{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a026540b-359e-4c3f-acce-e61d9d33944e",
   "metadata": {},
   "source": [
    "\n",
    "# Simulating stress-strain curves with DAMASK\n",
    "This notebook processes the results from the demo workflow `tension_DAMASK_Al`, and generates a plot of stress-strain.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e04174c-bfc9-44a6-8412-c656fd339ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matflow as mf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pathlib import Path\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a63da62-b6c3-4c1d-ae10-ac9112251201",
   "metadata": {},
   "source": [
    "## Load and process simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04e550a1-bdb9-4839-af3f-746587ed7f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "workflow_path = Path(\"tension_DAMASK_Al_2025-05-29_115126\")\n",
    "workflow = mf.Workflow(workflow_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c5337ac3-ba6c-46ae-bfba-36bb925056b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "VE_response = workflow.tasks.simulate_VE_loading_damask.elements[0].outputs.VE_response.value\n",
    "\n",
    "stress = np.array(VE_response['phase_data']['vol_avg_equivalent_stress']['data'])\n",
    "strain = np.array(VE_response['phase_data']['vol_avg_equivalent_strain']['data'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0460d09-ad3d-4678-959b-3f9585fb6777",
   "metadata": {},
   "source": [
    "## Plot stress-strain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5ca3e8bd-a127-4f74-a7a7-30c33885eca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Stress (MPa)')"
      ]
     },
     "execution_count": 29,
     "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": [
    "plt.plot(strain, stress/1e6, color='k')\n",
    "plt.xlabel(f\"Strain [-]\")\n",
    "plt.ylabel(f\"Stress (MPa)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "damask-venv",
   "language": "python",
   "name": "damask-venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}