# Faz o acumulado da estação
r = 31 * len(ntrimetres[i])
data_all_aux = np.nansum(np.swapaxes(data_all_aux, 1, 2) \
.reshape(r, 20, nlat, nlon), axis=0)
print(data_all_aux.shape)
data_all = np.full((20, nlat, nlon), np.nan)
for memb in range(20):
data_all[memb, ...], lons = shiftgrid(180., data_all_aux[memb, ...],
lons_aux, start=False)
# pm.plotmap(data_all[0, 23:33, 45:54], lats[23:33], lons[45:54])
# print(data_all[0, 23:33, 45:54].shape)
# raw_input()
y_season[a, :] = data_all[0, 23:33, 45:54]
y_season = np.swapaxes(y_season, 0, 1)
# Usado no hindcast
fname = 'pcp-seasonacc-rsm97-2016-{0}-{1}.nc' \
.format(fcst_month, tri.upper())
writenc4(y_season, lats[23:33], lons[45:54], fname, itime,
fcst_month, 11, 1981, 10, 9)
fname = 'pcp-seasonacc-rsm97-2016-{0}-{1}' \
.format(fcst_month, tri.upper())
np.save(fname, y_season)
# ano, membro, lat, lon
y_season = np.full((itime, 20, 72, 109), np.nan)
for a, y in enumerate(ltime):
data_all, seaccum, monaccum, seamean, monmean, \
lats, lons = post.PostModel(fcst_month, ntrimetres[i], y,
model='RSM97')
# Faz o acumulado da estação
r = 31 * len(ntrimetres[i])
data_all = np.nansum(np.swapaxes(data_all, 1, 2) \
.reshape(r, 20, nlat, nlon), axis=0)
# data_all = np.full((20, nlat, nlon), np.nan)
print(data_all.shape)
y_season[a, :] = data_all
y_season = np.swapaxes(y_season, 0, 1)
# Usado no hindcast
fname = 'pcp-seasonacc-rsm97-2016-{0}-{1}.nc' \
.format(fcst_month, tri.upper())
writenc4(y_season, lats, lons, fname, itime,
fcst_month, 11, 2016, nlat, nlon)
fname = 'pcp-seasonacc-rsm97-2016-{0}-{1}' \
.format(fcst_month, tri.upper())
np.save(fname, y_season)
