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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/networkx/drawing/nx_pylab.py

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Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
"""
**********
Matplotlib
**********
Draw networks with matplotlib.
See Also
--------
matplotlib: http://matplotlib.org/
pygraphviz: http://pygraphviz.github.io/
"""
from numbers import Number
import networkx as nx
from networkx.drawing.layout import (
shell_layout,
circular_layout,
kamada_kawai_layout,
spectral_layout,
spring_layout,
random_layout,
planar_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",
]
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.github.io/documentation/latest/auto_examples/index.html
"""
try:
import matplotlib.pyplot as plt
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
cf = plt.gcf()
else:
cf = ax.get_figure()
cf.set_facecolor("w")
if ax is None:
if cf._axstack() is None:
ax = cf.add_axes((0, 0, 1, 1))
else:
ax = cf.gca()
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
def draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds):
"""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, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrowsheads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
with_labels : bool, optional (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, 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 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, optional (default='o')
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8'.
alpha : float, optional (default=None)
The node and edge transparency
cmap : Matplotlib 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)
width : float, optional (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 (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, optional (default=None)
Node labels in a dictionary keyed by node of text labels
font_size : int, optional (default=12)
Font size for text labels
font_color : string, optional (default='k' black)
Font color string
font_weight : string, optional (default='normal')
Font weight
font_family : string, optional (default='sans-serif')
Font family
label : string, optional
Label for graph legend
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 of axis
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
valid_node_kwds = (
"nodelist",
"node_size",
"node_color",
"node_shape",
"alpha",
"cmap",
"vmin",
"vmax",
"ax",
"linewidths",
"edgecolors",
"label",
)
valid_edge_kwds = (
"edgelist",
"width",
"edge_color",
"style",
"alpha",
"arrowstyle",
"arrowsize",
"edge_cmap",
"edge_vmin",
"edge_vmax",
"ax",
"label",
"node_size",
"nodelist",
"node_shape",
"connectionstyle",
"min_source_margin",
"min_target_margin",
)
valid_label_kwds = (
"labels",
"font_size",
"font_color",
"font_family",
"font_weight",
"alpha",
"bbox",
"ax",
"horizontalalignment",
"verticalalignment",
)
valid_kwds = valid_node_kwds + valid_edge_kwds + valid_label_kwds
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()
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,
):
"""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, optional
Draw only specified nodes (default G.nodes())
node_size : scalar or array
Size of nodes (default=300). 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
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8' (default='o').
alpha : float or array of floats
The node transparency. This can be a single alpha value (default=None),
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
Colormap for mapping intensities of nodes (default=None)
vmin,vmax : floats
Minimum and maximum for node colormap scaling (default=None)
linewidths : [None | scalar | sequence]
Line width of symbol border (default =1.0)
edgecolors : [None | scalar | sequence]
Colors of node borders (default = node_color)
label : [None| string]
Label for legend
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.github.io/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
try:
import matplotlib.pyplot as plt
from matplotlib.collections import PathCollection
import numpy as np
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if nodelist is None:
nodelist = list(G)
if len(nodelist) == 0: # empty nodelist, no drawing
return PathCollection(None)
try:
xy = np.asarray([pos[v] for v in nodelist])
except KeyError as e:
raise nx.NetworkXError(f"Node {e} has no position.") from e
except ValueError as e:
raise nx.NetworkXError("Bad value in node positions.") from e
if isinstance(alpha, Iterable):
node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)
alpha = None
node_collection = ax.scatter(
xy[:, 0],
xy[:, 1],
s=node_size,
c=node_color,
marker=node_shape,
cmap=cmap,
vmin=vmin,
vmax=vmax,
alpha=alpha,
linewidths=linewidths,
edgecolors=edgecolors,
label=label,
)
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
node_collection.set_zorder(2)
return node_collection
def draw_networkx_edges(
G,
pos,
edgelist=None,
width=1.0,
edge_color="k",
style="solid",
alpha=None,
arrowstyle="-|>",
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
connectionstyle=None,
min_source_margin=0,
min_target_margin=0,
):
"""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 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
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=None)
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.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
connectionstyle : str, optional (default=None)
Pass the connectionstyle parameter to create curved arc of rounding
radius rad. For example, connectionstyle='arc3,rad=0.2'.
See :py:class: `matplotlib.patches.ConnectionStyle` and
:py:class: `matplotlib.patches.FancyArrowPatch` for more info.
label : [None| string]
Label for legend
min_source_margin : int, optional (default=0)
The minimum margin (gap) at the begining of the edge at the source.
min_target_margin : int, optional (default=0)
The minimum margin (gap) at the end of the edge at the target.
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
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])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if len(edgelist) == 0: # no edges!
if not G.is_directed() or not arrows:
return LineCollection(None)
else:
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.alltrue([isinstance(c, Number) for c in edge_color])
):
if edge_cmap is not None:
assert isinstance(edge_cmap, 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 = Normalize(vmin=edge_vmin, vmax=edge_vmax)
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
if not G.is_directed() or not arrows:
edge_collection = LineCollection(
edge_pos,
colors=edge_color,
linewidths=width,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
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)
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
# FancyArrowPatch doesn't handle color strings
arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if np.iterable(node_size): # many node sizes
source, target = edgelist[i][:2]
source_node_size = node_size[nodelist.index(source)]
target_node_size = node_size[nodelist.index(target)]
shrink_source = to_marker_edge(source_node_size, node_shape)
shrink_target = to_marker_edge(target_node_size, node_shape)
else:
shrink_source = shrink_target = to_marker_edge(node_size, node_shape)
if shrink_source < min_source_margin:
shrink_source = min_source_margin
if shrink_target < min_target_margin:
shrink_target = min_target_margin
if len(arrow_colors) == len(edge_pos):
arrow_color = arrow_colors[i]
elif len(arrow_colors) == 1:
arrow_color = arrow_colors[0]
else: # Cycle through colors
arrow_color = arrow_colors[i % len(arrow_colors)]
if np.iterable(width):
if len(width) == len(edge_pos):
line_width = width[i]
else:
line_width = width[i % len(width)]
else:
line_width = width
arrow = FancyArrowPatch(
(x1, y1),
(x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
connectionstyle=connectionstyle,
linestyle=style,
zorder=1,
) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update 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
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return arrow_collection
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,
):
"""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, optional (default=None)
Node labels in a dictionary keyed by node of text labels
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
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_family : string
Font family (default='sans-serif')
font_weight : string
Font weight (default='normal')
alpha : float or None
The text transparency (default=None)
horizontalalignment : {'center', 'right', 'left'}
Horizontal alignment (default='center')
verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
Vertical alignment (default='center')
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
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.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if labels is None:
labels = {n: n for n in G.nodes()}
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=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
transform=ax.transData,
bbox=bbox,
clip_on=True,
)
text_items[n] = t
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return text_items
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,
):
"""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.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha : float or None
The text transparency (default=None)
edge_labels : dictionary
Edge labels in a dictionary keyed by edge two-tuple of text
labels (default=None). Only labels for the keys in the dictionary
are drawn.
label_pos : float
Position of edge label along edge (0=head, 0.5=center, 1=tail)
font_size : int
Font size for text labels (default=12)
font_color : string
Font color string (default='k' black)
font_weight : string
Font weight (default='normal')
font_family : string
Font family (default='sans-serif')
bbox : Matplotlib bbox
Specify text box shape and colors.
clip_on : bool
Turn on clipping at axis boundaries (default=True)
horizontalalignment : {'center', 'right', 'left'}
Horizontal alignment (default='center')
verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
Vertical alignment (default='center')
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
Returns
-------
dict
`dict` of labels keyed on the edges
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.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
"""
try:
import matplotlib.pyplot as plt
import numpy as np
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edge_labels is None:
labels = {(u, v): d for u, v, d in G.edges(data=True)}
else:
labels = edge_labels
text_items = {}
for (n1, n2), label in labels.items():
(x1, y1) = pos[n1]
(x2, y2) = pos[n2]
(x, y) = (
x1 * label_pos + x2 * (1.0 - label_pos),
y1 * label_pos + y2 * (1.0 - label_pos),
)
if rotate:
# in degrees
angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360
# make label orientation "right-side-up"
if angle > 90:
angle -= 180
if angle < -90:
angle += 180
# transform data coordinate angle to screen coordinate angle
xy = np.array((x, y))
trans_angle = ax.transData.transform_angles(
np.array((angle,)), xy.reshape((1, 2))
)[0]
else:
trans_angle = 0.0
# use default box of white with white border
if bbox is None:
bbox = dict(boxstyle="round", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0))
if not isinstance(label, str):
label = str(label) # this makes "1" and 1 labeled the same
t = ax.text(
x,
y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
alpha=alpha,
horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
rotation=trans_angle,
transform=ax.transData,
bbox=bbox,
zorder=1,
clip_on=True,
)
text_items[(n1, n2)] = t
ax.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
return text_items
def draw_circular(G, **kwargs):
"""Draw the graph G with a circular layout.
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, circular_layout(G), **kwargs)
def draw_kamada_kawai(G, **kwargs):
"""Draw the graph G with a Kamada-Kawai force-directed layout.
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, kamada_kawai_layout(G), **kwargs)
def draw_random(G, **kwargs):
"""Draw the graph G with a random layout.
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, random_layout(G), **kwargs)
def draw_spectral(G, **kwargs):
"""Draw the graph G with a spectral 2D layout.
Using the unnormalized Laplacian, the layout shows possible clusters of
nodes which are an approximation of the ratio cut. The positions are the
entries of the second and third eigenvectors corresponding to the
ascending eigenvalues starting from the second one.
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, spectral_layout(G), **kwargs)
def draw_spring(G, **kwargs):
"""Draw the graph G with a spring layout.
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, spring_layout(G), **kwargs)
def draw_shell(G, **kwargs):
"""Draw networkx graph with shell layout.
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.
"""
nlist = kwargs.get("nlist", None)
if nlist is not None:
del kwargs["nlist"]
draw(G, shell_layout(G, nlist=nlist), **kwargs)
def draw_planar(G, **kwargs):
"""Draw a planar networkx graph with planar layout.
Parameters
----------
G : graph
A planar 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, planar_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
Color of element. Can be a single color format string (default='r'),
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 color mapping is
used.
Returns
-------
rgba_colors : numpy ndarray
Array containing RGBA format values for each of the node colours.
"""
from itertools import islice, cycle
try:
import numpy as np
from matplotlib.colors import colorConverter
import matplotlib.cm as cm
except ImportError as e:
raise ImportError("Matplotlib required for draw()") from e
# 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 = 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([colorConverter.to_rgba(colors)])
except ValueError:
rgba_colors = np.array([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
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