# 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: