def test_first_order_bounce_correction(self):
hw_corr = ZI_kf.first_order_bounce_corr(self.distorted_waveform,
self.bounce_delay,
self.bounce_amp,
self.sampling_rate)
ainv1 = ZI_kf.first_order_bounce_kern(self.bounce_delay,
-self.bounce_amp,
self.sampling_rate)
first_order_corr = signal.lfilter(ainv1, 1, self.distorted_waveform)
np.testing.assert_almost_equal(hw_corr, first_order_corr, 4)
def test_first_order_bounce_correction(self):
hw_corr = ZI_kf.first_order_bounce_corr(self.distorted_waveform,
self.bounce_delay,
self.bounce_amp,
self.sampling_rate)
b = ZI_kf.first_order_bounce_kern(
self.bounce_delay, ZI_kf.coef_round(self.bounce_amp,
force_bshift=0),
self.sampling_rate)
first_order_corr = signal.lfilter(b, 1.0, self.distorted_waveform)
np.testing.assert_almost_equal(hw_corr, first_order_corr, 6)
def distort_waveform(self,
waveform,
length_samples: int = None,
inverse: bool = False):
"""
Distorts a waveform using the models specified in the Kernel Object.
Args:
waveform (array) : waveform to be distorted
lenght_samples (int): number of samples after which to cut of wf
inverse (bool) : if True apply the inverse of the waveform.
Returns:
y_sig (array) : waveform with distortion filters applied
N.B. the bounce correction does not have an inverse implemented
(June 2018) MAR
"""
if length_samples is not None:
extra_samples = length_samples - len(waveform)
if extra_samples >= 0:
y_sig = np.concatenate([waveform, np.zeros(extra_samples)])
else:
y_sig = waveform[:extra_samples]
else:
y_sig = waveform
# Specific real-time filters are turned on below
self.set_realtime_distortions_zero()
nr_real_time_exp_models = 0
nr_real_time_hp_models = 0
nr_real_time_bounce_models = 0
for filt_id in range(self._num_models):
filt = self.get('filter_model_{:02}'.format(filt_id))
if not filt:
pass # dict is empty
else:
model = filt['model']
if model == 'high-pass':
if ('real-time' in filt.keys() and filt['real-time']):
# Implementation tested and found not working -MAR
raise NotImplementedError()
nr_real_time_hp_models += 1
if nr_real_time_hp_models > 1:
raise ValueError()
else:
y_sig = kf.bias_tee_correction(
y_sig,
sampling_rate=self.cfg_sampling_rate(),
inverse=inverse,
**filt['params'])
elif model == 'exponential':
if ('real-time' in filt.keys() and filt['real-time']):
AWG = self.instr_AWG.get_instr()
AWG.set(
'sigouts_{}_precompensation_exponentials'
'_{}_timeconstant'.format(
self.cfg_awg_channel() - 1,
nr_real_time_exp_models),
filt['params']['tau'])
AWG.set(
'sigouts_{}_precompensation_exponentials'
'_{}_amplitude'.format(self.cfg_awg_channel() - 1,
nr_real_time_exp_models),
filt['params']['amp'])
AWG.set(
'sigouts_{}_precompensation_exponentials'
'_{}_enable'.format(self.cfg_awg_channel() - 1,
nr_real_time_exp_models), 1)
nr_real_time_exp_models += 1
if nr_real_time_exp_models > 5:
raise ValueError()
else:
y_sig = kf.exponential_decay_correction(
y_sig,
sampling_rate=self.cfg_sampling_rate(),
inverse=inverse,
**filt['params'])
elif model == 'bounce':
if ('real-time' in filt.keys() and filt['real-time']):
AWG = self.instr_AWG.get_instr()
AWG.set(
'sigouts_{}_precompensation_bounces'
'_{}_delay'.format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models),
filt['params']['tau'])
AWG.set(
'sigouts_{}_precompensation_bounces'
'_{}_amplitude'.format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models),
filt['params']['amp'])
AWG.set(
'sigouts_{}_precompensation_bounces'
'_{}_enable'.format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models), 1)
nr_real_time_bounce_models += 1
if nr_real_time_bounce_models > 1:
raise ValueError()
else:
y_sig = kf.first_order_bounce_corr(
sig=y_sig,
delay=filt['params']['tau'],
amp=filt['params']['amp'],
awg_sample_rate=2.4e9)
else:
raise KeyError('Model {} not recognized'.format(model))
if inverse:
y_sig /= self.cfg_gain_correction()
else:
y_sig *= self.cfg_gain_correction()
return y_sig
step = np.zeros(len(time))
step[time >= 0.0] = 1.0
# Apply forward filter to impulse and step response
impulse_response = signal.lfilter([1.0], a, impulse)
step_response = signal.lfilter([1.0], a, step)
# Compute ideal inverted filter kernel
b_ideal = ZI_kf.ideal_inverted_fir_kernel(impulse_response, zero_ind)
# Apply ideal inverted filter to impulse response and step response
impulse_response_corr = signal.lfilter(b_ideal, 1.0, impulse_response)
step_response_corr = signal.lfilter(b_ideal, 1.0, step_response)
# Apply hardware-friendly filter to impulse response and step response
impulse_response_corr_hw = ZI_kf.first_order_bounce_corr(
impulse_response, bounce_delay, bounce_amp, fs)
step_response_corr_hw = ZI_kf.first_order_bounce_corr(step_response,
bounce_delay, bounce_amp,
fs)
# Plot impulse response comparison
plt.figure(1, figsize=(10, 14))
plt.subplot(3, 1, 1)
plt.plot(time * 1e9, impulse_response)
plt.xlabel('Time, t (ns)')
plt.ylabel('Amplitude (a.u)')
plt.title('(a) Impulse response')
plt.subplot(3, 1, 2)
plt.plot(time * 1e9, impulse_response_corr)
def distort_waveform(self,
waveform,
length_samples: int = None,
inverse: bool = False):
"""
Distorts a waveform using the models specified in the Kernel Object.
