def test_extrema02():
labels = cp.asarray([1, 2])
for type in types:
input = cp.asarray([[1, 2], [3, 4]], type)
# TODO: grlee77: avoid use of item()?
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_extrema04():
labels = cp.asarray([1, 2, 0, 4])
for type in types:
input = cp.asarray([[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_extrema03():
labels = cp.asarray([[1, 2], [2, 3]])
for type in types:
input = cp.asarray([[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_stat_funcs_2d():
a = cp.asarray([[5, 6, 0, 0, 0], [8, 9, 0, 0, 0], [0, 0, 0, 3, 5]])
lbl = cp.asarray([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 2, 2]])
index = cp.asarray([1, 2])
mean = ndimage.mean(a, labels=lbl, index=index)
assert_array_equal(mean, [7.0, 4.0])
var = ndimage.variance(a, labels=lbl, index=index)
assert_array_equal(var, [2.5, 1.0])
std = ndimage.standard_deviation(a, labels=lbl, index=index)
assert_array_almost_equal(std, cp.sqrt(cp.asarray([2.5, 1.0])))
med = ndimage.median(a, labels=lbl, index=index)
assert_array_equal(med, [7.0, 4.0])
min = ndimage.minimum(a, labels=lbl, index=index)
assert_array_equal(min, [5, 3])
max = ndimage.maximum(a, labels=lbl, index=index)
assert_array_equal(max, [9, 5])
def test_maximum05():
# Regression test for ticket #501 (Trac)
x = cp.asarray([-3, -2, -1])
assert_array_equal(ndimage.maximum(x), -1)
def test_maximum04():
labels = cp.asarray([[1, 2], [2, 3]])
for type in types:
input = cp.asarray([[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_maximum03():
labels = cp.asarray([1, 2])
for type in types:
input = cp.asarray([[1, 2], [3, 4]], type)
output = ndimage.maximum(input, labels=labels, index=2)
assert_almost_equal(output, 4.0)
def test_maximum02():
labels = cp.asarray([1, 0], bool)
input = cp.asarray([[2, 2], [2, 4]], bool)
output = ndimage.maximum(input, labels=labels)
assert_almost_equal(output, 1.0)
def test_maximum01():
labels = cp.asarray([1, 0], bool)
for type in types:
input = cp.asarray([[1, 2], [3, 4]], type)
output = ndimage.maximum(input, labels=labels)
assert_almost_equal(output, 3.0)