def pdf_convolver_bifurcation(cod_obj, force_drift, force_struc):
'set new vector'
diffxx = force_struc[0]
diffxy = force_struc[1]
diffxz = force_struc[2]
diffyy = force_struc[3]
diffyz = force_struc[4]
diffzz = force_struc[5]
struc_ten = np.array([[diffxx,diffxy,diffxz],[diffxy,diffyy,diffyz],[diffxz,diffyz,diffzz]])
eig = struc_ten_eig(struc_ten, cod_obj.max_eig, cod_obj.min_eig, cod_obj.proxy_reverse_eig)
cov = revers_eig(eig)
coff = np.linalg.solve(eig[1].T,cod_obj.vector_list[-1])
#print(coff)
vector_proj2 = coff[0]*eig[1][0]
vector_proj3 = coff[1]*eig[1][1]
vector_proj23 = (vector_proj2+vector_proj3)/np.linalg.norm((vector_proj2+vector_proj3))
vector_proj23, polar_angle = convert_vector_angle(vector_proj23)
montecarlo_iter = cod_obj.montecarlo_iterations
bifurcation_aperture = 20
vector_a = f*G.sphere_vector(bifurcation_aperture, polar_angle[0], cov, np.array([0,0,0]), montecarlo_iter, offset =cod_obj.branching_angle[0], points_on_sphere = 4000, plot = False)
#vector_a = f*G.sphere_vector(bifurcation_aperture, cod_obj.angle_list[-1], cov, np.array([0,0,0]), montecarlo_iter, offset =cod_obj.bifurcation_angle[0], points_on_sphere = 4000, plot = False)
vector_b = Quaternions.vec_angle_rot(vector_a,np.array([cod_obj.bifurcation_angle[0]*2]),np.cross(vector_a,cod_obj.vector_list[-1]))
'new unit vector and its sherical coordinates for growth direction'
branch_vector_a, branch_angle_a = convert_vector_angle(vector_a)
vector_b =vector_b+np.random.rand(3)*0.1*np.array([np.random.choice([-1,1]),np.random.choice([-1,1]),np.random.choice([-1,1])])
vector_b = vector_b/np.linalg.norm(vector_b)
branch_vector_b, branch_angle_b = convert_vector_angle(vector_b)
return branch_vector_a, branch_angle_a, branch_vector_b, branch_angle_b
def start_pos_orientation():
start_angle_list = np.array([np.random.rand(2)*np.array([180,360]) \
for i in range(4)])
start_pos_list = np.array([np.random.rand(3)*np.array([100,100,100]\
) + np.array([78,78,78]) for i in range(4)])
start_pos_list = np.array([[125, 125, 27], [125, 125, 223], [125, 27, 125],
[125, 223, 125], [27, 125, 125],
[223, 125, 125]])
start_angle_list = np.array([[0, 0], [0, 0], [90, 90], [90, 90], [90, 0],
[90, 0]])
start_pos_list = np.array([ #[42,42,85],[127,42,85],[212,42,85],
[42, 127, 85],
[127, 127, 85],
[212, 127, 85],
#[42,212,85],[127,212,85],[212,212,85],
#[42,42,170],[127,42,170],[212,42,170],
[42, 127, 170],
[127, 127, 170],
[212, 127, 170],
#[42,212,170],[127,212,170],[212,212,170]
])
start_angle_list = np.array([ #[0,0],[0,0],[0,0],
#[0,0],[0,0],[0,0],
#[0,0],[0,0],[0,0],
#[0,0],[0,0],[0,0],
[0, 0],
[0, 0],
[0, 0],
[0, 0],
[0, 0],
[0, 0]
])
start_pos_list = np.array([[124, 124, 124]])
start_angle_list = np.array([[0, 0]])
start_pos, start_angle,\
common_node = EsAG.start_point_generator(start_pos_list,
start_angle_list,
aperture = 180, offset = 0,
points_on_sphere = 30,
metric = np.array([[100,100,100]]),
common_starting_node = False,
tangent_orientation = 'pol')
return start_pos, start_angle, common_node
def pdf_convolver(cod_obj, force_drift, force_struc):
'Drift vector components'
'Drift signal//all directions'
driftx, drifty, driftz = force_drift
'Drift mean µ-vector'
drift_mu = np.array([driftx, drifty, driftz])
if cod_obj.max_drift != 0:
drift_mu = drift_mu / cod_obj.max_drift
#drift_mu = - drift_mu #to move away from borders instead getting attracted
'Drift/shift covariance //SIGMA'
struc_ten_drift = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) * 0.01
'Structure tensor components'
'Diffusion metric // Diffusive tensor components'
diffxx = force_struc[0]
diffxy = force_struc[1]
diffxz = force_struc[2]
diffyy = force_struc[3]
diffyz = force_struc[4]
diffzz = force_struc[5]
'Diffusion mena µ-vector'
diff_mu = np.array([0, 0, 0])
'Diffuison covariance //SIGMA'
struc_ten_diff = np.array([[diffxx, diffxy, diffxz],
[diffxy, diffyy, diffyz],
[diffxz, diffyz, diffzz]])
'Inverted length scales for real space opjects'
eigv_diff = struc_ten_eig(struc_ten_diff, cod_obj.max_eig, cod_obj.min_eig,
cod_obj.proxy_reverse_eig)
struc_ten_diff = revers_eig(eigv_diff)
'Correlation mean µ-vector'
corr_mu = cod_obj.vec_mem
'Correlation covariance //SIGMA'
struc_ten_corr = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) * 0.01
"""Proxy pdf parameter"""
if cod_obj.proxy_drift == True:
drift_mu = np.array([0, 0, 0]) #Proxy drift mean vector drift_mu
if cod_obj.proxy_tensor == True:
struc_ten_diff = np.array([[0.5, 0, 0], [0, 0.5, 0],
[0, 0, 1]]) #Proxy diffusion tensor
if cod_obj.proxy_corr == True:
corr_mu = np.array([0, 0, 0]) #Proxy correlation mean vector corr_mu
"""Calculate all sums:"""
'Sum of all three covariances'
struc_ten_sum = struc_ten_drift + struc_ten_diff + struc_ten_corr
'Sum of all three means'
shift_mu = drift_mu + diff_mu + corr_mu
"""
A Monte Carlo Simulation draws the new direction from a distribution
constructed out of the covarinaces and means. The cones internal parameter
like the aperture restricts the new growth direction inside a solid angle.
"""
new_posvec = f*G.sphere_vector(cod_obj.aperture,
cod_obj.angle_list[-1],
struc_ten_sum,
shift_mu,
cod_obj.montecarlo_iterations,
offset=0,
points_on_sphere=2000,
plot=False)
return new_posvec / np.linalg.norm(new_posvec)