def count_avg_sq():
districts = DistrictModel.query.all()
flats = RealtyModel.query.all()
for d in districts:
sum = 0.0
count = 0
for f in flats:
if f.type == 1:
if d.id == f.dist:
sum += f.price / f.area
count += 1
if count > 0:
d.avg_sq = sum / count
RealtyModel.update_dist()
def parse_ao():
# with open('ao.json', 'r') as myfile:
# data=myfile.read()
#
# obj = json.loads(data)
#
# for i in obj['features']:
# new_ao = AoModel(
# id=i['properties']['OKATO'],
# name=i['properties']['NAME'],
# type=i['geometry']['type']
# )
#
# new_ao.save_to_db()
#
districts = DistrictModel.query.all()
ao = AoModel.query.all()
for a in ao:
sumsq = 0.0
sumcoeff = 0
countsq = 0
countcoeff = 0
for d in districts:
if a.id == d.okato_ao:
if d.avg_sq:
sumsq += d.avg_sq
countsq += 1
if d.avg_coeff:
sumcoeff += d.avg_coeff
countcoeff += 1
if countsq != 0:
a.avg_sq = sumsq / countsq
if countcoeff != 0:
a.avg_coeff = int(sumcoeff / countcoeff)
RealtyModel.update_dist()
def count_avg_coeff():
districts = DistrictModel.query.all()
flats = RealtyModel.query.all()
flats_with_coeffs = TempModel.query.all()
for fwc in flats_with_coeffs:
for f in flats:
if fwc.id == f.id:
fwc.dist = f.dist
RealtyModel.update_dist()
for d in districts:
coeff = 0
count = 0
for fwc in flats_with_coeffs:
if d.id == fwc.dist:
coeff += fwc.coeff
count += 1
if count > 0:
d.avg_coeff = int(coeff / count)
RealtyModel.update_dist()
def check_point_is_in_polygon():
districts = DistrictModel.query.all()
flats = RealtyModel.query.filter(RealtyModel.dist == None)
res = 0
for d in districts:
for f in flats:
if d.type == "Polygon":
if ray_tracing(f.longitude, f.latitude,
np.array(d.coordinates)):
print('here')
res += 1
f.dist = d.id
continue
elif d.type == "MultiPolygon":
for p in d.coordinates:
if ray_tracing(f.longitude, f.latitude, np.array(p)):
print('here')
res += 1
f.dist = d.id
continue
RealtyModel.update_dist()
print(RealtyModel.query.filter(RealtyModel.dist == None).count())