"""
**********
Matplotlib
**********
Draw networks with matplotlib.
Examples
--------
>>> G = nx.complete_graph(5)
>>> nx.draw(G)
See Also
--------
- :doc:`matplotlib <matplotlib:index>`
- :func:`matplotlib.pyplot.scatter`
- :obj:`matplotlib.patches.FancyArrowPatch`
"""
import collections
import itertools
from numbers import Number
import networkx as nx
from networkx.drawing.layout import (
circular_layout,
forceatlas2_layout,
kamada_kawai_layout,
planar_layout,
random_layout,
shell_layout,
spectral_layout,
spring_layout,
)
__all__ = [
"draw",
"draw_networkx",
"draw_networkx_nodes",
"draw_networkx_edges",
"draw_networkx_labels",
"draw_networkx_edge_labels",
"draw_circular",
"draw_kamada_kawai",
"draw_random",
"draw_spectral",
"draw_spring",
"draw_planar",
"draw_shell",
"draw_forceatlas2",
]
[docs]
def draw(G, pos=None, ax=None, **kwds):
"""Draw the graph G with Matplotlib.
Draw the graph as a simple representation with no node
labels or edge labels and using the full Matplotlib figure area
and no axis labels by default. See draw_networkx() for more
full-featured drawing that allows title, axis labels etc.
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.
ax : Matplotlib Axes object, optional
Draw the graph in specified Matplotlib axes.
kwds : optional keywords
See networkx.draw_networkx() for a description of optional keywords.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> nx.draw(G)
>>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout
See Also
--------
draw_networkx
draw_networkx_nodes
draw_networkx_edges
draw_networkx_labels
draw_networkx_edge_labels
Notes
-----
This function has the same name as pylab.draw and pyplot.draw
so beware when using `from networkx import *`
since you might overwrite the pylab.draw function.
With pyplot use
>>> import matplotlib.pyplot as plt
>>> G = nx.dodecahedral_graph()
>>> nx.draw(G) # networkx draw()
>>> plt.draw() # pyplot draw()
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
"""
import matplotlib.pyplot as plt
if ax is None:
cf = plt.gcf()
else:
cf = ax.get_figure()
cf.set_facecolor("w")
if ax is None:
if cf.axes:
ax = cf.gca()
else:
ax = cf.add_axes((0, 0, 1, 1))
if "with_labels" not in kwds:
kwds["with_labels"] = "labels" in kwds
draw_networkx(G, pos=pos, ax=ax, **kwds)
ax.set_axis_off()
plt.draw_if_interactive()
return
[docs]
def draw_networkx(G, pos=None, arrows=None, with_labels=True, **kwds):
r"""Draw the graph G using Matplotlib.
Draw the graph with Matplotlib with options for node positions,
labeling, titles, and many other drawing features.
See draw() for simple drawing without labels or axes.
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 or None, optional (default=None)
If `None`, directed graphs draw arrowheads with
`~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
via `~matplotlib.collections.LineCollection` for speed.
If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
If `False`, draw edges using LineCollection (linear and fast).
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str (default='-\|>' for directed graphs)
For directed graphs, choose the style of the arrowsheads.
For undirected graphs default to '-'
See `matplotlib.patches.ArrowStyle` for more options.
arrowsize : int or list (default=10)
For directed graphs, choose the size of the arrow head's length and
width. A list of values can be passed in to assign a different size for arrow head's length and width.
See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale`
for more info.
with_labels : bool (default=True)
Set to True to draw labels on the nodes.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
nodelist : list (default=list(G))
Draw only specified nodes
edgelist : list (default=list(G.edges()))
Draw only specified edges
node_size : scalar or array (default=300)
Size of nodes. If an array is specified it must be the
same length as nodelist.
node_color : color or array of colors (default='#1f78b4')
Node color. Can be a single color or a sequence of colors with the same
length as nodelist. Color can be string or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
node_shape : string (default='o')
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8'.
alpha : float or None (default=None)
The node and edge transparency
cmap : Matplotlib colormap, optional
Colormap for mapping intensities of nodes
vmin,vmax : float, optional
Minimum and maximum for node colormap scaling
linewidths : scalar or sequence (default=1.0)
Line width of symbol border
width : float or array of floats (default=1.0)
Line width of edges
edge_color : color or array of colors (default='k')
Edge color. Can be a single color or a sequence of colors with the same
length as edgelist. Color can be string or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
edge_cmap : Matplotlib colormap, optional
Colormap for mapping intensities of edges
edge_vmin,edge_vmax : floats, optional
Minimum and maximum for edge colormap scaling
style : string (default=solid line)
Edge line style e.g.: '-', '--', '-.', ':'
or words like 'solid' or 'dashed'.
(See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
labels : dictionary (default=None)
Node labels in a dictionary of text labels keyed by node
font_size : int (default=12 for nodes, 10 for edges)
Font size for text labels
font_color : color (default='k' black)
Font color string. Color can be string or rgb (or rgba) tuple of
floats from 0-1.
font_weight : string (default='normal')
Font weight
font_family : string (default='sans-serif')
Font family
label : string, optional
Label for graph legend
hide_ticks : bool, optional
Hide ticks of axes. When `True` (the default), ticks and ticklabels
are removed from the axes. To set ticks and tick labels to the pyplot default,
use ``hide_ticks=False``.
