+ ܳj:a0t$Rt^RIHt^RIHt^RIHtHtH t H t H t ^RI t ^RIHt^RIHtHtHt^RIHtHtHtHtHt^RIHt^R IHt^R IHtH t H!t!H"t"H#t#^R I$H%t%H&t&H't'^R I(H)t)H*t*H+t+]'d!^R I,H-t-^RIH.t.^RI/H0t0] RS,t1R]2R&RRlt3RRlt4] RR/RRll4t5] RRl4t5RR/RRllt5.RTOt6.RUOt7RR lt8R!R"lt9R#R$lt:R%R&lt;R'R(ltR-R.lt?RWR/R0llt@RXR1R2lltARYR3R4lltBRZR5R6lltCR[R7R8lltDR\R:R;lltER]R<R=lltFR>R?ltG]GR^R@RAll4tH]GR^RBRCll4tI]GR^RDREll4tJ]GR^RFRGll4tKRHRIltLRJRKltMR9]HRL]I/tNR_RMRNlltORORPltPRQRRltQR#)`z$ Routines for filling missing data. ) annotations)wraps) TYPE_CHECKINGAnyLiteralcastoverloadN) is_nan_na)NaTalgoslib) ArrayLikeAxisIntF ReindexMethodnpt)import_optional_dependency)infer_dtype_from) is_array_like is_bool_dtypeis_numeric_dtypeis_object_dtypeneeds_i8_conversion) ArrowDtypeBaseMaskedDtypeDatetimeTZDtype)is_valid_na_for_dtypeisnana_value_for_dtype)Callable) TypeAlias)Indexr _CubicBCc V^8dQhRRRR/#)masknpt.NDArray[np.bool_]lengthint)formats"T/home/luca/screen-checking/.venv/lib/python3.14/site-packages/pandas/core/missing.py __annotate__r,>s  "7  c\V4'd3\V4V8wd\R\V4 RV 24hW,pV#)zB Validate the size of the values passed to ExtensionArray.fillna. z'Length of 'value' does not match. Got (z ) expected )rlen ValueError)valuer%r's&&&r+check_value_sizer2>sQU u: 9#e*F#H&   Lr-c V^8dQhRRRR/#)r$arrr returnr&r))r*s"r+r,r,MsLLiL+@Lr-c\V4wr!\VP\\34'EdA\ P !V4'Ed$\P!V4'Ed\4'gVPPR8Xd\VP\4'd9\P!VP4VP4(,pV#^RI HpVPVP 4P#R4P%4pV#VPPR9d)\P&!VP(\*R7pV#\V4'd \V4#\P&!VP(\*R7p\-VP4'd;\/VP4'g \ P0!V4'dV#\/VP4'd1\-V4'd \ P0!V4'gV#\-VP4'd\V\24'dV#\5VP4'd\V4(pW,V8HW5&V#W8Hp\V\P64'gVP%\*RR7pTpV#)a Return a masking array of same size/shape as arr with entries equaling value set to True. Parameters ---------- arr : ArrayLike value : scalar-like Caller has ensured `not is_list_like(value)` and that it can be held by `arr`. Returns ------- np.ndarray[bool] fNFiudtype)r:na_value)r isinstancer:rrr is_floatnpisnanr kind_datarpyarrow.computecomputeis_nan _pa_array fill_nullto_numpyzerosshapeboolrris_boolstrrndarray)r4r1r:r%pcarr_masknew_masks&& r+ mask_missingrQMs $E*LE 399 ;<< LL   HHUOO  99>>S #))_55xx *chhj[8 -yy/99%@IIK YY^^t #88CIIT2DK E{{Cy 88CIIT *D##cii(( KK   , K) cii  %5e%<%  ":9+VF8TUU Mr-c$V^8dQhRRRRRR/#)r$rTrLindexr!r5r))r*s"r+r,r,s!Er-c VPR4pVR9dVf \R4h\\,pW9d\RV RV R24hVR9d!VP'g\V R24hV#)orderz7You must specify the order of the spline or polynomial.zmethod must be one of z. Got 'z ' instead.z4 interpolation requires that the index be monotonic.)spline polynomial)kroghpiecewise_polynomialpchip)getr0 NP_METHODS SP_METHODSis_monotonic_increasing)rTrjkwargsrlvalids&&, r+clean_interp_methodrxs JJw E ))emRSS  #E 1%xzRSS ;;,,,(NO  Mr-c$V^8dQhRRRRRR/#)r$howrLis_validr&r5 int | Noner))r*s"r+r,r,s"$$#$)>$:$r-cVVR9gQh\V4^8XdR#VP^8XdVP^R7pVR8XdVR,P4pM8VR8Xd2\V4^, VRRR1,P4, pVX,pV'gR#V#)z 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 firstlastNaxisNNN)r~r)r/ndimanyargmax)rzr{idxpos chk_notnas&& r+find_valid_indexrs # ## # 8}}}<b B  r-c V^8dQhRRRR/#)r$ limit_area str | Noner5#Literal['inside', 'outside'] | Noner))r*s"r+r,r,%s  J 3V r-cfVe-RR.pVP4pW9d\RV RV R24hV#)Ninsideoutsidez%Invalid limit_area: expecting one of z, got .r)rvalid_limit_areass& r+validate_limit_arear%sS%y1%%'  .78I7J&,a!  r-c$V^8dQhRRRRRR/#)r$rz-Literal['backward', 'forward', 'both'] | NonerTrLr5z&Literal['backward', 'forward', 'both']r))r*s"r+r,r,3s$BLO+r-cVfVR9dRpV#RpV#VR9dVR8wd\RV R24hVR9dVR8wd\RV R24hV#)Nrrz0`limit_direction` must be 'forward' for method ``z1`limit_direction` must be 'backward' for method `)rcrb)rar`)r0)rrTs&&r+infer_limit_directionr3s * *(O (O  % %/Y*FB6(!L  * **/LCF81M  r-c V^8dQhRRRR/#)r$rjr!r5r))r*s"r+r,r,Hs  E e r-cVR8Xd^RIHpV!\V44pM0Rmp\VP4;'gD\ VP\ 4;'g"\P!VPR4p\\,pW9dW9dV'g\RV R24hM\RV R24h\V4P4'd \R4hV#) linear) RangeIndexmMz9Index column must be numeric or datetime type when using z_ method other than linear. Try setting a numeric or datetime index column before interpolating. Can not interpolate with method=rzkInterpolation with NaNs in the index has not been implemented. Try filling those NaNs before interpolating.>timerjvaluesrd)pandasrr/rr:r<rr is_np_dtypersrtr0rrNotImplementedError)rTrjrmethodsis_numeric_or_datetimerws&& r+get_interp_indexrHs %3u:&8 U[[ ) 2 2%++7 2 2u{{D1  Z' ?$-C #H%%%?xqIJ J E{! /  Lr-c<V^8dQhRRRRRRRRR R R RR R RRRR/ #)r$data np.ndarrayrjr!rrrTrLlimitr|rrr fill_value Any | Noner5Noner))r*s"r+r,r,ksb=*=* =* =* =*  =*  =*  =*=*=* =*r-c aaaaaa a a \SV3/S B\SVP4'd\VPRR7oSR8Xd)\ VP4'g \ R4hRo\ S4o\V4o \P!RSR7o\VS4o RVV V VV VVV3R llp \P!WV4R#) 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)compatrzStime-weighted interpolation only works on Series or DataFrames with a DatetimeIndexrN)nobsrc V^8dQhRRRR/#)r$yvaluesrr5rr))r*s"r+r,,interpolate_2d_inplace..