Python bokeh.models 模块，HoverTool() 实例源码

def make_plot(self, dataframe):
        self.source = ColumnDataSource(data=dataframe)
        self.plot = figure(
            x_axis_type="datetime", plot_width=400, plot_height=300,
            tools='', toolbar_location=None)

        vbar = self.plot.vbar(
            x='date', top='prcp', width=1, color='#fdae61', source=self.source)
        hover_tool = HoverTool(tooltips=[
            ('Value', '$y'),
            ('Date', '@date_readable'),
        ], renderers=[vbar])
        self.plot.tools.append(hover_tool)

        self.plot.xaxis.axis_label = None
        self.plot.yaxis.axis_label = None
        self.plot.axis.axis_label_text_font_style = 'bold'
        self.plot.x_range = DataRange1d(range_padding=0.0)
        self.plot.grid.grid_line_alpha = 0.3

        self.title = Paragraph(text=TITLE)
        return column(self.title, self.plot)

def make_plot(self, dataframe):
        self.source = ColumnDataSource(data=dataframe)
        self.plot = figure(
            x_axis_type="datetime", plot_width=600, plot_height=300,
            tools='', toolbar_location=None)
        self.plot.quad(
            top='max_temp', bottom='min_temp', left='left', right='right',
            color=Blues4[2], source=self.source, legend='Magnitude')
        line = self.plot.line(
            x='date', y='avg_temp', line_width=3, color=Blues4[1],
            source=self.source, legend='Average')
        hover_tool = HoverTool(tooltips=[
            ('Value', '$y'),
            ('Date', '@date_readable'),
        ], renderers=[line])
        self.plot.tools.append(hover_tool)

        self.plot.xaxis.axis_label = None
        self.plot.yaxis.axis_label = None
        self.plot.axis.axis_label_text_font_style = 'bold'
        self.plot.x_range = DataRange1d(range_padding=0.0)
        self.plot.grid.grid_line_alpha = 0.3

        self.title = Paragraph(text=TITLE)
        return column(self.title, self.plot)

def make_plot(self, dataframe):
        self.source = ColumnDataSource(data=dataframe)
        palette = all_palettes['Set2'][6]
        hover_tool = HoverTool(tooltips=[
            ("Value", "$y"),
            ("Year", "@year"),
        ])
        self.plot = figure(
            plot_width=600, plot_height=300, tools=[hover_tool],
            toolbar_location=None)
        columns = {
            'pm10': 'PM10 Mass (µg/m³)',
            'pm25_frm': 'PM2.5 FRM (µg/m³)',
            'pm25_nonfrm': 'PM2.5 non FRM (µg/m³)',
            'lead': 'Lead (¹/??? µg/m³)',
        }
        for i, (code, label) in enumerate(columns.items()):
            self.plot.line(
                x='year', y=code, source=self.source, line_width=3,
                line_alpha=0.6, line_color=palette[i], legend=label)

        self.title = Paragraph(text=TITLE)
        return column(self.title, self.plot)
# [END make_plot]

def get_states_plot():
    source = AjaxDataSource(
        data={'STATE': [], 'STNAME': [], 'STUSAB': [], 'TOT_POP': [], 'TOT_MALE': [], 'TOT_FEMALE': []},
        data_url='/api/states/', mode='replace', method='GET')

    hover = HoverTool(
        tooltips=[
            ("State", "@STNAME"),
            ("Population", "@TOT_POP"),
            ("Female Population", "@TOT_FEMALE"),
            ("Male Population", "@TOT_MALE"),
        ]
    )

    plot = figure(title='Population by State', plot_width=1200, plot_height=500,
                  x_range=FactorRange(factors=get_state_abbreviations()), y_range=(0, 40000000),
                  tools=[hover, 'tap','box_zoom','wheel_zoom','save','reset'])
    plot.toolbar.active_tap = 'auto'
    plot.xaxis.axis_label = 'State'
    plot.yaxis.axis_label = 'Population'
    plot.yaxis.formatter = NumeralTickFormatter(format="0a")
    plot.sizing_mode = 'scale_width'
    plot.vbar(bottom=0, top='TOT_POP', x='STUSAB', legend=None, width=0.5, source=source)

