def setUpClass(cls):
sc = 1e6
#Very branchy neuron (for testing morphology loop)
cls.data = LoadData(os.path.join(sample_data_path, "gang_7x7_200"))
cls.data.morphology[:,2:6] = cls.data.morphology[:,2:6]/sc
n_src = 1000
cls.cell = sKCSDcell(cls.data.morphology,cls.data.ele_pos/sc,n_src)
cls.branch_points = []
for loop in cls.cell.loops:
if loop[0] != loop[1]+1 and loop[0] != loop[1]-1:
cls.branch_points.append(loop.tolist())
cls.morpho = cls.cell.morphology[:,2:5]
cls.coor3D, cls.zero = cls.cell.point_coordinates(cls.morpho)
#ball and stick neuron
data = LoadData(os.path.join(sample_data_path, "ball_and_stick_8"))
data.morphology[:,2:6] = data.morphology[:,2:6]/sc
cls.cell_small = sKCSDcell(data.morphology,data.ele_pos/sc, 104)
cls.cell_small_segment_coordinates_loops = cls.cell_small.coordinates_3D_loops()
cls.small_points = np.zeros((len(cls.cell_small.morphology),))
dic = cls.cell_small_segment_coordinates_loops
for seg in dic:
ps = dic[seg]
for p in ps:
cls.small_points[p[2]] += 1
cls.cell_small_segment_coordinates = cls.cell_small.coordinates_3D_segments()
#Y-shaped neuron
data = LoadData(os.path.join(sample_data_path, "Y_shaped_neuron"))
data.morphology[:,2:6] = data.morphology[:,2:6]/sc
cls.cell_y = sKCSDcell(data.morphology,data.ele_pos/sc, 200)
cls.cell_y_segment_coordinates_loops = cls.cell_y.coordinates_3D_loops()
cls.cell_y_segment_coordinates = cls.cell_y.coordinates_3D_segments()
def make_larger_cell(data, n_sources=n_src):
if data.ele_pos[:, 0].max() > data.morphology[:, 2].max():
xmax = data.ele_pos[:, 0].max() + 50e-6
else:
xmax = data.morphology[:, 2].max() + 50e-6
if data.ele_pos[:, 0].min() < data.morphology[:, 2].min():
xmin = data.ele_pos[:, 0].min() - 50e-6
else:
xmin = data.morphology[:, 2].min() - 50e-6
if data.ele_pos[:, 1].max() > data.morphology[:, 3].max():
ymax = data.ele_pos[:, 1].max() + 50e-6
else:
ymax = data.morphology[:, 3].max() + 50e-6
if data.ele_pos[:, 1].min() < data.morphology[:, 3].min():
ymin = data.ele_pos[:, 1].min() - 50e-6
else:
ymin = data.morphology[:, 3].min() - 50e-6
if data.ele_pos[:, 2].max() > data.morphology[:, 4].max():
zmax = data.ele_pos[:, 2].max() + 50e-6
else:
zmax = data.morphology[:, 4].max() + 50e-6
if data.ele_pos[:, 2].min() < data.morphology[:, 4].min():
zmin = data.ele_pos[:, 2].min() - 50e-6
else:
zmin = data.morphology[:, 4].min() - 50e-6
return sKCSDcell(data.morphology, data.ele_pos, n_sources, xmin=xmin, xmax=xmax, zmin=zmin, zmax=zmax, ymin=ymin, ymax=ymax, tolerance=2e-6)
def load_sim(path):
"""
Load sKCSD estimation results (CSD, potential and cell specifics).
Parameters
----------
path: str
Returns
-------
est_csd : np.array
est_pot : np.array
cell_obj : sKCSDcell object
"""
est_csd = np.load(os.path.join(path, "csd.npy"))
est_pot = np.load(os.path.join(path, "pot.npy"))
try:
with open(os.path.join(path, "cell_data"), 'r') as handle:
cell_data = json.load(handle)
except Exception as error:
print('Could not load', os.path.join(path, "cell_data"))
return est_csd, est_pot, None
morphology = np.array(cell_data['morphology'])
ele_pos = np.array(cell_data['ele_pos'])
cell_obj = kcsd.sKCSDcell(morphology, ele_pos, cell_data['n_src'])
return est_csd, est_pot, cell_obj
def load_sim(path):
"""
Load sKCSD estimation results (CSD, potential and cell specifics).
