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"""
**********
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
__all__ = [
"display",
"apply_matplotlib_colors",
"draw",
"draw_networkx",
"draw_networkx_nodes",
"draw_networkx_edges",
"draw_networkx_labels",
"draw_networkx_edge_labels",
"draw_bipartite",
"draw_circular",
"draw_kamada_kawai",
"draw_random",
"draw_spectral",
"draw_spring",
"draw_planar",
"draw_shell",
"draw_forceatlas2",
]
def apply_matplotlib_colors(
G, src_attr, dest_attr, map, vmin=None, vmax=None, nodes=True
):
"""
Apply colors from a matplotlib colormap to a graph.
Reads values from the `src_attr` and use a matplotlib colormap
to produce a color. Write the color to `dest_attr`.
Parameters
----------
G : nx.Graph
The graph to read and compute colors for.
src_attr : str or other attribute name
The name of the attribute to read from the graph.
dest_attr : str or other attribute name
The name of the attribute to write to on the graph.
map : matplotlib.colormap
The matplotlib colormap to use.
vmin : float, default None
The minimum value for scaling the colormap. If `None`, find the
minimum value of `src_attr`.
vmax : float, default None
The maximum value for scaling the colormap. If `None`, find the
maximum value of `src_attr`.
nodes : bool, default True
Whether the attribute names are edge attributes or node attributes.
"""
import matplotlib as mpl
if nodes:
type_iter = G.nodes()
elif G.is_multigraph():
type_iter = G.edges(keys=True)
else:
type_iter = G.edges()
if vmin is None or vmax is None:
vals = [type_iter[a][src_attr] for a in type_iter]
if vmin is None:
vmin = min(vals)
if vmax is None:
vmax = max(vals)
mapper = mpl.cm.ScalarMappable(cmap=map)
mapper.set_clim(vmin, vmax)
def do_map(x):
# Cast numpy scalars to float
return tuple(float(x) for x in mapper.to_rgba(x))
if nodes:
nx.set_node_attributes(
G, {n: do_map(G.nodes[n][src_attr]) for n in G.nodes()}, dest_attr
)
else:
nx.set_edge_attributes(
G, {e: do_map(G.edges[e][src_attr]) for e in type_iter}, dest_attr
)
def display(
G,
canvas=None,
**kwargs,
):
"""Draw the graph G.
Draw the graph as a collection of nodes connected by edges.
The exact details of what the graph looks like are controled by the below
attributes. All nodes and nodes at the end of visible edges must have a
position set, but nearly all other node and edge attributes are options and
nodes or edges missing the attribute will use the default listed below. A more
complete discription of each parameter is given below this summary.
.. list-table:: Default Visualization Attributes
:widths: 25 25 50
:header-rows: 1
* - Parameter
- Default Attribute
- Default Value
* - pos
- `"pos"`
- If there is not position, a layout will be calculated with `nx.spring_layout`.
* - node_visible
- `"visible"`
- True
* - node_color
- `"color"`
- #1f78b4
* - node_size
- `"size"`
- 300
* - node_label
- `"label"`
- Dict describing the node label. Defaults create a black text with
the node name as the label. The dict respects these keys and defaults:
* size : 12
* color : black
* family : sans serif
* weight : normal
* alpha : 1.0
* h_align : center
* v_align : center
* bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
Default is None.
* - node_shape
- `"shape"`
- "o"
* - node_alpha
- `"alpha"`
- 1.0
* - node_border_width
- `"border_width"`
- 1.0
* - node_border_color
- `"border_color"`
- Matching node_color
* - edge_visible
- `"visible"`
- True
* - edge_width
- `"width"`
- 1.0
* - edge_color
- `"color"`
- Black (#000000)
* - edge_label
- `"label"`
- Dict describing the edge label. Defaults create black text with a
white bounding box. The dictionary respects these keys and defaults:
* size : 12
* color : black
* family : sans serif
* weight : normal
* alpha : 1.0
* bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
Default {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
* h_align : "center"
* v_align : "center"
* pos : 0.5
* rotate : True
* - edge_style
- `"style"`
- "-"
* - edge_alpha
- `"alpha"`
- 1.0
* - arrowstyle
- `"arrowstyle"`
- ``"-|>"`` if `G` is directed else ``"-"``
* - arrowsize
- `"arrowsize"`
- 10 if `G` is directed else 0
* - edge_curvature
- `"curvature"`
- arc3
* - edge_source_margin
- `"source_margin"`
- 0
* - edge_target_margin
- `"target_margin"`
- 0
Parameters
----------
G : graph
A networkx graph
canvas : Matplotlib Axes object, optional
Draw the graph in specified Matplotlib axes
pos : string or function, default "pos"
A string naming the node attribute storing the position of nodes as a tuple.
Or a function to be called with input `G` which returns the layout as a dict keyed
by node to position tuple like the NetworkX layout functions.
If no nodes in the graph has the attribute, a spring layout is calculated.
node_visible : string or bool, default visible
A string naming the node attribute which stores if a node should be drawn.
If `True`, all nodes will be visible while if `False` no nodes will be visible.
If incomplete, nodes missing this attribute will be shown by default.
node_color : string, default "color"
A string naming the node attribute which stores the color of each node.
Visible nodes without this attribute will use '#1f78b4' as a default.
node_size : string or number, default "size"
A string naming the node attribute which stores the size of each node.
Visible nodes without this attribute will use a default size of 300.
node_label : string or bool, default "label"
A string naming the node attribute which stores the label of each node.
The attribute value can be a string, False (no label for that node),
True (the node is the label) or a dict keyed by node to the label.
If a dict is specified, these keys are read to further control the label:
* label : The text of the label; default: name of the node
* size : Font size of the label; default: 12
* color : Font color of the label; default: black
* family : Font family of the label; default: "sans-serif"
* weight : Font weight of the label; default: "normal"
* alpha : Alpha value of the label; default: 1.0
* h_align : The horizontal alignment of the label.
one of "left", "center", "right"; default: "center"
* v_align : The vertical alignment of the label.
one of "top", "center", "bottom"; default: "center"
* bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
Visible nodes without this attribute will be treated as if the value was True.
node_shape : string, default "shape"
A string naming the node attribute which stores the label of each node.
The values of this attribute are expected to be one of the matplotlib shapes,
one of 'so^>v<dph8'. Visible nodes without this attribute will use 'o'.
node_alpha : string, default "alpha"
A string naming the node attribute which stores the alpha of each node.
The values of this attribute are expected to be floats between 0.0 and 1.0.
Visible nodes without this attribute will be treated as if the value was 1.0.
node_border_width : string, default "border_width"
A string naming the node attribute storing the width of the border of the node.
The values of this attribute are expected to be numeric. Visible nodes without
this attribute will use the assumed default of 1.0.
node_border_color : string, default "border_color"
A string naming the node attribute which storing the color of the border of the node.
Visible nodes missing this attribute will use the final node_color value.
edge_visible : string or bool, default "visible"
A string nameing the edge attribute which stores if an edge should be drawn.
If `True`, all edges will be drawn while if `False` no edges will be visible.
If incomplete, edges missing this attribute will be shown by default. Values
of this attribute are expected to be booleans.
edge_width : string or int, default "width"
A string nameing the edge attribute which stores the width of each edge.
Visible edges without this attribute will use a default width of 1.0.
edge_color : string or color, default "color"
A string nameing the edge attribute which stores of color of each edge.
Visible edges without this attribute will be drawn black. Each color can be
a string or rgb (or rgba) tuple of floats from 0.0 to 1.0.
edge_label : string, default "label"
A string naming the edge attribute which stores the label of each edge.
The values of this attribute can be a string, number or False or None. In
the latter two cases, no edge label is displayed.
If a dict is specified, these keys are read to further control the label:
* label : The text of the label, or the name of an edge attribute holding the label.
* size : Font size of the label; default: 12
* color : Font color of the label; default: black
* family : Font family of the label; default: "sans-serif"
* weight : Font weight of the label; default: "normal"
* alpha : Alpha value of the label; default: 1.0
* h_align : The horizontal alignment of the label.
one of "left", "center", "right"; default: "center"
* v_align : The vertical alignment of the label.
one of "top", "center", "bottom"; default: "center"
* bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
* rotate : Whether to rotate labels to lie parallel to the edge, default: True.
