def incidence_grabby(expname, hfca, rank, output_fname=None) :
calib_folder = calib_base + expname +"/"
if not output_fname:
output_fname = calib_folder + "rank{}_cases".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
am = AnalyzeManager()
# am.add_analyzer(IncidencePlotter(GriddedCalibSite(hfca),save_file=output_fname))
am.add_analyzer(IncidencePlotter(hfca, save_file=output_fname, save_csv=True))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
am.add_simulation(simid)
am.analyze()
def RDT_grabby(expname, rank, output_fname=None, plot_bairros=True) :
calib_folder = calib_base + expname +"/"
if not output_fname:
output_fname = calib_folder + "rank{}_rdt".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
start_date = "2009-01-01"
am = AnalyzeManager()
am.add_analyzer(PrevAnalyzer(start_date=start_date,
save_file=output_fname,
cait_output_mode=True,
plot_bairros=plot_bairros))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
# print("Sim ID: ",simid)
am.add_simulation(simid)
am.analyze()
def plot_vectors(exp_id, sample, save_file=None):
am = AnalyzeManager()
am.add_experiment(retrieve_experiment(exp_id))
am.add_analyzer(
VectorSpeciesReportAnalyzer(sample,
save_file=save_file,
channel='Daily HBR'))
am.analyze()
def vector(expname, calib_stage, rank) :
calib_folder = calib_base + expname +"/"
output_fname = calib_folder + "rank{}_vectors".format(rank)
LL_fname = calib_folder + "_plots/LL_all.csv"
LL_df = pd.read_csv(LL_fname)
LL_df.sort_values(by='total', ascending=False, inplace=True)
LL_df.reset_index(inplace=True)
sample = LL_df.loc[rank, 'sample']
iteration = LL_df.loc[rank, 'iteration']
am = AnalyzeManager()
am.add_analyzer(VectorSpeciesReportAnalyzer(save_file=output_fname, channel='Daily HBR'))
with open(calib_folder+"iter{}/IterationState.json".format(iteration)) as fin:
iteration_state = json.loads(fin.read())
siminfo = OrderedDict(iteration_state['simulations'])
for item in list(siminfo.items()) :
if item[1]['__sample_index__'] == sample :
simid = item[0]
am.add_simulation(simid)
am.analyze()
########################
# other configurations
########################
# 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 Default 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()
# am.add_experiment(retrieve_experiment("15a20ddd-2a36-e811-a2bf-c4346bcb7274")) # facazissa iter5. best 0. 4/1 10:30pm
# am.add_experiment(retrieve_experiment("86413a54-0d36-e811-a2bf-c4346bcb7274")) # magude iter3. best 10. 4/1 10:30pm X
# am.add_experiment(retrieve_experiment("15a1d9fe-2f36-e811-a2bf-c4346bcb7274")) # mahel iter9. best 0. 4/1
# am.add_experiment(retrieve_experiment("0fc16f8f-2636-e811-a2bf-c4346bcb7274")) # mapulanguene iter9. best 10. 4/1 10:30pm
# am.add_experiment(retrieve_experiment("f5873afe-1336-e811-a2bf-c4346bcb7274")) # moine iter6. best 0 4/1
# am.add_experiment(retrieve_experiment("19794550-c135-e811-a2bf-c4346bcb7274")) # motaze iter1. best 15 4/1
# am.add_experiment(retrieve_experiment("e6f8c635-2d36-e811-a2bf-c4346bcb7274")) # panjane iter6. best 0 4/1
# am.add_experiment(retrieve_experiment("6fe0132a-c135-e811-a2bf-c4346bcb7274")) # faca stage1, iter1, best 9
# am.add_experiment(retrieve_experiment("86413a54-0d36-e811-a2bf-c4346bcb7274")) # m-s stage 1. iter3, best 12
# am.add_experiment(retrieve_experiment("eb30545d-e536-e811-a2bf-c4346bcb7274")) # m-s stage 2. ite3, best 6
# am.add_experiment(retrieve_experiment("d4b08d09-1835-e811-a2bf-c4346bcb7274")) #caputine iter12. best 8.
# am.add_experiment(retrieve_experiment("0fc97f4a-4634-e811-a2bf-c4346bcb7274")) # chichuco iter0. best 3
# am.add_experiment(retrieve_experiment("f67437d5-4e34-e811-a2bf-c4346bcb7274")) # chicutso iter2. best 3
# am.add_experiment(retrieve_experiment("d7d2a0be-a234-e811-a2bf-c4346bcb7274")) # facazissa iter3. best 12
# am.add_experiment(retrieve_experiment("3240a906-9e33-e811-a2bf-c4346bcb7274")) # magude iter0. best 21.
# am.add_experiment(retrieve_experiment("6cd7957f-cb34-e811-a2bf-c4346bcb7274")) # mahel iter6. best 11.
# am.add_experiment(retrieve_experiment("0dbd4e00-cc34-e811-a2bf-c4346bcb7274")) # mapulanguene iter8. best 3
# am.add_experiment(retrieve_experiment("777c34a8-dc34-e811-a2bf-c4346bcb7274")) # moine iter6. best 8
# am.add_experiment(retrieve_experiment("5171d868-4634-e811-a2bf-c4346bcb7274")) # motaze iter0. best 11
# am.add_experiment(retrieve_experiment("7a5ab67b-dc34-e811-a2bf-c4346bcb7274")) # panjane iter8. best 17
# am.add_experiment(retrieve_experiment("2ecf9cd7-9c35-e811-a2bf-c4346bcb7274")) #aggregate 2014. iter2, best 20
# am.add_experiment(retrieve_experiment("d8cb3061-ae35-e811-a2bf-c4346bcb7274")) #aggregate 2014,2015. iter2, best 5
am.add_experiment(
retrieve_experiment("2f76368f-bc57-e811-a2bf-c4346bcb7274"))
am.add_analyzer(PrevAnalyzer(cait_output_mode=True, gatesreview=True))
am.analyze()
from dtk.utils.analyzers import TimeseriesAnalyzer, VectorSpeciesAnalyzer
from simtools.AnalyzeManager.AnalyzeManager import AnalyzeManager
from simtools.Utilities.Experiments import retrieve_experiment
if __name__ == "__main__":
# Retrieve a couple of test experiments
experiment1 = retrieve_experiment('158cc530-780e-e711-9400-f0921c16849c')
experiment2 = retrieve_experiment('c62aa746-780e-e711-9400-f0921c16849c')
# Create an analyze manager
# Note that we are adding the experiments that we want to analyze
am = AnalyzeManager(exp_list=[experiment1, experiment2])
# Add the TimeSeriesAnalyzer to the manager
am.add_analyzer(TimeseriesAnalyzer())
am.add_analyzer(VectorSpeciesAnalyzer())
# Analyze
am.analyze()
if __name__=="__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# Corrected stepd
# am.add_experiment(retrieve_experiment("43cac760-cbd6-e711-9414-f0921c16b9e5")) # bbondo
# am.add_experiment(retrieve_experiment("a31b516a-cbd6-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("1ecdf372-cbd6-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("957e6159-32d6-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("9669907b-cbd6-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("fbe40809-ccd6-e711-9414-f0921c16b9e5")) # munyumbwe
# am.add_experiment(retrieve_experiment("8aadd6a0-cbd6-e711-9414-f0921c16b9e5")) # nyanga chaamwe
# am.add_experiment(retrieve_experiment("d18a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinafala
am.add_experiment(retrieve_experiment("d28a9aa8-cbd6-e711-9414-f0921c16b9e5")) # sinamalima
# Old MBGSR
# am.add_experiment(retrieve_experiment("7f188957-2fe1-e711-9414-f0921c16b9e5")) # bbondo
# am.add_experiment(retrieve_experiment("f60d69eb-2fe1-e711-9414-f0921c16b9e5")) # chabbobboma
# am.add_experiment(retrieve_experiment("7aa30068-2fe1-e711-9414-f0921c16b9e5")) # chisanga
# am.add_experiment(retrieve_experiment("d57bccae-25e1-e711-9414-f0921c16b9e5")) # chiyabi
# am.add_experiment(retrieve_experiment("5d5cff6d-2fe1-e711-9414-f0921c16b9e5")) # luumbo
# am.add_experiment(retrieve_experiment("cf37cd7b-2fe1-e711-9414-f0921c16b9e5")) # munyumbwe
# am.add_experiment(retrieve_experiment("94aa85fb-2fe1-e711-9414-f0921c16b9e5")) # nyanga chaamwe
# am.add_experiment(retrieve_experiment("f5c0fb13-30e1-e711-9414-f0921c16b9e5")) # sinafala
# am.add_experiment(retrieve_experiment("33b92b39-30e1-e711-9414-f0921c16b9e5")) # sinamalima
am.add_analyzer(RDTPrevAnalyzer())
am.analyze()
def finalize(self):
# print self.my_data
print("")
def plot(self):
import matplotlib.pyplot as plt
# Plot histogram of trips
for sim_id, data in self.n_trips.items():
# data only contains data for travellers. Need to add in "zero trips" for everyone who didn't travel.
n_couch = self.pop_init[sim_id] - self.n_travellers[sim_id]
full_data = np.append(data, np.zeros(int(n_couch)))
plt.hist(full_data,histtype='stepfilled',alpha=0.4,log=True, label=self.metadata[sim_id])
plt.legend()
plt.show()
if __name__=="__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
am.add_experiment(retrieve_experiment("151f8b4b-867c-e711-9401-f0921c16849d"))
am.add_analyzer(MigrationAnalyzer())
am.analyze()
def plot_RDT(exp_id, sample, save_file=None, **kwargs):
am = AnalyzeManager()
am.add_experiment(retrieve_experiment(exp_id))
am.add_analyzer(
prevalence_plot_analyzer(catch, sample, save_file=save_file, **kwargs))
am.analyze()
plt.tight_layout()
# if self.save_file:
# # if self.cait_output_mode:
# # MozambiqueExperiment.save_figs_for_caitlin(fig,self.save_file)
# # else:
if not self.save_file:
self.save_file = save_file = "figs/{}".format(self.catch)
# plt.savefig(self.save_file + ".pdf")
# plt.savefig(self.save_file + ".png")
# else:
plt.show()
print("Done!")
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# am.add_experiment(retrieve_experiment("0a373d77-1f93-e811-a2c0-c4346bcb7275")) # chichuco
# am.add_experiment(retrieve_experiment("0d801fc0-3c92-e811-a2c0-c4346bcb7275")) # chicutso
am.add_experiment(
retrieve_experiment(
"c5c3c5bb-a79c-e811-a2c0-c4346bcb7275")) # magude-sede-facazissa
# am.add_experiment(retrieve_experiment("210bcb89-e696-e811-a2c0-c4346bcb7275")) # mahel
# am.add_experiment(retrieve_experiment("10238aac-7593-e811-a2c0-c4346bcb7275")) # mapulanguene
# am.add_experiment(retrieve_experiment("85bef741-2d97-e811-a2c0-c4346bcb7275")) # moine
# am.add_experiment(retrieve_experiment("140fe8a7-1194-e811-a2c0-c4346bcb7275")) # motaze
# am.add_experiment(retrieve_experiment("b1c79146-6194-e811-a2c0-c4346bcb7275")) # panjane-caputine
am.add_analyzer(PrevAnalyzer())
am.analyze()
experiment_manager.run_simulations(exp_name=COMPS_experiment_name,
exp_builder=experiment_builder,
suite_id=suite_id)
experiments.append(experiment_manager)
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)
# Every experiments are created at this point -> Analyze
am = AnalyzeManager(verbose=False, create_dir_map=False)
for em in experiments:
am.add_experiment(em.experiment)
# am.add_analyzer(DownloadAnalyzerTPI(['output\\DemographicsSummary.json', 'config.json', 'output\\ReportHIVART.csv', 'output\\ReportHIVByAgeAndGender.csv'],
# output_dir='Test HIV 1'))
am.add_analyzer(
DownloadAnalyzerTPI(['output\\ReportHIVByAgeAndGender.csv'],
output_dir='Nyanza Base Case'))
# While the experiments are running, we are analyzing every 15 seconds
while not all([em.finished() for em in experiments]):
map(lambda e: e.refresh_experiment(), experiments)
print("Analyzing !")
am.analyze()
print("Waiting 15 seconds")
time.sleep(15)
# Analyze one last time when everything is complete
am.analyze()
def catalyst(args, unknownArgs):
"""
Catalyst run-and-analyze process as ported from the test team.
Programmatic-only arguments:
args.mode : used by FidelityReportExperimentDefinition, default: 'prod'
args.report_label : attached to the experiment name
args.debug : True/False, passed into FidelityReportAnalyzer, default: False
:param args:
:param unknownArgs:
:return:
"""
from dtk.utils.builders.sweep import GenericSweepBuilder
from catalyst_report.fidelity_report_analyzer import FidelityReportAnalyzer
from catalyst_report.fidelity_report_experiment_definition import FidelityReportExperimentDefinition
