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arraypad.cpython-35.opt-1.pyc
W��fo� � @ s| d Z d d l m Z m Z m Z d d l Z d g Z d d d � Z d d � Z d3 d d � Z d4 d d � Z d5 d d � Z d6 d d � Z d7 d d � Z d8 d d � Z d9 d d � Z d: d d � Z d; d d � Z d< d d � Z d= d d � Z d> d! d" � Z d? d# d$ � Z d@ d% d& � Z dA d' d( � Z dB d) d* � Z dC d+ d, � Z d- d. d/ � Z d0 d1 � Z d2 d � Z d S)Dzl The arraypad module contains a group of functions to pad values onto the edges of an n-dimensional array. � )�division�absolute_import�print_functionN�padFc s� t � � f d d � t | j � D� � } | sN t j d � � d � } n t j � � d d � } | j | � } xB t � � D]4 \ } } | j | | k r� | j | d | �} q� W| S)a� 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. c 3 s1 | ]' \ } } | � k r! d n � � Vq d S)� N� )�.0�i�x)�axis�shaper � /arraypad.py� <genexpr>0 s z"_arange_ndarray.<locals>.<genexpr>r r r ���)�tuple� enumerater �npZarange�reshape�repeat)�arrr r �reverseZ initshapeZpadarrr Zdimr )r r r �_arange_ndarray s r c C s) t j | t j � r% | j d | � d S)z� Rounds arr inplace if destination dtype is integer. Parameters ---------- arr : ndarray Input array. dtype : dtype The dtype of the destination array. �outN)r Z issubdtypeZinteger�round)r �dtyper r r �_round_ifneeded= s r r c s� � d k r | St � � f d d � t | j � D� � } | d k rr t j t j | d | j �| f d � �St j t j | � | j | j � | f d � �Sd S)a 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`. r c 3 s- | ]# \ } } | � k r! | n � Vq d S)Nr )r r r )r �pad_amtr r r e s z!_prepend_const.<locals>.<genexpr>r r N)r r r r � concatenate�zerosr �astype)r r �valr �padshaper )r r r �_prepend_constM s $"r" c s� � d k r | St � � f d d � t | j � D� � } | d k rr t j | t j | d | j �f d � �St j | t j | � | j | j � f d � �Sd S)a 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`. r c 3 s- | ]# \ } } | � k r! | n � Vq d S)Nr )r r r )r r r r r � s z _append_const.<locals>.<genexpr>r r N)r r r r r r r r )r r r r r! r )r r r � _append_consto s $r# c s� | d k r | St � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j | � } t j | j | d � �| f d � �S)a� Prepend `pad_amt` to `arr` along `axis` by extending edge values. Parameters ---------- arr : ndarray Input array of arbitrary shape. pad_amt : int Amount of padding to prepend. axis : int Axis along which to pad `arr`. Returns ------- padarr : ndarray Output array, extended by `pad_amt` edge values appended along `axis`. r c 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr )�slice)r r r )r r r r � s z _prepend_edge.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r )r r r r r r r )r r r � edge_slice� pad_singleton�edge_arrr )r r � _prepend_edge� s !r( c s� | d k r � St � � f d d � t � j � D� � } t � f d d � t � j � D� � } � | j | � } t j � | j | d � �f d � �S)a� Append `pad_amt` to `arr` along `axis` by extending edge values. Parameters ---------- arr : ndarray Input array of arbitrary shape. pad_amt : int Amount of padding to append. axis : int Axis along which to pad `arr`. Returns ------- padarr : ndarray Output array, extended by `pad_amt` edge values prepended along `axis`. r c 3 s> | ]4 \ } } | � k r' t d � n � j � d Vq d S)Nr )r$ r )r r r )r r r r r � s z _append_edge.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r )r r r r r r r )r r r r% r& r' r )r r r � _append_edge� s !r) c s+ � d k r | St � � f d d � t | j � D� � } t | | � d d �j t j � } t � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j | � j � � � } | | t � � } | | } | | 7} t | | j � t j | j | j � | f d � �S) aF 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`. r c 3 s- | ]# \ } } | � k r! | n � Vq d S)Nr )r r r )r r r r r � s z _prepend_ramp.<locals>.