energies = np.genfromtxt(path + '_energies.txt') n_element = np.genfromtxt(path + '_chargeElement.txt') p_element = np.genfromtxt(path + '_fluxElement.txt')[-1] qp_element = np.genfromtxt(path + '_qpElement.txt') w = energies[-1, 1] - energies[-1, 0] C_chain = 36.0e-15 C_g = 36.0e-18 E_l = E_l_array[idx] E_c = E_c_array[idx] E_j = E_j_array[idx] chain_num = chain_num_array[idx] T1 = T1_array[idx] thermal_factor_diel = (1 + np.exp(-h * w * 1e9 / (kB * T_diel))) thermal_factor_qp = (1 + np.exp(-h * w * 1e9 / (kB * T_qp))) gamma_cap = r_cap(E_l, E_c, E_j, 1, w, p_element, T_diel) * thermal_factor_diel Q_diel = gamma_cap * T1 * 1e-6 tan_diel[idx] = 1.0 / Q_diel gamma_cap1 = r_cap_chain1(C_chain, chain_num, 1, w, p_element, T_diel) * thermal_factor_diel Q_diel1 = gamma_cap1 * T1 * 1e-6 tan_diel_chain[idx] = 1.0 / Q_diel1 gamma_qp_array = r_qp_array(E_l, E_c, E_j, 1, w, p_element) * thermal_factor_qp Q_qp = gamma_qp_array * T1 * 1e-6 x_qp_chain[idx] = 1.0 / Q_qp tan_ind[idx] = w**2 / (8 * E_c * E_l) * tan_diel[idx]
E_j = 4.86 E_l = 1.14 E_c = 0.84 energies = np.genfromtxt(path + '_energies.txt') n_element = np.genfromtxt(path + '_chargeElement.txt') p_element = np.genfromtxt(path + '_fluxElement.txt') qp_element = np.genfromtxt(path + '_qpElement.txt') w = energies[:, fState] - energies[:, iState] gamma_cap = np.zeros(len(p_element)) matrix_element_interp = np.genfromtxt(path + '_matrix_element_interp.txt') thermal_factor_diel = (1 + np.exp(-h * w * 1e9 / (kB * T_diel))) for Q_cap in [Q1, Q2]: for idx in range(len(p_element)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap * (5.0 / w[idx])**alpha, w[idx], p_element[idx], T_diel) * thermal_factor_diel[idx] plt.loglog(w, 1.0 / gamma_cap * 1e6 * p_element**2, linewidth=2.0, linestyle='--', color='magenta') for Q_cap in [Q1, Q2]: for idx in range(len(p_element)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap, w[idx], p_element[idx], T_diel) * thermal_factor_diel[idx] plt.loglog(w, 1.0 / gamma_cap * 1e6 * p_element**2, linewidth=2.0, linestyle='-',
path = root + "\\" + directory + "\\" + fname E_j = 4.86 E_l = 1.14 E_c = 0.84 energies = np.genfromtxt(path + '_energies.txt') n_element = np.genfromtxt(path + '_chargeElement.txt') p_element = np.genfromtxt(path + '_fluxElement.txt') qp_element = np.genfromtxt(path + '_qpElement.txt') w = energies[:, fState] - energies[:, iState] gamma_cap = np.zeros(len(p_element)) matrix_element_interp = np.genfromtxt(path + '_matrix_element_interp.txt') thermal_factor_diel = (1 + np.exp(-h * w * 1e9 / (kB * T_diel))) for Q_cap in [Q1, Q2]: for idx in range(len(p_element)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap * (6.0 / w[idx]) ** alpha, w[idx], p_element[idx], T_diel) * \ thermal_factor_diel[idx] plt.loglog(w, 1.0 / gamma_cap * 1e6 * p_element**2, linewidth=2.0, linestyle='--', color='orange') ''' for Q_cap in [Q1, Q2]: for idx in range(len(p_element)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap, w[idx], p_element[idx], T_diel)*thermal_factor_diel[idx] plt.loglog(w, 1.0 / gamma_cap*1e6 * p_element ** 2, linewidth=2.0, linestyle='-', color = 'orange') ''' directory = 'Box\Research\Paper Images\Coherence summary\\Fluxonium #28' fname = 'T1_summary_2018_04_13.txt' path = root + "\\" + directory + "\\" + fname
####################################################################################### phi_ext = np.genfromtxt(path + '_flux.txt') energies = np.genfromtxt(path + '_energies.txt') n_element = np.genfromtxt(path + '_chargeElement.txt') p_element = np.genfromtxt(path + '_fluxElement.txt') qp_element = np.genfromtxt(path + '_qpElement.txt') w = energies[:, fState] - energies[:, iState] T_diel = 20.0e-3 thermal_factor_diel = (1 + np.exp(-h * w * 1e9 / (kB * T_diel))) T_qp = 20.0e-3 thermal_factor_qp = (1 + np.exp(-h * w * 1e9 / (kB * T_qp))) alpha = 1 for Q_cap in [0.5e6]: for idx in range(len(phi_ext)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap, w[idx], p_element[idx], T_diel) * thermal_factor_diel[idx] plt.semilogy(w * 1000, 1.0 / gamma_cap * 1e6, linewidth=2.0, linestyle='--', color='blue', label='tan_diel = 2e-6') # plt.semilogy(w ** alpha, 1.0 / gamma_cap * 1e6 , linewidth=2.0, linestyle='--', color='orange') # plt.semilogy(phi_ext, 1.0/gamma_cap *1e6, linewidth= 2.0, linestyle ='--', color='orange') for x_qp in [1e-6]: Q_qp = 1.0 / x_qp for idx in range(len(phi_ext)): gamma_qp[idx] = r_qp(E_l, E_c, E_j, Q_qp, w[idx], qp_element[idx]) * thermal_factor_diel[idx] plt.semilogy(w * 1000,
####################################################################################### energies = np.genfromtxt(path + '_energies.txt') n_element = np.genfromtxt(path + '_chargeElement.txt') p_element = np.genfromtxt(path + '_fluxElement.txt') qp_element = np.genfromtxt(path + '_qpElement.txt') w = energies[:, fState] - energies[:, iState] T_diel = 50.0e-3 thermal_factor_diel = (1 + np.exp(-h * w * 1e9 / (kB * T_diel))) # T_qp=100.0e-3 # thermal_factor_qp = (1+np.exp(-h*w*1e9/(kB*T_qp))) plt.figure(figsize=[5, 5]) for Q_cap in [0.2e6]: for idx in range(len(phi_ext)): gamma_cap[idx] = r_cap(E_l, E_c, E_j, Q_cap * 5.0 / w[idx] ** 0.7, w[idx], p_element[idx], T_diel) * \ thermal_factor_diel[idx] plt.semilogy(w, 1.0 / gamma_cap * 1e6, linewidth=2.0, linestyle='--', color='orange') # for x_qp in [20e-7,100e-7]: # Q_qp = 1.0/x_qp # for idx in range(len(phi_ext)): # gamma_qp[idx] = r_qp(E_l, E_c, E_j, Q_qp, w[idx], qp_element[idx]) # plt.semilogy(w, 1.0/(gamma_qp)*1e6, linewidth = 2.0, linestyle='--') # plt.semilogy(w, 1.0/(gamma_qp+gamma_cap)*1e6, linewidth = 2.0, linestyle='-.', color='k') # for T_qp in [0.25, 0.28]: