class scipy.interpolate.interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=nan)

Interpolate over a 2-D grid.

x, y and z are arrays of values used to approximate some function f: z = f(x, y). This class returns a function whose call method uses spline interpolation to find the value of new points.

Parameters :

x, y : 1-D ndarrays

Arrays defining the data point coordinates.

If the points lie on a regular grid, x can specify the column coordinates and y the row coordinates, for example:

>>> x = [0,1,2];  y = [0,3]; z = [[1,2,3], [4,5,6]]

Otherwise, x and y must specify the full coordinates for each point, for example:

>>> x = [0,1,2,0,1,2];  y = [0,0,0,3,3,3]; z = [1,2,3,4,5,6]

If x and y are multi-dimensional, they are flattened before use.

z : 1-D ndarray

The values of the function to interpolate at the data points. If z is a multi-dimensional array, it is flattened before use.

kind : {‘linear’, ‘cubic’, ‘quintic’}, optional

The kind of spline interpolation to use. Default is ‘linear’.

copy : bool, optional

If True, then data is copied, otherwise only a reference is held.

bounds_error : bool, optional

If True, when interpolated values are requested outside of the domain of the input data, an error 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. Defaults to NaN.

See also

bisplrep, bisplev

a more recent wrapper of the FITPACK routines



The minimum number of data points required along the interpolation axis is (k+1)**2, with k=1 for linear, k=3 for cubic and k=5 for quintic interpolation.

The interpolator is constructed by bisplrep, with a smoothing factor of 0. If more control over smoothing is needed, bisplrep should be used directly.


Construct a 2-D grid and interpolate on it:

>>> x = np.arange(-5.01, 5.01, 0.25)
>>> y = np.arange(-5.01, 5.01, 0.25)
>>> xx, yy = np.meshgrid(x, y)
>>> z = np.sin(xx**2+yy**2)
>>> f = sp.interpolate.interp2d(x, y, z, kind='cubic')

Now use the obtained interpolation function and plot the result:

>>> xnew = np.arange(-5.01, 5.01, 1e-2)
>>> ynew = np.arange(-5.01, 5.01, 1e-2)
>>> znew = f(xnew, ynew)
>>> plt.plot(x, z[:, 0], 'ro-', xnew, znew[:, 0], 'b-')


__call__(x, y[, dx, dy]) Interpolate the function.

Previous topic


Next topic


This Page