locan.analysis.convex_hull_expectation.ConvexHullExpectationBatch¶
- class locan.analysis.convex_hull_expectation.ConvexHullExpectationBatch(meta=None, convex_hull_property='region_measure_ch', expected_variance=None)[source]¶
Bases:
_AnalysisAnalyze geometrical properties of the convex hull of localization coordinates in relation to expected values.
See also
- Parameters:
meta (locan.analysis.metadata_analysis_pb2.AMetadata) – Metadata about the current analysis routine.
convex_hull_property (Literal['region_measure_ch', 'subregion_measure_ch']) – One of ‘region_measure_ch’ (i.e. area or volume) or ‘subregion_measure_ch’ (i.e. circumference or surface.)
expected_variance (float | Iterable[float] | None) – The expected variance for all or each localization property. The expected variance equals the squared localization precision for localization position coordinates.
- Variables:
count (int) – A counter for counting instantiations (class attribute).
parameter (dict) – A dictionary with all settings for the current computation.
meta (locan.analysis.metadata_analysis_pb2.AMetadata) – Metadata about the current analysis routine.
results (ConvexHullExpectationResults) – Computed results.
distribution_statistics (Distribution_stats, None) – Distribution parameters derived from MLE fitting of results.
Methods
__init__([meta, convex_hull_property, ...])compute(locdatas)Run the computation.
from_batch(batch[, dimension])hist([ax, bins, n_bins, bin_size, ...])plot([ax])report(*args, **kwargs)Show a report about analysis results.
Attributes
countA counter for counting Analysis class instantiations (class attribute).
- compute(locdatas)[source]¶
Run the computation.
- Parameters:
locdatas (Iterable[LocData]) – Localization data.
- Return type:
Self