ogs-jupyter-lab-t1-new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "701c36ef-c7d0-498b-9218-fb51d685d348",
   "metadata": {
    "tags": []
   },
   "source": [
    "**ogs@jupyter-lab: heat diffusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102806cc-37f2-4ffe-b372-0d727eb0987f",
   "metadata": {},
   "source": [
    "<!--- ![tu-dresden-blue.png](attachment:tu-dresden-blue.png) --->\n",
    "<img src=\"ogs-workflows.png\" alt=\"drawing\" width=\"300\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752ca47e-b8f5-4a44-b556-caea7971f51b",
   "metadata": {},
   "source": [
    "**Running ogs using [ogs6py](https://github.com/joergbuchwald/ogs6py)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85143f21-f2ef-43ab-a2a3-9412f1ca55fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============\n",
      ">>> run ogs <<<\n",
      "OGS finished with project file square_1e2_axi.prj.\n",
      "Execution took 0.028557777404785156 s\n"
     ]
    }
   ],
   "source": [
    "from ogs6py import ogs\n",
    "#run ogs\n",
    "print(\"===============\")\n",
    "print(\">>> run ogs <<<\")\n",
    "model = ogs.OGS(PROJECT_FILE=\"square_1e2_axi.prj\", MKL=False)\n",
    "model.mesh.add_mesh(filename=\"square_1x1_quad_1e2.vtu\")\n",
    "model.geo.add_geom(filename=\"square_1x1_axi.gml\")\n",
    "model.run_model(path=\"/home/ok/ogs/build/release/bin/\",LOGFILE=\"example.log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb395b7-9b10-4a39-b914-929265094f56",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Extracting results using [VTUInterface](https://github.com/joergbuchwald/VTUinterface)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddae910b-4fc6-4384-bebf-723113924613",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================\n",
      ">>> print results <<<\n",
      "square_1e2_axi.pvd\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import vtuIO\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.tri as tri\n",
    "print(\"=====================\")\n",
    "print(\">>> print results <<<\")\n",
    "pvdfile=vtuIO.PVDIO(\"square_1e2_axi.pvd\", dim=2)\n",
    "# define xaxis\n",
    "#xaxis =  [(i,0,0) for i in np.linspace(start=0.0, stop=1.0, num=10)]\n",
    "#r_x = np.array(xaxis)[:,0]\n",
    "diagonal = [(i,i,0) for i in np.linspace(start=0.0, stop=1.4, num=100)]\n",
    "r_diag = np.sqrt(np.array(diagonal)[:,0]**2+np.array(diagonal)[:,1]**2)\n",
    "# define timestep\n",
    "time = [2.0]\n",
    "# read and interpolate from vtu-files listed in pvd\n",
    "#for t in time:\n",
    "#    xaxis_t = pvdfile.readPointSetData(t, 'temperature_interpolated', pointsetarray=xaxis)\n",
    "#    plt.plot(r_x, xaxis_t, label='t='+str(t))\n",
    "for t in time:\n",
    "    xaxis_t = pvdfile.read_point_set_data(t, 'temperature', pointsetarray=diagonal)\n",
    "    plt.plot(r_diag, xaxis_t, label='t='+str(t))\n",
    "# plot formatting\n",
    "titlestring=\"OGS-TH2M: T process\"\n",
    "plt.title(titlestring)\n",
    "#plt.xscale('log')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Temperature interpolated')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.savefig(\"result_TH2M_T_dirichlet.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a4390e-6829-4e80-a9d1-27438146702d",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Results have been shown using [matplotlib](https://matplotlib.org/)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d24afa0-17b8-436a-9bb7-8e5e2fa3e043",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8126362df0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_plot=vtuIO.VTUIO(\"square_1e2_axi_ts_2_t_2.000000.vtu\", dim=2)\n",
    "triang=tri.Triangulation(m_plot.points[:,0],m_plot.points[:,1])\n",
    "u_plot = m_plot.get_point_field(\"temperature\")#.T[0]\n",
    "#fig, ax = plt.subplots(ncols=2, figsize=(10,10))\n",
    "#contour_left = ax[0].tricontourf(triang, u_plot)\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "contour_left = ax.tricontourf(triang, u_plot)\n",
    "fig.colorbar(contour_left,label='$u_x$ / [m]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac2a681-f640-45a0-9d5b-c4e50f52a4d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}