def test_compute_allegation_metric(self):
officer = OfficerFactory(id=1, appointed_date=date(2010, 3, 14))
OfficerAllegationFactory(
officer=officer,
allegation__incident_date=datetime(2014, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer,
allegation__incident_date=datetime(2016, 1, 16, tzinfo=pytz.utc),
)
OfficerAllegationFactory(
officer=officer,
allegation__incident_date=datetime(2016, 7, 2, tzinfo=pytz.utc),
)
officers = officer_percentile._compute_metric(
2016, PERCENTILE_ALLEGATION_GROUP)
expect(officers).to.have.length(1)
validate_object(
officers[0], {
'officer_id': 1,
'year': 2016,
'start_date': date(2015, 1, 1),
'end_date': date(2016, 7, 1),
'service_year': 1.4986,
'num_allegation': 3,
'metric_allegation': 2.0019,
})
def test_extract_datum_most_common_category(self):
allegation = AllegationFactory()
cat1 = AllegationCategoryFactory(
category='Use Of Forces',
allegation_name='Sub Force'
)
cat2 = AllegationCategoryFactory(
category='Traffic',
allegation_name='Sub traffic'
)
OfficerAllegationFactory.create_batch(
2,
allegation=allegation,
allegation_category=cat1
)
OfficerAllegationFactory(
allegation=allegation,
allegation_category=cat2
)
rows = self.extract_data()
expect(rows).to.have.length(1)
expect(rows[0]['most_common_category']).to.eq({
'category': 'Use Of Forces',
'allegation_name': 'Sub Force'
})
def test_coaccused_count(self):
allegation_1 = AllegationFactory()
allegation_2 = AllegationFactory()
OfficerAllegationFactory.create_batch(6, allegation=allegation_1)
allegation_cache_manager.cache_data()
allegation_1.refresh_from_db()
allegation_2.refresh_from_db()
expect(allegation_1.coaccused_count).to.eq(6)
expect(allegation_2.coaccused_count).to.eq(0)
def test_coaccused_counts(self):
officer = OfficerFactory()
OfficerAllegationFactory.create_batch(2, officer=officer, final_finding='SU')
OfficerAllegationFactory(officer=officer, final_finding='NS')
rows = self.extract_data()
expect(rows).to.have.length(3)
for row in rows:
expect(row['coaccused']).to.have.length(1)
expect(row['coaccused'][0]['allegation_count']).to.eq(3)
expect(row['coaccused'][0]['sustained_count']).to.eq(2)
def test_new_timeline_item_no_join(self):
officer = OfficerFactory(id=123, appointed_date=None, rank='Police Officer')
unit = PoliceUnitFactory(unit_name='001', description='unit_001')
OfficerHistoryFactory(officer=officer, unit=unit, effective_date=date(2010, 1, 1), end_date=date(2011, 12, 31))
allegation = AllegationFactory(
crid='123456',
coaccused_count=4,
incident_date=datetime(2011, 8, 23, tzinfo=pytz.utc)
)
OfficerAllegationFactory(
final_finding='UN', final_outcome='Unknown',
officer=officer, allegation=allegation,
allegation_category=AllegationCategoryFactory(category='category', allegation_name='sub category')
)
OfficerAllegationFactory.create_batch(3, allegation=allegation)
SalaryFactory(officer=officer, year=2001, rank='Police Officer', spp_date=date(2001, 9, 23))
cache_managers.cache_all()
response = self.client.get(reverse('api-v2:officers-new-timeline-items', kwargs={'pk': 123}))
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data).to.eq([
{
'date': '2011-08-23',
'kind': 'CR',
'crid': '123456',
'category': 'category',
'subcategory': 'sub category',
'finding': 'Unfounded',
'outcome': 'Unknown',
'coaccused': 4,
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
'victims': [],
'attachments': [],
}, {
'date': '2010-01-01',
'kind': 'UNIT_CHANGE',
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2001-09-23',
'kind': 'RANK_CHANGE',
'unit_name': '',
'unit_description': '',
'rank': 'Police Officer',
}
])
def test_allegation_count(self):
officer_1 = OfficerFactory()
officer_2 = OfficerFactory()
OfficerAllegationFactory.create_batch(2, final_finding='NS', officer=officer_1)
OfficerAllegationFactory(final_finding='SU', officer=officer_1)
officer_cache_manager.build_cached_columns()
officer_1.refresh_from_db()
officer_2.refresh_from_db()
expect(officer_1.allegation_count).to.eq(3)
expect(officer_2.allegation_count).to.eq(0)
def test_discipline_count(self):
officer_1 = OfficerFactory()
officer_2 = OfficerFactory()
OfficerAllegationFactory.create_batch(2, officer=officer_1, disciplined=True)
OfficerAllegationFactory(officer=officer_1, disciplined=False)
OfficerAllegationFactory(officer=officer_2, disciplined=False)
officer_cache_manager.build_cached_columns()
officer_1.refresh_from_db()
officer_2.refresh_from_db()
expect(officer_1.discipline_count).to.eq(2)
expect(officer_2.discipline_count).to.eq(0)
def test_police_witness_counts(self):
officer = OfficerFactory()
OfficerAllegationFactory.create_batch(2, officer=officer, final_finding='SU')
OfficerAllegationFactory(officer=officer, final_finding='NS')
allegation = AllegationFactory()
PoliceWitnessFactory(officer=officer, allegation=allegation)
rows = self.extract_data()
expect(rows).to.have.length(4)
expect({len(row['involvements']) for row in rows}).to.eq({0, 1})
for row in rows:
if len(row['involvements']) == 1:
expect(row['involvements'][0]['allegation_count']).to.eq(3)
expect(row['involvements'][0]['sustained_count']).to.eq(2)
def test_most_common_category(self):
allegation = AllegationFactory()
category1, category2 = AllegationCategoryFactory.create_batch(2)
OfficerAllegationFactory(allegation=allegation,
allegation_category=category2)
OfficerAllegationFactory.create_batch(2,
allegation=allegation,
allegation_category=category1)
OfficerAllegationFactory.create_batch(3,
allegation=allegation,
allegation_category=None)
allegation_cache_manager.cache_data()
allegation.refresh_from_db()
expect(allegation.most_common_category).to.eq(category1)
def test_get_coaccused(self, coaccused_serializer_mock):
allegation = AllegationFactory()
officer_allegations = OfficerAllegationFactory.create_batch(2, allegation=allegation)
result = CRSerializer(allegation).data
officer_allegations_arg = coaccused_serializer_mock.call_args[0][0]
expect(set(officer_allegations_arg)).to.eq(set(officer_allegations))
expect(result['coaccused']).to.eq([{'id': 1}, {'id': 2}])
def test_count(self):
officer = OfficerFactory()
OfficerAllegationFactory.create_batch(2,
officer=officer,
final_finding='NS')
OfficerAllegationFactory.create_batch(2,
officer=officer,
final_finding='SU')
subquery = OfficerAllegation.objects.filter(
officer=models.OuterRef('officer_id'))
results = list(OfficerAllegation.objects.all().annotate(
allegation_count=SQCount(subquery.values('id'))).annotate(
sustained_count=SQCount(
subquery.filter(final_finding='SU').values('id'))).values(
'allegation_count', 'sustained_count'))
expect(results).to.have.length(4)
for obj in results:
expect(obj['allegation_count']).to.eq(4)
expect(obj['sustained_count']).to.eq(2)
def setUp(self):
violation_category = AllegationCategoryFactory(category='Operation/Personnel Violations')
use_of_force_category = AllegationCategoryFactory(category='Use Of Force')
illegal_search_category = AllegationCategoryFactory(category='Illegal Search')
false_arrest_category = AllegationCategoryFactory(category='False Arrest')
before_min_date = datetime(1987, 12, 31, tzinfo=pytz.utc)
valid_date = datetime(1988, 12, 31, tzinfo=pytz.utc)
today = datetime.now(pytz.utc)
for date in [before_min_date, valid_date, today]:
OfficerAllegationFactory.create_batch(
4,
allegation_category=violation_category,
allegation__incident_date=date,
disciplined=True)
OfficerAllegationFactory.create_batch(
3,
allegation_category=use_of_force_category,
allegation__incident_date=date,
disciplined=True)
OfficerAllegationFactory.create_batch(
2,
allegation_category=illegal_search_category,
allegation__incident_date=date,
disciplined=False)
OfficerAllegationFactory(
allegation_category=false_arrest_category,
allegation__incident_date=date,
final_outcome='900')
def test_extract_datum_percentiles(self):
officer1 = OfficerFactory(id=1, appointed_date=date(2013, 1, 1))
officer2 = OfficerFactory(id=2, appointed_date=date(2015, 3, 14))
OfficerFactory(id=3,
appointed_date=date(2014, 3, 1),
resignation_date=date(2015, 4, 14))
OfficerAllegationFactory(officer=officer1,
allegation__incident_date=datetime(
2015, 1, 1, tzinfo=pytz.utc),
start_date=date(2015, 1, 1),
allegation__is_officer_complaint=False)
OfficerAllegationFactory(officer=officer1,
start_date=date(2015, 1, 1),
allegation__incident_date=datetime(
2015, 1, 1, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
OfficerAllegationFactory(officer=officer1,
start_date=date(2016, 1, 22),
allegation__incident_date=datetime(
2016, 1, 1, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
OfficerAllegationFactory.create_batch(
2,
officer=officer2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(2016, 1, 16, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
