locan.analysis.radial_distribution.RadialDistributionBatch

class locan.analysis.radial_distribution.RadialDistributionBatch(bins=None, meta=None)[source]

Bases: _Analysis

Generate RadialDistribution results from a batch of data.

See also

RadialDistribution

Parameters:

  • bins (int | list[int | float] | None) – Bin specification as used in numpy.histogram()

  • meta (AMetadata | None) – Metadata about the current analysis routine.

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.

  • batch (list[RadialDistribution]) – The generated batch

  • dimension (int) – The dimension of original data

  • results (RadialDistributionResults) – Computed results.

Methods

__init__([bins, meta])

compute(locdatas[, other_locdatas])

Run the computation.

from_batch(batch)

hist([ax])

Provide step histogram as matplotlib.axes.Axes object showing results and the mean curve.

report(*args, **kwargs)

Show a report about analysis results.

Attributes

count

A counter for counting Analysis class instantiations (class attribute).
compute(locdatas, other_locdatas=None)[source]

Run the computation.

Parameters:

  • locdatas (Iterable[LocData]) – Localization data.

  • other_locdatas (Iterable[LocData] | None) – Localization data.

Return type:

RadialDistributionBatch

classmethod from_batch(batch)[source]
Return type:

TypeVar(T_RadialDistributionBatch, bound= RadialDistributionBatch)

hist(ax=None, **kwargs)[source]

Provide step histogram as matplotlib.axes.Axes object showing results and the mean curve.

Parameters:

  • ax (Axes | None) – The axes on which to show the image.

  • kwargs (Any) – Other parameters passed to matplotlib.step().

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes