def create_sim_map(burnin_id) :
am = AnalyzeManager(burnin_id, analyzers=SimulationDirectoryMapAnalyzer())
am.analyze()
sim_map_dict = am.analyzers[0].results
df = pd.concat([pd.DataFrame(exp) for exp_id, exp in sim_map_dict.items()])
return df
def run_single_analyzer(exp_id, analyzer, savefile_prefix=""):
SetupParser.default_block = 'HPC'
SetupParser.init()
am = AnalyzeManager()
am.add_analyzer(analyzer())
exp = retrieve_experiment(exp_id)
am.add_experiment(exp)
am.analyze()
df_return = am.analyzers[0].results
df_return.to_csv("{}_{}.csv".format(savefile_prefix, exp_id), index=False)
return df_return
def plot_inset_diagnostics(experiment_list,
channels=default_channels,
working_dir=".",
filename="output/InsetChart.json",
**kwargs):
from simtools.Analysis.AnalyzeManager import AnalyzeManager
am = AnalyzeManager()
am.add_analyzer(
inset_channel_plotter(channels,
working_dir=working_dir,
filename=filename,
**kwargs))
for expt in experiment_list:
am.add_experiment(expt)
am.analyze()
def run_experiment(configbuilder, experiment_name, experiment_builder,
analyzers):
run_sim_args = {
'config_builder': configbuilder,
'exp_name': experiment_name,
'exp_builder': experiment_builder
}
if not SetupParser.initialized:
SetupParser.init('HPC')
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(**run_sim_args)
exp_manager.wait_for_finished(verbose=True)
assert (exp_manager.succeeded())
am = AnalyzeManager(exp_manager.experiment)
for a in analyzers:
am.add_analyzer(a)
am.analyze()
def run_analyzers(exp_id, analyzers, savefile_prefix=""):
def _remove_duplicate_columns(df):
columns_to_keep = []
for c in df.columns:
if "_duplicated" not in c:
columns_to_keep.append(c)
return df[columns_to_keep]
SetupParser.default_block = 'HPC'
SetupParser.init()
am = AnalyzeManager()
for a in analyzers:
am.add_analyzer(a())
exp = retrieve_experiment(exp_id)
am.add_experiment(exp)
am.analyze()
if len(analyzers) == 1:
df_return = am.analyzers[0].results
elif len(analyzers) > 1:
df_list = [x.results for x in am.analyzers]
df_return = pd.merge(df_list[0],
df_list[1],
on="sim_id",
suffixes=["", "_duplicated"])
# Drop duplicated columns
# for c in df_result.columns:
# if "_duplicated" in c:
# df_result.drop(c, inplace=True)
df_return = _remove_duplicate_columns(df_return)
else:
raise ValueError
df_return.to_csv("{}_{}.csv".format(savefile_prefix, exp_id), index=False)
return df_return
else:
plt.plot(data, label=label)
plt.xlabel("Simulation Time")
plt.ylabel(self.channel)
plt.legend()
plt.show()
if __name__ == "__main__":
from simtools.Analysis.AnalyzeManager import AnalyzeManager
am = AnalyzeManager()
# am.add_analyzer(basic_inset_channel_plotter("True Prevalence"))
am.add_analyzer(
inset_channel_plotter("True Prevalence",
color_by_expt=True,
label_by_expt=True,
label_dict={
"7e3073b4-d9f1-e811-a2bd-c4346bcb1555":
"full campaign",
"a2e981fe-d9f1-e811-a2bd-c4346bcb1555":
"no 2011 bednets"
},
ref_date="2001-01-01"))
am.add_experiment("7e3073b4-d9f1-e811-a2bd-c4346bcb1555")
am.add_experiment("a2e981fe-d9f1-e811-a2bd-c4346bcb1555")
am.analyze()