<genexpr>r Tc 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr )r$ )r r r )r r r r � s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r ) r r r r r r �float64r r �floatr r r ) r r �endr r! �ramp_arrr% r& �edge_pad�sloper )r r r � _prepend_ramp� s r0 c s+ � d k r | St � � f d d � t | j � D� � } t | | � d d �j t j � } t � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j | � j � � � } | | t � � } | | } | | 7} t | | j � t j | | j | j � f d � �S) aB 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`. r c 3 s- | ]# \ } } | � k r! | n � Vq d S)Nr )r r r )r r r r r & s z _append_ramp.<locals>.<genexpr>r Fc 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr r )r$ )r r r )r r r r . s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r 2 s r ) r r r r r r r* r r r+ r r r ) r r r, r r! r- r% r& r. r/ r )r r r � _append_ramp s r1 c s� | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � t � � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j d � � j | � } t j | j | d � �| f d � �S)aS 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`. r r Nc 3 s9 | ]/ \ } } | � k r' t d � n t � � Vq d S)N)r$ )r r r )r �numr r r g s z _prepend_max.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r k s r ) r( r r r �maxr r r r )r r r2 r � max_slicer& � max_chunkr )r r2 r � _prepend_maxB s !r6 c s* | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � | j � d � � d k r� t � � � f d d � t | j � D� � } n t d d � | j D� � } t � f d d � t | j � D� � } | | j d � � j | � } t j | | j | d � �f d � �S) a_ 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`. r r Nc 3 sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S)Nr r )r$ )r r r )r r, r2 r r r � s z _append_max.<locals>.<genexpr>c s s | ] } t d � Vq d S)N)r$ )r r r r r r � s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r ) r) r r r r3 r r r r )r r r2 r r4 r& r5 r )r r, r2 r � _append_maxv s$ !r7 c s | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � t � � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j � � j | � } t | | j � t j | j | � � j | j � | f d � �S)aC 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`. r r Nc 3 s9 | ]/ \ } } | � k r' t d � n t � � Vq d S)N)r$ )r r r )r r2 r r r � s z _prepend_mean.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r ) r( r r r �meanr r r r r r r )r r r2 r � mean_slicer& � mean_chunkr )r r2 r � _prepend_mean� s !r; c sC | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � | j � d � � d k r� t � � � f d d � t | j � D� � } n t d d � | j D� � } t � f d d � t | j � D� � } | | j d � � j | � } t | | j � t j | | j | � � j | j � f d � �S) aB 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`. r r Nc 3 sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S)Nr r )r$ )r r r )r r, r2 r r r s z _append_mean.<locals>.<genexpr>c s s | ] } t d � Vq d S)N)r$ )r r r r r r s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r s r ) r) r r r r8 r r r r r r r )r r r2 r r9 r& r: r )r r, r2 r � _append_mean� s& r< c s | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � t � � f d d � t | j � D� � } t � f d d � t | j � D� � } t j | | d � �j | � } t | | j � t j | j | � � j | j � | f d � �S)aI 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`. r r Nc 3 s9 | ]/ \ } } | � k r' t d � n t � � Vq d S)N)r$ )r r r )r r2 r r r ? s z _prepend_med.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r C s r ) r( r r r r �medianr r r r r r )r r r2 r � med_slicer& � med_chunkr )r r2 r � _prepend_med s "r@ c sF | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � | j � d � � d k r� t � � � f d d � t | j � D� � } n t d d � | j D� � } t � f d d � t | j � D� � } t j | | d � �j | � } t | | j � t j | | j | � � j | j � f d � �S) aE 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`. r r Nc 3 sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S)Nr r )r$ )r r r )r r, r2 r r r v s z _append_med.<locals>.