added some useful comments
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3
force.py
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force.py
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@ -4,9 +4,12 @@ import matplotlib.pyplot as plt
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from coefficients import c
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from coefficients import c
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# This is the quite unreadable way to create
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# UFuncs with given parameters. FIXME: add this to another module.
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force_function = UFuncWrapper(0, c)
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force_function = UFuncWrapper(0, c)
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potential_function = UFuncWrapper(2, c)
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potential_function = UFuncWrapper(2, c)
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# Plot the force and potential.
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r = np.arange(0, 100, 0.02, dtype=np.float16)
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r = np.arange(0, 100, 0.02, dtype=np.float16)
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f, = plt.plot(r, force_function(r), label="force")
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f, = plt.plot(r, force_function(r), label="force")
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p, = plt.plot(r, potential_function(r), label="potential")
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p, = plt.plot(r, potential_function(r), label="potential")
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19
particles.py
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particles.py
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@ -11,23 +11,34 @@ from coefficients import c
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#force_function = UFuncWrapper(0, c)
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#force_function = UFuncWrapper(0, c)
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#interaction2D = UFuncWrapper(1, c)
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#interaction2D = UFuncWrapper(1, c)
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# Borders for both the plot and the boundary condition
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# (the boundary condition might be deactivated, when creating
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# the BrownIterator).
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borders_x = [-100, 100]
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borders_x = [-100, 100]
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borders_y = [-100, 100]
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borders_y = [-100, 100]
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n_particles = 600
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n_particles = 600
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# Idk, seems to not do anyting.
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frames = 100
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frames = 100
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# Only spawn in 1/x of the borders.
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spawn_restriction = 3
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spawn_restriction = 3
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# Time resolution. Note that setting this to a too
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# high value (i.e. low resolution) will lead to
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# erratic behaviour, because potentials can be skipped.
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dt = 0.1
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dt = 0.1
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c[-1] = dt
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c[-1] = dt
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# Initial positions.
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x_coords = np.random.uniform(borders_x[0] / spawn_restriction, borders_x[1] / spawn_restriction, n_particles).astype(np.float16)
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x_coords = np.random.uniform(borders_x[0] / spawn_restriction, borders_x[1] / spawn_restriction, n_particles).astype(np.float16)
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y_coords = np.random.uniform(borders_y[0] / spawn_restriction, borders_y[1] / spawn_restriction, n_particles).astype(np.float16)
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y_coords = np.random.uniform(borders_y[0] / spawn_restriction, borders_y[1] / spawn_restriction, n_particles).astype(np.float16)
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# Initial momenta are 0.
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x_momenta = np.zeros(n_particles, dtype=np.float16)
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x_momenta = np.zeros(n_particles, dtype=np.float16)
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y_momenta = np.zeros(n_particles, dtype=np.float16)
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y_momenta = np.zeros(n_particles, dtype=np.float16)
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# Prepare the plot, remove axis & stuff.
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fig = plt.figure(figsize=(7, 7))
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fig = plt.figure(figsize=(7, 7))
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ax = fig.add_axes([0, 0, 1, 1], frameon=False)
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ax = fig.add_axes([0, 0, 1, 1], frameon=False)
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ax.set_xlim(*borders_x)
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ax.set_xlim(*borders_x)
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@ -35,22 +46,28 @@ ax.set_xticks([])
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ax.set_ylim(*borders_y)
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ax.set_ylim(*borders_y)
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ax.set_yticks([])
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ax.set_yticks([])
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# Plot the initial values.
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plot, = ax.plot(x_coords, y_coords, "b.")
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plot, = ax.plot(x_coords, y_coords, "b.")
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center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-")
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center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-")
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# Keep track of the center of mass.
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center_of_mass_history_x = deque([x_coords.mean()])
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center_of_mass_history_x = deque([x_coords.mean()])
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center_of_mass_history_y = deque([y_coords.mean()])
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center_of_mass_history_y = deque([y_coords.mean()])
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brown = BrownIterator(-1, c
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brown = BrownIterator(-1, c # Max iterations, simulation parameters.
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, x_coords, y_coords
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, x_coords, y_coords
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, y_momenta, y_momenta
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, y_momenta, y_momenta
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# The boundary condition: reflect at the borders,
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, borders_x, borders_y
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, borders_x, borders_y
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# or just let propagate to infinity.
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#, [], []
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#, [], []
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# Let the border dampen the system, border_dampening < 1 => energy is absorbed.
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, border_dampening=1
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, border_dampening=1
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, dt=dt)
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, dt=dt)
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u = iter(brown)
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u = iter(brown)
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def update(i):
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def update(i):
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# Get the next set of positions.
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data = next(u)
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data = next(u)
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center_of_mass_history_x.append(x_coords.mean())
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center_of_mass_history_x.append(x_coords.mean())
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center_of_mass_history_y.append(y_coords.mean())
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center_of_mass_history_y.append(y_coords.mean())
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