def map_sample_to_model_input(cb, sample):
# arab_constant = sample["arab_constant_scale"]
arab_constant = 6.3
arab_rainfall = sample["arab_rainfall_scale"]
# funest_spline = sample["funest_spline_scale"]
log_spline_rainfall_ratio = sample["log_spline_rainfall_ratio"]
funest_spline = arab_rainfall + log_spline_rainfall_ratio
# funest_veg = sample["funest_veg_scale"]
funest_veg = 6.3
arab_constant_rescale = 1.0 #pow(10, arab_constant) / pow(10, burnin_params['arab_constant_scale'])
arab_rainfall_rescale = pow(10, arab_rainfall) / pow(
10, burnin_params['arab_rainfall_scale'])
funest_spline_rescale = pow(10, funest_spline) / pow(
10, burnin_params['funest_spline_scale'])
funest_veg_rescale = 1.0 #pow(10, funest_veg) / pow(10, burnin_params['funest_veg_scale'])
scale_larval_habitats(cb,
pd.DataFrame({
'CONSTANT.arabiensis': [arab_constant_rescale],
'TEMPORARY_RAINFALL.arabiensis':
[arab_rainfall_rescale],
'LINEAR_SPLINE.funestus':
[funest_spline_rescale],
'WATER_VEGETATION.funestus':
[funest_veg_rescale]
}),
start_day=0)
return sample
def simulation_setup(cb,
species_details,
site_vector_props,
max_larval_capacity=4e8):
site_dir = os.path.join('sites', 'all')
# directories
cb.update_params({
"Demographics_Filenames": [
os.path.join(site_dir, "demographics.json"),
os.path.join(site_dir, "demographics_net_overlay.json")
],
"Air_Temperature_Filename":
os.path.join(site_dir, "air_temperature_daily.bin"),
"Land_Temperature_Filename":
os.path.join(site_dir, "air_temperature_daily.bin"),
"Rainfall_Filename":
os.path.join(site_dir, "rainfall_daily.bin"),
"Relative_Humidity_Filename":
os.path.join(site_dir, "relative_humidity_daily.bin")
})
# Find vector proportions for each vector
set_params_by_species(cb.params, [name for name in species_details.keys()])
larval_habs_per_site = {"NodeID": site_vector_props["node_id"]}
for species_name, species_modifications in species_details.items():
set_species_param(cb, species_name, "Adult_Life_Expectancy", 20)
set_species_param(cb, species_name, 'Vector_Sugar_Feeding_Frequency',
'VECTOR_SUGAR_FEEDING_EVERY_DAY')
for param, val in species_modifications.items():
if param == "habitat_split":
new_vals = {
hab: hab_prop * max_larval_capacity
for hab, hab_prop in val.items()
}
set_species_param(cb, species_name, "Larval_Habitat_Types",
new_vals)
larval_habs_per_site.update({
".".join([species_name, hab]):
site_vector_props[species_name]
for hab in val.keys()
})
else:
set_species_param(cb, species_name, param, val)
scale_larval_habitats(cb, pd.DataFrame(larval_habs_per_site))
def scale_larval_habs_for_bairro(cb,
bairro_num,
bairro_df,
arab_mult,
funest_mult,
start_day=0):
# Get grid cells for this bairro
bairro_grid_cells = bairro_df[bairro_df['bairro'] ==
bairro_num]['grid_cell']
n_cells = len(bairro_grid_cells)
# Make a dataframe for these node ids, with the appropriate multipliers
scale_larval_habitats(
cb,
pd.DataFrame({
'LINEAR_SPLINE.arabiensis': [arab_mult] * n_cells,
'LINEAR_SPLINE.funestus': [funest_mult] * n_cells,
'Start_Day': [start_day] * n_cells,
'NodeID': [int(x) for x in bairro_grid_cells]
}))
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
cd = catch_specific_params(cb, catch)
arab_constant_rescale = pow(10, arab_constant) / pow(
10, burnin_params['arab_constant_scale'])
arab_rainfall_rescale = pow(10, arab_rainfall) / pow(
10, burnin_params['arab_rainfall_scale'])
funest_spline_rescale = pow(10, funest_spline) / pow(
10, burnin_params['funest_spline_scale'])
funest_veg_rescale = pow(10, funest_veg) / pow(
10, burnin_params['funest_veg_scale'])
scale_larval_habitats(cb,
pd.DataFrame({
'CONSTANT.arabiensis': [arab_constant_rescale],
'TEMPORARY_RAINFALL.arabiensis':
[arab_rainfall_rescale],
'LINEAR_SPLINE.funestus': [funest_spline_rescale],
'WATER_VEGETATION.funestus': [funest_veg_rescale]
}),
start_day=0)
# Add interventions
# zm.add_all_interventions(cb, catch)
zm.add_reports_for_likelihood_analyzers(cb,
catch,
filter_duration_days=sim_duration_days)
# Draw from serialized file (from rank0 burnin):
if catch == "bbondo":
cb.update_params({
"Serialized_Population_Path":
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