if len(Fiber_names) == 1 and not os.path.exists('/opt/Patient/' +
Name_to_save +
'.h5'): #stupid way
n_Ranviers_per_projection_list = [n_Ranviers_per_projection[0]]
elif len(Fiber_names) == 1 and os.path.exists('/opt/Patient/' +
Name_to_save +
'.h5'): #stupid way
n_Ranviers_per_projection_list = [int(n_Ranviers_per_projection + 0)
] # stupid trick
else:
n_Ranviers_per_projection_list = list(n_Ranviers_per_projection)
axon_dict['n_Ranvier'] = n_Ranviers_per_projection_list
from GUI_tree_files.GUI_tree_files.default_dict import d
d.update(axon_dict)
with open(
'/opt/OSS-DBS/OSS_platform/GUI_tree_files/GUI_tree_files/default_dict.py',
'w') as save_as_dict:
save_as_dict.write('"""@author: trieu,butenko"""\n')
#save_as_dict.write('\n')
save_as_dict.write("d = {\n")
for key in d:
if type(d[key]) != str:
save_as_dict.write(" '{}': {},\n".format(key, d[key]))
else:
save_as_dict.write(" '{}': '{}',\n".format(key, d[key]))
save_as_dict.write("}\n")
#return True
def get_input_from_LeadDBS(settings_location,index_side): # 0 - rhs, 1 - lhs
#index_side=1
#settings_location,index_side=side_and_settings[:]
index_side=int(index_side)
#these are input from Lead-DBS
input_dict = {
'MRI_data_name': "name_MRI.nii.gz", #segmented MRI data
'DTI_data_name': "name_DTI.nii.gz", #scaled tensor data
'CSF_index': 0.0, #index of the tissue in the segmented MRI data
'WM_index': 0.0,
'GM_index': 0.0,
'default_material': 'GM', # GM, WM or CSF
'Electrode_type': 'St_Jude6148',
'Implantation_coordinate_X': -10000000000.0, # in mm in the MRI data space
'Implantation_coordinate_Y': -10000000000.0,
'Implantation_coordinate_Z': -10000000000.0,
'Second_coordinate_X': -10000000000.0,
'Second_coordinate_Y': -10000000000.0,
'Second_coordinate_Z': -10000000000.0,
'Rotation_Z': 0.0, # rotation around the lead axis in degrees
'current_control': 0, # 0 - VC, 1 - CC
'Phi_vector': [None, None, None, None], #Signal vector: give an amplitude. If CC, 0.0 refers to 0 V (ground), other numbers are in A. None is for floating potentials
'Activation_threshold_VTA': 0.0 , # threshold for Astrom VTA (V/mm), compute using the Lead-DBS function.
'Full_Field_IFFT': 0,
'external_grounding': False,
'VTA_from_E': 1,
'VTA_from_divE': 0,
'Stim_side': 0, # 0 - rh, 1 - lh
'Neuron_model_array_prepared': 0,
}
#should add for 'Name_prepared_neuron_array' (you need only the name of the file, not the whole path)
#if 'Name_prepared_neuron_array'!='':
# 'Neuron_model_array_prepared'=1
#the same for the 'Brain_shape_name'
#also we need to choose whether the IFFT will be on neurons or VTA array (currently controlled by 'Full_Field_IFFT')
# and if VTA, then E-field threshold???
#file = tables.open_file('oss-dbs_parameters.mat') #hardwired
#file = tables.open_file('oss-dbs_parameters.mat') #hardwired
print("Input from ",settings_location)
file = h5py.File(str(settings_location), 'r')
#file = h5py.File('oss-dbs_parameters.mat')
#if file.root.settings.current_control[0][0]!=file.root.settings.current_control[0][1]:
if all(~np.isnan(file['settings']['current_control'][0])):
if file['settings']['current_control'][0][0] != file['settings']['current_control'][0][1]:
print("Simultaneous use of VC and CC is not allowed for safety reasons!")
raise SystemExit
input_dict['Stim_side']=index_side
#Phi_vector=file.root.settings.Phi_vector[:,index_side]
Phi_vector=file['settings']['Phi_vector'][:,index_side]
Phi_vector=list(Phi_vector)
import math
for i in range(len(Phi_vector)):
if math.isnan(Phi_vector[i]):
Phi_vector[i]=None
if file['settings']['current_control'][0][0]==1:
input_dict['current_control']=1
Phi_vector=Phi_vector*0.001 # because Lead-DBS uses mA as the input
# print(type(Phi_vector))
if all(v is None for v in Phi_vector):
print("No stimulation defined for this hemisphere")
return -1
input_dict['Phi_vector']=Phi_vector
# convert ascii from Matlab struct to a string
#array_ascii=file.root.settings.MRI_data_name[:]
array_ascii=file['settings']['MRI_data_name'][:]
list_ascii=[]
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
# list_ascii = map(lambda s: s.strip(), list_ascii)
name_split=''.join(chr(i) for i in list_ascii)
input_dict['MRI_data_name']=name_split.rsplit('/',1)[-1]
path_to_patient=name_split.rsplit('/',1)[:-1]
path_to_patient=path_to_patient[0]
# array_ascii=file.root.settings.DTI_data_name[:]
array_ascii=file['settings']['DTI_data_name'][:]
list_ascii=[]
if array_ascii[0]==0:
input_dict['DTI_data_name']=''
else:
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
input_dict['DTI_data_name']=''.join(chr(i) for i in list_ascii)
input_dict['CSF_index']=file['settings']['CSF_index'][0][0]
input_dict['WM_index']=file['settings']['WM_index'][0][0]
input_dict['GM_index']=file['settings']['GM_index'][0][0]
#input_dict['CSF_index']=file.root.settings.CSF_index[0][0]
#input_dict['WM_index']=file.root.settings.WM_index[0][0]
# input_dict['GM_index']=file.root.settings.GM_index[0][0]
#array_ascii=file.root.settings.default_material[:]
array_ascii=file['settings']['default_material'][:]
list_ascii=[]
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
default_material=''.join(chr(i) for i in list_ascii)
if default_material=='GM':
input_dict['default_material']=3
elif default_material=='WM':
input_dict['default_material']=2
elif default_material=='CSF':
input_dict['default_material']=1
else:
print("Unrecognized default material")
#array_ascii=file.root.settings.Electrode_type[:]
array_ascii=file['settings']['Electrode_type'][:]
list_ascii=[]
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
Electrode_type=''.join(chr(i) for i in list_ascii)
if Electrode_type == 'Medtronic 3389':
input_dict['Electrode_type']="Medtronic3389"#1
elif Electrode_type == 'Medtronic 3387':
input_dict['Electrode_type']="Medtronic3387"#1
elif Electrode_type == 'Medtronic 3391':
input_dict['Electrode_type']="Medtronic3391"#1
elif Electrode_type == 'St. Jude Directed 6172 (short)' or Electrode_type == 'St. Jude Directed 6180': #just different marker colors
input_dict['Electrode_type']="St_Jude6180"#1
elif Electrode_type == 'St. Jude Directed 6173 (long)':
input_dict['Electrode_type']="St_Jude6173"#1
elif Electrode_type == 'St. Jude ActiveTip (6142-6145)': # just different tail lenghts, but it does not matter here
input_dict['Electrode_type']="St_Jude6142"
elif Electrode_type == 'St. Jude ActiveTip (6146-6149)': # just different tail lenghts, but it does not matter here
input_dict['Electrode_type']="St_Jude6148" #1
elif Electrode_type == 'Boston Scientific Vercise':
input_dict['Electrode_type']="Boston_Scientific_Vercise" #1
elif Electrode_type == 'Boston Scientific Vercise Directed':
input_dict['Electrode_type']="Boston_Scientific_Vercise_Cartesia" #1
elif Electrode_type == 'PINS Medical L301':
input_dict['Electrode_type']="PINS_L301"
elif Electrode_type == 'PINS Medical L302':
input_dict['Electrode_type']="PINS_L302"
elif Electrode_type == 'PINS Medical L303':
input_dict['Electrode_type']="PINS_L303"
else:
print("The electrode is not yet implemented, but we will be happy to add it. Contact us via [email protected]")
#input_dict['Implantation_coordinate_X'],input_dict['Implantation_coordinate_Y'],input_dict['Implantation_coordinate_Z'] = file.root.settings.Implantation_coordinate[:,index_side]
#input_dict['Second_coordinate_X'],input_dict['Second_coordinate_Y'],input_dict['Second_coordinate_Z'] = file.root.settings.Second_coordinate[:,index_side]
input_dict['Implantation_coordinate_X'],input_dict['Implantation_coordinate_Y'],input_dict['Implantation_coordinate_Z'] = file['settings']['Implantation_coordinate'][:,index_side]
input_dict['Second_coordinate_X'],input_dict['Second_coordinate_Y'],input_dict['Second_coordinate_Z'] = file['settings']['Second_coordinate'][:,index_side]
#input_dict['Rotation_Z']=file.root.settings.Rotation_Z[0][0]
#input_dict['Activation_threshold_VTA']=file.root.settings.Activation_threshold_VTA[0][0]
input_dict['Rotation_Z']=file['settings']['Rotation_Z'][0][0] # this is not implemented, we need to extract rotation angles from markers
input_dict['Activation_threshold_VTA']=file['settings']['Activation_threshold_VTA'][0][0] #threshold is the same for both hemispheres
input_dict['external_grounding']=bool(file['settings']['Case_grounding'][:,index_side][0])
input_dict['Neuron_model_array_prepared']=int(file['settings']['calcAxonActivation'][0][0]) # external model (e.g. from fiber tractograpy)
if input_dict['Neuron_model_array_prepared']!=1:
input_dict['Full_Field_IFFT']=1 # for now we have only these two options
##just testing
#input_dict['Electrode_type']="Boston_Scientific_Vercise"
#input_dict['Phi_vector']=[0.0,-3.0,2.0,None,0.0,-3.0,2.0,None]
# input_dict['current_control']=1
# input_dict['Phi_vector']=[None,0.0005,None,None]
# input_dict['external_grounding']=True
# input_dict['Full_Field_IFFT']=1
# input_dict['Activation_threshold_VTA']: 0.12
from GUI_tree_files.GUI_tree_files.default_dict import d
d.update(input_dict)
with open('GUI_tree_files/GUI_tree_files/default_dict.py', 'w') as save_as_dict:
save_as_dict.write('"""@author: trieu,butenko"""\n')
#save_as_dict.write('\n')
save_as_dict.write("d = {\n")
for key in d:
if type(d[key])!=str:
save_as_dict.write(" '{}': {},\n".format(key, d[key]))
else:
save_as_dict.write(" '{}': '{}',\n".format(key, d[key]))
save_as_dict.write("}\n")
interactive_mode = int(file['settings']['interactiveMode'][0][0])
return path_to_patient,index_side,interactive_mode
def get_input_from_LeadDBS(settings_location, index_side): # 0 - rhs, 1 - lhs
#index_side=1
#settings_location,index_side=side_and_settings[:]
index_side = int(index_side)
#these are input from Lead-DBS
input_dict = {
'MRI_data_name': "name_MRI.nii.gz", #segmented MRI data
'DTI_data_name': "name_DTI.nii.gz", #scaled tensor data
'CSF_index': 0.0, #index of the tissue in the segmented MRI data
'WM_index': 0.0,
'GM_index': 0.0,
'default_material': 'GM', # GM, WM or CSF
'Electrode_type': 'St_Jude6148',
'Implantation_coordinate_X':
-10000000000.0, # in mm in the MRI data space
'Implantation_coordinate_Y': -10000000000.0,
'Implantation_coordinate_Z': -10000000000.0,
'Second_coordinate_X': -10000000000.0,
'Second_coordinate_Y': -10000000000.0,
'Second_coordinate_Z': -10000000000.0,
'Rotation_Z': 0.0, # rotation around the lead axis in degrees
'current_control': 0, # 0 - VC, 1 - CC
'Phi_vector': [
None, None, None, None
], #Signal vector: give an amplitude. If CC, 0.0 refers to 0 V (ground), other numbers are in A. None is for floating potentials
'Activation_threshold_VTA':
0.0, # threshold for Astrom VTA (V/mm), compute using the Lead-DBS function.
'Full_Field_IFFT': 0,
'external_grounding': False,
'VTA_from_E': 1,
'VTA_from_divE': 0,
'Stim_side': 0, # 0 - rh, 1 - lh
'Neuron_model_array_prepared': 0,
'stretch': 1.0,
'number_of_processors': 0,
'Approximating_Dimensions': [80.0, 80.0, 80.0],
'Aprox_geometry_center': [0.0, 0.0, 0.0],
'el_order': 2,
}
print("\nInput from ", settings_location, "\n")
path = os.path.normpath(settings_location)
path.split(os.sep)
print('Patient folder: ', path.split(os.sep)[-5])
patient_folder = path.split(os.sep)[-5]
file = h5py.File(str(settings_location), 'r')
#if file.root.settings.current_control[0][0]!=file.root.settings.current_control[0][1]:
if all(~np.isnan(file['settings']['current_control'][0])):
if file['settings']['current_control'][0][0] != file['settings'][
'current_control'][0][-1]:
print(
"Simultaneous use of VC and CC is not allowed for safety reasons!"
