def person_cosponsors(request, pk):
# Load the cosponsors.
from bill.models import Cosponsor
person = get_object_or_404(Person, pk=pk)
cosponsors = Cosponsor.objects.filter(bill__sponsor=person, withdrawn=None)\
.prefetch_related("bill", "bill__terms", "person", "person__roles")
# Pre-fetch all of the top-terms.
from bill.models import BillTerm
top_terms = set(BillTerm.get_top_term_ids())
# Aggregate.
total = 0
from collections import defaultdict
ret = defaultdict(lambda: {
"total": 0,
"by_issue": defaultdict(lambda: 0),
})
for cosp in cosponsors:
total += 1
ret[cosp.person]["total"] += 1
for t in cosp.bill.terms.all():
if t.id in top_terms:
ret[cosp.person]["by_issue"][t] += 1
if "first_date" not in ret[
cosp.person] or cosp.joined < ret[cosp.person]["first_date"]:
ret[cosp.person]["first_date"] = cosp.joined
if "last_date" not in ret[
cosp.person] or cosp.joined > ret[cosp.person]["last_date"]:
ret[cosp.person]["last_date"] = cosp.joined
# Sort.
for info in ret.values():
info['by_issue'] = sorted(info['by_issue'].items(),
key=lambda kv: kv[1],
reverse=True)
ret = sorted(ret.items(),
key=lambda kv:
(kv[1]['total'], kv[1]['last_date'], kv[0].sortname),
reverse=True)
# Total bills, date range.
from bill.models import Bill
total_bills = Bill.objects.filter(sponsor=person).count()
date_range = (None, None)
if len(ret) > 0:
date_range = (min(r["first_date"] for p, r in ret),
max(r["last_date"] for p, r in ret))
return {
"person": person,
"cosponsors": ret,
"total": total,
"total_bills": total_bills,
"date_range": date_range,
}
def person_cosponsors(request, pk):
# Load the cosponsors.
from bill.models import Cosponsor
person = get_object_or_404(Person, pk=pk)
cosponsors = Cosponsor.objects.filter(bill__sponsor=person, withdrawn=None)\
.prefetch_related("bill", "bill__terms", "person", "person__roles")
# Pre-fetch all of the top-terms.
from bill.models import BillTerm
top_terms = set(BillTerm.get_top_term_ids())
# Aggregate.
total = 0
from collections import defaultdict
ret = defaultdict(lambda : {
"total": 0,
"by_issue": defaultdict(lambda : 0),
})
for cosp in cosponsors:
total += 1
ret[cosp.person]["total"] += 1
for t in cosp.bill.terms.all():
if t.id in top_terms:
ret[cosp.person]["by_issue"][t] += 1
if "first_date" not in ret[cosp.person] or cosp.joined < ret[cosp.person]["first_date"]: ret[cosp.person]["first_date"] = cosp.joined
if "last_date" not in ret[cosp.person] or cosp.joined > ret[cosp.person]["last_date"]: ret[cosp.person]["last_date"] = cosp.joined
# Sort.
for info in ret.values():
info['by_issue'] = sorted(info['by_issue'].items(), key = lambda kv : kv[1], reverse=True)
ret = sorted(ret.items(), key = lambda kv : (kv[1]['total'], kv[1]['last_date'], kv[0].sortname), reverse=True)
# Total bills, date range.
from bill.models import Bill
total_bills = Bill.objects.filter(sponsor=person).count()
date_range = (None, None)
if len(ret) > 0:
date_range = (min(r["first_date"] for p, r in ret), max(r["last_date"] for p, r in ret))
return {
"person": person,
"cosponsors": ret,
"total": total,
"total_bills": total_bills,
"date_range": date_range,
}
def build_info():
global pronunciation_guide
if re.match(r"\d", pk):
person = get_object_or_404(Person, pk=pk)
else:
# support bioguide IDs for me
person = get_object_or_404(Person, bioguideid=pk)
# current role
role = person.get_current_role()
if role:
active_role = True
else:
active_role = False
try:
role = person.roles.order_by('-enddate')[0]
except IndexError:
role = None
# photo
photo_url, photo_credit = person.get_photo()
# analysis
analysis_data = analysis.load_data(person)
try:
