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316 lines
18 KiB
316 lines
18 KiB
import datetime
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from io import BytesIO
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import pandas as pd
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from django.contrib.gis.measure import Distance
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from django.db.models import F
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from postamates.settings import DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS, AGE_DAY_LIMIT
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from service import models
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from service.enums import PointStatus
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from service.utils import create_columns_dist, run_psql_command
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import base64
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import requests
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from postamates.settings import GEOCODER_API_KEY
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from service.enums import MatchingStatus
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from django.contrib.gis.db.models.functions import Distance as Dist
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from django.db.models import Avg, Sum, Count
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class PointService:
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def update_fact(self, postamat_id: str, fact: int):
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qs = self.get_point_by_postamat_id(postamat_id)
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qs.update(**{'fact': fact})
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def update_postamat_id(self, point_id: int, postamat_id: str):
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qs = self.get_point_by_id(point_id)
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qs.update(**{'postamat_id': postamat_id})
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def start_mathing(self, obj_id: int):
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file = models.TempFiles.objects.get(id=obj_id)
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excel_file = base64.b64decode(file.data)
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df = pd.read_excel(excel_file)
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total = df.shape[0]
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matched = 0
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problem = 0
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for _i, row in df.iterrows():
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addr = row['Адрес']
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cat = row['Категория объекта']
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req_url = f"https://geocode.search.hereapi.com/v1/geocode?q={addr}&apiKey={GEOCODER_API_KEY}"
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response = requests.get(req_url).json().get('items')
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if not response:
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models.PrePlacementPoint.objects.create(address=addr, matching_status=MatchingStatus.Error.name, status=PointStatus.Pending.name)
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problem += 1
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continue
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coords = response[0].get('position')
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if not coords:
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models.PrePlacementPoint.objects.create(address=addr, matching_status=MatchingStatus.Error.name, status=PointStatus.Pending.name)
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problem += 1
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continue
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wkt = "POINT(" + str(coords['lng']) + " " + str(coords['lat']) + ")"
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response = response[0]['address']
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obj = models.PlacementPoint.objects.filter(street=response.get('street'),
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house_number=response.get('houseNumber'),
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category=cat).values().first()
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rayon = models.Rayon.objects.filter(polygon__intersects=wkt).first()
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if obj:
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distances = models.PlacementPointPVZDistance.objects.filter(placement_point=obj.get('id')).all()
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obj.pop('id')
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pre_obj, _ = models.PrePlacementPoint.objects.get_or_create(
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**{**obj, "matching_status": MatchingStatus.Matched.name})
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for d in distances:
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models.PrePlacementPointPVZDistance.objects.get_or_create(placement_point=pre_obj,
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pvz_postamates_group=d.pvz_postamates_group,
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dist=d.dist)
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matched += 1
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elif not rayon:
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models.PrePlacementPoint.objects.get_or_create(address=addr, street=response.get('street'),
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house_number=response.get('houseNumber'),
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subject_rf=response.get('state'),
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city=response.get('city'),
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category=cat, geometry=wkt, sample_trn=False,
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is_vis=True,
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matching_status=MatchingStatus.Error.name,
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status=PointStatus.Unmatched.name)
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problem += 1
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else:
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models.PrePlacementPoint.objects.get_or_create(address=addr, street=response.get('street'),
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house_number=response.get('houseNumber'),
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subject_rf=response.get('state'),
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city=response.get('city'),
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category=cat, geometry=wkt, sample_trn=False,
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is_vis=True,
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matching_status=MatchingStatus.New.name,
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status=PointStatus.Pending.name, area=rayon,
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district=rayon.AO)
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return total, matched, problem
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def make_enrichment(self):
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points = models.PrePlacementPoint.objects.filter(matching_status=MatchingStatus.New.name).all()
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groups = models.Post_and_pvzGroup.objects.all()
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for point in points:
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origin = point.geometry
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qs = models.PlacementPoint.objects.filter(status=PointStatus.Working.name).annotate(
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dist=Dist('geometry', origin)).order_by('dist')
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point.target_dist = qs[0].dist.m
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point.target_post_cnt = qs.filter(
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dist__lt=Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS)
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).count()
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point.target_cnt_ao_mean = qs[0].target_cnt_ao_mean
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point.rival_post_cnt = models.Post_and_pvz.objects.filter(
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category__name="Постамат", include_in_ml=True,
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.rival_pvz_cnt = models.Post_and_pvz.objects.filter(
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category__name="ПВЗ", include_in_ml=True,
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.metro_dist = models.OtherObjects.objects.filter(group__name='metro_stations').annotate(
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dist=Dist('wkt', origin)).order_by('dist')[0].dist.m
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point.property_price_bargains = models.OtherObjects.objects.filter(
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group__name="bargains",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(Avg('param1'))[
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'param1__avg']
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offers_estate = models.OtherObjects.objects.filter(
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group__name="offers_estate",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(
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param1__avg=Avg('param1'), param3__avg=Avg('param3'))
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point.property_price_offers = offers_estate['param1__avg']
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point.property_mean_floor = offers_estate['param3__avg']
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point.property_era = models.OtherObjects.objects.filter(
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group__name="offers_estate").values('param2').annotate(cnt=Count('param2')).order_by('-cnt').first()[
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'param2']
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point.flats_cnt = models.OtherObjects.objects.filter(
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group__name="flats_cnt",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(
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param1__sum=Sum('param1'))['param1__sum']
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popul_home_job = models.OtherObjects.objects.filter(
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group__name="popul_home_job",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(
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param1__sum=Sum('param1'), param3__sum=Sum('param3'))
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point.popul_home = popul_home_job['param1__sum']
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point.popul_job = popul_home_job['param3__sum']
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yndx_food_cnt_amt = models.OtherObjects.objects.filter(
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group__name="yndx_food_cnt_amt",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(
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param1__sum=Sum('param1'), param3__sum=Sum('param3'))
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point.yndxfood_sum = yndx_food_cnt_amt['param1__sum']
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point.yndxfood_cnt = yndx_food_cnt_amt['param3__sum']
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point.school_cnt = models.OtherObjects.objects.filter(
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group__name="schools",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.kindergar_cnt = models.OtherObjects.objects.filter(
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group__name="kindergar",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.public_stop_cnt = models.OtherObjects.objects.filter(
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group__name="stops",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.sport_center_cnt = models.OtherObjects.objects.filter(
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group__name="sport_centers",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.pharmacy_cnt = models.OtherObjects.objects.filter(
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group__name="pharmacies",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.supermarket_cnt = models.OtherObjects.objects.filter(
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group__name="supermarkets",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.supermarket_premium_cnt = models.OtherObjects.objects.filter(
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group__name="supermarkets_premium",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.clinic_cnt = models.OtherObjects.objects.filter(
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group__name="clinics",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.bank_cnt = models.OtherObjects.objects.filter(
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group__name="banks",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.reca_cnt = models.OtherObjects.objects.filter(
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group__name="recas",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.lab_cnt = models.OtherObjects.objects.filter(
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group__name="labs",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.culture_cnt = models.OtherObjects.objects.filter(
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group__name="cultures",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.attraction_cnt = models.OtherObjects.objects.filter(
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group__name="attractions",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.mfc_cnt = models.OtherObjects.objects.filter(
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group__name="public_services",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.bc_cnt = models.OtherObjects.objects.filter(
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group__name="BC",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.tc_cnt = models.OtherObjects.objects.filter(
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group__name="TC",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).count()
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point.business_activity = models.OtherObjects.objects.filter(
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group__name="business_activity",
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wkt__distance_lt=(origin, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS))).aggregate(
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param1__sum=Sum('param1'))['param1__sum']
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point.age_day = AGE_DAY_LIMIT
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placement_point = models.PlacementPoint.objects.annotate(
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dist=Dist('geometry', origin)).order_by('dist')[0]
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point.target_cnt_ao_mean = placement_point.target_cnt_ao_mean
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point.save()
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for group in groups:
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self.calculate_dist_for_group(point, group, instance_type=models.PrePlacementPointPVZDistance)
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run_psql_command()
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@staticmethod
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def calculate_dist_for_group(point, group, instance_type=models.PlacementPointPVZDistance):
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post_object = models.Post_and_pvz.objects.filter(group__name=group.name).annotate(
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distance=Dist("wkt", point.geometry)).order_by('distance').first()
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d = instance_type.objects.filter(placement_point=point,
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pvz_postamates_group=group).first()
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if post_object:
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if d:
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if d.dist > post_object.distance.m:
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d.dist = post_object.distance.m
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d.save()
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else:
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instance_type.objects.create(placement_point=point,
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pvz_postamates_group=group,
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dist=post_object.distance.m)
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@staticmethod
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def delete_preplacement_points(ids: list):
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models.PrePlacementPoint.objects.filter(id__in=ids).all().delete()
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@staticmethod
