locan.analysis.localizations_per_frame.LocalizationsPerFrame#

class locan.analysis.localizations_per_frame.LocalizationsPerFrame(meta=None, norm=None, time_delta='integration_time', resample=None, **kwargs)[source]#

Bases: _Analysis

Compute localizations per frame.

Parameters:

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

  • norm (int | float | str | None) – Normalization factor that can be None, a number, or another property in locdata.

  • time_delta (int | float | str | pd.Timedelta | None) – Time per frame in milliseconds. String must specify the unit like ‘10ms’. For ‘integration_time’ the time is taken from locdata.meta.experiment.setups[0].optical_units[0].detection.camera.integration_time

  • resample (DateOffset | Timedelta | str) – Parameter for pandas.Series.resample(): The offset string or object representing target conversion.

  • kwargs (Any) – Other parameters passed to pandas.Series.resample().

Variables:

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

  • 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 (pandas.Series) – Computed results.

  • distribution_statistics (Distribution_fits object | None) – Distribution parameters derived from MLE fitting of results.

Methods

__init__([meta, norm, time_delta, resample])

compute(locdata)

Run the computation.

fit_distributions(**kwargs)

Fit probability density functions to the distributions of ` loc_property` values in the results using MLE (scipy.stats).

hist([ax, fit, bins])

Provide histogram as matplotlib.axes.Axes object showing hist(results).

plot([ax, window, cumulative, normalize])

Provide plot as matplotlib.axes.Axes object showing the running average of results over window size.

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 (Union[LocData, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – Points in time: either localization data that contains a column frame or an array with time points.

Return type:

Self

count: int = 0#

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

fit_distributions(**kwargs)[source]#

Fit probability density functions to the distributions of ` loc_property` values in the results using MLE (scipy.stats).

Parameters:

loc_property (str) – The LocData property for which to fit an appropriate distribution; if None all plots are shown.

Return type:

None

hist(ax=None, fit=True, bins='auto', **kwargs)[source]#

Provide histogram as matplotlib.axes.Axes object showing hist(results).

Parameters:

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

  • bins (int | Sequence[int | float] | str) – Bin specifications (passed to matplotlib.hist).

  • fit (bool) – Flag indicating if distributions fit are shown.

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

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes

plot(ax=None, window=1, cumulative=False, normalize=False, **kwargs)[source]#

Provide plot as matplotlib.axes.Axes object showing the running average of results over window size.

Parameters:

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

  • window (int) – Window for running average that is applied before plotting.

  • cumulative (bool) – Plot the cumulated results if true.

  • normalize (bool) – Normalize cumulative plot to the last value

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

Returns:

Axes object with the plot.

Return type:

matplotlib.axes.Axes