Module Scipy.InterpolateSource

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

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 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()

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 Points to evaluate the interpolator at. 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 : Bary centric 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 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()

Evaluate a bivariate B-spline and its derivatives.

Return a rank-2 array of spline function values (or spline derivative values) at points given by the cross-product of the rank-1 arrays `x` and `y`. In special cases, return an array or just a float if either `x` or `y` or both are floats. Based on BISPEV from FITPACK.

Parameters ---------- x, y : ndarray Rank-1 arrays specifying the domain over which to evaluate the spline or its derivative. tck : tuple A sequence of length 5 returned by `bisplrep` containing the knot locations, the coefficients, and the degree of the spline: tx, ty, c, kx, ky. dx, dy : int, optional The orders of the partial derivatives in `x` and `y` respectively.

Returns ------- vals : ndarray The B-spline or its derivative evaluated over the set formed by the cross-product of `x` and `y`.

See Also -------- splprep, splrep, splint, sproot, splev UnivariateSpline, BivariateSpline

Notes ----- See `bisplrep` to generate the `tck` representation.

References ---------- .. 1 Dierckx P. : An algorithm for surface fitting with spline functions Ima J. Numer. Anal. 1 (1981) 267-283. .. 2 Dierckx P. : An algorithm for surface fitting with spline functions report tw50, Dept. Computer Science,K.U.Leuven, 1980. .. 3 Dierckx P. : Curve and surface fitting with splines, Monographs on Numerical Analysis, Oxford University Press, 1993.

Find a bivariate B-spline representation of a surface.

Given a set of data points (xi, yi, zi) representing a surface z=f(x,y), compute a B-spline representation of the surface. Based on the routine SURFIT from FITPACK.

Parameters ---------- x, y, z : ndarray Rank-1 arrays of data points. w : ndarray, optional Rank-1 array of weights. By default ``w=np.ones(len(x))``. xb, xe : float, optional End points of approximation interval in `x`. By default ``xb = x.min(), xe=x.max()``. yb, ye : float, optional End points of approximation interval in `y`. By default ``yb=y.min(), ye = y.max()``. kx, ky : int, optional The degrees of the spline (1 <= kx, ky <= 5). Third order (kx=ky=3) is recommended. task : int, optional If task=0, find knots in x and y and coefficients for a given smoothing factor, s. If task=1, find knots and coefficients for another value of the smoothing factor, s. bisplrep must have been previously called with task=0 or task=1. If task=-1, find coefficients for a given set of knots tx, ty. s : float, optional A non-negative smoothing factor. If weights correspond to the inverse of the standard-deviation of the errors in z, then a good s-value should be found in the range ``(m-sqrt(2*m),m+sqrt(2*m))`` where m=len(x). eps : float, optional A threshold for determining the effective rank of an over-determined linear system of equations (0 < eps < 1). `eps` is not likely to need changing. tx, ty : ndarray, optional Rank-1 arrays of the knots of the spline for task=-1 full_output : int, optional Non-zero to return optional outputs. nxest, nyest : int, optional Over-estimates of the total number of knots. If None then ``nxest = max(kx+sqrt(m/2),2*kx+3)``, ``nyest = max(ky+sqrt(m/2),2*ky+3)``. quiet : int, optional Non-zero to suppress printing of messages. This parameter is deprecated; use standard Python warning filters instead.

Returns ------- tck : array_like A list tx, ty, c, kx, ky containing the knots (tx, ty) and coefficients (c) of the bivariate B-spline representation of the surface along with the degree of the spline. fp : ndarray The weighted sum of squared residuals of the spline approximation. ier : int An integer flag about splrep success. Success is indicated if ier<=0. If ier in 1,2,3 an error occurred but was not raised. Otherwise an error is raised. msg : str A message corresponding to the integer flag, ier.

See Also -------- splprep, splrep, splint, sproot, splev UnivariateSpline, BivariateSpline

Notes ----- See `bisplev` to evaluate the value of the B-spline given its tck representation.

References ---------- .. 1 Dierckx P.:An algorithm for surface fitting with spline functions Ima J. Numer. Anal. 1 (1981) 267-283. .. 2 Dierckx P.:An algorithm for surface fitting with spline functions report tw50, Dept. Computer Science,K.U.Leuven, 1980. .. 3 Dierckx P.:Curve and surface fitting with splines, Monographs on Numerical Analysis, Oxford University Press, 1993.

Interpolate unstructured D-D data.

