01_get_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8958b06f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gkrenzer/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.25.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import phonopy\n",
    "from monty.serialization import loadfn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import gc\n",
    "import scipy.constants\n",
    "from scipy import interpolate\n",
    "from scipy.signal import find_peaks \n",
    "import math\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54af126",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Read the database lookup file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6f8ed0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_db = #phonon_db directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd8a2b3f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/gabrielkrenzer/Documents/PhD/projects/unsupervised_ml/phononDB/phonon_db_lookup_df.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_196/1137639758.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Create a dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir_db\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/phonon_db_lookup_df.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/monty/serialization.py\u001b[0m in \u001b[0;36mloadfn\u001b[0;34m(fn, fmt, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmsgpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=E1101\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mzopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"yaml\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mYAML\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/monty/io.py\u001b[0m in \u001b[0;36mzopen\u001b[0;34m(filename, *args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mext\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\".XZ\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\".LZMA\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlzma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=R1732\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/gabrielkrenzer/Documents/PhD/projects/unsupervised_ml/phononDB/phonon_db_lookup_df.json'"
     ]
    }
   ],
   "source": [
    "# Create a dataframe\n",
    "df = loadfn(dir_db+'/phonon_db_lookup_df.json')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77bcfef3",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get the species with Li element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4f6fcf0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of the 10034 materials in this dataset, 873 contain lithium \n",
      "\n"
     ]
    },
    {
     "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>material_id</th>\n",
       "      <th>elements</th>\n",
       "      <th>formula_pretty_reduced</th>\n",
       "      <th>formula</th>\n",
       "      <th>num_elements</th>\n",
       "      <th>chemsys</th>\n",
       "      <th>spacegroup_symbol</th>\n",
       "      <th>spacegroup_number</th>\n",
       "      <th>FORCE_SETS_filepath</th>\n",
       "      <th>phonon_filepath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mp-8892</td>\n",
       "      <td>[Li, F, In]</td>\n",
       "      <td>LiInF4</td>\n",
       "      <td>Li4 In4 F16</td>\n",
       "      <td>3</td>\n",
       "      <td>F-In-Li</td>\n",
       "      <td>Pbcn</td>\n",
       "      <td>60</td>\n",
       "      <td>phonon_db/mp-8892-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-8892-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mp-772968</td>\n",
       "      <td>[Li, Tl, P, H, O]</td>\n",
       "      <td>LiTlPHO3</td>\n",
       "      <td>Li4 Tl4 P4 H4 O12</td>\n",
       "      <td>5</td>\n",
       "      <td>H-Li-O-P-Tl</td>\n",
       "      <td>C2</td>\n",
       "      <td>5</td>\n",
       "      <td>phonon_db/mp-772968-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-772968-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mp-756951</td>\n",
       "      <td>[Li, Sb, O]</td>\n",
       "      <td>LiSbO3</td>\n",
       "      <td>Li4 Sb4 O12</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-O-Sb</td>\n",
       "      <td>C2/m</td>\n",
       "      <td>12</td>\n",
       "      <td>phonon_db/mp-756951-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-756951-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mp-754856</td>\n",
       "      <td>[Li, Y, Zr, S]</td>\n",
       "      <td>Li7Y7ZrS16</td>\n",
       "      <td>Li7 Y7 Zr1 S16</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-S-Y-Zr</td>\n",
       "      <td>P2/m</td>\n",
       "      <td>10</td>\n",
       "      <td>phonon_db/mp-754856-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-754856-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mp-24610</td>\n",
       "      <td>[H, Li, O, P]</td>\n",
       "      <td>LiP(HO2)2</td>\n",
       "      <td>Li4 P4 H8 O16</td>\n",
       "      <td>4</td>\n",
       "      <td>H-Li-O-P</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "      <td>phonon_db/mp-24610-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-24610-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>mp-9919</td>\n",
       "      <td>[Li, Zn, Sb]</td>\n",
       "      <td>LiZnSb</td>\n",
       "      <td>Li2 Zn2 Sb2</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-Sb-Zn</td>\n",
       "      <td>P6_3mc</td>\n",
       "      <td>186</td>\n",
       "      <td>phonon_db/mp-9919-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-9919-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>mp-14704</td>\n",
       "      <td>[Li, B, O, Y]</td>\n",
       "      <td>Li6Y(BO3)3</td>\n",
       "      <td>Li24 Y4 B12 O36</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Li-O-Y</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>phonon_db/mp-14704-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-14704-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mp-557756</td>\n",
       "      <td>[Li, Zn, P, O]</td>\n",
       "      <td>Li4Zn(PO4)2</td>\n",
       "      <td>Li16 Zn4 P8 O32</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-O-P-Zn</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>phonon_db/mp-557756-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-557756-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>mp-966801</td>\n",
       "      <td>[Li, Ca, B, O]</td>\n",
       "      <td>LiCaBO3</td>\n",
       "      <td>Li8 Ca8 B8 O24</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Ca-Li-O</td>\n",
       "      <td>Ibca</td>\n",
       "      <td>73</td>\n",
       "      <td>phonon_db/mp-966801-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-966801-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>mp-772721</td>\n",
       "      <td>[Li, Si, Bi, O]</td>\n",
       "      <td>LiSi6Bi9O26</td>\n",
       "      <td>Li2 Si12 Bi18 O52</td>\n",
       "      <td>4</td>\n",
       "      <td>Bi-Li-O-Si</td>\n",
       "      <td>P-3</td>\n",
       "      <td>147</td>\n",
       "      <td>phonon_db/mp-772721-20180417/FORCE_SETS</td>\n",
       "      <td>phonon_db/mp-772721-20180417/phonon.yaml</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    material_id           elements formula_pretty_reduced            formula  \\\n",
       "0       mp-8892        [Li, F, In]                 LiInF4        Li4 In4 F16   \n",
       "1     mp-772968  [Li, Tl, P, H, O]               LiTlPHO3  Li4 Tl4 P4 H4 O12   \n",
       "2     mp-756951        [Li, Sb, O]                 LiSbO3        Li4 Sb4 O12   \n",
       "3     mp-754856     [Li, Y, Zr, S]             Li7Y7ZrS16     Li7 Y7 Zr1 S16   \n",
       "4      mp-24610      [H, Li, O, P]              LiP(HO2)2      Li4 P4 H8 O16   \n",
       "..          ...                ...                    ...                ...   \n",
       "868     mp-9919       [Li, Zn, Sb]                 LiZnSb        Li2 Zn2 Sb2   \n",
       "869    mp-14704      [Li, B, O, Y]             Li6Y(BO3)3    Li24 Y4 B12 O36   \n",
       "870   mp-557756     [Li, Zn, P, O]            Li4Zn(PO4)2    Li16 Zn4 P8 O32   \n",
       "871   mp-966801     [Li, Ca, B, O]                LiCaBO3     Li8 Ca8 B8 O24   \n",
       "872   mp-772721    [Li, Si, Bi, O]            LiSi6Bi9O26  Li2 Si12 Bi18 O52   \n",
       "\n",
       "     num_elements      chemsys spacegroup_symbol  spacegroup_number  \\\n",
       "0               3      F-In-Li              Pbcn                 60   \n",
       "1               5  H-Li-O-P-Tl                C2                  5   \n",
       "2               3      Li-O-Sb              C2/m                 12   \n",
       "3               4    Li-S-Y-Zr              P2/m                 10   \n",
       "4               4     H-Li-O-P            Pna2_1                 33   \n",
       "..            ...          ...               ...                ...   \n",
       "868             3     Li-Sb-Zn            P6_3mc                186   \n",
       "869             4     B-Li-O-Y            P2_1/c                 14   \n",
       "870             4    Li-O-P-Zn            P2_1/c                 14   \n",
       "871             4    B-Ca-Li-O              Ibca                 73   \n",
       "872             4   Bi-Li-O-Si               P-3                147   \n",
       "\n",
       "                         FORCE_SETS_filepath  \\\n",
       "0      phonon_db/mp-8892-20180417/FORCE_SETS   \n",
       "1    phonon_db/mp-772968-20180417/FORCE_SETS   \n",
