03_orientation_and_mapping_packages_neb.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.mapping import flag_and_map, atom_indexing, labelling, output_CIF, output_weirdos\n",
    "from positionism.plot import mapping_labelling, movement, structure_analysis\n",
    "from positionism.movement import move_by_occupancy, move_by_tuple_cage, move_by_distance\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 = \"neb\"\n",
    "type_movement = \"inTERcage_4\"         # 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",
    "activate_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 activate_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 activate_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 activate_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(\"activate_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": {},
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   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "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"
     ]
    }
   ],
   "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\n",
    "                                                                       , 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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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>path</th>\n",
       "      <th>subdir_new_system</th>\n",
       "      <th>g+p</th>\n",
       "      <th>g+p+1</th>\n",
       "      <th>g+p-1</th>\n",
       "      <th>perfect_system</th>\n",
       "      <th>p_s_mask</th>\n",
       "      <th>toten [eV]</th>\n",
       "      <th>subdir_positive_CONTCAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.368157</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</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>-272.365305</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.399452</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>-272.433050</td>\n",
       "      <td>/home/azka/Documents/01_Masterthesis/Atomic-Po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>-272.429366</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",
       "0       0.0   7.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  7.0   \n",
       "1       0.0   6.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  6.0   \n",
       "2       0.0   5.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  5.0   \n",
       "3       0.0   4.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  4.0   \n",
       "4       0.0   3.0  /home/azka/Documents/01_Masterthesis/Atomic-Po...  3.0   \n",
       "\n",
       "   g+p+1  g+p-1  perfect_system  p_s_mask  toten [eV]  \\\n",
       "0    0.0    6.0             NaN         0 -272.368157   \n",
       "1    7.0    5.0             NaN         0 -272.365305   \n",
       "2    6.0    4.0             NaN         0 -272.399452   \n",
       "3    5.0    3.0             NaN         0 -272.433050   \n",
       "4    4.0    2.0             NaN         0 -272.429366   \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": 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": 20,
   "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 activate_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)\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, activate_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_mapping_metainfo, \"Li\", activate_radius)\n",
    "elif activate_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, activate_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_mapping_metainfo, \"Li\", activate_radius)\n",
    "elif activate_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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 670x590 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "atom_indexing.weirdos_el(df_mapping_metainfo, \"Li\", activate_radius)\n",
    "\n",
    "atom_indexing.correct_idx_and_order_mapped_el(df_mapping_metainfo, \n",
    "                                                 \"Li\", activate_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, activate_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",
    "                                                         activate_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",
    "                                                 activate_radius, litype, el=\"Li\")\n",
    "\n",
    "labelling.get_label_mapping(df_mapping_metainfo, coor_ref_structure_48n24, \n",
    "                            \"Li\", activate_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, activate_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",
    "                                      activate_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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NECESSARY STEP: modify \"48htype1\" in \"idx_coor_limapped_weirdos_dict\" for litype=1\n",
    "move_by_occupancy.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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "category=Doubly occupied<br>idx_file=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Doubly occupied",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Doubly occupied",
         "offsetgroup": "Doubly occupied",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8
         ],
         "xaxis": "x",
         "y": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "category=Singly occupied<br>idx_file=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Singly occupied",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Singly occupied",
         "offsetgroup": "Singly occupied",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8
         ],
         "xaxis": "x",
         "y": [
          24,
          24,
          24,
          24,
          24,
          24,
          24,
          24
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "category=Empty<br>idx_file=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Empty",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Empty",
         "offsetgroup": "Empty",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8
         ],
         "xaxis": "x",
         "y": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "category=48h type 2<br>idx_file=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "48h type 2",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "48h type 2",
         "offsetgroup": "48h type 2",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8
         ],
         "xaxis": "x",
         "y": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "category=Unassigned<br>idx_file=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Unassigned",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Unassigned",
         "offsetgroup": "Unassigned",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8
         ],
         "xaxis": "x",
         "y": [
          0,
          0,
          0,
          0,
          0,
          0,
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          0
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "font": {
         "family": "serif",
         "size": 12
        },
        "legend": {
         "title": {
          "text": "category"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "b": 50,
         "l": 20,
         "r": 20,
         "t": 50
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "white",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
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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    36\n",
      "48htype1     0\n",
      "48htype2    61\n",
      "48htype3    69\n",
      "48htype4    55\n",
      "24g          7\n",
      "weirdo       0\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "dataframe = df_mapping_metainfo\n",
    "sorted = \"False\"\n",
    "\n",
    "move_by_occupancy.get_occupancy(dataframe, coor_ref_structure_48n24_expanded, tuple_cage_metainfo, el = \"Li\")\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",
    "move_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",
    "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    pass\n",
    "else:\n",
    "    dataframe = df_mapping_metainfo_sorted\n",
    "    sorted = \"True\"\n",
    "\n",
