def main(cfg):
"""Execute validation analysis and plotting."""
logger.setLevel(cfg['log_level'].upper())
input_data, grouped_input_data = do_preamble(cfg)
# select variables and their corresponding obs files
for short_name in grouped_input_data:
logger.info("Processing variable %s", short_name)
cmip_era = cfg["cmip_era"]
# get the control, experiment and obs dicts
ctrl, exper, obs = get_control_exper_obs(short_name, input_data, cfg,
cmip_era)
# set a plot key holding info on var and data set names
plot_key = "{}_{}_vs_{}".format(short_name, ctrl['dataset'],
exper['dataset'])
control_dataset_name = ctrl['dataset']
# get seasons if needed then apply analysis
if cfg['seasonal_analysis']:
ctrl_seasons = apply_seasons(ctrl)
exper_seasons = apply_seasons(exper)
ctrl_seasons = [
coordinate_collapse(cts, cfg) for cts in ctrl_seasons
]
exper_seasons = [
coordinate_collapse(exps, cfg) for exps in exper_seasons
]
plot_ctrl_exper_seasons(ctrl_seasons, exper_seasons, cfg, plot_key)
if obs:
for iobs in obs:
obs_seasons = apply_seasons(iobs)
obs_seasons = [
coordinate_collapse(obss, cfg) for obss in obs_seasons
]
plot_key_obs = "{}_{}_vs_{}".format(
short_name, ctrl['dataset'], iobs['dataset'])
plot_ctrl_exper_seasons(ctrl_seasons, obs_seasons, cfg,
plot_key_obs)
# apply the supermeans (MEAN on time), collapse a coord and plot
ctrl, exper, obs_list = apply_supermeans(ctrl, exper, obs)
ctrl = coordinate_collapse(ctrl, cfg)
exper = coordinate_collapse(exper, cfg)
plot_ctrl_exper(ctrl, exper, cfg, plot_key)
# apply desired analysis on obs's
if obs_list:
for obs_i, obsfile in zip(obs_list, obs):
obs_analyzed = coordinate_collapse(obs_i, cfg)
obs_name = obsfile['dataset']
plot_key = "{}_{}_vs_{}".format(short_name,
control_dataset_name, obs_name)
if cfg['analysis_type'] == 'lat_lon':
plot_latlon_cubes(ctrl,
obs_analyzed,
cfg,
plot_key,
obs_name=obs_name)
def main(cfg):
"""Execute the radiation rms diag."""
logger.setLevel(cfg['log_level'].upper())
input_data, grouped_input_data = do_preamble(cfg)
# select variables and their corresponding
# obs files
for short_name in grouped_input_data:
logger.info("Processing variable %s", short_name)
# control, experiment and obs's
ctrl, exper, obslist = get_control_exper_obs(short_name, input_data,
cfg, _CMIP_TYPE)
# apply the supermeans
ctrl_sm, exper_sm, obs_sm_list = apply_supermeans(ctrl, exper, obslist)
# assemble a dict that contains various params depending
# on the data combinations for RMS computations
# control-experiment
data_component_dict = {'ct-ex': {'ctrl': ctrl, 'exper': exper}}
logger.info("Computing CONTROL-EXPERIMENT RMS...")
apply_rms(ctrl_sm, exper_sm, cfg, data_component_dict['ct-ex'],
short_name)
if obs_sm_list:
for obs, obsfile in zip(obs_sm_list, obslist):
data_component_dict = {
'ct-obs': {
'ctrl': ctrl,
'obs': obsfile
},
'ex-obs': {
'exper': exper,
'obs': obsfile
}
}
# ctrl-obs
logger.info("Computing CONTROL-OBS RMS...")
apply_rms(ctrl_sm, obs, cfg, data_component_dict['ct-obs'],
short_name)
# exper-obs
logger.info("Computing EXPERIMENT-OBS RMS...")
apply_rms(exper_sm, obs, cfg, data_component_dict['ex-obs'],
short_name)
else:
# only ctrl-exper
data_component_dict = {'ct-ex': {'ctrl': ctrl, 'exper': exper}}
logger.info("Computing CONTROL-EXPERIMENT RMS...")
apply_rms(ctrl_sm, exper_sm, cfg, data_component_dict['ct-ex'],
short_name)