Args:
waveform (array) : waveform to be distorted
lenght_samples (int): number of samples after which to cut of wf
inverse (bool) : if True apply the inverse of the waveform.
Return:
y_sig (array) : waveform with distortion filters applied
N.B. The bounce correction does not have an inverse implemented
(June 2018) MAR
N.B.2 The real-time FIR also does not have an inverse implemented.
(May 2019) MAR
N.B.3 the real-time distortions are reset and set on the HDAWG every
time a waveform is distorted. This is a suboptimal workflow.
"""
if length_samples is not None:
extra_samples = length_samples - len(waveform)
if extra_samples >= 0:
y_sig = np.concatenate([waveform, np.zeros(extra_samples)])
else:
y_sig = waveform[:extra_samples]
else:
y_sig = waveform
# Specific real-time filters are turned on below
self.set_unused_realtime_distortions_zero()
nr_real_time_exp_models = 0
nr_real_time_hp_models = 0
nr_real_time_bounce_models = 0
for filt_id in range(self._num_models):
filt = self.get("filter_model_{:02}".format(filt_id))
if not filt:
pass # dict is empty
else:
model = filt["model"]
AWG = self.instr_AWG.get_instr()
if model == "high-pass":
if "real-time" in filt.keys() and filt["real-time"]:
# Implementation tested and found not working -MAR
raise NotImplementedError()
nr_real_time_hp_models += 1
if nr_real_time_hp_models > 1:
raise ValueError()
else:
y_sig = kf.bias_tee_correction(
y_sig,
sampling_rate=self.cfg_sampling_rate(),
inverse=inverse,
**filt["params"])
elif model == "exponential":
if "real-time" in filt.keys() and filt["real-time"]:
AWG.set(
"sigouts_{}_precompensation_exponentials"
"_{}_timeconstant".format(
self.cfg_awg_channel() - 1,
nr_real_time_exp_models),
filt["params"]["tau"],
)
AWG.set(
"sigouts_{}_precompensation_exponentials"
"_{}_amplitude".format(self.cfg_awg_channel() - 1,
nr_real_time_exp_models),
filt["params"]["amp"],
)
AWG.set(
"sigouts_{}_precompensation_exponentials"
"_{}_enable".format(self.cfg_awg_channel() - 1,
nr_real_time_exp_models),
1,
)
nr_real_time_exp_models += 1
if nr_real_time_exp_models > 5:
raise ValueError()
else:
y_sig = kf.exponential_decay_correction(
y_sig,
sampling_rate=self.cfg_sampling_rate(),
inverse=inverse,
**filt["params"])
elif model == "bounce":
if "real-time" in filt.keys() and filt["real-time"]:
AWG.set(
"sigouts_{}_precompensation_bounces"
"_{}_delay".format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models),
filt["params"]["tau"],
)
AWG.set(
"sigouts_{}_precompensation_bounces"
"_{}_amplitude".format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models),
filt["params"]["amp"],
)
AWG.set(
"sigouts_{}_precompensation_bounces"
"_{}_enable".format(self.cfg_awg_channel() - 1,
nr_real_time_bounce_models),
1,
)
nr_real_time_bounce_models += 1
if nr_real_time_bounce_models > 1:
raise ValueError()
else:
y_sig = kf.first_order_bounce_corr(
sig=y_sig,
delay=filt["params"]["tau"],
amp=filt["params"]["amp"],
awg_sample_rate=2.4e9,
)
elif model == "FIR":
fir_filter_coeffs = filt["params"]["weights"]
if "real-time" in filt.keys() and filt["real-time"]:
if len(fir_filter_coeffs) != 40:
raise ValueError(
"Realtime FIR filter must contain 40 weights")
else:
AWG.set(
"sigouts_{}_precompensation_fir_coefficients".
format(self.cfg_awg_channel() - 1),
fir_filter_coeffs,
)
AWG.set(
"sigouts_{}_precompensation_fir_enable".format(
self.cfg_awg_channel() - 1),
1,
)
else:
if not inverse:
y_sig = signal.lfilter(fir_filter_coeffs, 1, y_sig)
elif inverse:
y_sig = signal.lfilter(np.ones(1),
fir_filter_coeffs, y_sig)
else:
raise KeyError("Model {} not recognized".format(model))
if inverse:
y_sig /= self.cfg_gain_correction()
else:
y_sig *= self.cfg_gain_correction()
return y_sig
