def get_reference(metric_collection, metric):
if metric_collection in ["ENSO_tel"] and "Map" in metric:
my_met = metric.replace("Corr", "").replace("Rmse",
"").replace("Std", "")
else:
my_met = deepcopy(metric)
return plot_param(metric_collection, my_met)['metric_reference']
def cmip_plotter(metric_collection, metric, experiment, diagnostic_values,
diagnostic_units, metric_values, metric_units,
multi_member_ave, figure_name):
"""
Organizes plots for CMIP ensembles
Inputs:
------
:param metric_collection: string
name of the metric collection (e.g., "ENSO_perf")
:param metric: string
name of the metric (e.g., "EnsoAmpl")
:param diagnostic_values: dictionary
dictionary containing the diagnostic values
e.g., {"ref": {"ERA-Interim": 1, "Tropflux": 1.1}, "cmip5": [0.9, ...], "cmip6": [0.9, ...]}
:param diagnostic_units: string
diagnostic units (e.g., "C")
:param metric_values: dictionary
dictionary containing the metric values
e.g., {"cmip5": {"ERA-Interim": [10, ...], "Tropflux": [20, ...]},
"cmip6": {"ERA-Interim": [10, ...], "Tropflux": [20, ...]}}
:param metric_units: string
metric units (e.g., "%")
:param multi_member_ave: boolean
True if multi-member mean is used, False otherwise
:param figure_name: string
path and name of the plots (e.g., "path/to/directory/file_name")
Output:
------
:return:
"""
lmet = metric.replace("Corr", "").replace("Rmse", "").replace(
"Std", "") if "Map" in metric else deepcopy(metric)
dict_param = plot_param(metric_collection, lmet)
my_param = dict_param["diagnostic"]
reference = dict_param["metric_reference"]
diagnostic_units = diagnostic_units.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
metric_units = metric_units.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
if multi_member_ave is True:
info = "ensemble-mean\n" + experiment + " simulations"
else:
info = "only first\n" + experiment + " simulation"
# metric
cmip_boxplot(my_param, metric_values, metric_units, reference, "metric",
info, figure_name + "_divedown01")
# diagnostic
cmip_boxplot(my_param, diagnostic_values, diagnostic_units, reference,
"diagnostic", info, figure_name + "_divedown02")
return
def main_plotter(metric_collection,
metric,
model,
experiment,
filename_nc,
diagnostic_values,
diagnostic_units,
metric_values,
metric_units,
member=None,
path_png=None,
name_png=None,
models2=None,
shading=False,
plot_ref=False,
reference=None):
dict_param = plot_param(metric_collection, metric)
list_var = dict_param['metric_variables']
met_type = dict_param['metric_computation']
if reference is None:
reference = dict_param['metric_reference']
dict_reg = dict_param['metric_regions']
if isinstance(diagnostic_units, str) is True:
diagnostic_units = diagnostic_units.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
elif isinstance(diagnostic_units, list) is True:
for ii, uni in enumerate(diagnostic_units):
diagnostic_units[ii] = uni.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
if isinstance(metric_units, str) is True:
metric_units = metric_units.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
elif isinstance(metric_units, list) is True:
for ii, uni in enumerate(metric_units):
metric_units[ii] = uni.replace("C", "$^\circ$C").replace(
"long", "$^\circ$long")
if isinstance(name_png, str) is False:
name_png = metric_collection + "_" + metric + "_" + experiment + "_"
if isinstance(model, str):
name_png = name_png + model
elif isinstance(model, list) is True and shading is True:
name_png = name_png + str(len(model)) + "projects"
else:
name_png = name_png + str(len(model)) + "models"
if member is not None:
if (isinstance(model, str) is True and member
not in model) or isinstance(model, list) is True:
name_png = name_png + "_" + member
# diagnostic
dict_diag = dict_param['diagnostic']
if isinstance(path_png, str):
fig_name = OSpath__join(path_png, name_png + "_diagnostic")
else:
fig_name = deepcopy(name_png)
plt_typ = dict_diag['plot_type']
if plt_typ == "dot" and shading is True:
plt_typ = "dot_to_box"
t1 = datetime.now()
print(str().ljust(20) + plt_typ + " " + str(t1.hour).zfill(2) + ":" +
str(t1.minute).zfill(2))
dict_plot[plt_typ](model,
filename_nc,
dict_diag,
reference,
list_var,
fig_name + "_divedown01",
models2=models2,
member=member,
metric_type=met_type,
metric_values=metric_values,
metric_units=metric_units,
diagnostic_values=diagnostic_values,
diagnostic_units=diagnostic_units,
regions=dict_reg,
shading=shading)
if plot_ref is True:
model_new = model.replace("GPCPv2.3",
"GPCPv23").replace("SODA3.4.2", "SODA342")
fig_name_ref = fig_name.replace(model_new, "reference")
dict_plot[plt_typ](model,
filename_nc,
dict_diag,
reference,
list_var,
fig_name_ref + "_divedown01",
models2=None,
member=None,
metric_type=None,
metric_values=metric_values,
metric_units=metric_units,
diagnostic_values=diagnostic_values,
diagnostic_units=diagnostic_units,
regions=dict_reg,
shading=False,
plot_ref=plot_ref)
dt = datetime.now() - t1
dt = str(int(round(dt.seconds / 60.)))
print(str().ljust(30) + "took " + dt + " minute(s)")
# dive downs
list_dd = sorted(
[key for key in list(dict_param.keys()) if "dive_down" in key],
key=lambda v: v.upper())
for ii, dd in enumerate(list_dd):
dict_diag = dict_param[dd]
plt_typ = dict_diag['plot_type']
t1 = datetime.now()
print(str().ljust(20) + plt_typ + " " + str(t1.hour).zfill(2) + ":" +
str(t1.minute).zfill(2))
if metric_collection in ["ENSO_tel", "test_tel"] and "Map" in metric:
metype = deepcopy(met_type)
else:
metype = None
dict_plot[plt_typ](model,
filename_nc,
dict_diag,
reference,
list_var,
fig_name + "_divedown" + str(ii + 2).zfill(2),
models2=models2,
member=member,
metric_type=metype,
metric_values=metric_values,
metric_units=metric_units,
diagnostic_values=diagnostic_values,
diagnostic_units=diagnostic_units,
regions=dict_reg,
shading=shading)
if plot_ref is True:
dict_plot[plt_typ](model,
filename_nc,
dict_diag,
reference,
list_var,
fig_name_ref + "_divedown" +
str(ii + 2).zfill(2),
models2=None,
member=None,
metric_type=None,
metric_values=metric_values,
metric_units=metric_units,
diagnostic_values=diagnostic_values,
diagnostic_units=diagnostic_units,
regions=dict_reg,
shading=False,
plot_ref=plot_ref)
dt = datetime.now() - t1
dt = str(int(round(dt.seconds / 60.)))
print(str().ljust(30) + "took " + dt + " minute(s)")