def setUpClass(cls, n_src=5):
sc = 1e6
cls.data = LoadData(os.path.join(sample_data_path, "ball_and_stick_8"))
cls.data.morphology[:,2:6] = cls.data.morphology[:,2:6]/sc
cls.reco = sKCSD(cls.data.ele_pos/sc,cls.data.LFP[:,:10]/1e3,cls.data.morphology, n_src_init=n_src, exact=True)
cls.segments = cls.reco.values(transformation='segments')
cls.loops = cls.reco.values(transformation=None)
cls.cartesian = cls.reco.values(transformation='3D')
cls.data2 = LoadData(os.path.join(sample_data_path, "Simple_with_branches"))
cls.data2.morphology[:, 2:6] = cls.data2.morphology[:, 2:6]/sc
cls.data2.ele_pos = cls.data2.ele_pos/sc
cls.data2.LFP = cls.data2.LFP[:,:10]/1e3
cls.reco2 = sKCSD(cls.data2.ele_pos, cls.data2.LFP, cls.data2.morphology, n_src_init=n_src, dist_table_density=5)
def setUpClass(cls, n_src=5):
"""
Check, if it is possible to read in data.
This test will be expanded and more neurons read-in.
"""
cls.data2 = LoadData(os.path.join(sample_data_path, "Simple_with_branches"))
cls.data2.morphology[:, 2:6] = cls.data2.morphology[:, 2:6]/sc
cls.data2.ele_pos = cls.data2.ele_pos/sc
cls.data2.LFP = cls.data2.LFP[:,:10]/1e3
cls.reco2 = sKCSD(cls.data2.ele_pos, cls.data2.LFP, cls.data2.morphology, n_src_init=n_src, dist_table_density=5)
def setUpClass(cls, n_src=5):
sc = 1e6
cls.data = LoadData(os.path.join(sample_data_path, "ball_and_stick_8"))
cls.data.morphology[:, 2:6] = cls.data.morphology[:, 2:6] / sc
cls.reco = sKCSD(cls.data.ele_pos / sc,
cls.data.LFP[:, :10] / 1e3,
cls.data.morphology,
n_src_init=n_src,
exact=True)
cls.segments = cls.reco.values(transformation='segments')
cls.loops = cls.reco.values(transformation=None)
cls.cartesian = cls.reco.values(transformation='3D')
cls.data2 = LoadData(
os.path.join(sample_data_path, "Simple_with_branches"))
cls.data2.morphology[:, 2:6] = cls.data2.morphology[:, 2:6] / sc
cls.data2.ele_pos = cls.data2.ele_pos / sc
cls.data2.LFP = cls.data2.LFP[:, :10] / 1e3
cls.reco2 = sKCSD(cls.data2.ele_pos,
cls.data2.LFP,
cls.data2.morphology,
n_src_init=n_src,
dist_table_density=5)
ele_pos = data.ele_pos / scale_factor
data.LFP = data.LFP / scale_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6] / scale_factor
seglen = np.loadtxt(os.path.join(data_dir, 'seglength'))
ground_truth = np.loadtxt(os.path.join(
data_dir, 'membcurr')) / seglen[:, None] * 1e-3
t0 = int(500 / dt)
for i, R in enumerate(R_inits):
for j, l in enumerate(lambdas):
lambd = l * 2 * (2 * np.pi)**3 * R**2 * n_src
ker = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
dist_table_density=50,
skmonaco_available=False)
if not i and not j:
ground_truth_3D = ker.cell.transform_to_3D(ground_truth,
what="morpho")
vmax, vmin = pl.get_min_max(ground_truth_3D)
ker_dir = data_dir + '_R_%f_lambda_%f' % (R, lambd)
morpho, extent = ker.cell.draw_cell2D(axis=2)
est_skcsd = ker.values()
fig, ax = plt.subplots(1, 2)
if sys.version_info < (3, 0):
path = os.path.join(ker_dir, "preprocessed_data/Python_2")
else:
vmax=vmax,
alpha=.75)
vmax, vmin = pl.get_min_max(ground_truth_t0)
for i, di in enumerate(data_dir):
data = utils.LoadData(di)
ele_pos = data.ele_pos / scale_factor
data.LFP = data.LFP / scale_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6] / scale_factor
ker = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
tolerance=tolerance,
dist_table_density=20,
exact=True,
sigma=0.3)
path = os.path.join('simulation',
'%s_lambda_%f_R_%f' % (fname_base, l, R))
if sys.version_info < (3, 0):
path = os.path.join(path, "preprocessed_data/Python_2")
else:
path = os.path.join(path, "preprocessed_data/Python_3")
if not os.path.exists(path):
print("Creating", path)
os.makedirs(path)
ax_gt.set_xlabel('time (s)')
ax_gt.set_ylabel('#segment')
new_fname = fname_base + '.png'
fig_name = sKCSD_utils.make_fig_names(new_fname)
vmax, vmin = pl.get_min_max(ground_truth)
for i, datd in enumerate(data_dir):
data = utils.LoadData(datd)
