element-type.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from dynasaur.plugins.data_visualization_controller import DataVisualizationController\n",
    "from dynasaur.plugins.criteria_controller import CriteriaController\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[READ DYNASAUR DEF]\tread C:\\FE_Simulations\\Elemnt-type\\Auxilaries\\Calculation_procedures.def\n",
      "[READ DYNASAUR DEF]\tdone\n",
      "[READ DYNASAUR DEF]\tread C:\\FE_Simulations\\Elemnt-type\\Auxilaries\\Objects.def\n",
      "[READ DYNASAUR DEF]\tdone\n",
      "[READ BINOUT]\tread nodout data ...\n",
      "[READ BINOUT]\tdone reading nodout!\n",
      "[READ BINOUT]\tread glstat data ...\n",
      "[READ BINOUT]\tdone reading glstat!\n"
     ]
    }
   ],
   "source": [
    "#Auxilaries - where are your definition files located?\n",
    "input_dir = os.path.join(os.getcwd(),\"Auxilaries\")\n",
    "path_to_def = os.path.join(input_dir, \"Calculation_procedures.def\")\n",
    "path_to_def_id = os.path.join(input_dir, \"Objects.def\")\n",
    "\n",
    "#Simulation Results - which simulation do you want to analyse?\n",
    "path_to_data = os.path.join(os.getcwd(),\"01_Shell/3IP\")\n",
    "outputfiles = os.path.join(path_to_data,\"binout*\")\n",
    "\n",
    "data_vis_controller = DataVisualizationController(calculation_procedure_def_file=path_to_def,\n",
    "                                                  object_def_file=path_to_def_id,\n",
    "                                                  data_source=outputfiles)\n",
    "\n",
    "#Output Directory - where should resuls from analysis be stored?\n",
    "output_dir = os.path.join(path_to_data,\"Output\")\n",
    "if not os.path.exists(output_dir):\n",
    "    os.mkdir(output_dir)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load reference data\n",
    "path_to_1IP_data=os.path.join(os.getcwd(),\"01_Shell/1IP\")\n",
    "path_to_1IP_data=os.path.join(os.getcwd(),\"01_Shell/3IP\")\n",
    "#Reference_data = np.genfromtxt(os.path.join(path_to_ref_data,\"EN_AW-5083_stress_strain_curves_converted.csv\"),delimiter=';')\n",
    "#Reference_data = np.genfromtxt(os.path.join(path_to_ref_data,\"EN AW-5083_stress_strain_curves_raw_data.xlsx\"),delimiter=';')\n",
    "#strain_ref=Reference_data[:,0]\n",
    "#stress_ref=Reference_data[:,1] \n",
    "\n",
    "#print ()\n",
    "#print ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CALCULATION]\tCalculating Visualization: SHEET_Disp_z\n",
      "[DATA]\tread id 6 from channel name: z_displacement\n",
      "[CALCULATION]\tCalculating Visualization: MODEL_Timestep\n",
      "[DATA]\tENERGY_GLOBAL read from channel name: time_step\n",
      "[CALCULATION]\tCalculating Visualization: MODEL_Internal_Energy\n",
      "[DATA]\tENERGY_GLOBAL read from channel name: internal_energy\n",
      "[CALCULATION]\tCalculating Visualization: MODEL_Kinetic_Energy\n",
      "[DATA]\tENERGY_GLOBAL read from channel name: kinetic_energy\n",
      "[CALCULATION]\tCalculating Visualization: MODEL_Total_Energy\n",
      "[DATA]\tENERGY_GLOBAL read from channel name: total_energy\n"
     ]
    }
   ],
   "source": [
    "commands = [\n",
    "            {'visualization': \"SHEET_Disp_z\", 'x_label': 'Time[ms]', 'y_label': 'Displacement [mm]'},\n",
    "            {'visualization': \"MODEL_Timestep\", 'x_label': 'Time[ms]', 'y_label': 'timestep [ms]'},\n",
    "            {'visualization': \"MODEL_Internal_Energy\", 'x_label': 'Time[ms]', 'y_label': 'internal energy []'},\n",
    "            {'visualization': \"MODEL_Kinetic_Energy\", 'x_label': 'Time[ms]', 'y_label': 'hourglass energy []'},\n",
    "            {'visualization': \"MODEL_Total_Energy\", 'x_label': 'Time[ms]', 'y_label': 'total energy []'},\n",
    "            ]\n",
    "for command in commands:\n",
    "    data_vis_controller.calculate(command)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Time = data_vis_controller.get_data('SHEET', 'Disp_z')['X']\n",
    "x_label = data_vis_controller.get_data('SHEET', 'Disp_z')['x_name']\n",
    "\n",
    "Displacement = data_vis_controller.get_data('SHEET', 'Disp_z')['Y']\n",
    "y_label = data_vis_controller.get_data('SHEET', 'Disp_z')['y_name']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(Time, Displacement)\n",
    "#plt.plot(strain_ref, stress_ref)\n",
    "plt.xlabel(x_label)\n",
    "plt.ylabel(y_label)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = data_vis_controller.get_data(\"MODEL\",\"Timestep\")[\"X\"]\n",
