ó 2ÄČ[c"@`sždZddlmZmZmZddlZddlZddlZddl j Z ddl m Z ddlmZddlmZdd d d d d ddddddddddddddddddd d!d"d#d$d%d&d'd(d)g"ZejZd*„Zd+„Zd,„Zd-„Zd.„Zd/„Zd0„Zd1„Zejd2dgƒZejdgƒZ ejdgƒZ!ejddgƒZ"d3„Z#d4„Z$d5„Z%d6„Z&d7„Z'd8„Z(d9„Z)d:d;„Z*dddd<„Z+dgdddd=„Z,e-d>„Z.d?„Z/d@„Z0dA„Z1dB„Z2dC„Z3dD„Z4dE„Z5de7ddF„Z8dG„Z9dH„Z:dOdI„Z;dJ„Z<dK„Z=dL„Z>dM„Z?defdN„ƒYZ@dS(PsÚ Objects for dealing with Chebyshev series. This module provides a number of objects (mostly functions) useful for dealing with Chebyshev series, including a `Chebyshev` class that encapsulates the usual arithmetic operations. (General information on how this module represents and works with such polynomials is in the docstring for its "parent" sub-package, `numpy.polynomial`). Constants --------- - `chebdomain` -- Chebyshev series default domain, [-1,1]. - `chebzero` -- (Coefficients of the) Chebyshev series that evaluates identically to 0. - `chebone` -- (Coefficients of the) Chebyshev series that evaluates identically to 1. - `chebx` -- (Coefficients of the) Chebyshev series for the identity map, ``f(x) = x``. Arithmetic ---------- - `chebadd` -- add two Chebyshev series. - `chebsub` -- subtract one Chebyshev series from another. - `chebmul` -- multiply two Chebyshev series. - `chebdiv` -- divide one Chebyshev series by another. - `chebpow` -- raise a Chebyshev series to an positive integer power - `chebval` -- evaluate a Chebyshev series at given points. - `chebval2d` -- evaluate a 2D Chebyshev series at given points. - `chebval3d` -- evaluate a 3D Chebyshev series at given points. - `chebgrid2d` -- evaluate a 2D Chebyshev series on a Cartesian product. - `chebgrid3d` -- evaluate a 3D Chebyshev series on a Cartesian product. Calculus -------- - `chebder` -- differentiate a Chebyshev series. - `chebint` -- integrate a Chebyshev series. Misc Functions -------------- - `chebfromroots` -- create a Chebyshev series with specified roots. - `chebroots` -- find the roots of a Chebyshev series. - `chebvander` -- Vandermonde-like matrix for Chebyshev polynomials. - `chebvander2d` -- Vandermonde-like matrix for 2D power series. - `chebvander3d` -- Vandermonde-like matrix for 3D power series. - `chebgauss` -- Gauss-Chebyshev quadrature, points and weights. - `chebweight` -- Chebyshev weight function. - `chebcompanion` -- symmetrized companion matrix in Chebyshev form. - `chebfit` -- least-squares fit returning a Chebyshev series. - `chebpts1` -- Chebyshev points of the first kind. - `chebpts2` -- Chebyshev points of the second kind. - `chebtrim` -- trim leading coefficients from a Chebyshev series. - `chebline` -- Chebyshev series representing given straight line. - `cheb2poly` -- convert a Chebyshev series to a polynomial. - `poly2cheb` -- convert a polynomial to a Chebyshev series. - `chebinterpolate` -- interpolate a function at the Chebyshev points. Classes ------- - `Chebyshev` -- A Chebyshev series class. See also -------- `numpy.polynomial` Notes ----- The implementations of multiplication, division, integration, and differentiation use the algebraic identities [1]_: .. math :: T_n(x) = \frac{z^n + z^{-n}}{2} \\ z\frac{dx}{dz} = \frac{z - z^{-1}}{2}. where .. math :: x = \frac{z + z^{-1}}{2}. These identities allow a Chebyshev series to be expressed as a finite, symmetric Laurent series. In this module, this sort of Laurent series is referred to as a "z-series." References ---------- .. [1] A. T. Benjamin, et al., "Combinatorial Trigonometry with Chebyshev Polynomials," *Journal of Statistical Planning and Inference 14*, 2008 (preprint: http://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4) i(tdivisiontabsolute_importtprint_functionN(tnormalize_axis_indexi(t polyutils(t ABCPolyBasetchebzerotchebonetchebxt chebdomaintcheblinetchebaddtchebsubtchebmulxtchebmultchebdivtchebpowtchebvaltchebdertchebintt cheb2polyt poly2chebt chebfromrootst chebvandertchebfittchebtrimt chebrootstchebpts1tchebpts2t Chebyshevt chebval2dt chebval3dt chebgrid2dt chebgrid3dt chebvander2dt chebvander3dt chebcompaniont chebgausst chebweighttchebinterpolatecC`sP|j}tjd|dd|jƒ}|d||d)||ddd…S(sóCovert Chebyshev series to z-series. Covert a Chebyshev series to the equivalent z-series. The result is never an empty array. The dtype of the return is the same as that of the input. No checks are run on the arguments as this routine is for internal use. Parameters ---------- c : 1-D ndarray Chebyshev coefficients, ordered