def run_simulation(params):
"""Run a set of simulations using various parameter sets.
Args: params: A dict-like object that will be unpacked as the arguments to
schedule.make_schedule. In other words, it should be key-value pairs
defining the state variables for the simulations. The "interventions"
value should be an iterable of `Intervention` instances.
Returns: A pandas.DataFrame with three columns - step_number,
elapsed_time, and software_development_rate.
"""
attributes = ['software_development_rate']
output_stream = StringIO()
schedule = make_schedule(**params)
simulate(schedule, step, output_stream, attributes)
output_stream.seek(0)
frame = pandas.read_table(output_stream)
# This cleans up the null initial rate
frame.ix[0, 'software_development_rate'] = 0
return frame
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
arguments = docopt(__doc__, argv=argv, version="Brooks 0.5")
schedule_file_path = arguments['<schedule>']
loader = importlib.machinery.SourceFileLoader("schedule", schedule_file_path)
schedule = loader.load_module()
attributes = arguments['<attribute>']
with open_out_stream(arguments['--output']) as output_stream:
simulate(schedule, step, output_stream, attributes)
return 0
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
arguments = docopt(__doc__, argv=argv, version="Brooks 0.5")
schedule_file_path = arguments['<schedule>']
loader = importlib.machinery.SourceFileLoader("schedule",
schedule_file_path)
schedule = loader.load_module()
attributes = arguments['<attribute>']
with open_out_stream(arguments['--output']) as output_stream:
simulate(schedule, step, output_stream, attributes)
return 0