    "    move_by_occupancy.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",
    "    move_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": 26,
   "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 850x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n",
      "/home/azka/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:399: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_type = move_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_plot = 'type', activate_closest_tuple = False)\n",
    "    df_idx_tuple = move_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_plot = 'idx_tuple', activate_closest_tuple = False)\n",
    "    df_idx_cage = move_by_tuple_cage.get_df_movement(df_mapping_metainfo, to_plot = '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",
    "    move_by_tuple_cage.count_occupancy_cage(df_idx_cage, direc_restructure_destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2lrxnNspcH5S59uyWl8YUq0TtplRbVGrrLxaLgWVZrfW1XKXrElciiqLW4nS73XA6neB53vCSD1mWIcsy3G43GIYpWpzUtZYrKXagZi6XK9q9HIlEcPDgQd21hoYGvO/Tv28o80rOZV/C7NQctrZfhXPZl3T3JkdPr/n9a4Ey1wdlrj275a3FKRn1YqmWosfj0dblhcNhrdiFw2H4fD54PJ6CZQoAMDExoRuvW87IbM2l446SJBVMgAmFQujt7S35dZbnikajCAaDWguxs7OzoDWbTCZXzLTSgZrFNDQ0oLm5WfeqZvfpQwNfhtvzZu3ju/bdga8lrLW2cznKXB+UufbslJdaijXA8zxGRkYAXBpH9Hg8SKVSiEajaGtr09YoDg4OAsgXkWg0qi2tCIfDiMViGB8fRy6Xg9PpLFnUWJZFMBgEx3FwuVwFY3c+nw9jY2Ml86rjjeo4Yltbm5YLyK+v5DhOywFgxfFBIwdq1lty6GEE+vbgrn13AABu7HLhcwcSpmQxijLXB2WuPTvltfOYokNRFMXsEHYgyzLGx8eLTrIRBAEcx2FiYsKEZKvzbvKbHaEs6YUUZa4xu+UFKHO9pBfKO6m+GP/37zP8bOptD67561WTfct5HSztDk0mk4ZmnRJCyEa3AIfhl9VQUVwBx3HarNO17KlKCCEbiZ3HFKkorkCd6JNIJEqORQqCgMHBQYiiqG0PRwghG9lrC5sMv6zGshNtrMDtdpfchk2lrjskhBCSZ8UWoFFUFAkhhFSVFccKjaKiSAghpKqopUgIIYQsoqJICCGELKKiSAghhCyiokgIIYQsUmpYFNWlb+qBEcvPpY3FYtpBC7Isl70tJhVFQgghVVWr2accx+n2lA6FQvB6vdoxfGrBVPeVFgQBoVCorAPdrbdykhBCiK1dXNhk+GWULMsFpxGFQiEIgqCdcKSeTqTyeDzarmRGUVEkhBBSVbXa5m18fFx3ALx6rqwsy9oh9MXOqFXPpTWCuk8JIYRUVTljivPz85ifn9dda2hoKDgLlmEYTE1N6a6pxY5lWYyPjxd9f/WIQaOoKG4A1TgKpt4oc+3ZLS9Ame2inBZgNBrF4cOHddcOHTqEBx54wNDnxuPxoq1DldPpRC6XM5yHiuIGYMfz3OyY+abDnzU7hmFPHPoE3vrNT5kdoyw/vPczWDh3g9kxyrJp65O42ztgdoyynEz3r/k9yjmlNxKJ4ODBg7pry1uJxXAch56enhUPawdQVkEEqCgSQgipsnJmnxbrKl0Nz/NwuVy6gqiOLy4ny3LJe8XQRBtCCCFVpSgOw69yqeOIakFUJ9mwLAuGYXQTcVTlnGRERZEQQkhV1Wr2qSiKEEURbrcbkiRBkiQkEgntEPhIJKKbacrz/Krdq8utqSieOnUKBw4cwD333AMAmJ6exrFjx9byloQQQmxOUYy/jJJlGd3d3eA4Di6XS3txHKdNtAmHw5BlGTzPg+d5jI2NlbVwH1jDmOLx48cRj8fh9/u1QC0tLdi/fz9OnDiBvXv3VvrWhBBCbGyhjEX5RhVbklHM0m3dfD5f2V+n4qIoSRIee+wxAMDo6KjunlJO+SeEELKubMgNwV0uV8l7Rqo5IYSQ9cnO7aKK27iZTAYnTpwAADgcl34rOHHihLZ7OSGEkI2nlrNPa63ilmJfXx8CgYA2psiyLCRJQmdnJx599NFqZiSEEGIjVix2Rq1p8X4ymYQkSRgdHYUsy3C73eju7q5WtrKJogiO45DL5TAxMWFaDkII2chs3Hu69h1tWJYFy7LIZrMQRRFnzpzB9u3bqxAtb+lZWapEIqHthp7JZBCJRMAwDNxuNziOQygUquhrJRKJste01JIsy0gmkwDy3dWSJGF4eHjFff4IIcRsG7KleOTIEdx///3ax+3t7Whvb0c2m8WxY8ewf//+NYfjeb7gyI9YLIZgMKg7Wbm3txepVH7TXXURZyXS6bSliiLHceA4TtuiKBQKwe/3F/ySUE+Bvj2Yk18BADQyVyA59LBpWYyyeuamhga855YbcM/NO/HRfzlR9B4AXO9swfVMC/7yqwJml50qYIY/aL8LAHDtFW0AgIHH/63g/uyrFwAATa/bjH/O/kdd8y03Mwt889/z//3MWeC5s8Cn+4DmpkvPHPtXoLlx8fk5YP//qH/O1fTufxfOns1PZpydvYDv/MfPTE5UhI2bilVfTNLe3l6ViTayLBfdyDWdTutaSuUeC1JKIpEouj2QmSRJAs/z2scul6vk8Sj1EOjbAwB4ZFjAI8MCnhKz+PhR6/wSUYzVM9+89Sq855Yb0LS5AcyWzQX37/feie9LzyApnsZfC9/F9IUL+Bv/e01IqvdnN9yDf87+B/45+x9aMfzbzj/R7qsF89+eG8O/PTeGJ2bOov+W3zUh6SV/HQd+uxMI7AHuPwC0NAN/cejS/WP/mv//gT351803AIeOmJO1mCuvbMDR//vH+Ncv/QBff+RHePLJc3jgrz5odqyi7DzRpqyiODw8jEAggK6uLkSjUXR1dRW82traqhIsmUwiEAgUXGcYBl6vVyuE6p53y4miCJ7nEYvFwHHcil9LEASk02lIkoRYLIZYLAYg31Lt6OhAa2ur1mKVJAkulwterxeSJEEQBHR0dMDr9SKRSCCRSJT8mrFYTMu0tNiVkk6ndQtRx8bGytrDr9o+1P9BfD1xqeU+OXoa7wt6TctjhNUz/+TcS0iKp/Hc1HTR+9cxLbjn5p3ax8/kpnHrtqvrFa+oxss348bmbWi8/FIR//KzP8Rv/cZObNvSCgD4I/Zd+PKzY9r9sfMZ7L3+rXXPutSzZ4FHv3Xp4+u3AY8vaWQNfxEIvP/Sx2/rBJJftc4P7eD+d+Hfv/1TvPxyvpfgqadfxP3hL5mcqriFBYfhl9WU1X3a29uL3t5eJBIJCIJQdOyOZVm0t7evKZQgCCV/+A8PD2uFKhwOw+VyFWzjk8vlkMvltN0MXC4Xenp64Ha7i76n+rUkSSrYDYFhGIRCIe0ZlmURCoW051iWRSQSgd/vRzwe1wq0OrapZvP7/ejp6dEyeb1esCxbMtNyPM9DlmWtm7jetrZfhabWRrw8/UrBvV3dt2Fy9LQJqVZmx8zLLe9OvW3b1fiB9IxJaS55U8t1uPYKJ342cxYA8Pwr+V6dptdtwTYAza/bgrnXLhR8XlebC2PnzVmy9YX/o//4x08Av92R/+9nzwIzcw40NxX2+31/PF8gzfb+9+3Ch//gQVyztQXXXMNAnPw5xMmfmx2rOAu2AI2qaEwxGAzC5XLVbKapetRHsW5RhmHAcRzS6TRisRg8Hg8CgYCuS1WWZV1RVZeLGC1AS3k8HuRyOW0TWvX9lmdyu92665FIBK2trVqLked5XUFTi+hq+/Kpk21kWdZtqVdv17DFWyezU3NoZK6scxpj7Jh5Jfe8aSeaNjfgL1JfNzXH3GsX4Bn937prb23bASBfHN/Ucm3Rz5t59VdounxLzfMZ8ei38mOGf7N4tu2zZ4s/19yoYHaubrFKumZrCwBg586teOEFGS+8IOMTH78H3/7OE5YsjBty8X6tCmIikVhxvzp14kkqlUImk0Eul0NHR4fumWJFq9yDJpcKBoMYGRkBsHIrdvnXZBgGoihCEAQwDANBELSXOpvUyPsEg0GtZdra2lqVMdRqmc3NocnZaHaMstgtc1NDAwLu23Bdawse/clTlphks9wfse/EZ3785aKtQ9XMq6+g+XXmFsWZWSD5cL4I3vsu/SSbYlqagemZukRb0TXXMACAubkLeOrpF/HCuWkkjn0Lh/7SmmOKUMp4WUxNjo46cqSy0WlRFNHZWbqfQpIkXSuQZVlMTEyAYRhDY3TlWFqwenp6kEgktOvlttbUlq/H49Feg4ODK84ilWUZHMfpCqDH44EsywUzclXz8/OYmZnRveZr/APUTsVFZbfMs/PzSIqn8fnv5ydZ/Vf4PjSVeShrLf3ZDfdAOPff+LfnxlZ8rvl1V9Qp0QoZmvKTaNRZpb/13nyhLMUKBXGpnz15Tvvvl1+eR1PTZrh3vdHERMVtmIk2PT092tZuO3bswM6dOwteO3bsWHViSym5XA6CIGiTXdT3USemlCpIla5LXIkoitp/u91uOJ1O8DxveMmHLMvahgbq2V/FnilFnfSztIWrPl+qKEejUbS0tOhe0WjUUN7VvCC9WPR6U2tjyXtms2PmpZoaGvBJz526Avh96Rm0bNmMt7FvMDHZJXdffSueeyWnW26hji8u1/y6LXj+V5X32KzFzCxw5Ki+AP52Z34c8QcT+Uk3RT9vzoHrStyrpxdekIten529oLUiLcXGLcWyxhTb29u1H8gsy2JwcLDgGUVRMDAwUFEYtRWlEkURiURCN/llcHBQW7ivmpiYWHFszkh3ozruCKDo+GMoFEJvb2/Jzc5FUdTlikajCAaD2uYGnZ2d4Hle1zWcTCZLrot0u90Ih8O6ruCRkRG43e6S3beRSAQHDx7UXWtoaMD7Pv37K//hDTiXfQmzU3PY2n4VzmVf0t2z6oQVO2Ze6vrWFvS+vQsj46e1LtPmzfkCOXPB/C7Urrb8oQBqC7Hx8s1oft0WnP3VFGZe/RW2bWnF2V/p/72YNcnm2ReAz3/JgZ49itZlOrM4VtjUmC+KzY0Knj1bWCCtMMnmhXPTOHt2CtuuYfDU05d+oWtq2ownl7QercKKLUCjyiqKS4vd4OAgdu3aVfS5SCSytlTIT0xRx/E4joPX64XH40EqlUI0GkVbW5u2RlEtzqIoIhqNaq2scDiMWCyG8fFx5HI5OJ3OkuOVLMsiGAxqB1guL1Y+nw9jY6W7h9xutzZ2KIoi2tradL80pNNpbQs6tbW52kYBkUhEWx4C5Iv78mO6lmpoaEBDDbvVHhr4MtyeN+OR4Xz37V377sDXEuZtJGCEXTK3FFmj+JNzL2H4e2N4Tr60XOM9t96Ax8++iB9kzZ2BemPzNtzUvA2j536sLcPo3nqrtgzjH6Vv4a1tO7SCeffVt+LEsz80Le8tNwAf/T1FV/C+cRK4+QZFK3q9HwZ+MA5cn1/aike/BQTeb52mTOLYt/Cud71JK4rvuOtGjE9kdUXSMqzz11Y2h1Klww9nZmbQ3NxcjbeyJFmWMT4+XrSVJggCOI6z7H6r3k3+qr1XoG8PXpDyra4bu1w41v/Fqr23Kr2QsmXmmw5/tuzPU9ch/s4tN+CWbVdj+Htj+PHzL+LRnz4FYHGSTcdt2vNvcLbgSPq7a55s88ShT+Ct3/xURZ/bePlm/Ns7+4pOmln6nn/QfheefyXfUry55Vr8f0+u7aCAH977GSycu6Hiz5+ZBZJfvfTxs2eBT4YKd7RRC+fpJ/KL/Ndi09Yncbe3sp6zYt77O29BY2P+F6jm5i0YPvatqr236mS6f83vsf2fCnsRSznzh5UNt9WK4Zbi0NCQtptKV1eXtsXbiRMn0Nvbq00m8fl8VRvHMtvS7tCVujo3kqVbpP3H8f80MYlxVs78nDyNz39/XJtEs9zs/HzJe2YptiSjmKXjjCdf/HEtIxnS3LT6tm1L79/zrprGqcjXH/mR2RGMWTA7QOUMT7Tx+XxQFAWJREIriKOjo/D7/ejt7cXFixfx1FNPwePxVDz71Go4jtNmna5lT1VCCNlQFIfxl8UYLoqJRALJZBItLS3atcHBQbjdbt1YY3d3N6rUI2s6dVbrSmsnBUHA4OAgRFHUjf8RQshGpSjGX1ZjuPu0WEtJHUtbzuGwXvWvhLqcYiXLZ8wSQsiGZ8FiZ5Thori80KmzIL3ews2V10tLkRBCSAUs2C1qlOHuU0VRcOrUKQDAmTNnEAqF4HK5cPfdd+ueO3nyZMG2a4QQQjYOh2L8ZTWGi2JfXx+OHj0Kp9MJlmXBMAwee+wx7f7x48fR398Pn89nuXMJCSGE1NFG2dHm6NGjOHr0aNF76thaNRbuE0IIsTEbd59WdHRUMUtnpRJCCNnALNgCNKpqRZEQQggBsDEW7xNCCCGG1GjxvizLSCQSRVc9CIIAv9+PRCKhLRes5EhBaikSQgipqlrMKhVFEePj45Blueih8epZszzPg2VZcBy34oH1pVBRJIQQUl01KIrqZiortf6y2WzZh8AvV5Pu05kZix1XTQghhBhQk5ZiNBpdNydlEEIIKU853afz8/OYX3YUWqVnwyaTSTidTuRyOWQyGd2ZtkZVfJ7i5OQkent7kc1mddcVRcH09DQuXrxYydsSQgixOfZz/8fws384NYPDhw/rrh06dAgPPPBA0ed5nkc0Gi04v1bdNIZlWQD5gxzS6TRSqVQZydfQUoxGo4hEIgUbZiuKgv7+tR9SSaqnmgf21kO1Dxmuh/RCqqyDVc125g85fO4Je21k//GbBLz5E+Uf5Gym//7sJ2z5vbxmZTS1IpEIDh48qLtWSStRLYaqQCCAUCikOxfXiIqLYk9PD/bt21f0Hu1qQwghG1gZRbHSrtLleJ7XzTZVC6EkSauedrRUxRNtik2JVU1NTVX6toQQQmzOsWD8VQ2yLMPv9+v23ZZlGUBhC3I1FbcUe3t7tRahy+XS3UulUgWnZxBCCNkgarjNW7EGGcMwCIfDugKoHg5f7hKNiovigQMHIAgCWJZFJpPRrsuyXDAASgghZOOoxeJ9SZLA8zxGRkYgiiI4jkNXV5fWZRqJRBCLxbTnz58/X/YkG2ANRbGjo6PkiRlDQ0OVvi0hhBC7q8EpGSzLIhwOIxwOF72vthbXquKi6HQ6S97r6+ur9G0JIYTYnY1Pyah4oo3L5cLJkyeL3jty5EjFgQghhNibQzH+spqKW4r79++HLMvIZrO6wU1FUZDNZnH//fdXJSAhhBCbsWCxM2pN27zF4/GCblRFUTAwMLCmUJVSB19zuRxN9iGEEJNYsQVoVMVFcXBwEN3d3UXvVXPxvtfrRTqd1l1LJBLaLgWZTAaRSAQMw8DtdoPjOIRCoYq+ViKRQDAYrEbsqpFlGclkEqlUquDvgRBCLMnGhwxXXBTVgnjy5Enth3VXVxf27t2LXbt2VSUcz/MQBEF3LRaLIRgMamtPZFlGb2+vNvV2pQlAq0mn05YqiqudH2aGQN8ezMmvAAAamSuQHHrY5ESrs3rm5tc14L3bb8LvvPEm/IEworv3f9/xAXztzBN4Zk7GzK8v6O49Ozddz5gFJk/kT8OZPvcaAOBdH7v0b+/ZUxfwk0fncN3tm9F89eV47kcXcNWO18P19itMyQoATZsbsHvXDdj9lp0IHT1R9n0rsPr3smpDthQBYPfu3cjlctqYYjqdRjQaxdjY2JqDlSoE6XRaN