diff options
Diffstat (limited to 'src/silx/math/test/test_histogramnd_vs_np.py')
| -rw-r--r-- | src/silx/math/test/test_histogramnd_vs_np.py | 629 |
1 files changed, 302 insertions, 327 deletions
diff --git a/src/silx/math/test/test_histogramnd_vs_np.py b/src/silx/math/test/test_histogramnd_vs_np.py index d1fb8be..23167f6 100644 --- a/src/silx/math/test/test_histogramnd_vs_np.py +++ b/src/silx/math/test/test_histogramnd_vs_np.py @@ -25,7 +25,6 @@ Tests for the histogramnd function. Results are compared to numpy's histogramdd. """ -import os import unittest import operator import pytest @@ -38,8 +37,7 @@ from silx.math.chistogramnd import chistogramnd as histogramnd # ============================================================== # ============================================================== -_RTOL_DICT = {np.float64: 10**-13, - np.float32: 10**-5} +_RTOL_DICT = {np.float64: 10**-13, np.float32: 10**-5} # ============================================================== # ============================================================== @@ -51,16 +49,12 @@ def _add_values_to_array_if_missing(array, values, n_values): if len(array.shape) == 1: if not max_in_col: - rnd_idx = np.random.randint(0, - high=len(array)-1, - size=(n_values,)) + rnd_idx = np.random.randint(0, high=len(array) - 1, size=(n_values,)) array[rnd_idx] = values else: for i in range(len(max_in_col)): if not max_in_col[i]: - rnd_idx = np.random.randint(0, - high=len(array)-1, - size=(n_values,)) + rnd_idx = np.random.randint(0, high=len(array) - 1, size=(n_values,)) array[rnd_idx, i] = values[i] @@ -71,13 +65,10 @@ def _get_values_index(array, values, op=operator.lt): return np.where(idx)[0] -def _get_in_range_indices(array, - minvalues, - maxvalues, - minop=operator.ge, - maxop=operator.lt): - idx = np.logical_and(minop(array, minvalues), - maxop(array, maxvalues)) +def _get_in_range_indices( + array, minvalues, maxvalues, minop=operator.ge, maxop=operator.lt +): + idx = np.logical_and(minop(array, minvalues), maxop(array, maxvalues)) if array.ndim > 1: idx = np.all(idx, axis=1) return np.where(idx)[0] @@ -87,6 +78,7 @@ class _TestHistogramnd(unittest.TestCase): """ Unit tests of the histogramnd function. """ + __test__ = False # ignore abstract class sample_rng = None @@ -103,7 +95,6 @@ class _TestHistogramnd(unittest.TestCase): dtype_weights = None def generate_data(self): - self.longMessage = True int_min = 0 @@ -113,31 +104,33 @@ class _TestHistogramnd(unittest.TestCase): if self.n_dims == 1: shape = (n_elements,) else: - shape = (n_elements, self.n_dims,) + shape = ( + n_elements, + self.n_dims, + ) self.rng_state = np.random.get_state() - self.state_msg = ('Current RNG state :\n' - '{0}'.format(self.rng_state)) + self.state_msg = "Current RNG state :\n" "{0}".format(self.rng_state) - sample = np.random.randint(int_min, - high=int_max, - size=shape) + sample = np.random.randint(int_min, high=int_max, size=shape) sample = sample.astype(self.dtype_sample) - sample = (self.sample_rng[0] + - (sample-int_min) * - (self.sample_rng[1]-self.sample_rng[0]) / - (int_max-int_min)).astype(self.dtype_sample) - - weights = np.random.randint(int_min, - high=int_max, - size=(n_elements,)) + sample = ( + self.sample_rng[0] + + (sample - int_min) + * (self.sample_rng[1] - self.sample_rng[0]) + / (int_max - int_min) + ).astype(self.dtype_sample) + + weights = np.random.randint(int_min, high=int_max, size=(n_elements,)) weights = weights.astype(self.dtype_weights) - weights = (self.weights_rng[0] + - (weights-int_min) * - (self.weights_rng[1]-self.weights_rng[0]) / - (int_max-int_min)).astype(self.dtype_weights) + weights = ( + self.weights_rng[0] + + (weights - int_min) + * (self.weights_rng[1] - self.weights_rng[0]) + / (int_max - int_min) + ).astype(self.dtype_weights) # !!!!!!!!!!!!!!!!!!!!!!!!!!!! # !!!!!!!!!!!!!!!!!!!!!!!!!!!! @@ -149,21 +142,15 @@ class _TestHistogramnd(unittest.TestCase): # adding some values that are equal to the max # in order to test the opened/closed last bin bins_max = [b[1] for b in self.histo_range] - _add_values_to_array_if_missing(sample, - bins_max, - 100) + _add_values_to_array_if_missing(sample, bins_max, 100) # adding some values that are equal to the min weight value # in order to test the filters - _add_values_to_array_if_missing(weights, - self.weights_rng[0], - 100) + _add_values_to_array_if_missing(weights, self.weights_rng[0], 100) # adding some values that are equal to the max weight value # in order to test the filters - _add_values_to_array_if_missing(weights, - self.weights_rng[1], - 100) + _add_values_to_array_if_missing(weights, self.weights_rng[1], 100) return sample, weights @@ -179,150 +166,146 @@ class _TestHistogramnd(unittest.TestCase): return np.allclose(ar_a, ar_b, self.rtol) def test_bin_ranges(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + ) - result_np = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range + ) for i_edges, edges in enumerate(result_c[2]): # allclose for now until I can try with the latest version (TBD) # of numpy - self.assertTrue(np.allclose(edges, - result_np[1][i_edges]), - msg='{0}. Testing bin_edges for dim {1}.' - ''.format(self.state_msg, i_edges+1)) + self.assertTrue( + np.allclose(edges, result_np[1][i_edges]), + msg="{0}. Testing bin_edges for dim {1}." + "".format(self.state_msg, i_edges + 1), + ) def test_last_bin_closed(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + ) - result_np = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range + ) - result_np_w = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range, - weights=self.weights) + result_np_w = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range, weights=self.weights + ) # comparing "hits" - hits_cmp = np.array_equal(result_c[0], - result_np[0]) + hits_cmp = np.array_equal(result_c[0], result_np[0]) # comparing weights - weights_cmp = np.array_equal(result_c[1], - result_np_w[0]) + weights_cmp = np.array_equal(result_c[1], result_np_w[0]) self.assertTrue(hits_cmp, msg=self.state_msg) self.assertTrue(weights_cmp, msg=self.state_msg) bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample, - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.le) + inrange_idx = _get_in_range_indices( + self.sample, bins_min, bins_max, minop=operator.ge, maxop=operator.le + ) - self.assertEqual(result_c[0].sum(), inrange_idx.shape[0], - msg=self.state_msg) + self.assertEqual(result_c[0].sum(), inrange_idx.shape[0], msg=self.state_msg) # we have to sum the weights using the same precision as the # histogramnd function weights_sum = self.weights[inrange_idx].astype(result_c[1].dtype).sum() - self.assertTrue(self.array_compare(result_c[1].sum(), weights_sum), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c[1].sum(), weights_sum), msg=self.state_msg + ) def test_last_bin_open(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=False) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=False, + ) bins_max = [rng[1] for rng in self.histo_range] filtered_idx = _get_values_index(self.sample, bins_max) - result_np = np.histogramdd(self.sample[filtered_idx], - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample[filtered_idx], bins=self.n_bins, range=self.histo_range + ) - result_np_w = np.histogramdd(self.sample[filtered_idx], - bins=self.n_bins, - range=self.histo_range, - weights=self.weights[filtered_idx]) + result_np_w = np.histogramdd( + self.sample[filtered_idx], + bins=self.n_bins, + range=self.histo_range, + weights=self.weights[filtered_idx], + ) # comparing "hits" hits_cmp = np.array_equal(result_c[0], result_np[0]) # comparing weights - weights_cmp = np.array_equal(result_c[1], - result_np_w[0]) + weights_cmp = np.array_equal(result_c[1], result_np_w[0]) self.assertTrue(hits_cmp, msg=self.state_msg) self.assertTrue(weights_cmp, msg=self.state_msg) bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample, - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.lt) - - self.assertEqual(result_c[0].sum(), len(inrange_idx), - msg=self.state_msg) + inrange_idx = _get_in_range_indices( + self.sample, bins_min, bins_max, minop=operator.ge, maxop=operator.lt + ) + + self.assertEqual(result_c[0].sum(), len(inrange_idx), msg=self.state_msg) # we have to sum the weights using the same precision as the # histogramnd function weights_sum = self.weights[inrange_idx].astype(result_c[1].dtype).sum() - self.assertTrue(self.array_compare(result_c[1].sum(), weights_sum), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c[1].sum(), weights_sum), msg=self.state_msg + ) def test_filter_min(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True, - weight_min=self.filter_min) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + weight_min=self.filter_min, + ) # !!!!!!!!!!!!!!!!!!!!!!!!!!!! filter_min = self.dtype_weights(self.filter_min) - weight_idx = _get_values_index(self.weights, - filter_min, # <------ !!! - operator.ge) + weight_idx = _get_values_index( + self.weights, filter_min, operator.ge # <------ !!! + ) - result_np = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample[weight_idx], bins=self.n_bins, range=self.histo_range + ) - result_np_w = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range, - weights=self.weights[weight_idx]) + result_np_w = np.histogramdd( + self.sample[weight_idx], + bins=self.n_bins, + range=self.histo_range, + weights=self.weights[weight_idx], + ) # comparing "hits" - hits_cmp = np.array_equal(result_c[0], - result_np[0]) + hits_cmp = np.array_equal(result_c[0], result_np[0]) # comparing weights weights_cmp = np.array_equal(result_c[1], result_np_w[0]) @@ -331,53 +314,56 @@ class _TestHistogramnd(unittest.TestCase): bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample[weight_idx], - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.le) + inrange_idx = _get_in_range_indices( + self.sample[weight_idx], + bins_min, + bins_max, + minop=operator.ge, + maxop=operator.le, + ) inrange_idx = weight_idx[inrange_idx] - self.assertEqual(result_c[0].sum(), len(inrange_idx), - msg=self.state_msg) + self.assertEqual(result_c[0].sum(), len(inrange_idx), msg=self.state_msg) # we have to sum the weights using the same precision as the # histogramnd function weights_sum = self.weights[inrange_idx].astype(result_c[1].dtype).sum() - self.assertTrue(self.array_compare(result_c[1].sum(), weights_sum), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c[1].sum(), weights_sum), msg=self.state_msg + ) def test_filter_max(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True, - weight_max=self.filter_max) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + weight_max=self.filter_max, + ) # !!!!!!!!!!!!!!!!!!!!!!!!!!!! filter_max = self.dtype_weights(self.filter_max) - weight_idx = _get_values_index(self.weights, - filter_max, # <------ !!! - operator.le) + weight_idx = _get_values_index( + self.weights, filter_max, operator.le # <------ !!! + ) - result_np = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample[weight_idx], bins=self.n_bins, range=self.histo_range + ) - result_np_w = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range, - weights=self.weights[weight_idx]) + result_np_w = np.histogramdd( + self.sample[weight_idx], + bins=self.n_bins, + range=self.histo_range, + weights=self.weights[weight_idx], + ) # comparing "hits" - hits_cmp = np.array_equal(result_c[0], - result_np[0]) + hits_cmp = np.array_equal(result_c[0], result_np[0]) # comparing weights weights_cmp = np.array_equal(result_c[1], result_np_w[0]) @@ -386,57 +372,62 @@ class _TestHistogramnd(unittest.TestCase): bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample[weight_idx], - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.le) + inrange_idx = _get_in_range_indices( + self.sample[weight_idx], + bins_min, + bins_max, + minop=operator.ge, + maxop=operator.le, + ) inrange_idx = weight_idx[inrange_idx] - self.assertEqual(result_c[0].sum(), len(inrange_idx), - msg=self.state_msg) + self.assertEqual(result_c[0].sum(), len(inrange_idx), msg=self.state_msg) # we have to sum the weights using the same precision as the # histogramnd function weights_sum = self.weights[inrange_idx].astype(result_c[1].dtype).sum() - self.assertTrue(self.array_compare(result_c[1].sum(), weights_sum), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c[1].sum(), weights_sum), msg=self.state_msg + ) def test_filter_minmax(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True, - weight_min=self.filter_min, - weight_max=self.filter_max) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + weight_min=self.filter_min, + weight_max=self.filter_max, + ) # !!!!!!!!!!!!!!!!!!!!!!!!!!!! filter_min = self.dtype_weights(self.filter_min) filter_max = self.dtype_weights(self.filter_max) - weight_idx = _get_in_range_indices(self.weights, - filter_min, # <------ !!! - filter_max, # <------ !!! - minop=operator.ge, - maxop=operator.le) + weight_idx = _get_in_range_indices( + self.weights, + filter_min, # <------ !!! + filter_max, # <------ !!! + minop=operator.ge, + maxop=operator.le, + ) - result_np = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range) + result_np = np.histogramdd( + self.sample[weight_idx], bins=self.n_bins, range=self.histo_range + ) - result_np_w = np.histogramdd(self.sample[weight_idx], - bins=self.n_bins, - range=self.histo_range, - weights=self.weights[weight_idx]) + result_np_w = np.histogramdd( + self.sample[weight_idx], + bins=self.n_bins, + range=self.histo_range, + weights=self.weights[weight_idx], + ) # comparing "hits" - hits_cmp = np.array_equal(result_c[0], - result_np[0]) + hits_cmp = np.array_equal(result_c[0], result_np[0]) # comparing weights weights_cmp = np.array_equal(result_c[1], result_np_w[0]) @@ -445,122 +436,113 @@ class _TestHistogramnd(unittest.TestCase): bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample[weight_idx], - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.le) + inrange_idx = _get_in_range_indices( + self.sample[weight_idx], + bins_min, + bins_max, + minop=operator.ge, + maxop=operator.le, + ) inrange_idx = weight_idx[inrange_idx] - self.assertEqual(result_c[0].sum(), len(inrange_idx), - msg=self.state_msg) + self.assertEqual(result_c[0].sum(), len(inrange_idx), msg=self.state_msg) # we have to sum the weights using the same precision as the # histogramnd function weights_sum = self.weights[inrange_idx].astype(result_c[1].dtype).sum() - self.assertTrue(self.array_compare(result_c[1].sum(), weights_sum), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c[1].sum(), weights_sum), msg=self.state_msg + ) def test_reuse_histo(self): - """ - - """ - result_c_1 = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True) + """ """ + result_c_1 = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + ) - result_np_1 = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range) + result_np_1 = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range + ) - np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range, - weights=self.weights) + np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range, weights=self.weights + ) sample_2, weights_2 = self.generate_data() - result_c_2 = histogramnd(sample_2, - self.histo_range, - self.n_bins, - weights=weights_2, - last_bin_closed=True, - histo=result_c_1[0]) + result_c_2 = histogramnd( + sample_2, + self.histo_range, + self.n_bins, + weights=weights_2, + last_bin_closed=True, + histo=result_c_1[0], + ) - result_np_2 = np.histogramdd(sample_2, - bins=self.n_bins, - range=self.histo_range) + result_np_2 = np.histogramdd(sample_2, bins=self.n_bins, range=self.histo_range) - result_np_w_2 = np.histogramdd(sample_2, - bins=self.n_bins, - range=self.histo_range, - weights=weights_2) + result_np_w_2 = np.histogramdd( + sample_2, bins=self.n_bins, range=self.histo_range, weights=weights_2 + ) # comparing "hits" - hits_cmp = np.array_equal(result_c_2[0], - result_np_1[0] + - result_np_2[0]) + hits_cmp = np.array_equal(result_c_2[0], result_np_1[0] + result_np_2[0]) # comparing weights - weights_cmp = np.array_equal(result_c_2[1], - result_np_w_2[0]) + weights_cmp = np.array_equal(result_c_2[1], result_np_w_2[0]) self.assertTrue(hits_cmp, msg=self.state_msg) self.assertTrue(weights_cmp, msg=self.state_msg) def test_reuse_cumul(self): - """ - - """ - result_c = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True) + """ """ + result_c = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + ) - np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range) + np.histogramdd(self.sample, bins=self.n_bins, range=self.histo_range) - result_np_w = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range, - weights=self.weights) + result_np_w = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range, weights=self.weights + ) sample_2, weights_2 = self.generate_data() - result_c_2 = histogramnd(sample_2, - self.histo_range, - self.n_bins, - weights=weights_2, - last_bin_closed=True, - weighted_histo=result_c[1]) + result_c_2 = histogramnd( + sample_2, + self.histo_range, + self.n_bins, + weights=weights_2, + last_bin_closed=True, + weighted_histo=result_c[1], + ) - result_np_2 = np.histogramdd(sample_2, - bins=self.n_bins, - range=self.histo_range) + result_np_2 = np.histogramdd(sample_2, bins=self.n_bins, range=self.histo_range) - result_np_w_2 = np.histogramdd(sample_2, - bins=self.n_bins, - range=self.histo_range, - weights=weights_2) + result_np_w_2 = np.histogramdd( + sample_2, bins=self.n_bins, range=self.histo_range, weights=weights_2 + ) # comparing "hits" - hits_cmp = np.array_equal(result_c_2[0], - result_np_2[0]) + hits_cmp = np.array_equal(result_c_2[0], result_np_2[0]) # comparing weights self.assertTrue(hits_cmp, msg=self.state_msg) - self.assertTrue(self.array_compare(result_c_2[1], - result_np_w[0] + result_np_w_2[0]), - msg=self.state_msg) + self.assertTrue( + self.array_compare(result_c_2[1], result_np_w[0] + result_np_w_2[0]), + msg=self.state_msg, + ) def test_reuse_cumul_float(self): - """ - - """ + """ """ n_bins = np.array(self.n_bins, ndmin=1) if len(self.sample.shape) == 2: if len(n_bins) == self.sample.shape[1]: @@ -572,43 +554,46 @@ class _TestHistogramnd(unittest.TestCase): shp = (self.n_bins,) cumul = np.zeros(shp, dtype=np.float32) - result_c_1 = histogramnd(self.sample, - self.histo_range, - self.n_bins, - weights=self.weights, - last_bin_closed=True, - weighted_histo=cumul) + result_c_1 = histogramnd( + self.sample, + self.histo_range, + self.n_bins, + weights=self.weights, + last_bin_closed=True, + weighted_histo=cumul, + ) - result_np_1 = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range) + result_np_1 = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range + ) - result_np_w_1 = np.histogramdd(self.sample, - bins=self.n_bins, - range=self.histo_range, - weights=self.weights) + result_np_w_1 = np.histogramdd( + self.sample, bins=self.n_bins, range=self.histo_range, weights=self.weights + ) # comparing "hits" - hits_cmp = np.array_equal(result_c_1[0], - result_np_1[0]) + hits_cmp = np.array_equal(result_c_1[0], result_np_1[0]) self.assertTrue(hits_cmp, msg=self.state_msg) self.assertEqual(result_c_1[1].dtype, np.float32, msg=self.state_msg) bins_min = [rng[0] for rng in self.histo_range] bins_max = [rng[1] for rng in self.histo_range] - inrange_idx = _get_in_range_indices(self.sample, - bins_min, - bins_max, - minop=operator.ge, - maxop=operator.le) - weights_sum = \ - self.weights[inrange_idx].astype(np.float32).sum(dtype=np.float64) - self.assertTrue(np.allclose(result_c_1[1].sum(dtype=np.float64), - weights_sum), msg=self.state_msg) - self.assertTrue(np.allclose(result_c_1[1].sum(dtype=np.float64), - result_np_w_1[0].sum(dtype=np.float64)), - msg=self.state_msg) + inrange_idx = _get_in_range_indices( + self.sample, bins_min, bins_max, minop=operator.ge, maxop=operator.le + ) + weights_sum = self.weights[inrange_idx].astype(np.float32).sum(dtype=np.float64) + self.assertTrue( + np.allclose(result_c_1[1].sum(dtype=np.float64), weights_sum), + msg=self.state_msg, + ) + self.assertTrue( + np.allclose( + result_c_1[1].sum(dtype=np.float64), + result_np_w_1[0].sum(dtype=np.float64), + ), + msg=self.state_msg, + ) @pytest.mark.usefixtures("use_large_memory") def test_histo_big_array(self): @@ -622,35 +607,23 @@ class _TestHistogramnd(unittest.TestCase): n_repeat = (2**31 + 10) // self.sample.size sample = np.repeat(self.sample, n_repeat) n_bins = int(1e6) - result_c = histogramnd( - sample, - self.histo_range, - n_bins, - last_bin_closed=True - ) - result_np = np.histogramdd( - sample, - n_bins, - range=self.histo_range - ) + result_c = histogramnd(sample, self.histo_range, n_bins, last_bin_closed=True) + result_np = np.histogramdd(sample, n_bins, range=self.histo_range) for i_edges, edges in enumerate(result_c[2]): self.assertTrue( - np.allclose( - edges, - result_np[1][i_edges] - ), - msg='{0}. Testing bin_edges for dim {1}.'''.format(self.state_msg, i_edges+1) + np.allclose(edges, result_np[1][i_edges]), + msg="{0}. Testing bin_edges for dim {1}." + "".format(self.state_msg, i_edges + 1), ) - - class _TestHistogramnd_1d(_TestHistogramnd): """ Unit tests of the 1D histogramnd function. """ - sample_rng = [-55., 100.] - weights_rng = [-70., 150.] + + sample_rng = [-55.0, 100.0] + weights_rng = [-70.0, 150.0] n_dims = 1 filter_min = -15.6 filter_max = 85.7 @@ -665,13 +638,14 @@ class _TestHistogramnd_2d(_TestHistogramnd): """ Unit tests of the 1D histogramnd function. """ + sample_rng = [-50.2, 100.99] - weights_rng = [70., 150.] + weights_rng = [70.0, 150.0] n_dims = 2 filter_min = 81.7 filter_max = 135.3 - histo_range = [[10., 90.], [20., 70.]] + histo_range = [[10.0, 90.0], [20.0, 70.0]] n_bins = 30 dtype = None @@ -681,13 +655,14 @@ class _TestHistogramnd_3d(_TestHistogramnd): """ Unit tests of the 1D histogramnd function. """ + sample_rng = [10.2, 200.9] - weights_rng = [0., 100.] + weights_rng = [0.0, 100.0] n_dims = 3 filter_min = 31.5 filter_max = 83.7 - histo_range = [[30.8, 150.2], [20.1, 90.9], [10.1, 195.]] + histo_range = [[30.8, 150.2], [20.1, 90.9], [10.1, 195.0]] n_bins = 30 dtype = None |