def set_immune_forcing_builder(transition_matrix=None,
scale_factor_array=[2, 5, 10, 100]):
builder = ModBuilder.from_combos([
ModFn(set_transition_matrix, transition_matrix, scale_factor)
for scale_factor in scale_factor_array
])
return builder
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)
def test_vector_drug_param_sweep(self):
b = ModBuilder.from_combos([
ModFn(set_species_param,
'gambiae',
'Required_Habitat_Factor',
value=v) for v in [(100, 50), (200, 100)]
], [
ModFn(set_drug_param, 'Artemether', 'Max_Drug_IRBC_Kill', value=v)
for v in [4, 2]
])
md = [(4, (100, 50)), (2, (100, 50)), (4, (200, 100)), (2, (200, 100))]
ngenerated = 0
for i, ml in enumerate(b.mod_generator):
for m in ml:
m(self.cb)
self.assertListEqual([v for v in b.metadata.values()], list(md[i]))
self.assertEqual(
get_species_param(self.cb, 'gambiae',
'Required_Habitat_Factor'), md[i][1])
self.assertEqual(
get_drug_param(self.cb, 'Artemether', 'Max_Drug_IRBC_Kill'),
md[i][0])
ngenerated += 1
self.assertEqual(ngenerated, 4)
def set_immune_forcing_builder(transition_matrix=None,
scale_factor_array=[2, 5, 10, 100]):
"""Creates an experiment builder setting up immunity based on scale factor"""
builder = ModBuilder.from_combos([
ModFn(set_transition_matrix, transition_matrix, scale_factor)
for scale_factor in scale_factor_array
])
return builder
def test_multiple_site_exception(self):
def verify(b):
with self.assertRaises(ValueError):
for ml in b.mod_generator:
pass
b = ModBuilder.from_combos([ModFn(set_calibration_site, 'Matsari')],
[ModFn(configure_site, 'Namawala')])
verify(b)
b = ModBuilder.from_combos([ModFn(configure_site, 'Matsari')],
[ModFn(configure_site, 'Namawala')])
verify(b)
b = ModBuilder.from_combos([ModFn(set_calibration_site, 'Matsari')],
[ModFn(set_calibration_site, 'Namawala')])
verify(b)
def exp_builder_func(self, next_params, n_replicates=None):
if not n_replicates:
n_replicates = self.sim_runs_per_param_set
return ModBuilder.from_combos(
[ModFn(self.config_builder.__class__.set_param, 'Run_Number', i+1) for i in range(n_replicates)],
[ModFn(site.setup_fn) for site in self.sites],
[ModFn(self.map_sample_to_model_input_fn, index, samples) for index, samples in enumerate(next_params)]
)
def set_mods(cls, pv_pairs):
"""
Dictionary may include the special keys: '__site__' or '__calibsite___',
which are recognized as shorthand for site-configuration functions
with the site name as the "parameter value".
"""
def convert_to_mod_fn(pv_pair):
p, v = pv_pair
if p == '_site_':
return ModFn(configure_site, v)
return ModFn(DTKConfigBuilder.set_param, p, v)
return ModBuilder.set_mods([convert_to_mod_fn(pv_pair) for pv_pair in pv_pairs])
def add_test(self, test_name):
m = ModBuilder.ModList()
def configure(cb):
ModBuilder.metadata.update({'Config_Name': test_name})
test = DTKConfigBuilder.from_files(
os.path.join(self.regression_path, test_name, 'config.json'),
os.path.join(self.regression_path, test_name, 'campaign.json'))
cb.copy_from(test)
m.append(configure)
return m
SetupParser.set("HPC", "priority", priority)
SetupParser.set("HPC", "node_group", coreset)
modlists = []
new_modlist = [
ModFn(pickup_from_sample, i)
for i in range(len(arab_spline_list) * len(funest_spline_list))
]
modlists.append(new_modlist)
#
# new_modlist = [ModFn(counterfactuals, itn, irs, mda, msat, chw_hs, chw_rcd)
# for itn in [True]
# for irs in [True]
# for mda in [True]
# for msat in [True]
# for chw_hs in [False, True]
# for chw_rcd in [False, True]]
tuples_list = generate_input_variations()
new_modlist = [
ModFn(counterfactuals, x[0], x[1], x[2], x[3], x[4], x[5])
for x in tuples_list
]
modlists.append(new_modlist)
builder = ModBuilder.from_combos(*modlists)
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(config_builder=cb,
exp_name=experiment_name,
exp_builder=builder)
# create a config builder for the current experiment, derived from the base simulation input files
config_filepath = os.path.join(simulation_directory, 'config.json')
campaign_filepath = os.path.join(
simulation_directory,
'campaign_Swaziland_v05Sep2018_FINAL_reftracktopup_2011_v2.json')
cb = DTKConfigBuilder.from_files(config_name=config_filepath,
campaign_name=campaign_filepath)
cb.set_input_files_root(
path=simulation_directory
) # discoverability of other input files, e.g. PFA_Overlay.json
exp_name = EXP_NAME_PATTERN % (sweep['name'], i)
# Create a generator of the per-simulation differences from the base simulation
builder = ModBuilder.from_combos(sweep['mods'])
# keep track of each run_sim_args dictionary, one per experiment
run_sets.append({
'exp_name': exp_name,
'exp_builder': builder,
'config_builder': cb,
'base_simulation_id': sim_id,
'sweep_name': sweep['name']
})
