preJNB4-ReaxFF_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre JNB4 - Select optimization Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to this Jupyter Notebook. In this space, we import existing ReaxFF parameterizations and decide which interactions to optimize.\n",
    "\n",
    "This notebook serves as a preprocessing phase for the fourth step in our protocol, as illustrated in Figure 4. By repeating this step, we can strategically optimize a subset of parameters over time, effectively reducing the search space.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <img src=\"assets/img/wf-JNB4.png\" alt=\"JNB4 workflow\">\n",
    "    <figcaption><strong>Figure 4</strong>: Protocol workflow and part covered by this Jupyter Notebook highlighted in red.<figcaption>\n",
    "<figure> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is dedicated to defining constants and generators that will be utilized throughout this notebook:\n",
    "\n",
    "* `MAIN_DIR_JNB4`: Specifies the main directory where all results generated by this Jupyter Notebook will be stored.\n",
    "* `CHEM_FORMULA`: Represents the chemical formula of the SEI component.\n",
    "\n",
    "Additionally, we introduce boolean constants to toggle the optimization of specific interaction parameters:\n",
    "\n",
    "* `BLOCK_VDW`: Controls the optimization of Van der Waals interactions.\n",
    "* `BLOCK_BONDS`: Toggles the optimization of 2-body interactions.\n",
    "* `BLOCK_ANGLES`: Manages the optimization of 3-body interactions.\n",
    "* `BLOCK_TORSION`: Dictates the optimization of 4-body interactions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tools.misc import MAIN_DIR_JNB1, MAIN_DIR_JNB2, MAIN_DIR_JNB4\n",
    "\n",
    "CHEM_FORMULA = \"LiF\"\n",
    "\n",
    "BLOCK_VDW = True\n",
    "BLOCK_BONDS = False\n",
    "BLOCK_ANGLES = False\n",
    "BLOCK_TORSION = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scm.params as params\n",
    "import scm.plams as scm\n",
    "from prj_utils import get_all_angles, get_all_bonds, get_all_dihedrals, make_dir\n",
    "from pymatgen.core import Structure\n",
    "from pymatgen.io.ase import AseAtomsAdaptor\n",
    "from scm.plams.interfaces.molecule.ase import fromASE as ASEtoSCM\n",
    "from scm.plams.interfaces.molecule.ase import toASE as SCMtoASE\n",
    "\n",
    "matget2ase = AseAtomsAdaptor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the output folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_dir(os.path.join(MAIN_DIR_JNB4, CHEM_FORMULA))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load ReaxFF parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interface = params.ReaxParams(\"data/ffield.reax.CHOSiLiF_K.S.Yun_etal_2017.ff\", bounds_scale=1.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interface.generate_paramsdb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(interface)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, fix all parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interface.is_active = len(interface) * [False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramenter to train 0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Paramenter to train {interface.is_active.count(True)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5615b5",
   "metadata": {},
   "source": [
    "#### Select system for Geometry analize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd5e6941",
   "metadata": {},
   "outputs": [],
   "source": [
    "RUN_DIR = os.path.join(MAIN_DIR_JNB2, CHEM_FORMULA, \"1-supercells_SP\")\n",
    "TASK = \"SP\"\n",
    "\n",
    "settings_pkl_file = os.path.join(RUN_DIR, \"settings.pkl\")\n",
    "with open(settings_pkl_file, \"rb\") as file:\n",
    "    settings = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da6c2329",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paolo/Desktop/Batteries/Tier0-C+EC-DMC+Li/venv_ams2022/AMS2022.1.venv/lib/python3.6/site-packages/pymatgen/io/cif.py:1126: UserWarning:\n",
      "\n",
      "Issues encountered while parsing CIF: Some fractional co-ordinates rounded to ideal values to avoid issues with finite precision.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cis_dir = os.path.join(MAIN_DIR_JNB1, CHEM_FORMULA, \"1-supercell\")\n",
