Update all function doc strings

Author: constantinpape

scripts/cooper/full_reconstruction/qualitative_evaluation.py

@@ -75,6 +75,7 @@ def eval_compartments():
     print("Compartment Evaluation:")
     print("Avergage pieces per compartment:", avg, "+-", std)
     print("Max pieces per compartment:", max_)
+    print("Number of compartments:", len(pieces_per_compartment))
 
 
 def eval_mitos():
@@ -124,11 +125,11 @@ def check_mitos():
 def main():
     # check_mitos()
 
-    eval_mitos()
-    print()
+    # eval_mitos()
+    # print()
     eval_compartments()
-    print()
-    eval_az()
+    # print()
+    # eval_az()
 
 
 main()

scripts/inner_ear/processing/run_analyis.py

@@ -10,8 +10,9 @@
 
 from synaptic_reconstruction.file_utils import get_data_path
 from synaptic_reconstruction.distance_measurements import (
-    measure_segmentation_to_object_distances,
     filter_blocked_segmentation_to_object_distances,
+    load_distances,
+    measure_segmentation_to_object_distances,
 )
 
 from synaptic_reconstruction.morphology import compute_radii, compute_object_morphology
@@ -172,8 +173,9 @@ def load_dist(measurement_path, seg_ids=None):
 
     # Filter out the blocked vesicles.
     if apply_extra_filters:
+        rav_dists, ep1, ep2, all_rav_ids = load_distances(distance_paths["ribbon"])
         rav_ids = filter_blocked_segmentation_to_object_distances(
-            vesicles, distance_paths["ribbon"], seg_ids=rav_ids, line_dilation=4, verbose=True,
+            vesicles, rav_dists, ep1, ep2, all_rav_ids, filter_seg_ids=rav_ids, line_dilation=4, verbose=True,
         )
         rav_ids = filter_border_vesicles(vesicles, seg_ids=rav_ids)
 

synaptic_reconstruction/distance_measurements.py

@@ -1,6 +1,6 @@
 import os
 import multiprocessing as mp
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -18,7 +18,25 @@
     skfmm = None
 
 
-def compute_geodesic_distances(segmentation, distance_to, resolution=None, unsigned=True):
+def compute_geodesic_distances(
+    segmentation: np.ndarray,
+    distance_to: np.ndarray,
+    resolution: Optional[Union[int, float, Tuple[int, int, int]]] = None,
+    unsigned: bool = True,
+) -> np.ndarray:
+    """Compute the geodesic distances between a segmentation and a distance target.
+
+    This function require scikit-fmm to be installed.
+
+    Args:
+        segmentation: The binary segmentation.
+        distance_to: The binary distance target.
+        resolution: The voxel size of the data, used to scale the distances.
+        unsigned: Whether to return the unsigned or signed distances.
+
+    Returns:
+        Array with the geodesic distance values.
+    """
     assert skfmm is not None, "Please install scikit-fmm to use compute_geodesic_distance."
 
     invalid = segmentation == 0
@@ -43,14 +61,12 @@ def compute_geodesic_distances(segmentation, distance_to, resolution=None, unsig
     return distances
 
 
-# TODO update this
 def _compute_centroid_distances(segmentation, resolution, n_neighbors):
-    # TODO enable eccentricity centers instead
     props = regionprops(segmentation)
     centroids = np.array([prop.centroid for prop in props])
     if resolution is not None:
-        pass  # TODO scale the centroids
-
+        scale_factor = np.array(resolution)[:, None]
+        centroids *= scale_factor
     pair_distances = pairwise_distances(centroids)
     return pair_distances
 
@@ -313,11 +329,13 @@ def create_pairwise_distance_lines(
         endpoints1: One set of distance end points.
         endpoints2: The other set of distance end points.
         seg_ids: The segmentation pair corresponding to each distance.
-        n_neighbors: ...
-        pairs: ...
-        bb: ....
-        scale: ...
-        remove_duplicates: ...
+        n_neighbors: The number of nearest neighbors to take into consideration
+            for creating the distance lines.
+        pairs: Optional list of ids to use for creating the distance lines.
+        bb: Bounding box for restricing the distance line creation.
+        scale: Scale factor for resizing the distance lines.
+            Use this if the corresponding segmentations were downscaled for visualization.
+        remove_duplicates: Remove duplicate id pairs from the distance lines.
 
     Returns:
         The lines for plotting in napari.
@@ -386,8 +404,10 @@ def create_object_distance_lines(
         endpoints1: One set of distance end points.
         endpoints2: The other set of distance end points.
         seg_ids: The segmentation ids corresponding to each distance.
-        max_distance: ...
-        scale: ...
+        max_distance: Maximal distance for drawing the distance line.
+        filter_seg_ids: Segmentation ids to restrict the distance lines.
+        scale: Scale factor for resizing the distance lines.
+            Use this if the corresponding segmentations were downscaled for visualization.
 
     Returns:
         The lines for plotting in napari.
@@ -416,13 +436,32 @@ def create_object_distance_lines(
     return lines, properties
 
 
-def keep_direct_distances(segmentation, measurement_path, line_dilation=0, scale=None):
-    """Filter out all distances that are not direct.
-    I.e. distances that cross another segmented object.
-    """
+def keep_direct_distances(
+    segmentation: np.ndarray,
+    distances: np.ndarray,
+    endpoints1: np.ndarray,
+    endpoints2: np.ndarray,
+    seg_ids: np.ndarray,
+    line_dilation: int = 0,
+    scale: Optional[Tuple[int, int, int]] = None,
+) -> List[List[int, int]]:
+    """Filter out all distances that are not direct; distances that are occluded by another segmented object.
 
-    distances, ep1, ep2, seg_ids = load_distances(measurement_path)
-    distance_lines, properties = create_object_distance_lines(distances, ep1, ep2, seg_ids, scale=scale)
+    Args:
+        segmentation: The segmentation from which the distances are derived.
+        distances: The measurd distances.
+        endpoints1: One set of distance end points.
+        endpoints2: The other set of distance end points.
+        seg_ids: The segmentation ids corresponding to each distance.
+        line_dilation: Dilation factor of the distance lines for determining occlusions.
+        scale: Scaling factor of the segmentation compared to the distance measurements.
+
+    Returns:
+        The list of id pairs that are kept.
+    """
+    distance_lines, properties = create_object_distance_lines(
+        distances, endpoints1, endpoints2, seg_ids, scale=scale
+    )
 
     ids_a, ids_b = properties["id_a"], properties["id_b"]
     filtered_ids_a, filtered_ids_b = [], []
@@ -459,10 +498,35 @@ def keep_direct_distances(segmentation, measurement_path, line_dilation=0, scale
 
 
 def filter_blocked_segmentation_to_object_distances(
-    segmentation, measurement_path, line_dilation=0, scale=None, seg_ids=None, verbose=False,
-):
-    distances, ep1, ep2, seg_ids = load_distances(measurement_path)
-    distance_lines, properties = create_object_distance_lines(distances, ep1, ep2, seg_ids, scale=scale)
+    segmentation: np.ndarray,
+    distances: np.ndarray,
+    endpoints1: np.ndarray,
+    endpoints2: np.ndarray,
+    seg_ids: np.ndarray,
+    line_dilation: int = 0,
+    scale: Optional[Tuple[int, int, int]] = None,
+    filter_seg_ids: Optional[List[int]] = None,
+    verbose: bool = False,
+) -> List[int]:
+    """Filter out all distances that are not direct; distances that are occluded by another segmented object.
+
+    Args:
+        segmentation: The segmentation from which the distances are derived.
+        distances: The measurd distances.
+        endpoints1: One set of distance end points.
+        endpoints2: The other set of distance end points.
+        seg_ids: The segmentation ids corresponding to each distance.
+        line_dilation: Dilation factor of the distance lines for determining occlusions.
+        scale: Scaling factor of the segmentation compared to the distance measurements.
+        filter_seg_ids: Segmentation ids to restrict the distance lines.
+        verbose: Whether to print progressbar.
+
+    Returns:
+        The list of id pairs that are kept.
+    """
+    distance_lines, properties = create_object_distance_lines(
+         distances, endpoints1, endpoints2, seg_ids, scale=scale
+    )
     all_seg_ids = properties["id"]
 
     filtered_ids = []

synaptic_reconstruction/file_utils.py

@@ -1,7 +1,17 @@
 import os
+from typing import List, Optional, Union
 
 
-def get_data_path(folder, n_tomograms=1):
+def get_data_path(folder: str, n_tomograms: Optional[int] = 1) -> Union[str, List[str]]:
+    """Get the path to all tomograms stored as .rec or .mrc files in a folder.
+
+    Args:
+        folder: The folder with tomograms.
+        n_tomograms: The expected number of tomograms.
+
+    Returns:
+        The filepath or list of filepaths of the tomograms in the folder.
+    """
     file_names = os.listdir(folder)
     tomograms = []
     for fname in file_names:
@@ -11,7 +21,5 @@ def get_data_path(folder, n_tomograms=1):
 
     if n_tomograms is None:
         return tomograms
-
     assert len(tomograms) == n_tomograms, f"{folder}: {len(tomograms)}, {n_tomograms}"
-
     return tomograms[0] if n_tomograms == 1 else tomograms

synaptic_reconstruction/inference/active_zone.py

@@ -10,8 +10,7 @@
 
 
 def find_intersection_boundary(segmented_AZ: np.ndarray, segmented_compartment: np.ndarray) -> np.ndarray:
-    """
-    Find the cumulative intersection of the boundary of each label in segmented_compartment with segmented_AZ.
+    """Find the intersection of the boundaries of each objects in segmented_compartment with segmented_AZ.
 
     Args:
         segmented_AZ: 3D array representing the active zone (AZ).

synaptic_reconstruction/inference/compartments.py

@@ -157,8 +157,7 @@ def segment_compartments(
     n_slices_exclude: int = 0,
     **kwargs,
 ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
-    """
-    Segment synaptic compartments in an input volume.
+    """Segment synaptic compartments in an input volume.
 
     Args:
         input_volume: The input volume to segment.

synaptic_reconstruction/inference/cristae.py

@@ -43,8 +43,7 @@ def segment_cristae(
     scale: Optional[List[float]] = None,
     mask: Optional[np.ndarray] = None,
 ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
-    """
-    Segment cristae in an input volume.
+    """Segment cristae in an input volume.
 
     Args:
         input_volume: The input volume to segment. Expects 2 3D volumes: raw and mitochondria

synaptic_reconstruction/inference/mitochondria.py

@@ -66,8 +66,7 @@ def segment_mitochondria(
     scale: Optional[List[float]] = None,
     mask: Optional[np.ndarray] = None,
 ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
-    """
-    Segment mitochondria in an input volume.
+    """Segment mitochondria in an input volume.
 
     Args:
         input_volume: The input volume to segment.