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Commit bcd1d449 authored by Benedikt Zoennchen's avatar Benedikt Zoennchen
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add modified T-junction scenario calibrated via evacuation times which we used... 

add modified T-junction scenario calibrated via evacuation times which we used for the tgf2019 vadere paper, remove T-junction experiment data (they can be found on our nextcloud).
parent fd910ca2
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Tools/Notebooks/TrajectoryMetric.ipynb 100644 → 100755
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%% Cell type:markdown id: tags:
### Note
You can find the required data on the Nextcloud data/Paperdaten/2019/TGF2019-vadere
%% 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
from matplotlib.lines import Line2D
import seaborn as sns
sns.set(style="ticks")
import functools
import operator
sns.set_context("poster")
sns.set(style="whitegrid", font_scale=1.8)
from IPython.core.display import display, HTML
display(HTML('<style>.container { width:100% !important; }</style>'))
```
%% Cell type:markdown id: tags:
# 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'])
dataframe["distance"] = np.sqrt(np.square(dataframe["endX"] - dataframe["startX"]) + np.square(dataframe["endY"] - dataframe["startY"]))
dataframe["velocity"] = dataframe["distance"] / (dataframe["endTime"] - dataframe["startTime"])
return dataframe
```
%% 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:
s = row.split(" ");
pedId = int(s[0]);
footsteps = json.loads(s[1]);
trajectories[pedId] = footsteps[0]['footSteps'];
ptrajectories = trajectories_to_dataframe(trajectories)
ptrajectories.head()
def load_simulation_data(file):
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'];
return trajectories_to_dataframe(trajectories)
```
%% Cell type:markdown id: tags:
# Convert experiment data into a DataFrame
%% Cell type:code id: tags:
``` python
def load_experiment(file):
fps = 16
data = pd.read_csv(
file,
sep=' ',
names=['pedestrianId', 'timeStep', 'x', 'y', 'e'],
index_col=False,
header=None,
skiprows=0)
rows = []
#print(trajectories)
last_ped_id = None
lastX = None
lastY = None
for row in data.itertuples():
endX = row.x / 100 + 18.7
endY = row.y / 100 + 4.2
endX = row.x / 100.0 + 18.7
endY = row.y / 100.0 + 4.2
startTime = row.timeStep / fps - 1/fps
endTime = row.timeStep / fps
if last_ped_id is None or last_ped_id != row.pedestrianId:
startX = np.nan
startY = np.nan
distance = np.nan
velocity = np.nan
else:
startX = lastX / 100 + 18.7
startY = lastY / 100 + 4.2
startX = lastX / 100.0 + 18.7
startY = lastY / 100.0 + 4.2
distance = np.sqrt(np.square(endX - startX) + np.square(endY - startY))
velocity = distance / (endTime - startTime)
rows.append([row.pedestrianId, startX, startY, endX, endY, startTime, endTime, distance, velocity])
last_ped_id = row.pedestrianId
lastX = row.x
lastY = row.y
rows.append([row.pedestrianId, startX, startY, endX, endY, startTime, endTime, distance, velocity])
dataframe = pd.DataFrame(rows, columns=['pedestrianId', 'startX', 'startY', 'endX', 'endY','startTime','endTime','distance','velocity'])
return dataframe
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:markdown id: tags:
# Load all data
The following code loads the experiment data as well as the simulated data and transforms everything into the same format (data frame). The simulated trajectories are cut with respect to the camera bounds of the experiment.
