Merge 46db273 into 9690d37

localgraphclustering/GraphLocal.py

@@ -8,13 +8,17 @@
 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
+
 def _load_from_shared(sabuf, dtype, shape):
     return np.frombuffer(sabuf, dtype=dtype).reshape(shape)
 
@@ -558,8 +562,6 @@ 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 +618,193 @@ 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, nodes, edges, setnodes, setedges, groupnodes, groupedges
+        these are the handles to the actual plot elements, so that you could change 
+        values after the fact. 
+        """
+        if axs == 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)
+        N = nx.Graph()
+        coo = self.adjacency_matrix.tocoo()
+        #color, coords and alpha information are stored directly in node attributes
+        N.add_nodes_from(list(zip(nodelist_out,[{'pos':coords[i],'alpha':alpha*nodealpha,'color':nodecolor} for i in nodelist_out])))
+        N.add_nodes_from(list(zip(nodelist_in,[{'pos':coords[i],'alpha':alpha*setalpha,'color':setcolor} for i in nodelist_in])))
+        edge_list = [(coo.row[i],coo.col[i]) for i in range(self._num_edges)]
+        #color information is stored directly in edge information
+        N.add_edges_from(edge_list,color=edgecolor)
+        setedges = []
+        for i in range(self._num_edges):
+            if coo.row[i] in nodeset and coo.col[i] in nodeset:
+                setedges.append((coo.row[i],coo.col[i]))
+
+        #reassign node colors based on partition
+        groupedges = None
+        if groups is not None:
+            groupedges = [[] for i in range(len(groups))]
+            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):
+                nx.set_node_attributes(N,{k:{'color':v} for (k,v) in zip(g,[color[i]]*len(g))})
+                sg = set(g)
+                for j in g:
+                    for k in range(self.ai[j],self.ai[j+1]):
+                        if self.aj[k] >= j and self.aj[k] in sg:
+                            groupedges[i].append((j,self.aj[k]))
+
+        if len(coords[0]) == 3:
+            self.draw_nx_3d(N,axs,nodemarker,nodesize,alpha*edgealpha,linewidth)
+        else:
+            self.draw_nx(N,axs,nodemarker,nodesize,alpha*edgealpha,linewidth)
+
+        ret_dict = {"fig":fig,"ax":axs,"nodes":list(N.nodes),"edges":list(N.edges),
+                    "setnodes":nodelist_in,"setedges":setedges,"nx_graph":N,"groupnodes":groups,
+                    "groupedges":groupedges}
+        return ret_dict
+
+    @staticmethod
+    def draw_nx(N,axs,nodemarker='o',nodesize=5,edgealpha=0.01,linewidth=1):
+        """
+        a static method to draw a networkx instance, the plot will be based on node and edge attributes,
+        valid node attributes include "alpha", "color" and "pos". valid edge attributes include "color", 
+        designed to modify the plot returned by calling "draw" function
+
+        Parameters
+        ----------
+        N: networkx object
+
+        axs: matplotlib axes
+
+        nodemarker: 'o' by default
+
+        nodesize: 5 by default
+
+        edgealpha: 0.01 by default
+
+        linewidth: 1 by default
+        """
+        nnodes = nx.number_of_nodes(N)
+        node_alpha_list = [0]*nnodes
+        node_color_list = ['']*nnodes
+        for i,node in enumerate(N.nodes(data=True)):
+            node_alpha_list[i] = node[1]['alpha']
+            node_color_list[i] = node[1]['color']
+        nedges = nx.number_of_edges(N)
+        edge_color_list = ['']*nedges
+        for i,edge in enumerate(N.edges(data=True)):
+            edge_color_list[i] = edge[2]['color']
+        nx.draw_networkx_nodes(N,pos=nx.get_node_attributes(N,'pos'),node_size=nodesize,ax=axs,alpha=node_alpha_list,
+                               node_color=node_color_list,node_shape=nodemarker)
+        nx.draw_networkx_edges(N,pos=nx.get_node_attributes(N,'pos'),ax=axs,edge_color=edge_color_list,alpha=edgealpha,
+                              linewidths=linewidth)
+
+    @staticmethod
+    def draw_nx_3d(N,axs,nodemarker='o',nodesize=5,edgealpha=0.01,linewidth=1,angle=30):
+        """
+        a static method to draw a networkx instance, the plot will be based on node and edge attributes,
+        valid node attributes include "alpha", "color" and "pos". valid edge attributes include "color", 
+        designed to modify the plot returned by calling "draw" function
+
+        Parameters
+        ----------
+        N: networkx object
+
+        axs: matplotlib axes
+
+        nodemarker: 'o' by default
+
+        nodesize: 5 by default
+
+        edgealpha: 0.01 by default
+        
+        linewidth: 1 by default
+
+        angle: view angle, 30 by default
+        """
+        pos = nx.get_node_attributes(N,'pos')
+        # Loop on the pos dictionary to extract the x,y,z coordinates of each node
+        for key, value in N.nodes(data=True):
+            coord = value['pos']
+            # Scatter plot
+            axs.scatter(coord[0],coord[1],coord[2],c=value['color'],alpha=value['alpha'],
+                       marker=nodemarker,s=nodesize)
+
+        # Loop on the list of edges to get the x,y,z, coordinates of the connected nodes
+        # Those two points are the extrema of the line to be plotted
+        for i,edge in enumerate(N.edges(data=True)):
+            x = np.array((pos[edge[0]][0], pos[edge[1]][0]))
+            y = np.array((pos[edge[0]][1], pos[edge[1]][1]))
+            z = np.array((pos[edge[0]][2], pos[edge[1]][2]))
+
+            # Plot the connecting lines
+            axs.plot(x,y,z,c=edge[2]['color'],alpha=edgealpha,linewidth=linewidth)
+
+        # 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 +48,49 @@ 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 = ret_dict["nx_graph"]
+    # Change set nodes to blue
+    nx.set_node_attributes(N,{k:{'color':'b'} for k in ret_dict["setnodes"]})
+    GraphLocal.draw_nx(N,ret_dict['ax'])
+
+    # Change set edges to green
+    nx.set_edge_attributes(N,{k:{'color':'g'} for k in ret_dict["setedges"]})
+    GraphLocal.draw_nx(N,ret_dict['ax'])
+
+    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)])
+
+    N = ret_dict["nx_graph"]
+    # Change set nodes to blue
+    nx.set_node_attributes(N,{k:{'color':'b'} for k in ret_dict["setnodes"]})
+    GraphLocal.draw_nx_3d(N,ret_dict['ax'])
+
+    # Change set edges to green
+    nx.set_edge_attributes(N,{k:{'color':'g'} for k in ret_dict["setedges"]})
+    GraphLocal.draw_nx_3d(N,ret_dict['ax'])
+
+
 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,