# Get session stats for the previous year.
has_session_stats = person.get_session_stats(
str(datetime.now().year - 1))
except:
# Not everyone has current stats, obviously. They may have stats
# corresponding to their most recent role. Since stats are a
# session behind, even-year stats might not correspond to
# a legislator's most recent role, which is why I hard-coded
# the current session stats above.
has_session_stats = False
if role:
try:
has_session_stats = role.get_most_recent_session_stats()
except:
pass
links = []
if role.current:
if role.website:
links.append(("%s's Official Website" % person.lastname,
role.website, "fa fa-external-link"))
if person.twitterid:
links.append(("@" + person.twitterid,
"http://twitter.com/" + person.twitterid,
"fa fa-twitter"))
if person.osid:
links.append(
("OpenSecrets",
"http://www.opensecrets.org/politicians/summary.php?cid=" +
person.osid, "fa fa-money"))
if person.pvsid:
links.append(
("VoteSmart", "http://votesmart.org/candidate/" + person.pvsid,
"fa fa-th-list"))
if person.bioguideid:
links.append(
("Bioguide",
"http://bioguide.congress.gov/scripts/biodisplay.pl?index=" +
person.bioguideid, "fa fa-user"))
if person.cspanid:
links.append(("C-SPAN", "http://www.c-spanvideo.org/person/" +
str(person.cspanid), "fa fa-youtube-play"))
# Get a break down of the top terms this person's sponsored bills fall into,
# looking only at the most recent five years of bills.
from bill.models import BillTerm
most_recent_bill = person.sponsored_bills.order_by(
"-introduced_date").first()
bills_by_subject_counts = list(person.sponsored_bills.filter(
terms__id__in=BillTerm.get_top_term_ids(),
introduced_date__gt=(most_recent_bill.introduced_date if most_recent_bill else datetime.now())-timedelta(days=5*365.25))\
.values("terms")\
.annotate(count=Count('id')).order_by('-count')\
.filter(count__gt=1)\
[0:8])
terms = BillTerm.objects.in_bulk(item["terms"]
for item in bills_by_subject_counts)
total_count = sum(item["count"] for item in bills_by_subject_counts)
while len(bills_by_subject_counts) > 2 and bills_by_subject_counts[-1][
"count"] < bills_by_subject_counts[0]["count"] / 8:
bills_by_subject_counts.pop(-1)
for item in bills_by_subject_counts:
item["term"] = terms[item["terms"]]
item["pct"] = int(round(float(item["count"]) / total_count * 100))
del item["terms"]
# Missed vote explanations from ProPublica
try:
vote_explanations = http_rest_json(
"https://projects.propublica.org/explanations/api/members/%s.json"
% person.bioguideid)
except:
# squash all errors
vote_explanations = {}
# Misconduct - load and filter this person's entries, keeping original order.
# Choose 'Alleged misconduct', 'Misconduct', 'Misconduct/alleged misconduct' as appropriate.
from website.views import load_misconduct_data
misconduct = [
m for m in load_misconduct_data() if m["person"] == person
]
misconduct_any_alleged = (len([m for m in misconduct if m["alleged"]])
> 0)
misconduct_any_not_alleged = (len(
[m for m in misconduct if not m["alleged"]]) > 0)
# Load pronunciation from guide. Turn into a mapping from GovTrack IDs to data.
if pronunciation_guide is None:
import rtyaml
if not hasattr(settings, 'PRONUNCIATION_DATABASE_PATH'):
# debugging
pronunciation_guide = {}
else:
pronunciation_guide = {
p["id"]["govtrack"]: p
for p in rtyaml.load(
open(settings.PRONUNCIATION_DATABASE_PATH))
}
# Get this person's entry.
pronunciation = pronunciation_guide.get(person.id)
# TODO: Validate that the 'name' in the guide matches the name we're actually displaying.
if pronunciation and not pronunciation.get("key"):
# Show a key to the letters used in the pronunciation guide. Break apart the name
# into words which we'll show in columns.
pronunciation["key"] = []
for namepart in pronunciation["respell"].split(" // "):
for nameword in namepart.split(" "):
# Parse out the symbols actually used in the guide. Sweep from left to right chopping
# off valid respelling letter combinations, chopping off the longest one where possible.
pronunciation["key"].append([])
i = 0
while i < len(nameword):
for s in sorted(pronunciation_guide_key,
key=lambda s: -len(s)):
if nameword[i:i + len(s)] in (s, s.upper()):
pronunciation["key"][-1].append(
(nameword[i:i + len(s)],
pronunciation_guide_key[s]))
i += len(s)
break
else:
# respelling did not match any valid symbol, should be an error but we don't
# want to issue an Oops! for this
break
# Get their enacted bills.
enacted_bills_src_qs = person.sponsored_bills.exclude(
original_intent_replaced=True).order_by('-current_status_date')
return {
'person':
person,
'role':
role,
'active_role':
active_role,
'active_congressional_role':
active_role
and role.role_type in (RoleType.senator, RoleType.representative),
'pronunciation':
pronunciation,
'photo':
photo_url,
'photo_credit':
photo_credit,
'links':
links,
'analysis_data':
analysis_data,
'enacted_bills': [
b for b in enacted_bills_src_qs if b.was_enacted_ex(
cache_related_bills_qs=enacted_bills_src_qs)
],
'recent_bills':
person.sponsored_bills.all().order_by('-introduced_date')[0:7],
'committeeassignments':
get_committee_assignments(person),
'feed':
person.get_feed(),
'has_session_stats':
has_session_stats,
'bill_subject_areas':
bills_by_subject_counts,
'vote_explanations':
vote_explanations,
'key_votes':
load_key_votes(person),
'misconduct':
misconduct,
'misconduct_any_alleged':
misconduct_any_alleged,
'misconduct_any_not_alleged':
misconduct_any_not_alleged,
}
def build_info():
if re.match(r"\d", pk):
person = get_object_or_404(Person, pk=pk)
else:
# support bioguide IDs for me
person = get_object_or_404(Person, bioguideid=pk)
# current role
role = person.get_current_role()
if role:
active_role = True
else:
active_role = False
try:
role = person.roles.order_by('-enddate')[0]
except IndexError:
role = None
# photo
photo_url, photo_credit = person.get_photo()
# analysis
analysis_data = analysis.load_data(person)
try:
has_session_stats = person.get_session_stats('2016')
except:
# Not everyone has current stats, obviously. They may have stats
# corresponding to their most recent role. Since stats are a
# session behind, even-year stats might not correspond to
# a legislator's most recent role, which is why I hard-coded
# the current session stats above.
has_session_stats = False
if role:
try:
has_session_stats = role.get_most_recent_session_stats()
except:
pass
links = []
if role.current:
if role.website: links.append(("%s's Official Website" % person.lastname, role.website, "fa fa-external-link"))
if person.twitterid: links.append(("@" + person.twitterid, "http://twitter.com/" + person.twitterid, "fa fa-twitter"))
if person.osid: links.append(("OpenSecrets", "http://www.opensecrets.org/politicians/summary.php?cid=" + person.osid, "fa fa-money"))
if person.pvsid: links.append(("VoteSmart", "http://votesmart.org/candidate/" + person.pvsid, "fa fa-th-list"))
if person.bioguideid: links.append(("Bioguide", "http://bioguide.congress.gov/scripts/biodisplay.pl?index=" + person.bioguideid, "fa fa-user"))
if person.cspanid: links.append(("C-SPAN", "http://www.c-spanvideo.org/person/" + str(person.cspanid), "fa fa-youtube-play"))
# Get a break down of the top terms this person's sponsored bills fall into,
# looking only at the most recent five years of bills.
from bill.models import BillTerm
from datetime import datetime, timedelta
most_recent_bill = person.sponsored_bills.order_by("-introduced_date").first()
bills_by_subject_counts = list(person.sponsored_bills.filter(
terms__id__in=BillTerm.get_top_term_ids(),
introduced_date__gt=(most_recent_bill.introduced_date if most_recent_bill else datetime.now())-timedelta(days=5*365.25))\
.values("terms")\
.annotate(count=Count('id')).order_by('-count')\
.filter(count__gt=1)\
[0:8])
terms = BillTerm.objects.in_bulk(item["terms"] for item in bills_by_subject_counts)
total_count = sum(item["count"] for item in bills_by_subject_counts)
while len(bills_by_subject_counts) > 2 and bills_by_subject_counts[-1]["count"] < bills_by_subject_counts[0]["count"]/8: bills_by_subject_counts.pop(-1)
for item in bills_by_subject_counts:
item["term"] = terms[item["terms"]]
item["pct"] = int(round(float(item["count"]) / total_count * 100))
del item["terms"]
# Missed vote explanations from ProPublica
try:
vote_explanations = http_rest_json("https://projects.propublica.org/explanations/api/members/%s.json" % person.bioguideid)
except:
# squash all errors
vote_explanations = { }
return {'person': person,
'role': role,
'active_role': active_role,
'active_congressional_role': active_role and role.role_type in (RoleType.senator, RoleType.representative),
'photo': photo_url,
'photo_credit': photo_credit,
'links': links,
'analysis_data': analysis_data,
'enacted_bills': [b for b in person.sponsored_bills.order_by('-current_status_date') if b.was_enacted_ex()],
'recent_bills': person.sponsored_bills.all().order_by('-introduced_date')[0:7],
'committeeassignments': get_committee_assignments(person),
'feed': person.get_feed(),
'has_session_stats': has_session_stats,
'bill_subject_areas': bills_by_subject_counts,
'vote_explanations': vote_explanations,
'key_votes': load_key_votes(person),
}
def build_info():
if re.match(r"\d", pk):
person = get_object_or_404(Person, pk=pk)
else:
# support bioguide IDs for me
person = get_object_or_404(Person, bioguideid=pk)
# current role
role = person.get_current_role()
if role:
active_role = True
else:
active_role = False
try:
role = person.roles.order_by('-enddate')[0]
except IndexError:
role = None
# photo
photo_url, photo_credit = person.get_photo()
# analysis
analysis_data = analysis.load_data(person)
try:
has_session_stats = person.get_session_stats('2015')
except:
# Not everyone has 2014 stats, obviously. They may have stats
# corresponding to their most recent role. Since stats are a
# session behind, even-year stats might not correspond to
# a legislator's most recent role, which is why I hard-coded
# the current session stats above.
has_session_stats = False
if role:
try:
has_session_stats = role.get_most_recent_session_stats()
except:
pass
links = []
if role.current:
if role.website: links.append(("%s's Official Website" % person.lastname, role.website, "fa fa-external-link"))
if person.twitterid: links.append(("@" + person.twitterid, "http://twitter.com/" + person.twitterid, "fa fa-twitter"))
if person.osid: links.append(("OpenSecrets", "http://www.opensecrets.org/politicians/summary.php?cid=" + person.osid, "fa fa-money"))
if person.pvsid: links.append(("VoteSmart", "http://votesmart.org/candidate/" + person.pvsid, "fa fa-th-list"))
if person.bioguideid: links.append(("Bioguide", "http://bioguide.congress.gov/scripts/biodisplay.pl?index=" + person.bioguideid, "fa fa-user"))
if person.cspanid: links.append(("C-SPAN", "http://www.c-spanvideo.org/person/" + str(person.cspanid), "fa fa-youtube-play"))
# Get a break down of the top terms this person's sponsored bills fall into,
# looking only at the most recent five years of bills.
from bill.models import BillTerm
from datetime import datetime, timedelta
most_recent_bill = person.sponsored_bills.order_by("-introduced_date").first()
bills_by_subject_counts = list(person.sponsored_bills.filter(
terms__id__in=BillTerm.get_top_term_ids(),
introduced_date__gt=(most_recent_bill.introduced_date if most_recent_bill else datetime.now())-timedelta(days=5*365.25))\
.values("terms")\
.annotate(count=Count('id')).order_by('-count')\
.filter(count__gt=1)\
[0:8])
terms = BillTerm.objects.in_bulk(item["terms"] for item in bills_by_subject_counts)
total_count = sum(item["count"] for item in bills_by_subject_counts)
while len(bills_by_subject_counts) > 2 and bills_by_subject_counts[-1]["count"] < bills_by_subject_counts[0]["count"]/8: bills_by_subject_counts.pop(-1)
for item in bills_by_subject_counts:
item["term"] = terms[item["terms"]]
item["pct"] = int(round(float(item["count"]) / total_count * 100))
del item["terms"]
# Missed vote explanations from ProPublica
try:
vote_explanations = http_rest_json("https://projects.propublica.org/explanations/api/members/%s.json" % person.bioguideid)
except:
# squash all errors
vote_explanations = { }
return {'person': person,
'role': role,
'active_role': active_role,
'active_congressional_role': active_role and role.role_type in (RoleType.senator, RoleType.representative),
'photo': photo_url,
'photo_credit': photo_credit,
'links': links,
'analysis_data': analysis_data,
'recent_bills': person.sponsored_bills.all().order_by('-introduced_date')[0:7],
'committeeassignments': get_committee_assignments(person),
'feed': person.get_feed(),
'cities': get_district_cities("%s-%02d" % (role.state.lower(), role.district)) if role and role.district else None,
'has_session_stats': has_session_stats,
'bill_subject_areas': bills_by_subject_counts,
'vote_explanations': vote_explanations,
}
def build_info():
global pronunciation_guide
if re.match(r"\d", pk):
person = get_object_or_404(Person, pk=pk)
else:
# support bioguide IDs for me
person = get_object_or_404(Person, bioguideid=pk)
# current role
role = person.get_current_role()
if role:
active_role = True
else:
active_role = False
try:
role = person.roles.order_by('-enddate')[0]
except IndexError:
role = None
# photo
photo_url, photo_credit = person.get_photo()
# analysis
analysis_data = analysis.load_data(person)
try:
# Get session stats for the previous year.
has_session_stats = person.get_session_stats(str(datetime.now().year-1))
except:
# Not everyone has current stats, obviously. They may have stats
# corresponding to their most recent role. Since stats are a
# session behind, even-year stats might not correspond to
# a legislator's most recent role, which is why I hard-coded
# the current session stats above.
has_session_stats = False
if role:
try:
has_session_stats = role.get_most_recent_session_stats()
except:
pass
links = []
if role.current:
if role.website: links.append(("%s's Official Website" % person.lastname, role.website, "fa fa-external-link"))
if person.twitterid: links.append(("@" + person.twitterid, "http://twitter.com/" + person.twitterid, "fa fa-twitter"))
if person.osid: links.append(("OpenSecrets", "http://www.opensecrets.org/politicians/summary.php?cid=" + person.osid, "fa fa-money"))
if person.pvsid: links.append(("VoteSmart", "http://votesmart.org/candidate/" + person.pvsid, "fa fa-th-list"))
if person.bioguideid: links.append(("Bioguide", "http://bioguide.congress.gov/scripts/biodisplay.pl?index=" + person.bioguideid, "fa fa-user"))
if person.cspanid: links.append(("C-SPAN", "http://www.c-spanvideo.org/person/" + str(person.cspanid), "fa fa-youtube-play"))
# Get a break down of the top terms this person's sponsored bills fall into,
# looking only at the most recent five years of bills.
from bill.models import BillTerm
most_recent_bill = person.sponsored_bills.order_by("-introduced_date").first()
bills_by_subject_counts = list(person.sponsored_bills.filter(
terms__id__in=BillTerm.get_top_term_ids(),
introduced_date__gt=(most_recent_bill.introduced_date if most_recent_bill else datetime.now())-timedelta(days=5*365.25))\
.values("terms")\
.annotate(count=Count('id')).order_by('-count')\
.filter(count__gt=1)\
[0:8])
terms = BillTerm.objects.in_bulk(item["terms"] for item in bills_by_subject_counts)
total_count = sum(item["count"] for item in bills_by_subject_counts)
while len(bills_by_subject_counts) > 2 and bills_by_subject_counts[-1]["count"] < bills_by_subject_counts[0]["count"]/8: bills_by_subject_counts.pop(-1)
for item in bills_by_subject_counts:
item["term"] = terms[item["terms"]]
item["pct"] = int(round(float(item["count"]) / total_count * 100))
del item["terms"]
# Missed vote explanations from ProPublica
try:
vote_explanations = http_rest_json("https://projects.propublica.org/explanations/api/members/%s.json" % person.bioguideid)
except:
# squash all errors
vote_explanations = { }
# Misconduct - load and filter this person's entries, keeping original order.
# Choose 'Alleged misconduct', 'Misconduct', 'Misconduct/alleged misconduct' as appropriate.
from website.views import load_misconduct_data
misconduct = [m for m in load_misconduct_data() if m["person"] == person ]
misconduct_any_alleged = (len([ m for m in misconduct if m["alleged"] ]) > 0)
misconduct_any_not_alleged = (len([ m for m in misconduct if not m["alleged"] ]) > 0)
# Load pronunciation from guide. Turn into a mapping from GovTrack IDs to data.
if pronunciation_guide is None:
import rtyaml
if not hasattr(settings, 'PRONUNCIATION_DATABASE_PATH'):
# debugging
pronunciation_guide = { }
else:
pronunciation_guide = { p["id"]["govtrack"]: p for p in rtyaml.load(open(settings.PRONUNCIATION_DATABASE_PATH)) }
# Get this person's entry.
pronunciation = pronunciation_guide.get(person.id)
# TODO: Validate that the 'name' in the guide matches the name we're actually displaying.
if pronunciation and not pronunciation.get("key"):
# Show a key to the letters used in the pronunciation guide. Break apart the name
# into words which we'll show in columns.
pronunciation["key"] = []
for namepart in pronunciation["respell"].split(" // "):
for nameword in namepart.split(" "):
# Parse out the symbols actually used in the guide. Sweep from left to right chopping
# off valid respelling letter combinations, chopping off the longest one where possible.
pronunciation["key"].append([])
i = 0
while i < len(nameword):
for s in sorted(pronunciation_guide_key, key = lambda s : -len(s)):
if nameword[i:i+len(s)] in (s, s.upper()):
pronunciation["key"][-1].append( (nameword[i:i+len(s)], pronunciation_guide_key[s]) )
i += len(s)
break
else:
# respelling did not match any valid symbol, should be an error but we don't
# want to issue an Oops! for this
break
# Get their enacted bills.
enacted_bills_src_qs = person.sponsored_bills.exclude(original_intent_replaced=True).order_by('-current_status_date')
return {'person': person,
'role': role,
'active_role': active_role,
'active_congressional_role': active_role and role.role_type in (RoleType.senator, RoleType.representative),
'pronunciation': pronunciation,
'photo': photo_url,
'photo_credit': photo_credit,
'links': links,
'analysis_data': analysis_data,
'enacted_bills': [b for b in enacted_bills_src_qs if b.was_enacted_ex(cache_related_bills_qs=enacted_bills_src_qs)],
'recent_bills': person.sponsored_bills.all().order_by('-introduced_date')[0:7],
'committeeassignments': get_committee_assignments(person),
'feed': person.get_feed(),
'has_session_stats': has_session_stats,
'bill_subject_areas': bills_by_subject_counts,
'vote_explanations': vote_explanations,
'key_votes': load_key_votes(person),
'misconduct': misconduct,
'misconduct_any_alleged': misconduct_any_alleged,
'misconduct_any_not_alleged': misconduct_any_not_alleged,
}