Ib1 = Vb / rb1
Ib2 = Vb / rb2
Ib3 = Vb / rb3
I7 = Ib1
num_it = 20
I6_vec = np.zeros([num_it])
I4_vec = np.zeros([num_it])
Ia_vec = np.zeros([num_it])
for ii in range(num_it):
print('ii = {} of {}'.format(ii + 1, num_it))
La = (L1 + Ljj(Ic, Ia)) / 2
Lb = L2 + L8
Lc = L3 + L10
Ld = La + Lb
Lt = L6 + Ljj(Ic, I4)
Lq = L6 + Ljj(Ic, I6)
Le = ip(Ld, Lt)
Lf = Le + Lc
Lg = ip(Lf, Lq)
Lh = L4 + L11
Li = ip(Lh, Lf)
Lj = Lq + Li
Lk = ip(Lh, Lq)
Lm = Lc + Lk
Ln = ip(Lt, Lm)
Lp = Lb + Ln
#%% step through time
I1 = np.zeros([len(time_vec)])
I1[0] = I_bias
I2 = np.zeros([len(time_vec)])
state = 'subthreshold'
for ii in range(len(time_vec) - 1):
# if ii < 100:
# print('ii = {:d} of {:d}; time_vec[ii] = {:7.2f}ns; I1[ii] = {:5.2f}uA; I2[ii] = {:5.2f}uA; Ic = {:5.2f}uA'.format(ii+1,len(time_vec),time_vec[ii]*1e9,I1[ii]*1e6,I2[ii]*1e6,Ic*1e6))
# if Vj(I_bias-I2[ii],Ic,r) > 0:
# print('I2[ii] = {:5.2f}uA; Ic = {:5.2f}uA; Vj = {}uV'.format(I2[ii]*1e6,Ic*1e6,Vj(I_bias-I2[ii],Ic,r)*1e6))
# I2[ii+1] = (1-r*dt/Lt)*I2[ii] - ( M/Lt )*( I_drive[ii+1] - I_drive[ii] ) + ( dt/Lt )*Vj(I1[ii],Ic,rj)
Ltt = Lt + Ljj(Ic, I1[ii])
# if ii < 20:
# print('Ltt = {}pH'.format(Ltt*1e12))
if state == 'subthreshold':
V_j = 0
elif state == 'spiking':
# print('spiking')
V_j = Phi0
state = 'subthreshold'
I2[ii + 1] = (1 - r * dt / Ltt) * I2[ii] + (M / Ltt) * (
I_drive[ii + 1] - I_drive[ii]) + (1 / Ltt) * V_j
I1[ii + 1] = I_bias - I2[ii + 1]
if I1[ii + 1] >= Ic:
state = 'spiking'
#%% import WR
peaks_I_j_3, _ = find_peaks(I_j_3, height = 100e-9, distance = 20)
# plot_fq_peaks(time_vec,I_j_df,peaks_I_j_df)
I_j_df__init = I_j_df[initial_ind]
I_j_df__peaks = I_j_df[peaks_I_j_df]
I_j_df__fluxon_wr = I_j_df__peaks[0]-I_j_df__init
I_j_2__init = I_j_2[initial_ind]
I_j_2__peaks = I_j_2[peaks_I_j_2]
I_j_2__fluxon_wr = I_j_2__peaks[0]-I_j_2__init
I_j_3__init = I_j_3[initial_ind]
I_j_3__peaks = I_j_3[peaks_I_j_3]
I_j_3__fluxon_wr = I_j_3__peaks[0]-I_j_3__init
Ljdr1 = Ljj(Ic,Ic)
Ljdr2 = Ljj(Ic,Ic)
Lj2 = Ljj(Ic,Ic)
Lj3 = Ljj(Ic,Ic)
L_ppp = Lj3*L3/(Lj3+L3)
L_pp = L2+L_ppp
L_p = Lj2*L_pp/(Lj2+L_pp)
L_qqq = Ldr1+Lm2+Ljdr1
L_qq = Ldr2+Ljdr2
L_q = L_qq*L_qqq/(L_qq+L_qqq)
Phi0 = p['Phi0']
# I_j_df_fluxon_soen = Phi0/(L1+Ldr2+L_p)
# I_j_df_fluxon_soen = Phi0/(L1+L_p+L_q)
Ldr1 = 10e-12
Ldr2 = 26e-12
L1 = 200e-12
L2 = 77.5e-12
L3 = 77.5e-9
Ib = [71.5e-6, 36e-6, 35e-6]
Lm2 = 10e-12
M = np.sqrt(200e-12 * Lm2)
#%% calculate
I_drive_vec = np.arange(19e-6, 27e-6, 1e-6)
Idr1_vec = np.zeros([len(I_drive_vec), 1])
Idr2_vec = np.zeros([len(I_drive_vec), 1])
Lj0 = Ljj(Ic, 0)
Iflux = 0
Idr2_prev = (
(Lm2 + Ldr1 + Lj0) * Ib[0] + M * Iflux) / (Lm2 + Ldr1 + Ldr2 + 2 * Lj0 +
(Lm2 + Ldr1 + Lj0) *
(Ldr2 + Lj0) / L1)
Idr1_prev = Ib[0] - (1 + (Ldr2 + Lj0) / L1) * Idr2_prev
for ii in range(len(I_drive_vec)):
for jj in range(10):
Idr1_next, Idr2_next = dendrite_current_splitting(
Ic, I_drive_vec[ii], Ib[0], Ib[1], Ib[2], M, Lm2, Ldr1, Ldr2, L1,
L2, L3, Idr1_prev, Idr2_prev)
Idr1_prev = Idr1_next
Idr2_prev = Idr2_next
Idr1_vec[ii] = Idr1_next
#%% calculated quantities
M1 = np.sqrt(L3 * L4)
M2 = -np.sqrt(L5 * L6)
#%% iterate
Ia = 0
I3 = 0
num_it = 10
Ia_vec = np.zeros([num_it])
I3_vec = np.zeros([num_it])
for ii in range(num_it):
# print('ii = {} of {}'.format(ii+1,num_it))
La = L1 + Ljj(Ic, Ia)
Lb = L4 + Ljj(Ic, I3)
Lc = L2 + L6
Ld = 2 * (Lb + Lc) + La
Ia = (Lb / Ld) * Ib1 + (M1 / Ld) * Ib2 - (M2 / Ld) * Isy
Ia_vec[ii] = Ia
I3 = Ib1 - 2 * Ia
I3_vec[ii] = I3
print(
'Ib1 = {:9.4f}uA; Ib2 = {:9.4f}uA; Isy = {:9.4f}uA; I3 = {:9.4f}uA; Ia = {:9.4f}uA'
.format(Ib1, Ib2, Isy, I3, Ia))
#%% plot
I7a = 0
I7b = 0
Ib1 = Vb / rb1
Ib2 = Vb / rb2
Ib3 = Vb / rb3
num_it = 20
I6_vec = np.zeros([num_it])
I4_vec = np.zeros([num_it])
Ia_vec = np.zeros([num_it])
for ii in range(num_it):
print('ii = {} of {}'.format(ii + 1, num_it))
La = (L1 + Ljj(Ic, Ia)) / 2
Lb = L2 + La
Lc = ip(Lb, L6 + Ljj(Ic, I4))
Ld = L3 + Lc
Le = ip(Ld, Ljj(Ic, I6))
Lf = ip(Ld, L4)
Lg = Lf + L6 + Ljj(Ic, I6)
Lh = ip(L4, L6 + Ljj(Ic, I6))
Li = L3 + Lh
Lj = ip(Lb, Li)
Lk = Lj + L6 + Ljj(Ic, I4)
Lm = Ld + L6 + Ljj(Ic, I6)
Ln = Ld + L4
Lp = Lb + L6 + Ljj(Ic, I4)
Lq = L4 + L6 + Ljj(Ic, I6)
import numpy as np
from matplotlib import pyplot as plt
from _functions import Ljj
from _util import physical_constants, color_dictionary, set_plot_params
p = physical_constants()
colors = color_dictionary()
fig_size = set_plot_params('large') # 'publication' or 'large'
plt.close('all')
#%% inputs
Ic = 100e-6
L1 = 200e-12
L2 = 5.17e-12
k = 1
Ib = 140e-6
I_in = 10e-6
#%%
L_sq = 2 * L2
L_sq_tot = 2 * L2 + Ljj(Ic, 86e-6) + Ljj(Ic, 54e-6)
# L_sq_tot = 2*L2 # + Ljj(Ic,0) + Ljj(Ic,Ic)
I2__with_JJs = Ib / 2 + (k * np.sqrt(L1 * L2) / L_sq_tot) * I_in
I2__without_JJs = Ib / 2 + (k * np.sqrt(L1 * L2) / L_sq) * I_in
print('with JJs: I2 = {:7.4f}uA'.format(I2__with_JJs * 1e6))
print('without JJs: I2 = {:7.4f}uA'.format(I2__without_JJs * 1e6))
k2 = [0.5,0.75]
L_dc3 = 10e-12 # pH
L_di1 = [0,2e-9,10e-9]
I_ind_over_sat = np.zeros([len(k1),len(L_di1),len(N_vec)])
fig, ax = plt.subplots(nrows = 1, ncols = 1, sharex = True, sharey = False)
plt.suptitle('Cross talk when all loops are saturated\nIc = {:6.2f}uA, I_di_sat = {:6.2f}uA, L_dc1 = {:5.2f}pH, L_dc2 = {:4.2f}L_dc3, L_dc3 = {:6.2f}pH'.format(Ic*1e6,I_di_sat*1e6,L_dc1*1e12,alpha,L_dc3*1e12))
color_list = [['blue3','yellow3','green3'],['blue2','yellow2','green2']]
linestyle_list = ['solid','dotted']
for ii in range(len(k1)):
for jj in range(len(L_di1)):
L_di2 = (1/L_dc3) * ( (Phi_dr_max/(k1[ii]*k2[ii]*N_vec[:]*I_di_sat)) * ( N_vec[:] + (2*Ic/p['Phi0'])*L_dc3*(1+alpha) ) )**2
