a BCCf@sddlZddlZddlmZddlmZmZmZgdZ ddZ GdddZ Gd d d e Z Gd d d e Z dd dZdddZGddde ZdddddZdS)N) factorial)_asarray_validatedfloat_factorialcheck_random_state)KroghInterpolatorkrogh_interpolateBarycentricInterpolatorbarycentric_interpolateapproximate_taylor_polynomialcCst|pt|do|jdkS)z-Check whether x is if a scalar type, or 0-dimshape)npZisscalarhasattrr )xr r V/var/www/html/django/DPS/env/lib/python3.9/site-packages/scipy/interpolate/_polyint.py _isscalar src@s\eZdZdZdZdddZddZdd Zd d Zd d Z dddZ dddZ dddZ dS)_Interpolator1Da  Common features in univariate interpolation Deal with input data type and interpolation axis rolling. The actual interpolator can assume the y-data is of shape (n, r) where `n` is the number of x-points, and `r` the number of variables, and use self.dtype as the y-data type. Attributes ---------- _y_axis Axis along which the interpolation goes in the original array _y_extra_shape Additional trailing shape of the input arrays, excluding the interpolation axis. dtype Dtype of the y-data arrays. Can be set via _set_dtype, which forces it to be float or complex. Methods ------- __call__ _prepare_x _finish_y _reshape_yi _set_yi _set_dtype _evaluate )_y_axis_y_extra_shapedtypeNcCs.||_d|_d|_|dur*|j|||ddS)Nxiaxis)rrr_set_yi)selfryirr r r__init__4s z_Interpolator1D.__init__cCs$||\}}||}|||S)a Evaluate the interpolant Parameters ---------- x : array_like Point or points at which to evaluate the interpolant. Returns ------- y : array_like Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of `x`. Notes ----- Input values `x` must be convertible to `float` values like `int` or `float`. ) _prepare_x _evaluate _finish_y)rrx_shapeyr r r__call__;s z_Interpolator1D.__call__cCs tdS)zB Actually evaluate the value of the interpolator. NNotImplementedErrorrrr r rrTsz_Interpolator1D._evaluatecCs t|ddd}|j}||fS)zReshape input x array to 1-DFT)Z check_finiteZ as_inexact)rr Zravel)rrr r r rrZsz_Interpolator1D._prepare_xcCsz|||j}|jdkrv|dkrvt|}t|j}tt|||jtt|tt||j||}||}|S)z@Reshape interpolated y back to an N-D array similar to initial yrr )reshaperrlenlistrange transpose)rr!r nxnysr r rr`s   z_Interpolator1D._finish_yFcCsvtt||jd}|rb|jdd|jkrbd|j|j d|jd|j }td|||jddfS)Nrz{!r} + (N,) + {!r}zData must be of shape %s) r Zmoveaxisasarrayrr rformat ValueErrorr&)rrcheckZok_shaper r r _reshape_yiks z_Interpolator1D._reshape_yicCs|dur|j}|durtdt|}|j}|dkr:d}|durZ||t|krZtd||j|_|jd|j|j|jdd|_d|_| |jdS)Nzno interpolation axis specifiedr )r.z@x and y arrays must be equal in length along interpolation axis.r.) rr2r r0r r'ndimrr _set_dtype)rrrrr r r rrss  &z_Interpolator1D._set_yicCsDt|tjst|jtjr(tj|_n|r8|jtjkr@tj|_dS)N)r Z issubdtypeZcomplexfloatingrZ complex128float64)rrunionr r rr6s  z_Interpolator1D._set_dtype)NNN)F)NN)F) __name__ __module__ __qualname____doc__ __slots__rr"rrrr4rr6r r r rrs   rc@s*eZdZd ddZd ddZd ddZdS) _Interpolator1DWithDerivativesNcCs||\}}|||}||jdf||j}|jdkr|dkrt|}t|j}dgtt|d||jdttd|dtt|d|j||d}| |}|S)a Evaluate several derivatives of the polynomial at the point `x` Produce an array of derivatives evaluated at the point `x`. Parameters ---------- x : array_like Point or points at which to evaluate the derivatives der : int or list or None, optional How many derivatives to evaluate, or None for all potentially nonzero derivatives (that is, a number equal to the number of points), or a list of derivatives to evaluate. This number includes the function value as the '0th' derivative. Returns ------- d : ndarray Array with derivatives; ``d[j]`` contains the jth derivative. Shape of ``d[j]`` is determined by replacing the interpolation axis in the original array with the shape of `x`. Examples -------- >>> from scipy.interpolate import KroghInterpolator >>> KroghInterpolator([0,0,0],[1,2,3]).derivatives(0) array([1.0,2.0,3.0]) >>> KroghInterpolator([0,0,0],[1,2,3]).derivatives([0,0]) array([[1.0,1.0], [2.0,2.0], [3.0,3.0]]) rr r.) r_evaluate_derivativesr&r rrr'r(r)r*)rrderr r!r+r,r-r r r derivativess"    z*_Interpolator1DWithDerivatives.derivativesr.cCs.||\}}|||d}||||S)a Evaluate a single derivative of the polynomial at the point `x`. Parameters ---------- x : array_like Point or points at which to evaluate the derivatives der : integer, optional Which derivative to evaluate (default: first derivative). This number includes the function value as 0th derivative. Returns ------- d : ndarray Derivative interpolated at the x-points. Shape of `d` is determined by replacing the interpolation axis in the original array with the shape of `x`. Notes ----- This may be computed by evaluating all derivatives up to the desired one (using self.derivatives()) and then discarding the rest. r.rr?rrrr@r r!r r r derivativesz)_Interpolator1DWithDerivatives.derivativecCs tdS)a= Actually evaluate the derivatives. Parameters ---------- x : array_like 1D array of points at which to evaluate the derivatives der : integer, optional The number of derivatives to evaluate, from 'order 0' (der=1) to order der-1. If omitted, return all possibly-non-zero derivatives, ie 0 to order n-1. Returns ------- d : ndarray Array of shape ``(der, x.size, self.yi.shape[1])`` containing the derivatives from 0 to der-1 Nr#)rrr@r r rr?sz4_Interpolator1DWithDerivatives._evaluate_derivatives)N)r.)N)r9r:r;rArDr?r r r rr>s / r>cs4eZdZdZd fdd ZddZd dd ZZS) ra@ Interpolating polynomial for a set of points. The polynomial passes through all the pairs ``(xi, yi)``. One may additionally specify a number of derivatives at each point `xi`; this is done by repeating the value `xi` and specifying the derivatives as successive `yi` values. Allows evaluation of the polynomial and all its derivatives. For reasons of numerical stability, this function does not compute the coefficients of the polynomial, although they can be obtained by evaluating all the derivatives. Parameters ---------- xi : array_like, shape (npoints, ) Known x-coordinates. Must be sorted in increasing order. yi : array_like, shape (..., npoints, ...) Known y-coordinates. When an xi occurs two or more times in a row, the corresponding yi's represent derivative values. The length of `yi` along the interpolation axis must be equal to the length of `xi`. Use the `axis` parameter to select the correct axis. axis : int, optional Axis in the `yi` array corresponding to the x-coordinate values. Defaults to ``axis=0``. Notes ----- Be aware that the algorithms implemented here are not necessarily the most numerically stable known. Moreover, even in a world of exact computation, unless the x coordinates are chosen very carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice - polynomial interpolation itself is a very ill-conditioned process due to the Runge phenomenon. In general, even with well-chosen x values, degrees higher than about thirty cause problems with numerical instability in this code. Based on [1]_. References ---------- .. [1] Krogh, "Efficient Algorithms for Polynomial Interpolation and Numerical Differentiation", 1970. Examples -------- To produce a polynomial that is zero at 0 and 1 and has derivative 2 at 0, call >>> from scipy.interpolate import KroghInterpolator >>> KroghInterpolator([0,0,1],[0,2,0]) This constructs the quadratic :math:`2x^2-2x`. The derivative condition is indicated by the repeated zero in the `xi` array; the corresponding yi values are 0, the function value, and 2, the derivative value. For another example, given `xi`, `yi`, and a derivative `ypi` for each point, appropriate arrays can be constructed as: >>> import numpy as np >>> rng = np.random.default_rng() >>> xi = np.linspace(0, 1, 5) >>> yi, ypi = rng.random((2, 5)) >>> xi_k, yi_k = np.repeat(xi, 