def ann_cycle_obs_5var_aridity_update():
"seasonal cycle climatology"
keys = ['pan', 'rn', 'vpd', 'ea', 'tavg', 'wind'] # 'rh',
keynames = ['Epan', r'$R_n$', 'VPD', r'$e_a$', r'$T_a$', r'$u_2$'] # 'RH',
cols = [
'black', 'Orange', 'Purple', 'springgreen', 'Crimson', 'DeepSkyblue'
] # 'olive',
numbers = ['d', 'b', 'c', 'e', 'f']
arid_class = [
'average', r'humid (0<$\phi$<2)', r'sub-humid (2<$\phi$<4)',
r'semi-arid (4<$\phi$<8)', r'arid ($\phi$>8)'
]
def annual_all_records(df):
"This is a new function to calculate mean of each hours across all sites, not independently"
ann = [np.nanmean(g) for _, g, in df.groupby(df.index.month)]
return array(ann)
def zscore(ts):
return (ts - np.nanmean(ts)) / np.nanstd(ts)
ist = 0
met_station = []
arid = []
pan_obs_gapfill = load('%s/pan_obs_gapfill_good_stations' % workspace)
rn = load('%s/Rn_0.2_0.5_good_stations' % (workspace))
for ibasin in xrange(0, 10):
data = scipy.io.loadmat('%s/%s_AP.mat' % (datadir, ibasin + 1))
arid.append(
load('%s/aridity_station_%s' % (workspace, basinlongs[ibasin])))
for istation in good_stations[ibasin]:
# the PowerSpectrum method take the matrix as different segment, so shoule be a 1d array
dataplot = [
Gapfill(data[v][0, istation][0:tstep].flatten()).flatten()
for v in keys[2:6]
]
dataplot.insert(0, pan_obs_gapfill[ist, :].flatten())
dataplot.insert(1, rn[ist, :].flatten())
# met_station.append(array([zscore(annual_all_records(pd.Series(dataplot[i], index=dates))) for i in xrange(6)]))
met_station.append(
array([
annual_all_records((pd.Series(zscore(dataplot[i]),
index=dates)))
for i in xrange(6)
]))
ist = ist + 1
met_station = array(met_station)
arid = array(list(itertools.chain(*arid)))
index_DI = msc_groupby_DI(arid)
met_avg = [mean(met_station[index, :, :], axis=0) for index in index_DI]
met_avg.insert(0, mean(met_station, axis=0))
fig = plt.figure(figsize=(12, 7.5))
lons = load('%s/lons_good_stations' % workspace)
lats = load('%s/lats_good_stations' % workspace)
colors = r_[linspace(0, 1, 4), linspace(0, 1, 4)]
mymap = plt.get_cmap("coolwarm")
my_colors = mymap(colors)
arid_labels = ['humid', 'sub-humid', 'semi-arid', 'arid']
ax = fig.add_subplot(2, 3, 1)
[
Plotting.Mapshow_Aridity_subplot(ax, lons[index_DI[i]],
lats[index_DI[i]], 30, 0.8, 0, 1000,
my_colors[i], arid_labels[i])
for i in xrange(0, 4)
]
plot_loc = (4, 2, 3, 5, 6)
for i, mets in enumerate(met_avg):
ax = fig.add_subplot(2, 3, plot_loc[i])
lines = [
ax.plot(met,
'--',
dashes=(12, 5),
color=cols[im],
label=keynames[im],
linewidth=3,
alpha=0.9,
rasterized=True) for im, met in enumerate(mets)
] # '-o', ms=10, mew=0,
ax.set_xlim(0, 11)
ax.set_xticks(range(12))
ax.set_xticklabels(range(1, 13))
ax.set_ylim(-2, 2)
ymin, ymax = ax.get_ylim()
ax.text(10.8,
ymin + 0.03 * (ymax - ymin),
arid_class[i],
ha='right',
fontsize=16)
ax.text(0.8,
ymax - 0.13 * (ymax - ymin),
'(%s)' % numbers[i],
fontsize=16)
ax.tick_params(axis='y', labelsize=14)
ax.tick_params(axis='x', labelsize=14)
ax.grid('on')
lines = list(itertools.chain(*lines))
plt.figlegend(
lines, keynames, loc='lower center', ncol=6, fontsize=16
) # doesn't work bbox_to_anchor=(0, -0.1, 1, 1),, bbox_transform=plt.gcf().transFigure
# plt.subplots_adjust(bottom=0.2)
plt.tight_layout(rect=[0, 0.07, 1, 1])
# savefig('%s/fig1_ann_cycle_5var_aridity_good_station.tif' %figdir, dpi=300)
plt.show()
return