)
raise SystemExit
#IMPORTANT: it is actually not native but scrf!
head_native = file['settings']['headNative'][:, index_side]
y = file['settings']['yMarkerNative'][:, index_side] - head_native
y_postop = y / np.linalg.norm(y)
phi = np.arctan2(-y_postop[0], y_postop[1])
input_dict["Rotation_Z"] = phi * 180.0 / np.pi
input_dict['Stim_side'] = index_side
#Phi_vector=file.root.settings.Phi_vector[:,index_side]
Phi_vector = file['settings']['Phi_vector'][:, index_side]
if file['settings']['current_control'][0][0] == 1 or file['settings'][
'current_control'][0][-1] == 1:
input_dict['current_control'] = 1
Phi_vector = Phi_vector * 0.001 # because Lead-DBS uses mA as the input
input_dict['el_order'] = 3
Phi_vector = list(Phi_vector)
import math
for i in range(len(Phi_vector)):
if math.isnan(Phi_vector[i]):
Phi_vector[i] = None
StimSets = int(file['settings']['stimSetMode'][0][0])
if all(v is None for v in Phi_vector) and StimSets == 0:
print("No stimulation defined for this hemisphere")
return -1
input_dict['Phi_vector'] = Phi_vector
# convert ascii from Matlab struct to a string
#array_ascii=file.root.settings.MRI_data_name[:]
array_ascii = file['settings']['MRI_data_name'][:]
list_ascii = []
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
# list_ascii = map(lambda s: s.strip(), list_ascii)
name_split = ''.join(chr(i) for i in list_ascii)
input_dict['MRI_data_name'] = name_split.rsplit(os.sep, 1)[-1]
path_to_patient = name_split.rsplit(os.sep, 1)[:-1]
path_to_patient = path_to_patient[0]
# array_ascii=file.root.settings.DTI_data_name[:]
array_ascii = file['settings']['DTI_data_name'][:]
list_ascii = []
if array_ascii[0] == 0:
input_dict['DTI_data_name'] = ''
else:
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
input_dict['DTI_data_name'] = ''.join(chr(i) for i in list_ascii)
input_dict['CSF_index'] = file['settings']['CSF_index'][0][0]
input_dict['WM_index'] = file['settings']['WM_index'][0][0]
input_dict['GM_index'] = file['settings']['GM_index'][0][0]
#input_dict['CSF_index']=file.root.settings.CSF_index[0][0]
#input_dict['WM_index']=file.root.settings.WM_index[0][0]
# input_dict['GM_index']=file.root.settings.GM_index[0][0]
#array_ascii=file.root.settings.default_material[:]
array_ascii = file['settings']['default_material'][:]
list_ascii = []
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
default_material = ''.join(chr(i) for i in list_ascii)
if default_material == 'GM':
input_dict['default_material'] = 3
elif default_material == 'WM':
input_dict['default_material'] = 2
elif default_material == 'CSF':
input_dict['default_material'] = 1
else:
print("Unrecognized default material")
#array_ascii=file.root.settings.Electrode_type[:]
array_ascii = file['settings']['Electrode_type'][:]
list_ascii = []
for i in range(array_ascii.shape[0]):
list_ascii.append(array_ascii[i][0])
#list_ascii = map(lambda s: s.strip(), list_ascii)
Electrode_type = ''.join(chr(i) for i in list_ascii)
if Electrode_type == 'Medtronic 3389':
input_dict['Electrode_type'] = "Medtronic3389" #1
normal_array_length = 6.0 # center of the first to the center of the last
elif Electrode_type == 'Medtronic 3387':
normal_array_length = 9.0
input_dict['Electrode_type'] = "Medtronic3387" #1
elif Electrode_type == 'Medtronic 3391':
input_dict['Electrode_type'] = "Medtronic3391" #1
normal_array_length = 21.0
elif Electrode_type == 'St. Jude Directed 6172 (short)' or Electrode_type == 'St. Jude Directed 6180': #just different marker colors
input_dict['Electrode_type'] = "St_Jude6180" #1
normal_array_length = 6.0
elif Electrode_type == 'St. Jude Directed 6173 (long)':
normal_array_length = 9.0
input_dict['Electrode_type'] = "St_Jude6173" #1
elif Electrode_type == 'St. Jude ActiveTip (6142-6145)': # just different tail lenghts, but it does not matter here
input_dict['Electrode_type'] = "St_Jude6142"
normal_array_length = 9.75
elif Electrode_type == 'St. Jude ActiveTip (6146-6149)': # just different tail lenghts, but it does not matter here
input_dict['Electrode_type'] = "St_Jude6148" #1
normal_array_length = 6.75
elif Electrode_type == 'Boston Scientific Vercise':
input_dict['Electrode_type'] = "Boston_Scientific_Vercise" #1
normal_array_length = 6.0 # still the 4th level. BEWARE: Stretch is defined between the 1st and the 4th, but applied between 1st and 8th!!!
