def get_plays(season_id, game_id):
# Get the play-by-play data
url = "http://live.nhl.com/GameData/%s/%s/PlayByPlay.json" % (season_id, game_id)
data = requests.get(url).json()["data"]["game"]
game = schedule.get_game(game_id)
home = game["home_abbr"]
away = game["away_abbr"]
plays = pd.DataFrame(data["plays"]["play"])
# Figure out the UTC timestamp for each play
game_date = game["date"]
plays["localtime"] = pd.to_datetime(plays["localtime"].apply(lambda x: "%s %s" % (game_date, x))).dt.tz_localize(
schedule.tz_dict[home]
)
plays["utc_timestamp"] = plays["localtime"].astype(int) / 1e9
# Replace team ids with their abbreviations
plays["team_abbr"] = plays["teamid"].replace(
to_replace=[data["awayteamid"], data["hometeamid"]], value=[away, home]
)
# Extract the properties that I want from each play
plays = plays[["as", "hs", "desc", "utc_timestamp", "type", "time", "team_abbr"]]
return plays
def get_game_plotdata(id):
# Gather request parameters
start_timestamp = request.args.get('start')
end_timestamp = request.args.get('end')
# Get home and away team abbreviations
home_abbr = schedule.get_game(id).home_abbr
away_abbr = schedule.get_game(id).away_abbr
# Gather Redis set keys
sentiment_key = 'sentiment:%s' % id
home_sentiment_key = '%s:%s' % (sentiment_key, home_abbr)
away_sentiment_key = '%s:%s' % (sentiment_key, away_abbr)
plays_key = 'plays:%s' % id
# Get the minute timestamps when sentiment or plays were gathered
pipe = r.pipeline()
pipe.smembers(sentiment_key)
pipe.smembers(home_sentiment_key)
pipe.smembers(away_sentiment_key)
pipe.smembers(plays_key)
sentiment_timestamps, home_sentiment_timestamps, away_sentiment_timestamps, plays_timestamps = pipe.execute()
# Take union of sentiment and play timestamps
timestamps = list(sentiment_timestamps.union(plays_timestamps))
# Grab a subset of the timestamps if start and end parameters requested
if start_timestamp is not None:
if end_timestamp is not None:
timestamps = [timestamp for timestamp in timestamps if start_timestamp <= timestamp <= end_timestamp]
timestamps = [timestamp for timestamp in timestamps if timestamp >= start_timestamp]
# Helper function to format sentiment and play data into something convenient for the front end
def format_data(timestamp, sentiment, home_sentiment, away_sentiment, plays):
# Add the timestamp as a property
timestamp_data = {'timestamp': timestamp}
# Format the sentiment data - return a mean sentiment of 0 if there is no data for a timestamp
if sentiment:
timestamp_data['mean_sentiment'] = float(sentiment['sum'])
# timestamp_data['mean_sentiment'] = float(sentiment['sum']) / float(sentiment['count'])
timestamp_data['tweet_count'] = float(sentiment['count'])
else:
timestamp_data['mean_sentiment'] = 0.
timestamp_data['tweet_count'] = 0.
# Home plot data
if home_sentiment:
timestamp_data['home_mean_sentiment'] = float(home_sentiment['sum'])
# timestamp_data['home_mean_sentiment'] = float(home_sentiment['sum']) / float(home_sentiment['count'])
timestamp_data['home_tweet_count'] = float(home_sentiment['count'])
else:
timestamp_data['home_mean_sentiment'] = 0.
timestamp_data['home_tweet_count'] = 0.
# Away plot data
if away_sentiment:
timestamp_data['away_mean_sentiment'] = float(away_sentiment['sum'])
# timestamp_data['away_mean_sentiment'] = float(away_sentiment['sum']) / float(away_sentiment['count'])
timestamp_data['away_tweet_count'] = float(away_sentiment['count'])
else:
timestamp_data['away_mean_sentiment'] = 0.
timestamp_data['away_tweet_count'] = 0.
# Format the plays data - return an empty list if there is no data for a timestamp
if len(plays) != 0:
timestamp_data['plays'] = [ast.literal_eval(play) for play in plays]
else:
timestamp_data['plays'] = []
return timestamp_data
# Dict to store properly formatted data. This is ultimately jsonified
json_data = {}
# Get the sentiment and play data for each timestamp
pipe = r.pipeline()
for timestamp in timestamps:
pipe.hgetall('%s:%s' % (sentiment_key, timestamp))
pipe.hgetall('%s:%s' % (home_sentiment_key, timestamp))
pipe.hgetall('%s:%s' % (away_sentiment_key, timestamp))
pipe.lrange('%s:%s' % (plays_key, timestamp), 0, -1)
results = pipe.execute()
# Format the results into something convenient for the front end
for i in range(0, len(results), 4):
json_data[timestamps[i / 4]] = format_data(timestamps[i / 4], results[i], results[i + 1], results[i + 2],
results[i + 3])
return jsonify(json_data)
def game_details(id):
game = schedule.get_game(id)
return game.to_json()
def game(id):
game = schedule.get_game(id)
game['time_est'] = game['time'].strftime('%I:%M %p EST')
return render_template('game.html', game=game.to_dict())
