{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Styling Bokeh plots {#sec-styling-bokeh}\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true,
    "nbsphinx": "hidden",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#| code-fold: true\n",
    "\n",
    "# Colab setup ------------------\n",
    "import os, sys, subprocess\n",
    "if \"google.colab\" in sys.modules:\n",
    "    cmd = \"pip install --upgrade polars iqplot colorcet watermark\"\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    stdout, stderr = process.communicate()\n",
    "    data_path = \"https://s3.amazonaws.com/bebi103.caltech.edu/data/\"\n",
    "else:\n",
    "    data_path = \"../data/\"\n",
    "# ------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
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      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
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    }
   ],
   "source": [
    "import polars as pl\n",
    "\n",
    "import colorcet\n",
    "\n",
    "import bokeh.io\n",
    "import bokeh.plotting\n",
    "import bokeh.models\n",
    "import bokeh.themes\n",
    "\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<hr>\n",
    "\n",
    "We have seen how to use Bokeh (and the higher-level plotting package iqplot) to make interactive plots. We have seen how to adjust plot size, axis labels, glyph color, etc. We have also seen how to style plots generated with iqplot. But we have just started to touch the surface of how we might customize plots. In this lesson, we investigate ways to stylize Bokeh plots to our visual preferences.\n",
    "\n",
    "We will again make use of the face-matching data set. We'll naturally start by loading the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = os.path.join(data_path, \"gfmt_sleep.csv\")\n",
    "df = (\n",
    "    pl.read_csv(fname, null_values=\"*\")\n",
    "    .with_columns((pl.col('sci') <= 16).alias('insomnia'))\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styling Bokeh plots as they are built\n",
    "\n",
    "Bokeh figures and renderers (which are essentially the glyphs) have a plethora of attributes pertaining to visual appearance that may be adjusted at instantiation and after making a plot.\n",
    "\n",
    "A **color palette** an ordering of colors that are used for glyphs, usually corresponding to categorical data. [Colorcet's Glasbey Category 10](https://colorcet.holoviz.org/user_guide/Categorical.html#starting-colors) provides a good palette for categorical data, and we store this as our categorical colors for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_colors = colorcet.b_glasbey_category10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can build the plot. Since the data are percentages, we will set the axes to go from zero to 100 and enforce that the figure is square. We will also include a title as well so we can style that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = bokeh.plotting.figure(\n",
    "    frame_width=300,\n",
    "    frame_height=300,\n",
    "    x_axis_label=\"confidence when correct\",\n",
    "    y_axis_label=\"confidence when incorrect\",\n",
    "    title=\"GMFT with sleep conditions\",\n",
    "    x_range=[0, 100],\n",
    "    y_range=[0, 100],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In styling this plot, we will also put the legend outside of the plot area. This is a bit trickier than what we have been doing using the `legend_label` kwarg in `p.scatter()`. To get a legend outside of the plot area, we need to:\n",
    "\n",
    "1. Assign each glyph to a variable.\n",
    "2. Instantiate a `bokeh.models.Legend` object using the stored variables containing the glyphs. This is instantiated as `bokeh.models.Legend(items=legend_items)`, where `legend_items` is a list of 2-tuples. In each 2-tuple, the first entry is a string with the text used to label the glyph. The second entry is a list of glyphs that have the label.\n",
