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]: