update tutorials

grid2demand_tutorial.py

@@ -21,18 +21,11 @@
 
     # Step 2: Generate zone dictionary from node dictionary
     #   by specifying number of x blocks and y blocks
-    zone_dict = gd.net2zone(node_dict, num_x_blocks=10, num_y_blocks=10, use_zone_id=True)
+    zone_dict = gd.net2zone(node_dict, num_x_blocks=10, num_y_blocks=10)
 
-    # Step 2: Generate zone based on grid size with 10 km width and 10km height for each zone
+    # Step 2: Generate zone based on grid size with 10 km width and 10 km height for each zone
     # zone_dict = gd.net2zone(node_dict, cell_width=10, cell_height=10)
 
-    # Step 2: Generate zone dictionary from zone.csv file
-    #   please note, if you use this function,
-    #   you need to make sure the zone.csv file is in the input directory
-    #   this will generate zones based on your own TAZs
-    #   and zone.csv requires at least two columns: zone_id and geometry
-    # zone_dict = gd.taz2zone()
-
     # Step 3: synchronize geometry info between zone, node and poi
     #       add zone_id to node and poi dictionaries
     #       also add node_list and poi_list to zone dictionary
@@ -52,7 +45,7 @@
     zone_prod_attr = gd.calc_zone_prod_attr(node_prod_attr, zone_dict_update)
 
     # Step 7: Run gravity model to generate agent-based demand
-    df_demand = gd.run_gravity_model(zone_prod_attr, zone_od_dist_matrix, use_zone_id=True)
+    df_demand = gd.run_gravity_model(zone_prod_attr, zone_od_dist_matrix)
 
     # Step 8: generate agent-based demand
     df_agent = gd.gen_agent_based_demand(node_prod_attr, zone_prod_attr, df_demand=df_demand)

grid2demand_tutorial_use_zone_in_nodeCSV.py

@@ -0,0 +1,61 @@
+# -*- coding:utf-8 -*-
+##############################################################
+# Created Date: Monday, September 11th 2023
+# Contact Info: [email protected]
+# Author/Copyright: Mr. Xiangyong Luo
+##############################################################
+
+from __future__ import absolute_import
+from grid2demand import GRID2DEMAND
+
+if __name__ == "__main__":
+
+    # Step 0: Specify input directory, if not, use current working directory as default input directory
+    input_dir = "./datasets/dubai"
+
+    # Initialize a GRID2DEMAND object
+    gd = GRID2DEMAND(input_dir)
+
+    # Step 1: Load node and poi data from input directory
+    node_dict, poi_dict = gd.load_network.values()
+
+    # Step 2: Generate zone dictionary from node dictionary
+    #   by specifying number of x blocks and y blocks
+    zone_dict = gd.net2zone(node_dict, num_x_blocks=10, num_y_blocks=10, use_zone_id=True)
+
+    # Step 2: Generate zone based on grid size with 10 km width and 10km height for each zone
+    # zone_dict = gd.net2zone(node_dict, cell_width=10, cell_height=10, use_zone_id=True)
+
+    # Step 3: synchronize geometry info between zone, node and poi
+    #       add zone_id to node and poi dictionaries
+    #       also add node_list and poi_list to zone dictionary
+    updated_dict = gd.sync_geometry_between_zone_and_node_poi(zone_dict, node_dict, poi_dict)
+    zone_dict_update, node_dict_update, poi_dict_update = updated_dict.values()
+
+    # Step 4: Calculate zone-to-zone od distance matrix
+    zone_od_dist_matrix = gd.calc_zone_od_distance_matrix(zone_dict_update)
+
+    # Step 5: Generate poi trip rate for each poi
+    poi_trip_rate = gd.gen_poi_trip_rate(poi_dict_update)
+
+    # Step 6: Generate node production attraction for each node based on poi_trip_rate
+    node_prod_attr = gd.gen_node_prod_attr(node_dict_update, poi_trip_rate)
+
+    # Step 6.1: Calculate zone production and attraction based on node production and attraction
+    zone_prod_attr = gd.calc_zone_prod_attr(node_prod_attr, zone_dict_update)
+
+    # Step 7: Run gravity model to generate agent-based demand
+    df_demand = gd.run_gravity_model(zone_prod_attr, zone_od_dist_matrix, use_zone_id=True)
+
+    # Step 8: generate agent-based demand
+    df_agent = gd.gen_agent_based_demand(node_prod_attr, zone_prod_attr, df_demand=df_demand)
+
+    # You can also view and edit the package setting by using gd.pkg_settings
+    print(gd.pkg_settings)
+
+    # Step 9: Output demand, agent, zone, zone_od_dist_table, zone_od_dist_matrix files
+    gd.save_demand
+    gd.save_agent
+    gd.save_zone
+    gd.save_zone_od_dist_table
+    gd.save_zone_od_dist_matrix

grid2demand_tutorial_user_TAZ.py

@@ -0,0 +1,61 @@
+# -*- coding:utf-8 -*-
+##############################################################
+# Created Date: Monday, September 11th 2023
+# Contact Info: [email protected]
+# Author/Copyright: Mr. Xiangyong Luo
+##############################################################
+
+from __future__ import absolute_import
+from grid2demand import GRID2DEMAND
+
+if __name__ == "__main__":
+
+    # Step 0: Specify input directory, if not, use current working directory as default input directory
+    input_dir = "./datasets/dubai"
+
+    # Initialize a GRID2DEMAND object
+    gd = GRID2DEMAND(input_dir)
+
+    # Step 1: Load node and poi data from input directory
+    node_dict, poi_dict = gd.load_network.values()
+
+    # Step 2: Generate zone dictionary from zone.csv file
+    #   please note, if you use this function,
+    #   you need to make sure the zone.csv file is in the input directory
+    #   this will generate zones based on your own TAZs
+    #   and zone.csv requires at least two columns: zone_id and geometry
+    zone_dict = gd.taz2zone()
+
+    # Step 3: synchronize geometry info between zone, node and poi
+    #       add zone_id to node and poi dictionaries
+    #       also add node_list and poi_list to zone dictionary
+    updated_dict = gd.sync_geometry_between_zone_and_node_poi(zone_dict, node_dict, poi_dict)
+    zone_dict_update, node_dict_update, poi_dict_update = updated_dict.values()
+
+    # Step 4: Calculate zone-to-zone od distance matrix
+    zone_od_dist_matrix = gd.calc_zone_od_distance_matrix(zone_dict_update)
+
+    # Step 5: Generate poi trip rate for each poi
+    poi_trip_rate = gd.gen_poi_trip_rate(poi_dict_update)
+
+    # Step 6: Generate node production attraction for each node based on poi_trip_rate
+    node_prod_attr = gd.gen_node_prod_attr(node_dict_update, poi_trip_rate)
+
+    # Step 6.1: Calculate zone production and attraction based on node production and attraction
+    zone_prod_attr = gd.calc_zone_prod_attr(node_prod_attr, zone_dict_update)
+
+    # Step 7: Run gravity model to generate agent-based demand
+    df_demand = gd.run_gravity_model(zone_prod_attr, zone_od_dist_matrix)
+
+    # Step 8: generate agent-based demand
+    df_agent = gd.gen_agent_based_demand(node_prod_attr, zone_prod_attr, df_demand=df_demand)
+
+    # You can also view and edit the package setting by using gd.pkg_settings
+    print(gd.pkg_settings)
+
+    # Step 9: Output demand, agent, zone, zone_od_dist_table, zone_od_dist_matrix files
+    gd.save_demand
+    gd.save_agent
+    gd.save_zone
+    gd.save_zone_od_dist_table
+    gd.save_zone_od_dist_matrix