Parameters
----------
path: str
Returns
-------
est_csd : np.array
est_pot : np.array
cell_obj : sKCSDcell object
"""
est_csd = np.load(os.path.join(path, "csd.npy"))
est_pot = np.load(os.path.join(path, "pot.npy"))
try:
with open(os.path.join(path, "cell_data"), 'r') as handle:
cell_data = json.load(handle)
except Exception as error:
print('Could not load', os.path.join(path, "cell_data"))
return est_csd, est_pot, None
morphology = np.array(cell_data['morphology'])
ele_pos = np.array(cell_data['ele_pos'])
cell_obj = kcsd.sKCSDcell(morphology, ele_pos, cell_data['n_src'])
return est_csd, est_pot, cell_obj
def setUpClass(cls):
sc = 1e6
#Very branchy neuron (for testing morphology loop)
cls.data = LoadData(os.path.join(sample_data_path, "gang_7x7_200"))
cls.data.morphology[:,2:6] = cls.data.morphology[:,2:6]/sc
n_src = 1000
cls.cell = sKCSDcell(cls.data.morphology,cls.data.ele_pos/sc,n_src)
cls.cell.distribute_srcs_3D_morph()
cls.branch_points = []
for loop in cls.cell.loops:
if loop[0] != loop[1]+1 and loop[0] != loop[1]-1:
cls.branch_points.append(loop.tolist())
cls.morpho = cls.cell.morphology[:,2:5]
cls.coor3D, cls.zero = cls.cell.point_coordinates(cls.morpho)
#ball and stick neuron
data = LoadData(os.path.join(sample_data_path, "ball_and_stick_8"))
data.morphology[:,2:6] = data.morphology[:,2:6]/sc
cls.cell_small = sKCSDcell(data.morphology,data.ele_pos/sc,10)
cls.cell_small.distribute_srcs_3D_morph()
cls.cell_small_segment_coordinates_loops = cls.cell_small.coordinates_3D_loops()
cls.small_points = np.zeros((len(cls.cell_small.morphology),))
dic = cls.cell_small_segment_coordinates_loops
for seg in dic:
ps = dic[seg]
for p in ps:
cls.small_points[p[2]] += 1
cls.cell_small_segment_coordinates = cls.cell_small.coordinates_3D_segments()
#Y-shaped neuron
data = LoadData(os.path.join(sample_data_path, "Y_shaped_neuron"))
data.morphology[:,2:6] = data.morphology[:,2:6]/sc
cls.cell_y = sKCSDcell(data.morphology,data.ele_pos/sc,10)
cls.cell_y.distribute_srcs_3D_morph()
cls.cell_y_segment_coordinates_loops = cls.cell_y.coordinates_3D_loops()
cls.y_points = {}
dic = cls.cell_y_segment_coordinates_loops
for seg in dic:
ps = dic[seg]
for p in ps:
s = '%d%d%d'%(p[0],p[1],p[2])
if s not in cls.y_points:
cls.y_points[s] = 1
else:
cls.y_points[s] += 1
cls.cell_y_segment_coordinates = cls.cell_y.coordinates_3D_segments()
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)
L1 = []
cell_itself = sKCSDcell(morphology,
ele_pos,
n_src,
tolerance=tolerance,
xmin=-1000e-6,
xmax=1000e-6,
ymin=-1000e-6,
ymax=1000e-6)
ground_truth_3D = cell_itself.transform_to_3D(ground_truth, what="morpho")
ground_truth_t0 = ground_truth_3D[:, :, :, t0].sum(axis=2)
vmax, vmin = pl.get_min_max(ground_truth_t0)
morpho, extent = cell_itself.draw_cell2D(axis=2)
extent = [extent[-2], extent[-1], extent[0], extent[1]]
lambd = l / (2 * (2 * np.pi)**3 * R**2 * n_src)
fig, ax = plt.subplots(3, 3, figsize=(8, 20))
pl.make_map_plot(ax[0, 0], morpho, extent=extent, circles=False)
pl.make_map_plot(ax[0, 0],
ground_truth_t0,
extent=extent,
title="Ground truth",
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:
pass
skcsd, pot, morphology_file, ele_pos, n_src = utils.load_sim(path)
cell_obj = sKCSDcell(morphology_file, ele_pos, n_src)
csd = cell_obj.transform_to_segments(skcsd)
draw_ground_truth_skcsd_segments(ax, ground_truth, csd, time, gvmin, gvmax)
csd_3D = cell_itself.transform_to_3D(skcsd[:, toplot])
gt_3D = cell_itself.transform_to_3D(ground_truth[:, toplot],
what="morpho")
draw_ground_truth_skcsd_3D(ax, gt_3D, csd_3D, data,
gvmin, gvmax, time[toplot])
fig.subplots_adjust(wspace=0.5)
fig.subplots_adjust(hspace=0.5)
add_figure_labels(ax, ax_somav)
plt.savefig(fig_name,
bbox_inches='tight',
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")
vmax, vmin = pl.get_min_max(ground_truth_grid[:, :, :, t1].sum(axis=1))
gdt1 = ground_truth_grid[:, :, :, t1].sum(axis=1)
morpho, extent = cell.draw_cell2D(axis=1)
extent = [extent[-2], extent[-1], extent[0], extent[1]]
new_ele_pos = np.array([ele_pos[:, 2], ele_pos[:, 0]]).T
pl.make_map_plot(ax[0, 0],
gdt1,
vmin=vmin,
vmax=vmax,
extent=extent,
alpha=.9,
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)
L1 = []
cell_itself = sKCSDcell(morphology,
ele_pos,
n_src,
tolerance=tolerance,
xmin=-1000e-6,
xmax=1000e-6,
ymin=-1000e-6,
ymax=1000e-6)
cell_itself.distribute_srcs_3D_morph()
ground_truth_3D = cell_itself.transform_to_3D(ground_truth,
what="morpho")
ground_truth_t0 = ground_truth_3D[:, :, :, t0].sum(axis=2)
vmax, vmin = pl.get_min_max(ground_truth_t0)
morpho, extent = cell_itself.draw_cell2D(axis=2)
lambd = l/(2*(2*np.pi)**3*R**2*n_src)
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)
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
dt = c.cell_parameters['dt']
somav = np.loadtxt(os.path.join(data_dir, 'somav.txt'))
time = np.linspace(0, tstop, len(somav))
t0 = np.argmax(somav[int(400. / dt):int(600. / dt)]) + int(400. / dt)
cell_itself = sKCSDcell(morphology,
ele_pos,
n_src,
tolerance=tolerance,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax)
cell_itself.distribute_srcs_3D_morph()
ground_truth_3D = cell_itself.transform_to_3D(ground_truth, what="morpho")
vmax, vmin = pl.get_min_max(ground_truth_3D[:, :, :,
t0 - tt:t0 + tt].sum(axis=(2,
3)))
morpho, extent = cell_itself.draw_cell2D(axis=2)
gvmax, gvmin = pl.get_min_max(ground_truth)
l = 0.1
R = 64. / np.sqrt(2) / scale_factor
fig2 = plt.figure()
axfig21 = fig2.add_subplot(2, 1, 1)
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)
cell.distribute_srcs_3D_morph()
ground_truth_grid = cell.transform_to_3D(ground_truth, what="morpho")
vmax, vmin = pl.get_min_max(ground_truth_grid[:, :, :, t1].sum(axis=1))
gdt1 = ground_truth_grid[:,:,:,t1].sum(axis=1)
morpho, extent = cell.draw_cell2D(axis=1)
pl.make_map_plot(ax[0, 0],
gdt1,
vmin=vmin,
vmax=vmax,
extent=extent,
alpha=.9,
morphology=morpho,
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,
ele_pos,
n_src,
xmin=-120e-6,
xmax=120e-6,
ymin=-200e-6,
ymax=200e-6,
zmin=-150e-6,
zmax=550e-6))
morpho, extent = cell_itself[i].draw_cell2D(axis=1)
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,
ele_pos,
n_src,
xmin=-120e-6,
xmax=120e-6,
ymin=-200e-6,
ymax=200e-6,
zmin=-150e-6,
zmax=550e-6))
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
dt = c.cell_parameters['dt']
somav = np.loadtxt(os.path.join(data_dir,
'somav.txt'))
time = np.linspace(0, tstop, len(somav))
t0 = np.argmax(somav[int(400./dt):int(600./dt)])+int(400./dt)
cell_itself = sKCSDcell(morphology, ele_pos, n_src, tolerance=tolerance, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
cell_itself.distribute_srcs_3D_morph()
ground_truth_3D = cell_itself.transform_to_3D(ground_truth,
what="morpho")
vmax, vmin = pl.get_min_max(ground_truth_3D[:, :, :, t0-tt:t0+tt].sum(axis=(2,3)))
morpho, extent = cell_itself.draw_cell2D(axis=2)
gvmax, gvmin = pl.get_min_max(ground_truth)
l = 0.1
R = 64./np.sqrt(2)/scale_factor
fig2 = plt.figure()
axfig21 = fig2.add_subplot(2,1,1)
axfig22 = fig2.add_subplot(2,1,2)
axfig21.imshow(ground_truth, interpolation="none", origin="lower", vmin=gvmin, vmax=gvmax, cmap=plt.cm.bwr_r)
lambd = l/(2*(2*np.pi)**3*R**2*n_src)
fig = plt.figure(figsize=(8, 20))