def preprocess_node_types(network, node_population):
NodeAdaptor.preprocess_node_types(network, node_population)
node_type_ids = np.unique(node_population.type_ids)
node_types_table = node_population.types_table
if 'non_dom_params' in node_types_table.columns:
for nt_id in node_type_ids:
node_type = node_types_table[nt_id]
dynamics_params = node_type['non_dom_params']
if isinstance(dynamics_params, dict):
continue
if dynamics_params is None:
continue
params_dir = network.get_component('filter_models_dir')
params_path = os.path.join(params_dir, dynamics_params)
try:
params_val = json.load(open(params_path, 'r'))
node_type['non_dom_params'] = params_val
except Exception:
# TODO: Check dynamics_params before
network.io.log_exception(
'Could not find node dynamics_params file {}.'.format(
params_path))
def preprocess_node_types(network, node_population):
NodeAdaptor.preprocess_node_types(network, node_population)
node_types_table = node_population.types_table
if 'model_template' in node_types_table.columns and 'dynamics_params' in node_types_table.columns:
node_type_ids = np.unique(node_population.type_ids)
for nt_id in node_type_ids:
node_type_attrs = node_types_table[nt_id]
mtemplate = node_type_attrs['model_template']
dyn_params = node_type_attrs['dynamics_params']
if mtemplate.startswith('nest:glif') and dyn_params.get('type', None) == 'GLIF':
node_type_attrs['dynamics_params'] = convert_aibs2nest(mtemplate, dyn_params)
def patch_adaptor(cls, adaptor, node_group, network):
node_adaptor = NodeAdaptor.patch_adaptor(adaptor, node_group, network)
if 'non_dom_params' in node_group.all_columns:
node_adaptor.non_dom_params = types.MethodType(non_dom_params, node_adaptor)
else:
node_adaptor.non_dom_params = types.MethodType(return_none, node_adaptor)
jitter_lower = 'jitter_lower' in node_group.all_columns
jitter_upper = 'jitter_upper' in node_group.all_columns
if jitter_lower and jitter_upper:
node_adaptor.predefined_jitter = True
elif jitter_upper ^ jitter_lower:
raise Exception('Need to define both jitter_lower and jitter_upper (or leave both empty)')
else:
node_adaptor.predefined_jitter = False
if 'tuning_angle' in node_group.all_columns:
node_adaptor.tuning_angle = types.MethodType(tuning_angle_preset, node_adaptor)
else:
node_adaptor.tuning_angle = types.MethodType(tuning_angle_rand, node_adaptor)
find_weight_params(node_group, node_adaptor)
find_kpeaks_params(node_group, node_adaptor)
find_delays_params(node_group, node_adaptor)
find_nondom_weight_params(node_group, node_adaptor)
find_nondom_kpeaks_params(node_group, node_adaptor)
find_nondom_delays_params(node_group, node_adaptor)
return node_adaptor
def patch_adaptor(cls, adaptor, node_group, network):
node_adaptor = NodeAdaptor.patch_adaptor(adaptor, node_group, network)
# Position
if 'positions' in node_group.all_columns:
node_adaptor.position = types.MethodType(positions, adaptor)
elif 'position' in node_group.all_columns:
node_adaptor.position = types.MethodType(position, adaptor)
else:
node_adaptor.position = types.MethodType(positions_default,
adaptor)
# Rotation angles
if 'rotation_angle_xaxis' in node_group.all_columns:
node_adaptor.rotation_angle_xaxis = types.MethodType(
rotation_angle_x, node_adaptor)
else:
node_adaptor.rotation_angle_xaxis = types.MethodType(
rotation_angle_default, node_adaptor)
if 'rotation_angle_yaxis' in node_group.all_columns:
node_adaptor.rotation_angle_yaxis = types.MethodType(
rotation_angle_y, node_adaptor)
else:
node_adaptor.rotation_angle_yaxis = types.MethodType(
rotation_angle_default, node_adaptor)
if 'rotation_angle_zaxis' in node_group.all_columns:
node_adaptor.rotation_angle_zaxis = types.MethodType(
rotation_angle_z, node_adaptor)
else:
node_adaptor.rotation_angle_zaxis = types.MethodType(
rotation_angle_default, node_adaptor)
return node_adaptor
def patch_adaptor(adaptor, node_group, network):
node_adaptor = NodeAdaptor.patch_adaptor(adaptor, node_group, network)
# If dynamics params is stored in the nodes.h5 then we have to build each node separate
if node_group.has_dynamics_params:
node_adaptor.batch_process = False
# If there is a non-null value in the model_processing column then it potentially means that every cell is
# uniquly built (currently model_processing is applied to each individ. cell) and nodes can't be batched
if 'model_processing' in node_group.columns:
node_adaptor.batch_process = False
elif 'model_processing' in node_group.all_columns and not all_null(node_group, 'model_processing'):
node_adaptor.batch_process = False
if node_adaptor.batch_process:
io.log_info('Batch processing nodes for {}/{}.'.format(node_group.parent.name, node_group.group_id))
return node_adaptor