def simulate_parallel(config="../data/config/parallel.json", inData=None, params=None, search_space=None, **kwargs):
if inData is None: # othwerwise we use what is passed
from MaaSSim.data_structures import structures
inData = structures.copy() # fresh data
if params is None:
params = get_config(config, root_path = kwargs.get('root_path')) # load from .json file
if len(inData.G) == 0: # only if no graph in input
inData = load_G(inData, params, stats=True) # download graph for the 'params.city' and calc the skim matrices
if len(inData.passengers) == 0: # only if no passengers in input
inData = generate_demand(inData, params, avg_speed=True)
if len(inData.vehicles) == 0: # only if no vehicles in input
inData.vehicles = generate_vehicles(inData, params.nV)
if len(inData.platforms) == 0: # only if no platforms in input
inData.platforms = initialize_df(inData.platforms)
inData.platforms.loc[0] = empty_series(inData.platforms)
inData.platforms.fare = [1]
inData.vehicles.platform = 0
inData.passengers.platforms = inData.passengers.apply(lambda x: [0], axis=1)
inData = prep_shared_rides(inData, params.shareability) # obligatory to prepare schedules
brute(func=single_pararun,
ranges=slice_space(search_space, replications=params.parallel.get("nReplications",1)),
args=(inData, params, search_space),
full_output=True,
finish=None,
workers=params.parallel.get('nThread',1))
def simulate(config="../data/config/default.json",
inData=None,
params=None,
**kwargs):
"""
main runner and wrapper
loads or uses json config to prepare the data for simulation, run it and process the results
:param config: .json file path
:param inData: optional input data
:param params: loaded json file
:param kwargs: optional arguments
:return: simulation object with results
"""
if inData is None: # otherwise we use what is passed
from MaaSSim.data_structures import structures
inData = structures.copy() # fresh data
if params is None:
params = get_config(
config, root_path=kwargs.get('root_path')) # load from .json file
if kwargs.get('make_main_path', False):
from MaaSSim.utils import make_config_paths
params = make_config_paths(params,
main=kwargs.get('make_main_path', False),
rel=True)
if params.paths.get('requests', False):
inData = read_requests_csv(inData, path=params.paths.requests)
if params.paths.get('vehicles', False):
inData = read_vehicle_positions(inData, path=params.paths.vehicles)
if len(inData.G) == 0: # only if no graph in input
inData = load_G(
inData, params, stats=True
) # download graph for the 'params.city' and calc the skim matrices
if len(inData.passengers) == 0: # only if no passengers in input
inData = generate_demand(inData, params, avg_speed=True)
if len(inData.vehicles) == 0: # only if no vehicles in input
inData.vehicles = generate_vehicles(inData, params.nV)
if len(inData.platforms) == 0: # only if no platforms in input
inData.platforms = initialize_df(inData.platforms)
inData.platforms.loc[0] = empty_series(inData.platforms)
inData.platforms.fare = [1]
inData = prep_shared_rides(inData,
params.shareability) # prepare schedules
sim = Simulator(inData, params=params, **kwargs) # initialize
for day in range(params.get('nD', 1)): # run iterations
sim.make_and_run(run_id=day) # prepare and SIM
sim.output() # calc results
if sim.functions.f_stop_crit(sim=sim):
break
return sim