notebooks/07_annotations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"float:left; border:none\">\n",
    "   <tr style=\"border:none\">\n",
    "       <td style=\"border:none\">\n",
    "           <a href=\"https://bokeh.org/\" target=\"_blank\">\n",
    "           <img\n",
    "               src=\"assets/bokeh-transparent.png\"\n",
    "               style=\"width:50px\"\n",
    "           >\n",
    "           </a>\n",
    "       </td>\n",
    "       <td style=\"border:none\">\n",
    "           <h1>Bokeh Tutorial</h1>\n",
    "       </td>\n",
    "   </tr>\n",
    "</table>\n",
    "\n",
    "<div style=\"float:right;\"><a href=\"TOC.ipynb\" target=\"_blank\">Table of contents</a><br><h2>07 Adding annotations</h2></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load tutorial data\n",
    "from tutorial_data import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# activate notebook output\n",
    "from bokeh.io import output_notebook\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter, you will learn how to add annotations to your plots. Annotations are\n",
    "**visual elements that you add to your plot to provide additional information**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Span annotations\n",
    "\n",
    "As a first example, let's look at Span annotations. Spans are **horizontal or vertical\n",
    "lines**.\n",
    "You can use them to highlight specific values in your plot.\n",
    "For example, you can use them to highlight min or max values of your data.\n",
    "\n",
    "Like most of Bokeh's Annotations, adding Spans to your plot is a two-step process:\n",
    "1. You create a ``Span`` object\n",
    "2. You add the Span object to your plot using the ``add_layout`` method\n",
    "\n",
    "In the following example, you are using the number of passengers per month from the\n",
    "demo dataset. You add two horizontal Spans to your plot: One span highlights the\n",
    "minimum amount of passengers, and the other one highlights the maximum amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.models import NumeralTickFormatter, Span\n",
    "\n",
    "plot = figure(height=300)\n",
    "\n",
    "# load data from the monthly_values_df data set\n",
    "monthly_values_df = data.get_monthly_values()\n",
    "\n",
    "# use data from the monthly_values_df DataFrame\n",
    "x = monthly_values_df.index\n",
    "passengers = monthly_values_df[\"passengers\"]\n",
    "\n",
    "# add a line glyph and customize its appearance\n",
    "plot.line(x, passengers, line_width=3, line_color=\"orange\")\n",
    "plot.yaxis.formatter = NumeralTickFormatter(format=\"0,0\")\n",
    "\n",
    "max = Span(\n",
    "    location=monthly_values_df[\"passengers\"].max(),\n",
    "    dimension=\"width\",\n",
    "    line_color=\"red\",\n",
    "    line_width=2,\n",
    ")\n",
    "plot.add_layout(max)\n",
    "min = Span(\n",
    "    location=monthly_values_df[\"passengers\"].min(),\n",
    "    dimension=\"width\",\n",
    "    line_color=\"blue\",\n",
    "    line_width=2,\n",
    ")\n",
    "plot.add_layout(min)\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the usual [Line properties](05_styling.ipynb#Line-properties) to\n",
    "customize the look of your Spans. For example: ``line_alpha``, ``line_color``, or\n",
    "``line_dash``.\n",
    "\n",
    "The Span's `dimension` property defines the orientation of the Span:\n",
    "- ``dimension=\"width\"`` for a horizontal span\n",
    "- ``dimension=\"height\"`` for a vertical span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = figure(height=300)\n",
    "x = monthly_values_df.index\n",
    "passengers = monthly_values_df[\"passengers\"]\n",
    "\n",
    "# add a line glyph and customize its appearance\n",
    "plot.line(x, passengers, line_width=3, line_color=\"orange\")\n",
    "plot.yaxis.formatter = NumeralTickFormatter(format=\"0,0\")\n",
    "\n",
    "# add a vertical Span annotation with a dashed line\n",
    "max = Span(location=2, dimension=\"height\", line_color=\"red\", line_width=2, line_dash=\"dashed\")\n",
    "plot.add_layout(max)\n",
    "min = Span(location=7, dimension=\"height\", line_color=\"blue\", line_width=2, line_dash=\"dashed\")\n",
