geocat.comp.interpolation.interp_multidim#

geocat.comp.interpolation.interp_multidim(data_in, lat_out, lon_out, lat_in=None, lon_in=None, cyclic=False, missing_val=None, method='linear', fill_value=nan)#

Multidimensional interpolation of variables. Uses xarray.interp to perform interpolation. Will not perform extrapolation by default, returns missing values if any surrounding points contain missing values.

Warning

The output data type may be promoted to that of the coordinate data.

Parameters:

data_in (xarray.DataArray, numpy.ndarray) – Data array with data to be interpolated and associated coords. If it is a np array, then lat_in and lon_in must be provided. Length must be coordinated with given coordinates.
lat_out (numpy.ndarray) – List of latitude coordinates to be interpolated to.
lon_out (numpy.ndarray) – List of longitude coordinates to be interpolated to.
lat_in (numpy.ndarray) – List of latitude coordinates corresponding to data_in. Must be given if data_in is not an xarray.
lon_in (numpy.ndarray) – List of longitude coordinates corresponding to data_in. Must be given if data_in is not an xarray.
cyclic (bool, optional) – Set as true if lon values are cyclical but do not fully wrap around the globe (0, 1.5, 3, …, 354, 355.5) Default is false
missing_val (np.number, optional) – Provide a number to represent missing data. Alternative to using np.nan
method (str, optional) – Provide specific method of interpolation. Default is “linear” “linear” or “nearest” for multidimensional array
fill_value (str, optional) – Set as ‘extrapolate’ to allow extrapolation of data. Default is no extrapolation.

Returns:

data_out (numpy.ndarray, xarray.DataArray) – Returns the same type of object as input data_in. However, the type of the data in the array may be promoted to that of the coordinates. Shape will be the same as input array except for last two dimensions which will be equal to the coordinates given in data_out.

Examples

>>> import xarray as xr
>>> import numpy as np
>>> import geocat.comp
>>> data = np.asarray([[1, 2, 3, 4, 5, 99],
...                   [2, 4, 6, 8, 10, 12]])
>>> lat_in = [0, 1]
>>> lon_in = [0, 50, 100, 250, 300, 350]
>>> data_in = xr.DataArray(data,
...                        dims=['lat', 'lon'],
...                        coords={'lat':lat_in,
...                                'lon': lon_in})
>>> data_out = xr.DataArray(dims=['lat', 'lon'],
...                         coords={'lat': [0, 1],
...                                 'lon': [0, 50, 360]})
>>> do = interp_multidim(data_in,
...                      [0, 1],
...                      [0, 50, 360],
...                      cyclic=True,
...                      missing_val=99)
>>> print(do)
<xarray.DataArray (lat: 2, lon: 3)>
array([[ 1.,  2., 99.],
   [ 2.,  4., 99.]])
Coordinates:
  * lat      (lat) int64 0 1
  * lon      (lon) int64 0 50 360

geocat.comp.interpolation.interp_multidim

Contents

geocat.comp.interpolation.interp_multidim#