Python bokeh.models.widgets.Tabs() Examples

def pad_plots(plots):
    """
    Accepts a grid of bokeh plots in form of a list of lists and
    wraps any DataTable or Tabs in a WidgetBox with appropriate
    padding. Required to avoid overlap in gridplot.
    """
    widths = []
    for row in plots:
        row_widths = []
        for p in row:
            width = pad_width(p)
            row_widths.append(width)
        widths.append(row_widths)
    plots = [[WidgetBox(p, width=w) if isinstance(p, (DataTable, Tabs)) else p
              for p, w in zip(row, ws)] for row, ws in zip(plots, widths)]
    return plots 

def generate_model(self, figurepage_idx: int = 0) -> Model:
        """Returns a model generated from internal blueprints"""
        if figurepage_idx >= len(self.figurepages):
            raise RuntimeError(f'Cannot generate model for FigurePage with index {figurepage_idx} as there are only {len(self.figurepages)}.')

        figurepage = self.figurepages[figurepage_idx]
        if not self._is_optreturn:
            tabs = self.generate_model_tabs(figurepage)
        else:
            tabs = []

        # now append analyzer tab(s)
        analyzers = figurepage.analyzers
        panel_analyzer = self.get_analyzer_panel(analyzers)
        if panel_analyzer is not None:
            tabs.append(panel_analyzer)

        # append meta tab
        if not self._is_optreturn:
            assert figurepage.strategy is not None
            meta = Div(text=metadata.get_metadata_div(figurepage.strategy))
            metapanel = Panel(child=meta, title="Meta")
            tabs.append(metapanel)

        model = Tabs(tabs=tabs)

        # attach the model to the underlying figure for later reference (e.g. unit test)
        figurepage.model = model

        return model 

def __init__(self, doc: Document, push_fnc, bokeh_fac: callable, push_data_fnc:callable, strategy: bt.Strategy, figurepage_idx: int = 0, lookback: int = 20):
        self._slider_aspectratio = None
        self._push_data_fnc = push_data_fnc
        self._push_fnc = push_fnc
        self._figurepage_idx = figurepage_idx
        self.last_data_index = -1
        self._lookback = lookback
        self._strategy = strategy
        self._current_group = None
        self.document = doc

        self._bokeh_fac = bokeh_fac
        self._bokeh = None

        bokeh = self._bokeh_fac()  # temporary bokeh object to get tradingdomains and scheme
        self._scheme = copy(bokeh.p.scheme)  # preserve original scheme as originally provided by the user

        tradingdomains = bokeh.list_tradingdomains(strategy)
        self._current_group = tradingdomains[0]
        self._select_tradingdomain = Select(value=self._current_group, options=tradingdomains)
        self._select_tradingdomain.on_change('value', self._on_select_group)

        btn_refresh_analyzers = Button(label='Refresh Analyzers', width=100)
        btn_refresh_analyzers.on_click(self._on_click_refresh_analyzers)

        td_label = Div(text="Trading Domain:", margin=(9, 5, 15, 5))
        controls = row(children=[td_label, self._select_tradingdomain, btn_refresh_analyzers])
        self.model = column(children=[controls, Tabs(tabs=[])], sizing_mode=self._scheme.plot_sizing_mode)

        # append meta tab
        meta = Div(text=metadata.get_metadata_div(strategy))
        self._panel_metadata = Panel(child=meta, title="Meta")

        self._refreshmodel() 

def compute_plot_size(plot):
    """
    Computes the size of bokeh models that make up a layout such as
    figures, rows, columns, widgetboxes and Plot.
    """
    if isinstance(plot, GridBox):
        ndmapping = NdMapping({(x, y): fig for fig, y, x in plot.children}, kdims=['x', 'y'])
        cols = ndmapping.groupby('x')
        rows = ndmapping.groupby('y')
        width = sum([max([compute_plot_size(f)[0] for f in col]) for col in cols])
        height = sum([max([compute_plot_size(f)[1] for f in row]) for row in rows])
        return width, height
    elif isinstance(plot, (Div, ToolbarBox)):
        # Cannot compute size for Div or ToolbarBox
        return 0, 0
    elif isinstance(plot, (Row, Column, WidgetBox, Tabs)):
        if not plot.children: return 0, 0
        if isinstance(plot, Row) or (isinstance(plot, ToolbarBox) and plot.toolbar_location not in ['right', 'left']):
            w_agg, h_agg = (np.sum, np.max)
        elif isinstance(plot, Tabs):
            w_agg, h_agg = (np.max, np.max)
        else:
            w_agg, h_agg = (np.max, np.sum)
        widths, heights = zip(*[compute_plot_size(child) for child in plot.children])
        return w_agg(widths), h_agg(heights)
    elif isinstance(plot, (Figure, Chart)):
        if plot.plot_width:
            width = plot.plot_width
        else:
            width = plot.frame_width + plot.min_border_right + plot.min_border_left
        if plot.plot_height:
            height = plot.plot_height
        else:
            height = plot.frame_height + plot.min_border_bottom + plot.min_border_top
        return width, height
    elif isinstance(plot, (Plot, DataTable, Spacer)):
        return plot.width, plot.height
    else:
        return 0, 0 

def pad_width(model, table_padding=0.85, tabs_padding=1.2):
    """
    Computes the width of a model and sets up appropriate padding
    for Tabs and DataTable types.
    """
    if isinstance(model, Row):
        vals = [pad_width(child) for child in model.children]
        width = np.max([v for v in vals if v is not None])
    elif isinstance(model, Column):
        vals = [pad_width(child) for child in model.children]
        width = np.sum([v for v in vals if v is not None])
    elif isinstance(model, Tabs):
        vals = [pad_width(t) for t in model.tabs]
        width = np.max([v for v in vals if v is not None])
        for model in model.tabs:
            model.width = width
            width = int(tabs_padding*width)
    elif isinstance(model, DataTable):
        width = model.width
        model.width = int(table_padding*width)
    elif isinstance(model, (WidgetBox, Div)):
        width = model.width
    elif model:
        width = model.plot_width
    else:
        width = 0
    return width 

def plot_hail_hist_both(
    hist_data: hl.Struct, title: str, normalize: bool = True, log: bool = False
):
    p1 = plot_hail_hist(hist_data, title, log)
    p2 = plot_hail_hist_cumulative(
        hist_data, f"{title} (Cumulative)", normalize, log=log
    )
    return Tabs(
        tabs=[Panel(child=p1, title="Raw"), Panel(child=p2, title="Cumulative")]
    ) 

def linear_and_log_tabs(plot_func: Callable, **kwargs) -> Tabs:
    panels = []
    for axis_type in ["linear", "log"]:
        fig = plot_func(**kwargs, axis_type=axis_type)
        panel = Panel(child=fig, title=axis_type)
        panels.append(panel)

    return Tabs(tabs=panels)