docs/apps/van_der_waals/effect_of_a_and_b_reduced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of a and b in van der Waals isotherms (reduced variables)\n",
    "\n",
    "\n",
    "**Code:** #11C-000\n",
    "\n",
    "**File:** `apps/van_der_waals/effect_of_a_and_b_reduces.ipynb`\n",
    "\n",
    "**Run it online:** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jongablop/MinervaLab/master?urlpath=%2Fnotebooks%2Fapps%2Fvan_der_waals%2Feffect_of_a_and_b_reduced.ipynb)\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to visualize the effect of a and b parameters on van der Waals' isotherms (reduced variables)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface\n",
    "\n",
    "The main interface (``main_block_11C_000``) is divided in two HBox: ``top_block_11C_000`` and ``bottom_block_11C_000``. ``top_block_11C_000`` contains of 5 bqplot Figures: ``fig_11C_001``, ``fig_11C_002``, ``fig_11C_003``, ``fig_11C_004`` and ``fig_11C_005``.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../../static/images/apps/11C-000_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSS\n",
    "A custom `css` file is used to improve the interface of this application. It can be found [here](https://github.com/jongablop/MinervaLab/blob/master/static/custom.css)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<head><link rel='stylesheet' type='text/css' href='./../../static/custom.css'></head>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.widget-label { display: contents !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.slider-container { margin: 12px !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "display(HTML(\"<head><link rel='stylesheet' type='text/css' href='./../../static/custom.css'></head>\"))\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "display(HTML(\"<style>.widget-label { display: contents !important; }</style>\"))\n",
    "display(HTML(\"<style>.slider-container { margin: 12px !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bqplot import *\n",
    "import bqplot as bq\n",
    "import bqplot.marks as bqm\n",
    "import bqplot.scales as bqs\n",
    "import bqplot.axes as bqa\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import urllib.parse\n",
    "import webbrowser\n",
    "\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical functions\n",
    "\n",
    "This are the functions that have a physical meaning:\n",
    "\n",
    "- `get_relative_isotherms_params`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relative_isotherms_params(alpha, beta, v_range, T_range):\n",
    "    \"\"\"This function calculates the theoretical p(v, T) plane \n",
    "        (in reduced coordinates) according to van der Waals\n",
    "        equation of state from a given range of volumes\n",
    "        and tenperatures and taking into account the values\n",
    "        of alpha and beta.\n",
    "        \n",
    "    Args:\n",
    "        alpha: The value of the a parameter.\\n\n",
    "        alpha: The value of the b parameter.\\n\n",
    "        v_range: An array containing the values of v\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated.\\n\n",
    "        T_range: An array containing the values of T\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated.\\n\n",
    "        \n",
    "        \n",
    "    Returns:\n",
    "        isotherms: A list consisted of numpy arrays containing the\n",
    "        pressures of each isotherm.\n",
    "    \"\"\"\n",
    "\n",
    "    isotherms = []\n",
    "\n",
    "    for T in T_range:\n",
    "        p_R = []\n",
    "        for v in v_range:\n",
    "            val = (8.0/3.0*T/(v - beta) - alpha/v**2)\n",
    "            p_R = np.append(p_R, val)\n",
    "\n",
    "        isotherms.append(p_R)\n",
    "\n",
    "    return isotherms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions related to interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_isotherms(change):\n",
    "    \"\"\"This function update the lines 'alpha_line', 'beta_line' and\n",
    "    'unique_isotherm' when 'alpha_slider_11C_003' or 'beta_slider_11C_004' are\n",
    "    updated.\n",
    "    \"\"\"\n",
    "    \n",
    "    obj = change.owner\n",
    "    T_values = [0.95]\n",
    "\n",
    "    if obj is alpha_slider_11C_003:\n",
    "\n",
    "        v_values = np.linspace(0.4, 5.0, 500)\n",
    "        isotherms = get_relative_isotherms_params(\n",
    "            alpha_slider_11C_003.value,\n",
    "            beta_initial,\n",
    "            v_values,\n",
