Intelegentny_Pszczelarz/.venv/Lib/site-packages/scipy/ndimage/tests/test_measurements.py
2023-06-19 00:49:18 +02:00

1394 lines
47 KiB
Python

import os.path
import numpy as np
from numpy.testing import (assert_, assert_array_almost_equal, assert_equal,
assert_almost_equal, assert_array_equal,
suppress_warnings)
from pytest import raises as assert_raises
import scipy.ndimage as ndimage
from . import types
class Test_measurements_stats:
"""ndimage._measurements._stats() is a utility used by other functions."""
def test_a(self):
x = [0, 1, 2, 6]
labels = [0, 0, 1, 1]
index = [0, 1]
for shp in [(4,), (2, 2)]:
x = np.array(x).reshape(shp)
labels = np.array(labels).reshape(shp)
counts, sums = ndimage._measurements._stats(
x, labels=labels, index=index)
assert_array_equal(counts, [2, 2])
assert_array_equal(sums, [1.0, 8.0])
def test_b(self):
# Same data as test_a, but different labels. The label 9 exceeds the
# length of 'labels', so this test will follow a different code path.
x = [0, 1, 2, 6]
labels = [0, 0, 9, 9]
index = [0, 9]
for shp in [(4,), (2, 2)]:
x = np.array(x).reshape(shp)
labels = np.array(labels).reshape(shp)
counts, sums = ndimage._measurements._stats(
x, labels=labels, index=index)
assert_array_equal(counts, [2, 2])
assert_array_equal(sums, [1.0, 8.0])
def test_a_centered(self):
x = [0, 1, 2, 6]
labels = [0, 0, 1, 1]
index = [0, 1]
for shp in [(4,), (2, 2)]:
x = np.array(x).reshape(shp)
labels = np.array(labels).reshape(shp)
counts, sums, centers = ndimage._measurements._stats(
x, labels=labels, index=index, centered=True)
assert_array_equal(counts, [2, 2])
assert_array_equal(sums, [1.0, 8.0])
assert_array_equal(centers, [0.5, 8.0])
def test_b_centered(self):
x = [0, 1, 2, 6]
labels = [0, 0, 9, 9]
index = [0, 9]
for shp in [(4,), (2, 2)]:
x = np.array(x).reshape(shp)
labels = np.array(labels).reshape(shp)
counts, sums, centers = ndimage._measurements._stats(
x, labels=labels, index=index, centered=True)
assert_array_equal(counts, [2, 2])
assert_array_equal(sums, [1.0, 8.0])
assert_array_equal(centers, [0.5, 8.0])
def test_nonint_labels(self):
x = [0, 1, 2, 6]
labels = [0.0, 0.0, 9.0, 9.0]
index = [0.0, 9.0]
for shp in [(4,), (2, 2)]:
x = np.array(x).reshape(shp)
labels = np.array(labels).reshape(shp)
counts, sums, centers = ndimage._measurements._stats(
x, labels=labels, index=index, centered=True)
assert_array_equal(counts, [2, 2])
assert_array_equal(sums, [1.0, 8.0])
assert_array_equal(centers, [0.5, 8.0])
class Test_measurements_select:
"""ndimage._measurements._select() is a utility used by other functions."""
def test_basic(self):
x = [0, 1, 6, 2]
cases = [
([0, 0, 1, 1], [0, 1]), # "Small" integer labels
([0, 0, 9, 9], [0, 9]), # A label larger than len(labels)
([0.0, 0.0, 7.0, 7.0], [0.0, 7.0]), # Non-integer labels
]
for labels, index in cases:
result = ndimage._measurements._select(
x, labels=labels, index=index)
assert_(len(result) == 0)
result = ndimage._measurements._select(
x, labels=labels, index=index, find_max=True)
assert_(len(result) == 1)
assert_array_equal(result[0], [1, 6])
result = ndimage._measurements._select(
x, labels=labels, index=index, find_min=True)
assert_(len(result) == 1)
assert_array_equal(result[0], [0, 2])
result = ndimage._measurements._select(
x, labels=labels, index=index, find_min=True,
find_min_positions=True)
assert_(len(result) == 2)
assert_array_equal(result[0], [0, 2])
assert_array_equal(result[1], [0, 3])
assert_equal(result[1].dtype.kind, 'i')
result = ndimage._measurements._select(
x, labels=labels, index=index, find_max=True,
find_max_positions=True)
assert_(len(result) == 2)
assert_array_equal(result[0], [1, 6])
assert_array_equal(result[1], [1, 2])
assert_equal(result[1].dtype.kind, 'i')
def test_label01():
data = np.ones([])
out, n = ndimage.label(data)
assert_array_almost_equal(out, 1)
assert_equal(n, 1)
def test_label02():
data = np.zeros([])
out, n = ndimage.label(data)
assert_array_almost_equal(out, 0)
assert_equal(n, 0)
def test_label03():
data = np.ones([1])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [1])
assert_equal(n, 1)
def test_label04():
data = np.zeros([1])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [0])
assert_equal(n, 0)
def test_label05():
data = np.ones([5])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [1, 1, 1, 1, 1])
assert_equal(n, 1)
def test_label06():
data = np.array([1, 0, 1, 1, 0, 1])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [1, 0, 2, 2, 0, 3])
assert_equal(n, 3)
def test_label07():
data = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
assert_equal(n, 0)
def test_label08():
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0]])
out, n = ndimage.label(data)
assert_array_almost_equal(out, [[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]])
assert_equal(n, 4)
def test_label09():
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0]])
struct = ndimage.generate_binary_structure(2, 2)
out, n = ndimage.label(data, struct)
assert_array_almost_equal(out, [[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[2, 2, 0, 0, 0, 0],
[2, 2, 0, 0, 0, 0],
[0, 0, 0, 3, 3, 0]])
assert_equal(n, 3)
def test_label10():
data = np.array([[0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0]])
struct = ndimage.generate_binary_structure(2, 2)
out, n = ndimage.label(data, struct)
assert_array_almost_equal(out, [[0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0]])
assert_equal(n, 1)
def test_label11():
for type in types:
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0]], type)
out, n = ndimage.label(data)
expected = [[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]]
assert_array_almost_equal(out, expected)
assert_equal(n, 4)
def test_label11_inplace():
for type in types:
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0]], type)
n = ndimage.label(data, output=data)
expected = [[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]]
assert_array_almost_equal(data, expected)
assert_equal(n, 4)
def test_label12():
for type in types:
data = np.array([[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 0]], type)
out, n = ndimage.label(data)
expected = [[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 0]]
assert_array_almost_equal(out, expected)
assert_equal(n, 1)
def test_label13():
for type in types:
data = np.array([[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1],
[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
type)
out, n = ndimage.label(data)