%% 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()
```
trajectories240o050o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-050-240/ko-240-050-240_combined_MB.txt")
trajectories240o060o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-060-240/ko-240-060-240_combined_MB.txt")
trajectories240o080o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-080-240/ko-240-080-240_combined_MB.txt")
trajectories240o100o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-100-240/ko-240-100-240_combined_MB.txt")
trajectories240o120o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-120-240/ko-240-120-240_combined_MB.txt")
trajectories240o150o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-150-240/ko-240-150-240_combined_MB.txt")
trajectories240o240o240 = load_experiment("./T-junction-experiment-trajectories-files/KO/ko-240-240-240/ko-240-240-240_combined_MB.txt")
trajectoriesReal = pd.concat([trajectories240o050o240, trajectories240o060o240, trajectories240o080o240,
trajectories240o100o240, trajectories240o120o240, trajectories240o150o240,
trajectories240o240o240], ignore_index=True)
%% Cell type:markdown id: tags:
# trajectories starting from left
cut_minX = trajectoriesReal[trajectoriesReal["endX"] < 15].groupby(["pedestrianId"])["startX"].min().max() + 0.12
# Convert DataFrame to postvis DataFrame
# trajectories starting from right
cut_maxX = trajectoriesReal[trajectoriesReal["endX"] > 21].groupby(["pedestrianId"])["startX"].max().min() - 0.2
%% Cell type:code id: tags:
# trajectories ending at top
cut_maxY = trajectoriesReal.groupby(["pedestrianId"])["endY"].max().min() - 0.168
``` 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
```
trajectories240o050o240 = cut(trajectories240o050o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o060o240 = cut(trajectories240o060o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o080o240 = cut(trajectories240o080o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o100o240 = cut(trajectories240o100o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o120o240 = cut(trajectories240o120o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o150o240 = cut(trajectories240o150o240, cut_minX, cut_maxX, cut_maxY)
trajectories240o240o240 = cut(trajectories240o240o240, cut_minX, cut_maxX, cut_maxY)
%% Cell type:code id: tags:
osm_trajectories240o050o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-050-240.txt")
osm_trajectories240o050o240 = cut(osm_trajectories240o050o240, cut_minX, cut_maxX, cut_maxY)
``` python
to_postVis(trajectoriesReal).to_csv('expteriment_2.trajectories',index=False,sep=' ')
to_postVis(trajectoriesReal).head(10)
```
osm_trajectories240o060o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-060-240.txt")
osm_trajectories240o060o240 = cut(osm_trajectories240o060o240, cut_minX, cut_maxX, cut_maxY)
%% Cell type:markdown id: tags:
osm_trajectories240o080o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-080-240.txt")
osm_trajectories240o080o240 = cut(osm_trajectories240o080o240, cut_minX, cut_maxX, cut_maxY)
# Calculate evacution time
osm_trajectories240o100o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-100-240.txt")
osm_trajectories240o100o240 = cut(osm_trajectories240o100o240, cut_minX, cut_maxX, cut_maxY)
%% Cell type:markdown id: tags:
osm_trajectories240o120o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-120-240.txt")
osm_trajectories240o120o240 = cut(osm_trajectories240o120o240, cut_minX, cut_maxX, cut_maxY)
Evacuation time = endTime - startTime
osm_trajectories240o150o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-150-240.txt")
osm_trajectories240o150o240 = cut(osm_trajectories240o150o240, cut_minX, cut_maxX, cut_maxY)
%% Cell type:markdown id: tags:
osm_trajectories240o240o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-OSM-240-240-240.txt")
osm_trajectories240o240o240 = cut(osm_trajectories240o240o240, cut_minX, cut_maxX, cut_maxY)
trajectoriesOSM = pd.concat([osm_trajectories240o050o240, osm_trajectories240o060o240, osm_trajectories240o080o240, osm_trajectories240o100o240, osm_trajectories240o120o240, osm_trajectories240o150o240, osm_trajectories240o240o240], ignore_index=True)
bhm_trajectories240o050o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-050-240.txt")
bhm_trajectories240o050o240 = cut(bhm_trajectories240o050o240, cut_minX, cut_maxX, cut_maxY)
bhm_trajectories240o060o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-060-240.txt")
bhm_trajectories240o060o240 = cut(bhm_trajectories240o060o240, cut_minX, cut_maxX, cut_maxY)
bhm_trajectories240o080o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-080-240.txt")
bhm_trajectories240o080o240 = cut(bhm_trajectories240o080o240, cut_minX, cut_maxX, cut_maxY)
bhm_trajectories240o100o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-100-240.txt")
bhm_trajectories240o100o240 = cut(bhm_trajectories240o100o240, cut_minX, cut_maxX, cut_maxY)
bhm_trajectories240o120o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-120-240.txt")
bhm_trajectories240o120o240 = cut(bhm_trajectories240o120o240, cut_minX, cut_maxX, cut_maxY)
bhm_trajectories240o150o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-150-240.txt")
bhm_trajectories240o150o240 = cut(bhm_trajectories240o150o240, cut_minX, cut_maxX, cut_maxY)
## Real data
bhm_trajectories240o240o240 = load_simulation_data("./T-junction-sim-trajectory-files/trajectories-BHM-240-240-240.txt")
bhm_trajectories240o240o240 = cut(bhm_trajectories240o240o240, cut_minX, cut_maxX, cut_maxY)
trajectoriesBHM = pd.concat([bhm_trajectories240o050o240, bhm_trajectories240o060o240, bhm_trajectories240o080o240,
bhm_trajectories240o100o240, bhm_trajectories240o120o240, bhm_trajectories240o150o240,
bhm_trajectories240o240o240], ignore_index=True)
```
%% Cell type:code id: tags:
``` python
# Sum up all measured time deltas of a pedestrian to get the final evacuation time
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
cut_maxX = trajectoriesReal[trajectoriesReal["endX"] > 21].groupby(["pedestrianId"])["endX"].max().min()
cut_maxY = trajectoriesReal.groupby(["pedestrianId"])["endY"].max().min()
et = copy.groupby(["pedestrianId"])["timeDelta"].sum()
print("Evacuation time (real data)")
print("- mean: {:.2f} [s]".format(evacuation_time.mean()))
print("- std: {:.2f} [s]".format(evacuation_time.std()))
print("- min: {:.2f} [s]".format(evacuation_time.min()))
print("- max: {:.2f} [s]".format(evacuation_time.max()))
print("- mean: {:.2f} [s]".format(et.mean()))
print("- std: {:.2f} [s]".format(et.std()))
print("- min: {:.2f} [s]".format(et.min()))
print("- max: {:.2f} [s]".format(et.max()))
print("- minX: {:.2f} [m]".format(cut_minX))
print("- maxX: {:.2f} [m]".format(cut_maxX))
print("- maxY: {:.2f} [m]".format(cut_maxY))
```
%% Cell type:markdown id: tags:
# Convert DataFrame to postvis DataFrame
%% Cell type:code id: tags:
``` python
rightX = trajectoriesReal[trajectoriesReal.endX < 16].groupby(["pedestrianId"])["endX"].min().max()
leftX = trajectoriesReal[trajectoriesReal.endX > 16].groupby(["pedestrianId"])["endX"].max().min()
topY = trajectoriesReal.groupby(["pedestrianId"])["endY"].max().min()
topY
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:markdown id: tags:
## Simulation data
%% Cell type:markdown id: tags:
TODO: Use `PedestrianEvacuationTimeProcessor` to log evacuation time during simulation and analyze it here.