* pos : A float showing how far along the edge to put the label; default: 0.5.
edge_style : string, default "style"
A string naming the edge attribute which stores the style of each edge.
Visible edges without this attribute will be drawn solid. Values of this
attribute can be line styles, e.g. '-', '--', '-.' or ':' or words like 'solid'
or 'dashed'. If no edge in the graph has this attribute and it is a non-default
value, assume that it describes the edge style for all edges in the graph.
edge_alpha : string or float, default "alpha"
A string naming the edge attribute which stores the alpha value of each edge.
Visible edges without this attribute will use an alpha value of 1.0.
arrowstyle : string, default "arrow"
A string naming the edge attribute which stores the type of arrowhead to use for
each edge. Visible edges without this attribute use ``"-"`` for undirected graphs
and ``"-|>"`` for directed graphs.
See `matplotlib.patches.ArrowStyle` for more options
arrowsize : string or int, default "arrow_size"
A string naming the edge attribute which stores the size of the arrowhead for each
edge. Visible edges without this attribute will use a default value of 10.
edge_curvature : string, default "curvature"
A string naming the edge attribute storing the curvature and connection style
of each edge. Visible edges without this attribute will use "arc3" as a default
value, resulting an a straight line between the two nodes. Curvature can be given
as 'arc3,rad=0.2' to specify both the style and radius of curvature.
Please see `matplotlib.patches.ConnectionStyle` and
`matplotlib.patches.FancyArrowPatch` for more information.
edge_source_margin : string or int, default "source_margin"
A string naming the edge attribute which stores the minimum margin (gap) between
the source node and the start of the edge. Visible edges without this attribute
will use a default value of 0.
edge_target_margin : string or int, default "target_margin"
A string naming the edge attribute which stores the minimumm margin (gap) between
the target node and the end of the edge. Visible edges without this attribute
will use a default value of 0.
hide_ticks : bool, default True
Weather to remove the ticks from the axes of the matplotlib object.
Raises
------
NetworkXError
If a node or edge is missing a required parameter such as `pos` or
if `display` receives an argument not listed above.
ValueError
If a node or edge has an invalid color format, i.e. not a color string,
rgb tuple or rgba tuple.
Returns
-------
The input graph. This is potentially useful for dispatching visualization
functions.
"""
from collections import Counter
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
defaults = {
"node_pos": None,
"node_visible": True,
"node_color": "#1f78b4",
"node_size": 300,
"node_label": {
"size": 12,
"color": "#000000",
"family": "sans-serif",
"weight": "normal",
"alpha": 1.0,
"h_align": "center",
"v_align": "center",
"bbox": None,
},
"node_shape": "o",
"node_alpha": 1.0,
"node_border_width": 1.0,
"node_border_color": "face",
"edge_visible": True,
"edge_width": 1.0,
"edge_color": "#000000",
"edge_label": {
"size": 12,
"color": "#000000",
"family": "sans-serif",
"weight": "normal",
"alpha": 1.0,
"bbox": {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)},
"h_align": "center",
"v_align": "center",
"pos": 0.5,
"rotate": True,
},
"edge_style": "-",
"edge_alpha": 1.0,
"edge_arrowstyle": "-|>" if G.is_directed() else "-",
"edge_arrowsize": 10 if G.is_directed() else 0,
"edge_curvature": "arc3",
"edge_source_margin": 0,
"edge_target_margin": 0,
"hide_ticks": True,
}
# Check arguments
for kwarg in kwargs:
if kwarg not in defaults:
raise nx.NetworkXError(
f"Unrecongized visualization keyword argument: {kwarg}"
)
if canvas is None:
canvas = plt.gca()
if kwargs.get("hide_ticks", defaults["hide_ticks"]):
canvas.tick_params(
axis="both",
which="both",
bottom=False,
left=False,
labelbottom=False,
labelleft=False,
)
### Helper methods and classes
def node_property_sequence(seq, attr):
"""Return a list of attribute values for `seq`, using a default if needed"""
# All node attribute parameters start with "node_"
param_name = f"node_{attr}"
default = defaults[param_name]
attr = kwargs.get(param_name, attr)
if default is None:
# raise instead of using non-existant default value
for n in seq:
if attr not in node_subgraph.nodes[n]:
raise nx.NetworkXError(f"Attribute '{attr}' missing for node {n}")
# If `attr` is not a graph attr and was explicitly passed as an argument
# it must be a user-default value. Allow attr=None to tell draw to skip
# attributes which are on the graph
if (
attr is not None
and nx.get_node_attributes(node_subgraph, attr) == {}
and any(attr == v for k, v in kwargs.items() if "node" in k)
):
return [attr for _ in seq]
return [node_subgraph.nodes[n].get(attr, default) for n in seq]
def compute_colors(color, alpha):
if isinstance(color, str):
rgba = mpl.colors.colorConverter.to_rgba(color)
# Using a non-default alpha value overrides any alpha value in the color
if alpha != defaults["node_alpha"]:
return (rgba[0], rgba[1], rgba[2], alpha)
return rgba
if isinstance(color, tuple) and len(color) == 3:
return (color[0], color[1], color[2], alpha)
if isinstance(color, tuple) and len(color) == 4:
return color
raise ValueError(f"Invalid format for color: {color}")
# Find which edges can be plotted as a line collection
#
# Non-default values for these attributes require fancy arrow patches:
# - any arrow style (including the default -|> for directed graphs)
# - arrow size (by extension of style)
# - connection style
# - min_source_margin
# - min_target_margin
def collection_compatible(e):
return (
get_edge_attr(e, "arrowstyle") == "-"
and get_edge_attr(e, "curvature") == "arc3"
and get_edge_attr(e, "source_margin") == 0
and get_edge_attr(e, "target_margin") == 0
# Self-loops will use fancy arrow patches
and e[0] != e[1]
)
def edge_property_sequence(seq, attr):
"""Return a list of attribute values for `seq`, using a default if needed"""
param_name = f"edge_{attr}"
default = defaults[param_name]
attr = kwargs.get(param_name, attr)
if default is None:
# raise instead of using non-existant default value
for e in seq:
if attr not in edge_subgraph.edges[e]:
raise nx.NetworkXError(f"Attribute '{attr}' missing for edge {e}")
if (
attr is not None
and nx.get_edge_attributes(edge_subgraph, attr) == {}
and any(attr == v for k, v in kwargs.items() if "edge" in k)
):
return [attr for _ in seq]
return [edge_subgraph.edges[e].get(attr, default) for e in seq]
def get_edge_attr(e, attr):
"""Return the final edge attribute value, using default if not None"""
param_name = f"edge_{attr}"
default = defaults[param_name]
attr = kwargs.get(param_name, attr)
if default is None and attr not in edge_subgraph.edges[e]:
raise nx.NetworkXError(f"Attribute '{attr}' missing from edge {e}")
if (
attr is not None
and nx.get_edge_attributes(edge_subgraph, attr) == {}
and attr in kwargs.values()
):
return attr
return edge_subgraph.edges[e].get(attr, default)
def get_node_attr(n, attr, use_edge_subgraph=True):
"""Return the final node attribute value, using default if not None"""
subgraph = edge_subgraph if use_edge_subgraph else node_subgraph
param_name = f"node_{attr}"
default = defaults[param_name]
attr = kwargs.get(param_name, attr)
if default is None and attr not in subgraph.nodes[n]:
raise nx.NetworkXError(f"Attribute '{attr}' missing from node {n}")
if (
attr is not None
and nx.get_node_attributes(subgraph, attr) == {}
and attr in kwargs.values()
):
return attr
return subgraph.nodes[n].get(attr, default)
# Taken from ConnectionStyleFactory
def self_loop(edge_index, node_size):
def self_loop_connection(posA, posB, *args, **kwargs):
if not 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 = canvas.transData.inverted().transform(posA)
# Scale self loop based on the size of the base node
# Size of nodes are given in points ** 2 and each point is 1/72 of an inch
v_shift = np.sqrt(node_size) / 72
h_shift = v_shift * 0.5
# put the top of the loop first so arrow is not hidden by node
path = 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 = np.array([x, y]).T
return mpl.path.Path(
canvas.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
)
return self_loop_connection
def to_marker_edge(size, marker):
if marker in "s^>v<d":
return np.sqrt(2 * size) / 2
else:
return np.sqrt(size) / 2
def build_fancy_arrow(e):
source_margin = to_marker_edge(
get_node_attr(e[0], "size"),
get_node_attr(e[0], "shape"),
)
source_margin = max(
source_margin,
get_edge_attr(e, "source_margin"),
)
target_margin = to_marker_edge(
get_node_attr(e[1], "size"),
get_node_attr(e[1], "shape"),
)
target_margin = max(
target_margin,
get_edge_attr(e, "target_margin"),
)
return mpl.patches.FancyArrowPatch(
edge_subgraph.nodes[e[0]][pos],
edge_subgraph.nodes[e[1]][pos],
arrowstyle=get_edge_attr(e, "arrowstyle"),
connectionstyle=(
get_edge_attr(e, "curvature")
if e[0] != e[1]
else self_loop(
0 if len(e) == 2 else e[2] % 4,
get_node_attr(e[0], "size"),
)
),
color=get_edge_attr(e, "color"),
linestyle=get_edge_attr(e, "style"),
linewidth=get_edge_attr(e, "width"),
mutation_scale=get_edge_attr(e, "arrowsize"),
shrinkA=source_margin,
shrinkB=source_margin,
zorder=1,
)
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)
### Draw the nodes first
node_visible = kwargs.get("node_visible", "visible")
if isinstance(node_visible, bool):
if node_visible:
visible_nodes = G.nodes()
else:
visible_nodes = []
else:
visible_nodes = [
n for n, v in nx.get_node_attributes(G, node_visible, True).items() if v
]
node_subgraph = G.subgraph(visible_nodes)
# Ignore the default dict value since that's for default values to use, not
# default attribute name
pos = kwargs.get("node_pos", "pos")
default_display_pos_attr = "display's position attribute name"
if callable(pos):
nx.set_node_attributes(
node_subgraph, pos(node_subgraph), default_display_pos_attr
)
pos = default_display_pos_attr
kwargs["node_pos"] = default_display_pos_attr
elif nx.get_node_attributes(G, pos) == {}:
nx.set_node_attributes(
node_subgraph, nx.spring_layout(node_subgraph), default_display_pos_attr
)
pos = default_display_pos_attr
kwargs["node_pos"] = default_display_pos_attr
# Each shape requires a new scatter object since they can't have different
# shapes.
if len(visible_nodes) > 0:
node_shape = kwargs.get("node_shape", "shape")
for shape in Counter(
nx.get_node_attributes(
node_subgraph, node_shape, defaults["node_shape"]
).values()
):
# Filter position just on this shape.
nodes_with_shape = [
n
for n, s in node_subgraph.nodes(data=node_shape)
if s == shape or (s is None and shape == defaults["node_shape"])
]
# There are two property sequences to create before hand.
# 1. position, since it is used for x and y parameters to scatter
# 2. edgecolor, since the spaeical 'face' parameter value can only be
# be passed in as the sole string, not part of a list of strings.
position = np.asarray(node_property_sequence(nodes_with_shape, "pos"))
color = np.asarray(
[
compute_colors(c, a)
for c, a in zip(
node_property_sequence(nodes_with_shape, "color"),
node_property_sequence(nodes_with_shape, "alpha"),
)
]
)
border_color = np.asarray(
[
(
c
if (
c := get_node_attr(
n,
"border_color",
False,
)
)
!= "face"
else color[i]
)
for i, n in enumerate(nodes_with_shape)
]
)
canvas.scatter(
position[:, 0],
position[:, 1],
s=node_property_sequence(nodes_with_shape, "size"),
c=color,
marker=shape,
linewidths=node_property_sequence(nodes_with_shape, "border_width"),
edgecolors=border_color,
zorder=2,
)
### Draw node labels
node_label = kwargs.get("node_label", "label")
# Plot labels if node_label is not None and not False
if node_label is not None and node_label is not False:
default_dict = {}
if isinstance(node_label, dict):
default_dict = node_label
node_label = None
for n, lbl in node_subgraph.nodes(data=node_label):
if lbl is False:
continue
# We work with label dicts down here...
if not isinstance(lbl, dict):
lbl = {"label": lbl if lbl is not None else n}
lbl_text = lbl.get("label", n)
if not isinstance(lbl_text, str):
lbl_text = str(lbl_text)
lbl.update(default_dict)
x, y = node_subgraph.nodes[n][pos]
canvas.text(
x,
y,
lbl_text,
size=lbl.get("size", defaults["node_label"]["size"]),
color=lbl.get("color", defaults["node_label"]["color"]),
family=lbl.get("family", defaults["node_label"]["family"]),
weight=lbl.get("weight", defaults["node_label"]["weight"]),
horizontalalignment=lbl.get(
"h_align", defaults["node_label"]["h_align"]
),
verticalalignment=lbl.get("v_align", defaults["node_label"]["v_align"]),
transform=canvas.transData,
bbox=lbl.get("bbox", defaults["node_label"]["bbox"]),
)
### Draw edges
edge_visible = kwargs.get("edge_visible", "visible")
if isinstance(edge_visible, bool):
if edge_visible:
visible_edges = G.edges()
else:
visible_edges = []
else:
visible_edges = [
e for e, v in nx.get_edge_attributes(G, edge_visible, True).items() if v
]
edge_subgraph = G.edge_subgraph(visible_edges)
print(nx.get_node_attributes(node_subgraph, pos))
nx.set_node_attributes(
edge_subgraph, nx.get_node_attributes(node_subgraph, pos), name=pos
)
collection_edges = (
[e for e in edge_subgraph.edges(keys=True) if collection_compatible(e)]
if edge_subgraph.is_multigraph()
else [e for e in edge_subgraph.edges() if collection_compatible(e)]
)
non_collection_edges = (
[e for e in edge_subgraph.edges(keys=True) if not collection_compatible(e)]
if edge_subgraph.is_multigraph()
else [e for e in edge_subgraph.edges() if not collection_compatible(e)]
)
edge_position = np.asarray(
[
(
get_node_attr(u, "pos", use_edge_subgraph=True),
get_node_attr(v, "pos", use_edge_subgraph=True),
)
for u, v, *_ in collection_edges
]
)
# Only plot a line collection if needed
if len(collection_edges) > 0:
edge_collection = mpl.collections.LineCollection(
edge_position,
colors=edge_property_sequence(collection_edges, "color"),
linewidths=edge_property_sequence(collection_edges, "width"),
linestyle=edge_property_sequence(collection_edges, "style"),
alpha=edge_property_sequence(collection_edges, "alpha"),
antialiaseds=(1,),
zorder=1,
)
canvas.add_collection(edge_collection)
fancy_arrows = {}
if len(non_collection_edges) > 0:
for e in non_collection_edges:
# Cache results for use in edge labels
fancy_arrows[e] = build_fancy_arrow(e)
canvas.add_patch(fancy_arrows[e])
### Draw edge labels
edge_label = kwargs.get("edge_label", "label")
default_dict = {}
if isinstance(edge_label, dict):
default_dict = edge_label
# Restore the default label attribute key of 'label'
edge_label = "label"
# Handle multigraphs
edge_label_data = (
edge_subgraph.edges(data=edge_label, keys=True)
if edge_subgraph.is_multigraph()
else edge_subgraph.edges(data=edge_label)
)
if edge_label is not None and edge_label is not False:
for *e, lbl in edge_label_data:
e = tuple(e)
# I'm not sure how I want to handle None here... For now it means no label
if lbl is False or lbl is None:
continue
if not isinstance(lbl, dict):
lbl = {"label": lbl}
lbl.update(default_dict)
lbl_text = lbl.get("label")
if not isinstance(lbl_text, str):
lbl_text = str(lbl_text)
# In the old code, every non-self-loop is placed via a fancy arrow patch
# Only compute a new fancy arrow if needed by caching the results from
# edge placement.
try:
arrow = fancy_arrows[e]
except KeyError:
arrow = build_fancy_arrow(e)
if e[0] == e[1]:
# Taken directly from draw_networkx_edge_labels
connectionstyle_obj = arrow.get_connectionstyle()
posA = canvas.transData.transform(edge_subgraph.nodes[e[0]][pos])
path_disp = connectionstyle_obj(posA, posA)
path_data = canvas.transData.inverted().transform_path(path_disp)
x, y = path_data.vertices[0]
canvas.text(
x,
y,
lbl_text,
size=lbl.get("size", defaults["edge_label"]["size"]),
color=lbl.get("color", defaults["edge_label"]["color"]),
family=lbl.get("family", defaults["edge_label"]["family"]),
weight=lbl.get("weight", defaults["edge_label"]["weight"]),
alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
horizontalalignment=lbl.get(
"h_align", defaults["edge_label"]["h_align"]
),
verticalalignment=lbl.get(
"v_align", defaults["edge_label"]["v_align"]
),
rotation=0,
transform=canvas.transData,
bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
zorder=1,
)
continue
CurvedArrowText(
arrow,
lbl_text,
size=lbl.get("size", defaults["edge_label"]["size"]),
color=lbl.get("color", defaults["edge_label"]["color"]),
family=lbl.get("family", defaults["edge_label"]["family"]),
weight=lbl.get("weight", defaults["edge_label"]["weight"]),
alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
horizontalalignment=lbl.get(
"h_align", defaults["edge_label"]["h_align"]
),
verticalalignment=lbl.get("v_align", defaults["edge_label"]["v_align"]),
label_pos=lbl.get("pos", defaults["edge_label"]["pos"]),
labels_horizontal=lbl.get("rotate", defaults["edge_label"]["rotate"]),
transform=canvas.transData,
zorder=1,
ax=canvas,
)
# If we had to add an attribute, remove it here
if pos == default_display_pos_attr:
nx.remove_node_attributes(G, default_display_pos_attr)
return G
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
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()
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
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
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
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 = self.ax.transData
if arrow._posA_posB is None:
raise ValueError(
"Can only draw labels for fancy arrows with "
"posA and posB inputs, not custom path"
)
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,
)
# Return is in display coordinates
return _path
def _update_text_pos_angle(self, arrow):
# Fractional label position
# 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
path_disp = self._get_arrow_path_disp(arrow)
is_bar_style = isinstance(
arrow.get_connectionstyle(), mpl.patches.ConnectionStyle.Bar
)
# 1. Calculate x and y
if is_bar_style:
# Bar Connection Style - straight line
_, (cx1, cy1), (cx2, cy2), _ = path_disp.vertices
x = cx1 * tt + cx2 * t
y = cy1 * tt + cy2 * t
else:
# Arc or Angle type Connection Styles - Bezier curve
(x1, y1), (cx, cy), (x2, y2) = path_disp.vertices
x = tt**2 * x1 + 2 * t * tt * cx + t**2 * x2
y = tt**2 * y1 + 2 * t * tt * cy + t**2 * y2
# 2. Calculate Angle
if self.labels_horizontal:
# Horizontal text labels
angle = 0
else:
# Labels parallel to curve
if is_bar_style:
change_x = (cx2 - cx1) / 2
change_y = (cy2 - cy1) / 2
else:
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
elif 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
def draw_bipartite(G, **kwargs):
"""Draw the graph `G` with a bipartite layout.
This is a convenience function equivalent to::
nx.draw(G, pos=nx.bipartite_layout(G), **kwargs)
Parameters
----------
G : graph
A networkx graph
kwargs : optional keywords
See `draw_networkx` for a description of optional keywords.
Raises
------
NetworkXError :
If `G` is not bipartite.
Notes
-----
The layout is computed each time this function is called. For
repeated drawing it is much more efficient to call
`~networkx.drawing.layout.bipartite_layout` directly and reuse the result::
>>> G = nx.complete_bipartite_graph(3, 3)
>>> pos = nx.bipartite_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.complete_bipartite_graph(2, 5)
>>> nx.draw_bipartite(G)
See Also
--------
:func:`~networkx.drawing.layout.bipartite_layout`
"""
draw(G, pos=nx.bipartite_layout(G), **kwargs)
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, pos=nx.circular_layout(G), **kwargs)
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, pos=nx.kamada_kawai_layout(G), **kwargs)
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, pos=nx.random_layout(G), **kwargs)
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, pos=nx.spectral_layout(G), **kwargs)
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, pos=nx.spring_layout(G), **kwargs)
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, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
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, pos=nx.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, pos=nx.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
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