update metric notebook, add data for the notebook such that one can experiment with it.

Tools/Notebooks/TrajectoryMetric.ipynb

 %% Cell type:code id: tags:
 
 ``` python
-
-```
-
-%% Cell type:code id: tags:
-
-``` python
 # expand the cell of the notebook
 import json
 import numpy as np
 import pandas as pd
 import math
@@ -19,11 +13,11 @@
 display(HTML('<style>.container { width:100% !important; }</style>'))
 ```
 
 %% Cell type:markdown id: tags:
 
-# Methods to convert Vadere trajectories into a DataFrame
+# Convert Vadere trajectories into a DataFrame
 
 %% Cell type:code id: tags:
 
 ``` python
 def fs_append(pedestrianId, fs, llist):
@@ -37,12 +31,16 @@
     llist = []
     for pedId in trajectories:
         trajectory_append(pedId, trajectories[pedId], llist)
     dataframe = pd.DataFrame(llist, columns=['pedestrianId','startX','startY','startTime','endX','endY','endTime'])
     return dataframe
+```
 
-file = "./data/trajectories_distance.txt"
+%% Cell type:code id: tags:
+
+``` python
+file = "./data/TrajectoryMetric/trajectories_simulation.txt"
 f = open(file, "r")
 header = f.readline();
 trajectories = dict({});
 
 for row in f:
@@ -55,10 +53,99 @@
 ptrajectories.head()
 ```
 
 %% Cell type:markdown id: tags:
 
+# Convert experiment data into a DataFrame
+
+%% Cell type:code id: tags:
+
+``` python
+def load_experiment(file):
+    fps = 16
+    pad = pd.DataFrame([[np.nan, np.nan, np.nan, np.nan, np.nan]], columns=['pedestrianId', 'timeStep', 'x', 'y', 'e'])
+    data = pd.read_csv(
+        file, 
+        sep=' ', 
+        names=['pedestrianId', 'timeStep', 'x', 'y', 'e'], 
+        index_col=False, 
+        header=None, 
+        skiprows=0)
+    
+    cc = pd.concat([pad, data], ignore_index=True)
+        
+    data['endX'] = data['x'] / 100 + 18.7
+    data['endY'] = data['y'] / 100 + 4.2
+    data['startX'] = cc['x'] / 100 + 18.7
+    data['startY'] = cc['y'] / 100 + 4.2
+    data['startTime'] = data['timeStep'] / fps - 1/fps
+    data['endTime'] = data['timeStep'] / fps
+    data = data.drop(columns=['timeStep','x','y','e'])
+    return data
+    
+def to_trajectories(data):
+    trajectories = dict({})
+    trajectory = []
+    for i in range(len(data)-1):
+        pedId = data['pedestrianId'][i]
+        if pedId == data['pedestrianId'][i+1]:
+            pedId = data['pedestrianId'][i]
+            x1 = data['x'][i]
+            y1 = data['y'][i]
+            x2 = data['x'][i+1]
+            y2 = data['y'][i+1]
+            startTime = data['timeStep'][i] 
+            endTime = data['timeStep'][i+1]
+            fs = {'startTime':startTime, 'endTime': endTime, 'start':{'x':x1, 'y':y1}, 'end':{'x':x2, 'y':y2}}
+            trajectory.append(fs)
+        else:
+            trajectories[pedId] = trajectory
+            trajectory = []
+            pedId = data['pedestrianId'][i]
+    return trajectories
+```
+
+%% Cell type:code id: tags:
+
+``` python
+#times = np.linspace(4,10,10)
+#euclid_d(get_trajectory(1), get_trajectory(1), times)
+#to_trajectories(load_experiment(real_file))[1]
+
+real_file = "./data/TrajectoryMetric/KO/ko-240-120-240/ko-240-120-240_combined_MB.txt"
+trajectoriesReal = load_experiment(real_file)
+#trajectoriesReal = to_trajectories(data)
+trajectoriesReal.query('pedestrianId == 1').head()
+```
+
+%% Cell type:markdown id: tags:
+
+# Convert DataFrame to postvis DataFrame
+
+%% Cell type:code id: tags:
+
+``` python
+def to_postVis(df):
+    simTimeStep = 0.4
+    fps = 16
+    df['timeStep'] = np.ceil(df['endTime'] / (1/fps)).astype(np.int)
+    df['x'] = df['endX']
+    df['y'] = df['endY']
+    df['simTime'] = df['endTime']
+    df = df.drop(columns=['startX','startY','endX','endY','startTime', 'endTime'])    
+    return df
+```
+
+%% Cell type:code id: tags:
+
+``` python
+to_postVis(trajectoriesReal).to_csv('expteriment_2.trajectories',index=False,sep=' ')
+to_postVis(trajectoriesReal).head(10)
+```
+
+%% Cell type:markdown id: tags:
+
 # Helpler method to access parts of the trajectory
 
 %% Cell type:code id: tags:
 
 ``` python
@@ -259,11 +346,16 @@
         return s / c
     
 def total_inter_agent(trajectories1, trajectories2, times):
     """too expensive! TODO!"""
     return sum(map(lambda t: inter_agent_d(trajectories_position(trajectories1, [t])) - inter_agent_d(trajectories_position(trajectories2, [t])), times)) / len(times)
-    
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
 #start_time(get_trajectory(1, ptrajectories))
 #max_start_time(ptrajectories)
 #end_time(get_trajectory(1, ptrajectories))
 #foot_step_length(get_footstep(get_trajectory(1, ptrajectories), 0))
 #trajectory_length(get_trajectory(1, ptrajectories))
@@ -282,91 +374,10 @@
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
-trajPos2
-```
-
-%% Cell type:code id: tags:
-
-``` python
-
-```
-
-%% Cell type:code id: tags:
-
-``` python
-def position(trajectory, time):
-    fs = footstep(trajectory, time);
-    if fs != None:
-        startTime = fs['startTime'];
-        endTime = fs['endTime'];
-        dur = duration(fs);
-        partial_dur = time - startTime;
-        ratio = partial_dur / dur;
-        start = fs['start'];
-        x1 = start['x'];
-        y1 = start['y'];
-        l = length(fs);
-        if l == 0.0:
-            return np.array([x1, y1])
-        else:    
-            partial_l = l * ratio;
-            v = direction(fs) / l * partial_l;
-            return np.array([x1, y1]) + v;
-
-
-def euclid_d(traj1, traj2, times):
-    """Computes the total (Euclidean) distance between two trajectories at certain times."""
-    return 0
-    sT = max([start_time(traj1), start_time(traj2)])
-    eT = min([end_time(traj1), end_time(traj2)])
-    filtered_times = list(filter(lambda t: t >= sT and t <= eT, times))
-    overlaps = len(filtered_times)
-    if overlaps == 0:
-        return 0
-    return sum(map(lambda t: np.linalg.norm(position(traj1, t)- position(traj2, t)), filtered_times)) / overlaps
-    
-def euclid_path_length(traj1, traj2, times):
-    sT = max([start_time(traj1), start_time(traj2)]);
-    eT = min([end_time(traj1), end_time(traj2)]);
-    filtered_times = list(filter(lambda t: t >= sT and t <= eT, times));
-    s = np.array([0, 0])
-    for i in range(len(filtered_times)-1):
-        t1 = filtered_times[i]
-        t2 = filtered_times[i+1]
-        d1 = position(traj1, t1) - position(traj1, t2)
-        d2 = position(traj2, t1) - position(traj2, t2)
-        diff = d1 - d2
-        s = s + diff
-    return s;
-
-def inter_agent_d(trajectories, t):
-    s = 0
-    min_index = min(trajectories.keys())
-    c = 0
-    for i in range(len(trajectories)):
-        pos1 = position(trajectories[i+min_index], t)
-        for j in range(i+1, len(trajectories)):
-            pos2 = position(trajectories[j+min_index], t)
-            if pos1 is not None and pos2 is not None:
-                s = s + np.linalg.norm(pos1 - pos2)
-                c = c + 1
-    if c == 0:
-        return 0
-    else:
-        return s / c
-    
-def total_inter_agent(trajectories1, trajectories2, times):
-    return sum(map(lambda t: inter_agent_d(trajectories1, t) - inter_agent_d(trajectories2, t), times)) / len(times)
-    
-def euclid_len(trajectory, sTime, eTime):
-    """Computes the total (Euclidean) length of the trajectory in between [sTime;eTime]."""
-    cut_traj = cut_soft(trajectory, sTime, eTime);
-    return trajectory_length(cut_traj)
-
 def greedy_match(trajectories1, trajectories2, times, f):
     """Computes a match of trajectories by using a greedy algorithm."""
     assert len(trajectories1) == len(trajectories2)
     min_index1 = min(trajectories1.keys())
     min_index2 = min(trajectories2.keys())
@@ -384,125 +395,57 @@
                     minIndex = j
                     minVal = val
         match[i] = minIndex
         indexSet.remove(minIndex)
     return match
-        
-def overlap(traj1, traj2, dt):
-    return True
-    
-def load_experiment(file):
-    fps = 16
-    pad = pd.DataFrame([[np.nan, np.nan, np.nan, np.nan, np.nan]], columns=['pedestrianId', 'timeStep', 'x', 'y', 'e'])
-    data = pd.read_csv(
-        file, 
-        sep=' ', 
-        names=['pedestrianId', 'timeStep', 'x', 'y', 'e'], 
-        index_col=False, 
-        header=None, 
-        skiprows=0)
-    
-    cc = pd.concat([pad, data], ignore_index=True)
-        
-    data['endX'] = data['x'] / 100 + 18.7
-    data['endY'] = data['y'] / 100 + 4.2
-    data['startX'] = cc['x'] / 100 + 18.7
-    data['startY'] = cc['y'] / 100 + 4.2
-    data['startTime'] = data['timeStep'] / fps - 1/fps
-    data['endTime'] = data['timeStep'] / fps
-    data = data.drop(columns=['timeStep','x','y','e'])
-    return data
-    
-def to_trajectories(data):
-    trajectories = dict({})
-    trajectory = []
-    for i in range(len(data)-1):
-        pedId = data['pedestrianId'][i]
-        if pedId == data['pedestrianId'][i+1]:
-            pedId = data['pedestrianId'][i]
-            x1 = data['x'][i]
-            y1 = data['y'][i]
-            x2 = data['x'][i+1]
-            y2 = data['y'][i+1]
-            startTime = data['timeStep'][i] 
-            endTime = data['timeStep'][i+1]
-            fs = {'startTime':startTime, 'endTime': endTime, 'start':{'x':x1, 'y':y1}, 'end':{'x':x2, 'y':y2}}
-            trajectory.append(fs)
-        else:
-            trajectories[pedId] = trajectory
-            trajectory = []
-            pedId = data['pedestrianId'][i]
-    return trajectories
+```
 
-def to_postVis(df):
-    simTimeStep = 0.4
-    fps = 16
-    df['timeStep'] = np.ceil(df['endTime'] / (1/fps)).astype(np.int)
-    df['x'] = df['endX']
-    df['y'] = df['endY']
-    df['simTime'] = df['endTime']
-    df = df.drop(columns=['startX','startY','endX','endY','startTime', 'endTime'])    
-    return df
-#times = np.linspace(4,10,10)
-#euclid_d(get_trajectory(1), get_trajectory(1), times)
-#to_trajectories(load_experiment(real_file))[1]
+%% Cell type:markdown id: tags:
 
-file = "./data/trajectories_distance.txt"
-real_file = "./data/KO/ko-240-120-240/ko-240-120-240_combined_MB.txt"
-```
+# Plot trajectories
 
 %% Cell type:code id: tags:
 
 ``` python
-data = load_experiment(real_file)
-#trajectoriesReal = to_trajectories(data)
-data.query('pedestrianId == 1').head()
-to_postVis(data).to_csv('expteriment_2.trajectories',index=False,sep=' ')
-to_postVis(data).head(10)
+def to_line(trajectory, xleft):
+    """Transforms a trajectory into a Line2D."""
+    x = trajectory['endX'].values
+    y = trajectory['endY'].values
+    if x[0] < xleft:
+        c = current_palette[2]
+    else:
+        c = current_palette[0]
+    return x, y, Line2D(x, y, color=c, linewidth=0.2)
+
+def add_lines(trajectories, xleft, ax):
+    grouped = trajectories.groupby(['pedestrianId'])
+    for name, group in grouped:
+        x, y, line = to_line(group, xleft)
+        ax.add_line(line)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 import seaborn as sns
 sns.set(style="ticks")
 
 current_palette = sns.color_palette()
 
-def to_line(trajectory, xleft):
-    x = []
-    y = []
-    for fs in trajectory:
-        x.append(fs['start']['x'])
-        y.append(fs['start']['y'])
-    if x[0] < xleft:
-        c = current_palette[2]
-    else:
-        c = current_palette[0]
-    return x, y, Line2D(x, y, color=c, linewidth=0.2)
+x_vcenter = 17.5
+y_vcenter = 5.2
 
 fig1 = plt.figure(figsize=(10,10))
 ax1 = fig1.add_subplot(111)
-
-
-x_vcenter = 17.5
-y_vcenter = 5.2
-for i in range(len(trajectoriesReal)):
-    x, y, line = to_line(trajectoriesReal[i+1], 14)
-    ax1.add_line(line)
-    
+add_lines(trajectoriesReal, 14, ax1)
 ax1.set_xlim(x_vcenter-5, x_vcenter+5)
 ax1.set_ylim(y_vcenter-4, y_vcenter+4)
 ax1.set_aspect(1)
 
 fig2 = plt.figure(figsize=(10,10))
 ax2 = fig2.add_subplot(111)
-
-for i in range(len(trajectories)):
-    x, y, line = to_line(trajectories[i+1], 14)
-    ax2.add_line(line)
-
+add_lines(ptrajectories, 14, ax2)
 ax2.set_xlim(x_vcenter-5, x_vcenter+5)
 ax2.set_ylim(y_vcenter-4, y_vcenter+4)
 ax2.set_aspect(1)
 
 plt.show()