+2WYRht54K1SCQSut0QrMDI+WH1FOjbAwB4ZDj/i8qu7tvw8aNBfO5AwsxYK7J65lucV+PNbVvR/PoGMA2bC+7f2nY13rv9poLrXz/zBP70O1+pR8SifvCPMn77jxjt42/9XQ4P/9VL2PPpqwAAv355Ac/96AIy3/8Vmrdehl17m00tiG+67ircfP3VaN7SgJYrCv+eV7tvBVb/Xl4vKi6KR44cQTweR3t7u+66KIqIRCJrPjoqmUxqG7ouxTAMvF4vUqkUGIaBJElFt/ERRRGSJEGSJJw/f37FI0QEQUA6nYYkSdriz3A4rO3GLkkSUqkUPB4PJEmC1+sFy7KIx+OQJAkcx8HpdMLvz2/8K8ty0a8Zi8XAsqyWuZJToc30of4P4g/YP9U+nhw9jVj6ryz9j9LqmR/PvYjHcy/iPW+4sej9f33yFOKP/1B37fd2vgVfeupH9YhX1PzcAn6R+TXm5xbQ0JifwH7LPY1IHXwR0+deQ8vW/I+V309s0+6b7afPvYSfPvcSvG/ZWdF9K7D697LORmwptre3FxREIN+6WeskF0EQ4PEU38F/eHgYHR0daG1tRTgchsvlQjwe1z2Ty+WQy+W0ouNyudDT01NyU1j1a0mSpGuFqlsEhUIh7RmWZREKhbTnWJZFJBKB3+9HPB7XCrQ6tqlm8/v96Onp0TKphbWcjWrNtLX9KjS1NuLl6VcK7u3qvg2To6dNSLUyO2Ze7pGf/0z38duveSP++/w5k9Jc8ounf42ZF1/Dbza+HgDQfHX+R8mvX7bxYJKF2e172c7dpxX/GudwlN6xYKV7RsiyXHITV4ZhwHEcfD4fYrEYUqlUQfepLMu6oqq2zirh8XiQy+UgiqLu/ZZncrvduuuRSETrklW3J1raMlSLqF1cw15d9Prs1BwamSvrnMYYO2Zebum44fWNLXhDI4PHcy+amAhoaNyEj/7rdfhN1+u1a8/9KD/eqRZHAHj6e68g871X8Pijc/jBP8r1jrmu2O57WSnjZTEVF8VMJlN08f7Jkyfx9NNPVxxI3cS1FI7jwLIsUqkUMpkMcrkcOjo6dM8UK1prmagSDAYxMpKfALFSK3b512QYBqIoQhAEMAwDQRC0VyaTsdwYZiVmc3NocjaaHaMsdswMAPfdeoep3aYrEY/P4J0fa9W6S3/D9Xpc95bNcL39CtxyTyOat16ORwd/aXLK9cey38s2LooVd5/29fUhEAjA7/drRUiSJHg8Hq2AlEsURXR2dpa8L0mSrhXIsiwmJibQ0dFR0BJbq6VjlT09Peju7sbg4KD2ZyyH2vJd+nnlvocR8/PzmJ+f112rxjllK7HkP8hV2DHzLc7iLQUr+ME/ythxZ774qdRxRdWOt1+Bb//dlG4ckqydVb+X7dx9uqbZp8lkEpOTk9qyAY/Hs6blGGo3pboMQz19Q52gora+lqt0XeJKRFHUiqLb7YbT6QTP84aXfMiyDFmW4Xa7wTBM0YlH5Z4IvZpoNIrDhw/rrh06dKgq7/2CVLzLrqm1seQ9s9kxcykfvuF2/HzWeueUZr73Cpq3Xq4riOr1pbNN1UK4dBySGGe77+WNWBT7+/vhcDgQjUarti7R4/HoWlCiKCKRSOgmvwwODhYUk4mJiRXH54ws2Vg67ljspOZQKITe3t6SByiLoqjLFY1GEQwGwbIsWJZFZ2dnQWs2mUwaWhdptOs3Eong4MGDumsNDQ1436d/39Dnr+Rc9iXMTs1ha/tVOJd9SXfPaoP8KjtmLuXt17wRpy0wwWapZ0/lxxHVgjg/t4ALcwvY3LgJj8bO48Px12stxvm5/AScpWOOxDi7fS9X6/BgM6zpO7Snp6fo9TNnzmD79u1reWvt3CwgP47o9Xrh8XiQSqUQjUbR1tamrVFUlz6IoqgtoYjFYgiHw4jFYhgfH0cul4PT6SzZxcqyLILBIDiOg8vlKihWPp9vxfWXbrdbGzsURRFtbW26JRnpdFrbgk5tba5WEFc7P2y5hoaGmnaXPjTwZbg9b9bWSd217w58LWHtXXbskrnYGsWl3tjUimfm5PqEMeAXmV/jl9Kvwb7tCm3xvvT9V3Dz7kY0NG7Crr1Nui7Unzw2B9fbtpjeddq8yhrE1e6byS7fywBs3VJ0KIpSUfzR0VE4HA7cfffdBfeOHDmy7jYEl2UZ4+PjRccCBUEAx3GW3W/Vu8lftfcK9O3BC1L+N9Ubu1w41v/Fqr23Kr2QsmXm7f9Uei1sKdc3tuB33ngj3rf9TbitbSuO/vg/8aNfnsM3ntEvxfj2B4P42Le/UrWZp2f+kMPnnqhsXHt+bgH/EjyL+ZcLf3R87CvXa8/85LE57fqF2QXdYv9KfPwmAW/+xGcr+txr21qw+y07cc/tN+Dm66/GF0bH8PizLyL9o6cM3a/Uf3/2E7b8Xl6rWyLG/3d6PPqJNX+9aqq4KAYCAUiShMnJSW28D7h0Ssb58+ermdMUS7tDV9oXdSMVxXqodlGsh0qLolnWUhTNspaiaJZqF8V6qEpR7C+jKA5YqyhW3H0qSZK2EfdSiqJou8LYHcdx6OjoQDAYXNOeqoQQsqHYuPvUcFGMRCKQZRkulwsMw6x4SsZaF+9bRSgUwvj4+KqtxMHBQYiiqI1jEkLIRrYhlmTE43GcPHkSt99++6rPliqWdqNuyL2S5TNmCSFkw9sIRTEQCOgK4pkzZ3T3nU4nmpubq5WLEEKITdm5pWh4frTL5dJ9nMlkMDAwgI6ODt2+oIQQQja4jbjNW3d3N7q7u8EwDPbu3au7d/LkyaJLNQghhKx/G6KlWGryzG/8xm8UXKOWIyGEbGAboaV49OhRbS/SpcbHxwuuC4Kw7hbvE0IIMcbOLUXDRTGXy2FsbKxgvV5ra6uuKKoH/BJCCNmgNkJRDAaDGBgYMPTs0NBQxYEIIYTYXI2KoizLSCaTSKVSSKcL932NxWLahjKyLFe0btxwUSzneKZqnmtICCHEXmrRfSqKonZMYbHeSHUnNXWjFUEQEAqFVjxBqRjDRbG9vd3wm5bzLCGEkHWmBkVR3UyF5/mi96PRKLLZrPaxx+OB1+stuyjSEdiEEEKqyqEYf1WDJEklD21XD603ik78JIQQUl1lFLv5+XnMz8/rrpV7Nqx6QPxy6pm75aCiuAFU4yiYerNj5jN/yJkdoSwfv6m836Ct4L8/a61jhoyw4/fyWjkWjD8bjUZx+PBh3bVDhw7hgQceWHMOp9NZ9moIKoobgB3Pc7Nj5l+cvdbsGIb95rbncdNhe51N+MShT5R1eK0VPB79BO5t/hOzY5TlmzN/v/Y3KaOlGIlEcPDgQd21clqJK6lkeSAVRUIIIVVVzlhhuV2lxbAsW/S6LMsl75VCE20IIYRUV523eWNZFgzDFB1bLPdoPyqKhBBCqsqhKIZf5SrVJRqJRHQzTXmeL3k4/EqoKBJCCKmuGrQUJUlCLBZDPB6HKIrgOE63ZjEcDkOWZfA8D57nMTY2VvYaRYDGFAkhhFRZLXa0YVkW4XB4xa3blt6rdGc1KoqEEEKqayNsCE4IIYQYsSGOjiKEEEKMKGfxvtVQUSSEEFJdNm4prqvZp6Iowuv1oqOjw+wohBCyYdV7Q/BqsnxL0ev1FhwmmUgktB3RM5kMIpEIGIaB2+0Gx3Flnf24/H0rWddSL8X+Luot0LcHc/IrAIBG5gokhx42NY8Rdsj8xS+9DgDw/Nn876nhT84XvQ8A0zMOfCz06/qFK6KpoQHvueUG3HPzTnz0X04UvQcA1ztbcD3Tgr/8qoDZZZs+11vT5gbce9sN2H3bTvR+4UTB/Y+8oxNAPjMAHP630brmW82VLVvwjg++FXf9bhc+9btHzI6zsgrWH1qFpVuKPM8XHPsRi8UQCAQQDocRDAYRiUTQ29ur3Xc6nRV/PbMLzkqK/V3UW6BvDwDgkWEBjwwLeErM4uNHrftLBGCPzH8Xfz0+/Huv4sO/96pWDP/i/s3a/f/1wGY0NiraM9duW8DfxV9vVlzcvPUqvOeWG9C0uQHMls0F9+/33onvS88gKZ7GXwvfxfSFC/gb/3tNSHrJm7ZdhXtvuwFNWxrQckVh5oP33okvfGccX/jOuFYMhz+yt94xS9rxljfiHR98K65suQJNrVeaHWdVdm4pWrYoljpdOZ1O687MquRokGISiUTJ40fMVurvot4+1P9BfD1xqTBPjp7G+4JeExOtzuqZZ+eAJ5/ahNm5S9c+8P5XMT5xOZ4/68DzZx341rcvx93vfk27f/e7X8O/PvR63efU00/OvYSkeBrPTU0XvX8d04J7bt6pffxMbhq3bru6XvGK+unZl5AaO43ncoWZmzY34E3brkLT5kv7byZ/eBpv2/lGXNfaUs+YJT39o5/jG//wbZw78wuzoxhT523eqsmyRTGZTCIQCBRcZxgGXq9XK4SSJBXd8FUURfA8j1gsBo5b+UgfQRCQTqe1HRNisRiAfOuso6MDra2tWitNkiS4XC54vV5IkgRBENDR0QGv14tEIoFEIlHya8ZiMS1TqdOjy/m7qKet7VehqbURL0+/UnBvV/dtJiRanV0yP/Gzy3D27KV/itu25afuzc05cPaF/PWmxkvPq//9xM8uq1vGcnz0X07g898f1z6+bdvV+IH0jImJVnfLdVfjOuelAqgWz6Yt1TmtYaNxLBh/WY0lxxQFQSi5ievw8LBWqMLhMFwuV8FWPrlcDrlcTtvRwOVyoaenB263u+h7ql9LkqSCHREYhkEoFNKeYVkWoVBIe45lWUQiEfj9fsTjca1Aq2Obaja/34+enh4tk9frBcuyJTMZ+buop2vY4r/pz07NoZGxZneOHTI3NQLf/OrLumvjE/lit23bAhpnHADyLcqlhREAzp51ABafU3bPm3aiaXMD/iL1dbOjlDR7YR5v+/SDumu/veMNAFC0ZUkMsGAL0ChLthRXOu6DYRhwHAefz4dYLIZUKlXQfSrLsq6QsCxbcdeox+NBLpeDKIq691ueye12665HIhGtS1aSJPA8r9t2SC2iq6nk6JN6ms3NocnZuPqDFmL1zP/8xdej7+AFNDUC125T0NnxmlYoAWBswpotxKWaGhoQcN+G61pb8OhPnjJ9kk259r+rCw+cEDB7wV65rYLGFKsokUisuGcdx3FgWRapVAqZTAa5XK5gCUaxorWWMblgMIiRkREAxltuDMOAYRiIoghBEMAwDARB0F6ZTGbVQr3a38Vy8/PzmJmZ0b3ma/zDyMrFpRQrZ/67+OvR/e7X8IH3XxpD/JsjF/DTJy7DV756Of7925ehuSn/k2TbNgv+RFk0Oz+PpHha60b9r/B9aKrSwbG1dvDeO/HN/34SqbHTZkexLceCYvhlNZYqiqIoorOzs+R9SZJ0rUCWZTExMQGGYcoaozNiacHq6elBIpHQri+d6GOE2trzeDzaa3BwcMXZrqv9XRQTjUbR0tKie0Wj0bLeo5QXpBeLXm9qbSx5z2x2y/zv374M125bwId/79WCex8L/RofeP9rePc7L2pjjjfdeLHeEVfV1NCAT3ru1BXA70vPoGXLZryNfYOJyYzZfetOPHt+Gl/4zvjqD5PSbDzRxlJjimo3pTqpJZPJAMhPUFEPkSxWkCpdl7gSURS1Fqfb7YbT6QTP84aXfMiyDFmW4Xa7wTBM0eKkrrUsZrW/i2ItyEgkgoMHD+quNTQ04H2f/n1DmVdyLvsSZqfmsLX9KpzLvqS7Nzlqzd+o7ZRZ7RJVW4izc8DMjAPXblPwsyc34cYbLs1IGJ+4DO9652sFY4xWcH1rC3rf3oWR8dNal2nz4qzOGYt3Rd7hyhdttYXYtLkBLVs2l5xlS0qzYreoUZYqimorSiWKIhKJhG7yy+DgYEExmZiYWHF8zsiSjaXjjpIkFUyACYVC6O3txdTUVNHPF0VRlysajSIYDIJlWbAsi87OzoJxxWQyWXKzACN/F8s1NDSgoYZdVA8NfBluz5vxyHC+UN+17w58LWHdtZ2APTL/7MlNePLJTXjXO1/D82fzE2u+9e3Lsef9+RbjXx7ejL6D8+jqyLcMv/LV16HvoPkFpqXIGsWfnHsJw98bw3PypULynltvwONnX8QPsubPQC2WGcivY7z52qvw2OmntGUYu2/bidQPrfXLkx3WKAKw9eJ9SxXFpXie18bxOI6D1+uFx+NBKpVCNBpFW1ubtkZxcHAQQL5wRKNRbWlFOBxGLBbD+Pg4crkcnE5nyTE6lmURDAbBcRxcLldBsfL5fBgbGyuZ1+12a2OHoiiira1NywXk11dyHKflAGB495xSfxf1lhx6GIG+Pbhr3x0AgBu7XPjcgUTdc5TD6pln54CPf3IL5uYceDCh/4VG7UbtOziPJ5/chLNnHXj+7Cb0HZzHtSaOJ6rrEH/nlhtwy7ar8UnPnfjx8y/i0Z8+BQBI/McYPvq2S13/zZsb8Mf/dNysuACA61pbsPu2nbj3zTfglmuvxsF778SPn3sRj/34KTRtbsDn9+9Dy5bN+OR77tJ9nlW6Ubdu/03c+YFOvHPvW7Fz13Z85LAfT4pZfPcr1si3nJ1big5FsXFJryNZljE+Pl60GAmCAI7jMDExYUKy1Xk3+c2OUJb0QsqWmX9x9lqzYxj2m9uex02HP2t2jLI8cegTuCVir8yPRz+Be5v/xOwYZfnmzN+v+T3esWfI8LPfebhvzV+vmiw10cZqlna7JpNJS6wXJIQQq6MlGesUx3HarNO17KlKCCEbyoJi/GUxVBRXoM5qXWm9oCAIGBwchCiK2vZwhBCyodGSjPXJ7Xavug3b8lmihBCy0VlxUb5RVBQJIYRUlRXHCo2iokgIIaS6qCgSQggheQ4br/SjokgIIaS6LHhOolFUFAkhhFQVtRQJIYQQVY1qoiAIiMfj2iHt6XQaXV1dZR2xtxoqioQQQqqrRi1FWZYhCAJ4ngfLstqB89VERZEQQkhV1XJJRjabLftM23JQUSSEEFJVjos0pkgIIYTk1XCiTTKZhNPpRC6XQyaT0R3RVw1UFAkhhFRXGTVxfn4e8/P6Q7NLHZiubrvJsiyA/L7Ufr8fqVSq8qzL0HmKhBBCqmr3b/9vw8++7Z6LOHz4sO7aoUOH8MADD6z6ubIso7W1FVNTU1UbZ6SiuAHY8cBeO2a+9/a/MjuGYd889WlbHn77nmv/3OwYZfnG839ry+/ltdp9x6cNP/vVb3OGW4o8zxfMNnU4HJiYmFj18AajqPuUEEJIdZWxo02pAricLMvw+/3IZDJa96l6ELz6cTVQUSSEEFJVtdjRhmEYhMNhXQFUz7qt5hINKoqEEEKqq0ajcpFIRHeY+/nz56s6yQagokgIIaTaarROUW0t1hIVRUIIIVVFG4ITQgghKiqKhBBCyCIqioQQQsgiKoqEEELIojLWKVoNFcUaU6cPZzIZAEA8Hi/5rNfrRTqdrksuQgipFZpoQ4riOE63g3soFCpZ+HiehyAI9YxXkUDfHszJrwAAGpkrkBx62OREq7Nj5qU+c/SP8KkD/2h2jBVd2bIF7/jgW3HX73bhU797xOw4hvnu6wYAXLP9NwAAf8uNmBlnVbb5XrZxUdxkdoD1SpZliKKobUME5IuiIAiQJKng2VwuV+eE5Qv07QEAPDIs4JFhAU+JWXz8aNDkVCuzY+al7vTcDPcdLrNjrGjHW96Id3zwrbiy5Qo0tV5pdhzDPvKpPeAfHAX/4KhWDP+fL/2pyalKs9X38oJi/GUxVBRraHx8XFcAl+/Xp0omkwgEAgWfL8syQqEQEokEQqEQYrEYBEGA32/OBsMf6v8gvp641JqdHD2N9wW9pmQxyo6ZVVc2bUZTyxazY6zq6R/9HN/4h2/j3JlfmB3FsCubt8B12/W4svnS3+8j//w9uN9xE7a+oc3EZKXZ6nt5YcH4y2KoKNYIwzCYmprS7dyudo8u3btPEAR4PJ6i79Hb2wuv14tgMIjBwUHE43F4PJ6qb2tkxNb2q9DU2oiXp18puLer+7a65zHCjpmXesfuW/Cdxx43O8a6tfPNb9AVwHPPnAcAXaG0Ctt9LyuK8ZfFUFGso2g0ing8rtu8Vpblkju88zyvFVWGYSBJUkHXa71cw15d9Prs1BwaGWt2mdkxs2rXb7GY/E9z/rfeCF6e+RUCt3DI/Pg57dquu24EcKk4Wontvpep+5SshuM49PT0IBi8NAag7vBeitvtLuhqreYRKdUwm5tDk7PR7BhlsUPmK5s249zzU2bH2FACf+bF/xv+El6e+ZXZUQyz7PeysmD8ZTFUFOuA53m4XC7dRraiKKKzs3PFzwuFQohGo5BlGbFYbMXlHAAwPz+PmZkZ3Wv54Z3VZsl/kKuweub37OvAd4WfmB1jQ/nIp/bgOw+L+MYXv292lLJY9nvZxt2ntCSjxtRxRLWFqM40zeVyEEVRu6+uY4zFYmBZVmtBDg8PQxAE+Hy+VVuJ0WgUhw8f1l07dOhQVf4cL0gvFr3e1NpY8p7Z7Jh5x03X4KnHz5odY0O5872344Wf/9LSBdF238sW7BY1iopiDYmiCFEU4fP5tLFAnucRDAbBsqxugo0oikgkErrW5MTEBILB4IpdrEtFIhEcPHhQd62hoQHv+/Tvr/nPci77Eman5rC1/Sqcy76kuzc5enrN718Ldszc1LIFrjdtw+2LyzC2XdcKAPD98Z0491yOWpBVdvviOKJaEK9s3oIm5grLjSva7nvZgi1Ao6go1ogsy+ju7oYsy+A4Tndv+XlgPM9jZCS/TorjOHi9Xng8Hni9XjgcDjAMA6fTCYZhEAqFdOOSSzU0NKChoaE2fyAADw18GW7Pm/HIcL51e9e+O/C1hLV34LFb5sn/kjD5X5cm2Oy46Rr8jq8L/D9818RUxthpjSIAuG69DjtuvQ7f/fopbRbqne+93bItRlt9L1NRJMupSzKM8Pl8Ba1BWZYxMjKCqakpMAwDWZYhSZJWYEsVxlpKDj2MQN8e3LXvDgDAjV0ufO5Aou45ymHHzKo7PTfjnffcCgD4yMe9mPzPjK5gWsXW7b+JOz/QiXfufSt27tqOjxz240kxi+9+ZdzsaCVd2bwF0ZE/RxNzBT76v35Xd49/cNScUKuw1feyjYuiQ1FsnH4di8VicLvdBWsYRVFEPB5fddLNUt5N5iz2r1R6IWXLzPfe/ldmxzDsm6c+jXub/8TsGGX55szf4z3X/rnZMcryjef/1pbfy2v1nq0fM/zsN8793Zq/XjXR7FOLcrvdRRfpC4IAr9eiu1gQQghAs09J9aktRHU2KgBIkqSbmUoIIZZEs09JLXg8npJbwBFCiFUpFlyUbxQVRUIIIdVFLUVCCCFkkQXHCo2iokgIIaS6LHgklFFUFAkhhFQXtRQJIYSQPIVaioQQQsiii1QUCSGEkLwaLsmIxWLaQe2yLBfsJb1WtKMNIYSQqlIWFMOvcsRiMQD5vZ+DwSDcbjdCoVBVs1NLkRBCSHXVqKUYjUaRzWa1j9XThMrZC3o11FIkhBBSVbVoKUqSBFmWta7TpdTD2quBWoqEEEKqq4yW4vz8PObn53XXip0Nqx7Uvpx6tF7VKIRU4MKFC8qhQ4eUCxcumB3FELvlVRTKXC92y2y3vKs5dOiQAkD3OnToUMFz6XRaKVayWJZV4vF41fLQeYqkIjMzM2hpacH09DSam5vNjrMqu+UFKHO92C2z3fKuxmhLUT02b3nJam1txeDgYNUOXqfuU0IIIaYpVgCLUY/QW06W5ZL3KkETbQghhFgey7JgGKbo2GI1j9ijokgIIcQWIpGIbqYpz/NV6zZVUVEkFWloaMChQ4cMdXtYgd3yApS5XuyW2W55qykcDkOWZfA8D57nMTY2VtU1igBAE20IIYSQRdRSJIQQQhZRUSSEEEIWUVEkhBBCFlFRJOvSmTNncOrUKczMzBRcX37NimZmZnDixAmcPHnS7CgbwokTJwDAsn/fPT09K94/duwY7rvvPu3PQSpHRZGsmdV+kOzevRsulwtutxutra34/Oc/r91rbW1FPB7HZZddZmLCS2ZmZrB79260tbXhQx/6EGZnZwEA27dvh8/ng8fjQVdXly0K+XJ2+gGtrn1Lp9MmJylOXXZw5syZgnvDw8NIpVIIBoPIZDK2+nu3Ipp9SooyWuhkWUY0GsXY2FiNExlz4MABAEAoFALLskin0xgYGMCHPvQh3H///QDyPwB37NiBhQXzTwcPBAJwOp1wuVwYGxvD9PQ0/H4/FEWBx+PB1NQUkskkpqen8eCDD5odtyyRSATRaNTsGADyvyiNjo4aevbixYs1TlO+oaEhxONxSJIEh8OBeDyO/fv3AwC6urowPDyM22+/XXu2r6/PxLT2Rtu8kaJ8Ph9cLpdun0FRFOF2u3XPiaKIjo6Oesdb0dGjR7X/9vl88Pl86O/vx7Fjx7B//344HA44HA4TE17CsiwGBga0j48fP45sNqsV8Pb2drjdbgwNDZkVscDu3bsxNTW16nOiKFqmKD722GPgeR5TU1MIBAK6e4qioL+/H6FQyDJ5l8tkMpiYmEBLSwuAfOE7c+YMtm/fjqmpKd02Z9Xc8mwjoqJIigoEArricvz4cXg8Hu0fpWp6ehrj4+P1jlfSjh07il4fGBjA8ePHceLECezatavOqUpbnnffvn0YHh4ueM4qRRwA/H7/qvtNnj9/HolEoo6pVufz+TA9PY1UKgWPx4Pt27dr93bs2IFdu3atOnZnls7OTt2/vWAwiNHRUa0oLt0Y3ErfK3ZERZEUtbQgAvl/aMsLIgC0tLRY6h+h2rKdmZmBJElalxKQLzijo6Pged6kdIUURcGZM2fA87zWOlRbMjMzM8jlcti+fXvByQBmCgQCSKVS2Ldv34rPTU9P1ymRcS0tLdi/fz9GR0chiiL27t2ru7/an8ks4+PjcDqdcLvd2pCF+svT1NQUZmdn0dTUBKD0uYPEGJpoQwzJ5XIl71X1gM816uvrQ39/P3w+H7q7uwvud3d3o7u7u2iBN0MgEMDAwIBugoea7TOf+QxcLhcikUjV93dci5aWFkNd5j6frw5pKtPd3Y29e/dieHgYp06dMjvOqgYHB/HQQw+BZVn09vbC4/FgZGQE9913H1KpFPbv34+TJ0/ivvvuKxjiIOWhiTbEkKGhIbS2tmqD+6pjx45BlmWtlWMVaiulVPGbnp62TGEsZXp6GpIkWaq7dz2anJxELpcr+kuUXUxPTyORSMDtdtv6z2EFVBSJYf39/YjFYmhtbYXT6YQkSQgGg7abFUnIenDixAns3bsXJ0+exN133212nHWDxhSJYQMDA7qjW9xuN9rb201ORcjGtHRtJRXF6qGWIjHs1KlT2lqpRx99VJvJt7xLlRCydnZfW2lXNNGGGHL8+HGEw2G43W5tjEudyUc7aBBSfY899hhGRkZw9OhR5HI53ev8+fPo7e3F+Pi4ZWfM2hV1nxJDJEnCY489BgAFv71SZwMhtWHntZV2RS1FYojL5Sp5z8juJoSQyqg9MqX2NaWWYnVRUSSGLP0HuXSx/okTJ5DJZMyKtapTp07hwIEDuOeeewDkp64fO3bM5FSl2S0vQJnrxW5rK+2KiiIxpK+vDw899BAuu+wy+P1+dHV1oa2tDfF43LL7RarjoB0dHbYYB7VbXoAym6G3txeKotD61RqhMUViWDKZhCRJGB0dhSzLll8obLdxULvlBShzPZw5c0bba1bd43TXrl3IZrOYmZnR7XtK1o5aiqQs6jZTfX19WkG06m/XdhsHtVtegDLX2kpngzqdTkudDbpeUFEkhp06dQonTpzAsWPHdC+rdp/abRzUbnkBylxLBw4cAMuyGB8fx9TUFEZGRvDggw/iyJEjAPJdvvv27bNk69bOaPE+MaS/vx88z4NlWTAMo12XZRkTExM4f/68eeFWEAgEcPz4cTAMA5ZlIUkSOjs78eijj5odrSi75QUoc60cOHCg4LQaIP9vcceOHdi/fz+y2Sx27NhBi/eriIoiMWSl07ytftK3ncZBAfvlBShzLRw5cqTkRvvHjx+Hw+HArl27qChWGXWfEkNWOlDWygURADZt2oS2tja4XK4Vx5Oswm55AcpcC0vPBl2+BGPfvn1oaWmx1Nmg64ZCiAGTk5PK6Oho0XtDQ0N1TmNcKBRSHA6H0traqrS2tiqbNm1S7rvvPrNjlWS3vIpCmWuJ4zjF6/UqTqez6P2JiQmltbW1zqnWN2opEkP279+PYDCIyy67DDt37tReO3bsAMdxZscrqr+/HwzDYGpqSrdnZHNzszZZwUrslhegzLU2MDCAVCqlnYixnNvtRjabrXOq9Y2KIjEsHo9jfHwcyWRS97LqNlNtbW0YGBjQHSbMMAwGBgYsOWPPbnkBylwPLS0tKx6IbfXDsu2GFu8TQwYHB0tORIhEInVOY8zSWbLLud3u+gUxyG55AcpM1h9qKRJDEolEyXu7du3CsWPHcN9991lqIb/D4cCZM2cKrs/MzGB6elp3zQrdZnbLC1Bmsv7QkgxiyOjoKLq7u3HmzBnd8TUAMDw8DJ7nMTAwAEEQ4HK5sHfvXnOCLqEe0rr8t39RFHXXFEXB5OSk6dPa7ZYXoMxk/aHuU2KIKIoIhUKQJAkOhwPxeBz79+8HkG9FDg8P4/bbb8euXbswNDRkctq8XC6HZDK5YncZkP/hF4vF6hNqBXbLC1Bmsv5QUSSGZDIZTExMaIP6Q0NDWqtxampKt45xpTWN9bTSOOhyS7f7Movd8gKUmaw/NKZIDOns7NTNcgsGgxBFEUB+E+WlO/Vb5QdJOQuyrbCbid3yApSZrD9UFIkh4+PjOHHiBM6cOYNTp04hGAzC4/EAyBfF2dlZ7dlSa6rqLRQKmR2hLHbLC1Bmsv7QRBtiyPT0NHp7e8HzPDo6OhAMBgHkxxo9Hg+SySRCoRBSqRT8fj/uvvtukxMDO3bs0E4a8Hg8lj93zm55AcpM1h8qiqQqpqenkUgkLLWxcjabRXt7O4D87FlJktDW1maJmbHF2C0vQJnJOlTvfeUIMRPP84rT6VQCgUDJvVytxG55FYUyE3ujokjWzKobgqs/3CYnJ5X+/n7F6XQqO3bsUGKxmCJJkiLLspJIJJTjx4+bnDTPbnkVhTKT9Ye6T0lJPT096Onpwd69e7Fjx46is0oVRUE2m7XkAmen04m2tjbkcjkEg0EEAgHs2rWr6LPHjh3T1l2axW55AcpM1h9ap0hKam9v1xY4syyLwcHBgmcURcHAwECdkxmjZl5tjPPYsWN1SrQyu+UFKDNZh8xtqBK7EEWxontm4nl+1WdkWVZCoZAyOTlZ+0CrsFteRaHMZP2h7lNiiPpbc2dnJ26//XYcOXIEDz30EFwuF4aHh20xrT2bzWJychJut7tg/1YrsltegDIT+6PF+8SQqakpeDwe3H777RgaGkI8Hsfo6CgSicSKJ2iYafkJB+3t7di7dy8URbFk15jd8gKUmaw/NKZIDGFZVvstemRkBKFQSNv2TV3zZRft7e3IZDJmxzDMbnkBykzsi4oiMaS1tRVAfpG+KIq636itstcpkD/GKp1OI5vNQpIkjIyMFDwjSZK2I4/Z7JYXoMxkfaMxRWLI8PAwHA4HkskkAOCxxx7TXbfatPVEIgFBEIruc8myrOVat3bLC1Bmsj5RUSSGHT9+HACwb98+APnJN7Isg2EYyxVF4NLByHZht7wAZSbrDxVFYsh6mH1KCCGrodmnxJBis09Pnjxp6dmnhBBSLppoQwwpNvtUbR3SOAwhZL2gliIxZPnsU/WAYcBas08JIWQtqKVIDMlkMpAkCclkUutGBS7NPiWEkPWAWorEkN7eXrS2tiIUCumWY8iybG6wCp04ccLsCGWxW16AMhN7otmnpGLZbBaiKMLhcFj21PJTp05BkiTkcjnd9Xg8jrGxMZNSlWa3vABlJusLdZ8Sw2ZmZiAIQsEPknQ6bcmi2N/fD57nwbKsdgQWAMiyDEmSzAtWgt3yApSZrD9UFIkhk5OT8Pv9YFkWuVwOLMtClmVMTU0hlUqZHa+otrY2PP3000XvDQ0N1TnN6uyWF6DMZP2hokgMSSQS2g+SyclJsCyrbQh+8uRJSx65w7JsyXt9fX11TGKM3fIClJmsPzTRhhiydAkGy7KWbR0u5XK5cPLkyaL3lh8fZAV2ywtQZrL+0EQbYsjQ0BD8fj8EQcD+/fsRCATwP//n/8Rb3vIWRCIRRKNRsyOiq6urYDbs+fPnMT09rWsdKIqCbDaLixcv1jmhnt3yApSZrH/UfUoMCQaDiEajyGaz2L9/P/r7+/Hud78b09PTCIfDZscDkN9gYGBgAE6nc8XnFEXBwMBAnVKVZre8AGUm6x+1FEnFpqenIUkSdu3aZXYUAPmxTqNZynm2VuyWt9wclJnYERVFsmYnTpyw5JKM5dSF2XbICtgvL0CZif1RUSSG2WHB8/T0NHp7e7XxogcffBDT09Po7OwEwzAIBoMQRRGDg4OWOO7KbnkBykzWOYUQAziOU1wul+L1ehW/36+9vF6v4nQ6zY6n6e/vV2RZVhRFUSRJUhKJhLJ7926lo6ND99zQ0JAZ8QrYLa+iUGayvtFEG2KIXRY8L10/2d7ejkwmA0EQkMlkdM+pz5jNbnkBykzWN1qnSAyxy4Ln6elp3X/HYjH4fL6CzQWWPmcmu+UFKDNZ36goEkPssuC5paUFMzMzAKBtS+fxeHTZZ2Zm8Mtf/tKsiDp2ywtQZrK+0UQbYkhnZydkWUY2m7X8guehoSGk02kwDIPh4WG0tLSgv78f09PTaGlpweTkJFKplGUmVNgtL0CZyfpFRZEY0tnZicHBwYIF0MrigudkMmlSMuOy2SwkSUJ3d7fZUQyxW16AMhP7o+5TYsjg4CC6u7uxa9cu3cvtdiMSiZgdz5D29nZtzKhUV7CV2C0vQJmJ/VFRJEWdOnVK9/FKv0XbaQcQ9by8dDptchJj7JYXoMzE3qj7lBTV09ODkZERs2OUbffu3RgdHTX0rBXGQe2WF6DMZH2jokiKcjqdCIVCcLlcUBQFDocDQH4MUT1xwOFwIBgMWm5iAs/zmJqaQiAQ0F1XFAX9/f0IhUKIRqOWGQe1W16AMpN1rP77BRA74DjO0HOxWEzJZrO1DVMBWZaV4eHhgmzqjiU8z5uQqjS75VUUykzWJxpTJEUZPUKnr68PPM/XOE35WlpasH//fmQyGW3D56X27dtnQqrS7JYXoMxkfaJt3si6pk4QGh4eRldXl8lpVme3vABlJusLtRRJUeruH0acP3++hkmqo7e3F4qi2GamrN3yApSZrA800YYUdeTIEdx///2rPnfs2DFMTU1Zav9TQgipFBVFUtSOHTvg9XpL3pckCZIkgWVZPProo3VMRgghtUNFkRTldDrBsmzBtm4qlmXh9XppYgIhZF2hokiKGhoaoi5RQsiGQ0WRFJXNZtHe3m52DEIIqSsqioQQQsgiWpJBCCGELKKiSAghhCyiokgIIYQsoqJICCGELKK9TwmxGEmS0NHRAY/HA5ZlAeSPPWIYBj09PTh//jwEQQAATExMVPx1YrGY9t/nz59HKBQCz/MIh8Nr+wMQYmNUFAmxGFmWEYlEdMWJ53l4PB7dtZV2HFpNKBRCKBSC2+3Wrvn9ft0zHMdBkiSkUind9UQigWAwWPHXJsTKqPuUEIvJ5XIFRYdhmILnlhexciSTSV1BBPInRizl9XrR09NT8LnpdLrir0uI1VFLkRCLkWW5aBFcjmVZw88W+xrq3rUqhmF0xyh5PJ6Cz0skEpAkqeyvR4hd0OJ9QmxAHWMcHBwsuBeLxbTiJkmSoTHBjo4OyLKMeDxetPiJoqh1n2YyGQCAIAiIx+MQBAGRSAQAdF9LzaEWW5/PV9GflRBTKYQQy3O73Uo4HC647vP5lHQ6rX2cyWQUj8ez6vtlMhmFZVkFgAJA8Xg8uvdRFEWZmJhQWJbVXUun04rb7S6aI5VKaR97PB5lYmJi1RyEWA2NKRJiU6IoQhAEXUuPZVnkcjltdmopLMsik8kgnU4jHA4jl8vB6/WC5/myc0iSBJ7ndS1Dv9+PeDxe9nsRYjYaUyTEpsbHx3VjgiqWZZFOp4t2iy7n8Xi05ziOQ29vb9ndnoIggGEYXSHOZDI09khsiYoiITYly3LFnycIQkHxGxwcRCwWK2vyjiRJkGUZLMvqirCRgkyIFVH3KSE25fF4irbGJEnSzSItZmxsrOh1lmXLms0qiiLcbnfRHJUWbULMREWREBuQZbmgyLjdbng8Hl23pSiKALBqF2gikSgYdyzWelxOnV0K5IuvmqGzs7NgPDKZTK74XoRYES3JIMTCYrEYzp8/ry13CIVCBcsdOI6Dy+UCkB/LK7ZsYylZlpFMJsGyrFZEVeoSC1EUEY1GwfM8BgcHdUsvOI4DALhcLt0mA2oOp9MJYPXCTIgVUVEkhBBCFlH3KSGEELKIiiIhhBCyiIoiIYQQsoiKIiGEELKIiiIhhBCyiIoiIYQQsoiKIiGEELKIiiIhhBCyiIoiIYQQsoiKIiGEELKIiiIhhBCyiIoiIYQQsuj/B740btDNx+5DAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FswuqvVjzbZd8f7KPLR0scvEjk2Dk4kcmfmQSjFz8yAS7jak7WxgfH2+77Lpu4HbLy8uDaM6OJItJnfmnZ1oH8vQqdktMlmlpojAhc8SUNWYpPh5X4q6ELNPqc2sHh1z8yCQYufiRiR+ZBCMXPzLBbmNEfwtBI/hB6vX6LrdkZ+L/Oa78T+UD1/A1R8yOc/kaFxutbSSpmCyq/PNlWWOWKssVJYtJjf+H8cDRhP2AXPzIJBi5+JGJH5kEIxc/MsFAhH3Grr0ul8u5jz/+eOvy+v/3fOxjH3NPnTo1yGa12ersdakvptxyvdxx/8RCouMptfNLeVe/1nyZrD9tt2f14qqbWEi4xsPGNloeLnLxI5Ng5OJHJn5kEoxc/PZDJpwZNxoY0d/Cpz71Kf3Gb/yGpqam9Mgjj6hcLuupp57S6dOndfLkSR07dkyNRkP3339/2E0NlHs6p+T7k76f8OxVWxW7IkmKm/GO6/HWG/XWvhW70trHY1xvqJhsnthjP63pSy5+ZBKMXPzIxI9MgpGLH5lgkCj0u7C0tKRkMqnZ2Vnl83nF43ElEgnl83k9/PDDW545Nyyls6XW/L2Nai/WWj/7nXj/CTUuNgI7hNLXSkpPpFuXi18t+raRFPjT415FLn5kEoxc/MjEj0yCkYsfmWDQOBi3S7Ozs5qdndXy8rJs25Zpmq2VePai2os15at5Je9KqlC9uiSod7T+/FfnVU1VJTW//Z/6yCnfWrzeCTgSdyVa1y2cXVB6Mt3WgXj3ud2DiQaJXPzIJBi5+JGJH5kEIxc/MkEYhly3wzIy2DfOnz+v4eFhra2t6ejRo5KkkexI640exBwxVf+X7QcQV+yKyvWyxkfHW/vO/vBs63avg/FGGLwDgpzXnbbt9jJy8SOTYOTiRyZ+ZBKMXPz2WyZBtQX2Hwr9PnnggQf0uc99LpTH5s0IAAD6idoiGpi685Znn3122/s2Gg0tLCyEVugDAAAAG1Hov+Wee+7R2tpa4AmxvLX0N7sNAAAA2EtYdecto6OjWlpa0urqqq5cudL6t7q6qpmZmdZtG/898cQTmpmZ0erqathPAQAAAGhhRP8tiURCd999t+/6YrGoXC7XcX6aZVmamprSwsLCnl1LHwAAAAcPI/pvefjhhwOvd113y4NQhoeHA6f1AAAAAGGh0N9Ct3PwmasPAACAvYRCfwtf//rXu9quXq9vvREAAAAwIBT6W4jH43rggQc23ebkyZOampoaUIsAAACArXEw7haOHz+uJ598UseOHWsdeGsYhhzHUb1e18LCgk6cOKF777037KYCAAAALZwZt0uVSkUPPvigarVa6zrTNJXNZnXfffeF2DLOXgcAAPqL2iIaKPS3YXl5WWNjY2E3o4U3IwAA6Cdqi2hgjv427KUiHwAAAAhCod8n09PTYTcBAAAAaOFg3C49++yzajQagbc5jtM2dx8AAAAIG4X+FpaXlzUxMSHHcTbdjhNmAQAAYC9h6s4WMpmMTp06pdXVVV25cqXjv+PHj4fdVAAAAKCFEf0tTE1NdbV8ZjweH0BrAAAAgO4wor+FkZGRrrb71Kc+tcstAQAAALpHob8F13V1/vz5Lbc7ffr0AFoDAAAAdIdCfwszMzNaWFjQs88+u+l28/Pzg2kQAAAA0AXm6G/hQx/6kKTmQbmO48g0TRmG0baN4ziybTuE1gEAAADBKPS3sLi4KMuyNDMzo2PHjgVu88orr+ixxx4bcMsAAACAzij0t2CaphYWFrbcbnl5eQCtAQAAALrDHP0tFIvFrrY7derULrcEAAAA6B6F/hbGxsa62o4RfQAAAOwlFPp9kslkwm4CAAAA0MIc/XUeeOABjYyM6Dd+4zda101NTW25H6vuAAAAYK+h0F/nySef1KFDh9oK/dXVVVmWpfHx8Y77ua6rbDY7iCYCAAAAXaHQX6der/uuM01Tv/Vbv7XlvktLS7vRJAAAAGBbhlzXdcNuRBSsra1peHg4lMc+f/68hoeHtba2pqNHj4bSBgAAEB3UFtHAwbh9ElaRDwAAAASh0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9Lt07tw5PfbYYzp58mTrurW1NT311FMhtgoAAAAIRqHfhQcffFCmaWp2dlaFQqF1/fDwsEZGRvTII4+E2DoAAADAj0J/C6dOnZJt26rX62o0Gm2FviTdfffdmpmZ0WOPPRZSCwEAAAC/a8JuwF5Xr9e1sLDQujw0NOTbxhvZBwAAAPYKRvS3MD4+3nbZdd3A7ZaXlwfRHAAAAKArFPpbCBrBD1Kv13e5JQAAAED3KPS3sLq6qtOnT7cuBxX+DzzwgCYmJgbZLAAAAGBTQ26nuShomZyc1NDQkKanp1Wv15VMJuU4jhYXF1UoFGRZlubn50Nr3/nz5zU8PKy1tTUdPXo0tHYAAIBooLaIBgr9LuVyOT344IOSmqP6ruvKMAxls1nNzMyE2jbejAAAoJ+oLaKBQr9Hy8vLsm1bpmlqbGws7OZI4s0IAAD6i9oiGpijv4Uf+7Efa1sjf2xsTMePHw8s8k+fPq3JyUl96EMf0rPPPjvAVgIAAADtWEd/C8lkUvfff/+W2z3zzDO6//77derUKQ0PDyuTyahYLPItGAAAAKGg0O/S6dOnNT8/L9M09XM/93P64Ac/2Hb7gw8+qGw2q/vuu09Sc/39QqGgT37yk2E0FwAAAAccU3e2MDMzo9HRUSUSCdXrdRWLRcViMX3+859v225paUnxeLx1eWxsrOPJtQAAAIDdRqG/hccee0zZbFZXrlzR0tKSvv71r2tlZUVPPPGEzp8/39rOcRzdfvvtbfsahjHYxgIAAABvodDfwte//nXf8pnespqVSkWStLa2Frhvt2fVBQAAAPqNQn8Lx44dC7zeW0t/M47j7EKLAAAAgK1R6G/BMAw98sgjbdedO3dO6XS6NSfftu3Aov+VV14ZSBsBAACAjSj0tzAzM6O/+qu/0uHDh/X93//9OnbsmMbHxzU2NqZKpaJTp04pmUwqm822rbd/+vRpTU1NhdhyAAAAHGScGbdLlUpFlUpF4+PjsixLY2NjWltb09LSkiYnJzU8PKwHH3xQy8vLGh0dlW3beuKJJwbSNs5eBwAA+onaIhoo9HfgzJkzsm1bU1NT+oEf+AFJ0uOPP65Go+E7gHc38WYEAAD9RG0RDRT6ffDMM8+oWq12dQbd3cCbEQAA9BO1RTQwR78PTNNUsVgMuxkAAABAC4V+F5566ilNTU3p2LFjvn+HDx/W6Oho21lxAQAAgLBdE3YD9rpnnnlGiURCqVRKqVRK1WpVExMTGh0dVaPRULVaVTwe13333Rd2UwEAAIAWCv0tFAoFLS8va3h4WJI0OTmpkZER3X777ZKay28uLy/rqaee0j333BNiSwEAAICrmLqzhVgs1irypeYJtCqVSts2Y2Njsm170E0DAAAAOqLQ38LQ0FDb5bGxMVWr1ZBaAwAAAHSHQn8Lq6urkqRz587pqaeekiQNDw/r85//fNt25XJ54G0DAAAAOmEd/S2sra1pbm5OpVJJq6urWllZkeM4Mk1T3/d936fjx4+rUqlocnJSn/vc50JpI2vdAgCAfqK2iAYK/S4988wzMgxDY2NjkiTbtpVMJvXMM88oHo/riSeeCK1tvBkBAEA/UVtEA4V+BPBmBADggHFdacNxhP1EbRENzNEHAADYT86elX7915v/BTbBOvpvefzxx1sH3q43OTmpH/iBH2i77vTp07Isi2+4AABgsM6elR59VKrXpZdekj7+cemuu8JuFfYoCv23xGIxZTIZPf7445KkVColy7JkmqZv27vvvlvz8/OybVvpdLp18qxIWFmRLl/ufb/rrpOOHet/e/YCMvEjk2Dk4kcmwcjFj0y25hX5r7wiTU1Jzz/fvEyxjw6Yo7/O8vKyMpmMstls66DbrXz2s5/VxMTEts6KWygUVK/XVavV1Gg0ZFmWstlsz/fTt3l0KyvSZz4jOU7v+xqG9Cu/Er3Olkz8yCQYufiRSTBy8SOTra0v8u+8szk/33Wl556Tbrqp78U+c/SjgRH9dR588EEtLCz0tM+nPvUpPfjgg5qcnOzpjZDJZJROp5VKpSRJjuMomUxqZGREy8vLMgyjp3b0xeXLzU72hhukG2/sfr/XXmvut52RmL2OTPzIJBi5+JFJMHLxI5PNBRX5UvO/d97ZLPYZ2UcACv23PPLII3r44Ye3te/Jkyc1Nzenubm5rrYvlUqanp5umxZkGIaKxaJGRkaUTCbDPQHXjTdK73hHb/tcvLg7bdkryMSPTIKRix+ZBCMXPzLx61Tkeyj2sQlW3XnL4uJi19N1NhoeHpZt2z09ViwW811vGIZSqZQqlYqc7fx8CQAAomOrIt/jFfuvvNLcntV48BYK/RAUCgXF4/HA2yYmJiRJS0tLg2wSAADYS7ot8j0U+whAof+WXkbkd7r/5ORkx9u8kfzR0dEdtQcAAOxTvRb5Hop9bMAc/be4rqvz589v+8jyXhYv2mz+fb1el6TAqT2eS5cu6dKlS63L58+f7/qxt61QkF59Nfg215WuXJGefFI6FLHvjleuSGtrzefVyxkIycQvyplI5BKETIKRix+ZXPXd7zY/b69cka65Rvrylztv+/a3S28t6tHCnH2sQ6H/lunpaRUKBX3yk5/sed9HHnlE09PTfWlHoVBorcTTydzcnB566KG+PF7XXn1VunBh821ee20wbdlPyMSPTIKRix+ZBCMXPzJpNzQk3XGHtLgonT4tve99vX2BQmSwjv46o6Oj+qM/+iN98IMf7HqfZ555RpZlaXl5ecfrzGYyGZVKJVWr1U2X1wwa0b/11lt3vtbtiy9KJ0821yLeuOpBNyP6w8PRGVHx7HSUiUyuinImErkEIZNg5OJHJldtHNHfTNCIvtSX9fVZRz8aGNFfp1AoKBaLqVQq6aMf/eiW2z/++OM6ceKEisXijt8EtVpNhUJhyyJfko4cOaIjR47s6PF6ttmvDBcuNE92Mjcn3XLL4No0CJt9+dkMmfhFOROJXIKQSTBy8SOTdtudoy/t6km0sP9E5OtvfyQSCX3uc5/Tfffdp6mpKf27f/fv9Oyzz7bmwJ8/f17PPvusHnnkEU1NTenEiRN6+OGHde+99+74sZPJpM6cOdO2tj4AADiA7rqrWaTfdFOzaO928gVFPjZgRH+DVColy7KUTCb1qU99SkMB36Jd15VpmnryySd1/PjxHT9mPB5XPp/f9ABcAABwgHjF/qOPNov3rUb2KfIRgEI/gGmaqlarKpVKqlQqWlpakuM4MgxDk5OTisfjuu+++/ryWOl0WplMRpZl9eX+AABARHRb7FPkowMK/U0kEgklEoldu/9cLqdkMukr8m3blm3b4RX/va5ecBBWOyATPzIJRi5+ZBKMXPzIxG+rYp8iH5ug0A9JqVRSLBYLLOZrtVo403iuu04yDMlxpIsXe9vXMJr7Rw2Z+JFJMHLxI5Ng5OJHJpvrVOxT5GMLLK8Zglqtpkwmo2Qy2Xa9d1bc+fl5VavVru+vr0tgraxIly/3vt911zVXS4giMvEjk2Dk4kcmwcjFj0y2tn41njvukJ5/fteKfJbXjAYK/RCMjIy0ivogpmm2zpDbDd6MAAAcEF6xX69L4+O7NpJPbRENTN0JwerqathNAAAA+5E3jef0aenee5mug01R6AMAAOwnd90lve99vZ1ICwcSJ8wCAADYbyjy0QUKfQAAACCCKPQBAACACGKOftQVCs0j82s1qdGQLEvKZjtv7zhSJtNcl3iz7fY7cvEjk2Dk4kcmfmQSjFz8yAQDRKEfZZmMlE5LqVTzsuNIyaQ0MiItLzc7jfXb2rY0NSVVKs2OJ6rIxY9MgpGLH5n4kUkwcvEjEwwY6+hHQOBat6WSZJrSxjPsOk6zQ7EsqVwOvsOJCWlyUsrnd7XdoSAXPzIJRi5+ZOJHJsHIxW+fZcI6+tHAHP2oWlz0dyZSc7QglWqODmxy0q7IIhc/MglGLn5k4kcmwcjFj0wQAgr9qCoUpHg8+LaJieZ/l5YG1569glz8yCQYufiRiR+ZBCMXPzJBCCj0o2pysvNt3ojB6OhAmrKnkIsfmQQjFz8y8SOTYOTiRyYIAQfjRlWneX5S82h/KfgnxK3Y9tV5ho1Gs3MyzeZPkvthNQBy8SOTYOTiRyZ+ZBKMXPzIBCGg0D+ICoWrR/z3wlviq1j0359t96VpoSIXPzIJRi5+ZOJHJsHIxY9MsEuYunPQZDLNb/rb+ZZfqQT/rJhK7f+fG8nFj0yCkYsfmfiRSTBy8SMT7CIK/YOkVmt+yy+X29fq7ZZpSgsLzfvZKJnccfNCQy5+ZBKMXPzIxI9MgpGLH5lgl1HoHyTJpHTmTLNj2I5YrLnO78SEND7ePOlHqdS8bT+fyINc/MgkGLn4kYkfmQQjFz8ywS6j0D8o4vHmiTa2c6DPesVic+TBspo/GSaTzc4laDRhPyAXPzIJRi5+ZOJHJsHIxY9MMAAU+gdBOt2cA7jTb/fegT2W1eyc6nVpdbXZSR0/vvN2Dhq5+JFJMHLxIxM/MglGLn5kggGh0I+6XK757X5jZ2LbzW/+vahU/PsYRnM0YXR0fx3hTy5+ZBKMXPzIxI9MgpGLH5lggCj0o6xUujp/b6NabXtzAjcu4eXZ6U+Pg0QufmQSjFz8yMSPTIKRix+ZYMBYRz+qarXmz3jJZPOIfo939r35ealaDd7XcZon3QiysND8yXF9B+Ld53YPJhokcvEjk2Dk4kcmfmQSjFz8yAQhGHJd1w27EdiZ8+fPa3h4WGtrazp69GjzypGRq2/0IKZ59Ux8UvOnxMXF5s983gE8ltX8CXB6Wkokrp55z/sp0Ot0HEeane3zs9ol5OJHJsHIxY9M/MgkGLn47bNMAmsL7DsU+hHAmxEAAPQTtUU0MEcfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEIfAAAAiCAKfQAAACCCKPQBAACACKLQBwAAACKIQh8AAACIIAp9AAAAIIIo9AEAAIAIotAHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAii0AcAAAAiiEI/RI7jKJ1OK5PJhN2UrrzxRtgt2HvIxI9MgpGLH5kEIxc/MvEjE3SDQj8EmUxGyWRShUJBlUpFjuOE3aQtfeMb0mc+0/wvmsjEj0yCkYsfmQQjFz8y8SMTdOuasBtwEGWz2db/z8/Ph9iS7pXL0tKSdNtt0v33d7eP40iZjGQY0rqn7FOpSMViczvH2Xr7vfLY28kkbLudy37MxLOb2eznXKTdyWa/ZyKRy2b6mU1UMvH0I5uoZYLdQ6GPLZ07Jz39tDQ83PyvZUm33955+0xGsm1paqrZUVlW521zuWaHVS63X5dMNju4Xg3qsXvNJGyDyGW/ZeLZ7Wz2ay7S7mWznzORyGUz/c4mCpl4+pXNZz8bnUwwAC5CFYvF3FQqtaP7WFtbcyW5a2trfWpVu1OnXPejH3XdX/3V5n9Pnep+31jMdTs9vWrVdSXXrdf9txmG6xaL22vvIB57J5mEbbdy2c+ZeHYjmyjk4rr9zSYqmbguuWymH9lELRPPTrL52McGk8lu1xYYDOboY1PeaMp73iMNDTX/+/TTzet3am5OisUk0/TfduKElM/v/DF247F3M5OwbTeXKGfi2U42ByEXqbdsDkomErlspptsDlomns2y+YmfkL7whYOXCbaPQh+bqlSktTXp2LHm5WPHmpcrlf7cd1BHJkkTE/15jN147N3MJGzbzSXKmXi2k81ByEXqLZuDkolELpvpJpuDlolns2yuuUZ68cWDlwm2b8h1XTfsRhxkExMTmpycVL6H4etLly7p0qVLrcvnz5/XrbfeqrW1NR09erRvbTt3TnroIeltb5NOn5ZefbV5/ZUrkutKR49Khw9vfh8vvyxde600MuK/7Vvfkq6//mqHtd7Fi1KjIb3zndJ1122v/bvx2G++KZ0/3xxJObTua3IvmYSt37lEIRNPP7OJUi5Sf7I5fDhamUjkspmdZnP99c3nHaVMPNvJ5s03m7lcvizdeGPz+b/97dK990rf+Y706U/3d67++fPnNTw83PfaAoNFoR+y7RT6v/Zrv6aHHnrId32/34yPPSZ9+cvSBz4g/ft/L1240Le7BgAAO/S2t0k/9EPNz+c775T+/t9v/hqwuLjz1eso9KOBVXf2oZMnT+oTn/hE67I3ot9PG+dGvv3t7bd3O6qy2ajFK680/3vTTf7bLlxojnCNjkpHjkirq/7Rje98R3r99eARod147PUjtN/9bvPyDTdc3W+/jDT1MxfDaB+JvHy5PZf9komnX9k0Gs3nu36Edn02+y0XaefZGIZ06dLV0cj1Ll9uvqeOHNlfmUjkspntZrO21vwF+ciR5v6vv+7va994o/n+Ctp/P+g1G+/z5403mv+uv745jee735X+wT9o7vOd7zSPb3jyyebqPoBEob8vHTlyREeOHNnVx/DmRt52W/NyKtV+u+tKX/mK9OEPb76G78SENDkZfKBipSLF49Lf/q3/tmRSKpWaByWNjjaXGAu6j3S68wGi/X7s9b9wDA1JX/qS9JGPXN2v20zC1s9cpqbaM5Hac9kvmXj6lc1//I/NL4frc5GuZrPfcpF2ns1HPiI991yzMFn/vvF86UvS937v/spEIpfNbDebyUmpWm0uG3njjVK9HtzXrqxI//2/7177d1Ov2XifP//rf0lf+1ozmxtuaGYjNfubF15o7ptKNfMDJA7GRYCNo/lB+nG0v2U1O6RMpv36UqnZyUnNosk0pYUFqVbz30cyuYuPffS7rcf+/d/3Z/L+97fvG4UVELrK5YaLMsdc/c7vSF/8ov91sj6XKGTi6Sqbt13S9ddLf/EXzdG6je8fL5so5SJ1l80L33hDt98unT3bPJhwo/e/P1qZSN3lsvL1VY3d9t0DlYvUOZtHH716nMvIoTWNGFd82Xjvn+uvj1Ymno3ZeJ/Jr70mjY83rxs+fEEjhtvKZmOfst3PRkQPI/rw2TiaH+So87/1kacf0h8ftvSNhy7p9k//6LaOAsrnpUKh+c9jWVeLesNoLjNmWc0RENNs/n88LiUSm59wZNuP/flnJN0t4//+CcXe+dey3P+ij3wkrluueVk/NlTWsTuu11DivsBVEdaPquy30TdPx1z+8g1J18r4px9R7MY/1z/6bklf+NJP6L3XvqSp4ef1s9/5L7r+Jy3p/e2fMFHIxNMxmy/+raT3yvgnP6kfuWFR8TfntbDw43rvtX+nH3zH1/TTr8/r9X/8zyRzqrVflHKRNskm/1eSflAP/slP6P+84Rn93JX/qkLhQ3rvdX+nH7rxf+rH3/wDrc5kZJq3yHWjlYm0dR/3r/88qXv0lO47/HsqFH5at17zgn7w6HP6v675M/30kSf1279wRm8eui5yuUjB2aytNaepSNIvfOk+3TP0x/q7w7+nQuGndOu1L2rq6PP6sSt/qI++95t6aPw/q1IZilQmnvXZ/NmfNX/B+If/UHrppebtv/iFj+pHD/+p/m7otAqFn9St176kyeHn9aELJa2sHJP1xU+H+wSwZ1Doo003o/mS9NHf+wWNfeOPNaH/V6pKjXc/rNGHM5132MTGaUHS1aXCJieb/y0Wr54OvFJpdn6m2bwci23rYTs/9q0fbD72f/inknNOn3/utO558k/1pxd+QH/6+g/pt786rpEXmiMmt9zSvu/6UZX9fLbC4L/JtZKkycceUOPcTyr1e3+s487/1P9Ye7/+ovF/6Hev5HXb731LPz7anktUMvEEZnPD90iSbv/1Gf3u735I//qNsn5mraY/euUD+vPVD+i0+zl9z3/6tn7yF69mE7VcpOBsfudo80389X/4z6Rrv6H/59Uv66df+is91fig/sL5gEr6Tb37t87rZ/5JM5uoZSJ1yOV3mv/9nz/2Sb125Of0Ly78tY6/cE5/8vLf01+uvV+Pv/mP9Jv6x0qe/Yau/8D3RzIXqT0bb6U371iFcx/91/ry5YT+1YVF/dgLf6P//u336S+dv6/Tb87JXK1r8o439fTT10QuE08q1czkz/9c+uEfbv7CvbzcvO2bP/Mv9OTln9UnLvwP/fiLX9Mff/su/cXqB/S7b/4H3f6lc/qPvy/91E+F2nzsERT6aFOpSN/+dvPAL8fpvN3nPvwl3f03C/rqbT+ur7z4Tn3k+65VPwdVqtVmAW8YzYOKvJF8bwTfcaSZGen48eZBof1UfeZQ87H/xc/LtqWnnpL+wGn+ZHri0qocXVHlzCH99m9LH/uYf//Dh5sZRm30rfU3+aX7NDcnPf290vg90ruHpJ9xXV2z+rK+8OfvCcwlqpl4qrUhxWLSH79rWv/1HVfn5r9L0s+6V3TIeUVf/B/v9GUT9Vwk6YknD+noUelrd/9j/eV5aXi4ef27JH30zctyX13Vl/5spJXNQchEkp54otnPfuPv/bi+cl4aNiV9UHqPpI9KevM7F1V+6jb95h9cq4/dejBy8T5/XnlFete7pP/vtp/U02vS8PdJulu6RdLPSnLPn9cf/snt+sLvH5ZlHYxMvM/kc+ea2fz17T+jtTVp+I7mdjermc2h1RX9zpO3aXq6eXAuwBz9kDmOo0ajEXYzJDVXeKjXpXe/u7niwYULnf+tXHq7Krf9M72o9+imW65Vvd5cCaBfFhaaB9pKV0+csp5hNEfzR0f7v7rA+sf+wz+Uzpy5msnLb4zo8huH9CM/0pxH+q1v+bN59dXm9v3OJGxeLpcvS3/yJ82DB1uvk1eHtHrtu/QjPzIUmEtUM/EsLEi/9EvN53fpUvPgSu+5n3/1kJxrbgp8zUQ9l8uXpb/8y+aSf6++Kj3/fPNgQu/5O69dp7VDI23ZRD0TaetcLlyQXrtyg374R689MLms//x5/nnp+76vmUNQNq8OHdU/+L8O67rrrh6QGvVMvL52q2zWrjmmn/jwIV13XbMfAhjRD0Eul9Pi4qJs2279i8fjMgxD09PTSiQSobTruuukf/tvm4VKr7xl0HpRKjVH5TeOyJdKzQJ+/U+6xWLwfPztTtvp9rGvuab5QfLZz/rv45VXpJMnO/9kvJ1MwtZtLh/+cHOEv9dc9mMmnq2y+fjHmyNohYL013/dWzb7ORepczbewdpf+lLz8m//dvfZ7PdMJHLZzMZsvM+fYrH5K2ov2dx5ZzQy8azPZv1n8he/2H02jUa0MsH2UeiHYHZ2NuwmdPS2tzX/9YvjNDucII3G1Tn4nlqtudJAudx+vTeavL6w96YWdTpVeL8e+3d/V/rn/9z/2Nddt7PjA8LSj1yOHJG+8AXpX/7L6OQi7Twb7/1DNlezqVSaX4SkaGZDLp31ms1zz0mf+UzzV1Syaf6/16fUar1lc801nT8bcbBwZtwIWFtbk2EY+uY3v7knzl73m795nWq1wzp37pD++q+bZ3j50R/9roaHXd177xv62Z/9btu2jcaQ1taGtLo6pNtvv6JPfOKSDOPq/f3e712j22+/onPnmjPNVlebRwmvrQ3pX/2ry3vmsfcycumMbDojm2Dk0hnZdLbfsvFOxuk4joa9g2uw71DoR8Df/u3f9v3MuAAAAN/85jf13ve+N+xmYJso9CPgypUreuGFF/SOd7xDQ5utibkD3jf7vfKrwV5AJn5kEoxc/MgkGLn4kYnfIDJxXVcXLlzQe97zHh06xNot+xVz9CPg0KFDA/u2ffToUTraDcjEj0yCkYsfmQQjFz8y8dvtTJiys//xFQ0AAACIIAp9AAAAIIIo9NGVI0eO6NOf/rSOHDkSdlP2DDLxI5Ng5OJHJsHIxY9M/MgE3eJgXAAAACCCGNEHAAAAIohCHwAAAIggCn0AAAAggij0AQAAgAjihFnYVKVSUblc1vj4uBzHkSTNzs6G26gQOY6jTCYjwzCUzWbDbs6eUSgUVK/XVavV1Gg0ZFnWgc/HcRwVCgWtrKy0LjcaDZ08eVKxWCzk1u0duVxOsVhMlmWF3ZRQJJNJjY6OKp1OKxaLyXEcLS0tKZ/P81qRZNu2MpmMJGl0dPTA9r21Wk1zc3PKZrMyTTPs5mA/cYEOisWim0gk2q4rl8uuZVkhtSg8s7OzbiKRcLPZrGuapptKpcJu0p4xOzvr1uv11uXV1VXXsizXMAx3dXU1vIaFaHV11Z2dnfU9/2Kx6Epyy+VyOA3bY1ZXV11JbrFYDLspobEsy5XU9s8wDF4jbvP9EovFfP3L7OxsiK0Kh9d3bPVvfVaA67ouy2sikOM4Ghsb0/LysgzDaLttYmJC6XRaqVQqnMaFbGJiQpOTk8rn82E3JXSlUkmmafpGHR3H0cjIiCzLUrlcDql14SkUCspkMqpWq77Rt5GREZmmqWq1GlLr9o5cLqdMJqNisahEIhF2c0KRyWQUj8dVq9UkSaZpHtgs1qvVajp+/LjvMyiZTKpWq6ler4fXuBBkMhk5jqPx8fHA271fDg/irx3YHFN3EGhhYUGmafqKfEmanp5WPp8/sIU+rlpcXAwsSgzDUCqVUqFQkOM4ga+jKPOKe2+6G/wqlcqBna6zkWVZZLFBJpPRyZMnfX1HPB7X1NRUOI0K2WaDS5lMhiIfgTgYF4GKxaJGR0cDbzNNU7VajSIGKhQKisfjgbdNTExIkpaWlgbZpD3Bsiytrq4Gzq92HEeTk5MhtGpvqdVqB37+OYLVajVVKpXAwaRUKnUgjxPb7MtNoVBQOp0eYGuwn1DoI9DS0lLHA368623bHmSTsAdtVrB6XwQ7fWE8iHK5nCS1Di48qAqFwoEs1tCdfD7f8Rflg6rTdC7vc5gDdNEJU3cQqJvpFrZtMyJ3wG02/96bQ8trpMlxHOXzeRWLxQP9oWzbNl/+NrBtW5VKpXW5Xq8HTls5KCqVSus94n05NgzjwOcSJJvNcrwYNkWhj555nWyj0Qi3IdjTCoXCgT+Ow1tis16vq9FoqFwuH+giX2oewM1o/lW2batWq7W9V2q1miYmJlStVg9kUesNIuVyubbXim3bBzqXjZiyg24wdQdA32UyGZmmeeAPDjMMQ7Ozs8rn80qn00qn0yqVSmE3KzSlUokVZTYIWnEoFospFotpZmYmpFaFr1ar+XIxTVOWZR3oXNYrFov8YootUeijZ8y9xmZqtZoKhYLK5TKjbutYlqVisahkMqlCoRB2cwbOO2HYQf9Fo1vxePxAfymUguedT0xMqFQqHfjFIAqFAkU+ukKhD6Cvksmkzpw5Q0EXwDAMJRIJpdPpA1eoMJWrN95Aire+/kGz1WIQB3E1r/Xy+XzHFc+A9Sj0Ecg0zY6r6nhz8ynksFE8Hlc+n2ekaRPeMnnrD76MOpbSDJZOpzuuwHSQj4XqZsWdg7zqm3dcB5/B6AYH4yJQLBbrOOLodbB8cGM9r2jhxD9qnRW4WCz6bvMKmINUqNi2rfn5ed/qIF4fMzc3p/n5eY2Ojh6oFUQWFhY6vl+818dBPOdCLBbb8v1xEHPxeIMEFProBoU+AsXj8Y4HUtbrdYo5tMnlckomk77XhW3bsm37QL1eHMfZdFqOt+zoQfqQTiQSgQfhOo6jkZERnTx58kAepJtKpTr2s94KTQfxOJfp6Wklk8nA21g3XqpWq2E3AfsIU3cQ6MSJE2o0GoGjKqVSiSW90FIqlRSLxQKL+YP487JhGEqlUoGj+VJzNM6bq4+DbWpqKnAOvuM4KpVKB3bVqkQiIcMwAg9GLhaLSqVSB/ILkMe27QP9/NEbRvQRyDAMnTp1SplMpq1gKZVKMk3zQBcp3uohaBby+Xzet5KMN6I9Pz9/IEefMpmM0um0stls2wdyoVBQrVY7kJkE8QYSDur7yTswO51Ot02FPH78uFKp1IHuZ4vFotLptCzLar2HCoWCbNvu+CX6oDhI0/6wc0Ou67phNwJ7V6VSUblc1vj4eKt4O4gnu8nlclpcXGwdBCWp9QE0PT19YD+QR0ZGNp2mYppma6rKQeQdaOl9ORwdHfUV/wdRrVbT3Nxc6/1kGIYsy1I8Hj+QK/PkcjmtrKy0XicHuU9Zr1KpKJ/Pa3R0tLU060H9lWO9ZDIpx3E2PTM54KHQBwAAACKIOfoAAABABFHoAwAAABFEoQ8AAABEEIU+AAAAEEEU+gAAAEAEUegDAAAAEUShDwAAAEQQhT4AAAAQQRT6AAAAQARR6AMAAAARRKEP4EDLZDKKx+MaGhrS0NCQ4vG4kslk2M2KtEwmo/HxcY2MjKhWq4XdHACIrCHXdd2wGwEAYZuYmJBt21pdXQ27KaEbHx+XZVnK5/O79hi1Wk0TExOqVquKxWK79jgAcJAxog8AkkZHR8Nuwp5iGMau3r9pmrt6/wAA6ZqwGwAA2Fvq9fquP8Zuf5EAADCiDwAAAEQShT4AAAAQQUzdAYAObNtWMpmUbduanJxUuVxWoVCQ1JzeUqvVlM1mFYvFVKvVtLS0JEmqVqsaHx/X7Oys7z4dx1GhUGhNXalWq0qn0x0PSC0UCnIcRysrKxofH1cqlWq1oVgsqlwut7bN5XKt+63X663texGPx2XbtgzDULVa3TIHx3G0uLiobDbbcd59pVJRuVzW+Ph467oTJ05s2o7Nnks6ndbS0lJrxZ5isahEItFqz9jYmBzHkWmaymQyPWcAAJHhAgBcy7JcwzACb0skEq5lWW4+n3dXV1db1xeLRdcwDLdarbrlcrltH9M03Xw+77uv2dnZtsurq6uuYRi+/b3HzWazrcv5fL6tjevvPxaLucVi0bf/xsfrRiqVcmOxWGB7NsshyOzsrJtKpdquW11ddVOplCvJrVarvn26fS6JRCLwcS3LassNAA4qCn0AcDcv9GdnZ1sF/Xqrq6uuJF8h67rNYtmyrLbrqtWqa5qmW6/Xffe/sbAul8tu0FiMJF8RnM1mAwvzer3uSvI93lY63d9WOWy8vlwud8y0Wq0G7tPrczFNsy3/arUa+AULAA4i5ugDwBaOHTsmx3F802u8qSXrp6Ssv63RaAReZ9t22/Xj4+O+68rlcuBUmFgspvn5+bbrMpmMpqenfduapinDMFSpVDo/uR5slcPG55vJZGRZVuB9dZrm0+tzKRaLKhQKKpVKchxH8/PzTNUBgLcwRx8AurDZuu/dnvDJNM22E3LZti3HcVpz4dcbHx/3Fc6SWnPP19+H1JzHXiqVAh+3n8tl9rL+fa1WCyzaO9nOc4nFYsrn85qZmdGJEyd29SRfALDfUOgDQBf6te674ziam5uT4ziamJiQZVmamJjQwsJC23aWZclxHNm23Squvcvri2evOE4mk4Gj595Bqv3SbQ5eu3rJbbvPJZVKKZ/Ptw6GBgA0MXUHAAbEtm2NjY1pfHxc+XxeqVRKpmkGnpXXNE2lUiml02nZti3btjUzM6N8Pt/2C8L6LwF7yXbatd3nUqvVlE6n5TiOMplMT/sCQJRR6APAgCSTyVYBv97GKTqVSkWO4ygej6tYLKpWq6lWq+nUqVO+fb2564uLix0fN6wvAaZp9jRtaDvPxXEcVSoVpVIpFYtF5XK5vh2TAAD7HYU+AAxIrVYLnJJSrVbbCljbtrW0tKRGoyHDMJRIJJRIJDpOg8lms6117YMeM6wpLdls1jclydOpTb0+l7m5udb5Crz5+slkcs/9wgEAYaDQBwD5R9XXW1lZ2bRw7LaotCzLN9ps27bi8Xjrfmq1miYnJzU5OalsNqtarSbHcTZ9jFQqJcuylE6nfe2qVCodV77ZTNDj9ZpDIpHQiRMnfNNpHMdRsViU5M+9l+eSTCY1NTXl2980TR0/frxjOwHgoBhyXdcNuxEAEJZMJiPbtlurvFiWJdM0lc/n5TiOZmZmWlNpEomEpqenlUgklMvlVC6XValUZJqmYrGYisWibNtWJpNp2yedTrcK1HQ6rUaj0SruTdOUZVmt+0smk63pOblczlckG4bR+hKwcbWfQqHQOiuvaZpqNBo9LTW58flaltU6422vOaxXKpW0uLjoOzPuyMiIDMMIXC1ns+cSj8e1tLTUasv6x6vVajp+/HhrdaKg9gDAQUGhDwB7UCaT0fj4uK9Qt21blUpFmUxGp06d6vuqOgCA6KDQB4A9plQqaX5+ftOR6FKppLm5ucA1+AEAkJijDwB7TqPRCFxycz3DMLbcBgBwsFHoA8Aek0ql2o4b2Mi2bWWzWWWz2QG3DACwn/Q8defNN9/UG2+8sVvtAQC85YknntBXvvIVGYahd7zjHZKkCxcuSGoezHr06NEwmwcACMG1116rw4cPd7Vt14W+67p66aWXWJsYAAAACJFhGLr55ps1NDS06XbXdHuHXpH/rne9SzfeeOOWdwwAAACgf1zX1WuvvaaXX35ZknTLLbdsun1Xhf6bb77ZKvKPHTu281YCAAAA6NkNN9wgSXr55Zf1rne9a9NpPF0djOvNyb/xxhv70DwAAAAA2+XV5FsdN9vTqjtM1wEAAADC1W1NzvKaAAAAQARR6AMAAAARRKEPYE/J5XJKJpMaGRnR0NCQJiYmlEwmVavVwm7atmUyGY2Pj2tkZGRfP4+dIgcAGKyu1tF//fXXtby8rLGxMV1//fWDaBeAA25iYkK1Wk29nNNvfHxclmUpn8/39Fjb3a8XtVpNExMTqlarisViu/Y4e91Oc6jVagc6PwCQuq/NGdEHsCeNjo7KMIye99vOPjvZr1umae7q/e8XO81hN7+MAUDUdH3CLADY6+r1+kD368Vuf5HYL3aag23b/WkIABwAjOgDAPaFUqkUdhMAYF+h0AcA7Hm2bWtmZibsZgDAvsLUHQCREI/HZdu2DMNQtVrd0X62bSuZTMq2bU1OTqpcLqtQKEiSHMfR4uKistlsx/nmlUpF5XJZ4+PjretOnDixaTtyuVxrWku9Xtf4+LhSqZQkKZ1Oa2lpqbVSTbFYVCKRaLVnbGxMjuPINE1lMpnWfptxHEeFQqH1mNVqVel0uu1A1zByCFIoFFQulzU6OqqlpSUlk8nWbdlsVvl8XqVSqfV3PHXqVCsfqZltJpORYRhKpVJKp9Ntz6tYLLae18rKihzHUSaT6fi8NvtbAcCe4nbh4sWL7tmzZ92LFy92szkA7JhlWa5hGD3tk0ql3Fgs1vNjddovkUi4lmW5+XzeXV1dbV1fLBY7tm12dtZNpVJt162urrqpVMqV5FarVd8+sVjMLRaLvseenZ31XRf0uJZludlstuPz69TOjW00DMMtl8u+bQeVw1ZSqZRrWVbgbaurq66kjjlYluXW63XfdbFYzM3n823XV6tV1zAM39/Edbv/WwHAbuq2NmfqDoDIWD9y3I/9TNPU0tKSJicn2w4itSxLjuP41oKvVCoqFAq+lWEMw1A6nQ58jFwuJ0ltI9BSc6Q6l8u1HXxaLBY1Ojradl+1Wk3JZFKzs7NbP9F1+3gj4OvbmEqllMlkfNsPIoed8to/Pz8feHs8HveN0MdiMdm27RuNj8ViOnnypGZmZuQ4Tuv6Xv5WALAXUOgDQAfHjh2T4zi+ddu9YrfRaLRdn8lkZFlW4H11mgaSyWQ0PT0duL1hGKpUKm3Xe9NMSqWSHMfR/Px8z9NGDMNQo9HwFabj4+OBxeogcuiHdDqtWq0W+MVjY3G+VXtSqVRrepOn178VAISNOfoA9jXHcXZ16cpeCtNarRZYCHbiFdX1er3jijIbl/6MxWLK5/OamZnRiRMntrWuvGmaWl1dbWuH4zibHtuwmzn0SywWa+WzPpdardbxi0cnhmHIMAwtLi5K2t7fCgDCRqEPYN9yHEcLCwu7eiBkt18ivEKwly8d3j7JZDKwEO00Cp1KpZTP57W0tNT1Y23kOI7m5ubkOI4mJiZkWZYmJia0sLAQuP1u5tBP6XRa6XS6Veh7Bylvh2mareez3b8VAISJqTsA9i3btvfMGWe9dqyf070b+0jNEep0Ot1aHaZXtm1rbGxM4+PjyufzSqVSMk1To6OjPd/XRtt9Ttu1cXTd+9LnTbkpFArbLsLXv74G/bwAoB8o9AHsW/Pz83um0JeaxWAv0ze8ud3e9JAgGwtLx3FUqVSUSqVULBaVy+V6nhueTCZlmqbvl5CNc+23O+e81xx6sbGNQccUeL947ITjOHIcR1NTU5K297cCgLBR6APYl7wDJfsxCt0v2Wy249SXTtNsstls2wGf69VqNd9+c3NzrRV2vPnoyWSypyKz05z1arXadj/bXUVmOzl0Y3x8vKvn6R2Um8vlthzN73R/c3NzMgyjbTWjXv9WABA2Cn0Ae9LGkdv1vHnlQQfibndUNWg/7+RJ3e6TSCR04sQJ33Qax3FULBYl+Z9XKpWSZVm+ZSe9kfv1BXkymWyNMK/f3zRNHT9+vGM7N7Isyzdab9u24vF467FrtZomJyclDSaHbtvtHTgsNYvrjSsBSVcPyu32F5+N039qtZoKhYLOnDnTdn0vfysA2AuGXNd1t9ro9ddf1/LyssbGxnT99dcPol0ADqhcLqdyudwqRC3LaivmbdtuLZ9oGIZWV1flOI5mZmZUqVTkOI4sy1I2mw0sAtfrtJ9pmm3XJxIJTU9PK5FItLXPNE3FYrFW8eoplUpaXFz0nRF2ZGREhmEErpZTKBRUrVY1Pj4u0zTVaDRaU2vi8biWlpZabVn/eLVaTcePH28ddBrUniDpdFqNRqNV3JumKcuyWs8vmUzqxIkTA89hK6VSSeVyWRMTE5LU8UBsb+R9swO1M5mMKpWKzpw5o4WFBY2Ojsq2bdXrdWWz2Y4HFG/2twKAQei2NqfQBwBETi6X2/IkYl6hv9myogCwF3VbmzN1BwAAAIggCn0AwL5Wq9Xa5tlvdiZcADhIKPQBAPtaPp/X3Nxc63K351dgOUwAUcccfQDAvmbbduvgbcdxtpybb9t2a36+d5BxOp1m1RwA+wYH4wIAAAARxMG4AAAAwAFGoQ8AAABEEIU+AAAAEEEU+gAAAEAEUegDAAAAEUShDwAAAEQQhT4AAAAQQRT6AAAAQARdE3YD9qpcLqdYLBZ4psRSqaRyudx2XTablWEYkppnZkwmk1paWtLk5KRvWzQ5jqNCoaCVlZXW5UajoZMnTyoWi4XcuoOB13k4NssdO1MoFFSv11Wr1dRoNGRZlrLZbOC2lUpF5XJZ4+PjchxHkrY8qy76iz5ocBzHUSaTkWEYHd8T2Jle+h9pQH8TtwsXL150z5496168eLGbzfe91dVVV5JbLBZ9t83Ozrrlcrntunq97sZiMXd1dbXt+kQi4cZisb63r1qt9v0+B211ddWdnZ31ZVYsFl1Jvoyj4MqVK2E3oQ2v83Bsljt2ZnZ21q3X663Lq6urrmVZrmEYgX1NIpFou65cLruWZQ2iqXDpgwZldnbWTSQSbjabdU3TdFOpVNhNiqRe+p9+/E26rc2ZuhOgUCgEXl+r1STJN/JgmqZOnTqlmZkZ3/W7IZ/P78r9DtLCwoIKhYIajUbb9YlEQoZhKJPJhNSy3XH222f163/66zr77bNhN6WF13k4OuWOnSmVSpqenm57PRqGoWKx2Br59TiOo5mZGZ06dartPizLUqPR4G80IPRBg5HNZlUsFjU7O9v6NQT91Uv/Iw32b0Khv0GlUun4c3qlUtHU1FTgbbFYrNU5eY4dO9b39kmSbdu7cr+D5L0ZvJ/Lo+zst8/q0cVH9fT/flqPLj66J4p9Xufh2Cx37Mzi4mLglD/DMJRKpVSpVFr9zcLCgkzTDPyAnZ6ejkyBt5fRByFKeul/Bm1PFPpvvBF2C66q1Wqbzg/v9AFg2/aujSqsVyqVdv0xBsGyLK2urgZm7TiOJicnQ2hV/3lF/iuvvaKp75nSK6+9sieKfV7n4dgqd2xfoVBQPB4PvG1iYkKStLS0JEkqFosaHR0N3NY0TdVqtf07CLGyIr34Yu//3jpWalDogxAlvfQ/gxb6wbjf+Ib0+c9Lv/RL0vd+b7htKRQKmx6IlUgklMlklEwmderUqbbRoGw2u+vTTWzb1szMTGSK4CC5XE6SIjF1Z32Rf+exOzU0NKQ7j92p51ae06OLj+rjUx/XXe+8a+Dt4nUejq1yx85s9nrxinavuF9aWtKJEycCt/WKSNu299+XspUV6TOfkbbzJcUwpF/5FWmXRsfXow9C1PTS/wxa6IV+uSwtLUm33Sbdf3947bBte8s/gmmarU6mUqmoWCzKsqzWN7nNfpL35iI6jqPFxUVls9meRiUKhYLK5bJGR0e1tLTUNt8rm80qn8+rVCrJtm0ZhqFTp04pkUi0tsnlcq0ju1OplNLptJLJpGzb1uTkpIrFYquNKysrrSPBO7Uxl8u1Ot96va7x8XGlUqmun08Qx3GUz+dVLBYHMmKzm4KKfEmhF/u8zsN5nXeTO3ZmsxVX6vW6JLUKd8dxtpwXuy8L/cuXm0X+DTdIN97Y/X6vvdbc7/Ll3WpZC31Q+J+16L9e+p+B6+eRvb1aXnbdX/xF1/25n2v+d3m5r3ffk2w223ZZm6yK4a0MI8k1TbPtKOuN9xmLxdx8Pt92xHWxWHQNw9hWO1OpVMdVIbwVDDY+F49lWb62WpbVauN61WrVNQwjMINYLOa7PpFIuLOzs708lVabs9msm0ql3EQi0THL/eSrL3/V/eU/+GV3ujjt/upTv+p++o8+7fv3q0/9qjtdnHZ/+Q9+2f3qy18dWNt4nYfzOu8ld/SfpLZVLSR1/DvW63VXku+1si+88ILr/sIvuO4nPuG6n/509/8+8Ynmfi+8sOtNpA8Kpw9af5+sujNYG/ufjbb7N9kXq+5UKtLamnTHHc3/VirhtKNUKrV9I9+KYRianZ2VZVmybVvxeNx3cJDH+xa/fvTIsiw5jtNxn+3yRhDm5+cDb4/H475Rg1gsJtu2fSMEsVhMJ0+e1MzMTNtcVW9qzca8stmscrlczwcveVnm83ml02ml0+l9PTey00j+Rt7I/iDn7PM6D+d13mvu6C9vtJR1w8NHHxTeZy3CsRf6n9Cm7pw7Jz39tPSe90hDQ83/Pv20ZFnS7bcPrh3eSZq6/Wkvk8loamqq9UcrFApKp9OamJhQuVz2/aToOI7v5xqvI9q4tGQ/pNNpFQoF34FOlUqlYwfb6bmnUillMpm2+ZSZTCbwBeutYFGpVLb9s6JlWZqcnNTIyIjy+fy++3my2yLfM8hpPLzOw3md95p7mCYLk3rp1ZfCbkbLzW+/WUupnR28VqvVVCgUVK1Wu17CLuz5tFFFH7R3Pmv3rMlJ6aU90gfdfHNzXvkObKf/2Q2hFfreaP5ttzUvHzsmvfBC8/pBztXv5QA5b17d+jdxKpWSZVmKx+NKJpNaXl5u+4MO+gM+FospFospn8+3rVpQq9V6XtbPMAwZhqHFxUVJV5caq9frHUfdvblo22UYhhKJhNLptE6cOLFv1vzttcj3DKrY53Xe2W6+zvfTAbgvvfqSvnXhW2E3o6+SyaTOnDmzL75oRR19UGdhfNbuSS+9JH0rOn3QXul/Qin0N47mS+GM6ve61F02mw18c5mmqWq1qrGxMd+3+TAKVW8KjNf5OI6z7ReaaZqtTsf7bzKZDOzI+jU9YWpqSqVSadORkb1ku0W+Z7eLfV7nW9uN1/l+W0rz5rffHHYT2uy0PfF4XPl8PvBvsP7vvZE3+hv2h3OU0AdtLYzP2j3n5j3UB+2wLZv1P4MWSqG/cTTfM+hRfdu2NT8/71uv1/vpdm5uTvPz8xodHVU2m920IzEMQydPngxl3tzGeY/ekf6FQkGpVGpHo4q2bbc6mn6e5GpkZESWZalYLPpu83LeD3MQXdfV6a+dVr1R19T3TPVc5HuGhoZ0x7E7tPitRZ3+2mm976b3bfu+NuJ1vrXdeJ33kvteOEHTTqfJ7CXpdFqZTKbjyGosFuv49/Ve23vhAzoq6IO2tluftftKSOvM99tW/c+gDbzQDxrN9wx6VD+RSAR+M3YcRyMjIzp58mTb7d286QYxCrRxvmFQh5dKpXY8z91xHDmO0zpDoTc3cHFxseOIQjfL1nn324k3krMfRtSGhoZ07/vu1UuvvqTnV57f1oi+1PzC8PzK8xofHde977u3b0W+xOt8K7v1Ou81d/RHLpcLHAm1bbtVTMXj8Y4Hx9Xr9T3zAR0V9EGb260+CIPXTf8zaANfdccbze90To5jx8JdgWczpmmqsknDgg4Q6rfx8fGuOsF0Oq1araZcLrdlMdHp/ubm5lqrHniy2Wxr/uRGtVqtqzO/eSsWBI3mS82Dmby5+vvBXe+8Sx+f+rhuuvEmPbfynJqraXXPdV09t/KcbrrxptBOorUer/P+vM4xeKVSSbFYLPD1WavVWsXhiRMn1Gg0Agu3UqmkdDq9621FZ/RB9EH7Ubf9z6ANtNDfbDTfs35U/9y5QbbuKq/z3/htvlgstk7gsZ7jOK2fatZ/w/ZOhtHJdn6W85YZ8/btNPfRO1Bofn6+qxfXxgN+vKPFz5w503a9d0DUxg9Cx3FUqVS67nwzmYzS6bQvA28Vg42Pu9dtt9gPs8jndb77r/MgnXLHztRqNeXzedm2rUKh0PqXy+WUy+U0NzfXeo14JzraeIbVUqkk0zT3zSDDfkcfNPg+yFv9CP3VS/+z0W7/TYbcLiqS119/XcvLyxobG9P111+/7Qd77DHpy1+WPvCBzoW+JLmu9JWvSB/+8GBX4KnVapqbm5Nt26rVajIMo/Uz7/qf5TKZjK/jWD+v0HEczczMqFKpyHEcJRIJTU9PK5FIKJfLqVwuq1KpyDRNxWKxjiPbnZRKJZXLZU1MTEhSx58MvdGAzX5S9DrTM2fOaGFhQaOjo7JtW/V6fdO5kt6SUePj4zJNU41GY1s/XXoftN4LvZs5mntZLwfmhlXk8zof/Otc6j53bM/IyMimBZ1pmr4DPCuVisrlctvo7X5ZJSnQiy9KJ082fxp/xzu63+/CBWllRZqbk265Zffa9xb6oMH2QblcTouLi628peYXGcMwWnlhZ3rtf/rxN+m2Nh9YoX/unPTQQ9Lb3ibddNPW27/yivSd70if/vRg19WPklwut+WHltf5VKvVAbUq+rop9vfadJ39jNc58JZ9UuhHDX0QwtBtbT6wg3ErFenb35aOHpW6+RXt8OHm9oNeVx/YKW8az6OLj+q5led8xT5FPoBd9dpru7s9gH1jIIX+5ctSvS69+93Sq692v9+7393c7403pGuv3b32RUGtVpNt262fe/bLGvRR1anYp8jfGV7nwCauu04yjOZo2sWLve1rGM39sSn6IOw3Ayn0r7tO+rf/Vrp0qfd9jxyhyO9GPp/X0tJSq8PpdhmnA7dO7wBtLPbvOHaHnl95niJ/B3idA5s4dkz6lV9pjq716rrrOi+Hhxb6IOw3Az0YF7vHtu3WCgWO42w5X9C27dacQe8gpnQ6zfrRu8Cbs19v1DU+Ok6RvwO8zgGEiT4Ie8WeOxgXOMjOfvusTn/ttO59370U+QAAYEf23MG4wEF21zvv0vtuel9fz3gLAACwmYGfGRc4qCjyAQDAIFHoAwAAABFEoQ8AAABEEIU+AAAAEEEU+gAAAEAEUegDAAAAEcTymmqe9CKTycgwDGWz2Y7bVSoVFYtFGYYhx3G23B4AAAAIy4Eu9DOZjGzb1tTUlCqVyqZnqsvlciqXyyqXy23XJZNJFYvFQTQXAAAA6NqBLvTXj8bPz8933K5WqymTyaher7ddPzs7q5GREZVKJSUSiV1rJwAAANAr5uh3YW5uTrFYTKZp+m47ceKE8vl8CK0CAAAAOqPQ70KlUgks8iVpYmJClUplwC0CAAAANkeh3wXHcTreNjo6Kqk5vQcAAADYKyj0u2AYxpbb2La9+w0BAAAAukSh34XJycmOo/oU+AAAANiLKPS7kMlkOs7DX1xclCQ1Go1BNgkAAADYFIV+FyzLUiqVUiaTabu+VCopHo9LujpXHwAAANgLdryO/uSk9NJL/WjKzt18s7S0tDv3nc/nVSgUVCgUWtdZltU6CLebefwAAADAoOy40H/pJelb3+pHU/a+VCrlu86b0jM5OTno5gAAAAAd7bjQv/nmfjSjP8JoS7VaVSwWY0QfAAAAe8qOC/3dmiqzXywsLCibzYbdDAAAAKANB+N2oVQqaWRkJPD60dHRwCk9AAAAQJgo9N/iOE7HJTIbjYZvDn6tVlMmk1G5XB5E8wAAAICe7Hjqzn6Wy+W0uLgo27Zb/+LxuAzD0PT0tBKJhKTmQbiO4yiTybS+EJimqWq1ytx8AAAA7ElDruu6W230+uuva3l5WWNjY7r++usH0S4AAAAAAbqtzZm6AwAAAEQQhT4AAAAQQRT6AAAAQARR6AMAAAARRKEPAAAARBCFPgAAABBBFPoAAABABPVU6Hex5D4AAACAXdRtTd5VoX/ttddKkl577bXttwgAAADAjnk1uVejd3JNN3d2+PBhGYahl19+WZJ04403amhoaIdNBAAAANAt13X12muv6eWXX5ZhGDp8+PCm2w+5XY79u66rl156SY7j9KOdAAAAALbBMAzdfPPNWw68d13oe95880298cYbO2ocAAAAgN5de+21W47ke3ou9AEAAADsfSyvCQAAAEQQhT4AAAAQQRT6AAAAQARR6AMAAAARRKEPAAAARBCFPgAAABBBFPoAAABABP3/65MCIJW4VI4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x530 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "8",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/pandas/core/indexes/range.py:391\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m    392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[0;31mValueError\u001b[0m: 8 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m proceed_NEB \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrue\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m----> 2\u001b[0m     df_movement \u001b[38;5;241m=\u001b[39m \u001b[43mmove_by_tuple_cage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_df_movement_category\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_mapping_metainfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactivate_closest_tuple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m     movement\u001b[38;5;241m.