def _monthly_lb(category):
logger.info("generating monthly leaderboard")
if category == "scores":
key = "daily.score"
elif category == "missed":
key = "daily.incorrect"
else:
raise GenericError("Invalid category", 990)
today = datetime.datetime.now(datetime.timezone.utc).date()
past_month = pd.date_range( # pylint: disable=no-member
today - datetime.timedelta(29), today
).date
pipe = database.pipeline()
for day in past_month:
pipe.zrevrangebyscore(f"{key}:{day}", "+inf", "-inf", withscores=True)
result = pipe.execute()
totals = pd.Series(dtype="int64")
for daily_score in result:
daily_score = pd.Series(
{
e[0]: e[1]
for e in map(
lambda x: (x[0].decode("utf-8"), int(x[1])), daily_score
)
}
)
totals = totals.add(daily_score, fill_value=0)
totals = totals.sort_values(ascending=False)
return totals
def _server_total(ctx):
logger.info("fetching server totals")
channels = map(
lambda x: x.decode("utf-8").split(":")[1],
database.zrangebylex("channels:global", f"[{ctx.guild.id}",
f"({ctx.guild.id}\xff"),
)
pipe = database.pipeline() # use a pipeline to get all the scores
for channel in channels:
pipe.zscore("score:global", channel)
scores = pipe.execute()
return int(sum(scores))
def generate_dataframe(database_keys, titles):
"""Generates a pandas.DataFrame from multiple Redis sorted sets."""
pipe = database.pipeline()
for key in database_keys:
pipe.zrevrangebyscore(key, "+inf", "-inf", withscores=True)
result = pipe.execute()
df = pd.DataFrame()
for i, item in enumerate(result):
df.insert(
len(df.columns),
titles[i],
pd.Series(
{
e[0]: e[1]
for e in map(lambda x: (x[0].decode("utf-8"), int(x[1])), item)
}
),
)
df = df.fillna(value=0).astype(int)
return df
def generate_dataframe(database_keys, titles, index=None):
"""Generates a pandas.DataFrame from multiple Redis sorted sets."""
pipe = database.pipeline()
for key in database_keys:
pipe.zrevrangebyscore(key, "+inf", "-inf", withscores=True)
result = pipe.execute()
index = {v[0].decode("utf-8")
for r in result for v in r}.union(set(index if index else {}))
df = pd.DataFrame(index=index)
for title, item in zip(titles, result):
df.insert(
len(df.columns),
title,
pd.Series({
e[0]: e[1]
for e in map(lambda x: (x[0].decode("utf-8"), int(x[1])),
item)
}),
)
df = df.fillna(value=0).astype(int)
return df