DAMASK_postproc_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DAMASK postproc example\n",
    "When matflow goes arwy, your huge CP simulation has finished running, but you have no simulation results in `workflow.hdf5`, do not fear!\n",
    "You shouldn't have to rerun entire simulations just because you mispelt `volume_data` as `vulome_data`!\n",
    "This notebook is intended as an example of post-processing DAMASK simualtion data directly from the DAMASK simulation `geom_load.hdf5` file.\n",
    "\n",
    "More detail about the functions and methods used can be found here:https://damask.mpie.de/documentation/processing_tools/post-processing.html\n",
    "\n",
    "First, import required libraries, including some useful functions found in this repository..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Firstly, load in the required python libraries and functions:\n",
    "# libraries and functions from the web:\n",
    "import numpy as np\n",
    "import damask\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# libraries and functions local to this repository:\n",
    "from utils import import_diff_params\n",
    "from lattice_strain import VE_phase_mask\n",
    "from lattice_strain import lattice_strain\n",
    "from plotting import plot_latticestrain\n",
    "from plotting import plot_truestrain_peakint\n",
    "from plotting import plot_lattice_strain_dist_inc\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, define the path to the workflow in which your simulation is contained.\n",
    "\n",
    "Then, add on to this path the location of the `geom_load.hdf5` file.\n",
    "\n",
    "Finally, use the `Result` function from damask on the file to save all of it's attributes to a python variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define path to completed DAMASK geom_load.hdf5 file...\n",
    "workflow_dir = \"/mnt/iusers01/jf01/y15576gb/\\\n",
    "postprocessing/flowstress_CRSS_sensitivity_study/\\\n",
    "034_Ti64_TIFUN-T4_TD_910C_VE64x_elongated_Xprism_TensionY_1mms-1_CRSSratio_100beta_200basal_200prism_400pyram_aNsl_5_bNsl_5_2023-08-25-134000/\"\n",
    "\n",
    "# use damask result file to import the result.hdf5 file into jupyter as a variable...\n",
    "geom_load_path = workflow_dir+\"task_2_simulate_volume_element_loading/geom_load.hdf5\"\n",
    "\n",
    "results = damask.Result(geom_load_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here on where postprocessing with matflow or DAMASK is different.\n",
    "\n",
    "The initially outputted `geom_load.hdf5` file doesn't contain cauchy stress or strain.\n",
    "So we need to use the damask function `add_...()` to calculate these attributes and add them to the `geom_load.hdf5` file.\n",
    "\n",
    "Next, we can save the desired attribute to a python variable using the `.get()` damask function.\n",
    "This returns a dict of the attribute for all increments and phases.\n",
    "Therefore to get volume data will be a little more tricky..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# results.add_stress_cauchy\n",
    "# results.add_strain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cauchy_stress = results.get('sigma')\n",
    "epsilon = results.get('epsilon_V^0(F)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage : 0\tStart : inc0\tStop : inc100\tStep : 100\n",
      "Stage : 1\tStart : inc100\tStop : inc5030\tStep : 100\n",
      "Total incs to post process: 51\n"
     ]
    }
   ],
   "source": [
    "# Define changes in increment timestep throughout simulation\n",
    "final_inc = int(results.increments[-1][-4:])\n",
    "starts = [0, 100]\n",
    "stops = [100, final_inc]\n",
    "steps = [100, 100]\n",
    "\n",
    "reduced_incs = []\n",
    "for stage, step in enumerate(steps):\n",
    "    print(f\"Stage : {stage}\\tStart : inc{starts[stage]}\\tStop : inc{stops[stage]}\\tStep : {step}\")\n",
    "    # reduce output increment list to that desired\n",
