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)
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)
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}])
def perfect_health_seeking(cb):
add_health_seeking(cb, start_day=survey_day,
targets=[dict(trigger='NewClinicalCase', coverage=1.0, seek=1.0, rate=0.3)])
return cb.set_param('Config_Name', 'perfect_health_seeking')
def perfect_MSAT(cb):
add_drug_campaign(cb, campaign_type='MSAT', drug_code='DP',
start_days=[survey_day + 30],
coverage=1.0, repetitions=3, interval=60)
return cb.set_param('Config_Name', 'perfect_MSAT')
mod_fn_list = [[ModFn(baseline)], [ModFn(perfect_health_seeking)], [ModFn(perfect_MSAT)]]
builder = ModBuilder.from_list(mod_fn_list)
run_sim_args = {'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
"""
Analyzer to simply copy all survey-report JSON down from all simulations
"""
class MyDownloadAnalyzer(DownloadAnalyzer):
def __init__(self):
super(MyDownloadAnalyzer, self).__init__(
"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)
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,
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
}
add_InputEIR(cb, [x*scale_factor for x in monthly_EIR])
return { 'annual EIR' : scale_factor*sum(monthly_EIR)}
# builder = ModBuilder.from_list( [[ ModFn(sweep_scale_factor, x)] for x in [0.01,10,100]])
report_years = 1
report_start = 40
add_summary_report(cb,
start = 365*report_start,
description='Annual_Report',
interval = 365,
nreports = 1,
age_bins = [2, 10, 125],
parasitemia_bins = [0, 50, 200, 500, 2000000]
)
builder = ModBuilder.from_list( [[ ModFn(sweep_scale_factor, x)] for x in np.logspace(-3, 0, 200)])
# 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')
ModFn(add_summary_report, start_day=smc_start_day),
ModFn(DTKConfigBuilder.set_param, 'Serialized_Population_Path',
'{path}/output'.format(path=
get_outpath_for_serialized_file(simmap, seed)))
]
for smc_start_day in intervention_days
for smc_coverage in coverages
for seed in range(num_seeds)
for simmap in [sim_map]
for agemax in [5, 15]
for drug in ['DP']
for drug_code in [30, 90]
]
# builder = ModBuilder.from_list(SMC3)
builder = ModBuilder.from_list(SMC1+SMC2+SMC3)
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'],
] #HLC: 0.023478, .065638 CDC: 0.136566523, .098484065
}
}
},
duration=duration)
# add_ATSB(cb, coverage=coverage, start=100, duration=365, kill_cfg=killing_cfg[1])
return {'coverage': coverage, 'duration': duration, 'repetitions': reps}
# add_ATSB(cb, start=100, coverage=1, duration=365)
builder = ModBuilder.from_list([
[
ModFn(atsb_fn, cov, dur, reps),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', x)
# 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 cov in [0.0, 0.25, 0.5] for dur in [210] for reps in [1, 3]
for x in range(2)
])
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)
cb = DTKConfigBuilder.from_defaults('MALARIA_SIM',
Simulation_Duration=int(365 *
num_years))
update_config_params(cb, direc=dir, geography=geography)
add_SerializationTimesteps(cb, [num_years * 365], end_at_final=True)
########################## VECTOR GENETICS ####################################################
builder = ModBuilder.from_list([
[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(change_vector_params,
species='gambiae',
mutation_rate1=mutation_rate,
mutation_rate2=mutation_rate,
serialization_seed=seed)
] for seed in range(num_seeds)
for mutation_rate in [0] # pow(10, x) for x in range(-5, 0)
])
############################### REPORTERS ########################################
vector_genetics_report = 1
vector_stats_report = 0
malaria_summary_report = 0
add_reporters(cb, vector_genetics_report=vector_genetics_report)
################################ INTERVENTIONS ###################################
#
cb.update_params({
"Report_Event_Recorder":
1,
"Listed_Events": ["Immigrating", "Emigrating"],
"Report_Event_Recorder_Events": ["Immigrating", "Emigrating"],
"Report_Event_Recorder_Ignore_Events_In_List":
0
})
########################## VECTOR GENETICS ####################################################
# Add genes
genes = {'gambiae': [{"Alleles": {"a0": 1.0, "a1": 0.0}, "Mutations": {}}]}
set_species_genes(cb, genes)
builder = ModBuilder.from_list(
[[ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)]
for seed in range(num_seeds)])
################################ INTERVENTIONS ############################################
# Health seeking
add_health_seeking(cb,
targets=[{
'trigger': 'NewClinicalCase',
'coverage': 0.5,
'agemin': 0,
'agemax': 100,
'seek': 1,
'rate': 0.3
}, {
'trigger': 'NewSevereCase',
# '//internal.idm.ctr/IDM/Home/pselvaraj/output/Matsari_homogeneousbiting_serialization_2017_10_02_12_00_07_818000/89d/00c/08a/89d00c08-a4a7-e711-9414-f0921c16b9e5']
exp_name = ['2ac6fce6-9554-e811-a2bf-c4346bcb7274']
for exp in exp_name:
temp_sim_map = make_simmap(exp)
if 'sim_map' not in locals():
sim_map = temp_sim_map
else:
sim_map = pd.concat([sim_map, temp_sim_map])
builder = ModBuilder.from_list([[
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed),
ModFn(DTKConfigBuilder.set_param, 'Simulation_Duration',
start_day + 365),
ModFn(add_summary_report, start_day=start_day),
ModFn(
DTKConfigBuilder.set_param, 'Serialized_Population_Path',
'{path}/output'.format(
path=get_outpath_for_serialized_file(simmap, seed)))
] for seed in range(num_seeds) for simmap in [sim_map]
for start_day in intervention_days])
# configure_dapelogo(cb)
# cb.set_collection_id('1c189292-5471-e711-9401-f0921c16849d')
set_climate_constant(cb)
set_species_param(
cb,
'gambiae',
'Larval_Habitat_Types',
{
"LINEAR_SPLINE": {
old_builder = ModBuilder.from_list(
[[
ModFn(
DTKConfigBuilder.update_params, {
"Serialized_Population_Path":
os.path.join(df["outpath"][x], "output"),
"Serialized_Population_Filenames": [
name for name in os.listdir(
os.path.join(df["outpath"][x], "output"))
if "state" in name
],
"Run_Number":
df["Run_Number"][x],
"x_Temporary_Larval_Habitat":
df["x_Temporary_Larval_Habitat"][x]
}),
ModFn(add_annual_itns,
year_count=years,
n_rounds=1,
coverage=itn_cov / 100,
discard_halflife=180,
start_day=5,
IP=[{
"NetUsage": "LovesNets"
}]),
ModFn(assign_net_ip, hates_net_prop),
ModFn(add_irs_group,
coverage=irs_cov / 100,
decay=180,
start_days=[365 * start for start in range(years)]),
ModFn(add_healthseeking_by_coverage,
coverage=act_cov / 100,
rate=0.15),
] for x in df.index for hates_net_prop in net_hating_props
for itn_cov in intervention_coverages
for irs_cov in intervention_coverages
for act_cov in intervention_coverages])
}
}]
}
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"]]
}]))
cb.update_params({"x_Temporary_Larval_Habitat": scale_factor})
return {'larval_habitat_multiplier': scale_factor}
#Reporting -----------------------------------------------------------------------------------------------------------
add_summary_report(cb,
start=365 * report_start,
description='Annual_Report',
interval=365,
nreports=1,
age_bins=[2, 10, 125],
parasitemia_bins=[0, 50, 200, 500, 2000000])
builder = ModBuilder.from_list([[
ModFn(sweep_larval_habitat, scale_factor=habitat_multiplier),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)
] for habitat_multiplier in np.logspace(-2, 1, 50)
for seed in range(num_seeds)])
# builder = ModBuilder.from_list( [[ ModFn(sweep_scale_factor, x)] for x in [0.01,10,100]])
# report_years = 1
# report_start = 40
# Run args
run_sim_args = {
'config_builder': cb,
'exp_name': exp_name,
'exp_builder': builder
}
if __name__ == "__main__":
[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())
"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),
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(
cb.update_params({'Serialization_Time_Steps': [365*years]})
if pull_from_serialization :
set_forest_HS = False
burnin_df = create_sim_map(burnin_expid)
burnin_df = burnin_df[(burnin_df['dirus_Anthropophily'] == 0.5) &
(burnin_df['x_Temporary_Larval_Habitat'] == 2.4)]
update_serialization_params(cb)
builder = ModBuilder.from_list([[
ModFn(add_intervention, intervention, coverage, target, ivm_duration),
ModFn(sample_anthrop_and_outdoor, 'dirus', row['dirus_Anthropophily'], row['dirus_Outdoor_Fraction']),
ModFn(DTKConfigBuilder.update_params, {
'x_Temporary_Larval_Habitat': row['x_Temporary_Larval_Habitat'],
'Run_Number': y,
'Serialized_Population_Path': os.path.join(row['path'], 'output')
})
]
for y in range(starting_seed, starting_seed+num_seeds)
for coverage in np.linspace(0,1,6)
for intervention in interventions
for target in ['bothvill', 'vill1']
for ivm_duration in ivm_durations
for r,row in burnin_df.iterrows()
])
else :
set_forest_HS = True
builder = ModBuilder.from_list([[
ModFn(sample_anthrop_and_outdoor, 'dirus', anthropophily, outdoor),
ModFn(DTKConfigBuilder.set_param, 'x_Temporary_Larval_Habitat', x_LH),
ModFn(DTKConfigBuilder.set_param, 'Run_Number', y)]
for y in range(num_seeds) for anthropophily in np.linspace(0,1,11) for outdoor in [0.99]