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core_func.py
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from Time_line_proxy.clawers import *
from math import radians, cos, sin, asin, sqrt, hypot
import pandas as pd
from multiprocessing.dummy import Pool as ThreadPool
# from gevent import monkey
# from gevent.threadpool import ThreadPool
import numpy as np
# monkey.patch_all()
from PyQt5 import QtCore, QtGui, QtWidgets
mode_dict = {'步行': {'clawer': Walk_clawer,'params': Walk_params,'accuracy': 50, 'expand_dist': 200, 'result_dist': 100},
'公交': {'clawer': Bus_clawer, 'params': Bus_params,'accuracy': 200,'expand_dist': 1500, 'result_dist': 500},
'驾车': {'clawer': Drive_clawer,'params': Drive_params,'accuracy': 500, 'expand_dist': 3000, 'result_dist': 1000}}
class Geo_Point:
def __init__(self, lng, lat):
self.lng = lng
self.lat = lat
def calc_distance(self,another_point):
# 将十进制度转化为弧度
lng1, lat1, lng2, lat2 = map(radians, [self.lng, self.lat, another_point.lng, another_point.lat])
dlng = lng2 - lng1
dlat = lat2 - lat1
h = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlng/2)**2
earth_radius = 6371000
distance = 2 * asin(sqrt(h)) * earth_radius
return distance
def convert_to_rect(self,distance):
# distance 单位为m
lng_per_meter = 0.00001141
lat_per_meter = 0.00000899
lng1 = self.lng - (lng_per_meter * distance)
lat1 = self.lat - (lat_per_meter * distance)
lng2 = self.lng + (lng_per_meter * distance)
lat2 = self.lat + (lat_per_meter * distance)
return Rectangle(lng1, lat1, lng2, lat2)
def convert_to_destination(self):
a_dict = {}
a_dict['destination'] = str(self.lng)+ ',' + str(self.lat)
return a_dict
def convert_to_origin(self):
a_dict = {}
a_dict['origin'] = str(self.lng)+ ',' + str(self.lat)
return a_dict
def convert_to_location(self):
a_dict = {}
a_dict['location'] = str(self.lng) + ',' + str(self.lat)
return a_dict
class Geo_line:
def __init__(self,lng1, lat1, lng2, lat2):
self.point_1 = Geo_Point(lng1, lat1)
self.point_2 = Geo_Point(lng2, lat2)
self.length = self.point_1.calc_distance(self.point_2)
def convert_to_point(self, accuracy):
divide = int(self.length / accuracy) + 1
lng_min = min([self.point_1.lng, self.point_2.lng])
lng_max = max([self.point_1.lng, self.point_2.lng])
lat_min = min([self.point_1.lat, self.point_2.lat])
lat_max = max([self.point_1.lat, self.point_2.lat])
lng_list = np.linspace(lng_min, lng_max, divide)
lat_list = np.linspace(lat_min, lat_max, divide)
point_list = [Geo_Point(*i) for i in zip(lng_list, lat_list)]
return point_list
class Rectangle:
def __init__(self, lng1, lat1, lng2, lat2):
self.lng1 = round(lng1, 6)
self.lat1 = round(lat1, 6)
self.lng2 = round(lng2, 6)
self.lat2 = round(lat2, 6)
self.left_up = Geo_Point(lng1,lat2)
self.left_down = Geo_Point(lng1,lat1)
self.right_up = Geo_Point(lng2,lat2)
self.right_down = Geo_Point(lng2,lat1)
self.center = Geo_Point((lng1+lng2)/2,(lat1+lat2)/2)
self.wide = self.calc_wide()
self.high = self.calc_high()
self.radius = self.out_circle_radius()
def calc_wide(self):
return self.left_up.calc_distance(self.right_up)
def calc_high(self):
return self.left_up.calc_distance(self.left_down)
def out_circle_radius(self):
radius = hypot(self.wide, self.high)/2
return radius
def expand(self, distance, lng1=0, lat1=0, lng2=0, lat2=0):
# distance 单位为m
lng_per_meter = 0.00001141
lat_per_meter = 0.00000899
