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# coding: utf-8
# /*##########################################################################
# Copyright (C) 2016 European Synchrotron Radiation Facility
#
# 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.
#
# ############################################################################*/
import copy
import unittest
import numpy
import random
from silx.math.fit import bgtheories
from silx.math.fit.functions import sum_gauss
class TestBgTheories(unittest.TestCase):
"""
"""
def setUp(self):
self.x = numpy.arange(100)
self.y = 10 + 0.05 * self.x + sum_gauss(self.x, 10., 45., 15.)
# add a very narrow high amplitude peak to test strip and snip
self.y += sum_gauss(self.x, 100., 75., 2.)
self.narrow_peak_index = list(self.x).index(75)
random.seed()
def tearDown(self):
pass
def testTheoriesAttrs(self):
for theory_name in bgtheories.THEORY:
self.assertIsInstance(theory_name, str)
self.assertTrue(hasattr(bgtheories.THEORY[theory_name],
"function"))
self.assertTrue(hasattr(bgtheories.THEORY[theory_name].function,
"__call__"))
# Ensure legacy functions are not renamed accidentally
self.assertTrue(
{"No Background", "Constant", "Linear", "Strip", "Snip"}.issubset(
set(bgtheories.THEORY)))
def testNoBg(self):
nobgfun = bgtheories.THEORY["No Background"].function
self.assertTrue(numpy.array_equal(nobgfun(self.x, self.y),
numpy.zeros_like(self.x)))
# default estimate
self.assertEqual(bgtheories.THEORY["No Background"].estimate(self.x, self.y),
([], []))
def testConstant(self):
consfun = bgtheories.THEORY["Constant"].function
c = random.random() * 100
self.assertTrue(numpy.array_equal(consfun(self.x, self.y, c),
c * numpy.ones_like(self.x)))
# default estimate
esti_par, cons = bgtheories.THEORY["Constant"].estimate(self.x, self.y)
self.assertEqual(cons,
[[0, 0, 0]])
self.assertAlmostEqual(esti_par,
min(self.y))
def testLinear(self):
linfun = bgtheories.THEORY["Linear"].function
a = random.random() * 100
b = random.random() * 100
self.assertTrue(numpy.array_equal(linfun(self.x, self.y, a, b),
a + b * self.x))
# default estimate
esti_par, cons = bgtheories.THEORY["Linear"].estimate(self.x, self.y)
self.assertEqual(cons,
[[0, 0, 0], [0, 0, 0]])
self.assertAlmostEqual(esti_par[0], 10, places=3)
self.assertAlmostEqual(esti_par[1], 0.05, places=3)
def testStrip(self):
stripfun = bgtheories.THEORY["Strip"].function
anchors = sorted(random.sample(list(self.x), 4))
anchors_indices = [list(self.x).index(a) for a in anchors]
# we really want to strip away the narrow peak
anchors_indices_copy = copy.deepcopy(anchors_indices)
for idx in anchors_indices_copy:
if abs(idx - self.narrow_peak_index) < 5:
anchors_indices.remove(idx)
anchors.remove(self.x[idx])
width = 2
niter = 1000
bgtheories.THEORY["Strip"].configure(AnchorsList=anchors, AnchorsFlag=True)
bg = stripfun(self.x, self.y, width, niter)
# assert peak amplitude has been decreased
self.assertLess(bg[self.narrow_peak_index],
self.y[self.narrow_peak_index])
# default estimate
for i in anchors_indices:
self.assertEqual(bg[i], self.y[i])
# estimated parameters are equal to the default ones in the config dict
bgtheories.THEORY["Strip"].configure(StripWidth=7, StripIterations=8)
esti_par, cons = bgtheories.THEORY["Strip"].estimate(self.x, self.y)
self.assertTrue(numpy.array_equal(cons, [[3, 0, 0], [3, 0, 0]]))
self.assertEqual(esti_par, [7, 8])
def testSnip(self):
snipfun = bgtheories.THEORY["Snip"].function
anchors = sorted(random.sample(list(self.x), 4))
anchors_indices = [list(self.x).index(a) for a in anchors]
# we want to strip away the narrow peak, so remove nearby anchors
anchors_indices_copy = copy.deepcopy(anchors_indices)
for idx in anchors_indices_copy:
if abs(idx - self.narrow_peak_index) < 5:
anchors_indices.remove(idx)
anchors.remove(self.x[idx])
width = 16
bgtheories.THEORY["Snip"].configure(AnchorsList=anchors, AnchorsFlag=True)
bg = snipfun(self.x, self.y, width)
# assert peak amplitude has been decreased
self.assertLess(bg[self.narrow_peak_index],
self.y[self.narrow_peak_index],
"Snip didn't decrease the peak amplitude.")
# anchored data must remain fixed
for i in anchors_indices:
self.assertEqual(bg[i], self.y[i])
# estimated parameters are equal to the default ones in the config dict
bgtheories.THEORY["Snip"].configure(SnipWidth=7)
esti_par, cons = bgtheories.THEORY["Snip"].estimate(self.x, self.y)
self.assertTrue(numpy.array_equal(cons, [[3, 0, 0]]))
self.assertEqual(esti_par, [7])
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