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Add a unit test for ScaleVariance and ComputeNoiseCorrelation
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# This file is part of meas_algorithms. | ||
# | ||
# Developed for the LSST Data Management System. | ||
# This product includes software developed by the LSST Project | ||
# (https://www.lsst.org). | ||
# See the COPYRIGHT file at the top-level directory of this distribution | ||
# for details of code ownership. | ||
# | ||
# This program is free software: you can redistribute it and/or modify | ||
# it under the terms of the GNU General Public License as published by | ||
# the Free Software Foundation, either version 3 of the License, or | ||
# (at your option) any later version. | ||
# | ||
# This program is distributed in the hope that it will be useful, | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
# GNU General Public License for more details. | ||
# | ||
# You should have received a copy of the GNU General Public License | ||
# along with this program. If not, see <https://www.gnu.org/licenses/>. | ||
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import itertools | ||
import unittest | ||
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import lsst.utils.tests | ||
import numpy as np | ||
from lsst.afw import image as afwImage | ||
from lsst.meas.algorithms import ( | ||
ComputeNoiseCorrelationConfig, | ||
ComputeNoiseCorrelationTask, | ||
ScaleVarianceTask, | ||
) | ||
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class NoiseVarianceTestCase(lsst.utils.tests.TestCase): | ||
def _generateImage( | ||
self, rho1: float, rho2: float, size: int = 512, seed: int = 12345 | ||
): | ||
"""Create a correlated noise field using simple translations. | ||
Y[i,j] = X[i,j] + a1 X[i-1,j] + a2 X[i,j-1] | ||
Var(X[i,j]) = 1 | ||
Cov( Y[i,j], V[i-1,j] ) = a1 | ||
Cov ( Y[i,j], V[i,j-1] ) = a2 | ||
Var(Y[i,j]) = 1 + a1**2 + a2**2 | ||
rho_i = a_i / sqrt(1 + a1**2 + a2**2) for i = 1, 2 | ||
Parameters | ||
---------- | ||
rho1 : float | ||
Correlation coefficient along the horizontal (+x) direction. | ||
rho2 : float | ||
Correlation coefficient along the vertical (+y) direction. | ||
size : int, optional | ||
Size of the noise image to generate. | ||
seed : int, optional | ||
Seed for the random number generator. | ||
Returns | ||
------- | ||
mi: `lsst.afw.image.MaskedImage` | ||
MaskedImage containing the correlated noise field | ||
and a per-pixel variance plane. | ||
""" | ||
np.random.seed(seed) | ||
noise = np.random.randn(size, size).astype(np.float32) | ||
# Solve for the kernel parameters (a1, a2) & generate correlated noise. | ||
r2 = rho1**2 + rho2**2 | ||
if r2 > 0: | ||
k = 0.5 * (1 + np.sqrt(1 - 4 * r2)) / r2 | ||
a1, a2 = k * rho1, k * rho2 | ||
corr_noise = ( | ||
noise + a1 * np.roll(noise, 1, axis=0) + a2 * np.roll(noise, 1, axis=1) | ||
) | ||
else: | ||
a1, a2 = 0, 0 | ||
corr_noise = noise | ||
image = afwImage.ImageF(array=corr_noise[1:-1, 1:-1]) | ||
variance = afwImage.ImageF(size - 2, size - 2, (1 + a1**2 + a2**2)) | ||
mi = afwImage.MaskedImageF(image=image, variance=variance) | ||
return mi | ||
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@lsst.utils.tests.methodParameters( | ||
rho=((0.0, 0.0), (-0.2, 0.0), (0.0, 0.1), (0.15, 0.25), (0.25, -0.15)) | ||
) | ||
def testScaleVariance(self, rho): | ||
"""Test that the ScaleVarianceTask scales the variance plane correctly.""" | ||
task = ScaleVarianceTask() | ||
rho1, rho2 = rho | ||
mi = self._generateImage(rho1, rho2) | ||
scaleFactors = task.computeScaleFactors(mi) | ||
# Check for consistency between pixelFactor and imageFactor | ||
self.assertFloatsAlmostEqual( | ||
scaleFactors.pixelFactor, scaleFactors.imageFactor, atol=1e-6 | ||
) | ||
# Since the variance plane is adjusted for the correlation, the scale | ||
# factor should be 1.0 within statistical error. | ||
self.assertFloatsAlmostEqual(scaleFactors.pixelFactor, 1.0, rtol=2e-2) | ||
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@lsst.utils.tests.methodParametersProduct( | ||
rho=((0.0, 0.0), (0.2, 0.0), (0.0, -0.1), (0.15, 0.25), (-0.25, 0.15)), | ||
scaleEmpircalVariance=(True, False), | ||
subtractEmpiricalMean=(True, False), | ||
) | ||
def testComputeCorrelation(self, rho, scaleEmpircalVariance, subtractEmpiricalMean): | ||
"""Test that the noise correlation coefficients are computed correctly.""" | ||
config = ComputeNoiseCorrelationConfig(size=5) | ||
config.scaleEmpiricalVariance = scaleEmpircalVariance | ||
config.subtractEmpiricalMean = subtractEmpiricalMean | ||
task = ComputeNoiseCorrelationTask(config=config) | ||
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rho1, rho2 = rho | ||
mi = self._generateImage(rho1, rho2) | ||
corr_matrix = task.run(mi) | ||
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# corr_matrix elements should be zero except for (1,0), (0,1) & (0,0). | ||
# Use the other elements to get an estimate of the statistical | ||
# uncertainty in our estimates. | ||
err = np.std( | ||
[ | ||
corr_matrix(i, j) | ||
for i, j in itertools.product(range(5), range(5)) | ||
if (i + j > 1) | ||
] | ||
) | ||
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self.assertLess(abs(corr_matrix(1, 0) / corr_matrix(0, 0) - rho1), 2 * err) | ||
self.assertLess(abs(corr_matrix(0, 1) / corr_matrix(0, 0) - rho2), 2 * err) | ||
self.assertLess(err, 3e-3) # Check that the err is much less than rho. | ||
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class MemoryTestCase(lsst.utils.tests.MemoryTestCase): | ||
pass | ||
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def setup_module(module): | ||
lsst.utils.tests.init() | ||
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if __name__ == "__main__": | ||
lsst.utils.tests.init() | ||
unittest.main() |