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Editing: arraypad.cpython-310.pyc
o 6��a�y � @ s� d Z ddlZddlmZ ddlmZ dgZdd� Zdd � Z d d � Z d#d d �Z dd� Z dd� Z dd� Zdd� Zd$dd�Zdd� Zd$dd �Zd#d d �Zeed d �d%d"d��ZdS )&zl The arraypad module contains a group of functions to pad values onto the edges of an n-dimensional array. � N)�array_function_dispatch)�ndindex�padc C s" t �|t j�r| j| d� dS dS )z� Rounds arr inplace if destination dtype is integer. Parameters ---------- arr : ndarray Input array. dtype : dtype The dtype of the destination array. )�outN)�np� issubdtype�integer�round)�arr�dtype� r �4/usr/lib/python3/dist-packages/numpy/lib/arraypad.py�_round_if_needed s �r c C s t d�f| | f d S )a� Construct tuple of slices to slice an array in the given dimension. Parameters ---------- sl : slice The slice for the given dimension. axis : int The axis to which `sl` is applied. All other dimensions are left "unsliced". Returns ------- sl : tuple of slices A tuple with slices matching `shape` in length. Examples -------- >>> _slice_at_axis(slice(None, 3, -1), 1) (slice(None, None, None), slice(None, 3, -1), (...,)) N).��slice)�sl�axisr r r �_slice_at_axis! s r c C s* |d7 }t d�f| ||d� }| | S )a� Get a view of the current region of interest during iterative padding. When padding multiple dimensions iteratively corner values are unnecessarily overwritten multiple times. This function reduces the working area for the first dimensions so that corners are excluded. Parameters ---------- array : ndarray The array with the region of interest. original_area_slice : tuple of slices Denotes the area with original values of the unpadded array. axis : int The currently padded dimension assuming that `axis` is padded before `axis` + 1. Returns ------- roi : ndarray The region of interest of the original `array`. � Nr )�array�original_area_slicer r r r r � _view_roi: s r c C sx t dd� t| j|�D ��}| jjrdnd}tj|| j|d�}|dur'|�|� t dd� t| j|�D ��}| ||< ||fS )a� Pad array on all sides with either a single value or undefined values. Parameters ---------- array : ndarray Array to grow. pad_width : sequence of tuple[int, int] Pad width on both sides for each dimension in `arr`. fill_value : scalar, optional If provided the padded area is filled with this value, otherwise the pad area left undefined. Returns ------- padded : ndarray The padded array with the same dtype as`array`. Its order will default to C-style if `array` is not F-contiguous. original_area_slice : tuple A tuple of slices pointing to the area of the original array. c s s$ � | ] \}\}}|| | V qd S �Nr ��.0�size�left�rightr r r � <genexpr>m s � � �z _pad_simple.<locals>.<genexpr>�F�C)r �orderNc s s& � | ]\}\}}t ||| �V qd S r r r r r r r x s � � �) �tuple�zip�shape�flags�fncr �emptyr �fill)r � pad_width� fill_value� new_shaper! �paddedr r r r � _pad_simpleV s � �r- c C sN t td|d �|�}|d | |< t t| j| |d d�|�}|d | |< dS )a� Set empty-padded area in given dimension. Parameters ---------- padded : ndarray Array with the pad area which is modified inplace. axis : int Dimension with the pad area to set. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. value_pair : tuple of scalars or ndarrays Values inserted into the pad area on each side. It must match or be broadcastable to the shape of `arr`. Nr r �r r r$ )r, r � width_pair� value_pair� left_slice� right_slicer r r � _set_pad_area� s �r3 c C sZ |d }t t||d �|�}| | }| j| |d }t t|d |�|�}| | }||fS )aZ Retrieve edge values from empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the edges are considered. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. Returns ------- left_edge, right_edge : ndarray Edge values of the valid area in `padded` in the given dimension. Its shape will always match `padded` except for the dimension given by `axis` which will have a length of 1. r r r. ) r, r r/ � left_indexr1 � left_edge� right_indexr2 � right_edger r r � _get_edges� s r8 c sJ t �� |�}� �fdd�t|||�D �\}}|ttddd�� � }||fS )a� Construct linear ramps for empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the ramps are constructed. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. end_value_pair : (scalar, scalar) End values for the linear ramps which form the edge of the fully padded array. These values are included in the linear ramps. Returns ------- left_ramp, right_ramp : ndarray Linear ramps to set on both sides of `padded`. c 3 s4 � | ]\}}}t j||�� �|d �j� d�V qdS )F)�start�stop�num�endpointr r N)r �linspace�squeezer )r � end_value�edge�width�r r, r r r � s � �� �z$_get_linear_ramps.<locals>.<genexpr>N���)r8 r# r r )r, r r/ �end_value_pair� edge_pair� left_ramp� right_rampr rB r �_get_linear_ramps� s � �rH c C s |d }| j | |d }|| }|\}} |du s ||k r |}| du s'|| k r)|} |dks1| dkr=|tjtjhv r=td��tt||| �|�} | | } || |dd�} t| | j� || kre|krf| | fS tt|| |�|�} | | }|||dd�}t|| j� | |fS )al Calculate statistic for the empty-padded array in given dimension. Parameters ---------- padded : ndarray Empty-padded array. axis : int Dimension in which the statistic is calculated. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. length_pair : 2-element sequence of None or int Gives the number of values in valid area from each side that is taken into account when calculating the statistic. If None the entire valid area in `padded` is considered. stat_func : function Function to compute statistic. The expected signature is ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``. Returns ------- left_stat, right_stat : ndarray Calculated statistic for both sides of `padded`. r r Nz,stat_length of 0 yields no value for paddingT)r �keepdims) r$ r �amax�amin� ValueErrorr r r r )r, r r/ � length_pair� stat_funcr4 r6 � max_length� left_length� right_lengthr1 � left_chunk� left_statr2 � right_chunk� right_statr r r � _get_stats� s6 � �� rV Fc C st |\}}| j | | | }|rd}nd}|d8 }|dkrbt||�} || } | | } tt| | d�|�} | | } |dkrLtt||d �|�}d| | | } || } |} tt| | �|�}| | |< || 8 }|dkr�t||�} | | d } | | } tt| | d�|�}| | }|dkr�tt| d | �|�}d| | | }| j | | } | | } tt| | �|�}|| |< || 8 }||fS )a� Pad `axis` of `arr` with reflection. Parameters ---------- padded : ndarray Input array of arbitrary shape. axis : int Axis along which to pad `arr`. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. method : str Controls method of reflection; options are 'even' or 'odd'. include_edge : bool If true, edge value is included in reflection, otherwise the edge value forms the symmetric axis to the reflection. Returns ------- pad_amt : tuple of ints, length 2 New index positions of padding to do along the `axis`. If these are both 0, padding is done in this dimension. r r rC �odd� )r$ �minr r )r, r r/ �method� include_edge�left_pad� right_pad� old_length� edge_offset� chunk_lengthr: r9 r1 rR � edge_slice�pad_arear2 rT r r r �_set_reflect_both( sJ �rc c C s |\}}| j | | | }d}d}|dkrMtt| t||� |dkr%| nd�|�}| | } ||krAtt|| |�|�} || }nttd|�|�} | | | < |dkr�tt||t||� �|�} | | } ||krwtt| | | �|�} || }n tt| d�|�} | | | < ||fS )a� Pad `axis` of `arr` with wrapped values. Parameters ---------- padded : ndarray Input array of arbitrary shape. axis : int Axis along which to pad `arr`. width_pair : (int, int) Pair of widths that mark the pad area on both sides in the given dimension. Returns ------- pad_amt : tuple of ints, length 2 New index positions of padding to do along the `axis`. If these are both 0, padding is done in this dimension. r N)r$ r r rY ) r, r r/ r\ r] �period� new_left_pad� new_right_padr2 rT rb r1 rR r r r �_set_wrap_both} s< �� �� rg c C s� | du rd| S t �| �} |rt �| �jt jdd�} | jdk rh| jdkr=| �� } |r2| dk r2td��| d | d ff| S | jd krh| j d krh| �� } |r]| d dk sY| d dk r]td��| d | d ff| S |rt| � � dk rttd��t � | |d f�� � S ) a8 Broadcast `x` to an array with the shape (`ndim`, 2). A helper function for `pad` that prepares and validates arguments like `pad_width` for iteration in pairs. Parameters ---------- x : {None, scalar, array-like} The object to broadcast to the shape (`ndim`, 2). ndim : int Number of pairs the broadcasted `x` will have. as_index : bool, optional If `x` is not None, try to round each element of `x` to an integer (dtype `np.intp`) and ensure every element is positive. Returns ------- pairs : nested iterables, shape (`ndim`, 2) The broadcasted version of `x`. Raises ------ ValueError If `as_index` is True and `x` contains negative elements. Or if `x` is not broadcastable to the shape (`ndim`, 2). N))NNF)�copy� r r z#index can't contain negative valuesrX )rX r ) r r r �astype�intp�ndimr �ravelrL r$ rY � broadcast_to�tolist)�xrl �as_indexr r r � _as_pairs� s& rr c K s | fS r r )r r) �mode�kwargsr r r �_pad_dispatcher s ru �numpy)�module�constantc K s� t �| �} t �|�}|jjdkstd��t|| jdd�}t|�r\|}t| |dd�\}}t |j�D ])}t � ||d�}t |j dd� �} d d � | D �} | D ] } ||| || ||� qKq0|S g g g d gd gd gd gd gd gdgdgd� } z t |�t | | � } W n ty� td�|��d�w | r�td�|| ���t jt jt jt jd�} t| |�\}}t |j�}|dkr�|�d d�}t||j�}t|||�D ]\}}}t|||�}t||||� q�|S |dkr� |S | jdk�rt||�D ]\}}| j | dk�rt|��rtd�|���q�|S |dk�r,t||�D ]\}}t|||�}t|||�}t||||� �q|S |dk�r`|�d d�}t||j�}t|||�D ]\}}}t|||�}t ||||�}t||||� �qC|S || v �r�| | }|�d d�}t||jdd�}t|||�D ]\}}}t|||�}t |||||�}t||||� �q}|S |dv �r |�dd�}|dk�r�dnd} t||�D ]R\}\} } | j | d k�r�| dk�s�| dk�r�t||| | f�}t||| | f|� �q�t|||�}| dk�s�| dk�rt ||| | f|| �\} } | dk�s�| dk�s�q�|S |d k�r?t||�D ]+\}\} } t|||�}| dk�s)| dk�r=t ||| | f�\} } | dk�s)| dk�s)�q|S ) aZ Pad an array. Parameters ---------- array : array_like of rank N The array to pad. 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, optional One of the following string values or a user supplied function. 'constant' (default) 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. 'empty' Pads with undefined values. .. versionadded:: 1.17 <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 scalar, 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 ``constant`` is a shortcut for ``before = after = constant`` for all axes. Default is 0. end_values : sequence or scalar, 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 ``constant`` is a shortcut for ``before = after = constant`` 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 extended 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 modify a rank 1 array in-place. 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[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[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 : dict Any keyword arguments the function requires. Examples -------- >>> a = [1, 2, 3, 4, 5] >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6)) array([4, 4, 1, ..., 6, 6, 6]) >>> np.pad(a, (2, 3), 'edge') array([1, 1, 1, ..., 5, 5, 5]) >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4]) >>> np.pad(a, (2,), 'maximum') array([5, 5, 1, 2, 3, 4, 5, 5, 5]) >>> np.pad(a, (2,), 'mean') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> np.pad(a, (2,), 'median') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> a = [[1, 2], [3, 4]] >>> np.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.pad(a, (2, 3), 'reflect') array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2]) >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd') array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]) >>> np.pad(a, (2, 3), 'symmetric') array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3]) >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd') array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7]) >>> np.pad(a, (2, 3), 'wrap') array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3]) >>> def pad_with(vector, pad_width, iaxis, kwargs): ... pad_value = kwargs.get('padder', 10) ... vector[:pad_width[0]] = pad_value ... vector[-pad_width[1]:] = pad_value >>> a = np.arange(6) >>> a = a.reshape((2, 3)) >>> np.pad(a, 2, pad_with) 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]]) >>> np.pad(a, 2, pad_with, padder=100) array([[100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 0, 1, 2, 100, 100], [100, 100, 3, 4, 5, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100]]) �iz%`pad_width` must be of integral type.T)rq r )r* rC Nc s s � | ]}|t f V qd S r )�Ellipsis)r �indr r r r � s � zpad.<locals>.<genexpr>�constant_values� end_values� stat_length� reflect_type) r' r@ �wraprx � linear_ramp�maximum�mean�median�minimum�reflect� symmetriczmode '{}' is not supportedz/unsupported keyword arguments for mode '{}': {})r� r� r� r� rx r' zGcan't extend empty axis {} using modes other than 'constant' or 'empty'r@ r� > r� r� �evenr� Fr r� ) r �asarrayr �kind� TypeErrorrr rl �callabler- �range�moveaxisr r$ �set�KeyErrorrL �formatrJ rK r� r� �getr# r r3 r �anyr8 rH rV rc rg ) r r) rs rt �functionr, �_r �view�indsr{ �allowed_kwargs�unsupported_kwargs�stat_functionsr �axes�valuesr/ r0 �roirE r} � ramp_pair�func�lengthrM � stat_pairrZ r[ r4 r6 r r r r s� P �� ��� I�F ���> � 3 � + � " � $ � ��� � ���r )F)rx )�__doc__rv r �numpy.core.overridesr �numpy.lib.index_tricksr �__all__r r r r- r3 r8 rH rV rc rg rr ru r r r r r �<module> s& + , BU I C
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