damask_tensile_test_simulation.ipynb

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   "source": [
    "## <font style=\"font-family:roboto;color:#455e6c\"> Tensile Test Simulation with DAMASK </font>  "
   ]
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  {
   "cell_type": "markdown",
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    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background:#e3f2fd; padding: 10px\">\n",
    "<font style=\"font-family:roboto;color:#455e6c\"> <b> StahlDigital Tutorial: Creating and Running Simulations for Steel Development </b> </font> </br>\n",
    "<font style=\"font-family:roboto;color:#455e6c\"> 25 April 2024 </font>\n",
    "</div>"
   ]
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  {
   "cell_type": "markdown",
   "id": "31110557-6779-45d5-87a8-b34ca72bcc04",
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   "source": [
    "In this notebook, we will use `pyiron` to setup and run a workflow to calculate the stress-strain curve of a material with the continuum code [DAMASK](https://damask.mpie.de/release/). A damask simulation requires material specific information (`Elastic` and `Plastic` parameters of the material). We will also show, how to calculate the elastic parameters within pyiron with `Lammps` and use them to run `Damask` simulation."
   ]
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  {
   "cell_type": "markdown",
   "id": "357266ff-e392-41d9-9015-b4979755f8ca",
   "metadata": {},
   "source": [
    "### <font style=\"font-family:roboto;color:#455e6c\"> Import necessary libraries </font>  "
   ]
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from pyiron import Project\n",
    "from damask import Rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c43d2fb-d3ef-4b64-9410-3286753df259",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unit conversion factors: from megapascal(MPa) and gegapascal(GPa) to pascal(Pa)  \n",
    "MPa_to_Pa = 1e+6\n",
    "GPa_to_Pa = 1e+9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb608e83-9007-4e1a-a3eb-09bd2d7ae73f",
   "metadata": {},
   "source": [
    "#### <font style=\"font-family:roboto;color:#455e6c\"> Create a pyiron project </font>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f484be4-445c-4708-a72f-7b6691e518bb",
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   "outputs": [],
   "source": [
    "pr = Project(\"damask_tensile_simulation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b776af78-2bfd-431f-8948-cf28e302f164",
   "metadata": {},
   "source": [
    "### <font style=\"font-family:roboto;color:#455e6c\"> Running a continuum tensile test calculation (with DAMASK) </font>\n",
    "\n",
    "We can set up a simulation \"job\" of any continuum simulation that is intergrated within pyiron. In this section, we are going to use the continuum code [DAMASK](https://damask.mpie.de/release/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15853165-fc06-4324-b7c2-a155482efc7d",
   "metadata": {},
   "source": [
    "Let's start by creating a damask job. For tensile test simulation, we need to use the pyiron class `DAMASK`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d54c662-3745-49a9-a2f5-f63f5f8030f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a damask job\n",
    "job_damask = pr.create.job.DAMASK(\"damask_simulation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e374dd-d8a5-4326-90c1-8592723eb4c9",
   "metadata": {},
   "source": [
    "A DAMASK simulation requires two sets of configuration files: \n",
    "1. Generic files for all solvers in `YAML` style. A mandatory materialpoint configuration file (`material.yaml`) and a optional numerics configuration file (`numerics.yaml`)\n",
    "2. Solver-specific files for geometry and load case.\n",
    "\n",
    "More information are available [here](https://damask.mpie.de/release/documentation/index.html)\n",
    "\n",
    "Once, we pass the required parameter values to `pyiron` and generation of damask specific input files is handeled by `pyiron` internally when we call the `run()` method on our job. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7707c9c-5dab-4c33-b82e-20373cdd088c",
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   "source": [
    "# Elastic paramaters of the material\n",
    "elasticity = pr.continuum.damask.Elasticity(type = 'Hooke',\n",
    "                                            C_11 = 226000000000.0,\n",
    "                                            C_12 = 140000000000.0,\n",
    "                                            C_44 = 116000000000.0\n",
    "                                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b91a652-f6ce-47cf-92f9-d27fb64ec3e3",
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   "source": [
    "# Plastic parameters of the material\n",
    "plasticity = pr.continuum.damask.Plasticity(type = 'phenopowerlaw',\n",
    "                                            references = [\"https://doi.org/10.1016/j.actamat.2014.07.071\",\n",
    "                                                          \"https://doi.org/10.1007/BF02900224\"],\n",
    "                                            output = [\"xi_sl\", \"gamma_sl\"],\n",
    "                                            N_sl = [12, 12],\n",
    "                                            dot_gamma_0_sl = [0.001, 0.001],\n",
    "                                            n_sl = [20, 20],\n",
    "                                            a_sl = [3.3838750211834414, 2.71513775941709],\n",
    "                                            xi_0_sl = [58854884.55400854, 116398683.46869445],\n",
    "                                            xi_inf_sl = [245397260.8849832, 289183253.8512693],\n",
    "                                            h_0_sl_sl = [839702224.3104236, 797832430.2007155],\n",
    "                                            h_sl_sl = [1, 1.4, 1, 1.4, 1.4, 1.4, 1.4, 1.4,\n",
    "                                                       1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,\n",
    "                                                       1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4]\n",
    "                                            )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0455d697-04eb-4bd7-bb1e-8af90aac2ae9",
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   "source": [
    "# Define phase of the material\n",
    "phase = pr.continuum.damask.Phase(composition ='DX56D',\n",
    "                                  lattice = 'cI',\n",
    "                                  output_list = ['F', 'P'],\n",
    "                                  elasticity = elasticity, \n",
    "                                  plasticity = plasticity\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31322d37-e28a-4111-b81b-c31b79c1b8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define homogenization\n",
    "homogenization = pr.continuum.damask.Homogenization(method='SX',\n",
    "                                                    parameters={'N_constituents': 1,\n",
    "                                                                \"mechanical\": {\"type\": \"pass\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2040cbbc-be5e-4cd8-8aba-eba59142b468",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defines the number of grains and grids\n",
    "grains = 60\n",
