287 lines
9.5 KiB
Python
287 lines
9.5 KiB
Python
"""
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Shared property-based tests for all SpectrumComponent subclasses.
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These tests verify universal properties that every correction component
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must satisfy. Instead of duplicating `test_positive`, `test_output_shape`,
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`test_no_nan` in every component test file, each file imports and
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parametrizes these helpers.
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## Property test categories
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1. **Positivity** — multiplicative factors must be ≥ 0 (strictly > 0 except
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at physical boundaries like W=1 or W=W₀)
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2. **Shape preservation** — output shape must match input shape
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3. **Numerical stability** — no NaN or inf values across the full energy range
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4. **Type correctness** — output is always numpy.ndarray
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## Usage example
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```python
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import pytest
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from beta_spectrum.components.fermi import FermiFunction
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from tests.common.test_property_tests import (
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test_positive_values,
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test_output_shape_preserved,
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test_no_nan_across_range,
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)
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class TestFermiProperties:
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@pytest.mark.parametrize("W", [[1.5], [1.1, 2.0, 3.0], [1.01, 2.0, 3.0, 4.0, 5.0]])
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@pytest.mark.property
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def test_positive(self, W):
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ff = FermiFunction(Z=20, A=40)
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test_positive_values(ff, W, component_name="FermiFunction")
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@pytest.mark.parametrize("n", [1, 5, 100])
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@pytest.mark.property
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def test_shape(self, n):
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ff = FermiFunction(Z=20, A=40)
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W = np.linspace(1.1, 3.0, n)
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test_output_shape_preserved(ff, W, component_name="FermiFunction")
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```
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## Test markers
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- `@pytest.mark.property` — marks a test as checking a universal property
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- `@pytest.mark.quality` — marks a test as checking code quality (shape, type)
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These markers allow running only property tests (`pytest -m property`)
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or only quality tests (`pytest -m quality`) for fast feedback during development.
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"""
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import numpy as np
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import pytest
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# ---------------------------------------------------------------------------
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# Property tests: positivity
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# ---------------------------------------------------------------------------
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@pytest.mark.property
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def _test_positive_values(component, W, component_name="Component"):
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"""Component must return positive values at all W in the physical range.
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For multiplicative correction factors, negative values indicate a
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fundamental bug (wrong sign in formula, overflow to negative, etc.).
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Parameters
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----------
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component : SpectrumComponent or callable
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The component to test. Must be callable as `component(W)`.
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W : array_like
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Energy grid to evaluate at. Values should be in the physical range
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(W > 1 for β⁻, W < W₀ for endpoint).
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component_name : str
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Display name for error messages.
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"""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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assert not np.any(np.isnan(result)), (
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f"{component_name}: NaN values found in output. "
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f"W range: [{W_arr.min():.3f}, {W_arr.max():.3f}], "
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f"result range: [{np.nanmin(result):.6f}, {np.nanmax(result):.6f}]"
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)
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assert not np.any(
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np.isinf(result)
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), f"{component_name}: Inf values found in output."
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assert np.all(result > 0), (
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f"{component_name}: non-positive values found. "
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f"Min = {np.min(result):.10f} at W = {W_arr[np.argmin(result)][0]:.4f}"
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)
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@pytest.mark.property
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def _test_non_negative_values(
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component, W, component_name="Component", allow_zero=True
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):
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"""Component must return non-negative values (allows zero at physical boundaries).
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Use this for components that can legitimately be zero (e.g., phase_space
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at W=1 or W=W₀).
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Parameters
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----------
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allow_zero : bool
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If True, zero values are allowed. If False, strictly positive is required.
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"""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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assert not np.any(
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np.isnan(result)
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), f"{component_name}: NaN values found in output."
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assert not np.any(
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np.isinf(result)
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), f"{component_name}: Inf values found in output."
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if allow_zero:
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assert np.all(result >= 0), (
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f"{component_name}: negative values found. " f"Min = {np.min(result):.10f}"
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)
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else:
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assert np.all(result > 0), (
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f"{component_name}: non-positive values found. "
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f"Min = {np.min(result):.10f}"
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)
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# ---------------------------------------------------------------------------
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# Property tests: shape preservation
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# ---------------------------------------------------------------------------
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@pytest.mark.quality
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def _test_output_shape_preserved(component, W, component_name="Component"):
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"""Output shape must match input shape — no flattening or reshaping."""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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assert isinstance(
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result, np.ndarray
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), f"{component_name}: output is {type(result)}, expected np.ndarray"
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assert result.shape == W_arr.shape, (
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f"{component_name}: shape mismatch. "
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f"Input {W_arr.shape} → Output {result.shape}"
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)
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@pytest.mark.quality
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def _test_scalar_input_returns_array(component, W_scalar, component_name="Component"):
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"""Single-element array input should return array (not scalar)."""
