def test_schwartzberg_comparison_esri_shapefile(self):
shape_file = r"tests/tl_2014_55_sldl/tl_2014_55_sldl.shp"
driver = ogr.GetDriverByName('ESRI Shapefile')
data_source = driver.Open(shape_file, 0)
if data_source is None:
print ("Open failed.\n")
sys.exit(1)
layer = data_source.GetLayer()
layer.ResetReading()
compact = None
dispersed = None
for feature in layer:
# feature field one is the district ID
id = feature.GetField(1)
# compact
if id == "027":
# the feature's geometry
geom = feature.GetGeometryRef()
geometry = geom.Clone()
compact = District(id=27, geometry=geometry)
# dispersed
if id == "077":
# the feature's geometry
geom = feature.GetGeometryRef()
geometry = geom.Clone()
dispersed = District(id=77, geometry=geometry)
compact_score = schwartzberg(compact)
dispersed_score = schwartzberg(dispersed)
self.assertTrue(compact_score > dispersed_score)
def test_schwartzberg_square_3857(self):
# A square around Lake Winnebago
geom = ogr.CreateGeometryFromJson('{"type": "Polygon", "coordinates": [[[-9839815.088179024,5505529.83629639],[-9881396.831566159,5468840.062719505],[-9844707.057989275,5427258.31933237],[-9803125.31460214,5463948.092909254],[-9839815.088179024,5505529.83629639]]]}')
district = District(geometry=geom)
radius = math.sqrt(1/math.pi)
circumference = 2*math.pi*radius
schwartzberg_score = 1/(4/circumference)
self.assertAlmostEqual(schwartzberg_score, schwartzberg(district), places=5)
def test_schwartzberg_square_4326(self):
# A square around Lake Merritt: From PlanScore
geom = ogr.CreateGeometryFromJson('{"type": "Polygon", "coordinates": [[[-122.2631266, 37.7987797], [-122.2631266, 37.8103489], [-122.2484841, 37.8103489], [-122.2484841, 37.7987797], [-122.2631266, 37.7987797]]]}')
district = District(geometry=geom)
radius = math.sqrt(1/math.pi)
circumference = 2*math.pi*radius
schwartzberg_score = 1/(4/circumference)
self.assertAlmostEqual(schwartzberg_score, schwartzberg(district), places=5)
def test_schwartzberg_triangle_3857(self):
# An equilateral triangle around Lake Mendota
geom = ogr.CreateGeometryFromJson('{"type": "Polygon", "coordinates": [[[-9942183.378309947,5335705.868703798],[-9966678.038775941,5335248.39511508],[-9954034.524793552,5314264.133688814],[-9942183.378309947,5335705.868703798]]]}')
district = District(geometry=geom)
area_of_triangle = math.sqrt(3)/4
radius = math.sqrt(area_of_triangle/math.pi)
circumference = 2*math.pi*radius
schwartzberg_score = 1/(3/circumference)
self.assertAlmostEqual(schwartzberg_score, schwartzberg(district), places=5)
"""
convex_hull_scores = []
for district in district_plan:
score = convex_hull_ratio(district)
convex_hull_scores.append(score)
print("District: {}\tConvex Hull Ratio: {}".format(district.id, score))
print("Average Convex Hull Ratio: {}".format(
statistics.mean(convex_hull_scores)))
"""
Schwartzberg for each district and the district plan average
"""
print("\n\n")
schwartzberg_scores = []
for district in district_plan:
score = schwartzberg(district)
schwartzberg_scores.append(score)
print("District: {}\tSchwartzberg: {}".format(district.id, score))
print("Average Schwartzberg: {}".format(statistics.mean(schwartzberg_scores)))
"""
Polsby_Popper for each district and the district plan average
"""
print("\n\n")
polsby_popper_scores = []
for district in district_plan:
score = polsby_popper(district)
polsby_popper_scores.append(score)
print("District: {}\tPolsby-Popper: {}".format(district.id, score))
print("Average Polsby-Popper: {}".format(
def state_assembly(filename):
shape_file = filename
driver = ogr.GetDriverByName('ESRI Shapefile')
data_source = driver.Open(shape_file, 0)
if data_source is None:
print("Open failed.\n")
sys.exit(1)
layer = data_source.GetLayer()
layer.ResetReading()
district_plan = []
office = 'State Assembly'
for feature in layer:
# feature field one is the district ID
ida = feature['District_N']
# the feature's geometry
geom = feature.GetGeometryRef()
geometry = geom.Clone()
pre_districts = Pre_District.objects.filter(
district_no=int(ida),
office__contains='Assembly').values('population')
red_votes = Pre_District.objects.filter(
district_no=int(ida),
office__contains='Assembly').values('red_votes')
blue_votes = Pre_District.objects.filter(
district_no=int(ida),
office__contains='Assembly').values('blue_votes')
total_votes = Pre_District.objects.filter(
district_no=int(ida),
office__contains='Assembly').values('total_votes')
total_votes = total_votes[0]['total_votes']
party_votes = {
'rep': int(red_votes[0]['red_votes']),
'dem': int(blue_votes[0]['blue_votes'])
}
district_plan.append(
D(id=int(ida),
geometry=geometry,
population=pre_districts[0]['population'],
party_votes=party_votes,
votes=int(total_votes)))
total_polsby_popper = 0
total_sch = 0
total_hull = 0
## Django Stuff
for district in district_plan:
polsby_popper_score = polsby_popper(district)
sch_score = schwartzberg(district)
hull_score = convex_hull_ratio(district)
total_polsby_popper = total_polsby_popper + polsby_popper_score
total_sch = total_sch + sch_score
total_hull = total_hull + hull_score
district_model = District(district_no=district.id,
office=office,
polsby_popper=polsby_popper_score,
shwartzberg=sch_score,
convex_hull_ratio=hull_score)
district_model.save()
#district plan
avg_polsby = total_polsby_popper / 99.0
avg_sch = total_sch / 99.0
avg_hull = total_hull / 99.0
efficiency = efficiency_gap(district_plan, 'dem', 'rep')
mean_median = mean_median_diff(district_plan, 'dem', 'rep')
equi_pop = districts_in_percent_deviation(district_plan, 10.0)
district_plan_model = District_Plan(name=office,
year=2016,
avg_polsby_popper=avg_polsby,
avg_schwartzberg=avg_sch,
avg_convex_hull=avg_hull,
equal_population=equi_pop,
efficiency_gap=efficiency,
mean_median_diff=mean_median)
district_plan_model.save()