def test_species_params(self):
self.assertEqual(
vs.get_species_param(self.cb, 'arabiensis', 'Immature_Duration'),
2)
vs.set_species_param(self.cb, 'arabiensis', 'Egg_Batch_Size', 123)
self.assertEqual(
vs.get_species_param(self.cb, 'arabiensis', 'Egg_Batch_Size'), 123)
vs.set_larval_habitat(
self.cb,
{'arabiensis': {
'TEMPORARY_RAINFALL': 123,
'CONSTANT': 456
}})
self.assertDictEqual(
vs.get_species_param(self.cb, 'arabiensis',
'Larval_Habitat_Types'), {
'TEMPORARY_RAINFALL': 123,
'CONSTANT': 456
})
vs.scale_all_habitats(self.cb, 2)
self.assertDictEqual(
vs.get_species_param(self.cb, 'arabiensis',
'Larval_Habitat_Types'), {
'TEMPORARY_RAINFALL': 246,
'CONSTANT': 912
})
funestus_habitat = vs.get_species_param(
self.cb, 'funestus', 'Larval_Habitat_Types')['WATER_VEGETATION']
self.assertEqual(funestus_habitat, 4e7)
def set_ento(cb, a_sc, f_sc, arab_times, arab_spline, funest_times,
funest_spline):
hab = {
'arabiensis': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years":
int(len(arab_times) / 12),
"Capacity_Distribution_Over_Time": {
"Times": arab_times,
"Values": arab_spline
},
"Max_Larval_Capacity": pow(10, a_sc)
}
},
'funestus': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years":
int(len(funest_times) / 12),
"Capacity_Distribution_Over_Time": {
"Times": funest_times,
"Values": funest_spline
},
"Max_Larval_Capacity":
pow(10, f_sc)
}
}
}
set_larval_habitat(cb, hab)
def map_sample_to_model_input(cb, sample):
hab = {
'gambiae': {
'TEMPORARY_RAINFALL': 1.5e8 * sample["Temporary_Habitat"],
'CONSTANT': 5.5e6 * sample["Constant_Habitat"]
}
}
set_larval_habitat(cb, hab)
return sample
def map_sample_to_model_input(cb, sample):
tags = {}
for species in specs:
# Updating Spline values
hab = {
species: {
hab_type[species]: hab_val[species],
"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": [
0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2,
0.0, 0.0
]
},
"Max_Larval_Capacity": 1e8
}
}
}
for i in range(1, 13):
name = '%s%i' % (species, i)
if name in sample:
splinevalue = sample.pop(name)
hab[species]['LINEAR_SPLINE'][
'Capacity_Distribution_Per_Year']['Values'][
i - 1] = splinevalue
tags.update({name: splinevalue})
# Updating max habitat values
max_habitat_name = '%s_max' % species
if max_habitat_name in sample:
maxvalue = sample.pop(max_habitat_name)
hab[species]['LINEAR_SPLINE']['Max_Larval_Capacity'] = pow(
10, maxvalue)
tags.update({max_habitat_name: maxvalue})
set_larval_habitat(cb, hab)
for name, value in sample.items():
print('UNUSED PARAMETER:' + name)
assert (len(sample) == 0) # All params used
# For testing only, the duration should be handled by the site !! Please remove before running in prod!
tags.update(cb.set_param("Simulation_Duration", 2 * 365))
tags.update(cb.set_param('Run_Number', 0))
return tags
def set_habs_scale(cb, value=None):
temp_h = value[0]
const_h = value[1]
hab = {
'gambiae': {
'TEMPORARY_RAINFALL': 1.5e8 * temp_h,
'CONSTANT': 5.5e6 * const_h
}
}
set_larval_habitat(cb, hab)
return {'temp_h': temp_h, 'const_h': const_h}
def map_sample_to_model_input(cb, sample):
tags = {}
if 'arabiensis_scale' in sample:
a_sc = sample.pop('arabiensis_scale')
hab = {'arabiensis': {'TEMPORARY_RAINFALL': 1e8 * a_sc, 'CONSTANT': 2e6}}
set_larval_habitat(cb, hab)
tags.update({'arabiensis_scale': a_sc})
if 'funestus_scale' in sample:
f_sc = sample.pop('funestus_scale')
hab = {'funestus': {
"WATER_VEGETATION": 2e7,
"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": [0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2, 0.0, 0.0]
},
"Max_Larval_Capacity": 1e8 * f_sc
}
}
}
set_larval_habitat(cb, hab)
tags.update({'funestus_scale': f_sc})
for name, value in sample.iteritems():
print
'UNUSED PARAMETER:', name
assert (len(sample) == 0) # All params used
# Run for 10 years with a random random number seed
cb.set_param('Simulation_Duration', 3650) # 10*365
cb.set_param('Vector_Species_Names', ["arabiensis", "funestus"])
tags.update(cb.set_param('Run_Number', random.randint(0, 1e6)))
return tags
'''
def map_sample_to_model_input(cb, sample):
tags = {}
hab = copy.copy(ls_hab_ref)
timepoints = sorted([int(x.split('_')[1]) for x in sample.keys() if ('max' not in x and 'Multiplier' not in x)])
dates = [365*throwaway + (x-1)*365/12 for x in timepoints] + [365*(duration + throwaway)-1]
hab['Capacity_Distribution_Over_Time']['Times'] = [0] + dates
hab['Capacity_Distribution_Over_Time']['Values'] = [0.01]*len(hab['Capacity_Distribution_Over_Time']['Times'])
for mindex, month in enumerate(timepoints) :
name = '%s_%i' %(species, month)
if name in sample:
splinevalue = sample.pop(name)
hab['Capacity_Distribution_Over_Time']['Values'][mindex+1] = splinevalue
tags.update({name: splinevalue})
# Updating max habitat values
max_habitat_name = '%s_max' % species
if max_habitat_name in sample:
maxvalue = sample.pop(max_habitat_name)
hab['Max_Larval_Capacity'] = pow(10, maxvalue)
tags.update({max_habitat_name: maxvalue})
name = '%s.Multiplier' % hfca
if name in sample :
catchment_mult = sample[name]
tags.update(cb.set_param(name, catchment_mult))
set_larval_habitat(cb, { species : {'LINEAR_SPLINE' : hab}})
# for name,value in sample.items():
# print('UNUSED PARAMETER:'+name)
# assert( len(sample) == 0 ) # All params used
# For testing only, the duration should be handled by the site !! Please remove before running in prod!
tags.update(cb.set_param("Simulation_Duration", (throwaway+duration)*365))
tags.update(cb.set_param('Run_Number', 0))
return tags
def map_sample_to_model_input(cb, catch, sample):
a_sc = sample['arabiensis_scale']
f_sc = sample['funestus_scale']
arab_times, arab_spline = catch_3_yr_spline(catch, "gambiae")
funest_times, funest_spline = catch_3_yr_spline(catch, "funestus")
hab = {
'arabiensis': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": arab_times,
"Values": arab_spline
},
"Max_Larval_Capacity": pow(10, a_sc)
}
},
'funestus': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": funest_times,
"Values": funest_spline
},
"Max_Larval_Capacity": pow(10, f_sc)
# "Max_Larval_Capacity": pow(10,a_sc)/arab_funest_ratio
}
}
}
set_larval_habitat(cb, hab)
return {"arab": a_sc, "funest": f_sc}
def __call__(self, cb):
return set_larval_habitat(cb, {self.species: self.habitats})
def map_sample_to_model_input(cb, sample):
sim_time_dict = serialization_setup(cb, calib_stage)
add_interventions_and_reports(cb, sim_time_dict)
dd = best_run_so_far()
# =====================================================================================
# Global habitats
if calib_stage == 1 or calib_stage == 2:
a_sc = sample['arabiensis_scale']
f_sc = sample['funestus_scale']
if calib_stage == 1:
# LOAD FROM SPLINE:
arab_times, arab_spline = MozambiqueExperiment.catch_3_yr_spline(
catch, "gambiae")
funest_times, funest_spline = MozambiqueExperiment.catch_3_yr_spline(
catch, "funestus")
hab = {
'arabiensis': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": arab_times,
"Values": arab_spline
},
"Max_Larval_Capacity": pow(10, a_sc)
}
},
'funestus': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": funest_times,
"Values": funest_spline
},
"Max_Larval_Capacity": pow(10, f_sc)
# "Max_Larval_Capacity": pow(10,a_sc)/arab_funest_ratio
}
}
}
set_larval_habitat(cb, hab)
elif calib_stage >= 2:
arab_rescale = pow(10, a_sc) / pow(10, dd['a_sc_burnin'])
funest_rescale = pow(10, f_sc) / pow(10, dd['f_sc_burnin'])
if calib_stage == 2:
scale_larval_habitats(cb,
pd.DataFrame({
'LINEAR_SPLINE.arabiensis':
[arab_rescale],
'LINEAR_SPLINE.funestus':
[funest_rescale]
}),
start_day=0)
elif calib_stage == 3:
for bairro_num in bairro_df["bairro"].unique():
scale_larval_habs_for_bairro(
cb,
bairro_num,
bairro_df,
arab_rescale *
sample['b{}_vector_mult'.format(int(bairro_num))],
funest_rescale *
sample['b{}_vector_mult'.format(int(bairro_num))],
start_day=0)
# FOR TESTING ONLY:
if mode == 'fast':
cb.set_param('x_Temporary_Larval_Habitat', 0)
cb.set_param('x_Regional_Migration', 0)
return sample
def map_sample_to_model_input(cb, sample):
tags = {}
if 'gambiae_scale_dry' in sample and 'gambiae_scale_wet' in sample:
g_sc_dry = sample.pop('gambiae_scale_dry')
g_sc_wet = sample.pop('gambiae_scale_wet')
seasonal_TSI = np.zeros(12)
seasonal_TSI[np.array(list(wet_profile.keys()))] = g_sc_wet * np.array(
list(wet_profile.values()))
seasonal_TSI[np.array(list(dry_profile.keys()))] = g_sc_dry * np.array(
list(dry_profile.values()))
hab = {
'gambiae': {
'CONSTANT': 2e6,
"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":
list(seasonal_TSI)
},
"Max_Larval_Capacity": 1e8
}
}
}
set_larval_habitat(cb, hab)
tags.update({
'gambiae_scale_dry': g_sc_dry,
'gambiae_scale_wet': g_sc_wet
})
if 'funestus_scale_dry' in sample and 'funestus_scale_wet' in sample:
f_sc_dry = sample.pop('funestus_scale_dry')
f_sc_wet = sample.pop('funestus_scale_wet')
seasonal_TSI = np.zeros(12)
seasonal_TSI[np.array(list(wet_profile.keys()))] = f_sc_wet * np.array(
list(wet_profile.values()))
seasonal_TSI[np.array(list(dry_profile.keys()))] = f_sc_dry * np.array(
list(dry_profile.values()))
hab = {
'funestus': {
"WATER_VEGETATION": 2e7,
"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":
list(seasonal_TSI)
},
"Max_Larval_Capacity": 1e8
}
}
}
set_larval_habitat(cb, hab)
tags.update({
'funestus_scale_dry': f_sc_dry,
'funestus_scale_wet': f_sc_wet
})
for name, value in sample.items():
print('UNUSED PARAMETER:', name)
assert (len(sample) == 0) # All params used
return tags
'Capacity_Distribution_Number_Of_Years': 1,
'Capacity_Distribution_Over_Time': {
'Times': [
0, 30.4166666666667, 60.8333333333333, 91.25, 121.6666666666667,
152.0833333333334, 182.5, 213.9166666666666, 243.3333333333334,
273.75, 304.5833333333333, 335
],
'Values': [
0.001, 0.001236706, 0.001933152, 0.056693638, 0.057953358, 0.015,
0.95, 2.159928736, 3.205076212, 0.43290933, 0.391090655,
0.138816133
]
},
'Max_Larval_Capacity': 2.75e7
}
set_larval_habitat(cb, {species: {'LINEAR_SPLINE': hab}})
recurring_outbreak(cb,
outbreak_fraction=0.01,
start_day=152,
repetitions=years,
tsteps_btwn=365)
def atsb_fn(cb, killing, startDay):
# Start of June in Mali
start_day = startDay + 152
add_ATSB(cb,
start=start_day,
def map_sample_to_model_input(cb, sample):
sim_time_dict = serialization_setup(cb)
add_interventions_and_reports(cb, sim_time_dict)
dd = best_run_so_far()
# =====================================================================================
# Global habitats
a_sc = sample['arabiensis_scale']
f_sc = sample['funestus_scale']
# LOAD FROM SPLINE:
spline_base = "C:/Users/jsuresh/Dropbox (IDM)/Malaria Team Folder/projects/Mozambique/entomology_calibration/"
[times, funest_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180608/minicsv/Three_funestus_LifeAdj_rank0.csv'))
if catch == "Magude-Sede-Facazissa":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180608/minicsv/Magude-Sede_gambiae_LifeAdj_rank0.csv'))
elif catch == "Chichuco":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Chichuco_gambiae_frankenspline.csv'))
elif catch == "Chicutso":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Chicutso_gambiae_frankenspline.csv'))
elif catch == "Mapulanguene":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Mapulanguene_gambiae_frankenspline.csv'))
elif catch == "Motaze":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Motaze_gambiae_frankenspline.csv'))
elif catch == "Panjane-Caputine":
[times, arab_spline] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Panjane_gambiae_frankenspline.csv'))
elif catch == "Moine" or catch == "Mahel":
[times, arab_spline_pc] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Panjane_gambiae_frankenspline.csv'))
[times, arab_spline_ch] = magude_3_yr_spline(os.path.join(spline_base, 'Multi_year_calibration_by_HFCA_180808/minicsv/Chichuco_gambiae_frankenspline.csv'))
arab_spline = list((np.array(arab_spline_pc) + np.array(arab_spline_ch))/2.)
hab = {
'arabiensis': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": times,
"Values": arab_spline
},
"Max_Larval_Capacity": pow(10,a_sc)
}
},
'funestus': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": times,
"Values": funest_spline
},
"Max_Larval_Capacity": pow(10, f_sc)
# "Max_Larval_Capacity": pow(10,a_sc)/arab_funest_ratio
}
}
}
set_larval_habitat(cb, hab)
# # FOR TESTING ONLY:
# if mode == 'fast':
# cb.set_param('x_Temporary_Larval_Habitat',0)
# cb.set_param('x_Regional_Migration',0)
return sample
def map_sample_to_model_input(cb, sample):
sim_time_dict = serialization_setup(cb, calib_stage)
add_interventions_and_reports(cb, sim_time_dict)
# =====================================================================================
# Global habitats
if calib_stage == 1:
a_sc = sample['arabiensis_scale']
f_sc = sample['funestus_scale']
if sim_time_dict["burnin"]:
# LOAD FROM SPLINE:
arab_times, arab_spline = MozambiqueExperiment.catch_3_yr_spline(
catch, "gambiae")
funest_times, funest_spline = MozambiqueExperiment.catch_3_yr_spline(
catch, "funestus")
hab = {
'arabiensis': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": arab_times,
"Values": arab_spline
},
"Max_Larval_Capacity": pow(10, a_sc)
}
},
'funestus': {
"LINEAR_SPLINE": {
"Capacity_Distribution_Number_Of_Years": 3,
"Capacity_Distribution_Over_Time": {
# "Capacity_Distribution_Per_Year": {
"Times": funest_times,
"Values": funest_spline
},
"Max_Larval_Capacity": pow(10, f_sc)
# "Max_Larval_Capacity": pow(10,a_sc)/arab_funest_ratio
}
}
}
set_larval_habitat(cb, hab)
if not sim_time_dict["burnin"]:
# Get best-fit parameters from LL_all of Stage 1:
# Need to find best BURNIN to rescale to, not best run. Since the sims before 2009 are purely from burnins:
LL_all = pd.read_csv(LL_all_path)
LL_all_burnins = LL_all[LL_all["iteration"] == 0]
p1_list = list(LL_all_burnins['arabiensis_scale'])
p2_list = list(LL_all_burnins['funestus_scale'])
a_sc_burnin = p1_list[-1]
f_sc_burnin = p2_list[-1]
if calib_stage == 2: # Draw params from best RUN (may not be a burnin)
p1_list = list(LL_all['arabiensis_scale'])
p2_list = list(LL_all['funestus_scale'])
a_sc = p1_list[-1]
f_sc = p2_list[-1]
arab_rescale = pow(10, a_sc) / pow(10, a_sc_burnin)
funest_rescale = pow(10, f_sc) / pow(10, f_sc_burnin)
if calib_stage == 1:
scale_larval_habitats(cb,
pd.DataFrame({
'LINEAR_SPLINE.arabiensis':
[arab_rescale],
'LINEAR_SPLINE.funestus':
[funest_rescale]
}),
start_day=0)
elif calib_stage == 2:
for bairro_num in bairro_df["bairro"].unique():
scale_larval_habs_for_bairro(
cb,
bairro_num,
bairro_df,
arab_rescale *
sample['b{}_vector_mult'.format(int(bairro_num))],
funest_rescale *
sample['b{}_vector_mult'.format(int(bairro_num))],
start_day=0)
# FOR TESTING ONLY:
if mode == 'fast':
cb.set_param('x_Temporary_Larval_Habitat', 0)
cb.set_param('x_Regional_Migration', 0)
return sample
