def get_onset_indices(onset_nm, datafiles, years, data=None):
"""Return monsoon onset/retreat/length indices.
"""
# Options for CHP_MFC and CHP_PCP
lat1, lat2 = 10, 30
lon1, lon2 = 60, 100
chp_opts = [None, lat1, lat2, lon1, lon2]
if onset_nm == 'HOWI':
maxbreak = 10
npts = 100
ds = atm.combine_daily_years(['uq_int', 'vq_int'], datafiles, years,
yearname='year')
index, _ = indices.onset_HOWI(ds['uq_int'], ds['vq_int'], npts,
maxbreak=maxbreak)
index.attrs['title'] = 'HOWI (N=%d)' % npts
elif onset_nm == 'CHP_MFC':
if data is None:
tseries = get_mfc_box(datafiles, None, None, years, *chp_opts)
data = tseries['MFC_ACC']
index['ts_daily'] = tseries['MFC']
index = indices.onset_changepoint(data)
elif onset_nm == 'CHP_PCP':
if data is None:
tseries = get_mfc_box(None, datafiles, None, years, *chp_opts)
data = tseries['PCP_ACC']
index = indices.onset_changepoint(data)
index['ts_daily'] = tseries['PCP']
# Monsoon retreat and length indices
if 'retreat' in index:
index['length'] = index['retreat'] - index['onset']
else:
index['retreat'] = np.nan * index['onset']
index['length'] = np.nan * index['onset']
return index
north=(5, 30, 40, 100)
south=(-15, 5, 40, 100)
index['TT'] = indices.onset_TT(T, north=north, south=south)
# Some weirdness going on in 1991, for now just set to NaN
for nm in ['ttn', 'tts', 'tseries']:
vals = index['TT'][nm].values
vals = np.ma.masked_array(vals, abs(vals) > 1e30).filled(np.nan)
index['TT'][nm].values = vals
index['TT'].attrs['title'] = 'TT'
# ----------------------------------------------------------------------
# Changepoint method with accumulated MFC / precip (Cook & Buckley 2009)
mfc_acc = np.cumsum(mfcbar, axis=1)
precip_acc = np.cumsum(precipbar, axis=1)
index['CHP_MFC'] = indices.onset_changepoint(mfc_acc)
index['CHP_PCP'] = indices.onset_changepoint(precip_acc)
for key in ['CHP_MFC', 'CHP_PCP']:
index[key].attrs['title'] = key
# ----------------------------------------------------------------------
# Monsoon strength indices
def detrend(vals, index):
vals_det = scipy.signal.detrend(vals)
vals_det = vals_det / np.std(vals_det)
output = pd.Series(vals_det, index=index)
return output
# MERRA MFC
mfc_JJAS = atm.subset(mfcbar, {'day' : (atm.season_days('JJAS'), None)})
varid, filestr = 'MFC', 'MFC_ps-300mb'
elif varnm == 'precip':
varid, filestr = 'PRECTOT', 'precip'
files = [datadir + 'merra_%s_%d.nc' % (filestr, year) for year in years]
data[varnm] = atm.combine_daily_years(varid, files, years, yearname='year',
subset_dict=subset_dict)
data[varnm] = atm.mean_over_geobox(data[varnm], lat1, lat2, lon1, lon2)
data[varnm] = atm.precip_convert(data[varnm], data[varnm].attrs['units'],
'mm/day')
# Accumulated precip or MFC
data_acc[varnm] = np.cumsum(data[varnm], axis=1)
# Compute onset and retreat days
chp[varnm] = onset_changepoint(data_acc[varnm])
if incl_merged:
chpm = {}
for varnm in varnms:
datafile = datadir2 + 'merra_onset_changepoint_merged_%s.nc' % varnm.upper()
print('Reading ' + datafile)
chpm[varnm] = xray.open_dataset(datafile)
def plotyear(chp, y, xlims, ylims):
days = chp['day'].values
d_onset = chp['onset'][y]
d_retreat = chp['retreat'][y]
plt.plot(days, chp['tseries'][y])
plt.plot(days, chp['tseries_fit_onset'][y])
plt.plot(days, chp['tseries_fit_retreat'][y])