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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""Unit tests for kernel normalization features in convolution/deconvolution."""
from __future__ import annotations
import numpy as np
import pytest
from sigima.config import options as sigima_options
from sigima.objects import create_image, create_signal
from sigima.objects.image import ImageObj
from sigima.objects.signal import SignalObj
from sigima.proc.image.mathops import convolution as image_convolution
from sigima.proc.image.mathops import deconvolution as image_deconvolution
from sigima.proc.signal import convolution as signal_convolution
from sigima.proc.signal import deconvolution as signal_deconvolution
def _generate_test_signal(size: int = 100) -> SignalObj:
"""Generate a simple test signal.
Args:
size: The size of the signal to generate.
Returns:
A signal object.
"""
x = np.linspace(0, 10, size)
y = np.sin(x) + 0.5 * np.sin(3 * x)
return create_signal("Test Signal", x, y)
def _generate_unnormalized_signal_kernel(size: int = 100) -> SignalObj:
"""Generate an unnormalized Gaussian-like kernel for signal processing.
Args:
size: The size of the kernel.
Returns:
A signal object representing an unnormalized kernel.
Notes:
The kernel uses the same x-axis range and size as _generate_test_signal
to ensure compatible sample rates for convolution and deconvolution.
"""
x = np.linspace(0, 10, size)
y = np.exp(-((x - 5) ** 2) / 2) # Centered Gaussian
y *= 2.0 # Make it unnormalized (sum != 1.0)
return create_signal("Unnormalized Kernel", x, y)
def _generate_test_image(size: int = 64) -> ImageObj:
"""Generate a simple test image.
Args:
size: The dimension of the square image to generate.
Returns:
An image object.
"""
data = np.random.rand(size, size)
return create_image("Test Image", data)
def _generate_unnormalized_image_kernel(size: int = 5) -> ImageObj:
"""Generate an unnormalized Gaussian-like kernel for image processing.
Args:
size: The dimension of the square kernel to generate.
Returns:
An image object representing an unnormalized kernel.
"""
kernel = np.outer(
np.exp(-(np.linspace(-2, 2, size) ** 2)),
np.exp(-(np.linspace(-2, 2, size) ** 2)),
)
kernel *= 2.0 # Make it unnormalized (sum != 1.0)
return create_image("Unnormalized Kernel", kernel)
class TestKernelNormalizationSignal:
"""Test suite for signal kernel normalization in convolution."""
@pytest.fixture(autouse=True)
def setup_and_teardown(self):
"""Store initial option values and restore them after each test."""
# Store initial values
initial_auto_normalize = sigima_options.auto_normalize_kernel.get()
yield
# Restore initial values
sigima_options.auto_normalize_kernel.set(initial_auto_normalize)
def test_signal_convolution_auto_normalization_enabled(self):
"""Test that auto-normalization works correctly for convolution."""
# Setup: auto-normalization enabled
sigima_options.auto_normalize_kernel.set(True)
signal = _generate_test_signal()
kernel = _generate_unnormalized_signal_kernel()
# Execute with auto-normalization
result = signal_convolution(signal, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == signal.data.shape
def test_signal_convolution_auto_normalization_disabled(self):
"""Test convolution with auto-normalization disabled."""
# Setup: auto-normalization disabled
sigima_options.auto_normalize_kernel.set(False)
signal = _generate_test_signal()
kernel = _generate_unnormalized_signal_kernel()
# Execute without auto-normalization
result = signal_convolution(signal, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == signal.data.shape
def test_signal_deconvolution_auto_normalization_enabled(self):
"""Test that auto-normalization works correctly for deconvolution."""
# Setup: auto-normalization enabled
sigima_options.auto_normalize_kernel.set(True)
signal = _generate_test_signal()
kernel = _generate_unnormalized_signal_kernel()
# Execute with auto-normalization
result = signal_deconvolution(signal, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == signal.data.shape
def test_signal_deconvolution_auto_normalization_disabled(self):
"""Test deconvolution with auto-normalization disabled."""
# Setup: auto-normalization disabled
sigima_options.auto_normalize_kernel.set(False)
signal = _generate_test_signal()
kernel = _generate_unnormalized_signal_kernel()
# Execute without auto-normalization
result = signal_deconvolution(signal, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == signal.data.shape
class TestKernelNormalizationImage:
"""Test suite for image kernel normalization in convolution and deconvolution."""
@pytest.fixture(autouse=True)
def setup_and_teardown(self):
"""Store initial option values and restore them after each test."""
# Store initial values
initial_auto_normalize = sigima_options.auto_normalize_kernel.get()
yield
# Restore initial values
sigima_options.auto_normalize_kernel.set(initial_auto_normalize)
def test_image_convolution_auto_normalization_enabled(self):
"""Test that auto-normalization works correctly for convolution."""
# Setup: auto-normalization enabled
sigima_options.auto_normalize_kernel.set(True)
image = _generate_test_image()
kernel = _generate_unnormalized_image_kernel()
# Execute with auto-normalization
result = image_convolution(image, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == image.data.shape
def test_image_convolution_auto_normalization_disabled(self):
"""Test convolution with auto-normalization disabled."""
# Setup: auto-normalization disabled
sigima_options.auto_normalize_kernel.set(False)
image = _generate_test_image()
kernel = _generate_unnormalized_image_kernel()
# Execute without auto-normalization
result = image_convolution(image, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == image.data.shape
def test_image_deconvolution_auto_normalization_enabled(self):
"""Test that auto-normalization works correctly for deconvolution."""
# Setup: auto-normalization enabled
sigima_options.auto_normalize_kernel.set(True)
image = _generate_test_image()
kernel = _generate_unnormalized_image_kernel()
# Execute with auto-normalization
result = image_deconvolution(image, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == image.data.shape
def test_image_deconvolution_auto_normalization_disabled(self):
"""Test deconvolution with auto-normalization disabled."""
# Setup: auto-normalization disabled
sigima_options.auto_normalize_kernel.set(False)
image = _generate_test_image()
kernel = _generate_unnormalized_image_kernel()
# Execute without auto-normalization
result = image_deconvolution(image, kernel)
# Verify result exists and has same shape as input
assert result is not None
assert result.data is not None
assert result.data.shape == image.data.shape
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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