sfx_find_peaks

Classes for peak finding tasks in SFX.

Classes:

Name	Description
CxiWriter	utility class for writing peak finding results to CXI files.
FindPeaksPyAlgos	peak finding using psana's PyAlgos algorithm. Optional data compression and decompression with libpressio for data reduction tests.

CxiWriter

Source code in lute/tasks/sfx_find_peaks.py

class CxiWriter:

    def __init__(
        self,
        outdir: str,
        rank: int,
        exp: str,
        run: int,
        n_events: int,
        det_shape: Tuple[int, ...],
        raw_det_shape: Tuple[int, ...],
        min_peaks: int,
        max_peaks: int,
        i_x: Any,  # Not typed becomes it comes from psana
        i_y: Any,  # Not typed becomes it comes from psana
        ipx: Any,  # Not typed becomes it comes from psana
        ipy: Any,  # Not typed becomes it comes from psana
        tag: str,
    ):
        """
        Set up the CXI files to which peak finding results will be saved.

        Parameters:

            outdir (str): Output directory for cxi file.

            rank (int): MPI rank of the caller.

            exp (str): Experiment string.

            run (int): Experimental run.

            n_events (int): Number of events to process.

            det_shape (Tuple[int, int]): Shape of the numpy array storing the detector
                data. This must be aCheetah-stile 2D array.

            raw_det_shape (Tuple[int, ...]): Shape of the numpy array storing the
                detector in raw unassembled form. Length = 2, 3, or 4.

            min_peaks (int): Minimum number of peaks per image.

            max_peaks (int): Maximum number of peaks per image.

            i_x (Any): Array of pixel indexes along x

            i_y (Any): Array of pixel indexes along y

            ipx (Any): Pixel indexes with respect to detector origin (x component)

            ipy (Any): Pixel indexes with respect to detector origin (y component)

            tag (str): Tag to append to cxi file names.
        """
        self._det_shape: Tuple[int, ...] = det_shape
        self._raw_det_shape: Tuple[int, ...] = raw_det_shape
        self._i_x: Any = i_x
        self._i_y: Any = i_y
        self._ipx: Any = ipx
        self._ipy: Any = ipy
        self._index: int = 0

        # Create and open the HDF5 file
        fname: str = f"{exp}_r{run:0>4}_{rank}{tag}.cxi"
        Path(outdir).mkdir(exist_ok=True)
        self._outh5: Any = h5py.File(Path(outdir) / fname, "w")

        # Entry_1 entry for processing with CrystFEL
        entry_1: Any = self._outh5.create_group("entry_1")
        keys: List[str] = [
            "nPeaks",
            "peakXPosRaw",
            "peakYPosRaw",
            "rcent",
            "ccent",
            "rmin",
            "rmax",
            "cmin",
            "cmax",
            "peakTotalIntensity",
            "peakMaxIntensity",
            "peakRadius",
        ]
        ds_expId: Any = entry_1.create_dataset(
            "experimental_identifier", (n_events,), maxshape=(None,), dtype=int
        )
        ds_expId.attrs["axes"] = "experiment_identifier"
        data_1: Any = entry_1.create_dataset(
            "/entry_1/data_1/data",
            (n_events, det_shape[0], det_shape[1]),
            chunks=(1, det_shape[0], det_shape[1]),
            maxshape=(None, det_shape[0], det_shape[1]),
            dtype=numpy.float32,
        )
        data_1.attrs["axes"] = "experiment_identifier"
        key: str
        for key in ["powderHits", "powderMisses", "mask"]:
            entry_1.create_dataset(
                f"/entry_1/data_1/{key}",
                (det_shape[0], det_shape[1]),
                chunks=(det_shape[0], det_shape[1]),
                maxshape=(det_shape[0], det_shape[1]),
                dtype=float,
            )

        # Peak-related entries
        for key in keys:
            if key == "nPeaks":
                ds_x: Any = self._outh5.create_dataset(
                    f"/entry_1/result_1/{key}",
                    (n_events,),
                    maxshape=(None,),
                    dtype=int,
                )
                ds_x.attrs["minPeaks"] = min_peaks
                ds_x.attrs["maxPeaks"] = max_peaks
            else:
                ds_x: Any = self._outh5.create_dataset(
                    f"/entry_1/result_1/{key}",
                    (n_events, max_peaks),
                    maxshape=(None, max_peaks),
                    chunks=(1, max_peaks),
                    dtype=float,
                )
            ds_x.attrs["axes"] = "experiment_identifier:peaks"

        # Timestamp entries
        lcls_1: Any = self._outh5.create_group("LCLS")
        keys: List[str] = [
            "eventNumber",
            "machineTime",
            "machineTimeNanoSeconds",
            "fiducial",
            "photon_energy_eV",
        ]
        key: str
        for key in keys:
            if key == "photon_energy_eV":
                ds_x: Any = lcls_1.create_dataset(
                    f"{key}", (n_events,), maxshape=(None,), dtype=float
                )
            else:
                ds_x = lcls_1.create_dataset(
                    f"{key}", (n_events,), maxshape=(None,), dtype=int
                )
            ds_x.attrs["axes"] = "experiment_identifier"

        ds_x = self._outh5.create_dataset(
            "/LCLS/detector_1/EncoderValue", (n_events,), maxshape=(None,), dtype=float
        )
        ds_x.attrs["axes"] = "experiment_identifier"

    def write_event(
        self,
        img: NDArray[numpy.float_],
        peaks: Any,  # Not typed becomes it comes from psana
        timestamp_seconds: int,
        timestamp_nanoseconds: int,
        timestamp_fiducials: int,
        photon_energy: float,
        clen: float,
    ):
        """
        Write peak finding results for an event into the HDF5 file.

        Parameters:

            img (NDArray[numpy.float_]): Detector data for the event

            peaks: (Any): Peak information for the event, as recovered from the PyAlgos
                algorithm

            timestamp_seconds (int): Second part of the event's timestamp information

            timestamp_nanoseconds (int): Nanosecond part of the event's timestamp
                information

            timestamp_fiducials (int): Fiducials part of the event's timestamp
                information

            photon_energy (float): Photon energy for the event

            clen (float): Camera length/detector distance.
        """
        ch_rows: NDArray[numpy.float_] = (
            peaks[:, 0] * self._raw_det_shape[-2] + peaks[:, 1]
        )
        ch_cols: NDArray[numpy.float_] = peaks[:, 2]

        if self._outh5["/entry_1/data_1/data"].shape[0] <= self._index:
            self._outh5["entry_1/data_1/data"].resize(self._index + 1, axis=0)
            ds_key: str
            for ds_key in self._outh5["/entry_1/result_1"].keys():
                self._outh5[f"/entry_1/result_1/{ds_key}"].resize(
                    self._index + 1, axis=0
                )
            for ds_key in (
                "machineTime",
                "machineTimeNanoSeconds",
                "fiducial",
                "photon_energy_eV",
                "detector_1/EncoderValue",
            ):
                self._outh5[f"/LCLS/{ds_key}"].resize(self._index + 1, axis=0)

