#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# graph_tool -- a general graph manipulation python module
#
# Copyright (C) 2006-2023 Tiago de Paula Peixoto <tiago@skewed.de>
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
``graph_tool.draw``
-------------------
This module contains functions for graph drawing and layout.
Layout algorithms
=================
.. autosummary::
:nosignatures:
:toctree: autosummary
sfdp_layout
fruchterman_reingold_layout
arf_layout
radial_tree_layout
planar_layout
random_layout
Graph drawing
=============
.. autosummary::
:nosignatures:
:toctree: autosummary
graph_draw
draw_hierarchy
graphviz_draw
prop_to_size
get_hierarchy_control_points
Low-level graph drawing
^^^^^^^^^^^^^^^^^^^^^^^
.. autosummary::
:nosignatures:
:toctree: autosummary
cairo_draw
interactive_window
.. autosummary::
:nosignatures:
:toctree: autosummary
:template: class-no-inherit.rst
GraphWidget
GraphWindow
"""
from .. import Graph, GraphView, _check_prop_vector, _check_prop_scalar, \
group_vector_property, ungroup_vector_property, infect_vertex_property, \
_prop, _get_rng, VertexPropertyMap
from .. topology import max_cardinality_matching, max_independent_vertex_set, \
label_components, shortest_distance, make_maximal_planar, is_planar
from .. generation import predecessor_tree, condensation_graph
from .. inference.util import nested_contiguous_map
import numpy.random
from numpy import sqrt
from .. dl_import import dl_import
dl_import("from . import libgraph_tool_layout")
__all__ = ["graph_draw", "graphviz_draw", "fruchterman_reingold_layout",
"arf_layout", "sfdp_layout", "planar_layout", "random_layout",
"radial_tree_layout", "cairo_draw", "prop_to_size",
"get_hierarchy_control_points", "default_cm", "default_cm_bin",
"default_clrs"]
[docs]
def random_layout(g, shape=None, pos=None, dim=2):
r"""Performs a random layout of the graph.
Parameters
----------
g : :class:`~graph_tool.Graph`
Graph to be used.
shape : tuple or list (optional, default: ``None``)
Rectangular shape of the bounding area. The size of this parameter must
match `dim`, and each element can be either a pair specifying a range,
or a single value specifying a range starting from zero. If None is
passed, a square of linear size :math:`\sqrt{N}` is used.
pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Vector vertex property maps where the coordinates should be stored.
dim : int (optional, default: ``2``)
Number of coordinates per vertex.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm has complexity :math:`O(V)`.
Examples
--------
.. testcode::
:hide:
np.random.seed(42)
gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> shape = [[50, 100], [1, 2], 4]
>>> pos = gt.random_layout(g, shape=shape, dim=3)
>>> pos[g.vertex(0)].a
array([68.72700594, 1.03142919, 2.56812658])
"""
if pos is None:
pos = g.new_vertex_property("vector<double>")
_check_prop_vector(pos, name="pos")
pos = ungroup_vector_property(pos, list(range(0, dim)))
if shape is None:
shape = [sqrt(g.num_vertices())] * dim
for i in range(dim):
if hasattr(shape[i], "__len__"):
if len(shape[i]) != 2:
raise ValueError("The elements of 'shape' must have size 2.")
r = [min(shape[i]), max(shape[i])]
else:
r = [min(shape[i], 0), max(shape[i], 0)]
d = r[1] - r[0]
# deal with filtering
p = pos[i].fa
pos[i].fa = numpy.random.random(len(p)) * d + r[0]
pos = group_vector_property(pos)
return pos
[docs]
def planar_layout(g, pos=None):
r"""Performs a canonical layout of a planar graph.
Parameters
----------
g : :class:`~graph_tool.Graph`
Planar graph to be used.
pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Vector vertex property maps where the coordinates should be stored.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm has complexity :math:`O(V + E)`.
Examples
--------
>>> g = gt.lattice([10, 10])
>>> pos = gt.planar_layout(g)
>>> gt.graph_draw(g, pos=pos, output="lattice-planar.pdf")
<...>
.. testcleanup::
conv_png("lattice-planar.pdf")
.. figure:: lattice-planar.png
:align: center
:width: 60%
Straight-line drawing of planar graph (a 2D square lattice).
References
----------
.. [straight-line-boost] http://www.boost.org/doc/libs/release/libs/graph/doc/straight_line_drawing.html
.. [chrobak-linear-1995] M. Chrobak, T. Payne, "A Linear-time Algorithm for
Drawing a Planar Graph on the Grid", Information Processing Letters 54:
241-246, (1995), :doi:`10.1016/0020-0190(95)00020-D`
"""
if g.num_vertices() < 3:
raise ValueError("Graph must have at least 3 vertices.")
if not is_planar(g):
raise ValueError("Graph is not planar.")
u = Graph(GraphView(g, directed=False, skip_properties=True))
make_maximal_planar(u)
embed = is_planar(u, embedding=True)[1]
if pos is None:
pos = u.new_vp("vector<double>")
else:
pos = u.own_property(pos)
libgraph_tool_layout.planar_layout(u._Graph__graph,
_prop("v", u, embed),
_prop("v", u, pos))
pos = g.own_property(pos)
return pos
[docs]
def fruchterman_reingold_layout(g, weight=None, a=None, r=1., scale=None,
circular=False, grid=True, t_range=None,
n_iter=100, pos=None):
r"""Calculate the Fruchterman-Reingold spring-block layout of the graph.
Parameters
----------
g : :class:`~graph_tool.Graph`
Graph to be used.
weight : :class:`~graph_tool.EdgePropertyMap` (optional, default: ``None``)
An edge property map with the respective weights.
a : float (optional, default: :math:`V`)
Attracting force between adjacent vertices.
r : float (optional, default: 1.0)
Repulsive force between vertices.
scale : float (optional, default: :math:`\sqrt{V}`)
Total scale of the layout (either square side or radius).
circular : bool (optional, default: ``False``)
If ``True``, the layout will have a circular shape. Otherwise the shape
will be a square.
grid : bool (optional, default: ``True``)
If ``True``, the repulsive forces will only act on vertices which are on
the same site on a grid. Otherwise they will act on all vertex pairs.
t_range : tuple of floats (optional, default: ``(scale / 10, scale / 1000)``)
Temperature range used in annealing. The temperature limits the
displacement at each iteration.
n_iter : int (optional, default: ``100``)
Total number of iterations.
pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Vector vertex property maps where the coordinates should be stored. If
provided, this will also be used as the initial position of the
vertices.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm is defined in [fruchterman-reingold]_, and has
complexity :math:`O(\text{n-iter}\times V^2)` if `grid=False` or
:math:`O(\text{n-iter}\times (V + E))` otherwise.
Examples
--------
.. testcode::
:hide:
np.random.seed(42)
gt.seed_rng(42)
>>> g = gt.price_network(300)
>>> pos = gt.fruchterman_reingold_layout(g, n_iter=1000)
>>> gt.graph_draw(g, pos=pos, output="graph-draw-fr.pdf")
<...>
.. testcleanup::
conv_png("graph-draw-fr.pdf")
.. figure:: graph-draw-fr.png
:align: center
:width: 60%
Fruchterman-Reingold layout of a Price network.
References
----------
.. [fruchterman-reingold] Fruchterman, Thomas M. J.; Reingold, Edward M.
"Graph Drawing by Force-Directed Placement". Software - Practice & Experience
(Wiley) 21 (11): 1129-1164. (1991) :doi:`10.1002/spe.4380211102`
"""
if pos is None:
pos = random_layout(g, dim=2)
_check_prop_vector(pos, name="pos", floating=True)
if a is None:
a = float(g.num_vertices())
if scale is None:
scale = sqrt(g.num_vertices())
if t_range is None:
t_range = (scale / 10, scale / 1000)
ug = GraphView(g, directed=False)
libgraph_tool_layout.fruchterman_reingold_layout(ug._Graph__graph,
_prop("v", g, pos),
_prop("e", g, weight),
a, r, not circular, scale,
grid, t_range[0],
t_range[1], n_iter)
return pos
[docs]
def arf_layout(g, weight=None, d=0.5, a=10, dt=0.001, epsilon=1e-6,
max_iter=1000, pos=None, dim=2):
r"""Calculate the ARF spring-block layout of the graph.
Parameters
----------
g : :class:`~graph_tool.Graph`
Graph to be used.
weight : :class:`~graph_tool.EdgePropertyMap` (optional, default: ``None``)
An edge property map with the respective weights.
d : float (optional, default: ``0.5``)
Opposing force between vertices.
a : float (optional, default: ``10``)
Attracting force between adjacent vertices.
dt : float (optional, default: ``0.001``)
Iteration step size.
epsilon : float (optional, default: ``1e-6``)
Convergence criterion.
max_iter : int (optional, default: ``1000``)
Maximum number of iterations. If this value is ``0``, it runs until
convergence.
pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Vector vertex property maps where the coordinates should be stored.
dim : int (optional, default: ``2``)
Number of coordinates per vertex.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm is defined in [geipel-self-organization-2007]_, and has
complexity :math:`O(V^2)`.
Examples
--------
.. testcode::
:hide:
np.random.seed(43)
gt.seed_rng(45)
>>> g = gt.price_network(300)
>>> pos = gt.arf_layout(g)
>>> gt.graph_draw(g, pos=pos, output="graph-draw-arf.pdf")
<...>
.. testcleanup::
conv_png("graph-draw-arf.pdf")
.. figure:: graph-draw-arf.png
:align: center
:width: 60%
ARF layout of a Price network.
References
----------
.. [geipel-self-organization-2007] Markus M. Geipel, "Self-Organization
applied to Dynamic Network Layout", International Journal of Modern
Physics C vol. 18, no. 10 (2007), pp. 1537-1549,
:doi:`10.1142/S0129183107011558`, :arxiv:`0704.1748v5`
.. _arf: http://www.sg.ethz.ch/research/graphlayout
"""
if pos is None:
pos = random_layout(g, dim=dim, shape=[[0, 1]] * dim)
_check_prop_vector(pos, name="pos", floating=True)
ug = GraphView(g, directed=False)
libgraph_tool_layout.arf_layout(ug._Graph__graph, _prop("v", g, pos),
_prop("e", g, weight), d, a, dt, max_iter,
epsilon, dim)
return pos
def _coarse_graph(g, vweight, eweight, mivs=False, groups=None):
if groups is None:
if mivs:
mivs = max_independent_vertex_set(g, high_deg=True)
u = GraphView(g, vfilt=mivs, directed=False)
c = label_components(u)[0]
c.fa += 1
u = GraphView(g, directed=False)
infect_vertex_property(u, c,
list(range(1, c.fa.max() + 1)))
c = g.own_property(c)
else:
mivs = None
m = max_cardinality_matching(GraphView(g, directed=False),
heuristic=True, weight=eweight,
minimize=False, edges=True)
u = GraphView(g, efilt=m, directed=False)
c = label_components(u)[0]
c = g.own_property(c)
u = GraphView(g, directed=False)
else:
mivs = None
c = g.new_vp("int", vals=groups)
cg, cc, vcount, ecount = condensation_graph(g, c, vweight, eweight)[:4]
return cg, cc, vcount, ecount, c, mivs
def _propagate_pos(g, cg, c, cc, cpos, delta, mivs):
pos = g.new_vertex_property(cpos.value_type())
if mivs is not None:
g = GraphView(g, vfilt=mivs)
libgraph_tool_layout.propagate_pos(g._Graph__graph,
cg._Graph__graph,
_prop("v", g, c),
_prop("v", cg, cc),
_prop("v", g, pos),
_prop("v", cg, cpos),
delta if mivs is None else 0,
_get_rng())
if mivs is not None:
g = g.base
u = GraphView(g, directed=False)
try:
libgraph_tool_layout.propagate_pos_mivs(u._Graph__graph,
_prop("v", u, mivs),
_prop("v", u, pos),
delta, _get_rng())
except ValueError:
graph_draw(u, mivs, vertex_fillcolor=mivs)
return pos
def _avg_edge_distance(g, pos):
libgraph_tool_layout.sanitize_pos(g._Graph__graph, _prop("v", g, pos))
ad = libgraph_tool_layout.avg_dist(g._Graph__graph, _prop("v", g, pos))
if numpy.isnan(ad) or ad == 0:
ad = 1.
return ad
def coarse_graphs(g, method="hybrid", mivs_thres=0.9, ec_thres=0.75,
weighted_coarse=False, eweight=None, vweight=None,
groups=None, verbose=False):
cg = [[g, None, None, None, None, None]]
if weighted_coarse:
cg[-1][2], cg[-1][3] = vweight, eweight
mivs = not (method in ["hybrid", "ec"])
while True:
if groups is None or len(groups) < len(cg):
b = None
else:
b = groups[len(cg)-1]
u = _coarse_graph(cg[-1][0], cg[-1][2], cg[-1][3], mivs, b)
thres = mivs_thres if mivs else ec_thres
if u[0].num_vertices() >= thres * cg[-1][0].num_vertices():
if method == "hybrid" and not mivs:
mivs = True
else:
break
if u[0].num_vertices() <= 2:
break
cg.append(u)
if verbose:
print("Coarse level (%s):" % ("MIVS" if mivs else "EC"), end=' ')
print(len(cg), " num vertices:", end=' ')
print(u[0].num_vertices())
cg.reverse()
Ks = []
pos = random_layout(cg[0][0], dim=2)
for i in range(len(cg)):
if i == 0:
u = cg[i][0]
K = _avg_edge_distance(u, pos)
if K == 0:
K = 1.
Ks.append(K)
continue
if weighted_coarse:
gamma = 1.
else:
gamma = 0.75
Ks.append(Ks[-1] * gamma)
for i in range(len(cg)):
u, cc, vcount, ecount, c, mivs = cg[i]
if groups is None or len(cg) - i > len(groups):
b = None
else:
b = groups[len(cg) - 1 - i:]
yield u, pos, Ks[i], vcount, ecount, b
if verbose:
print("avg edge distance:", _avg_edge_distance(u, pos))
if i < len(cg) - 1:
if verbose:
print("propagating...", end=' ')
print(mivs.a.sum() if mivs is not None else "")
pos = _propagate_pos(cg[i + 1][0], u, c, cc, pos,
Ks[i] / 1000., mivs)
[docs]
def sfdp_layout(g, vweight=None, eweight=None, pin=None, groups=None, C=0.2,
K=None, p=2., theta=0.6, max_level=15, r=1., gamma=.3, mu=2.,
kappa=1., rmap=None, R=1, init_step=None, cooling_step=0.95,
adaptive_cooling=True, epsilon=1e-2, max_iter=0, pos=None,
multilevel=None, coarse_method="hybrid", mivs_thres=0.9,
ec_thres=0.75, weighted_coarse=False, verbose=False):
r"""Obtain the SFDP spring-block layout of the graph.
Parameters
----------
g : :class:`~graph_tool.Graph`
Graph to be used.
vweight : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
A vertex property map with the respective weights.
eweight : :class:`~graph_tool.EdgePropertyMap` (optional, default: ``None``)
An edge property map with the respective weights.
pin : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
A vertex property map with boolean values, which, if given,
specify the vertices which will not have their positions modified.
groups : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
A vertex property map with group assignments. Vertices belonging to the
same group will be put close together.
C : float (optional, default: ``0.2``)
Relative strength of repulsive forces.
K : float (optional, default: ``None``)
Optimal edge length. If not provided, it will be taken to be the average
edge distance in the initial layout.
p : float (optional, default: ``2``)
Repulsive force exponent.
theta : float (optional, default: ``0.6``)
Quadtree opening parameter, a.k.a. Barnes-Hut opening criterion.
max_level : int (optional, default: ``15``)
Maximum quadtree level.
r : float (optional, default: ``1.``)
Strength of attractive force between connected components.
gamma : float (optional, default: ``.3``)
Strength of the repulsive force between different groups.
mu : float (optional, default: ``2``)
Typical length of the repulsive force between different groups.
kappa : float (optional, default: ``1.0``)
Multiplicative factor on the attracttive force between nodes of the same group.
rmap : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Vertex rank to be used around to order them preferentially in the
:math:`y` direction.
R : float (optional, default: ``1.0``)
Strength of the rank ordering in the :math:`y` direction.
init_step : float (optional, default: ``None``)
Initial update step. If not provided, it will be chosen automatically.
cooling_step : float (optional, default: ``0.95``)
Cooling update step.
adaptive_cooling : bool (optional, default: ``True``)
Use an adaptive cooling scheme.
epsilon : float (optional, default: ``0.01``)
Relative convergence criterion.
max_iter : int (optional, default: ``0``)
Maximum number of iterations. If this value is ``0``, it runs until
convergence.
pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Initial vertex layout. If not provided, it will be randomly chosen.
multilevel : bool (optional, default: ``None``)
Use a multilevel layout algorithm. If ``None`` is given, it will be
activated based on the size of the graph.
coarse_method : str (optional, default: ``"hybrid"``)
Coarsening method used if ``multilevel == True``. Allowed methods are
``"hybrid"``, ``"mivs"`` and ``"ec"``.
mivs_thres : float (optional, default: ``0.9``)
If the relative size of the MIVS coarse graph is above this value, the
coarsening stops.
ec_thres : float (optional, default: ``0.75``)
If the relative size of the EC coarse graph is above this value, the
coarsening stops.
weighted_coarse : bool (optional, default: ``False``)
Use weighted coarse graphs.
verbose : bool (optional, default: ``False``)
Provide verbose information.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm is defined in [hu-multilevel-2005]_, and has
complexity :math:`O(V\log V)`.
Examples
--------
.. testcode::
:hide:
np.random.seed(42)
gt.seed_rng(42)
>>> g = gt.price_network(3000)
>>> pos = gt.sfdp_layout(g)
>>> gt.graph_draw(g, pos=pos, output="graph-draw-sfdp.pdf")
<...>
.. testcleanup::
conv_png("graph-draw-sfdp.pdf")
.. figure:: graph-draw-sfdp.png
:align: center
:width: 60%
SFDP layout of a Price network.
References
----------
.. [hu-multilevel-2005] Yifan Hu, "Efficient and High Quality Force-Directed
Graph", Mathematica Journal, vol. 10, Issue 1, pp. 37-71, (2005)
http://www.mathematica-journal.com/issue/v10i1/graph_draw.html
"""
if pos is None:
pos = random_layout(g, dim=2)
_check_prop_vector(pos, name="pos", floating=True)
if isinstance(groups, VertexPropertyMap):
groups = [numpy.asarray(groups.a, dtype="int32")]
elif groups is not None:
groups = nested_contiguous_map(groups)
g_ = g
g = GraphView(g, directed=False)
if pin is not None:
if pin.value_type() != "bool":
raise ValueError("'pin' property must be of type 'bool'.")
else:
pin = g.new_vertex_property("bool")
if K is None:
K = _avg_edge_distance(g, pos)
mu *= K
if rmap is None:
rmap = g.new_vp("double")
R = 0
elif rmap.value_type() != "double":
rmap = rmap.copy("double")
if init_step is None:
init_step = 2 * max(_avg_edge_distance(g, pos), K)
if multilevel is None:
multilevel = g.num_vertices() > 1000
if multilevel:
if eweight is not None or vweight is not None:
weighted_coarse = True
cgs = coarse_graphs(g, method=coarse_method,
mivs_thres=mivs_thres,
ec_thres=ec_thres,
weighted_coarse=weighted_coarse,
eweight=eweight,
vweight=vweight,
groups=groups,
verbose=verbose)
for count, (u, pos, K, vcount, ecount, groups) in enumerate(cgs):
if verbose:
print("Positioning level:", count, u.num_vertices(), end=' ')
print("with K =", K, "...")
count += 1
pos = sfdp_layout(u, pos=pos,
vweight=vcount if weighted_coarse else None,
eweight=ecount if weighted_coarse else None,
groups=groups,
C=C, K=K, p=p,
theta=theta, gamma=gamma, mu=mu, kappa=kappa,
epsilon=epsilon,
max_iter=max_iter,
cooling_step=cooling_step,
adaptive_cooling=False,
# init_step=max(2 * K,
# _avg_edge_distance(u, pos)),
multilevel=False,
verbose=False)
pos = g_.own_property(pos)
return pos
if g.num_vertices() <= 1:
return pos
if g.num_vertices() == 2:
vs = [g.vertex(0, False), g.vertex(1, False)]
pos[vs[0]] = [0, 0]
pos[vs[1]] = [1, 1]
return pos
if g.num_vertices() <= 50:
max_level = 0
if groups is None:
groups = [numpy.zeros(g.num_vertices(True), dtype="int32")]
c = label_components(g)[0]
libgraph_tool_layout.sanitize_pos(g._Graph__graph, _prop("v", g, pos))
libgraph_tool_layout.sfdp_layout(g._Graph__graph, _prop("v", g, pos),
_prop("v", g, vweight),
_prop("e", g, eweight),
_prop("v", g, pin),
(C, K, p, gamma, mu, kappa, groups, r,
_prop("v", g, c), R, _prop("v", g, rmap)),
theta, init_step, cooling_step, max_level,
epsilon, max_iter, not adaptive_cooling,
verbose, _get_rng())
pos = g_.own_property(pos)
return pos
[docs]
def radial_tree_layout(g, root, rel_order=None, rel_order_leaf=False,
weighted=False, node_weight=None, r=1.):
r"""Computes a radial layout of the graph according to the minimum spanning
tree centered at the ``root`` vertex.
Parameters
----------
g : :class:`~graph_tool.Graph`
Graph to be used.
root : :class:`~graph_tool.Vertex` or ``int``
The root of the radial tree.
rel_order : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
Relative order of the nodes at each respective branch.
rel_order_leaf : ``bool`` (optional, default: ``False``)
If ``True``, the relative order of the leafs will propagate to the
root. Otherwise they will propagate in the opposite direction.
weighted : ``bool`` (optional, default: ``False``)
If true, the angle between the child branches will be computed according
to weight of the entire sub-branches.
node_weight : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
If given, the relative spacing between leafs will correspond to the node
weights.
r : ``float`` (optional, default: ``1.``)
Layer spacing.
Returns
-------
pos : :class:`~graph_tool.VertexPropertyMap`
A vector-valued vertex property map with the coordinates of the
vertices.
Notes
-----
This algorithm has complexity :math:`O(V + E)`, or :math:`O(V\log V + E)` if
``rel_order`` is given.
Examples
--------
.. testcode::
:hide:
np.random.seed(42)
gt.seed_rng(42)
>>> g = gt.price_network(1000)
>>> pos = gt.radial_tree_layout(g, g.vertex(0))
>>> gt.graph_draw(g, pos=pos, output="graph-draw-radial.pdf")
<...>
.. testcleanup::
conv_png("graph-draw-radial.pdf")
.. figure:: graph-draw-radial.png
:align: center
:width: 60%
Radial tree layout of a Price network.
"""
levels, pred_map = shortest_distance(GraphView(g, directed=False), root,
pred_map=True)
t = predecessor_tree(g, pred_map)
pos = t.new_vertex_property("vector<double>")
levels = t.own_property(levels)
if rel_order is None:
rel_order = g.vertex_index.copy("int")
if node_weight is None:
node_weight = g.new_vertex_property("double", 1)
elif node_weight.value_type() != "double":
node_weight = node_weight.copy("double")
libgraph_tool_layout.get_radial(t._Graph__graph,
_prop("v", t, pos),
_prop("v", t, levels),
_prop("v", g, rel_order),
_prop("v", g, node_weight),
int(root), weighted, r,
rel_order_leaf)
return g.own_property(pos)
[docs]
def prop_to_size(prop, mi=0, ma=5, log=False, power=0.5):
r"""Convert property map values to be more useful as a vertex size, or edge
width. The new values are taken to be
.. math::
y_i = mi + (ma - mi) \left(\frac{x_i - \min(x)} {\max(x) - \min(x)}\right)^\text{power}
If ``log=True``, :math:`x_i` is replaced with :math:`\ln(x_i)`.
If :math:`\max(x) - \min(x)` is zero, :math:`y_i = mi`.
"""
prop = prop.copy(value_type="double")
if len(prop.fa) == 0:
return prop
if log:
vals = numpy.log(prop.fa)
else:
vals = prop.fa
delta = vals.max() - vals.min()
if delta == 0:
delta = 1
prop.fa = mi + (ma - mi) * ((vals - vals.min()) / delta) ** power
return prop
try:
from . cairo_draw import graph_draw, cairo_draw, \
get_hierarchy_control_points, default_cm, default_cm_bin, \
default_clrs, draw_hierarchy
__all__ += ["draw_hierarchy"]
except ImportError:
pass
try:
from . cairo_draw import GraphWidget, GraphWindow, \
interactive_window
__all__ += ["interactive_window", "GraphWidget", "GraphWindow"]
except ImportError:
pass
try:
from . graphviz_draw import graphviz_draw
except ImportError:
pass