    "            T_values\n",
    "        )\n",
    "        \n",
    "        alpha_line.y = isotherms[0]\n",
    "        \n",
    "    elif obj is beta_slider_11C_004:\n",
    "\n",
    "        v_values = np.linspace(0.4, 5.0, 500)\n",
    "        \n",
    "        if beta_slider_11C_004.value == 0.0:\n",
    "            \n",
    "            v_values = np.linspace(0.4, 5.0, 500)\n",
    "        \n",
    "        isotherms = get_relative_isotherms_params(\n",
    "            alpha_initial,\n",
    "            beta_slider_11C_004.value,\n",
    "            v_values,\n",
    "            T_values\n",
    "        )\n",
    "        \n",
    "        beta_line.x = v_values\n",
    "        beta_line.y = isotherms[0]\n",
    "        \n",
    "    if beta_slider_11C_004.value == 0.0:\n",
    "        v_values = np.linspace(0.4, 5.0, 500)        \n",
    "    \n",
    "    isotherms = get_relative_isotherms_params(\n",
    "        alpha_slider_11C_003.value,\n",
    "        beta_slider_11C_004.value,\n",
    "        v_values,\n",
    "        T_values\n",
    "    )\n",
    "    \n",
    "    unique_isotherm.x = v_values\n",
    "    unique_isotherm.y = isotherms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def restart(a):\n",
    "    \"\"\"This function sets the values of 'alpha_slider_11C_003'\n",
    "    and 'beta_slider_11C_004' to their initial ones.\n",
    "    \"\"\"\n",
    "    \n",
    "    alpha_slider_11C_003.value, beta_slider_11C_004.value = alpha_initial, beta_initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_view(change):\n",
    "    \"\"\"This function changes the visualization of all the\n",
    "    components of the application so they are suitable for\n",
    "    a projection.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    obj = change.owner\n",
    "\n",
    "    if obj.value:\n",
    "        \n",
    "        obj.description = 'Presentation mode (ON)'\n",
    "        \n",
    "        display(HTML(\n",
    "            \"<style>\" \\\n",
    "            \".widget-readout { font-size: 30px ; }\" \\\n",
    "            \".widget-label-basic {font-size: 30px;}\" \\\n",
    "            \"option {font-size: 25px;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.widget-slider.widget-vslider.widget-inline-vbox {width: auto}\" \\\n",
    "            \".p-Widget .jupyter-widgets .widgets-label {width: auto; height: auto; font-size: 30px;}\" \\\n",
    "            \".widget-label {font-size: 30px ; height: auto !important;}\" \\\n",
    "            \".p-Widget .bqplot .figure .jupyter-widgets {height: auto !important;}\" \\\n",
    "            \".widget-text input[type='number'] {font-size: 30px;height: auto;}\" \\\n",
    "            \".option { font-size: 30px ;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .jupyter-button.widget-button {font-size: 30px ; width: auto; height: auto;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.jupyter-button.widget-toggle-button{font-size: 30px ; width: auto; height: auto;}\" \\\n",
    "            \".p-Widget.p-Panel.jupyter-widgets.widget-container.widget-box.widget-vbox {padding-bottom: 30px}\" \\\n",
    "            \".bqplot > svg .axis text.axislabel, .bqplot > svg .axis tspan.axislabel {font-size: 30px;}\" \\\n",
    "            \".q-grid .slick-cell {font-size: 30px;}\" \\\n",
    "            \".slick-column-name {font-size: 30px;}\" \\\n",
    "            \".widget-html-content {font-size: 30px;}\"\n",
    "            \"</style>\"\n",
    "            )\n",
    "        )\n",
    "\n",
    "        for figure in figures:\n",
    "            \n",
    "            figure.legend_text = {'font-size': '30px'}\n",
    "            figure.title_style = {'font-size': '30px'}\n",
    "\n",
    "            for axis in figure.axes:\n",
    "                axis.tick_style = {'font-size': '30px'}\n",
    "                axis.label_style = {'font-size': '30px'}\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        obj.description = 'Presentation mode (OFF)'\n",
    "        \n",
    "        display(HTML(\n",
    "            \"<style>\" \\\n",
    "            \".widget-readout { font-size: 14px ;}\" \\\n",
    "            \".widget-label-basic {font-size: 14px;}\" \\\n",
    "            \"option {font-size: 12px;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .widgets-label {font-size: 14px;}\" \\\n",
    "            \".widget-label {font-size: 14px ;}\" \\\n",
    "            \".widget-text input[type='number'] {font-size: 14px;}\" \\\n",
