def housekeeping(var):
# Convert units
unit_dict={'m' : ('km', 1e-3)}
units_in = var.attrs.get('units')
if units_in in unit_dict:
attrs = var.attrs
attrs['units'] = unit_dict[units_in][0]
var = var * unit_dict[units_in][1]
var.attrs = attrs
# Fill Ro200 with NaNs near equator
if var.name == 'Ro200':
latbuf = 5
lat = atm.get_coord(var, 'lat')
latbig = atm.biggify(lat, var, tile=True)
vals = var.values
vals = np.where(abs(latbig)>latbuf, vals, np.nan)
var.values = vals
return var
var, _, _ = utils.load_dailyrel(datafiles[key][nm])
if 'year' in var.dims:
var = var.mean(dim='year')
data[key][nm] = var
# ----------------------------------------------------------------------
# Housekeeping
# Fill Ro200 with NaNs near equator
varnm = 'Ro200'
for key1 in data:
if varnm in data[key1]:
latbuf = 5
lat = atm.get_coord(data[key1][varnm], 'lat')
latbig = atm.biggify(lat, data[key1][varnm], tile=True)
vals = data[key1][varnm].values
vals = np.where(abs(latbig)>latbuf, vals, np.nan)
data[key1][varnm].values = vals
# ----------------------------------------------------------------------
# Sector mean data
lonname, latname = 'XDim', 'YDim'
sectordata = {}
for key1 in data:
sectordata[key1] = collections.OrderedDict()
for varnm in data[key1]:
var = atm.subset(data[key1][varnm], {lonname : (lon1, lon2)})
sectordata[key1][varnm] = var.mean(dim=lonname)
def applymask(data, mask):
_, _, coords, _ = atm.meta(data)
maskbig = atm.biggify(mask, data, tile=True)
vals = np.ma.masked_array(data, maskbig).filled(np.nan)
data_out = xray.DataArray(vals, coords=coords)
return data_out
onset, retreat, length = index['onset'], index['retreat'], index['length']
tseries[onset_nm] = index['tseries']
# ENSO
enso = utils.get_enso_indices(years)
enso = xray.DataArray(enso[enso_nm]).rename({'Year' : 'year'})
# ----------------------------------------------------------------------
# Climatology
index_clim = index.mean(dim='year')
tseries_clim = tseries.mean(dim='year')
enso_clim = enso.mean(dim='year').values
# Tile the climatology to each year for plot_tseries_together
vals = atm.biggify(tseries_clim[onset_nm], index['tseries'].values, tile=True)
_, _, coords, dims = atm.meta(index['tseries'])
ts_clim = xray.DataArray(vals, name=tseries_clim[onset_nm].name, coords=coords,
dims=dims)
tseries[onset_nm + '_clim'] = ts_clim
# ----------------------------------------------------------------------
# Timeseries relative to onset, shifted to 0 at onset day
npre, npost = 0, 200
tseries_rel = xray.Dataset()
for key in tseries.data_vars:
tseries_rel[key] = daily_rel2onset(tseries[key], onset, npre, npost,
yearnm='year', daynm='day')
if key.startswith('CHP') or key.endswith('ACC'):
tseries_rel[key] = tseries_rel[key] - tseries_rel[key][:, 0]
def applymask(ds, mask_in):
for nm in ds.data_vars:
mask = atm.biggify(mask_in, ds[nm], tile=True)
vals = np.ma.masked_array(ds[nm], mask=mask).filled(np.nan)
ds[nm].values = vals
return ds