diff options
Diffstat (limited to 'silx/math/test/test_HistogramndLut_nominal.py')
| -rw-r--r-- | silx/math/test/test_HistogramndLut_nominal.py | 586 |
1 files changed, 0 insertions, 586 deletions
diff --git a/silx/math/test/test_HistogramndLut_nominal.py b/silx/math/test/test_HistogramndLut_nominal.py deleted file mode 100644 index 9450326..0000000 --- a/silx/math/test/test_HistogramndLut_nominal.py +++ /dev/null @@ -1,586 +0,0 @@ -# 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") |