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_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
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()
# 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
from simtools.Analysis.AnalyzeManager import AnalyzeManager
from simtools.Analysis.BaseAnalyzers import DownloadAnalyzer
if __name__ == "__main__":
analyzer = DownloadAnalyzer(filenames=[
'campaign_Swaziland_v05Sep2018_FINAL_reftracktopup_2011_v2.json',
'config.json', 'Swaziland_Demographics_With_Properties.json',
'Risk_Assortivity_Overlay.json', 'PFA_Overlay.json',
'Accessibility_and_Risk_IP_Overlay.json'
])
am = AnalyzeManager(["6f6ff06d-fed6-e811-a2bd-c4346bcb1555"],
analyzers=analyzer)
am.analyze()
# This block will be used unless overridden on the command-line
SetupParser.default_block = 'HPC'
cb = DTKConfigBuilder.from_defaults('VECTOR_SIM')
configure_site(cb, 'Namawala')
cb.set_param('Simulation_Duration',365)
analyzers = (TimeseriesAnalyzer(),
VectorSpeciesAnalyzer())
builder = GenericSweepBuilder.from_dict({'Run_Number': range(5)})
run_sim_args = {
'exp_name': 'testrunandanalyze',
'exp_builder': builder,
'config_builder':cb
}
if __name__ == "__main__":
SetupParser.init(selected_block=SetupParser.default_block)
exp_manager = ExperimentManagerFactory.from_cb(config_builder=cb)
exp_manager.run_simulations(**run_sim_args)
exp_manager.wait_for_finished(verbose=True)
am = AnalyzeManager(exp_manager.experiment)
for a in analyzers:
am.add_analyzer(a)
am.analyze()
if len(selected) == 0:
print("No data have been returned... Exiting...")
return
d = pd.concat(selected).reset_index(drop=True)
d['year'] = d['time'].apply(lambda x : int(x/365))
d = d[d['time'] >= 100]
summed = d.groupby(self.sweep_variables + ['Run_Number'])[self.channel].agg(np.sum).reset_index()
df = summed.groupby(self.sweep_variables)[self.channel].agg(np.mean).reset_index()
print(df)
sns.set_style('white', {'axes.linewidth' : 0.5})
fig = plt.figure()
ax = fig.gca()
sns.heatmap(df.pivot(index=self.sweep_variables[0], columns=self.sweep_variables[1], values=self.channel),
ax=ax)
ax.set_ylabel(self.sweep_variables[0])
ax.set_xlabel(self.sweep_variables[1])
plt.show()
plt.close('all')
if __name__ == '__main__' :
expid = '63903283-1f7b-e811-a2c0-c4346bcb7275'
am = AnalyzeManager(expid,
analyzers=Sweep2DAnalyzer(['coverage', 'initial_killing']))
am.analyze()
cb = CMSConfigBuilder.from_files(model_file='inputs/models/simplemodel.emodl',
config_file='inputs/models/simplemodel.cfg')
# If the base collection containing CMS exists, use it
# If not, use the local
if SetupParser.default_block == "HPC":
try:
cb.set_collection_id('CMS 0.82 Pre-release')
except SimulationAssets.InvalidCollection:
cb.set_experiment_executable('inputs/compartments/compartments.exe')
cb.set_dll_root('inputs/compartments')
else:
cb.set_experiment_executable('inputs/compartments/compartments.exe')
cb.set_dll_root('inputs/compartments')
run_sim_args = {"config_builder": cb, "exp_name": "First CMS run"}
if __name__ == "__main__":
SetupParser.init()
em = ExperimentManagerFactory.from_cb(run_sim_args["config_builder"])
em.run_simulations(exp_name=run_sim_args["exp_name"])
# Wait for the simulation to complete
em.wait_for_finished(verbose=True)
# Analyze
am = AnalyzeManager(exp_list='latest')
am.add_analyzer(SimpleCMSAnalyzer())
am.analyze()
from COMPS.Data import QueryCriteria
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from PythonHelperFunctions.utils import load_output_path
from SCOutputAnalyzer import SCOutputAnalyzer
SetupParser.default_block = "HPC"
if __name__ == "__main__":
SetupParser.init()
exp_list = pd.read_csv(os.path.join(load_output_path(),
'Experiment_tracking.csv'),
index_col="Index")
for index, row in exp_list.iterrows():
exp = row['Experiment ID']
print('Checking experiment ' + exp)
tmp = get_experiment_by_id(
exp, query_criteria=QueryCriteria().select_children(["tags"]))
foldername = row['Description']
outDir = os.path.join(load_output_path(), foldername, 'simOutputs')
if not os.path.exists(outDir):
os.mkdir(outDir)
am = AnalyzeManager(exp,
analyzers=SCOutputAnalyzer(filenames=[
'output/InsetChart.json',
'output/PropertyReport.json'
],
output_path=outDir))
am.analyze()
]
for smc_coverage in coverages
for seed in range(num_seeds)
]
builder = ModBuilder.from_list(SMC)
run_sim_args = {'config_builder': cb,
'exp_name': expname,
'exp_builder': builder}
if __name__ == "__main__":
SetupParser.default_block = 'HPC'
SetupParser.init()
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(**run_sim_args)
# Wait for the simulations to be done
exp_manager.wait_for_finished(verbose=True)
assert (exp_manager.succeeded())
analyzer = PrevalenceAnalyzer(expt_name=expname,
sweep_variables=["Run_Number",
"Coverage"
])
am = AnalyzeManager(exp_manager.experiment, analyzers=analyzer)
am.analyze()
adf = pd.concat(selected).reset_index(drop=True)
adf.sort_values(by=self.sweep_variables + ['year'], inplace=True)
adf.to_csv(os.path.join(self.working_dir, '%s.csv' % self.expt_name),
index=False)
if __name__ == '__main__':
out_dir = os.path.join(wdir, 'data')
experiments = {"End60": "229a4440-8d1f-e911-a2be-c4346bcb1554"}
for expt_name, exp_id in experiments.items():
am = AnalyzeManager(exp_list=exp_id,
analyzers=[
IvermectinAnalyzer(
working_dir=out_dir,
expt_name=expt_name,
sweep_variables=[
"Run_Number",
"x_Temporary_Larval_Habitat",
'dirus_Anthropophily', 'target',
'coverage', "intervention"
])
],
force_analyze=True)
print(am.experiments)
am.analyze()
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)
df.to_csv(os.path.join(self.working_dir, '%s.csv' % self.dir_name), index=False)
if __name__ == "__main__":
SetupParser.init("HPC")
out_dir = os.path.join('E:/', 'Dropbox (IDM)', 'Malaria Team Folder', 'projects',
'map_intervention_impact', 'lookup_tables')
sites = pd.read_csv("site_details.csv")
experiments = {
"corr_itn_irs" :"e7fd04a2-a8d8-e811-a2bd-c4346bcb1555"
}
for dirname, exp_id in experiments.items():
am = AnalyzeManager(exp_list=exp_id, analyzers=[BurdenAnalyzer(working_dir=out_dir,
dir_name=dirname,
report_names = sites["name"].tolist(),
sweep_variables=["Run_Number",
"x_Temporary_Larval_Habitat",
"IRS_Coverage",
"ITN_Coverage",
"ITN_IRS_Coverage",
])],
force_analyze=True)
print(am.experiments)
am.analyze()
if sdf[self.channel][j] == sdf[self.channel][j+365]:
eliminated[str(c) + ' / ' + str(r)] += 1
# infections[str(c) + ' / ' + str(r)] = sdf[self.channel]
for num in range (1460):
infect[num] += sdf[self.channel][j-1000+num]
j += 4*365
for num in range(1460):
infect[num]
ax.plot(range(1460), infect, linewidth = 1, label='cov %.2f' % (c))
ax.legend()
ax.set_xlabel('Time')
ax.set_ylabel('Infected')
#plt.savefig(os.path.join(plotdir, '123myplot123.pdf'), format='PDF')
plt.title('ATSB Infection Reduction (CDC)')
plt.show()
plt.close('all')
if __name__ == '__main__' :
expid = '905a91f4-2e9b-e811-a2c0-c4346bcb7275'
am = AnalyzeManager(expid,
analyzers=VectorCountAnalyzer())
am.analyze()
value1 = tags[param1]
value2 = tags[param2]
sweep_set = '%s-%s--%s-%s' % (param1, value1, param2, value2)
return sweep_name, sweep_set
def get_sim_folder(self, simulation):
sweep_name, sweep_set = self.determine_sweep_set(simulation.tags)
return os.path.join(self.output_path, sweep_name, sweep_set,
simulation.id)
def select_simulation_data(self, data, simulation):
# Create a folder for the current simulation
sim_folder = self.get_sim_folder(simulation)
os.makedirs(sim_folder, exist_ok=True)
# Create the requested files
for filename in self.filenames:
file_path = os.path.join(sim_folder, os.path.basename(filename))
with open(file_path, 'wb') as outfile:
outfile.write(data[filename])
if __name__ == "__main__":
analyzer = DownloadAnalyzerWithTags(
filenames=['output\\ReportHIVByAgeAndGender.csv'])
experiments = retrieve_item(
itemid='5a088005-19f8-e911-a2c3-c4346bcb1551') # suite_id passed here
experiments = [e.id for e in experiments]
am = AnalyzeManager(exp_list=experiments, analyzers=analyzer)
am.analyze()
if __name__=="__main__":
# 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)]
from simtools.Analysis.AnalyzeManager import AnalyzeManager
from simtools.Analysis.BaseAnalyzers import BaseAnalyzer
class PopulationAnalyzer(BaseAnalyzer):
def __init__(self):
super().__init__(filenames=['output\\InsetChart.json'])
def select_simulation_data(self, data, simulation):
# Apply is called for every simulations included into the experiment
# We are simply storing the population data in the pop_data dictionary
return data[self.filenames[0]]["Channels"]["Statistical Population"]["Data"]
def finalize(self, all_data):
import matplotlib.pyplot as plt
for pop in list(all_data.values()):
plt.plot(pop)
plt.legend([s.id for s in all_data.keys()])
plt.show()
# This code will analyze the latest experiment ran with the PopulationAnalyzer
if __name__ == "__main__":
am = AnalyzeManager('latest', analyzers=PopulationAnalyzer())
am.analyze()
x = np.linspace(0, 0.25, 100)
y = np.linspace(
min(sdf2['pre-prevalence']), max(sdf2['pre-prevalence']), 100
) #np.arange(min(sdf['Log_x_Temp']), max(sdf['Log_x_Temp'])+(sdf['Log_x_Temp'][3]-sdf['Log_x_Temp'][2]), (sdf['Log_x_Temp'][3]-sdf['Log_x_Temp'][2]))
xx, yy = np.meshgrid(x, y)
inter = interpolate.Rbf(sdf['killing'], sdf2['pre-prevalence'],
sdf[self.channel])
zz = inter(xx, yy)
cmap = 'rainbow_r'
#plt.imshow(zz, cmap = plt.get_cmap(cmap, 10))
c = plt.contour(xx, yy, zz, cmap=plt.get_cmap((cmap)), vmin=0, vmax=1)
plt.clabel(c, inline=1, fontsize=8, fmt='%.2f')
palette = sns.color_palette(cmap, 100)
ax.scatter(
sdf['killing'],
sdf2['pre-prevalence'],
20,
color=[palette[int(x * 100)] for x in sdf[self.channel].values],
alpha=0.5)
#plt.savefig(os.path.join(plotdir, '123myplot123.pdf'), format='PDF')
plt.title('ATSB Infection Reduction Heatmap')
plt.show()
plt.close('all')
if __name__ == '__main__':
expid = '4ee73ebf-d59c-e811-a2c0-c4346bcb7275'
am = AnalyzeManager(expid, analyzers=HeatmapAnalyzer())
am.analyze()
class SCOutputAnalyzer(BaseAnalyzer):
def __init__(self, filenames=None, output_path=None):
super().__init__(filenames=filenames)
self.output_path = output_path or "output"
def select_simulation_data(self, data, simulation):
# Apply is called for every simulations included into the experiment
# We are simply storing the population data in the pop_data dictionary
selected_data = {}
selected_data['insetChart'] = data[self.filenames[0]]
selected_data['sim_id'] = simulation.id
if simulation.tags.get('__sample_index__') is not None:
selected_data['sample'] = simulation.tags.get('__sample_index__')
else:
selected_data['sample'] = simulation.id
selected_data['tags'] = simulation.tags
selected_data['propertyReport'] = data[self.filenames[1]]
return selected_data
def finalize(self, all_data):
with open(os.path.join(self.output_path, 'results.pkl'),
'wb') as pklfile:
pickle.dump(all_data, pklfile)
# This code will analyze the latest experiment ran with the PopulationAnalyzer
if __name__ == "__main__":
am = AnalyzeManager('latest', analyzers=SCOutputAnalyzer())
am.analyze()
# "IVM_final": "dd4ce507-c72f-e911-a2c5-c4346bcb7273",
# "No_interventions": "e9e19499-c52f-e911-a2c5-c4346bcb7273"
# "eSMC_IVM_everyone": "9609582b-7b3f-e911-a2c5-c4346bcb7273",
# "eSMC10_IVM": "a47b2382-7b3f-e911-a2c5-c4346bcb7273",
# "Ivermectin_excludingchildbearing": "1afccb61-8846-e911-a2c0-c4346bcb1554",
'SMC_10': '154263e1-c031-e911-a2c5-c4346bcb7273'
}
for expt_name, exp_id in experiments.items():
if 'Reference' in expt_name:
am = AnalyzeManager(
exp_list=exp_id,
analyzers=[
SummaryAnalyzer(expt_name=expt_name,
report_names=['Daily_Report'],
sweep_variables=["Run_Number"]),
InsetAnalyzer(expt_name=expt_name,
report_names=['InsetChart'],
sweep_variables=["Run_Number"])
],
force_analyze=False)
else:
am = AnalyzeManager(exp_list=exp_id,
analyzers=[
SummaryAnalyzer(
expt_name=expt_name,
report_names=['Daily_Report'],
sweep_variables=[
"Run_Number", "Coverage",
"Intervention_type"
'Malaria Team Folder', 'projects',
'map_intervention_impact', 'lookup_tables')
run_type = "exp"
if run_type == "exp":
experiments = ["6e91248b-dbbd-e811-a2bd-c4346bcb1555"]
for exp_id in experiments:
am = AnalyzeManager(exp_list=exp_id,
analyzers=[
PfPRAnalyzer(
working_dir=out_dir,
sweep_variables=[
"Site_Name", "Run_Number",
"x_Temporary_Larval_Habitat",
"ACT_Coverage", "IRS_Coverage",
"ITN_Coverage"
])
],
force_analyze=True)
print(am.experiments)
am.analyze()
elif run_type == "suite":
exps = exps_for_suite_id("537b9041-0fa3-e811-a2c0-c4346bcb7275")
print(exps)
am = AnalyzeManager(exp_list=exps,
analyzers=[
add_SerializationTimesteps(config_builder=cb_serializing,
timesteps=[timestep_to_reload],
end_at_final=True)
s_pop_filename = "state-{0:05d}.dtk".format(timestep_to_reload)
# Run the simulation
exp_manager.run_simulations(config_builder=cb_serializing,
exp_name="Sample serialization test")
exp_manager.wait_for_finished(verbose=True)
# Download the state file
simulation = exp_manager.experiment.simulations[0]
da = DownloadAnalyzer(filenames=["output\\{}".format(s_pop_filename)],
output_path="temp")
am = AnalyzeManager(sim_list=[simulation], analyzers=[da])
am.analyze()
# Add the state file
cb_reload = DTKConfigBuilder.from_defaults('VECTOR_SIM')
configure_site(cb_reload, 'Namawala')
cb_reload.set_param("Simulation_Duration", 365)
cb_reload.experiment_files.add_file(
os.path.join("temp", simulation.id, s_pop_filename))
load_Serialized_Population(cb_reload, 'Assets', [s_pop_filename])
# Run !
exp_manager.run_simulations(config_builder=cb_reload)
# Cleanup temp directory
color=palette[c],
label=coverage)
if i == num_interventions - 1:
ax.set_xlabel('%s no intervention' % self.data_channel)
if s == 0:
ax.set_ylabel('%s with intervention' % self.data_channel)
ax.set_title('%s %s' % (site, intervention))
axes[-1].legend(title='coverage')
plt.show()
if __name__ == "__main__":
from simtools.Analysis.AnalyzeManager import AnalyzeManager
from simtools.SetupParser import SetupParser
SetupParser.default_block = 'HPC'
SetupParser.init()
sites = pd.read_csv("site_details.csv")
analyzer = PfPRAnalyzer(expt_name='global_int_ex_test',
report_names=sites["name"].tolist(),
sweep_variables=[
"Run_Number", "x_Temporary_Larval_Habitat",
"intervention"
])
am = AnalyzeManager('d9bf3918-d8fc-e811-a2bd-c4346bcb1555',
analyzers=analyzer)
am.analyze()
index=False)
if __name__ == "__main__":
SetupParser.default_block = 'LOCAL'
SetupParser.init()
out_dir = os.path.join(projectdir, 'sim_data')
experiments = {
"SMC_ivermection_scenarios_uncorrelated":
"01768676-af24-e911-a2be-c4346bcb1554"
}
for expt_name, exp_id in experiments.items():
am = AnalyzeManager(exp_list=exp_id,
analyzers=[
InsetAnalyzer(working_dir=out_dir,
expt_name=expt_name,
report_names=['InsetChart'],
sweep_variables=[
"Run_Number", "Coverage",
"Intervention_type"
])
],
force_analyze=False)
print(am.experiments)
am.analyze()
df.to_csv('%s.csv' % self.output_fname)
fig = plt.figure()
ax = fig.gca()
max_EIR = np.max(df['aEIR'])
ax.scatter(np.log10(df['aEIR']), df['PfPR_2to10'])
ax.set_xlabel('aEIR')
ax.set_ylabel('PfPR [2 to 10]')
ax.set_ylim(0, 1)
plt.title('PfPR [2 to 10] v. aEIR')
plt.savefig('%s.png' % self.output_fname)
plt.savefig('%s.pdf' % self.output_fname, format='PDF')
plt.close('all')
if __name__ == '__main__':
expids = ['4f157763-d898-ea11-a2c5-c4346bcb1550']
expnames = ['EIR_sweep']
for expname, expid in zip(expnames, expids):
os.makedirs(os.path.join(wdir, expid), exist_ok=True)
output_fname = os.path.join(wdir, expid, expname)
am = AnalyzeManager(expid, analyzers=EIR_PfPR_Analyzer(output_fname))
am.analyze()
experiments_ids.append(experiment_manager.experiment.exp_id)
# 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
if "c" in locals():
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")
from simtools.Analysis.AnalyzeManager import AnalyzeManager
from simtools.Analysis.BaseAnalyzers import DownloadAnalyzer
if __name__ == "__main__":
analyzer = DownloadAnalyzer(
filenames=['output\\InsetChart.json', 'config.json'])
am = AnalyzeManager('latest', analyzers=analyzer)
am.analyze()
