from brown.interaction import UFuncWrapper
from brown.brown import BrownIterator
import numpy as np
from collections import deque
from copy import copy
import matplotlib.pyplot as plt
import matplotlib.animation as ani

c = np.array([5, 10, 20, 30, 0, 0, 0, 1, -20, 0, -2, -0.1, 2, 0, 0, 0, 0, 0, 0], dtype=np.float16)
#force_function = UFuncWrapper(0, c)
#interaction2D = UFuncWrapper(1, c)

borders_x = [-100, 100]
borders_y = [-100, 100]
n_particles = 6
frames = 1000

x_coords = np.random.uniform(borders_x[0] / 2, borders_x[1] / 2, n_particles).astype(np.float16)
y_coords = np.random.uniform(borders_y[0] / 2, borders_y[1] / 2, n_particles).astype(np.float16)


x_momenta = np.zeros(n_particles, dtype=np.float16)
y_momenta = np.zeros(n_particles, dtype=np.float16)



fig = plt.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1], frameon=False)
ax.set_xlim(*borders_x)
ax.set_xticks([])
ax.set_ylim(*borders_y)
ax.set_yticks([])

plot, = ax.plot(x_coords, y_coords, "b.")
center_of_mass, = ax.plot(x_coords.mean(), y_coords.mean(), "r-")
center_of_mass_history_x = deque([x_coords.mean()])
center_of_mass_history_y = deque([y_coords.mean()])

brown = BrownIterator(-1, c
			, x_coords, y_coords
			, y_momenta, y_momenta
			, borders_x, borders_y
			, border_dampening=1
			, dt=0.001)

u = iter(brown)

def update(i):
	data = next(u)
	center_of_mass_history_x.append(x_coords.mean())
	center_of_mass_history_y.append(y_coords.mean())

	plot.set_data(*data)
	center_of_mass.set_data(center_of_mass_history_x, center_of_mass_history_y)

animation = ani.FuncAnimation(fig, update, range(frames), interval=1)
plt.show()