def init_biophys_cell(env, pop_name, gid, load_connections=True, register_cell=True, write_cell=False,
cell_dict={}):
"""
Instantiates a BiophysCell instance and all its synapses.
:param env: an instance of env.Env
:param pop_name: population name
:param gid: gid
"""
rank = int(env.pc.id())
## Determine if a mechanism configuration file exists for this cell type
if 'mech_file_path' in env.celltypes[pop_name]:
mech_dict = env.celltypes[pop_name]['mech_dict']
else:
mech_dict = None
## Determine if correct_for_spines flag has been specified for this cell type
synapse_config = env.celltypes[pop_name]['synapses']
if 'correct_for_spines' in synapse_config:
correct_for_spines_flag = synapse_config['correct_for_spines']
else:
correct_for_spines_flag = False
## Determine presynaptic populations that connect to this cell type
presyn_names = env.projection_dict[pop_name]
## Load cell gid and its synaptic attributes and connection data
cell = load_biophys_cell(env, pop_name, gid, mech_dict=mech_dict, \
correct_for_spines=correct_for_spines_flag, \
load_connections=load_connections,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dicts=cell_dict.get('weight', None))
if register_cell:
cells.register_cell(env, pop_name, gid, cell)
cells.report_topology(cell, env)
env.cell_selection[pop_name] = [gid]
if is_interactive:
context.update(locals())
if write_cell:
write_selection_file_path = "%s/%s_%d.h5" % (env.results_path, env.modelName, gid)
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
io_utils.write_cell_selection(env, write_selection_file_path)
if load_connections:
io_utils.write_connection_selection(env, write_selection_file_path)
return cell
def init_biophys_cell(env,
pop_name,
gid,
load_weights=True,
load_connections=True,
register_cell=True,
write_cell=False,
validate_tree=True,
cell_dict={}):
"""
Instantiates a BiophysCell instance and all its synapses.
:param env: an instance of env.Env
:param pop_name: population name
:param gid: gid
:param load_connections: bool
:param register_cell: bool
:param validate_tree: bool
:param write_cell: bool
:param cell_dict: dict
Environment can be instantiated as:
env = Env(config_file, template_paths, dataset_prefix, config_prefix)
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
"""
rank = int(env.pc.id())
## Determine template name for this cell type
template_name = env.celltypes[pop_name]['template']
## Determine if a mechanism configuration file exists for this cell type
if 'mech_file_path' in env.celltypes[pop_name]:
mech_dict = env.celltypes[pop_name]['mech_dict']
else:
mech_dict = None
## Determine if correct_for_spines flag has been specified for this cell type
synapse_config = env.celltypes[pop_name]['synapses']
if 'correct_for_spines' in synapse_config:
correct_for_spines_flag = synapse_config['correct_for_spines']
else:
correct_for_spines_flag = False
## Load cell gid and its synaptic attributes and connection data
if template_name.lower() == 'izhikevich':
cell = cells.make_izhikevich_cell(
env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections)
elif template_name.lower() == 'pr_nrn':
cell = cells.make_PR_cell(env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get(
'connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections)
else:
cell = cells.make_biophys_cell(
env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections,
validate_tree=validate_tree)
cells.init_biophysics(cell,
reset_cable=True,
correct_cm=correct_for_spines_flag,
correct_g_pas=correct_for_spines_flag,
env=env)
synapses.init_syn_mech_attrs(cell, env)
if register_cell:
cells.register_cell(env, pop_name, gid, cell)
is_reduced = False
if hasattr(cell, 'is_reduced'):
is_reduced = cell.is_reduced
if not is_reduced:
cells.report_topology(cell, env)
env.cell_selection[pop_name] = [gid]
if is_interactive:
context.update(locals())
if write_cell:
write_selection_file_path = "%s/%s_%d.h5" % (env.results_path,
env.modelName, gid)
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
io_utils.write_cell_selection(env, write_selection_file_path)
if load_connections:
io_utils.write_connection_selection(env, write_selection_file_path)
return cell
def main(arena_id, bin_sample_count, config, config_prefix, dataset_prefix,
distances_namespace, distance_bin_extent, input_features_path,
input_features_namespaces, populations, spike_input_path,
spike_input_namespace, spike_input_attr, output_path, io_size,
trajectory_id, write_selection, verbose):
utils.config_logging(verbose)
logger = utils.get_script_logger(os.path.basename(__file__))
comm = MPI.COMM_WORLD
rank = comm.rank
env = Env(comm=comm,
config_file=config,
config_prefix=config_prefix,
dataset_prefix=dataset_prefix,
results_path=output_path,
spike_input_path=spike_input_path,
spike_input_namespace=spike_input_namespace,
spike_input_attr=spike_input_attr,
arena_id=arena_id,
trajectory_id=trajectory_id)
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
pop_ranges, pop_size = read_population_ranges(env.connectivity_file_path,
comm=comm)
distance_U_dict = {}
distance_V_dict = {}
range_U_dict = {}
range_V_dict = {}
selection_dict = defaultdict(set)
comm0 = env.comm.Split(2 if rank == 0 else 0, 0)
local_random = np.random.RandomState()
local_random.seed(1000)
if len(populations) == 0:
populations = sorted(pop_ranges.keys())
if rank == 0:
for population in populations:
distances = read_cell_attributes(env.data_file_path,
population,
namespace=distances_namespace,
comm=comm0)
soma_distances = {}
if input_features_path is not None:
num_fields_dict = {}
for input_features_namespace in input_features_namespaces:
if arena_id is not None:
this_features_namespace = '%s %s' % (
input_features_namespace, arena_id)
else:
this_features_namespace = input_features_namespace
input_features_iter = read_cell_attributes(
input_features_path,
population,
namespace=this_features_namespace,
mask=set(['Num Fields']),
comm=comm0)
count = 0
for gid, attr_dict in input_features_iter:
num_fields_dict[gid] = attr_dict['Num Fields']
count += 1
logger.info(
'Read feature data from namespace %s for %i cells in population %s'
% (this_features_namespace, count, population))
for (gid, v) in distances:
num_fields = num_fields_dict.get(gid, 0)
if num_fields > 0:
soma_distances[gid] = (v['U Distance'][0],
v['V Distance'][0])
else:
for (gid, v) in distances:
soma_distances[gid] = (v['U Distance'][0],
v['V Distance'][0])
numitems = len(list(soma_distances.keys()))
logger.info('read %s distances (%i elements)' %
(population, numitems))
if numitems == 0:
continue
gid_array = np.asarray([gid for gid in soma_distances])
distance_U_array = np.asarray(
[soma_distances[gid][0] for gid in gid_array])
distance_V_array = np.asarray(
[soma_distances[gid][1] for gid in gid_array])
U_min = np.min(distance_U_array)
U_max = np.max(distance_U_array)
V_min = np.min(distance_V_array)
V_max = np.max(distance_V_array)
range_U_dict[population] = (U_min, U_max)
range_V_dict[population] = (V_min, V_max)
distance_U = {
gid: soma_distances[gid][0]
for gid in soma_distances
}
distance_V = {
gid: soma_distances[gid][1]
for gid in soma_distances
}
distance_U_dict[population] = distance_U
distance_V_dict[population] = distance_V
min_dist = U_min
max_dist = U_max
distance_bins = np.arange(U_min, U_max, distance_bin_extent)
distance_bin_array = np.digitize(distance_U_array, distance_bins)
selection_set = set([])
for bin_index in range(len(distance_bins) + 1):
bin_gids = gid_array[np.where(
distance_bin_array == bin_index)[0]]
if len(bin_gids) > 0:
selected_bin_gids = local_random.choice(
bin_gids, replace=False, size=bin_sample_count)
for gid in selected_bin_gids:
selection_set.add(int(gid))
selection_dict[population] = selection_set
yaml_output_dict = {}
for k, v in utils.viewitems(selection_dict):
yaml_output_dict[k] = list(sorted(v))
yaml_output_path = '%s/DG_slice.yaml' % output_path
with open(yaml_output_path, 'w') as outfile:
yaml.dump(yaml_output_dict, outfile)
del (yaml_output_dict)
env.comm.barrier()
write_selection_file_path = None
if write_selection:
write_selection_file_path = "%s/%s_selection.h5" % (env.results_path,
env.modelName)
if write_selection_file_path is not None:
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
selection_dict = env.comm.bcast(dict(selection_dict), root=0)
env.cell_selection = selection_dict
io_utils.write_cell_selection(env,
write_selection_file_path,
populations=populations)
input_selection = io_utils.write_connection_selection(
env, write_selection_file_path, populations=populations)
if env.spike_input_ns is not None:
io_utils.write_input_cell_selection(env,
input_selection,
write_selection_file_path,
populations=populations)
env.comm.barrier()
MPI.Finalize()
def main(arena_id, config, config_prefix, dataset_prefix, distances_namespace, spike_input_path, spike_input_namespace, spike_input_attr, input_features_namespaces, input_features_path, selection_path, output_path, io_size, trajectory_id, verbose):
utils.config_logging(verbose)
logger = utils.get_script_logger(os.path.basename(__file__))
comm = MPI.COMM_WORLD
rank = comm.rank
if io_size == -1:
io_size = comm.size
env = Env(comm=comm, config_file=config,
config_prefix=config_prefix, dataset_prefix=dataset_prefix,
results_path=output_path, spike_input_path=spike_input_path,
spike_input_namespace=spike_input_namespace, spike_input_attr=spike_input_attr,
arena_id=arena_id, trajectory_id=trajectory_id, io_size=io_size)
selection = []
f = open(selection_path, 'r')
for line in f.readlines():
selection.append(int(line))
f.close()
selection = set(selection)
pop_ranges, pop_size = read_population_ranges(env.connectivity_file_path, comm=comm)
distance_U_dict = {}
distance_V_dict = {}
range_U_dict = {}
range_V_dict = {}
selection_dict = defaultdict(set)
comm0 = env.comm.Split(2 if rank == 0 else 0, 0)
if rank == 0:
for population in pop_ranges:
distances = read_cell_attributes(env.data_file_path, population, namespace=distances_namespace, comm=comm0)
soma_distances = { k: (v['U Distance'][0], v['V Distance'][0]) for (k,v) in distances }
del distances
numitems = len(list(soma_distances.keys()))
if numitems == 0:
continue
distance_U_array = np.asarray([soma_distances[gid][0] for gid in soma_distances])
distance_V_array = np.asarray([soma_distances[gid][1] for gid in soma_distances])
U_min = np.min(distance_U_array)
U_max = np.max(distance_U_array)
V_min = np.min(distance_V_array)
V_max = np.max(distance_V_array)
range_U_dict[population] = (U_min, U_max)
range_V_dict[population] = (V_min, V_max)
distance_U = { gid: soma_distances[gid][0] for gid in soma_distances }
distance_V = { gid: soma_distances[gid][1] for gid in soma_distances }
distance_U_dict[population] = distance_U
distance_V_dict[population] = distance_V
min_dist = U_min
max_dist = U_max
selection_dict[population] = set([ k for k in distance_U if k in selection ])
env.comm.barrier()
write_selection_file_path = "%s/%s_selection.h5" % (env.results_path, env.modelName)
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
selection_dict = env.comm.bcast(dict(selection_dict), root=0)
env.cell_selection = selection_dict
io_utils.write_cell_selection(env, write_selection_file_path)
input_selection = io_utils.write_connection_selection(env, write_selection_file_path)
if spike_input_path:
io_utils.write_input_cell_selection(env, input_selection, write_selection_file_path)
if input_features_path:
for this_input_features_namespace in sorted(input_features_namespaces):
for population in sorted(input_selection):
logger.info(f"Extracting input features {this_input_features_namespace} for population {population}...")
it = read_cell_attribute_selection(input_features_path, population,
namespace=f"{this_input_features_namespace} {arena_id}",
selection=input_selection[population], comm=env.comm)
output_features_dict = { cell_gid : cell_features_dict for cell_gid, cell_features_dict in it }
append_cell_attributes(write_selection_file_path, population, output_features_dict,
namespace=f"{this_input_features_namespace} {arena_id}",
io_size=io_size, comm=env.comm)
env.comm.barrier()
def main(arena_id, config, config_prefix, dataset_prefix, distances_namespace, distance_limits, spike_input_path, spike_input_namespace, spike_input_attr, output_path, io_size, trajectory_id, write_selection, verbose):
utils.config_logging(verbose)
logger = utils.get_script_logger(os.path.basename(__file__))
comm = MPI.COMM_WORLD
rank = comm.rank
if io_size == -1:
io_size = comm.size
env = Env(comm=comm, config_file=config,
config_prefix=config_prefix, dataset_prefix=dataset_prefix,
results_path=output_path, spike_input_path=spike_input_path,
spike_input_namespace=spike_input_namespace, spike_input_attr=spike_input_attr,
arena_id=arena_id, trajectory_id=trajectory_id, io_size=io_size)
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
pop_ranges, pop_size = read_population_ranges(env.connectivity_file_path, comm=comm)
distance_U_dict = {}
distance_V_dict = {}
range_U_dict = {}
range_V_dict = {}
selection_dict = defaultdict(set)
comm0 = env.comm.Split(2 if rank == 0 else 0, 0)
if rank == 0:
for population in pop_ranges:
distances = read_cell_attributes(env.data_file_path, population, namespace=distances_namespace, comm=comm0)
soma_distances = { k: (v['U Distance'][0], v['V Distance'][0]) for (k,v) in distances }
del distances
numitems = len(list(soma_distances.keys()))
logger.info('read %s distances (%i elements)' % (population, numitems))
if numitems == 0:
continue
distance_U_array = np.asarray([soma_distances[gid][0] for gid in soma_distances])
distance_V_array = np.asarray([soma_distances[gid][1] for gid in soma_distances])
U_min = np.min(distance_U_array)
U_max = np.max(distance_U_array)
V_min = np.min(distance_V_array)
V_max = np.max(distance_V_array)
range_U_dict[population] = (U_min, U_max)
range_V_dict[population] = (V_min, V_max)
distance_U = { gid: soma_distances[gid][0] for gid in soma_distances }
distance_V = { gid: soma_distances[gid][1] for gid in soma_distances }
distance_U_dict[population] = distance_U
distance_V_dict[population] = distance_V
min_dist = U_min
max_dist = U_max
if distance_limits:
min_dist = distance_limits[0]
max_dist = distance_limits[1]
selection_dict[population] = set([ k for k in distance_U if (distance_U[k] >= min_dist) and
(distance_U[k] <= max_dist) ])
yaml_output_dict = {}
for k, v in utils.viewitems(selection_dict):
yaml_output_dict[k] = list(v)
yaml_output_path = '%s/DG_slice.yaml' % output_path
with open(yaml_output_path, 'w') as outfile:
yaml.dump(yaml_output_dict, outfile)
del(yaml_output_dict)
env.comm.barrier()
write_selection_file_path = None
if write_selection:
write_selection_file_path = "%s/%s_selection.h5" % (env.results_path, env.modelName)
if write_selection_file_path is not None:
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
selection_dict = env.comm.bcast(dict(selection_dict), root=0)
env.cell_selection = selection_dict
io_utils.write_cell_selection(env, write_selection_file_path)
input_selection = io_utils.write_connection_selection(env, write_selection_file_path)
io_utils.write_input_cell_selection(env, input_selection, write_selection_file_path)
