def apply_along_axis(func1d, axis, arr, *args):
""" Execute func1d(arr[i],*args) where func1d takes 1-D arrays
and arr is an N-d array. i varies so as to apply the function
along the given axis for each 1-d subarray in arr.
"""
arr = asarray(arr)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." %
(axis, nd))
ind = [0] * (nd - 1)
i = zeros(nd, 'O')
indlist = range(nd)
indlist.remove(axis)
i[axis] = slice(None, None)
outshape = asarray(arr.shape).take(indlist)
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
def apply_along_axis(func1d,axis,arr,*args):
""" Execute func1d(arr[i],*args) where func1d takes 1-D arrays
and arr is an N-d array. i varies so as to apply the function
along the given axis for each 1-d subarray in arr.
"""
arr = asarray(arr)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d."
% (axis,nd))
ind = [0]*(nd-1)
i = zeros(nd,'O')
indlist = range(nd)
indlist.remove(axis)
i[axis] = slice(None,None)
outshape = asarray(arr.shape).take(indlist)
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())],*args)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape,asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist,ind)
res = func1d(arr[tuple(i.tolist())],*args)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape,asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())],*args)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
is a 1-D slice of `arr` along `axis`.
Parameters
----------
func1d : function
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arr : ndarray
Input array.
args : any
Additional arguments to `func1d`.
kwargs: any
Additional named arguments to `func1d`.
.. versionadded:: 1.9.0
Returns
-------
apply_along_axis : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension, where the length of `outarr`
is equal to the size of the return value of `func1d`. If `func1d`
returns a scalar `outarr` will have one fewer dimensions than `arr`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2., 5., 8.])
For a function that doesn't return a scalar, the number of dimensions in
`outarr` is the same as `arr`.
>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
[3, 4, 9],
[2, 5, 6]])
"""
arr = asarray(arr)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." %
(axis, nd))
ind = [0] * (nd - 1)
i = zeros(nd, 'O')
indlist = list(range(nd))
indlist.remove(axis)
i[axis] = slice(None, None)
outshape = asarray(arr.shape).take(indlist)
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
def apply_along_axis(func1d,axis,arr,*args):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
is a 1-D slice of `arr` along `axis`.
Parameters
----------
func1d : function
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arr : ndarray
Input array.
args : any
Additional arguments to `func1d`.
Returns
-------
apply_along_axis : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension, where the length of `outarr`
is equal to the size of the return value of `func1d`. If `func1d`
returns a scalar `outarr` will have one fewer dimensions than `arr`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2., 5., 8.])
For a function that doesn't return a scalar, the number of dimensions in
`outarr` is the same as `arr`.
>>> def new_func(a):
... \"\"\"Divide elements of a by 2.\"\"\"
... return a * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(new_func, 0, b)
array([[ 0.5, 1. , 1.5],
[ 2. , 2.5, 3. ],
[ 3.5, 4. , 4.5]])
"""
arr = asarray(arr)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d."
% (axis,nd))
ind = [0]*(nd-1)
i = zeros(nd,'O')
indlist = list(range(nd))
indlist.remove(axis)
i[axis] = slice(None,None)
outshape = asarray(arr.shape).take(indlist)
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())],*args)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape,asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist,ind)
res = func1d(arr[tuple(i.tolist())],*args)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape,asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())],*args)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
def apply_along_axes(func1d, axis, arrs, *args):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args)` where `func1d` operates on a set of 1-D arrays and `a`
is a 1-D slice of `arr` along `axis`.
Parameters
----------
func1d : function
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arrs : tuple
tuple of input arrays. All arrays must have the same shape
args : any
Additional arguments to `func1d`.
Returns
-------
outarr : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension, where the length of `outarr`
is equal to the size of the return value of `func1d`. If `func1d`
returns a scalar `outarr` will have one fewer dimensions than `arr`.
See Also
--------
apply_over_axis : Apply a function over 1-D slices of a single array.
apply_over_axes : Apply a function repeatedly over multiple axes.
"""
arrs = list(map(asarray, arrs))
arr = arrs[0]
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." %
(axis, nd))
ind = [0] * (nd - 1)
i = zeros(nd, 'O')
indlist = list(range(nd))
indlist.remove(axis)
i[axis] = slice(None, None)
outshape = asarray(arr.shape).take(indlist)
for arr in arrs[1:]:
if tuple(asarray(arr.shape).take(indlist)) != tuple(outshape):
raise ValueError(
"Shape of all input arrays must match in all but the selected dimension."
)
i.put(indlist, ind)
arglist = tuple([arr[tuple(i.tolist())] for arr in arrs]) + args
res = func1d(*arglist)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
arglist = tuple([arr[tuple(i.tolist())] for arr in arrs]) + args
res = func1d(*arglist)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
arglist = tuple([arr[tuple(i.tolist())] for arr in arrs]) + args
res = func1d(*arglist)
#res = func1d(arr[tuple(i.tolist())],*args)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
def apply_along_axis(func1d, axis, arr, *args):
"""
Apply function to 1-D slices along the given axis.
Execute `func1d(a[i],*args)` where `func1d` takes 1-D arrays, `a` is
the input array, and `i` is an integer that varies in order to apply the
function along the given axis for each 1-D subarray in `a`.
Parameters
----------
func1d : function
This function should be able to take 1-D arrays. It is applied to 1-D
slices of `a` along the specified axis.
axis : integer
Axis along which `func1d` is applied.
a : ndarray
Input array.
args : any
Additional arguments to `func1d`.
Returns
-------
out : ndarray
The output array. The shape of `out` is identical to the shape of `a`,
except along the `axis` dimension, whose length is equal to the size
of the return value of `func1d`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([2., 5., 8.])
"""
arr = asarray(arr)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." %
(axis, nd))
ind = [0] * (nd - 1)
i = zeros(nd, 'O')
indlist = range(nd)
indlist.remove(axis)
i[axis] = slice(None, None)
outshape = asarray(arr.shape).take(indlist)
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
# if res is a number, then we have a smaller output array
if isscalar(res):
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(ind)] = res
Ntot = product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
outarr[tuple(ind)] = res
k += 1
return outarr
else:
Ntot = product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = len(res)
outarr = zeros(outshape, asarray(res).dtype)
outarr[tuple(i.tolist())] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args)
outarr[tuple(i.tolist())] = res
k += 1
return outarr