       "2    phonon_db/mp-756951-20180417/FORCE_SETS   \n",
       "3    phonon_db/mp-754856-20180417/FORCE_SETS   \n",
       "4     phonon_db/mp-24610-20180417/FORCE_SETS   \n",
       "..                                       ...   \n",
       "868    phonon_db/mp-9919-20180417/FORCE_SETS   \n",
       "869   phonon_db/mp-14704-20180417/FORCE_SETS   \n",
       "870  phonon_db/mp-557756-20180417/FORCE_SETS   \n",
       "871  phonon_db/mp-966801-20180417/FORCE_SETS   \n",
       "872  phonon_db/mp-772721-20180417/FORCE_SETS   \n",
       "\n",
       "                              phonon_filepath  \n",
       "0      phonon_db/mp-8892-20180417/phonon.yaml  \n",
       "1    phonon_db/mp-772968-20180417/phonon.yaml  \n",
       "2    phonon_db/mp-756951-20180417/phonon.yaml  \n",
       "3    phonon_db/mp-754856-20180417/phonon.yaml  \n",
       "4     phonon_db/mp-24610-20180417/phonon.yaml  \n",
       "..                                        ...  \n",
       "868    phonon_db/mp-9919-20180417/phonon.yaml  \n",
       "869   phonon_db/mp-14704-20180417/phonon.yaml  \n",
       "870  phonon_db/mp-557756-20180417/phonon.yaml  \n",
       "871  phonon_db/mp-966801-20180417/phonon.yaml  \n",
       "872  phonon_db/mp-772721-20180417/phonon.yaml  \n",
       "\n",
       "[873 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def element_filtered_dataframe(df, element):\n",
    "    \"\"\"A function which filters a dataframe for a particular element and returns a new dataframe with only entries containing that element.\n",
    "    \n",
    "    Input:\n",
    "        df (pd.DataFrame): The original dataframe which describes the database\n",
    "        element (str): The element which you want to query the dataframe for.\n",
    "    Output:\n",
    "        filtered_df (pd.DataFrame): A new dataframe with only entries which contain the element of interest\"\"\"\n",
    "    \n",
    "    return df.loc[df['chemsys'].str.contains(element)].reset_index(drop=True)\n",
    "\n",
    "Li_df = element_filtered_dataframe(df,'Li')\n",
    "print(f'Of the {df.shape[0]} materials in this dataset, {Li_df.shape[0]} contain lithium \\n')\n",
    "\n",
    "Li_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe91102",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "An extra variable 'formula' for reading Li pdos later in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0155fd6c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "formula = Li_df['formula'].str.split(\" \", n=-1, expand=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af42c66c",
   "metadata": {},
   "source": [
    "# Get DOS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f8601c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The folder path will contain all dos and pdos files in dat format for 873 species containing lithium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b51f363e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "folder_path = #path of the folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3df1786d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def loading (index):\n",
    "    '''loading file from the database'''\n",
    "    phonon_filepath = dir_db+'/'+Li_df['phonon_filepath'][index]\n",
    "    print(phonon_filepath)\n",
    "    force_sets_filepath = dir_db+'/'+Li_df['FORCE_SETS_filepath'][index]\n",
    "    print(force_sets_filepath)\n",
    "\n",
    "    ph = phonopy.load(phonon_filepath, force_sets_filename=force_sets_filepath, log_level=1)\n",
    "    \n",
    "    return ph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3ea5766",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def separate_text (string):\n",
    "    '''Separate string e.g. Li2 into Li and 2'''\n",
    "    match = re.match(r\"([a-z]+)([0-9]+)\", string , re.I)\n",
    "    if match:\n",
    "        res = match.groups()\n",
    "        \n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5864be90",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_Li_entry (index):\n",
    "    '''Return a list of entries for Li atom pdos in dat file'''\n",
    "    temp = {}\n",
    "    shape = np.shape(formula)\n",
    "    length = shape[1]\n",
    "    for i in [*range (0, length)]:\n",
    "        if formula.loc[index,i] != None:\n",
    "            t = formula.loc[index,i]\n",
    "            res = separate_text(t)\n",
    "            temp[res[0]] = int(res[1])\n",
    "\n",
    "    keys = [*temp.keys()]\n",
    "\n",
    "    if keys[0] == 'Li':\n",
    "        no_of_entry = temp['Li']\n",
    "        entry = [*range(1,no_of_entry+1)]\n",
    "\n",
    "    elif keys[0] != 'Li':\n",
    "        Li_index = keys.index('Li')\n",
    "        index_list = [*range(0,Li_index)]\n",
    "        start = 0\n",
    "        for i in index_list:\n",
    "            start += temp[keys[i]]\n",
    "        start += 1\n",
    "        no_of_entry = temp['Li']\n",
    "        entry = [*range(start, start + no_of_entry)]\n",
    "\n",
    "    return entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "880a6137",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_unmatch ():\n",
    "    '''not necessary, only if pdos file has less atom data than actual formula'''\n",
    "    \n",
    "    all_index = [*range (0, Li_df.index[-1]+1)] \n",
    "    unmatch = []\n",
    "    \n",
    "    for index in all_index:\n",
    "        temp = {}\n",
    "        shape = np.shape(formula)\n",
    "        length = shape[1]\n",
    "        for i in [*range (0, length)]:\n",
    "            if formula.loc[index,i] != None:\n",
    "                t = formula.loc[index,i]\n",
    "                res = separate_text(t)\n",
    "                temp[res[0]] = int(res[1])\n",
    "\n",
    "        keys = [*temp.keys()]\n",
    "\n",
    "        total_entry = 0\n",
    "        for i in keys:\n",
    "            total_entry += temp[i]\n",
    "\n",
    "        total_entry += 1 #inclue the frequency row\n",
    "        \n",
    "        file = np.loadtxt(os.path.join(folder_path, \"projected_dos_{0}.dat\".format(index)),unpack = True)\n",
    "        shape = np.shape(file)\n",
    "        file_entry = shape[0]\n",
    "        \n",
    "        if total_entry != file_entry:\n",
    "            unmatch.append(index)\n",
    "            \n",
    "        return unmatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c18a6f8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_total_no_atoms ():\n",
    "    '''find total no of atoms in a unit cell'''\n",
    "\n",
    "    all_index = [*range (0, Li_df.index[-1]+1)]\n",
    "    total_no_atoms = {}\n",
    "    for index in all_index:\n",
    "        temp = {}\n",
    "        shape = np.shape(formula)\n",
    "        length = shape[1]\n",
    "        for i in [*range (0, length)]:\n",
    "            if formula.loc[index,i] != None:\n",
    "                t = formula.loc[index,i]\n",
    "                res = separate_text(t)\n",
    "                temp[res[0]] = int(res[1])\n",
    "\n",
    "        keys = [*temp.keys()]\n",
    "\n",
    "        total_entry = 0\n",
    "        for i in keys:\n",
    "            total_entry += temp[i]\n",
    "\n",
    "        total_no_atoms[index] = total_entry\n",
    "\n",
    "    return total_no_atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a41634f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_most_atom_species ():\n",
    "    '''find the index of the species that has the largest number of atoms in a unit cell'''\n",
    "\n",
    "    result = find_total_no_atoms()\n",
    "    species = list(result.keys())[list(result.values()).index(max(result.values()))]\n",
    "\n",
    "    return species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8466cb3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_total_dos(index):\n",
    "    '''Get total DOS values in a list'''\n",
    "    file = np.loadtxt(os.path.join(folder_path, \"total_dos_{0}.dat\".format(index)),unpack = True)\n",
    "    total_dos = file[1]\n",
    "    \n",
    "    return total_dos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f72d6cb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_total_f(index):\n",
    "    '''Get total frequency values in a list'''\n",
    "    file = np.loadtxt(os.path.join(folder_path, \"total_dos_{0}.dat\".format(index)),unpack = True)\n",
    "    total_f = file[0]\n",
    "    \n",
    "    return total_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "718832be",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_partial_dos (index):\n",
    "    '''Get partial DOS values in a numpy array'''\n",
    "    #entry should be a list of numbers corresponding to the columns of the species of interest\n",
    "    file = np.loadtxt(os.path.join(folder_path, \"projected_dos_{0}.dat\".format(index)),unpack = True)\n",
    "    entry = find_Li_entry(index)\n",
    "    partial_dos = 0\n",
    "    for i in entry:\n",
    "        partial_dos += file [i]\n",
    "    \n",
    "    return partial_dos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "531f598e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_partial_f (index):\n",
    "    '''Get partial DOS frequency in a numpy array'''\n",
    "    #entry should be a list of numbers corresponding to the columns of the species of interest\n",
    "    file = np.loadtxt(os.path.join(folder_path, \"projected_dos_{0}.dat\".format(index)),unpack = True)\n",
    "    partial_f = file[0]\n",
    "    \n",
    "    return partial_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4e1a808",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def run_all_dos( mesh = 50):\n",
    "    '''takes around 2 hrs for 800+ data, the larger the mesh, the better the quality of dos data'''\n",
    "\n",
    "    global fail_total\n",
    "    global fail_projected\n",
    "\n",
    "    fail_total = []\n",
    "    fail_projected = []\n",
    "\n",
    "    index = [*range (0, Li_df.index[-1]+1)]\n",
    "    for i in index:\n",
    "        \n",
    "        ph = loading(i)\n",
    "        ph.run_mesh(mesh, with_eigenvectors=True, is_mesh_symmetry=False)\n",
    "\n",
    "        filename_1 = os.path.join(folder_path, \"total_dos_{0}.dat\".format(i))\n",
    "        filename_2 = os.path.join(folder_path, \"projected_dos_{0}.dat\".format(i))\n",
    "\n",
    "        try:\n",
    "            ph.run_total_dos()\n",
    "            ph.write_total_dos(filename = filename_1)\n",
    "        except MemoryError as e:\n",
    "            fail_total.append(i)\n",
    "            pass\n",
    "\n",
    "        try:\n",
    "            ph.run_projected_dos()\n",
    "            ph.write_projected_dos(filename = filename_2)\n",
    "        except MemoryError as e:\n",
    "            fail_projected.append(i)\n",
    "            pass\n",
    "\n",
    "        print(i)\n",
    "\n",
    "        del ph\n",
    "        gc.collect()\n",
    "        \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ff56313",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "run_all_dos(mesh = 50) #comment out once DOS data is obtained\n",
    "#default is mesh = 50 which is low enough to run by regular computer, mesh can take either integer or list of three values\n",
    "#phonopy default is 100 or [20, 20, 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6eea33",
   "metadata": {},
   "source": [
    "# Data visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739b2889",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Prepare a dataframe for output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3d1e788",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "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>material_id</th>\n",
       "      <th>elements</th>\n",
       "      <th>formula_pretty_reduced</th>\n",
       "      <th>formula</th>\n",
       "      <th>num_elements</th>\n",
       "      <th>chemsys</th>\n",
       "      <th>spacegroup_symbol</th>\n",
       "      <th>spacegroup_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mp-8892</td>\n",
       "      <td>[Li, F, In]</td>\n",
       "      <td>LiInF4</td>\n",
       "      <td>Li4 In4 F16</td>\n",
       "      <td>3</td>\n",
       "      <td>F-In-Li</td>\n",
       "      <td>Pbcn</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mp-772968</td>\n",
       "      <td>[Li, Tl, P, H, O]</td>\n",
       "      <td>LiTlPHO3</td>\n",
       "      <td>Li4 Tl4 P4 H4 O12</td>\n",
       "      <td>5</td>\n",
       "      <td>H-Li-O-P-Tl</td>\n",
       "      <td>C2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mp-756951</td>\n",
       "      <td>[Li, Sb, O]</td>\n",
       "      <td>LiSbO3</td>\n",
       "      <td>Li4 Sb4 O12</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-O-Sb</td>\n",
       "      <td>C2/m</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mp-754856</td>\n",
       "      <td>[Li, Y, Zr, S]</td>\n",
       "      <td>Li7Y7ZrS16</td>\n",
       "      <td>Li7 Y7 Zr1 S16</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-S-Y-Zr</td>\n",
       "      <td>P2/m</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mp-24610</td>\n",
       "      <td>[H, Li, O, P]</td>\n",
       "      <td>LiP(HO2)2</td>\n",
       "      <td>Li4 P4 H8 O16</td>\n",
       "      <td>4</td>\n",
       "      <td>H-Li-O-P</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>mp-9919</td>\n",
       "      <td>[Li, Zn, Sb]</td>\n",
       "      <td>LiZnSb</td>\n",
       "      <td>Li2 Zn2 Sb2</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-Sb-Zn</td>\n",
       "      <td>P6_3mc</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>mp-14704</td>\n",
       "      <td>[Li, B, O, Y]</td>\n",
       "      <td>Li6Y(BO3)3</td>\n",
       "      <td>Li24 Y4 B12 O36</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Li-O-Y</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mp-557756</td>\n",
       "      <td>[Li, Zn, P, O]</td>\n",
       "      <td>Li4Zn(PO4)2</td>\n",
       "      <td>Li16 Zn4 P8 O32</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-O-P-Zn</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>mp-966801</td>\n",
       "      <td>[Li, Ca, B, O]</td>\n",
       "      <td>LiCaBO3</td>\n",
       "      <td>Li8 Ca8 B8 O24</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Ca-Li-O</td>\n",
       "      <td>Ibca</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>mp-772721</td>\n",
       "      <td>[Li, Si, Bi, O]</td>\n",
       "      <td>LiSi6Bi9O26</td>\n",
       "      <td>Li2 Si12 Bi18 O52</td>\n",
       "      <td>4</td>\n",
       "      <td>Bi-Li-O-Si</td>\n",
       "      <td>P-3</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    material_id           elements formula_pretty_reduced            formula  \\\n",
       "0       mp-8892        [Li, F, In]                 LiInF4        Li4 In4 F16   \n",
       "1     mp-772968  [Li, Tl, P, H, O]               LiTlPHO3  Li4 Tl4 P4 H4 O12   \n",
       "2     mp-756951        [Li, Sb, O]                 LiSbO3        Li4 Sb4 O12   \n",
       "3     mp-754856     [Li, Y, Zr, S]             Li7Y7ZrS16     Li7 Y7 Zr1 S16   \n",
       "4      mp-24610      [H, Li, O, P]              LiP(HO2)2      Li4 P4 H8 O16   \n",
       "..          ...                ...                    ...                ...   \n",
       "868     mp-9919       [Li, Zn, Sb]                 LiZnSb        Li2 Zn2 Sb2   \n",
       "869    mp-14704      [Li, B, O, Y]             Li6Y(BO3)3    Li24 Y4 B12 O36   \n",
       "870   mp-557756     [Li, Zn, P, O]            Li4Zn(PO4)2    Li16 Zn4 P8 O32   \n",
       "871   mp-966801     [Li, Ca, B, O]                LiCaBO3     Li8 Ca8 B8 O24   \n",
       "872   mp-772721    [Li, Si, Bi, O]            LiSi6Bi9O26  Li2 Si12 Bi18 O52   \n",
       "\n",
       "     num_elements      chemsys spacegroup_symbol  spacegroup_number  \n",
       "0               3      F-In-Li              Pbcn                 60  \n",
       "1               5  H-Li-O-P-Tl                C2                  5  \n",
       "2               3      Li-O-Sb              C2/m                 12  \n",
       "3               4    Li-S-Y-Zr              P2/m                 10  \n",
       "4               4     H-Li-O-P            Pna2_1                 33  \n",
       "..            ...          ...               ...                ...  \n",
       "868             3     Li-Sb-Zn            P6_3mc                186  \n",
       "869             4     B-Li-O-Y            P2_1/c                 14  \n",
       "870             4    Li-O-P-Zn            P2_1/c                 14  \n",
       "871             4    B-Ca-Li-O              Ibca                 73  \n",
       "872             4   Bi-Li-O-Si               P-3                147  \n",
       "\n",
       "[873 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = Li_df.copy()\n",
    "data = data.drop('FORCE_SETS_filepath', axis=1)\n",
    "data = data.drop('phonon_filepath', axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc163a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d3c22501",
   "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": [
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "for i in index:\n",
    "    dos = get_total_dos(i).reshape(1, -1)\n",
    "    dos = normalize(dos)\n",
    "    #dos = get_total_dos(i)/integral(i, 'total')\n",
    "    freq = get_total_f(i)\n",
    "    ax.plot(freq, dos[0], color='tomato', alpha=0.2)\n",
    "    \n",
    "ax.set_ylabel('Total DOS', fontsize=fs)\n",
    "ax.set_xlabel('Frequency [THz]', fontsize=fs)\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_xlim(-30, 125)\n",
    "#ax.set_ylim(0.0, 0.001)\n",
    "plt.tight_layout()\n",
    "plt.savefig('dos_total_overlay.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "216b3e9b",
   "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"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "for i in index:\n",
    "    dos = get_partial_dos(i).reshape(1, -1)\n",
    "    dos = normalize(dos)\n",
    "    #dos = get_partial_dos(i)/integral(i, 'partial')\n",
    "    freq = get_partial_f(i)\n",
    "    ax.plot(freq, dos[0], color='dodgerblue', alpha=0.2)\n",
    "    \n",
    "ax.set_ylabel('Li DOS', fontsize=fs)\n",
    "ax.set_xlabel('Frequency [THz]', fontsize=fs)\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_xlim(-30, 125)\n",
    "plt.tight_layout()\n",
    "plt.savefig('dos_partial_overlay.pdf')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "for i in index:\n",
    "    dos = get_partial_dos(i).reshape(1, -1)\n",
    "    dos = normalize(dos)\n",
    "    freq = get_partial_f(i)\n",
    "    ax.plot(freq, dos[0], color='dodgerblue', alpha=0.2)\n",
    "    \n",
    "ax.set_ylabel('Li DOS', fontsize=fs)\n",
    "ax.set_xlabel('Frequency [THz]', fontsize=fs)\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_xlim(-10, 35)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('dos_partial_overlay_zoom.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "635700ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 972x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(13.5, 5), sharey=True)\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "colors = ['tomato', 'dodgerblue']\n",
    "ylabel = ['Total DOS', 'Li DOS']\n",
    "\n",
    "for i in index:\n",
    "    for j in range(0, 2):\n",
    "        if j == 0:\n",
    "            dos = get_total_dos(i).reshape(1,-1)\n",
    "        else:\n",
    "            dos = get_partial_dos(i).reshape(1,-1)\n",
    "        dos = normalize(dos)\n",
    "        freq = get_total_f(i)\n",
    "        ax[j].plot(freq, dos[0], color=colors[j], alpha=0.2)\n",
    "        ax[j].set_xlabel('Frequency [THz]', fontsize=fs)\n",
    "        ax[j].set_ylabel(ylabel[j], fontsize=fs)\n",
    "        ax[j].tick_params(axis='both', which='major', labelsize=fs)\n",
    "        ax[j].set_xlim(-30, 125)\n",
    "        ax[j].set_ylim(-0.01, 1.0)\n",
    "        ax[j].axvline(-10, linestyle='--', color='k')\n",
    "        ax[j].axvline(35, linestyle='--', color='k')\n",
    "\n",
    "fig.text(0, 1, '(a)', fontsize=fs, weight='bold')\n",
    "fig.text(0.5, 1, '(b)', fontsize=fs, weight='bold')\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig('dos_overlay.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fca0ba6",
   "metadata": {},
   "source": [
    "# Get descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0400560d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_all_data (index):\n",
    "    '''output four lists of data based on index input'''\n",
    "    \n",
    "    total_dos = get_total_dos(index)\n",
    "    total_f = get_total_f(index)\n",
    "    projected_dos = get_partial_dos (index)\n",
    "    projected_f = get_partial_f (index)\n",
    "    \n",
    "    return total_dos,total_f,projected_dos,projected_f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5304e483",
   "metadata": {},
   "source": [
    "# Phonon band-centre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f4dc484",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_fav (index, dos):\n",
    "    '''Get a temperature independent phonon band centre value'''\n",
    "    if dos == 'total':\n",
    "        f = get_total_f(index)\n",
    "        d = get_total_dos(index)\n",
    "    if dos == 'partial':\n",
    "        f = get_partial_f(index)\n",
    "        d = get_partial_dos(index)\n",
    "    \n",
    "    fav_elements = f * d\n",
    "    fav = np.sum(fav_elements)\n",
    "    norm = np.sum(d)\n",
    "    fav = fav / norm\n",
    "    return fav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a52e7232",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "k = scipy.constants.k\n",
    "h = scipy.constants.h\n",
    "\n",
    "def get_favT (index, dos, Tmin, Tmax, Tstep):\n",
    "    '''Get a list of temperature dependent phonon band centre values or a single value at given T values'''\n",
    "    favT = []\n",
    "\n",
    "    if ((Tmin != None) and (Tmax == None) and (Tstep == None)):\n",
    "        Temperature = [Tmin]\n",
    "    elif ((Tmin != None) and (Tmax != None) and (Tstep != None)):\n",
    "        Temperature = [*range(Tmin, Tmax, Tstep)]\n",
    "    else:\n",
    "        print(\"Tmin is expected as a value, Tmax and Tstep could be None\")\n",
    "        Temperature = None\n",
    "\n",
    "    if dos == 'total':\n",
    "        f = get_total_f(index)\n",
    "        d = get_total_dos(index)\n",
    "    if dos == 'partial':\n",
    "        f = get_partial_f(index)\n",
    "        d = get_partial_dos(index)\n",
    "        \n",
    "    #index of first-positive value - only consider positive frequencies\n",
    "    idx_pos = [i for i, f_val in enumerate(f) if f_val>0]\n",
    "    idx = idx_pos[0]\n",
    "    f = f[idx:]\n",
    "    d = d[idx:]\n",
    "\n",
    "    for T in Temperature:\n",
    "        #calculate phonon numbers\n",
    "        #handling 0K\n",
    "        if T == 0:\n",
    "            ph_numbers = [0.5]*len(f)\n",
    "        else:\n",
    "            ph_numbers = []\n",
    "            for f_val in f:\n",
    "                energy = f_val*h*10**12\n",
    "                beta = 1/(k*T)\n",
    "                denominator = math.exp(energy*beta) - 1\n",
    "                ph_number = 1/denominator\n",
    "                ph_numbers.append(ph_number)\n",
    "\n",
    "\n",
    "        fav_elements = f * d * ph_numbers\n",
    "        norm_elements = d * ph_numbers\n",
    "\n",
    "        fav = np.sum(fav_elements)\n",
    "        norm = np.sum(norm_elements)\n",
    "        fav = fav / norm\n",
    "        favT.append(fav)\n",
    "\n",
    "    return favT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6979b324",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Add an empty column for temperature independent phonon band centre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35bd35d3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "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>material_id</th>\n",
       "      <th>elements</th>\n",
       "      <th>formula_pretty_reduced</th>\n",
       "      <th>formula</th>\n",
       "      <th>num_elements</th>\n",
       "      <th>chemsys</th>\n",
       "      <th>spacegroup_symbol</th>\n",
       "      <th>spacegroup_number</th>\n",
       "      <th>fav</th>\n",
       "      <th>fav_tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mp-8892</td>\n",
       "      <td>[Li, F, In]</td>\n",
       "      <td>LiInF4</td>\n",
       "      <td>Li4 In4 F16</td>\n",
       "      <td>3</td>\n",
       "      <td>F-In-Li</td>\n",
       "      <td>Pbcn</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mp-772968</td>\n",
       "      <td>[Li, Tl, P, H, O]</td>\n",
       "      <td>LiTlPHO3</td>\n",
       "      <td>Li4 Tl4 P4 H4 O12</td>\n",
       "      <td>5</td>\n",
       "      <td>H-Li-O-P-Tl</td>\n",
       "      <td>C2</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mp-756951</td>\n",
       "      <td>[Li, Sb, O]</td>\n",
       "      <td>LiSbO3</td>\n",
       "      <td>Li4 Sb4 O12</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-O-Sb</td>\n",
       "      <td>C2/m</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mp-754856</td>\n",
       "      <td>[Li, Y, Zr, S]</td>\n",
       "      <td>Li7Y7ZrS16</td>\n",
       "      <td>Li7 Y7 Zr1 S16</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-S-Y-Zr</td>\n",
       "      <td>P2/m</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mp-24610</td>\n",
       "      <td>[H, Li, O, P]</td>\n",
       "      <td>LiP(HO2)2</td>\n",
       "      <td>Li4 P4 H8 O16</td>\n",
       "      <td>4</td>\n",
       "      <td>H-Li-O-P</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>mp-9919</td>\n",
       "      <td>[Li, Zn, Sb]</td>\n",
       "      <td>LiZnSb</td>\n",
       "      <td>Li2 Zn2 Sb2</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-Sb-Zn</td>\n",
       "      <td>P6_3mc</td>\n",
       "      <td>186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>mp-14704</td>\n",
       "      <td>[Li, B, O, Y]</td>\n",
       "      <td>Li6Y(BO3)3</td>\n",
       "      <td>Li24 Y4 B12 O36</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Li-O-Y</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mp-557756</td>\n",
       "      <td>[Li, Zn, P, O]</td>\n",
       "      <td>Li4Zn(PO4)2</td>\n",
       "      <td>Li16 Zn4 P8 O32</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-O-P-Zn</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>mp-966801</td>\n",
       "      <td>[Li, Ca, B, O]</td>\n",
       "      <td>LiCaBO3</td>\n",
       "      <td>Li8 Ca8 B8 O24</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Ca-Li-O</td>\n",
       "      <td>Ibca</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>mp-772721</td>\n",
       "      <td>[Li, Si, Bi, O]</td>\n",
       "      <td>LiSi6Bi9O26</td>\n",
       "      <td>Li2 Si12 Bi18 O52</td>\n",
       "      <td>4</td>\n",
       "      <td>Bi-Li-O-Si</td>\n",
       "      <td>P-3</td>\n",
       "      <td>147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    material_id           elements formula_pretty_reduced            formula  \\\n",
       "0       mp-8892        [Li, F, In]                 LiInF4        Li4 In4 F16   \n",
       "1     mp-772968  [Li, Tl, P, H, O]               LiTlPHO3  Li4 Tl4 P4 H4 O12   \n",
       "2     mp-756951        [Li, Sb, O]                 LiSbO3        Li4 Sb4 O12   \n",
       "3     mp-754856     [Li, Y, Zr, S]             Li7Y7ZrS16     Li7 Y7 Zr1 S16   \n",
       "4      mp-24610      [H, Li, O, P]              LiP(HO2)2      Li4 P4 H8 O16   \n",
       "..          ...                ...                    ...                ...   \n",
       "868     mp-9919       [Li, Zn, Sb]                 LiZnSb        Li2 Zn2 Sb2   \n",
       "869    mp-14704      [Li, B, O, Y]             Li6Y(BO3)3    Li24 Y4 B12 O36   \n",
       "870   mp-557756     [Li, Zn, P, O]            Li4Zn(PO4)2    Li16 Zn4 P8 O32   \n",
       "871   mp-966801     [Li, Ca, B, O]                LiCaBO3     Li8 Ca8 B8 O24   \n",
       "872   mp-772721    [Li, Si, Bi, O]            LiSi6Bi9O26  Li2 Si12 Bi18 O52   \n",
       "\n",
       "     num_elements      chemsys spacegroup_symbol  spacegroup_number  fav  \\\n",
       "0               3      F-In-Li              Pbcn                 60  NaN   \n",
       "1               5  H-Li-O-P-Tl                C2                  5  NaN   \n",
       "2               3      Li-O-Sb              C2/m                 12  NaN   \n",
       "3               4    Li-S-Y-Zr              P2/m                 10  NaN   \n",
       "4               4     H-Li-O-P            Pna2_1                 33  NaN   \n",
       "..            ...          ...               ...                ...  ...   \n",
       "868             3     Li-Sb-Zn            P6_3mc                186  NaN   \n",
       "869             4     B-Li-O-Y            P2_1/c                 14  NaN   \n",
       "870             4    Li-O-P-Zn            P2_1/c                 14  NaN   \n",
       "871             4    B-Ca-Li-O              Ibca                 73  NaN   \n",
       "872             4   Bi-Li-O-Si               P-3                147  NaN   \n",
       "\n",
       "     fav_tot  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "..       ...  \n",
       "868      NaN  \n",
       "869      NaN  \n",
       "870      NaN  \n",
       "871      NaN  \n",
       "872      NaN  \n",
       "\n",
       "[873 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fav'] = np.nan\n",
    "data['fav_tot'] = np.nan\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0ebaaa",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get temperature independent phonon band centers for all the species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "27184500",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "index = [*range (0, data.index[-1]+1)]\n",
    "for i in index:\n",
    "    fav = get_fav(i, 'partial')\n",
    "    fav_tot = get_fav(i, 'total')\n",
    "    data.at[i,'fav'] = fav\n",
    "    data.at[i,'fav_tot'] = fav_tot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600eb12b",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get temperature dependent phonon band center (room temperature) for all the species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5910716",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "Tmin = 298\n",
    "Tmax = None\n",
    "Tstep = None\n",
    "\n",
    "if ((Tmin != None) and (Tmax == None) and (Tstep == None)):\n",
    "    Temperature = [Tmin]\n",
    "elif ((Tmin != None) and (Tmax != None) and (Tstep != None)):\n",
    "    Temperature = [*range(Tmin, Tmax, Tstep)]\n",
    "else:\n",
    "    print(\"Tmin is expected as a value, Tmax and Tstep could be None\")\n",
    "    Temperature = None\n",
    "\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "t_val = pd.DataFrame(columns=['298_tot'])\n",
    "li_val = pd.DataFrame(columns=['298'])\n",
    "for i in index:\n",
    "    fav_t = get_favT(i, 'partial', Tmin, Tmax, Tstep)\n",
    "    fav_li = get_favT(i, 'total', Tmin, Tmax, Tstep)\n",
    "    t_val.loc[i] = fav_t\n",
    "    li_val.loc[i] = fav_li\n",
    "    \n",
    "data = pd.concat([data, t_val], axis = 1)\n",
    "data = pd.concat([data, li_val], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c631fe1",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Get temperature dependent phonon band center in a temperature range for all the species, example here using 10 to 1000k with a step of 10k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "530524a6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "Tmin = 10\n",
    "Tmax = 1000\n",
    "Tstep = 10\n",
    "\n",
    "if ((Tmin != None) and (Tmax == None) and (Tstep == None)):\n",
    "    Temperature = [Tmin]\n",
    "elif ((Tmin != None) and (Tmax != None) and (Tstep != None)):\n",
    "    Temperature = [*range(Tmin, Tmax, Tstep)]\n",
    "else:\n",
    "    print(\"Tmin is expected as a value, Tmax and Tstep could be None\")\n",
    "    Temperature = None\n",
    "\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "T_val = pd.DataFrame(columns = Temperature)\n",
    "for i in index:\n",
    "    favT = get_favT(i, 'partial', Tmin, Tmax, Tstep)\n",
    "    T_val.loc[i] = favT\n",
    "\n",
    "data = pd.concat([data, T_val], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a4ed2d3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>material_id</th>\n",
       "      <th>elements</th>\n",
       "      <th>formula_pretty_reduced</th>\n",
       "      <th>formula</th>\n",
       "      <th>num_elements</th>\n",
       "      <th>chemsys</th>\n",
       "      <th>spacegroup_symbol</th>\n",
       "      <th>spacegroup_number</th>\n",
       "      <th>fav</th>\n",
       "      <th>fav_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>900</th>\n",
       "      <th>910</th>\n",
       "      <th>920</th>\n",
       "      <th>930</th>\n",
       "      <th>940</th>\n",
       "      <th>950</th>\n",
       "      <th>960</th>\n",
       "      <th>970</th>\n",
       "      <th>980</th>\n",
       "      <th>990</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mp-8892</td>\n",
       "      <td>[Li, F, In]</td>\n",
       "      <td>LiInF4</td>\n",
       "      <td>Li4 In4 F16</td>\n",
       "      <td>3</td>\n",
       "      <td>F-In-Li</td>\n",
       "      <td>Pbcn</td>\n",
       "      <td>60</td>\n",
       "      <td>11.132426</td>\n",
       "      <td>8.450140</td>\n",
       "      <td>...</td>\n",
       "      <td>9.622918</td>\n",
       "      <td>9.627107</td>\n",
       "      <td>9.631197</td>\n",
       "      <td>9.635190</td>\n",
       "      <td>9.639090</td>\n",
       "      <td>9.642901</td>\n",
       "      <td>9.646625</td>\n",
       "      <td>9.650265</td>\n",
       "      <td>9.653824</td>\n",
       "      <td>9.657305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mp-772968</td>\n",
       "      <td>[Li, Tl, P, H, O]</td>\n",
       "      <td>LiTlPHO3</td>\n",
       "      <td>Li4 Tl4 P4 H4 O12</td>\n",
       "      <td>5</td>\n",
       "      <td>H-Li-O-P-Tl</td>\n",
       "      <td>C2</td>\n",
       "      <td>5</td>\n",
       "      <td>12.800916</td>\n",
       "      <td>21.721623</td>\n",
       "      <td>...</td>\n",
       "      <td>9.804592</td>\n",
       "      <td>9.813504</td>\n",
       "      <td>9.822202</td>\n",
       "      <td>9.830693</td>\n",
       "      <td>9.838985</td>\n",
       "      <td>9.847085</td>\n",
       "      <td>9.854999</td>\n",
       "      <td>9.862734</td>\n",
       "      <td>9.870295</td>\n",
       "      <td>9.877688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mp-756951</td>\n",
       "      <td>[Li, Sb, O]</td>\n",
       "      <td>LiSbO3</td>\n",
       "      <td>Li4 Sb4 O12</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-O-Sb</td>\n",
       "      <td>C2/m</td>\n",
       "      <td>12</td>\n",
       "      <td>8.819405</td>\n",
       "      <td>11.408661</td>\n",
       "      <td>...</td>\n",
       "      <td>7.533812</td>\n",
       "      <td>7.536527</td>\n",
       "      <td>7.539180</td>\n",
       "      <td>7.541773</td>\n",
       "      <td>7.544308</td>\n",
       "      <td>7.546787</td>\n",
       "      <td>7.549212</td>\n",
       "      <td>7.551584</td>\n",
       "      <td>7.553905</td>\n",
       "      <td>7.556178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mp-754856</td>\n",
       "      <td>[Li, Y, Zr, S]</td>\n",
       "      <td>Li7Y7ZrS16</td>\n",
       "      <td>Li7 Y7 Zr1 S16</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-S-Y-Zr</td>\n",
       "      <td>P2/m</td>\n",
       "      <td>10</td>\n",
       "      <td>6.245816</td>\n",
       "      <td>6.055796</td>\n",
       "      <td>...</td>\n",
       "      <td>5.747567</td>\n",
       "      <td>5.748375</td>\n",
       "      <td>5.749165</td>\n",
       "      <td>5.749937</td>\n",
       "      <td>5.750691</td>\n",
       "      <td>5.751430</td>\n",
       "      <td>5.752152</td>\n",
       "      <td>5.752858</td>\n",
       "      <td>5.753550</td>\n",
       "      <td>5.754227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mp-24610</td>\n",
       "      <td>[H, Li, O, P]</td>\n",
       "      <td>LiP(HO2)2</td>\n",
       "      <td>Li4 P4 H8 O16</td>\n",
       "      <td>4</td>\n",
       "      <td>H-Li-O-P</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "      <td>38.676281</td>\n",
       "      <td>21.873600</td>\n",
       "      <td>...</td>\n",
       "      <td>19.990915</td>\n",
       "      <td>20.104714</td>\n",
       "      <td>20.216033</td>\n",
       "      <td>20.324942</td>\n",
       "      <td>20.431509</td>\n",
       "      <td>20.535803</td>\n",
       "      <td>20.637886</td>\n",
       "      <td>20.737823</td>\n",
       "      <td>20.835672</td>\n",
       "      <td>20.931493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>mp-9919</td>\n",
       "      <td>[Li, Zn, Sb]</td>\n",
       "      <td>LiZnSb</td>\n",
       "      <td>Li2 Zn2 Sb2</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-Sb-Zn</td>\n",
       "      <td>P6_3mc</td>\n",
       "      <td>186</td>\n",
       "      <td>6.350430</td>\n",
       "      <td>4.587253</td>\n",
       "      <td>...</td>\n",
       "      <td>5.693411</td>\n",
       "      <td>5.694497</td>\n",
       "      <td>5.695558</td>\n",
       "      <td>5.696595</td>\n",
       "      <td>5.697609</td>\n",
       "      <td>5.698600</td>\n",
       "      <td>5.699569</td>\n",
       "      <td>5.700516</td>\n",
       "      <td>5.701444</td>\n",
       "      <td>5.702351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>mp-14704</td>\n",
