def test_median5():
bm = np.zeros((4,2,2), dtype=np.bool8)
bm[2,...] = 1
result = combine.median(arrays, badmasks=bm)
expected = np.array([[ 0, 8],
[16, 24]])
assert_true((result == expected).all())
def test_median1():
"""
median() nominally computes the median pixels 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 median
on the low end of the pixel stack.
nhigh specifies the number of pixels to be excluded from median
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
median calculation.
"""
result = combine.median(arrays)
expected = np.array([[0, 6], [12, 18]])
assert (result == expected).all()
def test_median1():
"""
median() nominally computes the median pixels 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 median
on the low end of the pixel stack.
nhigh specifies the number of pixels to be excluded from median
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
median calculation.
"""
result = combine.median(arrays)
expected = np.array([[ 0, 6],
[12, 18]])
assert_true((result == expected).all())
def test_median6():
result = combine.median(arrays,
badmasks=combine.threshhold(arrays, high=25))
expected = np.array([[0, 6], [8, 12]])
assert (result == expected).all()
def test_median5():
bm = np.zeros((4, 2, 2), dtype=np.bool8)
bm[2, ...] = 1
result = combine.median(arrays, badmasks=bm)
expected = np.array([[0, 8], [16, 24]])
assert (result == expected).all()
def test_median4():
result = combine.median(arrays, outtype=np.float32)
expected = np.array([[0., 6.], [12., 18.]], dtype=np.float32)
assert (result == expected).all()
def test_median3():
result = combine.median(arrays, nlow=1)
expected = np.array([[0, 8], [16, 24]])
assert (result == expected).all()
def test_median2():
result = combine.median(arrays, nhigh=1)
expected = np.array([[0, 4], [8, 12]])
assert (result == expected).all()
def test_median6():
result = combine.median(arrays,
badmasks=combine.threshhold(arrays, high=25))
expected = np.array([[ 0, 6],
[ 8, 12]])
assert_true((result == expected).all())
def test_median4():
result = combine.median(arrays, outtype=np.float32)
expected = np.array([[ 0., 6.],
[ 12., 18.]], dtype=np.float32)
assert_true((result == expected).all())
def test_median3():
result = combine.median(arrays, nlow=1)
expected = np.array([[ 0, 8],
[16, 24]])
assert_true((result == expected).all())
def test_median2():
result = combine.median(arrays, nhigh=1)
expected = np.array([[ 0, 4],
[ 8, 12]])
assert_true((result == expected).all())