def test_percentile_with_custom_key(self):
object1 = create_object({
'id': 1,
'value': 0.1,
'metric_custom_value': 0.1
})
object2 = create_object({
'id': 2,
'value': 0.2,
'metric_custom_value': 0.1
})
object3 = create_object({
'id': 3,
'value': 0.4,
'metric_custom_value': 0.3
})
object4 = create_object({
'id': 4,
'value': 0.4,
'metric_custom_value': 0.4
})
data = [object1, object2, object3, object4]
results = percentile(data, percentile_type='custom_value')
expect(results).to.have.length(4)
expect(object1.percentile_custom_value).to.eq(0)
expect(object2.percentile_custom_value).to.eq(0)
expect(object3.percentile_custom_value).to.eq(50)
expect(object4.percentile_custom_value).to.eq(75)
def test_percentile_with_missing_field(self):
object1 = create_object({'id': 1, 'value': 0.1})
object2 = create_object({'id': 2, 'value': 0.2})
object3 = create_object({'id': 3, 'value': 0.4})
data = [object1, object2, object3]
results = percentile(data, percentile_type='custom_value')
expect(results).to.have.length(3)
expect(hasattr(object1, 'percentile_custom_value')).to.be.false()
expect(hasattr(object2, 'percentile_custom_value')).to.be.false()
expect(hasattr(object3, 'percentile_custom_value')).to.be.false()
def test_percentile(self):
object1 = create_object({'id': 1, 'metric_value': 0.1})
object2 = create_object({'id': 2, 'metric_value': 0.2})
object3 = create_object({'id': 3, 'metric_value': 0.4})
object4 = create_object({'id': 4, 'metric_value': 0.5})
data = [object2, object4, object3, object1]
results = percentile(data, percentile_type='value')
expect(results).to.have.length(4)
expect(object1.percentile_value).to.eq(0)
expect(object2.percentile_value).to.eq(25)
expect(object3.percentile_value).to.eq(50)
expect(object4.percentile_value).to.eq(75)
def top_percentile(year=now().year, percentile_groups=PERCENTILE_GROUPS):
""" This is calculate top percentile of top_percentile_value
:return: list of (officer_id, percentile_value)
# """
if any(t not in PERCENTILE_GROUPS for t in percentile_groups):
raise ValueError("percentile_group is invalid")
computed_data = []
for percentile_group in percentile_groups:
percentile_types = PERCENTILE_MAP[percentile_group][
'percentile_funcs'].keys()
new_data = _compute_metric(year, percentile_group)
computed_data = merge_metric(computed_data, new_data, percentile_types)
for percentile_type in percentile_types:
computed_data = percentile(computed_data,
percentile_type=percentile_type,
decimal_places=4)
return computed_data
def _compute_police_district_percentiles(self):
scores = Area.police_districts_with_allegation_per_capita()
return {
district.id: district.percentile_allegation_per_capita
for district in percentile(scores, key='allegation_per_capita')
}
def test_percentile_with_no_data(self):
expect(percentile([], 0)).to.be.eq([])