def indiv_contribution_section(entity_id, standardized_name, cycle, amount, external_ids):
section = {
'name': 'Campaign Finance',
'template': 'contributions.html',
}
section['contributions_data'] = True
recipient_candidates = api.indiv.pol_recipients(entity_id, cycle)
recipient_orgs = api.indiv.org_recipients(entity_id, cycle)
candidates_barchart_data = []
for record in recipient_candidates:
candidates_barchart_data.append({
'key': generate_label(str(PoliticianNameCleaver(record['recipient_name']).parse().plus_metadata(record['party'], record['state']))),
'value' : record['amount'],
'href' : barchart_href(record, cycle, entity_type="politician"),
})
section['candidates_barchart_data'] = json.dumps(bar_validate(candidates_barchart_data))
orgs_barchart_data = []
for record in recipient_orgs:
orgs_barchart_data.append({
'key': generate_label(str(OrganizationNameCleaver(record['recipient_name']).parse())),
'value' : record['amount'],
'href' : barchart_href(record, cycle, entity_type="organization"),
})
section['orgs_barchart_data'] = json.dumps(bar_validate(orgs_barchart_data))
party_breakdown = api.indiv.party_breakdown(entity_id, cycle)
for key, values in party_breakdown.iteritems():
party_breakdown[key] = float(values[1])
section['party_breakdown'] = json.dumps(pie_validate(party_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 section['candidates_barchart_data']
and not section['orgs_barchart_data']
and not section['party_breakdown']):
section['suppress_contrib_graphs'] = True
section['reason'] = 'empty'
section['external_links'] = external_sites.get_contribution_links('individual', standardized_name, external_ids, cycle)
bundling = api.entities.bundles(entity_id, cycle)
section['bundling_data'] = [ [x[key] for key in 'recipient_entity recipient_name recipient_type firm_entity firm_name amount'.split()] for x in bundling ]
return section
def build_section_data(self):
entity_id, cycle, standardized_name, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.standardized_name, self.entity.external_ids
self.contributions_data = True
candidates_barchart_data = []
for record in self.data['recipient_candidates']:
candidates_barchart_data.append({
'key': generate_label(str(PoliticianNameCleaver(record['recipient_name']).parse().plus_metadata(record['party'], record['state']))),
'value' : record['amount'],
'href' : barchart_href(record, cycle, entity_type="politician"),
})
self.candidates_barchart_data = json.dumps(bar_validate(candidates_barchart_data))
orgs_barchart_data = []
for record in self.data['recipient_orgs']:
orgs_barchart_data.append({
'key': generate_label(str(OrganizationNameCleaver(record['recipient_name']).parse())),
'value' : record['amount'],
'href' : barchart_href(record, cycle, entity_type="organization"),
})
self.orgs_barchart_data = json.dumps(bar_validate(orgs_barchart_data))
for key, values in self.data['party_breakdown'].iteritems():
self.data['party_breakdown'][key] = float(values[1])
self.party_breakdown = json.dumps(pie_validate(self.data['party_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
amount = int(float(self.entity.metadata['entity_info']['totals']['contributor_amount']))
if amount < 0:
self.suppress_contrib_graphs = True
self.reason = "negative"
elif (not self.candidates_barchart_data
and not self.orgs_barchart_data
and not self.party_breakdown):
self.suppress_contrib_graphs = True
self.reason = 'empty'
self.external_links = external_sites.get_contribution_links('individual', standardized_name, external_ids, cycle)
self.bundling_data = [[x[key] for key in 'recipient_entity recipient_name recipient_type firm_entity firm_name amount'.split()] for x in self.data['bundling']]
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
def org_contribution_section(entity_id, standardized_name, cycle, amount, type, external_ids):
section = {
'name': 'Campaign Finance',
'template': 'contributions.html',
}
if type == 'industry':
section['top_orgs'] = json.dumps([
