docs/apps/van_der_waals/stability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stability condition on van der Waals isotherms\n",
    "\n",
    "\n",
    "**Code:** #11A-000\n",
    "\n",
    "**File:** `apps/van_der_waals/stability.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%2Fstability.ipynb)\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to visualize the $\\left(\\frac{\\partial p}{\\partial v}\\right)_{T,N} < 0$ stability condition on van der Waals isotherms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main interface (``main_block_11A_000``) is divided in two HBox: ``top_block_11A_000`` and ``bottom_block_11A_000``. ``bottom_block_11A_000`` contains of 2 bqplot Figures: ``fig_11A_001`` and ``fig_11A_002``.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kCIQWSRGAJrmorqTnNQA5IHiREQMAAIKimiR09ZS+xtmKPtJ7ODl+4YOVtYI65jBJMaK07Uuk0unxoafa1GsiB95cc7avTvfrFdHOeHI8nU4l9uml2BOndRErin2mwSldIx/JGtMYPWvg2rn+mN5r36B60/R48rDZb+SJ005Uc++56F0K2QvJrtzhjSfjnVH1UdQxZJPi6nRcvY97u/tOkmbXMeWJoXH12X74i6vudzMXjud/Epopiu1D76uHG32CtYXfpF9Qz7T2vqC2zX2inu7a7/tD7dmdCFMU5Z3kE8mfh/MDj3QlF5w/O/vCMfWusW71B6r2nEp0q6VIzymvZuuNpAiEFkkRgCa5qK6k5zUAOSB4kREDAACCorokZGemjuxxTncz9EQtg6fn8+fpOUnRah+4mFtlslm2yZn8VXCmo71wfHe2OZpOlj23zlxo3d6unt834VQ40xgL2lnWX944FIvm7udoFLytOyO6Hxdzql8uuOWCnOzHOfnPffjO9mwnNLvO31dSc7+bPR0vbIpmo/OB/D5B8YXPJZ/I9XNxv4+zk93Hc59Iqqg83VzMfnAqt9GefWFHNNb384qbIkkRCC2SIgBNclFdSc9rAHJA8CIjBgAABMVmktD69Q8ujCd/mJ3vWZ8C1589Uc+Ur13HF5wlY+H4rmz9Mhfpuq9k1pyzEU03NNulIOrHu45PJXdnrxY2zyt+cXnuBlcS4LyToj11UL3GVdgUZ52zHxPgio7A9Ds5ZrNrV/dTCt7NSZLZQur0Ve/LuI2CirhBUnS6Ye64TBeVOvnRSJd6We4dHea1ztPNIUQe7T8997HXOaf+SIpAaJEUAWiSi+pKel4DkAOCFxkxAAAgKGpMQvbaHyf7H41kTzOU8lUY7Fz1q6iLCdcTLhxrlYKoV+qbDOo1Jn+ZdrdhUbTXVpbm3kqnU4PP9XW3mZMp5b3cAc79uIjnJrPS7Mfpc+biY5dXD+3IRlTz1MKeWrRL00zlPEyPolj+E2yYFJ3aKrdkdBdFiaCHXpVjFeYiaqecrp5P6Iu3DXPOaeoC91IEUAmSIgBNclFdSc9rAHJA8CIjBgAABEXFSejqz59siUT6z8mim8lbkrs2kxRdpzGagqjbmL5tommLpiSqjeYMP6lmpdYvjZqbNhrR2MPdfT88kD+oggBXFPncPDeZlWY/5lEZ+ScUvr54Vb4j6rrougra5xNsnBRdHbHw6nDzKm/5F69/MvdLfdKp0zDVuz82Wvgj2ABJEQgtkiIATXJRXUnPawByQPAiIwYAAARF5UlIx77C+wRmmVlTpDZunBT9LnzOnYs4q//jrDJ775uYltboRSYZOXDaPWWMu8G5A5xH98tyX8Sc5axz9lN858FiHrsuWWVW7B29as7HzBVF30/gkxSdw1R/Olf1Tyt/X0VzYmXheZMbsdf++LM+9ce84cmgBUiKQGiRFAFokovqSnpeA5ADghcZMQAAICiqSEKmYunsVXBlrL0ypaf7aI1Pmzq2cVIsPz1Ldm5mOSuxp6c1N9Ozfn2kvX1H9p6KpZwjc7c+e2XyYKtaJ9cYuw/B/biYOdmvcDKTiT596p7T8kpmb9GHn+yKRFq+88Zf1ILHrktXmRsf9hw40Orus76foDhnluzXOYvzwIGewoupzQ88Pye1ZuZniXa8MqdPPe1ra2l/YTb/tk71rTxCkhSB0CIpAtAkF/l450TiZ7//syxsOel5ftY+endizO3Xl1Y3Wr8JckA+lt999bnvVWJwKiMvCQIZMQAAICiqSUL2wqknYpYSaenoPfSc0ufM0FI8PcsGVW11ul9f3hvtjCfH0+lUYp9ZemwkX+mckxbdZ8qZyqXepVxRlHBmxboP652OJw/3PhK1IhFdB523dR9CaeRzc+ZDjjz6XbWn8WS8U/aTPT1QNrftS6TS+paHffpGhLnI6LFrj1XZD1hwxqffJ3B21NozMJ7+zcKa537lx1R8e0Znzmr5gas9m594NjJePdVjNvYN6s+jtnbrj9dzquyUMcVIikBokRQBaJKLfLz+zP333/9gxzPDv7q8LKu2kPQ8P3+aHvz3AoPTf9po/SbIAfn43cvfVD+OCnzz5d/JS4JARgwAAAiKapPQ+tLkYP6ue5GWtu548qzrtEW/pKioXSR6O5w9RGPd8dGZlYJUaK50dl/k7Fx77LrvYCl75e3B/D537R86u7SmXyUvch+CR4wrYK/MJPc7u1JHd2TqbX0WZf6KY7V5NJ79Aai36k1M5j6+x6693s05F7Lo4mKfT3BLT6Syx2w2L/Tar5Mli6Z3VswPfJczPbf5E3P/xO3MVG6j83mmMuXKrQeSIhBaJEUAmuQiHyYpGg/Eev9rYotPWJSe54ekWNZy5vKH+s9E/feDcswTaiEjBgAABAVJCLVg/AChRVIEoEku8pFPio4HO+I/m8ls1QmL0vP8kBTLuPL6My33P9D58rvLy+++3PmAvHmxZ16Xp2+WjBgAABAUJCHUgvEDhBZJEYAmuchHcVI0Hoj1vrwlF0JLz/NDUvTiqogtz7x+xemLXkiKAACgEEkItWD8AKFFUgSgSS7y4ZkUjQce+la81plbpOf5ISmWKgqI5lRF2ZQjf3gkRQAAUIgkhFowfoDQIikC0CQX+SifFB21zdwiPc8PSbHY5ZF9JZc5txz+lWzNIikCAABPJCHUgvEDhBZJEYAmuciHX1JUHog98/PL8vQqSc/zQ1Is8ecP9cQrv/qPdv1u//I/euFyyS0uSYoAAMATSQi1YPwAoUVSBKBJLvKxcVLUk7XUcu2z9Dw/JMUy5HhKmmFmavB7yt4d5mB27NULA7+8IlurJiMGAAAEBUkItWD8AKFFUgSgSS7yUS4p6hla0u/XdidFkmKtyibFNw4Wz9RiZnDZJBkxAAAgKEhCqAXjBwgtkiIATXKRD4+k+GBHfOSdrZjvmaRYKzke58LnvMuZZdd00Irn5C2VkxEDAACCgiSEWjB+gNAiKQLQJBf5KEiKWzHJcwHpeX5IimWUOx5zSmJuTuhaTlA0ZMQAAICgIAmhFowfILRIigA0yUU+sklRX+i8+Zmdy5Ge54ekWEb543ng74fnrutTFR+s7QRFQ0YMAAAICpIQasH4AUKLpAhAk1zk4/Vnap6DZQPS8/x8fPYVaYZC0mG59ZsgB+Rj7pVOSXY+Pqek+DmREQMAAILiB0BtZCQBCBmSIgBNcpGPP3/44WdTEw3peb7WVpcLrK5tvL56ckB+ljOX5X6FG7qc2erzOetKRgwAAAAAIMRIigA0yUV1JT2vAcgBwYuMGAAAAABAiJEUAWiSi+pKel4DkAOCFxkxAAAAAIAQIykC0CQX1ZX0vAYgBwQvMmIAAAAAACFGUgSgSS6qK+l5DUAOCF5kxAAAAAAAQoykCECTXFRX0vMagBwQvMiIAQAAAACEGEkRgCa5qK6k5zUAOSB4kREDAAAAAAgxkiIATXJRXUnPawByQPAiIwYAAAAAEGIkRQCa5KK6kp7XAOSA4EVGDAAAAAAgxEiKADTJRXUlPa8ByAHBi4wYAAAAAECIkRQBaJKL6kp6XgOQA4IXGTEAAAAAgBAjKQIAAAAAAACoAkkRAAAAAAAAQBVIigAAAAAAAACqQFIEAAAAAAAAUAWSIgAAAAAAAIAqkBQBAAAAAAAAVIGkCAAAAAAAAKAKJEUAAAAAAAAAVSApAgAAAAAAAKjYrVv/D4RUyQ3D7wCfAAAAAElFTkSuQmCC\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/11A-000_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slider ``T_slider`` updates the values of $T$ which updates the lines of ``fig_11A_001`` and ``fig_11A_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/11A-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": 3,
   "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"
    },
    {
     "data": {
      "text/html": [
       "<style>.jupyter-widgets { overflow: auto !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>\"))\n",
    "display(HTML(\"<style>.jupyter-widgets { overflow: auto !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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`\n",
    "- `get_derivative_y_by_x`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relative_isotherms(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.\n",
    "\n",
    "    Args:\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 - 1.0/3.0) - 3.0/v**2)\n",
    "            p_R = np.append(p_R, val)\n",
    "\n",
    "        isotherms.append(p_R)\n",
    "\n",
    "    return isotherms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_derivative_y_by_x(y_values, x_values):\n",
    "    \"\"\"This function calculates the derivative an y array\n",
    "    with respect to an x array calculated with the difference quotient.\n",
    "    \n",
    "    Args:\n",
    "        y_values: An array containing the values of y.\\n\n",
    "        x_values: An array containing the values of x.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        der: An array containing the values of the\n",
    "        derivative of y_values with respect to x_values.\n",
    "    \"\"\"\n",
    "    \n",
    "    der = []\n",
    "    \n",
    "    \n",
    "    for i in range(len(x_values)):\n",
    "\n",
    "        x = x_values[i]\n",
    "        y = y_values[i]\n",
    "        \n",
    "        d = []\n",
    "        \n",
    "        l = np.size(y)\n",
    "        \n",
    "        for j in range(1, l):\n",
    "            d.append((y[j] - y[j-1])/(x[j] - x[j-1]))\n",
    "        \n",
    "        der.append(d)\n",
    "        \n",
    "    return der"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions related to interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_tracer(change):\n",
    "    \"\"\"This function updates the marks 'tracer_11A_001' and\n",
    "    'lines_11A_002' from figures 'fig_11A_001' and\n",
    "    'fig_11A_002'.\n",
    "    \"\"\"\n",
    "    \n",
    "    tracer_11A_001.visible = True\n",
    "    \n",
    "    i = change.get('owner').value\n",
    "    v = dense_v_values_filtered[T_slider.index][i]\n",
    "    p = dense_p_values_filtered[T_slider.index][i]\n",
    "\n",
    "    tracer_11A_001.x, tracer_11A_001.y = [v], [p]\n",
