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The changelog entry for this release is six lines, not the fifty-four the development log had accumulated: what a user of rc.166 gets is native miniCBF input, masters from other facilities opening, the beam centre measured on every run, a 2theta-swung detector placed where the file says, the unmerged MTZ and a P1 merge written by default, and a substantially reworked symmetry determination. The per-change detail is in the commits. update_version.sh regenerates the three clients from broker/jfjoch_api.yaml, so this also carries the one API description that had drifted from the generated code - fft_high_resolution_A, which sizes the FFT's projection histogram and does not filter spots by resolution. Everything else in the regenerated tree is the version string. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EFEJG6WBQv8th4UJFNe53N
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3.3 KiB
IndexingSettings
Settings for crystallography indexing
Properties
| Name | Type | Description | Notes |
|---|---|---|---|
| algorithm | IndexingAlgorithm | [default to IndexingAlgorithm.FFBIDX] | |
| fft_max_unit_cell_a | float | Largest unit cell to be indexed by FFT algorithm; parameter value affects execution time of FFT | [default to 250] |
| fft_min_unit_cell_a | float | Smallest unit cell to be indexed by FFT algorithm; parameter value affects execution time of FFT | [default to 10.0] |
| fft_high_resolution_a | float | Sets how finely the FFT samples each search direction: it fixes the extent of the projection histogram, and with it the transform size, so it affects execution time and memory of FFT. It does NOT filter spots - every spot is projected whatever its resolution, so raising it does not make the transform see a coarser subset of the data. There is also correlation between smallest unit cell and this value, which need to be checked for very small systems. | [default to 2.0] |
| fft_num_vectors | int | Number of search directions for the FFT algorithm; parameter value affects execution time of FFT. | [default to 16384] |
| tolerance | float | Acceptance tolerance for spots after the indexing run - the larger the number, the more spots will be accepted | |
| thread_count | int | Thread count for indexing algorithm | |
| geom_refinement_algorithm | GeomRefinementAlgorithm | ||
| unit_cell_dist_tolerance | float | Relative distance tolerance for unit cell vs. reference; Lattices outside given tolerance will be ignored | [default to 0.05] |
| viable_cell_min_spots | int | Minimum number of indexed spots required for a cell to be considered viable | [default to 10] |
| index_ice_rings | bool | Include spots marked as ice rings in the indexing run. If `dataset_settings` doesn't have `detect_ice_rings` on, this option will have no effect on processing. | [default to False] |
| rotation_indexing | bool | [default to False] | |
| rotation_indexing_min_angular_range_deg | float | [default to 20.0] | |
| rotation_indexing_angular_stride_deg | float | [default to 0.5] | |
| blocking | bool | Indexing in Jungfraujoch goes with a dedicated thread pool. If set to false, the thread pool is non-blocking, i.e. if there are no threads available, image indexing will be skipped. This option is recommended for real-time processing at high frame rates. If set to true, the thread pool will block until a thread is available. | [default to True] |
Example
from jfjoch_client.models.indexing_settings import IndexingSettings
# TODO update the JSON string below
json = "{}"
# create an instance of IndexingSettings from a JSON string
indexing_settings_instance = IndexingSettings.from_json(json)
# print the JSON string representation of the object
print(IndexingSettings.to_json())
# convert the object into a dict
indexing_settings_dict = indexing_settings_instance.to_dict()
# create an instance of IndexingSettings from a dict
indexing_settings_from_dict = IndexingSettings.from_dict(indexing_settings_dict)