    "cis_dir_ref = os.path.join(MAIN_DIR_JNB1, CHEM_FORMULA, \"1-supercell\")\n",
    "data_pathr_ref = os.path.join(cis_dir_ref, \"properties.json\")\n",
    "properties_ref_df = pd.read_json(data_pathr_ref)\n",
    "supercell_size = properties_ref_df[\"unitcell_repetition\"].unique()\n",
    "refernce_cis = properties_ref_df.loc[properties_ref_df[\"unitcell_repetition\"] == max(supercell_size)].index\n",
    "\n",
    "systems = []\n",
    "\n",
    "for path in np.sort(glob.glob(os.path.join(cis_dir, \"*.cif\"))):\n",
    "    system_name = path.split(os.sep)[-1].split(\".cif\")[0]\n",
    "    system_mat = Structure.from_file(path)\n",
    "    system_ase = matget2ase.get_atoms(system_mat)\n",
    "    systems.append((system_name, ASEtoSCM(system_ase)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_1 = {}\n",
    "\n",
    "for name, system_obj in systems:\n",
    "    sim_name = f\"{TASK}-{name}\"\n",
    "    dir_jobs = os.path.abspath(os.path.join(RUN_DIR, \"jobs\", sim_name))\n",
    "    if not os.path.isfile(os.path.join(dir_jobs, \"ams.rkf\")):\n",
    "        print(f\"NO ams.rkf file in {dir_jobs}\")\n",
    "        continue\n",
    "    job = scm.AMSJob.load_external(dir_jobs, settings=settings)\n",
    "    # check\n",
    "    if not job.check():\n",
    "        print(f\"Simulation {dir_jobs} FAILED\")\n",
    "        continue\n",
    "    jobs_1[sim_name] = scm.AMSJob.load_external(dir_jobs, settings=settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d881994",
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry_ref = {}\n",
    "\n",
    "for sys_name in refernce_cis:\n",
    "    name = \"SP-\" + sys_name[:-4]\n",
    "    geometry_ref[name] = jobs_1[name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Off-diagonals (VdW) block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "activate = BLOCK_VDW\n",
    "bounds_scale = 0.15  # 15%\n",
    "\n",
    "# get List\n",
    "block = interface.get(block=\"OFD\", atoms=[\"Li\", \"F\"])\n",
    "block_names = [b.name for b in block]\n",
    "queries = interface.params_from_db(\n",
    "    block_names, Li=(\"Na\", \"K\"), F=(\"Cl\", \"Br\")\n",
    ")  # both Li and Na, and K are Alkali metal, both F, Cl, and Br are Halogen\n",
    "\n",
    "for b in block_names:\n",
    "    print(interface[b].__str__)\n",
    "\n",
    "\n",
    "for q in queries:\n",
    "    name = q.query\n",
    "    values = q.values\n",
    "    v_min = np.min(values)\n",
    "    v_max = np.max(values)\n",
    "    v_span = v_max - v_min\n",
    "    if not np.isclose(v_min, v_max):\n",
    "        interface[name].range = (v_min - v_span * bounds_scale, v_max + v_span * bounds_scale)\n",
    "        interface[name].is_active = activate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for b in block_names:\n",
    "    print(interface[b].__str__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966778c4",
   "metadata": {},
   "source": [
    "## Bond block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee2dae0",
   "metadata": {},
   "source": [
    "#### Search Bonds in the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f70291fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mol = {}\n",
    "\n",
    "for name, job in geometry_ref.items():\n",
    "    mol = job.results.get_input_molecule()\n",
    "    mol = SCMtoASE(mol)\n",
    "    data_mol[name] = mol.copy()\n",
    "\n",
    "for i, (name, ase_mol) in enumerate(data_mol.items()):\n",
    "    if i == 0:\n",
    "        bond_df = get_all_bonds(ase_mol, verbose=False)\n",
    "    else:\n",
    "        bond_df = bond_df.append(get_all_bonds(ase_mol, verbose=False))\n",
    "\n",
    "bond_df.reset_index(inplace=True)\n",
    "bond_df = bond_df.rename(columns={\"index\": \"bond_name\"})\n",
    "bond_df = bond_df.drop_duplicates(subset=[\"bond_name\"])\n",
    "\n",
    "# ADD F-F\n",
    "bond_df.loc[2, \"ai\"] = \"F\"\n",
    "bond_df.loc[2, \"aj\"] = \"F\"\n",
    "\n",
    "# serch in the system\n",
    "for ai, aj in zip(bond_df[\"ai\"], bond_df[\"aj\"]):\n",
