"""
火星坐标系(GCJ-02)转百度坐标系(BD-09)
谷歌、高德——>百度
:param lng:火星坐标经度
:param lat:火星坐标纬度
:return: bd_lng, bd_lat
"""
x_pi = 3.14159265358979324 * 3000.0 / 180.0
z = math.sqrt(lng * lng + lat * lat) + 0.00002 * math.sin(lat * x_pi)
theta = math.atan2(lat, lng) + 0.000003 * math.cos(lng * x_pi)
bd_lng = z * math.cos(theta) + 0.0065
bd_lat = z * math.sin(theta) + 0.006
return bd_lng, bd_lat
if __name__ == '__main__':
df = load_data_of(columns=['order_id', 'departure_time'])
day_list, count_list, hourcount_list = count_of_days(df)
result_dict = {}
result_dict['day_count'] = {}
result_dict['hour_count'] = {}
for a, b, c in zip(day_list, count_list, hourcount_list):
print(a, b, c)
result_dict['day_count'][a] = b
result_dict['hour_count'][a] = c
result_df = pd.DataFrame(result_dict)
result_df.to_csv("data_dayly_hourly_count.csv")
print("load data done")
c.add_control_panel(navigation_control_opts=opts.BMapNavigationControlOpts(offset_height=30),
scale_control_opts=opts.BMapScaleControlOpts(),)
return c
#可视化
geo = bmap_linemap()
geo.render( 'line_bmap.html')
if __name__ == '__main__':
data_file = "D:/CCF2019/data/selected_data/" + "DAY_WEEKDAY_0920" + ".csv" # 选择数据======================
date_imterval = ['2017-09-20', '2017-09-20'] # 选择时间===========================
time_interval = [0, 24] # 选择时间===========================
df = load_data_of(file=data_file,
dates=date_imterval, time_interval=time_interval,
columns=['order_id', 'departure_time', 'arrive_time', 'dest_lng', 'dest_lat', 'starting_lng', 'starting_lat','normal_time'])
# print(df)
print(df.shape)
df = get_datda_near(df, position='海口美兰机场', key='ARRIVE')
print(df.shape)
index_lngLat, route_pair = get_lng_lat_route(df)
# print(index_lngLat)
# print(route_pair)
index_value = make_value(index_lngLat)
# print(index_value)
draw_linemap(index_lngLat, index_value, route_pair)
elif '公司' in ss:
return '公司'
elif '购物' in ss or '餐饮' in ss or '生活' in ss:
return '休闲娱乐'
else:
return '其他'
if __name__ == '__main__':
data_file = "D:/CCF2019/data/selected_data/" + "DAY_WEEKDAY_0920" + ".csv" # 选择数据======================
date_imterval = ['2017-09-20',
'2017-09-20'] # 选择时间===========================
df = load_data_of(file=data_file,
columns=[
'order_id', 'departure_time', 'arrive_time',
'dest_lng', 'dest_lat', 'starting_lng',
'starting_lat', 'normal_time'
])
print("df total data:")
print(df.shape)
print(df.columns)
poi_counter_24 = []
for i in range(24):
time_interval = [i, i + 1] # 选择时间===========================
sub_df = select_df_of(df,
dates=date_imterval,
time_interval=time_interval)
sub_df['dest_near_poi'] = sub_df.apply(lambda row: find_near_poi(
(row['dest_lng'], row['dest_lat'])),