Description
When e.g. creating a Dataset from multiple DataArrays that are supposed to share the same grid, but are not exactly aligned (as is often the case with floating point coordinates), we usually end up with undesirable NaNs inserted in the data set.
For instance, consider the following data arrays that are not exactly aligned:
import xarray as xr
da1 = xr.DataArray([[0, 1, 2], [3, 4, 5], [6, 7, 8]], coords=[[0, 1, 2], [0, 1, 2]], dims=['x', 'y']).rename('da1')
da2 = xr.DataArray([[0, 1, 2], [3, 4, 5], [6, 7, 8]], coords=[[1.1, 2.1, 3.1], [1.1, 2.1, 3.1]], dims=['x', 'y']).rename('da2')
da1.plot.imshow()
da2.plot.imshow()
They show gaps when combined in a data set:
ds = xr.Dataset({'da1': da1, 'da2': da2})
ds['da1'].plot.imshow()
ds['da2'].plot.imshow()
I think this is a frequent enough situation that we would like a function to re-align all the data arrays together. There is a reindex_like method, which accepts a tolerance, but calling it successively on every data array, like so:
da1r = da1.reindex_like(da2, method='nearest', tolerance=0.2)
da2r = da2.reindex_like(da1r, method='nearest', tolerance=0.2)would result in the intersection of the coordinates, rather than their union. What I would like is a function like the following:
import numpy as np
from functools import reduce
def reindex_all(arrays, dims, tolerance):
coords = {}
for dim in dims:
coord = reduce(np.union1d, [array[dim] for array in arrays[1:]], arrays[0][dim])
diff = coord[:-1] - coord[1:]
keep = np.abs(diff) > tolerance
coords[dim] = np.append(coord[:-1][keep], coord[-1])
reindexed = [array.reindex(coords, method='nearest', tolerance=tolerance) for array in arrays]
return reindexed
da1r, da2r = reindex_all([da1, da2], ['x', 'y'], 0.2)
dsr = xr.Dataset({'da1': da1r, 'da2': da2r})
dsr['da1'].plot.imshow()
dsr['da2'].plot.imshow()
I have not found something equivalent. If you think this is worth it, I could try and send a PR to implement such a feature.