# 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")