interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=None)
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.
If `x` and `y` represent a regular grid, consider using RectBivariateSpline.
Note that calling `interp2d` with NaNs present in input values results in undefined behaviour.
Methods ------- __call__
Parameters ---------- x, y : array_like 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 : array_like The values of the function to interpolate at the data points. If `z` is a multi-dimensional array, it is flattened before use. The length of a flattened `z` array is either len(`x`)*len(`y`) if `x` and `y` specify the column and row coordinates or ``len(z) == len(x) == len(y)`` if `x` and `y` specify coordinates for each point. kind : 'linear', 'cubic', 'quintic', optional The kind of spline interpolation to use. Default is 'linear'. copy : bool, optional If True, the class makes internal copies of x, y and z. If False, references may be used. The default is to copy. bounds_error : bool, optional If True, when interpolated values are requested outside of the domain of the input data (x,y), 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 omitted (None), values outside the domain are extrapolated via nearest-neighbor extrapolation.
See Also -------- RectBivariateSpline : Much faster 2D interpolation if your input data is on a grid bisplrep, bisplev : Spline interpolation based on FITPACK BivariateSpline : a more recent wrapper of the FITPACK routines interp1d : one dimension version of this function
Notes ----- 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.
Examples -------- Construct a 2-D grid and interpolate on it:
>>> from scipy import interpolate >>> 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 = interpolate.interp2d(x, y, z, kind='cubic')
Now use the obtained interpolation function and plot the result:
>>> import matplotlib.pyplot as plt >>> 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, z0, :, 'ro-', xnew, znew0, :, 'b-') >>> plt.show()