def test_search_ctr(self, _build_request):
"""Test googleanalytics.search_ctr()."""
execute = _build_request.return_value.get.return_value.execute
execute.return_value = SEARCH_CTR_RESPONSE
ctr = googleanalytics.search_ctr(date(2013, 6, 6), date(2013, 6, 6))
eq_(1, len(ctr))
eq_(74.88925980111263, ctr['2013-06-06'])
def test_search_ctr(self, _build_request):
"""Test googleanalytics.search_ctr()."""
execute = _build_request.return_value.get.return_value.execute
execute.return_value = SEARCH_CTR_RESPONSE
ctr = googleanalytics.search_ctr(
date(2013, 6, 6), date(2013, 6, 6))
eq_(1, len(ctr))
eq_(74.88925980111263, ctr['2013-06-06'])
def handle(self, **options):
if settings.STAGE:
# Let's be nice to GA and skip on stage.
return
# Start updating the day after the last updated.
latest_metric = utils._get_latest_metric(SEARCH_CLICKS_METRIC_CODE)
if latest_metric is not None:
latest_metric_date = latest_metric.start
else:
latest_metric_date = date(2011, 1, 1)
start = latest_metric_date + timedelta(days=1)
# Collect up until yesterday
end = date.today() - timedelta(days=1)
# Get the CTR data from Google Analytics.
ctr_data = googleanalytics.search_ctr(start, end)
# Create the metrics.
clicks_kind = MetricKind.objects.get_or_create(
code=SEARCH_CLICKS_METRIC_CODE)[0]
searches_kind = MetricKind.objects.get_or_create(
code=SEARCH_SEARCHES_METRIC_CODE)[0]
for date_str, ctr in list(ctr_data.items()):
day = datetime.strptime(date_str, "%Y-%m-%d").date()
# Note: we've been storing our search data as total number of
# searches and clicks. Google Analytics only gives us the rate,
# so I am normalizing to 1000 searches (multiplying the % by 10).
# I didn't switch everything to a rate because I don't want to
# throw away the historic data.
Metric.objects.create(kind=searches_kind,
start=day,
end=day + timedelta(days=1),
value=1000)
Metric.objects.create(
kind=clicks_kind,
start=day,
end=day + timedelta(days=1),
value=round(ctr, 1) * 10,
)
def update_search_ctr_metric():
"""Get new search CTR data from Google Analytics and save."""
if settings.STAGE:
# Let's be nice to GA and skip on stage.
return
# Start updating the day after the last updated.
latest_metric = _get_latest_metric(SEARCH_CLICKS_METRIC_CODE)
if latest_metric is not None:
latest_metric_date = latest_metric.start
else:
latest_metric_date = date(2011, 01, 01)
start = latest_metric_date + timedelta(days=1)
# Collect up until yesterday
end = date.today() - timedelta(days=1)
# Get the CTR data from Google Analytics.
ctr_data = googleanalytics.search_ctr(start, end)
# Create the metrics.
clicks_kind = MetricKind.objects.get(code=SEARCH_CLICKS_METRIC_CODE)
searches_kind = MetricKind.objects.get(code=SEARCH_SEARCHES_METRIC_CODE)
for date_str, ctr in ctr_data.items():
day = datetime.strptime(date_str, '%Y-%m-%d').date()
# Note: we've been storing our search data as total number of
# searches and clicks. Google Analytics only gives us the rate,
# so I am normalizing to 1000 searches (multiplying the % by 10).
# I didn't switch everything to a rate because I don't want to
# throw away the historic data.
Metric.objects.create(
kind=searches_kind,
start=day,
end=day + timedelta(days=1),
value=1000)
Metric.objects.create(
kind=clicks_kind,
start=day,
end=day + timedelta(days=1),
value=round(ctr, 1) * 10)
def update_search_ctr_metric():
"""Get new search CTR data from Google Analytics and save."""
# Start updating the day after the last updated.
latest_metric = _get_latest_metric(SEARCH_CLICKS_METRIC_CODE)
if latest_metric is not None:
latest_metric_date = latest_metric.start
else:
latest_metric_date = date(2011, 01, 01)
start = latest_metric_date + timedelta(days=1)
# Collect up until yesterday
end = date.today() - timedelta(days=1)
# Get the CTR data from Google Analytics.
ctr_data = googleanalytics.search_ctr(start, end)
# Create the metrics.
clicks_kind = MetricKind.objects.get(code=SEARCH_CLICKS_METRIC_CODE)
searches_kind = MetricKind.objects.get(code=SEARCH_SEARCHES_METRIC_CODE)
for date_str, ctr in ctr_data.items():
day = datetime.strptime(date_str, '%Y-%m-%d').date()
# Note: we've been storing our search data as total number of
# searches and clicks. Google Analytics only gives us the rate,
# so I am normalizing to 1000 searches (multiplying the % by 10).
# I didn't switch everything to a rate because I don't want to
# throw away the historic data.
Metric.objects.create(
kind=searches_kind,
start=day,
end=day + timedelta(days=1),
value=1000)
Metric.objects.create(
kind=clicks_kind,
start=day,
end=day + timedelta(days=1),
value=round(ctr, 1) * 10)
