for i, p in zip(range(C.shape[0]), perm):
f.write("%d, %d, %g, %g\n" %
(i + 1, p + 1, C[i, perm[i]], OC[i, perm[i]]))
print("Rendering images")
for i in range(len(perm)):
if perm[i] >= 0:
f = plt.figure(figsize=(12, 6))
gs = matplotlib.gridspec.GridSpec(2,
2,
width_ratios=[5, 5],
height_ratios=[6, 4])
plt.subplot(gs[0, 0])
plot_data_robinson(np.reshape(comps1[:, i], (Nlats1, Nlons1)),
lats1,
lons1,
subplot=True,
euro_centered=True)
plt.title('Component %d' % (i + 1))
plt.subplot(gs[0, 1])
plot_data_robinson(np.reshape(comps2[:, perm[i]] * sign_flip[i],
(Nlats2, Nlons2)),
lats2,
lons2,
subplot=True,
euro_centered=True)
plt.title(
'Component %d%s' %
(perm[i] + 1, " (sign fliped)" if sign_flip[i] == -1 else ""))
plt.subplot(gs[1, :])
plt.plot(ts1[i, :].T, 'b-')
# we must match by time
coss = subsample_component_2x2(comp_orig, oNlats, oNlons)
dots = np.dot(coss, compsi)
comp_ndx = np.argmax(np.abs(dots))
sign_flip = -1.0 if dots[comp_ndx] < 0 else 1.0
time_series[:, i] = tsi[comp_ndx, :] / np.var(
tsi[comp_ndx, :]) * sign_flip - i * 0.4
comp = compsi[:, comp_ndx] * sign_flip
print("i = %d comp_ndx = %d\n" % (i, comp_ndx))
plt.subplot(Ncomps + 1, 1, i + 1)
plot_data_robinson(np.reshape(comp, (Nlats, Nlons)),
lats,
lons,
subplot=True,
euro_centered=euro_centered)
plt.text(0.0, 0.99 * plt.ylim()[1], '(%s)' % ("abcdefgh"[i]))
plt.subplot(Ncomps + 1, 1, Ncomps + 1)
plt.plot(time_series, linewidth=0.5)
plt.yticks([])
year_step = 8
years = range(1948, 2013, year_step)
sy = [str(y) for y in years]
plt.xticks(range(0, time_series.shape[0], 12 * year_step), sy, rotation=90)
plt.axis('tight')
plt.savefig(output_file)
ts_all[:,0] = standardize(tsi)
ts_all[:,1] = standardize(chop_series(ts1, ymin, ymax)) - 4.0
ts_all[:,2] = standardize(chop_series(ts2, ymin, ymax)) - 2*4.0
cc = np.corrcoef(ts_all, rowvar = 0)
# all time series data
# load data from component sets
fig = plt.figure(figsize = (12, 6))
plt.subplots_adjust(left = 0.025, right = 0.975, top = 0.97, wspace = 0.4, hspace = 0.3)
ax = plt.gca()
plt.subplot2grid((2,2), (0,0))
plot_data_robinson(np.reshape(U1, (len(lats1), len(lons1))), lats1, lons1,
subplot = True, add_colorbar = False, parallel_labels = 'left',
euro_centered = euro_centered)
plt.subplot2grid((2,2), (0,1))
plot_data_robinson(np.reshape(U2, (len(lats2), len(lons2))), lats2, lons2,
subplot = True, add_colorbar = False, parallel_labels = 'right',
euro_centered = euro_centered)
plt.subplot2grid((2,2), (1,0), colspan = 2)
plt.plot(ts_all, linewidth = 0.5)
plt.yticks([])
year_step = 8
years = range(1948, 2013, year_step)
sy = [str(y) for y in years]
plt.xticks(range(0, ts_all.shape[0], 12 * year_step), sy, rotation = 90)
# all time series data
# load data from component sets
fig = plt.figure(figsize=(12, 6))
plt.subplots_adjust(left=0.025,
right=0.975,
top=0.97,
wspace=0.4,
hspace=0.3)
ax = plt.gca()
plt.subplot2grid((2, 2), (0, 0))
plot_data_robinson(np.reshape(U1, (len(lats1), len(lons1))),
lats1,
lons1,
subplot=True,
add_colorbar=False,
parallel_labels='left',
euro_centered=euro_centered)
plt.subplot2grid((2, 2), (0, 1))
plot_data_robinson(np.reshape(U2, (len(lats2), len(lons2))),
lats2,
lons2,
subplot=True,
add_colorbar=False,
parallel_labels='right',
euro_centered=euro_centered)
plt.subplot2grid((2, 2), (1, 0), colspan=2)
plt.plot(ts_all, linewidth=0.5)
ts2 /= np.var(ts2, axis = 1)
with open(os.path.join(out_dir, 'matching'), 'w') as f:
f.write("# matching from %s (left) to %s (right) in %s\n" % (sys.argv[1], sys.argv[2], sys.argv[3]))
f.write("# third column - match similarity (correlation in time, dot product in space)\n")
f.write("# fourth column - complementary similarity (time -> dot product in space, space -> correlation in time\n")
for i, p in zip(range(C.shape[0]), perm):
f.write("%d, %d, %g, %g\n" % (i+1, p+1, C[i,perm[i]], OC[i,perm[i]]))
print("Rendering images")
for i in range(len(perm)):
if perm[i] >= 0:
f = plt.figure(figsize = (12, 6))
gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[5, 5],height_ratios=[6,4])
plt.subplot(gs[0,0])
plot_data_robinson(np.reshape(comps1[:, i], (Nlats1, Nlons1)),
lats1, lons1, subplot = True, euro_centered = True)
plt.title('Component %d' % (i+1))
plt.subplot(gs[0,1])
plot_data_robinson(np.reshape(comps2[:, perm[i]] * sign_flip[i], (Nlats2, Nlons2)),
lats2, lons2, subplot = True, euro_centered = True)
plt.title('Component %d%s' % (perm[i]+1, " (sign fliped)" if sign_flip[i] == -1 else ""))
plt.subplot(gs[1,:])
plt.plot(ts1[i,:].T, 'b-')
plt.plot(ts2[perm[i],:].T * sign_flip[i], 'g-')
if sys.argv[3] == 'time':
plt.figtext(0.25, 0.43, 'match by timeseries Pearson corr %g, components dot %g' % (C[i,perm[i]], OC[i,perm[i]]))
else:
plt.figtext(0.25, 0.43, 'match by component dot %g, time series Pearson corr %g' % (C[i,perm[i]], OC[i,perm[i]]))
plt.savefig(os.path.join(out_dir, "match_%d_to_%d.png" % (i+1, perm[i]+1)))
plt.clf()
sys.stdout.write("*")
gf = load_monthly_sat_all()
gf.detrend()
d = gf.data()
d = np.reshape(d, (768, 71*144))
# compute correlation of slp time series with sat index
Uc = np.zeros(U1.shape)
for i in range(Uc.shape[0]):
Uc[i] = np.corrcoef(ts1, d[:,i])[0,1]
plt.figure(figsize = (6, 3 * 4))
plt.subplots_adjust(left = 0.02, right = 0.96, hspace = 0.5)
plt.subplot(311)
plot_data_robinson(np.reshape(U1, (len(lats1), len(lons1))), lats1, lons1,
subplot = True, euro_centered = euro_centered)
plt.subplot(312)
plot_data_robinson(np.reshape(U2, (len(lats2), len(lons2))), lats2, lons2,
subplot = True, euro_centered = euro_centered)
plt.subplot(313)
plot_data_robinson(np.reshape(Uc, (len(lats2), len(lons2))), lats2, lons2,
subplot = True, euro_centered = euro_centered)
plt.savefig('slp_%02d_sat_%02d.eps' % (slp_ndx, best_match+1))
comp_ndx = np.argmax(np.abs(dots))
sign_flip = -1.0 if dots[comp_ndx] < 0 else 1.0
else:
# we must match by time
coss = subsample_component_2x2(comp_orig, oNlats, oNlons)
dots = np.dot(coss, compsi)
comp_ndx = np.argmax(np.abs(dots))
sign_flip = -1.0 if dots[comp_ndx] < 0 else 1.0
time_series[:, i] = tsi[comp_ndx, :] / np.var(tsi[comp_ndx, :]) * sign_flip - i*0.4
comp = compsi[:, comp_ndx] * sign_flip
print("i = %d comp_ndx = %d\n" % (i, comp_ndx))
plt.subplot(Ncomps+1, 1, i+1)
plot_data_robinson(np.reshape(comp, (Nlats, Nlons)), lats, lons,
subplot = True, euro_centered = euro_centered)
plt.text(0.0, 0.99*plt.ylim()[1], '(%s)' % ("abcdefgh"[i]))
plt.subplot(Ncomps+1, 1, Ncomps+1)
plt.plot(time_series, linewidth = 0.5)
plt.yticks([])
year_step = 8
years = range(1948,2013,year_step)
sy = [str(y) for y in years]
plt.xticks(range(0, time_series.shape[0], 12*year_step),sy,rotation = 90)
plt.axis('tight')
plt.savefig(output_file)