Merge branch 'master' of gitlab.lrz.de:vadere/vadere

Tools/Notebooks/TrajectoryMetric.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 # expand the cell of the notebook
 import json
+import gc
 import numpy as np
 import pandas as pd
 import math
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -177,12 +178,13 @@
 
 %% Cell type:code id: tags:
 
 ``` python
 # Sum up all measured time deltas of a pedestrian to get the final evacuation time
-trajectoriesReal["timeDelta"] = trajectoriesReal["endTime"] - trajectoriesReal["startTime"]
-evacuation_time = trajectoriesReal.groupby(["pedestrianId"])["timeDelta"].sum()
+copy = trajectoriesReal.copy(deep=True)
+copy["timeDelta"] = copy["endTime"] - copy["startTime"]
+evacuation_time = copy.groupby(["pedestrianId"])["timeDelta"].sum()
 
 # trajectories starting from left
 cut_minX = trajectoriesReal[trajectoriesReal["endX"] < 15].groupby(["pedestrianId"])["endX"].min().max()
 
 # trajectories starting from right
@@ -309,12 +311,12 @@
     df = df.append(df_head, ignore_index=True)
     return df
 
 def cuttail_trajectory_by(trajectory, xmin, xmax):
     #i1 = trajectory[np.logical_and(trajectory.endX >= xmax, trajectory.startX < xmax)].index.max()
-    i1 = trajectory[trajectory.endX >= xmax].index.max()
-    i2 = trajectory[trajectory.endX <= xmin].index.max()
+    i1 = trajectory[np.logical_or(trajectory.endX >= xmax, trajectory.startX is np.nan)].index.max()
+    i2 = trajectory[np.logical_or(trajectory.endX <= xmin, trajectory.startX is np.nan)].index.max()
     #assert (i1 is np.nan and i2 is not np.nan) or (i1 is not np.nan and i2 is np.nan)
     x = xmax if i2 is np.nan or (i1 is not np.nan and i1 > i2) else xmin
     i = i1 if x == xmax else i2
     i = i+1
     # cut the footstep at the tail to exactly fit xmin or xmax
@@ -330,11 +332,17 @@
         r = (x - fs["startX"]) / (fs["endX"] - fs["startX"])
         end = start + (d * r)
         time = fs["endTime"] - fs["startTime"]
         endTime = fs["startTime"] + (time * r)
         distance = np.linalg.norm(end - start)
-        velocity = distance / (endTime - startTime)
+        if abs(endTime - startTime) < 0.00001:
+            if distance < 0.00001:
+                velocity = 0
+            else:
+                raise exception
+        else:
+            velocity = distance / (endTime - startTime)
         
     df = trajectory.loc[i+1:]
     llist = [[fs["pedestrianId"],fs["startX"],fs["startY"],fs["startTime"],end[0],end[1],endTime,distance,velocity]]
     df_tail = pd.DataFrame(llist, columns=['pedestrianId','startX','startY','startTime','endX','endY','endTime','distance','velocity'])
     df_tail = df_tail.append(df, ignore_index=True)
@@ -361,14 +369,15 @@
 
 traj = get_trajectory(2, trajectoriesReal)
 ts = traj[traj.endX > 22].index.max()
 ts
 #cut(trajectoriesReal)
-#cuttail_by(trajectoriesReal, 13, 22).head()
+cuttail_by(trajectoriesReal, cut_minX, cut_maxX).head()
 #cuthead_by(trajectoriesReal, 0, 4).tail()
 #traj.loc[100]
 #trajectoriesReal.tail()
+trajectoriesReal.head()
 ```
 
 %% Cell type:markdown id: tags:
 
 # Helper methods to compute different metrices
@@ -642,11 +651,11 @@
 The following code plots the mean (over all agents / pedestrians) velocity at $t = 0, 0.5, \ldots 70$ and the corresponding standard deviation. 
 
 %% Cell type:code id: tags:
 
 ``` python
-times = np.arange(0, 70, 0.2)
+times = np.arange(0, 70, 5)
 velocity1 = list(map(lambda t: mean_velocity_at(t, c_real_trajectories), times))
 std1 = list(map(lambda t: get_trajectories(t, c_real_trajectories)['velocity'].std(),times))
 
 velocity2 = list(map(lambda t: mean_velocity_at(t, c_sim_trajecotories), times))
 std2 = list(map(lambda t: get_trajectories(t, c_sim_trajecotories)['velocity'].std(),times))
@@ -684,35 +693,42 @@
 The following code plots the density inside the measurement area at $t = 0, 0.5, \ldots 70$ and the corresponding standard deviation.
 
 %% Cell type:code id: tags:
 
 ``` python
+%%prun
+#gc.disable()
+print(len(times))
+gc.disable()
 def contains(x,y,rect):
     #ma = mpl.patches.Rectangle((16.3,6.0), 2.4, 2.0)
     return x >= rect.get_x() and y >= rect.get_y() and x <= rect.get_x() + rect.get_width() and y <= rect.get_y() + rect.get_height()
 
 def filter_by_time_and_place(t, rect, trajectories):
     """returns a subset of trajectories i.e. at most one footstep for each pedestrian / agent such that the footstep the position (x,y) is the position of the
     agent at the time t contained in the rectanlge rect. Two new colums will be added for x and y."""
     traj = get_trajectories(t, trajectories)
-    traj.loc[:,'x'] = traj['startX'] + (traj['endX'] - traj['startX']) * (t - traj['startTime']) / (traj['endTime'] - traj['startTime'])
-    traj.loc[:,'y'] = traj['startY'] + (traj['endY'] - traj['startY']) * (t - traj['startTime']) / (traj['endTime'] - traj['startTime'])
+    traj.loc[:,'x'] = traj.loc[:,'startX'] + (traj.loc[:,'endX'] - traj.loc[:,'startX']) * (t - traj.loc[:,'startTime']) / (traj.loc[:,'endTime'] - traj.loc[:,'startTime'])
+    traj.loc[:,'y'] = traj.loc[:,'startY'] + (traj.loc[:,'endY'] - traj.loc[:,'startY']) * (t - traj.loc[:,'startTime']) / (traj.loc[:,'endTime'] - traj.loc[:,'startTime'])
     traj = traj[traj.apply(lambda x: contains(x['x'], x['y'],rect), axis=1)]
     return traj
 
 def density(t, rect, trajectories):
     area = rect.get_width() * rect.get_height()
     traj = filter_by_time_and_place(t, rect, trajectories)
     number_of_peds = len(traj)
+    traj = None
+    #gc.collect()
     if number_of_peds == 0:
         return 0
     else:
         return number_of_peds / area
-    
+
 measurementArea = mpl.patches.Rectangle((16.3,6.0), 2.4, 2.0)
 density1 = list(map(lambda t: density(t, measurementArea, c_real_trajectories), times))
 density2 = list(map(lambda t: density(t, measurementArea, c_sim_trajecotories), times))
+#gc.enable()
 
 fig_density = plt.figure(figsize=(10,5))
 ax1_density = fig_density.add_subplot(121)
 ax1_density.set_xlim(min(times),max(times))
 ax1_density.set_ylim(0,6)
@@ -744,5 +760,17 @@
 ax1_fundamental.set_ylabel("density")
 plt.plot(velocity1, density1, '*')
 plt.plot(velocity2, density2, '*')
 plt.show()
 ```
+
+%% Cell type:code id: tags:
+
+``` python
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+```