    "    bonds_parm = interface.get(block=\"BND\", atoms=[ai, aj])\n",
    "    if len(bonds_parm) > 0:\n",
    "        print(f\"paramentrs for {ai}-{aj} exist!\")\n",
    "    else:\n",
    "        print(f\"paramentrs for {ai}-{aj} DO NOT EXIST! you may add this block manualy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a9e0a",
   "metadata": {},
   "source": [
    "#### Activate existin parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get List\n",
    "block = []\n",
    "for ai, aj in zip(bond_df[\"ai\"], bond_df[\"aj\"]):\n",
    "    block += interface.get(block=\"BND\", atoms=[ai, aj])\n",
    "block_names = [b.name for b in block]\n",
    "queries = interface.params_from_db(\n",
    "    block_names, Li=(\"Na\", \"K\"), F=(\"Cl\", \"Br\")\n",
    ")  # both Li and Na, and K are Alkali metal, both F, Cl, and Br are Halogen\n",
    "# queries\n",
    "\n",
    "missings = []\n",
    "missings_atoms = []\n",
    "active = BLOCK_BONDS\n",
    "\n",
    "for q in queries:\n",
    "    name = q.query\n",
    "    values = q.values\n",
    "    if len(values) == 0:\n",
    "        interface[name].is_active = False\n",
    "        missings.append(name)\n",
    "        continue\n",
    "    v_min = np.min(values)\n",
    "    v_max = np.max(values)\n",
    "    v_span = v_max - v_min\n",
    "    if not np.isclose(v_min, v_max):\n",
    "        interface[name].range = (v_min - v_span * bounds_scale, v_max + v_span * bounds_scale)\n",
    "        interface[name].is_active = active"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "parm = []\n",
    "missings = [m.split(\":\")[0] for m in missings]\n",
    "missings = np.unique(missings)\n",
    "for m in missings:\n",
    "    parm += interface.get(atoms=m)\n",
    "\n",
    "for p in parm:\n",
    "    interface._all.remove(p)\n",
    "\n",
    "# reinzialize\n",
    "interface._reinitialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fb523e",
   "metadata": {},
   "source": [
    "Get get bond parameters atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Paramenter to train {interface.is_active.count(True)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e20346",
   "metadata": {},
   "source": [
    "## Angles block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c413bf3",
   "metadata": {},
   "source": [
    "#### Search Angles in the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2c522de",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mol = {}\n",
    "\n",
    "for name, job in geometry_ref.items():\n",
    "    mol = job.results.get_input_molecule()\n",
    "    mol = SCMtoASE(mol)\n",
    "    data_mol[name] = mol.copy()\n",
    "\n",
    "for i, (name, ase_mol) in enumerate(data_mol.items()):\n",
    "    if i == 0:\n",
    "        angles_df = get_all_angles(ase_mol, values=True, verbose=False)\n",
    "    else:\n",
    "        angles_df = angles_df.append(get_all_angles(ase_mol, values=True, verbose=False))\n",
    "\n",
    "angles_df.reset_index(inplace=True)\n",
    "angles_df = angles_df.rename(columns={\"index\": \"angles_name\"})\n",
    "angles_df = angles_df.drop_duplicates(subset=[\"angles_name\"])\n",
    "\n",
    "for i in range(len(angles_df.index)):\n",
    "    print(angles_df.loc[i, \"angles_name\"], np.unique(np.round(angles_df.loc[i, \"angles_values\"], decimals=2)))\n",
    "\n",
    "# serch in the system\n",
    "for ai, aj, ak in zip(angles_df[\"ai\"], angles_df[\"aj\"], angles_df[\"ak\"]):\n",
    "    bonds_parm = interface.get(block=\"ANG\", atoms=[ai, aj, ak])\n",
    "    if len(bonds_parm) > 0:\n",
    "        print(f\"paramentrs for {ai}-{aj}-{ak} exist!\")\n",
    "    else:\n",
    "        print(f\"[W] paramentrs for {ai}-{aj}-{ak} DO NOT EXIST! it will be added\")\n",
    "        # interface.add_blocks([f'ANG:{ai}.{aj}.{ak}'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29887429",
   "metadata": {},
   "source": [
    "#### Activate existin parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get List\n",
    "block = []\n",
    "for ai, aj, ak in zip(angles_df[\"ai\"], angles_df[\"aj\"], angles_df[\"ak\"]):\n",
