bug: circular arg fixed, class rename (#279)

Co-authored-by: anna-grim <[email protected]>

src/deep_neurographs/neurograph.py

@@ -18,13 +18,12 @@
 from numpy import concatenate
 from scipy.spatial import KDTree
 
-from deep_neurographs import generate_proposals, geometry
+from deep_neurographs import generate_proposals, geometry, utils
 from deep_neurographs.groundtruth_generation import init_targets
-from deep_neurographs.utils import graph_util as gutil
 from deep_neurographs.utils import img_util, util
 
 
-class NeuroGraph(nx.Graph):
+class FragmentsGraph(nx.Graph):
     """
     A class of graphs whose nodes correspond to irreducible nodes from the
     predicted swc files.
@@ -48,7 +47,7 @@ def __init__(self, img_bbox=None, node_spacing=1):
         None
 
         """
-        super(NeuroGraph, self).__init__()
+        super(FragmentsGraph, self).__init__()
         # General class attributes
         self.leaf_kdtree = None
         self.node_cnt = 0
@@ -97,7 +96,7 @@ def set_proxy_soma_ids(self, k):
         None
 
         """
-        for i in gutil.largest_components(self, k):
+        for i in utils.graph_util.largest_components(self, k):
             self.soma_ids[self.nodes[i]["swc_id"]] = i
 
     def get_leafs(self):

src/deep_neurographs/utils/graph_util.py

@@ -5,7 +5,7 @@
 @email: [email protected]
 
 
-Routines for loading fragments and building a neurograph.
+Routines for loading fragments and building a fragments_graph.
 
 
 Terminology
@@ -31,7 +31,6 @@
 from tqdm import tqdm
 
 from deep_neurographs import geometry
-from deep_neurographs.neurograph import NeuroGraph
 from deep_neurographs.utils import img_util, swc_util, util
 
 MIN_SIZE = 30
@@ -82,8 +81,7 @@ def __init__(
 
         Returns
         -------
-        FragmentsGraph
-            FragmentsGraph generated from swc files.
+        None
 
         """
         self.anisotropy = anisotropy
@@ -120,6 +118,8 @@ def run(
             FragmentsGraph generated from swc files.
 
         """
+        from deep_neurographs.neurograph import FragmentsGraph
+
         # Load fragments and extract irreducibles
         self.img_bbox = img_util.init_bbox(img_patch_origin, img_patch_shape)
         swc_dicts = self.reader.load(fragments_pointer)
@@ -129,13 +129,13 @@ def run(
         if self.progress_bar:
             pbar = tqdm(total=len(irreducibles), desc="Combine Graphs")
 
-        neurograph = NeuroGraph(node_spacing=self.node_spacing)
+        fragments_graph = FragmentsGraph(node_spacing=self.node_spacing)
         while len(irreducibles):
             irreducible_set = irreducibles.pop()
-            neurograph.add_component(irreducible_set)
+            fragments_graph.add_component(irreducible_set)
             if self.progress_bar:
                 pbar.update(1)
-        return neurograph
+        return fragments_graph
 
     # --- Graph structure extraction ---
     def schedule_processes(self, swc_dicts):
@@ -645,7 +645,8 @@ def compute_dist(graph, i, j):
 
     Returns
     -------
-    Euclidean distance between i and j.
+    float
+        Euclidean distance between i and j.
 
     """
     return geometry.dist(graph.nodes[i]["xyz"], graph.nodes[j]["xyz"])
@@ -686,6 +687,7 @@ def get_leafs(graph):
     -------
     list
         Leaf nodes "graph".
+
     """
     return [i for i in graph.nodes if graph.degree[i] == 1]
 
@@ -746,20 +748,21 @@ def count_components(graph):
         Graph to be searched.
 
     Returns
-    -------
-    Number of connected components.
+    -------'
+    int
+        Number of connected components.
 
     """
     return nx.number_connected_components(graph)
 
 
-def largest_components(neurograph, k):
+def largest_components(graph, k):
     """
-    Finds the "k" largest connected components in "neurograph".
+    Finds the "k" largest connected components in "graph".
 
     Parameters
     ----------
-    neurograph : NeuroGraph
+    graph : nx.Graph
         Graph to be searched.
     k : int
         Number of largest connected components to return.
@@ -773,7 +776,7 @@ def largest_components(neurograph, k):
     """
     component_cardinalities = k * [-1]
     node_ids = k * [-1]
-    for nodes in nx.connected_components(neurograph):
+    for nodes in nx.connected_components(graph):
         if len(nodes) > component_cardinalities[-1]:
             i = 0
             while i < k:

src/deep_neurographs/visualization.py

@@ -16,7 +16,7 @@
 
 
 def visualize_connected_components(
-    graph, line_width=3, return_data=False, title=""
+    graph, width=3, return_data=False, title=""
 ):
     """
     Visualizes the connected components in "graph".
@@ -25,8 +25,8 @@ def visualize_connected_components(
     ----------
     graph : networkx.Graph
         Graph to be visualized.
-    line_width : int, optional
-        Line width used to plot "subset". The default is 5.
+    width : int, optional
+        Line width used to plot edges in "subset". The default is 5.
     return_data : bool, optional
         Indication of whether to return data object that is used to generate
         plot. The default is False.
@@ -50,7 +50,7 @@ def visualize_connected_components(
             color = colors[cnt % len(colors)]
             data.extend(
                 plot_edges(
-                    graph, subgraph.edges, color=color, line_width=line_width
+                    graph, subgraph.edges, color=color, width=width
                 )
             )
             cnt += 1
@@ -85,16 +85,18 @@ def visualize_graph(graph, title=""):
     plot(data, title)
 
 
-def visualize_proposals(graph, target_graph=None, title="Proposals"):
+def visualize_proposals(
+    graph, color=None, groundtruth_graph=None, title="Proposals"
+):
     """
-    Visualizes a graph with proposals.
+    Visualizes a graph and its proposals.
 
     Parameters
     ----------
     graph : networkx.Graph
         Graph to be visualized.
-    target_graph : networkx.Graph, optional
-        Graph generated from ground truth tracings. The default is None.
+    groundtruth_graph : networkx.Graph, optional
+        Graph generated from groundtruth tracings. The default is None.
     title : str, optional
         Title of the plot. Default is "Proposals".
 
@@ -106,24 +108,25 @@ def visualize_proposals(graph, target_graph=None, title="Proposals"):
     visualize_subset(
         graph,
         graph.proposals,
+        color=color,
         proposal_subset=True,
-        target_graph=target_graph,
+        groundtruth_graph=groundtruth_graph,
         title=title,
     )
 
 
-def visualize_targets(
-    graph, target_graph=None, title="Ground Truth - Accepted Proposals"
+def visualize_groundtruth(
+    graph, groundtruth_graph=None, title="Ground Truth - Accepted Proposals"
 ):
     """
-    Visualizes a graph and its ground truth accept proposals.
+    Visualizes a graph and its groundtruth accepted proposals.
 
     Parameters
     ----------
     graph : networkx.Graph
         Graph to be visualized.
-    target_graph : networkx.Graph, optional
-        Graph generated from ground truth tracings. The default is None.
+    groundtruth_graph : networkx.Graph, optional
+        Graph generated from groundtruth tracings. The default is None.
     title : str, optional
         Title of the plot. Default is "Ground Truth - Accepted Proposals".
 
@@ -136,17 +139,18 @@ def visualize_targets(
         graph,
         graph.target_edges,
         proposal_subset=True,
-        target_graph=target_graph,
+        groundtruth_graph=groundtruth_graph,
         title=title,
     )
 
 
 def visualize_subset(
     graph,
     subset,
-    line_width=5,
+    color=None,
+    width=5,
     proposal_subset=False,
-    target_graph=None,
+    groundtruth_graph=None,
     title="",
 ):
     """
@@ -158,12 +162,12 @@ def visualize_subset(
         Graph to be visualized.
     subset : container
         Subset of edges or proposals to be visualized.
-    line_width : int, optional
+    width : int, optional
         Line width used to plot "subset". The default is 5.
     proposals_subset : bool, optional
         Indication of whether "subset" is a subset of proposals. The default
         is False.
-    target_graph : networkx.Graph, optional
+    groundtruth_graph : networkx.Graph, optional
         Graph generated from ground truth tracings. The default is None.
     title : str, optional
         Title of the plot. Default is "Proposals".
@@ -177,13 +181,15 @@ def visualize_subset(
     data = plot_edges(graph, graph.edges, color="black")
     data.append(plot_nodes(graph))
     if proposal_subset:
-        data.extend(plot_proposals(graph, subset, line_width=line_width))
+        data.extend(
+            plot_proposals(graph, subset, color=color, width=width)
+        )
     else:
-        data.extend(plot_edges(graph, subset, line_width=line_width))
+        data.extend(plot_edges(graph, subset, width=width))
 
     # Add target graph (if applicable)
-    if target_graph:
-        cc = visualize_connected_components(target_graph, return_data=True)
+    if groundtruth_graph:
+        cc = visualize_connected_components(groundtruth_graph, return_data=True)
         data.extend(cc)
     plot(data, title)
 
@@ -202,12 +208,12 @@ def plot_nodes(graph):
     )
 
 
-def plot_proposals(graph, proposals, color=None, line_width=5):
+def plot_proposals(graph, proposals, color=None, width=5):
     # Set preferences
     if color is None:
-        line = dict(width=line_width)
+        line = dict(width=width)
     else:
-        line = dict(color=color, width=line_width)
+        line = dict(color=color, width=width)
 
     # Add traces
     traces = []
@@ -225,10 +231,10 @@ def plot_proposals(graph, proposals, color=None, line_width=5):
     return traces
 
 
-def plot_edges(graph, edges, color=None, line_width=3):
+def plot_edges(graph, edges, color=None, width=3):
     traces = []
     line = (
-        dict(width=5) if color is None else dict(color=color, width=line_width)
+        dict(width=5) if color is None else dict(color=color, width=width)
     )
     for i, j in edges:
         trace = go.Scatter3d(