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point_cloud Module

Contains the Spyral definition of a point cloud

PointCloud dataclass

Geometric representation of an AT-TPC event

A PointCloud is a geometric representation of an event in the AT-TPC The GET traces are converted into points in space within the AT-TPC

Overloads the length (len()) operator with the number of points in the cloud (number of rows in the data matrix)

Attributes:

Name Type Description
event_number int

The event number
cloud ndarray

The Nx8 array of points in AT-TPC space Each row is [x,y,z,amplitude,integral,pad id,time,scale]
Source code in src/spyral/core/point_cloud.py 
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@dataclass
class PointCloud:
    """Geometric representation of an AT-TPC event

    A PointCloud is a geometric representation of an event in the AT-TPC
    The GET traces are converted into points in space within the AT-TPC

    Overloads the length (len()) operator with the number of points in the cloud
    (number of rows in the data matrix)

    Attributes
    ----------
    event_number: int
        The event number
    cloud: numpy.ndarray
        The Nx8 array of points in AT-TPC space
        Each row is [x,y,z,amplitude,integral,pad id,time,scale]
    """

    event_number: int
    data: np.ndarray

    def __len__(self) -> int:
        """Number of points in the cloud

        AKA number of rows in the data matrix

        Returns
        -------
        int
            The number of points in the cloud
        """
        return len(self.data)

__len__()

Number of points in the cloud

AKA number of rows in the data matrix

Returns:

Type Description
int

The number of points in the cloud
Source code in src/spyral/core/point_cloud.py 
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def __len__(self) -> int:
    """Number of points in the cloud

    AKA number of rows in the data matrix

    Returns
    -------
    int
        The number of points in the cloud
    """
    return len(self.data)

calibrate_point_cloud_z(cloud, detector_params, efield_correction=None)

Calibrate the cloud z-position

Uses two time references, the micromegas and window. Also applies the electric field correction if given. Any points which were invalidated (NaN'ed) by this operation are removed.

WARNING: This modifies the point cloud data, including removing points from the point cloud which were invalidated (NaN'ed).

Parameters:

Name Type Description Default
cloud PointCloud

The point cloud to calibrate

 required
detector_params DetectorParameters

The detector parameters

 required
efield_correction ElectronCorrector | None

The optional Garfield electric field correction to the electron drift

 None
Source code in src/spyral/core/point_cloud.py 
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def calibrate_point_cloud_z(
    cloud: PointCloud,
    detector_params: DetectorParameters,
    efield_correction: ElectronCorrector | None = None,
):
    """Calibrate the cloud z-position

    Uses two time references, the micromegas and window. Also applies
    the electric field correction if given. Any points which were invalidated
    (NaN'ed) by this operation are removed.

    WARNING: This modifies the point cloud data, including removing points from the
    point cloud which were invalidated (NaN'ed).

    Parameters
    ----------
    cloud: PointCloud
        The point cloud to calibrate
    detector_params: DetectorParameters
        The detector parameters
    efield_correction: ElectronCorrector | None
        The optional Garfield electric field correction to the electron drift
    """
    # Maybe use mm as the reference because it is more stable?
    for idx, point in enumerate(cloud.data):
        cloud.data[idx][2] = (
            (detector_params.window_time_bucket - point[6])
            / (
                detector_params.window_time_bucket
                - detector_params.micromegas_time_bucket
            )
            * detector_params.detector_length
        )
        if efield_correction is not None:
            cloud.data[idx] = efield_correction.correct_point(cloud.data[idx])
    # Remove any invalid data after the calibration
    mask = np.any(np.isnan(cloud.data), axis=1)
    cloud.data = cloud.data[~mask]  # Invert the mask to reject the NaNs

point_cloud_from_get(event, pad_map)

Load a point cloud from a GetEvent

Loads the points from the signals in the traces and applies the pad relative gain correction and the pad time correction

Parameters:

Name Type Description Default
event GetEvent

The GetEvent whose data should be loaded

 required
pad_map PadMap

The PadMap used to get pad correction values

 required
Source code in src/spyral/core/point_cloud.py 
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def point_cloud_from_get(event: GetEvent, pad_map: PadMap) -> PointCloud:
    """Load a point cloud from a GetEvent

    Loads the points from the signals in the traces and applies
    the pad relative gain correction and the pad time correction

    Parameters
    ----------
    event: GetEvent
        The GetEvent whose data should be loaded
    pad_map: PadMap
        The PadMap used to get pad correction values
    """
    count = 0
    for trace in event.traces:
        count += trace.get_number_of_peaks()
    cloud_matrix = np.zeros((count, 8))
    idx = 0
    for trace in event.traces:
        if trace.get_number_of_peaks() == 0 or trace.get_number_of_peaks() > 5:
            continue

        pid = trace.hw_id.pad_id
        check = pad_map.get_pad_from_hardware(trace.hw_id)
        if check is None:
            spyral_warn(
                __name__,
                f"When checking pad number of hardware: {trace.hw_id}, recieved None!",
            )
            continue
        if (
            check != pid
        ):  # This is dangerous! We trust the pad map over the merged data!
            pid = check

        pad = pad_map.get_pad_data(check)
        if pad is None or pad_map.is_beam_pad(check):
            continue
        for peak in trace.get_peaks():
            cloud_matrix[idx, 0] = pad.x  # X-coordinate, geometry
            cloud_matrix[idx, 1] = pad.y  # Y-coordinate, geometry
            cloud_matrix[idx, 2] = (
                peak.centroid + pad.time_offset
            )  # Z-coordinate, time with correction until calibrated with calibrate_z_position()
            cloud_matrix[idx, 3] = peak.amplitude
            cloud_matrix[idx, 4] = peak.integral
            cloud_matrix[idx, 5] = trace.hw_id.pad_id
            cloud_matrix[idx, 6] = (
                peak.centroid + pad.time_offset
            )  # Time bucket with correction
            cloud_matrix[idx, 7] = pad.scale
            idx += 1
    cloud_matrix = cloud_matrix[cloud_matrix[:, 3] != 0.0]
    return PointCloud(event.number, cloud_matrix)

sort_point_cloud_in_z(cloud)

Sort the point cloud array by the z-coordinate

Note: This modifies the underlying data in the point cloud

Parameters:

Name Type Description Default
cloud PointCloud

The point cloud to sort

 required
Source code in src/spyral/core/point_cloud.py 
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def sort_point_cloud_in_z(cloud: PointCloud):
    """Sort the point cloud array by the z-coordinate

    Note: This modifies the underlying data in the point cloud

    Parameters
    ----------
    cloud: PointCloud
        The point cloud to sort
    """
    indicies = np.argsort(cloud.data[:, 2])
    cloud.data = cloud.data[indicies]