       "      <td>[Li, B, O, Y]</td>\n",
       "      <td>Li6Y(BO3)3</td>\n",
       "      <td>Li24 Y4 B12 O36</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Li-O-Y</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>11.867930</td>\n",
       "      <td>14.840322</td>\n",
       "      <td>...</td>\n",
       "      <td>8.646174</td>\n",
       "      <td>8.654130</td>\n",
       "      <td>8.661905</td>\n",
       "      <td>8.669505</td>\n",
       "      <td>8.676937</td>\n",
       "      <td>8.684205</td>\n",
       "      <td>8.691314</td>\n",
       "      <td>8.698270</td>\n",
       "      <td>8.705078</td>\n",
       "      <td>8.711743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mp-557756</td>\n",
       "      <td>[Li, Zn, P, O]</td>\n",
       "      <td>Li4Zn(PO4)2</td>\n",
       "      <td>Li16 Zn4 P8 O32</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-O-P-Zn</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>12.278804</td>\n",
       "      <td>14.092563</td>\n",
       "      <td>...</td>\n",
       "      <td>9.961839</td>\n",
       "      <td>9.968482</td>\n",
       "      <td>9.974967</td>\n",
       "      <td>9.981302</td>\n",
       "      <td>9.987490</td>\n",
       "      <td>9.993537</td>\n",
       "      <td>9.999447</td>\n",
       "      <td>10.005226</td>\n",
       "      <td>10.010877</td>\n",
       "      <td>10.016405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>mp-966801</td>\n",
       "      <td>[Li, Ca, B, O]</td>\n",
       "      <td>LiCaBO3</td>\n",
       "      <td>Li8 Ca8 B8 O24</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Ca-Li-O</td>\n",
       "      <td>Ibca</td>\n",
       "      <td>73</td>\n",
       "      <td>10.403979</td>\n",
       "      <td>14.003078</td>\n",
       "      <td>...</td>\n",
       "      <td>8.861610</td>\n",
       "      <td>8.865376</td>\n",
       "      <td>8.869055</td>\n",
       "      <td>8.872650</td>\n",
       "      <td>8.876164</td>\n",
       "      <td>8.879599</td>\n",
       "      <td>8.882959</td>\n",
       "      <td>8.886245</td>\n",
       "      <td>8.889461</td>\n",
       "      <td>8.892608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>mp-772721</td>\n",
       "      <td>[Li, Si, Bi, O]</td>\n",
       "      <td>LiSi6Bi9O26</td>\n",
       "      <td>Li2 Si12 Bi18 O52</td>\n",
       "      <td>4</td>\n",
       "      <td>Bi-Li-O-Si</td>\n",
       "      <td>P-3</td>\n",
       "      <td>147</td>\n",
       "      <td>5.711561</td>\n",
       "      <td>10.507305</td>\n",
       "      <td>...</td>\n",
       "      <td>4.298740</td>\n",
       "      <td>4.300452</td>\n",
       "      <td>4.302126</td>\n",
       "      <td>4.303763</td>\n",
       "      <td>4.305366</td>\n",
       "      <td>4.306935</td>\n",
       "      <td>4.308471</td>\n",
       "      <td>4.309975</td>\n",
       "      <td>4.311448</td>\n",
       "      <td>4.312891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    material_id           elements formula_pretty_reduced            formula  \\\n",
       "0       mp-8892        [Li, F, In]                 LiInF4        Li4 In4 F16   \n",
       "1     mp-772968  [Li, Tl, P, H, O]               LiTlPHO3  Li4 Tl4 P4 H4 O12   \n",
       "2     mp-756951        [Li, Sb, O]                 LiSbO3        Li4 Sb4 O12   \n",
       "3     mp-754856     [Li, Y, Zr, S]             Li7Y7ZrS16     Li7 Y7 Zr1 S16   \n",
       "4      mp-24610      [H, Li, O, P]              LiP(HO2)2      Li4 P4 H8 O16   \n",
       "..          ...                ...                    ...                ...   \n",
       "868     mp-9919       [Li, Zn, Sb]                 LiZnSb        Li2 Zn2 Sb2   \n",
       "869    mp-14704      [Li, B, O, Y]             Li6Y(BO3)3    Li24 Y4 B12 O36   \n",
       "870   mp-557756     [Li, Zn, P, O]            Li4Zn(PO4)2    Li16 Zn4 P8 O32   \n",
       "871   mp-966801     [Li, Ca, B, O]                LiCaBO3     Li8 Ca8 B8 O24   \n",
       "872   mp-772721    [Li, Si, Bi, O]            LiSi6Bi9O26  Li2 Si12 Bi18 O52   \n",
       "\n",
       "     num_elements      chemsys spacegroup_symbol  spacegroup_number  \\\n",
       "0               3      F-In-Li              Pbcn                 60   \n",
       "1               5  H-Li-O-P-Tl                C2                  5   \n",
       "2               3      Li-O-Sb              C2/m                 12   \n",
       "3               4    Li-S-Y-Zr              P2/m                 10   \n",
       "4               4     H-Li-O-P            Pna2_1                 33   \n",
       "..            ...          ...               ...                ...   \n",
       "868             3     Li-Sb-Zn            P6_3mc                186   \n",
       "869             4     B-Li-O-Y            P2_1/c                 14   \n",
       "870             4    Li-O-P-Zn            P2_1/c                 14   \n",
       "871             4    B-Ca-Li-O              Ibca                 73   \n",
       "872             4   Bi-Li-O-Si               P-3                147   \n",
       "\n",
       "           fav    fav_tot  ...        900        910        920        930  \\\n",
       "0    11.132426   8.450140  ...   9.622918   9.627107   9.631197   9.635190   \n",
       "1    12.800916  21.721623  ...   9.804592   9.813504   9.822202   9.830693   \n",
       "2     8.819405  11.408661  ...   7.533812   7.536527   7.539180   7.541773   \n",
       "3     6.245816   6.055796  ...   5.747567   5.748375   5.749165   5.749937   \n",
       "4    38.676281  21.873600  ...  19.990915  20.104714  20.216033  20.324942   \n",
       "..         ...        ...  ...        ...        ...        ...        ...   \n",
       "868   6.350430   4.587253  ...   5.693411   5.694497   5.695558   5.696595   \n",
       "869  11.867930  14.840322  ...   8.646174   8.654130   8.661905   8.669505   \n",
       "870  12.278804  14.092563  ...   9.961839   9.968482   9.974967   9.981302   \n",
       "871  10.403979  14.003078  ...   8.861610   8.865376   8.869055   8.872650   \n",
       "872   5.711561  10.507305  ...   4.298740   4.300452   4.302126   4.303763   \n",
       "\n",
       "           940        950        960        970        980        990  \n",
       "0     9.639090   9.642901   9.646625   9.650265   9.653824   9.657305  \n",
       "1     9.838985   9.847085   9.854999   9.862734   9.870295   9.877688  \n",
       "2     7.544308   7.546787   7.549212   7.551584   7.553905   7.556178  \n",
       "3     5.750691   5.751430   5.752152   5.752858   5.753550   5.754227  \n",
       "4    20.431509  20.535803  20.637886  20.737823  20.835672  20.931493  \n",
       "..         ...        ...        ...        ...        ...        ...  \n",
       "868   5.697609   5.698600   5.699569   5.700516   5.701444   5.702351  \n",
       "869   8.676937   8.684205   8.691314   8.698270   8.705078   8.711743  \n",
       "870   9.987490   9.993537   9.999447  10.005226  10.010877  10.016405  \n",
       "871   8.876164   8.879599   8.882959   8.886245   8.889461   8.892608  \n",
       "872   4.305366   4.306935   4.308471   4.309975   4.311448   4.312891  \n",
       "\n",
       "[873 rows x 111 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3e05a8",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## First frequency with non-negligible Li DOS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb954e2",
   "metadata": {},
   "source": [
    "Using scipy find_peaks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "24948080",
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_peak_f(index, frac_max=0.5):\n",
    "    '''Get frequency of the first peak in the Li DOS from scipy find_peaks() function'''\n",
    "    '''Threshold for peak height is a fraction frac_max of the maximum value'''\n",
    "    '''Default is set to a half the maximum'''\n",
    "    Li_f = get_partial_f(index)\n",
    "    Li_d = normalize(get_partial_dos(index).reshape(1, -1))[0]\n",
    "    max_h = max(Li_d)\n",
    "    h = max_h*frac_max\n",
    "    first_peak_index = find_peaks(Li_d, height=h)[0][0]\n",
    "    f = Li_f[first_peak_index]\n",
    "    return f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dcab890",
   "metadata": {},
   "source": [
    "Write frequencies to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e01a5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fractions = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "f_val = pd.DataFrame(columns = fractions)\n",
    "for i in index:\n",
    "    first_frequencies = []\n",
    "    for frac in fractions: \n",
    "        ff = first_peak_f(i, frac)\n",
    "        first_frequencies.append(ff)\n",
    "    f_val.loc[i] = first_frequencies\n",
    "\n",
    "data = pd.concat([data, f_val], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5932349",
   "metadata": {},
   "source": [
    "## Relative spread of the DOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "13c325c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_s (index, threshold=0.001):\n",
    "    '''get Li DOS spread above a threshold relative to the total spread'''\n",
    "    '''In phonopy, the DOS doesn't go to zero'''\n",
    "    '''so we need to set a threshold where we consider that the DOS is zero'''\n",
    "    '''Threshold is 0.001'''\n",
    "\n",
    "    total_d = get_total_dos(index)\n",
    "    total_d_n = normalize(total_d.reshape(1, -1))[0]\n",
    "    Li_d = get_partial_dos(index)\n",
