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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Fast Azimuthal integration
# https://github.com/pyFAI/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# 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 <http://www.gnu.org/licenses/>.
#
"test suite for utilities library"
__author__ = "Jérôme Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "20140106"
import unittest
import numpy
import logging
import sys
import os
import fabio
from utilstest import UtilsTest, getLogger
logger = getLogger(__file__)
pyFAI = sys.modules["pyFAI"]
import pyFAI.utils
if logger.getEffectiveLevel() <= logging.INFO:
import pylab
import scipy.ndimage
# TODO Test:
# gaussian_filter
# relabel
# boundingBox
# removeSaturatedPixel
# DONE:
# # binning
# # unbinning
# # shift
# # shiftFFT
# # measure_offset
# # averageDark
# # averageImages
class test_utils(unittest.TestCase):
unbinned = numpy.random.random((64, 32))
dark = unbinned.astype("float32")
flat = 1 + numpy.random.random((64, 32))
raw = flat + dark
tmp_dir = os.environ.get("PYFAI_TEMPDIR",os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp"))
tmp_file = os.path.join(tmp_dir, "testUtils_average.edf")
def setUp(self):
"""Download files & create tmp directory if needed"""
if not os.path.isdir(self.tmp_dir):
os.makedirs(self.tmp_dir)
def tearDown(self):
"""Remove tmp files if needed"""
if os.path.isfile(self.tmp_file):
try:
os.unlink(self.tmp_file)
except OSError as error:
logger.error("Unable to remove file %s" % self.tmp_file)
def test_binning(self):
"""
test the binning and unbinning functions
"""
binned = pyFAI.utils.binning(self.unbinned, (4, 2))
self.assertEqual(binned.shape, (64 / 4, 32 / 2), "binned size is OK")
unbinned = pyFAI.utils.unBinning(binned, (4, 2))
self.assertEqual(unbinned.shape, self.unbinned.shape, "unbinned size is OK")
self.assertAlmostEqual(unbinned.sum(), self.unbinned.sum(), 2, "content is the same")
def test_averageDark(self):
"""
Some testing for dark averaging
"""
one = pyFAI.utils.averageDark([self.dark])
self.assertEqual(abs(self.dark - one).max(), 0, "data are the same")
two = pyFAI.utils.averageDark([self.dark, self.dark])
self.assertEqual(abs(self.dark - two).max(), 0, "data are the same: mean test")
three = pyFAI.utils.averageDark([numpy.ones_like(self.dark), self.dark, numpy.zeros_like(self.dark) ], "median")
self.assertEqual(abs(self.dark - three).max(), 0, "data are the same: median test")
four = pyFAI.utils.averageDark([numpy.ones_like(self.dark), self.dark, numpy.zeros_like(self.dark) ], "min")
self.assertEqual(abs(numpy.zeros_like(self.dark) - four).max(), 0, "data are the same: min test")
five = pyFAI.utils.averageDark([numpy.ones_like(self.dark), self.dark, numpy.zeros_like(self.dark) ], "max")
self.assertEqual(abs(numpy.ones_like(self.dark) - five).max(), 0, "data are the same: max test")
six = pyFAI.utils.averageDark([numpy.ones_like(self.dark), self.dark, numpy.zeros_like(self.dark), self.dark, self.dark ], "median", .001)
self.assert_(abs(self.dark - six).max() < 1e-4, "data are the same: test threshold")
seven = pyFAI.utils.averageImages([self.raw], darks=[self.dark], flats=[self.flat], threshold=0, output=self.tmp_file)
self.assert_(abs(numpy.ones_like(self.dark) - fabio.open(seven).data).mean() < 1e-2, "averageImages")
def test_shift(self):
"""
Some testing for image shifting and offset measurement functions.
"""
ref = numpy.ones((11, 12))
ref[2, 3] = 5
res = numpy.ones((11, 12))
res[5, 7] = 5
delta = (5 - 2, 7 - 3)
self.assert_(abs(pyFAI.utils.shift(ref, delta) - res).max() < 1e-12, "shift with integers works")
self.assert_(abs(pyFAI.utils.shiftFFT(ref, delta) - res).max() < 1e-12, "shift with FFTs works")
self.assert_(pyFAI.utils.measure_offset(res, ref) == delta, "measure offset works")
def test_gaussian_filter(self):
"""
Check gaussian filters applied via FFT
"""
for sigma in [2, 9.0 / 8.0]:
for mode in ["wrap", "reflect", "constant", "nearest", "mirror" ]:
blurred1 = scipy.ndimage.filters.gaussian_filter(self.flat, sigma, mode=mode)
blurred2 = pyFAI.utils.gaussian_filter(self.flat, sigma, mode=mode)
delta = abs((blurred1 - blurred2) / (blurred1)).max()
logger.info("Error for gaussian blur sigma: %s with mode %s is %s" % (sigma, mode, delta))
self.assert_(delta < 6e-5, "Gaussian blur sigma: %s in %s mode are the same, got %s" % (sigma, mode, delta))
def test_suite_all_Utils():
testSuite = unittest.TestSuite()
testSuite.addTest(test_utils("test_binning"))
testSuite.addTest(test_utils("test_averageDark"))
testSuite.addTest(test_utils("test_shift"))
testSuite.addTest(test_utils("test_gaussian_filter"))
return testSuite
if __name__ == '__main__':
mysuite = test_suite_all_Utils()
runner = unittest.TextTestRunner()
runner.run(mysuite)
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