def main():
teams = []
teamList = gen.readTeamListCsv(2018)['Teams']
pool = Pool()
teams += pool.map(getTeamData, teamList)
teams = sorted(teams, key=lambda k: k['Sponsors'], reverse=True)
colOrder = ['Team', 'Name', 'Sponsors', 'Full Name']
gen.listOfDictToCSV("Team Sponsors", teams, colOrder)
normalEvents = [0, 1]
rookieVal = 7400
maxTeam = 7900
perPage = 500
pages = round(maxTeam / perPage)
yearStart = 2016
yearEnd = 2018
yearRange = range(yearStart, yearEnd + 1)
teamList = {}
eventList = {}
for year in yearRange:
teamList[year] = gen.readTeamListCsv(year)['Teams'].tolist()
eventList[year] = slff.fetchNormalEventData(year)
#teams = gen.readTeamListCsv(2019)['Teams'].tolist()
teams = tba.district_teams('2019tx', False, True)
#teams = ['frc125', 'frc401', 'frc7179']
eventFields = ['Code', 'Type', 'Week', 'Total Points', 'Award Points', 'Award Names', 'Draft Points', 'Rank Points', 'Elim Points', 'Rank']
teamData = {}
for team in tqdm(teams):
teamData[team] = {}
for year in yearRange:
teamData[team][year] = []
if team in teamList[year]:
teamEvents = gen.readTeamCsv(team, 'events', year)
import gen
teams = gen.readTeamListCsv(2018)['Teams']
records = []
for team in teams:
matches = gen.readTeamCsv(team, 'matches', 2018)
wins = 0
losses = 0
ties = 0
if matches is not None:
for idx, match in matches.iterrows():
result = gen.matchResult(team, match)
wins += result == 'WIN'
losses += result == 'LOSS'
ties += result == 'TIE'
total = wins + losses + ties
if total != 0:
if wins == 0:
wins = 1
total = total * 10
rating = wins / total
wins = 0
else:
rating = wins / total
recordString = str(wins) + ' / ' + str(losses) + ' / ' + str(ties)
records.append({
import gen
YEAR = 2018
tba = gen.setup()
teams = gen.readTeamListCsv(2018).Teams
def fetchNames(robotList):
print('memes')
robotNames = []
for team in teams:
robots = tba.team_robots(team)
for robot in robots:
if robot.year == 2018:
robotNames.append({
'name': robot.robot_name,
'length': len(robot.robot_name)
})
print(len(robotNames))
robotNames = sorted(robotNames, key=lambda k: k['Length'])
gen.listOfDictToCSV('robotNames', robotNames, ['Length', 'Name'])
import gen
import pandas as pd
from tqdm import tqdm
tba = gen.setup()
matchData = []
teamList = gen.readTeamListCsv(2018)['Teams'].tolist()
for team in tqdm(teamList):
teamMax = 0
yearMax = 0
for year in range(2008, 2019):
matchesPlayed = len(tba.team_matches(team, None, year, False, True))
if matchesPlayed > teamMax:
teamMax = matchesPlayed
yearMax = year
matchData.append({'Team': team, 'Max Matches': teamMax, 'Year': year})
matchDf = pd.DataFrame(matchData).sort_values('Max Matches').to_csv(
'TeamMaxMatches.csv', index=False)
import gen
import slff
currentYear = 2018
rookieYear = 2006
prevTeams = gen.readTeamListCsv(rookieYear - 1)['Teams']
currTeams = gen.readTeamListCsv(rookieYear)['Teams']
rookies = []
for team in currTeams:
prevYears = slff.getTeamYears(team, rookieYear - 1)
if len(prevYears) == 0:
print(team)
#Event data (ranks, opr)
events = gen.readTeamCsv(team, 'events', currentYear)
ranks = []
oprs = []
avgOpr = 0
avgRank = 99
if events is not None:
for event in events['Event']:
