`interpolate._fitpack_impl`¶

fitpack (dierckx in netlib) — A Python-C wrapper to FITPACK (by P. Dierckx).
FITPACK is a collection of FORTRAN programs for curve and surface fitting with splines and tensor product splines.
See
http://www.cs.kuleuven.ac.be/cwis/research/nalag/research/topics/fitpack.html
or
http://www.netlib.org/dierckx/index.html

Copyright 2002 Pearu Peterson all rights reserved, Pearu Peterson <pearu@cens.ioc.ee> Permission to use, modify, and distribute this software is given under the terms of the SciPy (BSD style) license. See LICENSE.txt that came with this distribution for specifics.

NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.

TODO: Make interfaces to the following fitpack functions:
For univariate splines: cocosp, concon, fourco, insert For bivariate splines: profil, regrid, parsur, surev

Module Contents¶

Functions¶

 `_intc_overflow`(x,msg=None) Cast the value to an intc and raise an OverflowError if the value `splprep`(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None,full_output=0,nest=None,per=0,quiet=1) Find the B-spline representation of an N-dimensional curve. `splrep`(x,y,w=None,xb=None,xe=None,k=3,task=0,s=None,t=None,full_output=0,per=0,quiet=1) Find the B-spline representation of 1-D curve. `splev`(x,tck,der=0,ext=0) Evaluate a B-spline or its derivatives. `splint`(a,b,tck,full_output=0) Evaluate the definite integral of a B-spline. `sproot`(tck,mest=10) Find the roots of a cubic B-spline. `spalde`(x,tck) Evaluate all derivatives of a B-spline. `bisplrep`(x,y,z,w=None,xb=None,xe=None,yb=None,ye=None,kx=3,ky=3,task=0,s=None,eps=1e-16,tx=None,ty=None,full_output=0,nxest=None,nyest=None,quiet=1) Find a bivariate B-spline representation of a surface. `bisplev`(x,y,tck,dx=0,dy=0) Evaluate a bivariate B-spline and its derivatives. `dblint`(xa,xb,ya,yb,tck) Evaluate the integral of a spline over area [xa,xb] x [ya,yb]. `insert`(x,tck,m=1,per=0) Insert knots into a B-spline. `splder`(tck,n=1) Compute the spline representation of the derivative of a given spline `splantider`(tck,n=1) Compute the spline for the antiderivative (integral) of a given spline.
`_intc_overflow`(x, msg=None)

Cast the value to an intc and raise an OverflowError if the value cannot fit.

`splprep`(x, w=None, u=None, ub=None, ue=None, k=3, task=0, s=None, t=None, full_output=0, nest=None, per=0, quiet=1)

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

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

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 x[0]. 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(x[0]))`.
u : array_like, optional

An array of parameter values. If not given, these values are calculated automatically as `M = len(x[0])`, where

v[0] = 0

v[i] = v[i-1] + distance(x[i], x[i-1])

u[i] = v[i] / v[M-1]

ub, ue : int, optional
The end-points of the parameters interval. Defaults to u[0] 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 `x[m-1] - x[0]` and a smooth periodic spline approximation is returned. Values of `y[m-1]` and `w[m-1]` are not used.
quiet : int, optional
Non-zero to suppress messages. This parameter is deprecated; use standard Python warning filters instead.
tck : tuple
A tuple (t,c,k) 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.

splrep, splev, sproot, spalde, splint, bisplrep, bisplev UnivariateSpline, BivariateSpline

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

 [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.
`splrep`(x, y, w=None, xb=None, xe=None, k=3, task=0, s=None, t=None, full_output=0, per=0, quiet=1)

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

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

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 x[0] and x[-1] respectively.
k : int, optional
The order of the spline fit. It is recommended to use cubic splines. Even order splines should be avoided especially with small s values. 1 <= k <= 5
task : {1, 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 x[m-1] - x[0] and a smooth periodic spline approximation is returned. Values of y[m-1] and w[m-1] are not used.
quiet : bool, optional
Non-zero to suppress messages. This parameter is deprecated; use standard Python warning filters instead.
tck : tuple
(t,c,k) a tuple 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 splev for evaluation of the spline and its derivatives.

