Python models.Plot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 Plot [as 别名]
def test_point_annotator_init_models():
    annot = PointAnnotator(point_columns=['Size'], points=sample_points)
    panel = annot.panel()
    root_model = panel.get_root()

    fig = root_model.select_one({'type': Plot})
    points = fig.renderers[-1]
    table = root_model.select_one({'type': DataTable})

    # Ensure points data matches
    for k in sample_points:
        np.testing.assert_allclose(points.data_source.data[k], sample_points[k])

    # Ensure point is linked to table
    point_cbs = points.data_source.js_property_callbacks['change:data']
    assert len(point_cbs) == 1
    assert point_cbs[0].code == PointTableLinkCallback.source_code

    table_cbs = table.source.js_property_callbacks['change:data']
    assert len(table_cbs) == 1
    assert table_cbs[0].code == PointTableLinkCallback.target_code 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def test_point_annotator_updates():
    annot = PointAnnotator(point_columns=['Size'], points=sample_points)
    panel = annot.panel()
    root_model = panel.get_root(comm=Comm()) # Pass comm to ensure update is applied

    updated_points = dict(sample_points, Size=sample_points['Size'][::-1])
    points = Points(updated_points, vdims=['Size'], crs=ccrs.GOOGLE_MERCATOR)
    annot.points = points

    fig = root_model.select_one({'type': Plot})
    points = fig.renderers[-1]
    table = root_model.select_one({'type': DataTable})

    # Ensure points data matches
    for k in updated_points:
        np.testing.assert_allclose(points.data_source.data[k], updated_points[k])

    # Ensure point is linked to table
    point_cbs = points.data_source.js_property_callbacks['change:data']
    assert sum([cb.code == PointTableLinkCallback.source_code
                for cb in point_cbs]) == 1

    table_cbs = table.source.js_property_callbacks['change:data']
    assert len(table_cbs) == 1
    assert table_cbs[0].code == PointTableLinkCallback.target_code 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def _construct_colorbars(self, color_mappers=None):
        if not color_mappers:
            color_mappers = self.color_mappers
        from bokeh.models import Plot, ColorBar, FixedTicker
        cbs = []
        for color_mapper in color_mappers:
            ticks = np.linspace(color_mapper.low, color_mapper.high, 5)
            cbs.append(ColorBar(
                color_mapper=color_mapper,
                title=color_mapper.name,
                ticker=FixedTicker(ticks=ticks),
                label_standoff=5, background_fill_alpha=0, orientation='horizontal', location=(0, 0)
            ))
        plot = Plot(toolbar_location=None, frame_height=0, sizing_mode='stretch_width',
                    outline_line_width=0)
        [plot.add_layout(cb, 'below') for cb in cbs]
        return plot 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def test_poly_annotator_init_models():
    annot = PolyAnnotator(poly_columns=['Group'], polys=[sample_poly], vertex_columns=['Weight'])
    panel = annot.panel()
    root_model = panel.get_root()

    fig = root_model.select_one({'type': Plot})
    polys = fig.renderers[1]
    table1, table2 = root_model.select({'type': DataTable})
    if 'xs' in table1.source.data:
        table1, table2 = table2, table1 # Ensure tables are correctly ordered

    # Ensure poly data matchs
    np.testing.assert_allclose(polys.data_source.data['xs'][0][:-1], sample_poly['Longitude'])
    np.testing.assert_allclose(polys.data_source.data['ys'][0][:-1], sample_poly['Latitude'])

    # Ensure table and poly data are lnked
    assert table2.source is polys.data_source

    # Ensure poly is linked to vertex table
    poly_cbs = polys.data_source.js_property_callbacks['change:data']
    assert len(poly_cbs) == 1
    assert poly_cbs[0].code == VertexTableLinkCallback.source_code

    table_cbs = table1.source.js_property_callbacks['change:data']
    assert len(table_cbs) == 1
    assert table_cbs[0].code == VertexTableLinkCallback.target_code 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def test_poly_annotator_update_models():
    annot = PolyAnnotator(poly_columns=['Group'], polys=[sample_poly], vertex_columns=['Weight'])
    panel = annot.panel()
    root_model = panel.get_root(comm=Comm()) # Pass comm to ensure update is applied

    poly = Polygons([dict(sample_poly, Test=1)], vdims=['Test'], crs=ccrs.GOOGLE_MERCATOR)
    annot.poly_columns = ['Test']
    annot.polys = poly

    fig = root_model.select_one({'type': Plot})
    polys = fig.renderers[1]
    table1, table2 = root_model.select({'type': DataTable})
    if 'xs' in table1.source.data:
        table1, table2 = table2, table1 # Ensure tables are correctly ordered

    # Ensure poly data matches
    np.testing.assert_allclose(polys.data_source.data['xs'][0][:-1], sample_poly['Longitude'])
    np.testing.assert_allclose(polys.data_source.data['ys'][0][:-1], sample_poly['Latitude'])
    np.testing.assert_allclose(polys.data_source.data['Test'][0], np.ones(6))

    # Ensure table and poly data are linked
    assert table2.source is polys.data_source

    # Ensure poly is linked to vertex table
    poly_cbs = polys.data_source.js_property_callbacks['change:data']
    assert sum([cb.code == VertexTableLinkCallback.source_code
                for cb in poly_cbs]) == 1

    table_cbs = table1.source.js_property_callbacks['change:data']
    assert len(table_cbs) == 1
    assert table_cbs[0].code == VertexTableLinkCallback.target_code 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def legend():
    # Set ranges
    xdr = Range1d(0, 100)
    ydr = Range1d(0, 500)
    # Create plot
    plot = Plot(
        x_range=xdr,
        y_range=ydr,
        title="",
        plot_width=100,
        plot_height=500,
        min_border=0,
        toolbar_location=None,
        outline_line_color="#FFFFFF",
    )

    # For each color in your palette, add a Rect glyph to the plot with the appropriate properties
    palette = RdBu11
    width = 40
    for i, color in enumerate(palette):
        rect = Rect(
            x=40, y=(width * (i + 1)),
            width=width, height=40,
            fill_color=color, line_color='black'
        )
        plot.add_glyph(rect)

    # Add text labels and add them to the plot
    minimum = Text(x=50, y=0, text=['-6 ºC'])
    plot.add_glyph(minimum)
    maximum = Text(x=50, y=460, text=['6 ºC'])
    plot.add_glyph(maximum)

    return plot 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def test_server_callback_resolve_attr_spec_tap_event(self):
        plot = Plot()
        event = Tap(plot, x=42)
        msg = Callback.resolve_attr_spec('cb_obj.x', event, plot)
        self.assertEqual(msg, {'id': plot.ref['id'], 'value': 42}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def test_plotsize_resolves(self):
        points = Points([1, 2, 3])
        PlotSize(source=points)
        plot = bokeh_server_renderer.get_plot(points)
        callback = plot.callbacks[0]
        model = namedtuple('Plot', 'inner_width inner_height ref')(400, 300, {'id': 'Test'})
        width_spec = callback.attributes['width']
        height_spec = callback.attributes['height']
        resolved = callback.resolve_attr_spec(width_spec, model, model=model)
        self.assertEqual(resolved, {'id': 'Test', 'value': 400})
        resolved = callback.resolve_attr_spec(height_spec, model, model=model)
        self.assertEqual(resolved, {'id': 'Test', 'value': 300}) 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [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 Plot [as 别名]
def plot(xLabel='x',yLabel='y',*args):

    from bokeh.models import DataRange1d, Plot, LinearAxis, Grid
    from bokeh.models import PanTool, WheelZoomTool

    xdr = DataRange1d()
    ydr = DataRange1d()

    plot = Plot(x_range=xdr, y_range=ydr, min_border=80)

    extra = list()
    if type(xLabel) is not str and type(yLabel) is not str:
        extra.append(xLabel)
        extra.append(yLabel)
        xLabel = 'x'
        yLabel = 'y'
    elif type(xLabel) is not str: 
        extra.append(xLabel)
        xLabel = 'x'
    elif type(yLabel) is not str:
        extra.append(yLabel)
        yLabel = 'y'
   
    args = extra+list(args) 
    for renderer in args:
         if type(renderer) is not list: 
             plot.renderers.append(renderer)
         else: 
             plot.renderers.extend(renderer)

    #axes
    xaxis = LinearAxis(axis_label=xLabel)
    plot.add_layout(xaxis, 'below')
    yaxis = LinearAxis(axis_label=yLabel)
    plot.add_layout(yaxis, 'left')
    #add grid to the plot 
    #plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
    #plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))

    #interactive tools
    plot.add_tools(PanTool(), WheelZoomTool()) #, SaveTool())

    return plot 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def interactiveG(G):
    from bokeh.models.graphs import NodesAndLinkedEdges,from_networkx
    from bokeh.models import Circle, HoverTool, MultiLine,Plot,Range1d,StaticLayoutProvider
    from bokeh.plotting import figure, output_file, show, ColumnDataSource
    from bokeh.io import output_notebook, show
    output_notebook()
    # We could use figure here but don't want all the axes and titles  
    #plot=Plot(plot_width=1600, plot_height=300, tooltips=TOOLTIPS,title="PHmi+landmarks+route+power(10,-5)",x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
    
    output_file("PHMI_network")
    source=ColumnDataSource(data=dict(
        x=locations[0].tolist(),
        #x=[idx for idx in range(len(PHMIList))],
        #y=locations[1].tolist(),
        y=PHMIList,
        #desc=[str(i) for i in PHMIList],
        #PHMI_value=PHMI_dic[0][0].tolist(),    
    ))
    TOOLTIPS=[
        ("index", "$index"),
        ("(x,y)", "($x, $y)"),
        #("desc", "@desc"),
        #("PHMI", "$PHMI_value"),
    ]
    
    
    plot=figure(x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1),plot_width=2200, plot_height=500,tooltips=TOOLTIPS,title="PHMI_network")
    
    #G_position={key:(G.position[key][1],G.position[key][0]) for key in G.position.keys()}
    graph = from_networkx(G,nx.spring_layout,scale=1, center=(0,0))  
    #plot.renderers.append(graph) 
    
    fixed_layout_provider = StaticLayoutProvider(graph_layout=G.position)
    graph.layout_provider = fixed_layout_provider
    plot.renderers.append(graph)
    
    # Blue circles for nodes, and light grey lines for edges  
    graph.node_renderer.glyph = Circle(size=5, fill_color='#2b83ba')  
    graph.edge_renderer.glyph = MultiLine(line_color="#cccccc", line_alpha=0.8, line_width=2)  
      
    # green hover for both nodes and edges  
    graph.node_renderer.hover_glyph = Circle(size=25, fill_color='#abdda4')  
    graph.edge_renderer.hover_glyph = MultiLine(line_color='#abdda4', line_width=4)  
      
    # When we hover over nodes, highlight adjecent edges too  
    graph.inspection_policy = NodesAndLinkedEdges()  
      
    plot.add_tools(HoverTool(tooltips=None))  
     
    colors=('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen')
    ScalePhmi=math.pow(10,1)
    i=0
    for val,idx in zip(phmi_breakPtsNeg, plot_x):
        plot.line(idx,np.array(val)*ScalePhmi,line_color=colors[i])
        i+=1    
        
    show(plot)
    
#06-single landmarks pattern 无人车位置点与对应landmarks栅格图
#convert location and corresponding landmarks to raster data format using numpy.histogram2d 

# 需要导入模块: from bokeh import models [as 别名]
# 或者: from bokeh.models import Plot [as 别名]
def interactiveG(G):
    from bokeh.models.graphs import NodesAndLinkedEdges,from_networkx
    from bokeh.models import Circle, HoverTool, MultiLine,Plot,Range1d,StaticLayoutProvider
    from bokeh.plotting import figure, output_file, show, ColumnDataSource
    from bokeh.io import output_notebook, show
    output_notebook()
    # We could use figure here but don't want all the axes and titles  
    #plot=Plot(plot_width=1600, plot_height=300, tooltips=TOOLTIPS,title="PHmi+landmarks+route+power(10,-5)",x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
    
    output_file("PHMI_network")
    source=ColumnDataSource(data=dict(
        x=locations[0].tolist(),
        #x=[idx for idx in range(len(PHMIList))],
        #y=locations[1].tolist(),
        y=PHMIList,
        #desc=[str(i) for i in PHMIList],
        #PHMI_value=PHMI_dic[0][0].tolist(),    
    ))
    TOOLTIPS=[
        ("index", "$index"),
        ("(x,y)", "($x, $y)"),
        #("desc", "@desc"),
        #("PHMI", "$PHMI_value"),
    ]
    
    
    plot=figure(x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1),plot_width=2200, plot_height=500,tooltips=TOOLTIPS,title="PHMI_network")
    
    #G_position={key:(G.position[key][1],G.position[key][0]) for key in G.position.keys()}
    graph = from_networkx(G,nx.spring_layout,scale=1, center=(0,0))  
    #plot.renderers.append(graph) 
    
    fixed_layout_provider = StaticLayoutProvider(graph_layout=G.position)
    graph.layout_provider = fixed_layout_provider
    plot.renderers.append(graph)
    
    # Blue circles for nodes, and light grey lines for edges  
    graph.node_renderer.glyph = Circle(size=5, fill_color='#2b83ba')  
    graph.edge_renderer.glyph = MultiLine(line_color="#cccccc", line_alpha=0.8, line_width=2)  
      
    # green hover for both nodes and edges  
    graph.node_renderer.hover_glyph = Circle(size=25, fill_color='#abdda4')  
    graph.edge_renderer.hover_glyph = MultiLine(line_color='#abdda4', line_width=4)  
      
    # When we hover over nodes, highlight adjecent edges too  
    graph.inspection_policy = NodesAndLinkedEdges()  
      
    plot.add_tools(HoverTool(tooltips=None))  
     
    colors=('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen')
    ScalePhmi=math.pow(10,1)
    i=0
    for val,idx in zip(phmi_breakPtsNeg, plot_x):
        plot.line(idx,np.array(val)*ScalePhmi,line_color=colors[i])
        i+=1    
        
    show(plot)
    
#05-single landmarks pattern 无人车位置点与对应landmarks栅格图
#convert location and corresponding landmarks to raster data format using numpy.histogram2d