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b/docs/_build/html/_modules/nellie/feature_extraction/hierarchical.html index 43b6d12..abf7329 100644 --- a/docs/_build/html/_modules/nellie/feature_extraction/hierarchical.html +++ b/docs/_build/html/_modules/nellie/feature_extraction/hierarchical.html @@ -47,6 +47,53 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Hierarchy: + """ + A class to handle the hierarchical structure of image data, including voxel, node, branch, and component features. + + Parameters + ---------- + im_info : ImInfo + Object containing metadata and pathways related to the image. + skip_nodes : bool, optional + Whether to skip node processing (default is True). + viewer : optional + Viewer for updating status messages (default is None). + + Attributes + ---------- + im_info : ImInfo + The ImInfo object containing the image metadata. + num_t : int + Number of time frames in the image. + spacing : tuple + Spacing between dimensions (Z, Y, X) or (Y, X) depending on the presence of Z. + im_raw : memmap + Raw image data loaded from disk. + im_struct : memmap + Preprocessed structural image data. + im_distance : memmap + Distance-transformed image data. + im_skel : memmap + Skeletonized image data. + label_components : memmap + Instance-labeled image data of components. + label_branches : memmap + Re-labeled skeleton data of branches. + im_border_mask : memmap + Image data with border mask. + im_pixel_class : memmap + Image data classified by pixel types. + im_obj_reassigned : memmap + Object reassigned labels across time. + im_branch_reassigned : memmap + Branch reassigned labels across time. + flow_interpolator_fw : FlowInterpolator + Forward flow interpolator. + flow_interpolator_bw : FlowInterpolator + Backward flow interpolator. + viewer : optional + Viewer to display status updates. + """ def __init__(self, im_info: ImInfo, skip_nodes=True, viewer=None): self.im_info = im_info @@ -84,6 +131,14 @@

Source code for nellie.feature_extraction.hierarchical

self.viewer = viewer def _get_t(self): + """ + Retrieves the number of time frames from image metadata, raising an error if the information is insufficient. + + Returns + ------- + int + Number of time frames. + """ if self.num_t is None and not self.im_info.no_t: # if self.im_info.no_t: # raise ValueError("No time dimension in image.") @@ -93,6 +148,10 @@

Source code for nellie.feature_extraction.hierarchical

return self.num_t def _allocate_memory(self): + """ + Loads the required image data into memory using memory-mapped arrays. This includes raw image data, structural + data, skeletons, component labels, and other features related to the image pipeline. + """ # getting reshaped image will load the image into memory.. should probably do this case by case self.im_raw = self.im_info.get_memmap(self.im_info.im_path) self.im_struct = self.im_info.get_memmap(self.im_info.pipeline_paths['im_preprocessed']) @@ -123,6 +182,10 @@

Source code for nellie.feature_extraction.hierarchical

# self.im_info.shape = self.shape def _get_hierarchies(self): + """ + Executes the hierarchical feature extraction process, which includes running voxel, node, branch, component, + and image analyses. + """ self.voxels = Voxels(self) logger.info("Running voxel analysis") start = time.time() @@ -165,6 +228,10 @@

Source code for nellie.feature_extraction.hierarchical

logger.debug(f"Image analysis took {i_time} seconds") def _save_dfs(self): + """ + Saves the extracted features to CSV files, including voxel, node, branch, component, and image features. + """ + if self.viewer is not None: self.viewer.status = f'Saving features to csv files.' voxel_features, voxel_headers = create_feature_array(self.voxels) @@ -189,6 +256,9 @@

Source code for nellie.feature_extraction.hierarchical

image_df.to_csv(self.im_info.pipeline_paths['features_image'], index=True) def _save_adjacency_maps(self): + """ + Constructs adjacency maps for voxels, nodes, branches, and components and saves them as a pickle file. + """ # edge list: v_n = [] v_b = [] @@ -279,6 +349,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the entire hierarchical feature extraction process, which includes memory allocation, + hierarchical analysis, and saving the results. + """ self._get_t() self._allocate_memory() self._get_hierarchies() @@ -295,6 +369,21 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def append_to_array(to_append): + """ + Converts feature dictionaries into lists of arrays and headers for saving to a CSV. + + Parameters + ---------- + to_append : dict + Dictionary containing feature names and values to append. + + Returns + ------- + list + List of feature arrays. + list + List of corresponding feature headers. + """ new_array = [] new_headers = [] for feature, stats in to_append.items(): @@ -317,6 +406,23 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def create_feature_array(level, labels=None): + """ + Creates a 2D feature array and corresponding headers for saving features to CSV. + + Parameters + ---------- + level : object + The level (e.g., voxel, node, branch, etc.) from which features are extracted. + labels : array-like, optional + Array of labels to use for the first column of the output (default is None). + + Returns + ------- + numpy.ndarray + 2D array of features. + list + List of corresponding feature headers. + """ full_array = None headers = None all_attr = [] @@ -367,6 +473,55 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Voxels: + """ + A class to extract and store voxel-level features from hierarchical image data. + + Parameters + ---------- + hierarchy : Hierarchy + The Hierarchy object containing the image data and metadata. + + Attributes + ---------- + hierarchy : Hierarchy + The Hierarchy object. + time : list + List of time frames associated with the extracted voxel features. + coords : list + List of voxel coordinates. + intensity : list + List of voxel intensity values. + structure : list + List of voxel structural values. + vec01 : list + List of vectors from frame t-1 to t. + vec12 : list + List of vectors from frame t to t+1. + lin_vel : list + List of linear velocity vectors. + ang_vel : list + List of angular velocity vectors. + directionality_rel : list + List of directionality features. + node_labels : list + List of node labels assigned to voxels. + branch_labels : list + List of branch labels assigned to voxels. + component_labels : list + List of component labels assigned to voxels. + node_dim0_lims : list + List of node bounding box limits in dimension 0 (Z or Y). + node_dim1_lims : list + List of node bounding box limits in dimension 1 (Y or X). + node_dim2_lims : list + List of node bounding box limits in dimension 2 (X). + node_voxel_idxs : list + List of voxel indices associated with each node. + stats_to_aggregate : list + List of statistics to aggregate for features. + features_to_save : list + List of voxel features to save. + """ def __init__(self, hierarchy: Hierarchy): self.hierarchy = hierarchy @@ -450,6 +605,16 @@

Source code for nellie.feature_extraction.hierarchical

self.features_to_save = self.stats_to_aggregate + ["x", "y", "z"] def _get_node_info(self, t, frame_coords): + """ + Gathers node-related information for each frame, including pixel classes, skeleton radii, and bounding boxes. + + Parameters + ---------- + t : int + The time frame index. + frame_coords : array-like + The coordinates of the voxels in the current frame. + """ # get all network pixels skeleton_pixels = np.argwhere(self.hierarchy.im_pixel_class[t] > 0) skeleton_radius = self.hierarchy.im_distance[t][tuple(skeleton_pixels.T)] @@ -527,6 +692,21 @@

Source code for nellie.feature_extraction.hierarchical

self.node_voxel_idxs.append(chunk_node_voxel_idxs) def _get_min_euc_dist(self, t, vec): + """ + Calculates the minimum Euclidean distance for voxels to their nearest branch. + + Parameters + ---------- + t : int + The time frame index. + vec : array-like + The vector field for the current time frame. + + Returns + ------- + pandas.Series + The indices of the minimum distance for each branch. + """ euc_dist = np.linalg.norm(vec, axis=1) branch_labels = self.branch_labels[t] @@ -541,6 +721,25 @@

Source code for nellie.feature_extraction.hierarchical

return idxmin def _get_ref_coords(self, coords_a, coords_b, idxmin, t): + """ + Retrieves the reference coordinates for calculating relative velocity and acceleration. + + Parameters + ---------- + coords_a : array-like + The coordinates in frame A. + coords_b : array-like + The coordinates in frame B. + idxmin : pandas.Series + Indices of minimum Euclidean distances. + t : int + The time frame index. + + Returns + ------- + tuple + The reference coordinates for frames A and B. + """ vals_a = idxmin[self.branch_labels[t]].values vals_a_no_nan = vals_a.copy() vals_a_no_nan[np.isnan(vals_a_no_nan)] = 0 @@ -559,6 +758,17 @@

Source code for nellie.feature_extraction.hierarchical

return ref_a, ref_b def _get_motility_stats(self, t, coords_1_px): + """ + Computes motility-related features for each voxel, including linear and angular velocities, accelerations, + and directionality. + + Parameters + ---------- + t : int + The time frame index. + coords_1_px : array-like + Coordinates of the voxels in pixel space for frame t. + """ coords_1_px = coords_1_px.astype('float32') if self.hierarchy.im_info.no_z: dims = 2 @@ -755,6 +965,21 @@