<genexpr>c s s | ] } t d � Vq d S)N)r$ )r r r r r r y s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r | s r ) r) r r r r r= r r r r r r )r r r2 r r> r& r? r )r r, r2 r � _append_medO s& "rA c s� | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � t � � f d d � t | j � D� � } t � f d d � t | j � D� � } | | j d � � j | � } t j | j | d � �| f d � �S)aT 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`. r r Nc 3 s9 | ]/ \ } } | � k r' t d � n t � � Vq d S)N)r$ )r r r )r r2 r r r � s z _prepend_min.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r ) r( r r r �minr r r r )r r r2 r � min_slicer& � min_chunkr )r r2 r � _prepend_min� s !rE c s* | d k r | S� d k r, t | | � � S� d k rQ � | j � k rQ d � | j � d � � d k r� t � � � f d d � t | j � D� � } n t d d � | j D� � } t � f d d � t | j � D� � } | | j d � � j | � } t j | | j | d � �f d � �S) aG 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`. r r Nc 3 sC | ]9 \ } } | � k r' t d � n t � � � d � Vq d S)Nr r )r$ )r r r )r r, r2 r r r � s z _append_min.<locals>.<genexpr>c s s | ] } t d � Vq d S)N)r$ )r r r r r r � s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s r ) r) r r r rB r r r r )r r r2 r rC r& rD r )r r, r2 r � _append_min� s$ !rF c s) � d d k r$ � d d k r$ | St � � f d d � t | j � D� � } | | } t � f d d � t | j � D� � } � d d k r� | j | � } d | k r� d d k rt � f d d � t | j � D� � } | | j | � } d | | } ~ | j � � d d � | j � d � t � � � f d d � t | j � D� � } t � f d d � t | j � D� � } | | | } � d d k r�| j | � } d | k r t � f d d � t | j � D� � } | | j | � } d | | } ~ t j | | | f d � �S) a� 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. r r c 3 sC | ]9 \ } } | � k r' t d � n t � d d d � Vq d S)Nr r r )r$ )r r r )r r r r r s z_pad_ref.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r " s �oddc 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr )r$ )r r r )r r r r ) s � c 3 s< | ]2 \ } } | � k r' t d � n t � � � Vq d S)N)r$ )r r r )r r, �startr r r 5 s c 3 s? | ]5 \ } } | � k r' t d � n t d d d � Vq d S)Nr r )r$ )r r r )r r r r 7 s c 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr r )r$ )r r r )r r r r ? s r )r r r r r r ) r r �methodr Z ref_sliceZ ref_chunk1r& � edge_slice1� edge_chunk�rev_idxZ ref_chunk2� edge_slice2r )r r, r rI r �_pad_ref� s> ! rO c s% � d d k r$ � d d k r$ | St � � f d d � t | j � D� � } t � f d d � t | j � D� � } | | | } t � f d d � t | j � D� � } � d d k r� | j | � } d | k r4� d d k r4t � f d d � t | j � D� � } | | j | � } d | | } ~ | j � � d � | j � � t � � � f d d � t | j � D� � } | | | } � d d k r�| j | � } d | k r t � f d d � t | j � D� � } | | j | � } d | | } ~ t j | | | f d � �S) a� 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. r r c 3 s@ | ]6 \ } } | � k r' t d � n t d � d � Vq d S)Nr )r$ )r r r )r r r r r q s z_pad_sym.<locals>.<genexpr>c 3 s? | ]5 \ } } | � k r' t d � n t d d d � Vq d S)Nr r )r$ )r r r )r r r r s s c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r x s rG c 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr )r$ )r r r )r r r r s rH c 3 s< | ]2 \ } } | � k r' t d � n t � � � Vq d S)N)r$ )r r r )r r, rI r r r � s c 3 s3 | ]) \ } } | � k r' t d � n d Vq d S)Nr r )r$ )r r r )r r r r � s r )r r r r r r ) r r rJ r Z sym_slicerM Z sym_chunk1r& rK rL Z sym_chunk2rN r )r r, r rI r �_pad_symI s> ! rP c s5 � d d k r$ � d d k r$ | S| j � � d � | j � � t � � � f d d � t | j � D� � } | | } t � f d d � t | j � D� � } � d d k r� | j | � } t � � f d d � t | j � D� � } | | } � d d k r| j | � } t j | | | f d � �S)a1 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. r r c 3 s< | ]2 \ } } | � k r' t d � n t � � � Vq d S)N)r$ )r r r )r r, rI r r r � s z _pad_wrap.<locals>.