def remove_ocean(data, data_obs, lat_bounds, lon_bounds): """ Masks out the ocean for land_only == True """ ### Import modules import numpy as np from netCDF4 import Dataset import calc_dataFunctions as df ### Read in land mask directorydata = '/Users/zlabe/Data/masks/' filename = 'lsmask_19x25.nc' datafile = Dataset(directorydata + filename) maskq = datafile.variables['nmask'][:] lats = datafile.variables['latitude'][:] lons = datafile.variables['longitude'][:] datafile.close() mask, lats, lons = df.getRegion(maskq, lats, lons, lat_bounds, lon_bounds) ### Mask out model and observations datamask = data * mask data_obsmask = data_obs * mask return datamask, data_obsmask
def read_primary_dataset(variq, dataset, lat_bounds=lat_bounds, lon_bounds=lon_bounds): data, lats, lons = df.readFiles(variq, dataset, monthlychoice) datar, lats, lons = df.getRegion(data, lats, lons, lat_bounds, lon_bounds) print('\nOur dataset: ', dataset, ' is shaped', data.shape) return datar, lats, lons
def read_obs_dataset(variq,dataset_obs,lat_bounds=lat_bounds,lon_bounds=lon_bounds): data_obs,lats_obs,lons_obs = df.readFiles(variq,dataset_obs,monthlychoice) data_obs,lats_obs,lons_obs = df.getRegion(data_obs,lats_obs,lons_obs, lat_bounds,lon_bounds) if dataset_obs == '20CRv3': if monthlychoice == 'DJF': year20cr = np.arange(1837,2015+1) else: year20cr = np.arange(1836,2015+1) year_obsall = np.arange(yearsall[sis].min(),yearsall[sis].max()+1,1) yearqq = np.where((year20cr >= year_obsall.min()) & (year20cr <= year_obsall.max()))[0] data_obs = data_obs[yearqq,:,:] print('our OBS dataset: ',dataset_obs,' is shaped',data_obs.shape) return data_obs,lats_obs,lons_obs