Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94b45554-3b1b-412f-a63d-fb2195742a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib import ARCDatabase as adb\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9333ce3d-7858-4b00-a02b-509a559e1f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = dict()\n",
    "\n",
    "options[\"atom1\"] = \"na\"\n",
    "options[\"atom2\"] = \"cs\"\n",
    "options[\"n1\"] = 53 # This value will be replaced!\n",
    "options[\"n2\"] = 54 # This value will be replaced!\n",
    "options[\"l1\"] = 0\n",
    "options[\"l2\"] = 0\n",
    "options[\"j1\"] = 0.5\n",
    "options[\"j2\"] = 0.5\n",
    "options[\"theta\"] = 0\n",
    "options[\"phi\"] = 0\n",
    "options[\"dn\"] = 10\n",
    "options[\"dl\"] = 5\n",
    "options[\"deltaMax\"] = 10e9\n",
    "options[\"nEig\"] = 250\n",
    "options[\"Bz\"] = 0\n",
    "\n",
    "calc = adb.Calculation('W:\\\\NaCsRydLab\\\\ryd_calculations.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a30b978-1afb-4aa4-8caa-64dbb6f4e7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Na: 0, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_45_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_45_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_45_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_45_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_45_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_45_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_45_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_45_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_45_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_45_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_45_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_45_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_45_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_45_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_45_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_45_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_45_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_45_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_45_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_45_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_45_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_45_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_45_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_45_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_45_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 0, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_45_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_46_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_46_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_46_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_46_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_46_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_46_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_46_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_46_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_46_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_46_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_46_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_46_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_46_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_46_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_46_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_46_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_46_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_46_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_46_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_46_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_46_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_46_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_46_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_46_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_46_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_46_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_47_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_47_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_47_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_47_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_47_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_47_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_47_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_47_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_47_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_47_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_47_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_47_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_47_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_47_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_47_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_47_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_47_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_47_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_47_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_47_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_47_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_47_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_47_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_47_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_47_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_47_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_48_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_48_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_48_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_48_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_48_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_48_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_48_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_48_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_48_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_48_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_48_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_48_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_48_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_48_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_48_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_48_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_48_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_48_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_48_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_48_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_48_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_48_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_48_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_48_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_48_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_48_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_49_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_49_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_49_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_49_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_49_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_49_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_49_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_49_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_49_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_49_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_49_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_49_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_49_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_49_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_49_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_49_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_49_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_49_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_49_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_49_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_49_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_49_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_49_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_49_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_49_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_49_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_50_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_50_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_50_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_50_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_50_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_50_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_50_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_50_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_50_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_50_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_50_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_50_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_50_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_50_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_50_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_50_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_50_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_50_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_50_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_50_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_50_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_50_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_50_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_50_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_50_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_50_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_51_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_51_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_51_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_51_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_51_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_51_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_51_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_51_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_51_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_51_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_51_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_51_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_51_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_51_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_51_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_51_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_51_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_51_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_51_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_51_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_51_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_51_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_51_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_51_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_51_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_51_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_52_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_52_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_52_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_52_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_52_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_52_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_52_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_52_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_52_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_52_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_52_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_52_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_52_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_52_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_52_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_52_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_52_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_52_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_52_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_52_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_52_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_52_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_52_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_52_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_52_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_52_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_53_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_53_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_53_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_53_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_53_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_53_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_53_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_53_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_53_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_53_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_53_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_53_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_53_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_53_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_53_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_53_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_53_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_53_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_53_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_53_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_53_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_53_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_53_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_53_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_53_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_53_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_54_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_54_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_54_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_54_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_54_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_54_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_54_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_54_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_54_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_54_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_54_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_54_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_54_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_54_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_54_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_54_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_54_