# # ck4, for catalyst this block
# run_sim_args = run_sets[1]
# run_sim_args.pop('base_simulation_id')
# run_sim_args.pop('sweep_name')
# Let's use a standard DTKConfigBuilder.
config_builder = DTKConfigBuilder()
# ignore_missing is required when using templates
config_builder.ignore_missing = True
# Use the default COMPS 2.10 and the SamplesInput folder for input files
# See examples\AssetManagement\use_custom_executable.py for more information
config_builder.set_exe_collection('EMOD 2.10')
# For the experiment builder in the example, we use a ModBuilder from_combos to run
# each of the configurations for two separate run numbers.
# The first line needs to be the modifier functions coming from the templates
experiment_builder = ModBuilder.from_combos(
templates.get_modifier_functions(), # <-- Do this first!
[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', rn)
for rn in range(2, 4)
])
# This dictionary is used by the `dtk run` command line and allow to pass it some parameters
# If you are running through `python example_templates.py`, this dictionary is unused
run_sim_args = {
'exp_builder': experiment_builder,
'exp_name': 'TemplateDemo',
'config_builder': config_builder
}
# This part is needed if you are running the script through `python example_templates.py`
# It is not used by the command line interface
if __name__ == "__main__":
# The first thing to do is always to initialize the environment
no_resistance = [
[ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(change_vector_params, species='gambiae', mutation_rate1=mutation_rate1,
mutation_rate2=mutation_rate2, serialization_seed=0),
ModFn(DTKConfigBuilder.set_param, 'Serialized_Population_Path', ''),
ModFn(add_ITNs, coverage=0.6, start=start, insecticide='carbamate', label='No_resistance'),
ModFn(add_ITNs, coverage=0.6, start=start + 3 * 365, insecticide='carbamate', label='No_resistance')
]
for seed in range(num_seeds)
for mutation_rate1 in [0.01]
for mutation_rate2 in [0]
for start in [180 + extra * 365]
]
builder = ModBuilder.from_list(no_rotation_three_year + no_nets + no_resistance)
############################### REPORTERS ###############################################
vector_genetics_report = 1
vector_stats_report = 0
malaria_summary_report = 0
add_reporters(cb, vector_genetics_report=vector_genetics_report)
############################## HEALTH SEEKING ###########################################
# Health seeking
add_health_seeking(cb,
targets=[{'trigger': 'NewClinicalCase',
'coverage': 0.5,
'agemin': 0,
'agemax': 100,
}
}]
}
set_species_genes(cb, genes)
copy_to_likelihoods = np.arange(0.5, 0.59, 0.1).tolist()
# Add gene trait modifiers
gene_drive = [[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(add_release, label='Gene_drive'),
ModFn(add_driver, copy_to_likelihood=likelihood),
] for seed in range(num_seeds) for likelihood in copy_to_likelihoods]
builder = ModBuilder.from_list(gene_drive)
# Call output reporters
cb.add_reports(
BaseVectorGeneticsReport(
type='ReportVectorGenetics',
species='arabiensis',
gender='VECTOR_FEMALE',
include_vector_state_columns=1,
stratify_by='SPECIFIC_GENOME',
combine_similar_genomes=1,
specific_genome_combinations_for_stratification=[{
"Allele_Combination": [["X", "X"], ["a1", "*"]]
}, {
"Allele_Combination": [["X", "X"], ["a0", "a0"]]
}]))
temp_h = [12.424280532176581]
habitat_scales = it.product(temp_h, const_h)
site = 'Hautkatanga'
demographics_filename = "Hautkatanga_node13.json"
air_temp_filename = "DemocraticRepublicOfTheCongo_30arcsec_air_temperature_daily.bin"
land_temp_filename = "DemocraticRepublicOfTheCongo_30arcsec_air_temperature_daily.bin"
rainfall_filename = "DemocraticRepublicOfTheCongo_30arcsec_rainfall_daily.bin"