import catalyst_report.utils as catalyst_utils
from simtools.Analysis.AnalyzeManager import AnalyzeManager
# we're going to do a dtk run, then a set-piece analysis. But first we need to do some overrides
# to get the run part to do the desired parameter sweep.
mod = args.loaded_module
# when run with 'dtk catalyst', run_sim_args['exp_name'] will have additional information appended.
mod.run_sim_args[
'exp_name'] = mod.run_sim_args['exp_name'] + '-development'
# lining up the arguments expected by FidelityReportExperimentDefinition
args.sweep = args.sweep_method
# hidden, programmatic arguments
args.mode = args.mode if hasattr(args, 'mode') else 'prod'
args.report_label = args.report_label if hasattr(args,
'report_label') else None
args.debug = args.debug if hasattr(args, 'debug') else False
# determine which report is being asked for. If not specified, default to what the config.json file says
# ck4, this should go somewhere else, on a Config object of some sort? (prob not the builder, though)
report_type_mapping = {
'DENGUE_SIM': 'dengue',
'GENERIC_SIM': 'generic',
'HIV_SIM': 'hiv',
'MALARIA_SIM': 'malaria',
'POLIO_SIM': 'polio',
'STI_SIM': 'sti',
'TB_SIM': 'tb',
'TYPHOID_SIM': 'typhoid',
'VECTOR_SIM': 'generic'
}
if args.report_type:
report_type = args.report_type
else:
sim_type = mod.run_sim_args['config_builder'].config['parameters'][
'Simulation_Type']
report_type = report_type_mapping.get(sim_type, None)
if not report_type:
raise KeyError(
'Default report type could not be determined for sim_type: %s. Report type must be specified'
' via -r flag.' % sim_type)
# Create and set a builder to sweep over population scaling or model timestep
reports = catalyst_utils.load_report_definitions(
definitions_filename=args.report_definitions)
if report_type in reports:
args.report_channel_list = reports[report_type]['inset_channel_names']
else:
raise Exception('Invalid report: %s. Available reports: %s' %
(report_type, sorted(reports.keys())))
catalyst_config = catalyst_utils.load_sweep_configs(
sweep_type=args.sweep_type, config_filename=args.sweep_definitions)
defn = FidelityReportExperimentDefinition(catalyst_config, args)
# redefine the experiment name so it doesn't conflict with the likely follow-up non-catalyst experiment
mod.run_sim_args['exp_name'] = 'Catalyst-' + mod.run_sim_args['exp_name']
# define the sweep to perform
sweep_dict = {
'Run_Number': range(1,
int(defn['nruns']) + 1),
defn['sweep_param']: defn['sweep_values']
}
mod.run_sim_args['exp_builder'] = GenericSweepBuilder.from_dict(sweep_dict)
# overwrite spatial output channels to those used in the catalyst report
spatial_channel_names = defn['spatial_channel_names']
if len(spatial_channel_names) > 0:
mod.run_sim_args['config_builder'].enable('Spatial_Output')
mod.run_sim_args['config_builder'].params[
'Spatial_Output_Channels'] = spatial_channel_names
else:
mod.run_sim_args['config_builder'].disable('Spatial_Output')
mod.run_sim_args['config_builder'].params[
'Spatial_Output_Channels'] = []
# now run if no preexisting experiment id was provided
if not args.experiment_id:
# we must always block so that we can run the analysis at the end; run and analyze!
args.blocking = True
experiment_manager = run(args, unknownArgs)
experiment = experiment_manager.experiment
print('Done running experiment: %s' % experiment.exp_id)
else:
experiment = retrieve_experiment(args.experiment_id)
# Create an analyze manager
am = AnalyzeManager(exp_list=[experiment], verbose=False)
# Add the TimeSeriesAnalyzer to the manager and do analysis
# ck4, is there a better way to specify the first 4 arguments? The DTKCase from Test-land might be nicer.
# After all, the names COULD be different
analyzer = FidelityReportAnalyzer(
'catalyst_report',
'config.json',
mod.run_sim_args['config_builder'].get_param(
'Demographics_Filenames')[0],
experiment_definition=defn,
experiment_id=experiment.exp_id,
experiment_name=experiment.exp_name,
label=args.report_label,
time_series_step_from=defn['step_from'],
time_series_step_to=defn['step_to'],
time_series_equal_step_count=True,
raw_data=True,
debug=args.debug)
am.add_analyzer(analyzer)
am.analyze()
import webbrowser
webbrowser.open_new("file:///{}".format(
os.path.join(os.getcwd(), "catalyst_report", "summary_report.html")))
int(x["year"]) + 2010,
