def fetch(self):
entity_id, cycle, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.external_ids
self.data['top_contributors'] = api.pol.contributors(entity_id, cycle)
self.data['top_industries'] = api.pol.industries(entity_id, cycle=cycle)
self.data['industries_unknown_amount'] = api.pol.industries_unknown(entity_id, cycle=cycle)
self.data['local_breakdown'] = api.pol.local_breakdown(entity_id, cycle)
self.data['entity_breakdown'] = api.pol.contributor_type_breakdown(entity_id, cycle)
self.partytime_link, self.partytime_data = external_sites.get_partytime_data(external_ids)
self.data['bundling'] = api.entities.bundles(entity_id, cycle)
self.fec_summary = api.pol.fec_summary(entity_id, cycle) if int(cycle) != -1 else None
if self.fec_summary:
self.data['fec_timeline'] = api.pol.fec_timeline(entity_id, cycle)
self.fec_indexp = api.pol.fec_indexp(entity_id, cycle)[:10]
return True
def fetch(self):
entity_id, cycle, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.external_ids
self.data['top_contributors'] = api.pol.contributors(entity_id, cycle)
self.data['top_industries'] = api.pol.industries(entity_id,
cycle=cycle)
self.data['industries_unknown_amount'] = api.pol.industries_unknown(
entity_id, cycle=cycle)
self.data['local_breakdown'] = api.pol.local_breakdown(
entity_id, cycle)
self.data['entity_breakdown'] = api.pol.contributor_type_breakdown(
entity_id, cycle)
self.partytime_link, self.partytime_data = external_sites.get_partytime_data(
external_ids)
self.data['bundling'] = api.entities.bundles(entity_id, cycle)
self.fec_summary = api.pol.fec_summary(
entity_id, cycle) if int(cycle) != -1 else None
if self.fec_summary:
self.data['fec_timeline'] = api.pol.fec_timeline(entity_id, cycle)
self.fec_indexp = api.pol.fec_indexp(entity_id, cycle)[:10]
return True
def pol_contribution_section(entity_id, standardized_name, cycle, amount, external_ids):
section = {
'name': 'Campaign Finance',
'template': 'contributions.html',
}
section['contributions_data'] = True
top_contributors = api.pol.contributors(entity_id, cycle)
top_industries = api.pol.industries(entity_id, cycle=cycle)
section['pct_known'] = pct_contribs_from_known_industries(entity_id, cycle, amount)
contributors_barchart_data = []
for record in top_contributors:
contributors_barchart_data.append({
'key': generate_label(str(OrganizationNameCleaver(record['name']).parse())),
'value' : record['total_amount'],
'value_employee' : record['employee_amount'],
'value_pac' : record['direct_amount'],
'href' : barchart_href(record, cycle, 'organization')
})
contributors_barchart_data = bar_validate(contributors_barchart_data)
section['contributors_barchart_data'] = json.dumps(contributors_barchart_data)
industries_barchart_data = []
for record in top_industries:
industries_barchart_data.append({
'key': generate_label(str(OrganizationNameCleaver(record['name']).parse())),
'href': barchart_href(record, cycle, 'industry'),
'value' : record['amount'],
})
industries_barchart_data = bar_validate(industries_barchart_data)
section['industries_barchart_data'] = json.dumps(industries_barchart_data)
local_breakdown = api.pol.local_breakdown(entity_id, cycle)
for key, values in local_breakdown.iteritems():
# values is a list of [count, amount]
local_breakdown[key] = float(values[1])
local_breakdown = pie_validate(local_breakdown)
section['local_breakdown'] = json.dumps(local_breakdown)
entity_breakdown = api.pol.contributor_type_breakdown(entity_id, cycle)
for key, values in entity_breakdown.iteritems():
# values is a list of [count, amount]
entity_breakdown[key] = float(values[1])
entity_breakdown = pie_validate(entity_breakdown)
section['entity_breakdown'] = json.dumps(entity_breakdown)
# if none of the charts have data, or if the aggregate total
# received was negative, then suppress that whole content
# section except the overview bar
if amount < 0:
section['suppress_contrib_graphs'] = True
section['reason'] = "negative"
elif not any((industries_barchart_data, contributors_barchart_data, local_breakdown, entity_breakdown)):
section['suppress_contrib_graphs'] = True
section['reason'] = 'empty'
partytime_link, section['partytime_data'] = external_sites.get_partytime_data(external_ids)
section['external_links'] = external_sites.get_contribution_links('politician', standardized_name.name_str(), external_ids, cycle)
if partytime_link:
section['external_links'].append({'url': partytime_link, 'text': 'Party Time'})
bundling = api.entities.bundles(entity_id, cycle)
section['bundling_data'] = [ [x[key] for key in 'lobbyist_entity lobbyist_name firm_entity firm_name amount'.split()] for x in bundling ]
if int(cycle) == LATEST_CYCLE:
section['fec_summary'] = api.pol.fec_summary(entity_id, cycle)
if section['fec_summary']:
section['include_fec'] = True
if section['fec_summary'] and 'date' in section['fec_summary']:
section['fec_summary']['clean_date'] = datetime.datetime.strptime(section['fec_summary']['date'], "%Y-%m-%d")
timelines = []
for pol in api.pol.fec_timeline(entity_id, cycle):
tl = {
'name': pol['candidate_name'],
'party': pol['party'],
'is_this': pol['entity_id'] == entity_id,
'timeline': map(lambda item: item if item >= 0 else 0, pol['timeline']),
'href': '/politician/%s/%s?cycle=%s' % (slugify(PoliticianNameCleaver(pol['candidate_name']).parse().name_str()), pol['entity_id'], cycle)
}
tl['sum'] = sum(tl['timeline'])
timelines.append(tl)
timelines.sort(key=lambda t: (int(t['is_this']), t['sum']), reverse=True)
# restrict to top 5, and only those receiving at least 10% of this pol's total
if timelines:
this_sum = timelines[0]['sum']
timelines = [timeline for timeline in timelines if timeline['sum'] > 0.1 * this_sum]
timelines = timelines[:5]
section['fec_timelines'] = json.dumps(timelines)
section['fec_indexp'] = api.pol.fec_indexp(entity_id, cycle)[:10]
return section