{
'key': generate_label(str(OrganizationNameCleaver(org['name']).parse())),
'value': org['total_amount'],
'value_employee': org['employee_amount'],
'value_pac': org['direct_amount'],
'href' : barchart_href(org, cycle, 'organization')
} for org in api.org.industry_orgs(entity_id, cycle, limit=10)
])
section['contributions_data'] = True
recipients = api.org.recipients(entity_id, cycle=cycle)
recipient_pacs = api.org.pac_recipients(entity_id, cycle)
pol_recipients_barchart_data = []
for record in recipients:
pol_recipients_barchart_data.append({
'key': generate_label(str(PoliticianNameCleaver(record['name']).parse().plus_metadata(record['party'], record['state']))),
'value' : record['total_amount'],
'value_employee' : record['employee_amount'],
'value_pac' : record['direct_amount'],
'href' : barchart_href(record, cycle, entity_type='politician')
})
section['pol_recipients_barchart_data'] = json.dumps(bar_validate(pol_recipients_barchart_data))
pacs_barchart_data = []
for record in recipient_pacs:
pacs_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, entity_type="organization"),
})
section['pacs_barchart_data'] = json.dumps(bar_validate(pacs_barchart_data))
party_breakdown = api.org.party_breakdown(entity_id, cycle)
for key, values in party_breakdown.iteritems():
party_breakdown[key] = float(values[1])
section['party_breakdown'] = json.dumps(pie_validate(party_breakdown))
level_breakdown = api.org.level_breakdown(entity_id, cycle)
for key, values in level_breakdown.iteritems():
level_breakdown[key] = float(values[1])
section['level_breakdown'] = json.dumps(pie_validate(level_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
if amount < 0:
section['reason'] = "negative"
elif (not section['pol_recipients_barchart_data']
and not section['party_breakdown']
and not section['level_breakdown']
and not section['pacs_barchart_data']):
section['suppress_contrib_graphs'] = True
section['reason'] = 'empty'
section['external_links'] = external_sites.get_contribution_links(type, standardized_name, external_ids, cycle)
bundling = api.entities.bundles(entity_id, cycle)
section['bundling_data'] = [ [x[key] for key in 'recipient_entity recipient_name recipient_type lobbyist_entity lobbyist_name firm_name amount'.split()] for x in bundling ]
if int(cycle) == LATEST_CYCLE:
section['fec_summary'] = api.org.fec_summary(entity_id)
if section['fec_summary']:
section['fec_summary']['clean_date'] = datetime.datetime.strptime(section['fec_summary']['date'], "%Y-%m-%d")
section['fec_top_contribs_data'] = json.dumps([dict(key=generate_label(row['contributor_name'], 27), value=row['amount'], href='')
for row in api.org.fec_top_contribs(entity_id)])
section['fec_indexp'] = api.org.fec_indexp(entity_id)[:10]
if section['fec_summary'] or section['fec_indexp']:
section['include_fec'] = True
return section
def build_section_data(self):
entity_id, cycle, type, standardized_name, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.type, self.entity.standardized_name, self.entity.external_ids
amount = int(float(self.entity.metadata['entity_info']['totals']['contributor_amount']))
if type == 'industry':
self.top_orgs = json.dumps([
{
'key': generate_label(str(OrganizationNameCleaver(org['name']).parse())),
'value': org['total_amount'],
'value_employee': org['employee_amount'],
'value_pac': org['direct_amount'],
'href' : barchart_href(org, cycle, 'organization')
} for org in self.data['industry_orgs']
])
self.contributions_data = True
pol_recipients_barchart_data = []
for record in self.data['recipients']:
pol_recipients_barchart_data.append({
'key': generate_label(str(PoliticianNameCleaver(record['name']).parse().plus_metadata(record['party'], record['state']))),
'value' : record['total_amount'],
'value_employee' : record['employee_amount'],
'value_pac' : record['direct_amount'],
'href' : barchart_href(record, cycle, entity_type='politician')
})
self.pol_recipients_barchart_data = json.dumps(bar_validate(pol_recipients_barchart_data))
