def Refine_CSF(MRI_param,
DTI_param,
Scaling,
Domains,
Field_calc_param,
rel_div,
CSF_frac_div,
CSF_ref_add,
EQS_mode,
cc_multicontact,
ref_freq,
Best_scaling=0,
scaling_old=0):
start_CSF_refinement = tim.clock()
if cc_multicontact == True:
from Math_module_hybrid_floating import compute_field_with_superposition, get_field_on_points
else:
from Math_module_hybrid import get_field, get_field_on_points
scaling_old = float(scaling_old)
Scaling = float(Scaling)
Best_scaling = float(Best_scaling)
if Field_calc_param.anisotropy == 1:
from Tissue_marking_new import get_cellmap_tensors
else:
from Tissue_marking_new import get_cellmap
'''load results and mesh from the most refined iteration if available'''
if Best_scaling != 0:
Phi_amp_on_neuron_old_get = read_csv(os.environ['PATIENTDIR'] +
'/CSF_ref/Field_on_points' +
str(Best_scaling) + '.csv',
delimiter=' ',
header=None)
Phi_amp_on_neuron_old = Phi_amp_on_neuron_old_get.values
mesh = Mesh(os.environ['PATIENTDIR'] + '/CSF_ref/mesh_adapt_CSF' +
str(scaling_old) + '.xml.gz')
boundaries = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' + str(scaling_old) + '.xml')
subdomains_assigned = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(scaling_old) +
'.xml')
else:
if Field_calc_param.external_grounding == False:
mesh = Mesh(os.environ['PATIENTDIR'] + "/Meshes/Mesh_unref.xml")
boundaries = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/Meshes/Mesh_unref_facet_region.xml')
subdomains_assigned = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
"/Meshes/Mesh_unref_physical_region.xml")
else:
mesh = Mesh(os.environ['PATIENTDIR'] +
'/Results_adaptive/mesh_adapt.xml.gz')
boundaries = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/Results_adaptive/boundaries_adapt.xml')
subdomains_assigned = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/Results_adaptive/subdomains_assigned_adapt.xml')
# load neuron compartments
Vertices_neur_get = read_csv(
os.environ['PATIENTDIR'] +
'/Neuron_model_arrays/Vert_of_Neural_model_NEURON.csv',
delimiter=' ',
header=None)
Vertices_neur = Vertices_neur_get.values
if Best_scaling == 0:
mesh_file = File(os.environ['PATIENTDIR'] + '/CSF_ref/mesh_adapt_CSF' +
str(Best_scaling) + '.xml.gz')
boundaries_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' +
str(Best_scaling) + '.xml')
subdomains_assigned_file = File(
os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(Best_scaling) +
'.xml')
mesh_file << mesh
boundaries_file << boundaries
subdomains_assigned_file << subdomains_assigned
print("Field calculation on the initial mesh")
if Field_calc_param.anisotropy == 1:
subdomains = get_cellmap_tensors(mesh, subdomains_assigned,
Domains, MRI_param, DTI_param,
Field_calc_param.default_material)
else:
subdomains = get_cellmap(mesh, subdomains_assigned, Domains,
MRI_param,
Field_calc_param.default_material)
save_mesh_and_subdomains_to_h5(mesh, subdomains, subdomains_assigned,
boundaries, Best_scaling)
print("CSF_Subdomains_unref file was created")
file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/CSF_Subdomains_unref.pvd')
file << subdomains, mesh
if cc_multicontact == True:
Phi_r_old, Phi_im_old, Field_r_old, Field_im_old, max_E_old, J_r_old, J_im_old, j_dens_real, j_dens_im = compute_field_with_superposition(
mesh, Domains, subdomains_assigned, subdomains, boundaries,
Field_calc_param)
else:
Phi_r_old, Phi_im_old, Field_r_old, Field_im_old, max_E_old, J_r_old, J_im_old, j_dens_real, j_dens_im = get_field(
mesh, Domains, subdomains, boundaries, Field_calc_param)
file = File(os.environ['PATIENTDIR'] + '/CSF_ref/Field_r_' +
str(Best_scaling) + '.pvd')
file << Field_r_old
Phi_amp_on_neuron_old = get_field_on_points(Phi_r_old, Phi_im_old,
Field_calc_param.c_c,
J_r_old, J_im_old)
np.savetxt(os.environ['PATIENTDIR'] + '/CSF_ref/Field_on_points' +
str(Best_scaling) + '.csv',
Phi_amp_on_neuron_old,
delimiter=" ")
# load array with the CSF voxels if available, otherwise extract it from the MRI data
if os.path.isfile(os.environ['PATIENTDIR'] + '/MRI_DTI_derived_data/' +
MRI_param.name[:-4] + '_voxel_array_CSF_' +
str(CSF_ref_add) + '.npy') or os.path.isfile(
os.environ['PATIENTDIR'] + '/MRI_DTI_derived_data/' +
MRI_param.name[:-7] + '_voxel_array_CSF_' +
str(CSF_ref_add) + '.npy'):
if MRI_param.name[-2:] == 'gz':
voxel_array_CSF = np.load(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/' +
MRI_param.name[:-7] +
'_voxel_array_CSF_' + str(CSF_ref_add) +
'.npy')
else:
voxel_array_CSF = np.load(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/' +
MRI_param.name[:-4] +
'_voxel_array_CSF_' + str(CSF_ref_add) +
'.npy')
print("voxel_array_CSF in ", str(CSF_ref_add),
" mm vicinity is loaded")
else:
start_voxel_array_CSF = tim.clock()
Tissue_array = np.load(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/Tissue_array_MRI.npy')
x_vect = np.genfromtxt(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/x_vector_MRI_Box.csv',
delimiter=' ')
y_vect = np.genfromtxt(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/y_vector_MRI_Box.csv',
delimiter=' ')
z_vect = np.genfromtxt(os.environ['PATIENTDIR'] +
'/MRI_DTI_derived_data/z_vector_MRI_Box.csv',
delimiter=' ')
voxel_array_CSF = np.zeros(
(Tissue_array.shape[0], 3),
float) #array to store all CSF voxels in the specified ROI
bb = mesh.bounding_box_tree()
#check the extent of the neuron array (loof for CSF only there + vicinity defined by CSF_ref_add)
x_neuron_max = max(Vertices_neur[:, 0])
y_neuron_max = max(Vertices_neur[:, 1])
z_neuron_max = max(Vertices_neur[:, 2])
x_neuron_min = min(Vertices_neur[:, 0])
y_neuron_min = min(Vertices_neur[:, 1])
z_neuron_min = min(Vertices_neur[:, 2])
#go over all voxels and check whether it contains CSF and intersect with the mesh
for x_coord in x_vect:
for y_coord in y_vect:
for z_coord in z_vect:
x_pos = x_coord - MRI_param.x_vox_size / 2.0
y_pos = y_coord - MRI_param.y_vox_size / 2.0
z_pos = z_coord - MRI_param.z_vox_size / 2.0
if (x_pos <= x_neuron_max + CSF_ref_add
and x_pos >= x_neuron_min - CSF_ref_add
and y_pos <= y_neuron_max + CSF_ref_add
and y_pos >= y_neuron_min - CSF_ref_add
and z_pos <= z_neuron_max + CSF_ref_add
and z_pos >= z_neuron_min - CSF_ref_add):
xv_mri = int(
(x_coord) / MRI_param.x_vox_size - 0.000000001
) #defines number of steps to get to the voxels containing x[0] coordinate
yv_mri = (
int((y_coord) / MRI_param.y_vox_size - 0.000000001)
) * MRI_param.M_x #defines number of steps to get to the voxels containing x[0] and x[1] coordinates
zv_mri = (
int((z_coord) / MRI_param.z_vox_size - 0.000000001)
) * MRI_param.M_x * MRI_param.M_y #defines number of steps to get to the voxels containing x[0], x[1] and x[2] coordinates
glob_index = int(xv_mri + yv_mri + zv_mri)
pnt = Point(x_pos, y_pos, z_pos)
if Tissue_array[
glob_index] == 1 and bb.compute_first_entity_collision(
pnt) < mesh.num_cells() * 100:
voxel_array_CSF[glob_index, 0] = x_pos
voxel_array_CSF[glob_index, 1] = y_pos
voxel_array_CSF[glob_index, 2] = z_pos
voxel_array_CSF = voxel_array_CSF[~np.all(
voxel_array_CSF == 0.0, axis=1)] #deletes all zero enteries
if MRI_param.name[-2:] == 'gz':
np.save(
os.environ['PATIENTDIR'] + '/MRI_DTI_derived_data/' +
MRI_param.name[:-7] + '_voxel_array_CSF_' + str(CSF_ref_add),
voxel_array_CSF)
else:
np.save(
os.environ['PATIENTDIR'] + '/MRI_DTI_derived_data/' +
MRI_param.name[:-4] + '_voxel_array_CSF_' + str(CSF_ref_add),
voxel_array_CSF)
del Tissue_array
print(
"----- voxel_array_CSF for ", str(CSF_ref_add),
" mm vicinity was prepared in %s seconds -----" %
(tim.clock() - start_voxel_array_CSF))
'''Here we pre-refine mesh on elements with CSF voxels'''
csf_ref = 0
loaded_from_h5 = 0
print("refining CSF voxels with scaling ", int(Scaling))
num_cell_old = mesh.num_cells()
if os.path.isfile(os.environ['PATIENTDIR'] +
'/CSF_ref/Mesh_to_solve_scaling_' + str(Scaling) +
'.h5'):
print(
"Mesh and tissue mapping was loaded from the refinement at the previous frequency"
)
mesh, subdomains, subdomains_assigned, boundaries = load_mesh_and_subdomains_from_h5(
Scaling)
loaded_from_h5 = 1
elif os.path.isfile(
os.environ['PATIENTDIR'] + '/CSF_ref/mesh_adapt_CSF' +
str(Scaling) + '.xml.gz'
): #this and not the case above could be only triggered if no computations took place at this scalling at the last frequency (because of the same mesh size)
if Best_scaling == 0:
print(
"No elements were refined during the full CSF refinement, skipping to adaptive mesh refinement (consider decreasing Minimum Element to Voxel Ratio)"
)
return 1
print("skipping scaling ", Scaling)
mesh_file = File(os.environ['PATIENTDIR'] + '/CSF_ref/mesh_adapt_CSF' +
str(Scaling) + '.xml.gz')
mesh_file << mesh
boundaries_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' + str(Scaling) +
'.xml')
subdomains_assigned_file = File(
os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(Scaling) + '.xml')
boundaries_file << boundaries
subdomains_assigned_file << subdomains_assigned
return 0
else:
while csf_ref == 0: #refine mesh until get the required edge size to voxel ratio
inx_pnt = 0 #to check, how much voxels were processed
cell_index_list = []
bb = mesh.bounding_box_tree()
for i_csf in range(voxel_array_CSF.shape[0]
): #find cells which contain the CSF voxels
pnt = Point(voxel_array_CSF[i_csf, 0],
voxel_array_CSF[i_csf, 1], voxel_array_CSF[i_csf,
2])
inx_pnt = inx_pnt + 1
cell_index_list.append(bb.compute_first_entity_collision(pnt))
cell_index_array = np.asarray(cell_index_list)
cell_ref = index_cell_marker(mesh, cell_index_array, MRI_param,
Scaling)
if not (
cell_ref.where_equal(True)
): #if any cell was marked for refinement, will return True
csf_ref = 1
mesh_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/mesh_adapt_CSF' + str(Scaling) +
'.xml.gz')
mesh_file << mesh
boundaries_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' +
str(Scaling) + '.xml')
subdomains_assigned_file = File(
os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(Scaling) +
'.xml')
boundaries_file << boundaries
subdomains_assigned_file << subdomains_assigned
print("Number of cells after CSF refinement iteration: ",
mesh.num_cells())
if num_cell_old == mesh.num_cells():
if Best_scaling == 0:
print(
"No elements were refined during the full CSF refinement, skipping to adaptive mesh refinement (consider decreasing Minimum Element to Voxel Ratio)"
)
return 1
print("skipping scaling ", Scaling)
return 0
else:
mesh_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/mesh_adapt_CSF' + str(Scaling) +
'_old.xml.gz')
mesh_file << mesh
boundaries_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' +
str(Scaling) + '_old.xml')
subdomains_assigned_file = File(
os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(Scaling) +
'_old.xml')
boundaries_file << boundaries
subdomains_assigned_file << subdomains_assigned
mesh, boundaries, subdomains_assigned = mesh_refiner(
mesh, boundaries, subdomains_assigned, cell_ref, Domains,
cc_multicontact)
if mesh.num_cells(
) > 10000000: #users can adjust for their hardware
print(
"Mesh is too large, will have to check with bigger scaling"
)
csf_refined = -1
return csf_refined
if loaded_from_h5 == 1:
print("CSF_Subdomains_refinement file with scaling ", int(Scaling),
" was loaded")
else:
if Field_calc_param.anisotropy == 1:
subdomains = get_cellmap_tensors(mesh, subdomains_assigned,
Domains, MRI_param, DTI_param,
Field_calc_param.default_material)
else:
subdomains = get_cellmap(mesh, subdomains_assigned, Domains,
MRI_param,
Field_calc_param.default_material)
print("CSF_Subdomains_refinement file with scaling ", int(Scaling),
" was created")
file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/CSF_Subdomains_refinement_' + str(int(Scaling)) +
'.pvd')
file << subdomains, mesh
save_mesh_and_subdomains_to_h5(mesh, subdomains, subdomains_assigned,
boundaries, Scaling)
if cc_multicontact == True:
Phi_r, Phi_im, Field_r, Field_im, max_E, J_r, J_im, j_dens_real, j_dens_im = compute_field_with_superposition(
mesh, Domains, subdomains_assigned, subdomains, boundaries,
Field_calc_param)
else:
Phi_r, Phi_im, Field_r, Field_im, max_E, J_r, J_im, j_dens_real, j_dens_im = get_field(
mesh, Domains, subdomains, boundaries, Field_calc_param)
if Scaling == 1:
file = File(os.environ['PATIENTDIR'] + '/CSF_ref/Field_r_' +
str(Scaling) + '.pvd')
file << Field_r
print("CSF_Subdomains full refinement was created")
file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/CSF_Subdomains_full_ref.pvd')
file << subdomains, mesh
#import subprocess
#subprocess.call('python Visualization_files/Paraview_CSFref.py', shell=True)
Phi_amp_on_neuron = get_field_on_points(Phi_r, Phi_im,
Field_calc_param.c_c, J_r, J_im)
np.savetxt(os.environ['PATIENTDIR'] + '/CSF_ref/Field_on_points' +
str(Scaling) + '.csv',
Phi_amp_on_neuron,
delimiter=" ")
csf_refined = 1
max_div = 0.0
# define the absolute error threshold
if Field_calc_param.c_c == True:
if EQS_mode == 'EQS':
#not the best approach, but both should be on the Dirichlet BCs
max_phi_r = max(Phi_r.vector()[:])
max_phi_im = max(Phi_im.vector()[:])
min_phi_r = min(Phi_r.vector()[:])
min_phi_im = min(Phi_im.vector()[:])
phi_error = abs(
(np.sqrt((max_phi_r - min_phi_r)**2 +
(max_phi_im - min_phi_im)**2)) * CSF_frac_div)
else:
phi_error = abs((max(Phi_r.vector()[:]) - min(Phi_r.vector()[:])) *
CSF_frac_div) #should be scaled
else:
Phi_vector = [x for x in Domains.fi if x is not None]
if Field_calc_param.external_grounding == True:
Phi_vector.append(0.0)
phi_error = abs(
(max(Phi_vector) - min(Phi_vector)) * CSF_frac_div
) #Absolute potential error defined as a 1% of the maximum potential difference, VC case
# compare solutions on the neuron compartments
for inx in range(Phi_amp_on_neuron_old.shape[0]):
if Best_scaling == 0: #first iteration
delim = abs(Phi_amp_on_neuron[inx, 3])
else:
delim = abs(Phi_amp_on_neuron_old[inx, 3])
if max_div < abs(Phi_amp_on_neuron_old[inx, 3] -
Phi_amp_on_neuron[inx, 3]):
max_div = abs(Phi_amp_on_neuron_old[inx, 3] -
Phi_amp_on_neuron[inx, 3])
if max_div > phi_error:
print("Deviation threshold: ", phi_error, "V")
print("Deviation at least: ", max_div, "V")
print("At point: ", Phi_amp_on_neuron_old[inx, 0],
Phi_amp_on_neuron_old[inx, 1], Phi_amp_on_neuron_old[inx, 2])
print("Need further refinement of CSF")
csf_refined = 0
break
if csf_refined == 1:
mesh_file = File(os.environ['PATIENTDIR'] + '/CSF_ref/mesh_adapt_CSF' +
str(Scaling) + '.xml.gz')
mesh_file << mesh
boundaries_file = File(os.environ['PATIENTDIR'] +
'/CSF_ref/boundaries_adapt_CSF' + str(Scaling) +
'.xml')
subdomains_assigned_file = File(
os.environ['PATIENTDIR'] +
'/CSF_ref/subdomains_assigned_adapt_CSF' + str(Scaling) + '.xml')
boundaries_file << boundaries
subdomains_assigned_file << subdomains_assigned
print("Max. deviation: ", max_div, "V")
print("Deviation threshold: ", phi_error, "V")
print("CSF is refined enough")
del voxel_array_CSF
minutes = int((tim.clock() - start_CSF_refinement) / 60)
secnds = int(tim.clock() - start_CSF_refinement) - minutes * 60
print("----- CSF refinement iteration took ", minutes, " min ", secnds,
" s -----\n")
return csf_refined
def adapt_mesh(region,mesh_initial,boundaries_initial,subdomains_assigned_initial,MRI_param,DTI_param,Domains,d,cc_multicontact,num_proc,anisotropy,Field_calc_param,previous_results):
print("----- Conducting "+region+" -----")
start_adapt_region=time_lib.clock()
save_mesh('initial_for_the_step',mesh_initial,boundaries_initial,subdomains_assigned_initial)
if d["FEniCS_MPI"]==True:
if cc_multicontact==True:
from Math_module_floating_MPI import get_solutions,get_field_on_points
else:
from Math_module_MPI_only_FEniCS import get_solutions,get_field_on_points
else:
if cc_multicontact==True:
from Math_module_hybrid_floating import compute_field_with_superposition,get_field_on_points
else:
from Math_module_hybrid import get_field,get_field_on_points
from CSF_refinement_new import mesh_refiner
if anisotropy==1:
from Tissue_marking_new import get_cellmap_tensors
subdomains=get_cellmap_tensors(mesh_initial,subdomains_assigned_initial,Domains,MRI_param,DTI_param,d["default_material"])
else:
from Tissue_marking_new import get_cellmap
subdomains=get_cellmap(mesh_initial,subdomains_assigned_initial,Domains,MRI_param,d["default_material"])
if d["current_control"]==1: #in this case we need to check current convergence
current_checked=0 #phi_error will be defined after phi evaluation on the initial mesh
else:
current_checked=1
Phi_vector=[x for x in d["Phi_vector"] if x is not None]
if Field_calc_param.external_grounding==True:
Phi_vector.append(0.0)
phi_error=abs((max(Phi_vector)-min(Phi_vector))*d["Adaptive_frac_div"])
if region == 'it_outside_ROI':
ref_mode = 0
elif region == 'it_on_contact':
ref_mode = 1
current_checked=0 #always check current when refining around contacts
if d["current_control"]==0: #in this case we need to check current convergence
d["rel_div_current"]=0.012
print("Although VC mode is used, current convergence will be checked during refinement around contacts with 1.2% rel. deviation. You can change the threshold in Mesh_adaption_hybrid.py")
if d["rel_div"]>=0.01:
d["rel_div"] = d["rel_div"]
else:
d["rel_div"] = 0.01 #always fixed to 1% to avoid flickering
print("Rel. error threshold during refinement around contacts is set to 1% (to discard flickering effect)")
elif region == 'it_in_ROI':
ref_mode = 2
ref_it=1 #first iteration (yes, here we start from 1)
if previous_results[0] == -1: #no previous results available
#Calculate on the initial mesh
if d["FEniCS_MPI"]==True:
save_mesh_and_kappa_to_h5(mesh_initial,subdomains,boundaries_initial,Field_calc_param)
if cc_multicontact==True:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_floating_MPI.py"])
else:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_MPI_only_FEniCS.py"])
Phi_r,Phi_im,Field_real,Field_imag,max_E,J_r,J_im,j_dens_real,j_dens_im=get_solutions(d["EQS_core"],Field_calc_param.frequenc,Field_calc_param.element_order)
else:
if cc_multicontact==True:
[Phi_r,Phi_im,Field_real,Field_imag,max_E,J_r,J_im,j_dens_real,j_dens_im]=compute_field_with_superposition(mesh_initial,Domains,subdomains_assigned_initial,subdomains,boundaries_initial,Field_calc_param)
else:
[Phi_r,Phi_im,Field_real,Field_imag,max_E,J_r,J_im,j_dens_real,j_dens_im]=get_field(mesh_initial,Domains,subdomains,boundaries_initial,Field_calc_param)
Phi_amp_on_neuron =get_field_on_points(Phi_r,Phi_im,d["current_control"],J_r,J_im) #To calculate the second norm of the Field
previous_results = [Phi_r,Phi_im,Field_real,Field_imag,J_r,J_im,j_dens_real,j_dens_im,Phi_amp_on_neuron,max_E]
else:
Phi_r,Phi_im,Field_real,Field_imag,J_r,J_im,j_dens_real,j_dens_im,Phi_amp_on_neuron,max_E=previous_results[:]
if d["current_control"] == True:
if d["EQS_core"]=='EQS':
#not the best approach, but both should be on the Dirichlet BCs
max_phi_r=max(Phi_r.vector()[:])
max_phi_im=max(Phi_im.vector()[:])
min_phi_r=min(Phi_r.vector()[:])
min_phi_im=min(Phi_im.vector()[:])
phi_error=abs((np.sqrt((max_phi_r-min_phi_r)**2+(max_phi_im-min_phi_im)**2))*d["Adaptive_frac_div"]) #should be scaled
else:
phi_error=abs((max(Phi_r.vector()[:])-min(Phi_r.vector()[:]))*d["Adaptive_frac_div"]) #should be scaled
print("Absolute error threshold on neuron compartments: ",phi_error,"V")
# mesh refined uniformly in the specified region by ref_mode
print("\n--- Initial uniform refinement step for "+region)
cells_ref=mark_cells_start(mesh_initial,ref_mode,subdomains_assigned_initial, Domains)
[mesh_new,boundaries_new,subdomains_assigned_new]=mesh_refiner(mesh_initial,boundaries_initial,subdomains_assigned_initial,cells_ref,Domains,cc_multicontact)
#here we start to refine
while (cells_ref.where_equal(True)): # if True, then there are some cells marked for refinement
if anisotropy==1:
subdomains_new=get_cellmap_tensors(mesh_new,subdomains_assigned_new,Domains,MRI_param,DTI_param,d["default_material"])
else:
subdomains_new=get_cellmap(mesh_new,subdomains_assigned_new,Domains,MRI_param,d["default_material"])
if d["FEniCS_MPI"]==True:
save_mesh_and_kappa_to_h5(mesh_new,subdomains_new,boundaries_new,Field_calc_param)
if cc_multicontact == True:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_floating_MPI.py"])
else:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_MPI_only_FEniCS.py"])
Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,max_E_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new=get_solutions(d["EQS_core"],Field_calc_param.frequenc,Field_calc_param.element_order)
else:
if cc_multicontact == True:
[Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,max_E_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new]=compute_field_with_superposition(mesh_new,Domains,subdomains_assigned_new,subdomains_new,boundaries_new,Field_calc_param)
else:
[Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,max_E_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new]=get_field(mesh_new,Domains,subdomains_new,boundaries_new,Field_calc_param)
Phi_amp_on_neuron_new=get_field_on_points(Phi_r_new,Phi_im_new,d["current_control"],J_r_new,J_im_new)
#check E-field convergence first (always, even if only current did not converge)
if ref_it==1: #on the first iteration we will mark cells on the initial mesh
cells_ref=mark_cells(Field_calc_param.external_grounding,mesh_initial,ref_mode,Field_real,Field_imag,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,Phi_amp_on_neuron,max_E_new,d["rel_div"],phi_error,subdomains_assigned_initial,Domains)
else:
cells_ref=mark_cells(Field_calc_param.external_grounding,mesh_new,ref_mode,Field_real,Field_imag,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,Phi_amp_on_neuron,max_E_new,d["rel_div"],phi_error,subdomains_assigned_new,Domains)
if (cells_ref.where_equal(True)):
ref_due_to_phi_dev=1
###### check current convergence if necessary #####
if current_checked==0 and not(cells_ref.where_equal(True)):
print("Checking current convergence for " + region)
if check_current_conv(J_r,J_im,J_r_new,J_im_new,d["rel_div_current"]): # True if current deviation above rel_div_current
if ref_it==1: #marking will be on the initial mesh
