Python models.BoxSelectTool方法代码示例

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import BoxSelectTool [as 别名]
def create_figure():
    # args = curdoc().session_context.request.arguments
    # with open('args.txt', 'w') as the_file:
    #     the_file.write(str(curdoc().session_context.request.arguments['batchid']))
    #     the_file.write(str(args))

    df = select_units()

    xs = df[x.value].values
    ys = df[y.value].values
    df['x'] = xs
    df['y'] = ys

    source.data = df.to_dict(orient='list')

    x_title = x.value.title()
    y_title = y.value.title()

    kw = dict()
    if x.value in discrete:
        kw['x_range'] = sorted(set(xs))
    if y.value in discrete:
        kw['y_range'] = sorted(set(ys))
    # kw['title'] = "%s" % (dir(args))
    kw['title'] = "%s vs %s (%i elements)" % (x_title, y_title, len(df))

    # hover = HoverTool(tooltips=[("Address", "@HostnameIP"), ("Malware", "@Malware"), ("Compromise", "@Compromise")])
    p = figure(plot_width=500, plot_height=500, tools=[BoxSelectTool(), ResetTool()], **kw)
    p.xaxis.axis_label = x_title
    p.yaxis.axis_label = y_title

    if x.value in discrete:
        p.xaxis.major_label_orientation = pd.np.pi / 4

        # c = np.where(pandata["Compromise"] > 0, "orange", "grey")
        # sz = np.where(pandata["Compromise"] > 0, 9 * , "grey")

    p.circle(x='x', y='y', source=source, size=15,
            selection_color="orange", alpha=0.8, nonselection_alpha=0.4, selection_alpha=0.6)

    return p 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import BoxSelectTool [as 别名]
def _add_select_tools(current_src, other_src, param_plot, point_glyph):
    select_js_kwargs = {"current_src": current_src, "other_src": other_src}
    select_js_code = """
    // adapted from https://stackoverflow.com/a/44996422

    var chosen = current_src.selected.indices;
    if (typeof(chosen) == "number"){
        var chosen = [chosen]
    };

    var chosen_models = [];

    for (var i = 0; i < chosen.length; ++ i){
        chosen_models.push(current_src.data['model'][chosen[i]])
    };

    var chosen_models_indices = [];
    for (var i = 0; i < current_src.data['index'].length; ++ i){
        if (chosen_models.includes(current_src.data['model'][i])){
            chosen_models_indices.push(i)
        };
    };
    current_src.selected.indices = chosen_models_indices;
    current_src.change.emit();

    for (var i = 0; i < other_src.length; ++i){
        var chosen_models_indices = [];
        for (var j = 0; j < other_src[i].data['index'].length; ++ j){
            if (chosen_models.includes(other_src[i].data['model'][j])){
                chosen_models_indices.push(j)
            };
        };
        other_src[i].selected.indices = chosen_models_indices;
        other_src[i].change.emit();
    };

    """
    select_callback = CustomJS(args=select_js_kwargs, code=select_js_code)
    # point_glyph as only renderer assures that when a point is chosen
    # only that point's model is chosen
    # this makes it impossible to choose models based on clicking confidence bands
    tap = TapTool(renderers=[point_glyph], callback=select_callback)
    param_plot.tools.append(tap)
    boxselect = BoxSelectTool(renderers=[point_glyph], callback=select_callback)
    param_plot.tools.append(boxselect) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import BoxSelectTool [as 别名]
def visualize_self_attention_scores(tokens, scores, filename="/notebooks/embedding/self-attention.png",
                                    use_notebook=False):
    mean_prob = np.mean(scores)
    weighted_edges = []
    for idx_1, token_prob_dist_1 in enumerate(scores):
        for idx_2, el in enumerate(token_prob_dist_1):
            if idx_1 == idx_2 or el < mean_prob:
                weighted_edges.append((tokens[idx_1], tokens[idx_2], 0))
            else:
                weighted_edges.append((tokens[idx_1], tokens[idx_2], el))
    max_prob = np.max([el[2] for el in weighted_edges])
    weighted_edges = [(el[0], el[1], (el[2] - mean_prob) / (max_prob - mean_prob)) for el in weighted_edges]

