allensdk.internal.brain_observatory.roi_filter_utils module

allensdk.internal.brain_observatory.roi_filter_utils.CRITERIA()[source]
class allensdk.internal.brain_observatory.roi_filter_utils.TrainingLabelClassifier(criteria)[source]

Bases: object

Very basic threshold_based classifier.

Has a decision function that is just the number of distinct criteria met by the classifier. Criteria are defined as a list of strings used with pandas.DataFrame.eval.

Parameters:
criterialist

List of evaluation strings.

decision_function(X)[source]

Get the distance from the decision boundary.

Parameters:
Xarray-like

Features for each ROI.

Returns:
Tarray-like

Distance for each sample from the decision boundary.

class allensdk.internal.brain_observatory.roi_filter_utils.TrainingMultiLabelClassifier(criteria=None)[source]

Bases: object

Multilabel classifier using groups of TrainingLabelClassifiers.

This was used to generate labeling for training the original SVM for classification.

Parameters:
criteriadictionary

Label names and criteria for each label.

get_eXcluded(X)[source]

Get the calculated value of the eXcluded column.

This is useful for comparison with the original classifier implementation.

Parameters:
Xpandas.DataFrame

Object features from the object list file.

Returns:
numpy.ndarray

Calculated eXcluded score from the classifier.

label_data(X, as_columns=True)[source]

Generate labels for each row in X.

Parameters:
Xpandas.DataFrame

Object features from the object list file.

Returns:
numpy.ndarray

Array of label codes representing the combination of labels found for each row.

allensdk.internal.brain_observatory.roi_filter_utils.calculate_max_border(motion_df, max_shift)[source]

Calculate motion boundary from frame offsets.

When the motion correction algorithm fails to find sufficient matches, it generates very large frame offsets. The use of max_shift avoids filtering too many cells due to the large offsets, with the tradeoff that those frames will be noise.

Parameters:
motion_dfpandas.DataFrame

Dataframe containing the x, y offsets from motion correction.

max_shiftfloat

Maximum shift to allow when considering motion correction. Any larger shifts are considered outliers.

Returns:
list

[right_shift, left_shift, down_shift, up_shift]

allensdk.internal.brain_observatory.roi_filter_utils.get_indices_by_distance(object_list_points, mask_points)[source]

Find indices of nearest neighbor matches.

Require a distance of 0 (perfect match) and a unique match between masks and object_list entries.

allensdk.internal.brain_observatory.roi_filter_utils.get_rois(segmentation_stack, border=None)[source]

Extract a list of rois from the segmentation data array.

Parameters:
segmentation_stacknumpy.ndarray

The array from the maxInt_masks file showing the object masks.

borderlist

[right_shift, left_shift, down_shift, up_shift] bounding box determined from motion correction.

Returns:
list

List of RoiMask objects.

allensdk.internal.brain_observatory.roi_filter_utils.order_rois_by_object_list(object_data, rois)[source]

Reorder rois by matching bounding boxes to object list.

Parameters:
object_datapandas.DataFrame

Object list data.

roislist

List of RoiMasks.

Returns:
list

The list of rois reordered to index the same as object_data.