<genexpr>c 3 s- | ]# \ } } | � k r! | n d Vq d S)r Nr )r r r )r r r r � s c 3 s@ | ]6 \ } } | � k r' t d � n t d � d � Vq d S)Nr r )r$ )r r r )r r r r r � s r )r r r r r r )r r r Z wrap_sliceZ wrap_chunk1r& Z wrap_chunk2r )r r, r rI r � _pad_wrap� s" rQ Tc C s� | j } | d k r d | St j | � } | j d k r� | j f k sY | j d k r~ t j | d f d | j �| } ql| j d k r� | t j d d � f j | d d �} qld } t | | f � � n� | j d k rS| j d d k r!| j d | k r!| j d d d �} ql| j d | k r7qld } t | | f � � n d } t | | f � � | d k r�t j | � j t � } t d d � | j � D� � S)aZ 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), ...) Nr rH r r r z/Unable to create correctly shaped tuple from %sTc s s | ] } t | � Vq d S)N)r )r r r r r r - s z#_normalize_shape.<locals>.<genexpr>�NN)rR )r )rH )�ndimr �asarrayr Zonesr Znewaxisr � ValueErrorr r �intr �tolist)Zndarrayr � cast_to_intZndimsr �fmtr r r �_normalize_shape� s. % %+& rZ c C s� t | | � } xm | D]e } d d � | D� } d d � | D� } | d d k sb | d d k r d } t | | f � � q W| S)a� 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), ...) c S s( g | ] } | d k r d n | � q S)Nr r )r r r r r � <listcomp>Q s z%_validate_lengths.<locals>.<listcomp>c S s( g | ] } | d k r d n d � q S)r r r r )r r r r r r[ R s r r z"%s cannot contain negative values.)rZ rU )�narrayZnumber_elementsZnormshpr ZchkrY r r r �_validate_lengths0 s r] c s� t j � � j j d k s' t d � � t j | � � t � � � � d d g d g d d g d d g d d g d d g d d g d d g d d g d g i } d d d d d d d d i } t | t j j � r�x8 | D]0 } | | | k r� t d | | | f � � q� Wx& | | D] } | j | | | � qWx | D]R } | d k rft � | | � | | <| d k r=t � | | d d �| | <q=Wn� | } t t t � j � � � } � f d d � | D� } � � f d d � | D� } t j � j � | } t j | � j � } � | | <x. | D]& } t j | | | � | | | � q(W| S� j � } | d k r�xt t � | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } q�Wn�| d k r-x�t � � D]6 \ } \ } } t | | | � } t | | | � } q�WnV| d k r�xGt t � | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } qSWn�| d k rx�t t � | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } q�Wnl| d k r�x]t t � | d � � D]H \ } \ \ } } \ } } t | | | | � } t | | | | � } q=Wn�| d k rx�t t � | d � � D]H \ } \ \ } } \ } } t | | | | � } t! | | | | � } q�Wn�| d k rvxst t � | d � � D]H \ } \ \ } } \ } } t" | | | | � } t# | | | | � } q'Wn | d k r�x�t � � D]$\ } \ } } | d k s�| d k r�| j | d k r�t | | | � } t | | | � } q�| d } | j | d } x� | | k s)| | k r�t$ | | | | � } t$ | | | | � } t% | | | f | | � } | | 8} | | 8} | | | 7} qWt% | | | f | | � } q�Wn�| d k r�x�t � � D]� \ } \ } } | d } | j | } x� | | k s| | k r�t$ | | | | � } t$ | | | | � } t& | | | f | | � } | | 8} | | 8} | | | 7} q�Wt& | | | f | | � } q�Wn� | d k r�x� t � � D]� \ } \ } } | j | } x� | | k s�| | k rft$ | | | | � } t$ | | | | � } t' | | | f | � } | | 8} | | 8} | | | 7} q�Wt' | | | f | � } q�W| S) a� 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]]) r z%`pad_width` must be of integral type.Zconstant�constant_valuesZedgeZ linear_ramp� end_valuesZmaximumZ stat_lengthr8 r= ZminimumZreflectZ reflect_typeZ symmetricZwrapNr Zevenz%%s keyword not in allowed keywords %srX Fc s# g | ] } t j � | � � q Sr )r �sum)r r )� pad_widthr r r[ U s zpad.<locals>.<listcomp>c s: g | ]0 } t � | d � | d � j | � � q S)r )r$ r )r r )r\ ra r r r[ V s r )r_ r^ )(r rT r �kind� TypeError�arrayr] � isinstance�compat� basestringrU � setdefaultrZ �list�range�lenr r Zapply_along_axis�copyr �zipr" r# r( r) r0 r1 r6 r7 r; r< r@ rA rE rF rB rO rP rQ )rd ra �mode�kwargsZ allowedkwargsZ kwdefaults�key�kwr �functionZrankZtotal_dim_increaseZ offset_slicesZ new_shapeZnewmatZiaxisr Z pad_beforeZ pad_afterZ before_valZ after_valZ chunk_beforeZ chunk_afterrJ Zsafe_padZ pad_iter_bZ pad_iter_ar )r\ ra r r ] s � 5 5 5 5 5 5 " " r r r r r r r r r r r r r r r r r ) �__doc__Z __future__r r r Znumpyr �__all__r r r" r# r( r) r0 r1 r6 r7 r; r< r@ rA rE rF rO rP rQ rZ r] r r r r r �<module> s2 + ""!"7748494948UT@S -