\u001b[39mplot_distance(df_movement, direc_restructure_destination, max_mapping_radius, litype, \n\u001b[1;32m      4\u001b[0m                            activate_shiftby1_labelatom\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, activate_shifting_x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, \n\u001b[1;32m      5\u001b[0m                            activate_diameter_line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, Li_idxs \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      6\u001b[0m                            counted_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmovement\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/01_Masterthesis/Atomic-Positionism/positionism/movement/move_by_tuple_cage.py:299\u001b[0m, in \u001b[0;36mget_df_movement_category\u001b[0;34m(dataframe, activate_closest_tuple)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(dataframe[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msize):  \u001b[38;5;66;03m# CHANGED HERE   # NO MORE -1\u001b[39;00m\n\u001b[1;32m    295\u001b[0m \n\u001b[1;32m    296\u001b[0m     \u001b[38;5;66;03m# idx_coor24li_tuple_cage_belongin_complete_closest_weight = dataframe.at[idx, col_idx_coor24li_tuple_cage_belongin_complete_closest_weight]\u001b[39;00m\n\u001b[1;32m    297\u001b[0m     \u001b[38;5;66;03m# idx_coor24li_tuple_cage_belongin_complete_closest_weight_next = dataframe.at[idx+1, col_idx_coor24li_tuple_cage_belongin_complete_closest_weight]\u001b[39;00m\n\u001b[1;32m    298\u001b[0m     idx_coor24li_tuple_cage_belongin_complete_closest \u001b[38;5;241m=\u001b[39m dataframe\u001b[38;5;241m.\u001b[39mat[idx, col_idx_coor24li_tuple_cage_belongin_complete_closest]\n\u001b[0;32m--> 299\u001b[0m     idx_coor24li_tuple_cage_belongin_complete_closest_next \u001b[38;5;241m=\u001b[39m \u001b[43mdataframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mat\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_idx_coor24li_tuple_cage_belongin_complete_closest\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    301\u001b[0m     \u001b[38;5;66;03m# for j in range(len(idx_coor24li_tuple_cage_belongin_complete_closest_weight)):\u001b[39;00m\n\u001b[1;32m    302\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(idx_coor24li_tuple_cage_belongin_complete_closest)):\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/pandas/core/indexing.py:2431\u001b[0m, in \u001b[0;36m_AtIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2428\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid call for scalar access (getting)!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2429\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mloc[key]\n\u001b[0;32m-> 2431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/pandas/core/indexing.py:2382\u001b[0m, in \u001b[0;36m_ScalarAccessIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2379\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid call for scalar access (getting)!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2381\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_key(key)\n\u001b[0;32m-> 2382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtakeable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_takeable\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/pandas/core/frame.py:3924\u001b[0m, in \u001b[0;36mDataFrame._get_value\u001b[0;34m(self, index, col, takeable)\u001b[0m\n\u001b[1;32m   3918\u001b[0m engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_engine\n\u001b[1;32m   3920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n\u001b[1;32m   3921\u001b[0m     \u001b[38;5;66;03m# CategoricalIndex: Trying to use the engine fastpath may give incorrect\u001b[39;00m\n\u001b[1;32m   3922\u001b[0m     \u001b[38;5;66;03m#  results if our categories are integers that dont match our codes\u001b[39;00m\n\u001b[1;32m   3923\u001b[0m     \u001b[38;5;66;03m# IntervalIndex: IntervalTree has no get_loc\u001b[39;00m\n\u001b[0;32m-> 3924\u001b[0m     row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3925\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m series\u001b[38;5;241m.\u001b[39m_values[row]\n\u001b[1;32m   3927\u001b[0m \u001b[38;5;66;03m# For MultiIndex going through engine effectively restricts us to\u001b[39;00m\n\u001b[1;32m   3928\u001b[0m \u001b[38;5;66;03m#  same-length tuples; see test_get_set_value_no_partial_indexing\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/positionism/lib/python3.11/site-packages/pandas/core/indexes/range.py:393\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[1;32m    392\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 393\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    394\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 8"
     ]
    }
   ],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_movement = move_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": null,
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if proceed_NEB == \"True\":\n",
    "    df_amount_movement = move_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": [
    "df_movement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_amount_movement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_filename = \"CONTCAR_positive\"\n",
    "\n",
    "if proceed_NEB==\"True\":\n",
    "    idx_ref = 0\n",
    "    idx_file_group = [0, 8]\n",
    "    mean_ref = False\n",
    "    df_distance, dataframe_group = move_by_distance.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": [
    "if proceed_NEB == \"True\":\n",
    "    idx_ref = 0\n",
    "    idx_file_group = [0, 8]\n",
    "    mean_ref = False\n",
    "    df_distance, dataframe_group = move_by_distance.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 = 30\n",
    "    idx_file_group = [0, 53]\n",
    "    mean_ref = False\n",
    "\n",
    "    df_distance, dataframe_group = move_by_distance.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 = move_by_distance.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 = move_by_distance.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 activate_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 activate_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 activate_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 activate_radius == 1:\n",
    "    df_mapping_metainfo.to_pickle(f'df_mapping_metainfo_{max_mapping_radius}_{file_perfect_poscar_48n24_wo_cif}.pkl') \n",
    "elif activate_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 activate_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 activate_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 activate_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 activate_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(\"activate_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 activate_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, activate_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_wo_weirdos_selectedcol, \"Li\", activate_radius)\n",
    "elif activate_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, activate_radius)\n",
    "    flag_and_map.li_48htypesmerged(df_wo_weirdos_selectedcol, \"Li\", activate_radius)\n",
    "elif activate_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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