def setup_node_size(nodes, node_size, node_size_range):
if node_size is None:
node_size = node_size_range[0]
if node_size in nodes.keys() and node_size_range is not None:
nodes['clamped_size'] = utility.clamp_values(nodes[node_size], node_size_range)
node_size = 'clamped_size'
return node_size
def get_positioned_nodes_as_dict(G, layout, node_size, node_size_range):
nodes = get_positioned_nodes(G, layout)
if node_size in nodes.keys() and node_size_range is not None:
nodes['clamped_size'] = clamp_values(nodes[node_size], node_size_range)
node_size = 'clamped_size'
label_y_offset = 'y_offset' if node_size in nodes.keys() else node_size + 8
if label_y_offset == 'y_offset':
nodes['y_offset'] = [ y + r for (y, r) in zip(nodes['y'], [ r / 2.0 + 8 for r in nodes[node_size] ]) ]
nodes = { k: list(nodes[k]) for k in nodes}
return nodes
def plot( # pylint: disable=W0102
network,
layout,
scale=1.0,
threshold=0.0,
node_description=None,
node_size=5,
node_size_range=[20, 40],
weight_scale=5.0,
normalize_weights=True,
node_opts=None,
line_opts=None,
text_opts=None,
element_id='nx_id3',
figsize=(900, 900),
tools=None,
palette=DFLT_PALETTE,
**figkwargs):
if threshold > 0:
network = get_sub_network(network, threshold)
edges = networkx_utility.get_positioned_edges(network, layout)
if normalize_weights and 'weight' in edges.keys():
max_weight = max(edges['weight'])
edges['weight'] = [float(x) / max_weight for x in edges['weight']]
if weight_scale != 1.0 and 'weight' in edges.keys():
edges['weight'] = [weight_scale * float(x) for x in edges['weight']]
edges = dict(source=u, target=v, xs=xs, ys=ys, weights=weights)
nodes = networkx_utility.get_positioned_nodes(network, layout)
#node_size = setup_node_size(nodes, node_size, node_size_range)
if node_size in nodes.keys() and node_size_range is not None:
nodes['clamped_size'] = utility.clamp_values(nodes[node_size],
node_size_range)
node_size = 'clamped_size'
label_y_offset = 'y_offset' if node_size in nodes.keys() else node_size + 8
if label_y_offset == 'y_offset':
nodes['y_offset'] = [
y + r
for (y,
r) in zip(nodes['y'], [r / 2.0 + 8 for r in nodes[node_size]])
]
edges = {k: list(edges[k]) for k in edges}
nodes = {k: list(nodes[k]) for k in nodes}
edges_source = bokeh.models.ColumnDataSource(edges)
nodes_source = bokeh.models.ColumnDataSource(nodes)
node_opts = utility.extend(DFLT_NODE_OPTS, node_opts or {})
line_opts = utility.extend(DFLT_EDGE_OPTS, line_opts or {})
p = figure(plot_width=figsize[0],
plot_height=figsize[1],
tools=tools or TOOLS,
**figkwargs)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
_ = p.multi_line('xs',
'ys',
line_width='weights',
source=edges_source,
**line_opts)
r_nodes = p.circle('x',
'y',
size=node_size,
source=nodes_source,
**node_opts)
if 'fill_color' in nodes.keys():
r_nodes.glyph.fill_color = 'fill_color'
if node_description is not None:
text_source = ColumnDataSource(
dict(text_id=node_description.index, text=node_description))
p.add_tools(
bokeh.models.HoverTool(
renderers=[r_nodes],
tooltips=None,
callback=widgets_config.glyph_hover_callback(
nodes_source,
'node_id',
text_source,
element_id=element_id)))
label_opts = utility.extend(
DFLT_TEXT_OPTS,
dict(y_offset=label_y_offset,
text_color='black',
text_baseline='bottom'), text_opts or {})
p.add_layout(bokeh.models.LabelSet(source=nodes_source, **label_opts))
return p