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pyhiperta.timing

Time related parameters (slop, intercept) algorithms.

Functions:

Name Description
slope_intercept

Computes slope, intercept by fitting a line (LSE) on the peak_times of each pixels along the ellipsis major axis

slope_intercept 

slope_intercept(peak_times, longitudinals, cleaning_mask)

Computes slope, intercept by fitting a line (LSE) on the peak_times of each pixels along the ellipsis major axis

This is essentially a less robust batched numpy.linalg.lstsq

Parameters:

Name Type Description Default
peak_times ndarray

The time of maximum signal for each pixels as computed by the integration. shape: ([N_batch,], N_pixels)

 required
longitudinals ndarray

The longitudinal coordinates (coordinate along the ellipsis major axis) of each pixels. shape: ([N_batch,], N_pixels)

 required

Returns:

Type Description
Tuple[ndarray, ndarray]

Slope and intercept values for each waveform. shape of each array: ([Batch,],)
Source code in src/pyhiperta/timing.py 
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def slope_intercept(
    peak_times: np.ndarray, longitudinals: np.ndarray, cleaning_mask
) -> tuple[np.ndarray, np.ndarray]:
    """Computes slope, intercept by fitting a line (LSE) on the `peak_times` of each pixels along the ellipsis major axis

    This is essentially a less robust __batched__ numpy.linalg.lstsq

    Parameters
    ----------
    peak_times : np.ndarray
        The time of maximum signal for each pixels as computed by the integration.
        shape: ([N_batch,], N_pixels)
    longitudinals : np.ndarray
        The longitudinal coordinates (coordinate along the ellipsis major axis) of each pixels.
        shape: ([N_batch,], N_pixels)

    Returns
    -------
    Tuple[np.ndarray, np.ndarray]
        Slope and intercept values for each waveform.
        shape of each array: ([Batch,],)
    """
    # Compute mean and sums only where the cleaning mask is True.
    longitudinals_mean = longitudinals.mean(axis=-1, keepdims=True, where=cleaning_mask)
    peak_times_mean = peak_times.mean(axis=-1, keepdims=True, where=cleaning_mask)

    slope = (cleaning_mask * (longitudinals - longitudinals_mean) * (peak_times - peak_times_mean)).sum(axis=-1) / (
        cleaning_mask * (longitudinals - longitudinals_mean) ** 2
    ).sum(axis=-1)
    intercept = peak_times_mean.squeeze() - slope * longitudinals_mean.squeeze()
    return slope, intercept