Fixed Aimsun Bugs (#750)

* Fix bugs in utils/aimsun/load.py

1. Updated the path to data.json
2. Added a check before querying centroid_config.origin_centroids and centroid_config.destination_centroids to prevent erroring out when the model doesn't have origin or destination centroids set.

* Fixed bug

* bug fixed

* deleted the json files

* PR fixes

docs/source/examples.rst

@@ -9,7 +9,7 @@ A few points of clarification:
 
 * Occasionally we describe environments as *fully observed*. By this we mean that all relevant pieces of information (speed, relative positions, traffic light states) of the system are available to the controller. *Partially observed* refers to only a subset being available. This is the default configuration of the environment and the set of observations can be customized.
 
-* Each environment has customizable environment/network parameters that can be used to configure it beyond what is described here. Particularly pertinent parameters are described. Additional parameters can be found by examining the documentation in the relevant scenario and environment files.
+* Each environment has customizable environment/network parameters that can be used to configure it beyond what is described here. Particularly pertinent parameters are described. Additional parameters can be found by examining the documentation in the relevant network and environment files.
 
 * In the figures below, the following key is used for vehicle colors, where AV stands for autonomous vehicle.
 
@@ -22,12 +22,12 @@ Figure Eight
 The figure-eight is a closed-ring version of an intersection. The goal is to maximize
 the system-wide velocity for fourteen vehicles,
 which necessitates spacing the vehicles so that they don't
-run into conflicts at the merging points. The scenario is fully observed: all vehicles
+run into conflicts at the merging points. The network is fully observed: all vehicles
 speeds and positions are visible to the controller.
-This scenario is also a benchmark, and has been
+This network is also a benchmark, and has been
 extensively tested at three penetration rates: 1 AV 13 humans, 7 AVs 7 humans, 14 AVs.
 
-The scenario, pictured below,
+The network, pictured below,
 is relatively light-weight and can be trained the quickest. It can serve both as a test
 that the training process is working correctly and as a study of the difficulty of controlling
 many vehicles simultaneously.
@@ -51,7 +51,7 @@ and the speed of the leading vehicle.
 
 To make this task more difficult, the environment has a configurable parameter, `ring_length`, which
 can be set to a list containing the minimum and maximum ring-size. The autonomous vehicle must
-learn to distinguish these scenarios from each other and pick the appropriate driving behavior.
+learn to distinguish these networks from each other and pick the appropriate driving behavior.
 
 .. image:: ../img/stabilizing_the_ring.png
    :width: 400

docs/source/flow_setup.rst

@@ -178,7 +178,7 @@ The latter command should return an output similar to:
 
     /path/to/envs/aimsun_flow/bin/python
 
-Copy the path up until right before /bin (i.e. /path/to/envs/aimsun_flow) and
+Copy the path up until right before /lib (i.e. /path/to/envs/aimsun_flow/bin/python) and
 place it under the `AIMSUN_SITEPACKAGES` variable in your bashrc, like this:
 
 ::

flow/core/kernel/network/aimsun.py

@@ -65,7 +65,7 @@ def generate_network(self, network):
             'render': self.sim_params.render,
             "sim_step": self.sim_params.sim_step,
             "traffic_lights": None,
-            "scenario_name": self.sim_params.scenario_name,
+            "network_name": self.sim_params.network_name,
             "experiment_name": self.sim_params.experiment_name,
             "replication_name": self.sim_params.replication_name,
             "centroid_config_name": self.sim_params.centroid_config_name,
@@ -276,6 +276,11 @@ def length(self):
         return sum(self.edge_length(edge_id)
                    for edge_id in self.get_edge_list())
 
+    def non_internal_length(self):
+        """See parent class."""
+        return sum(self.edge_length(edge_id)
+                   for edge_id in self.get_edge_list())
+
     def speed_limit(self, edge_id):
         """See parent class."""
         try:

flow/core/params.py

@@ -445,15 +445,15 @@ class AimsunParams(SimParams):
         specifies whether to render the radius of RL observation
     pxpm : int, optional
         specifies rendering resolution (pixel / meter)
-    scenario_name : str, optional
+    network_name : str, optional
         name of the network generated in Aimsun.
     experiment_name : str, optional
         name of the experiment generated in Aimsun
     replication_name : str, optional
         name of the replication generated in Aimsun. When loading
         an Aimsun template, this parameter must be set to the name
         of the replication to be run by the simulation; in this case,
-        the scenario_name and experiment_name parameters are not
+        the network_name and experiment_name parameters are not
         necessary as they will be obtained from the replication name.
     centroid_config_name : str, optional
         name of the centroid configuration to load in Aimsun. This
@@ -477,7 +477,7 @@ def __init__(self,
                  show_radius=False,
                  pxpm=2,
                  # set to match Flow_Aimsun.ang's scenario name
-                 scenario_name="Dynamic Scenario 866",
+                 network_name="Dynamic Scenario 866",
                  # set to match Flow_Aimsun.ang's experiment name
                  experiment_name="Micro SRC Experiment 867",
                  # set to match Flow_Aimsun.ang's replication name
@@ -488,7 +488,7 @@ def __init__(self,
         super(AimsunParams, self).__init__(
             sim_step, render, restart_instance, emission_path, save_render,
             sight_radius, show_radius, pxpm)
-        self.scenario_name = scenario_name
+        self.network_name = network_name
         self.experiment_name = experiment_name
         self.replication_name = replication_name
         self.centroid_config_name = centroid_config_name

flow/core/rewards.py

@@ -237,7 +237,7 @@ def penalize_near_standstill(env, thresh=0.3, gain=1):
 
     This reward function is used to penalize vehicles below a
     specified threshold. This assists with discouraging RL from
-    gamifying a scenario, which can result in standstill behavior
+    gamifying a network, which can result in standstill behavior
     or similarly bad, near-zero velocities.
 
     Parameters

flow/networks/base.py

@@ -1,4 +1,4 @@
-"""Contains the base scenario class."""
+"""Contains the base network class."""
 
 from flow.core.params import InitialConfig
 from flow.core.params import TrafficLightParams

flow/scenarios/multi_loop.py

@@ -1,6 +1,6 @@
 """Pending deprecation file.
 
-To view the actual content, go to: flow/scenarios/multi_ring.py
+To view the actual content, go to: flow/networks/multi_ring.py
 """
 from flow.utils.flow_warnings import deprecated
 from flow.networks.multi_ring import MultiRingNetwork

flow/utils/aimsun/generate.py

@@ -267,9 +267,9 @@ def generate_net(nodes,
     set_vehicles_color(model)
 
     # set API
-    scenario_name = data["scenario_name"]
+    network_name = data["network_name"]
     scenario = model.getCatalog().findByName(
-        scenario_name, model.getType("GKScenario"))  # find scenario
+        network_name, model.getType("GKScenario"))  # find scenario
     scenario_data = scenario.getInputData()
     scenario_data.addExtension(os.path.join(
         config.PROJECT_PATH, "flow/utils/aimsun/run.py"), True)
@@ -366,9 +366,9 @@ def generate_net_osm(file_name, inflows, veh_types):
     set_vehicles_color(model)
 
     # set API
-    scenario_name = data["scenario_name"]
+    network_name = data["network_name"]
     scenario = model.getCatalog().findByName(
-        scenario_name, model.getType("GKScenario"))  # find scenario
+        network_name, model.getType("GKScenario"))  # find scenario
     scenario_data = scenario.getInputData()
     scenario_data.addExtension(os.path.join(
         config.PROJECT_PATH, "flow/utils/aimsun/run.py"), True)

flow/utils/aimsun/load.py

@@ -174,7 +174,7 @@ def get_dict_from_objects(sections, nodes, turnings, cen_connections):
     scenario_data = load_network()
 
 # save template's scenario into a file to be loaded into Flow's scenario
-scenario_data_file = 'flow/core/kernel/network/scenario_data.json'
+scenario_data_file = 'flow/core/kernel/network/network_data.json'
 scenario_data_path = os.path.join(config.PROJECT_PATH, scenario_data_file)
 with open(scenario_data_path, 'w') as f:
     json.dump(scenario_data, f, sort_keys=True, indent=4)
@@ -183,7 +183,7 @@ def get_dict_from_objects(sections, nodes, turnings, cen_connections):
 
 # create a check file to announce that we are done
 # writing all the network data into the .json file
-check_file = 'flow/core/kernel/network/scenario_data_check'
+check_file = 'flow/core/kernel/network/network_data_check'
 check_file_path = os.path.join(config.PROJECT_PATH, check_file)
 open(check_file_path, 'a').close()
 

tutorials/tutorial05_networks.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "In this exercise, we will recreate the ring road network, seen in the figure below.\n",
     "\n",
-    "<img src=\"img/ring_scenario.png\">\n",
+    "<img src=\"img/ring_network.png\">\n",
     "\n",
     "In order to recreate this network, we will design a *network* class. This class creates the configuration files needed to produce a transportation network within the simulator. It also specifies the location of edge nodes in the network, as well as the positioning of vehicles at the start of a run.\n",
     "\n",

tutorials/tutorial10_traffic_lights.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "* Experiment script for RL version of traffic lights in grid: `examples/rllib/traffic_light_grid.py`\n",
     "* Experiment script for non-RL version of traffic lights in grid: `examples/sumo/traffic_light_grid.py`\n",
-    "* Network: `traffic_light_grid.py` (class TrafficLightGridScenario)\n",
+    "* Network: `traffic_light_grid.py` (class TrafficLightGridNetwork)\n",
     "* Environment for RL version of traffic lights in grid: (class TrafficLightGridEnv)\n",
     "* Environment for non-RL version of traffic lights in grid: (class AccelEnv)\n",
     "\n",