dvdt_cut = dvdt_sc[idx_start:idx_end]
t_cut = t_exp[idx_start:idx_end]
i_exp_cut = i_inj_sc[idx_start:idx_end]
currents_cut = np.zeros(len(t_cut))
# linear regression
weights, residual, y, X = linear_regression(dvdt_cut, i_exp_cut, currents_cut, i_pas=0, cell_area=cell_area)
# plots
pl.figure()
pl.plot(t_exp, dvdt_exp, 'k')
pl.plot(t_cut, dvdt_cut, 'r')
pl.title('Visualize cut of dV/dt')
pl.show()
plot_fit(y, X, weights, t_cut, [])
# transform into change in cell area
# 1) area_new = area_old * cm_fit
area_new = cell_area * weights[-1]
# 2) r=L = np.sqrt(area_new / (2*np.pi*1e-8))
r = np.sqrt(area_new / (2*np.pi*1e-8))
L = np.sqrt(area_new / (2*np.pi*1e-8))
# 3) diam = 2*r
diam = 2*r
fitter.cell.soma.L = L
fitter.cell.soma.diam = diam
cell_area = fitter.cell.soma(.5).area() * 1e-8
print 'L: ' + str(L)
print 'diam: ' + str(diam)
print 'cell area: ' + str(cell_area * 1e8)
# linear regression
weights, residual, y, X = linear_regression(dvdt_sc, i_inj_sc, currents_sc, i_pas=0, Cm=Cm)
#weights, residual, y, X = linear_regression(dvdt_sc, i_inj_sc, currents_sc, i_pas=0, Cm=None, cell_area=cell_area)
# output
print 'channels: ' + str(channel_list)
print 'weights: ' + str(weights)
# plot fit
# plot in three parts
merge_points = [0, 22999, 26240, 550528]
for i in range(len(merge_points)-1):
y_plot = y[merge_points[i]:merge_points[i+1]]
X_plot = X[merge_points[i]:merge_points[i+1], :]
t_plot = t_exp[merge_points[i]:merge_points[i+1]]
plot_fit(y_plot, X_plot, weights, t_plot, channel_list, save_dir=save_dir+str(i))
# save
np.savetxt(save_dir+'/best_candidate_'+str(trial)+'.txt', weights)
np.savetxt(save_dir+'/error_'+str(trial)+'.txt', np.array([residual]))
# simulate
#cm = weights[-1]
for i, w in enumerate(weights[:]):
keys = ['soma', '0.5', channel_list[i], 'gbar']
if channel_list[i] == 'pas':
keys = ['soma', '0.5', channel_list[i], 'g']
problem.cell.update_attr(keys, w)
elif 'ion' in channel_list[i]:
keys = None
else:
