cairo_draw.py 99.9 KB
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    ...               edge_color=ebet, eorder=eorder, edge_pen_width=ebet,
    ...               edge_control_points=control, # some curvy edges
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    ...               output="graph-draw.pdf")
    <...>

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    .. testcleanup::
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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:
            x, y, zoom = x, y, 1


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        if isinstance(output, (str, unicode)):
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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):
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        matplotlib.artist.Artist.__init__(self)
        self.g = g
        self.pos = pos
        self.vprops = vprops
        self.eprops = eprops
1504
        self.ax = ax
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        self.kwargs = kwargs

    def draw(self, renderer):
        if not isinstance(renderer, matplotlib.backends.backend_cairo.RendererCairo):
1509
            raise NotImplementedError("graph plotting is supported only on cairo backends")
1510
1511

        ctx = renderer.gc.ctx
1512
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        if not isinstance(ctx, cairo.Context):
            ctx = _UNSAFE_cairocffi_context_to_pycairo(ctx)

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        ctx.save()

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        pos = self.pos.copy()
        eprops = dict(self.eprops)
        vprops = dict(self.vprops)

1522
        if self.ax is not None:
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            transform = (self.ax.transData.get_affine() +
                         matplotlib.transforms.Affine2D().scale(1, -1).translate(0, renderer.height))
1525

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            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)
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            l, r = self.ax.get_xlim()
            b, t = self.ax.get_ylim()
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            ctx.new_path()
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            ctx.rectangle(l, b, r-l, t-b)
            ctx.clip()
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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)
1587
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        ctx.restore()
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#
# Drawing hierarchies
# ===================
#

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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,
1598
                   subsample_edges=None, rel_order="degree", deg_size=True,
1599
                   vsize_scale=1, hsize_scale=1, hshortcuts=0, hide=0,
1600
                   bip_aspect=1., empty_branches=False, **kwargs):
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    r"""Draw a nested block model state in a circular hierarchy layout with edge
    bundling.

    Parameters
    ----------
1606
    state : :class:`~graph_tool.inference.nested_blockmodel.NestedBlockState`
1607
        Nested block state to be drawn.
1608
    pos : :class:`~graph_tool.VertexPropertyMap` (optional, default: ``None``)
1609
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        If supplied, this specifies a vertex property map with the positions of
        the vertices in the layout.
1611
    layout : ``str`` or :class:`~graph_tool.VertexPropertyMap` (optional, default: ``"radial"``)
1612
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        If ``layout == "radial"`` :func:`~graph_tool.draw.radial_tree_layout`
        will be used. If ``layout == "sfdp"``, the hierarchy tree will be
1614
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        positioned using :func:`~graph_tool.draw.sfdp_layout`. If ``layout ==
        "bipartite"`` a bipartite layout will be used. If instead a
1616
        :class:`~graph_tool.VertexPropertyMap` is provided, it must correspond to the
1617
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        position of the hierarchy tree.
    beta : ``float`` (optional, default: ``.8``)
        Edge bundling strength.
1620
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    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.
1640
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    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.
1643
    rel_order : ``str`` or ``None`` or :class:`~graph_tool.VertexPropertyMap` (optional, default: ``"degree"``)
1644
1645
        If ``degree``, the vertices will be ordered according to degree inside
        each group, and the relative ordering of the hierarchy branches. If
1646
        instead a :class:`~graph_tool.VertexPropertyMap` is provided, its value will
1647
        be used for the relative ordering.
1648
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1650
    deg_size : ``bool`` (optional, default: ``True``)
        If ``True``, the (total) node degrees will be used for the default
        vertex sizes..
1651
    vsize_scale : ``float`` (optional, default: ``1.``)
1652
        Multiplicative factor for the default vertex sizes.
1653
    hsize_scale : ``float`` (optional, default: ``1.``)
1654
        Multiplicative factor for the default sizes of the hierarchy nodes.
1655
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    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.
1660
1661
    bip_aspect : ``float`` (optional, default: ``1.``)
        If ``layout == "bipartite"``, this will define the aspect ratio of layout.
1662
    empty_branches : ``bool`` (optional, default: ``False``)
1663
1664
        If ``empty_branches == False``, dangling branches at the upper layers
        will be pruned.
1665
    vertex_* : :class:`~graph_tool.VertexPropertyMap` or arbitrary types (optional, default: ``None``)
1666
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1669
        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.
1670
    edge_* : :class:`~graph_tool.EdgePropertyMap` or arbitrary types (optional, default: ``None``)
1671
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1673
        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.
1674
    hvertex_* : :class:`~graph_tool.VertexPropertyMap` or arbitrary types (optional, default: ``None``)
1675
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        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.
1679
    hedge_* : :class:`~graph_tool.EdgePropertyMap` or arbitrary types (optional, default: ``None``)
1680
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        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.
1683
    **kwargs :
1684
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        All remaining keyword arguments will be passed to the
        :func:`~graph_tool.draw.graph_draw` function.
1686
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1688

    Returns
    -------
1689
    pos : :class:`~graph_tool.VertexPropertyMap`
1690
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        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.
1694
    tpos : :class:`~graph_tool.VertexPropertyMap`
1695
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        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

1714
       conv_png("celegansneural_nested_mdl.pdf")
1715

1716
    .. figure:: celegansneural_nested_mdl.png
1717
       :align: center
1718
       :width: 80%
1719
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       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`
1731

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1734
1735
    """

    g = state.g

1736
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    overlap = state.levels[0].overlap
    if overlap:
1738
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        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)

1760
1761
    t, tb, tvorder = get_hierarchy_tree(state,
                                        empty_branches=empty_branches)
1762
1763

    if layout == "radial":
1764
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1766
        if rel_order == "degree":
            rel_order = g.degree_property_map("total")
        vorder = t.own_property(rel_order.copy())
1767
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        if pos is not None:
            x, y = ungroup_vector_property(pos, [0, 1])
1769
1770
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
1771
            angle = g.new_vertex_property("double")
1772
            angle.fa = (numpy.arctan2(y.fa, x.fa) + 2 * numpy.pi) % (2 * numpy.pi)
1773
            vorder = t.own_property(angle)
1774
1775
1776
        if node_weight is not None:
            node_weight = t.own_property(node_weight.copy())
            node_weight.a[node_weight.a == 0] = 1
1777
        tpos = radial_tree_layout(t, root=t.vertex(t.num_vertices() - 1,
1778
                                                   use_index=False),
1779
                                  node_weight=node_weight,
1780
1781
                                  rel_order=vorder,
                                  rel_order_leaf=True)
1782
    elif layout == "bipartite":
1783
        tpos = get_bip_hierachy_pos(state, aspect=bip_aspect,
1784
1785
                                    node_weight=node_weight)
        tpos = t.own_property(tpos)
1786
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1790
    elif layout == "sfdp":
        if pos is None:
            tpos = sfdp_layout(t)
        else:
            x, y = ungroup_vector_property(pos, [0, 1])
1791
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1793
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
            K = numpy.sqrt(x.fa.std() + y.fa.std()) / 10
1794
1795
            tpos = t.new_vertex_property("vector<double>")
            for v in t.vertices():
1796
                if int(v) < g.num_vertices(True):
1797
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1799
1800
                    tpos[v] = [x[v], y[v]]
                else:
                    tpos[v] = [0, 0]
            pin = t.new_vertex_property("bool")
1801
            pin.a[:g.num_vertices(True)] = True
1802
1803
1804
1805
            tpos = sfdp_layout(t, K=K, pos=tpos, pin=pin, multilevel=False)
    else:
        tpos = t.own_property(layout)

1806
    hvvisible = t.new_vertex_property("bool", True)
1807
1808
1809
1810
    if hide is None:
        L = len([s for s in state.levels if s.get_nonempty_B() > 0])
        hide = len(state.levels) - L

1811
    if hide > 0:
1812
        root = t.vertex(t.num_vertices(True) - 1)
1813
1814
1815
        dist = shortest_distance(t, source=root)
        hvvisible.fa = dist.fa >= hide

1816
1817
    pos = g.own_property(tpos.copy())

1818
    cts = get_hierarchy_control_points(g, t, tpos, beta,
1819
                                       max_depth=len(state.levels) - hshortcuts)
1820

1821
1822
1823
1824
1825
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1828
1829
1830
1831
1832
1833
    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)
1834
    vprops.setdefault("shape", _vdefaults["shape"] if not overlap else "pie")
1835
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1838
1839
1840
1841
1842

    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()))
1843
    vprops.setdefault("size", prop_to_size(g.degree_property_map("total"), s/5, s))
1844

1845
1846
    adjust_default_sizes(g, output_size, vprops, eprops)

1847
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1849
1850
    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
1851
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        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
1859
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1869
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1871

    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]


1872
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    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"])
1879
    eprops.setdefault("color", list(_edefaults["color"][:-1]) + [.6])
1880
    eprops.setdefault("end_marker", "arrow" if g.is_directed() else "none")
1881
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1894
    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")
1895
    hvprops.setdefault("size", s)
1896

1897
1898
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1900
    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
1901
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1904
1905
1906
1907
1908
        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
1909

1910
1911
1912
1913
1914
1915
1916
1917
    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")
1918
1919
    heprops.setdefault("marker_size", s * .8)
    heprops.setdefault("pen_width", s / 10)
1920
1921
1922

    heprops = _convert_props(heprops, "e", t, kwargs.get("ecmap", default_cm),
                             pmap_default=True)
1923

1924
1925
    vcmap = kwargs.get("vcmap", default_cm)
    ecmap = kwargs.get("ecmap", vcmap)
1926
1927
1928

    B = state.levels[0].B

1929
    if overlap and "pie_fractions" not in vprops:
1930
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1932
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1934
1935
1936
1937
1938
1939
1940
        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)
1941
1942
    if gradient is None:
        gradient = g.new_edge_property("double")
1943
        gradient = group_vector_property([gradient])
1944
1945
        ecolor = eprops.get("ecolor", _edefaults["color"])
        eprops["gradient"] = gradient
1946
        if overlap:
1947
            for e in g.edges():                       # ******** SLOW *******
1948
                r, s = be[e]
1949
                if not g.is_directed() and e.source() > e.target():
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                    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)