expected = [[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1],
[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
assert_array_almost_equal(out, expected)
assert_equal(n, 1)
def test_label_output_typed():
data = np.ones([5])
for t in types:
output = np.zeros([5], dtype=t)
n = ndimage.label(data, output=output)
assert_array_almost_equal(output, 1)
assert_equal(n, 1)
def test_label_output_dtype():
data = np.ones([5])
for t in types:
output, n = ndimage.label(data, output=t)
assert_array_almost_equal(output, 1)
assert output.dtype == t
def test_label_output_wrong_size():
data = np.ones([5])
for t in types:
output = np.zeros([10], t)
assert_raises((RuntimeError, ValueError),
ndimage.label, data, output=output)
def test_label_structuring_elements():
data = np.loadtxt(os.path.join(os.path.dirname(
__file__), "data", "label_inputs.txt"))
strels = np.loadtxt(os.path.join(
os.path.dirname(__file__), "data", "label_strels.txt"))
results = np.loadtxt(os.path.join(
os.path.dirname(__file__), "data", "label_results.txt"))
data = data.reshape((-1, 7, 7))
strels = strels.reshape((-1, 3, 3))
results = results.reshape((-1, 7, 7))
r = 0
for i in range(data.shape[0]):
d = data[i, :, :]
for j in range(strels.shape[0]):
s = strels[j, :, :]
assert_equal(ndimage.label(d, s)[0], results[r, :, :])
r += 1
def test_ticket_742():
def SE(img, thresh=.7, size=4):
mask = img > thresh
rank = len(mask.shape)
la, co = ndimage.label(mask,
ndimage.generate_binary_structure(rank, rank))
_ = ndimage.find_objects(la)
if np.dtype(np.intp) != np.dtype('i'):
shape = (3, 1240, 1240)
a = np.random.rand(np.prod(shape)).reshape(shape)
# shouldn't crash
SE(a)
def test_gh_issue_3025():
"""Github issue #3025 - improper merging of labels"""
d = np.zeros((60, 320))
d[:, :257] = 1
d[:, 260:] = 1
d[36, 257] = 1
d[35, 258] = 1
d[35, 259] = 1
assert ndimage.label(d, np.ones((3, 3)))[1] == 1
def test_label_default_dtype():
test_array = np.random.rand(10, 10)
label, no_features = ndimage.label(test_array > 0.5)
assert_(label.dtype in (np.int32, np.int64))
# Shouldn't raise an exception
ndimage.find_objects(label)
def test_find_objects01():
data = np.ones([], dtype=int)
out = ndimage.find_objects(data)
assert_(out == [()])
def test_find_objects02():
data = np.zeros([], dtype=int)
out = ndimage.find_objects(data)
assert_(out == [])
def test_find_objects03():
data = np.ones([1], dtype=int)
out = ndimage.find_objects(data)
assert_equal(out, [(slice(0, 1, None),)])
def test_find_objects04():
data = np.zeros([1], dtype=int)
out = ndimage.find_objects(data)
assert_equal(out, [])
def test_find_objects05():
data = np.ones([5], dtype=int)
out = ndimage.find_objects(data)
assert_equal(out, [(slice(0, 5, None),)])
def test_find_objects06():
data = np.array([1, 0, 2, 2, 0, 3])
out = ndimage.find_objects(data)
assert_equal(out, [(slice(0, 1, None),),
(slice(2, 4, None),),
(slice(5, 6, None),)])
def test_find_objects07():
data = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
out = ndimage.find_objects(data)
assert_equal(out, [])
def test_find_objects08():
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]])
out = ndimage.find_objects(data)
assert_equal(out, [(slice(0, 1, None), slice(0, 1, None)),
(slice(1, 3, None), slice(2, 5, None)),
(slice(3, 5, None), slice(0, 2, None)),
(slice(5, 6, None), slice(3, 5, None))])
def test_find_objects09():
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]])
out = ndimage.find_objects(data)
assert_equal(out, [(slice(0, 1, None), slice(0, 1, None)),
(slice(1, 3, None), slice(2, 5, None)),
None,
(slice(5, 6, None), slice(3, 5, None))])
def test_value_indices01():
"Test dictionary keys and entries"
data = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 4, 4, 0]])
vi = ndimage.value_indices(data, ignore_value=0)
true_keys = [1, 2, 4]
assert_equal(list(vi.keys()), true_keys)
truevi = {}
for k in true_keys:
