Cara menggunakan 2d extrapolation python

For legacy code, nearly bug-for-bug compatible replacements are on regular grids, and / for scattered 2D data.

In new code, for regular grids use instead. For scattered data, prefer or .

For more details see https://gist.github.com/ev-br/8544371b40f414b7eaf3fe6217209bff

Interpolate over a 2-D grid.

x, y and z are arrays of values used to approximate some function f:

>>> x = [0,1,2,0,1,2];  y = [0,0,0,3,3,3]; z = [1,4,2,5,3,6]

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

If z is a vector value, consider using .

Note that calling with NaNs present in input values, or with decreasing values in x an y results in undefined behaviour.

Arrays defining the data point coordinates. The data point coordinates need to be sorted by increasing order.

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 = [1,4,2,5,3,6]

If x and y are multidimensional, they are flattened before use.

The values of the function to interpolate at the data points. If z is a multidimensional array, it is flattened before use assuming Fortran-ordering (order=’F’). The length of a flattened z array is either len(x)*len(y) if x and y specify the column and row coordinates or

>>> x = [0,1,2,0,1,2];  y = [0,0,0,3,3,3]; z = [1,4,2,5,3,6]

The kind of spline interpolation to use. Default is ‘linear’.

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

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.

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

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

Spline interpolation based on FITPACK

a more recent wrapper of the FITPACK routines

1-D version of this function

interpolation on a regular or rectilinear grid in arbitrary dimensions.

Multidimensional interpolation on regular grids (wraps and ).

Notes

The minimum number of data points required along the interpolation axis is

>>> import numpy as np
>>> 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')

The interpolator is constructed by , with a smoothing factor of 0. If more control over smoothing is needed, should be used directly.

The coordinates of the data points to interpolate xnew and ynew have to be sorted by ascending order. is legacy and is not recommended for use in new code. New code should use instead.