quantify_scheduler.helpers.waveforms

Module Contents

Classes

GetWaveformPartial

Protocol type definition class for the get_waveform

Functions

get_waveform_size(→ int)	Returns the number of samples required to
resize_waveforms(→ None)	Resizes the waveforms to a multiple of the given
resize_waveform(→ numpy.ndarray)	Returns the waveform in a size that is a modulo of the given granularity.
shift_waveform(→ Tuple[int, numpy.ndarray])	Returns the waveform shifted with a number of samples
get_waveform(→ numpy.ndarray)	Returns the waveform of a pulse_info dictionary.
get_waveform_by_pulseid(→ Dict[int, GetWaveformPartial])	Returns a lookup dictionary of pulse_id and
exec_waveform_partial(→ numpy.ndarray)	Returns the result of the partial waveform function.
exec_waveform_function(→ numpy.ndarray)	Returns the result of the pulse's waveform function.
exec_custom_waveform_function(→ numpy.ndarray)	Load and import an ambiguous waveform function from a module by string.
apply_mixer_skewness_corrections(→ numpy.ndarray)	Takes a waveform and applies a correction for amplitude imbalances and
modulate_waveform(→ numpy.ndarray)	Generates a (single sideband) modulated waveform from a given envelope by
normalize_waveform_data(→ Tuple[numpy.ndarray, float, ...)	Rescales the waveform data so that the maximum amplitude is abs(amp) == 1.
area_pulses(→ float)	Calculates the area of a set of pulses.
area_pulse(→ float)	Calculates the area of a single pulse.

class GetWaveformPartial

Bases: Protocol

Protocol type definition class for the get_waveform partial function.

get_waveform_size(waveform: numpy.ndarray, granularity: int) → int

Returns the number of samples required to respect the granularity.

Parameters

• waveform –

• granularity –

resize_waveforms(waveforms_dict: Dict[int, numpy.ndarray], granularity: int) → None

Resizes the waveforms to a multiple of the given granularity.

Parameters

• waveforms_dict – The waveforms dictionary.

• granularity – The granularity.

resize_waveform(waveform: numpy.ndarray, granularity: int) → numpy.ndarray

Returns the waveform in a size that is a modulo of the given granularity.

Parameters

• waveform – The waveform array.

• granularity – The waveform granularity.

Returns

The resized waveform with a length equal to mod(len(waveform), granularity) == 0.

shift_waveform(waveform: numpy.ndarray, start_in_seconds: float, sampling_rate: int, resolution: int) → Tuple[int, numpy.ndarray]

Returns the waveform shifted with a number of samples to compensate for rounding errors that cause misalignment of the waveform in the clock time domain.

Note

when using this method be sure that the pulse starts at a round(start_in_sequencer_count).

waveform = np.ones(32)
sampling_rate = int(2.4e9)
resolution: int = 8

t0: float = 16e-9
#                 4.8 = 16e-9 / (8 / 2.4e9)
start_in_sequencer_count = (t0 // (resolution / sampling_rate))

start_waveform_at_sequencer_count(start_in_sequencer_count, waveform)

Parameters

• waveform –

• start_in_seconds –

• sampling_rate –

• resolution – The sequencer resolution.

get_waveform(pulse_info: Dict[str, Any], sampling_rate: float) → numpy.ndarray

Returns the waveform of a pulse_info dictionary.

Parameters

• pulse_info – The pulse_info dictionary.

• sampling_rate – The sample rate of the waveform.

Returns

The waveform.

get_waveform_by_pulseid(schedule: quantify_scheduler.schedules.schedule.Schedule) → Dict[int, GetWaveformPartial]

Returns a lookup dictionary of pulse_id and respectively its partial waveform function.

The keys are pulse info ids while the values are partial functions. Executing the waveform will return a numpy.ndarray.

Parameters

schedule – The schedule.

exec_waveform_partial(pulse_id: int, pulseid_waveformfn_dict: Dict[int, GetWaveformPartial], sampling_rate: int) → numpy.ndarray

Returns the result of the partial waveform function.

