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Opt year data

Brightcells преди 6 години
родител
3bf021fc9b
ревизия
76d59df977
променени са 2 файла, в които са добавени 86 реда и са изтрити 6 реда
Покажи статистика за разликите
  1. 2 0
      requirements_pywe.txt
  2. 84 6
      statistic/views.py

+ 2 - 0
requirements_pywe.txt
Преглед на файла 

@@ -1,3 +1,5 @@
1 
+pywe-component==1.0.1
2 
+pywe-component-preauthcode==1.0.3
1 3 
 pywe-jssdk==1.1.0
2 4 
 pywe-membercard==1.0.1
3 5 
 pywe-miniapp==1.1.5

+ 84 - 6
statistic/views.py
Преглед на файла 

@@ -2,12 +2,13 @@
2 2
3 3 
 from django.conf import settings
4 4 
 from django.db import transaction
5 
+from django.db.models import Count, F, Sum
5 6 
 from django_logit import logit
6 7 
 from django_models_ext.provincemodels import ProvinceShortModelMixin
7 8 
 from django_response import response
8 9 
 from TimeConvert import TimeConvert as tc
9 10
10 
-from mch.models import BrandInfo, DistributorInfo, ModelInfo
11 
+from mch.models import BrandInfo, DistributorInfo, ModelInfo, SaleclerkInfo
11 12 
 from statistic.models import (ConsumeDistributorSaleStatisticInfo, ConsumeModelSaleStatisticInfo,
12 13 
                               ConsumeProvinceSaleStatisticInfo, ConsumeSaleStatisticInfo, ConsumeUserStatisticInfo,
13 14 
                               DistributorSaleStatisticInfo, ModelSaleStatisticInfo, ProvinceSaleStatisticInfo,
 
@@ -203,6 +204,83 @@ def __tj_generate(ymd=None):
203 204 
         )
204 205
205 206
207 
+def ytj(brand_id):
208 
+    # [消费者维度] 周期内扫描用户人数
209 
+    cusis = ConsumeUserStatisticInfo.objects.filter(brand_id=brand_id, ymd__lt=9999)
210 
+    users = []
211 
+    for cusi in cusis:
212 
+        users += cusi.users
213 
+    scan_user_count = sum(users)
214 
+
215 
+    # [消费者维度] 周期内镜头销售支数
216 
+    sell_volume_count = ConsumeSaleStatisticInfo.objects.filter(brand_id=brand_id, ymd__lt=9999).aggregate(Sum('num')).get('num__sum', 0) or 0
217 
+
218 
+    startd = tc.local_string(months=-12, format='%Y%m%d')
219 
+    endd = tc.local_string(format='%Y%m%d')
220 
+
221 
+    # [消费者维度] 统计周期内型号扫描排行数据，请按顺序返回
222 
+    models = ConsumeModelSaleStatisticInfo.objects.filter(
223 
+        brand_id=brand_id,
224 
+        ymd__gt=startd,
225 
+        ymd__lte=endd,
226 
+        status=True
227 
+    ).order_by(
228 
+        'model_name'
229 
+    ).values(
230 
+        'model_id', 'model_name'
231 
+    ).annotate(
232 
+        num=Sum('num')
233 
+    ).order_by(
234 
+        '-num'
235 
+    )[:20]
236 
+
237 
+    # [经销商维度] 统计周期内销售员排行数据，请按顺序返回
238 
+    salesmen = SaleclerkSaleStatisticInfo.objects.filter(
239 
+        brand_id=brand_id,
240 
+        ymd__gt=startd,
241 
+        ymd__lte=endd,
242 
+        status=True
243 
+    ).order_by(
244 
+        'clerk_id'
245 
+    ).values(
246 
+        'clerk_id'
247 
+    ).annotate(
248 
+        num=Sum('num')
249 
+    ).order_by(
250 
+        '-num'
251 
+    )[:20]
252 
+    clerks = SaleclerkInfo.objects.filter(brand_id=brand_id, status=True)
253 
+    clerks = {clerk.clerk_id: {'distributor_id': clerk.distributor_id, 'distributor_name': clerk.distributor_name, 'clerk_name': clerk.clerk_name} for clerk in clerks}
254 
+    salesmen = [dict(sm, **clerks.get(sm.get('clerk_id', ''), {})) for sm in salesmen]
255 
+
256 
+    # [收费者维度] 统计周期内省份销量排行数据，请按顺序返回
257 
+    provinces = ConsumeProvinceSaleStatisticInfo.objects.filter(
258 
+        brand_id=brand_id,
259 
+        ymd__gt=startd,
260 
+        ymd__lte=endd,
261 
+        status=True
262 
+    ).order_by(
263 
+        'province_code'
264 
+    ).values(
265 
+        'province_code'
266 
+    ).annotate(
267 
+        num=Sum('num')
268 
+    ).order_by(
269 
+        '-num'
270 
+    )
271 
+    provinces = [dict(p, **{'province_name': ''}) for p in provinces]
272 
+
273 
+    return {
274 
+        'scan_user_count': scan_user_count,
275 
+        'sell_volume_count': sell_volume_count,
276 
+        'user_count_increase_pct': -1,  # 与上个统计周期数据的用户人数比例
277 
+        'volume_count_increase_pct': -1,  # 与上个统计周期数据的销售支数比例
278 
+        'models': list(models),
279 
+        'salesmen': salesmen,
280 
+        'provinces': provinces,
281 
+    }
282 
+
283 
+
206 284 
 def ymdtj(brand_id, ymd, lastymd):
207 285 
     # [消费者维度] 周期内扫描用户人数
208 286 
     try:
 
@@ -237,8 +315,8 @@ def ymdtj(brand_id, ymd, lastymd):
237 315 
     models = [m.data for m in current_models[:20]]
238 316
239 317 
     # [经销商维度] 统计周期内销售员排行数据，请按顺序返回
240 
-    salesmen = SaleclerkSaleStatisticInfo.objects.filter(brand_id=brand_id, ymd=ymd, status=True).order_by('-num')[:20]
241 
-    salesmen = [s.data for s in salesmen]
318 
+    salesmen = SaleclerkSaleStatisticInfo.objects.filter(brand_id=brand_id, ymd=ymd, status=True).order_by('-num')
319 
+    salesmen = [s.data for s in salesmen[:20]]
242 320
243 321 
     # [收费者维度] 统计周期内省份销量排行数据，请按顺序返回
244 322 
     provinces = ConsumeProvinceSaleStatisticInfo.objects.filter(brand_id=brand_id, ymd=ymd, status=True).order_by('-num')
 
@@ -259,15 +337,15 @@ def ymdtj(brand_id, ymd, lastymd):
259 337 
 def v2_tj_distributor(request):
260 338 
     brand_id = request.POST.get('brand_id', settings.KODO_DEFAULT_BRAND_ID)
261 339
262 
-    year = tc.local_string(format='%Y')
340 
+    # year = tc.local_string(format='%Y')
263 341 
     month = tc.local_string(format='%Y%m')
264 342 
     day = tc.local_string(format='%Y%m%d')
265 343
266 
-    lastyear = tc.local_string(tc.several_time_ago(years=1), format='%Y')
344 
+    # lastyear = tc.local_string(tc.several_time_ago(years=1), format='%Y')
267 345 
     lastmonth = tc.local_string(tc.several_time_ago(months=1), format='%Y%m')
268 346 
     lastday = tc.local_string(tc.several_time_ago(days=1), format='%Y%m%d')
269 347
270 
-    year_data = ymdtj(brand_id, year, lastyear)
348 
+    year_data = ytj(brand_id)
271 349 
     month_data = ymdtj(brand_id, month, lastmonth)
272 350 
     day_data = ymdtj(brand_id, day, lastday)
273 351