Feat s3 img reader

src/deep_neurographs/generate_proposals.py

@@ -4,7 +4,7 @@
 @author: Anna Grim
 @email: [email protected]
 
-Module used to generate edge proposals.
+Module used to generate edge proposals for a fragments graph.
 
 """
 

src/deep_neurographs/utils/graph_util.py

@@ -5,8 +5,20 @@
 @email: [email protected]
 
 
-Routines for loading fragments and building a fragments_graph.
+Overview
+--------
+Code that reads and preprocesses neuron fragments stored as swc files, then
+constructs a custom graph object called a "FragmentsGraph".
 
+    Graph Construction Algorithm:
+        1. Read Neuron Fragments
+            to do...
+
+        2. Preprocess Fragments and Extract Irreducibles
+            to do...
+
+        3. Build FragmentsGraph
+            to do...
 
 Terminology
 ------------
@@ -31,12 +43,7 @@
 from tqdm import tqdm
 
 from deep_neurographs import geometry
-from deep_neurographs.utils import img_util, swc_util, util
-
-MIN_SIZE = 30
-NODE_SPACING = 1
-SMOOTH_BOOL = True
-PRUNE_DEPTH = 20
+from deep_neurographs.utils import swc_util, util
 
 
 class GraphLoader:
@@ -48,36 +55,35 @@ class GraphLoader:
     def __init__(
         self,
         anisotropy=[1.0, 1.0, 1.0],
-        min_size=MIN_SIZE,
-        node_spacing=NODE_SPACING,
+        min_size=30,
+        node_spacing=1,
         progress_bar=False,
-        prune_depth=PRUNE_DEPTH,
-        smooth_bool=SMOOTH_BOOL,
+        prune_depth=20,
+        smooth_bool=True,
     ):
         """
         Builds a FragmentsGraph by reading swc files stored either on the
         cloud or local machine, then extracting the irreducible components.
 
         Parameters
         ----------
-        anisotropy : list[float], optional
-            Scaling factors applied to xyz coordinates to account for the
-            anisotropy of microscope. The default is [1.0, 1.0, 1.0].
+        anisotropy : List[float], optional
+            Image to physical coordinates scaling factors to account for the
+            anisotropy of the microscope. The default is [1.0, 1.0, 1.0].
         min_size : float, optional
             Minimum path length of swc files which are stored as connected
-            components in the FragmentsGraph. The default is 30ums.
+            components in the FragmentsGraph. The default is 30.0 (microns).
         node_spacing : int, optional
-            Spacing (in microns) between nodes. The default is the global
-            variable "NODE_SPACING".
+            Spacing (in microns) between nodes. The default is 1.
         progress_bar : bool, optional
             Indication of whether to print out a progress bar while building
             graph. The default is True.
         prune_depth : int, optional
             Branches less than "prune_depth" microns are pruned if "prune" is
-            True. The default is the global variable "PRUNE_DEPTH".
+            True. The default is 20.0 (microns).
         smooth_bool : bool, optional
             Indication of whether to smooth branches from swc files. The
-            default is the global variable "SMOOTH".
+            default is True.
 
         Returns
         -------
@@ -90,12 +96,9 @@ def __init__(
         self.progress_bar = progress_bar
         self.prune_depth = prune_depth
         self.smooth_bool = smooth_bool
-
         self.reader = swc_util.Reader(anisotropy, min_size)
 
-    def run(
-        self, fragments_pointer, img_patch_origin=None, img_patch_shape=None
-    ):
+    def run(self, fragments_pointer):
         """
         Builds a FragmentsGraph by reading swc files stored either on the
         cloud or local machine, then extracting the irreducible components.
@@ -105,12 +108,6 @@ def run(
         fragments_pointer : dict, list, str
             Pointer to swc files used to build an instance of FragmentsGraph,
             see "swc_util.Reader" for further documentation.
-        img_patch_origin : list[int], optional
-            An xyz coordinate which is the upper, left, front corner of the
-            image patch that contains the swc files. The default is None.
-        img_patch_shape : list[int], optional
-            Shape of the image patch which contains the swc files. The default
-            is None.
 
         Returns
         -------
@@ -120,12 +117,13 @@ def run(
         """
         from deep_neurographs.fragments_graph import FragmentsGraph
 
-        # Load fragments and extract irreducibles
-        self.img_bbox = img_util.init_bbox(img_patch_origin, img_patch_shape)
+        # Step 1: Read Neuron Fragments
         swc_dicts = self.reader.load(fragments_pointer)
+
+        # Step: Preprocess Fragments and Extract Irreducibles 
         irreducibles = self.schedule_processes(swc_dicts)
 
-        # Build FragmentsGraph
+        # Step 3: Build FragmentsGraph
         fragments_graph = FragmentsGraph(node_spacing=self.node_spacing)
         while len(irreducibles):
             irreducible_set = irreducibles.pop()
@@ -186,15 +184,14 @@ def get_irreducibles(self, swc_dict):
 
         Returns
         -------
-        list
+        List[dict]
             List of dictionaries such that each is the set of irreducibles in
             a connected component of the graph corresponding to "swc_dict".
 
         """
         # Build dense graph
         swc_dict["idx"] = dict(zip(swc_dict["id"], range(len(swc_dict["id"]))))
         graph, _ = swc_util.to_graph(swc_dict, set_attrs=True)
-        self.clip_branches(graph, swc_dict["swc_id"])
         self.prune_branches(graph)
 
         # Extract irreducibles
@@ -210,28 +207,6 @@ def get_irreducibles(self, swc_dict):
                         irreducibles.append(result)
         return irreducibles
 
-    def clip_branches(self, graph, swc_id):
-        """
-        Deletes all nodes from "graph" that are not contained in "img_bbox".
-
-        Parameters
-        ----------
-        graph : networkx.Graph
-            Graph to be searched
-
-        Returns
-        -------
-        None
-
-        """
-        if self.img_bbox:
-            delete_nodes = set()
-            for i in graph.nodes:
-                xyz = img_util.to_voxels(graph.nodes[i]["xyz"], self.to_anisotropy)
-                if not util.is_contained(self.img_bbox, xyz):
-                    delete_nodes.add(i)
-            graph.remove_nodes_from(delete_nodes)
-
     def prune_branches(self, graph):
         """
         Prunes all short branches from "graph". A short branch is a path
@@ -316,7 +291,6 @@ def get_component_irreducibles(self, graph, swc_dict):
             # Visit j
             attrs = upd_edge_attrs(swc_dict, attrs, j)
             if j in leafs or j in branchings:
-                # Check whether to smooth
                 attrs["length"] = branch_length
                 attrs = to_numpy(attrs)
                 if self.smooth_bool:

src/deep_neurographs/utils/img_util.py

@@ -4,7 +4,8 @@
 @author: Anna Grim
 @email: [email protected]
 
-Helper routines for working with images.
+
+Helper routines for processing images.
 
 """
 
@@ -313,29 +314,29 @@ def to_physical(voxel, anisotropy, shift=[0, 0, 0]):
     return tuple([voxel[i] * anisotropy[i] - shift[i] for i in range(3)])
 
 
-def to_voxels(xyz, anisotropy, downsample_factor=0):
+def to_voxels(xyz, anisotropy, multiscale=0):
     """
     Converts coordinates from world to voxel.
 
     Parameters
     ----------
     xyz : ArrayLike
-        xyz coordinate to be converted to voxels.
+        Physical coordiante to be converted to a voxel coordinate.
     anisotropy : ArrayLike
         Image to physical coordinates scaling factors to account for the
         anisotropy of the microscope.
-    downsample_factor : int, optional
-        Downsampling factor that accounts for which level in the image pyramid
-        the voxel coordinates must index into. The default is 0.
+    multiscale : int, optional
+        Level in the image pyramid that the voxel coordinate must index into.
+        The default is 0.
 
     Returns
     -------
     numpy.ndarray
-        Coordinates converted to voxels.
+        Voxel coordinate of the input.
 
     """
-    downsample_factor = 1.0 / 2 ** downsample_factor
-    voxel = downsample_factor * (xyz / np.array(anisotropy))
+    scaling_factor = 1.0 / 2 ** multiscale
+    voxel = scaling_factor * xyz / np.array(anisotropy)
     return np.round(voxel).astype(int)
 
 
@@ -348,9 +349,10 @@ def init_bbox(origin, shape):
     Parameters
     ----------
     origin : tuple[int]
-        Origin of bounding box which is assumed to be top, front, left corner.
-    shape : tuple[int]
-        Shape of bounding box.
+        Voxel coordinate of the origin of the bounding box, which is assumed
+        to be top-front-left corner.
+    shape : Tuple[int]
+        Shape of the bounding box.
 
     Returns
     -------

src/deep_neurographs/utils/util.py

@@ -4,7 +4,8 @@
 @author: Anna Grim
 @email: [email protected]
 
-General helper routines for various tasks.
+
+Miscellaneous helper routines.
 
 """