normal_array_length = 14.0
elif Electrode_type == 'Boston Scientific Vercise Directed':
input_dict['Electrode_type'] = "Boston_Scientific_Vercise_Cartesia" #1
normal_array_length = 6.0
elif Electrode_type == 'PINS Medical L301':
input_dict['Electrode_type'] = "PINS_L301"
normal_array_length = 6.0
elif Electrode_type == 'PINS Medical L302':
input_dict['Electrode_type'] = "PINS_L302"
normal_array_length = 9.0
elif Electrode_type == 'PINS Medical L303':
input_dict['Electrode_type'] = "PINS_L303"
normal_array_length = 18.0
elif Electrode_type == 'ELAINE Rat Electrode':
input_dict['Electrode_type'] = "AA_rodent_monopolar"
normal_array_length = 1.0 # irrelevant for monopolar electrodes
else:
print(
"The electrode is not yet implemented, but we will be happy to add it. Contact us via [email protected]"
)
input_dict['Implantation_coordinate_X'], input_dict[
'Implantation_coordinate_Y'], input_dict[
'Implantation_coordinate_Z'] = file['settings'][
'Implantation_coordinate'][:, index_side]
input_dict['Second_coordinate_X'], input_dict[
'Second_coordinate_Y'], input_dict['Second_coordinate_Z'] = file[
'settings']['Second_coordinate'][:, index_side]
input_dict['Aprox_geometry_center'] = [
input_dict['Implantation_coordinate_X'],
input_dict['Implantation_coordinate_Y'],
input_dict['Implantation_coordinate_Z']
]
el_array_length = np.sqrt((input_dict['Implantation_coordinate_X'] -
input_dict['Second_coordinate_X'])**2 +
(input_dict['Implantation_coordinate_Y'] -
input_dict['Second_coordinate_Y'])**2 +
(input_dict['Implantation_coordinate_Z'] -
input_dict['Second_coordinate_Z'])**2)
stretch = el_array_length / normal_array_length
if abs(
stretch - 1.0
) < 0.01 or Electrode_type == 'ELAINE Rat Electrode': #1% tolerance or monopolar electrodes
input_dict["stretch"] = 1.0
else:
input_dict["stretch"] = stretch
input_dict['Activation_threshold_VTA'] = file['settings'][
'Activation_threshold_VTA'][0][
0] #threshold is the same for both hemispheres
input_dict['external_grounding'] = bool(
file['settings']['Case_grounding'][:, index_side][0])
input_dict['Neuron_model_array_prepared'] = int(
file['settings']['calcAxonActivation'][0]
[0]) # external model (e.g. from fiber tractograpy)
if input_dict['Neuron_model_array_prepared'] != 1:
input_dict[
'Full_Field_IFFT'] = 1 # for now we have only these two options
from GUI_tree_files.GUI_tree_files.default_dict import d
d.update(input_dict)
with open('GUI_tree_files/GUI_tree_files/default_dict.py',
'w') as save_as_dict:
save_as_dict.write('"""@author: trieu,butenko"""\n')
#save_as_dict.write('\n')
save_as_dict.write("d = {\n")
for key in d:
if type(d[key]) != str:
save_as_dict.write(" '{}': {},\n".format(key, d[key]))
else:
save_as_dict.write(" '{}': '{}',\n".format(key, d[key]))
save_as_dict.write("}\n")
interactive_mode = int(file['settings']['interactiveMode'][0][0])
return path_to_patient, index_side, interactive_mode, patient_folder, StimSets