cairo_draw.py 98.5 KB
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    ...               output="graph-draw.pdf")
    <...>

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    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, vertex_size=deg, vertex_fill_color=deg, vorder=deg,
                     edge_color=ebet, eorder=eorder, edge_pen_width=ebet,
                     edge_control_points=control,
                     output="graph-draw.png")


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

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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
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        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:
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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)
        bg_color = kwargs.get("bg_color", [1., 1., 1., 1.])
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        vprops["text_color"] = auto_colors(g, bg,
                                           vprops.get("text_position",
                                                      _vdefaults["text_position"]),
                                           bg_color)

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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

        artist = GraphArtist(g, pos, vprops, eprops, vorder, eorder, nodesfirst,
                             ax, **kwargs)
        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:
                x, y = ungroup_vector_property(pos, [0, 1])
                l, r = x.a.min(), x.a.max()
                b, t = y.a.min(), y.a.max()
                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)
            ax.set_ylim(b - h * .1, t + h * .1)

        return pos
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    if inline:
        if fmt == "auto":
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            if output is None:
                fmt = "png"
            else:
                fmt = get_file_fmt(output)
        output_file = 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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        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])
        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)

        adjust_default_sizes(g, output_size, vprops, eprops)
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        if fit_view != False:
            try:
                x, y, w, h = fit_view
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                zoom = min(output_size[0] / w, output_size[1] / h)
                offset = (x * zoom, y * zoom)
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            except TypeError:
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                pad = fit_view if fit_view != True else 0.95
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                offset, zoom = fit_to_view(g, pos, output_size, vprops["size"],
                                           vprops["pen_width"], None,
                                           vprops.get("text", None),
                                           vprops.get("font_family",
                                                      _vdefaults["font_family"]),
                                           vprops.get("font_size",
                                                      _vdefaults["font_size"]),
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                                           pad, cr)
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            fit_view = False
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        else:
            offset, zoom = [0, 0], 1

        if "bg_color" in kwargs:
            bg_color = kwargs["bg_color"]
            del  kwargs["bg_color"]
            cr.set_source_rgba(bg_color[0], bg_color[1],
                               bg_color[2], bg_color[3])
            cr.paint()
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        cr.translate(offset[0], offset[1])
        cr.scale(zoom, zoom)

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

        if inline:
            img = None
            if fmt == "png":
                img = IPython.display.Image(data=out.getvalue())
            if fmt == "svg":
                img = IPython.display.SVG(data=out.getvalue())
            if 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
                img = IPython.display.Image(data=inl_out.getvalue())
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            srf.finish()
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            if output_file is not None:
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                if isinstance(output_file, (str, unicode)):
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                    ofile, auto_fmt = open_file(output_file, mode="wb")
                else:
                    ofile = output_file
                ofile.write(out.getvalue())
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                if isinstance(output_file, (str, unicode)):
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                    ofile.close()
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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


