a 鴫jƉ @sdZddlmZddlmZddlmZmZmZm Z m Z ddl Z ddl mZmZmZddlmZmZmZmZmZddlmZdd lmZdd lmZmZmZmZm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&m'Z'erdd l(m)Z)dddddZ*dddddZ+e ddddddddZ,e dd d!dd"dZ,d#ddd$d!dd%dZ,gd&Z-gd'Z.d(d)d(d*d+d,Z/d(dd-d.d/d0Z0d(d1d2d3d4Z1d5d6d7d8d9Z2d:d(d;d<d=d>Z3d)d)d?d@dAZ4ddDd)dEd(d-d(d5dFdGdH dIdJZ5d)d(dDdKdLdMZ6ddDdDd(d-d(d6dFd$d-dGdN dOdPZ7ddDdDdDd(d$dQdRdSZ8ddDdDdDdTd$dUdVdWZ9ddDdDdDdTdEdXdYdZZ:ddDdDdDdEd\d]d^d_Z;dDdd-d`dGdadbdcZdldldmdndoZ?e?ddDd-d6dhdpdqdrdsZ@e?ddDd-d6dhdpdqdtduZAe?ddDd-d6dhdvdwdxZBe?dd-d6dhdydzd{ZCdd|dGd}d~dZDdd|dGd}ddZEe@eAdZFdddddZGddddZHdd-d-dddZIddddddZJdS)z$ 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_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)valuer r!r%R/var/www/html/laksyashrimp/venv/lib/python3.9/site-packages/pandas/core/missing.pycheck_value_size3s  r'r )arrreturnc CsTt|\}}t|tjr(tj||d}n(|}t|s@|g}|j||dd}d}t |jrld}t |}t |}||}tj |j t d}t|jrt|jst|jrnt|jrt|jrt|jsnl|D]f} t|| rq|r tj |j tjd} ||| k| |<n$|| k} t| tjs0| jt dd} || O}q|rP|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_sequencerrZzerosshapeboolrrrZbool_ndarrayZto_numpyany) r(Zvalues_to_maskr*clsZ potential_naZarr_maskZna_maskZnonnar xZnew_maskr%r%r& mask_missingBsR          r5. allow_nearestz,Literal['ffill', 'pad', 'bfill', 'backfill']zLiteral[False]zLiteral['pad', 'backfill'])methodr7r)cCsdSNr%r8r7r%r%r&clean_fill_methodsr;z7Literal['ffill', 'pad', 'bfill', 'backfill', 'nearest']z Literal[True]z%Literal['pad', 'backfill', 'nearest']cCsdSr9r%r:r%r%r&r;sFr0cCsjt|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,strlowerappendr#)r8r7Z valid_methodsZ expectingr%r%r&r;s  )lineartimeindexvalues)r@zeroslinear quadraticcubic barycentrickroghspline polynomialfrom_derivativespiecewise_polynomialpchipakima cubicsplinerAr)r8rFr)cKsh|d}|dvr"|dur"tdtt}||vrHtd|d|d|dvrd|jsdt|d|S) Norder)rNrOz7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.)rMrQrRz4 interpolation requires that the index be monotonic.)getr# NP_METHODS SP_METHODSZis_monotonic_increasing)r8rFkwargsrUvalidr%r%r&clean_interp_methods r[ 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"ndimr2argmax)r]r^ZidxposZ chk_notnar%r%r&find_valid_indexs    rhz&Literal['forward', 'backward', 'both'])limit_directionr)cCs2gd}|}||vr.td|d|d|S)N)forwardbackwardZbothz*Invalid limit_direction: expecting one of z, got 'z'.rBr#)riZvalid_limit_directionsr%r%r&validate_limit_directionsrmz str | None#Literal['inside', 'outside'] | None) limit_arear)cCs:|dur6ddg}|}||vr6td|d|d|S)Ninsideoutsidez%Invalid limit_area: expecting one of z, got .rl)roZvalid_limit_areasr%r%r&validate_limit_areasrsz-Literal['backward', 'forward', 'both'] | Nonez&Literal['backward', 'forward', 'both'])rir8r)cCs`|dur|dvrd}q\d}n@|dvr<|dkrrkrj)r=r<z0`limit_direction` must be 'forward' for method ``z1`limit_direction` must be 'backward' for method `)r#)rir8r%r%r&infer_limit_direction#s  ru)rFr)cCs|dkr(ddlm}|tt|}nHhd}t|jpRt|jtpRt |jd}||vrp|spt d|dt | rtd|S) NrDrr>rEr@rGrFmMz9Index 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)r8rFrmethodsZis_numeric_or_datetimer%r%r&get_interp_index8s(      rzrDrj np.ndarrayr z Any | NoneNone) datarFrdr8limitriro 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)compatrEzStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexrGN)Znobsr~r{r|)yvaluesr)c s&tf|dd dS)NF) indicesrr8r~riror bounds_errorr )_interpolate_1d)rrrrYr~Zlimit_area_validatedrir r8r%r&funcs z$interpolate_2d_inplace..func) r[rr*rrr#rmrsr Zvalidate_limit_index_to_interp_indicesr-Zapply_along_axis) r}rFrdr8r~rirorr rYrr%rr&interpolate_2d_inplaceWs   "r)rFr8r)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. i8rD)rGrF) Z_valuesrr*viewrr-r1asarrayZobject_r Zmaybe_convert_objects)rFr8ZxarrZindsr%r%r&rs     r) rrr8r~rirorrrUr)c Ks| dur| } nt|} | } | s(dS| r4dStt| } td| d}|durZd}tt|}td| d}|durt|}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 }|tvr|t|| }t|| || ||| ||| <n.t|| || || f||||d | || <| durd| dd<d| |<n|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_r]r^rr`rbrjrkrprqrvr)r8rrrUFT)rr2allsetr-Z flatnonzerorhranger" _interp_limitsortedr*kindrrWZargsortZinterp_interpolate_scipy_wrapperr r$nan)rrr8r~rirorrrUr rYinvalidrZZall_nansZfirst_valid_indexZ start_nansZlast_valid_indexZend_nansZ preserve_nansZmid_nansZis_datetimelikeZindexerr%r%r&rsn               r)r4ynew_xr8rcKs|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)rLrMrPrQrTrSrR)r@rHrIrJrKrOrO)rrrrNz;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+)r4rrr8rrrUrYrrZ alt_methodsZinterp1d_methodsrZterpZnew_yr%r%r&r%sJ        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. rrrerb)Zordersr)rrZBPolyrPreshape) rrr4rUrrrr8mr%r%r&rls) r)rrr4rrdcCs(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, rrrc)nu)rrZAkima1DInterpolator)rrr4rrdrPr%r%r&rs* r not-a-knotzstr | tuple[Any, Any])rrr4rdbc_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)rdrr)rrZ CubicSpline)rrr4rdrrrrr%r%r&rs M  rzLiteral['inside', 'outside'])rGr8r~ror)cCst|}|}|std|d}|dur.d}td|d}|durJt|}t||||d|dkrtd|||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`)r8r~rorpFrbrqz*limit_area should be 'inside' or 'outside')rrrhr"pad_or_backfill_inplacer#r-r)rGr8r~rorr^r_r`r%r%r&_interpolate_with_limit_area%s*  rr=)rGr8rdr~ror)cCst|dkrddndd}|jdkrF|dkr2td|td|j}t|}||}t|dd }||||d d S) 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. rcSs|Sr9r%r4r%r%r&vz)pad_or_backfill_inplace..cSs|jSr9)Trr%r%r&rvrrbz0cannot interpolate on a ndim == 1 with axis != 0)rbrarfr~roN)rfAssertionErrorrtupler/r; get_fill_func)rGr8rdr~roZtransfZtvaluesrr%r%r&rZs  rznpt.NDArray[np.bool_] | None)r r)cCs|durt|}|Sr9)r)rGr r%r%r& _fillna_prepsrr)rr)cs(tddddfdd }tt|S)z> Wrapper to handle datetime64 and timedelta64 dtypes. Nr\rnrcsTt|jrD|durt|}|d|||d\}}||j|fS||||dS)Nrr~ror )rr*rr)rGr~ror resultrr%r&new_funcs  z&_datetimelike_compat..new_func)NNN)rrr)rrr%rr&_datetimelike_compats rz(tuple[np.ndarray, npt.NDArray[np.bool_]])rGr~ror r)cCs<t||}|dur$|s$t||tj|||d||fSNr~)rr_fill_limit_area_1dr Z pad_inplacerGr~ror r%r%r&_pad_1ds   rcCs<t||}|dur$|s$t||tj|||d||fSr)rrrr Zbackfill_inplacerr%r%r& _backfill_1ds   rrcCs<t||}|durt|||jr4tj|||dn||fSr)r_fill_limit_area_2dsizer Zpad_2d_inplacerr%r%r&_pad_2ds   rrcCs<t||}|durt|||jr4tj|||dn||fSr)rrrr Zbackfill_2d_inplacerr%r%r& _backfill_2ds   rzLiteral['outside', 'inside'])r ror)cCsn|}|}t||dddd}|dkrRd|d|<d||dd<n|dkrjd||d|<dS)aPrepare 1d mask for ffill/bfill with limit_area. Caller is responsible for checking at least one value of mask is False. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. NrerbrpFrq)rgr")r roneg_maskr_r`r%r%r&rs rcCs|j}|dkrFtjj|ddtjj|dddddddd@}n8tjj|ddtjj|ddddddddB}d||j<dS)aPrepare 2d mask for ffill/bfill with limit_area. When called, mask will no longer faithfully represent when the corresponding are NA or not. Parameters ---------- mask : np.ndarray[bool, ndim=1] Mask representing NA values when filling. limit_area : { "outside", "inside" } Whether to limit filling to outside or inside the outer most non-NA value. rqrrcNreF)rr-maximum accumulate)r rorZla_maskr%r%r&rs"$rr=r?rbrcCs&t|}|dkrt|Sttd|S)Nrbr)r; _fill_methodsrr)r8rfr%r%r&r*srzReindexMethod | None)r)cCs|dur dSt|ddS)NTr6)r;)r8r%r%r&clean_reindex_fill_method1sr)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)Nrbr)min_rolling_windowrrr-whereZcumsum)rr~ZwindowedidxNr%r&inner\s  "z_interp_limit..innerNrrerb)r"rr-rlistr)rrrZf_idxZb_idxrZ b_idx_invr%rr&r7s ! 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], ] Nrerb)r/strides)r/rr-r Z stride_tricksZ as_strided)rrr/rr%r%r&rxs $r)rDNrjNNN)rDNrjNNFNN)NFN)NrF)rr)rrN)r=rNN)N)NNN)NNN)NNN)NNN)rb)K__doc__ __future__r functoolsrtypingrrrrrnumpyr-Z pandas._libsr r r Zpandas._typingr r rrrZpandas.compat._optionalrZpandas.core.dtypes.castrZpandas.core.dtypes.commonrrrrrrZpandas.core.dtypes.dtypesrZpandas.core.dtypes.missingrrrrwrr'r5r;rWrXr[rhrmrsrurzrrrrrrrrrrrrrrrrrrrrrrr%r%r%r&s       I &# F"wK65V7-    A