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_54_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_54_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_54_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_54_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_54_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_54_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_54_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_54_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_54_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_55_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_55_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_55_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_55_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_55_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_55_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_55_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_55_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_55_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_55_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_55_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_55_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_55_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_55_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_55_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_55_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_55_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_55_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_55_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_55_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_55_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_55_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_55_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_55_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_55_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_55_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_56_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_56_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_56_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_56_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_56_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_56_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_56_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_56_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_56_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_56_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_56_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_56_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_56_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_56_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_56_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_56_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_56_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_56_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_56_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_56_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_56_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_56_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_56_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_56_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_56_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_56_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_57_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_57_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_57_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_57_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_57_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_57_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_57_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_57_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_57_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_57_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_57_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_57_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_57_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_57_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_57_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_57_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_57_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_57_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_57_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_57_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_57_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_57_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_57_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_57_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_57_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_57_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_58_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_58_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_58_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_58_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_58_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_58_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_58_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_58_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_58_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_58_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_58_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_58_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_58_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_58_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_58_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_58_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_58_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_58_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_58_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_58_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_58_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_58_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_58_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_58_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_58_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_58_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_59_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_59_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_59_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_59_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_59_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_59_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_59_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_59_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_59_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_59_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_59_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_59_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_59_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_59_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_59_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_59_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_59_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_59_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_59_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_59_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_59_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_59_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_59_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_59_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_59_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_59_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_60_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_60_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_60_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_60_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_60_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_60_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_60_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_60_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_60_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_60_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_60_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_60_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_60_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_60_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_60_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_60_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_60_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_60_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_60_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_60_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_60_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_60_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_60_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_60_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_60_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_60_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_61_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_61_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_61_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_61_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_61_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_61_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_61_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_61_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_61_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_61_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_61_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_61_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_61_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_61_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_61_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_61_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_61_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_61_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_61_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_61_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_61_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_61_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_61_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_61_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_61_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_61_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_62_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_62_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_62_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_62_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_62_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_62_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_62_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_62_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_62_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_62_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_62_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_62_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_62_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_62_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_62_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_62_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_62_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_62_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_62_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_62_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_62_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_62_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_62_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_62_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_62_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_62_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_63_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_63_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_63_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_63_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_63_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_63_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_63_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_63_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_63_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_63_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_63_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_63_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_63_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_63_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_63_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_63_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_63_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_63_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_63_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_63_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_63_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_63_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_63_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_63_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_63_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_63_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_64_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_64_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_64_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_64_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_64_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_64_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_64_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_64_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_64_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_64_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_64_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_64_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_64_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_64_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_64_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_64_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_64_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_64_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_64_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_64_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_64_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_64_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_64_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_64_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_64_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_64_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_65_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_65_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_65_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_65_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_65_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_65_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_65_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_65_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_65_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_65_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_65_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_65_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_65_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_65_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_65_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_65_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_65_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_65_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_65_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_65_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_65_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_65_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_65_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_65_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_65_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_65_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_66_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_66_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_66_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_66_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_66_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_66_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_66_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_66_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_66_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_66_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_66_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_66_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_66_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_66_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_66_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_66_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_66_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_66_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_66_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_66_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_66_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_66_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_66_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_66_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_66_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_66_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_67_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_67_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_67_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_67_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_67_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_67_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_67_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_67_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_67_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_67_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_67_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_67_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_67_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_67_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_67_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_67_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_67_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_67_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_67_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_67_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_67_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_67_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_67_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_67_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_67_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_67_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_68_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_68_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_68_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_68_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_68_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_68_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_68_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_68_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_68_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_68_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_68_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_68_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_68_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_68_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_68_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_68_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_68_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_68_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_68_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_68_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_68_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_68_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_68_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_68_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_68_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_68_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_69_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_69_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_69_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_69_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_69_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_69_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_69_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_69_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_69_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_69_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_69_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_69_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_69_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_69_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_69_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_69_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_69_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_69_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_69_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_69_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_69_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_69_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_69_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_69_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_69_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_69_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 0\n",
      "Calculating c6_na_cs_n1_70_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 1\n",
      "Calculating c6_na_cs_n1_70_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 2\n",
      "Calculating c6_na_cs_n1_70_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 3\n",
      "Calculating c6_na_cs_n1_70_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 4\n",
      "Calculating c6_na_cs_n1_70_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 5\n",
      "Calculating c6_na_cs_n1_70_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 6\n",
      "Calculating c6_na_cs_n1_70_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 7\n",
      "Calculating c6_na_cs_n1_70_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 8\n",
      "Calculating c6_na_cs_n1_70_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 9\n",
      "Calculating c6_na_cs_n1_70_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 10\n",
      "Calculating c6_na_cs_n1_70_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 11\n",
      "Calculating c6_na_cs_n1_70_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 12\n",
      "Calculating c6_na_cs_n1_70_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 13\n",
      "Calculating c6_na_cs_n1_70_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 14\n",
      "Calculating c6_na_cs_n1_70_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 15\n",
      "Calculating c6_na_cs_n1_70_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 16\n",
      "Calculating c6_na_cs_n1_70_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 17\n",
      "Calculating c6_na_cs_n1_70_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 18\n",
      "Calculating c6_na_cs_n1_70_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 19\n",
      "Calculating c6_na_cs_n1_70_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 20\n",
      "Calculating c6_na_cs_n1_70_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 21\n",
      "Calculating c6_na_cs_n1_70_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 22\n",
      "Calculating c6_na_cs_n1_70_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 23\n",
      "Calculating c6_na_cs_n1_70_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 24\n",
      "Calculating c6_na_cs_n1_70_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 25\n",
      "Calculating c6_na_cs_n1_70_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n"
     ]
    }
   ],
   "source": [
    "n_na_start = 45\n",
    "n_cs_start = 45\n",
    "\n",
    "delta_n_na = 26\n",
    "delta_n_cs = 26\n",
    "\n",
    "energy_values = np.zeros((delta_n_na, delta_n_cs))\n",
    "C6s = np.zeros((delta_n_na, delta_n_cs))\n",
    "\n",
    "for n_na_i in range(delta_n_na):\n",
    "    for n_cs_i in range(delta_n_cs):\n",
    "        print('Calculating Na: ' + str(n_na_i) + ', Calculating Cs: ' + str(n_cs_i))\n",
    "        n_na = n_na_start + n_na_i\n",
    "        n_cs = n_cs_start + n_cs_i\n",
    "\n",
    "        options[\"n1\"] = n_na\n",
    "        options[\"n2\"] = n_cs\n",
    "\n",
    "        C6, max_coupled_state, defect = calc.calculate_c6_perturbatively(options)\n",
    "        \n",
    "        energy_values[n_na_i, n_cs_i] = defect\n",
    "        C6s[n_na_i, n_cs_i] = C6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a75618f4-c87f-45fe-9f89-5a384ec88b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "style_sheet = 'C:\\\\Users\\\\goyan\\\\Downloads\\\\norm.mplstyle'\n",
    "plt.style.use(style_sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69bf6baf-a0ae-4f5a-9a6c-d3290929bdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$n_{Na}$')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot defects\n",
    "plt.imshow(np.abs(energy_values),extent=[45, 70, 70, 45], norm=LogNorm(), cmap='viridis_r')\n",
    "plt.gca().invert_yaxis()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('|Energy Defect| $\\delta$ (GHz)')\n",
    "plt.xlabel('$n_{Cs}$')\n",
    "plt.ylabel('$n_{Na}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1af86fe7-bc2b-498d-90ae-aa3a0bd2a4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$n_{Na}$')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot defects\n",
    "plt.imshow(energy_values,extent=[45, 70, 70, 45], cmap='viridis_r')\n",
    "plt.gca().invert_yaxis()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Energy Defect $\\delta$ (GHz)')\n",
    "plt.xlabel('$n_{Cs}$')\n",
    "plt.ylabel('$n_{Na}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f404f13-4596-4d11-a2cc-80f086a5d3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$n_{Na}$')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot C6s\n",
    "plt.imshow(C6s, extent=[45, 70, 70, 45], cmap='viridis_r')\n",
    "plt.gca().invert_yaxis()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('C6 $\\delta (GHz / um^{-6}) $')\n",
    "plt.xlabel('$n_{Cs}$')\n",
    "plt.ylabel('$n_{Na}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab9ae5c1-5a75-47aa-bafd-223621866386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$n_{Na}$')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot C6s\n",
    "plt.imshow(np.abs(C6s), extent=[45, 70, 70, 45], norm=LogNorm(), cmap='viridis_r')\n",
    "plt.gca().invert_yaxis()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('|C6| $\\delta (GHz / um^{-6}) $')\n",
    "plt.xlabel('$n_{Cs}$')\n",
    "plt.ylabel('$n_{Na}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f9c7162-bd67-44f8-a902-b134992c90c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24842e5e190>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(energy_values, C6s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ce619ef-19d6-4c4b-b4ad-05a8e9454293",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List the most promising pair states\n",
    "\n",
    "import heapq\n",
    "\n",
    "# create list of n_na, n_cs, c6 for heapq to sort through\n",
    "\n",
    "master_list = [(n_na_start + i, n_cs_start + j, C6s[i,j], np.abs(C6s[i,j])) for i in range(delta_n_na) for j in range(delta_n_cs)] \n",
    "results = heapq.nlargest(50, master_list, key=lambda x:x[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0813cbd8-0508-4542-8661-aa130009d650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(68, 68, 12196.788572031275, 12196.788572031275),\n",
       " (69, 69, -10115.217858076669, 10115.217858076669),\n",
       " (67, 67, 6848.39465147417, 6848.39465147417),\n",
       " (70, 70, -4910.060427823517, 4910.060427823517),\n",
       " (66, 70, 4881.469764746625, 4881.469764746625),\n",
       " (65, 69, 3454.1868986777686, 3454.1868986777686),\n",
       " (57, 62, -3165.9092628958883, 3165.9092628958883),\n",
       " (64, 68, 2488.0946158320385, 2488.0946158320385),\n",
       " (66, 66, 2281.85476105092, 2281.85476105092),\n",
       " (63, 67, 1814.0302378933113, 1814.0302378933113),\n",
       " (62, 66, 1333.7379057382484, 1333.7379057382484),\n",
       " (68, 70, -1303.6098189277532, 1303.6098189277532),\n",
       " (65, 65, 1262.6492422509418, 1262.6492422509418),\n",
       " (67, 69, -1105.1584424516666, 1105.1584424516666),\n",
       " (61, 65, 986.3302875520532, 986.3302875520532),\n",
       " (66, 68, -934.6360092101795, 934.6360092101795),\n",
       " (67, 70, -887.5936478256967, 887.5936478256967),\n",
       " (58, 63, -846.5403377368172, 846.5403377368172),\n",
       " (64, 64, 793.1308120825399, 793.1308120825399),\n",
       " (65, 67, -788.443209670842, 788.443209670842),\n",
       " (66, 69, -746.3830871790292, 746.3830871790292),\n",
       " (60, 64, 732.2721444636251, 732.2721444636251),\n",
       " (64, 66, -663.4011728339586, 663.4011728339586),\n",
       " (65, 68, -625.922353526526, 625.922353526526),\n",
       " (69, 70, 607.8679145508903, 607.8679145508903),\n",
       " (59, 64, -576.8476042957444, 576.8476042957444),\n",
       " (63, 65, -556.7064498227498, 556.7064498227498),\n",
       " (59, 63, 544.98211780413, 544.98211780413),\n",
       " (63, 63, 530.1822055821752, 530.1822055821752),\n",
       " (64, 67, -523.4222864874992, 523.4222864874992),\n",
