def draw(G, pos=None, **kwargs):
"""
Draw the graph G using hvPlot.
Draw the graph with hvPlot with options for node positions,
labeling, titles, and many other drawing features.
Parameters
----------
G : graph
A networkx graph
pos : dictionary, optional
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See :py:mod:`networkx.drawing.layout` for functions that
compute node positions.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowhead_length : float, optional (default=0.025)
The length of the arrows as fraction of the overall extent of
the graph
with_labels : bool, optional (default=True)
Set to True to draw labels on the nodes.
nodelist : list, optional (default G.nodes())
Draw only specified nodes
edgelist : list, optional (default=G.edges())
Draw only specified edges
node_size : scalar or array, optional (default=300)
Size of nodes. If an array is specified it must be the
same length as nodelist.
node_color : color string, node attribute, or array of floats, (default='r')
Can be a single color, the name of an attribute on the nodes or
sequence of colors with the same length as nodelist. If the
node_color references an attribute on the nodes or is a list of
values they will be colormapped using the cmap and vmin, vmax
parameters.
node_shape : string, optional (default='o')
The shape of the node. Specification is as valid bokeh marker.
alpha : float, optional (default=1.0)
The node and edge transparency
cmap : Colormap, optional (default=None)
Colormap for mapping intensities of nodes
vmin,vmax : float, optional (default=None)
Minimum and maximum for node colormap scaling
linewidths : [None | scalar | sequence]
Line width of symbol border (default =1.0)
edge_width : float, optional (default=1.0)
Line width of edges
edge_color : color string, or array of floats (default='r')
Can be a single color, the name of an attribute on the edges or
sequence of colors with the same length as the edges. If the
edge_color references an attribute on the edges or is a list of
values they will be colormapped using the edge_cmap and
edge_vmin, edge_vmax parameters.
edge_cmap : Matplotlib colormap, optional (default=None)
Colormap for mapping intensities of edges
edge_vmin,edge_vmax : floats, optional (default=None)
Minimum and maximum for edge colormap scaling
style : string, optional (default='solid')
Edge line style (solid|dashed|dotted,dashdot)
labels : dictionary or string, optional (default=None)
Node labels in a dictionary keyed by node of text labels or
a string referencing a node attribute
font_size : int, optional (default=12)
Font size for text labels
font_color : string, optional (default='black')
Font color string
font_family : string, optional (default='sans-serif')
Font family
label : string, optional
Label for graph legend
selection_policy : string, optional (default='nodes')
Whether to select 'nodes', 'edges' or None on tap and selection
events.
inspection_policy : string, optional (default='nodes')
Whether to select 'nodes', 'edges' or None on tap and selection
events.
geo : boolean, optional (default=False)
Whether to return a GeoViews graph
crs : cartopy.crs.CRS
A cartopy coordinate reference system (enables a geographic plot)
height : int, optional (default=400)
The height of the plot in pixels
width : int, optional (default=400)
The width of the plot in pixels
"""
if pos is None:
pos = nx.drawing.spring_layout
if not isinstance(pos, dict):
pos = pos(G, **kwargs.get('layout_kwargs', {}))
params, label_params = {}, {}
label_element = Labels
if kwargs.get('geo', False) or 'crs' in kwargs:
try:
import geoviews
except ImportError:
raise ImportError('In order to use geo-related features '
'the geoviews library must be available. '
'It can be installed with:\n conda '
'install -c pyviz geoviews')
crs = process_crs(kwargs.get('crs'))
label_element = geoviews.Labels
params['cls'] = geoviews.Graph
params['crs'] = crs
label_params['crs'] = crs
# Construct Graph object
g = _from_networkx(G, pos, **params)
if 'nodelist' in kwargs:
g.nodes.data = g.nodes.data.iloc[list(kwargs['nodelist'])]
if 'edgelist' in kwargs:
edges = g.array([0, 1])
comparisons = []
for edge in kwargs['edgelist']:
comparisons.append(edges == edge)
if len(comparisons):
selector = np.logical_and(*np.logical_or.reduce(comparisons).T)
g = g.iloc[selector]
else:
g = g.iloc[:0]
# Compute options
inspection_policy = kwargs.pop('inspection_policy', 'nodes')
opts = dict(
axiswise=True,
arrowhead_length=kwargs.get('arrowhead_length', 0.025),
directed=kwargs.pop('arrows', isinstance(G, nx.DiGraph)),
colorbar=kwargs.pop('colorbar', False),
padding=kwargs.get('padding', 0.1),
width=kwargs.pop('width', 400),
height=kwargs.pop('height', 400),
selection_policy=kwargs.pop('selection_policy', 'nodes'),
inspection_policy=inspection_policy,
node_fill_color='red')
if '_axis_defaults':
opts.update(xaxis=None, yaxis=None, show_frame=False)
opts.update({k: kwargs.pop(k) for k in list(kwargs) if k in GraphPlot.style_opts})
if 'node_size' in opts:
if isinstance(opts['node_size'], str):
opts['node_size'] = dim(opts['node_size'])
opts['node_size'] = np.sqrt(opts['node_size'])
if 'node_color' in opts:
opts['node_fill_color'] = opts.pop('node_color')
if 'edge_color' in opts:
opts['edge_line_color'] = opts.pop('edge_color')
if 'node_shape' in kwargs:
marker = kwargs.pop('node_shape')
if marker in markers:
marker_opts = markers[marker]
marker = marker_opts['marker']
if 'angle' in marker_opts:
Store.add_style_opts(Graph, ['node_angle'], 'bokeh')
opts['node_angle'] = marker_opts['angle']
opts['node_marker'] = marker
if 'alpha' in kwargs:
alpha = kwargs.pop('alpha')
opts['node_alpha'] = alpha
opts['edge_alpha'] = alpha
if 'linewidths' in kwargs:
opts['node_line_width'] = kwargs.pop('linewidths')
if 'edge_width' in kwargs:
opts['edge_line_width'] = kwargs.pop('edge_width')
if 'style' in kwargs:
opts['edge_line_dash'] = kwargs.pop('style')
node_styles = ('node_fill_color', 'node_size', 'node_alpha', 'node_line_width')
for node_style in node_styles:
if isinstance(opts.get(node_style), (np.ndarray, list, range)):
g = g.clone((g.data, g.nodes.add_dimension(node_style, len(g.nodes.vdims), opts[node_style], True)))
opts[node_style] = node_style
edge_styles = ('edge_line_color', 'edge_line_alpha', 'edge_alpha', 'edge_line_width')
for edge_style in edge_styles:
if isinstance(opts.get(edge_style), (np.ndarray, list, range)):
g = g.add_dimension(edge_style, len(g.vdims), opts[edge_style], True)
opts[edge_style] = edge_style
if opts.get('node_fill_color') in g.nodes.dimensions():
lims = (kwargs.get('vmin', None), kwargs.get('vmax', None))
if lims != (None, None):
dimension = g.nodes.get_dimension(opts.get('node_fill_color'))
dimension.range = lims
if opts.get('edge_line_color') in g.dimensions():
lims = (kwargs.get('edge_vmin', None), kwargs.get('edge_vmax', None))
if lims != (None, None):
dimension = g.get_dimension(opts.get('edge_line_color'))
dimension.range = lims
if inspection_policy == 'nodes':
tooltip_dims = [(d.label, 'index_hover' if d in g.nodes.kdims else d.name)
for d in g.nodes.kdims[2:] + g.nodes.vdims]
else:
tooltip_dims = [(d.label, d.name+'_values' if d in g.kdims else d.name)
for d in g.kdims + g.vdims]
tooltips = [(label, '@{%s}' % dimension_sanitizer(name))
for label, name in tooltip_dims if name not in node_styles + edge_styles]
opts['tools'] = [HoverTool(tooltips=tooltips), 'tap']
g.opts(**opts)
# Construct Labels
if kwargs.get('with_labels', kwargs.get('labels', False)):
label_opts = {k: kwargs.pop(k) for k in list(kwargs) if k in LabelsPlot.style_opts}
if 'xoffset' in kwargs:
label_opts['xoffset'] = kwargs.pop('xoffset')
if 'yoffset' in kwargs:
label_opts['yoffset'] = kwargs.pop('yoffset')
if 'font_size' in kwargs:
label_opts['text_font_size'] = kwargs.pop('font_size')
if 'font_color' in kwargs:
label_opts['text_color'] = kwargs.pop('font_color')
if 'font_family' in kwargs:
label_opts['text_font'] = kwargs.pop('font_family')
labels = kwargs.get('labels', g.nodes.kdims[2])
if isinstance(labels, dict):
values = g.nodes.array(g.nodes.kdims)
data = [(x, y, labels[i]) for (x, y, i) in values if i in labels]
labels = label_element(data, g.nodes.kdims[:2], 'text', **label_params)
else:
labels = label_element(g.nodes, g.nodes.kdims[:2], labels, **label_params)
g = g * labels.opts(**label_opts)
# Apply label
if 'label' in kwargs:
g = g.relabel(kwargs.pop('label'))
return g