ex_lng1 = round([(self.lng1 - (lng_per_meter * distance)), lng1][lng1 != 0],6)
ex_lng2 = round([(self.lng2 + (lng_per_meter * distance)), lng2][lng2 != 0],6)
ex_lat1 = round([(self.lat1 - (lat_per_meter * distance)), lat1][lat1 != 0],6)
ex_lat2 = round([(self.lat2 + (lat_per_meter * distance)), lat2][lat2 != 0],6)
logger.info('拓展矩形为(%s,%s,%s,%s)'%(ex_lng1, ex_lat1, ex_lng2, ex_lat2))
return Rectangle(ex_lng1, ex_lat1, ex_lng2, ex_lat2)
def convert_to_lines(self):
self.left_line = Geo_line(self.lng1, self.lat1, self.lng1, self.lat2)
self.right_line = Geo_line(self.lng2, self.lat1, self.lng2, self.lat2)
self.up_line = Geo_line(self.lng1, self.lat2, self.lng2, self.lat2)
self.down_line = Geo_line(self.lng1, self.lat1, self.lng2, self.lat1)
return {'left': self.left_line,
'right': self.right_line,
'up': self.up_line,
'down': self.down_line
}
def divided_into_four(self):
rect_left_down = Rectangle(self.left_down.lng,
self.left_down.lat,
self.center.lng,
self.center.lat)
rect_right_up = Rectangle(self.center.lng,
self.center.lat,
self.right_up.lng,
self.right_up.lat)
rect_left_up = Rectangle(self.left_down.lng,
self.center.lat,
self.center.lng,
self.right_up.lat)
rect_right_down = Rectangle(self.center.lng,
self.left_down.lat,
self.right_up.lng,
self.center.lat)
return [rect_left_down, rect_right_up, rect_left_up, rect_right_down]
def convert_to_eight_points(self):
self.middle_up = Geo_Point(self.center.lng,self.lat2)
self.middle_down = Geo_Point(self.center.lng,self.lat1)
self.middle_left = Geo_Point(self.lng1,self.center.lat)
self.middle_right = Geo_Point(self.lng2, self.center.lat)
return [self.left_up,
self.left_down,
self.right_up,
self.right_down,
self.middle_up,
self.middle_down,
self.middle_left,
self.middle_right
]
def convert_to_param_dict(self):
a_dict = {}
a_dict['location'] = str(self.center.lat) + ',' + str(self.center.lng)
a_dict['radius'] = str(self.radius)
return a_dict
def convert_to_df_dict(self):
a_dict = {}
a_dict['lng'] = self.center.lng
a_dict['lat'] = self.center.lat
a_dict['radius'] = self.radius
return a_dict
class Sample_Generator:
def __init__(self, origin_point, g_key, mode):
self.point = origin_point
self.g_key = g_key
self.mode = mode
self.rect = self.point.convert_to_rect(mode_dict[mode]['expand_dist'])
self.origin_city = point_to_citycode(origin_point, g_key)
def filter_duration(self, duration, accuracy, expand_dist):
"""获取最大耗时duration, 采样精度为accuracy，矩形扩张圈为expand_dist的抓取范围"""
# duration 单位为分钟
line_dict = self.rect.convert_to_lines()
key_list = ['left', 'down', 'right', 'up']
def filter_point(point):
a_clawer = clawer_init(self.point, self.g_key, self.mode)
a_clawer.params.update_destination(point.convert_to_destination())
if self.mode == '公交':
cityinfo = {'city': '广州', 'cityd': '广州'}
cityinfo['city'] = self.origin_city
cityinfo['cityd'] = point_to_citycode(point, self.g_key)
a_clawer.params.update_city(cityinfo)
return a_clawer.get_duration()
def filter_edge(line_name):
line = line_dict[line_name]
point_list = line.convert_to_point(accuracy)
pool_lv2 = ThreadPool(8)
duration_list = pool_lv2.map(filter_point, point_list)
pool_lv2.close()
pool_lv2.join()
none_num = duration_list.count(None)
try:
if none_num != 0:
for i in range(none_num):
duration_list.remove(None)
if min(duration_list) >= (duration*60):
return (line_name, 1)
