03_orientation_and_mapping_packages.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matrix transformation only for perfect file (with path 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys\n",
    "import shutil\n",
    "from itertools import islice\n",
    "from itertools import repeat\n",
    "from addict import Dict\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import math\n",
    "from collections import defaultdict\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from chart_studio import plotly\n",
    "from adjustText import adjust_text\n",
    "# from chart_studio import pyplot as ply\n",
    "# import plotly.plotly as py\n",
    "import plotly.offline as pyoff\n",
    "import re\n",
    "import mplcursors\n",
    "import mpldatacursor\n",
    "import warnings\n",
    "from varname import nameof\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# pymatgen libraries\n",
    "from pymatgen.core.structure import Structure\n",
    "from pymatgen.transformations.standard_transformations import SupercellTransformation\n",
    "from pymatgen.analysis.structure_matcher import StructureMatcher\n",
    "from pymatgen.io.cif import CifWriter\n",
    "from pymatgen.io.vasp.inputs import Poscar\n",
    "\n",
    "# from get_weirdos_ver3_new_cleaned import Operation, Orientation, PreProcessingCONTCAR, ReadStructure, Mapping, Movement, Plot, CreateDataFrame\n",
    "# splitall, copy_rename_single_file, copy_rename_files, check_folder_existance, weighing_movement\n",
    "from positionism.functional import func_cartesian, func_directory, func_distance, func_kmeans, func_file\n",
    "from positionism.preprocessing import create_dataframe, CONTCARorPOSCAR\n",
    "from positionism.orientation import orient_propagate\n",
    "from positionism.read import coordinate_and_el, metainfo\n",
    "from positionism.mapper import analysis, flag_and_map, atom_indexing, labelling, output_CIF, output_weirdos\n",
    "from positionism.plot import mapping_labelling, movement, structure_analysis\n",
    "from positionism.pathway_classifier import path_by_tuple_cage\n",
    "\n",
    "direc = os.getcwd() # get current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modif_dx_dz_cif_allvariables_litype\n",
    "\n",
    "# change_dx_dz_litype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "poscar_line_nr_start = 8        # index from 0\n",
    "poscar_line_nr_end = 60\n",
    "latticeconstantsmatrix_line_nr_start = 2\n",
    "cif_line_nr_start = 26          # index from 0\n",
    "\n",
    "calc_aim = \"mapping\"\n",
    "type_movement = \"intracage\"         # insert here for calc_aim neb: \"intracage_(n)\"\n",
    "xdatcar_temp = \"xdatcar_(temp)\"         # insert here for calc_aim xdatcar: \"xdatcar_(temp)\"\n",
    "\n",
    "if calc_aim == \"mapping\":\n",
    "    proceed_XDATCAR = \"False\"\n",
    "    proceed_NEB = \"False\"\n",
    "    calc_type = \"_mapping\"\n",
    "elif calc_aim == \"neb\":\n",
    "    proceed_XDATCAR = \"False\"\n",
    "    proceed_NEB = \"True\"\n",
    "    calc_type = f\"_neb/{type_movement}\"\n",
    "elif calc_aim == \"xdatcar\":\n",
    "    proceed_XDATCAR = \"True\"\n",
    "    proceed_NEB = \"True\"\n",
    "    calc_type = f\"_xdatcar/{xdatcar_temp}\"\n",
    "\n",
    "proceed_orientation=\"False\"\n",
    "\n",
    "amount_Li = 24\n",
    "reference_Li_nr = 23    # moved Li is placed in nr 23\n",
    "folder_name_init_system = \"/Init_System\"\n",
    "results_folder = \"_results\"\n",
    "file_new_system = \"CONTCAR\"\n",
    "file_init_system = \"POSCAR\"\n",
    "\n",
    "col_excel_geo = \"geometry\"\n",
    "col_excel_path = \"path\"\n",
    "col_excel_toten = \"toten [eV]\"\n",
    "\n",
    "amount_P = 4\n",
    "amount_S = 20\n",
    "amount_Cl = 4\n",
    "\n",
    "lattice_constant = 10.2794980000\n",
    "\n",
    "# litype = 0\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type0\"\n",
    "\n",
    "# litype = 1\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type1\"\n",
    "\n",
    "# litype = 2\n",
    "# # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type1_area2\" # \"Li6PS5Cl_optitype1\" # \"Li6PS5Cl_type2\" # same as: \"Li6PS5Cl_type2_2iia_115_new\"\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_optitype2\" # \"Li6PS5Cl_type2\"\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_optitype1n2_strictr_a\"\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type2\"\n",
    "\n",
    "# litype = 3\n",
    "# # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type3\" # use this\n",
    "# # # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type3_opti1\"\n",
    "# # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type3_init\"\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_optitype1n2_strictr_a_type3_B_noweirdnr\" # \"Li6PS5Cl_optitype1n2_strictr_a_type3\" # \"Li6PS5Cl_optitype1n2_strictr_a_type3_A\"\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type3\"\n",
    "\n",
    "litype = 4\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type4_trial2\"\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type4_DBSCAN_0_5_a_optitype2\"\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type4_31\" # \"Li6PS5Cl_optitype1n2_strictr_a_type4_weirdo1_var2\"\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_optitype1n2_strictr_a_type4_weirdo1_var2_opti_zoomedin\"\n",
    "file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type4\"\n",
    "\n",
    "# litype = 5\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type5\"           # better\n",
    "# # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type5_trial2_a\"\n",
    "# # # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type5_trial2_b\"\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type5\"\n",
    "\n",
    "# litype = 6\n",
    "# # file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type5_C_type6_b_type2\"\n",
    "# file_perfect_poscar_48n24_wo_cif = \"Li6PS5Cl_type6_init_opti\"\n",
    "\n",
    "file_perfect_poscar_48n24 = f\"{file_perfect_poscar_48n24_wo_cif}.cif\"\n",
    "\n",
    "level_radius = 1 # [1: r1 for 24g 48htype1 48htype2, 2: r2 for 48htype1 48htype2, 3: r3 for 48htype2]\n",
    "max_mapping_radius = 0.051 # 0.051 # 0.051 # 0.043 ### 0.018 # 0.015 # 0.043\n",
    "max_mapping_radius_48htype2 = 0.066 # 0.076 ### 0.075 # 0.051 # 0.076\n",
    "max_mapping_radius_48htype1_48htype2 = (max_mapping_radius + max_mapping_radius_48htype2) / 2\n",
    "file_perfect_poscar_24 = \"Li6PS5Cl_type0.cif\"\n",
    "\n",
    "file_name_toten = f\".{folder_name_init_system}/toten_final.ods\"\n",
    "\n",
    "if level_radius == 3:\n",
    "    folder_name_destination_restructure = f\"/{results_folder}/{calc_type}/restructure_{max_mapping_radius}_{max_mapping_radius_48htype2}_{max_mapping_radius_48htype1_48htype2}_{file_perfect_poscar_48n24_wo_cif}/\"\n",
    "elif level_radius == 2:\n",
    "    folder_name_destination_restructure = f\"/{results_folder}/{calc_type}/restructure_{max_mapping_radius}_{max_mapping_radius_48htype2}_{file_perfect_poscar_48n24_wo_cif}/\"\n",
    "elif level_radius == 1:\n",
    "    folder_name_destination_restructure = f\"/{results_folder}/{calc_type}/restructure_{max_mapping_radius}_{file_perfect_poscar_48n24_wo_cif}/\"\n",
    "else:\n",
    "    print(\"level_radius is not correct\")\n",
    "\n",
    "# # # # # folder_name_destination_lattice_coeff_input = \"/lattice_coeff_input/\"\n",
    "folder_name_perfect_poscar = \"/_reference_cif/\"\n",
    "file_restructure = \"CONTCAR\"\n",
    "file_restructure_positive = \"CONTCAR_positive\"\n",
    "element_restructure = \"P\"\n",
    "\n",
    "cif_columns = ['species', 'idx_species', 'unkownvar_1', 'coord_x', 'coord_y', 'coord_z', 'unkownvar_2']\n",
    "poscar_columns_type2 = ['coord_x', 'coord_y', 'coord_z', 'species']\n",
    "contcar_columns_type2 = ['coord_x', 'coord_y', 'coord_z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "var1 and var2 show that arc matters\n",
    "\n",
    "var2 and var3 show that distance by 0.02ish angstrom in 24g-48h doesn't matter\n",
    "\n",
    "var3 and var4 show that bigger radius by 0.007 reduce the weirdos each type by around 2\n",
    "\n",
    "var5 same as var3\n",
    "\n",
    "var5 and var6 show that bigger radius by 0.009 reduce the weirdos each type by around 4\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "04_important_outputs_var6 with radius 0.045 and 0.055 show no difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "direc_init_system = direc+str(folder_name_init_system)\n",
    "# # direc_perfect_system = direc+os.sep+str(file_perfect_poscar_24)\n",
    "direc_restructure_destination = direc+str(folder_name_destination_restructure)\n",
    "# # # # # direc_lattice_coeff_input = direc+str(folder_name_destination_lattice_coeff_input)\n",
    "direc_perfect_poscar = direc+str(folder_name_perfect_poscar)\n",
    "\n",
    "path_perfect_poscar_24 = os.path.join(direc_perfect_poscar, file_perfect_poscar_24)\n",
    "path_perfect_poscar_48n24 = os.path.join(direc_perfect_poscar, file_perfect_poscar_48n24)\n",
    "# path_perfect_poscar_P = os.path.join(direc_perfect_poscar, file_perfect_poscar_P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = {col_excel_geo: float, col_excel_path: float}\n",
    "\n",
    "data_toten = pd.read_excel(file_name_toten, dtype=dtype, engine=\"odf\")\n",
    "data_toten_ori = data_toten\n",
    "data_toten = data_toten.sort_values(by=[\"geometry\",\"path\"],ignore_index=True,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/create_dataframe.py:113: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/create_dataframe.py:115: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/create_dataframe.py:117: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_mapping_init = create_dataframe.base(data_toten, file_new_system)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete all elements in CONTCAR except Phospor"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Copy into new directory and rename files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just refreshing folder\n",
    "func_directory.check_folder_existance(direc_restructure_destination, empty_folder=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just copy reference file\n",
    "func_directory.copy_rename_single_file(direc_restructure_destination, direc_perfect_poscar, file_perfect_poscar_24, prefix=None)\n",
    "func_directory.copy_rename_single_file(direc_restructure_destination, direc_perfect_poscar, file_perfect_poscar_48n24, prefix=None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use get_translational_matrix\n",
    "alternative to get_transformation() from pymatgen: find_primitive() from spglib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # file_loc_mask_1, df_mapping_metainfo = propagate.calculate(file_loc, orientation=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now Processing with other folders that are with mask = 0 (not perfect system)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### copy the data of scaling and translation to the file_loc as initial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mapping_metainfo = file_loc.copy()\n",
    "func_directory.copy_rename_files(df_mapping_init, direc_restructure_destination, file_restructure, prefix=None, savedir = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/preprocessing/CONTCARorPOSCAR.py:147: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### ADJUSTMENT HERE !!!\n",
    "if proceed_NEB == \"True\":\n",
    "    CONTCARorPOSCAR.edit_to_normal_elements(df_mapping_init, direc_restructure_destination, file_restructure, prefix = None)\n",
    "else:\n",
    "    pass\n",
    "\n",
    "CONTCARorPOSCAR.positive_lessthan1(df_mapping_init, direc_restructure_destination, poscar_line_nr_start, poscar_line_nr_end, contcar_columns_type2, file_type = \"CONTCAR\", var_name_in = None, var_name_out = \"positive\", n_decimal=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'path', 'subdir_new_system', 'g+p', 'g+p+1', 'g+p-1',\n",
       "       'perfect_system', 'p_s_mask', 'toten [eV]', 'subdir_positive_CONTCAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapping_init.columns #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_init_mask_1, df_mapping_metainfo = orient_propagate.calculate(df_mapping_init, direc_restructure_destination, file_restructure_positive, \n",
    "                                                                       path_perfect_poscar_24, proceed_orientation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # just copy file\n",
    "# # Operation.File.copy_rename_single_file(direc_restructure_destination, direc_perfect_poscar, file_perfect_poscar_24, prefix=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-268.234411</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.296558</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geometry  path                                  subdir_new_system   g+p  \\\n",
       "0       8.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  11.0   \n",
       "1       8.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  10.0   \n",
       "2       8.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "3       8.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
       "4       7.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  12.0   \n",
       "\n",
       "   g+p+1  g+p-1  perfect_system  p_s_mask  toten [eV]  \\\n",
       "0    0.0   10.0             NaN         0 -271.373566   \n",
       "1   11.0    9.0             NaN         0 -271.923755   \n",
       "2   10.0    8.0             NaN         0 -271.907989   \n",
       "3    9.0   12.0             8.0         1 -268.234411   \n",
       "4    8.0   11.0             NaN         0 -271.296558   \n",
       "\n",
       "                             subdir_positive_CONTCAR  \n",
       "0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "1  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "2  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "3  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "4  /home/azka/Documents/01_Masterthesis/Atomic-Po...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapping_metainfo.head() #####"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert new cif file of orientated structure into only positive value"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TO DO: add subdir for lattice constant into file_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # def copy_rename_files_subpaths_latticecoeff(file_loc, source_directory, direc_lattice_coeff_input, var_filename, index):\n",
    "# # # # #     file_loc['subdir_lattice_coeff_input'] = None\n",
    "\n",
    "# # # # #     filename_to_copy = f\"{int(file_loc['geometry'][index])}_{int(file_loc['path'][index])}_POSCAR_{var_filename}\"\n",
    "# # # # #     source_path = os.path.join(source_directory, filename_to_copy)\n",
    "    \n",
