def do_measurement(phases, ontime=1, offtime=12):
voltages = table.get_voltages(phases)
dac.write_voltages(voltages)
print 'warming up...'
for j in range(ontime):
fpga.read()
counts = dict(zip(fpga.labels, fpga.read()))
dac.zero()
print 'cooling'
for j in range(offtime):
fpga.read()
return counts
def do_measurement(phases, ontime=1, offtime=12):
voltages=table.get_voltages(phases)
dac.write_voltages(voltages)
print 'warming up...'
for j in range(ontime):
fpga.read()
counts=fpga.read()
dac.zero()
print 'cooling'
for j in range(offtime):
fpga.read()
return counts
def do_experiment(voltages, ontime=2, offtime=12):
dac.write_voltages(voltages)
print 'warming up...',
for j in range(ontime):
fpga.read()
counts=fpga.read()
dac.zero()
print 'cooling...',
for j in range(offtime):
fpga.read()
# Get c00, c01, c10, c11
coincidences=np.array(counts[8:12])
accidentals=np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts=coincidences-accidentals
total_counts=np.sum(corrected_counts)
print 'total count rate %d' % total_counts
probabilities_expt=corrected_counts/float(np.sum(corrected_counts))
return probabilities_expt, total_counts
def do_experiment(voltages, ontime=2, offtime=12):
dac.write_voltages(voltages)
print 'warming up...',
for j in range(ontime):
fpga.read()
counts = fpga.read()
dac.zero()
print 'cooling...',
for j in range(offtime):
fpga.read()
# Get c00, c01, c10, c11
coincidences = np.array(counts[8:12])
accidentals = np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts = coincidences - accidentals
total_counts = np.sum(corrected_counts)
print 'total count rate %d' % total_counts
probabilities_expt = corrected_counts / float(np.sum(corrected_counts))
return probabilities_expt, total_counts
def acquire_data(heater_index, N=20, hold=[]):
min_voltage, max_voltage=0, 7
parameter_space=np.linspace(min_voltage, max_voltage, N)
experiment=np.zeros([N, 5])
theory=np.zeros([N, 4])
fidelity=np.zeros(N)
for index_1, parameter_1 in enumerate(parameter_space):
# Figure out the settings
phases=np.zeros(8)
for index, phase in hold:
print 'hold phase %d: %.3f' % (index, phase)
phases[index]=phase
voltages=np.array(table.get_voltages(phases))
print 'sweep voltage %d: %.3f' % (heater_index, parameter_1)
voltages[heater_index] = parameter_1
print 'final voltages', voltages.round(2)
parameters=table.get_parameters(heater_index)
#phase_voltage_2 for added linear dependance
phases[heater_index] = fitting.phase_voltage_2(parameters, parameter_1)
# Do the measurement
experiment[index_1] = do_experiment(voltages)
theory[index_1] = do_theory(phases, experiment[index_1][4])
fidelity[index_1]=statistical_fidelity(experiment[index_1][:4]/experiment[index_1][4], theory[index_1]/experiment[index_1][4])
print fidelity[index_1]
# Plot
plot_now(experiment, theory, parameter_space, fidelity, do_fit=False)
# Close hardware
dac.zero()
# Normalize, save and return data
#data=data/np.amax(data)
experiment_filename, theory_filename, param_filename = get_filenames(heater_index)
np.save(experiment_filename, experiment)
np.save(theory_filename, theory)
np.save(param_filename, parameter_space)
return experiment_filename, theory_filename, param_filename
def acquire_data(heater_index, N=20, hold=[]):
min_phase, max_phase = 0, 2 * np.pi
parameter_space = np.linspace(min_phase, max_phase, N)
experiment = np.zeros([N, 4])
theory = np.zeros([N, 4])
for index_1, parameter_1 in enumerate(parameter_space):
# Figure out the settings
phases = np.zeros(8)
for index, phase in hold:
print 'hold phase %d: %.3f' % (index, phase)
phases[index] = phase
print 'sweep phase %d: %.3f' % (heater_index, parameter_1)
phases[heater_index] = parameter_1
print 'final phases', phases.round(2)
# Do the measurement
experiment[index_1], total_counts = do_experiment(phases)
theory[index_1] = do_theory(phases, total_counts)
# Plot
plot_now(experiment, theory, parameter_space, do_fit=False)
# Close hardware
dac.zero()
# Normalize, save and return data
#data=data/np.amax(data)
experiment_filename, theory_filename, param_filename, hold_table_filename = get_filenames(
heater_index)
file = open(hold_table_filename, "w")
file.write(str(hold_table))
file.close()
np.save(experiment_filename, experiment)
np.save(theory_filename, theory)
np.save(param_filename, parameter_space)
return experiment_filename, theory_filename, param_filename
def acquire_data(heater_index, N=20, hold=[]):
min_phase, max_phase=0, 2*np.pi
parameter_space=np.linspace(min_phase, max_phase, N)
experiment=np.zeros([N, 4])
theory=np.zeros([N, 4])
for index_1, parameter_1 in enumerate(parameter_space):
# Figure out the settings
phases=np.zeros(8)
for index, phase in hold:
print 'hold phase %d: %.3f' % (index, phase)
phases[index]=phase
print 'sweep phase %d: %.3f' % (heater_index, parameter_1)
phases[heater_index] = parameter_1
print 'final phases', phases.round(2)
# Do the measurement
experiment[index_1], total_counts = do_experiment(phases)
theory[index_1] = do_theory(phases, total_counts)
# Plot
plot_now(experiment, theory, parameter_space, do_fit=False)
# Close hardware
dac.zero()
# Normalize, save and return data
#data=data/np.amax(data)
experiment_filename, theory_filename, param_filename, hold_table_filename = get_filenames(heater_index)
file=open(hold_table_filename, "w")
file.write(str(hold_table))
file.close()
np.save(experiment_filename, experiment)
np.save(theory_filename, theory)
np.save(param_filename, parameter_space)
return experiment_filename, theory_filename, param_filename
data_raw_coinc = []
data_raw_accid = []
fidelity_bins = [0] * bin_count
fidelity_samples = 0
fidelity_sum = 0
fidelity_mean = 0
stop = False
while not stop:
coincidences, accidentals, fidelity, phases, raw_coinc, raw_accid = new_sample(
device)
data_coincidences.append(coincidences)
data_accidentals.append(accidentals)
data_phases.append(phases)
print fidelity
data_raw_coinc.append(raw_coinc)
data_raw_accid.append(raw_accid)
fidelity_bins[get_bin(fidelity)] += 1
minf = min(fidelity, minf)
maxf = max(fidelity, maxf)
fidelity_sum += fidelity
fidelity_samples += 1.0
fidelity_mean = fidelity_sum / fidelity_samples
draw_graph()
if kbhit(): stop = getch() == 'q'
dac.zero()
data_phases=[]
data_raw_coinc=[]
data_raw_accid=[]
fidelity_bins=[0]*bin_count
fidelity_samples=0
fidelity_sum=0
fidelity_mean=0
stop=False
while not stop:
coincidences, accidentals, fidelity, phases, raw_coinc, raw_accid = new_sample(device)
data_coincidences.append(coincidences)
data_accidentals.append(accidentals)
data_phases.append(phases)
print fidelity
data_raw_coinc.append(raw_coinc)
data_raw_accid.append(raw_accid)
fidelity_bins[get_bin(fidelity)]+=1
minf=min(fidelity, minf)
maxf=max(fidelity, maxf)
fidelity_sum+=fidelity
fidelity_samples+=1.0
fidelity_mean=fidelity_sum/fidelity_samples
draw_graph()
if kbhit(): stop = getch()=='q'
dac.zero()
