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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.
#
# ############################################################################*/
"""
Nominal tests of the HistogramndLut function.
"""
import unittest
import numpy as np
from silx.math import HistogramndLut
def _get_bin_edges(histo_range, n_bins, n_dims):
edges = []
for i_dim in range(n_dims):
edges.append(histo_range[i_dim, 0] +
np.arange(n_bins[i_dim] + 1) *
(histo_range[i_dim, 1] - histo_range[i_dim, 0]) /
n_bins[i_dim])
return tuple(edges)
# ==============================================================
# ==============================================================
# ==============================================================
class _TestHistogramndLut_nominal(unittest.TestCase):
"""
Unit tests of the HistogramndLut class.
"""
ndims = None
def setUp(self):
ndims = self.ndims
self.tested_dim = ndims-1
if ndims is None:
raise ValueError('ndims class member not set.')
sample = np.array([5.5, -3.3,
0., -0.5,
3.3, 8.8,
-7.7, 6.0,
-4.0])
weights = np.array([500.5, -300.3,
0.01, -0.5,
300.3, 800.8,
-700.7, 600.6,
-400.4])
n_elems = len(sample)
if ndims == 1:
shape = (n_elems,)
else:
shape = (n_elems, ndims)
self.sample = np.zeros(shape=shape, dtype=sample.dtype)
if ndims == 1:
self.sample = sample
else:
self.sample[..., ndims-1] = sample
self.weights = weights
# the tests are performed along one dimension,
# all the other bins indices along the other dimensions
# are expected to be 2
# (e.g : when testing a 2D sample : [0, x] will go into
# bin [2, y] because of the bin ranges [-2, 2] and n_bins = 4
# for the first dimension)
self.other_axes_index = 2
self.histo_range = np.repeat([[-2., 2.]], ndims, axis=0)
self.histo_range[ndims-1] = [-4., 6.]
self.n_bins = np.array([4]*ndims)
self.n_bins[ndims-1] = 5
if ndims == 1:
def fill_histo(h, v, dim, op=None):
if op:
h[:] = op(h[:], v)
else:
h[:] = v
self.fill_histo = fill_histo
else:
def fill_histo(h, v, dim, op=None):
idx = [self.other_axes_index]*len(h.shape)
idx[dim] = slice(0, None)
if op:
h[idx] = op(h[idx], v)
else:
h[idx] = v
self.fill_histo = fill_histo
def test_nominal_bin_edges(self):
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
bin_edges = instance.bins_edges
expected_edges = _get_bin_edges(self.histo_range,
self.n_bins,
self.ndims)
for i_edges, edges in enumerate(expected_edges):
self.assertTrue(np.array_equal(bin_edges[i_edges],
expected_edges[i_edges]),
msg='Testing bin_edges for dim {0}'
''.format(i_edges+1))
def test_nominal_histo_range(self):
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
histo_range = instance.histo_range
self.assertTrue(np.array_equal(histo_range, self.histo_range))
def test_nominal_last_bin_closed(self):
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
last_bin_closed = instance.last_bin_closed
self.assertEqual(last_bin_closed, False)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins,
last_bin_closed=True)
last_bin_closed = instance.last_bin_closed
self.assertEqual(last_bin_closed, True)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins,
last_bin_closed=False)
last_bin_closed = instance.last_bin_closed
self.assertEqual(last_bin_closed, False)
def test_nominal_n_bins_array(self):
test_n_bins = np.arange(self.ndims) + 10
instance = HistogramndLut(self.sample,
self.histo_range,
test_n_bins)
n_bins = instance.n_bins
self.assertTrue(np.array_equal(test_n_bins, n_bins))
def test_nominal_n_bins_scalar(self):
test_n_bins = 10
expected_n_bins = np.array([test_n_bins] * self.ndims)
instance = HistogramndLut(self.sample,
self.histo_range,
test_n_bins)
n_bins = instance.n_bins
self.assertTrue(np.array_equal(expected_n_bins, n_bins))
def test_nominal_histo_ref(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
histo_ref = instance.histo(copy=False)
w_histo_ref = instance.weighted_histo(copy=False)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertTrue(np.array_equal(histo_ref, expected_h))
self.assertTrue(np.array_equal(w_histo_ref, expected_c))
histo_ref[0, ...] = histo_ref[0, ...] + 10
w_histo_ref[0, ...] = w_histo_ref[0, ...] + 20
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertFalse(np.array_equal(histo_ref, expected_h))
self.assertFalse(np.array_equal(w_histo_ref, expected_c))
histo_2 = instance.histo()
w_histo_2 = instance.weighted_histo()
self.assertFalse(np.array_equal(histo_2, expected_h))
self.assertFalse(np.array_equal(w_histo_2, expected_c))
self.assertTrue(np.array_equal(histo_2, histo_ref))
self.assertTrue(np.array_equal(w_histo_2, w_histo_ref))
def test_nominal_accumulate_once(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertTrue(np.array_equal(instance.histo(), expected_h))
self.assertTrue(np.array_equal(instance.weighted_histo(),
expected_c))
def test_nominal_accumulate_twice(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
# calling accumulate twice
expected_h *= 2
expected_c *= 2