2), np.ravel(np.dstack((yi,ypi))) >>> KroghInterpolator(xi_k, yi_k) To produce a vector-valued polynomial, supply a higher-dimensional array for `yi`: >>> KroghInterpolator([0,1],[[2,3],[4,5]]) This constructs a linear polynomial giving (2,3) at 0 and (4,5) at 1. rc st|||t||_|||_|jj\|_|_ |jj }dkr\t j |dddtj |jd|j f|jd}|jd|d<tj |j|j f|jd}td|jD]}d}||kr|||||kr|d7}q|d8}|j|t||d<t||D]} || ||kr td|dkrT|| || || |||| d<n,|| d|| || |||| d<q|||||<q||_dS) Nzv degrees provided, degrees higher than about thirty cause problems with numerical instability with 'KroghInterpolator') stacklevelr.rrz Elements of `xi` can't be equal.)superrr r0rr4rr nrsizewarningswarnzerosrr)rr2c) rrrrdegrPZVkkr-i __class__r rr@s2     *0zKroghInterpolator.__init__cCsd}tjt||jf|jd}||jdtjddf7}td|jD]>}||j |d}||}||ddtjf|j|7}qD|S)Nr.rHr) r rOr'rKrrPnewaxisr)rJr)rrpiprRwr r rr_s"zKroghInterpolator._evaluateNc Cs|j}|j}|dur|j}t|t|f}t|t|f}d|d<tjt||jf|jd}||jdtjddf7}td|D]\}||j |d||d<||d||d||<|||ddtjf|j|7}qtjt ||dt||f|jd} | d|dddddf|jd|dtjddf7<|| d<td|D]}td||dD]Z} ||| d|| d|| || <| ||| ddtjf| || | |<qn| |t |9<qXd| |ddddf<| d|S)Nr.rrH) rJrKr rOr'rrPrVr)rmaxr) rrr@rJrKrWrYrXrRcnrSr r rr?is.$$@(0z'KroghInterpolator._evaluate_derivatives)r)N)r9r:r;r<rrr? __classcell__r r rTrrsK rcCsTt|||d}|dkr||St|r4|j||dS|j|t|dd|SdS)a Convenience function for polynomial interpolation. See `KroghInterpolator` for more details. Parameters ---------- xi : array_like Interpolation points (known x-coordinates). yi : array_like Known y-coordinates, of shape ``(xi.size, R)``. Interpreted as vectors of length R, or scalars if R=1. x : array_like Point or points at which to evaluate the derivatives. der : int or list or None, optional How many derivatives to evaluate, or None for all potentially nonzero derivatives (that is, a number equal to the number of points), or a list of derivatives to evaluate. This number includes the function value as the '0th' derivative. axis : int, optional Axis in the `yi` array corresponding to the x-coordinate values. Returns ------- d : ndarray If the interpolator's values are R-D then the returned array will be the number of derivatives by N by R. If `x` is a scalar, the middle dimension will be dropped; if the `yi` are scalars then the last dimension will be dropped. See Also -------- KroghInterpolator : Krogh interpolator Notes ----- Construction of the interpolating polynomial is a relatively expensive process. If you want to evaluate it repeatedly consider using the class KroghInterpolator (which is what this function uses). Examples -------- We can interpolate 2D observed data using Krogh interpolation: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from scipy.interpolate import krogh_interpolate >>> x_observed = np.linspace(0.0, 10.0, 11) >>> y_observed = np.sin(x_observed) >>> x = np.linspace(min(x_observed), max(x_observed), num=100) >>> y = krogh_interpolate(x_observed, y_observed, x) >>> plt.plot(x_observed, y_observed, "o", label="observation") >>> plt.plot(x, y, label="krogh interpolation") >>> plt.legend() >>> plt.show() rrr@r.N)rrrDrAr amax)rrrr@rPr r rrs :rc Cs|dur |}|d}|ttjdtj||dd|}t|||}|j||dd}t|tt|ddddS)a\ Estimate the Taylor polynomial of f at x by polynomial fitting. Parameters ---------- f : callable The function whose Taylor polynomial is sought. Should accept a vector of `x` values. x : scalar The point at which the polynomial is to be evaluated. degree : int The degree of the Taylor polynomial scale : scalar The width of the interval to use to evaluate the Taylor polynomial. Function values spread over a range this wide are used to fit the polynomial. Must be chosen carefully. order : int or None, optional The order of the polynomial to be used in the fitting; `f` will be evaluated ``order+1`` times. If None, use `degree`. Returns ------- p : poly1d instance The Taylor polynomial (translated to the