kwds : optional keywords
See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and
networkx.draw_networkx_labels() for a description of optional keywords.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> nx.draw(G)
>>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout
>>> import matplotlib.pyplot as plt
>>> limits = plt.axis("off") # turn off axis
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx_nodes
draw_networkx_edges
draw_networkx_labels
draw_networkx_edge_labels
"""
from inspect import signature
import matplotlib.pyplot as plt
# Get all valid keywords by inspecting the signatures of draw_networkx_nodes,
# draw_networkx_edges, draw_networkx_labels
valid_node_kwds = signature(draw_networkx_nodes).parameters.keys()
valid_edge_kwds = signature(draw_networkx_edges).parameters.keys()
valid_label_kwds = signature(draw_networkx_labels).parameters.keys()
# Create a set with all valid keywords across the three functions and
# remove the arguments of this function (draw_networkx)
valid_kwds = (valid_node_kwds | valid_edge_kwds | valid_label_kwds) - {
"G",
"pos",
"arrows",
"with_labels",
}
if any(k not in valid_kwds for k in kwds):
invalid_args = ", ".join([k for k in kwds if k not in valid_kwds])
raise ValueError(f"Received invalid argument(s): {invalid_args}")
node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}
edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}
label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}
if pos is None:
pos = nx.drawing.spring_layout(G) # default to spring layout
draw_networkx_nodes(G, pos, **node_kwds)
draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)
if with_labels:
draw_networkx_labels(G, pos, **label_kwds)
plt.draw_if_interactive()
[docs]
def draw_networkx_nodes(
G,
pos,
nodelist=None,
node_size=300,
node_color="#1f78b4",
node_shape="o",
alpha=None,
cmap=None,
vmin=None,
vmax=None,
ax=None,
linewidths=None,
edgecolors=None,
label=None,
margins=None,
hide_ticks=True,
):
"""Draw the nodes of the graph G.
This draws only the nodes 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.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
nodelist : list (default list(G))
Draw only specified nodes
node_size : scalar or array (default=300)
Size of nodes. If an array it must be the same length as nodelist.
node_color : color or array of colors (default='#1f78b4')
Node color. Can be a single color or a sequence of colors with the same
length as nodelist. Color can be string or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
node_shape : string (default='o')
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8'.
alpha : float or array of floats (default=None)
The node transparency. This can be a single alpha value,
in which case it will be applied to all the nodes of color. Otherwise,
if it is an array, the elements of alpha will be applied to the colors
in order (cycling through alpha multiple times if necessary).
cmap : Matplotlib colormap (default=None)
Colormap for mapping intensities of nodes
vmin,vmax : floats or None (default=None)
Minimum and maximum for node colormap scaling
linewidths : [None | scalar | sequence] (default=1.0)
Line width of symbol border
edgecolors : [None | scalar | sequence] (default = node_color)
Colors of node borders. Can be a single color or a sequence of colors with the
same length as nodelist. Color can be string or rgb (or rgba) tuple of floats
from 0-1. If numeric values are specified they will be mapped to colors
using the cmap and vmin,vmax parameters. See `~matplotlib.pyplot.scatter` for more details.
label : [None | string]
Label for legend
margins : float or 2-tuple, optional
Sets the padding for axis autoscaling. Increase margin to prevent
clipping for nodes that are near the edges of an image. Values should
be in the range ``[0, 1]``. See :meth:`matplotlib.axes.Axes.margins`
for details. The default is `None`, which uses the Matplotlib default.
hide_ticks : bool, optional
Hide ticks of axes. When `True` (the default), ticks and ticklabels
are removed from the axes. To set ticks and tick labels to the pyplot default,
use ``hide_ticks=False``.
Returns
-------
matplotlib.collections.PathCollection
`PathCollection` of the nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx
draw_networkx_edges
draw_networkx_labels
draw_networkx_edge_labels
"""
from collections.abc import Iterable
import matplotlib as mpl
import matplotlib.collections # call as mpl.collections
import matplotlib.pyplot as plt
import numpy as np
if ax is None:
ax = plt.gca()
if nodelist is None:
nodelist = list(G)
if len(nodelist) == 0: # empty nodelist, no drawing
return mpl.collections.PathCollection(None)
try:
xy = np.asarray([pos[v] for v in nodelist])
except KeyError as err:
raise nx.NetworkXError(f"Node {err} has no position.") from err
if isinstance(alpha, Iterable):
node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)
alpha = None
if not isinstance(node_shape, np.ndarray) and not isinstance(node_shape, list):
node_shape = np.array([node_shape for _ in range(len(nodelist))])
for shape in np.unique(node_shape):
node_collection = ax.scatter(
xy[node_shape == shape, 0],
xy[node_shape == shape, 1],
s=node_size,
c=node_color,
marker=shape,
cmap=cmap,
vmin=vmin,
vmax=vmax,
alpha=alpha,
linewidths=linewidths,
edgecolors=edgecolors,
label=label,
)
if hide_ticks:
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
if margins is not None:
if isinstance(margins, Iterable):
ax.margins(*margins)
else:
ax.margins(margins)
node_collection.set_zorder(2)
return node_collection
class FancyArrowFactory:
"""Draw arrows with `matplotlib.patches.FancyarrowPatch`"""
class ConnectionStyleFactory:
def __init__(self, connectionstyles, selfloop_height, ax=None):
import matplotlib as mpl
import matplotlib.path # call as mpl.path
import numpy as np
self.ax = ax
self.mpl = mpl
self.np = np
self.base_connection_styles = [
mpl.patches.ConnectionStyle(cs) for cs in connectionstyles
]
self.n = len(self.base_connection_styles)