    "grids = 16 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f1923bb-6ee3-4af6-8d8d-a8c9134617ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define homogenization\n",
    "rotation = pr.continuum.damask.Rotation(Rotation.from_random,\n",
    "                                        grains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ceb379cf-fed5-4f75-97bb-f4403b30d400",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Materialpoint configuration\n",
    "material = pr.continuum.damask.Material([rotation],\n",
    "                                        ['DX56D'],\n",
    "                                        phase,\n",
    "                                        homogenization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a7ea72f-7685-4937-b0fb-d52d8f57a5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define grid\n",
    "grid = pr.continuum.damask.Grid.via_voronoi_tessellation(spatial_discretization = grids,\n",
    "                                                         num_grains = grains,\n",
    "                                                         box_size = 1.6e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8f30b8c-ad73-4a3d-9c99-c2700c0bb9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the material and grid to the damask job\n",
    "job_damask.material = material\n",
    "job_damask.grid = grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "666b027a-9993-4676-be3e-5b15e1621bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'mechanical': 'spectral_basic'},\n",
       " {'mechanical': 'spectral_polarization'},\n",
       " {'mechanical': 'FEM'}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List solvers available fo this job\n",
    "job_damask.list_solvers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17f60e4d-4bec-42ba-8429-8190bbc44230",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We choose the first solver\n",
    "solver = job_damask.list_solvers()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7379b818-4197-4623-93d5-4f545a390d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a load case for the grid solver\n",
    "load_step =[{'mech_bc_dict' : \n",
    "             {'dot_F' : [1e-3, 0, 0,\n",
    "                         0, 'x', 0,\n",
    "                         0, 0, 'x'],\n",
    "              'P' : ['x', 'x', 'x',\n",
    "                     'x', 0, 'x',\n",
    "                     'x', 'x', 0]\n",
    "              },\n",
    "             'discretization' : {'t' : 10., 'N' : 40},\n",
    "             'additional': {'f_out' : 4}\n",
    "             },\n",
    "            {'mech_bc_dict' : \n",
    "             {'dot_F' : [1e-3, 0, 0,\n",
    "                         0, 'x', 0,\n",
    "                         0, 0, 'x'],\n",
    "              'P':['x', 'x', 'x',\n",
    "                   'x', 0, 'x',\n",
    "                   'x', 'x', 0]\n",
    "             },\n",
    "             'discretization' : {'t' : 50., 'N' : 50}, # Use t=250. and N=250\n",
    "             'additional': {'f_out' : 4}\n",
    "             }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fc39251-aec0-448a-b6da-1909f6160df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign solver and load step to the damask job\n",
    "job_damask.loading = pr.continuum.damask.Loading(solver=solver,\n",
    "                                                 load_steps=load_step\n",
    "                                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85176a1b-0245-420e-bc3c-8df4b7683d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job damask_simulation was saved and received the ID: 27\n",
      "CPU times: user 648 ms, sys: 87.3 ms, total: 735 ms\n",
      "Wall time: 3min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Run the simulation. It might take 3 to 10 minutes.\n",
    "job_damask.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d691c5d-10df-465d-8270-294f07f0da64",
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
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       "      <td>27</td>\n",
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       "      <td>/damask_simulation</td>\n",
       "      <td>/home/jovyan/</td>\n",
       "      <td>00_pyiron_damask_tutorial/damask_tensile_simulation/</td>\n",
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       "   id    status chemicalformula                job              subjob  \\\n",
       "0  27  finished            None  damask_simulation  /damask_simulation   \n",
       "\n",
       "     projectpath                                               project  \\\n",
       "0  /home/jovyan/  00_pyiron_damask_tutorial/damask_tensile_simulation/   \n",
       "\n",
       "                   timestart                   timestop  totalcputime  \\\n",
       "0 2024-04-27 13:25:20.682144 2024-04-27 13:29:14.344912         233.0   \n",
       "\n",
       "                                              computer hamilton hamversion  \\\n",
       "0  pyiron@jupyter-pyiron-2ddsms-5fexample-2dnwvhe75z#1   DAMASK        0.4   \n",
       "\n",
       "  parentid masterid  \n",
       "0     None     None  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a quick overview of jobs within this project\n",
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65ae9a03-d4cf-4eb8-88f0-154105ca4ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
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   "execution_count": 21,
   "id": "8e7b0ce0-18e8-4b3c-a5bc-d3c37b9200be",
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Fodu3b2PJkiXYtGkTcnNzIYSotbxcLsfJkydx6tQpfPPNN+jTpw9mzJiBiRMn8n88RERGUlquwNmk+zhxqzL4XEjOgaL2f74BAN5ONujb6q/p7C090NydO7OT+dEqCD148ADz58/HsmXLUFZWBgBo06YNBg4ciN69e6N79+7w9PSEu7s77OzskJ2djezsbCQkJODUqVM4deoUoqKiEBMTg5MnT+Kbb77BN998gxEjRhj0wxERNUUKhUB8eh6OXsvAnisW+PDsYa1Wb3a0sUKflh7o39oD/dt4opWXI4MPmT2tglDr1q3x4MEDeHp64qWXXsLLL7+MkJAQjeW9vb3h7e2N9u3bY+TIkQCA/Px8bN++HRs3bkRkZCRGjRqFJUuW4O2339bPJyEiasKSs4tw7K9HXTG37iO7sOyvdywAqA9BMksJ3Zu7oX9rT/Rr7YmuzVxgxb26qInRKghZWFhg0aJFeOutt+Dg4KDTjZycnDB16lRMnToVp0+fxty5c5GTk6PTtYiImrrswjKcuJWF4zcrx/okZRdpdV4HP2f0b+2Bfq090SvYnbuzU5On1f8CEhMTdQ5A6vTq1Qv79+9HYaF2A/SIiJq64rIKnEnMVg5wvnw3T6vz3G0EhnZuhgFtvdG3lQc8HW0MXFOixkWrIKTPEKTrdRctWoSdO3fi6tWrsLOzQ9++ffH111+jXbt2Gs+JjIzEkCFDahyPj49H+/btdaozEZExKBQCf97NxdHrmTh+8z7O3XmAsopHj/NxtZehb6vKHp/eLVzxZ0wkRo/uBJlMZoRaEzU+jaZPNCoqCm+++SZ69uyJ8vJyfPLJJwgPD8eVK1ceGaiuXbsGZ2dn5WsvLy9DV5eIqM4y8ksQfT0LR29kIvpGVrVxPprZWFmgV7A7+rbyRP/Wnujo7wxLi8oBznK5HJc51pmoVo0mCB04cEDl9dq1a+Ht7Y1z585h4MCBtZ7r7e0NV1dXA9aOiKjuysoVOHfnAY7eyETUtUxcSXv04y5JAh4LcEG/1pXBp0eQG2xllkaoLZF50ksQSklJwYkTJ5CSkoLCwsJa1xf6/PPP9XFL5ObmAgDc3d0fWbZ79+4oKSlBx44d8emnn6p9XEZEZAxJ94sQ9VfwibmVpdVO7cGeDujX2gP9W3uiT0sPuNpbG6GmRE1DvYJQVlYW3njjDezevfuRiysKISBJkl6CkBACM2fORP/+/dG5c2eN5fz8/LBixQqEhISgtLQUGzduxNChQxEZGam2F6m0tBSlpX8vLZ+XV/n/zuRyOeRyeb3rXV3V9fR9XXPAtlGP7aJZQ26borJynEp4gOib9xF9IwuJ9x89u8vBxhJ9W3pgQBsPDGjtiWZuqlsVafs5G3K7mBrbRjNzaJu61F0Sj0owGhQWFqJ3796Ij4+HtbU1unbtitOnT8Pa2hq9evVCeno6bt68CaCy16Zqe40jR47ocjsVb775Jvbt24djx46hWbNmdTp3zJgxkCQJe/furfHe3LlzMW/evBrHN2/eDHt77qFDRI8mBJBWBFzNlRCfI+FWnoQK8eiBOs0cBDq4CrR3VSDYEeByPkS6KyoqwsSJE5Gbm6syRlgdnXuEli5diitXrqB9+/b4448/4OfnBwsLC7i7u+Po0aMAKjdj/fDDD7F9+3Y88cQT+PDDD3W9ndLbb7+NvXv34ujRo3UOQQDQp08fbNq0Se17c+bMwcyZM5Wv8/LyEBgYiPDw8Ec2ZF3J5XJERERg+PDhnM3xELaNemwXzUzdNjlFcpy4dR9Hb2bh2I37uKfFpqXuDjL0b+WJgW080K+1Yaa1m7pdGjK2jWbm0DZVT3S0oXMQ2rVrFyRJwqJFi+Dn56e2TFBQELZs2YKJEyfi448/RmhoKIYOHarT/YQQePvtt7Fr1y5ERkYiODhYp+vExsZqrK+NjQ1sbGr+YySTyQz2x2DIazd2bBv12C6aGattKhQCF1JycPR6JqKuZ2q1d5elhYSQ5m4Y1M4LA9t4oZO/MywsjDOli38zmrFtNGvMbVOXeuschK5evQoAeOKJJ1SOq3sut3DhQmzZsgU//PCDzkHozTffxObNm7Fnzx44OTkhPT0dAODi4gI7u8rn53PmzEFqaio2bNgAAFiyZAlatGiBTp06oaysDJs2bcKOHTuwY8cOnepARE3XvbwSRF3PxNHrmTh2Mws5RY8egxDgaoeBbb0wqK0X+rb2gLNt4/xSITJnOgehkpISuLq6qvSg2NraoqCgoEbZ4OBguLi44PTp07reDsuWLQMADB48WOX42rVr8corrwAA0tLSkJSUpHyvrKwMs2bNQmpqKuzs7NCpUyfs27cPo0aN0rkeRNQ0lFcoEJucgz/iMxB5LQNX0/MfeY6NlQX6tPRQhp9WXg7ctJSogdM5CPn4+CA1NRUKhQIWFpWj+ry8vJCSkoKUlBSV8TsVFRUoLCxEUZF2e+Goo82Y7nXr1qm8nj17NmbPnq3zPYmoackpKkPU9UwcvpqBqOuZWvX6tPF2VAafXsHuXNOHqJHROQgFBQUhOTkZd+/eVYaebt26ISUlBbt27VLZVX7v3r0oLy9HQEBA/WtMRKQnQgjczCjAH1czcDg+A+eSHqDiEYN9nGyt0L+1Jwa19cLAtl7wd7WrtTwRNWw6B6GhQ4fi2LFjOHz4MCZPngwAmDBhAn799VfMmTMHJSUl6NatGy5cuIAFCxZAkiSMHDlSbxUnItJFibwCpxKycTj+Hg5fy0BydvEjz+kc4Iwh7bwxqK0XugW6wopz24nMhs5BaMyYMZg7dy7+85//KIPQiy++iFWrViEyMhIfffSRsqwQAr6+vpg7d269K0xEVFf38kpw5GoGDl/NwLGbWSh6xGrO9taW6NfaE0Pbe2NIe2/4ONsaqaZEZGw6B6Hu3btDoVDdCVmSJOzbtw8LFizA1q1bkZycDBcXFzzxxBNYsGAB/P39611hIqJHUSgELqXm4vBf4edSau4jz2nmZoeh7b3xeAcf9OZYH6ImQ++brtrZ2WHhwoVYuHChvi9NRKRRSQVw6Mo9RN24j8NXM5FVUPuihhYSEBrkjsc7eGNoe2+09nbkDC+iJqjOQSgzMxPr1q3DmTNnkJeXB3d3d/Tp0wdTpkyBi4uLIepIRKTWnfuFOHw1A79fuYeTty1RcfpCreVd7GQY3M4Lj7evHO/DzUuJqE5B6LfffsMLL7xQY62grVu3YsGCBdizZw/CwsL0WkEioiryCgXO3XmAw1cz8Ef8PdzKLKz2rvrenLY+jni8vQ8eb++NHs050JmIVGkdhJKTkzFhwgRlCLKwsICHhweysrIghEBWVhaeeeYZXLlyBW5ubgarMBE1LbnFchy5moHf4+8h6nom8kvKay1vbWmBsFYeGNrBG0PaeSPQnRsmE5FmWgehH3/8EQUFBXB2dsZ3332Hl156CTY2NigqKsJPP/2Ezz77DBkZGVi7dq3KxqVERHWVnluCiPh7OHQ5HTG37qP8EWv7eDvZoJVdMSYP64GB7XzgYKP34Y9EZKa0/tfi999/hyRJ+PbbbzFt2jTlcXt7e8yaNQuFhYWYN28efv/9dwYhIqqzmxkFOHQlHYcu30Nccs4jy3cNdMXj7bwxtIM32nrZ4bfffsOwDt6QyRiCiEh7Wv+Lcfv2bQDAxIkT1b7/8ssvY968ecpyRES1USgELqbm4uDldBy6nP7QeJ+aHKwtMaCNFx7v4I3B7bzg7fT32j7qNnsmItKG1kEoLy8Pnp6ecHBwUPt+cHAwAKjddJWICKgc7Hzy9n0cunwPEVfuIT2vpNbyno7WGNbBByM6+SKslQfX9iEivdM6CAkhYGmp+R+hqo1XH15kkYiatsLSchy9nomDl9Pxx9WMRw52bu5ujxGdKsNP9+ZusLTg2j5EZDh8mE5Eene/oBR/xGfg0JV0HL2RhbLy2v8PUucAZ4R39EV4Jx+083HiwoZEZDR1CkIFBQWYP39+vcp8/vnndbklETUSydlFleN9rtzD2cRs1DbRy0ICegW7Y0QnXwzv6INmbpziTkSmUacgVDUzTBNJkh5ZhkGIyDwIIRCflo9DV9Jx8PI9xKfl1VrexsoCA9t6IbyjD4Z28IG7A1d1JiLTq1MQEqL2tTyIyLxVKATO3XnwV89POpKzi2st72Inw9AO3gjv6IuBbT1hb82n8UTUsGj9rxIHQRM1TfIKBU7cuo/9F9Pwe/w93C8sq7W8v4stwjv5IryjD3oGu0PGLS2IqAHj/z0johrkFQrE3LqP/ZfScOByOnKKal+np62PI8I7+mJEJ190DnDmYGciajQYhIgIAFBeocDJ29nYd+kuDvyZjge1hB9JAno0d0N4Rx+Ed/JFsKf69cWIiBo6BiGiJqy8QoFTCdn478U0HLycjuxaHntZWUjo29oTT3TyxbCO3iorOxMRNVZaBaEzZ86gZ8+eer1xcXExEhMT0aFDB71el4hqV6EQOHX7PvZdSsOBP9NrHfNjaSGhX2tPPNnFD+GdfOBqz5leRGRetApCvXv3xpNPPom5c+eiR48e9bphcXExli5disWLF2PGjBmcTk9kBBUKgdMJfz/2yiqoPfz0beWBJx/zQ3hHX7hxmjsRmTGtglC/fv3w3//+F/v27UPPnj3x8ssvY8KECfDy8tLqJkIIHD58GP/5z3+wc+dO5Ofnw8HBAd26datP3YmoFhUKgTOJ2dh3MQ2//ZmOrIJSjWUtJKBvK0+MfswPIzr5co0fImoytApC0dHR2LFjBz766COcPn0aZ86cwXvvvYe2bduiV69e6Nq1K7y8vODu7g4bGxs8ePAA2dnZuH37Nk6fPo2zZ8+isLBQuV/Za6+9hvnz58Pb29vQn4+oSalQCJxNzMb+S2nY/2c6MvNrDz9hrTwwuos/RnTygYejjRFrSkTUMGg9WHr8+PEYO3Ystm/fjuXLl+Po0aO4evUqrl27ho0bN2o8r2oRRi8vL0ydOhWvv/66cqd6Iqo/hULgXNID7LuYhv2X0pDxiPDTO9gDox/zwxOdfeHJ8ENETVydZo1ZWVnhhRdewAsvvICbN2/iwIEDOHr0KE6dOoW0tDSUl/+9q7SzszM6duyIgQMHYvDgwRg6dChkMpnePwBRU6RQCJxPeoB9lyrDz708zeFHkoDewe4Y/Zg/nujkCy8nhh8ioio6T59v3bo13nrrLbz11lvKYzk5OSgpKYGHhwdDD5GeKRQCCfnAwv1XceByBtLzSjSWlSSgZwt3PPlXzw+nuhMRqafXdYRcXV31eTkiAnAzIx+7YlOxOzYVqTlWAJLUlpMkoGeQO0Y/5oeRnX3h7czwQ0T0KFxQkagBysgvwa8X0rA7NhWXUnNrLRsa5PZX+PGDrwvDDxFRXTAIETUQhaXlOHQlHbti7+LYjUwohOayIUFuGN3FDyO7+MLPxc54lSQiMjMMQkQmVF6hwLGbWdgdm4qDl++hWF6hsWx7Xye0s8nBB88NQXNPJyPWkojIfDEIERmZEAKXUnOxKzYVv164W+sqz77Othjb3R9Pdw9AKw877N+/H358/EVEpDcMQkRGkpxdhD1xqdgVm4pbmYUayznZWGFkF1+M6x6APsEesLCQAAByuebd4ImISDcMQkQGlFNUhn2XKgc9n0l8oLGclYWEwe288XT3AAzt4A1bmaURa0lE1HQxCBHpWYm8AkeuZmBXbCqOXMuAvELzqOeQIDeM6x6A0V38uL8XEZEJMAgR6YFCIXA6MRu7Y1Ox71Ia8kvKNZYN9nTAuG4BGNfdH0EeDkasJRERPYxBiKgert+rXOxwT2wq7uZqXunZw8EaY7r6Y1z3AHRt5gJJkoxYSyIi0sRgQejSpUv4/fffYWlpiREjRqBdu3aGuhWRUWXklWDvhbvYeT4VV9LyNJazlVkgvKMvnu4egP5tPCGztDBiLYmISBs6B6HDhw9jwYIF6NOnD7788kuV97777jvMnj1bufO8hYUFvvvuO7z99tv1qy2RiZTIK3Doyj1sO5uM4zezNC52aCEB/Vp7Yly3AIzo7AtHG3a6EhE1ZDr/K71t2zZERUXhhRdeUDl+48YNfPjhh1AoFLCxsYGlpSWKiorw/vvvo3///ujevXu9K01kLNfS87HlTBJ2xaYip0jz9PWOfs54unsAnurmDx/u8UVE1GjoHIROnDgBABg5cqTK8ZUrV6KiogKDBg3Cf//7X1hbW+Oll17C9u3b8dNPP2HlypX1qzGRgRWUluO/F+5iy5lkxCXnaCzn72KLsd0DMK5bANr5cqVnIqLGSOcglJGRAUtLSzRr1kzl+IEDByBJEj7//HM4OFTOiFm0aBG2b9+Oo0eP1q+2RAYihEBscg62nk7GrxfvoqhM/VYX9taWePIxPzzToxl6tXBXLnZIRESNk85BKDs7G87OziqzX/Lz83H58mU4ODhg0KBByuOtWrWCra0tUlJS6ldbIj3LLizDrthUbD2ThOv3CjSW697cFS/0DMTox/w57oeIyIzo/C+6ra0tcnNzIYRQhqETJ05ACIHevXvDwkJ1hoydnR1KSjRPLyYyFoVC4MSt+9hyJgmHLt9DWYVCbTlXexme6d4ME3oG8tEXEZGZ0jkItW7dGnFxcYiKisLgwYMBADt37oQkSejfv79K2bKyMuTm5qJ58+b1qixRfaTlFmP72RRsPZuMlAfFGsv1b+2JCT0DEd7JBzZW3OqCiMic6RyERo8ejdjYWLz66qv48ssvkZaWhnXr1gEAnnnmGZWysbGxUCgUDEJkdPIKBf6Iz8AvZ5MReS1D47R3X2dbPBfaDM+HBiLQ3d64lSQiIpPROQjNnDkT69evR0JCAiZOnAigcsDphAkT0KVLF5Wye/bsUdtTVBeLFi3Czp07cfXqVdjZ2aFv3774+uuvH7lQY1RUFGbOnInLly/D398fs2fPxhtvvKFzPahxuJ1ZgK1nk7HjXCqyCkrVlrGykDC0gzde6NkcA9t6wZIDn4mImhydg5CrqytOnDiBL774AjExMXB1dcWTTz6J//f//p9KubKyMqxZswZCCAwZMkTnikZFReHNN99Ez549UV5ejk8++QTh4eG4cuWKcnbawxISEjBq1Ci89tpr2LRpE44fP44ZM2bAy8sL48eP17ku1DAVl1Xgtz/TsOVMMk4nZGssF+zpgAk9A/FMjwB4O3HNHyKipqxe018CAgKwatWqWstYW1sjPT29PrcBUDktv7q1a9fC29sb586dw8CBA9Wes3z5cjRv3hxLliwBAHTo0AFnz57F4sWLGYTMyJ+pudh6Jhm741I1bnZqY2WB0V38MKFnIHoFu3OvLyIiAtCIN13Nzc0FALi7u2ssExMTg/DwcJVjI0aMwOrVqyGXyyGTyQxaRzKc3GI59l64i61nkvBnqub9vjr5O+OFnoF4qlsAXOz4+yYiIlV6C0KZmZm4c+cOioqKNPbQ6IsQAjNnzkT//v3RuXNnjeXS09Ph4+OjcszHxwfl5eXIysqCn5+fynulpaUoLf17PEleXuUXrFwuh1yueXsFXVRdT9/XNQea2kYIgXNJOfjlbAp+u3wPJXL1094dbazwVFdfPB/SDJ38nWtct7Hi34xmbBv12C6asW00M4e2qUvd6x2E9u7di7lz5+LChQsAAEmSUF7+9+OJBw8e4MUXXwQA7NixQ+N4nrp46623cPHiRRw7duyRZR9+BFK1Eay6RyOLFi3CvHnzahw/dOgQ7O0NM5MoIiLCINc1B1VtU1oBnM2ScCzdAneLND/SauUk0MdHgW7u5bC2TMSduETciTNSZY2IfzOasW3UY7toxrbRrDG3TVFRkdZl6xWEvvrqK3zyySfKcKGOm5sb7O3tsWfPHuzfvx/PPfdcfW6Jt99+G3v37sXRo0drbO/xMF9f3xrjkzIyMmBlZQUPD48a5efMmYOZM2cqX+fl5SEwMBDh4eFwdnauUb4+5HI5IiIiMHz4cD6ie0hV27Tp0Q9bz6dh58W7Gsf+eDhY45nu/ni2RwBaetU/ZDdk/JvRjG2jHttFM7aNZubQNlVPdLShcxA6deoUPvnkE1hZWeGbb77BpEmT0KlTJ2RkZNQo+/LLL2P37t3Yu3evzkFICIG3334bu3btQmRkJIKDgx95TlhYGH799VeVY4cOHUJoaKjaX66NjQ1sbGxqHJfJZAb7YzDktRujCoVAVHwGfrpigWsxp9SWkSRgcFsvTOjZHEM7eENmaaG2nLni34xmbBv12C6asW00a8xtU5d66xyE/v3vfwOo7EV59913ay1bte/YmTNndL0d3nzzTWzevBl79uyBk5OTsqfHxcUFdnZ2yrqkpqZiw4YNAIA33ngDP/74I2bOnInXXnsNMTExWL16NX7++Wed60GGkVskx9azSVh/4g5Sc4oB1Aw3bvYyvNCrOSb2as5FD4mISC90DkJV43PeeuutR5b18PCAo6MjUlNTdb0dli1bBgDK7TyqrF27Fq+88goAIC0tDUlJScr3goODsX//frz//vtYunQp/P398f3333PqfANy414+1p1IxM7zqSiWq9/xvWugKyb3CcLox/xgK+OWF0REpD86B6GMjAw4OTnB09NTq/IymQwFBZp3936U2sYhVana4qO6QYMG4fz58zrfl/RPoRCIvJ6BtccTEX0jS20ZK0lgTLcAvNI3GF0DXY1bQSIiajJ0DkL29vYoKCiAQqGosdP8w/Ly8pCTkwMvLy9db0dmoLC0HNvOJmPdiUQk3lc/ot/fxRYTewXC7UE8nh/budE+nyYiosZB5yDUtm1bnDlzBhcvXkS3bt1qLbtjxw4IIdC1a1ddb0eNWHpuCdadSMTmU3eQp2H2V88WbpjaLxjhHX0gFBXYvz/eyLUkIqKmSOcgNGbMGJw+fRpfffUVtmzZorHczZs38dFHH0GSJIwbN07X21EjdPluLlZFJ+DXC3dRrmbbd2tLC4zp6o+p/Vqgc4CL8rhcoX6sEBERkb7pHITefvtt/PDDD9i2bRvs7Ozw4Ycfqrx/+/ZtbNmyBd9++y1yc3MRHByMadOm1bvC1LApFAJR1zOxMvo2Tty6r7aMp6MNJocFYWLv5vB0rLlcARERkbHoHIScnZ2xZ88ePPHEE9iwYYNyyjoAODo6ori4GEDlIGcPDw/s3LlT7Ro9ZB7kFQrsjbuL5VG3cCND/aD4dj5OeHVAMMZ284eNFWd/ERGR6dVrZenevXsjLi4Os2bNwq5du6BQVO79VLW0ddXjsH/9619aLYBIjU9xWQW2nEnCquiEv9b/qWlAG09MH9ASA9t4ctd3IiJqUOq911hQUBC2bduGBw8eICYmBnfv3kVFRQV8fX3Rt29fzhQzUzlFZdgQcwfrTiQiu7CsxvsySwljuwVg+oBgtPfV7/YkRERE+qK33efd3NwwatQofV2OGqh7eSVYefQ2Np9OQlFZzUHNjjZWeLlPEKb1awFvZ1sT1JCIiEh7egtCZN7u5ZVgWeQtbD6dhLJyRY33PR2tMa1/MF7qHQQXO679Q0REjYPOQaisrAzp6emwtraGr6+vynsFBQWYO3cuIiIiYGlpidGjR+Pjjz9W7glGjUdGXgl+qiUABbrb4R8DW+G5kGbc/oKIiBodnYPQqlWr8Pbbb2PKlClYs2aNynujR4/GsWPHlNtiXLhwAdHR0Thy5AgHyzYSGXklWBZ1C5tPJaFUTQBq7+uE/xncCqO7+MGqie3+TkRE5kPnIHTw4EEAwMSJE1WO7927F9HR0bCwsMDEiRNhZ2eHDRs2IDo6Ghs3bsTkyZPrV2MyqMz8UiyPuoVNJ+9oDEDvDWuL8I4+sLBgqCUiosZN5yAUH1+5BUJISIjK8c2bN0OSJHz44YdYuHAhAKBHjx6YMWMGNm/ezCDUQBWXVWBV9G0sj7qFQjWDoCsDUBuEd/RlACIiIrOhcxDKzMyEvb093NzcVI4fOXIEADB9+nTlsUmTJmHGjBm4cOGCrrcjA6lQCOw8n4LFh67hXl5pjffb+zrh3aFtMKITAxAREZkfnYNQYWFhjcHPiYmJyMzMRPPmzVUWUHRwcICrqyuys7N1rynp3bEbWVi4Px7xaXk13mvn44R3h7XBEwxARERkxnQOQu7u7sjMzEROTg5cXV0BAIcPHwYA9O3bt0b58vJyODo66no70qNr6flY9Fs8Iq9l1njPx9kGs8Lb4ZkezWDJAERERGZO5+k+PXr0AACsXr0aAKBQKLB69WpIkoQhQ4aolM3MzERBQUGNafZkXAWl5fh8z58Y+e+jNUKQvbUlPhjeFpGzhuC50ECGICIiahJ07hF65ZVXcODAAXz00Uf4/fffkZmZifPnz8PJyQnPPfecStno6GgAQIcOHepXW9JZ9I1MfLTjUo39wCwk4IVezfHesDbwduJK0ERE1LToHISef/55HDx4EGvXrlVOpbe1tcXy5cuVj8qqbN26VW1PERleXokcX+6Lx5YzyTXeG9LOC3NGdUBbHycT1IyIiMj06rXFxurVqzFt2jScOHECrq6uGDZsWI1d5svKyuDi4oLJkydzLzIjO3I1A3N2XkJ6XonK8WZudlj4dBcMassNcYmIqGmr915j/fr1Q79+/TS+b21tjRUrVtT3NlQHOUVlmP/fK9h5PrXGe5PDgvDhE+3hYMNt5oiIiPhtaGYir2Xg/22/iMx81TWBgjzs8c34x9C7pYeJakZERNTwMAiZkW1nk/HhjotQiL+PSRIwrV8wZoW3g501N0UlIiKqTqsgNG3aNACAn5+fctuMqmN1IUmScro96dfqYwn453+vqBxr5eWAb57tipAgNw1nERERNW1aBaF169ZBkiS0a9dOGYSqjlXtMF+bqnIMQvonhMD//n4D3/9xQ+X4i72a44sxHWErYy8QERGRJloFocmTJ0OSJPj5+dU4RqajUAjM/+8VrDuRqHL8rSGt8UF4W/5+iIiIHkHrHiFtjpHxyCsUmL39InbFqs4M+2RUB7w2sKWJakVERNS4cLB0I1Qir8Bbm2Pxe/w95TELCfjqmcfwfM9AE9aMiIiocWEQamTyS+R4bcNZnLydrTxmbWmBf7/QDSO7+NVyJhERET2MQagREULgHxvOqYQgO5klVkwOwYA2XCWaiIiorrQOQvPnz9fLDT///HO9XKcpiryWiZjb95WvnW2tsHZqL06PJyIi0pHWQWju3Ll6mYXEIKS7ZVG3lP9dZilh6+th6ODnbMIaERERNW51fjTm4eEBBwcHQ9SFanE+6QFOJ/z9SOzp7gEMQURERPVU5yBUUlKCMWPGYOrUqejfv78h6kRq/F+13iBJAv4xsJUJa0NERGQeLLQtuHXrVowYMQLFxcVYt24dBg0ahHbt2uGrr77C3bt3DVnHJu9WZgEOXfl7qvzwDj5o7e1owhoRERGZB62D0HPPPYf9+/fjzp07+Oc//4lWrVrhxo0b+OSTTxAUFITRo0dj+/btkMvlhqxvk7Qi6jaq72TyxmD2BhEREemD1kGoir+/Pz7++GNcv34dR48exeTJk2Fra4vffvsNEyZMgJ+fH959913ExsYaor5Nzr28EpXVo3sFu6NHc84SIyIi0oc6B6Hq+vfvj7Vr1yI9PR2rVq1CWFgYsrOz8cMPPyA0NBQ9evTAwYMH9VXXJmnNsQSUVSiUr/9nEHuDiIiI9KVeQaiKg4MDpk2bhmPHjuH69euYPXs2ZDIZLly4gD/++EMft2iScovl+M+pJOXr9r5OGNyOCycSERHpi15Xlr516xbWr1+Pn3/+GWVlZfq8dJP0n1N3UFBarnz9+qCW3FGeiIhIj+odhIqKirBt2zasXbsW0dHRACq3gujcuTOmTp2KSZMm1buSTVGJvAJrjiUqXwe42uHJx/xNVyEiIiIzpHMQOn78ONauXYtt27ahoKAAQgi4urrixRdfxNSpUxEaGqrPejY5O8+nIqugVPn61f7BkFnq5UkmERER/aVOQeju3bvYsGED1q5di5s3b0IIAQsLCwwdOhTTpk3D008/DRsbG0PVtcmoUAisOPr3Aoqu9jK80CvQhDUiIiIyT1oHoVGjRiEiIgIKhQJCCLRs2RJTpkzBK6+8gsBAfknr08HL6Ui8X6R8PTmsBeyt9Tqci4iIiFCHIHTgwAFIkoQWLVpgypQpGDRoECRJQkJCAhISErS+4cCBA3WqaFMhhMDyattp2Mos8ErfFqarEBERkRmrczdDYmIi5s2bp9PNJElCeXn5ows2YTG37uNiSq7y9YTQQLg7WJuwRkREROarTkFIVN/ngQzi/47eVv53SwsJ0we0NGFtiIiIzJvWQaguj78M4ejRo/j2229x7tw5pKWlYdeuXRg3bpzG8pGRkRgyZEiN4/Hx8Wjfvr0Ba6o7hULg+M0s5evRXfwQ6G5vwhoRERGZN62DUFBQkCHr8UiFhYXo2rUrpk6divHjx2t93rVr1+Ds7Kx87eXVcFdmLi1XoFzxd69b10BX01WGiIioCTD5VKT3338feXl5WL16da3lRo4ciZEjR9b5+t7e3nB1ddWxdsZVLK9QeW0nszRRTYiIiJoGk6/Qt2XLFqxbt85g1+/evTv8/PwwdOhQHDlyxGD30YcaQcja5L8eIiIis2byHiFD8fPzw4oVKxASEoLS0lJs3LgRQ4cORWRkpMYp/KWlpSgt/Xs157y8PACAXC6HXC7Xa/2qrlf9ugVFpSplrCTo/b6Ngbq2IbZLbdg26rFdNGPbaGYObVOXukvCxFPB/Pz8kJGRgYqKikcX/oskSY8cLK3OmDFjIEkS9u7dq/b9uXPnql0aYPPmzbC3N/yg5ZRC4NuLf2fT19tXoKMbZ+oRERHVRVFRESZOnIjc3FyVccLqmG2PkDp9+vTBpk2bNL4/Z84czJw5U/k6Ly8PgYGBCA8Pf2RD1pVcLkdERASGDx8OmUwGADh35wFw8YyyzIC+vdE72F2v920M1LUNsV1qw7ZRj+2iGdtGM3Nom6onOtpoUkEoNjYWfn5+Gt+3sbFRu1eaTCYz2B9D9WvLhaTynpOdTaP9I9QHQ7Z7Y8Z20Yxtox7bRTO2jWaNuW3qUu9GE4QKCgpw8+ZN5euEhATExcXB3d0dzZs3x5w5c5CamooNGzYAAJYsWYIWLVqgU6dOKCsrw6ZNm7Bjxw7s2LHDVB/hkUrkCpXXtpw1RkREZFCNJgidPXtWZYHEqkdYU6ZMwbp165CWloakpCTl+2VlZZg1axZSU1NhZ2eHTp06Yd++fRg1apTR664tTp8nIiIyrkYThAYPHlzrFh8PT8GfPXs2Zs+ebeBa6VdJmWoQsuX0eSIiIoPiN20Dwh4hIiIi42IQakBKHgpCHCNERERkWAxCDUj1HiErCwkyS/56iIiIDMnk37QmXs+xQakehPhYjIiIyPBMPlj6+++/R3Fxsamr0SBUHyxta80gREREZGgmD0LPP/+8qavQYFRfR8hWZvLOOiIiIrNX7yB069Yt/PLLL7h48SKys7Nr3ehMkiT88ccf9b2l2eKjMSIiIuOqVxCaN28eFixYAIVCodVYH0mSHlmmKWMQIiIiMi6dg9B//vMf5U7t/v7+GDFiBPz9/WFlZfKnbY1W9enznDpPRERkeDqnlqVLlwIAnnrqKfzyyy+wtrbWW6WaKgYhIiIi49J5RO6ff/4JSZLw008/MQTpCR+NERERGZfOQUiSJDg7O8Pf31+f9WnSiqtNn7fj9HkiIiKD0zkItW/fHkVFRSgtLdVnfZo01enzDEJERESGpnMQmj59OuRyObZt26bP+jRpqmOEuI4QERGRoen8bfvaa6/hqaeewjvvvIOjR4/qs05NFscIERERGZfOs8bmz5+Prl27Ijo6GkOGDEG/fv3Qu3dvODk51Xre559/rustzZoQgkGIiIjIyHQOQnPnzlUukCiEwLFjx3D8+PFHnscgpF5ZhQLV16TkYGkiIiLD0zkIDRw4kCtF61FJmULltQ17hIiIiAxO5yAUGRmpx2pQ9cdiAB+NERERGQOnJjUQDEJERETGxyDUQJQ8HISs+ashIiIyNL3skFpWVoaIiAicPXsWGRkZkCQJXl5e6NmzJ4YNG8YtOLTwcI+QrRV7hIiIiAyt3kFoxYoV+Oyzz5CVlaX2fU9PTyxYsACvvfZafW9l1krKHgpCnDVGRERkcPUKQh9++CEWL14M8de874CAADRr1gwAkJKSgtTUVGRmZuKNN97ArVu38NVXX9W/xmaKY4SIiIiMT+eBKFFRUfj2228hhMD48eNx5coVJCcnIyYmBjExMUhOTkZ8fDyeffZZCCHw7bffIjo6Wp91NyvV9xkDGISIiIiMQecgtHTpUgDAq6++im3btqF9+/Y1yrRr1w6//PILXn31VQgh8OOPP+peUzNXY4wQgxAREZHB6RyETpw4AQsLCyxcuPCRZRcsWABJkrRaebqp4qMxIiIi49M5CGVlZcHFxQXe3t6PLOvj4wNXV1eNA6pJ3WBpTp8nIiIyNJ2/bZ2cnJCfn4+SkpJHli0uLkZ+fj4cHR11vZ3Zq76OkCQB1pYMQkRERIam87ftY489hoqKCqxZs+aRZdesWYPy8nJ07dpV19uZvYd3nuc+bkRERIancxB66aWXIITABx98gNWrV2sst2rVKnzwwQeQJAmTJk3S9XZm7+EgRERERIan8zpCr7zyCjZu3IioqCj84x//wPz58zFkyBAEBARAkiQkJyfjyJEjSE1NhRACgwcPxpQpU/RZd7NS/dEYZ4wREREZh85ByMLCAnv27MG0adOwc+dOJCcnY+PGjSplqhZaHD9+PFavXs3HPbWovo6QrYzjg4iIiIyhXitLOzs7Y/v27Thz5gy2bNmi3GsMALy9vREaGooXXngBPXv21EtlzVlxtVljdtxeg4iIyCj0sulqz549GXbqiWOEiIiIjI/PYBqIYo4RIiIiMjqdg1Dbtm2xYMECJCYm6rE6TVcpgxAREZHR6RyEbt68iS+++AKtWrXC4MGDsXr1auTl5emzbk0KH40REREZn85B6JNPPkGLFi0ghMDRo0fxj3/8A76+vnjxxRexf/9+KBSKR1+ElBiEiIiIjE/nIPTPf/4Tt27dQnR0NF577TW4uLigpKQEv/zyC8aMGQN/f3/MnDkTsbGx+qyv2ao+fZ6zxoiIiIyj3oOl+/Xrh//7v/9Deno6tm/fjjFjxsDKygoZGRn497//jdDQUHTu3Bnffvst7t69q486m6Xqm67acB0hIiIio9DbN661tTWeeeYZ7N69G2lpafjxxx/Rq1cvCCFw5coVfPTRR2jRooW+bmd2+GiMiIjI+AzS9eDu7o4ZM2YgJiYGly9fRmhoKIQQqKioePTJTZC8QoFyhVC+ZhAiIiIyDr0sqKjO6dOnsXHjRmzduhX379831G3MQvV9xgCOESIiIjIWvQahO3fuYNOmTdi4cSNu3LgBoHK/MWtrazz55JOYPHmyPm9nNoofCkK2VgxCRERExlDvIJSXl4dffvkFGzduxPHjxyGEUG62GhYWhsmTJ2PChAlwdXWt763MVkmZ6lIDtuwRIiIiMgqdg9B///tfbNy4Eb/++itKS0uV4Sc4OBgvv/wyJk+ejFatWumtoubs4R4hjhEiIiIyDp0HSz/11FPYvn07SkpK4OzsjOnTp+Po0aO4desW5s2bp/cQdPToUeX6RJIkYffu3Y88JyoqCiEhIbC1tUXLli2xfPlyvdZJX2qMEWIQIiIiMgqdg5ClpSVGjRqFrVu3Ij09HStWrED//v31WTcVhYWF6Nq1K3788UetyickJGDUqFEYMGAAYmNj8fHHH+Odd97Bjh07DFZHXdUYI8R1hIiIiIxC50djd+/ehZeXV70rcPLkSZSVlWHgwIG1lhs5ciRGjhyp9XWXL1+O5s2bY8mSJQCADh064OzZs1i8eDHGjx9fnyrrXc0gxB4hIiIiY9C560EfIQgAnn76aTz++ON6uVZ1MTExCA8PVzk2YsQInD17FnK5XO/3q4/qq0oDnD5PRERkLAZbR6guqgZa61N6ejp8fHxUjvn4+KC8vBxZWVnw8/OrcU5paSlKS0uVr/Py8gAAcrlc7+Gp6npyuRyFJWUq78kk0eDCmjFVbxv6G9tFM7aNemwXzdg2mplD29Sl7g0iCBmKJEkqr6sC18PHqyxatAjz5s2rcfzQoUOwt7fXfwUBRERE4Mw9CcDfvUDRkYfhKDPI7RqViIgIU1ehQWK7aMa2UY/tohnbRrPG3DZFRUValzXbIOTr64v09HSVYxkZGbCysoKHh4fac+bMmYOZM2cqX+fl5SEwMBDh4eFwdnbWa/3kcjkiIiIwfPhw3DtzF7h9TfnemJEjmvTjseptI5MxEVZhu2jGtlGP7aIZ20Yzc2ibqic62jDbIBQWFoZff/1V5dihQ4cQGhqq8RdrY2MDGxubGsdlMpnB/hhkMhnkquspwtHOBhYW6nutmhJDtntjxnbRjG2jHttFM7aNZo25bepS70YzT7ugoABxcXGIi4sDUDk9Pi4uDklJSQAqe3Oqb+Hxxhtv4M6dO5g5cybi4+OxZs0arF69GrNmzTJF9WtVfR0hW5kFQxAREZGRNJoeobNnz2LIkCHK11WPsKZMmYJ169YhLS1NGYqAyhWu9+/fj/fffx9Lly6Fv78/vv/++wY3dR4AisuqB6Gm+0iMiIjI2BpNEBo8eHCts8vWrVtX49igQYNw/vx5A9ZKP6qvI8RVpYmIiIyn0TwaM2cMQkRERKbBINQAlFYbLc1HY0RERMbDINQAFD80WJqIiIiMg9+6DUD1wdJNef0gIiIiYzP5YOmwsDA8ePDA1NUwKY4RIiIiMg2DBaHy8nJcunQJlpaW6NKli8ZtLXbu3GmoKjQa1dcRsmEQIiIiMhqdH41du3YN8+fPx/r162u8FxkZiebNmyM0NBTdu3dHcHAwTpw4Ua+KmrMS9ggRERGZhM5BaMOGDZg3b57KIoYA8ODBA4wfPx7p6ekQQkAIgaSkJIwePbrG3l9UiY/GiIiITEPnIHT48GEAqLFS8+rVq/HgwQMEBQUhIiICx44dQ5cuXZCXl4fvv/++frU1UyXVps9zsDQREZHx6ByEUlNTAQCtW7dWOb5nzx5IkoRFixZh6NCh6Nu3L5YtWwYhBA4ePFi/2poplenzVpzIR0REZCw6f+tmZWXB1dUV1tbWymNyuRxnzpyBlZUVxowZozzet29fWFlZ4ebNm/WrrRmqUAiUlVdbUJE9QkREREajcxCSJAmFhYUqx2JjY1FWVoauXbvCwcFB5T0XFxeUlJToejuzVX2gNMAxQkRERMakcxBq1qwZ5HI54uPjlcf27dsHAOjXr59KWSEE8vLy4OXlpevtzFZJtd4ggEGIiIjImHQOQoMGDYIQAh988AEyMjIQFxeH5cuXQ5IkjBo1SqXstWvXIJfL4e/vX+8Km5uHe4S41xgREZHx6ByEPvjgA9jY2ODgwYPw8/NDSEgIMjMz0bVrVwwfPlyl7IEDBwAAvXr1ql9tzVD17TUABiEiIiJj0jkItWvXDnv37kXLli0hhIAkSRg+fDj27NlTo+zatWsBAEOGDNG9pmaq+tR5gNPniYiIjKleW2wMHz4cN27cQGZmJpycnGBra1ujjFwuV64f1LNnz/rcziyVlHOwNBERkanoZa+x2gZBy2QyDBo0SB+3MUvFNcYIcR0hIiIiYzHot+6DBw+Ql5dnyFs0eiVlnDVGRERkKjoHobt372LDhg3KgdDVXb58GaGhofD09ISbmxsGDBiA69ev16ui5qpmjxCDEBERkbHoHITWrFmDqVOnIjIyUuV4cXExRo0ahdjYWOWmq8ePH8ewYcPYO6RG6cNjhDhYmoiIyGh0DkK///47AGDChAkqx9evX4/k5GS4u7tj5cqV2LRpE5o1a4bU1FQsXbq0frU1Q8UPzRpjjxAREZHx6ByEEhMTAQDt27dXOb5z505IkoQvv/wSr776KiZOnIiVK1dCCIG9e/fWq7LmqMY6Qtx0lYiIyGjqtemqs7Mz7OzslMcUCgVOnDgBSZLw7LPPKo8PHz4cFhYWuHbtWv1qa4aqryxtbWkBK0sGISIiImPR+Vu3oqICpaWlKscuXbqEoqIidOrUCW5ubn/fxMICbm5uNTZpJdW9xjh1noiIyLh0/ub18/NDaWkpEhISlMcOHjwIAOjbt2+N8gUFBXB3d9f1dmareo8QxwcREREZl85BKCwsDAAwb948KBQKZGZmYtmyZZAkCSNGjFApm5CQgNLSUvj5+dWvtmao+vR5zhgjIiIyLp2D0LvvvgsA2LhxI1xdXREYGIg7d+4gODgYTz75pErZiIgIAECPHj3qUVXzVH1BRS6mSEREZFw6B6FevXphzZo1cHR0REFBAcrKytC+fXvs3LkTVlaqO3ds2LABADddVaf6XmN8NEZERGRc9dprbMqUKXj++efx559/wtXVFa1atYKFhWq2Kisrwz/+8Q+89tprGD16dL0qa46KVcYIcbA0ERGRMdV701U7O7tad5W3trbG5MmT63sbs1Ui56MxIiIiU2EXhIlVX1CRg6WJiIiMq949QkVFRVi1ahUOHjyIO3fuoLi4GLdu3VK+n5ubi3379kGSJLz44ov1vZ3ZKeUYISIiIpOpVxCKi4vD2LFjkZKSAiEEAECSJJUyzs7OWLhwIa5evYqWLVuid+/e9bml2am+1xiDEBERkXHp/Gjs/v37GD16NJKTk9GjRw8sXrwYzs7ONcpJkoRXX30VQgjs3r27PnU1S9UXVOQYISIiIuPSOQgtWbIEaWlpGDp0KE6dOoWZM2eq7DtWXdVssSNHjuh6O7PFIERERGQ6OgehX3/9FZIk4ZtvvqkxZf5h7dq1g0wmw82bN3W9nVkSQvXRGAdLExERGZfOQejWrVuwtrZGt27dtCrv7OyMvLw8XW9nlqplIACAjRUn8RERERmTzt+8CoUCVlZWNQZHayqbn58PBwcHXW9nlh4OQuwRIiIiMq567T5fVFSEjIyMR5Y9efIkysrK0KJFC11vZ5ZqBCGOESIiIjIqnYPQwIEDAQDr16+vtZxCocBnn30GSZIwdOhQXW9nlsoeCkKcPk9ERGRcOgehd955BwCwcOFCREZGqi2TnJyMcePG4ciRI7C0tMSMGTN0vZ1ZejgIsUeIiIjIuHQOQt26dcOnn36KvLw8DBs2DH369FEOhn7zzTcxcOBAtGrVCvv27QMALFq0CC1bttRPrc3Ew4/G2CNERERkXPVaWXrevHlwc3PDp59+itOnTyuPL1++XLnStJ2dHb766iu8/fbb9aupGSpTqA4052BpIiIi46r3XmPvvfceJk2ahP/85z84duwY7t69i4qKCvj6+qJfv36YNGkSfHx89FFXs1NtLUUAgK2M0+eJiIiMSecglJSUBADw9vaGh4cH3nnnHeW4IdIOxwgRERGZls5dEC1atEDLli2RnZ2tz/o0KQxCREREpqVzEHJ0dISLiwv8/f31WZ9a/fTTTwgODoatrS1CQkIQHR2tsWxkZCQkSarxc/XqVaPV91FqDJbmGCEiIiKjqlePUFFRESoqKh5dWA+2bt2K9957D5988gliY2MxYMAAjBw5UvmITpNr164hLS1N+dOmTRuj1FcbNYKQFYMQERGRMekchMaNG4eysjLl9HhD++677/Dqq69i+vTp6NChA5YsWYLAwEAsW7as1vO8vb3h6+ur/LG0bDhho6xahrS0kCCzfPR2JURERKQ/OgehDz/8EK1bt8b//M//4OLFi/qsUw1lZWU4d+4cwsPDVY6Hh4fjxIkTtZ7bvXt3+Pn5YejQoThy5Ighq1ln1afP28kstdq3jYiIiPRH51ljO3bswOuvv465c+ciNDQUTzzxBPr16wdvb+9ae10mT55c53tlZWWhoqKixjR8Hx8fpKenqz3Hz88PK1asQEhICEpLS7Fx40YMHToUkZGRyu1BHlZaWorS0lLl66oFIuVyOeRyeZ3rXRu5XK7yaMxWZqH3ezRWVe3A9lDFdtGMbaMe20Uzto1m5tA2dam7JKpWPqwjCwsLZQ+GEEKr3gxJklBeXl7ne929excBAQE4ceIEwsLClMcXLlyIjRs3aj0AesyYMZAkCXv37lX7/ty5czFv3rwaxzdv3gx7e/s61/tRttyyQExGZaecu43AFz2MM96KiIjInBUVFWHixInIzc2Fs7NzrWV17hFq3ry50R7leHp6wtLSskbvT0ZGRp0Wa+zTpw82bdqk8f05c+Zg5syZytd5eXkIDAxEeHj4IxuyruRyOTbc+EP52t3ZEaNG9dPrPRoruVyOiIgIDB8+HDKZzNTVaTDYLpqxbdRju2jGttHMHNqm6omONnQOQomJibqeWmfW1tYICQlBREQEnn76aeXxiIgIjB07VuvrxMbGws/PT+P7NjY2sLGxqXFcJpMZ5I+h+mBpexurRvsHZyiGavfGju2iGdtGPbaLZmwbzRpz29Sl3vXeYsNYZs6ciUmTJiE0NBRhYWFYsWIFkpKS8MYbbwCo7M1JTU3Fhg0bAABLlixBixYt0KlTJ5SVlWHTpk3YsWMHduzYYcqPoUJ1jFDDmc1GRETUVOgchI4ePQpra2v06dNHq/KnT59GSUmJxoHKjzJhwgTcv38f8+fPR1paGjp37oz9+/cjKCgIAJCWlqayplBZWRlmzZqF1NRU2NnZoVOnTti3bx9GjRql0/0NQV5t1hiDEBERkfHpHIQGDx4MPz8/pKamalV+woQJSE5O1mmwdJUZM2ZgxowZat9bt26dyuvZs2dj9uzZOt/LGKpvsWHHDVeJiIiMrl7fvnWdcKbjBDWzpRqE2CNERERkbEbrhigsLGy0g64MpfoYITvuM0ZERGR0RglC165dQ1ZWFry9vY1xu0ajeo+QDfcZIyIiMjqtxwjt2bMHe/bsUTmWm5uLadOmaTxHCIGcnBxER0dDkiQMGDBA95qaIXm16fPsESIiIjI+rYNQXFwc1q1bB0mSlGN9iouLawxS1sTLywtffPGFTpU0R0II1UdjHCNERERkdFoHoW7dumHKlCnK1+vXr4ednR2ef/55jedYWFjA2dkZnTt3xvjx4+Hq6lqvypoTeYWAAqqbrhIREZFxaR2Exo4dq7KK8/r16+Hi4oK1a9capGLmrkSuuq+YLafPExERGZ3O6wgdOXIE1tbW+qxLk1JcIwixR4iIiMjYdA5CgwYN0mc9mpyScoXKaw6WJiIiMj6dn8eUlZUhKSmpxo7wAFBQUIBZs2aha9eu6NGjBz777DMUFxfXq6LmpqRMtUeIY4SIiIiMT+ceoVWrVuHtt9/GlClTsGbNGpX3Ro8ejWPHjilnl124cAHR0dE4cuQIJElSd7kmh4/GiIiITE/nHqGDBw8CACZOnKhyfO/evcp1g1566SVMnz4dMpkM0dHR2LhxY/1qa0ZK5KqPxhiEiIiIjE/nIBQfHw8ACAkJUTm+efNmSJKEDz/8EBs3bsSKFSuwZMkSCCGwefPm+tXWjJSU89EYERGRqekchDIzM2Fvbw83NzeV40eOHAEATJ8+XXls0qRJACofkVGl4jJOnyciIjI1nb99CwsLYWGhenpiYiIyMzMRGBiI4OBg5XEHBwe4uroiOztb95qamYcfjXHWGBERkfHpHITc3d1RUFCAnJwc5bHDhw8DAPr27VujfHl5ORwdHXW9ndl5eLA0H40REREZn85BqEePHgCA1atXAwAUCgVWr14NSZIwZMgQlbKZmZkoKCiAr69vPapqXkrLOViaiIjI1HQOQq+88gqEEPjoo48wcuRI9OrVCzExMXB0dMRzzz2nUjY6OhoA0KFDh/rV1oxUHyMkSYCNFccIERERGZvO377PP/88pk6dioqKChw8eBDnz5+Hra0tli9fXmNz1a1bt6rtKWrKqu81ZmtlyfWViIiITEDnBRWBysdi06ZNw4kTJ+Dq6ophw4apDJIGKlegdnFxweTJkzFq1Kh6VdacVB8jxIHSREREplGvIAQA/fr1Q79+/TS+b21tjRUrVtT3Nman+l5jHChNRERkGhyYYiLV9xqz4RpCREREJsFvYBNReTTGHiEiIiKTYBAykeoLKjIIERERmQaDkIlU32uMg6WJiIhMg0HIRKo/GrOxYhAiIiIyBQYhEykpq/ZojD1CREREJsEgZCKqg6X5ayAiIjIFfgObSCnXESIiIjI5BiETqd4jxA1XiYiITINByERKGISIiIhMjkHIBMorFJBXCOVrDpYmIiIyDQYhE6i+zxjAMUJERESmwiBkAsXV9hkDAFvOGiMiIjIJfgObQPXxQQDHCBEREZkKg5AJPByE+GiMiIjINBiETKD44SDEwdJEREQmwSBkAjXHCDEIERERmQKDkAnU6BFiECIiIjIJBiETKJGrTp9njxAREZFpMAiZQI3B0hwjREREZBIMQibw8KMxWyv+GoiIiEyB38Am8PBgafYIERERmQaDkAmUlD/cI8QgREREZAoMQiZQUq1HyMbKAhYWkglrQ0RE1HQxCJlA9TFC3GeMiIjIdPgtbAKqQYiPxYiIiEylUQWhn376CcHBwbC1tUVISAiio6NrLR8VFYWQkBDY2tqiZcuWWL58uZFqWrvq6whxMUUiIiLTaTRBaOvWrXjvvffwySefIDY2FgMGDMDIkSORlJSktnxCQgJGjRqFAQMGIDY2Fh9//DHeeecd7Nixw8g1r0mlR4hT54mIiEym0XwLf/fdd3j11Vcxffp0dOjQAUuWLEFgYCCWLVumtvzy5cvRvHlzLFmyBB06dMD06dMxbdo0LF682Mg1r6n6YGlbTp0nIiIymUYRhMrKynDu3DmEh4erHA8PD8eJEyfUnhMTE1Oj/IgRI3D27FnI5XKD1VUb1XuE+GiMiIjIdKxMXQFtZGVloaKiAj4+PirHfXx8kJ6ervac9PR0teXLy8uRlZUFPz+/GueUlpaitLRU+TovLw8AIJfL9RqeisvKlf/dxlIyeTBraKrag+2iiu2iGdtGPbaLZmwbzcyhbepS90YRhKpIkup6O0KIGsceVV7d8SqLFi3CvHnzahw/dOgQ7O3t61pdjTLuWwKorENOdib279+vt2ubk4iICFNXoUFiu2jGtlGP7aIZ20azxtw2RUVFWpdtFEHI09MTlpaWNXp/MjIyavT6VPH19VVb3srKCh4eHmrPmTNnDmbOnKl8nZeXh8DAQISHh8PZ2bmen+Jv16xvIim7EEmpaRjeow1GDWylt2ubA7lcjoiICAwfPhwymczU1Wkw2C6asW3UY7toxrbRzBzapuqJjjYaRRCytrZGSEgIIiIi8PTTTyuPR0REYOzYsWrPCQsLw6+//qpy7NChQwgNDdX4i7WxsYGNjU2N4zKZTK9/DLNHdoBcLsf+/akYNbBVo/1DMzR9t7u5YLtoxrZRj+2iGdtGs8bcNnWpd6MYLA0AM2fOxKpVq7BmzRrEx8fj/fffR1JSEt544w0Alb05kydPVpZ/4403cOfOHcycORPx8fFYs2YNVq9ejVmzZpnqIxAREVED0yh6hABgwoQJuH//PubPn4+0tDR07twZ+/fvR1BQEAAgLS1NZU2h4OBg7N+/H++//z6WLl0Kf39/fP/99xg/frypPgIRERE1MI0mCAHAjBkzMGPGDLXvrVu3rsaxQYMG4fz58wauFRERETVWjebRGBEREZG+MQgRERFRk8UgRERERE0WgxARERE1WQxCRERE1GQxCBEREVGTxSBERERETRaDEBERETVZDEJERETUZDEIERERUZPVqLbYMDYhBAAgLy9P79eWy+UoKipCXl5eo93d11DYNuqxXTRj26jHdtGMbaOZObRN1fd21fd4bRiEapGfnw8ACAwMNHFNiIiIqK7y8/Ph4uJSaxlJaBOXmiiFQoG7d+/CyckJkiTp9dp5eXkIDAxEcnIynJ2d9Xrtxo5tox7bRTO2jXpsF83YNpqZQ9sIIZCfnw9/f39YWNQ+Cog9QrWwsLBAs2bNDHoPZ2fnRvuHZmhsG/XYLpqxbdRju2jGttGssbfNo3qCqnCwNBERETVZDEJERETUZDEImYiNjQ2++OIL2NjYmLoqDQ7bRj22i2ZsG/XYLpqxbTRram3DwdJERETUZLFHiIiIiJosBiEiIiJqshiEiIiIqMliENKTn376CcHBwbC1tUVISAiio6NrLR8VFYWQkBDY2tqiZcuWWL58eY0yO3bsQMeOHWFjY4OOHTti165dhqq+Qem7bS5fvozx48ejRYsWkCQJS5YsMWDtDUvfbbNy5UoMGDAAbm5ucHNzw7Bhw3D69GlDfgSD0He77Ny5E6GhoXB1dYWDgwO6deuGjRs3GvIjGIwh/q2psmXLFkiShHHjxum51oan73ZZt24dJEmq8VNSUmLIj2EQhvibycnJwZtvvgk/Pz/Y2tqiQ4cO2L9/v6E+gmEJqrctW7YImUwmVq5cKa5cuSLeffdd4eDgIO7cuaO2/O3bt4W9vb149913xZUrV8TKlSuFTCYT27dvV5Y5ceKEsLS0FF9++aWIj48XX375pbCyshInT5401sfSC0O0zenTp8WsWbPEzz//LHx9fcX//u//GunT6Jch2mbixIli6dKlIjY2VsTHx4upU6cKFxcXkZKSYqyPVW+GaJcjR46InTt3iitXroibN2+KJUuWCEtLS3HgwAFjfSy9METbVElMTBQBAQFiwIABYuzYsQb+JPpliHZZu3atcHZ2FmlpaSo/jY0h2qa0tFSEhoaKUaNGiWPHjonExEQRHR0t4uLijPWx9IpBSA969eol3njjDZVj7du3Fx999JHa8rNnzxbt27dXOfb666+LPn36KF8///zz4oknnlApM2LECPHCCy/oqdbGYYi2qS4oKKjRBiFDt40QQpSXlwsnJyexfv36+lfYSIzRLkII0b17d/Hpp5/Wr7JGZqi2KS8vF/369ROrVq0SU6ZMaXRByBDtsnbtWuHi4qL3uhqbIdpm2bJlomXLlqKsrEz/FTYBPhqrp7KyMpw7dw7h4eEqx8PDw3HixAm158TExNQoP2LECJw9exZyubzWMpqu2RAZqm3MgbHapqioCHK5HO7u7vqpuIEZo12EEPjjjz9w7do1DBw4UH+VNzBDts38+fPh5eWFV199Vf8VNzBDtktBQQGCgoLQrFkzPPnkk4iNjdX/BzAgQ7XN3r17ERYWhjfffBM+Pj7o3LkzvvzyS1RUVBjmgxgYg1A9ZWVloaKiAj4+PirHfXx8kJ6ervac9PR0teXLy8uRlZVVaxlN12yIDNU25sBYbfPRRx8hICAAw4YN00/FDcyQ7ZKbmwtHR0dYW1tj9OjR+OGHHzB8+HD9fwgDMVTbHD9+HKtXr8bKlSsNU3EDM1S7tG/fHuvWrcPevXvx888/w9bWFv369cONGzcM80EMwFBtc/v2bWzfvh0VFRXYv38/Pv30U/zrX//CwoULDfNBDIybrurJw7vTCyFq3bFeXfmHj9f1mg2VIdrGXBiybb755hv8/PPPiIyMhK2trR5qazyGaBcnJyfExcWhoKAAf/zxB2bOnImWLVti8ODB+qu4EeizbfLz8/Hyyy9j5cqV8PT01H9ljUjffzN9+vRBnz59lO/369cPPXr0wA8//IDvv/9eX9U2Cn23jUKhgLe3N1asWAFLS0uEhITg7t27+Pbbb/H555/rufaGxyBUT56enrC0tKyRrjMyMmqk6iq+vr5qy1tZWcHDw6PWMpqu2RAZqm3MgaHbZvHixfjyyy/x+++/47HHHtNv5Q3IkO1iYWGB1q1bAwC6deuG+Ph4LFq0qNEEIUO0zeXLl5GYmIgxY8Yo31coFAAAKysrXLt2Da1atdLzJ9EvY/07Y2FhgZ49ezaqHiFDtY2fnx9kMhksLS2VZTp06ID09HSUlZXB2tpaz5/EsPhorJ6sra0REhKCiIgIleMRERHo27ev2nPCwsJqlD906BBCQ0Mhk8lqLaPpmg2RodrGHBiybb799lv885//xIEDBxAaGqr/yhuQMf9mhBAoLS2tf6WNxBBt0759e1y6dAlxcXHKn6eeegpDhgxBXFwcAgMDDfZ59MVYfzNCCMTFxcHPz08/FTcCQ7VNv379cPPmTWVoBoDr16/Dz8+v0YUgAJw+rw9V0xNXr14trly5It577z3h4OAgEhMThRBCfPTRR2LSpEnK8lXTE99//31x5coVsXr16hrTE48fPy4sLS3FV199JeLj48VXX33VqKfP67NtSktLRWxsrIiNjRV+fn5i1qxZIjY2Vty4ccPon68+DNE2X3/9tbC2thbbt29XmfKbn59v9M+nK0O0y5dffikOHTokbt26JeLj48W//vUvYWVlJVauXGn0z1cfhmibhzXGWWOGaJe5c+eKAwcOiFu3bonY2FgxdepUYWVlJU6dOmX0z1cfhmibpKQk4ejoKN566y1x7do18d///ld4e3uLBQsWGP3z6QODkJ4sXbpUBAUFCWtra9GjRw8RFRWlfG/KlCli0KBBKuUjIyNF9+7dhbW1tWjRooVYtmxZjWtu27ZNtGvXTshkMtG+fXuxY8cOQ38Mg9B32yQkJAgANX4evk5joO+2CQoKUts2X3zxhRE+jf7ou10++eQT0bp1a2Frayvc3NxEWFiY2LJlizE+it4Z4t+a6hpjEBJC/+3y3nvviebNmwtra2vh5eUlwsPDxYkTJ4zxUfTOEH8zJ06cEL179xY2NjaiZcuWYuHChaK8vNzQH8UguPs8ERERNVkcI0RERERNFoMQERERNVkMQkRERNRkMQgRERFRk8UgRERERE0WgxARERE1WQxCRERE1GQxCBEREVGTxSBERGolJiZCkiRIkoTExERTV4fqafDgwZAkCXPnzjV1VYgaFAYhogZk9+7dmDt3Lnbv3m3qqpCJREZGKgOoJEl44403HnnOt99+q3LOunXrDF9RIjPBIETUgOzevRvz5s1rEEFIJpOhXbt2aNeuXa27uJNhbdmyBcXFxbWWWbt27SOv07x5c7Rr1w6enp76qhqRWbAydQWIqGEKCAjA1atXTV2NJq1FixZITEzErl27MHHiRLVlTp48ifj4eGVZTTZs2GCgWhI1buwRIiJqoKZMmQIAWLNmjcYyVe+98sorxqgSkdlhECIysF9++QUjR46Ej48PZDIZXF1d0aZNGzz11FNYunQpSkpKlONC1q9fDwBYv369ypgPSZIQGRmpvGaLFi2UY0EKCgrw+eefo0uXLnByclIZ3CyXyxEREYF33nkHoaGh8PPzg7W1Nby9vTFixAj8/PPPEEKorXdtg6Wrj2MBgJs3b2LatGkIDAyEjY0NmjVrhtdeew2pqan1br/z588r73Xx4sVay06aNAmSJGHYsGE13ispKcGSJUvQt29fuLm5wdbWFkFBQZg8eTLi4uI0XrN6W5eVleHbb79F165d4eDgABcXFzz++OM4cOBAfT+mWs899xwcHR1x+PBh3Llzp8b7RUVF2Lp1KyRJUoYmTWobLF1eXo4VK1Zg8ODB8PT0hEwmg4eHB9q1a4cJEybUGsTS09Px0UcfoWvXrnBxcYGtrS1atmyJ6dOn48qVKxrPS0lJwfvvv49OnTrBwcEBNjY28Pf3R0hICN5//32cOXOm1s9DpDeCiAxm2rRpAoDyx9HRUdjb26scS0hIEMePHxc+Pj7C1tZWABC2trbCx8dH5ef48ePK6wYFBQkAYvHixaJt27YCgLC2thaurq7KawohxJEjR1TuZWNjIxwdHVWOPffcc6KioqJG3RMSElTqWF316x4+fFh5TScnJ2FlZaV8z9/fX6SkpNS7HTt37iwAiFmzZmksU1BQIBwcHAQAsW7dOpX3UlJSlNcAIGQymXBxcVG+trCwEN9//73a61a19Q8//CB69+6tPL96O0qSJFavXl3vzymEatsmJCSIqVOnCgBi3rx5Ncpu2LBBABCPP/64EEIoz1u7dm2NsoMGDRIAxBdffKFyvLy8XAwfPlzlb8LFxUXY2NioHFPn119/VWkHmUym/B1U/U2uX7++xnlxcXHCzc1NWc7S0lK4ubkJSZKUx6ZMmVLntiPSBYMQkYFER0crv2S//vprcf/+feV7WVlZ4uDBg2LKlCkiNTVVeXzKlClafQlUfTk7OjoKX19fsXPnTlFWViaEECI5OVkUFhYKIYQ4efKkmDhxoti3b59IT08XCoVCCCHE/fv3xb///W/h7OwsAIh///vfNe6hbRByc3MTTz31lIiPjxdCCFFaWiq2bt0qnJycBAAxadKkOrfdw77++mtlsFIX2oQQYuPGjQKAcHBwEPn5+crj5eXlygDj4uIiNm3aJEpLS4UQQty6dUs8+eSTys+yf//+Gtetams3NzcREBAgdu/erWzrq1evij59+ih/Fzk5OfX+rA8Hoaq/o+DgYOXvr8rgwYMFALFp0yYhhG5BqKrdbG1txapVq5Rtp1AoxL1798TOnTvF+PHja1zv1KlTwtraWgAQr7/+uoiPjxfl5eVCCCHu3LkjZsyYIQAIKysrcebMGZVzhw4dKgCIHj16iJiYGOXnKi0tFdevXxeLFy8W33zzjU7tR1RXDEJEBlL15R0eHq71OXUNQpaWluL8+fM613Hbtm0CgGjVqlWN97QNQkOGDFEbTr7//nsBQNjZ2Qm5XK5zHYUQIjU1VVhYWAgA4uDBg2rLhIeHCwDi5ZdfVjm+ZcsWZV0PHDhQ4zy5XK4MSp07d67xflVb29jYKMNedRkZGcqevKpAUh8PByEhhGjTpo2y963K7du3hSRJwsXFRRQVFQkhdAtC//M//yMAiH/84x91qmfPnj0FAPHZZ59pLPPOO+8IAGLs2LEqx+3s7AQAceLEiTrdk8gQOEaIyEBcXV0BAJmZmaioqDDIPZ544gl0795d5/NHjx4NALh16xbS0tJ0usbHH38MC4ua/5SMHTsWAFBcXIwbN27oXEcA8Pf3x+OPPw4A2LhxY43309LS8McffwCoHCdU3datWwEAYWFhGDFiRI1zrays8MUXXwAA/vzzT1y6dEltHZ599lm0b9++xnEvLy+EhYUBwCPHMOlq6tSpAFSnya9duxZCCLzwwguws7PT+dpVf6fp6elan3PhwgWcOXMGMpkMH3zwgcZykydPBgD8/vvvKv8bqLqnrn9zRPrEIERkIMOGDYOtrS1iY2MxYMAArF69GgkJCXq9R79+/R5ZJj8/H99++y0GDRoEb29vWFtbKwcf29vbK8vpOrC5d+/eao/7+/sr/3t2drZO166u6kt1165dKCwsVHlv8+bNqKiogL+/f42B0mfPngUAtQOoqwwZMgSWlpYq5R+m6XMCf39WfXxOdSZPngxLS0vs2LEDeXl5UCgUyoH1VSFJV6NGjYIkSdi7dy9GjhyJn3/+GXfv3q31nGPHjgEAFAoF2rVrB19fX7U/TzzxBACgsLAQ9+/fV57/5JNPAqicFffBBx8gKioKRUVF9focRLpiECIykJYtW2LVqlVwdHRETEwMpk+fjpYtW8Lb2xsTJkzAnj17NM7Y0pa3t3et71+/fh0dO3bE7NmzcfToUWRmZkImk8HLyws+Pj7w8fFRln04XGjLyclJ7XErq7+XKZPL5Tpdu7pnnnkGjo6OKCwsxM6dO1Xeq+oleumll2r0TmVkZACoXBdJE1tbW+VCg1XlH6bpcwJ/f1Z9fE51AgICEB4erpwl9scffyApKQkdO3asNaBpo3///vj6669hbW2NAwcOYOLEiQgICEBgYCCmTp2KI0eO1DinKihVVFTg3r17Gn+ysrKU51QPOt988w2GDBmCgoICfPfddxg8eDCcnZ0RGhqKL774Qi+zDYm0xSBEZEAvvfQS7ty5g+XLl2PChAkIDAxEZmYmfvnlF4wbNw6DBg1CXl6eztev6sXQZOrUqUhJSUGLFi2wbds23L9/H4WFhcjIyEB6errKF059Q5mhOTg44OmnnwagujjgpUuXcOHCBQB/9xqpUzXV/1G0LWds1R+PVU1nr29vUJX/9//+HxISEvC///u/GDduHLy9vZGSkoJ169bh8ccfx3PPPacS8qoec7Vv3x6icqzpI39atGihPN/V1RWHDx9GdHQ0Zs+ejX79+sHKygrnzp3D/Pnz0aZNG/z88896+WxEj8IgRGRg7u7ueP3117FlyxYkJSXh5s2b+OijjyBJEqKjow22CWZycjJOnDgBAPj555/x7LPPwt3dXaVMXcaFNARVQefw4cPKEFfVG9StWzd07ty5xjlVvWbJyckar1tSUqJ8dOPl5aXXOuvL2LFj4eHhgZiYGOzYsQNWVlY1xkPVh7+/P9577z3s2rUL9+7dw8WLFzF9+nQAwPbt27Fs2TJlWV9fXwDA7du3de5JBP7ujTp27BhycnKwZ88edOnSBcXFxZg2bRru3btXvw9FpAUGISIja9WqFRYtWqTcMiEiIkL5XtVjHX30zlT/4tc0oPr333+v932M6fHHH0ezZs2gUCiwefNm5X8CmnuDQkNDAUA5mFqdyMhIlJeXAwB69uyp51rrh7W1tfJvRi6XY9SoUSqPNvWtS5cuWLlypXIcWvW/06pjZWVl2LVrl17uZ2tri6eeekr52LOkpEQ5FonIkBiEiAyktLS01verZvpUf7zl7OwMAMjJyan3/V1cXJT/verRUXX5+flYsGBBve9jTBYWFnjppZcAVPYEVfUMWVpaatyL64UXXgAAxMTE4NChQzXeLy8vx/z58wEAnTt3Vtur1FC89dZb+OCDD/DBBx9gzpw5ermmLn+noaGhynD9ySefIDMzs9ZrVB9EXl5eDoVC8cj7PXxPIkNhECIykLfeegvPP/88duzYoTIAt6CgAMuXL1eOcxk1apTyvaov4ejo6HpveNqxY0c0b94cADBt2jScO3dO+V5MTAwGDx6MBw8e1OseplDV83Pp0iVlGAgPD9fYOzJ+/HjlgOLnn38emzdvVo53SUhIwPjx4xETEwOgchBvQ9a2bVssXrwYixcvRp8+ffRyzXHjxmHatGn47bffVAJ4dnY2FixYoOxJq/53KkkSli9fDhsbGyQlJaF3797Yvn27yoDo1NRUbNq0CcOHD8eHH36oPJ6SkoI2bdpgwYIFiI2NVfbEAZXLD7z88ssAKseEDRw4UC+fkahWpli8iKgpqFocserH0dFRuQVG1U///v1FQUGB8pzs7Gzh5eWlfN/T01MEBQWJoKAgERMToyxXtcifuoXzqvv1119Vtrywt7dXbvFhb28vfv/9d+V7R44cUTlX2wUVa6Pp2vXVo0cPlXb8+eefay2fkpIiOnXqpLL1Q/XfhYWFhdrVtYXQrq21XQhTG+oWVNRW1Xl1WVCx6njVj7Ozs3LF8aqfZ599Vu2imYcOHRIeHh4qW2V4eHjU2EZm+vTpynOq/11VnePu7q5cpbrq97Nt27Y6fXYiXf09v5WI9Oqzzz5DSEgIjhw5gvj4eKSnp6OgoADe3t7o2rUrXnzxReX6MFXc3Nxw9OhRzJs3D9HR0cjIyFBOQS4pKalzHZ588kkcPXoUCxcuxPHjx1FUVARfX18MHToUH374Idq1a6e3z2tMkydPxvnz5wFUPk6sWrxRk4CAAJw9exbLli3DL7/8gvj4eBQVFSEwMBCDBw/GzJkz0a1bNyPUvOH54Ycf8NtvvyEqKgo3btxAeno6SkpK4O/vj9DQUEyZMgXPPPOM2nOHDx+OmzdvYvny5di3bx+uXLmCnJwc2NnZoWPHjggLC8PYsWMxfPhw5TkBAQHYu3cvjhw5gpiYGKSkpCAjIwNWVlZo3bo1hgwZgnfffRdt2rQxVhNQEycJ0cDnzBIREREZCMcIERERUZPFIERERERNFoMQERERNVkcLE1ERtOzZ89aV3hW58yZMwgMDDRQjQyjaop7XcyaNQuzZs0yUI2ISBMGISIymszMzDpvm1C1r1VjUlBQUOfPWVBQYKDaEFFtOGuMiIiImiyOESIiIqImi0GIiIiImiwGISIiImqyGISIiIioyWIQIiIioiaLQYiIiIiaLAYhIiIiarIYhIiIiKjJYhAiIiKiJuv/AzZRxABZ15GwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot stress vs strain curve\n",
