notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb

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    "# Introduction\n",
    "\n",
    "This is an example of how to plot a Crystal Orbital Hamilton Population (COHP) calculated with the Local-Orbital Basis Suite Towards Electronic-Structure Reconstruction (LOBSTER) code. See http://schmeling.ac.rwth-aachen.de/cohp/ for more information. (Credit: Marco Esters and Anubhav Jain)."
   ]
  },
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   "execution_count": 1,
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   "source": [
    "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n",
    "# !pip install pymatgen==2022.7.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (402,) + inhomogeneous part.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymatgen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlobster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Cohpcar\n\u001b[1;32m      5\u001b[0m COHPCAR_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlobster_data/GaAs/COHPCAR.lobster_new\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 6\u001b[0m cohpcar \u001b[38;5;241m=\u001b[39m \u001b[43mCohpcar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCOHPCAR_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m cdata \u001b[38;5;241m=\u001b[39m cohpcar\u001b[38;5;241m.\u001b[39mcohp_data\n\u001b[1;32m      8\u001b[0m cdata_processed \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/pymatgen/io/lobster/outputs.py:128\u001b[0m, in \u001b[0;36mCohpcar.__init__\u001b[0;34m(self, are_coops, are_cobis, are_multi_center_cobis, filename)\u001b[0m\n\u001b[1;32m    125\u001b[0m cohp_data: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mare_multi_center_cobis:\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;66;03m# The COHP data start in row num_bonds + 3\u001b[39;00m\n\u001b[0;32m--> 128\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcontents\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnum_bonds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtranspose()\n\u001b[1;32m    129\u001b[0m     cohp_data \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m    131\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m    132\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mICOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m    133\u001b[0m         }\n\u001b[1;32m    134\u001b[0m     }\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;66;03m# The COBI data start in row num_bonds + 3 if multi-center cobis exist\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (402,) + inhomogeneous part."
     ]
    }
   ],
   "source": [
    "from pymatgen.electronic_structure.cohp import Cohp\n",
    "from pymatgen.electronic_structure.plotter import CohpPlotter\n",
    "from pymatgen.io.lobster import Cohpcar\n",
    "\n",
    "COHPCAR_path = \"lobster_data/COHPCAR.lobster\"\n",
    "cohpcar = Cohpcar(filename=COHPCAR_path)\n",
    "cdata = cohpcar.cohp_data\n",
    "cdata_processed = {}\n",
    "for key in cdata:\n",
    "    c = cdata[key]\n",
    "    c[\"efermi\"] = 0\n",
    "    c[\"energies\"] = cohpcar.energies\n",
    "    c[\"are_coops\"] = False\n",
    "    cdata_processed[key] = Cohp.from_dict(c)\n",
    "cp = CohpPlotter()\n",
    "cp.add_cohp_dict(cdata_processed)\n",
    "ax = cp.get_plot()\n",
    "ax.set_ylim([-6, 6])"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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