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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Fable Input Output
# https://github.com/silx-kit/fabio
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,
# publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
__authors__ = ["Jérôme Kieffer"]
__contact__ = "Jerome.Kieffer@esrf.fr"
__license__ = "MIT"
__copyright__ = "2020 ESRF"
__date__ = "02/06/2022"
import unittest
import numpy
import logging
logger = logging.getLogger(__name__)
from ..sparseimage import densify, cython_densify
from ..ext.dense import distribution_uniform_mtc, distribution_normal_mtc
class TestDensification(unittest.TestCase):
def test_rng_uniform(self):
shape = (100, 100)
U = distribution_uniform_mtc(shape).ravel()
d, p = numpy.histogram(U, 100)
eps = 1e-3
self.assertGreater(p[0], 0)
self.assertLess(p[0], eps)
self.assertGreater(p[-1], 1 - eps)
self.assertLess(p[-1], 1)
self.assertGreater(d.min(), 50)
self.assertLess(d.max(), 150)
def test_rng_normal(self):
shape = (100, 100)
mu = 5.5
sigma = 1.5
one = numpy.ones(shape)
N = distribution_normal_mtc(one * mu, one * sigma)
self.assertAlmostEqual(N.mean(), mu, 1)
self.assertAlmostEqual(N.std(), sigma, 1)
def test_cython(self):
seed = 0
shape = 256, 256
nframes = 8
vsize = 181 # This is cheated to avoid interpolation issues with rounding 128*sqrt(2)
y, x = numpy.ogrid[-shape[0] // 2:-shape[0] // 2 + shape[0],
-shape[1] // 2:-shape[1] // 2 + shape[1]]
# To make this test "robust", those two radial position arrays needs to be in float64 ... in production float32 is more common
r2d = numpy.sqrt(x * x + y * y).astype(numpy.float64)
radius = numpy.linspace(0, r2d.max(), vsize).astype(numpy.float64)
npeak = numpy.random.randint(90, 110, size=nframes)
scale = numpy.random.randint(90, 110, size=nframes)
osc = numpy.random.randint(40, 100, size=nframes)
indptr = numpy.zeros(nframes + 1, dtype=int)
indptr[1:] = numpy.cumsum(npeak)
index = numpy.random.randint(0, numpy.prod(shape), size=npeak.sum()).astype(numpy.uint32)
intensity = numpy.random.randint(0, 10, size=npeak.sum()).astype(numpy.uint16)
frames = numpy.empty((nframes, *shape), dtype=numpy.uint16)
background = numpy.empty((nframes, vsize), dtype=numpy.float32)
noise = numpy.empty((nframes, vsize), dtype=numpy.float32)
python = []
cython = []
for i, f in enumerate(frames):
background[i] = numpy.arcsinh(scale[i] * numpy.sinc(radius / osc[i]) ** 2)
noise[i] = abs(numpy.diff(background[i], prepend=background[i, 0]))
f[...] = numpy.arcsinh(scale[i] * numpy.sinc(r2d / osc[i]) ** 2).round()
f.ravel()[index[indptr[i]:indptr[i + 1]]] = intensity[indptr[i]:indptr[i + 1]]
python = densify(r2d, radius, index[indptr[i]:indptr[i + 1]], intensity[indptr[i]:indptr[i + 1]], 0, background[i])
cython = cython_densify.densify(r2d, radius, index[indptr[i]:indptr[i + 1]], intensity[indptr[i]:indptr[i + 1]], 0, intensity.dtype, background[i], None)
self.assertTrue(numpy.all(python == cython), "python == cython #" + str(i))
# Rounding errors:
delta = (python.astype(int) - f)
self.assertLessEqual(abs(delta).max(), 1, "Maximum difference is 1 due to rounding errors")
bad = numpy.where(delta)
print("#####", i)
self.assertLess(len(bad[0]), numpy.prod(shape) / 500, "python differs from reference on less then 0.2% of the pixel #" + str(i))
# Now consider the noise ...
python = densify(r2d, radius, index[indptr[i]:indptr[i + 1]], intensity[indptr[i]:indptr[i + 1]], 0, background[i], noise[i], seed=seed)
cython = cython_densify.densify(r2d, radius, index[indptr[i]:indptr[i + 1]], intensity[indptr[i]:indptr[i + 1]], 0, intensity.dtype, background[i], noise[i], seed=seed)
self.assertTrue(abs(python.astype(int) - cython).max() <= 2 * max(1, noise[i].max()), "python is close to cython #" + str(i))
def suite():
loadTests = unittest.defaultTestLoader.loadTestsFromTestCase
testsuite = unittest.TestSuite()
testsuite.addTest(loadTests(TestDensification))
return testsuite
if __name__ == '__main__':
runner = unittest.TextTestRunner()
runner.run(suite())
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