    "            \".option { font-size: 14px ;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .jupyter-button.widget-button {font-size: 14px;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.jupyter-button.widget-toggle-button {font-size: 14px;}\" \\\n",
    "            \".bqplot > svg .axis text.axislabel, .bqplot > svg .axis tspan.axislabel {font-size: 14px;}\" \\\n",
    "            \".q-grid .slick-cell {font-size: 14px;}\" \\\n",
    "            \".slick-column-name {font-size: 14px;}\" \\\n",
    "            \".widget-html-content {font-size: 14px;}\"\n",
    "            \"</style>\"\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        for figure in figures:\n",
    "            \n",
    "            figure.legend_text = {'font-size': '14px'}\n",
    "            figure.title_style = {'font-size': '20px'}\n",
    "\n",
    "            for axis in figure.axes:\n",
    "                axis.tick_style = {'font-size': '14px'}\n",
    "                axis.label_style = {'font-size': '14px'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_export(button):\n",
    "    \"\"\"This function sends the selected plot to the 'export_plot'\n",
    "    function.\n",
    "    \"\"\"\n",
    "\n",
    "    if button is prepare_export_fig_11C_001_button:\n",
    "        \n",
    "        export_plot(fig_11C_001)\n",
    "        \n",
    "    elif button is prepare_export_fig_11C_005_button:\n",
    "        \n",
    "        export_plot(fig_11C_005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_plot(plot):\n",
    "    \"\"\"This function sends the selected plot to the export module.\n",
    "    \"\"\"\n",
    "    \n",
    "    global data\n",
    "    \n",
    "    text_lines = []\n",
    "    \n",
    "    np.set_printoptions(threshold=sys.maxsize)\n",
    "    \n",
    "    tooltips = []\n",
    "    \n",
    "    for mark in plot.marks:\n",
    "        tooltips.append(mark.tooltip)\n",
    "        mark.tooltip = None\n",
    "    \n",
    "    data =  repr((plot, text_lines))\n",
    "    \n",
    "    %store data\n",
    "    \n",
    "    rel_url = \"../../../apps/modules/export_module.ipynb\"\n",
    "    abs_url = urllib.parse.urljoin(notebook_url, rel_url)\n",
    "\n",
    "    if not webbrowser.open(abs_url):\n",
    "        go_to_export_button.value = \"<form action=\" + abs_url + \" target='_blank'><button type=''submit''>Open in export module</button></form>\"\n",
    "        \n",
    "    for i in range(len(plot.marks)):\n",
    "        mark = plot.marks[i]\n",
    "        mark.tooltip = tooltips[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript \n",
    "\n",
    "//Get the URL of the current notebook\n",
    "\n",
    "var kernel = Jupyter.notebook.kernel; \n",
    "var command = [\"notebook_url = \",\n",
    "               \"'\", window.location.href, \"'\" ].join('')\n",
    "\n",
    "kernel.execute(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "alpha_initial = 3.0 #0.0 < alpha < 3.0\n",
    "beta_initial = 0.33 #0.0 < beta < 0.33\n",
    "\n",
    "a, b = 5.536, 0.03049 #L^2 bar/mol^2, L/mol\n",
    "\n",
    "T_values = [0.95, 1.0, 1.2]\n",
    "v_values = np.linspace(0.4, 5.0, 500)\n",
    "colors = ['#0079c4','#f09205','#21c400']\n",
    "\n",
    "p_values = get_relative_isotherms_params(\n",
    "    alpha_initial,\n",
    "    beta_initial,\n",
    "    v_values,\n",
    "    T_values\n",
    ")\n",
    "\n",
    "#######################################\n",
    "#######CREATE THE FIGURES##############\n",
    "#######################################\n",
    "\n",
    "\n",
    "fig_11C_001 = bq.Figure(\n",
    "    title='p vs v (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=70, bottom=60, left=80, right=30),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(\n",
    "        width='100%',\n",
    "        height='500px'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig_11C_002 = bq.Figure(\n",
    "    title='',\n",
    "    marks=[],\n",
    "    axes=[],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=30, bottom=60, left=25, right=10),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(\n",
    "        width='90%',\n",
    "        height='40%'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig_11C_003 = bq.Figure(\n",
    "    title='',\n",
    "    marks=[],\n",
    "    axes=[],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=10, bottom=60, left=25, right=10),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(\n",
    "        width='90%',\n",