relative_humidity_filename = "DemocraticRepublicOfTheCongo_30arcsec_relative_humidity_daily.bin"
exp_name = site + '_baseline_single'
nyears = 50
builder = ModBuilder.from_combos(
[ModFn(set_habs_scale, habs)
for habs in habitat_scales], # set habitat scales
[ModFn(DTKConfigBuilder.set_param, 'Run_Number', r) for r in range(1)])
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM')
cb.update_params({
"Num_Cores": 1,
"Age_Initialization_Distribution_Type": 'DISTRIBUTION_COMPLEX',
"Simulation_Duration": nyears * 365,
"Enable_Demographics_Reporting": 1,
"Enable_Demographics_Builtin": 0,
"Valid_Intervention_States": [],
"New_Diagnostic_Sensitivity": 0.05 # 20/uL
})
# match demographics file for constant population size (with exponential age distribution)
from simtools.ModBuilder import ModFn, ModBuilder
from dtk.utils.core.DTKConfigBuilder import DTKConfigBuilder
from malaria.reports.MalariaReport import add_patient_report
from dtk.vector.study_sites import configure_site
from dtk.vector.species import set_species_param
from simtools.SetupParser import SetupParser
SetupParser.default_block = 'HPC'
exp_name = 'DrugCampaignVectorParamSweep'
builder = ModBuilder.from_combos([
ModFn(DTKConfigBuilder.set_param, 'x_Temporary_Larval_Habitat', h)
for h in [0.05]
], [ModFn(configure_site, 'Namawala')], [
ModFn(set_species_param, 'gambiae', 'Required_Habitat_Factor', value=v)
for v in [(100, 50), (200, 100)]
], [
ModFn(set_drug_param, 'Artemether', 'Max_Drug_IRBC_Kill', value=v)
for v in [4, 2]
])
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM')
cb.update_params({
'Num_Cores': 1,
'Base_Population_Scale_Factor': 0.01,
'Simulation_Duration': 30
})
add_drug_campaign(cb, 'DHA', start_days=[10])
add_patient_report(cb)
def submit_experiment(self,
cb,
num_seeds=1,
intervention_sweep=False,
migration_sweep=False,
vector_migration_sweep=False,
simple_intervention_sweep=False,
custom_name=None):
# Implement the actual (not dummy) baseline healthseeking
self.implement_baseline_healthseeking(cb)
modlists = []
if num_seeds > 1:
new_modlist = [
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)
for seed in range(num_seeds)
]
modlists.append(new_modlist)
if migration_sweep:
new_modlist = [
ModFn(DTKConfigBuilder.set_param, 'x_Local_Migration', x)
for x in [0.5, 1, 5, 10]
]
modlists.append(new_modlist)
if vector_migration_sweep:
new_modlist = [
ModFn(self.vector_migration_sweeper, vector_migration_on)
for vector_migration_on in [True, False]
]
modlists.append(new_modlist)
if simple_intervention_sweep:
new_modlist = [
ModFn(self.implement_interventions, True, False, False, False,
False),
ModFn(self.implement_interventions, False, True, False, False,
False),
ModFn(self.implement_interventions, False, False, True, False,
False),
ModFn(self.implement_interventions, False, False, False, True,
False),
ModFn(self.implement_interventions, False, False, False, False,
True),
ModFn(self.implement_interventions, True, True, True, True,
True)
]
modlists.append(new_modlist)
else:
# new_modlist = [ModFn(self.implement_interventions, True, True, True, True, True)]
new_modlist = [
ModFn(self.implement_interventions, True, True, False, False,
False)
]
modlists.append(new_modlist)
builder = ModBuilder.from_combos(*modlists)
run_name = self.exp_name
if custom_name:
run_name = custom_name
# SetupParser.init()
# SetupParser.set("HPC","priority","Normal")
# exp_manager = ExperimentManagerFactory.init()
# exp_manager.run_simulations(config_builder=self.cb, exp_name=run_name, exp_builder=builder)
# return self.cb
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(config_builder=cb,
exp_name=run_name,
exp_builder=builder)
return cb
coverages = [0.6, 0.7, 0.8, 0.9, 1.0]
SMC = [
[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(DTKConfigBuilder.set_param, 'Simulation_Duration', smc_start_day+365),
ModFn(add_smc_group,
start_days=[smc_start_day],
coverage=smc_coverage, drug='DP', agemax=5),
ModFn(add_summary_report_by_start_day, start_day=smc_start_day),
]
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)
prefix = "GarkiNE"
exp_name = prefix + "_pop_rep_3_locations_con_sw_w_seasonality"
num_cores = 24
num_years = 1
builder = ModBuilder.from_combos(
[ModFn(configure_site, site)],
[
ModFn(DTKConfigBuilder.set_param, "x_Vector_Migration_Local", vm)
for vm in [1]
], #, 5*1e-1, 1]],
[ModFn(DTKConfigBuilder.set_param, 'Run_Number', r) for r in range(1, 2)],
# [ModFn(DTKConfigBuilder.set_param, 'HEG_Infection_Modification', im) for im in [0.05]],
# [ModFn(DTKConfigBuilder.set_param, 'HEG_Fecundity_Limiting', fl) for fl in [0.05]],
# [ModFn(DTKConfigBuilder.set_param, 'HEG_Homing_Rate', hr) for hr in [0.95]],
# [ModFn(heg_release, num_released, num_repetitions = 10, released_species = "arabiensis", start_day = d, node_list = range(200, 300, 45)) for (num_released, d) in [(500, 480)]],
#[ModFn(heg_release, num_released, num_repetitions = 52, released_species = "arabiensis", start_day = d, node_list = [1]) for (num_released, d) in [(500, 365)]],
#[ModFn(heg_release, num_released, num_repetitions = 52, released_species = "arabiensis", start_day = d, node_list = range(1, 400, 20)) for (num_released, d) in [(500, 365)]],
)
cb = DTKConfigBuilder.from_defaults('VECTOR_SIM',
Num_Cores=num_cores,
Simulation_Duration=int(365 * num_years))
# set demographics file name
cb.update_params({
'Demographics_Filenames':
[ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(DTKConfigBuilder.set_param, 'Serialized_Population_Path', ''),
ModFn(add_nets, coverage=net_coverage, number=0, num_nodes=0, start_day=start_day),
ModFn(add_drivers, copy_to_likelihood=likelihood),
ModFn(add_trait_modifiers, transmission_to_human=transmission_prob)
]
for seed in range(num_seeds)
for start_day in [0]
for number in [0]
for numnodes in [0]
for net_coverage in [0.0]
for likelihood in [0.0]
for transmission_prob in [1.0]
]
builder = ModBuilder.from_list(VC_and_GM + VC + GM + No_interventions)
# builder = ModBuilder.from_list(VC)
################################ INTERVENTIONS ###################################
# Health seeking
add_health_seeking(cb,
targets=[{'trigger': 'NewClinicalCase',
'coverage': 0.5,
'agemin': 0,
'agemax': 100,
'seek': 1,
'rate': 0.3},
{'trigger': 'NewSevereCase',
'coverage': 0.8,
'agemin': 0,
from malaria.interventions.malaria_challenge import add_challenge_trial
from simtools.ModBuilder import ModFn, ModBuilder
from malaria.reports.MalariaReport import add_survey_report
from malaria.reports.MalariaReport import add_summary_report
burnin_duration = 25550
monthlyEIRs_1 = [1.9375, 3.375, 7.5, 1.9375, 0.5, 0.5, 0.5, 0.5, 0.25, 0.5, 0.5,1.0]
monthlyEIRs_2 = [3.875, 7.75, 15.0, 3.875, 1, 1, 1, 1, 0.5, 1, 1,2]
exp_name = 'Challenge_bite_20_350_Dan_fix'
builder = ModBuilder.from_list(
[[ModFn(add_challenge_trial, x, disable_vitals = False),
ModFn(add_survey_report,[x],reporting_interval = 730),
ModFn(add_summary_report,x,interval = 365/12,description = 'Monthly Report',
age_bins = [1.0, 4.0, 8.0, 18.0, 28.0, 43.0, 400000.0],
parasitemia_bins = [0.0, 16.0, 409.0, 4000000.0])
]
for x in range(730,740,10)])
# builder = ModBuilder.from_list(
# [[ModFn(add_InputEIR, monthlyEIRs_1, start_day=x, nodes={
# "Node_List": [
# 1
# ],
# "class": "NodeSetNodeList"
# }),
# ModFn(add_InputEIR, monthlyEIRs_2, start_day=x, nodes={
# "Node_List": [
# 2
"Decay_Time_Constant": 30
}
}]
add_ATSB(cb,
coverage=coverage,
start=100,
duration=365,
kill_cfg=killing_cfg[1])
return {'coverage': coverage, 'initial_killing': killing}
# add_ATSB(cb, start=100, coverage=1, duration=365)
builder = ModBuilder.from_list([[
ModFn(atsb_fn, cov, kill),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', x)
] for cov in [0, 1] for kill in [0.15] for x in range(1)])
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
if __name__ == "__main__":
SetupParser.init('HPC')
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())
# Construct the table
table = [list(c.values()) for c in combined]
# Change some things in the config.json
config.set_param('Config_Name', scenario_name)
# Initialize the template
tpl = TemplateHelper()
tpl.set_dynamic_header_table(headers, table)
tpl.active_templates = [
config, campaign_tpl, demog, demog_pfa, demog_asrt, demog_acc
]
# Create an experiment builder
experiment_builder = ModBuilder.from_combos(
tpl.get_modifier_functions())
experiment_manager = ExperimentManagerFactory.from_cb(
config_builder)
COMPS_experiment_name = scenario_name
# COMPS_experiment_name = suite_name # I want hover-over in COMPS to be the suite name
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)
RuralMultiplier[ind], UrbanMultiplier[ind], MCV1Days[ind],
mAbProfile[ind], Dropout[ind], MCV2Days[ind],
BirthRateScale[ind], basePop, 3.0
]
else:
values = [
0.4 + 0.59 * math.sqrt(random.uniform(0, 1)),
0.4 + 0.59 * math.sqrt(random.uniform(0, 1)), SIACov[ind],
random.randint(1, 1e6), Migration[ind],
RuralMultiplier[ind], UrbanMultiplier[ind], MCV1Days[ind],
mAbProfile[ind], Dropout[ind], MCV2Days[ind],
BirthRateScale[ind], basePop, 3.0
]
mod_fns.append(ModFn(sample_point_fn, names, values))
builder = ModBuilder.from_combos(mod_fns)
# Name the experiment
exp_name = 'Measles RI targets'
exp_manager = ExperimentManagerFactory.from_cb(cb)
# suite_id = exp_manager.create_suite("My experiment - Iteration 0")
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
exp_manager.experiment_tags = {}
for name, value in zip(names, values):
if name not in [
"Serialized_Population_Filenames": [
name for name in os.listdir(
os.path.join(df["outpath"][x], "output"))
if "state" in name
],
"Run_Number":
y,
"x_Temporary_Larval_Habitat":
df["x_Temporary_Larval_Habitat"][x]
}) for x in df.index for y in range(num_runs)
]
builder = ModBuilder.from_list(
[[
burnin_fn,
ModFn(add_intervention, intervention, coverage,
species_details)
] for burnin_fn in from_burnin_list
for intervention in interventions
for coverage in np.linspace(0, 1, num_coverage_bins)])
else:
print("building burnin")
builder = ModBuilder.from_list(
[[
ModFn(
DTKConfigBuilder.update_params, {
"Run_Number": run_num,
"x_Temporary_Larval_Habitat": 10**hab_exp
}),
] for run_num in range(10)
for hab_exp in np.concatenate((np.arange(-3.75, -2, 0.25),
smc_start_day + 365),
ModFn(
add_smc_group,
start_days=[smc_start_day],
coverage=smc_coverage,
drug=drug,
agemax=agemax,
),
ModFn(add_summary_report, start_day=smc_start_day),
ModFn(DTKConfigBuilder.set_param, 'Serialized_Population_Path',
'{path}/output'.format(path=path))
] for smc_start_day in intervention_days for smc_coverage in coverages
for seed in range(num_seeds) for path in serialized_file_output
for agemax in [5, 10] for drug in ['DP']]
builder = ModBuilder.from_list(expanded_SMC_IVM)
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":
[3, 0.8, 1.25, 0.1, 2.7, 8, 4, 35, 6.8, 6.5, 2.6, 2.1]
},
"Max_Larval_Capacity": 1e9
}
"class": 'WaningEffectConstant',
"Initial_Effect": killing,
}
},
duration=10000)
# add_ATSB(cb, coverage=coverage, start=100, duration=365, kill_cfg=killing_cfg[1])
return {'killing': killing, 'startDay': startDay}
# add_ATSB(cb, start=100, coverage=1, duration=365)
builder = ModBuilder.from_list([
[
ModFn(atsb_fn, killing, (years - 2) * 365),
ModFn(DTKConfigBuilder.set_param, 'x_Temporary_Larval_Habitat', hab)
