a  d} @s*dZddlmZddlmZmZddlmZmZm Z m Z ddl Z ddl mZmZmZddlmZmZmZmZmZddlmZdd lmZdd lmZmZmZmZm Z dd l!m"Z"dd l#m$Z$m%Z%m&Z&erdd l'm(Z(dddddZ)dddddZ*ddddddZ+gdZ,gdZ-dddd d!d"Z.ddd#d$d%d&Z/dd'd(d)d*Z0d+d,d-d.d/Z1d0d1Z2ddd2d3d4Z3dd7dd8dd#dd+d9d:d; dd?d@Z5dd7d7dd#dd,d9dd#d:dA dBdCZ6dd7d7d7dddDdEdFZ7dd7d7d7dGddHdIdJZ8dd7d7d7dGd8dKdLdMZ9dd7d7d7d8dOdPdQdRZ:d7dSd#dTd:dUdVdWZ;dd7dSd8d#d,d:dYdZd[Ze>dd7d#d\dddedfdgZ?e>dd7d#d\dddedhdiZ@e>dd7d#d\djdkdlZAe>dd#d\dmdndoZBe?e@dpZCdddrdsdtZDdudvdwdxZEdd#d#dydzd{ZFdddd|d}d~ZGdS)z$ Routines for filling missing data. ) annotations)partialwraps) TYPE_CHECKINGAnyLiteralcastN)NaTalgoslib) ArrayLikeAxisIntF ReindexMethodnpt)import_optional_dependency)infer_dtype_from) is_array_likeis_numeric_dtypeis_numeric_v_string_likeis_object_dtypeneeds_i8_conversion)DatetimeTZDtype)is_valid_na_for_dtypeisnana_value_for_dtypeIndexznpt.NDArray[np.bool_]int)masklengthcCs8t|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 )rlen ValueError)valuerr r$\/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/pandas/core/missing.pycheck_value_size4s  r&r )arrreturnc Cst|\}}t|tjr(tj||d}n(|}t|s@|g}|j||dd}d}t |jrld}t |}t |}||}tj |j t d}|D]b} t|| rq|rtj |j tjd} ||| k| |<n"|| k} t| tjs| jt dd} || O}q|r |t |O}|S)a  Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True Parameters ---------- arr : ArrayLike values_to_mask: list, tuple, or scalar Returns ------- np.ndarray[bool] )dtypeF)r)copyT)r)Zna_value)r isinstancenpr)arrayZconstruct_array_typer Z is_list_likeZ_from_sequencerrzerosshapeboolrZbool_ndarrayZto_numpyany) r'Zvalues_to_maskr)clsZ potential_naZarr_maskZna_maskZnonnarxZnew_maskr$r$r% mask_missingCs6           r5Fstrr0)method allow_nearestcCsjt|tr,|}|dkr d}n |dkr,d}ddg}d}|rJ|dd}||vrftd|d ||S) Nffillpadbfillbackfillzpad (ffill) or backfill (bfill)nearestz(pad (ffill), backfill (bfill) or nearestzInvalid fill method. Expecting z. Got )r+r6lowerappendr")r7r8Z valid_methodsZ expectingr$r$r%clean_fill_methods  r@)lineartimeindexvalues)r=zeroslinear quadraticcubic barycentrickroghspline polynomialfrom_derivativespiecewise_polynomialpchipakima cubicspliner)r7rCr(cKsh|d}|dvr"|dur"tdtt}||vrHtd|d|d|dvrd|jsdt|d|S) Norder)rKrLz7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.)rJrNrOz4 interpolation requires that the index be monotonic.)getr" NP_METHODS SP_METHODSZis_monotonic_increasing)r7rCkwargsrRvalidr$r$r%clean_interp_methods rX int | None)howis_validr(cCs|dvs Jt|dkrdS|jdkr2|jdd}|dkrL|dd}n&|dkrrt|d|ddd }||}|sdS|S) a+ Retrieves the positional index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. is_valid: np.ndarray Mask to find na_values. Returns ------- int or None )firstlastrNaxisr\r])r!ndimr2Zargmax)rZr[ZidxposZ chk_notnar$r$r%find_valid_indexs    rdz&Literal['forward', 'backward', 'both'])limit_directionr(cCs2gd}|}||vr.td|d|d|S)N)forwardbackwardZbothz*Invalid limit_direction: expecting one of z, got 'z'.r>r")reZvalid_limit_directionsr$r$r%validate_limit_directionsriz str | Nonez#Literal['inside', 'outside'] | None) limit_arear(cCs:|dur6ddg}|}||vr6td|d|d|S)Ninsideoutsidez%Invalid limit_area: expecting one of z, got .rh)rjZvalid_limit_areasr$r$r%validate_limit_areasrncCs`|dur|dvrd}q\d}n@|dvr<|dkrrDrBrCr=mMz9Index column must be numeric or datetime type when using z_ method other than linear. Try setting a numeric or datetime index column before interpolating.zkInterpolation with NaNs in the index has not been implemented. Try filling those NaNs before interpolating.)pandasrr,Zaranger!rr)r+rr Z is_np_dtyper"rr2NotImplementedError)r7rCrmethodsZis_numeric_or_datetimer$r$r%get_interp_indexs(      