def main():
out_folder = hp.create_folder('Release_check')
main_log = open(os.path.join(out_folder, 'Main.log'), 'w+')
for library in args.paths:
while library[-1] == '/':
library = library[:-1]
individual_log = open(os.path.join(out_folder, library.split('')), 'w+')
if not os.path.isdir(library):
individual_log.write('Folder not found\nFAIL')
individual_log.close()
main_log.write("{} folder doesn't exist: Fail")
# Skip because folder doesn't exist
continue
messages = []
# counts lib, db and aocv files
lib_count = hp.count_files(os.path.join(MODELS_FOLDER, '*/.lib'))
db_count = hp.count_files(os.path.join(MODELS_FOLDER, '*/.db'))
aocv_count = hp.count_files(os.path.join(MODELS_FOLDER, '*/.aocv'))
# checks if all dbs are newer than their lib counterparts
lib_files = glob(os.path.join(MODELS_FOLDER, '*/.lib'))
db_files = glob(os.path.join(MODELS_FOLDER, '*/.lib'))
for db in db_files:
for lib in lib_files:
if lib.replace('.lib', '.db') == db:
if os.path.getatime(lib) < os.path.getatime():
messages.append('Error lib is newer than db for {}'.format(lib))
break
def run():
num_classes = 2
image_shape = (160, 576)
data_dir = './data'
runs_dir = os.path.join('./runs', str(time.time()))
helper.create_folder(runs_dir)
tests.test_for_kitti_dataset(data_dir)
# Download pretrained vgg model
helper.maybe_download_pretrained_vgg(data_dir)
with tf.Session() as sess:
epochs = 48
batch_size = 5
correct_label = tf.placeholder(tf.int32,
[None, None, None, num_classes],
name='correct_label')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
# Path to vgg model
vgg_path = os.path.join(data_dir, 'vgg')
# Create function to get batches
get_batches_fn = helper.gen_batch_function(
os.path.join(data_dir, 'data_road/training'), image_shape)
image_input, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(
sess, vgg_path)
output = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out,
num_classes)
logits, train_op, cross_entropy_loss = optimize(
output, correct_label, learning_rate, num_classes)
train_nn(sess, epochs, batch_size, get_batches_fn, train_op,
cross_entropy_loss, image_input, correct_label, keep_prob,
learning_rate)
helper.save_inference_samples(runs_dir, data_dir, sess, image_shape,
logits, keep_prob, image_input)
def main():
# Display Banner
banner.banner()
# Define Target
target = input(Fore.GREEN + "[!] Enter Target IP: ")
# Call Helper and Menu Functions
file_location = h.dict_file()
ssl = h.check_ssl()
folder_name = h.create_folder()
inp = menu.display_menu()
# Start Scan
start.start_scan(file_location, folder_name, ssl, target, inp)
temples['imgUrl_5'] = xls.GetSheetValue(row, col_imgUrl_5)
temples['place'] = xls.GetSheetValue(row, col_place)
temples['surPlace'] = helper.remove_substring(
xls.GetSheetValue(row, col_place))
temples['longBrief'] = helper.capitalize_first(
xls.GetSheetValue(row, col_longBrief)) \
.replace('\t', ' ').replace(' ', ' ')
temples['srcUrl'] = xls.GetSheetValue(row, col_srcUrl)
temples['eparchyUrl'] = xls.GetSheetValue(row, col_eparchyUrl)
temples['abbots'] = helper.capitalize_first(
xls.GetSheetValue(row, col_abbots))
entities.append(temples)
except Exception as e:
print(f'Exception input line in row {row}: {temples}')
raise Exception(e)
helper.clear_folder(helper.get_full_path(root_folder))
helper.create_folder(helper.get_full_path(root_folder))
for i in range(len(entities)):
item = entities[i]
filename = 'file{}.json'.format(i)
filename = helper.get_full_path(os.path.join(root_folder, filename))
helper.save_json(item, filename)
print("Completed read to json files")
exit(0)
cell_path = os.path.join(general_path, file_name_all[:-5])
# load parameters
with file(os.path.join(general_path, file_name_all), 'r') as fid:
all_params = json.load(fid)
cell_param_path = os.path.join(cell_path, 'cell/params', file_name_cell)
with file(cell_param_path, 'r') as fid:
cell_params = json.load(fid)
new_cell_params, results = initiate_all(all_params=all_params, cell_params=cell_params,
sim_neuron=sim_neuron, sim_field=sim_field)
# save results v_ext, dipole, etc
cell_results_path = os.path.join(cell_path, 'cell/results')
hl.create_folder(cell_results_path)
save_file = os.path.join(cell_results_path, file_name_cell[:-5] + '.npz')
if sim_neuron and sim_field:
# simulate all
np.savez(save_file, xx=results['xx'],yy =results['yy'],Q=results['Q'],Q_len=results['Q_len'],
I=results['I'], I_axial=results['I_axial'],v_ext=results['v_ext'],ppt_vecs=results['ppt_vecs'],
seg_coords=results['seg_coords'], v_soma=results['v_soma'])
elif sim_neuron:
# simulate only neuron but not field
np.savez(save_file, I=results['I'], I_axial=results['I_axial'],ppt_vecs=results['ppt_vecs'],
seg_coords=results['seg_coords'], v_soma=results['v_soma'])
else:
# only get the structure of the neuron
np.savez(save_file, ppt_vecs=results['ppt_vecs'], seg_coords=results['seg_coords'])