from low to high Returns ------- zs : 1-D ndarray Odd length symmetric z-series, ordered from low to high. iitdtypeNi˙˙˙˙(tsizetnptzerosR((tctntzs((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt_cseries_to_zseriests  cC`s<|jdd}||djƒ}|d|c!d9+|S(s÷Covert z-series to a Chebyshev series. Covert a z series to the equivalent Chebyshev series. The result is never an empty array. The dtype of the return is the same as that of the input. No checks are run on the arguments as this routine is for internal use. Parameters ---------- zs : 1-D ndarray Odd length symmetric z-series, ordered from low to high. Returns ------- c : 1-D ndarray Chebyshev coefficients, ordered from low to high. ii(R)tcopy(R.R-R,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt_zseries_to_cseriesscC`stj||ƒS(sĹMultiply two z-series. Multiply two z-series to produce a z-series. Parameters ---------- z1, z2 : 1-D ndarray The arrays must be 1-D but this is not checked. Returns ------- product : 1-D ndarray The product z-series. Notes ----- This is simply convolution. If symmetric/anti-symmetric z-series are denoted by S/A then the following rules apply: S*S, A*A -> S S*A, A*S -> A (R*tconvolve(tz1tz2((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt _zseries_mulŚsc C`s­|jƒ}|jƒ}t|ƒ}t|ƒ}|dkrX||}||d dfS||krv|d d|fS||}|d}||}tj|dd|jƒ}d}|}x‚||kr@||} ||||<| |||<| |} ||||c!| 8+||||c!| 8+|d7}|d8}qżW||} | ||<| |} ||||c!| 8+||}||d|d|!jƒ} || fSdS(sťDivide the first z-series by the second. Divide `z1` by `z2` and return the quotient and remainder as z-series. Warning: this implementation only applies when both z1 and z2 have the same symmetry, which is sufficient for present purposes. Parameters ---------- z1, z2 : 1-D ndarray The arrays must be 1-D and have the same symmetry, but this is not checked. Returns ------- (quotient, remainder) : 1-D ndarrays Quotient and remainder as z-series. Notes ----- This is not the same as polynomial division on account of the desired form of the remainder. If symmetric/anti-symmetric z-series are denoted by S/A then the following rules apply: S/S -> S,S A/A -> S,A The restriction to types of the same symmetry could be fixed but seems like unneeded generality. There is no natural form for the remainder in the case where there is no symmetry. iiR(N(R0tlenR*temptyR(( R3R4tlen1tlen2tdlentscltquotitjtrttmptrem((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt _zseries_divÁs>!                 cC`sit|ƒd}tjdddgd|jƒ}|tj| |dƒd9}t||ƒ\}}|S(sŒDifferentiate a z-series. The derivative is with respect to x, not z. This is achieved using the chain rule and the value of dx/dz given in the module notes. Parameters ---------- zs : z-series The z-series to differentiate. Returns ------- derivative : z-series The derivative Notes ----- The zseries for x (ns) has been multiplied by two in order to avoid using floats that are incompatible with Decimal and likely other specialized scalar types. This scaling has been compensated by multiplying the value of zs by two also so that the two cancels in the division. ii˙˙˙˙iiR((R6R*tarrayR(tarangeRB(R.R-tnstdR?((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt _zseries_ders !cC`sdt|ƒd}tjdddgd|jƒ}t||ƒ}tj| |dƒd}||c || *||dc||d)d||<|S(sMIntegrate a z-series. The integral is with respect to x, not z. This is achieved by a change of variable using dx/dz given in the module notes. Parameters ---------- zs : z-series The z-series to integrate Returns ------- integral : z-series The indefinite integral Notes ----- The zseries for x (ns) has been multiplied by two in order to avoid using floats that are incompatible with Decimal and likely other specialized scalar types. This scaling has been compensated by dividing the resulting zs by two. iii˙˙˙˙iR((R6R*RCR(R5RD(R.R-REtdiv((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt _zseries_int$s! cC`setj|gƒ\}t|ƒd}d}x3t|ddƒD]}tt|ƒ||ƒ}q>W|S(s_ Convert a polynomial to a Chebyshev series. Convert an array representing the coefficients of a polynomial (relative to the "standard" basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Chebyshev series, ordered from lowest to highest degree. Parameters ---------- pol : array_like 1-D array containing the polynomial coefficients Returns ------- c : ndarray 1-D array containing the coefficients of the equivalent Chebyshev series. See Also -------- cheb2poly Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy import polynomial as P >>> p = P.Polynomial(range(4)) >>> p Polynomial([ 0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1]) >>> c = p.convert(kind=P.Chebyshev) >>> c Chebyshev([ 1. , 3.25, 1. , 0.75], domain=[-1, 1], window=[-1, 1]) >>> P.poly2cheb(range(4)) array([ 1. , 3.25, 1. , 0.75]) iii˙˙˙˙(tput as_seriesR6trangeR R (tpoltdegtresR=((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRJs *c C`sĎddlm}m}m}tj|gƒ\}t|ƒ}|dkrM|S|d}|d}xTt|dddƒD]<}|}|||d|ƒ}||||ƒdƒ}qxW||||ƒƒSdS(s´ Convert a Chebyshev series to a polynomial. Convert an array representing the coefficients of a Chebyshev series, ordered from lowest degree to highest, to an array of the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest to highest degree. Parameters ---------- c : array_like 1-D array containing the Chebyshev series coefficients, ordered from lowest order term to highest. Returns ------- pol : ndarray 1-D array containing the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest order term to highest. See Also -------- poly2cheb Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy import polynomial as P >>> c = P.Chebyshev(range(4)) >>> c Chebyshev([ 0., 1., 2., 3.], [-1., 1.]) >>> p = c.convert(kind=P.Polynomial) >>> p Polynomial([ -2., -8., 4., 12.], [-1., 1.]) >>> P.cheb2poly(range(4)) array([ -2., -8., 4., 12.]) i(tpolyaddtpolysubtpolymulxiiţ˙˙˙i˙˙˙˙iN(t polynomialRPRQRRRJRKR6RL( R,RPRQRRR-tc0tc1R=R@((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR|s,    i˙˙˙˙cC`s3|dkrtj||gƒStj|gƒSdS(s  Chebyshev series whose graph is a straight line. Parameters ---------- off, scl : scalars The specified line is given by ``off + scl*x``. Returns ------- y : ndarray This module's representation of the Chebyshev series for ``off + scl*x``. See Also -------- polyline Examples -------- >>> import numpy.polynomial.chebyshev as C >>> C.chebline(3,2) array([3, 2]) >>> C.chebval(-3, C.chebline(3,2)) # should be -3 -3.0 iN(R*RC(toffR;((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR Ës cC`s t|ƒdkrtjdƒStj|gdtƒ\}|jƒg|D]}t| dƒ^qK}t|ƒ}x‰|dkrţt|dƒ\}}gt |ƒD]!}t |||||ƒ^q¤}|rďt |d|dƒ|d>> import numpy.polynomial.chebyshev as C >>> C.chebfromroots((-1,0,1)) # x^3 - x relative to the standard basis array([ 0. , -0.25, 0. , 0.25]) >>> j = complex(0,1) >>> C.chebfromroots((-j,j)) # x^2 + 1 relative to the standard basis array([ 1.5+0.j, 0.0+0.j, 0.5+0.j]) iittrimii˙˙˙˙N( R6R*tonesRJRKtFalsetsortR tdivmodRLR(trootsR?tpR-tmR=R@((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRďs1  # 4 cC`sutj||gƒ\}}t|ƒt|ƒkrO||jc |7*|}n||jc |7*|}tj|ƒS(sţ Add one Chebyshev series to another. Returns the sum of two Chebyshev series `c1` + `c2`. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the Chebyshev series of their sum. See Also -------- chebsub, chebmul, chebdiv, chebpow Notes ----- Unlike multiplication, division, etc., the sum of two Chebyshev series is a Chebyshev series (without having to "reproject" the result onto the basis set) so addition, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial import chebyshev as C >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> C.chebadd(c1,c2) array([ 4., 4., 4.]) (RJRKR6R)ttrimseq(RUtc2tret((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR 1s( cC`s|tj||gƒ\}}t|ƒt|ƒkrO||jc |8*|}n | }||jc |7*|}tj|ƒS(sV Subtract one Chebyshev series from another. Returns the difference of two Chebyshev series `c1` - `c2`. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns ------- out : ndarray Of Chebyshev series coefficients representing their difference. See Also -------- chebadd, chebmul, chebdiv, chebpow Notes ----- Unlike multiplication, division, etc., the difference of two Chebyshev series is a Chebyshev series (without having to "reproject" the result onto the basis set) so subtraction, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial import chebyshev as C >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> C.chebsub(c1,c2) array([-2., 0., 2.]) >>> C.chebsub(c2,c1) # -C.chebsub(c1,c2) array([ 2., 0., -2.]) (RJRKR6R)R_(RUR`Ra((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR cs* cC`sÁtj|gƒ\}t|ƒdkr;|ddkr;|Stjt|ƒdd|jƒ}|dd|d<|d|d>> from numpy.polynomial import chebyshev as C >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> C.chebmul(c1,c2) # multiplication requires "reprojection" array([ 6.5, 12. , 12. , 4. , 1.5]) (RJRKR/R5R1R_(RUR`R3R4RbRa((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRŔs )   cC`sřtj||gƒ\}}|ddkr7tƒ‚nt|ƒ}t|ƒ}||krm|d d|fS|dkr“||d|d dfSt|ƒ}t|ƒ}t||ƒ\}}tjt|ƒƒ}tjt|ƒƒ}||fSdS(sa Divide one Chebyshev series by another. Returns the quotient-with-remainder of two Chebyshev series `c1` / `c2`. The arguments are sequences of coefficients from lowest order "term" to highest, e.g., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``. Parameters ---------- c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns ------- [quo, rem] : ndarrays Of Chebyshev series coefficients representing the quotient and remainder. See Also -------- chebadd, chebsub, chebmul, chebpow Notes ----- In general, the (polynomial) division of one C-series by another results in quotient and remainder terms that are not in the Chebyshev polynomial basis set. Thus, to express these results as C-series, it is typically necessary to "reproject" the results onto said basis set, which typically produces "unintuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial import chebyshev as C >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> C.chebdiv(c1,c2) # quotient "intuitive," remainder not (array([ 3.]), array([-8., -4.])) >>> c2 = (0,1,2,3) >>> C.chebdiv(c2,c1) # neither "intuitive" (array([ 0., 2.]), array([-2., -4.])) i˙˙˙˙iiN(RJRKtZeroDivisionErrorR6R/RBR_R1(RUR`tlc1tlc2R3R4R<RA((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRńs/       icC`sôtj|gƒ\}t|ƒ}||ks9|dkrHtdƒ‚n¨|dk ro||krotdƒ‚n|dkr”tjdgd|jƒS|dkr¤|St|ƒ}|}x-t d|dƒD]}tj ||ƒ}qĘWt |ƒSdS(sRaise a Chebyshev series to a power. Returns the Chebyshev series `c` raised to the power `pow`. The argument `c` is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series ``T_0 + 2*T_1 + 3*T_2.`` Parameters ---------- c : array_like 1-D array of Chebyshev series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainly to limit growth of the series to unmanageable size. Default is 16 Returns ------- coef : ndarray Chebyshev series of power. See Also -------- chebadd, chebsub, chebmul, chebdiv Examples -------- is%Power must be a non-negative integer.sPower is too largeiR(iN( RJRKtintt ValueErrortNoneR*RCR(R/RLR2R1(R,tpowtmaxpowertpowerR.RbR=((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR3s     c C`stj|ddddƒ}|jjdkrB|jtjƒ}ng||gD]}t|ƒ^qO\}}||krˆtdƒ‚n|dkrŁtdƒ‚n||kržtdƒ‚nt||j ƒ}|dkrŕ|Stj ||dƒ}t |ƒ}||kr|d d}náxŢt |ƒD]Đ}|d}||9}tj |f|jdd |jƒ} xXt |d d ƒD]D} d | || | | d<|| d c| || | d 7>> from numpy.polynomial import chebyshev as C >>> c = (1,2,3,4) >>> C.chebder(c) array([ 14., 12., 24.]) >>> C.chebder(c,3) array([ 96.]) >>> C.chebder(c,scl=-1) array([-14., -12., -24.]) >>> C.chebder(c,2,-1) array([ 12., 96.]) tndminiR0s ?bBhHiIlLqQpPs'The order of derivation must be integeris,The order of derivation must be non-negativesThe axis must be integerR(ii˙˙˙˙i(R*RCR(tchartastypetdoubleRfRgRtndimtmoveaxisR6RLR7tshape( R,R^R;taxistttcnttiaxisR-R=tderR>((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRgs<<+        &(  c C`s tj|ddddƒ}|jjdkrB|jtjƒ}ntj|ƒs]|g}ng||gD]}t|ƒ^qj\}}||krŁtdƒ‚n|dkržtdƒ‚nt |ƒ|krßtdƒ‚ntj |ƒdkrtd ƒ‚ntj |ƒdkr'td ƒ‚n||krBtd ƒ‚nt ||j ƒ}|dkrd|Stj ||dƒ}t |ƒdg|t |ƒ}xjt|ƒD]\} t |ƒ} ||9}| dkr˙tj|ddkƒr˙|dc|| 7 m``, ``np.ndim(lbnd) != 0``, or ``np.ndim(scl) != 0``. See Also -------- chebder Notes ----- Note that the result of each integration is *multiplied* by `scl`. Why is this important to note? Say one is making a linear change of variable :math:`u = ax + b` in an integral relative to `x`. Then :math:`dx = du/a`, so one will need to set `scl` equal to :math:`1/a`- perhaps not what one would have first thought. Also note that, in general, the result of integrating a C-series needs to be "reprojected" onto the C-series basis set. Thus, typically, the result of this function is "unintuitive," albeit correct; see Examples section below. Examples -------- >>> from numpy.polynomial import chebyshev as C >>> c = (1,2,3) >>> C.chebint(c) array([ 0.5, -0.5, 0.5, 0.5]) >>> C.chebint(c,3) array([ 0.03125 , -0.1875 , 0.04166667, -0.05208333, 0.01041667, 0.00625 ]) >>> C.chebint(c, k=3) array([ 3.5, -0.5, 0.5, 0.5]) >>> C.chebint(c,lbnd=-2) array([ 8.5, -0.5, 0.5, 0.5]) >>> C.chebint(c,scl=-2) array([-1., 1., -1., -1.]) RliR0s ?bBhHiIlLqQpPs(The order of integration must be integeris-The order of integration must be non-negativesToo many integration constantsslbnd must be a scalar.sscl must be a scalar.sThe axis must be integerR(ii(R*RCR(RmRnRotiterableRfRgR6RpRRqtlistRLtallR7RrR( R,R^tktlbndR;RsRtRuRvR=R-R@R>((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRÇsRU +    !  %* (! cC`s`tj|ddddƒ}|jjdkrB|jtjƒ}nt|ttfƒritj |ƒ}nt|tj ƒrĄ|rĄ|j |j d |j ƒ}nt|ƒdkrĆ|d}d}nŽt|ƒdkrď|d}|d}ned|}|d}|d}xDtd t|ƒdƒD])}|}|| |}|||}q'W|||S( sl Evaluate a Chebyshev series at points x. If `c` is of length `n + 1`, this function returns the value: .. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x) The parameter `x` is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either `x` or its elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If `c` is multidimensional, then the shape of the result depends on the value of `tensor`. If `tensor` is true the shape will be c.shape[1:] + x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that scalars have shape (,). Trailing zeros in the coefficients will be used in the evaluation, so they should be avoided if efficiency is a concern. Parameters ---------- x : array_like, compatible object If `x` is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, `x` or its elements must support addition and multiplication with with themselves and with the elements of `c`. c : array_like Array of coefficients ordered so that the coefficients for terms of degree n are contained in c[n]. If `c` is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients may be thought of as stored in the columns of `c`. tensor : boolean, optional If True, the shape of the coefficient array is extended with ones on the right, one for each dimension of `x`. Scalars have dimension 0 for this action. The result is that every column of coefficients in `c` is evaluated for every element of `x`. If False, `x` is broadcast over the columns of `c` for the evaluation. This keyword is useful when `c` is multidimensional. The default value is True. .. versionadded:: 1.7.0 Returns ------- values : ndarray, algebra_like The shape of the return value is described above. See Also -------- chebval2d, chebgrid2d, chebval3d, chebgrid3d Notes ----- The evaluation uses Clenshaw recursion, aka synthetic division. Examples -------- RliR0s ?bBhHiIlLqQpPiiiţ˙˙˙i˙˙˙˙i(i(R*RCR(RmRnRot isinstancettupleRytasarraytndarraytreshapeRrRpR6RL(txR,ttensorRTRUtx2R=R@((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRKs*>         cC`smy%tj||fddƒ\}}Wntk rDtdƒ‚nXt||ƒ}t||dtƒ}|S(sH Evaluate a 2-D Chebyshev series at points (x, y). This function returns the values: .. math:: p(x,y) = \sum_{i,j} c_{i,j} * T_i(x) * T_j(y) The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` is a 1-D array a one is implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points `(x, y)`, where `x` and `y` must have the same shape. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j is contained in ``c[i,j]``. If `c` has dimension greater than 2 the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional Chebyshev series at points formed from pairs of corresponding values from `x` and `y`. See Also -------- chebval, chebgrid2d, chebval3d, chebgrid3d Notes ----- .. versionadded:: 1.7.0 R0isx, y are incompatibleRƒ(R*RCt ExceptionRgRRY(R‚tyR,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR˘s.% cC`s"t||ƒ}t||ƒ}|S(sé Evaluate a 2-D Chebyshev series on the Cartesian product of x and y. This function returns the values: .. math:: p(a,b) = \sum_{i,j} c_{i,j} * T_i(a) * T_j(b), where the points `(a, b)` consist of all pairs formed by taking `a` from `x` and `b` from `y`. The resulting points form a grid with `x` in the first dimension and `y` in the second. The parameters `x` and `y` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x` and `y` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than two dimensions, ones are implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape[2:] + x.shape + y.shape. Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points in the Cartesian product of `x` and `y`. If `x` or `y` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j is contained in `c[i,j]`. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional Chebyshev series at points in the Cartesian product of `x` and `y`. See Also -------- chebval, chebval2d, chebval3d, chebgrid3d Notes ----- .. versionadded:: 1.7.0 (R(R‚R†R,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR Ús2cC`sˆy+tj|||fddƒ\}}}Wntk rJtdƒ‚nXt||ƒ}t||dtƒ}t||dtƒ}|S(sŁ Evaluate a 3-D Chebyshev series at points (x, y, z). This function returns the values: .. math:: p(x,y,z) = \sum_{i,j,k} c_{i,j,k} * T_i(x) * T_j(y) * T_k(z) The parameters `x`, `y`, and `z` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either `x`, `y`, and `z` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than 3 dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape[3:] + x.shape. Parameters ---------- x, y, z : array_like, compatible object The three dimensional series is evaluated at the points `(x, y, z)`, where `x`, `y`, and `z` must have the same shape. If any of `x`, `y`, or `z` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension greater than 3 the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the multidimensional polynomial on points formed with triples of corresponding values from `x`, `y`, and `z`. See Also -------- chebval, chebval2d, chebgrid2d, chebgrid3d Notes ----- .. versionadded:: 1.7.0 R0isx, y, z are incompatibleRƒ(R*RCR…RgRRY(R‚R†tzR,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRs0+ cC`s1t||ƒ}t||ƒ}t||ƒ}|S(sP Evaluate a 3-D Chebyshev series on the Cartesian product of x, y, and z. This function returns the values: .. math:: p(a,b,c) = \sum_{i,j,k} c_{i,j,k} * T_i(a) * T_j(b) * T_k(c) where the points `(a, b, c)` consist of all triples formed by taking `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form a grid with `x` in the first dimension, `y` in the second, and `z` in the third. The parameters `x`, `y`, and `z` are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either `x`, `y`, and `z` or their elements must support multiplication and addition both with themselves and with the elements of `c`. If `c` has fewer than three dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape[3:] + x.shape + y.shape + z.shape. Parameters ---------- x, y, z : array_like, compatible objects The three dimensional series is evaluated at the points in the Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ``c[i,j]``. If `c` has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of `x` and `y`. See Also -------- chebval, chebval2d, chebgrid2d, chebval3d Notes ----- .. versionadded:: 1.7.0 (R(R‚R†R‡R,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR!Ls5cC`st|ƒ}||kr'tdƒ‚n|dkrBtdƒ‚ntj|ddddƒd}|df|j}|j}tj|d|ƒ}|dd|d<|dkrd |}||dcC`sâtj|ƒd}tj|ƒd}tj|ƒ}|jdkse|jjdkse|jdkrttdƒ‚n|jƒdkr•tdƒ‚n|jdkrłtdƒ‚n|jdkrŃtdƒ‚n|jdksď|jd krţtd ƒ‚nt |ƒt |ƒkr%td ƒ‚n|jdkrV|}|d}t ||ƒ}nDtj |ƒ}|d }t |ƒ}t ||ƒd d …|f}|j } |j } |d k r'tj|ƒd}|jdkrétdƒ‚nt |ƒt |ƒkrtdƒ‚n| |} | |} n|d krUt |ƒtj|jƒj}nt| jjtjƒr¤tjtj| jƒtj| jƒjdƒƒ} n!tjtj| ƒjdƒƒ} d| | dk= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (`M`,), optional Weights. If not None, the contribution of each point ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the weights are chosen so that the errors of the products ``w[i]*y[i]`` all have the same variance. The default value is None. .. versionadded:: 1.5.0 Returns ------- coef : ndarray, shape (M,) or (M, K) Chebyshev coefficients ordered from low to high. If `y` was 2-D, the coefficients for the data in column k of `y` are in column `k`. [residuals, rank, singular_values, rcond] : list These values are only returned if `full` = True resid -- sum of squared residuals of the least squares fit rank -- the numerical rank of the scaled Vandermonde matrix sv -- singular values of the scaled Vandermonde matrix rcond -- value of `rcond`. For more details, see `linalg.lstsq`. Warns ----- RankWarning The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if `full` = False. The warnings can be turned off by >>> import warnings >>> warnings.simplefilter('ignore', RankWarning) See Also -------- polyfit, legfit, lagfit, hermfit, hermefit chebval : Evaluates a Chebyshev series. chebvander : Vandermonde matrix of Chebyshev series. chebweight : Chebyshev weight function. linalg.lstsq : Computes a least-squares fit from the matrix. scipy.interpolate.UnivariateSpline : Computes spline fits. Notes ----- The solution is the coefficients of the Chebyshev series `p` that minimizes the sum of the weighted squared errors .. math:: E = \sum_j w_j^2 * |y_j - p(x_j)|^2, where :math:`w_j` are the weights. This problem is solved by setting up as the (typically) overdetermined matrix equation .. math:: V(x) * c = w * y, where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the coefficients to be solved for, `w` are the weights, and `y` are the