rurArf np.ndarrayr z Any | NoneNone) datarCrar7limitrerj fill_valuer(c  st|fit|jr,t|jdddkrJt|jsFtddtt|tj ddt |dd d fd d } t | ||dS) z Column-wise application of _interpolate_1d. Notes ----- Alters 'data' in-place. The signature does differ from _interpolate_1d because it only includes what is needed for Block.interpolate. F)compatrBzStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexrDN)Znobsryrvrw)yvaluesr(c s$tf|dddS)NF)indicesr|r7ryrerjrz bounds_error)_interpolate_1d)r|rzr}rVryZlimit_area_validatedrer7r$r%func]s z$interpolate_2d_inplace..func) rXrr)rrr"rirnr Zvalidate_limit_index_to_interp_indicesr,apply_along_axis) rxrCrar7ryrerjrzrVrr$rr%interpolate_2d_inplace1s    r)rCr7r(cCs`|j}t|jr|d}|dkr4|}ttj|}n(t|}|dvr\|jtjkr\t |}|S)zE Convert Index to ndarray of indices to pass to NumPy/SciPy. i8rA)rDrC) Z_valuesrr)viewrr,r1asarrayZobject_r Zmaybe_convert_objects)rCr7ZxarrZindsr$r$r%rus     r) r}r|r7ryrerjrzr~rRr(c Kst|} | } | sdS| r&dStt| } td| d} | durLd} tt| }td| d}|durtt|}ttd|t| }|dkr|tt | |dB}n.|dkr|tt | d|B}ntt | ||}|d kr|||BO}n|d kr | ||}||O}t |}|j j d v}|r.| d }|tvrnt|| }t|| || ||| ||| <n.t|| || || f||||d | || <|rtj||<n tj||<dS)a Logic for the 1-d interpolation. The input indices and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. Notes ----- Fills 'yvalues' in-place. Nr\rZr[rr]r_rfrgrkrlrqr)r7rzr~rR)rr2allsetr,Z flatnonzerordranger! _interp_limitsortedr)kindrrTZargsortZinterp_interpolate_scipy_wrapperr r#nan)r}r|r7ryrerjrzr~rRrVinvalidrWZall_nansZfirst_valid_indexZ start_nansZlast_valid_indexZend_nansZ preserve_nansZmid_nansZis_datetimelikeZindexerr$r$r%rsd             r)r4ynew_xr7r~cKs|d}td|dddlm} t|}| j| jtttt | j d} gd} || vr|dkrd|} n|} | j ||| ||d } | |}n|d krt |s|dkrt d || j||fd |i|} | |}nL|jjs|}|jjs|}|jjs|}| |} | |||fi|}|S) z Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_method. z interpolation requires SciPy.scipy)extrar interpolate)rIrJrMrNrQrPrO)r=rErFrGrHrLrL)rrzr~rKz;order needs to be specified and greater than 0; got order: k)rrrr,rZbarycentric_interpolateZkrogh_interpolate_from_derivatives_cubicspline_interpolate_akima_interpolateZpchip_interpolateZinterp1drr"ZUnivariateSplineflagsZ writeabler*)r4rrr7rzr~rRrVrrZ alt_methodsZinterp1d_methodsrZterpZnew_yr$r$r%rsJ        rzint | list[int] | None)xiyir4der extrapolatec Cs4ddlm}|jj}|||dd||d}||S)a Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array-like sorted 1D array of x-coordinates yi : array-like or list of array-likes yi[i][j] is the j-th derivative known at xi[i] order: None or int or array-like of ints. Default: None. Specifies the degree of local polynomials. If not None, some derivatives are ignored. der : int or list How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. extrapolate : bool, optional Whether to extrapolate to ouf-of-bounds points based on first and last intervals, or to return NaNs. Default: True. See Also -------- scipy.interpolate.BPoly.from_derivatives Returns ------- y : scalar or array-like The result, of length R or length M or M by R. rrrbr_)Zordersr)rrZBPolyrMreshape) rrr4rRrrrr7mr$r$r%r>s) r)rrr4rracCs(ddlm}|j|||d}|||dS)aQ Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : np.ndarray A sorted list of x-coordinates, of length N. yi : np.ndarray A 1-D array of real values. `yi`'s length along the interpolation axis must be equal to the length of `xi`. If N-D array, use axis parameter to select correct axis. x : np.ndarray Of length M. der : int, optional How many derivatives to extract; None for all potentially nonzero derivatives (that is a number equal to the number of points), or a list of derivatives to extract. This number includes the function value as 0th derivative. axis : int, optional Axis in the yi array corresponding to the x-coordinate values. See Also -------- scipy.interpolate.Akima1DInterpolator Returns ------- y : scalar or array-like The result, of length R or length M or M by R, rrr`)nu)rrZAkima1DInterpolator)rrr4rrarPr$r$r%rps* r not-a-knotzstr | tuple[Any, Any])rrr4rabc_typecCs(ddlm}|j|||||d}||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)rarr)rrZ CubicSpline)rrr4rarrrrr$r$r%rs M  rzLiteral['pad', 'backfill']zLiteral['inside', 'outside'])rDr7ryrjr(cCst|}|}|std|d}|dur.d}td|d}|durJt|}t|||d|dkrrd|||d <n.|d krd|d|<||d d<ntd tj||<dS) a Apply interpolation and limit_area logic to values along a to-be-specified axis. Parameters ---------- values: np.ndarray Input array. method: str Interpolation method. Could be "bfill" or "pad" limit: int, optional Index limit on interpolation. limit_area: {'inside', 'outside'} Limit area for interpolation. Notes ----- Modifies values in-place. r\rNrr])r7ryrkFr_rlz*limit_area should be 'inside' or 'outside')rrrdr!pad_or_backfill_inplacer"r,r)rDr7ryrjrr[r\r]r$r$r%_interpolate_with_limit_areas(  rr:)rDr7raryrjr(cCs|dur&ttt|||d||dS|dkr6ddndd}|jdkrl|dkrXtd|td |j}t |}||}t |d d }|||d dS) a Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. Parameters ---------- values: np.ndarray Input array. method: str, default "pad" Interpolation method. Could be "bfill" or "pad" axis: 0 or 1 Interpolation axis limit: int, optional Index limit on interpolation. limit_area: str, optional Limit area for interpolation. Can be "inside" or "outside" Notes ----- Modifies values in-place. N)r7ryrjrcSs|SNr$r4r$r$r%\z)pad_or_backfill_inplace..cSs|jSr)Trr$r$r%r\rr_z0cannot interpolate on a ndim == 1 with axis != 0)r_r^rcry) r,rrrrcAssertionErrorrtupler/r@ get_fill_func)rDr7raryrjZtransfZtvaluesrr$r$r%r+s,    rznpt.NDArray[np.bool_] | None)rr(cCs |durt|}|tj}|Sr)rrr,Zuint8)rDrr$r$r% _fillna_prepms rr)rr(cs&tdddfdd }tt|S)z> Wrapper to handle datetime64 and timedelta64 dtypes. NrYrcsPt|jrB|durt|}|d||d\}}||j|fS|||dS)Nrryr)rr)rr)rDryrresultrr$r%new_func~s  z&_datetimelike_compat..new_func)NN)rrr)rrr$rr%_datetimelike_compatys rz(tuple[np.ndarray, npt.NDArray[np.bool_]])rDryrr(cCs"t||}tj|||d||fSNr)rr Z pad_inplacerDryrr$r$r%_pad_1ds rcCs"t||}tj|||d||fSr)rr Zbackfill_inplacerr$r$r% _backfill_1ds rrcCs*t||}|jr"tj|||dn||fSr)rsizer Zpad_2d_inplacerr$r$r%_pad_2ds rrcCs*t||}|jr"tj|||dn||fSr)rrr Zbackfill_2d_inplacerr$r$r% _backfill_2ds rr:r<r_rcCs&t|}|dkrt|Sttd|S)Nr_r)r@ _fill_methodsrr)r7rcr$r$r%rsrzReindexMethod | None)r(cCs|dur dSt|ddS)NT)r8)r@)r7r$r$r%clean_reindex_fill_methodsr)rfw_limitbw_limitcst|t}t}ddfdd }|durT|dkrJtt|d}n |||}|dur|dkrh|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 rrcs`t|}t||dd}tt|d|tt|d|ddkdB}|S)Nr_r)min_rolling_windowrrr,whereZcumsum)rryZwindowedidxNr$r%inners  "z_interp_limit..innerNrrbr_)r!rr,rlistr)rrrZf_idxZb_idxrZ b_idx_invr$rr%rs ! 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], ] Nrbr_)r/strides)r/rr,r Z stride_tricksZ as_strided)rrr/rr$r$r%rs $r)F)rANrfNN)rANrfNNFN)NFN)NrF)rr)rrN)r:rNN)N)NN)NN)NN)NN)r_)H__doc__ __future__r functoolsrrtypingrrrrnumpyr,Z pandas._libsr r r Zpandas._typingr r rrrZpandas.compat._optionalrZpandas.core.dtypes.castrZpandas.core.dtypes.commonrrrrrZpandas.core.dtypes.dtypesrZpandas.core.dtypes.missingrrrrrrr&r5r@rTrUrXrdrirnrprurrrrrrrrrrrrrrrrrrrrr$r$r$r%s     <&# D"qK65V6C    A