laywerrobot/lib/python3.6/site-packages/scipy/ndimage/tests/test_measurements.py
2020-08-27 21:55:39 +02:00

1106 lines
35 KiB
Python

from __future__ import division, print_function, absolute_import
import os.path
import numpy as np
from numpy.testing import (assert_, assert_array_almost_equal, assert_equal,
assert_almost_equal, assert_array_equal)
from pytest import raises as assert_raises
from scipy._lib._numpy_compat import suppress_warnings
import scipy.ndimage as ndimage
types = [np.int8, np.uint8, np.int16,
np.uint16, np.int32, np.uint32,
np.int64, np.uint64,
np.float32, np.float64]
np.mod(1., 1) # Silence fmod bug on win-amd64. See #1408 and #1238.
class Test_measurements_stats(object):
"""ndimage.measurements._stats() is a utility function 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(object):
"""ndimage.measurements._select() is a utility function 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_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_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_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)
olderr = np.seterr(all='ignore')
try:
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]))
finally:
np.seterr(**olderr)
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_variance01():
olderr = np.seterr(all='ignore')
try:
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))
finally:
np.seterr(**olderr)
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]
olderr = np.seterr(all='ignore')
try:
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])
finally:
np.seterr(**olderr)
def test_standard_deviation01():
olderr = np.seterr(all='ignore')
try:
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))
finally:
np.seterr(**olderr)
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]
olderr = np.seterr(all='ignore')
try:
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])
finally:
np.seterr(**olderr)
def test_standard_deviation07():
labels = [1]
olderr = np.seterr(all='ignore')
try:
for type in types:
input = np.array([-0.00619519], type)
output = ndimage.standard_deviation(input, labels, [1])
assert_array_almost_equal(output, [0])
finally:
np.seterr(**olderr)
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])