def test_invalid_histo_range(self):
data = np.random.random((60, 60))
nbins = 10
with self.assertRaises(ValueError):
histo_range = data.min(), np.inf
Histogramnd(sample=data.ravel(),
histo_range=histo_range,
n_bins=nbins)
histo_range = data.min(), np.nan
Histogramnd(sample=data.ravel(),
histo_range=histo_range,
n_bins=nbins)
def test_accumulate_no_weights(self):
"""
"""
expected_h_tpl = np.array([2, 3, 2, 2, 2])
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)
histo_inst = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=self.weights)
sample_2 = self.sample[:]
if len(sample_2.shape) == 1:
idx = (slice(0, None), )
else:
idx = slice(0, None), self.tested_dim
sample_2[idx] += 2
histo_inst.accumulate(sample_2) # <==== !!
histo = histo_inst.histo
cumul = histo_inst.weighted_histo
bin_edges = histo_inst.edges
self.assertEqual(cumul.dtype, np.float64)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.allclose(cumul, expected_c, rtol=10e-15))
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 = Histogramnd(sampleB,
self.histo_range,
self.n_bins,
weights=self.weights)
histo_inst = Histogramnd(sampleL,
self.histo_range,
self.n_bins,
weights=self.weights)
def test_nominal_uncontiguous_weights(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)
shape = list(self.weights.shape)
shape[0] *= 2
weights = np.zeros(shape, dtype=self.weights.dtype)
uncontig_weights = weights[::2, ...]
uncontig_weights[:] = self.weights
self.assertFalse(uncontig_weights.flags['C_CONTIGUOUS'],
msg='Making sure the array is not contiguous.')
histo, cumul, bin_edges = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=uncontig_weights)
self.assertEqual(cumul.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(cumul, expected_c))
def test_empty_init_accumulate(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)
histo_inst = Histogramnd(None, self.histo_range, self.n_bins)
histo_inst.accumulate(self.sample, weights=self.weights)
histo = histo_inst.histo
cumul = histo_inst.weighted_histo
bin_edges = histo_inst.edges
expected_edges = _get_bin_edges(self.histo_range, self.n_bins,
self.ndims)
self.assertEqual(cumul.dtype, np.float64)
self.assertEqual(histo.dtype, np.uint32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(cumul, expected_c))
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_no_sample(self):
"""
"""
histo_inst = Histogramnd(None, self.histo_range, self.n_bins)
histo, weighted_histo, edges = histo_inst
self.assertIsNone(histo)
self.assertIsNone(weighted_histo)
self.assertIsNone(edges)
self.assertIsNone(histo_inst.histo)
self.assertIsNone(histo_inst.weighted_histo)
self.assertIsNone(histo_inst.edges)
def test_nominal_wo_weights(self):
"""
"""
expected_h_tpl = np.array([2, 1, 1, 1, 1])
expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
histo, cumul = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=None)[0:2]
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(cumul is None)
def test_nominal_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)
histo, cumul = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=self.weights,
last_bin_closed=True)[0:2]
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(cumul, expected_c))
def test_nominal_wh_dtype(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.float32)
self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)
histo, cumul, bin_edges = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=self.weights,
wh_dtype=np.float32)
self.assertEqual(cumul.dtype, np.float32)
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.allclose(cumul, expected_c))
def test_int32_weights_double_weights_range(self):
"""
"""
weight_min = -299.9 # ===> will be cast to -299
weight_max = 499.9 # ===> will be cast to 499
expected_h_tpl = np.array([0, 1, 1, 1, 0])
expected_c_tpl = np.array([0., 0., 0., 300., 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)
histo, cumul = Histogramnd(self.sample,
self.histo_range,
self.n_bins,
weights=self.weights.astype(np.int32),
weight_min=weight_min,
weight_max=weight_max)[0:2]
self.assertTrue(np.array_equal(histo, expected_h))
self.assertTrue(np.array_equal(cumul, expected_c))