ele_pos = data.ele_pos/scaling_factor
data.LFP = data.LFP/scaling_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6]/scaling_factor
k = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
dist_table_density=100)
csd = k.values(transformation='segments')
cax = ax[i].imshow(csd,
extent=[0, tstop, 1, 52],
origin='lower',
aspect='auto',
cmap='seismic_r',
vmax=gvmax,
vmin=gvmin)
ax[3+i].set_title(electrode_number[i])
k.L_curve(lambdas=np.array(lambdas), Rs=np.array(R_inits))
csd_Lcurve = k.values(transformation='segments')
data_dir = simulate()
fig, ax_somav, ax = make_figure()
ground_truth, data, time, somav = read_in_data(data_dir)
toplot = np.argmax(somav)
gvmax, gvmin = pl.get_min_max(ground_truth[:, toplot])
new_fname = fname + '.png'
fig_name = sKCSD_utils.make_fig_names(new_fname)
cell_itself = make_larger_cell(data, n_src)
morphology, extent = cell_itself.draw_cell2D()
extent = [ex*1e6 for ex in extent]
draw_somav(ax_somav, time, somav)
k = sKCSD(data.ele_pos,
data.LFP,
data.morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
exact=True,
R_init=R,
sigma=0.3)
path = os.path.join(data_dir, 'lambda_%f_R_%f_n_src_%d' % (lambd, R, n_src))
if sys.version_info < (3, 0):
path = os.path.join(path, "preprocessed_data/Python_2")
else:
path = os.path.join(path, "preprocessed_data/Python_3")
if not os.path.exists(path):
print("Creating", path)
os.makedirs(path)
try:
utils.save_sim(path, k)
except NameError:
noise = numpy.random.normal(scale=std / nl, size=shape)
snr = np.round((new_LFP.var()**.5 / noise.var()**.5))
else:
noise = 0
snr = 0
snrs.append(snr)
new_LFP = data.LFP + noise
k = sKCSD(ele_pos,
new_LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
exact=True,
sigma=0.3)
if sys.version_info < (3, 0):
path = os.path.join(fname_base % nl, "preprocessed_data/Python_2")
else:
path = os.path.join(fname_base % nl, "preprocessed_data/Python_3")
if not os.path.exists(path):
print("Creating", path)
os.makedirs(path)
try:
utils.save_sim(path, k)
ax_gt.set_title('Ground truth')
ax_gt.set_xlabel('time (s)')
ax_gt.set_ylabel('#segment')
new_fname = fname_base + '.png'
fig_name = sKCSD_utils.make_fig_names(new_fname)
for i, datd in enumerate(data_dir):
print(datd)
data = utils.LoadData(datd)
ele_pos = data.ele_pos / scaling_factor
data.LFP = data.LFP / scaling_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6] / scaling_factor
k = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
dist_table_density=50,
R_init=R)
csd = k.values(transformation='segments')
print(csd.shape)
print(csd.max(), csd.min())
cax = ax[i].imshow(csd,
extent=[0, tstop, 1, csd.shape[0]],
origin='lower',
aspect='auto',
cmap='seismic_r',
vmax=gvmax,
vmin=gvmin)
ax[3 + i].set_title(electrode_number[i])
k.L_curve(lambdas=np.array(lambdas), Rs=np.array(R_inits))
fig, ax = plt.subplots(3, 3, figsize=(8, 20))
pl.make_map_plot(ax[0, 0], morpho, extent=extent)
pl.make_map_plot(ax[0, 0], ground_truth_t0, extent=extent, title="Ground truth", vmin=vmin, vmax=vmax, alpha=.75)
vmax, vmin = pl.get_min_max(ground_truth_t0)
for i, di in enumerate(data_dir):
data = utils.LoadData(di)
ele_pos = data.ele_pos/scale_factor
data.LFP = data.LFP/scale_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6]/scale_factor
ker = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
tolerance=tolerance,
dist_table_density=20,
exact=True)
path = os.path.join('simulation', '%s_lambda_%f_R_%f' % (fname_base, l, R))
if sys.version_info < (3, 0):
path = os.path.join(path, "preprocessed_data/Python_2")
else:
path = os.path.join(path, "preprocessed_data/Python_3")
if not os.path.exists(path):
print("Creating", path)
os.makedirs(path)
try:
ax4 = plt.subplot2grid((4, 2), (3, 0))
ax5 = plt.subplot2grid((4, 2), (3, 1))
cell_itself = sKCSDcell(morphology,
ele_pos,
n_src,
tolerance=tolerance,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax)
cell_itself.distribute_srcs_3D_morph()