%% Cell type:markdown id: tags:
# Helper 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_trajectories(t, trajectories):
return trajectories[np.logical_and(trajectories.startTime <= t, trajectories.endTime >= t)]
return trajectories[np.logical_and(trajectories.startTime <= t, trajectories.endTime > t)]
def get_pedestrianIds(trajectories):
return trajectories['pedestrianId'].unique()
#def get_velocity(trajectories, t, dt):
# trajectories[np.logical_and(trajectory.endX >= xmax, trajectory.startX < xmax)]
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)
def cuthead_trajectory_by(trajectory, ymin, ymax):
i1 = trajectory[trajectory.endY >= ymax].index.min()
i2 = trajectory[trajectory.endY <= ymin].index.min()
#assert (i1 is np.nan and i2 is not np.nan) or (i1 is not np.nan and i2 is np.nan)
y = ymax if i2 is np.nan or (i1 is not np.nan and i1 < i2) else ymin
i = i1 if y == ymax else i2
if i is np.nan:
print("i1:"+str(i1))
print("i2:"+str(i2))
assert i is not np.nan
#print(i)
# cut the footstep at the tail to exactly fit xmin or xmax
fs = trajectory.loc[i]
start = np.array([fs["startX"], fs["startY"]])
end = np.array([fs["endX"], fs["endY"]])
endTime = fs["endTime"]
startTime = fs["startTime"]
distance = fs["distance"]
velocity = fs["velocity"]
d = end - start
if abs(fs["endY"] - fs["startY"]) > 0.00001:
r = (y - fs["startY"]) / (fs["endY"] - fs["startY"])
end = start + (d * r)
time = fs["endTime"] - fs["startTime"]
endTime = fs["startTime"] + (time * r)
distance = np.linalg.norm(end - start)
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_head = pd.DataFrame(llist, columns=['pedestrianId','startX','startY','startTime','endX','endY','endTime','distance','velocity'])
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[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()
i1 = trajectory[trajectory.startX >= xmax].index.max()
i2 = trajectory[trajectory.startX <= xmin].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
assert i is not np.nan
# cut the footstep at the tail to exactly fit xmin or xmax
fs = trajectory.loc[i]
start = np.array([fs["startX"], fs["startY"]])
end = np.array([fs["endX"], fs["endY"]])
startTime = fs["startTime"]
endTime = fs["endTime"]
distance = fs["distance"]
velocity = fs["velocity"]
d = end - start
if abs(fs["endX"] - fs["startX"]) > 0.00001:
r = (x - fs["startX"]) / (fs["endX"] - fs["startX"])
end = start + (d * r)
r = (fs["endX"] - x) / (fs["endX"] - fs["startX"])
start = end - (d * r)
time = fs["endTime"] - fs["startTime"]
endTime = fs["startTime"] + (time * r)
startTime = fs["endTime"] - (time * r)
distance = np.linalg.norm(end - start)
if abs(endTime - startTime) < 0.00001:
if distance < 0.00001:
velocity = 0
else:
raise exception
else:
velocity = distance / (endTime - startTime)
velocity = distance / (endTime - startTime)
assert startTime <= endTime
df = trajectory.loc[i+1:]
llist = [[fs["pedestrianId"],fs["startX"],fs["startY"],fs["startTime"],end[0],end[1],endTime,distance,velocity]]
llist = [[fs["pedestrianId"],start[0],start[1],startTime,fs["endX"],fs["endY"],fs["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)
return df_tail
def cuthead_by(trajectories, ymin, ymax):
df = pd.DataFrame([], columns=['pedestrianId','startX','startY','startTime','endX','endY','endTime'])
pedIds = get_pedestrianIds(trajectories)
for pedId in pedIds:
df = df.append(cuthead_trajectory_by(get_trajectory(pedId, trajectories), ymin, ymax), ignore_index=True)
return df
def cuttail_by(trajectories, xmin, xmax):
df = pd.DataFrame([], columns=['pedestrianId','startX','startY','startTime','endX','endY','endTime'])
pedIds = get_pedestrianIds(trajectories)
for pedId in pedIds:
df = df.append(cuttail_trajectory_by(get_trajectory(pedId, trajectories), xmin, xmax), ignore_index=True)
return df
def cut(trajectories):
def cut(trajectories, cut_minX, cut_maxX, cut_maxY):
df = cuttail_by(trajectories, cut_minX, cut_maxX)
df = cuthead_by(df, -1000, cut_maxY)
return df
traj = get_trajectory(2, trajectoriesReal)
ts = traj[traj.endX > 22].index.max()
ts
#cut(trajectoriesReal)
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
%% 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 mean_velocity_at(t, trajectories):
return get_trajectories(t, trajectories)['velocity'].mean()
def evacuation_times(trajectories):
pedIds = get_pedestrianIds(trajectories)
rows = []
for pedId in pedIds:
evacTime = evacuation_time(pedId, trajectories)
rows.append([pedId, evacTime])
return pd.DataFrame(rows, columns=['pedestrianId', 'evacuationTime'])
def evacuation_time(pedId, trajectories):
traj = get_trajectory(pedId, trajectories)
start = traj.iloc[0]['endTime']
end = traj.iloc[len(traj)-1]['endTime']
return end - start
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']