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights)
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertTrue(np.array_equal(instance.histo(), expected_h))
self.assertTrue(np.array_equal(instance.weighted_histo(),
expected_c))
def test_nominal_apply_lut_once(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
histo, w_histo = instance.apply_lut(self.weights)
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertEqual(instance.histo(), None)
self.assertEqual(instance.weighted_histo(), None)
def test_nominal_apply_lut_twice(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
# calling apply_lut twice
expected_h *= 2
expected_c *= 2
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
histo, w_histo = instance.apply_lut(self.weights)
histo_2, w_histo_2 = instance.apply_lut(self.weights,
histo=histo,
weighted_histo=w_histo)
self.assertEqual(id(histo), id(histo_2))
self.assertEqual(id(w_histo), id(w_histo_2))
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
self.assertEqual(instance.histo(), None)
self.assertEqual(instance.weighted_histo(), None)
def test_nominal_accumulate_last_bin_closed(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 2])
expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 1101.1])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins,
last_bin_closed=True)
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def test_nominal_accumulate_weight_min_max(self):
"""
"""
weight_min = -299.9
weight_max = 499.9
expected_h_tpl = np.array([0, 1, 1, 1, 0])
expected_c_tpl = np.array([0., -0.5, 0.01, 300.3, 0.])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights,
weight_min=weight_min,
weight_max=weight_max)
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def test_nominal_accumulate_forced_int32(self):
"""
double weights, int32 weighted_histogram
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700, 0, 0, 300, 500])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins,
dtype=np.int32)
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.int32)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def test_nominal_accumulate_forced_float32(self):
"""
int32 weights, float32 weighted_histogram
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700., 0., 0., 300., 500.])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.float32)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins,
dtype=np.float32)
instance.accumulate(self.weights.astype(np.int32))
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.float32)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def test_nominal_accumulate_int32(self):
"""
int32 weights
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700, 0, 0, 300, 500])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.int32)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights.astype(np.int32))
histo = instance.histo()
w_histo = instance.weighted_histo()
self.assertEqual(w_histo.dtype, np.int32)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def test_nominal_accumulate_int32_double(self):
"""
int32 weights
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_c_tpl = np.array([-700, 0, 0, 300, 500])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
expected_c = np.zeros(shape=self.n_bins, dtype=np.int32)
self.fill_histo(expected_h, expected_h_tpl, self.ndims-1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims-1)
instance = HistogramndLut(self.sample,
self.histo_range,
self.n_bins)
instance.accumulate(self.weights.astype(np.int32))
instance.accumulate(self.weights)
histo = instance.histo()
w_histo = instance.weighted_histo()
expected_h *= 2
expected_c *= 2
self.assertEqual(w_histo.dtype, np.int32)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(w_histo, expected_c))
def testNoneNativeTypes(self):
type = self.sample.dtype.newbyteorder("B")
sampleB = self.sample.astype(type)
type = self.sample.dtype.newbyteorder("L")
sampleL = self.sample.astype(type)
histo_inst = HistogramndLut(sampleB,
self.histo_range,
self.n_bins)
histo_inst = HistogramndLut(sampleL,
self.histo_range,
self.n_bins)
class TestHistogramndLut_nominal_1d(_TestHistogramndLut_nominal):
ndims = 1
class TestHistogramndLut_nominal_2d(_TestHistogramndLut_nominal):
ndims = 2
class TestHistogramndLut_nominal_3d(_TestHistogramndLut_nominal):
ndims = 3
# ==============================================================
# ==============================================================
# ==============================================================
test_cases = (TestHistogramndLut_nominal_1d,
TestHistogramndLut_nominal_2d,
TestHistogramndLut_nominal_3d,)
def suite():
loader = unittest.defaultTestLoader
test_suite = unittest.TestSuite()
for test_class in test_cases:
tests = loader.loadTestsFromTestCase(test_class)
test_suite.addTests(tests)
return test_suite
if __name__ == '__main__':
unittest.main(defaultTest="suite")
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