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Get_DTP_FederalHighways.py
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Get_DTP_FederalHighways.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import requests as r
from json import loads,dumps
import pandas as pd
from numpy import random, array_split
from multiprocessing.pool import ThreadPool
import time
import re
import os
months=['MONTHS:12.2015','MONTHS:12.2016','MONTHS:12.2017','MONTHS:12.2018','MONTHS:12.2019','MONTHS:12.2020','MONTHS:12.2021','MONTHS:12.2022','MONTHS:11.2023']
desktop_agents = ['Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0']
inc=50
# In[2]:
def Get_OKTMOS():
regions_dict = {"maptype":1,"region":"877","date":"[\"11.2020\"]","pok":"1"}
k=1
while k==1:
try:
raw_data = r.post("http://stat.gibdd.ru/map/getMainMapData", json=regions_dict)
k=0
except:
time.sleep(10)
raw_reg_js=loads(raw_data.content)
raw_reg_js=loads(loads(raw_reg_js["metabase"])[0]["maps"])
oktmos=[(i["id"],i["name"]) for i in raw_reg_js]
region=[]
region_oktmo=[]
district=[]
district_oktmo=[]
for oktmo in oktmos:
subregions_dict = {"maptype":1,"region":"877","date":"[\"11.2020\"]","pok":"1","region":oktmo[0]}
raw_subdata=r.post("http://stat.gibdd.ru/map/getMainMapData", json=subregions_dict)
raw_subreg_js=loads(loads(loads(raw_subdata.content)["metabase"])[0]["maps"])
for js in raw_subreg_js:
region.append(oktmo[1])
region_oktmo.append(oktmo[0])
district.append(js["name"])
district_oktmo.append(js["id"])
df=pd.DataFrame([region,district,region_oktmo,district_oktmo]).T
df.columns=["Region","District","Region_OKTMO","District_OKTMO"]
df.to_excel("OKTMOS.xlsx",index=False)
Get_OKTMOS()
# In[3]:
def Get_Road_Info():
raw_data=r.post("http://stat.gibdd.ru/road/getMainMapData")
data=loads(loads(raw_data.content)["metabase"])["features"]
ids=[]
for i in data:
ids.append((i["properties"]["id"],i["properties"]["name"]))
ids=ids[:85]
mega_roads=[]
for id_ in ids:
roads=[]
raw_data1=r.post("http://stat.gibdd.ru/road/getRegionDorCombo",json={"reg":id_[0]})
try:
raw_data1=loads(raw_data1.content)
for rd in raw_data1:
if ("ИСКЛЮЧЕНА" in rd["text"]) or ("исключена" in rd["text"]):
pass
else:
roads.append((rd["value"],rd["text"]))
except:
roads.append(("0","0"))
mega_roads.append(roads)
mega_tuples=[]
for ind,id_ in enumerate(ids):
if (mega_roads[ind]==[("0","0")]) or (mega_roads[ind]==[]):
pass
else:
mega_tuples.append([id_,mega_roads[ind]])
reg_numbers=[]
reg_names=[]
road_numbers=[]
road_names=[]
for tup in mega_tuples:
reg_info,roads=tup
for road in roads:
reg_numbers.append(reg_info[0])
reg_names.append(reg_info[1])
road_value,road_text=road
road_numbers.append(road_value)
road_names.append(road_text)
df=pd.DataFrame([reg_numbers,reg_names,road_numbers,road_names]).T
df.columns=["Region_Number","Region","Road_Number","Road"]
df.to_excel("Roads.xlsx",index=False)
return df
df1=Get_Road_Info()
# In[4]:
def do_post(js,f0,t1,t2):
u=0
while u==0:
try:
post_content=f0.post("http://stat.gibdd.ru/road/getDorKMData",json=js,headers={"User-Agent":random.choice(desktop_agents)},verify=False)
u=1
except:
time.sleep(10)
print("Таймаут {},{}".format(t1,t2))
return post_content
# In[5]:
def get_km_months(rows_part):
num,rows_part=rows_part
km_and_months=[]
with r.Session() as f:
for ind,part in enumerate(rows_part):
indexes=list(df1[df1.Region==part].index)
for index in indexes:
x=0
tup=tuple(df1.iloc[index,:])
for month in months:
dicts={"dor":str(tup[2]),"reg":str(tup[0]),"date":month}
raw_data=do_post(dicts,f,tup[1],tup[3])
raw_data=raw_data.json()["data"]
x+=len(raw_data)
for d in raw_data:
km_and_months.append((tup[3],tup[1],tup[2],d["name"],month))
return km_and_months
# In[6]:
def make_km_frame():
regs=list(df1.Region.unique())
rows_divided=list(array_split(regs,4))
nums=[1,2,3,4]
rows_divided=list(zip(nums,rows_divided))
pool=ThreadPool(4)
l=pool.map(get_km_months,rows_divided)
res=[]
for l_l in l:
for l_l_l in l_l:
res.append(l_l_l)
names=[i[0] for i in res]
regs=[i[1] for i in res]
road_nums=[i[2] for i in res]
km_nums=[i[3] for i in res]
months=[i[4] for i in res]
df=pd.DataFrame([names,regs,road_nums,km_nums,months]).T
df.columns=["Name","Region","Road_Num","KM","Month"]
df.to_excel("Stats_by_region_roads.xlsx",index=False)
return df
# In[7]:
def do_post_card(js,f0,t1,t2):
u=0
while u==0:
try:
post_content=f0.post("http://stat.gibdd.ru/road/getDorKMKardList",json=js,headers={"User-Agent":random.choice(desktop_agents)},verify=False)
u=1
except:
time.sleep(10)
print("Таймаут {},{}".format(t1,t2))
return post_content
# In[8]:
def get_dtp_cards(rows_divided):
num,rows_part,df2=rows_divided
kard_data=[]
with r.Session() as fk:
for ind,part in enumerate(rows_part):
indexes=list(df2[df2.Region==part].index)
x=0
for index in indexes:
tup=tuple(df2.iloc[index,:])
st=1
j=True
while j:
dicts={"data":{"date":[tup[-1]],"order":{"type":"1","fieldName":"dat"},"dor":str(tup[2]),"km":str(tup[3]),"st":str(st),"en":str(st+inc)}}
js={}
js["data"]=dumps(dicts["data"],separators=(',', ':')).encode("utf8").decode('unicode-escape')
raw_data= do_post_card(js,fk,tup[1],tup[0])
try:
data=loads(loads(raw_data.content)["data"])["tab"]
st=st+inc+1
x+=len(data)
for i in data:
road=i["infoDtp"]["dor"]
date=i["date"]
district=i["District"]
reg=part
curr_km=tup[3]
type_acc=i["DTP_V"]
timing=i['Time']
identif=i['KartId']
deaths=i['POG']
wounded=i['RAN']
partic=i['K_UCH']
nofcars=i['K_TS']
street_type=i['infoDtp']['k_ul']
factor=" "
for f in i['infoDtp']['factor']:
factor+=f+" "
weather=" "
for f in i['infoDtp']['s_pog']:
weather+=f+" "
spch=i['infoDtp']['s_pch']
brightn=i['infoDtp']['osv']
change_motion=i['infoDtp']['change_org_motion']
nedostatk=" "
for f in i['infoDtp']['ndu']:
nedostatk+=f+" "
UDS_NA_DTP=" "
for f in i['infoDtp']['sdor']:
UDS_NA_DTP+=f+" "
UDS_NEAR_DTP=" "
for f in i['infoDtp']['OBJ_DTP']:
UDS_NEAR_DTP+=f+" "
kard_data.append((road,date,district,reg,curr_km,type_acc,timing,identif,deaths,wounded,partic,nofcars,street_type,factor,
weather,spch,brightn,change_motion,nedostatk,UDS_NA_DTP,UDS_NEAR_DTP))
except:
j=False
print(part,x)
time.sleep(20)
return kard_data
# In[10]:
def Federal_Highways_DTP_Parser():
df2=make_km_frame()
regs=list(df2.Region.unique())
rows_divided=list(array_split(regs,4))
nums=[1,2,3,4]
rd=list(zip(nums,rows_divided))
rd_n=[]
for r1 in rd:
rd_n.append((r1[0],r1[1],df2))
pool=ThreadPool(4)
l=pool.map(get_dtp_cards,rd_n)
tuples=[]
for l_l in l:
for l_l_l in l_l:
tuples.append(l_l_l)
print("Парсинг закончен.Работаю с данными")
roads=[i[0] for i in tuples]
dates=[i[1] for i in tuples]
districts=[i[2] for i in tuples]
regs=[i[3] for i in tuples]
kms=[i[4] for i in tuples]
types=[i[5] for i in tuples]
timings=[i[6] for i in tuples]
ids=[i[7] for i in tuples]
deaths=[i[8] for i in tuples]
woundeds=[i[9] for i in tuples]
participants=[i[10] for i in tuples]
nofcars=[i[11] for i in tuples]
street_types=[i[12] for i in tuples]
factors=[i[13] for i in tuples]
weathers=[i[14] for i in tuples]
spchs=[i[15] for i in tuples]
brightns=[i[16] for i in tuples]
change_motion=[i[17] for i in tuples]
nedostatk=[i[18] for i in tuples]
uds_na_dtp=[i[19] for i in tuples]
uds_near_dtp=[i[20] for i in tuples]
df_f=pd.DataFrame([ids,roads,dates,timings,districts,regs,kms,street_types,types,nofcars,participants,deaths,woundeds,factors,
weathers,spchs,brightns,change_motion,nedostatk,uds_na_dtp,uds_near_dtp]).T
df_f.columns=['Accident ID',"Road","Date","Time","District","Region","KM","Street type","Accident type","#,cars","#,participants","#,deaths","#,wounded","Accident factor"
,"Weather","Road condition","Lighting","Changes in dr.mode","Disadv.of road network","UDS on place","UDS near place"]
df_f["Road_Abbr"]=df_f["Road"].map(lambda x:re.findall(r"[АМР]-\d{1,4}\s?",x))
df_f["Road_Abbr"]=[i[0] if len(i)>0 else "" for i in df_f.Road_Abbr]
df_f.loc[df_f.Road=="Крым Москва - Тула - Орел - Курск - Белгород - граница с Украиной, подъезд к заповеднику Прохоровское поле","Road_Abbr"]="М-2"
df_f.loc[df_f.Road=="Самара – Пугачёв – Энгельс - Волгоград","Road_Abbr"]="Р-226"
df_f.loc[df_f.Road=="Чекшино - Тотьма - Котлас - Куратово","Road_Abbr"]="А-123"
df_f.loc[df_f.Road=="Обход г. Нижнего Новгорода","Road_Abbr"]="М-7"
df_f.loc[df_f.Road=="Кочубей-Зеленокумск через Нефтекумск","Road_Abbr"]="А-167"
df_f.loc[df_f.Road=="Объезд г. Бийска","Road_Abbr"]="Р-256"
df_f.loc[df_f.Road=="Барнаул - Павловск - граница с Республикой Казахстан","Road_Abbr"]="А-321"
df_f.loc[df_f.Road=="1Р 418 Иркутск - Усть-Ордынский","Road_Abbr"]="Р-418"
df_f.loc[df_f.Road=="Косолаповы – Новоурожайная – Наймушины (объезд г. Котельнича в составе а/д «Вятка»)","Road_Abbr"]="Р-176"
df_f.loc[df_f.Road.isin(["Внешняя сторона","Внутренняя сторона"]),"Road_Abbr"]="МКАД"
df_f.loc[df_f.Road=="«Таврида» Керчь – Симферополь - Севастополь","Road_Abbr"]="А-291"
df_f.loc[df_f.Road=="Южно-Сахалинск - Оха","Road_Abbr"]="А-393"
df_f.Road_Abbr=df_f.Road_Abbr.str.rstrip().str.lstrip()
df_f["KM"]=df_f["KM"].astype("category")
df_f["Accident type"]=df_f["Accident type"].astype("category")
df_f["Date"]=pd.to_datetime(df_f["Date"],format="%d.%m.%Y")
print("Сохраняю")
df_f.to_excel("Federal_Highways_Accidents.xlsx",index=False)
# In[11]:
Federal_Highways_DTP_Parser()