def prepare_triangles(vol_frac_goal, L, r_d, k_d, d_eq_d):
"""
Prepares a list of truncated prisms from the given distributions.
:param vol_frac_goal: Goal volume fraction.
:param L: Size of window.
:param r_d: Distribution (callback function) for r, truncation size.
:param k_d: Distribution (callback function) for k, elongation factor.
:param d_eq_d: Distribution (callback function) for d, equivalent diameter.
:return: List of prisms which contain random locations and a random r, k, and d.
"""
print("Prepare triangles")
trunc_triangles = list()
total_volume = L**3
volume = 0.
# Make random grains
while volume < vol_frac_goal * total_volume:
midpoint = L * np.random.random(3)
r = r_d()
k = k_d()
d_eq = d_eq_d()
rot_matrix = GeometryTools.random_rotation()
trunc_triangle = tt.TruncatedTriangle(midpoint, rot_matrix, r, k, d_eq)
trunc_triangles.append(trunc_triangle)
volume += trunc_triangle.volume
return trunc_triangles
def prepare_triangles(vol_frac_goal, L, r_min=0.1, r_max=0.4, k_min=0.2, k_max=0.6): """ Prepares random truncated_triangle objects. Grains are generated until the vol_frac_goal is exceeded. Geometry parameters r,k,d and rotation matrix are drawn from uniform distributions. Returns the list of truncated triangles sorted by size. """ print "Prepare triangles" d_eq_min = 0.5 d_eq_max = 2 trunc_triangles = [] total_volume = L**3 volume = 0. # Make random grains while volume/total_volume < vol_frac_goal: midpoint = L*np.random.random(3) r = (r_max-r_min)*np.random.random() + r_min k = (k_max-k_min)*np.random.random() + k_min d_eq = (d_eq_max-d_eq_min)*np.random.random() + d_eq_min rot_matrix = GeometryTools.random_rotation() trunc_triangle = tt.TruncatedTriangle(midpoint, rot_matrix, r, k, d_eq) trunc_triangles.append(trunc_triangle) volume += trunc_triangle.volume # Sort triangles w.r.t. volume, so that large triangles are added to the box first trunc_triangles.sort(key=lambda m: m.volume, reverse=True) return trunc_triangles
