locan.analysis.position_variance_expectation.PositionVarianceExpectation

class locan.analysis.position_variance_expectation.PositionVarianceExpectation(meta=None, loc_property='position_x', expectation=None, biased=True)

Bases: _Analysis

Analyze variation of localization properties in relation to expected variations.

Parameters:

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

• loc_property (str) – The localization property to analyze.

• expectation (int | float | Mapping[str, Any] | pd.Series[Any] | None) – The expected variance for all or each localization property. The expected variance equals the squared localization precision for localization position coordinates.

• biased (bool) – Flag to use biased or unbiased (Bessel-corrected) variance

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 (PositionVarianceExpectationResults) – Computed results.

• distribution_statistics (Distribution_stats, None) – Distribution parameters derived from MLE fitting of results.

Methods

__init__([meta, loc_property, expectation, ...])
compute(locdata)	Run the computation.
hist([ax, bins, n_bins, bin_size, ...])	Provide plot as matplotlib.axes.Axes object showing the 2-dimensional histogram of variances and localization counts.
plot([ax])	Provide plot as matplotlib.axes.Axes object showing the variances as function of localization counts.
report(*args, **kwargs)	Show a report about analysis results.

Attributes

count

A counter for counting Analysis class instantiations (class attribute).

compute(locdata)

Run the computation.

Parameters:

locdata (LocData) – Localization data.

Return type:

Self

hist(ax=None, bins=None, n_bins=None, bin_size=None, bin_edges=None, bin_range=None, log=True, fit=False, **kwargs)

Provide plot as matplotlib.axes.Axes object showing the 2-dimensional histogram of variances and localization counts.

Parameters:

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

• bins (Bins | Axis | AxesTuple | None) – The bin specification as defined in Bins

• bin_edges (Sequence[float] | Sequence[Sequence[float]] | None) – Bin edges for all or each dimension with shape (dimension, n_bin_edges).

• bin_range (tuple[float, float] | Sequence[float] | Sequence[Sequence[float]] | None) – Minimum and maximum edge for all or each dimensions with shape (2,) or (dimension, 2).

• n_bins (int | Sequence[int] | None) – The number of bins for all or each dimension. 5 yields 5 bins in all dimensions. (2, 5) yields 2 bins for one dimension and 5 for the other dimension.

• bin_size (float | Sequence[float] | Sequence[Sequence[float]] | None) – The size of bins for all or each bin and for all or each dimension with shape (dimension,) or (dimension, n_bins). 5 would describe bin_size of 5 for all bins in all dimensions. ((2, 5),) yield bins of size (2, 5) for one dimension. (2, 5) yields bins of size 2 for one dimension and 5 for the other dimension. ((2, 5), (1, 3)) yields bins of size (2, 5) for one dimension and (1, 3) for the other dimension. To specify arbitrary sequence of bin_size use bin_edges instead.

• log (bool) – Flag for plotting on a log scale.

• fit (bool) – Flag indicating if distribution fit is shown. The fit will only be computed if distribution_statistics is None.

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

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes

plot(ax=None, **kwargs)

Provide plot as matplotlib.axes.Axes object showing the variances as function of localization counts.

Parameters:

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

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

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes