{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08ee4c09-7fb5-4ce1-a7a3-5c5c52a6d4d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from ase import Atoms, Atom\n",
    "from ase.io import read, write\n",
    "from ase.data import chemical_symbols, covalent_radii, vdw_alvarez\n",
    "from ase.parallel import paropen as open\n",
    "from gpaw import GPAW, PW, FermiDirac, LCAO\n",
    "# from gpaw.new.ase_interface import GPAW\n",
    "from gpaw import Davidson\n",
    "from gpaw import Mixer, MixerSum, MixerDif\n",
    "from gpaw.directmin.etdm_lcao import LCAOETDM\n",
    "from gpaw.cdft.cdft import CDFT\n",
    "from pathlib import Path\n",
    "import os\n",
    "import numpy as np\n",
    "from pymatgen.core import Element\n",
    "from tqdm.auto import tqdm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90887faa-1601-4c4c-9c44-d16731471d7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b30d4d05feb144d9aaf062206dabc9c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='H2', pbc=True, cell=[15.0, 15.001, 15.002], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c69a861cba7040c494f062bea15216be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='C2', pbc=True, cell=[15.0, 15.001, 15.002], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe1c1d0c26394a54a3b734458f93f534",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='N2', pbc=True, cell=[15.0, 15.001, 15.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a651919fea245d1a1461adeccbd9941",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='O2', pbc=True, cell=[15.0, 15.001, 15.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ef80d3919514ac9828098f51d64b2c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Li2', pbc=True, cell=[15.0, 15.001, 15.002], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "399dc0992b304fd7a36f47864840123f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Be2', pbc=True, cell=[15.0, 15.001, 15.002], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f23732a547d42df82baa6b386d371cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c9ad8492a534483b249010062c128c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "068f21ad734e411c9bfae2f5fd059662",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did not converge!  See text output for help.\n",
      "Atoms(symbols='Cr2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002], initial_magmoms=..., calculator=SinglePointCalculator(...))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99d0786dd9f54142ba22c698b14418fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/29 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "magnetism = 'NM'\n",
    "\n",
    "# for symbol in tqdm(chemical_symbols):\n",
    "for symbol in tqdm(['H', 'C', 'N', 'O', 'Li', 'Be', 'Mg', 'Na', 'Cr', 'Fe', 'Mo', 'Cu', 'B', 'Si', 'As', 'Ge']):\n",
    "    \n",
    "    s = set([symbol])\n",
    "    \n",
    "    if 'X' in s:\n",
    "        continue\n",
    "        \n",
    "    try:\n",
    "        atom = Atom(symbol)\n",
    "        rmin = covalent_radii[atom.number] * 2 * 0.6\n",
    "        rvdw = vdw_alvarez.vdw_radii[atom.number] if atom.number < len(vdw_alvarez.vdw_radii) else np.nan \n",
    "        rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6\n",
    "        rstep = 0.2 #if rmin < 1 else 0.4\n",
    "\n",
    "        a = max(2 * rmax, 15)\n",
    "\n",
    "        da = symbol + symbol\n",
    "\n",
    "        # out_dir = Path(str(da + f\"_{magnetism}\"))\n",
    "        out_dir = Path(str(da))\n",
    "        os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "        npts = int((rmax - rmin)/rstep)\n",
    "        rs = np.linspace(rmin, rmax, npts)\n",
    "        e = np.zeros_like(rs)\n",
    "\n",
    "        r = rs[-1]\n",
    "\n",
    "        positions = [\n",
    "            [a/2-r/2, a/2, a/2],\n",
    "            [a/2+r/2, a/2, a/2],\n",
    "        ]\n",
    "\n",
    "        try:\n",
    "            element = Element(symbol)\n",
    "            m = element.valence[1]\n",
    "            if element.valence == (0, 2):\n",
    "                m = 0\n",
    "        except:\n",
    "            m = 0\n",
    "\n",
    "        skip = 0\n",
    "\n",
    "        traj_fpath = out_dir / \"PBE.extxyz\"\n",
    "\n",
    "        if traj_fpath.exists():\n",
    "            traj = read(traj_fpath, index=\":\")\n",
    "            skip = len(traj)\n",
    "            atoms = traj[-1]\n",
    "        else:\n",
    "            # Create the unit cell with two atoms\n",
    "            atoms = Atoms(\n",
    "                da, \n",
    "                positions=positions,\n",
    "                # magmoms=magmoms,\n",
    "                cell=[a, a+0.001, a+0.002], \n",
    "                pbc=True\n",
    "            )\n",
    "\n",
    "        # if magnetism == 'FM':\n",
    "        #     if m == 0:\n",
    "        #         continue\n",
    "        #     magmoms = [m, m]\n",
    "        # elif magnetism == 'AFM':\n",
    "        #     if m == 0:\n",
    "        #         continue\n",
    "        #     magmoms = [m, -m]\n",
    "        # elif magnetism == 'NM':\n",
    "        #     magmoms = [0, 0]\n",
    "\n",
    "\n",
    "\n",
    "        print(atoms)\n",
    "\n",
    "        restart_fpath = out_dir / 'restart.gpw'\n",
    "\n",
    "        calc = GPAW(\n",
    "            mode=PW(2000),\n",
    "            xc='PBE',\n",
    "            spinpol=True,\n",
    "            # basis='dzp'\n",
    "            basis='szp(dzp)',\n",
    "            # h=0.25,\n",
    "            # nbands=0 if element.is_noble_gas else '110%',\n",
    "            hund=False,\n",
    "            mixer=MixerDif(0.01, 1, 1) if element.is_transition_metal else MixerDif(0.25, 3, 10),\n",
    "            # eigensolver='rmm-diis', #Davidson(3),  # This solver can parallelize over bands Davidson(3), #\n",
    "            occupations=FermiDirac(0.2, fixmagmom=False), # if not element.is_metal else FermiDirac(0.2, fixmagmom=False),\n",
    "            # eigensolver=LCAOETDM(),\n",
    "            #     # searchdir_algo={'name': 'l-bfgs-p', 'memory': 10}),\n",
    "            # occupations={'name': 'fixed-uniform'},\n",
    "            # mixer={'backend': 'no-mixing'},\n",
    "            # nbands='nao',\n",
    "            symmetry={'point_group': True},\n",
    "            txt=out_dir / 'out.txt',\n",
    "            convergence={\n",
    "                'eigenstates': 1e-5,\n",
    "                'density': 5e-3,\n",
    "                'energy': 5e-4,\n",
    "                # 'bands': 4\n",
    "            },\n",
    "            # {'energy': 0.0005,  # eV / electron\n",
    "            #  'density': 1.0e-4,  # electrons / electron\n",
    "            #  'eigenstates': 4.0e-8,  # eV^2 / electron\n",
    "            #  'bands': 'occupied'},\n",
    "            # parallel={'gpu': True},\n",
    "            setups='paw'  \n",
    "        )\n",
    "        # calc = GPAW(\n",
    "        #     mode='pw', #PW(1500),\n",
    "        #     xc='PBE',\n",
    "        #     # spinpol=True,\n",
    "        #     # basis='szp(dzp)',\n",
    "        #     # h=0.25,\n",
    "        #     # nbands=0 if element.is_noble_gas else '110%',\n",
    "        #     hund=False,\n",
    "        #     # mixer=MixerDif(0.01, 1, 1) if element.is_transition_metal else MixerDif(0.25, 3, 10),\n",
    "        #     # eigensolver='rmm-diis', #Davidson(3),  # This solver can parallelize over bands Davidson(3), #\n",
    "        #     # occupations=FermiDirac(0.01, fixmagmom=False), # if not element.is_metal else FermiDirac(0.2, fixmagmom=False),\n",
    "        #     # eigensolver=LCAOETDM(),\n",
    "        #     #     # searchdir_algo={'name': 'l-bfgs-p', 'memory': 10}),\n",
    "        #     # occupations={'name': 'fixed-uniform'},\n",
    "        #     # mixer={'backend': 'no-mixing'},\n",
    "        #     # nbands='nao',\n",
    "        #     # symmetry={'point_group': True},\n",
    "        #     txt=out_dir / 'out.txt',\n",
    "        #     convergence={\n",
    "        #         'eigenstates': 1e-5,\n",
    "        #         'density': 5e-3,\n",
    "        #         'energy': 5e-4,\n",
    "        #         # 'bands': 4\n",
    "        #     },\n",
    "        #     # {'energy': 0.0005,  # eV / electron\n",
    "        #     #  'density': 1.0e-4,  # electrons / electron\n",
    "        #     #  'eigenstates': 4.0e-8,  # eV^2 / electron\n",
    "        #     #  'bands': 'occupied'},\n",
    "        #     parallel={'gpu': True},\n",
    "        #     setups='paw'\n",
    "        # )\n",
    "        # calc.attach(calc.write, 10, restart_fpath, mode='all')\n",
    "\n",
    "        atoms.calc = calc\n",
    "\n",
    "        # cdft = CDFT(calc=calc, atoms=atoms, spinspin_regions=      \n",
    "        # atoms.calc = cdft\n",
    "\n",
    "        for i, r in enumerate(tqdm(np.flip(rs))):\n",
    "\n",
    "            if i < skip:\n",
    "                continue\n",
    "\n",
    "            positions = [\n",
    "                [a/2-r/2, a/2, a/2],\n",
    "                [a/2+r/2, a/2, a/2],\n",
    "            ]\n",
    "\n",
    "            atoms.set_positions(positions)\n",
    "\n",
    "            if i == 0:\n",
    "                fm_magmoms = [m, m]\n",
    "                atoms.set_initial_magnetic_moments(fm_magmoms) \n",
    "                fm_energy = atoms.get_potential_energy()\n",
    "                # shutil.move(out_dir / \"WAVECAR\", out_dir / \"WAVECAR_FM\")\n",
    "\n",
    "                afm_magmoms = [m, -m]\n",
    "                atoms.set_initial_magnetic_moments(afm_magmoms) \n",
    "                afm_energy = atoms.get_potential_energy()\n",
    "                # shutil.move(out_dir / \"WAVECAR\", out_dir / \"WAVECAR_AFM\")\n",
    "\n",
    "                if fm_energy <= afm_energy:\n",
    "                    magmoms = fm_magmoms\n",
    "                    atoms.set_initial_magnetic_moments(magmoms) \n",
    "                    # shutil.move(out_dir / \"WAVECAR_FM\", out_dir / \"WAVECAR\")       \n",
    "                else:\n",
    "                    magmoms = afm_magmoms\n",
    "                    atoms.set_initial_magnetic_moments(magmoms)\n",
    "                    # shutil.move(out_dir / \"WAVECAR_AFM\", out_dir / \"WAVECAR\")\n",
    "\n",
    "#             if i > 0:   \n",
    "#                 magmoms = atoms.get_magnetic_moments()\n",
    "\n",
    "#             atoms.set_initial_magnetic_moments(magmoms)\n",
    "                # m = min(abs(magmoms[0])*1.2, m)\n",
    "                # magmoms = magmoms*m/np.abs(magmoms)\n",
    "\n",
    "            e[i] = atoms.get_potential_energy()\n",
    "\n",
    "            atoms.calc.results.update({\n",
    "                \"forces\": atoms.get_forces()\n",
    "            })\n",
    "\n",
    "            write(traj_fpath, atoms, append=True)\n",
    "    except Exception as e:\n",
    "        print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06118461-8db9-49f0-8aae-2150146ab9b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!echo $GPAW_NEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "df = pd.DataFrame(columns=['name', 'method', 'R', 'E', 'F', 'S^2'])\n",
    "\n",
    "\n",
    "\n",
    "for symbol in tqdm(chemical_symbols):\n",
    "    \n",
    "    for magnetism in ['AFM', 'FM', 'NM']:\n",
    "    \n",
    "        da = symbol + symbol\n",
    "\n",
    "        # out_dir = Path(da)\n",
    "        out_dir = Path(str(da + f\"_{magnetism}\"))\n",
    "\n",
    "        traj_fpath = out_dir / \"traj.extxyz\"\n",
    "\n",
    "        if traj_fpath.exists():\n",
    "            traj = read(traj_fpath, index=\":\")\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        Rs, Es, Fs, S2s = [], [], [], []\n",
    "        for atoms in traj:\n",
    "\n",
    "            vec = atoms.positions[1] - atoms.positions[0]\n",
    "            r = np.linalg.norm(vec)\n",
    "            e = atoms.get_potential_energy()\n",
    "            # f = np.inner(vec/r, atoms.get_forces()[1])\n",
    "            # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
    "\n",
    "            Rs.append(r)\n",
    "            Es.append(e)\n",
    "            # Fs.append(f)\n",
    "            # S2s.append(s2)\n",
    "\n",
    "        data = {\n",
    "            'name': da,\n",
    "            'method': f'GGA-PBE (GPAW): {magnetism}',\n",
    "            'R': Rs,\n",
    "            'E': Es,\n",
    "            'F': Fs,\n",
    "            'S^2': S2s\n",
    "        }\n",
    "\n",
    "        df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
    "\n",
    "json_fpath = 'homonuclear-diatomics.json'\n",
    "\n",
    "df.to_json(json_fpath, orient='records')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d4a7312-a619-411f-9c6f-36c40cd47a34",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlip-arena",
   "language": "python",
   "name": "mlip-arena"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "08bb114ae5674fd1af84c459d1407555": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_43a625312a50485faa2f10807c19a5c3",
       "style": "IPY_MODEL_7d3e41a01d7f4a3f997b510ad7be62c7",
       "value": "  0%"
      }
     },
     "16d09a1085ce4b7c94d3c9838e7b6047": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "378fd424e42c4a02a76319ddf1f4773e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_f6e60cbeb3a246a189de60bfddf1ea24",
       "style": "IPY_MODEL_6deae131201c46af8be20e2129d17176",
       "value": " 0/28 [00:00&lt;?, ?it/s]"
      }
     },
     "398c7fc38b92419a956e17bb3f69f61f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_9dbfeae19c864b1cb2df6c4b39bfa9c9",
        "IPY_MODEL_3bb3f03b83284c0bbdc316fdc313384f",
        "IPY_MODEL_378fd424e42c4a02a76319ddf1f4773e"
       ],
       "layout": "IPY_MODEL_cd8b7bf098b84df197fcd498084e5afd"
      }
     },
     "3bb3f03b83284c0bbdc316fdc313384f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "layout": "IPY_MODEL_c98b44b3cdfc4d1180786cf11521d9d3",
       "max": 28,
       "style": "IPY_MODEL_787e84a3b17d4f8ba132db1a5ed466b3"
      }
     },
     "43a625312a50485faa2f10807c19a5c3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "43e1040562d1439b801ecfedf5035aee": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "45675de1547d489d9f935cb432b4fadf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "bar_style": "danger",
       "layout": "IPY_MODEL_4d3c75a82de345ad9d01ef5507126023",
       "max": 29,
       "style": "IPY_MODEL_79037b808e294df393a993227a4ff8c3"
      }
     },
     "4cd7b5fa3e214f74a66ed4d99d5025a4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_67252eabaa4348d8a3fd4cf0c7653e97",
        "IPY_MODEL_988805506e274daaa19a059f19fb51b8",
        "IPY_MODEL_a0e545db7c0b4b8289e72e53c19fdc3c"
       ],
       "layout": "IPY_MODEL_b75fa1272c794c1cbb66cb39d61a5375"
      }
     },
     "4d3c75a82de345ad9d01ef5507126023": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "51a4c221d21544bdb91e98fe6e10dd43": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "67252eabaa4348d8a3fd4cf0c7653e97": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_b624b02837f44ae78ec7ecd29ed0f73b",
       "style": "IPY_MODEL_51a4c221d21544bdb91e98fe6e10dd43",
       "value": "100%"
      }
     },
     "6deae131201c46af8be20e2129d17176": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "787e84a3b17d4f8ba132db1a5ed466b3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "79037b808e294df393a993227a4ff8c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "7d3e41a01d7f4a3f997b510ad7be62c7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "805f79a3d7bd493092168b98de443e0b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8383a80b3a504b58a50458f237ecafd8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "91aa307807cd4abc909e1a0330674020": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "988805506e274daaa19a059f19fb51b8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "bar_style": "success",
       "layout": "IPY_MODEL_91aa307807cd4abc909e1a0330674020",
       "max": 28,
       "style": "IPY_MODEL_16d09a1085ce4b7c94d3c9838e7b6047",
       "value": 28
      }
     },
     "9dbfeae19c864b1cb2df6c4b39bfa9c9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_805f79a3d7bd493092168b98de443e0b",
       "style": "IPY_MODEL_bb9f29e22202434b9988e1850ac188fc",
       "value": "  0%"
      }
     },
     "a0e545db7c0b4b8289e72e53c19fdc3c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_8383a80b3a504b58a50458f237ecafd8",
       "style": "IPY_MODEL_faf0fccdc8044bc69f12c930c57d4e9f",
       "value": " 28/28 [7:25:40&lt;00:00, 2734.24s/it]"
      }
     },
     "ac2f01b8c798419cbd4daf5c12384219": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_08bb114ae5674fd1af84c459d1407555",
        "IPY_MODEL_45675de1547d489d9f935cb432b4fadf",
        "IPY_MODEL_e4a50abf99f3496f901a8bb16495cc91"
       ],
       "layout": "IPY_MODEL_cd191bca40d541228d4826c3885066d7"
      }
     },
     "ac7f987a9bcc4c558b6773d573d97134": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b624b02837f44ae78ec7ecd29ed0f73b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b75fa1272c794c1cbb66cb39d61a5375": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "bb9f29e22202434b9988e1850ac188fc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "c98b44b3cdfc4d1180786cf11521d9d3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cd191bca40d541228d4826c3885066d7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cd8b7bf098b84df197fcd498084e5afd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e4a50abf99f3496f901a8bb16495cc91": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "layout": "IPY_MODEL_ac7f987a9bcc4c558b6773d573d97134",
       "style": "IPY_MODEL_43e1040562d1439b801ecfedf5035aee",
       "value": " 0/29 [00:06&lt;?, ?it/s]"
      }
     },
     "f6e60cbeb3a246a189de60bfddf1ea24": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "faf0fccdc8044bc69f12c930c57d4e9f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}