rank = gen.readEventCsv(event, 'rankings')
opr = gen.readEventCsv(event, 'opr')
if opr is not None:
oprs.append(opr[opr.Team == team]['OPR'].values[0])
else:
print("Couldn't get OPR data for", event)
if rank is not None:
import gen
currentTeams = gen.readTeamListCsv(2019)
teamGaps = {x: {'Max Gap': 0, 'Current Gap': 0} for x in currentTeams['Teams']}
for year in range(1992, 2019):
yearTeams = gen.readTeamListCsv(year)
for team in currentTeams['Teams']:
entry = teamGaps[team]
if team in yearTeams['Teams'].unique():
teamYearEvents = gen.readTeamCsv(team, 'events', year)
champTypes = [3, 4]
if any(elem in champTypes for elem in teamYearEvents['Type']):
if entry['Current Gap'] > entry['Max Gap']:
entry['Max Gap'] = entry['Current Gap']
entry['Current Gap'] = 0
else:
entry['Current Gap'] += 1
for team in teamGaps:
entry = teamGaps[team]
if entry['Current Gap'] > entry['Max Gap']:
entry['Max Gap'] = entry['Current Gap']
def main():
global teams
pool = Pool()
teamData = {}
teamList = gen.readTeamListCsv(currentYear, ['Teams'])['Teams']
for team in teamList:
teams[team] = {'playValues': [], 'awardValues': [], 'totalValues': []}
for year in years:
teams[team][str(year)] = {'playPoints': 0, 'awardPoints': 0}
for idx, year in enumerate(years):
gen.progressBar(idx, len(years))
teamData[str(year)] = []
teamData[str(year)] = pool.map(partial(getData, year), teamList)
for year in teamData:
for t in teamData[year]:
teams[t['team']][year]['playPoints'] = t['playPoints']
teams[t['team']][year]['awardPoints'] = t['awardPoints']
outData = []
for team in teams:
playValues = [teams[team][str(year)]['playPoints'] for year in years]
awardValues = [teams[team][str(year)]['awardPoints'] for year in years]
totalValues = [
teams[team][str(year)]['playPoints'] +
teams[team][str(year)]['awardPoints'] for year in years
]
avgPlay = stat.mean(playValues)
avgAward = stat.mean(awardValues)
avgTotal = stat.mean(totalValues)
maxPlay = max(playValues)
maxAward = max(awardValues)
maxTotal = max(totalValues)
teamObj = {
'Team': team[3:],
'Average Play': avgPlay,
'Average Award': avgAward,
'Average Total': avgTotal,
'Max Play': maxPlay,
'Max Award': maxAward,
'Max Total': maxTotal
}
for idx, year in enumerate(years):
teamObj[str(year) + ' Play'] = playValues[idx]
teamObj[str(year) + ' Award'] = awardValues[idx]
teamObj[str(year) + ' Total'] = totalValues[idx]
outData.append(teamObj)
colOrder = [
'Team', 'Average Play', 'Average Award', 'Average Total', 'Max Play',
'Max Award', 'Max Total'
]
for year in range(currentYear - yearRange + 1, currentYear + 1):
colOrder.append(str(year) + ' Play')
colOrder.append(str(year) + ' Award')
colOrder.append(str(year) + ' Total')
gen.listOfDictToCSV("Worldwide Regional", outData, colOrder)
import gen
import pandas as pd
from tqdm import tqdm
tba = gen.setup()
teamDf = pd.read_csv('rawTeams.csv', header=None, index_col=0)
teams2019 = gen.readTeamListCsv(2019)['Teams']
names = []
for team in tqdm(teams2019):
if team in teamDf.index:
names.append({'Team': team[3:], 'Name': teamDf.loc[team][1]})
else:
names.append({'Team': team[3:], 'Name': tba.team(team)['nickname']})
gen.listOfDictToCSV('Team Names', names, ['Team', 'Name'])