Results#

FrameResult owns Python copies of native indexing output. A result with no identified patterns is valid and can still contain detected peaks and frame statistics.

Frame Result#

class lauelab.indexing.FrameResult(peaks, patterns, threshold_used, total_sum, sum_above_threshold, num_above_threshold, peaksearch_seconds, indexing_seconds, threshold_ratio=4.0, peak_minwidth=0.0, peak_maxwidth=0.0, peak_max_cent_to_fit=0.0, peak_boxsize=0, metadata={}, input_image=None, image_shape=(0, 0), start=(0, 0), group=(1, 1), depth=None, image=None)[source]#

Self-contained result of processing one diffraction frame.

Parameters:

  • peaks (numpy.ndarray) – Structured peak array with shape (n,). Fields are fit_x, fit_y, intens, integral, hwhm_x, hwhm_y, tilt, chisq, background, and the three-component qhat vector.

  • patterns (tuple[lauelab.indexing.indexer.Pattern, ...]) – Crystal orientations identified in the frame.

  • threshold_used (float) – Intensity threshold used by peak search. This is NaN when automatic thresholding receives no unmasked nonzero pixels.

  • total_sum (float) – Sum of unmasked raw frame pixel values.

  • sum_above_threshold (float) – Sum of pixel values above threshold_used.

  • num_above_threshold (int) – Number of pixels above threshold_used.

  • peak_minwidth (float) – Effective fitting parameters configured by the native peak search.

  • peak_maxwidth (float) – Effective fitting parameters configured by the native peak search.

  • peak_max_cent_to_fit (float) – Effective fitting parameters configured by the native peak search.

  • peak_boxsize (int) – Effective fitting parameters configured by the native peak search.

  • peaksearch_seconds (float) – Elapsed peak-search time in seconds.

  • indexing_seconds (float) – Elapsed orientation-indexing time in seconds. Pixel-to-q conversion is not included in either timing field.

  • threshold_ratio (float) – Resolved automatic-threshold ratio supplied to native peak search, or NaN when an absolute threshold made the ratio inactive.

  • metadata (Mapping[str, object]) – Experiment metadata copied into the result.

  • input_image (str | None) – Source HDF5 path, or None for an in-memory frame.

  • image_shape (tuple[int, int]) – Frame shape as (rows, columns).

  • start (tuple[int, int]) – Zero-based detector (x, y) origin of the frame.

  • group (tuple[int, int]) – Detector-pixel grouping factors as (x, y).

  • depth (float | None) – Optional sample depth in micrometres passed to the geometry conversion.

  • image (numpy.ndarray | None) – Retained contiguous uint16 frame, or None when image retention was disabled. This array can alias a contiguous array supplied by the caller. Native smoothing uses a separate working copy.

Notes

Native result memory is released before this object is returned. The dataclass is frozen, but contained arrays and the metadata mapping may remain mutable. A result with no patterns is valid and has indexed set to False.

property indexed: bool#

Whether at least one crystal pattern was identified.

property n_peaks: int#

Number of detected peaks.

property n_indexed: int#

Total peak assignments across all identified patterns.

property n_patterns: int#

Number of identified crystal patterns.

property elapsed_seconds: float#

Sum of the recorded peak-search and indexing times in seconds.

property indexed_peak_indices: numpy.ndarray#

Sorted unique peak indices assigned to any pattern.

Returns:

numpy.ndarray – One-dimensional array of zero-based indices into peaks.

property unindexed_peak_indices: numpy.ndarray#

Sorted peak indices not assigned to a pattern.

Returns:

numpy.ndarray – One-dimensional array of zero-based indices into peaks.

write_xml(path)[source]#

Write this result in the LaueGo XML format.

Parameters:

path (str | Path) – Destination XML file. An existing file is replaced.

Raises:

  • RuntimeError – If the result was constructed manually without an XML snapshot.

  • OSError – If the destination cannot be written.

Indexed Pattern#

See the results guide for the reciprocal-basis convention used by Pattern.reciprocal.

class lauelab.indexing.Pattern(euler_deg, rotation, reciprocal, goodness, rms_error_deg, hkl, pk_index, err_deg, energy_kev, pred_intens)[source]#

One crystal orientation identified in a diffraction frame.

Parameters:

  • euler_deg (numpy.ndarray) – Euler angles in degrees, with shape (3,).

  • rotation (numpy.ndarray) – Orientation rotation matrix with shape (3, 3).

  • reciprocal (numpy.ndarray) – Reciprocal-lattice matrix with shape (3, 3) in 1/nm. Rows are a*, b*, and c*; a Miller-index row maps to reciprocal space as q = hkl @ reciprocal.

  • goodness (float) – Native indexer’s goodness score for the pattern.

  • rms_error_deg (float) – Root-mean-square angular indexing error in degrees.

  • hkl (numpy.ndarray) – Integer Miller indices with shape (n, 3).

  • pk_index (numpy.ndarray) – Zero-based indices into the frame’s peak array, with shape (n,).

  • err_deg (numpy.ndarray) – Per-peak angular errors in degrees, with shape (n,).

  • energy_kev (numpy.ndarray) – Per-peak photon energies in keV, with shape (n,).

  • pred_intens (numpy.ndarray) – Per-peak predicted intensities, with shape (n,).

Notes

The dataclass is frozen, but its NumPy arrays remain mutable. Each pattern owns Python arrays copied from the native result.

property n_indexed: int#

Number of peaks assigned to this pattern.