def convolve(in1, in2, mode='full'):
"""Convolve two N-dimensional arrays.
Description:
Convolve in1 and in2 with output size determined by mode.
Inputs:
in1 -- an N-dimensional array.
in2 -- an array with the same number of dimensions as in1.
mode -- a flag indicating the size of the output
'valid' (0): The output consists only of those elements that
are computed by scaling the larger array with all
the values of the smaller array.
'same' (1): The output is the same size as the largest input
centered with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (Default)
Outputs: (out,)
out -- an N-dimensional array containing a subset of the discrete linear
convolution of in1 with in2.
"""
volume = asarray(in1)
kernel = asarray(in2)
if rank(volume) == rank(kernel) == 0:
return volume * kernel
if (product(kernel.shape, axis=0) > product(volume.shape, axis=0)):
temp = kernel
kernel = volume
volume = temp
del temp
slice_obj = [slice(None, None, -1)] * len(kernel.shape)
val = _valfrommode(mode)
return sigtools._correlateND(volume, kernel[slice_obj], val)
def convolve(in1, in2, mode='full'):
"""Convolve two N-dimensional arrays.
Description:
Convolve in1 and in2 with output size determined by mode.
Inputs:
in1 -- an N-dimensional array.
in2 -- an array with the same number of dimensions as in1.
mode -- a flag indicating the size of the output
'valid' (0): The output consists only of those elements that
are computed by scaling the larger array with all
the values of the smaller array.
'same' (1): The output is the same size as the largest input
centered with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (Default)
Outputs: (out,)
out -- an N-dimensional array containing a subset of the discrete linear
convolution of in1 with in2.
"""
volume = asarray(in1)
kernel = asarray(in2)
if rank(volume) == rank(kernel) == 0:
return volume*kernel
if (product(kernel.shape,axis=0) > product(volume.shape,axis=0)):
temp = kernel
kernel = volume
volume = temp
del temp
slice_obj = [slice(None,None,-1)]*len(kernel.shape)
val = _valfrommode(mode)
return sigtools._correlateND(volume,kernel[slice_obj],val)
def correlate(in1, in2, mode='full'):
"""Cross-correlate two N-dimensional arrays.
Description:
Cross-correlate in1 and in2 with the output size determined by mode.
Inputs:
in1 -- an N-dimensional array.
in2 -- an array with the same number of dimensions as in1.
mode -- a flag indicating the size of the output
'valid' (0): The output consists only of those elements that
do not rely on the zero-padding.
'same' (1): The output is the same size as the largest input
centered with respect to the 'full' output.
'full' (2): The output is the full discrete linear
cross-correlation of the inputs. (Default)
Outputs: (out,)
out -- an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
"""
# Code is faster if kernel is smallest array.
volume = asarray(in1)
kernel = asarray(in2)
if rank(volume) == rank(kernel) == 0:
return volume*kernel
if (product(kernel.shape,axis=0) > product(volume.shape,axis=0)):
temp = kernel
kernel = volume
volume = temp
del temp
val = _valfrommode(mode)
return sigtools._correlateND(volume, kernel, val)
def correlate(in1, in2, mode='full'):
"""Cross-correlate two N-dimensional arrays.
Description:
Cross-correlate in1 and in2 with the output size determined by mode.
Inputs:
in1 -- an N-dimensional array.
in2 -- an array with the same number of dimensions as in1.
mode -- a flag indicating the size of the output
'valid' (0): The output consists only of those elements that
do not rely on the zero-padding.
'same' (1): The output is the same size as the largest input
centered with respect to the 'full' output.
'full' (2): The output is the full discrete linear
cross-correlation of the inputs. (Default)
Outputs: (out,)
out -- an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
"""
# Code is faster if kernel is smallest array.
volume = asarray(in1)
kernel = asarray(in2)
if rank(volume) == rank(kernel) == 0:
return volume * kernel
if (product(kernel.shape, axis=0) > product(volume.shape, axis=0)):
temp = kernel
kernel = volume
volume = temp
del temp
val = _valfrommode(mode)
return sigtools._correlateND(volume, kernel, val)
def correlate(in1, in2, mode='full'):
"""
Cross-correlate two N-dimensional arrays.
Cross-correlate in1 and in2 with the output size determined by the mode
argument.
Parameters
----------
in1: array
first input.
in2: array
second input. Should have the same number of dimensions as in1.
mode: str {'valid', 'same', 'full'}, optional
A string indicating the size of the output:
- 'valid': the output consists only of those elements that do not
rely on the zero-padding.
- 'same': the output is the same size as ``in1`` centered
with respect to the 'full' output.
- 'full': the output is the full discrete linear cross-correlation
of the inputs (default).
Returns
-------
out: array
an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
Notes
-----
The correlation z of two arrays x and y of rank d is defined as::
z[...,k,...] = sum[..., i_l, ...]
x[..., i_l,...] * conj(y[..., i_l + k,...])
"""
val = _valfrommode(mode)
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
for i in range(len(ps)):
if ps[i] <= 0:
raise ValueError("Dimension of x(%d) < y(%d) " \
"not compatible with valid mode" % \
(in1.shape[i], in2.shape[i]))
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = [slice(0, i) for i in in1.shape]
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
else:
raise ValueError("Uknown mode %s" % mode)
return z
def correlate(in1, in2, mode='full'):
"""
Cross-correlate two N-dimensional arrays.
Cross-correlate in1 and in2 with the output size determined by the mode
argument.
Parameters
----------
in1: array
first input.
in2: array
second input. Should have the same number of dimensions as in1.
mode: str {'valid', 'same', 'full'}
a string indicating the size of the output:
- 'valid': the output consists only of those elements that do not
rely on the zero-padding.
- 'same': the output is the same size as the largest input centered
with respect to the 'full' output.
- 'full': the output is the full discrete linear cross-correlation
of the inputs. (Default)
Returns
-------
out: array
an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
Notes
-----
The correlation z of two arrays x and y of rank d is defined as
z[...,k,...] = sum[..., i_l, ...]
x[..., i_l,...] * conj(y[..., i_l + k,...])
"""
val = _valfrommode(mode)
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
for i in range(len(ps)):
if ps[i] <= 0:
raise ValueError("Dimension of x(%d) < y(%d) " \
"not compatible with valid mode" % \
(in1.shape[i], in2.shape[i]))
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = [slice(0, i) for i in in1.shape]
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
else:
raise ValueError("Uknown mode %s" % mode)
return z
def correlate(in1, in2, mode='full', old_behavior=True):
"""
Cross-correlate two N-dimensional arrays.
Cross-correlate in1 and in2 with the output size determined by the mode
argument.
Parameters
----------
in1: array
first input.
in2: array
second input. Should have the same number of dimensions as in1.
mode: str {'valid', 'same', 'full'}
a string indicating the size of the output:
- 'valid': the output consists only of those elements that do not
rely on the zero-padding.
- 'same': the output is the same size as the largest input centered
with respect to the 'full' output.
- 'full': the output is the full discrete linear cross-correlation
of the inputs. (Default)
old_behavior: bool
If True (default), the old behavior of correlate is implemented:
- if in1.size < in2.size, in1 and in2 are swapped (correlate(in1,
in2) == correlate(in2, in1))
- For complex inputs, the conjugate is not taken for in2
If False, the new, conventional definition of correlate is implemented.
Returns
-------
out: array
an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
Notes
-----
The correlation z of two arrays x and y of rank d is defined as
z[...,k,...] = sum[..., i_l, ...]
x[..., i_l,...] * conj(y[..., i_l + k,...])
"""
val = _valfrommode(mode)
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if np.iscomplexobj(in2):
in2 = in2.conjugate()
if in1.size < in2.size:
swp = in2
in2 = in1
in1 = swp
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
for i in range(len(ps)):
if ps[i] <= 0:
raise ValueError("Dimension of x(%d) < y(%d) " \
"not compatible with valid mode" % \
(in1.shape[i], in2.shape[i]))
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = [slice(0, i) for i in in1.shape]
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
else:
raise ValueError("Uknown mode %s" % mode)
return z
def correlate(in1, in2, mode='full', old_behavior=True):
"""
Cross-correlate two N-dimensional arrays.
Cross-correlate in1 and in2 with the output size determined by the mode
argument.
Parameters
----------
in1: array
first input.
in2: array
second input. Should have the same number of dimensions as in1.
mode: str {'valid', 'same', 'full'}
a string indicating the size of the output:
- 'valid': the output consists only of those elements that do not
rely on the zero-padding.
- 'same': the output is the same size as the largest input centered
with respect to the 'full' output.
- 'full': the output is the full discrete linear cross-correlation
of the inputs. (Default)
old_behavior: bool
If True (default), the old behavior of correlate is implemented:
- if in1.size < in2.size, in1 and in2 are swapped (correlate(in1,
in2) == correlate(in2, in1))
- For complex inputs, the conjugate is not taken for in2
If False, the new, conventional definition of correlate is implemented.
Returns
-------
out: array
an N-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
Notes
-----
The correlation z of two arrays x and y of rank d is defined as
z[...,k,...] = sum[..., i_l, ...]
x[..., i_l,...] * conj(y[..., i_l + k,...])
"""
val = _valfrommode(mode)
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if np.iscomplexobj(in2):
in2 = in2.conjugate()
if in1.size < in2.size:
swp = in2
in2 = in1
in1 = swp
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
for i in range(len(ps)):
if ps[i] <= 0:
raise ValueError("Dimension of x(%d) < y(%d) " \
"not compatible with valid mode" % \
(in1.shape[i], in2.shape[i]))
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = [slice(0, i) for i in in1.shape]
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
else:
raise ValueError("Uknown mode %s" % mode)
return z
