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New example: ContinuumSnakeWithLiftingWaveCase
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examples/ContinuumSnakeWithLiftingWaveCase/continuum_snake_postprocessing.py
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import numpy as np | ||
from matplotlib import pyplot as plt | ||
from matplotlib.colors import to_rgb | ||
from tqdm import tqdm | ||
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def plot_snake_velocity( | ||
plot_params: dict, | ||
period, | ||
filename="slithering_snake_velocity.png", | ||
SAVE_FIGURE=False, | ||
): | ||
time_per_period = np.array(plot_params["time"]) / period | ||
avg_velocity = np.array(plot_params["avg_velocity"]) | ||
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[ | ||
velocity_in_direction_of_rod, | ||
velocity_in_rod_roll_dir, | ||
_, | ||
_, | ||
] = compute_projected_velocity(plot_params, period) | ||
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fig = plt.figure(figsize=(10, 8), frameon=True, dpi=150) | ||
plt.rcParams.update({"font.size": 16}) | ||
ax = fig.add_subplot(111) | ||
ax.grid(visible=True, which="minor", color="k", linestyle="--") | ||
ax.grid(visible=True, which="major", color="k", linestyle="-") | ||
ax.plot( | ||
time_per_period[:], velocity_in_direction_of_rod[:, 2], "r-", label="forward" | ||
) | ||
ax.plot( | ||
time_per_period[:], | ||
velocity_in_rod_roll_dir[:, 0], | ||
c=to_rgb("xkcd:bluish"), | ||
label="lateral", | ||
) | ||
ax.plot(time_per_period[:], avg_velocity[:, 1], "k-", label="normal") | ||
ax.set_ylabel("Velocity [m/s]", fontsize=16) | ||
ax.set_xlabel("Time [s]", fontsize=16) | ||
fig.legend(prop={"size": 20}) | ||
plt.show() | ||
# Be a good boy and close figures | ||
# https://stackoverflow.com/a/37451036 | ||
# plt.close(fig) alone does not suffice | ||
# See https://github.com/matplotlib/matplotlib/issues/8560/ | ||
plt.close(plt.gcf()) | ||
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if SAVE_FIGURE: | ||
fig.savefig(filename) | ||
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def plot_video( | ||
plot_params: dict, | ||
video_name="video.mp4", | ||
fps=15, | ||
xlim=(0, 4), | ||
ylim=(-1, 1), | ||
): # (time step, x/y/z, node) | ||
import matplotlib.animation as manimation | ||
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positions_over_time = np.array(plot_params["position"]) | ||
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print("plot video") | ||
FFMpegWriter = manimation.writers["ffmpeg"] | ||
metadata = dict(title="Movie Test", artist="Matplotlib", comment="Movie support!") | ||
writer = FFMpegWriter(fps=fps, metadata=metadata) | ||
fig = plt.figure(figsize=(10, 8), frameon=True, dpi=150) | ||
ax = fig.add_subplot(111) | ||
ax.set_xlim(*xlim) | ||
ax.set_ylim(*ylim) | ||
ax.set_xlabel("z [m]", fontsize=16) | ||
ax.set_ylabel("x [m]", fontsize=16) | ||
rod_lines_2d = ax.plot(positions_over_time[0][0], positions_over_time[0][1])[0] | ||
# plt.axis("equal") | ||
with writer.saving(fig, video_name, dpi=150): | ||
for time in tqdm(range(1, len(plot_params["time"]))): | ||
rod_lines_2d.set_xdata(positions_over_time[time][0]) | ||
rod_lines_2d.set_ydata(positions_over_time[time][1]) | ||
writer.grab_frame() | ||
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# Be a good boy and close figures | ||
# https://stackoverflow.com/a/37451036 | ||
# plt.close(fig) alone does not suffice | ||
# See https://github.com/matplotlib/matplotlib/issues/8560/ | ||
plt.close(plt.gcf()) | ||
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def compute_projected_velocity(plot_params: dict, period): | ||
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time_per_period = np.array(plot_params["time"]) / period | ||
avg_velocity = np.array(plot_params["avg_velocity"]) | ||
center_of_mass = np.array(plot_params["center_of_mass"]) | ||
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# Compute rod velocity in rod direction. We need to compute that because, | ||
# after snake starts to move it chooses an arbitrary direction, which does not | ||
# have to be initial tangent direction of the rod. Thus we need to project the | ||
# snake velocity with respect to its new tangent and roll direction, after that | ||
# we will get the correct forward and lateral speed. After this projection | ||
# lateral velocity of the snake has to be oscillating between + and - values with | ||
# zero mean. | ||
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# Number of steps in one period. | ||
period_step = int(1.0 / (time_per_period[-1] - time_per_period[-2])) | ||
number_of_period = int(time_per_period[-1]) | ||
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# Center of mass position averaged in one period | ||
center_of_mass_averaged_over_one_period = np.zeros((number_of_period - 2, 3)) | ||
for i in range(1, number_of_period - 1): | ||
# position of center of mass averaged over one period | ||
center_of_mass_averaged_over_one_period[i - 1] = np.mean( | ||
center_of_mass[(i + 1) * period_step : (i + 2) * period_step] | ||
- center_of_mass[(i + 0) * period_step : (i + 1) * period_step], | ||
axis=0, | ||
) | ||
# Average the rod directions over multiple periods and get the direction of the rod. | ||
direction_of_rod = np.mean(center_of_mass_averaged_over_one_period, axis=0) | ||
direction_of_rod /= np.linalg.norm(direction_of_rod, ord=2) | ||
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# Compute the projected rod velocity in the direction of the rod | ||
velocity_mag_in_direction_of_rod = np.einsum( | ||
"ji,i->j", avg_velocity, direction_of_rod | ||
) | ||
velocity_in_direction_of_rod = np.einsum( | ||
"j,i->ji", velocity_mag_in_direction_of_rod, direction_of_rod | ||
) | ||
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# Get the lateral or roll velocity of the rod after subtracting its projected | ||
# velocity in the direction of rod | ||
velocity_in_rod_roll_dir = avg_velocity - velocity_in_direction_of_rod | ||
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# Compute the average velocity over the simulation, this can be used for optimizing snake | ||
# for fastest forward velocity. We start after first period, because of the ramping up happens | ||
# in first period. | ||
average_velocity_over_simulation = np.mean( | ||
velocity_in_direction_of_rod[period_step * 2 :], axis=0 | ||
) | ||
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return ( | ||
velocity_in_direction_of_rod, | ||
velocity_in_rod_roll_dir, | ||
average_velocity_over_simulation[2], | ||
average_velocity_over_simulation[0], | ||
) | ||
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def plot_curvature( | ||
plot_params: dict, | ||
rest_lengths, | ||
period, | ||
save_fig=False, | ||
filename="continuum_snake_curvature", | ||
): | ||
s = np.cumsum(rest_lengths) | ||
L0 = s[-1] | ||
s = s / L0 | ||
s = s[:-1].copy() | ||
x = np.linspace(0, 1, 100) | ||
curvature = np.array(plot_params["curvature"]) | ||
time = np.array(plot_params["time"]) | ||
peak_time = period * 0.125 | ||
dt = time[1] - time[0] | ||
peak_idx = int(peak_time / (dt)) | ||
plt.rcParams.update({"font.size": 16}) | ||
fig = plt.figure(figsize=(10, 8), frameon=True, dpi=150) | ||
ax = fig.add_subplot(111) | ||
try: | ||
for i in range(peak_idx * 8, peak_idx * 8 * 2, peak_idx): | ||
ax.plot(s, curvature[i, 0, :] * L0, "k") | ||
except: | ||
print("Simulation time not long enough to plot curvature") | ||
ax.plot( | ||
x, 7 * np.cos(2 * np.pi * x - 0.80), "--", label="stereotypical snake curvature" | ||
) | ||
ax.set_ylabel(r"$\kappa$", fontsize=16) | ||
ax.set_xlabel("s", fontsize=16) | ||
ax.set_xlim(0, 1) | ||
ax.set_ylim(-10, 10) | ||
fig.legend(prop={"size": 16}) | ||
plt.show() | ||
if save_fig: | ||
fig.savefig(filename) | ||
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# Be a good boy and close figures | ||
# https://stackoverflow.com/a/37451036 | ||
# plt.close(fig) alone does not suffice | ||
# See https://github.com/matplotlib/matplotlib/issues/8560/ | ||
plt.close(plt.gcf()) |
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