locan.analysis.position_variance_expectation.PositionVarianceExpectation#
- class locan.analysis.position_variance_expectation.PositionVarianceExpectation(meta=None, loc_property='position_x', expectation=None, biased=True)[source]#
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)[source]#
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)[source]#
Provide plot as
matplotlib.axes.Axes
object showing the 2-dimensional histogram of variances and localization counts.- Parameters:
ax (
Optional
[Axes
]) – The axes on which to show the imagebins (
UnionType
[Bins
,Axis
,AxesTuple
,None
]) – The bin specification as defined inBins
bin_edges (
UnionType
[Sequence
[float
],Sequence
[Sequence
[float
]],None
]) – Bin edges for all or each dimension with shape (dimension, n_bin_edges).bin_range (
UnionType
[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 (
UnionType
[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 (
UnionType
[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 tomatplotlib.pyplot.pcolormesh()
.
- Returns:
Axes object with the plot.
- Return type:
matplotlib.axes.Axes
- plot(ax=None, **kwargs)[source]#
Provide plot as
matplotlib.axes.Axes
object showing the variances as function of localization counts.- Parameters:
ax (
Optional
[Axes
]) – The axes on which to show the imagekwargs (
Any
) – Other parameters passed tomatplotlib.pyplot.plot()
.
- Returns:
Axes object with the plot.
- Return type:
matplotlib.axes.Axes