", ] [project.scripts] diff --git a/skills/README.md b/skills/README.md new file mode 100644 index 0000000..8b394ae --- /dev/null +++ b/skills/README.md @@ -0,0 +1,58 @@ +# Agent Skills Index (eo-processor) + +This directory contains **agentic skills** following the Agent Skills open standard (https://agentskills.io/specification). + +Each skill lives in its own folder and includes a required `SKILL.md` file with: +- **Metadata** (`name`, `description`, etc.) used for discovery +- **Instructions** loaded when the skill is activated +- Optional supporting files (e.g., `references/`, `scripts/`, `assets/`) + +## Skills in this workspace + +### `project-intake/` +**Use when:** starting a new task, requirements are unclear, or before multi-file Rust+Python changes. +**Focus:** repo map, acceptance criteria, risks, and a minimal verification plan. + +### `rust-pyo3-function/` +**Use when:** implementing or modifying Rust compute kernels exposed to Python via PyO3. +**Focus:** correctness, shape validation, numerical stability, avoiding unnecessary allocations, safe module registration. + +### `python-api-surface/` +**Use when:** changing anything users import from `eo_processor`. +**Focus:** exports (`__init__.py`), `__all__`, docstrings, and keeping `__init__.pyi` type stubs aligned with runtime behavior and docs. + +### `test-and-quality-gates/` +**Use when:** verifying changes, fixing CI failures, or before committing. +**Focus:** scope-driven gates across Rust (`fmt`, `clippy`, build), Python (lint, types, pytest/tox), integration, and coverage/docs checks. + +### `benchmark-and-perf/` +**Use when:** making performance changes or claims. +**Focus:** repeatable benchmark protocol (pinned settings, warmups, trials), correctness checks against a baseline, and result reporting. + +### `docs-and-release/` +**Use when:** changing public APIs, behavior/semantics, packaging, or preparing a release. +**Focus:** docs/typing/tests alignment, semver version decisions, changelog discipline, and truthful performance documentation. + +### `examples-and-smoke-tests/` +**Use when:** creating or updating examples under `examples/` for onboarding and smoke testing. +**Focus:** runnable scripts, minimal dependencies, predictable outputs, and keeping examples aligned with the public API. + +## Conventions & tips + +- Keep `SKILL.md` bodies concise (progressive disclosure); push deep reference material into `references/` if needed. +- When you add a new public function, assume you’ll update: + - Rust export + module registration + - Python exports and `__init__.pyi` + - tests + - README/docs + - (optionally) examples under `examples/` + +## Adding a new skill + +Create a directory named after the skill: +- lowercase letters, numbers, hyphens +- must match the `name:` field in `SKILL.md` + +Then add `SKILL.md` with frontmatter: +- `name` and `description` are required +- optional: `license`, `compatibility`, `metadata`, `allowed-tools` diff --git a/skills/benchmark-and-perf/SKILL.md b/skills/benchmark-and-perf/SKILL.md new file mode 100644 index 0000000..4660342 --- /dev/null +++ b/skills/benchmark-and-perf/SKILL.md @@ -0,0 +1,239 @@ +--- +name: benchmark-and-perf +description: Measure, compare, and improve eo-processor performance safely. Use when adding/changing Rust compute kernels, refactoring hot paths, or making performance claims. Provides a repeatable benchmark protocol with correctness checks, profiling hints, and reporting guidelines. +license: Proprietary. See repository LICENSE +compatibility: Assumes a local Rust toolchain and Python environment capable of building the PyO3 extension (maturin). Benchmarking may use repo scripts/artifacts under benchmarking/ and dist_bench*. Network access not required. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Benchmarking & performance skill (eo-processor) + +Use this skill to make performance work **repeatable**, **measurable**, and **safe** in a hybrid Rust+PyO3+Python Earth Observation library. + +The core objective is to answer, defensibly: +1) Did performance change (time, memory, allocations)? +2) Did correctness change (numerics, dtype/shape contract)? +3) Is the change worth the complexity? + +--- + +## When to activate + +Activate this skill when you: +- add a new Rust kernel or change an existing one +- refactor any hot loop / ndarray expression in Rust +- adjust parallelism, chunking, or algorithmic complexity +- change dtype handling (float32/float64) or NaN/Inf behavior (performance often couples to semantics) +- plan to claim speedups in docs/CHANGELOG/PR + +Do **not** activate for trivial doc updates or purely cosmetic refactors. + +--- + +## Principles (performance without breaking trust) + +1. **Correctness is a gate**: benchmarking without validating outputs is not acceptable. +2. **Measure the right thing**: avoid benchmarking builds that differ in optimization flags; compare like-for-like. +3. **Warm up**: compilation and caching effects can dominate first-run timings. +4. **Use representative shapes**: EO workloads are usually large (e.g., 1k²–10k² rasters), not just toy arrays. +5. **Avoid accidental regressions**: track both speed and memory; faster-but-alloc-heavy can be worse in real pipelines. +6. **No unverified claims**: do not claim performance improvements without before/after numbers and parameters. + +--- + +## Step 1: Define the benchmark question + +Write down: +- What operation is being measured? (function name and version) +- What data sizes/shapes? (e.g., 1000x1000, 5000x5000) +- What data distribution? (random uniform, realistic reflectance range, with/without NaNs) +- What environment? (CPU model optional, OS, Python version, Rust release build, number of threads) +- What metric? (wall time, throughput in pixels/s, peak RSS if available) + +### Acceptance criteria examples +- “New implementation must be ≥ 1.2× faster than baseline for 5000x5000 float64 arrays” +- “No more than +5% memory overhead compared to baseline” +- “Numerical difference within tolerance (float64: 1e-12; float32: 1e-5)” + +--- + +## Step 2: Always pin build mode & runtime settings + +### Build mode +- Benchmark only **release** builds for the Rust extension. +- Ensure you’re not comparing a debug build vs release build. +- If you compare against NumPy, ensure BLAS settings are stable. + +### Threading +Performance changes can be dominated by threading differences: +- Pin thread counts consistently across runs. +- If the repo uses Rayon or similar internally, control its thread count via environment (document what you used). +- For NumPy baselines, ensure you understand whether BLAS threads are involved. + +### Stability +- Close other heavy processes. +- Prefer running multiple trials and report median (and optionally p95). + +--- + +## Step 3: Correctness check BEFORE timing + +Before timing, validate: +- output shape matches contract +- dtype matches contract +- outputs are finite/NaN per contract +- values match baseline within tolerance + +### Baseline options +Pick the best available baseline: +- an existing eo-processor implementation (before refactor) +- a pure NumPy reference implementation of the formula +- a known-correct small example with asserted values + +If you can’t produce a correctness check, stop and add one first (usually a unit test in `tests/`). + +--- + +## Step 4: Benchmark protocol (repeatable) + +Use this protocol for each benchmark you report: + +1) **Generate inputs** + - Use seeded random generation for reproducibility. + - Use realistic ranges (e.g., reflectance in [0, 1]) unless the function expects something else. + +2) **Warm-up** + - Call the function at least once to warm caches and ensure the extension is loaded. + +3) **Time multiple trials** + - Run N trials (e.g., 5–20). + - Record the median and a dispersion metric (min/max or p95). + +4) **Report** + - Provide: shape, dtype, trials, median time, throughput, build mode, thread count. + +### Example Python timing skeleton (adapt to repo norms) +- Use `time.perf_counter()` not `time.time()` +- Avoid counting array creation time in the timed region +- Ensure outputs are consumed to avoid lazy evaluation traps (especially if using Dask wrappers) + +--- + +## Step 5: Evaluate results (interpretation) + +### Speedup thresholds +- < 1.05×: likely noise or not worth complexity unless it also reduces memory or fixes bugs. +- 1.05×–1.2×: consider whether it’s worth it; check memory/allocations and sustained performance. +- ≥ 1.2×: usually meaningful for EO rasters; still verify no semantic drift. + +### Check for regressions +- Small arrays sometimes get slower while large arrays get faster. That’s acceptable if the library targets large rasters—just document it. +- Watch for “fast median, slow tail”: if p95 worsened, investigate allocation spikes or scheduling. + +### Memory and allocations +If possible, assess: +- intermediate allocations (ndarray expression temporaries are common) +- peak memory usage (large rasters can OOM quickly) + +If you can’t measure memory precisely, at least reason about allocations: +- Did you add temporaries? +- Did you switch from in-place fill to multiple intermediate arrays? + +--- + +## Step 6: Profiling & root-cause techniques (use selectively) + +Use profiling only when the benchmark indicates a meaningful issue. + +### Common performance traps in this repo’s domain +- Extra temporaries from chained ndarray arithmetic +- Unintended dtype conversions / casts +- Poor cache locality due to iteration order +- Branch-heavy inner loops (NaN handling, conditional masking) +- Parallel overhead dominating small inputs + +### Suggested methods (choose what applies) +- Add lightweight internal instrumentation (counts, timing spans) temporarily; remove before commit. +- Compare “fused loop” vs “expression-based” implementations. +- Ensure the hottest path is in Rust (not Python glue). + +If you can’t profile locally, you can still: +- reduce the workload to isolate which step dominates +- compare variants with minimal changes to infer the cause + +--- + +## Step 7: How to make performance changes safely + +Preferred sequence: +1. Implement change with correctness tests. +2. Benchmark before/after with pinned settings. +3. If speedup is real, clean up code and document results. +4. If speedup is not real, revert or open a performance issue with data. + +### Safe optimizations (common wins) +- Fuse computations into a single pass over the array data. +- Precompute invariants outside inner loops. +- Reduce allocations: write into a preallocated output array. +- Avoid repeated bounds checks where safe and idiomatic (without `unsafe` unless justified). + +### Risky optimizations (require stronger evidence) +- Introducing `unsafe` +- Changing NaN/Inf behavior for speed +- Changing dtype semantics (float32 vs float64) +- Altering parallel thresholds or scheduling without benchmarks on multiple shapes + +--- + +## Reporting template (use in your response/PR) + +When you use this skill, report results in this format: + +### Benchmark setup +- Function(s): `...` +- Build: release (how built) +- Hardware/OS: (if known) +- Threads: (Rayon/BLAS/Python settings) +- Input: shape(s), dtype(s), distribution (seeded) + +### Correctness +- Baseline: (existing impl / NumPy reference) +- Tolerance: (e.g., rtol/atol) +- Result: pass/fail (and any notes about NaN behavior) + +### Performance results +For each shape/dtype: +- Before: median X ms (N trials) +- After: median Y ms (N trials) +- Speedup: X/Y (or %) +- Throughput: pixels/s (optional) +- Notes: memory/allocations qualitative notes + +### Decision +- Keep / revise / revert +- Follow-ups (tests, docs, perf issue) + +--- + +## Definition of done (for perf work) + +You’re done when: +- [ ] Correctness is validated against a baseline +- [ ] Benchmarks are repeatable and documented (inputs + settings) +- [ ] Any performance claim has before/after numbers +- [ ] No unacceptable memory regression is introduced (or it’s explicitly justified) +- [ ] Tests cover at least one representative correctness case and key edge cases +- [ ] The change doesn’t silently alter public semantics (or docs/versioning reflect it) + +--- + +## Local references (repo) + +- Engineering rules & quality gates: `AGENTS.md` +- User-facing docs and performance notes: `README.md`, `QUICKSTART.md` +- Complex workflows: `WORKFLOWS.md` +- Benchmark artifacts and harnesses: `benchmarking/`, `dist_bench.json`, `dist_bench.md`, `benchmark-*.json` +- Scripts and maintenance tooling: `scripts/` +- Tests: `tests/` diff --git a/skills/docs-and-release/SKILL.md b/skills/docs-and-release/SKILL.md new file mode 100644 index 0000000..c6bdf6b --- /dev/null +++ b/skills/docs-and-release/SKILL.md @@ -0,0 +1,258 @@ +--- +name: docs-and-release +description: Keep documentation, versioning, and release artifacts consistent for eo-processor. Use when changing public APIs, adding features, updating performance claims, or preparing a release (align README/QUICKSTART/docs, stubs, changelog, and pyproject/Cargo versions). +license: Proprietary. See repository LICENSE +compatibility: Designed for this repository’s hybrid Rust+PyO3+Python package. Assumes ability to build/test locally and update markdown/Sphinx docs when present. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Docs & release consistency skill (eo-processor) + +Use this skill to prevent the most common “shipped-but-confusing” failures: +- docs don’t match behavior +- types don’t match runtime +- versioning doesn’t reflect the change +- examples don’t run +- performance claims are unverified or outdated + +This repo is hybrid (Rust + PyO3 + Python packaging), so **documentation is part of the API**. Treat it with the same rigor as code. + +--- + +## When to activate + +Activate this skill when you: +- add or change a public function +- modify function semantics (NaN behavior, epsilon rules, dtype handling, shape rules) +- update performance characteristics or parallelism behavior +- modify packaging/build metadata (pyproject, Cargo, maturin config) +- prepare a release (or anything that looks like one: version bump, changelog update, docs refresh) + +Do *not* activate for purely internal refactors that produce no user-visible change—unless those refactors affect performance or behavior indirectly. + +--- + +## Core invariants (must always hold) + +1. **Public API is coherent across layers** + - Rust export (PyO3) ↔ Python re-export ↔ typing stubs ↔ docs ↔ tests + +2. **Examples are truthful** + - Code snippets in `README.md` / `QUICKSTART.md` should be runnable with minimal edits. + - Avoid pseudo-code that suggests features that do not exist. + +3. **Versioning matches semantics** + - Patch: bug fix, no API expansion + - Minor: new backward-compatible features + - Major: breaking changes (signature or contract changes) + +4. **Performance claims are substantiated** + - No “faster” claims without before/after numbers and workload context. + +--- + +## Step 1: Classify your change (to decide what to update) + +Choose the most specific category: + +### A) Public API addition (new function) +You must update: +- Python exports (`__init__.py`) + `__all__` (if used) +- typing stubs (`__init__.pyi`) +- `README.md` API summary (and/or index listing sections) +- tests (at least correctness + one edge case) +- docs (if repo has `docs/` API listings) + +Often update: +- `QUICKSTART.md` if it improves onboarding meaningfully (avoid bloat) +- `CHANGELOG` entry (if repo uses it for releases) + +### B) Public API behavior change (contract change) +You must update: +- docs describing behavior (formula, ranges, NaN/Inf handling, epsilon) +- tests to encode the new contract (and prevent regression) +- typing stubs if signature/dtype changes + +You must consider: +- semver implications (minor vs major depending on breakage) +- compatibility strategies (deprecation path, alias, warnings) + +### C) Bug fix +You must update: +- tests (regression test that fails before fix) +- docs only if user-facing behavior is clarified or corrected + +Usually: +- patch version bump (unless fix introduces new feature) +- changelog entry if repo practice expects it + +### D) Docs-only update +You must ensure: +- examples remain correct +- no stale references to removed/renamed functions +- claims are accurate (especially performance and supported environments) + +--- + +## Step 2: Documentation update checklist (minimum set) + +### README alignment +Update `README.md` when: +- a public function is added/removed/renamed +- formulas or semantics change (e.g., epsilon, masking rules) +- performance story changes + +Ensure: +- function name matches runtime import path (`from eo_processor import ...`) +- example inputs match expected dtype/range conventions +- outputs and interpretation notes are consistent (especially for indices) + +### Quickstart discipline +Update `QUICKSTART.md` only if: +- it materially reduces time-to-first-success for a new user +- it adds a simple, representative example (not a full tutorial) + +Avoid: +- duplicating large sections from README +- adding niche workflows that belong in `WORKFLOWS.md` or `docs/` + +### Workflows and examples +Update `WORKFLOWS.md` / `examples/` when: +- a new feature unlocks a compelling end-to-end workflow +- behavior changes would break published workflows + +Rule: +- workflows can be more narrative; README should stay concise. + +### Longer-form docs (`docs/`) +If this repo has generated docs (e.g., Sphinx): +- update API listings (autosummary pages, function lists) +- ensure cross-references resolve +- keep architecture/performance pages consistent with current implementation + +If docs build is part of CI, treat docs build failures as release blockers. + +--- + +## Step 3: Release readiness checklist (versioning + changelog + artifacts) + +### Version bump rules +Decide the version bump based on user-visible impact: +- Patch: + - bug fixes + - doc corrections + - internal refactor with no semantic change +- Minor: + - new function(s) + - optional parameters added with backward-compatible defaults + - performance improvements that do not change semantics +- Major: + - function removed + - signature changes that break callers + - semantic/contract changes that break expectations (dtype/range/NaN behavior) + +### Keep version sources consistent +In a hybrid Rust+Python repo, version may exist in multiple places (commonly): +- `pyproject.toml` (Python package version) +- Rust crate metadata (`Cargo.toml`) +- optionally a Python `__version__` constant (if present) + +The invariant is: **the version users see matches the release tag and changelog**. + +If the repo publishes to PyPI, ensure the Python package version is authoritative. + +### Changelog discipline +If the repo maintains a `CHANGELOG`: +- add an entry for user-visible changes +- write in terms of user impact (what changed, how to use it, any migration notes) +- include performance notes only when benchmarked and reproducible + +--- + +## Step 4: Performance documentation rules + +Any time you modify a hot path or claim performance changes: +- include benchmark context: + - function(s) + - array shapes + - dtype(s) + - build mode (release) + - thread settings (if applicable) + - before/after numbers (median of multiple trials) + +Do not: +- claim “X% faster” based on a single run +- compare debug vs release +- compare different thread counts or different BLAS settings without acknowledging it + +If benchmarks are stored in repo artifacts (e.g., `dist_bench.md`, `benchmark-*.json`), update them only if you actually re-ran them. + +--- + +## Step 5: Consistency sweep (the “find all references” mental pass) + +Before considering the work done: +- search for old function names and update them in: + - README + - quickstart + - docs + - examples + - tests + - type stubs +- ensure any added function is included in: + - Python exports + - typing stubs + - docs API list + +Also verify: +- no documentation references non-existent CLI flags or modules +- formulas match the implementation (including any epsilon or masking) +- input scaling guidance is explicit for EO data (reflectance vs scaled integers) + +--- + +## Output format: “Docs & release summary” + +When you use this skill, provide a short summary: + +1. **User-visible changes** +2. **Docs updated** (list files) +3. **Versioning decision** (patch/minor/major and why) +4. **Changelog entry** (what you added) +5. **Examples/workflows impacted** +6. **Verification plan** (what you will run) + +Example: + +- User-visible changes: Added `ndmi(a, b)` index and documented expected scaling. +- Docs updated: `README.md`, `docs/...` (API list), `QUICKSTART.md` (optional). +- Version: Minor (new backward-compatible function). +- Changelog: Added “New: NDMI index”. +- Examples: Updated `examples/...` to include NDMI usage. +- Verification: build extension, run pytest for new tests, run lint/type checks, optionally run docs build. + +--- + +## Definition of done + +You’re done when: +- [ ] Public API exports, typing stubs, docs, and tests all agree +- [ ] Version bump (if any) matches semver implications +- [ ] Changelog reflects user-visible change (if repo uses it) +- [ ] Published examples are correct and consistent +- [ ] No unverified performance claims remain in docs +- [ ] No references to removed/renamed functions remain + +--- + +## Local references (repo) + +- Engineering policies and mandatory checklist: `AGENTS.md` +- User docs: `README.md`, `QUICKSTART.md` +- Workflows: `WORKFLOWS.md` +- Docs (if present): `docs/` +- Packaging: `pyproject.toml`, `Cargo.toml` +- Python API surface and typing: `python/eo_processor/` +- Tests: `tests/` diff --git a/skills/examples-and-smoke-tests/SKILL.md b/skills/examples-and-smoke-tests/SKILL.md new file mode 100644 index 0000000..acb2ae3 --- /dev/null +++ b/skills/examples-and-smoke-tests/SKILL.md @@ -0,0 +1,266 @@ +--- +name: examples-and-smoke-tests +description: Create and maintain runnable examples under examples/ for onboarding and smoke testing. Use when adding/changing public APIs, when you need a quick “does it work end-to-end?” script, or when updating examples/README.md to match the current eo-processor API. +license: Proprietary. See repository LICENSE +compatibility: Intended for this repository’s examples/ scripts. Assumes a local Python environment with the built eo-processor extension available (e.g., via maturin/uv/tox) and optional xarray/dask for advanced examples. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Examples & smoke tests skill (eo-processor) + +Use this skill to add or update **examples** that serve two purposes: + +1. **User onboarding**: small, readable scripts that demonstrate “how do I use this?” +2. **Smoke testing**: quick, repeatable end-to-end checks that catch integration breakage (Rust ↔ PyO3 ↔ Python API). + +Examples are not a substitute for unit tests. They’re a *second line of defense* and a *first line of comprehension*. + +--- + +## Where this skill applies + +Primary directory: +- `examples/` + +Common example files in this repo: +- `examples/basic_usage.py` +- `examples/xarray_dask_usage.py` +- `examples/spectral_indices_extended.py` +- `examples/temporal_operations.py` +- `examples/zonal_stats_example.py` +- `examples/README.md` + +--- + +## When to activate + +Activate this skill when you: +- Add a new public function or change an existing one +- Modify behavior that examples rely on (dtype/shape rules, NaN handling, epsilon rules) +- Need a quick confirmation that the compiled extension, Python exports, and runtime behavior line up +- Want to provide a minimal “copy/paste runnable” onboarding path +- Need to demonstrate a workflow from `WORKFLOWS.md` in executable form + +Do not activate when: +- You’re only refactoring internals with no user-visible API impact (unless it might break runtime wiring) + +--- + +## Design goals (what “good” looks like) + +A good example is: +- **Runnable**: `python examples/.py` works in the repo’s standard dev environment +- **Small and focused**: demonstrates one concept (or a tightly-related set), not the entire library +- **Deterministic enough**: uses fixed seeds for random data and avoids flaky assertions +- **Honest**: no claims about features that don’t exist; no placeholder TODOs +- **Aligned with public API**: imports from `eo_processor` as users would +- **Light on dependencies**: uses only core deps unless the example explicitly targets an optional stack (xarray/dask) + +A bad example is: +- Too big to read +- Depends on local data files without telling the user +- Requires niche libraries without documenting them +- Prints pages of output without meaning +- Quietly passes while doing the wrong thing + +--- + +## Example types to create (pick the smallest one that fits) + +### Type A: “Hello world” onboarding script +Use when introducing a new user-facing function. + +Characteristics: +- Small arrays (1D or 2D) +- Clear printed output (min/max/mean, a small preview) +- A short explanation in comments +- No complex dependencies + +Example topics: +- Spectral indices (NDVI/NDWI/…) +- Masking utilities +- Simple temporal aggregations + +### Type B: Smoke test script for integration +Use when you need a quick end-to-end check that users can run. + +Characteristics: +- Imports the public API exactly as users do: `from eo_processor import ...