    url = "/counties/@STATE/"
    taptool = plot.select(type=TapTool)
    taptool.callback = OpenURL(url=url)

    return plot

def bokeh_scatter_plot(tsne_df):
    output_file("results\\Anime_similarity.html")
    # Prep plot
    plot_anime_sim = bp.figure(plot_width=700, plot_height=600, title="Anime Similarity plotted with tSNE",
        tools="pan,wheel_zoom,box_zoom,reset,hover,previewsave,tap",
        x_axis_type=None, y_axis_type=None, toolbar_location="below",
        toolbar_sticky=False)
    # Plotting the anime data
    plot_anime_sim.scatter(x='x', y='y', source=tsne_df)
    # Handle hover tools
    hover = plot_anime_sim.select(dict(type=HoverTool))
    hover.tooltips={"Anime":"@anime_name [@rating]"}
    # Add ability to click links:
    url = "http://www.myanimelist.net/anime/@anime_id"
    taptool = plot_anime_sim.select(type=TapTool)
    taptool.callback = OpenURL(url=url)
    # Show the file:
    show(plot_anime_sim)

    script, div = components(plot_anime_sim)
    with open("results\\sim_script.js", "w") as text_file:
        text_file.write(script[37:-10])
    with open("results\\sim_html.html", "w") as text_file:
        text_file.write(div)

#%% Main code:

def location_plot(title, colors):
    output_file(title+".html")
    location_source = ColumnDataSource(
        data={
            "x": whisky[" Latitude"],
            "y": whisky[" Longitude"],
            "colors": colors,
            "regions": whisky.Region,
            "distilleries": whisky.Distillery
        }
    )

    fig = figure(title = title,
        x_axis_location = "above", tools="resize, hover, save")
    fig.plot_width  = 400
    fig.plot_height = 500
    fig.circle("x", "y", 10, 10, size=9, source=location_source,
         color='colors', line_color = None)
    fig.xaxis.major_label_orientation = np.pi / 3
    hover = fig.select(dict(type = HoverTool))
    hover.tooltips = {
        "Distillery": "@distilleries",
        "Location": "(@x, @y)"
    }
    show(fig)

def location_plot(title, colors):
    output_file(title + ".html")
    location_source = ColumnDataSource(
        data={
            "x": whisky[" Latitude"],
            "y": whisky[" Longitude"],
            "colors": colors,
            "regions": whisky.Region,
            "distilleries": whisky.Distillery
        }
    )

    fig = figure(title=title,
                 x_axis_location="above", tools="resize, hover, save")
    fig.plot_width = 400
    fig.plot_height = 500
    fig.circle("x", "y", 10, 10, size=9, source=location_source,
               color='colors', line_color=None)
    fig.xaxis.major_label_orientation = np.pi / 3
    hover = fig.select(dict(type=HoverTool))
    hover.tooltips = {
        "Distillery": "@distilleries",
        "Location": "(@x, @y)"
    }
    show(fig)

def plot_bokeh(df,sublist,filename):
    lenlist=[0]
    df_sub = df[df['cuisine']==sublist[0]]
    lenlist.append(df_sub.shape[0])
    for cuisine in sublist[1:]:
        temp = df[df['cuisine']==cuisine]
        df_sub = pd.concat([df_sub, temp],axis=0,ignore_index=True)
        lenlist.append(df_sub.shape[0])
    df_X = df_sub.drop(['cuisine','recipeName'],axis=1)
    print df_X.shape, lenlist

    dist = squareform(pdist(df_X, metric='cosine'))
    tsne = TSNE(metric='precomputed').fit_transform(dist)
    #cannot use seaborn palette for bokeh
    palette =['red','green','blue','yellow']
    colors =[]
    for i in range(len(sublist)):
        for j in range(lenlist[i+1]-lenlist[i]):
            colors.append(palette[i])
    #plot with boken
    output_file(filename)
    source = ColumnDataSource(
            data=dict(x=tsne[:,0],y=tsne[:,1],
                cuisine = df_sub['cuisine'],
                recipe = df_sub['recipeName']))

    hover = HoverTool(tooltips=[
                ("cuisine", "@cuisine"),
                ("recipe", "@recipe")])

    p = figure(plot_width=1000, plot_height=1000, tools=[hover],
               title="flavor clustering")

    p.circle('x', 'y', size=10, source=source,fill_color=colors)

    show(p)

def _draw_contigCirclePlot(self):
        hover = HoverTool(tooltips=[('Length', '@contigs')])
        hover.point_policy = "follow_mouse"
        plot = figure(x_axis_type=None, y_axis_type=None, tools=[hover], title='Contig lengths')
        plot.annular_wedge(x=0, y=0, inner_radius=0.5, outer_radius=0.7,
                           start_angle='start', end_angle='stop',
                           color='colors', alpha=0.9, source=self.contig_dist_src)
        plot.yaxis.axis_label_text_font_size = '14pt'
        plot.xaxis.axis_label_text_font_size = '14pt'
        plot.yaxis.major_label_text_font_size = '14pt'
        plot.xaxis.major_label_text_font_size = '14pt'
        plot.title.text_font_size = '16pt'

        return plot

def plot_Trun(self):
        star = self.star
        inst_date = self.inst_date

        data = self.selected_df
        idx = data.groupby(['T']).apply(lambda x: x['ccf_max'].idxmax())
        highest = data.ix[idx].copy()
        source = ColumnDataSource(data=highest)
        self.current_source = source

        p = figure(
            title="{} - {}".format(star, inst_date),
            plot_width=800, plot_height=400,
            tools="pan,wheel_zoom,tap,hover,reset",
            title_text_font_size="20pt",
        )
        p.circle("T", "ccf_max",
                 size=10,
                 nonselection_alpha=0.6,
                 source=source
        )
        p.xaxis[0].axis_label = 'Temperature (K)'
        p.yaxis[0].axis_label = 'CCF Peak Value'

        hover = p.select(dict(type=HoverTool))
        hover.tooltips = OrderedDict([
            ("Temperature", "@T"),
            ("vsini", "@vsini"),
            ("[Fe/H]", "@feh"),
            ("log(g)", "@logg"),
            ("Radial Velocity (km/s)", "@vel_max"),
            ("ccf peak height", "@ccf_max"),
        ])
        return p, highest

def scatterplot_vis(df, **kwargs):
    plot_width = kwargs.get('plot_width',300)
    plot_height = kwargs.get('plot_height',300)
    size = kwargs.get('size',10)

    hover = HoverTool(
        tooltips="""
        <div>
            <div>

                <img
                    src="@img_filepath" height="200" alt="@img_filepath" width="200"
                    style="float: left; margin: 0px 15px 15px 0px;"
                    border="2"
                ></img>
            </div>

        </div>
        """
    )

    p = figure(plot_width=plot_width, plot_height=plot_height, 
               toolbar_location = 'right',
               tools='pan,box_zoom,wheel_zoom,reset,resize')
    p.add_tools(hover)

    df['label_color'] = df['label'].apply(lambda x: COLOR_PALETTE.as_hex()[int(x)])
    source = ColumnDataSource(df)
    circles = p.circle('x', 'y', size=size, source=source)
    circles.glyph.fill_color = 'label_color'

    output_notebook()
    show(p)

def scatterplot_text(df, **kwargs):
    plot_width = kwargs.get('plot_width',300)
    plot_height = kwargs.get('plot_height',300)
    size = kwargs.get('size',10)

    hover = HoverTool(
        tooltips="""
        <div>
            <div>
                <p>
                    @text
                </p>
            </div>            
        </div>
        """
    )

    p = figure(plot_width=plot_width, plot_height=plot_height, 
               toolbar_location = 'right',
               tools='pan,box_zoom,wheel_zoom,reset,resize')
    p.add_tools(hover)

    df['label_color'] = df['label'].apply(lambda x: COLOR_PALETTE.as_hex()[int(x)])
    source = ColumnDataSource(df)
    circles = p.circle('x', 'y', size=size, source=source)
    circles.glyph.fill_color = 'label_color'

