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GetData.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import datetime
import http.client
import urllib
import json
import time
from io import *
#最终数据写入地址
DATAPATH = os.path.join(os.path.dirname(os.path.abspath(__file__)))
file=os.path.join(DATAPATH, 'station.csv')
GET = 'GET'
POST = 'POST'
CITY_CODE = "370100"
url='jngffp.cn'
'''
通过请求的方式调用api接口
date:2017-8-3 14:40:01
'''
def getJsonData (method, uri, body=None):
connection = http.client.HTTPConnection('jngffp.cn') # 设置请求主机地址，如果不是80端口，后面多加一个参数
requestHeaders = {'Content-Type': 'text/html;charset=UTF-8', 'Accept': 'text/html,application/xhtml+xml, */*'} # 设置返回值类型
connection.request(method, uri, body, requestHeaders) # 开始请求
response = connection.getresponse() # 接收返回值
print("状态:",response.status) # 返回状态
datas = response.read(); # 读取返回值
return datas
#获取所有电站信息
def getPositionAll():
urlPosition='http://jngffp.cn/getMapController/mapData?StationStat=0&provinceIds=284'
print(urlPosition)
result = getJsonData(POST, urlPosition) # 调用请求函数
#print(type(BytesIO(result)))
#dataWeatherInfo = json.load(StringIO(result))["data"] # 请求返回的字符串转换为json字符串,result必须是str or none
dataPosition = json.load(BytesIO(result))["attributes"]["mapdata"] # 请求返回的字符串转换为json字符串
#print(dataPosition)
return dataPosition
'''
根据相应条件获取天气
date:2017-8-3 15:12:06
'''
def getWeatherInfo(nowDate,cityID):
paramWeatherInfo = {"city_Code": cityID, "startTime": nowDate+" 07",
"endTime": nowDate+" 18"} # 传递参数 为什么时间选择7点到18点，因为这个数据库存储的时间是天气更新时间
bodyWeatherInfo = urllib.parse.urlencode(paramWeatherInfo) # 参数转码
urlWeatherInfo = 'http://' + url + '/getSolarDataByPython/getWeatherList?' + bodyWeatherInfo
print(urlWeatherInfo)
result = getJsonData(POST, urlWeatherInfo) # 调用请求函数
#dataWeatherInfo = json.load(StringIO(result))["data"] # 请求返回的字符串转换为json字符串,result必须是str or none
dataWeatherInfo = json.load(BytesIO(result))["data"] # 请求返回的字符串转换为json字符串
#print(dataWeatherInfo)
return dataWeatherInfo
'''
获取日电量
date:2017-8-3 14:40:01
'''
def getPower(forecastDate,regionId):
paramPower = {"regionType": 5,"regionId": regionId,"dataType": 2, "batchType":0,"dayTime": forecastDate} # 传递参数 为什么时间选择7点到18点，因为这个数据库存储的时间是天气更新时间
bodyPower = urllib.parse.urlencode(paramPower) # 参数转码
urlPower = 'http://' + url + '/getSolarDataByPython/getSimilardayPower?' + bodyPower
print(urlPower)
result = getJsonData(POST, urlPower) # 调用请求函数
if(len(result) > 2):#返回为{}len(result)=2
#print(result)
data = json.load(BytesIO(result))["data"] # 请求返回的字符串转换为json字符串
return data
else:
print("为空")
return None
def saveStation():
data=getPositionAll()
df=pd.DataFrame(data)
df=df.sort_values(by='changZhanId')
df=df.reset_index()
df.drop(['index'],axis=1)
station=df[['stationCode','changZhanId','title']]
#把Series转为list获取其中的字典
list_temp=df['position'].values.tolist()
temp = pd.DataFrame(list_temp)
station['longitude']=temp['lng']
station['latitude']=temp['lat']
station.to_csv(file,index=False)
saveStation()
def getPowerAll():
file = os.path.join(DATAPATH, 'station.csv')
station=pd.read_csv(file,encoding='GB2312')
regionId=station['changZhanId']
startDate='2017-1-1 22'
endDate='2020-2-29 22'
startDate = datetime.datetime.strptime(startDate, '%Y-%m-%d %H')
endDate = datetime.datetime.strptime(endDate, '%Y-%m-%d %H')
for id in regionId:
print(id)
dayPowerAll=pd.DataFrame(columns=['id','time','dayPower','capacity'])
#根据id获得想要的范围区间
if(id>=41 and id<50):
i = datetime.timedelta(days=1)
while i <= (endDate - startDate + datetime.timedelta(days=1)):
listdata = startDate + i-datetime.timedelta(days=1)#在date1的基础上加i天
date=(listdata).strftime('%Y-%m-%d %H')
#print(date)
dayPower=getPower(date,id)
#对于空数据，先假设capacity为-1，后期在处理
capacity=-1
if(dayPower==None):
#默认值
dayPower=pd.DataFrame([[str(id),date,-1,capacity]],columns=['id','time','dayPower','capacity'])
i += datetime.timedelta(days=1)#i++
dayPowerAll=pd.concat([dayPowerAll,dayPower])
else:
dayPower=pd.DataFrame(dayPower)[['id','dayPower','capacity']]
dayPower['time']=date
i += datetime.timedelta(days=1)#i++
dayPowerAll=pd.concat([dayPowerAll,dayPower])
capacity=dayPower['capacity']
dayPowerAll=dayPowerAll[['id','time','dayPower','capacity']]
#路径按自己的要求来
dayPowerAll.to_csv('./station'+str(id)+'.csv',index=False)
#getPowerAll()
def getWeatherAll():
weatherInfo=pd.DataFrame(columns=['city_Code','city_Name','time','weatherCode','weatherTypeName','humidity','temperature','wind'])
startDate='2017-1-1'
endDate='2020-2-29'
startDate = datetime.datetime.strptime(startDate, '%Y-%m-%d')
endDate = datetime.datetime.strptime(endDate, '%Y-%m-%d')
i = datetime.timedelta(days=1)
count=0
while i <= (endDate - startDate+datetime.timedelta(days=1)):
listdata = startDate + i - datetime.timedelta(days=1)#在date1的基础上加i天
date=(listdata).strftime('%Y-%m-%d')
i += datetime.timedelta(days=1)#i++
count+=1
historyWeather = getWeatherInfo(date,'370100')
if len(historyWeather) == 0:
print(date, '没有数据')
if count>360:
#找前年的数据代替
date2=date[:2]+str(int(date[2:4])-1)+date[4:]
#print('date:',date2)
historyWeather=getWeatherInfo(date2,'370100')
if len(historyWeather) == 0:
print(date, '没有数据')
historyWeather=tmp
historyWeather['time']=date+' 12:00:00'
else:
historyWeather=pd.DataFrame(historyWeather)
historyWeather=historyWeather[['city_Code','city_Name','time','weatherCode','weatherTypeName','humidity','temperature','wind']]
historyWeather['time']=date+' 12:00:00'
else:
historyWeather=tmp
historyWeather['time']=date+' 12:00:00'
weatherInfo=pd.concat([weatherInfo,historyWeather])
continue
else :
historyWeather=pd.DataFrame(historyWeather)
historyWeather=historyWeather[['city_Code','city_Name','time','weatherCode','weatherTypeName','humidity','temperature','wind']]
weatherInfo=pd.concat([weatherInfo,historyWeather])
tmp=historyWeather
weatherInfo=pd.concat([weatherInfo,historyWeather])
#路径按自己的要求
weatherInfo.to_csv('./weather.csv',index=False)
#getWeatherAll()