pacs_barchart_data = []
for record in self.data['recipient_pacs']:
pacs_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, entity_type="organization"),
})
self.pacs_barchart_data = json.dumps(bar_validate(pacs_barchart_data))
for key, values in self.data['party_breakdown'].iteritems():
self.data['party_breakdown'][key] = float(values[1])
self.party_breakdown = json.dumps(pie_validate(self.data['party_breakdown']))
for key, values in self.data['level_breakdown'].iteritems():
self.data['level_breakdown'][key] = float(values[1])
self.level_breakdown = json.dumps(pie_validate(self.data['level_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:
self.suppress_contrib_graphs = True
if amount < 0:
self.reason = "negative"
elif (not self.pol_recipients_barchart_data
and not self.party_breakdown
and not self.level_breakdown
and not self.pacs_barchart_data):
self.suppress_contrib_graphs = True
self.reason = 'empty'
self.external_links = external_sites.get_contribution_links(type, standardized_name, external_ids, cycle)
self.bundling_data = [[x[key] for key in 'recipient_entity recipient_name recipient_type lobbyist_entity lobbyist_name firm_name amount'.split()] for x in self.data['bundling']]
if int(cycle) != -1:
self.fec_indexp = self.data['fec_indexp']
if self.data['fec_summary'] and self.data['fec_summary']['num_committee_filings'] > 0 and self.data['fec_summary'].get('first_filing_date'):
self.fec_summary = self.data['fec_summary']
self.fec_summary['clean_date'] = datetime.datetime.strptime(self.fec_summary['first_filing_date'], "%Y-%m-%d")
top_contribs_data = [dict(key=generate_label(row['contributor_name'] if row['contributor_name'] else '<Name Missing>', 27),
value=row['amount'], href='')
for row in self.data['fec_top_contribs']
if float(row['amount']) >= 100000]
if top_contribs_data:
self.fec_top_contribs_data = json.dumps(top_contribs_data)
if getattr(self, 'fec_indexp', False) or getattr(self, 'fec_summary', False):
self.include_fec = True
def build_section_data(self):
entity_id, standardized_name, cycle, external_ids = self.entity.entity_id, self.entity.standardized_name, self.entity.cycle, self.entity.external_ids
self.contributions_data = True
contributors_barchart_data = []
for record in self.data['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)
self.contributors_barchart_data = json.dumps(contributors_barchart_data)
industries_barchart_data = []
for record in self.data['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)
self.industries_barchart_data = json.dumps(industries_barchart_data)
for key, values in self.data['local_breakdown'].iteritems():
# values is a list of [count, amount]
self.data['local_breakdown'][key] = float(values[1])
self.data['local_breakdown'] = pie_validate(self.data['local_breakdown'])
self.local_breakdown = json.dumps(self.data['local_breakdown'])
for key, values in self.data['entity_breakdown'].iteritems():
# values is a list of [count, amount]
self.data['entity_breakdown'][key] = float(values[1])
self.data['entity_breakdown'] = pie_validate(self.data['entity_breakdown'])
self.entity_breakdown = json.dumps(self.data['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
amount = int(float(self.entity.metadata['entity_info']['totals']['recipient_amount']))
if amount < 0:
self.suppress_contrib_graphs = True
self.reason = "negative"
elif not any((industries_barchart_data, contributors_barchart_data, self.data['local_breakdown'], self.data['entity_breakdown'])):
self.suppress_contrib_graphs = True
self.reason = 'empty'
pct_unknown = 0
if amount:
pct_unknown = float(self.data['industries_unknown_amount'].get('amount', 0)) * 100 / amount
self.pct_known = int(round(100 - pct_unknown))
self.external_links = external_sites.get_contribution_links('politician', standardized_name.name_str(), external_ids, cycle)
if self.partytime_link:
self.external_links.append({'url': self.partytime_link, 'text': 'Party Time'})
self.bundling_data = [[x[key] for key in 'lobbyist_entity lobbyist_name firm_entity firm_name amount'.split()] for x in self.data['bundling']]
if self.fec_summary:
self.include_fec = True
if self.fec_summary and 'date' in self.fec_summary:
self.fec_summary['clean_date'] = datetime.datetime.strptime(self.fec_summary['date'], "%Y-%m-%d")
timelines = []
for pol in self.data['fec_timeline']:
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]
self.fec_timelines = json.dumps(timelines)
def build_section_data(self):
entity_id, cycle, standardized_name, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.standardized_name, self.entity.external_ids
self.contributions_data = True
candidates_barchart_data = []
for record in self.data['recipient_candidates']:
candidates_barchart_data.append({
'key':
generate_label(
str(
PoliticianNameCleaver(
record['recipient_name']).parse().plus_metadata(
record['party'], record['state']))),
'value':
record['amount'],
'href':
barchart_href(record, cycle, entity_type="politician"),
})
self.candidates_barchart_data = json.dumps(
bar_validate(candidates_barchart_data))
orgs_barchart_data = []
for record in self.data['recipient_orgs']:
orgs_barchart_data.append({
'key':
generate_label(
str(
OrganizationNameCleaver(
record['recipient_name']).parse())),
'value':
record['amount'],
'href':
barchart_href(record, cycle, entity_type="organization"),
})
self.orgs_barchart_data = json.dumps(bar_validate(orgs_barchart_data))
for key, values in self.data['party_breakdown'].iteritems():
self.data['party_breakdown'][key] = float(values[1])
self.party_breakdown = json.dumps(
pie_validate(self.data['party_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
amount = int(
float(self.entity.metadata['entity_info']['totals']
['contributor_amount']))
if amount < 0:
self.suppress_contrib_graphs = True
self.reason = "negative"
elif (not self.candidates_barchart_data and not self.orgs_barchart_data
and not self.party_breakdown):
self.suppress_contrib_graphs = True
self.reason = 'empty'
self.external_links = external_sites.get_contribution_links(
'individual', standardized_name, external_ids, cycle)
self.bundling_data = [[
x[key] for key in
'recipient_entity recipient_name recipient_type firm_entity firm_name amount'
.split()
] for x in self.data['bundling']]
def build_section_data(self):
entity_id, cycle, type, standardized_name, external_ids = self.entity.entity_id, self.entity.cycle, self.entity.type, self.entity.standardized_name, self.entity.external_ids
amount = int(
float(self.entity.metadata['entity_info']['totals']
['contributor_amount']))
if type == 'industry':
self.top_orgs = json.dumps([{
'key':
generate_label(
str(OrganizationNameCleaver(org['name']).parse())),
'value':
org['total_amount'],
'value_employee':
org['employee_amount'],
'value_pac':
org['direct_amount'],
'href':
barchart_href(org, cycle, 'organization')
} for org in self.data['industry_orgs']])
self.contributions_data = True
pol_recipients_barchart_data = []
for record in self.data['recipients']:
pol_recipients_barchart_data.append({
'key':
generate_label(
str(
PoliticianNameCleaver(
record['name']).parse().plus_metadata(
record['party'], record['state']))),
'value':
record['total_amount'],
'value_employee':
record['employee_amount'],
'value_pac':
record['direct_amount'],
'href':
barchart_href(record, cycle, entity_type='politician')
})
self.pol_recipients_barchart_data = json.dumps(
bar_validate(pol_recipients_barchart_data))
pacs_barchart_data = []
for record in self.data['recipient_pacs']:
pacs_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, entity_type="organization"),
})
self.pacs_barchart_data = json.dumps(bar_validate(pacs_barchart_data))
for key, values in self.data['party_breakdown'].iteritems():
self.data['party_breakdown'][key] = float(values[1])
self.party_breakdown = json.dumps(
pie_validate(self.data['party_breakdown']))
for key, values in self.data['level_breakdown'].iteritems():
self.data['level_breakdown'][key] = float(values[1])
self.level_breakdown = json.dumps(
pie_validate(self.data['level_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:
self.suppress_contrib_graphs = True
if amount < 0:
self.reason = "negative"
elif (not self.pol_recipients_barchart_data
and not self.party_breakdown and not self.level_breakdown
and not self.pacs_barchart_data):
self.suppress_contrib_graphs = True
self.reason = 'empty'
self.external_links = external_sites.get_contribution_links(
type, standardized_name, external_ids, cycle)
self.bundling_data = [[
x[key] for key in
'recipient_entity recipient_name recipient_type lobbyist_entity lobbyist_name firm_name amount'
.split()
] for x in self.data['bundling']]
if int(cycle) != -1:
self.fec_indexp = self.data['fec_indexp']
if self.data['fec_summary'] and self.data['fec_summary'][
'num_committee_filings'] > 0 and self.data[
'fec_summary'].get('first_filing_date'):
self.fec_summary = self.data['fec_summary']
self.fec_summary['clean_date'] = datetime.datetime.strptime(
self.fec_summary['first_filing_date'], "%Y-%m-%d")
top_contribs_data = [
dict(key=generate_label(
row['contributor_name']
if row['contributor_name'] else '<Name Missing>', 27),
value=row['amount'],
href='') for row in self.data['fec_top_contribs']
if float(row['amount']) >= 100000
]
if top_contribs_data:
self.fec_top_contribs_data = json.dumps(top_contribs_data)
if getattr(self, 'fec_indexp', False) or getattr(
self, 'fec_summary', False):
self.include_fec = True
def build_section_data(self):
entity_id, standardized_name, cycle, external_ids = self.entity.entity_id, self.entity.standardized_name, self.entity.cycle, self.entity.external_ids
self.contributions_data = True
contributors_barchart_data = []
for record in self.data['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)
self.contributors_barchart_data = json.dumps(
contributors_barchart_data)
industries_barchart_data = []
for record in self.data['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)
self.industries_barchart_data = json.dumps(industries_barchart_data)
for key, values in self.data['local_breakdown'].iteritems():
# values is a list of [count, amount]
self.data['local_breakdown'][key] = float(values[1])
self.data['local_breakdown'] = pie_validate(
self.data['local_breakdown'])
self.local_breakdown = json.dumps(self.data['local_breakdown'])
for key, values in self.data['entity_breakdown'].iteritems():
# values is a list of [count, amount]
self.data['entity_breakdown'][key] = float(values[1])
self.data['entity_breakdown'] = pie_validate(
self.data['entity_breakdown'])
self.entity_breakdown = json.dumps(self.data['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
amount = int(
float(self.entity.metadata['entity_info']['totals']
['recipient_amount']))
if amount < 0:
self.suppress_contrib_graphs = True
self.reason = "negative"
elif not any(
(industries_barchart_data, contributors_barchart_data,
self.data['local_breakdown'], self.data['entity_breakdown'])):
self.suppress_contrib_graphs = True
self.reason = 'empty'
pct_unknown = 0
if amount:
pct_unknown = float(self.data['industries_unknown_amount'].get(
'amount', 0)) * 100 / amount
self.pct_known = int(round(100 - pct_unknown))
self.external_links = external_sites.get_contribution_links(
'politician', standardized_name.name_str(), external_ids, cycle)
if self.partytime_link:
self.external_links.append({
'url': self.partytime_link,
'text': 'Party Time'
})
self.bundling_data = [[
x[key] for key in
'lobbyist_entity lobbyist_name firm_entity firm_name amount'.split(
)
] for x in self.data['bundling']]
if self.fec_summary:
self.include_fec = True
if self.fec_summary and 'date' in self.fec_summary:
self.fec_summary['clean_date'] = datetime.datetime.strptime(
self.fec_summary['date'], "%Y-%m-%d")
timelines = []
for pol in self.data['fec_timeline']:
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]
self.fec_timelines = json.dumps(timelines)