    "# # # # #     subdir_lattice_coeff_input = direc_lattice_coeff_input + str(int(file_loc['geometry'][index])) +  \"/\" + str(int(file_loc['path'][index])) + \"/\"\n",
    "# # # # #     new_filename = \"POSCAR\"\n",
    "# # # # #     destination_path = os.path.join(subdir_lattice_coeff_input, new_filename)\n",
    "\n",
    "# # # # #     # check existance and emptying it\n",
    "# # # # #     Operation.File.check_folder_existance(subdir_lattice_coeff_input)\n",
    "# # # # #     # Copy the file to the destination directory with the new name\n",
    "# # # # #     shutil.copy2(source_path, destination_path)\n",
    "# # # # #     print(f\"File copied and renamed: {filename_to_copy} -> {destination_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # var_filename = \"trf_w_linalg_orientated_positive_lessthan1_diagonalizedconstants\"\n",
    "\n",
    "# # # # # for index in range(df_mapping_metainfo[\"geometry\"].size):\n",
    "# # # # #     copy_rename_files_subpaths_latticecoeff(df_mapping_metainfo, direc_restructure_destination, direc_lattice_coeff_input, var_filename, index)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mapping to the closest neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pymatgen.analysis.local_env import CrystalNN\n",
    "\n",
    "# max_mapping_radius = 0.05282658993283027\n",
    "# max_mapping_radius = 0.045\n",
    "# max_mapping_radius = 0.055\n",
    "# max_mapping_radius = 0.04197083906\n",
    "\n",
    "ref_structure_48n24 = Structure.from_file(path_perfect_poscar_48n24)\n",
    "cif_structure = Structure(ref_structure_48n24.lattice, ref_structure_48n24.species, ref_structure_48n24.frac_coords)\n",
    "cif = CifWriter(cif_structure)\n",
    "cif.write_file(f\"{direc_restructure_destination}{file_perfect_poscar_48n24_wo_cif}_expanded.cif\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reverse df_mapping_metainfo for NEB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_mapping_metainfo = df_mapping_metainfo[::-1].reset_index()\n",
    "    df_mapping_metainfo = df_mapping_metainfo.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.549916</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "      <th>12</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.542982</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.447232</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "      <td>-272.253248</td>\n",
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       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.311081</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "    <tr>\n",
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       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.232154</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-272.023661</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.208913</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.347557</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.414633</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>-272.330529</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.443512</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.362421</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.232047</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-272.160414</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.086533</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.283727</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.005719</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.990037</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.234390</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-271.991508</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.460801</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.269562</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.004952</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.255326</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.319999</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.234085</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.758771</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-271.517129</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.992602</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.776769</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.231430</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-271.959006</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-272.368408</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.327751</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.231844</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.095991</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-272.368157</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.334495</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.553958</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.337269</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-268.232224</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    geometry  path                                  subdir_new_system   g+p  \\\n",
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       "1        8.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  10.0   \n",
       "2        8.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "3        8.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
       "4        7.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  12.0   \n",
       "5        7.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  11.0   \n",
       "6        7.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  10.0   \n",
       "7        7.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "8        7.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
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       "11       6.0   6.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  12.0   \n",
       "12       6.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  11.0   \n",
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       "14       6.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "15       6.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
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       "17       6.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   6.0   \n",
       "18       5.0   7.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  12.0   \n",
       "19       5.0   6.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  11.0   \n",
       "20       5.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  10.0   \n",
       "21       5.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
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       "24       5.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   6.0   \n",
       "25       5.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   5.0   \n",
       "26       4.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "27       4.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
       "28       4.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   7.0   \n",
       "29       4.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   6.0   \n",
       "30       4.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   5.0   \n",
       "31       4.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   4.0   \n",
       "32       3.0   7.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  10.0   \n",
       "33       3.0   6.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   9.0   \n",
       "34       3.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   8.0   \n",
       "35       3.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   7.0   \n",
       "36       3.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   6.0   \n",
       "37       3.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   5.0   \n",
       "38       3.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   4.0   \n",
       "39       3.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   3.0   \n",
       "40       2.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   6.0   \n",
       "41       2.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   5.0   \n",
       "42       2.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   4.0   \n",
       "43       2.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   3.0   \n",
       "44       2.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   2.0   \n",
       "45       1.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   4.0   \n",
       "46       1.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   3.0   \n",
       "47       1.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   2.0   \n",
       "48       1.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   1.0   \n",
       "49       0.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   5.0   \n",
       "50       0.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   4.0   \n",
       "51       0.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   3.0   \n",
       "52       0.0   2.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   2.0   \n",
       "53       0.0   1.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   1.0   \n",
       "54       0.0   0.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...   0.0   \n",
       "\n",
       "    g+p+1  g+p-1  perfect_system  p_s_mask  toten [eV]  \\\n",
       "0     0.0   10.0             NaN         0 -271.373566   \n",
       "1    11.0    9.0             NaN         0 -271.923755   \n",
       "2    10.0    8.0             NaN         0 -271.907989   \n",
       "3     9.0   12.0             8.0         1 -268.234411   \n",
       "4     8.0   11.0             NaN         0 -271.296558   \n",
       "5    12.0   10.0             NaN         0 -272.228547   \n",
       "6    11.0    9.0             NaN         0 -272.025225   \n",
       "7    10.0    8.0             NaN         0 -272.239606   \n",
       "8     9.0    7.0             NaN         0 -271.990043   \n",
       "9     8.0   13.0             7.0         1 -268.234004   \n",
       "10    7.0   12.0             NaN         0 -271.999187   \n",
       "11   13.0   11.0             NaN         0 -272.549916   \n",
       "12   12.0   10.0             NaN         0 -272.542982   \n",
       "13   11.0    9.0             NaN         0 -272.447232   \n",
       "14   10.0    8.0             NaN         0 -272.253248   \n",
       "15    9.0    7.0             NaN         0 -272.311081   \n",
       "16    8.0    6.0             NaN         0 -268.232154   \n",
       "17    7.0   12.0             6.0         1 -272.023661   \n",
       "18    6.0   11.0             NaN         0 -272.208913   \n",
       "19   12.0   10.0             NaN         0 -272.347557   \n",
       "20   11.0    9.0             NaN         0 -272.414633   \n",
       "21   10.0    8.0             NaN         0 -272.330529   \n",
       "22    9.0    7.0             NaN         0 -272.443512   \n",
       "23    8.0    6.0             NaN         0 -272.362421   \n",
       "24    7.0    5.0             NaN         0 -268.232047   \n",
       "25    6.0    9.0             5.0         1 -272.160414   \n",
       "26    5.0    8.0             NaN         0 -272.086533   \n",
       "27    9.0    7.0             NaN         0 -271.283727   \n",
       "28    8.0    6.0             NaN         0 -272.005719   \n",
       "29    7.0    5.0             NaN         0 -271.990037   \n",
       "30    6.0    4.0             NaN         0 -268.234390   \n",
       "31    5.0   10.0             4.0         1 -271.991508   \n",
       "32    4.0    9.0             NaN         0 -272.460801   \n",
       "33   10.0    8.0             NaN         0 -272.269562   \n",
       "34    9.0    7.0             NaN         0 -272.004952   \n",
       "35    8.0    6.0             NaN         0 -272.255326   \n",
       "36    7.0    5.0             NaN         0 -272.319999   \n",
       "37    6.0    4.0             NaN         0 -268.234085   \n",
       "38    5.0    3.0             NaN         0 -271.758771   \n",
       "39    4.0    6.0             3.0         1 -271.517129   \n",
       "40    3.0    5.0             NaN         0 -271.992602   \n",
       "41    6.0    4.0             NaN         0 -271.776769   \n",
       "42    5.0    3.0             NaN         0 -268.231430   \n",
       "43    4.0    2.0             NaN         0 -271.959006   \n",
       "44    3.0    4.0             2.0         1 -272.368408   \n",
       "45    2.0    3.0             NaN         0 -272.327751   \n",
       "46    4.0    2.0             NaN         0 -268.231844   \n",
       "47    3.0    1.0             NaN         0 -272.095991   \n",
       "48    2.0    5.0             1.0         1 -272.368157   \n",
       "49    1.0    4.0             NaN         0 -272.334495   \n",
       "50    5.0    3.0             NaN         0 -272.553958   \n",
       "51    4.0    2.0             NaN         0 -272.337269   \n",
       "52    3.0    1.0             NaN         0 -268.232224   \n",
       "53    2.0    0.0             NaN         0         NaN   \n",
       "54    1.0    0.0             0.0         1         NaN   \n",
       "\n",
       "                              subdir_positive_CONTCAR  \n",
       "0   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "1   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "2   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "3   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "4   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "5   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "6   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "7   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "8   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "9   /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "10  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "11  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "12  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "13  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "14  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "15  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "16  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "17  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "18  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "19  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "20  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "21  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "22  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "23  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "24  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "25  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "26  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "27  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "28  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "29  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
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       "31  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "32  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "33  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "34  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "35  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "36  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "37  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "38  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "39  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "40  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "41  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "42  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "43  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "44  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "45  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "46  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "47  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "48  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "49  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "50  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "51  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "52  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "53  /home/azka/Documents/01_Masterthesis/Atomic-Po...  \n",
       "54  /home/azka/Documents/01_Masterthesis/Atomic-Po...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapping_metainfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filter only moving the Li-ions from 24g to 24g+48h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "coor_ref_structure_48n24 = coordinate_and_el.single_structure(ref_structure_48n24)\n",
    "coor_ref_structure_48n24_expanded = coordinate_and_el.single_structure(Structure.from_file(f\"{direc_restructure_destination}{file_perfect_poscar_48n24_wo_cif}_expanded.cif\"))\n",
    "\n",
    "\n",
    "coordinate_and_el.structures(df_mapping_metainfo, mapping = \"False\")\n",
    "\n",
    "if level_radius == 3:\n",
    "    flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "    flag_and_map.li_48htype1_48htype2(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype1_48htype2, litype)\n",
    "    flag_and_map.li_48htypesmerged_level1(df_mapping_metainfo, \"Li\")\n",
    "    flag_and_map.li_48htype2(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype2, level_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_mapping_metainfo, \"Li\", level_radius)\n",
    "elif level_radius == 2:\n",
    "    flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "    flag_and_map.li_48htype2(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype2, level_radius, litype)\n",
    "    flag_and_map.li_48htypesmerged(df_mapping_metainfo, \"Li\", level_radius)\n",
    "elif level_radius == 1:\n",
    "    flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "\n",
    "# flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"P\", max_mapping_radius)\n",
    "# flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"S\", max_mapping_radius)\n",
    "# flag_and_map.all_atoms_of_el(df_mapping_metainfo, coor_ref_structure_48n24, \"Cl\", max_mapping_radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'path', 'subdir_new_system', 'g+p', 'g+p+1', 'g+p-1',\n",
       "       'perfect_system', 'p_s_mask', 'toten [eV]', 'subdir_positive_CONTCAR',\n",
       "       'coor_structure_init_dict', 'flag_Li', 'coor_weirdos_Li',\n",
       "       'sum_weirdos_Li', 'duplicate_closest24_w_data_Li',\n",
       "       'coor_reducedreference_Li_closestduplicate',\n",
       "       'sum_mapped_Li_closestduplicate', 'sum_sanitycheck_Li_closestduplicate',\n",
       "       'atom_mapping_Li_closestduplicate',\n",
       "       'atom_mapping_Li_w_dist_closestduplicate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapping_metainfo.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Intel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD.\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "SVD did not converge in Linear Least Squares",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 64\u001b[0m\n\u001b[1;32m     56\u001b[0m CONTCARorPOSCAR\u001b[38;5;241m.\u001b[39mconvert_to_cif_pymatgen(df_mapping_metainfo, direc_restructure_destination, \n\u001b[1;32m     57\u001b[0m                                         file_restructure \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCONTCAR_positive\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m     58\u001b[0m                                         var_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCONTCAR_positive_pymatgen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     60\u001b[0m CONTCARorPOSCAR\u001b[38;5;241m.\u001b[39mget_latticeconstant_dict(df_mapping_metainfo, direc_restructure_destination, \n\u001b[1;32m     61\u001b[0m                                          proceed_XDATCAR, \n\u001b[1;32m     62\u001b[0m                                          var_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCONTCAR\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 64\u001b[0m \u001b[43mstructure_analysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergy_vs_latticeconstant\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_mapping_metainfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirec_restructure_destination\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mlitype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_filename\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCONTCAR\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m structure_analysis\u001b[38;5;241m.\u001b[39mweirdos_directcoor(df_mapping_metainfo, direc_restructure_destination, \n\u001b[1;32m     68\u001b[0m                                       level_radius, litype)\n",
      "File \u001b[0;32m~/Documents/01_Masterthesis/Atomic-Positionism/positionism/plot/structure_analysis.py:43\u001b[0m, in \u001b[0;36menergy_vs_latticeconstant\u001b[0;34m(dataframe, direc_restructure_destination, litype, var_filename, interpolate)\u001b[0m\n\u001b[1;32m     37\u001b[0m total_energies \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(total_energies)        \n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# # Linear interpolation\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# interp_func = interp1d(lattice_constants, total_energies, kind='linear')\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# Perform linear regression to find the slope (m) and intercept (c)\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m m, c \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolyfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlattice_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_energies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Set the figure size to 5x3\u001b[39;00m\n\u001b[1;32m     46\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m3\u001b[39m))\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mpolyfit\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/numpy/lib/polynomial.py:668\u001b[0m, in \u001b[0;36mpolyfit\u001b[0;34m(x, y, deg, rcond, full, w, cov)\u001b[0m\n\u001b[1;32m    666\u001b[0m scale \u001b[38;5;241m=\u001b[39m NX\u001b[38;5;241m.\u001b[39msqrt((lhs\u001b[38;5;241m*\u001b[39mlhs)\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n\u001b[1;32m    667\u001b[0m lhs \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m scale\n\u001b[0;32m--> 668\u001b[0m c, resids, rank, s \u001b[38;5;241m=\u001b[39m \u001b[43mlstsq\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlhs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrhs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrcond\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    669\u001b[0m c \u001b[38;5;241m=\u001b[39m (c\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m/\u001b[39mscale)\u001b[38;5;241m.\u001b[39mT  \u001b[38;5;66;03m# broadcast scale coefficients\u001b[39;00m\n\u001b[1;32m    671\u001b[0m \u001b[38;5;66;03m# warn on rank reduction, which indicates an ill conditioned matrix\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mlstsq\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/numpy/linalg/linalg.py:2285\u001b[0m, in \u001b[0;36mlstsq\u001b[0;34m(a, b, rcond)\u001b[0m\n\u001b[1;32m   2282\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_rhs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   2283\u001b[0m     \u001b[38;5;66;03m# lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis\u001b[39;00m\n\u001b[1;32m   2284\u001b[0m     b \u001b[38;5;241m=\u001b[39m zeros(b\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m+\u001b[39m (m, n_rhs \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), dtype\u001b[38;5;241m=\u001b[39mb\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m-> 2285\u001b[0m x, resids, rank, s \u001b[38;5;241m=\u001b[39m \u001b[43mgufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrcond\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   2287\u001b[0m     x[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/numpy/linalg/linalg.py:101\u001b[0m, in \u001b[0;36m_raise_linalgerror_lstsq\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_lstsq\u001b[39m(err, flag):\n\u001b[0;32m--> 101\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSVD did not converge in Linear Least Squares\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mLinAlgError\u001b[0m: SVD did not converge in Linear Least Squares"
     ]
    }
   ],
   "source": [
    "atom_indexing.weirdos_el(df_mapping_metainfo, \"Li\", level_radius)\n",
    "\n",
    "atom_indexing.correct_idx_and_order_mapped_el(df_mapping_metainfo, \n",
    "                                                 \"Li\", level_radius)\n",
    "# output_CIF.write_merged_structure(df_mapping_metainfo, direc_restructure_destination, \n",
    "#                                     amount_Li, amount_P, amount_S, \n",
    "#                                     var_savefilename = \"mapLi\")\n",
    "\n",
    "labelling.weirdos_to_top_n_closestcoorref_el(df_mapping_metainfo, \n",
    "                                             coor_ref_structure_48n24, \n",
    "                                             \"Li\", litype)\n",
    "\n",
    "output_CIF.write_merged_structure(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                    amount_Li, amount_P, amount_S, level_radius, \n",
    "                                    var_savefilename = \"mapLi\")\n",
    "\n",
    "output_CIF.correct_Li_idx(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                        amount_Li, amount_P, amount_S, amount_Cl, \n",
    "                                        var_savefilename_init = \"mapLi\", \n",
    "                                        var_savefilename_new = \"mapLi_reindexed\")\n",
    "\n",
    "output_CIF.Edit.format_spacing_cif(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                   var_savefilename_init = \"mapLi_reindexed\", \n",
    "                                   var_savefilename_new = \"mapLi_reindexed\")\n",
    "# # # # Operation.File.delete_files(df_mapping_metainfo, direc_restructure_destination, file_name_w_format = \"mapLi_reindexed.cif\")\n",
    "\n",
    "output_CIF.correct_Li_idx_weirdos_appended(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                                         amount_Li, amount_P, amount_S, amount_Cl, \n",
    "                                                         level_radius,var_savefilename_init = \"mapLi\", \n",
    "                                                         var_savefilename_new = \"mapLi_reindexed_weirdos_appended\")\n",
    "\n",
    "output_CIF.Edit.format_spacing_cif(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                   var_savefilename_init = \"mapLi_reindexed_weirdos_appended\", \n",
    "                                   var_savefilename_new = \"mapLi_reindexed_weirdos_appended\")\n",
    "# # # Operation.File.delete_files(df_mapping_metainfo, direc_restructure_destination, file_name_w_format = \"mapLi_reindexed_weirdos_appended.cif\")\n",
    "\n",
    "output_CIF.ascending_Li(df_mapping_metainfo, direc_restructure_destination, \n",
    "                        var_filename_init = \"mapLi_reindexed_weirdos_appended\", \n",
    "                        var_savefilename_new = \"mapLi_reindexed_weirdos_appended_reordered\")\n",
    "# # # Mapping.OutputCIF.format_spacing_cif(df_mapping_metainfo, direc_restructure_destination, var_savefilename_init = \"mapLi_reindexed_weirdos_appended_reordered\", var_savefilename_new = \"mapLi_reindexed_weirdos_appended_reordered\")\n",
    "\n",
    "atom_indexing.get_idx_coor_limapped_weirdos_dict(df_mapping_metainfo, coor_ref_structure_48n24, \n",
    "                                                 level_radius, litype, el=\"Li\")\n",
    "\n",
    "labelling.get_label_mapping(df_mapping_metainfo, coor_ref_structure_48n24, \n",
    "                            \"Li\", level_radius, litype)\n",
    "# NECESSARY STEP \n",
    "labelling.relabel_48htype1(df_mapping_metainfo, litype, el=\"Li\")\n",
    "\n",
    "coor_weirdos_Li = output_weirdos.as_array(df_mapping_metainfo, level_radius)\n",
    "\n",
    "output_weirdos.create_POSCAR(coor_weirdos_Li, direc_restructure_destination, \n",
    "                                     lattice_constant, \n",
    "                                     filename = \"POSCAR_weirdos\")\n",
    "\n",
    "CONTCARorPOSCAR.convert_to_cif_pymatgen(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                        file_restructure = \"CONTCAR_positive\", \n",
    "                                        var_name = \"CONTCAR_positive_pymatgen\")\n",
    "\n",
    "CONTCARorPOSCAR.get_latticeconstant_dict(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                         proceed_XDATCAR, \n",
    "                                         var_filename = \"CONTCAR\")\n",
    "\n",
    "structure_analysis.energy_vs_latticeconstant(df_mapping_metainfo, direc_restructure_destination,\n",
    "                                             litype, var_filename = \"CONTCAR\", interpolate = True)\n",
    "\n",
    "structure_analysis.weirdos_directcoor(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                      level_radius, litype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get_dx_dz_init_litype(litype)\n",
    "\n",
    "\n",
    "create_file_name_allvariables_litype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 670x590 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure_analysis.weirdos_directcoor(df_mapping_metainfo, direc_restructure_destination, \n",
    "                                      level_radius, litype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/azka/anaconda3/envs/positionism/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuple_metainfo = metainfo.tuple(coor_ref_structure_48n24_expanded, litype, el = \"Li\")\n",
    "# # NECESSARY STEP: modify \"48htype1\" in \"idx_coor_limapped_weirdos_dict\" for litype=1\n",
    "metainfo.relabel_48htype1(tuple_metainfo, litype)\n",
    "\n",
    "coor_48htype2_metainfo = metainfo.coor_48htype2(coor_ref_structure_48n24_expanded, litype, el = 'Li')\n",
    "\n",
    "idx_coor_cage_order = {0: np.array([0.97111, 0.25   , 0.25   ]), 3: np.array([0.02889, 0.75   , 0.25   ]),\n",
    "                       1: np.array([0.02889, 0.25   , 0.75   ]), 2: np.array([0.97111, 0.75   , 0.75   ])}\n",
    "\n",
    "# idx_coor_cage_order = {0: np.array([0.02889, 0.25   , 0.75   ]), 3: np.array([0.02889, 0.75   , 0.25   ]),\n",
    "#                        1: np.array([0.97111, 0.25   , 0.25   ]), 2: np.array([0.97111, 0.75   , 0.75   ])}\n",
    "\n",
    "coor_24g_array = np.array([item['coor'] for sublist in tuple_metainfo.values() for item in sublist if item['type'] == '24g'])\n",
    "centroids, labels = func_kmeans.kmeans_cluster_atoms(coor_24g_array, amount_clusters = 4)\n",
    "func_kmeans.create_POSCAR_atoms_centroids_appended(coor_24g_array, centroids, direc_restructure_destination, lattice_constant, filename = \"POSCAR_24g_centroids4\")\n",
    "\n",
    "idx_cage_coor_24g = metainfo.idx_cage_coor_24g(coor_24g_array, labels, idx_coor_cage_order, amount_clusters = 4)\n",
    "tuple_cage_metainfo = metainfo.tuple_cage(tuple_metainfo, idx_cage_coor_24g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NECESSARY STEP: modify \"48htype1\" in \"idx_coor_limapped_weirdos_dict\" for litype=1\n",
    "analysis.relabel_48htype1(df_mapping_metainfo, litype)\n",
    "\n",
    "df_mapping_metainfo_sorted = df_mapping_metainfo.sort_values(\"toten [eV]\", ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "$\\text{Structure index}$"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "$\\text{Amount of occupancy}$"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAABgskDB4smTJ7JtW6ZpjkxXcl134mhDFM4LFZJUqVT03Xff+e+Pj4918+bNS6sHAAAAwAeBgsXKyorevHkz9tyzZ89CFSR9WKsxjuu6E89J0uLiohYXF0PfHwAAAMCnCbSPxXkf7h8/fhy4mNPXNwxj7OgHC7cBAACA2RMoWKRSKe3t7Y099/HmeReZNL2pUqmo0+n4723bnvpRtgAAAAA+r0BToR48eCDXddXv90dGLzzPU7/f16NHjy68huM4sm1bzWZT3W5X5XJZd+7cUS6XkySVSiXV63V/n4v9/X32sAAAAABmVOCdtxuNhhKJxMgxz/P09OnTqb7eNE2VSiWVSqWJbU6fGwYOAAAAALMnULCo1WoT97GoVCqhCgIAAAAQP4HWWEwKFZLU7/cDFwMAAAAgngKNWLx48WLscdd1L3WDPAAAAACzKVCwKJVKWltb8zfHc11Xg8FAjuMom81GWR8AAACAGAgULAqFwthF2kdHRyOPiAUAAABwPQRaYzHpyU/Ly8taWFgIVRAAAACA+AkULM4zbrdsAAAAAPMt0FSor776auzIhOM4qtVqoYsCAAAAEC+BgoVpmiqXy2c2yDNNU8vLy5EUBgAAACA+Am+Qt7q6GnUtAAAAAGIqULAYhoq9vT21221J0p07d9i/AgAAALimAgULSfr22281GAxkmqYkqd1uq1qtan9/P7LiAAAAAMRDoGCxtbWlRqOhZDI5crzb7apSqaharUZSHAAAAIB4CPS42WQyeSZUSFI6nfZHMAAAAABcH4GCxXmb4LFBHgAAAHD9BAoWvV5Pe3t7Z47v7e3pzZs3oYsCAAAAEC+B1lg8fvxYGxsbyufz/tQnx3GUyWTUbDYjLRAAAADA7Av8VKhWq6VXr17p4OBArusqk8mwtwUAAABwTQUOFtKH/Sw+DhPHx8daWloKVRQAAACAeAm0xuI8PGoWAAAAuH4CBYtXr15pbW1NKysrI69EIqF6vR51jQAAAABmXKCpUNVqVZVKRel0euS453l68uRJJIUBAAAAiI9AweL+/fu6d+/e2HOVSiVUQQAAAADiJ9BUqMFgMPHc4eFh4GIAAAAAxFOgEYvNzU1/ZCKVSo2c29nZ0TfffBO+MgAAAACxEShYPHz4UJ1OR6Zpqtfr+cdd19XLly8jKw4AAABAPAQKFrdv39bz58/Hnnv27FmoggAAAADET6A1FolEYuK5x48fBy4GAAAAQDwFChapVEp7e3tjz21tbYUqCAAAAED8BJoK9eDBA7muq36/L9M0/eOe56nf7+vRo0eRFQgAAABg9gUKFpLUaDTOTInyPE9Pnz4NXRQAAACAeAkULGq1mtbX18eeY4M8AAAA4PoJtMZiUqiQpH6/H7gYAAAAAPEUKFicp9lsRn1JAAAAADMu8BqL016/fq1Go6FWqyXXdaO4JAAAAIAYCRwsXr9+rWazKcuydHh4qHQ6rSdPnqjdbkdWXKfTUaPRUDablWmaarfbunPnjnK5XGT3AAAAABDeJwWLt2/fqtFoyLZtOY6j1dVVJZNJOY6j5eVlSVI6nY6sONd11el0ZNu2TNNUuVwmVAAAAAAzaKpg8ezZM1mWJcdxlEwmlcvlVCwWlUwm9ezZMz9USOcv7A6i3+/LMIxIrwkAAAAgWlMFi3Q6rdXVVa2vr6ter2tpack/t7CwcGnFAQAAAIiHqYLF+vq6PxLx448/+qMIGxsbl1qcJLVaLSUSCQ0GA/V6PdVqtYltT05OdHJy4r8/Pj6+9PoAAAAABFi8fe/ePUnS0dGRms2m/vGPf2h3d1d3796VJG1tbenRo0eRFDdcr2GapiTJsizl83nt7OyMbV+tVvXDDz9Ecm8AAAAA0wu8j8Xy8rI2NzfVarW0vr6u7e1tPXz4UOVyObLiTNP0Q4UkbWxsyLbtiY+0rVQqOjo68l/v3r2LrBYAAAAAk0WyQd4wZDx//lyrq6tRXFKSZNv2yPvhIm7Hcca2X1xc1NLS0sgLAAAAwOWLfOft89ZAfArXdZXP50dCxHCk4vQoBgAAAICrF3mwiOpxs4ZhqFQqjYQIy7KUy+V4/CwAAAAwYwLvvP05VCoV1et1//379+8nLtwGAAAAcHVmOlgMRy0AAAAAzLbIp0IBAAAAuH4IFgAAAABCI1gAAAAACG2m11jgcvzz73+cruGfji63EAAAAMwNRiwAAAAAhEawAAAAABAawQIAAABAaAQLAAAAAKERLAAAAACERrAAAAAAEBrBAgAAAEBoBAsAAAAAobFB3mfwqRvSXXZ7AAAAIGqMWAAAAAAIjWABAAAAIDSCBQAAAIDQCBYAAAAAQiNYAAAAAAiNYAEAAAAgNIIFAAAAgNAIFgAAAABCI1gAAAAACI1gAQAAACA0ggUAAACA0AgWAAAAAEIjWAAAAAAIjWABAAAAIDSCBQAAAIDQCBYAAAAAQiNYAAAAAAiNYAEAAAAgtN9fdQHAdfO//uf/mK7h//n3yy0EiEDc/z3Hvf5//v2P0zX809HlFgIAYsQCAAAAQAQIFgAAAABCI1gAAAAACG3m11jU63UZhiFJcl1XpVLpagsCAAAAcMZMj1jU63VJUqFQUKFQUDqdVrFYvOKqAAAAAHxspoNFtVpVoVDw32cyGVmWdYUVAQAAABhnZoOF4zhyXdefBnVap9P5/AUBAAAAmGhm11g4jjP2uGEYcl137LmTkxOdnJz474+OPjy3+/j4OPL6PsmJN127YZ20j1X73/7ff0zZ/EP7//jP//yk9p96fdrT/nO2j/u/57jXf9k/r2hPe9rPTvurMry/503x88abUe122xtXnmmaXqPRGPs133//vSeJFy9evHjx4sWLFy9eEb7evXt34ef3mR2xmGQwGEw8V6lU9N133/nvf/vtNw0GA62srGhhYeHSajo+PtbNmzf17t07LS0tXdp9cDXo3/lG/843+ne+0b/zjf6dDZ7n6ddff9WXX355YduZDRamaY497rruxHOLi4taXFwcOTZujcZlWVpa4h/+HKN/5xv9O9/o3/lG/843+vfqLS8vT9VuZhdvm6YpwzDGrrXIZDJXUBEAAACASWY2WEgfpjadfgKUbdsjj58FAAAAMBtmOliUSiW5rivbtmXbtvb399VoNK66rDMWFxf1/fffn5mGhflA/843+ne+0b/zjf6db/Rv/Cx43jTPjgIAAACAyWZ6xAIAAABAPBAsAAAAAIRGsAAAAAAQ2szuYxEX9Xrd3yvDdV2VSqWrLQiBua6rVqulnZ0dtdvtM+fp6/ir1+uSpF6vJ0lnHgZBH8fX8PtX+tC/juNoe3t7ZC8j+nd+ZLPZMz+n6d/46nQ6ajQaymazMk1T7XZbd+7cUS6X89vQv/HAiEUIww8phUJBhUJB6XRaxWLxiqtCEN1uV61WS67rjt3dnb6Ov3K5rFKppFKp5AeKbDbrn6eP461cLiuTyahQKKhWqymRSCifz/vn6d/5Ydv2yKPoJfo37lzXVafTUbFYVLFYVCqVOhMqJPo3DngqVAg3btxQv98f+Y3YwsKC+CuNL9u2Va1W9fLly5Hj9HW8ua6rfD6vnZ0dvw+73a5u376tXq8n0zTp45jLZrPKZrP+bzHr9bqq1aoODw8l8T08L4YjU8VicaTv6N94s21bmUxmpP9Oo3/jgxGLgBzHkeu6Y78JPv5NCuKNvp4PBwcHchzHf2+apqQPH1To4/hrt9sjUyP29/eVyWQk8T08T1qtljY2NkaO0b/zjf6NF9ZYBHT6A8pphmHIdd3PWwwuFX0df4Zh+L+5Hhr+B8k0TR0cHEz8Ovo4fmzbluu62tnZkcT38LzodDp+WDyN/p0PrVZLiURCg8FAvV5PtVpNEv0bNwSLiA2/KTD/6Ot4q1arajQaE4feJfo4bobTZIZT387rW4n+jRvXdWWa5tQfJunf+Ein05L+NZJsWZY/fXUS+nc2MRUqYvwjvz7o6/gql8u6f/++CoXCue3o43gxDEOFQsGfEnXjxo1zP4TSv/FhWdbIYt5p0L/xYZqmHyokaWNjwx95nIT+nU0Ei4BOfwOcNvyNCuYHfT1fbNtWKpUamY9PH8eb67oql8sjH0IymYz/pBn6N9663a7W1tYmnqd/48+27ZH3w9FGx3Ho35ghWARkmqYMwxg792/cHFDEF309P4brKoYjFcOF2/RxvDmOo3q9PvIbzGHIMAyD/o25wWCgTqejer2uer2ucrks6cOTv2zbpn9jbjh18XT/Db9/hyMZ9G98ECxCqFQqI08ksG37wqkVmG2Thlbp6/jrdrvqdrtKp9NyHEeO48iyLCUSCUn0cZyl02mVSqWR3142m02l02n/gwf9G1+ZTMbfg6ZUKvn7F5RKJX96FP0bX4ZhnPn+HU59G45c0L/xwT4WIdXrdf+bYX9/33+KAeLFcRzZtq1ms6lut6tSqTR210/6Op5c11UymRw7X/f0j0D6OL5c15VlWf774VNlPt55m/6Nt+HPadu2VSqVlM1m/fBI/8bXx9+/79+/P9N/9G88ECwAAAAAhMZUKAAAAAChESwAAAAAhEawAAAAABAawQIAAABAaAQLAAAAAKERLAAAAACERrAAAAAAEBrBAgAAAEBov7/qAgAA06vX6/6f379/r2Kx6O9CjGgUi0W1Wi3t7Oz4uzoDAC5GsACAmCgWiyoWi0qn0/6xfD5/pp1lWSoUCp+trs99v0nK5bIcx9HOzk6o6zQaDR0cHERUFQBcH0yFAoCYaLVaI6FCkra3t8+0a7fbn6ukK7nfJNlsVvfv37/qMgDg2mLEAgBiwnVdOY4j0zT9Y4Zh6M6dO/57y7LkOM5nq+lz3+88TFsCgKvFiAUAxEQ6nVY2m1Wn0xk5Plxf0el01G635TiO6vW6vx6j0+no9u3byufz6nQ6KpfLKpfL6na7ymazSqVS/rXK5bJu3Lghy7JG7mFZ1sjrvPtNc91JNQ3V63XZtu3/70XG3XN4j9u3b6vb7frXO32f0/ezLEu2bU+837iaLMvS7du3tbCw4B8rFou6ceOGisWiXNe9sHYAmBseACAWer2eZ5qmJ8mT5GUyGa/dbo+0abfbXjqdPvO1Ozs7Xjqd9trttvfy5UuvVCr57U3THGmbyWS8RqPhv6/Van774bV2dnbOvd80151UUy6X868//LqXL1+e/5fjed7Lly/P3HNYx+m/J9M0R65XKpVG6jo8PPQkjXzNRTUZhuG/bzQaU9ULAPOGqVAAEBOmaarX6/kjBZ1OR9lsVjs7O8rlcud+rWEY6na7/nSh4VqNRCIxtu2Q67oql8s6PDz0jzWbzZHpV+NcdN1JNTmOI9u2RxZg5/N5NRoNNRqNc+85qQ7HcUamSZmmKcdxlE6n5bqu6vW6PM8bqev0WpZpatrZ2VE+n1e73VYikTizFgYArgOCBQDETCaT8T8ol8tlbW5uXhgsJI2szZjWwcGBDMMYCQVhn7p0Xk2dTkeGYYxM9+r1eqHWcXx8D8MwNBgMRu53nmlqGvZJPp/Xy5cvA9cKAHFGsACAGHBdV51O50yAqNVqqtfrcl137Afk04u9L/oAffpe4/48jY8Xl0+67tDHNbmuK9M0R0YYLntR9rjRlSA13b59W51OR91ulxELANcSi7cBICb29/fHHjdNc2Jo6Ha7n3yf4W/zJfnThT42KXCcd7/T151kOB1q2vuFNel+n1rTMFA1Go2xe4sAwHVAsACAmLAs68wToT4exRiuH5DkryM4z+n20r8eaTv80GyapnK53MiO367rqtVqnXu/i647SSaT0dra2pknMw3vF4XTNZimqUKhMPIULNd11e12/XbT1NRoNPzpUJlMRsViMbJ6ASAuFrzTK9YAADNp+GHeNM0zowLDx80ODR+nmkqlVCgU1Ol0VKvVdHBwoEqlolwuNzJdaRgahseazaa63a5qtZofWsrlslZWVmSapgaDwchO2x/fb5rrGoZxbk3lclmpVMqfpnTRGpJut6tqtSrbtlWr1VQqlcYeq9frqlarMk3Tv++w1tMjP8P/T7VabWQ9y8c11et1fwF3r9eT9GFht23bKhQKZ3ZKB4B5RrAAAAAAEBpToQAAAACERrAAAAAAEBrBAgAAAEBoBAsAAAAAoREsAAAAAIRGsAAAAAAQGsECAAAAQGgECwAAAAChESwAAAAAhEawAAAAABAawQIAAABAaAQLAAAAAKH9f87S00+WnI7ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAEg4gh0AAAAAJBzBDgAAAAASbu7B7uDgQE+fPtUXX3wx76IBAAAAAGPEDnaPHz8eery2tqa7d++q3+/ryZMncYsHAAAAAJzg3KZirq2tqdvtnlfxAAAAAIDvnekG5dvb22q32zo4OJDv+9rZ2RnZx/d9FQqF2BUEAAAAAEx3pmC3tbWlra0tNRoNua6rYrE4so9pmlpbWztVuUEQqNlsqtVqqd1uj2yv1WoyDCPat1QqnaX6AAAAAHClnCnYhQqFgtLptDY2NsZuf/r0qe7evTtTWZ7nqdPpKAgC9Xq9ke21Wi06p6QoUNbr9TPWHgAAAACuhljBTpI2Njb0/Plz+b4/Esjq9frMwc6yLFmWJcdxxm6vVCo6ODiIHmcyGWWzWYIdAAAAgKUXO9g9fPhQjuPINM1omqT0Zqqk7/txi5f05nq9IAiGyg+5rqtMJjOX8wAAAABAEsUOdqurq/r888/Hbvv444/jFi9JEwOiYRgKgmDica9fv9br16+jx69evZpLfQAAAADgMol9uwPTNCdue/DgQdzip0qlUmOvxwtVKhXduHEj+nr33XfPtT4AAAAAcBFiB7t0Oq3d3d2x247fvHzepoU6SSqXyzo6Ooq+Xrx4ca71AQAAAICLEHsq5gcffKAgCHRwcDA0etfv93VwcKD79+/HPcXEUcEgCKaOGF67dk3Xrl2LfX4AAAAAuMxiBzvpzeqXqVRq6Ll+v69Hjx7No/hoYRbf90eCHAunAAAAAFh2sYNdtVqdeB+7crl86vImTa8sl8tyXTe6j53jONH/AQAAAGCZxb7GblKokzR037mT+L6vWq2mer0uz/Nk2/bQPe1KpZKCIJDjOHIcR3t7e9zDDgAAAAA0hxG7J0+ejH0+CIJT3aDcNE2VSiWVSqWJ+wxuy+Vyp6soAAAAAFxRsYNdqVTS7du3o5uHB0GgXq8n3/eVzWbjFg8AAAAAOEHsYFcoFMYuknJ0dCTXdeMWDwAAAAA4Qexr7CatfHnjxg2trKzELR4AAAAAcILYwW4a3/fPs3gAAAAAgOYwFfOXv/zl2JE53/dVrVbjFg8AAAAAOEHsYGeapmzbHrlBuWmaunHjRtziAQAAAAAnmMsNym/dujWPugAAAAAAziB2sAtD3e7urtrttiTpzp07M9+/DgAAAAAQT+xgJ0m/+c1v1Ov1ZJqmJKndbqtSqWhvb28exQMAAAAApogd7B4/fqx6va61tbWh5z3PU7lcVqVSiXsKAAAAAMAUsW93sLa2NhLqJMmyrGgEDwAAAABwfmIHu2k3IecG5QAAAABw/mIHu263q93d3ZHnd3d39fnnn8ctHgAAAABwgtjX2D148ED37t1TPp+Ppl76vq9MJqOdnZ3YFQQAAAAATDeXVTGbzaaePXumTqejIAiUyWS4tx0AAAAALMhcgp305n52x8Pcq1evdP369XmdAgAAAAAwRuxr7KbhVgcAAAAAcP5iB7tnz57p9u3bWl1dHfpKpVKq1WrzqCMAAAAAYIrYUzErlYrK5bIsyxp6vt/v6+HDh3GLBwAAAACcIHaw29zc1Pvvvz92W7lcjls8AAAAAOAEsadi9nq9idsODw/jFg8AAAAAOEHsEbutra1oZC6dTg9ta7Va+vWvfx33FAAAAACAKWIHuw8//FCu68o0TXW73ej5IAi0v78ft3gAAAAAwAliB7v19XV98sknY7d9/PHHcYsHAAAAAJwg9jV2qVRq4rYHDx7ELR4AAAAAcILYwS6dTmt3d3fstsePH8ctHgAAAABwgthTMT/44AMFQaCDgwOZphk93+/3dXBwoPv378c9BQAAAABgitjBTpLq9frIlMx+v69Hjx7No3gAAAAAwBSxg121WtXGxsbYbdygHAAAAADOX+xr7CaFOkk6ODiIWzwAAAAA4ASxg900Ozs751k8AAAAAEBzusZu0PPnz1Wv19VsNhUEwbyLBwAAAAAcM5cRu+fPn6tcLmt1dVWWZWlvb08PHz6cOk0TAAAAADAfZx6x++KLL1Sv1+U4jnzf161bt7S2tibf93Xjxg1JkmVZc6soAAAAAGC8U4/Yffzxx/rLv/xLpdNptVotvf/++/r888/V6XS0ubkZhTpp+sIqAAAAAID5OPWInWVZunXrljY2NlSr1XT9+vVo28rKylwrBwAAAAA42amD3cbGRjQS99vf/lYHBwcyDEP37t2be+UAAAAAACeLtXjK+++/r/v37yufz2tnZ0f//M//rKdPn0bbHz9+HLuCAAAAAIDp5rIq5o0bN7S1taVms6mNjQ1tb2/rww8/lG3b8ygeAAAAADDF3G9QHoa8Tz75RLdu3Zp38QAAAACAY+Ye7AZVq9XzLB4AAAAAoBj3sZvFvG934Lqu6vW6stmsTNNUu93WnTt3lMvl5noeAAAAAEiScw128xYEgVzXleM4Mk1Ttm0T6gAAAAAsvUQFO0nR7RUAAAAAAG+c6zV2AAAAAIDzl7gRu2azqVQqpV6vp263O3WBltevX+v169fR41evXi2iigAAAACwUIkKdpZlSZJM05QkNRoN5fN5tVqtsftXKhV99NFHC6sfAAAAAFyERE3FNE0zCnWSdO/ePTmOoyAIxu5fLpd1dHQUfb148WJBNQUAAACAxUlUsHMcZ+hxuIiK7/tj97927ZquX78+9AUAAAAAV01igl0QBMrn80MhLhypGxzFAwAAAIBlk5hgZxiGSqXSUIhrNBrK5XLc/gAAAADAUkvU4inlclm1Wi16/PLly4kLpwAAAADAskhUsAtH7QAAAAAAP0jMVEwAAAAAwHgEOwAAAABIOIIdAAAAACQcwQ4AAAAAEi5Ri6dgMb78p7+ebcdfHZ1vRQAAAADMhBE7AAAAAEg4gh0AAAAAJBzBDgAAAAASjmAHAAAAAAlHsAMAAACAhCPYAQAAAEDCEewAAAAAIOEIdgAAAACQcAQ7AAAAAEi4H110BZbVl//017Pv/Kuj0x3z/f5nPQYAAABAsjBiBwAAAAAJR7ADAAAAgIQj2AEAAABAwhHsAAAAACDhCHYAAAAAkHAEOwAAAABIOIIdAAAAACQcwQ4AAAAAEo5gBwAAAAAJR7ADAAAAgIQj2AEAAABAwhHsAAAAACDhCHYAAAAAkHAEOwAAAABIOIIdAAAAACQcwQ4AAAAAEo5gBwAAAAAJR7ADAAAAgIQj2AEAAABAwhHsAAAAACDhfnTRFQBwsv/+X/7T7Dv/7385v4oACzZz309Av79Kr+XLf/rr2Xb81dH5VgQAEGHEDgAAAAASjmAHAAAAAAlHsAMAAACAhCPYAQAAAEDCEewAAAAAIOEStypmrVaTYRiSpCAIVCqVLrZCAAAAAHDBEjViV6vVJEmFQkGFQkGWZalYLF5wrQAAAADgYiUq2FUqFRUKhehxJpNRo9G4wBoBAAAAwMVLTLDzfV9BEETTMAe5rrv4CgEAAADAJZGYa+x83x/7vGEYCoJg7LbXr1/r9evX0eOjoyNJ0qtXr+Zev1N73Z9937C+sx4z+PoWccwyvpaY9fru//3fGQ95c8z//fbb2c4xcMxpz7GoY5Jer7Mcw2s5/76fhO/xVXoti/i9d5ZjLms/vko/k0mv11mO4bXEO+YihfXo90/xXjXBVvoJeaWu6yqbzY40TDqdlm3bQ1M0Q3//93+vjz76aFFVBAAAAHDJvHjxQr/4xS8uuhrnLjEjdpP0er2J28rlsv72b/82evzdd9+p1+tpdXVVKysr51qvV69e6d1339WLFy90/fr1cz0XLhfafnnR9suN9l9etP3you0vt36/rz/84Q/6+c9/ftFVWYjEBDvTNMc+HwTBxG3Xrl3TtWvXhp4bd43eebp+/To/6EuKtl9etP1yo/2XF22/vGj7y+vGjRsXXYWFScziKaZpyjCMsdfaZTKZC6gRAAAAAFwOiQl20puplYMrYDqOM/baOgAAAABYJokKdqVSSUEQyHEcOY6jvb091ev1i67WWNeuXdPf/d3fjUwFxdVH2y8v2n650f7Li7ZfXrQ9LpPErIoJAAAAABgvUSN2AAAAAIBRBDsAAAAASDiCHQAAAAAkHMEOAAAAABIuMTcoT5JarRbdCD0IApVKpYutEM5FEARqNptqtVpqt9sj2+kHV1utVpMkdbtdSRpZoZf2v5rCn3vpTdv7vq/t7e2orSXafplks9mR3/+0/9Xkuq7q9bqy2axM01S73dadO3eUy+WifWh7XDRG7OYsfLNXKBRUKBRkWZaKxeIF1wrz5nmems2mgiBQr9cb2U4/uNps21apVFKpVIoCXTabjbbT/leXbdvKZDIqFAqqVqtKpVLK5/PRdtp+eTiOM3RvXYn2v8qCIJDruioWiyoWi0qn0yOhTqLtcbG43cGc3bx5UwcHB0Of3q6srIhv89XkOI4qlYr29/eHnqcfXF1BECifz6vVakXt63me1tfX1e12ZZom7X+FZbNZZbPZ6JP4Wq2mSqWiw8NDSfzsL4tw5LZYLA61Le1/dTmOo0wmM9S2g2h7XAaM2M2R7/sKgmDsD/3xT/VwddEPrr5OpyPf96PHpmlKevNmj/a/2trt9tD0qr29PWUyGUn87C+TZrOpe/fuDT1H+y8v2h6XBdfYzdHgG71BhmEoCILFVgYXhn5wtRmGEY3OhMI/3KZpqtPpTDyO9r9aHMdREARqtVqS+NlfFq7rRmF+EO1/9TWbTaVSKfV6PXW7XVWrVUm0PS4Pgt0ChL8EsNzoB1dXpVJRvV6fOEVHov2vknAaXjgtd1q7S7T9VRMEgUzTnPkNO+1/NViWJemHGRqNRiOalj8JbY9FYyrmAvBDDYl+cFXZtq3NzU0VCoWp+9H+V4dhGCoUCtGUzJs3b059k0/bXx2NRmNowYxZ0P5Xg2maUaiTpHv37kWj9pPQ9lg0gt0cDf7ADwo/3cNyoB8sD8dxlE6nh665ov2vriAIZNv20Bu5TCYTrZZH219tnufp9u3bE7fT/leb4zhDj8ORet/3aXtcGgS7OTJNU4ZhjJ1rPW4+Pq4m+sFyCK+rC0fqwoVTaP+ry/d91Wq1oU/hw5BnGAZtf8X1ej25rqtaraZarSbbtiW9WRnVcRza/woLp10Ptm34sx+O5NH2uAwIdnNWLpeHVkByHOfEKVpIrknTLOgHV5vnefI8T5Zlyfd9+b6vRqOhVColifa/qizLUqlUGvoEfmdnR5ZlRW/eaPurK5PJRPevLJVK0T3KSqVSND2T9r+aDMMY+dkPp+WGI3e0PS4D7mN3Dmq1WvTDv7e3F62ahKvD9305jqOdnR15nqdSqaQ7d+6M3KyUfnD1BEGgtbW1sddVDP46pf2vpiAI1Gg0osfhyniDC6jQ9ldf+PvfcRyVSiVls9ko3NP+V9Pxn/2XL1+OtC1tj4tGsAMAAACAhGMqJgAAAAAkHMEOAAAAABKOYAcAAAAACUewAwAAAICEI9gBAAAAQMIR7AAAAAAg4Qh2AAAAAJBwP7roCgAALpdarRb9/+XLlyoWi9GNmDEfxWJRzWZTrVYrurE1AABxEOwAAJFisahisSjLsqLn8vn8yH6NRkOFQmFh9Vr0+SaxbVu+76vVasUqp16vq9PpzKlWAAAwFRMAMKDZbA6FOkna3t4e2a/dbi+qShdyvkmy2aw2NzcvuhoAAIxgxA4AEAmCQL7vyzTN6DnDMHTnzp3ocaPRkO/7C6vTos83DdMmAQCXFSN2AICIZVnKZrNyXXfo+fD6Otd11W635fu+arVadD2e67paX19XPp+X67qybVu2bcvzPGWzWaXT6ags27Z18+ZNNRqNoXM0Go2hr2nnm6XcSXUK1Wo1OY4T/XuScecMz7G+vi7P86LyBs8zeL5GoyHHcSaeb1ydGo2G1tfXtbKyEj1XLBZ18+ZNFYtFBUFwYt0BAEugDwDA97rdbt80zb6kvqR+JpPpt9vtoX3a7XbfsqyRY1utVt+yrH673e7v7+/3S6VStL9pmkP7ZjKZfr1ejx5Xq9Vo/7CsVqs19XyzlDupTrlcLio/PG5/f3/6N6ff7+/v74+cM6zH4PfJNM2h8kql0lC9Dg8P+5KGjjmpToZhRI/r9fpM9QUALA+mYgIAIqZpqtvtRiNlrusqm82q1Wopl8tNPdYwDHmeF01XDK/VS6VSY/cNBUEg27Z1eHgYPbezszM0/XOck8qdVCff9+U4ztACKPl8XvV6XfV6feo5J9XD9/2haZqmacr3fVmWpSAIVKvV1O/3h+o1eC3jLHVqtVrK5/Nqt9tKpVIj10ICAJYbwQ4AMCKTyURBxbZtbW1tnRjsJA1dmzerTqcjwzCGQlncVSen1cl1XRmGMTTdtNvtxrqO7/g5DMNQr9cbOt80s9QpbJN8Pq/9/f0z1xUAcDUR7AAAkt6MnLmuOxLgqtWqarWagiAYG1AGF1s5KcAMnmvc/2dxfHGXSeWGjtcpCAKZpjk0wnbei6KMG108S53W19fluq48z2PEDgAwhMVTAACRvb29sc+bpjkxtHmed+rzhKNZkqLpisdNCnzTzjdY7iThdMxZzxfXpPOdtk5hoK3X