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-rw-r--r--src/silx/math/test/test_HistogramndLut_nominal.py242
1 files changed, 95 insertions, 147 deletions
diff --git a/src/silx/math/test/test_HistogramndLut_nominal.py b/src/silx/math/test/test_HistogramndLut_nominal.py
index 907a592..fba0778 100644
--- a/src/silx/math/test/test_HistogramndLut_nominal.py
+++ b/src/silx/math/test/test_HistogramndLut_nominal.py
@@ -34,10 +34,12 @@ 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])
+ 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)
@@ -50,6 +52,7 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
"""
Unit tests of the HistogramndLut class.
"""
+
__test__ = False # ignore abstract class
ndims = None
@@ -58,22 +61,16 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
ndims = self.ndims
if ndims is None:
self.skipTest("Abstract class")
- self.tested_dim = ndims-1
+ self.tested_dim = ndims - 1
if ndims is None:
- raise ValueError('ndims class member not set.')
+ 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])
+ sample = np.array([5.5, -3.3, 0.0, -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])
+ 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)
@@ -86,7 +83,7 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
if ndims == 1:
self.sample = sample
else:
- self.sample[..., ndims-1] = sample
+ self.sample[..., ndims - 1] = sample
self.weights = weights
@@ -97,124 +94,106 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
# 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.histo_range = np.repeat([[-2.0, 2.0]], ndims, axis=0)
+ self.histo_range[ndims - 1] = [-4.0, 6.0]
- self.n_bins = np.array([4]*ndims)
- self.n_bins[ndims-1] = 5
+ 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 = [self.other_axes_index] * len(h.shape)
idx[dim] = slice(0, None)
idx = tuple(idx)
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)
+ 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)
+ 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))
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
instance.accumulate(self.weights)
@@ -245,20 +224,17 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
instance.accumulate(self.weights)
@@ -270,28 +246,24 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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))
+ 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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
instance.accumulate(self.weights)
@@ -305,24 +277,20 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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))
+ 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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
histo, w_histo = instance.apply_lut(self.weights)
@@ -334,29 +302,26 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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)
+ 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)
+ 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))
@@ -368,21 +333,19 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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 = HistogramndLut(
+ self.sample, self.histo_range, self.n_bins, last_bin_closed=True
+ )
instance.accumulate(self.weights)
@@ -395,27 +358,22 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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_c_tpl = np.array([0.0, -0.5, 0.01, 300.3, 0.0])
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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
- instance.accumulate(self.weights,
- weight_min=weight_min,
- weight_max=weight_max)
+ instance.accumulate(self.weights, weight_min=weight_min, weight_max=weight_max)
histo = instance.histo()
w_histo = instance.weighted_histo()
@@ -435,13 +393,12 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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 = HistogramndLut(
+ self.sample, self.histo_range, self.n_bins, dtype=np.int32
+ )
instance.accumulate(self.weights)
@@ -458,18 +415,17 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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_c_tpl = np.array([-700.0, 0.0, 0.0, 300.0, 500.0])
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)
+ 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 = HistogramndLut(
+ self.sample, self.histo_range, self.n_bins, dtype=np.float32
+ )
instance.accumulate(self.weights.astype(np.int32))
@@ -491,12 +447,10 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
instance.accumulate(self.weights.astype(np.int32))
@@ -518,12 +472,10 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
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)
+ 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 = HistogramndLut(self.sample, self.histo_range, self.n_bins)
instance.accumulate(self.weights.astype(np.int32))
instance.accumulate(self.weights)
@@ -546,13 +498,9 @@ class _TestHistogramndLut_nominal(unittest.TestCase):
type = self.sample.dtype.newbyteorder("L")
sampleL = self.sample.astype(type)
- histo_inst = HistogramndLut(sampleB,
- self.histo_range,
- self.n_bins)
+ histo_inst = HistogramndLut(sampleB, self.histo_range, self.n_bins)
- histo_inst = HistogramndLut(sampleL,
- self.histo_range,
- self.n_bins)
+ histo_inst = HistogramndLut(sampleL, self.histo_range, self.n_bins)
class TestHistogramndLut_nominal_1d(_TestHistogramndLut_nominal):
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-24 16:28:06 +0000