| Current File : //proc/self/root/proc/self/root/lib64/python2.7/site-packages/numpy/lib/arraypad.py |
"""
The arraypad module contains a group of functions to pad values onto the edges
of an n-dimensional array.
"""
import numpy as np
__all__ = ['pad']
################################################################################
# Private utility functions.
def _create_vector(vector, pad_tuple, before_val, after_val):
'''
Private function which creates the padded vector.
Parameters
----------
vector : ndarray of rank 1, length N + pad_tuple[0] + pad_tuple[1]
Input vector including blank padded values. `N` is the lenth of the
original vector.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding along
this particular iaxis.
before_val : scalar or ndarray of rank 1, length pad_tuple[0]
This is the value(s) that will pad the beginning of `vector`.
after_val : scalar or ndarray of rank 1, length pad_tuple[1]
This is the value(s) that will pad the end of the `vector`.
Returns
-------
_create_vector : ndarray
Vector with before_val and after_val replacing the blank pad values.
'''
vector[:pad_tuple[0]] = before_val
if pad_tuple[1] > 0:
vector[-pad_tuple[1]:] = after_val
return vector
def _normalize_shape(narray, shape):
'''
Private function which does some checks and normalizes the possibly
much simpler representations of 'pad_width', 'stat_length',
'constant_values', 'end_values'.
Parameters
----------
narray : ndarray
Input ndarray
shape : {sequence, int}, optional
The width of padding (pad_width) or the number of elements on the
edge of the narray used for statistics (stat_length).
((before_1, after_1), ... (before_N, after_N)) unique number of
elements for each axis where `N` is rank of `narray`.
((before, after),) yields same before and after constants for each
axis.
(constant,) or int is a shortcut for before = after = constant for
all axes.
Returns
-------
_normalize_shape : tuple of tuples
int => ((int, int), (int, int), ...)
[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
((int1, int2), (int3, int4), ...) => no change
[[int1, int2], ] => ((int1, int2), (int1, int2), ...]
((int1, int2), ) => ((int1, int2), (int1, int2), ...)
[[int , ), ) => ((int, int), (int, int), ...)
((int , ), ) => ((int, int), (int, int), ...)
'''
normshp = None
shapelen = len(np.shape(narray))
if (isinstance(shape, int)):
normshp = ((shape, shape), ) * shapelen
elif (isinstance(shape, (tuple, list))
and isinstance(shape[0], (tuple, list))
and len(shape) == shapelen):
normshp = shape
for i in normshp:
if len(i) != 2:
fmt = "Unable to create correctly shaped tuple from %s"
raise ValueError(fmt % (normshp,))
elif (isinstance(shape, (tuple, list))
and isinstance(shape[0], (int, float, long))
and len(shape) == 1):
normshp = ((shape[0], shape[0]), ) * shapelen
elif (isinstance(shape, (tuple, list))
and isinstance(shape[0], (int, float, long))
and len(shape) == 2):
normshp = (shape, ) * shapelen
if normshp == None:
fmt = "Unable to create correctly shaped tuple from %s"
raise ValueError(fmt % (shape,))
return normshp
def _validate_lengths(narray, number_elements):
'''
Private function which does some checks and reformats pad_width and
stat_length using _normalize_shape.
Parameters
----------
narray : ndarray
Input ndarray
number_elements : {sequence, int}, optional
The width of padding (pad_width) or the number of elements on the edge
of the narray used for statistics (stat_length).
((before_1, after_1), ... (before_N, after_N)) unique number of
elements for each axis.
((before, after),) yields same before and after constants for each
axis.
(constant,) or int is a shortcut for before = after = constant for all
axes.
Returns
-------
_validate_lengths : tuple of tuples
int => ((int, int), (int, int), ...)
[[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...)
((int1, int2), (int3, int4), ...) => no change
[[int1, int2], ] => ((int1, int2), (int1, int2), ...]
