Examples And Smoke Tests

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/<file>.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/

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