Merge 225edb6 into 9690d37

localgraphclustering/GraphLocal.py

@@ -8,13 +8,20 @@
 import warnings
 import collections as cole
 from .cpp import *
+import random
 
 import gzip
 import bz2
 import lzma
 
 import multiprocessing as mp
 
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib.collections import LineCollection
+from mpl_toolkits.mplot3d.art3d import Line3DCollection
+from matplotlib.colors import to_rgb,to_rgba
+
 def _load_from_shared(sabuf, dtype, shape):
     return np.frombuffer(sabuf, dtype=dtype).reshape(shape)
 
@@ -558,8 +565,6 @@ def local_extrema(self,vals,strict=False,reverse=False):
         Parameters
         ----------
 
-        G: GraphLocal
-
         vals: Sequence[float]
             a feature value per node used to find the ex against each other, i.e. conductance
 
@@ -616,3 +621,142 @@ def local_extrema(self,vals,strict=False,reverse=False):
         minvals = vals[minverts]
 
         return minverts, minvals
+
+    def draw(self,coords,alpha=1.0,nodesize=5,linewidth=1,
+         nodealpha=1.0,edgealpha=0.01,nodecolor='r',
+         edgecolor='k',nodemarker='o',setalpha=1.0,
+         setcolor='y',axs=None,fig=None,nodeset=None,
+         groups=None):
+        """
+        standard drawing function of GraphLocal object
+
+        Parameters
+        ----------
+
+        coords: a n-by-2 or n-by-3 array with coordinates for each node of the graph.
+
+        Optional parameters
+        ------------------
+
+        alpha: float (1.0 by default)
+            the overall alpha scaling of the plot, [0,1]
+
+        nodealpha: float (1.0 by default)
+            the overall node alpha scaling of the plot, [0, 1]
+
+        edgealpha: float (1.0 by default)
+            the overall edge alpha scaling of the plot, [0, 1]
+
+        setalpha: float (1.0 by default)
+            the overall set alpha scaling of the plot, [0, 1]
+
+        nodecolor: string or RGB ('r' by default)
+
+        edgecolor: string or RGB ('k' by default)
+
+        setcolor: string or RGB ('y' by default)
+
+        nodemarker: string ('o' by default)
+
+        nodesize: float (5.0 by default)
+
+        linewidth: float (1.0 by default)
+
+        nodeset: Sequence[int] (None by default)
+            a set of nodes to highlight
+
+        groups: Sequence[Sequence[int]] (None by default)
+            node partitions, different colors will be assigned to different groups
+
+        axs,fig: None,None (default) 
+            by default it will create a new figure, or this will plot in axs if not None.
+
+        Returns
+        -------
+
+        a dictionary with: 
+        fig, ax, setnodes, groupnodes
+        """
+        if axs is None:
+            fig = plt.figure()
+            if len(coords[0]) == 3:
+                axs = fig.add_subplot(111, projection='3d')
+            else:
+                axs = fig.add_subplot(111)
+        axs.set_axis_off()
+        nodeset = set(nodeset) if nodeset is not None else set()
+        nodelist_in = []
+        nodelist_out = []
+        for i in range(self._num_vertices):
+            if i in nodeset:
+                nodelist_in.append(i)
+            else:
+                nodelist_out.append(i)
+        #store color information for each node
+        node_color_list = np.empty((self._num_vertices,4))
+        node_color_list[nodelist_out] = to_rgba(nodecolor,alpha*nodealpha)
+        node_color_list[nodelist_in] = to_rgba(setcolor,alpha*setalpha)
+        #reassign node colors based on partition
+        if groups is not None:
+            number_of_colors = len(groups)
+            color = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])
+                     for i in range(number_of_colors)]
+            for i,g in enumerate(groups):
+                for k in g:
+                    node_color_list[k] = to_rgba(color[i],alpha*nodealpha)               
+        if len(coords[0]) == 3:
+            self.draw_3d(coords,axs,nodemarker=nodemarker,nodesize=nodesize,edgealpha=alpha*edgealpha,
+                    linewidth=linewidth,node_color_list=node_color_list)
+        else:
+            self.draw_2d(coords,axs,nodemarker=nodemarker,nodesize=nodesize,edgealpha=alpha*edgealpha,
+                    linewidth=linewidth,node_color_list=node_color_list)
+
+        ret_dict = {"fig":fig,"ax":axs,"setnodes":nodelist_in,"groupnodes":groups}
+        return ret_dict
+
+    @staticmethod
+    def draw_star(center,points,pos,edge_pos):
+        for i,p in enumerate(points):
+            if p >= center:
+                edge_pos.append([pos[center],pos[p]])
+
+    def draw_2d(self,pos,axs,nodemarker='o',nodesize=5,edgealpha=0.01,linewidth=1,
+                node_color_list=None,edgecolor='k',nodecolor='r',node_list=None):
+        if node_color_list is not None:
+            node_collection = axs.scatter([p[0] for p in pos],[p[1] for p in pos],c=node_color_list,s=nodesize,marker=nodemarker)
+        else:
+            node_collection = axs.scatter([p[0] for p in pos],[p[1] for p in pos],c=nodecolor,s=nodesize,marker=nodemarker)
+        #make sure nodes are on the top
+        node_collection.set_zorder(2)
+        node_list = range(self._num_vertices) if node_list is None else node_list
+        edge_pos = []
+        for i in node_list:
+            self.draw_star(i,self.aj[self.ai[i]:self.ai[i+1]],pos,edge_pos)
+        edge_pos = np.asarray(edge_pos)
+        edge_collection = LineCollection(edge_pos,colors=to_rgba(edgecolor,edgealpha),linewidths=linewidth)
+        #make sure edges are at the bottom
+        edge_collection.set_zorder(1)
+        axs.add_collection(edge_collection)
+        axs.autoscale()
+
+    def draw_3d(self,pos,axs,nodemarker='o',nodesize=5,edgealpha=0.01,linewidth=1,
+                node_color_list=None,angle=30,edgecolor='k',nodecolor='r',node_list=None):
+        if node_color_list is not None:
+            node_collection = axs.scatter([p[0] for p in pos],[p[1] for p in pos],[p[2] for p in pos],c=node_color_list,
+                                          s=nodesize,marker=nodemarker,zorder=2)
+        else:
+            node_collection = axs.scatter([p[0] for p in pos],[p[1] for p in pos],[p[2] for p in pos],c=nodecolor,
+                                          s=nodesize,marker=nodemarker,zorder=2)
+        #make sure nodes are on the top
+        node_list = range(self._num_vertices) if node_list is None else node_list
+        edge_pos = []
+        for i in node_list:
+            self.draw_star(i,self.aj[self.ai[i]:self.ai[i+1]],pos,edge_pos)
+        edge_pos = np.asarray(edge_pos)
+        edge_collection = Line3DCollection(edge_pos,colors=to_rgba(edgecolor,edgealpha),linewidths=linewidth)
+        #make sure edges are at the bottom
+        edge_collection.set_zorder(1)
+        axs.add_collection(edge_collection)
+        axs.autoscale()
+        # Set the initial view
+        axs.view_init(30, angle)