    "job_damask.plot_stress_strain(von_mises=True)\n",
    "plt.xlabel(\"strain_von_Mises\")\n",
    "plt.ylabel(\"stress_von_Mises (Pa)\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142da152-66d0-4027-b9ee-0259b07b7c18",
   "metadata": {},
   "source": [
    "Let's compare our damask simulated stress-strain curve with the experimental curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "265a5fcd-b911-4c99-9244-f858623392aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the experimental stress-strain data\n",
    "exp_data = np.loadtxt(\"../resources/true_stress_strain.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "930be774-f528-4bec-b038-b00045ddbe2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot damask calculated stress-strain curve along with the experimental curve\n",
    "plt.plot(job_damask.output.strain[:, 0, 0], job_damask.output.stress[:, 0, 0],\n",
    "         '--o', label=\"DAMASK\")\n",
    "plt.plot(exp_data[:,1][:12000], exp_data[:,0][:12000]*MPa_to_Pa, label=\"Experimental\")\n",
    "plt.xlabel(\"Strain\")\n",
    "plt.ylabel(\"Stress (Pa)\")\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "raw",
   "id": "02618ca3-9d2e-4de5-b1a2-8f6ac3f2001d",
   "metadata": {},
   "source": [
    "job_damask.output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f5dfc6-445e-46c0-87c1-46b3e22361f6",
   "metadata": {},
   "source": [
    "### <font style=\"font-family:roboto;color:#455e6c\"> Run DAMASK simulation with LAMMPS calculated elastic parameters </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b646711f-34d2-4c49-92c1-e78c7ed27d6c",
   "metadata": {},
   "source": [
    "Let's create two python functions to make our life easier\n",
    "- A first python function which takes any bulk structure and corresponding potential file name as input and calculate the elastic tensors with `LAMMPS`\n",
    "- A second python function which takes the elastic tensors from the first python function as input and perform a tensile test simulation with `DAMASK`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38980540-33f9-469b-8c1b-4191a72ca652",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A python function to run LAMMPS\n",
    "def calc_elastic_tensors_lammps(structure, \n",
    "                                potential=\"2005--Mendelev-M-I--Al-Fe--LAMMPS--ipr1\",\n",
    "                                delete_existing_job=False\n",
    "                               ):\n",
    "    \"\"\"\n",
    "    This function takes atomic structure as input argument and returns\n",
    "    elastic tensor values for the structure.\n",
    "    \"\"\"\n",
    "    # Create and optimizate the structure\n",
    "    job_mini = pr.create.job.Lammps(job_name='lammps_mini_job',\n",
    "                                    delete_existing_job=delete_existing_job)\n",
    "    job_mini.structure = structure\n",
    "    job_mini.potential = potential\n",
    "    job_mini.calc_static()\n",
    "    job_mini.run()\n",
    "    \n",
    "    # Create a reference job \n",
    "    job_ref = pr.create.job.Lammps(job_name='lammps_ref_job',\n",
    "                                   delete_existing_job=delete_existing_job)\n",
    "    job_ref.structure = job_mini.get_structure(-1) # Assign the already optimized structure to this job\n",
    "    job_ref.potential = potential\n",
    "    job_ref.calc_static()\n",
    "    \n",
    "    # Create a `ElasticTensor` job and assign `job_ref` to this job\n",
    "    job_elastic = pr.create.job.ElasticTensor(job_name=\"elastic_tensor_job\",\n",
    "                                              delete_existing_job=delete_existing_job)\n",
    "    job_elastic.ref_job = job_ref\n",
    "    \n",
    "    # Call the run function\n",
    "    job_elastic.run()\n",
    "    \n",
    "    output = {\"type\": \"Hooke\",\n",
    "              \"C_11\": job_elastic['output/elastic_tensor'][0][0]*GPa_to_Pa,\n",
    "              \"C_12\": job_elastic['output/elastic_tensor'][0][1]*GPa_to_Pa,\n",
    "              \"C_44\": job_elastic['output/elastic_tensor'][3][3]*GPa_to_Pa}\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07b81ac4-a99c-495c-b8b4-75a57c044c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A python function to run DAMASK\n",
    "def run_damask_simulation(elasticity_data, \n",
    "                          plasticity_data,\n",
    "                          delete_existing_job=False):\n",
    "    \"\"\"\n",
    "    This function takes elastic tensors as input argument and returns\n",
    "    strain and stress values of damask tensile simulation.\n",
    "    \"\"\"\n",
    "    # Create a damask job\n",
    "    job_damask = pr.create.job.DAMASK(\"damask_simulation_lammps\", \n",
    "                                      delete_existing_job=delete_existing_job)\n",
    "    \n",
    "    # Elastic paramaters of the material\n",
    "    elasticity = pr.continuum.damask.Elasticity(**elasticity_data)\n",
    "    \n",
    "    # Plastic parameters of the material\n",
    "    plasticity = pr.continuum.damask.Plasticity(**plasticity_data)\n",
    "    \n",
    "    # Define phase of the material\n",
    "    phase = pr.continuum.damask.Phase(composition ='DX56D',\n",
    "                                      lattice = 'cI',\n",
    "                                      output_list = ['F', 'P'],\n",
    "                                      elasticity = elasticity, \n",
    "                                      plasticity = plasticity\n",
    "                                     )\n",
    "    # Define homogenization\n",
    "    homogenization = pr.continuum.damask.Homogenization(method='SX',\n",
    "                                                        parameters={'N_constituents': 1,\n",
    "                                                                    \"mechanical\": {\"type\": \"pass\"}})\n",
    "    # Defines the number of grains and grids\n",
    "    grains = 60\n",
    "    grids = 16 \n",
    "    \n",
    "    # Define homogenization\n",
    "    rotation = pr.continuum.damask.Rotation(Rotation.from_random,\n",
    "                                            grains)\n",
    "    # Materialpoint configuration\n",
    "    material = pr.continuum.damask.Material([rotation],\n",
    "                                            ['DX56D'],\n",
    "                                            phase,\n",
    "                                            homogenization)\n",
    "    # Define grid\n",
    "    grid = pr.continuum.damask.Grid.via_voronoi_tessellation(spatial_discretization=grids,\n",
    "                                                             num_grains=grains,\n",
    "                                                             box_size=1.6e-5)\n",
    "    \n",
    "    # Assign the material and grid to the damask job\n",
    "    job_damask.material = material\n",
    "    job_damask.grid = grid\n",
    "    \n",
    "    # Generate a load case for the grid solver\n",
