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_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
plt.plot(data, label=label, c=c)
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()
# Write special serialized_files_sim_map for runs that need it
for c in range(56):
catch = catch_list[c]
c_folder = "C:/Users/jsuresh/Dropbox (IDM)/Malaria Team Folder/projects/zambia_gridded_sims/kariba_gridded_sims/calibs/{}".format(
catch)
sim_map_filename = os.path.join(c_folder,
"serialized_files_sim_map.csv")
# See if you can find an experiment named CATCH_project_climatefix
try:
name_try = "{}_project_climatefix".format(catch)
proj_exp = DataStore.get_most_recent_experiment(
id_or_name=name_try)
# If you can find the experiment, find the corresponding parent simulation and output its sim map
if proj_exp:
name_try = "{}_serialize".format(catch)
orig_exp = DataStore.get_most_recent_experiment(
id_or_name=name_try)
am = AnalyzeManager()
am.add_experiment(orig_exp)
am.add_analyzer(
SimulationDirectoryMapAnalyzer(save_file=sim_map_filename))
am.analyze()
print("Wrote serialized_files_sim_map for {}".format(catch))
except:
pass
# Dump the experiment ids for resume
with open('ids.json', 'w') as out:
json.dump(experiments_ids, out)
# While the experiments are running, we are analyzing every 30 seconds
while True:
print("Analyzing !")
# Determine if we are done at the beginning of the loop
# We will still analyze everything even if we are done
finished = all([em.finished() for em in experiments])
# Create a new AnalyzeManager and add experiment and analyzer
am = AnalyzeManager(verbose=False)
for em in experiments:
am.add_experiment(em.experiment)
analyzer = DownloadAnalyzerTPI(
filenames=['output\\ReportHIVByAgeAndGender.csv'],
TPI_tag="TPI",
ignore_TPI=False,
REP_tag="TPI",
ignore_REP=True,
output_path=output_directory)
am.add_analyzer(analyzer)
# Make sure we refresh our set of experiments
for e in experiments:
e.refresh_experiment()
plt.ylabel("Fraction of sims eliminating")
plt.title(vc_packs[i])
plt.legend()
plt.show()
if __name__=="__main__":
if True:
# analyzer_list = [ExtractInfectionResult()]
analyzer_list = [ExtractInfectionResult(),
SimulationDirectoryMapAnalyzer(save_file="sim_map.csv")]
exp_list = ["520818ca-ae3b-e911-a2c5-c4346bcb7273"]
am = AnalyzeManager(force_analyze=True)
for exp_name in exp_list:
am.add_experiment(retrieve_experiment(exp_name))
for a in analyzer_list:
am.add_analyzer(a)
am.analyze()
if True:
convert_infection_csv_to_elim()
if False:
plot_elim_curves(y=2)
# Run endpoint analyzer on original run, and output as endpoint_original.csv
for c in catch_nums:
catch = catch_list[c]
catch_folder = os.path.join(cf_folder, catch)
if run_endpoint_original:
sim_map_filename = os.path.join(catch_folder, "sim_map_original.csv")
endpoint_filename = os.path.join(catch_folder, "endpoint_original.csv")
analyzer_list = []
analyzer_list += [SaveEndpoint(save_file=endpoint_filename, year_to_use=6),
SimulationDirectoryMapAnalyzer(save_file=sim_map_filename)]
am = AnalyzeManager()
exp = retrieve_experiment(orig_exp["exp_id"])
am.add_experiment(exp)
for a in analyzer_list:
am.add_analyzer(a)
am.analyze()
if run_endpoint_cf:
sim_map_filename = os.path.join(catch_folder, "sim_map_cf.csv")
endpoint_filename = os.path.join(catch_folder, "endpoint_cf.csv")
analyzer_list = []
analyzer_list += [SaveEndpoint(save_file=endpoint_filename, year_to_use=6),
SimulationDirectoryMapAnalyzer(save_file=sim_map_filename)]
am = AnalyzeManager()
