def save_data(sim_type, output_dir):
save_file = 'expected/sim_output_{}.h5'.format(sim_type)
sample_data = h5py.File(save_file, 'w')
# spikes data
input_spikes = SpikesFile(os.path.join(output_dir, 'spikes.h5'))
spikes_df = input_spikes.to_dataframe()
sample_data.create_dataset('/spikes/gids',
data=np.array(spikes_df['gids']))
sample_data.create_dataset('/spikes/timestamps',
data=np.array(spikes_df['timestamps']))
sample_data['/spikes/gids'].attrs['sorting'] = 'time'
# soma data
soma_reports = CellVarsFile(os.path.join(output_dir, 'soma_vars.h5'))
soma_grp = sample_data.create_group('/soma')
soma_grp.create_dataset('mapping/time', data=[2500.0, 3000.0, 0.1])
soma_grp.create_dataset('mapping/gids', data=[3])
for var_name in soma_reports.variables:
soma_grp.create_dataset(var_name,
data=soma_reports.data(var_name,
gid=3,
time_window=(2500.0,
3000.0)))
# compartmental report
ecp_file = h5py.File(os.path.join(output_dir, 'ecp.h5'), 'r')
ecp_grp = sample_data.create_group('/ecp')
ecp_grp.create_dataset('data', data=ecp_file['data'][:, 0])
ecp_grp.create_dataset('channel_id', data=ecp_file['channel_id'])
def plot_report(config_file=None,
report_file=None,
report_name=None,
variables=None,
gids=None):
if report_file is None:
report_name, report_file = _get_cell_report(config_file, report_name)
var_report = CellVarsFile(report_file)
variables = listify(
variables) if variables is not None else var_report.variables
gids = listify(gids) if gids is not None else var_report.gids
time_steps = var_report.time_trace
def __units_str(var):
units = var_report.units(var)
if units == CellVarsFile.UNITS_UNKNOWN:
units = missing_units.get(var, '')
return '({})'.format(units) if units else ''
n_plots = len(variables)
if n_plots > 1:
# If more than one variale to plot do so in different subplots
f, axarr = plt.subplots(n_plots, 1)
for i, var in enumerate(variables):
for gid in gids:
axarr[i].plot(time_steps,
var_report.data(gid=gid, var_name=var),
label='gid {}'.format(gid))
axarr[i].legend()
axarr[i].set_ylabel('{} {}'.format(var, __units_str(var)))
if i < n_plots - 1:
axarr[i].set_xticklabels([])
axarr[i].set_xlabel('time (ms)')
elif n_plots == 1:
# For plotting a single variable
plt.figure()
for gid in gids:
plt.plot(time_steps,
var_report.data(gid=0, var_name=variables[0]),
label='gid {}'.format(gid))
plt.ylabel('{} {}'.format(variables[0], __units_str(variables[0])))
plt.xlabel('time (ms)')
else:
return
plt.show()
#for gid in gids:
# plt.plot(times, var_report.data(gid=0, var_name='v'), label='gid {}'.format(gid))
'''
def get_variable_report(config_file=None, report_file=None, report_name=None, variable=None, gid=None):
"""Returns variable reports for specified gids
Function will return the report for a specific cell's variable.
"""
if report_file is None:
report_name, report_file = _get_cell_report(config_file, report_name)
var_report = CellVarsFile(report_file)
time_steps = var_report.time_trace
return var_report.data(gid=gid, var_name=variable), time_steps
def plot_vars(file_names, cell_var='v', gid_list=[], t_min=None, t_max=None):
"""Plots variable traces for a SONATA h5 file. If multiple spike files are specified will do a side-by-side
comparsion for each gid.
:param file_names: list of cell_var file names
:param cell_var: cell variable to plot trace
:param gid_list: used to set what gid/subplots to show (if empty list just plot all possible gids)
"""
# convert to list if single spike file passed in
file_names = [file_names] if not isinstance(file_names, (tuple, list)) else file_names
assert(len(file_names) > 0)
# Use bmtk to parse the cell-var files
cell_var_files = []
for fn in file_names:
try:
cell_var_files.append(CellVarsFile(fn, h5_root="/report/internal"))
except KeyError:
cell_var_files.append(CellVarsFile(fn))
# get first spike file and properties
cvf_base = cell_var_files[0]
xlim = [t_min or cvf_base.time_trace[0], t_max or cvf_base.time_trace[-1]] # Use the same x-axis across all subplots
gid_list = cvf_base.gids if not gid_list else gid_list # if gid_list is None just get all gids in first file
n_cells = len(cvf_base.gids)
fig, ax = plt.subplots(n_cells, 1, figsize=(10, 10))
for subplot, gid in enumerate(gid_list):
for i, cvf in enumerate(cell_var_files):
# plot all traces
ax[subplot].plot(cvf.time_trace, cvf.data(gid, cell_var), label=file_names[i])
ax[subplot].yaxis.set_label_position("right")
ax[subplot].set_ylabel('gid {}'.format(gid), fontsize='xx-small')
ax[subplot].set_xlim(xlim)
if subplot + 1 < n_cells:
# remove x-axis labels on all but the last plot
ax[subplot].set_xticklabels([])
else:
# Use the last plot to get the legend
handles, labels = ax[subplot].get_legend_handles_labels()
fig.legend(handles, labels, loc='upper right')
plt.show()
def save_data(sim_type, conn_type, output_dir):
"""Saves the expected results"""
from bmtk import __version__ as bmtk_version
from neuron import h
import platform
save_file = 'expected/sim_output_{}.h5'.format(sim_type)
sample_data = h5py.File(save_file, 'w')
root_group = sample_data['/']
root_group.attrs['bmtk'] = bmtk_version
root_group.attrs['date'] = str(datetime.datetime.now())
root_group.attrs['python'] = '{}.{}'.format(*sys.version_info[0:2])
root_group.attrs['NEURON'] = h.nrnversion()
root_group.attrs['system'] = platform.system()
root_group.attrs['arch'] = platform.machine()
# spikes data
input_spikes = SpikesFile(os.path.join(output_dir, 'spikes.h5'))
spikes_df = input_spikes.to_dataframe()
sample_data.create_dataset('/spikes/gids',
data=np.array(spikes_df['gids']))
sample_data.create_dataset('/spikes/timestamps',
data=np.array(spikes_df['timestamps']))
sample_data['/spikes/gids'].attrs['sorting'] = 'time'
# soma data
soma_reports = CellVarsFile(os.path.join(output_dir, 'soma_vars.h5'))
soma_grp = sample_data.create_group('/soma')
soma_grp.create_dataset('mapping/time', data=[2500.0, 3000.0, 0.1])
soma_grp.create_dataset('mapping/gids',
data=soma_reports.h5['mapping/gids']) # data=[3])
soma_grp.create_dataset('mapping/element_id',
data=soma_reports.h5['mapping/element_id'])
soma_grp.create_dataset('mapping/element_pos',
data=soma_reports.h5['mapping/element_pos'])
soma_grp.create_dataset('mapping/index_pointer',
data=soma_reports.h5['mapping/index_pointer'])
for var_name in soma_reports.variables:
ds_name = '{}/data'.format(var_name)
soma_grp.create_dataset(ds_name,
data=soma_reports.h5[ds_name][-5000:, :])
# compartmental report
soma_reports = CellVarsFile(os.path.join(output_dir, 'full_cell_vars.h5'))
soma_grp = sample_data.create_group('/compartmental')
soma_grp.create_dataset('mapping/time', data=[2500.0, 3000.0, 0.1])
soma_grp.create_dataset('mapping/gids',
data=soma_reports.h5['mapping/gids']) # data=[3])
soma_grp.create_dataset('mapping/element_id',
data=soma_reports.h5['mapping/element_id'])
soma_grp.create_dataset('mapping/element_pos',
data=soma_reports.h5['mapping/element_pos'])
soma_grp.create_dataset('mapping/index_pointer',
data=soma_reports.h5['mapping/index_pointer'])
for var_name in soma_reports.variables:
ds_name = '{}/data'.format(var_name)
soma_grp.create_dataset(ds_name,
data=soma_reports.h5[ds_name][-5000:, :])
# ecp data
ecp_file = h5py.File(os.path.join(output_dir, 'ecp.h5'), 'r')
ecp_grp = sample_data.create_group('/ecp')
ecp_grp.create_dataset('data', data=ecp_file['data'][:, 0])
ecp_grp.create_dataset('channel_id', data=ecp_file['channel_id'])
def test_bionet(input_type='virt',
conn_type='nsyns',
capture_output=True,
tol=1e-05):
if MPI_rank == 0:
print(
'Testing BioNet with {} inputs and {} synaptic connections (nodes: {})'
.format(input_type, conn_type, MPI_size))
output_dir = 'output' if capture_output else tempfile.mkdtemp()
config_base = json.load(open('config_base.json'))
config_base['manifest']['$OUTPUT_DIR'] = output_dir
config_base['inputs'] = get_inputs(input_type)
config_base['manifest']['$NETWORK_DIR'] = os.path.join(
'$BASE_DIR', get_network_path(conn_type))
conf = bionet.Config.from_dict(config_base, validate=True)
conf.build_env()
net = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, net)
sim.run()
barrier()
if MPI_rank == 0:
print('Verifying output.')
expected_file = get_expected_results(input_type, conn_type)
# Check spikes file
assert (SpikesFile(os.path.join(
output_dir, 'spikes.h5')) == SpikesFile(expected_file))
# soma reports
soma_report_expected = CellVarsFile(expected_file, h5_root='/soma')
soma_reports = CellVarsFile(os.path.join(output_dir, 'soma_vars.h5'))
t_window = soma_report_expected.t_start, soma_report_expected.t_stop
assert (soma_report_expected.dt == soma_reports.dt)
assert (soma_report_expected.gids == soma_reports.gids)
assert (soma_report_expected.variables == soma_reports.variables)
for gid in soma_report_expected.gids:
assert (soma_reports.compartment_ids(gid) ==
soma_report_expected.compartment_ids(gid)).all()
for var in soma_report_expected.variables:
assert (np.allclose(
soma_reports.data(gid=gid,
var_name=var,
time_window=t_window),
soma_report_expected.data(gid=gid, var_name=var), tol))
# Compartmental reports
compart_report_exp = CellVarsFile(expected_file,
h5_root='/compartmental')
compart_report = CellVarsFile(
os.path.join(output_dir, 'full_cell_vars.h5'))
t_window = compart_report_exp.t_start, compart_report_exp.t_stop
assert (compart_report_exp.dt == compart_report.dt)
assert (compart_report_exp.variables == compart_report.variables)
for gid in compart_report_exp.gids:
assert ((compart_report.compartment_ids(gid) ==
compart_report_exp.compartment_ids(gid)).all())
for var in compart_report_exp.variables:
assert (np.allclose(
compart_report.data(gid,
var_name=var,
time_window=t_window,
compartments='all'),
compart_report_exp.data(gid,
var_name=var,
compartments='all'), tol))
# ecp
ecp_report = h5py.File(os.path.join(output_dir, 'ecp.h5'), 'r')
ecp_report_exp = h5py.File(expected_file, 'r')
ecp_grp_exp = ecp_report_exp['/ecp']
assert (np.allclose(np.array(ecp_report['/data'][:, 0]),
np.array(ecp_grp_exp['data']), tol))
print('Success!')
barrier()
bionet.nrn.quit_execution()
numIND = 10 numHypo = 10 numINmplus = 10 numINmminus = 10 numPGN = 10 numFB = 10 numIMG = 10 numMPG = 10 numEUSmn = 10 numBladmn = 10 config_file = "simulation_config.json" report_name = None report_name, report_file = _get_cell_report(config_file, report_name) var_report = CellVarsFile(report_file) time_steps = var_report.time_trace # Plot spike raster --------------------------------------- Blad_gids = np.arange(0,numBladaff) EUS_gids = Blad_gids + numBladaff PAG_gids = EUS_gids + numEUSaff IND_gids = PAG_gids + numPAGaff Hypo_gids = IND_gids + numIND INmplus_gids = Hypo_gids + numHypo INmminus_gids = INmplus_gids + numINmplus PGN_gids = INmminus_gids + numINmminus FB_gids = PGN_gids + numPGN IMG_gids = FB_gids + numFB MPG_gids = IMG_gids + numIMG EUSmn_gids = MPG_gids + numMPG