fosanalysis.preprocessing.masking.ZscoreOutlierDetection Class Reference

Class for the standard z-score approach for spike detection. More...

Inheritance diagram for fosanalysis.preprocessing.masking.ZscoreOutlierDetection:

Protected Member Functions
np.array	_get_z_score (self, np.array z)
	Calculates the z-score of the given strain array with mean and standard deviation.
Protected Member Functions inherited from fosanalysis.preprocessing.masking.ZSOD
np.array	_get_outlier_mask (self, np.array z_score)
	Mask entries as SRA, whose z-scores exceed threshold.
tuple	_run_1d (self, np.array x, np.array z, np.array SRA_array, *args, **kwargs)
	Estimate which entries are strain reading anomalies in 1D.
tuple	_run_2d (self, np.array x, np.array y, np.array z, np.array SRA_array, *args, **kwargs)
	Estimate which entries are strain reading anomalies in 2D.
Protected Member Functions inherited from fosanalysis.preprocessing.masking.AnomalyMasker
tuple	_map_2d (self, np.array x, np.array y, np.array z, np.array SRA_array, str timespace=None, *args, **kwargs)
	Estimate, which entries are strain reading anomalies, in 2D.

Additional Inherited Members
Public Member Functions inherited from fosanalysis.preprocessing.masking.ZSOD
	__init__ (self, float threshold=3.5, str timespace="1d_space", *args, **kwargs)
	Construct an instance of the class.
Public Member Functions inherited from fosanalysis.preprocessing.masking.AnomalyMasker
np.array	run (self, np.array x, np.array y, np.array z, bool make_copy=True, str timespace=None, bool identify_only=False, *args, **kwargs)
	Mask strain reading anomalies with NaNs.
Public Member Functions inherited from fosanalysis.utils.base.Task
	__init__ (self, *args, **kwargs)
Public Member Functions inherited from fosanalysis.utils.base.Base
	__init__ (self, *args, **kwargs)
	Construct the object and warn about unused/unknown arguments.
Public Attributes inherited from fosanalysis.preprocessing.masking.ZSOD
	threshold = threshold
	Relative height threshold above which a pixel is flagged as SRA.

Detailed Description

Class for the standard z-score approach for spike detection.

Describing a data point in terms of its relationship to the mean and standard deviation of strain values. The method can be mainly used for constant (noise) signals. See [4].

Definition at line 644 of file masking.py.

Member Function Documentation

_get_z_score()

np.array fosanalysis.preprocessing.masking.ZscoreOutlierDetection._get_z_score ( self, np.array z )

protected

Calculates the z-score of the given strain array with mean and standard deviation.

Parameters

z	Array containing strain data.

Returns

Returns a z-score array.

Reimplemented from fosanalysis.preprocessing.masking.ZSOD.

Definition at line 652 of file masking.py.

---

The documentation for this class was generated from the following file:

• src/fosanalysis/preprocessing/masking.py