def __init__(self, state, parameters, config, timeseries):
"""
Initialize a company to manage the fixed crews (e.g. a contracting company).
"""
self.name = 'fixed'
self.state = state
self.parameters = parameters
self.config = config
self.timeseries = timeseries
self.crews = []
self.deployment_days = self.state['weather'].deployment_days('fixed')
self.timeseries['fixed_prop_sites_avail'] = []
self.timeseries['fixed_cost'] = np.zeros(self.parameters['timesteps'])
self.timeseries['fixed_eff_flags'] = np.zeros(
self.parameters['timesteps'])
self.timeseries['fixed_flags_redund1'] = np.zeros(
self.parameters['timesteps'])
self.timeseries['fixed_flags_redund2'] = np.zeros(
self.parameters['timesteps'])
self.timeseries['fixed_flags_redund3'] = np.zeros(
self.parameters['timesteps'])
# Assign the correct follow-up threshold
if self.config['follow_up_thresh'][1] == "absolute":
self.config['follow_up_thresh'] = self.config['follow_up_thresh'][
0]
elif self.config['follow_up_thresh'][1] == "proportion":
self.config['follow_up_thresh'] = get_prop_rate(
self.config['follow_up_thresh'][0],
self.state['empirical_leaks'])
else:
print(
'Follow-up threshold type not recognized. Must be "absolute" or "proportion".'
)
# Initialize 2D matrices to store deployment day (DD) counts and MCBs
self.DD_map = np.zeros((len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))
self.MCB_map = np.zeros((len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))
# Initialize the individual fixed crews (the agents) - each is a single fixed sensor
for site in self.state['sites']:
n_fixed = int(site['fixed_sensors'])
for i in range(n_fixed):
self.crews.append(
fixed_crew(state,
parameters,
config,
timeseries,
site,
self.deployment_days,
id=site['facility_ID'] + '-' + str(i + 1)))
self.timeseries['fixed_cost'][self.state['t'].current_timestep] += \
self.parameters['methods']['fixed']['up_front_cost']
return
def __init__(self, state, parameters, config, timeseries):
"""
Initialize a company to manage the aircraft crews (e.g. a contracting company).
"""
self.name = 'aircraft'
self.state = state
self.parameters = parameters
self.config = config
self.timeseries = timeseries
self.crews = []
self.deployment_days = self.state['weather'].deployment_days('aircraft')
self.timeseries['aircraft_prop_sites_avail'] = []
self.timeseries['aircraft_cost'] = np.zeros(self.parameters['timesteps'])
self.timeseries['aircraft_eff_flags'] = np.zeros(self.parameters['timesteps'])
self.timeseries['aircraft_flags_redund1'] = np.zeros(self.parameters['timesteps'])
self.timeseries['aircraft_flags_redund2'] = np.zeros(self.parameters['timesteps'])
self.timeseries['aircraft_flags_redund3'] = np.zeros(self.parameters['timesteps'])
self.timeseries['aircraft_sites_visited'] = np.zeros(self.parameters['timesteps'])
# Assign the correct follow-up threshold
if self.config['follow_up_thresh'][1] == "absolute":
self.config['follow_up_thresh'] = self.config['follow_up_thresh'][0]
elif self.config['follow_up_thresh'][1] == "proportion":
self.config['follow_up_thresh'] = get_prop_rate(
self.config['follow_up_thresh'][0],
self.state['empirical_leaks'])
else:
print('Follow-up threshold type not recognized. Must be "absolute" or "proportion".')
# Additional variable(s) for each site
for site in self.state['sites']:
site.update({'aircraft_t_since_last_LDAR': 0})
site.update({'aircraft_surveys_conducted': 0})
site.update({'attempted_today_aircraft?': False})
site.update({'surveys_done_this_year_aircraft': 0})
site.update({'aircraft_missed_leaks': 0})
# Initialize 2D matrices to store deployment day (DD) counts and MCBs
self.DD_map = np.zeros(
(len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))
self.MCB_map = np.zeros(
(len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))
# Initialize the individual aircraft crews (the agents)
for i in range(config['n_crews']):
self.crews.append(aircraft_crew(state, parameters, config,
timeseries, self.deployment_days, id=i + 1))
return
def __init__(self, state, parameters, config, timeseries):
"""
Initialize a company to manage the satellite
"""
self.name = 'satellite'
self.state = state
self.parameters = parameters
self.config = config
self.timeseries = timeseries
self.crews = []
self.deployment_days = self.state['weather'].deployment_days('satellite')
self.timeseries['satellite_prop_sites_avail'] = []
self.timeseries['satellite_cost'] = np.zeros(self.parameters['timesteps'])
self.timeseries['satellite_eff_flags'] = np.zeros(self.parameters['timesteps'])
self.timeseries['satellite_flags_redund1'] = np.zeros(self.parameters['timesteps'])
self.timeseries['satellite_flags_redund2'] = np.zeros(self.parameters['timesteps'])
self.timeseries['satellite_flags_redund3'] = np.zeros(self.parameters['timesteps'])
self.timeseries['satellite_sites_visited'] = np.zeros(self.parameters['timesteps'])
# load pre-defined orbit grids
Dataset = nc.Dataset(self.parameters['methods']['Satellite']['Satellite_grid'], 'r')
self.satgrid = Dataset.variables['sat'][:]
Dataset.close()
# load cloud cover data
Dataset = nc.Dataset(self.parameters['methods']['Satellite']['CloudCover'], 'r')
self.cloudcover = Dataset.variables['tcc'][:]
Dataset.close()
# build a satellite orbit object
sat = self.parameters['methods']['Satellite']['name']
tlefile = self.parameters['methods']['Satellite']['TLE_file']
TLEs = []
with open(tlefile) as f:
for line in f:
TLEs.append(line.rstrip())
i = 0
for x in TLEs:
if x == sat:
break
i += 1
TLE_LINES = (TLEs[i+1], TLEs[i+2])
self.predictor = get_predictor_from_tle_lines(TLE_LINES)
return
# Assign the correct follow-up threshold
if self.config['follow_up_thresh'][1] == "absolute":
self.config['follow_up_thresh'] = self.config['follow_up_thresh'][0]
elif self.config['follow_up_thresh'][1] == "proportion":
self.config['follow_up_thresh'] = get_prop_rate(
self.config['follow_up_thresh'][0],
self.state['empirical_leaks'])
else:
print('Follow-up threshold type not recognized. Must be "absolute" or "proportion".')
# Additional variable(s) for each site
for site in self.state['sites']:
site.update({'satellite_t_since_last_LDAR': 0})
site.update({'satellite_surveys_conducted': 0})
site.update({'attempted_today_satellite?': False})
site.update({'surveys_done_this_year_satellite': 0})
site.update({'satellite_missed_leaks': 0})
# Initialize 2D matrices to store deployment day (DD) counts and MCBs
self.DD_map = np.zeros(
(len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))
self.MCB_map = np.zeros(
(len(self.state['weather'].longitude),
len(self.state['weather'].latitude)))