    "    \n",
    "    x_values = np.append(lines_11A_002.x, v)\n",
    "    y_values = np.append(lines_11A_002.y, der[T_slider.index][i])\n",
    "    \n",
    "    lines_11A_002.x, lines_11A_002.y = x_values, y_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def restart_derivative(a):\n",
    "    \"\"\"This function clears the mark 'lines_11A_002'\n",
    "    from figure 'fig_11A_002' when 'restart_button' is pressed.\n",
    "    \"\"\"\n",
    "    \n",
    "    lines_11A_002.x, lines_11A_002.y = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_tenperature(change):\n",
    "    \"\"\"This function changes the tenperature of the\n",
    "    marks 'lines_11A_002' and 'tracer_11A_001' from figures\n",
    "    'fig_11A_002' and 'fig_11A_003'.\n",
    "    \"\"\"\n",
    "    \n",
    "    v_slider.max = len(dense_v_values_rounded[T_slider.index])-2\n",
    "    restart_derivative(None)\n",
    "    \n",
    "    obj = change.owner\n",
    "    \n",
    "    opacities = [0.0 for t in T_values]\n",
    "    opacities[T_slider.index] = 1.0\n",
    "    lines_11A_001.opacities = opacities\n",
    "\n",
    "    lines_11A_002.x, lines_11A_002.y = p_values[T_slider.index], v_values\n",
    "    \n",
    "    tracer_11A_001.x, tracer_11A_001.y = [lines_11A_002.x[0]], [lines_11A_002.y[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_all_derivatives(change):\n",
    "    \"\"\"This function shows all the calculated derivatives in \n",
    "    'lines_11A_002'.\n",
    "    \"\"\"\n",
    "    \n",
    "    lines_11A_002.x, lines_11A_002.y = [v.tolist()[:-1] for v in dense_v_values_filtered], der"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_text(change):\n",
    "    \"\"\"This function update the volume of the 'tracer_11A_001'\n",
    "    shown in 'v_text' widget.\n",
    "    \"\"\"\n",
    "    \n",
    "    obj = change.owner\n",
    "    i = obj.value\n",
    "    \n",
    "    v_text.value = '<p>' + str(dense_v_values_rounded[T_slider.index][i])+ '</p>'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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_11A_001_button:\n",
    "        \n",
    "        export_plot(fig_11A_001)\n",
    "        \n",
    "    elif button is prepare_export_fig_11A_002_button:\n",
    "        \n",
    "        export_plot(fig_11A_002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "v_values = np.linspace(0.4, 5.0, 500)\n",
    "T_values = [0.85, 0.9, 0.95, 1.0, 1.05, 1.1, 1.15, 1.2]\n",
    "\n",
    "p_values = get_relative_isotherms(v_values, T_values)\n",
    "\n",
    "################################\n",
    "######TOP BLOCK#################\n",
    "################################\n",
    "\n",
    "top_block = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "T_slider = widgets.SelectionSlider(\n",
    "    options= T_values,\n",
    "    value=T_values[0],\n",
    "    description=r'\\( T \\)',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout = widgets.Layout(\n",
    "        width = '300px',\n",
    "        height = 'auto',\n",
    "        margin='0 0 0 50px'\n",
    "    )\n",
    ")\n",
    "\n",
    "T_slider.observe(change_tenperature, 'value')\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",
    "top_block.children = [\n",
    "    change_view_button,\n",
    "    T_slider\n",
    "]\n",
    "\n",
    "################################\n",
    "######BOTTOM BLOCK##############\n",
    "################################\n",
    "\n",
    "bottom_block = widgets.HBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        width='100%',\n",
    "        align_items='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "dense_v_values = np.linspace(min(v_values), max(v_values), 10000)\n",
    "dense_p_values = get_relative_isotherms(dense_v_values, T_values)\n",
    "\n",
    "dense_v_values_filtered = []\n",
    "dense_p_values_filtered = []\n",
    "\n",
    "dense_v_values_inverted = []\n",
    "dense_p_values_inverted = []\n",
    "\n",
    "dense_v_values_rounded = []\n",
    "\n",
    "for i in range(len(T_values)):    \n",
    "\n",
    "    i_in_range, = np.where(dense_p_values[i] < 2.0)\n",
    "\n",
    "    dense_v_values_filtered.append(np.take(dense_v_values, i_in_range))\n",
    "    dense_p_values_filtered.append(np.take(dense_p_values[i], i_in_range))\n",
    "    dense_v_values_rounded.append(np.round(dense_v_values_filtered[i], 3))\n",
    "    \n",
    "v_text = widgets.HTML(\n",
    "    value=\"<p>\" + str(dense_v_values_rounded[T_slider.index][i]) + \"</p>\",\n",
    "    layout = widgets.Layout(\n",
    "        height='auto',\n",
    "        margin='8px 0 0 10px',\n",
    "        width='initial'\n",
    "    )\n",
    ")\n",
    "\n",
    "der = get_derivative_y_by_x(dense_p_values_filtered, dense_v_values_filtered)\n",
    "\n",
    "v_slider = widgets.IntSlider(\n",
    "    min=0,\n",
    "    max=len(der[T_slider.index])-1,\n",
    "    value=0,\n",
    "    description=r'\\( v \\)',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=False,\n",
    "    layout = widgets.Layout(width = '75%', height='auto', margin='0 0 0 100px')\n",
    ")\n",
    "\n",
    "v_slider.observe(update_tracer, 'value')\n",
    "v_slider.observe(update_text, 'value')\n",
    "\n",
    "play = widgets.Play(\n",
    "    interval=1,\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=v_slider.max,\n",
    "    step=1,\n",
    "    description=\"Press play\",\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "widgets.jslink((play, 'value'), (v_slider, 'value'));\n",
    "\n",
    "scale_x = bqs.LinearScale(min = min(v_values), max = max(v_values))\n",
    "scale_y = bqs.LinearScale(min = 0.0, max = 2.0)\n",
    "\n",
    "color_scale = bqs.ColorScale(\n",
    "    colors = ['#FF0000', '#FFfa00'],\n",
    "    min=min(T_values),\n",
    "    max=max(T_values)\n",
    ")\n",
    "\n",
    "axis_x_001 = 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_001 = bqa.Axis(\n",
    "    scale=scale_y,\n",
    "    tick_format='.1f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 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_color = bqa.ColorAxis(\n",
    "    label = 'T',\n",
    "    scale=color_scale,\n",
    "    tick_format='.2f',\n",
    "    orientation='vertical',\n",
    "    side='right'\n",
    ")\n",
    "\n",
    "fig_11A_001 = Figure(\n",
    "    title='p vs v (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_001, axis_y_001, axis_color],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=70, bottom=75, left=80, right=100),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(width='85%')\n",
    ")\n",
    "\n",
    "lines_11A_001 = bqm.Lines(\n",
    "    x = v_values, \n",
    "    y = p_values, \n",
    "    scales = {'x': scale_x, 'y': scale_y, 'color': color_scale}, \n",
    "    opacities = [1.0] + [0.0 for i in range(len(T_values)-1)],\n",
    "    visible = True,\n",
    "    color = T_values,\n",
    ")\n",
    "\n",
    "tracer_11A_001 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = [0.0],\n",
    "    y = [0.0],\n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacity = [1.0],\n",
    "    visible = False,\n",
    "    colors = ['#2807a3'],\n",
    ")\n",
    "\n",
    "fig_11A_001.marks = [\n",
    "    lines_11A_001,\n",
    "    tracer_11A_001\n",
    "]\n",
