def home(request):
base = base_ctx(request=request)
period = Period.objects.filter(computed=True).order_by('-start')[0]
entries = filter_active_ratings(Rating.objects.filter(period=period).order_by('-rating'))
entries = entries.select_related('team', 'teammembership')[0:10]
for entry in entries:
teams = entry.player.teammembership_set.filter(current=True)
if teams.exists():
entry.team = teams[0].team.shortname
entry.teamfull = teams[0].team.name
entry.teamid = teams[0].team.id
blogs = Post.objects.order_by('-date')[0:3]
base.update({'entries': entries, 'blogposts': blogs})
return render_to_response('index.html', base)
st = cdf(rt-rp) + cdf(rt-rz)
sz = cdf(rz-rp) + cdf(rz-rt)
period.dom_p = sp
period.dom_t = st
period.dom_z = sz
# Write some period statistics
period.num_retplayers = num_retplayers
period.num_newplayers = num_newplayers
period.num_games = num_games
period.computed = True
period.needs_recompute = False
period.save()
# Write ranks
ratings = list(filter_active_ratings(Rating.objects.filter(period=period)))
for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating, reverse=True)):
rating.position = index + 1
for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vp, reverse=True)):
rating.position_vp = index + 1
for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vt, reverse=True)):
rating.position_vt = index + 1
for index, rating in enumerate(sorted(ratings, key=lambda r: r.rating + r.rating_vz, reverse=True)):
rating.position_vz = index + 1
for rating in ratings:
rating.save()
# Recompute the hall of fame
# NOTE: If you compute several periods after one another, it might be wise to comment this and run it only
# after the last rating computation, as it takes time to run and adds up quickly.
# os.system('./domination.py')
# Setup
nplayers_max = 6 if proleague else 5
nplayers_needed = 6 if proleague else 1
Simulator = TeamPL if proleague else TeamAK
# Get a list of all teams that can compete
current_period = Period.objects.filter(computed=True).order_by('-id')[0]
teams = Group.objects.filter(active=True, is_team=True)
allowed_teams = []
for team in teams:
ratings = Rating.objects.filter(period=current_period,
player__groupmembership__group=team,
player__groupmembership__current=True,
player__groupmembership__playing=True)\
.exclude(player__race='S').exclude(player__race='R')
if filter_active_ratings(ratings).count() >= nplayers_needed:
allowed_teams.append(team)
nteams = len(allowed_teams)
# Prepare the score table
scores = dict()
for t in allowed_teams:
scores[t] = 0.0
# Loop over all pairs of teams
for (team_a, team_b) in combinations(allowed_teams, 2):
print team_a.name, '--', team_b.name
# Get player lists for both teams
players = []
for team in [team_a, team_b]:
def period(request, period_id, page='1'):
psize = 40
try:
page = int(request.GET['page'])
except:
page = 1
period = get_object_or_404(Period, id=period_id, computed=True)
# Best and most specialised players
best = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2).order_by('-rating')[0]
bestvp = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vp'}).order_by('-d')[0]
bestvt = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vt'}).order_by('-d')[0]
bestvz = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vz'}).order_by('-d')[0]
specvp = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vp/dev_vp*rating'}).order_by('-d')[0]
specvt = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vt/dev_vt*rating'}).order_by('-d')[0]
specvz = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vz/dev_vz*rating'}).order_by('-d')[0]
# Matchup statistics
ntz = lambda k: k if k is not None else 0
def wl(rca, rcb):
ms = Match.objects.filter(period=period, rca=rca, rcb=rcb).aggregate(Sum('sca'), Sum('scb'))
w = ntz(ms['sca__sum'])
l = ntz(ms['scb__sum'])
ms = Match.objects.filter(period=period, rca=rcb, rcb=rca).aggregate(Sum('sca'), Sum('scb'))
w += ntz(ms['scb__sum'])
l += ntz(ms['sca__sum'])
return w, l
pvt_wins, pvt_loss = wl('P', 'T')
pvz_wins, pvz_loss = wl('P', 'Z')
tvz_wins, tvz_loss = wl('T', 'Z')
# Filtering the ratings
entries = Rating.objects.filter(period=period).select_related('team','teammembership')
entries = filter_active_ratings(entries)
try:
race = request.GET['race']
except:
race = 'ptzrs'
q = None
for r in race:
qt = Q(player__race=r.upper())
if q == None:
q = qt
else:
q = q | qt
entries = entries.filter(q)
try:
nats = request.GET['nats']
except:
nats = 'all'
if nats == 'kr':
entries = entries.filter(player__country='KR')
elif nats == 'foreigners':
entries = entries.exclude(player__country='KR')
try:
sort = request.GET['sort']
except:
sort = ''
if sort not in ['', 'vp', 'vt', 'vz']:
sort = ''
if sort == '':
entries = entries.order_by('-rating', 'player__tag')
elif sort == 'vp':
entries = entries.extra(select={'d':'rating+rating_vp'}).order_by('-d', 'player__tag')
elif sort == 'vt':
entries = entries.extra(select={'d':'rating+rating_vt'}).order_by('-d', 'player__tag')
elif sort == 'vz':
entries = entries.extra(select={'d':'rating+rating_vz'}).order_by('-d', 'player__tag')
# Pages
nperiods = Period.objects.filter(computed=True).count()
nitems = entries.count()
npages = nitems/psize + (1 if nitems % psize > 0 else 0)
page = min(max(page, 1), npages)
entries = entries[(page-1)*psize:page*psize]
# Collect team data
for entry in entries:
teams = entry.player.teammembership_set.filter(current=True)
if teams.exists():
entry.team = teams[0].team.shortname
entry.teamfull = teams[0].team.name
entry.teamid = teams[0].team.id
# Render
base = base_ctx('Ranking', 'Current', request)
base.update({'period': period, 'entries': entries, 'page': page, 'npages': npages, 'nperiods': nperiods,
'best': best, 'bestvp': bestvp, 'bestvt': bestvt, 'bestvz': bestvz, 'specvp': specvp,
'specvt': specvt, 'specvz': specvz, 'sortable': True, 'startcount': (page-1)*psize,
'localcount': True, 'sort': sort, 'race': race, 'nats': nats,
'pvt_wins': pvt_wins, 'pvt_loss': pvt_loss, 'pvz_wins': pvz_wins,
'pvz_loss': pvz_loss, 'tvz_wins': tvz_wins, 'tvz_loss': tvz_loss})
if period.id != base['curp'].id:
base['curpage'] = ''
return render_to_response('period.html', base)
def period(request, period_id, page='1'):
psize = 40
try:
page = int(request.GET['page'])
except:
page = 1
period = get_object_or_404(Period, id=period_id, computed=True)
best = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2).order_by('-rating')[0]
bestvp = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vp'}).order_by('-d')[0]
bestvt = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vt'}).order_by('-d')[0]
bestvz = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating+rating_vz'}).order_by('-d')[0]
specvp = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vp/dev_vp*rating'}).order_by('-d')[0]
specvt = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vt/dev_vt*rating'}).order_by('-d')[0]
specvz = Rating.objects.filter(period=period, decay__lt=4, dev__lte=0.2)\
.extra(select={'d':'rating_vz/dev_vz*rating'}).order_by('-d')[0]
entries = Rating.objects.filter(period=period).select_related('team','teammembership')
entries = filter_active_ratings(entries)
try:
race = request.GET['race']
except:
race = 'ptzrs'
q = None
for r in race:
qt = Q(player__race=r.upper())
if q == None:
q = qt
else:
q = q | qt
entries = entries.filter(q)
try:
nats = request.GET['nats']
except:
nats = 'all'
if nats == 'kr':
entries = entries.filter(player__country='KR')
elif nats == 'foreigners':
entries = entries.exclude(player__country='KR')
try:
sort = request.GET['sort']
except:
sort = ''
if sort not in ['', 'vp', 'vt', 'vz']:
sort = ''
if sort == '':
entries = entries.order_by('-rating', 'player__tag')
elif sort == 'vp':
entries = entries.extra(select={'d':'rating+rating_vp'}).order_by('-d', 'player__tag')
elif sort == 'vt':
entries = entries.extra(select={'d':'rating+rating_vt'}).order_by('-d', 'player__tag')
elif sort == 'vz':
entries = entries.extra(select={'d':'rating+rating_vz'}).order_by('-d', 'player__tag')
nperiods = Period.objects.filter(computed=True).count()
nitems = entries.count()
npages = nitems/psize + (1 if nitems % psize > 0 else 0)
page = min(max(page, 1), npages)
entries = entries[(page-1)*psize:page*psize]
for entry in entries:
teams = entry.player.teammembership_set.filter(current=True)
if teams.exists():
entry.team = teams[0].team.shortname
entry.teamfull = teams[0].team.name
entry.teamid = teams[0].team.id
base = base_ctx('Ranking', 'Current', request)
base.update({'period': period, 'entries': entries, 'page': page, 'npages': npages, 'nperiods': nperiods,\
'best': best, 'bestvp': bestvp, 'bestvt': bestvt, 'bestvz': bestvz, 'specvp': specvp,\
'specvt': specvt, 'specvz': specvz, 'sortable': True, 'startcount': (page-1)*psize,
'localcount': True, 'sort': sort, 'race': race, 'nats': nats})
if period.id != base['curp'].id:
base['curpage'] = ''
return render_to_response('period.html', base)
limit = 7 # The benchmark position on the rating list. Above here, players will gain points.
# Below, players will lose points.
mean = False # Set to True to use the mean of the top N players as a benchmark, False to just
# use the rating of the Nth player.
first_period = 25 # Before this it doesn't count.
# First, clear everything
Rating.objects.update(domination=None)
# Evaluate the domination scores for every player in every period
print 'Evaluating domination scores...'
for period in Period.objects.filter(computed=True, id__gte=first_period):
bench = filter_active_ratings(Rating.objects.filter(period=period)).order_by('-rating')[limit-1].rating
if mean:
bench = filter_active_ratings(Rating.objects.filter(period=period, rating__gte=bench))
bench = bench.aggregate(Avg('rating'))['rating__avg']
filter_active_ratings(Rating.objects.filter(period=period)).update(domination=F('rating')-bench)
# Evaluate the hall of fame scores
print 'Evaluating Hall of Fame. This might take a while...'
for player in Player.objects.all():
ratings = list(Rating.objects.filter(player=player, period__id__gte=first_period).order_by('period__id'))
# Ignore players without ratings
if len(ratings) == 0:
continue
# Collect a list of indices where the rating domination switches from positive to negative or vice versa
