iconarray.core.latlonhash.Icon2latlon

class iconarray.core.latlonhash.Icon2latlon(grid: xarray.Dataset, scale_factor: float = 0.3)

Creates a field in a Cartesian lat/lon grid whose elements contain the ICON grid indices of the element whose lat/lon coordinates are contained within the cartesian element.

It can be used as a hashing of the ICON grid indices in order to search for nearest neighbor ICON indices with O(1) complexity.

Parameters:

• grid (xr.Dataset) – the ICON grid information, it should contain the following coordinates: clon,clat,elon,elat,vlon,vlat

• scale_factor (float) – factor of resolution between the icon grid and an auxiliary latlon grid needed by the cropping algorithm.

Example

open the ICON grid:

>>> ds_grid = xr.open_dataset("icon_grid_0001_R19B08_lon:0.1525-0.1535_lat:0.8748-0.8752.nc")

create the 2D array that maps ICON grid indices into a lat/lon Cartesian grid

>>> i2ll = Icon2latlon(ds_grid)
>>> cartgrid_ind = i2ll.latlon_grid("cell")
>>> cartgrid_ind
... array([[ 3,  0,  0,  1],
...     [ 0,  0,  0,  0],
...     [ 0,  4,  2,  0],
...     [ 0,  0,  0,  0],
...     [ 5,  0,  0,  9],
...     [ 0,  0,  0,  0],
...     [ 6,  0,  8,  0],
...     [ 0,  0,  0,  0],
...     [10,  0,  0,  0],
...     [ 0,  0,  0,  7]])
... Dimensions without coordinates: x, y

the lat/lon bounds of the grid are lon:[0.1525,0.1535], lat[0.8748,0.8752]

we can search for the ICON indices of the following coordinates:

>>> lons = xr.DataArray([0.152871, 0.153016])
>>> lats = xr.DataArray([0.875108, 0.874878])
>>> inds_lon, inds_lat = i2ll.latlon_indices_of_coords("cell", lons, lats)
>>> inds_lon
... <xarray.DataArray (cindex: 2)>
... array([2, 4])
... Dimensions without coordinates: cindex
>>> inds_lat
...  <xarray.DataArray (cindex: 2)>
... array([2, 0])
... Dimensions without coordinates: cindex

retrieve the ICON indices:

>>> icon_inds = cartgrid_ind[inds_lon, inds_lat]
>>> icon_inds
... <xarray.DataArray (cindex: 2)>
... array([2, 5])
... Dimensions without coordinates: cindex

__init__(grid: xarray.Dataset, scale_factor: float = 0.3)

Methods

__init__(grid[, scale_factor])
latlon_grid(loc)	Generate a lat/lon grid that covers the entire ICON grid.
latlon_indices_of_coords(loc, lons, lats)	Retrieve the indices in the lat/lon grid associated with a sequence of lon/lat coordinates.