def var_calcs(var, jday=0, latlon=(-90, 90, 40, 120), plevs=(850, 200),
dp_vars=['U', 'OMEGA'], sector_lons=(60, 100)):
"""Process a single variable from a single day."""
lat1, lat2, lon1, lon2 = latlon
opts = merra.url_opts(var.name)
vertical = opts['vertical']
if vertical == 'X':
plevs = [None]
if dp_vars is not None and var.name in dp_vars:
dp = True
else:
dp = False
data = xray.Dataset()
# Lat-lon data
print('Lat-lon data')
for plev in plevs:
print('plev', plev)
var_out = latlon_data(var, lat1, lat2, lon1, lon2, plev)
data[var_out.name] = var_out
if dp:
print('Computing d/dp')
var_out = pgradient(var, lat1, lat2, lon1, lon2, plev)
data[var_out.name] = var_out
# Sector and zonal mean data
print('Computing zonal mean')
var_out = sector_mean(var, 0, 360)
data[var_out.name] = var_out
if vertical == 'P':
print('Computing sector mean')
var_out = sector_mean(var, sector_lons[0], sector_lons[1])
data[var_out.name] = var_out
# Compute daily data from subdaily data
nperday = len(atm.get_coord(data, 'time'))
data = atm.daily_from_subdaily(data, nperday, dayname='day',
dayvals=[jday])
# Make sure output is in a Dataset
if isinstance(data, xray.DataArray):
data = data.to_dataset()
return data
def get_group(varnm, version):
opts = merra.url_opts(varnm, version)
group = '%s%s_%s_%s' % (opts['res'], opts['vertical'], opts['kind'],
opts['time_kind'])
return group