       " (55, 60, 493.4372750623515, 493.4372750623515),\n",
       " (60, 65, -478.428047637623, 478.428047637623),\n",
       " (68, 69, 477.27314693857653, 477.27314693857653),\n",
       " (62, 64, -465.8921220277758, 465.8921220277758),\n",
       " (63, 66, -436.43272162136145, 436.43272162136145),\n",
       " (61, 66, -432.3985851722786, 432.3985851722786),\n",
       " (62, 67, -409.39602470736935, 409.39602470736935),\n",
       " (58, 62, 406.1051845779012, 406.1051845779012),\n",
       " (63, 68, -398.4483055325088, 398.4483055325088),\n",
       " (64, 69, -394.345628934606, 394.345628934606),\n",
       " (65, 70, -394.1170512763732, 394.1170512763732),\n",
       " (61, 63, -388.79144277697634, 388.79144277697634),\n",
       " (67, 68, 372.584759678093, 372.584759678093),\n",
       " (70, 69, 369.43369119332016, 369.43369119332016),\n",
       " (62, 62, 367.6805022547047, 367.6805022547047),\n",
       " (62, 65, -362.80549356206166, 362.80549356206166),\n",
       " (60, 62, -323.5063387329519, 323.5063387329519),\n",
       " (57, 61, 302.7015662514185, 302.7015662514185),\n",
       " (61, 64, -300.6616912573602, 300.6616912573602),\n",
       " (69, 68, 298.6062469522113, 298.6062469522113)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fcaef8c2-f104-47e6-afc7-77ea67941cd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Na: 25, Calculating Cs: 0\n",
      "c6_cs_cs_n1_45_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000 is already calculated\n",
      "Calculating Na: 25, Calculating Cs: 1\n",
      "Calculating c6_cs_cs_n1_46_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 2\n",
      "Calculating c6_cs_cs_n1_47_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 3\n",
      "Calculating c6_cs_cs_n1_48_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 4\n",
      "Calculating c6_cs_cs_n1_49_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 5\n",
      "Calculating c6_cs_cs_n1_50_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 6\n",
      "Calculating c6_cs_cs_n1_51_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 7\n",
      "Calculating c6_cs_cs_n1_52_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 8\n",
      "Calculating c6_cs_cs_n1_53_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 9\n",
      "Calculating c6_cs_cs_n1_54_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 10\n",
      "Calculating c6_cs_cs_n1_55_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 11\n",
      "Calculating c6_cs_cs_n1_56_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 12\n",
      "Calculating c6_cs_cs_n1_57_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 13\n",
      "Calculating c6_cs_cs_n1_58_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 14\n",
      "Calculating c6_cs_cs_n1_59_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 15\n",
      "Calculating c6_cs_cs_n1_60_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 16\n",
      "Calculating c6_cs_cs_n1_61_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 17\n",
      "Calculating c6_cs_cs_n1_62_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 18\n",
      "Calculating c6_cs_cs_n1_63_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 19\n",
      "Calculating c6_cs_cs_n1_64_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 20\n",
      "Calculating c6_cs_cs_n1_65_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 21\n",
      "Calculating c6_cs_cs_n1_66_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 22\n",
      "Calculating c6_cs_cs_n1_67_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 23\n",
      "Calculating c6_cs_cs_n1_68_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 24\n",
      "Calculating c6_cs_cs_n1_69_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 25\n",
      "Calculating c6_cs_cs_n1_70_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n"
     ]
    }
   ],
   "source": [
    "n_cs_start2 = 45\n",
    "\n",
    "delta_n_cs2 = 26\n",
    "\n",
    "energy_values_cs = np.zeros(delta_n_cs2)\n",
    "C6s_cs = np.zeros(delta_n_cs2)\n",
    "\n",
    "for n_cs_i in range(delta_n_cs2):\n",
    "    print('Calculating Na: ' + str(n_na_i) + ', Calculating Cs: ' + str(n_cs_i))\n",
    "    n_cs = n_cs_start2 + n_cs_i\n",
    "\n",
    "    options[\"atom1\"] = \"cs\"\n",
    "    options[\"atom2\"] = \"cs\"\n",
    "    options[\"n1\"] = n_cs\n",
    "    options[\"n2\"] = n_cs\n",
    "\n",
    "    C6, max_coupled_state, defect = calc.calculate_c6_perturbatively(options)\n",
    "    \n",
    "    energy_values_cs[n_cs_i] = defect\n",
    "    C6s_cs[n_cs_i] = C6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "60ef540c-c28e-433b-9173-56548125594d",
   "metadata": {},
   "outputs": [],
   "source": [
    "master_list_cs = [(n_cs_start2 + i, n_cs_start2 + i, C6s_cs[i], np.abs(C6s_cs[i])) for i in range(delta_n_cs2)] \n",
    "results_cs = heapq.nlargest(25, master_list_cs, key=lambda x:x[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c01d5181-b70a-4b5d-b1be-c77cb77ef341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(70, 70, -656.1241280068665, 656.1241280068665),\n",
       " (69, 69, -552.0734989821038, 552.0734989821038),\n",
       " (68, 68, -463.2586869829961, 463.2586869829961),\n",
       " (67, 67, -387.6395796781789, 387.6395796781789),\n",
       " (66, 66, -323.42312164760534, 323.42312164760534),\n",
       " (65, 65, -269.0360998254868, 269.0360998254868),\n",
       " (64, 64, -223.10186962258857, 223.10186962258857),\n",
       " (63, 63, -184.4179999454215, 184.4179999454215),\n",
       " (62, 62, -151.93660449400744, 151.93660449400744),\n",
       " (61, 61, -124.74689710886476, 124.74689710886476),\n",
       " (60, 60, -102.05911547387834, 102.05911547387834),\n",
       " (59, 59, -83.19019230280877, 83.19019230280877),\n",
       " (58, 58, -67.55090401756306, 67.55090401756306),\n",
       " (57, 57, -54.63437256812423, 54.63437256812423),\n",
       " (56, 56, -44.005593678519375, 44.005593678519375),\n",
       " (55, 55, -35.29316914194952, 35.29316914194952),\n",
       " (54, 54, -28.179165417185228, 28.179165417185228),\n",
       " (53, 53, -22.39508393466971, 22.39508393466971),\n",
       " (52, 52, -17.71271792101159, 17.71271792101159),\n",
       " (51, 51, -13.938367232435304, 13.938367232435304),\n",
       " (50, 50, -10.911140051474433, 10.911140051474433),\n",
       " (49, 49, -8.49459285793711, 8.49459285793711),\n",
       " (48, 48, -6.575343263737485, 6.575343263737485),\n",
       " (47, 47, -5.059217137988677, 5.059217137988677),\n",
       " (46, 46, -3.8682269435593444, 3.8682269435593444)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4f1b7e67-657e-4602-98e9-5fb6155b50e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating Na: 0, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_45_n2_45_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 1, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_46_n2_46_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 2, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_47_n2_47_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 3, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_48_n2_48_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 4, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_49_n2_49_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 5, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_50_n2_50_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 6, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_51_n2_51_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 7, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_52_n2_52_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 8, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_53_n2_53_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 9, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_54_n2_54_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 10, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_55_n2_55_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 11, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_56_n2_56_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 12, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_57_n2_57_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 13, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_58_n2_58_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 14, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_59_n2_59_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 15, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_60_n2_60_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 16, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_61_n2_61_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 17, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_62_n2_62_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 18, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_63_n2_63_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 19, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_64_n2_64_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 20, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_65_n2_65_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 21, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_66_n2_66_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 22, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_67_n2_67_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 23, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_68_n2_68_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 24, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_69_n2_69_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n",
      "Calculating Na: 25, Calculating Cs: 25\n",
      "Calculating c6_na_na_n1_70_n2_70_l1_0_l2_0_j1_0_5_j2_0_5_dn_10_deltaMax_10000000000\n"
     ]
    }
   ],
   "source": [
    "n_na_start2 = 45\n",
    "\n",
    "delta_n_na2 = 26\n",
    "\n",
    "energy_values_na = np.zeros(delta_n_na2)\n",
    "C6s_na = np.zeros(delta_n_na2)\n",
    "\n",
    "for n_na_i in range(delta_n_na2):\n",
    "    print('Calculating Na: ' + str(n_na_i) + ', Calculating Cs: ' + str(n_cs_i))\n",
    "    n_na = n_na_start2 + n_na_i\n",
    "\n",
    "    options[\"atom1\"] = \"na\"\n",
    "    options[\"atom2\"] = \"na\"\n",
    "    options[\"n1\"] = n_na\n",
    "    options[\"n2\"] = n_na\n",
    "\n",
    "    C6, max_coupled_state, defect = calc.calculate_c6_perturbatively(options)\n",
    "    \n",
    "    energy_values_na[n_na_i] = defect\n",
    "    C6s_na[n_na_i] = C6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b3f3bff8-43be-4578-b23f-da2c881c94c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(70, 70, -1862.063271364251, 1862.063271364251),\n",
       " (69, 69, -1574.110263730699, 1574.110263730699),\n",
       " (68, 68, -1327.287923852684, 1327.287923852684),\n",
       " (67, 67, -1116.2215628194394, 1116.2215628194394),\n",
       " (66, 66, -936.1713889641392, 936.1713889641392),\n",
       " (65, 65, -782.9657320362855, 782.9657320362855),\n",
       " (64, 64, -652.9403778641686, 652.9403778641686),\n",
       " (63, 63, -542.8836979908972, 542.8836979908972),\n",
       " (62, 62, -449.9865600313986, 449.9865600313986),\n",
       " (61, 61, -371.79744542199313, 371.79744542199313),\n",
       " (60, 60, -306.18258770536295, 306.18258770536295),\n",
       " (59, 59, -251.28794106202403, 251.28794106202403),\n",
       " (58, 58, -205.50735058648314, 205.50735058648314),\n",
       " (57, 57, -167.45274688376526, 167.45274688376526),\n",
       " (56, 56, -135.92771468687917, 135.92771468687917),\n",
       " (55, 55, -109.90385951145754, 109.90385951145754),\n",
       " (54, 54, -88.49972542990614, 88.49972542990614),\n",
       " (53, 53, -70.96203092460622, 70.96203092460622),\n",
       " (52, 52, -56.6490096947859, 56.6490096947859),\n",
       " (51, 51, -45.01563420180311, 45.01563420180311),\n",
       " (50, 50, -35.60057073287096, 35.60057073287096),\n",
       " (49, 49, -28.01476696911358, 28.01476696911358),\n",
       " (48, 48, -21.92979994316243, 21.92979994316243),\n",
       " (47, 47, -17.074583011494994, 17.074583011494994),\n",
       " (46, 46, -13.219152523102016, 13.219152523102016)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_list_na = [(n_na_start2 + i, n_na_start2 + i, C6s_na[i], np.abs(C6s_na[i])) for i in range(delta_n_na2)] \n",
    "results_na = heapq.nlargest(25, master_list_na, key=lambda x:x[3])\n",
    "results_na"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b593d6af-b391-460e-bae8-4ef5db2c0f7b",
   "metadata": {},
   "source": [
    "# Looking for attractive interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8c21eed-0d89-4368-b084-0ae22945647e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[67, 67]\n",
      "0.0060534855085647155\n"
     ]
    }
   ],
   "source": [
    "logs = (energy_values < 1) & (energy_values > 0)\n",
    "xs, ys = np.where(logs)\n",
    "# Find minimum defect state\n",
    "idx = np.argmin(energy_values[xs, ys])\n",
    "n1 = n_na_start + xs[idx]\n",
    "n2 = n_cs_start + ys[idx]\n",
    "print([n1,n2])\n",
    "print(energy_values[xs[idx], ys[idx]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f20ea20a-1da1-4d3a-8154-af00425e63cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question: Do all these states have the same sign of C6\n",
    "these_defects = energy_values[xs, ys]\n",
    "these_C6s = C6s[xs, ys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "084aff38-86cb-405e-81cd-fdd4feedf0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b3382f6880>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.scatter(these_defects, these_C6s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "974153cf-3136-425d-a26a-b36c8df16681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56, 61]\n",
      "-0.007391950989803952\n"
     ]
    }
   ],
   "source": [
    "logs2 = (energy_values > -1) & (energy_values < 0)\n",
    "xs2, ys2 = np.where(logs2)\n",
    "# Find minimum defect state\n",
    "idx2 = np.argmax(energy_values[xs2, ys2])\n",
    "n12 = n_na_start + xs2[idx2]\n",
    "n22 = n_cs_start + ys2[idx2]\n",
    "print([n12,n22])\n",
