# popdstack=np.dstack(pop_3state) # # pop3state_f=popdstack.reshape(seq.num_patterns*3,steps) # np.savetxt('popdstack', pop3state_f) # np.savetxt('fpop', fpop) # fig = plt.figure() # plt.imshow(fpop) ## plt.imshow(pop_3state[:,1,:]) # plt.show seq.comment = "t1 ge" seq.num_patterns = 51 seq.sweep_time = 1000 seq.num_records_per_pattern = 1000 seq.times = np.linspace(0., seq.sweep_time, seq.num_patterns) * 1e-3 black_nonHermitian.ramsey_ef() wx_programs.wx_set_and_amplitude_and_offset() daq, msmt.rec_readout_vs_pats, msmt.p_readout = daq_programs.run_daq( num_patterns=seq.num_patterns, num_records_per_pattern=seq.num_records_per_pattern) # msmt.p_post = analysis.p_readout_postselected(msmt.p_readout) msmt.p_post = analysis.p_readout_postselected(msmt.p_readout) # x = seq.times y = msmt.p_post[1] plt.plot(x, y) plt.ylim([0, 1]) # msmt.popt, msmt.perr, _, _ = analysis.fit_exp_decay(x, y, guess_vals=None) msmt.popt, msmt.perr, _, _ = analysis.fit_sine_decay(x, y, guess_vals=None) # save_by_pickle((expt, seq, daq, msmt))
seq.num_records_per_pattern = 200 seq.num_avgs = 1 seq.sweep_time = 2000 seq.rabi_amp = 0.5 seq.times = np.linspace(0., seq.sweep_time, seq.num_patterns)*1e-3 #seq_experiments.rabi_ef_prep_f(seq.num_patterns, seq.sweep_time, seq.rabi_amp) # sweep = Nop("sweep") sweep.comment = "wx amps, ch1ch2" sweep.vals = np.linspace(0.1, 0.3, 2) sweep.num_steps = sweep.vals.size # sweep.p = [] sweep.p_post = [] for step_num, v in enumerate(sweep.vals): # print("\nstep_num: {}, sweep.val: {}".format(step_num, v)) # amp = [v*1.5, v*1.77, 1.5, 1.5] wx_programs.wx_set_and_amplitude_and_offset(amp=amp) for k in range(seq.num_avgs): # daq_params, _, p = daq_programs.run_daq( num_patterns=seq.num_patterns, num_records_per_pattern=seq.num_records_per_pattern) if k is 0: p_readout = p else: