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added option to draw arrows

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This commit is contained in:
Daniel Knüttel 2019-07-31 12:05:33 +02:00
parent b7f276a8dd
commit 6927e8cf76
1 changed files with 21 additions and 5 deletions
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26
particles.py
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@ -16,17 +16,20 @@ from coefficients import c
# the BrownIterator). # the BrownIterator).
borders_x = [-100, 100] borders_x = [-100, 100]
borders_y = [-100, 100] borders_y = [-100, 100]
n_particles = 600 n_particles = 60
# Idk, seems to not do anyting. # Idk, seems to not do anyting.
frames = 100 frames = 100
# Only spawn in 1/x of the borders. # Only spawn in 1/x of the borders.
spawn_restriction = 3 spawn_restriction = 2
# Time resolution. Note that setting this to a too # Time resolution. Note that setting this to a too
# high value (i.e. low resolution) will lead to # high value (i.e. low resolution) will lead to
# erratic behaviour, because potentials can be skipped. # erratic behaviour, because potentials can be skipped.
dt = 0.1 dt = 0.01
c[-1] = dt c[-1] = dt
# Draw arrows, makes the simulation fucking slow.
draw_arrows = True
# Initial positions. # Initial positions.
x_coords = np.random.uniform(borders_x[0] / spawn_restriction, borders_x[1] / spawn_restriction, n_particles).astype(np.float32) x_coords = np.random.uniform(borders_x[0] / spawn_restriction, borders_x[1] / spawn_restriction, n_particles).astype(np.float32)
y_coords = np.random.uniform(borders_y[0] / spawn_restriction, borders_y[1] / spawn_restriction, n_particles).astype(np.float32) y_coords = np.random.uniform(borders_y[0] / spawn_restriction, borders_y[1] / spawn_restriction, n_particles).astype(np.float32)
@ -49,6 +52,12 @@ ax.set_yticks([])
# Plot the initial values. # Plot the initial values.
plot, = ax.plot(x_coords, y_coords, "b.") plot, = ax.plot(x_coords, y_coords, "b.")
center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-") center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-")
if(draw_arrows):
arrows = [ plt.Arrow(x, y, dx, dy) for x, y, dx, dy in zip(x_coords, y_coords, x_momenta, y_momenta)]
for arrow in arrows:
ax.add_patch(arrow)
# Keep track of the center of mass. # Keep track of the center of mass.
center_of_mass_history_x = deque([x_coords.mean()]) center_of_mass_history_x = deque([x_coords.mean()])
center_of_mass_history_y = deque([y_coords.mean()]) center_of_mass_history_y = deque([y_coords.mean()])
@ -57,9 +66,9 @@ brown = BrownIterator(-1, c # Max iterations, simulation parameters.
, x_coords, y_coords , x_coords, y_coords
, y_momenta, y_momenta , y_momenta, y_momenta
# The boundary condition: reflect at the borders, # The boundary condition: reflect at the borders,
#, borders_x, borders_y , borders_x, borders_y
# or just let propagate to infinity. # or just let propagate to infinity.
, [], [] #, [], []
# Let the border dampen the system, border_dampening < 1 => energy is absorbed. # Let the border dampen the system, border_dampening < 1 => energy is absorbed.
, border_dampening=1 , border_dampening=1
, dt=dt) , dt=dt)
@ -67,6 +76,7 @@ brown = BrownIterator(-1, c # Max iterations, simulation parameters.
u = iter(brown) u = iter(brown)
def update(i): def update(i):
global arrows
# Get the next set of positions. # Get the next set of positions.
data = next(u) data = next(u)
center_of_mass_history_x.append(x_coords.mean()) center_of_mass_history_x.append(x_coords.mean())
@ -74,6 +84,12 @@ def update(i):
plot.set_data(*data) plot.set_data(*data)
center_of_mass.set_data(center_of_mass_history_x, center_of_mass_history_y) center_of_mass.set_data(center_of_mass_history_x, center_of_mass_history_y)
if(draw_arrows):
for arrow in arrows:
ax.patches.remove(arrow)
arrows = [plt.Arrow(x, y, dx, dy) for x, y, dx, dy in zip(data[0], data[1], u.px, u.py)]
for arrow in arrows:
ax.add_patch(arrow)
animation = ani.FuncAnimation(fig, update, range(frames), interval=1) animation = ani.FuncAnimation(fig, update, range(frames), interval=1)
plt.show() plt.show()