cairo_draw.py 99.7 KB
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       conv_png("graph-draw.pdf")
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    .. figure:: graph-draw.png
       :align: center
       :width: 80%
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       SFDP force-directed layout of a Price network with 1500 nodes. The
       vertex size and color indicate the degree, and the edge color and width
       the edge betweenness centrality.
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    """

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    vprops = vprops.copy() if vprops is not None else {}
    eprops = eprops.copy() if eprops is not None else {}

    props, kwargs = parse_props("vertex", kwargs)
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    props = _convert_props(props, "v", g, kwargs.get("vcmap", default_cm))
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    vprops.update(props)
    props, kwargs = parse_props("edge", kwargs)
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    props = _convert_props(props, "e", g, kwargs.get("ecmap", default_cm))
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    eprops.update(props)

    if pos is None:
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        if (g.num_vertices() > 2 and output is None and
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            not inline and kwargs.get("update_layout", True) and
            mplfig is None):
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            L = np.sqrt(g.num_vertices())
            pos = random_layout(g, [L, L])
            if g.num_vertices() > 1000:
                if "multilevel" not in kwargs:
                    kwargs["multilevel"] = True
            if "layout_K" not in kwargs:
                kwargs["layout_K"] = _avg_edge_distance(g, pos) / 10
        else:
            pos = sfdp_layout(g)
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    else:
        _check_prop_vector(pos, name="pos", floating=True)
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        if output is None and not inline and mplfig is None:
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            if "layout_K" not in kwargs:
                kwargs["layout_K"] = _avg_edge_distance(g, pos)
            if "update_layout" not in kwargs:
                kwargs["update_layout"] = False
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    if "pen_width" in eprops and "marker_size" not in eprops:
        pw = eprops["pen_width"]
        if isinstance(pw, PropertyMap):
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            pw = pw.copy("double")
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            pw.fa *= 2.75
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            eprops["marker_size"] = pw
        else:
            eprops["marker_size"] = pw * 2.75
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    if "text" in eprops and "text_distance" not in eprops and "pen_width" in eprops:
        pw = eprops["pen_width"]
        if isinstance(pw, PropertyMap):
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            pw = pw.copy("double")
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            pw.fa *= 2
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            eprops["text_distance"] = pw
        else:
            eprops["text_distance"] = pw * 2

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    if "text" in vprops and ("text_color" not in vprops or vprops["text_color"] == "auto"):
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        vcmap = kwargs.get("vcmap", default_cm)
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        bg = _convert(vertex_attrs.fill_color,
                      vprops.get("fill_color", _vdefaults["fill_color"]),
                      vcmap)
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        vprops["text_color"] = auto_colors(g, bg,
                                           vprops.get("text_position",
                                                      _vdefaults["text_position"]),
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                                           bg_color if bg_color is not None else [1., 1., 1., 1.])
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    if mplfig is not None:
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        ax = None
        if isinstance(mplfig, matplotlib.figure.Figure):
            ctr = ax = mplfig.gca()
        elif isinstance(mplfig, matplotlib.axes.Axes):
            ctr = ax = mplfig
        else:
            ctr = mplfig

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        x, y = ungroup_vector_property(pos, [0, 1])
        l, r = x.a.min(), x.a.max()
        b, t = y.a.min(), y.a.max()

        adjust_default_sizes(g, (r - l, t - b), vprops, eprops)

        artist = GraphArtist(g, pos, vprops, eprops, ax, vorder=vorder,
                             eorder=eorder, nodesfirst=nodesfirst, **kwargs)

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        ctr.artists.append(artist)

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        if fit_view != False and ax is not None:
            try:
                x, y, w, h = fit_view
            except TypeError:
                w = r - l
                h = t - b
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            if fit_view != True:
                w *= float(fit_view)
                h *= float(fit_view)
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            ax.set_xlim(l - w * .1, r + w * .1)
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            ax.set_ylim(t + h * .1, b - h * .1)
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        return pos
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    output_file = output
    if inline and output is None:
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        if fmt == "auto":
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            if output is None:
                fmt = "png"
            else:
                fmt = get_file_fmt(output)
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        output = io.BytesIO()

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    if output is None:
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        return interactive_window(g, pos, vprops, eprops, vorder, eorder,
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                                  nodesfirst, geometry=output_size,
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                                  fit_view=fit_view, **kwargs)
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    else:
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        adjust_default_sizes(g, output_size, vprops, eprops)

        if ink_scale != 1:
            scale_ink(ink_scale, vprops, eprops)

        if inline and fmt != "svg":
            output_size = [int(x * inline_scale) for x in output_size]
            scale_ink(inline_scale, vprops, eprops)

        if fit_view != False:
            try:
                x, y, w, h = fit_view
                zoom = min(output_size[0] / w, output_size[1] / h)
            except TypeError:
                pad = fit_view if fit_view is not True else 0.9
                output_size = list(output_size)
                x, y, zoom = fit_to_view_ink(g, pos, output_size, vprops,
                                             eprops, adjust_aspect, pad=pad)
        else:
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            x, y, zoom = 0, 0, 1
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        if isinstance(output, str):
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            out, auto_fmt = open_file(output, mode="wb")
        else:
            out = output
            if fmt == "auto":
                raise ValueError("File format must be specified.")
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        if fmt == "auto":
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            fmt = auto_fmt
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        if fmt == "pdf":
            srf = cairo.PDFSurface(out, output_size[0], output_size[1])
        elif fmt == "ps":
            srf = cairo.PSSurface(out, output_size[0], output_size[1])
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        elif fmt == "eps":
            srf = cairo.PSSurface(out, output_size[0], output_size[1])
            srf.set_eps(True)
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        elif fmt == "svg":
            srf = cairo.SVGSurface(out, output_size[0], output_size[1])
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            srf.restrict_to_version(cairo.SVG_VERSION_1_2)
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        elif fmt == "png":
            srf = cairo.ImageSurface(cairo.FORMAT_ARGB32, output_size[0],
                                     output_size[1])
        else:
            raise ValueError("Invalid format type: " + fmt)

        cr = cairo.Context(srf)

        cr.scale(zoom, zoom)
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        cr.translate(-x, -y)
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        if bg_color is not None:
            cr.set_source_rgba(bg_color[0], bg_color[1],
                               bg_color[2], bg_color[3])
            cr.paint()

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        cairo_draw(g, pos, cr, vprops, eprops, vorder, eorder,
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                   nodesfirst, **kwargs)
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        srf.flush()
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        if fmt == "png":
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            srf.write_to_png(out)
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        elif fmt == "svg":
            srf.finish()
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        del cr

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        if inline and output_file is None:
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            img = None
            if fmt == "png":
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                img = IPython.display.Image(data=out.getvalue(),
                                            width=int(output_size[0]/inline_scale),
                                            height=int(output_size[1]/inline_scale))
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            elif fmt == "svg":
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                img = IPython.display.SVG(data=out.getvalue())
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            elif img is None:
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                inl_out = io.BytesIO()
                inl_srf = cairo.ImageSurface(cairo.FORMAT_ARGB32,
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                                             output_size[0],
                                             output_size[1])
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                inl_cr = cairo.Context(inl_srf)
                inl_cr.set_source_surface(srf, 0, 0)
                inl_cr.paint()
                inl_srf.write_to_png(inl_out)
                del inl_srf
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                img = IPython.display.Image(data=inl_out.getvalue(),
                                            width=int(output_size[0]/inline_scale),
                                            height=int(output_size[1]/inline_scale))
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            srf.finish()
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            IPython.display.display(img)
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        del srf
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        return pos
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def adjust_default_sizes(g, geometry, vprops, eprops, force=False):
    if "size" not in vprops or force:
        A = geometry[0] * geometry[1]
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        N = max(g.num_vertices(), 1)
        vprops["size"] = np.sqrt(A / N) / 3.5
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    if "pen_width" not in vprops or force:
        size = vprops["size"]
        if isinstance(vprops["size"], PropertyMap):
            size = vprops["size"].fa.mean()
        vprops["pen_width"] = size / 10
        if "pen_width" not in eprops or force:
            eprops["pen_width"] = size / 10
        if "marker_size" not in eprops or force:
            eprops["marker_size"] = size * 0.8

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    if "font_size" not in vprops or force:
        size = vprops["size"]
        if isinstance(vprops["size"], PropertyMap):
            size = vprops["size"].fa.mean()
        vprops["font_size"] =  size * .6
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    if "font_size" not in eprops or force:
        size = vprops["size"]
        if isinstance(vprops["size"], PropertyMap):
            size = vprops["size"].fa.mean()
        eprops["font_size"] =  size * .6


def scale_ink(scale, vprops, eprops, copy=False):
    vink_props = ["size", "pen_width", "font_size", "text_out_width"]
    eink_props = ["marker_size", "pen_width", "font_size", "text_distance",
                  "text_out_width"]
    if copy:
        vprops = dict(vprops)
        eprops = dict(eprops)
    for p in vink_props:
        if p not in vprops:
            vprops[p] = _vdefaults[p]
        if isinstance(vprops[p], PropertyMap):
            if copy:
                vprops[p] = vprops[p].copy()
            vprops[p].fa *= scale
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        else:
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            vprops[p] = vprops[p] * scale
    for p in eink_props:
        if p not in eprops:
            eprops[p] = _edefaults[p]
        if isinstance(eprops[p], PropertyMap):
            if copy:
                eprops[p] = eprops[p].copy()
            eprops[p].fa *= scale
        else:
            eprops[p] = eprops[p] * scale
    if copy:
        return vprops, eprops
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def get_bb(g, pos):
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    pos_x, pos_y = ungroup_vector_property(pos, [0, 1])
    x_range = [pos_x.fa.min(), pos_x.fa.max()]
    y_range = [pos_y.fa.min(), pos_y.fa.max()]
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    return x_range[0], y_range[1], x_range[1] - x_range[0], y_range[1] - y_range[0]

def fit_to_view(rec, output_size, adjust_aspect=False, pad=.9):
    x, y, w, h = rec
    d = max(w, h)
    if adjust_aspect:
        if h > w:
            output_size[0] = int(round(float(output_size[1] * w / h)))
        else:
            output_size[1] = int(round(float(output_size[0] * h / w)))

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    zoom = min(output_size[0] / w, output_size[1] / h)

    x -= (output_size[0] / zoom - w) / 2
    y -= (output_size[1] / zoom - h) / 2

    zoom *= pad
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    x -= (1-pad) / 2 * output_size[0] / zoom
    y -= (1-pad) / 2 * output_size[1] / zoom

    return x, y, zoom

def fit_to_view_ink(g, pos, output_size, vprops, eprops, adjust_aspect=False,
                    pad=0.9):
    x, y, zoom = fit_to_view(get_bb(g, pos), output_size, pad=pad)

    srf = cairo.RecordingSurface(cairo.Content.COLOR_ALPHA,
                                 cairo.Rectangle(-output_size[0] * 5,
                                                 -output_size[1] * 5,
                                                 output_size[0] * 10,
                                                 output_size[1] * 10))
    cr = cairo.Context(srf)

    cr.scale(zoom, zoom)
    cr.translate(-x, -y)

    cairo_draw(g, pos, cr, vprops, eprops)

    bb = list(srf.ink_extents())

    bb[0], bb[1] = cr.device_to_user(bb[0], bb[1])
    bb[2], bb[3] = cr.device_to_user_distance(bb[2], bb[3])

    x, y, zoom = fit_to_view(bb, output_size,
                             adjust_aspect=adjust_aspect, pad=pad)
    return x, y, zoom
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def transform_scale(M, scale):
    p = M.transform_distance(scale / np.sqrt(2),
                             scale / np.sqrt(2))
    return np.sqrt(p[0] ** 2 + p[1] ** 2)

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def get_hierarchy_control_points(g, t, tpos, beta=0.8, cts=None, is_tree=True,
                                 max_depth=None):
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    r"""Return the Bézier spline control points for the edges in ``g``, given the hierarchical structure encoded in graph `t`.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be drawn.
    t : :class:`~graph_tool.Graph`
        Directed graph containing the hierarchy of ``g``. It must be a directed
        tree with a single root. The direction of the edges point from the root
        to the leaves, and the vertices in ``t`` with index in the range
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        :math:`[0, N-1]`, with :math:`N` being the number of vertices in ``g``,
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        must correspond to the respective vertex in ``g``.
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    tpos : :class:`~graph_tool.VertexPropertyMap`
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        Vector-valued vertex property map containing the x and y coordinates of
        the vertices in graph ``t``.
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    beta : ``float`` (optional, default: ``0.8`` or :class:`~graph_tool.EdgePropertyMap`)
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        Edge bundling strength. For ``beta == 0`` the edges are straight lines,
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        and for ``beta == 1`` they strictly follow the hierarchy. This can be
        optionally an edge property map, which specified a different bundling
        strength for each edge.
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    cts : :class:`~graph_tool.EdgePropertyMap` (optional, default: ``None``)
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        Edge property map of type ``vector<double>`` where the control points
        will be stored.
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    is_tree : ``bool`` (optional, default: ``True``)
        If ``True``, ``t`` must be a directed tree, otherwise it can be any
        connected graph.
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    max_depth : ``int`` (optional, default: ``None``)
        If supplied, only the first ``max_depth`` bottom levels of the hierarchy
        will be used.
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    Returns
    -------

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    cts : :class:`~graph_tool.EdgePropertyMap`
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        Vector-valued edge property map containing the Bézier spline control
        points for the edges in ``g``.

    Notes
    -----
    This is an implementation of the edge-bundling algorithm described in
    [holten-hierarchical-2006]_.


    Examples
    --------
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    .. testsetup:: nested_cts

       gt.seed_rng(42)
       np.random.seed(42)

    .. doctest:: nested_cts

       >>> g = gt.collection.data["netscience"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> g.purge_vertices()
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       >>> state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True)
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       >>> t = gt.get_hierarchy_tree(state)[0]
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       >>> tpos = pos = gt.radial_tree_layout(t, t.vertex(t.num_vertices() - 1), weighted=True)
       >>> cts = gt.get_hierarchy_control_points(g, t, tpos)
       >>> pos = g.own_property(tpos)
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       >>> b = state.levels[0].b
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       >>> shape = b.copy()
       >>> shape.a %= 14
       >>> gt.graph_draw(g, pos=pos, vertex_fill_color=b, vertex_shape=shape, edge_control_points=cts,
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       ...               edge_color=[0, 0, 0, 0.3], vertex_anchor=0, output="netscience_nested_mdl.pdf")
       <...>

    .. testcleanup:: nested_cts

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       conv_png("netscience_nested_mdl.pdf")
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    .. figure:: netscience_nested_mdl.png
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       :align: center
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       :width: 80%
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       Block partition of a co-authorship network, which minimizes the description
       length of the network according to the nested (degree-corrected) stochastic blockmodel.

