def merge_labels_within_lonlatbox(precur, lonlatbox=list):
prec_labels = precur.prec_labels.copy()
corr_xr = precur.corr_xr
new = core_pp.get_selbox(prec_labels, lonlatbox)
regions = np.unique(new)[~np.isnan(np.unique(new))]
pregs = [] ; nregs = [] # positive negative regions
for r in regions:
m = view_or_replace_labels(prec_labels, regions=[r])
s = np.sign(np.mean(corr_xr.values[~np.isnan(m).values]))
if s == 1:
pregs.append(r)
elif s == -1:
nregs.append(r)
for regs in [pregs, nregs]:
if len(regs) != 0:
maskregions = view_or_replace_labels(prec_labels, regions=regs)
prec_labels.values[~np.isnan(maskregions).values] = min(regs)
return prec_labels
plt.savefig(os.path.join(rg.path_outsub1, f'snapshots_{var}_rm{rm}.pdf'))
#%% Correlation PNA-like RW with Wavenumber 6 phase 2 # only for eastern
import core_pp, find_precursors
values = []
if west_or_east == 'eastern':
lags_list = range(-10,10)
for lag in lags_list:
selbox = (0,360,25,60)
# selbox = (140,300,20,73)
tfreq = 1
# lag = 0
dates_RV = core_pp.get_subdates(pd.to_datetime(rg.fulltso.time.values),
start_end_date=rg.start_end_TVdate)
RV_ts = rg.fulltso.sel(time=dates_RV)
ds_v300 = core_pp.import_ds_lazy(rg.list_precur_pp[1][1])
dslocal = core_pp.get_selbox(ds_v300, selbox=selbox)
datesRW = core_pp.get_subdates(pd.to_datetime(dslocal.time.values),
start_end_date=rg.start_end_TVdate)
datesRW = datesRW + pd.Timedelta(f'{lag}d')
dslocal = dslocal.sel(time=datesRW)
wv6local = core_pp.get_selbox(xarray.sel(lag=5), selbox=selbox)
patternlocal = wv6local.mean(dim='lag')
ts = find_precursors.calc_spatcov(dslocal, patternlocal)
ts_15, d = functions_pp.time_mean_bins(ts, tfreq, start_end_date=start_end_TVdate,
closed_on_date=start_end_TVdate[-1])
RV_15, d = functions_pp.time_mean_bins(RV_ts, tfreq, start_end_date=start_end_TVdate,
closed_on_date=start_end_TVdate[-1])
'col_dim': 'tfreq',
'x_ticks': np.arange(240, 300, 20),
'y_ticks': np.arange(0, 61, 10)
})
f_name = 'clustering_dendogram_{}'.format(xrclustered.attrs['hash']) + '.pdf'
path_fig = os.path.join(rg.path_outmain, f_name)
fig.savefig(path_fig, bbox_inches='tight') # dpi auto 600
#%%
if region != 'init':
# try:
# ds_cl_ts = core_pp.get_selbox(ds_cl['xrclusteredall'].sel(q=q, n_clusters=c),
# selbox)
# ds_new = cl.spatial_mean_clusters(var_filename,
# ds_cl_ts,
# selbox=selbox)
# ds_new['xrclusteredall'] = xrclustered
# f_name = 'q{}_nc{}'.format(int(q), int(c))
# except:
ds_cl_ts = core_pp.get_selbox(
ds_cl['xrclusteredall'].sel(tfreq=t, n_clusters=c), selbox)
ds_new = cl.spatial_mean_clusters(var_filename, ds_cl_ts, selbox=selbox)
ds_new['xrclusteredall'] = xrclustered
f_name = 'tf{}_nc{}'.format(int(t), int(c))
filepath = os.path.join(rg.path_outmain, f_name)
cl.store_netcdf(ds_new,
filepath=filepath,
append_hash='dendo_' + xrclustered.attrs['hash'])
TVpath = filepath + '_' + 'dendo_' + xrclustered.attrs['hash'] + '.nc'