Rasterize_function=rasterize_points_griddata, Rasterize_function=rasterize_points_radial,ĭefault griddata interpolation: geo_griddata = make_geocube( With radial interpolation: geo_radial = make_geocube( Rasterize_function=partial(rasterize_points_griddata, method="linear"), You can also fill in the missing data: geo_grid_cubic = make_geocube(Īnd you can use linear interpolation as well: geo_grid_linear = make_geocube( "long": ,ĭf, geometry=geopandas.points_from_xy(df, df), crs="EPSG:4326",įrom import make_geocubeįrom geocube.rasterize import rasterize_points_griddata Idx = pd.om_arrays(arrays=, names=)īut when it is interpolated, I get only nan: dsi = da.interp(Latitude=Latitude, Longitude=Longitude,method='linear') Then I have tried also using xarray to interpolate, I could plot my data but could not interpolate: Latitude=points3d.values Rasterize_function=partial(rasterize_points_griddata, method="cubic"), I'm also open to use other libraries, but seems like scipy is the best one.Įdit: I have tried solution, however it return empty raster: geo_grid = make_geocube( My end goal is to interpolate these points to get raster with the given dimensions (3586, 2284) with the correct coordinates. TypeError: griddata() missing 1 required positional argument: 'xi' I have tried to do something similar to this post : xt,yt = df.values, df.valuesĬONC = griddata((xt,yt), zt, method='cubic')īut then it says I'm missing the xi argument: I want my final raster to have size that I have already defined (3586, 2284). I have dataframe which contains coordinates and measurements, something similar to this (this is fake): id lat long mes