fig, AX = ge.figure(axes=(3, 1))
for i, color in enumerate(['blue', 'green', 'red']):
array = calib[correc][color]
array = (array - np.min(array)) / (np.max(array) - np.min(array))
def to_minimize(coefs):
return np.sum(np.abs(array - func(lum, coefs))**2)
residual = minimize(to_minimize, [1, 1], bounds=[(0.5, 2), (0.1, 3.)])
print('For %s and %s, gamma=' % (correc, color), residual.x[1])
# ge.title(AX[i], "a=%.2f, k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1], residual.x[2]), color=getattr(ge, color), size='small')
ge.title(AX[i],
"k=%.2f, $\gamma$=%.2f" % (residual.x[0], residual.x[1]),
color=getattr(ge, color),
size='small')
ge.scatter(lum,
array,
ax=AX[i],
color=getattr(ge, color),
label='data',
ms=3)
ge.plot(lum,
func(lum, residual.x),
ax=AX[i],
lw=3,
alpha=.5,
color=getattr(ge, color),
label='fit')
ge.annotate(AX[i],
if sys.argv[-1] == 'syn-demo':
from datavyz import nrnvyz, ges as ge
_, neuron, SEGMENTS = initialize_sim(Model)
vis = nrnvyz(SEGMENTS, ge=ge)
fig, AX = ge.figure(axes=(len(NSYNs), 1),
figsize=(.8, 1.2),
wspace=0,
left=0,
top=0.3,
bottom=0,
right=0)
for nsyn, ax in zip(NSYNs, AX):
ge.title(ax, '$N_{syn}$=%i' % nsyn, size='xx-small')
vis.plot_segments(SEGMENTS['comp_type'] != 'axon',
bar_scale_args=dict(Ybar=50,
Xbar_label='50um',
Xbar=50,
Ybar_label='',
loc='top-right',
xyLoc=(-110, 90),
size='xx-small'),
ax=ax)
vis.add_dots(ax, loc_syn0 + np.arange(nsyn), 10, ge.orange)
ge.show()
elif sys.argv[-1] == 'run-demo':
syn_loc0 = 9
]
fig, AX = ge.figure(figsize=(.6, .6),
axes=(int(len(file_list) / 8) + 1, 8))
n = 0
for fn, ax in zip(file_list, ge.flat(AX)):
morpho = ntwk.Morphology.from_swc_file(
os.path.join(args.directory, fn))
SEGMENTS = ntwk.morpho_analysis.compute_segments(morpho)
colors = [
ge.green if comp_type == 'axon' else ge.red
for comp_type in SEGMENTS['comp_type']
]
plot_nrn_shape(ge, SEGMENTS, colors=colors, ax=ax)
ge.title(ax, fn.split('-')[0], bold=True, style='italic', size='')
n += 1
while n < len(ge.flat(AX)):
ge.flat(AX)[n].axis('off')
n += 1
else:
if args.movie_demo:
t = np.arange(100) * 1e-3
Quant = np.array([.5*(1-np.cos(20*np.pi*t))*i/len(SEGMENT_LIST['xcoords']) \
for i in np.arange(len(SEGMENT_LIST['xcoords']))])*20-70
ani = show_animated_time_varying_trace(
1e3 * t,
Quant,
SEGMENT_LIST,
fig,
vis.add_dots(ax, DIST, 50, ge.orange)
ge.show()
elif args.protocol == 'syn-number':
tstop, t0_stim = 300, 20
if args.plot:
data = load_dict(
os.path.join('data', 'branco-syn-number-%s.npz' % suffix))
fig, AX = ge.figure(axes=(1, 2),
figsize=(.7, 1),
right=4.,
hspace=0.1,
bottom=0.1)
ge.title(AX[0],
'%i to %i synapses (by 2)' % (N_PULSES[0], N_PULSES[-1]))
for synapses_loc, label, ax in zip([PROX, DIST], ['prox', 'dist'],
AX):
for i in range(len(data['v-%s' % label])):
ax.plot(data['t'],
data['v-%s' % label][i],
label=label,
color='k',
lw=1)
ge.set_plot(ax, [], ylim=[-76, -45], xlim=[0, tstop])
ge.draw_bar_scales(ax,
Xbar=50.,
Xbar_label='50ms',
Ybar=5.,
Ybar_label='5mV',