Interpolation

enstools.interpolation.downsize(arr, fac)

Reduce resolution of an array by neighbourhood averaging - 2D averaging of fac x fac element

Parameters

arrxarray.DataArray or np.ndarray

array to downsize by neighbourhood averaging

facint

factor of downsizing, 2D averaging of fac x fac element

Returns

xarray.DataArray or np.ndarray

class enstools.interpolation.model2pressure(src_p, dst_p, vertical_dim=None)

Methods

__call__(data)

perform the actual interpolation

enstools.interpolation.nearest_neighbour(src_lon, src_lat, dst_lon, dst_lat, src_grid='regular', dst_grid='unstructured', npoints=1, method='mean')

Find the coordinates of station locations within gridded model data. Supported are 1d- and 2d-coordinates of regular grids (e.g. rotated lat-lon) or ‘unstructured’ grids like the ICON grid.

Parameters

src_lonnp.ndarray or xarray.DataArray

1d or 2d coordinate in x-direction of the source grid

src_latnp.ndarray or xarray.DataArray

1d or 2d coordinate in y-direction of the source grid

dst_lonnp.ndarray or xarray.DataArray

1d coordinate in x-direction of the station locations

dst_latnp.ndarray or xarray.DataArray

1d coordinate in y-direction of the station locations

src_grid{‘regular’, ‘unstructured’}

Type of input grid. Possible values are: “regular”: a regular grid with 1d or 2d coordinates. 1d-coordinates are internally converted to 2d- coordinates using meshgrid. This selection is the default. “unstructured”: The grid is given as a 1d list of points (e.g., station data or ICON model output).

dst_grid{‘regular’, ‘unstructured’}

Type of output grid. Possible values are: “regular”: a regular grid with 1d coordinates. “unstructured”: The grid is given as a 1d list of points (e.g., station data or ICON model output). This selection is the default.

npointsint

Number of nearest points to be used in the interpolation. For a regular grid useful values are 4 or 12. For data on the ICON grid 3 or 6 might be used. The actual geometry of the grid is not taken into account. The points are selected based merely on distance.

method{‘mean’, ‘d-mean’}

“mean” : the mean value of the neighbour points is used with equal weight. “d-mean”: each point is weighted by the reciprocal of the squared distance. The minimum distance within this calculation is half of the mean grid spacing.

Returns

NearestNeighbourInterpolator

callable interpolator object. Each call returns interpolated values

Examples

>>> import numpy
>>> if numpy.__version__ > "1.13.9": numpy.set_printoptions(legacy="1.13")      # numpy version independent printing
>>> lon = numpy.arange(10)
>>> lat = numpy.arange(15)
>>> gridded_data = numpy.zeros((15, 10))
>>> gridded_data[8, 4] = 3
>>> f = nearest_neighbour(lon, lat, 4.4, 7.6)
>>> f(gridded_data)
<xarray.DataArray 'interpolated' (cell: 1)>
array([ 3.])
Coordinates:
    lat      (cell) float64 7.6
    lon      (cell) float64 4.4
Dimensions without coordinates: cell
Attributes:
    grid_type:    unstructured_grid
    coordinates:  lon lat

enstools.interpolation.unstagger(data)

Interpolate variables on staggered grid (e.g., U and V) to grid cell centers. Every model has different names for staggered coordinates and variables. Currently, only the names of the model COSMO are known and supported.

Parameters

dataxarray.DataArray or xarray.DataSet

Array or Dataset with staggered variables. For datasets, all staggered variables are unstaggered. The staggered coordinate arrays are removed

Returns

xarray.DataArray or xarray.DataSet

The return type depends on the input type.