localgraphclustering/tests/test_algs.py

@@ -1,10 +1,25 @@
 from localgraphclustering import *
 import time
 import numpy as np
+import networkx as nx
+import random
 
 def load_example_graph(vtype,itype):
     return GraphLocal("localgraphclustering/tests/data/dolphins.edges",separator=" ",vtype=vtype,itype=itype)
 
+def generate_random_3Dgraph(n_nodes, radius, seed=None):
+
+    if seed is not None:
+        random.seed(seed)
+
+    # Generate a dict of positions
+    pos = {i: (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)) for i in range(n_nodes)}
+
+    # Create random 3D network
+    G = nx.random_geometric_graph(n_nodes, radius, pos=pos)
+
+    return G
+
 def test_GraphLocal_methods():
     g = load_example_graph(np.uint32,np.uint32)
     g.largest_component()
@@ -33,6 +48,31 @@ def test_GraphLocal_methods():
     # Test graph with more than one components
     G = GraphLocal("notebooks/datasets/neuro-fmri-01.edges",file_type = "edgelist", separator = " ", header = True)
 
+    # Test drawing fuinctions
+    g = GraphLocal('notebooks/datasets/JohnsHopkins.graphml','graphml','\t')
+    ld_coord = np.loadtxt('notebooks/datasets/JohnHopkins_coord.xy', dtype = 'Float64')
+    idx = np.argsort(ld_coord[:,0])
+    coords = []
+    for i in range(g._num_vertices):
+        coords.append(ld_coord[idx[i],1:3])
+    coords = np.array(coords)
+    # Call the global spectral partitioning algorithm.
+    eig2 = fiedler(g)[0]
+
+    # Round the eigenvector
+    output_sc = sweep_cut(g,eig2)
+
+    # Extract the partition for g and store it.
+    eig2_rounded = output_sc[0]
+    ret_dict = g.draw(coords,edgealpha=0.01,nodealpha=0.5,nodeset=eig2_rounded)
+
+    N = generate_random_3Dgraph(n_nodes=200, radius=0.25, seed=1)
+    pos = list(nx.get_node_attributes(N,'pos').values())
+    G = GraphLocal()
+    G = G.from_networkx(N)
+    ret_dict = G.draw(pos,edgealpha=0.01,nodealpha=0.5,nodeset=range(100,150),groups=[range(50),range(50,100)])
+
+
 def test_sweepcut_self_loop():
     """ This is a regression test for sweep-cuts with self-loops """
     g = GraphLocal()

notebooks/examples_with_visualization.ipynb

@@ -1410,7 +1410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,