OfficerAllegationFactory(officer=officer2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(
2016, 3, 15, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory(officer=officer2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(
2017, 3, 15, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
TRRFactory(
officer=officer1,
trr_datetime=datetime(2017, 3, 15, tzinfo=pytz.utc),
)
TRRFactory(
officer=officer1,
trr_datetime=datetime(2016, 3, 15, tzinfo=pytz.utc),
)
rows = sorted(self.extract_data(), key=itemgetter('id'))
expect(rows).to.have.length(3)
expect(rows[0]['current_allegation_percentile']).to.eq('33.3333')
expect(rows[0]['percentiles']).to.eq([{
'id':
1,
'year':
2014,
'percentile_allegation':
'0.0000',
'percentile_allegation_civilian':
'0.0000',
'percentile_allegation_internal':
'0.0000',
'percentile_trr':
'0.0000'
}, {
'id':
1,
'year':
2015,
'percentile_allegation':
'50.0000',
'percentile_allegation_civilian':
'50.0000',
'percentile_allegation_internal':
'0.0000',
'percentile_trr':
'0.0000'
}, {
'id':
1,
'year':
2016,
'percentile_allegation':
'33.3333',
'percentile_allegation_civilian':
'33.3333',
'percentile_allegation_internal':
'0.0000',
'percentile_trr':
'66.6667'
}])
expect(rows[1]['current_allegation_percentile']).to.eq('66.6667')
expect(rows[1]['percentiles']).to.eq([{
'id':
2,
'year':
2016,
'percentile_allegation':
'66.6667',
'percentile_allegation_civilian':
'66.6667',
'percentile_allegation_internal':
'66.6667',
'percentile_trr':
'0.0000'
}])
expect(rows[2]['current_allegation_percentile']).to.eq('0.0000')
expect(rows[2]['percentiles']).to.eq([{
'id':
3,
'year':
2015,
'percentile_allegation':
'0.0000',
'percentile_allegation_civilian':
'0.0000',
'percentile_allegation_internal':
'0.0000',
'percentile_trr':
'0.0000'
}])
def test_extract_datum(self):
allegation = AllegationFactory(
crid='123456',
incident_date=datetime(2017, 7, 27, tzinfo=pytz.utc),
summary='abc',
add1='3000',
add2='Michigan Ave',
city='Chicago IL'
)
officer = OfficerFactory(
id=10,
first_name='Luke',
last_name='Skywalker',
allegation_count=4,
complaint_percentile='99.70',
trr_percentile='99.88',
civilian_allegation_percentile='77.66',
internal_allegation_percentile='66.55'
)
officer2 = OfficerFactory(
id=11,
first_name='John', last_name='Doe',
allegation_count=2,
complaint_percentile='70.33',
trr_percentile='66.88',
civilian_allegation_percentile='33.66',
internal_allegation_percentile='22.55'
)
OfficerAllegationFactory(allegation=allegation, officer=officer)
category1 = AllegationCategoryFactory(
category='Operation/Personnel Violations',
allegation_name='Secondary/Special Employment'
)
category2 = AllegationCategoryFactory(category='Use of Force', allegation_name='sub category')
OfficerAllegationFactory(allegation=allegation, allegation_category=category2, officer=officer2)
OfficerAllegationFactory.create_batch(2, allegation=allegation, allegation_category=category1, officer=None)
OfficerAllegationFactory.create_batch(3, allegation=allegation, allegation_category=None, officer=None)
VictimFactory(allegation=allegation, gender='F', race='Black', age=25)
VictimFactory(allegation=allegation, gender='', race='Black', age=25)
VictimFactory(allegation=allegation, gender='F', race='Black', age=None)
AttachmentFileFactory(id=1, allegation=allegation, text_content='')
AttachmentFileFactory(
id=2, allegation=allegation, show=False,
text_content="CHICAGO POLICE DEPARTMENT RD I HT334604"
)
AttachmentFileFactory(id=3, allegation=allegation, text_content='CHICAGO POLICE DEPARTMENT RD I HT334604')
setattr(allegation, 'investigator_names', ['Jerome Finnigan'])
allegation_cache_manager.cache_data()
allegation.refresh_from_db()
datum = CrIndexer().extract_datum(allegation)
datum['victims'] = sorted(
datum['victims'],
key=lambda victim: (victim['gender'], victim['race'], victim.get('age', 0))
)
expect(datum).to.eq({
'crid': '123456',
'category': 'Operation/Personnel Violations',
'sub_category': 'Secondary/Special Employment',
'incident_date': '2017-07-27',
'address': '3000 Michigan Ave, Chicago IL',
'summary': 'abc',
'to': '/complaint/123456/',
'investigator_names': ['Jerome Finnigan'],
'victims': [
{'gender': '', 'race': 'Black', 'age': 25},
{'gender': 'Female', 'race': 'Black'},
{'gender': 'Female', 'race': 'Black', 'age': 25},
],
'coaccused': [
{
'id': 10, 'full_name': 'Luke Skywalker', 'allegation_count': 4,
