# ----------------------------------------------------------------------
# Average values over JJAS and LRS - interannual variability
keys = ['PCP', 'EVAP', 'ANA', 'dW', 'P-E+dW-ANA', 'MFC']
fmts = {'PCP' : 'b', 'EVAP' : 'g', 'ANA' : 'r', 'dW' : 'c',
'P-E+dW-ANA' : 'm', 'MFC' : 'k--'}
ssn[keys].plot(figsize=(10, 10), style=fmts, legend=False, grid=True)
plt.legend(loc='upper left', fontsize=10)
plt.ylabel('mm/day')
plt.title('JJAS Mean')
plt.figure(figsize=(7, 10))
plt.suptitle('JJAS mean standardized timeseries')
nrow, ncol = len(keys), 1
for i, col in enumerate(ssn_st[keys].columns):
plt.subplot(nrow, ncol, i + 1)
plt.plot(years, ssn_st[col], 'k')
plt.title(col, loc='left', fontsize=10)
plt.autoscale(tight=True)
plt.grid(True)
if i < nrow - 1:
plt.gca().set_xticklabels([])
else:
plt.xlabel('Year')
atm.scatter_matrix(ssn[keys], figsize=(14, 10), pmax_bold=0.05, incl_p=True,
annotation_pos=(0.05, 0.6), incl_line=True, suptitle='JJAS Mean')
handles = [cs.collections[0], cs2.collections[0]] + handles
plt.legend(handles, labels, fontsize=12)
plt.title('Averaging Regions for Onset Indices')
saveclose('map_', isave, exts)
# ----------------------------------------------------------------------
# Monsoon strength
styles = {'MERRA' : 'r--', 'MERRA_DET' : 'r', 'ERAI' : 'b--', 'ERAI_DET' : 'b',
'AIR' : 'g--', 'AIR_DET' : 'g'}
strength.plot(figsize=(10,10), style=styles)
plt.title('JJAS Monsoon Strength')
plt.xlabel('Year')
plt.ylabel('Index (mm/day)')
plt.grid()
plt.legend(fontsize=12)
atm.scatter_matrix(strength)
plt.suptitle('JJAS Monsoon Strength')
saveclose('strength_', isave, exts)
# ----------------------------------------------------------------------
# Histograms of each index
keys = index.keys()
for key in keys:
ind = index[key]
if 'retreat' in ind.keys():
retreat = ind.retreat
else:
retreat = None
indices.summarize_indices(ind.year, ind.onset, retreat, ind.title)
saveclose('onset_retreat_hist_', isave, exts)
i_detrend = True
# ----------------------------------------------------------------------
# Correlations between indices
opts = {'figsize' : (12, 9), 'annotation_pos' : (0.05, 0.75), 'incl_p' : True,
'incl_line' : True, 'pmax_bold' : 0.05}
for i_detrend in [True, False]:
# Correlations between onset/retreat/length
if i_detrend:
suptitle = onset_nm + ' (Detrended Indices)'
atm.scatter_matrix(detrend(index), suptitle=suptitle, **opts)
else:
atm.scatter_matrix(index, suptitle=onset_nm, **opts)
# Cumulative and average rainfall over monsoon season
df1 = ssn[['onset', 'retreat', 'length']]
nms = ['MFC_JJAS', 'MFC_LRS', 'PCP_JJAS', 'PCP_LRS']
suptitle = 'Season Totals (%s Monsoon Onset/Retreat)' % onset_nm
for key in ['_TOT', '_AVG']:
keys = [nm + key for nm in nms]
df2 = ssn[keys]
if i_detrend:
atm.scatter_matrix_pairs(detrend(df1), detrend(df2),
suptitle=suptitle + ' (Detrended)')
else:
atm.scatter_matrix_pairs(df1, df2, suptitle=suptitle)
row, col = atm.subplot_index(nrow, ncol, yplot)
if row == nrow:
plt.xlabel("Day")
else:
plt.gca().set_xticklabels("")
if col > 1:
plt.gca().set_yticklabels("")
if isavefigs:
atm.savefigs("onset_changepoint_merged_" + varnm.upper(), "pdf")
plt.close("all")
df = pd.DataFrame()
for key in ["onset", "retreat"]:
for varnm in varnms:
df["%s_%s" % (key, varnm.upper())] = chp[varnm][key].to_series()
atm.scatter_matrix(df, incl_p=True, incl_line=True, annotation_pos=(0.05, 0.7))
plt.suptitle("Merged Changepoints")
plt.figure(figsize=(8, 10))
for i, key in enumerate(["onset", "retreat"]):
plt.subplot(2, 1, i + 1)
for varnm in varnms:
plt.plot(years, df["%s_%s" % (key, varnm.upper())], label=varnm.upper())
plt.legend()
plt.xlabel("Year")
plt.ylabel("Day of Year")
plt.title(key.capitalize() + " from Merged Changepoint Method")
plt.grid()
plt.autoscale(tight=True)
if isave:
if isave:
atm.savefigs('onset_changepoint_' + varnm.upper(), 'pdf')
plt.close('all')
df = pd.DataFrame()
for key in ['onset', 'retreat']:
for varnm in varnms:
df['%s_%s' % (key, varnm.upper())] = chp[varnm][key].to_series()
if incl_merged:
key2 = '%s_%s_m' % (key, varnm.upper())
df[key2] = chpm[varnm][key].to_series()
opts = {'incl_p' : True, 'incl_line' : True, 'annotation_pos' : (0.05, 0.7)}
if incl_merged:
for nm in ['onset', 'retreat']:
keys = [key for key in df.columns if key.startswith(nm)]
atm.scatter_matrix(df[keys], **opts)
else:
atm.scatter_matrix(df, **opts)
plt.figure(figsize=(8, 10))
clrs = ['b', 'r']
for i, key in enumerate(['onset', 'retreat']):
plt.subplot(2, 1, i + 1)
for varnm, clr in zip(varnms, clrs):
key1 = '%s_%s' % (key, varnm.upper())
plt.plot(years, df[key1], clr, label=varnm.upper())
if incl_merged:
plt.plot(years, df[key1 + '_m'], clr + '--', label=varnm.upper() + '_m')
plt.legend(fontsize=10)
plt.xlabel('Year')
plt.ylabel('Day of Year')
