def get_targets_by_chamber(chamber):
if chamber not in targets_by_chamber:
# Get all current representatives or all senators.
role_type = RoleType.representative if chamber == "house" else RoleType.senator
targets = PersonRole.objects.filter(
current=True,
role_type=role_type)\
.exclude(phone=None)\
.select_related('person')
# Create a priority score for each. We prefer to first call
# legislators in the middle of the ideology spectrum because
# they are least predictable, and also we prefer to call leaders
# because they are more important.
analysis = load_sponsorship_analysis2(settings.CURRENT_CONGRESS,
role_type, None)
target_priorities = {}
all_ideology_scores = [float(p["ideology"]) for p in analysis["all"]]
mean_ideology = sum(all_ideology_scores) / float(len(analysis["all"]))
absmax_ideology = max(abs(max(all_ideology_scores) - mean_ideology),
abs(min(all_ideology_scores) - mean_ideology))
for p in analysis["all"]:
score = float(
p["leadership"]) - abs(float(p["ideology"]) -
mean_ideology) / absmax_ideology
target_priorities[p["id"]] = score
targets_by_chamber[chamber] = [
(target, target_priorities.get(target.person.id, 0.0))
for target in targets
]
return targets_by_chamber[chamber]
def get_targets_by_chamber(chamber):
if chamber not in targets_by_chamber:
# Get all current representatives or all senators.
role_type = RoleType.representative if chamber == "house" else RoleType.senator
targets = PersonRole.objects.filter(
current=True,
role_type=role_type)\
.exclude(phone=None)\
.select_related('person')
# Create a priority score for each. We prefer to first call
# legislators in the middle of the ideology spectrum because
# they are least predictable, and also we prefer to call leaders
# because they are more important.
analysis = load_sponsorship_analysis2(settings.CURRENT_CONGRESS, role_type, None)
target_priorities = { }
all_ideology_scores = [float(p["ideology"]) for p in analysis["all"]]
mean_ideology = sum(all_ideology_scores) / float(len(analysis["all"]))
absmax_ideology = max(abs(max(all_ideology_scores)-mean_ideology), abs(min(all_ideology_scores)-mean_ideology))
for p in analysis["all"]:
score = float(p["leadership"]) - abs(float(p["ideology"]) - mean_ideology)/absmax_ideology
target_priorities[p["id"]] = score
targets_by_chamber[chamber] = [
(target, target_priorities.get(target.person.id, 0.0))
for target in targets
]
return targets_by_chamber[chamber]
def make_chart_series(role_type):
data = load_sponsorship_analysis2(CURRENT_CONGRESS, role_type, None)
if not data: return None
ret = { }
for p in data["all"]:
ret.setdefault(p["party"], {
"type": "party",
"party": p["party"],
"data": [],
})["data"].append({
"x": float(p["ideology"]),
"y": float(p["leadership"]),
"name": p["name"],
})
ret = list(ret.values())
ret.sort(key = lambda s : len(s["data"]), reverse=True)
data = dict(data) # clone before modifying, just in case
data["series"] = json.dumps(ret)
return data
def make_chart_series(role_type):
data = load_sponsorship_analysis2(CURRENT_CONGRESS, role_type, None)
if not data: return None
ret = {}
for p in data["all"]:
ret.setdefault(p["party"], {
"type": "party",
"party": p["party"],
"data": [],
})["data"].append({
"x": float(p["ideology"]),
"y": float(p["leadership"]),
"name": p["name"],
})
ret = list(ret.values())
ret.sort(key=lambda s: len(s["data"]), reverse=True)
data = dict(data) # clone before modifying, just in case
data["series"] = json.dumps(ret)
return data
def fetch_statements(bill):
from person.models import Person
from person.views import http_rest_json
from parser.processor import Processor
# load statements from ProPublica API, ignoring any network errors
try:
statements = http_rest_json(
"https://api.propublica.org/congress/v1/{congress}/bills/{bill}/statements.json"
.format(
congress=bill.congress,
bill=bill.bill_type_slug + str(bill.number),
),
headers={
'X-API-Key': settings.PROPUBLICA_CONGRESS_API_KEY,
'Content-Type': 'application/json',
'Accept': 'application/json'
})
if statements["status"] != "OK": raise Exception()
statements = statements["results"]
except:
return []
# bulk-fetch all legislators mentioned and make a mapping from bioguide ID to Person object
legislators = {
p.bioguideid: p
for p in Person.objects.filter(
bioguideid__in=[s['member_id'] for s in statements])
}
# make simplified statements records
statements = [{
"date": Processor.parse_datetime(s["date"]).date(),
"person": legislators.get(s["member_id"]),
"type": s["statement_type"],
"title": s["title"],
"url": s["url"],
} for s in statements if s["date"]]
# downcase all-caps titles
for s in statements:
if s["title"] != s["title"].upper(): continue
s["title"] = s["title"].lower()
if s["person"]:
s["title"] = s["title"].replace(
s["person"].lastname.lower(),
s["person"].lastname) # common easy case fix
# sort by date because we want to give a diversity of viewpoints by showing only one
# statement per legislator
statements.sort(key=lambda s: s["date"], reverse=True)
# Put all of the statements that are the most recent for the legislator first, then all of the second-most-recent, and so on.
# Since sort is stable, it will remain in reverse-date order within each group.
seen = {}
for s in statements:
seen[s["person"]] = seen.get(s["person"], 0) + 1
s["legislator_ordinal"] = seen[s["person"]]
statements.sort(key=lambda s: s["legislator_ordinal"])
# bulk-fetch ideology & leadership scores
from person.models import RoleType
from person.analysis import load_sponsorship_analysis2
scores = load_sponsorship_analysis2(
bill.congress,
RoleType.representative, None)["all"] + load_sponsorship_analysis2(
bill.congress, RoleType.senator, None)["all"]
leadership_scores = {s["id"]: float(s["leadership"]) for s in scores}
ideology_scores = {s["id"]: float(s["ideology"]) for s in scores}
# Tag relevance of the person making the press statement. (We'd love to prioritize relevant
# committee chairs but we only have current committee info so we couldn't do it for
# past bills.)
cosponsors = set(bill.cosponsors.all())
for s in statements:
if s["person"] == bill.sponsor:
s["relevance"] = "Sponsor"
s["priority"] = (0, -leadership_scores.get(s["person"].id, 0))
elif s["person"] in cosponsors:
s["relevance"] = "Co-sponsor"
s["priority"] = (1, -leadership_scores.get(
s["person"].id if s["person"] else 0, 0))
# Get the prioritized statements to show.
ret = []
# For up to the first two, show the sponsor and the cosponsor with the highest leadership
# score (or if there is no sponsor statement, the two top cosponsors), but prioritizing
# the most recent statements for each legislator
statements.sort(
key=lambda s: (s["legislator_ordinal"], s.get("priority", (2, ))))
while statements and statements[0].get("priority") and statements[0][
"legislator_ordinal"] == 1 and len(ret) < 2:
ret.append(statements.pop(0))
# For the third statement, take the legislator with the ideology score furthest from
# the sponsor's score, to hopefully get an opposing viewpoint.
if bill.sponsor and bill.sponsor.id in ideology_scores:
statements.sort(key=lambda s: (
s["legislator_ordinal"
], # most recent statements by legislators first
not (s["person"] and s["person"].id in ideology_scores
), # people with ideology scores first
"relevance" in s, # people who aren't a sponsor/cosponsor first
-abs(
ideology_scores.get(s["person"].id if s["person"] else 0, 0) -
ideology_scores[bill.sponsor.id])))
while statements and len(ret) < 3:
ret.append(statements.pop(0))
return ret
