docs/apps/van_der_waals/p_v_T_3D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Van der Waals isotherms in 3D\n",
    "\n",
    "\n",
    "**Code:** #116-000\n",
    "\n",
    "**File:** `apps/van_der_waals/p_v_T_3D.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%2Fp_v_T_3D.ipynb)\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main interface (``main_block_116_000``) contains the 3D ipyvolume figure ``fig3d`` and the ``play\n",
    "`` and ``degrees_slider`` widgets. Those widgets update the angle of the camera of the figure."
   ]
  },
  {
   "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/116-000_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "import ipyvolume as ipv\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical functions\n",
    "\n",
    "This are the functions that have a physical meaning:\n",
    "\n",
    "- `get_relative_isotherms`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Functions related to interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_camera_angle(change):\n",
    "    \"\"\"This function updates the camera angle taking\n",
    "    the values from dregees_slider widget.\n",
    "    \"\"\"\n",
    "    \n",
    "    ipv.pylab.view(azimuth=degrees_slider.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_values = np.linspace(0.8, 1.0, 10)\n",
    "v_values = np.linspace(0.45, 5.0, 500)\n",
    "\n",
    "p_values = get_relative_isotherms(v_values, T_values)\n",
    "\n",
    "fig3d = ipv.pylab.figure(\n",
    "    key=None,\n",
    "    width=600,\n",
    "    height=500,\n",
    "    lighting=True,\n",
    "    controls=True,\n",
    "    controls_vr=False,\n",
    "    controls_light=False,\n",
    "    debug=False\n",
    ")\n",
    "\n",
    "ipv.pylab.xlim(min(v_values), max(v_values))\n",
    "ipv.pylab.ylim(0.0, 2.0)\n",
    "ipv.pylab.zlim(min(T_values), max(T_values))\n",
    "\n",
    "ipv.pylab.xlabel('v')\n",
    "ipv.pylab.ylabel('p')\n",
    "ipv.pylab.zlabel('T')\n",
    "\n",
    "ipv.pylab.view(azimuth=180, elevation=None, distance=None)\n",
    "\n",
    "for i in range(len(T_values)):\n",
    "    \n",
    "    x_values = np.asarray(v_values)\n",
    "    y_values = np.asarray(p_values[i])\n",
    "    z_values = np.asarray(\n",
    "        [T_values[i] for elem in v_values]\n",
    "    )\n",
    "    \n",
    "    ipv.pylab.plot(\n",
    "        x_values,\n",
    "        y_values,\n",
    "        z_values\n",
    "    )\n",
    "    \n",
    "degrees_slider = widgets.IntSlider(\n",
    "    value=180,\n",
    "    min=0,\n",
    "    max=360,\n",
    "    step=1,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    ")\n",
    "\n",
    "degrees_slider.observe(update_camera_angle, 'value')\n",
    "\n",
    "play = widgets.Play(\n",
    "    value=180,\n",
    "    min=0,\n",
    "    max=360,\n",
    "    step=1,\n",
    "    description=\"Press play\",\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "widgets.jslink((play, 'value'), (degrees_slider, 'value'));\n",
    "\n",
    "main_block_116_000 = widgets.VBox([\n",
    "    fig3d,\n",
    "    widgets.HBox([\n",
    "        play,\n",
    "        degrees_slider\n",
    "    ])\n",
    "    ],\n",
    "    layout=widgets.Layout(\n",
    "        align_items='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "main_block_116_000"
   ]
  }
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