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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.
-#
-# ############################################################################*/
-"""
-Tests for the histogramnd function.
-Results are compared to numpy's histogramdd.
-"""
-
-import unittest
-import operator
-
-import numpy as np
-
-from silx.math.chistogramnd import chistogramnd as histogramnd
-
-# ==============================================================
-# ==============================================================
-# ==============================================================
-
-_RTOL_DICT = {np.float64: 10**-13,
- np.float32: 10**-5}
-
-# ==============================================================
-# ==============================================================
-# ==============================================================
-
-
-def _add_values_to_array_if_missing(array, values, n_values):
- max_in_col = np.any(array[:, ...] == values, axis=0)
-
- if len(array.shape) == 1:
- if not max_in_col:
- 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,))
- array[rnd_idx, i] = values[i]
-
-
-def _get_values_index(array, values, op=operator.lt):
- idx = op(array[:, ...], values)
- if array.ndim > 1:
- idx = np.all(idx, axis=1)
- 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))
- if array.ndim > 1:
- idx = np.all(idx, axis=1)
- return np.where(idx)[0]
-
-
-class _TestHistogramnd(unittest.TestCase):
-
- """
- Unit tests of the histogramnd function.
- """
- sample_rng = None
- weights_rng = None
- n_dims = None
-
- filter_min = None
- filter_max = None
-
- histo_range = None
- n_bins = None
-
- dtype_sample = None
- dtype_weights = None
-
- def generate_data(self):
-
- self.longMessage = True
-
- int_min = 0
- int_max = 100000
- n_elements = 10**5
-
- if self.n_dims == 1:
- shape = (n_elements,)
- else:
- 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))
-
- 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,))
- 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)
-
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- # the bins range are cast to the same type as the sample
- # in order to get the same results as numpy
- # (which doesnt cast the range)
- self.histo_range = np.array(self.histo_range).astype(self.dtype_sample)
-
- # 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)
-
- # 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)
-
- # 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)
-
- return sample, weights
-
- def setUp(self):
- self.sample, self.weights = self.generate_data()
- self.rtol = _RTOL_DICT.get(self.dtype_weights, None)
-
- def array_compare(self, ar_a, ar_b):
- if self.rtol is None:
- return np.array_equal(ar_a, ar_b)
- 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_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))
-
- 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_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)
-
- # 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])
-
- 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)
-
- 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)
-
- def test_last_bin_open(self):
- """
-
- """
- 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_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])
-
- 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)
- # 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)
-
- 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)
-
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- filter_min = self.dtype_weights(self.filter_min)
-
- 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_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])
- # comparing weights
- 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[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)
-
- # 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)
-
- 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)
-
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- filter_max = self.dtype_weights(self.filter_max)
-
- 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_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])
- # comparing weights
- 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[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)
-
- # 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)
-
- 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)
-
- # !!!!!!!!!!!!!!!!!!!!!!!!!!!!
- 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)
-
- 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])
-
- # 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])
-
- self.assertTrue(hits_cmp)
- self.assertTrue(weights_cmp)
-
- 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 = weight_idx[inrange_idx]
-
- 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)
-
- 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_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)
-
- 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_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)
-
- # comparing "hits"
- 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])
-
- 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)
-
- 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)
-
- 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_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)
-
- # comparing "hits"
- 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)
-
- 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]:
- shp = tuple([x for x in n_bins])
- else:
- shp = (self.n_bins,) * self.sample.shape[1]
- cumul = np.zeros(shp, dtype=np.float32)
- else:
- 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_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)
-
- # comparing "hits"
- 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)
-
-
-class _TestHistogramnd_1d(_TestHistogramnd):
-
- """
- Unit tests of the 1D histogramnd function.
- """
-
- sample_rng = [-55., 100.]
- weights_rng = [-70., 150.]
- n_dims = 1
- filter_min = -15.6
- filter_max = 85.7
-
- histo_range = [[-30.2, 90.3]]
- n_bins = 30
-
- dtype = None
-
-
-class _TestHistogramnd_2d(_TestHistogramnd):
-
- """
- Unit tests of the 1D histogramnd function.
- """
-
- sample_rng = [-50.2, 100.99]
- weights_rng = [70., 150.]
- n_dims = 2
- filter_min = 81.7
- filter_max = 135.3
-
- histo_range = [[10., 90.], [20., 70.]]
- n_bins = 30
-
- dtype = None
-
-
-class _TestHistogramnd_3d(_TestHistogramnd):
-
- """
- Unit tests of the 1D histogramnd function.
- """
-
- sample_rng = [10.2, 200.9]
- weights_rng = [0., 100.]
- n_dims = 3
- filter_min = 31.5
- filter_max = 83.7
-
- histo_range = [[30.8, 150.2], [20.1, 90.9], [10.1, 195.]]
- n_bins = 30
-
- dtype = None
-
-
-# ################################################################
-# ################################################################
-# ################################################################
-# ################################################################
-
-
-class TestHistogramnd_1d_double_double(_TestHistogramnd_1d):
- dtype_sample = np.double
- dtype_weights = np.double
-
-
-class TestHistogramnd_1d_double_float(_TestHistogramnd_1d):
- dtype_sample = np.double
- dtype_weights = np.float32
-
-
-class TestHistogramnd_1d_double_int32(_TestHistogramnd_1d):
- dtype_sample = np.double
- dtype_weights = np.int32
-
-
-class TestHistogramnd_1d_float_double(_TestHistogramnd_1d):
- dtype_sample = np.float32
- dtype_weights = np.double
-
-
-class TestHistogramnd_1d_float_float(_TestHistogramnd_1d):
- dtype_sample = np.float32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_1d_float_int32(_TestHistogramnd_1d):
- dtype_sample = np.float32
- dtype_weights = np.int32
-
-
-class TestHistogramnd_1d_int32_double(_TestHistogramnd_1d):
- dtype_sample = np.int32
- dtype_weights = np.double
-
-
-class TestHistogramnd_1d_int32_float(_TestHistogramnd_1d):
- dtype_sample = np.int32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_1d_int32_int32(_TestHistogramnd_1d):
- dtype_sample = np.int32
- dtype_weights = np.int32
-
-
-class TestHistogramnd_2d_double_double(_TestHistogramnd_2d):
- dtype_sample = np.double
- dtype_weights = np.double
-
-
-class TestHistogramnd_2d_double_float(_TestHistogramnd_2d):
- dtype_sample = np.double
- dtype_weights = np.float32
-
-
-class TestHistogramnd_2d_double_int32(_TestHistogramnd_2d):
- dtype_sample = np.double
- dtype_weights = np.int32
-
-
-class TestHistogramnd_2d_float_double(_TestHistogramnd_2d):
- dtype_sample = np.float32
- dtype_weights = np.double
-
-
-class TestHistogramnd_2d_float_float(_TestHistogramnd_2d):
- dtype_sample = np.float32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_2d_float_int32(_TestHistogramnd_2d):
- dtype_sample = np.float32
- dtype_weights = np.int32
-
-
-class TestHistogramnd_2d_int32_double(_TestHistogramnd_2d):
- dtype_sample = np.int32
- dtype_weights = np.double
-
-
-class TestHistogramnd_2d_int32_float(_TestHistogramnd_2d):
- dtype_sample = np.int32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_2d_int32_int32(_TestHistogramnd_2d):
- dtype_sample = np.int32
- dtype_weights = np.int32
-
-
-class TestHistogramnd_3d_double_double(_TestHistogramnd_3d):
- dtype_sample = np.double
- dtype_weights = np.double
-
-
-class TestHistogramnd_3d_double_float(_TestHistogramnd_3d):
- dtype_sample = np.double
- dtype_weights = np.float32
-
-
-class TestHistogramnd_3d_double_int32(_TestHistogramnd_3d):
- dtype_sample = np.double
- dtype_weights = np.int32
-
-
-class TestHistogramnd_3d_float_double(_TestHistogramnd_3d):
- dtype_sample = np.float32
- dtype_weights = np.double
-
-
-class TestHistogramnd_3d_float_float(_TestHistogramnd_3d):
- dtype_sample = np.float32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_3d_float_int32(_TestHistogramnd_3d):
- dtype_sample = np.float32
- dtype_weights = np.int32
-
-
-class TestHistogramnd_3d_int32_double(_TestHistogramnd_3d):
- dtype_sample = np.int32
- dtype_weights = np.double
-
-
-class TestHistogramnd_3d_int32_float(_TestHistogramnd_3d):
- dtype_sample = np.int32
- dtype_weights = np.float32
-
-
-class TestHistogramnd_3d_int32_int32(_TestHistogramnd_3d):
- dtype_sample = np.int32
- dtype_weights = np.int32
-
-
-# ==============================================================
-# ==============================================================
-# ==============================================================
-
-
-test_cases = (TestHistogramnd_1d_double_double,
- TestHistogramnd_1d_double_float,
- TestHistogramnd_1d_double_int32,
- TestHistogramnd_1d_float_double,
- TestHistogramnd_1d_float_float,
- TestHistogramnd_1d_float_int32,
- TestHistogramnd_1d_int32_double,
- TestHistogramnd_1d_int32_float,
- TestHistogramnd_1d_int32_int32,
- TestHistogramnd_2d_double_double,
- TestHistogramnd_2d_double_float,
- TestHistogramnd_2d_double_int32,
- TestHistogramnd_2d_float_double,
- TestHistogramnd_2d_float_float,
- TestHistogramnd_2d_float_int32,
- TestHistogramnd_2d_int32_double,
- TestHistogramnd_2d_int32_float,
- TestHistogramnd_2d_int32_int32,
- TestHistogramnd_3d_double_double,
- TestHistogramnd_3d_double_float,
- TestHistogramnd_3d_double_int32,
- TestHistogramnd_3d_float_double,
- TestHistogramnd_3d_float_float,
- TestHistogramnd_3d_float_int32,
- TestHistogramnd_3d_int32_double,
- TestHistogramnd_3d_int32_float,
- TestHistogramnd_3d_int32_int32,)
-
-
-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")
generated by cgit v1.2.3 (git 2.25.1) at 2024-04-25 03:53:54 +0000