    "    load_step =[{'mech_bc_dict' : \n",
    "                 {'dot_F' : [1e-3, 0, 0,\n",
    "                             0, 'x', 0,\n",
    "                             0, 0, 'x'],\n",
    "                  'P' : ['x', 'x', 'x',\n",
    "                         'x', 0, 'x',\n",
    "                         'x', 'x', 0]\n",
    "                  },\n",
    "                 'discretization' : {'t' : 10., 'N' : 40},\n",
    "                 'additional': {'f_out' : 4}\n",
    "                 },\n",
    "                {'mech_bc_dict' : \n",
    "                 {'dot_F' : [1e-3, 0, 0,\n",
    "                             0, 'x', 0,\n",
    "                             0, 0, 'x'],\n",
    "                  'P':['x', 'x', 'x',\n",
    "                       'x', 0, 'x',\n",
    "                       'x', 'x', 0]\n",
    "                 },\n",
    "                 'discretization' : {'t' : 50., 'N' : 50}, # Use t=250. and N=250\n",
    "                 'additional': {'f_out' : 4}\n",
    "                 }]\n",
    "    \n",
    "    # Assign solver and load step to the damask job\n",
    "    job_damask.loading = pr.continuum.damask.Loading(solver=job_damask.list_solvers()[0],\n",
    "                                                     load_steps=load_step\n",
    "                                                     )\n",
    "    \n",
    "    # Run the simulation\n",
    "    job_damask.run()\n",
    "\n",
    "    return job_damask.output.strain[:, 0, 0], job_damask.output.stress[:, 0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72592753-a361-4251-bcd6-8ef4c52efcdb",
   "metadata": {},
   "source": [
    "#### <font style=\"font-family:roboto;color:#455e6c\"> We will use the python functions defined above to perform our simulation </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a0f1ca-2c4a-44f4-b1b5-38beed706639",
   "metadata": {},
   "source": [
    "First, create a cubic unit cell of bcc iron and use `calc_elastic_tensors_lammps()` to calculate the elastic tensors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b13d48c-8bed-4a8e-bd1f-5181ac609e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Complete the code below to create a atomic structure of bcc iron\n",
    "my_structure = pr.create.structure.bulk(\"Fe\", cubic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68b593f-b075-4867-a318-f518c3a32586",
   "metadata": {},
   "source": [
    "Pass `my_structure` to `calc_elastic_tensors_lammps()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d59b512-b41d-40fc-9d32-e80d25ded61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lammps_mini_job was saved and received the ID: 28\n",
      "The job elastic_tensor_job was saved and received the ID: 29\n",
      "The job elastic_tensor_job_0 was saved and received the ID: 30\n",
      "The job elastic_tensor_job_1 was saved and received the ID: 31\n",
      "The job elastic_tensor_job_2 was saved and received the ID: 32\n",
      "The job elastic_tensor_job_3 was saved and received the ID: 33\n",
      "The job elastic_tensor_job_4 was saved and received the ID: 34\n",
      "The job elastic_tensor_job_5 was saved and received the ID: 35\n",
      "The job elastic_tensor_job_6 was saved and received the ID: 36\n",
      "The job elastic_tensor_job_7 was saved and received the ID: 37\n",
      "The job elastic_tensor_job_8 was saved and received the ID: 38\n",
      "The job elastic_tensor_job_9 was saved and received the ID: 39\n",
      "The job elastic_tensor_job_10 was saved and received the ID: 40\n",
      "The job elastic_tensor_job_11 was saved and received the ID: 41\n",
      "The job elastic_tensor_job_12 was saved and received the ID: 42\n",
      "The job elastic_tensor_job_13 was saved and received the ID: 43\n",
      "The job elastic_tensor_job_14 was saved and received the ID: 44\n",
      "The job elastic_tensor_job_15 was saved and received the ID: 45\n",
      "The job elastic_tensor_job_16 was saved and received the ID: 46\n",
      "The job elastic_tensor_job_17 was saved and received the ID: 47\n",
      "The job elastic_tensor_job_18 was saved and received the ID: 48\n",
      "The job elastic_tensor_job_19 was saved and received the ID: 49\n",
      "The job elastic_tensor_job_20 was saved and received the ID: 50\n",
      "The job elastic_tensor_job_21 was saved and received the ID: 51\n",
      "The job elastic_tensor_job_22 was saved and received the ID: 52\n"
     ]
    }
   ],
   "source": [
    "# Calculate elastic tensors\n",
    "elasticity_data = calc_elastic_tensors_lammps(structure=my_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3cbfafd8-d28b-4876-81bd-f5dee497e4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set plasticity parameters of the material\n",
    "plasticity_data = {\"type\": \"phenopowerlaw\",\n",
    "                   \"references\": [\"https://doi.org/10.1016/j.actamat.2014.07.071\",\n",
    "                                   \"https://doi.org/10.1007/BF02900224\"],\n",
    "                   \"output\": [\"xi_sl\", \"gamma_sl\"],\n",
    "                   \"N_sl\": [12, 12],\n",
    "                   \"dot_gamma_0_sl\": [0.001, 0.001],\n",
    "                   \"n_sl\": [20, 20],\n",
    "                   \"a_sl\": [3.3838750211834414, 2.71513775941709],\n",
    "                   \"xi_0_sl\": [58854884.55400854, 116398683.46869445],\n",
    "                   \"xi_inf_sl\": [245397260.8849832, 289183253.8512693],\n",
    "                   \"h_0_sl_sl\": [839702224.3104236, 797832430.2007155],\n",
    "                   \"h_sl_sl\": [1, 1.4, 1, 1.4, 1.4, 1.4, 1.4, 1.4,\n",
    "                                1.4,1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,\n",
    "                                1.4,1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4]\n",
    "                   }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41526b9e-8b63-4b27-81cc-b111989137f4",
   "metadata": {},
   "source": [
    "Pass the `elasticity_data` and `elasticity_data` to `run_damask_simulation()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d53f26a-84a7-49aa-ac19-76bb4c706462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job damask_simulation_lammps was saved and received the ID: 53\n"
     ]
    }
   ],
   "source": [
    "# Execute this cell to get damask calculated stress and strain\n",
    "result = run_damask_simulation(elasticity_data, plasticity_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "062e9df8-d940-46fe-a4af-e71f637a4517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot damask calculated stress-strain curve along with the experimental curve\n",
    "plt.plot(result[0], result[1], '--o', label=\"DAMASK_LAMMPS\")\n",
    "plt.plot(exp_data[:,1][:12000], exp_data[:,0][:12000]*MPa_to_Pa, label=\"Experimental\")\n",
    "plt.xlabel(\"Strain\")\n",
    "plt.ylabel(\"Stress (Pa)\")\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7eb74b-6731-4e19-90d3-c78af3d916ee",
   "metadata": {},
   "source": [
    "### <font style=\"font-family:roboto;color:#455e6c\"> Software used in this notebook </font>\n",
    "\n",
    "- [pyiron_atomistics](https://github.com/pyiron/pyiron_atomistics)\n",
    "- [pyiron_continuum](https://github.com/pyiron/pyiron_continuum)\n",
    "- [LAMMPS](https://www.lammps.org/)\n",
    "- [DAMASK](https://damask.mpie.de/release)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}