    "plot.add_layout(min)\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spans are one example of many similar annotations. See the entries for\n",
    "[Slopes](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#slopes),\n",
    "[Bands](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#bands),\n",
    "[Boxes](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#box-annotations),\n",
    "[Polygons](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#polygon-annotations),\n",
    "and [Whiskers](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#whisker-annotations)\n",
    "for more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labels\n",
    "\n",
    "Labels are annotations that you can use to **display text inside your plot**.\n",
    "\n",
    "You can [place labels manually](#placing-labels-manually) at specific locations in your\n",
    "plot. You can also connect labels to our data to automatically\n",
    "[place them relative to specific data points](#placing-labels-relative-to-data-points).\n",
    "\n",
    "Labels work similarly to Spans: You first create a ``Label`` object.\n",
    "Then you add it to your plot, using the ``add_layout`` method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Placing labels manually\n",
    "\n",
    "To define where a label is located in your plot, use the ``Label``'s ``x`` and ``y``\n",
    "properties.\n",
    "\n",
    "By default, these coordinates are in **data-space units**. This means they use the same\n",
    "units as the x- and y-axes of your plot. This is helpful if you want to place a label\n",
    "relative to a specific data point. To define the position of a label in pixels, set the\n",
    "``x_units`` and ``y_units`` parameters to **screen units**. \n",
    "Screen and data-space units is a concept that you can use in many places in Bokeh\n",
    "(including with Spans). See\n",
    "[Screen units and data-space units](https://docs.bokeh.org/en/latest/docs/user_guide/styling/visuals.html#screen-units-and-data-space-units)\n",
    "in the Bokeh user guide for more information.\n",
    "\n",
    "Three properties define how the label is oriented relative to the point\n",
    "defined by ``x`` and ``y``:\n",
    "\n",
    "- ``x_offset`` and ``y_offset`` define the label's offset from the position\n",
    "  defined by ``x`` and ``y``.\n",
    "- ``text_align`` defines the horizontal alignment of the text. This can be ``left``,\n",
    "  ``center`` or ``right``.\n",
    "- ``text_baseline`` defines the vertical alignment of the text. This can be ``top``,\n",
    "  ``middle`` or ``bottom``.\n",
    "\n",
    "The text itself is defined by the ``text`` property.\n",
    "\n",
    "Additionally, you can use the usual\n",
    "[Text properties](05_styling.ipynb#Text-properties) to define the look of your\n",
    "text. For example: ``text_font``, ``text_font_size``, ``text_color``, or\n",
    "``text_alpha``.\n",
    "\n",
    "The following example contains two labels:\n",
    "- The first label is placed at the same coordinates as one of the data points. It uses\n",
    "data-space units to match the position of the point. This label also uses the `x_offset`\n",
    "and `text_baseline` properties to position the text relative to the point.\n",
    "- The second label is placed at a fixed position defined in pixels. This label use\n",
    "screen coordinates in pixels (`x_units` and `y_units` are set to \"screen\").\n",
    "\n",
    "Adjust the different properties of the two `Label` objects in the following code cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models.annotations import Label\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "p = figure(x_range=(0, 10), y_range=(0, 10), height=300)\n",
    "p.scatter([2, 5, 8], [4, 7, 6], color=\"olive\", size=10)\n",
    "\n",
    "# 🔁 adjust some of the properties of this label\n",
    "# first label, using the same coordinates as the circle (5 on the x-axis and 7 on the y-axis)\n",
    "data_label = Label(\n",
    "    x=5,  # place the label at the same x coordinates as the circle\n",
    "    y=7,  # place the label at the same y coordinates as the circle\n",
    "    x_offset=12,  # offset the label from the circle center by 12 pixels in the x direction\n",
    "    text=\"Maximum\",\n",
    "    text_baseline=\"middle\",  # align the text vertically to the middle of the circle\n",
    "    text_color=\"olive\",  # use a standard text property for the text color\n",
    ")\n",
    "p.add_layout(data_label)\n",
    "\n",
    "# 🔁 adjust some of the properties of this label\n",
    "# second label, located 5 by 5 pixels from the bottom left corner of the plot\n",
    "source_label = Label(\n",
    "    x_units=\"screen\",  # use screen units (pixels) for the x coordinate\n",
    "    y_units=\"screen\",  # use screen units (pixels) for the y coordinate\n",
    "    x=5,  # since we use screen units, these are measured in pixels\n",
    "    y=5,  # since we use screen units, these are measured in pixels\n",
    "    text=\"Source: Bokeh user guide\",\n",
    "    text_font_size=\"0.7em\",\n",
    ")\n",
    "p.add_layout(source_label)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Placing labels relative to data points\n",
    "\n",
    "In the example above, you defined concrete coordinates to place your labels.\n",
    "This works well to manually place one or two labels. \n",
    "\n",
    "However, if you want to place many labels, it is easier to define the position of the\n",
    "labels relative to your data. This means that Bokeh automatically places the labels at\n",
    "the correct position on your plot.\n",
    "\n",
    "To do this, your data needs to be in a [ColumnDataSource](06_data_sources.ipynb).\n",
    "Instead of defining concrete values for a label's ``x`` and ``y`` properties, pass\n",
    "the column names of your ColumnDataSource:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColumnDataSource, LabelSet\n",
    "\n",
    "# set up the data source\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        temp=[166, 171, 172, 168, 174, 162],\n",
    "        pressure=[165, 189, 220, 141, 260, 174],\n",
    "        names=[\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"],\n",
    "    )\n",
    ")\n",
    "\n",
    "# set up the figure\n",
    "p = figure(x_range=(160, 175), height=300)\n",
    "\n",
    "# add circle scatter glyphs to the figure\n",
    "p.scatter(\n",
    "    x=\"temp\",  # use the temp column as the x coordinates\n",
    "    y=\"pressure\",  # use the pressure column as the y coordinates\n",
    "    size=8,\n",
    "    source=source,\n",
    ")\n",
    "\n",
    "# add axis labels\n",
    "p.xaxis.axis_label = \"Temperature (C)\"\n",
    "p.yaxis.axis_label = \"Pressure (lbs)\"\n",
    "\n",
    "# add labels based on the data in the ColumnDataSource\n",
    "labels = LabelSet(\n",
    "    x=\"temp\",  # use the temp column as the x coordinates\n",
    "    y=\"pressure\",  # use the pressure column as the y coordinates\n",
    "    text=\"names\",  # use the strings from the names column as text\n",
    "    text_baseline=\"middle\",\n",
    "    x_offset=10,\n",
    "    source=source,  # use the ColumnDataSource as the data source for the label positions\n",
    ")\n",
    "\n",
    "p.add_layout(labels)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrows\n",
    "\n",
    "Arrows are annotations that you can use to **display arrows inside your plot**.\n",
    "They can be especially useful in combination with labels.\n",
    "\n",
    "Arrow annotations generally work similarly to Spans and Labels. However: An Arrow\n",
    "annotation is a **compound annotation**. It consists of the main `Arrow` object that\n",
    "can be combined with additional arrowhead objects as its start or end.\n",
    "\n",
    "These are the most important properties of an `Arrow` object:\n",
    "\n",
    "* `x_start` and `y_start` define the start point of the arrow (in data-space units)\n",
    "* `x_end` and `y_end` define the end point of the arrow (in data-space units)\n",
    "* `start` and `end` define the arrowhead objects at the start and end of the arrow.\n",
    "\n",
    "You can chose from four different **arrowhead types**:\n",
    "\n",
    "![ArrowHead types](assets/arrowheads.png)\n",
