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_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_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_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_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_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 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)
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 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_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_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_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_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_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))
