def make_tables(self):
start, end = promote.get_total_run(self.thing)
if not start or not end:
self.history = []
return
now = datetime.datetime.utcnow().replace(minute=0, second=0,
microsecond=0)
end = min(end, now)
cutoff = end - datetime.timedelta(days=31)
start = max(start, cutoff)
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(fullname,
start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(fullname,
start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = _clickthrough_rate(u_imps, u_clicks)
ctr = _clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = _clickthrough_rate(self.total_impressions,
self.total_clicks)
# XXX: _is_promo_preliminary correctly expects tz-aware datetimes
# because it's also used with datetimes from promo code. this hack
# relies on the fact that we're storing UTC w/o timezone info.
# TODO: remove this when traffic is correctly using timezones.
end_aware = end.replace(tzinfo=g.tz)
self.is_preliminary = _is_promo_preliminary(end_aware)
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history
def make_tables(self):
start, end = promote.get_total_run(self.thing)
if not start or not end:
self.history = []
return
cutoff = end - datetime.timedelta(days=31)
start = max(start, cutoff)
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(fullname,
start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(fullname,
start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = _clickthrough_rate(u_imps, u_clicks)
ctr = _clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = _clickthrough_rate(self.total_impressions,
self.total_clicks)
# XXX: _is_promo_preliminary correctly expects tz-aware datetimes
# because it's also used with datetimes from promo code. this hack
# relies on the fact that we're storing UTC w/o timezone info.
# TODO: remove this when traffic is correctly using timezones.
end_aware = end.replace(tzinfo=g.tz)
self.is_preliminary = _is_promo_preliminary(end_aware)
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history
def get_tables(self):
start, end = promote.get_total_run(self.thing)
if not start or not end:
self.history = []
return
cutoff = end - datetime.timedelta(days=31)
start = max(start, cutoff)
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(
fullname, start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(
fullname, start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = self.calculate_clickthrough_rate(u_imps, u_clicks)
ctr = self.calculate_clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = (
(float(self.total_clicks) / self.total_impressions) * 100.)
# the results are preliminary until 1 day after the promotion ends
now = datetime.datetime.utcnow()
self.is_preliminary = end + datetime.timedelta(days=1) > now
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history
def get_tables(self):
start, end = promote.get_total_run(self.thing)
if not start or not end:
self.history = []
return
cutoff = end - datetime.timedelta(days=31)
start = max(start, cutoff)
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(fullname,
start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(fullname,
start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = self.calculate_clickthrough_rate(u_imps, u_clicks)
ctr = self.calculate_clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = ((float(self.total_clicks) /
self.total_impressions) * 100.)
# the results are preliminary until 1 day after the promotion ends
now = datetime.datetime.utcnow()
self.is_preliminary = end + datetime.timedelta(days=1) > now
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history
def make_tables(self):
now = datetime.datetime.utcnow().replace(minute=0, second=0,
microsecond=0)
promo_start, promo_end = promote.get_total_run(self.thing)
promo_end = min(now, promo_end)
if not promo_start or not promo_end:
self.history = []
return
if self.period:
start = self.after
end = self.before
if not start and not end:
end = promo_end
start = end - self.period
elif not end:
end = start + self.period
elif not start:
start = end - self.period
if start > promo_start:
p = request.get.copy()
p.update({'after':None, 'before':start.strftime('%Y%m%d%H')})
self.prev = '%s?%s' % (request.path, urllib.urlencode(p))
else:
start = promo_start
if end < promo_end:
p = request.get.copy()
p.update({'after':end.strftime('%Y%m%d%H'), 'before':None})
self.next = '%s?%s' % (request.path, urllib.urlencode(p))
else:
end = promo_end
else:
start, end = promo_start, promo_end
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(fullname,
start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(fullname,
start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = _clickthrough_rate(u_imps, u_clicks)
ctr = _clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = _clickthrough_rate(self.total_impressions,
self.total_clicks)
# XXX: _is_promo_preliminary correctly expects tz-aware datetimes
# because it's also used with datetimes from promo code. this hack
# relies on the fact that we're storing UTC w/o timezone info.
# TODO: remove this when traffic is correctly using timezones.
end_aware = end.replace(tzinfo=g.tz)
self.is_preliminary = _is_promo_preliminary(end_aware)
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history
def make_tables(self):
now = datetime.datetime.utcnow().replace(minute=0, second=0,
microsecond=0)
promo_start, promo_end = promote.get_total_run(self.thing)
promo_end = min(now, promo_end)
if not promo_start or not promo_end:
self.history = []
return
if self.period:
start = self.after
end = self.before
if not start and not end:
end = promo_end
start = end - self.period
elif not end:
end = start + self.period
elif not start:
start = end - self.period
if start > promo_start:
p = request.get.copy()
p.update({'after':None, 'before':start.strftime('%Y%m%d%H')})
self.prev = '%s?%s' % (request.path, urllib.urlencode(p))
else:
start = promo_start
if end < promo_end:
p = request.get.copy()
p.update({'after':end.strftime('%Y%m%d%H'), 'before':None})
self.next = '%s?%s' % (request.path, urllib.urlencode(p))
else:
end = promo_end
else:
start, end = promo_start, promo_end
fullname = self.thing._fullname
imps = traffic.AdImpressionsByCodename.promotion_history(fullname,
start, end)
clicks = traffic.ClickthroughsByCodename.promotion_history(fullname,
start, end)
# promotion might have no clicks, zip_timeseries needs valid columns
if imps and not clicks:
clicks = [(imps[0][0], (0, 0))]
history = traffic.zip_timeseries(imps, clicks, order="ascending")
computed_history = []
self.total_impressions, self.total_clicks = 0, 0
for date, data in history:
u_imps, imps, u_clicks, clicks = data
u_ctr = _clickthrough_rate(u_imps, u_clicks)
ctr = _clickthrough_rate(imps, clicks)
self.total_impressions += imps
self.total_clicks += clicks
computed_history.append((date, data + (u_ctr, ctr)))
self.history = computed_history
if self.total_impressions > 0:
self.total_ctr = _clickthrough_rate(self.total_impressions,
self.total_clicks)
# XXX: _is_promo_preliminary correctly expects tz-aware datetimes
# because it's also used with datetimes from promo code. this hack
# relies on the fact that we're storing UTC w/o timezone info.
# TODO: remove this when traffic is correctly using timezones.
end_aware = end.replace(tzinfo=g.tz)
self.is_preliminary = _is_promo_preliminary(end_aware)
# we should only graph a sane number of data points (not everything)
self.max_points = traffic.points_for_interval("hour")
return computed_history