__init__.py 21.5 KB
Newer Older
Tiago Peixoto's avatar
Tiago Peixoto committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
#! /usr/bin/env python
# graph_tool.py -- a general graph manipulation python module
#
# Copyright (C) 2007 Tiago de Paula Peixoto <tiago@forked.de>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU 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 General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

19
"""
20
``graph_tool.draw`` - Graph drawing
21
-----------------------------------
22
23
24
25
26
27
28
29
30
31
32
33
34

Summary
+++++++

.. autosummary::
   :nosignatures:

   graph_draw
   arf_layout
   random_layout

Contents
++++++++
35
36
"""

37
import sys, os, os.path, time, warnings
38
39
40
from .. core import _degree, _prop, PropertyMap, _check_prop_vector,\
     _check_prop_scalar, _check_prop_writable, group_vector_property,\
     ungroup_vector_property
Tiago Peixoto's avatar
Tiago Peixoto committed
41
from .. decorators import _limit_args
42
import numpy.random
43
44
45
46
from numpy import *

from .. dl_import import dl_import
dl_import("import libgraph_tool_layout")
47
48
49
50
51
52

try:
    import gv
except ImportError:
    warnings.warn("error importing gv module... graph_draw() will not work.",
                  ImportWarning)
53
54
55
56
57
58
try:
    import matplotlib.cm
    import matplotlib.colors
except ImportError:
    warnings.warn("error importing matplotlib module... " + \
                  "graph_draw() will not work.", ImportWarning)
Tiago Peixoto's avatar
Tiago Peixoto committed
59

60
61
__all__ = ["graph_draw", "arf_layout", "random_layout"]

62
63
64
65
def graph_draw(g, pos=None, size=(15, 15), pin=False, layout= "neato",
               maxiter=None, ratio= "fill", overlap="prism", sep=None,
               splines=False, vsize=0.1, penwidth=1.0, elen=None, gprops={},
               vprops={}, eprops={}, vcolor=None, ecolor=None,
Tiago Peixoto's avatar
Tiago Peixoto committed
66
               vcmap=matplotlib.cm.jet, vnorm=True, ecmap=matplotlib.cm.jet,
67
               enorm=True, output= "", output_format= "auto", returngv=False,
68
               fork=False, seed=0):
69
70
71
72
73
74
    r"""Draw a graph using graphviz.

    Parameters
    ----------
    g : Graph
        Graph to be used.
75
    pos : PropertyMap or tuple of PropertyMaps (optional, default: None)
76
77
78
79
80
81
82
        Vertex property maps containing the x and y coordinates of the vertices.
    size : tuple of scalars (optional, default: (15,15))
        Size (in centimeters) of the canvas.
    pin : bool (default: False)
        If True, the vertices are not moved from their initial position.
    layout : string (default: "neato")
        Layout engine to be used. Possible values are "neato", "fdp", "dot",
83
        "circo", "twopi" and "arf".
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    maxiter : int (default: None)
        If specified, limits the maximum number of iterations.
    ratio : string or float (default: "fill")
        Sets the aspect ratio (drawing height/drawing width) for the
        drawing. Note that this is adjusted before the 'size' attribute
        constraints are enforced.

        If ratio is numeric, it is taken as the desired aspect ratio. Then, if
        the actual aspect ratio is less than the desired ratio, the drawing
        height is scaled up to achieve the desired ratio; if the actual ratio is
        greater than that desired ratio, the drawing width is scaled up.

        If ratio = "fill" and the size attribute is set, node positions are
        scaled, separately in both x and y, so that the final drawing exactly
        fills the specified size.

        If ratio = "compress" and the size attribute is set, dot attempts to
        compress the initial layout to fit in the given size. This achieves a
        tighter packing of nodes but reduces the balance and symmetry.
        This feature only works in dot.

        If ratio = "expand", the size attribute is set, and both the width and
        the height of the graph are less than the value in size, node positions
        are scaled uniformly until at least one dimension fits size exactly.
        Note that this is distinct from using size as the desired size, as here
        the drawing is expanded before edges are generated and all node and text
        sizes remain unchanged.

        If ratio = "auto", the page attribute is set and the graph cannot be
113
        drawn on a single page, then size is set to an "ideal" value. In
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
        particular, the size in a given dimension will be the smallest integral
        multiple of the page size in that dimension which is at least half the
        current size. The two dimensions are then scaled independently to the
        new size. This feature only works in dot.
    overlap : bool or string (default: "prism")
        Determines if and how node overlaps should be removed. Nodes are first
        enlarged using the sep attribute. If True, overlaps are retained. If
        the value is "scale", overlaps are removed by uniformly scaling in x and
        y. If the value is False, node overlaps are removed by a Voronoi-based
        technique. If the value is "scalexy", x and y are separately scaled to
        remove overlaps.

        If sfdp is available, one can set overlap to "prism" to use a proximity
        graph-based algorithm for overlap removal. This is the preferred
        technique, though "scale" and False can work well with small graphs.
        This technique starts with a small scaling up, controlled by the
        overlap_scaling attribute, which can remove a significant portion of the
        overlap. The prism option also accepts an optional non-negative integer
        suffix. This can be used to control the number of attempts made at
        overlap removal. By default, overlap="prism" is equivalent to
        overlap="prism1000". Setting overlap="prism0" causes only the scaling
        phase to be run.

        If the value is "compress", the layout will be scaled down as much as
        possible without introducing any overlaps, obviously assuming there are
        none to begin with.
    sep : float (default: None)
        Specifies margin to leave around nodes when removing node overlap. This
        guarantees a minimal non-zero distance between nodes.
    splines : bool (default: False)
        If True, the edges are drawn as splines and routed around the vertices.
145
146
147
148
    vsize : float, PropertyMap, or tuple (default: 0.1)
        Default vertex size (width and height). If a tuple is specified, the
        first value should be a property map, and the second is a scale factor.
    penwidth : float, PropertyMap or tuple (default: 1.0)
149
150
        Specifies the width of the pen, in points, used to draw lines and
        curves, including the boundaries of edges and clusters. It has no effect
151
152
153
        on text. Default vertex size (width and height). If a tuple is
        specified, the first value should be a property map, and the second is a
        scale factor.
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
    elen : float or PropertyMap (default: None)
        Preferred edge length, in inches.
    gprops : dict (default: {})
        Additional graph properties, as a dictionary. The keys are the property
        names, and the values must be convertible to string.
    vprops : dict (default: {})
        Additional vertex properties, as a dictionary. The keys are the property
        names, and the values must be convertible to string, or vertex property
        maps, with values convertible to strings.
    eprops : dict (default: {})
        Additional edge properties, as a dictionary. The keys are the property
        names, and the values must be convertible to string, or edge property
        maps, with values convertible to strings.
    vcolor : string or PropertyMap (default: None)
        Drawing color for vertices. If the valued supplied is a property map,
        the values must be scalar types, whose color values are obtained from
        the 'vcmap' argument.
    ecolor : string or PropertyMap (default: None)
        Drawing color for edges. If the valued supplied is a property map,
        the values must be scalar types, whose color values are obtained from
        the 'ecmap' argument.
    vcmap : matplotlib.colors.Colormap (default: matplotlib.cm.jet)
        Vertex color map.
    vnorm : bool (default: True)
        Normalize vertex color values to the [0,1] range.
    ecmap : matplotlib.colors.Colormap (default: matplotlib.cm.jet)
        Edge color map.
    enorm : bool (default: True)
        Normalize edge color values to the [0,1] range.
    output : string (default: "")
        Output file name.
    output_format : string (default: "auto")
        Output file format. Possible values are "auto", "xlib", "ps", "svg",
        "svgz", "fig", "mif", "hpgl", "pcl", "png", "gif", "dia", "imap",
        "cmapx". If the value is "auto", the format is guessed from the 'output'
189
190
        parameter, or 'xlib' if it is empty. If the value is None, no output is
        produced.
191
192
193
194
195
196
197
198
199
200
201
202
203
    returngv : bool (default: False)
        Return the graph object used internally with the gv module.
    fork : bool (default: False)
        If true, the program is forked before drawing. This is used as a
        work-around for a bug in graphviz, where the exit() function is called,
        which would cause the calling program to end. This is always assumed
        'True', if output_format = 'xlib'.
    seed : int (default: 0)
        Seed for the random number generator. If the value 0, a different random
        value is used each time.

    Returns
    -------
204
205
    pos : PropertyMap
        Vector vertex property map with the x and y coordinates of the vertices.
206
207
208
209
210
211
    gv : gv.digraph or gv.graph (optional, only if returngv == True)
        Internally used graphviz graph.


    Notes
    -----
212
    This function is a wrapper for the [graphviz] python
213
214
215
216
217
218
    routines. Extensive additional documentation for the graph, vertex and edge
    properties is available at: http://www.graphviz.org/doc/info/attrs.html.


    Examples
    --------
219
    >>> from numpy import *
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
    >>> from numpy.random import seed, zipf
    >>> seed(42)
    >>> g = gt.random_graph(1000, lambda: min(zipf(2.4), 40),
    ...                     lambda i,j: exp(abs(i-j)), directed=False)
    >>> # extract largest component
    >>> comp = gt.label_components(g)
    >>> h = gt.vertex_hist(g, comp)
    >>> max_comp = h[1][list(h[0]).index(max(h[0]))]
    >>> g.remove_vertex_if(lambda v: comp[v] != max_comp)
    >>>
    >>> deg = g.degree_property_map("out")
    >>> deg.get_array()[:] = 2*(sqrt(deg.get_array()[:])*0.5 + 0.4)
    >>> ebet = gt.betweenness(g)[1]
    >>> ebet.get_array()[:] *= 4000
    >>> ebet.get_array()[:] += 10
    >>> gt.graph_draw(g, vsize=deg, vcolor=deg, elen=10, ecolor=ebet,
    ...               penwidth=ebet, overlap="prism", output="graph-draw.png")
237
    <...>
238
239
240
241
242
243
244
245
246
247
248

    .. figure:: graph-draw.png
        :align: center

        Kamada-Kawai force-directed layout of a graph with a power-law degree
        distribution, and dissortative degree correlation. The vertex size and
        color indicate the degree, and the edge color and width the edge
        betweeness centrality.

    References
    ----------
249
    .. [graphviz] http://www.graphviz.org
250
251

    """
Tiago Peixoto's avatar
Tiago Peixoto committed
252

253
    if output != "" and output != None:
254
        output = os.path.expanduser(output)
255
        # check opening file for writing, since graphviz will bork if it is not
256
257
258
259
260
        # possible to open file
        if os.path.dirname(output) != "" and \
               not os.access(os.path.dirname(output), os.W_OK):
            raise IOError("cannot write to " + os.path.dirname(output))

Tiago Peixoto's avatar
Tiago Peixoto committed
261
262
263
264
265
    if g.is_directed():
        gvg = gv.digraph("G")
    else:
        gvg = gv.graph("G")

266
267
268
269
270
    if layout == "arf":
        layout = "neato"
        pos = arf_layout(g, pos=pos)
        pin = True

271
272
    if pos != None:
        # copy user-supplied property
273
274
275
276
        if isinstance(pos, PropertyMap):
            pos = ungroup_vector_property(g, pos, [0,1])
        else:
            pos = (g.copy_property(pos[0]), g.copy_property(pos[1]))
277

278
279
280
281
282
283
284
    if type(vsize) == tuple:
        s = g.new_vertex_property("double")
        g.copy_property(vsize[0], s)
        s.a *= vsize[1]
        vsize = s

    if type(penwidth) == tuple:
285
        s = g.new_edge_property("double")
286
287
288
289
        g.copy_property(penwidth[0], s)
        s.a *= penwidth[1]
        penwidth = s

Tiago Peixoto's avatar
Tiago Peixoto committed
290
291
    # main graph properties
    gv.setv(gvg,"outputorder", "edgesfirst")
292
    gv.setv(gvg,"mode", "major")
293
    if overlap == False:
294
        overlap = "false"
295
296
297
    else:
        overlap = "true"
    if isinstance(overlap,str):
Tiago Peixoto's avatar
Tiago Peixoto committed
298
        gv.setv(gvg,"overlap", overlap)
299
300
    if sep != None:
        gv.setv(gvg,"sep", str(sep))
Tiago Peixoto's avatar
Tiago Peixoto committed
301
302
303
    if splines:
        gv.setv(gvg,"splines", "true")
    gv.setv(gvg,"ratio", str(ratio))
304
    gv.setv(gvg,"size", "%f,%f" % (size[0]/2.54,size[1]/2.54)) # centimeters
Tiago Peixoto's avatar
Tiago Peixoto committed
305
306
    if maxiter != None:
        gv.setv(gvg,"maxiter", str(maxiter))
307
308
309
310
311
312
313

    if seed == 0:
        seed = numpy.random.randint(sys.maxint)
    if type(seed) == int:
        gv.setv(gvg, "start", "%d" % seed)
    else:
        gv.setv(gvg, "start", seed)
Tiago Peixoto's avatar
Tiago Peixoto committed
314
315

    # apply all user supplied properties
316
    for k,val in gprops.iteritems():
Tiago Peixoto's avatar
Tiago Peixoto committed
317
318
319
320
321
322
323
324
325
326
327
328
        if isinstance(val, PropertyMap):
            gv.setv(gvg, k, str(val[g]))
        else:
            gv.setv(gvg, k, str(val))

    # normalize color properties
    if vcolor != None and not isinstance(vcolor, str):
        minmax = [float("inf"), -float("inf")]
        for v in g.vertices():
            c = vcolor[v]
            minmax[0] = min(c,minmax[0])
            minmax[1] = max(c,minmax[1])
329
330
        if minmax[0] == minmax[1]:
            minmax[1] += 1
Tiago Peixoto's avatar
Tiago Peixoto committed
331
332
        if vnorm:
            vnorm = matplotlib.colors.normalize(vmin=minmax[0], vmax=minmax[1])
333
334
        else:
            vnorm = lambda x: x
Tiago Peixoto's avatar
Tiago Peixoto committed
335
336
337
338

    if ecolor != None and not isinstance(ecolor, str):
        minmax = [float("inf"), -float("inf")]
        for e in g.edges():
339
            c = ecolor[e]
Tiago Peixoto's avatar
Tiago Peixoto committed
340
341
            minmax[0] = min(c,minmax[0])
            minmax[1] = max(c,minmax[1])
342
343
        if minmax[0] == minmax[1]:
            minmax[1] += 1
Tiago Peixoto's avatar
Tiago Peixoto committed
344
345
        if enorm:
            enorm = matplotlib.colors.normalize(vmin=minmax[0], vmax=minmax[1])
346
347
        else:
            enorm = lambda x: x
Tiago Peixoto's avatar
Tiago Peixoto committed
348

349
    nodes = {}
Tiago Peixoto's avatar
Tiago Peixoto committed
350
351
352
353
354
    edges = []

    # add nodes
    for v in g.vertices():
        n = gv.node(gvg,str(g.vertex_index[v]))
355
356
357

        if type(vsize) == PropertyMap:
            vw = vh = vsize[v]
358
        else:
359
            vw = vh = vsize
360
361
362

        gv.setv(n, "width", "%g" % vw)
        gv.setv(n, "height", "%g" % vh)
Tiago Peixoto's avatar
Tiago Peixoto committed
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
        gv.setv(n, "style", "filled")
        gv.setv(n, "color", "black")
        # apply color
        if vcolor != None:
            if isinstance(vcolor,str):
                gv.setv(n, "fillcolor", vcolor)
            else:
                color = tuple([int(c*255.0) for c in vcmap(vnorm(vcolor[v]))])
                gv.setv(n, "fillcolor", "#%.2x%.2x%.2x%.2x" % color)
        else:
            gv.setv(n, "fillcolor", "red")
        gv.setv(n, "label", "")

        # user supplied position
        if pos != None:
            gv.setv(n, "pos", "%f,%f" % (pos[0][v],pos[1][v]))
            gv.setv(n, "pin", str(pin))

        # apply all user supplied properties
382
        for k,val in vprops.iteritems():
Tiago Peixoto's avatar
Tiago Peixoto committed
383
384
385
386
            if isinstance(val, PropertyMap):
                gv.setv(n, k, str(val[v]))
            else:
                gv.setv(n, k, str(val))
387
388
        nodes[g.vertex_index[v]] = n

Tiago Peixoto's avatar
Tiago Peixoto committed
389
390
391
392
    for e in g.edges():
        ge = gv.edge(nodes[g.vertex_index[e.source()]],
                     nodes[g.vertex_index[e.target()]])
        gv.setv(ge, "arrowsize", "0.3")
393
394
        if g.is_directed():
            gv.setv(ge, "arrowhead", "vee")
Tiago Peixoto's avatar
Tiago Peixoto committed
395
396
397
398
399
400
401
402
        # apply color
        if ecolor != None:
            if isinstance(ecolor,str):
                gv.setv(ge, "color", ecolor)
            else:
                color = tuple([int(c*255.0) for c in ecmap(enorm(ecolor[e]))])
                gv.setv(ge, "color", "#%.2x%.2x%.2x%.2x" % color)

403
404
405
406
        # apply edge length
        if elen != None:
            if isinstance(elen, PropertyMap):
                gv.setv(ge, "len", str(elen[e]))
Tiago Peixoto's avatar
Tiago Peixoto committed
407
            else:
408
                gv.setv(ge, "len", str(elen))
Tiago Peixoto's avatar
Tiago Peixoto committed
409
410

        # apply width
411
412
413
        if penwidth != None:
            if isinstance(penwidth, PropertyMap):
                gv.setv(ge, "penwidth", str(penwidth[e]))
Tiago Peixoto's avatar
Tiago Peixoto committed
414
            else:
415
                gv.setv(ge, "penwidth", str(penwidth))
Tiago Peixoto's avatar
Tiago Peixoto committed
416
417

        # apply all user supplied properties
418
        for k,v in eprops.iteritems():
Tiago Peixoto's avatar
Tiago Peixoto committed
419
420
421
422
            if isinstance(v, PropertyMap):
                gv.setv(ge, k, str(v[e]))
            else:
                gv.setv(ge, k, str(v))
423

Tiago Peixoto's avatar
Tiago Peixoto committed
424
425

    gv.layout(gvg, layout)
426
    gv.render(gvg, "dot", "/dev/null") # retrieve positions
Tiago Peixoto's avatar
Tiago Peixoto committed
427
428
429

    if pos == None:
        pos = (g.new_vertex_property("double"), g.new_vertex_property("double"))
430
431
    for n, n_gv in nodes.iteritems():
        p = gv.getv(n_gv, "pos")
Tiago Peixoto's avatar
Tiago Peixoto committed
432
433
434
        p = p.split(",")
        pos[0][g.vertex(n)] = float(p[0])
        pos[1][g.vertex(n)] = float(p[1])
435
436
437
438

    if output_format == "auto":
        if output == "":
            output_format = "xlib"
439
        elif output != None:
440
            output_format = output.split(".")[-1]
Tiago Peixoto's avatar
Tiago Peixoto committed
441
442
443

    # if using xlib we need to fork the process, otherwise good ol' graphviz
    # will call exit() when the window is closed
444
    if output_format == "xlib" or fork:
Tiago Peixoto's avatar
Tiago Peixoto committed
445
446
447
        pid = os.fork()
        if pid == 0:
            gv.render(gvg, output_format, output)
448
449
            os._exit(0) # since we forked, it's good to be sure
        if output_format != "xlib":
450
            os.wait()
451
    elif output != None:
Tiago Peixoto's avatar
Tiago Peixoto committed
452
        gv.render(gvg, output_format, output)
453

454
455
456
457
    # I don't get this, but it seems necessary
    pos[0].get_array()[:] /= 100
    pos[1].get_array()[:] /= 100

458
459
    pos = group_vector_property(g, pos)

460
461
462
463
    if returngv:
        return pos, gv
    else:
        gv.rm(gvg)
464
        del gvg
465
        return pos
466
467

def random_layout(g, shape=None, pos=None, dim=2):
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
    r"""Performs a random layout of the graph.

    Parameters
    ----------
    g : Graph
        Graph to be used.
    shape : tuple (optional, default: None)
        Rectangular shape of the bounding area. If None, a square of linear size
        :math:`\sqrt{N}` is used.
    pos : PropertyMap (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 : A vector vertex property map
        Vertex property map with the coordinates of the vertices.

    Notes
    -----
    This algorithm has complexity :math:`O(V)`.
    """

492
493
494
495
496
497
498
    if pos == None:
        pos = [g.new_vertex_property("double") for i in xrange(dim)]

    if isinstance(pos, PropertyMap) and "vector" in pos.value_type():
        pos = ungroup_vector_property(pos)

    if shape == None:
499
        shape = [sqrt(g.num_vertices())]*dim
500
501
502
503
504
505
506
507
508
509
510
511

    for i in xrange(dim):
        _check_prop_scalar(pos[i], name="pos[%d]" % i)
        _check_prop_writable(pos[i], name="pos[%d]" % i)
        a = pos[i].get_array()
        a[:] = numpy.random.random(len(a))*shape[i]

    pos = group_vector_property(g, pos)
    return pos

def arf_layout(g, weight=None, d=0.1, a=10, dt=0.001, epsilon=1e-6,
               max_iter=1000, pos=None, dim=2):
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
    r"""Calculate the ARF spring-block layout of the graph.

    Parameters
    ----------
    g : Graph
        Graph to be used.
    weight : PropertyMap (optional, default: None)
        An edge property map with the respective weights.
    d : float (optional, default: 0.1)
        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 : PropertyMap (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 : A vector vertex property map
        Vertex property map with the coordinates of the vertices.

    Notes
    -----
543
    This algorithm is defined in [geipel-self-organization-2007]_, and has
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
    complexity :math:`O(V^2)`.

    Examples
    --------
    >>> from numpy.random import seed, zipf
    >>> seed(42)
    >>> g = gt.random_graph(100, lambda: 3, directed=False)
    >>> t = gt.min_spanning_tree(g)
    >>> g.set_edge_filter(t)
    >>> pos = gt.graph_draw(g, output=None) # initial configuration
    >>> pos = gt.arf_layout(g, pos=pos, max_iter=0)
    >>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-arf.png")
    <...>

    .. figure:: graph-draw-arf.png
        :align: center

        ARF layout of a minimum spanning tree of a random graph.

    References
    ----------
565
    .. [geipel-self-organization-2007] Markus M. Geipel, "Self-Organization
566
567
568
569
570
       applied to Dynamic Network Layout" , International Journal of Modern
       Physics C vol. 18, no. 10 (2007), pp. 1537-1549, arXiv:0704.1748v5
    .. _arf: http://www.sg.ethz.ch/research/graphlayout
    """

571
    if pos == None:
572
573
574
575
        if dim != 2:
            pos = random_layout(g, dim=dim)
        else:
            pos = graph_draw(g, output=None)
576
577
578
    _check_prop_vector(pos, name="pos", floating=True)

    g.stash_filter(directed=True)
579
580
581
582
583
584
585
    try:
        g.set_directed(False)
        libgraph_tool_layout.arf_layout(g._Graph__graph, _prop("v", g, pos),
                                        _prop("e", g, weight), d, a, dt,
                                        max_iter, epsilon, dim)
    finally:
        g.pop_filter(directed=True)
586
    return pos