Edit File: shape_base.cpython-37.pyc
B ��Fd�k � @ s> d dl mZmZmZ d dlZd dlm mZ d dlm Z m Z m Z m Z m Z mZmZ d dlmZmZmZ d dlmZ d dlmZmZ d dlmZ d dlm Z d d d d d dddddddddgZ dd� Z dd� Z dd� Z eZ!dd � Z"dd � Z#d d � Z$d(d d �Z%d)d d �Z&d d� Z'd!d� Z(d"d� Z)d#d$� Z*d%d� Z+d&d� Z,d'd� Z-dS )*� )�division�absolute_import�print_functionN)�asarray�zeros�outer� concatenate�isscalar�array� asanyarray)�product�reshape� transpose)�normalize_axis_index)�vstack� atleast_3d)�ndindex)�matrix� column_stack� row_stack�dstack� array_split�split�hsplit�vsplit�dsplit�apply_over_axes� expand_dims�apply_along_axis�kron�tile�get_array_wrapc O s� t |�}|j}t||�}tt|��}t||d|� ||d d� |g �}t|jdd� �}dd� |D �}y t|�} W n t k r� t d��Y nX t | || f|�|��} t |jdd� | j | j �} tt| j��} | d|� | | j| j | j� | || j| j � } t | t��s$| �| �} | | | < x*|D ]"}t | || f|�|��| |< �q2W t | t��sx| �| �} t| | �S t| | �}| �|�S dS )ak Apply a function to 1-D slices along the given axis. Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`. Parameters ---------- func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis. axis : integer Axis along which `arr` is sliced. arr : ndarray Input array. args : any Additional arguments to `func1d`. kwargs : any Additional named arguments to `func1d`. .. versionadded:: 1.9.0 Returns ------- apply_along_axis : ndarray The output array. The shape of `outarr` is identical to the shape of `arr`, except along the `axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of `func1d`. So if `func1d` returns a scalar `outarr` will have one fewer dimensions than `arr`. See Also -------- apply_over_axes : Apply a function repeatedly over multiple axes. Examples -------- >>> def my_func(a): ... """Average first and last element of a 1-D array""" ... return (a[0] + a[-1]) * 0.5 >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> np.apply_along_axis(my_func, 0, b) array([ 4., 5., 6.]) >>> np.apply_along_axis(my_func, 1, b) array([ 2., 5., 8.]) For a function that returns a 1D array, the number of dimensions in `outarr` is the same as `arr`. >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]]) >>> np.apply_along_axis(sorted, 1, b) array([[1, 7, 8], [3, 4, 9], [2, 5, 6]]) For a function that returns a higher dimensional array, those dimensions are inserted in place of the `axis` dimension. >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> np.apply_along_axis(np.diag, -1, b) array([[[1, 0, 0], [0, 2, 0], [0, 0, 3]], [[4, 0, 0], [0, 5, 0], [0, 0, 6]], [[7, 0, 0], [0, 8, 0], [0, 0, 9]]]) N� ���c s s | ]}|t f V qd S )N)�Ellipsis)�.0�ind� r' �G/opt/alt/python37/lib64/python3.7/site-packages/numpy/lib/shape_base.py� <genexpr>m s z#apply_along_axis.<locals>.<genexpr>z;Cannot apply_along_axis when any iteration dimensions are 0r )r �ndimr �list�ranger r �shape�next� StopIteration� ValueErrorr �dtype� isinstancer �__array_prepare__�__array_wrap__)Zfunc1d�axis�arr�args�kwargs�ndZin_dimsZ inarr_viewZindsZind0�resZbuffZ buff_dimsZ buff_permuter& Zout_arrr' r' r( r s4 K ( " c C s� t |�}|j}t|�jdkr"|f}xb|D ]Z}|dk r<|| }||f}| |� }|j|jkr^|}q(t||�}|j|jkrz|}q(td��q(W |S )az Apply a function repeatedly over multiple axes. `func` is called as `res = func(a, axis)`, where `axis` is the first element of `axes`. The result `res` of the function call must have either the same dimensions as `a` or one less dimension. If `res` has one less dimension than `a`, a dimension is inserted before `axis`. The call to `func` is then repeated for each axis in `axes`, with `res` as the first argument. Parameters ---------- func : function This function must take two arguments, `func(a, axis)`. a : array_like Input array. axes : array_like Axes over which `func` is applied; the elements must be integers. Returns ------- apply_over_axis : ndarray The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input. See Also -------- apply_along_axis : Apply a function to 1-D slices of an array along the given axis. Notes ------ This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been available since version 1.7.0. Examples -------- >>> a = np.arange(24).reshape(2,3,4) >>> a array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) Sum over axes 0 and 2. The result has same number of dimensions as the original array: >>> np.apply_over_axes(np.sum, a, [0,2]) array([[[ 60], [ 92], [124]]]) Tuple axis arguments to ufuncs are equivalent: >>> np.sum(a, axis=(0,2), keepdims=True) array([[[ 60], [ 92], [124]]]) r z7function is not returning an array of the correct shape)r r* r r r0 )�func�aZaxes�val�Nr5 r7 r: r' r' r( r � s A c C sp t | �} | j}|| jks(|| j d k r8tjdtdd� |dk rN|| j d }| �|d|� d ||d� �S )a� Expand the shape of an array. Insert a new axis that will appear at the `axis` position in the expanded array shape. .. note:: Previous to NumPy 1.13.0, neither ``axis < -a.ndim - 1`` nor ``axis > a.ndim`` raised errors or put the new axis where documented. Those axis values are now deprecated and will raise an AxisError in the future. Parameters ---------- a : array_like Input array. axis : int Position in the expanded axes where the new axis is placed. Returns ------- res : ndarray Output array. The number of dimensions is one greater than that of the input array. See Also -------- squeeze : The inverse operation, removing singleton dimensions reshape : Insert, remove, and combine dimensions, and resize existing ones doc.indexing, atleast_1d, atleast_2d, atleast_3d Examples -------- >>> x = np.array([1,2]) >>> x.shape (2,) The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``: >>> y = np.expand_dims(x, axis=0) >>> y array([[1, 2]]) >>> y.shape (1, 2) >>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,newaxis] >>> y array([[1], [2]]) >>> y.shape (2, 1) Note that some examples may use ``None`` instead of ``np.newaxis``. These are the same objects: >>> np.newaxis is None True r" zcBoth axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.� )� stacklevelr N)r" )r r- r* �warnings�warn�DeprecationWarningr )r< r5 r- r' r' r( r � s ; c C sR g }x@| D ]8}t |ddd�}|jdk r8t |dddd�j}|�|� q W t�|d�S )a� Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with `hstack`. 1-D arrays are turned into 2-D columns first. Parameters ---------- tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns ------- stacked : 2-D array The array formed by stacking the given arrays. See Also -------- hstack, vstack, concatenate Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.column_stack((a,b)) array([[1, 2], [2, 3], [3, 4]]) FT)�copy�subokr? )rD rE �ndminr" )r r* �T�append�_nxr )�tupZarrays�vr6 r' r' r( r : s ! c C s t �dd� | D �d�S )a� Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by `dsplit`. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. This function continues to be supported for backward compatibility, but you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack`` function was added in NumPy 1.10. Parameters ---------- tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack along first axis. hstack : Stack along second axis. concatenate : Join a sequence of arrays along an existing axis. dsplit : Split array along third axis. Notes ----- Equivalent to ``np.concatenate(tup, axis=2)`` if `tup` contains arrays that are at least 3-dimensional. Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[[1, 2], [2, 3], [3, 4]]]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]]) c S s g | ] }t |��qS r' )r )r% �_mr' r' r( � <listcomp>� s zdstack.<locals>.<listcomp>r? )rI r )rJ r' r' r( r c s 6c C sz xtt t| ��D ]d}t�| | �dkr>tjd| | jd�| |< qt�t�t�| | �d��rtjd| | jd�| |< qW | S )Nr )r1 ) r, �lenrI r* �emptyr1 Zsometrue�equalr- )�sub_arys�ir' r' r( �_replace_zero_by_x_arrays� s rS c C s y| j | }W n tk r* t| �}Y nX y$t|�d }dgt|� |g }W nj tk r� t|�}|dkrxtd��t||�\}}dg||d g || |g }t� |�� � }Y nX g } t� | |d�} x@t |�D ]4} || } || d } | � t� | | | � |d�� q�W | S )a Split an array into multiple sub-arrays. Please refer to the ``split`` documentation. The only difference between these functions is that ``array_split`` allows `indices_or_sections` to be an integer that does *not* equally divide the axis. See Also -------- split : Split array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(8.0) >>> np.array_split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])] r" r z&number sections must be larger than 0.)r- �AttributeErrorrN r+ � TypeError�intr0 �divmodrI r ZcumsumZswapaxesr, rH )�ary�indices_or_sectionsr5 ZNtotalZ NsectionsZ div_pointsZ Neach_section�extrasZ section_sizesrQ ZsaryrR �st�endr' r' r( r � s, c C sP y t |� W n2 tk r> |}| j| }|| r:td��Y nX t| ||�}|S )a� Split an array into multiple sub-arrays. Parameters ---------- ary : ndarray Array to be divided into sub-arrays. indices_or_sections : int or 1-D array If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays along `axis`. If such a split is not possible, an error is raised. If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where along `axis` the array is split. For example, ``[2, 3]`` would, for ``axis=0``, result in - ary[:2] - ary[2:3] - ary[3:] If an index exceeds the dimension of the array along `axis`, an empty sub-array is returned correspondingly. axis : int, optional The axis along which to split, default is 0. Returns ------- sub-arrays : list of ndarrays A list of sub-arrays. Raises ------ ValueError If `indices_or_sections` is given as an integer, but a split does not result in equal division. See Also -------- array_split : Split an array into multiple sub-arrays of equal or near-equal size. Does not raise an exception if an equal division cannot be made. hsplit : Split array into multiple sub-arrays horizontally (column-wise). vsplit : Split array into multiple sub-arrays vertically (row wise). dsplit : Split array into multiple sub-arrays along the 3rd axis (depth). concatenate : Join a sequence of arrays along an existing axis. stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third dimension). Examples -------- >>> x = np.arange(9.0) >>> np.split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7., 8.])] >>> x = np.arange(8.0) >>> np.split(x, [3, 5, 6, 10]) [array([ 0., 1., 2.]), array([ 3., 4.]), array([ 5.]), array([ 6., 7.]), array([], dtype=float64)] z0array split does not result in an equal division)rN rU r- r0 r )rX rY r5 �sectionsr> r: r' r' r( r � s B c C s< t �| �dkrtd��| jdkr,t| |d�S t| |d�S dS )a� Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.hsplit(x, 2) [array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [ 12., 13.]]), array([[ 2., 3.], [ 6., 7.], [ 10., 11.], [ 14., 15.]])] >>> np.hsplit(x, np.array([3, 6])) [array([[ 0., 1., 2.], [ 4., 5., 6.], [ 8., 9., 10.], [ 12., 13., 14.]]), array([[ 3.], [ 7.], [ 11.], [ 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the second axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.hsplit(x, 2) [array([[[ 0., 1.]], [[ 4., 5.]]]), array([[[ 2., 3.]], [[ 6., 7.]]])] r z3hsplit only works on arrays of 1 or more dimensionsr" N)rI r* r0 r )rX rY r' r' r( r ! s 7 c C s" t �| �dk rtd��t| |d�S )aI Split an array into multiple sub-arrays vertically (row-wise). Please refer to the ``split`` documentation. ``vsplit`` is equivalent to ``split`` with `axis=0` (default), the array is always split along the first axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.vsplit(x, 2) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]]), array([[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]])] >>> np.vsplit(x, np.array([3, 6])) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]]), array([[ 12., 13., 14., 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the first axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.vsplit(x, 2) [array([[[ 0., 1.], [ 2., 3.]]]), array([[[ 4., 5.], [ 6., 7.]]])] r? z3vsplit only works on arrays of 2 or more dimensionsr )rI r* r0 r )rX rY r' r' r( r _ s /c C s" t �| �dk rtd��t| |d�S )a Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the `split` documentation. `dsplit` is equivalent to `split` with ``axis=2``, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]], [[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]]) >>> np.dsplit(x, 2) [array([[[ 0., 1.], [ 4., 5.]], [[ 8., 9.], [ 12., 13.]]]), array([[[ 2., 3.], [ 6., 7.]], [[ 10., 11.], [ 14., 15.]]])] >>> np.dsplit(x, np.array([3, 6])) [array([[[ 0., 1., 2.], [ 4., 5., 6.]], [[ 8., 9., 10.], [ 12., 13., 14.]]]), array([[[ 3.], [ 7.]], [[ 11.], [ 15.]]]), array([], dtype=float64)] � z3dsplit only works on arrays of 3 or more dimensionsr? )rI r* r0 r )rX rY r' r' r( r � s )c G s* t dd� t| �D ��}|r&|d d S dS )z�Find the wrapper for the array with the highest priority. In case of ties, leftmost wins. If no wrapper is found, return None c s s2 | ]*\}}t |d �rt|dd�| |jfV qdS )r3 �__array_priority__r N)�hasattr�getattrr3 )r% rR �xr' r' r( r) � s z$get_array_prepare.<locals>.<genexpr>r# N)�sorted� enumerate)r7 �wrappersr' r' r( �get_array_prepare� s rf c G s* t dd� t| �D ��}|r&|d d S dS )z�Find the wrapper for the array with the highest priority. In case of ties, leftmost wins. If no wrapper is found, return None c s s2 | ]*\}}t |d �rt|dd�| |jfV qdS )r4 r_ r N)r` ra r4 )r% rR rb r' r' r( r) � s z!get_array_wrap.<locals>.<genexpr>r# N)rc rd )r7 re r' r' r( r! � s c C s$ t |�}t| dd|jd�} |j| j }}|dks8|dkrDt�| |�S | j}|j}| jjsbt| |�} |jjstt||�}|}||kr�||kr�d|| | }nd|| | }|}t | |��|| �}|d }x t |�D ]} t ||d�}q�W t | |�} | dk �r| |�}t | |�} | dk �r | |�}|S ) a Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters ---------- a, b : array_like Returns ------- out : ndarray See Also -------- outer : The outer product Notes ----- The function assumes that the number of dimensions of `a` and `b` are the same, if necessary prepending the smallest with ones. If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`, the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`. The elements are products of elements from `a` and `b`, organized explicitly by:: kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] where:: kt = it * st + jt, t = 0,...,N In the common 2-D case (N=1), the block structure can be visualized:: [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], [ ... ... ], [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] Examples -------- >>> np.kron([1,10,100], [5,6,7]) array([ 5, 6, 7, 50, 60, 70, 500, 600, 700]) >>> np.kron([5,6,7], [1,10,100]) array([ 5, 50, 500, 6, 60, 600, 7, 70, 700]) >>> np.kron(np.eye(2), np.ones((2,2))) array([[ 1., 1., 0., 0.], [ 1., 1., 0., 0.], [ 0., 0., 1., 1.], [ 0., 0., 1., 1.]]) >>> a = np.arange(100).reshape((2,5,2,5)) >>> b = np.arange(24).reshape((2,3,4)) >>> c = np.kron(a,b) >>> c.shape (2, 10, 6, 20) >>> I = (1,3,0,2) >>> J = (0,2,1) >>> J1 = (0,) + J # extend to ndim=4 >>> S1 = (1,) + b.shape >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1)) >>> c[K] == a[I]*b[J] True FT)rD rE rF r )r" r" )r5 N)r r r* rI �multiplyr- �flags� contiguousr r r, r rf r! ) r< �bZndbZndaZas_�bsr9 �resultr5 �_�wrapperr' r' r( r � s8 D c C s� y t |�}W n tk r& |f}Y nX t|�}tdd� |D ��r`t| tj�r`tj| dd|d�S tj| dd|d�}||jk r�d|j| | }t dd� t |j |�D ��}|j }|dkr�x<t |j |�D ],\}}|d kr�|� d |�� |d�}|| }q�W |� |�S ) a Construct an array by repeating A the number of times given by reps. If `reps` has length ``d``, the result will have dimension of ``max(d, A.ndim)``. If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote `A` to d-dimensions manually before calling this function. If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it. Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as (1, 1, 2, 2). Note : Although tile may be used for broadcasting, it is strongly recommended to use numpy's broadcasting operations and functions. Parameters ---------- A : array_like The input array. reps : array_like The number of repetitions of `A` along each axis. Returns ------- c : ndarray The tiled output array. See Also -------- repeat : Repeat elements of an array. broadcast_to : Broadcast an array to a new shape Examples -------- >>> a = np.array([0, 1, 2]) >>> np.tile(a, 2) array([0, 1, 2, 0, 1, 2]) >>> np.tile(a, (2, 2)) array([[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]]) >>> np.tile(a, (2, 1, 2)) array([[[0, 1, 2, 0, 1, 2]], [[0, 1, 2, 0, 1, 2]]]) >>> b = np.array([[1, 2], [3, 4]]) >>> np.tile(b, 2) array([[1, 2, 1, 2], [3, 4, 3, 4]]) >>> np.tile(b, (2, 1)) array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> c = np.array([1,2,3,4]) >>> np.tile(c,(4,1)) array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]) c s s | ]}|d kV qdS )r" Nr' )r% rb r' r' r( r) � s ztile.<locals>.<genexpr>T)rD rE rF F)r" c s s | ]\}}|| V qd S )Nr' )r% �s�tr' r' r( r) � s r r" r# )�tuplerU rN �allr2 rI Zndarrayr r* �zipr- �sizer �repeat) �A�repsrJ �d�cZ shape_out�nZdim_inZnrepr' r' r( r : s$ B )r )r ).� __future__r r r rA Znumpy.core.numeric�core�numericrI r r r r r r r Znumpy.core.fromnumericr r r Znumpy.core.multiarrayr Z numpy.corer r Znumpy.lib.index_tricksr Znumpy.matrixlib.defmatrixr �__all__r r r r r r rS r r r r r rf r! r r r' r' r' r( �<module> s8 $ UI)8 1 M>3- c