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import numpy as num
from _combine import combine as _comb
import operator as _operator
def _combine_f(funcstr, arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None):
arrays = [ num.asarray(a) for a in arrays ]
shape = arrays[0].shape
if output is None:
if outtype is not None:
out = arrays[0].astype(outtype)
else:
out = arrays[0].copy()
else:
out = output
for a in tuple(arrays[1:])+(out,):
if a.shape != shape:
raise ValueError("all arrays must have identical shapes")
_comb(arrays, out, nlow, nhigh, badmasks, funcstr)
if output is None:
return out
def median( arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None):
"""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.
>>> a = num.arange(4)
>>> a = a.reshape((2,2))
>>> arrays = [a*16, a*4, a*2, a*8]
>>> median(arrays)
array([[ 0, 6],
[12, 18]])
>>> median(arrays, nhigh=1)
array([[ 0, 4],
[ 8, 12]])
>>> median(arrays, nlow=1)
array([[ 0, 8],
[16, 24]])
>>> median(arrays, outtype=num.float32)
array([[ 0., 6.],
[ 12., 18.]], dtype=float32)
>>> bm = num.zeros((4,2,2), dtype=num.bool8)
>>> bm[2,...] = 1
>>> median(arrays, badmasks=bm)
array([[ 0, 8],
[16, 24]])
>>> median(arrays, badmasks=threshhold(arrays, high=25))
array([[ 0, 6],
[ 8, 12]])
"""
return _combine_f("median", arrays, output, outtype, nlow, nhigh, badmasks)
def average( arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None):
"""average() nominally computes the average 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 average
on the low end of the pixel stack.
nhigh specifies the number of pixels to be excluded from average
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
average calculation.
>>> a = num.arange(4)
>>> a = a.reshape((2,2))
>>> arrays = [a*16, a*4, a*2, a*8]
>>> average(arrays)
array([[ 0, 7],
[15, 22]])
>>> average(arrays, nhigh=1)
array([[ 0, 4],
[ 9, 14]])
>>> average(arrays, nlow=1)
array([[ 0, 9],
[18, 28]])
>>> average(arrays, outtype=num.float32)
array([[ 0. , 7.5],
[ 15. , 22.5]], dtype=float32)
>>> bm = num.zeros((4,2,2), dtype=num.bool8)
>>> bm[2,...] = 1
>>> average(arrays, badmasks=bm)
array([[ 0, 9],
[18, 28]])
>>> average(arrays, badmasks=threshhold(arrays, high=25))
array([[ 0, 7],
[ 9, 14]])
"""
return _combine_f("average", arrays, output, outtype, nlow, nhigh, badmasks)
def minimum( arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None):
"""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.
>>> a = num.arange(4)
>>> a = a.reshape((2,2))
>>> arrays = [a*16, a*4, a*2, a*8]
>>> minimum(arrays)
array([[0, 2],
[4, 6]])
>>> minimum(arrays, nhigh=1)
array([[0, 2],
[4, 6]])
>>> minimum(arrays, nlow=1)
array([[ 0, 4],
[ 8, 12]])
>>> minimum(arrays, outtype=num.float32)
array([[ 0., 2.],
[ 4., 6.]], dtype=float32)
>>> bm = num.zeros((4,2,2), dtype=num.bool8)
>>> bm[2,...] = 1
>>> minimum(arrays, badmasks=bm)
array([[ 0, 4],
[ 8, 12]])
>>> minimum(arrays, badmasks=threshhold(arrays, low=10))
array([[ 0, 16],
[16, 12]])
"""
return _combine_f("minimum", arrays, output, outtype, nlow, nhigh, badmasks)
def threshhold(arrays, low=None, high=None, outputs=None):
"""threshhold() computes a boolean array 'outputs' with
corresponding elements for each element of arrays. The
boolean value is true where each of the arrays values
is < the low or >= the high threshholds.
>>> a=num.arange(100)
>>> a=a.reshape((10,10))
>>> (threshhold(a, 1, 50)).astype(num.int8)
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, 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],
[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]], dtype=int8)
>>> (threshhold([ range(10)]*10, 3, 7)).astype(num.int8)
array([[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 1, 1]], dtype=int8)
>>> (threshhold(a, high=50)).astype(num.int8)
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, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[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]], dtype=int8)
>>> (threshhold(a, low=50)).astype(num.int8)
array([[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],
[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, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int8)
"""
if not isinstance(arrays[0], num.ndarray):
return threshhold( num.asarray(arrays), low, high, outputs)
if outputs is None:
outs = num.zeros(shape=(len(arrays),)+arrays[0].shape,
dtype=num.bool8)
else:
outs = outputs
for i in range(len(arrays)):
a, out = arrays[i], outs[i]
out[:] = 0
if high is not None:
num.greater_equal(a, high, out)
if low is not None:
num.logical_or(out, a < low, out)
else:
if low is not None:
num.less(a, low, out)
if outputs is None:
return outs
def _bench():
"""time a 10**6 element median"""
import time
a = num.arange(10**6)
a = a.reshape((1000, 1000))
arrays = [a*2, a*64, a*16, a*8]
t0 = time.clock()
median(arrays)
print "maskless:", time.clock()-t0
a = num.arange(10**6)
a = a.reshape((1000, 1000))
arrays = [a*2, a*64, a*16, a*8]
t0 = time.clock()
median(arrays, badmasks=num.zeros((1000,1000), dtype=num.bool8))
print "masked:", time.clock()-t0
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