Module `quantum_inferno.utilities.picker`

A set of functions to pick key portions of a signal.

Functions

def apply_bandpass(timeseries: numpy.ndarray, filter_band: Tuple[float, float], sample_rate_hz: float, filter_order: int = 7) ‑> numpy.ndarray: Apply a bandpass filter to the timeseries data. Apparently you need at least 46 values in the timeseries for this to work

:param timeseries: input signal :param filter_band: bandpass filter band :param sample_rate_hz: sample rate of the signal :param filter_order: order of the filter :return: filtered signal
def extract_signal_index_with_buffer(sample_rate_hz: float, peak: int, intro_buffer_s: float, outro_buffer_s: float) ‑> Tuple[int, int]: Extract start and end index of the extracted signal with a buffer around the peak

:param sample_rate_hz: sample rate of the signal :param peak: peak location :param intro_buffer_s: intro buffer in seconds :param outro_buffer_s: outro buffer in seconds :return: start and end index of the extracted signal
def extract_signal_with_buffer_seconds(timeseries: numpy.ndarray, sample_rate_hz: float, peak: int, intro_buffer_s: float, outro_buffer_s: float) ‑> numpy.ndarray: Extract a signal with a buffer in seconds around the peak

:param timeseries: input signal :param sample_rate_hz: sample rate of the signal :param peak: peak location :param intro_buffer_s: intro buffer in seconds :param outro_buffer_s: outro buffer in seconds :return: extracted signal
def find_peaks_by_extraction_type(timeseries: numpy.ndarray, extraction_type: str = 'sigmax', height: Optional[float] = 0.7, *args) ‑> numpy.ndarray: Find peaks in the timeseries data by extraction type

:param timeseries: input signal :param extraction_type: extraction type (default SIGMAX) :param height: minimum height for the peaks (default 0.7) :param args: additional arguments for scipy's find_peaks :return: location of peaks in the timeseries data
def find_peaks_by_extraction_type_with_bandpass(timeseries: numpy.ndarray, filter_band: Tuple[float, float], sample_rate_hz: float, filter_order: int = 7, extraction_type: str = 'sigmax', height: Optional[float] = 0.7, *args) ‑> numpy.ndarray: Find peaks in the timeseries data using a normalized bandpass filter

:param timeseries: input signal :param filter_band: bandpass filter band :param sample_rate_hz: sample rate of the signal :param filter_order: order of the filter (default 7) :param extraction_type: extraction type (default SIGMAX) :param height: minimum height for the peaks (default 0.7) :param args: additional arguments for scipy's find_peaks :return: location of peaks in the timeseries data
def find_peaks_to_comb_function(timeseries: numpy.ndarray, peaks: Union[list, int, numpy.ndarray]) ‑> numpy.ndarray: Returns a comb function of the same length as the timeseries with 1s at the peak locations and 0s elsewhere

:param timeseries: input signal :param peaks: peak locations :return: a comb function with the peak locations
def find_peaks_with_bits(timeseries: numpy.ndarray, sample_rate_hz: float, scaling_type: str = 'amplitude', threshold_bits: Optional[int] = 1, time_distance_seconds: Optional[float] = 0.1, *args) ‑> numpy.ndarray: Find peaks in the timeseries data with a threshold in bits (originally picker_signal_finder)

:param timeseries: time series :param sample_rate_hz: sample rate of the signal :param scaling_type: scaling type of the signal; either "amplitude" or "log2". (default "amplitude") :param threshold_bits: threshold in bits (default 1) :param time_distance_seconds: minimum time distance between peaks in seconds (default 0.1) :param args: additional arguments for scipy's find_peaks :return: location of peaks in the timeseries data
def find_sample_rate_hz_from_timestamps(timestamps: numpy.ndarray, time_unit: str = 's') ‑> float: Find the sample rate from timestamps in a given time unit (picoseconds to years defined in convert_time_unit())

:param timestamps: input timestamps :param time_unit: time unit of the timestamps (default to "s") :return: sample rate in Hz
def scale_signal_by_extraction_type(in_signal: numpy.ndarray, extraction_type: str = 'sigmax') ‑> numpy.ndarray: Normalize the signal based on the extraction type

:param in_signal: input signal :param extraction_type: extraction type :return: normalized signal