def test_generic_sweep(self):
def verify(b):
md = [(0.05, 100, 'Namawala'), (0.05, 100, 'Matsari'),
(0.1, 100, 'Namawala'), (0.1, 100, 'Matsari')]
ngenerated = 0
for i, ml in enumerate(b.mod_generator):
for m in ml:
m(self.cb)
mdd = dict(
zip(('x_Temporary_Larval_Habitat', 'Simulation_Duration',
'_site_'), md[i]))
mdd.update({'population_scale': 1})
self.assertEqual(b.metadata, mdd)
self.assertEqual(
self.cb.get_param('x_Temporary_Larval_Habitat'), md[i][0])
ngenerated += 1
self.assertEqual(ngenerated, 4)
b = ModBuilder.from_combos([
ModFn(DTKConfigBuilder.set_param, 'x_Temporary_Larval_Habitat', v)
for v in [0.05, 0.1]
], [ModFn(DTKConfigBuilder.set_param, 'Simulation_Duration', 100)
], [ModFn(configure_site, s) for s in ['Namawala', 'Matsari']])
verify(b)
b = GenericSweepBuilder.from_dict({
'x_Temporary_Larval_Habitat': [0.05, 0.1],
'Simulation_Duration': [100],
'_site_': ['Namawala', 'Matsari']
})
verify(b)
from dtk.generic.climate import set_climate_constant
from simtools.ExperimentManager.ExperimentManagerFactory import ExperimentManagerFactory
from simtools.SetupParser import SetupParser
if __name__ == "__main__":
expname = 'Serialized_file_drug_testing'
sim_duration = 365 * 70
num_seeds = 50
SetupParser('HPC')
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM')
builder = GenericSweepBuilder.from_dict({'Run_Number': range(num_seeds)})
set_climate_constant(cb)
set_species_param(cb, 'gambiae', 'Larval_Habitat_Types',
{"LINEAR_SPLINE": {
"Capacity_Distribution_Per_Year": {
"Times": [0.0, 30.417, 60.833, 91.25, 121.667, 152.083,
182.5, 212.917, 243.333, 273.75, 304.167, 334.583],
# "Values": [1, 0.25, 0.1, 1, 1, 0.5, 12, 5, 3, 2, 1.5, 1.5]
"Values": [3, 0.8, 1.25, 0.1, 2.7, 8, 4, 35, 6.8, 6.5, 2.6, 2.1]
},
"Max_Larval_Capacity": 1e9
}})
set_species_param(cb, "gambiae", "Indoor_Feeding_Fraction", 0.9)
cb.update_params({"Demographics_Filenames": ['Calibration/single_node_demographics.json'],
from simtools.SetupParser import SetupParser
from simtools.ExperimentManager.ExperimentManagerFactory import ExperimentManagerFactory
# Run on HPC
SetupParser.default_block = "HPC"
# Configure a default 5 years simulation
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM', Simulation_Duration=365 * 5)
# Set it in Namawala
configure_site(cb, 'Namawala')
# Name of the experiment
exp_name = 'ExampleSweep'
# Create a builder to sweep over the birth rate multiplier
builder = GenericSweepBuilder.from_dict({'x_Birth': np.arange(1, 1.5, .1)})
run_sim_args = {
'exp_name': exp_name,
'exp_builder': builder,
'config_builder': cb
}
if __name__ == "__main__":
SetupParser.init()
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(**run_sim_args)
exp_manager.wait_for_finished(verbose=True)
assert (exp_manager.succeeded())
'CONSTANT': 1.0 * pop_multiplier,
'WATER_VEGETATION': 1.0 * pop_multiplier,
'PIECEWISE_MONTHLY': fun_multiplier
}
return demographics
location = 'HPC' #'LOCAL'
geography = 'Zambia/Gwembe_Sinazongwe_115_nodes'
sites = ['Gwembe_Sinazongwe_115_nodes']
SetupParser.init(location)
builder = GenericSweepBuilder.from_dict({
'_site_': sites, # study sites
'x_Local_Migration': [1e-2],
'Run_Number': range(1) # random seeds
})
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM',
Num_Cores=24,
Simulation_Duration=365 * 5)
# migration
cb.update_params(single_roundtrip_params)
# set demographics file name
cb.update_params({
'Demographics_Filenames': [
os.path.join(geography,
"Zambia_Gwembe_Sinazongwe_115_nodes_demographics.json")
from simtools.ExperimentManager.ExperimentManagerFactory import ExperimentManagerFactory
from simtools.SetupParser import SetupParser
# 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:
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")))
from dtk.interventions.outbreakindividual import recurring_outbreak
from dtk.utils.builders.sweep import GenericSweepBuilder
from dtk.utils.core.DTKConfigBuilder import DTKConfigBuilder
from malaria.reports.MalariaReport import add_summary_report, add_immunity_report
exp_name = 'burnin'
builder = GenericSweepBuilder.from_dict({
'Run_Number':
range(1),
'x_Temporary_Larval_Habitat': (0.1, 0.2, 0.3, 0.4, 0.5),
'_site_': ('Sinazongwe.static', )
})
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")
