Module pylars.processing.fixwindow

Classes

class fixed_window_processing

Functions for LED window analysis. Since no peak finding is required, all the waveforms can be computed in a single step for each channel.

Expand source code

class fixed_window_processing():
    """Functions for LED window analysis. Since no peak finding is required,
    all the waveforms can be computed in a single step for each channel.
    """

    @classmethod
    def process_all_waveforms(cls, waveforms: np.ndarray,
                              baseline_samples: int,
                              led_window: Tuple[int, int],
                              dt: int = 10,
                              negative_polarity: bool = True):
        """Main process function on the waveform level for LED window
        analysis. All wfs are processed in a single step.

        Args:
            waveforms (np.ndarray): ADC counts of the waveform;
            baseline_samples (int): how many samples to take for the
        baseline calculation (at the begining of the waveform);
            led_window (tuple, optional): LED window in samples to consider.

        Returns:
            tuple: amplitudes, areas, ADCcounts of max/min
        """

        baselines = np.median(waveforms[:, :baseline_samples], axis=1)

        if negative_polarity:
            polarity = -1
            ADCcounts = np.min(waveforms[:, led_window[0]:led_window[1]],
                               axis=1)
            amplitudes = polarity * np.min(waveforms[:, led_window[0]:led_window[1]]
                                           - baselines[:, np.newaxis],
                                           axis=1)
        else:
            polarity = 1
            ADCcounts = np.max(waveforms[:, led_window[0]:led_window[1]],
                               axis=1)
            amplitudes = np.max(waveforms[:, led_window[0]:led_window[1]]
                                - baselines[:, np.newaxis],
                                axis=1)

        sum = np.sum(waveforms[:, led_window[0]:led_window[1]]
                     - baselines[:, np.newaxis],
                     axis=1)

        areas = (polarity * dt * sum)

        return amplitudes, areas, ADCcounts

Static methods

def process_all_waveforms(waveforms: numpy.ndarray, baseline_samples: int, led_window: Tuple[int, int], dt: int = 10, negative_polarity: bool = True)

Main process function on the waveform level for LED window analysis. All wfs are processed in a single step.

Args

waveforms : np.ndarray

ADC counts of the waveform;

baseline_samples : int

how many samples to take for the

baseline calculation (at the begining of the waveform); led_window (tuple, optional): LED window in samples to consider.

Returns

tuple

amplitudes, areas, ADCcounts of max/min

Expand source code

@classmethod
def process_all_waveforms(cls, waveforms: np.ndarray,
                          baseline_samples: int,
                          led_window: Tuple[int, int],
                          dt: int = 10,
                          negative_polarity: bool = True):
    """Main process function on the waveform level for LED window
    analysis. All wfs are processed in a single step.

    Args:
        waveforms (np.ndarray): ADC counts of the waveform;
        baseline_samples (int): how many samples to take for the
    baseline calculation (at the begining of the waveform);
        led_window (tuple, optional): LED window in samples to consider.

    Returns:
        tuple: amplitudes, areas, ADCcounts of max/min
    """

    baselines = np.median(waveforms[:, :baseline_samples], axis=1)

    if negative_polarity:
        polarity = -1
        ADCcounts = np.min(waveforms[:, led_window[0]:led_window[1]],
                           axis=1)
        amplitudes = polarity * np.min(waveforms[:, led_window[0]:led_window[1]]
                                       - baselines[:, np.newaxis],
                                       axis=1)
    else:
        polarity = 1
        ADCcounts = np.max(waveforms[:, led_window[0]:led_window[1]],
                           axis=1)
        amplitudes = np.max(waveforms[:, led_window[0]:led_window[1]]
                            - baselines[:, np.newaxis],
                            axis=1)

    sum = np.sum(waveforms[:, led_window[0]:led_window[1]]
                 - baselines[:, np.newaxis],
                 axis=1)

    areas = (polarity * dt * sum)

    return amplitudes, areas, ADCcounts