numpy.interp#

numpy.interp(x,xp,fp,left=None,right=None,period=None)[source]#

One-dimensional linear interpolation for monotonically increasing sample points.

Returns the one-dimensional piecewise linear interpolant to a functionwith given discrete data points (xp,fp), evaluated atx.

Parameters:

xarray_like

The x-coordinates at which to evaluate the interpolated values.

xp1-D sequence of floats

The x-coordinates of the data points, must be increasing if argumentperiod is not specified. Otherwise,xp is internally sorted afternormalizing the periodic boundaries withxp=xp%period.

fp1-D sequence of float or complex

The y-coordinates of the data points, same length asxp.

leftoptional float or complex corresponding to fp

Value to return forx < xp[0], default isfp[0].

rightoptional float or complex corresponding to fp

Value to return forx > xp[-1], default isfp[-1].

periodNone or float, optional

A period for the x-coordinates. This parameter allows the properinterpolation of angular x-coordinates. Parametersleft andrightare ignored ifperiod is specified.

Returns:

yfloat or complex (corresponding to fp) or ndarray

The interpolated values, same shape asx.

Raises:

ValueError

Ifxp andfp have different lengthIfxp orfp are not 1-D sequencesIfperiod == 0

Warning

The x-coordinate sequence is expected to be increasing, but this is notexplicitly enforced. However, if the sequencexp is non-increasing,interpolation results are meaningless.

Note that, since NaN is unsortable,xp also cannot contain NaNs.

A simple check forxp being strictly increasing is:

np.all(np.diff(xp)>0)

See also

scipy.interpolate

Examples

Try it in your browser!

>>>importnumpyasnp>>>xp=[1,2,3]>>>fp=[3,2,0]>>>np.interp(2.5,xp,fp)1.0>>>np.interp([0,1,1.5,2.72,3.14],xp,fp)array([3.  , 3.  , 2.5 , 0.56, 0.  ])>>>UNDEF=-99.0>>>np.interp(3.14,xp,fp,right=UNDEF)-99.0

Plot an interpolant to the sine function:

>>>x=np.linspace(0,2*np.pi,10)>>>y=np.sin(x)>>>xvals=np.linspace(0,2*np.pi,50)>>>yinterp=np.interp(xvals,x,y)>>>importmatplotlib.pyplotasplt>>>plt.plot(x,y,'o')[<matplotlib.lines.Line2D object at 0x...>]>>>plt.plot(xvals,yinterp,'-x')[<matplotlib.lines.Line2D object at 0x...>]>>>plt.show()

Interpolation with periodic x-coordinates:

>>>x=[-180,-170,-185,185,-10,-5,0,365]>>>xp=[190,-190,350,-350]>>>fp=[5,10,3,4]>>>np.interp(x,xp,fp,period=360)array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])

Complex interpolation:

>>>x=[1.5,4.0]>>>xp=[2,3,5]>>>fp=[1.0j,0,2+3j]>>>np.interp(x,xp,fp)array([0.+1.j , 1.+1.5j])

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