    "    block += interface.get(block=\"ANG\", atoms=[ai, aj, ak])\n",
    "block_names = [b.name for b in block]\n",
    "queries = interface.params_from_db(\n",
    "    block_names, Li=(\"Na\", \"K\"), F=(\"Cl\", \"Br\")\n",
    ")  # both Li and Na, and K are Alkali metal, both F, Cl, and Br are Halogen\n",
    "# queries\n",
    "\n",
    "\n",
    "missings = []\n",
    "missings_atoms = []\n",
    "active = BLOCK_ANGLES\n",
    "\n",
    "for q in queries:\n",
    "    name = q.query\n",
    "    values = q.values\n",
    "    if len(values) == 0:\n",
    "        interface[name].is_active = False\n",
    "        missings.append(name)\n",
    "        continue\n",
    "    v_min = np.min(values)\n",
    "    v_max = np.max(values)\n",
    "    v_span = v_max - v_min\n",
    "    if not np.isclose(v_min, v_max):\n",
    "        interface[name].range = (v_min - v_span * bounds_scale, v_max + v_span * bounds_scale)\n",
    "        interface[name].is_active = active"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "parm = []\n",
    "missings = [m.split(\":\")[0] for m in missings]\n",
    "missings = np.unique(missings)\n",
    "for m in missings:\n",
    "    parm += interface.get(atoms=m)\n",
    "\n",
    "for p in parm:\n",
    "    interface._all.remove(p)\n",
    "\n",
    "# reinzialize\n",
    "interface._reinitialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f14a5d2",
   "metadata": {},
   "source": [
    "Get get angle parameters atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Paramenter to train {interface.is_active.count(True)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b959d3e",
   "metadata": {},
   "source": [
    "## Torsions block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8125968f",
   "metadata": {},
   "source": [
    "#### Search Diehedral in the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ab2eae04",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mol = {}\n",
    "\n",
    "for name, job in geometry_ref.items():\n",
    "    mol = job.results.get_input_molecule()\n",
    "    mol = SCMtoASE(mol)\n",
    "    data_mol[name] = mol.copy()\n",
    "\n",
    "for i, (name, ase_mol) in enumerate(data_mol.items()):\n",
    "    if i == 0:\n",
    "        torsion_df = get_all_dihedrals(ase_mol, verbose=False)\n",
    "    else:\n",
    "        torsion_df = torsion_df.append(get_all_dihedrals(ase_mol, verbose=False))\n",
    "\n",
    "torsion_df.reset_index(inplace=True)\n",
    "torsion_df = torsion_df.rename(columns={\"index\": \"torsion_name\"})\n",
    "torsion_df = torsion_df.drop_duplicates(subset=[\"torsion_name\"])\n",
    "\n",
    "# serch in the system\n",
    "for ai, aj, ak, al in zip(torsion_df[\"ai\"], torsion_df[\"aj\"], torsion_df[\"ak\"], torsion_df[\"al\"]):\n",
    "    bonds_parm = interface.get(block=\"TOR\", atoms=[ai, aj, ak, al])\n",
    "    if len(bonds_parm) > 0:\n",
    "        print(f\"paramentrs for {ai}-{aj}-{ak}-{al} exist!\")\n",
    "    else:\n",
    "        print(f\"[W] paramentrs for {ai}-{aj}-{ak}-{al} DO NOT EXIST! it will be added\")\n",
    "        # interface.add_blocks([f'TOR:{ai}.{aj}.{ak}.{al}'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29887429",
   "metadata": {},
   "source": [
    "#### Activate existin parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "active = BLOCK_TORSION\n",
    "\n",
    "# get List\n",
    "block = []\n",
    "for ai, aj, ak, al in zip(torsion_df[\"ai\"], torsion_df[\"aj\"], torsion_df[\"ak\"], torsion_df[\"al\"]):\n",
    "    block += interface.get(block=\"TOR\", atoms=[ai, aj, ak, al])\n",
    "block_names = [b.name for b in block]\n",
    "queries = interface.params_from_db(\n",
    "    block_names, Li=(\"Na\", \"K\"), F=(\"Cl\", \"Br\")\n",
    ")  # both Li and Na, and K are Alkali metal, both F, Cl, and Br are Halogen\n",
    "# queries\n",
    "\n",
    "missings = []\n",
    "missings_atoms = []\n",
    "\n",
    "for q in queries:\n",
    "    name = q.query\n",
    "    values = q.values\n",
    "    if len(values) == 0:\n",
    "        interface[name].is_active = False\n",
    "        missings.append(name)\n",
    "        continue\n",
