Source code for coseda.nexus.detector

"""Helpers for detector geometry metadata in NeXus."""

from __future__ import annotations

import h5py
import numpy as np

from coseda.nexus.groups import ensure_nexus_parents
from coseda.nexus.paths import SAMPLE_TRANSFORM_PATH


def _safe_dataset(group: h5py.Group, name: str, value, units: str, long_name: str) -> None:
    if name in group:
        del group[name]
    ds = group.create_dataset(name, data=value)
    ds.attrs["units"] = units
    ds.attrs["long_name"] = long_name




def write_detector_geometry(
    h5file: h5py.File,
    camera_length_m: float,
    correction_factor: float = 1.0,
) -> None:
    """
    Store camera length metadata and NXtransformations for detector placement.

    - camera_length: given value from microscope (m)
    - camera_length_correction: dimensionless multiplier
    - camera_length_effective: corrected length (m)
    - transformations/det_z: translation along +Z by corrected length
    """
    if camera_length_m is None or camera_length_m <= 0:
        return
    if correction_factor is None or correction_factor <= 0:
        correction_factor = 1.0

    ensure_nexus_parents(h5file)

    det_group = h5file.require_group("entry/instrument/detector")
    det_group.attrs["NX_class"] = "NXdetector"

    corrected = float(camera_length_m) * float(correction_factor)

    _safe_dataset(det_group, "camera_length", float(camera_length_m), "m", "Given camera length")
    _safe_dataset(
        det_group,
        "camera_length_correction",
        float(correction_factor),
        "1",
        "Camera length correction factor",
    )
    _safe_dataset(det_group, "camera_length_effective", corrected, "m", "Corrected camera length")

    trans_group = det_group.require_group("transformations")
    trans_group.attrs["NX_class"] = "NXtransformations"
    if "det_z" in trans_group:
        del trans_group["det_z"]
    det_z = trans_group.create_dataset("det_z", data=corrected)
    det_z.attrs["transformation_type"] = "translation"
    det_z.attrs["vector"] = np.array([0.0, 0.0, 1.0], dtype=np.float32)
    det_z.attrs["units"] = "m"
    det_z.attrs["depends_on"] = SAMPLE_TRANSFORM_PATH

    det_group.attrs["depends_on"] = "/entry/instrument/detector/transformations/det_z"