__annotate__s  j T r-cB<\R RSRVRSRSRSRSRSRRR S/ SBR #) indicesrrTrrrr bounds_errorFr%Nr))_interpolate_1d) rrrrvrlimit_area_validatedrr%rTs &r+func$interpolate_2d_inplace..funcsm      , , "    r-) rxrr:rrr0rrr validate_limit_index_to_interp_indicesr>apply_along_axis) rrjrrTrrrrr%rvrrrs &&&fff&ffl @@r+interpolate_2d_inplacerks.00Z44' 5A  "5;;//   .?O.z:  d% 8E&uf5G  D)r-c$V^8dQhRRRRRR/#)r$rjr!rTrLr5rr))r*s"r+r,r,s!E3:r-cdVPp\VP4'dVPR4pVR8XdTp\ \ P V4pV#\ P!V4pVR9d6VP\ P8Xd\P!V4pV#)z= Convert Index to ndarray of indices to pass to NumPy/SciPy. i8r)rrj) _valuesrr:viewrr>rMasarrayobject_r maybe_convert_objects)rjrTxarrindss&& r+rrs ==D4::&&yy BJJ% K zz$ ( (zzRZZ'006 Kr-c@V^8dQhRRRRRRRRRRR R R R R RRRRR/ #)r$rrrrTrLrr|rrrrrrrJrlr5rr))r*s"r+r,r,sxm m m m  m   m  m 4 m m m  m  m r-c V eT p M \V4p V (p V P4'gR#V P4'dR#\P!V 4p \ RV R7pVf^p\P !V4p\ RV R7pVf \V4p\P !^V,\V 44pVR8Xd#\P!V\W^44pMJVR8Xd$\P!V\V ^V44pM \P!\WV44pVR8Xd0\P!VV4p\P!VV4pMOVR8XdI\P!WR R 7p\P!VVR R 7p\P!VV4pVPPR 9pV'dVPR 4pV\9dX\P !W ,4p\P"!W ,W ,V,W,V,4W&M*\%W ,W,W ,3R VRVRVRV/V BW&V e RV R&R V V&R#V'd\&P(VV&R#\P*VV&R#)z 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{rrrrrT assume_uniquerrrTrrrlFr)rrallr> flatnonzeroraranger/union1d _interp_limitunique setdiff1dr:r@rrsargsortinterp_interpolate_scipy_wrapperr r1nan)rrrTrrrrrrlr%rvinvalidrwall_nansfirst_valid_index start_nanslast_valid_indexend_nans preserve_nansmid_nansis_datetimelikeindexers&&&&&&&&&&, r+rrsk0 w- HE 99;; yy{{~~g&H(WuE ,-J'FUCw<yy--s5z:H)# :}WQ/OP J & 8]7Au-MN  -"FG X =*=  =(; y <<DI<<($G =(; mm((D0O,,t$ **W^,99  gnW5w~g7N 6 N N      "  &       Q"]  !$  "$  r-c ,V^8dQhRRRRRRRRRR/#) r$xrynew_xrTrLrrJr))r*s"r+r,r,1sAJJJJ J  J  Jr-c hV R2p\RVR7^RIHp \P!V4pRV P RV P R\R\R \R \R V P/p .ROp W;9d+VR 8XdTp MTp V PWWVR 7p V !V4pV#VR8XdF\V4'gV^8:d\RV 24hV P!W3RV/VBp V !V4pV#VPP 'gVP#4pVPP 'gVP#4pVPP 'gVP#4pV P%VR4p V f\RV R24hVP'RR4V !WV3/VBpV#)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)extra interpolate barycentricrofrom_derivativesrp cubicsplineakimarqrn)r@rrrmz;order needs to be specified and greater than 0; got order: kNrrdowncast)rdzeroslinear quadraticcubicrn)rrrr>rbarycentric_interpolatekrogh_interpolate_from_derivatives_cubicspline_interpolate_akima_interpolatepchip_interpolateinterp1drr0UnivariateSplineflags writeablecopyrrpop)rrrrTrrrlrvrr alt_methodsinterp1d_methodsr@terpnew_ys&&&&&&&, r+rr1sh4 5Ewe4! JJu E {::..- 1/#..9K! \ !DD## t$ U 2 L1 8  ;;5A:MeWU ++ADEDVDU " Lww   Aww   A{{$$$JJLEvt, <?xqIJ J  :t$Q5+F+ Lr-c ,V^8dQhRRRRRRRRRR/#) r$xiryirderzint | list[int] | None extrapolaterJr))r*s"r+r,r,~s:///// /  /r-c~^RIHpVPPpV!WP R^4W5R7pV!V4#)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)ordersrr)rrBPolyrreshape) rrrrlrrrrTms &&&&&& r+rr~s;R"   / /Fr::b!$ULA Q4Kr-c ,V^8dQhRRRRRRRRRR/#) r$rrrrrr(rrr))r*s"r+r,r,s:,,,,, ,  ,r-cF^RIHpVPWVR7pV!W#R7#)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. 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)nu)rrAkima1DInterpolator)rrrrrrPs&&&&& r+rrs'P"''T':A Q<r-c4V^8dQhRRRRRRRRRRR R R R/#) r$rrrrrrbc_typez_CubicBC | tuple[Any, Any]rz!Literal['periodic'] | bool | Noner5r))r*s"r+r,r,sWSSSSS  S ( S 3 SSr-cF^RIHpVPWW4VR7pV!V4#)ak 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. r)rrr)rr CubicSpline)rrrrrrrrs&&&&&& r+rrs/Z" T   A Q4Kr-rac 0V^8dQhRRRRRRRRR R R R /#) r$rrrTrUrrrr|rrr5rr))r*s"r+r,r,5sD)6)6 )6 &)6 )6  )6 4 )6  )6r-cV^8XdRMRpVP^8Xd2V^8wd \R4hVP^.VPO54p\ V4pV!V4p\ V^R7pV!WcVR7R#)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. cV#rWr)rs&r+)pad_or_backfill_inplace..Qsr-cVP#rW)Tr#s&r+r$r%Qsr-z1cannot interpolate on an ndim == 1 with axis != 0)r)rrN)rAssertionErrorrrIrY get_fill_func)rrTrrrtransftvaluesrs&&&&& r+pad_or_backfill_inplacer,5su8#aikmF{{a 19 !TU U 2V\\ 23 v &FVnG a (D*5r-c V^8dQhRRRR/#)r$r%npt.NDArray[np.bool_] | Noner5r&r))r*s"r+r,r,as.r-c$Vf \V4pV#rW)r)rr%s&&r+ _fillna_prepr0as  |F| Kr-c V^8dQhRRRR/#)r$rrr5r))r*s"r+r,r,lsqQr-cVa\S4RRV3Rlll4p\\V4#)z6 Wrapper to handle datetime64 and timedelta64 dtypes. c V^8dQhRRRR/#)r$rr|rrr))r*s"r+r,*_datetimelike_compat..__annotate__rs"KKK8Kr-c<\VP4'dIVf \V4pS!VPR4WVR7wrCVPVP4V3#S!WW#R7#)Nr)rrr%)rr:rr)rrrr%resultrs&&&& r+new_func&_datetimelike_compat..new_funcqse v|| , ,|F| D!DLF;;v||,d2 2FJJJr-NNN)rrr)rr7sf r+_datetimelike_compatr:ls2  4[KKK$ 8 r-c ,V^8dQhRRRRRRRRR R /# r$rrrr|rrr%r.r5z(tuple[np.ndarray, npt.NDArray[np.bool_]]r))r*s"r+r,r,:      4  '  . r-c\W4pVe"VP4'g \W24\P!WVR7W3#N)r)r0r_fill_limit_area_1dr pad_inplacerrrr%s&&&&r+_pad_1drCs>  %DdhhjjD- f%0 <r-c ,V^8dQhRRRRRRRRR R /#r<r))r*s"r+r,r,r=r-c\W4pVe"VP4'g \W24\P!WVR7W3#r?)r0rr@r backfill_inplacerBs&&&&r+ _backfill_1drGs>  %DdhhjjD- 6u5 <r-c ,V^8dQhRRRRRRRRR R /#r<r))r*s"r+r,r,s:      4  '  . r-c\W4pVe \W24VP'd\P!WVR7W3#r?)r0_fill_limit_area_2dsizer pad_2d_inplacerBs&&&&r+_pad_2drMs=  %DD- {{{ V7 <r-c$V^8dQhRRRRRR/#)r$rr|rrr%r.r))r*s"r+r,r,s( 4 ' r-c\W4pVe \W24VP'd\P!WVR7W3#W3#r?)r0rJrKr backfill_2d_inplacerBs&&&&r+ _backfill_2drQsL  %DD- {{{ !!&e< < <r-c$V^8dQhRRRRRR/#r$r%r&rzLiteral['outside', 'inside']r5rr))r*s"r+r,r,s$'' '-I' 'r-cV(pVP4p\V4VRRR1,P4, ^, pVR8XdRVRV%RW^,R%R#VR8XdRW^,V%R#R#)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. NrFrr)rr/)r%rneg_maskr~rs&& r+r@r@sw uH OO E x=8DbD>002 2Q 6DXVe  AXZ y !&QY !r-c$V^8dQhRRRRRR/#rSr))r*s"r+r,r,s$ -I r-cVP(pVR8Xd]\PPV^R7\PPVRRR1,^R7RRR1,,pM]\PPV^R7(\PPVRRR1,^R7RRR1,(,pRWP&R#)agPrepare 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. rrNFr)r'r>maximum accumulate)r%rrUla_masks&& r+rJrJswHY JJ ! !( ! 3jj##HTrTN#;DbDA B ZZ " "8! " 4 4zz$$Xdd^!$>>r-cj\V4pV^8Xd\V,#R\R\/V,#)rarc)rY _fill_methodsrMrQ)rTrs&&r+r)r)s2 v &F qyV$$ 7J 5f ==r-cV^8dQhRR/#)r$r5zReindexMethod | Noner))r*s"r+r,r, s99)=9r-c(VfR#\VRR7#)NT)rR)rY)rTs&r+clean_reindex_fill_methodra s ~ V4 88r-c(V^8dQhRRRRRRRR/#)r$rr&fw_limitr|bw_limitr5rr))r*s"r+r,r,s8@E@E "@E.8@EDN@E@Er-ca\V4o\P!.\PR7p\P!.\PR7pRpRV3RllpVe0V^8Xd!\P!V4^,pRpMV!W4pVe4V^8XdV#S^, V!VRRR1,V4, pV^8XdV#\P !W4VR7#) a 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 r9TcV^8dQhRR/#)r$rr(r))r*s"r+r,#_interp_limit..__annotate__5scr-c <\VS^, 4p\PPP W^,4P ^4p\P !\P!V4^,V,\P!VRV^,(P4^8H4^,4pV#)r]N) minr>r stride_trickssliding_window_viewrrwherecumsum)rrwindowedidxNs&& r+inner_interp_limit..inner5sE1q5!66'';;GQYOSSTUVjj HHX q !E ) HHw{++335: ;A >  r-NFrr)r/r>arrayint64rl intersect1d)rrcrdf_idxb_idxrrqrps&&& @r+rrsB G A HHRrxx (E HHRrxx (EM q=HHW%a(E!M',E q=LEE'$B$-::E1} >>%m DDr-) not-a-knotclampednaturalperiodic)rrrjr)rdrrrrrrormrnrrprqrr)rNrNNN)rNrNNFNN)NFN)NF)r|r|)r|rxN)rar|NNrWr9)r])R__conditional_annotations____doc__ __future__r functoolsrtypingrrrrrnumpyr>pandas._configr pandas._libsr r r pandas._typingr rrrrpandas.compat._optionalrpandas.core.dtypes.castrpandas.core.dtypes.commonrrrrrpandas.core.dtypes.dtypesrrrpandas.core.dtypes.missingrrrcollections.abcrr rr!r"__annotations__r2rQrYrsrtrxrrrrrrrrrrrrr,r0r:rCrGrMrQr@rJr^r)rar)r}s@r+rs#$ ?4  ( !"PQHiQ L^ %%(% % 0 0 43  $&$N  * F=*@,m `JZ/d,^Sl)6X6      $'4>\: >9 @Er-