else:
return (line_name, 0)
except:
print(duration_list)
pool_lv1 = ThreadPool(2)
bool_list = pool_lv1.map(filter_edge, key_list)
pool_lv1.close()
pool_lv1.join()
# for i in range(bool_list.count(None)):
# bool_list.remove(None)
judge_dict = {'left': [0, self.rect.lng1],
'right': [0, self.rect.lng2],
'up': [0, self.rect.lat2],
'down': [0, self.rect.lat1]
}
link_dict = {'left': 'lng1',
'right': 'lng2',
'up': 'lat2',
'down': 'lat1'
}
bool_values = [i[1] for i in bool_list]
if min(bool_values) == 0:
coord = dict(lng1=0, lat1=0, lng2=0, lat2=0)
for i in bool_list:
if i[1] == 1:
logger.info(u'======已经找到%s边界======'%i[0])
coord[link_dict[i[0]]] = judge_dict[i[0]][i[1]]
self.rect = self.rect.expand(expand_dist,**coord)
self.filter_duration(duration, accuracy, expand_dist)
else:
logger.info(u'======已经找到所有边界======')
return self.rect
def filter_radius(self, distance):
radius_correct = []
rect_list = [self.rect]
while rect_list:
rect = rect_list.pop()
if rect.radius > distance:
rect_list.extend(rect.divided_into_four())
else:
radius_correct.append(rect)
logger.info(u"生成少于 %s m采样点 %s 个"%(distance, len(radius_correct)))
print(u"生成少于 %s m采样点 %s 个"%(distance, len(radius_correct)))
self.save_as_csv(radius_correct,'d:/radius_correct.csv')
return radius_correct
def save_as_csv(self,rect_list,file_path):
a_list = [i.convert_to_df_dict() for i in rect_list]
df = pd.DataFrame(a_list)
df.to_csv(file_path, encoding= 'utf-8')
def point_to_citycode(point,keys):
location = point.convert_to_location()
params = Recode_params()
params.update_location(location)
recode_clawer = Regeocode(params,keys)
return recode_clawer.get_citycode()
def clawer_init(point, g_key, mode):
origin = point.convert_to_origin()
param_mode = mode_dict[mode]['params']
key_dict = {'key': g_key[0]}
thread_params = param_mode(origin, key_dict)
a_clawer = mode_dict[mode]['clawer'](thread_params, g_key)
return a_clawer
def main(place_name,point, g_key, mode, path):
start_time = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
origin_city = point_to_citycode(point, g_key)
generator = Sample_Generator(point, g_key, mode)
generator.filter_duration(60, mode_dict[mode]['accuracy'], mode_dict[mode]['expand_dist'])
rect_list = generator.filter_radius(mode_dict[mode]['result_dist'])
def thread_clawer(rect):
a_clawer = clawer_init(point, g_key, mode)
a_clawer.params.update_destination(rect.center.convert_to_destination())
if mode == '公交':
cityinfo = {'city': '广州', 'cityd': '广州'}
cityinfo['city'] = origin_city
cityinfo['cityd'] = point_to_citycode(rect.center,g_key)
a_clawer.params.update_city(cityinfo)
return a_clawer.process()
pool = ThreadPool()
results = pool.map(thread_clawer, rect_list)
pool.close()
pool.join()
end_time = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
try:
df = pd.DataFrame(results)
except:
logger.info('%s'%results)
df.to_csv(path + '/%s_%s_%s_%s.csv'%(place_name, mode, start_time, end_time))
logger.info('抓取成功，结果已保存')
# class ClawerThread(QtCore.QThread):
# progressSignal = QtCore.pyqtSignal(float, str)
# statusSignal = QtCore.pyqtSignal(str)
# finishSignal = QtCore.pyqtSignal()
#
# def __init__(self, ):
if __name__ == "__main__":
a_dict = {
# '湛江CBD': (110.386677, 21.235136),
# '天河CBD': (113.333026, 23.120495),
# '福田CBD': (114.030657, 22.538324),
'茂南区CBD': (110.929024, 21.644562),
'南片区CBD_建设中_': (110.929459, 21.649175),
# '江城区CBD': (111.96681, 21.854052),
# '蓬江区CBD': (113.084279, 22.617895),
# '云城区CBD': (112.046295, 22.931489),
# '端州区CBD': (112.470865, 23.055311),
# '清城区CBD': (113.030492, 23.707625),
# '欧浦御龙湾': (113.575603, 24.793524),
# '石岐区CBD_大信新都汇': (113.247341, 22.67052),
# '十字门CBD': (113.526414, 22.174303),
# '九洲城_城市之心': (113.580927, 22.257145),
# '佛山新城': (113.140384, 22.962309),
# '季华CBD_建设中_': (113.053983, 22.993636),
# '南城CBD': (113.727088, 22.977552),
# '惠城区江北_华贸天地_': (114.41473, 23.104025),
# '越王大道沿线': (114.720984, 23.769719),
'汕尾新区CBD_万盛广场_': (115.362296, 22.780107)
# '新津片区_建设中_': (116.780237, 23.33503),
# '榕城区CBD_金门龙庭_': (116.372887, 23.563248),
# '梅江CBD_南门商业广场_': (116.113731, 24.305589),
# '湘桥区CBD': (116.643753, 23.662817)
}
# a_dict = {'广州南站': (113.26908, 22.989033),
# '虎门站': (113.673131, 22.861129),
# '深圳北站': (113.673131, 22.861129),
# '前海湾站': (113.897868, 22.537042),
# '机场北站': (113.797299, 22.652712),
# '滨海湾站': (113.700712, 22.765153),
# '珠海北站': (113.551849, 22.400757),
# '中山北站': (113.382768, 22.557043),
# '南朗镇站': (113.432948, 22.544041),
# '横琴站': (113.54462, 22.136599),
# '庆盛站': (113.490336, 22.866953),
# '万顷沙站': (113.546924, 22.699854),
# '城轨松山湖北站': (113.896687, 22.95779),
# '容桂站': (113.312357, 22.751182)}
# for i in list(a_dict.keys()):
for i in a_dict:
point = Geo_Point(*a_dict[i])
logger.info('_________开始抓取%s________'%i)
g_key = ['4a8f97b5b29c20bc21e8d58e0122281b',
'ceb9aaa9e1692f3ca497b58163e12de7',
'd93d6632b7d90134a2d4e949ca69bc1f',
'70d06b71c196d7c3e71ed084b6beb014',
'95eba7f5e12828d5699e3d19d95659be',
'ed15337b525ec9097b1fd35d476b992d',
'159e5e70247db21e0884e9fc2cc48a83',
'70de561d24ed370ab68d0434d834d106'
]
mode = '驾车'
path = 'D:\program_lib\Time_line_proxy'
# print(point_to_citycode(point,g_key))
logger.info("高德等时线生成工具V1.0 QQ:575548935")
main(i, point, g_key, mode, path)
# bus_params = Bus_params({'origin': '1,2'},{'key': '70de561d24ed370ab68d0434d834d106'},{'city':'北京'})
# walk_params = Walk_params({'origin': '2,3'},{'key': '70de561d24ed370ab68d0434d834d106'})
# drive_params = Drive_params({'origin': '3,4'},{'key': '70de561d24ed370ab68d0434d834d106'})
# print(bus_params)
# print(walk_params)
# print(drive_params)
# ['Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50',
# 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50',
# 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)',
# 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)',
# 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)',
# 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) Gecko/20100101',
# 'Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/20100101 Firefox/4.0.1',
# 'Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; en) Presto/2.8.131 Version/11.11',
# 'Opera/9.80 (Windows NT 6.1; U; en) Presto/2.8.131 Version/11.11',
# 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Maxthon 2.0)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; TencentTraveler 4.0)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; The World)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SE 2.X MetaSr 1.0; SE 2.X MetaSr 1.0; .NET CLR 2.0.50727; SE 2.X MetaSr 1.0)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; 360SE)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Avant Browser)',
# 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)']