# Run simulation with coverage, duration, or repetitions spread.
# Don't leave duration over 210 because we don't know how ATSBs act after 210 days, or how they act after Dec 31.
] for killing in np.linspace(0, 0.25, 20)
for hab in np.arange(0.2, 1, 0.478 / 10)
])
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
if __name__ == "__main__":
SetupParser.init('HPC')
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(**run_sim_args)
# +-------------------+----------------+
# | 0 | 0 |
# | 5 | 0 |
# | 10 | 0 |
# | ... | ... |
# | 5 | 1 |
# | 10 | 1 |
# | 365 | 1 |
# | ... | ... |
# | 365 | 4 |
# | 730 | 4 |
# +-------------------+----------------+
builder = ModBuilder.from_combos(
[ModFn(set_larvicides, start_time) for start_time in (0, 5, 10, 365, 730)],
[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)
for seed in range(5)
],
)
# The run_sim_args is a dictionary informing the command line of:
# - What config builder to use
# - What is the name we want for the experiment
# - Which experiment builder to use
run_sim_args = {
'config_builder': cb,
'exp_name': 'Sample larvicides epxeriment',
'exp_builder': builder
}
# In this file we also decided to include the analyzers.
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)]
builder = ModBuilder.from_list(healthseeking)
# Run args
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
if __name__ == "__main__":
if not SetupParser.initialized:
SetupParser.init('HPC')
exp_manager = ExperimentManagerFactory.init()
exp_manager.run_simulations(**run_sim_args)
set_species_param(cb, 'funestus', 'Larval_Habitat_Types',
{"CONSTANT": 11250000000})
set_species_param(cb, 'funestus', 'Male_Life_Expectancy', 5)
set_species_param(cb, 'funestus', 'Adult_Life_Expectancy', 10)
set_species_param(cb, 'funestus', 'Transmission_Rate', 0.5)
set_species_param(cb, 'funestus', 'Indoor_Feeding_Fraction', 1.0)
########################## VECTOR GENETICS ####################################################
add_genes(cb)
mendelian = [[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(add_release, label=label, number=10000),
] for seed in range(num_seeds) for label in ['No_inrogression']]
builder = ModBuilder.from_list(mendelian)
# Call output reporters
cb.add_reports(
BaseVectorGeneticsReport(
type='ReportVectorGenetics',
species='arabiensis',
gender='VECTOR_FEMALE',
include_vector_state_columns=1,
stratify_by='SPECIFIC_GENOME',
combine_similar_genomes=1,
specific_genome_combinations_for_stratification=[{
"Allele_Combination": [["X", "X"], ["a0", "*"]]
}]))
cb.add_reports(
BaseVectorGeneticsReport(
def add_my_outbreak(cb, start_day):
recurring_outbreak(cb, start_day=start_day, repetitions=1)
return {'outbreak_start_day': start_day}
builder = ModBuilder.from_list([[
ModFn(add_my_outbreak, start_day=x * 365),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', y),
ModFn(DTKConfigBuilder.set_param, 'Simulation_Duration',
(x * 365) + (3 * 365)),
ModFn(add_filtered_report, start=x * 365, end=(x * 365) + (3 * 365)),
ModFn(add_health_seeking,
start_day=0,
targets=[{
'trigger': 'NewClinicalCase',
'coverage': 1,
'agemin': 0,
'agemax': 200,
'seek': 1,
'rate': 0.3
}, {
'trigger': 'NewSevereCase',
'coverage': 1,
'seek': 1,
'rate': 0.5
}])
] for y in range(num_seeds) for x in range(51)])
#ModFn(add_my_outbreak, start_day=x),
#ModFn(add_health_seeking, start_day=0, targets=[
#{'trigger': 'NewClinicalCase', 'coverage': 1, 'agemin': 0, 'agemax': 200, 'seek': 1,'rate': 0.3},
#{'trigger': 'NewSevereCase', 'coverage': 1, 'seek': 1, 'rate': 0.5}])