    "\n",
    "The `Arrow`object and the different arrowhead objects use the usual\n",
    "[line properties](05_styling.ipynb#Line-properties) that you have worked with\n",
    "before. For example: ``line_color``, ``line_width``, ``line_alpha``, or\n",
    "``line_dash``.\n",
    "\n",
    "This is a minimal example of an Arrow annotation:\n",
    "\n",
    "``` python\n",
    "arrow = Arrow(\n",
    "    end=OpenHead(),\n",
    "    x_start=1, y_start=1,\n",
    "    x_end=4, y_end=4,\n",
    ")\n",
    "```\n",
    "\n",
    "The following example contains an Arrow annotation that points to the minimum of a\n",
    "quadratic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import Arrow, NormalHead, OpenHead, VeeHead\n",
    "\n",
    "plot = figure(height=300)\n",
    "\n",
    "x = list(range(-100, 100, 1))\n",
    "y = [pow(i, 2) for i in x]\n",
    "\n",
    "plot.line(x, y)\n",
    "\n",
    "arrow = Arrow(\n",
    "    end=OpenHead(line_color=\"firebrick\", line_width=3),  # 🔁 try using  OpenHead or VeeHead instead of OpenHead\n",
    "    line_width=3,\n",
    "    x_start=20,\n",
    "    y_start=6000,  # the start of the arrow, defined in data coordinates\n",
    "    x_end=0,\n",
    "    y_end=0,  # the tip of the arrow is at the minimum of the curve\n",
    ")\n",
    "plot.add_layout(arrow)\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale Bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bokeh's `ScaleBar` allows you to add a reference measurement for scale. This is particularly useful for images, maps, time series, or any plot where you want to communicate the scale of various features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a ScaleBar to a Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate the use of a `ScaleBar`, we'll add one to a microscopy image. The `ScaleBar` requires a Range to determine the scale, which can either be the x-range or y-range of the plot. You can also specify various properties such as the `bar_length`, `label` format, and `unit` of measurement. Here, we apply the `ScaleBar` to the `x_range` and specify that one `unit` of measurement on this axis corresponds to a millimeter (`\"mm\"`). Refer to the [reference docs](https://docs.bokeh.org/en/latest/docs/reference/models/annotations.html#bokeh.models.ScaleBar) for `ScaleBar` for all customization options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from bokeh.models import MetricLength, ScaleBar\n",
    "\n",
    "TOOLS = \"pan,wheel_zoom,box_zoom,reset\"\n",
    "\n",
    "p = figure(\n",
    "    tools=TOOLS,\n",
    "    active_scroll=\"wheel_zoom\",\n",
    ")\n",
    "\n",
    "p.x_range.range_padding = 0\n",
    "p.y_range.range_padding = 0\n",
    "\n",
    "p.x_range.bounds = (0, 1)\n",
    "p.y_range.bounds = (0, 1)\n",
    "\n",
    "pollen_png = Path(\"assets/pollen.png\")\n",
    "img = pollen_png.read_bytes()\n",
    "\n",
    "p.image_url(x=0, y=0, w=1, h=1, url=[img], anchor=\"bottom_left\")\n",
    "\n",
    "scale_bar = ScaleBar(\n",
    "    range=p.x_range,\n",
    "    unit=\"mm\",\n",
    "    dimensional=MetricLength(),\n",
    "    orientation=\"horizontal\",\n",
    "    location=\"top_right\",\n",
    "    label=\"@{value} @{unit}\",\n",
    "    label_location=\"above\",\n",
    "    label_align=\"center\",\n",
    "    bar_length=0.2,\n",
    "    bar_line_width=2,\n",
    "    background_fill_alpha=0.8,\n",
    ")\n",
    "p.add_layout(scale_bar)\n",
    "\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Go ahead and scroll over the image to activate the wheel-zoom and see the scale bar dynamically adjust."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Custom Scale Bar Units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the ScaleBar uses metric length units, but you can define custom units of measurement using the `CustomDimensional` model. This allows you to create scale bars with units such as angular measurements or any other custom units you might need. To learn more about customizing units and other advanced features of the ScaleBar, refer to the [Bokeh User Guide on Annotations](https://docs.bokeh.org/en/latest/docs/user_guide/basic/annotations.html#scale-bars)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LaTeX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bokeh supports LaTeX equations in a number of different elements.\n",