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

UnivariateSpline, BivariateSpline splprep, splev, sproot, spalde, splint bisplrep, bisplev

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

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

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

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.

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 : tuple
A sequence of length 3 returned by splrep or splprep containing the knots, coefficients, and degree of the spline.
der : int, optional
The order of derivative of the spline to compute (must be less than or equal to k).
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.

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 N-dimensional space.

splprep, splrep, sproot, spalde, splint bisplrep, bisplev

 [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.
`splint`(a, b, tck, full_output=0)

Evaluate the definite integral of a B-spline.

Given the knots and coefficients of a B-spline, evaluate the definite integral of the smoothing polynomial between two given points.

a, b : float
The end-points of the integration interval.
tck : tuple
A tuple (t,c,k) 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.
integral : float
The resulting integral.
wrk : ndarray
An array containing the integrals of the normalized B-splines defined on the set of knots.

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

splprep, splrep, sproot, spalde, splev bisplrep, bisplev UnivariateSpline, BivariateSpline

 [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.
`sproot`(tck, mest=10)

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.

tck : tuple
A tuple (t,c,k) 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).
zeros : ndarray
An array giving the roots of the spline.

splprep, splrep, splint, spalde, splev bisplrep, bisplev UnivariateSpline, BivariateSpline

 [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.
`spalde`(x, tck)

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).

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

splprep, splrep, splint, sproot, splev, bisplrep, bisplev, UnivariateSpline, BivariateSpline

 [1] de Boor C : On calculating with b-splines, J. Approximation Theory 6 (1972) 50-62.
 [2] Cox M.G. : The numerical evaluation of b-splines, J. Inst. Maths applics 10 (1972) 134-149.
 [3] Dierckx P. : Curve and surface fitting with splines, Monographs on Numerical Analysis, Oxford University Press, 1993.
`bisplrep`(x, y, z, w=None, xb=None, xe=None, yb=None, ye=None, kx=3, ky=3, task=0, s=None, eps=1e-16, tx=None, ty=None, full_output=0, nxest=None, nyest=None, quiet=1)

Find a bivariate B-spline representation of a surface.

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

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.
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.

splprep, splrep, splint, sproot, splev UnivariateSpline, BivariateSpline

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

 [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.
`bisplev`(x, y, tck, dx=0, dy=0)

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.

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

splprep, splrep, splint, sproot, splev UnivariateSpline, BivariateSpline

See bisplrep to generate the tck representation.
 [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.
`dblint`(xa, xb, ya, yb, tck)

Evaluate the integral of a spline over area [xa,xb] x [ya,yb].

xa, xb : float
The end-points of the x integration interval.
ya, yb : float
The end-points of the y integration interval.
tck : list [tx, ty, c, kx, ky]
A sequence of length 5 returned by bisplrep containing the knot locations tx, ty, the coefficients c, and the degrees kx, ky of the spline.
integ : float
The value of the resulting integral.
`insert`(x, tck, m=1, per=0)

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.

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 : tuple
A tuple (t,c,k) returned by `splrep` or `splprep` 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.
tck : tuple
A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the new spline. `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)`.

Based on algorithms from [1]_ and [2]_.

 [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.
`splder`(tck, n=1)

Compute the spline representation of the derivative of a given spline

tck : tuple of (t, c, k)
Spline whose derivative to compute
n : int, optional
Order of derivative to evaluate. Default: 1
tck_der : tuple of (t2, c2, k2)
Spline of order k2=k-n representing the derivative of the input spline.

New in version 0.13.0.

splantider, splev, spalde

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 of .

`splantider`(tck, n=1)

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

tck : tuple of (t, c, k)
Spline whose antiderivative to compute
n : int, optional
Order of antiderivative to evaluate. Default: 1
tck_ader : tuple of (t2, c2, k2)
Spline of order k2=k+n representing the antiderivative of the input spline.

splder, splev, spalde

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

New in version 0.13.0.

```>>> 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 :

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