Merge pull request #6 from stepeos/multiple-mop

finalize mulitple mop

Author: Domsall

carmodel_calibration/cmd.py

@@ -168,7 +168,8 @@ def _get_parser(args_to_parse):
                                             type=str,
                                             help="measure of performance, "
                                             "comma separated list, default:"
-                                            "`distance`")
+                                            "`distance`, options are `distance`"
+                                            "`speed`, `acceleration`")
     args_to_check = ["--action=read_matrix", "--action=create_matrix",
                      "--action=create_data_config"]
     if _check_for_args(args_to_parse, args_to_check):

carmodel_calibration/optimization.py

@@ -23,7 +23,7 @@ def measure_of_performance_factory(objectives=["distance"],
     case2:  leader-follower-pair, mmop: distance and speed
     :param identification:  (leader identification, follower identification,
                             recordingId)
-    :param objectives:      ['distance', 'speed', 'speedVariance',...] the
+    :param objectives:      ['distance', 'speed', 'acceleration'] the
                             objectives to calibrate, weighted equally if not
                             specified differently by the `weights` parameter
     :param weights:         `tuple` of len(objectives), each item of type float
@@ -46,10 +46,21 @@ def measure_of_performance_factory(objectives=["distance"],
     except AssertionError as exc:
         _LOGGER.error("Failed creating objective function with given args.")
         raise exc
+    weights_renamed = []
+    for sop in objectives:
+        if sop not in ["distance", "speed", "acceleration"]:
+            raise ValueError(f"Objective {sop} not supported.")
+        if sop == "acceleration":
+            sop = "accFollower"
+        if sop == "speed":
+            sop = "speedFollower"
+        weights_renamed.append(sop)
+    objectives = weights_renamed
     for weights_value, objective in zip(weights_values, objectives):
         weights[objective] = weights_value
     def rmse(ground_truth, prediction):
-        """RMSE(v)
+        """
+        RMSE(v)
         see 'About calibration of car-following dynamics of automated and
         human-driven vehicles: Methodology, guidelines and codes'
         """
@@ -75,7 +86,8 @@ def rmspe(ground_truth, prediction):
             sums[idx] = np.sqrt(sums[idx] / len(ground_truth)) * weights[sop]
         return np.sum(sums)
     def theils_u(ground_truth, prediction):
-        """theils U(v)
+        """
+        theils U(v)
         see 'About calibration of car-following dynamics of automated and
         human-driven vehicles: Methodology, guidelines and codes'
         """

carmodel_calibration/sumo/simulation_module.py

@@ -72,7 +72,7 @@ def _interpolate_jumpy_start(leader_jumpy, follower_jumpy):
                 follower_synced.iloc[1:], follower_synced.iloc[:-1])
     free_follower = (set(leader_synced["trackId"])
                      == set(follower_synced["trackId"]))
-    columns = ["xCenter", "yCenter", "speed", "lon", "lat", "accel"]
+    columns = ["xCenter", "yCenter", "speed", "lon", "lat", "acc"]
     jumpy_points = []
     if np.any(np.abs(covered_distance_l) > 1):
         jumpy_points.extend(np.where(np.abs(covered_distance_l) > 1)[0])
@@ -480,6 +480,11 @@ def _process_chunk(self, selection_data, chunk):
             follower_synced = pd.concat((follower_ss, follower_synced))
             follower_synced.reset_index(drop=True, inplace=True)
             follower_synced["time"] = new_time
+            follower_speed = np.clip(follower_synced["speed"].values, 0, np.inf)
+            if not "acc" in follower_synced:
+                follower_synced["acc"] = np.insert(
+                    (follower_speed[1:] - follower_speed[:-1]) / self._timestep,
+                    0, 0, axis=0)
 
             #  covered distance per vehicle
             distance = (_get_distance_df(leader_synced, follower_synced)
@@ -500,7 +505,11 @@ def _process_chunk(self, selection_data, chunk):
             new_speed_padded = np.pad(new_speed, (5//2, 5-1-5//2), mode="edge")
             new_speed_smooth = np.convolve(new_speed_padded, np.ones((5,))/5,
                                            mode="valid")
-            leader_synced["speed"] = np.clip(new_speed_smooth,0, np.inf)
+            leader_synced["speed"] = np.clip(new_speed_smooth, 0, np.inf)
+            if not "acc" in leader_synced:
+                leader_synced["acc"] = np.insert(
+                    (new_speed_smooth[1:] - new_speed_smooth[:-1]) / self._timestep,
+                    0, 0, axis=0)
 
             if leader != "":
                 # distance = (_get_distance_df(leader_synced, follower_synced)
@@ -525,7 +534,9 @@ def _process_chunk(self, selection_data, chunk):
                 "coveredDistanceFollower": accumulated_distance_f,
                 "distance": distance,
                 "speedLeader": leader_synced["speed"],
-                "speedFollower": follower_synced["speed"]})
+                "speedFollower": follower_synced["speed"],
+                "accLeader": leader_synced["acc"],
+                "accFollower": follower_synced["acc"]})
             ground_truth["counter"] = counter
             ground_truth["leader"] = leader
             ground_truth["follower"] = follower

setup.py

@@ -4,10 +4,10 @@
 setup_dic = dict(
     name="carmodel_calibration",
     description="a tool for calibration car-following models using sumo",
-    author="Steve Oswald",
+    author="Steve Oswald, Dominik Salles",
     author_email="stevepeter.oswald@gmail.com",
     packages=find_packages(exclude=["scripting", "tests", 'tests.*', ".*"]),
-    version=0.1,
+    version=0.2,
     entry_points={
         'console_scripts': [
             'carmodel-calibration = carmodel_calibration.cmd:main'