fname = save_path + ".pickle"
print(fname)
with open(fname, "wb") as open_file:
pickle.dump(data_in, open_file)
if __name__ == '__main__':
seq = Nop("seq")
expt_cal = seq_programs.get_expt_cal()
#
seq.comment = "rabi_ge, sweep wx amps"
seq.num_patterns = 101
seq.num_records_per_pattern = 200
seq.sweep_time = 1000
seq.times = np.linspace(0., seq.sweep_time, seq.num_patterns)*1e-3
#seq_experiments.rabi_ge(seq.num_patterns, seq.sweep_time)
#
sweep = Nop("sweep")
sweep.comment = "wx amps, ch1ch2"
sweep.vals = np.linspace(0.1, 1.1, 21)
sweep.num_steps = sweep.vals.size
#
daq = Nop("daq")
sweep.p = []
for step_num, v in enumerate(sweep.vals):
#
print("\nstep_num: {}".format(step_num))
def readout():
expt = expt_parameters.expt_parameters()
seq = Nop("seq")
msmt = Nop("measurement")
# seq.comment = "ramsey_ge calibration"
# seq.num_patterns = 51
# seq.sweep_time = 1000
# seq.num_records_per_pattern = 500
# seq.rabi_amp = 0.05
# seq.times = np.linspace(0., seq.sweep_time, seq.num_patterns)*1e-3
# steps=13
#
# sweep = Nop("sweep")
# sweep.comment = "rabi ssb freq near EP"
# sweep.vals = np.linspace(-3, 3, steps)*1e-3 + 0.092 # ge ramsey
## sweep.vals = np.linspace(-0.5, 0.5, 3)*1e-3 + expt_cal.ssm.ef
# pop_3state = []
# fpop=[]
#
# for idx, ssm_ef in enumerate(sweep.vals):
# print(idx)
#
# black_nonHermitian.ramsey_ef(ssm_ef)
# wx_programs.wx_set_and_amplitude_and_offset()
#
## seq_experiments.rabi_ef_prep_f(seq.num_patterns, seq.sweep_time, seq.rabi_amp, rabi_ssb_freq)
# daq_params, rec_readout_vs_pats, p_readout = daq_programs.run_daq(
# num_patterns=seq.num_patterns, num_records_per_pattern=seq.num_records_per_pattern)
# p_post = analysis.p_readout_postselected(p_readout)
# pop_3state.append(p_readout)
## msmt.popt, msmt.perr, _, _ = analysis.fit_sine_decay(x, y, guess_vals=None)
# x = seq.times
# y = p_post[1]
## analysis.fit_sine_decay(x,y,)
# fpop.append(p_post[1])
## save_by_pickle((expt, seq, daq, msmt))
# plt.plot(seq.times, p_post[1])
# plt.ylim([0,4])
# plt.show()
## pop_3state = np.stack(pop_3state)
# 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))
# test = [1, 2, 3]
return msmt.p_readout