def main(cfg):
"""Plot weighted temperature graph."""
input_files = cfg['input_files']
filename = cfg['weights']
weights_path = Path(input_files[0]) / filename
weights = read_weights(weights_path)
models = read_metadata(cfg)['tas']
model_data, model_data_files = read_model_data(models)
# if a historical period is given calculate the change
if 'tas_reference' in read_metadata(cfg).keys():
models_hist = read_metadata(cfg)['tas_reference']
model_data_hist, model_data_files_hist = read_model_data(models_hist)
model_data_files += model_data_files_hist
model_data = model_data - model_data_hist
metric = cfg.get('model_aggregation', 'mean')
unweighted_mean = model_aggregation(model_data, metric)
weighted_mean = model_aggregation(model_data, metric, weights)
visualize_and_save_temperature(
weighted_mean,
cfg,
model_data_files,
)
visualize_and_save_difference(
weighted_mean - unweighted_mean,
cfg,
model_data_files,
)
def mapplot(dataarray, cfg, title_pattern, filename_part, ancestors,
**colormesh_args):
"""Visualize weighted temperature."""
period = '{start_year}-{end_year}'.format(**read_metadata(cfg)['tas'][0])
if 'tas_reference' in read_metadata(cfg).keys():
meta = read_metadata(cfg)['tas_reference']
period = 'change: {} minus {start_year}-{end_year}'.format(
period, **meta[0])
metric = cfg['model_aggregation']
if isinstance(metric, int):
metric = f'{metric}perc'
proj = ccrs.PlateCarree(central_longitude=0)
figure, axes = plt.subplots(subplot_kw={'projection': proj})
dataarray = set_antimeridian(dataarray, cfg.get('antimeridian', 'pacific'))
dataarray = dataarray.dropna('lon', how='all').dropna('lat', how='all')
dataarray.plot.pcolormesh(
ax=axes,
transform=ccrs.PlateCarree(),
levels=9,
robust=True,
extend='both',
**colormesh_args
# colorbar size often does not fit nicely
# https://stackoverflow.com/questions/18195758/set-matplotlib-colorbar-size-to-match-graph
# cbar_kwargs={'fraction': .021}
)
lons = dataarray.lon.values
lats = dataarray.lat.values
longitude_formatter = LongitudeFormatter()
latitude_formatter = LatitudeFormatter()
default_xticks = np.arange(np.floor(lons.min()), np.ceil(lons.max()), 10)
default_yticks = np.arange(np.floor(lats.min()), np.ceil(lats.max()), 10)
axes.coastlines()
axes.set_xticks(cfg.get('xticks', default_xticks), crs=proj)
axes.set_yticks(cfg.get('yticks', default_yticks), crs=proj)
axes.xaxis.set_ticks_position('both')
axes.yaxis.set_ticks_position('both')
axes.xaxis.set_major_formatter(longitude_formatter)
axes.yaxis.set_major_formatter(latitude_formatter)
axes.set_xlabel('')
axes.set_ylabel('')
title = title_pattern.format(metric=metric, period=period)
axes.set_title(title)
filename_plot = get_plot_filename(filename_part, cfg)
figure.savefig(filename_plot, dpi=300, bbox_inches='tight')
plt.close(figure)
filename_data = get_diagnostic_filename(filename_part, cfg, extension='nc')
dataarray.to_netcdf(filename_data)
log_provenance(title, filename_plot, cfg, ancestors)
log_provenance(title, filename_data, cfg, ancestors)
def main(cfg):
"""Plot weighted temperature graph."""
input_files = cfg['input_files']
filename = cfg['weights']
weights_path = Path(input_files[0]) / filename
weights = read_weights(weights_path)
models = read_metadata(cfg, 'short_name')['tas']
model_data, model_data_files = read_model_data(models)
percentiles = np.array([25, 75])
iqr = calculate_percentiles(
model_data,
percentiles,
)
iqr_weighted = calculate_percentiles(
model_data,
percentiles,
weights=weights,
)
visualize_and_save_temperatures(
model_data,
iqr,
iqr_weighted,
cfg,
model_data_files,
)