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Commit ec049e49 authored by Benedikt Zoennchen's avatar Benedikt Zoennchen
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update metric notebook, add data for the notebook such that one can experiment with it.

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Tools/Notebooks/TrajectoryMetric.ipynb
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%% 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
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from IPython.core.display import display, HTML
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):
llist.append([pedestrianId, fs['start']['x'], fs['start']['y'], fs['startTime'], fs['end']['x'], fs['end']['y'], fs['endTime']])
def trajectory_append(pedestrianId, trajectory, llist):
for fs in trajectory:
fs_append(pedestrianId, fs, llist)
def trajectories_to_dataframe(trajectories):
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
```
%% Cell type:code id: tags:
file = "./data/trajectories_distance.txt"
``` python
file = "./data/TrajectoryMetric/trajectories_simulation.txt"
f = open(file, "r")
header = f.readline();
trajectories = dict({});
for row in f:
s = row.split(" ");
pedId = int(s[0]);
footsteps = json.loads(s[1]);
trajectories[pedId] = footsteps[0]['footSteps'];
ptrajectories = trajectories_to_dataframe(trajectories)
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
def get_trajectory(pedId, trajectories):
"""returns a data frame containing the trajectory of one specific agent."""
query = 'pedestrianId == ' + str(pedId)
return trajectories.query(query)
def get_pedestrianIds(trajectories):
return trajectories['pedestrianId'].unique()
def get_footstep(trajectory, i):
"""returns the i-ths footstep."""
return trajectory.iloc[i];
def get_footstep_by_time(trajectory, time):
"""returns the footstep which happens at time or nothing (None)."""
query = 'startTime <= ' + str(time) + ' and ' + str(time) + ' < endTime'
fs = trajectories.query(query)
assert len(fs) >= 1
return fs
def start_time(trajectory):
"""returns the time of the first footstep of the trajectory."""
return get_footstep(trajectory, 0)['startTime'];
def end_time(trajectory):
return get_footstep(trajectory, len(trajectory)-1)['endTime'];
def max_start_time(trajectories):
"""returns the time of the first footstep of the trajectory which starts last."""
pedestrianIds = get_pedestrianIds(trajectories)
return max(map(lambda pedId: start_time(get_trajectory(pedId, trajectories)), pedestrianIds))
def min_end_time(trajectories):
"""returns the time of the last footstep of the trajectory which ends first."""
pedestrianIds = get_pedestrianIds(trajectories)
return min(map(lambda pedId: end_time(get_trajectory(pedId, trajectories)), pedestrianIds))
def footstep_is_between(fs, time):
"""true if the foostep and the intersection with time is not empty."""
startTime = fs['startTime'];
endTime = fs['endTime'];
return startTime <= time and time < endTime;
def cut(trajectory, sTime, eTime):
query = 'startTime >= ' + str(sTime) + ' and endTime < ' + str(eTime)
return trajectory.query(query)
def cut_soft(trajectory, sTime, eTime):
query = 'endTime > ' + str(sTime) + ' and startTime < ' + str(eTime)
return trajectory.query(query)
```
%% Cell type:markdown id: tags:
# Helper methods to compute different metrices
%% Cell type:code id: tags:
``` python
def footstep_length(fs):
"""Euclidean length of a footstep."""
x1 = fs['startX'];
y1 = fs['startY'];
x2 = fs['endX'];
y2 = fs['endY'];
dx = x1-x2;
dy = y1-y2;
return np.sqrt(dx*dx + dy*dy);
def trajectory_length(trajectory):
"""Euclidean length of a trajectory."""
dx = trajectory['startX']-trajectory['endX']
dy = trajectory['startY']-trajectory['endY']
return np.sqrt(dx*dx + dy*dy).sum();
def footstep_direction(fs):
"""Vector from start to end position."""
x1 = fs['startX'];
y1 = fs['startY'];
x2 = fs['endX'];
y2 = fs['endY'];
return np.array([x2-x1, y2-y1]);
def footstep_duration(fs):
"""Duration of a footstep."""
startTime = fs['startTime'];
endTime = fs['endTime'];
return endTime-startTime;
def trajectory_duration(trajectory):
"""Euclidean length of a trajectory."""
return (trajectory['endTime'] - trajectory['startTime']).sum();
def footstep_speed(fs):
"""Speed of the footstep."""
return footstep_length(fs) / footstep_duration(fs);
def trajectory_speed(fs):
"""Speed of the trajectory."""
return trajectory_length(fs) / trajectory_duration(fs);
#def trajectory_positions(trajectory, times):
# mask = trajectory[['startTime', 'endTime']].mask(lambda x: x**2)
# (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10')
# duration = trajectory['endTime'] - trajectory['startTime']
# dx = trajectory['endX'] - trajectory['startX']
# dy = trajectory['endY'] - trajectory['startY']
# direction =
def filter_trajectories(trajectories, times):
"""Filters trajectory by times."""
rows = []
for row in trajectories.itertuples():
if len(list(filter(lambda b: b, map(lambda t: row.startTime <= t and t < row.endTime, times)))) > 0:
rows.append(row)
return pd.DataFrame(rows)
def trajectories_position(trajectories, times):
"""Transforms trajectories into positions at each time in times such that each position is computed by linear interpolation."""
rows = []
#print(trajectories)
for row in trajectories.itertuples():
llist = list(filter(lambda t: row.startTime <= t and t < row.endTime, times))
assert len(llist) == 1 or len(llist) == 0
if len(llist) > 0:
time = llist[0]
dur = row.endTime - row.startTime
partial_dur = time - row.startTime
ratio = partial_dur / dur
direction = np.array([row.endX - row.startX, row.endY - row.startY])
l = np.linalg.norm(direction)
if l > 0:
partial_l = l * ratio;
v = direction / l * partial_l;
pos = np.array([row.startX, row.startY]) + v;
rows.append([row.pedestrianId, pos[0], pos[1], time])
else:
rows.append([row.pedestrianId, np.nan, np.nan, time])
dataframe = pd.DataFrame(rows, columns=['pedestrianId','x','y','time'])
return dataframe
def euclid_d(trajPos1, trajPos2):
"""Computes the total (Euclidean) distance between two trajectories.
Assumption: trajectories are both cut acccordingly!
"""
assert len(trajPos1) == len(trajPos2)
dx = trajPos1['x'] - trajPos2['x']
dy = trajPos1['y'] - trajPos2['y']
norm = np.sqrt(dx**2 + dy**2)
return norm.sum() / len(dx)
def euclid_path_length(trajPos1, trajPos2):
"""Computes the total (Euclidean) path length difference between two trajectories.
Assumption: trajectories are both cut acccordingly!
"""
count = len(trajPos1)
pad = pd.DataFrame([[np.nan, np.nan, np.nan, np.nan]], columns=['pedestrianId','x','y','time'])
trajPos1Pad = pd.concat([pad, trajPos1], ignore_index=True)
trajPos2Pad = pd.concat([pad, trajPos1], ignore_index=True)
dx1 = trajPos1['x'] - trajPos1Pad['x']
dy1 = trajPos1['y'] - trajPos1Pad['y']
dx2 = trajPos2['x'] - trajPos2Pad['x']
dy2 = trajPos2['y'] - trajPos2Pad['y']
dx = dx1 - dx2
dy = dy1 - dy2
diff = np.sqrt(dx**2 + dy**2)
return diff.sum()
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 inter_agent_d(trajPos):
"""Computes the inter agent (Euclidean) distance between all pairs of agents.
Assumption: the trajectory is cut accordingly, ie the time is equal for
each position.
"""
s = 0
min_index = min(trajectories.keys())
c = 0
llen = len(trajPos)
for index1, row1 in trajPos.iterrows():
for index2, row2 in trajPos.tail(llen-1-index1).iterrows():
x1 = row1['x']
y1 = row1['y']
x2 = row2['x']
y2 = row2['y']
dx = x1 - x2
dy = y1 - y2
s = s + np.sqrt(dx**2 + dy**2)
c = c + 1
if c == 0:
return 0
else:
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))
#trajectory_speed(get_trajectory(1, ptrajectories))
cutTraj = cut(get_trajectory(1, ptrajectories), 0.0, 10.0)[['startTime', 'endTime']]
#cutTraj.mask(cutTraj['startTime'] <= 4 and 4 > cutTraj['endTime'])
#start_time(get_trajectory(1, ptrajectories))
#trajectories_position(ptrajectories, [1,2,3,4]).head()
trajPos1 = trajectories_position(get_trajectory(2, ptrajectories), [1,2,3,4,5,6,8,9,10,11,12,13])
trajPos2 = trajectories_position(get_trajectory(7, ptrajectories), [1,2,3,4,5,6,8,9,10,11,12,13])
trajPos1 = trajPos1[~np.isnan(trajPos1.x)]
trajPos2 = trajPos2[~np.isnan(trajPos2.x)]
euclid_path_length(trajPos1, trajPos2)
euclid_len(ptrajectories,0,10000)
#print(total_inter_agent(ptrajectories, ptrajectories, [1,2]))
```
%% 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;