def scale_ink(scale, vprops, eprops):
    if "size" not in vprops:
        vprops["size"] = _vdefaults["size"]
    if "pen_width" not in vprops:
        vprops["pen_width"] = _vdefaults["pen_width"]
    if "font_size" not in vprops:
        vprops["font_size"] = _vdefaults["font_size"]
    if "pen_width" not in eprops:
        eprops["pen_width"] = _edefaults["pen_width"]
    if "marker_size" not in eprops:
        eprops["marker_size"] = _edefaults["marker_size"]
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    if "font_size" not in eprops:
        eprops["font_size"] = _edefaults["font_size"]
    if "text_distance" not in eprops:
        eprops["text_distance"] = _edefaults["text_distance"]
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    for props in [vprops, eprops]:
        if isinstance(props["pen_width"], PropertyMap):
            props["pen_width"].fa *= scale
        else:
            props["pen_width"] *= scale
    if isinstance(vprops["size"], PropertyMap):
        vprops["size"].fa *= scale
    else:
        vprops["size"] *= scale
    if isinstance(vprops["font_size"], PropertyMap):
        vprops["font_size"].fa *= scale
    else:
        vprops["font_size"] *= scale
    if isinstance(eprops["marker_size"], PropertyMap):
        eprops["marker_size"].fa *= scale
    else:
        eprops["marker_size"] *= scale
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    if isinstance(eprops["font_size"], PropertyMap):
        eprops["font_size"].fa *= scale
    else:
        eprops["font_size"] *= scale
    if isinstance(eprops["text_distance"], PropertyMap):
        eprops["text_distance"].fa *= scale
    else:
        eprops["text_distance"] *= scale
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def get_bb(g, pos, size, pen_width, size_scale=1, text=None, font_family=None,
           font_size=None, cr=None):
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    size = size.fa if isinstance(size, PropertyMap) else size
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    pen_width = pen_width.fa if isinstance(pen_width, PropertyMap) else pen_width
    pos_x, pos_y = ungroup_vector_property(pos, [0, 1])
    if text is not None and text != "":
        if not isinstance(size, PropertyMap):
            uniform = (not isinstance(font_size, PropertyMap) and
                       not isinstance(font_family, PropertyMap))
            size = np.ones(len(pos_x.fa)) * size
        else:
            uniform = False
        for i, v in enumerate(g.vertices()):
            ff = font_family[v] if isinstance(font_family, PropertyMap) \
               else font_family
            cr.select_font_face(ff)
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            fs = font_size[v] if isinstance(font_size, PropertyMap) \
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               else font_size
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            if not isinstance(font_size, PropertyMap):
                cr.set_font_size(fs)
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            t = text[v] if isinstance(text, PropertyMap) else text
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            if not isinstance(t, (str, unicode)):
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                t = str(t)
            extents = cr.text_extents(t)
            s = max(extents[2], extents[3]) * 1.4
            size[i] = max(size[i] * size_scale, s) / size_scale
            if uniform:
                size[:] = size[i]
                break
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    sl = label_self_loops(g)
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    slm = sl.fa.max() * 0.75 if g.num_edges() > 0 else 0
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    delta = (size * size_scale * (slm + 1)) / 2 + pen_width * 2
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    x_range = [pos_x.fa.min(), pos_x.fa.max()]
    y_range = [pos_y.fa.min(), pos_y.fa.max()]
    x_delta = [x_range[0] - (pos_x.fa - delta).min(),
               (pos_x.fa + delta).max() - x_range[1]]
    y_delta = [y_range[0] - (pos_y.fa - delta).min(),
               (pos_y.fa + delta).max() - y_range[1]]
    return x_range, y_range, x_delta, y_delta


def fit_to_view(g, pos, geometry, size, pen_width, M=None, text=None,
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                font_family=None, font_size=None, pad=0.95, cr=None):
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    if g.num_vertices() == 0:
        return [0, 0], 1
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    if M is not None:
        pos_x, pos_y = ungroup_vector_property(pos, [0, 1])
        P = np.zeros((2, len(pos_x.fa)))
        P[0, :] = pos_x.fa
        P[1, :] = pos_y.fa
        T = np.zeros((2, 2))
        O = np.zeros(2)
        T[0, 0], T[1, 0], T[0, 1], T[1, 1], O[0], O[1] = M
        P = np.dot(T, P)
        P[0] += O[0]
        P[1] += O[1]
        pos_x.fa = P[0, :]
        pos_y.fa = P[1, :]
        pos = group_vector_property([pos_x, pos_y])
    x_range, y_range, x_delta, y_delta = get_bb(g, pos, size, pen_width,
                                                1, text, font_family,
                                                font_size, cr)
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    dx = (x_range[1] - x_range[0])
    dy = (y_range[1] - y_range[0])
    if dx == 0:
        dx = 1
    if dy == 0:
        dy = 1
    zoom_x = (geometry[0] - sum(x_delta)) / dx
    zoom_y = (geometry[1] - sum(y_delta)) / dy
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    if np.isnan(zoom_x) or np.isinf(zoom_x) or zoom_x == 0:
        zoom_x = 1
    if np.isnan(zoom_y) or np.isinf(zoom_y) or zoom_y == 0:
        zoom_y = 1
    zoom = min(zoom_x, zoom_y) * pad
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    empty_x = (geometry[0] - sum(x_delta)) - dx * zoom
    empty_y = (geometry[1] - sum(y_delta)) - dy * zoom
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    offset = [-x_range[0] * zoom + empty_x / 2 + x_delta[0],
              -y_range[0] * zoom + empty_y / 2 + y_delta[0]]
    return offset, zoom


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``.
    tpos : :class:`~graph_tool.PropertyMap`
        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.PropertyMap`)
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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.PropertyMap` (optional, default: ``None``)
        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.PropertyMap`
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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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       gt.graph_draw(g, pos=pos, vertex_fill_color=b, vertex_shape=shape, edge_control_points=cts, edge_color=[0, 0, 0, 0.3], vertex_anchor=0, output="netscience_nested_mdl.png")
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    .. figure:: netscience_nested_mdl.*
       :align: center

       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())
1462
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1465

    u = GraphView(g, directed=True)
    tu = GraphView(t, directed=True)

1466
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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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1473
    if max_depth is None:
        max_depth = t.num_vertices()

1474
    tu = GraphView(tu, skip_vfilt=True)
1475
    tpos = tu.own_property(tpos)
1476
1477
    libgraph_tool_draw.get_cts(u._Graph__graph,
                               tu._Graph__graph,
1478
1479
                               _prop("v", tu, tpos),
                               _prop("e", u, beta),
1480
                               _prop("e", u, cts),
1481
                               is_tree, max_depth)
1482
    return cts
1483
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1487
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1489

#
# The functions and classes below depend on GTK
# =============================================
#

try:
1490
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    import gi
    gi.require_version('Gtk', '3.0')
1492
    from gi.repository import Gtk, Gdk, GdkPixbuf
1493
    from gi.repository import GObject as gobject
1494
1495
    from .gtk_draw import *
except (ImportError, RuntimeError) as e:
1496
    msg = "Error importing Gtk module: %s; GTK+ drawing will not work." % str(e)
1497
    warnings.warn(msg, RuntimeWarning)
1498
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1504
1505
1506
1507
1508
1509
1510
1511

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
1512
#
1513
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# matplotlib
# ==========
1515
#
1516

1517
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class GraphArtist(matplotlib.artist.Artist):
    """:class:`matplotlib.artist.Artist` specialization that draws
       :class:`graph_tool.Graph` instances.

    .. warning::

        Only Cairo-based backends are supported.
1524
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1527

    """

    def __init__(self, g, pos, vprops, eprops, vorder, eorder,
1528
                nodesfirst, ax=None, **kwargs):
1529
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        matplotlib.artist.Artist.__init__(self)
        self.g = g
        self.pos = pos
        self.vprops = vprops
        self.eprops = eprops
        self.vorder = vorder
        self.eorder = eorder
        self.nodesfirst = nodesfirst
1537
        self.ax = ax
1538
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        self.kwargs = kwargs

    def draw(self, renderer):
        if not isinstance(renderer, matplotlib.backends.backend_cairo.RendererCairo):
            raise NotImplementedError("graph plotting is supported only on Cairo backends")
1543
1544

        ctx = renderer.gc.ctx
1545
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1547
1548

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

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        ctx.save()

        if self.ax is not None:
            m = self.ax.transData.get_affine().get_matrix()
            m = cairo.Matrix(m[0,0], m[1, 0], m[0, 1], m[1, 1], m[0, 2], m[1,2])
            ctx.set_matrix(m)

            l, r = self.ax.get_xlim()
            b, t = self.ax.get_ylim()
            ctx.rectangle(l, b, r-l, t-b)
            ctx.clip()

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        # flip y direction
        x, y = ungroup_vector_property(self.pos, [0, 1])
1563
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        l, t, r, b = ctx.clip_extents()
        y.fa = b + t - y.fa
1565
        pos = group_vector_property([x, y])
1566

1567
        cairo_draw(self.g, pos, ctx, self.vprops, self.eprops,
1568
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                   self.vorder, self.eorder, self.nodesfirst, self.kwargs)

        ctx.restore()
1571
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1575
1576
1577


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

1578
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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,
1580
                   subsample_edges=None, rel_order="degree", deg_size=True,
1581
                   vsize_scale=1, hsize_scale=1, hshortcuts=0, hide=0,
1582
                   bip_aspect=1., empty_branches=False, **kwargs):
1583
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    r"""Draw a nested block model state in a circular hierarchy layout with edge
    bundling.