    "\n",
    "scale_x_002 = bqs.LinearScale(min = 0.0, max = 2.0)\n",
    "scale_y_002 = bqs.LinearScale(min = -2.0, max = 2.0)\n",
    "\n",
    "axis_y_002 = bqa.Axis(\n",
    "    scale=scale_y_002,\n",
    "    tick_format='.2f',#'0.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [-2, -1, 0, 1, 2],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='dp/dv',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "fig_11A_002 = Figure(\n",
    "    title='dp/dv vs v (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_001, axis_y_002],\n",
    "    animation_duration=0, \n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    fig_margin=dict(top=70, bottom=75, left=80, right=100),\n",
    "    toolbar = True,\n",
    "    layout = widgets.Layout(width='85%')\n",
    ")\n",
    "\n",
    "lines_11A_002 = bqm.Lines(\n",
    "    x = [0.4], \n",
    "    y = [1.0], \n",
    "    scales = {'x': scale_x, 'y': scale_y_002, 'color':color_scale}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    color = T_values,\n",
    ")\n",
    "\n",
    "zero_11A_002 = bqm.Lines(\n",
    "    x = v_values, \n",
    "    y = [0.0 for v in v_values], \n",
    "    scales = {'x': scale_x, 'y': scale_y_002}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['#FF0000'],\n",
    "    line_style = 'dotted'\n",
    ")\n",
    "\n",
    "fig_11A_002.marks = [\n",
    "    zero_11A_002,\n",
    "    lines_11A_002\n",
    "]\n",
    "\n",
    "restart_button = widgets.Button(\n",
    "    description='Clean figure',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip=\"\",\n",
    "    layout = widgets.Layout(height='auto')\n",
    ")\n",
    "\n",
    "restart_button.on_click(restart_derivative)\n",
    "\n",
    "show_all_button = widgets.Button(\n",
    "    description='Show all derivatives',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip=\"\",\n",
    "    layout = widgets.Layout(height='auto', width='initial')\n",
    ")\n",
    "\n",
    "show_all_button.on_click(show_all_derivatives)\n",
    "\n",
    "prepare_export_fig_11A_001_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_11A_001_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_11A_002_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_11A_002_button.on_click(prepare_export)\n",
    "\n",
    "slider_box = widgets.HBox([\n",
    "    v_slider, v_text\n",
    "    ],\n",
    "    layout=widgets.Layout(\n",
    "        height='auto',\n",
    "        width='100%',\n",
    "        align_items='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "bottom_block.children = [\n",
    "    widgets.VBox([\n",
    "        fig_11A_001,\n",
    "        prepare_export_fig_11A_001_button,\n",
    "        slider_box,\n",
    "        play\n",
    "    ], \n",
    "    layout=widgets.Layout(\n",
    "        height='auto',\n",
    "        width='50%',\n",
    "        align_items='center'\n",
    "    )\n",
    "    ),\n",
    "    widgets.VBox([\n",
    "        fig_11A_002,\n",
    "        prepare_export_fig_11A_002_button,\n",
    "        restart_button,\n",
    "        show_all_button\n",
    "    ], \n",
    "    layout=widgets.Layout(\n",
    "        height='auto',\n",
    "        width='50%',\n",
    "        align_items='center'\n",
    "    )\n",
    "    )\n",
    "]\n",
    "\n",
    "main_block_11A_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "main_block_11A_000.children = [\n",
    "    top_block,\n",
    "    bottom_block\n",
    "]\n",
    "\n",
    "figures = [\n",
    "    fig_11A_001,\n",
    "    fig_11A_002\n",
    "]\n",
    "\n",
    "main_block_11A_000"
   ]
  }
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