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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_datasource.cpython-35.opt-1.pyc
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_datasource.cpython-35.pyc
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_iotools.cpython-35.opt-1.pyc
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_version.cpython-35.opt-1.pyc
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arraypad.cpython-35.opt-1.pyc
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arraypad.cpython-35.pyc
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arraysetops.cpython-35.opt-1.pyc
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arraysetops.cpython-35.pyc
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arrayterator.cpython-35.opt-1.pyc
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arrayterator.cpython-35.pyc
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financial.cpython-35.opt-1.pyc
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financial.cpython-35.pyc
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format.cpython-35.opt-1.pyc
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format.cpython-35.pyc
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function_base.cpython-35.opt-1.pyc
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function_base.cpython-35.pyc
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index_tricks.cpython-35.opt-1.pyc
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index_tricks.cpython-35.pyc
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info.cpython-35.opt-1.pyc
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info.cpython-35.pyc
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nanfunctions.cpython-35.opt-1.pyc
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nanfunctions.cpython-35.pyc
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npyio.cpython-35.opt-1.pyc
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npyio.cpython-35.pyc
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polynomial.cpython-35.opt-1.pyc
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polynomial.cpython-35.pyc
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recfunctions.cpython-35.opt-1.pyc
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recfunctions.cpython-35.pyc
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scimath.cpython-35.opt-1.pyc
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scimath.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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setup.cpython-35.pyc
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shape_base.cpython-35.opt-1.pyc
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shape_base.cpython-35.pyc
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stride_tricks.cpython-35.opt-1.pyc
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stride_tricks.cpython-35.pyc
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twodim_base.cpython-35.opt-1.pyc
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twodim_base.cpython-35.pyc
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type_check.cpython-35.opt-1.pyc
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type_check.cpython-35.pyc
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ufunclike.cpython-35.opt-1.pyc
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ufunclike.cpython-35.pyc
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user_array.cpython-35.opt-1.pyc
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user_array.cpython-35.pyc
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utils.cpython-35.opt-1.pyc
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utils.cpython-35.pyc
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N4ST4R_ID | Naxtarrr