Source code for nellie.feature_extraction.hierarchical

# self.directionality_acc_rel.append(directionality_acc_rel) def _get_linear_velocity(self, ra, rb): + """ + Computes the linear velocity, its magnitude, and orientation between two sets of coordinates. + + Parameters + ---------- + ra : array-like + Coordinates in the earlier frame (frame t-1 or t). + rb : array-like + Coordinates in the later frame (frame t or t+1). + + Returns + ------- + tuple + Tuple containing linear velocity vectors, magnitudes, and orientations. + """ lin_disp = rb - ra lin_vel = lin_disp / self.hierarchy.im_info.dim_res['T'] lin_vel_mag = np.linalg.norm(lin_vel, axis=1) @@ -767,6 +992,22 @@

Source code for nellie.feature_extraction.hierarchical

return lin_vel, lin_vel_mag, lin_vel_orient def _get_angular_velocity_2d(self, ra, rb): + """ + Computes the angular velocity, its magnitude, and orientation between two sets of coordinates. + Uses either 2D or 3D calculations depending on the image dimensionality. + + Parameters + ---------- + ra : array-like + Coordinates in the earlier frame (frame t-1 or t). + rb : array-like + Coordinates in the later frame (frame t or t+1). + + Returns + ------- + tuple + Tuple containing angular velocity vectors, magnitudes, and orientations. + """ # calculate angles of ra and rb relative to x-axis theta_a = np.arctan2(ra[:, 1], ra[:, 0]) theta_b = np.arctan2(rb[:, 1], rb[:, 0]) @@ -846,6 +1087,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the extraction of voxel features over all time frames. + Iterates over each frame to extract coordinates, intensity, structural features, and motility statistics. + """ if self.hierarchy.num_t is None: self.hierarchy.num_t = 1 for t in range(self.hierarchy.num_t): @@ -920,6 +1165,47 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Nodes: + """ + A class to extract and store node-level features from hierarchical image data. + + Parameters + ---------- + hierarchy : Hierarchy + The Hierarchy object containing the image data and metadata. + + Attributes + ---------- + hierarchy : Hierarchy + The Hierarchy object. + time : list + List of time frames associated with the extracted node features. + nodes : list + List of node coordinates for each frame. + z, x, y : list + List of node coordinates in 3D or 2D space. + node_thickness : list + List of node thickness values. + divergence : list + List of divergence values for nodes. + convergence : list + List of convergence values for nodes. + vergere : list + List of vergere values (convergence + divergence). + aggregate_voxel_metrics : list + List of aggregated voxel metrics for each node. + voxel_idxs : list + List of voxel indices associated with each node. + branch_label : list + List of branch labels assigned to nodes. + component_label : list + List of component labels assigned to nodes. + image_name : list + List of image file names. + stats_to_aggregate : list + List of statistics to aggregate for nodes. + features_to_save : list + List of node features to save. + """ def __init__(self, hierarchy): self.hierarchy = hierarchy @@ -960,10 +1246,26 @@

Source code for nellie.feature_extraction.hierarchical

self.node_x_lims = self.hierarchy.voxels.node_dim2_lims def _get_aggregate_voxel_stats(self, t): + """ + Aggregates voxel-level statistics for each node in the frame. + + Parameters + ---------- + t : int + The time frame index. + """ frame_agg = aggregate_stats_for_class(self.hierarchy.voxels, t, self.hierarchy.voxels.node_voxel_idxs[t]) self.aggregate_voxel_metrics.append(frame_agg) def _get_node_stats(self, t): + """ + Computes node-level statistics, including thickness, divergence, convergence, and vergere. + + Parameters + ---------- + t : int + The time frame index. + """ radius = distance_check(self.hierarchy.im_border_mask[t], self.nodes[t], self.hierarchy.spacing) self.node_thickness.append(radius * 2) @@ -1043,6 +1345,14 @@

