t1decay.times = np.linspace(0., seq.sweep_time,
seq.num_patterns_each) * 1e-3
t1decay.freq = dum.copy()
t1decay.gamma = dum.copy()
t1decay.p = []
#
daq = Nop("daq")
for k, bias_val in enumerate(keithley_current.setpoint_mA):
mA_start, mA_end = keithley2401.set_current(bias_val)
keithley_current.msmt_mA_start[k] = mA_start
keithley_current.msmt_mA_end[k] = mA_end
daq.daq_params, daq.rec_readout_vs_pats, daq.p_readout = (
daq_programs.run_daq(
num_patterns=seq.num_patterns_each,
num_records_per_pattern=seq.num_records_per_pattern))
t1decay.p.append(daq.p_readout[0])
#
keithley_current.msmt_mA_end = np.array(keithley_current.msmt_mA_end)
t1decay.p = np.array(t1decay.p)
#
t1decay.fit = []
for k in range(keithley_current.num_steps):
#.
popt, perr, _, _ = analysis.fit_exp_decay(t1decay.times, t1decay.p[k])
t1decay.fit.append(popt)
#
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))
#
amp = [v*1.5, v*1.77, 1.5, 1.5]
wx_programs.wx_set_and_amplitude_and_offset(amp=amp)
#
daq.daq_params, daq.rec_readout_vs_pats, daq.p_readout = daq_programs.run_daq(
num_patterns=seq.num_patterns, num_records_per_pattern=seq.num_records_per_pattern)
sweep.p.append(daq.p_readout)
wx_programs.wx_set_and_amplitude_and_offset()
sweep.p = np.array(sweep.p)
#
sweep.fit = []
for k in range(sweep.num_steps):
#.
popt, perr, _, _ = analysis.fit_sine_decay(seq.times, sweep.p[k][0])
sweep.fit.append(popt)
#
sweep.fit = np.array(sweep.fit)
# 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))
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
import numpy as np
import daq_programs
import daq_processing
import keithley2401
import matplotlib.pyplot as plt
if __name__ == "__main__":
# freq_sweep = []
# gamma_sweep = []
# current_sweep = np.linspace(7.20, 7.25, 6)
#
# for val_mA in current_sweep:
# keithley2401.set_current(val_mA)
# daq_params, rec_readout_vs_pats, p_readout = daq_programs.run_daq();
# times = np.linspace(0, 0.5, daq_params.num_patterns)
# fit, y_vals = daq_processing.fit_sine_decay(times, p_readout[1])
# freq_sweep.append(fit[0][0])
# gamma_sweep.append(fit[0][1])
# plt.figure(figsize=(6,4))
# plt.plot(current_sweep, freq_sweep)
# plt.show()
#
# plt.figure(figsize=(6,4))
# plt.plot(current_sweep, gamma_sweep)
# plt.show()
#
daq_params, rec_readout_vs_pats, p_readout = daq_programs.run_daq()
times = np.linspace(0, 1.0, daq_params.num_patterns)
fit, y_vals = daq_processing.fit_sine_decay(times, p_readout[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:
p_readout += p
p_readout = p_readout/seq.num_avgs
p_post = analysis.p_readout_postselected(p_readout)
sweep.p.append(p_readout)
sweep.p_post.append(p_post)
daq = Nop("daq")
daq.daq_params = daq_params
wx_programs.wx_set_and_amplitude_and_offset()
sweep.p = np.array(sweep.p)