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

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 : array_like

A 1-D array of monotonically increasing real values.

y : 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, an error is thrown 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

A more recent wrapper of the FITPACK routines.

splrep, splev, interp2d


>>> 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, '-')


__call__(x_new) Find interpolated y_new = f(x_new).

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