def weekly_play_counts_js(self, user, start, end):
wpcs = self.weekly_play_counts(user, start, end)
n = 0 # number of data points so far
# Cumulative average:
# CA_i+1 = CA_i + ((x_i+1 - CA_i) / i+1)
# where CA_i = last average,
# x_i+1 = new entry's value.
last_avg = 0.0
for date_idx, wpc in wpcs:
n += 1
average = last_avg + (( wpc - last_avg) / n )
last_avg = average
yield (ldates.js_timestamp_of_index(date_idx), wpc, average)
def user_weekly_plays_of_artists(self, user, artists, start, end):
# initialise the results to a dictionary or artist -> week/playcount,
# with all playcounts set to zero. means no need to handle missing
# weeks in query set loop.
prelim_data = [0] * ((end+1) - start) #dict((ldates.js_timestamp_of_index(x), 0) for x in xrange(start, end+1))
dates = [ ldates.js_timestamp_of_index(idx) for idx in xrange(start, end+1) ]
results = dict((artist.id, prelim_data[:]) for artist in artists)
for artist_wd in self.user_weeks_between(user, artists, start, end):
# place in the appropriate slot in the result list (subtract start)
results[artist_wd.artist_id][artist_wd.week_idx - start] = artist_wd.plays
out = {}
for artist in artists:
out[artist] = zip(dates, results[artist.id])
return out