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result = component(W_scalar)
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assert isinstance(result, np.ndarray), (
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f"{component_name}: single-element input returned {type(result)}, "
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f"expected np.ndarray"
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)
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assert result.ndim == 1, f"{component_name}: expected 1D output, got {result.ndim}D"
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# ---------------------------------------------------------------------------
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# Property tests: numerical stability
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# ---------------------------------------------------------------------------
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@pytest.mark.quality
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def _test_no_nan_across_range(component, W, component_name="Component"):
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"""No NaN values across the entire energy range."""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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nan_mask = np.isnan(result)
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if np.any(nan_mask):
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nan_W = W_arr[nan_mask]
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pytest.fail(
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f"{component_name}: NaN values at W = {nan_W}. "
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f"Result min = {np.nanmin(result):.6f}, max = {np.nanmax(result):.6f}"
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)
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@pytest.mark.quality
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def _test_no_inf_across_range(component, W, component_name="Component"):
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"""No infinite values across the entire energy range."""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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inf_mask = np.isinf(result)
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if np.any(inf_mask):
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inf_W = W_arr[inf_mask]
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pytest.fail(
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f"{component_name}: Inf values at W = {inf_W}. "
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f"Result min = {np.nanmin(result):.6f}, max = {np.nanmax(result):.6f}"
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)
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@pytest.mark.quality
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def _test_full_range_stability(component, W, component_name="Component"):
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"""Full energy range: no NaN, no inf, finite values."""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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assert np.all(np.isfinite(result)), (
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f"{component_name}: non-finite values in full range. "
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f"NaN count: {np.sum(np.isnan(result))}, "
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f"Inf count: {np.sum(np.isinf(result))}"
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)
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# ---------------------------------------------------------------------------
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# Property tests: type correctness
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# ---------------------------------------------------------------------------
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@pytest.mark.quality
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def _test_output_is_ndarray(component, W, component_name="Component"):
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"""Output must be numpy.ndarray for any valid input."""
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W_arr = np.asarray(W, dtype=float)
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result = component(W_arr)
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assert isinstance(result, np.ndarray), (
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f"{component_name}: output type is {type(result).__name__}, "
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f"expected np.ndarray"
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)
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# ---------------------------------------------------------------------------
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# Parametrized test templates for quick component integration
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# ---------------------------------------------------------------------------
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def make_positive_test(component_class, test_params, component_name=None):
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"""Factory function to create a parametrized positivity test.
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Parameters
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----------
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component_class : type
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The component class to instantiate (e.g., FermiFunction).
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test_params : list[dict]
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List of keyword arguments for component construction.
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Each dict is tested at multiple W values.
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component_name : str, optional
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Display name. Defaults to component_class.__name__.
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Example
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-------
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tests = make_positive_test(
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FermiFunction,
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[{"Z": 1, "A": 1}, {"Z": 20, "A": 40}, {"Z": 92, "A": 238}],
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)
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"""
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if component_name is None:
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component_name = component_class.__name__
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W_values = [
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np.array([1.5]),
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np.linspace(1.1, 3.0, 5),
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np.linspace(1.01, 5.0, 20),
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]
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@pytest.mark.property
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@pytest.mark.parametrize("params", test_params)
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@pytest.mark.parametrize("W", W_values)
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def test_positive(component_params, W_arr):
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comp = component_class(**component_params)
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_test_positive_values(comp, W_arr, component_name)
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return test_positive
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def make_stability_test(component_class, test_params, component_name=None):
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"""Factory function to create a parametrized stability test."""
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if component_name is None:
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component_name = component_class.__name__
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W_values = [
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np.linspace(1.01, 6.0, 50),
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np.linspace(1.001, 10.0, 100),
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]
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@pytest.mark.quality
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@pytest.mark.parametrize("params", test_params)
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@pytest.mark.parametrize("W", W_values)
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def test_stability(component_params, W_arr):
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comp = component_class(**component_params)
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_test_full_range_stability(comp, W_arr, component_name)
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return test_stability
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