    output_notebook()
    show(p)

def _add_hover(p):
    p.select_one(HoverTool).tooltips = [
        ('names', '@yname, @xname'),
        ('weight', '@weight'),
    ]
    return p

def make_plot(results, title, xlabel, ylabel, baseline, ycolname, yaxis_format):
    p = plotting.figure(plot_width=WIDTH, plot_height=250, title=title,
        x_axis_label=xlabel, y_axis_label=ylabel,
        toolbar_location="above", tools='box_zoom,reset')

    legend_items = []

    baseline_times = [t * 1e6 for t in results[baseline]['times']]

    for i, (impl_name, impl_data) in enumerate(results.items()):
        color = IMPL_COLORS[i % len(IMPL_COLORS)]
        style = IMPL_STYLES[i % len(IMPL_STYLES)]

        data = dict(x=impl_data['x'])
        # convert to microseconds
        data['times'] = [t * 1e6 for t in impl_data['times']]
        data['name'] = [impl_name] * len(data['x'])
        data['speedup'] = [b/t for (t,b) in zip(data['times'], baseline_times)]
        # not this is items/sec
        data['throughput'] = [items/t for (t, items) in zip(impl_data['times'], impl_data['x'])]

        source = plotting.ColumnDataSource(data=data)
        line = p.line('x', ycolname, source=source,
            line_width=2, line_color=color, line_dash=style)
        marker = p.circle('x', ycolname, source=source,
            size=10, fill_color=color)
        legend_items.append( (impl_name, [line, marker]) )

    hover = HoverTool(
        tooltips=[
            ('implementation', '@name'),
            ('x', '@x{%1.0e}'),
            ('time per call', '@times{%1.1e} usec'),
            ('speedup', '@{speedup}{%1.1f}x'),
            ('throughput', '@{throughput}{%1.1e}'),
        ],
        formatters={
            'x': 'printf',
            'times': 'printf',
            'speedup': 'printf',
            'throughput': 'printf',
        }
    )
    p.add_tools(hover)
    p.xaxis[0].formatter = PrintfTickFormatter(format='%1.0e')
    p.yaxis[0].formatter = PrintfTickFormatter(format=yaxis_format)

    legend = Legend(items=legend_items, location=(0, -30))
    p.add_layout(legend, 'right')

    return p

def map(h2, show=True, cmap="gray", cmap_reverse=True, **kwargs):
    """Heat map."""
    density = kwargs.pop("density", False)
    data = get_data(h2, density=density)
    show_colorbar = kwargs.pop("show_colorbar", True)

    X, Y = np.meshgrid(h2.get_bin_centers(0), h2.get_bin_centers(1))
    dX, dY = np.meshgrid(h2.get_bin_widths(0), h2.get_bin_widths(1))

    source = ColumnDataSource({
        "frequency": data.T.flatten(),
        "x": X.flatten(),
        "y": Y.flatten(),
        "width": dX.flatten(),
        "height": dY.flatten()
    })

    import bokeh.palettes
    if cmap in named_palettes:
        palette_generator = getattr(bokeh.palettes, cmap)
        palette = palette_generator(256)
    elif cmap in bokeh.palettes.all_palettes:
        palette = bokeh.palettes.all_palettes[cmap]
    else:
        raise RuntimeError("Unknown palette")
    if cmap_reverse:
        palette = palette[::-1]

    mapper = LinearColorMapper(palette=palette, low=data.min(), high=data.max())

    p = kwargs.pop("figure", _create_figure(h2))

    p.rect(source=source,
           x="x", y="y", 
           width="width", height="height",
           fill_color={"field" : "frequency", "transform" : mapper},
           line_color="white")

    p.select_one(HoverTool).tooltips = [
        ("frequency", "@frequency")
    ]

    if show_colorbar:
        from bokeh.models import ColorBar, BasicTicker
        color_bar = ColorBar(color_mapper=mapper, #, major_label_text_font_size="5pt",
                         ticker=BasicTicker(desired_num_ticks=6),
                         # formatter=PrintfTickFormatter(format="%d%%"),
                         # label_standoff=6, border_line_color=None,
                         location=(0, 0)
                         )
        p.add_layout(color_bar, 'right')

    if show:
        bokeh_show(p)
    return p

def create_figure(df,x,y,discrete,quantileable,continuous,size,color,controls):
    xs = df[x.value].values
    ys = df[y.value].values

    # x_title = x.value.title()
    # y_title = y.value.title()
    x_title = "Marginal Effective Tax Rate"
    y_title = "Asset Category"

    source = ColumnDataSource(ColumnDataSource.from_df(df))

    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 vs %s" % (x_title, y_title)
    #kw['title'] = "Marginal Effective Tax Rates on Typically Financed Corporate Investments, 2016 Law"
    # kw['title'] = "Marginal Effective Tax Rates on Corporate Investments, 2016 Law"
    kw['title'] = "METRs on Corporate Investments, 2016 Law"

    p = figure(plot_height=400, plot_width=600, tools='pan,box_zoom,reset,hover', **kw)
    p.xaxis.axis_label = x_title
    p.yaxis.axis_label = y_title

    hover = p.select(dict(type=HoverTool))
    hover.tooltips = [('Asset', '@Asset')]

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

    sz = 9
    if size.value != 'None':
        groups = pd.qcut(df[size.value].values, len(SIZES))
        sz = [SIZES[xx] for xx in groups.codes]

    c = "#73000A"
    if color.value != 'None':
        groups = pd.qcut(df[color.value].values, len(COLORS))
        c = [COLORS[xx] for xx in groups.codes]
    p.circle(x=xs, y=ys, source=source, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)

    # p.title.text_color = "black"
    # p.title.text_font = "Georgia"

    return p

def scatter_with_hover(x, y, in_notebook=True, show_plt=True,
                       fig=None, name=None, marker='o',
                       fig_width=500, fig_height=500, x_label=None,
                       y_label=None, title=None, color="blue"):
    """
    Plots an interactive scatter plot of `x` vs `y` using bokeh, with automatic
    tooltips showing columns from `df`. Modified from: 
    http://blog.rtwilson.com/bokeh-plots-with-dataframe-based-tooltips/

    Args:
        x (numpy.ndarray): The data for the x-axis.
        y (numpy.ndarray): The data for the y-axis.

        fig (bokeh.plotting.Figure, optional): Figure on which to plot 
          (if not given then a new figure will be created)

        name (str, optional): Series name to give to the scattered data
        marker (str, optional): Name of marker to use for scatter plot

    Returns:
        fig (bokeh.plotting.Figure): Figure (the same as given, or the newly created figure) 
            if show is False
    """
    # Make it so it works for ipython.
    if in_notebook: #pragma: no cover
        output_notebook()
    # insert the correct hover identifier.
    hover = HoverTool(tooltips=[("entry#", "@label"),])
    # If we haven't been given a Figure obj then create it with default
    # size etc.
    if fig is None:
        # if title is None:
        #     fig = figure(width=fig_width, height=fig_height, tools=['box_zoom', 'reset',hover])
        # else:
        fig = figure(width=fig_width, height=fig_height,
                     tools=['box_zoom', 'reset',hover],title=title)
    # We're getting data from the given dataframe
    source = ColumnDataSource(data=dict(x=x,y=y,label=range(1,len(x)+1)))

    # Actually do the scatter plot - the easy bit
    # (other keyword arguments will be passed to this function)
    fig.scatter('x', 'y', source=source, marker=marker,color=color,name=name)

    if x_label is not None:
        fig.xaxis.axis_label = x_label
    if y_label is not None:
        fig.yaxis.axis_label = y_label
    if show_plt: # pragma: no cover
        show(fig)
    else:
        return(fig)

def plot_detection_history():
    # Rectangle grid with detection history
    cols = np.shape(audio.predictions)[1]

    plt = figure(plot_width=WIDTHS[0], plot_height=HEIGHTS[1],
                 toolbar_location=None, tools="hover",
                 x_range=[-cols, 0], y_range=labels[::-1])

    plt.rect(x='x', y='y', width=0.95, height=0.8, color='color', source=HISTORY)