ker = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
tolerance=tolerance,
exact=True)
path = os.path.join(data_dir, 'lambda_%f_R_%f_n_src_%d' % (l, R, n_src))
if sys.version_info < (3, 0):
path = os.path.join(path, "preprocessed_data/Python_2")
else:
path = os.path.join(path, "preprocessed_data/Python_3")
if not os.path.exists(path):
print("Creating", path)
os.makedirs(path)
try:
utils.save_sim(path, ker)
except NameError:
aspect='auto',
cmap='seismic_r',
vmax=gvmax,
vmin=gvmin)
new_fname = fname_base + '.png'
fig_name = sKCSD_utils.make_fig_names(new_fname)
for i, datd in enumerate(data_dir):
data = utils.LoadData(datd)
ele_pos = data.ele_pos/scaling_factor
data.LFP = data.LFP/scaling_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6]/scaling_factor
k = sKCSD(ele_pos,
data.LFP,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
exact=True)
csd = k.values(transformation='segments')
cax = ax[1, i].imshow(csd,
extent=[0, tstop, 1, 52],
origin='lower',
aspect='auto',
cmap='seismic_r',
vmax=gvmax,
vmin=gvmin)
ax[1, i].set_title(electrode_number[i])
fig.savefig(fig_name,
bbox_inches='tight',
pots = data.LFP / scale_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6] / scale_factor
ground_truth = np.loadtxt(os.path.join(new_path, 'membcurr'))
seglen = np.loadtxt(os.path.join(new_path, 'seglength'))
ground_truth = ground_truth / seglen[:, None] * 1e-3
outs = np.zeros((len(n_srcs), len(lambdas), len(R_inits)))
for i, n_src in enumerate(n_srcs):
for j, l in enumerate(lambdas):
for k, R in enumerate(R_inits):
lambd = l
ker = sKCSD(ele_pos,
pots,
morphology,
n_src_init=n_src,
src_type='gauss_lim',
lambd=lambd,
R_init=R,
skmonaco_available=False)
est_skcsd = ker.values(estimate='CSD',
transformation='segments')
outs[i, j, k] = sKCSD_utils.L1_error(ground_truth, est_skcsd)
print(outs[i, j, k])
fig, ax = plt.subplots(1, 4, sharey=True)
vmax = outs.max()
vmin = outs.min()
for i, ax_i in enumerate(ax):
title = "M = %d" % n_srcs[i]
if not i:
pl.make_map_plot(ax_i,
outs[i],
pots = data.LFP / scale_factor_LFP
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6] / scale_factor
ground_truth = np.loadtxt(os.path.join(new_path, 'membcurr'))
seglen = np.loadtxt(os.path.join(new_path, 'seglength'))
ground_truth = ground_truth / seglen[:, None] * 1e-3
outs = np.zeros((len(n_srcs), len(lambdas), len(R_inits)))
for i, n_src in enumerate(n_srcs):
for j, l in enumerate(lambdas):
for k, R in enumerate(R_inits):
lambd = l / (16 * np.pi**3 * R**2 * n_src)
ker = sKCSD(ele_pos,
pots,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
exact=True)
est_skcsd = ker.values(estimate='CSD',
transformation='segments')
outs[i, j, k] = sKCSD_utils.L1_error(ground_truth, est_skcsd)
print(outs[i, j, k], est_skcsd.min(), est_skcsd.max(),
ground_truth.min(), ground_truth.max(), n_src, l, R)
fig, ax = plt.subplots(1, 4, sharey=True)
vmax = outs.max()
vmin = outs.min()
for i, ax_i in enumerate(ax):
title = "M = %d" % n_srcs[i]
if not i:
morphology[:, 2:6] = morphology[:, 2:6]/scale_factor
ground_truth = np.loadtxt(os.path.join(new_path,
'membcurr'))
seglen = np.loadtxt(os.path.join(new_path,
'seglength'))
ground_truth = ground_truth/seglen[:, None]*1e-3
outs = np.zeros((len(n_srcs), len(lambdas), len(R_inits)))
for i, n_src in enumerate(n_srcs):
for j, l in enumerate(lambdas):
for k, R in enumerate(R_inits):
lambd = l/(16*np.pi**3*R**2*n_src)
ker = sKCSD(ele_pos,
pots,
morphology,
n_src_init=n_src,
src_type='gauss',
lambd=lambd,
R_init=R,
exact=True)
est_skcsd = ker.values(estimate='CSD',
transformation='segments')
outs[i, j, k] = sKCSD_utils.L1_error(ground_truth,
est_skcsd)
print(outs[i, j, k], est_skcsd.min(), est_skcsd.max(), ground_truth.min(), ground_truth.max(), n_src, l, R)
fig, ax = plt.subplots(1, 4, sharey=True)
vmax = outs.max()
vmin = outs.min()
for i, ax_i in enumerate(ax):
title = "M = %d" % n_srcs[i]
if not i: