for i, tsdata in enumerate([tseries, tseries_rel]):
for varnm in varnms:
if i == 0:
ymin, ymax = ylims[varnm]
else:
ymin, ymax = ylims_rel[varnm]
if varnm in ['MFC', 'PCP', 'HOWI'] and i == 1:
legend_loc = 'lower left'
else:
legend_loc = 'upper left'
suptitle = '%s (%s Onset/Retreat)' % (varnm, onset_nm)
ts_plot_all(tsdata, comp_yrs, comp_keys, varnm, index, enso, ymin, ymax,
suptitle, figsize, legend_loc=legend_loc)
filestr = 'daily_tseries-onset_%s-enso_%s-%s' % (onset_nm, enso_nm, savestr)
atm.savefigs(savedir + filestr, 'pdf', merge=True)
plt.close('all')
# ----------------------------------------------------------------------
# Variability in Accumulated precip / MFC over climatology
if plot_acc_clim:
for ts, daynm in zip([tseries, tseries_rel], ['day', 'dayrel']):
plt.figure(figsize=(8, 10))
for i, key in enumerate(['MFC_ACC', 'PCP_ACC']):
plt.subplot(2, 1, i + 1)
tsbar = ts[key].mean(dim='year')
tsplus = tsbar + ts[key].std(dim='year')
tsminus = tsbar - ts[key].std(dim='year')
tsmax = ts[key].max(dim='year')
tsmin = ts[key].min(dim='year')
days = tsbar[daynm]
fig_kw = {'figsize' : (14, 10), 'sharex' : True, 'sharey' : True}
gridspec_kw = {'left' : 0.05, 'right' : 0.98, 'bottom' : 0.05, 'top' : 0.95,
'wspace' : 0.04, 'hspace' : 0.1}
stipple_kw = {'markersize' : 2, 'markeredgewidth' : 1.5}
coeff = 'm'
suptitle = '%d-%dE Reg onto %s Onset - Reg Coefficients' % (lon1, lon2, onset_nm)
ylim, yticks = (-60, 60), np.arange(-60, 61, 20)
grp = atm.FigGroup(nrow, ncol, fig_kw=fig_kw, gridspec_kw=gridspec_kw,
suptitle=suptitle)
for varnm in varnms:
grp.next()
var = data['reg'][varnm + '_sector'][coeff]
p = data['reg'][varnm + '_sector']['p']
sector_plot(var, p, stipple_kw, grp, ylim, yticks)
atm.savefigs(savestr + 'sector_onset_' + onset_nm, 'pdf', merge=True)
plt.close('all')
# Lat-lon maps
plt_list = regdays + seasons
nrow = 2
ncol = len(plt_list)
fig_kw = {'figsize' : (14, 10), 'sharex' : True, 'sharey' : True}
gridspec_kw = {'left' : 0.05, 'right' : 0.95, 'bottom' : 0.05, 'top' : 0.95,
'wspace' : 0.15, 'hspace' : 0.1}
stipple_kw = {'markersize' : 2, 'markeredgewidth' : 1.5}
coeff = 'm'
suptitle = 'Reg onto %s Onset - Reg Coefficients' % onset_nm
grp = atm.FigGroup(nrow, ncol, fig_kw=fig_kw, gridspec_kw=gridspec_kw,
suptitle=suptitle)
for varnm in varnms:
tt = onset_TT(T, north=north, south=south)
# Some weirdness going on in 1991, for now just set to NaN
# Troubleshoot later
for nm in ['ttn', 'tts', 'tseries']:
vals = tt[nm].values
vals = np.ma.masked_array(vals, abs(vals) > 1e30).filled(np.nan)
tt[nm].values = vals
# Plot TTN and TTS
plot_tseries_together(tt[['ttn', 'tts']], tt['onset'].values,
suptitle=suptitle, standardize=False)
# Summary plot and timeseries in individual years
summarize_indices(years, tt['onset'])
plt.suptitle(suptitle)
plot_index_years(tt, suptitle=suptitle, yearnm='year', daynm='day')
if save:
atm.savefigs(varname + '_', 'png')
# Plot contour map of pressure-level data
p_plot = 400
T_plot = T_p[p_plot]
y, d = 0, 80
lat = atm.get_coord(T_plot, 'lat')
lon = atm.get_coord(T_plot, 'lon')
axlims = (lat.min(), lat.max(), lon.min(), lon.max())
plt.figure()
atm.pcolor_latlon(T_plot[y, d], axlims=axlims)
def saveclose(name, isave, exts):
if isave:
for ext in exts:
atm.savefigs(name, ext)
plt.close('all')
def index_tseries(days, ind, ind_roll, titlestr):
plt.plot(days, ind, label='daily')
plt.plot(days, ind_roll, label='%d-day rolling' % nroll)
plt.grid()
plt.legend(loc='lower right')
plt.title(titlestr)
plt.figure(figsize=(12, 10))
plt.subplot(221)
index_tseries(days, ds.howi_clim_norm, ds.howi_clim_norm_roll,
'HOWI ' + yearstr + ' Climatology')
for yr in [0, 1, 2]:
plt.subplot(2, 2, yr + 2)
index_tseries(days, ds.howi_norm[yr], ds.howi_norm_roll[yr],
'HOWI %d' % years[yr])
# ----------------------------------------------------------------------
# Onset and retreat indices
summarize_indices(years, onset, retreat, 'HOWI')
# ----------------------------------------------------------------------
# Plot timeseries of each year
plot_index_years(howi)
# ----------------------------------------------------------------------
# Save figures
if isave:
for ext in exts:
atm.savefigs(namestr, ext)
plt.suptitle(suptitle)
yplot = 1
else:
yplot += 1
plt.subplot(nrow, ncol, yplot)
plotyear(chp[varnm], y, xlims, ylims[varnm])
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.suptitle(suptitle)
yplot = 1
else:
yplot += 1
plt.subplot(nrow, ncol, yplot)
plotyear(chp[varnm], y, xlims, ylims[varnm])
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 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:
