added a nice way to tweak the simulation

coefficients.py

@@ -0,0 +1,23 @@
+import numpy as np
+c = np.array(
+		[ 15      # (x**0
+		, 1      #  x**1
+		, -6     #  x**2
+		, 0      #  x**3
+		, 0      #  x**4
+		, 0      #  x**5
+		, 0      #  x**6)
+		, 1      #       *c*exp(
+		, -1     #              c
+		, 0      #               (r - c))
+		, -1     # + c*exp(
+		, -.1    #         c
+		, 2      #          (r - c))
+		, -4     # + c*exp(
+		, -0.05  #         c
+		, 40     #          (r - c)**2)
+		, 10     # + c*exp(
+		, -0.05  #         c
+		, 20]    #          (r - c)**2)
+		, dtype=np.float16)
+

force.py

@@ -0,0 +1,11 @@
+from brown.interaction import UFuncWrapper
+import numpy as np
+import matplotlib.pyplot as plt
+
+from coefficients import c
+
+force_function = UFuncWrapper(0, c)
+
+r = np.arange(0, 100, 0.02, dtype=np.float16)
+plt.plot(r, force_function(r))
+plt.show()

particles.py

@@ -6,17 +6,19 @@ 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)
+from coefficients import c
+
 #force_function = UFuncWrapper(0, c)
 #interaction2D = UFuncWrapper(1, c)
 
 borders_x = [-100, 100]
 borders_y = [-100, 100]
-n_particles = 6
+n_particles = 600
 frames = 1000
+spawn_restriction = 1.1
 
-x_coords = np.random.uniform(borders_x[0] / 2, borders_x[1] / 2, n_particles).astype(np.float16)
+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] / 2, borders_y[1] / 2, 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)
 
 
 x_momenta = np.zeros(n_particles, dtype=np.float16)
@@ -41,7 +43,7 @@ brown = BrownIterator(-1, c
 			, y_momenta, y_momenta
 			, borders_x, borders_y
 			, border_dampening=1
-			, dt=0.001)
+			, dt=0.0001)
 
 u = iter(brown)