def add_scenario_reports(cb,
include_summary=True,
include_inset=True,
include_bednet_events_in_counter=False):
if include_summary:
add_summary_report(cb, age_bins=summary_age_bins, start=365)
events_to_count = [
"Received_Treatment", "Received_Test", "Received_Campaign_Drugs",
"Received_RCD_Drugs", "Received_SMC", "Received_Ivermectin",
"Received_Primaquine"
]
if include_bednet_events_in_counter:
events_to_count += [
"Bednet_Discarded", "Bednet_Got_New_One", "Bednet_Using"
]
add_event_counter_report(cb, event_trigger_list=events_to_count)
# drug_events = ["Received_Treatment", "Diagnostic_Survey_0", "Received_Test", "Received_RCD_Drugs",
# "Received_Campaign_Drugs"]
# cb.set_param("Listed_Events", drug_events)
# cb.set_param("Custom_Individual_Events", drug_events)
if include_inset:
cb.set_param("Enable_Default_Reporting", 1)
else:
cb.set_param("Enable_Default_Reporting", 0)
def add_summary_report_by_start_day(cb, start_day=0):
'''
Add daily summary report to output
:param cb: config builder
:param start_day: start day for report
:return: None
'''
add_summary_report(cb, start=start_day+1, interval=1.0, description='Daily_Report',
age_bins=[5.0, 10.0, 100.0])
return {'report_start_day': start_day}
def add_testing_reports(cb):
add_node_demographics_report(cb, IP_key_to_collect='SchoolStatus')
add_summary_report(cb,
ipfilter="SchoolStatus:AttendsSchool",
description="AttendsSchool",
age_bins=summary_age_bins)
add_summary_report(cb,
ipfilter="SchoolStatus:DoesNotAttendSchool",
description="DoesNotAttendSchool",
age_bins=summary_age_bins)
cb.set_param("Report_Detection_Threshold_True_Parasite_Density", 0)
cb.set_param("Report_Detection_Threshold_PCR_Gametocytes", 0)
def add_burnin_reports(cb, include_inset=False):
add_summary_report(cb, start=45 * 365)
if include_inset:
cb.set_param("Enable_Default_Reporting", 1)
else:
cb.set_param("Enable_Default_Reporting", 0)
events_to_count = [
"Bednet_Discarded", "Bednet_Got_New_One", "Bednet_Using",
"Received_Treatment", "Received_SMC"
]
add_event_counter_report(cb,
event_trigger_list=events_to_count,
duration=365 * 50)
def __call__(self, cb):
from malaria.reports.MalariaReport import add_summary_report
return add_summary_report(cb,
start=self.start,
interval=self.interval,
nreports=self.nreports,
description=self.description,
age_bins=self.age_bins,
nodes=self.nodes)
def __call__(self, cb):
from malaria.reports.MalariaReport import add_summary_report
return add_summary_report(cb,
start=self.start,
interval=self.interval,
nreports=self.nreports,
description=self.description,
age_bins=self.age_bins,
parasitemia_bins=self.parasitemia_bins,
infection_bins=self.infection_bins,
nodes=self.nodes,
ipfilter=self.ip_filter)
def add_testing_reports(cb):
add_summary_report(cb)
add_filtered_spatial_report(
cb,
channels=["Population", "Blood_Smear_Parasite_Prevalence"],
start=start,
end=(start + duration))
add_vector_migration_report(cb)
cb.set_param("Enable_Vector_Species_Report", 1)
cb.update_params({
"Report_Event_Recorder":
1,
"Report_Event_Recorder_Ignore_Events_In_List":
0,
"Report_Event_Recorder_Events": [
"Received_Treatment", "Diagnostic_Survey_0", "Received_Test",
"Received_RCD_Drugs", "Received_Campaign_Drugs"
]
})
# InsetChart
cb.update_params({"Enable_Default_Reporting": 1})
##################### modlists = [] num_seeds = 3 modlist = modfn_sweep_over_seeds(num_seeds) modlists.append(modlist) modlist = modfn_sweep_over_timings(archetype, num_years=num_years) modlists.append(modlist) #################### # Reports and logs # #################### # add_scenario_reports(cb, include_inset=False) # add_testing_reports(cb) add_summary_report(cb, start=365) ############################### # Submission/COMPs parameters # ############################### comps_experiment_name = "central_sac_ipt_timing_sweep" # comps_priority = "BelowNormal" comps_priority = "AboveNormal" # comps_priority = "AboveNormal" comps_coreset = "emod_abcd" # comps_coreset = "emod_32cores" ################## # Job submission # ##################
scaled_habitats["LINEAR_SPLINE"]["Max_Larval_Capacity"] = habitats[
"LINEAR_SPLINE"]["Max_Larval_Capacity"] * scale_factor
species_params["Larval_Habitat_Types"] = scaled_habitats
return {'larval_habitat_multiplier': scale_factor}
# builder = ModBuilder.from_list( [[ ModFn(sweep_scale_factor, x)] for x in [0.01,10,100]])
report_years = 1
report_start = 50
survey_days = list(range(365 * report_start, (365 * (report_start + 1)), 30))
# add_survey_report(cb, survey_days, reporting_interval=1)
add_summary_report(cb,
start=365 * report_start,
description='Annual_Report',
interval=365,
