leonarski_fandClaude Opus 5
90d0d3c3f9
docs/python_client/docs is generated from the API spec and copied in by update_version.sh, so it goes stale between releases. The calibration convergence gate and the beam size both added properties without it; this is the same generator run those commits should have carried. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
45 lines
4.1 KiB
Markdown
45 lines
4.1 KiB
Markdown
# PowderCalibrationQuality
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What a powder calibration knows about the geometry it produced. A calibration that has gone wrong - the wrong standard named, a header too far out for the rings to be found - looks exactly like one that has not until these are read.
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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**converged** | **bool** | Whether the geometry beside this is a measurement of the data at all. False means at least one of its parameters was carried over from the input file rather than fitted - which looks exactly like a good fit in every other field here - and see not_converged_reason for which. rugnux --mode calibration writes no .poni file in that case, a PONI file having no field in which to say it, and exits non-zero. | [optional]
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**not_converged_reason** | **str** | What made converged false, in words. Absent when the calibration converged. | [optional]
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**calibrant** | **str** | The powder standard the rings were fitted to, or the unit cell given in its place | [optional]
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**method** | **str** | How the rings were measured. rings fits the arcs of the run-summed (q x azimuth) azimuthal profile; spots fits the pooled per-image spot lists. | [optional]
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**ring_points** | **int** | Ring measurements the fit used | [optional]
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**rms_radial_pxl** | **float** | Scatter of those measurements about the fitted rings, as a radial distance [pixels]. The single number that separates a calibration that worked from one that did not. | [optional]
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**beam_sigma_pxl** | **float** | Standard error the scatter implies on the beam centre [pixels], for a ring of that many points. See powder_calibration_fit_sigma for what the fit itself says, which is not the same and is the larger of the two whenever the tilt is poorly separated. | [optional]
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**direct_beam_x_pxl** | **float** | Where the direct beam lands [pixels]. NOT dataset_settings.beam_x_pxl, which is the PONI: the two part company by distance*tan(tilt)/pixel as soon as the detector is tilted. | [optional]
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**direct_beam_y_pxl** | **float** | Where the direct beam lands, y [pixels] | [optional]
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**header_distance_mm** | **float** | The detector distance the input file declared, for comparison [mm] | [optional]
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**ring_seed_distance_mm** | **float** | The distance measured from the ring radii before the fit ran [mm]. It is taken from the radii, the wavelength and the pixel size alone, so a large gap from header_distance_mm is the answer to the question the calibration was run to ask. Absent when the profile showed too few rings to fix a scale. | [optional]
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**tilt_refined** | **bool** | Whether the reported rot1/rot2 were measured by this fit. False means they were declined and pinned at the input file's values, because the rings could not separate a tilt from a shift of the beam centre. | [optional]
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**tilt_significance** | **float** | How many of its own sigmas the fitted tilt stands from zero. Below about 3 it is not a measurement of a tilt but of a beam-centre shift, and the tilt is declined. Passing does not certify a tilt: that estimator is limited by systematics rather than by this sigma. | [optional]
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**fit_sigma** | [**PowderCalibrationFitSigma**](PowderCalibrationFitSigma.md) | | [optional]
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**spot_cross_check** | [**PowderCalibrationSpotCheck**](PowderCalibrationSpotCheck.md) | | [optional]
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## Example
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```python
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from jfjoch_client.models.powder_calibration_quality import PowderCalibrationQuality
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# TODO update the JSON string below
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json = "{}"
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# create an instance of PowderCalibrationQuality from a JSON string
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powder_calibration_quality_instance = PowderCalibrationQuality.from_json(json)
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# print the JSON string representation of the object
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print(PowderCalibrationQuality.to_json())
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# convert the object into a dict
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powder_calibration_quality_dict = powder_calibration_quality_instance.to_dict()
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# create an instance of PowderCalibrationQuality from a dict
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powder_calibration_quality_from_dict = PowderCalibrationQuality.from_dict(powder_calibration_quality_dict)
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```
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
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