    "    Li_d_n = normalize(Li_d.reshape(1, -1))[0]\n",
    "\n",
    "    #count above threshold\n",
    "    c_total = sum(d > threshold for d in total_d_n)\n",
    "    c_partial = sum(d > threshold for d in Li_d_n)\n",
    "\n",
    "    #relative spread is (step*c_partial)/(step*c_total)\n",
    "    relative_s = c_partial / c_total\n",
    "\n",
    "    return relative_s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38bf0769",
   "metadata": {},
   "source": [
    "Write relative spread using thresholds of 0.001 and 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f8ea1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds = [0.00001, 0.0001, 0.001, 0.01]\n",
    "\n",
    "index = [*range (0, data.index[-1]+1)]\n",
    "p_val = pd.DataFrame(columns = thresholds)\n",
    "for i in index:\n",
    "    spread = []\n",
    "    for t in thresholds: \n",
    "        s = relative_s(i, t)\n",
    "        spread.append(s)\n",
    "    p_val.loc[i] = spread\n",
    "\n",
    "data = pd.concat([data, p_val], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3b92ff17",
   "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>material_id</th>\n",
       "      <th>elements</th>\n",
       "      <th>formula_pretty_reduced</th>\n",
       "      <th>formula</th>\n",
       "      <th>num_elements</th>\n",
       "      <th>chemsys</th>\n",
       "      <th>spacegroup_symbol</th>\n",
       "      <th>spacegroup_number</th>\n",
       "      <th>fav</th>\n",
       "      <th>fav_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>1e-05</th>\n",
       "      <th>0.0001</th>\n",
       "      <th>0.001</th>\n",
       "      <th>0.01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mp-8892</td>\n",
       "      <td>[Li, F, In]</td>\n",
       "      <td>LiInF4</td>\n",
       "      <td>Li4 In4 F16</td>\n",
       "      <td>3</td>\n",
       "      <td>F-In-Li</td>\n",
       "      <td>Pbcn</td>\n",
       "      <td>60</td>\n",
       "      <td>11.132426</td>\n",
       "      <td>8.450140</td>\n",
       "      <td>...</td>\n",
       "      <td>9.190986</td>\n",
       "      <td>9.927689</td>\n",
       "      <td>9.927689</td>\n",
       "      <td>9.927689</td>\n",
       "      <td>9.927689</td>\n",
       "      <td>9.927689</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.981707</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.788732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mp-772968</td>\n",
       "      <td>[Li, Tl, P, H, O]</td>\n",
       "      <td>LiTlPHO3</td>\n",
       "      <td>Li4 Tl4 P4 H4 O12</td>\n",
       "      <td>5</td>\n",
       "      <td>H-Li-O-P-Tl</td>\n",
       "      <td>C2</td>\n",
       "      <td>5</td>\n",
       "      <td>12.800916</td>\n",
       "      <td>21.721623</td>\n",
       "      <td>...</td>\n",
       "      <td>11.140249</td>\n",
       "      <td>11.140249</td>\n",
       "      <td>11.140249</td>\n",
       "      <td>14.066884</td>\n",
       "      <td>14.066884</td>\n",
       "      <td>14.066884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mp-756951</td>\n",
       "      <td>[Li, Sb, O]</td>\n",
       "      <td>LiSbO3</td>\n",
       "      <td>Li4 Sb4 O12</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-O-Sb</td>\n",
       "      <td>C2/m</td>\n",
       "      <td>12</td>\n",
       "      <td>8.819405</td>\n",
       "      <td>11.408661</td>\n",
       "      <td>...</td>\n",
       "      <td>4.864107</td>\n",
       "      <td>5.543978</td>\n",
       "      <td>5.543978</td>\n",
       "      <td>5.543978</td>\n",
       "      <td>5.543978</td>\n",
       "      <td>6.903721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993865</td>\n",
       "      <td>0.906832</td>\n",
       "      <td>0.560606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mp-754856</td>\n",
       "      <td>[Li, Y, Zr, S]</td>\n",
       "      <td>Li7Y7ZrS16</td>\n",
       "      <td>Li7 Y7 Zr1 S16</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-S-Y-Zr</td>\n",
       "      <td>P2/m</td>\n",
       "      <td>10</td>\n",
       "      <td>6.245816</td>\n",
       "      <td>6.055796</td>\n",
       "      <td>...</td>\n",
       "      <td>4.319241</td>\n",
       "      <td>4.319241</td>\n",
       "      <td>4.319241</td>\n",
       "      <td>4.319241</td>\n",
       "      <td>5.047983</td>\n",
       "      <td>5.047983</td>\n",
       "      <td>0.987952</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.921569</td>\n",
       "      <td>0.735294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mp-24610</td>\n",
       "      <td>[H, Li, O, P]</td>\n",
       "      <td>LiP(HO2)2</td>\n",
       "      <td>Li4 P4 H8 O16</td>\n",
       "      <td>4</td>\n",
       "      <td>H-Li-O-P</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "      <td>38.676281</td>\n",
       "      <td>21.873600</td>\n",
       "      <td>...</td>\n",
       "      <td>29.153401</td>\n",
       "      <td>29.153401</td>\n",
       "      <td>32.155099</td>\n",
       "      <td>32.155099</td>\n",
       "      <td>32.155099</td>\n",
       "      <td>32.155099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988506</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>0.537500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>mp-9919</td>\n",
       "      <td>[Li, Zn, Sb]</td>\n",
       "      <td>LiZnSb</td>\n",
       "      <td>Li2 Zn2 Sb2</td>\n",
       "      <td>3</td>\n",
       "      <td>Li-Sb-Zn</td>\n",
       "      <td>P6_3mc</td>\n",
       "      <td>186</td>\n",
       "      <td>6.350430</td>\n",
       "      <td>4.587253</td>\n",
       "      <td>...</td>\n",
       "      <td>4.292991</td>\n",
       "      <td>4.292991</td>\n",
       "      <td>7.108637</td>\n",
       "      <td>7.108637</td>\n",
       "      <td>7.108637</td>\n",
       "      <td>7.108637</td>\n",
       "      <td>0.987578</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.876623</td>\n",
       "      <td>0.664286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>mp-14704</td>\n",
       "      <td>[Li, B, O, Y]</td>\n",
       "      <td>Li6Y(BO3)3</td>\n",
       "      <td>Li24 Y4 B12 O36</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Li-O-Y</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>11.867930</td>\n",
       "      <td>14.840322</td>\n",
       "      <td>...</td>\n",
       "      <td>5.185502</td>\n",
       "      <td>5.979421</td>\n",
       "      <td>6.508700</td>\n",
       "      <td>7.567257</td>\n",
       "      <td>11.272210</td>\n",
       "      <td>11.272210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982143</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.742574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mp-557756</td>\n",
       "      <td>[Li, Zn, P, O]</td>\n",
       "      <td>Li4Zn(PO4)2</td>\n",
       "      <td>Li16 Zn4 P8 O32</td>\n",
       "      <td>4</td>\n",
       "      <td>Li-O-P-Zn</td>\n",
       "      <td>P2_1/c</td>\n",
       "      <td>14</td>\n",
       "      <td>12.278804</td>\n",
       "      <td>14.092563</td>\n",
       "      <td>...</td>\n",
       "      <td>4.985237</td>\n",
       "      <td>8.758697</td>\n",
       "      <td>8.758697</td>\n",
       "      <td>8.758697</td>\n",
       "      <td>8.758697</td>\n",
       "      <td>10.855064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967480</td>\n",
       "      <td>0.811475</td>\n",
       "      <td>0.700935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>mp-966801</td>\n",
       "      <td>[Li, Ca, B, O]</td>\n",
       "      <td>LiCaBO3</td>\n",
       "      <td>Li8 Ca8 B8 O24</td>\n",
       "      <td>4</td>\n",
       "      <td>B-Ca-Li-O</td>\n",
       "      <td>Ibca</td>\n",
       "      <td>73</td>\n",
       "      <td>10.403979</td>\n",
       "      <td>14.003078</td>\n",
       "      <td>...</td>\n",
       "      <td>5.331724</td>\n",
       "      <td>7.424524</td>\n",
       "      <td>10.040524</td>\n",
       "      <td>10.040524</td>\n",
       "      <td>10.040524</td>\n",
       "      <td>10.040524</td>\n",
       "      <td>0.989011</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.771084</td>\n",
       "      <td>0.735294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>mp-772721</td>\n",
       "      <td>[Li, Si, Bi, O]</td>\n",
       "      <td>LiSi6Bi9O26</td>\n",
       "      <td>Li2 Si12 Bi18 O52</td>\n",
       "      <td>4</td>\n",
       "      <td>Bi-Li-O-Si</td>\n",
       "      <td>P-3</td>\n",
       "      <td>147</td>\n",
       "      <td>5.711561</td>\n",
       "      <td>10.507305</td>\n",
       "      <td>...</td>\n",
       "      <td>2.082729</td>\n",
       "      <td>2.817394</td>\n",
       "      <td>2.817394</td>\n",
       "      <td>3.552058</td>\n",
       "      <td>3.552058</td>\n",
       "      <td>3.919391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957983</td>\n",
       "      <td>0.815126</td>\n",
       "      <td>0.540541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    material_id           elements formula_pretty_reduced            formula  \\\n",
       "0       mp-8892        [Li, F, In]                 LiInF4        Li4 In4 F16   \n",
       "1     mp-772968  [Li, Tl, P, H, O]               LiTlPHO3  Li4 Tl4 P4 H4 O12   \n",
       "2     mp-756951        [Li, Sb, O]                 LiSbO3        Li4 Sb4 O12   \n",
       "3     mp-754856     [Li, Y, Zr, S]             Li7Y7ZrS16     Li7 Y7 Zr1 S16   \n",
       "4      mp-24610      [H, Li, O, P]              LiP(HO2)2      Li4 P4 H8 O16   \n",
       "..          ...                ...                    ...                ...   \n",
       "868     mp-9919       [Li, Zn, Sb]                 LiZnSb        Li2 Zn2 Sb2   \n",
       "869    mp-14704      [Li, B, O, Y]             Li6Y(BO3)3    Li24 Y4 B12 O36   \n",
       "870   mp-557756     [Li, Zn, P, O]            Li4Zn(PO4)2    Li16 Zn4 P8 O32   \n",
       "871   mp-966801     [Li, Ca, B, O]                LiCaBO3     Li8 Ca8 B8 O24   \n",
       "872   mp-772721    [Li, Si, Bi, O]            LiSi6Bi9O26  Li2 Si12 Bi18 O52   \n",
       "\n",
       "     num_elements      chemsys spacegroup_symbol  spacegroup_number  \\\n",
       "0               3      F-In-Li              Pbcn                 60   \n",
       "1               5  H-Li-O-P-Tl                C2                  5   \n",
       "2               3      Li-O-Sb              C2/m                 12   \n",
       "3               4    Li-S-Y-Zr              P2/m                 10   \n",
       "4               4     H-Li-O-P            Pna2_1                 33   \n",
       "..            ...          ...               ...                ...   \n",
       "868             3     Li-Sb-Zn            P6_3mc                186   \n",
       "869             4     B-Li-O-Y            P2_1/c                 14   \n",
       "870             4    Li-O-P-Zn            P2_1/c                 14   \n",
       "871             4    B-Ca-Li-O              Ibca                 73   \n",
       "872             4   Bi-Li-O-Si               P-3                147   \n",
       "\n",
       "           fav    fav_tot  ...        0.1        0.2        0.3        0.4  \\\n",
       "0    11.132426   8.450140  ...   9.190986   9.927689   9.927689   9.927689   \n",
       "1    12.800916  21.721623  ...  11.140249  11.140249  11.140249  14.066884   \n",
       "2     8.819405  11.408661  ...   4.864107   5.543978   5.543978   5.543978   \n",
       "3     6.245816   6.055796  ...   4.319241   4.319241   4.319241   4.319241   \n",
       "4    38.676281  21.873600  ...  29.153401  29.153401  32.155099  32.155099   \n",
       "..         ...        ...  ...        ...        ...        ...        ...   \n",
       "868   6.350430   4.587253  ...   4.292991   4.292991   7.108637   7.108637   \n",
       "869  11.867930  14.840322  ...   5.185502   5.979421   6.508700   7.567257   \n",
       "870  12.278804  14.092563  ...   4.985237   8.758697   8.758697   8.758697   \n",
       "871  10.403979  14.003078  ...   5.331724   7.424524  10.040524  10.040524   \n",
       "872   5.711561  10.507305  ...   2.082729   2.817394   2.817394   3.552058   \n",
       "\n",
       "           0.5        0.6     1e-05    0.0001     0.001      0.01  \n",
       "0     9.927689   9.927689  0.993939  0.981707  0.962025  0.788732  \n",
       "1    14.066884  14.066884  1.000000  0.925000  0.725000  0.555556  \n",
       "2     5.543978   6.903721  1.000000  0.993865  0.906832  0.560606  \n",
       "3     5.047983   5.047983  0.987952  0.962963  0.921569  0.735294  \n",
       "4    32.155099  32.155099  1.000000  0.988506  0.941860  0.537500  \n",
       "..         ...        ...       ...       ...       ...       ...  \n",
       "868   7.108637   7.108637  0.987578  0.968750  0.876623  0.664286  \n",
       "869  11.272210  11.272210  1.000000  0.982143  0.812500  0.742574  \n",
       "870   8.758697  10.855064  1.000000  0.967480  0.811475  0.700935  \n",
       "871  10.040524  10.040524  0.989011  0.933333  0.771084  0.735294  \n",
       "872   3.552058   3.919391  1.000000  0.957983  0.815126  0.540541  \n",
       "\n",
       "[873 rows x 122 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5451122",
   "metadata": {},
   "source": [
    "## Write the result dataframe into a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "58f02961",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06125c4",
   "metadata": {},
   "source": [
    "## Featurising the DOS into a vector using interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3aaae8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "material_id                     mp-557328\n",
       "elements                  [Li, Zn, Sb, O]\n",
       "formula_pretty_reduced         Li3Zn2SbO6\n",
       "formula                   Li6 Zn4 Sb2 O12\n",
       "num_elements                            4\n",
       "                               ...       \n",
       "0.6                              6.163993\n",
       "1e-05                                 1.0\n",
       "0.0001                           0.993939\n",
       "0.001                            0.969136\n",
       "0.01                              0.84106\n",
       "Name: 652, Length: 120, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[652]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c977072",
   "metadata": {},
   "source": [
    "### Check different point densities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3731b4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,6))\n",
    "gs = fig.add_gridspec(2, 3, hspace=0, wspace=0)\n",
    "ax = gs.subplots(sharex=True, sharey=True)\n",
    "#fig, ax = plt.subplots(2,3, sharex=True, sharey=True, figsize=(13,6))\n",
    "fig_subnum = ['(a)']\n",
    "\n",
    "for j, density in enumerate([100, 4, 1]):\n",
    "\n",
    "    for i, idx in enumerate([45, 652]):\n",
    "        fr = get_total_f(idx)\n",
    "        d = get_partial_dos(idx)\n",
    "        f = interpolate.interp1d(fr, d, fill_value=0, bounds_error=False)\n",
    "\n",
    "        start = -10\n",
    "        end = 35\n",
    "        step = 1/density\n",
    "        fr_new = np.arange(start, end, step)\n",
    "        d_interp = f(fr_new)\n",
    "\n",
    "        ax[i][j].plot(fr, d, label='True')\n",
    "        ax[i][j].plot(fr_new, d_interp, color='orange', label='Interpolated')\n",
    "        ax[i][j].scatter(fr_new, d_interp, color='orange', s=10)\n",
    "        ax[i][j].tick_params(axis='both', which='major', labelsize=fs)\n",
    "        ax[i][j].set_ylim(-0.1,3.9)\n",
    "        ax[i][j].set_xlim(-6,26)\n",
    "        if i == 0:\n",
    "            if j == 2:\n",
    "                ax[i][j].annotate(r'Density = '+str(density)+' point THZ$^{-1}$', xy=(-5.5, 3.5), fontsize=fs)\n",
    "            else:\n",
    "                ax[i][j].annotate(r'Density = '+str(density)+' points THZ$^{-1}$', xy=(-5.5, 3.5), fontsize=fs)\n",
    "\n",
    "ax[0][0].annotate('Li$_2$CaTa$_2$O$_7$', xy=(-5.5, 3), fontsize=fs)\n",
    "ax[1][0].annotate('Li$_3$Zn$_2$SbO$_6$', xy=(-5.5, 3.5), fontsize=fs)\n",
    "ax[1][1].set_xlabel('Frequency [THz]', fontsize=fs)\n",
    "fig.text(0.08, 0.5, 'DOS', rotation=90, fontsize=fs)\n",
    "plt.savefig('dos_featurisation_ptdensity.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885dd72e",
   "metadata": {},
   "source": [
    "### Get DOS feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1837658c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dos_featuriser(index, dos, density=4):\n",
    "    '''Featurise the DOS into a vector using interpolation'''\n",
    "    '''density=4 is good'''\n",
    "    fr = get_total_f(i)\n",
    "    if dos == 'total':\n",
    "        d = get_total_dos(i)\n",
    "    elif dos == 'partial':\n",
    "        d = get_partial_dos(i)\n",
    "    f = interpolate.interp1d(fr, d, fill_value=0, bounds_error=False)\n",
    "    \n",
    "    start = -10\n",
    "    end = 35\n",
    "    step = 1/density\n",
    "    fr_new = np.arange(start, end, step)\n",
    "    d_interp = f(fr_new)\n",
    "    \n",
    "    return d_interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b02abf47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Int64Index([831], dtype='int64'),\n",
       " Int64Index([832], dtype='int64'),\n",
       " Int64Index([199], dtype='int64'),\n",
       " Int64Index([287], dtype='int64'),\n",
       " Int64Index([307], dtype='int64'),\n",
       " Int64Index([5], dtype='int64')]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_id = ['mp-867700', 'mp-613442', 'mp-867699', 'mp-867677', 'mp-561623', 'mp-867712']\n",
    "missing_idx = []\n",
    "\n",
    "for mat in missing_id: \n",
    "    idx = data[data.material_id == mat].index\n",
    "    missing_idx.append(idx)\n",
    "    \n",
    "missing_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8fbbaf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [*range(0, data.index[-1]+1)]\n",
    "for idx in missing_idx:\n",
    "    index.remove(idx)\n",
    "tot_list = []\n",
    "li_list = []\n",
    "all_list = []\n",
    "for i in index:\n",
    "    fvt = dos_featuriser(i, 'total')\n",
    "    fvli = dos_featuriser(i, 'partial')\n",
    "    all_list.append(np.concatenate((fvt, fvli)))\n",
    "    tot_list.append(fvt)\n",
    "    li_list.append(fvli)\n",
    "\n",
    "tot_arr = np.array(tot_list)\n",
    "li_arr = np.array(li_list)\n",
    "all_arr = np.array(all_list)\n",
    "#print(np.shape(all_arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9662f144",
   "metadata": {},
   "source": [
    "Write the result to a binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5a390383",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('vibtot_feature', tot_arr)\n",
    "np.save('vibli_feature', li_arr)\n",
    "np.save('viball_feature', all_arr)"
   ]
  }
 ],
 "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}