    "        height='40%'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig_11C_004 = bq.Figure(\n",
    "    title='',\n",
    "    marks=[],\n",
    "    axes=[],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=10, bottom=60, left=25, right=10),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(\n",
    "        width='90%',\n",
    "        height='40%'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig_11C_005 = bq.Figure(\n",
    "    title='p vs v (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=70, bottom=60, left=80, right=30),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(\n",
    "        width='100%',\n",
    "        height='500px'\n",
    "    )\n",
    ")\n",
    "\n",
    "scale_x = bqs.LinearScale(min = 0.4, max = 5.0)\n",
    "scale_y = bqs.LinearScale(min = 0, max = 2.0)\n",
    "\n",
    "axis_x = bqa.Axis(\n",
    "    scale=scale_x,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.5, 2.5, 5.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='v',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y = bqa.Axis(\n",
    "    scale=scale_y,\n",
    "    tick_format='.1f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0, 1, 2],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_x_no_ticks = bqa.Axis(\n",
    "    scale=scale_x,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks=0,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='v',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='15px'\n",
    ")\n",
    "\n",
    "axis_y_no_ticks = bqa.Axis(\n",
    "    scale=scale_y,\n",
    "    tick_format='.0f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks=0,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='15px'\n",
    ")\n",
    "\n",
    "fig_11C_001.axes = [axis_x, axis_y]\n",
    "fig_11C_002.axes = [axis_x_no_ticks, axis_y_no_ticks]\n",
    "fig_11C_003.axes = [axis_x_no_ticks, axis_y_no_ticks]\n",
    "fig_11C_004.axes = [axis_x_no_ticks, axis_y_no_ticks]\n",
    "fig_11C_005.axes = [axis_x, axis_y]\n",
    "\n",
    "\n",
    "#######################################\n",
    "##############MARKS####################\n",
    "#######################################\n",
    "\n",
    "x_values = [v_values for i in range(len(p_values))]\n",
    "y_values = []\n",
    "color_values = []\n",
    "label_values = []\n",
    "\n",
    "for i in range(len(p_values)):\n",
    "    \n",
    "    y_values.append(p_values[i])\n",
    "    color_values.append(colors[i])\n",
    "    label_values.append(str(T_values[i]))\n",
    "\n",
    "new_state = bqm.Lines(\n",
    "    x = x_values, \n",
    "    y = y_values, \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "old_state = bqm.Lines(\n",
    "    x = x_values, \n",
    "    y = y_values, \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "current_state = bqm.Lines(\n",
    "    x = x_values[0], \n",
    "    y = y_values[0], \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "alpha_line = bqm.Lines(\n",
    "    x = x_values[0], \n",
    "    y = y_values[0], \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "beta_line = bqm.Lines(\n",
    "    x = x_values[0], \n",
    "    y = y_values[0], \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "ideal_isotherms = get_relative_isotherms_params(\n",
    "    0,\n",
    "    0,\n",
    "    v_values,\n",
    "    T_values\n",
    ")\n",
    "\n",
    "ideal_line = bqm.Lines(\n",
    "    x = x_values, \n",
    "    y = ideal_isotherms, \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0 for elem in p_values],\n",
    "    visible = True,\n",
    "    colors = color_values,\n",
    "    labels = label_values,\n",
    ")\n",
    "\n",
    "unique_isotherm = bqm.Lines(\n",
    "    x = x_values[0], \n",
    "    y = y_values[0], \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [0.6],\n",
    "    visible = True,\n",
    "    colors = ['#c90000'],\n",
    "    labels = [label_values[0]],\n",
    "    stroke_width = 5\n",
    ")\n",
    "\n",
    "fig_11C_001.marks = [old_state]\n",
    "fig_11C_002.marks = [current_state]\n",
    "fig_11C_003.marks = [alpha_line]\n",
    "fig_11C_004.marks = [beta_line]\n",
    "fig_11C_005.marks = [ideal_line, unique_isotherm]\n",
    "\n",
    "alpha_slider_11C_003 = widgets.FloatSlider(\n",
    "    min=0.0,\n",
    "    max=5.0,\n",
    "    step=0.01,\n",
    "    value=alpha_initial,\n",
    "    description=r\"\\( \\alpha \\)\",\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout=widgets.Layout(width='90%')\n",
    ")\n",
    "\n",
    "alpha_slider_11C_003.observe(update_isotherms, 'value')\n",
