import os,codecs
import pandas as pd
import numpy as np

PATH = './dcic2021_data/'


# 读取共享单车停车点位（电子围栏）数据
def bike_fence_format(s):
    s = s.replace('[', '').replace(']', '').split(',')
    s = np.array(s).astype(float).reshape(5, -1)
    return s
    
# 共享单车停车点位（电子围栏）数据
bike_fence = pd.read_csv(PATH + 'gxdc_tcd.csv')
bike_fence['FENCE_LOC'] = bike_fence['FENCE_LOC'].apply(bike_fence_format)

# 读取共享单车订单数据
bike_order = pd.read_csv(PATH + 'gxdc_dd.csv')
bike_order = bike_order.sort_values(['BICYCLE_ID', 'UPDATE_TIME'])

#/*---------停车点处理---------*/
# 得出停车点 LATITUDE 范围
bike_fence['MIN_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 1]))
bike_fence['MAX_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 1]))

# 得到停车点 LONGITUDE 范围
bike_fence['MIN_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 0]))
bike_fence['MAX_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 0]))

from geopy.distance import geodesic
# 根据停车点 范围 计算具体的面积
bike_fence['FENCE_AREA'] = bike_fence.apply(lambda x: geodesic(
    (x['MIN_LATITUDE'], x['MIN_LONGITUDE']), (x['MAX_LATITUDE'], x['MAX_LONGITUDE'])
).meters, axis=1)

# 根据停车点 计算中心经纬度
bike_fence['FENCE_CENTER'] = bike_fence['FENCE_LOC'].apply(
    lambda x: np.mean(x[:-1, ::-1], 0)
)

#/*---------时间统计---------*/
# 对订单数据进行时间提取
bike_order['UPDATE_TIME'] = pd.to_datetime(bike_order['UPDATE_TIME'])
bike_order['DAY'] = bike_order['UPDATE_TIME'].dt.day.astype(object)
bike_order['DAY'] = bike_order['DAY'].apply(str)

bike_order['HOUR'] = bike_order['UPDATE_TIME'].dt.hour.astype(object)
bike_order['HOUR'] = bike_order['HOUR'].apply(str)
bike_order['HOUR'] = bike_order['HOUR'].str.pad(width=2,side='left',fillchar='0')

# 日期和时间进行拼接
bike_order['DAY_HOUR'] = bike_order['DAY'] + bike_order['HOUR']

#/*---------距离匹配计算潮汐点---------*/
# 调用knn
from sklearn.neighbors import NearestNeighbors
knn = NearestNeighbors(metric = "haversine", n_jobs=-1, algorithm='auto')
knn.fit(np.stack(bike_fence['FENCE_CENTER'].values))
# 计算离当前单车最近的一个停车点
dist, index = knn.kneighbors(bike_order[['LATITUDE','LONGITUDE']].values[:], n_neighbors=1)
# 标记该停车点
bike_order['fence'] = bike_fence.iloc[index.flatten()]['FENCE_ID'].values

# 计算所有停车点的潮汐流量
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_remain = (bike_inflow - bike_outflow).fillna(0)
bike_remain[bike_remain < 0] = 0  
bike_remain = bike_remain.sum(1)

# 计算停车点的密度
bike_density = bike_remain / bike_fence.set_index('FENCE_ID')['FENCE_AREA']
bike_density = bike_density.sort_values(ascending=False).reset_index()
bike_density = bike_density.fillna(0)

#/*---------输出---------*/
bike_density['label'] = '0'
bike_density.iloc[:40, -1] = '1'

bike_density['BELONG_AREA'] ='厦门'
bike_density = bike_density.drop(0, axis=1)
bike_density.columns = ['FENCE_ID', 'FENCE_TYPE', 'BELONG_AREA']
bike_density.to_csv('./result2.txt', index=None, sep='|')