    "print(energy_values[xs2[idx2], ys2[idx2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffbed260-4354-4230-b6d3-9fb66ac8fb28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b338341640>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGYCAYAAACqH13pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA9g0lEQVR4nO3df3xU1YH///dkAgmBzPBLCEqKoMBHpCiEiWjE5KNQVILbottSF8Tqo34tg2VBlyJaAa0FHn6oK3YKuPVRRG35bD+rlQQrFVgsUiwS7FJAFkVkAUmAhGbAQDCT+/2DndlMMjO5M5mf3Nfz8eChuXPuueeeTOa+59x7z7UZhmEIAADAQrJS3QAAAIBkIwABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLyU51A9JRc3OzvvjiC+Xn58tms6W6OQAAwATDMHTmzBldfvnlysqKPMZDAArhiy++UGFhYaqbAQAAYnDkyBH1798/YhkCUAj5+fmSLnagw+HocH1er1eFhYVxq681l8ulDz/8MGPqTWTdmdrXmVp3pvY3fd1WJvZJIuvO1P62el/76/IfxyMhAIXgP+112223yW63B73mdrvldrtjqtfhcCTkD8lut2dUvYmuW8q8vs7kuqXM62/6uq1M7RP6Ozn1JrpuKfq+9ng88ng8Qct8Pp8kmbp8hQAUwYcffpjQXzYAAIhNqAEJr9crp9Npan3uAgMAAJZDAAIAAJZDALoExHpNUqrqTXTdiZSpfUJ/J6feRNedSJnaJ/R3cupNdN2pYDMMw0h1I9KN/xxifX193O4Ci2d9CI++Ti76O3no6+Siv5Mnnn0dTV2MAAEAAMshAAEAAMshAAEAAMthHqAkyMnJ0YIFC5STk5Pqplzy6Ovkor+Th75OLvo7cXzNhnYcqtOJM+fVJz9X113eNSV9zUXQIXDxGwAA8ffOnuNaVLFPx+vPB5b1c+ZqwaRhun14vw7Xz0XQEbz55ptyuVwaO3asSktLtXfv3lQ3CRblaza0/WCt3vrLMW0/WCtfs2F6Wbj1ASBdvbPnuH7w2q6g8CNJ1fXn9YPXdumdPceT2h5LnQLbsWOHpk+frqqqKg0ePFhr1qzRhAkT9PHHH5t6cFpHtR72KxrQQx9+XqftB2slGbpxUG+NuaqX7Fltn2HSet3igT1Dlot3GxO1ndbbq64/p7ovL6hntxwVONpuNxHtCldnqN9T1eHTOnHmvHp3zZFs0qmzjRHbcaGpWa/86XPtOFSrhgs+Db/coTONX+nEmQvKz8lWYc88/evOo6r2/s8HQV5nu7JsNp1tbAos657XSZL0t4avAsvyc7P17dGFevuvx9v9FuXfly9ON+gvR/8myaYre+Xp3hsG6C9H/hbYp2bD0J8P1am992Go/mvvd2e23ztaNh7by6R6O7L9eLc9XT6fzLSjo+8/SaY+N9LpvZoKoT5HF1XsU6ivaYYkm6RFFfs0flhB0vbZUqfAJk+erJycHP3mN7+RJDU3N+vyyy/XE088oUceeSRQLhGnwEIN+9lsUuve757XSUsmfz3oIJboIcNkbyfS9kJtNxHtClfnXdf107r/CA4WWTYp3OBKqHYsfnufXtp6qM3vNtH8Hxkrpo4K229m+d+H44cVhPxAbu939+OJw9Sja2dT6/n7sPW2Tn/ZqGfWf9yh33ui3tOprrcj249329Pl88lMO6Jpa6iyob6QhPvcSJf3aiqE2peeXTup7suvIqx10W++P0Y3XtUr5m1Hc/y2VAByOp166qmn9OijjwaWTZw4UdnZ2XrrrbcCy+IdgPzDftF09MoWB7FQ67Y+2EVi5ltFPLYTDTN9YpP00C0D9dIfD8W1XbH8PiK1sWU7Fr+9T6v+eCgONcfengJnrn488Rq5f/1Rh/exe16nkB/2oX4nkURaz6aL3wBbbyuUlv0dLpz5Rfo92xT7ezpRfytm6+3I9uPd9mR9brS3HTOfE5JMtzUenxHR9EGyP38TqaN998KU6/V3118R8/YJQCHU1taqd+/eevXVVzV16tTA8gcffFAffvihdu/eHVgWzwDkazZ089LNUX8LL3Dk6I9zb1Xpc/8edl3/we79H90adsjQzLeK9trY3naiHbaNpk8ijb6Y2f+ObNssfzs2P1qmaxe8E7a9yWT221Ymskly5nVSbrY96BRiy/e1md9zj7xO2vnk+KhOkXT0byUcs/W+90//O+bPhHi3PVF9Ee12pPY/J/o6ciTZgt4v4doqKW6fEWb6IFn9mAzx+HxN5giQZa4BamhokKQ2t9nl5OQEXmvN6/WaqjsnJyfs7Xs7DtXF9Gao9jbq1e2fR1zXkHS8/rx2HKoL+YYJl8T9F5z5v1W018ZI24ll2DaaPokUJtrb/45u2yx/O3769r60CD+SLtnwI13s74sjRcH72PJ97ezSud3f8+mGr/TzzZ9o1rghgWXtvZ+j/Vsx++XAbL0d+UzoyN95KPGuL9btSO1/TlR7GyOu37Kt+u//jwczfZCsfkyGjn6+9nP+z3VWrTU2NqqxMfLvUTJ/3JYsFIDy8vIkqU0HNjY2Bl5rrbCw0FTdCxYs0MKFC0O+duJM7G+Gw3Whg5mZbfiaDdMXnJltY+tyZgOWmfZ2RDT1xXvbLX1ea+73hcRo+b6ee/v/MrXOr7Z9rpm3Dg5cn9Te+7mxqdlUvSfOnI/qy4HZ92VHPhNi/TtPVrlErZ8O24pUb7L6MRk62sYFk4aFHeVavHixFi1a1KH6W7NMAOrVq5ecTqdqamqClldXV2vQoEEh1zly5IipU2CRJm/qk58bXUNbGNAzdDAzs41ovlWYbWPLctEErNZv6I70SXvtas/np76M67ZbSoezyTZJPbt2Vu2XF1LdlJTwv6/rzrb/TVGS/nbuK+04VKfigT1NvZ//z99fZ6rez099qX/e+ImpLwe+ZkOnzphrb0c+E2L5O09muUStnw7bilRvsvoxGTrSxgdLrox4ndPjjz+uOXPmtFuP1+s1PXhhqXmAbr31VlVVVQV+NgxDu3bt0rhx40KWdzgcpv5FCkDFA3uqnzNX0Z65LXDkaNqNV0Zc16bwQ4bRfKtor42hthNNwGrNv72OirT/obyz57ie3/hJh7cbzvuf1iasbjP8v79n/m54XPo3k/Xs2lndu3QyVfbEmfOm38/NPkPtXYaRZZN+/ef/ChumpIthytds6J09x3Xz0s16Zv3HEeu0Ser13/vUs2vnmD4TYvk7jyTe9cW6Helin0dqR4Hj4hQNZtoa62d2e/WGk6x+TIaO9N24YQURX8/JyTF9TDbLUgFo3rx5Wr9+vT799FNJ0uuvvy673a7p06cnbJv2LJsWTBoW9XoL77pWnbOzAuu2fkP5fw43ZBjNt4qWbTS7nY4M2/q319EPGCNEu8Lxj1hlmpxs83+iBc5crZg6SneO6BeX/k2UZLSrwNlF3ysZaKpsn/xc0+/nP39e2+51Xs2GVBNhRMcfpn6++dOQk8KFW6f2ywt69P/tVt2XF8LeYWNImuL6WsjXYvk7jyTe9cW6HZuk748dGLEdC++6VgvvMtfWSNuLhtk+SFY/JkMsfZfKgGepAFRcXKzVq1drypQpGjt2rP7lX/5FGzZsSPgkiLcP76cVU0epwNH+c05skn5x78jAUGBg3Vbf6P0Hu3BDhtF+q4h2Ox0dtvVvryMjFQ+0M2TaktmL88pH9GvTpkR97uR1ztKkEQVhRyp6du2s5799vb4/9so2v8csm/T9sVfqN98foxemXK/ffH+M3v/RrRo/rEDbD9aqsalZ/zhusKn3XChdO9slhT/g/H+3DIzqd9dyvdbvsR7/PbeKmW4ucOSoe14nU+/rmbdeHZi3pb2y5ofu4/dm+NW26KYSMOv5jQd089LNIWfVjfXzJJx41xfrdh6/c1i77YimreHK9sjr1OY91c+ZG/LvIZo+SFY/JkO4fQkl1QHPMrfBRyNRzwLzNRv6+eZP9fzGA2HL/OLei9/gQ60b7Qyh/os6JQV90EaaWyKaWWhvXrpZ1fXnw86zYubWzVCzCdedbWz3lIAU3e2Sb/3lmGat/Uu75V6Ycr3KR1ze7kzQ2w+e0s///WC79RV9rbtyO9nbzAQ9eVR/3XR1b9mzbHp793HN+PWuNuu2/D3d+r/66tXtn+twXYMG9MzTtBuvVOdWo0OhLrotcOTqu8VfU//uuVHPBP3uvuqIF/GGmwm6vQkMQ73Hwm0r1ISK7+6rNv2+fmfPcT38WuS+bXnbfHvv5//z99fpH3755xAlEiM/N1tnzjeFfb1rjl1fNvraLG+5f6HmS5JCz2YcK2aCZibo1lrvy+kvL+iZ9Ymf6JF5gDoo0Q9DTeaMn4ncViwBy4x4hauWth+s1Xf/5YN2y5kNVdEEqkiTesVrDpB4TaQW6TEg0Xwgx/JBHs06HZ3RN9zsyu29n8cPK2j3vemfc6bGG76Ms0sn/e1cYqcqsKn9+ZKAZEpGwCMAdVAyngafzKSfyG0l8pEA8QxX8Q5V8QpU8agnniEqk6biT8Qzxcw+TqG996akiGX+cdzghF6QH0l7o0OZOuIASASgDktGALqUJCpgJeKZRfEKVfEKVPEYSYpHiLqUpuLvKLOPjunIc6fMjCT1SOBs3owOpYbZU2qhlsXrNNuljgDUQQSg9BHvP/h4hqp4BKp4hJeOhqh4jSBZ7cO5o9ebtPf+8dw7Us+s/zhsSEoEKwbeZDH7cNVYH7hqtb+/cAhAHUQAurTF84Oio4EqHiNJHQ1R8RpByqTTZ+nCzBPOw4Uk/wNk6xu+imtA8r/n3vun/x3T9V9oK54PYG7J/9t46JaBpp5Gb4WQRADqIAIQotHRD5WOjiR1NER1dASJ02cd0977J1JIkkJfaxQPPbt2Vl2LGcUJtLFJxAOYzWj992eVLykEoA4iACHZOvrh1JEQ1ZERoI6ePrPCN9J4aO9UWtvpD3J0vqk5rqNDBNrYmP37SgT/39+PJ14j968/ssSXFAJQBxGAkArxGEmKJUR1ZASpI+HJKt9IkyHc3ErxHh3i9Fj0zI6wJlLPCBfUxzKtSDqL5vhtmYehAunOnmUzPbFjKLcP7xfTbc3+6et/8NquwLUlfu3N1BrrI1HMPHmdEGReqPeOf0beeI4O+R/jMWbxJk6PmZQODzGNdDdhy+c23nhVL0uNyhKAgEtIrCEq7MGynQNbLI9E8T+Xrb0nr48fVsBpsw4KF4r9o0OtA69ZLcOPRHCNxP9YomTezReLE2fOW25UllNgIfiH0IYMGSK73R70mtvtltvtTlHLgMSKNlzEcvqM02bpIVRfRjpV0h7/LNjLvn29Tp1tJJy2EO4avY5qL8DadPFi9tpWgTWU2eMG6583fpJR1wl5PB55PJ6gZT6fTwcOHOAaoFhxDRBgXrQXYMd61xl3m8VfqEeflD7373EbrSCc/o9QgbNHXicZCp7zJ9SySPMA3XVdP730x0OSws0nNUrPrN/X7pcUwzBU7W0M2fZMuk6Ii6A7iAAERCeakZlYRoDiNVkj2hfP0QrCabBEzQTdkfmkJOkfxw2J+JBuv2geQJ0qBKAOIgAB0TN7+izZp824Zih68Tw9RjhNjo7MJ9XY1ByXhzunA+4CA5B0Zi/AjuWus47cbcY1Q9ELdfF0rKfHWt9lhMRo7+8v0l2i2w/WmtpGOtzRFk9ZqW4AAOvx33VW4Az+QC1w5oY8XRLL3Wb+Yf/Wp838dyy9s+d4jK23Bv8B9e+uv0I3XtVLnbOzArNPxzKO8/s9x7X9YK18zZx0SJXWv1P/lwz/nWrhfq82Xfzi4D89d6ngFFgInAIDkiNRp824ZihxQo2qRYMRuPQU62zy6XaKmWuAOogABKSfaD6g4/GAV4TX8qDXu2uOHv3tf6jGa+7UGBdGp69oTxmn4ylmAlAHEYCA9GT2AzfWW+3T7dtspoj2zjFG4NKX2b+BdJ2WgougAVySzD7uI9ZrhtLt22ymCDeTeDj+C6NXbzuk3vk5hM00YuZmho7O5p4uGAEKgREgILNFe81Qun6bzTT+0YPf7zmuNdsPR7UuYTNzpPMp5miO39wFBuCS47/VXmp7x1LrW+3b+zYrXfw2y91L7fOPHtwRQ4jh7rzMEeu0FOmGAATgkmT2Vvsdh+oinrZpOY8NzGnvtupQCJuZI5ZTzOmIa4AAXLLMXDN0qXybTSeRJruMhEkTM0N7T7j3n2JO93mDGAECcEkLN/mb36XybTbdhBuBM4Owmd6iOcWczhgBAmBpl8q32XTUegTu1JlGPbP+43bX+6TmrLYfrOXOsDQW7s6/ggy6mJ27wELgLjDAWmKdBRfRae/uvNa4Myz9pdvcWUyE2EH+DhwyZIjsdnvQa263W263O0UtA5AozAOUHNFMmkgARSQej0cejydomc/n04EDBwhAsWIECLCmdPs2e6mK5nlizBqNaDATNADEwMwsuOi4ltcGbfv0pH7+7wfDluXOMCQKAQgAkHT+sMk0BEgVboMHAKQM0xAgVQhAAICUMTNrdM+unVTtPa/tB2uZJRpxQwACAKRMpEn1/Oq+/Eqz/+9f9N1/+UA3L93M88IQFwQgAEBKRTNrNA9NRbxwG3wI3AYPAMnnn4aguv6cnln/seq+vBCyHLfGI5xojt+MAAEA0oL/zrACZ5ew4UcKvjUeiBUBCACQVrg1HslAAAIApBVujUcyEIAAAGmlvVvjbbr4nLbigT2T2SxcYghAAIC0EunWeP/PCyYN4wJodAgBCACQdsLdGl/gzOXp8IgLngUGAEhLLR+aeuLMefXJv3jai5EfxAMBKAKXyyW73R60zO12y+12p6hFAGAt/lvjgdY8Ho88Hk/QMp/PZ3p9JkIMgYkQAQDIPEyECAAAEAEBCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWE7KAlBdXZ0WLlyom2++WWVlZRo5cqR++tOfqqmpKajcsWPHVF5erpKSEo0aNUorV65sU9f777+vMWPGqLS0VGPGjNHWrVvblFm1apWKiopUUlKiiRMn6tixYwnbNwAAkN5S9iywt99+W//6r/+q7du3y+l06tixYxo1apQuXLighQsXSpKam5tVXl6ue+65R0888YROnjypr3/96+rTp48mT54sSTp8+LAmTpyoyspKjR07Vu+9957Ky8u1e/duDRgwQJL0xhtvaNGiRdq9e7d69+6tp59+WuXl5aqqqlJWFoNgAABYTcqO/r169dJjjz0mp9MpSbriiiv093//9/rNb34TKFNZWam9e/dq1qxZkqTLLrtM9913n5599tlAmRdeeEHDhg3T2LFjJUmlpaUaOnSoli9fHijzk5/8RNOnT1fv3r0lSbNmzdKePXu0fv36hO8nAABIPykLQHfccYceeOCBoGW5ublqbGwM/Lxp0yYNHTpU3bp1CyxzuVzatWuXTp8+HSgzevTooHpcLpc2btwo6eKpto8++iiojNPp1JAhQwJlAACAtaTV+Z/t27fr29/+duDnzz77TH379g0qU1BQIEk6dOhQxDL+1/3/jVQGAABYS8quAWpt8+bNOnr0qJ588snAsoaGBuXm5gaVy8nJCbzm/69/WcsyLV9vuV6oMuF4vV5Tbc/JyWlTPwAAiI/GxsagM0ThmD1uSwkYAZo3b55sNlvEf/v37w9a59ixY5oxY4beeustORyOwPK8vLw2O+z/OS8vL2KZlq+3XC9UmXAKCwvldDrb/bd48WKz3QMAAKK0ePFiU8fjwsJC03XGfQRo/vz5mjlzZsQy/tNYklRbW6tvfvObWrVqla6//vqgcoMGDdLmzZuDllVXV0uSBg4cGChTU1PTpsygQYMCr0sKWWb8+PER23nkyJGgQBYOoz8AACTO448/rjlz5rRbzuv1mg5BcQ9ADofDVGiQpDNnzuiuu+7SggULVFpaKkl66aWX9NBDD0mSbrvtNq1YsUJnz54NXAi9c+dOFRUVqUePHoEyH3zwQVC9O3fu1Lhx4yRJPXr00MiRI1VVVaW7775b0sUOOnDggJYuXRq3fQEAAImRiEtNUnYR9Pnz53XXXXfpxhtvVEFBgXbu3KmdO3dq1apVgTITJ07UtddeqxdffFGSdOrUKa1Zs0bz588PlJk1a5b27t2rbdu2SZK2bt2q/fv365FHHgmUefLJJ/XKK6+otrZWkrR8+XINHz5cd955ZzJ2FQAApJmUXQT98ssva8uWLdqyZYuWLVsWsozdbldFRYUefvhhlZSU6Ny5c3rqqacCkyBK0oABA1RZWalHH31UnTt3VmNjoyorKwOTIErS5MmTdeLECY0fP165ubnq0aOHKioqmAQRAACLshmGYaS6EenG6/XK6XSqvr6eU2AAAGSIaI7fDIEAAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLSdmzwDKBy+WS3W4PWuZ2u+V2u1PUIgAAIEkej0cejydomc/nM70+zwILgWeBAQCQeXgWGAAAQAQEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDnZqW5AOnO5XLLb7UHL3G633G53iloEAEBm8zUb2nGoTifOnFef/FwVD+wpe5Yt6no8Ho88Hk9w3T6f6fVthmEYUW/1Euf1euV0OlVfXy+Hw5Hq5gAAcEl4Z89xLarYp+P15wPL+jlztWDSMN0+vF+H64/m+M0pMAAAkHDv7DmuH7y2Kyj8SFJ1/Xn94LVdemfP8aS2hwAEAAASytdsaFHFPoU65eRftqhin3zNyTspRQACAAAJteNQXZuRn5YMScfrz2vHobqktYkABAAAEurEmfDhJ5Zy8UAAAgAACdUnPzeu5eKBAAQAABKqeGBP9XPmKtzN7jZdvBuseGDPpLWJAAQAABLKnmXTgknDJKlNCPL/vGDSsJjmA4oVAQgAACTc7cP7acXUUSpwBp/mKnDmasXUUXGZBygazAQNAACS4vbh/TR+WEFcZoLuKAIQAABIGnuWTTde1SvVzeAUGAAAsB4CEAAAsBwCEAAAsBwCEAAAsBwugo7A5XLJbrcHLXO73XK73SlqEQAAkCSPxyOPxxO0zOfzmV7fZhhG8h69miG8Xq+cTqfq6+vlcDhS3RwAAGBCNMdvToEBAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLSYsA1NzcrBtuuEFXXnllm9fq6+s1bdo0FRcXa9SoUVq0aJFaT169b98+lZWV6ZZbbtHo0aP1xhtvtKnnzTfflMvl0tixY1VaWqq9e/cmancAAECaS4tngXk8Hh04cEBOp7PNa9OmTVPfvn21Y8cONTQ0qLi4WPn5+ZozZ44k6cyZM/rGN76hpUuX6h/+4R904MABFRUVqX///iouLpYk7dixQ9OnT1dVVZUGDx6sNWvWaMKECfr444+Vn5+f1H0FAACpl/IRoGPHjunll1/WQw891Oa13bt3q6KiQo899pgkKS8vTzNmzNCSJUsCDzz71a9+pebmZt17772SpCFDhuiOO+7Q0qVLA/UsWbJEEydO1ODBgyVJU6dOVVNTk1avXp3gvQMAAOko5QHohz/8oZYsWaIuXbq0eW3Tpk3q1q2bhg4dGljmcrl08uRJ7d69O1CmqKhINpstqMymTZuC6hk9enTg56ysLBUVFWnjxo2J2CUAAJDmUhqAKioqlJ2drdtvvz3k65999pn69u0btKygoECSdOjQoYhl6uvrVVdXp9raWnm93pBl/HUAAABrSdk1QGfPntX8+fP1hz/8IWyZhoYG5eTkBC3z/9zQ0GCqjP+C6VBl/HWE4/V6TezJxbpa1w8AAOKjsbFRjY2N7ZYze9yWEjACNG/ePNlstoj/9u/frx//+Md6+OGH1a9fv7B15eXltdlh/895eXmmyvjLhSrjfy2cwsJCOZ3Odv8tXrzYRM8AAIBYLF682NTxuLCw0HSdcR8Bmj9/vmbOnBmxTEFBgTZv3qyqqir99re/lSR9/vnnqq6uVllZma6++mr98pe/1KBBg1RTUxO0bnV1tSRp0KBBgf+GKuN0OtWzZ09JktPpDFnGX0c4R44ckcPhaGeP244uAQCA+Hn88ccDd39H4vV6TYeguAcgh8NhKjT8x3/8R9DPCxcu1OrVq7Vly5bAsttuu01z5szRgQMHNGTIEEnSzp071adPH40YMSJQZunSpTIMI3Ah9M6dOzVu3LhAPbfeequqqqoCPxuGoV27dumJJ56Iy74AAIDEScSlJim/CyySESNGaNKkSVq2bJkk6dy5c1qxYoV+9KMfKSvrYtO/973vyWazae3atZKkTz75RL///e81d+7cQD3z5s3T+vXr9emnn0qSXn/9ddntdk2fPj3JewQAANKBzWg9rXKSVVdXa8qUKYFTYGPGjNH999+v+++/X5L0t7/9TTNnztQnn3yiCxcu6Jvf/KaeeuqpoNve9+7dqxkzZqi5uVnnzp3T/PnzNXny5KDtvPnmm3r22WfVpUsXZWVl6Re/+IWuvfbakG3yer1yOp2qr69nBAgAgAwRzfE75QEoHRGAAADIPNEcv9P6FBgAAEAiEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlEIAAAIDlxP1p8JcSl8slu90etMztdsvtdqeoRQAAQJI8Ho88Hk/QMp/PZ3p9ngUWAs8CAwAg8/AsMAAAgAgIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHKYCBEAAMSFr9nQjkN1OnHmvPrk56p4YE/Zs2ypblZIBCAAANBh7+w5rkUV+3S8/nxgWT9nrhZMGqbbh/dLYctC4xQYAADokHf2HNcPXtsVFH4kqbr+vH7w2i69s+d4iloWHgEIAADEzNdsaFHFPoV6rpZ/2aKKffI1p9eTtwhAAAAgZjsO1bUZ+WnJkHS8/rx2HKpLXqNMIAABAICYnTgTPvzEUi5ZCEAAACBmffJz41ouWQhAAAAgZsUDe6qfM1fhbna36eLdYMUDeyazWe3iNvgIXC6X7HZ70DK32y23252iFgEAkF7sWTYtmDRMP3htl2xS0MXQ/lC0YNKwuM8H5PF45PF4gpb5fD7T69sMw0ivy7LTgNfrldPpVH19vRwOR6qbAwBA2kuHeYCiOX4zAgQAADrs9uH9NH5YATNBAwAAa7Fn2XTjVb1S3QxTuAgaAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDg9DjcDlcslutwctc7vdcrvdKWoRAACQJI/HI4/HE7TM5/OZXt9mGIYR70ZlOq/XK6fTqfr6ejkcjlQ3BwAAmBDN8ZtTYAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHJSHoDWrl2rsrIylZaW6qqrrtJ3vvOdoNePHTum8vJylZSUaNSoUVq5cmWbOt5//32NGTNGpaWlGjNmjLZu3dqmzKpVq1RUVKSSkhJNnDhRx44dS9g+AQCA9JbSZ4G9/vrrWr58ud599105HA7953/+p1wuV+D15uZmlZeX65577tETTzyhkydP6utf/7r69OmjyZMnS5IOHz6siRMnqrKyUmPHjtV7772n8vJy7d69WwMGDJAkvfHGG1q0aJF2796t3r176+mnn1Z5ebmqqqqUlZXyDAgAAJIsZUf/pqYmPfbYY3rmmWcCz+sYOnSoKisrA2UqKyu1d+9ezZo1S5J02WWX6b777tOzzz4bKPPCCy9o2LBhGjt2rCSptLRUQ4cO1fLlywNlfvKTn2j69Onq3bu3JGnWrFnas2eP1q9fn/D9BAAA6SdlAehPf/qTampqAsHF75Zbbgn8/6ZNmzR06FB169YtsMzlcmnXrl06ffp0oMzo0aOD6nC5XNq4caMkqa6uTh999FFQGafTqSFDhgTKAAAAa0lZANqzZ4+6d++uP/zhDxo/frxuuukmPfDAA/riiy8CZT777DP17ds3aL2CggJJ0qFDhyKW8b/u/2+kMgAAwFpSdg3Q6dOn5fV6tXLlSq1bt06dO3fWQw89pFtuuUV79uxRbm6uGhoalJubG7ReTk6OJKmhoSHwX/+ylmVavt5yvVBlwvF6vab2JScnp039AAAgPhobG9XY2NhuObPHbSkBI0Dz5s2TzWaL+G///v2y2+3y+XyaO3euunTpIrvdrmeeeUYHDx4MXAeUl5fXZof9P+fl5UUs0/L1luuFKhNOYWGhnE5nu/8WL14cY28BAID2LF682NTxuLCw0HSdcR8Bmj9/vmbOnBmxTEFBgfr37y9JuuKKKwLLL7/8cmVnZwdOTQ0aNEibN28OWre6ulqSNHDgwECZmpqaNmUGDRoUeF1SyDLjx4+P2M4jR44ELtCOhNEfAAAS5/HHH9ecOXPaLef1ek2HoLgHIIfDYSo0+C92Pn78uIYMGSLp4gXLTU1N+trXviZJuu2227RixQqdPXs2cCH0zp07VVRUpB49egTKfPDBB0F179y5U+PGjZMk9ejRQyNHjlRVVZXuvvtuSRc76MCBA1q6dGlc9gUAACROIi41SdlF0F/72td077336uc//7mam5slSc8//7wGDBig8vJySdLEiRN17bXX6sUXX5QknTp1SmvWrNH8+fMD9cyaNUt79+7Vtm3bJElbt27V/v379cgjjwTKPPnkk3rllVdUW1srSVq+fLmGDx+uO++8Myn7CgAA0ktKJ0J86aWXNHv2bI0cOVIOh0M9e/bUpk2b1LVrV0mS3W5XRUWFHn74YZWUlOjcuXN66qmnApMgStKAAQNUWVmpRx99VJ07d1ZjY6MqKysDkyBK0uTJk3XixAmNHz9eubm56tGjhyoqKpgEEQAAi7IZhmGkuhHpxuv1yul0qr6+nlNgAABkiGiO3wyBAAAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy0nps8DSncvlkt1uD1rmdrvldrtT1CIAACBJHo9HHo8naJnP5zO9Ps8CC4FngQEAkHl4FhgAAEAEBCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA52aluQDpzuVyy2+1By9xut9xud4paBAAAJMnj8cjj8QQt8/l8pte3GYZhxLtRmc7r9crpdKq+vl4OhyPVzQEAACZEc/zmFBgAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALCc7FQ3IJ25XC7Z7fagZW63W263O0UtAgAAkuTxeOTxeIKW+Xw+0+vbDMMw4t2oTOf1euV0OlVfXy+Hw5Hq5gAAABOiOX5zCgwAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFhOSgPQ7t27NWHCBI0ZM0YlJSWaPHmyDh8+HFTm2LFjKi8vV0lJiUaNGqWVK1e2qef999/XmDFjVFpaqjFjxmjr1q1tyqxatUpFRUUqKSnRxIkTdezYsYTtFwAASG8pC0CGYai8vFzXXnutPvjgA23btk1XXnml7rnnnkCZ5uZmlZeX68Ybb9S2bdu0YcMGLVy4UG+88UagzOHDhzVx4kQ999xzeu+997R06VKVl5cHBak33nhDixYt0oYNG7Rt2zbdcMMNKi8vV3Nzc1L3GQAApIeUBaC6ujodOXJE48aNCywbN26cdu7cqdOnT0uSKisrtXfvXs2aNUuSdNlll+m+++7Ts88+G1jnhRde0LBhwzR27FhJUmlpqYYOHarly5cHyvzkJz/R9OnT1bt3b0nSrFmztGfPHq1fvz7h+wkAANJPygJQr169VFZWprVr16qpqUlNTU1au3atunbtqq5du0qSNm3apKFDh6pbt26B9Vwul3bt2hUISZs2bdLo0aOD6na5XNq4caOki0Hro48+CirjdDo1ZMiQQBkAAGAtKb0GaN26dTp9+rT69++v/v3763e/+51Wrlypzp07S5I+++wz9e3bN2idgoICSdKhQ4cilvG/7v9vpDIAAMBaslO1YZ/Pp7vuukuFhYU6cuSIJOmVV17RwIEDA2UaGhqUm5sbtF5OTk7gNf9//ctalmn5esv1QpUJx+v1mtqXnJycNvUDAID4aGxsVGNjY7vlzB63pQQEoHnz5mnp0qURy3z88cfav3+/tmzZoqNHj6pTp06SpAkTJmjw4MHau3evrrrqKuXl5encuXNB6/o7IC8vL/Df1p3S2NgY9HrL9VqW8Z9qC6ewsDDi634LFizQwoULTZUFAADRWbx4sRYtWhTXOuMegObPn6+ZM2dGLFNQUKB169YpOztbV1xxRWB5YWGhfD6fKisrNWvWLA0aNEibN28OWre6ulqSAiNFgwYNUk1NTZsygwYNCrwuKWSZ8ePHR2znkSNH5HA4IpaR2o4uAQCA+Hn88cc1Z86cdst5vV7TgxdxD0AOh8NUaLjiiivU1NSkU6dOBe7OOnnypJqamgKjNrfddptWrFihs2fPBi6E3rlzp4qKitSjR49AmQ8++CCo7p07dwbuLuvRo4dGjhypqqoq3X333ZIudtCBAwfaHakyuy8AACBxEnGpScougp44caL69u2r5557LrBsyZIlcjgcuv322wNlrr32Wr344ouSpFOnTmnNmjWaP39+YJ1Zs2Zp79692rZtmyRp69at2r9/vx555JFAmSeffFKvvPKKamtrJUnLly/X8OHDdeeddyZ8PwEAQPpJ2UXQ3bt317vvvqu5c+dqzJgxam5uVrdu3bRhw4bA8JXdbldFRYUefvhhlZSU6Ny5c3rqqac0efLkQD0DBgxQZWWlHn30UXXu3FmNjY2qrKzUgAEDAmUmT56sEydOaPz48crNzVWPHj1UUVGhrCyeBAIAgBXZDMMwUt2IdOP1euV0OlVfX88pMAAAMkQ0x2+GQAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOWk7FlgmcDlcslutwctc7vdcrvdKWoRAACQJI/HI4/HE7TM5/OZXp9ngYXAs8AAAMg8PAsMAAAgAgIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnOxUNyCduVwu2e32oGVut1tutztFLQIAAJLk8Xjk8XiClvl8PtPr2wzDMOLdqEzn9XrldDpVX18vh8OR6uYAAAATojl+cwoMAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYDgEIAABYTnaqG5DOXC6X7HZ70DK32y23252iFgEAAEnyeDzyeDxBy3w+n+n1bYZhGPFuVKbzer1yOp2qr6+Xw+FIdXMAAIAJ0Ry/OQUGAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAsJ6EB6JNPPtFNN92ksrKykK/X19dr2rRpKi4u1qhRo7Ro0SK1nph63759Kisr0y233KLRo0frjTfeaFPPm2++KZfLpbFjx6q0tFR79+4Net0wDD399NMaNWqUiouLNXXqVNXX18dtPwEAQGZJWAB69dVXdd999ykrK/wmpk2bptzcXO3YsUPvv/++fvvb3+r5558PvH7mzBl94xvf0Pe//3398Y9/1K9//WtNnz5dO3bsCJTZsWOHpk+frl//+tfaunWrHnzwQU2YMEFnzpwJlHn++ef1b//2b9q2bZt27Nihzp07a9q0aYnZcQAAkPYSFoB69eql9957T1dffXXI13fv3q2Kigo99thjkqS8vDzNmDFDS5YsCTzM7Fe/+pWam5t17733SpKGDBmiO+64Q0uXLg3Us2TJEk2cOFGDBw+WJE2dOlVNTU1avXq1pIsPRluyZIlmzJihLl26SJIee+wxVVRU6K9//WtC9h0AAKS3hAWgO++8U507dw77+qZNm9StWzcNHTo0sMzlcunkyZPavXt3oExRUZFsNltQmU2bNgXVM3r06MDPWVlZKioq0saNGyVdDFonT54MKnPNNdeoa9eugTIAAMBaUnYR9Geffaa+ffsGLSsoKJAkHTp0KGKZ+vp61dXVqba2Vl6vN2SZlnVICipjs9nUt2/fQBkAAJBYvmZD2w/W6q2/HNP2g7XyNRvtr5RA2anacENDg3JycoKW+X9uaGgwVcZ/wXSoMi3raK9MOF6v19S+5OTktKkfAABc9M6e41pUsU/H688HlvVz5mrBpGG6fXi/dtdvbGxUY2Nju+XMHrelKEeA5s2bJ5vNFvHf/v37TdWVl5fXZmf8P+fl5Zkq4y8XqkzLOtorE05hYaGcTme7/xYvXmxqnwEAsJp39hzXD17bFRR+JKm6/rx+8NouvbPneLt1LF682NTxuLCw0HS7ohoBmj9/vmbOnBmxjP80VnsGDRqkmpqaoGXV1dWB1yKVcTqd6tmzpyTJ6XSGLNOyDkmqqalR//79A2VqamoCr4Vz5MgRORyOdveF0R8AANryNRtaVLFPoU52GZJskhZV7NP4YQWyZ9lClLro8ccf15w5c9rdntfrNR2CogpADofDVCAw47bbbtOcOXN04MABDRkyRJK0c+dO9enTRyNGjAiUWbp0qQzDCFwIvXPnTo0bNy5Qz6233qqqqqrAz4ZhaNeuXXriiSckSSNGjNBll12mqqoqFRUVSZI+/vhjffnll0H1JHp/AQCwmh2H6tqM/LRkSDpef147DtXpxqt6hS2XiEtNUnYR9IgRIzRp0iQtW7ZMknTu3DmtWLFCP/rRjwJzB33ve9+TzWbT2rVrJV2cWPH3v/+95s6dG6hn3rx5Wr9+vT799FNJ0uuvvy673a7p06dLkux2u+bNm6df/OIXOnfunCRp2bJlmjRpkoYPH560/QUAwGpOnAkffmIpF08Juwh63bp1+tnPfqb9+/fr/PnzKisr07Rp0/Tggw8GyqxZs0YzZ87UDTfcoAsXLujuu+/W7NmzA6/n5+drw4YNmjFjRiDArF69WsXFxYEyxcXFWr16taZMmaIuXbooKytLGzZsUH5+fqDM7NmzdfbsWZWUlCg7O1uDBw/WmjVrErXrAABAUp/83LiWiyeb0frZE5DX65XT6VR9fT2nwAAAiJGv2dDNSzeruv58yOuAbJIKnLl6/0e3RrwGyKxojt88DBUAACSEPcumBZOGSboYdlry/7xg0rC4hJ9oEYAAAEDC3D68n1ZMHaUCZ/BprgJnrlZMHWVqHqBESNlEiAAAwBpuH95P44cVaMehOp04c1598nNVPLBnSkZ+/BgBSoLGxkYtXLjQ1CyW6Bj6Orno7+Shr5OL/o4/e5ZNN17VS393/RW68apegfCTqr7mIugQ4n0RNBdVJw99nVz0d/LQ18lFfydPPPuai6ABAAAiIAABAADLIQABAADLIQBdAjweT0bVm+i6EylT+4T+Tk69ia47kTK1T+jv5NSb6LpTwkAb9fX1hiSjvr4+Letr7ZprrsmoehNZd6b2dabWnan9TV+3lYl9ksi6M7W/rd7X0dTFPEARuFwu2e32oGVut1tutztFLQIAANLFEanWo1I+n8/0+gSgCD788ENufwQAIA2FGpDw3wZvBtcAAQAAy2EEKATjv+eG9Hq9canPX0+86mvN5/MlpO5E1ZvIujO1rzO17kztb/q6rUzsk0TWnan9bfW+9tdhmJjjmZmgQzh69KgKCwtT3QwAABCDI0eOqH///hHLEIBCaG5u1hdffKH8/HzZbKl7UBsAADDPMAydOXNGl19+ubKyIl/lQwACAACWw0XQAADAcghAAADAcghACfLJJ5/opptuUllZmanyhmHo6aef1qhRo1RcXKypU6eqvr4+sY28RFy4cEGzZs3S6NGjVVRUpB/+8Ie6cOFCxHV2796tCRMmaMyYMSopKdHkyZN1+PDhJLU4s8XS35K0du1alZWVqbS0VFdddZW+853vJKG1mS3WvpYuXst4ww036Morr0xsIy8R0fZ1XV2dFi5cqJtvvlllZWUaOXKkfvrTn6qpqSmJrc4cb775plwul8aOHavS0lLt3bs3ruVj0uF5p9HGmjVrjDFjxhglJSVGaWmpqXWWLVtmjBgxwmhoaDAMwzC+973vGZMmTUpgKy8djzzyiDFhwgSjqanJaGpqMsaNG2c88sgjYcs3NzcbhYWFxuzZswPLZs+ebYwePToZzc140fa3YRjGa6+9ZhQXFwemp9+/f7+Rn5+fjOZmtFj62m/58uVG9+7djQEDBiS2kZeIaPv61VdfNa655hrjb3/7m2EYhnH06FGjT58+xoIFC5LU4szx5z//2cjPzzcOHDhgGIZhvPLKK8YVV1xheL3euJSPFQEoAdavX280NjYa06dPNxWAmpqajMsuu8xYuXJlYNnevXsNScbu3bsT2NLMd+rUKaNTp07GO++8E1i2fv16o1OnTkZtbW3YdSQZ69evD1pHklFXV5fwNmeyWPr7q6++MgoKCowNGzYELX/vvfcS2tZMF0tf+x09etS47rrrjLlz5xKATIilr99++23j5ZdfDlrmdruNIUOGJLStmehb3/qWMWXKlMDPPp/P6Nu3r7F8+fK4lI8Vp8AS4M4771Tnzp1Nl9+9e7dOnjyp0aNHB5Zdc8016tq1qzZu3JiIJl4y/vjHP+qrr74K6juXy6WvvvpK7733Xsh1evXqpbKyMq1du1ZNTU1qamrS2rVr1bVrV3Xt2jVZTc9IsfT3n/70J9XU1Gjs2LFBy2+55ZaEtjXTxdLXfj/84Q+1ZMkSdenSJdHNvCTE0td33HGHHnjggaBlubm5amxsTGhbM9GmTZuC+jYrK0tFRUVhj2/Rlo8VASgNfPbZZ5Kkvn37BpbZbDb17dtXhw4dSlWzMsJnn32m7Oxs9erVK7Dssssuk91uj9h369at0+nTp9W/f3/1799fv/vd77Ry5cqogqsVxdLfe/bsUffu3fWHP/xB48eP10033aQHHnhAX3zxRbKanZFifW9XVFQoOztbt99+ezKaeUmIta9b2759u7797W8nookZq7a2Vl6vN+j4JkkFBQUh+zba8h1BAEoDDQ0NkqScnJyg5Tk5OYHXEFpDQ0PI0NK5c+ewfefz+XTXXXepR48eOnLkiI4cOaKf/exnGjhwYKKbm/Fi6e/Tp0/L6/Vq5cqVWrdunbZu3SqbzaZbbrlF58+fT3STM1YsfX327FnNnz9f//zP/5zg1l1aYunr1jZv3qyjR4/qySefjHfzMlq0x7dkHg8JQCbNmzdPNpst4r/9+/fHVHdeXp4ktRk6bWxsDLxmNWb7Oy8vL+SdGhcuXAjbdxUVFdqyZYsWL16sTp06qVOnTpowYYJuu+02HTx4MNG7lpYS2d92u10+n09z585Vly5dZLfb9cwzz+jgwYOqrKxM9K6lnUT29Y9//GM9/PDD6tevX6J3IyMksq9bOnbsmGbMmKG33npLDocjEbuSsaI9viXzeMjDUE2aP3++Zs6cGbFMQUFBTHUPGjRIklRTUxP07JKamprAa1Zjtr8HDRqkpqYm1dbWBoavT548KZ/PF7bvDhw4oOzsbF1xxRWBZYWFhfL5fKqsrNSsWbPityMZIpH97X9Pt+zvyy+/XNnZ2ZY8xZvIvt68ebOqqqr029/+VpL0+eefq7q6WmVlZbr66qv1y1/+Mr47k+YS2dd+tbW1+uY3v6lVq1bp+uuvj1fTLxm9evWS0+lUTU1N0PLq6uqQfRtt+Q6J6yXVCBLtXWCrVq0KLNu3b58hyfjrX/+awBZmPv/dGy3vMHr77bcj3r3x2muvGZKMkydPBpadOHHCkGS89NJLCW9zJoulvw8fPmxIMrZs2RJYVltba0gy1q5dm/A2Z6pY+rq1BQsWcBeYCbH2tdfrNW666SajoqIisKzl5zgu+ta3vmV897vfDfzc3NxsFBQUGC+++GJcyseKAJRA4QLQ0aNHjf79+xuVlZWBZcuWLTOuu+66wDxADz74IPMAmfTII48Yd9xxh+Hz+Qyfz2d84xvfCJq/o6qqyrj88suNXbt2GYZhGKdPnzb69u1rzJ07N1Bmzpw5hsPhMP7rv/4r6e3PNNH2t2EYxr333mvcc889hs/nMwzDMJ588kljwIABxtmzZ5Pe/kwSS1+3RAAyL9q+PnfunFFWVmY8+uijxocffhj4N2rUqFTtQtr685//bDgcDuOTTz4xDOPiHEot5/UpKSkx5s+fb7p8vHAKLAHWrVunn/3sZ9q/f7/Onz+vsrIyTZs2TQ8++KCkixfhnjt3Tl999VVgndmzZ+vs2bMqKSlRdna2Bg8erDVr1qRqFzLKc889p3/6p3+Sy+WSJN1000167rnnAq83NTWpoaEhMENr9+7d9e6772ru3LkaM2aMmpub1a1bN23YsEGFhYUp2YdMEm1/S9JLL72k2bNna+TIkXI4HOrZs6c2bdrEtAPtiKWvpYunC6ZMmRJ0Cuz+++/X/fffn8zmZ5Ro+/rll1/Wli1btGXLFi1btiwlbc4UxcXFWr16taZMmaIuXbooKytLGzZsUH5+vqSLFz63vOanvfLxwtPgAQCA5XAXGAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsJz/H3vU1rMijMOsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "these_defects = energy_values[xs2, ys2]\n",
    "these_C6s = C6s[xs2, ys2]\n",
    "plt.scatter(these_defects, these_C6s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7b2b1f5-f795-4e08-a12f-7c28d0bfae93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "23\n",
      "12196.788572031275\n",
      "68\n",
      "68\n"
     ]
    }
   ],
   "source": [
    "# We should probably find the states with the largest C6s\n",
    "idx1, idx2 = np.unravel_index(np.argmax(C6s), C6s.shape)\n",
    "print(idx1)\n",
    "print(idx2)\n",
    "print(C6s[idx1, idx2])\n",
    "n_na_highestC6 = n_na_start + idx1\n",
    "n_cs_highestC6 = n_cs_start + idx2\n",
    "print(n_na_highestC6)\n",
    "print(n_cs_highestC6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2f82dcd-12f6-4441-951c-a40bdbf8715e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 has already been calculated!\n",
      "3.07 has already been calculated!\n",
      "3.14 has already been calculated!\n",
      "3.21 has already been calculated!\n",
      "3.2800000000000002 has already been calculated!\n",
      "3.35 has already been calculated!\n",
      "3.42 has already been calculated!\n",
      "3.49 has already been calculated!\n",
      "3.56 has already been calculated!\n",
      "3.63 has already been calculated!\n",
      "3.7 has already been calculated!\n",
      "3.77 has already been calculated!\n",
      "3.84 has already been calculated!\n",
      "3.91 has already been calculated!\n",
      "3.98 has already been calculated!\n",
      "4.05 has already been calculated!\n",
      "4.12 has already been calculated!\n",
      "4.19 has already been calculated!\n",
      "4.26 has already been calculated!\n",
      "4.33 has already been calculated!\n",
      "4.4 has already been calculated!\n",
      "4.470000000000001 has already been calculated!\n",
      "4.54 has already been calculated!\n",
      "4.61 has already been calculated!\n",
      "4.68 has already been calculated!\n",
      "4.75 has already been calculated!\n",
      "4.82 has already been calculated!\n",
      "4.890000000000001 has already been calculated!\n",
      "4.96 has already been calculated!\n",
      "5.03 has already been calculated!\n",
      "5.1 has already been calculated!\n",
      "5.17 has already been calculated!\n",
      "5.24 has already been calculated!\n",
      "5.3100000000000005 has already been calculated!\n",
      "5.380000000000001 has already been calculated!\n",
      "5.45 has already been calculated!\n",
      "5.5200000000000005 has already been calculated!\n",
      "5.59 has already been calculated!\n",
      "5.66 has already been calculated!\n",
      "5.73 has already been calculated!\n",
      "5.800000000000001 has already been calculated!\n",
      "5.87 has already been calculated!\n",
      "5.94 has already been calculated!\n",
      "6.01 has already been calculated!\n",
      "6.08 has already been calculated!\n",
      "6.15 has already been calculated!\n",
      "6.220000000000001 has already been calculated!\n",
      "6.290000000000001 has already been calculated!\n",
      "6.36 has already been calculated!\n",
      "6.43 has already been calculated!\n",
      "6.5 has already been calculated!\n",
      "6.57 has already been calculated!\n",
      "6.640000000000001 has already been calculated!\n",
      "6.710000000000001 has already been calculated!\n",
      "6.78 has already been calculated!\n",
      "6.8500000000000005 has already been calculated!\n",
      "6.92 has already been calculated!\n",
      "6.99 has already been calculated!\n",
      "7.0600000000000005 has already been calculated!\n",
      "7.130000000000001 has already been calculated!\n",
      "7.2 has already been calculated!\n",
      "7.2700000000000005 has already been calculated!\n",
      "7.340000000000001 has already been calculated!\n",
      "7.41 has already been calculated!\n",
      "7.48 has already been calculated!\n",
      "7.550000000000001 has already been calculated!\n",
      "7.62 has already been calculated!\n",
      "7.69 has already been calculated!\n",
      "7.760000000000001 has already been calculated!\n",
      "7.83 has already been calculated!\n",
      "7.9 has already been calculated!\n",
      "7.970000000000001 has already been calculated!\n",
      "8.040000000000001 has already been calculated!\n",
      "8.11 has already been calculated!\n",
      "8.18 has already been calculated!\n",
      "8.25 has already been calculated!\n",
      "8.32 has already been calculated!\n",
      "8.39 has already been calculated!\n",
      "8.46 has already been calculated!\n",
      "8.530000000000001 has already been calculated!\n",
      "8.600000000000001 has already been calculated!\n",
      "8.670000000000002 has already been calculated!\n",
      "8.74 has already been calculated!\n",
      "8.81 has already been calculated!\n",
      "8.88 has already been calculated!\n",
      "8.95 has already been calculated!\n",
      "9.02 has already been calculated!\n",
      "9.09 has already been calculated!\n",
      "9.16 has already been calculated!\n",
      "9.23 has already been calculated!\n",
      "9.3 has already been calculated!\n",
      "9.370000000000001 has already been calculated!\n",
      "9.440000000000001 has already been calculated!\n",
      "9.510000000000002 has already been calculated!\n",
      "9.580000000000002 has already been calculated!\n",
      "9.65 has already been calculated!\n",
      "9.72 has already been calculated!\n",
      "9.790000000000001 has already been calculated!\n",
      "9.86 has already been calculated!\n",
      "9.93 has already been calculated!\n",
      "10.0 has already been calculated!\n"
     ]
    }
   ],
   "source": [
    "# Perform full diagonalization\n",
    "options[\"n1\"] = n_na_highestC6\n",
    "options[\"n2\"] = n_cs_highestC6\n",
    "options[\"dn\"] = 5\n",
    "\n",
    "rs = np.linspace(3,10, 101)\n",
    "res_rs, res_evals, res_evecs, res_basis = calc.calculate_pair_interaction(rs, options, tolerance=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d4a4cdf-85cb-4960-a153-8b707860911c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[68, 0, 0.5, 0.5, 68, 0, 0.5, 0.5]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we need to extract the correct eigenvector at each r\n",
    "# first identify the correct basis vector\n",
    "main_idx = -1\n",
    "for idx, val in enumerate(res_basis):\n",
    "    if val[0] == options[\"n1\"] and val[1] == options[\"l1\"] and val[2] == options[\"j1\"] and val[4] == options[\"n2\"] and val[5] == options[\"l2\"] and val[6] == options[\"j2\"]:\n",
    "        main_idx = idx\n",
    "        break\n",
    "res_basis[main_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8fa3db87-f79f-4aa2-b452-fb9bcd766a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "evals = []\n",
    "comps = []\n",
    "for idx, evec in enumerate(res_evecs):\n",
    "    comp = np.abs(evec[main_idx, :])**2\n",
    "    this_idx = np.argmax(comp)\n",
    "    evals.append(res_evals[idx][this_idx])\n",
    "    comps.append(comp[this_idx])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa83c795-9829-4adf-81da-02b2499b01f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b34cef1160>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGYCAYAAAC3YWNyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAABRAUlEQVR4nO3deVzUdf4H8NfcMNwgcisiHnmRwqh5oJllx9q23Zdptb+yME3XSq1WbStzrc3aKHVts9N2207Tsqw8OhQEb/EEFQGRcwYYmPP7+2OYURJlOGa+c7yejweP5Dvfz3fe35H8vvlcb4kgCAKIiIiIvJRU7ACIiIiIOoPJDBEREXk1JjNERETk1ZjMEBERkVdjMkNERERejckMEREReTUmM0REROTVmMwQERGRV5OLHYCrWa1WlJaWIiQkBBKJROxwiIiIyAmCIKCurg7x8fGQSi/d9+LzyUxpaSmSkpLEDoOIiIg6oLi4GImJiZc8x+eTmZCQEAC2DyM0NPSS52o0GuTm5jp9bZ1Oh6SkJKeu3Zn3ae/57mrjrvv31Db+fv+A730G/n7/HWnjyZ+Bv9+/u9p05DNw5j3s17U/xy/F55MZ+9BSaGhomx+yTCZr1w+jnTPX7sz7dCQud7UBXH//nt7G3+8f8J3PwN/vv6NtAM/8DPz9/t3ZBmjfZ9Ce93BmiggnABMREZFX8/meGTuNRgOZTNbiWFZWFrKyskSKiIiIiAAgOzsb2dnZLY5ZLBan2/tNMpObm9uhbjMiIiJyrdY6F3Q6HcLCwpxqz2Gm87irl6a979ORuNzVpiM8+X7c8Rl48r34+8+Av99/R9t0hKf+ffrS/buzjdjvIREEQejSK3oYe2an1Wq7vGfGldf2Brx//75/gJ+Bv98/wM/A3+8fcN1n0J7rsmeGiIiIvBqTGSIiIvJqTGaIiIjIqzGZ6QSVSoWFCxdCpVKJHYooeP/+ff8APwN/v3+An4G/3z/gGZ+BWyYAf/7553jxxRcREBAAqVSKN998EwMHDuzQ+Xq9HitWrMAXX3wBmUwGrVaLCRMmYPHixQgKCrrgWpycRURE5H3a8/x2+T4zOTk5mDp1KvLy8tCnTx+89957mDRpEgoKClqtt9DW+fn5+Vi6dCny8vKQmJiI2tpajB49GpWVlVizZo2rb4eIiIg8jMuHmV566SXccMMN6NOnDwDg3nvvhdlsvmji0db5ISEhmDlzpqOCZnh4OO6//37897//bddugUREROQbXJ7M/PDDD8jIyDj3hlIp0tPTsWnTpg6dn5aWhqeffrpFm4CAAJjNZlitVhfcAREReTJBELB80xH89cv92LCvDFX1BrFDIjdz6TBTVVUVdDodYmJiWhyPjY1ttfR3e8+3++2333DTTTdBoVBc9BydTudUzCqVyq8nchEReZvjFQ1YvukoAOC9304CAPrFhGBkSiSG94qCplcEuocEiBkincdgMMBgaDvhdPa5Dbg4mdHr9QBwQXKgUqkcr3XmfAA4dOgQvvvuO+zcufOSsSQlJTkV88KFC7Fo0SKnziUiIvFpG00AALVShh6Rahw6U4fD5bavd5uTm5RuQRjeKxKaZNtXUmQgJBKJmGH7rSVLlmDx4sVdek2XJjNqtRoALsjADAaD47XOnF9XV4e7774b77//Pnr27HnJWIqLi51azcReGSIi79JotM2X7BGpxrePZ6K6wYicoipsL6xGTlE1Cs7oUFjZgMLKBnycWwwA6B6igiY5Euk9I5DeMwID4kOhkHG3EneYP38+5syZ0+Z5Op3O6Y4IlyYzUVFRCAsLQ3l5eYvjZ86cQUpKSqfOb2pqwk033YQnnngC1157bZuxhIaGcmk2EZEP0hvNAIBApQwAEBmkxLWD4nDtoDgAtp6bvJPV2FFYjdwT1dhXosXZOgPW7yvD+n1lAIAAhRRDEsIxrGcEhvUIx+U9wjk05SKumM7h8qXZEyZMQF5enuN7QRCQn59/wSTe9pxvNptx++234/bbb8ddd90FAPjkk08wceJEREREuOhOiIjIE+mbe2aClK0/0sICFZjQPwYT+tvmYzaZLNhTXIudJ2uw80Q18k/VQttoQs6JauScqHa0SwgPxNAe4bg8KRxDe4RjYHwYAhQy198QtZvLk5l58+bh6quvxrFjx5CamooPP/wQMpkMU6dOBQCMGTMG48aNwwsvvODU+VarFVOnTkVwcDDS09Mdc2Xee+89aDQaJjNERH7GnszYe2baEqCQYURKFEakRAEArFYBhZUNyD9Vg/yTNdh1qhZHztahpLYRJbWN+HqvrfdGJpWgX0wI0pLCkZYYhsGJYegbE8LhKQ/g8mRm+PDhWLNmDe68804EBgZCKpVi48aNjg3z9Hp9izkybZ3/zTff4KOPPgIArF27tsV7/fOf/3T17RARkYexDzMFOZnM/J5UKkFq92Ckdg/G7Rm2ORp1TSbsPa3F7uJa7DpVg93FWlTWG3CwTIeDZTqszbG1VcqlGBAXiiGJYRiUEIZB8WHoExPMBMfN3FLOQEwsZ0BE5Nte/+Eo/vH9Edw1vAeW3DzYJe8hCALKtE3YU1yL3adrse+0FvtKtKhrMl9wrlImRf+4EAyMD8WA+DAMiAvFZXEhUF9kGIxa51HlDIiIiFypoblnRt3BnhlnSCQSxIcHIj48ENcNtk0stloFnKzWY29zcnOgVIf9pbYEZ+9pLfae1gIobm4P9OoWhMviQh3JzWVxoYgNDeAS8S7AZIaIiLxao2MCsHsn50qlEvTqFoRe3YLwx8sTANgSnOIaPfaVaHGw1DYkdaBUh4o6AworGlBY0YD1zXNwANvk5H6xIbgsNgT9YkMxOCEMgxJCmeC0E5MZIiLyaucmAIv/SJNKJegZFYSeUUH4w5B4x/GzdU0oKKtDQZnO8XW8osG2iqrIth+O3d9vGYLbNc7tr0I24v/NExERdYJjArDKc5dNdw8JQPeQAIzrG+041mSy4HhFPQ6V2XYr/vHQWRw7W4+DZc5v4082TGaIiMirOXpmvGwPmACFDAPjwzAwPgyAbVfi59cXoKrBKHJk3odrx4iIyKvpDc1zZlTe/ft5VLASAFDDZKbdvPtvvh00Gg1kspZZe1ZWFrKyskSKiIiIuoLe1LKcgbeKUNuSGX/smcnOzkZ2dnaLYxaLxen2fpPM5Obmcp8ZIiIfZO+ZUXvZMNPvRQXZ6hVVNxjaONP3tNa5YN9nxhkcZiIiIq/mqM3k5cNMkY5hJhN8fD/bLsdkhoiIvNrvq2Z7q8jmYSajxYp6w4U7C9PFMZkhIiKv1lbVbG8RqJQ5VmTVNJhEjsa7MJkhIiKvZTRbYbbahmS8vWcGACKD7JOA/W/eTGcwmSEiIq9lH2ICXFubyV3syUyN3v9WNHUGkxkiIvJa9iEmpUwKhcz7H2kR9p6ZeiYz7eH9f/NEROS3fGXyr11UczJT7Yd7zXQGkxkiIvJaepEqZruKfZipmsNM7cJkhoiIvNa5itk+lsxwmKldmMwQEZHXOlcx27uXZdtxAnDHMJkhIiKv5a0Vsy/Gn+szdYZvpLJOYKFJIiLf4ysVs+38tXI2C006iYUmiYh8j6+tZvLXnhkWmiQiIr/VYPSNitl29qXZdU1mGM1WkaPxHkxmiIjIazX6SMVsu7BABaQS259rOQnYaUxmiIjIa/na0mypVOK3Q02dwWSGiIi8lmNpto8kM8B5y7OZzDiNyQwREXmtcz0zvjHMBJxXn4nJjNOYzBARkdfyxZ6ZKG6c125MZoiIyGv52pwZgJWzO4LJDBEReS3H0mwfGmZi5ez2YzJDREReq9EHh5lYObv9mMwQEZHXajD43jATK2e3H5MZIiLyWo0m39o0D2Dl7I7wnb/9NrDQJBGR73HUZvKRcgaAf9ZnYqFJJ7HQJBGRb7FYBTSZbPWLfKln5vzK2YIgQCKRiByR67HQJBER+SX7EBMAqH1ozoy9Z8ZsFaBrMoscjXdgMkNERF5Jb7A96CUSQCX3ncdZgELmWJ3F5dnO8Z2/fSIi8iv2DfOClHKfG4qJDOZeM+3BZIaIiLxSg33yrw8NMdlFqpnMtAeTGSIi8kqNjp4ZH0xmWDm7XZjMEBGRV/LFitl2rJzdPm5JZj7//HNoNBqMHTsW48aNw4EDBzp1viAIeO655zBs2DAMHz4c9957L7RarStvgYiIPIwvVsy2Y+Xs9nF5MpOTk4OpU6fio48+wrZt2/Dggw9i0qRJqKur6/D5r776Kj799FP88ssvyMnJgVKpxJQpU1x9K0RE5EF8sWK2HStnt4/Lk5mXXnoJN9xwA/r06QMAuPfee2E2m7FmzZoOnW+xWPDSSy/h0UcfRWBgIABg7ty5WLduHfbt2+fq2yEiIg/RcN5qJl9zrnK2QeRIvIPLk5kffvgBGRkZ595QKkV6ejo2bdrUofP37t2LioqKFudcdtllCAoKuug1iYjI99grZvvShnl2kUEqAEC13iRyJN7BpelsVVUVdDodYmJiWhyPjY1Fbm5uh84vLCwEgBbnSCQSxMTEoKio6KKx6HQ6p2JWqVRQqVROnUtEROLxxYrZdpFBCgC+2TNjMBhgMLR9X84+twEXJzN6vR4ALkgOVCqV47X2nt/ea9olJSU5FfPChQuxaNEip84lIiLx+GLFbDt7z0xNg+/1zCxZsgSLFy/u0mu69CdArVYDwAUZmMFgcLzW3vPbe0274uJipwpNsleGiMg7+GLFbDv7pnn1BjMMZgtUct+5x/nz52POnDltnqfT6ZzuiHBpMhMVFYWwsDCUl5e3OH7mzBmkpKR06Hz7f8vLy5GYmOg4p7y8vNVr2oWGhrJqNhGRD9Eb7D0zvvOgtwsNlEMmlcBiFVDTYEJsmO/coyumc7h8AvCECROQl5fn+F4QBOTn52PixIkdOn/IkCGIjo5ucU5BQQEaGhouek0iIvI9vrxpnkQicVTPrvLBeTNdzeXJzLx587B+/XocO3YMAPDhhx9CJpNh6tSpAIAxY8bg6aefdvp8mUyGefPm4c0330RjYyMA4JVXXsHkyZMxaNAgV98OERF5iAYf3jQPOH95NveaaYvL09nhw4djzZo1uPPOOxEYGAipVIqNGzciJCQEgG1C7/nzX9o6HwBmz56N+vp6jB49GnK5HH369MF7773n6lshIiIPYq/N5ItLs4Fz9ZmYzLRNIgiCIHYQrqTT6RAWFgatVss5M0REPuS617ahoEyHdx8YjnF9o8UOp8tlfZiP9fvKsHDyANw/upfY4bhde57fLDRJREReqdHHh5lYOdt5TGaIiMgr+XJtJoCVs9uDyQwREXklvQ/XZgJYObs9mMwQEZHXEQTBsWmer04AZuVs5/lmOktERD7NYLbC2rx8Re2D5QwA1y/NtlgFNJos0BvNaDRaoDfa/qz/3Z/trzW0dp7BdvzagbF47Ko+LonTGb75E0BERD7NPsQE+GY5A+DcBODKegOOV9Sj0WiBwWxBo9GKRpPF9tWcYDSarLY/m2zJhu01y7nvjeeSFvsxg9naZbEOiBN3tTCTGSIi8joNBtsQk0ouhUwqETka13CsZtKbcNUrW1z2PhKJbd5RoFIGtVKGQIXtv+rmY0FKGQKVcgTZX1fKEaSynReksp2TGB7osvic4TfJjEajgUzWMnvPyspCVlaWSBEREVFH+XLFbLvoYBVGp0Yhp6gagQoZApUyBChkCJDb/mxPOgKUtmPq5mQjoPncc4mJHIFKKQIVcsc5gc3Jilopg0ouhUQibkKYnZ2N7OzsFscsFstFzr4QN80jIiKvs7u4Fjdl/4KE8ED8Mm+C2OGQC3DTPCIi8mn65mEmX6yYTe3HZIaIiLyOL1fMpvZjMkNERF7H1ytmU/swmSEiIq/j6xWzqX2YzBARkddp4DATnYfJDBEReR1fr5hN7cNkhoiIvI6vV8ym9mEyQ0REXsfXK2ZT+zCZISIir2OvmM2eGQKYzBARkRdqcPTMMJkhJjNEROSFzi3N5jATsdAkC00SEXkhe9VsDjP5hs4WmvSbZCY3N5eFJomIfMS5qtlMZnxBa50L9kKTzuAwExEReR3H0myF3/xOTpfAZIaIiLwOq2bT+ZjMEBGR19GbWJuJzmEyQ0REXkdv4GomOofJDBEReRWTxQqjxQqAPTNkw2SGiIi8in3yL8Cl2WTDZIaIiLyKfcM8uVQCpYyPMWIyQ0REXub8ukwSiUTkaMgTMJkhIiKvworZ9HtMZoiIyKvojVyWTS0xmSEiIq/S0DzMpOaGedTMb/roWGiSiMg3OCpms5SBz2ChSSex0CQRkW9gxWzfw0KTRETkV1gxm36PyQwREXkVVsym32MyQ0REXoUVs+n3mMwQEZFXcfTMcM4MNWMyQ0REXqWBm+bR7/AngYiIPF6TyYKj5fUoKNNh16kaANw0j85xaTJjNBrxxBNP4JdffoEgCBg9ejRefvllKJXKDrc5dOgQXnnlFRw+fBiCIMBoNGLevHn405/+5MpbISIiN9HqTThQpsWBEh0OlGpxoFSHwsoGWKxCi/NiwwJEipA8jUuTmblz5+LIkSPYsWMHAODaa6/F3Llz8frrr3e4zcsvvwyj0YiffvoJMpkM33//Pa699lps2rQJV155pStvh4iIuphWb8K+Ei32lWixv0SLvSW1KK5ubPXccLUCl8WG4rK4UAztEY7rBsW5OVryVBJBEIS2T2u/qqoqxMXFYd26dZg0aRIAYMOGDbjppptw5swZREZGdqjN888/j8mTJyMtLc3RbtCgQRg9ejRWrlx5wTXtm+5otVpumkdEJKImkwUHSrXYU6zFntO12Htai6LKhlbPTYoMxMC4MAyID8XA+FAMiA9FbGgAq2T7kfY8v13WM7N161aYTCZkZGQ4jmk0GphMJmzZsqXVYSFn2jzzzDMXtAsICIDBYHDNjRARUbsJgoDTNY3IP1WDXadqsetUDQ6W6WCyXPj7c49INQYnhmFwgu1rUHwYwtQKEaImb+WyZKawsBByuRxRUVGOY9HR0ZDJZCgqKuqyNjqdDgcOHMBzzz13yXh0Op1TcatUKqhUKqfOJSIiG5PFigOlOuw8UY28kzXYebIGFXUX/pLZLViJy5PCkZYYjiFJ4RiSEIaIoIvPoyTfYzAYnOqAcPa5DbgwmdHr9a1O9FUqldDr9V3W5uWXX8Y111yD66+//pLxJCUlORE1sHDhQixatMipc4mI/FWj0YJdp2qQc6IauSeqkX+y1lFmwE4hk2BAXCiG9ojA0B7hGNYjAokRgRwq8nNLlizB4sWLu/Sa7U5m5s2bh6VLl17ynIKCAqjVahiNxgteMxqNUKvVrbZrb5vvv/8eX331FbZs2dJm3MXFxU7NmWGvDBHRhfRGM/JP1mJ7YRW2F1Zhz+naC4aMwtUKpPeIQHpyBNJ7RCAtKRwBCi6fppbmz5+POXPmtHmeTqdzuiOi3cnMggULMGPGjEueExsbi5SUFJjNZlRVVTmGjSoqKmCxWJCSktJqu/a0yc3NxRNPPIFvvvnGqaqaoaGhnABMROQko9mK3cW1+OVYJX49XondxRcmL7GhARjeKxKaXpEY0SsSqdHBkErZ60KX5orpHO1OZpxNCjIzM6FQKJCXl4drrrkGALBz504oFApkZmZ2qs2BAwfw4IMP4ssvv0RcnG1p3qpVq/DQQw+193aIiAi2CbtHz9Zj65EK/HysEjlF1Y6yAXZxYQG4IiUKI5u/kiI5ZESewWVLswFg5syZOHbsGL7++msAwHXXXYd+/fo59ozJz8/H5MmT8fXXX2Po0KFOtSksLMSECRPw3HPPYcCAAY73mjt3LjZv3nxBDFyaTUTUupoGI7YercDWI5X4+VgFynUtJ2VGBSlxRe8ojE7thlG9o9AjUs3khdzGI5ZmA8CyZcvwxBNPQKPRAABGjRqFZcuWOV43m83Q6/Uwm81Ot3nqqadw8uRJTJ06tcV7jRs3zpW3QkTk9axWAftKtPjp8FlsOVKBPcW1OH9TXZVcihEpUcjs0w2jU7uhX0wIh43IK7i0Z8YTsGeGiPxZvcGMn49W4IeCs/jpcAUq61v2vvSPDcG4vtEY2ycaGckRnLBLHsNjemaIiMj9yrSN2HSwHN8dLMf2wqoWE3eDVXKMSe2G8f2iMa5fNOLCAkWMlKhrMJkhIvJygiDg2Nl6fLv/DL4vKMfe09oWr/eMUuOq/jG46rLu0CRHQimXihQpkWswmSEi8kKCIGB/iQ7fHijDN/vPoLDiXI0jiQQY1iMCVw+IwcTLYtA7OogTd8mnMZkhIvISgmCbwLt+bxnW7yvD6Zpz1aWVMinG9OmGSQNjMKF/DKJDuAEo+Q8mM0REHkwQBBSU1eGrPaVYv68UxdXnEphAhQxX9o/GpIGxmNC/O0ICWJyR/BOTGSIiD3SyqgFf7S7Fl3tKcexsveN4oEKGCZd1xw2D43Blv+4IVHL1EZHfJDMajQYyWcv/6bOyspCVlSVSRERELVU3GLFuTyk+31WC3cW1juNKuRQT+nXHH9LiMKF/d6iVfvNPN/mJ7OxsZGdntzhmsVgucvaFuM8MEZGIDGYLNh08i893ncbmwxUwN+9iJ5NKMKp3FG5Mi8ekQbEI5RAS+RnuM0NE5MHsK5E+ySvGl7tLoW00OV4bnBCGPw1NwOS0eE7iJXISkxkiIjepaTDis10l+GRnMQ6dqXMcjwsLwJ+GJuBPQxPQJyZExAiJvBOTGSIiF7JaBWwvrMLa3GJs3H8GRosVgG0ezKSBsbgtPRGjU7tBxhpIRB3GZIaIyAWq6g34JO801uacwskqveP4wPhQ3KlJwo1pCQhTcx4MUVdgMkNE1EUEQcDOkzX4YPtJfLPvXC9MsEqOP14ej7uG98CghDCRoyTyPUxmiIg6qdFowRe7S/DurydazIUZnBCGe0f2wOS0eC6nJnIh/t9FRNRBxdV6vL/9JP6TW+xYkRSgkOLGtHjcO7InhiSGixsgkZ9gMkNE1A6CICD3RA3e/rkQ3x8sR/O2MEiKDMR9I5Nxe0YS58IQuRmTGSIiJ5gsVmzYV4a3fy7C3tNax/Gxfbph2qhkjO/XnSuSiETCZIaI6BLqDWZ8nHMKb/9chDJtEwBAJZfi5mEJeGB0L+4LQ+QBmMwQEbXibF0T1vxyAu9vP4m6JjMAoFuwClOv6Im7R/RAVDB35yXyFH6TzLDQJBE541SVHiu3HscneadhNNuWVqdEB+HhzBTcNDQBKjmrVBN1NRaabAMLTRKRM46U1+Gtzcfx1Z5SWJpn9ab3jMD0cb1xVf/ukHI+DJFbsdAkEZGTDpRq8c8fjuHbA2ccx8b1jUbWlakY3itSxMiIyFlMZojIL+0v0eL1H47iu4PljmPXDoxF1pWpGJzIXXqJvAmTmQ46UKrF5sMVSIpU48a0eLHDISInHSzV4dVNR/B9cxIjkQB/GBKPxyakoi9XJhF5JSYzHbT3tBbLNh7GxMu6M5kh8gLHztbh1U1HsX5vGQBAKgEmp9mSmNTuTGKIvBmTmQ6KDFICAKoajCJHQkSXcqpKj+U/HMEXu0pgFWw9MZOHxGPmVX2Q2j1Y7PCIqAswmemgqOZkpprJDJFHqqgz4I0fj+KjnFMwWWyrk64ZEIM51/RF/1iubCTyJUxmOijCnszUM5kh8iT1BjNWbS3E6m2F0Btt+1SM7dMNT0zqx8KPRD6KyUwH2Xtm6gxmGMwWbqRFJDKTxYqPc05h+aajjuHftMQwPHVtf4xK7SZydETkSkxmOig0QAGZVAKLVUBNgwmxYUxmiMQgCAJ+KDiLJd8U4HhFAwAgpVsQ5k7qh+sGxUIi4WZ3RL6OyUwHSaUSRKiVqKw3oKrBgNiwALFDIvI7B0q1eP7rAvxWWAXANjF/9sQ+uHN4DyhkUpGjIyJ3YTLTCVFBtmSGk4CJ3Kuy3oBXvjuMj3OLIQiAUi7FA6N74dEreyM0QCF2eETkZn6TzLii0GQkVzQRuZXRbMV7v53Aa5uOos5gq2Q9OS0eT07qh6RItcjREVFHdbbQpN8kM7m5uV1eaDIyuHmvGa5oInK5rUcqsGjdARQ2z4sZlBCKhZMHQpPM+klE3q61zgV7oUln+E0y4wr2FU01eiYzRK5SWtuIv319EN/stxWCjApS4olJ/XBbRhJkrGRNRGAy0yncBZjIdYxmK97+uQiv/3AUjSYLpBJg6qhkPD6xL8ICOS+GiM5hMtMJUdw4j8gldhRW4ekv9uPY2XoAgCY5As/9cRAui+POvUR0ISYznRAZpALACcBEXaVWb8SSDYfwn53FAIBuwUrMv+4y3DwsgfvFENFFMZnphIggW1d3VYNB5EiIvJsgCPhidwme/7rAMWx794geeGpSf4SpOaRERJfGZKYTotgzQ9RpxdV6LPh8H7YdrQQA9I0Jxot/GowMrlIiIie5bItMo9GIWbNmISMjA+np6Zg5cyaMxks/9NvTprS0FGFhYZg2bZoLoneOfQJwbaMJFqsgWhxE3shqFbDmlyJMWr4V245WQimX4olJ/fD1Y2OZyBBRu7gsmZk7dy4OHz6MHTt2ICcnBwUFBZg7d26XtZk5cyakUnG3K49o7v4WBC7PJmqPY2frcfvK37Bo3UHojRYMT47EN7PGIuvKVCjlLENARO3jkn81qqqqsGLFCsyePRsymQwymQyzZ8/GihUrUF1d3ek269atg0KhQFpamivCd5pcJkV4c0LDoSaitlmsAv61tRDXv74NO0/WIEgpw9/+OBAfPzQSvaODxQ6PiLyUS5KZrVu3wmQyISMjw3FMo9HAZDJhy5YtnWrT0NCAp59+Gq+++qorQm83x14zXJ5NdElFlQ24Y+VveGFDAYxmKzL7RuO7OeMw5YpkSLn5HRF1gksmABcWFkIulyMqKspxLDo6GjKZDEVFRZ1q8+yzz+KRRx5BbGxsu2LS6XROnadSqaBSqZy+blSQEoUVDeyZIboIq1XAe7+dwEvfHkKTyYpglRzP3HAZ7tAkcbk1kR8yGAwwGNpeBezscxtwUTKj1+uhVCovOK5UKqHX6zvcZteuXcjJycHLL7/c7piSkpKcOm/hwoVYtGiR09c9V2ySy7OJfq9M24i5n+zBL8eqAACjekfh77cOQWIEi0IS+aslS5Zg8eLFXXrNdiUz8+bNw9KlSy95TkFBAdRqdaurkIxGI9Tq1v8Ra6uN1WrFo48+ihUrVnRo4m9xcbFThSbb0ysDnNs4jyUNiFr6em8pnv58P7SNJgQopFhw/WW4d0RPDikR+bn58+djzpw5bZ6n0+mc7ohoVzKzYMECzJgx45LnxMbGIiUlBWazGVVVVY5ho4qKClgsFqSkpLTarq02hw8fRlVVFWbNmuVos3v3bhw6dAjjx4/HrbfeesnYQkNDu7xqNnBesUkmM0QAAF2TCQu/PIDPd5UAANISw/DqHZcjhRN8iQjtn87hjHYlM84mBJmZmVAoFMjLy8M111wDANi5cycUCgUyMzM71CYyMhJHjhxp0Wb8+PFITk7GmjVr2nMbXYrFJonOyTtZjZlrd6OkthFSCTBjQh88NiEVChmXWxOR67jkX5ioqChMnz4dy5cvh9VqhdVqxfLlyzF9+nRERto2w8rPz0dCQgJ27drldBtPdG7ODJMZ8l8Wq4A3fjyK21duR0ltI3pEqvHJ9FGYc3VfJjJE5HIu+1dm2bJlSE1NhUajgUajQd++fbFs2TLH62azGXq9Hmaz2ek2dt9++y3Gjx+P3bt3t/izGJjMkL8r0zbintXb8fJ3R2CxCrjp8nisnzkG6T0jxA6NiPyERBAEn96HX6fTISwsDFqt1iVzZvaXaPGHf/6M6BAVcp+e2OXXJ/JkPxSU4y+f7EGt3gS1Uoa//XEQbklPFDssIvIB7Xl+s9BkJ0UFn5sALAgC980gv2CyWPHyxsNYubUQADAoIRSv3zmUk3yJSBRMZjrJPsxktgrQNZoR1lzegMhXlWkb8dhHu7DzZA0AYNqoZMy/vj9UcpnIkRGRv2Iy00kquQzBKjnqDWZUNRiYzJBP23KkArP/sxvVDUaEqORYeusQXD84TuywiMjPMZnpApFBStQbzKhuMCIlWuxoiLqe1SrgjZ+O4dVNRyAIwMD4ULx5zzD0jAoSOzQiIiYzXSEySIlT1XruNUM+Sdtowpz/7MYPh84CAO4e0QN//cMABCg4rEREnoHJTBeI4vJs8lEFZTpM/yAPJ6v0UMqleOGmQbgtw7ntxYmI3MVvkhmNRgOZrOVvkllZWcjKyur0tbnXDPmiL3eX4KlP96LJZEVCeCBWTknHoIQwscMiIh+UnZ2N7OzsFscsFovT7f0mmcnNzXXJPjMAEBnMZIZ8h8UqYNnGw1ix5TgAYGyfbnj9zqGICLqwqj0RUVdorXPBvs+MM/wmmXGlSDWTGfIN2kYTZn28C5sPVwAAHhnfG3Ov6QcZK10TkQdjMtMFWGySfMGxs/V46L2dKKxsQIBCiqW3DMEfL08QOywiojYxmekCUY5hJoPIkRB1zNYjFcj6KB91TWbEhwVg1X0ZnB9DRF6DyUwXiAxSAQCq69kzQ97n/e0nseirA7BYBWT0jMBb96YjOkQldlhERE5jMtMFos4bZmJ9JvIWZosVz68vwJpfTwAAbh6WgCU3D2ZZAiLyOkxmuoB9zozBbIXeaEGQih8reba6JhMeW3tuou8Tk/rh0fG9mYgTkVfiU7cLqJUyqORSGMxWVDcYmcyQRyvTNuL+d3Jx6EwdAhRS/OP2y1lfiYi8mlTsAHyBRCJpMdRE5KkKynT4U/avOHSmDt2CVfjPQ1cwkSEir8cuhC4SGaxEqbaJK5rIY207WoFHPshHvcGM1O7BeGeaBkmRarHDIiLqNCYzXcS+oqmKK5rIA/0v7zTmfboXZquAEb0isWpKBsLUCrHDIiLqEkxmugiLTZInEgQBb24+jmUbDwMAbkyLx7LbhnDFEhH5FL9JZlxZaBIAIuwlDfRMZsgzWK0Cnvv6oGPp9cPjUvDUpP6QsjQBEXkYFpp0kisLTQLn7QLMYSbyAAazBXM/2Yt1e0oBAM/+YQAeHNNL5KiIiFrHQpMeIpLDTOQh6g1mTH8/Dz8fq4RCJsHLt6WxxhIR+TQmM12ExSbJE9Q0GDHtnRzsOa2FWinDinvTkdk3WuywiIhcislMF+EEYBLbWV0Tprydg8PldYhQK7Dm/uFISwoXOywiIpdjMtNFOMxEYiqu1uOe1TtwqlqPmFAVPnhwBPrEhIgdFhGRWzCZ6SJRzfvM1BvMMJgtXPpKbnPsbB3uXZ2DM7om9IhU48M/j+BmeETkV1jOoIuEBsohb17yyt4ZcpeDpTrcvnI7zuia0DcmGJ9Mv4KJDBH5HSYzXUQikSDCPgmYy7PJDfaersVd/9qO6gYjhiSG4T8PXYGY0ACxwyIicjsmM10oJtQ21FSuaxI5EvJ1+adqcM+/dkDbaMKwHuH44M8jHMk0EZG/YTLThWJDAwEAZVomM+Q6OUXVmLJ6B+oMZgzvFYn3HhyB0ADWWSIi/8UJwF0oLszWxX+GyQy5yK/HK/Hgmp1oNFkwqncUVk/NgFrJ/42JyL/xX8EuFBduS2ZKtY0iR0K+6LfjVXhgTS6aTFaM6xuNlVPSEaDgqjkiIr9JZlxdaBJgzwy5zvbCc4nM+H7RWHEvExki8h2dLTQpEQRB6OqgPIm9UJVWq3VpoUnA9pvzXf/ajpRuQfhx7niXvhf5jx2FVZj2Ti4aTRb2yBCR32jP85sTgLtQ/HnDTD6eI5Kb5BRV4/41tkRmbJ9uTGSIiFrBZKYL2ff4aDJZoW00iRwNebu8kzWY9k4O9EZbIvOv+zKYyBARtYLJTBcKUMgcNZq4PJs6Y3+J1pHIjE6NYiJDRHQJTGa6mH0ScBlXNFEHHT5Thylv70Bdkxma5AgmMkREbWAy08XOJTPsmaH2K6yoxz2rd6BGb0JaYhj+PU3DfWSIiNrAZKaLxXJ5NnVQcbUe96zegcp6A/rHhuDdB4YjhDv7EhG1yaXJjNFoxKxZs5CRkYH09HTMnDkTRuOlizA608ZkMmHRokUYO3YsRo8ejdTUVPz973935a04LS7MVtKgtJbJDDnvbF0Tpry9A2XaJvSODsIHfx6BcDVrLREROcOlyczcuXNx+PBh7NixAzk5OSgoKMDcuXM73ebRRx9FeXk5tm7dil9++QULFizAjz/+6MpbcZpj4zwd58yQc7SNJtz3dg5OVOmRGBGID/88Et2CVWKHRUTkNVy2aV5VVRXi4uKwbt06TJo0CQCwYcMG3HTTTThz5gwiIyM71Gbfvn0YPnw4ysvLHZvoGAwG7NmzB8OHD7/gmu7cNA+w1c65+187kBIdhB//Mt7l70feTW80Y8rbOcg7WYPoEBX+N/0K9IwKEjssIiLRecSmeVu3boXJZEJGRobjmEajgclkwpYtWzrc5vPPP0daWlqLG1OpVK0mMmKIbx5mKqtt4sZ5dElGsxWPfJCPvJM1CA2Q470HhjORISLqAJctkygsLIRcLkdUVJTjWHR0NGQyGYqKijrcZv/+/YiNjcXSpUuxYcMGmM1mXHXVVXj66aehUl28a16n0zkVt0qluuR12mKfANxoskDXaEaYmhM46UJWq4A5/92NLUcqEKiQ4Z37NbgszvU9h0REYjMYDDAYDG2e5+xzG3BhMqPX66FUXjiBUalUQq/Xd7hNTU0NtmzZgiFDhmDz5s2oqanBxIkTcfz4cXz44YcXjScpKcmpuBcuXIhFixY5dW5rAhQyRKgVqNGbUKZrZDJDFxAEAc99fRBf7y2DQibBiinpSO954bArEZEvWrJkCRYvXtyl12z3MNO8efMgkUgu+XXo0CGo1epWVy4ZjUao1epWr+1MG5lMBolEgqeffhoSiQSRkZGYO3cuPvroI1RUVFw07uLiYmi12ja/5s+f396P5AKx5w01Ef3eW1uOY82vJwAAr9x+Ocb1jRY3ICIiN5o/f75Tz+Pi4mKnr9nunpkFCxZgxowZlzwnNjYWKSkpMJvNqKqqcgwbVVRUwGKxICUlpdV2zrRJTExEVFRUi6Ggnj17AgBOnDiB6OjWHwyhoaFumQAMAPFhASgo03HjPLrAJzuL8fdvDwMA/vqHAbgxLV7kiIiI3Kuz0zla0+6emdDQUCQmJl7ySy6XIzMzEwqFAnl5eY62O3fuhEKhQGZmZqvXdqbNuHHjUFVVBZPpXCHH8vJyAECPHj3aezsucW7jPC7PpnN+OnwW8z7bBwB4eFwKHhjTS+SIiIh8g8tWM0VFRWH69OlYvnw5rFYrrFYrli9fjunTpzuWZefn5yMhIQG7du1yus1tt92GxMREvPXWWwBsQ1BvvvkmbrnlFsTExLjqdtqFJQ3o93YX1+LRD/JhsQq4eWgCnprUX+yQiIh8hks3zVu2bBlSU1Oh0Wig0WjQt29fLFu2zPG62WyGXq+H2Wx2uk1AQAA2btyI9evXY/jw4Rg7diwGDBiAd955x5W30i72XYCZzBAAnKxqwINrctFosiCzbzSW3joEUqlE7LCIiHyGyzbN8xTu3jQPAH49Vom7V+9A7+gg/MCN8/xaTYMRt7z1KworGzAwPhT/efgKBKtYOJKIqC0esWmeP4s9b5jJx3NFuoQmkwUPvb8ThZUNSAgPxL+naZjIEBG5AJMZF7APM+mNFuiazG2cTb7IahXwl0/2IPdEDUIC5Hjnfg1iQgPEDouIyCcxmXGBQKUM4c2b5Z3hvBm/tHTjIaxv3hRv5b3p6BsTInZIREQ+i8mMi8SG2oeauDzb36zNOYWVWwoBAEtvGYJRqd1EjoiIyLcxmXGR+HCuaPJHvxyrxLNf7AcAPD6xD24elihyREREvo/JjIvEcq8Zv3PsbD0e+SAPZquAmy6Px6yr+ogdEhGRX/CbpRUajQYymazFsaysLGRlZbnk/eJCuQuwP6luMOLBd3OhazIjvWcEXrplCCQS7iVDROSM7OxsZGdntzhmsVicbu83yUxubq7b9pkBgDgOM/kNg9mC6e/n4WSVHkmRgVg1JR0BClnbDYmICEDrnQv2fWacwWEmF2FJA/8gCAKe/nw/ck5UI0Qlx7+nahAV3LUF1IiI6NKYzLjIuWKTTGZ82ds/F+F/eachk0qQfc8w9OESbCIit2My4yL2npl6gxm6JlMbZ5M3+unwWby4oQAA8MwNlyGzb7TIERER+ScmMy6iVsoRFsiN83zVsbP1mPnRLlgF4E5NEqaNShY7JCIiv8VkxoU4b8Y3afUm/N97O1FnMEOTHIHn/jiIK5eIiETEZMaFHMlMLZdn+wqzxYoZa/NR1Fw88q1706GU838jIiIx8V9hF4oN4/JsX7P020PYdrQSaqUM/7ovA924comISHRMZlwojiuafMqXu0vwr21FAICXb0vDgHj37VtEREQXx2TGhezJTAmHmbze/hItnvzfXgBA1pW9cf3gOJEjIiIiOyYzLpTcLQgAUFTZIHIk1BlV9QY8/H4eDGYrxveLxpyr+4kdEhERnYfJjAv1ak5mSrWNaDI5X2OCPIfJYsWMj3ahpLYRvboF4bU7h0Im5colIiJP4je1mdxdaBIAooKUCAmQo67JjJNVevSL5e6w3ualbw7ht8IqBCllWDUl3bF3EBERdZ3OFpqUCIIgdHVQnsReqEqr1bq10KTdH9/4GXtOa7Hi3mG4dhDnWXiTdXtK8djaXQCAFfem49pBsSJHRETkP9rz/OYwk4vZh5oKOW/Gqxwpr8NTn9om/D46vjcTGSIiD8ZkxsV6dQsGABRVMJnxFnVNJkx/Pw96owWjU6Pwl2s44ZeIyJMxmXGxXtG2npkTVUxmvIEgCHjik70orGxAXFgAXueEXyIij8dkxsVSuDzbq6zaWohvD5yBUibFW/emI4o7/BIReTwmMy5m32umst4IbaNJ5GjoUn47XoWl3x4CACy8cQAuTwoXNyAiInIKkxkXC1bJ0T3E9tv9CfbOeKyzuiY8tnYXrAJwy7BE3D28h9ghERGRk5jMuEEvDjV5NHPzxniV9Qb0jw3B8zcNgkTCeTJERN6CyYwbpERzebYnW7bxMHJOVCNYJceb9wxDoFLWdiMiIvIYTGbcgD0znmvjgTNYubUQALDs1iFIiQ4WOSIiImovJjNu4NhrprJe5EjofCerGjD3v3sAAA+O6YXrWAmbiMgrMZlxA0fPTEUDfLx6hNdoMlnwyAf5qDOYkd4zAvOu6y92SERE1EEsNOnCQpN2PSLVkEqABqMFFXUGdA8NcPl70qX97euDOFimQ2SQEm/cPRQKGfN6IiKxsNBkG8QuNGk3btlPOFmlx8cPjcTIlCjR4iDgy90lmPXxbkgkwJr7h2Nc32ixQyIiot9hoUkPxEnAnuF4RT0WfLYPAJA1PpWJDBGRD2Ay4yZMZsTXZLIg68N8NBgtGNErEo9P7CN2SERE1AWYzLiJvUZTIatni2bxugM4dKYOUUFKvH7XUMg5T4aIyCfwX3M34fJscX25uwRrc4ohkQCv3TkUMZyETUTkM5jMuEmv5l2AT1XrYbZYRY7GvxRVNjjmyTx2ZSrG9OkmckRERNSVmMy4SVxoAFRyKUwWASW1jWKH4zd+P09m1sS+YodERERdzKXJjNFoxKxZs5CRkYH09HTMnDkTRqOx020+++wzaDQaZGZmYvjw4Zg9ezaamppceSudJpVKkBzFGk3utmRDgWM/mdfuHAqZlAUkiYh8jUuTmblz5+Lw4cPYsWMHcnJyUFBQgLlz53aqzeHDh3H77bfj2WefxdatW7Ft2zb89ttvePbZZ115K13i/J2AyfW+3V+Gd387CQB45fY0xIZxngwRkS9yWTJTVVWFFStWYPbs2ZDJZJDJZJg9ezZWrFiB6urqDrfZv38/LBYLJk6cCABQqVTIzMzEd99956pb6TL2eTNcnu16xdV6PPG/vQCAh8el4Mp+3UWOiIiIXMVlyczWrVthMpmQkZHhOKbRaGAymbBly5YOtxk7diy6d++ODz74AABQXV2NDRs2ICYmxlW30mW414x7mCxWPLZ2F+qazBjWIxxzr+kndkhERORCLqvNVFhYCLlcjqioc1v3R0dHQyaToaioqMNtunfvjp9++gm33XYbli1bhpKSEsTGxmLZsmWXjEen0zkVt0qlgkqlcurc9kphMuMWr3x3BLuLaxEaIMfrd7HuEhGRJzEYDDAYDG2e5+xzG3Bhz4xer4dSqbzguFKphF6v73CbU6dO4ZprrsGsWbNw9OhRnD59Gg8//DC6dbv0ctukpCSEhYW1+bVkyZIO3K1z7D0zJbWNaDI5X0CLnLf1SAVWbDkOAPj7rUOQGKEWOSIiIjrfkiVLnHoeJyUlOX3NdvfMzJs3D0uXLr3kOQUFBVCr1a2uXDIajVCrW3/AONPmH//4BwIDA/HQQw8BACIjIxEfH4+JEydi3759kMtbv6Xi4mKnCk26qlcGACKDlAgNkEPXZEZRZQMuixOv8KUvqqgzYM5/9wAA7h3ZA9cOihM5IiIi+r358+djzpw5bZ6n0+mcTmjancwsWLAAM2bMuOQ5sbGxSElJgdlsRlVVlWPYqKKiAhaLBSkpKa22c6bNkSNHkJyc3KJdr169cOjQIRw4cABpaWmtXjs0NFTUqtkAIJFI0DcmBDtP1qCgTMdkpgtZrQLm/Hc3KusN6B8bgmduGCB2SERE1ApXTOdo9zBTaGgoEhMTL/kll8uRmZkJhUKBvLw8R9udO3dCoVAgMzOz1Ws70yYhIQFlZWUt2tm/v1iPjycZlBAGANhXohU5Et+yalshth2tRIBCin/eNRQBCpnYIRERkZu4bM5MVFQUpk+fjuXLl8NqtcJqtWL58uWYPn06IiMjAQD5+flISEjArl27nG4zbdo0HDx4EBs2bAAANDY24rXXXkN6ejp69+7tqtvpMoObk5kDJc5PbKJL23WqBi9vPAwAWDR5IPrEhIgcERERuZNLl3ksW7YMqamp0Gg00Gg06Nu3b4tVR2azGXq9Hmaz2ek2o0ePxueff47FixdjzJgxuOKKK5CcnIwvvvgCUqnnr1qx98wcKNXCahVEjsb76ZpMeGztLpitAm4YEoc7NM5PGCMiIt8gEQTBp5+oOp0OYWFh0Gq1os+ZAQCzxYpBizaiyWTFpjnjkNo9WOyQvJYgCHhs7S58vbcMSZGBWD9zLEIDFGKHRUREXaA9z2/P78rwMXKZFAOaJ/4eKOW8mc74785ifL23DHKpBK/fOZSJDBGRn2IyIwLHJODTTGY66tjZOiz86gAA4C/X9MPQHhEiR0RERGJhMiMCrmjqnCaTBTM+2oUmkxVj+3TDw5mtL/UnIiL/wGRGBPYVTQdLdZwE3AEvrC/AoTN16BasxCu3p0EqlYgdEhERiYjJjAhSuwdDKZeizmDGyerWSztQ677dfwbvbz8JAHj5tjR0DwkQOSIiIhIbkxkRKGRSx+6/HGpyXkltI578n61cwcOZKRjfr7vIERERkSdwWdVsT6PRaCCTtdwVNisrC1lZWaLEMzghFHuKa3GgRIsb0+JFicGbmC1WzFq7C7omM9KSwvGXa/qJHRIREXWR7OxsZGdntzhmsThfkNlvkpnc3FyP2GfGblA8JwG3x2s/HMXOkzUIUcnxzzuHQilnpyIRka9orXPBvs+MM/hEEIl9RdP+Ei18fN/CTvv1WCXe+OkYAODFmwejR5Tn1+AiIiL3YTIjkr4xIVDKpNA1mVFc3Sh2OB6rqt6Ax/+zG4IA3KlJwmQOyRER0e8wmRGJUi5F/zhbQUQONbXOahUw5797cLbOgNTuwVg4eaDYIRERkQdiMiOigZw3c0mrthViy5EKqORSZN89DIFKWduNiIjI7zCZEdHg8+bNUEt5J6uxbONhAMDiGweiX2yIyBEREZGnYjIjIkcyU8pJwOer1Rsxc+1uWKwCbkyLxx2aJLFDIiIiD8ZkRkR9Y4OhkElQqzfhdA0nAQOAIAh48n97UVLbiJ5Rarzwp0GQSFiugIiILo7JjIhUchn6xtiGTzjUZLPm1xP47mA5lDLbPJmQAIXYIRERkYdjMiOy84ea/N2uUzV4cUMBAGD+9f0de/EQERFdCpMZkQ1sfmDvKfbvZKZWb8SMj3bBZBFw3aBYTBuVLHZIRETkJZjMiGxEr0gAwM6T1WgyOV+HwpfY95MpqW1EcpQaS28dwnkyRETkNL+pzeRphSbt+nQPRnSIChV1BuSfqsGo3t1EjUcMK7cW4sdDZ6GUS5F9zzCEcp4MEZFf6WyhSYng42uC7YWqtFqtRxWaPN/s/+zG57tKkHVlbzwxqb/Y4bjVjsIq3L16ByxWAUtuHoy7hvcQOyQiIvIA7Xl+c5jJA4xOtfXG/HysSuRI3OtsXRMeW7sLFquAm4cm4E7uJ0NERB3AZMYDjE6NAgDsO10Lrd4kcjTuYbJYMePDXThbZ0Cf7sF4nvvJEBFRBzGZ8QBxYYHoHR0EqwD8VugfvTMvrC9AzolqhKjkWDElHWql30zfIiKiLsZkxkOMaR5q+uVYpciRuN5n+aex5tcTAIB/3HE5ekcHixsQERF5NSYzHmKUnyQz+0u0mP/ZPgDAzAmpuHpAjMgRERGRt2My4yFGpkRBKgEKKxtQUuubdZqqG4x4+P08GMxWXNkvGo9P7Ct2SERE5AOYzHiIsEAFhiSGA/DN3hmTxYoZH+U7Ckguv2MopFJO+CUios5jMuNB7PNmfvWxZEYQBDz7xX78erwKQUoZVk5JR5iaG+MREVHXYDLjQc7fb8aX9jJcva0IH+cWQyoB/nn3UPSP9czNC4mIyDsxmfEgw3qGI0AhRWW9AUfK68UOp0t8d+AMXvzGVgn76RsGYEJ/TvglIqKuxWTGg6jkMgzvZdtA72cfGGraX6LFrI93QxCAe0b0wAOjk8UOiYiIfBCTGQ8zpnk3YG+fBFxa24g/v7sTjSYLxvbphkU3DuQOv0RE5BJ+s+2qp1bN/j37vJnthVUwWaxQyLwv36yoM+De1TtwRteE1O7BeOPuYV55H0RE5B6smt0Gb6iafT6rVUDGC5tQ3WDER38e4dhMz1to9Sbcseo3HDpTh4TwQHwy/QrEhweKHRYREXkZVs32YlKpBFdfZpsk+9WeUpe/3w8F5Zj/2T7omjpf4LLeYMbUd3Jw6EwdokNU+PDPI5jIEBGRyzGZ8UB/HBoPANiwrwwGs/PdbB2x9NtDWJtzCv/47kinrtNksuCh93Zid3EtwtUKfPDgCCR3C+qiKImIiC6OyYwHGtkrCrGhAdA1mfHToQqXvY/ZYkVRZQMA4P3tJ3HsbF2HrlPXZML/vbfTsSneu/cPR7/YkK4MlYiI6KKYzHggqVSCGy+39c58ubvEZe9zuqYRJottypTFKuD59QXtvkaZthG3rfgN245WIlAhw9vTNEhLCu/iSImIiC6OyYyH+mNzMvPDobNdMp+lNccrbBvzdQ9RQSGTYPPhCvx0+KzT7QvKdPhT9q+OOTL/ffgKjEyJckmsREREF8NkxkMNiAtFavdgGM1WfLvvjEveo7DCNsSk6RWJaaOSAQAvrC+AyWJts+3WIxW4bcVvjuXXnz86CoMTw1wSJxER0aW4LJkxGo2YNWsWMjIykJ6ejpkzZ8JoNLbZ7ujRoxg1ahTGjx/f6utarRZTpkzB8OHDMWzYMCxevNin6hjZSSQS3NTcO/OFi4aa7D0zvbsFYcaEPogMUuLY2Xp8tOPURdtU1RvwzBf7MO2dHNQbzBiZEolPp49CYoTaJTESERG1xWXJzNy5c3H48GHs2LEDOTk5KCgowNy5cy/Z5v3338d9990HqfTiYU2ZMgUBAQHIycnBzz//jE8++QSvvvpqV4fvEf54eQIA4LfCKpzRNnX59R3JTPdghAUqMOfqvgCAVzcdQa2+ZeJpMFuwcstxjF+2GR9sPwWrANyanoh3HxjOCthERCQql+wAXFVVhRUrVmDdunWOXXdnz56Nm266CYsWLUJkZGSr7aKiorBlyxY89NBDOHHixAWv7927F+vWrcOhQ4cAAGq1Go8++igWLVqEWbNmXbDDr7dLilQjvWcE8k7WYN2eUvxfZkqXXt8+zJTSLRgAcKcmCR9sP4lDZ+ow+Y2fERcWiGCVHMEqOXYX1+JUtR4AMCghFM/cMIDzY4iIyCO4pGdm69atMJlMyMjIcBzTaDQwmUzYsmXLRdtdf/31UCqVF339hx9+QHBwMPr169fiuhUVFdi7d2/XBO9h7ENNX+7p2qGmWr0RVQ223peUaNt+MHKZFH/9wwBIJEBxdSNyiqrx46Gz+GpPKU5V69E9RIVltw7BV1ljmMgQEZHHcEnPTGFhIeRyOaKizj3woqOjIZPJUFRU1KnrxsTEtDgWGxsLACgqKsLQoUMv2lan0zn1HiqVCiqVqsMxdrUbhsRj8bqD2F+iw7Gz9UjtHtwl1z3e3CsTFxaAINW5H4NRqd3w/exxKKltRH2TGQ0GM+oMZgQqZPjj5fEtziUiImovg8EAg8HQ5nnOPrcBFyUzer2+1R4WpVIJvV7fqev+PtGwf9/WdZOSkpx6j4ULF2LRokUdis8VIoOUyOwbjR8PncWXu0vwl2v6td3ICfb5MvZemfOldg/usqSJiIjofEuWLMHixYu79JrtSmbmzZuHpUuXXvKcgoICqNXqVlcuGY1GqNUdX/WiVqsvyObs37d13eLiYqcKTXpSr4zdTUMT8OOhs/jvzmI8NqEPlPLOjw46Jv9GM2khIiL3mT9/PubMmdPmeTqdzumOiHYlMwsWLMCMGTMueU5sbCxSUlJgNptRVVXlGGqqqKiAxWJBSkrHJ7GmpKSgvLy8xbEzZ844XruU0NBQr6ia3ZprB8aie4gK5ToD1u0pxS3piZ2+5rnJv6yfRERE7uOK6Rzt+hU/NDQUiYmJl/ySy+XIzMyEQqFAXl6eo+3OnTuhUCiQmZnZ4WCvuuoq1NfX48iRc0URd+7cie7du2PIkCEdvq6nU8qlmDY6GQDwr22FXbKvzvnLsomIiLyZS1YzRUVFYfr06Vi+fDmsViusViuWL1+O6dOnO5Zl5+fnIyEhAbt27XL6ukOGDMHkyZPxyiuvAAAaGxvx1ltv4amnnrrk3jS+4J7hPaFWynDoTB1+PlbZqWuZLFacqrLNMeIwExEReTuXZQDLli1DamoqNBoNNBoN+vbti2XLljleN5vN0Ov1MJvNjmNfffUVxo8fj2+//Ra7d+/G+PHj8fbbb7e47nvvvYeGhgaMGDECo0aNwi233ILZs2e76jY8RphagdszbGOH/9rW8RVhAHCqWg+zVUCgQobY0ICuCI+IiEg0EsEXawGcR6fTISwsDFqt1mvnzNidqtJj/Ms/wSoA3z4+Fv1jO3Y/3x04g4fez8PA+FCsnzm2i6MkIiLqvPY8v317bMbH9IhS49pBtn11Vneid6aw0jb5l0NMRETkC5jMeJn/G2tbtfXl7hKc1XWsXtPxsxffY4aIiMjbMJnxMkN7RCCjZwRMFgFrfj3RoWuwZ4aIiHwJkxkv9Ofm3pkPd5xCg8F80fOe/nwfhj73HY6drXMcEwQBx9gzQ0REPoTJjBe6ekAMkqPU0Daa8L+8062ec6pKj49yTqFGb8JL3xx2HK9uMELbaAJwrlo2ERGRN2My44VkUgkebO6dWf1zISzWCxekrfn1BOzr1DYVlCPvZDWAc0NMCeGBCFTK3BMwERGRC/lNMqPRaDBgwIAWX9nZ2WKH1WG3DktEhFqB4upGbDxwpsVrdU0m/HdnMQCgf2wIAGDpN4chCAIn/xIRkcfJzs6+4Bmt0Wicbu+SqtmeKDc31+v3mTlfoFKGKSN74vUfj2HV1kJcNygWEokEAPC/vNOoN5jROzoI/56mwZUvb0bOiWpsPlzByb9ERORxsrKykJWV1eKYfZ8ZZ/hNz4wvmnJFMpRyKXYX1yLvZA0AwGI9t8rp/tG9EB8eiGmjkgEAS789hKPltsnAvdkzQ0REPoLJjBeLDlHhlmEJAIBVWwsBAD8dOouTVXqEBshxc/Nrj4zvjZAAOQ6dqcOWIxUA2DNDRES+g8mMl3twjG0i8PcF5SiqbMC/f7HtDHzXiB5QK22jiOFqJaaP6w0AsM8VZrVsIiLyFUxmvFxq92Bc1b87BAFY8Nk+/Hq8CjKpBPddkdzivPtHJyM6RAUACFLK0L35z0RERN6OyYwP+L9MW+/Mb4VVAIBrB8YiITywxTlqpRwzr+oDABgYH+aYLExEROTt/GY1ky8b0SsSgxPCsK9EC8DWC9Oae0f0QFigAoMTnJsdTkRE5A3YM+MDJBIJHhlvmxMztEc40ntGXPS8G9Pi0asbVzIREZHvYM+Mj7h+cBzW/t9I9IkJ5hASERH5FSYzPuSK3lFih0BEROR2HGYiIiIir8ZkhoiIiLya3wwzaTQayGQtq0S3VguCiIiI3Cs7O/uC4s8Wi8Xp9n7TM5Obm4uDBw+2+OpsImMwGLBo0SIYDIYuitK78P79+/4Bfgb+fv8APwN/v3+gaz6DrKysC57Rubm5TreXCIIgdPjdvYC96qZWq+3yqtmuvLY34P379/0D/Az8/f4Bfgb+fv+A6z6D9lzXb3pmiIiIyDcxmSEiIiKvxmSGiIiIvBqTmfP8fia1p7xPR+JyV5uO8OT7ccdn4Mn34u8/A/5+/x1t0xGe+vfpS/fvzjaiv4fg47RarQBA0Gq1bZ572WWXuezanXmf9p7vrjbuun9PbePv9y8IvvcZ+Pv9d6SNJ38G/n7/7mrTkc/Amfdoz3XZM0NERERejckMEREReTUmM0REROTVfL6cgdC8J6BOp2vzXIvF4tR5dvZz29OmI+/T3vPd1cZd9++pbfz9/gHf+wz8/f470saTPwN/v393tenIZ+DMe9hfF5zY29fndwA+ffo0kpKSxA6DiIiIOqC4uBiJiYmXPMfnkxmr1YrS0lKEhIRAIpGIHQ4RERE5QRAE1NXVIT4+HlLppWfF+HwyQ0RERL6NE4CJiIjIqzGZISIiIq/GZKadvvzyS1x33XW46qqrMGbMGAwbNgxr164VOyzRvPHGG5BIJNi8ebPYobjNokWLcPnll2P8+PGOr5tvvlnssNyusLAQt9xyC6688koMHDgQI0eOxM6dO8UOyy369+/f4u9//PjxSE1NRWZmptihuY3BYMDs2bORlpaGcePGYcSIEfj888/FDsutjEYjFi9ejJEjR+KKK67AmDFjfPr/AaPRiHnz5kEul+PEiRMXvL5y5Uqkp6dj9OjRuOGGG1BSUuK+4Jzee5gEQRCESZMmCe+++67j+6+++kqQSCTCnj17RIxKHCUlJUKPHj0EAMJPP/0kdjhus3DhQr+639acPXtWSE5OFrZs2SIIgiCYTCbhyiuvFNauXStyZO4xbty4C47dcsstwhtvvOH+YETyzDPPCMnJyUJtba0gCIKQn58vKJVKYffu3SJH5j5z5swRBg0aJOh0OkEQBGHt2rVCRESEUF5eLnJkXa+oqEgYOXKkcN999wkAhKKiohavf/rpp0JcXJxQUVEhCIIgLF68WLj88ssFi8XilvjYM9NOL7zwAu6++27H9+PHj4cgCCgsLBQxKnE89thjWLBggdhhkAiWLl2KK664wtETIZfLsWrVKr/pmXjnnXdafF9dXY3vv/++xb8Nvm737t3QaDQICwsDAAwdOhRhYWH48ccfRY7MPaxWK1asWIEHHngAISEhAIA777wTKpUKK1euFDm6rldfX4/3338f999/f6uvP//885g6dSq6desGAJg1axb279+P9evXuyU+JjPtlJ6eDrncttegyWTCyy+/jAEDBmDixIkiR+Ze69atg0KhwKRJk8QOhUTw2WefXZC4pKamIj4+XqSI3KtXr14tvl+7di2uu+46REREiBSR+91yyy3Ytm0bTp06BQDYuHEjKioqEBMTI3Jk7lFZWQm9Xn/B/cbGxmLr1q0iReU6gwYNQmpqaquvVVdXY9euXcjIyHAcCwsLQ9++fbFp0ya3xOfzOwC7SlZWFj788EMMHDgQGzduRHBwsNghuU1DQwOefvppbNy4EQaDQexwRPHvf/8bixYtgslkQmpqKv7617+id+/eYoflFg0NDSgqKoLFYsE999yDEydOIDg4GI8//jiuu+46scMTxZo1a/D888+LHYZbTZs2DXq9HkOGDEFcXByOHDmCW2+9FbfffrvYoblFdHQ0goKCHMkcYNsXpaysDE1NTSJG5n5FRUUA0GpiZ3/N1dgz00HZ2dmorKzE+PHjMXr0aJSVlYkdkts8++yzmD59OuLi4sQORRQ9evTA0KFDsWnTJmzbtg29evVCenq6eye7iai2thaA7efgySefxC+//IInn3wSkydPxvfffy9ucCI4ePAgzpw5g6uvvlrsUNxq9erVeOmll5CXl4eCggLk5+dj5MiRbW5u5iskEglmzJiBVatWOf7ff+utt1BdXQ2LxSJydO6l1+sBACqVqsVxlUrleM3V/OOnzkXkcjn+9re/wWq14h//+IfY4bhFfn4+duzYgenTp4sdimgeeOABzJ49G3K5HFKpFM8++ywCAgLw5ptvih2aW8hkMgDA5MmTkZaWBgC46qqrMGHCBLz22mtihiaKNWvW4L777vObhzhg64F48skn8fDDDzt6JNPS0rBhwwa8+OKLIkfnPi+88AIeeeQR3HHHHRg7dixKS0tx1113+dVwIwCo1WoAuKCn3mAwOF5zNQ4ztZPRaIRSqXR8L5VK0bdvXxw8eFDEqNxn/fr1aGxsxIQJEwDA0Z36+OOPIzw8HKtXr77ouKqvkslkSE5OxvHjx8UOxS2io6OhUqmQkJDQ4njPnj3x66+/ihSVOCwWCz788ENs2bJF7FDcqqKiAjU1NUhOTm5xvFevXvj000/xzDPPiBOYm8lkMjzxxBN44oknHMf+8Ic/YPDgwSJG5X4pKSkAgPLy8hbH3dlj6T+/SnSRYcOGXXCsrKzMbyY+Pvvss8jPz8fmzZuxefNmfPzxxwCA5cuXY/PmzX6RyMyaNeuCY6WlpejRo4cI0bifTCZrdWi1vLzcbz4Du++++w69e/f2i5/783Xr1g0qleqCn4GysjK3/SbuCfbu3Yvq6mrH9yaTCdu3b8ett94qYlTuFxERgaFDhyIvL89xTKfT4ciRI25bHMNkpp0OHjzYYqnZBx98gMOHD2Pq1KkiRkXu9NVXX+Grr75yfL969WpUVFTggQceEDEq93rqqafw5ZdfOiY/Hjx4EN999x2ysrJEjsy91qxZc9Glqr5MKpVi6tSpWL16NWpqagDYhqC///57v5kADACrVq3CG2+84fj++eefh0ajwbXXXitiVOJ45pln8O6776KqqgoA8Prrr2PQoEG4/vrr3fL+LDTZTv/85z+xdu1aSKVSWK1WSCQSLFiwADfccIPYobnd448/ju3bt2PHjh1IS0tD//79HT01vuyjjz7C6tWrYbVaYTQaoVKp8Pzzz2P06NFih+ZWH3zwAV555RUEBwfDbDbj8ccfxx133CF2WG5TW1uL1NRUx2ouf6PX67Fo0SJs2rQJarUadXV1mDp1KmbPng2JRCJ2eG7x/vvv48UXX0RoaCgUCgXS09Px4osvIigoSOzQupzRaMQ111yD2tpa7NmzByNGjEBSUhI++eQTxzkrVqzAqlWrEBAQgIiICKxcuRKJiYluiY/JDBEREXk1DjMRERGRV2MyQ0RERF6NyQwRERF5NSYzRERE5NWYzBAREZFXYzJDREREXo3JDBEREXk1JjNERETk1ZjMEBERkVdjMkNERERejckMEREReTUmM0REROTV/h9oT1shAx7wUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res_rs, evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ff303db-3f3f-44ee-b679-8f61c179b487",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc3bf708-2802-4a54-b43d-06cf2d3ad24e",
   "metadata": {},
   "outputs": [],
   "source": [
    "these_evals, these_comps, these_idxs = utils.get_maximum_overlap_eigenstate(res_evals, res_evecs, res_basis, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2614ce5-7ba7-4783-8cc5-043b97528873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b34d4e3ca0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res_rs, these_evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd1a0226-3e0b-4d19-93b6-3f800acda577",
   "metadata": {},
   "outputs": [],
   "source": [
    "these_states, largest_elements = utils.find_highest_n_comps(res_evecs[100][:, 150], 10, res_basis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "381f936e-4eef-4fee-88c8-7ff2b0822274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[68, 0, 0.5, 0.5, 68, 0, 0.5, 0.5], [67, 1, 0.5, 0.5, 68, 1, 0.5, 0.5], [67, 1, 1.5, 0.5, 68, 1, 0.5, 0.5], [67, 1, 1.5, 1.5, 68, 1, 0.5, -0.5], [66, 2, 2.5, 0.5, 67, 2, 1.5, 0.5], [66, 2, 2.5, 1.5, 67, 2, 1.5, -0.5], [67, 1, 1.5, 0.5, 68, 1, 1.5, 0.5], [66, 2, 1.5, 0.5, 67, 2, 1.5, 0.5], [66, 2, 2.5, 2.5, 67, 2, 1.5, -1.5], [67, 1, 0.5, 0.5, 68, 1, 1.5, 0.5]]\n",
      "(0.46626590512424415, 0.21350771095481258, 0.18292043244617387, 0.13679864837409283, 0.0001096140286999332, 9.727846208503756e-05, 6.495727131682854e-05, 5.50705113490552e-05, 4.049042850470671e-05, 3.4721582325226405e-05)\n"
     ]
    }
   ],
   "source": [
    "print(these_states)\n",
    "print(largest_elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e9aa7d4-470c-49ba-8879-bbdcd7cb5387",
   "metadata": {},
   "outputs": [],
   "source": [
    "these_evals2, these_comps2, these_idxs2 = utils.get_maximum_overlap_eigenstate(res_evals, res_evecs, res_basis, n1 = 67, l1 = 1, j1 = 0.5, n2 = 68, l2 = 1, j2 = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "666c37e1-e697-4687-a0fe-bfa25518197d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b34eaa9d90>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res_rs, these_evals)\n",
    "plt.plot(res_rs, these_evals2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ca4841a4-9181-470e-ad94-32393039ddad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "comps = np.abs(res_evecs[100][:, 0])**2\n",
    "\n",
    "largest_with_indices = heapq.nlargest(10, enumerate(comps), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4ee90f46-8b4e-47d7-9020-466cbf96edf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(620, 0.48892600122391566),\n",
       " (614, 0.3666955331219451),\n",
       " (619, 0.10302405397156464),\n",
       " (613, 0.04120966591567214),\n",
       " (586, 3.94750581634841e-05),\n",
       " (578, 2.631678172069106e-05),\n",
       " (605, 1.8017843705738548e-05),\n",
       " (585, 1.4704314843843503e-05),\n",
       " (595, 1.4414317045585524e-05),\n",
       " (596, 5.032504674699259e-06)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "largest_with_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2c9a8eab-245c-4311-87c6-e16725756b07",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[49], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m indices, largest_elements \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mlargest_with_indices)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mres_basis\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindices\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "indices, largest_elements = zip(*largest_with_indices)\n",
    "res_basis[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80515880-4d1e-4b59-9928-893c8fdbe678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(np.array([1 + 1j, 1 + 2j]))**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2e8aa122-f3a4-48cd-b417-65b3dccd4b50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating for [3.0, 3.07, 3.14, 3.21, 3.2800000000000002, 3.35, 3.42, 3.49, 3.56, 3.63, 3.7, 3.77, 3.84, 3.91, 3.98, 4.05, 4.12, 4.19, 4.26, 4.33, 4.4, 4.470000000000001, 4.54, 4.61, 4.68, 4.75, 4.82, 4.890000000000001, 4.96, 5.03, 5.1, 5.17, 5.24, 5.3100000000000005, 5.380000000000001, 5.45, 5.5200000000000005, 5.59, 5.66, 5.73, 5.800000000000001, 5.87, 5.94, 6.01, 6.08, 6.15, 6.220000000000001, 6.290000000000001, 6.36, 6.43, 6.5, 6.57, 6.640000000000001, 6.710000000000001, 6.78, 6.8500000000000005, 6.92, 6.99, 7.0600000000000005, 7.130000000000001, 7.2, 7.2700000000000005, 7.340000000000001, 7.41, 7.48, 7.550000000000001, 7.62, 7.69, 7.760000000000001, 7.83, 7.9, 7.970000000000001, 8.040000000000001, 8.11, 8.18, 8.25, 8.32, 8.39, 8.46, 8.530000000000001, 8.600000000000001, 8.670000000000002, 8.74, 8.81, 8.88, 8.95, 9.02, 9.09, 9.16, 9.23, 9.3, 9.370000000000001, 9.440000000000001, 9.510000000000002, 9.580000000000002, 9.65, 9.72, 9.790000000000001, 9.86, 9.93, 10.0]\n",
      "\n",
      "Calculating Hamiltonian matrix...\n",
      "\n",
      "\n",
      "\tmatrix (dimension  707 )\n",
      "\n",
      "Matrix R3 100.0 % (state 183 of 183)\n",
      "\n",
      "\n",
      "\n",
      "Diagonalizing interaction matrix...\n",
      "\n",
      "99%"
     ]
    }
   ],
   "source": [
    "# Perform full diagonalization\n",
    "options[\"n1\"] = 48\n",
    "options[\"n2\"] = 62\n",
    "options[\"dn\"] = 5\n",
    "\n",
    "rs = np.linspace(3,10, 101)\n",
    "res_rs, res_evals, res_evecs, res_basis = calc.calculate_pair_interaction(rs, options, tolerance=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "62353834-3a75-4b2f-9216-15f52f6ec75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[48, 0, 0.5, 0.5, 62, 0, 0.5, 0.5]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we need to extract the correct eigenvector at each r\n",
    "# first identify the correct basis vector\n",
    "main_idx = -1\n",
    "for idx, val in enumerate(res_basis):\n",
    "    if val[0] == options[\"n1\"] and val[1] == options[\"l1\"] and val[2] == options[\"j1\"] and val[4] == options[\"n2\"] and val[5] == options[\"l2\"] and val[6] == options[\"j2\"]:\n",
    "        main_idx = idx\n",
    "        break\n",
    "res_basis[main_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "027dd07d-e4c0-4bf2-8acb-46d46bc3b605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21c1f1a6fd0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evals = []\n",
    "comps = []\n",
    "for idx, evec in enumerate(res_evecs):\n",
    "    comp = np.abs(evec[main_idx, :])**2\n",
    "    this_idx = np.argmax(comp)\n",
    "    evals.append(res_evals[idx][this_idx])\n",
    "    comps.append(comp[this_idx])\n",
    "plt.plot(res_rs, evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f30e74-348f-4b1d-af42-bb3635d0add8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}