Repository: ni1o1/transbigdata (language: python)

Issue:
merge



Discussion / hints:

Fix patch:
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ab0d915..38ce6bc 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,8 +22,8 @@
 author = 'Qing Yu'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4.8'
-version = '0.4.8'
+release = '0.4.9'
+version = '0.4.9'
 html_logo = "_static/logo-wordmark-light.png"
 html_favicon = '_static/logo2.ico'
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/metroline.rst b/docs/source/metroline.rst
index 83ac93f..edaf14d 100644
--- a/docs/source/metroline.rst
+++ b/docs/source/metroline.rst
@@ -3,45 +3,107 @@
 公交地铁网络拓扑建模
 ******************************
 
-.. function:: transbigdata.split_subwayline(line,stop)
+.. function:: transbigdata.metro_network(line, stop, transfertime=5, nxgraph=True)
 
-用公交/地铁站点对公交/地铁线进行切分，得到断面
+输入站点信息，输出网络信息，可用于最短路径与最短k路径生成。该方法依赖于NetworkX。
+地铁出行时间由以下部分构成：地铁运行时间（距离/速度）+停站时间（一般取固定值）+换乘时间（步行时间+等车时间）
 
 **输入**
 
 line : GeoDataFrame
-    公交/地铁线路
+    地铁线路，`line`列存储地铁线路名称，`speed`存储每条地铁线路运行车速，`stoptime`每条线路停站时间
 stop : GeoDataFrame
-    公交/地铁站点
+    公交站点
+transfertime : number
+    每个轨道换乘的时长
+nxgraph : bool
+    默认True，如果True则直接输出由NetworkX构建的网络G，如果为False，则输出网络的边edge1,edge2和
+    节点node，以便用于精确定义边权
+    
+**输出**
+
+*nxgraph为True时*
+G : networkx.classes.graph.Graph
+    networkx构建的网络G
+
+*nxgraph为False时*
+edge1 : DataFrame
+    轨道断面的边
+edge2 : DataFrame
+    轨道换乘的边
+node : List
+    网络节点
+
+.. function:: transbigdata.get_shortest_path(G, stop, ostation, dstation)
+
+获取最短路径
+
+**输入**
+
+G : networkx.classes.graph.Graph
+    networkx构建的地铁网络G
+stop : DataFrame
+    地铁站点信息表
+ostation : str
+    O站点名称
+dstation : str
+    D站点名称
 
 **输出**
 
-metro_line_splited : GeoDataFrame
-    生成的断面线型
+path : list
+    路径，一个包含路径经过节点名称的list
+
+.. function:: transbigdata.get_k_shortest_paths(G, stop, ostation, dstation, k)
+
+获取前k个最短路径
+
+**输入**
+
+G : networkx.classes.graph.Graph
+    networkx构建的地铁网络G
+stop : DataFrame
+    地铁站点信息表
+ostation : str
+    O站点名称
+dstation : str
+    D站点名称
+k : int
+    获取前k个路径
+
+**输出**
+
+paths : list
+    包含前k个路径的list
+
+.. function:: transbigdata.get_path_traveltime(G, path)
+
+通过路径获得出行时间
+
+**输入**
 
+G : networkx.classes.graph.Graph
+    networkx构建的地铁网络G
+path : list
+    路径，一个包含路径经过节点名称的list
 
-.. function:: transbigdata.metro_network(stop,traveltime = 3,transfertime = 5,nxgraph = True)
+**输出**
 
-输入站点信息，输出网络信息，该方法依赖于NetworkX
+traveltime : float
+    该路径的出行时间
+
+.. function:: transbigdata.split_subwayline(line,stop)
+
+用公交/地铁站点对公交/地铁线进行切分，得到断面，可用于可视化
 
 **输入**
 
+line : GeoDataFrame
+    公交/地铁线路
 stop : GeoDataFrame
-    公交站点
-traveltime : number
-    每个轨道断面的出行时长
-transfertime : number
-    每个轨道换乘的时长
-nxgraph : bool
-    默认True，如果True则直接输出由NetworkX构建的网络G，如果为False，则输出网络的边edge1,edge2,和节点node
-    
+    公交/地铁站点
+
 **输出**
 
-G : networkx.classes.graph.Graph
-    networkx构建的网络G，nxgraph参数为True时只输出这个
-edge1 : DataFrame
-    轨道断面的边，nxgraph参数为False时输出这个
-edge2 : DataFrame
-    轨道换乘的边，nxgraph参数为False时输出这个
-node : List
-    网络节点，nxgraph参数为False时输出这个
\ No newline at end of file
+metro_line_splited : GeoDataFrame
+    生成的断面线型
\ No newline at end of file
diff --git a/docs/source/metromodel/metromodel.rst b/docs/source/metromodel/metromodel.rst
index ea41e07..5b4b17a 100644
--- a/docs/source/metromodel/metromodel.rst
+++ b/docs/source/metromodel/metromodel.rst
@@ -14,15 +14,24 @@
 
 ::
 
+    import pandas as pd
     import transbigdata as tbd
     line,stop = tbd.getbusdata('厦门',['1号线','2号线','3号线'])
 
 
 
-获取城市id: 厦门成功
-1号线成功
-2号线成功
-3号线成功
+Obtaining city id: 厦门success
+1号线
+地铁1号线(镇海路-岩内) success
+地铁1号线(岩内-镇海路) success
+2号线
+地铁2号线(五缘湾-天竺山) success
+地铁2号线(天竺山-五缘湾) success
+3号线
+地铁3号线(厦门火车站-蔡厝) success
+地铁3号线(蔡厝-厦门火车站) success
+地铁3号线南延段(厦门火车站-沙坡尾) success
+地铁3号线南延段(沙坡尾-厦门火车站) success
 
 
 ::
@@ -61,7 +70,7 @@ tbd.split_subwayline方法可以用轨道站点切分轨道线路，得到轨道
 ::
 
     metroline_splited = tbd.split_subwayline(line,stop)
-    metroline_splited.plot(column = 'o_project')
+    metroline_splited.plot(column = pd.Series(metroline_splited.index))
 
 
 
@@ -78,11 +87,101 @@ tbd.split_subwayline方法可以用轨道站点切分轨道线路，得到轨道
 ::
 
     #构建拓扑模型
+    line['speed'] = 36 #每条线路运营车速 36km/h
+    line['stoptime'] = 0.5 #每条线路停站时间 30s
     import networkx as nx
-    G = tbd.metro_network(stop)
-    nx.draw(G)
-
+    G = tbd.metro_network(line,stop, transfertime=5)
+    nx.draw(G,node_size=20)
 
