潜力

# -*- coding: utf-8 -*-
"""
Created on Thu Dec  7 13:10:20 2017

@author: Administrator
"""

import pymongo
import pandas

import pandas as pd
import matplotlib.pyplot as plt  
import numpy as np 
import pylab as pl
import datetime

ct='上海'
rl='长宁'
lk='天山河畔花园'


client1 = pymongo.MongoClient('192.168.0.136',27017)
db1 = client1.fangjia_stat
stat = db1.test2_1013_lg

client = pymongo.MongoClient('192.168.0.136',27017) #'192.168.0.65',27777
db = client.fangjia
seawater = db.district_stat


def some_district_information(ct,rl,lk):
    #client = pymongo.MongoClient('192.168.0.60',27017) #'192.168.0.65',27777
    #db = client.fangjia
    #seawater = db.district_stat
    #seawater.find_one()
    query = {"city":ct,"region":rl,"district_name":lk}
    fields1 = {"city":1,"region":1, "district_name":1,
               'calc_value':1,'calc_value':1,'weekend':1}
    lt= seawater.count(query)
    #print(lt)
    pos = list()
    for s in seawater.find(query,fields1).limit(lt):
        pos.append(s)    
    data=pandas.DataFrame(pos)
    del pos
    return data




dd1=some_district_information(ct,rl,lk)


aset={'weekend','region','district_name','city','calc_value'}
bset=set(dd1.columns)

if   aset & bset == aset:

    dd=dd1[['weekend','region','district_name','city','calc_value']].dropna()

    if dd.shape[0]>5:

        vv=dd.sort_values(by=['weekend'])

        #df=vv.loc[380:]
        fr=vv.shape[0]
        print('fr=',fr)
        print('****************')

        num=min(120,fr)
        df=vv.iloc[fr-num:fr]
        print('PPPPPPPPPPPPPPPPP')
        df.index=list(range(num))

        x=list(df.index)

        #x=list(df['weekend'])

        y=list(df['calc_value'])

        z1 = np.polyfit(x, y, 10)
        # 生成多项式对象{
        p1 = np.poly1d(z1)
        print(z1)
        print(p1)

        xx=df['weekend']
        plt.figure(num=None, figsize=(10, 10))
        plt.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
        plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问
        plt.figure()
        plt.plot(xx,y,label= '原数据')
        #plt.scatter(x,y,s=20,c='red',marker='o',alpha=0.9,label= '原数据')  


c=p1(x)



def choose(x,f,cal):
    a=f(x)
    if abs(a-cal)/cal<0.03:
        return cal
    else:
        return a
cc=list()
for i in df.index:
    xx=i
    f=p1
    cal=df['calc_value'][i]
    #print(cal)
    b=choose(xx,f,cal)
    #print('b=',b)
    cc.append(int(b))

df['拟合过滤']=cc
"""
timeStamp = int(time.mktime(timeArray))
timeStamp == 1381419600
"""
leng=list(range(len(cc)-1))

def rate(i):
    a0=cc[i]
    a1=cc[i+1]
    return int(10000*(a1-a0)/a0)

dd=list(map(rate,leng))

dg=[0]

dg.extend(dd)

del dd,leng,y,vv

df['增长率']=dg

xx=df['weekend']            
plt.plot(xx,c,c='black',label= '拟合后')


#plt.plot(df['weekend'],c,c='black',label= '拟合后')

#plt.scatter(x,cc,s=20,c='green',marker='*',alpha=0.9,label= '原数据')
pl.legend()
plt.show()

import  dateutil

def before_month_lastday(ti):

    today=dateutil.parser.parse(str(ti))

    first = datetime.date(day=1, month=today.month, year=today.year)

    lastMonth = first + datetime.timedelta(days=ti*31)

    print(lastMonth)

    cc=str(lastMonth.year)+str(lastMonth.month)+str(lastMonth.day)

    return (lastMonth)
    #return int(cc)


def fga(p):
    return p*4+120


bb=df[['calc_value','weekend']]

bb1=bb.iloc[80:120]

bby=list(bb1['calc_value'])
bbx=list(bb1.index)



bbz1 = np.polyfit(bbx, bby,2)
# 生成多项式对象{
bbp1 = np.poly1d(bbz1)

xxx=x[80:120]
bbc=bbp1(xxx)
xcx=df['weekend'][80:120]

plt.figure(num=None, figsize=(8, 6))
plt.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问
plt.plot(xcx,bbc,c='black',label='拟合后')
plt.plot(xcx,bb1['calc_value'])

#plt.scatter(xcx,bb1['calc_value'],s=20,c='green',marker='*',alpha=0.9,label= '原数据')
bnb=[1,2,3,4,5]
bnb1=list(map(before_month_lastday,bnb))

bnb2=list(map(fga,bnb))

vvb=bbp1(bnb2)

vvb=list(map(int,vvb))

print('预测价格',vvb)
print('日期',bnb1)
plt.figure(num=None, figsize=(10, 6))

plt.plot(bnb1,vvb)


#bbc1=

"""
xcx=df['weekend']
plt.plot(xcx,c,c='black',label= '拟合后')

"""

print(before_month_lastday(1))

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 12 16:21:34 2017

@author: Administrator
"""

import pymongo
import pandas

import pandas as pd
import matplotlib.pyplot as plt  
import numpy as np 

import pylab as pl

import sys, time

import pp


ct='上海'
rl='闵行'

lk='和风家园'

def pply(ct,rl,lk):


    client1 = pymongo.MongoClient('192.168.0.136',27017)
    db1 = client1.fangjia_stat
    stat = db1.test2_1013_lg

    client = pymongo.MongoClient('192.168.0.60',27017) #'192.168.0.65',27777
    db = client.fangjia
    seawater = db.district_stat


    def some_district_information(ct,rl,lk):
        #client = pymongo.MongoClient('192.168.0.60',27017) #'192.168.0.65',27777
        #db = client.fangjia
        #seawater = db.district_stat
        #seawater.find_one()
        query = {"city":ct,"region":rl,"district_name":lk}
        fields1 = {"city":1,"region":1, "district_name":1,
                   'calc_value':1,'calc_value':1,'weekend':1}
        lt= seawater.count(query)
        #print(lt)
        pos = list()
        for s in seawater.find(query,fields1).limit(lt):
            pos.append(s)    
        data=pandas.DataFrame(pos)
        del pos
        return data




    dd1=some_district_information(ct,rl,lk)





    aset={'weekend','region','district_name','city','calc_value'}
    bset=set(dd1.columns)

    if   aset & bset == aset:

        dd=dd1[['weekend','region','district_name','city','calc_value']].dropna()

        if dd.shape[0]>5:

            vv=dd.sort_values(by=['weekend'])

            #df=vv.loc[380:]
            fr=vv.shape[0]
            print('fr=',fr)
            print('****************')

            num=min(120,fr)
            df=vv.iloc[fr-num:fr]
            print('PPPPPPPPPPPPPPPPP')
            df.index=list(range(num))

            x=list(df.index)

            #x=list(df['weekend'])

            y=list(df['calc_value'])

            z1 = np.polyfit(x, y, 36)
            # 生成多项式对象{
            p1 = np.poly1d(z1)
            print(z1)
            print(p1)

            plt.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
            plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问
            plt.figure()
            plt.plot(x,y,label= '原数据')
            plt.scatter(x,y,s=20,c='red',marker='o',alpha=0.9,label= '原数据')

            """
            p=p1
            def ff(x):
                a=len(p)
                b=p[0]*1+p[1]*x+p[2]*x**2+p[3]*x**3+p[4]*x**4+p[5]*x**5
                return b

            c=list(map(ff,x))
            """
            c=p1(x)



            def choose(x,f,cal):
                a=f(x)
                if abs(a-cal)/cal<0.03:
                    return cal
                else:
                    return a
            cc=list()
            for i in df.index:
                xx=i
                f=p1
                cal=df['calc_value'][i]
                #print(cal)
                b=choose(xx,f,cal)
                #print('b=',b)
                cc.append(int(b))

            df['拟合过滤']=cc
            """
            timeStamp = int(time.mktime(timeArray))
            timeStamp == 1381419600
            """
            leng=list(range(len(cc)-1))

            def rate(i):
                a0=cc[i]
                a1=cc[i+1]
                return int(10000*(a1-a0)/a0)

            dd=list(map(rate,leng))

            dg=[0]

            dg.extend(dd)

            del dd,leng,y,vv

            df['增长率']=dg
            """
            def fgg(df,cc):

                leng=len(cc)-1

                df['增长率']=0
                for i in range(leng):

                    a0=cc[i]
                    a1=cc[i+1]
                    ff=(a1-a0)/a0

                    df['增长率'][i+1]=ff
                return df

            #dff=fgg(df,cc)

            """


            def save_to_mongo(i):

                ct=df['city'][i]
                rl=df['region'][i]
                d_name=df['district_name'][i]

                py=cc[i]
                ratio=dg[i]





                date=df['weekend'][i]

                stat.save({"city":ct,"region":rl,"district_name":d_name,
                          "weeedend":date,"拟合后的价格":py,'增长率':ratio})           
                       #"增长率":df['增长率'][i]})

            #map(save_to_mongo,list(df.index))



            #cti=['上海']

            """

            region=region_list(i)[0:]
            for j in region:
                lf=region_district_list(i,j)

                testFL=lf['name']
                import time
                from multiprocessing import Pool
                if __name__ == "__main__" :
                  startTime = time.time()
                  testFL = df.index
                  pool = Pool(10)#可以同时跑10个进程
                  pool.map(save_to_mongo,testFL)
                  pool.close()
                  pool.join()   
                  endTime = time.time()
                  print ("time :", endTime - startTime)
            """        

            """
            import time
            startTime = time.time()

            list(map(save_to_mongo,df.index))

            endTime = time.time()
            print ("time :", endTime - startTime)
            """


            """
            import time
            startTime = time.time()
            for i in df.index:
                save_to_mongo(i)

            endTime = time.time()
            print ("time :", endTime - startTime)
            """    

            """
            import time
            from multiprocessing import Pool
            if __name__ == "__main__" :
              startTime = time.time()
              testFL = df.index
              pool = Pool(10)#可以同时跑10个进程
              pool.map(save_to_mongo,testFL)
              pool.close()
              pool.join()   
              endTime = time.time()
              print ("time :", endTime - startTime)
            """ 


            """
            #print(df)
            for i in df.index:

                ct=df['city'][i]
                rl=df['region'][i]
                d_name=df['district_name'][i]

                py=cc[i-380]
                ratio=dg[i-380]



                date="2017-11-17"
                client1 = pymongo.MongoClient('192.168.0.136',27017)
                db1 = client1.fangjia_stat
                stat = db1.test2_1013_lg
                stat.save({"city":ct,"region":rl,"district_name":d_name,
                           "weeedend":date,"pp":py
                           })
                #stat.save({"city":ct,"region":rl,
                           #"district_name":d_name,
                       #"拟合过滤":python_cal, "weeedend":date})
            """     







            plt.plot(x,c,c='black',label= '拟合后')

            plt.scatter(x,cc,s=20,c='green',marker='*',alpha=0.9,label= '原数据')
            pl.legend()
            plt.show()



def region_list(city):
    client = pymongo.MongoClient('192.168.0.136',27017) 
    db = client.fangjia
    region = db.region_block
    #seawater.find_one()
    query = {"city":city,"category":"region"}
    fields1 = {"name":1}
    pos = list()
    for s in region.find(query,fields1):
        pos.append(s)
    data=pandas.DataFrame(pos)
    del pos
    return data['name']

def region_district_list(ct,rl):
    import pandas
    client1 = pymongo.MongoClient('192.168.0.136',27017)
    db1 = client1.fangjia
    seaweed1 = db1.seaweed

    query1 = {"status":0,"cat":"district","city":ct,"region":rl}
    fields1 = {"lat2":1,"lng2":1, "city":1,"region":1,
               "cat":1,"name":1,'estate_type2':1,'alias':1}

    lct= list()
    for s in seaweed1.find(query1, fields1):
        lct.append(s)

    lf=pandas.DataFrame(lct)
    return lf

#cti=['上海','北京','广州','深圳']

"""
cti=['上海']
for ct in cti:

    region=region_list(ct)

    for rl in region[10:]:
        dictn=region_district_list(ct,rl)
        disname=dictn['name']

        for lk in disname:
            print('ct=',ct,'rl=',rl,'lk=',lk)
            pply(ct,rl,lk)

"""
ct='上海'
rl='闵行'
lk='上海康城'

print('ct=',ct,'rl=',rl,'lk=',lk)
pply(ct,rl,lk)