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Data vs theory.py
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# Plot intensity data
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import rc
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
from qutip import*
rc('text', usetex=False)
plt.close("all")
fig=plt.figure()
ax = plt.gca()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
##############################################################################################################################
################################################### Data ###############################################################
##############################################################################################################################
contrast_max = 1
contrast_min = -2
#File path
directory = 'D:\Data\Fluxonium #28\Two_tone'
measurement = '042218_Two_tone_spec_YOKO_2to0mA_Cav_7.3374GHz&-25dBm_QuBit2.5to5.8GHz&5dBm'
path = directory + '\\' + measurement
#Read data
current = np.genfromtxt(path + '_CURRENT.csv')#*1e3 b
current = current[1:-1]
freq = np.genfromtxt(path + '_FREQ.csv')
freq = freq[1:]
data = np.genfromtxt(path + '_PHASEMAG.csv')
phase = data[1:,0] #phase is recorded in rad
mag = data[1:,1]
Z = np.zeros((len(current),len(freq)))
for idx in range(len(current)):
temp = np.unwrap(phase[idx*len(freq):(idx+1)*len(freq)])
temp = temp*180/(np.pi)
# temp = mag[idx*len(freq):(idx+1)*len(freq)]
Z[idx,:] = temp - np.mean(temp)
plt.figure(1)
X,Y = np.meshgrid(current,freq)
plt.pcolormesh(X,Y,Z.transpose(), cmap= 'GnBu', vmin = contrast_min, vmax = contrast_max)
measurement = '042218_Two_tone_spec_YOKO_1.18to1.55mA_Cav_7.3374GHz&-25dBm_QuBit2to4GHz&15dBm'
path = directory + '\\' + measurement
#Read data
current = np.genfromtxt(path + '_CURRENT.csv')#*1e3 b
current = current[1:-1]
freq = np.genfromtxt(path + '_FREQ.csv')
freq = freq[1:]
data = np.genfromtxt(path + '_PHASEMAG.csv')
phase = data[1:,0] #phase is recorded in rad
mag = data[1:,1]
Z = np.zeros((len(current),len(freq)))
for idx in range(len(current)):
temp = np.unwrap(phase[idx*len(freq):(idx+1)*len(freq)])
temp = temp*180/(np.pi)
# temp = mag[idx*len(freq):(idx+1)*len(freq)]
Z[idx,:] = temp - np.mean(temp)
plt.figure(1)
X,Y = np.meshgrid(current,freq)
plt.pcolormesh(X,Y,Z.transpose(), cmap= 'GnBu', vmin = contrast_min, vmax = contrast_max)
measurement = '042318_Two_tone_spec_YOKO_2to2.5mA_Cav_7.3374GHz&-25dBm_QuBit5.3to5.7GHz&0dBm'
path = directory + '\\' + measurement
#Read data
current = np.genfromtxt(path + '_CURRENT.csv')#*1e3 b
current = current[1:-1]
freq = np.genfromtxt(path + '_FREQ.csv')
freq = freq[1:]
data = np.genfromtxt(path + '_PHASEMAG.csv')
phase = data[1:,0] #phase is recorded in rad
mag = data[1:,1]
Z = np.zeros((len(current),len(freq)))
for idx in range(len(current)):
temp = np.unwrap(phase[idx*len(freq):(idx+1)*len(freq)])
temp = temp*180/(np.pi)
# temp = mag[idx*len(freq):(idx+1)*len(freq)]
Z[idx,:] = temp - np.mean(temp)
plt.figure(1)
measurement = '042618_Two_tone_spec_YOKO_1.2to1.4mA_Cav_7.33756GHz&-10dBm_QuBit0.3to2GHz&25dBm'
path = directory + '\\' + measurement
#Read data
current = np.genfromtxt(path + '_CURRENT.csv')#*1e3 b
current = current[1:-1]
freq = np.genfromtxt(path + '_FREQ.csv')
freq = freq[1:]
data = np.genfromtxt(path + '_PHASEMAG.csv')
phase = data[1:,0] #phase is recorded in rad
mag = data[1:,1]
Z = np.zeros((len(current),len(freq)))
for idx in range(len(current)):
temp = np.unwrap(phase[idx*len(freq):(idx+1)*len(freq)])
temp = temp*180/(np.pi)
# temp = mag[idx*len(freq):(idx+1)*len(freq)]
Z[idx,:] = temp - np.mean(temp)
plt.figure(1)
X,Y = np.meshgrid(current,freq)
plt.pcolormesh(X,Y,Z.transpose(), cmap= 'GnBu', vmin = contrast_min, vmax = contrast_max)
measurement = '042618_Two_tone_spec_YOKO_1.375to1.2mA_Cav_7.33756GHz&-25dBm_QuBit0.6to2GHz&25dBm'
path = directory + '\\' + measurement
#Read data
current = np.genfromtxt(path + '_CURRENT.csv')#*1e3 b
current = current[1:-1]
freq = np.genfromtxt(path + '_FREQ.csv')
freq = freq[1:]
data = np.genfromtxt(path + '_PHASEMAG.csv')
phase = data[1:,0] #phase is recorded in rad
mag = data[1:,1]
Z = np.zeros((len(current),len(freq)))
for idx in range(len(current)):
temp = np.unwrap(phase[idx*len(freq):(idx+1)*len(freq)])
temp = temp*180/(np.pi)
# temp = mag[idx*len(freq):(idx+1)*len(freq)]
Z[idx,:] = temp - np.mean(temp)
plt.figure(1)
X,Y = np.meshgrid(current,freq)
plt.pcolormesh(X,Y,Z.transpose(), cmap= 'GnBu', vmin = contrast_min, vmax = contrast_max)
##############################################################################################################################
directory = 'D:\Data\Fluxonium #28\Summary'
measurement = 'T1summary0mA 4018_25_04.txt'
path = directory + '\\' + measurement
data = np.genfromtxt(path)
current = data[1:,0]
freq = data[1:,1]
T1 = data[1:,2]
T1sp = data[1:,4]
# T1_error = data[:,3]
plt.figure(1)
plt.plot(current, freq, 'r.')
plt.xlabel('I (mA)')
plt.ylabel('freq (GHz)')
plt.figure(2)
plt.ylabel('t (uS)')
plt.xlabel('freq (GHz)')
plt.errorbar(freq, T1sp, yerr= data[1:,5], fmt='bs', label='50us Saturation Pulse')
plt.errorbar(freq, T1, yerr= data[1:,3], fmt='r.', label='Pi Pulse')
plt.legend()
plt.yscale('log')
# measurement = 'T1_summary_2018_04_13.txt'
# path = directory + '\\' + measurement
# data = np.genfromtxt(path)
# current = data[1:,0]
# freq = data[1:,1]
# T1 = data[1:,2]
# plt.figure(1)
# T1err = data[1:,3]
# #.plot(current, freq, 'r.')
# plt.figure(2)
# plt.errorbar(freq, T1, yerr=T1err, fmt='c^', label='45mA')
# plt.legend()
# plt.savefig('T1_0ma_vs_45ma')
# measurement = 'T1 wrong data4_4_18.txt'
# path = directory + '\\' + measurement
# data = np.genfromtxt(path)
# current = data[1:,0]
# freq = data[1:,1]
# T1 = data[1:,2]
# plt.figure(1)
# #T1_error = data[:,3]
# plt.plot(current, freq, 'm^')
# plt.figure(2)
# plt.plot(freq, T1, 'm^')
# measurement = 'T1_auto_31.07to31.41mA.csv'
# path = directory + '\\' + measurement
# data = np.genfromtxt(path)
# current = data[:,0]
# freq = data[:,1]
# T1 = data[:,2]
# T1_error = data[:,3]
# plt.plot(current, freq, 'r.')
##############################################################################################################################
##############################################################################################################################
# e = 1.602e-19 #Fundamental charge
# h = 6.62e-34 #Placnk's constant
# phi_o = h/(2*e) #Flux quantum
# N = 50
# B_coeff = 25.5
# def trans_energy(current, E_l, E_c, E_j, A, offset):
# energy = np.zeros(len(current))
# flux = current * B_coeff * A * 1e-4
# phi_ext = (flux/phi_o-offset) * 2 * np.pi
# a = tensor(destroy(N))
# phi = (a + a.dag()) * (8.0 * E_c / E_l) ** (0.25) / np.sqrt(2.0)
# na = 1.0j * (a.dag() - a) * (E_l / (8 * E_c)) ** (0.25) / np.sqrt(2.0)
# for idx in range(len(current)):
# ope = 1.0j * (phi - phi_ext[idx])
# H = 4.0 * E_c * na ** 2.0 + 0.5 * E_l * phi ** 2.0 - 0.5 * E_j * (ope.expm() + (-ope).expm())
# energy[idx] = H.eigenenergies()[1] - H.eigenenergies()[0]
# return energy
#
# E_l = 1.0168105
# E_c = 0.835632
# E_j = 2.9964865
# A = 2.738042e-10
# offset = 0.017897167
# current = np.linspace(59,62,301)*1e-3
# energy = trans_energy(current, E_l, E_c, E_j, A, offset)
# plt.plot(current*1e3+0.39, energy)
plt.show()