Parameters ---------- points : 2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,). Data point coordinates. values : ndarray of float or complex, shape (n,) Data values. xi : 2-D ndarray of floats with shape (m, D), or length D tuple of ndarrays broadcastable to the same shape. Points at which to interpolate data. method : 'linear', 'nearest', 'cubic', optional Method of interpolation. One of

``nearest`` return the value at the data point closest to the point of interpolation. See `NearestNDInterpolator` for more details.

``linear`` tessellate the input point set to N-D simplices, and interpolate linearly on each simplex. See `LinearNDInterpolator` for more details.

``cubic`` (1-D) return the value determined from a cubic spline.

``cubic`` (2-D) return the value determined from a piecewise cubic, continuously differentiable (C1), and approximately curvature-minimizing polynomial surface. See `CloughTocher2DInterpolator` for more details. fill_value : float, optional Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is ``nan``. This option has no effect for the 'nearest' method. rescale : bool, optional Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude.

.. versionadded:: 0.14.0

Returns ------- ndarray Array of interpolated values.

Notes -----

.. versionadded:: 0.9

Examples --------

Suppose we want to interpolate the 2-D function

>>> def func(x, y): ... return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2

on a grid in 0, 1x0, 1

>>> grid_x, grid_y = np.mgrid0:1:100j, 0:1:200j

but we only know its values at 1000 data points:

>>> points = np.random.rand(1000, 2) >>> values = func(points:,0, points:,1)

This can be done with `griddata` -- below we try out all of the interpolation methods:

>>> from scipy.interpolate import griddata >>> grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest') >>> grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear') >>> grid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic')

One can see that the exact result is reproduced by all of the methods to some degree, but for this smooth function the piecewise cubic interpolant gives the best results:

>>> import matplotlib.pyplot as plt >>> plt.subplot(221) >>> plt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower') >>> plt.plot(points:,0, points:,1, 'k.', ms=1) >>> plt.title('Original') >>> plt.subplot(222) >>> plt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Nearest') >>> plt.subplot(223) >>> plt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Linear') >>> plt.subplot(224) >>> plt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Cubic') >>> plt.gcf().set_size_inches(6, 6) >>> plt.show()

Insert knots into a B-spline.

Given the knots and coefficients of a B-spline representation, create a new B-spline with a knot inserted `m` times at point `x`. This is a wrapper around the FORTRAN routine insert of FITPACK.

Parameters ---------- x (u) : array_like A 1-D point at which to insert a new knot(s). If `tck` was returned from ``splprep``, then the parameter values, u should be given. tck : a `BSpline` instance or a tuple If tuple, then it is expected to be a tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline. m : int, optional The number of times to insert the given knot (its multiplicity). Default is 1. per : int, optional If non-zero, the input spline is considered periodic.

Returns ------- BSpline instance or a tuple A new B-spline with knots t, coefficients c, and degree k. ``t(k+1) <= x <= t(n-k)``, where k is the degree of the spline. In case of a periodic spline (``per != 0``) there must be either at least k interior knots t(j) satisfying ``t(k+1)<t(j)<=x`` or at least k interior knots t(j) satisfying ``x<=t(j)<t(n-k)``. A tuple is returned iff the input argument `tck` is a tuple, otherwise a BSpline object is constructed and returned.

Notes ----- Based on algorithms from 1_ and 2_.

Manipulating the tck-tuples directly is not recommended. In new code, prefer using the `BSpline` objects.

References ---------- .. 1 W. Boehm, 'Inserting new knots into b-spline curves.', Computer Aided Design, 12, p.199-201, 1980. .. 2 P. Dierckx, 'Curve and surface fitting with splines, Monographs on Numerical Analysis', Oxford University Press, 1993.

Multidimensional interpolation on regular grids.

Parameters ---------- points : tuple of ndarray of float, with shapes (m1, ), ..., (mn, ) The points defining the regular grid in n dimensions.

values : array_like, shape (m1, ..., mn, ...) The data on the regular grid in n dimensions.

xi : ndarray of shape (..., ndim) The coordinates to sample the gridded data at

method : str, optional The method of interpolation to perform. Supported are 'linear' and 'nearest', and 'splinef2d'. 'splinef2d' is only supported for 2-dimensional data.

bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data, a ValueError is raised. If False, then `fill_value` is used.

fill_value : number, optional If provided, the value to use for points outside of the interpolation domain. If None, values outside the domain are extrapolated. Extrapolation is not supported by method 'splinef2d'.

Returns ------- values_x : ndarray, shape xi.shape:-1 + values.shapendim: Interpolated values at input coordinates.

Notes -----

.. versionadded:: 0.14

Examples -------- Evaluate a simple example function on the points of a regular 3-D grid:

>>> from scipy.interpolate import interpn >>> def value_func_3d(x, y, z): ... return 2 * x + 3 * y - z >>> x = np.linspace(0, 5) >>> y = np.linspace(0, 5) >>> z = np.linspace(0, 5) >>> points = (x, y, z) >>> values = value_func_3d( *np.meshgrid( *points))

Evaluate the interpolating function at a point

>>> point = np.array(2.21, 3.12, 1.15) >>> print(interpn(points, values, point)) 11.72

See also -------- NearestNDInterpolator : Nearest neighbor interpolation on unstructured data in N dimensions

LinearNDInterpolator : Piecewise linear interpolant on unstructured data in N dimensions

RegularGridInterpolator : Linear and nearest-neighbor Interpolation on a regular grid in arbitrary dimensions

RectBivariateSpline : Bivariate spline approximation over a rectangular mesh

Convenience function for polynomial interpolation.

See `KroghInterpolator` for more details.

Parameters ---------- xi : array_like 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, 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.

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 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()

Return a Lagrange interpolating polynomial.

Given two 1-D arrays `x` and `w,` returns the Lagrange interpolating polynomial through the points ``(x, w)``.

Warning: This implementation is numerically unstable. Do not expect to be able to use more than about 20 points even if they are chosen optimally.

Parameters ---------- x : array_like `x` represents the x-coordinates of a set of datapoints. w : array_like `w` represents the y-coordinates of a set of datapoints, i.e., f(`x`).

Returns ------- lagrange : `numpy.poly1d` instance The Lagrange interpolating polynomial.

Examples -------- Interpolate :math:`f(x) = x^3` by 3 points.

>>> from scipy.interpolate import lagrange >>> x = np.array(0, 1, 2) >>> y = x**3 >>> poly = lagrange(x, y)

Since there are only 3 points, Lagrange polynomial has degree 2. Explicitly, it is given by

.. math::

\beginaligned L(x) &= 1\times \fracx (x - 2)

1

}

1. 8\times \fracx (x-1)

   \\ &= x (-2 + 3x) \endaligned

>>> from numpy.polynomial.polynomial import Polynomial >>> Polynomial(poly).coef array( 3., -2., 0.)

Compute the (coefficients of) interpolating B-spline.

Parameters ---------- x : array_like, shape (n,) Abscissas. y : array_like, shape (n, ...) Ordinates. k : int, optional B-spline degree. Default is cubic, k=3. t : array_like, shape (nt + k + 1,), optional. Knots. The number of knots needs to agree with the number of datapoints and the number of derivatives at the edges. Specifically, ``nt - n`` must equal ``len(deriv_l) + len(deriv_r)``. bc_type : 2-tuple or None Boundary conditions. Default is None, which means choosing the boundary conditions automatically. Otherwise, it must be a length-two tuple where the first element sets the boundary conditions at ``x0`` and the second element sets the boundary conditions at ``x-1``. Each of these must be an iterable of pairs ``(order, value)`` which gives the values of derivatives of specified orders at the given edge of the interpolation interval. Alternatively, the following string aliases are recognized:

* ``'clamped'``: The first derivatives at the ends are zero. This is equivalent to ``bc_type=((1, 0.0), (1, 0.0))``. * ``'natural'``: The second derivatives at ends are zero. This is equivalent to ``bc_type=((2, 0.0), (2, 0.0))``. * ``'not-a-knot'`` (default): The first and second segments are the same polynomial. This is equivalent to having ``bc_type=None``.

axis : int, optional Interpolation axis. Default is 0. check_finite : bool, optional Whether to check that the input arrays contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default is True.

Returns ------- b : a BSpline object of the degree ``k`` and with knots ``t``.

Examples --------

Use cubic interpolation on Chebyshev nodes:

>>> def cheb_nodes(N): ... jj = 2.*np.arange(N) + 1 ... x = np.cos(np.pi * jj / 2 / N)::-1 ... return x

>>> x = cheb_nodes(20) >>> y = np.sqrt(1 - x**2)

>>> from scipy.interpolate import BSpline, make_interp_spline >>> b = make_interp_spline(x, y) >>> np.allclose(b(x), y) True

Note that the default is a cubic spline with a not-a-knot boundary condition

>>> b.k 3

Here we use a 'natural' spline, with zero 2nd derivatives at edges:

>>> l, r = (2, 0.0), (2, 0.0) >>> b_n = make_interp_spline(x, y, bc_type=(l, r)) # or, bc_type='natural' >>> np.allclose(b_n(x), y) True >>> x0, x1 = x0, x-1 >>> np.allclose(b_n(x0, 2), b_n(x1, 2), 0, 0) True

Interpolation of parametric curves is also supported. As an example, we compute a discretization of a snail curve in polar coordinates

>>> phi = np.linspace(0, 2.*np.pi, 40) >>> r = 0.3 + np.cos(phi) >>> x, y = r*np.cos(phi), r*np.sin(phi) # convert to Cartesian coordinates

Build an interpolating curve, parameterizing it by the angle

>>> from scipy.interpolate import make_interp_spline >>> spl = make_interp_spline(phi, np.c_x, y)

Evaluate the interpolant on a finer grid (note that we transpose the result to unpack it into a pair of x- and y-arrays)

>>> phi_new = np.linspace(0, 2.*np.pi, 100) >>> x_new, y_new = spl(phi_new).T

Plot the result

>>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o') >>> plt.plot(x_new, y_new, '-') >>> plt.show()

See Also -------- BSpline : base class representing the B-spline objects CubicSpline : a cubic spline in the polynomial basis make_lsq_spline : a similar factory function for spline fitting UnivariateSpline : a wrapper over FITPACK spline fitting routines splrep : a wrapper over FITPACK spline fitting routines

Compute the (coefficients of) an LSQ B-spline.

The result is a linear combination

.. math::

S(x) = \sum_j c_j B_j(x; t)

of the B-spline basis elements, :math:`B_j(x; t)`, which minimizes

.. math::

\sum_j \left( w_j \times (S(x_j) - y_j) \right)^2

Parameters ---------- x : array_like, shape (m,) Abscissas. y : array_like, shape (m, ...) Ordinates. t : array_like, shape (n + k + 1,). Knots. Knots and data points must satisfy Schoenberg-Whitney conditions. k : int, optional B-spline degree. Default is cubic, k=3. w : array_like, shape (n,), optional Weights for spline fitting. Must be positive. If ``None``, then weights are all equal. Default is ``None``. axis : int, optional Interpolation axis. Default is zero. check_finite : bool, optional Whether to check that the input arrays contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default is True.

Returns ------- b : a BSpline object of the degree `k` with knots `t`.

Notes -----

The number of data points must be larger than the spline degree `k`.

Knots `t` must satisfy the Schoenberg-Whitney conditions, i.e., there must be a subset of data points ``xj`` such that ``tj < xj < tj+k+1``, for ``j=0, 1,...,n-k-2``.

Examples -------- Generate some noisy data:

>>> x = np.linspace(-3, 3, 50) >>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)

Now fit a smoothing cubic spline with a pre-defined internal knots. Here we make the knot vector (k+1)-regular by adding boundary knots:

>>> from scipy.interpolate import make_lsq_spline, BSpline >>> t = -1, 0, 1 >>> k = 3 >>> t = np.r_(x[0],)*(k+1), ... t, ... (x[-1],)*(k+1) >>> spl = make_lsq_spline(x, y, t, k)

For comparison, we also construct an interpolating spline for the same set of data:

>>> from scipy.interpolate import make_interp_spline >>> spl_i = make_interp_spline(x, y)

Plot both:

>>> import matplotlib.pyplot as plt >>> xs = np.linspace(-3, 3, 100) >>> plt.plot(x, y, 'ro', ms=5) >>> plt.plot(xs, spl(xs), 'g-', lw=3, label='LSQ spline') >>> plt.plot(xs, spl_i(xs), 'b-', lw=3, alpha=0.7, label='interp spline') >>> plt.legend(loc='best') >>> plt.show()

**NaN handling**: If the input arrays contain ``nan`` values, the result is not useful since the underlying spline fitting routines cannot deal with ``nan``. A workaround is to use zero weights for not-a-number data points:

>>> y8 = np.nan >>> w = np.isnan(y) >>> yw = 0. >>> tck = make_lsq_spline(x, y, t, w=~w)

Notice the need to replace a ``nan`` by a numerical value (precise value does not matter as long as the corresponding weight is zero.)

See Also -------- BSpline : base class representing the B-spline objects make_interp_spline : a similar factory function for interpolating splines LSQUnivariateSpline : a FITPACK-based spline fitting routine splrep : a FITPACK-based fitting routine

Return Pade approximation to a polynomial as the ratio of two polynomials.

Parameters ---------- an : (N,) array_like Taylor series coefficients. m : int The order of the returned approximating polynomial `q`. n : int, optional The order of the returned approximating polynomial `p`. By default, the order is ``len(an)-m``.

Returns ------- p, q : Polynomial class The Pade approximation of the polynomial defined by `an` is ``p(x)/q(x)``.

Examples -------- >>> from scipy.interpolate import pade >>> e_exp = 1.0, 1.0, 1.0/2.0, 1.0/6.0, 1.0/24.0, 1.0/120.0 >>> p, q = pade(e_exp, 2)

>>> e_exp.reverse() >>> e_poly = np.poly1d(e_exp)

Compare ``e_poly(x)`` and the Pade approximation ``p(x)/q(x)``

>>> e_poly(1) 2.7166666666666668

>>> p(1)/q(1) 2.7179487179487181

Convenience function for pchip interpolation.

xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. The interpolant uses monotonic cubic splines to find the value of new points x and the derivatives there.

See `scipy.interpolate.PchipInterpolator` for details.

Parameters ---------- xi : array_like A sorted list of x-coordinates, of length N. yi : array_like 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 : scalar or array_like Of length M. der : int or list, optional Derivatives to extract. The 0th derivative can be included to return the function value. axis : int, optional Axis in the yi array corresponding to the x-coordinate values.

See Also -------- PchipInterpolator : PCHIP 1-D monotonic cubic interpolator.

Returns ------- y : scalar or array_like The result, of length R or length M or M by R,

Examples -------- We can interpolate 2D observed data using pchip interpolation:

>>> import matplotlib.pyplot as plt >>> from scipy.interpolate import pchip_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 = pchip_interpolate(x_observed, y_observed, x) >>> plt.plot(x_observed, y_observed, 'o', label='observation') >>> plt.plot(x, y, label='pchip interpolation') >>> plt.legend() >>> plt.show()

Evaluate all derivatives of a B-spline.

Given the knots and coefficients of a cubic B-spline compute all derivatives up to order k at a point (or set of points).

Parameters ---------- x : array_like A point or a set of points at which to evaluate the derivatives. Note that ``t(k) <= x <= t(n-k+1)`` must hold for each `x`. tck : tuple A tuple ``(t, c, k)``, containing the vector of knots, the B-spline coefficients, and the degree of the spline (see `splev`).

Returns ------- results : ndarray, list of ndarrays An array (or a list of arrays) containing all derivatives up to order k inclusive for each point `x`.

See Also -------- splprep, splrep, splint, sproot, splev, bisplrep, bisplev, BSpline

References ---------- .. 1 C. de Boor: On calculating with b-splines, J. Approximation Theory 6 (1972) 50-62. .. 2 M. G. Cox : The numerical evaluation of b-splines, J. Inst. Maths applics 10 (1972) 134-149. .. 3 P. Dierckx : Curve and surface fitting with splines, Monographs on Numerical Analysis, Oxford University Press, 1993.

Compute the spline for the antiderivative (integral) of a given spline.

Parameters ---------- tck : BSpline instance or a tuple of (t, c, k) Spline whose antiderivative to compute n : int, optional Order of antiderivative to evaluate. Default: 1

Returns ------- BSpline instance or a tuple of (t2, c2, k2) Spline of order k2=k+n representing the antiderivative of the input spline. A tuple is returned iff the input argument `tck` is a tuple, otherwise a BSpline object is constructed and returned.

See Also -------- splder, splev, spalde BSpline

Notes ----- The `splder` function is the inverse operation of this function. Namely, ``splder(splantider(tck))`` is identical to `tck`, modulo rounding error.

.. versionadded:: 0.13.0

Examples -------- >>> from scipy.interpolate import splrep, splder, splantider, splev >>> x = np.linspace(0, np.pi/2, 70) >>> y = 1 / np.sqrt(1 - 0.8*np.sin(x)**2) >>> spl = splrep(x, y)

The derivative is the inverse operation of the antiderivative, although some floating point error accumulates:

>>> splev(1.7, spl), splev(1.7, splder(splantider(spl))) (array(2.1565429877197317), array(2.1565429877201865))

Antiderivative can be used to evaluate definite integrals:

>>> ispl = splantider(spl) >>> splev(np.pi/2, ispl) - splev(0, ispl) 2.2572053588768486

This is indeed an approximation to the complete elliptic integral :math:`K(m) = \int_0^\pi/2 1 - m\sin^2 x^

1/2

}

dx`:

>>> from scipy.special import ellipk >>> ellipk(0.8) 2.2572053268208538

Compute the spline representation of the derivative of a given spline

Parameters ---------- tck : BSpline instance or a tuple of (t, c, k) Spline whose derivative to compute n : int, optional Order of derivative to evaluate. Default: 1

Returns ------- `BSpline` instance or tuple Spline of order k2=k-n representing the derivative of the input spline. A tuple is returned iff the input argument `tck` is a tuple, otherwise a BSpline object is constructed and returned.

Notes -----

.. versionadded:: 0.13.0

See Also -------- splantider, splev, spalde BSpline

Examples -------- This can be used for finding maxima of a curve:

>>> from scipy.interpolate import splrep, splder, sproot >>> x = np.linspace(0, 10, 70) >>> y = np.sin(x) >>> spl = splrep(x, y, k=4)

Now, differentiate the spline and find the zeros of the derivative. (NB: `sproot` only works for order 3 splines, so we fit an order 4 spline):

>>> dspl = splder(spl) >>> sproot(dspl) / np.pi array( 0.50000001, 1.5 , 2.49999998)

This agrees well with roots :math:`\pi/2 + n\pi` of :math:`\cos(x) = \sin'(x)`.

Evaluate a B-spline or its derivatives.

Given the knots and coefficients of a B-spline representation, evaluate the value of the smoothing polynomial and its derivatives. This is a wrapper around the FORTRAN routines splev and splder of FITPACK.

Parameters ---------- x : array_like An array of points at which to return the value of the smoothed spline or its derivatives. If `tck` was returned from `splprep`, then the parameter values, u should be given. tck : 3-tuple or a BSpline object If a tuple, then it should be a sequence of length 3 returned by `splrep` or `splprep` containing the knots, coefficients, and degree of the spline. (Also see Notes.) der : int, optional The order of derivative of the spline to compute (must be less than or equal to k, the degree of the spline). ext : int, optional Controls the value returned for elements of ``x`` not in the interval defined by the knot sequence.

* if ext=0, return the extrapolated value. * if ext=1, return 0 * if ext=2, raise a ValueError * if ext=3, return the boundary value.

The default value is 0.

Returns ------- y : ndarray or list of ndarrays An array of values representing the spline function evaluated at the points in `x`. If `tck` was returned from `splprep`, then this is a list of arrays representing the curve in an N-D space.

Notes ----- Manipulating the tck-tuples directly is not recommended. In new code, prefer using `BSpline` objects.

See Also -------- splprep, splrep, sproot, spalde, splint bisplrep, bisplev BSpline

References ---------- .. 1 C. de Boor, 'On calculating with b-splines', J. Approximation Theory, 6, p.50-62, 1972. .. 2 M. G. Cox, 'The numerical evaluation of b-splines', J. Inst. Maths Applics, 10, p.134-149, 1972. .. 3 P. Dierckx, 'Curve and surface fitting with splines', Monographs on Numerical Analysis, Oxford University Press, 1993.

Evaluate the definite integral of a B-spline between two given points.

Parameters ---------- a, b : float The end-points of the integration interval. tck : tuple or a BSpline instance If a tuple, then it should be a sequence of length 3, containing the vector of knots, the B-spline coefficients, and the degree of the spline (see `splev`). full_output : int, optional Non-zero to return optional output.

Returns ------- integral : float The resulting integral. wrk : ndarray An array containing the integrals of the normalized B-splines defined on the set of knots. (Only returned if `full_output` is non-zero)

Notes ----- `splint` silently assumes that the spline function is zero outside the data interval (`a`, `b`).

Manipulating the tck-tuples directly is not recommended. In new code, prefer using the `BSpline` objects.

See Also -------- splprep, splrep, sproot, spalde, splev bisplrep, bisplev BSpline

References ---------- .. 1 P.W. Gaffney, The calculation of indefinite integrals of b-splines', J. Inst. Maths Applics, 17, p.37-41, 1976. .. 2 P. Dierckx, 'Curve and surface fitting with splines', Monographs on Numerical Analysis, Oxford University Press, 1993.

Find the B-spline representation of an N-D curve.

Given a list of N rank-1 arrays, `x`, which represent a curve in N-D space parametrized by `u`, find a smooth approximating spline curve g(`u`). Uses the FORTRAN routine parcur from FITPACK.

Parameters ---------- x : array_like A list of sample vector arrays representing the curve. w : array_like, optional Strictly positive rank-1 array of weights the same length as `x0`. The weights are used in computing the weighted least-squares spline fit. If the errors in the `x` values have standard-deviation given by the vector d, then `w` should be 1/d. Default is ``ones(len(x0))``. u : array_like, optional An array of parameter values. If not given, these values are calculated automatically as ``M = len(x0)``, where

v0 = 0

vi = vi-1 + distance(`xi`, `xi-1`)

ui = vi / vM-1

ub, ue : int, optional The end-points of the parameters interval. Defaults to u0 and u-1. k : int, optional Degree of the spline. Cubic splines are recommended. Even values of `k` should be avoided especially with a small s-value. ``1 <= k <= 5``, default is 3. task : int, optional If task==0 (default), find t and c for a given smoothing factor, s. If task==1, find t and c for another value of the smoothing factor, s. There must have been a previous call with task=0 or task=1 for the same set of data. If task=-1 find the weighted least square spline for a given set of knots, t. s : float, optional A smoothing condition. The amount of smoothness is determined by satisfying the conditions: ``sum((w * (y - g))**2,axis=0) <= s``, where g(x) is the smoothed interpolation of (x,y). The user can use `s` to control the trade-off between closeness and smoothness of fit. Larger `s` means more smoothing while smaller values of `s` indicate less smoothing. Recommended values of `s` depend on the weights, w. If the weights represent the inverse of the standard-deviation of y, then a good `s` value should be found in the range ``(m-sqrt(2*m),m+sqrt(2*m))``, where m is the number of data points in x, y, and w. t : int, optional The knots needed for task=-1. full_output : int, optional If non-zero, then return optional outputs. nest : int, optional An over-estimate of the total number of knots of the spline to help in determining the storage space. By default nest=m/2. Always large enough is nest=m+k+1. per : int, optional If non-zero, data points are considered periodic with period ``xm-1 - x0`` and a smooth periodic spline approximation is returned. Values of ``ym-1`` and ``wm-1`` are not used. quiet : int, optional Non-zero to suppress messages. This parameter is deprecated; use standard Python warning filters instead.

Returns ------- tck : tuple (t,c,k) a tuple containing the vector of knots, the B-spline coefficients, and the degree of the spline. u : array An array of the values of the parameter. fp : float The weighted sum of squared residuals of the spline approximation. ier : int An integer flag about splrep success. Success is indicated if ier<=0. If ier in 1,2,3 an error occurred but was not raised. Otherwise an error is raised. msg : str A message corresponding to the integer flag, ier.

See Also -------- splrep, splev, sproot, spalde, splint, bisplrep, bisplev UnivariateSpline, BivariateSpline BSpline make_interp_spline

Notes ----- See `splev` for evaluation of the spline and its derivatives. The number of dimensions N must be smaller than 11.

The number of coefficients in the `c` array is ``k+1`` less then the number of knots, ``len(t)``. This is in contrast with `splrep`, which zero-pads the array of coefficients to have the same length as the array of knots. These additional coefficients are ignored by evaluation routines, `splev` and `BSpline`.

References ---------- .. 1 P. Dierckx, 'Algorithms for smoothing data with periodic and parametric splines, Computer Graphics and Image Processing', 20 (1982) 171-184. .. 2 P. Dierckx, 'Algorithms for smoothing data with periodic and parametric splines', report tw55, Dept. Computer Science, K.U.Leuven, 1981. .. 3 P. Dierckx, 'Curve and surface fitting with splines', Monographs on Numerical Analysis, Oxford University Press, 1993.

Examples -------- Generate a discretization of a limacon curve in the polar coordinates:

>>> phi = np.linspace(0, 2.*np.pi, 40) >>> r = 0.5 + np.cos(phi) # polar coords >>> x, y = r * np.cos(phi), r * np.sin(phi) # convert to cartesian

And interpolate:

>>> from scipy.interpolate import splprep, splev >>> tck, u = splprep(x, y, s=0) >>> new_points = splev(u, tck)

Notice that (i) we force interpolation by using `s=0`, (ii) the parameterization, ``u``, is generated automatically. Now plot the result:

>>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots() >>> ax.plot(x, y, 'ro') >>> ax.plot(new_points0, new_points1, 'r-') >>> plt.show()

Find the B-spline representation of a 1-D curve.

Given the set of data points ``(xi, yi)`` determine a smooth spline approximation of degree k on the interval ``xb <= x <= xe``.

Parameters ---------- x, y : array_like The data points defining a curve y = f(x). w : array_like, optional Strictly positive rank-1 array of weights the same length as x and y. The weights are used in computing the weighted least-squares spline fit. If the errors in the y values have standard-deviation given by the vector d, then w should be 1/d. Default is ones(len(x)). xb, xe : float, optional The interval to fit. If None, these default to x0 and x-1 respectively. k : int, optional The degree of the spline fit. It is recommended to use cubic splines. Even values of k should be avoided especially with small s values. 1 <= k <= 5 task :

, 0, -1

, optional If task==0 find t and c for a given smoothing factor, s.

If task==1 find t and c for another value of the smoothing factor, s. There must have been a previous call with task=0 or task=1 for the same set of data (t will be stored an used internally)

If task=-1 find the weighted least square spline for a given set of knots, t. These should be interior knots as knots on the ends will be added automatically. s : float, optional A smoothing condition. The amount of smoothness is determined by satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x) is the smoothed interpolation of (x,y). The user can use s to control the tradeoff between closeness and smoothness of fit. Larger s means more smoothing while smaller values of s indicate less smoothing. Recommended values of s depend on the weights, w. If the weights represent the inverse of the standard-deviation of y, then a good s value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m is the number of datapoints in x, y, and w. default : s=m-sqrt(2*m) if weights are supplied. s = 0.0 (interpolating) if no weights are supplied. t : array_like, optional The knots needed for task=-1. If given then task is automatically set to -1. full_output : bool, optional If non-zero, then return optional outputs. per : bool, optional If non-zero, data points are considered periodic with period xm-1 - x0 and a smooth periodic spline approximation is returned. Values of ym-1 and wm-1 are not used. quiet : bool, optional Non-zero to suppress messages. This parameter is deprecated; use standard Python warning filters instead.

Returns ------- tck : tuple A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline. fp : array, optional The weighted sum of squared residuals of the spline approximation. ier : int, optional An integer flag about splrep success. Success is indicated if ier<=0. If ier in 1,2,3 an error occurred but was not raised. Otherwise an error is raised. msg : str, optional A message corresponding to the integer flag, ier.

See Also -------- UnivariateSpline, BivariateSpline splprep, splev, sproot, spalde, splint bisplrep, bisplev BSpline make_interp_spline

Notes ----- See `splev` for evaluation of the spline and its derivatives. Uses the FORTRAN routine ``curfit`` from FITPACK.

The user is responsible for assuring that the values of `x` are unique. Otherwise, `splrep` will not return sensible results.

If provided, knots `t` must satisfy the Schoenberg-Whitney conditions, i.e., there must be a subset of data points ``xj`` such that ``tj < xj < tj+k+1``, for ``j=0, 1,...,n-k-2``.

This routine zero-pads the coefficients array ``c`` to have the same length as the array of knots ``t`` (the trailing ``k + 1`` coefficients are ignored by the evaluation routines, `splev` and `BSpline`.) This is in contrast with `splprep`, which does not zero-pad the coefficients.

References ---------- Based on algorithms described in 1_, 2_, 3_, and 4_:

.. 1 P. Dierckx, 'An algorithm for smoothing, differentiation and integration of experimental data using spline functions', J.Comp.Appl.Maths 1 (1975) 165-184. .. 2 P. Dierckx, 'A fast algorithm for smoothing data on a rectangular grid while using spline functions', SIAM J.Numer.Anal. 19 (1982) 1286-1304. .. 3 P. Dierckx, 'An improved algorithm for curve fitting with spline functions', report tw54, Dept. Computer Science,K.U. Leuven, 1981. .. 4 P. Dierckx, 'Curve and surface fitting with splines', Monographs on Numerical Analysis, Oxford University Press, 1993.

Examples --------

>>> import matplotlib.pyplot as plt >>> from scipy.interpolate import splev, splrep >>> x = np.linspace(0, 10, 10) >>> y = np.sin(x) >>> spl = splrep(x, y) >>> x2 = np.linspace(0, 10, 200) >>> y2 = splev(x2, spl) >>> plt.plot(x, y, 'o', x2, y2) >>> plt.show()

Find the roots of a cubic B-spline.

Given the knots (>=8) and coefficients of a cubic B-spline return the roots of the spline.

Parameters ---------- tck : tuple or a BSpline object If a tuple, then it should be a sequence of length 3, containing the vector of knots, the B-spline coefficients, and the degree of the spline. The number of knots must be >= 8, and the degree must be 3. The knots must be a montonically increasing sequence. mest : int, optional An estimate of the number of zeros (Default is 10).

Returns ------- zeros : ndarray An array giving the roots of the spline.

Notes ----- Manipulating the tck-tuples directly is not recommended. In new code, prefer using the `BSpline` objects.

See also -------- splprep, splrep, splint, spalde, splev bisplrep, bisplev BSpline

References ---------- .. 1 C. de Boor, 'On calculating with b-splines', J. Approximation Theory, 6, p.50-62, 1972. .. 2 M. G. Cox, 'The numerical evaluation of b-splines', J. Inst. Maths Applics, 10, p.134-149, 1972. .. 3 P. Dierckx, 'Curve and surface fitting with splines', Monographs on Numerical Analysis, Oxford University Press, 1993.