def draw_colocalization(G,
seed_nodes_1,
seed_nodes_2,
edge_cmap=plt.cm.autumn_r,
export_file='colocalization.json',
export_network=False,
highlight_nodes=None,
k=None,
largest_connected_component=False,
node_cmap=plt.cm.autumn_r,
node_size=10,
num_nodes=None,
physics_enabled=False,
Wprime=None,
**kwargs):
'''
Implements and displays the network propagation for a given graph and two
sets of seed nodes. Additional kwargs are passed to visJS_module.
Inputs:
- G: a networkX graph
- seed_nodes_1: first set of nodes on which to initialize the simulation
- seed_nodes_2: second set of nodes on which to initialize the simulation
- edge_cmap: matplotlib colormap for edges, optional, default: matplotlib.cm.autumn_r
- export_file: JSON file to export graph data, default: 'colocalization.json'
- export_network: export network to Cytoscape, default: False
- highlight_nodes: list of nodes to place borders around, default: None
- k: float, optional, optimal distance between nodes for nx.spring_layout(), default: None
- largest_connected_component: boolean, optional, whether or not to display largest_connected_component,
default: False
- node_cmap: matplotlib colormap for nodes, optional, default: matplotlib.cm.autumn_r
- node_size: size of nodes, default: 10
- num_nodes: the number of the hottest nodes to graph, default: None (all nodes will be graphed)
- physics_enabled: enable physics simulation, default: False
- Wprime: Normalized adjacency matrix (from normalized_adj_matrix)
Returns:
- VisJS html network plot (iframe) of the colocalization.
'''
# check for invalid nodes in seed_nodes
invalid_nodes = [(node, 'seed_nodes_1') for node in seed_nodes_1
if node not in G.nodes()]
invalid_nodes.extend([(node, 'seed_nodes_2') for node in seed_nodes_2
if node not in G.nodes()])
for node in invalid_nodes:
print('Node {} in {} not in graph'.format(node[0], node[1]))
if invalid_nodes:
return
# perform the colocalization
if Wprime is None:
Wprime = normalized_adj_matrix(G)
prop_graph_1 = network_propagation(G, Wprime, seed_nodes_1).to_dict()
prop_graph_2 = network_propagation(G, Wprime, seed_nodes_2).to_dict()
prop_graph = {
node: (prop_graph_1[node] * prop_graph_2[node])
for node in prop_graph_1
}
nx.set_node_attributes(G, name='node_heat', values=prop_graph)
# find top num_nodes hottest nodes and connected component if requested
G = set_num_nodes(G, num_nodes)
if largest_connected_component:
G = max(nx.connected_component_subgraphs(G), key=len)
nodes = list(G.nodes())
edges = list(G.edges())
# check for empty nodes and edges after getting subgraph of G
if not nodes:
print('There are no nodes in the graph. Try increasing num_nodes.')
return
if not edges:
print('There are no edges in the graph. Try increasing num_nodes.')
return
# set position of each node
if k is None:
pos = nx.spring_layout(G)
else:
pos = nx.spring_layout(G, k=k)
xpos, ypos = zip(*pos.values())
nx.set_node_attributes(G,
name='xpos',
values=dict(
zip(pos.keys(), [x * 1000 for x in xpos])))
nx.set_node_attributes(G,
name='ypos',
values=dict(
zip(pos.keys(), [y * 1000 for y in ypos])))
# set the border width of nodes
if 'node_border_width' not in kwargs.keys():
kwargs['node_border_width'] = 2
border_width = {}
for n in nodes:
if n in seed_nodes_1 or n in seed_nodes_2:
border_width[n] = kwargs['node_border_width']
elif highlight_nodes is not None and n in highlight_nodes:
border_width[n] = kwargs['node_border_width']
else:
border_width[n] = 0
nx.set_node_attributes(G, name='nodeOutline', values=border_width)
# set the shape of each node
nodes_shape = []
for node in G.nodes():
if node in seed_nodes_1:
nodes_shape.append('triangle')
elif node in seed_nodes_2:
nodes_shape.append('square')
else:
nodes_shape.append('dot')
node_to_shape = dict(zip(G.nodes(), nodes_shape))
nx.set_node_attributes(G, name='nodeShape', values=node_to_shape)
# add a field for node labels
if highlight_nodes:
node_labels = {}
for node in nodes:
if node in seed_nodes_1 or n in seed_nodes_2:
node_labels[node] = str(node)
elif node in highlight_nodes:
node_labels[node] = str(node)
else:
node_labels[node] = ''
else:
node_labels = {n: str(n) for n in nodes}
nx.set_node_attributes(G, name='nodeLabel', values=node_labels)
# set the title of each node
node_titles = [
str(node[0]) + '<br/>heat = ' + str(round(node[1]['node_heat'], 10))
for node in G.nodes(data=True)
]
node_titles = dict(zip(nodes, node_titles))
nx.set_node_attributes(G, name='nodeTitle', values=node_titles)
# set the color of each node
node_to_color = visJS_module.return_node_to_color(
G,
field_to_map='node_heat',
cmap=node_cmap,
color_vals_transform='log')
# set heat value of edge based off hottest connecting node's value
node_attr = nx.get_node_attributes(G, 'node_heat')
edge_weights = {}
for e in edges:
if node_attr[e[0]] > node_attr[e[1]]:
edge_weights[e] = node_attr[e[0]]
else:
edge_weights[e] = node_attr[e[1]]
nx.set_edge_attributes(G, name='edge_weight', values=edge_weights)
# set the color of each edge
edge_to_color = visJS_module.return_edge_to_color(
G,
field_to_map='edge_weight',
cmap=edge_cmap,
color_vals_transform='log')
# create the nodes_dict with all relevant fields
nodes_dict = [{
'id': str(n),
'border_width': border_width[n],
'degree': G.degree(n),
'color': node_to_color[n],
'node_label': node_labels[n],
'node_size': node_size,
'node_shape': node_to_shape[n],
'title': node_titles[n],
'x': np.float64(pos[n][0]).item() * 1000,
'y': np.float64(pos[n][1]).item() * 1000
} for n in nodes]
# map nodes to indices for source/target in edges
node_map = dict(zip(nodes, range(len(nodes))))
# create the edges_dict with all relevant fields
edges_dict = [{
'source': node_map[edges[i][0]],
'target': node_map[edges[i][1]],
'color': edge_to_color[edges[i]]
} for i in range(len(edges))]
# set node_size_multiplier to increase node size as graph gets smaller
if 'node_size_multiplier' not in kwargs.keys():
if len(nodes) > 500:
kwargs['node_size_multiplier'] = 1
elif len(nodes) > 200:
kwargs['node_size_multiplier'] = 3
else:
kwargs['node_size_multiplier'] = 5
kwargs['physics_enabled'] = physics_enabled
# if node hovering color not set, set default to black
if 'node_color_hover_background' not in kwargs.keys():
kwargs['node_color_hover_background'] = 'black'
# node size determined by size in nodes_dict, not by id
if 'node_size_field' not in kwargs.keys():
kwargs['node_size_field'] = 'node_size'
# node label determined by value in nodes_dict
if 'node_label_field' not in kwargs.keys():
kwargs['node_label_field'] = 'node_label'
# export the network to JSON for Cytoscape
if export_network:
node_colors = map_node_to_color(G, 'node_heat', True)
nx.set_node_attributes(G, name='nodeColor', values=node_colors)
edge_colors = map_edge_to_color(G, 'edge_weight', True)
nx.set_edge_attributes(G, name='edgeColor', values=edge_colors)
visJS_module.export_to_cytoscape(G=G, export_file=export_file)
return visJS_module.visjs_network(nodes_dict, edges_dict, **kwargs)
def draw_graph_overlap(G1,
G2,
edge_cmap=plt.cm.coolwarm,
export_file='graph_overlap.json',
export_network=False,
highlight_nodes=None,
k=None,
node_cmap=plt.cm.autumn,
node_name_1='graph 1',
node_name_2='graph 2',
node_size=10,
physics_enabled=False,
**kwargs):
'''
Takes two networkX graphs and displays their overlap, where intersecting
nodes are triangles. Additional kwargs are passed to visjs_module.
Inputs:
- G1: a networkX graph
- G2: a networkX graph
- edge_cmap: matplotlib colormap for edges, default: matplotlib.cm.coolwarm
- export_file: JSON file to export graph data, default: 'graph_overlap.json'
- export_network: export network to Cytoscape, default: False
- highlight_nodes: list of nodes to place borders around, default: None
- k: float, optimal distance between nodes for nx.spring_layout(), default: None
- node_cmap: matplotlib colormap for nodes, default: matplotlib.cm.autumn
- node_name_1: string to name first graph's nodes, default: 'graph 1'
- node_name_2: string to name second graph's nodes, default: 'graph 2'
- node_size: size of nodes, default: 10
- physics_enabled: enable physics simulation, default: False
Returns:
- VisJS html network plot (iframe) of the graph overlap.
'''
G_overlap = create_graph_overlap(G1, G2, node_name_1, node_name_2)
# create nodes dict and edges dict for input to visjs
nodes = list(G_overlap.nodes())
edges = list(G_overlap.edges())
# set the position of each node
if k is None:
pos = nx.spring_layout(G_overlap)
else:
pos = nx.spring_layout(G_overlap, k=k)
xpos, ypos = zip(*pos.values())
nx.set_node_attributes(G_overlap,
name='xpos',
values=dict(
zip(pos.keys(), [x * 1000 for x in xpos])))
nx.set_node_attributes(G_overlap,
name='ypos',
values=dict(
zip(pos.keys(), [y * 1000 for y in ypos])))
# set the border width of nodes
if 'node_border_width' not in kwargs.keys():
kwargs['node_border_width'] = 2
border_width = {}
for n in nodes:
if highlight_nodes is not None and n in highlight_nodes:
border_width[n] = kwargs['node_border_width']
else:
border_width[n] = 0
nx.set_node_attributes(G_overlap, name='nodeOutline', values=border_width)
# set the shape of each node
nodes_shape = []
for node in G_overlap.nodes(data=True):
if node[1]['node_overlap'] == 0:
nodes_shape.append('dot')
elif node[1]['node_overlap'] == 2:
nodes_shape.append('square')
elif node[1]['node_overlap'] == 1:
nodes_shape.append('triangle')
node_to_shape = dict(zip(G_overlap.nodes(), nodes_shape))
nx.set_node_attributes(G_overlap, name='nodeShape', values=node_to_shape)
# set the node label of each node
if highlight_nodes:
node_labels = {}
for node in nodes:
if node in highlight_nodes:
node_labels[node] = str(node)
else:
node_labels[node] = ''
else:
node_labels = {n: str(n) for n in nodes}
nx.set_node_attributes(G_overlap, name='nodeLabel', values=node_labels)
# set the node title of each node
node_titles = [
node[1]['node_name_membership'] + '<br/>' + str(node[0])
for node in G_overlap.nodes(data=True)
]
node_titles = dict(zip(G_overlap.nodes(), node_titles))
nx.set_node_attributes(G_overlap, name='nodeTitle', values=node_titles)
# set color of each node
node_to_color = visJS_module.return_node_to_color(
G_overlap,
field_to_map='node_overlap',
cmap=node_cmap,
color_max_frac=.9,
color_min_frac=.1)
# set color of each edge
edge_to_color = visJS_module.return_edge_to_color(
G_overlap, field_to_map='edge_weight', cmap=edge_cmap, alpha=.3)
# create the nodes_dict with all relevant fields
nodes_dict = [{
'id': str(n),
'border_width': border_width[n],
'color': node_to_color[n],
'degree': G_overlap.degree(n),
'node_label': node_labels[n],
'node_shape': node_to_shape[n],
'node_size': node_size,
'title': node_titles[n],
'x': np.float64(pos[n][0]).item() * 1000,
'y': np.float64(pos[n][1]).item() * 1000
} for n in nodes]
# map nodes to indices for source/target in edges
node_map = dict(zip(nodes, range(len(nodes))))
# create the edges_dict with all relevant fields
edges_dict = [{
'source': node_map[edges[i][0]],
'target': node_map[edges[i][1]],
'color': edge_to_color[edges[i]]
} for i in range(len(edges))]
# set node_size_multiplier to increase node size as graph gets smaller
if 'node_size_multiplier' not in kwargs.keys():
if len(nodes) > 500:
kwargs['node_size_multiplier'] = 3
elif len(nodes) > 200:
kwargs['node_size_multiplier'] = 5
else:
kwargs['node_size_multiplier'] = 7
kwargs['physics_enabled'] = physics_enabled
# if node hovering color not set, set default to black
if 'node_color_hover_background' not in kwargs.keys():
kwargs['node_color_hover_background'] = 'black'
# node size determined by size in nodes_dict, not by id
if 'node_size_field' not in kwargs.keys():
kwargs['node_size_field'] = 'node_size'
# node label determined by value in nodes_dict
if 'node_label_field' not in kwargs.keys():
kwargs['node_label_field'] = 'node_label'
# export the network to JSON for Cytoscape
if export_network:
node_colors = map_node_to_color(G_overlap, 'node_overlap', False)
nx.set_node_attributes(G_overlap, name='nodeColor', values=node_colors)
edge_colors = map_edge_to_color(G_overlap, 'edge_weight', False)
nx.set_edge_attributes(G_overlap, name='edgeColor', values=edge_colors)
visJS_module.export_to_cytoscape(G=G_overlap, export_file=export_file)
return visJS_module.visjs_network(nodes_dict, edges_dict, **kwargs)