Current File : //proc/self/root/opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pyo
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The arraypad module contains a group of functions to pad values onto the edges
of an n-dimensional array.
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Create an ndarray of `shape` with increments along specified `axis`
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
shape : tuple of ints
Shape of desired array. Should be equivalent to `arr.shape` except
`shape[axis]` which may have any positive value.
axis : int
Axis to increment along.
reverse : bool
If False, increment in a positive fashion from 1 to `shape[axis]`,
inclusive. If True, the bounds are the same but the order reversed.
Returns
-------
padarr : ndarray
Output array sized to pad `arr` along `axis`, with linear range from
1 to `shape[axis]` along specified `axis`.
Notes
-----
The range is deliberately 1-indexed for this specific use case. Think of
this algorithm as broadcasting `np.arange` to a single `axis` of an
arbitrarily shaped ndarray.
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Rounds arr inplace if destination dtype is integer.
Parameters
----------
arr : ndarray
Input array.
dtype : dtype
The dtype of the destination array.
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Prepend constant `val` along `axis` of `arr`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
val : scalar
Constant value to use. For best results should be of type `arr.dtype`;
if not `arr.dtype` will be cast to `arr.dtype`.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` constant `val` prepended along `axis`.
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Append constant `val` along `axis` of `arr`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
val : scalar
Constant value to use. For best results should be of type `arr.dtype`;
if not `arr.dtype` will be cast to `arr.dtype`.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` constant `val` appended along `axis`.
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Prepend `pad_amt` to `arr` along `axis` by extending edge values.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, extended by `pad_amt` edge values appended along `axis`.
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Append `pad_amt` to `arr` along `axis` by extending edge values.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, extended by `pad_amt` edge values prepended along
`axis`.
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Prepend linear ramp along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
end : scalar
Constal value to use. For best results should be of type `arr.dtype`;
if not `arr.dtype` will be cast to `arr.dtype`.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values prepended along `axis`. The
prepended region ramps linearly from the edge value to `end`.
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Append linear ramp along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
end : scalar
Constal value to use. For best results should be of type `arr.dtype`;
if not `arr.dtype` will be cast to `arr.dtype`.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
appended region ramps linearly from the edge value to `end`.
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Prepend `pad_amt` maximum values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
num : int
Depth into `arr` along `axis` to calculate maximum.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
prepended region is the maximum of the first `num` values along
`axis`.
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Pad one `axis` of `arr` with the maximum of the last `num` elements.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
num : int
Depth into `arr` along `axis` to calculate maximum.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
appended region is the maximum of the final `num` values along `axis`.
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Prepend `pad_amt` mean values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
num : int
Depth into `arr` along `axis` to calculate mean.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values prepended along `axis`. The
prepended region is the mean of the first `num` values along `axis`.
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| | j | � � j | j � f d � �S( sB
Append `pad_amt` mean values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
num : int
Depth into `arr` along `axis` to calculate mean.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
appended region is the maximum of the final `num` values along `axis`.
i i c 3` sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S( i����N( R# R$ ( R R R ( R R- R4 ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr> s c s` s | ] } t d � Vq d S( N( R# R$ ( R R ( ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr> s c 3` s- | ]# \ } } | � k r! | n d Vq d S( i N( ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr> s R N(
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| j | � � j | j � | f d � �S( sI
Prepend `pad_amt` median values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
num : int
Depth into `arr` along `axis` to calculate median.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values prepended along `axis`. The
prepended region is the median of the first `num` values along `axis`.
i i c 3` s9 | ]/ \ } } | � k r' t d � n t � � Vq d S( N( R# R$ ( R R R ( R R4 ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>? s c 3` s- | ]# \ } } | � k r! | n d Vq d S( i N( ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>C s R N(
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| | j | � � j | j � f d � �S( sE
Append `pad_amt` median values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
num : int
Depth into `arr` along `axis` to calculate median.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
appended region is the median of the final `num` values along `axis`.
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Prepend `pad_amt` minimum values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to prepend.
num : int
Depth into `arr` along `axis` to calculate minimum.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values prepended along `axis`. The
prepended region is the minimum of the first `num` values along
`axis`.
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Append `pad_amt` median values along `axis`.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : int
Amount of padding to append.
num : int
Depth into `arr` along `axis` to calculate minimum.
Range: [1, `arr.shape[axis]`] or None (entire axis)
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt` values appended along `axis`. The
appended region is the minimum of the final `num` values along `axis`.
i i c 3` sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S( i����N( R# R$ ( R R R ( R R- R4 ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s c s` s | ] } t d � Vq d S( N( R# R$ ( R R ( ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s c 3` s- | ]# \ } } | � k r! | n d Vq d S( i N( ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s R N(
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Pad `axis` of `arr` by reflection.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : tuple of ints, length 2
Padding to (prepend, append) along `axis`.
method : str
Controls method of reflection; options are 'even' or 'odd'.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
values appended along `axis`. Both regions are padded with reflected
values from the original array.
Notes
-----
This algorithm does not pad with repetition, i.e. the edges are not
repeated in the reflection. For that behavior, use `mode='symmetric'`.
