def test_index_chunk_task(self):
responses = [response(save=True) for i in range(10)]
# With live indexing, that'll create items in the index. Since
# we want to test index_chunk_test, we need a clean index to
# start with so we delete and recreate it.
self.setup_indexes(empty=True)
# Verify there's nothing in the index.
eq_(len(ResponseMappingType.search()), 0)
# Create the record and the chunk and then run it through
# celery.
batch_id = 'ou812'
rec = record(batch_id=batch_id, save=True)
chunk = (ResponseMappingType, [item.id for item in responses])
index_chunk_task.delay(get_index(), batch_id, rec.id, chunk)
ResponseMappingType.refresh_index()
# Verify everything is in the index now.
eq_(len(ResponseMappingType.search()), 10)
# Verify the record was marked succeeded.
rec = Record.objects.get(pk=rec.id)
eq_(rec.status, Record.STATUS_SUCCESS)
def test_purge(self):
now = datetime.datetime.now()
cutoff = now - datetime.timedelta(days=5)
# Create 10 ResponseEmail objs--one for each day for the last
# 10 days.
for i in range(10):
ResponseEmailFactory(
opinion__created=(now - datetime.timedelta(days=i))
)
ResponseContextFactory(
opinion__created=(now - datetime.timedelta(days=i))
)
# Since creating the objects and indexing them happens very
# quickly in tests, we hit a race condition and the has_email
# column ends up being false. So instead we just drop the
# index and rebuild it.
self.setup_indexes()
# Make sure everything is in the db
eq_(Response.objects.count(), 20)
eq_(ResponseEmail.objects.count(), 10)
eq_(ResponseContext.objects.count(), 10)
# Make sure everything is in the index
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 20)
eq_(resp_s.filter(has_email=True).count(), 10)
# Now purge everything older than 5 days and make sure things
# got removed that should have gotten removed
cutoff = now - datetime.timedelta(days=5)
purge_data(cutoff=cutoff)
self.refresh()
eq_(Response.objects.count(), 20)
eq_(ResponseEmail.objects.count(), 5)
eq_(ResponseEmail.objects.filter(
opinion__created__gte=cutoff).count(),
5)
eq_(ResponseContext.objects.count(), 5)
eq_(ResponseContext.objects.filter(
opinion__created__gte=cutoff).count(),
5)
# Everything should still be in the index, but the number of
# things with has_email=True should go down
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 20)
eq_(resp_s.filter(has_email=True).count(), 5)
def analytics_hourly_histogram(request):
"""Shows an hourly histogram for the last 5 days of all responses"""
template = 'analytics/analyzer/hourly_histogram.html'
date_end = smart_date(request.GET.get('date_end', None), fallback=None)
if date_end is None:
date_end = date.today()
date_start = date_end - timedelta(days=5)
search = ResponseMappingType.search()
filters = F(created__gte=date_start, created__lte=date_end)
search.filter(filters)
hourly_histogram = search.facet_raw(
hourly={
'date_histogram': {
'interval': 'hour',
'field': 'created'
},
'facet_filter': search._process_filters(filters.filters)
}).facet_counts()
hourly_data = dict(
(p['time'], p['count']) for p in hourly_histogram['hourly'])
hour = 60 * 60 * 1000.0
zero_fill(date_start, date_end, [hourly_data], spacing=hour)
# FIXME: This is goofy. After zero_fill, we end up with a bunch of
# trailing zeros for reasons I don't really understand, so instead
# of fixing that, I'm just going to remove them here.
hourly_data = sorted(hourly_data.items())
while hourly_data and hourly_data[-1][1] == 0:
hourly_data.pop(-1)
histogram = [
{
'label': 'Hourly',
'name': 'hourly',
'data': hourly_data
},
]
return render(request, template, {
'histogram': histogram,
'start_date': date_start,
'end_date': date_end
})
def analytics_hourly_histogram(request):
"""Shows an hourly histogram for the last 5 days of all responses"""
template = 'analytics/analyzer/hourly_histogram.html'
date_end = smart_date(request.GET.get('date_end', None), fallback=None)
if date_end is None:
date_end = date.today()
date_start = date_end - timedelta(days=5)
search = ResponseMappingType.search()
filters = F(created__gte=date_start, created__lte=date_end)
search.filter(filters)
hourly_histogram = search.facet_raw(
hourly={
'date_histogram': {
'interval': 'hour',
'field': 'created'
},
'facet_filter': search._process_filters(filters.filters)
}).facet_counts()
hourly_data = dict(
(p['time'], p['count']) for p in hourly_histogram['hourly'])
hour = 60 * 60 * 1000.0
zero_fill(date_start, date_end, [hourly_data], spacing=hour)
# FIXME: This is goofy. After zero_fill, we end up with a bunch of
# trailing zeros for reasons I don't really understand, so instead
# of fixing that, I'm just going to remove them here.
hourly_data = sorted(hourly_data.items())
while hourly_data and hourly_data[-1][1] == 0:
hourly_data.pop(-1)
histogram = [
{
'label': 'Hourly',
'name': 'hourly',
'data': hourly_data
},
]
return render(request, template, {
'histogram': histogram,
'start_date': date_start,
'end_date': date_end
})
def timezone_view(request):
"""Admin view showing times and timezones in data."""
# Note: This is an admin page that gets used once in a blue moon.
# As such, I'm taking some liberties (hand-indexing the response,
# time.sleep, etc) that I would never take if it was used more
# often or was viewable by users. If these two assumptions ever
# change, then this should be rewritten.
from elasticutils.contrib.django import get_es
from fjord.feedback.models import Response, ResponseMappingType
from fjord.feedback.tests import ResponseFactory
from fjord.search.index import get_index
server_time = datetime.now()
# Create a new response.
resp = ResponseFactory.create()
resp_time = resp.created
# Index the response by hand so we know it gets to Elasticsearch. Otherwise
# it gets done by celery and we don't know how long that'll take.
doc = ResponseMappingType.extract_document(resp.id)
ResponseMappingType.index(doc, resp.id)
# Fetch the response from the db.
resp = Response.objects.get(id=resp.id)
resp2_time = resp.created
# Refresh and sleep 5 seconds as a hand-wavey way to make sure
# that Elasticsearch has had time to refresh the index.
get_es().indices.refresh(get_index())
time.sleep(5)
es_time = ResponseMappingType.search().filter(id=resp.id)[0].created
# Delete the test response we created.
resp.delete()
return render(request, 'admin/timezone_view.html', {
'server_time': server_time,
'resp_time': resp_time,
'resp2_time': resp2_time,
'es_time': es_time
})
def analytics_duplicates(request):
"""Shows all duplicate descriptions over the last n days"""
template = 'analytics/analyzer/duplicates.html'
n = 14
responses = (ResponseMappingType.search()
.filter(created__gte=datetime.now() - timedelta(days=n))
.values_dict('description', 'happy', 'created', 'locale',
'user_agent', 'id')
.order_by('created').everything())
responses = ResponseMappingType.reshape(responses)
total_count = len(responses)
response_dupes = {}
for resp in responses:
response_dupes.setdefault(resp['description'], []).append(resp)
response_dupes = [
(key, val) for key, val in response_dupes.items()
if len(val) > 1
]
# convert the dict into a list of tuples sorted by the number of
# responses per tuple largest number first
response_dupes = sorted(response_dupes, key=lambda item: len(item[1]) * -1)
# duplicate_count -> count
# i.e. "how many responses had 2 duplicates?"
summary_counts = defaultdict(int)
for desc, responses in response_dupes:
summary_counts[len(responses)] = summary_counts[len(responses)] + 1
summary_counts = sorted(summary_counts.items(), key=lambda item: item[0])
return render(request, template, {
'n': 14,
'response_dupes': response_dupes,
'render_time': datetime.now(),
'summary_counts': summary_counts,
'total_count': total_count,
})
def analytics_duplicates(request):
"""Shows all duplicate descriptions over the last n days"""
template = 'analytics/analyzer/duplicates.html'
n = 14
responses = (ResponseMappingType.search().filter(
created__gte=datetime.now() - timedelta(days=n)).values_dict(
'description', 'happy', 'created', 'locale', 'user_agent',
'id').order_by('created').everything())
responses = ResponseMappingType.reshape(responses)
total_count = len(responses)
response_dupes = {}
for resp in responses:
response_dupes.setdefault(resp['description'], []).append(resp)
response_dupes = [(key, val) for key, val in response_dupes.items()
if len(val) > 1]
# convert the dict into a list of tuples sorted by the number of
# responses per tuple largest number first
response_dupes = sorted(response_dupes, key=lambda item: len(item[1]) * -1)
# duplicate_count -> count
# i.e. "how many responses had 2 duplicates?"
summary_counts = defaultdict(int)
for desc, responses in response_dupes:
summary_counts[len(responses)] = summary_counts[len(responses)] + 1
summary_counts = sorted(summary_counts.items(), key=lambda item: item[0])
return render(
request, template, {
'n': 14,
'response_dupes': response_dupes,
'render_time': datetime.now(),
'summary_counts': summary_counts,
'total_count': total_count,
})
def analytics_search(request):
template = 'analytics/analyzer/search.html'
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_has_email = request.GET.get('has_email', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_country = request.GET.get('country', None)
search_product = request.GET.get('product', None)
search_domain = request.GET.get('domain', None)
search_api = smart_int(request.GET.get('api', None), fallback=None)
search_version = request.GET.get('version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_date(request.GET.get('date_start', None),
fallback=None)
search_date_end = smart_date(request.GET.get('date_end', None),
fallback=None)
search_bigram = request.GET.get('bigram', None)
search_source = request.GET.get('source', None)
search_campaign = request.GET.get('campaign', None)
search_organic = request.GET.get('organic', None)
selected = request.GET.get('selected', None)
filter_data = []
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
# If search has_email is '0' or '1', set it to False or True,
# respectively.
search_has_email = {'0': False, '1': True}.get(search_has_email, None)
if search_has_email in [False, True]:
f &= F(has_email=search_has_email)
current_search['has_email'] = int(search_has_email)
def unknown_to_empty(text):
"""Convert "Unknown" to "" to support old links"""
return u'' if text.lower() == u'unknown' else text
if search_platform is not None:
f &= F(platform=unknown_to_empty(search_platform))
current_search['platform'] = search_platform
if search_locale is not None:
f &= F(locale=unknown_to_empty(search_locale))
current_search['locale'] = search_locale
if search_product is not None:
f &= F(product=unknown_to_empty(search_product))
current_search['product'] = search_product
# Only show the version if there's a product.
if search_version is not None:
# Note: We only filter on version if we're filtering on
# product.
f &= F(version=unknown_to_empty(search_version))
current_search['version'] = search_version
# Only show the country if the product is Firefox OS.
if search_country is not None and search_product == 'Firefox OS':
f &= F(country=unknown_to_empty(search_country))
current_search['country'] = search_country
if search_domain is not None:
f &= F(url_domain=unknown_to_empty(search_domain))
current_search['domain'] = search_domain
if search_api is not None:
f &= F(api=search_api)
current_search['api'] = search_api
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = datetime.now()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
# Add one day, so that the search range includes the entire day.
end = search_date_end + timedelta(days=1)
# Note 'less than', not 'less than or equal', because of the added
# day above.
f &= F(created__lt=end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
current_search['q'] = search_query
search = search.query(description__sqs=search_query)
if search_bigram is not None:
f &= F(description_bigrams=search_bigram)
filter_data.append({
'display':
'Bigram',
'name':
'bigram',
'options': [{
'count': 'all',
'name': search_bigram,
'display': search_bigram,
'value': search_bigram,
'checked': True
}]
})
if search_source is not None:
f &= F(source=search_source)
current_search['source'] = search_source
if search_campaign is not None:
f &= F(campaign=search_campaign)
current_search['campaign'] = search_campaign
search_organic = {'0': False, '1': True}.get(search_organic, None)
if search_organic in [False, True]:
f &= F(organic=search_organic)
current_search['organic'] = int(search_organic)
search = search.filter(f).order_by('-created')
# If they're asking for a CSV export, then send them to the export
# screen.
if output_format == 'csv':
return _analytics_search_export(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 50
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
search_results = search.values_list('id')[start:end]
opinion_page_ids = [mem[0][0] for mem in search_results]
# We convert what we get back from ES to what's in the db so we
# can get all the information.
opinion_page = Response.objects.filter(id__in=opinion_page_ids)
# Navigation facet data
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'has_email': {},
'platform': {},
'locale': {},
'country': {},
'product': {},
'version': {},
'url_domain': {},
'api': {},
'source': {},
'campaign': {},
'organic': {},
}
facets = search.facet(*(counts.keys()),
size=1000,
filtered=bool(search._process_filters(f.filters)))
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name == 'T':
name = True
elif name == 'F':
name = False
counts[param][name] = term['count']
def empty_to_unknown(text):
return 'Unknown' if text == u'' else text
filter_data.extend([
counts_to_options(counts['happy'].items(),
name='happy',
display='Sentiment',
display_map={
True: 'Happy',
False: 'Sad'
},
value_map={
True: 1,
False: 0
},
checked=search_happy),
counts_to_options(counts['has_email'].items(),
name='has_email',
display='Has email',
display_map={
True: 'Yes',
False: 'No'
},
value_map={
True: 1,
False: 0
},
checked=search_has_email),
counts_to_options(counts['product'].items(),
name='product',
display='Product',
display_map=empty_to_unknown,
checked=search_product)
])
# Only show the version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(counts['version'].items(),
name='version',
display='Version',
display_map=empty_to_unknown,
checked=search_version))
# Only show the country if the product is Firefox OS.
if search_product == 'Firefox OS':
filter_data.append(
counts_to_options(counts['country'].items(),
name='country',
display='Country',
checked=search_country,
display_map=country_name), )
filter_data.extend([
counts_to_options(counts['platform'].items(),
name='platform',
display='Platform',
display_map=empty_to_unknown,
checked=search_platform),
counts_to_options(counts['locale'].items(),
name='locale',
display='Locale',
checked=search_locale,
display_map=locale_name),
counts_to_options(counts['url_domain'].items(),
name='domain',
display='Domain',
checked=search_domain,
display_map=empty_to_unknown),
counts_to_options(counts['api'].items(),
name='api',
display='API version',
checked=search_api,
display_map=empty_to_unknown),
counts_to_options(counts['organic'].items(),
name='organic',
display='Organic',
display_map={
True: 'Yes',
False: 'No'
},
value_map={
True: 1,
False: 0
},
checked=search_organic),
counts_to_options(counts['source'].items(),
name='source',
display='Source',
checked=search_source,
display_map=empty_to_unknown),
counts_to_options(counts['campaign'].items(),
name='campaign',
display='Campaign',
checked=search_campaign,
display_map=empty_to_unknown),
])
return render(
request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
})
def analytics_occurrences(request):
template = 'analytics/analyzer/occurrences.html'
first_facet_bi = None
first_params = {}
first_facet_total = 0
second_facet_bi = None
second_params = {}
second_facet_total = 0
if 'product' in request.GET:
form = OccurrencesComparisonForm(request.GET)
if form.is_valid():
cleaned = form.cleaned_data
# First item
first_resp_s = (ResponseMappingType.search().filter(
product=cleaned['product']).filter(locale__startswith='en'))
first_params['product'] = cleaned['product']
if cleaned['first_version']:
first_resp_s = first_resp_s.filter(
version=cleaned['first_version'])
first_params['version'] = cleaned['first_version']
if cleaned['first_start_date']:
first_resp_s = first_resp_s.filter(
created__gte=cleaned['first_start_date'])
first_params['date_start'] = cleaned['first_start_date']
if cleaned['first_end_date']:
first_resp_s = first_resp_s.filter(
created__lte=cleaned['first_end_date'])
first_params['date_end'] = cleaned['first_end_date']
if cleaned['first_search_term']:
first_resp_s = first_resp_s.query(
description__match=cleaned['first_search_term'])
first_params['q'] = cleaned['first_search_term']
if ('date_start' not in first_params
and 'date_end' not in first_params):
