tasks/task05.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation and post-processing\n",
    "\n",
    "In this task, we'll learn how to visualise FESTIM results.\n",
    "\n",
    "Let's create a 1D simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import festim as F\n",
    "import numpy as np\n",
    "\n",
    "my_model = F.Simulation()\n",
    "\n",
    "my_model.mesh = F.MeshFromVertices(\n",
    "    vertices=np.linspace(0, 1, num=1000)\n",
    ")\n",
    "\n",
    "my_material = F.Material(id=1, D_0=1, E_D=0.2)\n",
    "my_model.materials = my_material\n",
    "\n",
    "my_model.traps = F.Trap(k_0=1, E_k=0, p_0=0.1, E_p=0, materials=my_material, density=0.5)\n",
    "\n",
    "# temperature varying in space and time\n",
    "my_model.T = F.Temperature(500 + F.x + F.t)\n",
    "\n",
    "my_model.boundary_conditions = [\n",
    "    F.DirichletBC(\n",
    "        surfaces=[1, 2],\n",
    "        value=1 + F.x,  # 1 in x=0, 2 in x=1\n",
    "        field=0\n",
    "        )\n",
    "]\n",
    "\n",
    "my_model.settings = F.Settings(\n",
    "    absolute_tolerance=1e-10,\n",
    "    relative_tolerance=1e-10,\n",
    "    final_time=100\n",
    "    )\n",
    "\n",
    "my_model.dt = F.Stepsize(0.1, stepsize_change_ratio=1.1, dt_min=1e-5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to export the temperature, the mobile concentration field, and the total retention (mobile + trapped).\n",
    "\n",
    ">\n",
    "> Note: in 1D, to visualise the XDMF files in Paraview, the `checkpoint` argument needs to be `False` (see [#134](https://github.com/RemDelaporteMathurin/FESTIM/issues/134))\n",
    ">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_folder = \"task05\"\n",
    "\n",
    "xdmf_exports = [\n",
    "    F.XDMFExport(field=\"solute\", filename=export_folder + \"/hydrogen_concentration.xdmf\", checkpoint=False),\n",
    "    F.XDMFExport(field=\"retention\", filename=export_folder + \"/retention.xdmf\", checkpoint=False),\n",
    "    F.XDMFExport(field=\"T\", filename=export_folder + \"/T.xdmf\", checkpoint=False),\n",
    "]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would also like to visualise the total hydrogen inventory, the particle fluxes at the surfaces, and the average temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "derived_quantities = F.DerivedQuantities(\n",
    "    [\n",
    "        F.TotalVolume(field=\"retention\", volume=1),\n",
    "        F.HydrogenFlux(surface=1),\n",
    "        F.HydrogenFlux(surface=2),\n",
    "        F.AverageVolume(field=\"T\", volume=1)\n",
    "    ],\n",
    "    filename=export_folder + \"/derived_quantities.csv\",\n",
    "    show_units=True,\n",
    ")\n",
    "\n",
    "my_model.exports = [derived_quantities] + xdmf_exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defining initial values\n",
      "Defining variational problem\n",
      "Defining source terms\n",
      "Defining boundary conditions\n",
      "Time stepping...\n",
      "100.0 %        1.0e+02 s    Elapsed time so far: 0.5 s\n"
     ]
    }
   ],
   "source": [
    "my_model.initialise()\n",
    "\n",
    "my_model.run()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several files were created.\n",
    "\n",
    "The XDMF files can be opened in [Paraview](https://www.paraview.org/) for visualisation.\n",
    "\n",
    "The derived_quantities.csv file can be read in Excel, or programmatically in python.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ts', 'Total_retention_volume_1_H_m2', 'solute_flux_surface_1_H_m2_s1', 'solute_flux_surface_2_H_m2_s1', 'Average_T_volume_1_K')\n"
     ]
    }
   ],
   "source": [
    "# load the csv file with numpy\n",
    "data = np.genfromtxt(export_folder + \"/derived_quantities.csv\", delimiter=\",\", names=True)\n",
    "\n",
    "# print the columns names\n",
    "print(data.dtype.names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.1          0.21         0.331        0.4641       0.61051\n",
      "   0.771561     0.9487171    1.14358881   1.35794769   1.59374246\n",
      "   1.85311671   2.13842838   2.45227121   2.79749834   3.17724817\n",
      "   3.59497299   4.05447028   4.55991731   5.11590904   5.72749995\n",
      "   6.40024994   7.14027494   7.95430243   8.84973268   9.83470594\n",
      "  10.91817654  12.10999419  13.42099361  14.86309297  16.44940227\n",
      "  18.1943425   20.11377675  22.22515442  24.54766986  27.10243685\n",
      "  29.91268053  33.00394859  36.40434344  40.14477779  44.25925557\n",
      "  48.78518112  53.76369924  59.24006916  65.26407608  71.89048369\n",
      "  79.17953205  87.19748526  96.01723378 100.        ]\n"
     ]
    }
   ],
   "source": [
    "print(data[\"ts\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'H flux')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(data[\"ts\"], data[\"solute_flux_surface_2_H_m2_s1\"], label=\"right surf.\")\n",
    "plt.plot(data[\"ts\"], data[\"solute_flux_surface_1_H_m2_s1\"], label=\"left surf.\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "\n",
    "plt.ylabel(\"H flux\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, one can use the `plot` function of FEniCS to plot fields directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa9ef102910>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fenics import plot\n",
    "\n",
    "plot(my_model.T.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa9ef194c10>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(my_model.h_transport_problem.mobile.solution)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the workshop if being run in GitHub codespaces, the XDMF files will not be readable in Paraview, however this is how they should look:\n",
    "\n",
    "Temperature field:\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://github.com/festim-dev/FESTIM-workshop/assets/65899899/3db25229-a752-49b9-9ba4-2b19d1dc3252\" width=\"30%\">\n",
    "</p>\n",
    "\n",
    "Temperature profile:\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://github.com/festim-dev/FESTIM-workshop/assets/65899899/2c135663-a1aa-4c1d-9eef-d4a615b34bc0\" width=\"30%\">\n",
    "</p>\n",
    "\n",
    "Mobile concentration and retention profiles:\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://github.com/festim-dev/FESTIM-workshop/assets/65899899/2ca1b424-652b-4b6d-8b37-51905cc9f7b3\" width=\"30%\">\n",
    "</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}