((int1, int2), ) => ((int1, int2), (int1, int2), ...)
[[int , ), ) => ((int, int), (int, int), ...)
((int , ), ) => ((int, int), (int, int), ...)
'''
shapelen = len(np.shape(narray))
normshp = _normalize_shape(narray, number_elements)
for i in normshp:
if i[0] < 0 or i[1] < 0:
fmt ="%s cannot contain negative values."
raise ValueError(fmt % (number_elements,))
return normshp
def _create_stat_vectors(vector, pad_tuple, iaxis, kwargs):
'''
Returns the portion of the vector required for any statistic.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Only 'stat_length' is used. 'stat_length'
defaults to the entire vector if not supplied.
Return
------
_create_stat_vectors : ndarray
The values from the original vector that will be used to calculate
the statistic.
'''
# Can't have 0 represent the end if a slice... a[1:0] doesnt' work
pt1 = -pad_tuple[1]
if pt1 == 0:
pt1 = None
# Default is the entire vector from the original array.
sbvec = vector[pad_tuple[0]:pt1]
savec = vector[pad_tuple[0]:pt1]
if kwargs['stat_length']:
stat_length = kwargs['stat_length'][iaxis]
sl0 = min(stat_length[0], len(sbvec))
sl1 = min(stat_length[1], len(savec))
sbvec = np.arange(0)
savec = np.arange(0)
if pad_tuple[0] > 0:
sbvec = vector[pad_tuple[0]:pad_tuple[0] + sl0]
if pad_tuple[1] > 0:
savec = vector[-pad_tuple[1] - sl1:pt1]
return (sbvec, savec)
def _maximum(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_maximum.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Only 'stat_length' is used. 'stat_length'
defaults to the entire vector if not supplied.
Return
------
_maximum : ndarray
Padded vector
'''
sbvec, savec = _create_stat_vectors(vector, pad_tuple, iaxis, kwargs)
return _create_vector(vector, pad_tuple, max(sbvec), max(savec))
def _minimum(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_minimum.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Only 'stat_length' is used. 'stat_length'
defaults to the entire vector if not supplied.
Return
------
_minimum : ndarray
Padded vector
'''
sbvec, savec = _create_stat_vectors(vector, pad_tuple, iaxis, kwargs)
return _create_vector(vector, pad_tuple, min(sbvec), min(savec))
def _median(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_median.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Only 'stat_length' is used. 'stat_length'
defaults to the entire vector if not supplied.
Return
------
_median : ndarray
Padded vector
'''
sbvec, savec = _create_stat_vectors(vector, pad_tuple, iaxis, kwargs)
return _create_vector(vector, pad_tuple, np.median(sbvec),
np.median(savec))
def _mean(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_mean.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Only 'stat_length' is used. 'stat_length'
defaults to the entire vector if not supplied.
Return
------
_mean : ndarray
Padded vector
'''
sbvec, savec = _create_stat_vectors(vector, pad_tuple, iaxis, kwargs)
return _create_vector(vector, pad_tuple, np.average(sbvec),
np.average(savec))
def _constant(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for
pad_constant.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across.
kwargs : keyword arguments
Keyword arguments. Need 'constant_values' keyword argument.
Return
------
_constant : ndarray
Padded vector
'''
nconstant = kwargs['constant_values'][iaxis]
return _create_vector(vector, pad_tuple, nconstant[0], nconstant[1])
def _linear_ramp(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for
pad_linear_ramp.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across. Not used in _linear_ramp.
kwargs : keyword arguments
Keyword arguments. Not used in _linear_ramp.
