Python bokeh.models.ColorBar() Examples

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 

def visualize_between_sentences(sentences, vec_list, palette="Viridis256",
                                filename="/notebooks/embedding/between-sentences.png",
                                use_notebook=False):
    df_list, score_list = [], []
    for sent1_idx, sentence1 in enumerate(sentences):
        for sent2_idx, sentence2 in enumerate(sentences):
            vec1, vec2 = vec_list[sent1_idx], vec_list[sent2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': sentence1, 'y': sentence2, 'similarity': score[0][0]})
                score_list.append(score[0][0])
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=np.max(score_list), high=np.min(score_list))
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=sentences, y_range=list(reversed(sentences)),
                x_axis_location="above", plot_width=900, plot_height=900,
                toolbar_location='below', tools=TOOLS,
                tooltips=[('sentences', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
            source=df,
            fill_color={'field': 'similarity', 'transform': color_mapper},
            line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                        color_mapper=color_mapper, major_label_text_font_size="7pt",
                        label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

def visualize_between_words(words, vecs, palette="Viridis256", filename="/notebooks/embedding/between-words.png",
                            use_notebook=False):
    df_list = []
    for word1_idx, word1 in enumerate(words):
        for word2_idx, word2 in enumerate(words):
            vec1 = vecs[word1_idx]
            vec2 = vecs[word2_idx]
            if np.any(vec1) and np.any(vec2):
                score = cosine_similarity(X=[vec1], Y=[vec2])
                df_list.append({'x': word1, 'y': word2, 'similarity': score[0][0]})
    df = pd.DataFrame(df_list)
    color_mapper = LinearColorMapper(palette=palette, low=1, high=0)
    TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
    p = figure(x_range=list(words), y_range=list(reversed(list(words))),
               x_axis_location="above", plot_width=900, plot_height=900,
               toolbar_location='below', tools=TOOLS,
               tooltips=[('words', '@x @y'), ('similarity', '@similarity')])
    p.grid.grid_line_color = None
    p.axis.axis_line_color = None
    p.axis.major_tick_line_color = None
    p.axis.major_label_standoff = 0
    p.xaxis.major_label_orientation = 3.14 / 3
    p.rect(x="x", y="y", width=1, height=1,
           source=df,
           fill_color={'field': 'similarity', 'transform': color_mapper},
           line_color=None)
    color_bar = ColorBar(ticker=BasicTicker(desired_num_ticks=5),
                         color_mapper=color_mapper, major_label_text_font_size="7pt",
                         label_standoff=6, border_line_color=None, location=(0, 0))
    p.add_layout(color_bar, 'right')
    if use_notebook:
        output_notebook()
        show(p)
    else:
        export_png(p, filename)
        print("save @ " + filename) 

def test_quadmesh_colorbar(self):
        n = 21
        xs = np.logspace(1, 3, n)
        ys = np.linspace(1, 10, n)
        qmesh = QuadMesh((xs, ys, np.random.rand(n-1, n-1))).options(colorbar=True)
        plot = bokeh_renderer.get_plot(qmesh)
        self.assertIsInstance(plot.handles['colorbar'], ColorBar)
        self.assertIs(plot.handles['colorbar'].color_mapper, plot.handles['color_mapper']) 

def test_radial_heatmap_colorbar(self):
        hm = HeatMap([(0, 0, 1), (0, 1, 2), (1, 0, 3)]).options(radial=True, colorbar=True)
        plot = bokeh_renderer.get_plot(hm)
        self.assertIsInstance(plot.handles.get('colorbar'), ColorBar) 

def HeatTable(self):
        ready = {}
        for item in self.sharedCount:
            ready.update({ (item.split(',')[0],item.split(',')[1]): self.sharedCount[item] })

        k = np.array([item for item in ready])
        v = np.array([ready[item] for item in ready])

        unq_keys, key_idx = np.unique(k, return_inverse=True)
        key_idx = key_idx.reshape(-1, 2)
        n = len(unq_keys)
        adj = np.zeros((n, n), dtype=v.dtype)
        adj[key_idx[:, 0], key_idx[:, 1]] = v
        adj += adj.T

        adj = adj.astype(float)
        for i in range(0,adj.shape[0]):
            for k in range(0,adj.shape[1]):
                if k<=i:
                    adj[i,k]=np.nan

        dfout = pd.DataFrame(data=np.log10(adj+0.01),index = unq_keys, columns = unq_keys)
        dfout.index.name = 'Sam1'
        dfout.columns.name = 'Sam2'
        df = pd.DataFrame(dfout.stack(), columns=['Neoantigens']).reset_index()

        source = ColumnDataSource(df)

        import bokeh.palettes as p
        colors = p.Plasma256
        mapper = LinearColorMapper(palette=colors, low=df.Neoantigens.min(), high=df.Neoantigens.max())

        p = figure(title = "log10( Shared Neoantigens )", plot_height=400, plot_width=400, x_range=list(dfout.index), y_range=list(reversed(dfout.columns)),
           toolbar_location=None, tools="", x_axis_location="below")
        p.grid.grid_line_color = None
        p.axis.axis_line_color = None
        p.axis.major_tick_line_color = None
        p.axis.major_label_text_font_size = "5pt"
        p.axis.major_label_standoff = 0
        p.xaxis.major_label_orientation = np.pi / 3
        p.rect(x='Sam1',y='Sam2', source=source, width=1, height=1, fill_color={'field':'Neoantigens','transform':mapper}, line_color=None)

        color_bar = ColorBar(color_mapper=mapper, location=(0, 0),
                             ticker=BasicTicker(desired_num_ticks=int(len(colors)/10)))

        p.add_layout(color_bar, 'right')
        return(p) 

def _plot_contour(self, p, contour_data, x_range, y_range):
        """Plot contour data.

