ehƉdZddlmZddlmZddlmZmZmZm Z m Z ddl Z ddl mZmZmZddlmZmZmZmZmZddlmZdd lmZdd lmZmZmZmZm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&m'Z'erdd l(m)Z)d4dZ*d5dZ+e dd d6dZ,e d7dZ,dd d8dZ,gdZ-gdZ.d9dZ/d:dZ0 d;dZ1ddZ4 d? d@dZ5dAdZ6 dB dCd Z7 dD dEd!Z8 dF dGd"Z9 dH dId#Z: dJ dKd$Z; dLd%Z< dM dNd&Z= dO dPd'Z>dQd(Z?e? dR dSd)Z@e? dR dSd*ZAe? dR dTd+ZBe? dR dUd,ZC dVd-ZD dVd.ZEe@eAd/ZFdWdXd0ZGdYd1ZH dZd2ZId[d3ZJy)\z$ Routines for filling missing data. ) annotations)wraps) TYPE_CHECKINGAnyLiteralcastoverloadN)NaTalgoslib) ArrayLikeAxisIntF ReindexMethodnpt)import_optional_dependency)infer_dtype_from) is_array_like is_bool_dtypeis_numeric_dtypeis_numeric_v_string_likeis_object_dtypeneeds_i8_conversion)DatetimeTZDtype)is_valid_na_for_dtypeisnana_value_for_dtypeIndexcvt|r-t||k7rtdt|d|||}|S)zJ Validate the size of the values passed to ExtensionArray.fillna. z'Length of 'value' does not match. Got (z ) expected )rlen ValueError)valuemasklengths Q/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/pandas/core/missing.pycheck_value_sizer'3sPU u: 9#e*F#H& d  Lct|\}}t|tjrtj||}n<|j }t j|s|g}|j||d}d}t|jrd}t|}t|}||}tj|jt}t|jr+t|jst|jrnt|jr+t|jrt|jsn|D]} t!|| r|r;tj|jtj"} || k(| |<n6|| k(} t| tj$s| j'td} || z}|j)r|t|z}|S)a  Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True Parameters ---------- arr : ArrayLike values_to_mask: list, tuple, or scalar Returns ------- np.ndarray[bool] )dtypeF)r*copyT)r*na_value)r isinstancenpr*arrayconstruct_array_typer is_list_like_from_sequencerrzerosshapeboolrrrbool_ndarrayto_numpyany) arrvalues_to_maskr*cls potential_naarr_maskna_masknonnar$xnew_masks r& mask_missingrCBs"-^<E>%".>((*/,-N++N%e+TLsyy! I:>"G G8 $E 88CIIT *D#cii( %++ & cii U[[ )ekk*  !A'Q/!xx BH),X!);HX&"axH%h ;#+#4#44%#4#P  ! {{} S  Kr(. allow_nearestcyNmethodrEs r&clean_fill_methodrK r(cyrGrHrIs r&rKrKrLr(Fct|tr|j}|dk(rd}n|dk(rd}ddg}d}|r|jdd}||vrt d|d ||S) Nffillpadbfillbackfillzpad (ffill) or backfill (bfill)nearestz(pad (ffill), backfill (bfill) or nearestzInvalid fill method. Expecting z. Got )r-strlowerappendr")rJrE valid_methods expectings r&rKrKs &# W F w FJ'M1IY'>  ]":9+VF8TUU Mr()lineartimeindexvalues)rSzeroslinear quadraticcubic barycentrickroghspline polynomialfrom_derivativespiecewise_polynomialpchipakima cubicsplinec |jd}|dvr | tdttz}||vrtd|d|d|dvr|jst|d|S) Norder)rcrdz7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.)rbrfrgz4 interpolation requires that the index be monotonic.)getr" NP_METHODS SP_METHODSis_monotonic_increasing)rJr[kwargsrkvalids r&clean_interp_methodrrs JJw E ))emRSS  #E U1%xzRSS ;;,,(NO  Mr(c|dvsJt|dk(ry|jdk(r|jd}|dk(r|ddj}n*|dk(r%t|dz |ddd jz }|}|sy|S) a+ Retrieves the positional index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. is_valid: np.ndarray Mask to find na_values. Returns ------- int or None )firstlastrNaxisrtru)r!ndimr9argmax)howis_valididxpos chk_notnas r&find_valid_indexrs # ## # 8}}}<b B  r(c^|*ddg}|j}||vrtd|d|d|S)Ninsideoutsidez%Invalid limit_area: expecting one of z, got .r) limit_areavalid_limit_areass r&validate_limit_arearsV%y1%%' . .78I7J&,a!  r(c| |dvrd}|Sd}|S|dvr|dk7rtd|d|dvr|dk7rtd|d|S)N)rRrQrr)rPrOz0`limit_direction` must be 'forward' for method ``z1`limit_direction` must be 'backward' for method `)r")rrJs r&infer_limit_directionr#s * *(O (O  % %/Y*FB6(!L  * **/LCF81M  r(c|dk(r+ddlm}|tjt |}nlhd}t |j xs<t|j txs tj|j d}||vr|std|dt|jr td|S) NrYrr>rZr[r\rSmMz9Index column must be numeric or datetime type when using z_ method other than linear. Try setting a numeric or datetime index column before interpolating.zkInterpolation with NaNs in the index has not been implemented. Try filling those NaNs before interpolating.)pandasrr.aranger!rr*r-rr is_np_dtyper"rr9NotImplementedError)rJr[rmethodsis_numeric_or_datetimes r&get_interp_indexr8s  biiE +,8 U[[ ) 2%++7 2u{{D1   )?!!!  E{! /  Lr(c  t|fi t|jrt|jddk(r"t |js t ddt t| tjdt| d   fd } tj| ||y) 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)compatrZzStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexr\N)nobslimitc 0td|dd y)NF) indicesyvaluesrJrrr fill_value bounds_errorr$rH)_interpolate_1d) rrrrprlimit_area_validatedrr$rJs r&funcz$interpolate_2d_inplace..funcs7  ++!  r()r np.ndarrayreturnNone) rrrr*rrr"rrr validate_limit_index_to_interp_indicesr.apply_along_axis) datar[ryrJrrrrr$rprrrs ``` ``` @@r&interpolate_2d_inplacerWs.00Z4' 5A  "5;;/   .?O.z:  d% 8E&uf5G  ,dD)r(cF|j}t|jr|jd}|dk(r|}t t j |}|St j|}|dvr2|jt jk(rtj|}|S)zE Convert Index to ndarray of indices to pass to NumPy/SciPy. i8rY)r\r[) _valuesrr*viewrr.r7asarrayobject_r maybe_convert_objects)r[rJxarrindss r&rrs ==D4::&yy BJJ% K zz$ ( (zzRZZ'006 Kr(c  | | } n t|} | } | jsy| jrytt j | } t d| }|d}tt|}t d| }| t|}ttd|zt| }|dk(r|tt| |dz}n5|dk(r|tt| d|z}ntt| ||}|d k(r |||zz}n|d k(r | |z |z }||z}t|}|jjd v}|r|jd }|tvrBt j|| }t j || || ||| ||| <nt#|| || || f||||d | || <| d| ddd| |<y|rt$j&||<ytj(||<y)a Logic for the 1-d interpolation. The input indices and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. Notes ----- Fills 'yvalues' in-place. Nrtr}r~rrurwrrrrrr)rJrrrkFT)rr9allsetr. flatnonzerorranger! _interp_limitsortedr*kindrrmargsortinterp_interpolate_scipy_wrapperr r#nan)rrrJrrrrrrkr$rpinvalidrqall_nansfirst_valid_index start_nanslast_valid_indexend_nans preserve_nansmid_nansis_datetimelikeindexers r&rrsK0 w- HE 99; yy{2>>'*+H(WuE U,-.J'FUCw<5--s5z:;H)#"Swq)I%JJ J & 3}Wa'G#HH M'5%@A Xh.. y j(83! =)Mmm((D0O,,t$ **WU^,99 G genW5wu~g7N 6 EN EN G   !%     Q"]  !$  "$  r(c |d}td|ddlm} tj|}| j | j tttt| jd} gd} || vr*|dk(r|} n|} | j||| || } | |}|S|d k(r>t|s|dkrtd || j||fd |i|} | |}|S|jj s|j#}|jj s|j#}|jj s|j#}| |} | |||fi|}|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)rarbrerfrirhrg)rSr]r^r_r`rdrd)rrrrcz;order needs to be specified and greater than 0; got order: k)rrrr.rbarycentric_interpolatekrogh_interpolate_from_derivatives_cubicspline_interpolate_akima_interpolatepchip_interpolateinterp1drr"UnivariateSplineflags writeabler+)rAynew_xrJrrrkrprr alt_methodsinterp1d_methodsrterpnew_ys r&rr%s}h4 5Ewe4! JJu E#::..- 1/#..K!! \ !DD## qt $ U ( L' 8  ;5A:MeWU ,{++AqDEDVDU  Lww  Aww  A{{$$JJLE6"Q5+F+ Lr(cddlm}|jj}|||j dd||}||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. rrrzrw)orders extrapolate)rrBPolyrereshape) xiyirArkderrrrJms r&rrls?R"   / /Fr2::b!$U LA Q4Kr(cJddlm}|j|||}|||S)aQ 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 : np.ndarray A sorted list of x-coordinates, of length N. yi : np.ndarray 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 : np.ndarray 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, rrrx)nu)rrAkima1DInterpolator)rrrArryrPs r&rrs+T"''BT':A Q3<r(cJddlm}|j|||||}||S)ag Convenience function for cubic spline data interpolator. See `scipy.interpolate.CubicSpline` for details. Parameters ---------- xi : np.ndarray, shape (n,) 1-d array containing values of the independent variable. Values must be real, finite and in strictly increasing order. yi : np.ndarray 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 : np.ndarray, 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)rybc_typer)rr CubicSpline)rrrAryrrrrs r&rrs3Z" BT7   A Q4Kr(c4t|}|}|jsztd|}|d}td|}| t|}t |||||dk(r d|||d zn|d k(rdx|d|||d zdn t d t j||<yy) 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: {'inside', 'outside'} Limit area for interpolation. Notes ----- Modifies values in-place. rtrNrru)rJrrrFrwrz*limit_area should be 'inside' or 'outside')rrrr!pad_or_backfill_inplacer"r.r)r\rJrrrr~rtrus r&_interpolate_with_limit_arear%s26lGxH ;;= Wx@ =EFX> <v;D !    !(-GED1H % 9 $49 9GFUOgdQhj1IJ J&&w- r(c|dk(rdnd}|jdk(r7|dk7r td|jtd|jz}t |}||}t |d}|||| y ) 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. rc|SrGrHrAs r&z)pad_or_backfill_inplace..vsr(c|jSrG)Trs r&rz)pad_or_backfill_inplace..vs r(rwz0cannot interpolate on a ndim == 1 with axis != 0rwrv)r{)rrN)r{AssertionErrorrtupler4rK get_fill_func)r\rJryrrtransftvaluesrs r&rrZsy8#aikmF{{a 19 !ST TdV\\&9 :; v &FVnG a (D*5r(c | t|}|SrG)r)r\r$s r& _fillna_preprs  |F| Kr(cZt d dfd }tt|S)z> Wrapper to handle datetime64 and timedelta64 dtypes. ct|jrH| t|}|jd|||\}}|j|j|fS||||S)Nr)rrr$)rr*rr)r\rrr$resultrs r&new_funcz&_datetimelike_compat..new_funcsj v|| ,|F| D!:DLFD;;v||,d2 2F%JTJJr(NNN)r int | Noner#Literal['inside', 'outside'] | None)rrr)rr s` r&_datetimelike_compatrsK  4[!:>  KK8KK$ 8 r(ct||}||js t||tj|||||fSN)r)rr_fill_limit_area_1dr pad_inplacer\rrr$s r&_pad_1drsD  %DdhhjD*- fd%0 4<r(ct||}||js t||tj|||||fSr)rrrr backfill_inplacers r& _backfill_1drsD  %DdhhjD*- 64u5 4<r(ct||}| t|||jrtj|||||fS ||fSr)r_fill_limit_area_2dsizer pad_2d_inplacers r&_pad_2drsT  %DD*- {{ VT7 4< 4<r(ct||}| t|||jrtj|||||fS ||fSr)rrrr backfill_2d_inplacers r& _backfill_2dr sT  %DD*- {{ !!&$e< 4< 4<r(c|}|j}t||dddjz dz }|dk(rd|d|d||dzdy|dk(r d||dz|yy)aPrepare 1d mask for ffill/bfill with limit_area. Caller is responsible for checking at least one value of mask is False. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. NrzrwrFr)r|r!)r$rneg_maskrtrus r&rrs{ uH OO E x=8DbD>002 2Q 6DXVe  TAXZ y !&UQY !r(c|j}|dk(rPtjj|dtjj|dddddddz}nQtjj|dtjj|dddddddz}d||j<y)aPrepare 2d mask for ffill/bfill with limit_area. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. rrrxNrzF)rr.maximum accumulate)r$rr"la_masks r&rrswHY JJ ! !( ! 3jj##HTrTN#;DbDA B ZZ " "8! " 4 4zz$$Xdd^!$.inner\sE1 "7EAI6::1="((8$Q'%/03 HHw{++335: ;A >4   r(Nrrzrw)rint)r!rr.r0listr)rfw_limitbw_limitf_idxb_idxr5 b_idx_invr4s @r&rr7sB G A EE EE q=)!,-E'8,E q=LU74R4=(; [ [True, True], [True, False], [False, True], [True, False], ] Nrzrw)r4strides)r4r>r.r stride_tricks as_strided)awindowr4r>s r&r/r/xsj GGCRLAGGBK&014f= =Eii199R=**G 66   * *1E7 * KKr()r$npt.NDArray[np.bool_]r%r6)r:r rrC)rJz,Literal['ffill', 'pad', 'bfill', 'backfill']rEzLiteral[False]rLiteral['pad', 'backfill'])rJ7Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']rEz Literal[True]r%Literal['pad', 'backfill', 'nearest'])rJrErEr5rrF)rJrTr[rrrT)r}rTr~rCrr )rrTrz&Literal['forward', 'backward', 'both'])r str | Nonerr)rz-Literal['backward', 'forward', 'both'] | NonerJrTrz&Literal['backward', 'forward', 'both'])r[rrr)rYNrNNN)rrr[rryrrJrTrr rrTrrGr Any | Nonerr)r[rrJrTrr)rYNrNNFNN)rrrrrJrTrr rrTrrrrHrr5rkr rr)NFN) rArrrrrrJrTrr5)NrF) rrrrrArrint | list[int] | Nonerr5)rr) rrrrrArrrIryr)rz not-a-knotN) rrrrrArryrrzstr | tuple[Any, Any]) r\rrJrDrr rzLiteral['inside', 'outside']rr)rPrNN) r\rrJrDryrrr rrrrrG)r$npt.NDArray[np.bool_] | NonerrC)rrrrr ) r\rrr rrr$rJrz(tuple[np.ndarray, npt.NDArray[np.bool_]])r\rrr rrr$rJ)rr rrr$rJ)r$rCrzLiteral['outside', 'inside']rrr)r{r6)rzReindexMethod | None)rrCr8r r9r )rArCrBr6rrC)K__doc__ __future__r functoolsrtypingrrrrr numpyr. pandas._libsr r r pandas._typingr rrrrpandas.compat._optionalrpandas.core.dtypes.castrpandas.core.dtypes.commonrrrrrrpandas.core.dtypes.dtypesrpandas.core.dtypes.missingrrrrrr'rCrKrmrnrrrrrrrrrrrrrrrrrrrrrr rrr)rr+rr/rHr(r&rWs# ?46  FR %( 8"    C!+    C+ 43  $&#L  +  BLO+*F$!! C* C* C* C*  C*  C*  C*C*C* C*L2$6:! o o o  o   o  o 4 o o o  o  o n DDD D  D  DV "# //// /  /l#$ .... .  .j%1 SSSS  S # Sl2! 2! &2! 2!- 2!  2!n*/6: )6 )6 &)6 )6  )6 4 )6  )6Z26.66:)-     4  '  .  6:)-     4  '  .  6:)-   4 ' $6:)-  4 ' $' '-I' '4 -I > \: >9 > ">.8>DN>BLr(