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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 convolution cython code"
__author__ = "Jérôme Kieffer"
__contact__ = "Jérôme Kieffer"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "20/10/2014"
import sys
import unittest
import numpy
from utilstest import getLogger # UtilsTest, Rwp, getLogger
logger = getLogger(__file__)
pyFAI = sys.modules["pyFAI"]
from pyFAI import _convolution
import scipy.ndimage, scipy.misc, scipy.signal
class TestConvolution(unittest.TestCase):
def setUp(self):
self.sigma = 1
self.width = 8*self.sigma+1
if self.width%2==0:
self.width+=1
self.gauss = scipy.signal.gaussian(self.width, self.sigma)
self.gauss/=self.gauss.sum()
self.lena = scipy.misc.lena().astype("float32")
def test_gaussian(self):
gauss = _convolution.gaussian(self.sigma)
self.assert_(numpy.allclose(gauss,self.gauss), "gaussian curves are the same")
def test_horizontal_convolution(self):
gauss = self.gauss.astype(numpy.float32)
ref = scipy.ndimage.filters.convolve1d(self.lena, self.gauss, axis= -1)
obt = _convolution.horizontal_convolution(self.lena, gauss)
self.assert_(numpy.allclose(ref, obt), "horizontal filtered images are the same")
def test_vertical_convolution(self):
gauss = self.gauss.astype(numpy.float32)
ref = scipy.ndimage.filters.convolve1d(self.lena, self.gauss, axis=0)
obt = _convolution.vertical_convolution(self.lena, gauss)
self.assert_(numpy.allclose(ref, obt), "vertical filtered images are the same")
def test_gaussian_filter(self):
ref = scipy.ndimage.filters.gaussian_filter(self.lena, self.sigma)
obt = _convolution.gaussian_filter(self.lena, self.sigma)
self.assert_(numpy.allclose(ref, obt), "gaussian filtered images are the same")
def test_suite_all_convolution():
testSuite = unittest.TestSuite()
testSuite.addTest(TestConvolution("test_horizontal_convolution"))
testSuite.addTest(TestConvolution("test_vertical_convolution"))
testSuite.addTest(TestConvolution("test_gaussian"))
testSuite.addTest(TestConvolution("test_gaussian_filter"))
return testSuite
if __name__ == '__main__':
mysuite = test_suite_all_convolution()
runner = unittest.TextTestRunner()
runner.run(mysuite)
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