scipy.interpolate.

# SmoothSphereBivariateSpline#

class scipy.interpolate.SmoothSphereBivariateSpline(theta, phi, r, w=None, s=0.0, eps=1e-16)[source]#

Smooth bivariate spline approximation in spherical coordinates.

Parameters:
theta, phi, rarray_like

1-D sequences of data points (order is not important). Coordinates must be given in radians. Theta must lie within the interval `[0, pi]`, and phi must lie within the interval `[0, 2pi]`.

warray_like, optional

Positive 1-D sequence of weights.

sfloat, optional

Positive smoothing factor defined for estimation condition: `sum((w(i)*(r(i) - s(theta(i), phi(i))))**2, axis=0) <= s` Default `s=len(w)` which should be a good value if `1/w[i]` is an estimate of the standard deviation of `r[i]`.

epsfloat, optional

A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value within the open interval `(0, 1)`, the default is 1e-16.

Methods

 `__call__`(theta, phi[, dtheta, dphi, grid]) Evaluate the spline or its derivatives at given positions. `ev`(theta, phi[, dtheta, dphi]) Evaluate the spline at points Return spline coefficients. Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) `partial_derivative`(dx, dy) Construct a new spline representing a partial derivative of this spline.

`BivariateSpline`

a base class for bivariate splines.

`UnivariateSpline`

a smooth univariate spline to fit a given set of data points.

`SmoothBivariateSpline`

a smoothing bivariate spline through the given points

`LSQBivariateSpline`

a bivariate spline using weighted least-squares fitting

`RectSphereBivariateSpline`

a bivariate spline over a rectangular mesh on a sphere

`LSQSphereBivariateSpline`

a bivariate spline in spherical coordinates using weighted least-squares fitting

`RectBivariateSpline`

a bivariate spline over a rectangular mesh.

`bisplrep`

a function to find a bivariate B-spline representation of a surface

`bisplev`

a function to evaluate a bivariate B-spline and its derivatives

Notes

Examples

Suppose we have global data on a coarse grid (the input data does not have to be on a grid):

```>>> import numpy as np
>>> theta = np.linspace(0., np.pi, 7)
>>> phi = np.linspace(0., 2*np.pi, 9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
```

We need to set up the interpolator object

```>>> lats, lons = np.meshgrid(theta, phi)
>>> from scipy.interpolate import SmoothSphereBivariateSpline
>>> lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
...                                   data.T.ravel(), s=3.5)
```

As a first test, we’ll see what the algorithm returns when run on the input coordinates

```>>> data_orig = lut(theta, phi)
```

Finally we interpolate the data to a finer grid

```>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2 * np.pi, 90)
```
```>>> data_smth = lut(fine_lats, fine_lons)
```
```>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2.imshow(data_orig, interpolation='nearest')