"""Draw hypergraphs and simplicial complexes with matplotlib."""
from inspect import signature
from itertools import chain
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb # This cannot be removed because it is used for cmap "crest"
from matplotlib import cm
from matplotlib.colors import is_color_like
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d.art3d import (
Line3DCollection,
LineCollection,
PatchCollection,
Poly3DCollection,
)
from scipy.spatial import ConvexHull
chaini = chain.from_iterable
from .. import convert
from ..algorithms import max_edge_order, unique_edge_sizes
from ..convert import to_bipartite_edgelist
from ..core import DiHypergraph, Hypergraph, SimplicialComplex
from ..exception import XGIError
from ..utils import subfaces
from .draw_utils import (
_CCW_sort,
_draw_arg_to_arr,
_draw_init,
_interp_draw_arg,
_parse_color_arg,
_update_lims,
)
from .layout import barycenter_spring_layout, bipartite_spring_layout
__all__ = [
"draw",
"draw_nodes",
"draw_hyperedges",
"draw_simplices",
"draw_node_labels",
"draw_hyperedge_labels",
"draw_multilayer",
"draw_bipartite",
"draw_undirected_dyads",
"draw_directed_dyads",
]
[docs]def draw(
H,
pos=None,
ax=None,
node_fc="white",
node_ec="black",
node_lw=1,
node_size=7,
node_shape="o",
node_fc_cmap="Reds",
vmin=None,
vmax=None,
max_order=None,
dyad_color="black",
dyad_lw=1.5,
dyad_style="solid",
dyad_color_cmap="Greys",
dyad_vmin=None,
dyad_vmax=None,
edge_fc=None,
edge_fc_cmap="crest_r",
edge_vmin=None,
edge_vmax=None,
alpha=0.4,
hull=False,
radius=0.05,
node_labels=False,
hyperedge_labels=False,
rescale_sizes=True,
aspect="equal",
**kwargs,
):
"""Draw hypergraph or simplicial complex.
Parameters
----------
H : Hypergraph or SimplicialComplex.
Hypergraph to draw
pos : dict, optional
If passed, this dictionary of positions node_id:(x,y) is used for placing the
0-simplices. If None (default), use the `barycenter_spring_layout` to compute
the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
node_fc : str, dict, iterable, or NodeStat, optional
Color of the nodes. If str, use the same color for all nodes. If a dict, must
contain (node_id: color_str) pairs. If other iterable, assume the colors are
specified in the same order as the nodes are found in H.nodes. If NodeStat, use
the colormap specified with node_fc_cmap. By default, "white".
node_ec : str, dict, iterable, or NodeStat, optional
Color of node borders. If str, use the same color for all nodes. If a dict,
must contain (node_id: color_str) pairs. If other iterable, assume the colors
are specified in the same order as the nodes are found in H.nodes. If NodeStat,
use the colormap specified with node_ec_cmap. By default, "black".
node_lw : int, float, dict, iterable, or NodeStat, optional
Line width of the node borders in pixels. If int or float, use the same width
for all node borders. If a dict, must contain (node_id: width) pairs. If
iterable, assume the widths are specified in the same order as the nodes are
found in H.nodes. If NodeStat, use a monotonic linear interpolation defined
between min_node_lw and max_node_lw. By default, 1.
node_size : int, float, dict, iterable, or NodeStat, optional
Radius of the nodes in pixels. If int or float, use the same radius for all
nodes. If a dict, must contain (node_id: radius) pairs. If iterable, assume
the radiuses are specified in the same order as the nodes are found in
H.nodes. If NodeStat, use a monotonic linear interpolation defined between
min_node_size and max_node_size. By default, 15.
node_shape : string, optional
The shape of the node. Specification is as matplotlib.scatter
marker. Default is "o".
node_fc_cmap : colormap
Colormap for mapping node colors. By default, "Reds". Ignored, if `node_fc` is
a str (single color).
vmin : float or None
Minimum for the node_fc_cmap scaling. By default, None.
vmax : float or None
Maximum for the node_fc_cmap scaling. By default, None.
max_order : int, optional
Maximum of hyperedges to plot. If None (default), plots all orders.
dyad_color : str, dict, iterable, or EdgeStat, optional
Color of the dyadic links. If str, use the same color for all edges. If a dict,
must contain (edge_id: color_str) pairs. If iterable, assume the colors are
specified in the same order as the edges are found in H.edges. If EdgeStat, use
a colormap (specified with dyad_color_cmap) associated to it. By default,
"black".
dyad_lw : int, float, dict, iterable, or EdgeStat, optional
Line width of edges of order 1 (dyadic links). If int or float, use the same
width for all edges. If a dict, must contain (edge_id: width) pairs. If
iterable, assume the widths are specified in the same order as the edges are
found in H.edges. If EdgeStat, use a monotonic linear interpolation defined
between min_dyad_lw and max_dyad_lw. By default, 1.5.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap: matplotlib colormap
Colormap used to map the dyad colors. By default, "Greys".
dyad_vmin, dyad_vmax : float, optional
Minimum and maximum for dyad colormap scaling. By default, None.
edge_fc : color or list of colors or array-like or dict or EdgeStat, optional
Color of the hyperedges. The accepted formats are the same as
matplotlib's scatter, with the addition of dict and IDStat.
Those with colors:
* single color as a string
* single color as 3- or 4-tuple
* list of colors of length len(ids)
* dict of colors containing the `ids` as keys
Those with numerical values (will be mapped to colors):
* array of floats
* dict of floats containing the `ids` as keys
* IDStat containing the `ids` as keys
If None (default), color by edge size.
edge_fc_cmap: matplotlib colormap
Colormap used to map the edge colors. By default, "cres_r".
edge_vmin, edge_vmax : float, optional
Minimum and maximum for edge colormap scaling. By default, None.
alpha : float, optional
The edge transparency. By default, 0.4.
hull : bool, optional
Wether to draw hyperedes as convex hulls. By default, False.
radius: float, optional
Radius margin around the nodes when drawing convex hulls. Ignored if
`hull is False`. Default is 0.05.
node_labels : bool or dict, optional
If True, draw ids on the nodes. If a dict, must contain (node_id: label) pairs.
By default, False. The default node_size (7) is too small to display the default
labels well. The user may need to set it to a size of a least 15.
hyperedge_labels : bool or dict, optional
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, False.
aspect : {"auto", "equal"} or float, optional
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
description at
https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_aspect.html
rescale_sizes: bool, optional
If True, linearly interpolate `node_size`, `node_lw` and `dyad_lw`
between min/max values that can be changed in the other argument `params`.
If those are single values, `interpolate_sizes` is ignored
for it. By default, True.
**kwargs : optional args
Alternate default values. Values that can be overwritten are the following:
* "min_node_size" (default: 5)
* "max_node_size" (default: 30)
* "min_node_lw" (default: 0)
* "max_node_lw" (default: 5)
* "min_dyad_lw" (default: 1)
* "max_dyad_lw" (default: 10)
Returns
-------
ax : matplotlib Axes
Axes plotted on
collections : a tuple of 3 collections:
* node_collection : matplotlib PathCollection
Collection containing the nodes
* dyad_collection : matplotlib LineCollection
Collection containing the dyads
* edge_collection : matplotlib PathCollection
Collection containing the edges
Examples
--------
>>> import xgi
>>> H = xgi.Hypergraph()
>>> H.add_edges_from([[1,2,3],[3,4],[4,5,6,7],[7,8,9,10,11]])
>>> ax = xgi.draw(H, pos=xgi.barycenter_spring_layout(H))
See Also
--------
draw_nodes
draw_hyperedges
draw_simplices
draw_node_labels
draw_hyperedge_labels
"""
settings = {
"min_node_size": 5,
"max_node_size": 30,
"min_dyad_lw": 1,
"max_dyad_lw": 10,
"min_node_lw": 0,
"max_node_lw": 5,
}
settings.update(kwargs)
ax, pos = _draw_init(H, ax, pos)
if not max_order:
max_order = max_edge_order(H)
if isinstance(H, SimplicialComplex):
ax, (dyad_collection, edge_collection) = draw_simplices(
SC=H,
pos=pos,
ax=ax,
dyad_color=dyad_color,
dyad_lw=dyad_lw,
dyad_style=dyad_style,
dyad_color_cmap=dyad_color_cmap,
dyad_vmin=dyad_vmin,
dyad_vmax=dyad_vmax,
alpha=alpha,
edge_fc=edge_fc,
edge_fc_cmap=edge_fc_cmap,
edge_vmin=edge_vmin,
edge_vmax=edge_vmax,
max_order=max_order,
hyperedge_labels=hyperedge_labels,
rescale_sizes=rescale_sizes,
**kwargs,
)
elif isinstance(H, Hypergraph):
ax, (dyad_collection, edge_collection) = draw_hyperedges(
H=H,
pos=pos,
ax=ax,
dyad_color=dyad_color,
dyad_lw=dyad_lw,
dyad_style=dyad_style,
dyad_color_cmap=dyad_color_cmap,
dyad_vmin=dyad_vmin,
dyad_vmax=dyad_vmax,
alpha=alpha,
edge_fc=edge_fc,
edge_fc_cmap=edge_fc_cmap,
edge_vmin=edge_vmin,
edge_vmax=edge_vmax,
max_order=max_order,
hyperedge_labels=hyperedge_labels,
hull=hull,
radius=radius,
rescale_sizes=rescale_sizes,
**kwargs,
)
else:
raise XGIError("The input must be a SimplicialComplex or Hypergraph")
ax, node_collection = draw_nodes(
H=H,
pos=pos,
ax=ax,
node_fc=node_fc,
node_ec=node_ec,
node_lw=node_lw,
node_size=node_size,
node_shape=node_shape,
node_fc_cmap=node_fc_cmap,
vmin=vmin,
vmax=vmax,
zorder=max_order,
params=settings,
node_labels=node_labels,
rescale_sizes=rescale_sizes,
**kwargs,
)
# compute axis limits
_update_lims(pos, ax)
ax.set_aspect(aspect, "datalim")
return ax, (node_collection, dyad_collection, edge_collection)
[docs]def draw_nodes(
H,
pos=None,
ax=None,
node_fc="white",
node_ec="black",
node_lw=1,
node_size=7,
node_shape="o",
node_fc_cmap="Reds",
vmin=None,
vmax=None,
zorder=None,
params=dict(),
node_labels=False,
rescale_sizes=True,
**kwargs,
):
"""Draw the nodes of a hypergraph
Parameters
----------
H : Hypergraph or SimplicialComplex
Higher-order network to plot.
pos : dict, optional
If passed, this dictionary of positions node_id:(x,y) is used for placing the
0-simplices. If None (default), use the `barycenter_spring_layout` to compute
the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
node_fc : str, iterable, or NodeStat, optional
Color of the nodes. If str, use the same color for all nodes. If other iterable,
or NodeStat, assume the colors are specified in the same order as the nodes are
found in H.nodes. By default, "white".
node_ec : color or sequence of colors, optional
Color of node borders. If color, use the same color for all nodes. If sequence
of colors, assume the colors are specified in the same order as the nodes are
found in H.nodes. By default, "black".
node_lw : int, float, iterable, or NodeStat, optional
Line width of the node borders in pixels. If int or float, use the same width
for all node borders. If iterable or NodeStat, assume the widths are specified
in the same order as the nodes are found in H.nodes. Values are clipped below
and above by min_node_lw and max_node_lw, respectively. By default, 1.
node_size : int, float, iterable, or NodeStat, optional
Radius of the nodes in pixels. If int or float, use the same radius for all
nodes. If iterable or NodeStat, assume the radiuses are specified in the same
order as the nodes are found in H.nodes. Values are clipped below
and above by min_node_size and max_node_size, respectively. By default, 15.
node_shape : string, optional
The shape of the node. Specification is as matplotlib.scatter
marker. Default is "o".
node_fc_cmap : colormap
Colormap for mapping node colors. By default, "Reds". Ignored, if `node_fc` is
a str (single color).
vmin : float or None
Minimum for the node_fc_cmap scaling. By default, None.
vmax : float or None
Maximum for the node_fc_cmap scaling. By default, None.
zorder : int
The layer on which to draw the nodes.
node_labels : bool or dict
If True, draw ids on the nodes. If a dict, must contain (node_id: label) pairs.
The default node_size (7) is too small to display the default labels well.
The user may need to set it to a size of a least 15.
rescale_sizes: bool, optional
If True, linearly interpolate `node_size` and `node_lw` between min/max values
(5/30 for size, 0/5 for lw) that can be changed in the other argument `params`.
If `node_size` (`node_lw`) is a single value, `interpolate_sizes` is ignored
for it. By default, True.
params : dict
Default parameters used if `rescale_sizes` is True.
Keys to override default settings:
* "min_node_size" (default: 5)
* "max_node_size" (default: 30)
* "min_node_lw" (default: 0)
* "max_node_lw" (default: 5)
kwargs : optional keywords
See `draw_node_labels` for a description of optional keywords.
Returns
-------
ax : matplotlib Axes
Axes plotted on
node_collection : matplotlib PathCollection
Collection containing the nodes
See Also
--------
draw
draw_hyperedges
draw_simplices
draw_node_labels
draw_hyperedge_labels
Notes
-----
If nodes are colored with a cmap, the `node_collection` returned
can be used to easily plot a colorbar corresponding to the node
colors. Simply do `plt.colorbar(node_collection)`.
Nodes with nonfinite `node_fc` (i.e. `inf`, `-inf` or `nan` are drawn
with the bad colormap color (see `plotnonfinitebool` in `plt.scatter` and
Colormap.set_bad from Matplotlib).
"""
settings = {
"min_node_size": 5,
"max_node_size": 30,
"min_node_lw": 0,
"max_node_lw": 5,
}
settings.update(params)
settings.update(kwargs)
ax, pos = _draw_init(H, ax, pos)
# convert pos to format convenient for scatter
try:
xy = np.asarray([pos[v] for v in H.nodes])
except KeyError as err:
raise XGIError(f"Node {err} has no position.") from err
# convert all formats to ndarray
node_size = _draw_arg_to_arr(node_size)
node_fc = _draw_arg_to_arr(node_fc)
node_lw = _draw_arg_to_arr(node_lw)
# avoid matplotlib scatter UserWarning "Parameters 'cmap' will be ignored"
if isinstance(node_fc, str) or (
isinstance(node_fc, np.ndarray) and is_color_like(node_fc[0])
):
node_fc_cmap = None
# check validity of input values
if np.any(node_size < 0):
raise ValueError("node_size cannot contain negative values.")
if np.any(node_lw < 0):
raise ValueError("node_lw cannot contain negative values.")
# interpolate if needed
if rescale_sizes and isinstance(node_size, np.ndarray):
node_size = _interp_draw_arg(
node_size, settings["min_node_size"], settings["max_node_size"]
)
if rescale_sizes and isinstance(node_lw, np.ndarray):
node_lw = _interp_draw_arg(
node_lw, settings["min_node_lw"], settings["max_node_lw"]
)
node_size = np.array(node_size) ** 2
# plot
node_collection = ax.scatter(
x=xy[:, 0],
y=xy[:, 1],
s=node_size,
marker=node_shape,
c=node_fc,
cmap=node_fc_cmap,
vmin=vmin,
vmax=vmax,
edgecolors=node_ec,
linewidths=node_lw,
zorder=zorder,
plotnonfinite=True, # plot points with nonfinite color
)
if node_labels:
# Get all valid keywords by inspecting the signatures of draw_node_labels
valid_label_kwds = signature(draw_node_labels).parameters.keys()
# Remove the arguments of this function (draw_networkx)
valid_label_kwds = valid_label_kwds - {"H", "pos", "ax", "node_labels"}
if any([k not in valid_label_kwds for k in kwargs]):
invalid_args = ", ".join([k for k in kwargs if k not in valid_label_kwds])
raise ValueError(f"Received invalid argument(s): {invalid_args}")
label_kwds = {k: v for k, v in kwargs.items() if k in valid_label_kwds}
draw_node_labels(H, pos, node_labels, ax_nodes=ax, **label_kwds)
# compute axis limits
_update_lims(pos, ax)
return ax, node_collection
[docs]def draw_hyperedges(
H,
pos=None,
ax=None,
dyad_color="black",
dyad_lw=1.5,
dyad_style="solid",
dyad_color_cmap="Greys",
dyad_vmin=None,
dyad_vmax=None,
edge_fc=None,
edge_fc_cmap="crest_r",
edge_vmin=None,
edge_vmax=None,
alpha=0.4,
max_order=None,
params=dict(),
hyperedge_labels=False,
hull=False,
radius=0.05,
rescale_sizes=True,
**kwargs,
):
"""Draw hyperedges.
Parameters
----------
H : Hypergraph
Hypergraph to plot
pos : dict, optional
If passed, this dictionary of positions node_id:(x,y) is used for placing the
0-simplices. If None (default), use the `barycenter_spring_layout` to compute
the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
dyad_color : str, dict, iterable, or EdgeStat, optional
Color of the dyadic links. If str, use the same color for all edges. If a dict,
must contain (edge_id: color_str) pairs. If iterable, assume the colors are
specified in the same order as the edges are found in H.edges. If EdgeStat, use
a colormap (specified with dyad_color_cmap) associated to it. By default,
"black".
dyad_lw : int, float, dict, iterable, or EdgeStat, optional
Line width of edges of order 1 (dyadic links). If int or float, use the same
width for all edges. If a dict, must contain (edge_id: width) pairs. If
iterable, assume the widths are specified in the same order as the edges are
found in H.edges. If EdgeStat, use a monotonic linear interpolation defined
between min_dyad_lw and max_dyad_lw. By default, 1.5.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap: matplotlib colormap
Colormap used to map the dyad colors. By default, "Greys".
dyad_vmin, dyad_vmax : float, optional
Minimum and maximum for dyad colormap scaling. By default, None.
edge_fc : color or list of colors or array-like or dict or EdgeStat, optional
Color of the hyperedges. The accepted formats are the same as
matplotlib's scatter, with the addition of dict and IDStat.
Those with colors:
* single color as a string
* single color as 3- or 4-tuple
* list of colors of length len(ids)
* dict of colors containing the `ids` as keys
Those with numerical values (will be mapped to colors):
* array of floats
* dict of floats containing the `ids` as keys
* IDStat containing the `ids` as keys
If None (default), color by edge size.
edge_fc_cmap: matplotlib colormap
Colormap used to map the edge colors. By default, "crest_r".
edge_vmin, edge_vmax : float, optional
Minimum and maximum for edge colormap scaling. By default, None.
alpha : float, optional
The edge transparency. By default, 0.4.
max_order : int, optional
Maximum of hyperedges to plot. By default, None.
hyperedge_labels : bool or dict, optional
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, None.
hull : bool, optional
Wether to draw hyperedes as convex hulls. By default, False.
radius: float, optional
Radius margin around the nodes when drawing convex hulls. Ignored if
`hull is False`. Default is 0.05.
rescale_sizes: bool, optional
If True, linearly interpolate `dyad_lw` and between min/max values
(1/10) that can be changed in the other argument `params`.
If `dyad_lw` is a single value, `interpolate_sizes` is ignored
for it. By default, True.
params : dict
Default parameters. Keys that may be useful to override default settings:
* "min_dyad_lw" (default: 1)
* "max_dyad_lw" (default: 10)
kwargs : optional keywords
See `draw_hyperedge_labels` for a description of optional keywords.
Returns
-------
ax : matplotlib Axes
Axes plotted on
collections : a tuple of 2 collections:
* dyad_collection : matplotlib LineCollection
Collection containing the dyads
* edge_collection : matplotlib PathCollection
Collection containing the edges
Raises
------
XGIError
If a SimplicialComplex is passed.
See Also
--------
draw
draw_nodes
draw_simplices
draw_node_labels
draw_hyperedge_labels
"""
settings = {
"min_dyad_lw": 1,
"max_dyad_lw": 10,
}
settings.update(params)
settings.update(kwargs)
ax, pos = _draw_init(H, ax, pos)
# filter edge sizes
if max_order is None:
max_order = max_edge_order(H)
dyads = H.edges.filterby("order", 1)
edges = H.edges.filterby("order", (2, max_order), "between")
if edge_fc is None: # color is proportional to size
edge_fc = edges.size
# convert all formats to ndarray
dyad_lw = _draw_arg_to_arr(dyad_lw)
# parse colors
dyad_color, dyad_c_mapped = _parse_color_arg(dyad_color, list(dyads))
edge_fc, edge_c_mapped = _parse_color_arg(edge_fc, list(edges))
# check validity of input values
if np.any(dyad_lw < 0):
raise ValueError("dyad_lw cannot contain negative values.")
# interpolate if needed
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
# convert dyad pos to format convenient for scatter
dyad_pos = np.asarray([(pos[list(e)[0]], pos[list(e)[1]]) for e in dyads.members()])
# plot dyads
if dyad_c_mapped:
dyad_c_arr = dyad_color
dyad_colors = None
else:
dyad_c_arr = None
dyad_colors = dyad_color
dyad_collection = LineCollection(
dyad_pos,
colors=dyad_colors,
array=dyad_c_arr, # colors if mapped, ie arr of floats
linewidths=dyad_lw,
antialiaseds=(1,),
linestyle=dyad_style,
cmap=dyad_color_cmap,
zorder=max_order - 1,
)
# dyad_collection.set_cmap(dyad_color_cmap)
if dyad_c_mapped:
dyad_collection.set_clim(dyad_vmin, dyad_vmax)
# dyad_collection.set_zorder(max_order - 1) # edges go behind nodes
ax.add_collection(dyad_collection)
# reorder to plot larger hyperedges first
ids_sorted = np.argsort(edges.size.aslist())[::-1]
# plot other hyperedges
if edge_c_mapped:
edge_fc_arr = edge_fc[ids_sorted]
edge_fc_colors = None
else:
edge_fc_arr = None
edge_fc_colors = edge_fc[ids_sorted] if len(edge_fc) > 1 else edge_fc
patches = []
for he in np.array(edges.members())[ids_sorted]:
d = len(he) - 1
he = list(he)
coordinates = [[pos[n][0], pos[n][1]] for n in he]
# Sorting the points counterclockwise (needed to have the correct filling)
sorted_coordinates = _CCW_sort(coordinates)
if hull:
# add points in circle with radius around each node
thetas = np.linspace(0, 2 * np.pi, num=100, endpoint=False)
offsets = radius * np.array([np.cos(thetas), np.sin(thetas)]).T
points = np.vstack([p + offsets for p in sorted_coordinates])
points = np.vstack([sorted_coordinates, points])
hull = ConvexHull(points)
pts = points[hull.vertices]
patch = plt.Polygon(pts, capstyle="round")
else:
patch = plt.Polygon(sorted_coordinates)
patches.append(patch)
edge_collection = PatchCollection(
patches,
facecolors=edge_fc_colors,
array=edge_fc_arr,
cmap=edge_fc_cmap,
alpha=alpha,
zorder=max_order - 2, # below dyads
)
# edge_collection.set_cmap(edge_fc_cmap)
if edge_c_mapped:
edge_collection.set_clim(edge_vmin, edge_vmax)
ax.add_collection(edge_collection)
if hyperedge_labels:
# Get all valid keywords by inspecting the signatures of draw_node_labels
valid_label_kwds = signature(draw_hyperedge_labels).parameters.keys()
# Remove the arguments of this function (draw_networkx)
valid_label_kwds = valid_label_kwds - {"H", "pos", "ax", "hyperedge_labels"}
if any([k not in valid_label_kwds for k in kwargs]):
invalid_args = ", ".join([k for k in kwargs if k not in valid_label_kwds])
raise ValueError(f"Received invalid argument(s): {invalid_args}")
label_kwds = {k: v for k, v in kwargs.items() if k in valid_label_kwds}
draw_hyperedge_labels(H, pos, hyperedge_labels, ax_edges=ax, **label_kwds)
# compute axis limits
_update_lims(pos, ax)
return ax, (dyad_collection, edge_collection)
[docs]def draw_simplices(
SC,
pos=None,
ax=None,
dyad_color="black",
dyad_lw=1.5,
dyad_style="solid",
dyad_color_cmap="Greys",
dyad_vmin=None,
dyad_vmax=None,
edge_fc=None,
edge_fc_cmap="crest_r",
edge_vmin=None,
edge_vmax=None,
alpha=0.4,
max_order=None,
params=dict(),
hyperedge_labels=False,
rescale_sizes=True,
**kwargs,
):
"""Draw maximal simplices and pairwise faces.
Parameters
----------
SC : SimplicialComplex
Simplicial complex to draw
pos : dict, optional
If passed, this dictionary of positions node_id:(x,y) is used for placing the
0-simplices. If None (default), use the `barycenter_spring_layout` to compute
the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
dyad_color : str, dict, iterable, or EdgeStat, optional
Color of the dyadic links. If str, use the same color for all edges. If a dict,
must contain (edge_id: color_str) pairs. If iterable, assume the colors are
specified in the same order as the edges are found in H.edges. If EdgeStat, use
a colormap (specified with dyad_color_cmap) associated to it. By default,
"black".
dyad_lw : int, float, dict, iterable, or EdgeStat, optional
Line width of edges of order 1 (dyadic links). If int or float, use the same
width for all edges. If a dict, must contain (edge_id: width) pairs. If
iterable, assume the widths are specified in the same order as the edges are
found in H.edges. If EdgeStat, use a monotonic linear interpolation defined
between min_dyad_lw and max_dyad_lw. By default, 1.5.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap: matplotlib colormap
Colormap used to map the dyad colors. By default, "Greys".
dyad_vmin, dyad_vmax : float, optional
Minimum and maximum for dyad colormap scaling. By default, None.
edge_fc : color or list of colors or array-like or dict or EdgeStat, optional
Color of the hyperedges. The accepted formats are the same as
matplotlib's scatter, with the addition of dict and IDStat.
Those with colors:
* single color as a string
* single color as 3- or 4-tuple
* list of colors of length len(ids)
* dict of colors containing the `ids` as keys
Those with numerical values (will be mapped to colors):
* array of floats
* dict of floats containing the `ids` as keys
* IDStat containing the `ids` as keys
If None (default), color by edge size.
edge_fc_cmap: matplotlib colormap
Colormap used to map the edge colors. By default, "crest_r".
edge_vmin, edge_vmax : float, optional
Minimum and maximum for edge colormap scaling. By default, None.
alpha : float, optional
The edge transparency. By default, 0.4.
max_order : int, optional
Maximum of hyperedges to plot. By default, None.
hyperedge_labels : bool or dict, optional
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, None.
rescale_sizes: bool, optional
If True, linearly interpolate `dyad_lw` and between min/max values
(1/10) that can be changed in the other argument `params`.
If `dyad_lw` is a single value, `interpolate_sizes` is ignored
for it. By default, True.
params : dict
Default parameters. Keys that may be useful to override default settings:
* "min_dyad_lw" (default: 1)
* "max_dyad_lw" (default: 10)
kwargs : optional keywords
See `draw_hyperedge_labels` for a description of optional keywords.
Returns
-------
ax
collections : a tuple of 2 collections:
* dyad_collection : matplotlib LineCollection
Collection containing the dyads
* edge_collection : matplotlib PathCollection
Collection containing the edges
Raises
------
XGIError
If a Hypergraph is passed.
See Also
--------
draw
draw_nodes
draw_hyperedges
draw_node_labels
draw_hyperedge_labels
"""
if max_order:
max_edges = SC.edges.filterby("order", max_order, "leq").members()
SC = SimplicialComplex(max_edges) # SC without simplices larger than max_order
# Plot only the maximal simplices, thus let's convert the SC to H
H_ = convert.from_max_simplices(SC)
# add the projected pairwise interactions
dyads = subfaces(H_.edges.members(), order=1)
H_.add_edges_from(dyads)
H_.cleanup(
multiedges=False,
isolates=True,
connected=False,
relabel=False,
in_place=True,
singletons=True,
) # remove multi-dyads
if not max_order:
max_order = max_edge_order(H_)
ax, (dyad_collection, edge_collection) = draw_hyperedges(
H_,
pos=pos,
ax=ax,
dyad_color=dyad_color,
dyad_lw=dyad_lw,
dyad_style=dyad_style,
dyad_color_cmap=dyad_color_cmap,
dyad_vmin=dyad_vmin,
dyad_vmax=dyad_vmax,
edge_fc=edge_fc,
edge_fc_cmap=edge_fc_cmap,
edge_vmin=edge_vmin,
edge_vmax=edge_vmax,
alpha=alpha,
max_order=max_order,
params=params,
hyperedge_labels=hyperedge_labels,
rescale_sizes=rescale_sizes,
**kwargs,
)
return ax, (dyad_collection, edge_collection)
[docs]def draw_node_labels(
H,
pos,
node_labels=False,
font_size_nodes=10,
font_color_nodes="black",
font_family_nodes="sans-serif",
font_weight_nodes="normal",
alpha_nodes=None,
bbox_nodes=None,
horizontalalignment_nodes="center",
verticalalignment_nodes="center",
ax_nodes=None,
clip_on_nodes=True,
zorder=None,
):
"""Draw node labels on the hypergraph or simplicial complex.
Parameters
----------
H : Hypergraph or SimplicialComplex.
pos : dict
Dictionary of positions node_id:(x,y).
node_labels : bool or dict, optional
If True, draw ids on the nodes. If a dict, must contain (node_id: label) pairs.
By default, False.
font_size_nodes : int, optional
Font size for text labels, by default 10.
font_color_nodes : str, optional
Font color string, by default "black".
font_family_nodes : str, optional
Font family, by default "sans-serif".
font_weight_nodes : str (default='normal')
Font weight.
alpha_nodes : float, optional
The text transparency, by default None.
bbox_nodes : Matplotlib bbox, optional
Specify text box properties (e.g. shape, color etc.) for node labels.
When it is None (default), use Matplotlib's ax.text default
horizontalalignment_nodes : str, optional
Horizontal alignment {'center', 'right', 'left'}.
By default, "center".
verticalalignment_nodes : str, optional
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}.
By default, "center".
ax_nodes : matplotlib.pyplot.axes, optional
Draw the graph in the specified Matplotlib axes.
By default, None.
clip_on_nodes : bool, optional
Turn on clipping of node labels at axis boundaries.
By default, True.
zorder : int, optional
The vertical order on which to draw the labels. By default, None,
in which case it is plotted above the last plotted object.
Returns
-------
dict
`dict` of labels keyed by node id.
See Also
--------
draw
draw_nodes
draw_hyperedges
draw_simplices
draw_hyperedge_labels
"""
if ax_nodes is None:
ax = plt.gca()
else:
ax = ax_nodes
if node_labels is True:
node_labels = {id: id for id in H.nodes}
# Plot the labels in the last layer
if zorder is None:
zorder = max_edge_order(H) + 1
text_items = {}
for idx, label in node_labels.items():
(x, y) = pos[idx]
if not isinstance(label, str):
label = str(label)
t = ax.text(
x,
y,
label,
size=font_size_nodes,
color=font_color_nodes,
family=font_family_nodes,
weight=font_weight_nodes,
alpha=alpha_nodes,
horizontalalignment=horizontalalignment_nodes,
verticalalignment=verticalalignment_nodes,
transform=ax.transData,
bbox=bbox_nodes,
clip_on=clip_on_nodes,
zorder=zorder,
)
text_items[idx] = t
return text_items
[docs]def draw_hyperedge_labels(
H,
pos,
hyperedge_labels=False,
font_size_edges=10,
font_color_edges="black",
font_family_edges="sans-serif",
font_weight_edges="normal",
alpha_edges=None,
bbox_edges=None,
horizontalalignment_edges="center",
verticalalignment_edges="center",
ax_edges=None,
rotate_edges=False,
clip_on_edges=True,
):
"""Draw hyperedge labels on the hypegraph or simplicial complex.
Parameters
----------
H : Hypergraph.
pos : dict
Dictionary of positions node_id:(x,y).
hyperedge_labels : bool or dict, optional
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, False.
font_size_edges : int, optional
Font size for text labels, by default 10.
font_color_edges : str, optional
Font color string, by default "black".
font_family_edges : str (default='sans-serif')
Font family.
font_weight_edges : str (default='normal')
Font weight.
alpha_edges : float, optional
The text transparency, by default None.
bbox_edges : Matplotlib bbox, optional
Specify text box properties (e.g. shape, color etc.) for edge labels.
By default, {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}
horizontalalignment_edges : str, optional
Horizontal alignment {'center', 'right', 'left'}.
By default, "center".
verticalalignment_edges: str, optional
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}.
By default, "center".
ax_edges : matplotlib.pyplot.axes, optional
Draw the graph in the specified Matplotlib axes. By default, None.
rotate_edges : bool, optional
Rotate edge labels for dyadic links to lie parallel to edges, by default False.
clip_on_edges: bool, optional
Turn on clipping of hyperedge labels at axis boundaries, by default True.
Returns
-------
dict
`dict` of labels keyed by hyperedge id.
See Also
--------
draw
draw_nodes
draw_hyperedges
draw_simplices
draw_node_labels
"""
if ax_edges is None:
ax = plt.gca()
else:
ax = ax_edges
if hyperedge_labels is True:
hyperedge_labels = {id: id for id in H.edges}
text_items = {}
for id, label in hyperedge_labels.items():
he = H.edges.members(id)
coordinates = [[pos[n][0], pos[n][1]] for n in he]
x, y = np.mean(coordinates, axis=0)
if len(he) == 2:
# Rotate edge labels for dyadic links to lie parallel to edges
if rotate_edges:
x_diff, y_diff = np.subtract(coordinates[1], coordinates[0])
angle = np.arctan2(y_diff, x_diff) / (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
else:
trans_angle = 0.0
# Use default box of white with white border
if bbox_edges is None:
bbox = dict(boxstyle="round", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0))
else:
bbox = bbox_edges
if not isinstance(label, str):
label = str(label)
t = ax.text(
x,
y,
label,
size=font_size_edges,
color=font_color_edges,
family=font_family_edges,
weight=font_weight_edges,
alpha=alpha_edges,
horizontalalignment=horizontalalignment_edges,
verticalalignment=verticalalignment_edges,
rotation=trans_angle,
transform=ax.transData,
bbox=bbox,
clip_on=clip_on_edges,
)
text_items[id] = t
return text_items
[docs]def draw_multilayer(
H,
pos=None,
ax=None,
node_fc="white",
node_ec="black",
node_lw=1,
node_size=5,
node_shape="o",
node_fc_cmap="Reds",
vmin=None,
vmax=None,
dyad_color="grey",
dyad_lw=1.5,
dyad_style="solid",
edge_fc=None,
edge_fc_cmap="crest_r",
edge_vmin=None,
edge_vmax=None,
alpha=0.4,
layer_color="grey",
layer_cmap="crest_r",
max_order=None,
conn_lines=True,
conn_lines_style="dotted",
h_angle=10,
v_angle=20,
sep=0.4,
rescale_sizes=True,
**kwargs,
):
"""Draw a hypergraph or simplicial complex visualized in 3D
showing hyperedges/simplices of different orders on superimposed layers.
Parameters
----------
H : Hypergraph or SimplicialComplex.
Higher-order network to plot.
pos : dict or None, optional
The positions of the nodes in the multilayer network.
If None, a default layout will be computed using
xgi.barycenter_spring_layout(). Default is None.
ax : matplotlib Axes3DSubplot or None, optional
The subplot to draw the visualization on.
If None, a new subplot will be created. Default is None.
node_fc : str, iterable, or NodeStat, optional
Color of the nodes. If str, use the same color for all nodes. If other iterable,
or NodeStat, assume the colors are specified in the same order as the nodes are
found in H.nodes. By default, "white".
node_ec : color or sequence of colors, optional
Color of node borders. If color, use the same color for all nodes. If sequence
of colors, assume the colors are specified in the same order as the nodes are
found in H.nodes. By default, "black".
node_lw : int, float, iterable, or NodeStat, optional
Line width of the node borders in pixels. If int or float, use the same width
for all node borders. If iterable or NodeStat, assume the widths are specified
in the same order as the nodes are found in H.nodes. Values are clipped below
and above by min_node_lw and max_node_lw, respectively. By default, 1.
node_size : int, float, iterable, or NodeStat, optional
Radius of the nodes in pixels. If int or float, use the same radius for all
nodes. If iterable or NodeStat, assume the radiuses are specified in the same
order as the nodes are found in H.nodes. Values are clipped below
and above by min_node_size and max_node_size, respectively. By default, 5.
node_shape : string, optional
The shape of the node. Specification is as matplotlib.scatter
marker. Default is "o".
node_fc_cmap : colormap
Colormap for mapping node colors. By default, "Reds". Ignored, if `node_fc` is
a str (single color).
vmin : float or None
Minimum for the node_fc_cmap scaling. By default, None.
vmax : float or None
Maximum for the node_fc_cmap scaling. By default, None.
dyad_color : color or list of colors
Color of the dyadic links. If str, use the same color for all edges. If iterable,
assume the colors are specified in the same order as the edges are found in H.edges.
By default, "black".
dyad_lw : int, float, dict, iterable, or EdgeStat, optional
Line width of edges of order 1 (dyadic links). If int or float, use the same
width for all edges. If a dict, must contain (edge_id: width) pairs. If
iterable, assume the widths are specified in the same order as the edges are
found in H.edges. If EdgeStat, use a monotonic linear interpolation defined
between min_dyad_lw and max_dyad_lw. By default, 1.5.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
edge_fc : color or list of colors or array-like or dict or EdgeStat, optional
Color of the hyperedges. The accepted formats are the same as
matplotlib's scatter, with the addition of dict and IDStat.
Those with colors:
* single color as a string
* single color as 3- or 4-tuple
* list of colors of length len(ids)
* dict of colors containing the `ids` as keys
Those with numerical values (will be mapped to colors):
* array of floats
* dict of floats containing the `ids` as keys
* IDStat containing the `ids` as keys
If None (default), color by edge size.
edge_fc_cmap: matplotlib colormap, optional
Colormap used to map the edge colors. By default, "crest_r".
edge_vmin, edge_vmax : float, optional
Minimum and maximum for edge colormap scaling. By default, None.
alpha : float, optional
The edge transparency. By default, 0.4.
layer_color : color or list of colors, optional
Color of layers. By default, "grey".
layer_cmap : colormap, optional
Colormap to use in case of numerical values in layer_color. Ignored if layer_color
does not contain numerical values to be mapped. By default, "crest_r", but ignored.
max_order : int, optional
Maximum of hyperedges to plot. If None (default), plots all orders.
conn_lines : bool, optional
Whether to draw connections between layers. Default is True.
conn_lines_style : str, optional
The linestyle of the connections between layers. Default is 'dotted'.
h_angle : float, optional
The rotation angle around the horizontal axis in degrees. Default is 10.
v_angle : float, optional
The rotation angle around the vertical axis in degrees. Default is 0.
sep : float, optional
The separation between layers. Default is 0.4.
**kwargs : optional args
Alternate default values. Values that can be overwritten are the following:
* "min_node_size" (default: 10)
* "max_node_size" (default: 30)
* "min_node_lw" (default: 2)
* "max_node_lw" (default: 10)
* "min_dyad_lw" (default: 1)
* "max_dyad_lw" (default: 5)
Returns
-------
ax : matplotlib Axes3DSubplot
The subplot with the multilayer network visualization.
collections : a tuple of 2 collections:
* node_collection : matplotlib PathCollection
Collection containing the nodes one the top layer
* edge_collection : matplotlib PathCollection
Collection containing the edges of size > 2
"""
settings = {
"min_node_size": 10,
"max_node_size": 30,
"min_dyad_lw": 2,
"max_dyad_lw": 10,
"min_node_lw": 1,
"max_node_lw": 5,
}
settings.update(kwargs)
if ax is None:
_, ax = plt.subplots(subplot_kw={"projection": "3d"})
if pos is None:
pos = barycenter_spring_layout(H)
s = unique_edge_sizes(H)
if max_order is None:
max_order = max(s) - 1
else:
max_order = min(max_order, max(s) - 1)
min_order = min(s) - 1
orders = list(range(min_order, max_order + 1))
xs, ys = zip(*pos.values())
dyads = H.edges.filterby("order", 1)
edges = H.edges.filterby("order", (2, max_order), "between")
if edge_fc is None: # color is proportional to size
edge_fc = edges.size
# convert pos to format convenient for scatter
try:
xy = np.asarray([pos[v] for v in H.nodes])
except KeyError as err:
raise XGIError(f"Node {err} has no position.") from err
# convert all formats to ndarray
node_size = _draw_arg_to_arr(node_size)
node_fc = _draw_arg_to_arr(node_fc)
node_lw = _draw_arg_to_arr(node_lw)
dyad_lw = _draw_arg_to_arr(dyad_lw)
layer_color = _draw_arg_to_arr(layer_color)
# avoid matplotlib scatter UserWarning "Parameters 'cmap' will be ignored"
if isinstance(node_fc, str) or (
isinstance(node_fc, np.ndarray) and is_color_like(node_fc[0])
):
node_fc_cmap = None
# check validity of input values
if np.any(node_size < 0):
raise ValueError("node_size cannot contain negative values.")
if np.any(node_lw < 0):
raise ValueError("node_lw cannot contain negative values.")
# interpolate if needed
if rescale_sizes and isinstance(node_size, np.ndarray):
node_size = _interp_draw_arg(
node_size, settings["min_node_size"], settings["max_node_size"]
)
if rescale_sizes and isinstance(node_lw, np.ndarray):
node_lw = _interp_draw_arg(
node_lw, settings["min_node_lw"], settings["max_node_lw"]
)
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
# check validity of input values
if np.any(dyad_lw < 0):
raise ValueError("dyad_lw cannot contain negative values.")
# parse colors
dyad_color, dyad_c_mapped = _parse_color_arg(dyad_color, list(dyads))
edge_fc, edge_c_mapped = _parse_color_arg(edge_fc, list(edges))
layer_color, layer_c_mapped = _parse_color_arg(layer_color, orders)
node_size = np.array(node_size) ** 2
# compute ax limits
xdiff = np.max(xs) - np.min(xs)
ydiff = np.max(ys) - np.min(ys)
ymin = np.min(ys) - ydiff * 0.1
ymax = np.max(ys) + ydiff * 0.1
xmin = np.min(xs) - xdiff * 0.1 # * (width / height)
xmax = np.max(xs) + xdiff * 0.1 # * (width / height)
xx, yy = np.meshgrid([xmin, xmax], [ymin, ymax])
# plot layers
for jj, d in enumerate(orders):
z = [sep * d] * H.num_nodes
# draw surfaces corresponding to the different orders
zz = np.zeros(xx.shape) + d * sep
if layer_c_mapped:
layer_c = None
else:
layer_c = layer_color[jj] if len(layer_color) > 1 else layer_color
layer_cmap = None
ax.plot_surface(
xx,
yy,
zz,
color=layer_c,
cmap=layer_cmap,
vmin=min_order * sep,
vmax=max_order * sep,
alpha=0.1,
zorder=0,
)
# convert dyad pos to format convenient for scatter
dyad_pos = [
(np.append(pos[list(e)[0]], sep), np.append(pos[list(e)[1]], sep))
for e in dyads.members()
]
# plot dyads
if dyad_c_mapped:
raise ValueError(
"dyad_color needs to be a color or list of colors, not numerical values."
)
dyad_collection = Line3DCollection(
dyad_pos,
colors=dyad_color,
linewidths=dyad_lw,
antialiaseds=(1,),
linestyle=dyad_style,
zorder=1, # above layer
)
ax.add_collection3d(dyad_collection)
# reorder to plot larger hyperedges first
ids_sorted = np.argsort(edges.size.aslist())[::-1]
# plot other hyperedges
if edge_c_mapped:
edge_fc_arr = edge_fc[ids_sorted]
edge_fc_colors = None
else:
edge_fc_arr = None
edge_fc_colors = edge_fc[ids_sorted] if len(edge_fc) > 1 else edge_fc
patches = []
zs = []
for he in np.array(edges.members())[ids_sorted]:
d = len(he) - 1
zs.append(d * sep)
he = list(he)
coordinates = [[pos[n][0], pos[n][1], d * sep] for n in he]
# Sorting the points counterclockwise (needed to have the correct filling)
sorted_coordinates = _CCW_sort(coordinates)
patches.append(sorted_coordinates)
edge_collection = Poly3DCollection(
patches,
facecolors=edge_fc_colors,
array=edge_fc_arr,
cmap=edge_fc_cmap,
alpha=alpha,
zorder=max_order - 2, # below dyads
)
edge_collection.set_cmap(edge_fc_cmap)
if edge_c_mapped:
edge_collection.set_clim(edge_vmin, edge_vmax)
ax.add_collection3d(edge_collection)
# draw inter-layer links between nodes
if conn_lines:
lines3d_between = [
(list(pos[i]) + [min_order * sep], list(pos[i]) + [max_order * sep])
for i in H.nodes
]
between_lines = Line3DCollection(
lines3d_between,
zorder=5,
color=".5",
alpha=0.4,
linestyle=conn_lines_style,
linewidth=1,
)
ax.add_collection3d(between_lines)
# draw nodes (last)
for d in orders:
z = [sep * d] * H.num_nodes
node_collection = ax.scatter(
xs=xy[:, 0],
ys=xy[:, 1],
zs=z,
s=node_size,
marker=node_shape,
c=node_fc,
cmap=node_fc_cmap,
vmin=vmin,
vmax=vmax,
edgecolors=node_ec,
linewidths=node_lw,
zorder=max_order + 1,
plotnonfinite=True, # plot points with nonfinite color
alpha=1,
)
ax.view_init(h_angle, v_angle)
ax.set_ylim(np.min(ys) - ydiff * 0.1, np.max(ys) + ydiff * 0.1)
ax.set_xlim(np.min(xs) - xdiff * 0.1, np.max(xs) + xdiff * 0.1)
ax.set_axis_off()
ax.set_aspect("equal")
return ax, (node_collection, edge_collection)
[docs]def draw_bipartite(
H,
pos=None,
ax=None,
node_fc="white",
node_ec="black",
node_lw=1,
node_size=7,
node_shape="o",
node_fc_cmap="Reds",
edge_marker_fc=None,
edge_marker_ec="black",
edge_marker_lw=1,
edge_marker_size=7,
edge_marker_shape="s",
edge_marker_fc_cmap="crest_r",
max_order=None,
dyad_color=None,
dyad_lw=1,
dyad_style="solid",
dyad_color_cmap="crest_r",
node_labels=None,
hyperedge_labels=None,
arrowsize=10,
arrowstyle="->",
connectionstyle="arc3",
rescale_sizes=True,
aspect="equal",
**kwargs,
):
"""Draw a hypergraph as a bipartite network.
Parameters
----------
H : Hypergraph or DiHypergraph
The hypergraph to draw.
pos : tuple of two dicts, optional
The tuple should contain a (1) dictionary of positions node_id:(x,y) for
placing node markers, and (2) a dictionary of positions edge_id:(x,y) for
placing the edge markers. If None (default), use the `bipartite_spring_layout`
to compute the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
node_fc : str, dict, iterable, or NodeStat, optional
Color of the nodes. If str, use the same color for all nodes. If a dict, must
contain (node_id: color_str) pairs. If other iterable, assume the colors are
specified in the same order as the nodes are found in H.nodes. If NodeStat, use
the colormap specified with node_fc_cmap. By default, "white".
node_ec : str, dict, iterable, or NodeStat, optional
Color of node borders. If str, use the same color for all nodes. If a dict,
must contain (node_id: color_str) pairs. If other iterable, assume the colors
are specified in the same order as the nodes are found in H.nodes. If NodeStat,
use the colormap specified with node_ec_cmap. By default, "black".
node_lw : int, float, dict, iterable, or NodeStat, optional
Line width of the node borders in pixels. If int or float, use the same width
for all node borders. If a dict, must contain (node_id: width) pairs. If
iterable, assume the widths are specified in the same order as the nodes are
found in H.nodes. If NodeStat, use a monotonic linear interpolation defined
between min_node_lw and max_node_lw. By default, 1.
node_size : int, float, dict, iterable, or NodeStat, optional
Radius of the nodes in pixels. If int or float, use the same radius for all
nodes. If a dict, must contain (node_id: radius) pairs. If iterable, assume
the radiuses are specified in the same order as the nodes are found in
H.nodes. If NodeStat, use a monotonic linear interpolation defined between
min_node_size and max_node_size. By default, 7.
node_shape : str, optional
Marker used for the nodes. By default 'o' (circle marker).
node_fc_cmap : colormap
Colormap for mapping node colors. By default, "Reds". Ignored, if `node_fc` is
a str (single color).
edge_marker_fc: str, dict, iterable, optional
Filling color of the hyperedges (markers). If str, use the same color for all hyperedges.
If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
assume the colors are specified in the same order as the hyperedges are found
in H.edges. If None, colors markers by edge size. By default, None.
edge_marker_ec: str, dict, iterable, optional
Edge color of the hyperedges (markers). If str, use the same color for all hyperedges.
If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
assume the colors are specified in the same order as the hyperedges are found
in H.edges. By default, "black".
edge_marker_lw : int, float, dict, iterable, or EdgeStat, optional
Line width of the edge marker borders in pixels. If int or float, use the same width
for all edge marker borders. If a dict, must contain (edge_id: width) pairs. If
iterable, assume the widths are specified in the same order as the nodes are
found in H.edges. If EdgeStat, use a monotonic linear interpolation defined
between min_edge_marker_lw and max_edge_marker_lw. By default, 1.
edge_marker_size : int, float, dict, iterable, or EdgeStat, optional
Radius of the edge markers in pixels. If int or float, use the same radius for all
edge markers. If a dict, must contain (edge_id: radius) pairs. If iterable, assume
the radii are specified in the same order as the edges are found in
H.edges. If EdgeStat, use a monotonic linear interpolation defined between
min_edge_marker_size and max_edge_marker_size. By default, 7.
edge_marker_shape: str, optional
Marker used for the hyperedges. By default 's' (square marker). If "", no marker is
displayed.
edge_marker_fc_cmap : colormap
Colormap for mapping edge marker colors. By default, "Blues".
Ignored, if `edge_marker_fc` is a str (single color) or an iterable of colors.
max_order : int, optional
Maximum of hyperedges to plot. If None (default), plots all orders.
dyad_color : str, dict, iterable, optional
Color of the bipartite edges. If str, use the same color for all edges.
If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
assume the colors are specified in the same order as the hyperedges are found
in H.edges. By default, "black".
dyad_lw : int, float, dict, iterable, optional
Line width of the bipartite edges. If int or float, use the same width for
all hyperedges. If a dict, must contain (hyperedge_id: width) pairs. If other
iterable, assume the widths are specified in the same order as the hyperedges
are found in H.edges. By default, 1.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap : colormap
Colormap for mapping bipartite edge colors. By default, "Greys".
Ignored, if `dyad_color` is a str (single color) or an iterable of colors.
node_labels : bool or dict, optional
If True, draw ids on the nodes. If a dict, must contain (node_id: label) pairs.
By default, False.
hyperedge_labels : bool or dict, optional
If True, draw ids on the hyperedges. If a dict, must contain (edge_id: label)
pairs. By default, False.
arrowsize : int (default=10)
Size of the arrow head's length and width. See `matplotlib.patches.FancyArrowPatch`
for attribute `mutation_scale` for more info. Only used if the higher-order network
is a `DiHypergraph`.
arrowstyle : str, optional
By default: '->'. See `matplotlib.patches.ArrowStyle` for more options.
Only used if the higher-order network is a `DiHypergraph`.
connectionstyle : string (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.
Only used if the higher-order network is a `DiHypergraph`.
rescale_sizes: bool, optional
If True, linearly interpolate `node_size` and between min/max values
that can be changed in the other argument `params`.
If `node_size` is a single value, this is ignored. By default, True.
aspect : {"auto", "equal"} or float, optional
Set the aspect ratio of the axes scaling, i.e. y/x-scale. `aspect` is passed
directly to matplotlib's `ax.set_aspect()`. Default is `equal`. See full
description at
https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_aspect.html
**kwargs : optional args
Alternate default values. Values that can be overwritten are the following:
* min_node_size (default: 5)
* max_node_size (default: 30)
* min_node_lw (default: 0)
* max_node_lw (default: 5)
* min_edge_marker_size (default: 5)
* max_edge_marker_size (default: 30)
* min_edge_marker_lw (default: 0)
* max_edge_marker_lw (default: 5)
* min_dyad_lw (default: 1)
* max_dyad_lw (default: 10)
* min_source_margin (default: 0)
* min_target_margin (default: 0)
Returns
-------
ax : matplotlib.pyplot.axes
The axes corresponding the visualization
collections : a tuple of 3 collections:
* node_collection : matplotlib PathCollection
Collection containing the nodes
* edge_marker_collection : matplotlib PathCollection
Collection containing the edge markers
* dyad_collection : matplotlib LineCollection if undirected,
list of FancyArrowPatches if not
Collection containing the edges
Raises
------
XGIError
If the network is not a Hypergraph, SimplicialComplex or a DiHypergraph.
See Also
--------
draw
draw_multilayer
"""
is_directed = False
if isinstance(H, DiHypergraph):
from ..convert import to_hypergraph
DH = H.copy()
H = to_hypergraph(DH)
is_directed = True
if not isinstance(H, Hypergraph):
raise XGIError("The input must be a Hypergraph")
settings = {
"min_node_lw": 0,
"max_node_lw": 5,
"min_node_size": 5,
"max_node_size": 30,
"min_edge_marker_lw": 0,
"max_edge_marker_lw": 5,
"min_edge_marker_size": 0,
"max_edge_marker_size": 5,
"min_dyad_lw": 1,
"max_dyad_lw": 10,
"min_source_margin": 0,
"min_target_margin": 0,
}
settings.update(kwargs)
node_settings = {
"min_node_lw": settings["min_node_lw"],
"max_node_lw": settings["max_node_lw"],
"min_node_size": settings["min_node_size"],
"max_node_size": settings["max_node_size"],
}
edge_marker_settings = {
"min_node_lw": settings["min_edge_marker_lw"],
"max_node_lw": settings["max_edge_marker_lw"],
"min_node_size": settings["min_edge_marker_size"],
"max_node_size": settings["max_edge_marker_size"],
}
if not pos:
pos = bipartite_spring_layout(H)
elif not (isinstance(pos[0], dict) and isinstance(pos[1], dict)):
raise XGIError("Position must be a 2-tuple of dictionaries!")
node_pos, edge_pos = pos
if ax is None:
ax = plt.gca()
if not max_order:
max_order = max_edge_order(H)
D = H.dual()
if edge_marker_fc is None:
edge_marker_fc = D.nodes.degree
ax, node_collection = draw_nodes(
H=H,
pos=node_pos,
ax=ax,
node_fc=node_fc,
node_ec=node_ec,
node_lw=node_lw,
node_size=node_size,
node_shape=node_shape,
node_fc_cmap=node_fc_cmap,
zorder=2,
params=node_settings,
node_labels=node_labels,
rescale_sizes=rescale_sizes,
**kwargs,
)
ax, edge_marker_collection = draw_nodes(
H=D,
pos=edge_pos,
ax=ax,
node_fc=edge_marker_fc,
node_ec=edge_marker_ec,
node_lw=edge_marker_lw,
node_size=edge_marker_size,
node_shape=edge_marker_shape,
node_fc_cmap=edge_marker_fc_cmap,
zorder=1,
params=edge_marker_settings,
node_labels=hyperedge_labels,
rescale_sizes=rescale_sizes,
**kwargs,
)
if is_directed:
ax = draw_directed_dyads(
DH,
pos=pos,
ax=ax,
max_order=max_order,
dyad_color=dyad_color,
dyad_lw=dyad_lw,
dyad_style=dyad_style,
dyad_color_cmap=dyad_color_cmap,
rescale_sizes=rescale_sizes,
arrowsize=arrowsize,
arrowstyle=arrowstyle,
connectionstyle=connectionstyle,
node_size=node_size,
node_shape=node_shape,
edge_marker_size=edge_marker_size,
edge_marker_shape=edge_marker_shape,
**kwargs,
)
else:
ax, dyad_collection = draw_undirected_dyads(
H,
pos=pos,
ax=ax,
max_order=max_order,
dyad_color=dyad_color,
dyad_lw=dyad_lw,
dyad_style=dyad_style,
dyad_color_cmap=dyad_color_cmap,
rescale_sizes=rescale_sizes,
**kwargs,
)
pos = {}
for i, n in enumerate(node_pos):
pos[i] = node_pos[n]
n = H.num_nodes
for i, e in enumerate(edge_pos):
pos[i + n] = edge_pos[e]
# compute axis limits
_update_lims(pos, ax)
ax.set_aspect(aspect, "datalim")
if is_directed:
return ax, (node_collection, edge_marker_collection)
else:
return ax, (node_collection, edge_marker_collection, dyad_collection)
[docs]def draw_undirected_dyads(
H,
pos=None,
ax=None,
max_order=None,
dyad_color=None,
dyad_lw=1,
dyad_style="solid",
dyad_color_cmap="crest_r",
rescale_sizes=True,
**kwargs,
):
"""Draw the bipartite edges of an undirected hypergraph.
Parameters
----------
H : Hypergraph
The hypergraph to draw.
pos : tuple of two dicts, optional
The tuple should contains a (1) dictionary of positions node_id:(x,y) for
placing node markers, and (2) a dictionary of positions edge_id:(x,y) for
placing the edge markers. If None (default), use the `bipartite_spring_layout`
to compute the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
max_order : int, optional
Maximum of hyperedges to plot. If None (default), plots all orders.
dyad_color : str, dict, iterable, optional
Color of the bipartite edges. If str, use the same color for all edges.
If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
assume the colors are specified in the same order as the hyperedges are found
in H.edges. By default, "black".
dyad_lw : int, float, dict, iterable, optional
Line width of the bipartite edges. If int or float, use the same width for
all hyperedges. If a dict, must contain (hyperedge_id: width) pairs. If other
iterable, assume the widths are specified in the same order as the hyperedges
are found in H.edges. By default, 1.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap : colormap
Colormap for mapping bipartite edge colors. By default, "Greys".
Ignored, if `dyad_color` is a str (single color) or an iterable of colors.
rescale_sizes: bool, optional
If True, linearly interpolate `node_size` and between min/max values
that can be changed in the other argument `params`.
If `node_size` is a single value, this is ignored. By default, True.
**kwargs : optional args
Alternate default values. Values that can be overwritten are the following:
* min_dyad_lw (default: 1)
* max_dyad_lw (default: 10)
Returns
-------
ax : matplotlib.pyplot.axes
The axes corresponding the visualization
* dyad_collection : matplotlib LineCollection
of bipartite edges
Raises
------
XGIError
If DiHypergraph is passed.
See Also
--------
draw_bipartite
draw_directed_dyads
"""
settings = {
"min_dyad_lw": 1,
"max_dyad_lw": 10,
}
settings.update(kwargs)
if not isinstance(H, Hypergraph):
raise XGIError("The input must be a Hypergraph")
if not pos:
pos = bipartite_spring_layout(H)
if ax is None:
ax = plt.gca()
if dyad_color is None: # color is proportional to size
dyad_color = H.edges.size
if not max_order:
edge_ids = list(H.edges)
max_order = max_edge_order(H)
else:
edge_ids = list(H.edges.filterby("order", max_order, "leq"))
node_pos, edge_pos = pos
dyads = to_bipartite_edgelist(H)
dyad_pos = np.asarray(
[(node_pos[e[0]], edge_pos[e[1]]) for e in dyads if e[1] in edge_ids]
)
dyad_lw = _draw_arg_to_arr(dyad_lw)
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
# parse colors
dyad_color, dyads_c_mapped = _parse_color_arg(dyad_color, H.edges)
# The following two list comprehensions map colors assigned to a hyperedge to
# all of the bipartite edges, so that users need not specify colors for every
# node-edge incidence.
if isinstance(dyad_lw, np.ndarray):
dyad_lw = np.array(
list(
chaini([lw] * int(s) for s, lw in zip(H.edges.size.aslist(), dyad_lw)),
dtype=float,
)
)
if isinstance(dyad_color, np.ndarray):
dyad_color = np.array(
list(
chaini(
[dc] * int(s) for s, dc in zip(H.edges.size.aslist(), dyad_color)
),
)
)
# convert numbers to colors for FancyArrowPatch
if dyads_c_mapped:
norm = mpl.colors.Normalize()
m = cm.ScalarMappable(norm=norm, cmap=dyad_color_cmap)
dyad_color = m.to_rgba(dyad_color)
# check validity of input values
if np.any(dyad_lw < 0):
raise ValueError("dyad_lw cannot contain negative values.")
# interpolate if needed
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
dyad_collection = LineCollection(
dyad_pos,
colors=dyad_color,
linewidths=dyad_lw,
antialiaseds=(1,),
linestyle=dyad_style,
cmap=dyad_color_cmap,
zorder=0,
)
ax.add_collection(dyad_collection)
return ax, dyad_collection
[docs]def draw_directed_dyads(
H,
pos=None,
ax=None,
max_order=None,
dyad_color=None,
dyad_lw=1,
dyad_style="solid",
dyad_color_cmap="crest_r",
arrowsize=10,
arrowstyle="->",
connectionstyle="arc3",
node_size=5,
node_shape="o",
edge_marker_size=5,
edge_marker_shape="s",
rescale_sizes=True,
**kwargs,
):
"""Draw the bipartite edges of a directed hypergraph.
