def test_dipole():
"""Test dipole object."""
hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'default.json')
dpl_out_fname = '/tmp/dpl1.txt'
params = read_params(params_fname)
times = np.random.random(6000)
data = np.random.random((6000, 3))
dipole = Dipole(times, data)
dipole.baseline_renormalize(params)
dipole.convert_fAm_to_nAm()
dipole.scale(params['dipole_scalefctr'])
dipole.smooth(params['dipole_smooth_win'] / params['dt'])
dipole.plot(show=False)
viz.plot_dipole([dipole, dipole], show=False)
dipole.write(dpl_out_fname)
dipole_read = read_dipole(dpl_out_fname)
assert_allclose(dipole_read.times, dipole.times, rtol=0, atol=0.00051)
for dpl_key in dipole.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole.data[dpl_key], rtol=0, atol=0.000051)
# average two identical dipole objects
dipole_avg = average_dipoles([dipole, dipole_read])
for dpl_key in dipole_avg.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole_avg.data[dpl_key], rtol=0, atol=0.000051)
with pytest.raises(ValueError, match="Dipole at index 0 was already an "
"average of 2 trials"):
dipole_avg = average_dipoles([dipole_avg, dipole_read])
def test_dipole(tmpdir, run_hnn_core_fixture):
"""Test dipole object."""
hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'default.json')
dpl_out_fname = tmpdir.join('dpl1.txt')
params = read_params(params_fname)
times = np.random.random(6000)
data = np.random.random((6000, 3))
dipole = Dipole(times, data)
dipole.baseline_renormalize(params['N_pyr_x'], params['N_pyr_y'])
dipole.convert_fAm_to_nAm()
dipole.scale(params['dipole_scalefctr'])
dipole.smooth(params['dipole_smooth_win'] / params['dt'])
dipole.plot(show=False)
plot_dipole([dipole, dipole], show=False)
dipole.write(dpl_out_fname)
dipole_read = read_dipole(dpl_out_fname)
assert_allclose(dipole_read.times, dipole.times, rtol=0, atol=0.00051)
for dpl_key in dipole.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole.data[dpl_key],
rtol=0,
atol=0.000051)
# average two identical dipole objects
dipole_avg = average_dipoles([dipole, dipole_read])
for dpl_key in dipole_avg.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole_avg.data[dpl_key],
rtol=0,
atol=0.000051)
with pytest.raises(ValueError,
match="Dipole at index 0 was already an "
"average of 2 trials"):
dipole_avg = average_dipoles([dipole_avg, dipole_read])
# test postproc
dpls_raw, net = run_hnn_core_fixture(backend='joblib',
n_jobs=1,
reduced=True,
record_isoma=True,
record_vsoma=True,
postproc=False)
dpls, _ = run_hnn_core_fixture(backend='joblib',
n_jobs=1,
reduced=True,
record_isoma=True,
record_vsoma=True,
postproc=True)
with pytest.raises(AssertionError):
assert_allclose(dpls[0].data['agg'], dpls_raw[0].data['agg'])
dpls_raw[0].post_proc(net.params['N_pyr_x'], net.params['N_pyr_y'],
net.params['dipole_smooth_win'] / net.params['dt'],
net.params['dipole_scalefctr'])
assert_allclose(dpls_raw[0].data['agg'], dpls[0].data['agg'])
dpl.scale(dpl_scalefctr)
###############################################################################
# Finally, we plot the driving spike histogram, empirical and simulated median
# nerve evoked response waveforms, and output spike histogram.
fig, axes = plt.subplots(3,
1,
sharex=True,
figsize=(6, 6),
constrained_layout=True)
net.cell_response.plot_spikes_hist(ax=axes[0],
spike_types=['evprox', 'evdist'],
show=False)
axes[1].axhline(0, c='k', ls=':', label='_nolegend_')
axes[1].plot(1e3 * stc.times, dipole_tc, 'r--')
average_dipoles(dpls).plot(ax=axes[1], show=False)
axes[1].legend(['MNE label average', 'HNN simulation'])
axes[1].set_ylabel('Current Dipole (nAm)')
net.cell_response.plot_spikes_raster(ax=axes[2])
###############################################################################
# References
# ----------
# .. [1] Jones, S. R., Pritchett, D. L., Stufflebeam, S. M., Hämäläinen, M.
# & Moore, C. I. Neural correlates of tactile detection: a combined
# magnetoencephalography and biophysically based computational modeling
# study. J. Neurosci. 27, 10751–10764 (2007).
# .. [2] Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT,
# Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical
# Neurosolver (HNN), a new software tool for interpreting the cellular and
# network origin of human MEG/EEG data. eLife 9, e51214 (2020).
def test_dipole(tmpdir, run_hnn_core_fixture):
"""Test dipole object."""
hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'default.json')
dpl_out_fname = tmpdir.join('dpl1.txt')
params = read_params(params_fname)
times = np.arange(0, 6000 * params['dt'], params['dt'])
data = np.random.random((6000, 3))
dipole = Dipole(times, data)
dipole._baseline_renormalize(params['N_pyr_x'], params['N_pyr_y'])
dipole._convert_fAm_to_nAm()
# test smoothing and scaling
dipole_raw = dipole.copy()
dipole.scale(params['dipole_scalefctr'])
dipole.smooth(window_len=params['dipole_smooth_win'])
with pytest.raises(AssertionError):
assert_allclose(dipole.data['agg'], dipole_raw.data['agg'])
assert_allclose(
dipole.data['agg'],
(params['dipole_scalefctr'] *
dipole_raw.smooth(params['dipole_smooth_win']).data['agg']))
dipole.plot(show=False)
plot_dipole([dipole, dipole], show=False)
# Test IO
dipole.write(dpl_out_fname)
dipole_read = read_dipole(dpl_out_fname)
assert_allclose(dipole_read.times, dipole.times, rtol=0, atol=0.00051)
for dpl_key in dipole.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole.data[dpl_key],
rtol=0,
atol=0.000051)
# average two identical dipole objects
dipole_avg = average_dipoles([dipole, dipole_read])
for dpl_key in dipole_avg.data.keys():
assert_allclose(dipole_read.data[dpl_key],
dipole_avg.data[dpl_key],
rtol=0,
atol=0.000051)
with pytest.raises(ValueError,
match="Dipole at index 0 was already an "
"average of 2 trials"):
dipole_avg = average_dipoles([dipole_avg, dipole_read])
# average an n_of_1 dipole list
single_dpl_avg = average_dipoles([dipole])
for dpl_key in single_dpl_avg.data.keys():
assert_allclose(dipole_read.data[dpl_key],
single_dpl_avg.data[dpl_key],
rtol=0,
atol=0.000051)
# average dipole list with one dipole object and a zero dipole object
n_times = len(dipole_read.data['agg'])
dpl_null = Dipole(np.zeros(n_times, ), np.zeros((n_times, 3)))
dpl_1 = [dipole, dpl_null]
dpl_avg = average_dipoles(dpl_1)
for dpl_key in dpl_avg.data.keys():
assert_allclose(dpl_1[0].data[dpl_key] / 2., dpl_avg.data[dpl_key])
# Test experimental dipole
dipole_exp = Dipole(times, data[:, 1])
dipole_exp.write(dpl_out_fname)
dipole_exp_read = read_dipole(dpl_out_fname)
assert_allclose(dipole_exp.data['agg'],
dipole_exp_read.data['agg'],
rtol=1e-2)
dipole_exp_avg = average_dipoles([dipole_exp, dipole_exp])
assert_allclose(dipole_exp.data['agg'], dipole_exp_avg.data['agg'])
# XXX all below to be deprecated in 0.3
dpls_raw, net = run_hnn_core_fixture(backend='joblib',
n_jobs=1,
reduced=True,
record_isoma=True,
record_vsoma=True)
# test deprecation of postproc
with pytest.warns(DeprecationWarning,
match='The postproc-argument is deprecated'):
dpls, _ = run_hnn_core_fixture(backend='joblib',
n_jobs=1,
reduced=True,
record_isoma=True,
record_vsoma=True,
postproc=True)
with pytest.raises(AssertionError):
assert_allclose(dpls[0].data['agg'], dpls_raw[0].data['agg'])
dpls_raw[0]._post_proc(net._params['dipole_smooth_win'],
net._params['dipole_scalefctr'])
assert_allclose(dpls_raw[0].data['agg'], dpls[0].data['agg'])