Parameters

• pulse_id – The pulse uuid.

• pulseid_waveformfn_dict – The partial waveform lookup dictionary.

• sampling_rate – The sampling rate.

Returns

The waveform array.

exec_waveform_function(wf_func: str, t: numpy.ndarray, pulse_info: dict) → numpy.ndarray

Returns the result of the pulse’s waveform function.

If the wf_func is defined outside quantify-scheduler then the wf_func is dynamically loaded and executed using exec_custom_waveform_function().

Parameters

• wf_func – The custom waveform function path.

• t – The linear timespace.

• pulse_info – The dictionary containing pulse information.

Returns

Returns the computed waveform.

exec_custom_waveform_function(wf_func: str, t: numpy.ndarray, pulse_info: dict) → numpy.ndarray

Load and import an ambiguous waveform function from a module by string.

The parameters of the dynamically loaded wf_func are extracted using inspect.signature() while the values are extracted from the pulse_info dictionary.

Parameters

• wf_func – The custom waveform function path.

• t – The linear timespace.

• pulse_info – The dictionary containing pulse information.

Returns

Returns the computed waveform.

apply_mixer_skewness_corrections(waveform: numpy.ndarray, amplitude_ratio: float, phase_shift: float) → numpy.ndarray

Takes a waveform and applies a correction for amplitude imbalances and phase errors when using an IQ mixer from previously calibrated values.

Phase correction is done using:

\[Re(z_{corrected}) (t) = Re(z (t)) + Im(z (t)) \tan(\phi) Im(z_{corrected}) (t) = Im(z (t)) / \cos(\phi)\]

The amplitude correction is achieved by rescaling the waveforms back to their original amplitudes and multiplying or dividing the I and Q signals respectively by the square root of the amplitude ratio.

Parameters

• waveform – The complex valued waveform on which the correction will be applied.

• amplitude_ratio – The ratio between the amplitudes of I and Q that is used to correct for amplitude imbalances between the different paths in the IQ mixer.

• phase_shift – The phase error (in deg) used to correct the phase between I and Q.

Returns

The complex valued waveform with the applied phase and amplitude corrections.

modulate_waveform(t: numpy.ndarray, envelope: numpy.ndarray, freq: float, t0: float = 0) → numpy.ndarray

Generates a (single sideband) modulated waveform from a given envelope by multiplying it with a complex exponential.

\[z_{mod} (t) = z (t) \cdot e^{2\pi i f (t+t_0)}\]

The signs are chosen such that the frequencies follow the relation RF = LO + IF for LO, IF > 0.

Parameters

• t – A numpy array with time values

• envelope – The complex-valued envelope of the modulated waveform

• freq – The frequency of the modulation

• t0 – Time offset for the modulation

Returns

The modulated waveform

normalize_waveform_data(data: numpy.ndarray) → Tuple[numpy.ndarray, float, float]

Rescales the waveform data so that the maximum amplitude is abs(amp) == 1.

Parameters

data – The waveform data to rescale.

Returns

• rescaled_data – The rescaled data.

• amp_real – The original amplitude of the real part.

• amp_imag – The original amplitude of the imaginary part.

area_pulses(pulses: List[Dict[str, Any]], sampling_rate: float) → float

Calculates the area of a set of pulses.

For details of the calculation see area_pulse.

Parameters

• pulses – List of dictionary with information of the pulses

• sampling_rate – Sampling rate for the pulse

Returns

The area formed by all the pulses

area_pulse(pulse: Dict[str, Any], sampling_rate: float) → float

Calculates the area of a single pulse.

The sampled area is calculated, which means that the area calculated is based on the sampled waveform. This can differ slightly from the ideal area of the parameterized pulse.

The duration used for calculation is the duration of the pulse. This duration is equal to the duration of the sampled waveform for pulse durations that are integer multiples of the 1/sampling_rate.

Parameters

• pulse – The dictionary with information of the pulse

• sampling_rate – Sampling rate for the pulse

Returns

• The area defined by the pulse