truevi[k] = np.where(data == k)
vi = ndimage.value_indices(data, ignore_value=0)
assert_equal(vi, truevi)
def test_value_indices02():
"Test input checking"
data = np.zeros((5, 4), dtype=np.float32)
msg = "Parameter 'arr' must be an integer array"
with assert_raises(ValueError, match=msg):
ndimage.value_indices(data)
def test_value_indices03():
"Test different input array shapes, from 1-D to 4-D"
for shape in [(36,), (18, 2), (3, 3, 4), (3, 3, 2, 2)]:
a = np.array((12*[1]+12*[2]+12*[3]), dtype=np.int32).reshape(shape)
trueKeys = np.unique(a)
vi = ndimage.value_indices(a)
assert_equal(list(vi.keys()), list(trueKeys))
for k in trueKeys:
trueNdx = np.where(a == k)
assert_equal(vi[k], trueNdx)
def test_sum01():
for type in types:
input = np.array([], type)
output = ndimage.sum(input)
assert_equal(output, 0.0)
def test_sum02():
for type in types:
input = np.zeros([0, 4], type)
output = ndimage.sum(input)
assert_equal(output, 0.0)
def test_sum03():
for type in types:
input = np.ones([], type)
output = ndimage.sum(input)
assert_almost_equal(output, 1.0)
def test_sum04():
for type in types:
input = np.array([1, 2], type)
output = ndimage.sum(input)
assert_almost_equal(output, 3.0)
def test_sum05():
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input)
assert_almost_equal(output, 10.0)
def test_sum06():
labels = np.array([], bool)
for type in types:
input = np.array([], type)
output = ndimage.sum(input, labels=labels)
assert_equal(output, 0.0)
def test_sum07():
labels = np.ones([0, 4], bool)
for type in types:
input = np.zeros([0, 4], type)
output = ndimage.sum(input, labels=labels)
assert_equal(output, 0.0)
def test_sum08():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([1, 2], type)
output = ndimage.sum(input, labels=labels)
assert_equal(output, 1.0)
def test_sum09():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input, labels=labels)
assert_almost_equal(output, 4.0)
def test_sum10():
labels = np.array([1, 0], bool)
input = np.array([[1, 2], [3, 4]], bool)
output = ndimage.sum(input, labels=labels)
assert_almost_equal(output, 2.0)
def test_sum11():
labels = np.array([1, 2], np.int8)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input, labels=labels,
index=2)
assert_almost_equal(output, 6.0)
def test_sum12():
labels = np.array([[1, 2], [2, 4]], np.int8)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.sum(input, labels=labels, index=[4, 8, 2])
assert_array_almost_equal(output, [4.0, 0.0, 5.0])
def test_sum_labels():
labels = np.array([[1, 2], [2, 4]], np.int8)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output_sum = ndimage.sum(input, labels=labels, index=[4, 8, 2])
output_labels = ndimage.sum_labels(
input, labels=labels, index=[4, 8, 2])
assert (output_sum == output_labels).all()
assert_array_almost_equal(output_labels, [4.0, 0.0, 5.0])
def test_mean01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.mean(input, labels=labels)
assert_almost_equal(output, 2.0)
def test_mean02():
labels = np.array([1, 0], bool)
input = np.array([[1, 2], [3, 4]], bool)
output = ndimage.mean(input, labels=labels)
assert_almost_equal(output, 1.0)
def test_mean03():
labels = np.array([1, 2])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.mean(input, labels=labels,
index=2)
assert_almost_equal(output, 3.0)
def test_mean04():
labels = np.array([[1, 2], [2, 4]], np.int8)
with np.errstate(all='ignore'):
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.mean(input, labels=labels,
index=[4, 8, 2])
assert_array_almost_equal(output[[0, 2]], [4.0, 2.5])
assert_(np.isnan(output[1]))
def test_minimum01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.minimum(input, labels=labels)
assert_almost_equal(output, 1.0)
def test_minimum02():
labels = np.array([1, 0], bool)
input = np.array([[2, 2], [2, 4]], bool)
output = ndimage.minimum(input, labels=labels)
assert_almost_equal(output, 1.0)
def test_minimum03():
labels = np.array([1, 2])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.minimum(input, labels=labels,
index=2)
assert_almost_equal(output, 2.0)
def test_minimum04():
labels = np.array([[1, 2], [2, 3]])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.minimum(input, labels=labels,
index=[2, 3, 8])
assert_array_almost_equal(output, [2.0, 4.0, 0.0])
def test_maximum01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.maximum(input, labels=labels)
assert_almost_equal(output, 3.0)
def test_maximum02():
labels = np.array([1, 0], bool)
input = np.array([[2, 2], [2, 4]], bool)
output = ndimage.maximum(input, labels=labels)
assert_almost_equal(output, 1.0)
def test_maximum03():
labels = np.array([1, 2])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.maximum(input, labels=labels,
index=2)
assert_almost_equal(output, 4.0)
def test_maximum04():
labels = np.array([[1, 2], [2, 3]])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.maximum(input, labels=labels,
index=[2, 3, 8])
assert_array_almost_equal(output, [3.0, 4.0, 0.0])
def test_maximum05():
# Regression test for ticket #501 (Trac)
x = np.array([-3, -2, -1])
assert_equal(ndimage.maximum(x), -1)
def test_median01():
a = np.array([[1, 2, 0, 1],
[5, 3, 0, 4],
[0, 0, 0, 7],
[9, 3, 0, 0]])
labels = np.array([[1, 1, 0, 2],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
output = ndimage.median(a, labels=labels, index=[1, 2, 3])
assert_array_almost_equal(output, [2.5, 4.0, 6.0])
def test_median02():
a = np.array([[1, 2, 0, 1],
[5, 3, 0, 4],
[0, 0, 0, 7],
[9, 3, 0, 0]])
output = ndimage.median(a)
assert_almost_equal(output, 1.0)
def test_median03():
a = np.array([[1, 2, 0, 1],
[5, 3, 0, 4],
[0, 0, 0, 7],
[9, 3, 0, 0]])
labels = np.array([[1, 1, 0, 2],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
output = ndimage.median(a, labels=labels)
assert_almost_equal(output, 3.0)
def test_median_gh12836_bool():
# test boolean addition fix on example from gh-12836
a = np.asarray([1, 1], dtype=bool)
output = ndimage.median(a, labels=np.ones((2,)), index=[1])
assert_array_almost_equal(output, [1.0])
def test_median_no_int_overflow():
# test integer overflow fix on example from gh-12836
a = np.asarray([65, 70], dtype=np.int8)
output = ndimage.median(a, labels=np.ones((2,)), index=[1])
assert_array_almost_equal(output, [67.5])
def test_variance01():
with np.errstate(all='ignore'):
for type in types:
input = np.array([], type)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "Mean of empty slice")
output = ndimage.variance(input)
assert_(np.isnan(output))
def test_variance02():
for type in types:
input = np.array([1], type)
output = ndimage.variance(input)
assert_almost_equal(output, 0.0)
def test_variance03():
for type in types:
input = np.array([1, 3], type)
output = ndimage.variance(input)
assert_almost_equal(output, 1.0)
def test_variance04():
input = np.array([1, 0], bool)
output = ndimage.variance(input)
assert_almost_equal(output, 0.25)
def test_variance05():
labels = [2, 2, 3]
for type in types:
input = np.array([1, 3, 8], type)
output = ndimage.variance(input, labels, 2)
assert_almost_equal(output, 1.0)
def test_variance06():
labels = [2, 2, 3, 3, 4]
with np.errstate(all='ignore'):
for type in types:
input = np.array([1, 3, 8, 10, 8], type)
output = ndimage.variance(input, labels, [2, 3, 4])
assert_array_almost_equal(output, [1.0, 1.0, 0.0])
def test_standard_deviation01():
with np.errstate(all='ignore'):
for type in types:
input = np.array([], type)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "Mean of empty slice")
output = ndimage.standard_deviation(input)
assert_(np.isnan(output))
def test_standard_deviation02():
for type in types:
input = np.array([1], type)
output = ndimage.standard_deviation(input)
assert_almost_equal(output, 0.0)
def test_standard_deviation03():
for type in types:
input = np.array([1, 3], type)
output = ndimage.standard_deviation(input)
assert_almost_equal(output, np.sqrt(1.0))
def test_standard_deviation04():
input = np.array([1, 0], bool)
output = ndimage.standard_deviation(input)
assert_almost_equal(output, 0.5)
def test_standard_deviation05():
labels = [2, 2, 3]
for type in types:
input = np.array([1, 3, 8], type)
output = ndimage.standard_deviation(input, labels, 2)
assert_almost_equal(output, 1.0)
def test_standard_deviation06():
labels = [2, 2, 3, 3, 4]
with np.errstate(all='ignore'):
for type in types:
input = np.array([1, 3, 8, 10, 8], type)
output = ndimage.standard_deviation(input, labels, [2, 3, 4])
assert_array_almost_equal(output, [1.0, 1.0, 0.0])
def test_standard_deviation07():
labels = [1]
with np.errstate(all='ignore'):
for type in types:
input = np.array([-0.00619519], type)
output = ndimage.standard_deviation(input, labels, [1])
assert_array_almost_equal(output, [0])
def test_minimum_position01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.minimum_position(input, labels=labels)
assert_equal(output, (0, 0))
def test_minimum_position02():
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 0, 2],
[1, 5, 1, 1]], type)
output = ndimage.minimum_position(input)
assert_equal(output, (1, 2))
def test_minimum_position03():
input = np.array([[5, 4, 2, 5],
[3, 7, 0, 2],
[1, 5, 1, 1]], bool)
output = ndimage.minimum_position(input)
assert_equal(output, (1, 2))
def test_minimum_position04():
input = np.array([[5, 4, 2, 5],
[3, 7, 1, 2],
[1, 5, 1, 1]], bool)
output = ndimage.minimum_position(input)
assert_equal(output, (0, 0))
def test_minimum_position05():
labels = [1, 2, 0, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 0, 2],
[1, 5, 2, 3]], type)
output = ndimage.minimum_position(input, labels)
assert_equal(output, (2, 0))
def test_minimum_position06():
labels = [1, 2, 3, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 0, 2],
[1, 5, 1, 1]], type)
output = ndimage.minimum_position(input, labels, 2)
assert_equal(output, (0, 1))
def test_minimum_position07():
labels = [1, 2, 3, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 0, 2],
[1, 5, 1, 1]], type)
output = ndimage.minimum_position(input, labels,
[2, 3])
assert_equal(output[0], (0, 1))
assert_equal(output[1], (1, 2))
def test_maximum_position01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output = ndimage.maximum_position(input,
labels=labels)
assert_equal(output, (1, 0))
def test_maximum_position02():
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output = ndimage.maximum_position(input)
assert_equal(output, (1, 2))
def test_maximum_position03():
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], bool)
output = ndimage.maximum_position(input)
assert_equal(output, (0, 0))
def test_maximum_position04():
labels = [1, 2, 0, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output = ndimage.maximum_position(input, labels)
assert_equal(output, (1, 1))
def test_maximum_position05():
labels = [1, 2, 0, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output = ndimage.maximum_position(input, labels, 1)
assert_equal(output, (0, 0))
def test_maximum_position06():
labels = [1, 2, 0, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output = ndimage.maximum_position(input, labels,
[1, 2])
assert_equal(output[0], (0, 0))
assert_equal(output[1], (1, 1))
def test_maximum_position07():
# Test float labels
labels = np.array([1.0, 2.5, 0.0, 4.5])
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output = ndimage.maximum_position(input, labels,
[1.0, 4.5])
assert_equal(output[0], (0, 0))
assert_equal(output[1], (0, 3))
def test_extrema01():
labels = np.array([1, 0], bool)
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output1 = ndimage.extrema(input, labels=labels)
output2 = ndimage.minimum(input, labels=labels)
output3 = ndimage.maximum(input, labels=labels)
output4 = ndimage.minimum_position(input,
labels=labels)
output5 = ndimage.maximum_position(input,
labels=labels)
assert_equal(output1, (output2, output3, output4, output5))
def test_extrema02():
labels = np.array([1, 2])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output1 = ndimage.extrema(input, labels=labels,
index=2)
output2 = ndimage.minimum(input, labels=labels,
index=2)
output3 = ndimage.maximum(input, labels=labels,
index=2)
output4 = ndimage.minimum_position(input,
labels=labels, index=2)
output5 = ndimage.maximum_position(input,
labels=labels, index=2)
assert_equal(output1, (output2, output3, output4, output5))
def test_extrema03():
labels = np.array([[1, 2], [2, 3]])
for type in types:
input = np.array([[1, 2], [3, 4]], type)
output1 = ndimage.extrema(input, labels=labels,
index=[2, 3, 8])
output2 = ndimage.minimum(input, labels=labels,
index=[2, 3, 8])
output3 = ndimage.maximum(input, labels=labels,
index=[2, 3, 8])
output4 = ndimage.minimum_position(input,
labels=labels, index=[2, 3, 8])
output5 = ndimage.maximum_position(input,
labels=labels, index=[2, 3, 8])
assert_array_almost_equal(output1[0], output2)
assert_array_almost_equal(output1[1], output3)
assert_array_almost_equal(output1[2], output4)
assert_array_almost_equal(output1[3], output5)
def test_extrema04():
labels = [1, 2, 0, 4]
for type in types:
input = np.array([[5, 4, 2, 5],
[3, 7, 8, 2],
[1, 5, 1, 1]], type)
output1 = ndimage.extrema(input, labels, [1, 2])
output2 = ndimage.minimum(input, labels, [1, 2])
output3 = ndimage.maximum(input, labels, [1, 2])
output4 = ndimage.minimum_position(input, labels,
[1, 2])
output5 = ndimage.maximum_position(input, labels,
[1, 2])
assert_array_almost_equal(output1[0], output2)
assert_array_almost_equal(output1[1], output3)
assert_array_almost_equal(output1[2], output4)
assert_array_almost_equal(output1[3], output5)
def test_center_of_mass01():
expected = [0.0, 0.0]
for type in types:
input = np.array([[1, 0], [0, 0]], type)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass02():
expected = [1, 0]
for type in types:
input = np.array([[0, 0], [1, 0]], type)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass03():
expected = [0, 1]
for type in types:
input = np.array([[0, 1], [0, 0]], type)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass04():
expected = [1, 1]
for type in types:
input = np.array([[0, 0], [0, 1]], type)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass05():
expected = [0.5, 0.5]
for type in types:
input = np.array([[1, 1], [1, 1]], type)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass06():
expected = [0.5, 0.5]
input = np.array([[1, 2], [3, 1]], bool)
output = ndimage.center_of_mass(input)
assert_array_almost_equal(output, expected)
def test_center_of_mass07():
labels = [1, 0]
expected = [0.5, 0.0]
input = np.array([[1, 2], [3, 1]], bool)
output = ndimage.center_of_mass(input, labels)
assert_array_almost_equal(output, expected)
def test_center_of_mass08():
labels = [1, 2]
expected = [0.5, 1.0]
input = np.array([[5, 2], [3, 1]], bool)
output = ndimage.center_of_mass(input, labels, 2)
assert_array_almost_equal(output, expected)
def test_center_of_mass09():
labels = [1, 2]
expected = [(0.5, 0.0), (0.5, 1.0)]
input = np.array([[1, 2], [1, 1]], bool)
output = ndimage.center_of_mass(input, labels, [1, 2])
assert_array_almost_equal(output, expected)
def test_histogram01():
expected = np.ones(10)
input = np.arange(10)
output = ndimage.histogram(input, 0, 10, 10)
assert_array_almost_equal(output, expected)
def test_histogram02():
labels = [1, 1, 1, 1, 2, 2, 2, 2]
expected = [0, 2, 0, 1, 1]
input = np.array([1, 1, 3, 4, 3, 3, 3, 3])
output = ndimage.histogram(input, 0, 4, 5, labels, 1)
assert_array_almost_equal(output, expected)
def test_histogram03():
labels = [1, 0, 1, 1, 2, 2, 2, 2]
expected1 = [0, 1, 0, 1, 1]
expected2 = [0, 0, 0, 3, 0]
input = np.array([1, 1, 3, 4, 3, 5, 3, 3])
output = ndimage.histogram(input, 0, 4, 5, labels, (1, 2))
assert_array_almost_equal(output[0], expected1)
assert_array_almost_equal(output[1], expected2)
def test_stat_funcs_2d():
a = np.array([[5, 6, 0, 0, 0], [8, 9, 0, 0, 0], [0, 0, 0, 3, 5]])
lbl = np.array([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 2, 2]])
mean = ndimage.mean(a, labels=lbl, index=[1, 2])
assert_array_equal(mean, [7.0, 4.0])
var = ndimage.variance(a, labels=lbl, index=[1, 2])
assert_array_equal(var, [2.5, 1.0])
std = ndimage.standard_deviation(a, labels=lbl, index=[1, 2])
assert_array_almost_equal(std, np.sqrt([2.5, 1.0]))
med = ndimage.median(a, labels=lbl, index=[1, 2])
assert_array_equal(med, [7.0, 4.0])
min = ndimage.minimum(a, labels=lbl, index=[1, 2])
assert_array_equal(min, [5, 3])
max = ndimage.maximum(a, labels=lbl, index=[1, 2])
assert_array_equal(max, [9, 5])
class TestWatershedIft:
def test_watershed_ift01(self):
data = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.int8)
out = ndimage.watershed_ift(data, markers, structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
expected = [[-1, -1, -1, -1, -1, -1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift02(self):
data = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.int8)
out = ndimage.watershed_ift(data, markers)
expected = [[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, 1, 1, 1, -1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, 1, 1, 1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift03(self):
data = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1]], np.int8)
out = ndimage.watershed_ift(data, markers)
expected = [[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, 2, -1, 3, -1, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, -1, 2, -1, 3, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift04(self):
data = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 3, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1]],
np.int8)
out = ndimage.watershed_ift(data, markers,
structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
expected = [[-1, -1, -1, -1, -1, -1, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, 2, 2, 3, 3, 3, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift05(self):
data = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 3, 0, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1]],
np.int8)
out = ndimage.watershed_ift(data, markers,
structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
expected = [[-1, -1, -1, -1, -1, -1, -1],
[-1, 3, 3, 2, 2, 2, -1],
[-1, 3, 3, 2, 2, 2, -1],
[-1, 3, 3, 2, 2, 2, -1],
[-1, 3, 3, 2, 2, 2, -1],
[-1, 3, 3, 2, 2, 2, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift06(self):
data = np.array([[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.int8)
out = ndimage.watershed_ift(data, markers,
structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
expected = [[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift07(self):
shape = (7, 6)
data = np.zeros(shape, dtype=np.uint8)
data = data.transpose()
data[...] = np.array([[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], np.int8)
out = np.zeros(shape, dtype=np.int16)
out = out.transpose()
ndimage.watershed_ift(data, markers,
structure=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
output=out)
expected = [[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, 1, 1, -1],
[-1, -1, -1, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, -1, -1]]
assert_array_almost_equal(out, expected)
def test_watershed_ift08(self):
# Test cost larger than uint8. See gh-10069.
shape = (2, 2)
data = np.array([[256, 0],
[0, 0]], np.uint16)
markers = np.array([[1, 0],
[0, 0]], np.int8)
out = ndimage.watershed_ift(data, markers)
expected = [[1, 1],
[1, 1]]
assert_array_almost_equal(out, expected)