Features

class cellector.features.FeaturePipeline(name: str, method: callable, dependencies: List[str])[source]

Bases: object

Pipeline that defines a feature computation method and its dependencies on attributes of roi_processor instances.

name

Name of the feature pipeline.

Type:

str

method

Method that computes the feature, accepting an roi_processor instance as an input and returns a np.ndarray which associates each ROI with a feature value.

Type:

callable

dependencies

List of attributes of roi_processor that the feature computation method depends on.

Type:

List[str]

cellector.features.compute_phase_correlation(roi_processor: RoiProcessor, functional: bool = False) → ndarray[source]

Compute the phase correlation between the masks and reference images.

Parameters:

  • roi_processor (RoiProcessor) – The roi_processor instance to compute the phase correlation for.

  • functional (bool, optional) – Whether to compute the phase correlation between the masks and functional reference images. Default is False.

Returns:

The phase correlation between the masks and reference images across planes.

Return type:

np.ndarray

See also

utils.phase_correlation_zero

Function that computes the phase correlation values.

cellector.features.compute_dot_product(roi_processor: RoiProcessor, functional: bool = False) → ndarray[source]

Compute the dot product between the masks and filtered reference images.

Parameters:

  • roi_processor (RoiProcessor) – The roi_processor instance to compute the dot product for.

  • functional (bool, optional) – Whether to compute the dot product between the masks and functional reference images. Default is False.

Returns:

The dot product between the masks and reference images across planes, normalized by the norm of the mask intensity values.

Return type:

np.ndarray

See also

utils.dot_product

Function that computes the dot product values.

cellector.features.compute_corr_coef(roi_processor: RoiProcessor, functional: bool = False) → ndarray[source]

Compute the correlation coefficient between the masks and reference images.

Parameters:

  • roi_processor (RoiProcessor) – The roi_processor instance to compute the correlation coefficient for.

  • functional (bool, optional) – Whether to compute the correlation coefficient between the masks and functional reference images. Default is False.

Returns:

The correlation coefficient between the masks and reference images across planes.

Return type:

np.ndarray

See also

utils.compute_correlation

Function that computes the correlation coefficient values.

cellector.features.compute_in_vs_out(roi_processor: RoiProcessor, functional: bool = False) → ndarray[source]

Compute the in vs. out feature for each ROI.

The in vs. out feature is the ratio of the dot product of the mask and reference image inside the mask to the dot product inside plus outside the mask.

Parameters:

  • roi_processor (RoiProcessor) – The roi_processor instance to compute the in vs. out feature for.

  • functional (bool, optional) – Whether to compute the in vs. out feature between the masks and functional reference images. Default is False.

Returns:

The in vs. out feature for each ROI.

Return type:

np.ndarray

See also

utils.in_vs_out

Function that computes the in vs. out feature values.