self.selfloop_height = selfloop_height
def curved(self, edge_index):
return self.base_connection_styles[edge_index % self.n]
def self_loop(self, edge_index):
def self_loop_connection(posA, posB, *args, **kwargs):
if not self.np.all(posA == posB):
raise nx.NetworkXError(
"`self_loop` connection style method"
"is only to be used for self-loops"
)
# this is called with _screen space_ values
# so convert back to data space
data_loc = self.ax.transData.inverted().transform(posA)
v_shift = 0.1 * self.selfloop_height
h_shift = v_shift * 0.5
# put the top of the loop first so arrow is not hidden by node
path = self.np.asarray(
[
# 1
[0, v_shift],
# 4 4 4
[h_shift, v_shift],
[h_shift, 0],
[0, 0],
# 4 4 4
[-h_shift, 0],
[-h_shift, v_shift],
[0, v_shift],
]
)
# Rotate self loop 90 deg. if more than 1
# This will allow for maximum of 4 visible self loops
if edge_index % 4:
x, y = path.T
for _ in range(edge_index % 4):
x, y = y, -x
path = self.np.array([x, y]).T
return self.mpl.path.Path(
self.ax.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
)
return self_loop_connection
def __init__(
self,
edge_pos,
edgelist,
nodelist,
edge_indices,
node_size,
selfloop_height,
connectionstyle="arc3",
node_shape="o",
arrowstyle="-",
arrowsize=10,
edge_color="k",
alpha=None,
linewidth=1.0,
style="solid",
min_source_margin=0,
min_target_margin=0,
ax=None,
):
import matplotlib as mpl
import matplotlib.patches # call as mpl.patches
import matplotlib.pyplot as plt
import numpy as np
if isinstance(connectionstyle, str):
connectionstyle = [connectionstyle]
elif np.iterable(connectionstyle):
connectionstyle = list(connectionstyle)
else:
msg = "ConnectionStyleFactory arg `connectionstyle` must be str or iterable"
raise nx.NetworkXError(msg)
self.ax = ax
self.mpl = mpl
self.np = np
self.edge_pos = edge_pos
self.edgelist = edgelist
self.nodelist = nodelist
self.node_shape = node_shape
self.min_source_margin = min_source_margin
self.min_target_margin = min_target_margin
self.edge_indices = edge_indices
self.node_size = node_size
self.connectionstyle_factory = self.ConnectionStyleFactory(
connectionstyle, selfloop_height, ax
)
self.arrowstyle = arrowstyle
self.arrowsize = arrowsize
self.arrow_colors = mpl.colors.colorConverter.to_rgba_array(edge_color, alpha)
self.linewidth = linewidth
self.style = style
if isinstance(arrowsize, list) and len(arrowsize) != len(edge_pos):
raise ValueError("arrowsize should have the same length as edgelist")
def __call__(self, i):
(x1, y1), (x2, y2) = self.edge_pos[i]
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if (
self.np.iterable(self.min_source_margin)
and not isinstance(self.min_source_margin, str)
and not isinstance(self.min_source_margin, tuple)
):
min_source_margin = self.min_source_margin[i]
else:
min_source_margin = self.min_source_margin
if (
self.np.iterable(self.min_target_margin)
and not isinstance(self.min_target_margin, str)
and not isinstance(self.min_target_margin, tuple)
):
min_target_margin = self.min_target_margin[i]
else:
min_target_margin = self.min_target_margin
if self.np.iterable(self.node_size): # many node sizes
source, target = self.edgelist[i][:2]
source_node_size = self.node_size[self.nodelist.index(source)]
target_node_size = self.node_size[self.nodelist.index(target)]
shrink_source = self.to_marker_edge(source_node_size, self.node_shape)
shrink_target = self.to_marker_edge(target_node_size, self.node_shape)
else:
shrink_source = self.to_marker_edge(self.node_size, self.node_shape)
shrink_target = shrink_source
shrink_source = max(shrink_source, min_source_margin)
shrink_target = max(shrink_target, min_target_margin)
# scale factor of arrow head
if isinstance(self.arrowsize, list):
mutation_scale = self.arrowsize[i]
else:
mutation_scale = self.arrowsize
if len(self.arrow_colors) > i:
arrow_color = self.arrow_colors[i]
elif len(self.arrow_colors) == 1:
arrow_color = self.arrow_colors[0]
else: # Cycle through colors
arrow_color = self.arrow_colors[i % len(self.arrow_colors)]
if self.np.iterable(self.linewidth):
if len(self.linewidth) > i:
linewidth = self.linewidth[i]
else:
linewidth = self.linewidth[i % len(self.linewidth)]
else:
linewidth = self.linewidth
if (
self.np.iterable(self.style)
and not isinstance(self.style, str)
and not isinstance(self.style, tuple)
):
if len(self.style) > i:
linestyle = self.style[i]
else: # Cycle through styles
linestyle = self.style[i % len(self.style)]
else:
linestyle = self.style
if x1 == x2 and y1 == y2:
connectionstyle = self.connectionstyle_factory.self_loop(
self.edge_indices[i]
)
else:
connectionstyle = self.connectionstyle_factory.curved(self.edge_indices[i])
if (
self.np.iterable(self.arrowstyle)
and not isinstance(self.arrowstyle, str)
and not isinstance(self.arrowstyle, tuple)
):
arrowstyle = self.arrowstyle[i]
else:
arrowstyle = self.arrowstyle
return self.mpl.patches.FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=linewidth,
connectionstyle=connectionstyle,
linestyle=linestyle,
zorder=1, # arrows go behind nodes
)
def to_marker_edge(self, marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return self.np.sqrt(2 * marker_size) / 2
else:
return self.np.sqrt(marker_size) / 2
[docs]
def draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle=None,
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=None,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle="arc3",
min_source_margin=0,
min_target_margin=0,
hide_ticks=True,
):
r"""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 (default=G.edges())
Draw only specified edges
width : float or array of floats (default=1.0)
Line width of edges
edge_color : color or array of colors (default='k')
Edge color. Can be a single color or a sequence of colors with the same
length as edgelist. Color can be string or rgb (or rgba) tuple of
floats from 0-1. If numeric values are specified they will be
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string or array of strings (default='solid')
Edge line style e.g.: '-', '--', '-.', ':'
or words like 'solid' or 'dashed'.
