def run_hnn_core(backend=None, n_jobs=1):
"""Test to check if hnn-core does not break."""
# small snippet of data on data branch for now. To be deleted
# later. Data branch should have only commit so it does not
# pollute the history.
data_url = ('https://raw.githubusercontent.com/jonescompneurolab/'
'hnn-core/test_data/dpl.txt')
if not op.exists('dpl.txt'):
_fetch_file(data_url, 'dpl.txt')
dpl_master = loadtxt('dpl.txt')
hnn_core_root = op.dirname(hnn_core.__file__)
# default params
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
# run the simulation
net = Network(params)
if backend == 'mpi':
with MPIBackend(n_procs=2, mpi_cmd='mpiexec'):
dpl = simulate_dipole(net)[0]
elif backend == 'joblib':
with JoblibBackend(n_jobs=n_jobs):
dpl = simulate_dipole(net)[0]
else:
dpl = simulate_dipole(net)[0]
# write the dipole to a file and compare
fname = './dpl2.txt'
dpl.write(fname)
dpl_pr = loadtxt(fname)
assert_array_equal(dpl_pr[:, 2], dpl_master[:, 2]) # L2
assert_array_equal(dpl_pr[:, 3], dpl_master[:, 3]) # L5
# Test spike type counts
spiketype_counts = {}
for spikegid in net.spikes.gids[0]:
if net.gid_to_type(spikegid) not in spiketype_counts:
spiketype_counts[net.gid_to_type(spikegid)] = 0
else:
spiketype_counts[net.gid_to_type(spikegid)] += 1
assert 'common' not in spiketype_counts
assert 'exgauss' not in spiketype_counts
assert 'extpois' not in spiketype_counts
assert spiketype_counts == {
'evprox1': 269,
'L2_basket': 54,
'L2_pyramidal': 113,
'L5_pyramidal': 395,
'L5_basket': 85,
'evdist1': 234,
'evprox2': 269
}
def __call__(self, new_param_values):
new_params = dict(
zip(self.param_names,
new_param_values.detach().cpu().numpy()))
self.params.update(new_params)
net = Network(self.params)
with JoblibBackend(n_jobs=1):
dpl = simulate_dipole(net, n_trials=1)
summstats = torch.as_tensor(dpl[0].data['agg'])
return summstats
def _run_hnn_core_fixture(backend=None,
n_procs=None,
n_jobs=1,
reduced=False,
record_vsoma=False,
record_isoma=False,
postproc=True):
hnn_core_root = op.dirname(hnn_core.__file__)
# default params
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
if reduced:
params.update({
'N_pyr_x': 3,
'N_pyr_y': 3,
'tstop': 25,
't_evprox_1': 5,
't_evdist_1': 10,
't_evprox_2': 20,
'N_trials': 2
})
net = Network(params)
# number of trials simulated
assert all(
len(src_feed_times) == params['N_trials']
for src_type, src_feed_times in net.feed_times.items()
if src_type != 'tonic')
if backend == 'mpi':
with MPIBackend(n_procs=n_procs, mpi_cmd='mpiexec'):
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
elif backend == 'joblib':
with JoblibBackend(n_jobs=n_jobs):
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
else:
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
return dpls, net
def __call__(self, new_param_values):
new_params = dict(
zip(self.param_names,
new_param_values.detach().cpu().numpy()))
self.params.update(new_params)
net = Network(self.params)
with JoblibBackend(n_jobs=1):
dpl = simulate_dipole(net, n_trials=1)
summstats = dpl[0].data['agg']
spike_times = net.cell_response.spike_times
spike_gids = net.cell_response.spike_gids
spike_types = net.cell_response.spike_types
return summstats, spike_times, spike_gids, spike_types
def _run_hnn_core_fixture(backend=None,
n_procs=None,
n_jobs=1,
reduced=False,
record_vsoma=False,
record_isoma=False,
postproc=True):
hnn_core_root = op.dirname(hnn_core.__file__)
# default params
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
if reduced:
params.update({
'N_pyr_x': 3,
'N_pyr_y': 3,
'tstop': 40,
't_evprox_1': 5,
't_evdist_1': 10,
't_evprox_2': 20,
'N_trials': 2
})
net = Network(params, add_drives_from_params=True)
# number of trials simulated
for drive in net.external_drives.values():
assert len(drive['events']) == params['N_trials']
if backend == 'mpi':
with MPIBackend(n_procs=n_procs, mpi_cmd='mpiexec'):
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
elif backend == 'joblib':
with JoblibBackend(n_jobs=n_jobs):
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
else:
dpls = simulate_dipole(net,
record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc)
return dpls, net
def _run_hnn_core_fixture(backend=None, n_procs=None, n_jobs=1,
reduced=False, record_vsoma=False,
record_isoma=False, postproc=False,
electrode_array=None):
hnn_core_root = op.dirname(hnn_core.__file__)
# default params
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
tstop = 170.
if reduced:
params.update({'N_pyr_x': 3,
'N_pyr_y': 3,
't_evprox_1': 5,
't_evdist_1': 10,
't_evprox_2': 20,
'N_trials': 2})
tstop = 40.
net = jones_2009_model(params, add_drives_from_params=True)
if electrode_array is not None:
for name, positions in electrode_array.items():
net.add_electrode_array(name, positions)
if backend == 'mpi':
with MPIBackend(n_procs=n_procs, mpi_cmd='mpiexec'):
dpls = simulate_dipole(net, record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc, tstop=tstop)
elif backend == 'joblib':
with JoblibBackend(n_jobs=n_jobs):
dpls = simulate_dipole(net, record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc, tstop=tstop)
else:
dpls = simulate_dipole(net, record_vsoma=record_isoma,
record_isoma=record_vsoma,
postproc=postproc, tstop=tstop)
# check that the network object is picklable after the simulation
pickle.dumps(net)
# number of trials simulated
for drive in net.external_drives.values():
assert len(drive['events']) == params['N_trials']
return dpls, net
numspikes=1,
location='distal',
n_drive_cells=1,
cell_specific=False,
weights_ampa=weights_ampa_d,
weights_nmda=weights_nmda_d,
synaptic_delays=synaptic_delays_d,
event_seed=2)
###############################################################################
# Now we run the simulation over 2 trials so that we can plot the average
# aggregate dipole. For a better match to the empirical waveform, set
# ``n_trials`` to be >=25.
n_trials = 2
# n_trials = 25
with JoblibBackend(n_jobs=2):
dpls = simulate_dipole(net, tstop=170., n_trials=n_trials)
###############################################################################
# Since the model is a reduced representation of the larger network
# contributing to the response, the model response is noisier than it would be
# in the net activity from a larger network where these effects are averaged
# out, and the dipole amplitude is smaller than the recorded data. The
# post-processing steps of smoothing and scaling the simulated dipole response
# allow us to more accurately approximate the true signal responsible for the
# recorded macroscopic evoked response [1]_, [2]_.
dpl_smooth_win = 20
dpl_scalefctr = 12
for dpl in dpls:
dpl.smooth(dpl_smooth_win)
dpl.scale(dpl_scalefctr)
# This is a lot of parameters! We can also filter the
# parameters using unix-style wildcard characters
print(params['L2Pyr_soma*'])
###############################################################################
# Let us first create our network from the params file and visualize the cells
# inside it.
net = Network(params)
net.plot_cells()
###############################################################################
# Now let's simulate the dipole, running 2 trials with the Joblib backend.
# To run them in parallel we could set n_jobs to equal the number of trials.
from hnn_core import JoblibBackend
with JoblibBackend(n_jobs=1):
dpls = simulate_dipole(net, n_trials=2)
###############################################################################
# and then plot it
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))
plot_dipole(dpls, ax=axes[0], layer='agg', show=False)
net.plot_input(ax=axes[1])
###############################################################################
# Also, we can plot the spikes and write them to txt files.
# Note that we can use formatting syntax to specify the filename pattern
# with which each trial will be written. To read spikes back in, we can use
# wildcard expressions.
net.spikes.plot()