{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b58fa431-18d0-444c-a0c0-b07d8f70fbb4",
   "metadata": {},
   "source": [
    "# Exercise 3: Visualizing Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7077098d-6b6b-47ed-88a6-1d18133fab7f",
   "metadata": {},
   "source": [
    "You'll need to run the Unpack.ipynb notebook before this one!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e785fb5-81c3-4608-bc8f-e159c366c82a",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee1d8390-7d8c-4306-a6d0-31c87ee40fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron import Project\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0415ea7a-509d-4473-8365-ca31568a555a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('figure', figsize=(16,8))\n",
    "plt.rc('font', size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc7814d6-8fcf-46cc-bdbf-efd01748fa2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project('training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a0e908e-de03-414a-b998-ac8ba12410a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17168587</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>full</td>\n",
       "      <td>/full</td>\n",
       "      <td>/cmmc/u/</td>\n",
       "      <td>zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/</td>\n",
       "      <td>2022-06-03 00:02:34.816533</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17168588</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>basic</td>\n",
       "      <td>/basic</td>\n",
       "      <td>/cmmc/u/</td>\n",
       "      <td>zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/</td>\n",
       "      <td>2022-06-03 00:02:47.281055</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17168589</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>Al_basic_atomicrex</td>\n",
       "      <td>/Al_basic_atomicrex</td>\n",
       "      <td>/cmmc/u/</td>\n",
       "      <td>zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/</td>\n",
       "      <td>2022-06-03 00:02:48.226369</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id    status chemicalformula                 job  \\\n",
       "2  17168587  finished            None                full   \n",
       "0  17168588  finished            None               basic   \n",
       "1  17168589  finished            None  Al_basic_atomicrex   \n",
       "\n",
       "                subjob projectpath  \\\n",
       "2                /full    /cmmc/u/   \n",
       "0               /basic    /cmmc/u/   \n",
       "1  /Al_basic_atomicrex    /cmmc/u/   \n",
       "\n",
       "                                                                                              project  \\\n",
       "2  zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/   \n",
       "0  zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/   \n",
       "1  zora/pyiron/projects/2022/POTENTIALS/TutorialNotebooks/workshop_preparation/introduction/training/   \n",
       "\n",
       "                   timestart timestop totalcputime        computer  \\\n",
       "2 2022-06-03 00:02:34.816533     None         None  zora@cmti001#1   \n",
       "0 2022-06-03 00:02:47.281055     None         None  zora@cmti001#1   \n",
       "1 2022-06-03 00:02:48.226369     None         None  zora@cmti001#1   \n",
       "\n",
       "            hamilton hamversion parentid masterid  \n",
       "2  TrainingContainer        0.4     None     None  \n",
       "0  TrainingContainer        0.4     None     None  \n",
       "1  TrainingContainer        0.4     None     None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff50d46-c4f9-440a-bc88-5a8fec27d4f7",
   "metadata": {},
   "source": [
    "## Loading Container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0ebcdf7-3ac8-4359-8840-1036ee3388b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "container = pr.load('basic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95792b0-f5b6-450b-b08c-f67ba45e060b",
   "metadata": {},
   "source": [
    "## Some Predefined Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad795198-d689-4b2e-96dc-9e9b4b687aa6",
   "metadata": {},
   "source": [
    "### Cell Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e91af194-919e-4268-8f15-56106bd2748b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "container.plot.cell();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3353c813-426c-4532-8b57-74500ecb4373",
   "metadata": {},
   "source": [
    "### Cell Symmetries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deb03a6b-cf55-44cb-b47e-050f4ff2b997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "container.plot.spacegroups();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10131a1b-3d2e-4b5e-8f8f-993256a0486c",
   "metadata": {},
   "source": [
    "### E-V Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70e779bf-296a-4843-ad12-127c389337e1",
   "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": [
    "container.plot.energy_volume();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c73c13-37b6-46fd-80ce-8cbfe4f5ab0f",
   "metadata": {},
   "source": [
    "### Custom Plots from Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3521262-079a-45b7-b50f-43578068e3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = container.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734ab66b-1725-41b0-a96a-d25049469167",
   "metadata": {},
   "source": [
    "### E-V by Concentration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55049592-b4b7-48a3-8f80-f6bc2a0ac3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['concentration'] = df.atoms.map(lambda s: (s.get_chemical_symbols()=='Li').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "552bd06b-39ff-40ac-96e9-bf171a21c588",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['energy_atom'] = (df.energy / df.number_of_atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2f40ee4-818f-4e40-84b6-7844fca037a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['volume_atom'] = df.atoms.map(lambda s: s.get_volume(per_atom=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "483e90a9-c4a6-4ec6-aba3-c017450e08e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1502c1833a60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(\n",
    "    data=df,\n",
    "    x='volume_atom',\n",
    "    y='energy_atom',\n",
    "    hue='concentration'\n",
    ")\n",
    "plt.xlabel(r'Atomic Volume [$\\mathrm{\\AA}^3$/atom')\n",
    "plt.ylabel(r'Atomic Energy [eV/atom]')\n",
    "plt.legend(title='Li')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f53c741-75ee-4847-82ac-1640c045b144",
   "metadata": {},
   "source": [
    "### Convex Hull"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344b6061-09ca-4175-988a-05b1b2f08e2d",
   "metadata": {},
   "source": [
    "First find the equilibrium energy at the terminal concentrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f68739c-1f55-45a3-80aa-16bb081aac08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.4827513025 -1.757035875\n"
     ]
    }
   ],
   "source": [
    "e_min_al = df.query('concentration == 0').energy_atom.min()\n",
    "e_min_li = df.query('concentration == 1').energy_atom.min()\n",
    "print(e_min_al, e_min_li)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "759447cb-8cef-4b87-8da0-c46db64ecd30",
   "metadata": {},
   "source": [
    "Next calculate the deviation to the \"ideal\" mixing enthalpy\n",
    "\n",
    "$$\n",
    "e(c_\\mathrm{Li}) = e_\\mathrm{Al} + c_\\mathrm{Li} (e_\\mathrm{Li} - e_\\mathrm{Al})\n",
    "$$\n",
    "\n",
    "and call that the energy excess, where $e$ are the per atom equilibrium energies of the pure phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "efcac90d-abcc-423f-ae19-0b5ad763f897",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['energy_atom_excess'] = df.energy_atom - df.concentration * (e_min_li - e_min_al) - e_min_al"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c309e2f-f9f9-4098-8332-c845cf621619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Excess Energy [eV/atom]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.lineplot(data=df,\n",
    "             marker='o',\n",
    "             x='concentration', y='energy_atom_excess',\n",
    "             estimator=np.min)\n",
    "plt.scatter(df.concentration, df.energy_atom_excess, marker='.')\n",
    "plt.ylim(df.energy_atom_excess.min() * 1.2, .4)\n",
    "plt.xlabel('Li concentration')\n",
    "plt.ylabel('Excess Energy [eV/atom]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0690c44c-6c2c-4b23-888a-950eb510bffd",
   "metadata": {},
   "source": [
    "## Extra Credit\n",
    "\n",
    "1. Plot the energy against smallest nearest neighbor distance in a structure.  You can get the neighbor information with the [get_neighbor method](https://pyiron-atomistics.readthedocs.io/en/latest/apidoc/pyiron_atomistics.atomistics.structure.atoms.html?highlight=Atoms.get_neighbors#pyiron_atomistics.atomistics.structure.atoms.Atoms.get_neighbors) on a structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a917ba97-57c3-48ec-ae3c-bb6cf6191902",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}