    G = nx.Graph()
    G.add_nodes_from([el for el in tokens])
    G.add_weighted_edges_from(weighted_edges)

    plot = Plot(plot_width=500, plot_height=500,
                x_range=Range1d(-1.1, 1.1), y_range=Range1d(-1.1, 1.1))
    plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())

    graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0, 0))

    graph_renderer.node_renderer.data_source.data['colors'] = Spectral8[:len(tokens)]
    graph_renderer.node_renderer.glyph = Circle(size=15, line_color=None, fill_color="colors")
    graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color="colors")
    graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color="grey")

    graph_renderer.edge_renderer.data_source.data["line_width"] = [G.get_edge_data(a, b)['weight'] * 3 for a, b in
                                                                   G.edges()]
    graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_width={'field': 'line_width'})
    graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color="grey", line_width=5)
    graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color="grey", line_width=5)

    graph_renderer.selection_policy = NodesAndLinkedEdges()
    graph_renderer.inspection_policy = EdgesAndLinkedNodes()

    plot.renderers.append(graph_renderer)

    x, y = zip(*graph_renderer.layout_provider.graph_layout.values())
    data = {'x': list(x), 'y': list(y), 'connectionNames': tokens}
    source = ColumnDataSource(data)
    labels = LabelSet(x='x', y='y', text='connectionNames', source=source, text_align='center')
    plot.renderers.append(labels)
    plot.add_tools(SaveTool())
    if use_notebook:
        output_notebook()
        show(plot)
    else:
        export_png(plot, filename)
        print("save @ " + filename) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import BoxSelectTool [as 别名]
def create_plot(self, updateCoordinates):
        load_x = []
        load_y = []
        load_lines_xs = []
        load_lines_ys = []
        if updateCoordinates:
            edges = [list(x) for x in self.nxGraph.edges()]

            self.plotdata = {
                'Xs': [],
                'Ys': [],
            }
            for edge in edges:
                node1, node2 = edge
                if 'x' in self.nxGraph.node[node1] and 'x' in self.nxGraph.node[node2]:
                    x1 = self.nxGraph.node[node1]['x']
                    y1 = self.nxGraph.node[node1]['y']
                    x2 = self.nxGraph.node[node2]['x']
                    y2 = self.nxGraph.node[node2]['y']
                    if None not in [x1, x2, y1, y2]:
                        self.plotdata['Xs'].append([x1, x2])
                        self.plotdata['Ys'].append([y1, y2])

                for node in [node1, node2]:
                    if 'loads' in self.nxGraph.node[node]:
                        for load_name, load_properties in self.nxGraph.node[node]['loads'].items():
                            if 'x' in self.nxGraph.node[node] and self.nxGraph.node[node]:
                                x1 = self.nxGraph.node[node]['x']
                                y1 = self.nxGraph.node[node]['y']
                                x2 = load_properties['x']
                                y2 = load_properties['y']
                                dist = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
                                if 0 not in [x1, x2, y1, y2] and dist < 500:
                                    load_x.append(x2)
                                    load_y.append(y2)
                                    load_lines_xs.append([x1, x2])
                                    load_lines_ys.append([y1, y2])

        Linesource = ColumnDataSource(self.plotdata)
        plot = figure(title=None, plot_width=900, plot_height=900,
                      tools=[ResetTool(), BoxSelectTool(), SaveTool(), BoxZoomTool(), WheelZoomTool(),
                             PanTool()])

        # plot edges (lines, fuses, switches, transformers, regulators)
        plot.multi_line(xs="Xs", ys="Ys", source=Linesource, line_width=2, legend='Edges', line_color='#00008B') # , line_color=ColorBy
        # plot loads
        plot.triangle(x=load_x, y=load_y, legend='Loads', color='orange')
        # plot substation
        Xs, Ys = self.get_class_type_locations('substation')
        plot.circle_x(x=Xs, y=Ys, size=14, legend='Substation', fill_color='red', line_color='black')
        Xs, Ys = self.get_class_type_locations('regulator')
        plot.hex(x=Xs, y=Ys, size=14, legend='Regulators', fill_color='lightgreen', line_color='black')
        plot.multi_line(xs=load_lines_xs, ys=load_lines_ys, line_width=2, legend='Drop lines', line_color='black')

        plot.legend.location = "top_left"
        plot.legend.click_policy = "hide"
        curdoc().add_root(plot)

        show(plot)
        return