str(int(x["month"])).zfill(2)),
axis=1)
# print("mdate")
df["mdate"] = df.apply(lambda x: date_to_mdate(x["date"]), axis=1)
# print("plot")
# ax.plot(df["year"] * 12 + df["month"], df["cases"], *args, **kwargs)
ax.plot_date(df["mdate"], df["cases"], *args, **kwargs)
ax.set_xlabel("Date")
ax.set_ylabel("Cases")
ax.set_xlim([date_to_mdate("2010-01-01"), date_to_mdate("2017-01-01")])
# ax.tick_params(direction="inout")
def uid(self):
''' A unique identifier of site-name and analyzer-name. '''
return '_'.join([self.site.name, self.name])
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
# Calibration experiments:
am.add_experiment(
retrieve_experiment("a0bee2bd-f8b5-e811-a2c0-c4346bcb7275"))
am.add_analyzer(incidence_likelihood(zambia_calib_site("bbondo")))
am.analyze()
for exp_id in self.pop_data.keys():
plt.plot(
np.array(self.raw_pop_data[exp_id][-2]) /
self.tot_pop[exp_id][-2])
ax.set_title("Late")
ax.set_xticks(range(24))
ax.set_xticklabels(self.age_bins)
plt.show()
#
# for exp_id in self.pop_data.keys():
# plt.plot_date(self.report_times, self.pop_data[exp_id],fmt='-',c=c,linewidth=lw,label=label,alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.plot_date(self.report_times, self.pop_data[exp_id], fmt='-', c=c, linewidth=lw, label=label, alpha=0.4)
# plt.legend([s['environment'] for s in self.metadata.values()])
if __name__ == "__main__":
SetupParser.init('HPC')
am = AnalyzeManager()
am.add_experiment(
retrieve_experiment("f4ecdcc6-768c-e711-9401-f0921c16849d")) # L1
# am.add_experiment(retrieve_experiment("001a9f44-758c-e711-9401-f0921c16849d")) # L5
am.add_experiment(
retrieve_experiment("4188b9de-e28c-e711-9401-f0921c16849d")) # L6
am.add_analyzer(AgeStratificationAnalyzer())
am.analyze()
experiment_manager.run_simulations(exp_name=COMPS_experiment_name,
exp_builder=experiment_builder,
suite_id=suite_id)
experiments.append(experiment_manager)
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)
# Every experiments are created at this point -> Analyze
am = AnalyzeManager(verbose=False, create_dir_map=False)
for em in experiments:
am.add_experiment(em.experiment)
# am.add_analyzer(DownloadAnalyzerTPI(['output\\DemographicsSummary.json', 'config.json', 'output\\ReportHIVART.csv', 'output\\ReportHIVByAgeAndGender.csv'],
# output_dir='Test HIV 1'))
am.add_analyzer(
DownloadAnalyzerTPI(['output\\ReportHIVByAgeAndGender.csv'],
output_dir='..\\Output\\ReportHIVByAgeAndGender'))
# While the experiments are running, we are analyzing every 15 seconds
while not all([em.finished() for em in experiments]):
map(lambda e: e.refresh_experiment(), experiments)
print("Analyzing !")
am.analyze()
print("Waiting 15 seconds")
time.sleep(15)
# Analyze one last time when everything is complete
am.analyze()
selected = [
p.selected_data[id(self)] for p in parsers.values()
if id(self) in p.selected_data
]
self.data = pd.concat(selected)
def finalize(self):
import seaborn as sns
sns.set_style("darkgrid")
fig = plt.figure(figsize=(10, 6))
ax = fig.gca()
for a, adf in self.data.groupby('sim_id'):
for s, sdf in adf.groupby('species'):
ax.plot(sdf['date'], sdf[self.channel], label=s)
ax.legend()
plt.ylabel(self.channel)
if self.save_file:
plt.savefig(self.save_file + ".png")
plt.savefig(self.save_file + ".pdf")
else:
plt.show()
if __name__ == '__main__':
am = AnalyzeManager()
am.add_analyzer(VectorSpeciesReportAnalyzer())
am.add_simulation('4047a20f-b33d-e811-a2bf-c4346bcb7274')
am.analyze()
date_to_mdate("2013-01-01"),
date_to_mdate("2014-01-01"),
date_to_mdate("2015-01-01"),
date_to_mdate("2016-01-01"),
date_to_mdate("2017-01-01"),
date_to_mdate("2018-01-01"),
date_to_mdate("2019-01-01")
])
# plt.ylim([-0.01,0.25])
plt.ylabel("RDT Prevalence")
plt.legend(frameon=True)
plt.tight_layout()
if self.save_file:
plt.savefig(self.save_file + ".pdf")
plt.savefig(self.save_file + ".png")
else:
plt.show()
if __name__ == "__main__":
am = AnalyzeManager()
# am.add_experiment(retrieve_experiment("cdb12c2d-61c3-e811-a2bd-c4346bcb1555"))
am.add_experiment(
retrieve_experiment("9df3a55a-63c3-e811-a2bd-c4346bcb1555"))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C0", 3))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C1", 4))
am.add_analyzer(custom_prev_plot_analyzer("chiyabi", "C2", 5))
# am.add_analyzer(custom_prev_plot_analyzer("chiyabi","C3", 6))
am.analyze()