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