62PvLQgAWG4EOwBApNFojKyIeXwUL7x+TFJ0Hdk0g/tLP9xSIQwtpmkql8tFK16G+zSbzannO6ncSTKZjG7fvj2yMmV4vnkYrINpmioUCkOrgAZBIM/zov1mqVO9Xo+mY2YyGRWLxbnVFwCQfCv9wau5AQBLKwxTpmmOjIqFtzsIhcv5p9NpFQoFua6rarWqTqejcrmsXC43NF0yDG3hczs7O/I8T9VqNQqNtm1rdXVVpmmq1+upUChMPN8s5RqGMbVOtm0rnU5H0yRPuobQ8zxVKhU5jqNqtapSqTT2uVqtpkqlItM0o/OGdR0c+QxfU7VaHbqe8XidarVatIBKt9uV9GZhFcdxVCgUVCwWmZYJACDYAQAAAEDSMRUTAAAAABKOYAcAAAAACUewAwAAAICEO/V97L799lv96U9/Oo+6AAAAAAAkvf3223rrrbdm3n/mYNfv9/Xll1+e231+AAAAAAA/MAxDP/vZz7SysnLivjMHuzDU/fSnP9U777wzU+EAAAAAgNPp9/v6+uuv9dVXX0mS/uIv/uLEY2YKdt9++20U6lZXV+PVEgAAAAAw1Z//+Z9Lkr766iv99Kc/PXFa5kyLp4TX1L3zzjsxqwcAAAAAmEWYv2ZZ4+RUq2Iy/RIAAAAAFuM0+evUq2ICABCXbduSpNXVVRmGoVQqpVwuJ9u2Va1WL7h2AAAkD/exAwAsjOd5Wl9fVzabVbVaValUUqFQkGVZyufzchznVOU1Go1zqikAAMlCsAMALEw+n1e1WlUmkxl63jRNFYvFU5fXbrfnVTUAABKNYAcAWIhw+uXxUBfKZDIyTXPm8hqNhnzfn0vdAABIOq6xAwAshOM4E0NdKAx/khQEgRqNhkzTVLvdVrFYlGVZkiTXddVut+X7vmq1miSpVCpFx9ZqNZmmKd/3ZZqmcrlcVKZt21pfX9f+/r7S6bQsy1K9Xler1Ro6VpJ834/KdV1Xtm1Ho4vhaOGdO3dUqVTk+75arZYymYx831c2m5VpmqrX66cKrAAAnEl/Bt98803/d7/7Xf+bb76ZZXcAAEZI6ler1Zn3L5VK/W63Gz02TbN/eHgYPW63233LskaOy+Vy/VarFT3OZDL9/f39kW2Hh4d90zRHjm2329Hjbrfbz2Qy0eNWq9W3LKvfbrf7+/v7/VKpFNXleFmnea0AAIxzmhzGVEwAwKXk+75c140em6Y59HjSMY7jRCN00pvr+ur1uqQ3o4bhqJ9hGPJ9P5rO6XmeXNcdGlU0TVO9Xi86r2EY8jxPmUxGlmVFK3hmMhn1ej15njd0LAAAi8JUTADAQpimqW63O3WfcOqkpGhqZBAE8n1fvV5PvV5v6vGu68owjKEA2O12o/BmWZaCIBiplyR1Op2xYSycChoGvkmBrVAoaGdnR5ZljQREAADOGyN2AICFyOVyJ464DW73PE/5fF7NZlOmaZ44Aub7voIgkGmaymQy0Ve1Wo2uhysWi6pUKgqCQLVaLRrJkzQS+CYxDGPs85ubm9HtF3zfn7gfAADngWAHAFiIcNripHAXBIFSqVT0/42NDZXLZRUKBRmGEQWvSSthep4ny7LGbh8Mbdvb23JdV7lcToVCIXo+XPTkON/3defOnRNfn2VZSqVSchwneh0AACwKwQ4AsDCtVku2bY+Eu3AFzPDauHD0LbweTlI0DTO8ji1c9TLc37IsZTIZ3b59e+RG581mU5K0v78vwzCUy+VGRgDD44+PGkoaumZvmmKxqK2trZn3BwBgXlb6/X7/pJ3++Mc/6uDgQGtra/rxj3+8iHoBAK6w8LYGq6ur0ZTFwdGzwX2y2aykN0HOtm1tbm5GwSncJ51ODx1v27bS6XQ0chbu7ziO8vm8DMNQKpWSYRgqFotjj5XeXJ83ONJYrVbV6XRULpfHhkPf92XbdnR9IAAAcZwmhxHsAABLIQgCbW1taXt7O5raGQaxfD4/EizPeo5Op8PCKQCAuThNDmMqJgBgKTQaDRWLxWiE0DCM6JYF+/v7Zy538Pq9ZrNJqAMAXAiCHQBgKViWNXaKpOu60XTPs7BtO1oNk0VTAAAXhamYAICl4bquPM+Lro0L75sXZ7ETz/PU6XQkjV4nCABAHFxjBwAAAAAJxzV2AAAAALBECHYAAAAAkHAEOwAAAABIOIIdAAAAACQcwQ4AAAAAEo5gBwAAAAAJR7ADAAAAgIT70VxK+fsbcykmXh2OLroGl9a//Jt/e9FV0L/93/9yIef1PE+2bavX62l/f/9C6rBM3nv43y66CpKkLx79x4Wfk762eP/uv/67i66C/td//l8Xcl7622J9tpu+6CpIkjZ+3V34Oelri/ez//78oqugL3/1V6c+xvd9OY6jSqWiVCqlYrGoQqEgwzAkSbZtq9FoKJPJqFwuy7Ks+VY6QYrFoprNplqtljKZzNzKZcROUq1WU61WU7FYVLFYnLpvNptdUK0wybg2aDQaqtVqajQasm1bQRBIkizLGnp8Wo1GI0ZNz8dp+iviWfa+FgSBGo0Gv/cWZJn7W9jXwteZz+fP/NpwsmXua8fx+21+TNNUqVSSaZrKZDIqlUpRqJOkarWqQqGgVqt1oaEu/B1zker1ukzTnHu58xmxSzDbtlWtVqPHxWJR2WxW7XZ7ZF/HceS67iKrh2PGtUGtVhv6RCgIAm1tbanVakmSUqnUmc/XbrdVKBTOfPy8naa/Ip5l72ue56nT6SgIAvV6vYuuzpW37P3Ntm3Zth290SkWi8rn8/xuOwfL3tcG8b7ufEzrL+n0xY9+Z7PZK/vB0VKP2AVBIM/zhhq3WCzKdV35vj+yL29uLtakNmi320OfCBmGMZcf2EajMdIPLtJp+iviWfa+Jr35lL5QKJzLJ4oYRn/7YQpXKJ1Oq9PpXGCNrib62g94X7e8MpmMcrncRVfjXCz9iF2n05Hv+9GQcPgm5vgvtGazqXv37o1MfQuCQLZta319Xfv7+0qn07IsS/V6PfqkC/MxqQ0Mw1A2m1Wr1ZJhGPJ9f+ybUc/z5Pu+fN/Xy5cvh0a+jnNdV+12W77vq1arSZJKpVI0d9z3/WhetO/7ymazMk1T9Xpdvu/Ltm2lUqloqD8IgrHnrNVqMk0zqvNJv2hm7a+Ih76GRaK/aWRkbm9vb67XneAN+trJ3wssjuu6sm1bkrS9vT2xb4VTtU3TVLvdVrFYHJrKGW4LgkC+78swDBUKhbHP3759W7Zty/d9dbvdoXNMej9fLBZnqqc0vT/WajUZhhFrBPwkSx3sDMPQ4eHh0HPhkPzgLzTXdSf+gdna2tLm5qZyuZyCIND6+rq63S5/kOZsWhtsb29rfX1dN2/eVKlUUjqdVr1eH9qn1+up1+tFP2DpdFqbm5sT53iH5/J9X6VSKXo+l8vJMAwVi8VoH9M0VSwWo/1M01S5XFY+nx+aQ23btorFYlS3fD4f9R1J0R+1SXWatb8iHvoaFon+NspxHAVBwIejc0Zfm+17gcXJZDKqVqsqFotT+1alUlGxWIyCUjqd1v7+vgzDiEb6B/ub67oTn7csS9VqdeQau5Pez89Sz2n90bZtpdPpaFpyEATncp3fUk/FHKdSqaherw9NSQiCYOIbZ8dxogYNP+W6rNMOkmxaGxiGIdu2lcvlVKvV1Gq1RkawgiAY+iUefppyFplMRr1eT57nDZV3vE6WZQ09Xy6Xo2kp4bSjwU9ywj9gpzGuvyIe+hoWif42/FrCcvL5PL/X5oy+Nvxa+ED0ckilUvJ9f2rfCkPZ4PbBx4P91TRN3b59e+rz45z0fv6kek7rj0EQRNexhsL+PW8EuwG2bWtzc3PoG99oNKYO61uWNfLLj18W83VSG4QX3LdaLXW7XfV6Pa2vrw/tM+4PRpy59YVCQTs7O5Jm/+TPMAwZhiHP8+S6rgzDkOu60Ve32z3VH8lx/RXx0NewSPS30XIKhUI0anPz5k2mmc8Jfe0HJ30vcL7G/Uyf1LdarZYKhUK01kA4eiwpasubN29qfX1dtVpNlmVNfH6SWd7PT6vntP4YblsEgt33HMdROp0emi7ged7UdC+9WbyiUqlEaZxPwefrpDbwfX/oU0TTNEeG5+dl8I/F5uZmtIRzOG/7NMJPCzOZTPRVrVZnXgFuXH9FPPQ1LBL9bfiY48vpZzIZBUHAioVzQF/7wSzv6xBfnNHccTzPUz6fV7PZlGmaIwGr3W5rf39fm5ubqtfr0TWdk54fJ+77+ZP643leVzdoqa+xC4V/OAbnvYafBoSfCkmKLrIML4wMPw3Y3t6W67rK5XKM1s3ZSW0Qfnp33HlcDO15XtS+lmUplUrJcZyZf1iDIFAQBLIsS4ZhqFKpjN3npD9uk/orfS8e+hoWif72g3BxjWKxGO0Thjz6aHz0tR/M+r4O8WSz2WixkbiCINDGxoY+++yzaMQt/P0QjoYVCgVZlhWt5ryxsRHNADj+/LQPxOO8n7csa2J/tCxrYbNklj7YeZ4nz/OUy+Wib7rjONEy34NTAzzPU6PRGOoU+/v7KhQK/DI4J+GnHqFxbVCtVkd+ke/v70/9tGWW6T3H504fH8IvFova2toaWdBksK6D9apUKlG/Cud6H5+P3Ww2p06tnNZfEQ99bTyWAz8f9LcfWJYV3dQ4tLOzI8uyWOBiDuhrP5jle4H4crmc6vW6Go3GUFscv85sksG+FY4oD/ad8O9S2D+OnydcCXPc85Oc5f388VkG0/pjuErn4Ifyx29hNQ9LHezCTwHCaSCDjv+QO44TzQW3bVvZbFaZTEbZbFYrKysyvl++1Ph+pSfeaM/fpDZotVqqVCpaXV2V8f29d8LlZz3Pi5ZVrtVqKpVKqtVq6nQ66vV6SqVSE3+ITdNUoVAYWckolMvltLe3N7G+lmVF86o9z9Pq6urQsrjtdlu2bUf1kDS135ymvyKeZe9r0g8Xgu/s7MjzPNm2rTt37vAh1jmgv71ZFGNwmlQQBPrss8+mHoPTo6+d/L3AfLTbbdVqNdm2rdXVVUk/rIgamrVvlUqlqI2kN9fchesMhOWF04Z939f29raazebY58edU9LU9/Oz1nNafwyngjqOE9U5HOUzDGN+fa8/g2+++ab/u9/9rv/NN9/MsvvSODw87Odyuf7h4WH0eH9/v5/JZPr1ev1iK4dzd3h42G+322O3tdvtvmVZC64Rrir6GhaJ/oZFoa/hMrjs7+dPk8NYPCWGRqMxdE2A8f3SpdVqVfv7+xdbOZyLwSHzZrPJp3s4N/Q1LBL9DYtCX8Nlc5XezxPsYrAsa+wNVF3XjYaLcbXYth2t4rWoFY6wnOhrWCT6GxaFvobL5iq9n1/p9/v9k3b64x//qIODA62trenHP/7xIuqVGK7rDq3y5Ps+KytdYZ7nqdPpSJo8j991XVWr1ehfrn/DWdDXsEj0NywKfQ2X0WV+P3+aHEawAwAAAIBL6DQ5jKmYAAAAAJBwBDsAAAAASLhTBbsZZm0CAAAAAObgNPlrpmD39ttvS5K+/vrrs9UIAAAAAHAqYf4K89g0P5qlwLfeekuGYeirr76SJL3zzjtaWVmJUUUAAAAAwDj9fl9ff/21vvrqKxmGobfeeuvEY2ZaFTMs/Msvvxy6sSQAAAAA4HwYhqGf/exnMw2qzRzsQt9++63+9Kc/nblyAAAAAIDp3n777ZlG6kKnDnYAAAAAgMuF2x0AAAAAQMIR7AAAAAAg4Qh2AAAAAJBwBDsAAAAASDiCHQAAAAAkHMEOAAAAABKOYAcAAAAACff/AdjZSEq25umtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 330x210 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx_file    1540\n",
      "48htype1       3\n",
      "48htype2       0\n",
      "48htype3       0\n",
      "48htype4       0\n",
      "24g          153\n",
      "weirdo      1164\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "dataframe = df_mapping_metainfo\n",
    "sorted = \"False\"\n",
    "\n",
    "analysis.get_occupancy(dataframe, coor_ref_structure_48n24_expanded, tuple_cage_metainfo, el = \"Li\")\n",
    "if proceed_NEB == \"True\":\n",
    "    df_occupancy_strict = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=False, strict_count=True)\n",
    "    df_occupancy = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=False, strict_count=False)\n",
    "else:\n",
    "    df_occupancy_strict = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, strict_count=True)\n",
    "    df_occupancy = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, strict_count=False)\n",
    "\n",
    "path_by_tuple_cage.get_complete_closest_tuple_cage(dataframe, tuple_cage_metainfo, coor_48htype2_metainfo)\n",
    "# weighing_movement(dataframe, litype)\n",
    "# df_weighted = tuple_cage.get_df_movement(dataframe, to_plot = 'weight')\n",
    "# # Plot.Movement.Distance.plot_distance(df_weighted, max_mapping_radius, Li_idxs=\"all\")\n",
    "# Plot.Movement.Distance.plot_distance(df_weighted, max_mapping_radius, activate_shifting_x = False, activate_diameter_line=False, Li_idxs='all')\n",
    "\n",
    "labelling.get_amount_type(dataframe, litype, el = \"Li\")\n",
    "# df_amount_type = mapping_labelling.plot_amount_type(dataframe, litype, el, style, category_labels)\n",
    "df_amount_type = mapping_labelling.get_df_amount_type(dataframe, litype, el = \"Li\")\n",
    "if proceed_NEB == \"True\":\n",
    "    mapping_labelling.plot_amount_type(df_amount_type, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=False, style = \"bar\")\n",
    "else:\n",
    "    mapping_labelling.plot_amount_type(df_amount_type, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, style = \"bar\")\n",
    "print(df_amount_type.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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        "plotlyServerURL": "https://plot.ly"
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         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "$\\text{Amount of occupancy}$"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 937x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 330x210 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx_file    1540\n",
      "48htype1       3\n",
      "48htype2       0\n",
      "48htype3       0\n",
      "48htype4       0\n",
      "24g          153\n",
      "weirdo      1164\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    pass\n",
    "else:\n",
    "    dataframe = df_mapping_metainfo_sorted\n",
    "    sorted = \"True\"\n",
    "\n",
    "    analysis.get_occupancy(dataframe, coor_ref_structure_48n24_expanded, tuple_cage_metainfo, el = \"Li\")\n",
    "    df_occupancy_strict_sorted = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, strict_count=True)\n",
    "    df_occupancy_sorted = movement.plot_occupancy(dataframe, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, strict_count=False)\n",
    "\n",
    "    path_by_tuple_cage.get_complete_closest_tuple_cage(dataframe, tuple_cage_metainfo, coor_48htype2_metainfo)\n",
    "    # weighing_movement(df_mapping_metainfo, litype)\n",
    "    # df_weighted = tuple_cage.get_df_movement(df_mapping_metainfo, to_plot = 'weight')\n",
    "    # # Plot.Movement.Distance.plot_distance(df_weighted, max_mapping_radius, Li_idxs=\"all\")\n",
    "    # Plot.Movement.Distance.plot_distance(df_weighted, max_mapping_radius, activate_shifting_x = False, activate_diameter_line=False, Li_idxs='all')\n",
    "\n",
    "    labelling.get_amount_type(dataframe, litype, el = \"Li\")\n",
    "    df_amount_type_sorted = mapping_labelling.get_df_amount_type(dataframe, litype, el = \"Li\")\n",
    "    mapping_labelling.plot_amount_type(df_amount_type_sorted, sorted, direc_restructure_destination, litype, activate_shiftby1_idxfile=True, style = \"bar\")\n",
    "    print(df_amount_type_sorted.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# chunk_size = 3\n",
    "# total_items = amount_Li  # Adjust this if your total number of items changes\n",
    "\n",
    "# # Loop through the range in steps of chunk_size\n",
    "# for start_idx in range(0, total_items, chunk_size):\n",
    "#     end_idx = start_idx + chunk_size\n",
    "#     Li_idxs_range = range(start_idx, end_idx)\n",
    "    \n",
    "#     # Call your function with the current range of Li_idxs\n",
    "#     Plot.Movement.Distance.plot_distance(df_weighted, max_mapping_radius, activate_shifting_x = False, activate_diameter_line = False, Li_idxs = Li_idxs_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "idx_coor24li_tuple_cage_belongin_complete_id48htype2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Function to count changes in each row\n",
    "# def count_type_changes(row):\n",
    "#     changes = 0\n",
    "#     for i in range(len(row) - 1):\n",
    "#         if row[i].startswith('48htype') and row[i + 1].startswith('48htype') and row[i] != row[i + 1]:\n",
    "#             changes += 1\n",
    "#     return changes\n",
    "\n",
    "# # Apply the function to each row and create a new column with the counts\n",
    "# df_type['changes_count'] = df_type.apply(count_48htype_changes, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_type = path_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_attain = 'type', activate_closest_tuple = False)\n",
    "    df_idx_tuple = path_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_attain = 'idx_tuple', activate_closest_tuple = False)\n",
    "    df_idx_cage = path_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_attain = 'idx_cage', activate_closest_tuple = False)\n",
    "\n",
    "    # # Plot.Movement.Distance.plot_distance(df_idx_cage, max_mapping_radius, Li_idxs=\"all\")\n",
    "    movement.plot_cage_tuple_label(df_idx_cage, df_type, df_idx_tuple, direc_restructure_destination, \n",
    "                                   max_mapping_radius, litype, activate_shiftby1=True, \n",
    "                                   activate_diameter_line=False, activate_relabel_s_i = True, Li_idxs=\"all\")\n",
    "\n",
    "    path_by_tuple_cage.count_occupancy_cage(df_idx_cage, direc_restructure_destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    movement.get_and_plot_transition_matrix_relabeled(df_type, direc_restructure_destination, litype, \n",
    "                                                      normalize_amount=\"False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    chunk_size = 3\n",
    "    total_items = amount_Li  # Adjust this if your total number of items changes\n",
    "\n",
    "    # Loop through the range in steps of chunk_size\n",
    "    for start_idx in range(0, total_items, chunk_size):\n",
    "        end_idx = start_idx + chunk_size\n",
    "        Li_idxs_range = range(start_idx, end_idx)\n",
    "        \n",
    "        # Call your function with the current range of Li_idxs\n",
    "        movement.plot_cage_tuple_label(df_idx_cage, df_type, df_idx_tuple, direc_restructure_destination, max_mapping_radius, litype, activate_shiftby1=True, activate_diameter_line=False, activate_relabel_s_i=True, Li_idxs=Li_idxs_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_movement = path_by_tuple_cage.get_df_movement_category(df_mapping_metainfo, activate_closest_tuple=False)\n",
    "    movement.plot_distance(df_movement, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                           activate_shiftby1_labelatom=True, activate_shifting_x = True, \n",
    "                           activate_diameter_line = False, Li_idxs = 'all',\n",
    "                           counted_object=\"movement\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    chunk_size = 3\n",
    "    total_items = amount_Li  # Adjust this if your total number of items changes\n",
    "\n",
    "    # Loop through the range in steps of chunk_size\n",
    "    for start_idx in range(0, total_items, chunk_size):\n",
    "        end_idx = start_idx + chunk_size\n",
    "        Li_idxs_range = range(start_idx, end_idx)\n",
    "        \n",
    "        # Call your function with the current range of Li_idxs\n",
    "        movement.plot_distance(df_movement, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                               activate_shiftby1_labelatom=True, activate_shifting_x = True, \n",
    "                               activate_diameter_line = False, Li_idxs = Li_idxs_range,\n",
    "                               counted_object=\"movement\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # labelling.get_amount_type(df_mapping_metainfo_sorted, litype, el = \"Li\")\n",
    "\n",
    "# # df = mapping_labelling.plot_amount_type(df_mapping_metainfo_sorted, litype, el, style, category_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "weirdo exists at file nr.:\n",
