def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""Cross-correlate two 2-dimensional arrays.
Description:
Cross correlate in1 and in2 with output size determined by mode
and boundary conditions determined by boundary and fillvalue.
Inputs:
in1 -- a 2-dimensional array.
in2 -- a 2-dimensional array.
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 input centered
with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (*Default*)
boundary -- a flag indicating how to handle boundaries
'fill' : pad input arrays with fillvalue. (*Default*)
'wrap' : circular boundary conditions.
'symm' : symmetrical boundary conditions.
fillvalue -- value to fill pad input arrays with (*Default* = 0)
Outputs: (out,)
out -- a 2-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
"""
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 0,val,bval,fillvalue)
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""Cross-correlate two 2-dimensional arrays.
Description:
Cross correlate in1 and in2 with output size determined by mode
and boundary conditions determined by boundary and fillvalue.
Inputs:
in1 -- a 2-dimensional array.
in2 -- a 2-dimensional array.
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 input centered
with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (*Default*)
boundary -- a flag indicating how to handle boundaries
'fill' : pad input arrays with fillvalue. (*Default*)
'wrap' : circular boundary conditions.
'symm' : symmetrical boundary conditions.
fillvalue -- value to fill pad input arrays with (*Default* = 0)
Outputs: (out,)
out -- a 2-dimensional array containing a subset of the discrete linear
cross-correlation of in1 with in2.
"""
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 0, val, bval, fillvalue)
def convolve2d(in1,
in2,
mode='full',
boundary='fill',
fillvalue=0,
old_behavior=True):
"""Convolve two 2-dimensional arrays.
Description:
Convolve in1 and in2 with output size determined by mode and boundary
conditions determined by boundary and fillvalue.
Inputs:
in1 -- a 2-dimensional array.
in2 -- a 2-dimensional array.
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 input centered
with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (*Default*)
boundary -- a flag indicating how to handle boundaries
'fill' : pad input arrays with fillvalue. (*Default*)
'wrap' : circular boundary conditions.
'symm' : symmetrical boundary conditions.
fillvalue -- value to fill pad input arrays with (*Default* = 0)
Outputs: (out,)
out -- a 2-dimensional array containing a subset of the discrete linear
convolution of in1 with in2.
"""
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if (product(np.shape(in2), axis=0) > product(np.shape(in1), axis=0)):
temp = in1
in1 = in2
in2 = temp
del temp
else:
if mode == 'valid':
for d1, d2 in zip(np.shape(in1), np.shape(in2)):
if not d1 >= d2:
raise ValueError(
"in1 should have at least as many items as in2 in " \
"every dimension for valid mode.")
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0, old_behavior=True):
"""Convolve two 2-dimensional arrays.
Description:
Convolve in1 and in2 with output size determined by mode and boundary
conditions determined by boundary and fillvalue.
Inputs:
in1 -- a 2-dimensional array.
in2 -- a 2-dimensional array.
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 input centered
with respect to the 'full' output.
'full' (2): The output is the full discrete linear convolution
of the inputs. (*Default*)
boundary -- a flag indicating how to handle boundaries
'fill' : pad input arrays with fillvalue. (*Default*)
'wrap' : circular boundary conditions.
'symm' : symmetrical boundary conditions.
fillvalue -- value to fill pad input arrays with (*Default* = 0)
Outputs: (out,)
out -- a 2-dimensional array containing a subset of the discrete linear
convolution of in1 with in2.
"""
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
if (product(np.shape(in2),axis=0) > product(np.shape(in1),axis=0)):
temp = in1
in1 = in2
in2 = temp
del temp
else:
if mode == 'valid':
for d1, d2 in zip(np.shape(in1), np.shape(in2)):
if not d1 >= d2:
raise ValueError(
"in1 should have at least as many items as in2 in " \
"every dimension for valid mode.")
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1,in2,1,val,bval,fillvalue)
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""Convolve two 2-dimensional arrays.
Convolve `in1` and `in2` with output size determined by mode and boundary
conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1, in2 : ndarray
Two-dimensional input arrays to be convolved.
mode: str, 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)
boundary : str, optional
A flag indicating how to handle boundaries:
- 'fill' : pad input arrays with fillvalue. (default)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
"""
if mode == 'valid':
for d1, d2 in zip(np.shape(in1), np.shape(in2)):
if not d1 >= d2:
raise ValueError(
"in1 should have at least as many items as in2 in " \
"every dimension for valid mode.")
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""Convolve two 2-dimensional arrays.
Convolve `in1` and `in2` with output size determined by mode and boundary
conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1, in2 : ndarray
Two-dimensional input arrays to be convolved.
mode: str, 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 the largest input centered
with respect to the 'full' output.
``full`` : the output is the full discrete linear cross-correlation
of the inputs. (Default)
boundary : str, optional
A flag indicating how to handle boundaries:
- 'fill' : pad input arrays with fillvalue. (default)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
"""
if mode == 'valid':
for d1, d2 in zip(np.shape(in1), np.shape(in2)):
if not d1 >= d2:
raise ValueError(
"in1 should have at least as many items as in2 in " \
"every dimension for valid mode.")
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0, old_behavior=True):
"""Cross-correlate two 2-dimensional arrays.
Cross correlate in1 and in2 with output size determined by mode and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1, in2 : ndarray
Two-dimensional input arrays to be convolved.
mode: str, 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 the largest input centered
with respect to the 'full' output.
``full`` : the output is the full discrete linear cross-correlation
of the inputs. (Default)
boundary : str, optional
A flag indicating how to handle boundaries:
- 'fill' : pad input arrays with fillvalue. (default)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
"""
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 0,val,bval,fillvalue)
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""Cross-correlate two 2-dimensional arrays.
Cross correlate in1 and in2 with output size determined by mode and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1, in2 : ndarray
Two-dimensional input arrays to be convolved.
mode: str, 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)
boundary : str, optional
A flag indicating how to handle boundaries:
- 'fill' : pad input arrays with fillvalue. (default)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
"""
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
return sigtools._convolve2d(in1, in2, 0, val, bval, fillvalue)