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]
# ----------------------------------------------------------------------
# Composite timeseries
def lowest_highest(ind, nyrs):
ind_sorted = ind.to_series()
ind_sorted.sort()
lowest = ind_sorted[:nyrs].index.values
highest = ind_sorted[-1:-nyrs-1:-1].index.values
return lowest, highest
# Composite definitions
nyrs = 5
data = utils.wrapyear(ds, ds_pre, ds_post, daymin, daymax, year=year)
data.attrs = ds.attrs
return data
for plev in plevs:
for y, year in enumerate(years):
datafile = datafiles[plev][y]
d_onset, d_retreat = onset[y], retreat[y]
d0 = int(index[ind_nm][y].values)
ds_rel = xray.Dataset()
ds = get_data(datafile, year, d0, npre, npost)
ds_rel.attrs = ds.attrs
for nm in ds.data_vars:
var = atm.expand_dims(ds[nm], 'year', year)
ds_rel[nm] = utils.daily_rel2onset(var, d_onset, npre, npost)
ds_rel.attrs['d_onset'] = d_onset
ds_rel.attrs['d_retreat'] = d_retreat
savefile = savefiles[plev][y]
print('Saving to ' + savefile)
ds_rel.to_netcdf(savefile)
# ----------------------------------------------------------------------
# Compute climatologies and save
yearstr = '%d-%d' % (years.min(), years.max())
for plev in plevs:
relfiles = savefiles[plev]
savefile = savestr % plev + '_' + yearstr + '.nc'
ds = atm.combine_daily_years(None, relfiles, years, yearname='year')
with xray.open_dataset(onsetfile) as ds:
u = ds['U'].load()
oci = indices.onset_OCI(u)
d_onset = oci['onset']
# Daily data fields
var = 'precip'
yearnm, daynm = 'year', 'day'
cmap = 'hot_r'
axlims = (-10, 50, 40, 120)
with xray.open_dataset(datafile) as ds:
data = ds['PRECTOT'].load()
# Align daily data relative to onset day
ndays = 30
data = daily_rel2onset(data, d_onset, ndays, ndays, yearnm=yearnm, daynm=daynm)
years = data[yearnm].values
databar = data.mean(dim=yearnm)
# Days for pre/onset/post composites
compdays = comp_days_centered(5)
#compdays = comp_days_centered(1, 9)
# Calculate composites
comp = composite(data, compdays, daynm=daynm + 'rel')
compbar = collections.OrderedDict()
for key in comp:
compbar[key] = comp[key].mean(dim=yearnm)
# Plot pre/onset/post composites - climatology
cmin, cmax = 0, 20
saveclose('corr_tseries_', isave, exts)
# ----------------------------------------------------------------------
# Composite indices relative to onset day
keys = ['HOWI_100', 'OCI', 'SJKE', 'TT', 'WLH_MERRA_MFC_nroll7',
'WLH_MERRA_PRECIP_nroll7']
npre, npost = 30, 80
tseries_rel = xray.Dataset()
for key in keys:
print(key)
ts = index[key]['tseries']
d_onset = index[key]['onset'].values
tseries_rel[key] = utils.daily_rel2onset(ts, d_onset, npre, npost,
yearnm='year', daynm='day')
dayrel = tseries_rel['dayrel'].values
def plot_tseries(dayrel, ind, std, clr, key, xlabel, ylabel):
plt.plot(dayrel, ind, clr, label=key)
plt.fill_between(dayrel, ind-std, ind+std, color=clr, alpha=0.2)
plt.title(key, fontsize=12)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.autoscale(tight=True)
plt.figure(figsize=(9, 12))
nrow, ncol = 3, 2
clr = 'k'
for i, key in enumerate(keys):
# -- Interpolate to daily resolution
days = np.arange(1, 367)
pcp_i = np.nan * np.ones((len(years), len(days)))
for y, year in enumerate(years):
pcp_i[y] = np.interp(days, pcp_box['day'], pcp_box[y])
coords = {'day' : days, 'year' : years}
pcp = xray.DataArray(pcp_i, dims=['year', 'day'], coords=coords)
# Monsoon onset, retreat indices
index = utils.get_onset_indices(onset_nm, indfiles, years)
mfc = atm.rolling_mean(index['ts_daily'], nroll, center=True)
onset = index['onset']
ssn_length=index['length'].mean(dim='year')
data = {}
data['MFC'] = utils.daily_rel2onset(mfc, onset, npre, npost)
data[pcp_nm] = utils.daily_rel2onset(pcp, onset, npre, npost)
data['MFC_ACC'] = utils.daily_rel2onset(index['tseries'], onset, npre, npost)
for nm in varnms:
print('Loading ' + relfiles[nm])
with xray.open_dataset(relfiles[nm]) as ds:
if nm == 'PSI':
data[nm] = atm.streamfunction(ds['V'])
psimid = atm.subset(data[nm], {'plev' : (pmid, pmid)},
squeeze=True)
psimid.name = 'PSI%d' % pmid
data['PSI%d' % pmid] = psimid
elif nm == 'VFLXLQV':
var = atm.dim_mean(ds['VFLXQV'], 'lon', lon1, lon2)
data[nm] = var * atm.constants.Lv.values
for key in enso_keys:
if key not in enso.columns:
months = atm.season_months(key)
month_names = [(atm.month_str(m)).capitalize() for m in months]
enso[key] = enso[month_names].mean(axis=1)
enso = enso[enso_keys]
col_names = [enso_nm + ' ' + nm for nm in enso.columns]
enso.columns = col_names
# ----------------------------------------------------------------------
# Daily timeseries
ts = xray.Dataset()
for nm in ['GPCP', 'PRECTOT']:
ts[nm] = atm.mean_over_geobox(data[nm], lat1, lat2, lon1, lon2)
ts['MFC'] = utils.daily_rel2onset(index_all['CHP_MFC']['daily_ts'],
index[ind_nm], npre, npost)
ts['CMFC'] = utils.daily_rel2onset(index_all['CHP_MFC']['tseries'],
index[ind_nm], npre, npost)
# Extract variables at specified latitudes
for nm, lat0 in lat_extract.iteritems():
var = atm.dim_mean(data[nm], 'lon', lon1, lon2)
lat = atm.get_coord(var, 'lat')
lat0_str = atm.latlon_labels(lat0, 'lat', deg_symbol=False)
# key = nm + '_' + lat0_str
key = nm
lat_closest, _ = atm.find_closest(lat, lat0)
print '%s %.2f %.2f' % (nm, lat0, lat_closest)
ts[key] = atm.subset(var, {'lat' : (lat_closest, None)}, squeeze=True)
index.load()
index = index.sel(year=years)
d0 = index[ind_nm].values
# Precip data
if pcp_nm == 'cmap':
pcp = precipdat.read_cmap(pcpfiles, yearmin=min(years), yearmax=max(years))
# Interpolate to daily resolution
name, attrs, coords, dimnames = atm.meta(pcp)
days = np.arange(3, 364)
interp_func = scipy.interpolate.interp1d(pcp['day'], pcp, axis=1)
vals = interp_func(days)
coords['day'] = xray.DataArray(days, coords={'day' : days})
pcp = xray.DataArray(vals, dims=dimnames, coords=coords, name=name,
attrs=attrs)
else:
pcp = atm.combine_daily_years(None, pcpfiles, years, yearname='year',
subset_dict=subset_dict)
# Wrap from following year to get extended daily range
daymin = min(d0) - npre
daymax = max(d0) + npost
pcp = utils.wrapyear_all(pcp, daymin=daymin, daymax=daymax)
# Daily relative to onset/withdrawal
pcp_rel = utils.daily_rel2onset(pcp, d0, npre, npost)
print('Saving to ' + savefile)
atm.save_nc(savefile, pcp_rel)