    "Timestep = data_vis_controller.get_data(\"MODEL\",\"Timestep\")[\"Y\"]\n",
    "\n",
    "\n",
    "fig5, ax = plt.subplots(figsize=(10,5))# a figure with a single Axes\n",
    "ax.plot(time, Timestep, label=\"Timestep\")\n",
    "\n",
    "\n",
    "ax.set(xlabel='Time [ms]',ylabel='Timestep [ms]', title='Timestep', xlim=0)\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = data_vis_controller.get_data(\"MODEL\",\"Internal_Energy\")[\"X\"]\n",
    "#hourglass_energy = data_vis_controller.get_data(\"MODEL\",\"Hourglass_Energy\")[\"Y\"]\n",
    "internal_energy = data_vis_controller.get_data(\"MODEL\",\"Internal_Energy\")[\"Y\"]\n",
    "kinetic_energy=data_vis_controller.get_data(\"MODEL\",\"Kinetic_Energy\")[\"Y\"]\n",
    "total_energy=data_vis_controller.get_data(\"MODEL\",\"Total_Energy\")[\"Y\"]\n",
    "#sliding_interface_energy=data_vis_controller.get_data(\"MODEL\",\"Sliding_Interface_Energy\")[\"Y\"]\n",
    "#system_damping_energy=data_vis_controller.get_data(\"MODEL\",\"System_Damping_Energy\")[\"Y\"]\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(10,5))# a figure with a single Axes\n",
    "ax.plot(time, internal_energy, label=\"Internal Energy\")\n",
    "#ax.plot(time, hourglass_energy, label=\"Hourglass Energy\")\n",
    "ax.plot(time, kinetic_energy, label=\"Kinetic Energy\")\n",
    "ax.plot(time, total_energy, label=\"Total Energy\")\n",
    "ax.set(xlabel='Time [ms]',ylabel='Energy [J]', title='Energy Balance', xlim=0)\n",
    "ax.grid()\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[READ DYNASAUR DEF]\tread C:\\FE_Simulations\\Elemnt-type\\Auxilaries\\Calculation_procedures.def\n",
      "[READ DYNASAUR DEF]\tdone\n",
      "[READ DYNASAUR DEF]\tread C:\\FE_Simulations\\Elemnt-type\\Auxilaries\\Objects.def\n",
      "[READ DYNASAUR DEF]\tdone\n",
      "[READ BINOUT]\tread nodout data ...\n",
      "[READ BINOUT]\tdone reading nodout!\n",
      "[CALCULATION]\tCalculating Criteria: SHEET_Disp_Min\n",
      "[DATA]\tread id 6 from channel name: z_displacement\n"
     ]
    }
   ],
   "source": [
    "crit_controller = CriteriaController(calculation_procedure_def_file=path_to_def, \n",
    "                                     object_def_file=path_to_def_id,\n",
    "                                     data_source=outputfiles)\n",
    "\n",
    "commands = [\n",
    "#    {'criteria': 'SHEET_Disp_Max'},\n",
    "            {'criteria': 'SHEET_Disp_Min'}\n",
    "           ]\n",
    "           \n",
    "\n",
    "for command in commands:\n",
    "    crit_controller.calculate(command)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "d = crit_controller.get_data()\n",
    "col_labels = [\"Name\", \"Value\", \"Units\",\"Elform\",\"IP\"]\n",
    "criteria_table =  []\n",
    "\n",
    "for part_of in d.keys():\n",
    "    for crit_type in d[part_of].keys():\n",
    "        for criteria in d[part_of][crit_type].keys():\n",
    "            criteria_table.append([criteria, d[part_of][crit_type][criteria][\"Value\"]])\n",
    "\n",
    "units = ['[mm]','[mm]']\n",
    "for criteria, unit in zip(criteria_table, units):\n",
    "    criteria.append(unit)\n",
    " \n",
    "\n",
    "fig6, ax = plt.subplots()\n",
    "\n",
    "ax.axis('off')\n",
    "bbox = [0, 0, 3, 1]\n",
    "mpl_table = ax.table(cellText = criteria_table, bbox = bbox, colLabels = col_labels, cellLoc='center')\n",
    "mpl_table.auto_set_font_size(False)\n",
    "mpl_table.set_fontsize(13)\n",
    "mpl_table[0,0].get_text().set_weight('bold')\n",
    "mpl_table[0,1].get_text().set_weight('bold')\n",
    "mpl_table[0,2].get_text().set_weight('bold')           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CALCULATION]\tDATA VISUALIZATION writing csv to C:\\FE_Simulations\\Elemnt-type\\01_Shell/3IP\\Output\n",
      "[CALCULATION]\tDATA VISUALIZATION done writing csv to C:\\FE_Simulations\\Elemnt-type\\01_Shell/3IP\\Output\\DATA VISUALIZATION_2021-01-18-21-36-50.csv\n",
      "[CALCULATION]\tCRITERIA writing csv to C:\\FE_Simulations\\Elemnt-type\\01_Shell/3IP\\Output\n",
      "[CALCULATION]\tCRITERIA done writing csv to C:\\FE_Simulations\\Elemnt-type\\01_Shell/3IP\\Output\\criteria_table.csv\n"
     ]
    }
   ],
   "source": [
    "#writes data from data_vis_controller to csv\n",
    "data_vis_controller.write_CSV(output_dir)\n",
    "path_to_text_out = os.path.join(output_dir, \"criteria_table.csv\")\n",
    "crit_controller.write_CSV(output_dir, \"criteria_table.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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