` +- Exercises the main code path (compiled extension) +- Validates invariants: + - shape preserved + - dtype as expected (or at least float) + - finite results (or NaN behavior is explicitly expected) +- Exits non-zero on failure (use `assert` or explicit checks) + +Important: +- Keep it fast (seconds, not minutes). +- Avoid huge arrays unless specifically testing performance. + +### Type C: “Workflow” example script +Use when demonstrating multi-step pipelines (e.g., temporal compositing + index + morphology). + +Characteristics: +- A few logical steps, each with a comment header +- Prints intermediate summaries (shape/dtype/min/max/percentiles) +- Avoids large IO; uses synthetic data unless the repo explicitly includes sample data + +### Type D: Optional-stack example (xarray/dask) +Use only when: +- the feature is about `xr.apply_ufunc`, dask parallelism, or chunk behavior +- the dependency is documented clearly at the top of the example + +Characteristics: +- A small chunked array to keep runtime short +- Emphasizes correct usage patterns (`xr.apply_ufunc`, `dask="parallelized"`, etc.) +- Avoids requiring a distributed cluster (local threads is enough) + +--- + +## Step-by-step workflow + +### Step 1: Decide the example’s purpose and audience +Write down, in one sentence: +- “This example is for ____ and demonstrates ____.” + +Also decide: +- Does it target beginners (Type A) or is it a smoke/integration check (Type B)? + +### Step 2: Choose file naming and placement +Rules: +- Put examples in `examples/`. +- Prefer descriptive names (e.g., `ndmi_example.py`, `masking_example.py`). +- If updating, prefer editing an existing related example rather than creating duplicates. + +### Step 3: Write the script with a consistent structure + +Recommended structure: + +1. Header comment: + - what it demonstrates + - how to run it + - optional dependencies (if any) + +2. Imports: + - `numpy` first (almost always) + - `eo_processor` imports next + - optional packages last + +3. Deterministic input: + - use `np.random.default_rng(seed)` if randomness is needed + - keep sizes modest (e.g., 256x256 or similar) + +4. Compute: + - call the function(s) under test + +5. Validate invariants: + - `assert out.shape == in.shape` + - `assert np.isfinite(out).all()` where appropriate + - if NaNs are expected: assert their presence and explain why + +6. Print a human-readable summary: + - dtype, shape + - min/max/mean + - small preview slice if helpful (`out[:3, :3]`) + +7. Exit cleanly: + - if using asserts, Python will raise on failure + +### Step 4: Keep outputs stable +Guidelines: +- Prefer summaries over full arrays. +- If printing numeric summaries, consider rounding for readability. +- Seed randomness so results don’t change unexpectedly. + +### Step 5: Update `examples/README.md` if appropriate +If you add a new example or make a meaningful change: +- Add a short bullet: + - what it demonstrates + - how to run it + - notes about optional deps + +--- + +## Assertions and correctness guidance + +Examples can include lightweight checks, but avoid heavy logic. + +Recommended checks: +- Shape preservation +- Basic finiteness (when contract expects it) +- Known-value sanity for tiny hand-constructed arrays (best for onboarding) + +Avoid in examples: +- Tight tolerance comparisons against complex baselines (put those in `tests/`) +- Large exhaustive test matrices (again, `tests/`) + +If you need stronger correctness validation: +- Add/extend a `tests/test_*.py` unit test, and keep the example focused on usability. + +--- + +## Dependency discipline + +### Default dependency set for most examples +- `numpy` +- `eo_processor` + +### Optional dependencies (only when needed) +- `xarray`, `dask[array]` for xarray/dask examples +- Any ML libs only for ML-specific demos (keep optional and documented) + +Rules: +- If an example needs optional deps, say so at the top with install hints. +- Don’t introduce new dependencies solely for an example unless the repo already supports them. + +--- + +## Common pitfalls (avoid) + +- Importing private/internal modules instead of the public API +- Using huge arrays that make examples slow or memory-heavy +- Relying on local files without documenting where to get them +- Silent failures (no asserts, no meaningful output) +- Flaky results due to unseeded randomness +- Examples drifting out of sync with `README.md` and stubs + +--- + +## “Definition of done” for a new/updated example + +You’re done when: +- [ ] The example runs from the repo root with the standard dev environment +- [ ] It imports from `eo_processor` (public API) rather than internals +- [ ] It includes small but meaningful invariant checks (shape/dtype/finite as appropriate) +- [ ] Output is readable and stable (seeded randomness if used) +- [ ] `examples/README.md` is updated if a new example was added or behavior changed +- [ ] If the example reflects a new/changed public API, docs and stubs are also aligned (see `docs-and-release` and `python-api-surface` skills) + +--- + +## Suggested cookbook patterns (choose one) + +### Pattern 1: Minimal onboarding +- Tiny inputs (hand-coded arrays) +- Known outputs or easy-to-interpret summaries + +### Pattern 2: Small realistic raster +- Synthetic 2D arrays (e.g., 256x256 reflectance in [0, 1]) +- Print min/max/mean and a tiny window + +### Pattern 3: Smoke test mode +- Minimal runtime +- Assertions + short summary +- Clear failure signal + +--- + +## Local references (repo) +- Examples directory: `examples/` +- Example index: `examples/README.md` +- User docs: `README.md`, `QUICKSTART.md`, `WORKFLOWS.md` +- Engineering rules: `AGENTS.md` +- Public API surface: `python/eo_processor/__init__.py`, `python/eo_processor/__init__.pyi` +- Tests: `tests/` diff --git a/skills/project-intake/SKILL.md b/skills/project-intake/SKILL.md new file mode 100644 index 0000000..6394794 --- /dev/null +++ b/skills/project-intake/SKILL.md @@ -0,0 +1,172 @@ +--- +name: project-intake +description: Build a fast, accurate mental model of the eo-processor repo and the user’s task. Use when starting work, when requirements are unclear, or before making multi-file changes (Rust+PyO3+Python packaging/tests/docs). +license: Proprietary. See repository LICENSE +compatibility: Designed for agentic engineering in this repository; assumes local filesystem access and ability to run Rust/Python tooling. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Project intake & repo map (eo-processor) + +Use this skill to (1) clarify what you’re building, (2) map the repository, and (3) choose the smallest safe change set. + +## Goals + +1. Produce a **repo map**: key directories, entrypoints, and “where to change what”. +2. Translate the user request into **concrete acceptance criteria**. +3. Identify **risk areas** (performance, numerical stability, API surface, safety) and a **verification plan**. + +Non-goals: +- Don’t start coding until you can point to the exact files you’ll touch and why. +- Don’t broaden scope. If the request implies refactors, propose them separately. + +--- + +## 1) Clarify the task (requirements triage) + +Write down: +- **User intent**: What outcome do they want? (feature, bug fix, perf, docs) +- **Inputs/outputs**: data shapes (1D/2D), dtypes, expected ranges, NaN handling. +- **API surface**: new public function? change existing behavior? CLI change? +- **Constraints**: performance target, memory limits, backwards compatibility. +- **Examples**: at least one concrete example call and expected result properties. + +### Minimal clarifying questions (ask only if blocked) +Ask *at most 3* questions, only when needed to proceed safely: +1. “Is this intended to be a new public API or internal refactor?” +2. “What shapes/dtypes should be supported (1D/2D, float32/float64)?” +3. “Any expected behavior for nodata/NaNs or divide-by-zero?” + +If you can infer defaults from existing patterns in the repo, proceed without asking. + +--- + +## 2) Repository map (what’s where) + +Build a short map like this (fill with repo-specific paths): + +### Core implementation +- `src/` — Rust crate implementing fast EO ops via PyO3 (native extension). +- `Cargo.toml` — Rust crate config and dependencies. +- `python/eo_processor/` — Python package wrapper: exports, docstrings, typing stubs. +- `pyproject.toml` / `uv.lock` — Python packaging + dependency lock. + +### Tests & quality +- `tests/` — Python tests (pytest). +- `tox.ini` / `pytest.ini` — test environments and config. +- Lint/type tooling likely defined in `pyproject.toml` (ruff/mypy) and Rust (`clippy`, `fmt`). + +### Docs & user guides +- `README.md` — primary API & examples. +- `QUICKSTART.md` — onboarding quickstart. +- `WORKFLOWS.md` — complex usage examples / pipelines. +- `docs/` — longer-form docs (if present; check for Sphinx/MD pipeline). + +### Scripts / maintenance +- `scripts/` — maintenance