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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Statistics unit test
Testing the following:
- Create a signal
- Compute statistics on signal and compare with expected results
- Create an image
- Compute statistics on image and compare with expected results
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
from __future__ import annotations
import numpy as np
import pytest
import scipy.integrate as spt
import sigima.objects
import sigima.proc.image
import sigima.proc.signal
def get_analytical_stats(data: np.ndarray) -> dict[str, float]:
"""Compute analytical statistics for data
Args:
data: Array of data
Returns:
Dictionary with analytical statistics
"""
results = {}
if data.shape[0] == 2:
# This is a signal data (row 0: x, row 1: y)
results["trapz"] = spt.trapezoid(data[1], data[0])
data = data[1]
results.update(
{
"min": np.min(data),
"max": np.max(data),
"mean": np.mean(data),
"median": np.median(data),
"std": np.std(data),
"snr": np.mean(data) / np.std(data),
"ptp": np.ptp(data),
"sum": np.sum(data),
}
)
return results
def create_reference_signal() -> sigima.objects.SignalObj:
"""Create reference signal"""
param = sigima.objects.GaussParam()
sig = sigima.objects.create_signal_from_param(param)
sig.roi = sigima.objects.create_signal_roi(
[len(sig.x) // 2, len(sig.x) - 1], indices=True
)
return sig
def create_reference_image() -> sigima.objects.ImageObj:
"""Create reference image"""
param = sigima.objects.Gauss2DParam.create(title="2D-Gaussian")
ima = sigima.objects.create_image_from_param(param)
dy, dx = ima.data.shape
ima.roi = sigima.objects.create_image_roi(
"rectangle",
[
[dx // 2, 0, dx, dy],
[0, 0, dx // 3, dy // 3],
[dx // 2, dy // 2, dx, dy],
],
)
return ima
@pytest.mark.validation
def test_signal_stats_unit() -> None:
"""Validate computed statistics for signals"""
obj = create_reference_signal()
table = sigima.proc.signal.stats(obj)
ref = get_analytical_stats(obj.xydata)
for key, val in ref.items():
assert key in table
assert np.isclose(table[key][0], val), f"Incorrect value for {key}"
# Given the fact that signal ROI is set to [len(sig.x) // 2, len(sig.x) - 1],
# we may check the relationship between the results on the whole signal and the ROI:
for key, val in ref.items():
if key in ("trapz", "sum"):
assert np.isclose(table[key][1], val / 2, rtol=0.02)
elif key == "median":
continue
else:
assert np.isclose(table[key][1], val, rtol=0.01)
@pytest.mark.validation
def test_image_stats_unit() -> None:
"""Validate computed statistics for images"""
obj = create_reference_image()
# Ignore "RuntimeWarning: invalid value encountered in scalar divide" in the test
# (this warning is due to the fact that the 2nd ROI has zero sum of pixel values,
# hence the mean/std is NaN)
with np.errstate(invalid="ignore"):
res = sigima.proc.image.stats(obj)
ref = get_analytical_stats(obj.data)
for key, val in ref.items():
assert key in res
assert np.isclose(res[key][0], val, rtol=1e-4, atol=1e-5), (
f"Incorrect value for {key}"
)
# Given the fact that image ROI is set to
# [[dx // 2, 0, dx, dy], [0, 0, dx // 3, dy // 3], [dx // 2, dy // 2, dx, dy]],
# we may check the relationship between the results on the whole image and the ROIs:
for key, val in ref.items():
if key == "sum":
assert np.isclose(res[key][1], val / 2, rtol=0.02)
assert np.isclose(res[key][3], val / 4, rtol=0.02)
elif key == "median":
continue
else:
assert np.isclose(res[key][1], val, rtol=0.01)
assert np.isclose(res[key][3], val, rtol=0.01)
if key != "snr":
assert np.isclose(res[key][2], 0.0, atol=0.001)
if __name__ == "__main__":
test_signal_stats_unit()
test_image_stats_unit()
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