def fn(self, cb):
coverage_by_age = {'min': 0, 'max': 200, 'coverage': self.coverage}
add_IRS(cb,
start=self.start,
coverage_by_ages=[coverage_by_age],
waning=self.waning,
nodeIDs=self.nodeIDs)
def add_irs(self):
irs_events = pd.read_csv(self.irs_filename)
irs_events['simday'] = [convert_to_day(x, self.sim_start_date, "%Y-%m-%d") for x in irs_events.fulldate]
irs_events = irs_events[np.in1d(irs_events['grid_cell'], self.demo_cells)]
irs_events.reset_index(inplace=True, drop=True)
irs_events = irs_events[['grid_cell','event','fulldate','simday','cov_all','killing','exp_duration','box_duration']]
[binned_and_grouped, grouped_by_fields] = self.try_campaign_compression(irs_events)
for tup, group in binned_and_grouped:
table = self.convert_group_tuple_to_dict(tup, grouped_by_fields)
node_list = list(group['grid_cell'])
if sorted(node_list) == self.demo_cells:
nodeIDs = []
else:
nodeIDs = node_list
cov_all = float(table['cov_all'])
if cov_all > 0:
add_IRS(self.cb,
start=int(table['simday']),
coverage_by_ages=[{'coverage': float(table['cov_all'])}],
waning={"Killing_Config": {
"Initial_Effect": float(table['killing']),
"Decay_Time_Constant": float(table['exp_duration']),
"Box_Duration": float(table['box_duration']),
"class": "WaningEffectBoxExponential"
}},
nodeIDs=nodeIDs)
def add_irs_group(cb, coverage=1.0, start_days=[0], decay=270):
waning = {
"Killing_Config": {
"Initial_Effect": 0.6,
"Decay_Time_Constant": decay,
"class": "WaningEffectExponential"
},
"Blocking_Config": {
"Initial_Effect": 0.0,
"Decay_Time_Constant": 730,
"class": "WaningEffectExponential"
}
}
for start in start_days:
add_IRS(cb,
start, [{
"min": 0,
"max": 200,
"coverage": coverage
}],
waning=waning)
return {"IRS_Start": start_days[0], "IRS_Coverage": coverage}
def add_irs(cb, events_df):
irs_field_list = ["cov_all", "killing", "exp_duration", "box_duration"]
binned_intervene_events, binned_and_grouped, data_fields = try_campaign_compression(events_df, irs_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
add_IRS(cb, start=int(table_dict['simday']),
coverage_by_ages=[{'coverage': float(table_dict['cov_all'])}],
waning={"Killing_Config": {
"Initial_Effect": float(table_dict['killing']),
"Decay_Time_Constant": float(table_dict['exp_duration']),
"Box_Duration": float(table_dict['box_duration']),
"class": "WaningEffectBoxExponential"
}},
nodeIDs=node_list)
def add_irs(self, cb, filename=None):
if not filename:
filename = self.dropbox_base + "inputs/grid_csv/grid_all_irs_events.csv"
intervene_events = pd.read_csv(filename)
# Restrict to catchment of interest
intervene_events = intervene_events[np.in1d(
intervene_events['grid_cell'], self.catch_cells)]
intervene_events.reset_index(inplace=True)
# Compute simulation days relative to start date or use default in file
intervene_events['simday'] = [
convert_to_day_365(x, self.start_date, "%Y-%m-%d")
for x in intervene_events.fulldate
]
irs_field_list = ["cov_all", "killing", "exp_duration", "box_duration"]
binned_intervene_events, binned_and_grouped, data_fields = self.try_campaign_compression_v2(
intervene_events, irs_field_list)
for table, group in binned_and_grouped:
table_dict = dict(zip((data_fields), table))
node_list = sorted(group['grid_cell'])
if node_list == sorted(self.catch_cells):
nodeIDs = []
else:
nodeIDs = node_list
add_IRS(cb,
start=int(table_dict['simday']),
coverage_by_ages=[{
'coverage': float(table_dict['cov_all'])
}],
waning={
"Killing_Config": {
"Initial_Effect":
float(table_dict['killing']),
"Decay_Time_Constant":
float(table_dict['exp_duration']),
"Box_Duration":
float(table_dict['box_duration']),
"class":
"WaningEffectBoxExponential"
}
},
nodeIDs=nodeIDs)
#####################
# Experiment sweeps #
#####################
add_simple_hs(cb, 0.75)
# annual actellic spray
for y in range(6):
spray_date = 9 * 30 + 365 * y
add_IRS(cb,
start=spray_date,
coverage_by_ages=[{
'coverage': 0.75
}],
killing_config={
"Initial_Effect": 0.9,
"Decay_Time_Constant": 30,
"Box_Duration": 210,
"class": "WaningEffectBoxExponential"
})
modlists = []
num_seeds = 5
modlist = modfn_sweep_over_seeds(num_seeds)
modlists.append(modlist)
# modlist = [ModFn(itn_scenario, s) for s in [0,1,2,3,4,5,6]]
modlist = [ModFn(itn_scenario, s) for s in [0, 7]] # no-ITN scenarios
modlists.append(modlist)