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def get_min_distances_to_group(postamat_id: str):
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return {d['pvz_postamates_group']: d['dist'] for d in list(
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models.PlacementPointPVZDistance.objects.filter(placement_point=postamat_id).values(
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'pvz_postamates_group', 'dist'))}
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@staticmethod
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def update_points_in_radius(qs: models.PlacementPoint, new_status: str):
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for point in qs:
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if new_status == PointStatus.Installation.name:
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if point.status == PointStatus.Pending.name:
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pnts = models.PlacementPoint.objects.filter(
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geometry__distance_lt=(point.geometry, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS)),
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)
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pnts.update(prediction_first=F('prediction_current'), target_post_cnt=F('target_post_cnt') + 1)
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elif new_status == PointStatus.Cancelled.name or new_status == PointStatus.Pending.name:
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if point.status == PointStatus.Installation.name:
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pnts = models.PlacementPoint.objects.filter(
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geometry__distance_lt=(point.geometry, Distance(m=DEFAULT_PLACEMENT_POINT_UPDATE_RADIUS)),
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)
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pnts.update(target_post_cnt=F('target_post_cnt') - 1 if F('target_post_cnt') != 0 else 0)
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@staticmethod
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def update_status(qs: models.PlacementPoint, new_status: str) -> models.PlacementPoint:
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for q in qs:
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if q.status == PointStatus.Installation.name and new_status == PointStatus.Working.name:
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qs.update(**{'age_day': 0, 'start_date': datetime.datetime.now(), 'status': new_status})
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else:
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qs.update(**{'status': new_status})
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@staticmethod
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def get_point_by_id(point_id: int):
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return models.PlacementPoint.objects.filter(pk=point_id)
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@staticmethod
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def get_point_by_postamat_id(postamat_id: str):
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return models.PlacementPoint.objects.filter(postamat_id=postamat_id)
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@staticmethod
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def to_excel(serializer):
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data = pd.DataFrame(serializer.data)
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if not data.empty:
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if data['start_date'].any():
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data['start_date'] = data.get('start_date').dt.tz_localize(None)
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if data['sample_trn'].any():
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data['sample_trn'] = data['sample_trn'].astype(int)
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data.rename(columns={'district_id': 'district', 'area_id': 'area'}, inplace=True)
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data['min_distance_to_group'] = data['min_distance_to_group'].apply(lambda x: list(x.items()))
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new_columns = data.apply(create_columns_dist, axis=1)
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for ind in new_columns.columns:
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expanded = new_columns[ind].apply(pd.Series)
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group = models.Post_and_pvzGroup.objects.get(id=int(expanded.loc[0, 0]))
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expanded[[f"group_{ind + 1}_name", f"group_{ind + 1}_category"]] = group.name, group.category.name
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expanded = expanded.rename(columns={1: f"dist_to_group_{ind + 1}"})
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expanded = expanded.drop(0, axis=1)
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new_columns = pd.concat([new_columns, expanded], axis=1)
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new_columns = new_columns.drop(ind, axis=1)
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data.drop('min_distance_to_group', axis=1, inplace=True)
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data = pd.concat([data, new_columns], axis=1)
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with BytesIO() as b:
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with pd.ExcelWriter(b) as writer:
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data.to_excel(
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writer, sheet_name='Placement Points',
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index=False,
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)
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return b.getvalue()
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@staticmethod
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def to_json(serializer):
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data = pd.DataFrame(serializer.data)
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data['start_date'] = pd.to_datetime(data['start_date'], errors='coerce')
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data['start_date'] = data['start_date'].dt.tz_localize(None)
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data['sample_trn'] = data['sample_trn'].astype(int)
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data['geometry'] = data['geometry'].apply(lambda x: {'latitude': x[1], 'longtitude': x[0]})
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data.rename(columns={'district_id': 'district', 'area_id': 'area'}, inplace=True)
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data['min_distance_to_group'] = data['min_distance_to_group'].apply(lambda x: list(x.items()))
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new_columns = data.apply(create_columns_dist, axis=1)
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for ind in new_columns.columns:
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expanded = new_columns[ind].apply(pd.Series)
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group = models.Post_and_pvzGroup.objects.get(id=int(expanded.loc[0, 0]))
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expanded[[f"group_{ind + 1}_name", f"group_{ind + 1}_category"]] = group.name, group.category.name
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expanded = expanded.rename(columns={1: f"dist_to_group_{ind + 1}"})
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expanded = expanded.drop(0, axis=1)
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new_columns = pd.concat([new_columns, expanded], axis=1)
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new_columns = new_columns.drop(ind, axis=1)
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data.drop('min_distance_to_group', axis=1, inplace=True)
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data = pd.concat([data, new_columns], axis=1)
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return data.to_json(orient='records')
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@staticmethod
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def get_first_10_k():
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if models.PlacementPoint.objects.count() > 10000:
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qs = models.PlacementPoint.objects.order_by('-prediction_current').all()[10000]
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return qs.prediction_current
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else:
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return models.PlacementPoint.objects.order_by('prediction_current').first().prediction_current
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