    "\n",
    "This is based on the [Bessel equation example](https://docs.bokeh.org/en/latest/docs/examples/styling/mathtext/latex_bessel.html) in the Bokeh documentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from bokeh.io import output_notebook\n",
    "# from bokeh.models import ColorBar, CustomJS, Div, FixedTicker, Label, LinearColorMapper, Paragraph, Slider\n",
    "from bokeh.palettes import TolPRGn, PiYG\n",
    "# from bokeh.plotting import column, figure, show\n",
    "import numpy as np\n",
    "from scipy.special import jv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure(\n",
    "    width=700, height=450,\n",
    "    title=r\"$$\\text{Bessel functions of the first kind: } J_\\alpha(x) = \\sum_{m=0}^{\\infty}\"\n",
    "          r\"\\frac{(-1)^m}{m!\\:\\Gamma(m+\\alpha+1)} \\left(\\frac{x}{2}\\right)^{2m+\\alpha}$$\",\n",
    ")\n",
    "p.x_range.range_padding = 0\n",
    "p.xaxis.axis_label = r\"$$x$$\"\n",
    "p.yaxis.axis_label = r\"$$J_\\alpha(x)$$\"\n",
    "p.title.text_font_size = \"14px\"\n",
    "\n",
    "x = np.linspace(0.0, 14.0, 100)\n",
    "\n",
    "for i, (xlabel, ylabel) in enumerate(zip([0.5, 1.6, 2.8, 4.2], [0.95, 0.6, 0.5, 0.45])):\n",
    "    p.line(x, jv(i, x), line_width=3, color=PiYG[4][i])\n",
    "    p.add_layout(Label(text=rf\"$$J_{i}(x)$$\", x=xlabel, y=ylabel))\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:\n",
    "\n",
    "- Use of standard LaTeX delimeters of ``$$``. Other options are available.\n",
    "- Use raw Python strings e.g. ``r\"$$\\alpha$$\"`` so that backslashes are interpreted as normal characters rather than control sequences. \n",
    "    - Raw strings can be used in conjunction with f-strings by using 'rf'.\n",
    "- ``Div`` and ``Paragraph`` accept LaTeX for just part of their contents, but for all other elements the whole contents must be LaTeX.\n",
    "    - To put normal text in a LaTeX string use ``\\text{...}``.\n",
    "    - We are actively working on improvements in this area.\n",
    "\n",
    "#### Where can LaTeX be used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColorBar, Div, FixedTicker, LinearColorMapper, Paragraph, Slider\n",
    "from bokeh.plotting import column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure(width=500, height=400)\n",
    "p.scatter(1, 1, size=0)\n",
    "\n",
    "p.title = r\"$$\\LaTeX \\text{ figure title}$$\"\n",
    "p.axis.axis_label = r\"$$\\LaTeX \\text{ axis label}$$\"\n",
    "p.axis.ticker = FixedTicker(ticks=[1])\n",
    "p.axis.major_label_overrides = {1: r\"$$\\LaTeX \\text{ tick label}$$\"}\n",
    "p.yaxis.major_label_orientation = \"vertical\"\n",
    "p.add_layout(Label(text=r\"$$\\LaTeX \\text{ label}$$\", text_font_size=\"26px\",\n",
    "                   angle=0.4, text_baseline=\"middle\", text_align=\"center\", x=1, y=1))\n",
    "\n",
    "slider = Slider(start=0, end=100, value=50, step=1, title=r\"$$\\LaTeX \\text{ slider}$$\")\n",
    "div = Div(text=r\"$$\\LaTeX$$ div\")\n",
    "paragraph = Paragraph(text=r\"$$\\LaTeX$$ paragraph\")\n",
    "\n",
    "color_mapper = LinearColorMapper(palette=PiYG[8])\n",
    "colorbar = ColorBar(color_mapper=color_mapper, title=r\"$$\\LaTeX \\text{ colorbar title}$$\")\n",
    "p.add_layout(colorbar, \"right\")\n",
    "\n",
    "show(column(p, slider, div, paragraph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further information see the [LaTeX section](https://docs.bokeh.org/en/latest/docs/user_guide/styling/mathtext.html#latex) in the User Guide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next section\n",
    "\n",
    "<a href=\"08_plot_tools.ipynb\" target=\"_blank\">\n",
    "    <img src=\"assets/arrow.svg\" alt=\"Next section\" width=\"100\" align=\"right\">\n",
    "</a>\n",
    "\n",
    "In the [next chapter](08_plot_tools.ipynb), you'll learn how to use and configure\n",
    "the tools in Bokeh's toolbar."
   ]
  }
 ],
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