phLBS/rSSKrSSKJrJrJr SSKJr \(aSSK r Sr Sr Sr "SS \ 5r\"\5rS rS \lS rS rg)bsN) have_pandas no_picklingassert_no_pickling)stateful_transformc^SSKJn [R"[R "U[ S95nURS:XdeUR5 [U5n[R"U5nURS:XaURSS:Xa USS2S4nURS:Xde[R"U5[R"U5:d.[R"U5[R"U5:a [S5e[U5US-- n[R"URSU4[ S9n[!U5H/n[R""U45nSXv'U"XXr45USS2U4'M1 U$![a [S5ef=f)Nr)splevz#spline functionality requires scipy)dtypezksome data points fall outside the outermost knots, and I'm not sure how to handle them. (Patches accepted!))scipy.interpolater ImportErrornp atleast_1dasarrayfloatndimsortintshapeminmaxNotImplementedErrorlenemptyrangezeros)xknotsdegreer n_basesbasisicoefss eC:\Users\julio\OneDrive\Documentos\Trabajo\IdeasFrscas\Cabanna\env\Lib\site-packages\patsy/splines.py_eval_bspline_basisr&scA+ MM"**U%8 9E ::?? JJL [F aAvv{qwwqzQ adG 66Q;;  vvay266%= BFF1Iu $=! 2  "%jFQJ'G HHaggaj'*% 8E 7^'$Au56ad  LS A?@@As FF,c [R"U5n[R"URSS9Vs/sHn[R"USU-5PM sn5nUR UR SS9$s snf)NC)orderd)rrravel percentilereshaper)rprobsprob quantiless r%_R_compat_quantiler1Bsn JJu E 27++C+2HI2H$q#* %2HII   U[[  44 Js$A=cSnU"SS/SS5 U"SS/SS5 U"SS/SS/SS /5 U"[[S55SS/S S /5 g) NcR[R"[X5U5(degN)rallcloser1)rr/expecteds r%t"test__R_compat_quantile..tLs{{-a6AAAA g?g333333? gffffff?g@g333333@)listr)r7s r%test__R_compat_quantiler@Ks^Br2hRr2hRr2hc RH%d59oSzC:.r9cT\rSrSrSrSrS SjrSrS Sjr\ r Sr g) BSUa bs(x, df=None, knots=None, degree=3, include_intercept=False, lower_bound=None, upper_bound=None) Generates a B-spline basis for ``x``, allowing non-linear fits. The usual usage is something like:: y ~ 1 + bs(x, 4) to fit ``y`` as a smooth function of ``x``, with 4 degrees of freedom given to the smooth. :arg df: The number of degrees of freedom to use for this spline. The return value will have this many columns. You must specify at least one of ``df`` and ``knots``. :arg knots: The interior knots to use for the spline. If unspecified, then equally spaced quantiles of the input data are used. You must specify at least one of ``df`` and ``knots``. :arg degree: The degree of the spline to use. :arg include_intercept: If ``True``, then the resulting spline basis will span the intercept term (i.e., the constant function). If ``False`` (the default) then this will not be the case, which is useful for avoiding overspecification in models that include multiple spline terms and/or an intercept term. :arg lower_bound: The lower exterior knot location. :arg upper_bound: The upper exterior knot location. A spline with ``degree=0`` is piecewise constant with breakpoints at each knot, and the default knot positions are quantiles of the input. So if you find yourself in the situation of wanting to quantize a continuous variable into ``num_bins`` equal-sized bins with a constant effect across each bin, you can use ``bs(x, num_bins - 1, degree=0)``. (The ``- 1`` is because one degree of freedom will be taken by the intercept; alternatively, you could leave the intercept term out of your model and use ``bs(x, num_bins, degree=0, include_intercept=True)``. A spline with ``degree=1`` is piecewise linear with breakpoints at each knot. The default is ``degree=3``, which gives a cubic b-spline. This is a stateful transform (for details see :ref:`stateful-transforms`). If ``knots``, ``lower_bound``, or ``upper_bound`` are not specified, they will be calculated from the data and then the chosen values will be remembered and re-used for prediction from the fitted model. Using this function requires scipy be installed. .. note:: This function is very similar to the R function of the same name. In cases where both return output at all (e.g., R's ``bs`` will raise an error if ``degree=0``, while patsy's will not), they should produce identical output given identical input and parameter settings. .. warning:: I'm not sure on what the proper handling of points outside the lower/upper bounds is, so for now attempting to evaluate a spline basis at such points produces an error. Patches gratefully accepted. .. versionadded:: 0.2.0 c.0UlSUlSUlgr4)_tmp_degree _all_knots)selfs r%__init__ BS.