nreports=1,
age_bins=[5, 125],
parasitemia_bins=[0, 50, 200, 500, 2000000])
healthseeking_coverage_pairs = [[0.30, 0.30]] # as U5 and then >5 year olds
# creates a sweep of simulations with varying coverages and Run_Number seeds
healthseeking = [[
ModFn(sweep_coverages, coverages),
ModFn(scale_linear_spline_max_habitat, x),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)
] for x in np.logspace(-3, -0.3, 50)
for coverages in healthseeking_coverage_pairs
for seed in range(num_seeds)]
template_asset = asset_expt.simulations[0].tags
cb.set_exe_collection(template_asset["exe_collection_id"])
cb.set_dll_collection(template_asset["dll_collection_id"])
cb.set_input_collection(template_asset["input_collection_id"])
# Find vector proportions for each vector in our site
site_vectors = pd.read_csv(
os.path.join(site_input_dir, "vector_proportions.csv"))
simulation_setup(cb, species_details, site_vectors)
# reporting
for idx, row in site_vectors.iterrows():
add_summary_report(cb,
age_bins=list(range(10, 130, 10)),
nodes={
"class": "NodeSetNodeList",
"Node_List": [int(row["node_id"])]
},
description=row["name"])
if pull_from_serialization:
print("building from pickup")
# serialization
print("retrieving burnin")
expt = retrieve_experiment(burnin_id)
df = pd.DataFrame([x.tags for x in expt.simulations])
df["outpath"] = pd.Series([sim.get_path() for sim in expt.simulations])
df = df[df['Run_Number'] == 0]
num_seeds = 100
modlist = modfn_sweep_over_seeds(num_seeds)
modlists.append(modlist)
modlist = modfn_sweep_over_burnins(archetype)
modlists.append(modlist)
# modlist = modfn_sweep_over_scenarios(archetype, specific_scenarios_to_run=[13,30,34,35,36])
# modlists.append(modlist)
modlist = [ModFn(add_ipt_with_iver_skipping_one_term, t) for t in [1, 2, 3]]
modlists.append(modlist)
####################
# Reports and logs #
####################
add_summary_report(cb, age_bins=summary_age_bins, start=365)
events_to_count = [
"Received_Treatment", "Received_Test", "Received_Campaign_Drugs_Term_1",
"Received_Campaign_Drugs_Term_2", "Received_Campaign_Drugs_Term_3",
"Received_RCD_Drugs", "Received_SMC", "Received_Ivermectin",
"Received_Primaquine"
]
events_to_count += ["Bednet_Discarded", "Bednet_Got_New_One", "Bednet_Using"]
add_event_counter_report(cb, event_trigger_list=events_to_count)
cb.set_param("Enable_Default_Reporting", 1)
###############################
nyears = 50 # for sim duration AND reporting interval
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM')
cb.update_params({
'Num_Cores': 1,
'Simulation_Duration': nyears * 365,
'New_Diagnostic_Sensitivity': 0.025 # 40/uL
})
# recurring outbreak to avoid fadeout
recurring_outbreak(cb, outbreak_fraction=0.001, tsteps_btwn=180)
# custom reporting
add_summary_report(cb,
start=0,
interval=365,
nreports=nyears,
description="AnnualAverage")
add_immunity_report(cb,
start=365 * (nyears - 1),
interval=365,
nreports=1,
description="FinalYearAverage",
age_bins=[
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20,
25, 30, 40, 50, 60, 1000
])
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
num_seeds = 10
modlist = modfn_sweep_over_seeds(num_seeds)
modlists.append(modlist)
modlist = modfn_sweep_over_burnins(archetype)
modlists.append(modlist)
modlist = [ModFn(add_smc_variant, s) for s in [0, 1, 2, 3, 4]]
modlists.append(modlist)
####################
# Reports and logs #
####################
add_summary_report(cb,
age_bins=summary_age_bins,
start=365,
duration_days=sim_duration)
cb.set_param("Enable_Demographics_Reporting", 1)
events_to_count = [
"Received_Treatment", "Received_Test", "Received_Campaign_Drugs",
"Received_RCD_Drugs", "Received_SMC", "Received_Ivermectin",
"Received_Primaquine"
]
events_to_count += ["Bednet_Discarded", "Bednet_Got_New_One", "Bednet_Using"]
add_event_counter_report(cb,
event_trigger_list=events_to_count,
duration=sim_duration)
'gambiae': {
'TEMPORARY_RAINFALL': 1.5e8 * sample["Temporary_Habitat"],
'CONSTANT': 5.5e6 * sample["Constant_Habitat"]
}
}
set_larval_habitat(cb, hab)
return sample
optimtool = OptimTool(params, center_repeats=1, samples_per_iteration=25)
calib_manager = CalibManager(
name=exp_name,
config_builder=cb,
map_sample_to_model_input_fn=map_sample_to_model_input,
sites=[PointPrevalenceSite(site)],
next_point=optimtool,
sim_runs_per_param_set=1,
max_iterations=3,
plotters=[OptimToolPlotter()])
add_summary_report(
cb,
description='AnnualAverage') ## add summary report of annual prevalence
run_calib_args = {"calib_manager": calib_manager}
if __name__ == "__main__":
SetupParser.init()
cm = run_calib_args["calib_manager"]
cm.run_calibration()
