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# -*- coding: utf-8 -*-
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"""
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Created on Sun Nov 10 15:42:46 2024
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@author: shen_t2
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"""
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import os
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os.chdir(os.path.abspath(os.path.dirname(__file__)))
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import numpy as np
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import matplotlib.pyplot as plt
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plt.rcParams.update({'font.size': 14})
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#%%
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MAIN_EXP_folder = 'C:/RE_qubit_TS/202504_CW_PLE_167Er/20250408_BalongcScan_sigma/'
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os.chdir(MAIN_EXP_folder)
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timestampsuffix = '_04080258.npy'
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actual_counts_all = np.load('actual_counts_All_Bfields_rep0' + timestampsuffix)
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raw_counts_all = np.load('actual_counts_All_Bfields_rep0' + timestampsuffix)
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B_field_all = np.load('wl_scan_Bfields' + timestampsuffix)
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wl_scan_all = np.load('wl_scan_x_axis_20.00_120.00_speed0.5000_CWGatedD' + timestampsuffix)
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B_field_all = B_field_all.reshape(len(B_field_all), 1)
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B_field_all = np.repeat(B_field_all, len(wl_scan_all), axis=1)
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wl_scan_all = np.tile(wl_scan_all, (len(B_field_all), 1) )
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wn_PLE_c = 6535.0997 # OK w/o precise fitting
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wn_FTIR_c = 6534.356431
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wn_scan_all = wl_scan_all#1e7 / wl_scan_all - wn_PLE_c + wn_FTIR_c
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#%%
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counts_all = raw_counts_all
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counts_threshold = 2500
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counts_all = actual_counts_all
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counts_threshold = 40000
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plt.figure(11, figsize=[9,6], dpi=100)
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plt.clf()
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plt.pcolormesh(B_field_all, wn_scan_all, counts_all, norm='linear',
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vmin=0,
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# vmax=counts_threshold, # to see satellites
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cmap='RdBu')
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plt.colorbar(label='Photon Counting (cps)')
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# to see the full
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# plt.contourf(B_field_all, wn_scan_all, counts_all,
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# cmap='RdBu', levels=200)
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# plt.colorbar(label='Photon Counting (cps)')
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# to see satellites
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# plt.contourf(B_field_all, wn_scan_all, counts_all,
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# levels=np.linspace(0, counts_threshold, 201),
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# cmap='RdBu',
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# extend='both')
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# plt.colorbar(label='Photon Counting (cps)', ticks=np.linspace(0, counts_threshold, 6))
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# plt.grid()
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# plt.legend(loc=1)
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plt.title('Gated CW PLE @ #22, 0.01%, 167Er, 3.4 K, OD 0.5,\n 1/2 V/s, AOM, DAQ = 10 ms, sigma $E\perp c$, $B_{ext}\parallel c$')
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plt.xlabel('Magnetic field (Gauss)')
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plt.ylabel('Calibrated wavenumber (cm$^{-1}$)')
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plt.ylabel('Piezo voltage (V)')
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# plt.ylabel('Wavelength (nm)')
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# plt.xlim(-100, )
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# plt.ylim(6533.5, 6536.0)
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plt.tight_layout()
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plt.savefig( MAIN_EXP_folder + '20250408_plot_BalongcScan_sigma_167Er_full.jpg' )
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plt.show()
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@@ -0,0 +1,67 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Feb 5 13:08:12 2025
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@author: shen_t2
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"""
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import os
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# os.chdir(os.path.abspath(os.path.dirname(__file__)))
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import numpy as np
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import matplotlib.pyplot as plt
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plt.rcParams.update({'font.size': 14})
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from scipy import optimize
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# %%
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MAIN_EXP_folder = 'C:/RE_qubit_TS/202510_T1_pair/20250208_T1/'
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os.chdir(MAIN_EXP_folder)
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timestampsuffix = '_02081615.npy'
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time_scan = np.load('detection_delay_x_axis_0-120ms' + timestampsuffix) # in [us]
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time_scan *= 1e-3 # in [ms]
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# raw_count_rep = np.load('raw_counts_B150Gs_rep0_exc30ms_count3ms' + timestampsuffix)
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actual_count_rep = np.load('actual_counts_B150Gs_rep0_exc30ms_count3ms' + timestampsuffix)
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actual_count_rep /= np.max(actual_count_rep)
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# %%
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def _ExpDecay(t, tau):
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return np.exp(-t/tau)
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(popt, pcov, infodict, mesg, ier) = optimize.curve_fit(_ExpDecay, time_scan, actual_count_rep,
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# p0 = [0, 1.88, 1.94, 5.5],
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full_output=True)
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# (popt, pcov, infodict, mesg, ier)
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# %%
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plt.figure(131, figsize=[9,6], dpi=100)
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plt.clf()
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# plt.plot(time_scan, raw_count_rep, '.-b', label= 'raw counts' )
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plt.plot(time_scan, actual_count_rep, '.-r', label='Experiment' )
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plt.plot(time_scan, _ExpDecay(time_scan, *popt), '--b', label='Fitted $T_1$ = {:.2f} ms'.format(popt[0]) )
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plt.grid()
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plt.legend(loc=1)
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plt.title('$T_1$ measurement')
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plt.xlabel('Detection delay time/ms')
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plt.ylabel('Actual photon counts (normalized)')
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plt.tight_layout()
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plt.savefig( MAIN_EXP_folder + 'fitted_T1_{:.0f}ms{:}.jpg'.format(popt[0], timestampsuffix[:-4]) )
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plt.show()
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@@ -0,0 +1,101 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Feb 5 13:08:12 2025
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@author: shen_t2
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"""
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import os
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# os.chdir(os.path.abspath(os.path.dirname(__file__)))
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import numpy as np
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import matplotlib.pyplot as plt
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plt.rcParams.update({'font.size': 14})
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from scipy.fftpack import fft, ifft, fftfreq
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# %%
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MAIN_EXP_folder = 'C:/RE_qubit_TS/202503_Pulse_echo_sample2/02_thicker_Cu_plate/61_left3_left_splitting/run21_Rabi_smallApt_1500ns_noEOM_count10ms_avg100/'
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os.chdir(MAIN_EXP_folder)
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timestampsuffix = '_04102217.npy'
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time_scan = np.load('excitation_time_x_axis_0-0ms_Pulse_Rabi' + timestampsuffix) # in [us]
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raw_count_sum = np.zeros(len(time_scan))
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actual_count_sum = np.zeros(len(time_scan))
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total_average = 35
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for ii in range(total_average):
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raw_count_rep = np.load('raw_counts_B1000Gs_rep{:d}_count10ms'.format(ii) + timestampsuffix)
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raw_count_sum += raw_count_rep
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actual_count_rep = np.load('actual_counts_B1000Gs_rep{:d}_count10ms'.format(ii) + timestampsuffix)
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actual_count_sum += actual_count_rep
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raw_count = raw_count_sum / total_average
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actual_count = actual_count_sum / total_average
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# %% FFT
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time_step = time_scan[1] - time_scan[0] # [us]
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fs = 1e6 / time_step # Hz
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x, y = time_scan, actual_count
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fft_x = fftfreq(len(x)) * fs
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fft_y = fft(y)
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# %%
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plt.figure(1320, figsize=[9,6], dpi=100)
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plt.clf()
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plt.plot(time_scan * 1e3, raw_count, '.-b', label= 'raw counts' )
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plt.plot(time_scan * 1e3, actual_count, '.-r', label='Experiment' )
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plt.grid()
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plt.legend(loc=2)
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plt.title('Total average = {:d}'.format(total_average))
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plt.xlabel('Excitation time (ns)')
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plt.ylabel('Actual photon counts (cps)')
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# plt.xscale('log')
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plt.tight_layout()
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# plt.savefig( MAIN_EXP_folder + 'plot_{:}Averaged.jpg'.format(total_average) )
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plt.show()
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# %%
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plt.figure(1321, figsize=[9,6], dpi=100)
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plt.clf()
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# plt.plot(time_scan, raw_count, '.-b', label= 'raw counts' )
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# plt.plot(time_scan, actual_count, '.-r', label='Experiment' )
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# plt.plot(fft_x, abs(fft_y), '.-b', label='FFT')
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plt.plot(fft_x[:len(fft_x)//2], abs(fft_y)[:len(fft_x)//2] / len(x) * 2, '.-r', label='FFT')
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plt.grid()
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plt.legend(loc=1)
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# plt.title('Rabi measurement')
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plt.xlabel('Frequency (Hz)')
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plt.ylabel('FFT')
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plt.yscale('log')
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plt.tight_layout()
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# plt.savefig( MAIN_EXP_folder + 'plot_FFT.jpg' )
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plt.show()
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Feb 5 13:08:12 2025
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@author: shen_t2
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"""
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import os
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# os.chdir(os.path.abspath(os.path.dirname(__file__)))
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import numpy as np
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import matplotlib.pyplot as plt
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plt.rcParams.update({'font.size': 14})
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from scipy.fftpack import fft, ifft, fftfreq
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# %%
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MAIN_EXP_folder = 'C:/RE_qubit_TS/202503_Pulse_echo_sample2/02_thicker_Cu_plate/19_echo_halfpi_population/run1_2us_noEOM_avg100/'
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os.chdir(MAIN_EXP_folder)
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timestampsuffix = '_04071904.npy'
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time_scan = np.load('half_pi_x_axis_300-1500ns_Pulse_echo_halfpi' + timestampsuffix) # in [us]
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raw_count_sum = np.zeros(len(time_scan))
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actual_count_sum = np.zeros(len(time_scan))
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total_average = 55
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for ii in range(total_average):
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raw_count_rep = np.load('raw_counts_B680Gs_rep{:d}_tau500ns_count3ms'.format(ii) + timestampsuffix)
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raw_count_sum += raw_count_rep
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actual_count_rep = np.load('actual_counts_B680Gs_rep{:d}_tau500ns_count3ms'.format(ii) + timestampsuffix)
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actual_count_sum += actual_count_rep
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raw_count = raw_count_sum / total_average
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actual_count = actual_count_sum / total_average
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# %% FFT
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time_step = 1/409.6 # [us]
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fs = 1e6 / time_step # Hz
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x, y = time_scan, actual_count
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fft_x = fftfreq(len(x)) * fs
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fft_y = fft(y)
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# %%
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plt.figure(1330, figsize=[9,6], dpi=100)
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plt.clf()
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# plt.plot(time_scan, raw_count, '.-b', label= 'raw counts' )
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plt.plot(time_scan, actual_count, '.-r', label='Experiment' )
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plt.grid()
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plt.legend(loc=1)
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# plt.title('Rabi measurement')
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plt.xlabel('Excitation time (us)')
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plt.ylabel('Actual photon counts (cps)')
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plt.tight_layout()
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# plt.savefig( MAIN_EXP_folder + 'fitted_T1_{:.0f}ms{:}.jpg'.format(popt[0], timestampsuffix[:-4]) )
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plt.show()
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# %%
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plt.figure(1331, figsize=[9,6], dpi=100)
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plt.clf()
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# plt.plot(time_scan, raw_count, '.-b', label= 'raw counts' )
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# plt.plot(time_scan, actual_count, '.-r', label='Experiment' )
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# plt.plot(fft_x, abs(fft_y), '.-b', label='FFT')
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plt.plot(fft_x[:len(fft_x)//2], abs(fft_y)[:len(fft_x)//2] / len(x) * 2, '.-r', label='FFT')
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plt.grid()
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plt.legend(loc=1)
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# plt.title('Rabi measurement')
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plt.xlabel('Frequency (Hz)')
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plt.ylabel('FFT')
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plt.yscale('log')
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plt.tight_layout()
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# plt.savefig( MAIN_EXP_folder + 'fitted_T1_{:.0f}ms{:}.jpg'.format(popt[0], timestampsuffix[:-4]) )
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plt.show()
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