    "3. Add the legend to the figure using the `add_layout()` method.\n",
    "\n",
    "Now, we add the glyphs, storing them as variables `normal_glyph` and `insom_glyph`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_glyph = p.scatter(\n",
    "    source=df.filter(~pl.col('insomnia')).to_dict(),\n",
    "    x=\"confidence when correct\",\n",
    "    y=\"confidence when incorrect\",\n",
    "    color=cat_colors[0],\n",
    ")\n",
    "\n",
    "insom_glyph = p.scatter(\n",
    "    source=df.filter(pl.col('insomnia')).to_dict(),\n",
    "    x=\"confidence when correct\",\n",
    "    y=\"confidence when incorrect\",\n",
    "    color=cat_colors[1],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can construct and add the legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct legend items\n",
    "legend_items = [('normal', [normal_glyph]), ('insomnia', [insom_glyph])]\n",
    "\n",
    "# Instantiate legend\n",
    "legend = bokeh.models.Legend(items=legend_items, click_policy='hide')\n",
    "\n",
    "# Add the legend to the right of the plot\n",
    "p.add_layout(legend, 'right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at this beauty!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styling Bokeh plots after they are built\n",
    "\n",
    "After building a plot, we sometimes want to adjust styling. To do so, we need to change attributes of the object `p`. For example, let's look at the font of the x-axis label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'helvetica'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.xaxis.axis_label_text_font"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the style and size of the font."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('italic', '13px')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.xaxis.axis_label_text_font_style, p.xaxis.axis_label_text_font_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the default axis labels for Bokeh are italicized 13 pt Helvetica. I personally think this choice is fine, but we may have other preferences.\n",
    "\n",
    "To find out all of the available options to tweak, I usually type something like `p.` and hit tab to see what the options are. Finding `p.xaxis` is an option, then type `p.xaxis.` and hit tab again to see the styling option there.\n",
    "\n",
    "Using this technique, we can set some obnoxious styling for this plot. I will make all of the fonts non-italicized, large [papyrus](https://www.youtube.com/watch?v=jVhlJNJopOQ). I can also set the background and grid colors. Note that in Bokeh, any [named CSS color](https://css-tricks.com/snippets/css/named-colors-and-hex-equivalents/) or any valid [HEX code](https://en.wikipedia.org/wiki/Web_colors#Hex_triplet), entered as a string, is a valid color.\n",
    "\n",
    "Before we do the obnoxious styling, we will do one adjustment that is useful. Note in the above plot that the glyphs at the end of the plot are cropped. We would like the whole glyph to show. To do that, we set the **level** of the glyphs to be `'overlay'`. To do that, we extract the first two elements of the list of renderers, which contains the glyphs, and set the `level` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.renderers[0].level = 'overlay'\n",
    "p.renderers[1].level = 'overlay'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can proceed to make our obnoxious styling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obnoxious fonts\n",
    "p.xaxis.major_label_text_font = 'papyrus'\n",
    "p.xaxis.major_label_text_font_size = '14pt'\n",
    "p.xaxis.axis_label_text_font = 'papyrus'\n",
    "p.xaxis.axis_label_text_font_style = 'normal'\n",
    "p.xaxis.axis_label_text_font_size = '20pt'\n",
    "p.yaxis.major_label_text_font = 'papyrus'\n",
    "p.yaxis.major_label_text_font_size = '14pt'\n",
    "p.yaxis.axis_label_text_font = 'papyrus'\n",
    "p.yaxis.axis_label_text_font_style = 'normal'\n",
    "p.yaxis.axis_label_text_font_size = '20pt'\n",
    "p.title.text_font = 'papyrus'\n",
    "p.title.text_font_size = '18pt'\n",
    "p.legend.label_text_font = 'papyrus'\n",
    "\n",
    "# Align the title center\n",
    "p.title.align = 'center'\n",
    "\n",
    "# Set background and grid color\n",
    "p.background_fill_color = 'blanchedalmond'\n",
    "p.legend.background_fill_color = 'chartreuse'\n",
    "p.xgrid.grid_line_color = 'azure'\n",
    "p.ygrid.grid_line_color = 'azure'\n",
    "\n",
    "# Make the ticks point inward (I *hate* this!)\n",
    "# Units are pixels that the ticks extend in and out of plot\n",
    "p.xaxis.major_tick_out = 0\n",
    "p.xaxis.major_tick_in = 10\n",
    "p.xaxis.minor_tick_out = 0\n",
    "p.xaxis.minor_tick_in = 5\n",
    "p.yaxis.major_tick_out = 0\n",
    "p.yaxis.major_tick_in = 10\n",
    "p.yaxis.minor_tick_out = 0\n",
    "p.yaxis.minor_tick_in = 5\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is truly hideous, but it demonstrates how we can go about styling plots after they are made."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh themes\n",
    "\n",
    "Bokeh has several built-in [themes](https://docs.bokeh.org/en/latest/docs/reference/themes.html) which you can apply to all plots in a given document (e.g., in a notebook). Please see the [documentation](https://docs.bokeh.org/en/latest/docs/reference/themes.html) for details about the built-in themes. I personally prefer the default styling to all of their themes, but your opinion may differ.\n",
    "\n",
    "You may also specify custom themes using [JSON](https://www.json.org/json-en.html) or [YAML](https://yaml.org). As an example, we can specify a theme such that plots are styled like the default style of the excellent plotting packages [Vega-Altair](https://altair-viz.github.io/)/[Vega-Lite](http://vega.github.io/vega-lite)/[Vega](http://vega.github.io/vega). If we use JSON formatting, we can specify a theme as a dictionary of dictionaries, as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "altair_theme_dict = {\n",
    "    \"attrs\": {\n",
    "        \"Axis\": {\n",
    "            \"axis_line_color\": \"dimgray\",\n",
    "            \"minor_tick_out\": 0,\n",
    "            \"major_tick_in\": 0,\n",
    "            \"major_tick_line_color\": \"dimgray\",\n",
    "            \"major_label_text_font_size\": \"7.5pt\",\n",
    "            \"axis_label_text_font_size\": \"8pt\",\n",
    "            \"axis_label_text_font_style\": \"bold\",\n",
    "        },\n",
    "        \"Scatter\": {\n",
    "            \"fill_alpha\": 0, \n",
    "            \"line_width\": 2, \n",
    "            \"size\": 5, \n",
    "            \"line_alpha\": 0.7,\n",
    "        },\n",
    "        \"ContinuousTicker\": {\n",
    "            \"desired_num_ticks\": 10\n",
    "        },\n",
    "        \"figure\": {\n",
    "            \"frame_width\": 350, \n",
    "            \"frame_height\": 300,\n",
    "        },\n",
    "        \"Grid\": {\n",
    "            \"grid_line_color\": \"lightgray\",\n",
    "            \"level\": \"underlay\",\n",
    "        },\n",
    "        \"Legend\": {\n",
    "            \"border_line_color\": None,\n",
    "            \"background_fill_color\": None,\n",
    "            \"label_text_font_size\": \"7.5pt\",\n",
    "            \"title_text_font_size\": \"8pt\",\n",
    "            \"title_text_font_style\": \"bold\",\n",
    "        },\n",
    "        \"Renderer\": {\n",
    "            \"level\": \"overlay\"\n",
    "        },\n",
    "        \"Title\": {\n",
    "            \"align\": \"center\",\n",
    "        },\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To activate the theme, we convert it to a Bokeh theme and then add it to the `curdoc()`, or the current document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "altair_theme = bokeh.themes.Theme(json=altair_theme_dict)\n",
    "\n",
    "bokeh.io.curdoc().theme = altair_theme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the theme is activated, and future plots will have this theme by default. Let's remake our plot using this theme. For convenience later on, I will write a function to generate this scatter plot that we will use to test various styles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def gfmt_plot():\n",
    "    \"\"\"Make a plot for testing out styles in this notebook.\"\"\"\n",
    "    p = bokeh.plotting.figure(\n",
    "        frame_width=300,\n",
    "        frame_height=300,\n",
    "        x_axis_label=\"confidence when correct\",\n",
    "        y_axis_label=\"confidence when incorrect\",\n",
    "        title=\"GMFT with sleep conditions\",\n",
    "        x_range=[0, 100],\n",
    "        y_range=[0, 100],\n",
    "    )\n",
    "\n",
    "    normal_glyph = p.scatter(\n",
    "        source=df.filter(~pl.col('insomnia')).to_dict(),\n",
    "        x=\"confidence when correct\",\n",
    "        y=\"confidence when incorrect\",\n",
    "        color=cat_colors[0],\n",
    "    )\n",
    "\n",
    "    insom_glyph = p.scatter(\n",
    "        source=df.filter(pl.col('insomnia')).to_dict(),\n",
    "        x=\"confidence when correct\",\n",
    "        y=\"confidence when incorrect\",\n",
    "        color=cat_colors[1],\n",
    "    )\n",
    "\n",
    "    # Construct legend items\n",
    "    legend_items = [('normal', [normal_glyph]), ('insomnia', [insom_glyph])]\n",
    "\n",
    "    # Instantiate legend\n",
    "    legend = bokeh.models.Legend(items=legend_items, click_policy='hide')\n",
    "\n",
    "    # Add the legend to the right of the plot\n",
    "    p.add_layout(legend, 'right')\n",
    "    \n",
    "    return p\n",
    "\n",
    "bokeh.io.show(gfmt_plot())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also style our plots to resemble the default \"dark\" styling of [Seaborn](https://seaborn.pydata.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seaborn_theme_dict = {\n",
    "    \"attrs\": {\n",
    "        \"figure\": {\n",
    "            \"background_fill_color\": \"#eaeaf2\",\n",
    "            \"frame_height\": 300,\n",
    "            \"frame_width\": 350,\n",
    "        },\n",
    "        \"Axis\": {\n",
    "            \"axis_line_color\": None,\n",
    "            \"minor_tick_out\": 0,\n",
    "            \"major_tick_in\": 0,\n",
    "            \"major_tick_out\": 0,\n",
    "            \"major_label_text_font_size\": \"7.5pt\",\n",
    "            \"axis_label_text_font_size\": \"7.5pt\",\n",
    "            \"axis_label_text_font_style\": \"normal\",\n",
    "        },\n",
    "        \"Legend\": {\n",
    "            \"border_line_color\": \"darkgray\",\n",
    "            \"background_fill_color\": \"#eaeaf2\",\n",
    "            \"border_line_width\": 0.75,\n",
    "            \"label_text_font_size\": \"7.5pt\",\n",
    "        },\n",
    "        \"Grid\": {\n",
    "            \"grid_line_color\": \"#FFFFFF\", \n",
    "            \"grid_line_width\": 0.75,\n",
    "        },\n",
    "        \"Title\": {\n",
    "            \"align\": \"center\",\n",
    "            'text_font_style': 'normal',\n",
    "            'text_font_size': \"8pt\",\n",
    "        },\n",
    "    }\n",
    "}\n",
    "\n",
    "seaborn_theme = bokeh.themes.Theme(json=seaborn_theme_dict)\n",
    "bokeh.io.curdoc().theme = seaborn_theme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the plot, yet again, with this new styling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
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   "cell_type": "markdown",
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   "source": [
    "Finally, we can specify a style I like. Note that I do not specify that the glyphs are at an overlay level, since by default Bokeh will scale the axes such that the glyphs are fully contained in the plot area. I also put the toolbar above the plot, which is usually not a problem because I generally prefer not to title my plots, opting instead for good textual description in captions or in surrounding text."