    # X ticks
    plt.xaxis[0].ticker = FixedTicker(ticks=np.arange(-cols, 1, 1).tolist())
    plt.xaxis[0].formatter = FuncTickFormatter(code="""
        return (tick * {} / 1000).toFixed(1) + " s"
    """.format(PREDICTION_STEP_IN_MS))
    plt.xaxis.major_tick_line_color = GRID_COLOR
    plt.xaxis.major_label_text_font_size = '7pt'
    plt.xaxis.major_label_text_font = TEXT_FONT
    plt.xaxis.major_label_text_color = TEXT_COLOR

    # X axis
    plt.xaxis.axis_line_color = None

    # Y ticks
    plt.yaxis.major_tick_line_color = None
    plt.yaxis.major_label_text_font_size = '7pt'
    plt.yaxis.major_label_text_font = TEXT_FONT
    plt.yaxis.major_label_text_color = TEXT_COLOR

    # Y axis
    plt.yaxis.axis_line_color = GRID_COLOR

    # Grid
    plt.ygrid.grid_line_color = None
    plt.xgrid.grid_line_color = None

    # Plot fill/border
    plt.background_fill_color = GRID_COLOR
    plt.outline_line_color = GRID_COLOR
    plt.min_border = 10

    # Plot title
    plt.title.text = 'Detection history:'
    plt.title.align = 'left'
    plt.title.text_color = TEXT_COLOR
    plt.title.text_font = TEXT_FONT
    plt.title.text_font_size = '9pt'
    plt.title.text_font_style = 'normal'

    # Hover tools
    hover = plt.select(dict(type=HoverTool))
    hover.tooltips = [
        ("Event", "@label"),
        ('Probability', '@pretty_value'),
    ]

    return plt

def plot_detection_last():
    # Horizontal bars with current probabilities
    plt = figure(plot_width=WIDTHS[1], plot_height=HEIGHTS[1],
                 toolbar_location=None, tools='hover',
                 x_range=[0., 1.], y_range=labels[::-1])

    plt.hbar(y='pos', height=0.9, left=0, right='value', color='color', source=DETECTION,
             name='bars', line_color=None)

    # Threshold annotation
    plt.quad(left=-0.1, right='threshold', bottom=-0.1, top=len(labels) + 1, source=THRESHOLD,
             fill_color='#000000', fill_alpha=0.1, line_color='red', line_dash='dashed')

    # X ticks
    plt.xaxis[0].ticker = FixedTicker(ticks=[0, 1])
    plt.xaxis.major_tick_line_color = GRID_COLOR
    plt.xaxis.major_label_text_font_size = '7pt'
    plt.xaxis.major_label_text_font = TEXT_FONT
    plt.xaxis.major_label_text_color = TEXT_COLOR

    # X axis
    plt.xaxis.axis_line_color = None

    # Y ticks
    plt.yaxis[0].ticker = FixedTicker(ticks=np.arange(1, len(labels) + 1, 1).tolist())
    plt.yaxis.major_label_text_font_size = '0pt'
    plt.yaxis.major_tick_line_color = None

    # Y axis
    plt.yaxis.axis_line_color = GRID_COLOR

    # Grid
    plt.xgrid.grid_line_color = None
    plt.ygrid.grid_line_color = None

    # Band fill
    plt.hbar(y=np.arange(1, len(labels) + 1, 2), height=1., left=0, right=1., color='#000000',
             alpha=0.1, level='image', name='bands')

    # Plot fill/border
    plt.outline_line_color = GRID_COLOR
    plt.min_border = 10

    # Plot title
    plt.title.text = 'Current frame:'
    plt.title.text_color = TEXT_COLOR
    plt.title.text_font = TEXT_FONT
    plt.title.text_font_size = '9pt'
    plt.title.text_font_style = 'normal'

    # Hover tools
    hover = plt.select(dict(type=HoverTool))
    hover.names = ['bars']
    hover.tooltips = [
        ('Event', '@label'),
        ('Probability', '@pretty_value'),
    ]

    return plt