def predict_tournament(matchlist_file, expos=None, start=0):
"""
- Given a matchlist for a tournament, predict the winner of each match.
- If only the matchlist file is given, predict the tournament based
on previous match data.
- If a starting round is specified, predict the tournament based on
previously available match data when available, falling back on
old data when necessary.
"""
# Parse Matchlist
matches = roboscout.getData(matchlist_file, True)
# Get a list of teams from the matchlist
teams = []
for match in matches:
positions = ['Red 1', 'Red 2', 'Blue 1', 'Blue 2']
for n in positions:
if match[n] not in teams:
teams.append(match[n])
if expos is None:
# Pre-scout from the main match pool
expos, vars, caps = metatournament.best_robots(
officialparser.get_tournaments())
# filter expo to only include teams we care about
expos = filter_dict(lambda t: t in teams, expos)
# Run roboscout on the first n rounds to get more accurate
# expected output when possible.
s = roboscout.scout(matches[:start])
for team in teams:
if team in s['expo']:
expos[team] = s['expo'][team]
# Get an average for teams with no data
expo_avg = avg(expos.values())
for team in teams:
if team not in expos:
expos[team] = expo_avg
# Fill in match predictions based on expected output for each team
for match in matches:
rs = round(expos[match['Red 1']] + expos[match['Red 2']])
bs = round(expos[match['Blue 1']] + expos[match['Blue 2']])
match['Red Score'] = rs
match['Blue Score'] = bs
return matches
def predict_tournament(matchlist_file, expos=None, start=0):
"""
- Given a matchlist for a tournament, predict the winner of each match.
- If only the matchlist file is given, predict the tournament based
on previous match data.
- If a starting round is specified, predict the tournament based on
previously available match data when available, falling back on
old data when necessary.
"""
# Parse Matchlist
matches = roboscout.getData(matchlist_file, True)
# Get a list of teams from the matchlist
teams = []
for match in matches:
positions = ['Red 1', 'Red 2', 'Blue 1', 'Blue 2']
for n in positions:
if match[n] not in teams:
teams.append(match[n])
if expos is None:
# Pre-scout from the main match pool
expos, vars, caps = metatournament.best_robots(
officialparser.get_tournaments())
# filter expo to only include teams we care about
expos = filter_dict(lambda t: t in teams, expos)
# Run roboscout on the first n rounds to get more accurate
# expected output when possible.
s = roboscout.scout(matches[:start])
for team in teams:
if team in s['expo']:
expos[team] = s['expo'][team]
# Get an average for teams with no data
expo_avg = avg(expos.values())
for team in teams:
if team not in expos:
expos[team] = expo_avg
# Fill in match predictions based on expected output for each team
for match in matches:
rs = round(expos[match['Red 1']] + expos[match['Red 2']])
bs = round(expos[match['Blue 1']] + expos[match['Blue 2']])
match['Red Score'] = rs
match['Blue Score'] = bs
return matches
def robot_stats(t):
s = roboscout.scout(t)
highcap = capabilities(s, HIGH_GOAL)
midcap = capabilities(s, MID_GOAL)
lowcap = capabilities(s, LOW_GOAL)
hangcap = capabilities(s, HANG)
auto = auto_expo(s)
autoend = futil.mapd(max, auto_ending_pos(s))
teams = {}
for team in highcap.keys():
teams[team] = {
'expo': s['expo'][team],
'variance': s['variance'][team],
'caps': {
'high': highcap[team],
'mid': midcap[team],
'low': lowcap[team],
'hang': hangcap[team],
'auto': auto[team],
'autopos': autoend[team]
}
}
return teams
for x in xrange(len(m1)):
p = m1[x]
r = m2[x]
pw = 'Red' if p['Red Score'] > p['Blue Score'] else 'Blue'
rw = 'Red' if int(r['Red Score']) > int(r['Blue Score']) else 'Blue'
confirmation.append({
'Round #': p['Round #'],
'Red Diff': p['Red Score'] - int(r['Red Score']),
'Blue Diff': p['Blue Score'] - int(r['Blue Score']),
'Win Predicted': pw == rw
})
return confirmation
if __name__ == '__main__':
s = roboscout.scout(roboscout.getData('matchlists/hopper1.csv'))
t = predict_tournament('matchlists/hopper1.csv', s['expo'])
d = map(lambda r: [r['Round #'],
r['Red Score'],
r['Blue Score'],
'Blue' if r['Blue Score'] > r['Red Score'] else 'Red'], t)
h = ['#', 'Red', 'Blue', 'Winner']
scratch.print_table(h, d)
# confirmation = compare_matchlists(
# predict_tournament('scoreboard_tesla.csv'),
# roboscout.getData('scoreboard_tesla.csv'))
#
# h = ['Round #', 'RD', 'BD', 'Right?']
#
def capabilities(s, i):
tc = futil.mapd(
lambda matches: map(lambda m: m[m['team'] + ' Breakdown'][i], matches),
s['m'])
cap = roboscout.scout({}, s['m'], tc)
return cap['expo']
def auto_expo(s):
ac = futil.mapd(
lambda matches: map(lambda m: m[m['team'] + ' Auto'], matches), s['m'])
auto = roboscout.scout({}, s['m'], ac)
return auto['expo']
p = m1[x]
r = m2[x]
pw = 'Red' if p['Red Score'] > p['Blue Score'] else 'Blue'
rw = 'Red' if int(r['Red Score']) > int(r['Blue Score']) else 'Blue'
confirmation.append({
'Round #': p['Round #'],
'Red Diff': p['Red Score'] - int(r['Red Score']),
'Blue Diff': p['Blue Score'] - int(r['Blue Score']),
'Win Predicted': pw == rw
})
return confirmation
if __name__ == '__main__':
s = roboscout.scout(roboscout.getData('matchlists/hopper1.csv'))
t = predict_tournament('matchlists/hopper1.csv', s['expo'])
d = map(
lambda r: [
r['Round #'], r['Red Score'], r['Blue Score'], 'Blue'
if r['Blue Score'] > r['Red Score'] else 'Red'
], t)
h = ['#', 'Red', 'Blue', 'Winner']
scratch.print_table(h, d)
# confirmation = compare_matchlists(
# predict_tournament('scoreboard_tesla.csv'),
# roboscout.getData('scoreboard_tesla.csv'))
#
# h = ['Round #', 'RD', 'BD', 'Right?']
if __name__ == '__main__':
# mode = sys.argv[2]
url = sys.argv[1]
filename = sys.argv[2]
if url.startswith('http'):
html_doc = urllib2.urlopen(url).read()
else:
html_doc = open(url, 'r').read()
html_doc = html_doc.replace("\xc2\xa0", " ")
# if mode == 'match':
# matches = html_to_matchlist(html_doc)
# elif mode in ['Tot', 'Auto', 'EndG', 'Tele']:
# matches = details_to_matchlist(parse_details(html_doc), mode)
details = parse_details(html_doc)
tms = {}
for mode in ['Tot', 'Auto', 'EndG', 'Tele']:
matches = details_to_matchlist(details, mode)
save_matchlist(matches, filename + mode + '.csv')
s = scout(matches)
for k, v in s['expo'].iteritems():
if k not in tms:
tms[k] = {}
tms[k][mode] = v
save_details(tms, filename)
# save_matchlist(matches, filename)
if __name__ == '__main__':
# mode = sys.argv[2]
url = sys.argv[1]
filename = sys.argv[2]
if url.startswith('http'):
html_doc = urllib2.urlopen(url).read()
else:
html_doc = open(url, 'r').read()
html_doc = html_doc.replace("\xc2\xa0", " ")
# if mode == 'match':
# matches = html_to_matchlist(html_doc)
# elif mode in ['Tot', 'Auto', 'EndG', 'Tele']:
# matches = details_to_matchlist(parse_details(html_doc), mode)
details = parse_details(html_doc)
tms = {}
for mode in ['Tot', 'Auto', 'EndG', 'Tele']:
matches = details_to_matchlist(details, mode)
save_matchlist(matches, filename+mode+'.csv')
s = scout(matches)
for k, v in s['expo'].iteritems():
if k not in tms:
tms[k] = {}
tms[k][mode] = v
save_details(tms, filename)
# save_matchlist(matches, filename)