        # Entry_1 entry for processing with CrystFEL
        self._outh5["/entry_1/data_1/data"][self._index, :, :] = img.reshape(
            -1, img.shape[-1]
        )
        self._outh5["/entry_1/result_1/nPeaks"][self._index] = peaks.shape[0]
        self._outh5["/entry_1/result_1/peakXPosRaw"][self._index, : peaks.shape[0]] = (
            ch_cols.astype("int")
        )
        self._outh5["/entry_1/result_1/peakYPosRaw"][self._index, : peaks.shape[0]] = (
            ch_rows.astype("int")
        )
        self._outh5["/entry_1/result_1/rcent"][self._index, : peaks.shape[0]] = peaks[
            :, 6
        ]
        self._outh5["/entry_1/result_1/ccent"][self._index, : peaks.shape[0]] = peaks[
            :, 7
        ]
        self._outh5["/entry_1/result_1/rmin"][self._index, : peaks.shape[0]] = peaks[
            :, 10
        ]
        self._outh5["/entry_1/result_1/rmax"][self._index, : peaks.shape[0]] = peaks[
            :, 11
        ]
        self._outh5["/entry_1/result_1/cmin"][self._index, : peaks.shape[0]] = peaks[
            :, 12
        ]
        self._outh5["/entry_1/result_1/cmax"][self._index, : peaks.shape[0]] = peaks[
            :, 13
        ]
        self._outh5["/entry_1/result_1/peakTotalIntensity"][
            self._index, : peaks.shape[0]
        ] = peaks[:, 5]
        self._outh5["/entry_1/result_1/peakMaxIntensity"][
            self._index, : peaks.shape[0]
        ] = peaks[:, 4]

        # Calculate and write pixel radius
        peaks_cenx: NDArray[numpy.float_] = (
            self._i_x[
                numpy.array(peaks[:, 0], dtype=numpy.int64),
                numpy.array(peaks[:, 1], dtype=numpy.int64),
                numpy.array(peaks[:, 2], dtype=numpy.int64),
            ]
            + 0.5
            - self._ipx
        )
        peaks_ceny: NDArray[numpy.float_] = (
            self._i_y[
                numpy.array(peaks[:, 0], dtype=numpy.int64),
                numpy.array(peaks[:, 1], dtype=numpy.int64),
                numpy.array(peaks[:, 2], dtype=numpy.int64),
            ]
            + 0.5
            - self._ipy
        )
        peak_radius: NDArray[numpy.float_] = numpy.sqrt(
            (peaks_cenx**2) + (peaks_ceny**2)
        )
        self._outh5["/entry_1/result_1/peakRadius"][
            self._index, : peaks.shape[0]
        ] = peak_radius

        # LCLS entry dataset
        self._outh5["/LCLS/machineTime"][self._index] = timestamp_seconds
        self._outh5["/LCLS/machineTimeNanoSeconds"][self._index] = timestamp_nanoseconds
        self._outh5["/LCLS/fiducial"][self._index] = timestamp_fiducials
        self._outh5["/LCLS/photon_energy_eV"][self._index] = photon_energy

        # Add clen distance
        self._outh5["/LCLS/detector_1/EncoderValue"][self._index] = clen
        self._index += 1

    def write_non_event_data(
        self,
        powder_hits: NDArray[numpy.float_],
        powder_misses: NDArray[numpy.float_],
        mask: NDArray[numpy.uint16],
    ):
        """
        Write to the file data that is not related to a specific event (masks, powders)

        Parameters:

            powder_hits (NDArray[numpy.float_]): Virtual powder pattern from hits

            powder_misses (NDArray[numpy.float_]): Virtual powder pattern from hits

            mask: (NDArray[numpy.uint16]): Pixel ask to write into the file

        """
        # Add powders and mask to files, reshaping them to match the crystfel
        # convention
        self._outh5["/entry_1/data_1/powderHits"][:] = powder_hits.reshape(
            -1, powder_hits.shape[-1]
        )
        self._outh5["/entry_1/data_1/powderMisses"][:] = powder_misses.reshape(
            -1, powder_misses.shape[-1]
        )
        self._outh5["/entry_1/data_1/mask"][:] = (1 - mask).reshape(
            -1, mask.shape[-1]
        )  # Crystfel expects inverted values

    def optimize_and_close_file(
        self,
        num_hits: int,
        max_peaks: int,
    ):
        """
        Resize data blocks and write additional information to the file

        Parameters:

            num_hits (int): Number of hits for which information has been saved to the
                file

            max_peaks (int): Maximum number of peaks (per event) for which information
                can be written into the file
        """

        # Resize the entry_1 entry
        data_shape: Tuple[int, ...] = self._outh5["/entry_1/data_1/data"].shape
        self._outh5["/entry_1/data_1/data"].resize(
            (num_hits, data_shape[1], data_shape[2])
        )
        self._outh5[f"/entry_1/result_1/nPeaks"].resize((num_hits,))
        key: str
        for key in [
            "peakXPosRaw",
            "peakYPosRaw",
            "rcent",
            "ccent",
            "rmin",
            "rmax",
            "cmin",
            "cmax",
            "peakTotalIntensity",
            "peakMaxIntensity",
            "peakRadius",
        ]:
            self._outh5[f"/entry_1/result_1/{key}"].resize((num_hits, max_peaks))

        # Resize LCLS entry
        for key in [
            "eventNumber",
            "machineTime",
            "machineTimeNanoSeconds",
            "fiducial",
            "detector_1/EncoderValue",
            "photon_energy_eV",
        ]:
            self._outh5[f"/LCLS/{key}"].resize((num_hits,))
        self._outh5.close()

__init__(outdir, rank, exp, run, n_events, det_shape, raw_det_shape, min_peaks, max_peaks, i_x, i_y, ipx, ipy, tag)

Set up the CXI files to which peak finding results will be saved.

Parameters:

outdir (str): Output directory for cxi file.

rank (int): MPI rank of the caller.

exp (str): Experiment string.

run (int): Experimental run.

n_events (int): Number of events to process.

det_shape (Tuple[int, int]): Shape of the numpy array storing the detector
    data. This must be aCheetah-stile 2D array.

raw_det_shape (Tuple[int, ...]): Shape of the numpy array storing the
    detector in raw unassembled form. Length = 2, 3, or 4.

min_peaks (int): Minimum number of peaks per image.

max_peaks (int): Maximum number of peaks per image.

i_x (Any): Array of pixel indexes along x

i_y (Any): Array of pixel indexes along y

ipx (Any): Pixel indexes with respect to detector origin (x component)

ipy (Any): Pixel indexes with respect to detector origin (y component)

tag (str): Tag to append to cxi file names.

Source code in lute/tasks/sfx_find_peaks.py

def __init__(
    self,
    outdir: str,
    rank: int,
    exp: str,
    run: int,
    n_events: int,
    det_shape: Tuple[int, ...],
    raw_det_shape: Tuple[int, ...],
    min_peaks: int,
    max_peaks: int,
    i_x: Any,  # Not typed becomes it comes from psana
    i_y: Any,  # Not typed becomes it comes from psana
    ipx: Any,  # Not typed becomes it comes from psana
    ipy: Any,  # Not typed becomes it comes from psana
    tag: str,
):
    """
    Set up the CXI files to which peak finding results will be saved.

    Parameters:

        outdir (str): Output directory for cxi file.

        rank (int): MPI rank of the caller.

        exp (str): Experiment string.

        run (int): Experimental run.

        n_events (int): Number of events to process.

        det_shape (Tuple[int, int]): Shape of the numpy array storing the detector
            data. This must be aCheetah-stile 2D array.

        raw_det_shape (Tuple[int, ...]): Shape of the numpy array storing the
            detector in raw unassembled form. Length = 2, 3, or 4.

        min_peaks (int): Minimum number of peaks per image.

        max_peaks (int): Maximum number of peaks per image.

        i_x (Any): Array of pixel indexes along x

        i_y (Any): Array of pixel indexes along y

        ipx (Any): Pixel indexes with respect to detector origin (x component)

        ipy (Any): Pixel indexes with respect to detector origin (y component)

        tag (str): Tag to append to cxi file names.
    """
    self._det_shape: Tuple[int, ...] = det_shape
    self._raw_det_shape: Tuple[int, ...] = raw_det_shape
    self._i_x: Any = i_x
    self._i_y: Any = i_y
    self._ipx: Any = ipx
    self._ipy: Any = ipy
    self._index: int = 0

    # Create and open the HDF5 file
    fname: str = f"{exp}_r{run:0>4}_{rank}{tag}.cxi"
    Path(outdir).mkdir(exist_ok=True)
    self._outh5: Any = h5py.File(Path(outdir) / fname, "w")

    # Entry_1 entry for processing with CrystFEL
    entry_1: Any = self._outh5.create_group("entry_1")
    keys: List[str] = [
        "nPeaks",
        "peakXPosRaw",
        "peakYPosRaw",
        "rcent",
        "ccent",
        "rmin",
        "rmax",
        "cmin",
        "cmax",
        "peakTotalIntensity",
        "peakMaxIntensity",
        "peakRadius",
    ]
    ds_expId: Any = entry_1.create_dataset(
        "experimental_identifier", (n_events,), maxshape=(None,), dtype=int
    )
    ds_expId.attrs["axes"] = "experiment_identifier"
    data_1: Any = entry_1.create_dataset(
        "/entry_1/data_1/data",
        (n_events, det_shape[0], det_shape[1]),
        chunks=(1, det_shape[0], det_shape[1]),
        maxshape=(None, det_shape[0], det_shape[1]),
        dtype=numpy.float32,
    )
    data_1.attrs["axes"] = "experiment_identifier"
    key: str
    for key in ["powderHits", "powderMisses", "mask"]:
        entry_1.create_dataset(
            f"/entry_1/data_1/{key}",
            (det_shape[0], det_shape[1]),
            chunks=(det_shape[0], det_shape[1]),
            maxshape=(det_shape[0], det_shape[1]),
            dtype=float,
        )

    # Peak-related entries
    for key in keys:
        if key == "nPeaks":
            ds_x: Any = self._outh5.create_dataset(
                f"/entry_1/result_1/{key}",
                (n_events,),
                maxshape=(None,),
                dtype=int,
            )
            ds_x.attrs["minPeaks"] = min_peaks
            ds_x.attrs["maxPeaks"] = max_peaks
        else:
            ds_x: Any = self._outh5.create_dataset(
                f"/entry_1/result_1/{key}",
                (n_events, max_peaks),
                maxshape=(None, max_peaks),
                chunks=(1, max_peaks),
                dtype=float,
            )
        ds_x.attrs["axes"] = "experiment_identifier:peaks"

    # Timestamp entries
    lcls_1: Any = self._outh5.create_group("LCLS")
    keys: List[str] = [
        "eventNumber",
        "machineTime",
        "machineTimeNanoSeconds",
        "fiducial",
        "photon_energy_eV",
    ]
    key: str
    for key in keys:
        if key == "photon_energy_eV":
            ds_x: Any = lcls_1.create_dataset(
                f"{key}", (n_events,), maxshape=(None,), dtype=float
            )
        else:
            ds_x = lcls_1.create_dataset(
                f"{key}", (n_events,), maxshape=(None,), dtype=int
            )
        ds_x.attrs["axes"] = "experiment_identifier"

    ds_x = self._outh5.create_dataset(
        "/LCLS/detector_1/EncoderValue", (n_events,), maxshape=(None,), dtype=float
    )
    ds_x.attrs["axes"] = "experiment_identifier"

optimize_and_close_file(num_hits, max_peaks)

Resize data blocks and write additional information to the file

Parameters:

num_hits (int): Number of hits for which information has been saved to the
    file

max_peaks (int): Maximum number of peaks (per event) for which information
    can be written into the file

Source code in lute/tasks/sfx_find_peaks.py

def optimize_and_close_file(
    self,
    num_hits: int,
    max_peaks: int,
):
    """
    Resize data blocks and write additional information to the file

    Parameters:

        num_hits (int): Number of hits for which information has been saved to the
            file

        max_peaks (int): Maximum number of peaks (per event) for which information
            can be written into the file
    """

    # Resize the entry_1 entry
    data_shape: Tuple[int, ...] = self._outh5["/entry_1/data_1/data"].shape
    self._outh5["/entry_1/data_1/data"].resize(
        (num_hits, data_shape[1], data_shape[2])
    )
    self._outh5[f"/entry_1/result_1/nPeaks"].resize((num_hits,))
    key: str
    for key in [
        "peakXPosRaw",
        "peakYPosRaw",
        "rcent",
        "ccent",
        "rmin",
        "rmax",
        "cmin",
        "cmax",
        "peakTotalIntensity",
        "peakMaxIntensity",
        "peakRadius",
    ]:
        self._outh5[f"/entry_1/result_1/{key}"].resize((num_hits, max_peaks))

    # Resize LCLS entry
    for key in [
        "eventNumber",
        "machineTime",
        "machineTimeNanoSeconds",
        "fiducial",
        "detector_1/EncoderValue",
        "photon_energy_eV",
    ]:
        self._outh5[f"/LCLS/{key}"].resize((num_hits,))
    self._outh5.close()

write_event(img, peaks, timestamp_seconds, timestamp_nanoseconds, timestamp_fiducials, photon_energy, clen)

Write peak finding results for an event into the HDF5 file.

Parameters:

img (NDArray[numpy.float_]): Detector data for the event

peaks: (Any): Peak information for the event, as recovered from the PyAlgos
    algorithm

timestamp_seconds (int): Second part of the event's timestamp information

timestamp_nanoseconds (int): Nanosecond part of the event's timestamp
    information

timestamp_fiducials (int): Fiducials part of the event's timestamp
    information

photon_energy (float): Photon energy for the event

clen (float): Camera length/detector distance.

Source code in lute/tasks/sfx_find_peaks.py

def write_event(
    self,
    img: NDArray[numpy.float_],
    peaks: Any,  # Not typed becomes it comes from psana
    timestamp_seconds: int,
    timestamp_nanoseconds: int,
    timestamp_fiducials: int,
    photon_energy: float,
    clen: float,
):
    """
    Write peak finding results for an event into the HDF5 file.

    Parameters:

        img (NDArray[numpy.float_]): Detector data for the event

        peaks: (Any): Peak information for the event, as recovered from the PyAlgos
            algorithm

        timestamp_seconds (int): Second part of the event's timestamp information

        timestamp_nanoseconds (int): Nanosecond part of the event's timestamp
            information

        timestamp_fiducials (int): Fiducials part of the event's timestamp
            information

        photon_energy (float): Photon energy for the event

        clen (float): Camera length/detector distance.
    """
    ch_rows: NDArray[numpy.float_] = (
        peaks[:, 0] * self._raw_det_shape[-2] + peaks[:, 1]
    )
    ch_cols: NDArray[numpy.float_] = peaks[:, 2]

    if self._outh5["/entry_1/data_1/data"].shape[0] <= self._index:
        self._outh5["entry_1/data_1/data"].resize(self._index + 1, axis=0)
        ds_key: str
        for ds_key in self._outh5["/entry_1/result_1"].keys():
            self._outh5[f"/entry_1/result_1/{ds_key}"].resize(
                self._index + 1, axis=0
            )
        for ds_key in (
            "machineTime",
            "machineTimeNanoSeconds",
            "fiducial",
            "photon_energy_eV",
            "detector_1/EncoderValue",
        ):
            self._outh5[f"/LCLS/{ds_key}"].resize(self._index + 1, axis=0)

    # Entry_1 entry for processing with CrystFEL
    self._outh5["/entry_1/data_1/data"][self._index, :, :] = img.reshape(
        -1, img.shape[-1]
    )
    self._outh5["/entry_1/result_1/nPeaks"][self._index] = peaks.shape[0]
    self._outh5["/entry_1/result_1/peakXPosRaw"][self._index, : peaks.shape[0]] = (
        ch_cols.astype("int")
    )
    self._outh5["/entry_1/result_1/peakYPosRaw"][self._index, : peaks.shape[0]] = (
        ch_rows.astype("int")
    )
    self._outh5["/entry_1/result_1/rcent"][self._index, : peaks.shape[0]] = peaks[
        :, 6
    ]
    self._outh5["/entry_1/result_1/ccent"][self._index, : peaks.shape[0]] = peaks[
        :, 7
    ]
    self._outh5["/entry_1/result_1/rmin"][self._index, : peaks.shape[0]] = peaks[
        :, 10
    ]
    self._outh5["/entry_1/result_1/rmax"][self._index, : peaks.shape[0]] = peaks[
        :, 11
    ]
    self._outh5["/entry_1/result_1/cmin"][self._index, : peaks.shape[0]] = peaks[
        :, 12
    ]
    self._outh5["/entry_1/result_1/cmax"][self._index, : peaks.shape[0]] = peaks[
        :, 13
    ]
    self._outh5["/entry_1/result_1/peakTotalIntensity"][
        self._index, : peaks.shape[0]
    ] = peaks[:, 5]
    self._outh5["/entry_1/result_1/peakMaxIntensity"][
        self._index, : peaks.shape[0]
    ] = peaks[:, 4]

    # Calculate and write pixel radius
    peaks_cenx: NDArray[numpy.float_] = (
        self._i_x[
            numpy.array(peaks[:, 0], dtype=numpy.int64),
            numpy.array(peaks[:, 1], dtype=numpy.int64),
            numpy.array(peaks[:, 2], dtype=numpy.int64),
        ]
        + 0.5
        - self._ipx
    )
    peaks_ceny: NDArray[numpy.float_] = (
        self._i_y[
            numpy.array(peaks[:, 0], dtype=numpy.int64),
            numpy.array(peaks[:, 1], dtype=numpy.int64),
            numpy.array(peaks[:, 2], dtype=numpy.int64),
        ]
        + 0.5
        - self._ipy
    )
    peak_radius: NDArray[numpy.float_] = numpy.sqrt(
        (peaks_cenx**2) + (peaks_ceny**2)
    )
    self._outh5["/entry_1/result_1/peakRadius"][
        self._index, : peaks.shape[0]
    ] = peak_radius

    # LCLS entry dataset
    self._outh5["/LCLS/machineTime"][self._index] = timestamp_seconds
    self._outh5["/LCLS/machineTimeNanoSeconds"][self._index] = timestamp_nanoseconds
    self._outh5["/LCLS/fiducial"][self._index] = timestamp_fiducials
    self._outh5["/LCLS/photon_energy_eV"][self._index] = photon_energy

    # Add clen distance
    self._outh5["/LCLS/detector_1/EncoderValue"][self._index] = clen
    self._index += 1

write_non_event_data(powder_hits, powder_misses, mask)

Write to the file data that is not related to a specific event (masks, powders)

Parameters:

powder_hits (NDArray[numpy.float_]): Virtual powder pattern from hits

powder_misses (NDArray[numpy.float_]): Virtual powder pattern from hits

mask: (NDArray[numpy.uint16]): Pixel ask to write into the file

Source code in lute/tasks/sfx_find_peaks.py

def write_non_event_data(
    self,
    powder_hits: NDArray[numpy.float_],
    powder_misses: NDArray[numpy.float_],
    mask: NDArray[numpy.uint16],
):
    """
    Write to the file data that is not related to a specific event (masks, powders)

    Parameters:

        powder_hits (NDArray[numpy.float_]): Virtual powder pattern from hits

        powder_misses (NDArray[numpy.float_]): Virtual powder pattern from hits

        mask: (NDArray[numpy.uint16]): Pixel ask to write into the file

    """
    # Add powders and mask to files, reshaping them to match the crystfel
    # convention
    self._outh5["/entry_1/data_1/powderHits"][:] = powder_hits.reshape(
        -1, powder_hits.shape[-1]
    )
    self._outh5["/entry_1/data_1/powderMisses"][:] = powder_misses.reshape(
        -1, powder_misses.shape[-1]
    )
    self._outh5["/entry_1/data_1/mask"][:] = (1 - mask).reshape(
        -1, mask.shape[-1]
    )  # Crystfel expects inverted values

FindPeaksPyAlgos

Bases: Task

Task that performs peak finding using the PyAlgos peak finding algorithms and writes the peak information to CXI files.

Source code in lute/tasks/sfx_find_peaks.py

class FindPeaksPyAlgos(Task):
    """
    Task that performs peak finding using the PyAlgos peak finding algorithms and
    writes the peak information to CXI files.
    """

    def __init__(self, *, params: TaskParameters, use_mpi: bool = True) -> None:
        super().__init__(params=params, use_mpi=use_mpi)
        if self._task_parameters.compression is not None:
            from libpressio import PressioCompressor

    def _run(self) -> None:
        ds: Any = MPIDataSource(
            f"exp={self._task_parameters.lute_config.experiment}:"
            f"run={self._task_parameters.lute_config.run}:smd"
        )
        if self._task_parameters.n_events != 0:
            ds.break_after(self._task_parameters.n_events)

        det: Any = Detector(self._task_parameters.det_name)
        det.do_reshape_2d_to_3d(flag=True)

        evr: Any = Detector(self._task_parameters.event_receiver)

        i_x: Any = det.indexes_x(self._task_parameters.lute_config.run).astype(
            numpy.int64
        )
        i_y: Any = det.indexes_y(self._task_parameters.lute_config.run).astype(
            numpy.int64
        )
        ipx: Any
        ipy: Any
        ipx, ipy = det.point_indexes(
            self._task_parameters.lute_config.run, pxy_um=(0, 0)
        )

        alg: Any = None
        num_hits: int = 0
        num_events: int = 0
        num_empty_images: int = 0
        tag: str = self._task_parameters.tag
        if (tag != "") and (tag[0] != "_"):
            tag = "_" + tag

        evt: Any
        for evt in ds.events():

            evt_id: Any = evt.get(EventId)
            timestamp_seconds: int = evt_id.time()[0]
            timestamp_nanoseconds: int = evt_id.time()[1]
            timestamp_fiducials: int = evt_id.fiducials()
            event_codes: Any = evr.eventCodes(evt)

            if isinstance(self._task_parameters.pv_camera_length, float):
                clen: float = self._task_parameters.pv_camera_length
            else:
                clen = (
                    ds.env().epicsStore().value(self._task_parameters.pv_camera_length)
                )

            if self._task_parameters.event_logic:
                if not self._task_parameters.event_code in event_codes:
                    continue

            img: Any = det.calib(evt)

            if img is None:
                num_empty_images += 1
                continue

            if alg is None:
                det_shape: Tuple[int, ...] = img.shape
                if len(det_shape) == 3:
                    det_shape = (det_shape[0] * det_shape[1], det_shape[2])
                else:
                    det_shape = img.shape

                mask: NDArray[numpy.uint16] = numpy.ones(det_shape).astype(numpy.uint16)

                if self._task_parameters.psana_mask:
                    mask = det.mask(
                        self.task_parameters.run,
                        calib=False,
                        status=True,
                        edges=False,
                        centra=False,
                        unbond=False,
                        unbondnbrs=False,
                    ).astype(numpy.uint16)

                hdffh: Any
                if self._task_parameters.mask_file is not None:
                    with h5py.File(self._task_parameters.mask_file, "r") as hdffh:
                        loaded_mask: NDArray[numpy.int] = hdffh["entry_1/data_1/mask"][
                            :
                        ]
                        mask *= loaded_mask.astype(numpy.uint16)

                file_writer: CxiWriter = CxiWriter(
                    outdir=self._task_parameters.outdir,
                    rank=ds.rank,
                    exp=self._task_parameters.lute_config.experiment,
                    run=self._task_parameters.lute_config.run,
                    n_events=self._task_parameters.n_events,
                    det_shape=det_shape,
                    raw_det_shape=img.shape,
                    i_x=i_x,
                    i_y=i_y,
                    ipx=ipx,
                    ipy=ipy,
                    min_peaks=self._task_parameters.min_peaks,
                    max_peaks=self._task_parameters.max_peaks,
                    tag=tag,
                )
                alg: Any = PyAlgos(mask=mask, pbits=0)  # pbits controls verbosity
                alg.set_peak_selection_pars(
                    npix_min=self._task_parameters.npix_min,
                    npix_max=self._task_parameters.npix_max,
                    amax_thr=self._task_parameters.amax_thr,
                    atot_thr=self._task_parameters.atot_thr,
                    son_min=self._task_parameters.son_min,
                )

                if self._task_parameters.compression is not None:

                    libpressio_config = generate_libpressio_configuration(
                        compressor=self._task_parameters.compression.compressor,
                        roi_window_size=self._task_parameters.compression.roi_window_size,
                        bin_size=self._task_parameters.compression.bin_size,
                        abs_error=self._task_parameters.compression.abs_error,
                        libpressio_mask=mask,
                    )

                powder_hits: NDArray[numpy.float_] = numpy.zeros(det_shape)
                powder_misses: NDArray[numpy.float_] = numpy.zeros(det_shape)

            peaks: Any = alg.peak_finder_v3r3(
                img,
                rank=self._task_parameters.peak_rank,
                r0=self._task_parameters.r0,
                dr=self._task_parameters.dr,
                nsigm=self._task_parameters.nsigm,
            )

            num_events += 1

            if (peaks.shape[0] >= self._task_parameters.min_peaks) and (
                peaks.shape[0] <= self._task_parameters.max_peaks
            ):

                if self._task_parameters.compression is not None:

                    libpressio_config_with_peaks = (
                        add_peaks_to_libpressio_configuration(libpressio_config, peaks)
                    )
                    compressor = PressioCompressor.from_config(
                        libpressio_config_with_peaks
                    )
                    compressed_img = compressor.encode(img)
                    decompressed_img = numpy.zeros_like(img)
                    decompressed = compressor.decode(compressed_img, decompressed_img)
                    img = decompressed_img

                photon_energy: float
                try:
                    photon_energy = Detector("EBeam").get(evt).ebeamPhotonEnergy()
                    if numpy.isinf(photon_energy):
                        raise ValueError
                except (AttributeError, ValueError):
                    photon_energy = (
                        1.23984197386209e-06
                        / ds.env().epicsStore().value("SIOC:SYS0:ML00:AO192")
                    ) * 1e9

                file_writer.write_event(
                    img=img,
                    peaks=peaks,
                    timestamp_seconds=timestamp_seconds,
                    timestamp_nanoseconds=timestamp_nanoseconds,
                    timestamp_fiducials=timestamp_fiducials,
                    photon_energy=photon_energy,
                    clen=clen,
                )
                num_hits += 1

            # TODO: Fix bug here
            # generate / update powders
            if peaks.shape[0] >= self._task_parameters.min_peaks:
                powder_hits = numpy.maximum(
                    powder_hits,
                    img.reshape(-1, img.shape[-1]),
                )
            else:
                powder_misses = numpy.maximum(
                    powder_misses,
                    img.reshape(-1, img.shape[-1]),
                )

        if num_empty_images != 0:
            msg: Message = Message(
                contents=f"Rank {ds.rank} encountered {num_empty_images} empty images."
            )
            self._report_to_executor(msg)

        file_writer.write_non_event_data(
            powder_hits=powder_hits,
            powder_misses=powder_misses,
            mask=mask,
        )

        file_writer.optimize_and_close_file(
            num_hits=num_hits, max_peaks=self._task_parameters.max_peaks
        )

        COMM_WORLD.Barrier()

        num_hits_per_rank: List[int] = COMM_WORLD.gather(num_hits, root=0)
        num_hits_total: int = COMM_WORLD.reduce(num_hits, SUM)
        num_events_total: int = COMM_WORLD.reduce(num_events, SUM)

        if ds.rank == 0:
            master_fname: Path = write_master_file(
                mpi_size=ds.size,
                outdir=self._task_parameters.outdir,
                exp=self._task_parameters.lute_config.experiment,
                run=self._task_parameters.lute_config.run,
                tag=tag,
                n_hits_per_rank=num_hits_per_rank,
                n_hits_total=num_hits_total,
            )

            # Write final summary file
            f: TextIO
            with open(
                Path(self._task_parameters.outdir) / f"peakfinding{tag}.summary", "w"
            ) as f:
                print(f"Number of events processed: {num_events_total}", file=f)
                print(f"Number of hits found: {num_hits_total}", file=f)
                print(
                    "Fractional hit rate: " f"{(num_hits_total/num_events_total):.2f}",
                    file=f,
                )
                print(f"No. hits per rank: {num_hits_per_rank}", file=f)

            with h5py.File(master_fname, "r") as f:
                final_powder_hits: numpy.ndarray[numpy.float64] = f[
                    "entry_1/data_1/powderHits"
                ][:]
                final_powder_misses: numpy.ndarray[numpy.float64] = f[
                    "entry_1/data_1/powderMisses"
                ][:]
                f.close()

            powder_plots: pn.Tabs = self._create_powder_plots(
                det, final_powder_hits, final_powder_misses
            )
            text_summary: Dict[str, str] = {
                "Number of events processed": str(num_events_total),
                "Number of hits found": str(num_hits_total),
                "Fractional hit rate": f"{num_hits_total/num_events_total:.2f}",
            }
            self._result.summary = (
                text_summary,
                ElogSummaryPlots(
                    f"r{self._task_parameters.lute_config.run}/powders", powder_plots
                ),
            )
            with open(Path(self._task_parameters.out_file), "w") as f:
                print(f"{master_fname}", file=f)

            # Write out_file

    def _post_run(self) -> None:
        super()._post_run()
        self._result.task_status = TaskStatus.COMPLETED

    def _assemble_image(
        self, det: Detector, img: numpy.ndarray[numpy.float64]
    ) -> numpy.ndarray[numpy.float64]:
        """Assemble an image based on psana geometry.

        Args:
            det (psana.Detector): The detector object for the associated image.
                Used to access the geometry.

            img (numpy.ndarray[np.float64]): The image to assemble. Should
                generally be of shape (n_panels, ss, fs)

        Returns:
            assembled_img(numpy.ndarray[np.float64]): Assembled 2D image.
        """
        geom: GeometryAccess = det.geometry(self._task_parameters.lute_config.run)
        tmp: Tuple[numpy.ndarray[numpy.uint64], ...] = geom.get_pixel_coord_indexes()
        pixel_map: numpy.ndarray[numpy.unit64] = numpy.zeros(
            tmp[0].shape[1:] + (2,), dtype=numpy.uint64
        )
        pixel_map[..., 0] = tmp[0][0]
        pixel_map[..., 1] = tmp[1][0]
        unflattened_img: numpy.ndarray[numpy.float64] = img.reshape(
            pixel_map.shape[:-1]
        )
        idx_max_y: int = int(numpy.max(pixel_map[..., 0]) + 1)  # Adding one
        idx_max_x: int = int(numpy.max(pixel_map[..., 1]) + 1)  # casts to float
        assembled_img: numpy.ndarray[numpy.float64] = numpy.zeros(
            (idx_max_y, idx_max_x)
        )
        assembled_img[pixel_map[..., 0], pixel_map[..., 1]] = unflattened_img

        return assembled_img

    def _create_powder_plots(
        self,
        det: Detector,
        powder_hits: numpy.ndarray[numpy.float64],
        powder_misses: numpy.ndarray[numpy.float64],
    ) -> pn.Tabs:
        """Create a tabbed display of hits and misses 'powder' plots.

        Args:
            det (psana.Detector): The detector object for the associated image.
                Used to access the geometry.

            powder_hits (numpy.ndarray[np.float64]): Total max/sum projection of
                hits across the run.

            powder_misses (numpy.ndarray[np.float64]): Total max/sum projection of
                misses across the run.

        Returns:
            tabs (pn.Tabs): Tabbed display of the image plots.
        """
        assembled_powder_hits: numpy.ndarray[numpy.float64] = self._assemble_image(
            det, powder_hits
        )
        assembled_powder_misses: numpy.ndarray[numpy.float64] = self._assemble_image(
            det, powder_misses
        )

        grid_hits: pn.GridSpec = pn.GridSpec(
            sizing_mode="stretch_both",
            max_width=700,
            name=f"{self._task_parameters.det_name} - Hits",
        )
        dim: hv.Dimension = hv.Dimension(
            ("image", "Hits"),
            range=(
                numpy.nanpercentile(assembled_powder_hits, 1),
                numpy.nanpercentile(assembled_powder_hits, 99),
            ),
        )
        grid_hits[0, 0] = pn.Row(
            hv.Image(assembled_powder_hits, vdims=[dim], name=dim.label).options(
                colorbar=True, cmap="rainbow"
            )
        )

        grid_misses: pn.GridSpec = pn.GridSpec(
            sizing_mode="stretch_both",
            max_width=700,
            name=f"{self._task_parameters.det_name} - Misses",
        )
        dim = hv.Dimension(
            ("image", "Misses"),
            range=(
                numpy.nanpercentile(assembled_powder_misses, 1),
                numpy.nanpercentile(assembled_powder_misses, 99),
            ),
        )
        grid_misses[0, 0] = pn.Row(
            hv.Image(assembled_powder_misses, vdims=[dim], name=dim.label).options(
                colorbar=True, cmap="rainbow"
            )
        )

        tabs: pn.Tabs = pn.Tabs(grid_hits)
        tabs.append(grid_misses)
        return tabs

_assemble_image(det, img)

Assemble an image based on psana geometry.

Parameters:

Name	Type	Description	Default
det	Detector	The detector object for the associated image. Used to access the geometry.	required
img	ndarray[float64]	The image to assemble. Should generally be of shape (n_panels, ss, fs)	required

Returns:

Name	Type	Description
assembled_img	ndarray[float64]	Assembled 2D image.

Source code in lute/tasks/sfx_find_peaks.py

def _assemble_image(
    self, det: Detector, img: numpy.ndarray[numpy.float64]
) -> numpy.ndarray[numpy.float64]:
    """Assemble an image based on psana geometry.

    Args:
        det (psana.Detector): The detector object for the associated image.
            Used to access the geometry.

        img (numpy.ndarray[np.float64]): The image to assemble. Should
            generally be of shape (n_panels, ss, fs)

    Returns:
        assembled_img(numpy.ndarray[np.float64]): Assembled 2D image.
    """
    geom: GeometryAccess = det.geometry(self._task_parameters.lute_config.run)
    tmp: Tuple[numpy.ndarray[numpy.uint64], ...] = geom.get_pixel_coord_indexes()
    pixel_map: numpy.ndarray[numpy.unit64] = numpy.zeros(
        tmp[0].shape[1:] + (2,), dtype=numpy.uint64
    )
    pixel_map[..., 0] = tmp[0][0]
    pixel_map[..., 1] = tmp[1][0]
    unflattened_img: numpy.ndarray[numpy.float64] = img.reshape(
        pixel_map.shape[:-1]
    )
    idx_max_y: int = int(numpy.max(pixel_map[..., 0]) + 1)  # Adding one
    idx_max_x: int = int(numpy.max(pixel_map[..., 1]) + 1)  # casts to float
    assembled_img: numpy.ndarray[numpy.float64] = numpy.zeros(
        (idx_max_y, idx_max_x)
    )
    assembled_img[pixel_map[..., 0], pixel_map[..., 1]] = unflattened_img

    return assembled_img

_create_powder_plots(det, powder_hits, powder_misses)

Create a tabbed display of hits and misses 'powder' plots.

Parameters:

Name	Type	Description	Default
det	Detector	The detector object for the associated image. Used to access the geometry.	required
powder_hits	ndarray[float64]	Total max/sum projection of hits across the run.	required
powder_misses	ndarray[float64]	Total max/sum projection of misses across the run.	required

Returns:

Name	Type	Description
tabs	Tabs	Tabbed display of the image plots.

Source code in lute/tasks/sfx_find_peaks.py

def _create_powder_plots(
    self,
    det: Detector,
    powder_hits: numpy.ndarray[numpy.float64],
    powder_misses: numpy.ndarray[numpy.float64],
) -> pn.Tabs:
    """Create a tabbed display of hits and misses 'powder' plots.

    Args:
        det (psana.Detector): The detector object for the associated image.
            Used to access the geometry.

        powder_hits (numpy.ndarray[np.float64]): Total max/sum projection of
            hits across the run.

        powder_misses (numpy.ndarray[np.float64]): Total max/sum projection of
            misses across the run.

    Returns:
        tabs (pn.Tabs): Tabbed display of the image plots.
    """
    assembled_powder_hits: numpy.ndarray[numpy.float64] = self._assemble_image(
        det, powder_hits
    )
    assembled_powder_misses: numpy.ndarray[numpy.float64] = self._assemble_image(
        det, powder_misses
    )

    grid_hits: pn.GridSpec = pn.GridSpec(
        sizing_mode="stretch_both",
        max_width=700,
        name=f"{self._task_parameters.det_name} - Hits",
    )
    dim: hv.Dimension = hv.Dimension(
        ("image", "Hits"),
        range=(
            numpy.nanpercentile(assembled_powder_hits, 1),
            numpy.nanpercentile(assembled_powder_hits, 99),
        ),
    )
    grid_hits[0, 0] = pn.Row(
        hv.Image(assembled_powder_hits, vdims=[dim], name=dim.label).options(
            colorbar=True, cmap="rainbow"
        )
    )

    grid_misses: pn.GridSpec = pn.GridSpec(
        sizing_mode="stretch_both",
        max_width=700,
        name=f"{self._task_parameters.det_name} - Misses",
    )
    dim = hv.Dimension(
        ("image", "Misses"),
        range=(
            numpy.nanpercentile(assembled_powder_misses, 1),
            numpy.nanpercentile(assembled_powder_misses, 99),
        ),
    )
    grid_misses[0, 0] = pn.Row(
        hv.Image(assembled_powder_misses, vdims=[dim], name=dim.label).options(
            colorbar=True, cmap="rainbow"
        )
    )

    tabs: pn.Tabs = pn.Tabs(grid_hits)
    tabs.append(grid_misses)
    return tabs

add_peaks_to_libpressio_configuration(lp_json, peaks)

Add peak infromation to libpressio configuration

Parameters:

lp_json: Dictionary storing the configuration JSON structure for the libpressio
    library.

peaks (Any): Peak information as returned by psana.

Returns:

lp_json: Updated configuration JSON structure for the libpressio library.

Source code in lute/tasks/sfx_find_peaks.py

def add_peaks_to_libpressio_configuration(lp_json, peaks) -> Dict[str, Any]:
    """
    Add peak infromation to libpressio configuration

    Parameters:

        lp_json: Dictionary storing the configuration JSON structure for the libpressio
            library.

        peaks (Any): Peak information as returned by psana.

    Returns:

        lp_json: Updated configuration JSON structure for the libpressio library.
    """
    lp_json["compressor_config"]["pressio"]["roibin"]["roibin:centers"] = (
        numpy.ascontiguousarray(numpy.uint64(peaks[:, [2, 1, 0]]))
    )
    return lp_json

generate_libpressio_configuration(compressor, roi_window_size, bin_size, abs_error, libpressio_mask)

Create the configuration JSON for the libpressio library

Parameters:

compressor (Literal["sz3", "qoz"]): Compression algorithm to use
    ("qoz" or "sz3").

abs_error (float): Bound value for the absolute error.

bin_size (int): Bining Size.

roi_window_size (int): Default size of the ROI window.

libpressio_mask (NDArray): mask to be applied to the data.

Returns:

lp_json (Dict[str, Any]): Dictionary storing the JSON configuration structure
for the libpressio library

Source code in lute/tasks/sfx_find_peaks.py

def generate_libpressio_configuration(
    compressor: Literal["sz3", "qoz"],
    roi_window_size: int,
    bin_size: int,
    abs_error: float,
    libpressio_mask,
) -> Dict[str, Any]:
    """
    Create the configuration JSON for the libpressio library

    Parameters:

        compressor (Literal["sz3", "qoz"]): Compression algorithm to use
            ("qoz" or "sz3").

        abs_error (float): Bound value for the absolute error.

        bin_size (int): Bining Size.

        roi_window_size (int): Default size of the ROI window.

        libpressio_mask (NDArray): mask to be applied to the data.

    Returns:

        lp_json (Dict[str, Any]): Dictionary storing the JSON configuration structure
        for the libpressio library
    """

    if compressor == "qoz":
        pressio_opts: Dict[str, Any] = {
            "pressio:abs": abs_error,
            "qoz": {"qoz:stride": 8},
        }
    elif compressor == "sz3":
        pressio_opts = {"pressio:abs": abs_error}

    lp_json = {
        "compressor_id": "pressio",
        "early_config": {
            "pressio": {
                "pressio:compressor": "roibin",
                "roibin": {
                    "roibin:metric": "composite",
                    "roibin:background": "mask_binning",
                    "roibin:roi": "fpzip",
                    "background": {
                        "binning:compressor": "pressio",
                        "mask_binning:compressor": "pressio",
                        "pressio": {"pressio:compressor": compressor},
                    },
                    "composite": {
                        "composite:plugins": [
                            "size",
                            "time",
                            "input_stats",
                            "error_stat",
                        ]
                    },
                },
            }
        },
        "compressor_config": {
            "pressio": {
                "roibin": {
                    "roibin:roi_size": [roi_window_size, roi_window_size, 0],
                    "roibin:centers": None,  # "roibin:roi_strategy": "coordinates",
                    "roibin:nthreads": 4,
                    "roi": {"fpzip:prec": 0},
                    "background": {
                        "mask_binning:mask": None,
                        "mask_binning:shape": [bin_size, bin_size, 1],
                        "mask_binning:nthreads": 4,
                        "pressio": pressio_opts,
                    },
                }
            }
        },
        "name": "pressio",
    }

    lp_json["compressor_config"]["pressio"]["roibin"]["background"][
        "mask_binning:mask"
    ] = (1 - libpressio_mask)

    return lp_json

write_master_file(mpi_size, outdir, exp, run, tag, n_hits_per_rank, n_hits_total)

Generate a virtual dataset to map all individual files for this run.

Parameters:

mpi_size (int): Number of ranks in the MPI pool.

outdir (str): Output directory for cxi file.

exp (str): Experiment string.

run (int): Experimental run.

tag (str): Tag to append to cxi file names.

n_hits_per_rank (List[int]): Array containing the number of hits found on each
    node processing data.

n_hits_total (int): Total number of hits found across all nodes.

Returns:

The path to the the written master file

Source code in lute/tasks/sfx_find_peaks.py

def write_master_file(
    mpi_size: int,
    outdir: str,
    exp: str,
    run: int,
    tag: str,
    n_hits_per_rank: List[int],
    n_hits_total: int,
) -> Path:
    """
    Generate a virtual dataset to map all individual files for this run.

    Parameters:

        mpi_size (int): Number of ranks in the MPI pool.

        outdir (str): Output directory for cxi file.

        exp (str): Experiment string.

        run (int): Experimental run.

        tag (str): Tag to append to cxi file names.

        n_hits_per_rank (List[int]): Array containing the number of hits found on each
            node processing data.

        n_hits_total (int): Total number of hits found across all nodes.

    Returns:

        The path to the the written master file
    """
    # Retrieve paths to the files containing data
    fnames: List[Path] = []
    fi: int
    for fi in range(mpi_size):
        if n_hits_per_rank[fi] > 0:
            fnames.append(Path(outdir) / f"{exp}_r{run:0>4}_{fi}{tag}.cxi")
    if len(fnames) == 0:
        sys.exit("No hits found")

    # Retrieve list of entries to populate in the virtual hdf5 file
    dname_list, key_list, shape_list, dtype_list = [], [], [], []
    datasets = ["/entry_1/result_1", "/LCLS/detector_1", "/LCLS", "/entry_1/data_1"]
    f = h5py.File(fnames[0], "r")
    for dname in datasets:
        dset = f[dname]
        for key in dset.keys():
            if f"{dname}/{key}" not in datasets:
                dname_list.append(dname)
                key_list.append(key)
                shape_list.append(dset[key].shape)
                dtype_list.append(dset[key].dtype)
    f.close()

    # Compute cumulative powder hits and misses for all files
    # Copy mask as well
    mask: Optional[NDArray[numpy.uint16]] = None
    powder_hits, powder_misses = None, None
    for fn in fnames:
        f = h5py.File(fn, "r")
        if mask is None:
            mask = f["entry_1/data_1/mask"][:].copy()
        if powder_hits is None:
            powder_hits = f["entry_1/data_1/powderHits"][:].copy()
            powder_misses = f["entry_1/data_1/powderMisses"][:].copy()
        else:
            powder_hits = numpy.maximum(
                powder_hits, f["entry_1/data_1/powderHits"][:].copy()
            )
            powder_misses = numpy.maximum(
                powder_misses, f["entry_1/data_1/powderMisses"][:].copy()
            )
        f.close()

    vfname: Path = Path(outdir) / f"{exp}_r{run:0>4}{tag}.cxi"
    with h5py.File(vfname, "w") as vdf:

        # Write the virtual hdf5 file
        for dnum in range(len(dname_list)):
            dname = f"{dname_list[dnum]}/{key_list[dnum]}"
            if key_list[dnum] not in ["mask", "powderHits", "powderMisses"]:
                layout = h5py.VirtualLayout(
                    shape=(n_hits_total,) + shape_list[dnum][1:], dtype=dtype_list[dnum]
                )
                cursor = 0
                for i, fn in enumerate(fnames):
                    vsrc = h5py.VirtualSource(
                        fn, dname, shape=(n_hits_per_rank[i],) + shape_list[dnum][1:]
                    )
                    if len(shape_list[dnum]) == 1:
                        layout[cursor : cursor + n_hits_per_rank[i]] = vsrc
                    else:
                        layout[cursor : cursor + n_hits_per_rank[i], :] = vsrc
                    cursor += n_hits_per_rank[i]
                vdf.create_virtual_dataset(dname, layout, fillvalue=-1)

        vdf["entry_1/data_1/powderHits"] = powder_hits
        vdf["entry_1/data_1/powderMisses"] = powder_misses
        vdf["entry_1/data_1/mask"] = mask

    return vfname