from analysis.lib.lde import tail_cts_per_shot_v4 def find_nearest(array, value): idx = (abs(array - value)).argmin() return idx d = numpy.loadtxt( r'D:\measuring\data\LDE\analysis_data\opt_rabi_vs_CR\2012-10-24-CRvsRabi_LT1\183600_rabi_vs_cr\pulseshape_50ns_250nW.txt', skiprows=10) counts0 = d[:, 0] counts1 = d[:, 1] counts_rebin0 = tail_cts_per_shot_v4.rebin(counts0, 4) counts_rebin1 = tail_cts_per_shot_v4.rebin(counts1, 4) time_ax = arange(len(counts_rebin0)) * 4 * 0.128 fit_min = 58.0 #ns fit_max = 100.0 #ns idx_min = find_nearest(time_ax, fit_min) idx_max = find_nearest(time_ax, fit_max) fig = plt.figure() plt.plot(time_ax, counts_rebin1, 'bo') plt.xlabel('time (ns)')
# we assume that at the pulse begin the population should be 1 # i think this means assuming infinetely sharp pulses LT1_pulse_start = 30.6 LT2_pulse_start = 4.8 LT1_params = LT1_data[0]['fit_result'][0]['params_dict'] LT2_params = LT2_data[0]['fit_result'][0]['params_dict'] norm_amp_LT1 = LT1_params['a'] + LT1_params['b']*(LT1_pulse_start-LT1_params['x0'])+\ np.abs(LT1_params['A']) * np.exp(-(LT1_pulse_start-LT1_params['x0'])/LT1_params['tau']) norm_amp_LT2 = LT2_params['a'] + LT2_params['b']*(LT2_pulse_start-LT2_params['x0'])+\ np.abs(LT2_params['A']) * np.exp(-(LT2_pulse_start-LT2_params['x0'])/LT2_params['tau']) LT1_counts = tail_cts_per_shot_v4.rebin(LT1_data[0]['counts'],rebins)/(norm_amp_LT1*rebins) LT1_time = np.arange(len(LT1_counts))*0.128*rebins-26 LT1_fit = tail_cts_per_shot_v4.rebin(LT1_data[0]['fit_result'][0]['fitdata'],rebins)/(norm_amp_LT1*rebins) LT1_fit_time = np.arange(len(LT1_fit))*0.128*rebins+LT1_data[0]['time_fit'][0]-26 idx_LT1_min = find_nearest(LT1_time,x_range[0]) idx_LT1_max = find_nearest(LT1_time,x_range[1]) LT2_counts = tail_cts_per_shot_v4.rebin(LT2_data[0]['counts'],rebins)/(norm_amp_LT2*rebins) LT2_time = np.arange(len(LT2_counts))*0.128*rebins LT2_fit = tail_cts_per_shot_v4.rebin(LT2_data[0]['fit_result'][0]['fitdata'],rebins)/(norm_amp_LT2*rebins) LT2_fit_time = np.arange(len(LT2_fit))*0.128*rebins+LT2_data[0]['time_fit'][0]
from analysis.lib.lde import tail_cts_per_shot_v4 def find_nearest(array,value): idx=(abs(array-value)).argmin() return idx d=numpy.loadtxt(r'D:\measuring\data\LDE\analysis_data\opt_rabi_vs_CR\2012-10-24-CRvsRabi_LT1\183600_rabi_vs_cr\pulseshape_50ns_250nW.txt', skiprows = 10) counts0=d[:,0] counts1=d[:,1] counts_rebin0=tail_cts_per_shot_v4.rebin(counts0,4) counts_rebin1=tail_cts_per_shot_v4.rebin(counts1,4) time_ax = arange(len(counts_rebin0))*4*0.128 fit_min = 58.0 #ns fit_max = 100.0 #ns idx_min = find_nearest(time_ax,fit_min) idx_max = find_nearest(time_ax,fit_max) fig=plt.figure() plt.plot(time_ax,counts_rebin1,'bo') plt.xlabel('time (ns)') plt.ylabel('counts') plt.title('pulse shape')