U +ift@sdZddlmZddlmZmZddlmZmZm Z ddl Z ddl m Z mZddlmZmZmZmZddlmZdd lmZdd lmZmZmZdd lmZmZmZerdd l m!Z!d ddddZ"dddddZ#dzddddZ$ddddgZ%d d!d"d#d$d%d&d'd(d)d*d+d,d-gZ&d.d/d.d0d1d2Z'd.d3d4d5d6Z(d{d d.dd9d3d.d:d;dd:dd?Z)d|d d/dd.d3d.d:d;dtdt|| |||dn,|dk s^ttf||||||||d| dS)z Wrapper to dispatch to either interpolate_2d or _interpolate_2d_with_fill. Notes ----- Alters 'data' in-place. Nz&Cannot pass both fill_value and method)r5r[r\r^)rZr9r[r5r\r]r^r_)r6rinterpolate_2drU_interpolate_2d_with_fill) rZr5r[r9r\r]r^r_r`rarLmrrrinterpolate_array_2ds8     re) rZr9r[r5r\r]r^r_r"c  st|ft|jr(t|jdddkrFt|jsBtdddddg} | krvtd | d d d k rd dg} | krtd| ddtjd dt |dddfdd } t | ||d S)z Column-wise application of _interpolate_1d. Notes ----- Alters 'data' in-place. The signature does differ from _interpolate_1d because it only includes what is needed for Block.interpolate. F)compatr8zStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexr:rXbackwardZbothz*Invalid limit_direction: expecting one of z, got 'z'.Ninsideoutsidez%Invalid limit_area: expecting one of z, got .)Znobsr\rrY)yvaluesr"c s$tf|dddS)NF)indicesrkr5r\r]r^r_ bounds_error)_interpolate_1d)rkr_rlrLr\r^r]r5rrfuncAs z'_interpolate_2d_with_fill..func) rNrr#rrrr3rZvalidate_limit_index_to_interp_indicesr$apply_along_axis) rZr9r[r5r\r]r^r_rLZvalid_limit_directionsZvalid_limit_areasrprrorrcs6     rc)r9r5r"cCs`|j}t|jr|d}|dkr4|}ttj|}n(t|}|dkr\|jtjkr\t |}|S)zE Convert Index to ndarray of indices to pass to NumPy/SciPy. i8r7)r:r9) _valuesrr#viewrr$r)asarrayZobject_r Zmaybe_convert_objects)r9r5ZxarrZindsrrrrq\s     rq) rlrkr5r\r]r^r_rmrHc Kst|} | } | sdS| r&dStt| } t|dd} | dkrLd} tt| }t|dd}|dkrtt|}ttd|t| }|dkr|tt | |dB}n.|dkr|tt | d|B}ntt | ||}|d kr|||BO}n|d kr | ||}||O}t |}|t krRt || }t || || ||| ||| <n.t|| || || f||||d | || <tj||<dS) a Logic for the 1-d interpolation. The input indices and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. Notes ----- Fills 'yvalues' in-place. NrPrOrrQrSrXrgrhri)r5r_rmrH)rr*allsetr$Z flatnonzerorWranger _interp_limitsortedrJZargsortZinterp_interpolate_scipy_wrappernan)rlrkr5r\r]r^r_rmrHrLinvalidrMZall_nansZfirst_valid_indexZ start_nansZlast_valid_indexZend_nansZ preserve_nansZmid_nansZindexerrrrrnrs^         rncKsx|d}td|dddlm} t|}| j| jttd} t|ddrb|j d | d }}|d krv| j | d <n"|d krt | d <n|d krt | d <d dddddg} || kr|dkr|}| j|||||d} | |} n|dkr&t|s|dkrtd|| j||fd|i|} | |} nN|jjs8|}|jjsJ|}|jjs\|}| |}||||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@rCrDZ _is_all_datesFrsrErFrGr1r;r<r=r>rB)kindr_rmrAz;order needs to be specified and greater than 0; got order: k)rrrr$rvZbarycentric_interpolateZkrogh_interpolate_from_derivativesgetattrrtZastypeZpchip_interpolate_akima_interpolate_cubicspline_interpolateZinterp1drrZUnivariateSplineflagsZ writeablecopy)r+yZnew_xr5r_rmrHrLrrZ alt_methodsZinterp1d_methodsZterpZnew_yrrrr}sf             r}c Cs4ddlm}|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. rrrTrS)Zorders extrapolate)rrZBPolyrCreshape) xiyir+rHderrrr5rdrrrrs" rcCs(ddlm}|j|||d}|||dS)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, rr)r[)nu)rrZAkima1DInterpolator)rrr+rr[rPrrrrGs$ r not-a-knotcCs(ddlm}|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 `_ on Wikiversity. .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978. rr)r[bc_typer)rrZ CubicSpline)rrr+r[rrrrrrrrrsF r)r:r5r\r^r"cCst|}|st|dd}|dkr(d}t|dd}|dkrDt|}t|||d|dkrld|||d <n$|d krd|d|<||d d<tj||<dS) a Apply interpolation and limit_area logic to values along a to-be-specified axis. Parameters ---------- values: np.ndarray Input array. method: str Interpolation method. Could be "bfill" or "pad" limit: int, optional Index limit on interpolation. limit_area: str Limit area for interpolation. Can be "inside" or "outside" Notes ----- Modifies values in-place. rPrwNrrQ)r5r\rhFrSri)rrxrWrrbr$r~)r:r5r\r^rrPrQrrr_interpolate_with_limit_areas&   rr )r:r5r[r\r^r"cCs|dk r&ttt|||d||dS|dkr6ddndd}|jdkrl|dkrXtd|td |j}t |}||}|d krt ||d n t ||d dS) a Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. Parameters ---------- values: np.ndarray 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" Notes ----- Modifies values in-place. N)r5r\r^rcSs|SNrr+rrr#z interpolate_2d..cSs|jSr)Trrrrr#rrSz0cannot interpolate on a ndim == 1 with axis != 0)rSr/r\) r$rrrrrVrUrtupler&r6_pad_2d _backfill_2d)r:r5r[r\r^ZtransfZtvaluesrrrrbs.    rbznpt.NDArray[np.bool_] | None)rr"cCs |dkrt|}|tj}|Sr)rrur$Zuint8r:rrrr _fillna_prep7s rr )rpr"cs tdfdd }tt|S)z> Wrapper to handle datetime64 and timedelta64 dtypes. NcsPt|jrB|dkrt|}|d||d\}}||j|fS|||dS)Nrs)r\r)rr#rru)r:r\rresultrprrnew_funcHs  z&_datetimelike_compat..new_func)NN)rrr )rprrrr_datetimelike_compatCs rz(tuple[np.ndarray, npt.NDArray[np.bool_]])r:r\rr"cCs"t||}tj|||d||fSNr)rrZ pad_inplacer:r\rrrr_pad_1dWs rcCs"t||}tj|||d||fSr)rrZbackfill_inplacerrrr _backfill_1dbs rrcCs0t||}t|jr(tj|||dn||fSr)rr$rxr&rZpad_2d_inplacerrrrrms  r)rcCs0t||}t|jr(tj|||dn||fSr)rr$rxr&rZbackfill_2d_inplacerrrrrys  rr/r0rS)rVcCs&t|}|dkrt|Sttd|S)NrSr)r6 _fill_methodsrr)r5rVrrr get_fill_funcsrcCs t|ddS)NTr-)r6)r5rrrclean_reindex_fill_methodsr)rcst|t}t}fdd}|dk rN|dkrDtt|d}n |||}|dk r|dkrb|St||ddd|}tdt|}|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 cs`t|}t||dd}tt|d|tt|d|ddkdB}|S)NrSr)min_rolling_windowrxryr$whereZcumsum)rr\ZwindowedidxNrrinners  "z_interp_limit..innerNrrTrS)rryr$rlistrv)rZfw_limitZbw_limitZf_idxZb_idxrZ b_idx_invrrrr{s   r{)awindowr"cCsJ|jdd|jd|d|f}|j|jdf}tjjj|||dS)z [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] NrTrS)r&strides)r&rr$r Z stride_tricksZ as_strided)rrr&rrrrrs $r)F) r/rNNrXNNFN)r7NrXNN)r7NrXNNFN)NFN)NrF)rr)rrN)r/rNN)N)NN)NN)NN)NN)rS)>__doc__ __future__r functoolsrrtypingrrrnumpyr$Z pandas._libsrr Zpandas._typingr r r r Zpandas.compat._optionalrZpandas.core.dtypes.castrZpandas.core.dtypes.commonrrrZpandas.core.dtypes.missingrrrZpandasrr r,r6rJrKrNrWrercrqrnr}rrrrrbrrrrrrrrrr{rrrrrs    . 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