Merge branch 'master' into use_momentum_changes

This commit is contained in:
Daniel Knüttel 2019-07-16 21:22:22 +02:00
commit a9450179be
4 changed files with 24 additions and 2 deletions

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@ -10,7 +10,7 @@ c = np.array(
, 1 # *c*exp(
, -0.7 # c
, 0 # (r - c))
, 0 # + c*exp(
, +5 # + c*exp(
, -.1 # c
, 2 # (r - c))
, -0 # + c*exp(

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@ -4,9 +4,12 @@ import matplotlib.pyplot as plt
from coefficients import c
# This is the quite unreadable way to create
# UFuncs with given parameters. FIXME: add this to another module.
force_function = UFuncWrapper(0, c)
potential_function = UFuncWrapper(2, c)
# Plot the force and potential.
r = np.arange(0, 100, 0.02, dtype=np.float16)
f, = plt.plot(r, force_function(r), label="force")
p, = plt.plot(r, potential_function(r), label="potential")

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@ -11,23 +11,34 @@ from coefficients import c
#force_function = UFuncWrapper(0, c)
#interaction2D = UFuncWrapper(1, c)
# Borders for both the plot and the boundary condition
# (the boundary condition might be deactivated, when creating
# the BrownIterator).
borders_x = [-100, 100]
borders_y = [-100, 100]
n_particles = 600
# Idk, seems to not do anyting.
frames = 100
# Only spawn in 1/x of the borders.
spawn_restriction = 3
# Time resolution. Note that setting this to a too
# high value (i.e. low resolution) will lead to
# erratic behaviour, because potentials can be skipped.
dt = 0.1
c[-1] = dt
# Initial positions.
x_coords = np.random.uniform(borders_x[0] / spawn_restriction, borders_x[1] / spawn_restriction, n_particles).astype(np.float16)
y_coords = np.random.uniform(borders_y[0] / spawn_restriction, borders_y[1] / spawn_restriction, n_particles).astype(np.float16)
# Initial momenta are 0.
x_momenta = np.zeros(n_particles, dtype=np.float16)
y_momenta = np.zeros(n_particles, dtype=np.float16)
# Prepare the plot, remove axis & stuff.
fig = plt.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1], frameon=False)
ax.set_xlim(*borders_x)
@ -35,22 +46,28 @@ ax.set_xticks([])
ax.set_ylim(*borders_y)
ax.set_yticks([])
# Plot the initial values.
plot, = ax.plot(x_coords, y_coords, "b.")
center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-")
# Keep track of the center of mass.
center_of_mass_history_x = deque([x_coords.mean()])
center_of_mass_history_y = deque([y_coords.mean()])
brown = BrownIterator(-1, c
brown = BrownIterator(-1, c # Max iterations, simulation parameters.
, x_coords, y_coords
, y_momenta, y_momenta
# The boundary condition: reflect at the borders,
, borders_x, borders_y
# or just let propagate to infinity.
#, [], []
# Let the border dampen the system, border_dampening < 1 => energy is absorbed.
, border_dampening=1
, dt=dt)
u = iter(brown)
def update(i):
# Get the next set of positions.
data = next(u)
center_of_mass_history_x.append(x_coords.mean())
center_of_mass_history_y.append(y_coords.mean())

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@ -42,8 +42,10 @@ class BrownIterator(object):
self.py = delta_py + self.py
# XXX: We need the (-1)**i to make the problem
# symmetric.
# FIXME: is this necessary?
self.px[np.isnan(self.px)] = self.speed_of_light * (-1)**self._i
self.py[np.isnan(self.py)] = self.speed_of_light * (-1)**self._i
self._reflect_at_borders()
self.x += self.dt * self.px