Module quantum_inferno.synth.benchmark_signals

This module provides benchmark synthetic functions

Global variables

var DEFAULT_OVERSAMPLE_SCALE

Signal conditioning

Functions

def oversample_time(time_duration: float, time_sample_interval: float, oversample_scale: float) ‑> numpy.ndarray

Return an oversampled time by a factor oversample_scale

:param time_duration: duration of waveform :param time_sample_interval: length of an interval :param oversample_scale: oversample waveform, then decimate by scale :return: oversampled timestamps

def quantum_chirp(omega: float, order: float = 12.0, gamma: float = 0.0, gauss: bool = True, oversample_scale: int = 2) ‑> Tuple[numpy.ndarray, int]

Constructs a tone or a sweep with a gaussian window option and a duration of 2^n points

:param omega: center frequency < pi. Resets to pi/4 if >= 1 (Nyquist). :param order: fractional octave band, sets the Q and duration. Default of 12 for chromatic scale. :param gamma: sweep index, could be positive or negative. Default of zero yields the atom. :param gauss: Apply the Gauss envelope, True as default. False constructs constant amplitude CW or sweep :param oversample_scale: oversample waveform, then decimate by scale :return: waveform, scale factor

def signal_gate(wf: numpy.ndarray, t: numpy.ndarray, tmin: float, tmax: float, fraction_cosine: float = 0) ‑> numpy.ndarray

Time gate and apply Tukey window, rectangular is the default

:param wf: waveform :param t: timestamp array :param tmin: lower time limit :param tmax: upper time limit :param fraction_cosine: 0 = rectangular, 1 = Hann :return: waveform

def synth_00(frequency_0: float = 100.0, frequency_1: float = 200.0, frequency_2: float = 400.0, time_start_2: float = 0.25, time_stop_2: float = 0.4, time_sample_interval: float = 0.001, time_duration: float = 1.0, oversample_scale: int = 2) ‑> Tuple[numpy.ndarray, numpy.ndarray]

Generate three sine waves, oversample and decimate to AA Always work with non-dimensionalized units (number of points, Nyquist, etc.)

:param frequency_0: first frequency :param frequency_1: second frequency :param frequency_2: third frequency :param time_start_2: start time of third frequency :param time_stop_2: end time of third frequency :param time_sample_interval: length of an interval :param time_duration: length of waveform :param oversample_scale: oversample synthetic, then decimate by scale :return: synth waveform and timestamps

def synth_01(a: float = 100.0, b: float = 20.0, f: float = 5.0, time_sample_interval: float = 0.001, time_duration: float = 1.0, oversample_scale: int = 2) ‑> Tuple[numpy.ndarray, numpy.ndarray]

Example Synthetic 1

:param a: first value :param b: second value :param f: third value :param time_sample_interval: length of an interval :param time_duration: length of waveform :param oversample_scale: oversample synthetic, then decimate by scale :return: synth waveform and timestamps

def synth_02(t1: float = 0.3, t2: float = 0.7, t3: float = 0.5, f1: float = 45.0, f2: float = 75.0, f3: float = 15.0, time_sample_interval: float = 0.001, time_duration: float = 1.0, oversample_scale: int = 2) ‑> Tuple[numpy.ndarray, numpy.ndarray]

Example Synthetic 2

:param t1: first time :param t2: second time :param t3: third time :param f1: first frequency :param f2: second frequency :param f3: third frequency :param time_sample_interval: length of an interval :param time_duration: length of waveform :param oversample_scale: oversample synthetic, then decimate by scale :return: synth waveform and timestamps

def synth_03(a: float = 30.0, b: float = 40.0, c: float = 150.0, time_sample_interval: float = 0.001, time_duration: float = 1.0, oversample_scale: int = 2) ‑> Tuple[numpy.ndarray, numpy.ndarray]

Example Synthetic 3

:param a: first value :param b: second value :param c: third value :param time_sample_interval: length of an interval :param time_duration: length of waveform :param oversample_scale: oversample synthetic, then decimate by scale :return: synth waveform and timestamps

def well_tempered_tone(frequency_sample_rate_hz: float = 800.0, frequency_center_hz: float = 60.0, time_duration_s: float = 10.24, time_fft_s: float = 0.64, use_fft_frequency: bool = True, add_noise_taper_aa: bool = False, output_desc: bool = False) ‑> Tuple[numpy.ndarray, numpy.ndarray, int, float, float, float]

Return a tone of unit amplitude and fixed frequency with a constant sample rate

:param frequency_sample_rate_hz: sample rate of frequency in Hz :param frequency_center_hz: center frequency in Hz :param time_duration_s: duration in seconds :param time_fft_s: length of segments. Default 1 second :param use_fft_frequency: use FFT frequency. Default True :param add_noise_taper_aa: add noise taper. Default False :param output_desc: if True, output description of waveform. Default False :return: waveform, timestamps, fft duration, sample rate, center frequency, frequency resolution