docs/apps/van_der_waals/parameters_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical analysis of the effect of a and b parameters\n",
    "\n",
    "\n",
    "**Code:** #119-000\n",
    "\n",
    "**File:** `apps/van_der_waals/parameters_analysis.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%2Fparameters_analysis.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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main interface (``main_block_119_000``) is divided in two HBox: ``top_block_119_000`` and ``bottom_block_119_000``. ``top_block_119_000`` contains of 2 bqplot Figures: ``fig_119_001`` and ``fig_119_002``.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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/119-000_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sliders ``a_slider`` and ``b_slider`` update the values of $a$ and $b$ which updates the isotherms of ``fig_119_001`` and ``fig_119_002``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='../../static/images/apps/119-000_2.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": 1,
   "metadata": {
    "scrolled": true
   },
   "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"
    }
   ],
   "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>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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_absolute_isotherms`\n",
    "- `calculate_critic`\n",
    "- `bar_to_atm`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_absolute_isotherms(a, b, v_values, T_values):\n",
    "    \"\"\"This function calculates the theoretical p(v, T) plane \n",
    "        (in absolute coordinates) according to van der Waals\n",
    "        equation of state from a given range of volumes\n",
    "        and tenperatures.\n",
    "\n",
    "    Args:\n",
    "        a: Term related with the attraction between particles in\n",
    "           L^2 bar/mol^2.\\n\n",
    "        b: Term related with the volume that is occupied by one \n",
    "        mole of the molecules in L/mol.\\n\n",
    "        v_values: An array containing the values of v\n",
    "        for which the isotherms must be calculated.\\n\n",
    "        T_values: An array containing the values of T for which\n",
    "        the isotherms must be calculated.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        isotherms: A list consisted of numpy arrays containing the\n",
    "        pressures of each isotherm.\n",
    "    \"\"\"\n",
    "    isotherms = []\n",
    "\n",
    "    R = 0.082 * 1.01325 #bar L mol^-1 K^-1\n",
    "\n",
    "    for T in T_values:\n",
    "        \n",
    "        isot = []\n",
    "        \n",
    "        for v in v_values:\n",
    "            \n",
    "            p = R*T/(v - b) - (a/v**2)\n",
    "            isot = np.append(isot, p)\n",
    "            \n",
    "        isotherms.append(isot)\n",
    "        \n",
    "    return isotherms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_critic(a, b):\n",
    "    \n",
    "    \"\"\"\n",
    "        This function calculates the critic point \n",
    "        (p_c, v_c, T_c) from given a and b parameters of \n",
    "        the Van der Waals equation of state for real gases.\n",
    "        \n",
    "        :math:`(P + a \\\\frac{n^2}{V^2})(V - nb) = nRT`\n",
    "        \n",
    "        :math:`p_c = \\\\frac{a}{27 b^2}`\n",
    "        :math:`v_c = 3b`\n",
    "        :math:`T_c = \\\\frac{8a}{27 b R}`\n",
    "        \n",
    "   Args:\n",
    "       a: Term related with the attraction between particles in\n",
    "       L^2 bar/mol^2.\\n\n",
    "       b: Term related with the volume that is occupied by one \n",
    "       mole of the molecules in L/mol.\\n\n",
    "       \n",
    "   Returns:\n",
    "       p_c: Critical pressure in bar.\\n\n",
    "       v_c: Critical volume in L/mol.\\n\n",
    "       T_c: Critical tenperature in K.\\n\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    if b == 0.0:\n",
    "        return None\n",
    "    \n",
    "    k_B = 1.3806488e-23 #m^2 kg s^-2 K^-1\n",
    "    N_A = 6.02214129e23 \n",
    "    R = 0.082 * 1.01325 #bar L mol^-1 K^-1\n",
    "    \n",
    "    p_c = a/27.0/(b**2)\n",
    "    v_c = 3.0*b\n",
    "    T_c = 8.0*a/27.0/b/R\n",
    "    \n",
    "    return p_c, v_c, T_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_to_atm(p_values):\n",
    "    \"\"\"This function changes the pressures of an array\n",
    "    form bars to atm.\n",