    Parameters
    ----------
    state : :class:`~graph_tool.community.NestedBlockState`
        Nested block state to be drawn.
    pos : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
        If supplied, this specifies a vertex property map with the positions of
        the vertices in the layout.
    layout : ``str`` or :class:`~graph_tool.PropertyMap` (optional, default: ``"radial"``)
        If ``layout == "radial"`` :func:`~graph_tool.draw.radial_tree_layout`
        will be used. If ``layout == "sfdp"``, the hierarchy tree will be
1596
1597
        positioned using :func:`~graph_tool.draw.sfdp_layout`. If ``layout ==
        "bipartite"`` a bipartite layout will be used. If instead a
1598
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1601
        :class:`~graph_tool.PropertyMap` is provided, it must correspond to the
        position of the hierarchy tree.
    beta : ``float`` (optional, default: ``.8``)
        Edge bundling strength.
1602
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1615
1616
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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.
1622
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1624
    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.
1625
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1628
1629
    rel_order : ``str`` or ``None`` or :class:`~graph_tool.PropertyMap` (optional, default: ``"degree"``)
        If ``degree``, the vertices will be ordered according to degree inside
        each group, and the relative ordering of the hierarchy branches. If
        instead a :class:`~graph_tool.PropertyMap` is provided, its value will
        be used for the relative ordering.
1630
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1632
    deg_size : ``bool`` (optional, default: ``True``)
        If ``True``, the (total) node degrees will be used for the default
        vertex sizes..
1633
    vsize_scale : ``float`` (optional, default: ``1.``)
1634
        Multiplicative factor for the default vertex sizes.
1635
    hsize_scale : ``float`` (optional, default: ``1.``)
1636
        Multiplicative factor for the default sizes of the hierarchy nodes.
1637
1638
1639
1640
1641
    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.
1642
1643
    bip_aspect : ``float`` (optional, default: ``1.``)
        If ``layout == "bipartite"``, this will define the aspect ratio of layout.
1644
    empty_branches : ``bool`` (optional, default: ``False``)
1645
1646
        If ``empty_branches == False``, dangling branches at the upper layers
        will be pruned.
1647
1648
1649
1650
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1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
    vertex_* : :class:`~graph_tool.PropertyMap` or arbitrary types (optional, default: ``None``)
        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.
    edge_* : :class:`~graph_tool.PropertyMap` or arbitrary types (optional, default: ``None``)
        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.
    hvertex_* : :class:`~graph_tool.PropertyMap` or arbitrary types (optional, default: ``None``)
        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.
    hedge_* : :class:`~graph_tool.PropertyMap` or arbitrary types (optional, default: ``None``)
        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.
1665
    **kwargs :
1666
1667
        All remaining keyword arguments will be passed to the
        :func:`~graph_tool.draw.graph_draw` function.
1668
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1670
1671
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1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
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1689
1690
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1693
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1695
1696
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1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711

    Returns
    -------
    pos : :class:`~graph_tool.PropertyMap`
        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.
    tpos : :class:`~graph_tool.PropertyMap`
        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

       gt.draw_hierarchy(state, output="celegansneural_nested_mdl.png")

    .. figure:: celegansneural_nested_mdl.*
       :align: center

       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`
1712

1713
1714
1715
1716
    """

    g = state.g

1717
1718
    overlap = state.levels[0].overlap
    if overlap:
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
        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)

1741
1742
    t, tb, tvorder = get_hierarchy_tree(state,
                                        empty_branches=empty_branches)
1743
1744

    if layout == "radial":
1745
1746
1747
        if rel_order == "degree":
            rel_order = g.degree_property_map("total")
        vorder = t.own_property(rel_order.copy())
1748
1749
        if pos is not None:
            x, y = ungroup_vector_property(pos, [0, 1])
1750
1751
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
1752
            angle = g.new_vertex_property("double")
1753
            angle.fa = (numpy.arctan2(y.fa, x.fa) + 2 * numpy.pi) % (2 * numpy.pi)
1754
            vorder = angle
1755
1756
1757
        if node_weight is not None:
            node_weight = t.own_property(node_weight.copy())
            node_weight.a[node_weight.a == 0] = 1
1758
        tpos = radial_tree_layout(t, root=t.vertex(t.num_vertices() - 1,
1759
                                                   use_index=False),
1760
                                  node_weight=node_weight,
1761
1762
                                  rel_order=vorder,
                                  rel_order_leaf=True)
1763
    elif layout == "bipartite":
1764
        tpos = get_bip_hierachy_pos(state, aspect=bip_aspect,
1765
1766
                                    node_weight=node_weight)
        tpos = t.own_property(tpos)
1767
1768
1769
1770
1771
    elif layout == "sfdp":
        if pos is None:
            tpos = sfdp_layout(t)
        else:
            x, y = ungroup_vector_property(pos, [0, 1])
1772
1773
1774
            x.fa -= x.fa.mean()
            y.fa -= y.fa.mean()
            K = numpy.sqrt(x.fa.std() + y.fa.std()) / 10
1775
1776
            tpos = t.new_vertex_property("vector<double>")
            for v in t.vertices():
1777
                if int(v) < g.num_vertices(True):
1778
1779
1780
1781
                    tpos[v] = [x[v], y[v]]
                else:
                    tpos[v] = [0, 0]
            pin = t.new_vertex_property("bool")
1782
            pin.a[:g.num_vertices(True)] = True
1783
1784
1785
1786
            tpos = sfdp_layout(t, K=K, pos=tpos, pin=pin, multilevel=False)
    else:
        tpos = t.own_property(layout)