__init__s  r9NcBUUUUUUS.nXRS'[R"U5nURS:XaURSS:Xa USS2S4nURS:a [ S5eURR S/5RU5 g)N)dfrr include_intercept lower_bound upper_boundargsr r rz1input to 'bs' must be 1-d, or a 2-d column vectorxs)rErrrr ValueError setdefaultappend) rHrrLrr rMrNrOrPs r%memorize_chunkBS.memorize_chunks!2&&  ! & MM!  66Q;1771:?!Q$A 66A:ST T T2&--a0r9c ,URnUSnU?USS:a[SUS<S35e[US5US:wa[SUR<S35e[R "US5nUScUS c [S 5eUSS -nUSbUSU- nUS (dUS - nUS:a*[S US<SUS<SUS <SUSU- <35eUS bA[ US 5U:wa.[S US<SUS<SU<S[ US 5<S3 5eO)[R"SS US-5S Sn[X65nUS bUS nUSbUSnO[R"U5nUSbUSn O[R"U5n X:a[SU<SU <S35e[R"W5nURS :a [S5e[R"Xx:5(a[SXwU:<SU<S35e[R"Xy:5(a[SXwU :<SU <S35e[R "X/U-U45n U R5 USUlXlg)NrPr rz#degree must be greater than 0 (not )zdegree must be an integer (not rQrLrzmust specify either df or knotsr rMzdf=z is too small for degree=z and include_intercept=z ; must be >= z with degree=z implies z knots, but z knots were providedr rNrOzlower_bound > upper_bound (z > zknots must be 1 dimensionalzsome knot values (z) fall below lower bound (z) fall above upper bound ()rErRrrFr concatenaterlinspacer1rrrranyrrG) rHtmprPrr) n_inner_knotsknot_quantiles inner_knotsrNrO all_knotss r%memorize_finishBS.memorize_finishsii6{ I >A <@NL  tH~ $x. 0T\\ST T NN3t9 % : $w-"7>? ?X" : ! J.M+," q  T X01T ]2   G}(tG}%6$!J N)W .   7"$Q=13D!Ea!K0C = $w-K   *}-K&&)K   *}-K&&)K  $9DkR jj-   a :; ; 66++ , ,%K&?@+O  66++ , ,%K&?@+O NN[$>$F #TU H~ #r9c,[XRUR5nU(d USS2SS24n[(aV[ U[ R [ R45(a'[ R"U5nURUlU$)Nr ) r&rGrFr isinstancepandasSeries DataFrameindex) rHrrLrr rMrNrOr"s r% transform BS.transformsk$A E !QR%LE ;!fmmV-=-=>??((/gg  r9)rGrFrE)NNFNN) __name__ __module__ __qualname____firstlineno____doc__rIrUrbrjr __getstate____static_attributes__r9r%rBrBUsM9v 1:R$n &Lr9rBcnSSKJn SSKJnJnJn UR S5nSnURS5nXFS:XdOUS- nURSU5nXFUn0n UHn U R SS5upXU 'M [U S 5[U S 5[U S 5S .n U S S:wa[U S 5upXS'XS'U SS:HU S'[R"[U S55nU S bURSU S :XdeU"[SUU40U D6 US- nUS-nMXS:Xdeg)Nr)check_stateful) R_bs_test_xR_bs_test_dataR_bs_num_tests z--BEGIN TEST CASE--r z--END TEST CASE--=r rLr)r rLrzBoundary.knotsNonerNrO interceptTRUErMoutputF)patsy.test_statervpatsy.test_splines_bs_datarwrxrysplitrirevalrrrrB)rvrwrxrylines tests_ran start_idxstop_idxblock test_datalinekeyvaluekwargslowerupperrs r%test_bs_compatr s{/VV   &EI 12I #88 Q ;;2I>) DC+JC"cN )H-.y')G,-   % && 0 *: ;>&A, 3 33 3"J%&JQ1q5!" >>&A, 3 33 3"JJAv >>&A, 3 33 3 >> 9QqcTB Q!Q!Q     AaVAFJqAqEJM >>*QU+] ; ;; ;r9c `SSKn[R"SSS5nUR[[ USSS9 UR[[ USSS9 UR[ [ U5 [ USSS/S -S 9 [ USS S/S -S 9 [ USSS/S -SS9 [ USS S/S-SS9 UR[ [ USSS/S-S 9 UR[ [ USS S/S -S 9 UR[ [ USSS/S-SS9 UR[ [ USS S/S -SS9 UR[ [ USSS/S -S 9 UR[ [ USS S/S-S 9 UR[ [ USSS/S-SS9 UR[ [ USS S/S -SS9 UR[ [ USSS9 UR[ [ USSS9 UR[ [ USSS9 UR[ [ USSS9 UR[ [ USSSS9 UR[ [ [R"X45S5 [R"[ USS/S9[ USS/S95(deUR[ [ US/S//S9 UR[ [ USS/S9 UR[ [ USS/SS9 UR[ [ USS/S9 UR[ [ USS/SS9 g)Nrir:r;rl)rN)rOF)rLrMrT r )rLrMrr )rLr rYg?)rNrOr)r)rrOi)rrN) pytestrr[raisesrrrR column_stackr)rrs r%test_bs_errorsrms CR A MM%r1aQM? MM%r1aQM? MM*b!$qR5a8qR4sQw7qR5aBqR4sQwqA MM*b!eA3QR7MS MM*b!d1#PQ'MR MMBbE!rRS MMBbDaPQ MM*b!eA3QR7MS MM*b!d1#PQ'MR MMBbE!qQR MMBbDaPQ MM*b!!M4 MM*b!!M4 MM*b!2M6 MM*b!3M7 MM*b!QA2MF MM*b"//1&"91= >>"Qq!f-r!Aq6/B C CC C MM*b!QC";M7 MM*b!Ar7M3 MM*b!Aq6qMA MM*b!C8M4 MM*b!B7MCr9)__all__numpyr patsy.utilrrr patsy.staterrfr&r1r@objectrBrrslowrrrtr9r%rsf &CC*,^5/EEP*'Z <65Dr9