I'm new to stackexchange, so I'm sorry if I make any mistakes in posting here. I'm using metpy.calc.windchill in order to calculate wind chill values, and it automatically spits out an array with a numpy mask on it. wind_chill = metpy.calc.windchill( t2m * units.K, wind_mag * units('m/s'), ) windchill_ma = wind_chill.to_masked_array() mask = ma.getmask(windchill_ma) The mask is a numpy ndarray with boolean values. I'd like to convert all values that are masked (set as True in the mask) to np.nan while retaining the DataArray structure. For example: wind_chill = xr.DataArray(array([[[1,6,4,8],[2,4,3,5]],[[2,5,3,6],[2,6,4,8]]])) mask = array([[[True,True,False,False],[........ masked_wind_chill = xr.DataArray(array([[[NaN,NaN,4,8...... masked_wind_chill = wind_chill.where(~mask) and other variations of .where(mask=False) and whatnot just end up not applying the mask at all to the data. If I use .where(mask=True), all data points are masked regardless of if the value is True or False for that data point. Am I using mask wrong? Thank you for reading! Continue reading...