Parameters
----------
H : DiHypergraph
The hypergraph to draw.
pos : tuple of two dicts, optional
The tuple should contains a (1) dictionary of positions node_id:(x,y) for
placing node markers, and (2) a dictionary of positions edge_id:(x,y) for
placing the edge markers. If None (default), use the `bipartite_spring_layout`
to compute the positions.
ax : matplotlib.pyplot.axes, optional
Axis to draw on. If None (default), get the current axes.
max_order : int, optional
Maximum of hyperedges to plot. If None (default), plots all orders.
dyad_color : str, dict, iterable, optional
Color of the bipartite edges. If str, use the same color for all edges.
If a dict, must contain (hyperedge_id: color_str) pairs. If other iterable,
assume the colors are specified in the same order as the hyperedges are found
in H.edges. By default, "black".
dyad_lw : int, float, dict, iterable, optional
Line width of the bipartite edges. If int or float, use the same width for
all hyperedges. If a dict, must contain (hyperedge_id: width) pairs. If other
iterable, assume the widths are specified in the same order as the hyperedges
are found in H.edges. By default, 1.
dyad_style : str or list of strings, optional
Line style of the dyads, e.g. ‘-’, ‘–’, ‘-.’, ‘:’ or words like ‘solid’ or ‘dashed’.
See matplotlib's documentation for all accepted values. By default, "solid".
dyad_color_cmap : colormap
Colormap for mapping bipartite edge colors. By default, "Greys".
Ignored, if `dyad_color` is a str (single color) or an iterable of colors.
arrowsize : int (default=10)
Size of the arrow head's length and width. See `matplotlib.patches.FancyArrowPatch`
for attribute `mutation_scale` for more info. Only used if the higher-order network
is a `DiHypergraph`.
arrowstyle : str, optional
By default: '->'. See `matplotlib.patches.ArrowStyle` for more options.
Only used if the higher-order network is a `DiHypergraph`.
connectionstyle : string (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.
Only used if the higher-order network is a `DiHypergraph`.
rescale_sizes: bool, optional
If True, linearly interpolate `node_size` and between min/max values
that can be changed in the other argument `params`.
If `node_size` is a single value, this is ignored. By default, True.
node_size : int, float, dict, iterable, or NodeStat, optional
Radius of the nodes in pixels. If int or float, use the same radius for all
nodes. If a dict, must contain (node_id: radius) pairs. If iterable, assume
the radiuses are specified in the same order as the nodes are found in
H.nodes. If NodeStat, use a monotonic linear interpolation defined between
min_node_size and max_node_size. Used for arrow spacing. By default, 7.
node_shape : str, optional
Marker used for the nodes. Used for arrow spacing. By default 'o' (circle marker).
edge_marker_size : int, float, dict, iterable, or EdgeStat, optional
Radius of the edge markers in pixels. If int or float, use the same radius for all
edge markers. If a dict, must contain (edge_id: radius) pairs. If iterable, assume
the radii are specified in the same order as the edges are found in
H.edges. If EdgeStat, use a monotonic linear interpolation defined between
min_edge_marker_size and max_edge_marker_size. Used for arrow spacing. By default, 7.
edge_marker_shape: str, optional
Marker used for the hyperedges. If "", no marker is
displayed. Used for arrow spacing. By default 's' (square marker).
**kwargs : optional args
Alternate default values. Values that can be overwritten are the following:
* min_dyad_lw (default: 1)
* max_dyad_lw (default: 10)
Returns
-------
ax : matplotlib.pyplot.axes
The axes corresponding the visualization
dyad_collection : list of FancyArrowPatches
representing directed bipartite edges
Raises
------
XGIError
If something different than a DiHypergraph is passed.
See Also
--------
draw_bipartite
draw_directed_dyads
"""
settings = {
"min_dyad_lw": 1,
"max_dyad_lw": 10,
"min_source_margin": 0,
"min_target_margin": 0,
}
settings.update(kwargs)
if not isinstance(H, DiHypergraph):
raise XGIError("Input must be a DiHypergraph")
if not pos:
from ..convert import to_hypergraph
pos = bipartite_spring_layout(to_hypergraph(H))
if ax is None:
ax = plt.gca()
if dyad_color is None: # color is proportional to size
dyad_color = H.edges.size
if not max_order:
edge_ids = list(H.edges)
max_order = H.edges.order.max()
else:
edge_ids = list(H.edges.filterby("order", max_order, "leq"))
dyad_lw = _draw_arg_to_arr(dyad_lw)
node_size = _draw_arg_to_arr(node_size)
edge_marker_size = _draw_arg_to_arr(edge_marker_size)
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
# parse colors
dyad_color, dyads_c_mapped = _parse_color_arg(dyad_color, H.edges)
# convert numbers to colors for FancyArrowPatch
if dyads_c_mapped:
norm = mpl.colors.Normalize()
m = cm.ScalarMappable(norm=norm, cmap=dyad_color_cmap)
dyad_color = m.to_rgba(dyad_color)
# check validity of input values
if np.any(dyad_lw < 0):
raise ValueError("dyad_lw cannot contain negative values.")
# interpolate if needed
if rescale_sizes and isinstance(dyad_lw, np.ndarray):
dyad_lw = _interp_draw_arg(
dyad_lw, settings["min_dyad_lw"], settings["max_dyad_lw"]
)
node_pos, edge_pos = pos
"""Helper functions"""
def _arrow_shrink(
source="node", target="edge", node_size=None, edge_marker_size=None
):
"""Compute the shrink factor for the arrows based on node sizes."""
def to_marker_edge(marker_size, marker):
# from networkx
# https://networkx.org/documentation/stable/_modules/networkx/drawing/nx_pylab.html#draw_networkx_edges
if marker in "s^>v<d": # `large` markers need extra space
return marker_size / 1.6
else:
return marker_size / 2
shrink_source = to_marker_edge(
node_size, node_shape
) # space from source to tail
shrink_target = to_marker_edge(
edge_marker_size, edge_marker_shape
) # space from head to target
if shrink_source < settings["min_source_margin"]:
shrink_source = settings["min_source_margin"]
if shrink_target < settings["min_target_margin"]:
shrink_target = settings["min_target_margin"]
if source == "node" and target == "edge":
return shrink_source, shrink_target
elif source == "edge" and target == "node":
return shrink_target, shrink_source
else:
raise ValueError("Wrong input arguments.")
# We are using single patches rather than a PatchCollection of arrows
# because Matplotlib has an old bug: FancyArrowPatch has incompatibilities
# with PatchCollection (https://github.com/matplotlib/matplotlib/issues/2341)
# We are thus following the approach used by NetworkX
# https://github.com/networkx/networkx/pull/2760
edges = H.edges
nodes = H.nodes
edge_to_idx = dict(zip(edges, range(len(edges))))
node_to_idx = dict(zip(nodes, range(len(nodes))))
patches = []
for e, (tail, head) in edges.dimembers(dtype=dict).items():
if e in edge_ids:
if isinstance(dyad_lw, np.ndarray): # many node sizes
dlw = dyad_lw[edge_to_idx[n]]
else:
dlw = dyad_lw
if dyads_c_mapped:
d_color = dyad_color[edge_to_idx[e]]
else:
d_color = dyad_color
if isinstance(edge_marker_size, np.ndarray): # many node sizes
ems = edge_marker_size[edge_to_idx[e]]
else:
ems = edge_marker_size
for n in tail: # lines going towards the center
xy_source = node_pos[n]
xy_target = edge_pos[e]
if isinstance(node_size, np.ndarray): # many node sizes
ns = node_size[node_to_idx[n]]
else:
ns = node_size
shrink_source, shrink_target = _arrow_shrink(
source="node",
target="edge",
node_size=ns,
edge_marker_size=ems,
)
patch = FancyArrowPatch(
xy_source,
xy_target,
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=arrowsize,
linewidth=dlw,
linestyle=dyad_style,
zorder=0,
color=d_color,
connectionstyle=connectionstyle,
) # arrows go behind nodes
patches.append(patch)
ax.add_patch(patch)
for n in head: # lines going out from the center
xy_source = edge_pos[e]
xy_target = node_pos[n]
if isinstance(node_size, np.ndarray): # many node sizes
ns = node_size[node_to_idx[n]]
else:
ns = node_size
shrink_source, shrink_target = _arrow_shrink(
source="edge",
target="node",
node_size=ns,
edge_marker_size=ems,
)
patch = FancyArrowPatch(
xy_source,
xy_target,
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=arrowsize,
linewidth=dlw,
linestyle=dyad_style,
zorder=0,
color=d_color,
connectionstyle=connectionstyle,
) # arrows go behind nodes
patches.append(patch)
ax.add_patch(patch)
return ax