origin, so that for example p(0)=f(x)). Notes ----- The appropriate choice of "scale" is a trade-off; too large and the function differs from its Taylor polynomial too much to get a good answer, too small and round-off errors overwhelm the higher-order terms. The algorithm used becomes numerically unstable around order 30 even under ideal circumstances. Choosing order somewhat larger than degree may improve the higher-order terms. Examples -------- We can calculate Taylor approximation polynomials of sin function with various degrees: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from scipy.interpolate import approximate_taylor_polynomial >>> x = np.linspace(-10.0, 10.0, num=100) >>> plt.plot(x, np.sin(x), label="sin curve") >>> for degree in np.arange(1, 15, step=2): ... sin_taylor = approximate_taylor_polynomial(np.sin, 0, degree, 1, ... order=degree + 2) ... plt.plot(x, sin_taylor(x), label=f"degree={degree}") >>> plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', ... borderaxespad=0.0, shadow=True) >>> plt.tight_layout() >>> plt.axis([-10, 10, -10, 10]) >>> plt.show() Nr.r)Zendpointr^r/) r cosZlinspacerWrrAZpoly1drarange) frZdegreescaleorderrJZxsr`dr r rr s;&r csbeZdZdZddddfdd ZdddZdd d Zd d Zd dZdddZ dddZ Z S)ra%Interpolating polynomial for a set of points. Constructs a polynomial that passes through a given set of points. Allows evaluation of the polynomial and all its derivatives, efficient changing of the y-values to be interpolated, and updating by adding more x- and y-values. For reasons of numerical stability, this function does not compute the coefficients of the polynomial. The values `yi` need to be provided before the function is evaluated, but none of the preprocessing depends on them, so rapid updates are possible. Parameters ---------- xi : array_like, shape (npoints, ) 1-D array of x coordinates of the points the polynomial should pass through yi : array_like, shape (..., npoints, ...), optional N-D array of y coordinates of the points the polynomial should pass through. If None, the y values will be supplied later via the `set_y` method. The length of `yi` along the interpolation axis must be equal to the length of `xi`. Use the ``axis`` parameter to select correct axis. axis : int, optional Axis in the yi array corresponding to the x-coordinate values. Defaults to ``axis=0``. wi : array_like, optional The barycentric weights for the chosen interpolation points `xi`. If absent or None, the weights will be computed from `xi` (default). This allows for the reuse of the weights `wi` if several interpolants are being calculated using the same nodes `xi`, without re-computation. random_state : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional If `seed` is None (or `np.random`), the `numpy.random.RandomState` singleton is used. If `seed` is an int, a new ``RandomState`` instance is used, seeded with `seed`. If `seed` is already a ``Generator`` or ``RandomState`` instance then that instance is used. Notes ----- This class uses a "barycentric interpolation" method that treats the problem as a special case of rational function interpolation. This algorithm is quite stable, numerically, but even in a world of exact computation, unless the x coordinates are chosen very carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice - polynomial interpolation itself is a very ill-conditioned process due to the Runge phenomenon. Based on Berrut and Trefethen 2004, "Barycentric Lagrange Interpolation". Examples -------- To produce a quintic barycentric interpolant approximating the function :math:`\sin x`, and its first four derivatives, using six randomly-spaced nodes in :math:`(0, \frac{\pi}{2})`: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from scipy.interpolate import BarycentricInterpolator >>> rng = np.random.default_rng() >>> xi = rng.random(6) * np.pi/2 >>> f, f_d1, f_d2, f_d3, f_d4 = np.sin, np.cos, lambda x: -np.sin(x), lambda x: -np.cos(x), np.sin >>> P = BarycentricInterpolator(xi, f(xi), random_state=rng) >>> fig, axs = plt.subplots(5, 1, sharex=True, layout='constrained', figsize=(7,10)) >>> x = np.linspace(0, np.pi, 100) >>> axs[0].plot(x, P(x), 'r:', x, f(x), 'k--', xi, f(xi), 'xk') >>> axs[1].plot(x, P.derivative(x), 'r:', x, f_d1(x), 'k--', xi, f_d1(xi), 'xk') >>> axs[2].plot(x, P.derivative(x, 2), 'r:', x, f_d2(x), 'k--', xi, f_d2(xi), 'xk') >>> axs[3].plot(x, P.derivative(x, 3), 'r:', x, f_d3(x), 'k--', xi, f_d3(xi), 'xk') >>> axs[4].plot(x, P.derivative(x, 4), 'r:', x, f_d4(x), 'k--', xi, f_d4(xi), 'xk') >>> axs[0].set_xlim(0, np.pi) >>> axs[4].set_xlabel(r"$x$") >>> axs[4].set_xticks([i * np.pi / 4 for i in range(5)], ... ["0", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$\frac{3\pi}{4}$", r"$\pi$"]) >>> axs[0].set_ylabel("$f(x)$") >>> axs[1].set_ylabel("$f'(x)$") >>> axs[2].set_ylabel("$f''(x)$") >>> axs[3].set_ylabel("$f^{(3)}(x)$") >>> axs[4].set_ylabel("$f^{(4)}(x)$") >>> labels = ['Interpolation nodes', 'True function $f$', 'Barycentric interpolation'] >>> axs[0].legend(axs[0].get_lines()[::-1], labels, bbox_to_anchor=(0., 1.02, 1., .102), ... loc='lower left', ncols=3, mode="expand", borderaxespad=0., frameon=False) >>> plt.show() Nr)wi random_statec st|||t|}tj|tjd|_||t|j|_ d|_ |durV||_ ndt |jt |j|_||j }tj|j tjd}t|j ||<t|j |_ t|j D]R}|j|j||j|} d| ||<t| } | dkrtdd| |j |<qdS)NrHg@g?gz)Interpolation points xi must be distinct.)rIrrr r0r7rset_yir'rJ _diff_cijrgrZmin _inv_capacityZ permutationrOZint32rbr)prodr2) rrrrrgrhZpermuteZ inv_permuterSdistrmrTr rrns(      z BarycentricInterpolator.__init__cCsJ|durd|_dS|j||j|d|||_|jj\|_|_d|_dS)a Update the y values to be interpolated The barycentric interpolation algorithm requires the calculation of weights, but these depend only on the `xi`. The `yi` can be changed at any time. Parameters ---------- yi : array_like The y-coordinates of the points the polynomial will pass through. If None, the y values must be supplied later. axis : int, optional Axis in the `yi` array corresponding to the x-coordinate values. Nr)rrrr4r rJrK _diff_baryint)rrrr r rris zBarycentricInterpolator.set_yic Cs.|dur<|jdurtd|j|dd}t|j|f|_n|jdurNtd|j}t|j|f|_t|j|_|j dC_ |j }t |j|_ ||j d|<t ||jD]`}|j d||j |j||jd|9<tj |j |jd||j||j |<q|j dC_ d|_d|_dS)a  Add more x values to the set to be interpolated The barycentric interpolation algorithm allows easy updating by adding more points for the polynomial to pass through. Parameters ---------- xi : array_like The x coordinates of the points that the polynomial should pass through. yi : array_like, optional The y coordinates of the points the polynomial should pass through. Should have shape ``(xi.size, R)``; if R > 1 then the polynomial is vector-valued. If `yi` is not given, the y values will be supplied later. `yi` should be given if and only if the interpolator has y values specified. Notes ----- The new points added by `add_xi` are not randomly permuted so there is potential for numerical instability, especially for a large number of points. If this happens, please reconstruct interpolation from scratch instead. NzNo previous yi value to update!T)r3zNo update to yi provided!r/)rr2r4r ZvstackrJZ concatenaterr'rgrOr)rlmultiplyreducerjro)rrrZold_nZold_wijr r radd_xis,   0 zBarycentricInterpolator.add_xicCs t||S)avEvaluate the interpolating polynomial at the points x Parameters ---------- x : array_like Point or points at which to evaluate the interpolant. Returns ------- y : array_like Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of `x`. Notes ----- Currently the code computes an outer product between `x` and the weights, that is, it constructs an intermediate array of size ``(N, len(x))``, where N is the degree of the polynomial. )rr"r%r r rr"sz BarycentricInterpolator.__call__cCs|jdkr"tjd|jf|jd}n|dtjf|j}|dk}d||<|j|}tjdd6t ||j tj |dddtjf}Wdn1s0Yt |}t |dkrt |ddkr|j |dd}n|j |d||dd<|S) NrrH.r.ignore)divider/r])rLr rOrKrrVrrgZerrstatedotrsumZnonzeror')rrrXrPzrKr r rrs  D  z!BarycentricInterpolator._evaluater.cCs.||\}}|j||ddd}|||S)aj Evaluate a single derivative of the polynomial at the point x. Parameters ---------- x : array_like Point or points at which to evaluate the derivatives der : integer, optional Which derivative to evaluate (default: first derivative). This number includes the function value as 0th derivative. Returns ------- d : ndarray Derivative interpolated at the x-points. Shape of `d` is determined by replacing the interpolation axis in the original array with the shape of `x`. r.F all_lowerrBrCr r rrD sz"BarycentricInterpolator.derivativeTcCs|s.|jdks|jdkr.tjd|jf|jdS|sD|dkrD||S|sl||jkrltjt||jf|jdS|durz|j}|r|jdks|jdkrtj|t||jf|jdS|jdur&|j ddtj f|j }t |d|j ||j dtj f}t |d|j dd }t ||||_|jdurXt|j |j|j|j d|_|j|j_|rtj|t||jf|jd}t|D],}|j||ddd||ddddf<q|S|jj||dddS) NrrHr..r])rrrgFry)rLrKr rOrrrJr'rjrrVZ fill_diagonalrgrwrorrr)r?)rrr@rzrPrfr[r r rr? s<          *z-BarycentricInterpolator._evaluate_derivatives)Nr)N)N)r.)NT) r9r:r;r<rrirsr"rrDr?r\r r rTrrsW&  3 rr^cCsTt|||d}|dkr||St|r4|j||dS|j|t|dd|SdS)aS Convenience function for polynomial interpolation. Constructs a polynomial that passes through a given set of points, then evaluates the polynomial. For reasons of numerical stability, this function does not compute the coefficients of the polynomial. This function uses a "barycentric interpolation" method that treats the problem as a special case of rational function interpolation. This algorithm is quite stable, numerically, but even in a world of exact computation, unless the `x` coordinates are chosen very carefully - Chebyshev zeros (e.g., cos(i*pi/n)) are a good choice - polynomial interpolation itself is a very ill-conditioned process due to the Runge phenomenon. Parameters ---------- xi : array_like 1-D array of x coordinates of the points the polynomial should pass through yi : array_like The y coordinates of the points the polynomial should pass through. x : scalar or array_like Point or points at which to evaluate the interpolant. der : int or list or None, optional How many derivatives to evaluate, or None for all potentially nonzero derivatives (that is, a number equal to the number of points), or a list of derivatives to evaluate. This number includes the function value as the '0th' derivative. axis : int, optional Axis in the `yi` array corresponding to the x-coordinate values. Returns ------- y : scalar or array_like Interpolated values. Shape is determined by replacing the interpolation axis in the original array with the shape of `x`. See Also -------- BarycentricInterpolator : Barycentric interpolator Notes ----- Construction of the interpolation weights is a relatively slow process. If you want to call this many times with the same xi (but possibly varying yi or x) you should use the class `BarycentricInterpolator`. This is what this function uses internally. Examples -------- We can interpolate 2D observed data using barycentric interpolation: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from scipy.interpolate import barycentric_interpolate >>> x_observed = np.linspace(0.0, 10.0, 11) >>> y_observed = np.sin(x_observed) >>> x = np.linspace(min(x_observed), max(x_observed), num=100) >>> y = barycentric_interpolate(x_observed, y_observed, x) >>> plt.plot(x_observed, y_observed, "o", label="observation") >>> plt.plot(x, y, label="barycentric interpolation") >>> plt.legend() >>> plt.show() r]rr^r.N)rrrDrAr r_)rrrrr@r`r r rr as Cr )rr)N)r)rMnumpyr Z scipy.specialrZscipy._lib._utilrrr__all__rrr>rrr rr r r r rs ~d C KM