def main(project_info):
"""Diagnostics and plotting script for Southern Hemisphere radiation."""
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(
diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
[""], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Check which parts of the code to run
if (modelconfig.getboolean('general', 'plot_scatter')):
info("Starting scatter plot", verbosity, 2)
process_scatter(E, modelconfig)
def main(project_info):
"""Diagnostics and plotting script for Southern Hemisphere radiation."""
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
datakeys = E.get_currVars()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
[""], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Check at which stage of the program we are
clouds = False
fluxes = False
radiation = False
if ('clt' in datakeys or 'clivi' in datakeys or 'clwvi' in datakeys):
clouds = True
elif ('hfls' in datakeys or 'hfss' in datakeys):
fluxes = True
else:
radiation = True
# Check which parts of the code to run
if (modelconfig.getboolean('general', 'plot_clouds') and clouds is True):
info("Starting to plot clouds", verbosity, 2)
process_clouds(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_fluxes') and fluxes is True):
info("Starting to plot turbulent fluxes", verbosity, 2)
process_fluxes(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_radiation') and radiation is True):
info("Starting to plot radiation graphs", verbosity, 2)
process_radiation(E, modelconfig)
def main(project_info):
"""Diagnostics and plotting script for Tropical Variability.
We use ts as a proxy for Sea Surface Temperature. """
# ESMValProject provides some easy methods to access information in
# project_info but you can also access it directly (as in main.py)
# First we get some general configurations (ESMValProject)
# and then we read in the model configuration file
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
# A-laue_ax+
diag_script = E.get_diag_script_name()
res = E.write_references(
diag_script, # diag script name
["A_maek_ja"], # authors
["A_eval_ma", "A_jone_co"], # contributors
["D_li14jclim"], # diag_references
[""], # obs_references
["P_embrace"], # proj_references
project_info,
verbosity,
False)
# A-laue_ax-
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
E.ensure_directory(plot_dir)
# Here we check and process only desired parts of the diagnostics
if (modelconfig.getboolean('general', 'plot_zonal_means')):
info("Starting to plot zonal means", verbosity, 2)
process_zonal_means(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_scatter')):
info("Starting scatterplot of temperature and precipitation",
verbosity, 2)
process_scatterplot(E, modelconfig)
if (modelconfig.getboolean('general', 'plot_equatorial')):
info("Starting to gather values for equatorial means", verbosity, 2)
process_equatorial_means(E, modelconfig)
def main(project_info):
# print(">>>>>>>> entering ww09_ESMValTool.py <<<<<<<<<<<<")
# create instance of a wrapper that allows easy access to data
E = ESMValProject(project_info)
config_file = E.get_configfile()
plot_dir = E.get_plot_dir()
verbosity = E.get_verbosity()
plot_type = E.get_graphic_format()
diag_script = E.get_diag_script_name()
res = E.write_references(diag_script, # diag script name
["A_will_ke"], # authors
[""], # contributors
["D_Williams09climdyn"], # diag_references
["E_isccp_d1"], # obs_references
["P_cmug"], # proj_references
project_info,
verbosity,
False)
modelconfig = ConfigParser.ConfigParser()
modelconfig.read(config_file)
# create list of model names (plot labels)
models = []
for model in E.project_info['MODELS']:
models.append(model.split_entries()[1])
# number of models
nummod = len(E.project_info['MODELS'])
crems = np.empty(nummod)
# get filenames of preprocessed climatological mean files (all models)
fn_alb = get_climo_filenames(E, variable='albisccp')
fn_pct = get_climo_filenames(E, variable='pctisccp')
fn_clt = get_climo_filenames(E, variable='cltisccp')
fn_su = get_climo_filenames(E, variable='rsut')
fn_suc = get_climo_filenames(E, variable='rsutcs')
fn_lu = get_climo_filenames(E, variable='rlut')
fn_luc = get_climo_filenames(E, variable='rlutcs')
fn_snc = get_climo_filenames(E, variable='snc')
fn_sic = get_climo_filenames(E, variable='sic')
if not fn_snc:
print("no data for variable snc found, using variable snw instead")
fn_snw = get_climo_filenames(E, variable='snw')
# loop over models and calulate CREM
for i in range(nummod):
if fn_snc:
snc = fn_snc[i]
else:
snc = ""
pointers = {'albisccp_nc': fn_alb[i],
'pctisccp_nc': fn_pct[i],
'cltisccp_nc': fn_clt[i],
'rsut_nc' : fn_su[i],
'rsutcs_nc' : fn_suc[i],
'rlut_nc' : fn_lu[i],
'rlutcs_nc' : fn_luc[i],
'snc_nc' : snc,
'sic_nc' : fn_sic[i]}
if not fn_snc:
pointers['snw_nc'] = fn_snw[i]
# calculate CREM
(CREMpd, __) = crem_calc(E, pointers)
crems[i] = CREMpd
print("------------------------------------")
print(crems)
print("------------------------------------")
# plot results
fig = plt.figure()
ypos = np.arange(nummod)
plt.barh(ypos, crems)
plt.yticks(ypos + 0.5, models)
plt.xlabel('Cloud Regime Error Metric')
# draw observational uncertainties (dashed red line)
plt.plot([0.96, 0.96], [0, nummod], 'r--')
# if needed, create directory for plots
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plt.savefig(plot_dir + 'ww09_metric_multimodel.' + plot_type)
print("Wrote " + plot_dir + "ww09_metric_multimodel." + plot_type)