def read_primary_dataset(variq,dataset,lat_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
for i in range(len(variq)):
### Read in data for selected region
lat_bounds,lon_bounds = UT.regions(reg_name)
dataall,lats,lons = read_primary_dataset(variq[i],dataset,
lat_bounds,lon_bounds)
### Remove ensemble mean
if rm_ensemble_mean == True:
data= dSS.remove_ensemble_mean(dataall)
print('*Removed ensemble mean*')
elif rm_ensemble_mean == False:
data = dataall
### Calculate ensemble mean
meandata = np.nanmean(data,axis=0)
del data #save storage
### Composite over selected period (x2)
if monthlychoice == 'DJF':
years = np.arange(meandata.shape[0]) + 1921
else:
years = np.arange(meandata.shape[0]) + 1920
length = years.shape[0]//2
test_output_mat = np.empty(
(np.max(expList) + 1, foldsN, 180 * int(
np.round(
np.shape(data_all)[0] *
(1.0 - segment_data_factor)))))
for exp in expList:
# get the data together
data, data_obs, = data_all, data_obs_all,
if rm_annual_mean == True:
data, data_obs = dSS.remove_annual_mean(
data, data_obs, lats, lons, lats_obs, lons_obs)
print('*Removed annual mean*')
if rm_ensemble_mean == True:
datae = dSS.remove_ensemble_mean(data)
print('*Removed ensemble mean*')
if rm_standard_dev == True:
data = dSS.rm_standard_dev(datae, window)
print('*Removed standard deviation*')
if land_only == True:
data, data_obs = dSS.remove_ocean(
data, data_obs, lat_bounds, lon_bounds)
if ocean_only == True:
data, data_obs = dSS.remove_land(
data, data_obs, lat_bounds, lon_bounds)
# ### Loop over folds