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updated parameters and updated visualization function to better display the flow

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lbaumberger
2026-04-28 00:42:22 +02:00
parent adb1b492df
commit 6b8d5bc94c
3 changed files with 57 additions and 17 deletions
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forces.hpp
+2 -2
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@@ -22,7 +22,7 @@ const double R = 0.2;
const double c = 0.5;
// $\varphi$
const double phi = 100.0 / 180.0 * M_PI;
const double phi = 45.0 / 180.0 * M_PI;
const double cos_phi = cos(phi);
@@ -30,7 +30,7 @@ const double cos_phi = cos(phi);
// step size = 0.2 seconds
const double delta_t = 2;
const double dt = 0.001;
const double dt = 0.2;
// # Optional (forces.cpp: compute_repulsive_effects_from_pedestrians)
// const double DISTANCE = 2.0;
main.cpp
+37 -12
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@@ -28,12 +28,13 @@ Pedestrian *init_pedestrians(int n_p) {
// std::mt19937 gen(rd()); // use rd instead of 42 as seed for real randomness
std::mt19937 gen(42); // use fixed seed for reproducibility
std::normal_distribution<double> velocity_gen(1.34, 0.26); // 1.34 and 0.26 are the mean and stdrd deviation used in paper
std::uniform_real_distribution<double> x_gen(2, BOARD_LENGTH - 2);
std::uniform_real_distribution<double> y_gen(2, BOARD_WIDTH - 2);
std::uniform_real_distribution<double> x_gen_left(2, BOARD_LENGTH / 2);
std::uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2, BOARD_LENGTH - 2);
std::uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
for (int i = 0; i < n_p / 2; i++) {
double y = y_gen(gen);
Vector2d start_pos = Vector2d(x_gen(gen), y);
Vector2d destination = Vector2d(BOARD_LENGTH + 30, BOARD_WIDTH / 2);
Vector2d start_pos = Vector2d(x_gen_left(gen), y);
Vector2d destination = Vector2d(BOARD_LENGTH + 30, y);
// double desired_v0 = velocity_gen(gen);
double desired_v0;
do {
@@ -45,8 +46,8 @@ Pedestrian *init_pedestrians(int n_p) {
for (int i = n_p / 2; i < n_p; i++) {
double y = y_gen(gen);
Vector2d start_pos = Vector2d(x_gen(gen), y);
Vector2d destination = Vector2d(-30, BOARD_WIDTH / 2);
Vector2d start_pos = Vector2d(x_gen_right(gen), y);
Vector2d destination = Vector2d(-30, y);
// double desired_v0 = velocity_gen(gen);
double desired_v0;
do {
@@ -85,7 +86,7 @@ int main(int argc, const char *argv[]) {
Board board = Board(BOARD_LENGTH, BOARD_WIDTH, 0, obs);
// Logic to get/dump the simulation data
const int dump_interval = 50; // dump every 5 iterations -> 20 fps (since dt = 0.01)
const int dump_interval = 1; // dump every 5 iterations -> 20 fps (since dt = 0.01)
const char *dump_path = (argc >= 4) ? argv[3] : nullptr;
std::vector<std::tuple<int, int, double, double, double, double, int>> samples; // t, id, x, y, e.x, e.y, color
@@ -130,6 +131,11 @@ int main(int argc, const char *argv[]) {
// # 4 flops = 2 ADDs, 2 MULTs
// integrate preferred velocity: dw/dt = F (Eq. 10)
p.w = p.w + p.f_i * dt;
double w_norm = p.w.norm();
if (w_norm > p.v_max) {
p.w = p.w * (p.v_max / w_norm);
}
// Vector2d w_alpha = p.curr_vel + p.f_i * dt; // commented since this was using p.curr_vel instead of p.w (different from paper)
// # 8 flops = 2 MULTs, 2 DIVs, 1 SQRT, 2 MULTs, 1 ADD
@@ -142,7 +148,7 @@ int main(int argc, const char *argv[]) {
p.curr_pos = p.curr_pos + p.curr_vel * dt;
// # 10 flops = 5 ADDs, 2 DIVs, 1 SQRT, 2 MULTs
p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
// p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
// how to handle if a pedestrian reached the end of our simulation/end of the hallway
// The option we do here is to just "teleport" the person back to the beginning
@@ -150,17 +156,36 @@ int main(int argc, const char *argv[]) {
if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
// basically just keep the y position and reset the x
p.curr_pos.x = 1.0;
// p.destination.y = p.curr_pos.y;
p.destination.y = p.curr_pos.y;
p.w = Vector2d(p.desired_v0, 0);
p.curr_vel = p.w;
}
if (p.color == BLACK && p.curr_pos.x < 1.5) {
p.curr_pos.x = BOARD_LENGTH - 1.0;
// p.destination.y = p.curr_pos.y;
p.destination.y = p.curr_pos.y;
p.w = Vector2d(-p.desired_v0, 0);
p.curr_vel = p.w;
}
// # 10 flops = 5 ADDs, 2 DIVs, 1 SQRT, 2 MULTs
// p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
// p.e = (p.curr_vel - p.destination) / (p.curr_vel - p.destination).norm();
p.e = (p.destination - (p.curr_vel / p.curr_vel.norm())) / (p.destination - (p.curr_vel / p.curr_vel.norm())).norm();
// p.e = (p.destination + (p.curr_vel / p.curr_vel.norm())) / (p.destination + (p.curr_vel / p.curr_vel.norm())).norm();
const double WALL_EPS = 1e-3;
if (p.curr_pos.y < 0.0) {
p.curr_pos.y = WALL_EPS;
if (p.curr_vel.y < 0)
p.curr_vel.y = 0;
if (p.w.y < 0)
p.w.y = 0;
}
if (p.curr_pos.y > BOARD_WIDTH) {
p.curr_pos.y = BOARD_WIDTH - WALL_EPS;
if (p.curr_vel.y > 0)
p.curr_vel.y = 0;
if (p.w.y > 0)
p.w.y = 0;
}
// if we have a dump path/file we wanna save the data for visualisation
if (dump_path && (t % dump_interval == 0)) {
visualization.py
+18 -3
View File
@@ -1,4 +1,5 @@
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
@@ -9,6 +10,20 @@ if len(sys.argv) < 2:
df = pd.read_csv(sys.argv[1])
frames = sorted(df["time"].unique())
# Replace p.e with direction-of-next-hop: where the ped actually moves to next frame.
# Falls back to p.e for the last frame and for teleport jumps.
df = df.sort_values(["id", "time"]).reset_index(drop=True)
df["dx"] = df.groupby("id")["x"].shift(-1) - df["x"]
df["dy"] = df.groupby("id")["y"].shift(-1) - df["y"]
mag = np.sqrt(df["dx"]**2 + df["dy"]**2)
TELEPORT_THRESHOLD = 5.0 # any per-step jump larger than this is the teleport, not motion
fallback = (mag > TELEPORT_THRESHOLD) | mag.isna() | (mag < 1e-9)
df.loc[fallback, "dx"] = df.loc[fallback, "p.e.x"]
df.loc[fallback, "dy"] = df.loc[fallback, "p.e.y"]
mag = np.sqrt(df["dx"]**2 + df["dy"]**2).replace(0, 1.0)
df["dx"] = df["dx"] / mag
df["dy"] = df["dy"] / mag
fig, ax = plt.subplots(figsize=(10, 2.2))
ax.set_xlim(0, 50); ax.set_ylim(0, 10)
ax.set_aspect("equal"); ax.set_facecolor("white")
@@ -39,13 +54,13 @@ def update(t):
# Draw fresh arrows each frame
arrow_scale = 1.5 # Tune this to change arrow length visually
if len(w):
q_white = ax.quiver(w["x"], w["y"], w["p.e.x"], w["p.e.y"],
q_white = ax.quiver(w["x"], w["y"], w["dx"], w["dy"],
color="black", angles="xy", scale_units="xy",
scale=1/arrow_scale, width=0.003,
headwidth=4, headlength=5, zorder=3)
quivers.append(q_white)
if len(b):
q_black = ax.quiver(b["x"], b["y"], b["p.e.x"], b["p.e.y"],
q_black = ax.quiver(b["x"], b["y"], b["dx"], b["dy"],
color="gray", angles="xy", scale_units="xy",
scale=1/arrow_scale, width=0.003,
headwidth=4, headlength=5, zorder=3)
@@ -57,7 +72,7 @@ def update(t):
anim = FuncAnimation(fig, update, frames=frames, interval=50, blit=False)
if len(sys.argv) >= 3:
anim.save(sys.argv[2], writer="ffmpeg", fps=20)
anim.save(sys.argv[2], writer="ffmpeg", fps=5)
print(f"Saved {sys.argv[2]}")
else:
plt.show()