scipy.interpolate.interp1d

class scipy.interpolate.interp1d(x, y, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=np.nan)

Interpolate a 1-D function.

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

Parameters :

x : (N,) array_like

A 1-D array of monotonically increasing real values.

y : (...,N,...) array_like

A N-D array of real values. The length of y along the interpolation axis must be equal to the length of x.

kind : str or int, optional

Specifies the kind of interpolation as a string (‘linear’,’nearest’, ‘zero’, ‘slinear’, ‘quadratic, ‘cubic’) or as an integer specifying the order of the spline interpolator to use. Default is ‘linear’.

axis : int, optional

Specifies the axis of y along which to interpolate. Interpolation defaults to the last axis of y.

copy : bool, optional

If True, the class makes internal copies of x and y. If False, references to x and y are used. The default is to copy.

bounds_error : bool, optional

If True, a ValueError is raised any time interpolation is attempted on a value outside of the range of x (where extrapolation is necessary). If False, out of bounds values are assigned fill_value. By default, an error is raised.

fill_value : float, optional

If provided, then this value will be used to fill in for requested points outside of the data range. If not provided, then the default is NaN.

See also

UnivariateSpline

A more recent wrapper of the FITPACK routines.

splrep, splev, interp2d

Examples

>>> from scipy import interpolate
>>> x = np.arange(0, 10)
>>> y = np.exp(-x/3.0)
>>> f = interpolate.interp1d(x, y)

>>> xnew = np.arange(0,9, 0.1)
>>> ynew = f(xnew)   # use interpolation function returned by `interp1d`
>>> plt.plot(x, y, 'o', xnew, ynew, '-')
>>> plt.show()

Methods

__call__(x)

Evaluate the interpolant