observed values. This equation is then solved using the singular value decomposition of `V`. If some of the singular values of `V` are so small that they are neglected, then a `RankWarning` will be issued. This means that the coefficient values may be poorly determined. Using a lower order fit will usually get rid of the warning. The `rcond` parameter can also be set to a value smaller than its default, but the resulting fit may be spurious and have large contributions from roundoff error. Fits using Chebyshev series are usually better conditioned than fits using power series, but much can depend on the distribution of the sample points and the smoothness of the data. If the quality of the fit is inadequate splines may be a good alternative. References ---------- .. [1] Wikipedia, "Curve fitting", http://en.wikipedia.org/wiki/Curve_fitting Examples -------- gitiuis0deg must be an int or non-empty 1-D array of intsexpected deg >= 0sexpected 1D vector for xsexpected non-empty vector for xisexpected 1D or 2D array for ys$expected x and y to have same lengthi˙˙˙˙Nsexpected 1D vector for ws$expected x and w to have same lengthR(s!The fit may be poorly conditionedt stacklevel( R*RRpR(tkindR)t TypeErrortminRgR6RRZtTRhtfinfotepst issubclassttypetcomplexfloatingtsqrttsquaretrealtimagtsumtlatlstsqR+RrtwarningstwarnRJt RankWarning(R‚R†RNtrcondtfulltwtlmaxtordertvantlhstrhsR;R,tresidstranktstcctmsg((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR>sjw0         "7!+,  cC`sctj|gƒ\}t|ƒdkr6tdƒ‚nt|ƒdkrhtj|d |dggƒSt|ƒd}tj||fd|jƒ}tjdgtjdƒg|dƒ}|j dƒdd |d…}|j dƒ|d |d…}tjdƒ|d>> import numpy.polynomial.chebyshev as cheb >>> cheb.chebroots((-1, 1,-1, 1)) # T3 - T2 + T1 - T0 has real roots array([ -5.00000000e-01, 2.60860684e-17, 1.00000000e+00]) iR(ii( RJRKR6R*RCR(R$RĽteigvalsRZ(R,R^R?((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR/s,  cC`sÚtj|ƒ}|jdks?|jjdks?|jdkrNtdƒ‚n|dkritdƒ‚n|d}t|ƒ}|||Œ}t ||ƒ}tj |j |ƒ}|dc|<|dcd|)|S(s2Interpolate a function at the Chebyshev points of the first kind. Returns the Chebyshev series that interpolates `func` at the Chebyshev points of the first kind in the interval [-1, 1]. The interpolating series tends to a minmax approximation to `func` with increasing `deg` if the function is continuous in the interval. .. versionadded:: 1.14.0 Parameters ---------- func : function The function to be approximated. It must be a function of a single variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are extra arguments passed in the `args` parameter. deg : int Degree of the interpolating polynomial args : tuple, optional Extra arguments to be used in the function call. Default is no extra arguments. Returns ------- coef : ndarray, shape (deg + 1,) Chebyshev coefficients of the interpolating series ordered from low to high. Examples -------- >>> import numpy.polynomial.chebyshev as C >>> C.chebfromfunction(lambda x: np.tanh(x) + 0.5, 8) array([ 5.00000000e-01, 8.11675684e-01, -9.86864911e-17, -5.42457905e-02, -2.71387850e-16, 4.51658839e-03, 2.46716228e-17, -3.79694221e-04, -3.26899002e-16]) Notes ----- The Chebyshev polynomials used in the interpolation are orthogonal when sampled at the Chebyshev points of the first kind. If it is desired to constrain some of the coefficients they can simply be set to the desired value after the interpolation, no new interpolation or fit is needed. This is especially useful if it is known apriori that some of coefficients are zero. For instance, if the function is even then the coefficients of the terms of odd degree in the result can be set to zero. iR•sdeg must be an intsexpected deg >= 0igŕ?( R*RRpR(R—R)R˜RgRRtdotRš(tfuncRNtargsRŽtxchebtyfuncR^R,((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR'gs00   cC`sˆt|ƒ}||ks$|dkr3tdƒ‚ntjtjtjdd|dƒd|ƒ}tj|ƒtj|}||fS(s“ Gauss-Chebyshev quadrature. Computes the sample points and weights for Gauss-Chebyshev quadrature. These sample points and weights will correctly integrate polynomials of degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with the weight function :math:`f(x) = 1/\sqrt{1 - x^2}`. Parameters ---------- deg : int Number of sample points and weights. It must be >= 1. Returns ------- x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes ----- .. versionadded:: 1.7.0 The results have only been tested up to degree 100, higher degrees may be problematic. For Gauss-Chebyshev there are closed form solutions for the sample points and weights. If n = `deg`, then .. math:: x_i = \cos(\pi (2 i - 1) / (2 n)) .. math:: w_i = \pi / n is"deg must be a non-negative integerig@(RfRgR*tcostpiRDRX(RNRˆR‚RŹ((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR%Şs # 1cC`s,dtjd|ƒtjd|ƒ}|S(sţ The weight function of the Chebyshev polynomials. The weight function is :math:`1/\sqrt{1 - x^2}` and the interval of integration is :math:`[-1, 1]`. The Chebyshev polynomials are orthogonal, but not normalized, with respect to this weight function. Parameters ---------- x : array_like Values at which the weight function will be computed. Returns ------- w : ndarray The weight function at `x`. Notes ----- .. versionadded:: 1.7.0 gđ?(R*R (R‚RŹ((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR&×s(cC`s}t|ƒ}||kr'tdƒ‚n|dkrBtdƒ‚ntjtj d|dtƒtjd|}tj|ƒS(sź Chebyshev points of the first kind. The Chebyshev points of the first kind are the points ``cos(x)``, where ``x = [pi*(k + .5)/npts for k in range(npts)]``. Parameters ---------- npts : int Number of sample points desired. Returns ------- pts : ndarray The Chebyshev points of the first kind. See Also -------- chebpts2 Notes ----- .. versionadded:: 1.5.0 snpts must be integerisnpts must be >= 1itendpointi(RfRgR*tlinspaceRÁRYRŔ(tnptst_nptsR‚((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRós   .cC`sht|ƒ}||kr'tdƒ‚n|dkrBtdƒ‚ntjtj d|ƒ}tj|ƒS(s– Chebyshev points of the second kind. The Chebyshev points of the second kind are the points ``cos(x)``, where ``x = [pi*k/(npts - 1) for k in range(npts)]``. Parameters ---------- npts : int Number of sample points desired. Returns ------- pts : ndarray The Chebyshev points of the second kind. Notes ----- .. versionadded:: 1.5.0 snpts must be integerisnpts must be >= 2i(RfRgR*RĂRÁRŔ(RÄRĹR‚((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyRs   cB`s×eZdZeeƒZeeƒZeeƒZ ee ƒZ ee ƒZ eeƒZeeƒZeeƒZeeƒZeeƒZeeƒZeeƒZeddd„ƒZdZe j!e"ƒZ#e j!e"ƒZ$RS(sňA Chebyshev series class. The Chebyshev class provides the standard Python numerical methods '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the methods listed below. Parameters ---------- coef : array_like Chebyshev coefficients in order of increasing degree, i.e., ``(1, 2, 3)`` gives ``1*T_0(x) + 2*T_1(x) + 3*T_2(x)``. domain : (2,) array_like, optional Domain to use. The interval ``[domain[0], domain[1]]`` is mapped to the interval ``[window[0], window[1]]`` by shifting and scaling. The default value is [-1, 1]. window : (2,) array_like, optional Window, see `domain` for its use. The default value is [-1, 1]. .. versionadded:: 1.6.0 c`sOˆdkrˆj‰n‡‡‡‡fd†}t||ƒ}ˆ|dˆƒS(sÝInterpolate a function at the Chebyshev points of the first kind. Returns the series that interpolates `func` at the Chebyshev points of the first kind scaled and shifted to the `domain`. The resulting series tends to a minmax approximation of `func` when the function is continuous in the domain. .. versionadded:: 1.14.0 Parameters ---------- func : function The function to be interpolated. It must be a function of a single variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are extra arguments passed in the `args` parameter. deg : int Degree of the interpolating polynomial. domain : {None, [beg, end]}, optional Domain over which `func` is interpolated. The default is None, in which case the domain is [-1, 1]. args : tuple, optional Extra arguments to be used in the function call. Default is no extra arguments. Returns ------- polynomial : Chebyshev instance Interpolating Chebyshev instance. Notes ----- See `numpy.polynomial.chebfromfunction` for more details. c`sˆtj|ˆjˆƒˆŒS(N(RJt mapdomaintwindow(R‚(R˝tclstdomainRź(s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt‡sRÉN(RhRÉR'(RČRźRNRÉR˝txfunctcoef((R˝RČRÉRźs9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyt interpolateas $  tchebN((%t__name__t __module__t__doc__t staticmethodR t_addR t_subRt_mulRt_divRt_powRt_valRt_intRt_derRt_fitR t_lineRt_rootsRt _fromrootst classmethodRhRÍtnicknameR*RCR RÉRÇ(((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pyR=s$            *((ARŃt __future__RRRtnumbersR§tnumpyR*t numpy.linalgtlinalgRĽtnumpy.core.multiarrayRtRRJt _polybaseRt__all__ttrimcoefRR/R1R5RBRGRIRRRCR RRRR RR R R RRRRRtTrueRRR RR!RR"R#RhRYRR$RR'R%R&RRR(((s9/tmp/pip-build-fiC0ax/numpy/numpy/polynomial/chebyshev.pytXsn         C & 2 C $ B 2 5 ( 1 B 4`„ W 8 7 ; ; 7 ? AÄ - 8 C -  % %