        Parameters
        ----------
        p: bokeh.plotting.figure
            figure to be drawn upon
        contour_data: Dict[str -> np.array]
            dict from labels to array with contour data
        x_range: List[float, float]
            min and max of x-axis
        y_range: List[float, float]
            min and max of y-axis

        Returns
        -------
        handles: dict[str -> tuple(ImageGlyph, tuple(float, float))]
            mapping from label to image glyph and min/max-tuple
        """
        unique = np.unique(np.concatenate([contour_data[label][2] for label in contour_data.keys()]))
        color_mapper = LinearColorMapper(palette="Viridis256", low=np.min(unique), high=np.max(unique))
        handles = {}
        default_label = 'combined' if 'combined' in contour_data.keys() else list(contour_data.keys())[0]
        for label, data in contour_data.items():
            unique = np.unique(contour_data[label][2])
            handles[label] = (p.image(image=contour_data[label], x=x_range[0], y=y_range[0],
                                      dw=x_range[1] - x_range[0], dh=y_range[1] - y_range[0],
                                      color_mapper=color_mapper),
                              (np.min(unique), np.max(unique)))

            if not label == default_label and len(contour_data) > 1:
                handles[label][0].visible = False
        color_bar = ColorBar(color_mapper=color_mapper,
                             ticker=BasicTicker(desired_num_ticks=15),
                             label_standoff=12,
                             border_line_color=None, location=(0, 0))
        color_bar.major_label_text_font_size = '12pt'
        p.add_layout(color_bar, 'right')
        return handles, color_mapper 

def image(img, x=None, y=None, colormap='Plasma256', clim=None, clabel=None, title=None, xlabel=None, ylabel=None, xlim=None, ylim=None, xtype='auto', ytype='auto', width=None, height=None, hold=False, interactive=None):
    """Plot a heatmap of 2D scalar data.

    :param img: 2D image data
    :param x: x-axis range for image data (min, max)
    :param y: y-axis range for image data (min, max)
    :param colormap: named color palette or Bokeh ColorMapper (see `Bokeh palettes <https://bokeh.pydata.org/en/latest/docs/reference/palettes.html>`_)
    :param clim: color axis limits (min, max)
    :param clabel: color axis label
    :param title: figure title
    :param xlabel: x-axis label
    :param ylabel: y-axis label
    :param xlim: x-axis limits (min, max)
    :param ylim: y-axis limits (min, max)
    :param xtype: x-axis type ('auto', 'linear', 'log', etc)
    :param ytype: y-axis type ('auto', 'linear', 'log', etc)
    :param width: figure width in pixels
    :param height: figure height in pixels
    :param interactive: enable interactive tools (pan, zoom, etc) for plot
    :param hold: if set to True, output is not plotted immediately, but combined with the next plot

    >>> import arlpy.plot
    >>> import numpy as np
    >>> arlpy.plot.image(np.random.normal(size=(100,100)), colormap='Inferno256')
    """
    global _figure
    if x is None:
        x = (0, img.shape[1]-1)
    if y is None:
        y = (0, img.shape[0]-1)
    if xlim is None:
        xlim = x
    if ylim is None:
        ylim = y
    if _figure is None:
        _figure = _new_figure(title, width, height, xlabel, ylabel, xlim, ylim, xtype, ytype, interactive)
    if clim is None:
        clim = [_np.amin(img), _np.amax(img)]
    if not isinstance(colormap, _bmodels.ColorMapper):
        colormap = _bmodels.LinearColorMapper(palette=colormap, low=clim[0], high=clim[1])
    _figure.image([img], x=x[0], y=y[0], dw=x[-1]-x[0], dh=y[-1]-y[0], color_mapper=colormap)
    cbar = _bmodels.ColorBar(color_mapper=colormap, location=(0,0), title=clabel)
    _figure.add_layout(cbar, 'right')
    if not hold and not _hold:
        _show(_figure)
        _figure = None 

def test_vtk_pane_more_complex(document, comm, tmp_path):
    renWin = pyvista_render_window()
    pane = VTK(renWin)

    # Create pane
    model = pane.get_root(document, comm=comm)
    assert isinstance(model, VTKSynchronizedPlot)
    assert pane._models[model.ref['id']][0] is model

    colorbars_infered = pane.construct_colorbars().object

    assert len(colorbars_infered.below) == 2 # infer only actor color bars
    assert all(isinstance(cb, ColorBar) for cb in colorbars_infered.below)

    colorbars_in_scene = pane.construct_colorbars(infer=False).object()
    assert len(colorbars_in_scene.below) == 3
    assert all(isinstance(cb, ColorBar) for cb in colorbars_in_scene.below)
    # add axes
    pane.axes = dict(
        origin = [-5, 5, -2],
        xticker = {'ticks': np.linspace(-5,5,5)},
        yticker = {'ticks': np.linspace(-5,5,5)},
        zticker = {'ticks': np.linspace(-2,2,5),
                   'labels': [''] + [str(int(item)) for item in np.linspace(-2,2,5)[1:]]},
        fontsize = 12,
        digits = 1,
        grid_opacity = 0.5,
        show_grid=True
    )
    assert isinstance(model.axes, VTKAxes)

    # test export to file
    tmpfile = os.path.join(*tmp_path.joinpath('scene').parts)
    exported_file = pane.export_scene(filename=tmpfile)
    assert exported_file.endswith('.synch')

    # test import from file
    # (TODO test if the scene imported is identical to the one exported)
    imported_scene = VTK.import_scene(filename=exported_file)
    assert isinstance(imported_scene, VTKRenderWindowSynchronized)

    # Cleanup
    pane._cleanup(model)
    assert pane._contexts == {}
    assert pane._models == {}