1787
1788
    hvvisible = t.new_vertex_property("bool", True)
    if hide > 0:
1789
        root = t.vertex(t.num_vertices(True) - 1)
1790
1791
1792
        dist = shortest_distance(t, source=root)
        hvvisible.fa = dist.fa >= hide

1793
1794
    pos = g.own_property(tpos.copy())

1795
    cts = get_hierarchy_control_points(g, t, tpos, beta,
1796
                                       max_depth=len(state.levels) - hshortcuts)
1797

1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
    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)
1811
    vprops.setdefault("shape", _vdefaults["shape"] if not overlap else "pie")
1812
1813
    s = max(200 / numpy.sqrt(g.num_vertices()), 5)
    vprops.setdefault("size", prop_to_size(g.degree_property_map("total"), s/5, s))
1814
1815
1816
1817
1818

    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
1819
1820
1821
1822
1823
1824
1825
1826
        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
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839

    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]


1840
1841
1842
1843
1844
1845
1846
    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"])
1847
    eprops.setdefault("color", list(_edefaults["color"][:-1]) + [.6])
1848
    eprops.setdefault("end_marker", "arrow" if g.is_directed() else "none")
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
    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")
    hvprops.setdefault("size", 10)

1865
1866
1867
1868
    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
1869
1870
1871
1872
1873
1874
1875
1876
        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
1877

1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
    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")
    heprops.setdefault("marker_size", 8.)
    heprops.setdefault("pen_width", 1.)

    heprops = _convert_props(heprops, "e", t, kwargs.get("ecmap", default_cm),
                             pmap_default=True)
1891

1892
1893
    vcmap = kwargs.get("vcmap", default_cm)
    ecmap = kwargs.get("ecmap", vcmap)
1894
1895
1896

    B = state.levels[0].B

1897
    if overlap and "pie_fractions" not in vprops:
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
        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)
1909
1910
    if gradient is None:
        gradient = g.new_edge_property("double")
1911
        gradient = group_vector_property([gradient])
1912
1913
        ecolor = eprops.get("ecolor", _edefaults["color"])
        eprops["gradient"] = gradient
1914
        if overlap:
1915
            for e in g.edges():                       # ******** SLOW *******
1916
                r, s = be[e]
1917
                if not g.is_directed() and e.source() > e.target():
1918
1919
1920
                    r, s = s, r
                gradient[e] = [0] + list(vcmap(r / (B - 1))) + \
                              [1] + list(vcmap(s / (B - 1)))
1921
1922
1923
1924
                if isinstance(ecolor, PropertyMap):
                    gradient[e][4] = gradient[e][9] = ecolor[e][3]
                else:
                    gradient[e][4] = gradient[e][9] = ecolor[3]
1925
1926
1927


    t_orig = t
1928
    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)

                bstack = state.get_bstack()
                bs = [s.vp["b"].a for s in bstack[:l+1]]
                bs[-1][:] = 0

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                if not overlap:
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                    b = state.project_level(l).b
                    u = GraphView(g, vfilt=b.a == tb[picked])
                    u.vp["b"] = state.levels[0].b
                    u = Graph(u, prune=True)
                    b = u.vp["b"]
                    bs[0] = b.a
                else:
                    be = orig_state.project_level(l).get_edge_blocks()
                    emask = g.new_edge_property("bool")
                    for e in g.edges():
                        rs = be[e]
                        if rs[0] == tb[picked] and rs[1] == tb[picked]:
                            emask[e] = True
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                    u = GraphView(g, efilt=emask)
                    d = u.degree_property_map("total")
                    u = GraphView(u, vfilt=d.fa > 0)
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                    u.ep["be"] = orig_state.levels[0].get_edge_blocks()
                    u = Graph(u, prune=True)
                    be = u.ep["be"]