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Commit 41985de4 authored by Benedikt Kleinmeier's avatar Benedikt Kleinmeier
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Merge branch 'master' of gitlab.lrz.de:vadere/vadere

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Tools/Notebooks/TrajectoryMetric.ipynb
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%% 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")
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()
```
%% 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
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
distance = np.sqrt(np.square(endX - startX) + np.square(endY - startY))
velocity = distance / (endTime - startTime)
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: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:
# Calculate evacution time
%% Cell type:markdown id: tags:
Evacuation time = endTime - startTime
%% Cell type:markdown id: tags:
## Real data
%% 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
cut_maxX = trajectoriesReal[trajectoriesReal["endX"] > 21].groupby(["pedestrianId"])["endX"].max().min()
cut_maxY = trajectoriesReal.groupby(["pedestrianId"])["endY"].max().min()
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("- minX: {:.2f} [m]".format(cut_minX))
print("- maxX: {:.2f} [m]".format(cut_maxX))
print("- maxY: {:.2f} [m]".format(cut_maxY))
```
%% 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
```
%% 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)]
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
#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[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
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)
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)
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):
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, 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
%% 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 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.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]))
t = 0.5
ttraj = ptrajectories[np.logical_and(ptrajectories.startTime <= t, ptrajectories.endTime >= t)]
#ptrajectories["velocity"] = numpy.linalg.norm(
get_trajectories(0.5, ptrajectories).head()
```
%% Cell type:code id: tags:
``` python
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())
match = {}
indexSet = set(range(min_index2, len(trajectories2)))
for i in range(min_index1, len(trajectories1)):
traj1 = trajectories1[i]
minVal = None
minIndex = None
for j in indexSet:
traj2 = trajectories2[j]
if overlap(traj1, traj2, 0.4):
val = f(traj1, traj2, times)
if(minVal == None or val < minVal):
minIndex = j
minVal = val
match[i] = minIndex
indexSet.remove(minIndex)
return match
```
%% Cell type:markdown id: tags:
Here we cut all trajectory data such each left trajectory starts at the same $x$-coordinate and each right trajectory starts at the same $x$-coordinate. In addition each trajectory ends a the same $y$-coordinate.
%% Cell type:code id: tags:
``` python
c_real_trajectories = cut(trajectoriesReal)
c_sim_trajecotories = cut(ptrajectories)
```
%% Cell type:markdown id: tags:
# Plot trajectories
Now we plot the cut trajectories.
%% Cell type:code id: tags:
``` python
def to_line(trajectory, xleft):
"""Transforms a trajectory into a Line2D."""
current_palette = sns.color_palette()
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=0.3)
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
x_vcenter = 17.5
y_vcenter = 5.2
fig_trajectories = plt.figure(figsize=(10,10))
ax1_trajectories = fig_trajectories.add_subplot(121)
add_lines(c_real_trajectories, 16, ax1_trajectories)
ax1_trajectories.set_xlim(x_vcenter-5, x_vcenter+6)
ax1_trajectories.set_ylim(y_vcenter-4, y_vcenter+4)
ax1_trajectories.set_aspect(1)
ax2_trajectories = fig_trajectories.add_subplot(122, sharey=ax1)
add_lines(c_sim_trajecotories, 16, ax2_trajectories)
plt.setp(ax2_trajectories.get_yticklabels(), visible=False)
ax2_trajectories.set_xlim(x_vcenter-5, x_vcenter+6)
ax2_trajectories.set_ylim(y_vcenter-4, y_vcenter+4)
ax2_trajectories.set_aspect(1)
plt.show()
```
%% Cell type:markdown id: tags:
# Plot velocities
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))
#df1 = pd.DataFrame({'velocity':vel1, 'time':times})
#df2 = pd.DataFrame({'velocity':vel2, 'time':times})
fig_velocities = plt.figure(figsize=(10,5))
ax1_velocities = fig_velocities.add_subplot(121)
ax1_velocities.set_xlim(min(times),max(times))
ax1_velocities.set_ylim(0,2)
ax1_velocities.set_xlabel("time")
ax1_velocities.set_ylabel("velocity")
plt.errorbar(times, velocity1, std1, ecolor=sns.color_palette()[2])
ax2_velocities = fig_velocities.add_subplot(122)
ax2_velocities.set_xlim(min(times),max(times))
ax2_velocities.set_ylim(0,2)
ax2_velocities.set_xlabel("time")
ax2_velocities.set_ylabel("velocity")
plt.errorbar(times, velocity2, std2, ecolor=sns.color_palette()[2])
plt.show()
#ax6 = fig2.add_subplot(212)
#sns.relplot(x="startTime", y="velocity", kind="line", ci="sd", data=c_real_trajectories, ax=ax) #linewidth=0.5
#sns.relplot(x="startTime", y="velocity", kind="line", ci="sd", data=c_sim_trajecotories, ax=ax6)
#fmri = sns.load_dataset("fmri")
#fmri
#c_real_trajectories[c_real_trajectories.endTime == 5.0]
#c_real_trajectories
```
%% Cell type:markdown id: tags:
# Plot densities
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)
ax1_density.set_xlabel("time")
ax1_density.set_ylabel("density")
plt.plot(times, density1)
ax2_density = fig_density.add_subplot(122)
ax2_density.set_xlim(min(times),max(times))
ax2_density.set_xlabel("time")
ax2_density.set_ylabel("density")
ax2_density.set_ylim(0,6)
plt.plot(times, density2)
plt.show()
```
%% Cell type:markdown id: tags:
# Plot of fundamental diagrams using method c
%% Cell type:code id: tags:
``` python
fig_fundamental = plt.figure(figsize=(10,5))
ax1_fundamental = fig_fundamental.add_subplot(111)
#ax1_fundamental.set_xlim(min(times),max(times))
ax1_fundamental.set_ylim(0,6)
ax1_fundamental.set_xlabel("velocity")
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
```
......
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