    "    v_min = np.min(values)\n",
    "    v_max = np.max(values)\n",
    "    v_span = v_max - v_min\n",
    "    if not np.isclose(v_min, v_max):\n",
    "        interface[name].range = (v_min - v_span * bounds_scale, v_max + v_span * bounds_scale)\n",
    "        interface[name].is_active = active"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Missings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "parm = []\n",
    "missings = [m.split(\":\")[0] for m in missings]\n",
    "missings = np.unique(missings)\n",
    "for m in missings:\n",
    "    parm += interface.get(atoms=m)\n",
    "\n",
    "for p in parm:\n",
    "    interface._all.remove(p)\n",
    "\n",
    "# reinzialize\n",
    "interface._reinitialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Paramenter to train {interface.is_active.count(True)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad85fdf",
   "metadata": {},
   "source": [
    "## Storing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0b99fd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 parametes to retraining.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{interface.is_active.count(True)} parametes to retraining.\")\n",
    "interface.pickle_dump(os.path.join(MAIN_DIR_JNB4, \"reaxff_parameters.pkl\"))\n",
    "interface.yaml_store(os.path.join(MAIN_DIR_JNB4, \"reaxff_parameters.yaml\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43190883",
   "metadata": {},
   "source": [
    "## Plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "362ed623",
   "metadata": {},
   "outputs": [],
   "source": [
    "parametes_name = interface.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eb1425eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for parm in parametes_name:\n",
    "    parm_ = parm.split(\":\")\n",
    "    if len(parm_) < 2:\n",
    "        rows.append(\"General\")\n",
    "    else:\n",
    "        if \";;\" in parm_[0]:\n",
    "            rows.append(\"General\")\n",
    "        else:\n",
    "            rows.append(\"-\".join(parm_[0].split(\".\")).replace(\"0\", \"*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "63807ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_name = {}\n",
    "cols = []\n",
    "for rows_, parm in zip(rows, parametes_name):\n",
    "    if rows_ in row_name.keys():\n",
    "        row_name[rows_] += [parm]\n",
    "    else:\n",
    "        row_name[rows_] = [parm]\n",
    "        # print(row_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e6398f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc = max(len(p) for p in row_name.values())\n",
    "nr = len(row_name)\n",
    "\n",
    "active_mtx = np.ones((nr, nc)) * np.nan\n",
    "\n",
    "for i, (par_type, par_name) in enumerate(row_name.items()):\n",
    "    for j, par in enumerate(par_name):\n",
    "        active_mtx[i, j] = float(interface[par].is_active)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5c93fd7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_mtx = np.zeros((nr, nc), dtype=\"<U7\")\n",
    "idx = np.where(active_mtx == 0.0)\n",
    "color_mtx[idx] = \"#ffffff\"\n",
    "idx = np.where(active_mtx == 1.0)\n",
    "color_mtx[idx] = \"#7bccc4\"\n",
    "idx = np.where(active_mtx != active_mtx)\n",
    "color_mtx[idx] = \"#d9d9d9\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3b914365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[7, 4], dpi=100, facecolor=\"white\")\n",
    "\n",
    "bbox_props = dict(boxstyle=\"square,pad=0.1\", fc=\"w\", ec=\"none\", lw=0.72)\n",
    "kw = dict(arrowprops=dict(arrowstyle=\"-\"), bbox=bbox_props, zorder=0, va=\"center\")\n",
    "\n",
    "\n",
    "with plt.style.context(\"seaborn\"):\n",
    "    spec = gridspec.GridSpec(ncols=1, nrows=1, figure=fig)\n",
    "    ax = fig.add_subplot(spec[0])\n",
    "    ax.axis(\"off\")\n",
    "    ax.axis(\"tight\")\n",
    "    ax.table(rowLabels=list(row_name.keys()), cellColours=color_mtx, loc=\"center\")\n",
    "\n",
    "fig.savefig(os.path.join(\"plots\", \"parameters.png\"), dpi=300, bbox_inches=\"tight\", facecolor=\"none\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}