def test_minimum1():
"""
minimum() nominally computes the minimum pixel value for a stack of
identically shaped images.
arrays specifies a sequence of inputs arrays, which are nominally a
stack of identically shaped images.
output may be used to specify the output array. If none is specified,
either arrays[0] is copied or a new array of type 'outtype'
is created.
outtype specifies the type of the output array when no 'output' is
specified.
nlow specifies the number of pixels to be excluded from minimum
on the low end of the pixel stack.
nhigh specifies the number of pixels to be excluded from minimum
on the high end of the pixel stack.
badmasks specifies boolean arrays corresponding to 'arrays', where true
indicates that a particular pixel is not to be included in the
minimum calculation.
"""
result = combine.minimum(arrays)
expected = np.array([[0, 2], [4, 6]])
assert (result == expected).all()
def test_minimum5():
bm = np.zeros((4,2,2), dtype=np.bool8)
bm[2,...] = 1
result = combine.minimum(arrays, badmasks=bm)
expected = np.array([[ 0, 4],
[ 8, 12]])
assert_true((result == expected).all())
def test_minimum1():
"""
minimum() nominally computes the minimum pixel value for a stack of
identically shaped images.
arrays specifies a sequence of inputs arrays, which are nominally a
stack of identically shaped images.
output may be used to specify the output array. If none is specified,
either arrays[0] is copied or a new array of type 'outtype'
is created.
outtype specifies the type of the output array when no 'output' is
specified.
nlow specifies the number of pixels to be excluded from minimum
on the low end of the pixel stack.
nhigh specifies the number of pixels to be excluded from minimum
on the high end of the pixel stack.
badmasks specifies boolean arrays corresponding to 'arrays', where true
indicates that a particular pixel is not to be included in the
minimum calculation.
"""
result = combine.minimum(arrays)
expected = np.array([[0, 2],
[4, 6]])
assert_true((result == expected).all())
def test_minimum6():
result = combine.minimum(arrays,
badmasks=combine.threshhold(arrays, low=10))
expected = np.array([[ 0, 16],
[16, 12]])
assert_true((result == expected).all())
def test_minimum4():
result = combine.minimum(arrays, outtype=np.float32)
expected = np.array([[ 0., 2.],
[ 4., 6.]], dtype=np.float32)
assert_true((result == expected).all())
def test_minimum3():
result = combine.minimum(arrays, nlow=1)
expected = np.array([[ 0, 4],
[ 8, 12]])
assert_true((result == expected).all())
def test_minimum2():
result = combine.minimum(arrays, nhigh=1)
expected = np.array([[0, 2],
[4, 6]])
assert_true((result == expected).all())
def test_minimum6():
result = combine.minimum(arrays,
badmasks=combine.threshhold(arrays, low=10))
expected = np.array([[0, 16], [16, 12]])
assert (result == expected).all()
def test_minimum5():
bm = np.zeros((4, 2, 2), dtype=np.bool8)
bm[2, ...] = 1
result = combine.minimum(arrays, badmasks=bm)
expected = np.array([[0, 4], [8, 12]])
assert (result == expected).all()
def test_minimum4():
result = combine.minimum(arrays, outtype=np.float32)
expected = np.array([[0., 2.], [4., 6.]], dtype=np.float32)
assert (result == expected).all()
def test_minimum3():
result = combine.minimum(arrays, nlow=1)
expected = np.array([[0, 4], [8, 12]])
assert (result == expected).all()
def test_minimum2():
result = combine.minimum(arrays, nhigh=1)
expected = np.array([[0, 2], [4, 6]])
assert (result == expected).all()