    "16 =\n",
    "17 =\n",
    "\n",
    "48htype2 exists at:\n",
    "17 =\n",
    "20 ="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_amount_movement = path_by_tuple_cage.get_df_movement_category_counted(df_movement)\n",
    "    print(df_amount_movement.sum())\n",
    "\n",
    "    movement.plot_movement_category_counted(df_amount_movement, direc_restructure_destination, litype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_filename = \"CONTCAR_positive\"\n",
    "\n",
    "if proceed_NEB==\"True\":\n",
    "    idx_ref = 0\n",
    "    idx_file_group = [0, 9]\n",
    "    mean_ref = False\n",
    "    df_distance, dataframe_group = analysis.get_distance_li(df_mapping_metainfo, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "    movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                        activate_shiftby1_labelatom=True, activate_shifting_x = False, \n",
    "                        activate_diameter_line=True, Li_idxs=\"all\",\n",
    "                        counted_object=\"distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_distance' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf_distance\u001b[49m\u001b[38;5;241m.\u001b[39miloc[:,:\u001b[38;5;241m12\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df_distance' is not defined"
     ]
    }
   ],
   "source": [
    "df_distance.iloc[:,:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance.iloc[:,12:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    idx_ref = 0\n",
    "    idx_file_group = [0, 9]\n",
    "    mean_ref = False\n",
    "    df_distance, dataframe_group = analysis.get_distance_li(df_mapping_metainfo, max_mapping_radius, \n",
    "                                                                    direc_restructure_destination, idx_file_group, \n",
    "                                                                    idx_ref, mean_ref, var_filename)\n",
    "    chunk_size = 3\n",
    "    total_items = amount_Li  # Adjust this if your total number of items changes\n",
    "\n",
    "    # Loop through the range in steps of chunk_size\n",
    "    for start_idx in range(0, total_items, chunk_size):\n",
    "        end_idx = start_idx + chunk_size\n",
    "        Li_idxs_range = range(start_idx, end_idx)\n",
    "    \n",
    "        movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                            activate_shiftby1_labelatom=True, activate_shifting_x = False, \n",
    "                            activate_diameter_line=True, Li_idxs=Li_idxs_range,\n",
    "                            counted_object=\"distance\")\n",
    "        \n",
    "else:\n",
    "    var_filename = \"CONTCAR_positive\"\n",
    "    idx_ref = 0\n",
    "    idx_file_group = [0, 53]\n",
    "    mean_ref = False\n",
    "\n",
    "    df_distance, dataframe_group = analysis.get_distance_li(df_mapping_metainfo_sorted, max_mapping_radius, \n",
    "                                                                    direc_restructure_destination, idx_file_group, \n",
    "                                                                    idx_ref, mean_ref, var_filename)\n",
    "    movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                        activate_shiftby1_labelatom=False, activate_shifting_x = False, \n",
    "                        activate_diameter_line=True, Li_idxs=\"all\", counted_object=\"distance\")\n",
    "\n",
    "    # # df_distance_average_rms = df_distance.copy()\n",
    "    # # column_averages = df_distance.mean()\n",
    "    # # df = df.append(column_averages, ignore_index=True)\n",
    "\n",
    "    df_distance.transpose().to_excel(f'06_test_distance_LitoLi_intercage_BD_litype{litype}_{var_filename}_{idx_ref}.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xxxxx - xxxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intra triad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_filename = \"CONTCAR_positive\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idx_ref = 0\n",
    "# idx_file_group = [0, 16]\n",
    "# mean_ref = False\n",
    "# df_distance, dataframe_group = distance.get_distance_li(df_mapping_metainfo_sorted, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "# # Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, Li_idxs=\"all\")\n",
    "\n",
    "# df_distance_average_rms = df_distance.copy()\n",
    "# column_averages = df_distance.mean()\n",
    "# df = df.append(column_averages, ignore_index=True)\n",
    "\n",
    "# df_distance.transpose().to_excel(f'06_distance_LitoLi_intratriad_litype{litype}_{var_filename}.xlsx', index=True)\n",
    "\n",
    "# Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_shifting_x = False, activate_diameter_line=True, Li_idxs='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance.iloc[:, :12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_distance.iloc[:, 12:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted['geometry'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_ref = 30\n",
    "idx_file_group = [0, 53]\n",
    "mean_ref = False\n",
    "\n",
    "df_distance, dataframe_group = analysis.get_distance_li(df_mapping_metainfo_sorted, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                       activate_shiftby1_labelatom=False, activate_shifting_x = False, \n",
    "                       activate_diameter_line=True, Li_idxs=\"all\", counted_object=\"distance\")\n",
    "\n",
    "# # df_distance_average_rms = df_distance.copy()\n",
    "# # column_averages = df_distance.mean()\n",
    "# # df = df.append(column_averages, ignore_index=True)\n",
    "\n",
    "df_distance.transpose().to_excel(f'06_test_distance_LitoLi_intercage_BD_litype{litype}_{var_filename}_{idx_ref}.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance.iloc[:, :12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance.iloc[:, 12:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_ref = 0\n",
    "idx_file_group = [0, 16]\n",
    "mean_ref = True\n",
    "\n",
    "# # df_distance, dataframe_group = distance.get_distance_li(df_mapping_metainfo_sorted, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "# # Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_shifting_x = False, activate_diameter_line=True, Li_idxs=\"all\")\n",
    "\n",
    "# # df_distance_average_rms = df_distance.copy()\n",
    "# # column_averages = df_distance.mean()\n",
    "# # df = df.append(column_averages, ignore_index=True)\n",
    "\n",
    "# # df_distance.transpose().to_excel(f'06_distance_LitoLi_intratriad_litype{litype}_{var_filename}_meanref.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(amount_Li):\n",
    "#     Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_diameter_line=False, Li_idxs=[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe = df_mapping_metainfo\n",
    "# idx_ref = 0\n",
    "# destination_directory = direc_restructure_destination\n",
    "# coor_Li_ref = []\n",
    "\n",
    "# if 'CONTCAR' in var_filename:\n",
    "#     file_ref = f\"{int(dataframe['geometry'][idx_ref])}_{int(dataframe['path'][idx_ref])}_{var_filename}\"\n",
    "# else:\n",
    "#     file_ref = f\"{int(dataframe['geometry'][idx_ref])}_{int(dataframe['path'][idx_ref])}_{var_filename}.cif\"\n",
    "# file_path_ref = os.path.join(destination_directory, file_ref)\n",
    "\n",
    "# structure_ref = Structure.from_file(file_path_ref)\n",
    "\n",
    "# for idx, coor in enumerate(structure_ref):\n",
    "#     if coor.species_string == \"Li\":\n",
    "#         coor_Li_ref.append(coor.frac_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coor_Li_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coor_Li_ref_mean = np.mean(coor_Li_ref, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coor_Li_ref_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_ref = 31\n",
    "idx_file_group = [0, 53]\n",
    "mean_ref = False\n",
    "df_distance, dataframe_group = analysis.get_distance_li(df_mapping_metainfo, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                       activate_shiftby1_labelatom=True, activate_shifting_x = False, \n",
    "                       activate_diameter_line=True, Li_idxs=\"all\",\n",
    "                       counted_object=\"distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_distance.iloc[:, 0:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_size = 3\n",
    "total_items = amount_Li  # Adjust this if your total number of items changes\n",
    "\n",
    "# Loop through the range in steps of chunk_size\n",
    "for start_idx in range(0, total_items, chunk_size):\n",
    "    end_idx = start_idx + chunk_size\n",
    "    Li_idxs_range = range(start_idx, end_idx)\n",
    "    \n",
    "    # Call your function with the current range of Li_idxs\n",
    "    movement.plot_distance(df_distance, direc_restructure_destination, max_mapping_radius, litype, \n",
    "                       activate_shiftby1_labelatom=True, activate_shifting_x = False, \n",
    "                       activate_diameter_line=True, Li_idxs=\"all\",\n",
    "                       counted_object=\"distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intra cage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # idx_ref = 3 # 20\n",
    "# # idx_file_group = [0, 16]\n",
    "# # mean_ref = False\n",
    "# # df_distance, dataframe_group = get_distance_litoli(df_mapping_metainfo_sorted, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "# # Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_shifting_x=False, activate_diameter_line=False, Li_idxs=\"all\")\n",
    "\n",
    "# # df_distance.transpose().to_excel(f'06_distance_LitoLi_intracage_litype{litype}_{var_filename}.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # idx_ref = 3 # 20\n",
    "# # idx_file_group = [0, 16]\n",
    "# # mean_ref = True\n",
    "# # df_distance, dataframe_group = get_distance_litoli(df_mapping_metainfo_sorted, max_mapping_radius, direc_restructure_destination, idx_file_group, idx_ref, mean_ref, var_filename)\n",
    "# # Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_shifting_x=False, activate_diameter_line=False, Li_idxs=\"all\")\n",
    "\n",
    "# # df_distance.transpose().to_excel(f'06_distance_LitoLi_intracage_litype{litype}_{var_filename}_meanref.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(amount_Li):\n",
    "#    Plot.Movement.Distance.plot_distance(df_distance, max_mapping_radius, activate_diameter_line=False, Li_idxs=[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor_limapped_weirdos_dict'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor_weirdos_Li'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['coor_reduced120_sorted_Li'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df_mapping_metainfo['coor_reduced120_sorted_Li'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['atom_mapping_Li_w_dist_closestduplicate'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df_mapping_metainfo['atom_mapping_Li_w_dist_closestduplicate'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['atom_mapping_Li_w_dist_label'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor_limapped_weirdos_dict'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor_limapped_weirdos_dict_init'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor_limapped_weirdos_dict'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe = df_mapping_metainfo_sorted\n",
    "# el = \"Li\"\n",
    "# idx = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# col_top1_sorted_idxweirdo_label_el = f\"top1_sorted_idxweirdo_label_{el}\"\n",
    "\n",
    "# # for idx in range(dataframe[\"geometry\"].size):\n",
    "# top1_sorted_idxweirdo_label_el = dataframe.at[idx, col_top1_sorted_idxweirdo_label_el]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = dataframe[col_top1_sorted_idxweirdo_label_el]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.DataFrame(data.items(), columns=['index', 'col_name'])\n",
    "\n",
    "# # Count empty dictionaries\n",
    "# empty_count = df['col_name'].apply(lambda x: len(x) == 0).sum()\n",
    "\n",
    "# # Count occurrences of certain types\n",
    "# type_count = df['col_name'].apply(lambda x: next(iter(x.values()))[0] if x else None).value_counts()\n",
    "\n",
    "# print(f\"Number of empty dictionaries: {empty_count}\")\n",
    "# print(f\"Occurrences of certain types:\\n{type_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-means analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if np.array_equal(coor_weirdos_Li, np.array([])) == False:\n",
    "\n",
    "    centroids_2, labels_2 = func_kmeans.kmeans_cluster_atoms(coor_weirdos_Li, amount_clusters = 2)\n",
    "    centroids_3, labels_3 = func_kmeans.kmeans_cluster_atoms(coor_weirdos_Li, amount_clusters = 3)\n",
    "    centroids_4, labels_4 = func_kmeans.kmeans_cluster_atoms(coor_weirdos_Li, amount_clusters = 4)\n",
    "    centroids_5, labels_5 = func_kmeans.kmeans_cluster_atoms(coor_weirdos_Li, amount_clusters = 5)\n",
    "\n",
    "    print(f\"centroids: \\n {centroids_2}\")\n",
    "    print(f\"centroids: \\n {centroids_3}\")\n",
    "    print(f\"centroids: \\n {centroids_4}\")\n",
    "    print(f\"centroids: \\n {centroids_5}\")\n",
    "\n",
    "    func_kmeans.create_POSCAR_atoms_centroids_appended(coor_weirdos_Li, centroids_2, direc_restructure_destination, lattice_constant, filename = \"POSCAR_weirdos_centroids2\")\n",
    "    func_kmeans.create_POSCAR_atoms_centroids_appended(coor_weirdos_Li, centroids_3, direc_restructure_destination, lattice_constant, filename = \"POSCAR_weirdos_centroids3\")\n",
    "    func_kmeans.create_POSCAR_atoms_centroids_appended(coor_weirdos_Li, centroids_4, direc_restructure_destination, lattice_constant, filename = \"POSCAR_weirdos_centroids4\")\n",
    "\n",
    "    structure_analysis.plot_distweirdos(df_mapping_metainfo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualization of mapped atom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mapping_labelling.plot_mapped_label_vs_dist_and_histogram(df_mapping_metainfo, litype, category_data = \"mapping\", el = \"Li\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_weirdos_Li\",\"sum_weirdos_48htype2_Li\",\"dist_weirdos_atom\"dist_weirdos_48htype2_atom72_Li\",\"idx1_weirdos_Li\",\"#weirdos_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "if level_radius == 3:\n",
    "    df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"sum_weirdos_Li\",\"sum_mapped_48htype1_48htype2_Li_closestduplicate\",\"sum_weirdos_48htype1_48htype2_Li\",\"sum_mapped_48htype2_Li_closestduplicate\",\"#weirdos_Li\",\"sum_mapped_48htypesmerged_Li\",\"sum_sanitycheck_48htypesmerged_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "    df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"sum_weirdos_Li\",\"sum_mapped_48htype1_48htype2_Li_closestduplicate\",\"sum_weirdos_48htype1_48htype2_Li\",\"sum_mapped_48htype2_Li_closestduplicate\",\"#weirdos_Li\",\"sum_mapped_48htypesmerged_Li\",\"sum_sanitycheck_48htypesmerged_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"toten [eV]\"]]\n",
    "    \n",
    "    sum_weirdos_Li = float(df_mapping_metainfo_sorted_toten[\"#weirdos_Li\"].sum())\n",
    "\n",
    "    var_excel_file = f\"map2type2_{sum_weirdos_Li}_{max_mapping_radius}_{max_mapping_radius_48htype2}_{max_mapping_radius_48htype1_48htype2}_{file_perfect_poscar_48n24_wo_cif}\"\n",
    "\n",
    "elif level_radius == 2:\n",
    "    if litype == 2:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 3:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 4:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 5:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 6:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_24g_Li\",\"toten [eV]\"]] \n",
    "    elif litype == 7:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_24g_Li\",\"toten [eV]\"]] \n",
    "    elif litype == 8:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_48htype8_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_48htype8_Li\",\"#closest_24g_Li\",\"toten [eV]\"]]   \n",
    "\n",
    "    sum_weirdos_Li = float(df_mapping_metainfo_sorted_toten[\"#weirdos_Li\"].sum())\n",
    "\n",
    "    var_excel_file = f\"map2type2_{sum_weirdos_Li}_{max_mapping_radius}_{max_mapping_radius_48htype2}_{file_perfect_poscar_48n24_wo_cif}\"\n",
    "\n",
    "elif level_radius == 1:\n",
    "    if litype == 2:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 3:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 4:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 5:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_24g_Li\",\"atom_mapping_Li_w_dist_label\",\"toten [eV]\"]]\n",
    "    elif litype == 6:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_24g_Li\",\"toten [eV]\"]] \n",
    "    elif litype == 7:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_24g_Li\",\"toten [eV]\"]] \n",
    "    elif litype == 8:\n",
    "        df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_48htype8_Li\",\"#closest_24g_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "        df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"sum_mapped_Li_closestduplicate\",\"#weirdos_Li\",\"idx0_weirdos_Li\",\"top3_sorted_idxweirdo_dist_Li\",\"top3_sorted_idxweirdo_label_Li\",\"#closest_48htype1_Li\",\"#closest_48htype2_Li\",\"#closest_48htype3_Li\",\"#closest_48htype4_Li\",\"#closest_48htype5_Li\",\"#closest_48htype6_Li\",\"#closest_48htype7_Li\",\"#closest_48htype8_Li\",\"#closest_24g_Li\",\"toten [eV]\"]]   \n",
    "\n",
    "    sum_weirdos_Li = float(df_mapping_metainfo_sorted_toten[\"#weirdos_Li\"].sum())\n",
    "\n",
    "    var_excel_file = f\"map2type2_{sum_weirdos_Li}_{max_mapping_radius}_{file_perfect_poscar_48n24_wo_cif}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mapping_labelling.plot_mapped_label_vs_dist_and_histogram(df_mapping_metainfo, litype, category_data = \"mapping\", el = \"Li\")\n",
    "\n",
    "df_48htype2 = df.loc[df['label'] == '48htype2']\n",
    "\n",
    "fig = px.scatter(df_48htype2, \n",
    "                x = 'idx_nr', \n",
    "                y = 'dist',\n",
    "                title = 'Distance of mapped 48htype2 vs its file index nr'\n",
    "                )\n",
    "\n",
    "fig.show(config={'scrollZoom': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find radius that removes all weirdos in the first groups"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## all weirdos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mapping_labelling.plot_mapped_label_vs_dist_and_histogram(df_mapping_metainfo, litype, category_data = \"weirdo\", el = \"Li\")\n",
    "\n",
    "fig = px.scatter(df, \n",
    "                x = 'idx_nr', \n",
    "                y = 'dist',\n",
    "                # mode = \"markers\"\n",
    "                color = 'label',\n",
    "                title = 'Distance of weirdos vs its file index nr (0: lowest energy)',\n",
    "                hover_data = ['dist', 'label', 'idx_nr', 'coor']\n",
    "                )\n",
    "\n",
    "fig.show(config={'scrollZoom': True}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# weirdos of 0:17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mapping_labelling.plot_mapped_label_vs_dist_and_histogram(df_mapping_metainfo[:17], litype, category_data = \"weirdo\", el = \"Li\")\n",
    "\n",
    "fig = px.scatter(df, \n",
    "                x = 'idx_nr', \n",
    "                y = 'dist',\n",
    "                # mode = \"markers\"\n",
    "                color = 'label',\n",
    "                title = 'Distance of weirdos vs its file index nr'\n",
    "                )\n",
    "\n",
    "fig.show(config={'scrollZoom': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_toten.to_excel(f'04_important_outputs_{var_excel_file}_sorted.xlsx', index=False)\n",
    "df_mapping_metainfo_not_sorted_toten.to_excel(f'04_important_outputs_{var_excel_file}.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if level_radius == 1:\n",
    "    df_mapping_metainfo.to_pickle(f'df_mapping_metainfo_{max_mapping_radius}_{file_perfect_poscar_48n24_wo_cif}.pkl') \n",
    "elif level_radius == 2:\n",
    "    df_mapping_metainfo.to_pickle(f'df_mapping_metainfo_{max_mapping_radius}_{max_mapping_radius_48htype2}_{file_perfect_poscar_48n24_wo_cif}.pkl')\n",
    "elif level_radius == 3:\n",
    "    df_mapping_metainfo.to_pickle(f'df_mapping_metainfo_{max_mapping_radius}_{max_mapping_radius_48htype2}_{max_mapping_radius_48htype1_48htype2}_{file_perfect_poscar_48n24_wo_cif}.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['atom_mapping_Li_w_dist_label'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_complete = df_mapping_metainfo.sort_values(\"toten [eV]\", ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_complete['idx_coor24li_tuple_cage_belongin_complete_id48htype2'][26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_complete['idx_coor24li_tuple_cage_belongin_complete_id48htype2'][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo_sorted_complete['idx_coor24li_tuple_cage_belongin_complete_id48htype2'][20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with warnings.catch_warnings():\n",
    "#     warnings.simplefilter(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = df_mapping_metainfo_sorted_complete\n",
    "\n",
    "col_idx_coor24li_tuple_cage_belongin_complete_id48htype2 = 'idx_coor24li_tuple_cage_belongin_complete_id48htype2'\n",
    "\n",
    "col_idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered = \"idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered\"\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=FutureWarning)\n",
    "    \n",
    "    for idx in range(dataframe[\"geometry\"].size):\n",
    "\n",
    "        # idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered = defaultdict(list)\n",
    "\n",
    "        idx_coor24li_tuple_cage_belongin_complete_id48htype2 = dataframe[col_idx_coor24li_tuple_cage_belongin_complete_id48htype2][idx]\n",
    "\n",
    "        # Filtered dictionary\n",
    "        idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered = {key: value for key, value in idx_coor24li_tuple_cage_belongin_complete_id48htype2.items() if value['type'] in ['weirdos', '48htype1']}\n",
    "\n",
    "        # # Check if filtered dictionary is empty, if so, set it to {}\n",
    "        # if not idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered:\n",
    "        #     idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered = {}\n",
    "\n",
    "        print(idx)\n",
    "        print(idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered)\n",
    "\n",
    "        dataframe.at[idx, col_idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered] = idx_coor24li_tuple_cage_belongin_complete_id48htype2_filtered\n",
    "\n",
    "        # # Iterate through each key, value pair in the dictionary\n",
    "        # for key, value in idx_coor24li_tuple_cage_belongin_complete_id48htype2.items():\n",
    "        #     # Check if the 'type' is 'weirdos' or '48htype4'\n",
    "        #     if value['type'] in ['weirdos', '48htype4']:\n",
    "        #         # Print the key and value\n",
    "        #         print(f\"Key: {key}, Value: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{key: value for key, value in df_mapping_metainfo_sorted_complete['idx_coor24li_tuple_cage_belongin_complete_id48htype2'][42].items() if value['type'] in ['weirdos', '48htype1']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coor_48htype1_metainfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_cage_dict = {key: value['idx_cage'] for key, value in df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][0].items()}\n",
    "print(idx_cage_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_cage_dict = {key: value['idx_cage'] for key, value in df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][8].items()}\n",
    "print(idx_cage_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "different_idx_cage = {\n",
    "    key: value['idx_cage']\n",
    "    for key, value in df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][8].items()\n",
    "    if value['idx_cage'] != df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][0][key]['idx_cage']\n",
    "}\n",
    "\n",
    "print(different_idx_cage)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_distance.mic_eucledian_distance([0.66882, 0.47147, 0.16882], [0.33118, 0.52853, 0.16882])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_distance.mic_eucledian_distance([0.97111, 0.25000, 0.25000], [0.97147, 0.33118, 0.33118])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_distance.mic_eucledian_distance([0.75000, 0.25000, 0.25000], [0.25000, 0.75000, 0.25000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_distance.mic_eucledian_distance([0.79503, 0.43060, 0.43057], [0.93057, 0.70497, 0.56940])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_distance.mic_eucledian_distance([0.06943, 0.79503, 0.06940], [0.97111, 0.75000, 0.75000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['idx_coor24li_tuple_cage_belongin_complete_closest'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xxxx - xx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimize dx_dz of 48htype1, r_2 for files w/o weirdos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wo_weirdos_selectedcol = df_mapping_metainfo[['geometry','path','subdir_new_system', 'toten [eV]']].sort_values(\"toten [eV]\", ascending=True)[0:17].reset_index().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wo_weirdos_selectedcol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if level_radius == 3:\n",
    "    folder_name_destination_restructure_wo_weirdos = f\"/restructure_{max_mapping_radius}_{max_mapping_radius_48htype2}_{max_mapping_radius_48htype1_48htype2}_{file_perfect_poscar_48n24_wo_cif}_wo_weirdos/\"\n",
    "elif level_radius == 2:\n",
    "    folder_name_destination_restructure_wo_weirdos = f\"/restructure_{max_mapping_radius}_{max_mapping_radius_48htype2}_{file_perfect_poscar_48n24_wo_cif}_wo_weirdos/\"\n",
    "elif level_radius == 1:\n",
    "    folder_name_destination_restructure_wo_weirdos = f\"/restructure_{max_mapping_radius}_{file_perfect_poscar_48n24_wo_cif}_wo_weirdos/\"\n",
    "else:\n",
    "    print(\"level_radius is not correct\")\n",
    "\n",
    "direc_restructure_destination_wo_weirdos = direc+str(folder_name_destination_restructure_wo_weirdos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just refreshing folder\n",
    "func_directory.check_folder_existance(direc_restructure_destination_wo_weirdos, empty_folder=True)\n",
    "\n",
    "# # # # # # just copy file\n",
    "# # # # # Operation.File.copy_rename_single_file(direc_restructure_destination_wo_weirdos, direc_perfect_poscar, file_perfect_poscar_24, prefix=None)\n",
    "# # # # # Operation.File.copy_rename_single_file(direc_restructure_destination_wo_weirdos, direc_perfect_poscar, file_perfect_poscar_48n24, prefix=None)\n",
    "\n",
    "# # # # # Operation.File.copy_rename_files(df_wo_weirdos_selectedcol, direc_restructure_destination_wo_weirdos, file_restructure, prefix=None, savedir = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coor_ref_structure_48n24 = coordinate_and_el.single_structure(ref_structure_48n24)\n",
    "\n",
    "CONTCARorPOSCAR.positive_lessthan1(df_wo_weirdos_selectedcol, direc_restructure_destination_wo_weirdos, poscar_line_nr_start, poscar_line_nr_end, contcar_columns_type2, file_type = \"CONTCAR\", var_name_in = None, var_name_out = \"positive\", n_decimal=16)\n",
    "coordinate_and_el.structures(df_wo_weirdos_selectedcol, mapping = \"False\")\n",
    "\n",
    "if level_radius == 3:\n",
    "    flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "    flag_and_map.li_48htype1_48htype2(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype1_48htype2)\n",
    "    flag_and_map.li_48htypesmerged_level1(df_wo_weirdos_selectedcol, \"Li\")\n",
    "    flag_and_map.li_48htype2(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype2, level_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_wo_weirdos_selectedcol, \"Li\", level_radius)\n",
    "elif level_radius == 2:\n",
    "    flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "    flag_and_map.li_48htype2(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius_48htype2, level_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_wo_weirdos_selectedcol, \"Li\", level_radius)\n",
    "elif level_radius == 1:\n",
    "    flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Li\", max_mapping_radius)\n",
    "\n",
    "# flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"P\", max_mapping_radius)\n",
    "# flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"S\", max_mapping_radius)\n",
    "# flag_and_map.all_atoms_of_el(df_wo_weirdos_selectedcol, coor_ref_structure_48n24, \"Cl\", max_mapping_radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xxx - xxxx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = df_mapping_metainfo\n",
    "\n",
    "col_top1_sorted_idxweirdo_dist_el = \"top1_sorted_idxweirdo_dist_Li\"\n",
    "col_top1_sorted_idxweirdo_label_el = \"top1_sorted_idxweirdo_label_Li\"\n",
    "col_top1_sorted_idxweirdo_coor_el = \"top1_sorted_idxweirdo_coor_Li\"\n",
    "col_top1_sorted_idxweirdo_file_el = \"top1_sorted_idxweirdo_file_Li\"\n",
    "\n",
    "val = 0.\n",
    "\n",
    "dist_weirdos_el_appendend = []\n",
    "y_appended = []\n",
    "label_weirdos_el_appended = []\n",
    "coor_weirdos_el_appended = []\n",
    "file_weirdos_el_appended = []\n",
    "\n",
    "for idx in range(dataframe[\"geometry\"].size):\n",
    "    dist_weirdos_el = dataframe.at[idx, col_top1_sorted_idxweirdo_dist_el].values()\n",
    "    label_weirdos_el = dataframe.at[idx, col_top1_sorted_idxweirdo_label_el].values()\n",
    "    coor_weirdos_el = dataframe.at[idx, col_top1_sorted_idxweirdo_coor_el].values()\n",
    "    file_weirdos_el = dataframe.at[idx, col_top1_sorted_idxweirdo_file_el].values()\n",
    "\n",
    "    for single_dist in dist_weirdos_el:\n",
    "        dist_weirdos_el_appendend.append(single_dist[0])\n",
    "        y_appended.append(np.zeros_like(single_dist[0]) + val)\n",
    "\n",
    "    for single_label in label_weirdos_el:\n",
    "        label_weirdos_el_appended.append(single_label[0])\n",
    "\n",
    "    for single_coor in coor_weirdos_el:\n",
    "        coor_weirdos_el_appended.append(single_coor[0])\n",
    "    \n",
    "    for single_file in file_weirdos_el:\n",
    "        file_weirdos_el_appended.append(single_file[0])\n",
    "\n",
    "df = pd.DataFrame({'dist': dist_weirdos_el_appendend, 'label': label_weirdos_el_appended, 'y': y_appended, 'coor': coor_weirdos_el_appended, 'file': file_weirdos_el_appended})\n",
    "\n",
    "# fig = px.scatter(df, x = 'dist', y = 'y', color='label', color_discrete_map={'48htype1': 'red', '48htype2': 'blue', '24g': 'green'})\n",
    "fig = px.scatter(df, \n",
    "                 x = 'dist', \n",
    "                 y = 'y', \n",
    "                 color ='label', \n",
    "                 hover_data = ['dist', 'label', 'coor', 'file'])\n",
    "\n",
    "\n",
    "fig.show(config={'scrollZoom': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "0.04718813922672343"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "\n",
    "# x = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]\n",
    "\n",
    "# X = np.array(list(zip(x,np.zeros(len(x)))), dtype='int')\n",
    "# bandwidth = estimate_bandwidth(X, quantile=0.1)\n",
    "# ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "# ms.fit(X)\n",
    "# labels = ms.labels_\n",
    "# cluster_centers = ms.cluster_centers_\n",
    "\n",
    "# labels_unique = np.unique(labels)\n",
    "# n_clusters_ = len(labels_unique)\n",
    "\n",
    "# for k in range(n_clusters_):\n",
    "#     my_members = labels == k\n",
    "#     print(\"cluster {0}: {1}\".format(k, X[my_members, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DBSCAN\n",
    "# source: https://stackoverflow.com/questions/11513484/1d-number-array-clustering\n",
    "\n",
    "# points = [0.1, 0.31,  0.32, 0.45, 0.35, 0.40, 0.5 ]\n",
    "points = dist_weirdos_el_appendend\n",
    "\n",
    "clusters = []\n",
    "# eps = 0.2\n",
    "eps = 0.043\n",
    "points_sorted = sorted(points)\n",
    "curr_point = points_sorted[0]\n",
    "curr_cluster = [curr_point]\n",
    "for point in points_sorted[1:]:\n",
    "    if point <= curr_point + eps:\n",
    "        curr_cluster.append(point)\n",
    "    else:\n",
    "        clusters.append(curr_cluster)\n",
    "        curr_cluster = [point]\n",
    "    curr_point = point\n",
    "clusters.append(curr_cluster)\n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in clusters:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters[0][int(len(clusters[0])/2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(clusters[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(clusters)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## another idea: plot based on litype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.datasets import make_swiss_roll\n",
    "\n",
    "# X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "        # # source: https://plotly.com/python/v3/3d-point-clustering/\n",
    "        # scatter = dict(\n",
    "        #     mode = \"markers\",\n",
    "        #     name = \"y\",\n",
    "        #     type = \"scatter3d\",\n",
    "        #     x = coordinates[0], y = coordinates[1], z = coordinates[2],\n",
    "        #     # x = tuple(coordinates[0]), y = tuple(coordinates[1]), z = tuple(coordinates[2]),\n",
    "        #     marker = dict( size=2, color=\"rgb(23, 190, 207)\" )\n",
    "        # )\n",
    "\n",
    "        # clusters = dict(\n",
    "        #     alphahull = 7,\n",
    "        #     name = \"y\",\n",
    "        #     opacity = 0.1,\n",
    "        #     type = \"mesh3d\",\n",
    "        #     x = coordinates[0], y = coordinates[1], z = coordinates[2]\n",
    "        # )\n",
    "\n",
    "        # layout = dict(\n",
    "        #     title = '3d point clustering',\n",
    "        #     scene = dict(\n",
    "        #         xaxis = dict( zeroline=False ),\n",
    "        #         yaxis = dict( zeroline=False ),\n",
    "        #         zaxis = dict( zeroline=False ),\n",
    "        #     )\n",
    "        # )\n",
    "\n",
    "        # fig = dict( data=[scatter, clusters], layout=layout )\n",
    "\n",
    "        # # Use py.iplot() for IPython notebook\n",
    "        # pyoff.iplot(fig, filename='3d point clustering')\n",
    "\n",
    "        # scatter = go.Scatter3d(\n",
    "        #     mode=\"markers\",\n",
    "        #     name=\"y\",\n",
    "        #     x=coordinates[0],\n",
    "        #     y=coordinates[1],\n",
    "        #     z=coordinates[2],\n",
    "        #     marker=dict(size=2, color=\"rgb(23, 190, 207)\")\n",
    "        # )\n",
    "\n",
    "        # clusters = go.Mesh3d(\n",
    "        #     alphahull=7,\n",
    "        #     name=\"y\",\n",
    "        #     opacity=0.1,\n",
    "        #     x=coordinates[0],\n",
    "        #     y=coordinates[1],\n",
    "        #     z=coordinates[2]\n",
    "        # )\n",
    "\n",
    "        # layout = go.Layout(\n",
    "        #     title='3d point clustering',\n",
    "        #     scene=dict(\n",
    "        #         xaxis=dict(zeroline=False),\n",
    "        #         yaxis=dict(zeroline=False),\n",
    "        #         zaxis=dict(zeroline=False),\n",
    "        #     )\n",
    "        # )\n",
    "\n",
    "        # fig = go.Figure(data=[scatter, clusters], layout=layout)\n",
    "\n",
    "        # # Use iplot() for offline plotting\n",
    "        # # iplot(fig, filename='3d point clustering')\n",
    "        # pyoff(fig, filename='3d point clustering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONTCARorPOSCAR.get_latticeconstant_dict(df_mapping_metainfo, direc_restructure_destination, var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_matrix(df_mapping_metainfo, var_filename = \"trf_w_linalg_orientated_positive_lessthan1\", angle_in_degrees=True)\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_coor(df_mapping_metainfo, direc_restructure_destination, var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_neighbors_radius = 5\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Li\", var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"P\", var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"S\", var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Cl\", var_filename = \"trf_w_linalg_orientated_positive_lessthan1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mapping_metainfo[\"distance_cartesian_trf_w_linalg_orientated_positive_S\"][45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my_dict = df_mapping_metainfo[\"distance_cartesian_trf_w_linalg_orientated_positive_Li\"][45]\n",
    "\n",
    "# max_length = max(len(lst) for lst in my_dict.values())\n",
    "\n",
    "# # Fill in missing values with None to make all lists the same length\n",
    "# for key in my_dict:\n",
    "#     my_dict[key] += [None] * (max_length - len(my_dict[key]))\n",
    "\n",
    "# # Create a DataFrame from the modified dictionary\n",
    "# df_my_dict = pd.DataFrame(my_dict)\n",
    "\n",
    "# df_my_dict.columns = ['_'.join(map(str, col)) for col in df_my_dict.columns]\n",
    "\n",
    "# df_my_dict = df_my_dict.sort_index(axis=1)\n",
    "# df_my_dict = df_my_dict.transpose()\n",
    "# # Save the DataFrame to an Excel file without the index\n",
    "# df_my_dict.to_excel('neighbors_Li_trf_w_linalg_orientated_positive_45.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONTCARorPOSCAR.get_latticeconstant_dict(df_mapping_metainfo, direc_restructure_destination, var_filename = \"CONTCAR\")\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_matrix(df_mapping_metainfo, var_filename = \"CONTCAR\", angle_in_degrees=True)\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_coor(df_mapping_metainfo, direc_restructure_destination, var_filename = \"CONTCAR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Li\", var_filename = \"CONTCAR\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"P\", var_filename = \"CONTCAR\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"S\", var_filename = \"CONTCAR\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Cl\", var_filename = \"CONTCAR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # df_mapping_metainfo[\"distance_cartesian_CONTCAR_S\"][45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # my_dict = df_mapping_metainfo[\"distance_cartesian_CONTCAR_Li\"][45]\n",
    "\n",
    "# # # max_length = max(len(lst) for lst in my_dict.values())\n",
    "\n",
    "# # # # Fill in missing values with None to make all lists the same length\n",
    "# # # for key in my_dict:\n",
    "# # #     my_dict[key] += [None] * (max_length - len(my_dict[key]))\n",
    "\n",
    "# # # # Create a DataFrame from the modified dictionary\n",
    "# # # df_my_dict = pd.DataFrame(my_dict)\n",
    "\n",
    "# # # df_my_dict.columns = ['_'.join(map(str, col)) for col in df_my_dict.columns]\n",
    "\n",
    "# # # df_my_dict = df_my_dict.sort_index(axis=1)\n",
    "# # # df_my_dict = df_my_dict.transpose()\n",
    "# # # # Save the DataFrame to an Excel file without the index\n",
    "# # # df_my_dict.to_excel(f'neighbors_Li_CONTCAR_45_{max_mapping_radius}.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONTCARorPOSCAR.get_latticeconstant_structure_dict_iterated(df_mapping_metainfo, direc_restructure_destination, var_filename = \"trf_w_linalg_orientated\")\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_matrix(df_mapping_metainfo, var_filename = \"trf_w_linalg_orientated\", angle_in_degrees=True)\n",
    "func_cartesian.Cartesian.get_fractional_to_cartesian_coor(df_mapping_metainfo, direc_restructure_destination, var_filename = \"trf_w_linalg_orientated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Li\", var_filename = \"trf_w_linalg_orientated\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"P\", var_filename = \"trf_w_linalg_orientated\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"S\", var_filename = \"trf_w_linalg_orientated\")\n",
    "func_cartesian.Cartesian.get_closest_neighbors_el_cartesian_coor(df_mapping_metainfo, max_neighbors_radius, el = \"Cl\", var_filename = \"trf_w_linalg_orientated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # my_dict = df_mapping_metainfo[\"distance_cartesian_trf_w_linalg_orientated_Li\"][45]\n",
    "\n",
    "# # # max_length = max(len(lst) for lst in my_dict.values())\n",
    "\n",
    "# # # # Fill in missing values with None to make all lists the same length\n",
    "# # # for key in my_dict:\n",
    "# # #     my_dict[key] += [None] * (max_length - len(my_dict[key]))\n",
    "\n",
    "# # # # Create a DataFrame from the modified dictionary\n",
    "# # # df_my_dict = pd.DataFrame(my_dict)\n",
    "\n",
    "# # # df_my_dict.columns = ['_'.join(map(str, col)) for col in df_my_dict.columns]\n",
    "\n",
    "# # # df_my_dict = df_my_dict.sort_index(axis=1)\n",
    "# # # df_my_dict = df_my_dict.transpose()\n",
    "# # # # Save the DataFrame to an Excel file without the index\n",
    "# # # df_my_dict.to_excel(f'neighbors_Li_trf_w_linalg_orientated_45_{max_mapping_radius}.xlsx', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"coor_weirdos_Li\",\"dist_weirdos_Li\",\"dist_weirdos_atom72_Li\",\"idx1_weirdos_Li\",\"#weirdos_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "df_mapping_metainfo_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"coor_weirdos_Li\",\"dist_weirdos_Li\",\"idx0_weirdos_Li\",\"sum_weirdos_Li\",\"sum_mapped_Li_new\",\"sum_sanitycheck_Li_new\",\"duplicate_closest24_w_data_Li\",\"toten [eV]\"]].sort_values(\"toten [eV]\", ascending=True)\n",
    "df_mapping_metainfo_not_sorted_toten = df_mapping_metainfo[[\"geometry\",\"path\",\"coor_weirdos_Li\",\"dist_weirdos_Li\",\"idx0_weirdos_Li\",\"sum_weirdos_Li\",\"sum_mapped_Li_new\",\"sum_sanitycheck_Li_new\",\"duplicate_closest24_w_data_Li\",\"toten [eV]\"]]\n",
    "\n",
    "df_mapping_metainfo_sorted_toten.to_excel(f'04_important_outputs_{var_excel_file}_sorted.xlsx', index=False)\n",
    "df_mapping_metainfo_not_sorted_toten.to_excel(f'04_important_outputs_{var_excel_file}.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['scaling'][48]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mapping_metainfo['translation'][48]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lattice_constants_1_0 = [[10.0222501477270232, -0.0006786050655888, -0.0018947275007341], [-0.0006784775666570, 10.0236290992971000, 0.0006784775666570], [-0.0018947275007341, 0.0006786050655888, 10.0222501477270232]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.dot(lattice_constants_1_0, df_mapping_metainfo['scaling'][48].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_lattice_constant_1_0 = np.round(np.dot(lattice_constants_1_0, df_mapping_metainfo['scaling'][48].T) + df_mapping_metainfo['translation'][48], decimals=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('new_constants_trial', new_lattice_constant_1_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "-0.11331824 + 1.89472750e-03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1.00236291e+01 + 0.2500525"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "-6.78477567e-04 + 0.38668173"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_lattice_constant_1_0_precision = np.round(new_lattice_constant_1_0, decimals=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_lattice_constant_1_0_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "direc_restructure_destination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_CONTCAR').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_CONTCAR_pymatgen.cif').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_trf_w_lib.cif').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_trf_w_linalg.cif').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_lattice_constant_1_0_nottransposed = np.round(np.dot(lattice_constants_1_0, df_mapping_metainfo['scaling'][48]) + df_mapping_metainfo['translation'][48], decimals=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('new_constants_trial_not_transposed', new_lattice_constant_1_0_nottransposed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_CONTCAR_edit').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.from_file('/home/azka/01_Masterthesis/01_Precondition/01_En_calc_13thiter_analysis/restructure_0.09_selected/1_0_CONTCAR_edit_not_transposed').lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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