meta/views.py

   1 import datetime
   2 import difflib
   3 import json
   4 from django.shortcuts import render
   5 from django.template import RequestContext
   6 from django.http import HttpResponse
   7 from meta.models import TournamentModel, RecordModel
   8 from mlpccg.CardDb import CARDDB
   9 from mlpccg.DeckList import DeckList
  10 from mlpccg.Clustering import Clustering
  11
  12 def index(request):
  13     tournament_models = TournamentModel.objects.all().order_by('-date')
  14     tournament_data = []
  15     for tournament in tournament_models:
  16         tournament_records = tournament.recordmodel_set.all().order_by('placement')
  17         tournament_decks = [DeckList(name=record.decklist.name, url=record.decklist.url) for record in tournament_records]
  18         tournament_data += [{'tournament': tournament, 'decklists': tournament_decks}]
  19
  20     records = RecordModel.objects.all()
  21     placements = [{'decklist': DeckList(name=record.decklist.name, url=record.decklist.url), 'placement': record.placement} for record in records if record.decklist.url]
  22     clustering = Clustering(records=placements)
  23     ranking = [(int(avg), label, [decklist for decklist in clustering.clusters[label]]) for avg, label in clustering.ranking()]
  24
  25     return render(request, 'index.html', {
  26         'ranking': ranking,
  27         'tournaments': tournament_data},
  28                   context_instance=RequestContext(request))
  29
  30 def tournaments(request):
  31     return HttpResponse(json.dumps([{'name': t.name} for t in TournamentModel.objects.all()]), content_type='application/json')
  32
  33 def rate(request):
  34     decklist = DeckList(url=request.POST['url'])
  35
  36     records = RecordModel.objects.all()
  37     placements = [{'decklist': DeckList(name=record.decklist.name, url=record.decklist.url), 'placement': record.placement, 'record': record} for record in records if record.decklist.url]
  38     clustering = Clustering(records=placements)
  39     label = clustering.predict(decklist)
  40
  41     similiar = []
  42     for placement in placements:
  43         for deck in clustering.clusters[label]:
  44             if placement['decklist'] == deck:
  45                 similiar += [(deck, placement['record'])]
  46
  47     return render(request, 'rate.html', {
  48         'deck': decklist,
  49         'similiar': similiar}, context_instance=RequestContext(request))