 
 .. image:: output_12_0.png
 
+给定起终点，即可获取最短路径和前k个最短路径
+
+::
+
+    path = tbd.get_shortest_path(G,stop,'镇海路','蔡厝')
+    path
+
+::
+
+    ['地铁1号线镇海路',
+    '地铁1号线中山公园',
+    '地铁1号线将军祠',
+    '地铁1号线文灶',
+    '地铁1号线湖滨东路',
+    '地铁3号线湖滨东路',
+    '地铁3号线体育中心',
+    '地铁3号线人才中心',
+    '地铁3号线湖里公园',
+    '地铁3号线华荣路',
+    '地铁3号线火炬园',
+    '地铁3号线小东山',
+    '地铁3号线安兜',
+    '地铁3号线坂尚',
+    '地铁3号线湖里创新园',
+    '地铁3号线五缘湾',
+    '地铁3号线林前',
+    '地铁3号线鼓锣',
+    '地铁3号线后村',
+    '地铁3号线蔡厝']
+
+::
+
+    # 前k最短路径
+    paths =  tbd.get_k_shortest_paths(G,stop,'镇海路','蔡厝',2)
+    paths
+
+::
+
+    [['地铁1号线镇海路',
+    '地铁1号线中山公园',
+    '地铁1号线将军祠',
+    '地铁1号线文灶',
+    '地铁1号线湖滨东路',
+    '地铁3号线湖滨东路',
+    '地铁3号线体育中心',
+    '地铁3号线人才中心',
+    '地铁3号线湖里公园',
+    '地铁3号线华荣路',
+    '地铁3号线火炬园',
+    '地铁3号线小东山',
+    '地铁3号线安兜',
+    '地铁3号线坂尚',
+    '地铁3号线湖里创新园',
+    '地铁3号线五缘湾',
+    '地铁3号线林前',
+    '地铁3号线鼓锣',
+    '地铁3号线后村',
+    '地铁3号线蔡厝'],
+    ['地铁1号线镇海路',
+    '地铁1号线中山公园',
+    '地铁1号线将军祠',
+    '地铁1号线文灶',
+    '地铁1号线湖滨东路',
+    '地铁1号线莲坂',
+    '地铁1号线莲花路口',
+    '地铁1号线吕厝',
+    '地铁1号线乌石浦',
+    '地铁1号线塘边',
+    '地铁1号线火炬园',
+    '地铁3号线火炬园',
+    '地铁3号线小东山',
+    '地铁3号线安兜',
+    '地铁3号线坂尚',
+    '地铁3号线湖里创新园',
+    '地铁3号线五缘湾',
+    '地铁3号线林前',
+    '地铁3号线鼓锣',
+    '地铁3号线后村',
+    '地铁3号线蔡厝']]
+
+获取路径出行时长
+
+::
+
+    tbd.get_path_traveltime(G,path)
+
+::
+
+    68.00206888083389
\ No newline at end of file
diff --git a/docs/source/metromodel/output_12_0.png b/docs/source/metromodel/output_12_0.png
index 481bced..bead45c 100644
Binary files a/docs/source/metromodel/output_12_0.png and b/docs/source/metromodel/output_12_0.png differ
diff --git a/example/Example 3-Modeling for subway network topology.ipynb b/example/Example 3-Modeling for subway network topology.ipynb
index cfeff8f..943a4c9 100644
--- a/example/Example 3-Modeling for subway network topology.ipynb
+++ b/example/Example 3-Modeling for subway network topology.ipynb
@@ -49,18 +49,23 @@
      "text": [
       "Obtaining city id: 厦门success\n",
       "1号线\n",
-      "地铁1号线(岩内-镇海路) success\n",
-      "地铁3号线(厦门火车站-蔡厝) success\n",
-      "地铁3号线(蔡厝-厦门火车站) success\n",
       "地铁1号线(镇海路-岩内) success\n",
+      "地铁1号线(岩内-镇海路) success\n",
       "2号线\n",
-      "地铁2号线(天竺山-五缘湾) success\n",
       "地铁2号线(五缘湾-天竺山) success\n",
-      "3号线\n"
+      "地铁2号线(天竺山-五缘湾) success\n",
+      "3号线\n",
+      "地铁3号线(厦门火车站-蔡厝) success\n",
+      "地铁3号线(蔡厝-厦门火车站) success\n",
+      "地铁3号线南延段(厦门火车站-沙坡尾) success\n",
+      "地铁3号线南延段(沙坡尾-厦门火车站) success\n"
      ]
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
     "import transbigdata as tbd\n",
     "line,stop = tbd.getbusdata('厦门',['1号线','2号线','3号线'])"
    ]
@@ -88,7 +93,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -126,7 +131,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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r7wU+Rc5ALknAWuDxZNEXgPV5jmFWdXXrEVO3862CXG0AkqaBS4Gdkq4FDkfEXnVvwX+zpFkaNYnNEbEFeBvwekQcT7Z5iVNBxUqqzNMRV1Hd5tCv2/lWQeZeQJLOB54APk7jZn4HcFeGXd+RDED4EPAbklbmKaCkWyTNSpqdn5/Ps6sNkd/5Onx16xFTt/OtgkwBQNLZNG7+j0TEk8BKYDmwV9JBYAmwW9KF7ftGxOHk3wPA0zRqEK8BF0hq1kCWAIc7fXdE3BcRMxExMzU1lePUbJicvx2+vN1JmxPQTd/+FCs3bnXwtYFl6QUk4AFgf0TcAxARcxGxOCKmI2KaRgrnsoh4pW3ft0o6N/m8CLgS2BeN+Se2Ax9INv0w8KUhnZONgPO3o9HeI+jhHYc63twnoQbmh4jyydIGcCVwIzAnaU+y7I6I6DgXrqQZ4F9ExM3Au4DflvQGjWCzOSL2JZt+GviipE3Ad2gEGSsp529HJ8tbxXrdPKvQNuOHiPLpGQAi4ls0xmt022a65fMscHPy+X8CHf8Sk5TQ5TnKagXq9VLyuhhFQ3i3m3vz2N1unllfLF80P0SUj6eCsEza89VwagRrXfLRo0rDZHkyznuTLGNaxY3A5eMAYJnVdQRrU5E57Lw3yWbwKFPD8SBzKNloeC4gyy1LymISZXlSH9VYifY5hJrHbp1TqNUCKVPbwijL3M7jSMrHAcByG6Qxb9Q3gVEePy2H3fxeoK98fNbceNoEdGltM1kCddYg0dy232ub53tsfJwCstz6ndN91F0ZR338bmmYh3ccSn1PcPuNuD0ts2LqvNzf19QtrZIlUGdNaw16bd0FtJwcACy3fhvzRn0TGPXxmzfbvNpTRO030ueOHGXV4oV958abbTMHN1/DC3evO7lflkCdtTY36LV1F9BycgrIckvLR8Opd752ShGM+iYwjpvMpvWrU5/007TecNNumAfmjw39nbhZuu5mTT9lvbZpaSJ3AS0nBwDrS3s+OkuOd9Q3gWEcfxRtCK033HE+CacF6tbzyTq+I8u17fY34HEk5eQUkA1FlhTBqPuBD3r8YbQh9OrmOO534qalh1rXZ+mameXa9mp0dhfQ8nENwIYiy5NtlifSQfQ6fq+n+6zdW29Ys6zj02zzhtbtfMr4JJzl9ZZZfne9/gb6fY2mjY4DgA3FoF0ZhyXt+FlSVFnTM3kDWXvgWbV4IQfmj1WuP3yv353z/NXjAGBDUcYn21ZZnu7z3MA63Qw71TDgzLEBzx05OpHpj7L/DdiZ3AZgQ1H2HG+Wp/tB2hDS2g+yjg2YBGX/G7AzuQZgQ1PmHG+Wp/tB2igeydk1dFL7v5f5b8DO5ABgtZA1PdHvDSzv7dx5cSsDBwCrhVH3QMrLeXErAwcAq42i0hM3rFlWmsBj1soBwGwI0sYGQKMX0KrFC9l221XjLZRZDw4AZjmkDSZrTzG1e+7IUX72nqcdBKxU3A3ULKNeU0U0p11I89yRo2Mpp1lWDgBmGXlOe5s0DgBmGXlOe5s0DgBmGWWdyXPV4oUdt0tbblYUBwCzjLJOFbHttqvOuNm7F5CVkXsBmfXQ2vNHnBr1261Pv2/2VgUOAGZdtE8j3bz5e5IzmwROAZl14Z4/NskcAMy6cM8fm2QOAGZdjPsdvmbj5DYAq7Ve7wn2W65skjkAWG1leU9w2aaRNhsmRYVymTMzMzE7O1t0MWxCrNy4NfUtYd3m9DGrGkm7ImKmfbnbAKy23MBrdecUkNVCp1x/lvcEm00y1wBs4qVN47xi6ryO27uB1+rCAcAmXtqgrQPzx7hhzbKTT/wLJI/wtVrpmQKStBR4CPi7NEbC3xcR/7Fl/Qbg14GpiHg15Rg/BuwDtkTEx5JlTwNvB36YbHZ1RBzp/1TMOuuW6y/qPcFmZZClDeA4sCEidkt6C7BL0raI2JcEh6uBzi9DPeUzwDc7LL8+Itytx0bKuX6zznqmgCLi5YjYnXz+PrAfuChZfS/wKU7NkXUGSe+hUXv4+sClNetD1mmczeomVxuApGngUmCnpGuBwxGxt8v2bwL+A/CJlE1+V9IeSf9a6vw4JukWSbOSZufn5/MU1wxoDOZyrt/sTJkHgkk6H/hj4LPAV4HtNPL235N0EJhpbwOQ9DHgvIj495JuSrZptgFcFBGHk7TSE8DDEfFQtzJ4IJiZWX5pA8EyjQOQdDaNm/QjEfGkpNXAcmBv8uC+BNgt6fKIeKVl1/cC75P0UeB84BxJP4iI2yPiMDTSSpJ+D7icRmOz2cj0mvvHrE6y9AIS8ACwPyLuAYiIOWBxyzYH6VADiIjrW7a5KdnmdklnARdExKtJcPl54I8GPx2zdFnm/jGrkyxtAFcCNwJrk3z9HkmpE6VImpF0f49jngt8TdJ3gT3AYeDzGcts1he/3MXsdD1rABHxLaBrf7mImG75PAvc3GGbB4EHk89HgffkKqnZgDz3j9npPBeQ1UZZxgO4HcLKwlNBWG2UYTxA2rxEd26ZG1sZzJpcA7Da6PZyl3E9lXdrh3AtwMbNAcBqpdPcP+PsHeR2CCsTp4Cs9sbZO8gvmbcycQCw2hvnU3kZ2iHMmpwCstobtHfQIO0HAq73vERWENcArPYGeSrP06unva0BukyjazYGDgBWe4PMFpqn/cAjka1snAIyo3PvoCzytB+4B5CVjWsAZgPI06vHPYCsbBwAzAaQp/3APYCsbJwCMhtAt9HFg2xrNg6Z3whWBn4jmJlZfmlvBHMKyMysphwAzMxqygHAzKymHADMzGrKAcDMrKYq1QtI0jzw5wV9/SLg1YK+ux9VKy9Ur8xVKy9Ur8xVKy+Us8zviIip9oWVCgBFkjTbqRtVWVWtvFC9MletvFC9MletvFCtMjsFZGZWUw4AZmY15QCQ3X1FFyCnqpUXqlfmqpUXqlfmqpUXKlRmtwGYmdWUawBmZjXlAGBmVlO1CQCSfkfSEUnPtCz7JUnPSnpD0kzL8usl7Wn5eUPSJR2O+VOS/pekOUl/IOnHkuXTkn7Ysv/nRlzesyV9ISnHfkkbU465XNJOSc9LekzSOcnyc5P/P5+sn85b3gLKfJOk+ZZrfHNJyvuxpKwhaVHLckn6zWTddyVdVvLyXiXpey3X96685R1hmR+R9KeSnkmOf3ayvKzXOK28Q7nGA4mIWvwA/wi4DHimZdm7gL8HPA3MpOy3GnghZd23gX+cfP5l4DPJ5+nW7xl1eYEPAV9MPp8HHASmOxzzvwIfTD5/DviXyeePAp9LPn8QeKwCZb4J+K0SXuNLk9//QWBRy/J1wB8CAtYAO0te3quArwxyfUdY5nXJdRTwaMvfRFmvcVp5h3KNB/mpTQ0gIr4J/GXbsv0R8ac9dr0O+GLKuncC30w+bwPeP1AhTy9bnvIGsFDSWcDfAv4f8NetG0gSsBZ4PFn0BWB98vna5P8k638m2b7MZR7YsMub7P+diDjYYf9rgYeiYQdwgaS3l7i8QzGiMm9NrmMA/xtYkqwq6zVOK2/hahMABvDPaETtTp6l8UcH8EtA67v9lkv6jqQ/lvS+URaQxg3yKPAycAj49Yj4y7Zt3ga8HhHHk/+/BFyUfL4IeBEgWf+9ZPsylxng/UlV/3FJo36vYpbydnPyGifaz2XYBi0vwHsl7ZX0h5L+/tBLeKZcZU5SKTcCX00WlfoadygvjP8an8YBoAtJVwDHIuKZlE1+GfiopF3AW2g8AUDjD2JZRFwK3Ab8npL2gRG5HDgB/DiwHNggacUIv28YBi3zH9Cobr+bRu3rCz22H1TVrvGg5d1NY/6YnwL+E7Bl6CU8U94y/xfgmxHx38dQtk4GLW8R1/g0DgDdfZD0p38i4k8i4uqIeE+y3QvJ8h9FxGvJ513J8neOsJwfAr4aEX8TEUeA/wG0z0XyGo0qcfM90EuAw8nnwyS1l2T93062H6WByhwRr0XEj5Ll9wPvKUF5uzl5jROt138UBipvRPx1RPwg+bwVOLu1kXhEMpdZ0r8Bpmg8YDWV9hp3Km9B1/g0DgApJL0J+Kek5/+RtLhl2ztpNFIiaUrSguTzCmAVcGCExT1EI1eOpIU0GsD+pHWDJP+4HfhAsujDwJeSz19O/k+y/r8l24/SQGVuy+3+ArC/6PL28GXgnyc9VdYA34uIl4dfzJMGKq+kC5vtQJIup3GvGPVDQaYyq9Hj6+eA6yLijZZVpbzGaeUt6BqfbpQtzGX6ofGE/jLwNzRygx8BfjH5/CPg/wJfa9n+KmBHh+PcT9ITALgV+D/Jz2ZOjax+P432gT00qnn/ZJTlBc4Hfj/5zn3AJ1uOsxX48eTzChqNUM8n25+bLH9z8v/nk/UrRn2Nh1Dmu5N999IIEj9ZkvL+q2T/48BfAPcnywX8Zxq1wTlSep2VqLwfa7m+O4B/UKK/iePJddyT/NxV8mucVt6hXONBfjwVhJlZTTkFZGZWUw4AZmY15QBgZlZTDgBmZjXlAGBmVlMOAGZmNeUAYGZWU/8fMzIHfX1GrhsAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -180,7 +185,7 @@
     },
     {
      "data": {
-      "image/png": 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58hER/Hwr546AaTN/YMZtn+FoEsTUlW/QrGHZx7ExxaEYGxY9Tbuu09m1pSnn9JxNcPipw1IY3ubdhSt4e9s6uvnXY87fb7E7jlGN/HPi9/yyaDOu45lMeOtGLunX2e5I//X5rEV8dtNUfNvV4qvVb1MrJLhM6ztTcSjJeA7NRGSZiMSIyDYRefCU5eNFREUkooj++QUe9f1dgfkrCsw/KCJzrfkDRSStwLLiRpsrsx6XvsiOzbcS2fEAafv788fil8jPdxXf0bDNA5f3p5OzDpuyE9mfmGJ3HKMacLnyuf/+T1k643d8fZxM+c8jXlUYAMaMvJSr3huJa1sqYy55jBMZmRX2XiW5NsoFjFfVjkBv4D4R6QiewgEMAfadoX+WqnazfoadnKmq/U/OB1YDcwr0WVGgzwul3Kaz0v3S5zmW/CFpx8Jo2/ljtq/oRdLBPyvjrY2zdHf/3uAQ3vtppd1RjCou7Xgmo69/j50r99Coc0O+mvsQzRqX34nf8vTgXdfT/+XLyFp7lFEDx5OUerxC3qfY4qCqh1R1gzV9AogFmliLJwGPAWd9bEpEwvCMEDf3bNdRXlp2vZy2/deyY8tY6jdNISX+ejJSTx3czvAWl3dvR2iuk/n7dpJ0PN3uOEYVtXvPUUaPeIeUPSmcN7Qjn3x2FwEBfnbHOqNnnhzL4DeH4c7Lx3HG0XbOXqnuqhCRSKA7sFZEhgMJqrqpmG4BIhItImtEZEQhy0cAS1W1YPnrIyKbRGShiHQqIss4a73RiYmJpdmMM/LxcdJ9yDMkJT5Hg+ZpxKx4qNzWbZQvh8PB4xf0J9fXzTUffsGh5Ir5BmVUX7+u3M49N31Iblo2I+8bwCsv31Blnvz7xMNj+G79v6lTK6xC1l/iE9IiEgL8ArwM/IhnDOghqpomIvFAlKqe9gAcEWmiqgki0gr4GRisqrsKLF8ITFHV2dbrMMCtqunWcKTvqGqbM2WrqJvgoudfReuuW8jWOTSM7FHu6zfKxyOff8e8Iztx5Au9/Rty18W9uaBzK7tjGV7u+4V/8O7T8xAfYcIr13LJYO86v1AZyny1koj4At8Di1T1LRHpAiwFTp4NaQocBHqqapHHYUTkU+B7VZ1lvY4AtgNNrOFBC+sTTxGF56SKKg5JCbE4coYSH9uGHlf8VO7rN8rPD+tj+XjFOnYkHgMRwtSXro0aMOTctlwW1Z6QEl5PbtQMJwuDw9+HN6fcTqcOTYrvVA2VqTiIiACfAcmq+lARbeIp5A+4iNQGMlU1xyoEq4HhqhpjLb8b6KOqfyvQpyFwRFVVRHoCs4AWeoagFfn4jN/n/42OUcs5mvghLbteXiHvYZSfuIREJn33K2v3J5ApLhABVfzcPtTxD6BxWCitG0TQNbIhPdu1oGlELbsjG5WsYGF4e9pY2rdtbHck25S1OFwArAC2AG5r9lOquqBAm3is4iAiUcDdqnqHiPQFPrL6OYC3VXVqgX7LgddU9ccC8+4H7sFzlVQW8IiqrjpTxoosDhmphzn058UcSGnCBRd/h9PXfAOtKvYdTWHWys1s2XeY/alpJGdnke3I9xQMi08+hDn9aBQaSrsGEZzfujkXdmlJnbCyXT9ueKcfFm7knafnmsJgMTfBldHK5ZMIbDWJo5sv47IrJ1fY+xgVLzvXxYa4/fyx6yCxCUfYm5zKkYwMMtSFnjwPqYq/24d6gUF0btyAp64bREStEFtzG2VXsDBM+mQsHdrV7MIApjiUix9+uIoGnTYhh17ivD7mjtzqxuVys3F3Aitj9rB53yH2JKdyLCeLPB/FkQ/D27Xj2ZuH4Oc0gydWRd/9sIH3n/0Oh58Pk6aZwnCSKQ7lIDM9hVUbBxJYK4Pmod/SLLJ7hb6f4R3mrtzKxB9/xZWTT31nMLNeuMUUiCokN8/F//3jGzYu3o4j0GkKwynK9PgMwyMopDZtGk0BUfak3MDOmJ/tjmRUghH9OvPbi/fSrUEjEhLTuP7Zz3G53MV3NGy3ZdsBRl7xJpsWb6d2y9pMnXWfKQylYIpDKbQ453waOD9F3UIid7Blw1y7IxmV5MPxIzmvXVP2H03l+uenk+syz97yZm+/u5Dxf5tCdnIWl4zpycxZD9DESx+H4a1McSil1u3707b+f8hKCyIj6GE2Rc+yO5JRST585Fq6t2nC3sMpXP1/n5KVm2t3JOMUh4+kMuq6d1g4bQ3+4QH885PbmfDIFVXmrmdvYj6xs9CoWUc6R/6HjOQQ0gMe58A++wYBNyqPw+FgymPX069LSw4fO8FVT3xCWnqh924aNpj5zRr+NvxdjsUl03lQG2b9MIFuXZvbHavKMsXhLDVo1IY2DT9FRImJv5XsLPPgt5ri3QdGcHmv9qScyGLYU1M5knzC7kg12rHkdG6/9UM+fXUhDqfw0GtX8+abN+Pvby4cKAtTHMqgeavz8El5nIjIJJYuNZe31iQv3XE5NwzqRnpWLtc8/Snxh5PtjlQjzZn3O6OvnMSBTYdoEdWUrxaMZ+il3eyOVS2Y4lBGvS64iyObL6ZR12h+WfKy3XGMSvTYqIu486peZOe6uPH5z4mJP2J3pBoj7Xgmd42bwofPfQ8odz17JZM/vpNaYUF2R6s2THEoB5dc9hFHtjcjoPnHbN+2xO44RiW6e1hfHrn+QvJcbm59dSbR2/fbHalac7vdfDtnLTcOfYv43/fTpGsjZnz/CNeOON/uaNWOuQmunBxL3MvWfUNw5/vQvc0Swmub66lrkvmrtvH8tJ9AhIn3XsnAbq3tjlStZGbl8tHkpSyZ+weu1Bzw9+GWhy5m9I197Y5WpZk7pCvJto3zyQi9n6S4llx68RJ8fMwJsZpk+cY4Hv3gexTlpbGXc1mv9nZHqvL27T/Gu+/+yJZf4yDXjSPYSd/LO/PA/ZdSq5Y5hFRWpjhUoqWL/kHtDp+TuOVKLr3iA7vjGJVsbew+7n97Dm638txtQ7iqb6EDGRpn4Ha7Wb5iO9OnLOfgtsOIQlCjEIaP6s3No/ridPrYHbHaKOsju5sB04EGeMaKnqyq7xRYPh6YCNQrYiS4fDyP+wbYp6rDrPmfAgOANGvZraq60Ro/4h1gKJ7BhG49OYZ1UbypOLjdbhYuvIoGnbawf8cDDL/4EXMDTg3zx84D3DVxFvlu5dFRA7lxkHkO15lkZGSzZFkMq1fuIG7bQY4fOo64FBVo1KkBd9wzmP5929kds1oqa3FoBDRS1Q0iEgqsB0aoaoxVOKYA7YHziigO6ap62vOOTx0VrsD8ocDf8RSHXniGCe11pozeVBzA85C+ucsfZU/LQ+Tvacj1ncfTPrKj3bGMSrRtz2Fuf/1rXPlu7h7ehzuv7G13JK+RlpbJvO/Xs25VHPt2HCX7WCZi/RmSICf1I+vQ8dzm3DSqL82b1bU3bDV3puJQ7EFxVT0EHLKmT4hILNAEiAEmAY8B88ovLsOB6dbIb2tEJFxEGlk5qoSgkNpcO+RffL7kXxxsuJxvUx7HZ3MLRAW3uHCLC0RxuP3wUT98CcDPEUiQM4ywgDqEB9WlblgD6tduSIOIhvg6fe3eJKOUOrVsyJdP38yYV77kw3mrScvIZsINA+2OZQuXK59ffvuTn37czPaN+8k8moGo5zCEX+0AWkU1o0fPVlxySRdatqhnd1zDUqpzDiISCfwKdAYuAgap6oNnGudZRFzARjwju72mqnOt+Z8CfYAcPONRP2ENJ/q91e43q91S4HFVjT5lveOAcQDNmzc/b+/evSXejsq07+Bevoh+g9w6ByHfgeY7kHwfz2+G0wV++Tj8XTgC3EghR580H/IznLgzAnDk+eFwBeB0B+BHEAGOYIJ8axHqX5vw4LoM6H4xvr5+lb6NRtEOJR/nhuc+JyMrlyv6dOCF2y+zO1KlSEnN4JNpv7B+zS6O7UmGPM+TbB2hvjRv35D+F3XgqqHdzUllm5XLCWkRCQF+AV4GfgSWAUNUNa2Y4tBEVRNEpBXwMzBYVXdZh6sOA37AZGCXqr5Q0uJQkLcdVjobea48jqUmkZh8mGMnEknJOEZaZhIn8lLIcqeS60gn3ycH9c0B/zwkwIUzOP8v63DEteTJ4e/ZtAVGUVLSMxn59HRS07Pof24r3r5/uN2RKtSOnYd56I6p5B/PRX0d1G1Rmx69z2H4sPNo26ah3fGMAsp0WMlagS8wG5ihqnNEpAvQEtjkOX9MU2CDiPRU1cMF+6pqgvXf3daY0d3xFIKTh4lyRGQaMMF6nQA0K7CKpta8as3X6UvDiEY0jGhU4j55ebkkpiSSmHqUxbEzyWkdw8pNy+l37sCKC2qUWu2QIOa/ejvXPvMZKzbt5tlpi3j+tkvtjlUhfl25nZfHf43mubnu7xdx2y39zdVFVVSxl9FYVw9NBWJV9S0AVd2iqvVVNVJVI4EDQI9TC4OI1BYRf2s6AuiH51zFyRPdJ9c/AthqdfsOuEU8egNpVel8Q2Xy9fWjcf0mnNu2O3cOeoq8NF8WH5qC220Go/E2QQF+zHvlNkIC/fhhdQwJiWnFd6piZn6zhpcenAmqPP7mddx5+0BTGKqwklxj2Q8YAwwSkY3Wz9CiGotIlIhMsV52AKJFZBOew1CvqWqMtWyGiGzBc5lrBPCSNX8BsBuIAz4G7i3tRtVEtULDaZ1xGb7NUln6+492xzEK4ed08tSYwajChH/PtztOuZr41g9Me3UhPkFO3vr0DgYPNPd3VHUluVrpN0CKaRNZYDoauMOaXgV0KaLPoCLmK3BfcbmM07Vt3JW9zCcr1zw+3Ftden57Pp6/lh37E/ll4y4GdDvH7khl4na7GT9+BjHL4whqEMxH0++iQf1adscyyoG5O6saycrJACDQz1wB4s3euOdKBHj6kx+r/HjUzzw7m5jlcdRrV48v//OQKQzViCkO1UhWrqc4BJji4NVaNqrLFX07kpGVy0tfLLY7zlnbvHUf6xZsJaRpGNO/vJegQHMZdXViikM1kp3vIudoEP6mOHi9p8dcQnCgH0vW72Tf0RS745Sa2+3muce/AeCF128wj4iphsz/0WrkWH4uKbV8CQqtY3cUoxhOp4MnRl9EbkouL31e9fYe3v9gMRkHT9Dris506tjU7jhGBTDFoRrJzMsEoFZwqM1JjJIY2qsj4fWC2LzlABnZuXbHKbGEg8n8MH0NznB/nn76arvjGBXEFIdqJDs/GwB/H3Pst6q4akBncMHcVVuKb+wlnnx0JprnZvxzV+Pna8Ysqa5McahGujU6F4BXVr7C3OhF5ma4KuCSqLYAbIrz/vs8Xa48nrzsBsKT19C6TwsGDehgdySjApmyX40M7tSXVYtWcSR8K0sd01m8eC4N3edQP7ABdQLrEBFSl4jQOoQH1aJ2SBiqbpLSj3P0RDKREY1oEGbOVVS28KAAAPJcLpuTFO/o4YMkHc5m75Z8Hv4w1e44RgUzxaEacTgcPHv5Y2Tl5PDp6q/Y6ljFkTp/cPTkEwxyPD+ZRyPIrp35186JcE5aD14Yck9lx67RUtKzAM/jNbzdE5/9xvb+fTnffx3/enAlXS7oT9MO59sdy6ggpjhUQ4H+/twz8G/A38jMzmJv0iGOHD9KUnoyadlpZAa4OZbmuYs62DeIML8QolM2sKveBn6KWceQjj3t3YAaZEPcAQCa1Q+3N0gxvl+1jc2HjtCqdn2em/0aYzs9zvt/f5PXlnxldzSjgpjiUM0FBQTSoWkrOtDqjO2uSx/MfdGP8WXSbFMcKtGCVbEoMKJvZ7ujFCkzK5eXvlqKDw7ef/AaIiJqMeapznz0ZCxrv5tOr2G32B3RqADmhLQBQJ2QMLq4epIXkcy365faHadGSEpLJ27HUSIah9KwTpjdcYr08L/mka35jB0UReMIz+Mxhj30BE1a5fPRo3PJy82yOaFREUxxMP7rwf6jcB5tyPt7fmVP0mnjNhnl7OUvlyJuGDvce8eXXrZ+J2vjD9AkJIR7r7ngv/P9/IMY99oV7N/pw6zXXrAxoVFRSjKeQzMRWSYiMSKyTUQePGX5eBFRa7yGwvrnF3jU93cF5s8Qke0islVEPrEGFEJEBopIWoE+z5R1I42SCfYPZGTLkaT5uRmzdDLHs7LtjlRtHUo6zup1uwmq7c81/Qp9cLHtlkTv4IlPF+AA3v/7Nact733NbVxwlT+fv7qDvTFrKz+gUaFKcs7BBYxX1Q0iEgqsF5HFqhojIs2AIcC+M/TPUtVuhcyfAdxsTX+J5zHf/7Zer1DVK0u0BUa5uqrzuexMPsp0n18ZPX8y80beb56bUwH+b/IPOLPdPHL3QLuj/FfcgUTm/baNtX/uZe+xVHJw41B4+MoLaNm47mntHQ4H9/3rWTLveJ5J97/LxEWf4PT1tyG5URFKMp7DIeCQNX1CRGKBJnhGdJsEPAbMK+0bq+qCk9Misg7PcKCGF3jkwkvYPu8Qa4N38s7KpTzc/xK7I1Urrnw32/9IoHbjUK7sZd+gOCknMpn962Z+3hLHjmPHcGW5QUAU6vgH0LNZAx4ddRHNG9Quch0RTdsw4IbzeOfvG3niqjt46rOXqNOgRSVuhVFRSnW1kohE4hkDeq2IDAcSVPXkONJFCRCRaDx7IK+p6txT1umLZ6S5goer+lijxx0EJqjqtkKyjAPGATRv3rw0m2GUwDtDR3HRnNf5PPs3bkg9n8bh4XZHqjZi9h5GXNC6Vb1Kf+/MrFwmzlrO8tjdHMnL8hxYdit1fQLo3akpF3RuxZBe7QnwK/mfhqG3PYzbPZEPH93EPX2eZNwr/bno+rvMHmcVJ56B10rQUCQE+AV4GfgRz7CfQ1Q1TUTigShVPe0spog0UdUEEWkF/AwMVtVdBZZ/DGSo6kPW6zDArarp1nCk76hqmzNli4qK0ujo6BJth1FyC2K28NSub2id3YhZ15nRWsuLK9/N4FvfQd0w9993USesch6xvnnXQe78aDbp4sI3X2hXty5DurZh5IBzqRUSWOb1b9+wlDfvmsreWF869cnn3ol30frc/uWQ3KgoIrJeVaMKW1ai0m59u58NzFDVOcA5QEtgk1UYmgIbRKThqX1VNcH6725gOZ49j5PrfRaoBzxSoP1xVU23phcAvkWd7DYq1tCOXeiY24y4oEPM3rTB7jjVhtPHwW2j++HOzOf252dU+Pvl5rl44YufGP3h12Soi7FR3dnwzwf4+skxjL2id7kUBoB2PQbzwerPGPdaW+JjlL9fOJl3Hryf48ne/9wo43TF7jmI55jRZ0DyyW/3hbSJp5A9BxGpDWSqao71B341MNw6mX0HcDuePYmsAn0aAkdUVUWkJzALaKFnCGr2HCpOUno6l/44Eafbh19GPE6An/c/5qGquOeNb9i6ah/XjIri4esHlqpvTq6LTXEH2Hc0haQTGRzPzCY7z0Xi8Uxy8lwE+vlSKyiApBOZrE7YT7av0tIRyv9dO5i+XVpWzAYVkJq4n6nPvMLiLzIIq+vinn8O4KLr76rw9zVK50x7DiUpDhcAK4AtwMnHfD51ygnleKziICJRwN2qeoeI9AU+svo5gLdVdarVxwXsBU5Yq5mjqi+IyP3APXjOUWQBj6jqqjNlNMWhYr2/8mempC6jv3bgvStvsjtOtZGT5+KS297D6efDz588UKI+rnw3Ez6ex4+H96A+fz3XF5zjIMtx+pN4Q1w+3Ni9Cw9fO6DSzwPE/r6Edx/8hN2bfbnxyabc/OjT+PoFVGoGo2hlKg5VgSkOFe/iryZyLOA4iwZNoH6Y997NW9X8fdIsNv4az+R3xtCheYMzto07cJQ7P57NQWc2jVz+DGrdiqZ1w6kdGkTd0EDqhgbTICKMkKAAUk9kcjQlndqhgTSrX/TVRpUhLzeLaS8+zbxpGbTulsfTU/+PiAaRtmYyPExxMMrs+22b+b/4b7mIzky64ga741Qb81ZuYeLERYwcfT4Pjhzwl2Vut5s1MfHMX7eN6P0H2a8ZqMCVDVvxxp3DqtzVQD/M+Bcf/WMLIbVd/GPKKDqdN9juSDXemYqDefCeUSJXdurKP7cs4FdnLNm5uebcQzlp3cRzrcWxtAwAduw/yje//cFv8XuJd6cTmu/kuOTj64L2geE8MXwQvTtF2pj47F0x+j4i2y/l5Ttm8uS133L/xN0MGXmn3bGMIpg9B6PEPlr9C/9OXsItoRfyyIXmxrjycCIzm8tHv094i2CS6rpIDMgFEZy5SjOC6dO0GVec14meHZpXuT2FoiQdiee5W19i9yY/bn6yCTf9/R92R6qxzGElo1zk5rvoM/claueFsOTGCXbH8Wpvz1nCrB0xZJFPhASSlJeNAornygwHgg9CPoruycXHBSfa+NAnvBHX9ezKxed1wOlTPYpBYbIyj/PSXY+xfb2bQaMbcPeTz1eb4leVlPk+B8MA8PNx0lGakhScRnzyMbvjeK2vl0fz9uGNJPrk4AM4xYEfDgLEhyDxwQ8HDgQXigCB6sA/Sflw2HCm3j+Ky3p2qtaFASAwKIznp73HhTdEMP/jdN78x5NmzHMvY845GKXyt079mBC3lw/XLee1y661O45XWrA1FkRZeefdRISHFtt+16EkbrvrE/45eREXvl9z7kR3On25/+mXyMt9iiUzcvAPfJoHnnnZ7liGpXp/PTHK3cXtOhCQ4ceKtO12R/FauS4Xkk+JCgPAOY0i6NS/BSf2pPPKFz9VcDrv4nA4ePjFV7jwWh8WfpLJN9PetjuSYTHFwSi1nkGtyQjJYk38brujeCURgTM+i/J0/7xvOH4NA1g0cyPjXv2yRh1icTgcPPr6q7TvlcsXr+1m354tdkcyMMXBOAt39RgAClM3rbA7ileq5e9LcLaW6g98aFAAc94fR72Otdmx5RB3vzKzRhUIp9OXCW/8HYDZ02banMYAUxyMs9CpcWNqpYewMTe+Rv0BK6m8fBcnags5ea5S9QsNDOCb18fSJLI2cb/s54E3v62ghN6pSfP29Lk2hGVzc8jJybQ7To1nioNxVgbX60hekIu5WzfaHcXrhAd4nnK6/0jpr+hyOBx89uKt1O4YTsxPe5i+qGYNv9m6U3Pysp3s3RNrd5QazxQH46zcff4AcMHMHTXrj1dJtIrwDKkZszfhrPo7fRx88sotaLCDL79eU57RvN6qRdsJCM2jafO2dkep8UxxMM5K/bAwGmfXJc7nEJm5OXbH8SoZ2Z7PIyTg7MdTrhUcRPPO9clJyCIrJ7e8onm1Fcvn8ecaP664rRFBQSW70suoOKY4GGdtRIvuqJ8y7fczPlG9xvn94AFwKz3alm3chI7tmyBuWLVtTzkl827fffErIXVzGXPnfXZHMShBcRCRZiKyTERiRGSbiDx4yvLxIqJFjdYmIvkistH6+a7A/JYislZE4kTkaxHxs+b7W6/jrOWRZdxGo4Lc0qMPjhwH8w/8YXcUrxGzJ4F1ASm0TvGnTq2QMq0rqqNnbPTomH3lEc3rpSa6aHwO+PtXzrCpxpmVZM/BBYxX1Y5Ab+A+EekInsIBDAHO9K83S1W7WT/DCsx/HZikqq2BFGCsNX8skGLNn2S1M7xQgJ8fbdyNOByYwsHUVLvjeIVHvp0DIrw+bFjxjYvRs30LnPV8OZBw2tDs1VKO24kj2NfuGIal2OKgqodUdYM1fQKIBZpYiycBj+F5nliJWUOPDsIzBCh4hiEdYU0Pt15jLR9stTe80G2d+oMPvLLiB7uj2G7hms3E1smmT3ZtojqUfSjO0KAA3Fn5xO9ILId03s/hA/n5Vf9BoNVFqc45WId4ugNrRWQ4kKCqm4rpFiAi0SKyRkRGWPPqAqmqevJC8AP8r+A0AfYDWMvTrPanZhlnrTc6MbFm/PJ4o8s6dKLOiVBW5u0gOSPD7ji2evmXJfhkuZl083Xlts46zcPI2J9RI+4nCQoTMo9X/+2sKkpcHEQkBJgNPITnUNNTwDMl6NrCeiTsTcDbInLOWeQ8japOVtUoVY2qV69eeazSOEv3dbgY9XfzwvL5dkexzead+9hfN5+B0oCGEeHltt7OXZvhyFF+27qr3NbprULCnaSn2p3COKlExUFEfPEUhhmqOgc4B2gJbBKReKApsEFEGp7aV1UTrP/uBpbj2fM4BoSLyMmnwjYFTl4UngA0s97XCdSy2hte6tpze9A6uzFL03eyen+83XFssXjjNgCu7n5uua730r6dAPhpVUy5rtcb+fr54Mo1R5C9RbGP7LaO908FYlX1LQBV3QLUL9AmHohS1aRT+tYGMlU1x7qaqR/wT1VVEVkGjAS+Av4GzLO6fWe9Xm0t/1mrw4hE1dw/+lzF6JWfcP/Kr/n9+kdr3MAtCWlpALRrdtr3ozI5v11z3EFC7Na/3lB3PDuLT1etZFXMHo4czUAVfPPd1G8QxJjBvbmsc7dyzVEZ8twOzokKsDuGYSnJb3A/YAwwqMAlqUOLaiwiUSIyxXrZAYgWkU3AMuA1VT35Fehx4BERicNzTmGqNX8qUNea/wjwRKm3yqh03Rs35bLwzmT6Z7MsfqfdcSpdh4aeorB+R3y5rtfhcFArMpSU3cdxu91sObifWz6axqDxH/DZzC3ExRzH7YbAQM/3vLgt6Tzzxs9c8fw7/LB5Q7lmqWg7N7pAfOyOYViK3XNQ1d8o5gHEqhpZYDoauMOaXgV0KaLPbqBnIfOzgfI7o2dUmk2pB8ABLWrVsTtKpRvcvSMvfb+OH7bFMOriPuW67k7nNmG5bGPIS++RciAfgMatA7m2f1du6tkbP5///RofPXGc1+bOZ9Vvh3j+zeW8Gv4zTZoF0aZxON3bRDKoY1dqB5ft/ouK4Ha7yUz1JaJBsN1RDIsZCc4oN8n5mTjUQZ473+4ola5Vk/o0TfFhtzOZnNw8/P3O/nr97Lw8Fu/4k59jdxITf5QjBzJxBPuSk+ji3J4RPDx0EF0aNyu0b/3QMN4aM5qkEcd596efWL81gaTELPZtyGIph5joWEVYI+hxbiPuHTqEZnW842IOh8OBw6G4XOZqJW9hioNRbm5v1ZcP9i1j5LKPee3ca7iqXSe7I1WqG9t3YlLWBt777kcmjLyqxP0yc3P4ISaGJdt28Gf8UdKO5kG+Z2ddAhSHXx7+x138NOkBQgMDS7TOiNAwXrh2JFgjue5NOsKKP2NYG7OH7duPsXzBYX5Z/Cl9BtbnmRtGUivI/m/swXXySD5qHtXtLaQ6nOuNiorS6Ohou2MYwKK4WJ6J/oFkzealLldyg80nRg+mHWfJ7l1sPXKUm7p2pVvjRhX2Xrl5Lrp//Bp+LmH9/U8WeVLe7XazdOd25m/axqadh0k7mvvfYuAbBi2a1eL81s0IyXfyn1dWIEE+vP3hbXSKbFxuWVfH/cmbMxeRdCgHR7Yy+NLmXHJeNyLrNSQ4IAAfcSAOBz7iwOlTOecBxo5+AnEIUz5/tVLezwARWW/danAas+dglKtLW3cgMrwuwxdN5blNC7mibUdC/PxKvZ5sl4vNhw6Tmp3FiZxc0nNzOZGTQ0ZuHpl5uWTk5pLlcpGZl0e2y0W2K49sVz65+Z55J3Jzyc534S5w8/6s2G10rdeAKVdfTURw+T+/x8/XyZXBrfnWbzffLF/NjYP6/XfZtiOH+WbjRrYnHGX7tiQ0xyoGodCmQ236tYtkRNeuRNb13O/545qtTHxsDhLkw1sf3FquhQGgT+v2zHq6PfPWreSjz39j4dcJLPy68EeMNzjHTfpBF6F1HTRpEULrlvWJbNyQFg0bck7DpgQHlP2zdLvdnEiGBpE16yo3b2b2HIwK8a/ff2PSnuV09GnE3KtvK9Glrek52byzejVzYmJJycku3TjM6mkuIjjFQZCvL7X8A2gWFkZU4ya0qlubN1euYv+JNDrUiWD+LWPOetvOJOV4Or2+eZv6x52M7tOfpbFx7NqfQl665/fMP0yoHxxIr/bNuLZ7N7o0Ov2P/oYde3ns7ungI7zz8e3lXhgKs3ZnDOv+3EHy8XRycl24VUHB7VZ8RTm0P5Xko7mkHRTceX/9f+kbnE9IBHTrHEb3zm24pFd/ggJKdvjrpJ9XL+Xjt5cx/Ib23HjNzeW5acYZnGnPwRQHo0K43W6umfspW10HaSURzB1xO0G+Re9BrN6/l7FLZ5PnduNzwklkrdp0adCA8IAAgnz9CPb3JczPnxB/P8L8Awjz9ycswJ/wgADCAgJwlvC+itHffsPa3QlMHHYpIzp0LK/NJdfl4vvtsczf9ifRO/eTn+PGkeUDooRF+NG1ZUOGdenEkDbtcPoUnTUhMZn7Hv2M9D0nePa9UfTv2qbcMpaHPJeL2AO72ZWQwMGjxziclEpSUgaJhzJJ3Z6P5vrg459Pi64+9O/fluEXXUytkFrFrveRJ1/mzzV5fP39w4QGF9/eKB+mOBi2cLvd3Pz9l6zLjqe2K5g5V9xGs1rhp7V7+bdlTIlbB8B97foyoU//CsuUcPw4Az6cSuPaofx65x1lWtfvB/bzzabNrNt9gMTEjP+dNwgScoKy8MfN0nEPElGKS0dvGPc2KeuPcecbw7hu0PllylfZ0rPSWbR6BSt+28aO9VnkHHfi8HXTspuDwRd35qoLLy50j+JgYgJjR35M5wv9eOPFp2xIXnOZ4mDY6qllC/jmyAb88315/fxhXNm2A+A5jHTd3JnE5hwl2O3H9CHXc17jJsWsreyumP452w8lsuLeO2kUWvIRxzLzcpm5aSNzN8YQfzCV/BzP7474QpOGofRrHclN3bvRpm4EL8+cxafOP7khsxUv/e2mEq1/35Ek7rzsHRoPasK0N+8+q23zFnmuPJatX8WPS9YTuyaD3HQnzkAXvS4OpWe31vTo0p3klGTWrF/Pwq93kXHMyRtTR9Clbfk+fsQ4M1McDNt9uWUDkzatIDE/mwtrt6R1eF2+3LWRbB8XXYMa8eVVNxLiX/oT12djcvTv/HPpb9zXvycP9+13xraJGel8HP07i2PjOJiUTlCgk5zUPGrXCaRHy8Zc07UzAyJbnnZOxe120++9V0gJz2fB4HG0alr8VVIvT57Hin9F8/Dk67m0V6H3jlZJOXk5LFi5jMVLNnE8IYOjf/71HpDwJrnc++hgBvYaaE/AGswUB8Mr7ExKYszCbzisJwBw5ju4r0MfHu59QaXmOJGTQ/d3PiAs0J9Ft99CveAQcl0uDhxPIz41lf1paWw9fITfdu3lWHImgoADmtYPY2jHtow9L4rwEtxv8NumGG6LmU3bIwH88NCjxba/buwk0mJT+PG356rts6kyszL5Zd0v7Nq9n7CwYDq3b0e3Dj2q7fZ6O3Mpq+EV2kREsGbMvSzcuZ1MVx6XtGxNWEDlP2gt1N+f4V3bM2/Tn/R+fzKCoCg+OfKXC6QcTmjfPIJrunbixq7nEuAs3a/LBed2pM+Kn1ndJJWvf17BDYOKPpeSm+ciLSaF8M51qvUfyqDAIC4fcDkMsDuJURxTHIxKd3mbdnZH4M3LLqdbw0Z8v/1Psl35BPv6UicwgIigYBqHhdKmbgQDIiPL/If67TG30P/rSbya+DPD+vUk0N//L8u37TnA98v+YPXSP3FkKhcOrll3lRveyxxWMowK9q95C3g7dwNXpDXhxZtuZO6yaH5dtpUDGw7BoTwcbYPJS8qj+QVNmPzqHdV6z8HwLmU6rCQizYDpQAM8Y0VPVtV3CiwfD0wE6p06nkOBNmFADDBXVe8XkVBgRYEmTYEvVPUhEbkVeIP/Df7zvqpOwTCqILfbTbfmLWn17io2HtvJyHdewOEC9QG/NsG0G9KGoRf3YECPDqYoGF6lJIeVXMB4Vd1g/VFfLyKLVTXGKhxDgH3FrONF4NeTL1T1BNDt5GsRWQ/MKdD+a1W9v4TbYBheZXt8AvMXr+WPlTs49kcikpKPOMGnaSC57WDUqMGMvKQ3dcK879HZhnFSScZzOAQcsqZPiEgs0ATPnsAk4DH+N4rbaUTkPDx7HT8Cp+2+iEhbPKPKrTh1mWFUJetj4nhhwmfkbvVcjeUOFsK61KFLv3O46rLezFy1nB/q7CM1O8UUBsPrleqEtIhE4hkDeq2IDAcSVHWTZyTRQts7gDeBm4GLi1jtjXj2FAqe/LhWRC4EdgAPq+r+QtY9DhgH0Lx589JshmFUiBfGf0b2rnQ63tqRyy47n8G9uv7liaadzmnGio9f5IuQzYw6MoBmDeqfYW2GYa8SH+QUkRBgNvAQnkNNTwHPFNPtXmCBqh44Q5sbgZkFXs8HIlW1K7AY+KywTqo6WVWjVDWqXj3vGLDEqNlydpyg0UVNmPTCOC7t2/20R137OX15MWoE+cEO/v71JzalNIySKVFxEBFfPIVhhqrOAc4BWgKbRCQezwnlDSJy6ujqfYD7rTYTgVtE5LUC6z0XcKrq+pPzVPWYquZYL6cA553NhhlGZfNpFkjSzuQztrn4/B5EHa6Njzhw5de8EfOMqqMkVysJMBWIVdW3AFR1C57zBCfbxANRp16tpKqjC7S51WrzRIEmo/jrXgMi0sg6zwEwDIgtxfYYhm3aXNCCHT/vJiYuno6tI4tsN+3uB8yVSYbXK8m/0H7AGGCQiGy0foYW1VhEokSkpJeeXs8pxQF4QES2icgm4AHg1hKuyzBsNer6AeiuE8z8dskZ25nCYFQF5iY4wyhHQ3s8SFCTYGbNf8XuKIZRrDPdBGe+whhGOflpZTTuxFx8A81TaYyqzxQHwygHcfsPMumOT5EwJy+/dY/dcQyjzExxMIwy2rxjNw8Mew1Nz2fC1NtKNHaDYXg7s/9rGGcp1+Xi9TenseqzTWiWm3FTb2Jw7x52xzKMcmGKg2GchXmLf2HyhC9xxWXR6NqWPPjUGLq3b213LMMoN6Y4GEYpxOzaw0uP/JtjSw4jEU6ueucK7r39OnN5qlHtmOJgGGdw5FgyC5esZO/ugyQfTePP2bFoupsO4zrz3PP3UDss1O6IhlEhTHEwjCLsO3SEcX2eRBNdAKgDfFsE8Pisu7mwpzm3YFRvpjgYRhFefvxtnOFOrn5lGIMH9KJRvbr4+/nZHcswKoUpDoZRiEnvTmX3F3F0e6g7Y28aYXccw6h05iyaYZxi2oxZLJjwI6E9w3n59Ql2xzEMW5g9B8Ow/Bm3ixnT5rLmn2sIbB/Mv79/BT9fcxjJqJlMcTBqvB279/DI5c+SszMLgNoXRvDBnJeJqFPH5mSGYZ+SjOfQDJiOZxxoBSar6jsFlo/HM5BPvVPHcyjQJgzPmNNzVfV+a95yoBGQZTUboqpHRcTfer/zgGPADaoaf1ZbZxgl0KpFM2qdE07bUT0YMKQPF/Y+39y3YNR4JdlzcAHjVXWDiIQC60VksarGWIVjCLCvmHW8CPxayPzRqnrqs7bHAimq2lpEbgReB24oQU7DOCtOHyczFrxrdwzD8CrFfj1S1UOqusGaPoFnZLYm1uJJwGN49igKJSLn4dnr+KmEmYbzv3GjZwGDrdHoDMMwjEpSqn1nEYkEugNrRWQ4kKCqm87Q3gG8CRR1ycc0a2S5pwsUgCbAfgBVdQFpQN1C1j1ORKJFJDoxMbE0m2EYhmEUo8TFQURCgNnAQ3gONT0FPFNMt3uBBap6oJBlo1W1C9Df+hlT0iwAqjpZVaNUNapevXql6WoYhmEUo0TFQUR88RSGGao6BzgHaAlsEpF4oCmwQUQantK1D3C/1WYicIuIvAagqgnWf08AXwI9rT4JQDPrfZ1ALTwnpg3DMIxKUpKrlQSYCsSq6lsAqroFqF+gTTwQderVSqo6ukCbW602T1h/9MNVNckqPFcCJ0dl/w74G7AaGAn8rNVhoGvDMIwqpCR7Dv3wHPIZZJ0f2CgiQ4tqLCJRIjKlmHX6A4tEZDOwEc/ewsfWsqlAXRGJAx4BnihBRsMwDKMcSXX4Uh4VFaXR0adeEWsYhmGciYisV9WoQpdVh+IgIonAXpvePgIo9OY/L1bVMle1vFD1Mle1vFD1Mntj3haqWugVPdWiONhJRKKLqrzeqqplrmp5oeplrmp5oeplrmp5zTMCDMMwjNOY4mAYhmGcxhSHsptsd4CzUNUyV7W8UPUyV7W8UPUyV6m85pyDYRiGcRqz52AYhmGcxhQHwzAM4zQ1vjiIyCciclREthaYd52IbBMRt4hEFZg/usBd4hut5d0KWee5IrJaRLaIyHxrsCNEJFJEsgr0/7ASMvuKyGdWllgRebKIdbYUkbUiEiciX4uInzXf33odZy2P9PK8t4pIYoHP+A4vyXu/lVVFJKLAfBGRd61lm0WkR2nz2pB5oIikFfiMi3sAZ2XlnSEi20Vkq7V+X2u+N3/GRWUu82dcZqpao3+AC4EewNYC8zoA7YDleJ4HVVi/LsCuIpb9Dgywpm8HXrSmIwu+T2VkBm4CvrKmg4B4ILKQdX4D3GhNfwjcY03fC3xoTd8IfO3leW8F3vfCz7e79f8/HogoMH8osBAQoDewtgpkHgh874Wf8VDrcxRgZoF/E978GReVucyfcVl/avyeg6r+CiSfMi9WVbcX03UU8FURy9ryv5HvFgPXlinkKUqZWYFg8TzsMBDIBY4XbCAiAgzCM7gSeAZbGmFNl3nwpUrOW2blndfq/4cWPtztcGC6eqwBwkWkkZdnLrMKyrvA+hwVWIfnadHg3Z9xUZltV+OLQxncgKfSF2Ybnn+QANdhPYLc0lJE/hCRX0Skf0UGtMwCMoBDeIZznaiqyae0qQukqmdwJYAD/G+0vxINvuRFeQGutQ4fzBLPULYVqSR5z+S/n6/l1G2pCGXNDNBHRDaJyEIR6VTuCf+qVHmtQzNjgB+tWV7/GReSGSr3Mz6NKQ5nQUR6AZmqurWIJrcD94rIeiAUz7cG8PxDaa6q3fE8cfZLsc5HVKCeQD7QGM8YHONFpFUFv2dZlDXvfDy7713x7LV9Vkz7sqpqny+UPfMGPM/kORd4D5hb7gn/qrR5PwB+VdUVFZzrTMqaubI/49OY4nB2bqTovQZU9U9VHaKq51ntdlnzc1T1mDW93prftoKz3gT8qKp5qnoUWAmc+nyXY3h2tU+O79EUz2PUofIHXypTXlU9pqo51vwpwHkVmLWkec/kv5+vpeBnX1HKlFlVj6tqujW9APAteMK6ApQ4r4g8C9TD8+XrJK/+jAvLbMNnfBpTHEpJPONiX0/R5xsQkfoF2v4fnhOmiEg9EfGxplsBbYDdFRx5H57j84hIMJ4Tcn8WbGAd71yGZ3Al8Ay2NM+aPjn4ElTO4EtlynvKseRhQGwFZi1R3mJ8h2eERBGR3kCaqh4q/5h/UabMItLw5HknEemJ5+9IRX5hKFFe8VyZdikwSlXdBRZ57WdcVGYbPuPTVeTZ7qrwg+eb/SEgD8+xyLHA1dZ0DnAEWFSg/UBgTSHrmYJ1tQLwILDD+nmN/92Jfi2e8xEb8ew2XlXRmYEQ4FvrfWOARwusZwHQ2JpuheeEWJzV3t+aH2C9jrOWt/LyvK9afTfhKSDtvSTvA1Z/F3AQmGLNF+BfePYit1DE1XFelvn+Ap/xGqCvl+R1WZ/jRuvnmSrwGReVucyfcVl/zOMzDMMwjNOYw0qGYRjGaUxxMAzDME5jioNhGIZxGlMcDMMwjNOY4mAYhmGcxhQHwzAM4zSmOBiGYRin+X/cLKndgnBFNQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -193,7 +198,7 @@
    ],
    "source": [
     "metroline_splited = tbd.split_subwayline(line,stop)\n",
-    "metroline_splited.plot(column = 'o_project')"
+    "metroline_splited.plot(column = pd.Series(metroline_splited.index))"
    ]
   },
   {
@@ -225,7 +230,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -236,23 +241,163 @@
    ],
    "source": [
     "#Modeling for subway network topology\n",
+    "line['speed'] = 36 #operation speed 36km/h\n",
+    "line['stoptime'] = 0.5 #stop time at each stations 30s\n",
     "import networkx as nx\n",
-    "G = tbd.metro_network(stop)\n",
-    "nx.draw(G)"
+    "G = tbd.metro_network(line,stop, transfertime=5)\n",
+    "nx.draw(G,node_size=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c8a8b429",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['地铁1号线镇海路',\n",
+       " '地铁1号线中山公园',\n",
+       " '地铁1号线将军祠',\n",
+       " '地铁1号线文灶',\n",
+       " '地铁1号线湖滨东路',\n",
+       " '地铁3号线湖滨东路',\n",
+       " '地铁3号线体育中心',\n",
+       " '地铁3号线人才中心',\n",
+       " '地铁3号线湖里公园',\n",
+       " '地铁3号线华荣路',\n",
+       " '地铁3号线火炬园',\n",
+       " '地铁3号线小东山',\n",
+       " '地铁3号线安兜',\n",
+       " '地铁3号线坂尚',\n",
+       " '地铁3号线湖里创新园',\n",
+       " '地铁3号线五缘湾',\n",
+       " '地铁3号线林前',\n",
+       " '地铁3号线鼓锣',\n",
+       " '地铁3号线后村',\n",
+       " '地铁3号线蔡厝']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path = tbd.get_shortest_path(G,stop,'镇海路','蔡厝')\n",
+    "path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9bcc0ad1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "68.00206888083389"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbd.get_path_traveltime(G,path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "090a7c85",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['地铁1号线镇海路',\n",
+       "  '地铁1号线中山公园',\n",
+       "  '地铁1号线将军祠',\n",
+       "  '地铁1号线文灶',\n",
+       "  '地铁1号线湖滨东路',\n",
+       "  '地铁3号线湖滨东路',\n",
+       "  '地铁3号线体育中心',\n",
+       "  '地铁3号线人才中心',\n",
+       "  '地铁3号线湖里公园',\n",
+       "  '地铁3号线华荣路',\n",
+       "  '地铁3号线火炬园',\n",
+       "  '地铁3号线小东山',\n",
+       "  '地铁3号线安兜',\n",
+       "  '地铁3号线坂尚',\n",
+       "  '地铁3号线湖里创新园',\n",
+       "  '地铁3号线五缘湾',\n",
+       "  '地铁3号线林前',\n",
+       "  '地铁3号线鼓锣',\n",
+       "  '地铁3号线后村',\n",
+       "  '地铁3号线蔡厝'],\n",
+       " ['地铁1号线镇海路',\n",
+       "  '地铁1号线中山公园',\n",
+       "  '地铁1号线将军祠',\n",
+       "  '地铁1号线文灶',\n",
+       "  '地铁1号线湖滨东路',\n",
+       "  '地铁1号线莲坂',\n",
+       "  '地铁1号线莲花路口',\n",
+       "  '地铁1号线吕厝',\n",
+       "  '地铁1号线乌石浦',\n",
+       "  '地铁1号线塘边',\n",
+       "  '地铁1号线火炬园',\n",
+       "  '地铁3号线火炬园',\n",
+       "  '地铁3号线小东山',\n",
+       "  '地铁3号线安兜',\n",
+       "  '地铁3号线坂尚',\n",
+       "  '地铁3号线湖里创新园',\n",
+       "  '地铁3号线五缘湾',\n",
+       "  '地铁3号线林前',\n",
+       "  '地铁3号线鼓锣',\n",
+       "  '地铁3号线后村',\n",
+       "  '地铁3号线蔡厝']]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# k th shortest paths\n",
+    "paths =  tbd.get_k_shortest_paths(G,stop,'镇海路','蔡厝',2)\n",
+    "paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9bf50736",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "68.176777400316"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbd.get_path_traveltime(G,paths[1])"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.8",
    "language": "python",
    "name": "python3"
   },
@@ -280,6 +425,11 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
+   }
   }
  },
  "nbformat": 4,
diff --git a/setup.py b/setup.py
index c9c7174..277a6bc 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="transbigdata",
-    version="0.4.8",
+    version="0.4.9",
     author="Qing Yu",
     author_email="qingyu0815@foxmail.com",
     description="A Python package developed for transportation spatio-temporal big data processing and analysis.",
diff --git a/src/transbigdata/__init__.py b/src/transbigdata/__init__.py
index 44a74ba..2efbe23 100644
--- a/src/transbigdata/__init__.py
+++ b/src/transbigdata/__init__.py
@@ -33,7 +33,7 @@
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 
-__version__ = '0.4.8'
+__version__ = '0.4.9'
 __author__ = 'Qing Yu <qingyu0815@foxmail.com>'
 
 # module level doc-string
@@ -181,8 +181,13 @@
     getbusdata,
     get_isochrone_amap,
     get_isochrone_mapbox,
+)
+from .metro import (
     split_subwayline,
-    metro_network
+    metro_network,
+    get_path_traveltime,
+    get_shortest_path,
+    get_k_shortest_paths
 )
 from .visualization import (
     visualization_trip,
diff --git a/src/transbigdata/busgps.py b/src/transbigdata/busgps.py
index 846771a..57e5cf8 100644
--- a/src/transbigdata/busgps.py
+++ b/src/transbigdata/busgps.py
@@ -116,7 +116,7 @@ def busgps_arriveinfo(data, line, stop, col=[
     data = data.to_crs(epsg=project_epsg)
     print('.', end='')
     if method == 'project':
-        data['project'] = data['geometry'].apply(lambda r: lineshp.project(r))
+        data['project'] = data['geometry'].apply(lineshp.project)
     elif method == 'dislimit':
         tmps = []
         # Distance limit method
@@ -149,7 +149,7 @@ def busgps_arriveinfo(data, line, stop, col=[
     print('.', end='')
     # Project bus stop to bus line
     stop = stop.to_crs(epsg=project_epsg)
-    stop['project'] = stop['geometry'].apply(lambda r: lineshp.project(r))
+    stop['project'] = stop['geometry'].apply(lineshp.project)
     print('.', end='')
     starttime = data[GPSDateTime].min()
     data['time_st'] = (data[GPSDateTime]-starttime).dt.total_seconds()
diff --git a/src/transbigdata/crawler.py b/src/transbigdata/crawler.py
index a96e4c9..9d70420 100644
--- a/src/transbigdata/crawler.py
+++ b/src/transbigdata/crawler.py
@@ -48,7 +48,7 @@
 )
 
 
-def getadmin(keyword, ak,jscode='', subdistricts=False):
+def getadmin(keyword, ak, jscode='', subdistricts=False):
     '''
     Input the keyword and the Amap ak. The output is the GIS file of
     the administrative boundary (Only in China)
@@ -83,7 +83,7 @@ def getadmin(keyword, ak,jscode='', subdistricts=False):
         'showbiz': 'false',
         'extensions': 'all',
         'key': ak,
-        'jscode':jscode,
+        'jscode': jscode,
         's': 'rsv3',
         'output': 'json',
         'level': 'district',
@@ -286,7 +286,7 @@ def coodconvert(coo):
     return data, stop
 
 
-def get_isochrone_amap(lon, lat, reachtime, ak,jscode='', mode=2):
+def get_isochrone_amap(lon, lat, reachtime, ak, jscode='', mode=2):
     '''
     Obtain the isochrone from Amap reachcricle
 
@@ -318,7 +318,7 @@ def get_isochrone_amap(lon, lat, reachtime, ak,jscode='', mode=2):
     url = 'http://restapi.amap.com/v3/direction/reachcircle?'
     dict1 = {
         'key': ak,
-        'jscode':jscode,
+        'jscode': jscode,
         'location': str(round(float(lon), 6))+','+str(round(float(lat), 6)),
         'time': reachtime,
         'output': 'json',
@@ -391,117 +391,3 @@ def get_isochrone_mapbox(lon, lat, reachtime, access_token='auto',
     isochrone['lat'] = lat
     isochrone['reachtime'] = reachtime
     return isochrone[['lon', 'lat', 'reachtime', 'geometry']]
-
-
-def split_subwayline(line, stop):
-    '''
-    To slice the metro line with metro stations to obtain metro section
-    information (This step is useful in subway passenger flow visualization)
-
-    Parameters
-    -------
-    line : GeoDataFrame
-        Bus/metro lines
-    stop : GeoDataFrame
-        Bus/metro stations
-
-    Returns
-    -------
-    metro_line_splited : GeoDataFrame
-        Generated section line shape
-    '''
-    def getline(r2, line_geometry):
-        ls = []
-        if r2['o_project'] <= r2['d_project']:
-            tmp1 = np.linspace(r2['o_project'], r2['d_project'], 10)
-        if r2['o_project'] > r2['d_project']:
-            tmp1 = np.linspace(
-                r2['o_project']-line_geometry.length, r2['d_project'], 10)
-            tmp1[tmp1 < 0] = tmp1[tmp1 < 0]+line_geometry.length
-        for j in tmp1:
-            ls.append(line_geometry.interpolate(j))
-        return LineString(ls)
-    lss = []
-    for k in range(len(line)):
-        r = line.iloc[k]
-        line_geometry = r['geometry']
-        tmp = stop[stop['linename'] == r['linename']].copy()
-        for i in tmp.columns:
-            tmp[i+'1'] = tmp[i].shift(-1)
-        tmp = tmp.iloc[:-1]
-        tmp = tmp[['stationnames', 'stationnames1',
-                   'geometry', 'geometry1', 'linename']]
-        tmp['o_project'] = tmp['geometry'].apply(
-            lambda r1: r['geometry'].project(r1))
-        tmp['d_project'] = tmp['geometry1'].apply(
-            lambda r1: r['geometry'].project(r1))
-        tmp['geometry'] = tmp.apply(
-            lambda r2: getline(r2, line_geometry), axis=1)
-        lss.append(tmp)
-    metro_line_splited = pd.concat(lss).drop('geometry1', axis=1)
-    return metro_line_splited
-
-
-def metro_network(stop, traveltime=3, transfertime=5, nxgraph=True):
-    '''
-    Inputting the metro station data and outputting the network topology
-    model. The graph generated relies on NetworkX.
-
-    Parameters
-    -------
-    stop : GeoDataFrame
-        Bus/metro stations
-    traveltime : number
-        Travel time per section
-    transfertime : number
-        Travel time per transfer
-    nxgraph : bool
-        Default True, if True then output the network G constructed by
-        NetworkX, if False then output the edges1(line section),
-        edge2(station transfer), and the node of the network
-
-    Returns
-    -------
-    G : networkx.classes.graph.Graph
-        Network G built by networkx.
-        Output when the nxgraph parameter is True
-    edge1 : DataFrame
-        Network edge for line section.
-        Output when the nxgraph parameter is False
-    edge2 : DataFrame
-        Network edge for transfering.
-        Output when the nxgraph parameter is False
-    node : List
-        Network nodes. Output when the nxgraph parameter is False
-    '''
-    linestop = stop.copy()
-    for i in linestop.columns:
-        linestop[i+'1'] = linestop[i].shift(-1)
-    linestop = linestop[linestop['linename'] == linestop['linename1']].copy()
-    linestop = linestop.rename(
-        columns={'stationnames': 'ostop', 'stationnames1': 'dstop'})
-    linestop['ostation'] = linestop['line']+linestop['ostop']
-    linestop['dstation'] = linestop['line']+linestop['dstop']
-    edge1 = linestop[['ostation', 'dstation']].copy()
-    edge1['duration'] = traveltime
-    linestop = stop.copy()
-    linestop['station'] = linestop['line'] + linestop['stationnames']
-    tmp = linestop.groupby(['stationnames'])[
-        'linename'].count().rename('count').reset_index()
-    tmp = pd.merge(linestop, tmp[tmp['count'] > 2]
-                   ['stationnames'], on='stationnames')
-    tmp = tmp[['stationnames', 'line', 'station']].drop_duplicates()
-    tmp = pd.merge(tmp, tmp, on='stationnames')
-    edge2 = tmp[tmp['line_x'] != tmp['line_y']][['station_x', 'station_y']]
-    edge2['duration'] = transfertime
-    edge2.columns = edge1.columns
-    edge = edge1.append(edge2)
-    node = list(edge['ostation'].drop_duplicates())
-    if nxgraph:
-        import networkx as nx
-        G = nx.Graph()
-        G.add_nodes_from(node)
-        G.add_weighted_edges_from(edge.values)
-        return G
-    else:
-        return edge1, edge2, node
diff --git a/src/transbigdata/gisprocess.py b/src/transbigdata/gisprocess.py
index 8835638..e9344e5 100644
--- a/src/transbigdata/gisprocess.py
+++ b/src/transbigdata/gisprocess.py
@@ -270,7 +270,7 @@ def polyexterior(p):
             return MultiPolygon(geometries)
         if type(p) == Polygon:
             return Polygon(p.exterior)
-    data1['geometry'] = data1['geometry'].apply(lambda r: polyexterior(r))
+    data1['geometry'] = data1['geometry'].apply(polyexterior)
     data1 = data1[-data1['geometry'].is_empty]
     return data1
 
diff --git a/src/transbigdata/grids.py b/src/transbigdata/grids.py
index 68262b4..399d782 100644
--- a/src/transbigdata/grids.py
+++ b/src/transbigdata/grids.py
@@ -1370,7 +1370,7 @@ def geohash_decode(geohash):
     lat : Series
         decoded latitude Series
     '''
-    lonslats = geohash.apply(lambda r: decode(r))
+    lonslats = geohash.apply(decode)
     lon = lonslats.apply(lambda r: r[0])
     lat = lonslats.apply(lambda r: r[1])
     return lon, lat
@@ -1390,7 +1390,7 @@ def geohash_togrid(geohash):
     poly : Series
         grid cell polygon for geohash
     '''
-    lonslats = geohash.apply(lambda r: decode_exactly(r))
+    lonslats = geohash.apply(decode_exactly)
 
     def topoly(r):
         (lon, lat, lon_err, lat_err) = r
@@ -1401,5 +1401,5 @@ def topoly(r):
                         [lon+lon_err, lat-lat_err],
                         [lon-lon_err, lat-lat_err],
                         ])
-    poly = lonslats.apply(lambda r: topoly(r))
+    poly = lonslats.apply(topoly)
     return poly
diff --git a/src/transbigdata/metro.py b/src/transbigdata/metro.py
new file mode 100644
index 0000000..39d7242
--- /dev/null
+++ b/src/transbigdata/metro.py
@@ -0,0 +1,271 @@
+'''
+BSD 3-Clause License
+
+Copyright (c) 2021, Qing Yu
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+'''
+
+import pandas as pd
+import numpy as np
+from shapely.geometry import LineString
+
+
+def split_subwayline(line, stop):
+    '''
+    To slice the metro line with metro stations to obtain metro section
+    information (This step is useful in subway passenger flow visualization)
+
+    Parameters
+    -------
+    line : GeoDataFrame
+        Bus/metro lines
+    stop : GeoDataFrame
+        Bus/metro stations
+
+    Returns
+    -------
+    metro_line_splited : GeoDataFrame
+        Generated section line shape
+    '''
+    def getline(r2, line_geometry):
+        ls = []
+        if r2['o_project'] <= r2['d_project']:
+            tmp1 = np.linspace(r2['o_project'], r2['d_project'], 10)
+        if r2['o_project'] > r2['d_project']:
+            tmp1 = np.linspace(
+                r2['o_project']-line_geometry.length, r2['d_project'], 10)
+            tmp1[tmp1 < 0] = tmp1[tmp1 < 0]+line_geometry.length
+        for j in tmp1:
+            ls.append(line_geometry.interpolate(j))
+        return LineString(ls)
+    lss = []
+    for k in range(len(line)):
+        r = line.iloc[k]
+        line_geometry = r['geometry']
+        tmp = stop[stop['linename'] == r['linename']].copy()
+        for i in tmp.columns:
+            tmp[i+'1'] = tmp[i].shift(-1)
+        tmp = tmp.iloc[:-1]
+        tmp = tmp[['stationnames', 'stationnames1',
+                   'geometry', 'geometry1', 'linename', 'line']]
+        tmp['o_project'] = tmp['geometry'].apply(
+            r['geometry'].project)
+        tmp['d_project'] = tmp['geometry1'].apply(
+            r['geometry'].project)
+        tmp['geometry'] = tmp.apply(
+            lambda r2: getline(r2, line_geometry), axis=1)
+        lss.append(tmp)
+    metro_line_splited = pd.concat(lss).drop('geometry1', axis=1)
+    metro_line_splited.crs = 'epsg:4326'
+    metro_line_splited['length'] = metro_line_splited.to_crs(epsg=3857).length
+    metro_line_splited = metro_line_splited.drop(
+        ['o_project', 'd_project'], axis=1)
+    return metro_line_splited
+
+
+def metro_network(line, stop, transfertime=5, nxgraph=True):
+    '''
+    Inputting the metro station data and outputting the network topology
+    model. The graph generated relies on NetworkX. The travel time is consist of:
+    operation time between stations + stop time at each station + transfer time
+
+    Parameters
+    -------
+
+    line : GeoDataFrame
+        Lines. Should have `line` column to store line name `speed` column to
+        store metro speed and `stoptime` column to store stop time at each
+        station.
+    stop : GeoDataFrame
+        Bus/metro stations
+    transfertime : number
+        Travel time per transfer
+    nxgraph : bool
+        Default True, if True then output the network G constructed by
+        NetworkX, if False then output the edges1(line section),
+        edge2(station transfer), and the node of the network
+
+    Returns
+    -------
+
+    When the nxgraph parameter is True
+    ================================================================
+    G : networkx.classes.graph.Graph
+        Network G built by networkx.
+
+    When the nxgraph parameter is False (This is for detail design of the network)
+    ================================================================
+    edge1 : DataFrame
+        Network edge for line section.
+    edge2 : DataFrame
+        Network edge for transfering.
+    node : List
+        Network nodes.
+    '''
+    # Obtain edge1: Network edge for line section.
+    linestop = stop.copy()
+    if ('speed' not in line.columns) | ('stoptime' not in line.columns):
+        raise ValueError(
+            'Lines should have `line` column to store line name,'
+            '`speed` column to store metro speed and'
+            '`stoptime` column to store stop time at each station'
+        )
+    for i in linestop.columns:
+        linestop[i+'1'] = linestop[i].shift(-1)
+    linestop = linestop[linestop['linename'] == linestop['linename1']].copy()
+    linestop = linestop.rename(
+        columns={'stationnames': 'ostop', 'stationnames1': 'dstop'})
+    linestop['ostation'] = linestop['line']+linestop['ostop']
+    linestop['dstation'] = linestop['line']+linestop['dstop']
+    edge1 = linestop[['ostation', 'dstation']].copy()
+
+    # calculate travel time for edge1
+    # calculate distance
+    metrolinesplit = split_subwayline(line, stop)
+    metrolinesplit['ostation'] = metrolinesplit['line'] + \
+        metrolinesplit['stationnames']
+    metrolinesplit['dstation'] = metrolinesplit['line'] + \
+        metrolinesplit['stationnames1']
+    metrolinesplit = metrolinesplit[['ostation', 'dstation', 'line', 'length']]
+    edge1 = pd.merge(edge1, metrolinesplit, how='left')
+    edge1 = pd.merge(edge1, line[['line', 'speed', 'stoptime']])
+
+    # calculate duration
+    edge1['duration'] = 60*(edge1['length']/1000) / \
+        edge1['speed']+edge1['stoptime']
+    edge1 = edge1[['ostation', 'dstation', 'duration']].drop_duplicates(
+        subset=['ostation', 'dstation'])
+
+    # Obtain edge2: Network edge for transfering.
+    linestop = stop.copy()
+    linestop['station'] = linestop['line'] + linestop['stationnames']
+    tmp = linestop.groupby(['stationnames'])[
+        'linename'].count().rename('count').reset_index()
+    tmp = pd.merge(linestop, tmp[tmp['count'] > 2]
+                   ['stationnames'], on='stationnames')
+    tmp = tmp[['stationnames', 'line', 'station']].drop_duplicates()
+    tmp = pd.merge(tmp, tmp, on='stationnames')
+
+    edge2 = tmp[tmp['line_x'] != tmp['line_y']][['station_x', 'station_y']]
+    # All transfer time are set as the same, export `edge2` for further degign
+    edge2['duration'] = transfertime
+    edge2.columns = edge1.columns
+    edge = edge1.append(edge2)
+    node = list(edge['ostation'].drop_duplicates())
+    if nxgraph:
+        import networkx as nx
+        G = nx.Graph()
+        G.add_nodes_from(node)
+        G.add_weighted_edges_from(edge.values)
+        return G
+    else:
+        return edge1, edge2, node
+
+
+def get_shortest_path(G, stop, ostation, dstation):
+    '''
+    Obtain the travel path of shortest path from the metro nextwork
+
+    Parameters
+    -------
+
+    G : networkx.classes.graph.Graph
+        metro network
+    stop : DataFrame
+        metro stop dataframe
+    ostation : str
+        O station name
+    dstation : str
+        D station name
+
+
+    Returns
+    -------
+    path : list
+        travel path: list of station names
+    '''
+    import networkx as nx
+    o = stop[stop['stationnames'] == ostation]['line'].iloc[0]+ostation
+    d = stop[stop['stationnames'] == dstation]['line'].iloc[0]+dstation
+    return nx.shortest_path(G, source=o, target=d, weight='weight')
+
+
+def get_k_shortest_paths(G, stop, ostation, dstation, k):
+    from itertools import islice
+    '''
+    Obtain the k th shortest paths from the metro nextwork
+
+    Parameters
+    -------
+
+    G : networkx.classes.graph.Graph
+        metro network
+    stop : DataFrame
+        metro stop dataframe
+    ostation : str
+        O station name
+    dstation : str
+        D station name
+    k : int
+        the k th shortest paths
+
+
+    Returns
+    -------
+    paths : list
+        travel path: list of travel paths
+    '''
+    import networkx as nx
+    o = stop[stop['stationnames'] == ostation]['line'].iloc[0]+ostation
+    d = stop[stop['stationnames'] == dstation]['line'].iloc[0]+dstation
+    return list(
+        islice(nx.shortest_simple_paths(G, o, d, weight='weight'), k)
+    )
+
+
+def get_path_traveltime(G, path):
+    '''
+    Obtain the travel time of the path
+
+    Parameters
+    -------
+
+    G : networkx.classes.graph.Graph
+        metro network
+    path : list
+        list of stationnames
+
+    Returns
+    -------
+    traveltime : float
+        travel time of the path
+    '''
+    traveltime = 0
+    for i in range(len(path)-1):
+        traveltime += G.get_edge_data(path[i], path[i+1])['weight']
+    return traveltime


Test patch:
diff --git a/src/transbigdata/tests/test_getbusdata.py b/src/transbigdata/tests/test_getbusdata.py
index 376747a..1c14df3 100644
--- a/src/transbigdata/tests/test_getbusdata.py
+++ b/src/transbigdata/tests/test_getbusdata.py
@@ -1,6 +1,5 @@
 import transbigdata as tbd
-import networkx as nx
-
+import numpy as np
 
 class TestGetbusdata:
     def test_getbusdata(self):
@@ -8,8 +7,16 @@ def test_getbusdata(self):
         data, stop = tbd.getbusdata('深圳市', ['地铁1号线'])
         assert '地铁1号线' in data['linename'].sum()
         assert '世界之窗' in tbd.split_subwayline(data, stop)['stationnames'].sum()
-        G = tbd.metro_network(stop)
-        assert type(G) == nx.classes.graph.Graph
+
+        data['speed'] = 36 #operation speed 36km/h
+        data['stoptime'] = 0.5 #stop time at each stations 30s
+        G = tbd.metro_network(data,stop, transfertime=10)
+        path = tbd.get_shortest_path(G,stop,'罗湖','竹子林')
+        assert len(path) == 12
+        traveltime = tbd.get_path_traveltime(G,path)
+        assert np.allclose(traveltime,28.3142682125485)
+        paths = tbd.get_k_shortest_paths(G,stop,'罗湖','竹子林',20)
+        assert len(paths) == 1
 
     def test_getadmin(self):
         admin, _ = tbd.getadmin(