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 test_hnn_core():
"""Test to check if MNE neuron 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.join(op.dirname(hnn_core.__file__), '..')
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
net = Network(params, n_jobs=1)
dpl = simulate_dipole(net)[0]
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.spikegids[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 'extinput' 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
}
print(net.gid_ranges)
###############################################################################
# Simulated voltage in the soma is stored in :class:`~hnn_core.CellResponse`
# as a dictionary. The CellResponse object stores data produced by individual
# cells including spikes, somatic voltages and currents.
trial_idx = 0
vsoma = net.cell_response.vsoma[trial_idx]
print(vsoma.keys())
###############################################################################
# We can plot the firing pattern of individual cells by indexing with the gid
gid = 170
plt.figure(figsize=(4, 4), constrained_layout=True)
plt.plot(net.cell_response.times, vsoma[gid])
plt.title('%s (gid=%d)' % (net.gid_to_type(gid), gid))
plt.xlabel('Time (ms)')
plt.ylabel('Voltage (mV)')
plt.show()
###############################################################################
# Also, we can plot the spikes in the network and write them to text files.
# Note that we can use formatting syntax to specify the filename pattern
# with which each trial will be written ('spk_1.txt', 'spk_2.txt, ...). To
# read spikes back in, we can use wildcard expressions.
net.cell_response.plot_spikes_raster()
with tempfile.TemporaryDirectory() as tmp_dir_name:
net.cell_response.write(op.join(tmp_dir_name, 'spk_%d.txt'))
cell_response = read_spikes(op.join(tmp_dir_name, 'spk_*.txt'))
cell_response.plot_spikes_raster()