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# scipy.interpolate.interp2d

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.

  If x and y represent a regular grid, consider using RectBivariateSpline.

  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.

  Returns:

  values_x : ndarray, shape xi.shape[:-1] + values.shape[ndim:]

  > Interpolated values at input coordinates.

  See also

  `RectBivariateSpline`
    Much faster 2D interpolation if your input data is on a grid

  `bisplrep`, `bisplev`

  `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, z[0, :], 'ro-', xnew, znew[0, :], 'b-')
      >>> plt.show()

  (Source code)

  Methods

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