def read_in_data(ddir):
seglen = np.loadtxt(os.path.join(ddir, 'seglength'))
n_seg = len(seglen)
ground_truth = np.loadtxt(os.path.join(ddir, 'membcurr'))
time = np.loadtxt(os.path.join(ddir, 'tvec.txt'))
somav = np.loadtxt(os.path.join(ddir, 'somav.txt'))
ground_truth = ground_truth/seglen[:, None]*1e-3
Data = utils.LoadData(ddir)
Data.ele_pos = Data.ele_pos/scaling_factor
Data.LFP = Data.LFP/scaling_factor_LFP
Data.morphology[:, 2:6] = Data.morphology[:, 2:6]/scaling_factor
return ground_truth, Data, time, somav
def load_data(data_dir):
"""
Load sKCSD estimation data and measured LFPs
pots are the measured LFPs
Parameters
----------
data_dir : str
Returns
-------
pots : np.array
est_csd : np.array
est_pot : np.array
cell_obj : sKCSDcell object
"""
try:
data = sKCSD_utils.LoadData(data_dir)
except KeyError:
print('Could not load %s LFP from' % data_dir)
data = None
if data:
pots = data.LFP
else:
pots = None
try:
est_csd, est_pot, morphology, ele_pos, n_src = sKCSD_utils.load_sim(
data_dir)
cell_object = kcsd.sKCSDcell(morphology, ele_pos, n_src)
except IOError:
if sys.version_info < (3, 0):
path = os.path.join(data_dir, "preprocessed_data/Python_2")
else:
path = os.path.join(data_dir, "preprocessed_data/Python_3")
est_csd, est_pot, morphology, ele_pos, n_src = sKCSD_utils.load_sim(
path)
cell_object = kcsd.sKCSDcell(morphology, ele_pos, n_src)
return (pots, est_csd, est_pot, cell_obj)
weight=0.01,
n_syn=1000,
simulate_what='oscillatory',
electrode_distribution=1,
electrode_orientation=3,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
colnb=colnb,
rownb=rownb,
dt=dt)
c.save_for_R_kernel()
data_dir.append(c.return_paths_skCSD_python())
data = utils.LoadData(data_dir[0])
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[0], 'seglength'))
ground_truth = np.loadtxt(os.path.join(
data_dir[0], 'membcurr')) / seglen[:, None] * 1e-3
somav = np.loadtxt(os.path.join(data_dir[0], 'somav.txt'))
time = np.linspace(0, tstop, len(somav))
plt.figure()
plt.plot(time, somav)
dt = c.cell_parameters['dt']
t0 = int(247.5 / dt) #np.argmax(somav)
print(t0 * dt)
electrode_distribution=1,
dt=dt)
data_dir = c.return_paths_skCSD_python()
seglen = np.loadtxt(os.path.join(data_dir, 'seglength'))
ground_truth = np.loadtxt(os.path.join(data_dir, 'membcurr'))
ground_truth = ground_truth / seglen[:, None] * 1e-3
fig, ax = plt.subplots(2, 3, figsize=(8, 20))
fname = "Figure_9.png"
fig_name = sKCSD_utils.make_fig_names(fname)
data = utils.LoadData(data_dir)
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
std = data.LFP.var()**.5
shape = data.LFP.shape
cell = sKCSDcell(morphology,
ele_pos,
n_src,
tolerance=2e-6,
xmin=-120e-6,
xmax=120e-6,
zmin=-50e-6,
zmax=550e-6)
ground_truth_grid = cell.transform_to_3D(ground_truth, what="morpho")
cax = ax_gt.imshow(ground_truth,
extent=[0, tstop, 1, ground_truth.shape[0]],
origin='lower',
aspect='auto',
cmap='seismic_r',
vmax=gvmax,
vmin=gvmin)
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())
morphology=2,
colnb=rownb[i],
rownb=colnb[i],
xmin=-100,
xmax=xmax[i],
ymin=-100,
ymax=ymax[i],
tstop=tstop,
seed=1988,
weight=0.01,
n_syn=100,
simulate_what="symmetric",
electrode_orientation=electrode_orientation,
dt=dt)
data_dir.append(c.return_paths_skCSD_python())
data = utils.LoadData(data_dir[i])
ele_pos = data.ele_pos/scaling_factor
morphology = data.morphology
morphology[:, 2:6] = morphology[:, 2:6]/scaling_factor
if i == 0:
cell_itself.append(sKCSDcell(morphology,
ele_pos,
n_src,
xmin=-120e-6,
xmax=120e-6,
ymin=-200e-6,
ymax=200e-6,
zmin=-150e-6,
zmax=550e-6))
else:
cell_itself.append(sKCSDcell(morphology,
morphology=2,
colnb=colnb,
rownb=rownb,
xmin=0,
xmax=500,
ymin=-100,
ymax=100,
tstop=tstop,
seed=1988,
weight=0.04,
n_syn=100,
electrode_orientation=2,
simulate_what='symmetric',
dt=dt)
new_path = c.return_paths_skCSD_python()
data = utils.LoadData(new_path)
ele_pos = data.ele_pos / scale_factor
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,
