def main():
parser = argparse.ArgumentParser()
parser.add_argument('--first_inputs', nargs='+',
help="""List of files containing variable to plot""")
parser.add_argument('--second_inputs', nargs='+',
help="""List of files containing variable to plot""")
parser.add_argument('--variable', default='temp',
help='The variable to plot.')
parser.add_argument('--mask', default=None,
help="""
A mask file specifing the grid points to use in
the timeseries.""")
parser.add_argument('--mask_var', default=None,
help='The variable name within the mask file to use.')
parser.add_argument('--region', default=None, action='store', type=region,
help="""A region within to use instead of the mask.""")
parser.add_argument('--plot_mask', default=None, help='Plot the mask used.')
parser.add_argument('--output_file', default='sst_timeseries.png',
help='Name of plot to output.')
args = parser.parse_args()
assert not (args.mask is None) or (args.mask_var is None)
assert not (args.mask is None) or not (args.region is None)
if args.region:
slat = args.region[0]
nlat = args.region[1]
wlon = args.region[2]
elon = args.region[3]
blayer = args.region[4]
tlayer = args.region[5]
mask = make_mask_from_region(slat, nlat, wlon, elon, blayer, tlayer,
args.first_inputs[0], args.variable)
else:
with nc.Dataset(args.mask) as m:
mask = m.variables[args.mask_var][0, :]
if args.plot_mask:
plt.imshow(np.flipud(mask))
plt.savefig(args.plot_mask)
plt.close()
pool = mp.Pool()
data_to_plot = []
for name, data_files in zip(['A', 'B'], [args.first_inputs, args.second_inputs]):
inputs = zip(data_files, [args.variable]*len(data_files),
[mask]*len(data_files),
[args.mask_var]*len(data_files))
results = pool.map(make_timeseries, inputs)
#results = map(make_timeseries, inputs)
ts = pd.Series()
for t in results:
ts = ts.append(t)
ts = ts.sort_index()
data_to_plot.append((ts, name))
fig, axarr = plt.subplots(2, sharex=True)
# Extend colours list when necessary.
colours = ['red', 'blue', 'green', 'black']
assert(len(colours) >= 2)
min_len = min(len(data_to_plot[0][0]), len(data_to_plot[1][0]))
for colour, (ts, name) in zip(colours, data_to_plot):
dates = [datetime.date(i.year, i.month, i.day) for i in ts.index]
axarr[0].plot(dates[:min_len], ts.values[:min_len], colour,
label=name, lw=1.0)
title = 'Spatial average {} in region'.format(args.variable)
axarr[0].set_title(title)
if args.variable == 'temp':
axarr[0].set_ylabel('Temperature C')
else:
axarr[1].set_ylabel('Wind Stress N/m^2')
axarr[0].legend()
diff = data_to_plot[1][0] - data_to_plot[0][0]
diff_smoothed = lowess_smooth(diff, smooth_years=8.0)
# In a separate pane plot the difference between datasets
dates = [datetime.date(i.year, i.month, i.day) for i in diff.index]
axarr[1].plot(dates[:min_len], diff.values[:min_len], 'blue', lw=1.0)
axarr[1].plot(dates[:min_len], diff_smoothed.values[:min_len], 'blue', lw=3.0)
axarr[1].set_title('B - A')
if args.variable == 'temp':
axarr[1].set_ylabel('Temperature C')
else:
axarr[1].set_ylabel('Wind Stress N/m^2')
axarr[1].set_xlabel('Time (years)')
fig.set_size_inches(12, 6)
plt.tight_layout()
plt.savefig(args.output_file)
plt.close()
def plot_timeseries(data_file, output_file, offset=0):
# Read data and plot
with h5py.File(data_file) as f:
time = f['/time'][:]
amoc_index_sst = f['/amoc_index_sst'][:]
nh_sst_average = f['/nh_sst_average'][:]
amoc_psi_max = f['/amoc_psi_max'][:]
amoc_psi_max_at_26n = f['/amoc_psi_max_at_26n'][:]
start_date = datetime.datetime(year=1, month=1, day=1)
time_dim = [start_date + datetime.timedelta(days=int(t)) for t in time]
amoc_index = amoc_index_sst - nh_sst_average
if offset != 0:
time_dim = time_dim[:-offset]
amoc_index = amoc_index[:-offset]
amoc_psi_max = amoc_psi_max[offset:]
# Do some smoothing
amoc_index_sm = lowess_smooth(amoc_index)
amoc_psi_max_anomaly = amoc_psi_max - np.mean(amoc_psi_max)
amoc_psi_max_anomaly_sm = lowess_smooth(amoc_psi_max_anomaly)
amoc_psi_max_sm = lowess_smooth(amoc_psi_max)
# Time series of the maximum overturning stream function anomaly
fig, axarr = plt.subplots(2, sharex=True)
axarr[0].plot(time_dim, amoc_psi_max_anomaly, 'red', lw=1.0, alpha=0.5)
axarr[0].plot(time_dim, amoc_psi_max_anomaly_sm, 'darkred', lw=3.0)
axarr[0].set_ylabel('AMOC Maximum Anomaly (Sv)', color='darkred')
axarr[0].set_title('Connection between Max Overturning Streamfunction and AMOC Index')
# Time series of the AMOC index
ax2 = axarr[0].twinx()
ax2.plot(time_dim, amoc_index, 'blue', lw=1.0, alpha=0.5)
ax2.plot(time_dim, amoc_index_sm, 'darkblue', lw=3.0)
ax2.set_ylabel('AMOC Index (K)', color='darkblue')
p_corr_sm = pearsonr(amoc_psi_max_anomaly_sm, amoc_index_sm)
print('p_corr_sm: {}'.format(p_corr_sm))
p_corr = pearsonr(amoc_psi_max_anomaly, amoc_index)
print('p_corr: {}'.format(p_corr))
text = 'Pearson correlation (smoothed): {0:.2f} ({1:.2f})'. \
format(p_corr[0], p_corr_sm[0])
print(text)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
axarr[0].text(0.05, 0.95, text, transform=axarr[0].transAxes,
verticalalignment='top', bbox=props)
# Time series of AMOC mean
time_dim = [start_date + datetime.timedelta(days=int(t)) for t in time]
amoc_psi_max_at_26n_sm = lowess_smooth(amoc_psi_max_at_26n)
axarr[1].plot(time_dim, amoc_psi_max_at_26n, 'green', lw=1.0)
axarr[1].plot(time_dim, amoc_psi_max_at_26n_sm, 'darkgreen', lw=3.0)
axarr[1].set_ylabel('Sv', color='darkgreen')
axarr[1].set_xlabel('Year')
axarr[1].set_title('AMOC monthly maximum at 26.5N')
fig.set_size_inches(14,10)
plt.tight_layout()
plt.savefig(output_file)
plt.close()