    "\n",
    "beta_slider_11C_004 = widgets.FloatSlider(\n",
    "    min=0.0,\n",
    "    max=0.66,\n",
    "    step=0.001,\n",
    "    value=beta_initial,\n",
    "    description=r\"\\( \\beta \\)\",\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout=widgets.Layout(width='90%')\n",
    ")\n",
    "\n",
    "beta_slider_11C_004.observe(update_isotherms, 'value')\n",
    "\n",
    "return_button = widgets.Button(\n",
    "    description='Reset',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='Return to the original state',\n",
    ")\n",
    "\n",
    "return_button.on_click(restart)\n",
    "\n",
    "change_view_button = widgets.ToggleButton(\n",
    "    value=False,\n",
    "    description='Presentation mode (OFF)',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    icon='desktop',\n",
    "    layout=widgets.Layout(\n",
    "        width='initial',\n",
    "        align_self='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "change_view_button.observe(change_view, 'value')\n",
    "\n",
    "prepare_export_fig_11C_001_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_11C_001_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_11C_005_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_11C_005_button.on_click(prepare_export)\n",
    "\n",
    "top_block_11C_000 = widgets.HBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        width='100%',\n",
    "        align_self='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "top_block_11C_000.children = [\n",
    "    widgets.VBox([\n",
    "        fig_11C_001,\n",
    "        prepare_export_fig_11C_001_button\n",
    "    ],\n",
    "        layout=widgets.Layout(\n",
    "            width='33%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    ),\n",
    "    widgets.VBox([\n",
    "        fig_11C_002,\n",
    "        fig_11C_003,\n",
    "        alpha_slider_11C_003\n",
    "    ], \n",
    "        layout=widgets.Layout(\n",
    "            width='16%',\n",
    "            height='500px',\n",
    "            align_items='center',\n",
    "            margin='40px 0 0 0'\n",
    "        )\n",
    "    ),\n",
    "    widgets.VBox([\n",
    "        fig_11C_002,\n",
    "        fig_11C_004,\n",
    "        beta_slider_11C_004\n",
    "    ],\n",
    "        layout=widgets.Layout(\n",
    "            width='16%',\n",
    "            height='500px',\n",
    "            align_items='center',\n",
    "            margin='40px 0 0 0'\n",
    "        )\n",
    "    ),\n",
    "    widgets.VBox([\n",
    "        fig_11C_005,\n",
    "        prepare_export_fig_11C_005_button\n",
    "    ],\n",
    "        layout=widgets.Layout(\n",
    "            width='33%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    ),\n",
    "]\n",
    "\n",
    "bottom_block_11C_000 = widgets.HBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        width='100%',\n",
    "        height='60px',\n",
    "        align_self='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "bottom_block_11C_000.children = [\n",
    "    widgets.VBox([\n",
    "        widgets.HTMLMath(\n",
    "            value=r\"\\( (p_r + \\frac{\\alpha}{v_r^2})(v_r - \\beta) = \\frac{8}{3} T_r \\)\"\n",
    "        )],\n",
    "            layout=widgets.Layout(\n",
    "                width='33%',\n",
    "                align_items='center'\n",
    "            )\n",
    "    ),\n",
    "    widgets.VBox(\n",
    "        [return_button],\n",
    "        layout=widgets.Layout(\n",
    "            width='33%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    ),\n",
    "    widgets.VBox([\n",
    "        widgets.HTMLMath(\n",
    "            value=r\"\\( p_r v_r = R T_r \\)\"\n",
    "        )],\n",
    "            layout=widgets.Layout(\n",
    "                width='33%',\n",
    "                align_items='center'\n",
    "            )\n",
    "    )\n",
    "]\n",
    "\n",
    "main_block_11C_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        width='100%',\n",
    "        align_items='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "main_block_11C_000.children = [\n",
    "    change_view_button,\n",
    "    top_block_11C_000,\n",
    "    bottom_block_11C_000\n",
    "]\n",
    "\n",
    "figures = [\n",
    "    fig_11C_001,\n",
    "    fig_11C_002,\n",
    "    fig_11C_003,\n",
    "    fig_11C_004,\n",
    "    fig_11C_005,\n",
    "]\n",
    "\n",
    "main_block_11C_000"
   ]
  }
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