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 test_zerofill(self):
start = datetime(2012, 1, 1)
end = datetime(2012, 1, 7)
data1 = {
epoch_milliseconds(datetime(2012, 1, 3)): 1,
epoch_milliseconds(datetime(2012, 1, 5)): 1,
}
data2 = {
epoch_milliseconds(datetime(2012, 1, 2)): 1,
epoch_milliseconds(datetime(2012, 1, 5)): 1,
epoch_milliseconds(datetime(2012, 1, 10)): 1,
}
zero_fill(start, end, [data1, data2])
for day in range(1, 7):
millis = epoch_milliseconds(datetime(2012, 1, day))
assert millis in data1, "Day %s was not zero filled." % day
assert millis in data2, "Day %s was not zero filled." % day
def test_zerofill(self):
start = datetime(2012, 1, 1)
end = datetime(2012, 1, 7)
data1 = {
epoch_milliseconds(datetime(2012, 1, 3)): 1,
epoch_milliseconds(datetime(2012, 1, 5)): 1,
}
data2 = {
epoch_milliseconds(datetime(2012, 1, 2)): 1,
epoch_milliseconds(datetime(2012, 1, 5)): 1,
epoch_milliseconds(datetime(2012, 1, 10)): 1,
}
zero_fill(start, end, [data1, data2])
for day in range(1, 7):
millis = epoch_milliseconds(datetime(2012, 1, day))
assert millis in data1, "Day %s was not zero filled." % day
assert millis in data2, "Day %s was not zero filled." % day
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
if search_product is not None:
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
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)
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',
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': {}
}
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['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)
)
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 product_dashboard_firefox(request, prod):
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):
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):
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])
if sum([p['count']
for p in platform_counts[key]]) < (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])
if sum([p['count']
for p in version_counts[key]]) < (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):
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
if search_product is not None:
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
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)
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',
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': {}
}
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 facet')
})
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),
})
