locan.analysis.localization_property_correlations.LocalizationPropertyCorrelations#

class locan.analysis.localization_property_correlations.LocalizationPropertyCorrelations(meta=None, loc_properties=None)[source]#

Bases: _Analysis

Compute and analyze correlation coefficients between any two localization

properties.

Parameters:

  • meta (locan.analysis.metadata_analysis_pb2.AMetadata) – Metadata about the current analysis routine.

  • loc_properties (list[str] | None) – Localization properties to be analyzed. If None all are used.

Variables:

  • count (int) – A counter for counting instantiations (class attribute).

  • parameter (dict[str, Any]) – A dictionary with all settings for the current computation.

  • meta (locan.analysis.metadata_analysis_pb2.AMetadata | None) – Metadata about the current analysis routine.

  • results (pandas.DataFrame | None) – The correlation coefficients..

Methods

__init__([meta, loc_properties])

compute(locdata)

Run the computation.

plot([ax, cbar, colorbar_kws])

Provide heatmap of all correlation values as matplotlib.axes.Axes object.

report()

Show a report about analysis results.

Attributes

count

A counter for counting Analysis class instantiations (class attribute).
compute(locdata)[source]#

Run the computation.

Parameters:

locdata (LocData) – Localization data.

Return type:

Self

plot(ax=None, cbar=True, colorbar_kws=None, **kwargs)[source]#

Provide heatmap of all correlation values as matplotlib.axes.Axes object.

Parameters:

  • ax (Optional[Axes]) – The axes on which to show the image

  • cbar (bool) – If true draw a colorbar.

  • colorbar_kws (Optional[dict[str, Any]]) – Keyword arguments for matplotlib.pyplot.colorbar().

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

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes

report()[source]#

Show a report about analysis results.

Return type:

None