# FIXME - If there's no start date, then we want
# "everything" so we use a hard-coded 2013-01-01 date
# here to hack that.
#
# Better way might be to change the dashboard to allow
# for an "infinite" range, but there's no other use
# case for that and the ranges are done in the ui--not
# in the backend.
first_params['date_start'] = '2013-01-01'
first_resp_s = first_resp_s.facet('description_bigrams',
size=30,
filtered=True)
first_resp_s = first_resp_s[0:0]
first_facet_total = first_resp_s.count()
first_facet = first_resp_s.facet_counts()
first_facet_bi = first_facet['description_bigrams']
first_facet_bi = sorted(first_facet_bi,
key=lambda item: -item['count'])
if (cleaned['second_version'] or cleaned['second_search_term']
or cleaned['second_start_date']):
second_resp_s = (ResponseMappingType.search().filter(
product=cleaned['product']).filter(
locale__startswith='en'))
second_params['product'] = cleaned['product']
if cleaned['second_version']:
second_resp_s = second_resp_s.filter(
version=cleaned['second_version'])
second_params['version'] = cleaned['second_version']
if cleaned['second_start_date']:
second_resp_s = second_resp_s.filter(
created__gte=cleaned['second_start_date'])
second_params['date_start'] = cleaned['second_start_date']
if cleaned['second_end_date']:
second_resp_s = second_resp_s.filter(
created__lte=cleaned['second_end_date'])
second_params['date_end'] = cleaned['second_end_date']
if form.cleaned_data['second_search_term']:
second_resp_s = second_resp_s.query(
description__match=cleaned['second_search_term'])
second_params['q'] = cleaned['second_search_term']
if ('date_start' not in second_params
and 'date_end' not in second_params):
# FIXME - If there's no start date, then we want
# "everything" so we use a hard-coded 2013-01-01 date
# here to hack that.
#
# Better way might be to change the dashboard to allow
# for an "infinite" range, but there's no other use
# case for that and the ranges are done in the ui--not
# in the backend.
second_params['date_start'] = '2013-01-01'
# Have to do raw because we want a size > 10.
second_resp_s = second_resp_s.facet('description_bigrams',
size=30,
filtered=True)
second_resp_s = second_resp_s[0:0]
second_facet_total = second_resp_s.count()
second_facet = second_resp_s.facet_counts()
second_facet_bi = second_facet['description_bigrams']
second_facet_bi = sorted(second_facet_bi,
key=lambda item: -item['count'])
permalink = request.build_absolute_uri()
else:
permalink = ''
form = OccurrencesComparisonForm()
# FIXME - We have responses that have no product set. This ignores
# those. That's probably the right thing to do for the Occurrences Report
# but maybe not.
products = [prod for prod in ResponseMappingType.get_products() if prod]
return render(
request, template, {
'permalink': permalink,
'form': form,
'products': products,
'first_facet_bi': first_facet_bi,
'first_params': first_params,
'first_facet_total': first_facet_total,
'first_normalization': round(first_facet_total * 1.0 / 1000, 3),
'second_facet_bi': second_facet_bi,
'second_params': second_params,
'second_facet_total': second_facet_total,
'second_normalization': round(second_facet_total * 1.0 / 1000, 3),
'render_time': datetime.now(),
})
def test_purge(self):
now = datetime.datetime.now()
cutoff = now - datetime.timedelta(days=5)
# Create 10 objs of each type--one for each day for the last
# 10 days.
for i in range(10):
ResponseEmailFactory(
opinion__created=(now - datetime.timedelta(days=i))
)
ResponseContextFactory(
opinion__created=(now - datetime.timedelta(days=i))
)
ResponsePIFactory(
opinion__created=(now - datetime.timedelta(days=i))
)
# Note that this creates 30 Response objects.
# Since creating the objects and indexing them happens very
# quickly in tests, we hit a race condition and the has_email
# column ends up being false. So instead we just drop the
# index and rebuild it.
self.setup_indexes()
# Make sure everything is in the db
eq_(Response.objects.count(), 30)
eq_(ResponseEmail.objects.count(), 10)
eq_(ResponseContext.objects.count(), 10)
eq_(ResponsePI.objects.count(), 10)
# Make sure everything is in the index
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 30)
eq_(resp_s.filter(has_email=True).count(), 10)
# Now purge everything older than 5 days and make sure things
# got removed that should have gotten removed. Also check if
# there is a journal entry for the purge operation.
cutoff = now - datetime.timedelta(days=5)
purge_data(cutoff=cutoff)
self.refresh()
eq_(Response.objects.count(), 30)
eq_(ResponseEmail.objects.count(), 5)
eq_(ResponseEmail.objects.filter(
opinion__created__gte=cutoff).count(),
5)
eq_(ResponseContext.objects.count(), 5)
eq_(ResponseContext.objects.filter(
opinion__created__gte=cutoff).count(),
5)
eq_(ResponsePI.objects.count(), 5)
eq_(ResponsePI.objects.filter(
opinion__created__gte=cutoff).count(),
5)
eq_(1,
Record.objects.filter(action='purge_data').count())
expected_msg = ('feedback_responseemail: 5, '
'feedback_responsecontext: 5, '
'feedback_responsepi: 5')
eq_(expected_msg,
Record.objects.get(action='purge_data').msg)
# Everything should still be in the index, but the number of
# things with has_email=True should go down
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 30)
eq_(resp_s.filter(has_email=True).count(), 5)
def dashboard(request, template):
page = smart_int(request.GET.get('page', 1), 1)
search_happy = request.GET.get('happy', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_query = request.GET.get('q', None)
search_date_start = smart_datetime(request.GET.get('date_start', None),
fallback=None)
search_date_end = smart_datetime(request.GET.get('date_end', None),
fallback=None)
selected = request.GET.get('selected', None)
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
if search_platform:
f &= F(platform=search_platform)
current_search['platform'] = search_platform
if search_locale:
f &= F(locale=search_locale)
current_search['locale'] = search_locale
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = datetime.now()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
# Add one day, so that the search range includes the entire day.
end = search_date_end + timedelta(days=1)
# Note 'less than', not 'less than or equal', because of the added
# day above.
f &= F(created__lt=end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
fields = ['text', 'text_phrase', 'fuzzy']
query = dict(('description__%s' % f, search_query) for f in fields)
search = search.query(or_=query)
current_search['q'] = search_query
search = search.filter(f).order_by('-created')
facets = search.facet('happy', 'platform', 'locale',
filtered=bool(f.filters))
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {'happy': {}, 'platform': {}, 'locale': {}}
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name == 'T':
name = True
elif name == 'F':
name = False
counts[param][name] = term['count']
filter_data = [
counts_to_options(counts['happy'].items(), name='happy',
display=_('Sentiment'),
display_map={True: _('Happy'), False: _('Sad')},
value_map={True: 1, False: 0}, checked=search_happy),
counts_to_options(counts['platform'].items(),
name='platform', display=_('Platform'), checked=search_platform),
counts_to_options(counts['locale'].items(),
name='locale', display=_('Locale'), checked=search_locale,
display_map=locale_name)
]
# Histogram data
happy_data = []
sad_data = []
histograms = search.facet_raw(
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': (f & F(happy=True)).filters
},
sad={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': (f & F(happy=False)).filters
},
).facet_counts()
# p['time'] is number of milliseconds since the epoch. Which is
# convenient, because that is what the front end wants.
happy_data = dict((p['time'], p['count']) for p in histograms['happy'])
sad_data = dict((p['time'], p['count']) for p in histograms['sad'])
_zero_fill(search_date_start, search_date_end, [happy_data, sad_data])
histogram = [
{'label': _('Happy'), 'name': 'happy',
'data': sorted(happy_data.items())},
{'label': _('Sad'), 'name': 'sad',
'data': sorted(sad_data.items())},
]
# Pagination
if page < 1:
page = 1
page_count = 20
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
opinion_page = search[start:end]
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'histogram': histogram,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
})
def dashboard(request):
template = 'analytics/dashboard.html'
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_product = request.GET.get('product', None)
search_version = request.GET.get('version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_date(
request.GET.get('date_start', None), fallback=None)
search_date_end = smart_date(
request.GET.get('date_end', None), fallback=None)
search_bigram = request.GET.get('bigram', None)
selected = request.GET.get('selected', None)
filter_data = []
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
def unknown_to_empty(text):
"""Convert "Unknown" to "" to support old links"""
return u'' if text.lower() == u'unknown' else text
if search_platform is not None:
f &= F(platform=unknown_to_empty(search_platform))
current_search['platform'] = search_platform
if search_locale is not None:
f &= F(locale=unknown_to_empty(search_locale))
current_search['locale'] = search_locale
visible_products = [
prod.encode('utf-8')
for prod in Product.objects.public().values_list('db_name', flat=True)
]