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    References
    ----------

    .. [holten-hierarchical-2006] Holten, D. "Hierarchical Edge Bundles:
       Visualization of Adjacency Relations in Hierarchical Data.", IEEE
       Transactions on Visualization and Computer Graphics 12, no. 5, 741–748
       (2006). :doi:`10.1109/TVCG.2006.147`
    """

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    if cts is None:
        cts = g.new_edge_property("vector<double>")
    if cts.value_type() != "vector<double>":
        raise ValueError("cts property map must be of type 'vector<double>' not '%s' " % cts.value_type())
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    u = GraphView(g, directed=True)
    tu = GraphView(t, directed=True)

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    if not isinstance(beta, PropertyMap):
        beta = u.new_edge_property("double", beta)
    else:
        beta = beta.copy("double")

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    if max_depth is None:
        max_depth = t.num_vertices()

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    tu = GraphView(tu, skip_vfilt=True)
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    tpos = tu.own_property(tpos)
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    libgraph_tool_draw.get_cts(u._Graph__graph,
                               tu._Graph__graph,
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                               _prop("v", tu, tpos),
                               _prop("e", u, beta),
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                               _prop("e", u, cts),
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                               is_tree, max_depth)
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    return cts
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#
# The functions and classes below depend on GTK
# =============================================
#

try:
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    import gi
    gi.require_version('Gtk', '3.0')
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    from gi.repository import Gtk, Gdk, GdkPixbuf
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    from gi.repository import GObject as gobject
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    from .gtk_draw import *
except (ImportError, RuntimeError) as e:
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    msg = "Error importing Gtk module: %s; GTK+ drawing will not work." % str(e)
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    warnings.warn(msg, RuntimeWarning)
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def gen_surface(name):
    fobj, fmt = open_file(name)
    if fmt in ["png", "PNG"]:
        sfc = cairo.ImageSurface.create_from_png(fobj)
        return sfc
    else:
        pixbuf = GdkPixbuf.Pixbuf.new_from_file(name)
        surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, pixbuf.get_width(),
                                     pixbuf.get_height())
        cr = cairo.Context(surface)
        Gdk.cairo_set_source_pixbuf(cr, pixbuf, 0, 0)
        cr.paint()
        return surface
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#
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# matplotlib
# ==========
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#
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class GraphArtist(matplotlib.artist.Artist):
    """:class:`matplotlib.artist.Artist` specialization that draws
       :class:`graph_tool.Graph` instances.

    .. warning::

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        Only cairo-based backends are supported.
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    """

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    def __init__(self, g, pos, vprops, eprops, ax=None, **kwargs):
1492
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        matplotlib.artist.Artist.__init__(self)
        self.g = g
        self.pos = pos
        self.vprops = vprops
        self.eprops = eprops
1497
        self.ax = ax
1498
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1500
1501
        self.kwargs = kwargs

    def draw(self, renderer):
        if not isinstance(renderer, matplotlib.backends.backend_cairo.RendererCairo):
1502
            raise NotImplementedError("graph plotting is supported only on cairo backends")
1503
1504

        ctx = renderer.gc.ctx
1505
1506
1507
1508

        if not isinstance(ctx, cairo.Context):
            ctx = _UNSAFE_cairocffi_context_to_pycairo(ctx)

1509
1510
        ctx.save()

1511
1512
1513
1514
        pos = self.pos.copy()
        eprops = dict(self.eprops)
        vprops = dict(self.vprops)

1515
        if self.ax is not None:
1516
1517
            transform = (self.ax.transData.get_affine() +
                         matplotlib.transforms.Affine2D().scale(1, -1).translate(0, renderer.height))
1518

1519
1520
1521
1522
1523
            m = transform.get_matrix()

            m_s = ctx.get_matrix()
            cm = cairo.Matrix(m[0,0], m[1,0], m[0,1], m[1,1], m[0,2], m[1,2])
            ctx.transform(cm)
1524
1525
            l, r = self.ax.get_xlim()
            b, t = self.ax.get_ylim()
1526
            ctx.new_path()
1527
1528
            ctx.rectangle(l, b, r-l, t-b)
            ctx.clip()
1529
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            ctx.set_matrix(m_s)

            vprops, eprops = scale_ink(np.mean([m[0,0], m[1,1]]), vprops, eprops,
                                       copy=True)

            x = np.ones(3)
            for v in self.g.vertices():
                x[:2] = pos[v].a
                pos[v].a = np.dot(m, x)[:2]

            cp = eprops.get("control_points", None)
            if isinstance(cp, PropertyMap):
                ctx.save()
                cp = cp.copy()
                for e in self.g.edges():
                    s = self.pos[e.source()].a
                    t = self.pos[e.target()].a
                    a = np.arctan2(t[1] - s[1],
                                   t[0] - s[0])
                    l = np.sqrt((t[1] - s[1]) ** 2 +
                                (t[0] - s[0]) ** 2)
                    c = cp[e]
                    for i in range(len(c) // 2):
                        x[:2] = c.a[i*2:(i+1)*2]
                        ctx.identity_matrix()
                        ctx.translate(s[0], s[1])
                        ctx.rotate(a)
                        ctx.scale(l, 1)
                        x[:2] = ctx.user_to_device(x[0], x[1])
                        x = np.dot(m, x)
                        c.a[i*2:(i+1)*2] = x[:2]

                    s = pos[e.source()].a
                    t = pos[e.target()].a
                    a = np.arctan2(t[1] - s[1],
                                   t[0] - s[0])
                    l = np.sqrt((t[1] - s[1]) ** 2 +
                                (t[0] - s[0]) ** 2)

                    for i in range(len(c) // 2):
                        x[:2] = c.a[i*2:(i+1)*2]
                        ctx.identity_matrix()
                        ctx.scale(1/l, 1)
                        ctx.rotate(-a)
                        ctx.translate(-s[0], -s[1])
                        x[:2] = ctx.user_to_device(x[0], x[1])
                        c.a[i*2:(i+1)*2] = x[:2]
                ctx.restore()
                eprops["control_points"] = cp

        cairo_draw(self.g, pos, ctx, vprops, eprops, **self.kwargs)
1580
1581

        ctx.restore()
1582
1583
1584
1585
1586
1587
1588


#
# Drawing hierarchies
# ===================
#

1589
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def draw_hierarchy(state, pos=None, layout="radial", beta=0.8, node_weight=None,
                   vprops=None, eprops=None, hvprops=None, heprops=None,
1591
                   subsample_edges=None, rel_order="degree", deg_size=True,
1592
                   vsize_scale=1, hsize_scale=1, hshortcuts=0, hide=0,
1593
                   bip_aspect=1., empty_branches=False, **kwargs):
1594
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1598
    r"""Draw a nested block model state in a circular hierarchy layout with edge
    bundling.

    Parameters
    ----------
1599
    state : :class:`~graph_tool.inference.nested_blockmodel.NestedBlockState`
1600
        Nested block state to be drawn.
1601
    pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
1602
1603
        If supplied, this specifies a vertex property map with the positions of
        the vertices in the layout.
1604
    layout : ``str`` or :class:`~graph_tool.VertexPropertyMap` (optional, default: ``"radial"``)
1605
1606
        If ``layout == "radial"`` :func:`~graph_tool.draw.radial_tree_layout`
        will be used. If ``layout == "sfdp"``, the hierarchy tree will be
1607
1608
        positioned using :func:`~graph_tool.draw.sfdp_layout`. If ``layout ==
        "bipartite"`` a bipartite layout will be used. If instead a
1609
        :class:`~graph_tool.VertexPropertyMap` is provided, it must correspond to the
1610
1611
1612
        position of the hierarchy tree.
    beta : ``float`` (optional, default: ``.8``)
        Edge bundling strength.
1613
1614
1615
1616
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1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
    vprops : dict (optional, default: ``None``)
        Dictionary with the vertex properties. Individual properties may also be
        given via the ``vertex_<prop-name>`` parameters, where ``<prop-name>`` is
        the name of the property. See :func:`~graph_tool.draw.graph_draw` for
        details.
    eprops : dict (optional, default: ``None``)
        Dictionary with the edge properties. Individual properties may also be
        given via the ``edge_<prop-name>`` parameters, where ``<prop-name>`` is
        the name of the property. See :func:`~graph_tool.draw.graph_draw` for
        details.
    hvprops : dict (optional, default: ``None``)
        Dictionary with the vertex properties for the *hierarchy tree*.
        Individual properties may also be given via the ``hvertex_<prop-name>``
        parameters, where ``<prop-name>`` is the name of the property. See
        :func:`~graph_tool.draw.graph_draw` for details.
    heprops : dict (optional, default: ``None``)
        Dictionary with the edge properties for the *hierarchy tree*. Individual
        properties may also be given via the ``hedge_<prop-name>`` parameters,
        where ``<prop-name>`` is the name of the property. See
        :func:`~graph_tool.draw.graph_draw` for details.
1633
1634
1635
    subsample_edges : ``int`` or list of :class:`~graph_tool.Edge` instances (optional, default: ``None``)
        If provided, only this number of random edges will be drawn. If the
        value is a list, it should include the edges that are to be drawn.
1636
    rel_order : ``str`` or ``None`` or :class:`~graph_tool.VertexPropertyMap` (optional, default: ``"degree"``)
1637
1638
        If ``degree``, the vertices will be ordered according to degree inside
        each group, and the relative ordering of the hierarchy branches. If
1639
        instead a :class:`~graph_tool.VertexPropertyMap` is provided, its value will
1640
        be used for the relative ordering.
1641
1642
1643
    deg_size : ``bool`` (optional, default: ``True``)
        If ``True``, the (total) node degrees will be used for the default
        vertex sizes..
1644
    vsize_scale : ``float`` (optional, default: ``1.``)
1645
        Multiplicative factor for the default vertex sizes.
1646
    hsize_scale : ``float`` (optional, default: ``1.``)
1647
        Multiplicative factor for the default sizes of the hierarchy nodes.
1648
1649
1650
1651
1652
    hshortcuts : ``int`` (optional, default: ``0``)
        Include shortcuts to the number of upper layers in the hierarchy
        determined by this parameter.
    hide : ``int`` (optional, default: ``0``)
        Hide upper levels of the hierarchy.
1653
1654
    bip_aspect : ``float`` (optional, default: ``1.``)
        If ``layout == "bipartite"``, this will define the aspect ratio of layout.
1655
    empty_branches : ``bool`` (optional, default: ``False``)
1656
1657
        If ``empty_branches == False``, dangling branches at the upper layers
        will be pruned.
1658
    vertex_* : :class:`~graph_tool.VertexPropertyMap` or arbitrary types (optional, default: ``None``)
1659
1660
1661
1662
        Parameters following the pattern ``vertex_<prop-name>`` specify the
        vertex property with name ``<prop-name>``, as an alternative to the
        ``vprops`` parameter. See :func:`~graph_tool.draw.graph_draw` for
        details.
1663
    edge_* : :class:`~graph_tool.EdgePropertyMap` or arbitrary types (optional, default: ``None``)
1664
1665
1666
        Parameters following the pattern ``edge_<prop-name>`` specify the edge
        property with name ``<prop-name>``, as an alternative to the ``eprops``
        parameter. See :func:`~graph_tool.draw.graph_draw` for details.
1667
    hvertex_* : :class:`~graph_tool.VertexPropertyMap` or arbitrary types (optional, default: ``None``)
1668
1669
1670
1671
        Parameters following the pattern ``hvertex_<prop-name>`` specify the
        vertex property with name ``<prop-name>``, as an alternative to the
        ``hvprops`` parameter. See :func:`~graph_tool.draw.graph_draw` for
        details.
1672
    hedge_* : :class:`~graph_tool.EdgePropertyMap` or arbitrary types (optional, default: ``None``)
1673
1674
1675
        Parameters following the pattern ``hedge_<prop-name>`` specify the edge
        property with name ``<prop-name>``, as an alternative to the ``heprops``
        parameter. See :func:`~graph_tool.draw.graph_draw` for details.
1676
    **kwargs :
1677
1678
        All remaining keyword arguments will be passed to the
        :func:`~graph_tool.draw.graph_draw` function.
1679
1680
1681

    Returns
    -------
1682
    pos : :class:`~graph_tool.VertexPropertyMap`
1683
1684
1685
1686
        This is a vertex property map with the positions of
        the vertices in the layout.
    t : :class:`~graph_tool.Graph`
        This is a the hierarchy tree used in the layout.
1687
    tpos : :class:`~graph_tool.VertexPropertyMap`
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
        This is a vertex property map with the positions of
        the hierarchy tree in the layout.

    Examples
    --------
    .. testsetup:: draw_hierarchy

       gt.seed_rng(42)
       np.random.seed(42)

    .. doctest:: draw_hierarchy

       >>> g = gt.collection.data["celegansneural"]
       >>> state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True)
       >>> gt.draw_hierarchy(state, output="celegansneural_nested_mdl.pdf")
       (...)

    .. testcleanup:: draw_hierarchy

1707
       conv_png("celegansneural_nested_mdl.pdf")
1708

1709
    .. figure:: celegansneural_nested_mdl.png
1710
       :align: center
1711
       :width: 80%
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723

       Hierarchical block partition of the C. elegans neural network, which
       minimizes the description length of the network according to the nested
       (degree-corrected) stochastic blockmodel.


    References
    ----------
    .. [holten-hierarchical-2006] Holten, D. "Hierarchical Edge Bundles:
       Visualization of Adjacency Relations in Hierarchical Data.", IEEE
       Transactions on Visualization and Computer Graphics 12, no. 5, 741–748
       (2006). :doi:`10.1109/TVCG.2006.147`
1724

1725
1726
1727
1728
    """

    g = state.g

1729
1730
    overlap = state.levels[0].overlap
    if overlap:
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
        ostate = state.levels[0]
        bv, bcin, bcout, bc = ostate.get_overlap_blocks()
        be = ostate.get_edge_blocks()
        orig_state = state
        state = state.copy()
        b = ostate.get_majority_blocks()
        state.levels[0] = BlockState(g, b=b)
    else:
        b = state.levels[0].b

    if subsample_edges is not None:
        emask = g.new_edge_property("bool", False)
        if isinstance(subsample_edges, int):
            eidx = g.edge_index.copy("int").fa.copy()
            numpy.random.shuffle(eidx)
            emask = g.new_edge_property("bool")
            emask.a[eidx[:subsample_edges]] = True
        else:
            for e in subsample_edges:
                emask[e] = True
        g = GraphView(g, efilt=emask)

1753
1754
    t, tb, tvorder = get_hierarchy_tree(state,
                                        empty_branches=empty_branches)
1755
1756

    if layout == "radial":
1757
1758
1759
        if rel_order == "degree":
            rel_order = g.degree_property_map("total")
        vorder = t.own_property(rel_order.copy())
1760
1761
        if pos is not None:
            x, y = ungroup_vector_property(pos, [0, 1])
1762
1763
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
1764
            angle = g.new_vertex_property("double")
1765
            angle.fa = (numpy.arctan2(y.fa, x.fa) + 2 * numpy.pi) % (2 * numpy.pi)
1766
            vorder = t.own_property(angle)
1767
1768
1769
        if node_weight is not None:
            node_weight = t.own_property(node_weight.copy())
            node_weight.a[node_weight.a == 0] = 1
1770
        tpos = radial_tree_layout(t, root=t.vertex(t.num_vertices() - 1,
1771
                                                   use_index=False),
1772
                                  node_weight=node_weight,
1773
1774
                                  rel_order=vorder,
                                  rel_order_leaf=True)
1775
    elif layout == "bipartite":
1776
        tpos = get_bip_hierachy_pos(state, aspect=bip_aspect,
1777
1778
                                    node_weight=node_weight)
        tpos = t.own_property(tpos)
1779
1780
1781
1782
1783
    elif layout == "sfdp":
        if pos is None:
            tpos = sfdp_layout(t)
        else:
            x, y = ungroup_vector_property(pos, [0, 1])
1784
1785
1786
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
            K = numpy.sqrt(x.fa.std() + y.fa.std()) / 10
1787
1788
            tpos = t.new_vertex_property("vector<double>")
            for v in t.vertices():
1789
                if int(v) < g.num_vertices(True):
1790
1791
1792
1793
                    tpos[v] = [x[v], y[v]]
                else:
                    tpos[v] = [0, 0]
            pin = t.new_vertex_property("bool")
1794
            pin.a[:g.num_vertices(True)] = True
1795
1796
1797
1798
            tpos = sfdp_layout(t, K=K, pos=tpos, pin=pin, multilevel=False)
    else:
        tpos = t.own_property(layout)

1799
    hvvisible = t.new_vertex_property("bool", True)
1800
1801
1802
1803
    if hide is None:
        L = len([s for s in state.levels if s.get_nonempty_B() > 0])
        hide = len(state.levels) - L

1804
    if hide > 0:
1805
        root = t.vertex(t.num_vertices(True) - 1)
1806
1807
1808
        dist = shortest_distance(t, source=root)
        hvvisible.fa = dist.fa >= hide

1809
1810
    pos = g.own_property(tpos.copy())

1811
    cts = get_hierarchy_control_points(g, t, tpos, beta,
1812
                                       max_depth=len(state.levels) - hshortcuts)
1813

1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
    vprops_orig = vprops
    eprops_orig = eprops
    hvprops_orig = vprops
    heprops_orig = eprops
    kwargs_orig = kwargs

    vprops = vprops.copy() if vprops is not None else {}
    eprops = eprops.copy() if eprops is not None else {}

    props, kwargs = parse_props("vertex", kwargs)
    vprops.update(props)
    vprops.setdefault("fill_color", b)
    vprops.setdefault("color", b)
1827
    vprops.setdefault("shape", _vdefaults["shape"] if not overlap else "pie")
1828
1829
1830
1831
1832
1833
1834
1835

    output_size = kwargs.get("output_size", (600, 600))
    if kwargs.get("mplfig", None) is not None:
        x, y = ungroup_vector_property(pos, [0, 1])
        w = x.a.max() - x.a.min()
        h = y.a.max() - y.a.min()
        output_size = (w, h)
    s = numpy.mean(output_size) / (4 * numpy.sqrt(g.num_vertices()))
1836
    vprops.setdefault("size", prop_to_size(g.degree_property_map("total"), s/5, s))
1837

1838
1839
    adjust_default_sizes(g, output_size, vprops, eprops)

1840
1841
1842
1843
    if vprops.get("text_position", None) == "centered":
        angle, text_pos = centered_rotation(g, pos, text_pos=True)
        vprops["text_position"] = text_pos
        vprops["text_rotation"] = angle
1844
1845
1846
1847
1848
1849
1850
1851
        toffset = vprops.get("text_offset", None)
        if toffset is not None:
            if not isinstance(toffset, PropertyMap):
                toffset = g.new_vp("vector<double>", val=toffset)
            xo, yo = ungroup_vector_property(toffset, [0, 1])
            xo.a[text_pos.a == numpy.pi] *= -1
            toffset = group_vector_property([xo, yo])
            vprops["text_offset"] = toffset
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864

    self_loops = label_self_loops(g, mark_only=True)
    if self_loops.fa.max() > 0:
        parallel_distance = vprops.get("size", _vdefaults["size"])
        if isinstance(parallel_distance, PropertyMap):
            parallel_distance = parallel_distance.fa.mean()
        cts_p = position_parallel_edges(g, pos, numpy.nan,
                                        parallel_distance)
        gu = GraphView(g, efilt=self_loops)
        for e in gu.edges():
            cts[e] = cts_p[e]


1865
1866
1867
1868
1869
1870
1871
    vprops = _convert_props(vprops, "v", g, kwargs.get("vcmap", default_cm),
                            pmap_default=True)

    props, kwargs = parse_props("edge", kwargs)
    eprops.update(props)
    eprops.setdefault("control_points", cts)
    eprops.setdefault("pen_width", _edefaults["pen_width"])
1872
    eprops.setdefault("color", list(_edefaults["color"][:-1]) + [.6])
1873
    eprops.setdefault("end_marker", "arrow" if g.is_directed() else "none")
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
    eprops = _convert_props(eprops, "e", g, kwargs.get("ecmap", default_cm),
                            pmap_default=True)

    hvprops = hvprops.copy() if hvprops is not None else {}
    heprops = heprops.copy() if heprops is not None else {}

    props, kwargs = parse_props("hvertex", kwargs)
    hvprops.update(props)

    blue = list(color_converter.to_rgba("#729fcf"))
    blue[-1] = .6
    hvprops.setdefault("fill_color", blue)
    hvprops.setdefault("color", [1, 1, 1, 0])
    hvprops.setdefault("shape", "square")
1888
    hvprops.setdefault("size", s)
1889

1890
1891
1892
1893
    if hvprops.get("text_position", None) == "centered":
        angle, text_pos = centered_rotation(t, tpos, text_pos=True)
        hvprops["text_position"] = text_pos
        hvprops["text_rotation"] = angle
1894
1895
1896
1897
1898
1899
1900
1901
        toffset = hvprops.get("text_offset", None)
        if toffset is not None:
            if not isinstance(toffset, PropertyMap):
                toffset = t.new_vp("vector<double>", val=toffset)
            xo, yo = ungroup_vector_property(toffset, [0, 1])
            xo.a[text_pos.a == numpy.pi] *= -1
            toffset = group_vector_property([xo, yo])
            hvprops["text_offset"] = toffset
1902

1903
1904
1905
1906
1907
1908
1909
1910
    hvprops = _convert_props(hvprops, "v", t, kwargs.get("vcmap", default_cm),
                             pmap_default=True)

    props, kwargs = parse_props("hedge", kwargs)
    heprops.update(props)

    heprops.setdefault("color", blue)
    heprops.setdefault("end_marker", "arrow")
1911
1912
    heprops.setdefault("marker_size", s * .8)
    heprops.setdefault("pen_width", s / 10)
1913
1914
1915

    heprops = _convert_props(heprops, "e", t, kwargs.get("ecmap", default_cm),
                             pmap_default=True)
1916

1917
1918
    vcmap = kwargs.get("vcmap", default_cm)
    ecmap = kwargs.get("ecmap", vcmap)
1919
1920
1921

    B = state.levels[0].B

1922
    if overlap and "pie_fractions" not in vprops:
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
        vprops["pie_fractions"] = bc.copy("vector<double>")
        if "pie_colors" not in vprops:
            vertex_pie_colors = g.new_vertex_property("vector<double>")
            nodes = defaultdict(list)
            def conv(k):
                clrs = [vcmap(r / (B - 1) if B > 1 else 0) for r in k]
                return [item for l in clrs for item in l]
            map_property_values(bv, vertex_pie_colors, conv)
            vprops["pie_colors"] = vertex_pie_colors

    gradient = eprops.get("gradient", None)
1934
1935
    if gradient is None:
        gradient = g.new_edge_property("double")
1936
        gradient = group_vector_property([gradient])
1937
1938
        ecolor = eprops.get("ecolor", _edefaults["color"])
        eprops["gradient"] = gradient
1939
        if overlap:
1940
            for e in g.edges():                       # ******** SLOW *******
1941
                r, s = be[e]
1942
                if not g.is_directed() and e.source() > e.target():
1943
1944
1945
                    r, s = s, r
                gradient[e] = [0] + list(vcmap(r / (B - 1))) + \
                              [1] + list(vcmap(s / (B - 1)))
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                if isinstance(ecolor, PropertyMap):
                    gradient[e][4] = gradient[e][9] = ecolor[e][3]
                else:
                    gradient[e][4] = gradient[e][9] = ecolor[3]
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    t_orig = t
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    t = GraphView(t,
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                  vfilt=lambda v: int(v) >= g.num_vertices(True) and hvvisible[v])
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    t_vprops = {}
    t_eprops = {}
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    props = []
    for k in set(list(vprops.keys()) + list(hvprops.keys())):
        t_vprops[k] = (vprops.get(k, None), hvprops.get(k, None))
        props.append(t_vprops[k])
    for k in set(list(eprops.keys()) + list(heprops.keys())):
        t_eprops[k] = (eprops.get(k, None), heprops.get(k, None))
        props.append(t_eprops[k])
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    props.append((pos, tpos))
    props.append((g.vertex_index, tb))
    props.append((b, None))
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    if "eorder" in kwargs:
        eorder = kwargs["eorder"]
        props.append((eorder,
                      t.new_ep(eorder.value_type(),
                               eorder.fa.max() + 1)))
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    u, props = graph_union(g, t, props=props)
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    for k in set(list(vprops.keys()) + list(hvprops.keys())):
        t_vprops[k] = props.pop(0)
    for k in set(list(eprops.keys()) + list(heprops.keys())):
        t_eprops[k] = props.pop(0)
    pos = props.pop(0)
    tb = props.pop(0)
    b = props.pop(0)
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    if "eorder" in kwargs:
        eorder = props.pop(0)
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    def update_cts(widget, gg, picked, pos, vprops, eprops):
        vmask = gg.vertex_index.copy("int")
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        u = GraphView(gg, directed=False, vfilt=vmask.fa < g.num_vertices(True))
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        cts = eprops["control_points"]
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        get_hierarchy_control_points(u, t_orig, pos, beta, cts=cts,
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                                     max_depth=len(state.levels) - hshortcuts)
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    def draw_branch(widget, gg, key_id, picked, pos, vprops, eprops):
        if key_id == ord('b'):
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            if picked is not None and not isinstance(picked, PropertyMap) and int(picked) > g.num_vertices(True):
                p = shortest_path(t_orig, source=t_orig.vertex(t_orig.num_vertices(True) - 1),
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                                  target=picked)[0]
                l = len(state.levels) - max(len(p), 1)