doc/tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dolfinh5tools tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updated: 16 October 2015\n",
    "Author: Marijan Beg\n",
    "\n",
    "This tutorial demonstates the use of dolfinh5tools for saving data obtained using FEniCS.\n",
    "\n",
    "The time dependent heat equation is used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, the time-dependent heat equation problem is defined (courtesy of Fenics project http://fenicsproject.org/pub/course/lectures/2012-06-29-rognes-imperial-uk/lecture_04_time_dependent_pdes.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n"
     ]
    }
   ],
   "source": [
    "# Time dependent heat equation.\n",
    "from fenics import *\n",
    "\n",
    "# Create mesh and define function space\n",
    "mesh = UnitSquareMesh(5, 5)\n",
    "V = FunctionSpace(mesh, 'Lagrange', 1)\n",
    "\n",
    "# Define initial condition expression g (will also be used as boundary\n",
    "# condition), and interpolate into initial function u0\n",
    "alpha = 3.0\n",
    "beta = 1.2\n",
    "g = Expression('1 + x[0]*x[0] + alpha*x[1]*x[1] + beta*t',\n",
    "               alpha=alpha, beta=beta, t=0, degree=2)\n",
    "u0 = interpolate(g, V)\n",
    "\n",
    "# Define boundary condition\n",
    "bc = DirichletBC(V, g, \"on_boundary\")\n",
    "\n",
    "# Define timestep and end-time\n",
    "dt = 0.2\n",
    "T = 1.8\n",
    "\n",
    "# Define variational problem for each time-step\n",
    "u = TrialFunction(V)\n",
    "v = TestFunction(V)\n",
    "f = Constant(beta - 2 - 2*alpha)\n",
    "a = u*v*dx + dt*inner(grad(u), grad(v))*dx\n",
    "L = (u0 + dt*f)*v*dx\n",
    "\n",
    "# Assemble once before the time-stepping begins\n",
    "A = assemble(a)\n",
    "\n",
    "# Define function for unknown at this time step\n",
    "u1 = Function(V)\n",
    "\n",
    "t = dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the savingdata module is imported, files to which the data would be saved are created, and the mesh is saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Debug: (0/1) opening file heat_equation_data.h5\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n"
     ]
    }
   ],
   "source": [
    "# Import dolfinh5tools\n",
    "from lib import openh5\n",
    "\n",
    "# Create a saving object.\n",
    "h5file = openh5('heat_equation_data', functionspace=V, mode='w')\n",
    "\n",
    "# Save mesh to h5 file.\n",
    "h5file.save_mesh(name='mesh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two files are created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "heat_equation_data.h5  heat_equation_data.json\r\n"
     ]
    }
   ],
   "source": [
    "!ls *.h5 *.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the data is saved at every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time:  0.2\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  0.4\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  0.6\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  0.8\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  1.0\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  1.2\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  1.4\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  1.6\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n",
      "Time:  1.8\n",
      "Debug: (0/1) writing json file heat_equation_data.json\n"
     ]
    }
   ],
   "source": [
    "while t <= T:\n",
    "    print 'Time: ', t\n",
    "    # Assemble right-hand side vector\n",
    "    b = assemble(L)\n",
    "\n",
    "    # Update and apply boundary condition\n",
    "    g.t = t\n",
    "    bc.apply(A, b)\n",
    "\n",
    "    # Solve linear system of equations\n",
    "    solve(A, u1.vector(), b)\n",
    "    \n",
    "    # Save data in u1 at time step t.\n",
    "    h5file.write(u0, field_name='u', t=t)\n",
    "    \n",
    "    # Update time and previous function\n",
    "    t += dt\n",
    "    u0.assign(u1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the saving is completed, the hf file is closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h5file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data from the saved h5 file.\n",
    "\n",
    "The data can be loaded using the loading object at any time steps that was previously saved. The loaded data is plotted along $y=5$ line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n",
      "DEBUG:FFC:Reusing form from cache.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved fields:  [u'u']\n",
      "Times at which the field was saved:  [0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2, 1.4, 1.5999999999999999, 1.7999999999999998]\n"
     ]
    },
    {
     "data": {
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BM5xOqK42VEF5OXR1GYHgRz+Cm28Gi/rNgAj329CCQU/PaerrH+f8+cdITp7JjBmbWbTo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff82bbd2f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Create a loading object.\n",
    "h5file = openh5('heat_equation_data', mode='r')\n",
    "\n",
    "# Load the mesh.\n",
    "mesh = h5file.load_mesh(name='mesh')\n",
    "\n",
    "# Get field names and times at which they were saved.\n",
    "fields_list = h5file.fields()\n",
    "print 'Saved fields: ', fields_list\n",
    "\n",
    "t_list = h5file.times(field_name='u')\n",
    "print 'Times at which the field was saved: ', t_list\n",
    "\n",
    "def plot_along_line(u, xmin=0, xmax=1):\n",
    "    x_array = np.linspace(0, 1, 50)\n",
    "    u_values = []\n",
    "    for x in x_array:\n",
    "        u_values.append(u(x, 0))\n",
    "        \n",
    "    plt.plot(x_array, u_values)\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('u')\n",
    "    plt.grid()\n",
    "    \n",
    "for t in t_list:\n",
    "    # Load the field.\n",
    "    u = h5file.read(t=t, field_name='u')\n",
    "    plot_along_line(u)"
   ]
  }
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