Apologies, the method is in data_process, not utils, as incorrectly stated in the header of the previous email.
On Sun, Nov 26, 2017 at 6:29 PM, Michael Anderson < michael.arthur.ander...@gmail.com> wrote: > I'm working on https://issues.apache.org/jira/browse/CLIMATE-797. > > The comments in the method in question state: > > If any of dataset in dataset_array has missing values at a grid point, > the values at the grid point in all other datasets are masked. > > > The problem here is that the method assumes a masked array is passed as an > input. > > If a regular numpy array (e.g. OCW dataset) is passed, it does not have a > mask attribute and an error is thrown > > > 1. I could tidy up the error handling to make it more clear to the caller > that a masked array was expected. > > > 2. I could check if a mask exists and use that. In the case of the mask not > being supplied, I could carry out the intent of the function and manually > check the array for "missing values". Other than None or NaN, are there any > other values that by convention constitute missing? The netCDF default fill > values? > > > Preferences on the approach and / or suggestions on the second approach? > > > Thanks, > > > Michael A. Anderson > > > > >