# This covers the "unknown" product which is also visible.
visible_products.append('')
if search_product in visible_products:
f &= F(product=unknown_to_empty(search_product))
current_search['product'] = search_product
if search_version is not None:
# Note: We only filter on version if we're filtering on
# product.
f &= F(version=unknown_to_empty(search_version))
current_search['version'] = search_version
else:
f &= F(product__in=visible_products)
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = date.today()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
# If the start and end dates are inverted, switch them into proper
# chronoligcal order
search_date_start, search_date_end = sorted(
[search_date_start, search_date_end])
# Restrict the frontpage dashboard to only show the last 6 months
# of data
six_months_ago = date.today() - timedelta(days=180)
search_date_start = max(six_months_ago, search_date_start)
search_date_end = max(search_date_start, search_date_end)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
f &= F(created__lte=search_date_end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
current_search['q'] = search_query
search = search.query(description__sqs=search_query)
if search_bigram is not None:
f &= F(description_bigrams=search_bigram)
filter_data.append({
'display': _('Bigram'),
'name': 'bigram',
'options': [{
'count': 'all',
'name': search_bigram,
'display': search_bigram,
'value': search_bigram,
'checked': True
}]
})
search = search.filter(f).order_by('-created')
# If the user asked for a feed, give him/her a feed!
if output_format == 'atom':
return generate_atom_feed(request, search)
elif output_format == 'json':
return generate_json_feed(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 20
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
opinion_page = search[start:end]
# Navigation facet data
facets = search.facet(
'happy', 'platform', 'locale', 'product', 'version',
size=1000,
filtered=bool(search._process_filters(f.filters)))
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'platform': {},
'locale': {},
'product': {},
'version': {}
}
happy_sad_filter = request.GET.get('happy', None)
if happy_sad_filter:
if happy_sad_filter == '1':
counts['happy'] = {True: 0}
elif happy_sad_filter == '0':
counts['happy'] = {False: 0}
if search_platform:
counts['platform'] = {search_platform: 0}
if search_locale:
counts['locale'] = {search_locale: 0}
if search_product:
counts['product'] = {search_product: 0}
if search_version:
counts['version'] = {search_version: 0}
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name.upper() == 'T':
name = True
elif name.upper() == 'F':
name = False
counts[param][name] = term['count']
def empty_to_unknown(text):
return _('Unknown') if text == u'' else text
filter_data.extend([
counts_to_options(
counts['happy'].items(),
name='happy',
display=_('Sentiment'),
display_map={True: _('Happy'), False: _('Sad')},
value_map={True: 1, False: 0},
checked=search_happy),
counts_to_options(
counts['product'].items(),
name='product',
display=_('Product'),
display_map=empty_to_unknown,
checked=search_product)
])
# Only show the version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(
counts['version'].items(),
name='version',
display=_('Version'),
display_map=empty_to_unknown,
checked=search_version)
)
else:
filter_data.append({
'display': _('Version'),
'note': _('Select product to see version breakdown')
})
filter_data.extend(
[
counts_to_options(
counts['platform'].items(),
name='platform',
display=_('Platform'),
display_map=empty_to_unknown,
checked=search_platform),
counts_to_options(
counts['locale'].items(),
name='locale',
display=_('Locale'),
checked=search_locale,
display_map=locale_name),
]
)
# Histogram data
happy_data = []
sad_data = []
happy_f = f & F(happy=True)
sad_f = f & F(happy=False)
histograms = search.facet_raw(
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(happy_f.filters)
},
sad={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(sad_f.filters)
},
).facet_counts()
# p['time'] is number of milliseconds since the epoch. Which is
# convenient, because that is what the front end wants.
happy_data = dict((p['time'], p['count']) for p in histograms['happy'])
sad_data = dict((p['time'], p['count']) for p in histograms['sad'])
zero_fill(search_date_start, search_date_end, [happy_data, sad_data])
histogram = [
{'label': _('Happy'), 'name': 'happy',
'data': sorted(happy_data.items())},
{'label': _('Sad'), 'name': 'sad',
'data': sorted(sad_data.items())},
]
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'histogram': histogram,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
'atom_url': generate_dashboard_url(request),
})
def analytics_search(request):
template = 'analytics/analyzer/search.html'
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_has_email = request.GET.get('has_email', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_country = request.GET.get('country', None)
search_product = request.GET.get('product', None)
search_domain = request.GET.get('domain', None)
search_api = smart_int(request.GET.get('api', None), fallback=None)
search_version = request.GET.get('version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_date(
request.GET.get('date_start', None), fallback=None)
search_date_end = smart_date(
request.GET.get('date_end', None), fallback=None)
search_bigram = request.GET.get('bigram', None)
search_source = request.GET.get('source', None)
search_campaign = request.GET.get('campaign', None)
search_organic = request.GET.get('organic', None)
selected = request.GET.get('selected', None)
filter_data = []
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
# If search has_email is '0' or '1', set it to False or True,
# respectively.
search_has_email = {'0': False, '1': True}.get(search_has_email, None)
if search_has_email in [False, True]:
f &= F(has_email=search_has_email)
current_search['has_email'] = int(search_has_email)
def unknown_to_empty(text):
"""Convert "Unknown" to "" to support old links"""
return u'' if text.lower() == u'unknown' else text
if search_platform is not None:
f &= F(platform=unknown_to_empty(search_platform))
current_search['platform'] = search_platform
if search_locale is not None:
f &= F(locale=unknown_to_empty(search_locale))
current_search['locale'] = search_locale
if search_product is not None:
f &= F(product=unknown_to_empty(search_product))
current_search['product'] = search_product
# Only show the version if there's a product.
if search_version is not None:
# Note: We only filter on version if we're filtering on
# product.
f &= F(version=unknown_to_empty(search_version))
current_search['version'] = search_version
# Only show the country if the product is Firefox OS.
if search_country is not None and search_product == 'Firefox OS':
f &= F(country=unknown_to_empty(search_country))
current_search['country'] = search_country
if search_domain is not None:
f &= F(url_domain=unknown_to_empty(search_domain))
current_search['domain'] = search_domain
if search_api is not None:
f &= F(api=search_api)
current_search['api'] = search_api
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = datetime.now()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
# Add one day, so that the search range includes the entire day.
end = search_date_end + timedelta(days=1)
# Note 'less than', not 'less than or equal', because of the added
# day above.
f &= F(created__lt=end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
current_search['q'] = search_query
search = search.query(description__sqs=search_query)
if search_bigram is not None:
f &= F(description_bigrams=search_bigram)
filter_data.append({
'display': 'Bigram',
'name': 'bigram',
'options': [{
'count': 'all',
'name': search_bigram,
'display': search_bigram,
'value': search_bigram,
'checked': True
}]
})
if search_source is not None:
f &= F(source=search_source)
current_search['source'] = search_source
if search_campaign is not None:
f &= F(campaign=search_campaign)
current_search['campaign'] = search_campaign
search_organic = {'0': False, '1': True}.get(search_organic, None)
if search_organic in [False, True]:
f &= F(organic=search_organic)
current_search['organic'] = int(search_organic)
search = search.filter(f).order_by('-created')
# If they're asking for a CSV export, then send them to the export
# screen.
if output_format == 'csv':
return _analytics_search_export(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 50
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
search_results = search.values_list('id')[start:end]
opinion_page_ids = [mem[0][0] for mem in search_results]
# We convert what we get back from ES to what's in the db so we
# can get all the information.
opinion_page = Response.objects.filter(id__in=opinion_page_ids)
# Navigation facet data
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'has_email': {},
'platform': {},
'locale': {},
'country': {},
'product': {},
'version': {},
'url_domain': {},
'api': {},
'source': {},
'campaign': {},
'organic': {},
}
facets = search.facet(*(counts.keys()),
filtered=bool(search._process_filters(f.filters)),
size=25)
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name == 'T':
name = True
elif name == 'F':
name = False
counts[param][name] = term['count']
def empty_to_unknown(text):
return 'Unknown' if text == u'' else text
filter_data.extend([
counts_to_options(
counts['happy'].items(),
name='happy',
display='Sentiment',
display_map={True: 'Happy', False: 'Sad'},
value_map={True: 1, False: 0},
checked=search_happy),
counts_to_options(
counts['has_email'].items(),
name='has_email',
display='Has email',
display_map={True: 'Yes', False: 'No'},
value_map={True: 1, False: 0},
checked=search_has_email),
counts_to_options(
counts['product'].items(),
name='product',
display='Product',
display_map=empty_to_unknown,
checked=search_product)
])
# Only show the version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(
counts['version'].items(),
name='version',
display='Version',
display_map=empty_to_unknown,
checked=search_version)
)
# Only show the country if the product is Firefox OS.
if search_product == 'Firefox OS':
filter_data.append(
counts_to_options(
counts['country'].items(),
name='country',
display='Country',
checked=search_country,
display_map=country_name),
)
filter_data.extend(
[
counts_to_options(
counts['platform'].items(),
name='platform',
display='Platform',
display_map=empty_to_unknown,
checked=search_platform),
counts_to_options(
counts['locale'].items(),
name='locale',
display='Locale',
checked=search_locale,
display_map=locale_name),
counts_to_options(
counts['url_domain'].items(),
name='domain',
display='Domain',
checked=search_domain,
display_map=empty_to_unknown),
counts_to_options(
counts['api'].items(),
name='api',
display='API version',
checked=search_api,
display_map=empty_to_unknown),
counts_to_options(
counts['organic'].items(),
name='organic',
display='Organic',
display_map={True: 'Yes', False: 'No'},
value_map={True: 1, False: 0},
checked=search_organic),
counts_to_options(
counts['source'].items(),
name='source',
display='Source',
checked=search_source,
display_map=empty_to_unknown),
counts_to_options(
counts['campaign'].items(),
name='campaign',
display='Campaign',
checked=search_campaign,
display_map=empty_to_unknown),
]
)
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
})
def dashboard(request, template):
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_product = request.GET.get('product', None)
search_version = request.GET.get('browser_version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_datetime(request.GET.get('date_start', None),
fallback=None)
search_date_end = smart_datetime(request.GET.get('date_end', None),
fallback=None)
selected = request.GET.get('selected', None)
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
if search_platform:
f &= F(platform=search_platform)
current_search['platform'] = search_platform
if search_locale:
f &= F(locale=search_locale)
current_search['locale'] = search_locale
if search_product:
f &= F(product=search_product)
current_search['product'] = search_product
if search_version:
# Note: We only filter on version if we're filtering on
# product.
f &= F(browser_version=search_version)
current_search['browser_version'] = search_version
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = datetime.now()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
# Add one day, so that the search range includes the entire day.
end = search_date_end + timedelta(days=1)
# Note 'less than', not 'less than or equal', because of the added
# day above.
f &= F(created__lt=end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
current_search['q'] = search_query
es_query = generate_query_parsed('description', search_query)
search = search.query_raw(es_query)
search = search.filter(f).order_by('-created')
# If the user asked for a feed, give him/her a feed!
if output_format == 'atom':
return generate_atom_feed(request, search)
elif output_format == 'json':
return generate_json_feed(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 20
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
opinion_page = search[start:end]
# Navigation facet data
facets = search.facet(
'happy', 'platform', 'locale', 'product', 'browser_version',
filtered=bool(search._process_filters(f.filters)))
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'platform': {},
'locale': {},
'product': {},
'browser_version': {}
}
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name == 'T':
name = True
elif name == 'F':
name = False
counts[param][name] = term['count']
filter_data = [
counts_to_options(
counts['happy'].items(),
name='happy',
display=_('Sentiment'),
display_map={True: _('Happy'), False: _('Sad')},
value_map={True: 1, False: 0},
checked=search_happy),
counts_to_options(
counts['product'].items(),
name='product',
display=_('Product'),
checked=search_product)
]
# Only show the browser_version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(
counts['browser_version'].items(),
name='browser_version',
display=_('Version'),
checked=search_version)
)
filter_data.extend(
[
counts_to_options(
counts['platform'].items(),
name='platform',
display=_('Platform'),
checked=search_platform),
counts_to_options(
counts['locale'].items(),
name='locale',
display=_('Locale'),
checked=search_locale,
display_map=locale_name),
]
)
# Histogram data
happy_data = []
sad_data = []
happy_f = f & F(happy=True)
sad_f = f & F(happy=False)
histograms = search.facet_raw(
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(happy_f.filters)
},
sad={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(sad_f.filters)
},
).facet_counts()
# p['time'] is number of milliseconds since the epoch. Which is
# convenient, because that is what the front end wants.
happy_data = dict((p['time'], p['count']) for p in histograms['happy'])
sad_data = dict((p['time'], p['count']) for p in histograms['sad'])
_zero_fill(search_date_start, search_date_end, [happy_data, sad_data])
histogram = [
{'label': _('Happy'), 'name': 'happy',
'data': sorted(happy_data.items())},
{'label': _('Sad'), 'name': 'sad',
'data': sorted(sad_data.items())},
]
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'histogram': histogram,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
'atom_url': generate_dashboard_atom_url(request)
})
def product_dashboard_firefox(request, prod):
# Note: Not localized because it's ultra-alpha.
template = 'analytics/product_dashboard_firefox.html'
current_search = {}
search_query = request.GET.get('q', None)
if search_query:
current_search['q'] = search_query
search_date_end = smart_date(
request.GET.get('date_end', None), fallback=None)
if search_date_end is None:
search_date_end = date.today()
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
search_date_start = smart_date(
request.GET.get('date_start', None), fallback=None)
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
histogram = generate_totals_histogram(
search_date_start, search_date_end, search_query, prod)
# FIXME: This is lame, but we need to make sure the item we're
# looking at is the totals.
assert histogram[1]['name'] == 'total'
totals_sum = sum([p[1] for p in histogram[1]['data']])
search = ResponseMappingType.search()
if search_query:
search = search.query(description__sqs=search_query)
base_f = F()
base_f &= F(product=prod.db_name)
base_f &= F(created__gte=search_date_start)
base_f &= F(created__lt=search_date_end)
search = search.filter(base_f)
# Figure out the list of platforms and versions for this range.
plats_and_vers = (search
.facet('platform', 'version', size=50)
.facet_counts())
# Figure out the "by platform" histogram
platforms = [part['term'] for part in plats_and_vers['platform']]
platform_facet = {}
for plat in platforms:
plat_f = base_f & F(platform=plat)
platform_facet[plat if plat else 'unknown'] = {
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(plat_f.filters)
}
platform_counts = search.facet_raw(**platform_facet).facet_counts()
platforms_histogram = []
for key in platform_counts.keys():
data = dict((p['time'], p['count']) for p in platform_counts[key])
sum_counts = sum([p['count'] for p in platform_counts[key]])
if sum_counts < (totals_sum * 0.02):
# Skip platforms where the number of responses is less than
# 2% of the total.
continue
zero_fill(search_date_start, search_date_end, [data])
platforms_histogram.append({
'name': key,
'label': key,
'data': sorted(data.items()),
'lines': {'show': True, 'fill': False},
'points': {'show': True},
})
# Figure out the "by version" histogram
versions = [part['term'] for part in plats_and_vers['version']]
version_facet = {}
for vers in versions:
vers_f = base_f & F(version=vers)
version_facet['v' + vers if vers else 'unknown'] = {
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(vers_f.filters)
}
version_counts = search.facet_raw(**version_facet).facet_counts()
versions_histogram = []
for key in version_counts.keys():
data = dict((p['time'], p['count']) for p in version_counts[key])
sum_counts = sum([p['count'] for p in version_counts[key]])
if sum_counts < (totals_sum * 0.02):
# Skip versions where the number of responses is less than
# 2% of the total.
continue
zero_fill(search_date_start, search_date_end, [data])
versions_histogram.append({
'name': key,
'label': key,
'data': sorted(data.items()),
'lines': {'show': True, 'fill': False},
'points': {'show': True},
})
return render(request, template, {
'start_date': search_date_start,
'end_date': search_date_end,
'current_search': current_search,
'platforms_histogram': platforms_histogram,
'versions_histogram': versions_histogram,
'histogram': histogram,
'product': prod
})
def generate_totals_histogram(search_date_start, search_date_end,
search_query, prod):
