def plot():
fig = plt.figure()
axis = fig.add_subplot(1, 1, 1)
col = PolyCollection(
[], facecolors="none", edgecolors="red", linestyle="--", linewidth=1.2
)
col.set_zorder(1)
axis.add_collection(col)
axis.set_xlim(0.5, 2.5)
axis.set_ylim(0.5, 2.5)
axis.collections[0].set_verts([[[1, 1], [1, 2], [2, 2], [2, 1]]])
return fig
def _init_axis_2d(axis, info):
axis.set_xlabel(info['labels'][0])
axis.set_ylabel(info['labels'][1])
if 'xlim' in info and info['xlim'] is not None:
axis.set_xlim(info['xlim'][0], info['xlim'][1])
if 'ylim' in info and info['ylim'] is not None:
axis.set_ylim(info['ylim'][0], info['ylim'][1])
if 'lines' in info:
for line in info['lines']:
axis.plot([], [], zorder=0, **line)[0]
if 'surfaces' in info:
for surf in info['surfaces']:
col = PolyCollection([], **surf)
col.set_zorder(1)
axis.add_collection(col)
def plot():
fig = plt.figure()
axis = fig.add_subplot(1, 1, 1)
col = PolyCollection([],
facecolors="none",
edgecolors="red",
linestyle="--",
linewidth=1.2)
col.set_zorder(1)
axis.add_collection(col)
axis.set_xlim(0.5, 2.5)
axis.set_ylim(0.5, 2.5)
axis.collections[0].set_verts([[[1, 1], [1, 2], [2, 2], [2, 1]]])
return fig
def draw_nx_tapered_edges(G,
pos,
edgelist=None,
width=0.5,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
label=None,
highlight=None,
tapered=False,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.github.io/documentation/latest/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if highlight is not None and (isinstance(edge_color, basestring)
or not cb.iterable(edge_color)):
idMap = {}
nodes = G.nodes()
for i in range(len(nodes)):
idMap[nodes[i]] = i
ecol = [edge_color] * len(edgelist)
eHighlight = [
highlight[idMap[edge[0]]] or highlight[idMap[edge[1]]]
for edge in edgelist
]
for i in range(len(eHighlight)):
if eHighlight[i]:
ecol[i] = '0.0'
edge_color = ecol
# set edge positions
if not cb.iterable(width):
lw = np.full(len(edgelist), width)
else:
lw = width
edge_pos = []
wdScale = 0.01
for i in range(len(edgelist)):
e = edgelist[i]
w = wdScale * lw[i] / 2
p0 = pos[e[0]]
p1 = pos[e[1]]
dx = p1[0] - p0[0]
dy = p1[1] - p0[1]
l = math.sqrt(dx * dx + dy * dy)
edge_pos.append(
((p0[0] + w * dy / l, p0[1] - w * dx / l),
(p0[0] - w * dy / l, p0[1] + w * dx / l), (p1[0], p1[1])))
edge_vertices = np.asarray(edge_pos)
if not isinstance(edge_color, basestring) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_vertices):
if np.alltrue([isinstance(c, basestring) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif np.alltrue([not isinstance(c, basestring) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if isinstance(edge_color, basestring) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
if tapered:
edge_collection = PolyCollection(
edge_vertices,
facecolors=edge_colors,
linewidths=0,
antialiaseds=(1, ),
transOffset=ax.transData,
)
else:
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
# update view
minx = np.amin(np.ravel(edge_vertices[:, :, 0]))
maxx = np.amax(np.ravel(edge_vertices[:, :, 0]))
miny = np.amin(np.ravel(edge_vertices[:, :, 1]))
maxy = np.amax(np.ravel(edge_vertices[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
return edge_collection