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
single function, lest the indexing tricks in non-integer multiples of the
original shape would violate repetition in the final iteration.
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Pad `axis` of `arr` by symmetry.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : tuple of ints, length 2
Padding to (prepend, append) along `axis`.
method : str
Controls method of symmetry; options are 'even' or 'odd'.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
values appended along `axis`. Both regions are padded with symmetric
values from the original array.
Notes
-----
This algorithm DOES pad with repetition, i.e. the edges are repeated.
For padding without repeated edges, use `mode='reflect'`.
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
single function, lest the indexing tricks in non-integer multiples of the
original shape would violate repetition in the final iteration.
i i c 3` s@ | ]6 \ } } | � k r' t d � n t d � d � Vq d S( i N( R# R$ ( R R R ( R R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>q s c 3` s? | ]5 \ } } | � k r' t d � n t d d d � Vq d S( i����N( R# R$ ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>s s c 3` s- | ]# \ } } | � k r! | n d Vq d S( i N( ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>x s RI c 3` s3 | ]) \ } } | � k r' t d � n d Vq d S( i N( R# R$ ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr> s i c 3` s< | ]2 \ } } | � k r' t d � n t � � � Vq d S( N( R# R$ ( R R R ( R R- RJ ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s c 3` s3 | ]) \ } } | � k r' t d � n d Vq d S( i����N( R# R$ ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s R ( R R
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Pad `axis` of `arr` via wrapping.
Parameters
----------
arr : ndarray
Input array of arbitrary shape.
pad_amt : tuple of ints, length 2
Padding to (prepend, append) along `axis`.
axis : int
Axis along which to pad `arr`.
Returns
-------
padarr : ndarray
Output array, with `pad_amt[0]` values prepended and `pad_amt[1]`
values appended along `axis`. Both regions are padded wrapped values
from the opposite end of `axis`.
Notes
-----
This method of padding is also known as 'tile' or 'tiling'.
The modes 'reflect', 'symmetric', and 'wrap' must be padded with a
single function, lest the indexing tricks in non-integer multiples of the
original shape would violate repetition in the final iteration.
i i c 3` s< | ]2 \ } } | � k r' t d � n t � � � Vq d S( N( R# R$ ( R R R ( R R- RJ ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s c 3` s- | ]# \ } } | � k r! | n d Vq d S( i N( ( R R R ( R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s c 3` s@ | ]6 \ } } | � k r' t d � n t d � d � Vq d S( i i N( R# R$ ( R R R ( R R ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>� s R ( R R R
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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, array_like, float, int}, optional
The width of padding (pad_width), the number of elements on the
edge of the narray used for statistics (stat_length), the constant
value(s) to use when filling padded regions (constant_values), or the
endpoint target(s) for linear ramps (end_values).
((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 val is a shortcut for before = after = constant for
all axes.
cast_to_int : bool, optional
Controls if values in ``shape`` will be rounded and cast to int
before being returned.
Returns
-------
normalized_shape : tuple of tuples
val => ((val, val), (val, val), ...)
[[val1, val2], [val3, val4], ...] => ((val1, val2), (val3, val4), ...)
((val1, val2), (val3, val4), ...) => no change
[[val1, val2], ] => ((val1, val2), (val1, val2), ...)
((val1, val2), ) => ((val1, val2), (val1, val2), ...)
[[val , ], ] => ((val, val), (val, val), ...)
((val , ), ) => ((val, val), (val, val), ...)
i i R NR i s/ Unable to create correctly shaped tuple from %sc s` s | ] } t | � Vq d S( N( R ( R R ( ( sE /opt/alt/python27/lib64/python2.7/site-packages/numpy/lib/arraypad.pys <genexpr>- s ( NN( ( NN( ( i ( i ( t ndimR$ R t asarrayR t onesR t newaxisR t
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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), ...)
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| | | | | | � qpW| S| j � } | d k r xt t | | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } q�Wn�| d k rux�t | � D]6 \ } \ } } t | | | � } t | | | � } q8Wn\| d k r�xMt t | | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } q�Wn�| d k r_x�t t | | d � � D]H \ } \ \ } } \ } } t | | | | � } t! | | | | � } qWnr| d
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} | j | d } x� | | k st| | k r�t( | | | | � } t( | | | | � } t) | | | f | | � } | | 8} | | 8} | | | 7} q\Wt) | | | f | | � } q�Wn�| d k r�x�t | � D]� \ } \ } } | d
} | j | } x� | | k sb| | k r�t( | | | | � } t( | | | | � } t* | | | f | | � } | | 8} | | 8} | | | 7} qJWt* | | | f | | � } q Wn� | d k r�x� t | � D]� \ } \ } } | j | } x� | | k sF| | k r�t( | | | | � } t( | | | | � } t+ | | | f | � } | | 8} | | 8} | | | 7} q.Wt+ | | | f | � } q Wn | S( s�
Pads an array.
Parameters
----------
array : array_like of rank N
Input array
pad_width : {sequence, array_like, 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 or 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 or 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 or 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 or 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 : {'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]])
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