    "    for increment in range(starts[stage], stops[stage], step):\n",
    "        reduced_incs.append(f\"increment_{increment}\")\n",
    "print(f\"Total incs to post process: {len(new_inc_list)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first double check the total increments, loadcase, and strain rate of the model..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dictionaries we aquire in the previous step, we can extract the data and plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Axis : X\n",
      "\tphase : Ti_alpha\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n",
      "\tphase : Ti_beta\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n",
      "\n",
      "Axis : Y\n",
      "\tphase : Ti_alpha\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n",
      "\tphase : Ti_beta\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n",
      "\n",
      "Axis : Z\n",
      "\tphase : Ti_alpha\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n",
      "\tphase : Ti_beta\n",
      "\tphase_strain : (51,)\n",
      "\tphase_stress : (51,)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3900x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(1,3, figsize=(13, 4), dpi=300, facecolor='w')\n",
    "fig.suptitle('Stress-Strain')\n",
    "\n",
    "stress_strain = {}\n",
    "\n",
    "for i, axis in enumerate([\"X\", \"Y\", \"Z\"]): # loop over axes directions\n",
    "    print(f\"\\nAxis : {axis}\")\n",
    "    stress_strain[axis] = {}\n",
    "    vol_stress = np.ndarray(shape=0)\n",
    "    vol_strain = np.ndarray(shape=0)\n",
    "    \n",
    "#     # some method for averaging data over all phases for each increment.\n",
    "#     for increment, phase in epsilon.items():\n",
    "#         vol_stress = np.append(vol_stress, epsilon[increment])/len(epsilon[increment][phase]))\n",
    "    \n",
    "    for phase in results.phases:\n",
    "        print(f\"\\tphase : {phase}\")\n",
    "        phase_stress = np.ndarray(shape=0)\n",
    "        phase_strain = np.ndarray(shape=0)\n",
    "        \n",
    "        for increment in reduced_incs:\n",
    "            phase_stress = np.append(phase_stress, cauchy_stress[increment][phase][:,i,i].mean(axis=0))\n",
    "            phase_strain = np.append(phase_strain, epsilon[increment][phase][:,i,i].mean(axis=0))\n",
    "        print(f\"\\tphase_strain : {phase_strain.shape}\")\n",
    "        print(f\"\\tphase_stress : {phase_stress.shape}\")\n",
    "            \n",
    "        stress_strain[axis][f'{phase}_stress'] = phase_stress/1e6 # MPa\n",
    "        stress_strain[axis][f'{phase}_strain'] = phase_strain\n",
    "\n",
    "        if phase == \"Ti_alpha\":\n",
    "            axs[i].plot(stress_strain[axis][f'{phase}_strain'], stress_strain[axis][f'{phase}_stress'],\n",
    "                        marker='h', markersize=8, color=\"tab:blue\",\n",
    "                        label = phase, zorder=1)\n",
    "        if phase == \"Ti_beta\":\n",
    "            axs[i].plot(stress_strain[axis][f'{phase}_strain'], stress_strain[axis][f'{phase}_stress'],\n",
    "                        marker='s', markersize=8, color=\"tab:orange\",\n",
    "                        label = phase, zorder=0)\n",
    "\n",
    "#     axs[i].plot(stress_strain[axis]['strain'], abs(stress_strain[axis]['stress']),\n",
    "#                 linestyle=\"solid\", marker='None', color=\"k\",\n",
    "#                 label = \"Volume\")\n",
    "\n",
    "    axs[i].title.set_text(f\"{axis}\")\n",
    "    axs[i].set_facecolor(\"white\")\n",
    "    axs[i].set_xlabel(f\"Strain $\\epsilon_{{{str(i+1)}{str(i+1)}}}$ [-]\")\n",
    "    axs[i].set_ylabel(f\"Stress $\\sigma_{{{str(i+1)}{str(i+1)}}}$ (MPa)\")\n",
    "    axs[i].legend()\n",
    "\n",
    "# save stress_strain dict as .pkl file to avoid reloading workflow.hdf5 file:\n",
    "# pickle.dump(stress_strain, open(workflow_dir+'stress_strain.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}