def apply_filters(self, request, applicable_filters):
"""Implement advanced filters.
- Implement 'groups' filter.
- Implement 'languages' filter.
- Implement 'skills' filter.
"""
if (request.GET.get('restricted', False)
and 'email__text' not in applicable_filters
and len(applicable_filters) != 1):
raise ImmediateHttpResponse(response=http.HttpForbidden())
if request.GET.get('restricted', False):
applicable_filters.append(F(allows_community_sites=True))
mega_filter = F()
for filter in applicable_filters:
mega_filter &= filter
return S(UserProfile).filter(mega_filter)
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 test_filter_not(self):
eq_(len(S(FakeDjangoMappingType).filter(~F(tag='awesome'))), 2)
eq_(
len(
S(FakeDjangoMappingType).filter(~(F(tag='boring')
| F(tag='boat')))), 3)
eq_(
len(
S(FakeDjangoMappingType).filter(~F(tag='boat')).filter(~F(
foo='bar'))), 3)
eq_(len(S(FakeDjangoMappingType).filter(~F(tag='boat', foo='barf'))),
5)
def from_search(cls, cat=None, region=None, gaia=False):
filters = dict(type=amo.ADDON_WEBAPP,
status=amo.STATUS_PUBLIC,
is_disabled=False)
if cat:
filters.update(category=cat.id)
srch = S(cls).query(**filters)
if region:
excluded = cls.get_excluded_in(region)
if excluded:
srch = srch.filter(~F(id__in=excluded))
if waffle.switch_is_active('disabled-payments') or not gaia:
srch = srch.filter(premium_type__in=amo.ADDON_FREES, price=0)
return srch
def queryset(self):
res = SearchSuggestionsAjax.queryset(self)
if self.category:
res = res.filter(category__in=[self.category])
if waffle.switch_is_active('disabled-payments'):
res = res.filter(premium_type__in=amo.ADDON_FREES, price=0)
region = getattr(self.request, 'REGION', mkt.regions.WORLDWIDE)
if region:
excluded = Webapp.get_excluded_in(region)
if excluded:
if isinstance(res, S):
# ES? Do fanciness.
return res.filter(~F(id__in=excluded))
else:
# Django ORM? Do an `exclude`.
return res.exclude(id__in=excluded)
if getattr(self.request, 'MOBILE', False):
res = res.filter(device=amo.DEVICE_MOBILE.id)
return res
def test_filter_complicated(self):
eq_(
len(
S(FakeDjangoMappingType).filter(
F(tag='awesome', foo='bar') | F(tag='boring'))), 2)
def test_filter_or(self):
eq_(
len(
S(FakeDjangoMappingType).filter(
F(tag='awesome') | F(tag='boat'))), 4)
def test_filter(self):
eq_(len(S(FakeDjangoMappingType).filter(tag='awesome')), 3)
eq_(len(S(FakeDjangoMappingType).filter(F(tag='awesome'))), 3)
def test_filter_empty_f(self):
eq_(len(S(FakeDjangoMappingType).filter(F() | F(tag='awesome'))), 3)
eq_(len(S(FakeDjangoMappingType).filter(F() & F(tag='awesome'))), 3)
eq_(len(S(FakeDjangoMappingType).filter(F() | F() | F(tag='awesome'))),
3)
eq_(len(S(FakeDjangoMappingType).filter(F() & F() & F(tag='awesome'))),
3)
eq_(len(S(FakeDjangoMappingType).filter(F())), 5)
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)
})
def get(self, request):
"""Returns JSON feed of first 10000 results
This feels like a duplication of the front-page dashboard search
logic, but it's separate which allows us to handle multiple
values.
"""
search = models.ResponseMappingType.search()
f = F()
if 'happy' in request.GET:
happy = {'0': False, '1': True}.get(request.GET['happy'], None)
if happy is not None:
f &= F(happy=happy)
if 'platforms' in request.GET:
platforms = request.GET['platforms'].split(',')
if platforms:
f &= F(platform__in=platforms)
if 'locales' in request.GET:
locales = request.GET['locales'].split(',')
if locales:
f &= F(locale__in=locales)
if 'products' in request.GET:
products = request.GET['products'].split(',')
if products:
f &= F(product__in=products)
if 'versions' in request.GET:
versions = request.GET['versions'].split(',')
if versions:
f &= F(version__in=versions)
date_start = smart_date(request.GET.get('date_start', None))
date_end = smart_date(request.GET.get('date_end', None))
delta = smart_timedelta(request.GET.get('date_delta', None))
if delta is not None:
if date_end is not None:
date_start = date_end - delta
elif date_start is not None:
date_end = date_start + delta
else:
date_end = date.today()
date_start = date_end - delta
# We restrict public API access to the last 6 months.
six_months_ago = date.today() - timedelta(days=180)
if date_start:
date_start = max(six_months_ago, date_start)
f &= F(created__gte=date_start)
if date_end:
date_end = max(six_months_ago, date_end)
f &= F(created__lte=date_end)
search = search.filter(f)
search_query = request.GET.get('q', None)
if search_query is not None:
search = search.query(description__sqs=search_query)
# FIXME: Probably want to make this specifyable
search = search.order_by('-created')
# Explicitly include only publicly visible fields
search = search.values_dict(*models.ResponseMappingType.public_fields())
maximum = smart_int(request.GET.get('max', None))
maximum = maximum or 1000
maximum = min(max(1, maximum), 10000)
responses = models.ResponseMappingType.reshape(search[:maximum])
return rest_framework.response.Response({
'count': len(responses),
'results': list(responses)
})
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_filter_bad_field_action(self):
with self.assertRaises(InvalidFieldActionError):
len(S(FakeDjangoMappingType).filter(F(tag__faux='awesome')))
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 test_filter_and(self):
eq_(len(S(FakeDjangoMappingType).filter(tag='awesome', foo='bar')), 1)
eq_(len(S(FakeDjangoMappingType).filter(tag='awesome').filter(foo='bar')), 1)
eq_(len(S(FakeDjangoMappingType).filter(F(tag='awesome') & F(foo='bar'))), 1)