Can be a single style or a sequence of styles with the same
length as the edge list.
If less styles than edges are given the styles will cycle.
If more styles than edges are given the styles will be used sequentially
and not be exhausted.
Also, `(offset, onoffseq)` tuples can be used as style instead of a strings.
(See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
alpha : float or array of floats (default=None)
The edge transparency. This can be a single alpha value,
in which case it will be applied to all specified edges. Otherwise,
if it is an array, the elements of alpha will be applied to the colors
in order (cycling through alpha multiple times if necessary).
edge_cmap : Matplotlib colormap, optional
Colormap for mapping intensities of edges
edge_vmin,edge_vmax : floats, optional
Minimum and maximum for edge colormap scaling
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool or None, optional (default=None)
If `None`, directed graphs draw arrowheads with
`~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
via `~matplotlib.collections.LineCollection` for speed.
If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
If `False`, draw edges using LineCollection (linear and fast).
Note: Arrowheads will be the same color as edges.
arrowstyle : str or list of strs (default='-\|>' for directed graphs)
For directed graphs and `arrows==True` defaults to '-\|>',
For undirected graphs default to '-'.
See `matplotlib.patches.ArrowStyle` for more options.
arrowsize : int or list of ints(default=10)
For directed graphs, choose the size of the arrow head's length and
width. See `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : string or iterable of strings (default="arc3")
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See `matplotlib.patches.ConnectionStyle` and
`matplotlib.patches.FancyArrowPatch` for more info.
If Iterable, index indicates i'th edge key of MultiGraph
node_size : scalar or array (default=300)
Size of nodes. Though the nodes are not drawn with this function, the
node size is used in determining edge positioning.
nodelist : list, optional (default=G.nodes())
This provides the node order for the `node_size` array (if it is an array).
node_shape : string (default='o')
The marker used for nodes, used in determining edge positioning.
Specification is as a `matplotlib.markers` marker, e.g. one of 'so^>v<dph8'.
label : None or string
Label for legend
min_source_margin : int or list of ints (default=0)
The minimum margin (gap) at the beginning of the edge at the source.
min_target_margin : int or list of ints (default=0)
The minimum margin (gap) at the end of the edge at the target.
hide_ticks : bool, optional
Hide ticks of axes. When `True` (the default), ticks and ticklabels
are removed from the axes. To set ticks and tick labels to the pyplot default,
use ``hide_ticks=False``.
Returns
-------
matplotlib.collections.LineCollection or a list of matplotlib.patches.FancyArrowPatch
If ``arrows=True``, a list of FancyArrowPatches is returned.
If ``arrows=False``, a LineCollection is returned.
If ``arrows=None`` (the default), then a LineCollection is returned if
`G` is undirected, otherwise returns a list of FancyArrowPatches.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False or by passing an arrowstyle without
an arrow on the end.
Be sure to include `node_size` as a keyword argument; arrows are
drawn considering the size of nodes.
Self-loops are always drawn with `~matplotlib.patches.FancyArrowPatch`
regardless of the value of `arrows` or whether `G` is directed.
When ``arrows=False`` or ``arrows=None`` and `G` is undirected, the
FancyArrowPatches corresponding to the self-loops are not explicitly
returned. They should instead be accessed via the ``Axes.patches``
attribute (see examples).
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
The FancyArrowPatches corresponding to self-loops are not always
returned, but can always be accessed via the ``patches`` attribute of the
`matplotlib.Axes` object.
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> G = nx.Graph([(0, 1), (0, 0)]) # Self-loop at node 0
>>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax)
>>> self_loop_fap = ax.patches[0]
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx
draw_networkx_nodes
draw_networkx_labels
draw_networkx_edge_labels
"""