    "    \n",
    "    Args:\n",
    "        p_values: List consisted of pressures in bars.\\n\n",
    "        \n",
    "    Returns:\n",
    "        p_values: List consisted of pressures in atm.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    p_values = np.array(p_values) * 0.9869 \n",
    "        \n",
    "    return p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions related to interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_zoom_arrays(initial_x_range, final_x_range, initial_y_range, final_y_range, size):\n",
    "    \"\"\"This function calculates the arrays of the max/min values of\n",
    "    x/y for some given limits.\n",
    "    \n",
    "    Args:\n",
    "        initial_x_range: List consisted of initial values of x.\\n\n",
    "        final_x_range: List consisted of final values of x.\\n\n",
    "        initial_y_range: List consisted of initial values of y.\\n\n",
    "        final_y_range: List consisted of final values of y.\\n\n",
    "        \n",
    "    Returns:\n",
    "        x_min: Array consisted of minimun values of x.\\n\n",
    "        x_max: Array consisted of maximun values of x.\\n\n",
    "        y_min: Array consisted of minimun values of y.\\n\n",
    "        y_max: Array consisted of maximun values of y.\\n\n",
    "    \"\"\"\n",
    "        \n",
    "    x_min = np.linspace(min(initial_x_range), min(final_x_range), size)\n",
    "    x_max = np.linspace(max(initial_x_range), max(final_x_range), size)\n",
    "    \n",
    "    y_min = np.linspace(min(initial_y_range), min(final_y_range), size)\n",
    "    y_max = np.linspace(max(initial_y_range), max(final_y_range), size)\n",
    "    \n",
    "    return x_min, x_max, y_min, y_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_isotherms(change):\n",
    "    \"\"\"This function updates the isotherms of bqplot Figure\n",
    "    'fig_119_002' when 'a_slider' of 'b_slider' are updated.\n",
    "    \"\"\"\n",
    "    \n",
    "    p_values = get_absolute_isotherms(\n",
    "        a_slider.value,\n",
    "        b_slider.value,\n",
    "        v_values,\n",
    "        T_values\n",
    "    )\n",
    "    \n",
    "    p_values = bar_to_atm(p_values)\n",
    "    \n",
    "    marks = [\n",
    "        bqm.Lines(\n",
    "            x = [v_values for elem in p_values], \n",
    "            y = p_values, \n",
    "            scales = {'x': scale_x_119_001, 'y': scale_y_119_001}, \n",
    "            opacities = [1.0],\n",
    "            visible = True,\n",
    "            colors = colors,\n",
    "        )\n",
    "    ]\n",
    "    \n",
    "    fig_119_002.marks = marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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_119_001_button:\n",
    "        \n",
    "        export_plot(fig_119_001)\n",
    "        \n",
    "    elif button is prepare_export_fig_119_002_button:\n",
    "        \n",
    "        export_plot(fig_119_002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": {},
   "outputs": [],
   "source": [
    "#In this program we are going to use water's parameters\n",
    "a = 5.536 #L^2 bar mol^-2\n",
    "b = 0.03049 #L mol^-1\n",
    "\n",
    "colors = ['#0079c4','#f09205','#21c400', '#850082']\n",
    "\n",
    "p_c, v_c, T_c = calculate_critic(a, b)\n",
    "\n",
    "p_c = p_c * 0.9869 #unit change from bar to atm\n",
    "\n",
    "v_values = np.linspace(-5, 5, 3000) #L/mol\n",
    "T_values = [0.9*T_c, 1.0*T_c, 1.1*T_c]\n",
    "\n",
    "p_values = get_absolute_isotherms(a, b, v_values, T_values)\n",
    "p_values = bar_to_atm(p_values)\n",
    "\n",
    "#####################\n",
    "######TOP BLOCK######\n",
    "#####################\n",
    "\n",
    "top_block_119_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        align_items='center',\n",
    "    )\n",
    ")\n",
    "\n",
    "scale_x_119_001 = bqs.LinearScale(min = -0.5, max = 0.5)\n",
    "scale_y_119_001 = bqs.LinearScale(min = -300, max = 300)\n",
    "\n",
    "axis_x_119_001 = bqa.Axis(\n",
    "    scale=scale_x_119_001,\n",
    "    tick_format='.1f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks=5,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='v (L/mol)',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y_119_001 = bqa.Axis(\n",
    "    scale=scale_y_119_001,\n",
    "    tick_format='.0f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks=4,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='p (atm)',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "fig_119_001 = Figure(\n",
    "    title='',\n",
    "    marks=[],\n",
    "    axes=[axis_x_119_001, axis_y_119_001],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=10, bottom=60, left=80, right=30),\n",
    "    toolbar = True,\n",
    "    layout=widgets.Layout(\n",
    "        height='350px',\n",
    "    )\n",
    ")\n",
    "\n",
    "marks = [\n",
    "    bqm.Lines(\n",
    "        x = [v_values for elem in p_values], \n",
    "        y = p_values, \n",
    "        scales = {'x': scale_x_119_001, 'y': scale_y_119_001}, \n",
    "        opacities = [1.0],\n",
    "        visible = True,\n",
    "        colors = colors,\n",
    "    )\n",
    "]\n",
    "\n",
    "fig_119_001.marks = marks\n",
    "\n",
    "\n",
    "tb_119_001 = Toolbar(figure=fig_119_001, layout=widgets.Layout(align_self='center'))\n",
    "\n",
    "fig_119_002 = Figure(\n",
    "    title='',\n",
    "    marks=[],\n",
    "    axes=[axis_x_119_001, axis_y_119_001],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=10, bottom=60, left=80, right=30),\n",
    "    toolbar = True,\n",
    "    layout=widgets.Layout(\n",
    "        height='350px',\n",
    "    )\n",
    ")\n",
    "\n",
    "fig_119_002.marks = marks\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_119_001_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    layout=widgets.Layout(\n",
    "        align_self='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "prepare_export_fig_119_001_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_119_002_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    layout=widgets.Layout(\n",
    "        align_self='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "prepare_export_fig_119_002_button.on_click(prepare_export)\n",
    "\n",
    "top_block_119_000.children = [\n",
    "    change_view_button,\n",
    "    tb_119_001,\n",
    "    widgets.HBox([\n",
    "        widgets.VBox([\n",
    "            prepare_export_fig_119_001_button,\n",
    "            fig_119_001\n",
    "        ]),\n",
    "        widgets.VBox([\n",
    "            prepare_export_fig_119_002_button,\n",
    "            fig_119_002\n",
    "        ])\n",
    "    ])\n",
    "]\n",
    "\n",
    "#####################\n",
    "######BOTTOM BLOCK###\n",
    "#####################\n",
    "\n",
    "bottom_block_119_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        align_items='center',\n",
    "        width='100%',\n",
    "        margin='30px 0 0 0'\n",
    "    )\n",
    ")\n",
    "\n",
    "a_slider = widgets.FloatSlider(\n",
    "    min=0,\n",
    "    max=34.0,\n",
    "    step=0.001,\n",
    "    value=a,\n",
    "    description='a',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout=widgets.Layout(width='90%'),\n",
    ")\n",
    "\n",
    "a_slider.observe(update_isotherms, 'value')\n",
    "\n",
    "b_slider = widgets.FloatSlider(\n",
    "    min=0,\n",
    "    max=0.1735,\n",
    "    step=0.0001,\n",
    "    value=b,\n",
    "    description='b',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout=widgets.Layout(width='90%'),\n",
    ")\n",
    "\n",
    "b_slider.observe(update_isotherms, 'value')\n",
    "\n",
    "bottom_block_119_000.children = [\n",
    "    widgets.HBox([\n",
    "        a_slider,\n",
    "        widgets.HTMLMath(\n",
    "            value=r\"\\( \\frac{L^2 bar}{mol^2} \\)\",\n",
    "            layout=widgets.Layout(height='60px')\n",
    "        )],\n",
    "        layout=widgets.Layout(\n",
    "            width='50%',\n",
    "            height='100%'\n",
    "        )\n",
    "    ),\n",
    "    widgets.HBox([\n",
    "        b_slider,\n",
    "        widgets.HTMLMath(\n",
    "            value=r\"\\( \\frac{L}{mol} \\)\",\n",
    "            layout=widgets.Layout(height='60px')\n",
    "        )],\n",
    "        layout=widgets.Layout(\n",
    "            width='50%',\n",
    "            height='100%'\n",
    "        )\n",
    "    )\n",
    "]\n",
    "\n",
    "#####################\n",
    "####MAIN BLOCK#####\n",
    "#####################\n",
    "\n",
    "main_block_119_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "main_block_119_000.children = [\n",
    "    top_block_119_000,\n",
    "    bottom_block_119_000\n",
    "]\n",
    "\n",
    "figures = [\n",
    "    fig_119_001,\n",
    "    fig_119_002\n",
    "]\n",
    "\n",
    "main_block_119_000"
   ]
  }
 ],
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