scripts (e.g., coverage badge generation). + +--- + +## 3) Change classification (choose the correct workflow) + +Pick exactly one dominant category and follow it: + +### A) New function (most common) +- Implement in Rust, expose through PyO3, export in Python, add typing stub, add tests, update docs. + +### B) Bug fix +- Reproduce -> minimal fix -> regression test -> confirm no API break. + +### C) Performance improvement +- Benchmark before/after, verify correctness, avoid unsafe, document speedup context. + +### D) Docs-only / packaging +- Keep changes isolated; ensure examples remain runnable. + +--- + +## 4) Safety/performance checklist (pre-implementation) + +Before you change code, write: +- **Numerical stability plan**: EPS usage, division-by-zero behavior, NaNs, clipping rules. +- **Shape validation**: ensure consistent behavior for mismatched shapes. +- **Threading / parallelism**: confirm any parallel approach matches project conventions. +- **No side effects**: core ops should not do file I/O or network I/O. + +Common pitfalls in EO numeric code: +- silent `NaN` propagation vs deliberate masking +- dtype promotion surprises (float32 vs float64) +- divide-by-zero in normalized differences +- avoid unnecessary allocations for large rasters + +--- + +## 5) Verification plan (what you will run) + +Define a verification plan proportional to risk: + +### Always +- Rust formatting and linting +- Python linting and typing (when Python layer touched) +- Unit tests relevant to changed behavior + +### When touching Rust core +- `cargo fmt` +- `cargo clippy` (treat warnings as errors) +- `cargo test` or at least `cargo check` + +### When touching Python API/tests +- run targeted `pytest` for affected test module(s) +- run `tox` env used by the project (pick the most current default) + +### When coverage impacted +- run coverage job and regenerate badge if this repo mandates it + +--- + +## 6) Output format: the “Intake Summary” you should produce + +When this skill is used, produce a short “Intake Summary” before coding: + +1. **Problem statement** +2. **Proposed approach** +3. **Files likely to change** (list exact paths) +4. **Acceptance criteria** (bullet list) +5. **Test/verification plan** +6. **Risks & mitigations** + +Example template: + +- Problem: … +- Approach: … +- Files: + - `src/...` + - `python/eo_processor/...` + - `tests/...` + - `README.md` +- Acceptance criteria: + - … +- Verification: + - … +- Risks: + - … + +--- + +## 7) Heuristics for good agent behavior in this repo + +- Prefer small, reviewable commits. +- Preserve public API stability. +- Keep Rust+Python exports/stubs/docs synchronized. +- Don’t claim performance improvements without benchmarks. +- Don’t introduce new dependencies unless clearly justified. + +--- + +## References (local) + +- Repo operations guide: `AGENTS.md` +- User-facing docs: `README.md`, `QUICKSTART.md` +- Workflows: `WORKFLOWS.md` diff --git a/skills/python-api-surface/SKILL.md b/skills/python-api-surface/SKILL.md new file mode 100644 index 0000000..22a4ef3 --- /dev/null +++ b/skills/python-api-surface/SKILL.md @@ -0,0 +1,185 @@ +--- +name: python-api-surface +description: Maintain the public Python API for eo-processor, including exports, __all__, docstrings, and type stubs. Use when adding/renaming functions, changing signatures/behavior, or ensuring Python typing and user-facing docs stay consistent with the Rust/PyO3 core. +license: Proprietary. See repository LICENSE +compatibility: Intended for this repository’s Python package (python/eo_processor) paired with a Rust PyO3 extension; assumes numpy/xarray usage patterns and standard Python tooling (pytest, ruff, mypy, tox/uv). +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Python API surface & typing skill (eo-processor) + +Use this skill when you need to change anything users import from `eo_processor`, or when you change behavior/signatures in the Rust core and must keep **Python exports + typing stubs + docs + tests** aligned. + +This skill is intentionally opinionated: **public API stability and consistency beat cleverness**. + +--- + +## What “Python API surface” means in this repo + +The user-visible API typically includes: + +- `python/eo_processor/__init__.py` + - imports from the compiled extension module + - `__all__` + - module docstring / high-level documentation + - occasionally `__version__` or metadata (if present in this repo) +- `python/eo_processor/__init__.pyi` + - the canonical typing surface for users and CI type checks +- Any Python helpers/wrappers that sit on top of the Rust core (if present) +- Documentation that enumerates functions and guides usage: + - `README.md` (always) + - `QUICKSTART.md` (only when it materially improves onboarding) + - `docs/` (if the repo generates longer-form docs) + +**Key invariant:** if it’s public, it must be importable, typed, tested, and documented (at least minimally). + +--- + +## Activation checklist (before you edit) + +1. Identify whether the change is: + - **new public function** + - **renamed function** + - **signature change** + - **behavior change** + - **docs-only change** +2. Identify the **single source of truth** for the behavior (usually Rust core), and confirm: + - input shapes (1D/2D), dtype(s), and expected ranges + - NaN/Inf behavior + - divide-by-zero / epsilon strategy (if applicable) +3. Decide if the function should be **public**: + - If it’s stable, broadly useful, and not experimental → public. + - If it’s internal glue or unstable → keep internal, don’t export. + +If any of those are unclear and you cannot infer them from existing functions, stop and ask the user for clarification (max 3 targeted questions). + +--- + +## Step-by-step workflow + +### Step 1: Wire exports correctly + +When a function is intended to be public: + +1. Import it into `python/eo_processor/__init__.py` from the extension module (or its wrapper module). +2. Add it to `__all__`. +3. Ensure naming matches docs and tests exactly. + +**Rules:** +- Re-export under a single canonical name. Avoid multiple aliases unless there’s a compatibility story. +- Keep ordering/grouping consistent (e.g., indices together, masking together, temporal stats together). + +### Step 2: Define the typing contract in `__init__.pyi` + +The `.pyi` signature should be: +- accurate to runtime behavior (including accepted dtypes/shapes) +- helpful to users (clear parameter names, optional defaults) +- consistent across the library (similar functions should have similar typing style) + +**Typing guidance:** +- Prefer `numpy.typing.NDArray` for NumPy arrays. +- If the project supports `xarray.DataArray` directly at runtime, either: + - provide overloads, or + - document that `xarray` usage is via `xr.apply_ufunc` and keep types NumPy-focused. +- Don’t promise more than the function actually supports (e.g., don’t type as accepting `ArrayLike` if it fails on lists). + +**Return type:** +- If the function returns a new array: `NDArray[np.floating[Any]]` or a specific `np.float64` / `np.float32` depending on behavior. +- If dtype is guaranteed (common in Rust kernels), reflect that explicitly. + +### Step 3: Update docstrings and high-level docs + +For user-facing completeness: +- Ensure `README.md` mentions the function in the right section (API summary / feature list). +- Provide a minimal example snippet in docs if this is a new capability. +- If it’s an EO index, include: + - formula + - band meanings + - expected input scaling (0–1 reflectance vs scaled integers) + - output range and interpretation notes + +Keep docs concise; move deep explanations to `docs/` if the repo uses it. + +### Step 4: Add/adjust tests + +Any public API change requires tests. At minimum: +- a correctness test on a small array +- shape mismatch behavior (if relevant) +- stability (near-zero denominator) tests for ratio/normalized difference functions + +When behavior changes: +- update or add a regression test that captures the intended new behavior +- avoid “brittle” tests that encode implementation quirks rather than contract + +--- + +## Compatibility and deprecation rules + +### Renames +If renaming a public function: +- Prefer a deprecation window: + - keep old name available and forward it to the new name + - add a deprecation note in docs + - (optionally) emit a `DeprecationWarning` from the old alias +- Update: + - docs, tests, `__all__`, stubs + - any examples referencing the old name + +### Signature changes +Avoid breaking signature changes. If necessary: +- make new args optional with safe defaults +- document default behavior carefully +- update stubs and tests + +If a breaking change is unavoidable, it must be explicit and coordinated with versioning policy. + +--- + +## Quality bar (must pass before “done”) + +- [ ] Public function is importable from `eo_processor` +- [ ] `__all__` includes it (if this repo uses `__all__` as canonical) +- [ ] `.pyi` typing matches runtime behavior +- [ ] Tests cover correctness + key edge cases +- [ ] Docs mention the function and provide guidance +- [ ] No redundant exports or conflicting names +- [ ] No undocumented behavior change + +--- + +## Common pitfalls (avoid) + +- Adding a Rust function but forgetting: + - `python/eo_processor/__init__.py` export + - `python/eo_processor/__init__.pyi` stub + - docs update + - tests +- Typing promises that runtime can’t fulfill (e.g., `ArrayLike` when only `ndarray` works) +- Silent behavioral changes without a corresponding doc/test update +- Inconsistent naming (e.g., `normalizedDifference` vs `normalized_difference`) +- Returning different dtype than typed/documented +- “Convenience” broadcasting rules in one function that don’t exist elsewhere + +--- + +## Suggested “API change summary” format (use in your response/PR) + +When you use this skill, summarize the API outcome: + +1. **What changed** +2. **How to use it** (minimal snippet) +3. **Typing contract** (inputs/outputs) +4. **Edge cases** (NaN, divide-by-zero, shape mismatch) +5. **Tests added/updated** + +--- + +## Local references (repo) + +- Engineering and checklists: `AGENTS.md` +- User docs: `README.md`, `QUICKSTART.md` +- Workflows/examples: `WORKFLOWS.md`, `examples/` +- Python surface: `python/eo_processor/__init__.py`, `python/eo_processor/__init__.pyi` +- Tests: `tests/` diff --git a/skills/rust-pyo3-function/SKILL.md b/skills/rust-pyo3-function/SKILL.md new file mode 100644 index 0000000..73c7a1c --- /dev/null +++ b/skills/rust-pyo3-function/SKILL.md @@ -0,0 +1,192 @@ +--- +name: rust-pyo3-function +description: Implement or modify a Rust function exposed to Python via PyO3 in eo-processor. Use when adding new compute kernels, fixing numerical/shape bugs in the Rust core, or wiring Rust functions into the Python module safely and consistently. +license: Proprietary. See repository LICENSE +compatibility: Requires Rust toolchain and Python packaging via maturin; assumes NumPy arrays passed through PyO3/numpy bindings. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Rust + PyO3 function skill (eo-processor) + +Use this skill when you need to add or change a Rust implementation that is exposed to Python. The goal is: **correct math + stable API + high performance + predictable behavior**. + +## Activation checklist (before you code) + +1. Identify the *public Python name* of the function (e.g., `ndvi`) and confirm whether this is: + - a new function, or + - a behavior change, or + - a bug fix (no API change). +2. Find existing patterns in: + - `src/` for Rust/PyO3 style + - `python/eo_processor/` for exports/docstrings + - `python/eo_processor/__init__.pyi` for typing + - `tests/` for numerical expectations. +3. Define expected behavior for: + - shape mismatches + - division by zero / near-zero denominators + - NaN/Inf handling + - dtype support and output dtype. + +If anything is ambiguous, decide based on existing functions in this repo (stay consistent). + +--- + +## Design rules (repo conventions) + +### 1) Keep the core pure +- Rust compute kernels should be **pure functions**: no file I/O, no network access, no global state. +- Deterministic results for deterministic inputs. + +### 2) Validate shapes early, fail clearly +- If the function expects aligned shapes, check and raise a Python-friendly error. +- Do not silently broadcast unless the repo already does that everywhere. + +### 3) Numerical stability is not optional +- For normalized differences and ratios, guard denominators with a small epsilon. +- Use a *consistent* epsilon strategy across similar functions. +- Make NaN behavior explicit (propagate vs sanitize) and match existing functions. + +### 4) Avoid unnecessary allocations +- Prefer single-pass loops where possible. +- Avoid creating multiple temporaries for large rasters. +- If using ndarray operations, be mindful of intermediate allocations. + +### 5) Don’t introduce `unsafe` without a compelling reason +- If you think you need `unsafe`, stop and provide: + - justification (benchmark evidence), + - a safety argument, + - tests that would catch UB-like symptoms. + +--- + +## Implementation workflow (step-by-step) + +### Step A: Specify the API contract +Write down: +- signature at Python level (args, defaults, return) +- expected input shapes and dtypes +- output dtype +- error behavior & messages + +Example contract for a normalized difference: +- Inputs: `a`, `b` float arrays (1D or 2D depending on existing patterns) +- Output: float array same shape +- Math: `(a - b) / (a + b + EPS)` +- Errors: shape mismatch -> `ValueError` (or the project’s standard) + +### Step B: Implement Rust function with PyO3 glue +Typical structure: +1. Accept `Python` token and `PyReadonlyArray*` inputs. +2. Convert to `ndarray::ArrayView*` using `.as_array()`. +3. Validate shape compatibility. +4. Allocate output (or create new array) and fill it. +5. Return `PyArray*` via `into_pyarray(py)` (or existing conventions). + +Prefer returning a new array rather than mutating inputs. + +### Step C: Register with the module +- Ensure the `#[pyfunction]` is added to the module in the `#[pymodule]` initializer. +- Keep ordering, naming, and grouping consistent with neighboring functions. + +### Step D: Maintain Python surface coherence +Whenever you add/rename a Rust-exposed function, you almost always need to update: +- `python/eo_processor/__init__.py` (exports and `__all__`) +- `python/eo_processor/__init__.pyi` (typing stub) +- docs (`README.md` at minimum; `docs/` if used by the repo) +- tests (`tests/`) + +If the change is internal-only, do not export it publicly. + +--- + +## Error handling guidance (PyO3) + +- Prefer returning `PyResult`. +- Use Pythonic errors; typical choices: + - `ValueError` for invalid shapes/values + - `TypeError` for wrong types (rare if signature enforces arrays) +- Error messages should be: + - actionable + - short + - consistent across functions. + +--- + +## Performance guidance + +### What to optimize first +1. Avoid extra allocations / temporaries. +2. Ensure tight loops with minimal branching in the inner loop. +3. Use contiguous iteration patterns when possible. + +### When to benchmark +Benchmark if: +- you’re adding a new kernel, or +- you changed the inner loop, or +- you changed dtype handling. + +Don’t claim speedups without before/after numbers and array sizes. + +--- + +## Testing expectations (what to add) + +Add tests that cover: +1. **Correctness**: known inputs with known outputs (small arrays). +2. **Stability**: near-zero denominators; ensure outputs are finite if expected. +3. **Shape behavior**: mismatch raises the correct error. +4. **NaN behavior**: if inputs contain NaN, outcome matches contract. +5. **Dtype behavior**: float32/float64 if supported by project conventions. + +For numerical comparisons: +- use tolerances appropriate for float64/float32 +- avoid exact equality for floats unless intentionally exact. + +--- + +## Documentation expectations + +For a new EO index function, document: +- the formula +- what each band represents +- recommended input scaling (e.g., reflectance 0–1 vs scaled ints) +- typical output range and interpretation + +Keep docs short in `README.md` and move deeper material to `docs/` if present. + +--- + +## “Done” checklist (must be true before you stop) + +- [ ] Rust function implemented with correct math and shape checks +- [ ] Function is registered in the PyO3 module +- [ ] Python exports updated (if public) +- [ ] Typing stubs updated (if public) +- [ ] Tests added/updated and pass +- [ ] Docs updated (if public) +- [ ] Lint/format gates pass for Rust (+ Python if touched) +- [ ] No unnecessary new dependencies + +--- + +## Common pitfalls (avoid these) + +- Returning wrong shape due to accidental broadcast or flattening +- Implicit dtype casts causing precision loss +- Division by zero producing noisy Inf/NaN without being documented +- Adding a Rust function but forgetting Python `__init__.py` / `__init__.pyi` +- Performance regressions from extra temporaries +- Changing behavior without updating tests and docs + +--- + +## Local references (repo) + +- Engineering rules & checklists: `AGENTS.md` +- User docs and API overview: `README.md`, `QUICKSTART.md` +- Existing workflows/examples: `WORKFLOWS.md`, `examples/` +- Rust crate entrypoints and module registration: `src/` +- Python exports/stubs: `python/eo_processor/` +- Tests: `tests/` diff --git a/skills/test-and-quality-gates/SKILL.md b/skills/test-and-quality-gates/SKILL.md new file mode 100644 index 0000000..19c3b6a --- /dev/null +++ b/skills/test-and-quality-gates/SKILL.md @@ -0,0 +1,257 @@ +--- +name: test-and-quality-gates +description: Run and interpret eo-processor quality gates (Rust+Python) and add the right tests for changes. Use when verifying work, fixing CI failures, or before committing PR-ready changes (fmt/clippy, ruff/mypy, pytest/tox, coverage, and minimal regression tests). +license: Proprietary. See repository LICENSE +compatibility: Assumes local Rust toolchain and Python tooling (tox/uv/pytest/ruff/mypy) are available; intended for this repository’s hybrid Rust+PyO3+Python package. +metadata: + author: eo-processor maintainers + version: "1.0" +--- + +# Test & quality gates skill (eo-processor) + +Use this skill to keep changes safe and shippable by running the correct checks, adding the **right** tests, and diagnosing failures efficiently across **Rust**, **Python**, and **packaging** boundaries. + +This repo is a hybrid: +- Rust core (PyO3 extension) +- Python package surface +- Tests primarily in Python +- Multiple quality gates (format, lint, type, unit, coverage, docs) + +## When to activate +Activate this skill when you are: +- about to make a commit / open a PR +- fixing CI failures +- adding or changing a public API +- modifying numerical kernels (risk of subtle regressions) +- touching build/packaging (maturin, pyproject, Cargo) + +--- + +## Gate 0: Decide what changed (scope-driven checklist) + +First, classify your change. This determines which gates must run. + +### A) Docs only +- Run: markdown sanity (optional), and ensure examples still look correct +- You can usually skip compilation checks unless docs reference generated API content + +### B) Python-only logic (no Rust changes) +- Run: ruff + mypy (if configured) + pytest (or tox) +- Coverage if tests changed or coverage is tracked for this change + +### C) Rust core change (PyO3 extension) +- Run: cargo fmt + clippy + (test/check) +- Rebuild extension and run Python tests that exercise the path +- If any Python API was touched, also run Python gates + +### D) Public API change (new function / signature / behavior) +- Run: **everything relevant**: + - Rust gates (if Rust involved) + - Python gates (exports, typing, tests) + - Docs updates and (if present) docs build + - Coverage gates if the change impacts coverage tracking + +**Rule:** the broader the public impact, the more gates you run. + +--- + +## Gate 1: Test design (what tests to add) + +### Always add at least one regression test for: +- a bug fix (reproduces old failure, asserts new behavior) +- a behavior change (encodes the new contract) +- a new public function (correctness + edge case) + +### Recommended test coverage for numerical kernels (EO indices / ratios) +Add tests that cover: + +1) **Correctness on tiny arrays** +- Hand-computable examples (2x2, 1D length 4) +- Assert shape and values within tolerance + +2) **Stability edge cases** +- denominators near zero +- extremely small/large floats if relevant +- confirm behavior for divide-by-zero matches contract (finite vs inf vs NaN) + +3) **Shape behavior** +- mismatched shapes should raise a predictable, Python-friendly error + +4) **NaN / nodata behavior** +- if inputs can contain NaN: ensure behavior is consistent with the repo contract +- only sanitize NaNs if that is explicitly part of the function contract + +5) **Dtype behavior** +- if float32 and float64 are supported, include both (or at least confirm output dtype is stable) + +### Tolerances +- Prefer `np.testing.assert_allclose` (or equivalent) +- Use tighter tolerances for float64 than float32 +- Avoid exact float equality unless the operation is guaranteed exact + +### Avoid brittle tests +- Don’t test implementation details (loop order, internal eps value) unless the contract depends on it. +- Test behavior and contract, not incidental formatting. + +--- + +## Gate 2: Rust quality gates (when Rust is touched) + +### Required +1. Format: +- Run Rust formatter (repo standard) + +2. Lint: +- Run clippy with warnings treated as errors (repo standard) + +3. Compile/test: +- Run Rust tests if they exist +- If no Rust tests apply, at least ensure the crate checks/compiles + +### Failure triage (Rust) +When a Rust gate fails: +- **fmt**: apply format; do not hand-fight formatting +- **clippy**: + - treat warnings as real issues + - prefer clear, idiomatic fixes + - don’t suppress warnings unless there’s a documented reason +- **borrow checker / lifetime**: + - simplify ownership at API boundaries (convert views once, avoid unnecessary clones) +- **performance concerns**: + - avoid introducing extra allocations + - watch for accidental temporaries in ndarray expressions + +--- + +## Gate 3: Python quality gates (when Python/stubs/tests are touched) + +### Required (as applicable) +1. Lint: +- run the repo’s linter (ruff is common here) + +2. Types: +- run mypy (if configured) focusing on `python/eo_processor` and stubs + +3. Unit tests: +- run pytest for relevant test modules +- run the canonical tox environment if that’s the repo standard + +### Failure triage (Python) +- **ruff**: fix high-signal issues (unused imports, incorrect comparisons, unsafe patterns) +- **mypy**: + - update stubs to match runtime behavior + - prefer precise types over `Any` unless you truly can’t express it +- **pytest failures**: + - confirm it’s not a stale compiled artifact (rebuild extension if Rust changed) + - isolate failing test with a targeted run + - inspect dtype/shape differences first for numerical failures + +--- + +## Gate 4: Integration gate (Rust ↔ Python) + +Use this gate whenever Rust core changed or new functions were added. + +Checklist: +- Can you import `eo_processor` successfully? +- Can you call the changed/new function from Python? +- Do the Python exports and stubs match the compiled module? +- Do tests exercise the actual compiled path (not a fallback)? + +Common integration failures: +- forgot to register the function in the PyO3 `#[pymodule]` +- forgot to re-export in `python/eo_processor/__init__.py` +- stubs (`__init__.pyi`) are out of sync +- stale build wheel/extension loaded in the environment + +--- + +## Gate 5: Coverage gate (when tests changed) + +Run coverage gates if: +- you added or removed tests +- you changed Python logic +- the repo’s CI enforces coverage thresholds +- coverage badges/artifacts are tracked + +If coverage is updated and the repo expects it: +- run the coverage job +- regenerate any badge or report artifacts as per repo conventions + +Do not “game” coverage with meaningless tests. Add tests that encode real behavior. + +--- + +## Gate 6: Docs gate (when public API changes) + +At minimum, ensure: +- `README.md` is updated if the public API surface changed +- quickstart/examples remain consistent with the API + +If the repo has a docs build: +- run it when: + - new functions are added to API listings + - cross-references might break + - docs tooling or dependencies changed + +--- + +## Minimal “pre-commit” checklist for this repo + +If you are about to commit, you should be able to say “yes” to all applicable items: + +- [ ] Rust formatted (if Rust touched) +- [ ] Rust clippy passes with warnings as errors (if Rust touched) +- [ ] Rust compiles / tests pass (if Rust touched) +- [ ] Python lint passes (if Python touched) +- [ ] Type check passes (if Python/stubs touched) +- [ ] Unit tests pass (always run relevant subset; full tox if risky change) +- [ ] Public API exports/stubs/docs/tests consistent (if public API touched) +- [ ] Coverage updated if required by repo policy (if test/coverage impacted) +- [ ] No unrelated changes staged +- [ ] No secrets, no large binary artifacts + +--- + +## Debugging playbook (fast triage) + +### 1) Reproduce quickly +- run the smallest failing command (single test module, single lint tool) +- avoid running the full suite until you have a hypothesis + +### 2) Investigate likely boundaries first +For EO numeric code, the usual suspects are: +- dtype conversion (float32 vs float64) +- integer-scaled inputs vs reflectance (0–1) assumptions +- divide-by-zero / epsilon differences +- shape mismatch or unexpected broadcasting + +### 3) Reduce the case +- create a tiny reproducer array in a test +- assert intermediate invariants (shape, dtype, finite) + +### 4) Fix root cause, then strengthen the test +- if you fixed a bug, add a test that would have failed before + +--- + +## Expected output when this skill is used + +When you activate this skill, produce a short actionable plan like: + +1. **Gates to run** (based on change type) +2. **Tests to add/update** (what and why) +3. **Failure triage steps** (if something fails) +4. **Definition of done** (what must be green) + +--- + +## Local references (repo) +- Core engineering rules & mandatory checklist: `AGENTS.md` +- User docs: `README.md`, `QUICKSTART.md` +- Workflows: `WORKFLOWS.md` +- Rust core: `src/`, `Cargo.toml` +- Python package surface: `python/eo_processor/` +- Tests: `tests/` +- Tool configuration: `pyproject.toml`, `tox.ini`, `pytest.ini` diff --git a/src/workflows.rs b/src/workflows.rs index 251b87d..370da3d 100644 --- a/src/workflows.rs +++ b/src/workflows.rs @@ -1,12 +1,16 @@ use crate::CoreError; use nalgebra::{DMatrix, DVector}; -use ndarray::{Axis, IxDyn, Zip}; +use ndarray::{Axis, IxDyn}; use numpy::{IntoPyArray, PyArrayDyn, PyReadonlyArrayDyn}; use pyo3::prelude::*; use rayon::prelude::*; const TWO_PI: f64 = 2.0 * std::f64::consts::PI; +// Chunk size (in pixels) for parallel work units in bfast_monitor. +// Larger chunks reduce scheduling overhead and enable safe disjoint mutable writes. +const BFAST_PIXELS_PER_CHUNK: usize = 1024; + // --- 1. BFAST Monitor Workflow --- /// Represents the fitted harmonic model. @@ -159,55 +163,31 @@ fn dates_to_frac_years(dates: &[i64]) -> Vec { } // This is the main logic function that runs for each pixel. -fn run_bfast_monitor_per_pixel( - pixel_ts: &[f64], - dates: &[f64], - history_start: f64, - monitor_start: f64, - order: usize, - h: f64, // h parameter for MOSUM window size - alpha: f64, // Significance level -) -> (f64, f64) { - // 1. Find the indices for the history and monitoring periods - let history_indices: Vec = dates - .iter() - .enumerate() - .filter(|(_, &d)| d >= history_start && d < monitor_start) - .map(|(i, _)| i) - .collect(); - - let monitor_indices: Vec = dates - .iter() - .enumerate() - .filter(|(_, &d)| d >= monitor_start) - .map(|(i, _)| i) - .collect(); - - if history_indices.is_empty() || monitor_indices.is_empty() { - return (0.0, 0.0); - } - // 2. Extract the data for these periods - let history_ts: Vec = history_indices.iter().map(|&i| pixel_ts[i]).collect(); - let history_dates: Vec = history_indices.iter().map(|&i| dates[i]).collect(); - let monitor_ts: Vec = monitor_indices.iter().map(|&i| pixel_ts[i]).collect(); - let monitor_dates: Vec = monitor_indices.iter().map(|&i| dates[i]).collect(); - // 3. Fit model on the historical period - let model_result = fit_harmonic_model(&history_ts, &history_dates, order); - let model = match model_result { +fn run_bfast_monitor_per_pixel_windows( + history_ts: &[f64], + history_dates: &[f64], + monitor_ts: &[f64], + monitor_dates: &[f64], + order: usize, + h: f64, + alpha: f64, +) -> (f64, f64) { + // Fit model on the historical period + let model = match fit_harmonic_model(history_ts, history_dates, order) { Ok(m) => m, - Err(_) => return (0.0, 0.0), // Return no-break if model fails + Err(_) => return (0.0, 0.0), }; - // 4. Predict for the monitoring period - let predicted_ts = predict_harmonic_model(&model, &monitor_dates, order); + // Predict for the monitoring period + let predicted_ts = predict_harmonic_model(&model, monitor_dates, order); - // 5. Detect break using MOSUM process on residuals + // Detect break using MOSUM process on residuals detect_mosum_break( - &monitor_ts, + monitor_ts, &predicted_ts, - &monitor_dates, + monitor_dates, history_ts.len(), model.sigma, h, @@ -259,37 +239,120 @@ pub fn bfast_monitor( let mut out_array = ndarray::ArrayD::::zeros(IxDyn(&[2, height, width])); // Flatten spatial dimensions for parallel processing + // + // IMPORTANT: In the input layout (time, y, x), each pixel's time series is strided + // along the time axis. After reshaping to (time, num_pixels), iterating over Axis(1) + // yields 1D views that are typically *not* contiguous, so `as_slice()` can return None. + // + // To avoid per-pixel allocations (and avoid panics), we instead fetch each time sample + // by indexing the (time, num_pixels) array. let num_pixels = height * width; let stack_flat = stack_arr .into_shape((time_len, num_pixels)) .map_err(|e| CoreError::ComputationError(e.to_string()))?; + // Precompute the (time) indices for history/monitor windows once (they are the same + // for all pixels), which avoids collecting per-pixel vectors in the hot loop. + let history_indices: Vec = frac_dates + .iter() + .enumerate() + .filter(|(_, &d)| d >= history_start_frac && d < monitor_start_frac) + .map(|(i, _)| i) + .collect(); + + let monitor_indices: Vec = frac_dates + .iter() + .enumerate() + .filter(|(_, &d)| d >= monitor_start_frac) + .map(|(i, _)| i) + .collect(); + + if history_indices.is_empty() || monitor_indices.is_empty() { + // No valid windows: output stays zeros (no break, no magnitude) + return Ok(out_array.into_pyarray(py).to_owned()); + } + let mut out_flat = out_array .view_mut() .into_shape((2, num_pixels)) .map_err(|e| CoreError::ComputationError(e.to_string()))?; - // Get mutable 1D views for each output channel + // Get mutable 1D views for each output channel (avoid panics; return Python-friendly errors) let mut out_slices = out_flat.axis_iter_mut(Axis(0)); - let mut break_dates = out_slices.next().unwrap(); - let mut magnitudes = out_slices.next().unwrap(); - - // Iterate over each pixel's time series in parallel - Zip::from(&mut break_dates) - .and(&mut magnitudes) - .and(stack_flat.axis_iter(Axis(1))) - .par_for_each(|break_date, magnitude, pixel_ts| { - let (bk_date, mag) = run_bfast_monitor_per_pixel( - pixel_ts.as_slice().unwrap(), - &frac_dates, - history_start_frac, - monitor_start_frac, - order, - h, - alpha, - ); - *break_date = bk_date; - *magnitude = mag; + + let mut break_dates = out_slices.next().ok_or_else(|| { + CoreError::ComputationError( + "Internal error: failed to create break_dates output view (missing channel 0)".to_string(), + ) + })?; + + let mut magnitudes = out_slices.next().ok_or_else(|| { + CoreError::ComputationError( + "Internal error: failed to create magnitudes output view (missing channel 1)".to_string(), + ) + })?; + + // NOTE: Pixel time series for a (time, y, x) input are strided, so we avoid relying on + // `pixel_ts.as_slice()` entirely. Instead, we index into `stack_flat[(t, pix)]`. + // + // To write outputs in parallel safely, we: + // 1) ensure each output channel is a contiguous 1D slice + // 2) split each output slice into disjoint chunks + // 3) process chunks in parallel, writing only within the current chunk + let break_dates_slice = break_dates + .as_slice_mut() + .ok_or_else(|| CoreError::ComputationError("break_dates output buffer is not contiguous".to_string()))?; + let magnitudes_slice = magnitudes + .as_slice_mut() + .ok_or_else(|| CoreError::ComputationError("magnitudes output buffer is not contiguous".to_string()))?; + + // Parallelize over disjoint mutable chunks for both outputs. + break_dates_slice + .par_chunks_mut(BFAST_PIXELS_PER_CHUNK) + .zip(magnitudes_slice.par_chunks_mut(BFAST_PIXELS_PER_CHUNK)) + .enumerate() + .for_each(|(chunk_idx, (break_chunk, mag_chunk))| { + let start = chunk_idx * BFAST_PIXELS_PER_CHUNK; + let end = start + break_chunk.len(); // last chunk may be shorter + + // Reusable buffers for this worker/chunk. + let mut history_ts: Vec = Vec::with_capacity(history_indices.len()); + let mut history_dates: Vec = Vec::with_capacity(history_indices.len()); + let mut monitor_ts: Vec = Vec::with_capacity(monitor_indices.len()); + let mut monitor_dates: Vec = Vec::with_capacity(monitor_indices.len()); + + for pix in start..end { + history_ts.clear(); + history_dates.clear(); + monitor_ts.clear(); + monitor_dates.clear(); + + // Fill history buffers + for &ti in &history_indices { + history_ts.push(stack_flat[(ti, pix)]); + history_dates.push(frac_dates[ti]); + } + + // Fill monitoring buffers + for &ti in &monitor_indices { + monitor_ts.push(stack_flat[(ti, pix)]); + monitor_dates.push(frac_dates[ti]); + } + + let (bk_date, mag) = run_bfast_monitor_per_pixel_windows( + &history_ts, + &history_dates, + &monitor_ts, + &monitor_dates, + order, + h, + alpha, + ); + + let local = pix - start; + break_chunk[local] = bk_date; + mag_chunk[local] = mag; + } }); 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