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- alexsalr · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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456960684 | https://github.com/pydata/xarray/issues/2042#issuecomment-456960684 | https://api.github.com/repos/pydata/xarray/issues/2042 | MDEyOklzc3VlQ29tbWVudDQ1Njk2MDY4NA== | alexsalr 7217358 | 2019-01-23T20:49:40Z | 2019-01-23T21:07:43Z | NONE | Hi @guillaumeeb https://github.com/guillaumeeb , I have also created geotiff files from xarray using rasterio. I was working in with a a to_tiff method adapted to my workflow ( https://github.com/alexsalr/ciat_monitor_crops/blob/master/b_Temporal_Stack/xr_eotemp.py ), based in these methods https://github.com/robintw/XArrayAndRasterio. This was with rasterio prior to 1.0, so I don't know if the new version changes the behaviour. |
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Anyone working on a to_tiff? Alternatively, how do you write an xarray to a geotiff? 312203596 | |
400800345 | https://github.com/pydata/xarray/issues/2249#issuecomment-400800345 | https://api.github.com/repos/pydata/xarray/issues/2249 | MDEyOklzc3VlQ29tbWVudDQwMDgwMDM0NQ== | alexsalr 7217358 | 2018-06-27T19:25:10Z | 2018-06-27T19:38:46Z | NONE | I was trying to apply the same groupby('lat','long').apply() strategy for interpolating time series of optical remote sensing images. With @shoyer 's suggestions I managed to apply and paralellize a ufunc, which was significantly faster than operating by pixels. However I am still looking for a way to optimize the spline fitting and evaluation (maybe numba, as suggested). Any other suggestions would be appreciated. Im working with dask arrays, and my data looks like this:
``` def _cubic_spline(y, orig_times, new_times): # Filter NaNs nans = np.isnan(y)#.values)[:,0] # Try to fit cubic spline with filtered y values try: spl = interpolate.CubicSpline(orig_times.astype('d')[~nans], y[~nans])
``` |
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stacked_xarray.groupby('lat','lon').apply(func) over 3D array takes too long 335523891 |
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