2016 FRC Scheduling With/Against

2016-against.csv

2016-with.csv

schedstats.py

#! /usr/bin/python3

import argparse
import json
import urllib.request
import statistics
import csv
from collections import defaultdict


"""
A script to calculate distributions of unique partners and opponents for 2016 FRC events
"""

BASE_URL = 'https://www.thebluealliance.com/api/v2/{}'
APP_HEADER = 'X-TBA-App-Id'
APP_ID = 'plnyyanks:schedstats:v0.1'

# See https://github.com/the-blue-alliance/the-blue-alliance/blob/master/consts/event_type.py
VALID_EVENT_TYPES = [0, 1, 2, 3]


def fetch_endpoint(endpoint):
    full_url = BASE_URL.format(endpoint)
    url = urllib.request.Request(full_url, headers={APP_HEADER: APP_ID, 'User-agent': 'Mozilla/5.0'})
    response = urllib.request.urlopen(url)
    return json.loads(response.read().decode('utf-8'))


def fetch_event_keys_in_year(year):
    api_events = fetch_endpoint("events/{}".format(year))
    return [event["key"] for event in api_events if event["event_type"] in VALID_EVENT_TYPES]


def fetch_event_matches(event_key):
    matches = fetch_endpoint("event/{}/matches".format(event_key))
    return [match for match in matches if match["comp_level"] == "qm"]


def fetch_event_team_keys(event_key):
    teams = fetch_endpoint("event/{}/teams".format(event_key))
    return [team["key"] for team in teams]


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--year", help="Competition season to test", type=int, default=2016)
    args = parser.parse_args()

    # Build list of all relevent event keys
    print("Fetching events in {}".format(args.year))
    event_keys = []
    event_keys.extend(fetch_event_keys_in_year(args.year))

    # Build dict of each event's matches
    matches = {}
    teams = {}
    for event_key in event_keys:
        print("Fetching qual matches for {}".format(event_key))
        matches[event_key] = fetch_event_matches(event_key)
        teams[event_key] = fetch_event_team_keys(event_key)

    # Calculate stats for schedule
    # {# with, # against} x {mean, median, max, min}
    teams_with = defaultdict(lambda: defaultdict(set))
    teams_against = defaultdict(lambda: defaultdict(set))
    count_with = defaultdict(lambda: defaultdict(int))
    count_against = defaultdict(lambda: defaultdict(int))

    for event_key in event_keys:
        if len(matches[event_key]) == 0:
            continue

        for match in matches[event_key]:
            for team in match["alliances"]["blue"]["teams"]:
                for other in match["alliances"]["blue"]["teams"]:
                    if team != other:
                        teams_with[event_key][team].add(other)
                for other in match["alliances"]["red"]["teams"]:
                        teams_against[event_key][team].add(other)
            for team in match["alliances"]["red"]["teams"]:
                for other in match["alliances"]["red"]["teams"]:
                    if team != other:
                        teams_with[event_key][team].add(other)
                for other in match["alliances"]["blue"]["teams"]:
                        teams_against[event_key][team].add(other)

        for team in teams[event_key]:
            count_with[event_key][team] = len(teams_with[event_key][team])
            count_against[event_key][team] = len(teams_against[event_key][team])

    with_res = [["Event Key", "Mean", "Median", "Max", "Min"]]
    against_res = [["Event Key", "Mean", "Median", "Max", "Min"]]
    for event_key in event_keys:
        if len(matches[event_key]) == 0:
            continue
        withs = [count for team, count in count_with[event_key].items() if count != 0]
        ags = [count for team, count in count_against[event_key].items() if count != 0]
        with_res.append([event_key, statistics.mean(withs), statistics.median(withs), max(withs), min(withs)])
        against_res.append([event_key, statistics.mean(ags), statistics.median(ags), max(ags), min(ags)])

    with open('2016-with.csv', 'w') as f:
        writer = csv.writer(f)
        writer.writerows(with_res)
    with open('2016-against.csv', 'w') as f:
        writer = csv.writer(f)
        writer.writerows(against_res)