p = proxy(**vargs)
"""
initialise output file list
"""
images = []
"""
process the proxy
"""
# 1: find the analog seasons
if verbose:
save_progress(opath, 'Process the proxy', 0)
p.find_analogs()
# 2: save the proxy in the JSON file
p.proxy_repr()
# 3: plot the time-series of seasonal values with the analog years and save
if verbose:
save_progress(opath, 'Time Series', 10)
f = p.plot_season_ts()
f.savefig(os.path.join(opath, 'time_series.png'))
images.append({'id': 'time_series', 'title' : 'Analog Seasons', 'filename': 'time_series.png'})
plt.close(f)
"""
"""
HGT at 1000 hPa, NZ domain, composite
"""
f = scalar_plot(hgt, test=0.1, proj='cyl', domain=[165, 180, -50., -30], res='h').plot(subplots=False)
f.savefig(os.path.join(opath,'hgt_1000_proxy_NZ.png'))
images.append({'id': 'hgt_1000_NZ', 'title' : 'Geopotential at 1000 hPa, NZ domain', 'filename': 'HGT_1000_NZ_proxy.png'})
plt.close(f)
if verbose:
save_progress(opath, 'HGT 1000 NZ domain composite', 40)
"""
HGT at 1000 hPa, NZ domain, one map per year
"""
f = scalar_plot(hgt, test=0.1, proj='cyl', domain=[165, 180, -50., -30], res='h').plot(subplots=True)
f.savefig(os.path.join(opath,'hgt_1000_proxy_NZ_years.png'))
images.append({'id': 'hgt_1000_NZ_samples', 'title' : 'Geopotential at 1000 hPa, NZ domain, analog years', 'filename': 'HGT_1000_NZ_sample_proxy.png'})
plt.close(f)
if verbose:
save_progress(opath, 'HGT 1000 NZ domain analogs', 50)
"""
opath = vargs.pop('opath')
"""
pop `verbose` out of the dictionnary
"""
verbose = vargs.pop('verbose')
"""
instantiates an `ensemble` class, pass the `vargs` dict of keyword arguments to the class
"""
ens = ensemble(**vargs)
if verbose:
save_progress(opath, 'Process the ensemble', 0)
"""
Creates output file array
"""
images = []
"""
instantiate the analog classes with the proxy for each dataset + variable we
want to map
"""
# ==============================================================================
"""
HGT 1000