# Note: Not localized because it's ultra-alpha.
search_date_start = search_date_start - timedelta(days=1)
search = ResponseMappingType.search()
if search_query:
search = search.query(description__sqs=search_query)
f = F()
f &= F(product=prod.db_name)
f &= F(created__gte=search_date_start)
f &= F(created__lt=search_date_end)
happy_f = f & F(happy=True)
totals_histogram = search.facet_raw(
total={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(f.filters)
},
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(happy_f.filters)
},
).facet_counts()
totals_data = dict((p['time'], p['count'])
for p in totals_histogram['total'])
zero_fill(search_date_start, search_date_end, [totals_data])
totals_data = sorted(totals_data.items())
happy_data = dict((p['time'], p['count'])
for p in totals_histogram['happy'])
zero_fill(search_date_start, search_date_end, [happy_data])
happy_data = sorted(happy_data.items())
up_deltas = []
down_deltas = []
for i, hap in enumerate(happy_data):
if i == 0:
continue
yesterday = 0
today = 0
# Figure out yesterday and today as a percent to one
# significant digit.
if happy_data[i-1][1] and totals_data[i-1][1]:
yesterday = (
int(happy_data[i-1][1] * 1.0
/ totals_data[i-1][1] * 1000)
/ 10.0
)
if happy_data[i][1] and totals_data[i][1]:
today = (
int(happy_data[i][1] * 1.0
/ totals_data[i][1] * 1000)
/ 10.0
)
if (today - yesterday) >= 0:
up_deltas.append((happy_data[i][0], today - yesterday))
else:
down_deltas.append((happy_data[i][0], today - yesterday))
# Nix the first total because it's not in our date range
totals_data = totals_data[1:]
histogram = [
{
'name': 'zero',
'data': [(totals_data[0][0], 0), (totals_data[-1][0], 0)],
'yaxis': 2,
'lines': {'show': True, 'fill': False, 'lineWidth': 1,
'shadowSize': 0},
'color': '#dddddd',
},
{
'name': 'total',
'label': 'Total # responses',
'data': totals_data,
'yaxis': 1,
'lines': {'show': True, 'fill': False},
'points': {'show': True},
'color': '#3E72BF',
},
{
'name': 'updeltas',
'label': 'Percent change in sentiment upwards',
'data': up_deltas,
'yaxis': 2,
'bars': {'show': True, 'lineWidth': 3},
'points': {'show': True},
'color': '#55E744',
},
{
'name': 'downdeltas',
'label': 'Percent change in sentiment downwards',
'data': down_deltas,
'yaxis': 2,
'bars': {'show': True, 'lineWidth': 3},
'points': {'show': True},
'color': '#E73E3E',
}
]
return histogram
def dashboard(request):
template = 'analytics/dashboard.html'
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_product = request.GET.get('product', None)
search_version = request.GET.get('version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_date(
request.GET.get('date_start', None), fallback=None)
search_date_end = smart_date(
request.GET.get('date_end', None), fallback=None)
search_bigram = request.GET.get('bigram', None)
selected = request.GET.get('selected', None)
filter_data = []
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
def unknown_to_empty(text):
"""Convert "Unknown" to "" to support old links"""
return u'' if text.lower() == u'unknown' else text
if search_platform is not None:
f &= F(platform=unknown_to_empty(search_platform))
current_search['platform'] = search_platform
if search_locale is not None:
f &= F(locale=unknown_to_empty(search_locale))
current_search['locale'] = search_locale
visible_products = [
prod.encode('utf-8')
for prod in Product.objects.public().values_list('db_name', flat=True)
]
# This covers the "unknown" product which is also visible.
visible_products.append('')
if search_product in visible_products:
f &= F(product=unknown_to_empty(search_product))
current_search['product'] = search_product
if search_version is not None:
# Note: We only filter on version if we're filtering on
# product.
f &= F(version=unknown_to_empty(search_version))
current_search['version'] = search_version
else:
f &= F(product__in=visible_products)
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = date.today()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
# If the start and end dates are inverted, switch them into proper
# chronoligcal order
search_date_start, search_date_end = sorted(
[search_date_start, search_date_end])
# Restrict the frontpage dashboard to only show the last 6 months
# of data
six_months_ago = date.today() - timedelta(days=180)
search_date_start = max(six_months_ago, search_date_start)
search_date_end = max(search_date_start, search_date_end)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
f &= F(created__lte=search_date_end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
current_search['q'] = search_query
search = search.query(description__sqs=search_query)
if search_bigram is not None:
f &= F(description_bigrams=search_bigram)
filter_data.append({
'display': _('Bigram'),
'name': 'bigram',
'options': [{
'count': 'all',
'name': search_bigram,
'display': search_bigram,
'value': search_bigram,
'checked': True
}]
})
search = search.filter(f).order_by('-created')
# If the user asked for a feed, give him/her a feed!
if output_format == 'atom':
return generate_atom_feed(request, search)
elif output_format == 'json':
return generate_json_feed(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 20
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
opinion_page = search[start:end]
# Navigation facet data
facets = search.facet(
'happy', 'platform', 'locale', 'product', 'version',
size=1000,
filtered=bool(search._process_filters(f.filters)))
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'platform': {},
'locale': {},
'product': {},
'version': {}
}
happy_sad_filter = request.GET.get('happy', None)
if happy_sad_filter:
if happy_sad_filter == '1':
counts['happy'] = {True: 0}
elif happy_sad_filter == '0':
counts['happy'] = {False: 0}
if search_platform:
counts['platform'] = {search_platform: 0}
if search_locale:
counts['locale'] = {search_locale: 0}
if search_product:
counts['product'] = {search_product: 0}
if search_version:
counts['version'] = {search_version: 0}
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name.upper() == 'T':
name = True
elif name.upper() == 'F':
name = False
counts[param][name] = term['count']
def empty_to_unknown(text):
return _('Unknown') if text == u'' else text
filter_data.extend([
counts_to_options(
counts['happy'].items(),
name='happy',
display=_('Sentiment'),
display_map={True: _('Happy'), False: _('Sad')},
value_map={True: 1, False: 0},
checked=search_happy),
counts_to_options(
counts['product'].items(),
name='product',
display=_('Product'),
display_map=empty_to_unknown,
checked=search_product)
])
# Only show the version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(
counts['version'].items(),
name='version',
display=_('Version'),
display_map=empty_to_unknown,
checked=search_version)
)
else:
filter_data.append({
'display': _('Version'),
'note': _('Select product to see version breakdown')
})
filter_data.extend(
[
counts_to_options(
counts['platform'].items(),
name='platform',
display=_('Platform'),
display_map=empty_to_unknown,
checked=search_platform),
counts_to_options(
counts['locale'].items(),
name='locale',
display=_('Locale'),
checked=search_locale,
display_map=locale_name),
]
)
# Histogram data
happy_data = []
sad_data = []
happy_f = f & F(happy=True)
sad_f = f & F(happy=False)
histograms = search.facet_raw(
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(happy_f.filters)
},
sad={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(sad_f.filters)
},
).facet_counts()
# p['time'] is number of milliseconds since the epoch. Which is
# convenient, because that is what the front end wants.
happy_data = dict((p['time'], p['count']) for p in histograms['happy'])
sad_data = dict((p['time'], p['count']) for p in histograms['sad'])
zero_fill(search_date_start, search_date_end, [happy_data, sad_data])
histogram = [
{'label': _('Happy'), 'name': 'happy',
'data': sorted(happy_data.items())},
{'label': _('Sad'), 'name': 'sad',
'data': sorted(sad_data.items())},
]
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'histogram': histogram,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
'atom_url': generate_dashboard_url(request),
})
def test_purge(self):
now = datetime.datetime.now()
cutoff = now - datetime.timedelta(days=5)
# Create 10 objs of each type--one for each day for the last
# 10 days.
for i in range(10):
ResponseEmailFactory(opinion__created=(now -
datetime.timedelta(days=i)))
ResponseContextFactory(
opinion__created=(now - datetime.timedelta(days=i)))
ResponsePIFactory(opinion__created=(now -
datetime.timedelta(days=i)))
# Note that this creates 30 Response objects.
# Since creating the objects and indexing them happens very
# quickly in tests, we hit a race condition and the has_email
# column ends up being false. So instead we just drop the
# index and rebuild it.
self.setup_indexes()
# Make sure everything is in the db
eq_(Response.objects.count(), 30)
eq_(ResponseEmail.objects.count(), 10)
eq_(ResponseContext.objects.count(), 10)
eq_(ResponsePI.objects.count(), 10)
# Make sure everything is in the index
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 30)
eq_(resp_s.filter(has_email=True).count(), 10)
# Now purge everything older than 5 days and make sure things
# got removed that should have gotten removed. Also check if
# there is a journal entry for the purge operation.
cutoff = now - datetime.timedelta(days=5)
purge_data(cutoff=cutoff)
self.refresh()
eq_(Response.objects.count(), 30)
eq_(ResponseEmail.objects.count(), 5)
eq_(
ResponseEmail.objects.filter(opinion__created__gte=cutoff).count(),
5)
eq_(ResponseContext.objects.count(), 5)
eq_(
ResponseContext.objects.filter(
opinion__created__gte=cutoff).count(), 5)
eq_(ResponsePI.objects.count(), 5)
eq_(ResponsePI.objects.filter(opinion__created__gte=cutoff).count(), 5)
eq_(1, Record.objects.filter(action='purge_data').count())
expected_msg = ('feedback_responseemail: 5, '
'feedback_responsecontext: 5, '
'feedback_responsepi: 5')
eq_(expected_msg, Record.objects.get(action='purge_data').msg)
# Everything should still be in the index, but the number of
# things with has_email=True should go down
resp_s = ResponseMappingType.search()
eq_(resp_s.count(), 30)
eq_(resp_s.filter(has_email=True).count(), 5)
def generate_totals_histogram(search_date_start, search_date_end,
search_query, prod):
# Note: Not localized because it's ultra-alpha.
search_date_start = search_date_start - timedelta(days=1)
search = ResponseMappingType.search()
if search_query:
search = search.query(description__sqs=search_query)
f = F()
f &= F(product=prod.db_name)
f &= F(created__gte=search_date_start)
f &= F(created__lt=search_date_end)
happy_f = f & F(happy=True)
totals_histogram = search.facet_raw(
total={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(f.filters)
},
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(happy_f.filters)
},
).facet_counts()
totals_data = dict((p['time'], p['count'])
for p in totals_histogram['total'])
zero_fill(search_date_start, search_date_end, [totals_data])
totals_data = sorted(totals_data.items())
happy_data = dict((p['time'], p['count'])
for p in totals_histogram['happy'])
zero_fill(search_date_start, search_date_end, [happy_data])
happy_data = sorted(happy_data.items())
up_deltas = []
down_deltas = []
for i, hap in enumerate(happy_data):
if i == 0:
continue
yesterday = 0
today = 0
# Figure out yesterday and today as a percent to one
# significant digit.
if happy_data[i-1][1] and totals_data[i-1][1]:
yesterday = (
int(happy_data[i-1][1] * 1.0
/ totals_data[i-1][1] * 1000)
/ 10.0
)
if happy_data[i][1] and totals_data[i][1]:
today = (
int(happy_data[i][1] * 1.0
/ totals_data[i][1] * 1000)
/ 10.0
)
if (today - yesterday) >= 0:
up_deltas.append((happy_data[i][0], today - yesterday))
else:
down_deltas.append((happy_data[i][0], today - yesterday))
# Nix the first total because it's not in our date range
totals_data = totals_data[1:]
histogram = [
{
'name': 'zero',
'data': [(totals_data[0][0], 0), (totals_data[-1][0], 0)],
'yaxis': 2,
'lines': {'show': True, 'fill': False, 'lineWidth': 1,
'shadowSize': 0},
'color': '#dddddd',
},
{
'name': 'total',
'label': 'Total # responses',
'data': totals_data,
'yaxis': 1,
'lines': {'show': True, 'fill': False},
'points': {'show': True},
'color': '#3E72BF',
},
{
'name': 'updeltas',
'label': 'Percent change in sentiment upwards',
'data': up_deltas,
'yaxis': 2,
'bars': {'show': True, 'lineWidth': 3},
'points': {'show': True},
'color': '#55E744',
},
{
'name': 'downdeltas',
'label': 'Percent change in sentiment downwards',
'data': down_deltas,
'yaxis': 2,
'bars': {'show': True, 'lineWidth': 3},
'points': {'show': True},
'color': '#E73E3E',
}
]
return histogram
def product_dashboard_firefox(request, prod):
# Note: Not localized because it's ultra-alpha.
template = 'analytics/product_dashboard_firefox.html'
current_search = {}
search_query = request.GET.get('q', None)
if search_query:
current_search['q'] = search_query
search_date_end = smart_date(
request.GET.get('date_end', None), fallback=None)
if search_date_end is None:
search_date_end = date.today()
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
search_date_start = smart_date(
request.GET.get('date_start', None), fallback=None)
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
histogram = generate_totals_histogram(
search_date_start, search_date_end, search_query, prod)
# FIXME: This is lame, but we need to make sure the item we're
# looking at is the totals.
assert histogram[1]['name'] == 'total'
totals_sum = sum([p[1] for p in histogram[1]['data']])
search = ResponseMappingType.search()
if search_query:
search = search.query(description__sqs=search_query)
base_f = F()
base_f &= F(product=prod.db_name)
base_f &= F(created__gte=search_date_start)
base_f &= F(created__lt=search_date_end)
search = search.filter(base_f)
# Figure out the list of platforms and versions for this range.
plats_and_vers = (search
.facet('platform', 'version', size=50)
.facet_counts())
# Figure out the "by platform" histogram
platforms = [part['term'] for part in plats_and_vers['platform']]
platform_facet = {}
for plat in platforms:
plat_f = base_f & F(platform=plat)
platform_facet[plat if plat else 'unknown'] = {
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(plat_f.filters)
}
platform_counts = search.facet_raw(**platform_facet).facet_counts()
platforms_histogram = []
for key in platform_counts.keys():
data = dict((p['time'], p['count']) for p in platform_counts[key])
sum_counts = sum([p['count'] for p in platform_counts[key]])
if sum_counts < (totals_sum * 0.02):
# Skip platforms where the number of responses is less than
# 2% of the total.
continue
zero_fill(search_date_start, search_date_end, [data])
platforms_histogram.append({
'name': key,
'label': key,
'data': sorted(data.items()),
'lines': {'show': True, 'fill': False},
'points': {'show': True},
})
# Figure out the "by version" histogram
versions = [part['term'] for part in plats_and_vers['version']]
version_facet = {}
for vers in versions:
vers_f = base_f & F(version=vers)
version_facet['v' + vers if vers else 'unknown'] = {
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': search._process_filters(vers_f.filters)
}
version_counts = search.facet_raw(**version_facet).facet_counts()
versions_histogram = []
for key in version_counts.keys():
data = dict((p['time'], p['count']) for p in version_counts[key])
sum_counts = sum([p['count'] for p in version_counts[key]])
if sum_counts < (totals_sum * 0.02):
# Skip versions where the number of responses is less than
# 2% of the total.
continue
zero_fill(search_date_start, search_date_end, [data])
versions_histogram.append({
'name': key,
'label': key,
'data': sorted(data.items()),
'lines': {'show': True, 'fill': False},
'points': {'show': True},
})
return render(request, template, {
'start_date': search_date_start,
'end_date': search_date_end,
'current_search': current_search,
'platforms_histogram': platforms_histogram,
'versions_histogram': versions_histogram,
'histogram': histogram,
'product': prod
})
def analytics_occurrences(request):
template = 'analytics/analyzer/occurrences.html'
first_facet_bi = None
first_params = {}
first_facet_total = 0
second_facet_bi = None
second_params = {}
second_facet_total = 0
if 'product' in request.GET:
form = OccurrencesComparisonForm(request.GET)
if form.is_valid():
cleaned = form.cleaned_data
# First item
first_resp_s = (ResponseMappingType.search()
.filter(product=cleaned['product'])
.filter(locale__startswith='en'))
first_params['product'] = cleaned['product']
if cleaned['first_version']:
first_resp_s = first_resp_s.filter(
version=cleaned['first_version'])
first_params['version'] = cleaned['first_version']
if cleaned['first_start_date']:
first_resp_s = first_resp_s.filter(
created__gte=cleaned['first_start_date'])
first_params['date_start'] = cleaned['first_start_date']
if cleaned['first_end_date']:
first_resp_s = first_resp_s.filter(
created__lte=cleaned['first_end_date'])
first_params['date_end'] = cleaned['first_end_date']
if cleaned['first_search_term']:
first_resp_s = first_resp_s.query(
description__match=cleaned['first_search_term'])
first_params['q'] = cleaned['first_search_term']
if ('date_start' not in first_params
and 'date_end' not in first_params):
# FIXME - If there's no start date, then we want
# "everything" so we use a hard-coded 2013-01-01 date
# here to hack that.
#
# Better way might be to change the dashboard to allow
# for an "infinite" range, but there's no other use
# case for that and the ranges are done in the ui--not
# in the backend.
first_params['date_start'] = '2013-01-01'
# Have to do raw because we want a size > 10.
first_resp_s = first_resp_s.facet('description_bigrams',
size=30, filtered=True)
first_resp_s = first_resp_s[0:0]
first_facet_total = first_resp_s.count()
first_facet = first_resp_s.facet_counts()
first_facet_bi = first_facet['description_bigrams']
first_facet_bi = sorted(
first_facet_bi, key=lambda item: -item['count'])
if (cleaned['second_version']
or cleaned['second_search_term']
or cleaned['second_start_date']):
second_resp_s = (ResponseMappingType.search()
.filter(product=cleaned['product'])
.filter(locale__startswith='en'))
second_params['product'] = cleaned['product']
if cleaned['second_version']:
second_resp_s = second_resp_s.filter(
version=cleaned['second_version'])
second_params['version'] = cleaned['second_version']
if cleaned['second_start_date']:
second_resp_s = second_resp_s.filter(
created__gte=cleaned['second_start_date'])
second_params['date_start'] = cleaned['second_start_date']
if cleaned['second_end_date']:
second_resp_s = second_resp_s.filter(
created__lte=cleaned['second_end_date'])
second_params['date_end'] = cleaned['second_end_date']
if form.cleaned_data['second_search_term']:
second_resp_s = second_resp_s.query(
description__match=cleaned['second_search_term'])
second_params['q'] = cleaned['second_search_term']
if ('date_start' not in second_params
and 'date_end' not in second_params):
# FIXME - If there's no start date, then we want
# "everything" so we use a hard-coded 2013-01-01 date
# here to hack that.
#
# Better way might be to change the dashboard to allow
# for an "infinite" range, but there's no other use
# case for that and the ranges are done in the ui--not
# in the backend.
second_params['date_start'] = '2013-01-01'
# Have to do raw because we want a size > 10.
second_resp_s = second_resp_s.facet('description_bigrams',
size=30, filtered=True)
second_resp_s = second_resp_s[0:0]
second_facet_total = second_resp_s.count()
second_facet = second_resp_s.facet_counts()
second_facet_bi = second_facet['description_bigrams']
second_facet_bi = sorted(
second_facet_bi, key=lambda item: -item['count'])
permalink = request.build_absolute_uri()
else:
permalink = ''
form = OccurrencesComparisonForm()
# FIXME - We have responses that have no product set. This ignores
# those. That's probably the right thing to do for the Occurrences Report
# but maybe not.
products = [prod for prod in ResponseMappingType.get_products() if prod]
return render(request, template, {
'permalink': permalink,
'form': form,
'products': products,
'first_facet_bi': first_facet_bi,
'first_params': first_params,
'first_facet_total': first_facet_total,
'first_normalization': round(first_facet_total * 1.0 / 1000, 3),
'second_facet_bi': second_facet_bi,
'second_params': second_params,
'second_facet_total': second_facet_total,
'second_normalization': round(second_facet_total * 1.0 / 1000, 3),
'render_time': datetime.now(),
})
def dashboard(request, template):
output_format = request.GET.get('format', None)
page = smart_int(request.GET.get('page', 1), 1)
# Note: If we add additional querystring fields, we need to add
# them to generate_dashboard_url.
search_happy = request.GET.get('happy', None)
search_platform = request.GET.get('platform', None)
search_locale = request.GET.get('locale', None)
search_product = request.GET.get('product', None)
search_version = request.GET.get('browser_version', None)
search_query = request.GET.get('q', None)
search_date_start = smart_datetime(request.GET.get('date_start', None),
fallback=None)
search_date_end = smart_datetime(request.GET.get('date_end', None),
fallback=None)
selected = request.GET.get('selected', None)
current_search = {'page': page}
search = ResponseMappingType.search()
f = F()
# If search happy is '0' or '1', set it to False or True, respectively.
search_happy = {'0': False, '1': True}.get(search_happy, None)
if search_happy in [False, True]:
f &= F(happy=search_happy)
current_search['happy'] = int(search_happy)
if search_platform:
f &= F(platform=search_platform)
current_search['platform'] = search_platform
if search_locale:
f &= F(locale=search_locale)
current_search['locale'] = search_locale
if search_product:
f &= F(product=search_product)
current_search['product'] = search_product
if search_version:
# Note: We only filter on version if we're filtering on
# product.
f &= F(browser_version=search_version)
current_search['browser_version'] = search_version
if search_date_start is None and search_date_end is None:
selected = '7d'
if search_date_end is None:
search_date_end = datetime.now()
if search_date_start is None:
search_date_start = search_date_end - timedelta(days=7)
current_search['date_end'] = search_date_end.strftime('%Y-%m-%d')
# Add one day, so that the search range includes the entire day.
end = search_date_end + timedelta(days=1)
# Note 'less than', not 'less than or equal', because of the added
# day above.
f &= F(created__lt=end)
current_search['date_start'] = search_date_start.strftime('%Y-%m-%d')
f &= F(created__gte=search_date_start)
if search_query:
fields = ['text', 'text_phrase', 'fuzzy']
query = dict(('description__%s' % f, search_query) for f in fields)
search = search.query(or_=query)
current_search['q'] = search_query
search = search.filter(f).order_by('-created')
# If the user asked for a feed, give him/her a feed!
if output_format == 'atom':
return generate_atom_feed(request, search)
elif output_format == 'json':
return generate_json_feed(request, search)
# Search results and pagination
if page < 1:
page = 1
page_count = 20
start = page_count * (page - 1)
end = start + page_count
search_count = search.count()
opinion_page = search[start:end]
# Navigation facet data
facets = search.facet(
'happy', 'platform', 'locale', 'product', 'browser_version',
filtered=bool(f.filters))
# This loop does two things. First it maps 'T' -> True and 'F' ->
# False. This is probably something EU should be doing for
# us. Second, it restructures the data into a more convenient
# form.
counts = {
'happy': {},
'platform': {},
'locale': {},
'product': {},
'browser_version': {}
}
for param, terms in facets.facet_counts().items():
for term in terms:
name = term['term']
if name == 'T':
name = True
elif name == 'F':
name = False
counts[param][name] = term['count']
filter_data = [
counts_to_options(
counts['happy'].items(),
name='happy',
display=_('Sentiment'),
display_map={True: _('Happy'), False: _('Sad')},
value_map={True: 1, False: 0},
checked=search_happy),
counts_to_options(
counts['product'].items(),
name='product',
display=_('Product'),
checked=search_product)
]
# Only show the browser_version if we're showing a specific
# product.
if search_product:
filter_data.append(
counts_to_options(
counts['browser_version'].items(),
name='browser_version',
display=_('Version'),
checked=search_version)
)
filter_data.extend(
[
counts_to_options(
counts['platform'].items(),
name='platform',
display=_('Platform'),
checked=search_platform),
counts_to_options(
counts['locale'].items(),
name='locale',
display=_('Locale'),
checked=search_locale,
display_map=locale_name),
]
)
# Histogram data
happy_data = []
sad_data = []
histograms = search.facet_raw(
happy={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': (f & F(happy=True)).filters
},
sad={
'date_histogram': {'interval': 'day', 'field': 'created'},
'facet_filter': (f & F(happy=False)).filters
},
).facet_counts()
# p['time'] is number of milliseconds since the epoch. Which is
# convenient, because that is what the front end wants.
happy_data = dict((p['time'], p['count']) for p in histograms['happy'])
sad_data = dict((p['time'], p['count']) for p in histograms['sad'])
_zero_fill(search_date_start, search_date_end, [happy_data, sad_data])
histogram = [
{'label': _('Happy'), 'name': 'happy',
'data': sorted(happy_data.items())},
{'label': _('Sad'), 'name': 'sad',
'data': sorted(sad_data.items())},
]
return render(request, template, {
'opinions': opinion_page,
'opinion_count': search_count,
'filter_data': filter_data,
'histogram': histogram,
'page': page,
'prev_page': page - 1 if start > 0 else None,
'next_page': page + 1 if end < search_count else None,
'current_search': current_search,
'selected': selected,
'atom_url': generate_dashboard_atom_url(request)
})