cells_ref=mark_cell_loc_J(Field_calc_param.external_grounding,subdomains_assigned_initial,j_dens_real,j_dens_im,j_dens_real_new,j_dens_im_new,mesh_initial,mesh_new,ref_mode,1,Domains,d["rel_div_current"])
elif ref_it==2:
cells_ref=mark_cell_loc_J(Field_calc_param.external_grounding,subdomains_assigned_initial,j_dens_real,j_dens_im,j_dens_real_new,j_dens_im_new,mesh_initial,mesh_new,ref_mode,2,Domains,d["rel_div_current"])
else:
mesh_old = Mesh(os.environ['PATIENTDIR']+"/Results_adaptive/mesh_adapt.xml.gz")
subdomains_assigned_old = MeshFunction('size_t',mesh_old,os.environ['PATIENTDIR']+'/Results_adaptive/subdomains_assigned_adapt.xml')
if ref_due_to_phi_dev==0: #if previous refinement due to current, refine on mesh_new
cells_ref=mark_cell_loc_J(Field_calc_param.external_grounding,subdomains_assigned_old,j_dens_real,j_dens_im,j_dens_real_new,j_dens_im_new,mesh_old,mesh_new,ref_mode,2,Domains,d["rel_div_current"])
else: #else, refine on mesh
cells_ref=mark_cell_loc_J(Field_calc_param.external_grounding,subdomains_assigned_old,j_dens_real,j_dens_im,j_dens_real_new,j_dens_im_new,mesh_old,mesh_new,ref_mode,1,Domains,d["rel_div_current"])
if not (cells_ref.where_equal(True)):
current_checked=1
print("Current did not converge, but nothing marked to refine! Consider decreasing threshold_current_in_element in Mesh_adaptation.py")
else:
if ref_it>2:
if ref_due_to_phi_dev==1: #if this is a refinement due to current right after phi_dev check, refine on mesh (resaved as mesh_new). Otherwise, on mesh_new
Phi_r_new,Phi_im_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,max_E_new=(Phi_r,Phi_im,J_r,J_im,j_dens_real,j_dens_im,Field_real,Field_imag,Phi_amp_on_neuron,max_E)
del mesh_new,boundaries_new,subdomains_assigned_new
mesh_new,boundaries_new,subdomains_assigned_new=load_mesh('adapt')
ref_due_to_phi_dev=0 #the refinement will be conducted due to current deviation
else:
current_checked=1
print("Current converged\n")
if not(cells_ref.where_equal(True)) and ref_it==2: # written on the envelope. if we refine adaptively only once, we want to evaluate mesh that we just refined. Otherwise, we will evaluate already saved mesh
save_mesh('adapt_it2',mesh_new,boundaries_new,subdomains_assigned_new)
Phi_r,Phi_im,J_r,J_im,j_dens_real,j_dens_im,Field_real,Field_imag,Phi_amp_on_neuron,max_E=(Phi_r_new,Phi_im_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,max_E_new)
previous_results = [Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Phi_amp_on_neuron_new,max_E_new]
# if we got local convergence, let us check with uniform refinement again and mark cells, where high deviation occurs
if not (cells_ref.where_equal(True)) and ref_it>1:
print("\n--- Uniform refinement step for "+ region)
if ref_it==2:
mesh,boundaries,subdomains_assigned=load_mesh('adapt_it2')
else:
mesh,boundaries,subdomains_assigned=load_mesh('adapt')
#uniform refinement
cells_ref=mark_cells_start(mesh,ref_mode,subdomains_assigned, Domains)
[mesh_uni,boundaries_uni,subdomains_assigned_uni]=mesh_refiner(mesh,boundaries,subdomains_assigned,cells_ref,Domains,cc_multicontact)
if not(os.path.isfile(os.environ['PATIENTDIR']+'/Results_adaptive/cells_to_ref_after_uni_'+str(mesh_uni.num_cells())+'.h5')): #check whether calculations for this uniform refinement were already conducted
if anisotropy==1:
subdomains_uni=get_cellmap_tensors(mesh_uni,subdomains_assigned_uni,Domains,MRI_param,DTI_param,d["default_material"])
else:
subdomains_uni=get_cellmap(mesh_uni,subdomains_assigned_uni,Domains,MRI_param,d["default_material"])
if d["FEniCS_MPI"]==True:
save_mesh_and_kappa_to_h5(mesh_uni,subdomains_uni,boundaries_uni,Field_calc_param)
if cc_multicontact == True:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_floating_MPI.py"])
else:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_MPI_only_FEniCS.py"])
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=get_solutions(d["EQS_core"],Field_calc_param.frequenc,Field_calc_param.element_order)
else:
if cc_multicontact == True:
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=compute_field_with_superposition(mesh_uni,Domains,subdomains_assigned_uni,subdomains_uni,boundaries_uni,Field_calc_param)
else:
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=get_field(mesh_uni,Domains,subdomains_uni,boundaries_uni,Field_calc_param)
Phi_amp_on_neuron_uni=get_field_on_points(Phi_r_uni,Phi_im_uni,d["current_control"],J_r_uni,J_im_uni)
cells_ref=mark_cells(Field_calc_param.external_grounding,mesh,ref_mode,Field_real,Field_imag,Field_real_uni,Field_imag_uni,Phi_amp_on_neuron_uni,Phi_amp_on_neuron,max_E_uni,d["rel_div"],phi_error,subdomains_assigned,Domains)
hdf = HDF5File(mesh.mpi_comm(), os.environ['PATIENTDIR']+'/Results_adaptive/cells_to_ref_after_uni_'+str(mesh_uni.num_cells())+'.h5', 'w')
hdf.write(cells_ref, "/cells_ref_after_uni")
hdf.close()
np.savetxt(os.environ['PATIENTDIR']+'/Results_adaptive/current_in_uni_ref_'+str(mesh_uni.num_cells())+'.csv', np.array([J_r_uni,J_im_uni]), delimiter=" ")
else: #the field was already computed for this uniform refinement
print("Cells for refinement after uniform check were loaded from the previous iteration\n")
hdf = HDF5File(mesh.mpi_comm(), os.environ['PATIENTDIR']+'/Results_adaptive/cells_to_ref_after_uni_'+str(mesh_uni.num_cells())+'.h5', 'r')
cells_ref = MeshFunction('bool', mesh,3)
hdf.read(cells_ref, "/cells_ref_after_uni")
hdf.close()
[J_r_uni,J_im_uni]=np.genfromtxt(os.environ['PATIENTDIR']+'/Results_adaptive/current_in_uni_ref_'+str(mesh_uni.num_cells())+'.csv', delimiter=' ')
mesh_check=refine(mesh, cells_ref) #we need to check whether it leads to mesh_new
num_cells_mesh_check=mesh_check.num_cells()
file=File(os.environ['PATIENTDIR']+'/Results_adaptive/cells_ref_after_uni.pvd')
file<<cells_ref
del mesh_check
if mesh_new.num_cells()<num_cells_mesh_check or (ref_due_to_phi_dev==0 and mesh_new.num_cells()!=num_cells_mesh_check): #use mesh to refine further (for this you will have to resave it as mesh_new)
Phi_r_new,Phi_im_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,max_E_new=(Phi_r,Phi_im,J_r,J_im,j_dens_real,j_dens_im,Field_real,Field_imag,Phi_amp_on_neuron,max_E)
del mesh_new,boundaries_new,subdomains_assigned_new
hdf = HDF5File(mesh.mpi_comm(), os.environ['PATIENTDIR']+'/Results_adaptive/cells_to_ref_after_uni_'+str(mesh_uni.num_cells())+'.h5', 'r')
cells_ref = MeshFunction('bool', mesh,3)
hdf.read(cells_ref, "/cells_ref_after_uni")
hdf.close()
if ref_it==2:
mesh_new,boundaries_new,subdomains_assigned_new=load_mesh('adapt_it2')
else:
mesh_new,boundaries_new,subdomains_assigned_new=load_mesh('adapt')
elif mesh_new.num_cells()==num_cells_mesh_check and cells_ref.where_equal(True): #if mesh_uni and mesh_new are the same after current-based ref, switch status to phi-based
ref_due_to_phi_dev=1
if check_current_conv(J_r,J_im,J_r_uni,J_im_uni,d["rel_div_current"]):
print("Current ref. will be conducted only if deviation on neuron compartments was high")
current_checked=0
else:
if ref_mode==1: #
if cells_ref.where_equal(True):
print("After uniform check, the current converged, the iteration is complete (only when refining around the contacts)")
cells_ref.set_all(False)
###
#if uniform refinement on mesh revealed deviation, but number of marked cells is <= than in mesh_new, then uniformly refine mesh_new. Do this step only if the last local refinement was due to phi ref. If current ref, refine on mesh (no danger of repetition as we have a different criterion)
if (cells_ref.where_equal(True) and ref_it!=2 and (mesh_new.num_cells()>=num_cells_mesh_check) and ref_due_to_phi_dev==1): #if mesh and mesh_uni have a high deviation in the solution, then create new mesh_uni from mesh_new using uniform refinement (otherwise it might stuck in the loop mesh-mesh_uni-mesh_new-mesh)
cells_ref=mark_cells_start(mesh_new,ref_mode,subdomains_assigned_new, Domains)
print("Deviation is high, now uniformly refining mesh_new")
save_mesh('adapt',mesh_new,boundaries_new,subdomains_assigned_new)
[mesh_uni,boundaries_uni,subdomains_assigned_uni]=mesh_refiner(mesh_new,boundaries_new,subdomains_assigned_new,cells_ref,Domains,cc_multicontact)
if anisotropy==1:
subdomains_uni=get_cellmap_tensors(mesh_uni,subdomains_assigned_uni,Domains,MRI_param,DTI_param,d["default_material"])
else:
subdomains_uni=get_cellmap(mesh_uni,subdomains_assigned_uni,Domains,MRI_param,d["default_material"])
if d["FEniCS_MPI"]==True:
save_mesh_and_kappa_to_h5(mesh_uni,subdomains_uni,boundaries_uni,Field_calc_param)
if cc_multicontact==True:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_floating_MPI.py"])
else:
subprocess.call(["mpirun", "-np", "{}".format(num_proc), "python3", "Math_module_MPI_only_FEniCS.py"])
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=get_solutions(d["EQS_core"],Field_calc_param.frequenc,Field_calc_param.element_order)
else:
if cc_multicontact==True:
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=compute_field_with_superposition(mesh_uni,Domains,subdomains_assigned_uni,subdomains_uni,boundaries_uni,Field_calc_param)
else:
[Phi_r_uni,Phi_im_uni,Field_real_uni,Field_imag_uni,max_E_uni,J_r_uni,J_im_uni,j_dens_real_uni,j_dens_im_uni]=get_field(mesh_uni,Domains,subdomains_uni,boundaries_uni,Field_calc_param)
Phi_amp_on_neuron_uni=get_field_on_points(Phi_r_uni,Phi_im_uni,d["current_control"],J_r_uni,J_im_uni)
#for stability
del mesh_new,boundaries_new,subdomains_assigned_new
mesh_new,boundaries_new,subdomains_assigned_new=load_mesh('adapt')
cells_ref=mark_cells(Field_calc_param.external_grounding,mesh_new,ref_mode,Field_real_new,Field_imag_new,Field_real_uni,Field_imag_uni,Phi_amp_on_neuron_uni,Phi_amp_on_neuron_new,max_E_uni,d["rel_div"],phi_error,subdomains_assigned_new,Domains)
if d["current_control"]==1 or ref_mode==1:
if check_current_conv(J_r_new,J_im_new,J_r_uni,J_im_uni,d["rel_div_current"]):
print("Current ref. will be conducted only if deviation on neuron compartments was high")
current_checked=0
#save field solution
hdf = HDF5File(mesh.mpi_comm(), os.environ['PATIENTDIR']+'/Results_adaptive/cells_to_ref_after_uni_'+str(mesh_uni.num_cells())+'.h5', 'w')
hdf.write(cells_ref, "/cells_ref_after_uni")
hdf.close()
np.savetxt(os.environ['PATIENTDIR']+'/Results_adaptive/current_in_uni_ref_'+str(mesh_uni.num_cells())+'.csv', np.array([J_r_uni,J_im_uni]), delimiter=" ")
if not (cells_ref.where_equal(True)):
previous_results = [Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Phi_amp_on_neuron_new,max_E_new]
#exiting from here if no cells marked, otherwise a new iteration
if not (cells_ref.where_equal(True)):
if ref_it==1:
print("mesh meets the requirements (or no cells were marked for refinement) "+region+" was completed\n")
save_mesh(region,mesh_initial,boundaries_initial,subdomains_assigned_initial)
if ref_mode == 2:
save_mesh('adapt',mesh_initial,boundaries_initial,subdomains_assigned_initial)
elif ref_it==2:
print("mesh_new meets the requirements (or no cells were marked for refinement) "+region+" was completed\n")
mesh_2it,boundaries_2it,subdomains_assigned_2it=load_mesh('adapt_it2')
save_mesh(region,mesh_2it,boundaries_2it,subdomains_assigned_2it)
if ref_mode == 2:
save_mesh('adapt',mesh_initial,boundaries_initial,subdomains_assigned_initial)
else:
print("mesh_new meets the requirements (or no cells were marked for refinement) "+region+" was completed\n")
mesh_adapted,boundaries_adapted,subdomains_assigned_adapted=load_mesh('adapt')
save_mesh(region,mesh_adapted,boundaries_adapted,subdomains_assigned_adapted)
else:
print("--- Local refinement step for "+region)
if ref_it==1:
mesh_initial,boundaries_initial,subdomains_assigned_initial=load_mesh('initial_for_the_step') #for stability
[mesh_new,boundaries_new,subdomains_assigned_new]=mesh_refiner(mesh_initial,boundaries_initial,subdomains_assigned_initial,cells_ref,Domains,cc_multicontact)
print("mesh size after adaptive refinement: ", mesh_new.num_cells())
ref_it=ref_it+1
else:
Phi_r,Phi_im,J_r,J_im,j_dens_real,j_dens_im,Field_real,Field_imag,Phi_amp_on_neuron,max_E=(Phi_r_new,Phi_im_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Field_real_new,Field_imag_new,Phi_amp_on_neuron_new,max_E_new)
previous_results = [Phi_r_new,Phi_im_new,Field_real_new,Field_imag_new,J_r_new,J_im_new,j_dens_real_new,j_dens_im_new,Phi_amp_on_neuron_new,max_E_new]
ref_it=ref_it+1
save_mesh('adapt',mesh_new,boundaries_new,subdomains_assigned_new)
[mesh_new,boundaries_new,subdomains_assigned_new]=mesh_refiner(mesh_new,boundaries_new,subdomains_assigned_new,cells_ref,Domains,cc_multicontact)
minutes=int((time_lib.clock() - start_adapt_region)/60)
secnds=int(time_lib.clock() - start_adapt_region)-minutes*60
print("--- For "+region+" was adapted in ",minutes," min ",secnds," s \n")
status=1 #for now always 1
return previous_results,status
def calculate_in_parallel(d, freq_list, Domains, MRI_param, DTI_param,
anisotropy, number_of_points, cc_multicontact):
start_paral = tm.time()
Field_on_VTA = 0 #temp solution
if d["Full_Field_IFFT"] == 1:
Field_on_VTA = 1
d["Full_Field_IFFT"] = 0
#load adapted mesh
mesh = Mesh(os.environ['PATIENTDIR'] +
'/Results_adaptive/mesh_adapt.xml.gz')
boundaries = MeshFunction(
'size_t', mesh,
os.environ['PATIENTDIR'] + '/Results_adaptive/boundaries_adapt.xml')
subdomains_assigned = MeshFunction(
'size_t', mesh, os.environ['PATIENTDIR'] +
'/Results_adaptive/subdomains_assigned_adapt.xml')
#load the neuron array
Vertices_get = read_csv(
os.environ['PATIENTDIR'] +
'/Neuron_model_arrays/Vert_of_Neural_model_NEURON.csv',
delimiter=' ',
header=None)
Vertices = Vertices_get.values
#load CPE parameters if necessary
CPE_param = [] # just initialization
if d["CPE_activ"] == 1:
CPE_param = [d["K_A"], d["beta"], d["K_A_ground"], d["beta_ground"]]
#load unscaled tensors if DTI data are provided
if anisotropy == 1:
from Tissue_marking_new import get_cellmap_tensors
subdomains = get_cellmap_tensors(
mesh, subdomains_assigned, Domains, MRI_param, DTI_param,
d["default_material"]) #mapping of tissue onto the mesh
#initiating with isotropic tensor
c00 = MeshFunction("double", mesh, 3, 1.0)
c01 = MeshFunction("double", mesh, 3, 0.0)
c02 = MeshFunction("double", mesh, 3, 0.0)
c11 = MeshFunction("double", mesh, 3, 1.0)
c12 = MeshFunction("double", mesh, 3, 0.0)
c22 = MeshFunction("double", mesh, 3, 1.0)
hdf = HDF5File(
mesh.mpi_comm(), os.environ['PATIENTDIR'] +
"/Results_adaptive/Tensors_to_solve_num_el_" +
str(mesh.num_cells()) + ".h5", "r")
hdf.read(c00, "/c00")
hdf.read(c01, "/c01")
hdf.read(c02, "/c02")
hdf.read(c11, "/c11")
hdf.read(c12, "/c12")
hdf.read(c22, "/c22")
hdf.close()
DTI_tensor = [c00, c01, c02, c11, c12, c22]
else:
from Tissue_marking_new import get_cellmap
subdomains = get_cellmap(
mesh, subdomains_assigned, Domains, MRI_param,
d["default_material"]) #mapping of tissue onto the mesh
DTI_tensor = [0, 0, 0, 0, 0, 0] #initiating
print(
"Subdomains file for parallel is saved in Field_solutions/parallel_Subdomains.pvd"
)
file = File(os.environ['PATIENTDIR'] +
'/Field_solutions/parallel_Subdomains.pvd')
file << subdomains, mesh
# choose solver
if d['Solver_Type'] == 'Default':
from Math_module_hybrid import choose_solver_for_me
Solver_type = choose_solver_for_me(
d["EQS_core"], Domains.Float_contacts
) #choses solver basing on the Laplace formulation and whether the floating conductors are used
else:
Solver_type = d['Solver_Type'] # just get the solver directly
print("Solver: ", Solver_type)
#with open(os.devnull, 'w') as FNULL: subprocess.call('python Paraview_adapted.py', shell=True, stdout=FNULL, stderr=subprocess.STDOUT)
i = 0 #frequency index in the frequency list of the signal spectrum
complete_solution = []
# this snippet will check at which frequency the FFEM computations were interrupted
if d["Parallel_comp_interrupted"] == 1:
pack_to_start_after = np.genfromtxt(
os.environ['PATIENTDIR'] +
'/Field_solutions/last_completed_pack.csv',
delimiter=' ')
if pack_to_start_after.size == 1:
rslt = np.where(freq_list == pack_to_start_after)
i = rslt[0][0] + 1
elif pack_to_start_after[-1] == freq_list[-1]:
i = freq_list.shape[0]
print(
"All computations in frequency spectrum were already conducted"
)
else:
rslt = np.where(freq_list == pack_to_start_after[-1])
i = rslt[0][0] + 1
#FFEM calculations are conducted in parallel
while i < freq_list.shape[0]:
proc = []
j = 0 #counter for processes
output = mp.Queue()
freq_pack = []
while j < d["number_of_processors"] and i < freq_list.shape[0]:
sine_freq = freq_list[i]
freq_pack.append(sine_freq)
i = i + 1
[cond_GM, perm_GM] = DielectricProperties(3).get_dielectrics(
sine_freq) #1 for grey matter and so on
[cond_WM,
perm_WM] = DielectricProperties(2).get_dielectrics(sine_freq)
[cond_CSF,
perm_CSF] = DielectricProperties(1).get_dielectrics(sine_freq)
[cond_default, perm_default] = DielectricProperties(
d["default_material"]).get_dielectrics(sine_freq)
[cond_encap, perm_encap] = DielectricProperties(
d["encap_tissue_type"]).get_dielectrics(sine_freq)
cond_encap = cond_encap * d["encap_scaling_cond"]
perm_encap = perm_encap * d["encap_scaling_perm"]
cond_vector = [
cond_default, cond_GM, cond_WM, cond_CSF, cond_encap
]
perm_vector = [
perm_default, perm_GM, perm_WM, perm_CSF, perm_encap
]
Sim_setup = Simulation_setup(
sine_freq, d["freq"], mesh, boundaries, subdomains,
cond_vector, perm_vector, d["el_order"], anisotropy,
d["current_control"], DTI_tensor, d["CPE_activ"], CPE_param,
d["EQS_core"], d["external_grounding"])
import Contact_ground_calc
processes = mp.Process(
target=Contact_ground_calc.compute_fields_from_unit_currents,
args=(Sim_setup, Solver_type, Vertices, Domains, j,
Field_on_VTA, output))
# if cc_multicontact==True:
# import FEM_in_spectrum_multicontact
# processes=mp.Process(target=FEM_in_spectrum_multicontact.solve_Laplace_multicontact,args=(Sim_setup,Solver_type,Vertices,Domains,j,Field_on_VTA,output))
# else:
# import FEM_in_spectrum
# processes=mp.Process(target=FEM_in_spectrum.solve_Laplace,args=(Sim_setup,Solver_type,Vertices,Domains,j,Field_on_VTA,output))
proc.append(processes)
j = j + 1
for p in proc:
p.start()
for p in proc:
p.join()
last_completed_pack = np.asarray(freq_pack)
np.savetxt(os.environ['PATIENTDIR'] +
'/Field_solutions/last_completed_pack.csv',
last_completed_pack,
delimiter=" "
) #to recover the last frequency of FFEM was interrupted
if d["freq"] in freq_pack:
print("Processed frequencies: ")
print(freq_pack)
if d["number_of_processors"] > freq_list.shape[0]:
n_files = freq_list.shape[0]
else:
n_files = d["number_of_processors"]
complete_impedance = np.zeros((freq_list.shape[0], 3), float)
if d["Full_Field_IFFT"] == 1: # for Full IFFT we have to re-sort only impedance results
cnt_freq = 0
for core in range(n_files):
if (d["CPE_activ"] == 1
or d["current_control"] == 1) and cc_multicontact == False:
impedance_get = read_csv(os.environ['PATIENTDIR'] +
'/Field_solutions/Impedance' +
str(core) + '.csv',
delimiter=' ',
header=None)
impedance = impedance_get.values
complete_impedance[cnt_freq:cnt_freq + impedance.shape[0], :]
cnt_freq = cnt_freq + impedance.shape[0]
if (
d["CPE_activ"] == 1 or d["current_control"] == 1
) and cc_multicontact == False: # we calculate impedance with FFEM only for these cases
sorted_impedance = complete_impedance[
complete_impedance[:, 2].argsort(axis=0)] #sort by freq
np.savetxt(os.environ['PATIENTDIR'] +
'/Field_solutions/sorted_impedance.csv',
sorted_impedance,
delimiter=" ")
minutes = int((tm.time() - start_paral) / 60)
secnds = int(tm.time() - start_paral) - minutes * 60
print("----- parallel calculations took ", minutes, " min ", secnds,
" s -----")
return True
else:
inx_compl_sol = 0
complete_solution = np.zeros(
(freq_list.shape[0] * Vertices.shape[0], len(d["Phi_vector"]) + 1),
float)
cnt_freq = 0
for core in range(n_files):
hf = h5py.File(
os.environ['PATIENTDIR'] +
'/Field_solutions/sol_per_contact_cor' + str(core) + '.h5',
'r')
lst = list(hf.keys())
result_total = []
for i in lst:
a = hf.get(i)
a = np.array(a)
result_total.append(a)
result = np.concatenate(result_total)
hf.close()
complete_solution[inx_compl_sol:inx_compl_sol +
result.shape[0], :] = result
inx_compl_sol = inx_compl_sol + result.shape[0]
if (
d["CPE_activ"] == 1 or d["current_control"] == 1
) and cc_multicontact == False: # we calculate impedance with FFEM only for these cases
impedance_get = read_csv(os.environ['PATIENTDIR'] +
'/Field_solutions/Impedance' +
str(core) + '.csv',
delimiter=' ',
header=None)
impedance = impedance_get.values
complete_impedance[cnt_freq:cnt_freq +
impedance.shape[0], :] = impedance
cnt_freq = cnt_freq + impedance.shape[0]
if (
d["CPE_activ"] == 1 or d["current_control"] == 1
) and cc_multicontact == False: # we calculate impedance with FFEM only for these cases
sorted_impedance = complete_impedance[
complete_impedance[:, 2].argsort(axis=0)] #sort by freq
np.savetxt(os.environ['PATIENTDIR'] +
'/Field_solutions/sorted_impedance.csv',
sorted_impedance,
delimiter=" ")
plt.figure(101010)
plt.plot(sorted_impedance[:, 0],
sorted_impedance[:, 1],
marker='o')
#plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,ncol=2, mode="expand", borderaxespad=0.)
### ncol=2, mode="expand", borderaxespad=0.)
plt.xlabel(r'Re(Z), $\Omega$')
plt.ylabel(r'Im(Z), $\Omega$')
plt.grid(True)
plt.savefig(os.environ['PATIENTDIR'] + '/Images/Imp_plot.eps',
format='eps',
dpi=1000)
Ampl_imp = np.zeros(sorted_impedance.shape[0], dtype=float)
for i_fr in range(sorted_impedance.shape[0]):
Ampl_imp[i_fr] = np.sqrt(
sorted_impedance[i_fr, 0] * sorted_impedance[i_fr, 0] +
sorted_impedance[i_fr, 1] * sorted_impedance[i_fr, 1])
plt.figure(2)
plt.plot(sorted_impedance[:, 2], Ampl_imp[:], marker='o')
plt.xscale("log")
plt.xlabel('f, Hz')
plt.ylabel(r'Ampl(Z), $\Omega$')
plt.grid(True)
plt.savefig(os.environ['PATIENTDIR'] + '/Images/Imp_Ampl_plot.eps',
format='eps',
dpi=1000)
minutes = int((tm.time() - start_paral) / 60)
secnds = int(tm.time() - start_paral) - minutes * 60
print("----- Parallel calculations took ", minutes, " min ", secnds,
" s -----\n")
sort_full_solution(d, freq_list, complete_solution, number_of_points)
del complete_solution
if Field_on_VTA == 1:
d["Full_Field_IFFT"] = 1
return True