Return
------
_linear_ramp : ndarray
Padded vector
'''
end_values = kwargs['end_values'][iaxis]
before_delta = ((vector[pad_tuple[0]] - end_values[0])
/ float(pad_tuple[0]))
after_delta = ((vector[-pad_tuple[1] - 1] - end_values[1])
/ float(pad_tuple[1]))
before_vector = np.ones((pad_tuple[0], )) * end_values[0]
before_vector = before_vector.astype(vector.dtype)
for i in range(len(before_vector)):
before_vector[i] = before_vector[i] + i * before_delta
after_vector = np.ones((pad_tuple[1], )) * end_values[1]
after_vector = after_vector.astype(vector.dtype)
for i in range(len(after_vector)):
after_vector[i] = after_vector[i] + i * after_delta
after_vector = after_vector[::-1]
return _create_vector(vector, pad_tuple, before_vector, after_vector)
def _reflect(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_reflect.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across. Not used in _reflect.
kwargs : keyword arguments
Keyword arguments. Not used in _reflect.
Return
------
_reflect : ndarray
Padded vector
'''
# Can't have pad_tuple[1] be used in the slice if == to 0.
if pad_tuple[1] == 0:
after_vector = vector[pad_tuple[0]:None]
else:
after_vector = vector[pad_tuple[0]:-pad_tuple[1]]
reverse = after_vector[::-1]
before_vector = np.resize(
np.concatenate(
(after_vector[1:-1], reverse)), pad_tuple[0])[::-1]
after_vector = np.resize(
np.concatenate(
(reverse[1:-1], after_vector)), pad_tuple[1])
if kwargs['reflect_type'] == 'even':
pass
elif kwargs['reflect_type'] == 'odd':
before_vector = 2 * vector[pad_tuple[0]] - before_vector
after_vector = 2 * vector[-pad_tuple[-1] - 1] - after_vector
else:
raise ValueError("The keyword '%s' cannot have the value '%s'."
% ('reflect_type', kwargs['reflect_type']))
return _create_vector(vector, pad_tuple, before_vector, after_vector)
def _symmetric(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for
pad_symmetric.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across. Not used in _symmetric.
kwargs : keyword arguments
Keyword arguments. Not used in _symmetric.
Return
------
_symmetric : ndarray
Padded vector
'''
if pad_tuple[1] == 0:
after_vector = vector[pad_tuple[0]:None]
else:
after_vector = vector[pad_tuple[0]:-pad_tuple[1]]
before_vector = np.resize( np.concatenate( (after_vector,
after_vector[::-1])), pad_tuple[0])[::-1]
after_vector = np.resize( np.concatenate( (after_vector[::-1],
after_vector)), pad_tuple[1])
if kwargs['reflect_type'] == 'even':
pass
elif kwargs['reflect_type'] == 'odd':
before_vector = 2 * vector[pad_tuple[0]] - before_vector
after_vector = 2 * vector[-pad_tuple[1] - 1] - after_vector
else:
raise ValueError("The keyword '%s' cannot have the value '%s'."
% ('reflect_type', kwargs['reflect_type']))
return _create_vector(vector, pad_tuple, before_vector, after_vector)
def _wrap(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_wrap.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across. Not used in _wrap.
kwargs : keyword arguments
Keyword arguments. Not used in _wrap.
Return
------
_wrap : ndarray
Padded vector
'''
if pad_tuple[1] == 0:
after_vector = vector[pad_tuple[0]:None]
else:
after_vector = vector[pad_tuple[0]:-pad_tuple[1]]
before_vector = np.resize(after_vector[::-1], pad_tuple[0])[::-1]
after_vector = np.resize(after_vector, pad_tuple[1])
return _create_vector(vector, pad_tuple, before_vector, after_vector)
def _edge(vector, pad_tuple, iaxis, kwargs):
'''
Private function to calculate the before/after vectors for pad_edge.
Parameters
----------
vector : ndarray
Input vector that already includes empty padded values.
pad_tuple : tuple
This tuple represents the (before, after) width of the padding
along this particular iaxis.
iaxis : int
The axis currently being looped across. Not used in _edge.
kwargs : keyword arguments
Keyword arguments. Not used in _edge.
Return
------
_edge : ndarray
Padded vector
'''
return _create_vector(vector, pad_tuple, vector[pad_tuple[0]],
vector[-pad_tuple[1] - 1])
################################################################################
# Public functions
def pad(array, pad_width, mode=None, **kwargs):
"""
Pads an array.
Parameters
----------
array : array_like of rank N
Input array
pad_width : {sequence, int}
Number of values padded to the edges of each axis.
((before_1, after_1), ... (before_N, after_N)) unique pad widths
for each axis.
((before, after),) yields same before and after pad for each axis.
(pad,) or int is a shortcut for before = after = pad width for all
axes.
mode : {str, function}
One of the following string values or a user supplied function.
'constant' Pads with a constant value.
'edge' Pads with the edge values of array.
'linear_ramp' Pads with the linear ramp between end_value and the
array edge value.
'maximum' Pads with the maximum value of all or part of the
vector along each axis.
'mean' Pads with the mean value of all or part of the
vector along each axis.
'median' Pads with the median value of all or part of the
vector along each axis.
'minimum' Pads with the minimum value of all or part of the
vector along each axis.
'reflect' Pads with the reflection of the vector mirrored on
the first and last values of the vector along each
axis.
'symmetric' Pads with the reflection of the vector mirrored
along the edge of the array.
'wrap' Pads with the wrap of the vector along the axis.
The first values are used to pad the end and the
end values are used to pad the beginning.
<function> Padding function, see Notes.
stat_length : {sequence, int}, optional
Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
values at edge of each axis used to calculate the statistic value.
((before_1, after_1), ... (before_N, after_N)) unique statistic
lengths for each axis.
((before, after),) yields same before and after statistic lengths
for each axis.
(stat_length,) or int is a shortcut for before = after = statistic
length for all axes.
Default is ``None``, to use the entire axis.
constant_values : {sequence, int}, optional
Used in 'constant'. The values to set the padded values for each
axis.
((before_1, after_1), ... (before_N, after_N)) unique pad constants
for each axis.
((before, after),) yields same before and after constants for each
axis.
(constant,) or int is a shortcut for before = after = constant for
all axes.
Default is 0.
end_values : {sequence, int}, optional
Used in 'linear_ramp'. The values used for the ending value of the
linear_ramp and that will form the edge of the padded array.
((before_1, after_1), ... (before_N, after_N)) unique end values
for each axis.
((before, after),) yields same before and after end values for each
axis.
(constant,) or int is a shortcut for before = after = end value for
all axes.
Default is 0.
reflect_type : str {'even', 'odd'}, optional
Used in 'reflect', and 'symmetric'. The 'even' style is the
default with an unaltered reflection around the edge value. For
the 'odd' style, the extented part of the array is created by
subtracting the reflected values from two times the edge value.
Returns
-------
pad : ndarray
Padded array of rank equal to `array` with shape increased
according to `pad_width`.
Notes
-----
.. versionadded:: 1.7.0
For an array with rank greater than 1, some of the padding of later
axes is calculated from padding of previous axes. This is easiest to
think about with a rank 2 array where the corners of the padded array
are calculated by using padded values from the first axis.
The padding function, if used, should return a rank 1 array equal in
length to the vector argument with padded values replaced. It has the
following signature:
padding_func(vector, iaxis_pad_width, iaxis, **kwargs)
where
vector: ndarray
A rank 1 array already padded with zeros. Padded values are
vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].
iaxis_pad_width: tuple
A 2-tuple of ints, iaxis_pad_width[0] represents the number of
values padded at the beginning of vector where
iaxis_pad_width[1] represents the number of values padded at
the end of vector.
iaxis : int
The axis currently being calculated.
kwargs : misc
Any keyword arguments the function requires.
Examples
--------
>>> a = [1, 2, 3, 4, 5]
>>> np.lib.pad(a, (2,3), 'constant', constant_values=(4,6))
array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
>>> np.lib.pad(a, (2,3), 'edge')
array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
>>> np.lib.pad(a, (2,3), 'linear_ramp', end_values=(5,-4))
array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
>>> np.lib.pad(a, (2,), 'maximum')
array([5, 5, 1, 2, 3, 4, 5, 5, 5])
>>> np.lib.pad(a, (2,), 'mean')
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> np.lib.pad(a, (2,), 'median')
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> a = [[1,2], [3,4]]
>>> np.lib.pad(a, ((3, 2), (2, 3)), 'minimum')
array([[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[3, 3, 3, 4, 3, 3, 3],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1]])
>>> a = [1, 2, 3, 4, 5]
>>> np.lib.pad(a, (2,3), 'reflect')
array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
>>> np.lib.pad(a, (2,3), 'reflect', reflect_type='odd')
array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
>>> np.lib.pad(a, (2,3), 'symmetric')
array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
>>> np.lib.pad(a, (2,3), 'symmetric', reflect_type='odd')
array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
>>> np.lib.pad(a, (2,3), 'wrap')
array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
>>> def padwithtens(vector, pad_width, iaxis, kwargs):
... vector[:pad_width[0]] = 10
... vector[-pad_width[1]:] = 10
... return vector
>>> a = np.arange(6)
>>> a = a.reshape((2,3))
>>> np.lib.pad(a, 2, padwithtens)
array([[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 0, 1, 2, 10, 10],
[10, 10, 3, 4, 5, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10]])
"""
narray = np.array(array)
pad_width = _validate_lengths(narray, pad_width)
modefunc = {
'constant': _constant,
'edge': _edge,
'linear_ramp': _linear_ramp,
'maximum': _maximum,
'mean': _mean,
'median': _median,
'minimum': _minimum,
'reflect': _reflect,
'symmetric': _symmetric,
'wrap': _wrap,
}
allowedkwargs = {
'constant': ['constant_values'],
'edge': [],
'linear_ramp': ['end_values'],
'maximum': ['stat_length'],
'mean': ['stat_length'],
'median': ['stat_length'],
'minimum': ['stat_length'],
'reflect': ['reflect_type'],
'symmetric': ['reflect_type'],
'wrap': [],
}
kwdefaults = {
'stat_length': None,
'constant_values': 0,
'end_values': 0,
'reflect_type': 'even',
}
if isinstance(mode, str):
function = modefunc[mode]
# Make sure have allowed kwargs appropriate for mode
for key in kwargs:
if key not in allowedkwargs[mode]:
raise ValueError('%s keyword not in allowed keywords %s' %
(key, allowedkwargs[mode]))
# Set kwarg defaults
for kw in allowedkwargs[mode]:
kwargs.setdefault(kw, kwdefaults[kw])
# Need to only normalize particular keywords.
for i in kwargs:
if i == 'stat_length' and kwargs[i]:
kwargs[i] = _validate_lengths(narray, kwargs[i])
if i in ['end_values', 'constant_values']:
kwargs[i] = _normalize_shape(narray, kwargs[i])
elif mode == None:
raise ValueError('Keyword "mode" must be a function or one of %s.' %
(modefunc.keys(),))
else:
# User supplied function, I hope
function = mode
# Create a new padded array
rank = range(len(narray.shape))
total_dim_increase = [np.sum(pad_width[i]) for i in rank]
offset_slices = [slice(pad_width[i][0],
pad_width[i][0] + narray.shape[i])
for i in rank]
new_shape = np.array(narray.shape) + total_dim_increase
newmat = np.zeros(new_shape).astype(narray.dtype)
# Insert the original array into the padded array
newmat[offset_slices] = narray
# This is the core of pad ...
for iaxis in rank:
np.apply_along_axis(function,
iaxis,
newmat,
pad_width[iaxis],
iaxis,
kwargs)
return newmat