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B �5�g�p � @ s� d Z ddlmZ ddlmZmZ ddlmZmZm Z ddl Z ddl m Z mZ ddlmZmZmZ ddlmZ dd lmZ dd lmZmZmZ dd lm Z m Z m Z er�dd l m Z d dd�dd�Z!dd d�dd�Z"dsdd�dd�Z#dd d d gZ$d d d!d"d#d$d%d&d'd(d)d*d+d,gZ%d-d.d-d/�d0d1�Z&d-d2d3�d4d5�Z'dtd d-dd8d2d-d9d:dd9d;� d<d=�Z(dud d.dd-d2d-d9d:d d>� d?d@�Z)dvd.d d9d2d-d9d:dd2dA� dBdC�Z*dwdDdE�Z+dxdFdG�Z,dydHdI�Z-dzdKdL�Z.dd-d2d9ddM�dNdO�Z/d{d-dPd2d9dQ�dRdS�Z0d|dTd dU�dVdW�Z1dXdXdY�dZd[�Z2e2d}d d2dTd\d]�d^d_��Z3e2d~d d2dTd\d]�d`da��Z4e2ddbdc��Z5e2d�ddde��Z6e3e4df�Z7d�ddh�didj�Z8dkdl� Z9d dm�dndo�Z:d ddp�dqdr�Z;dS )�z$ Routines for filling missing data. � )� annotations)�partial�wraps)� TYPE_CHECKING�Any�castN)�algos�lib)� ArrayLike�Axis�F)�import_optional_dependency)�infer_dtype_from)� is_array_like�is_numeric_v_string_like�needs_i8_conversion)�is_valid_na_for_dtype�isna�na_value_for_dtype)�Indexz np.ndarray�int)�mask�lengthc C s8 t | �r4t| �|kr,tdt| �� d|� ���| | } | S )zJ Validate the size of the values passed to ExtensionArray.fillna. z'Length of 'value' does not match. Got (z ) expected )r �len� ValueError)�valuer r � r �I/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/missing.py�check_value_size- s r r )�arr�returnc C sz t |�\}}tj||d�}t|�}|| }tj| jtd�}x$|D ] }t| |�rRqB|| |kO }qBW |�� rv|t| �O }|S )a Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True Parameters ---------- arr : ArrayLike values_to_mask: list, tuple, or scalar Returns ------- np.ndarray[bool] )�dtype) r �np�arrayr Zzeros�shape�boolr �any)r Zvalues_to_maskr! Zna_maskZnonnar �xr r r � mask_missing< s r( Fr% )� allow_nearestc C sv | dkr d S t | t�r8| �� } | dkr,d} n | dkr8d} ddg}d}|rV|�d� d}| |krrtd |� d | � ���| S ) N)NZasfreqZffill�padZbfill�backfillz pad (ffill) or backfill (bfill)�nearestz(pad (ffill), backfill (bfill) or nearestz Invalid fill method. Expecting z. Got )� isinstance�str�lower�appendr )�methodr) Z valid_methodsZ expectingr r r �clean_fill_methodf s r2 �linear�time�index�valuesr, �zero�slinear� quadratic�cubic� barycentric�krogh�spline� polynomial�from_derivatives�piecewise_polynomial�pchip�akima� cubicspliner. r )r1 r5 r c K sh |� d�}| dkr"|d kr"td��tt }| |krHtd|� d| � d���| dkrd|jsdt| � d���| S ) N�order)r= r> z7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.)r< r@ rA z4 interpolation requires that the index be monotonic.)�getr � NP_METHODS� SP_METHODSZ is_monotonic)r1 r5 �kwargsrD �validr r r �clean_interp_method� s rJ z int | None)�howr c C s� |dks t �t| �dkr dS t| � }| jdkr:|�d�}|dkrT|dd� �� }n&|dkrzt| �d |ddd� �� }|| }|s�dS |S ) a Retrieves the index of the first valid value. Parameters ---------- values : ndarray or ExtensionArray how : {'first', 'last'} Use this parameter to change between the first or last valid index. Returns ------- int or None )�first�lastr N� � rL rM ���)�AssertionErrorr r �ndimr& Zargmax)r6 rK Zis_validZidxposZ chk_notnar r r �find_valid_index� s rS r* �forwardz Index | Nonez str | Nonez Any | None) �datar1 �axisr5 �limit�limit_direction� limit_area� fill_value�coerce�downcastc K s� y t |�} W n tk r$ d} Y nX | dk rR|dk r>td��t| | |||d�} n,|dk s^t�tf | |||||||d�| ��} | S )zS Wrapper to dispatch to either interpolate_2d or interpolate_2d_with_fill. Nz&Cannot pass both fill_value and method)r1 rV rW rY )rU r5 rV r1 rW rX rY rZ )r2 r �interpolate_2drQ �interpolate_2d_with_fill) rU r1 rV r5 rW rX rY rZ r[ r\ rH �mZ interp_valuesr r r �interpolate_array_2d� s2 r` ) rU r5 rV r1 rW rX rY rZ r c sV t ��f�� t� | j�r(t| jdd�� ddd�� ������fdd� } t�| || �S )z� Column-wise application of interpolate_1d. Notes ----- The signature does differs from interpolate_1d because it only includes what is needed for Block.interpolate. F)�compatz np.ndarray)�yvaluesr c s t f �| ����� dd����S )NF)�xvaluesrb r1 rW rX rY rZ � bounds_error)�interpolate_1d)rb )rZ r5 rH rW rY rX r1 r r �func s z&interpolate_2d_with_fill.<locals>.func)rJ r r! r r" �apply_along_axis) rU r5 rV r1 rW rX rY rZ rH rf r )rZ r5 rH rW rY rX r1 r r^ � s r^ ) rc rb r1 rW rX rY rZ rd rD c K s� t |�} | } | �� s8tj| jtjd�} | �tj� | S | �� rD|S |dkrbt | j �s^t d��d}dddg} |� � }|| kr�t d| � d |� d ���|d k r�d d g}|� � }||kr�t d|� d|� d���t jd |d�}tt�| ��}t|dd�}|d kr�d}tt|��}t|dd�}|d k�r&t|�}ttd| t| ���}|| | }|dk�rh|tt| |d��B }n0|dk�r�|tt| d|��B }ntt| ||��}|d k�r�|||B O }n|d k�r�||O }t|�}|�� } | j}t |j ��r�|�d�}|dk�r�|}n,t�|�}|dk�r*|j tjk�r*t� |�}|t k�rjt� || �}t� || || | || | �| | < n.t || || || f||||d�| ��| | < tj| |< | S )z� Logic for the 1-d interpolation. The result should be 1-d, inputs xvalues and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. )r! r4 zStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexr6 rT ZbackwardZbothz*Invalid limit_direction: expecting one of z, got 'z'.N�inside�outsidez%Invalid limit_area: expecting one of z, got �.)ZnobsrW rL )rK r rM rO �i8r3 )r6 r5 )r1 rZ rd rD )!r r& r" �emptyr$ Zfloat64�fill�nan�allr r! r r/ r Zvalidate_limit�setZ flatnonzerorS �ranger � _interp_limit�sorted�copy�_values�view�asarrayZobject_r Zmaybe_convert_objectsrF ZargsortZinterp�_interpolate_scipy_wrapper)rc rb r1 rW rX rY rZ rd rD rH �invalidrI �resultZvalid_limit_directionsZvalid_limit_areasZall_nansZfirst_valid_indexZ start_nansZlast_valid_indexZend_nansZmid_nansZ preserve_nansZxarrZindsZindexerr r r re , s� $ re c K sx |� d�}t d|d� ddlm} t�|�}| j| jttd�} t| dd�rb| j � d �|� d � } }|d krv| j | d < n"|d kr�t | d < n|d kr�t | d < d dddddg} || kr�|dkr�|}| j| ||||d�} | |�} n�|dk�r&t|�s�|dk�rtd|� ���| j| |fd|i|��} | |�} nN| jj�s8| �� } |jj�sJ|�� }|jj�s\|�� }| | }|| ||f|�} | S )z� Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_method. z interpolation requires SciPy.�scipy)�extrar )� interpolate)r; r< r? r@ Z _is_all_datesFrk rA rB rC r, r7 r8 r9 r: r> )�kindrZ rd r= z;order needs to be specified and greater than 0; got order: �k)r r{ r} r" rw Zbarycentric_interpolateZkrogh_interpolate�_from_derivatives�getattrru ZastypeZpchip_interpolate�_akima_interpolate�_cubicspline_interpolateZinterp1dr r ZUnivariateSpline�flagsZ writeablert )r' �yZnew_xr1 rZ rd rD rH r| r} Z alt_methodsZinterp1d_methodsZterpZnew_yr r r rx � sV rx c C s4 ddl m} |jj}|| |�dd�||d�}||�S )a� Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array-like sorted 1D array of x-coordinates yi : array-like or list of array-likes yi[i][j] is the j-th derivative known at xi[i] order: None or int or array-like of ints. Default: None. Specifies the degree of local polynomials. If not None, some derivatives are ignored. der : int or list How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. extrapolate : bool, optional Whether to extrapolate to ouf-of-bounds points based on first and last intervals, or to return NaNs. Default: True. See Also -------- scipy.interpolate.BPoly.from_derivatives Returns ------- y : scalar or array-like The result, of length R or length M or M by R. r )r} rP rO )Zorders� extrapolate)r{ r} ZBPolyr? �reshape) �xi�yir' rD �derr� r} r1 r_ r r r r� s " r� c C s( ddl m} |j| ||d�}|||d�S )a[ Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : array-like A sorted list of x-coordinates, of length N. yi : array-like A 1-D array of real values. `yi`'s length along the interpolation axis must be equal to the length of `xi`. If N-D array, use axis parameter to select correct axis. x : scalar or array-like Of length M. der : int, optional How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. axis : int, optional Axis in the yi array corresponding to the x-coordinate values. See Also -------- scipy.interpolate.Akima1DInterpolator Returns ------- y : scalar or array-like The result, of length R or length M or M by R, r )r} )rV )�nu)r{ r} ZAkima1DInterpolator)r� r� r' r� rV r} �Pr r r r� 1 s $ r� � not-a-knotc C s( ddl m} |j| ||||d�}||�S )aq Convenience function for cubic spline data interpolator. See `scipy.interpolate.CubicSpline` for details. Parameters ---------- xi : array-like, shape (n,) 1-d array containing values of the independent variable. Values must be real, finite and in strictly increasing order. yi : array-like Array containing values of the dependent variable. It can have arbitrary number of dimensions, but the length along ``axis`` (see below) must match the length of ``x``. Values must be finite. x : scalar or array-like, shape (m,) axis : int, optional Axis along which `y` is assumed to be varying. Meaning that for ``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``. Default is 0. bc_type : string or 2-tuple, optional Boundary condition type. Two additional equations, given by the boundary conditions, are required to determine all coefficients of polynomials on each segment [2]_. If `bc_type` is a string, then the specified condition will be applied at both ends of a spline. Available conditions are: * 'not-a-knot' (default): The first and second segment at a curve end are the same polynomial. It is a good default when there is no information on boundary conditions. * 'periodic': The interpolated functions is assumed to be periodic of period ``x[-1] - x[0]``. The first and last value of `y` must be identical: ``y[0] == y[-1]``. This boundary condition will result in ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``. * 'clamped': The first derivative at curves ends are zero. Assuming a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition. * 'natural': The second derivative at curve ends are zero. Assuming a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition. If `bc_type` is a 2-tuple, the first and the second value will be applied at the curve start and end respectively. The tuple values can be one of the previously mentioned strings (except 'periodic') or a tuple `(order, deriv_values)` allowing to specify arbitrary derivatives at curve ends: * `order`: the derivative order, 1 or 2. * `deriv_value`: array-like containing derivative values, shape must be the same as `y`, excluding ``axis`` dimension. For example, if `y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D and have the shape (n0, n1). extrapolate : {bool, 'periodic', None}, optional If bool, determines whether to extrapolate to out-of-bounds points based on first and last intervals, or to return NaNs. If 'periodic', periodic extrapolation is used. If None (default), ``extrapolate`` is set to 'periodic' for ``bc_type='periodic'`` and to True otherwise. See Also -------- scipy.interpolate.CubicHermiteSpline Returns ------- y : scalar or array-like The result, of shape (m,) References ---------- .. [1] `Cubic Spline Interpolation <https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation>`_ on Wikiversity. .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978. r )r} )rV �bc_typer� )r{ r} Z CubicSpline)r� r� r' rV r� r� r} r� r r r r� \ s F r� )r6 r1 rW rY r c C s� t | �}|�� s�t| dd�}|dkr(d}t| dd�}|dkrDt| �}t| ||d�} |dkrld|||d �< n$|d kr�d |d|�< ||d d�< tj| |< | S ) a� Apply interpolation and limit_area logic to values along a to-be-specified axis. Parameters ---------- values: array-like Input array. method: str Interpolation method. Could be "bfill" or "pad" limit: int, optional Index limit on interpolation. limit_area: str Limit area for interpolation. Can be "inside" or "outside" Returns ------- values: array-like Interpolated array. rL )rK Nr rM )r1 rW rh FrO ri )r ro rS r r] r" rn )r6 r1 rW rY ry rL rM r r r � _interpolate_with_limit_area� s$ r� r )r1 rV rW rY c C s� |dk r"t �tt|||d�|| �S |dkr2dd� ndd� }| j}| jdkrn|dkrZtd��| �td | j ��} t |�}|| �}|d kr�t ||d �\}} nt ||d �\}} ||�}|dkr�|d }|S ) a7 Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. Parameters ---------- values: array-like Input array. method: str, default "pad" Interpolation method. Could be "bfill" or "pad" axis: 0 or 1 Interpolation axis limit: int, optional Index limit on interpolation. limit_area: str, optional Limit area for interpolation. Can be "inside" or "outside" Returns ------- values: array-like Interpolated array. N)r1 rW rY r c S s | S )Nr )r' r r r �<lambda> � z interpolate_2d.<locals>.<lambda>c S s | j S )N)�T)r' r r r r� r� rO z0cannot interpolate on a ndim == 1 with axis != 0)rO r* )rW ) r" rg r r� rR rQ r� �tupler$ r2 �_pad_2d� _backfill_2d) r6 r1 rV rW rY ZtransfrR Ztvaluesrz �_r r r r] � s0 r] znp.ndarray | None)r r c C s |d krt | �}|�tj�}|S )N)r rv r" Zuint8)r6 r r r r � _fillna_prep s r� r )rf r c s t � �d� fdd� �}tt|�S )z> Wrapper to handle datetime64 and timedelta64 dtypes. Nc sP t | j�rB|d krt| �}� | �d�||d�\}}|�| j�|fS � | ||d�S )Nrk )rW r )r r! r rv )r6 rW r rz )rf r r �new_func, s z&_datetimelike_compat.<locals>.new_func)NN)r r r )rf r� r )rf r �_datetimelike_compat' s r� z tuple[np.ndarray, np.ndarray])r6 rW r r c C s" t | |�}tj| ||d� | |fS )N)rW )r� r Z pad_inplace)r6 rW r r r r �_pad_1d; s r� c C s" t | |�}tj| ||d� | |fS )N)rW )r� r Zbackfill_inplace)r6 rW r r r r � _backfill_1dF s r� c C s0 t | |�}t�| j�r(tj| ||d� n | |fS )N)rW )r� r" ro r$ r Zpad_2d_inplace)r6 rW r r r r r� Q s r� c C s0 t | |�}t�| j�r(tj| ||d� n | |fS )N)rW )r� r" ro r$ r Zbackfill_2d_inplace)r6 rW r r r r r� ] s r� )r* r+ rO )rR c C s& t | �} |dkrt| S ttd�| S )NrO )r* r+ )r2 � _fill_methodsr� r� )r1 rR r r r � get_fill_funcl s r� c C s t | dd�S )NT)r) )r2 )r1 r r r �clean_reindex_fill_methods s r� )ry c s� t | �� t� }t� }� fdd�}|dk rN|dkrDtt�| �d �}n || |�}|dk r�|dkrb|S t|| ddd� |��}t� d t�|� �}|dkr�|S ||@ S )ak Get indexers of values that won't be filled because they exceed the limits. Parameters ---------- invalid : np.ndarray[bool] fw_limit : int or None forward limit to index bw_limit : int or None backward limit to index Returns ------- set of indexers Notes ----- This is equivalent to the more readable, but slower .. code-block:: python def _interp_limit(invalid, fw_limit, bw_limit): for x in np.where(invalid)[0]: if invalid[max(0, x - fw_limit):x + bw_limit + 1].all(): yield x c s` t |� �}t| |d ��d�}tt�|�d | �tt�| d |d � �� dk�d �B }|S )NrO r )�min�_rolling_windowro rp r" �whereZcumsum)ry rW Zwindowed�idx)�Nr r �inner� s (z _interp_limit.<locals>.innerNr rP rO )r rp r" r� �listrw )ry Zfw_limitZbw_limitZf_idxZb_idxr� Z b_idx_invr )r� r rr w s rr )�a�windowc C sJ | j dd� | j d | d |f }| j| jd f }tjjj| ||d�S )z� [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] NrP rO )r$ �strides)r$ r� r" r Z stride_tricksZ as_strided)r� r� r$ r� r r r r� � s $r� )F) r* r NNrT NNFN)r3 NrT NN)r3 NrT NNFN)NFN)Nr F)r r )r r� N)r* r NN)N)NN)NN)NN)NN)rO )<�__doc__� __future__r � functoolsr r �typingr r r �numpyr" Z pandas._libsr r Zpandas._typingr r r Zpandas.compat._optionalr Zpandas.core.dtypes.castr Zpandas.core.dtypes.commonr r r Zpandas.core.dtypes.missingr r r Zpandasr r r( r2 rF rG rJ rS r` r^ re rx r� r� r� r� r] r� r� r� r� r� r� r� r� r� rr r� r r r r �<module> s� * ' ", ) E + + O3 < A
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