import warnings
import matplotlib as mpl
import matplotlib.collections # call as mpl.collections
import matplotlib.colors # call as mpl.colors
import matplotlib.pyplot as plt
import numpy as np
# The default behavior is to use LineCollection to draw edges for
# undirected graphs (for performance reasons) and use FancyArrowPatches
# for directed graphs.
# The `arrows` keyword can be used to override the default behavior
if arrows is None:
use_linecollection = not (G.is_directed() or G.is_multigraph())
else:
if not isinstance(arrows, bool):
raise TypeError("Argument `arrows` must be of type bool or None")
use_linecollection = not arrows
if isinstance(connectionstyle, str):
connectionstyle = [connectionstyle]
elif np.iterable(connectionstyle):
connectionstyle = list(connectionstyle)
else:
msg = "draw_networkx_edges arg `connectionstyle` must be str or iterable"
raise nx.NetworkXError(msg)
# Some kwargs only apply to FancyArrowPatches. Warn users when they use
# non-default values for these kwargs when LineCollection is being used
# instead of silently ignoring the specified option
if use_linecollection:
msg = (
"\n\nThe {0} keyword argument is not applicable when drawing edges\n"
"with LineCollection.\n\n"
"To make this warning go away, either specify `arrows=True` to\n"
"force FancyArrowPatches or use the default values.\n"
"Note that using FancyArrowPatches may be slow for large graphs.\n"
)
if arrowstyle is not None:
warnings.warn(msg.format("arrowstyle"), category=UserWarning, stacklevel=2)
if arrowsize != 10:
warnings.warn(msg.format("arrowsize"), category=UserWarning, stacklevel=2)
if min_source_margin != 0:
warnings.warn(
msg.format("min_source_margin"), category=UserWarning, stacklevel=2
)
if min_target_margin != 0:
warnings.warn(
msg.format("min_target_margin"), category=UserWarning, stacklevel=2
)
if any(cs != "arc3" for cs in connectionstyle):
warnings.warn(
msg.format("connectionstyle"), category=UserWarning, stacklevel=2
)
# NOTE: Arrowstyle modification must occur after the warnings section
if arrowstyle is None:
arrowstyle = "-|>" if G.is_directed() else "-"
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges) # (u, v, k) for multigraph (u, v) otherwise
if len(edgelist):
if G.is_multigraph():
key_count = collections.defaultdict(lambda: itertools.count(0))
edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
else:
edge_indices = [0] * len(edgelist)
else: # no edges!
return []
if nodelist is None:
nodelist = list(G.nodes())
# FancyArrowPatch handles color=None different from LineCollection
if edge_color is None:
edge_color = "k"
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
# Check if edge_color is an array of floats and map to edge_cmap.
# This is the only case handled differently from matplotlib
if (
np.iterable(edge_color)
and (len(edge_color) == len(edge_pos))
and np.all([isinstance(c, Number) for c in edge_color])
):
if edge_cmap is not None:
assert isinstance(edge_cmap, mpl.colors.Colormap)
else:
edge_cmap = plt.get_cmap()
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = mpl.colors.Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
# compute initial view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
# Self-loops are scaled by view extent, except in cases the extent
# is 0, e.g. for a single node. In this case, fall back to scaling
# by the maximum node size
selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
fancy_arrow_factory = FancyArrowFactory(
edge_pos,
edgelist,
nodelist,
edge_indices,
node_size,
selfloop_height,
connectionstyle,
node_shape,
arrowstyle,
arrowsize,
edge_color,
alpha,
width,
style,
min_source_margin,
min_target_margin,
ax=ax,
)
# Draw the edges
if use_linecollection:
edge_collection = mpl.collections.LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
alpha=alpha,
)
edge_collection.set_cmap(edge_cmap)
edge_collection.set_clim(edge_vmin, edge_vmax)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
edge_viz_obj = edge_collection
# Make sure selfloop edges are also drawn
# ---------------------------------------
selfloops_to_draw = [loop for loop in nx.selfloop_edges(G) if loop in edgelist]
if selfloops_to_draw:
edgelist_tuple = list(map(tuple, edgelist))
arrow_collection = []
for loop in selfloops_to_draw:
i = edgelist_tuple.index(loop)
arrow = fancy_arrow_factory(i)
arrow_collection.append(arrow)
ax.add_patch(arrow)
else:
edge_viz_obj = []
for i in range(len(edgelist)):
arrow = fancy_arrow_factory(i)
ax.add_patch(arrow)
edge_viz_obj.append(arrow)
# update view after drawing
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
if hide_ticks:
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return edge_viz_obj
[docs]
def draw_networkx_labels(
G,
pos,
labels=None,
font_size=12,
font_color="k",
font_family="sans-serif",
font_weight="normal",
alpha=None,
bbox=None,
horizontalalignment="center",
verticalalignment="center",
ax=None,
clip_on=True,
hide_ticks=True,
):
"""Draw node labels on 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.
labels : dictionary (default={n: n for n in G})
Node labels in a dictionary of text labels keyed by node.
Node-keys in labels should appear as keys in `pos`.
If needed use: `{n:lab for n,lab in labels.items() if n in pos}`
font_size : int or dictionary of nodes to ints (default=12)
Font size for text labels.
font_color : color or dictionary of nodes to colors (default='k' black)
Font color string. Color can be string or rgb (or rgba) tuple of
floats from 0-1.
font_weight : string or dictionary of nodes to strings (default='normal')
Font weight.
font_family : string or dictionary of nodes to strings (default='sans-serif')
Font family.
alpha : float or None or dictionary of nodes to floats (default=None)
The text transparency.
bbox : Matplotlib bbox, (default is Matplotlib's ax.text default)
Specify text box properties (e.g. shape, color etc.) for node labels.
horizontalalignment : string or array of strings (default='center')
Horizontal alignment {'center', 'right', 'left'}. If an array is
specified it must be the same length as `nodelist`.
verticalalignment : string (default='center')
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}.
If an array is specified it must be the same length as `nodelist`.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
clip_on : bool (default=True)
Turn on clipping of node labels at axis boundaries
hide_ticks : bool, optional
Hide ticks of axes. When `True` (the default), ticks and ticklabels
are removed from the axes. To set ticks and tick labels to the pyplot default,
use ``hide_ticks=False``.
Returns
-------
dict
`dict` of labels keyed on the nodes
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx
draw_networkx_nodes
draw_networkx_edges
draw_networkx_edge_labels
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
if labels is None:
labels = {n: n for n in G.nodes()}
individual_params = set()
def check_individual_params(p_value, p_name):
if isinstance(p_value, dict):
if len(p_value) != len(labels):
raise ValueError(f"{p_name} must have the same length as labels.")
individual_params.add(p_name)
def get_param_value(node, p_value, p_name):
if p_name in individual_params:
return p_value[node]
return p_value
check_individual_params(font_size, "font_size")
check_individual_params(font_color, "font_color")
check_individual_params(font_weight, "font_weight")
check_individual_params(font_family, "font_family")
check_individual_params(alpha, "alpha")
text_items = {} # there is no text collection so we'll fake one
for n, label in labels.items():
(x, y) = pos[n]
if not isinstance(label, str):
label = str(label) # this makes "1" and 1 labeled the same
t = ax.text(
x,
y,
label,
size=get_param_value(n, font_size, "font_size"),
color=get_param_value(n, font_color, "font_color"),
family=get_param_value(n, font_family, "font_family"),
weight=get_param_value(n, font_weight, "font_weight"),
alpha=get_param_value(n, alpha, "alpha"),
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
transform=ax.transData,
bbox=bbox,
clip_on=clip_on,
)
text_items[n] = t
if hide_ticks:
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return text_items
[docs]
def draw_networkx_edge_labels(
G,
pos,
edge_labels=None,
label_pos=0.5,
font_size=10,
font_color="k",
font_family="sans-serif",
font_weight="normal",
alpha=None,
bbox=None,
horizontalalignment="center",
verticalalignment="center",
ax=None,
rotate=True,
clip_on=True,
node_size=300,
nodelist=None,
connectionstyle="arc3",
hide_ticks=True,
):
"""Draw edge labels.
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.
edge_labels : dictionary (default=None)
Edge labels in a dictionary of labels keyed by edge two-tuple.
Only labels for the keys in the dictionary are drawn.
label_pos : float (default=0.5)
Position of edge label along edge (0=head, 0.5=center, 1=tail)
font_size : int (default=10)
Font size for text labels
font_color : color (default='k' black)
Font color string. Color can be string or rgb (or rgba) tuple of
floats from 0-1.
font_weight : string (default='normal')
Font weight
font_family : string (default='sans-serif')
Font family
alpha : float or None (default=None)
The text transparency
bbox : Matplotlib bbox, optional
Specify text box properties (e.g. shape, color etc.) for edge labels.
Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}.
horizontalalignment : string (default='center')
Horizontal alignment {'center', 'right', 'left'}
verticalalignment : string (default='center')
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
rotate : bool (default=True)
Rotate edge labels to lie parallel to edges
clip_on : bool (default=True)
Turn on clipping of edge labels at axis boundaries
node_size : scalar or array (default=300)
Size of nodes. If an array it must be the same length as nodelist.
nodelist : list, optional (default=G.nodes())
This provides the node order for the `node_size` array (if it is an array).
connectionstyle : string or iterable of strings (default="arc3")
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See `matplotlib.patches.ConnectionStyle` and
`matplotlib.patches.FancyArrowPatch` for more info.
If Iterable, index indicates i'th edge key of MultiGraph
hide_ticks : bool, optional
Hide ticks of axes. When `True` (the default), ticks and ticklabels
are removed from the axes. To set ticks and tick labels to the pyplot default,
use ``hide_ticks=False``.
Returns
-------
dict
`dict` of labels keyed by edge
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
https://networkx.org/documentation/latest/auto_examples/index.html
See Also
--------
draw
draw_networkx
draw_networkx_nodes
draw_networkx_edges
draw_networkx_labels
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
class CurvedArrowText(mpl.text.Text):
def __init__(
self,
arrow,
*args,
label_pos=0.5,
labels_horizontal=False,
ax=None,
**kwargs,
):
# Bind to FancyArrowPatch
self.arrow = arrow
# how far along the text should be on the curve,
# 0 is at start, 1 is at end etc.
self.label_pos = label_pos
self.labels_horizontal = labels_horizontal
if ax is None:
ax = plt.gca()
self.ax = ax
self.x, self.y, self.angle = self._update_text_pos_angle(arrow)
# Create text object
super().__init__(self.x, self.y, *args, rotation=self.angle, **kwargs)
# Bind to axis
self.ax.add_artist(self)
def _get_arrow_path_disp(self, arrow):
"""
This is part of FancyArrowPatch._get_path_in_displaycoord
It omits the second part of the method where path is converted
to polygon based on width
The transform is taken from ax, not the object, as the object
has not been added yet, and doesn't have transform
"""
dpi_cor = arrow._dpi_cor
# trans_data = arrow.get_transform()
trans_data = self.ax.transData
if arrow._posA_posB is not None:
posA = arrow._convert_xy_units(arrow._posA_posB[0])
posB = arrow._convert_xy_units(arrow._posA_posB[1])
(posA, posB) = trans_data.transform((posA, posB))
_path = arrow.get_connectionstyle()(
posA,
posB,
patchA=arrow.patchA,
patchB=arrow.patchB,
shrinkA=arrow.shrinkA * dpi_cor,
shrinkB=arrow.shrinkB * dpi_cor,
)
else:
_path = trans_data.transform_path(arrow._path_original)
# Return is in display coordinates
return _path
def _update_text_pos_angle(self, arrow):
# Fractional label position
path_disp = self._get_arrow_path_disp(arrow)
(x1, y1), (cx, cy), (x2, y2) = path_disp.vertices
# Text position at a proportion t along the line in display coords
# default is 0.5 so text appears at the halfway point
t = self.label_pos
tt = 1 - t
x = tt**2 * x1 + 2 * t * tt * cx + t**2 * x2
y = tt**2 * y1 + 2 * t * tt * cy + t**2 * y2
if self.labels_horizontal:
# Horizontal text labels
angle = 0
else:
# Labels parallel to curve
change_x = 2 * tt * (cx - x1) + 2 * t * (x2 - cx)
change_y = 2 * tt * (cy - y1) + 2 * t * (y2 - cy)
angle = (np.arctan2(change_y, change_x) / (2 * np.pi)) * 360
# Text is "right way up"
if angle > 90:
angle -= 180
if angle < -90:
angle += 180
(x, y) = self.ax.transData.inverted().transform((x, y))
return x, y, angle
def draw(self, renderer):
# recalculate the text position and angle
self.x, self.y, self.angle = self._update_text_pos_angle(self.arrow)
self.set_position((self.x, self.y))
self.set_rotation(self.angle)
# redraw text
super().draw(renderer)
# use default box of white with white border
if bbox is None:
bbox = {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
if isinstance(connectionstyle, str):
connectionstyle = [connectionstyle]
elif np.iterable(connectionstyle):
connectionstyle = list(connectionstyle)
else:
raise nx.NetworkXError(
"draw_networkx_edges arg `connectionstyle` must be"
"string or iterable of strings"
)
if ax is None:
ax = plt.gca()
if edge_labels is None:
kwds = {"keys": True} if G.is_multigraph() else {}
edge_labels = {tuple(edge): d for *edge, d in G.edges(data=True, **kwds)}
# NOTHING TO PLOT
if not edge_labels:
return {}
edgelist, labels = zip(*edge_labels.items())
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if G.is_multigraph():
key_count = collections.defaultdict(lambda: itertools.count(0))
edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
else:
edge_indices = [0] * len(edgelist)
# Used to determine self loop mid-point
# Note, that this will not be accurate,
# if not drawing edge_labels for all edges drawn
h = 0
if edge_labels:
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
h = maxy - miny
selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
fancy_arrow_factory = FancyArrowFactory(
edge_pos,
edgelist,
nodelist,
edge_indices,
node_size,
selfloop_height,
connectionstyle,
ax=ax,
)
individual_params = {}
def check_individual_params(p_value, p_name):
# TODO should this be list or array (as in a numpy array)?
if isinstance(p_value, list):
if len(p_value) != len(edgelist):
raise ValueError(f"{p_name} must have the same length as edgelist.")
individual_params[p_name] = p_value.iter()
# Don't need to pass in an edge because these are lists, not dicts
def get_param_value(p_value, p_name):
if p_name in individual_params:
return next(individual_params[p_name])
return p_value
check_individual_params(font_size, "font_size")
check_individual_params(font_color, "font_color")
check_individual_params(font_weight, "font_weight")
check_individual_params(alpha, "alpha")
check_individual_params(horizontalalignment, "horizontalalignment")
check_individual_params(verticalalignment, "verticalalignment")
check_individual_params(rotate, "rotate")
check_individual_params(label_pos, "label_pos")
text_items = {}
for i, (edge, label) in enumerate(zip(edgelist, labels)):
if not isinstance(label, str):
label = str(label) # this makes "1" and 1 labeled the same
n1, n2 = edge[:2]
arrow = fancy_arrow_factory(i)
if n1 == n2:
connectionstyle_obj = arrow.get_connectionstyle()
posA = ax.transData.transform(pos[n1])
path_disp = connectionstyle_obj(posA, posA)
path_data = ax.transData.inverted().transform_path(path_disp)
x, y = path_data.vertices[0]
text_items[edge] = ax.text(
x,
y,
label,
size=get_param_value(font_size, "font_size"),
color=get_param_value(font_color, "font_color"),
family=get_param_value(font_family, "font_family"),
weight=get_param_value(font_weight, "font_weight"),
alpha=get_param_value(alpha, "alpha"),
horizontalalignment=get_param_value(
horizontalalignment, "horizontalalignment"
),
verticalalignment=get_param_value(
verticalalignment, "verticalalignment"
),
rotation=0,
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=clip_on,
)
else:
text_items[edge] = CurvedArrowText(
arrow,
label,
size=get_param_value(font_size, "font_size"),
color=get_param_value(font_color, "font_color"),
family=get_param_value(font_family, "font_family"),
weight=get_param_value(font_weight, "font_weight"),
alpha=get_param_value(alpha, "alpha"),
horizontalalignment=get_param_value(
horizontalalignment, "horizontalalignment"
),
verticalalignment=get_param_value(
verticalalignment, "verticalalignment"
),
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=clip_on,
label_pos=get_param_value(label_pos, "label_pos"),
labels_horizontal=not get_param_value(rotate, "rotate"),
ax=ax,
)
if hide_ticks:
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return text_items
[docs]
def draw_circular(G, **kwargs):
"""Draw the graph `G` with a circular layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.circular_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
The layout is computed each time this function is called. For
repeated drawing it is much more efficient to call
`~networkx.drawing.layout.circular_layout` directly and reuse the result::
>>> G = nx.complete_graph(5)
>>> pos = nx.circular_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(5)
>>> nx.draw_circular(G)
See Also
--------
:func:`~networkx.drawing.layout.circular_layout`
"""
draw(G, circular_layout(G), **kwargs)
[docs]
def draw_kamada_kawai(G, **kwargs):
"""Draw the graph `G` with a Kamada-Kawai force-directed layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.kamada_kawai_layout` directly and reuse the
result::
>>> G = nx.complete_graph(5)
>>> pos = nx.kamada_kawai_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(5)
>>> nx.draw_kamada_kawai(G)
See Also
--------
:func:`~networkx.drawing.layout.kamada_kawai_layout`
"""
draw(G, kamada_kawai_layout(G), **kwargs)
[docs]
def draw_random(G, **kwargs):
"""Draw the graph `G` with a random layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.random_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.random_layout` directly and reuse the result::
>>> G = nx.complete_graph(5)
>>> pos = nx.random_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.lollipop_graph(4, 3)
>>> nx.draw_random(G)
See Also
--------
:func:`~networkx.drawing.layout.random_layout`
"""
draw(G, random_layout(G), **kwargs)
[docs]
def draw_spectral(G, **kwargs):
"""Draw the graph `G` with a spectral 2D layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.spectral_layout(G), **kwargs)
For more information about how node positions are determined, see
`~networkx.drawing.layout.spectral_layout`.
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.spectral_layout` directly and reuse the result::
>>> G = nx.complete_graph(5)
>>> pos = nx.spectral_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(5)
>>> nx.draw_spectral(G)
See Also
--------
:func:`~networkx.drawing.layout.spectral_layout`
"""
draw(G, spectral_layout(G), **kwargs)
[docs]
def draw_spring(G, **kwargs):
"""Draw the graph `G` with a spring layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.spring_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
`~networkx.drawing.layout.spring_layout` is also the default layout for
`draw`, so this function is equivalent to `draw`.
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.spring_layout` directly and reuse the result::
>>> G = nx.complete_graph(5)
>>> pos = nx.spring_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(20)
>>> nx.draw_spring(G)
See Also
--------
draw
:func:`~networkx.drawing.layout.spring_layout`
"""
draw(G, spring_layout(G), **kwargs)
[docs]
def draw_shell(G, nlist=None, **kwargs):
"""Draw networkx graph `G` with shell layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
Parameters
----------
G : graph
A networkx graph
nlist : list of list of nodes, optional
A list containing lists of nodes representing the shells.
Default is `None`, meaning all nodes are in a single shell.
See `~networkx.drawing.layout.shell_layout` for details.
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Notes
-----
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.shell_layout` directly and reuse the result::
>>> G = nx.complete_graph(5)
>>> pos = nx.shell_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(4)
>>> shells = [[0], [1, 2, 3]]
>>> nx.draw_shell(G, nlist=shells)
See Also
--------
:func:`~networkx.drawing.layout.shell_layout`
"""
draw(G, shell_layout(G, nlist=nlist), **kwargs)
[docs]
def draw_planar(G, **kwargs):
"""Draw a planar networkx graph `G` with planar layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.planar_layout(G), **kwargs)
Parameters
----------
G : graph
A planar networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Raises
------
NetworkXException
When `G` is not planar
Notes
-----
The layout is computed each time this function is called.
For repeated drawing it is much more efficient to call
`~networkx.drawing.layout.planar_layout` directly and reuse the result::
>>> G = nx.path_graph(5)
>>> pos = nx.planar_layout(G)
>>> nx.draw(G, pos=pos) # Draw the original graph
>>> # Draw a subgraph, reusing the same node positions
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
Examples
--------
>>> G = nx.path_graph(4)
>>> nx.draw_planar(G)
See Also
--------
:func:`~networkx.drawing.layout.planar_layout`
"""
draw(G, planar_layout(G), **kwargs)
def draw_forceatlas2(G, **kwargs):
"""Draw a networkx graph with forceatlas2 layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.forceatlas2_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See networkx.draw_networkx() for a description of optional keywords,
with the exception of the pos parameter which is not used by this
function.
"""
draw(G, forceatlas2_layout(G), **kwargs)
def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
"""Apply an alpha (or list of alphas) to the colors provided.
Parameters
----------
colors : color string or array of floats (default='r')
Color of element. Can be a single color format string,
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
alpha : float or array of floats
Alpha values for elements. This can be a single alpha value, in
which case it will be applied to all the elements of color. Otherwise,
if it is an array, the elements of alpha will be applied to the colors
in order (cycling through alpha multiple times if necessary).
elem_list : array of networkx objects
The list of elements which are being colored. These could be nodes,
edges or labels.
cmap : matplotlib colormap
Color map for use if colors is a list of floats corresponding to points
on a color mapping.
vmin, vmax : float
Minimum and maximum values for normalizing colors if a colormap is used
Returns
-------
rgba_colors : numpy ndarray
Array containing RGBA format values for each of the node colours.
"""
from itertools import cycle, islice
import matplotlib as mpl
import matplotlib.cm # call as mpl.cm
import matplotlib.colors # call as mpl.colors
import numpy as np
# If we have been provided with a list of numbers as long as elem_list,
# apply the color mapping.
if len(colors) == len(elem_list) and isinstance(colors[0], Number):
mapper = mpl.cm.ScalarMappable(cmap=cmap)
mapper.set_clim(vmin, vmax)
rgba_colors = mapper.to_rgba(colors)
# Otherwise, convert colors to matplotlib's RGB using the colorConverter
# object. These are converted to numpy ndarrays to be consistent with the
# to_rgba method of ScalarMappable.
else:
try:
rgba_colors = np.array([mpl.colors.colorConverter.to_rgba(colors)])
except ValueError:
rgba_colors = np.array(
[mpl.colors.colorConverter.to_rgba(color) for color in colors]
)
# Set the final column of the rgba_colors to have the relevant alpha values
try:
# If alpha is longer than the number of colors, resize to the number of
# elements. Also, if rgba_colors.size (the number of elements of
# rgba_colors) is the same as the number of elements, resize the array,
# to avoid it being interpreted as a colormap by scatter()
if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
rgba_colors[1:, 0] = rgba_colors[0, 0]
rgba_colors[1:, 1] = rgba_colors[0, 1]
rgba_colors[1:, 2] = rgba_colors[0, 2]
rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))
except TypeError:
rgba_colors[:, -1] = alpha
return rgba_colors