'percentile_allegation': '99.7000',
'percentile_allegation_civilian': '77.6600',
'percentile_allegation_internal': '66.5500',
'percentile_trr': '99.8800',
},
{
'id': 11, 'full_name': 'John Doe', 'allegation_count': 2,
'percentile_allegation': '70.3300',
'percentile_allegation_civilian': '33.6600',
'percentile_allegation_internal': '22.5500',
'percentile_trr': '66.8800',
}
],
'attachment_files': [
{'id': 3, 'text_content': 'CHICAGO POLICE DEPARTMENT RD I HT334604'}
]
})
def test_latest_year_percentile(self):
officer1 = OfficerFactory(id=1, appointed_date=date(1990, 3, 14))
officer2 = OfficerFactory(id=2,
appointed_date=date(1990, 3, 14),
resignation_date=date(2014, 7, 1))
officer3 = OfficerFactory(id=3, appointed_date=date(1990, 3, 14))
officer4 = OfficerFactory(id=4,
appointed_date=date(1990, 3, 14),
resignation_date=date(2010, 7, 1))
# officer1 have all data
OfficerAllegationFactory.create_batch(
2,
officer=officer1,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
4,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(2,
officer=officer1,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
AwardFactory(officer=officer1,
award_type='Complimentary Letter',
start_date=datetime(2013, 1, 1))
AwardFactory.create_batch(3,
officer=officer1,
award_type='Honorable Mention',
start_date=datetime(2015, 1, 1))
# officer2 don't have trr
OfficerAllegationFactory(
officer=officer2,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
AwardFactory(officer=officer2,
award_type='Complimentary Letter',
start_date=datetime(2013, 1, 1))
AwardFactory.create_batch(3,
officer=officer2,
award_type='Honorable Mention',
start_date=datetime(2014, 1, 1))
# officer3 don't have allegation in ALLEGATION_MIN - ALLEGATION_MAX
OfficerAllegationFactory.create_batch(
2,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
3,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(3,
officer=officer3,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer4 have all data but is out of data range
OfficerAllegationFactory.create_batch(
2,
officer=officer4,
allegation__incident_date=datetime(2009, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer4,
allegation__incident_date=datetime(2009, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
4,
officer=officer4,
allegation__incident_date=datetime(2009, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(2,
officer=officer4,
trr_datetime=datetime(2009,
2,
1,
tzinfo=pytz.utc))
expected_dict = {
1: {
'officer_id': 1,
'year': 2017,
'metric_allegation': 3.0,
'metric_allegation_civilian': 2.0,
'metric_allegation_internal': 1.0,
'metric_trr': 0.6667,
'metric_honorable_mention': 1.0,
'percentile_allegation': 66.6667,
'percentile_allegation_civilian': 66.6667,
'percentile_allegation_internal': 66.6667,
'percentile_trr': 33.3333,
'percentile_honorable_mention': 33.3333,
},
2: {
'officer_id': 2,
'year': 2014,
'metric_allegation': 2.0055,
'metric_allegation_civilian': 1.337,
'metric_allegation_internal': 0.6685,
'metric_trr': 0.0,
'metric_honorable_mention': 2.0055,
'percentile_allegation': 66.6667,
'percentile_allegation_civilian': 66.6667,
'percentile_allegation_internal': 66.6667,
'percentile_trr': 0.0,
'percentile_honorable_mention': 66.6667
},
3: {
'officer_id': 3,
'year': 2017,
'metric_allegation': 1.6667,
'metric_allegation_civilian': 1.0,
'metric_allegation_internal': 0.6667,
'metric_trr': 1.0,
'metric_honorable_mention': 0.0,
'percentile_allegation': 0.0,
'percentile_allegation_civilian': 0.0,
'percentile_allegation_internal': 0.0,
'percentile_trr': 66.6667,
'percentile_honorable_mention': 0.0
}
}
officers = officer_percentile.latest_year_percentile()
expect(officers).to.have.length(3)
for officer in officers:
validate_object(officer, expected_dict[officer.id])
def test_top_percentile_not_enough_service_year(self):
officer1 = OfficerFactory(id=1, appointed_date=date(1990, 3, 14))
officer2 = OfficerFactory(id=2,
appointed_date=date(1990, 3, 14),
resignation_date=date(2014, 5, 1))
officer3 = OfficerFactory(id=3, appointed_date=date(2013, 3, 14))
officer4 = OfficerFactory(id=4,
appointed_date=date(1990, 3, 14),
resignation_date=date(2015, 5, 1))
# officer1 have all data
OfficerAllegationFactory.create_batch(
2,
officer=officer1,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer1,
allegation__incident_date=datetime(2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
4,
officer=officer1,
allegation__incident_date=datetime(2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(2,
officer=officer1,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer2 don't have trr
OfficerAllegationFactory(
officer=officer2,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2014, 2, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2014, 3, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
# officer3 don't have allegation in ALLEGATION_MIN - ALLEGATION_MAX
OfficerAllegationFactory.create_batch(
2,
officer=officer3,
allegation__incident_date=datetime(2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
3,
officer=officer3,
allegation__incident_date=datetime(2014, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(3,
officer=officer3,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer4 don't have allegation in INTERNAL_CIVILIAN_ALLEGATION year range
OfficerAllegationFactory.create_batch(
5,
officer=officer4,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
TRRFactory.create_batch(6,
officer=officer4,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
expected_dict = {
1: {
'officer_id': 1,
'year': 2016,
'metric_allegation': 2,
'metric_allegation_civilian': 4,
'metric_allegation_internal': 3,
'metric_trr': 2,
'percentile_allegation': 33.3333,
'percentile_allegation_civilian': 66.6667,
'percentile_allegation_internal': 66.6667,
'percentile_trr': 0.0
},
2: {
'officer_id': 2,
'year': 2016,
'metric_allegation': 1,
'metric_allegation_civilian': None,
'metric_allegation_internal': None,
'metric_trr': None,
'percentile_allegation': 0.0,
'percentile_allegation_civilian': None,
'percentile_allegation_internal': None,
'percentile_trr': None
},
3: {
'officer_id': 3,
'year': 2016,
'metric_allegation': None,
'metric_allegation_civilian': 3,
'metric_allegation_internal': 2,
'metric_trr': 3,
'percentile_allegation': None,
'percentile_allegation_civilian': 33.3333,
'percentile_allegation_internal': 33.3333,
'percentile_trr': 50.0
},
4: {
'officer_id': 4,
'year': 2016,
'metric_allegation': 5,
'metric_allegation_civilian': 0.0,
'metric_allegation_internal': 0.0,
'metric_trr': None,
'percentile_allegation': 66.6667,
'percentile_allegation_civilian': 0.0,
'percentile_allegation_internal': 0.0,
'percentile_trr': None
}
}
officers = officer_percentile.top_percentile(2016)
for officer in officers:
validate_object(officer, expected_dict[officer.id])
def test_top_percentile_with_types(self):
officer1 = OfficerFactory(id=1, appointed_date=date(1990, 3, 14))
officer2 = OfficerFactory(id=2, appointed_date=date(1990, 3, 14))
officer3 = OfficerFactory(id=3, appointed_date=date(1990, 3, 14))
officer4 = OfficerFactory(id=4, appointed_date=date(1990, 3, 14))
# officer1 have all data
OfficerAllegationFactory.create_batch(
2,
officer=officer1,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
4,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(2,
officer=officer1,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer2 don't have trr
OfficerAllegationFactory(
officer=officer2,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
# officer3 don't have allegation in ALLEGATION_MIN - ALLEGATION_MAX
OfficerAllegationFactory.create_batch(
2,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
3,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(3,
officer=officer3,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer4 don't have allegation in INTERNAL_CIVILIAN_ALLEGATION year range
OfficerAllegationFactory.create_batch(
5,
officer=officer4,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
TRRFactory.create_batch(6,
officer=officer4,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
expected_dict = {
1: {
'officer_id': 1,
'year': 2016,
'metric_allegation_civilian': 4,
'metric_allegation_internal': 3,
'metric_trr': 2,
'percentile_allegation': None,
'percentile_allegation_civilian': 75.0,
'percentile_allegation_internal': 75.0,
'percentile_trr': 25.0
},
2: {
'officer_id': 2,
'year': 2016,
'metric_allegation_civilian': 1,
'metric_allegation_internal': 1,
'metric_trr': 0.0,
'percentile_allegation': None,
'percentile_allegation_civilian': 25.0,
'percentile_allegation_internal': 25.0,
'percentile_trr': 0.0
},
3: {
'officer_id': 3,
'year': 2016,
'metric_allegation_civilian': 3,
'metric_allegation_internal': 2,
'metric_trr': 3,
'percentile_allegation': None,
'percentile_allegation_civilian': 50.0,
'percentile_allegation_internal': 50.0,
'percentile_trr': 50.0
},
4: {
'officer_id': 4,
'year': 2016,
'metric_allegation_civilian': 0.0,
'metric_allegation_internal': 0.0,
'metric_trr': 6,
'percentile_allegation': None,
'percentile_allegation_civilian': 0.0,
'percentile_allegation_internal': 0.0,
'percentile_trr': 75.0
}
}
visual_token_percentile_groups = [
PERCENTILE_ALLEGATION_INTERNAL_CIVILIAN_GROUP, PERCENTILE_TRR_GROUP
]
officers = officer_percentile.top_percentile(
2016, percentile_groups=visual_token_percentile_groups)
for officer in officers:
validate_object(officer, expected_dict[officer.id])
def test_top_percentile(self):
officer1 = OfficerFactory(id=1, appointed_date=date(1990, 3, 14))
officer2 = OfficerFactory(id=2, appointed_date=date(1990, 3, 14))
officer3 = OfficerFactory(id=3, appointed_date=date(1990, 3, 14))
# officer1 have all data
OfficerAllegationFactory.create_batch(
2,
officer=officer1,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory.create_batch(
3,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
4,
officer=officer1,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(2,
officer=officer1,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
# officer2 don't have trr
OfficerAllegationFactory(
officer=officer2,
allegation__incident_date=datetime(2013, 12, 31, tzinfo=pytz.utc),
)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory(officer=officer2,
allegation__incident_date=datetime(
2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
# officer3 don't have allegation in ALLEGATION_MIN - ALLEGATION_MAX
OfficerAllegationFactory.create_batch(
2,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=True)
OfficerAllegationFactory.create_batch(
3,
officer=officer3,
allegation__incident_date=datetime(2015, 7, 2, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
TRRFactory.create_batch(3,
officer=officer3,
trr_datetime=datetime(2015,
2,
1,
tzinfo=pytz.utc))
expected_dict = {
1: {
'officer_id': 1,
'year': 2016,
'metric_allegation': 2,
'metric_allegation_civilian': 4,
'metric_allegation_internal': 3,
'metric_trr': 2,
'percentile_allegation': 66.6667,
'percentile_allegation_civilian': 66.6667,
'percentile_allegation_internal': 66.6667,
'percentile_trr': 33.3333
},
2: {
'officer_id': 2,
'year': 2016,
'metric_allegation': 1,
'metric_allegation_civilian': 1,
'metric_allegation_internal': 1,
'metric_trr': 0.0,
'percentile_allegation': 33.3333,
'percentile_allegation_civilian': 0.0000,
'percentile_allegation_internal': 0.0000,
'percentile_trr': 0.0
},
3: {
'officer_id': 3,
'year': 2016,
'metric_allegation': 0.0,
'metric_allegation_civilian': 3,
'metric_allegation_internal': 2,
'metric_trr': 3,
'percentile_allegation': 0.0,
'percentile_allegation_civilian': 33.3333,
'percentile_allegation_internal': 33.3333,
'percentile_trr': 66.6667,
}
}
officers = officer_percentile.top_percentile(2016)
for officer in officers:
validate_object(officer, expected_dict[officer.id])
def test_build_cached_yearly_percentiles(self):
officer_1 = OfficerFactory(id=1, appointed_date=date(2013, 1, 1))
officer_2 = OfficerFactory(id=2, appointed_date=date(2015, 3, 14))
officer_3 = OfficerFactory(id=3, appointed_date=date(2014, 3, 1), resignation_date=date(2015, 4, 14))
OfficerAllegationFactory(
officer=officer_1,
allegation__incident_date=datetime(2015, 1, 1, tzinfo=pytz.utc),
start_date=datetime(2015, 1, 1),
allegation__is_officer_complaint=False)
OfficerAllegationFactory(
officer=officer_1,
start_date=date(2015, 1, 1),
allegation__incident_date=datetime(2015, 1, 1, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
OfficerAllegationFactory(
officer=officer_1,
start_date=date(2016, 1, 22),
allegation__incident_date=datetime(2016, 1, 1, tzinfo=pytz.utc),
allegation__is_officer_complaint=False)
OfficerAllegationFactory.create_batch(
2,
officer=officer_2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(2016, 1, 16, tzinfo=pytz.utc),
allegation__is_officer_complaint=False
)
OfficerAllegationFactory(
officer=officer_2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(2016, 3, 15, tzinfo=pytz.utc),
allegation__is_officer_complaint=True
)
OfficerAllegationFactory(
officer=officer_2,
start_date=date(2017, 10, 19),
allegation__incident_date=datetime(2017, 3, 15, tzinfo=pytz.utc),
allegation__is_officer_complaint=True
)
TRRFactory(
officer=officer_1,
trr_datetime=datetime(2017, 3, 15, tzinfo=pytz.utc),
)
TRRFactory(
officer=officer_1,
trr_datetime=datetime(2016, 3, 15, tzinfo=pytz.utc),
)
officer_cache_manager.build_cached_yearly_percentiles()
expected_officer_yearly_percentiles = {
officer_1.id: {
2014: {
'percentile_allegation': Decimal(0.0),
'percentile_allegation_civilian': Decimal(0.0),
'percentile_allegation_internal': Decimal(0.0),
'percentile_trr': Decimal(0.0),
},
2015: {
'percentile_allegation': Decimal(50.0),
'percentile_allegation_civilian': Decimal(50.0),
'percentile_allegation_internal': Decimal(0.0),
'percentile_trr': Decimal(0.0),
},
2016: {
'percentile_allegation': Decimal(33.3333),
'percentile_allegation_civilian': Decimal(33.3333),
'percentile_allegation_internal': Decimal(0.0),
'percentile_trr': Decimal(66.6667),
}
},
officer_2.id: {
2016: {
'percentile_allegation': Decimal(66.6667),
'percentile_allegation_civilian': Decimal(66.6667),
'percentile_allegation_internal': Decimal(66.6667),
'percentile_trr': Decimal(0.0),
}
},
officer_3.id: {
2015: {
'percentile_allegation': Decimal(0.0),
'percentile_allegation_civilian': Decimal(0.0),
'percentile_allegation_internal': Decimal(0.0),
'percentile_trr': Decimal(0.0),
}
}
}
for officer_id, expected_yearly_percentiles in expected_officer_yearly_percentiles.items():
yearly_percentiles = OfficerYearlyPercentile.objects.filter(officer_id=officer_id)
expect(yearly_percentiles.count()).to.eq(len(expected_yearly_percentiles.keys()))
for year, expected_percentile in expected_yearly_percentiles.items():
percentile = yearly_percentiles.get(year=year)
for attr, value in expected_percentile.items():
expect(f'{getattr(percentile, attr):.2f}').to.eq(f'{value:.2f}')
def test_new_timeline_item(self):
officer = OfficerFactory(id=123, appointed_date=date(2000, 1, 1), rank='Police Officer')
unit1 = PoliceUnitFactory(unit_name='001', description='unit_001')
unit2 = PoliceUnitFactory(unit_name='002', description='unit_002')
OfficerHistoryFactory(officer=officer, unit=unit1, effective_date=date(2010, 1, 1), end_date=date(2011, 12, 31))
OfficerHistoryFactory(officer=officer, unit=unit2, effective_date=date(2012, 1, 1), end_date=None)
AwardFactory(officer=officer, start_date=date(2011, 3, 23), award_type='Honorable Mention')
AwardFactory(officer=officer, start_date=date(2015, 3, 23), award_type='Complimentary Letter')
AwardFactory(officer=officer, start_date=date(2011, 3, 23), award_type='Life Saving Award')
allegation = AllegationFactory(
crid='123456',
coaccused_count=4,
incident_date=datetime(2011, 8, 23, tzinfo=pytz.utc)
)
VictimFactory(allegation=allegation, gender='M', race='White', age=34)
OfficerAllegationFactory(
final_finding='UN', final_outcome='Unknown',
officer=officer, allegation=allegation,
allegation_category=AllegationCategoryFactory(category='category', allegation_name='sub category')
)
OfficerAllegationFactory.create_batch(3, allegation=allegation)
allegation2 = AllegationFactory(
crid='654321',
point=Point(35.5, 68.9),
coaccused_count=1,
incident_date=datetime(2015, 8, 23, tzinfo=pytz.utc)
)
OfficerAllegationFactory(
final_finding='UN', final_outcome='9 Day Suspension',
officer=officer, allegation=allegation2,
allegation_category=AllegationCategoryFactory(category='Use of Force', allegation_name='sub category')
)
trr2011 = TRRFactory(
officer=officer,
trr_datetime=datetime(2011, 9, 23, tzinfo=pytz.utc),
taser=True,
firearm_used=False
)
trr2015 = TRRFactory(
officer=officer,
trr_datetime=datetime(2015, 9, 23, tzinfo=pytz.utc),
taser=False,
firearm_used=False
)
SalaryFactory(officer=officer, year=2001, rank='Police Officer', spp_date=date(2001, 9, 23))
SalaryFactory(officer=officer, year=2000, rank='Junior Police Officer', spp_date=date(2000, 1, 1))
cache_managers.cache_all()
response = self.client.get(reverse('api-v2:officers-new-timeline-items', kwargs={'pk': 123}))
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data).to.eq([
{
'trr_id': trr2015.id,
'date': '2015-09-23',
'kind': 'FORCE',
'taser': False,
'firearm_used': False,
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
}, {
'date': '2015-08-23',
'kind': 'CR',
'crid': '654321',
'category': 'Use of Force',
'subcategory': 'sub category',
'finding': 'Unfounded',
'outcome': '9 Day Suspension',
'coaccused': 1,
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
'point': {
'lon': 35.5,
'lat': 68.9
},
'victims': [],
'attachments': []
}, {
'date': '2012-01-01',
'kind': 'UNIT_CHANGE',
'unit_name': '002',
'unit_description': 'unit_002',
'rank': 'Police Officer',
}, {
'trr_id': trr2011.id,
'date': '2011-09-23',
'kind': 'FORCE',
'taser': True,
'firearm_used': False,
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2011-08-23',
'kind': 'CR',
'crid': '123456',
'category': 'category',
'subcategory': 'sub category',
'finding': 'Unfounded',
'outcome': 'Unknown',
'coaccused': 4,
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
'victims': [
{
'race': 'White',
'age': 34,
'gender': 'Male',
}
],
'attachments': [],
}, {
'date': '2011-03-23',
'kind': 'AWARD',
'award_type': 'Life Saving Award',
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2010-01-01',
'kind': 'UNIT_CHANGE',
'unit_name': '001',
'unit_description': 'unit_001',
'rank': 'Police Officer',
}, {
'date': '2001-09-23',
'kind': 'RANK_CHANGE',
'unit_name': '',
'unit_description': '',
'rank': 'Police Officer',
}, {
'date': '2000-01-01',
'kind': 'JOINED',
'unit_name': '',
'unit_description': '',
'rank': 'Junior Police Officer',
},
])
def test_list_item_content(self):
officer1 = OfficerFactory(
first_name='Jerome',
last_name='Finnigan',
birth_year=1950,
race='Asian',
gender='M',
appointed_date=datetime(2011, 1, 1, tzinfo=pytz.utc),
complaint_percentile=50.0,
allegation_count=6,
sustained_count=2,
rank='Police Officer',
)
officer2 = OfficerFactory(
first_name='Raymond',
last_name='Piwinicki',
birth_year=1960,
race='White',
gender='M',
appointed_date=datetime(2012, 1, 1, tzinfo=pytz.utc),
complaint_percentile=0.0,
allegation_count=1,
sustained_count=1,
rank='Police Officer',
)
allegation = AllegationFactory(incident_date=datetime(2014, 1, 1, tzinfo=pytz.utc))
OfficerAllegationFactory(
officer=officer1,
allegation=allegation,
final_finding='SU',
start_date=date(2014, 1, 1),
)
OfficerAllegationFactory(
officer=officer2,
allegation=allegation,
final_finding='SU',
start_date=date(2014, 1, 1),
)
OfficerAllegationFactory(
officer=officer1,
final_finding='SU',
allegation__incident_date=datetime(2016, 1, 1, tzinfo=pytz.utc),
start_date=date(2016, 1, 1)
)
OfficerAllegationFactory.create_batch(
4,
officer=officer1,
final_finding='NS',
start_date=date(2015, 1, 1),
allegation__incident_date=datetime(2015, 2, 20, tzinfo=pytz.utc)
)
ActivityCardFactory(officer=officer1, last_activity=datetime(2018, 12, 22, tzinfo=pytz.utc))
ActivityCardFactory(officer=officer2, last_activity=datetime(2018, 10, 15, tzinfo=pytz.utc))
ActivityPairCardFactory(
officer1=officer1, officer2=officer2, last_activity=datetime(2018, 5, 20, tzinfo=pytz.utc)
)
cache_all()
url = reverse('api-v2:activity-grid-list')
response = self.client.get(url)
expect(response.status_code).to.eq(status.HTTP_200_OK)
expect(response.data).to.eq([{
'id': officer1.id,
'full_name': 'Jerome Finnigan',
'complaint_count': 6,
'sustained_count': 2,
'birth_year': 1950,
'race': 'Asian',
'gender': 'Male',
'rank': 'Police Officer',
'percentile_trr': '0.0000',
'percentile_allegation_internal': '0.0000',
'percentile_allegation_civilian': '50.0000',
'percentile_allegation': '50.0000',
'kind': 'single_officer',
}, {
'id': officer2.id,
'full_name': 'Raymond Piwinicki',
'complaint_count': 1,
'sustained_count': 1,
'birth_year': 1960,
'race': 'White',
'gender': 'Male',
'rank': 'Police Officer',
'percentile_trr': '0.0000',
'percentile_allegation_internal': '0.0000',
'percentile_allegation_civilian': '0.0000',
'percentile_allegation': '0.0000',
'kind': 'single_officer',
}, {
'officer1': {
'id': officer1.id,
'full_name': 'Jerome Finnigan',
'birth_year': 1950,
'race': 'Asian',
'gender': 'Male',
'rank': 'Police Officer',
'percentile_trr': '0.0000',
'percentile_allegation_internal': '0.0000',
'percentile_allegation_civilian': '50.0000',
'percentile_allegation': '50.0000',
'complaint_count': 6,
'sustained_count': 2,
},
'officer2': {
'id': officer2.id,
'full_name': 'Raymond Piwinicki',
'birth_year': 1960,
'race': 'White',
'gender': 'Male',
'rank': 'Police Officer',
'percentile_trr': '0.0000',
'percentile_allegation_internal': '0.0000',
'percentile_allegation_civilian': '0.0000',
'percentile_allegation': '0.0000',
'complaint_count': 1,
'sustained_count': 1,
},
'coaccusal_count': 1,
'kind': 'coaccused_pair',
}])