L_di_tot = Ljj(Ic,0) + L_di1[jj] + L_di2
L_dc_tot = N_vec[:]*L_dc1 + L_dc3*(1+alpha)
# print('L_di2[0] = {:5.2f}pH, L_di2[-1] = {:5.2f}pH, L_di_tot[0] = {:5.2f}nH, L_di_tot[-1] = {:5.2f}nH'.format(L_di2[0]*1e12,L_di2[-1]*1e12,L_di_tot[0]*1e9,L_di_tot[-1]*1e9))
I_ind_over_sat[ii,jj,:] = ( (k1[ii]**2 * L_di2 * L_dc1) / (L_di_tot*L_dc_tot) ) * N_vec[:]
ax.loglog(N_vec[:],I_ind_over_sat[ii,jj,:], color = colors[color_list[ii][jj]], linestyle = linestyle_list[ii], label = 'k = {:4.2f}, L_di1 = {:5.2f}nH'.format(k1[ii],L_di1[jj]*1e9))
ax.set_ylabel(r'$I^{di}_{ind}/I^{di}_{sat}$')
ax.set_xlabel(r'$N$')
ax.tick_params(axis = 'both')
ax.grid(which = 'major', axis = 'both')
ax.set_xlim([N_vec[0],N_vec[-1]])
ax.set_ylim([1e-4,1])
# plt.subplots_adjust(wspace=0, hspace=0)
ax.legend()
plt.tight_layout()
Ib1 = Vb / rb1
Ib2 = Vb / rb2
Ib3 = Vb / rb3
I7 = Ib1
num_it = 20
I6_vec = np.zeros([num_it])
I4_vec = np.zeros([num_it])
Ia_vec = np.zeros([num_it])
for ii in range(num_it):
print('ii = {} of {}'.format(ii + 1, num_it))
La = (L1 + Ljj(Ic, Ia)) / 2
Ld = L6 + Ljj(Ic, I4)
Le = L6 + Ljj(Ic, I6)
Lf = Le + Lc + Ld
alpha = 2 * (1 + Lb / Ld - Ld / Lf) - La / Ld
Ia = (-M1 * (1 / Lf + 1 / Ld) * Ib3 + (1 - ((Lc + Ld) / Lf)) * Ib1 +
(M1 / Lf) * Ib2 - (M2 / Ld) * Isy1 + (M3 / Lf) * Isy2) / alpha
Ia_vec[ii] = Ia
I6 = Ib1 + (M1 / Ld) * Ib3 + (M2 / Ld) * Isy1 - (2 * (1 + (Lb / Ld)) -
La / Ld) * Ia
I6_vec[ii] = I6
I4 = Ib1 - I6 - 2 * Ia
error_mat_master__mu1_mu2__exp_pls_trn_per100ns)
# error_mat_master__mu1_mu2 = ( error_mat_master__mu1_mu2__no_leak__vary_Ldi
# +error_mat_master__mu1_mu2__vary_taudi
# +error_mat_master__mu1_mu2__vary_Idrive_20uA
# +error_mat_master__mu1_mu2__vary_Idrive_25uA
# +error_mat_master__mu1_mu2__vary_Idrive_30uA
# +error_mat_master__mu1_mu2__sq_pls_trn_per20ns
# +error_mat_master__mu1_mu2__sq_pls_trn_per100ns
# +error_mat_master__mu1_mu2__exp_pls_trn_per100ns
# +error_mat_master__mu1_mu2__exp_pls_trn_per1000ns )
ind_best = np.where(error_mat_master__mu1_mu2 == np.amin(
error_mat_master__mu1_mu2)) #error_mat.argmin()
mu1_best = mu1_vec[ind_best[0]][0]
mu2_best = mu2_vec[ind_best[1]][0]
print('mu1_best = {}'.format(mu1_best))
print('mu2_best = {}\n\n'.format(mu2_best))
pre_str = 'dend_fit_master'
title_string = '{}; mu1_best = {:1.2f}, mu2_best = {:1.2f}'.format(
pre_str, mu1_best, mu2_best)
save_str_1 = '{}__master_error__mu1_mu2'.format(pre_str)
plot_error_mat(error_mat_master__mu1_mu2[:, :], mu1_vec, mu2_vec, 'mu1', 'mu2',
title_string, save_str_1)
#%% load data
# load_string = 'session_data__wr_fits__no_leak__vary_L_di__finding_amp_mu1_mu2+mu3_mu4__2020-04-08_14-29-15.dat'
# data_array_imported = load_session_data(load_string)
Ljj(40e-6, 35e-6)