Merge remote-tracking branch 'origin/smail' into lbaumberger
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lbaumberger
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-6
@@ -28,7 +28,7 @@ void compute_acceleration_term(Pedestrian *p) {
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// # 55(N - 1) = 55N - 55 flops
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//
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// $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
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// $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta})$
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//
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void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestrian *p, int N) {
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@@ -68,25 +68,25 @@ void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestr
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// # 7 flops = 1 SQRT, 3 ADDs, 2 MULTs, 1 DIV
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//
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// $(4):2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
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// $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
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//
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double b = sqrt((r_ab_norm + r_ab2_norm) * (r_ab_norm + r_ab2_norm) - s_b * s_b) / 2;
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// # 12 flops = 6 DIVs, 3 ADDs, 3 MULTs
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//
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// $(7):\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$
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// $\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert) (\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{4b}$
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//
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Vector2d b_grad = (r_ab / r_ab_norm + r_ab2 / r_ab2_norm) * (r_ab_norm + r_ab2_norm) / (4 * b);
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Vector2d b_grad = ((r_ab / r_ab_norm + r_ab2 / r_ab2_norm) * (r_ab_norm + r_ab2_norm)) / (4 * b);
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// # 6 flops = 3 MULTs, 2 DIVs, 1 EXP
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//
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// $(5):\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$
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// $\vec{f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{V^0_{\alpha \beta}}{\sigma}e^{-b / \sigma} \grad_{\vec{r}_{\alpha \beta}}b$
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//
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Vector2d f_ped = b_grad * ((V0_AB / SIGMA) * (exp(-b / SIGMA)));
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// # 14 flops: 9 MULTs, 4 ADDs, 1 SQRT
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//
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// $(11):\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta}) = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta})\vec{f}_{\alpha\beta}(\vec{r}_{\alpha} - \vec{r}_{\beta})$
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// $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{\beta}) = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta})\vec{f}_{\alpha\beta}(\vec{r}_{\alpha} - \vec{r}_{\beta})$
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//
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p->f_i += f_ped * w(p->e, f_ped * -1);
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}
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@@ -94,6 +94,9 @@ void compute_repulsive_effects_from_pedestrians(Pedestrian *pedestrians, Pedestr
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// # Assume that the four borders are also 4 obstacles
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// # 48 n_obstacles flops = 192 flops
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//
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// $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
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//
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void compute_repulsive_effects_from_borders(Board &board, Pedestrian *p) {
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Obstacle *obstacles = board.obstacles;
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@@ -33,7 +33,7 @@ Pedestrian *init_pedestrians(int n_p) {
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for (int i = 0; i < n_p / 2; i++) {
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double y = y_gen(gen);
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Vector2d start_pos = Vector2d(x_gen(gen), y);
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Vector2d destination = Vector2d(BOARD_LENGTH + 300, BOARD_WIDTH / 2);
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Vector2d destination = Vector2d(BOARD_LENGTH + 30, BOARD_WIDTH / 2);
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// double desired_v0 = velocity_gen(gen);
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double desired_v0;
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do {
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@@ -46,7 +46,7 @@ Pedestrian *init_pedestrians(int n_p) {
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for (int i = n_p / 2; i < n_p; i++) {
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double y = y_gen(gen);
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Vector2d start_pos = Vector2d(x_gen(gen), y);
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Vector2d destination = Vector2d(-300, BOARD_WIDTH / 2);
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Vector2d destination = Vector2d(-30, BOARD_WIDTH / 2);
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// double desired_v0 = velocity_gen(gen);
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double desired_v0;
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do {
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@@ -88,7 +88,7 @@ int main(int argc, const char *argv[]) {
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const int dump_interval = 50; // dump every 5 iterations -> 20 fps (since dt = 0.01)
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const char *dump_path = (argc >= 4) ? argv[3] : nullptr;
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std::vector<std::tuple<int, int, double, double, int>> samples; // t, id, x, y, color
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std::vector<std::tuple<int, int, double, double, double, double, int>> samples; // t, id, x, y, e.x, e.y, color
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if (dump_path)
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samples.reserve((size_t)n_iter / dump_interval * n_p);
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@@ -137,7 +137,7 @@ int main(int argc, const char *argv[]) {
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// # 4 flops = 2 ADDs, 2 MULTs
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//
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// $\vec{r}_i^{t+1} += \vec{r}^t_i + v^t_idt$
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// $\vec{r}_i^{t+1} = \vec{r}^t_i + v^t_idt$
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//
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p.curr_pos = p.curr_pos + p.curr_vel * dt;
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@@ -157,9 +157,14 @@ int main(int argc, const char *argv[]) {
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// p.destination.y = p.curr_pos.y;
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}
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// # 10 flops = 5 ADDs, 2 DIVs, 1 SQRT, 2 MULTs
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// p.e = (p.destination - p.curr_pos) / (p.destination - p.curr_pos).norm();
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// p.e = (p.curr_vel - p.destination) / (p.curr_vel - p.destination).norm();
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p.e = (p.destination - (p.curr_vel / p.curr_vel.norm())) / (p.destination - (p.curr_vel / p.curr_vel.norm())).norm();
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// if we have a dump path/file we wanna save the data for visualisation
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if (dump_path && (t % dump_interval == 0)) {
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samples.emplace_back(t, i, p.curr_pos.x, p.curr_pos.y, p.color);
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samples.emplace_back(t, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
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}
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}
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}
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@@ -182,9 +187,9 @@ int main(int argc, const char *argv[]) {
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// now we wanna save the data to a csv
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if (dump_path) {
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std::ofstream out(dump_path);
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out << "time,id,x,y,color\n";
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for (auto &[t, id, x, y, c] : samples) {
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out << t << ',' << id << ',' << x << ',' << y << ',' << c << '\n';
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out << "time,id,x,y,p.e.x,p.e.y,color\n";
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for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
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out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
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}
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}
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+1
-1
@@ -14,7 +14,7 @@ struct Pedestrian {
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// $\vec{r}_\alpha(t)$
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Vector2d curr_pos;
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// $\vec{e}_{\alpha}(t)$
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Vector2d e;
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Vector2d e; // desired direction vector
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// $v_{\alpha}^{max} = 1.3 v^{0}_{\alpha}$
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double v_max;
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// $v_\alpha^0$
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Binary file not shown.
+33
-22
@@ -1,52 +1,63 @@
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import sys
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation
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if len(sys.argv)<2:
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sys.exit("Use it like the following: python visualization.py positions.csv (out.gif)")
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if len(sys.argv) < 2:
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sys.exit("Use it like the following: python visualization.py positions.csv (out.mp4)")
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df = pd.read_csv(sys.argv[1])
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frames = sorted(df["time"].unique())
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fig, ax = plt.subplots(figsize = (10,2.2))
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fig, ax = plt.subplots(figsize=(10, 2.2))
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ax.set_xlim(0, 50); ax.set_ylim(0, 10)
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ax.set_aspect("equal"); ax.set_facecolor("white")
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ax.set_xticks([]); ax.set_yticks([])
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for s in ("top", "right", "left", "bottom"):
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ax.spines[s].set_color("gray")
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# Lets define the 2 groups
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# WHITE = 1 (left -> right, drawn as filled black circles, like paper)
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# BLACK = 0 (right -> left, drawn as empty circles)
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s_white = ax.scatter([],[], s=30, facecolors = "black", edgecolor = "black", zorder = 2)
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s_black = ax.scatter([],[], s=30, facecolors = "white", edgecolor = "black", zorder = 2)
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# update our graphic from t to t+1
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s_white = ax.scatter([], [], s=30, facecolors="black", edgecolor="black", zorder=2)
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s_black = ax.scatter([], [], s=30, facecolors="white", edgecolor="black", zorder=2)
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title = ax.set_title("")
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# Keep a reference to quiver objects so we can remove them each frame
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quivers = []
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def update(t):
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sub = df[df["time"] == t]
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w = sub[sub["color"] == 1]
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b = sub[sub["color"] == 0]
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s_white.set_offsets(w[["x", "y"]].values)
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s_black.set_offsets(b[["x", "y"]].values)
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# Remove old quiver arrows
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for q in quivers:
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q.remove()
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quivers.clear()
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# Draw fresh arrows each frame
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arrow_scale = 1.5 # Tune this to change arrow length visually
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if len(w):
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q_white = ax.quiver(w["x"], w["y"], w["p.e.x"], w["p.e.y"],
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color="black", angles="xy", scale_units="xy",
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scale=1/arrow_scale, width=0.003,
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headwidth=4, headlength=5, zorder=3)
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quivers.append(q_white)
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if len(b):
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q_black = ax.quiver(b["x"], b["y"], b["p.e.x"], b["p.e.y"],
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color="gray", angles="xy", scale_units="xy",
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scale=1/arrow_scale, width=0.003,
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headwidth=4, headlength=5, zorder=3)
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quivers.append(q_black)
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title.set_text(f"t = {t}")
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return s_white, s_black, title
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anim = FuncAnimation(fig,update, frames = frames, interval=50, blit=False)
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anim = FuncAnimation(fig, update, frames=frames, interval=50, blit=False)
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if len(sys.argv) >= 3:
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anim.save(sys.argv[2], writer="ffmpeg", fps=20)
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print(f"saved {sys.argv[2]}")
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print(f"Saved {sys.argv[2]}")
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else:
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plt.show()
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