Domain name

.gitignore

@@ -6,4 +6,4 @@ pyvenv.cfg
 *.pyc
 node_modules
 cache/
-share/man/man1/ttx.1
+venv/

Database/fetchGraph/.ipynb_checkpoints/coppyGenerate-checkpoint.py

@@ -0,0 +1,105 @@
+import matplotlib.pyplot as plt
+import networkx as nx
+import osmnx as ox
+import json
+from entrancePoint import entrancePoint
+import geopandas as gpd
+from shapely.geometry import  Point
+import pandas as pd
+import geopandas as gpd
+
+def testPlotRoute(RPI):
+      orig = ox.nearest_nodes(RPI, 42.7324294, -73.6905807)
+      dest = ox.nearest_nodes(RPI, 42.7338301, -73.6847063)
+      #1208042175, "lat": 42.7268945, "lon": -73.6737245
+      route = nx.shortest_path(RPI, orig, dest, 'travel_time')
+      print(route)
+      #route_map = ox.plot_route_folium(RPI, route)
+      #route_map.save('test.html')
+      return route
+def createNodeArray(filename):
+      f = open(filename)
+      data = json.load(f)
+      add_nulls = lambda number, zero_count : "{0:0{1}d}".format(number, zero_count)
+      counter = 0
+      nodeArr = []
+      for i in data['Entrances']:
+            for j in i['Points']:
+                  counter += 1
+                  buidling = i['NAME']
+                  tmp_id = add_nulls(counter, 5)
+                  pointName = buidling + str(tmp_id)
+                  #print(pointName)
+                  lon, lat = j.strip(' ').split(',')
+                  #print(float(lon), float(lat))
+                  tmp_p = entrancePoint(tmp_id, lon, lat, pointName, buidling)
+                  nodeArr.append(tmp_p.serialize())
+
+
+      # '001': 
+      #       {
+      #             'y': 7.367210, 
+      #             'x': 151.838487,
+      #             'street_count': 1
+      #       }
+      # }
+      gdf = gpd.GeoDataFrame(nodeArr)
+      return gdf
+
+
+def plotGraph(new_nodes):
+      #ox.config(use_cache=True, log_console=False)
+      ox.settings.log_console = False
+      ox.settings.use_cache = True
+      # RPI = ox.graph_from_place('Troy, NY', network_type="walk")
+      # RPI = ox.speed.add_edge_speeds(RPI)
+      # RPI = ox.speed.add_edge_travel_times(RPI)
+      #point1 = "lat": 42.7334294, "lon": -73.6905807
+      #point2 = "lat": 42.7338301, "lon": -73.6887063
+      north = 42.73201
+      south = 42.72492
+      east = -73.67119
+      west = -73.68663
+      RPI = ox.graph_from_bbox(north, south, east, west, network_type = "walk")
+      RPI = ox.add_edge_speeds(RPI,5)
+      RPI = ox.add_edge_travel_times(RPI)
+
+      origin_node = ox.nearest_nodes(RPI, north, south)
+      destination_node = list(RPI.nodes())[-1]
+      shortest_path = nx.shortest_path(RPI,origin_node,destination_node) 
+      for u, v in zip(shortest_path[0:], shortest_path[1:]):
+            edge_data = RPI.get_edge_data(u, v)
+            min_weight = min([edge_data[edge]['length'] for edge in edge_data])
+            #print(f"{u}---({edge_data, min_weight})---{v}")
+      nodes, edges = ox.graph_to_gdfs(RPI, nodes=True, edges=True)      
+      new_node_gdf = gpd.GeoDataFrame(new_nodes)
+      nodes = pd.concat([nodes, new_node_gdf], ignore_index=True)
+      #print(nodes.loc[nodes['highway'] == 'custom_entrance'])
+      RPI = ox.graph_from_gdfs(nodes, edges)
+      #graph_attrs = {'crs': 'epsg:4326', 'simplified': True}
+      ox.plot.plot_graph_route(RPI,shortest_path)#, graph_attrs)
+      return RPI
+
+def weight(RPI):
+      RPI = ox.graph_to_gdfs(RPI, nodes=True, edges=True)
+      return RPI
+def addNodes(RPI, filename):
+      tmp_list = createNodeArray(filename)
+
+      my_nodes = gpd.GeoDataFrame(tmp_list)
+      return RPI
+
+
+place = 'Rensselaer Polytechnic Institute'
+new_nodes = createNodeArray('Database/fetchGraph/buildingEntrance.json')
+test = plotGraph(new_nodes).edges(data=True)
+
+#for i in arr:
+#      print(i.serialize())
+# for u, v, highway in RPI.edges(highway='highway'):
+      #       if highway != 'footpath' or highway != 'path': 
+      #             RPI.remove_edge(u, v)
+
+      #print(RPI.edges)
+      #fig, ax = ox.plot_graph(RPI, bbox=(north, south, east, west), edge_color = 'g', node_color='b')
+

Database/fetchGraph/.ipynb_checkpoints/entrancePoint-checkpoint.py

@@ -0,0 +1,24 @@
+from shapely.geometry import  Point
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+
+class entrancePoint:
+      def __init__(self, id, lng, lat, name, building):
+            self.type = 'node'
+            self.id = id
+            self.lng = lng
+            self.lat = lat
+            self.tags = {"Entrance" : building}
+      def serialize(self):
+            dict ={
+                  'id' : id,
+                  'y' : self.lat,
+                  'x' : self.lng,
+                  'highway' : 'custom_entrance',
+                  'street_count' : 1,
+                  'geometry' : Point(self.lng, self.lat),
+                  0 : np.NaN,
+                  'osmid' : self.id
+            }
+            return dict

Database/fetchGraph/buildingEntrance.json

@@ -0,0 +1,105 @@
+{
+      "Entrances" : 
+      [
+            {
+                  "NAME" : "Rensselaer Union", 
+                  "Points": ["42.73008909771817, -73.67704454765556", "42.72986744543403, -73.67709148631033"]
+            },
+            {
+                  "NAME" : "CBIS", 
+                  "Points" : ["42.7280194663293, -73.67837529170055"]
+            },
+            {
+                  "NAME" : "RPI Playhouse", 
+                  "Points": ["42.7291677088131, -73.67797446087425"]
+            },
+            {
+                  "NAME" : "Academy Hall", 
+                  "Points" : ["42.727594996082445, -73.67877098483189", "42.727406830406196, -73.67853092714033"]
+            },
+            {
+                  "NAME" : "Sage dining Hall" ,
+                  "Points": ["42.72961158163338, -73.67811398380265"]
+            },
+            {
+                  "NAME" : "87 Gym" , 
+                  "Points" :["42.73069362735673, -73.67893406504051"]
+            },
+            {
+                  "NAME" : "Ricketts",
+                  "Points" : ["42.731028834936374, -73.68003372882379"]
+            },
+            {
+                  "NAME" : "Troy Building",
+                  "Points" : ["42.73094387705421, -73.68058770013498", "42.73109558318403, -73.68041067434208"]
+            },
+            {
+                  "NAME" : "Sage Labs",
+                  "Points" :  ["42.730765480933485, -73.68167733541351", "42.73098811511924, -73.6811509518876"]
+            },
+            {
+                  "NAME" : "DCC",
+                  "Points" : ["42.72985060400276, -73.67950427536064", "42.72940825115112, -73.67958095188204"] 
+            },
+            {
+                  "NAME" : "Low",
+                  "Points" : ["42.729498136339146, -73.67838850778122"] 
+            },
+            {
+                  "NAME" : "JEC",
+                  "Points" : ["42.72966163418423, -73.67965642486313"]
+            },
+            {
+                  "NAME" : "JROWL",
+                  "Points" : ["42.728354027584466, -73.68051256452605", "42.72890209771069, -73.68020399822392"]
+            },
+            {
+                  "NAME" : "VCC",
+                  "Points" : ["42.72929467529175, -73.68221647357326"]
+            },
+            {
+                  "NAME" : "Folsom Library",
+                  "Points" : ["42.72937890018547, -73.68241790188574"]
+            },
+            {
+                  "NAME" : "Greene Building",
+                  "Points" : ["42.729891792437606, -73.68086878731187", "42.73011738511107, -73.68114572537503"]
+            },
+            {
+                  "NAME" : "Amos Eaton",
+                  "Points" : ["42.730198618376356, -73.68284716687016"]
+            },
+            {
+                  "NAME" : "Lally",
+                  "Points" : ["42.73013834900463, -73.68189151650563"]
+            },
+            {
+                  "NAME" : "Walker Labs",
+                  "Points" : ["42.7307475293064, -73.68241334334832"]
+            },
+            {
+                  "NAME" : "Cogswell Labs",
+                  "Points" : ["42.72831706292051, -73.6811600143054"]
+            },
+            {
+                  "NAME" : "EMPAC",
+                  "Points" : ["42.72886165658477, -73.68332059485383"]
+            },
+            {
+                  "NAME" : "Carnegie",
+                  "Points" : ["42.730515354781396, -73.68331526803681", "42.730415837049144, -73.68306538178464"]
+            },
+            {
+                  "NAME" : "Pittsburg",
+                  "Points" : ["42.731021303557796, -73.68333244233531"]
+            },
+            {
+                  "NAME" : "West Hall",
+                  "Points" : ["42.73166096445073, -73.68276797289366"]
+            },
+            {
+                  "NAME" : "Commons dining hall",
+                  "Points" : ["42.72844630836017, -73.67450286751094"]
+            }
+      ]
+}

Database/fetchGraph/coppyGenerate.py

@@ -0,0 +1,104 @@
+import matplotlib.pyplot as plt
+import networkx as nx
+import osmnx as ox
+import json
+from entrancePoint import entrancePoint
+import geopandas as gpd
+from shapely.geometry import  Point
+import pandas as pd
+import geopandas as gpd
+import numpy as np
+import math
+
+def testPlotRoute(RPI):
+      orig = ox.nearest_nodes(RPI, 42.7324294, -73.6905807)
+      dest = ox.nearest_nodes(RPI, 42.7338301, -73.6847063)
+      #1208042175, "lat": 42.7268945, "lon": -73.6737245
+      route = nx.shortest_path(RPI, orig, dest, 'travel_time')
+      print(route)
+      return route
+
+def closestNode(G, x, y, orig_id):
+    min_dist = float('inf')
+    closest_node_id = None
+    for node in G.nodes:
+        tmp_node = G.nodes[node]
+        tmp_x = float(tmp_node['x'])
+        tmp_y = float(tmp_node['y'])
+        tmp_dist = math.sqrt((tmp_y - y)**2 + (tmp_x - x)**2)
+
+        if tmp_dist < min_dist and orig_id != node and tmp_dist != 0.0:
+            print(f"({x}, {y}) : {orig_id} --> ({tmp_x}, {tmp_y}){tmp_dist} --> {node}")
+            closest_node_id = node
+            min_dist = tmp_dist
+    return closest_node_id
+
+def createNodeArray(filename):
+      f = open(filename)
+      data = json.load(f)
+      #add_nulls = lambda number, zero_count : "{0:0{1}d}".format(number, zero_count)
+      counter = 0
+      nodeArr = []
+      for i in data['Entrances']:
+            for j in i['Points']:
+                  counter += 1
+                  buidling = i['NAME']
+                  tmp_id = counter#add_nulls(counter, 5)
+                  pointName = buidling + str(tmp_id)
+                  #print(pointName)
+                  lat, lon = j.strip(' ').split(',')
+                  #print(float(lon), float(lat))
+                  tmp_p = entrancePoint(int(tmp_id), lon, lat, pointName, buidling)
+                  nodeArr.append(tmp_p.serialize())
+      gdf = gpd.GeoDataFrame(nodeArr)
+      return gdf
+
+
+def plotGraph(new_nodes):
+      ox.settings.log_console = False
+      ox.settings.use_cache = True
+      north = 42.73201
+      south = 42.72492
+      east = -73.67119
+      west = -73.68663
+      RPI = ox.graph_from_bbox(north, south, east, west, network_type = "walk")
+      RPI = ox.add_edge_speeds(RPI,5)
+      RPI = ox.add_edge_travel_times(RPI)
+      nodes, edges = ox.graph_to_gdfs(RPI, nodes=True, edges=True)  
+      new_node_gdf = gpd.GeoDataFrame(new_nodes)
+      new_node_gdf = new_node_gdf.set_crs(4326, allow_override=True)
+      nodes = nodes.set_crs(4326, allow_override=True)  
+      non_new_nodes = nodes  
+      nodes = pd.concat([nodes, new_node_gdf], ignore_index=False)
+      entrance_df = nodes[nodes['highway'].str.contains("Entrance", na=False)]
+      counter = len(new_nodes)
+      new_edge_list = []
+      RPI = ox.graph_from_gdfs(nodes, edges)
+      for index, row in entrance_df.iterrows():
+            tmp_orig_id = int(row['id'])
+            tmp_x = float(row['x'])  
+            tmp_y = float(row['y'])          
+            tmp_dest_id = closestNode(RPI, tmp_x, tmp_y, tmp_orig_id)
+            if tmp_orig_id != 33:
+                  RPI.add_edge(tmp_orig_id, tmp_dest_id)   
+      RPI = ox.add_edge_speeds(RPI,5)   
+      G = RPI
+      print(RPI.nodes)
+      ox.save_graphml(G, "test.graphml")
+      return RPI
+
+if __name__ == "__main__":
+      place = 'Rensselaer Polytechnic Institute'
+      new_nodes = createNodeArray('Database/fetchGraph/buildingEntrance.json')
+      test = plotGraph(new_nodes)
+      print(test.nodes)
+      start_node = 31
+      end_node = 1208041960
+      route = nx.shortest_path(test, start_node, end_node, 'travel_time')
+      #shortest_path = nx.shortest_path(test, source=start_node, target=end_node)
+      print(route)
+      #ox.plot_graph(test)
+      filepath = "./data/piedmont.graphml"
+#ox.save_graphml(test, filepath)
+#G = ox.load_graphml(filepath)
+