mirror of
https://github.com/a-b-street/abstreet.git
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83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
import requests
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import statistics
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def get(args, cmd, **kwargs):
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resp = requests.get(args.api + cmd, **kwargs)
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if resp.status_code != requests.codes.ok:
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raise Exception(resp.text)
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return resp
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def post(args, cmd, **kwargs):
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resp = requests.post(args.api + cmd, **kwargs)
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if resp.status_code != requests.codes.ok:
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raise Exception(resp.text)
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return resp
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# Returns Results
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def run_sim(args, modifiers=[], edits=None):
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post(args, '/sim/load', json={
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'scenario': 'data/system/scenarios/{}/weekday.bin'.format(args.map_name),
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'modifiers': modifiers,
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'edits': edits,
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})
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post(args, '/sim/goto-time',
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params={'t': '{}:00:00'.format(args.hours)})
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raw_trips = get(args, '/data/get-finished-trips').json()
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# Map trip ID to the duration (in seconds) of the trip. Filter out
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# cancelled trips.
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num_cancelled = 0
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trip_times = {}
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capped_trips = set()
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for trip in raw_trips:
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if trip['mode'] is None:
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num_cancelled += 1
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else:
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trip_times[trip['id']] = trip['duration']
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if trip['capped']:
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capped_trips.add(trip['id'])
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return Results(num_cancelled, trip_times, capped_trips)
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class Results:
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def __init__(self, num_cancelled, trip_times, capped_trips):
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self.num_cancelled = num_cancelled
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# Maps trip ID to seconds
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self.trip_times = trip_times
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# A set of trip IDs
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self.capped_trips = capped_trips
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# self is the baseline, results2 is the experiment
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def compare(self, results2):
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faster = []
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slower = []
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for trip, after_dt in results2.trip_times.items():
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before_dt = self.trip_times.get(trip)
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if not before_dt:
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# The trip didn't finish in time in the baseline run
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continue
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if before_dt:
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if before_dt > after_dt:
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faster.append(before_dt - after_dt)
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elif after_dt > before_dt:
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slower.append(after_dt - before_dt)
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print('{:,} trips faster, average {:.1f}s savings'.format(
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len(faster), avg(faster)))
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print('{:,} trips slower, average {:.1f}s loss'.format(
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len(slower), avg(slower)))
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print('{:,} trips cancelled before, {:,} after'.format(
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self.num_cancelled, results2.num_cancelled))
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def avg(data):
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if data:
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return statistics.mean(data)
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else:
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return 0.0
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