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.bokehjs_exec.v0+json": ""
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   "source": [
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    "            \"axis_line_color\": \"dimgray\",\n",
    "            \"major_tick_line_color\": \"dimgray\",\n",
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    "            \"axis_label_text_font_style\": \"bold\",\n",
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    "        \"Scatter\": {\n",
    "            \"size\": 5, \n",
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    "            \"frame_width\": 350,\n",
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    "jb_theme = bokeh.themes.Theme(json=jb_theme_dict)\n",
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if I were to make this particular plot, I would do it without a title and with axes leaving a little buffer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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when incorrect\",\"axis_label_text_font_size\":\"9pt\",\"axis_label_text_font_style\":\"bold\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1430\"},\"major_label_text_font_size\":\"7.5pt\",\"axis_line_color\":\"dimgray\",\"major_tick_line_color\":\"dimgray\"}}],\"right\":[{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1469\",\"attributes\":{\"level\":\"overlay\",\"title_text_font\":\"helvetica\",\"title_text_font_size\":\"8pt\",\"title_text_font_style\":\"bold\",\"border_line_color\":\"darkgray\",\"border_line_width\":0.75,\"background_fill_color\":\"#ffffff\",\"background_fill_alpha\":0.7,\"click_policy\":\"hide\",\"label_text_font\":\"helvetica\",\"label_text_font_size\":\"7.5pt\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1470\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"normal\"},\"renderers\":[{\"id\":\"p1454\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1471\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"insomnia\"},\"renderers\":[{\"id\":\"p1466\"}]}}]}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1422\",\"attributes\":{\"level\":\"overlay\",\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1423\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1424\"},\"axis_label\":\"confidence when correct\",\"axis_label_text_font_size\":\"9pt\",\"axis_label_text_font_style\":\"bold\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1425\"},\"major_label_text_font_size\":\"7.5pt\",\"axis_line_color\":\"dimgray\",\"major_tick_line_color\":\"dimgray\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1426\",\"attributes\":{\"level\":\"underlay\",\"axis\":{\"id\":\"p1422\"},\"grid_line_color\":\"lightgray\"}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1431\",\"attributes\":{\"level\":\"underlay\",\"dimension\":1,\"axis\":{\"id\":\"p1427\"},\"grid_line_color\":\"lightgray\"}}],\"frame_width\":300,\"frame_height\":300}}]}};\n  const render_items = [{\"docid\":\"3f0a45b9-116c-4f4a-abca-60727cd7aa12\",\"roots\":{\"p1409\":\"fe1440ac-0ba3-4945-83ab-04878d1b0612\"},\"root_ids\":[\"p1409\"]}];\n  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1409"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = bokeh.plotting.figure(\n",
    "    frame_width=300,\n",
    "    frame_height=300,\n",
    "    x_axis_label=\"confidence when correct\",\n",
    "    y_axis_label=\"confidence when incorrect\",\n",
    "    x_range=[-2.5, 102.5],\n",
    "    y_range=[-2.5, 102.5],\n",
    ")\n",
    "\n",
    "normal_glyph = p.scatter(\n",
    "    source=df.filter(~pl.col('insomnia')).to_dict(),\n",
    "    x=\"confidence when correct\",\n",
    "    y=\"confidence when incorrect\",\n",
    "    color=cat_colors[0],\n",
    ")\n",
    "\n",
    "insom_glyph = p.scatter(\n",
    "    source=df.filter(pl.col('insomnia')).to_dict(),\n",
    "    x=\"confidence when correct\",\n",
    "    y=\"confidence when incorrect\",\n",
    "    color=cat_colors[1],\n",
    ")\n",
    "\n",
    "# Construct legend items\n",
    "legend_items = [('normal', [normal_glyph]), ('insomnia', [insom_glyph])]\n",
    "\n",
    "# Instantiate legend\n",
    "legend = bokeh.models.Legend(items=legend_items, click_policy='hide')\n",
    "\n",
    "# Add the legend to the right of the plot\n",
    "p.add_layout(legend, 'right')\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can play with these themes and develop your own style as you see fit. As you can see, Bokeh is highly configurable, and you can really make the plots your own!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.13.7\n",
      "IPython version      : 9.5.0\n",
      "\n",
      "numpy     : 2.2.6\n",
      "polars    : 1.33.1\n",
      "bokeh     : 3.7.3\n",
      "jupyterlab: 4.4.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,polars,bokeh,jupyterlab"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "default",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}