Source code for nellie.feature_extraction.hierarchical

self.x.append(x) def _run_frame(self, t): + """ + Extracts node features for a single time frame, including voxel metrics, node coordinates, and node statistics. + + Parameters + ---------- + t : int + The time frame index. + """ frame_skel_coords = np.argwhere(self.hierarchy.im_pixel_class[t] > 0) self.nodes.append(frame_skel_coords) @@ -1064,6 +1374,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the extraction of node features over all time frames. + Iterates over each frame to extract node features and calculate metrics. + """ if self.hierarchy.skip_nodes: return for t in range(self.hierarchy.num_t): @@ -1087,6 +1401,51 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Branches: + """ + A class to extract and store branch-level features from hierarchical image data. + + Parameters + ---------- + hierarchy : Hierarchy + The Hierarchy object containing the image data and metadata. + + Attributes + ---------- + hierarchy : Hierarchy + The Hierarchy object. + time : list + List of time frames associated with the extracted branch features. + branch_label : list + List of branch labels for each frame. + aggregate_voxel_metrics : list + List of aggregated voxel metrics for each branch. + aggregate_node_metrics : list + List of aggregated node metrics for each branch. + z, x, y : list + List of branch centroid coordinates in 3D or 2D space. + branch_length : list + List of branch length values. + branch_thickness : list + List of branch thickness values. + branch_aspect_ratio : list + List of aspect ratios for branches. + branch_tortuosity : list + List of tortuosity values for branches. + branch_area : list + List of branch area values. + branch_axis_length_maj, branch_axis_length_min : list + List of major and minor axis lengths for branches. + branch_extent : list + List of extent values for branches. + branch_solidity : list + List of solidity values for branches. + reassigned_label : list + List of reassigned branch labels across time. + stats_to_aggregate : list + List of statistics to aggregate for branches. + features_to_save : list + List of branch features to save. + """ def __init__(self, hierarchy): self.hierarchy = hierarchy @@ -1122,6 +1481,14 @@

Source code for nellie.feature_extraction.hierarchical

self.features_to_save = self.stats_to_aggregate + ["x", "y", "z"] def _get_aggregate_stats(self, t): + """ + Aggregates voxel and node-level statistics for each branch in the frame. + + Parameters + ---------- + t : int + The time frame index. + """ voxel_labels = self.hierarchy.voxels.branch_labels[t] grouped_vox_idxs = [np.argwhere(voxel_labels == label).flatten() for label in np.unique(voxel_labels) if label != 0] @@ -1136,6 +1503,14 @@

Source code for nellie.feature_extraction.hierarchical

self.aggregate_node_metrics.append(node_agg) def _get_branch_stats(self, t): + """ + Computes branch-level statistics, including length, thickness, aspect ratio, tortuosity, and solidity. + + Parameters + ---------- + t : int + The time frame index. + """ branch_idx_array_1 = np.array(self.branch_idxs[t]) branch_idx_array_2 = np.array(self.branch_idxs[t])[:, None, :] dist = np.linalg.norm(branch_idx_array_1 - branch_idx_array_2, axis=-1) @@ -1264,6 +1639,15 @@

Source code for nellie.feature_extraction.hierarchical

self.x.append(x) def _run_frame(self, t): + """ + Extracts branch features for a single time frame, including voxel and node metrics, branch coordinates, and + branch statistics. + + Parameters + ---------- + t : int + The time frame index. + """ frame_branch_idxs = np.argwhere(self.hierarchy.im_skel[t] > 0) self.branch_idxs.append(frame_branch_idxs) @@ -1300,6 +1684,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the extraction of branch features over all time frames. + Iterates over each frame to extract branch features and calculate metrics. + """ for t in range(self.hierarchy.num_t): if self.hierarchy.viewer is not None: self.hierarchy.viewer.status = f'Extracting branch features. Frame: {t + 1} of {self.hierarchy.num_t}.' @@ -1311,6 +1699,45 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Components: + """ + A class to extract and store component-level features from hierarchical image data. + + Parameters + ---------- + hierarchy : Hierarchy + The Hierarchy object containing the image data and metadata. + + Attributes + ---------- + hierarchy : Hierarchy + The Hierarchy object. + time : list + List of time frames associated with the extracted component features. + component_label : list + List of component labels for each frame. + aggregate_voxel_metrics : list + List of aggregated voxel metrics for each component. + aggregate_node_metrics : list + List of aggregated node metrics for each component. + aggregate_branch_metrics : list + List of aggregated branch metrics for each component. + z, x, y : list + List of component centroid coordinates in 3D or 2D space. + organelle_area : list + List of component area values. + organelle_axis_length_maj, organelle_axis_length_min : list + List of major and minor axis lengths for components. + organelle_extent : list + List of extent values for components. + organelle_solidity : list + List of solidity values for components. + reassigned_label : list + List of reassigned component labels across time. + stats_to_aggregate : list + List of statistics to aggregate for components. + features_to_save : list + List of component features to save. + """ def __init__(self, hierarchy): self.hierarchy = hierarchy @@ -1339,6 +1766,14 @@

Source code for nellie.feature_extraction.hierarchical

self.features_to_save = self.stats_to_aggregate + ["x", "y", "z"] def _get_aggregate_stats(self, t): + """ + Aggregates voxel, node, and branch-level statistics for each component in the frame. + + Parameters + ---------- + t : int + The time frame index. + """ voxel_labels = self.hierarchy.voxels.component_labels[t] grouped_vox_idxs = [np.argwhere(voxel_labels == label).flatten() for label in np.unique(voxel_labels) if label != 0] @@ -1359,6 +1794,14 @@

Source code for nellie.feature_extraction.hierarchical

self.aggregate_branch_metrics.append(branch_agg) def _get_component_stats(self, t): + """ + Computes component-level statistics, including area, axis lengths, extent, and solidity. + + Parameters + ---------- + t : int + The time frame index. + """ regions = regionprops(self.hierarchy.label_components[t], spacing=self.hierarchy.spacing) areas = [] axis_length_maj = [] @@ -1406,6 +1849,15 @@

Source code for nellie.feature_extraction.hierarchical

self.x.append(x) def _run_frame(self, t): + """ + Extracts component features for a single time frame, including voxel, node, and branch metrics, and component + statistics. + + Parameters + ---------- + t : int + The time frame index. + """ smallest_label = int(np.min(self.hierarchy.label_components[t][self.hierarchy.label_components[t] > 0])) largest_label = int(np.max(self.hierarchy.label_components[t])) frame_component_labels = np.arange(smallest_label, largest_label + 1) @@ -1424,6 +1876,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the extraction of component features over all time frames. + Iterates over each frame to extract component features and calculate metrics. + """ for t in range(self.hierarchy.num_t): if self.hierarchy.viewer is not None: self.hierarchy.viewer.status = f'Extracting organelle features. Frame: {t + 1} of {self.hierarchy.num_t}.' @@ -1435,6 +1891,35 @@

Source code for nellie.feature_extraction.hierarchical

[docs] class Image: + """ + A class to extract and store global image-level features from hierarchical image data. + + Parameters + ---------- + hierarchy : Hierarchy + The Hierarchy object containing the image data and metadata. + + Attributes + ---------- + hierarchy : Hierarchy + The Hierarchy object. + time : list + List of time frames associated with the extracted image-level features. + image_name : list + List of image file names. + aggregate_voxel_metrics : list + List of aggregated voxel metrics for the entire image. + aggregate_node_metrics : list + List of aggregated node metrics for the entire image. + aggregate_branch_metrics : list + List of aggregated branch metrics for the entire image. + aggregate_component_metrics : list + List of aggregated component metrics for the entire image. + stats_to_aggregate : list + List of statistics to aggregate for the entire image. + features_to_save : list + List of image-level features to save. + """ def __init__(self, hierarchy): self.hierarchy = hierarchy @@ -1448,6 +1933,14 @@

Source code for nellie.feature_extraction.hierarchical

self.features_to_save = [] def _get_aggregate_stats(self, t): + """ + Aggregates voxel, node, branch, and component-level statistics for the entire image in the frame. + + Parameters + ---------- + t : int + The time frame index. + """ voxel_agg = aggregate_stats_for_class(self.hierarchy.voxels, t, [np.arange(len(self.hierarchy.voxels.coords[t]))]) self.aggregate_voxel_metrics.append(voxel_agg) @@ -1465,6 +1958,15 @@

Source code for nellie.feature_extraction.hierarchical

self.aggregate_component_metrics.append(component_agg) def _run_frame(self, t): + """ + Extracts image-level features for a single time frame, including aggregated voxel, node, branch, and + component metrics. + + Parameters + ---------- + t : int + The time frame index. + """ self.time.append(t) self.image_name.append(self.hierarchy.im_info.file_info.filename_no_ext) @@ -1473,6 +1975,10 @@

Source code for nellie.feature_extraction.hierarchical

[docs] def run(self): + """ + Main function to run the extraction of image-level features over all time frames. + Iterates over each frame to extract and aggregate voxel, node, branch, and component-level features. + """ for t in range(self.hierarchy.num_t): if self.hierarchy.viewer is not None: self.hierarchy.viewer.status = f'Extracting image features. Frame: {t + 1} of {self.hierarchy.num_t}.' @@ -1508,6 +2014,10 @@

Nellie

Navigation

+

Contents:

+

Related Topics

diff --git a/docs/_build/html/modules.html b/docs/_build/html/modules.html index e505b71..bacd88f 100644 --- a/docs/_build/html/modules.html +++ b/docs/_build/html/modules.html @@ -13,6 +13,8 @@ + + @@ -249,11 +251,23 @@

Nellie

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+

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diff --git a/docs/_build/html/nellie.feature_extraction.html b/docs/_build/html/nellie.feature_extraction.html index d9744f5..56168ca 100644 --- a/docs/_build/html/nellie.feature_extraction.html +++ b/docs/_build/html/nellie.feature_extraction.html @@ -13,6 +13,8 @@ + + @@ -41,66 +43,340 @@

Submodules class nellie.feature_extraction.hierarchical.Branches(hierarchy)[source]

Bases: object

+

A class to extract and store branch-level features from hierarchical image data.

+
+

Parameters

+
+
hierarchyHierarchy

The Hierarchy object containing the image data and metadata.

+
+
+
+
+

Attributes

+
+
hierarchyHierarchy

The Hierarchy object.

+
+
timelist

List of time frames associated with the extracted branch features.

+
+
branch_labellist

List of branch labels for each frame.

+
+
aggregate_voxel_metricslist

List of aggregated voxel metrics for each branch.

+
+
aggregate_node_metricslist

List of aggregated node metrics for each branch.

+
+
z, x, ylist

List of branch centroid coordinates in 3D or 2D space.

+
+
branch_lengthlist

List of branch length values.

+
+
branch_thicknesslist

List of branch thickness values.

+
+
branch_aspect_ratiolist

List of aspect ratios for branches.

+
+
branch_tortuositylist

List of tortuosity values for branches.

+
+
branch_arealist

List of branch area values.

+
+
branch_axis_length_maj, branch_axis_length_minlist

List of major and minor axis lengths for branches.

+
+
branch_extentlist

List of extent values for branches.

+
+
branch_soliditylist

List of solidity values for branches.

+
+
reassigned_labellist

List of reassigned branch labels across time.

+
+
stats_to_aggregatelist

List of statistics to aggregate for branches.

+
+
features_to_savelist

List of branch features to save.

+
+
run()[source]
-
+

Main function to run the extraction of branch features over all time frames. +Iterates over each frame to extract branch features and calculate metrics.

+
+
class nellie.feature_extraction.hierarchical.Components(hierarchy)[source]

Bases: object

+

A class to extract and store component-level features from hierarchical image data.

+
+

Parameters

+
+
hierarchyHierarchy

The Hierarchy object containing the image data and metadata.

+
+
+
+
+

Attributes

+
+
hierarchyHierarchy

The Hierarchy object.

+
+
timelist

List of time frames associated with the extracted component features.

+
+
component_labellist

List of component labels for each frame.

+
+
aggregate_voxel_metricslist

List of aggregated voxel metrics for each component.

+
+
aggregate_node_metricslist

List of aggregated node metrics for each component.

+
+
aggregate_branch_metricslist

List of aggregated branch metrics for each component.

+
+
z, x, ylist

List of component centroid coordinates in 3D or 2D space.

+
+
organelle_arealist

List of component area values.

+
+
organelle_axis_length_maj, organelle_axis_length_minlist

List of major and minor axis lengths for components.

+
+
organelle_extentlist

List of extent values for components.

+
+
organelle_soliditylist

List of solidity values for components.

+
+
reassigned_labellist

List of reassigned component labels across time.

+
+
stats_to_aggregatelist

List of statistics to aggregate for components.

+
+
features_to_savelist

List of component features to save.

+
+
run()[source]
-
+

Main function to run the extraction of component features over all time frames. +Iterates over each frame to extract component features and calculate metrics.

+
+
class nellie.feature_extraction.hierarchical.Hierarchy(im_info: ImInfo, skip_nodes=True, viewer=None)[source]

Bases: object

+

A class to handle the hierarchical structure of image data, including voxel, node, branch, and component features.

+
+

Parameters

+
+
im_infoImInfo

Object containing metadata and pathways related to the image.

+
+
skip_nodesbool, optional

Whether to skip node processing (default is True).

+
+
vieweroptional

Viewer for updating status messages (default is None).

+
+
+
+
+

Attributes

+
+
im_infoImInfo

The ImInfo object containing the image metadata.

+
+
num_tint

Number of time frames in the image.

+
+
spacingtuple

Spacing between dimensions (Z, Y, X) or (Y, X) depending on the presence of Z.

+
+
im_rawmemmap

Raw image data loaded from disk.

+
+
im_structmemmap

Preprocessed structural image data.

+
+
im_distancememmap

Distance-transformed image data.

+
+
im_skelmemmap

Skeletonized image data.

+
+
label_componentsmemmap

Instance-labeled image data of components.

+
+
label_branchesmemmap

Re-labeled skeleton data of branches.

+
+
im_border_maskmemmap

Image data with border mask.

+
+
im_pixel_classmemmap

Image data classified by pixel types.

+
+
im_obj_reassignedmemmap

Object reassigned labels across time.

+
+
im_branch_reassignedmemmap

Branch reassigned labels across time.

+
+
flow_interpolator_fwFlowInterpolator

Forward flow interpolator.

+
+
flow_interpolator_bwFlowInterpolator

Backward flow interpolator.

+
+
vieweroptional

Viewer to display status updates.

+
+
run()[source]
-
+

Main function to run the entire hierarchical feature extraction process, which includes memory allocation, +hierarchical analysis, and saving the results.

+
+
class nellie.feature_extraction.hierarchical.Image(hierarchy)[source]

Bases: object

+

A class to extract and store global image-level features from hierarchical image data.

+
+

Parameters

+
+
hierarchyHierarchy

The Hierarchy object containing the image data and metadata.

+
+
+
+
+

Attributes

+
+
hierarchyHierarchy

The Hierarchy object.

+
+
timelist

List of time frames associated with the extracted image-level features.

+
+
image_namelist

List of image file names.

+
+
aggregate_voxel_metricslist

List of aggregated voxel metrics for the entire image.

+
+
aggregate_node_metricslist

List of aggregated node metrics for the entire image.

+
+
aggregate_branch_metricslist

List of aggregated branch metrics for the entire image.

+
+
aggregate_component_metricslist

List of aggregated component metrics for the entire image.

+
+
stats_to_aggregatelist

List of statistics to aggregate for the entire image.

+
+
features_to_savelist

List of image-level features to save.

+
+
run()[source]
-
+

Main function to run the extraction of image-level features over all time frames. +Iterates over each frame to extract and aggregate voxel, node, branch, and component-level features.

+
+
class nellie.feature_extraction.hierarchical.Nodes(hierarchy)[source]

Bases: object

+

A class to extract and store node-level features from hierarchical image data.

+
+

Parameters

+
+
hierarchyHierarchy

The Hierarchy object containing the image data and metadata.

+
+
+
+
+

Attributes

+
+
hierarchyHierarchy

The Hierarchy object.

+
+
timelist

List of time frames associated with the extracted node features.

+
+
nodeslist

List of node coordinates for each frame.

+
+
z, x, ylist

List of node coordinates in 3D or 2D space.

+
+
node_thicknesslist

List of node thickness values.

+
+
divergencelist

List of divergence values for nodes.

+
+
convergencelist

List of convergence values for nodes.

+
+
vergerelist

List of vergere values (convergence + divergence).

+
+
aggregate_voxel_metricslist

List of aggregated voxel metrics for each node.

+
+
voxel_idxslist

List of voxel indices associated with each node.

+
+
branch_labellist

List of branch labels assigned to nodes.

+
+
component_labellist

List of component labels assigned to nodes.

+
+
image_namelist

List of image file names.

+
+
stats_to_aggregatelist

List of statistics to aggregate for nodes.

+
+
features_to_savelist

List of node features to save.

+
+
run()[source]
-
+

Main function to run the extraction of node features over all time frames. +Iterates over each frame to extract node features and calculate metrics.

+
+
class nellie.feature_extraction.hierarchical.Voxels(hierarchy: Hierarchy)[source]

Bases: object

+

A class to extract and store voxel-level features from hierarchical image data.

+
+

Parameters

+
+
hierarchyHierarchy

The Hierarchy object containing the image data and metadata.

+
+
+
+
+

Attributes

+
+
hierarchyHierarchy

The Hierarchy object.

+
+
timelist

List of time frames associated with the extracted voxel features.

+
+
coordslist

List of voxel coordinates.

+
+
intensitylist

List of voxel intensity values.

+
+
structurelist

List of voxel structural values.

+
+
vec01list

List of vectors from frame t-1 to t.

+
+
vec12list

List of vectors from frame t to t+1.

+
+
lin_vellist

List of linear velocity vectors.

+
+
ang_vellist

List of angular velocity vectors.

+
+
directionality_rellist

List of directionality features.

+
+
node_labelslist

List of node labels assigned to voxels.

+
+
branch_labelslist

List of branch labels assigned to voxels.

+
+
component_labelslist

List of component labels assigned to voxels.

+
+
node_dim0_limslist

List of node bounding box limits in dimension 0 (Z or Y).

+
+
node_dim1_limslist

List of node bounding box limits in dimension 1 (Y or X).

+
+
node_dim2_limslist

List of node bounding box limits in dimension 2 (X).

+
+
node_voxel_idxslist

List of voxel indices associated with each node.

+
+
stats_to_aggregatelist

List of statistics to aggregate for features.

+
+
features_to_savelist

List of voxel features to save.

+
+
run()[source]
-
+

Main function to run the extraction of voxel features over all time frames. +Iterates over each frame to extract coordinates, intensity, structural features, and motility statistics.

+
+
@@ -111,12 +387,48 @@

Submodules
nellie.feature_extraction.hierarchical.append_to_array(to_append)[source]
-

+

Converts feature dictionaries into lists of arrays and headers for saving to a CSV.

+
+

Parameters

+
+
to_appenddict

Dictionary containing feature names and values to append.

+
+
+
+
+

Returns

+
+
list

List of feature arrays.

+
+
list

List of corresponding feature headers.

+
+
+
+
nellie.feature_extraction.hierarchical.create_feature_array(level, labels=None)[source]
-
+

Creates a 2D feature array and corresponding headers for saving features to CSV.

+
+

Parameters

+
+
levelobject

The level (e.g., voxel, node, branch, etc.) from which features are extracted.

+
+
labelsarray-like, optional

Array of labels to use for the first column of the output (default is None).

+
+
+
+
+

Returns

+
+
numpy.ndarray

2D array of features.

+
+
list

List of corresponding feature headers.

+
+
+
+
@@ -146,11 +458,27 @@

Nellie

Navigation

+

Contents:

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[10, 11], "test_frangi_filt": 11, "test_im_info": 11, "torch_xp": 8, "track": 7, "unit": 11, "util": 8, "verifi": 5, "voxel_reassign": 7}}) \ No newline at end of file diff --git a/nellie/feature_extraction/hierarchical.py b/nellie/feature_extraction/hierarchical.py index f257924..d538d94 100644 --- a/nellie/feature_extraction/hierarchical.py +++ b/nellie/feature_extraction/hierarchical.py @@ -1052,6 +1052,26 @@ def run(self): def aggregate_stats_for_class(child_class, t, list_of_idxs): + """ + Aggregates statistical metrics (mean, standard deviation, min, max, sum) for features of a given class at + a specific time frame. + + Parameters + ---------- + child_class : object + The class from which the feature data is extracted. It should contain a list of statistics to aggregate + (i.e., `stats_to_aggregate`) and corresponding feature arrays. + t : int + The time frame index for which the aggregation is performed. + list_of_idxs : list of lists + A list where each sublist contains the indices of data points that should be grouped together for aggregation. + + Returns + ------- + dict + A dictionary where the keys are feature names and the values are dictionaries containing aggregated + statistics for each feature (mean, standard deviation, min, max, sum). + """ # initialize a dictionary to hold lists of aggregated stats for each stat name # aggregate_stats = { # stat_name: {"mean": [], "std_dev": [], "25%": [], "50%": [], "75%": [], "min": [], "max": [], "range": [], @@ -1330,6 +1350,23 @@ def run(self): def distance_check(border_mask, check_coords, spacing): + """ + Calculates the minimum distance between given coordinates and a border mask using a KD-tree. + + Parameters + ---------- + border_mask : numpy.ndarray + A binary mask where the border is marked as `True`. + check_coords : numpy.ndarray + Coordinates of the points for which distances to the border will be calculated. + spacing : tuple or list + The spacing of the image dimensions (used to scale the coordinates). + + Returns + ------- + numpy.ndarray + An array of distances from each point in `check_coords` to the nearest border point. + """ border_coords = np.argwhere(border_mask) * spacing border_tree = spatial.cKDTree(border_coords) dist, _ = border_tree.query(check_coords * spacing, k=1)