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 new_sample(device):
#phases=[0,pi,0,0,0,0,0,0]
#phases=[pi,pi,0,0,0,0,0,0]
phases=np.random.uniform(0,np.pi*2,8)
phases[6]=0.93361981
phases[0]=np.pi
counts=do_measurement(phases, ontime=2)
coincidences=np.array(counts[8:12])
accidentals=np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts=coincidences-accidentals
probabilities_expt=corrected_counts/float(np.sum(corrected_counts))
print probabilities_expt
'''get some simulated probabilities'''
device.set_phases(phases)
simulator=lo.simulator(device, nphotons=2)
simulator.set_input_state([1,3])
probabilities_theory=simulator.get_probabilities(patterns=[[1,3],[1,4],[2,3],[2,4]])
sum_probs=sum(probabilities_theory)
probabilities_theory=np.array(probabilities_theory)/sum_probs
fidelity=statistical_fidelity(probabilities_expt, probabilities_theory)
return np.sum(coincidences), np.sum(accidentals), np.sum(fidelity), phases, coincidences, accidentals
def new_sample(device):
#phases=[0,pi,0,0,0,0,0,0]
#phases=[pi,pi,0,0,0,0,0,0]
phases = np.random.uniform(0, np.pi * 2, 8)
phases[6] = 0.93361981
phases[0] = np.pi
counts = do_measurement(phases, ontime=2)
coincidences = np.array(counts[8:12])
accidentals = np.array([counts[12], counts[17], counts[16], counts[13]])
corrected_counts = coincidences - accidentals
probabilities_expt = corrected_counts / float(np.sum(corrected_counts))
print probabilities_expt
'''get some simulated probabilities'''
device.set_phases(phases)
simulator = lo.simulator(device, nphotons=2)
simulator.set_input_state([1, 3])
probabilities_theory = simulator.get_probabilities(
patterns=[[1, 3], [1, 4], [2, 3], [2, 4]])
sum_probs = sum(probabilities_theory)
probabilities_theory = np.array(probabilities_theory) / sum_probs
fidelity = statistical_fidelity(probabilities_expt, probabilities_theory)
return np.sum(coincidences), np.sum(accidentals), np.sum(
fidelity), phases, coincidences, accidentals