{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the packages we'll need\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib.pyplot import *\n",
    "import string\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "from pyobjcryst import loadCrystal\n",
    "from diffpy.srfit.pdf import DebyePDFGenerator, PDFGenerator, PDFParser\n",
    "from diffpy.srfit.fitbase import Profile\n",
    "from diffpy.srfit.fitbase import FitContribution, FitRecipe\n",
    "from diffpy.srfit.fitbase import FitResults, initializeRecipe\n",
    "from diffpy.Structure import Structure\n",
    "from diffpy.Structure import loadStructure\n",
    "import time\n",
    "from scipy.optimize.minpack import leastsq\n",
    "#from ase.io import write\n",
    "#from ase.io import read\n",
    "import copy\n",
    "from diffpy.Structure import Atom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for optimization and plotting that we will use later\n",
    "\n",
    "def scipyOptimize(recipe):\n",
    "    from scipy.optimize.minpack import leastsq\n",
    "    print(\"Fit using scipy's LM optimizer\")\n",
    "    leastsq(recipe.residual, recipe.getValues())\n",
    "    return\n",
    "\n",
    "def plotRecipe(recipe, ax=None):\n",
    "    \"\"\"Plot recipe in the specified axes `ax`.\n",
    "\n",
    "    If `ax` is None, create a new figure.\n",
    "\n",
    "    Return `ax`\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = subplots()\n",
    "    r = recipe.pdf.profile.x\n",
    "    g = recipe.pdf.profile.y\n",
    "    gcalc = recipe.pdf.evaluate()\n",
    "    diffzero = -0.8 * max(g) * np.ones_like(g)\n",
    "    diff = g - gcalc + diffzero\n",
    "    ax.plot(r,g,'o',label=\"G(r) Data\", mfc='none', mec='blue')\n",
    "    ax.plot(r, gcalc,'r-',label=\"G(r) Fit\")\n",
    "    ax.plot(r,diff,'g-',label=\"G(r) diff\")\n",
    "    ax.plot(r, diffzero,'k-')\n",
    "    ax.set_xlabel(\"$r (\\AA)$\")\n",
    "    ax.set_ylabel(\"$G (\\AA^{-2})$\")\n",
    "    ax.legend(loc=1)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the data and make it a PDFprofile. Define the range of the data that will be \n",
    "# used in the fit.\n",
    "\n",
    "grdata =  'MoO2_t-BuOH_200-00050.gr'\n",
    "pdfprofile = Profile()\n",
    "pdfparser = PDFParser()\n",
    "pdfparser.parseFile(grdata)\n",
    "pdfprofile.loadParsedData(pdfparser)\n",
    "\n",
    "# Setup the PDFgenerator 1 that calculates the PDF from the CIF-file\n",
    "\n",
    "pdfgenerator_cluster1 = PDFGenerator(\"G1\")\n",
    "pdfgenerator_cluster1.setQmax(17.0)\n",
    "pdfgenerator_cluster1.setQmin(0.5)\n",
    "pdfgenerator_cluster1._calc.evaluatortype = 'OPTIMIZED'\n",
    "pdfgenerator_cluster1.parallel(2)\n",
    "# Load structure from the XYZ file.\n",
    "\n",
    "xyzfile1 = \"disrut_cluster_4x5x1.xyz\"\n",
    "xyz_structure1 = loadStructure(xyzfile1)\n",
    "pdfgenerator_cluster1.setStructure(xyz_structure1, periodic = False)\n",
    "\n",
    "\n",
    "# Add the profile and generator to the PDFcontribution\n",
    "pdfcontribution = FitContribution(\"pdf\")\n",
    "pdfcontribution.setProfile(pdfprofile, xname=\"r\") \n",
    "pdfcontribution.addProfileGenerator(pdfgenerator_cluster1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the wave function that arises from solvent-particle interaction in the sample\n",
    "\n",
    "def npwave(r, wA, wlam, wphi, wr0, wsig, wasym):\n",
    "    \"\"\"Dampened sine-wave from equation (M-2) in Zoebel science supplement.\n",
    "    \"\"\"\n",
    "    ysin = np.sin(2 * np.pi * (r/wlam - wphi))\n",
    "    sigeff = np.zeros_like(r, dtype=float)\n",
    "    lo = (r < wr0); hi = ~lo;\n",
    "    sigeff[lo] = wsig / wasym\n",
    "    sigeff[hi] = wsig * wasym\n",
    "    yexp = np.exp(-1 * ((r - wr0) / (2 * sigeff))**2)\n",
    "    rv = wA * ysin * yexp\n",
    "    return rv\n",
    "\n",
    "# Register the npwave function for the dispersed sample background.\n",
    "pdfcontribution.registerFunction(npwave, name='npwave')\n",
    "\n",
    "# Setup initial values for the wave-related parameters.\n",
    "pdfcontribution.wA << 0\n",
    "pdfcontribution.wlam << 5\n",
    "pdfcontribution.wphi << 0\n",
    "pdfcontribution.wr0 << 4\n",
    "pdfcontribution.wsig << 2\n",
    "pdfcontribution.wasym << 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#original parameters:\n",
    "#    # Setup initial values for the wave-related parameters.\n",
    "#pdfcontribution.wA << 0\n",
    "#pdfcontribution.wlam << 5\n",
    "#pdfcontribution.wphi << 0\n",
    "#pdfcontribution.wr0 << 4\n",
    "#pdfcontribution.wsig << 2\n",
    "#pdfcontribution.wasym << 1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust partial PDF scaling so that refined scaling factors\n",
    "are proportional to molar content of each phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the pdfcontribution equation to account for the npwave\n",
    "# Use scaling factors proportional to molar content\n",
    "pdfcontribution.setEquation('mc1*G1 + npwave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<diffpy.srfit.fitbase.restraint.Restraint at 0x7fab98091690>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the recipe to do the fit and add it to the PDFcontribution\n",
    "\n",
    "recipe = FitRecipe()\n",
    "recipe.addContribution(pdfcontribution)\n",
    "\n",
    "# Avoid too much output during fitting \n",
    "recipe.clearFitHooks()\n",
    "\n",
    "# Add the scale factor.\n",
    "recipe.addVar(pdfcontribution.mc1, 1.0, tag = \"scale\")\n",
    "\n",
    "recipe.restrain('mc1', lb=0, ub = 3, sig=0.0001)\n",
    "\n",
    "# set qdamp, qbroad for the instrument\n",
    "qdamp = 0.034\n",
    "#qbroad = 0.01\n",
    "\n",
    "# Add the instrumental parameters s\n",
    "pdfgenerator_cluster1.qdamp.value = qdamp\n",
    "#pdfgenerator_cluster1.qbroad.value = qbroad\n",
    "\n",
    "# Add the delta2 parameters for the two phases, and make sure they cannot take unphysical values\n",
    "recipe.addVar(pdfgenerator_cluster1.delta2, 0, name = \"delta2_cluster1\", tag = \"delta2\")\n",
    "recipe.restrain(\"delta2_cluster1\", lb=0, ub = 7, sig=0.001) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add variables related to the wave background:\n",
    "recipe.addVar(pdfcontribution.wA, fixed=True, tag='wave')\n",
    "recipe.addVar(pdfcontribution.wlam, fixed=True, tag='wave')\n",
    "recipe.addVar(pdfcontribution.wphi, fixed=True, tag='wave')\n",
    "recipe.addVar(pdfcontribution.wr0, fixed=True, tag='wave')\n",
    "recipe.addVar(pdfcontribution.wsig, fixed=True, tag='wave')\n",
    "recipe.addVar(pdfcontribution.wasym, fixed=True, tag='wave');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add ADP and \"cell\" for the cluster\n",
    "phase_cluster1 = pdfgenerator_cluster1.phase\n",
    "atoms1 = phase_cluster1.getScatterers()\n",
    "\n",
    "lat1 = phase_cluster1.getLattice()\n",
    "\n",
    "recipe.newVar(\"zoomscale1\", 1.0, tag = \"lat1\")\n",
    "recipe.newVar(\"zoomscale2\", 1.0, tag = \"lat1\")\n",
    "recipe.newVar(\"zoomscale3\", 1.0, tag = \"lat1\")\n",
    "recipe.constrain(lat1.a, 'zoomscale1')\n",
    "recipe.constrain(lat1.b, 'zoomscale2')\n",
    "recipe.constrain(lat1.c, 'zoomscale3')\n",
    "recipe.restrain(\"zoomscale1\", lb=0.97, ub = 1.03, sig=0.001)\n",
    "recipe.restrain(\"zoomscale2\", lb=0.97, ub = 1.03, sig=0.001)\n",
    "recipe.restrain(\"zoomscale3\", lb=0.97, ub = 1.03, sig=0.001)\n",
    "\n",
    "Mo_cluster1 = recipe.newVar(\"Mo_Biso_cluster1\", 0.4, tag = 'adp_mo')\n",
    "O_cluster1 = recipe.newVar(\"O_Biso_cluster1\", 0.4, tag = 'adp_o')\n",
    "#Cl_cluster1 = recipe.newVar(\"Cl_Biso_cluster1\", 0.4, tag = 'adp_cl')\n",
    "\n",
    "for atom in atoms1:\n",
    "    if atom.element.title() == \"Mo\":\n",
    "        recipe.constrain(atom.Biso, Mo_cluster1)\n",
    "    elif atom.element.title() == \"O\":\n",
    "        recipe.constrain(atom.Biso, O_cluster1)\n",
    "   # elif atom.element.title() == \"Cl\":\n",
    "    #    recipe.constrain(atom.Biso, Cl_cluster1)\n",
    "        \n",
    "recipe.restrain(\"Mo_Biso_cluster1\", lb=0.08, ub = 3, sig=0.001)\n",
    "\n",
    "pos_limit1 = 0.22\n",
    "\n",
    "for atom in atoms1:\n",
    "    if atom.element == 'Mo':\n",
    "        recipe.addVar(atom.x, name=atom.name+'_x1', tag='xyz1')\n",
    "        recipe.addVar(atom.y, name=atom.name+'_y1', tag='xyz1')\n",
    "        recipe.addVar(atom.z, name=atom.name+'_z1', tag='xyz1')       \n",
    "        #restrain atomic positions\n",
    "        lbx = atom.x.value-pos_limit1\n",
    "        ubx = atom.x.value+pos_limit1\n",
    "        recipe.restrain(atom.x, lb=lbx, ub = ubx, sig=0.001)\n",
    "        lby = atom.y.value-pos_limit1\n",
    "        uby = atom.y.value+pos_limit1\n",
    "        recipe.restrain(atom.y, lb=lby, ub = uby, sig=0.001)\n",
    "        lbz = atom.z.value-pos_limit1\n",
    "        ubz = atom.z.value+pos_limit1\n",
    "        recipe.restrain(atom.y, lb=lby, ub = uby, sig=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pdfprofile.setCalculationRange(xmin = 1.55, xmax = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Done with setting up, start the actual refinements!\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "recipe.fix('all')\n",
    "recipe.free('mc1')\n",
    "scipyOptimize(recipe)\n",
    "plotRecipe(recipe, ax=ax)\n",
    "plt.show()\n",
    "# print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='$r (\\\\AA)$', ylabel='$G (\\\\AA^{-2})$'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recipe.free('lat1')\n",
    "scipyOptimize(recipe)\n",
    "plotRecipe(recipe)\n",
    "# print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recipe.fix ('lat1')\n",
    "recipe.free('xyz1')\n",
    "scipyOptimize(recipe)\n",
    "FitResults(recipe)\n",
    "# print(FitResults(recipe))\n",
    "plotRecipe(recipe);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='$r (\\\\AA)$', ylabel='$G (\\\\AA^{-2})$'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recipe.free('adp_mo')\n",
    "scipyOptimize(recipe)\n",
    "plotRecipe(recipe)\n",
    "# print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit using scipy's LM optimizer\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       33.03006737\n",
      "Contributions  33.01839561\n",
      "Restraints     0.01167176\n",
      "Chi2           33.01839561\n",
      "Reduced Chi2   0.03889093\n",
      "Rw             0.63236804\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Mo0_x1            1.93795762e+00 +/- 1.12513271e+06\n",
      "Mo0_y1            2.57501257e+00 +/- 1.68112180e+06\n",
      "Mo0_z1            5.93995562e+00 +/- 4.72278224e+05\n",
      "Mo10_x1           -8.47076903e-01 +/- 4.16395423e+05\n",
      "Mo10_y1           -1.65970269e-01 +/- 5.88561437e+05\n",
      "Mo10_z1           5.79901278e+00 +/- 1.23292574e+05\n",
      "Mo11_x1           1.84019450e+00 +/- 1.37814176e+06\n",
      "Mo11_y1           4.72655934e+00 +/- 1.60803356e+06\n",
      "Mo11_z1           9.41905592e-01 +/- 6.54101644e+05\n",
      "Mo12_x1           2.03125427e+00 +/- 1.21403195e+06\n",
      "Mo12_y1           -1.59316070e-01 +/- 1.36517830e+06\n",
      "Mo12_z1           1.07916517e+01 +/- 6.12516875e+05\n",
      "Mo13_x1           4.96217954e+00 +/- 3.20340413e+05\n",
      "Mo13_y1           4.90148556e+00 +/- 3.63219152e+05\n",
      "Mo13_z1           5.78300668e+00 +/- 2.79418524e+05\n",
      "Mo14_x1           7.88111851e+00 +/- 7.92569975e+05\n",
      "Mo14_y1           4.91152166e+00 +/- 8.70318208e+05\n",
      "Mo14_z1           1.08041172e+01 +/- 4.96447073e+05\n",
      "Mo15_x1           7.10820578e-01 +/- 1.46883356e+05\n",
      "Mo15_y1           2.64150302e+00 +/- 7.07106782e-04\n",
      "Mo15_z1           3.70325062e+00 +/- 6.28900942e+04\n",
      "Mo16_x1           3.69057414e+00 +/- 1.74417892e+05\n",
      "Mo16_y1           2.64150969e+00 +/- 7.07106786e-04\n",
      "Mo16_z1           8.55044751e+00 +/- 1.03459911e+05\n",
      "Mo17_x1           6.37727098e+00 +/- 1.55638000e+05\n",
      "Mo17_y1           7.48450201e+00 +/- 7.07106783e-04\n",
      "Mo17_z1           3.59538207e+00 +/- 1.05877837e+05\n",
      "Mo18_x1           6.88139223e+00 +/- 9.99999996e-04\n",
      "Mo18_y1           2.61161149e+00 +/- 3.00203850e+05\n",
      "Mo18_z1           1.36806469e+01 +/- 1.06178068e+05\n",
      "Mo19_x1           9.61264478e+00 +/- 1.00000001e-03\n",
      "Mo19_y1           7.35080432e+00 +/- 2.95497415e+04\n",
      "Mo19_z1           8.78996100e+00 +/- 2.36195248e+05\n",
      "Mo1_x1            4.72431387e+00 +/- 1.28084345e+06\n",
      "Mo1_y1            7.43414915e+00 +/- 9.02704748e+05\n",
      "Mo1_z1            8.00134039e-01 +/- 4.95833088e+05\n",
      "Mo2_x1            4.77232582e+00 +/- 7.53170015e+05\n",
      "Mo2_y1            2.56917739e+00 +/- 1.23726359e+06\n",
      "Mo2_z1            1.08775070e+01 +/- 5.79612482e+05\n",
      "Mo3_x1            7.58858141e+00 +/- 1.68793987e+05\n",
      "Mo3_y1            7.48450381e+00 +/- 7.07106785e-04\n",
      "Mo3_z1            5.87396429e+00 +/- 1.63267946e+05\n",
      "Mo4_x1            1.07576632e+01 +/- 9.99999997e-04\n",
      "Mo4_y1            7.13982398e+00 +/- 1.34045209e+05\n",
      "Mo4_z1            1.10705344e+01 +/- 1.15267701e+05\n",
      "Mo5_x1            -1.81285835e+00 +/- 1.61463951e+06\n",
      "Mo5_y1            1.77306941e-01 +/- 2.07446015e+06\n",
      "Mo5_z1            3.45047848e+00 +/- 6.62578447e+05\n",
      "Mo6_x1            9.33231646e-01 +/- 1.87603883e+05\n",
      "Mo6_y1            9.74536145e-03 +/- 1.52103189e+05\n",
      "Mo6_z1            8.49814791e+00 +/- 3.30032919e+05\n",
      "Mo7_x1            3.77381529e+00 +/- 1.14024268e+06\n",
      "Mo7_y1            4.99545465e+00 +/- 1.45800787e+06\n",
      "Mo7_z1            3.49878974e+00 +/- 6.64241448e+05\n",
      "Mo8_x1            4.07639985e+00 +/- 1.00000000e-03\n",
      "Mo8_y1            2.20001579e-01 +/- 7.07106784e-04\n",
      "Mo8_z1            1.31147709e+01 +/- 1.41695644e+05\n",
      "Mo9_x1            6.77831462e+00 +/- 1.64778092e+05\n",
      "Mo9_y1            5.06302067e+00 +/- 7.07106782e-04\n",
      "Mo9_z1            8.48948699e+00 +/- 2.80507160e+05\n",
      "Mo_Biso_cluster1  7.99782976e-02 +/- 1.00000000e-03\n",
      "O_Biso_cluster1   5.35959240e-01 +/- 3.19443398e+05\n",
      "mc1               2.94508231e-01 +/- 1.54544153e+03\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "delta2_cluster1  0.00000000e+00\n",
      "wA               0.00000000e+00\n",
      "wasym            1.00000000e+00\n",
      "wlam             5.00000000e+00\n",
      "wphi             0.00000000e+00\n",
      "wr0              4.00000000e+00\n",
      "wsig             2.00000000e+00\n",
      "zoomscale1       1.03003354e+00\n",
      "zoomscale2       9.69981023e-01\n",
      "zoomscale3       1.03005248e+00\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(Mo0_x1, Mo0_y1)             -0.9998\n",
      "corr(Mo7_y1, Mo7_z1)             0.9996\n",
      "corr(Mo0_x1, Mo2_y1)             0.9996\n",
      "corr(Mo1_x1, Mo2_z1)             0.9996\n",
      "corr(Mo1_x1, Mo1_z1)             -0.9995\n",
      "corr(Mo0_z1, Mo1_x1)             -0.9995\n",
      "corr(Mo0_z1, Mo1_z1)             0.9994\n",
      "corr(Mo11_y1, Mo14_z1)           -0.9994\n",
      "corr(Mo0_y1, Mo2_y1)             -0.9992\n",
      "corr(Mo0_y1, Mo0_z1)             0.9990\n",
      "corr(Mo2_x1, Mo2_z1)             -0.9988\n",
      "corr(Mo11_x1, Mo11_z1)           -0.9988\n",
      "corr(Mo1_z1, Mo2_z1)             -0.9987\n",
      "corr(mc1, Mo8_z1)                0.9986\n",
      "corr(Mo0_y1, Mo1_z1)             0.9986\n",
      "corr(Mo11_z1, Mo14_y1)           -0.9986\n",
      "corr(Mo11_x1, Mo11_y1)           -0.9986\n",
      "corr(Mo5_x1, Mo5_z1)             -0.9985\n",
      "corr(Mo13_x1, Mo13_y1)           -0.9984\n",
      "corr(Mo0_x1, Mo0_z1)             -0.9984\n",
      "corr(Mo14_z1, Mo15_x1)           0.9983\n",
      "corr(Mo11_y1, Mo15_x1)           -0.9983\n",
      "corr(Mo0_z1, Mo2_z1)             -0.9982\n",
      "corr(Mo0_x1, Mo1_z1)             -0.9981\n",
      "corr(Mo1_x1, Mo2_x1)             -0.9981\n",
      "corr(Mo0_y1, Mo1_x1)             -0.9979\n",
      "corr(Mo3_z1, Mo19_z1)            0.9977\n",
      "corr(Mo11_x1, Mo14_z1)           0.9977\n",
      "corr(Mo5_x1, Mo5_y1)             -0.9974\n",
      "corr(Mo1_y1, Mo2_x1)             0.9973\n",
      "corr(Mo0_x1, Mo1_x1)             0.9969\n",
      "corr(Mo1_y1, Mo2_z1)             -0.9968\n",
      "corr(Mo0_z1, Mo2_x1)             0.9968\n",
      "corr(Mo0_z1, Mo2_y1)             -0.9967\n",
      "corr(Mo1_z1, Mo2_y1)             -0.9963\n",
      "corr(Mo1_z1, Mo2_x1)             0.9960\n",
      "corr(Mo0_y1, Mo2_z1)             -0.9958\n",
      "corr(O_Biso_cluster1, Mo2_y1)    0.9957\n",
      "corr(Mo16_x1, Mo17_x1)           -0.9957\n",
      "corr(Mo11_z1, Mo14_z1)           -0.9955\n",
      "corr(Mo11_y1, Mo11_z1)           0.9955\n",
      "corr(Mo11_x1, Mo15_x1)           0.9954\n",
      "corr(Mo8_z1, Mo12_y1)            0.9952\n",
      "corr(Mo4_y1, Mo18_z1)            0.9950\n",
      "corr(Mo11_x1, Mo14_y1)           0.9950\n",
      "corr(Mo8_z1, Mo9_z1)             0.9947\n",
      "corr(Mo1_x1, Mo2_y1)             0.9947\n",
      "corr(Mo1_x1, Mo1_y1)             -0.9945\n",
      "corr(Mo0_x1, Mo2_z1)             0.9944\n",
      "corr(O_Biso_cluster1, Mo13_z1)   0.9942\n",
      "corr(O_Biso_cluster1, Mo0_x1)    0.9941\n",
      "corr(Mo5_z1, Mo7_y1)             -0.9939\n",
      "corr(Mo2_x1, Mo12_x1)            0.9935\n",
      "corr(Mo9_z1, Mo12_x1)            -0.9935\n",
      "corr(Mo5_z1, Mo7_z1)             -0.9934\n",
      "corr(Mo12_z1, Mo19_z1)           0.9933\n",
      "corr(Mo15_z1, Mo18_y1)           0.9933\n",
      "corr(Mo5_x1, Mo7_x1)             -0.9933\n",
      "corr(Mo1_y1, Mo1_z1)             0.9929\n",
      "corr(Mo1_y1, Mo9_z1)             -0.9928\n",
      "corr(Mo0_y1, Mo2_x1)             0.9926\n",
      "corr(O_Biso_cluster1, Mo0_y1)    -0.9925\n",
      "corr(Mo5_z1, Mo7_x1)             0.9925\n",
      "corr(Mo5_y1, Mo7_x1)             0.9924\n",
      "corr(mc1, Mo12_y1)               0.9924\n",
      "corr(Mo5_y1, Mo5_z1)             0.9924\n",
      "corr(Mo12_x1, Mo12_y1)           -0.9924\n",
      "corr(Mo3_x1, Mo14_y1)            -0.9920\n",
      "corr(mc1, Mo9_z1)                0.9919\n",
      "corr(Mo14_y1, Mo14_z1)           0.9919\n",
      "corr(Mo11_z1, Mo15_x1)           -0.9917\n",
      "corr(Mo0_z1, Mo1_y1)             0.9917\n",
      "corr(Mo2_y1, Mo2_z1)             0.9916\n",
      "corr(Mo9_z1, Mo12_y1)            0.9911\n",
      "corr(Mo2_x1, Mo9_z1)             -0.9908\n",
      "corr(Mo3_x1, Mo11_z1)            0.9908\n",
      "corr(Mo0_x1, Mo2_x1)             -0.9907\n",
      "corr(Mo10_x1, Mo11_z1)           0.9906\n",
      "corr(Mo11_y1, Mo14_y1)           -0.9903\n",
      "corr(Mo10_x1, Mo14_y1)           -0.9897\n",
      "corr(Mo10_x1, Mo11_x1)           -0.9895\n",
      "corr(O_Biso_cluster1, Mo18_y1)   -0.9891\n",
      "corr(Mo1_y1, Mo12_x1)            0.9891\n",
      "corr(Mo15_z1, Mo17_z1)           0.9885\n",
      "corr(Mo3_x1, Mo14_z1)            -0.9885\n",
      "corr(O_Biso_cluster1, Mo1_z1)    -0.9883\n",
      "corr(Mo3_z1, Mo12_z1)            0.9883\n",
      "corr(Mo3_x1, Mo15_x1)            -0.9878\n",
      "corr(Mo5_x1, Mo7_y1)             0.9874\n",
      "corr(Mo2_x1, Mo2_y1)             -0.9873\n",
      "corr(Mo3_x1, Mo11_x1)            -0.9872\n",
      "corr(Mo2_z1, Mo12_x1)            -0.9869\n",
      "corr(Mo2_y1, Mo15_z1)            -0.9869\n",
      "corr(O_Biso_cluster1, Mo0_z1)    -0.9866\n",
      "corr(Mo2_z1, Mo9_z1)             0.9864\n",
      "corr(O_Biso_cluster1, Mo15_z1)   -0.9863\n",
      "corr(Mo8_z1, Mo12_x1)            -0.9862\n",
      "corr(Mo5_x1, Mo7_z1)             0.9862\n",
      "corr(Mo0_y1, Mo1_y1)             0.9860\n",
      "corr(Mo14_x1, Mo18_y1)           0.9859\n",
      "corr(Mo14_y1, Mo15_x1)           0.9859\n",
      "corr(mc1, Mo3_z1)                0.9856\n",
      "corr(Mo7_x1, Mo7_y1)             -0.9855\n",
      "corr(Mo1_x1, Mo12_x1)            -0.9852\n",
      "corr(Mo4_z1, Mo15_x1)            -0.9850\n",
      "corr(Mo3_x1, Mo11_y1)            0.9850\n",
      "corr(Mo13_z1, Mo18_y1)           -0.9846\n",
      "corr(O_Biso_cluster1, Mo1_x1)    0.9845\n",
      "corr(Mo5_y1, Mo10_y1)            0.9844\n",
      "corr(Mo10_x1, Mo11_y1)           0.9842\n",
      "corr(Mo0_x1, Mo1_y1)             -0.9841\n",
      "corr(Mo0_z1, Mo12_x1)            0.9835\n",
      "corr(Mo4_z1, Mo6_z1)             0.9834\n",
      "corr(Mo1_y1, Mo8_z1)             -0.9833\n",
      "corr(Mo17_z1, Mo18_y1)           0.9833\n",
      "corr(Mo7_x1, Mo7_z1)             -0.9830\n",
      "corr(Mo0_x1, Mo15_z1)            -0.9828\n",
      "corr(Mo2_y1, Mo13_z1)            0.9828\n",
      "corr(Mo10_x1, Mo14_z1)           -0.9826\n",
      "corr(Mo4_z1, Mo11_y1)            0.9825\n",
      "corr(Mo2_y1, Mo18_y1)            -0.9825\n",
      "corr(Mo1_x1, Mo9_z1)             0.9824\n",
      "corr(Mo13_z1, Mo15_z1)           -0.9820\n",
      "corr(Mo4_z1, Mo11_x1)            -0.9818\n",
      "corr(Mo5_x1, Mo10_y1)            -0.9814\n",
      "corr(Mo7_x1, Mo10_y1)            0.9811\n",
      "corr(Mo0_y1, Mo15_z1)            0.9808\n",
      "corr(O_Biso_cluster1, Mo2_z1)    0.9804\n",
      "corr(Mo13_z1, Mo14_x1)           -0.9802\n",
      "corr(mc1, Mo12_x1)               -0.9800\n",
      "corr(Mo0_x1, Mo13_z1)            0.9800\n",
      "corr(Mo1_z1, Mo12_x1)            0.9799\n",
      "corr(Mo3_z1, Mo8_z1)             0.9793\n",
      "corr(Mo14_x1, Mo17_z1)           0.9793\n",
      "corr(Mo1_y1, Mo2_y1)             -0.9792\n",
      "corr(mc1, Mo1_y1)                -0.9789\n",
      "corr(Mo13_x1, Mo14_x1)           0.9782\n",
      "corr(Mo14_x1, Mo15_x1)           -0.9780\n",
      "corr(Mo10_x1, Mo10_y1)           -0.9777\n",
      "corr(Mo0_x1, Mo18_y1)            -0.9774\n",
      "corr(Mo0_z1, Mo9_z1)             -0.9774\n",
      "corr(Mo4_z1, Mo14_z1)            -0.9773\n",
      "corr(Mo1_y1, Mo12_y1)            -0.9771\n",
      "corr(Mo1_z1, Mo9_z1)             -0.9771\n",
      "corr(Mo2_x1, Mo8_z1)             -0.9770\n",
      "corr(Mo0_y1, Mo13_z1)            -0.9764\n",
      "corr(Mo2_x1, Mo12_y1)            -0.9764\n",
      "corr(Mo13_x1, Mo15_x1)           -0.9763\n",
      "corr(Mo16_x1, Mo16_z1)           -0.9759\n",
      "corr(Mo13_y1, Mo13_z1)           0.9757\n",
      "corr(Mo0_y1, Mo12_x1)            0.9756\n",
      "corr(Mo0_y1, Mo18_y1)            0.9755\n",
      "corr(Mo5_y1, Mo14_y1)            0.9754\n",
      "corr(Mo4_z1, Mo11_z1)            0.9753\n",
      "corr(Mo11_y1, Mo14_x1)           0.9748\n",
      "corr(Mo5_y1, Mo7_y1)             -0.9747\n",
      "corr(Mo5_z1, Mo10_y1)            0.9742\n",
      "corr(Mo13_y1, Mo14_x1)           -0.9740\n",
      "corr(mc1, Mo19_z1)               0.9738\n",
      "corr(Mo4_y1, Mo16_z1)            -0.9736\n",
      "corr(Mo14_x1, Mo14_z1)           -0.9736\n",
      "corr(Mo0_z1, Mo15_z1)            0.9734\n",
      "corr(Mo10_y1, Mo14_y1)           0.9728\n",
      "corr(Mo10_x1, Mo15_x1)           -0.9727\n",
      "corr(Mo4_z1, Mo17_z1)            0.9727\n",
      "corr(Mo5_y1, Mo7_z1)             -0.9726\n",
      "corr(Mo7_x1, Mo14_y1)            0.9724\n",
      "corr(Mo0_x1, Mo12_x1)            -0.9721\n",
      "corr(O_Biso_cluster1, Mo2_x1)    -0.9717\n",
      "corr(Mo14_x1, Mo15_z1)           0.9710\n",
      "corr(Mo1_z1, Mo13_z1)            -0.9706\n",
      "corr(Mo13_x1, Mo14_z1)           -0.9705\n",
      "corr(Mo3_z1, Mo12_y1)            0.9700\n",
      "corr(mc1, Mo2_x1)                -0.9700\n",
      "corr(Mo1_z1, Mo15_z1)            0.9698\n",
      "corr(Mo2_z1, Mo8_z1)             0.9698\n",
      "corr(Mo16_z1, Mo18_z1)           -0.9695\n",
      "corr(O_Biso_cluster1, Mo14_x1)   -0.9693\n",
      "corr(Mo5_y1, Mo11_z1)            -0.9693\n",
      "corr(Mo0_y1, Mo9_z1)             -0.9689\n",
      "corr(Mo13_x1, Mo13_z1)           -0.9687\n",
      "corr(Mo0_z1, Mo13_z1)            -0.9679\n",
      "corr(Mo3_x1, Mo5_y1)             -0.9677\n",
      "corr(Mo2_y1, Mo12_x1)            -0.9673\n",
      "corr(Mo13_z1, Mo17_z1)           -0.9670\n",
      "corr(Mo1_x1, Mo15_z1)            -0.9667\n",
      "corr(Mo8_z1, Mo19_z1)            0.9663\n",
      "corr(Mo3_x1, Mo7_x1)             -0.9662\n",
      "corr(Mo18_z1, Mo19_z1)           0.9660\n",
      "corr(Mo2_z1, Mo12_y1)            0.9659\n",
      "corr(Mo10_y1, Mo11_z1)           -0.9657\n",
      "corr(Mo11_y1, Mo13_x1)           0.9656\n",
      "corr(Mo3_x1, Mo4_z1)             0.9650\n",
      "corr(Mo0_z1, Mo18_y1)            0.9650\n",
      "corr(Mo3_x1, Mo10_x1)            0.9650\n",
      "corr(Mo12_z1, Mo18_z1)           0.9649\n",
      "corr(Mo0_x1, Mo9_z1)             0.9648\n",
      "corr(O_Biso_cluster1, Mo1_y1)    -0.9646\n",
      "corr(Mo13_y1, Mo15_x1)           0.9640\n",
      "corr(Mo1_z1, Mo18_y1)            0.9638\n",
      "corr(Mo1_x1, Mo13_z1)            0.9636\n",
      "corr(Mo1_x1, Mo8_z1)             0.9634\n",
      "corr(Mo4_z1, Mo14_y1)            -0.9633\n",
      "corr(mc1, Mo12_z1)               0.9632\n",
      "corr(Mo3_x1, Mo13_x1)            0.9631\n",
      "corr(Mo6_z1, Mo11_x1)            -0.9630\n",
      "corr(Mo7_y1, Mo10_y1)            -0.9626\n",
      "corr(mc1, Mo2_z1)                0.9625\n",
      "corr(Mo11_x1, Mo14_x1)           -0.9618\n",
      "corr(Mo1_x1, Mo12_y1)            0.9611\n",
      "corr(Mo7_x1, Mo11_z1)            -0.9609\n",
      "corr(Mo12_y1, Mo12_z1)           0.9603\n",
      "corr(Mo1_x1, Mo18_y1)            -0.9603\n",
      "corr(Mo4_z1, Mo14_x1)            0.9599\n",
      "corr(Mo5_x1, Mo14_y1)            -0.9597\n",
      "corr(O_Biso_cluster1, Mo17_z1)   -0.9597\n",
      "corr(Mo2_z1, Mo15_z1)            -0.9591\n",
      "corr(Mo15_x1, Mo17_z1)           -0.9589\n",
      "corr(Mo2_y1, Mo9_z1)             0.9585\n",
      "corr(Mo12_y1, Mo19_z1)           0.9585\n",
      "corr(Mo5_y1, Mo11_x1)            0.9585\n",
      "corr(Mo5_y1, Mo10_x1)            -0.9585\n",
      "corr(Mo6_z1, Mo11_z1)            0.9582\n",
      "corr(Mo8_z1, Mo12_z1)            0.9581\n",
      "corr(Mo3_z1, Mo9_z1)             0.9581\n",
      "corr(Mo2_z1, Mo13_z1)            0.9578\n",
      "corr(Mo1_z1, Mo8_z1)             -0.9571\n",
      "corr(Mo0_z1, Mo8_z1)             -0.9571\n",
      "corr(Mo0_z1, Mo12_y1)            -0.9570\n",
      "corr(Mo7_z1, Mo9_x1)             -0.9565\n",
      "corr(Mo7_z1, Mo10_y1)            -0.9564\n",
      "corr(Mo6_z1, Mo11_y1)            0.9563\n",
      "corr(Mo13_y1, Mo14_z1)           0.9561\n",
      "corr(mc1, Mo1_x1)                0.9555\n",
      "corr(Mo10_y1, Mo11_x1)           0.9554\n",
      "corr(Mo16_z1, Mo17_x1)           0.9554\n",
      "corr(Mo6_z1, Mo15_x1)            -0.9551\n",
      "corr(Mo4_y1, Mo12_z1)            0.9551\n",
      "corr(Mo2_x1, Mo15_z1)            0.9538\n",
      "corr(Mo4_z1, Mo10_x1)            0.9537\n",
      "corr(Mo1_z1, Mo12_y1)            -0.9536\n",
      "corr(Mo11_x1, Mo13_x1)           -0.9534\n",
      "corr(Mo7_y1, Mo9_x1)             -0.9532\n",
      "corr(Mo4_y1, Mo19_z1)            0.9529\n",
      "corr(Mo2_z1, Mo18_y1)            -0.9528\n",
      "corr(Mo2_y1, Mo17_z1)            -0.9526\n",
      "corr(Mo3_x1, Mo5_x1)             0.9510\n",
      "corr(Mo11_y1, Mo17_z1)           0.9509\n",
      "corr(Mo11_y1, Mo13_y1)           -0.9507\n",
      "corr(Mo5_x1, Mo11_z1)            0.9505\n",
      "corr(Mo7_x1, Mo10_x1)            -0.9500\n",
      "corr(Mo11_z1, Mo14_x1)           0.9495\n",
      "corr(Mo3_z1, Mo18_z1)            0.9495\n",
      "corr(mc1, Mo1_z1)                -0.9489\n",
      "corr(Mo13_x1, Mo17_z1)           0.9487\n",
      "corr(Mo2_y1, Mo14_x1)            -0.9485\n",
      "corr(Mo10_z1, Mo17_z1)           0.9485\n",
      "corr(mc1, Mo0_z1)                -0.9484\n",
      "corr(Mo10_z1, Mo15_z1)           0.9482\n",
      "corr(Mo6_z1, Mo14_z1)            -0.9480\n",
      "corr(Mo3_x1, Mo13_y1)            -0.9476\n",
      "corr(Mo11_z1, Mo13_x1)           0.9474\n",
      "corr(Mo5_z1, Mo14_y1)            0.9472\n",
      "corr(O_Biso_cluster1, Mo13_y1)   0.9471\n",
      "corr(Mo7_x1, Mo11_x1)            0.9466\n",
      "corr(Mo13_y1, Mo17_z1)           -0.9463\n",
      "corr(Mo6_z1, Mo14_y1)            -0.9459\n",
      "corr(Mo2_x1, Mo13_z1)            -0.9457\n",
      "corr(Mo0_x1, Mo17_z1)            -0.9450\n",
      "corr(Mo3_x1, Mo10_y1)            -0.9447\n",
      "corr(Mo14_z1, Mo17_z1)           -0.9446\n",
      "corr(Mo6_z1, Mo10_x1)            0.9445\n",
      "corr(Mo0_y1, Mo8_z1)             -0.9445\n",
      "corr(Mo5_y1, Mo14_z1)            0.9441\n",
      "corr(Mo0_y1, Mo12_y1)            -0.9440\n",
      "corr(Mo2_x1, Mo18_y1)            0.9436\n",
      "corr(Mo4_z1, Mo13_x1)            0.9436\n",
      "corr(Mo13_y1, Mo18_y1)           -0.9432\n",
      "corr(Mo5_y1, Mo11_y1)            -0.9430\n",
      "corr(Mo13_x1, Mo18_y1)           0.9421\n",
      "corr(Mo13_x1, Mo14_y1)           -0.9419\n",
      "corr(O_Biso_cluster1, Mo12_x1)   -0.9417\n",
      "corr(Mo0_y1, Mo17_z1)            0.9409\n",
      "corr(Mo5_x1, Mo10_x1)            0.9407\n",
      "corr(Mo0_x1, Mo14_x1)            -0.9406\n",
      "corr(Mo5_y1, Mo6_z1)             -0.9405\n",
      "corr(Mo3_x1, Mo5_z1)             -0.9402\n",
      "corr(Mo10_y1, Mo11_y1)           -0.9397\n",
      "corr(Mo0_x1, Mo8_z1)             0.9396\n",
      "corr(Mo10_y1, Mo14_z1)           0.9396\n",
      "corr(Mo3_x1, Mo6_z1)             0.9393\n",
      "corr(Mo1_y1, Mo13_z1)            -0.9393\n",
      "corr(Mo0_x1, Mo12_y1)            0.9389\n",
      "corr(Mo9_z1, Mo19_z1)            0.9388\n",
      "corr(Mo11_x1, Mo17_z1)           -0.9388\n",
      "corr(O_Biso_cluster1, Mo13_x1)   -0.9386\n",
      "corr(Mo3_x1, Mo14_x1)            0.9382\n",
      "corr(Mo6_z1, Mo17_z1)            0.9379\n",
      "corr(Mo5_y1, Mo15_x1)            0.9374\n",
      "corr(Mo5_z1, Mo9_x1)             0.9374\n",
      "corr(Mo3_z1, Mo12_x1)            -0.9372\n",
      "corr(Mo14_x1, Mo14_y1)           -0.9370\n",
      "corr(Mo7_x1, Mo14_z1)            0.9369\n",
      "corr(O_Biso_cluster1, Mo9_z1)    0.9364\n",
      "corr(Mo5_x1, Mo11_x1)            -0.9363\n",
      "corr(Mo15_x1, Mo18_y1)           -0.9362\n",
      "corr(Mo0_y1, Mo14_x1)            0.9362\n",
      "corr(Mo1_y1, Mo15_z1)            0.9360\n",
      "corr(Mo13_y1, Mo15_z1)           -0.9360\n",
      "corr(Mo11_x1, Mo13_y1)           0.9359\n",
      "corr(Mo5_z1, Mo11_z1)            -0.9354\n",
      "corr(mc1, Mo0_y1)                -0.9347\n",
      "corr(Mo10_x1, Mo14_x1)           0.9337\n",
      "corr(Mo3_z1, Mo4_y1)             0.9320\n",
      "corr(Mo7_x1, Mo11_y1)            -0.9318\n",
      "corr(Mo13_x1, Mo15_z1)           0.9317\n",
      "corr(Mo11_y1, Mo18_y1)           0.9314\n",
      "corr(Mo1_y1, Mo3_z1)             -0.9312\n",
      "corr(Mo2_y1, Mo8_z1)             0.9312\n",
      "corr(Mo2_y1, Mo12_y1)            0.9311\n",
      "corr(Mo4_z1, Mo18_y1)            0.9311\n",
      "corr(Mo12_x1, Mo15_z1)           0.9305\n",
      "corr(Mo13_z1, Mo15_x1)           0.9303\n",
      "corr(Mo9_z1, Mo12_z1)            0.9300\n",
      "corr(Mo4_z1, Mo13_y1)            -0.9300\n",
      "corr(mc1, Mo0_x1)                0.9297\n",
      "corr(Mo4_z1, Mo15_z1)            0.9287\n",
      "corr(Mo11_z1, Mo13_y1)           -0.9283\n",
      "corr(Mo0_z1, Mo17_z1)            0.9282\n",
      "corr(Mo4_z1, Mo5_y1)             -0.9271\n",
      "corr(Mo14_z1, Mo18_y1)           -0.9264\n",
      "corr(Mo1_y1, Mo18_y1)            0.9264\n",
      "corr(Mo10_x1, Mo19_y1)           0.9263\n",
      "corr(Mo7_x1, Mo15_x1)            0.9259\n",
      "corr(Mo10_y1, Mo15_x1)           0.9248\n",
      "corr(Mo5_x1, Mo9_x1)             -0.9246\n",
      "corr(Mo5_z1, Mo10_x1)            -0.9242\n",
      "corr(Mo1_z1, Mo17_z1)            0.9234\n",
      "corr(Mo4_y1, Mo16_x1)            0.9225\n",
      "corr(Mo11_z1, Mo17_z1)           0.9223\n",
      "corr(Mo1_z1, Mo14_x1)            0.9217\n",
      "corr(Mo13_y1, Mo14_y1)           0.9217\n",
      "corr(Mo7_y1, Mo14_y1)            -0.9209\n",
      "corr(mc1, Mo2_y1)                0.9202\n",
      "corr(Mo15_x1, Mo15_z1)           -0.9198\n",
      "corr(Mo0_z1, Mo14_x1)            0.9198\n",
      "corr(Mo5_x1, Mo14_z1)            -0.9197\n",
      "corr(Mo6_z1, Mo10_y1)            -0.9191\n",
      "corr(Mo12_x1, Mo12_z1)           -0.9190\n",
      "corr(Mo2_y1, Mo13_y1)            0.9190\n",
      "corr(Mo5_z1, Mo11_x1)            0.9189\n",
      "corr(Mo12_x1, Mo19_z1)           -0.9185\n",
      "corr(Mo13_z1, Mo14_z1)           0.9183\n",
      "corr(Mo11_y1, Mo13_z1)           -0.9180\n",
      "corr(Mo5_x1, Mo6_z1)             0.9178\n",
      "corr(Mo5_x1, Mo11_y1)            0.9177\n",
      "corr(Mo1_x1, Mo17_z1)            -0.9176\n",
      "corr(Mo2_x1, Mo3_z1)             -0.9153\n",
      "corr(Mo7_z1, Mo14_y1)            -0.9148\n",
      "corr(Mo2_y1, Mo10_z1)            -0.9134\n",
      "corr(Mo1_x1, Mo14_x1)            -0.9133\n",
      "corr(Mo0_x1, Mo13_y1)            0.9131\n",
      "corr(Mo11_x1, Mo18_y1)           -0.9130\n",
      "corr(Mo12_x1, Mo18_y1)           0.9128\n",
      "corr(Mo3_x1, Mo17_z1)            0.9110\n",
      "corr(Mo5_x1, Mo15_x1)            -0.9109\n",
      "corr(Mo3_x1, Mo7_y1)             0.9108\n",
      "corr(Mo10_x1, Mo13_x1)           0.9107\n",
      "corr(Mo0_x1, Mo10_z1)            -0.9104\n",
      "corr(Mo10_z1, Mo18_y1)           0.9102\n",
      "corr(Mo11_y1, Mo15_z1)           0.9098\n",
      "corr(Mo4_z1, Mo13_z1)            -0.9092\n",
      "corr(Mo6_z1, Mo14_x1)            0.9091\n",
      "corr(Mo1_y1, Mo19_z1)            -0.9082\n",
      "corr(Mo2_y1, Mo13_x1)            -0.9079\n",
      "corr(Mo9_z1, Mo15_z1)            -0.9071\n",
      "corr(Mo3_x1, Mo7_z1)             0.9071\n",
      "corr(Mo0_y1, Mo10_z1)            0.9071\n",
      "corr(Mo12_x1, Mo13_z1)           -0.9068\n",
      "corr(Mo2_z1, Mo17_z1)            -0.9061\n",
      "corr(Mo4_z1, Mo10_y1)            -0.9059\n",
      "corr(Mo0_y1, Mo13_y1)            -0.9058\n",
      "corr(Mo0_z1, Mo10_z1)            0.9046\n",
      "corr(Mo16_x1, Mo18_z1)           0.9040\n",
      "corr(Mo2_z1, Mo14_x1)            -0.9038\n",
      "corr(Mo7_y1, Mo11_z1)            0.9036\n",
      "corr(Mo14_z1, Mo15_z1)           -0.9036\n",
      "corr(Mo2_z1, Mo3_z1)             0.9033\n",
      "corr(Mo10_z1, Mo13_z1)           -0.9031\n",
      "corr(O_Biso_cluster1, Mo8_z1)    0.9031\n",
      "corr(Mo5_z1, Mo14_z1)            0.9020\n",
      "corr(Mo14_y1, Mo17_z1)           -0.9020\n",
      "corr(O_Biso_cluster1, Mo15_x1)   0.9012\n",
      "corr(Mo0_x1, Mo13_x1)            -0.9006\n",
      "corr(Mo12_z1, Mo16_z1)           -0.9003\n",
      "corr(O_Biso_cluster1, Mo10_z1)   -0.8995\n",
      "corr(Mo9_z1, Mo13_z1)            0.8992\n",
      "corr(Mo4_z1, Mo5_x1)             0.8988\n",
      "corr(Mo5_z1, Mo11_y1)            -0.8987\n",
      "corr(Mo6_z1, Mo7_x1)             -0.8983\n",
      "corr(Mo9_z1, Mo18_y1)            -0.8981\n",
      "corr(O_Biso_cluster1, Mo12_y1)   0.8979\n",
      "corr(Mo2_x1, Mo17_z1)            0.8977\n",
      "corr(Mo7_z1, Mo11_z1)            0.8972\n",
      "corr(Mo7_y1, Mo10_x1)            0.8968\n",
      "corr(Mo9_x1, Mo17_x1)            0.8967\n",
      "corr(Mo5_y1, Mo9_x1)             0.8966\n",
      "corr(Mo1_z1, Mo13_y1)            -0.8966\n",
      "corr(Mo4_z1, Mo7_x1)             -0.8966\n",
      "corr(Mo5_z1, Mo6_z1)             -0.8965\n",
      "corr(Mo11_x1, Mo13_z1)           0.8959\n",
      "corr(Mo10_x1, Mo17_z1)           0.8958\n",
      "corr(Mo14_y1, Mo19_y1)           -0.8949\n",
      "corr(Mo7_x1, Mo9_x1)             0.8944\n",
      "corr(Mo0_y1, Mo13_x1)            0.8930\n",
      "corr(Mo11_z1, Mo18_y1)           0.8930\n",
      "corr(Mo1_z1, Mo10_z1)            0.8929\n",
      "corr(Mo1_x1, Mo3_z1)             0.8922\n",
      "corr(Mo5_z1, Mo15_x1)            0.8921\n",
      "corr(Mo1_y1, Mo12_z1)            -0.8920\n",
      "corr(mc1, O_Biso_cluster1)       0.8920\n",
      "corr(Mo4_y1, Mo17_x1)            -0.8919\n",
      "corr(O_Biso_cluster1, Mo11_y1)   -0.8909\n",
      "corr(Mo0_z1, Mo13_y1)            -0.8909\n",
      "corr(Mo1_x1, Mo10_z1)            -0.8908\n",
      "corr(Mo2_x1, Mo19_z1)            -0.8906\n",
      "corr(Mo11_x1, Mo15_z1)           -0.8903\n",
      "corr(Mo10_y1, Mo19_y1)           -0.8901\n",
      "corr(Mo14_z1, Mo19_y1)           -0.8894\n",
      "corr(O_Biso_cluster1, Mo14_z1)   0.8889\n",
      "corr(Mo11_z1, Mo19_y1)           0.8886\n",
      "corr(Mo7_z1, Mo10_x1)            0.8877\n",
      "corr(Mo2_x1, Mo14_x1)            0.8874\n",
      "corr(Mo10_x1, Mo13_y1)           -0.8874\n",
      "corr(Mo11_y1, Mo19_y1)           0.8867\n",
      "corr(Mo4_z1, Mo10_z1)            0.8862\n",
      "corr(Mo11_x1, Mo19_y1)           -0.8857\n",
      "corr(O_Biso_cluster1, Mo4_z1)    -0.8852\n",
      "corr(Mo2_x1, Mo10_z1)            0.8836\n",
      "corr(mc1, Mo18_z1)               0.8835\n",
      "corr(Mo1_x1, Mo13_y1)            0.8832\n",
      "corr(Mo9_x1, Mo10_y1)            0.8828\n",
      "corr(Mo7_y1, Mo11_x1)            -0.8825\n",
      "corr(Mo1_z1, Mo3_z1)             -0.8825\n",
      "corr(Mo16_z1, Mo19_z1)           -0.8824\n",
      "corr(Mo6_z1, Mo13_x1)            0.8823\n",
      "corr(Mo1_z1, Mo13_x1)            0.8814\n",
      "corr(Mo0_z1, Mo3_z1)             -0.8813\n",
      "corr(Mo2_x1, Mo12_z1)            -0.8810\n",
      "corr(Mo3_x1, Mo13_z1)            -0.8805\n",
      "corr(Mo2_z1, Mo10_z1)            -0.8797\n",
      "corr(Mo6_z1, Mo18_y1)            0.8795\n",
      "corr(Mo12_y1, Mo15_z1)           -0.8788\n",
      "corr(Mo10_x1, Mo18_y1)           0.8786\n",
      "corr(Mo11_z1, Mo13_z1)           -0.8783\n",
      "corr(Mo10_z1, Mo12_x1)           0.8780\n",
      "corr(Mo6_z1, Mo15_z1)            0.8770\n",
      "corr(Mo4_z1, Mo5_z1)             -0.8763\n",
      "corr(Mo2_z1, Mo19_z1)            0.8762\n",
      "corr(Mo0_z1, Mo13_x1)            0.8759\n",
      "corr(Mo7_z1, Mo11_x1)            -0.8757\n",
      "corr(Mo10_z1, Mo14_x1)           0.8749\n",
      "corr(Mo2_z1, Mo13_y1)            0.8741\n",
      "corr(Mo3_x1, Mo18_y1)            0.8733\n",
      "corr(Mo1_y1, Mo17_z1)            0.8732\n",
      "corr(Mo14_y1, Mo18_y1)           -0.8725\n",
      "corr(Mo17_x1, Mo18_z1)           -0.8721\n",
      "corr(Mo1_y1, Mo14_x1)            0.8707\n",
      "corr(Mo8_z1, Mo18_z1)            0.8699\n",
      "corr(Mo8_z1, Mo15_z1)            -0.8690\n",
      "corr(Mo2_y1, Mo15_x1)            0.8689\n",
      "corr(Mo11_z1, Mo15_z1)           0.8683\n",
      "corr(Mo1_x1, Mo13_x1)            -0.8675\n",
      "corr(Mo7_x1, Mo13_x1)            -0.8667\n",
      "corr(Mo12_x1, Mo17_z1)           0.8664\n",
      "corr(O_Biso_cluster1, Mo11_x1)   0.8661\n",
      "corr(Mo7_y1, Mo14_z1)            -0.8655\n",
      "corr(Mo5_y1, Mo13_x1)            -0.8651\n",
      "corr(Mo15_x1, Mo19_y1)           -0.8641\n",
      "corr(Mo2_y1, Mo4_z1)             -0.8640\n",
      "corr(Mo1_x1, Mo19_z1)            0.8639\n",
      "corr(Mo6_z1, Mo13_y1)            -0.8629\n",
      "corr(Mo2_z1, Mo12_z1)            0.8622\n",
      "corr(Mo6_z1, Mo10_z1)            0.8622\n",
      "corr(Mo13_z1, Mo14_y1)           0.8617\n",
      "corr(Mo8_z1, Mo13_z1)            0.8615\n",
      "corr(Mo0_y1, Mo3_z1)             -0.8614\n",
      "corr(Mo10_z1, Mo13_y1)           -0.8608\n",
      "corr(mc1, Mo4_y1)                0.8603\n",
      "corr(Mo1_y1, Mo10_z1)            0.8600\n",
      "corr(Mo7_y1, Mo11_y1)            0.8597\n",
      "corr(Mo12_y1, Mo18_z1)           0.8596\n",
      "corr(Mo14_x1, Mo19_y1)           0.8584\n",
      "corr(Mo7_z1, Mo14_z1)            -0.8583\n",
      "corr(Mo2_z1, Mo13_x1)            -0.8574\n",
      "corr(Mo2_y1, Mo11_y1)            -0.8572\n",
      "corr(Mo0_x1, Mo15_x1)            0.8567\n",
      "corr(Mo12_y1, Mo18_y1)           -0.8565\n",
      "corr(Mo3_x1, Mo15_z1)            0.8553\n",
      "corr(Mo12_y1, Mo13_z1)           0.8550\n",
      "corr(Mo3_z1, Mo16_z1)            -0.8545\n",
      "corr(Mo9_x1, Mo16_x1)            -0.8543\n",
      "corr(Mo0_x1, Mo3_z1)             0.8543\n",
      "corr(Mo2_x1, Mo13_y1)            -0.8541\n",
      "corr(Mo8_z1, Mo18_y1)            -0.8538\n",
      "corr(Mo1_z1, Mo19_z1)            -0.8532\n",
      "corr(mc1, Mo15_z1)               -0.8532\n",
      "corr(Mo2_y1, Mo14_z1)            0.8528\n",
      "corr(Mo0_z1, Mo19_z1)            -0.8527\n",
      "corr(Mo7_z1, Mo11_y1)            0.8523\n",
      "corr(Mo10_z1, Mo13_x1)           0.8522\n",
      "corr(Mo0_x1, Mo4_z1)             -0.8509\n",
      "corr(Mo1_x1, Mo12_z1)            0.8508\n",
      "corr(Mo7_y1, Mo15_x1)            -0.8506\n",
      "corr(mc1, Mo13_z1)               0.8503\n",
      "corr(Mo0_y1, Mo15_x1)            -0.8492\n",
      "corr(Mo3_x1, Mo19_y1)            0.8488\n",
      "corr(Mo1_y1, Mo13_y1)            -0.8488\n",
      "corr(Mo5_y1, Mo14_x1)            -0.8464\n",
      "corr(Mo10_x1, Mo13_z1)           -0.8461\n",
      "corr(Mo10_y1, Mo14_x1)           -0.8447\n",
      "corr(Mo14_y1, Mo15_z1)           -0.8443\n",
      "corr(Mo7_z1, Mo15_x1)            -0.8442\n",
      "corr(O_Biso_cluster1, Mo11_z1)   -0.8442\n",
      "corr(Mo0_x1, Mo11_y1)            -0.8441\n",
      "corr(Mo0_y1, Mo4_z1)             0.8441\n",
      "corr(Mo4_y1, Mo8_z1)             0.8441\n",
      "corr(Mo6_z1, Mo7_y1)             0.8437\n",
      "corr(Mo10_z1, Mo15_x1)           -0.8430\n",
      "corr(Mo7_x1, Mo19_y1)            -0.8426\n",
      "corr(Mo10_x1, Mo15_z1)           0.8421\n",
      "corr(Mo2_y1, Mo3_z1)             0.8410\n",
      "corr(Mo0_z1, Mo12_z1)            -0.8410\n",
      "corr(Mo6_z1, Mo7_z1)             0.8401\n",
      "corr(Mo0_x1, Mo14_z1)            0.8400\n",
      "corr(Mo7_x1, Mo13_y1)            0.8397\n",
      "corr(Mo12_x1, Mo14_x1)           0.8395\n",
      "corr(Mo4_y1, Mo12_y1)            0.8391\n",
      "corr(Mo10_y1, Mo13_x1)           -0.8390\n",
      "corr(mc1, Mo18_y1)               -0.8380\n",
      "corr(Mo1_z1, Mo12_z1)            -0.8377\n",
      "corr(Mo5_y1, Mo13_y1)            0.8376\n",
      "corr(Mo6_z1, Mo13_z1)            -0.8374\n",
      "corr(Mo0_y1, Mo11_y1)            0.8370\n",
      "corr(Mo2_x1, Mo13_x1)            0.8362\n",
      "corr(O_Biso_cluster1, Mo3_x1)    -0.8360\n",
      "corr(Mo1_y1, Mo6_x1)             0.8353\n",
      "corr(Mo9_z1, Mo17_z1)            -0.8334\n",
      "corr(Mo7_x1, Mo14_x1)            -0.8330\n",
      "corr(Mo5_x1, Mo13_x1)            0.8330\n",
      "corr(Mo0_y1, Mo14_z1)            -0.8325\n",
      "corr(Mo10_z1, Mo12_y1)           -0.8311\n",
      "corr(Mo2_y1, Mo11_x1)            0.8302\n",
      "corr(Mo0_y1, Mo19_z1)            -0.8301\n",
      "corr(Mo9_z1, Mo10_z1)            -0.8295\n",
      "corr(Mo1_y1, Mo13_x1)            0.8280\n",
      "corr(Mo1_z1, Mo15_x1)            -0.8272\n",
      "corr(Mo0_z1, Mo15_x1)            -0.8261\n",
      "corr(Mo9_z1, Mo14_x1)            -0.8259\n",
      "corr(Mo5_y1, Mo17_z1)            -0.8255\n",
      "corr(O_Biso_cluster1, Mo14_y1)   0.8237\n",
      "corr(Mo4_z1, Mo7_y1)             0.8236\n",
      "corr(Mo9_z1, Mo18_z1)            0.8232\n",
      "corr(Mo13_x1, Mo19_y1)           0.8230\n",
      "corr(Mo0_z1, Mo4_z1)             0.8229\n",
      "corr(Mo0_x1, Mo19_z1)            0.8225\n",
      "corr(Mo10_z1, Mo11_y1)           0.8212\n",
      "corr(Mo5_y1, Mo19_y1)            -0.8200\n",
      "corr(O_Biso_cluster1, Mo10_x1)   -0.8197\n",
      "corr(mc1, Mo6_x1)                -0.8194\n",
      "corr(Mo4_z1, Mo7_z1)             0.8188\n",
      "corr(Mo1_z1, Mo4_z1)             0.8185\n",
      "corr(Mo2_z1, Mo6_x1)             -0.8180\n",
      "corr(Mo0_y1, Mo12_z1)            -0.8174\n",
      "corr(Mo0_x1, Mo11_x1)            0.8159\n",
      "corr(Mo5_z1, Mo13_x1)            -0.8149\n",
      "corr(Mo1_x1, Mo15_x1)            0.8146\n",
      "corr(Mo1_z1, Mo6_x1)             0.8137\n",
      "corr(Mo1_z1, Mo11_y1)            0.8137\n",
      "corr(Mo6_x1, Mo9_z1)             -0.8135\n",
      "corr(Mo6_x1, Mo8_z1)             -0.8129\n",
      "corr(Mo0_z1, Mo11_y1)            0.8124\n",
      "corr(Mo10_z1, Mo14_z1)           -0.8120\n",
      "corr(O_Biso_cluster1, Mo6_z1)    -0.8119\n",
      "corr(Mo1_z1, Mo14_z1)            -0.8100\n",
      "corr(Mo0_x1, Mo12_z1)            0.8088\n",
      "corr(Mo0_y1, Mo11_x1)            -0.8083\n",
      "corr(Mo1_x1, Mo4_z1)             -0.8081\n",
      "corr(Mo2_y1, Mo19_z1)            0.8080\n",
      "corr(Mo0_z1, Mo14_z1)            -0.8077\n",
      "corr(Mo1_x1, Mo6_x1)             -0.8074\n",
      "corr(Mo10_y1, Mo13_y1)           0.8074\n",
      "corr(Mo5_x1, Mo14_x1)            0.8073\n",
      "corr(Mo4_z1, Mo19_y1)            0.8071\n",
      "corr(Mo10_z1, Mo11_x1)           -0.8051\n",
      "corr(Mo2_y1, Mo11_z1)            -0.8047\n",
      "corr(O_Biso_cluster1, Mo3_z1)    0.8041\n",
      "corr(Mo10_y1, Mo17_z1)           -0.8034\n",
      "corr(Mo5_x1, Mo13_y1)            -0.8031\n",
      "corr(Mo5_x1, Mo19_y1)            0.8031\n",
      "corr(Mo1_x1, Mo11_y1)            -0.8015\n",
      "corr(Mo2_x1, Mo6_x1)             0.8004\n",
      "corr(Mo2_z1, Mo15_x1)            0.8002\n",
      "corr(Mo13_y1, Mo19_y1)           -0.8000\n",
      "corr(O_Biso_cluster1, Mo6_x1)    -0.7997\n",
      "corr(Mo12_y1, Mo17_z1)           -0.7994\n",
      "corr(Mo3_z1, Mo6_x1)             -0.7993\n",
      "corr(Mo18_y1, Mo19_y1)           0.7985\n",
      "corr(Mo1_x1, Mo14_z1)            0.7970\n",
      "corr(Mo8_z1, Mo10_z1)            -0.7969\n",
      "corr(Mo12_x1, Mo13_y1)           -0.7955\n",
      "corr(Mo2_y1, Mo12_z1)            0.7949\n",
      "corr(Mo4_y1, Mo9_z1)             0.7937\n",
      "corr(Mo2_y1, Mo3_x1)             -0.7934\n",
      "corr(Mo0_z1, Mo6_x1)             0.7931\n",
      "corr(Mo12_z1, Mo16_x1)           0.7928\n",
      "corr(Mo2_z1, Mo4_z1)             -0.7922\n",
      "corr(Mo12_x1, Mo18_z1)           -0.7918\n",
      "corr(Mo2_y1, Mo6_z1)             -0.7911\n",
      "corr(Mo0_y1, Mo6_x1)             0.7905\n",
      "corr(Mo0_x1, Mo6_x1)             -0.7902\n",
      "corr(Mo6_x1, Mo13_z1)            -0.7899\n",
      "corr(Mo0_x1, Mo11_z1)            -0.7897\n",
      "corr(Mo7_x1, Mo17_z1)            -0.7897\n",
      "corr(Mo5_z1, Mo19_y1)            -0.7882\n",
      "corr(Mo2_z1, Mo11_y1)            -0.7870\n",
      "corr(Mo3_x1, Mo10_z1)            0.7864\n",
      "corr(Mo9_z1, Mo13_y1)            0.7861\n",
      "corr(Mo9_x1, Mo14_y1)            0.7853\n",
      "corr(Mo8_z1, Mo17_z1)            -0.7847\n",
      "corr(Mo7_y1, Mo19_y1)            0.7844\n",
      "corr(Mo5_z1, Mo13_y1)            0.7843\n",
      "corr(mc1, Mo10_z1)               -0.7839\n",
      "corr(Mo5_x1, Mo17_z1)            0.7829\n",
      "corr(Mo1_z1, Mo11_x1)            -0.7828\n",
      "corr(Mo2_z1, Mo14_z1)            0.7827\n",
      "corr(Mo7_z1, Mo17_x1)            -0.7826\n",
      "corr(Mo2_y1, Mo6_x1)             -0.7821\n",
      "corr(Mo0_z1, Mo11_x1)            -0.7821\n",
      "corr(Mo10_z1, Mo11_z1)           0.7816\n",
      "corr(Mo0_y1, Mo11_z1)            0.7815\n",
      "corr(Mo5_z1, Mo14_x1)            -0.7806\n",
      "corr(Mo2_y1, Mo14_y1)            0.7798\n",
      "corr(Mo0_x1, Mo3_x1)             -0.7796\n",
      "corr(Mo2_x1, Mo15_x1)            -0.7791\n",
      "corr(Mo2_y1, Mo10_x1)            -0.7789\n",
      "corr(Mo2_x1, Mo4_z1)             0.7773\n",
      "corr(Mo0_x1, Mo6_z1)             -0.7755\n",
      "corr(Mo12_x1, Mo13_x1)           0.7752\n",
      "corr(Mo7_y1, Mo17_x1)            -0.7744\n",
      "corr(Mo8_z1, Mo14_x1)            -0.7730\n",
      "corr(Mo6_x1, Mo19_z1)            -0.7728\n",
      "corr(Mo7_y1, Mo13_x1)            0.7724\n",
      "corr(Mo13_z1, Mo19_y1)           -0.7714\n",
      "corr(Mo1_y1, Mo18_z1)            -0.7705\n",
      "corr(Mo1_x1, Mo11_x1)            0.7701\n",
      "corr(Mo16_x1, Mo19_z1)           0.7700\n",
      "corr(Mo0_y1, Mo3_x1)             0.7697\n",
      "corr(Mo12_y1, Mo14_x1)           -0.7690\n",
      "corr(Mo7_z1, Mo19_y1)            0.7686\n",
      "corr(Mo0_y1, Mo6_z1)             0.7686\n",
      "corr(Mo9_x1, Mo10_x1)            -0.7685\n",
      "corr(Mo7_z1, Mo13_x1)            0.7666\n",
      "corr(Mo4_y1, Mo12_x1)            -0.7664\n",
      "corr(O_Biso_cluster1, Mo19_z1)   0.7664\n",
      "corr(Mo9_x1, Mo11_z1)            -0.7657\n",
      "corr(Mo2_x1, Mo11_y1)            0.7651\n",
      "corr(mc1, Mo17_z1)               -0.7651\n",
      "corr(Mo3_x1, Mo9_x1)             -0.7648\n",
      "corr(Mo0_x1, Mo14_y1)            0.7644\n",
      "corr(Mo9_z1, Mo13_x1)            -0.7638\n",
      "corr(Mo5_y1, Mo18_y1)            -0.7634\n",
      "corr(Mo10_y1, Mo18_y1)           -0.7634\n",
      "corr(Mo0_x1, Mo10_x1)            -0.7628\n",
      "corr(mc1, Mo16_z1)               -0.7610\n",
      "corr(Mo6_z1, Mo19_y1)            0.7604\n",
      "corr(O_Biso_cluster1, Mo19_y1)   -0.7598\n",
      "corr(Mo2_x1, Mo14_z1)            -0.7596\n",
      "corr(Mo17_z1, Mo19_y1)           0.7593\n",
      "corr(Mo1_y1, Mo15_x1)            -0.7562\n",
      "corr(Mo0_y1, Mo10_x1)            0.7559\n",
      "corr(Mo0_y1, Mo14_y1)            -0.7555\n",
      "corr(Mo6_x1, Mo12_x1)            0.7553\n",
      "corr(Mo1_z1, Mo11_z1)            0.7552\n",
      "corr(mc1, Mo14_x1)               -0.7552\n",
      "corr(Mo2_z1, Mo11_x1)            0.7544\n",
      "corr(Mo6_x1, Mo12_y1)            -0.7542\n",
      "corr(Mo0_z1, Mo11_z1)            0.7538\n",
      "corr(Mo3_z1, Mo15_z1)            -0.7535\n",
      "corr(Mo5_z1, Mo17_z1)            -0.7522\n",
      "corr(Mo6_z1, Mo9_x1)             -0.7521\n",
      "corr(Mo10_z1, Mo14_y1)           -0.7519\n",
      "corr(Mo3_z1, Mo13_z1)            0.7519\n",
      "corr(Mo6_x1, Mo13_y1)            -0.7503\n",
      "corr(Mo5_y1, Mo13_z1)            0.7464\n",
      "corr(Mo1_z1, Mo3_x1)             0.7461\n",
      "corr(Mo8_z1, Mo16_z1)            -0.7459\n",
      "corr(O_Biso_cluster1, Mo12_z1)   0.7458\n",
      "corr(Mo1_y1, Mo4_z1)             0.7456\n",
      "corr(Mo2_x1, Mo18_z1)            -0.7455\n",
      "corr(Mo12_y1, Mo16_z1)           -0.7454\n",
      "corr(Mo0_z1, Mo6_z1)             0.7443\n",
      "corr(Mo0_z1, Mo3_x1)             0.7430\n",
      "corr(Mo1_x1, Mo11_z1)            -0.7413\n",
      "corr(Mo12_z1, Mo17_x1)           -0.7403\n",
      "corr(Mo1_y1, Mo11_y1)            0.7403\n",
      "corr(Mo1_y1, Mo4_y1)             -0.7400\n",
      "corr(Mo7_y1, Mo13_y1)            -0.7393\n",
      "corr(Mo8_z1, Mo13_y1)            0.7389\n",
      "corr(Mo5_y1, Mo15_z1)            -0.7389\n",
      "corr(Mo9_x1, Mo11_x1)            0.7385\n",
      "corr(Mo7_x1, Mo18_y1)            -0.7374\n",
      "corr(Mo1_y1, Mo14_z1)            -0.7368\n",
      "corr(Mo1_z1, Mo6_z1)             0.7355\n",
      "corr(Mo3_z1, Mo18_y1)            -0.7351\n",
      "corr(Mo7_x1, Mo13_z1)            0.7344\n",
      "corr(Mo7_z1, Mo13_y1)            -0.7337\n",
      "corr(Mo15_z1, Mo19_y1)           0.7329\n",
      "corr(Mo2_x1, Mo11_x1)            -0.7318\n",
      "corr(Mo9_x1, Mo16_z1)            0.7312\n",
      "corr(Mo4_z1, Mo12_x1)            0.7309\n",
      "corr(Mo3_z1, Mo16_x1)            0.7301\n",
      "corr(Mo7_z1, Mo16_x1)            0.7298\n",
      "corr(Mo1_x1, Mo3_x1)             -0.7293\n",
      "corr(Mo1_z1, Mo14_y1)            -0.7292\n",
      "corr(Mo10_y1, Mo13_z1)           0.7291\n",
      "corr(Mo10_x1, Mo10_z1)           0.7285\n",
      "corr(Mo7_y1, Mo14_x1)            0.7284\n",
      "corr(Mo1_z1, Mo10_x1)            0.7276\n",
      "corr(Mo6_x1, Mo13_x1)            0.7274\n",
      "corr(Mo12_y1, Mo13_y1)           0.7270\n",
      "corr(Mo1_x1, Mo6_z1)             -0.7266\n",
      "corr(mc1, Mo13_y1)               0.7265\n",
      "corr(Mo0_z1, Mo14_y1)            -0.7264\n",
      "corr(Mo0_z1, Mo10_x1)            0.7261\n",
      "corr(Mo2_z1, Mo11_z1)            -0.7251\n",
      "corr(Mo2_z1, Mo18_z1)            0.7246\n",
      "corr(Mo10_y1, Mo15_z1)           -0.7224\n",
      "corr(Mo6_x1, Mo18_y1)            0.7212\n",
      "corr(Mo7_y1, Mo16_x1)            0.7211\n",
      "corr(Mo17_x1, Mo19_z1)           -0.7200\n",
      "corr(Mo12_x1, Mo15_x1)           -0.7195\n",
      "corr(Mo7_z1, Mo14_x1)            0.7188\n",
      "corr(Mo5_z1, Mo17_x1)            0.7184\n",
      "corr(Mo15_z1, Mo19_z1)           -0.7162\n",
      "corr(Mo1_x1, Mo10_x1)            -0.7157\n",
      "corr(Mo5_x1, Mo18_y1)            0.7152\n",
      "corr(Mo6_x1, Mo14_x1)            0.7147\n",
      "corr(Mo2_x1, Mo4_y1)             -0.7144\n",
      "corr(Mo1_x1, Mo14_y1)            0.7141\n",
      "corr(Mo2_z1, Mo3_x1)             -0.7131\n",
      "corr(Mo8_z1, Mo13_x1)            -0.7125\n",
      "corr(Mo13_z1, Mo19_z1)           0.7115\n",
      "corr(Mo2_y1, Mo19_y1)            -0.7115\n",
      "corr(Mo12_z1, Mo15_z1)           -0.7110\n",
      "corr(Mo2_z1, Mo6_z1)             -0.7071\n",
      "corr(Mo1_x1, Mo18_z1)            0.7069\n",
      "corr(Mo11_y1, Mo12_x1)           0.7047\n",
      "corr(Mo7_x1, Mo15_z1)            -0.7047\n",
      "corr(Mo1_y1, Mo11_x1)            -0.7045\n",
      "corr(Mo9_x1, Mo14_z1)            0.7038\n",
      "corr(Mo9_x1, Mo11_y1)            -0.7024\n",
      "corr(Mo6_x1, Mo12_z1)            -0.7017\n",
      "corr(Mo12_y1, Mo13_x1)           -0.7015\n",
      "corr(Mo2_x1, Mo11_z1)            0.7008\n",
      "corr(Mo2_z1, Mo10_x1)            -0.6998\n",
      "corr(Mo5_x1, Mo13_z1)            -0.6993\n",
      "corr(mc1, Mo13_x1)               -0.6981\n",
      "corr(Mo2_z1, Mo14_y1)            0.6977\n",
      "corr(Mo0_x1, Mo19_y1)            -0.6977\n",
      "corr(Mo12_x1, Mo14_z1)           -0.6963\n",
      "corr(Mo9_z1, Mo15_x1)            0.6950\n",
      "corr(O_Biso_cluster1, Mo5_y1)    0.6944\n",
      "corr(Mo2_x1, Mo6_z1)             0.6940\n",
      "corr(Mo0_y1, Mo19_y1)            0.6938\n",
      "corr(Mo18_y1, Mo19_z1)           -0.6927\n",
      "corr(Mo1_z1, Mo18_z1)            -0.6914\n",
      "corr(Mo6_x1, Mo15_z1)            0.6911\n",
      "corr(Mo2_z1, Mo4_y1)             0.6906\n",
      "corr(Mo0_z1, Mo18_z1)            -0.6906\n",
      "corr(Mo4_z1, Mo9_x1)             -0.6896\n",
      "corr(Mo4_z1, Mo9_z1)             -0.6892\n",
      "corr(Mo9_z1, Mo16_z1)            -0.6886\n",
      "corr(Mo5_x1, Mo15_z1)            0.6885\n",
      "corr(O_Biso_cluster1, Mo10_y1)   0.6878\n",
      "corr(Mo9_x1, Mo15_x1)            0.6876\n",
      "corr(Mo2_x1, Mo3_x1)             0.6874\n",
      "corr(Mo3_z1, Mo10_z1)            -0.6864\n",
      "corr(Mo5_y1, Mo10_z1)            -0.6851\n",
      "corr(Mo5_x1, Mo17_x1)            -0.6850\n",
      "corr(Mo12_z1, Mo13_z1)           0.6847\n",
      "corr(Mo7_y1, Mo17_z1)            0.6841\n",
      "corr(Mo12_z1, Mo18_y1)           -0.6816\n",
      "corr(Mo5_z1, Mo18_y1)            -0.6814\n",
      "corr(Mo9_z1, Mo11_y1)            -0.6813\n",
      "corr(Mo7_z1, Mo17_z1)            0.6773\n",
      "corr(O_Biso_cluster1, Mo7_x1)    0.6770\n",
      "corr(Mo3_z1, Mo17_x1)            -0.6767\n",
      "corr(Mo1_z1, Mo19_y1)            0.6765\n",
      "corr(Mo6_x1, Mo18_z1)            -0.6761\n",
      "corr(Mo9_z1, Mo14_z1)            0.6755\n",
      "corr(Mo2_x1, Mo10_x1)            0.6748\n",
      "corr(Mo1_y1, Mo11_z1)            0.6736\n",
      "corr(Mo1_x1, Mo4_y1)             0.6733\n",
      "corr(Mo10_z1, Mo12_z1)           -0.6731\n",
      "corr(Mo2_x1, Mo14_y1)            -0.6713\n",
      "corr(Mo5_z1, Mo13_z1)            0.6702\n",
      "corr(Mo11_x1, Mo12_x1)           -0.6698\n",
      "corr(Mo1_y1, Mo3_x1)             0.6662\n",
      "corr(Mo1_x1, Mo19_y1)            -0.6654\n",
      "corr(Mo0_z1, Mo19_y1)            0.6649\n",
      "corr(Mo0_y1, Mo18_z1)            -0.6604\n",
      "corr(Mo12_x1, Mo16_z1)           0.6603\n",
      "corr(Mo10_z1, Mo19_z1)           -0.6601\n",
      "corr(Mo0_z1, Mo4_y1)             -0.6582\n",
      "corr(Mo2_z1, Mo19_y1)            -0.6577\n",
      "corr(Mo5_z1, Mo16_x1)            -0.6574\n",
      "corr(Mo1_z1, Mo4_y1)             -0.6572\n",
      "corr(Mo7_x1, Mo17_x1)            0.6564\n",
      "corr(Mo5_z1, Mo15_z1)            -0.6536\n",
      "corr(Mo6_z1, Mo12_x1)            0.6527\n",
      "corr(Mo1_y1, Mo6_z1)             0.6524\n",
      "corr(Mo6_x1, Mo19_y1)            0.6510\n",
      "corr(Mo0_x1, Mo18_z1)            0.6496\n",
      "corr(Mo3_z1, Mo17_z1)            -0.6462\n",
      "corr(Mo1_y1, Mo14_y1)            -0.6454\n",
      "corr(Mo2_y1, Mo5_y1)             0.6450\n",
      "corr(Mo1_y1, Mo10_x1)            0.6442\n",
      "corr(Mo9_z1, Mo11_x1)            0.6436\n",
      "corr(Mo4_z1, Mo12_y1)            -0.6428\n",
      "corr(O_Biso_cluster1, Mo5_x1)    -0.6424\n",
      "corr(Mo5_x1, Mo10_z1)            0.6367\n",
      "corr(Mo2_y1, Mo10_y1)            0.6361\n",
      "corr(Mo3_z1, Mo14_x1)            -0.6359\n",
      "corr(Mo11_z1, Mo12_x1)           0.6349\n",
      "corr(Mo2_y1, Mo18_z1)            0.6308\n",
      "corr(Mo5_y1, Mo17_x1)            0.6307\n",
      "corr(Mo12_y1, Mo15_x1)           0.6304\n",
      "corr(Mo4_y1, Mo9_x1)             -0.6302\n",
      "corr(Mo8_z1, Mo15_x1)            0.6301\n",
      "corr(Mo7_x1, Mo10_z1)            -0.6280\n",
      "corr(Mo4_z1, Mo8_z1)             -0.6274\n",
      "corr(Mo2_x1, Mo19_y1)            0.6266\n",
      "corr(Mo0_x1, Mo5_y1)             0.6261\n",
      "corr(Mo4_y1, Mo6_x1)             -0.6261\n",
      "corr(Mo0_y1, Mo4_y1)             -0.6257\n",
      "corr(Mo9_x1, Mo19_y1)            -0.6244\n",
      "corr(Mo10_y1, Mo10_z1)           -0.6223\n",
      "corr(Mo5_x1, Mo16_x1)            0.6207\n",
      "corr(Mo2_y1, Mo7_x1)             0.6195\n",
      "corr(Mo0_x1, Mo10_y1)            0.6164\n",
      "corr(Mo3_x1, Mo12_x1)            0.6163\n",
      "corr(Mo10_y1, Mo17_x1)           0.6159\n",
      "corr(Mo7_y1, Mo18_y1)            0.6156\n",
      "corr(Mo0_y1, Mo5_y1)             -0.6151\n",
      "corr(Mo0_x1, Mo4_y1)             0.6151\n",
      "corr(Mo8_z1, Mo11_y1)            -0.6139\n",
      "corr(Mo6_x1, Mo17_z1)            0.6137\n",
      "corr(Mo1_y1, Mo16_z1)            0.6136\n",
      "corr(Mo11_y1, Mo12_y1)           -0.6135\n",
      "corr(Mo6_x1, Mo6_y1)             -0.6114\n",
      "corr(Mo1_y1, Mo19_y1)            0.6108\n",
      "corr(mc1, Mo16_x1)               0.6103\n",
      "corr(Mo10_x1, Mo12_x1)           0.6098\n",
      "corr(Mo7_y1, Mo13_z1)            -0.6094\n",
      "corr(Mo9_z1, Mo11_z1)            -0.6089\n",
      "corr(O_Biso_cluster1, Mo5_z1)    0.6089\n",
      "corr(mc1, Mo15_x1)               0.6086\n",
      "corr(Mo3_z1, Mo13_y1)            0.6083\n",
      "corr(Mo8_z1, Mo14_z1)            0.6080\n",
      "corr(Mo0_y1, Mo10_y1)            -0.6072\n",
      "corr(Mo7_z1, Mo18_y1)            0.6050\n",
      "corr(Mo12_y1, Mo14_z1)           0.6049\n",
      "corr(Mo17_z1, Mo19_z1)           -0.6040\n",
      "corr(Mo5_z1, Mo10_z1)            -0.6034\n",
      "corr(Mo6_x1, Mo14_z1)            -0.6022\n",
      "corr(mc1, Mo4_z1)                -0.6012\n",
      "corr(Mo0_x1, Mo7_x1)             0.6011\n",
      "corr(Mo12_z1, Mo17_z1)           -0.6009\n",
      "corr(Mo7_z1, Mo13_z1)            -0.6007\n",
      "corr(Mo12_x1, Mo14_y1)           -0.6006\n",
      "corr(Mo6_z1, Mo9_z1)             -0.6004\n",
      "corr(Mo6_x1, Mo15_x1)            -0.5995\n",
      "corr(Mo9_x1, Mo18_z1)            -0.5962\n",
      "corr(Mo2_y1, Mo4_y1)             0.5951\n",
      "corr(Mo7_x1, Mo16_x1)            -0.5943\n",
      "corr(Mo12_y1, Mo16_x1)           0.5925\n",
      "corr(Mo2_x1, Mo16_z1)            0.5923\n",
      "corr(mc1, Mo11_y1)               -0.5911\n",
      "corr(Mo3_x1, Mo9_z1)             -0.5907\n",
      "corr(Mo8_z1, Mo16_x1)            0.5903\n",
      "corr(Mo0_y1, Mo7_x1)             -0.5893\n",
      "corr(Mo2_y1, Mo5_x1)             -0.5891\n",
      "corr(Mo6_x1, Mo11_y1)            0.5886\n",
      "corr(Mo9_z1, Mo10_x1)            -0.5886\n",
      "corr(Mo14_x1, Mo19_z1)           -0.5869\n",
      "corr(mc1, Mo14_z1)               0.5862\n",
      "corr(Mo0_z1, Mo5_y1)             -0.5814\n",
      "corr(Mo1_z1, Mo5_y1)             -0.5812\n",
      "corr(Mo7_y1, Mo15_z1)            0.5802\n",
      "corr(O_Biso_cluster1, Mo18_z1)   0.5776\n",
      "corr(Mo9_z1, Mo14_y1)            0.5771\n",
      "corr(Mo11_x1, Mo12_y1)           0.5744\n",
      "corr(Mo3_z1, Mo13_x1)            -0.5741\n",
      "corr(Mo1_z1, Mo10_y1)            -0.5730\n",
      "corr(Mo8_z1, Mo11_x1)            0.5730\n",
      "corr(Mo7_z1, Mo15_z1)            0.5716\n",
      "corr(Mo0_z1, Mo10_y1)            -0.5715\n",
      "corr(Mo0_x1, Mo5_x1)             -0.5692\n",
      "corr(Mo7_z1, Mo16_z1)            -0.5685\n",
      "corr(Mo9_z1, Mo19_y1)            -0.5671\n",
      "corr(Mo12_z1, Mo14_x1)           -0.5649\n",
      "corr(Mo9_x1, Mo13_x1)            -0.5641\n",
      "corr(Mo1_x1, Mo5_y1)             0.5639\n",
      "corr(Mo2_z1, Mo16_z1)            -0.5632\n",
      "corr(Mo5_y1, Mo16_x1)            -0.5624\n",
      "corr(Mo13_y1, Mo19_z1)           0.5619\n",
      "corr(Mo1_z1, Mo7_x1)             -0.5586\n",
      "corr(Mo1_x1, Mo10_y1)            0.5578\n",
      "corr(Mo0_y1, Mo5_x1)             0.5575\n",
      "corr(Mo7_y1, Mo16_z1)            -0.5568\n",
      "corr(Mo6_z1, Mo12_y1)            -0.5568\n",
      "corr(Mo0_z1, Mo7_x1)             -0.5545\n",
      "corr(Mo12_x1, Mo19_y1)           0.5529\n",
      "corr(Mo2_y1, Mo5_z1)             0.5524\n",
      "corr(Mo10_y1, Mo16_x1)           -0.5499\n",
      "corr(Mo6_x1, Mo11_x1)            -0.5494\n",
      "corr(mc1, Mo11_x1)               0.5490\n",
      "corr(Mo3_x1, Mo6_x1)             0.5488\n",
      "corr(mc1, Mo17_x1)               -0.5467\n",
      "corr(Mo1_x1, Mo16_z1)            -0.5438\n",
      "corr(Mo2_z1, Mo5_y1)             0.5430\n",
      "corr(O_Biso_cluster1, Mo7_y1)    -0.5430\n",
      "corr(Mo1_x1, Mo7_x1)             0.5390\n",
      "corr(Mo2_z1, Mo10_y1)            0.5385\n",
      "corr(O_Biso_cluster1, Mo4_y1)    0.5378\n",
      "corr(Mo8_z1, Mo11_z1)            -0.5365\n",
      "corr(Mo11_z1, Mo12_y1)           -0.5364\n",
      "corr(Mo6_x1, Mo11_z1)            0.5333\n",
      "corr(O_Biso_cluster1, Mo7_z1)    -0.5324\n",
      "corr(Mo0_x1, Mo5_z1)             0.5322\n",
      "corr(Mo6_x1, Mo14_y1)            -0.5293\n",
      "corr(Mo6_z1, Mo8_z1)             -0.5290\n",
      "corr(Mo9_x1, Mo14_x1)            -0.5264\n",
      "corr(Mo0_z1, Mo16_z1)            0.5260\n",
      "corr(Mo8_z1, Mo17_x1)            -0.5255\n",
      "corr(Mo13_x1, Mo19_z1)           -0.5255\n",
      "corr(Mo12_y1, Mo17_x1)           -0.5238\n",
      "corr(Mo1_z1, Mo16_z1)            0.5225\n",
      "corr(Mo1_z1, Mo5_x1)             0.5224\n",
      "corr(Mo12_z1, Mo13_y1)           0.5222\n",
      "corr(Mo0_z1, Mo5_x1)             0.5221\n",
      "corr(Mo3_x1, Mo8_z1)             -0.5218\n",
      "corr(Mo9_x1, Mo13_y1)            0.5215\n",
      "corr(Mo9_z1, Mo16_x1)            0.5201\n",
      "corr(Mo0_y1, Mo5_z1)             -0.5198\n",
      "corr(Mo2_z1, Mo7_x1)             0.5193\n",
      "corr(Mo3_x1, Mo12_y1)            -0.5189\n",
      "corr(Mo2_x1, Mo5_y1)             -0.5156\n",
      "corr(Mo15_z1, Mo18_z1)           -0.5156\n",
      "corr(Mo7_y1, Mo10_z1)            0.5154\n",
      "corr(Mo10_z1, Mo19_y1)           0.5148\n",
      "corr(Mo6_x1, Mo10_x1)            0.5146\n",
      "corr(Mo7_z1, Mo10_z1)            0.5140\n",
      "corr(mc1, Mo11_z1)               -0.5121\n",
      "corr(Mo8_z1, Mo10_x1)            -0.5118\n",
      "corr(Mo10_x1, Mo12_y1)           -0.5099\n",
      "corr(Mo2_x1, Mo10_y1)            -0.5093\n",
      "corr(Mo9_x1, Mo17_z1)            -0.5078\n",
      "corr(Mo13_z1, Mo18_z1)           0.5076\n",
      "corr(Mo4_z1, Mo6_x1)             0.5043\n",
      "corr(Mo1_x1, Mo5_x1)             -0.5039\n",
      "corr(Mo6_x1, Mo10_z1)            0.5033\n",
      "corr(Mo8_z1, Mo14_y1)            0.5031\n",
      "corr(Mo8_z1, Mo19_y1)            -0.5019\n",
      "corr(mc1, Mo6_z1)                -0.5018\n",
      "corr(mc1, Mo3_x1)                -0.5003\n",
      "corr(Mo12_y1, Mo14_y1)           0.4997\n",
      "corr(Mo6_x1, Mo16_z1)            0.4932\n",
      "corr(Mo0_y1, Mo16_z1)            0.4911\n",
      "corr(Mo12_x1, Mo16_x1)           -0.4908\n",
      "corr(Mo18_y1, Mo18_z1)           -0.4899\n",
      "corr(Mo2_x1, Mo7_x1)             -0.4874\n",
      "corr(Mo12_z1, Mo13_x1)           -0.4872\n",
      "corr(mc1, Mo10_x1)               -0.4858\n",
      "corr(Mo1_z1, Mo5_z1)             -0.4848\n",
      "corr(Mo5_z1, Mo16_z1)            0.4847\n",
      "corr(Mo0_z1, Mo5_z1)             -0.4836\n",
      "corr(Mo2_z1, Mo5_x1)             -0.4822\n",
      "corr(Mo1_y1, Mo5_y1)             -0.4819\n",
      "corr(Mo4_y1, Mo15_z1)            -0.4815\n",
      "corr(Mo2_y1, Mo7_y1)             -0.4793\n",
      "corr(mc1, Mo14_y1)               0.4790\n",
      "corr(mc1, Mo19_y1)               -0.4776\n",
      "corr(Mo0_x1, Mo16_z1)            -0.4774\n",
      "corr(Mo1_y1, Mo10_y1)            -0.4743\n",
      "corr(Mo4_y1, Mo13_z1)            0.4696\n",
      "corr(Mo12_y1, Mo19_y1)           -0.4692\n",
      "corr(Mo2_y1, Mo7_z1)             -0.4688\n",
      "corr(Mo14_y1, Mo17_x1)           0.4679\n",
      "corr(Mo3_z1, Mo15_x1)            0.4672\n",
      "corr(Mo1_x1, Mo5_z1)             0.4652\n",
      "corr(Mo10_z1, Mo18_z1)           -0.4643\n",
      "corr(Mo1_y1, Mo7_x1)             -0.4607\n",
      "corr(Mo4_y1, Mo10_z1)            -0.4600\n",
      "corr(Mo0_x1, Mo7_y1)             -0.4585\n",
      "corr(Mo3_z1, Mo4_z1)             -0.4584\n",
      "corr(Mo2_y1, Mo16_z1)            -0.4573\n",
      "corr(Mo2_x1, Mo5_x1)             0.4533\n",
      "corr(Mo3_x1, Mo17_x1)            -0.4504\n",
      "corr(Mo9_z1, Mo17_x1)            -0.4499\n",
      "corr(Mo3_z1, Mo11_y1)            -0.4483\n",
      "corr(Mo0_x1, Mo7_z1)             -0.4480\n",
      "corr(Mo4_y1, Mo18_y1)            -0.4471\n",
      "corr(Mo0_y1, Mo7_y1)             0.4454\n",
      "corr(Mo3_z1, Mo14_z1)            0.4435\n",
      "corr(Mo2_z1, Mo5_z1)             0.4432\n",
      "corr(Mo5_y1, Mo12_x1)            -0.4423\n",
      "corr(Mo5_x1, Mo16_z1)            -0.4423\n",
      "corr(Mo10_x1, Mo17_x1)           -0.4382\n",
      "corr(Mo1_y1, Mo16_x1)            -0.4367\n",
      "corr(Mo10_y1, Mo12_x1)           -0.4351\n",
      "corr(Mo0_y1, Mo7_z1)             0.4347\n",
      "corr(Mo11_z1, Mo17_x1)           -0.4319\n",
      "corr(Mo4_y1, Mo7_z1)             0.4235\n",
      "corr(Mo1_y1, Mo5_x1)             0.4193\n",
      "corr(Mo12_x1, Mo17_x1)           0.4163\n",
      "corr(Mo4_y1, Mo7_y1)             0.4135\n",
      "corr(Mo15_x1, Mo19_z1)           0.4131\n",
      "corr(Mo2_x1, Mo5_z1)             -0.4128\n",
      "corr(Mo2_x1, Mo16_x1)            -0.4110\n",
      "corr(Mo1_z1, Mo7_y1)             0.4108\n",
      "corr(Mo7_x1, Mo16_z1)            0.4097\n",
      "corr(Mo4_z1, Mo19_z1)            -0.4091\n",
      "corr(Mo9_z1, Mo10_y1)            0.4077\n",
      "corr(Mo0_z1, Mo7_y1)             0.4071\n",
      "corr(Mo9_x1, Mo18_y1)            -0.4065\n",
      "corr(Mo4_z1, Mo12_z1)            -0.4050\n",
      "corr(Mo5_y1, Mo9_z1)             0.4048\n",
      "corr(Mo7_x1, Mo12_x1)            -0.4043\n",
      "corr(Mo3_z1, Mo11_x1)            0.4018\n",
      "corr(Mo1_z1, Mo7_z1)             0.4000\n",
      "corr(Mo0_z1, Mo7_z1)             0.3965\n",
      "corr(Mo14_y1, Mo16_x1)           -0.3955\n",
      "corr(Mo11_y1, Mo19_z1)           -0.3926\n",
      "corr(O_Biso_cluster1, Mo16_z1)   -0.3908\n",
      "corr(Mo12_z1, Mo15_x1)           0.3907\n",
      "corr(Mo6_x1, Mo7_x1)             -0.3905\n",
      "corr(Mo11_x1, Mo17_x1)           0.3905\n",
      "corr(Mo1_x1, Mo7_y1)             -0.3895\n",
      "corr(Mo6_z1, Mo17_x1)            -0.3875\n",
      "corr(Mo14_z1, Mo19_z1)           0.3874\n",
      "corr(Mo17_z1, Mo18_z1)           -0.3822\n",
      "corr(Mo7_z1, Mo18_z1)            0.3814\n",
      "corr(Mo9_x1, Mo12_z1)            -0.3813\n",
      "corr(Mo1_y1, Mo5_z1)             -0.3801\n",
      "corr(Mo1_x1, Mo7_z1)             -0.3783\n",
      "corr(Mo3_x1, Mo16_x1)            0.3783\n",
      "corr(Mo7_x1, Mo9_z1)             0.3777\n",
      "corr(Mo2_z1, Mo16_x1)            0.3774\n",
      "corr(Mo9_x1, Mo15_z1)            -0.3767\n",
      "corr(Mo5_x1, Mo12_x1)            0.3767\n",
      "corr(Mo5_y1, Mo16_z1)            0.3766\n",
      "corr(Mo9_x1, Mo13_z1)            0.3759\n",
      "corr(Mo9_x1, Mo19_z1)            -0.3729\n",
      "corr(Mo11_y1, Mo12_z1)           -0.3716\n",
      "corr(Mo7_y1, Mo18_z1)            0.3693\n",
      "corr(Mo2_z1, Mo7_y1)             -0.3679\n",
      "corr(Mo6_x1, Mo10_y1)            -0.3678\n",
      "corr(Mo1_y1, Mo17_x1)            0.3654\n",
      "corr(Mo10_x1, Mo16_x1)           0.3644\n",
      "corr(Mo14_x1, Mo18_z1)           -0.3638\n",
      "corr(Mo12_z1, Mo14_z1)           0.3624\n",
      "corr(Mo3_z1, Mo11_z1)            -0.3618\n",
      "corr(Mo6_x1, Mo6_z1)             0.3605\n",
      "corr(Mo3_z1, Mo19_y1)            -0.3584\n",
      "corr(Mo11_z1, Mo16_x1)           0.3569\n",
      "corr(Mo9_x1, Mo10_z1)            -0.3568\n",
      "corr(Mo2_z1, Mo7_z1)             -0.3564\n",
      "corr(Mo10_y1, Mo16_z1)           0.3563\n",
      "corr(Mo1_x1, Mo16_x1)            0.3560\n",
      "corr(Mo14_z1, Mo17_x1)           0.3537\n",
      "corr(Mo3_z1, Mo6_z1)             -0.3515\n",
      "corr(Mo3_x1, Mo3_z1)             -0.3500\n",
      "corr(Mo4_y1, Mo17_z1)            -0.3480\n",
      "corr(Mo11_x1, Mo19_z1)           0.3449\n",
      "corr(Mo11_y1, Mo17_x1)           -0.3449\n",
      "corr(Mo5_y1, Mo6_x1)             -0.3430\n",
      "corr(Mo17_x1, Mo19_y1)           -0.3420\n",
      "corr(Mo15_z1, Mo16_z1)           0.3403\n",
      "corr(Mo6_x1, Mo16_x1)            -0.3393\n",
      "corr(Mo5_x1, Mo9_z1)             -0.3387\n",
      "corr(Mo0_z1, Mo16_x1)            -0.3376\n",
      "corr(Mo3_z1, Mo10_x1)            -0.3373\n",
      "corr(Mo4_y1, Mo5_z1)             -0.3371\n",
      "corr(Mo2_x1, Mo17_x1)            0.3361\n",
      "corr(Mo2_x1, Mo7_y1)             0.3340\n",
      "corr(Mo13_y1, Mo18_z1)           0.3334\n",
      "corr(Mo1_z1, Mo16_x1)            -0.3331\n",
      "corr(Mo5_z1, Mo12_x1)            -0.3330\n",
      "corr(Mo5_y1, Mo12_y1)            0.3294\n",
      "corr(Mo6_y1, Mo10_z1)            0.3289\n",
      "corr(Mo15_x1, Mo17_x1)           0.3282\n",
      "corr(Mo3_z1, Mo14_y1)            0.3271\n",
      "corr(Mo11_x1, Mo12_z1)           0.3256\n",
      "corr(Mo2_x1, Mo7_z1)             0.3226\n",
      "corr(Mo5_y1, Mo8_z1)             0.3226\n",
      "corr(Mo10_y1, Mo12_y1)           0.3224\n",
      "corr(Mo8_z1, Mo10_y1)            0.3223\n",
      "corr(Mo3_z1, Mo9_x1)             -0.3178\n",
      "corr(Mo4_y1, Mo14_x1)            -0.3158\n",
      "corr(Mo11_x1, Mo16_x1)           -0.3134\n",
      "corr(Mo10_z1, Mo16_z1)           0.3130\n",
      "corr(Mo6_z1, Mo12_z1)            -0.3099\n",
      "corr(Mo16_z1, Mo18_y1)           0.3096\n",
      "corr(Mo4_z1, Mo17_x1)            -0.3086\n",
      "corr(Mo13_z1, Mo16_z1)           -0.3062\n",
      "corr(O_Biso_cluster1, Mo9_x1)    0.3059\n",
      "corr(Mo6_y1, Mo6_z1)             0.3041\n",
      "corr(Mo6_x1, Mo17_x1)            0.3041\n",
      "corr(Mo1_y1, Mo7_y1)             0.3041\n",
      "corr(Mo11_z1, Mo19_z1)           -0.3039\n",
      "corr(Mo6_z1, Mo16_x1)            0.3031\n",
      "corr(Mo2_z1, Mo17_x1)            -0.3027\n",
      "corr(Mo6_z1, Mo19_z1)            -0.3011\n",
      "corr(Mo0_y1, Mo16_x1)            -0.2981\n",
      "corr(Mo19_y1, Mo19_z1)           -0.2978\n",
      "corr(Mo5_x1, Mo6_x1)             0.2972\n",
      "corr(Mo4_y1, Mo5_x1)             0.2963\n",
      "corr(mc1, Mo5_y1)                0.2961\n",
      "corr(Mo7_x1, Mo8_z1)             0.2960\n",
      "corr(Mo5_z1, Mo9_z1)             0.2959\n",
      "corr(Mo5_z1, Mo18_z1)            -0.2958\n",
      "corr(Mo4_y1, Mo13_y1)            0.2955\n",
      "corr(mc1, Mo10_y1)               0.2941\n",
      "corr(Mo3_x1, Mo19_z1)            -0.2940\n",
      "corr(Mo1_y1, Mo7_z1)             0.2928\n",
      "corr(Mo13_x1, Mo18_z1)           -0.2916\n",
      "corr(Mo7_x1, Mo12_y1)            0.2915\n",
      "corr(Mo16_x1, Mo19_y1)           0.2880\n",
      "corr(Mo0_x1, Mo16_x1)            0.2837\n",
      "corr(Mo11_z1, Mo12_z1)           -0.2822\n",
      "corr(Mo1_x1, Mo17_x1)            -0.2799\n",
      "corr(Mo6_y1, Mo19_y1)            -0.2775\n",
      "corr(Mo14_z1, Mo16_x1)           -0.2773\n",
      "corr(Mo5_z1, Mo6_x1)             -0.2770\n",
      "corr(Mo10_x1, Mo19_z1)           -0.2767\n",
      "corr(mc1, Mo7_x1)                0.2718\n",
      "corr(Mo14_y1, Mo19_z1)           0.2679\n",
      "corr(Mo11_y1, Mo16_x1)           0.2672\n",
      "corr(Mo3_x1, Mo12_z1)            -0.2635\n",
      "corr(Mo2_y1, Mo16_x1)            0.2613\n",
      "corr(Mo5_x1, Mo12_y1)            -0.2607\n",
      "corr(Mo0_z1, Mo17_x1)            0.2603\n",
      "corr(Mo12_z1, Mo19_y1)           -0.2600\n",
      "corr(Mo10_x1, Mo12_z1)           -0.2586\n",
      "corr(Mo1_z1, Mo17_x1)            0.2573\n",
      "corr(Mo5_x1, Mo8_z1)             -0.2546\n",
      "corr(Mo5_x1, Mo18_z1)            0.2540\n",
      "corr(Mo6_x1, Mo7_y1)             0.2537\n",
      "corr(Mo4_y1, Mo7_x1)             -0.2535\n",
      "corr(Mo4_y1, Mo13_x1)            -0.2510\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recipe.free('adp_o')\n",
    "scipyOptimize(recipe)\n",
    "plotRecipe(recipe)\n",
    "print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#recipe.free('wave')\n",
    "#scipyOptimize(recipe)\n",
    "#plotRecipe(recipe)\n",
    "# print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       33.03006755\n",
      "Contributions  33.01839578\n",
      "Restraints     0.01167176\n",
      "Chi2           33.01839578\n",
      "Reduced Chi2   0.03889093\n",
      "Rw             0.63236804\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Mo0_x1            1.93795762e+00 +/- 1.13719108e+06\n",
      "Mo0_y1            2.57501257e+00 +/- 1.69716302e+06\n",
      "Mo0_z1            5.93995562e+00 +/- 4.75729377e+05\n",
      "Mo10_x1           -8.47076903e-01 +/- 4.24398387e+05\n",
      "Mo10_y1           -1.65970269e-01 +/- 5.95087829e+05\n",
      "Mo10_z1           5.79901278e+00 +/- 1.22340721e+05\n",
      "Mo11_x1           1.84019450e+00 +/- 1.40650572e+06\n",
      "Mo11_y1           4.72655934e+00 +/- 1.64516149e+06\n",
      "Mo11_z1           9.41905592e-01 +/- 6.68086302e+05\n",
      "Mo12_x1           2.03125427e+00 +/- 1.20488273e+06\n",
      "Mo12_y1           -1.59316070e-01 +/- 1.34636031e+06\n",
      "Mo12_z1           1.07916517e+01 +/- 5.94593152e+05\n",
      "Mo13_x1           4.96217954e+00 +/- 3.32108783e+05\n",
      "Mo13_y1           4.90148556e+00 +/- 3.76782504e+05\n",
      "Mo13_z1           5.78300668e+00 +/- 2.86397447e+05\n",
      "Mo14_x1           7.88111851e+00 +/- 8.12488240e+05\n",
      "Mo14_y1           4.91152166e+00 +/- 8.90785035e+05\n",
      "Mo14_z1           1.08041172e+01 +/- 5.09235219e+05\n",
      "Mo15_x1           7.10820578e-01 +/- 1.50384097e+05\n",
      "Mo15_y1           2.64150302e+00 +/- 7.07106782e-04\n",
      "Mo15_z1           3.70325062e+00 +/- 6.34428774e+04\n",
      "Mo16_x1           3.69057414e+00 +/- 1.67511339e+05\n",
      "Mo16_y1           2.64150969e+00 +/- 7.07106786e-04\n",
      "Mo16_z1           8.55044751e+00 +/- 9.89885943e+04\n",
      "Mo17_x1           6.37727098e+00 +/- 1.49848079e+05\n",
      "Mo17_y1           7.48450201e+00 +/- 7.07106783e-04\n",
      "Mo17_z1           3.59538207e+00 +/- 1.06846163e+05\n",
      "Mo18_x1           6.88139223e+00 +/- 9.99999996e-04\n",
      "Mo18_y1           2.61161149e+00 +/- 3.04396237e+05\n",
      "Mo18_z1           1.36806469e+01 +/- 1.02136367e+05\n",
      "Mo19_x1           9.61264478e+00 +/- 1.00000001e-03\n",
      "Mo19_y1           7.35080432e+00 +/- 3.06935881e+04\n",
      "Mo19_z1           8.78996091e+00 +/- 2.30414392e+05\n",
      "Mo1_x1            4.72431387e+00 +/- 1.29007686e+06\n",
      "Mo1_y1            7.43414915e+00 +/- 9.07385956e+05\n",
      "Mo1_z1            8.00134039e-01 +/- 5.00934088e+05\n",
      "Mo2_x1            4.77232582e+00 +/- 7.55233086e+05\n",
      "Mo2_y1            2.56917739e+00 +/- 1.25147090e+06\n",
      "Mo2_z1            1.08775070e+01 +/- 5.83786430e+05\n",
      "Mo3_x1            7.58858141e+00 +/- 1.73256818e+05\n",
      "Mo3_y1            7.48450381e+00 +/- 7.07106785e-04\n",
      "Mo3_z1            5.87396429e+00 +/- 1.59767576e+05\n",
      "Mo4_x1            1.07576632e+01 +/- 9.99999997e-04\n",
      "Mo4_y1            7.13982398e+00 +/- 1.28511916e+05\n",
      "Mo4_z1            1.10705344e+01 +/- 1.16254237e+05\n",
      "Mo5_x1            -1.81285835e+00 +/- 1.62343609e+06\n",
      "Mo5_y1            1.77306941e-01 +/- 2.09140473e+06\n",
      "Mo5_z1            3.45047848e+00 +/- 6.65888225e+05\n",
      "Mo6_x1            9.33231646e-01 +/- 1.95145633e+05\n",
      "Mo6_y1            9.74536145e-03 +/- 1.55181565e+05\n",
      "Mo6_z1            8.49814791e+00 +/- 3.29190776e+05\n",
      "Mo7_x1            3.77381529e+00 +/- 1.15967761e+06\n",
      "Mo7_y1            4.99545465e+00 +/- 1.46617756e+06\n",
      "Mo7_z1            3.49878974e+00 +/- 6.67044069e+05\n",
      "Mo8_x1            4.07639985e+00 +/- 1.00000000e-03\n",
      "Mo8_y1            2.20001579e-01 +/- 7.07106784e-04\n",
      "Mo8_z1            1.31147709e+01 +/- 1.40555808e+05\n",
      "Mo9_x1            6.77831462e+00 +/- 1.60757845e+05\n",
      "Mo9_y1            5.06302067e+00 +/- 7.07106782e-04\n",
      "Mo9_z1            8.48948699e+00 +/- 2.78845699e+05\n",
      "Mo_Biso_cluster1  7.99782976e-02 +/- 1.00000000e-03\n",
      "O_Biso_cluster1   5.35959240e-01 +/- 3.25380952e+05\n",
      "mc1               2.94508231e-01 +/- 1.52820268e+03\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "delta2_cluster1  0.00000000e+00\n",
      "wA               0.00000000e+00\n",
      "wasym            1.00000000e+00\n",
      "wlam             5.00000000e+00\n",
      "wphi             0.00000000e+00\n",
      "wr0              4.00000000e+00\n",
      "wsig             2.00000000e+00\n",
      "zoomscale1       1.03003354e+00\n",
      "zoomscale2       9.69981023e-01\n",
      "zoomscale3       1.03005248e+00\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(Mo0_x1, Mo0_y1)             -0.9998\n",
      "corr(Mo0_x1, Mo2_y1)             0.9997\n",
      "corr(Mo7_y1, Mo7_z1)             0.9997\n",
      "corr(Mo1_x1, Mo2_z1)             0.9996\n",
      "corr(Mo0_z1, Mo1_x1)             -0.9995\n",
      "corr(Mo1_x1, Mo1_z1)             -0.9995\n",
      "corr(Mo11_y1, Mo14_z1)           -0.9994\n",
      "corr(Mo0_z1, Mo1_z1)             0.9993\n",
      "corr(Mo0_y1, Mo2_y1)             -0.9993\n",
      "corr(Mo0_y1, Mo0_z1)             0.9990\n",
      "corr(Mo11_x1, Mo11_z1)           -0.9989\n",
      "corr(Mo1_z1, Mo2_z1)             -0.9988\n",
      "corr(Mo2_x1, Mo2_z1)             -0.9988\n",
      "corr(mc1, Mo8_z1)                0.9988\n",
      "corr(Mo11_x1, Mo11_y1)           -0.9987\n",
      "corr(Mo11_z1, Mo14_y1)           -0.9987\n",
      "corr(Mo0_y1, Mo1_z1)             0.9987\n",
      "corr(Mo5_x1, Mo5_z1)             -0.9986\n",
      "corr(Mo13_x1, Mo13_y1)           -0.9986\n",
      "corr(Mo0_x1, Mo0_z1)             -0.9984\n",
      "corr(Mo14_z1, Mo15_x1)           0.9984\n",
      "corr(Mo0_z1, Mo2_z1)             -0.9984\n",
      "corr(Mo11_y1, Mo15_x1)           -0.9984\n",
      "corr(Mo0_x1, Mo1_z1)             -0.9982\n",
      "corr(Mo0_y1, Mo1_x1)             -0.9981\n",
      "corr(Mo1_x1, Mo2_x1)             -0.9981\n",
      "corr(Mo11_x1, Mo14_z1)           0.9978\n",
      "corr(Mo5_x1, Mo5_y1)             -0.9976\n",
      "corr(Mo3_z1, Mo19_z1)            0.9975\n",
      "corr(Mo1_y1, Mo2_x1)             0.9973\n",
      "corr(Mo0_x1, Mo1_x1)             0.9972\n",
      "corr(Mo1_y1, Mo2_z1)             -0.9970\n",
      "corr(Mo0_z1, Mo2_x1)             0.9969\n",
      "corr(Mo0_z1, Mo2_y1)             -0.9969\n",
      "corr(Mo1_z1, Mo2_y1)             -0.9966\n",
      "corr(Mo11_z1, Mo14_z1)           -0.9961\n",
      "corr(Mo0_y1, Mo2_z1)             -0.9961\n",
      "corr(Mo11_y1, Mo11_z1)           0.9961\n",
      "corr(Mo1_z1, Mo2_x1)             0.9960\n",
      "corr(O_Biso_cluster1, Mo2_y1)    0.9959\n",
      "corr(Mo11_x1, Mo15_x1)           0.9957\n",
      "corr(Mo11_x1, Mo14_y1)           0.9954\n",
      "corr(Mo16_x1, Mo17_x1)           -0.9953\n",
      "corr(Mo1_x1, Mo2_y1)             0.9952\n",
      "corr(Mo0_x1, Mo2_z1)             0.9948\n",
      "corr(Mo1_x1, Mo1_y1)             -0.9948\n",
      "corr(Mo8_z1, Mo12_y1)            0.9948\n",
      "corr(Mo8_z1, Mo9_z1)             0.9946\n",
      "corr(O_Biso_cluster1, Mo13_z1)   0.9946\n",
      "corr(O_Biso_cluster1, Mo0_x1)    0.9944\n",
      "corr(Mo5_z1, Mo7_y1)             -0.9942\n",
      "corr(Mo4_y1, Mo18_z1)            0.9942\n",
      "corr(Mo9_z1, Mo12_x1)            -0.9941\n",
      "corr(Mo5_z1, Mo7_z1)             -0.9938\n",
      "corr(Mo15_z1, Mo18_y1)           0.9936\n",
      "corr(Mo1_y1, Mo1_z1)             0.9935\n",
      "corr(Mo5_x1, Mo7_x1)             -0.9932\n",
      "corr(Mo2_x1, Mo12_x1)            0.9931\n",
      "corr(Mo0_y1, Mo2_x1)             0.9930\n",
      "corr(Mo5_y1, Mo5_z1)             0.9930\n",
      "corr(Mo14_y1, Mo14_z1)           0.9929\n",
      "corr(O_Biso_cluster1, Mo0_y1)    -0.9928\n",
      "corr(mc1, Mo12_y1)               0.9927\n",
      "corr(Mo5_z1, Mo7_x1)             0.9926\n",
      "corr(Mo1_y1, Mo9_z1)             -0.9926\n",
      "corr(Mo11_z1, Mo15_x1)           -0.9926\n",
      "corr(Mo12_x1, Mo12_y1)           -0.9925\n",
      "corr(Mo12_z1, Mo19_z1)           0.9925\n",
      "corr(Mo5_y1, Mo7_x1)             0.9925\n",
      "corr(Mo3_x1, Mo14_y1)            -0.9924\n",
      "corr(Mo2_y1, Mo2_z1)             0.9923\n",
      "corr(Mo0_z1, Mo1_y1)             0.9923\n",
      "corr(mc1, Mo9_z1)                0.9920\n",
      "corr(Mo2_x1, Mo9_z1)             -0.9916\n",
      "corr(Mo11_y1, Mo14_y1)           -0.9915\n",
      "corr(Mo0_x1, Mo2_x1)             -0.9912\n",
      "corr(Mo3_x1, Mo11_z1)            0.9911\n",
      "corr(Mo10_x1, Mo11_z1)           0.9910\n",
      "corr(Mo9_z1, Mo12_y1)            0.9908\n",
      "corr(Mo10_x1, Mo11_x1)           -0.9901\n",
      "corr(Mo10_x1, Mo14_y1)           -0.9900\n",
      "corr(Mo15_z1, Mo17_z1)           0.9898\n",
      "corr(Mo3_x1, Mo14_z1)            -0.9896\n",
      "corr(O_Biso_cluster1, Mo18_y1)   -0.9895\n",
      "corr(O_Biso_cluster1, Mo1_z1)    -0.9891\n",
      "corr(Mo3_x1, Mo15_x1)            -0.9891\n",
      "corr(Mo5_x1, Mo7_y1)             0.9882\n",
      "corr(Mo1_y1, Mo12_x1)            0.9882\n",
      "corr(Mo2_x1, Mo2_y1)             -0.9881\n",
      "corr(Mo2_y1, Mo15_z1)            -0.9880\n",
      "corr(Mo3_x1, Mo11_x1)            -0.9878\n",
      "corr(Mo14_y1, Mo15_x1)           0.9875\n",
      "corr(O_Biso_cluster1, Mo0_z1)    -0.9872\n",
      "corr(Mo0_y1, Mo1_y1)             0.9871\n",
      "corr(O_Biso_cluster1, Mo15_z1)   -0.9871\n",
      "corr(Mo5_x1, Mo7_z1)             0.9870\n",
      "corr(Mo2_z1, Mo9_z1)             0.9870\n",
      "corr(Mo3_z1, Mo12_z1)            0.9868\n",
      "corr(Mo14_x1, Mo18_y1)           0.9868\n",
      "corr(Mo8_z1, Mo12_x1)            -0.9866\n",
      "corr(Mo7_x1, Mo7_y1)             -0.9866\n",
      "corr(Mo3_x1, Mo11_y1)            0.9862\n",
      "corr(Mo4_z1, Mo15_x1)            -0.9861\n",
      "corr(Mo2_z1, Mo12_x1)            -0.9861\n",
      "corr(mc1, Mo3_z1)                0.9856\n",
      "corr(O_Biso_cluster1, Mo1_x1)    0.9854\n",
      "corr(Mo17_z1, Mo18_y1)           0.9854\n",
      "corr(Mo0_x1, Mo1_y1)             -0.9854\n",
      "corr(Mo10_x1, Mo11_y1)           0.9852\n",
      "corr(Mo5_y1, Mo10_y1)            0.9850\n",
      "corr(Mo1_x1, Mo12_x1)            -0.9846\n",
      "corr(Mo13_z1, Mo18_y1)           -0.9844\n",
      "corr(Mo0_x1, Mo15_z1)            -0.9844\n",
      "corr(Mo7_x1, Mo7_z1)             -0.9842\n",
      "corr(Mo4_z1, Mo11_y1)            0.9839\n",
      "corr(Mo2_y1, Mo18_y1)            -0.9836\n",
      "corr(Mo2_y1, Mo13_z1)            0.9836\n",
      "corr(Mo10_x1, Mo14_z1)           -0.9834\n",
      "corr(Mo1_x1, Mo9_z1)             0.9834\n",
      "corr(Mo1_y1, Mo8_z1)             -0.9834\n",
      "corr(Mo0_z1, Mo12_x1)            0.9832\n",
      "corr(Mo4_z1, Mo11_x1)            -0.9832\n",
      "corr(Mo0_y1, Mo15_z1)            0.9827\n",
      "corr(Mo5_x1, Mo10_y1)            -0.9823\n",
      "corr(Mo13_z1, Mo14_x1)           -0.9822\n",
      "corr(Mo13_z1, Mo15_z1)           -0.9820\n",
      "corr(O_Biso_cluster1, Mo2_z1)    0.9817\n",
      "corr(mc1, Mo12_x1)               -0.9814\n",
      "corr(Mo7_x1, Mo10_y1)            0.9814\n",
      "corr(Mo0_x1, Mo13_z1)            0.9811\n",
      "corr(Mo4_z1, Mo6_z1)             0.9810\n",
      "corr(Mo14_x1, Mo15_x1)           -0.9810\n",
      "corr(Mo1_y1, Mo2_y1)             -0.9810\n",
      "corr(Mo10_x1, Mo10_y1)           -0.9799\n",
      "corr(Mo14_x1, Mo17_z1)           0.9799\n",
      "corr(Mo3_z1, Mo8_z1)             0.9799\n",
      "corr(mc1, Mo1_y1)                -0.9794\n",
      "corr(Mo13_x1, Mo14_x1)           0.9793\n",
      "corr(Mo1_z1, Mo12_x1)            0.9791\n",
      "corr(Mo4_z1, Mo14_z1)            -0.9791\n",
      "corr(Mo0_z1, Mo9_z1)             -0.9790\n",
      "corr(Mo0_x1, Mo18_y1)            -0.9790\n",
      "corr(Mo1_z1, Mo9_z1)             -0.9782\n",
      "corr(Mo13_y1, Mo13_z1)           0.9782\n",
      "corr(Mo11_y1, Mo14_x1)           0.9781\n",
      "corr(Mo2_x1, Mo8_z1)             -0.9779\n",
      "corr(Mo13_x1, Mo15_x1)           -0.9779\n",
      "corr(Mo0_y1, Mo13_z1)            -0.9777\n",
      "corr(Mo0_y1, Mo18_y1)            0.9773\n",
      "corr(Mo4_z1, Mo11_z1)            0.9770\n",
      "corr(Mo14_x1, Mo14_z1)           -0.9770\n",
      "corr(Mo5_y1, Mo14_y1)            0.9769\n",
      "corr(Mo5_y1, Mo7_y1)             -0.9765\n",
      "corr(Mo4_z1, Mo17_z1)            0.9760\n",
      "corr(Mo0_z1, Mo15_z1)            0.9760\n",
      "corr(Mo2_x1, Mo12_y1)            -0.9758\n",
      "corr(Mo1_y1, Mo12_y1)            -0.9756\n",
      "corr(Mo5_z1, Mo10_y1)            0.9755\n",
      "corr(Mo0_y1, Mo12_x1)            0.9755\n",
      "corr(Mo7_x1, Mo14_y1)            0.9752\n",
      "corr(Mo13_y1, Mo14_x1)           -0.9751\n",
      "corr(Mo5_y1, Mo7_z1)             -0.9746\n",
      "corr(Mo10_x1, Mo15_x1)           -0.9743\n",
      "corr(Mo10_y1, Mo14_y1)           0.9740\n",
      "corr(O_Biso_cluster1, Mo2_x1)    -0.9731\n",
      "corr(mc1, Mo19_z1)               0.9730\n",
      "corr(O_Biso_cluster1, Mo14_x1)   -0.9729\n",
      "corr(Mo1_z1, Mo13_z1)            -0.9728\n",
      "corr(Mo1_z1, Mo15_z1)            0.9726\n",
      "corr(Mo13_x1, Mo14_z1)           -0.9726\n",
      "corr(Mo13_x1, Mo13_z1)           -0.9725\n",
      "corr(Mo16_x1, Mo16_z1)           -0.9723\n",
      "corr(Mo14_x1, Mo15_z1)           0.9722\n",
      "corr(Mo0_x1, Mo12_x1)            -0.9721\n",
      "corr(mc1, Mo2_x1)                -0.9718\n",
      "corr(Mo3_z1, Mo12_y1)            0.9717\n",
      "corr(Mo5_y1, Mo11_z1)            -0.9717\n",
      "corr(Mo0_y1, Mo9_z1)             -0.9710\n",
      "corr(Mo2_z1, Mo8_z1)             0.9707\n",
      "corr(Mo1_x1, Mo15_z1)            -0.9701\n",
      "corr(Mo0_z1, Mo13_z1)            -0.9697\n",
      "corr(Mo3_x1, Mo7_x1)             -0.9693\n",
      "corr(Mo13_z1, Mo17_z1)           -0.9691\n",
      "corr(Mo4_y1, Mo16_z1)            -0.9691\n",
      "corr(Mo3_x1, Mo5_y1)             -0.9689\n",
      "corr(Mo10_y1, Mo11_z1)           -0.9680\n",
      "corr(Mo3_x1, Mo13_x1)            0.9679\n",
      "corr(Mo11_y1, Mo13_x1)           0.9678\n",
      "corr(Mo2_y1, Mo12_x1)            -0.9676\n",
      "corr(Mo0_z1, Mo18_y1)            0.9674\n",
      "corr(Mo0_x1, Mo9_z1)             0.9672\n",
      "corr(O_Biso_cluster1, Mo1_y1)    -0.9671\n",
      "corr(Mo13_y1, Mo15_x1)           0.9669\n",
      "corr(Mo3_x1, Mo4_z1)             0.9668\n",
      "corr(Mo11_x1, Mo14_x1)           -0.9666\n",
      "corr(Mo1_z1, Mo18_y1)            0.9663\n",
      "corr(Mo3_x1, Mo10_x1)            0.9662\n",
      "corr(Mo8_z1, Mo19_z1)            0.9660\n",
      "corr(Mo1_x1, Mo13_z1)            0.9660\n",
      "corr(Mo4_z1, Mo14_y1)            -0.9659\n",
      "corr(Mo4_z1, Mo14_x1)            0.9653\n",
      "corr(Mo7_y1, Mo10_y1)            -0.9650\n",
      "corr(Mo2_z1, Mo12_y1)            0.9650\n",
      "corr(Mo7_x1, Mo11_z1)            -0.9648\n",
      "corr(Mo1_x1, Mo8_z1)             0.9647\n",
      "corr(Mo12_z1, Mo18_z1)           0.9644\n",
      "corr(mc1, Mo2_z1)                0.9642\n",
      "corr(O_Biso_cluster1, Mo17_z1)   -0.9639\n",
      "corr(Mo1_x1, Mo18_y1)            -0.9633\n",
      "corr(Mo18_z1, Mo19_z1)           0.9631\n",
      "corr(Mo2_z1, Mo15_z1)            -0.9630\n",
      "corr(Mo5_x1, Mo14_y1)            -0.9626\n",
      "corr(Mo16_z1, Mo18_z1)           -0.9625\n",
      "corr(Mo5_y1, Mo11_x1)            0.9623\n",
      "corr(Mo5_y1, Mo10_x1)            -0.9620\n",
      "corr(Mo15_x1, Mo17_z1)           -0.9615\n",
      "corr(Mo2_y1, Mo9_z1)             0.9615\n",
      "corr(Mo6_z1, Mo11_x1)            -0.9610\n",
      "corr(Mo2_z1, Mo13_z1)            0.9608\n",
      "corr(Mo1_x1, Mo12_y1)            0.9605\n",
      "corr(Mo13_y1, Mo14_z1)           0.9597\n",
      "corr(Mo12_y1, Mo19_z1)           0.9596\n",
      "corr(mc1, Mo12_z1)               0.9596\n",
      "corr(Mo7_z1, Mo10_y1)            -0.9591\n",
      "corr(Mo12_y1, Mo12_z1)           0.9590\n",
      "corr(Mo0_z1, Mo8_z1)             -0.9590\n",
      "corr(Mo10_y1, Mo11_x1)           0.9589\n",
      "corr(Mo1_z1, Mo8_z1)             -0.9587\n",
      "corr(Mo3_z1, Mo9_z1)             0.9583\n",
      "corr(Mo2_x1, Mo15_z1)            0.9580\n",
      "corr(Mo2_y1, Mo17_z1)            -0.9577\n",
      "corr(mc1, Mo1_x1)                0.9577\n",
      "corr(Mo4_z1, Mo10_x1)            0.9571\n",
      "corr(Mo0_z1, Mo12_y1)            -0.9569\n",
      "corr(Mo11_x1, Mo13_x1)           -0.9565\n",
      "corr(Mo2_z1, Mo18_y1)            -0.9564\n",
      "corr(Mo11_z1, Mo14_x1)           0.9561\n",
      "corr(Mo6_z1, Mo11_z1)            0.9557\n",
      "corr(Mo3_x1, Mo13_y1)            -0.9545\n",
      "corr(Mo5_x1, Mo11_z1)            0.9545\n",
      "corr(Mo8_z1, Mo12_z1)            0.9544\n",
      "corr(Mo11_y1, Mo13_y1)           -0.9544\n",
      "corr(Mo7_z1, Mo9_x1)             -0.9544\n",
      "corr(Mo7_x1, Mo10_x1)            -0.9544\n",
      "corr(Mo11_y1, Mo17_z1)           0.9543\n",
      "corr(Mo6_z1, Mo11_y1)            0.9541\n",
      "corr(Mo3_x1, Mo5_x1)             0.9537\n",
      "corr(Mo2_y1, Mo14_x1)            -0.9533\n",
      "corr(Mo4_y1, Mo12_z1)            0.9530\n",
      "corr(Mo1_z1, Mo12_y1)            -0.9529\n",
      "corr(Mo6_z1, Mo15_x1)            -0.9526\n",
      "corr(Mo7_x1, Mo11_x1)            0.9521\n",
      "corr(O_Biso_cluster1, Mo13_y1)   0.9520\n",
      "corr(Mo11_z1, Mo13_x1)           0.9519\n",
      "corr(Mo5_z1, Mo14_y1)            0.9514\n",
      "corr(mc1, Mo0_z1)                -0.9514\n",
      "corr(mc1, Mo1_z1)                -0.9514\n",
      "corr(Mo7_y1, Mo9_x1)             -0.9512\n",
      "corr(Mo0_x1, Mo17_z1)            -0.9509\n",
      "corr(Mo16_z1, Mo17_x1)           0.9501\n",
      "corr(Mo13_x1, Mo17_z1)           0.9494\n",
      "corr(Mo5_y1, Mo14_z1)            0.9492\n",
      "corr(Mo2_x1, Mo13_z1)            -0.9488\n",
      "corr(Mo14_z1, Mo17_z1)           -0.9483\n",
      "corr(Mo5_y1, Mo11_y1)            -0.9483\n",
      "corr(Mo13_x1, Mo14_y1)           -0.9476\n",
      "corr(Mo2_x1, Mo18_y1)            0.9475\n",
      "corr(Mo4_z1, Mo13_x1)            0.9474\n",
      "corr(Mo0_y1, Mo17_z1)            0.9474\n",
      "corr(Mo4_y1, Mo19_z1)            0.9473\n",
      "corr(Mo0_y1, Mo8_z1)             -0.9470\n",
      "corr(Mo13_y1, Mo17_z1)           -0.9469\n",
      "corr(Mo3_x1, Mo10_y1)            -0.9468\n",
      "corr(Mo3_x1, Mo14_x1)            0.9462\n",
      "corr(Mo0_x1, Mo14_x1)            -0.9462\n",
      "corr(Mo10_z1, Mo17_z1)           0.9460\n",
      "corr(Mo6_z1, Mo14_z1)            -0.9458\n",
      "corr(Mo5_x1, Mo10_x1)            0.9458\n",
      "corr(Mo10_z1, Mo15_z1)           0.9455\n",
      "corr(Mo13_y1, Mo18_y1)           -0.9451\n",
      "corr(Mo14_x1, Mo14_y1)           -0.9451\n",
      "corr(O_Biso_cluster1, Mo13_x1)   -0.9451\n",
      "corr(Mo3_z1, Mo18_z1)            0.9450\n",
      "corr(Mo13_x1, Mo18_y1)           0.9448\n",
      "corr(Mo10_y1, Mo11_y1)           -0.9446\n",
      "corr(Mo6_z1, Mo10_x1)            0.9445\n",
      "corr(Mo10_y1, Mo14_z1)           0.9443\n",
      "corr(Mo0_y1, Mo12_y1)            -0.9443\n",
      "corr(Mo3_x1, Mo5_z1)             -0.9442\n",
      "corr(Mo1_y1, Mo13_z1)            -0.9441\n",
      "corr(Mo7_x1, Mo14_z1)            0.9440\n",
      "corr(Mo6_z1, Mo14_y1)            -0.9437\n",
      "corr(Mo11_x1, Mo17_z1)           -0.9436\n",
      "corr(Mo15_x1, Mo18_y1)           -0.9434\n",
      "corr(Mo5_y1, Mo15_x1)            0.9431\n",
      "corr(Mo5_y1, Mo6_z1)             -0.9424\n",
      "corr(Mo0_x1, Mo8_z1)             0.9424\n",
      "corr(O_Biso_cluster1, Mo12_x1)   -0.9424\n",
      "corr(Mo5_x1, Mo11_x1)            -0.9421\n",
      "corr(Mo0_y1, Mo14_x1)            0.9421\n",
      "corr(Mo1_y1, Mo15_z1)            0.9418\n",
      "corr(Mo4_z1, Mo18_y1)            0.9412\n",
      "corr(Mo5_z1, Mo11_z1)            -0.9409\n",
      "corr(Mo11_x1, Mo13_y1)           0.9409\n",
      "corr(Mo6_z1, Mo17_z1)            0.9409\n",
      "corr(Mo10_x1, Mo14_x1)           0.9403\n",
      "corr(Mo3_z1, Mo12_x1)            -0.9401\n",
      "corr(O_Biso_cluster1, Mo9_z1)    0.9399\n",
      "corr(Mo0_x1, Mo12_y1)            0.9394\n",
      "corr(Mo13_z1, Mo15_x1)           0.9392\n",
      "corr(Mo11_y1, Mo18_y1)           0.9392\n",
      "corr(Mo7_x1, Mo11_y1)            -0.9392\n",
      "corr(mc1, Mo0_y1)                -0.9384\n",
      "corr(Mo9_z1, Mo19_z1)            0.9381\n",
      "corr(Mo13_y1, Mo15_z1)           -0.9377\n",
      "corr(Mo4_z1, Mo15_z1)            0.9374\n",
      "corr(Mo5_z1, Mo9_x1)             0.9369\n",
      "corr(Mo0_z1, Mo17_z1)            0.9356\n",
      "corr(Mo3_x1, Mo6_z1)             0.9356\n",
      "corr(Mo4_z1, Mo13_y1)            -0.9353\n",
      "corr(Mo11_z1, Mo13_y1)           -0.9350\n",
      "corr(Mo12_x1, Mo15_z1)           0.9349\n",
      "corr(Mo2_y1, Mo8_z1)             0.9347\n",
      "corr(Mo14_z1, Mo18_y1)           -0.9344\n",
      "corr(Mo13_x1, Mo15_z1)           0.9344\n",
      "corr(Mo7_x1, Mo15_x1)            0.9340\n",
      "corr(mc1, Mo0_x1)                0.9337\n",
      "corr(Mo4_z1, Mo5_y1)             -0.9326\n",
      "corr(Mo1_y1, Mo3_z1)             -0.9322\n",
      "corr(Mo2_y1, Mo12_y1)            0.9321\n",
      "corr(Mo1_y1, Mo18_y1)            0.9317\n",
      "corr(Mo1_z1, Mo17_z1)            0.9312\n",
      "corr(Mo5_z1, Mo10_x1)            -0.9307\n",
      "corr(Mo10_y1, Mo15_x1)           0.9306\n",
      "corr(Mo13_y1, Mo14_y1)           0.9297\n",
      "corr(Mo10_x1, Mo19_y1)           0.9294\n",
      "corr(Mo1_z1, Mo14_x1)            0.9292\n",
      "corr(Mo11_z1, Mo17_z1)           0.9286\n",
      "corr(Mo13_z1, Mo14_z1)           0.9283\n",
      "corr(Mo7_y1, Mo14_y1)            -0.9281\n",
      "corr(Mo11_y1, Mo13_z1)           -0.9278\n",
      "corr(Mo5_x1, Mo14_z1)            -0.9271\n",
      "corr(Mo0_z1, Mo14_x1)            0.9269\n",
      "corr(Mo15_x1, Mo15_z1)           -0.9269\n",
      "corr(Mo1_x1, Mo17_z1)            -0.9263\n",
      "corr(Mo5_z1, Mo11_x1)            0.9263\n",
      "corr(Mo9_z1, Mo12_z1)            0.9258\n",
      "corr(Mo5_x1, Mo11_y1)            0.9253\n",
      "corr(Mo2_y1, Mo13_y1)            0.9252\n",
      "corr(mc1, Mo2_y1)                0.9251\n",
      "corr(Mo5_x1, Mo9_x1)             -0.9250\n",
      "corr(Mo3_z1, Mo4_y1)             0.9242\n",
      "corr(Mo11_x1, Mo18_y1)           -0.9232\n",
      "corr(Mo7_z1, Mo14_y1)            -0.9224\n",
      "corr(Mo6_z1, Mo10_y1)            -0.9218\n",
      "corr(Mo5_x1, Mo6_z1)             0.9215\n",
      "corr(Mo1_x1, Mo14_x1)            -0.9214\n",
      "corr(Mo4_z1, Mo13_z1)            -0.9210\n",
      "corr(Mo12_x1, Mo19_z1)           -0.9205\n",
      "corr(Mo0_x1, Mo13_y1)            0.9201\n",
      "corr(Mo5_x1, Mo15_x1)            -0.9191\n",
      "corr(Mo3_x1, Mo7_y1)             0.9183\n",
      "corr(Mo2_x1, Mo3_z1)             -0.9181\n",
      "corr(Mo3_x1, Mo17_z1)            0.9178\n",
      "corr(Mo11_y1, Mo15_z1)           0.9178\n",
      "corr(Mo12_x1, Mo12_z1)           -0.9175\n",
      "corr(Mo12_x1, Mo18_y1)           0.9169\n",
      "corr(Mo10_x1, Mo13_x1)           0.9161\n",
      "corr(Mo2_y1, Mo13_x1)            -0.9160\n",
      "corr(Mo9_z1, Mo15_z1)            -0.9157\n",
      "corr(Mo2_z1, Mo17_z1)            -0.9157\n",
      "corr(Mo3_x1, Mo7_z1)             0.9149\n",
      "corr(O_Biso_cluster1, Mo15_x1)   0.9136\n",
      "corr(Mo0_y1, Mo13_y1)            -0.9132\n",
      "corr(Mo2_z1, Mo14_x1)            -0.9128\n",
      "corr(Mo4_z1, Mo10_y1)            -0.9125\n",
      "corr(Mo7_y1, Mo11_z1)            0.9124\n",
      "corr(Mo14_z1, Mo15_z1)           -0.9118\n",
      "corr(Mo6_z1, Mo14_x1)            0.9118\n",
      "corr(Mo2_y1, Mo10_z1)            -0.9114\n",
      "corr(Mo5_z1, Mo14_z1)            0.9113\n",
      "corr(Mo14_y1, Mo17_z1)           -0.9101\n",
      "corr(Mo0_x1, Mo13_x1)            -0.9096\n",
      "corr(Mo0_x1, Mo10_z1)            -0.9091\n",
      "corr(Mo12_x1, Mo13_z1)           -0.9091\n",
      "corr(Mo10_z1, Mo18_y1)           0.9084\n",
      "corr(Mo4_y1, Mo16_x1)            0.9083\n",
      "corr(Mo5_z1, Mo11_y1)            -0.9082\n",
      "corr(Mo11_x1, Mo13_z1)           0.9081\n",
      "corr(Mo1_y1, Mo19_z1)            -0.9079\n",
      "corr(Mo2_x1, Mo17_z1)            0.9077\n",
      "corr(O_Biso_cluster1, Mo8_z1)    0.9075\n",
      "corr(Mo7_z1, Mo11_z1)            0.9065\n",
      "corr(Mo4_z1, Mo5_x1)             0.9064\n",
      "corr(Mo0_y1, Mo10_z1)            0.9062\n",
      "corr(Mo7_y1, Mo10_x1)            0.9061\n",
      "corr(Mo2_z1, Mo3_z1)             0.9059\n",
      "corr(Mo1_z1, Mo13_y1)            -0.9058\n",
      "corr(Mo9_z1, Mo18_y1)            -0.9057\n",
      "corr(Mo11_z1, Mo18_y1)           0.9055\n",
      "corr(Mo9_z1, Mo13_z1)            0.9052\n",
      "corr(Mo4_z1, Mo7_x1)             -0.9050\n",
      "corr(Mo0_z1, Mo10_z1)            0.9044\n",
      "corr(O_Biso_cluster1, Mo11_y1)   -0.9042\n",
      "corr(Mo10_x1, Mo17_z1)           0.9037\n",
      "corr(Mo0_y1, Mo13_x1)            0.9025\n",
      "corr(Mo14_y1, Mo19_y1)           -0.9024\n",
      "corr(O_Biso_cluster1, Mo14_z1)   0.9024\n",
      "corr(Mo5_z1, Mo15_x1)            0.9023\n",
      "corr(Mo5_z1, Mo6_z1)             -0.9013\n",
      "corr(O_Biso_cluster1, Mo4_z1)    -0.9007\n",
      "corr(Mo6_z1, Mo7_x1)             -0.9007\n",
      "corr(Mo11_x1, Mo15_z1)           -0.9004\n",
      "corr(Mo0_z1, Mo13_y1)            -0.8994\n",
      "corr(O_Biso_cluster1, Mo12_y1)   0.8993\n",
      "corr(Mo5_y1, Mo9_x1)             0.8985\n",
      "corr(Mo10_z1, Mo13_z1)           -0.8982\n",
      "corr(Mo7_z1, Mo10_x1)            0.8976\n",
      "corr(mc1, O_Biso_cluster1)       0.8976\n",
      "corr(Mo14_z1, Mo19_y1)           -0.8971\n",
      "corr(Mo3_x1, Mo13_z1)            -0.8968\n",
      "corr(Mo2_x1, Mo14_x1)            0.8967\n",
      "corr(O_Biso_cluster1, Mo10_z1)   -0.8967\n",
      "corr(Mo11_z1, Mo19_y1)           0.8963\n",
      "corr(Mo10_y1, Mo19_y1)           -0.8959\n",
      "corr(Mo1_x1, Mo3_z1)             0.8954\n",
      "corr(Mo10_x1, Mo13_y1)           -0.8950\n",
      "corr(Mo11_y1, Mo19_y1)           0.8945\n",
      "corr(Mo7_x1, Mo9_x1)             0.8943\n",
      "corr(Mo7_y1, Mo11_x1)            -0.8936\n",
      "corr(Mo11_z1, Mo13_z1)           -0.8931\n",
      "corr(Mo11_x1, Mo19_y1)           -0.8931\n",
      "corr(Mo1_z1, Mo13_x1)            0.8929\n",
      "corr(Mo1_x1, Mo13_y1)            0.8929\n",
      "corr(Mo9_x1, Mo17_x1)            0.8929\n",
      "corr(Mo1_z1, Mo10_z1)            0.8923\n",
      "corr(Mo2_x1, Mo19_z1)            -0.8923\n",
      "corr(Mo1_x1, Mo10_z1)            -0.8915\n",
      "corr(Mo10_x1, Mo18_y1)           0.8913\n",
      "corr(Mo6_z1, Mo18_y1)            0.8891\n",
      "corr(Mo12_z1, Mo16_z1)           -0.8889\n",
      "corr(Mo4_z1, Mo10_z1)            0.8880\n",
      "corr(Mo3_x1, Mo18_y1)            0.8873\n",
      "corr(Mo7_z1, Mo11_x1)            -0.8872\n",
      "corr(Mo14_y1, Mo18_y1)           -0.8870\n",
      "corr(Mo0_z1, Mo13_x1)            0.8867\n",
      "corr(Mo1_y1, Mo12_z1)            -0.8865\n",
      "corr(Mo1_z1, Mo3_z1)             -0.8861\n",
      "corr(Mo9_x1, Mo10_y1)            0.8861\n",
      "corr(Mo0_z1, Mo3_z1)             -0.8857\n",
      "corr(Mo4_z1, Mo5_z1)             -0.8856\n",
      "corr(Mo1_y1, Mo17_z1)            0.8856\n",
      "corr(Mo2_x1, Mo10_z1)            0.8855\n",
      "corr(Mo6_z1, Mo15_z1)            0.8855\n",
      "corr(Mo12_y1, Mo15_z1)           -0.8851\n",
      "corr(Mo2_z1, Mo13_y1)            0.8850\n",
      "corr(Mo16_x1, Mo18_z1)           0.8848\n",
      "corr(Mo2_y1, Mo15_x1)            0.8832\n",
      "corr(Mo1_y1, Mo14_x1)            0.8826\n",
      "corr(Mo10_z1, Mo12_x1)           0.8822\n",
      "corr(O_Biso_cluster1, Mo11_x1)   0.8821\n",
      "corr(Mo7_x1, Mo13_x1)            -0.8815\n",
      "corr(Mo6_z1, Mo13_x1)            0.8813\n",
      "corr(Mo2_y1, Mo4_z1)             -0.8808\n",
      "corr(Mo11_z1, Mo15_z1)           0.8808\n",
      "corr(Mo2_z1, Mo10_z1)            -0.8806\n",
      "corr(Mo1_x1, Mo13_x1)            -0.8796\n",
      "corr(Mo7_y1, Mo14_z1)            -0.8788\n",
      "corr(Mo13_z1, Mo14_y1)           0.8787\n",
      "corr(Mo8_z1, Mo15_z1)            -0.8780\n",
      "corr(Mo2_x1, Mo12_z1)            -0.8776\n",
      "corr(Mo2_z1, Mo19_z1)            0.8775\n",
      "corr(Mo12_x1, Mo17_z1)           0.8768\n",
      "corr(mc1, Mo18_z1)               0.8767\n",
      "corr(Mo5_y1, Mo13_x1)            -0.8767\n",
      "corr(Mo4_y1, Mo17_x1)            -0.8751\n",
      "corr(Mo15_x1, Mo19_y1)           -0.8739\n",
      "corr(Mo7_y1, Mo11_y1)            0.8733\n",
      "corr(Mo2_y1, Mo11_y1)            -0.8724\n",
      "corr(Mo0_x1, Mo15_x1)            0.8724\n",
      "corr(Mo7_z1, Mo14_z1)            -0.8721\n",
      "corr(Mo10_z1, Mo14_x1)           0.8715\n",
      "corr(Mo2_z1, Mo13_x1)            -0.8708\n",
      "corr(Mo14_x1, Mo19_y1)           0.8703\n",
      "corr(Mo0_x1, Mo4_z1)             -0.8693\n",
      "corr(Mo3_x1, Mo15_z1)            0.8689\n",
      "corr(Mo8_z1, Mo13_z1)            0.8688\n",
      "corr(Mo6_z1, Mo10_z1)            0.8686\n",
      "corr(Mo2_y1, Mo14_z1)            0.8684\n",
      "corr(Mo0_y1, Mo3_z1)             -0.8666\n",
      "corr(Mo7_z1, Mo11_y1)            0.8664\n",
      "corr(Mo1_x1, Mo19_z1)            0.8659\n",
      "corr(Mo0_y1, Mo15_x1)            -0.8655\n",
      "corr(Mo2_x1, Mo13_y1)            -0.8651\n",
      "corr(Mo7_y1, Mo15_x1)            -0.8651\n",
      "corr(mc1, Mo15_z1)               -0.8645\n",
      "corr(Mo8_z1, Mo18_z1)            0.8641\n",
      "corr(Mo16_z1, Mo19_z1)           -0.8641\n",
      "corr(Mo6_z1, Mo13_y1)            -0.8635\n",
      "corr(O_Biso_cluster1, Mo11_z1)   -0.8632\n",
      "corr(Mo0_y1, Mo4_z1)             0.8632\n",
      "corr(Mo5_y1, Mo14_x1)            -0.8628\n",
      "corr(Mo1_y1, Mo10_z1)            0.8627\n",
      "corr(Mo1_y1, Mo13_y1)            -0.8626\n",
      "corr(Mo8_z1, Mo18_y1)            -0.8621\n",
      "corr(Mo12_y1, Mo18_y1)           -0.8620\n",
      "corr(Mo10_x1, Mo13_z1)           -0.8612\n",
      "corr(Mo0_x1, Mo11_y1)            -0.8608\n",
      "corr(Mo3_x1, Mo19_y1)            0.8607\n",
      "corr(Mo10_y1, Mo14_x1)           -0.8602\n",
      "corr(Mo0_x1, Mo3_z1)             0.8598\n",
      "corr(Mo7_z1, Mo15_x1)            -0.8592\n",
      "corr(Mo14_y1, Mo15_z1)           -0.8590\n",
      "corr(mc1, Mo13_z1)               0.8589\n",
      "corr(Mo12_y1, Mo13_z1)           0.8586\n",
      "corr(Mo2_z1, Mo12_z1)            0.8579\n",
      "corr(Mo7_x1, Mo13_y1)            0.8577\n",
      "corr(Mo0_x1, Mo14_z1)            0.8570\n",
      "corr(O_Biso_cluster1, Mo3_x1)    -0.8569\n",
      "corr(Mo12_y1, Mo18_z1)           0.8568\n",
      "corr(Mo0_z1, Mo19_z1)            -0.8558\n",
      "corr(Mo1_z1, Mo19_z1)            -0.8556\n",
      "corr(Mo10_x1, Mo15_z1)           0.8555\n",
      "corr(Mo7_x1, Mo19_y1)            -0.8547\n",
      "corr(Mo10_z1, Mo13_y1)           -0.8547\n",
      "corr(Mo0_y1, Mo11_y1)            0.8543\n",
      "corr(Mo7_x1, Mo14_x1)            -0.8526\n",
      "corr(Mo5_y1, Mo13_y1)            0.8523\n",
      "corr(Mo6_z1, Mo7_y1)             0.8512\n",
      "corr(Mo10_y1, Mo13_x1)           -0.8511\n",
      "corr(Mo17_x1, Mo18_z1)           -0.8504\n",
      "corr(Mo0_y1, Mo14_z1)            -0.8501\n",
      "corr(Mo2_x1, Mo13_x1)            0.8498\n",
      "corr(mc1, Mo4_y1)                0.8495\n",
      "corr(Mo9_z1, Mo17_z1)            -0.8495\n",
      "corr(Mo12_x1, Mo14_x1)           0.8487\n",
      "corr(Mo9_x1, Mo16_x1)            -0.8485\n",
      "corr(Mo2_y1, Mo11_x1)            0.8483\n",
      "corr(mc1, Mo18_y1)               -0.8482\n",
      "corr(Mo5_x1, Mo13_x1)            0.8478\n",
      "corr(Mo6_z1, Mo7_z1)             0.8478\n",
      "corr(Mo2_y1, Mo3_z1)             0.8477\n",
      "corr(Mo1_x1, Mo12_z1)            0.8472\n",
      "corr(Mo10_z1, Mo13_x1)           0.8470\n",
      "corr(Mo6_z1, Mo13_z1)            -0.8465\n",
      "corr(Mo1_z1, Mo15_x1)            -0.8460\n",
      "corr(O_Biso_cluster1, Mo14_y1)   0.8451\n",
      "corr(Mo1_y1, Mo13_x1)            0.8447\n",
      "corr(Mo0_z1, Mo15_x1)            -0.8441\n",
      "corr(Mo1_y1, Mo6_x1)             0.8438\n",
      "corr(Mo0_z1, Mo4_z1)             0.8435\n",
      "corr(Mo10_z1, Mo15_x1)           -0.8434\n",
      "corr(Mo5_y1, Mo17_z1)            -0.8411\n",
      "corr(Mo1_z1, Mo4_z1)             0.8401\n",
      "corr(Mo9_z1, Mo14_x1)            -0.8400\n",
      "corr(O_Biso_cluster1, Mo10_x1)   -0.8386\n",
      "corr(Mo13_x1, Mo19_y1)           0.8384\n",
      "corr(Mo0_z1, Mo12_z1)            -0.8384\n",
      "corr(Mo10_z1, Mo12_y1)           -0.8377\n",
      "corr(Mo9_z1, Mo10_z1)            -0.8374\n",
      "corr(Mo4_z1, Mo7_y1)             0.8373\n",
      "corr(Mo0_x1, Mo11_x1)            0.8356\n",
      "corr(Mo1_x1, Mo15_x1)            0.8343\n",
      "corr(Mo1_z1, Mo12_z1)            -0.8340\n",
      "corr(Mo0_y1, Mo19_z1)            -0.8340\n",
      "corr(Mo4_y1, Mo8_z1)             0.8340\n",
      "corr(Mo1_z1, Mo11_y1)            0.8335\n",
      "corr(Mo4_y1, Mo12_y1)            0.8330\n",
      "corr(Mo3_z1, Mo16_z1)            -0.8329\n",
      "corr(Mo4_z1, Mo7_z1)             0.8327\n",
      "corr(Mo5_z1, Mo13_x1)            -0.8322\n",
      "corr(Mo0_z1, Mo11_y1)            0.8315\n",
      "corr(Mo5_y1, Mo19_y1)            -0.8308\n",
      "corr(Mo1_x1, Mo4_z1)             -0.8307\n",
      "corr(Mo1_z1, Mo14_z1)            -0.8302\n",
      "corr(Mo0_y1, Mo11_x1)            -0.8286\n",
      "corr(Mo2_z1, Mo6_x1)             -0.8286\n",
      "corr(Mo5_x1, Mo14_x1)            0.8275\n",
      "corr(Mo0_z1, Mo14_z1)            -0.8271\n",
      "corr(Mo0_x1, Mo19_z1)            0.8267\n",
      "corr(Mo1_z1, Mo6_x1)             0.8264\n",
      "corr(Mo2_y1, Mo11_z1)            -0.8259\n",
      "corr(O_Biso_cluster1, Mo6_z1)    -0.8255\n",
      "corr(Mo10_z1, Mo11_y1)           0.8229\n",
      "corr(Mo10_y1, Mo13_y1)           0.8228\n",
      "corr(Mo1_x1, Mo11_y1)            -0.8222\n",
      "corr(Mo2_z1, Mo15_x1)            0.8214\n",
      "corr(Mo5_x1, Mo13_y1)            -0.8213\n",
      "corr(Mo4_z1, Mo19_y1)            0.8205\n",
      "corr(Mo10_y1, Mo17_z1)           -0.8195\n",
      "corr(mc1, Mo6_x1)                -0.8195\n",
      "corr(Mo1_x1, Mo6_x1)             -0.8185\n",
      "corr(Mo1_x1, Mo14_z1)            0.8181\n",
      "corr(Mo13_y1, Mo19_y1)           -0.8179\n",
      "corr(O_Biso_cluster1, Mo6_x1)    -0.8166\n",
      "corr(Mo2_y1, Mo3_x1)             -0.8166\n",
      "corr(Mo6_x1, Mo8_z1)             -0.8163\n",
      "corr(Mo2_z1, Mo4_z1)             -0.8163\n",
      "corr(Mo9_z1, Mo18_z1)            0.8159\n",
      "corr(Mo6_x1, Mo9_z1)             -0.8157\n",
      "corr(Mo5_x1, Mo19_y1)            0.8154\n",
      "corr(Mo0_y1, Mo12_z1)            -0.8153\n",
      "corr(Mo10_z1, Mo14_z1)           -0.8139\n",
      "corr(Mo18_y1, Mo19_y1)           0.8139\n",
      "corr(Mo2_y1, Mo19_z1)            0.8133\n",
      "corr(Mo0_x1, Mo11_z1)            -0.8126\n",
      "corr(Mo12_y1, Mo17_z1)           -0.8124\n",
      "corr(O_Biso_cluster1, Mo3_z1)    0.8114\n",
      "corr(Mo6_x1, Mo13_z1)            -0.8113\n",
      "corr(Mo2_x1, Mo6_x1)             0.8100\n",
      "corr(Mo2_z1, Mo11_y1)            -0.8091\n",
      "corr(Mo10_z1, Mo11_x1)           -0.8090\n",
      "corr(Mo7_x1, Mo17_z1)            -0.8088\n",
      "corr(Mo0_x1, Mo12_z1)            0.8068\n",
      "corr(Mo2_y1, Mo6_z1)             -0.8065\n",
      "corr(Mo0_z1, Mo6_x1)             0.8059\n",
      "corr(Mo12_x1, Mo13_y1)           -0.8058\n",
      "corr(Mo1_z1, Mo11_x1)            -0.8057\n",
      "corr(Mo2_z1, Mo14_z1)            0.8052\n",
      "corr(Mo5_z1, Mo13_y1)            0.8051\n",
      "corr(Mo0_y1, Mo11_z1)            0.8050\n",
      "corr(Mo0_x1, Mo6_x1)             -0.8048\n",
      "corr(Mo0_x1, Mo3_x1)             -0.8045\n",
      "corr(Mo0_y1, Mo6_x1)             0.8043\n",
      "corr(Mo0_z1, Mo11_x1)            -0.8042\n",
      "corr(Mo8_z1, Mo10_z1)            -0.8040\n",
      "corr(Mo2_y1, Mo14_y1)            0.8036\n",
      "corr(Mo5_z1, Mo14_x1)            -0.8035\n",
      "corr(Mo5_z1, Mo19_y1)            -0.8022\n",
      "corr(Mo5_x1, Mo17_z1)            0.8020\n",
      "corr(Mo9_z1, Mo13_y1)            0.8017\n",
      "corr(Mo2_x1, Mo4_z1)             0.8016\n",
      "corr(Mo8_z1, Mo17_z1)            -0.8014\n",
      "corr(Mo7_y1, Mo19_y1)            0.8009\n",
      "corr(Mo2_x1, Mo15_x1)            -0.8006\n",
      "corr(Mo2_y1, Mo10_x1)            -0.8002\n",
      "corr(Mo2_y1, Mo6_x1)             -0.7982\n",
      "corr(Mo7_y1, Mo13_x1)            0.7953\n",
      "corr(Mo0_y1, Mo3_x1)             0.7952\n",
      "corr(mc1, Mo10_z1)               -0.7944\n",
      "corr(Mo3_z1, Mo6_x1)             -0.7944\n",
      "corr(Mo1_x1, Mo11_x1)            0.7940\n",
      "corr(Mo2_y1, Mo12_z1)            0.7938\n",
      "corr(Mo13_z1, Mo19_y1)           -0.7927\n",
      "corr(Mo9_x1, Mo14_y1)            0.7927\n",
      "corr(Mo0_x1, Mo6_z1)             -0.7925\n",
      "corr(Mo3_x1, Mo10_z1)            0.7909\n",
      "corr(Mo7_z1, Mo13_x1)            0.7900\n",
      "corr(Mo0_x1, Mo14_y1)            0.7899\n",
      "corr(Mo12_x1, Mo18_z1)           -0.7889\n",
      "corr(Mo8_z1, Mo14_x1)            -0.7888\n",
      "corr(Mo5_y1, Mo18_y1)            -0.7886\n",
      "corr(Mo12_x1, Mo13_x1)           0.7882\n",
      "corr(Mo2_x1, Mo11_y1)            0.7877\n",
      "corr(Mo10_y1, Mo18_y1)           -0.7875\n",
      "corr(Mo10_z1, Mo11_z1)           0.7869\n",
      "corr(Mo0_y1, Mo6_z1)             0.7863\n",
      "corr(Mo7_z1, Mo19_y1)            0.7861\n",
      "corr(Mo0_x1, Mo10_x1)            -0.7856\n",
      "corr(mc1, Mo17_z1)               -0.7846\n",
      "corr(Mo6_x1, Mo13_y1)            -0.7839\n",
      "corr(Mo2_x1, Mo14_z1)            -0.7826\n",
      "corr(Mo9_z1, Mo13_x1)            -0.7826\n",
      "corr(O_Biso_cluster1, Mo19_y1)   -0.7825\n",
      "corr(Mo4_y1, Mo9_z1)             0.7820\n",
      "corr(Mo0_y1, Mo14_y1)            -0.7818\n",
      "corr(Mo1_z1, Mo11_z1)            0.7816\n",
      "corr(Mo1_y1, Mo15_x1)            -0.7813\n",
      "corr(Mo12_y1, Mo14_x1)           -0.7805\n",
      "corr(Mo2_z1, Mo11_x1)            0.7798\n",
      "corr(Mo9_x1, Mo10_x1)            -0.7796\n",
      "corr(Mo0_y1, Mo10_x1)            0.7794\n",
      "corr(Mo0_z1, Mo11_z1)            0.7793\n",
      "corr(Mo9_x1, Mo11_z1)            -0.7750\n",
      "corr(Mo1_z1, Mo3_x1)             0.7748\n",
      "corr(Mo17_z1, Mo19_y1)           0.7745\n",
      "corr(Mo5_z1, Mo17_z1)            -0.7738\n",
      "corr(Mo1_y1, Mo4_z1)             0.7738\n",
      "corr(Mo5_y1, Mo13_z1)            0.7737\n",
      "corr(Mo7_z1, Mo17_x1)            -0.7737\n",
      "corr(mc1, Mo14_x1)               -0.7730\n",
      "corr(O_Biso_cluster1, Mo19_z1)   0.7724\n",
      "corr(Mo3_x1, Mo9_x1)             -0.7715\n",
      "corr(Mo0_z1, Mo3_x1)             0.7706\n",
      "corr(Mo1_x1, Mo11_z1)            -0.7687\n",
      "corr(Mo3_z1, Mo15_z1)            -0.7677\n",
      "corr(Mo12_z1, Mo16_x1)           0.7668\n",
      "corr(Mo1_y1, Mo11_y1)            0.7665\n",
      "corr(Mo6_x1, Mo19_z1)            -0.7664\n",
      "corr(Mo7_y1, Mo13_y1)            -0.7660\n",
      "corr(Mo7_x1, Mo18_y1)            -0.7657\n",
      "corr(Mo7_x1, Mo13_z1)            0.7657\n",
      "corr(Mo7_y1, Mo17_x1)            -0.7656\n",
      "corr(Mo6_z1, Mo19_y1)            0.7654\n",
      "corr(Mo6_x1, Mo13_x1)            0.7647\n",
      "corr(Mo0_z1, Mo6_z1)             0.7636\n",
      "corr(Mo1_y1, Mo14_z1)            -0.7635\n",
      "corr(Mo5_y1, Mo15_z1)            -0.7632\n",
      "corr(Mo1_y1, Mo18_z1)            -0.7632\n",
      "corr(Mo6_z1, Mo9_x1)             -0.7628\n",
      "corr(Mo3_z1, Mo13_z1)            0.7627\n",
      "corr(Mo7_z1, Mo13_y1)            -0.7610\n",
      "corr(Mo6_x1, Mo12_x1)            0.7607\n",
      "corr(Mo4_y1, Mo12_x1)            -0.7597\n",
      "corr(Mo10_z1, Mo14_y1)           -0.7590\n",
      "corr(Mo1_x1, Mo3_x1)             -0.7590\n",
      "corr(Mo1_z1, Mo14_y1)            -0.7585\n",
      "corr(Mo2_x1, Mo11_x1)            -0.7577\n",
      "corr(Mo7_y1, Mo14_x1)            0.7570\n",
      "corr(Mo8_z1, Mo13_y1)            0.7569\n",
      "corr(Mo10_y1, Mo13_z1)           0.7559\n",
      "corr(Mo4_z1, Mo12_x1)            0.7554\n",
      "corr(Mo6_x1, Mo12_y1)            -0.7553\n",
      "corr(Mo1_z1, Mo6_z1)             0.7551\n",
      "corr(Mo0_z1, Mo14_y1)            -0.7547\n",
      "corr(Mo2_z1, Mo11_z1)            -0.7541\n",
      "corr(Mo1_z1, Mo10_x1)            0.7538\n",
      "corr(Mo15_z1, Mo19_y1)           0.7517\n",
      "corr(Mo0_z1, Mo10_x1)            0.7515\n",
      "corr(Mo9_x1, Mo11_x1)            0.7505\n",
      "corr(Mo7_z1, Mo14_x1)            0.7481\n",
      "corr(Mo1_x1, Mo6_z1)             -0.7478\n",
      "corr(Mo3_z1, Mo18_y1)            -0.7476\n",
      "corr(Mo10_y1, Mo15_z1)           -0.7466\n",
      "corr(mc1, Mo13_y1)               0.7459\n",
      "corr(Mo6_x1, Mo14_x1)            0.7457\n",
      "corr(O_Biso_cluster1, Mo12_z1)   0.7448\n",
      "corr(Mo5_x1, Mo18_y1)            0.7446\n",
      "corr(Mo2_z1, Mo3_x1)             -0.7445\n",
      "corr(Mo1_x1, Mo14_y1)            0.7443\n",
      "corr(Mo1_x1, Mo10_x1)            -0.7428\n",
      "corr(Mo6_x1, Mo18_y1)            0.7420\n",
      "corr(Mo12_x1, Mo15_x1)           -0.7409\n",
      "corr(Mo2_x1, Mo18_z1)            -0.7408\n",
      "corr(Mo12_y1, Mo13_y1)           0.7398\n",
      "corr(Mo2_y1, Mo19_y1)            -0.7368\n",
      "corr(Mo10_x1, Mo10_z1)           0.7366\n",
      "corr(Mo16_x1, Mo19_z1)           0.7349\n",
      "corr(mc1, Mo16_z1)               -0.7343\n",
      "corr(Mo1_y1, Mo11_x1)            -0.7341\n",
      "corr(Mo8_z1, Mo13_x1)            -0.7338\n",
      "corr(Mo7_x1, Mo15_z1)            -0.7329\n",
      "corr(Mo5_x1, Mo13_z1)            -0.7310\n",
      "corr(Mo2_x1, Mo11_z1)            0.7302\n",
      "corr(Mo2_z1, Mo14_y1)            0.7296\n",
      "corr(Mo2_z1, Mo6_z1)             -0.7293\n",
      "corr(Mo15_z1, Mo19_z1)           -0.7289\n",
      "corr(Mo2_z1, Mo10_x1)            -0.7283\n",
      "corr(Mo1_y1, Mo4_y1)             -0.7276\n",
      "corr(Mo11_y1, Mo12_x1)           0.7272\n",
      "corr(O_Biso_cluster1, Mo5_y1)    0.7272\n",
      "corr(Mo12_y1, Mo16_z1)           -0.7251\n",
      "corr(Mo0_x1, Mo19_y1)            -0.7243\n",
      "corr(Mo9_z1, Mo15_x1)            0.7233\n",
      "corr(Mo4_z1, Mo9_z1)             -0.7216\n",
      "corr(mc1, Mo13_x1)               -0.7211\n",
      "corr(Mo13_z1, Mo19_z1)           0.7210\n",
      "corr(Mo0_y1, Mo19_y1)            0.7206\n",
      "corr(Mo8_z1, Mo16_z1)            -0.7200\n",
      "corr(O_Biso_cluster1, Mo10_y1)   0.7195\n",
      "corr(Mo7_z1, Mo16_x1)            0.7193\n",
      "corr(Mo12_x1, Mo14_z1)           -0.7192\n",
      "corr(Mo2_x1, Mo3_x1)             0.7191\n",
      "corr(Mo2_z1, Mo18_z1)            0.7190\n",
      "corr(Mo9_x1, Mo14_z1)            0.7184\n",
      "corr(Mo12_y1, Mo13_x1)           -0.7174\n",
      "corr(Mo12_z1, Mo15_z1)           -0.7171\n",
      "corr(Mo9_x1, Mo11_y1)            -0.7170\n",
      "corr(Mo2_x1, Mo6_z1)             0.7170\n",
      "corr(Mo5_x1, Mo15_z1)            0.7170\n",
      "corr(Mo6_x1, Mo15_z1)            0.7149\n",
      "corr(O_Biso_cluster1, Mo7_x1)    0.7139\n",
      "corr(Mo5_z1, Mo18_y1)            -0.7138\n",
      "corr(Mo9_x1, Mo16_z1)            0.7133\n",
      "corr(Mo5_z1, Mo17_x1)            0.7122\n",
      "corr(Mo7_y1, Mo17_z1)            0.7112\n",
      "corr(Mo12_z1, Mo17_x1)           -0.7111\n",
      "corr(Mo7_y1, Mo16_x1)            0.7108\n",
      "corr(Mo9_z1, Mo11_y1)            -0.7107\n",
      "corr(Mo1_y1, Mo11_z1)            0.7071\n",
      "corr(Mo1_z1, Mo19_y1)            0.7062\n",
      "corr(Mo5_z1, Mo13_z1)            0.7052\n",
      "corr(Mo9_z1, Mo14_z1)            0.7051\n",
      "corr(Mo7_z1, Mo17_z1)            0.7049\n",
      "corr(Mo2_x1, Mo4_y1)             -0.7048\n",
      "corr(Mo2_x1, Mo10_x1)            0.7039\n",
      "corr(Mo18_y1, Mo19_z1)           -0.7037\n",
      "corr(Mo2_x1, Mo14_y1)            -0.7035\n",
      "corr(Mo9_x1, Mo15_x1)            0.7028\n",
      "corr(Mo1_y1, Mo3_x1)             0.7022\n",
      "corr(Mo1_x1, Mo18_z1)            0.7020\n",
      "corr(Mo4_z1, Mo9_x1)             -0.7018\n",
      "corr(Mo5_y1, Mo10_z1)            -0.7013\n",
      "corr(Mo3_z1, Mo10_z1)            -0.7004\n",
      "corr(Mo6_x1, Mo19_y1)            0.6981\n",
      "corr(Mo11_x1, Mo12_x1)           -0.6957\n",
      "corr(Mo1_x1, Mo19_y1)            -0.6951\n",
      "corr(Mo0_z1, Mo19_y1)            0.6938\n",
      "corr(Mo3_z1, Mo16_x1)            0.6907\n",
      "corr(Mo6_x1, Mo12_z1)            -0.6892\n",
      "corr(Mo2_z1, Mo19_y1)            -0.6888\n",
      "corr(Mo0_z1, Mo18_z1)            -0.6872\n",
      "corr(Mo1_z1, Mo18_z1)            -0.6868\n",
      "corr(Mo12_z1, Mo13_z1)           0.6864\n",
      "corr(Mo12_z1, Mo18_y1)           -0.6860\n",
      "corr(Mo5_z1, Mo15_z1)            -0.6850\n",
      "corr(Mo1_y1, Mo14_y1)            -0.6819\n",
      "corr(Mo10_z1, Mo12_z1)           -0.6818\n",
      "corr(Mo17_x1, Mo19_z1)           -0.6818\n",
      "corr(Mo2_y1, Mo5_y1)             0.6802\n",
      "corr(Mo5_x1, Mo17_x1)            -0.6802\n",
      "corr(O_Biso_cluster1, Mo5_x1)    -0.6799\n",
      "corr(Mo2_z1, Mo4_y1)             0.6798\n",
      "corr(Mo1_y1, Mo6_z1)             0.6783\n",
      "corr(Mo6_z1, Mo12_x1)            0.6771\n",
      "corr(Mo1_y1, Mo10_x1)            0.6770\n",
      "corr(Mo9_z1, Mo11_x1)            0.6766\n",
      "corr(Mo10_z1, Mo19_z1)           -0.6728\n",
      "corr(Mo4_z1, Mo12_y1)            -0.6709\n",
      "corr(Mo2_y1, Mo10_y1)            0.6705\n",
      "corr(Mo3_z1, Mo17_z1)            -0.6693\n",
      "corr(Mo11_z1, Mo12_x1)           0.6641\n",
      "corr(Mo0_x1, Mo5_y1)             0.6633\n",
      "corr(Mo1_x1, Mo4_y1)             0.6632\n",
      "corr(Mo4_z1, Mo8_z1)             -0.6609\n",
      "corr(Mo9_z1, Mo16_z1)            -0.6608\n",
      "corr(Mo8_z1, Mo15_x1)            0.6604\n",
      "corr(Mo2_y1, Mo7_x1)             0.6592\n",
      "corr(Mo2_x1, Mo19_y1)            0.6580\n",
      "corr(Mo0_y1, Mo18_z1)            -0.6575\n",
      "corr(Mo3_z1, Mo14_x1)            -0.6567\n",
      "corr(Mo5_x1, Mo10_z1)            0.6563\n",
      "corr(Mo12_y1, Mo15_x1)           0.6553\n",
      "corr(Mo6_x1, Mo18_z1)            -0.6553\n",
      "corr(Mo7_y1, Mo18_y1)            0.6540\n",
      "corr(Mo0_y1, Mo5_y1)             -0.6530\n",
      "corr(Mo0_x1, Mo10_y1)            0.6526\n",
      "corr(Mo7_y1, Mo13_z1)            -0.6511\n",
      "corr(Mo7_x1, Mo17_x1)            0.6508\n",
      "corr(Mo6_x1, Mo14_z1)            -0.6505\n",
      "corr(Mo6_x1, Mo6_y1)             -0.6497\n",
      "corr(O_Biso_cluster1, Mo5_z1)    0.6497\n",
      "corr(Mo0_z1, Mo4_y1)             -0.6497\n",
      "corr(Mo5_z1, Mo16_x1)            -0.6496\n",
      "corr(Mo6_x1, Mo17_z1)            0.6482\n",
      "corr(Mo6_x1, Mo15_x1)            -0.6479\n",
      "corr(Mo3_x1, Mo12_x1)            0.6476\n",
      "corr(Mo1_z1, Mo4_y1)             -0.6473\n",
      "corr(Mo0_x1, Mo18_z1)            0.6471\n",
      "corr(Mo7_x1, Mo10_z1)            -0.6465\n",
      "corr(Mo1_y1, Mo19_y1)            0.6461\n",
      "corr(Mo9_z1, Mo11_z1)            -0.6456\n",
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      "corr(Mo0_y1, Mo10_y1)            -0.6442\n",
      "corr(Mo7_z1, Mo18_y1)            0.6440\n",
      "corr(Mo7_z1, Mo13_z1)            -0.6431\n",
      "corr(Mo0_x1, Mo7_x1)             0.6428\n",
      "corr(mc1, Mo15_x1)               0.6415\n",
      "corr(Mo8_z1, Mo14_z1)            0.6399\n",
      "corr(Mo9_x1, Mo19_y1)            -0.6398\n",
      "corr(Mo11_y1, Mo12_y1)           -0.6395\n",
      "corr(Mo10_y1, Mo10_z1)           -0.6394\n",
      "corr(Mo10_x1, Mo12_x1)           0.6389\n",
      "corr(Mo12_x1, Mo16_z1)           0.6384\n",
      "corr(mc1, Mo4_z1)                -0.6382\n",
      "corr(Mo6_x1, Mo11_y1)            0.6374\n",
      "corr(Mo3_z1, Mo17_x1)            -0.6341\n",
      "corr(Mo12_x1, Mo14_y1)           -0.6327\n",
      "corr(Mo6_z1, Mo9_z1)             -0.6318\n",
      "corr(Mo0_y1, Mo7_x1)             -0.6318\n",
      "corr(Mo12_y1, Mo14_z1)           0.6313\n",
      "corr(Mo3_z1, Mo13_y1)            0.6311\n",
      "corr(Mo3_x1, Mo9_z1)             -0.6299\n",
      "corr(Mo2_y1, Mo18_z1)            0.6295\n",
      "corr(Mo2_y1, Mo5_x1)             -0.6289\n",
      "corr(Mo5_y1, Mo17_x1)            0.6280\n",
      "corr(Mo17_z1, Mo19_z1)           -0.6254\n",
      "corr(Mo5_z1, Mo10_z1)            -0.6252\n",
      "corr(mc1, Mo11_y1)               -0.6251\n",
      "corr(Mo9_z1, Mo10_x1)            -0.6243\n",
      "corr(Mo1_z1, Mo5_y1)             -0.6223\n",
      "corr(Mo0_z1, Mo5_y1)             -0.6214\n",
      "corr(mc1, Mo14_z1)               0.6204\n",
      "corr(Mo7_y1, Mo15_z1)            0.6177\n",
      "corr(Mo0_y1, Mo4_y1)             -0.6175\n",
      "corr(Mo9_z1, Mo14_y1)            0.6167\n",
      "corr(Mo12_z1, Mo17_z1)           -0.6148\n",
      "corr(Mo10_y1, Mo17_x1)           0.6144\n",
      "corr(Mo5_x1, Mo16_x1)            0.6143\n",
      "corr(Mo1_z1, Mo10_y1)            -0.6130\n",
      "corr(Mo0_x1, Mo5_x1)             -0.6111\n",
      "corr(Mo0_z1, Mo10_y1)            -0.6106\n",
      "corr(Mo7_z1, Mo15_z1)            0.6097\n",
      "corr(Mo3_x1, Mo6_x1)             0.6093\n",
      "corr(Mo8_z1, Mo11_x1)            0.6079\n",
      "corr(Mo0_x1, Mo4_y1)             0.6072\n",
      "corr(Mo14_x1, Mo19_z1)           -0.6064\n",
      "corr(Mo1_x1, Mo5_y1)             0.6062\n",
      "corr(Mo1_z1, Mo7_x1)             -0.6045\n",
      "corr(Mo11_x1, Mo12_y1)           0.6038\n",
      "corr(Mo9_z1, Mo19_y1)            -0.6020\n",
      "corr(Mo6_x1, Mo11_x1)            -0.6018\n",
      "corr(Mo3_z1, Mo13_x1)            -0.6007\n",
      "corr(Mo0_y1, Mo5_x1)             0.6001\n",
      "corr(Mo0_z1, Mo7_x1)             -0.5992\n",
      "corr(Mo1_x1, Mo10_y1)            0.5989\n",
      "corr(Mo4_y1, Mo6_x1)             -0.5976\n",
      "corr(Mo4_y1, Mo9_x1)             -0.5962\n",
      "corr(Mo2_y1, Mo5_z1)             0.5955\n",
      "corr(O_Biso_cluster1, Mo7_y1)    -0.5906\n",
      "corr(Mo6_x1, Mo11_z1)            0.5900\n",
      "corr(Mo2_y1, Mo4_y1)             0.5884\n",
      "corr(Mo6_x1, Mo14_y1)            -0.5883\n",
      "corr(Mo9_x1, Mo13_x1)            -0.5878\n",
      "corr(Mo7_x1, Mo16_x1)            -0.5877\n",
      "corr(Mo2_z1, Mo5_y1)             0.5869\n",
      "corr(mc1, Mo11_x1)               0.5866\n",
      "corr(Mo1_x1, Mo7_x1)             0.5859\n",
      "corr(Mo6_z1, Mo12_y1)            -0.5844\n",
      "corr(Mo13_y1, Mo19_z1)           0.5834\n",
      "corr(Mo12_x1, Mo19_y1)           0.5833\n",
      "corr(Mo1_y1, Mo16_z1)            0.5833\n",
      "corr(Mo2_z1, Mo10_y1)            0.5811\n",
      "corr(O_Biso_cluster1, Mo7_z1)    -0.5808\n",
      "corr(Mo0_x1, Mo5_z1)             0.5774\n",
      "corr(O_Biso_cluster1, Mo18_z1)   0.5763\n",
      "corr(Mo12_z1, Mo14_x1)           -0.5760\n",
      "corr(Mo8_z1, Mo11_z1)            -0.5753\n",
      "corr(Mo11_z1, Mo12_y1)           -0.5693\n",
      "corr(Mo1_z1, Mo5_x1)             0.5683\n",
      "corr(Mo2_z1, Mo7_x1)             0.5681\n",
      "corr(Mo6_x1, Mo10_x1)            0.5672\n",
      "corr(Mo0_z1, Mo5_x1)             0.5668\n",
      "corr(Mo0_y1, Mo5_z1)             -0.5658\n",
      "corr(Mo2_x1, Mo16_z1)            0.5655\n",
      "corr(mc1, Mo16_x1)               0.5650\n",
      "corr(Mo3_x1, Mo8_z1)             -0.5635\n",
      "corr(Mo4_z1, Mo6_x1)             0.5604\n",
      "corr(Mo6_z1, Mo8_z1)             -0.5601\n",
      "corr(Mo2_x1, Mo5_y1)             -0.5598\n",
      "corr(Mo5_y1, Mo16_x1)            -0.5582\n",
      "corr(Mo9_x1, Mo14_x1)            -0.5567\n",
      "corr(Mo9_x1, Mo18_z1)            -0.5559\n",
      "corr(Mo12_y1, Mo16_x1)           0.5550\n",
      "corr(Mo3_x1, Mo12_y1)            -0.5541\n",
      "corr(mc1, Mo11_z1)               -0.5536\n",
      "corr(Mo2_x1, Mo10_y1)            -0.5523\n",
      "corr(Mo1_x1, Mo5_x1)             -0.5510\n",
      "corr(Mo13_x1, Mo19_z1)           -0.5507\n",
      "corr(Mo8_z1, Mo10_x1)            -0.5495\n",
      "corr(Mo9_x1, Mo13_y1)            0.5492\n",
      "corr(Mo10_y1, Mo16_x1)           -0.5469\n",
      "corr(Mo8_z1, Mo16_x1)            0.5459\n",
      "corr(Mo8_z1, Mo14_y1)            0.5450\n",
      "corr(mc1, Mo3_x1)                -0.5446\n",
      "corr(Mo7_z1, Mo16_z1)            -0.5428\n",
      "corr(Mo10_x1, Mo12_y1)           -0.5424\n",
      "corr(Mo7_y1, Mo10_z1)            0.5417\n",
      "corr(Mo7_z1, Mo10_z1)            0.5407\n",
      "corr(Mo8_z1, Mo19_y1)            -0.5406\n",
      "corr(mc1, Mo6_z1)                -0.5368\n",
      "corr(Mo2_x1, Mo7_x1)             -0.5364\n",
      "corr(Mo12_y1, Mo14_y1)           0.5356\n",
      "corr(Mo9_x1, Mo17_z1)            -0.5353\n",
      "corr(Mo12_z1, Mo13_y1)           0.5346\n",
      "corr(Mo2_z1, Mo16_z1)            -0.5346\n",
      "corr(Mo1_z1, Mo5_z1)             -0.5341\n",
      "corr(Mo0_z1, Mo5_z1)             -0.5317\n",
      "corr(Mo7_y1, Mo16_z1)            -0.5312\n",
      "corr(Mo2_z1, Mo5_x1)             -0.5310\n",
      "corr(O_Biso_cluster1, Mo4_y1)    0.5308\n",
      "corr(Mo1_y1, Mo5_y1)             -0.5305\n",
      "corr(Mo2_y1, Mo7_y1)             -0.5293\n",
      "corr(Mo10_z1, Mo19_y1)           0.5283\n",
      "corr(mc1, Mo10_x1)               -0.5260\n",
      "corr(mc1, Mo14_y1)               0.5235\n",
      "corr(Mo15_z1, Mo18_z1)           -0.5224\n",
      "corr(Mo1_y1, Mo10_y1)            -0.5215\n",
      "corr(Mo6_x1, Mo10_z1)            0.5206\n",
      "corr(Mo2_y1, Mo7_z1)             -0.5195\n",
      "corr(mc1, Mo19_y1)               -0.5173\n",
      "corr(Mo1_x1, Mo16_z1)            -0.5161\n",
      "corr(Mo1_x1, Mo5_z1)             0.5155\n",
      "corr(Mo1_y1, Mo7_x1)             -0.5143\n",
      "corr(Mo0_x1, Mo7_y1)             -0.5105\n",
      "corr(Mo13_z1, Mo18_z1)           0.5098\n",
      "corr(Mo3_z1, Mo15_x1)            0.5039\n",
      "corr(Mo12_z1, Mo13_x1)           -0.5032\n",
      "corr(Mo12_y1, Mo19_y1)           -0.5025\n",
      "corr(Mo2_x1, Mo5_x1)             0.5021\n",
      "corr(Mo0_x1, Mo7_z1)             -0.5007\n",
      "corr(Mo0_z1, Mo16_z1)            0.4998\n",
      "corr(Mo3_z1, Mo4_z1)             -0.4994\n",
      "corr(mc1, Mo17_x1)               -0.4985\n",
      "corr(Mo0_y1, Mo7_y1)             0.4982\n",
      "corr(Mo2_z1, Mo5_z1)             0.4953\n",
      "corr(Mo1_z1, Mo16_z1)            0.4946\n",
      "corr(Mo18_y1, Mo18_z1)           -0.4942\n",
      "corr(Mo0_y1, Mo7_z1)             0.4882\n",
      "corr(Mo5_y1, Mo12_x1)            -0.4861\n",
      "corr(Mo3_z1, Mo11_y1)            -0.4860\n",
      "corr(Mo4_y1, Mo15_z1)            -0.4833\n",
      "corr(Mo12_y1, Mo17_x1)           -0.4832\n",
      "corr(Mo3_z1, Mo14_z1)            0.4814\n",
      "corr(Mo8_z1, Mo17_x1)            -0.4784\n",
      "corr(Mo10_y1, Mo12_x1)           -0.4778\n",
      "corr(Mo9_z1, Mo16_x1)            0.4736\n",
      "corr(Mo10_z1, Mo18_z1)           -0.4732\n",
      "corr(Mo1_y1, Mo5_x1)             0.4727\n",
      "corr(Mo14_y1, Mo17_x1)           0.4724\n",
      "corr(Mo1_z1, Mo7_y1)             0.4672\n",
      "corr(Mo4_y1, Mo10_z1)            -0.4671\n",
      "corr(Mo4_y1, Mo13_z1)            0.4658\n",
      "corr(Mo6_x1, Mo7_x1)             -0.4658\n",
      "corr(Mo0_y1, Mo16_z1)            0.4653\n",
      "corr(Mo2_x1, Mo5_z1)             -0.4650\n",
      "corr(Mo0_z1, Mo7_y1)             0.4620\n",
      "corr(Mo5_z1, Mo16_z1)            0.4619\n",
      "corr(Mo9_z1, Mo10_y1)            0.4580\n",
      "corr(Mo1_z1, Mo7_z1)             0.4570\n",
      "corr(Mo5_y1, Mo9_z1)             0.4569\n",
      "corr(Mo3_x1, Mo17_x1)            -0.4547\n",
      "corr(Mo7_x1, Mo12_x1)            -0.4525\n",
      "corr(Mo0_z1, Mo7_z1)             0.4520\n",
      "corr(Mo0_x1, Mo16_z1)            -0.4518\n",
      "corr(Mo12_x1, Mo16_x1)           -0.4514\n",
      "corr(Mo15_x1, Mo19_z1)           0.4482\n",
      "corr(Mo4_z1, Mo19_z1)            -0.4480\n",
      "corr(Mo1_x1, Mo7_y1)             -0.4468\n",
      "corr(Mo9_x1, Mo18_y1)            -0.4467\n",
      "corr(Mo10_x1, Mo17_x1)           -0.4463\n",
      "corr(Mo4_y1, Mo18_y1)            -0.4460\n",
      "corr(Mo6_x1, Mo16_z1)            0.4434\n",
      "corr(Mo3_z1, Mo11_x1)            0.4430\n",
      "corr(Mo11_z1, Mo17_x1)           -0.4386\n",
      "corr(Mo1_y1, Mo5_z1)             -0.4370\n",
      "corr(Mo1_x1, Mo7_z1)             -0.4362\n",
      "corr(Mo6_x1, Mo10_y1)            -0.4353\n",
      "corr(Mo4_z1, Mo12_z1)            -0.4346\n",
      "corr(Mo7_x1, Mo9_z1)             0.4341\n",
      "corr(Mo2_y1, Mo16_z1)            -0.4328\n",
      "corr(Mo11_y1, Mo19_z1)           -0.4286\n",
      "corr(Mo2_z1, Mo7_y1)             -0.4270\n",
      "corr(Mo5_x1, Mo12_x1)            0.4248\n",
      "corr(Mo14_z1, Mo19_z1)           0.4237\n",
      "corr(Mo5_x1, Mo16_z1)            -0.4209\n",
      "corr(Mo9_x1, Mo13_z1)            0.4186\n",
      "corr(Mo12_z1, Mo15_x1)           0.4163\n",
      "corr(Mo2_z1, Mo7_z1)             -0.4162\n",
      "corr(Mo5_y1, Mo6_x1)             -0.4149\n",
      "corr(Mo9_x1, Mo15_z1)            -0.4143\n",
      "corr(Mo6_x1, Mo6_z1)             0.4085\n",
      "corr(Mo3_z1, Mo11_z1)            -0.4069\n",
      "corr(Mo9_z1, Mo17_x1)            -0.4006\n",
      "corr(Mo3_z1, Mo19_y1)            -0.4002\n",
      "corr(Mo11_x1, Mo17_x1)           0.4002\n",
      "corr(Mo14_y1, Mo16_x1)           -0.3994\n",
      "corr(Mo3_x1, Mo3_z1)             -0.3982\n",
      "corr(Mo11_y1, Mo12_z1)           -0.3982\n",
      "corr(Mo17_z1, Mo18_z1)           -0.3975\n",
      "corr(Mo5_x1, Mo9_z1)             -0.3953\n",
      "corr(Mo6_z1, Mo17_x1)            -0.3950\n",
      "corr(Mo2_x1, Mo7_y1)             0.3929\n",
      "corr(Mo3_z1, Mo6_z1)             -0.3896\n",
      "corr(Mo12_z1, Mo14_z1)           0.3892\n",
      "corr(Mo1_y1, Mo16_x1)            -0.3878\n",
      "corr(Mo7_x1, Mo16_z1)            0.3876\n",
      "corr(Mo9_x1, Mo10_z1)            -0.3847\n",
      "corr(Mo11_x1, Mo19_z1)           0.3843\n",
      "corr(Mo5_z1, Mo12_x1)            -0.3841\n",
      "corr(Mo2_x1, Mo7_z1)             0.3823\n",
      "corr(Mo3_x1, Mo16_x1)            0.3821\n",
      "corr(Mo3_z1, Mo10_x1)            -0.3805\n",
      "corr(Mo4_y1, Mo7_z1)             0.3778\n",
      "corr(Mo5_y1, Mo12_y1)            0.3764\n",
      "corr(Mo14_x1, Mo18_z1)           -0.3762\n",
      "corr(Mo5_y1, Mo8_z1)             0.3758\n",
      "corr(Mo3_z1, Mo14_y1)            0.3751\n",
      "corr(Mo12_x1, Mo17_x1)           0.3741\n",
      "corr(Mo8_z1, Mo10_y1)            0.3741\n",
      "corr(Mo5_x1, Mo6_x1)             0.3738\n",
      "corr(Mo10_x1, Mo16_x1)           0.3715\n",
      "corr(Mo10_y1, Mo12_y1)           0.3682\n",
      "corr(Mo4_y1, Mo7_y1)             0.3680\n",
      "corr(Mo1_y1, Mo7_y1)             0.3679\n",
      "corr(Mo14_z1, Mo17_x1)           0.3664\n",
      "corr(Mo2_x1, Mo16_x1)            -0.3661\n",
      "corr(O_Biso_cluster1, Mo16_z1)   -0.3653\n",
      "corr(Mo11_z1, Mo16_x1)           0.3628\n",
      "corr(Mo4_y1, Mo17_z1)            -0.3582\n",
      "corr(Mo11_y1, Mo17_x1)           -0.3577\n",
      "corr(Mo5_y1, Mo16_z1)            0.3573\n",
      "corr(Mo1_y1, Mo7_z1)             0.3573\n",
      "corr(Mo5_z1, Mo6_x1)             -0.3568\n",
      "corr(Mo5_z1, Mo9_z1)             0.3557\n",
      "corr(Mo11_x1, Mo12_z1)           0.3554\n",
      "corr(Mo7_x1, Mo8_z1)             0.3545\n",
      "corr(O_Biso_cluster1, Mo9_x1)    0.3540\n",
      "corr(Mo17_x1, Mo19_y1)           -0.3528\n",
      "corr(mc1, Mo5_y1)                0.3524\n",
      "corr(Mo6_y1, Mo19_y1)            -0.3512\n",
      "corr(mc1, Mo10_y1)               0.3485\n",
      "corr(Mo13_y1, Mo18_z1)           0.3480\n",
      "corr(Mo11_z1, Mo19_z1)           -0.3472\n",
      "corr(Mo7_x1, Mo12_y1)            0.3431\n",
      "corr(Mo15_x1, Mo17_x1)           0.3418\n",
      "corr(Mo3_x1, Mo19_z1)            -0.3404\n",
      "corr(Mo6_x1, Mo7_y1)             0.3394\n",
      "corr(Mo19_y1, Mo19_z1)           -0.3386\n",
      "corr(Mo6_z1, Mo12_z1)            -0.3384\n",
      "corr(Mo10_y1, Mo16_z1)           0.3384\n",
      "corr(Mo6_z1, Mo19_z1)            -0.3369\n",
      "corr(Mo9_x1, Mo12_z1)            -0.3349\n",
      "corr(mc1, Mo7_x1)                0.3327\n",
      "corr(Mo2_z1, Mo16_x1)            0.3306\n",
      "corr(Mo7_z1, Mo18_z1)            0.3283\n",
      "corr(Mo6_x1, Mo7_z1)             0.3264\n",
      "corr(Mo15_z1, Mo16_z1)           0.3250\n",
      "corr(Mo11_x1, Mo16_x1)           -0.3223\n",
      "corr(Mo4_y1, Mo14_x1)            -0.3221\n",
      "corr(Mo10_x1, Mo19_z1)           -0.3180\n",
      "corr(Mo4_z1, Mo17_x1)            -0.3179\n",
      "corr(Mo9_x1, Mo19_z1)            -0.3166\n",
      "corr(Mo7_y1, Mo18_z1)            0.3164\n",
      "corr(Mo11_z1, Mo12_z1)           -0.3156\n",
      "corr(Mo1_y1, Mo17_x1)            0.3145\n",
      "corr(Mo14_y1, Mo19_z1)           0.3140\n",
      "corr(Mo5_x1, Mo8_z1)             -0.3123\n",
      "corr(Mo5_x1, Mo12_y1)            -0.3119\n",
      "corr(Mo1_x1, Mo16_x1)            0.3103\n",
      "corr(Mo13_x1, Mo18_z1)           -0.3100\n",
      "corr(Mo6_z1, Mo16_x1)            0.3083\n",
      "corr(Mo10_z1, Mo16_z1)           0.3059\n",
      "corr(Mo7_y1, Mo12_x1)            0.3043\n",
      "corr(Mo4_y1, Mo13_y1)            0.3033\n",
      "corr(Mo3_x1, Mo12_z1)            -0.2993\n",
      "corr(Mo16_x1, Mo19_y1)           0.2988\n",
      "corr(Mo4_y1, Mo5_z1)             -0.2949\n",
      "corr(Mo2_y1, Mo9_x1)             0.2944\n",
      "corr(Mo7_z1, Mo12_x1)            0.2937\n",
      "corr(Mo0_z1, Mo16_x1)            -0.2936\n",
      "corr(Mo16_z1, Mo18_y1)           0.2912\n",
      "corr(Mo12_z1, Mo19_y1)           -0.2912\n",
      "corr(Mo10_x1, Mo12_z1)           -0.2908\n",
      "corr(Mo14_z1, Mo16_x1)           -0.2894\n",
      "corr(Mo2_x1, Mo17_x1)            0.2890\n",
      "corr(mc1, Mo5_x1)                -0.2885\n",
      "corr(Mo1_z1, Mo16_x1)            -0.2872\n",
      "corr(Mo13_z1, Mo16_z1)           -0.2836\n",
      "corr(Mo7_y1, Mo9_z1)             -0.2821\n",
      "corr(Mo11_y1, Mo16_x1)           0.2793\n",
      "corr(Mo12_z1, Mo14_y1)           0.2780\n",
      "corr(Mo5_z1, Mo8_z1)             0.2723\n",
      "corr(Mo0_x1, Mo9_x1)             0.2717\n",
      "corr(Mo7_z1, Mo9_z1)             -0.2700\n",
      "corr(Mo5_z1, Mo12_y1)            0.2698\n",
      "corr(Mo6_x1, Mo16_x1)            -0.2691\n",
      "corr(Mo6_y1, Mo10_z1)            0.2648\n",
      "corr(Mo15_x1, Mo16_x1)           -0.2630\n",
      "corr(Mo4_y1, Mo13_x1)            -0.2625\n",
      "corr(Mo0_y1, Mo9_x1)             -0.2592\n",
      "corr(Mo3_z1, Mo9_x1)             -0.2571\n",
      "corr(Mo4_y1, Mo5_x1)             0.2559\n",
      "corr(Mo0_y1, Mo16_x1)            -0.2546\n",
      "corr(Mo2_z1, Mo17_x1)            -0.2540\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(FitResults(recipe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot partial contributions in PDF simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the fit for plotting ## Here, we would also like to get mc1*G1*f1, mc2*G2*f2, and npwave independently\n",
    "\n",
    "r = recipe.pdf.profile.x\n",
    "g = recipe.pdf.profile.y\n",
    "gcalc = recipe.pdf.evaluate()\n",
    "diffzero = -0.8 * max(g) * np.ones_like(g)\n",
    "diff = g - gcalc + diffzero\n",
    "g1 = recipe.pdf.evaluateEquation('mc1 * G1')\n",
    "gwave = recipe.pdf.evaluateEquation('npwave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jie+++w53d3deeuklpk+fXmx+RGxsbIlNBvv06UN+fj779u0r9vXy2rlzp34+a8jPz+eHH36gT58+pKen88ADDzB06FD99WbNmuHk5MT69es5ceKE/nx2djaPPfZYibNCoaGhAMTExBR5zVi++LfffrPIQbl27RqTJk3Sk+UdjSTJC6vYcXkHWfnaJxWeLp7cVO+mCh1/f8T9fB/9PbmGXBKyElh+ajl3tbjLFkMVDuDvc3/rjzsEdyDIs/hykoW1D26Pr6svaXlp5Bny2Bm3k/4N+ttolEIIUfN5esKff2od4ufN05Z7bd9uuY+rqzZz8vHHUM7UDJvq3Lkza9euZcyYMcycOZNZs2bRrVs3QkNDycrK4sKFC+zbtw+DwUBkZGSRGY7bb7+dX3/9lb///puePXtWehxR/9RbNg8iymvp0qV6ont2djZXr15l9+7dpKam4uTkxLPPPsvbb79tcUxwcDCTJk1izpw5tG/fnptvvhlPT082bdpEQUEBEyZMKLbXyYgRI9iwYQO33HILAwYMwNvbm6CgIGbOnEmXLl0YOHAga9asoUWLFnrzyaioKIKCghg5ciTLli0rck57kxkUYRV/nzfdcPau31tPfC+vIM8gi14X8w7Pk74o17Ftl7bpjysSYLg6udIjtIe+veXillL2FkIIAVqQ8uWXcP48vPmm1pTR2LzxzTe157/80jGCE6PevXtz6tQpPvroI3r27MmxY8dYvHgx69atIyMjg/Hjx7N8+XIOHDig9ysxGjt2LP7+/vz444+Vvn5WVhbLli2jbdu2egnjijhw4AALFixgwYIF/P7778TExHDTTTcxY8YMTp8+zfvvv4+rq2uR4z7//HM++OADmjRpwrp169i0aRO33noru3fvplGjRsVe66mnnuLll1/Gx8eHxYsXM3fuXD3HBbTu9C+99BJ16tThzz//ZM+ePdxzzz1s376dgICACr+36qA4Wtb+jURRlCMRERERR44csfdQqkRVVQYsHEBidiIAb/V+i+HhpdcWL875tPMMWzIMg6olxc0aMKvcuQmi5kjNTaXPz330P+dfh/9Kq1qtyjjK5Nfjv/L6ttcBaBbQjCUjl9hknEIIYWsGg0FfltOyZUucnORzY2t5+umnmTVrFnv27KlUf4+ffvqJe++9l88++4wpU4rv6Xajqcjva2RkJNHR0dGqqpbcb6IU8jdBVNnJayf14ERBoU/9yq3VbODbgFsbmnJXfjr2k1XGJxzL7rjdenAS6B5Ii8AWFTq+S90u+uOT106SnJ1cyt5CCCFuRNOnT8fHx4eZM2dW+FhVVXnnnXcIDw9nkj1Lmt3AJEARVbbj8g79catarQjwCKj0ue5tfa/FeU9fc8zyd6LyzH9fuoZ0xUmp2D9Djf0aW+Ss7InfY7WxCSGEuD4EBwfz/PPPs3jxYqKjoyt07LJlyzhw4ABvvfWWXiFLVC8JUESVmd9wdq/XvUrn6hTcyeITdZlFuf6Y/75UtJgCaF3lu9btqm/vittllXEJIYS4vrzyyisUFBQQERFRoeNGjRqlN00U9iEBiqiSfEM+u+N369uVueE0pygK41qN07eXn1oujRuvI1cyr3Aq5ZS+3aNej1L2LlmXENMyr13xEqAIIYQQ1xMJUESVHEk8QnqeFkC4OLnQMbhjlc85tMlQfN18AcjMz2T5qeVVPqdwDOazJ6HeoYT5Fm1GVR7mAcqJ5BOk5KRUeWxCCCGEcAwSoIgqMV9e0y6oXbm7x5fGy9WL0c1G69s/HfsJqTZ3fSi8vEtRlEqdp4lfEwLcA/TtQwmHqjo0IYQQQjgICVBElRy4ckB/bP6pdlXd0/IeFLSb17OpZyXP4Dqgqio74qqWf2KkKArt6rTTtw9cPVDK3kIIIYSoSSRAEZWmqir7r+7XtzvU6WC1czfwa0DP+qbur7+d/M1q57aFxKxE5hycw4ytM/j1+K/kFeTZe0gOJzY1lriMOH27qvlK7eu01x8fvHqwSucSQgghhONwsfcARM11JvUM13KuAVr/k/bB7Us/oILubH6n3il8bexaUrql4O/ub9VrWMORhCM8vu5xkrKTAFh8YjGLji9izqA5+Ln52Xl0jsN8eVezgGYWpYIrwzxAOXT1EAbVUOGSxUIIIYRwPPK/uai0/Vf264/DA8KtfjPeP6w/tTxqAZBTkMPKMyuten5rSMxK5F/r/6UHJ0bRidH8e+O/JXfGjPnyrqqWowZoE9RGD0jS8tKkZ44QQghxnZAARVTaviv79MfWqN5VmKuzK8ObDte3l5xYYvVrVNW7u94lISsB0KqYRdaO1F/bcnELf539y15DcygFhgJ2xu3Ut60RoHi7etMsoJm+LXkoQgghxPVBAhRRaeYzKB2CO9jkGnc0v0N/fDTpKNGJFesGa0vRidEWszrPdXmOn27/id71e+vPfbb/MwyqwR7DcyjHko/ppYCdFWc61+1slfNa5KEkSB6KEEIIcT2QAEVUSlpuGmdTz+rb5jeK1tQ0oKlF8v1vJxwnWf6bw9/oj1sEttAqjykKL3R9waICWdT5KPsM0IGY55+0CWqDj5uPVc5r/ntnXlFOCCGEEDWXBCiiUo4lHdMf+7r60tC3oc2uZT6LsvL0SrLys2x2rfKKz4hnTewafXtK+yk4OzkD0Ni/Mbc2ulV/bfGJxdU+PkdTuP+JtZgHKKdSTpGam2q1cwshhLCvzMxMPvnkEwYNGkS9evVwc3PD19eXiIgIJk6cyIoVKzAYSl6l8Nprr+Hs7Ex0dMVWXyxZsgRFUfj1118rPOYZM2agKEqpX1FRUYBWMr9x48YVvsaNQKp4iUoxX2rVunbrSjfcK4/BjQczc+dMMvMzSctLI+p8FEOaDLHZ9cpj5ZmV+tKt+j71GdBggMXrd7W4Sw9gNl/cTEJWQpWrVtVUuQW57I3fq29bI//EqJFfI/zd/fXlY4evHrYoTy2EEKJm2rZtG2PGjOHSpUt4eHjQrVs3QkNDyc7O5sSJE8yfP5/58+fTrl07DhwoOoMeHx/P+++/z1133UVERESFrj1q1Cjat2/P9OnTGTlyJG5ubhUef/v27enQoUOxr4WEhJR4XFRUFAMGDODBBx9k/vz5Fb7u9UICFFEpRxKP6I8jalfsL35Febl6MbDRQJadWgbA76d/t3uAsuL0Cv3xsKbD9NkTo5vq3USIdwhxGXEYVANR56MY02JMNY/SMRy4eoDsgmwAPJw9rLocUFEU2ga1ZfPFzYDWUV4CFCGEqNn279/PzTffTE5ODi+++CL/+c9/8PX1tdjn7NmzfPjhh8ybN6/Yc7z11lukp6czffr0Cl9fURRefPFFxo0bx9y5c5kyZUqFzzFq1ChmzJhR6j5Hjx7F1dW1wue+EcgSL1EpRxOP6o9tHaAADA83VfPacnELiVmJNr9mSWKSYjiRfELfHtZ0WJF9nBQnbm5ws759I+ehbL+8XX/cqW4n3Jwr/klUadoGtdUfH044bNVzCyGEqF6qqnLfffeRnZ3NW2+9xdtvv10kOAFo3Lgxs2fP1pdLmcvMzGTBggW0a9eO9u0r96HYyJEj8fX15YsvvqjU8eXRqlUrwsPDbXb+mkwCFFFh6bnpFgnyrWu1tvk1u9TtQrBXMAAFaoFdy/f+ceYP/XHboLY09m9c7H79GvTTH2+/vN0hcmfswVb5J0Ztgtrojw8mHJTeM0IIUYOtXLmSI0eO0KhRI/7973+XuX/nzkWrQv7666+kpKQwfvz4Yo8x5n7k5uby+uuv06pVK9zd3Rk1apS+j6enJ6NGjeLgwYPs2LGj2PNUVeEclAkTJjBggLZkfMGCBRZ5K2XNxlxvJEARFWaeIO/t6k1DP9slyBs5Ozlze9Pb9e0/Tv9Ryt629fe5v/XH5mMqrGvdrni7egNao8ntl7aXuO/1Kj033WJWw5r5J0bmMyhJ2Ulczrhs9WsIIYSoHn/++ScAY8aMwcmpcrepv//+OwD9+/cvcR+DwcCoUaN49913CQ8PZ+TIkdSrV89iH+Pxf/xRPfccvXv3ZvDgwQCEh4fz4IMP6l8l5bNcryQHRVSYRYJ8rdZ6N29bG9Z0GPMOa2tNDyUc4kzKGZr4N6mWaxudTTlrMXtUODnenKuzK71Ce7E6djUAGy5sYEDDkve/Hu2O302BWgCAv7s/rWq1svo1Aj0CCfMJ40L6BUD73Qj1CbX6dYQQotqpKqSk2HsUFefvD5UsnmNMeO/YsfINoDdv3oyrq2upy7vOnz+Pu7s7MTEx1K9fv9h9unXrBsCmTZsqPZaKmDx5Ms2aNWPVqlX07t1bkuSFqIjoJFOAUh35J0YtAlvQMrAlMckxgDaL8q+O/6q264MWZBg1D2xe5o1wvwb99ADFfKnTjcL8PXcL6WazYLZtUFs9QDmccJjBjQfb5DpCCFGtUlIgMNDeo6i45GQICKjUoQkJCQAEBRVf+XLChAlFnpsyZQo33aQtIb5y5QpxcXE0b94cd3f3Uq/19ttvlxicgJYjAhRbJawsr732Gq+99lqR56dOncqsWbMqfL4bjQQoosLMZ1CqM0ABbRYlZo8WoPx++nee6PCETUscF2YeoPQP61/m/t1CuumPL6RfIC4jjhDvkssLXm+2XNqiP7bF8i6jNkFt+POstizg4FXpKC+EEDWVMY+wpP/bFyxYUOS52267zSJAAQgsI7BTFIXhw4eXuo+Liwu+vr5cu3aN/Px8XFzKf9tcUplh46yMKJ0EKKJCMvIyOJtyVt+u7gBlSJMhfLjnQ1RULqZf5MDVA3QI7lAt107NTbXo52GeBF+SEO8Q6vvU52L6RQD2xO8pNW/lenIp/RJnUs7o2z1DbVf+t12ddvrjo0lHyTfk4+Ik/7wJIURNExQURExMjD6TUph5IZQJEyYUCVhS/lkSV1zlL3PBwcFlzrAA+Pn5kZaWRmpqKrVq1Spzf6PylBkWJZP/wUWFHEs6hor2j4O3qzeN/BpV6/XretelW0g3dsRpS4dWnV1VbQHKrsu79HyKQPdAi+Ts0nSu21kPUHbH775hAhTz2ZPGfo0J8w2z2bVa1WqFs+JMgVpAVn4Wp66domWtlja7nhBCVAt/f225VE3j71/pQ9u3b8+WLVvYu3cv9957byUurV07NTW11P08PDzKdb6UlBQURcHPz6/CYxGVJ1W8RIWY9z9pVatVtSXImxvcxJRfsPrsar2ju61tu7xNf9y9Xvdyv/cudbvoj/fE77H6uBzV1otb9ce2nD0B8HDxoEVgC31b+qEIIa4LiqLlctS0ryosvR4yRGvEvGjRIgyGiv//HhystSRISkqq9BiM8vLySE9PJyAgoELLu0TVSYAiKqRwBS97uLXhrTgrWuf2K1lXLJZd2ZJ5wnf30PLnU5gHKGdSzpCQVfy09fUk35Bv8fPqVb+Xza9p3g/lUMIhm19PCCGE9Q0dOpTWrVsTGxvLO++8U+Hjg4ODCQkJITY2lqysqvUfO3ZMa6tQnSV+3dy0Zsb5+fnVdk1HJAGKqBB7JsgbBXoEWjT8q46mjXEZcRblhSuS8B3mG6Y3mQQ4cLXi1UBqmkMJh0jLSwPA1cnVIkizFfMldxKgCCFEzeTk5MR3332Hu7s7L730EtOnTyctLa3IfrGxsRw/frzYc/Tp04f8/Hz27dtXpbHs3LlTP191CQ3VqoPGxMRU2zUdkQQootwy8zI5k2pKeo6sHWm3sdzW+Db98ZrYNeQbbPtJw/bLpiaLDX0bVqjPhqIotK9jqsXuCMuPcgtyWXV2FR/v/Zh5h+dZJLNbw5aLpvyTTsGd8HL1sur5i2MeoJy8dpLMvEybX1MIIYT1de7cmbVr1xIcHMzMmTMJDg6mX79+jBs3jlGjRtGlSxeaNm3Ktm3biIyMLDLDcfvtWq7n33//XczZyy8qKgrQZnWqS+PGjWnXrh27d++mW7duTJw4kcmTJ7N8+fJqG4MjkABFlFtMcoye7+Hp4lntCfLmbm54s16lKSk7yea5HeYBSmXK5VosP7pq30/3TySf4M7ld/Lchuf4+tDXfLjnQ0YuHcnMnTPJM+RZ5RrmAUp1LO8CaOLfBC8XLRAyqAaOJh0t4wghhBCOqnfv3pw6dYqPPvqInj17cuzYMRYvXsy6devIyMhg/PjxLF++nAMHDuj9SozGjh2Lv78/P/74Y6Wvn5WVxbJly2jbtq1ewri6LF68mFGjRnH69Gm+/fZb5s6dy9691bOc3VFIxo8ot8L5J85OznYbi7+7Pz1De7LxwkZAW+ZlvuzLmlRVZfslswClAvknRuaf7h9OPIxBNdilwMC51HM8tOohruVcs3heReWHoz9wPu08Hw/4uEoleuMy4jicaJolqq4AxdnJmcigSHbF7QK0QLBz3c7Vcm0hhBDW5+3tzbRp05g2bVqFjvP09GTixInMmjWLvXv30qlTJ4vXzUsVl2Tp0qWkpaUxZcqUCl17xowZ5S4vXNI4mjVrxpIlSyp03euNzKCIcnOE/BNz5t3C18autdqn/4WdvHaSxOxEABQUi+aL5RVZO1IPSDLyMqy+pKo88gryeCbqGT04UVC4ucHNNPZrrO+z8cJG3trxVrn+8S7JunPr9McNfRvSPKB5pc9VUZIoL4QQAmD69On4+Pgwc+bMCh+rqirvvPMO4eHhTJo0yQajE2WRAEWUm6MFKAMaDMDVyRWAaznX2Hl5p02uY768q3Xt1vi7V7y+u5erF+EB4fq2PW6evz/6PTHJpqS7t/q8xcc3f8yiEYu4peEt+vO/Hv+VxScWV/o65gHKLY1uKbEbsC20CzI1bHSEXB8hhBD2ERwczPPPP8/ixYuJjo4u+wAzy5Yt48CBA7z11lt6VS1RvSRAEeWSlZ/F6ZTT+rYjBCi+br70rt9b3151dpVNrmNRXrgS+SdGFlWmqjkPJSMvgzmH5ujbdza/k2FNhwHg7uzOzD4zLbqxv7vrXc6nna/wdQrnA93a8NYqjLrizGdQLmVcuiFKOgshhCjeK6+8QkFBARERFbtnGTVqFKqqMnbsWBuNTJRFAhRRLjFJlgny5suC7Mlimde5teQVWHeZV74hn93xu/XtquS5FM5DqU6Lji8iLVcr0+jt6s20TtMsXvdw8WBW/1n67FBWfhYvb365wk0w15xdox8T7BVsETBUh7pedanjWUffllkUIYQQouaRAEWUi/nyrpaBLe2aIG9uQIMBuDu7A5CWm2bR7d0aDiccJiMvA9D6eXQM7ljpc7WubWpseSL5hM1yZgrLK8jju+jv9O27WtxFgEdAkf3qeNXh5e4v69t7r+xl0fFFFbrW0pNL9ceDGw+u9kIAiqJIHooQAtA+aFl+ajn/2/Y/Hln9CFPXT+Wdne+w5eIWq3+YJYSwLqniJcrF0fJPjLxcvegb1pc1sWsA+PPMn/QN62u185sv72pfpz2eLp6VPlfzgOa4KC7kq/nkGfI4fe00LWu1tMYwS7Xu3DriM+MBcHFy4b7W95W4722Nb2P12dX6z/OjPR/Rv0F/i0aTJTmefNxiZmh0s9FVHHnltKvTjr/Pa7XvZQZFiBtPgaGA749+z1cHvyI1N7XI698f/Z66XnWZ3HYyd7a4U89lFEI4DplBEeVi3lPCkQIUsFzm9ff5v8kpyLHauXfGmRLvq1rG2M3ZjWaBzfTt6urTseL0Cv3x0CZDqetdt9T9p3ebjq+rLwDpeem8vePtcl3HfPYksnYkzQOrr3qXucIzKFWpSCaEqFkSshKYuGoi7+9+v9jgxCg+M543d7zJ+D/GE5N0Y3fsFsIRSYAiypSdn82pa6f0bUcLUPqG9dVnNjLyMth8YbNVzpudn83+K/v17aokyBu1qmVqJnU00fYBSlJ2kkXTxBHhI8o8po5XHZ7p8oy+vfbcWovKXMVJy01j6Yml+ra9Zk9AC46M0nLTiE2NtdtYhP2dSz3Hh7s/5M7ld9L3574MWDiAR9c8yoIjC7iWfc3ewxNWdDH9Ig/++SD7ruzTn/N08WR0s9G82O1Fnun8DAMaDLDo83Q06Sj3/nEvy04us8eQhRAlkCVeokzHk49ToBYA4OHsQRP/JnYekSVPF0/6N+jPn2f+BLSmjbc0uqWMo8q278o+cg25AHi5eBEZFFnGEWVrXas1S1kKwLGkY1U+X1n+OvOX/mcX7BVMl7pdynXcHc3v4PfTv+sVud7a/hbdQrrh6+Zb7P4/Hv2RtDwtCd/H1YehTYdaYfSV4+vmSxP/JnqvmUMJh2js39hu4xH2kW/I58uDXzL30Nwi+V4JWQlsvbSVT/d/yrhW43i03aN4uXrZaaTCGhKyEpi0ahIX0y/qzw1tMpTp3aZb5NxNZCJxGXF8tv8zlpzUGuHlGnJ5ecvLHE06ynNdnqtSo1ohhHXIDIook0WCfK2WDvmP922Nb9Mfb7iwgcy8zCqf0zz/pHPdzlZZp2yeKH8s6ViFq2RV1B9n/tAf397k9nIXN3BSnHi1x6u4OWn1369kXeGdne8Uu29GXgbfHTUl4Y9vPb7EQKa6WFRMkzyUG05uQS7PRj3LFwe+KLUYRVZ+Ft8c/oYRS0ew7ZJ1C2yI6pOVn8WT6560CE6e7vw07/R9p9iCICHeIbze63W+HPglge6B+vM/HP2B5zY8Z9VlwkKIypEARZTJURPkzfWu3xsfVx9A+89q48WNVT6neYBS1fwTo5aBLVHQGhdm5mdyLvWcVc5bnKuZVzl49aC+XdFZjSb+TXik3SP69rJTy1h8vGgDx0/3f0pKTgqgzTTdH3F/JUdsPRKg3LgKDAU8E/UM68+v158L9grmha4v8N2Q7/hq4FdMajPJ4sY0PjOeR9c8yqw9s6qtup6wnpk7Z1oU6Hi287M81OahMo/rGdqTn4f9TOtapg+O1p1bx+NrHyc9N90mYxVClI8EKKJM5gGK+T/kjsTN2Y2bG96sb686U7Wmjam5qUQnmd63NfJPQKs6Zr7cyJaJ8hsvmIK0UO9QWgZWvGLYQ20eon2d9vr2mzvetDjv3+f+5vvo7/XtByIf0Hup2JN5gHI06Si5Bbl2HI2oTp8f+JwNFzbo2/3D+rNi1Arui7iPDsEd6BHag2mdp7FqzCoeafeIPjOqojL38Fwm/DmBC2kX7DV8UUG/n/6d3078pm/f3fJuHox8sNzHh/qEsmDIAvqF9dOf2xm3k4dWPURiVqJVxyqEKD8JUESpHD1B3px5Na9NFzfp/UsqY1fcLn35VaB7oFUrUlVXonzU+Sj9cb8G/VAUpcLncHV25YN+H1DLoxYAeYY8nlz/JG9sf4MPdn/AMxueQUWrktXYrzGT2062xtCrrEVgC/3GM8+QJ1V6bhBbL23ly4Nf6tv9G/TnwwEfFptf4uniyZMdn+TH23+0aDx7MOEgY1eMZfXZ1dUxZFEFVzKv8Ob2N/XtdnXa8UK3Fyr8b52niycfDfjIoojI0aSjTPhrApfTL1ttvMIxFBgKyCvII7cgl7yCPKn06KAkQCmGoiheiqKMUhRlrqIoBxVFSVUUJUNRlAOKoryiKIqPvcdYXWKSY8hX8wEtQT48INzOIypZj3o98HPzAyCnIEfvhVEZ5pWvuoZ0tWrDwYhapiDPVjMoWflZbL+8Xd/uH9a/0ueq612X2TfPxstFu8kzqAZ+ifmF+Ufmk2/Qfje8XLz4sP+HetNMe3N1drUIpvde2WvH0YjqkJWfxevbXte3G/s15u3eb5eZO9aqVit+GfYLdzS/Q38uLS+NZzc8y/+2/Y/s/GybjVlUzcydM0nP05Zi+br68m7fdyudK+jq5Mr/ev3PYonq2dSz3P/n/ZxOOW2V8YrqZ1ANpOemcyXzCmdTzhKTFMOxpGMcTz7OieQTHE8+TnRiNMeTjnMu9RwJWQnyd95BSIBSvHuBJcBDaD+jv4BNQBPgNWCXoihld667Dpiv329du7VDJsgbuTq7MrDRQH27ssu8VFVl08VN+nafsD5VHps580T5o0lHbfLpzc7LO8ku0P6R9Xb1pktI+ap3laR9nfbMv20+9X3qF3ktyDOIOYPm2K3vSUk61+2sPzZWIxPXry8OfKEnSbsoLrzf73183Mr3WZKXqxev9XyNd/u+i7ert/78wuMLGb9yPKevyQ2qo4k6H6U3lAV4usvTxf77VBFOihPPd3meJzs+qT8XnxnPhD8ncCTxSJXOLSonMzOTTz75hEGDBlGvXj3c3Nzw9fUlIiKCiRMnsmLFCgwGy2IzqqqSnpvOxfSLPPXiU/h7+rN933Yy8jL0D9UKyzPkkZabRnxGPKeuneKz7z5DURR++uUnq76fs2fPoigK/fv3t3h+/vz5KIrCjBkzihxz6tQpRo8eTVBQEE5OTiiKQlRUFAAFBQW88sorhIeH4+bmhqIoTJgwwapjthfHvdu0r1zgc+AjVVVPGJ9UFKUe8AfQEZiFFshc144kmP5RNu8v4agGNx7M4hNaIvfmS5tJzU3VZ1XK6+S1k8RlxOnbvev3tuoYzZd4peSkEJcRRz2fela9hvnsSY96PXBzdqvyOVvXbs3SkUtZdnIZO+J2kGfIo2NwR8a0GFPhn3F16Fy3M98c/gbQZlAMqsGqM2HCcZxPO8+30d/q2w9EPkDLWhXPuRrSZAiRtSN5fuPzeu7d8eTj3LXiLh5t/ygTIyfi6ixdx+0tIy+DN3eYlnZ1Cu7Enc3vtMq5FUXhkXaP4O/mz5s73kRFJTknmUmrJvHJzZ/QNaSrVa4jyrZt2zbGjBnDpUuX8PDwoFu3boSGhpKdnc2JEyeYP38+8+fPp127duzfv5+s/CxSclNIzUkl35BPwpUE5n06j0EjBhHesmKrP/re1peWkS15YfoL3HTrTYT4hdilFLnBYGDMmDHs37+f7t2707x5c5ycnAgJCQHg448/5n//+x+hoaHccccdeHh40Lu3de9Z7EUClGKoqvot8G0xz19WFOUJYCtwh6IobqqqXtfZt+afGlmjD4itdQ3pSi2PWiRlJ5FvyGdd7DpGN69Y00Dz2ZPI2pEEeQZZdYz+7v7U96mvf9obnRRt9QBlR5ypApm1EvwBPFw8uLvV3dzd6m6rndNWOgZ3xElxwqAaSMlJ4dS1Uw43yyOs49P9n+qfjNbzrsdj7R+r9Lka+jXkuyHf8dGej/j+qFYAIteQyyf7PuGvs38xo8cM2tVpZ5Vxi8r5v33/p3+I5OLkwqs9XrX6hw93t7obXzdfXtr8EvlqPhl5GTy25jE+6P8B/Rv0t+q1RFH79+/n5ptvJicnhxdffJH//Oc/+Ppalq8/c+YM733wHt8u+JaT104WKYYyZ9YcMjMyeXjaw/i4+eDl4oWniyduzm64OLmgoKCikmfQ8lEy8zJJz0snOz8bRVGYNHUS/37k38z7Zh73TLwHT1dPgjyC8HXzrVROZ2lGjx5N9+7dCQqyvN84e/Ys+/fvp0+fPmzcWLQ66dKlSwHYtGkTTZs2teqY7E0+Tqy4A/98dwdq23MgtpaRl6E3uwNoU7uNHUdTPi5OLgxqNEjfNq/uUl6bLthueZeRLRPlE7MSOZGsT/zRrV43q56/pvB187WoXLY7frcdRyNsJSYphpWnV+rbT3R4Ak8Xzyqd083ZjRe6vcD/3fx/BHuaVvOeSD7B+JXjmbF1BsnZyVW6hqicwwmH+fHYj/r25LaTaRpgmxuzoU2H8vHNH+u5dbmGXKb9PY0Vp1bY5HrXG1VVycnP4Vr2NeIy4jiXeo7TKac5kXyCE8knOHntJKdTTnM+9TyX0i9xJfMKSdlJpGSncO/4e8nOzuaNN9/gjTffwNPbk5z8HNJz00nMSuRC2gVy/XN5YsYTfLP0myLBSXZmNssXLieybSQj+4ykkV8j6njVwcfNBzdnN5wUbamUk+KEu7M7vm6+1PWuS3hAOM0DmxPsFcygoYPw9vFm4YKFAGTlZXE+7Twnrp0gMSuRAkOB1X5W/v7+tGrVqkiAcuGCVlGwpOCjrNdrMglQKs74W5AHJNlzILYWnRitV2jycfWhoV9DO4+ofMxnTPZf3W9RhawsiVmJFgnVferbJkAxL9ds7UT5XfG79MfBnsEWFYpuNOZ5KLvjJEC5Hn196Gv936lmAc0Y1nSY1c7dr0E/lo5aytgWYy2eX3xiMcOWDOOXY79Y9SZFlC7PkMeMrTP0CovVUTmwb1hfvhz4pd5nq0At4D+b/8MXB76Q6k/FUFWVjLwMLqVf4njycU5eO8nF9IskZiWSlptGVl4WuQW55BbkkpOfQ1ZeFqm5qSRnJ3M18yqX0y/zw28/cDT6KKENQhk+aTjHko4RkxTDyWsniU2NJS4jjpScFH3WNLK9aXWHl6sX9XzqcWDdAdJS0njgvgeKbVCsKAqNGzcmNzeX119/nVatWuHu7s6oUaNwc3ajjlcd2oW2Y/iI4Rw/cpyDe0w9xfIK8ojLiONE8gniM+LJKyjaOykhIYFHH32UkJAQvLy86NixI99+W2Rhjq64HBRFUejXTyt/vWDBAhRF0fNXJkyYgKIonDlzRt/X+HX27NkK/Zk5KlniVXFT//n+l6qq5Wo3qyhKSdl1jlsSC8sE+cjakTVm/X5E7Qha12qt3/j/duI3nu/6fLmOXXdunf6fX12vurQJss2skUVH+cRjVj33zss79cfd6nWz+lR0TdI1pKu+TGdn3E7JQ7nOXEy/aJEoPaX9lGJvRqrC182X//b4L0ObDuWN7W9w8tpJQOuV9MaON1h8YjEvdX/Jol+QsI3vo78nJtlUMvyVHq9US+XAznU7883gb3hs7WMkZWufS366/1NiU2N5redrVsnxK4mqqnojXEeWU5BDak4qKbkp5BXkVWkZ1OZ1mwEYOHwgTk5l/3vt4eKBv7s//m7+eo7Ynyv/BCiSjG7OYDAwatQoNm7cSL9+/WjXrh21a5sWxiiKwsBbBvLzjz9zcONB+vTsY/FnUaAWkJCVQGJ2Iv5u/gR4BODl4kVSUhK9evXi+PHjhIWFMWLECOLi4pg4cSKPPVb+5acPPvggcXFxrFq1ivDwcD23xHymZdGiRWRkZPDgg6bePz4+10ehWQlQKkBRlKHAJLTZk//aeTg2V9PyT8zd0fwOPYlyxakVTO00tVz/iaw6a6r8NajxIJvdzJqXwL2SdYWErASr5brsjDMLUEJuzOVdRl1DuuKsOFOgFnAt5xpHE4/a7HdZVVXiM+NJy03Tlgt41b2hg8Pq8H309xSo2gxGmE8YtzS8xWbX6ly3MwuHL+TnYz/z2f7P9PK2R5OOct/K+xjdbDTTOk/TewYJ67qQdoHP9n+mb9/R/I5qTVhvXbs13w35jifWPcHZ1LOA1iTyUvolZg2YRaBHoE2um5KTQuA7tjm3LW2dvBU/d614iruLO57Onri7uOOiuOgfIqiqikE1kK/mk28wfZ2I1pYot25r2RhaURScFWfcnd3xcPHA08UTb1fvYquLbt68GVdXV9q3L/mDg/Pnz+Pu7k5MTAz16xdfAa5bN+3/0O1bt/O279sEewWTlJ1Ecnay/mGmqqpcy7nGtZxruDi58Mbzb3D8+HFGjBzBwl8W4u6uBdF//vknI0aMKPY6xZk/fz5RUVGsWrWK3r17M3/+fIvXJ0+eTFRUFBkZGUVeux5IgFJOiqK0Br4HFOB5VVUPlHGITlXVYu+I/plZcdjOh4VnUGqSoU2H8v7u98kpyCE5J5m/z/9t0cixOAlZCRZ5CmXtXxVBnkEEewZzJesKoC2n6xvWt8rnjcuIIzY1Vt++qd5NVT5nTebr5ku7Ou3Yd2UfAFsubbF6gHIt+xrfHP6Gv87+xeUMU1O3Wh61GNhoIHe3vFuS820gNTfVIsfsvoj7rD57Upirkyv3R9zPkCZD+HD3h6w4bcpFWHJyCWvPreWVHq9wW+PbbDqOG42qqvxv+//00um1PGrxTOdnqn0cDf0a8v3Q73km6hn9g6C9V/YyfuV4/u+W/6Op//WXB1BZ3q7e1Pepj7ebd4V706Rf04L/do3b6dX4FBQ9b6S4MrpTpkzhppu0/++uXLlCXFwczZs314ODkrz99tslBiegzVYAHDig3fK5ObsR4h1CHc86JOckk5iVaFG6ODU1lUU/L8LFxYUnX3uSy9mX8cj3wMPFgwEDBzBmzBh+/vnn8v8wKsmgGrRmlIZcDKoBf3d/m1/T2mStQzkoihKG1gslEPhQVdWP7Twkm7uWfU2vMgXYbKmTrfi5+Vkky/949MdS9tasPL1S/0Sknnc92gXZtlKPRT8UKyXKm8+eNPBtQKhPqFXOW5P1DO2pP956aatVz73y9EqGLx3OvCPzLIITgKTsJH6J+YU7lt/BS5tf4mrmVate+0a3+PhiMvMzAS0QHd2sYtX6qiLIM4i3+rzF/NvmWwSfablpPL/heV7b9po0e7OipSeXWvzdfaHrC3a74fJ39+eLW79gVLNR+nPn085z38r7rP7vS03W0K8hAR4BlWqcacztcXZyxsXJBRcnbdbFOCO9YMGCIl/GXAzQAhSAwMDSZ54URWH48OGl7uPi4oKvry/Xrl0jP98UiDg7ORPkGUTzwObU962v91yKPhhNdlY2bTu1pV79emTkZZCYlcjFtIucunaK3sO0ZVpZ+VnEpsZyIe0ClzMuk5qTCkB2fjYpOVqp5PTcdLLyswAoMBSQmZdJZl4mGXkZpOemk5KTot+zxGXEcSHtAqdTThOTFMPRxKOcvHaSc6nnuJR+qXw/eAcjMyhlUBQlCFgDNATmAc/Zd0TV42CCKSGslkct6nlbtwxudbir5V36J5x7r+xl/5X9dAjuUOy+xu7oRkOaDLH58pzWtVuz4cIGAL3nQlXtuGwqL3yjL+8y6hXai0/3fwrAgSsHSM9NL3cDv5KoqsoXB7+wWHJi5O7sTk6BZXra8lPL+fv837x808vV8rt1vcsz5PHD0R/07bEtxtqlR0Hnup1ZOGwhv8T8wif7PiEjLwOARccXcSzxGJ/c8onVy5TfaOIz4nlv13v6dp/6fRjSZIgdR6Q1BX695+s08mvEx3u1zyvTctOYsnYKz3V5jvta32e1v+P+7v4kv1CxinGZeZkkZiXqyxBtycXJBV9XX/w9/PFw9tDfd1UCyKCgIGJiYkhISCj2dfPiBBMmTGDBggUWr6ekaHkihcsSFxYcHFzmDAuAn58faWlppKamUquW5RJOJ8WJAPcAAtwDyDfks/WaFqSGhhX/4WBIfa1/ibHDvVFabhqgzQxfSLugP28sp52Wm2ZRVdXIuMQ1MSuxxPEbVAMFhgKbzzBbmwQopVAUxRf4E2gF/AY8rN4gZTv2X9mvP25fp32NvKHqGNyRjsEd9eU9cw/P5ZObPyl2322XtnEu7RygTSWPbTm22P2sKaKWaXWfNSp5qapqEaDc6Mu7jCJqR+Dv7q9VfVHz2XZ5GwMbDazSOT/Z9wlzDs3Rt71cvHik3SOMbDaSIM8gkrOTiTofxbfR3+pJ1Wm5abyw6QXWnVvHy91fttma9RvBqrOriM+MB7QbpHtb269nrouTC+Nbj6dvWF+e3/C8nrt3OPEw9/5xL5/e8qks8ask49KutDzt5s3H1YdXerziEP8fKYrC5LaTaejbkJc2v0R2QTYG1cC7u94lJimGV3q8YpXkeUVRCPAIKNe+eYY84jPiSclJwcnJSc8BMT+XMQdEz/8w5OtV8MrLzdkNb1dv/N388XL1svqfR/v27dmyZQt79+7l3nsr/nfb318LjlJTU0vdz8PDo1znS0lJQVEU/PxKb0js4uSiV3vz8/CjiX8TMvMzyc7PJrsgu0gp5Org6uyKm5MbBtWAMzUrQJElXiVQFMUdWAZ0AVYB41RVvWHqSRpv6kG70a+pzEtQRp2P4tDVQ8Xu99Oxn/TH/cL6Ud+n5DWp1mK+xOtyxuUq91U4l3ZOv2kDpOPxP5ydnC3KRZtXfaqMn479ZBGcNPZrzKIRi5jUdpL+aXmgRyCjm4/m1+G/8vJNL+PlYvp0f3XsakYvG826c+uqNI4blaqqfHvEVK5zaJOhBHsFl3JE9Wjg24DvhnzH3S1NTUwvZ1zmwb8etMjnE+X3c8zP+iwzwPNdnyfEO8SOIypqUONBLBiywGJcy04t46FVD5GQVfwMgC1cy7nGqeRTRSp+ubu46/09WtdqTYtaLQgPCKdZYDNa1GpB69qtaRHYgib+TQjzDaOud12CPIMI9AgkwD2AQI9AannWIsQ7hIZ+DWkR2ILmgc0J9QnF283bJsHikCHaDNmiRYswGAwVPj44WPv3ICmp6p0g8vLySE9PJyAgABeXsj/TDw3VZk7OxZ7Dy9WLIM8gwnzDaBbQjNa1WuN0Tbvtdnd213JZvOpQy7MWnq5a7yZXJ1c8XbSCAm7Obrgo2jUVRdGXvLk6ueLm7IaHiwcK2s8/0COQOl51CPUJpbF/Y5oHNieidgQtAlvQ2L+xXt2sJpEApRiKojgDPwEDgE3AHdd7x3hzeYY8i/9QS1oWVRP0qd/HolnfmzveLNK34NDVQxb/Cd7T6p5qGVtdr7oWFX+qOotiPnvSLKCZLC0xYz5jsuH8hiJLsMprV9wuZu6cqW83C2jGd0O+o4Fvg2L3d3Fy4e5Wd7N4xGKLniyJ2YlM+3saz2943mI6X5RtV9wui78rD0Q8YMfRWHJ1duXl7i/zYrcX9RuHtNw0Hl79MAeulruuikBb9mq+tKtXaK9qzTOqiIjaEfx0+08WH+YduHqAu3+/myMJJXUZsA6DauBS+iUupl3Ul/uAVnq3oV9Dwv3DCfIM0m5miwkmFEXB1dkVL1cv/N39CfIMoq53XUJ9QqnvW59Qn1DqedejtmdtfN18q+VGd+jQobRu3ZrY2FjeeeedCh8fHBxMSEgIsbGxZGVlVWksx45pbQA6dOhQrv07d+6Mh4cHO3bs4Pz58xavKYrCol8XAdr/DbU9axPsFUw973r6vUCgRyBNA5rSLKAZzQOb08BP+7/F392fVrVa0bJWS1rU0oLE8IBwvYJZqE8owV7BBHoE4u3qjZuzm0PMNFaFBCjF+xdg/JcwAfhMUZT5xXxdl3eAMUkxerUUVydXi5K4NY2iKLzQ7QV9+0jiEX6OMVXQyDfk6+WIQWug2CO0R7WNzbxhY1XzUKS8cMl6hvbUZzEy8zPZerHiyawJWQn8e+O/9aTEEO8Qvrj1i3ItvwjzDeObwd/wXJfnLPo2/HX2L4YvHc4b29/gRPKJCo/pRjT/yHz9cY96PfQqP45kfOvxfNT/I/3mIT0vnUdWP8Le+L1lHClAyzt5cv2T5Bm0BnhBnkG80fsNh77hCvIMYu6gudzZ/E79uSuZV3jwrwf54/QfNrlmbkEuZ1LOWMy+OylOhHiH0NS/aZV6kdiTk5MT3333He7u7rz00ktMnz6dtLS0IvvFxsZy/PjxYs/Rp08f8vPz2bdvX7Gvl9fOnTv185WHj48P48ePJz8/n6lTp5KTY/owbPXq1SxcuLBK47mRSA5K8cwXh5f2kc0MtADmumK+vCuidkS1NMKypa4hXbm96e36fxLv736fMJ8w+oT1YebOmRb9Xp7u/HS1NvKLqB3BlktbgKpV8jKoBnbFmTrId6snAYo5DxcP+oX148+zWvOuVbGrGNBwQLmPLzAU8MLGF/QlG65OrnzU/yPqetct9zmcFCcejHyQvmF9+e+W/+qfqOcb8vkl5hd+ifmF+j71aRPUhmCvYJxwokAtIDM/k7TcNFJzU0nLTSM9N530vHR8XH0I9QmlXZ129Knfp8bmilXE6Wun2XRxk779YOSDpextX7c0uoVZ/WfxdNTT5BnyyMzPZMraKXw58MsaPStta0nZSTy+7nGuZGqVmJwUJ2b2mVkjZoRdnV15tcertAhswbu73qVALSCnIIcXN73I8eTjPNXxKaslKqfmpHIx/aL+gQlo/8418G1g08aR1aVz586sXbuWMWPGMHPmTGbNmkW3bt0IDQ0lKyuLCxcusG/fPgwGA5GRkUVmOG6//XZ+/fVX/v77b3r27Fn8RcohKioK0GZ1ymvmzJls2LCBJUuW0Lx5c3r16kV8fDwbNmzg0Ucf5fPPP6/0eG4kEqAUQ1XVGWjBxw3JPEDpUKeD/QZiRc91eY7tl7aTmK3VLP/X+n8R6B5Ico7pk6cR4SOqbfbEyDwPpSozKCevndQ7HDspTpJ/UoyBjQfqAcra2LWkdEspd6WZrw59ZTFD9XzX5ytderuJfxMW3LaAFadX8On+T/UqLaB1Rjcv712apOwkzqWdY/vl7Xx18Csa+TViQuQERjYbWanSnhWVZ8jTCg8Y8rWAqhoC+2+jTbknzQKaWZSQdkT9GvRj9s2zmbp+KrmGXD1I+XrQ1zWu+W1hx5OP8/e5v9kVv4sz186QlJNEgaGAQI9AwnzDaBfUjk51O9EtpFu5/55dTL/IlLVTLKoV/afbf2pUwQ9FUbi39b2EB4Tz7IZn9ZyQbw5/w8lrJ3mz15vlTnovjkE1EJ8ZT1KWZX5FLY9a1PWuW60fsNla7969OXXqFHPmzGHFihUcPnyYbdu24e7uTlhYGOPHj+euu+5i6NChODtbBn5jx45l6tSp/Pjjj7z00kuVun5WVhbLli2jbdu2eo+V8ggKCmLLli289NJLLF++nKVLl9KiRQvmzJnDzTffLAFKOSk3SFEqh6QoypGIiIiII0dsu0a1IgyqgX6/9ONazjUAPh7wMTc3vNm+g7KSA1cP8OiaR/VSoOYiakcwb/C8ai9VejH9IrctNjV123T3pkr95/Vd9He8u+tdQGuq+fMw2zeCqmlyC3IZuGigHsg91+W5cn0Cvyd+Dw+tekj/pHJw48G81/c9q8xW5BTksPTEUn47+ZvVSk038mvEqz1etUmQmm/I588zf/LH6T/YGbdTX4Lj7epN93rdua/1fXSu29kmMzkJWQkMXjSYXIOWDvh6z9cZ3dwxcxIK23Jxi8WSJT83P+YOnkurWq3sPLKKUVWV9efX883hbzh49WDZBwDOijPt67Snb1hf+ob1pVlAsyK/H3mGPJafXM77u9+3KI07qc0kpnWeZs23UK3Op53nqfVP6ZX8AII9g3mj9xv6h2EGg4GYmBgAWrZsiZNTyQFGTkEOF9IuWPTYcVKcCPUJrZGN+Gzt6aefZtasWezZs4dOnTpV+PiffvqJe++9l88++4wpU6bYYIQ1T0V+XyMjI4mOjo4uqVl5WSRAsSNHDFCOJR3jrhV3Ado/fJvu2YSfW+ml9WqSY0nH+O+W/3Is6Zj+3ODGg3m1x6v4upVeM90WVFWl/8L++k3zZ7d8Rp+w8q11NffkuieJuhAFwMQ2E+3SZbkm+Hjvx3x96GsAGvo2ZMXoFaV+4piSk8KYFWP0WY76PvX5dfivNvldicuIY2/8XmLTYvWa9s6KM96u3vi4+eDr5ouvmy9+rn54uXqRmpvK8eTjbLu0jV1xu4qUCr2z+Z082+VZq41144WNzNw5k/Np50vd79aGt/JKj1esXkb5k32f8NXBrwBtvf+qO1fVqKUsUeejePrvp8lXtWZvge6BfDP4G5oFNrPvwMpp35V9fLD7gyon+9fzrkfP0J409GuIgsL5tPNsurjJYiYR4IkOT/Bou0dr/LLFjLwM/rPpP6w/v97i+dHNRjO101QC3QPLvOEzqAaSspO4mnnVYkmXu4s7DXwa4O5Ss5dh28qVK1cIDw9nyJAhFc79UFWVjh07kp6eTnR0NG5uNeffGluqzgBFlngJC+aVoCJrR15XwQlAq1qt+GXYLxxLOsbVzKuEB4QT5htmt/EoikK7oHZ6cHEw4WCFA5R8Qz6743fr25IgX7IxLcYw99BcVFTOpZ0j6nxUiTOEqqry6tZX9RsnF8WF9/q+Z7NANsQ7hKFNy7/OGaBvWF8mt53MhbQLfH3oa5acXKLfwCw+sZhtl7bxVp+3LCqIVVRGXgbv7XqPxScWl2v/tefWcvDqQT4f+DktAltU+rrm0nPTLUqB39vq3hoVnAD0b9Cfd/u9y/MbnqdALSA5J5nJqycz77Z5NPFvYu/hlSgjL4OP9nxk0cjWKLJ2JAMaDKBtnbbU866Hk+JEQlYCMUkx7Luyj51xO/UPX4wuZ1wu9XfJx9WHl7u/zO1Nb7f6e7EHb1dvPhrwEd9Ff8fHez/WZ9GWnFzC2ti1PND6AXq59dILKphTVZXU3FSuZl0lJ9+y8mCgRyAh3iHX1ZIuawsODub555/ntddeIzo6moiI8hf8WbZsGQcOHOCXX36R4MROZAbFjhxxBuXxtY/rSag1fXq9pphzcA6z980GtGpTXw78skLH77+yn/v/vB/Qkrc337PZLl21a4qn1j/F3+f/BqB5YHMWDltY7M3B14e+1rtEg1ZA4aE2D1XbOCvjSOIRXt3yKjHJMfpzCgoT20zkiQ5PVPimfnfcbl7e8rJFXoyz4sztTW9nVLNRRNaOxKAa2BO/h7mH51rkr/m6+fL5rZ/Tvk77Kr+veYfn8eGeDwHthm/1mNU19sOTladXMn3zdD2QDPYMZv5t8/Vyoo5ky8UtvLbtNS5nXLZ4vl9YP6Z0mEJk7dI/GDWoBo4kHGHjxY1surDJoiBJYS6KC7c3vZ3HOzxOqE/xXbhrumNJx5i+abrFki9XxZWXmr5EeEA4dRvVxdXFFVVVySnIIT0vvUhZfGcnZ+p515MlXcIuZAZF2EWeIY898Xv07ZqUmFiTta3TVn98KOEQBtVQoU/Ftl3epj/uENxBgpMyPN7hcT1AOZF8gu+iv2Nim4kW+/x55k+L4KRHvR5MiJxQncOslMjakfw07Ce+PvQ1Xx74kgK1ABWVbw5/w5aLW5jZZ2a5lhRl5mXy2f7P+Db6W4ulY61rtebN3m8W6Yzer0E/+oT14bvo7/hwz4cYVANpuWlMWTuFb2/7tkrLmHIKciyS4+9ueXeNDU4AhjYdSp4hj5e3vAzAlawrTFo9ifm3zXeYG/OUnBTe2/Uey04ts3i+WUAzXrrpJbqEdCnXeZwUJ9rWaUvbOm15osMTJGQl6IHK1cyrOClOBHgE0C6oHb3q93KIhpu21KpWK34d/iu/xPzCp/s/JS1XK52ronV1T8xKRHEqeUlboEcgwV7BxX6gIsT1Rn7LhW7/lf1k5mcC2ifxNbmDfE3SpnYbFBRUVNJy0zibepam/k3Lffz2S9v1xz3qVW8VspqoVa1WjGo2iqUnlwIwe+9sWgS2oFf9XqiqysKYhRa9cRr6NuTdvu/WmKUUrk6uTGk/hd6hvZm+eTqxqbEAxCTHcPfvdzO101TGtR5XbKUvVVXZfHEzb+54s8isycPtHuaRdo+UWCHMWEY5zCeM5zc+T54hj7TcNB5b+xjfD/2+0h3Al51cppd3dnNy4/6I+yt1HkcystlIcg25vL7tdUBb9jRp1SS+GfwN9Xzq2W1cqqqy8sxK3t31rsXSLBcnFx5p+wiT206uUqO+IM8gRjcfXWOKG9iCi5ML41uP5/Ymt/NzzM/8dvy3UvdXFAU/Nz9qe9bG08WzmkYphP1JgCJ068+Zkvi6hXTDw8XDjqO5cfi4+RAeEK5P+x+4cqDcAUpGXoZFNZ3u9brbZIzXm2c7P8vWS1u5knmFfDWfJ9Y9wS0Nb+FK5hX2X92v7+fv7s//3fJ/VSoLai9t67Rl4bCFfLjnQz1/INeQy3u73+PHYz9yZ/M76Rnak7redUnNSWXvlb0sObmkSHWmxn6NebvP2+Uuq3xLo1t4t++7PBP1DCoq8ZnxPLbmMb4d+m2FZz6y8rP48qBpyePo5qNrRD+M8rirxV3kFuQyc+dMAC6kX2DcH+P4+OaPrbIsrqLOpJzhnV3vsOXiFovnI2tH8nqv162WTyQ0AR4BPNb+MSa3mcyBIwdwdXLF390fVVG17u5Orni6eOLj6mO13ilC1CQSoAhA++TMuOwFuG5KC9cU7eu01wOUvVf2lvsTxt1xu/WqQH5ufkTULn8S4I0swCOAj/p/xOTVk8nKz6JALWB17GqLfYK9gvny1i8dOoG5LF6uXrzc/WX6hfXjla2v6DMRF9MvMnvfbD33qTguigsT2kzg0XaPVvjDilsb3cpLN73EGzveAOBUyimejXqWz279rEI9Wn44+oPesM/VybXIUryabnzr8eQV5PHBng8ASMxO5KG/HuLFm15kTPMx1VLB6lL6JeYcmsOSE0soUE35Dh7OHjzR4Qnui7hPlhTZkJPipC/Lre9bv9Q1/ULcSORvggC0plvmSzr6N+hvv8HcgMyrLJl3hC+Lef7JTfVukk/aKqBdnXZ8M/gbGvgWTU7uUa8HPwz9ocaUgC1Ln7A+/DbiN0Y3G42LUvbN5k0hN/HL8F+Y2mlqpWdS7251Nw+3fVjf3n55O29uf5PyFmZJyErQS0KDVrmrvk/9So3FkU1oM4HXe76u/7kYl35NWTuFy+mXyzi6cvIMeWy5uIWn/36aIb8NYdHxRRbBSa/QXiwZuYQJbSZIcCKEsAv5l0cAWNRobxvU9rpPVnQ05qWBL6Zf5FL6pXIlzJrnn8jyroprE9SGpSOXEnU+imNJx/Bw8aB7ve60DWpb4/svFBboEcjrvV7n0faPsvzUcqLOR3H62mmyC7JxVpwJ8w2jZ2hPRoSPKPdyrrL8q+O/OJ92nr/O/gVopY8b+TUqcyZEVVXe2P6G3lTV182Xh9s9XOoxNdno5qMJ8w3jmahn9Ca5Wy5tYdiSYYxtOZZxrcbR0K9hpc+fnZ/N2dSzHLhygD1X9rD54mY9QdtcA98GTO00lUGNBl13v/9CiJpFAhSBqqr8cfoPfXtAgwF2HM2Nqa53XRr5NdITmnfG7WRUs1GlHhOXEceplFP6tiTIV46bsxuDGg9iUONB9h5KtajvU58p7acwpf0UVFUlMz8TTxdPmxQBcFKceKP3G1zOuKw3+Ptoz0eE+oQyuPHgEo9beWYl686t07endpx63ZdV7RrSlcUjFvPattfYeGEjoM2mfH/0e74/+j1tarfhpno3ER4QTiO/Rvi6+eLp4kmeIY+c/Byy8rNIzE4kIStB/7qQfoGzKWe5lH6pSCNPc2E+YUyInMAdLe6o0BI8IYSwFQlQBLvjd+s3xgrKddMgq6bpUreL/uewK25XmQGKec5QA98Gdm04KWomRVHwdvW26TXcnd2ZffNs7v3jXi6mX0RF5YWNL2BQDQxpMqTI/geuHuDVra/q291CunFXy7tsOkZHEewVzP/d/H8sP7Wcj/d+zNWsq/prhxMPczjxsNWu5e7sTs/QntzZ/E561+8ty0OFEA5FAhTBouOL9Mc9Q3s6TC3+G023kG56h+WdcTtRVbXUZRbmVddubnCzLMkQDquWRy0+u+Uz7v/zflJzUylQC3hh4wscSzrGvzr8Sy9duyd+D9P+nkZOgdY129fVlxk9Z9SYEs/WoCgKI5uNZHDjwSw+sZhfY361mCmtLFcnV5oFNKNT3U50rtuZXqG9pGeSEMJhSYByg0vOTmZt7Fp9+84Wd9pxNDe2bvVMeShxGXEcTz5Oy1oti903JSeF3XG79W2puiYcXdOApswdPJeHVz/MtZxregPJlWdW0jO0J1cyr7Dl4hZ9KZKLkwuzBswqtojBjcDDxYPxrcdzb6t7OXHtBFsubuFo0lHOpJzhUvolMvMy9Qp+7s7ueLh4EOgeSB2vOgR5BBHkFURdr7o08W9CY7/GhPqESsK7EKLGkH+tbnALjiwg15ALaJ9y9g/rb98B3cCCPINoF9SOgwlaH4p159aVGKBsurhJvzmp5VHLLn0ThKioVrVaseC2BUyLmsaZlDOAFoz/dsKyWZ2Lkwtv93nbImi/USmKQovAFkX6kKiqSr4hHxcnF5k9FUJcd26ceXNRxMX0i/xw9Ad9+4GIB6rUJVhUnflMiHmScGGrzqzSHw9oMEDWj4sao2lAU36+/WfuaXlPsSWPmwU0Y/5t87mt8W12GF3NoSgKrs6uEpwIIa5LMoNyg8o35PPfLf8luyAbgNoetRnXapydRyVuaXgLs/bOArTeNOfTzhdZ4hKfEc/Gixv17Rul+pS4fni5evFS95d4uN3DRJ2P4nzaeTxdPOkQ3IHu9brfUDknQgghipIA5QaUZ8jjxY0vWjQEfLbLs5Iw6QAa+zcm3D9cT4pddXYVk9tOtthn2allGFQDoJWMlf4noqYK9gpmbMux9h6GEEIIByMfU92A3t/1PqtjV+vbQ5oMYVjTYXYckTBn3h9i8fHFejACUGAosFivP7rZaPm0WQghxHUjOzsbDw8PmjRpUuS1YcOGoSgKAwYU7dfWpk0bXFxcSE1NBeCPP/7goYceonXr1vj5+eHt7U379u156623yMnJsTj2gw8+QFEUXnzxxRLHNXToUBRFYc2aNRbPX716leeee46WLVvi4eFBYGAgQ4YMYePGjSWcSZSH3NncgB6MfJBQb62UcK/6vXi95+uyjtmBjG5uCjoupF/g73OmfierY1dzMf0ioDXBG9lspF3GKIQQQtiCh4cHN910E2fPnuXs2bP68wUFBWzevBmAbdu2kZ2drb+WkJBAdHQ0HTp0wM/PD4BJkybx66+/4u/vz2233UafPn04f/48L730EkOHDqWgoEA/fty4cTg5OfHTTz+hqkWbmiYkJLBmzRpCQkK4+WZTruixY8fo2LEjH3zwAQUFBQwdOpR27dqxfv16BgwYwI8//mjtH88NQwKUG1CoTyhfD/6aO5vfyewBs/Fw8bD3kISZEO8Qbm5g+gfw//b/H3kFeWTmZTJ772z9+UGNBhHiHWKPIQohhKgOqgpZ12reVzE3+RXRv39/AKKiovTn9u3bR0pKCpGRkeTk5LB9+3b9taioKFRV1Y8D+OKLL4iLi2P79u0sXLiQv/76i9jYWIYNG8b69ev54QdTkaDQ0FD69+/PuXPn9CDI3MKFC8nPz+eee+7B2VkrSlNQUMBdd93FxYsX+fjjjzlx4gS//fYbGzZsYPv27QQGBvLII49w5cqVKv0sblSSg3KDauDbgBk9Z9h7GKIEj7V/jLXntP40J6+dZPrm6WTlZ3Eh/QIAzoozU9pPsecQhRBC2Fp2CrzTyN6jqLgXYsEzoNKH9+vXD9ACjwkTJgCwYcMGAF555RXuvvtuoqKi9IDE+Jp5gDJq1Kgi5/X19eWjjz7i999/Z9myZTzwwAP6a+PHj9cDlz59+lgcZ5wJGT9+vP7cihUrOHz4MOPGjeOpp56y2L9jx47897//Zdq0aXz//fc888wzFf8h3OAkQBHCAbWs1ZKxLcay8PhCQEuWNzchcgJNA5raY2hCCCGETfXs2RN3d3eLGZSoqCgCAgIYM2YMYWFhRV5zcnKid+/eFuc5ceIEK1eu5OTJk2RkZGAwGPQlXCdOnLDY98477+SJJ55g0aJFfPLJJ7i6am0Xzp07x9atW2nRogVdunTR9zfmohQXCAH6WHbt2lXs66J0EqAI4aCe7fIsx5KO6Y0bjXqG9uSJDk/YaVRCCCGEbXl4eNCtWzc2bdrE2bNnadiwIZs3b6Zv3744OTnRr18/Fi1aRHZ2Nunp6Rw5coSOHTsSEBAAaI1Mn3vuOT766KNic0oA0tLSLLb9/f25/fbbWbx4MX/99RfDhw8HtNkTVVUtZk8APT/m7rvv5u677y7xvSQkJFTyp3BjkwBFCAfl5erFnEFz+Gz/Z6yJXYOLkwvDmg5jUttJ0lBTCCFuBB7+2nKpmsbDv8qn6NevH5s2bSIqKop27dpx7do1fQlX//79+eGHH9i+fTtJSUlF8k9++eUXPvzwQ8LCwpg1axY9evSgTp06uLq6kpubi7u7e7GBy/jx41m8eDE//vijRYACcO+991rsa0yyHzJkCMHBwSW+j1atWlXlx3DDkgBFCAfm5erFc12f47muz9l7KEIIIaqbolQpl6Mm69+/P2+88QZRUVEkJSXpz5l/N3/NmLcCsGTJEgA+//xzhg2zbKNw+vTpEq95++23ExAQwPLly0lPTyc2NpZDhw5x00030axZM4t9w8LCAHjssccYMWJE5d+oKJZU8RJCCCGEEA6lZ8+euLm5ERUVRVRUFIGBgbRv3x6AZs2a6XkoxvyTvn376scmJycD0KBBgyLnXbhwYYnXdHNzY8yYMWRmZrJ06VK90lfh5V0At956KwBLly6t9HsUJZMARQghhBBCOBRPT0+6du1KbGwsa9as0fNPjPr168e2bds4fPgw7du31/NPAFq0aAHAV199ZbGUa9OmTbz33nulXtcYjPzwww/8/PPPODs7F5tjMmbMGFq1asX8+fN55513yMvLs3g9NzeX3377jUOHDlX4vQsJUIQQQgghhAMyLuXKzs62yDExvpabm4uqqhbLuwCeeuopvL29+eyzz2jTpg3jxo2jb9++9OvXj8cee6zUa/br14+wsDD++usvzpw5w8CBA4vNMXFxcWHJkiU0aNCAF198kUaNGnHbbbcxduxYevToQd26dbnzzjs5depUlX4GNyoJUIQQQgghhMMxD0qKC1BKeq1Fixbs2rWL4cOHk5CQoOeUfPnll2XOoCiKwrhx4/Ttwsnx5lq1asX+/fuZMWMGwcHBbN68mT/++IOrV6/St29f5s2bpy8FExWjlFR+TdieoihHIiIiIo4cOWLvoQghhBCimhkMBmJiYgBo2bKlxRImIRxNRX5fIyMjiY6OjlZVNbIy15K/CUIIIYQQQgiHIQGKEEIIIYQQwmFIgCKEEEIIIYRwGBKgCCGEEEIIIRyGBChCCCGEEEIIhyEBihBCCCGEEMJhSIAihBBCCCGEcBgSoAghhBBCCCEchgQoQgghhBBCCIchAYoQQgghhBDCYUiAIoQQQgghhHAYEqAIIYQQQgghHIYEKEIIIYQQQgiHIQGKEEIIIYSocSZMmICiKERFRdl7KMLKJEARQgghhBCinBo3boyiKPYexnVNAhQhhBBCCCGEw5AARQghhBBCCOEwJEARQgghhBAO5dy5c/zrX/+iefPmeHh4ULt2bbp168Zbb71FVlZWqccqikLjxo2LfW3+/PkoisKMGTMsns/IyOCdd96hQ4cOBAQE4OPjQ3h4OHfddRerVq0CICoqCkVRiI2N1a9j/Cp8vdzcXD7++GO6du2Kr68v3t7edOvWjblz56Kqaoljzs3N5fXXX6dVq1a4u7szatSocv28rjcu9h6AEEIIIYQQRhs3bmTEiBGkpKTQtGlTRo4cSUZGBtHR0bz00kvce++9JQYglVFQUMCgQYPYunUrYWFh9O/fHzc3Ny5cuMDvv/+Ot7c3gwcPJiQkhAcffJBFixaRkZHBgw8+qJ8jKChIf5yRkcGQIUPYtGkTQUFB9O7dGycnJ7Zt28bkyZPZtWsXX3zxRZFxGAwGRo0axcaNG+nXrx/t2rWjdu3aVnufNYkEKEIIIYQQDkhVVdLy0uw9jArzdfWtdBJ5cnIyY8aMISUlhY8++oipU6danGvjxo0EBgZaa6gAbNq0ia1btzJy5Eh+++03nJxMC4xSUlI4efIkAK1atWL+/PlERUWRkZHB/Pnziz3f888/z6ZNm7j//vv57LPP8PHxAeDq1asMHz6cL7/8kuHDh3P77bdbHHf+/Hnc3d2JiYmhfv36Vn2PNY0EKEIIIYQQDigtL41eP/Wy9zAqbMu4Lfi5+VXq2Dlz5nD16lWGDRvGtGnTirzet2/fKo6uqCtXrgDQv39/i+AEwN/fn86dO1foXF9//TVNmjRhzpw5uLu766/VqVOHL7/8kg4dOvDll18WCVAA3n777Rs+OAHJQRFCCCGEEA5i7dq1ADz66KPVds0OHTrg5OTEe++9x88//0xaWuVnrTZs2EBeXh633XabRXBi1L59e3x9fdm1a1eR1xRFYfjw4ZW+9vVEAhQhhBBCCOEQzp8/D0B4eHi1XbNFixa89957XL16lXHjxhEYGEiHDh147rnnOHLkSIXOdfbsWQA+//xziyR686+0tDQSEhKKHBscHFxsUHMjkiVeQgghhBAOyNfVly3jtth7GBXm6+pb5XPYqhGiwWAo9vlnnnmGu+66i6VLl7JmzRo2bdrEBx98wEcffcTs2bN54oknynX+goICADp27Ei7du0qNDYPD48K7X89kwBFCCGEEMIBKYpS6VyOmqpBgwYcO3aMkydP0qpVq0qdw9XVlfT09GJfM87QlHTtJ598kieffJL8/Hx+/vlnJk6cyDPPPMP48eMJCAgo89phYWGAls/y4YcfVmr8QpZ4CSGEEEIIB3HrrbcC8NVXX1X6HPXq1SMxMZGkpKQir61evbpc53BxceG+++6ja9eu5Obmcvz4cf01Nzc3APLz84scN2DAAJydnfn999/12RRRcRKgCCGEEEIIhzB58mSCgoJYsWIF//d//1ekqeGmTZtISUkp9Rz9+vUD4H//+5/+nKqqvP3222zdurXI/n///Tdr164tsvwrNjaWo0ePoiiKPjMCEBoaCkBMTEyRc9WvX58JEyZw4sQJ7r///mJzTbZu3crKlStLfQ83OlniJYQQQgghHEKtWrVYuHAhI0eO5Mknn2TWrFl07tyZzMxMjhw5wpkzZzhz5gz+/v4lnuOFF15g0aJFzJo1i6ioKMLDwzl06BDnz5/n8ccf57PPPrPY/8CBAzz99NPUqVOHzp07U7t2ba5evcrGjRvJzs5m2rRpelACMGLECDZs2MAtt9zCgAED8Pb2JigoiJkzZwIwe/ZsTp8+zU8//cTvv/9Ohw4dCA0NJS4ujpMnT3Lx4kWmTp3K0KFDbfNDvA5IgCKEEEIIIRzGgAED2L9/P++88w6rVq1i6dKl+Pn5ER4eziOPPEJISEipx0dGRrJ+/XqmT5/Ozp07OX36NL169WLhwoXs27evyP7Dhg0jMTGRv//+mwMHDpCYmEidOnXo06cPjz/+OKNGjbLY/6mnniI5OZmffvqJxYsXk5eXR6NGjfQAxcvLi9WrV7NgwQK+++47Dh48yI4dOwgODiY8PJypU6cybtw4q/28rkdK4akzUX0URTkSERERUdESdkIIIYSo+QwGg75MqGXLlkWaBArhSCry+xoZGUl0dHS0qqqRlbmW/E0QQgghhBBCOAwJUIQQQgghhBAOQwIUIYQQQgghhMOQAEUIIYQQQgjhMCRAEUIIIYQQQjgMCVCEEEIIIYQQDkMCFCGEEEIIIYTDkABFCCGEEMIOFEXRHxsMBjuORIiymf+Omv/u2oIEKEIIIYQQdqAoCm5ubgBkZGTYeTRClM74O+rm5mbzAMXFpmcXQgghhBAl8vX1JTExkfj4eAC8vb2lo7xwKAaDgYyMDP131NfX1+bXlABFCCGEEMJOateuTUZGBtnZ2Vy6dMnewxGiVB4eHtSuXdvm15EARQghhBDCTpydnWnYsCGJiYmkpaWRm5tr7yEJUYSbmxu+vr7Url0bZ2dnm19PAhQhhBBCCDtydnYmODiY4OBgVFVFVVV7D0kInaIoNs85KUwCFCGEEEIIB2GPm0EhHI1kYQkhhBBCCCEchgQoQgghhBBCCIchAYoQQgghhBDCYUiAIoQQQgghhHAYEqAIIYQQQgghHIZU8SqBoiidgYFAN+AmIBTIUVXVw64DE0IIIYQQ4jomAUrJ/guMtPcghBBCCCGEuJFIgFKybcABYNc/X3H2HY4QQgghhBDXPwlQSqCq6jvm29I0SQghhBBCCNuTJHkhhBBCCCGEw5AZlBuRqkJ2ir1HIYQQQgghbM3DH2rYSiAJUKqBoihHSngpvFoHYpSdAu80ssulhRBCCCFENXohFjwD7D2KCpElXkIIIYQQQgiHITMo1UBV1cjinv9nZiWimocjhBBCCCGEw5IA5Ubk4a9N9wnhILKy4V//gsWLS95HUWDMGPjkE/Bwr76xCSGEEDWah7+9R1BhEqDciBSlxq1FFNevpCSIiID4+LL3/fp7WLYKYmIgMND2YxNCCCFE9ZMcFCGE3WRllT84Mbp6FcLCIDnZduMSQgghhP1IgCKEsJuJEysWnBhlZkKrVlqAI4QQQojriwQoQgi7OHoUfvmlfPt6eBR97soVmDTJumMSQgghhP1JgFICRVFuVxRlu/Hrn6fdzJ9TFOV2uw5SiBoqKwu6dSv//tnZxT//00+Vm4ERQgghhOOSAKVkdYCbzL4AlELP1bHP0ISo2R5+GNLTrXOu99+3znmEEEII4RgkQCmBqqrzVVVVyviab+9xClHTxMVpMx8lcXau2Pm+/LJq4xFCCCGEY6m2MsOKorgCLdFmHfyBFOAqEKOqal51jUMIYV9z54LBUPLrBQXad19fSEsr+3xpaXD2LDRubI3RCSGEEMLebBqgKIpSB5gA3A50A4prr5atKMpO4A9ggaqqV205JiGEff31V/n2S0vTWvaoatn7jhkDu3dXbVxCCCGEcAw2CVAURWkOvA6MBtz+eToB2AMkAalosyiBQCug3z9fbyiK8hvwiqqqJ20xNiGEfe3bV/Y+xsCkPMEJwJ49MosihBBCXC+sHqAoivIJ8AjgDPwN/AhEqap6ppRjmgIDgHuBscCdiqJ8parqk9YenxDCfs6cgYwM07aLC+TnF91PVcs/e2I0dizs3Fn1MQohhBDCvmyRJD8J+BxoqKrqQFVV55UWnACoqnpaVdW5qqreAjQCvgAessHYhBB2NHas5XZ+fvE9TqBiwQloS7yk5LAQQghR89kiQGmqquo0VVUvVeZgVVUvqqo6FQi38riEEHYUF6ctxSqspB4nFaWqWgK+EEIIIWo2qwcoqqrGOdJ5hBCO4Ysvis6KKIrpsZdXxc7n7V30uaioCg9LCCGEEA5G+qAIIarF11+bHhsDE/OAJTOzYuczz2UxSkys+LiEEEII4VgkQBFC2FxcHFwyW/RZ0fwSo8BAaNDAtO3pafn6tWuVO68QQgghHIfVAhRFUe5SFOVXRVE+VxSlfaHXghRFOW2tawlhb/HxMH26VtbWwwNcXbXvTZrAf/4jydqFzZ1bNCgxX9IVGFhysry55GQ4f16bgQkIgNxcy9cTEqo8VCGEEELYmVUCFEVR7gV+BryAdsAORVEeMNvFGa06lxA1WlYWPPQQhITAzJkQGws5OVo1qpwcrRfH229DvXowebL1EsBrug0bij6Xk6MFGYqiBR6l/aw8PbXO8uZLw65dM3WdN0pNleBQCCGEqOmsNYPyDPCCqqq3q6raC3gU+FJRlPutdH4h7C4rCwYNgnnzyt7XWFHq1lu14250SUlFnyso0IIM85kVX9/ik+WzsrTO8ub7OjtDZKT23dwXX1hlyEIIIYSwE2sFKC2A34wbqqouAO4BvlIU5S4rXUM4qMOHoW9f7VNuJyftU24XF2jY8Ppa7vTEE7B5c+n7FM6J2LIF/vUv242ppkhOttx2coI2bbTfE3NpaaUny4eEQPv2MGKE9v3IES3QMf+5z5ljvXELIYQQovopamWzVc1PoihXgCGqqu4p9Pw9wDfAVOALVVWdizv+RqUoypGIiIiII0eO2HsolZKUBF27wukysosUBcaN02YUypNn4IjOnIGmTYs+7+VVdvUpRYHLl6FuXduMrSbw99eWX4H2czAGrb6+UL++NguSmKgl0xdWWkd5RYHatbXcE+N+8vMWQggh7CsyMpLo6OhoVVUjK3O8tWZQDgO9Cz+pqurPaMHJZ1a6jnAQFy9qn2aXFZyAdtP4449aAnnhT9JrisId0I1KCk7M+3uoKrz/vvXHVFPExZmCE7CcUUtLg2PHtJmQ4oIT0H5+zZvDjBlaMFP4Z1s4MV4aNgohhBA1m7UClG+BLsW9oKrqHOAF4JyVriXsLCtLu2HMy6vYcXFxEBFR83IySuqADtrNcnCw5U0zFP3E/8svbTO2mqBwToizMzz1FMyfry3V8vc3vVa3rrb8q7ATJ+CDD7TAWFW12azGjU2v+/pa/szXrbPmOxBCCCFEdbJKgKKq6nxVVUtMiFdV9UNVVZtY41rC/u65p+wgIzi4+BvNuDiYNMk247KV4krkGqkqXLmifQ8IKHkJW1qaVuHrRmTeoNHVVcsZmT0bpk7Vnps1SwtYnJ212RWDofjzpKWZHp8+rf08XV3hkUfgp58s9z182JrvQAghhBDVySo5KKJyamIOSkm5GKAFJD4+lst5ShIXV3NyBPr0KT453t8fUlK0G2tjuVvz/IrCunaFnTttN05HFBcHoaGVa8yoKNoywsuXTc916qR9T0nRXvf318595IhWtjgyUnsseShCCCGE/ThKDooFRVHqKIryjS3OLexrzJiSXzMYTMGJcfakcJUmo5qUk7FvX/HPp6Ro3+fPhyFDtMelVSzbtev6qWhWXl98UTQ48fCA554ruryrcWMt+B3KH2yhJ8kEsJsujOUXfZ8LF+DAATh1Ck6e1Jbe7d2rBSeKos1mgeShCCGEEDWZTQIUwA94UFEUW51f2MGZM9rNYHHc3S23jTel+fnF719TcjLi4iAjo/R9HngA/vyzfOe70Xp0mC/vCg/XgpDsbC1A3bAB+vXTghUPD23J1qjTH/AHw+jJNvzVFEIv7+EX7uE1XgG0AKRwc0YnJ21JoarC1aum56OibP3uhBBCCGELEkCIciupkhVon2CD1vsEtJtFYwO9AaxnMXcQTWui6MdTfExeWlaNyMkoK6Dw9DQFY23bQosWpe9/I/XoiIuDS5e0x4qizXqY/5mnpMDy5Vqwkp0NQ1jJBzxX7Lle4X+M4VdcXbVlXt27w8CB8Oab2jW+/75og0fznBUhhBBC1BwSoIhyKa2Slblz50yBSUGBykxeYD23cAdLaM0x+rGRj5nGbrowbdhJ2w7aCsxnAEBrLmjOWCxAUeDQITh+vOg5atc2Pb506cZZ5mVeXMD4vaQKXl5k8DWT9WPPOjeFX37Rkkr+8RmP45mXgpsbjB4NdepogUmzZjBokFbyuVUr0/Xd3Gz8BoUQQghhExKgiHIprZJVYcYlOC/zBi/wbrH7RBLNZ0f6cmHTGSuN0PrMZwCMjh7VCgEUVtrPJjHRcj9750bEx8P06dpyK3d3LWhwctKWWTVpAv/5j3WCqA0bTI+dnKBdO1MFry++0PJ25s/XApbHlS8IRcuGz8adlY8u16bsli0jy9kbgDok8ILyHtu3wwsvaL11jh6F9HTTdU6dMuU9FV52KIQQQoiaQQIUUS7mN5ulCQjQvvdgK6/xqv78QdpyNz/zKY/rz4VymYzb7nDYxijFBWUFBZY3xKVp2dI0m2TOXrkRWVnw0ENaZayZMyE2FnJzteIGqqot0zt7Ft5+G+rVg8mTtaVXlZWUZHpsMGgBirHR4vbtMGWKNhMyZ3Ymz6mmQPYTnuTOV/6ZOQkP56ew5/XX/qX8Hz5oa7d8fLQZk/Hj4Z13tGVfeXmmvCcH/bUSQgghRBkkQBHlYn6z6edX8n7XroETBXzG4zih3d2fJJx+bGAhd/MvPuVhvtL3b5m5n6zJT9po1FXz999FnzPONpTFywtiYrSAxryhINgnNyIrC269FebNK9/+xpmeW2+t/I3+tWvad1dX7fv335saLRopCjzvP4e6aOW3MvHkS5/nLMoD/1DnaVLQfun8DClMdv2W1au1n+PRo9p5//1veP11yzyU4pbbCSGEEMLxSYAiysV4swll9zkZy0I6cEDffoBvuUagvv01D/MxT+nbnj/OdcjW34cOWW4blygZDNCgQfGzI0aZmdrNd1CQZR8PsE9uxGOPwdatFT9uyxbt2MowBiJ5eZbPt2wJ48ZpBQVUVWVsiilg/YLHUIMtm5c4B/oxF1N3z3vyvuWVV+Ddd7XZk4gIrZO8MQ/FGNxcvXrj5PsIIYQQ1xNbBiiKDc8tqlnhpU5OTlrCeNE+Jyr/4S196yfuYRs99e3QUO37c7zPbjqbDpsypWrriawsLs5UstaYp33mDAwdqs0InD9ftNxtYaoKCQmmHh32yo04cwa+/bbyx3/7rTYbVFHGPjEAt9xiWt4VE6N1fj9+HLqyi0ii9f0+5Ql9maBRv34wnwn69k3sJHH78RLzUKQXihBCCFGz2SpASQJeU1XVYKPzi2pmfrPZpIk2i3D4sDarMGKElmAN0INttOWwvu//+C+gBTRBQaZPtF08XHmErygw/gqeOAEff1wdb6VczPNPvL2hRw9tSdHKlVqAEhRkWrpUGvMeHfbKjRg5surnaNMGkpPLv39cnOWywHXril/e9YT3fH17I304TbhF1TOASZPgsNKOg7TVnxvPD0DRPJTWrS2v4YATc0IIIYQog00CFFVVk1VVfc0W5xbVr/DN5sWL2g26omiNG5cvN01+TMZUl3c9AzjhEoGiaAFNQoJp1iE7G/bRif/jX6YTz5xZsbtgGzIWBXB2hp07YccO02uZmdp7Kbx0yahz59J7dFRnbsSZM0WXqlVGfr5281/e4Kq4DvJgubzLVc1hRMZP+mvzmAhA//6Wx4SE/FNSmPv05x5Qvmf1KtUiD6VjR+13E0yzVYcPI4QQQogaRnJQRJkKV7PKzdVu0AvfgPqSyt38om/P4WHy8037KYqWK6CYLf57nVf0BGiuXdMSCxyAMZHdWBDAYNBu0N95R/u0vnXr4nNQXFy06lePPw61ammzF8bcCOPyturMjRgzxnrnio+HRx8t376F+8cYA1rz5V03s55ArgFacvwixqAo2oxJYW3bwo/cq283UU+TtDma+fO1n7G/v/ZzTk3VihLc908sI3koQgghRM0jAYooU+FqVgEBxd+c38WveJMJQBKBLGE0YApKVFW78TcPbJKozXuYysjyySeWjUPsxNdX+56cbHqvR4/CkiXQuze89RbcfnvR4/LzTeVzp0zRZl4UBbp21ZZ6QfXlRpw5o81wWdP335d9w19c/5jCAa2iwH1eS/TtPxlCOr7UqYNFBS+jAQPgImEWeUu7/7eSiRO1GTzzwg1nz8KCBdpjyUMRQgghah4JUESZjEuEjDfqBoOWnPzmm9pSpu7doVMnuAPTDedPjCMHLTHFPChxctKWQBlzVgBmMY2rBGkbGRnwf/9n67dUpk6dTI+XLoVHHtFyTsz7dyxfXvS4evUsZ4hAe++7dsH+/abnqqMXytix1j+nqmrLt0pTeHmXeVnqJk20L0Ut4NbMZfrzSxkFaLkuxZk0Sfu5rmSo/txQVgLa7MmIEVrTx88/lzwUIYQQoqaza4CiKIq7oigdFUV5QFGUdxVFWWnP8YiizKtZPfqoVlEpNRXuvhtWrIA77tC6ek+5P51blbX6cb9xh/5YUaBhQy2Z2dsb9uyxLNiVgQ+zzcoOM3t2+bshVoPMTG38PXpo5YVr1dK+9+unvR9zly9rN8c9emg3y0uXap3SC884mRcdsIW4OO3nbAuff1766+bLu5o3h6efNlXwOnNG+7qJHXrvk3yc+QNtOuqWW4o/pzEPxTxA6c1m1i9J4do1WLYMHnwQwsMlD0UIIYSo6YoUia0MRVEGAUOBWOBzVVWzC72uAM2AtkCbf763BcIxBUkKUExarbAn8/yTHTtg2DA4eVJbwrN9u/YFcAermEwOAMkEsJG++jlUFc6dK/06n/IE/+ZdfEnXMvK//hqmTbPBOyof86VRd99d9PWkJC0I8PPTJn3MPfWUtiQpLk7rym5MsG/bVitWlp1tuom2lcJ5Q9YUH68toyrcgBIsl3cpivZ+XyumXMYoTLMnUfQnmVol5p8YtW0Lf6/rSgK1CSIRV/LJXbmWpdxJXJw2g2L8Wffrp5WH/uwzUx5KcUvHhBBCCOF4qjyDoijKZOBPoD0wFViqKMogRVGeURRlnqIou4F04BjwK/AqcCfQAnAGzgNrgNnA41Udj7AuYzUrRdE+kX/tteLLxY7zNN1w/sHt5OOqv2bk4WG5tMu8I3sytfgSswzsDz7QsvHtpLgZjiZNtKaFjz6qPc7LKz5dZvZsyxwUV1dtidjOnaab+rg42yZvr15tu3NDycn35oGR8buzsxa0LV2qzb507w63skY/ZgXDAa2IQGlBxIABYMCZv7hNf+7snNXF5vssWGCaQZI8FCGEEKJmUdQqfsyqKMpp4Iqqqt0VRWkNHME0E2K+Gv8qcPif148ATwGtVFUtpR/39U1RlCMRERERR44csfdQSnTTTdqNtZGzMzzxBNx8s7acacEC2LHdwFXqUButFvFdLGQRd1X4WqFc5KzSBFf1n/q9P/6o1aS1g7AwLRBzd9eWHa1ZU3JZYXPNmmnBS1qalmjfv782K1C3rnaOQYNMBQPefBP+8x/bjL9BA7hwwTbnNjpzpugsyqBB2vsE7X0GB5sCMX9/bWbjnlsTuPupYJz++WcigiMcJYIZM+DVV0u+XlycFsQ8qM5jHg8BcIyWtOZYkX1dXLSCBcaf9cCBtg/ahBBCCKGJjIwkOjo6WlXVyMocb40cFD/AuGDFfOHKZbQZlQFAsKqqdVVVvUVV1adUVf0SSEI4PONSJHd3rSljQYE2QzBzpvb8Cy/AO/fs14OTfJxZzSCLc5jPlAQGajfPRsZqWQCXqM+fAaZSsvZKlo+L04IvgKZN4Y8/tM7x5kUBjH1OfvnF8tg+fbQb4aVLtZvxqCgYNUorDDBcmyigVy/tuy0T5Y15Q0Y+Pta/RnFJ+Ob9clTVcpYoJUUrLPDbU3/rwckl6nGU1oA2O1UaYx5KFP3151oRw7DOl/V8H2OSvLEpprErvbFstBBCCCEcnzVyUH4BHlIU5THgn1svMoFQ4GHg36qqJljhOqKaFb5R375dSwuZN88y/+Q5TGWSdtGVVPwtzmMwmB4nJ2tfiqLdbNavD/v2mV7/MPdfjOCfGrFbt2rJIOYltarB3LnamBVFKy08caIWqBlnRYYPN82KgJacbUz6nzdPC2gSE01NKc0pihbsge1umuPiICfH8jlb1BzYtatobkfhPpvGGbdOneC337Qlg7ekmH5f1nMzoODnV74ckbZtYd26xsTSkEZoiU1Ptd9ARsg9XL6s5aEcPWq6tnE85oGwEEIIIRybNWZQngE+BV5EC1CeABqi5ZS0BP5QFGWtoigdrXAtUY0K36ivWFF8Nau7Ak03nOu4BWfnop/Yd+igLfcx5qSoKly5YhmcAGzI6EJu5+6mJz791DZvrhTGvBtj35L587VlS9u3a99fekl7348+qgUjxlQZFxft/V25UjQ4cXLSzqeqprK35t3lralwGWDz6zRsqM1iWUvh3I4Es48i6tY1zbhNnao9N2sW3Olv+fsCWiPH8ujSBUBhA/30505+s6HYPJT58wsfJ4QQQoiaoMoBiqqqOaqqPqeqamNVVZuqqvq5qqrJqqpOAyKBZcDNwC5FUb5XFKVhVa8pqofxRr37P/HC3XfDyy/Dxo3akqekJIg7n0tk8ib9mHXcQkiIVl7W3KlT2o27qmo3ycalN2C5BAzg90b/Mm38+GO1N240JsgXTmJv2VJLiWnRQstH+eorraqZ+QyRqmozJJ06WS4Fu3RJO9/q1aaSw7aaQSncxd3ZGYYO1ZL1z50rOstRFSvNCoPHxVk2TCxuederE2Opk3JSf94YoJj/PpSH+TKv/kRZvGbsOzNxYsXOKYQQQgjHYJUywyVRVfUkcIeiKH2AD4B7gTsVRfkUkEUXDs54A33tmuXzTZrA4MHajWDy8h14X9a6x2fhwTZ60L6+lhBd3LmgaMlh8xt8gGe3jeGO4Ge0iCY7G775Bp5/nupizLvx8IBff9VmjubNg5gY7auwOnW0nA9j3sOKFVpgUhLj7Mr+/dYvf1u4zK+qaj/7lSu1ZU7h4VrAkpmpXTszs/jzKIo281K4hHJh5jNgxTVwDAnRZtoMBu3nekuaafbkOM05j/Z5Re3a5Xt/xvLP5gFKa44xpMtVRkyqQ7162rLE2bNNS73AVH5YCCGEEI6vWho1qqq6SVXVbsADwBW0ZWFtARRFcauOMYiKM67bP3pUe2z8FP7MGe1m9MsvocXlKH3/LfQiBw+9WlNhpSXLm7929rI718Y+Ynris8+KT+iwAfO8myZNtBmSL78sPkne+Am9+bKmwg0Zza1ZYyrPGxyszcJYu/xtcWV+jdLS4NgxOHJE+zMsKTgxHltccOLqarmdman1RAGYM6fo/nFxEB2tXTczE+4oZnkXaNXOykMPmgOacJkQ/fknu24nJIQieSjG3ytp1iiEEELUHNXaSV5V1e/R+p+8jNYbRQFOKorykKIodu1qL4oyz01fvLj4alYjam/V9zE2Z+zSRUsiN++BAkWT5c+f1/YJCio6i3LfpkdNd/tnz1ZbjdjCeTfGkrl162olgVevhm3btO/ffAOtWlkGAgUFWqndzp1h5Egt96ZpU21GadAgbQlUv37aUjmwfiWvv/+23PbzMz0ODLTsQ+NWiY8Giiu1PGaMFvAYZ26MvL0ttwsKVDqnrNe3jQFKWQ0azRmD5tQ0he2YcpX2fb6t2DyUBx7QXjc2a7SG+Hitr0vt2lrA5uKizTb17y+BkBBCCGEN1R4U/JOz8hZaZ/mvgHrAHOCwoiijq3s8omIsbtS3GOiav01/bYtexE1b2tO5s+k44yfZtWtbBi6qajkDYbTyYBjZg0eYnqimTnvGvBtjKeDhw7X30aOHFmC89ZbpRnfNGtPsQSuXk3zENLYEjeRl3uD43jSWL4cDB7Sbd2NuhpOT1ivFuKzL2nkohw5Zbv/wg9Yk0tVVCwqN1cZAS+4vHESaM58tMX9srEJmtGePFowVVngGJoJo6hEHgAGFvxkAlN2g0ZxxZq6gAC427KE/353tFvsZ81AWLDBtV/VX6OxZaNNG+93+5BMtBys/XxtLVpb2u9O2rfY7fvx41a4lhBBC3MjsNmuhqupVVVUfQ+tAvwpohdZpXjgI43p/0D4lN84m6I4c0TPKC3BiJ90A2L1be3nhQtOuBoN2c56YWL5EeVWFZUGTTU8sW6blpNiYMWBwc9Nu3nNytJ+DeQUv4w31oEHaDf8A1rMzvyPT+JieCcv5H/9lL51o7htHkybQvj2MGKEFOgaDdqNsnEGxZvnbuDhT/5PIf9oijRmj/Xm0basFRs2aaeMwzo4ZZ3/MZ1qMzGdLzB8XLmEMRQMj86VugYHan/etrNWf20dHktASTx5+uDzvTmM+M5fbyRSgdGMnvbvnF+mHYj67tW4dlZKVpc3ENGmi/cqXJSlJK6hw552WAaEQQgghysfuy6pUVY1WVXUoMBit07xwEMab9chIbQZg0CAtqGjaVFu69Pl9W/R9D9CeRpG+Fsc1aaLdzBuZL+M6d84y+b7wEi+AeZcGa41SQPuo+rvvqvyeymJclrR+vXZzGxxcNK/EYNBiJUWB0Z1iWcyd+GLZaKQ5Jzne5g5OnzSwf78WX+3erc08+fnBiRPafuXNvSgP8/wT4/iMAdbevXDypPa1b1/RLvMPP1z6bIq/f9HCB6UxTxlKTtb+vG+h+PyTsho0mjM2awR4dXln8v6p8+FDBps+P8xjj2mzOeHhpmIHLv+UAqnM8qusLLj11sr96v32m/Z3wJpV04QQQogbgdUDFEVRWlXmOFVV1wB6r5TKnkdYj/HTfV9f0wxHSoq2ZOnAAfA5aApQkiN68eijlsdB0U/HzWdKCt/0Fg4E4hOcLWvFfv110cxvKzOWGHZ21oKJ+HjtRtc876ZTJ22Zk6rCo4efJJBrAGTgxUqGmE62bVuRdUUDB8L06abtESOwGuPyNNBmUsoKsMw9/7zlkrzCUlJMP5vyUBQtkB00SAtSncmnH6YBGgOU+vUrXsWsbVvte7rBiwO015/f9uE2Ro60zPdp3Bjuu097vTJ5KI89pvULray4OIiI0AIdIYQQQpSPLWZQDiuK8oOiKG0qeqCqqqqiKB0URfkZOFTmAcKmevbUvm/frt3Udu6s3VC3b699MtzbyXTntkXtqX/KbD4rUPjTcfOZksI3vYULdZ06hWWAcuyYdtNvI3FxWunfwmMpnCC/Z49WSrgXmxmcu0LfbwqfczsrWexyt+ngN96wWB+1Zg28/bbp5eXLrTf+wvksXl7w/ffFB1jmOSXBwdp7NF+SZ1Q436S8VBVOn9Z+ZnFxML7lbvzREnFycGMzvYGKLe8yGjDA9Hg7pmVex7/bzvLllr1Yzp6tfB7KmTPw7bcVH19hcXEwYULVzyOEEELcKGwRoPwPGA4cUBRlr6IozyqK0kVRFNfidlYUxV1RlO6KokxXFOUQsAcYCrxug7GJCjhp6qfH229rS5SWLdNu4k/vSqSJ4bT++jdHe7Brl3bja16RKSSkaH6DeZf5hg3hnXdgxgzLClOg3XCfdWoKt5iWAxXpQmhFc+dqK8mMHeSLzbsx8wLv6o+P+PfgO+6neXOYlv+evvSIc+d4sdmiIpW8jI0srVnFy7wqV0SEVtZ30CAt7+S777T3NXq09mdknlPSsqX2vUkTy8ptUHy+SVnq1tV6nxiXjBkM0CDGlH+yjR5koq2lq8jyLiPzPBTXvqYApQfb8PfXguj586ueh1Jc4n9lLVxoKqgghBBCiDKoqmr1LyAY+BhIBgxAAZANHAO2AauB7cBxIOef1w3/7P8hUMcW43K0L+BIRESE6oguX1ZVV1dV1W7vVNXPT1VXrzbbYc0a/cVMz1oqGFRQ1fvvL3qujh1N5zF+eXio6quvquo776hqWJjpeR8fy/26dlVV9ccfTU94ealqaqpN3vPAgdolPv5YVfv1M13S319VmzRR1fbtVXXECFXt1ElVG3PaYqDTWq5UQVV/+UVVH3pIVefzgP7aGm6xeE9OTqp6yy3a4+7drTf+W2/Vzunion0V/pmbf7m6mvYZONB0jtOnSz/O/EtRyt7HyUlVg4JU9W/660++zOsqqKq3d+Xfa3CwdrpmTqcsL3j1qr7P6tXa763xZwLaceURHW3dnwOoart2lX+/QgghRE0SERGhAkfUSt4j2yRJXlXVK6qqTgVCgYnAb0ASWg+Um4BbgW5opYYTgUXABKCeqqrPqKp61RbjEuU3d672KXu3blppV2OSfI8eWpPGI9/u0ffdlNUZraWNlpxcWFBQ0eeys+G11+CFFywTttMtc83ZtQvie47WSkGBNi2weHEV313xjEukQkK0/Ini8m6WL9cSzu9XvtePi/VoyZEGtwHacp5Tp+ArTI0mb2Y9g1ufs6jkZfwk38vLeuM3VozKz9e+evTQZqfGj9dmEsxnrsLDtX3AcpakSZOi/UtKopYjHchggIyETHpgWg5ozD/p2LGko8pmzEM5aWhCPMH68++N2UFEhJYHZZyt6tFDK7UM5ctDycrSfu/Lqzw/B4CDByEmpvznFUIIIW5UNq3ipapqlqqqC1RVvUtV1VCgLloH+d7/fK+rqmqoqqp3q6r6raqqUpTTQRgTridO1JbLdOmiLavZvl1rhnfkO1OAst+pM721lAK2bCl6ruK6ypsnbnt4WOY6FK4m9cV8D7jbLK/j+++xBWNy/8svw7x5xefdaEn9KvepprJO68IeoF9/bdBvvqn97A779iQztBkATqj89fhyi0pexvdvzT4o5r03vLy0APDf/9Z+XNHR2rVWr9ZeO3bMtG9uruV5qhI4FKcXW3BHu0gqvuyiKwBDhpR2VOlMeSiWDRszN+zk6FHLQHf3bli0SHusliMP5eGHiwbK1tK1qyTMCyGEEGWp7k7yV1VVPaKq6tZ/vstMiYMy3jj/9hu0aKHd5Jl/UtwZU4Ay8f868+yzlseZK66rvHkSena25af4hT+RnjMHuP9+0xPr1xetk2sFxkDqxAktb2b16kJ5N6e10sjbZ++iBSf04zJHjWfSJK2crbE61qLFCl73jjKdvFA2vPH9799vnQ7n5j1Q6tc35Z8YZ7yWLtW+//e/2mvmCvdiue22qo/HnHn/kw30Ix8tHa283eOLY/47ddTHNN3Rw3knrVpps0bvvKMVBcjLs6xaVlreT1wc/PRT5cdVlrQ09Gp3QgghhCieTQIURVHcyt5LODLjMp81a7QbvB490JvgzX0/mXBMCfJPzu/MuXPa4+IaDxbuKl84WPH1hVatoFGj4ntxXLwI8U17aOuuQItgbHAXOXy46fH06VrVq+LcdPIH/fEG+tL/wUaEhGi9YcBsdsj8hFFRkJrKmjVa8j1oSet5eVXvcA6WPVCCg4vOeI0erX3fsUNLkg82rYoq0ouluICyKorrf9K1a8XLC5sz74eyLt0UoAzy38nRaJXvv9dmj15/vegyutJmrebOLb4njzV9/711glIhhBDiemWLPiguQIyiKOcURelc6LVbFUW5TVGUEGtfV1hX4X4gW7eiN8F7qIOpxXwygfyyszFvvqltl9R40LyErfFG2t1dqxqVmaktOYqNLXk9/9xvFFNDC7DJMq8VporBvP12CRW8VJWsX5bpmz9yrz45YuzpUlCgzV70/ndPsjz/yZ3Jy+O+uqst8iKM3eStUcnLuCRPUbRGjIVnvIyvde2qzRDdc4/pucIzGYUDyqoIJIlOmH5f1nIrAL/+WvVzt2ihfTcuGQMgKYlnRp6yyEPJzNQCYCO3Uj4++euvqo+rLKqqzWYJIYQQoni2mEEZDTQC1ququqfQa+OBP4CLiqLEKYryl6IoMxVFuUdRlOAiZxJ2UVI/EN0e0x+roaOWIH/lStESw+ZKKmG7d6/pGoqi3VQW9+n9ypVYBigHD2pfVmS8yW/e3FQUICBAm7jp0EELzsZGHsEzPlY/Zjkj9AAjI0P7PmiQttxryw4XFmcN1fcdmG2KgHbvht9/1x5bIw/FGFAagxJnZ3jqKW3G6/PPtaVOqqoVHRg+/J9lc2izEMXNZBTXE6UkhRtBmutPFE5og4qjLkeIpHNnbbasqoxlqTNcAjhGS/35uBVF81BOnTJ1lC+tt8u+fVUfV3l8/nn1XEcIIYSoiWwRoIwCVOC1El5X0Cp3BQODgH8DPwALbDAWUQll9gPZa/pEPLV5J/0GtUOH0pftGBOVCzMGJaqq3awXN4uybx9a5HDTTaYnrTyLYgwUGjQouYJX+FFTkJHUtAtx1NOPMy5vu/12baYC4HeG6fsP81rPOzNVPS9i9WrteWtU8rp4Ufvu7g7t2mlB3+zZMHOm9vwLL2gBi7MzHDpkStRuU0I71SZNLOsSlKbYIPYf5vkn67kZUKxWhM2Y3J+fDzH+pmVevVyLz0MxVi0rKUn9zBlTkAnWXeZWWHy89EURQojqcPgw9O0Lnp7av+vGLycn7bn+/bV9hGOxRYDSFTisquqZEl5XVVUNBuoDQ4AXgQPAIEVRWttgPKKCjDMJL71UfInh9I2mGZQXFnbWb1CLyz8x16RJ8Z9emwclJc2iZGb+c0NnPovyww+l3x1XkDHvZv36kit4DccUoBxvqeWYGN+3Mcn+zTe1jvN+fjDl5/76/rUzL/DvsWfZts26lbzi4uDyZe1x06Za3skjj2gzWuY5KLNnF/1xmffALGzePMuO85UxmFX647Xciq+vdWZPwLL62JoUU4DycPud3Heflhi/ZIm2n/n7MK92Zs6YG2RU3vLBlVX4ekIIIawjPh6mTtVmztu2hU2bTKX4jVRVe27DBm2f2rVL/v9BVD9bBCihYFbiqASqql5WVXWVqqrvAs+jzazIf9kOwHjD3KgR/Pmn5c3uC1NS8LlsajF/0KUzgwZpjwtXhyqOMbG5MONNfmmzKGPGoH2sb1yrc+kSbNxYvjdVDoXzbopU8DqZQA9lu77/S9u1AMWYd1Okktci6Hd3iClZAkzRH9ar5GVM7FYUOHoUHn9cmw1o21brIt+smRZsDRyoBU/NmpneZ2mVtDw94fnnKz+ucE5aFFNYxeBKdY4viXlAvBNTgGLYvZfXXs5lzRrtd3bvXm0GxejKlaI/7zNnLCYGdYpS/r4wFbVnj8yiCCGENWVlwbhxWi5lcR/KlSYpCVq21FZBFA5mRPWzRYDijNYZvjg5GDv6mVFVdR1wGehjg/GICjLe+F2+rM2eNGyozZ40aAD9fU13cVkeAWw435TRoy2PK42xEFdh5ZlF2LMHzmbUQY+IAH75pewDy6HMvBuAdetQ/omcLlGP9ckdLPJuiq3kBdrcstHGjVav5GWMeYxL8ubP15bk7d0LJ09qXwcParNA7dubKjSHhJRdSevJJ03xYEXdhinj/BBtuER9vRy1NZj317kU1J4ctOx3D3JoyyH9NScny6plUDRJfezY4q+hqpbLviojONi0ZLAwmUURQgjrSErS7ld+/rlq51m5UusNbVyZIOzDFgHKVaBxcS+oqvoYEFbCcceBCBuMR1SQ8cbv1Ve1oOTll7WJivPnoXmaaXlXbK1O+AcozJ+vbZdUwcuceWwBRZd81a8PgwdrgUxxOQBjxmB5N7l4sSm5oArKzLsBWGvKp1irDASUInk3hSt59egB6/JNd9IXftygV/Lq1886lbyMMz+FZwVattQ+SWrRQguCvvoKhg0zfTJUv37Z5w4J0Zp1Vob58q5VDObee6tWWrgw83LIAXXd2U8H/bW7G+/UZ4wuXSqarmQsEgBacGpW98FqgoO18V25UnLp4j17pOSwEEJUVVIShIZCQoJ1zpedrf0feemSdc4nKs4WAcpuoLOiKKHFvaiqakl/3JeAWjYYz/+3d97hUVRdHP7NphNCqCH03juhhN6kg1SR3qsoRSkqKIogKp+CCNIFVEREBBWQLkV6753QCYEQWnqy5/vjZmZ2kt3N7mY3u0nO+zz32al3zszuzN4zpzFW0ru3OrBKXgNlRC11JPfXgyDUqaPW1rCk8F7yGhtygUYPD5HlKzQU2LZNuFQZc/M6cQIIq99ZzRX75Anw7782n6uMYdxNvXpq3I2fH1ChAtC3D+H5elVj2UEiXW5yq5FhJi/ZLW7gSlVBKRx7E4VxD7VrA6tWCQUQSFscihwg7+0tUiXLLnlXrohyMcl9agOTknzLylRqfPst0LChdTJ5IA7NoH4vx3K1tku9F0MMa6FcuKB183qn7lE0aSIUv2bN1Kr1VauKzwcPVMXAsIaMPQkLs6xfOZEBwzAMYz3374v/AsOCz/aASPxHR0TYt1/GMhyhoKxJ6ne2lfv5AXBwWCpjCbNmqQOrbNmATz9Va6CUea4qKOc9g3AuyZNm0CDL3o6bqrERH69NOWxIckvK7CX+2nLndnDzkhWETZtEKl6ZV69EjZZDv9yEf4SaXjihsYguN1WVvUsXoRjUqgXcl4ogxMCoGIxDOHZMJCX7+mvtftZiGCBfooSwkCxeLKxdM2eKuJPgYPEpW0LkgbklFi9AxKJs3w7072+5XA1wANkhtLVo+GDxxUZKWmB7UqWKOm2ooISsPYKpU4UV7NIl9Xd14YL4JFLd6ozVPjEMqjecNpbkoXNny2Q1lRXsu+9MZxZjGIZhTHP/PlC4sOMK7Or1wjLDSkr64wgFZT2AUwB6SpJkKtWwBkmSPAAEA7jjAHkYKwgNheKyVbWqGIDLrkrL57wQVf6SOBgnNA1JAj780PJjJK+xIQ/cTAUkJ38LvXgxtDlw//hDm9LJBuTj7tghXL3q1RMpavv0ERaU9l5qNfSbPhVRr5swECZXLGT3uBUrgIED1YKJ1/MEK9tMee2okvr2v//EsgYNbJM7eYD8b78JxaRfP7XwZMeOwE8/AT/8IAoWEgG5dM/xNuYLk9aECcCBA2aP4+MjLD4hIaZTExti6N7l2bopcgY6QDuBsI7IHDNQUMrjMnJA+L65uQkLUJ06WgV4V9JXaqz2iWFQveF08jd0QUHAL78A2bMbl0+S1HotpqwpiYnA0KHG1zEMwzDGefpU9UJwJDEx4r+TXySlM0Rk9wagHIBQiGD5vwCUSGX7aUnbznOEPK7aAFyoWLEiuRIzZhABRMHBRFFRRMOHE3l4iGWNsUdMAPQMOcjTPZECAsSimTOtO07NmkpXJltAAJFOJ6YlSbvu1rkXRN7e6oLNm9N03rVqiW7c3Ii2bzeywRtvKMeaizEmz/vhQ/V6AUQ5ciT198036sJGjYiIaNYsdVG/frbJ3bKl2L9ePfPX0sODqF07ccnq4QCF6gJTbtSzJ9GrVxYdNzSUaMwYopw5td+NmxtRkSJEDwKqqgvnzrXt5Czg4UP1+NWqJNJT5FSO26fALipdmigoSFynQYNS/r5u3jR+reTpbNmML1d+h7eEHBcvpv57Tq2FhDjsMjEMw2Qq7t1LOS5wdOvRw9lnnbGoWLEiAbhAto6Rbd0x1Y6BsgBuA9ADiAewBcA7AOoBKJnU2gBYm6ScvAJQ0lHyuGJzRQVFHvAuXKguCw0VA/FFZb9W7tRbJZpSaKjYDhD7WUPygaH8oJEkMXBM7cETFEREXbuqC/r3t/mcHz4kcndXu0qhoCQmEuXOrWzQAX8pA9bQ0JT99emj9jVrVtLCAwfUhdmy0Y5/4ilHDnWRqb5SIzhY7F+hgvb6lChBNHIk0YgRYlpeXg2n6CV8TV/Y+vWFZpoWrl/X9nnlStr6S4VChdRDbUNLZWYyZhk9xVy51N9alSrmf2fmWrZsWjn8/GzvCyCqUcOhl4lhGCZTcO9e2p61aWmXLzv77DMOaVVQHOHiJVtmrgKoAmAeAEpSRuYC+A+iTso1AJsBvAGRfrgfEd002hmTbsixGAUKiM9Hj4Bly0SwcfFwNf4kb+sg5M+vBlxbG+RdooTWPUroa+LTMLjY11frAiZz4gQQ1szAzWvjRpsj5FLN4HX6tLAlA9Dr3PCfrgkApMjgJSPXGQGADz4Q7mJLj9eA3i0pX29UFN5re0HJ5FW7tu2phuVreOmSmG7XTsRMhISIVLqLF4tpAPBBFNbhDSU2BJ6eIqK+bl21w4MHgQED0ubQu2GDOl2pkrYOjAMwdI8yjENp7nsEVauKOCpDZF9iIigxVDKGMSam4lBkatbUzg8fboXQRjh1SiQ2YBiGYYwjpxJ2FpUrczxKeuEwBQUAiOgFEY0DUArAZACHAMhJ4CQIN7CVAGoQ0QZjfTDpizzgvX1bDLjkNMM7dgDFDBSUkUuDMGKECMY23M8akg/oDBUQeToyUqu8GNJ9RXt19PnihUj/ZQOGGbyaNFEzeNWrJwb5579V0wsf1tfBM30OAKbP+eBB8dmwoZrJa/hYH5xKrKpsU8/tKIYPFwHagweLZbakGjYcJK9fD2zebDxAfuZM4Fi3L1AGosimHpJQJBYvFvEnshAAsG6dGr1vC3/8oU537Wp7PxYycqT6ezFUUCpFHsXZs5YVEJUx1HFNxaHIyJnBZCZMsPw4pqhUif/8GMYRPHoEjBkj6lvodOKZYa65u4uB8IcfcipwV+HpU/Hy1FEB8ZaQkMDxKOmGraaXtDQA7gDcnXFsV2pwQRcvOQbF0F2lXj2iZXNekN7A76oMrmi2szYGhUi4VqVmTtXphDuXMf9/gCj69R7qTO/eNp2z7Ca1caOIA6hVS+tithWtlJkZuo+oVdJscHDq/YWGEn3wAVGpUkTLPEcq/RytNkRx6dqwwXx/5pg8WZVz7Vrx/bVsKfpq2VJ8L6GhRBQeTvHeqmvX3roTtB3FxxO1bq125ulJdO6c9QLdv6/9gk6dsr4PG8iTRxwuP7Q/qrK+96h8eaJKlYgCjYTdmGr+/qKZ28aYS54lsVWpNTc3diNgGHtx7BgpMYNpab6+RBMn2uaKy6Sd8HAiL6+0f4/2ahyPkjouG4PCLWMqKA8fqoHp2bIZxGPs26femX5+tH1rohJArNPZ/tA2NqCTJKKSJYlatSIqUEC7LvkD6vfe69WZ7Nltip+Q424aNEipCHkihiLhoyx4+OueVONu5P6+/ZZo2DC1z4H4QennJKqTh4dIQjBvnvn+LJHdXPPwIFpV9jNlwRPkpo7NXqbs7OlTooIF1R1r1BCKizUYJgMoUYJIr7f+pGygZEn1sLdQVJnppvvDIX9OtWsbl8NY0L09miSJ2JkxY3iAxDCWEB5OVLy4Y+5HVlbSl/Bw8c7MEd9lWhq/SDKPy8agMBkXIiMLDUtt16wpbOR24PffjR//5k1Re0Ou8SGTPMxk0e22ao7XV6+ALVuslqF+ffF54EDKwpS/jT+MbBC23Cj4oN+CYPzwg9jeVB0ROdXw1KmiYrncZ9spagGYKtJ5SPGxWLJEuJaZ688cchV5Q0qUEG5PI0aIabf4aLS5Ok9Z/x3eweNoI3lxc+XSBsKcOgXMmWO5METa/Xv2NF38w84YHsbQzauW/ohmOz8/IEcO431YU/tk3TrjfZQooc2AbS+IhOvXvHki7qtKFeGGyTBMSk6fBvLmBV7deoz38D+sR1ecQnUcQH38jD7ohx+VNOS2EBkJzJ4t7sVKlfhedCRyEcY0VhJwCByP4mBs1Wy4ZU4LijEXr+Bgoit1+ioLVge+q6xLi4uXTGpuMZIkjmMss1ehQqRNm/XGG1Yf32jWLZmPP1ZW7nJrqWxnLuuWYepbjRUqLk5jAjo0/3iarVByBisvL5FG2Jgr3ACsUGaidT6UB4/NW2sGD1Z39vEhunHDMmGOHtUe+OpV60/IRoKC1MNO9fpKmdnr1szuqSiDgszLEhUl3LTS4w1ejx5E0dHpc40ZxtWJihIp270RRV9iIsXA9Gv3CPjTNEyjbHhll3sxIEC4kzH249Qpxzw3DS3u9vje05r4MrPCFhTGrsgB4zNmiCB2Ocg74ahqQdkcWhMeHmL9zJlimS0B3jLGrCiAeNstSeIx8PKl+EzO48fQvrLetElYUiwkNFRbOHLWrGQZvORqfgCkli2U6Z49jWfwUrY1Zjjw8NCUPs9x45TFchrDsIp8yZKmA+SnlfpZ2WeVvj/Ckde8tWb2bDWlWXS0MMUYu/jJkU1LANCoEVCmjNXnZCu5c6vT+2JVC0r1xOOQKBFFioiCm4ULW9evsexd69eb38fHBxg92rrj2Mpvv4lEFvwWz0LkcQWT6YiOBl57Dfjvp5s4jlqYhNnwgunX7jnxHJ/gU5xDFTTBnjQfPyxMZGTMnRu4ejXN3WVpoqOBN94AatSwf99nzwI3bgAXL9qnv7AwUe+YcQC2ajbcMqcFxTDAm0i81f/q45eUCPU19KJxl+wS4G2Ir5nSHIB5K0rI5RhtRPOaNRYfV7YY1alD1KSJ2kVwMNHSOS8p0U0tkBKEY8r6Tz5Jvc/kVqiFC4luthquLFyAUZrtrLVCyceRr4nRApPJqlkF4yC5uVlgrfn1V+1FXrXK/PZhYdqqhitWWHcyaUS+FgCRL15SAnTKgkrSBYvehFni45y89okpDK1o6dG8vUUIEWOEo0eJBgwgyp9fXCydjqhaNaJJk0TQEJMp6N+fqDLO0gNos2HcRwH6HO9TD/xK/bGSFmMYRcBfs00CdPQeZhOgt9s92a4dWzdt4dIl7V+JvZpOJ57LhhjUX05z43iklHCQfAZurqigGCvUSPv3q3dh9uyicGESthZqTE7DhsZvelNKiWELCiKigQPVBZ0723S+snuA7J7TFpuVPp8iJ3m5JyhymjtfwyD54cO1blcjsFCZOSQFpylIXj6OLJOXl3CXM8zg9eLj2crxbkglCdALt7jU0OuJ2rdXBc+TRyghpnj/fXXbvHktrkhvL5IrBGegVmAciB/s9ifUsKHlMr35pn2OaWljJSUZkZGiWqm5B4hORzRoENGjR86WlkkDFy8SFcdNCoU2Xdc3GEfeiErxtfsjgj7H+xQPrS/mavQiL0Tb9Z588MDZVydjEBIisi064tkoSUQRESmPGRVFlC+ffY4xblx6XzHXh128GLsiB3ivWiVyv8+cCXw/VHXvupO3Bh49Vn82K1eKT1sCvA1p08b4ckPXLp0OCAoCvL2125w4ATxqauDm9c8/opiJBcgFJnPnBsaOBX79FUhMFMtaQHXv2oumeKOnG955R7ufuT6LFRNlRu7eFQUbS5UCrvqqhUtqup3B9I8TUKRI6n2aO46np3Api40FTp4ULnk7dojg++vTVyvb/0x9AEgoVMiCziUJ+P57NQFBeLhpV6/wcGD+fHV+wgRRYTMdCQwUvw0Zw0D5Ojhqt+Mkr31ijhUrRHBnehETAxQqxO5eAMT937atKGRk7Dcro9eLL6pcOXHzMxmOp0+BRlWeYTPaIz/CAACJ0GEQfsC7mIMY+KTY5zly4kPMQl0cQQiKK8t7Yw3+QVv4wbL/j9SIiQEKFgTOnLFLd5mSW7dEXZESJYALF+zfvyQBDx4AOXOmXOfjA1y+LGrepJW5c1Mm9THF+fNA48bi+Kbq8Oh0Yn3TpmL7LImtmg23zGlBefhQfeMvWxJWor/ymuAbjFPS427aJBabCxi35rjGXnRKElHRokTly2tdpuR18nRwUJxaDAMg+vFHi44rWyHKlFF3rVdPWFSeFa+mLByN7zTbWWJBka0yhqmGvRGleWtXzf28RVYZY7Roob0eAQHa4OyiuKXZoFf1i9Yf59tvtQf56quU2xgG1efOTfTihXUnYicMU/wOw2Jl5jhq2u1NnLW/8/BwInd3+xzb0pY/fxYP2oyNFTnDDS9KpUpEv/xCdO0a0ZkzIh12qVIpL97gwelu/WNsJyqKKF9ePf2Orprv0RqraR48pp1orll4AjUoAKF2vS85oYWWkBCiChUc/zxM7tZljIsX7XMsSRKlwIwRGirSxNuaQCV3bqIrV+z6FTgcdvHKwM0VFZSoKG3tkQoViMILqHbXaaV+UtbJN9rw4fY5tpyRypqHgeF0ZD81voPat7fomIaxCzlyGMRxPH6sOdiBpRcoRw51kbl4EVNxLbLi87xoZWVhH6jX01xcizFq1VK/B1nu0FAhW8uWRLOLz1eOcwVllGJlVsW6JCRoCzjqdFrlb8UK7Zcye7Z1J2Fn5IxwVXFakSkO7kbdPKxtpmqfpIa9/vysaf362fe6ZihGj9ZejIEDRQa95MTHC//K5G8+ypVLtwKjTNp44w2itzBf8/1Nx1Sr7xc3xNMKDNAsvIrSVAwhdr0v8+ZlN0xHunIlb9bUKbFXPIokab/jkBCiKlXs0zdAVLYs0a1b9v5WHAMrKBm4uaKCMmyY+FXIykc2vNIEHK/5+CKVL6/eLAUK2O+t0LRplOJGl6d9fEShOnk++ZgCIPp58C51xt1dvL5OhTNn1F00KYbXrtWepF5Ps2api8wVWTe0QgHJFB8iEc2ZtPJIg/HKdtYMKh8+1L6ZNxogb6BYzMZ7BNho7XryhKhYMe3F7t5dnIfhl1SzpvGBYDoiW1HcEK8psFkf/6X5jyEtfwoGX3m6NEnKokGbv/+uvRADBmhi5oxy4wZR3bra/by8iBYsSLdCo4z1XLxIVAI3NPf5NrQkCYm23TNIpNl4T7PwHgpSBViWZMPS5uWVNZWUkBCh+6fXM7B/fxOCJCQIYU6dIjpyhOjkSaJHjyjqVaLd4lEkieiffxx7vpUqub6iwgqKAxsAbwCfArgKIAbAAwA/AChsp/5dSkExHFj//bewjDR1UyvIv0B20iGBAHVw7O5uv4GQJZmPJEm4MtWokXJd69cS1Ew9ANGyZake06QFZdAgdUXfvrR9O1lsQSFKpbbKnDnKyn1uTZTtrFEeZLllq0jyAPnZH78gvUFaqma6PQTYbgWg8+fF6z9TX0zevC6TEUnOCLcHjRX5JuELjbiGGbssccFKrfZJakRFEdWvn/px7NmstchleCIiiAINMjjVrGn525O4OKIPPkj5AOrWzXh0LeNUwsOJ3N30tBWtlO8qFAF2cMvS02TM0ix8gtxUG0fsem/qdFkneP7YMfsFolvaGjQwuPWfPhWZPYcPF6N6U+kavbwork59+kqaRM2xk9wQn64y29qMuQ6GhooxQsuW2qQ56f3SihUUB7Uk5eQAAEpSTNYCOJI0HwaglB2O4VIKijzoNUwZ/OKTr5U74YRfE80PXX7pmJYijcmR3ZbkVr26GIRbkrK1Zk3SundYEGxhLAYlZ45EeuSmDnQ+KP6Lss6SGBSilNYgOc3whg1EG9/dq6x4hhzUtHEi1a5t3bWU5Q4ONn5tumC9MhOOXMrDtnlzy/o3yrVrxjXDsmXF60wXQY7pmY6pioyb0C5NfwL2eFMVFZW+lpT8+dMuc4Zi1Cj15L29bSsUumOH9iUHIKyHBw7YXVzGNqKixH9Cb/ys+Z7exBq73TvDsFiTWv8lfKk5dtr1/jQXr5DRkeMtdDr7XS9L24ABRNFPXomYs44djVcvtqCFIoDm4W27W9Ac0bJlE+5sUVFEffuavu5y/HB6xUKxguKgBmB6kjJyEEB2g+XvJi3fa4djuJSCYjTFcK9e6q97wgTN9vZKMWyIYaAzoHXlKlqUKGdOdT75TViyJBHtUy0+5OZmPjUuqXVfmjdX+6uBE0ofCdBRbjwhnY5oyBDV8yu1ui+GKYCTPx/98FyzIObCdauvZZ062j6TB8jPx1vKzC/oqQTUp7VeDSUkCDeaUaOIhg4VMSgxMWns1L7IinYL7FCuwTPkUKx/hj8PS6wn9h7oOyrPv7EWEmJf2V2W8+e1D4S0vDUJDVVvYLlJkhhxOSkBBKPSpw9RbjyhR1Bfy29GW7JnDROA6A2spVioD+8YeFJn/GHXYySPV8joREUR9eyZPs+25M0DsRTy3d9izGLJAzZHDmH5T60IG0A70Zw6YUOK/5CM2ho1Sp9EKqygOKAB8AAQkaSI1DCy/kzSuqA0HselFJTkRRqJiKh0afVXvXatZnt7FWlMjrH4Ekuanx8Jf3PDaPv5880eK3kmrKAgop/Kf6YsOORWX1nXpIma1Co1RcLwWsqB640bExUpIrJx3HRXr2vE0nVWX0v5FL29jQfIh3irgUKDsczmTGEZkYcPxVg1G15RHFQNpAZO2PS7coSrVFSUVvd3VEura1qGoVMn9aTLlhWZvNJCYqLwzUyecqdIEeH/yjgF+d5eDtUF9xWy2RTM7uZmPCvSuXPq87sVttIrqIPdBOjsWlcJyDzuXvv3p3/GQk/EUHPspEUYQYm5cpveMCBABHn+8APR2bOiRpKMXk/07JmIR/nmG6J27UhvwupyFaVpFBaQDyLT9Twd0QYPdvxvghUUBzQAzZIUkOsm1n+UtP6TNB7HpRQUQwtKaCjR/6Y81fyiF7x3Q+PD6AgLClFKLyLDF6O5cokxgjzv46PdNiSEiN59V11Qs6bZYxnLhKX8OwFEM2Zo4k8szYRlLtUwQPQreigzX+g+sEqBkP+kAZFlLQX372suSnHcVNzA7OmO58rIMUCHoAY/j8Ucqx/ibm6O9dsNDRWhD8WL2+yJkGrL9FaUgwe1J/z77/bt2zAriNw6dhTB9Uy68uGHRE2xW/NdvIv/WX1PeHlZFlp07hzRsEoH6ClyGjmmfS02p0/b91qFhhK9847wOrAktjNXLmEktOZ5d+5cyvwShs0DsdQYe2gCvqJV6EeHUJeuoRQ9QW56gtwUigC6itK0Dw1pHbrRdxhNUzGdhmExdcSfVAeHqQyuUGlcpQq4QE3wL/XCavoc79NWtNIojymav7+IJd2+XWTss4anT2lxrSV0CtWM9v0YeWg6pto9FbWjm+FYSqdzfEwKKygOaADGJSkgv5lY3z5p/YY0HselFBTDwGsPD6LXsF35NT9GHgL0Gh9GR8SgEKX0rjD1QM2ZM+ULztq1Sbh7GC48ccLocYxmwrp7V7vvyZNEJNYpD10LgtlTSzV8oZ8aiPkPWivrLXlbL/ct/+mkyOD100/qAYsXV8ZXjh5suxKyn/oXmKRciz/Q2eoHur1SaNt6DvZIfWlzYoSMQseO2pO1d+atmBiijz9OqUF6eYlgsyxddCb9iIoiyu4eTVegBgyeQA2rg5nd3GzIe3DmDMXl1sYmfYfRdg+kTkutlNBQovfftz5dv6VNkiyLBfVCNPXAr7QRr9NLpO4+Zdfm7S0emn/8keZAi6goojy59dQA+2kt3tBkM5VbDDxpKYZkiDgVY+2DD9J0iVKFFRQHNADfJCkg35hYXy1p/QkL+7tgosW4koJy86b2AfR90c+VmdsVW2sMC3Jeb3sUaUyOYWYtufn4CBcuSx6QoaEkNAF5wahRZo8jW0Vy5CC6PHKOul+pUspgZ/t2VRmyZMCXaqrhbduUla9885H8Ns6SVMOG8S2AuC4DB6oZO/4pqLo/3Gs9mLy9xWyhQqn3nZkIDyfq7LlZuRYR8LdqQNGokWsUVhs71jJ5U70nMiOXL2tPdPNmxx3r/Hnt2wa5lShB9NdfjjsuQ0REb76pTXyRAB3VxHGr7wVramNouH5dfNcGnW1BG/LDc7sOGuWAZ0tJr4KH5puegnGQFmE4RcA/fQ/u40PUrh3RqlVEz5/b+OUaJzxctToUx02ag7Emla7NaEvd8Ztdam6lVytd2q6XKwWsoDigAViSpIDMMLG+dNL6Kxb2lyEUFLkGivyQDG3QRV0wZQoRiUG2YfyZI94wW5JuGBAKg7GCT598QkTLl6sLcuTQ+pwmIQ/0v/1W1Wf+g5oL9s9KH9CXX6Y0YVuaCctsquFkhSAL4S4Blil8sqL400/aopryH8UtFFUW9MbPyro6dSyTOzMRfueVJtC1Hg5Y9OAeMMAG5SQmhuj4caJdu8SnnZIHGLr02doyrWvfiBHqSVas6Pi6JXo90erVxm48og4d2O3LQdy8SVQR5zUxZV9jvNX3wZtvplGQhw9JSbmY1M6iMhXFLbsPHs1ZU0JDxXs3W6uS26uVxHX6EDM0Vq3kLQretAMt6CtMoAFYQa2wlWrhKNXEcaqLQ9QGW2gAVtBkzKK5GENr8CbtRlO6gAr0BLkpDu4UB3eKgSfdRSE6JdWg5+17En3xhXjWOjhJy7172lPKiac0CV/QPRQ0er4vkJ1+Qh/qjZ8pPx469ftJrfn7O/TSsYLiiAZgaZIC8pmJ9WWsUVDMHMdlXLwM3/hXrSo+76Cw8ks+9P5GWrhQO1iXJMcVCjI0U8vFjtzdhcZfqpT4LFNGfUAbFkQqVIiIXr4kyp5dXbhqVYpjyAP9li1F34VxR3P3VsdJZdbDg6hVUsp9SwPZzaUaXriQ6IGnqkh8UOlPi1MNG0uNLCt0JXBDc9ACuK/Ez2SFAHljJDRpplyPT/Cx2Qd25co2/KZDQkT+4OTZHTw9ibp2Jdq9O80DZ0Nl15bWoEGaDu+ahIWRYh4ELKp7ZDeePxexbslHiF5eojq9HRSl1GIIbI0bSKtM778vMi8n93hzpDy1aiRoXh7dQlHyxUur7oGAADtZRCMjRX0cg84fIr/da6XI13T4cBHrkTb3LT0VxS1qjp3UC6tpJL6nQVhOb2INNcG/VBw3yQOxqfbjhniqhaM0CV/QMQSZ3PAVstFK9KdW2EpeiLbb9ejd2zlW7Xv3Ut6DHoilflhFp1HVrNDnUZFWoR+Nx9f0GrZTKVwjT8TY/bdi2HRIoMK4Q42wl/phFY3E90a3YwUlAzZ7u3iZOY7LKCiGNVCioogm9H2o+SUXwH31xvSwPFjcVpIP7gFhLTGVJcTw4aFU0TY0CQUFpRg0JM/gtbDITGXmlkdpyu6rBkHWq2d5Bi8Zc6mGAaI/0FmZiRjzscVJB4wVl5QzeH1dYamy8r5/BY2OlmnfoqfGF18oF+FmYDDlzKlaJNI0qNLrxY8ieaYGY6116zT4liT53xt8l/LvXZIsypJJ2bLZfGjX5ZNP1BO02+jTSs6fJ2raNOUF79LF6vyxaY0hCAgQRfEcgS3pY21S9o3w8CHRWMzRdN4Wm60bsOnsXG8zMVF8WQYHiYK3XWuxpL3pqTH20GIMo/swYvFL1hIh0T0UpMOoQ5vRln5EX1qIEfQDBtJ6dKFTqGY+KB2gXWhG/bHSauXRXHN3Jxo/3vluquHhpjw79NQcO2kl+tMLZLfopBIh0X0UoKOoRVvRin5BT5qPt2g6ptJ7mE2j8R0NxRLqix/pDayljviT2mIzdcSf1AXrqQd+pT74iUbjO/oIn9JcjKEf0Zd2oAVdR0mNpZEAeg4/MpbUgV28MmDLikHyKWqgyHlvAXrsVTBFNVJHZfCSscTNy81NDP4NrSdymzmTRIC74cKdOzXHMMzgteOfeG16sKRodVsyeMkYphoOCRHHMzynKVDTGW+SOlhsoTlzRu0jheuYYe7a0aNplkFR5HPnLJM703HqlHoRdDr7FB5ISNAWBZWbt7co2GOsWrG3N9GcOWJwYwMXL5q/HwybMYU4U2XziorSlqeePt15suj1olJ18gKPZcuKuAUTyAqJ4WPHHi1XLuPpc20lrXV70hL4TUQ0vf81ioT6EmA1elktQ5KHsv1ZvjzFW7OZ+IAkJNr1O7Wu6el1bKTDqJMuBzyHSjQFn9ndzc3Dw/XqoxqzpBg2b0RRN6yjZRhMt2HnGzuNLRfCUyzmIPkM2KxIM/xpGo/jMgpKihooEyaov+Lu3VNs76gaKIYkryrv7i4yfhoqSoYKhOGDQ1Gc5FE/IEwmSSTP4HVq2gbtgQwS01ubwUvGMMbFWCavg1PVAO57KKisTy3GxZgFhYjEQMlgkHT64/XKtQGysAUlMVHVLgGin39OW396vdY6B4jr/sMPaqxTbKyol2EsqLppU5tfLVtiLTHVMlVNlCVL1BPz8RExXc4mNFT7vAGI8uShGz/+R40aab3R0qO1a5c2xSAqSjz67SGLtYHfMuGPE+lfNFE6CkUA5cFjq46tWNQdxe7dQis0OOhf6GD34HlLWilcox1oYXKDOyhMR1CbtqEl/YsmdAxB9ACBVh0kFh60Hw3oE3xMFXHeIefhLFcuS7h3z9J7WU+lcZX64CeajfdoJ5rTXRSiRFgQYGunFooAOow6tAZvarxgAE4znGEbAE8Azywo1Fg7jcdxGQUlhQWlvurvS3PmpNje0RYUopRV5cuXF8ddsYLo9ddJM/hO/vJSKX+yW5szn3btIqKUGbz2uqlxCtSjh0YOazN4yRjLEqbJ5PVQ60ZXUPfQomMYi0Hx9ydqVUhNr5wIiXLjiWa7rBqDQkREQ4aoF6tLl7T1ZehaBIjsA48eGd9Wrydavz7lD9TPj2jxYqtjFeTMbYbN0FpiqMAUNBLDmSmsKImJ2tokI0c6WyKF0AeJtKOh9vcRCR9qgR3pNSbRNHd3YXG1lvBwUWTb3vJYk3wiKoroc19tSsduWGf1MS3JjJhmrl8XSRoMDnwBFag0rqbTd62nsZhDUUg5ct6O16gPfqK8CDO5vydiqCSuU1Pspl5YTW9jHk3DNPoCk+hTfESTMYt64heqjSMOK1LoKq5clhAVRdS+vW3n6YFYKoEb9JpuF61q9ys9/+J7MVh4911Rt6VrV6L27SkiqAXtR0M6ilp0BlXoIsrTGVShE6hBR1CbDqAebUEb+hm96Vu8Qx/jExqFBdQWm6kCLqT6PXGhxgzcAMxIUkIOAPA1WP5u0vL9djiGyygohjEoobeiKd5NdVF5u84RxWIh46gaKMmxtqq8PFhTfCv1eq2yVaECUWysxroxuqJWielbdC/16UNpyuBFZKLOSnIMRpGyX3VqVhrZ2tW8uTa709uYp8ycQA3S6cS4fO1a9bvNsvzzj3qhvL1FEgVb+PNP7Q+iXj2iV69S3+/x4xSBtQQQNWtmsk6PMYyl4E7eTNUIAjJJTZRNm7Qna09/JhuJiiLq3FkVqxdWUwzUZ2g0vKg1/nHIwM6S1rWr5YpBeLiI9XeULA0aWFY65rPX9mhqT6zFG1YfK3/+dHwT//y5eHNmIMBT5KTXsN2h360/Img9uqRYsQVtKAjHnPabs7Q5MnbK0Vy6RJTbTAH7tJ6vvWphGbsH0+O+YAXFQQ2AN4DDScrIAwBrDeafAChth2O4jIJimMWroe6A8kuOhA+5I44AUoo0yuMDR9RASU7JksZvsGzZRLYxU+tLljTo5Phxrf/Xp58qg/x2LWLoPNQ3X3vRiJIHk9mSwUvGaKV6Qzp0UA40BZ9ZFOeSPLg/KEj8L+7K0VlZOBvvESA8jKwN7s+UxMaKUbt80dautb6Pmze1fZQrJ0ZzlqLXC/eyZO4gBAhfml27RGyLGeTYrOQ+7j4+lqXmdri7S3rQzMDa2amTs6WhU6eMK4NN8K+mZkIMPKk9/k73AaDc8uVLPfzq3r20p7S2pPXvb16OsANX6QnUkd8NlCB/RFh1DEmyc2C8JSQmEk2dqhEkAToaizkp/lfs0arjJF2H9k/wAQKpO35zyPHs2cqXd1wW0PTm3Dmixo2Nu37ZI7tdWuPADJtNKfRthBUUBzYAPgCmA7gOIBZAKICVAIrYqX+XUVCiotTU/hPwlfJrflypMS1cSJoijfKfcXpU2Q4ynclQ8wAoVIjotdfUZXnzJuto1CjNDl9V+IE8EUO/QE1No5ck+rj1IapQQZsxyZYMXjLJlYnkqYaXFvpYWbk3bxeaNy/14xhVehISNIPnkzM22xzcn2kZMED9Irp2tW7fmBhtUJSvr4hat4UHD7TVzw1bjhwiRqVPH+Gb0quX2LZRI1EdtUgReqkTZsXn8KPTqEqLMJw6YYNFaUIz/O/gxAntyezb5zRRoqJEnIe5a90A+zWZfWLhQW2wxWmDQnd307EghrkkrG06JJA/IigPHltcDNVkTMqtW/QkpzrojoYX1cJRq2UaP95R37wFrF2bIrvfDxhox/SyehqGxRQNranrH7S2OkYnvZu9srtlNaKitDlwMsJ1ZwUlAzdXUlDkmF83N6KNUM3UJ1q/rwymDd2+CxRIHy1cdsWSW4sWQhmxuKK8zNOnKaoApwhWGztWc+y0ZPCSkRW7Vq2MZ1Z6HRuVGX2RoqkmHzDpNnb8uLrQ3Z3oxQubg/szLYauQe7u4mJayrhx2i9u9eq0yaLXi6rnctEhO7Qw5KUPMYOy44XZTe1mSQsLIzp9Wvz2LHFzswe9e6snUru24wszmuDePcvdoIJxkJ5BDZiLhhc1x06nDRABbWatqCihC1uzv4REao6d9C3eobOorHFnS4CObqAE/YzeNBLfU0lcN9qHm5sRi86ZM5RQsLBmwx741aZzdPrz7uTJFCnaDiI4zdmu/PBc82JNvuZT8JmTs4eZb5UqsWJiD0JDRfat4sWNjymUe9QJdZKSwwpKBm6uoqAYundt2hhPUR7qn2nyt33y4NjdPX1+9OZ87suVE28UDIPFDVtSpmCV8+e1qUkNW/Pmwg0oGWkd5BvGuvTtm9IVpBDuahYsnRFKgOlBZPLAey8vkRDgu6Kq1Su2dn1FdluC+zMt8fFEhQ0GP599Ztl+hooNIKqX24vERBFE366d6SI/VrZQBFAPrCW/7Hqjinz16mmQ9+VLov/9j+Kq1NB0mgiJDkr16a18v9GH7yc65tlw5472Bvr1VwccJHVssTTUwWGNJSUSPtQIe506YMyWTeiXyQqjm23uiKNhWEzXUMqqg11COfof3qXm2Kmx9Hl5JSkpL14QzZxJ+mTpucfjawKsdztzmYx1oaHC4d9AuAj4U0/8YtN3VhPHU1z7UARQM+xy6m/JXKtYkRWTrAorKBm4uYqCYhggT0eOKE+WBJ07dWz2MkUNlPQKkCfS1kMx5rovN0lKmXWmUCEjHd64QTdKqWaZGHjSQs93aOcm4+agtA7y5WtrGOwvpxneuFF8hnmogfI9fP40e21lhSc4WGtF2oI2ysxnmKrJqgtYF9yfqZk+Xb0oRYoIpcUc9+9rf1iVK1sW4WsLr16JOJRFi4R2/emn4gc0dy7RypXiB7NnD9GpU3Tn3+sUhGM0ED/QGrxpNHvPRrxO+fEwxb3i52eDbHo90YoVpLcgtdMeNKaiuEVdutjZyvruu+pxihVL/buzM7ZYGgxbQ+zTFLp7gewUjINOH0Ba2l7HRrqMsmnu6AWy00a8TrPxHn2FCbRR15n0fjk02yRCMln92pLmUgPi2NiUackhUhGXwjWLzscXL2kWJqcowPcvmqRIH+sKzdeXaOJEF7BiMU6FFZQM3FxFQdGkGDas7NewodHt0yPFsCHJB9v9+pl38zJcbuwB2bKlsFysefcota//VNnW3194gVWrJoLOa9bU9mvLIN9QwcqWzUSgfNeuykFm4gOz+cnrJKu9FRBA5KOL0Qx85LdpkqTGwGTpDF6GPHigtVSYc9VKSBBfurytj4+wwrkIhkpvLoTTDHyoKWhHAD2R8lAX/JHiHrEq3XBoaIrsRHJ7hWwpjkkQ2YsaYD95e2tKCtnOkyfaHMpGUp87kqgobTJAW1sz7NIok8+Qw+UzLeXHQ1qHlBnoEqCjv9GehmIJVcEZyomnlAPPqAyuUHf8RnMxhs6hktUHvI8C1BLbbJa3d+90/WlYhl5P9NNPKdJSxsCT5uMtKovLRs8lO17QaHxHd6B1e0uERNMwjXRIsOia5Mxp3NUnNFQsz5nTMrdpU83NTbzv+eADVkoYFVZQMnBzFQVFU6TRMNL844+Nbp8eRRoNSR5obviML1lS+GIaWwcYcfMi9XzXrhVpx825D+h0aRvkP3yo9m9SQflKdc/aieZmFZRChcSm3t5qX+Hr/1X2j9b5UHD1aMU/Xq6bkaUzeCXHMI6hdGkRAG8Mwzf2gKhb4kLUqJHy91oS1+lft+YpVizHIE3hOItdYNavT2GafIVs9CUmUnlcJEBPEhKpNo7QH+is2S4K3kpq3dOn03iy06apfefJkyLmRa7KXqqUeNHg7y++WnsNmPr3N/2MsLa1wlZNzEY4clFVnLZb//ZreuqLHykcWtN1AnS0FEOoGEIs6qcYQmgUFtDfaG/U0ie35/CjWZis1G+ypXl4uG6BPyISmQCbNjUq/HlUpBUYQF9gEs3FGNqBFprfidxCUIyaYneq18Ie6XvleIfSpR1zXzGZG1ZQMnBzFQVFtqAsmRetzZO3d6/R7dPbgmJp7YdChYRXTOnS6nJjbl7GCh3KqXqrVRNWFH9/dV1aCh0ac/FKnslrRIW9ysoXyE46JBh18TJUdipUMFgxZYraeatWRKTGzshvxTJ05iZ7c/myNpZhypSU23z3nfYH1r270wKyTZE8gYThwHI4FmmsagTQTRSnBtivLDJrRXn8WARNJet8G1pSYdwxeR/2wU+azEKR8KG6OERAGtJbvnih9e+cPl1ZFRIikpul9nwIDLR9sHbxYur9W9s64C+Nu04Y8jqsKrctrQhu0yakTFG2F42oAi7Y3K83oqgtNtNH+JQWYgR9j5E0HVOpHTaRL16mWW5jt7LLodeL+KnCha06uRh40meYYrYAn4cH0VtvsfLAuAasoGTg5ioKijyIHlN+m/Kki/PwoXYtYlLEnxClbwwKkdZNSh6g58lje90HQ4UnRXV3AwyzeNl6voZB8sOHG8+6kQ2vKB7qgLkyzhpVhmS55fNW5Db0+5o9W9lezrrm5sZ/WCkYO1b7I5FdvRIStG/rAaG12lrY0YGYUtwliahoUaI2Ja/QMZ3WJzABOvoc71N2vDAeU5WQQLRihbjBDPaLhA+NwgKypLZCY+yh51A18ifITeVwiQDLi/Rp+N//1M6zZyd6+pSiokxnajbXcuWyrq7jvXtpc30x17rid819/xD5qRLOOeRYljYdEmgM5mrqtxDEi5NRWODSWaIyXI2fqChhlU0lk184ctE3GGcy+5cp9y2GcTasoGTg5ioKipzFawHUWiEb8XqKB6GHh5r3P73T1squTYC2YJFhwPmYMcYLpiVXLM6cUdfNmmX+uIYhOefOWS+3xn2OVJN5cleUe/lVf52hWGLUnUxWdmS3LS8voiZVn2qqLT/ZeYqIhPIiG8OMJgvI6rx4kTL9W5MmRGWTBQEXLiyyR7kghoq7qeaGePoYn2gGwvKgZy7G0tOfNwv/qz17hMZjaH5MagdQj0rjqlWDxabYrXFPCUExCoDIUDd4sBUn+eyZ1sVs4kSrUvyaau3apW7NuXcvbcewpPXEL5r79zn8jNZJMRyEhoSk/Jnao9XDATqKWilW/IPWZlPjOkqBs7b165eWu8nJXLtGtHw50YQJFNl/BO2r8hZN8f4fNcW/SqFk+Vp7e4tHlS3/RwyTnrCCkoGbqygoRESDBybSXahawIxSP2gyTRkWarR6kGEHkr/UdnMTf9iym5Rs1QGEy4ehopLcGmH45tnPj2jgQLFNcmuRPS0oCxeKF2bDhhm3oszHW8rMj+hr1IIifwfNm6uDgi5Yr+z3CPnITUpMkVSgTh1brngW4MKFlKnfDFv58kS3bztbSrPUSjaeNBy4+/mJUyhWjKguDtMVmMjHbaIl+vjSWMyxuPBe8vYG1mpqDR1GHfJBpHVvuj/4QO3Q15fO7UiZlczW5u4uXlYYIzw8/Qbe/bFSc50SoKOPpM+oacN4s4PQU6fsU/W9Bk7QBnRKseIx8lA/rCJzVjO5nsqlS8ZfDqVXy5/fxWNPGCYLwgpKBm6upKB82uGo8rRPgI7aBIVp4iQMFQAg/RWUhw9T/5OS41CSB7TWrKntS1YacuQw35c8bY8YlDp1xFsvuc/kqYanlFmrrLyLQvTJtJTxDsmTBQQEEC2URioLVqOXRn456xAHyJvh8uWUo3xAVHJ/8sTZ0qXKzZspRZdr4yQfMGbDK5qJDzT1OIw2nY5iuvakEm630zxwHIdvNAvWoRtJSKRx4yw4uZAQTUzcH1WnOWRwm9ya4ki3LlOtq/uf9ErSulVRrVqmNagkoqKEAmrt8fwRQX3xI+1DQ6MbrET/VCuSnz2rlSU83G6lfKxqOh1RRISVNw7DMA6HFZQM3FxFQXn4kGiW7kPlib9PamT0j8CZLl5EqkIhSamnGpZlBITXiiHJU/Wa66NAAaIffxTTtmbxMrSYmIx5efRIc+D3Ol1LsYk8jnZzU/uIL6G65MwouZxq1lTfosuWFA6QT4XERKJ9+0S9kaVLia5fd7ZEVpE8HXbyJknCmqLUE0I4Dcci2oR2dN+jmFACChQQvoPTp1P0hRsm65la3/Qa11ECaBYmkyQZqSJuiF6v0cifeOS3SxC1qSZbU2wpwmhpSzUV65kzIngo+ZfXqxfRwYMmkzRERaVWbFFPhXCXWmIbfYxPaAdaUCyMl6A+jDrUBP+aPQ+dTjzXjOGIhAKptQwRGM8wWRBWUDJwcxUFZeb0BLoF9Y/xxSdf08yZxt2eiNI/SF6mZEnjf1CpVZQvWVLbT/JUvaGhlOJ8Bw0S8bhA2lP19umjymIq5mX7dqKLuorKhiPclmgGMQ8fat9Obt9OYiBteKJJ7khyBi/AOYokk74Ys6IAWqXEVDNWtPHNN83vY/XAHPGaQqIE0Eh8TwEBZgLm58/XbN8bP6f7wNderX17K9yPIiLEw8dYR0WLinVff030119CqT5+nOjgQYrZsou+afYXDcYy+hAzaB7epnXoRsdRM0XAu7F2EMHUBesptSQInp6pWyvsmZI5tZbhAuMZJgvBCkoGbq6ioHxQ4x/1ie/mlmpltfROMywTFGTZH1ahQtpyLnnzqn2YTNVrBHul6k0eP5M8zbCs8H2H0cpGq9FLczzZVUy2ivj5Ef1S+2tl+1u+FTSxM7J7j9FMTUymI3n9H2P3hSmFxTDdsCWB97Y0PzynM9DmAx6N76hXLyMns3Wrxj9NJOxIPXuYqzVJSukGZTFbthBVrOhQAR8hHy3GMKqNIxbt4u1tmSuVvYpaWtIydGA8w2RyWEHJwM1VFJTdudVK5tS5c6rbp3ehRpnkdR9atEjdzUtu8lu25Kl6TQXIy9gjVa9h9i1jAfKyPJNK/a4seIBAavVaYoo+Zs8W3jgA0V40UrafgQ815yW35s3TcMGZDIOxoo2AZVYUw6KNY8akfi/Z2orgNt1DQc3CHzCQHl17rgrwxx+aivGhUn4KxIN0Gezas1liaUiVhARRL6NZM7sIdR8FaAva0IeYQQ2xz+Iq5ABRvnzWnU9UFNEbbzj2GhcowIHxDOPKpFVBcQeTtbl/H40i/lLnhw1LdZfQUPHp5+cgmUzQpAmwY4c6v2uXOl2uHFCzJnD8OHDtWsp9Fy0Cpk0D9u4V8wEBwKNHwMqV2u127AA++QQYNAh4/XXg1i2xPDAQyJ/fNrlfvhSfEyYA1aoBb7wBnDgh/mZliIDlN5rgc+jgBj0KIBSFQ48DqKPpY+VK4OFDIB/C0AAHlP03orPSDwDkygVERABRUbbJzGQs8uY1vlz+3cj4+gLR0YBery47cUL8zvPnBxYscJiIuIuiaIx92I3mKIY7AIBBWInoShuA1o2BBw+EMEnEuGVDu8TNCEUBxwnlALy8xDMyZ840duTmBrz5pmihocD27cCpU8ClS+ILe/UKiIwEPD0BHx8gWzYgXz6gQAEgMBBhboH4cH4hnIkrj6soixfwt0mMbt2An38GvL0t38fHB/jtN6BHD2DdOpsOa5aAAODiRetkYhgmg2GrZsMtk1hQ3nlHeSX10LMIhd5PoBkzzFsVnBWDYuh+YlhYOnmTpJTZY+VaIMkD5OUmx7GYqi+QllS9hsUazWXyCg4m2oPGygari72fog9ABNqff3e5siDMqxDVq5uYInYG4AxeWQVTRRstbbVr2z/2xFQrjDt0EMFmN0r0zU4tsS1d5LFn8/JyrYxSUVHiuWbLueTObV1RS1PHt7e7V4kSbDlhmIwAu3hl4OZ0BeXiRU3k9Wh8ZzKXvYeHqIS+aZM674zgxOQ1PlLL5mW4PDRUGyD/99+mq7vLLTBQfKZloJ88fsRc9frLI+coBw/xLqcsnzxZlWn7dtIG2bz1lqYPwyD5Dz6wXW4m4+Co2BFHNDc3InfE0VRMN57yuGlT6lz+ktPltLa5crpbuUBs8eLm3UwdUQQwKsp+gfO1a7NywjAZhbQqKDpnWm8YJxIdDfTrByQkAADuuhXDMgxFYiJQoQKwcCGwcaP4DA4G4uOBJUuATp3E7oMG2e7ylBaqVNHOb9wI3L8v/r5KlgSKF1fX+fmJ5TKzZwv3KAAoUgQ4cwYICRF9li4tWlAQ0LKlOD9AuIEBQNOmtss8ZAjg7g6EhYn5338XxzBGWP3OynTxmCt4sv9Sim3iLt8Edu5U5t892hOff67KyjiGey/u4cv/vkT/Df3x1ua38OflP5GoT3S2WACEC2JQkPltChUS2+ic/NRPTAQS4IEZ+AgF8BCdsQET8RVW1VkAnDmDS9//i42XyztXSCuRJPEcSrNbl4PInx/4/HPxvIuLMz781+vF38KePUDlyvY7to8PsGqV8EzLls32fgYMAPbtY7cuhsky2KrZcMvAFpTIyBRV/zrgL431JHmmKTlYHHBucKIlrixyJq9PPxU1UOTl5gozyk22FEVH2ydAXsZYDZPQUNK408k1TE5AjXje1+xjItK6eH2GKcrMRZQnOcORXKfG8DzZxSvt6PV6+vrg1+T1mRfhE2harSW16Fp4ypo1zsBUumEfH6JKlVzfwiJJoiK5M4r9paWZqwvCaLHF5axyZaJbt5wtOcMw1pJWC4pERE5WkbIukiRdqFixYsULFy6k74FHjRJR40l8rxuN0fr5+Ptv4O+/gRUrhMUkOe7uwuDi7g7cu+ccC0poKFCwoPjr0unEW788eYCnT8Uya3BzA0aPBpo3F5aVVauAw4fFuipVRLB9TIx483zvXtrkfu01bVB/QAAQHi7eJidnMr7EF3gfAPDYqxDyvbqFug3ccfQo4I543EJxFMIDAMDv9f6HnVXfw/bt4u2oTIUK4o1lcDBw6FDaZM/qTNoxCbMPzja5Ppd3LuzotwNBBVMxYaQDQUHAyZPpf9zTp4EGDUTMdlrw8RFv8TMKkiSePa5qOXFVHj0Cvv0WWLNGWJ4M/28kSVzPfv2ADz90zv8MwzBpp1KlSrh48eJFIqpky/7s4pUVmT4dqCR+L+er9sYY/VwEBwMdOgCLFwN37wIzZwo3pOBg8Tlzphik160rlJTly50jemCgUFAAoZxkyyYG+kRAvXqqa9qYMUIBMYa3N1C1qlAO5s0DvvhCLJ88Wd3v3DmhnABCQUkr8sCtRQvxBxwWllI50emE4rICAxGflGAvX+x94Ndfcf++2GaI+4+KchIHD7x1qB8WL9YqJ4DI4gWkf6a1zMbSE0s1ykmB7AUwucFkdCnfRVkWEROBNqvb4N6LNGqxduD337XzkuT4Y9aoIbLTHTuW9r7soZx4eopsfabuf3shSSLxGCsn1mPO5UyvF0rft9+ycsIwWRlOM5wVyZdPvM6fPx8TDn+CRLhhwAB1df784s3Vhx+m3HXgQODIEeGnbGx9ejB0KPDpp2I6Kkq1hNSoAWzYIGR78UKsz58fePxYm1Z1+HBg/Hg13e/hw6rlxBj+/mmXWVYUYmPFn7CXl9ARPT3FuqZNRaxK/vzAjh35sa5NT/TW/wwASJg6Dc8fdIE/4jEdHyl9Xgzqh+q5A/DypdpH6dIiK6lsNUlL7ExW51r4NYzbNk6Zrx5YHdv7bkc+33wAgF/P/4p+G/ohQZ+AJ1FP8Ma6N7B34F54unk6SWKgRAmRSlhWiNPDQL5hg/isUAGoXl1YU5yFYYrfly/FPb55s/2Po9OJN/+Bgfbvm2EYhmELStYlf37gs8/w/JV4zVjAwlID8h9y8voK6cnIkdp52RIyaBDw11+qcgIIVwJD5QQAfv0VKFtW1ExJPoCTJKB2bTHQkrHHIL9JE/F5IKl0yd9/C+Xo0CFR3sDQlaFlS+Cn4h8hMen2dL99E+upCzahAwISRJR/nM4LnyYKZaVjR+Cnn0QfPXoA5cuL83JzE0oPYz160qP/xv6IiheFZPL45MHm3psV5QQAelbuicUdFivzh+8dxrR/p6W7rMmpUSP9jtW7N1CsmDr/xx/pd2xjGNYf8fEBNm0SpUPsaU3x8hJWW1ZOGIZhHAcrKFkc+c2+nN0qNZxVpNGQwEAgRw7tvIy/vyiwuHKlaTevsDDh82zoEiZnKyMSripyxi1Jss8gf8gQ8daVSLxpNpXFCxDFIrfeLIv/YaKyrBV2oKFBYcaP9Z9g4+ni2LEDmDJFZCUbMUK8LbamuOTV8KuYvGMyGv7QELWX1sbAjQOx4tQKZWCeVVlzbg0O31PNaks6LkFBv4IpthtcYzCG1hiqzM8+OBsnHzohCMSANm3S5zhubildPUuUEBY8ZzB+vHF3q+rVxQuV9u3Tfoy8ee1UhJFhGIYxCwfJOxGnBckbMHMmMHWqGJxv3AgsWyb8tw3dhmTXI0Bsd+SI2M9ZLl4AUKoUcPOmOp+am1fyFLweHiIQPrmL1dmzQOfOagV2ueK8PShcWLiFeHsLS0/Vqimvd+HCovLyq1dAs/qxmHSwE9pgm6afNeiJvvgZZcq5oWZNYYm5ejXl8erUEd+VMeIS4zBj3wzM+m8WEvQJKdYX8iuEJR2XoF2ZdvY49QxFTEIMys0vhzvPRbXz7hW7Y90bpsthR8ZFouqiqrgZIX6Q1QOr49iwY3DXOceDNjRUxE0ltxzam/HjgW++Sbk8Olq8KDCWaMORhIamrpBfviyC+Z8+tb7/3r2FQsZpbhmGYVInrUHyrKA4EVdQUEJDgaJFxWDCzc14VikPD+E+9frrIpDew0ME0jszgLFWLTEwtyc6nXhDKltPAKBRI5F73x7UrQscParOS5LpGIECBYCvvgIG94vDe7q56Kxfj0TJA34j+2BB/HD8sMrN5AAwMFB8ry1bCvex5MQmxKLjmo7YcXNHqjJPbjAZs1rMgpQe0dYuwpxDc/Du9ncBAB46D1wafQmlcpcyu8/ukN1o8WMLZX5em3l4p+47DpXTHH37AqtXO/YY5hSCKVNEEHR60bu3ded7/rx4oXH0qJoMIzmeniIhR69ewNixHLDNMAxjDaygZGBcQUGJjhbWCNnFq0IF4RpVoEDK1LuyAjN8uMj25UxatRKuUIAY6OfPr3U/K1RIyBseri43xFyaX0kS+yYkmB7kp0XmHDm0cTLJjy3fkmXKiFTHMhUqABcviulHj8Tb3D17tNaXFSvUPoxZuRL1iei5vid+v6imeyrmXwzjgschl3cuHLh7AKvOrEJcYpyyfnD1wVj6+lLopMzvERqbEIuS80riwUuRKW1s3bGY22auRfsO+nMQVp5eCQDI4ZUDV9++ivzZnTOqjY4Wv/FXrxzTf2oKQXpZcQBxnrdvs2WDYRjGleA0w0yaGDtWKCJyrMalS0IpefhQvIkfMEAEXQNiMF+ggEj/6GzkoHNADMZDQ4UFpFIlMTi7fBm4cMG4cuLpKdKiVqumrSBfs6YIgCUSygkgsm4ZIzo+Gluvb8VfV/7Ci1gT2oYJmV+8EArFoEEpUzk/fCgUohw5tMqJJInvZtAgoeh07iyUk6ZNhWve9u3ADz+kHiD/9aGvNcpJ94rdcf6t8xgXPA4Dqg/Ako5LcHL4SVQOUEtJ/3D6B0zaMcmic8zorD63WlFOPN08MbnBZIv3/fK1L+HvJVK+vYh9gUk7nXfNfHy01jp7EhCQeprxwEBheXA07u7AlSusnDAMw2Q22ILiRJxtQTF078oIRRoNMSzYaM5Vys1NWIiMxWgYIruxFSkCfKRm8jVa6PD4g+Po9ls3JUYhl3curO2+Fi1LmYl8h4hvqVZNTM+aBbz/vrCEGIv7iYzUusgYi6NJLvvrrwPdu5suLnn20VnUXlpbsY60Kd0Gf/b802ha3Gcxz/D6mtex/85+ZdmCdgvwVu23zJ5jRkZPelRcUBFXwq8AAIbXHI7FHa0zFS44ugBv//O2Mr9v4D40KtbIrnJaQ9WqoqaPvXB3F2m7LQkSj44GihfXukzam8uXgXLlHNc/wzAMYxvs4pWBcbaCYhggLw/CjbkOyQHknTq5RoC8jBx0DqhuU+7uYlAkKy6SBFy/blyByZZNWE8iI4EbN4wfI7mL193nd1FjcQ2ER4drtsvumR3Hhh1D+bzlTcorX29AXNeGDYGdO20LJi5XDlYFyOtJj7rL6uL4g+MARBD82VFnkdsnt8ljvIh9gSYrm+B06GkAIh7j4JCDqFWwlvUCZwA2XNqArr91BQBIkHDl7Ssok6eMVX0k6hNRe2ltnAo9BQCoElAFJ0ecdFrAfEgIULKk/fqzViF4+lS8SDBliUwLb74pUoYzDMMwrge7eDE2s3ev+DRWpHH79pQ1OgYOFNvs2ZPekhpnqJrdVYnpKFdOpNm9cUMoJteumbauREUJq0Zy5cQwbXHyGigTd0xMoZwAwKu4V3hr81swp/DL17tUKaH8/fOPUE5KlhQpgkeOFMqVMby9hYVr+HBhMblyBVizJqVyIsuevLjk+ovrFeUEAH7o9INZ5QQQcRSbe29GgeyiSE68Ph491vXAs5hnZvfLqHx18CtlulvFblYrJwDgpnPD9+2/V+bPhZ3D/KPz7SKfLZQoAfTvb5+++ve33lqRO7eotq6z8z9NYKBIJc4wDMNkTlhBycLIxRYzUpFGQ0aOFBYSQy5cUONHUiNbNhFzIiO7rZmqgXL20VmsvbBWmV/UfhHW91ivzP9761+zmbHk65ZcCbl5UyQdWLRIKFceHkC7dlq/+hIlRAHJokVF/ZYiRcTgr0gREdsyaJDYTnYDM1Ss4hPjMWX3FGW+a4WuaFWqlUk5DSnoVxBruq1RAuRDnoVgyF9DzCpiGZHD9w5r6p5Mqm97/Ehw4WAMqaH+cD7+92M8fGlhoSEHsGgRUL9+2vpo0MD2xBi5cwN37qS8V20lMFDEY3HcCcMwTOaFFZQsTEYs0mhIYKBwHzFFoULago7JiYoSridubkIpePRI+NbLmYcKFtTG2sw5PEeZrpa/GoYFDUOX8l3QsqQae/LlgS9NHk++brIFau1a4faVPFD+7l1RcNGwIvilS+J8pk4VaY/v3hXuM3fvAgcPCvnLljUeIL/m/Bpceyoi7nWSDjOazTB9UYzQpHgTTG86XZn/49If+OHUD1b14erMPTxXmW5UtBFqF6qdpv5mtZiFXN65AAAv415i4o6JqezhOHx8hCuhrZaUAQPE/mlRCAoVEr/VtCoV3boJtzUulMgwDJO5YQUlCyNnlVq1yrLtZZeK5G5PzsTQzUvGy0vEZzx8aDqdryGJiWocyLNn6vJhw9TpyLhIrD2vWk8m1p8InaSDJEmY0ki1TuwO2Y2r4cYj8uXrnZgoFJIePYT73E8/ifoygHDj6tdPBMifP59SzgoVRNX7jRvFZ3CwkH3JEuHSBmgryBMRvjmkVtPrV7UfKuSrkPpFScYHjT7AayVfU+bHbB1j8jwzGnef39VkNhsXPC7NfebzzYdZLWYp86vPrcbeW3vT3K+t+PiI+zwkBKhcOfXtAbHdrVvivreHtaJQIaFU21LR3d1duGP+/jtbThiGYbICrKBkYYYMEW/eDx8GfvtNvL1v1Uq4ELVqJQbJssvQjh0i6NrDw3j6WmcxcmTKZbGxwMmTaa/BYNj35mubEZ0QDQDw9/JH94rdlXWNizVGlYAqyrxcCyM5Q4aobi516ogsR8OHCzetqVPFNT58WHxOmWLcle7SJWDiRJEB7L//gC5d1DTQ8vkWKqRuv+fWHpx5dEaZn1jftjf5OkmHVZ1XKXErUfFR6PNHH8QnpnO5cAew4NgCJJIoiFM8Z3F0KtfJLv0OrTlUk1Bg9JbRTr9exYuLrF6hoaLeUc6canyIJAG5conloaFiu2LF7Ht8Hx9g0ybxO85tPgRKoUsXcS9UqZL6tgzDMEzmgBWULExgoKg4DYiMOMYGyUWKiDee3ZPG44MGOT/FsCGBgVo3LlNvV631fy9USHuev134TZnuXL4zvNzV4BVJkjCo+iBl3vBtfHJZ8+UT00uWCOvH0qXCAlKvnmoZGTNGrUtjjFevRDal1auByZPFtLtBkigfH3Xa0C2tdanWqBRgUzINACIeZVnHZcr88QfH8cmeT2zuzxWIjIvEkhNLlPkxdcbATWfm4luBm84N37f7HhLEj+/C4wsaa5YzyZ9f1DOKiBCWOSKh4D59KpY7+h4vX14USj13DmjcWNy38j3q5iaeOx98IBSlP/5gqwnDMExWgxWULEx0tLYYICCCsUeOFFmlSpQQg+ctW4SrVKNGrlGkMTmlSqnTMTHGt7E2ptvQvetV3CtsvrZZme9RqUeK7btV7KZMX3t6DVeeXDHar/wWOCZGuKy4uQmFZPhwkdWrf39g3jwxaPTw0O5brx7w5ZdAnz7C1St7dnVd7dqAr6+Y9kwqa/Lg5QON3OODx1tw5ubpUqELhtZQ/epm/TcL+27vS3O/zuLHMz8iIiYCgEgVPbjGYLv2X7tQbQyrqf6Ypuyegt0hu+16jIxM5coiu110tFCQ5CKpd+4IC64rvQxhGIZh0g9WULIwY8cKNyE/P5E1ysND+KgvWiQy9oSEaLcvU8Y132TmzWvZdkFBKQf9pjB079pybQtiEoTmk8s7lyYWQ6aof1FUy19Nmf/76t9G+23WTDufmCgUkkGDgL/+0sbMJK+PsmEDMGkS8PPPwMWLwu1Frjp/6JCo5wKoNSd+OvMT9CT8vornLJ5qIUlLmdNmDsrkFil4CYS+f/TNkKmH9aTHt0dUjXtw9cHw9/Y3s4dtfN7icxTOURgAkEiJeGPdGzj18JTdj8MwDMMwmQVWULIooaFq0Pv69SJr1N27xrNKrU2KDf/pJ9PVzJ2JHHxuCtn96cQJMeiXLQ2m8PXVvrndeXOnMt2hbAejldcBoGPZjsr0X1f+MrrNkCGqz3+pUsJKJePvL6rBr1wJfPFFyn2rV08ZH9SypXCFMSQuTgTHrzyzUlk2oNoAJVVwWsnumR2ru65Wig/efXEXIzeNtFvq4ZiEGOy6uQtf/vcl3t/5Pr747wv8G/Kv3eM3tl7fqlSNlyBhTN0xdu1fJk+2PNjw5gZ4uwvt/mn0UzRb1Qzbb2xPZU+GYRiGyZpwJXkn4sxK8saqyJsjOFhbRf6/O/9hwvYJuPzkMoILB2NO6zk2ZYeyB6GhImYkrUHxMg0bAvv3q/Ol55XGjQhRzXFV51XoX814vtaj94+i7rK6AERQediEMOTJlifFdoULA/fvC/euxERhAVm6VLjb7d0rLCO3b6ee/lmnE9YjuW6LTMuWwGc/HEHw8mBl2c0xN1EiVwnYk1n7Z+HD3R8q8z92/hH9qvWzub+n0U8x59AcfHf0OzyPfZ5ifeEchTE+eDzeqfMOPNwsNIWZocnKJop7WqdynbCx58Y092mO3y/+jp6/91QC8gGgT5U+GBE0ArUL1VYUGCJCbGIs9KSHj7sPJHsVEGEYhmGYdIIryTM2YayKvDkMq8gfuHMAzVc1x5H7R/A89jm23diGpqua4vaz2w6QNHUCA4Fevcxv4+mprStijrZt1enbz24rygkANCvezMgegloFayG/rzC96EmPf2/9a3Q7OctWYtI4VZKA3r21SQosqU2j1wvlRJK0hfiaNhW1T2SaFGtid+UEACY1mITGxRor86O3jMbNiJs29fXHpT9QYUEFzNg/w6hyAgD3XtzDe9vfQ73l9UzG+FjK/tv7NbEz79V7L039WUL3it3xx5t/wMtNTbCw+txqNF7ZGL6f+8L3c1/4zPSB+2fu8JnpoywLXhaMz/Z+hkevXNB8yTAMwzAOgBWULIqtVeSfv4rDwD8HIl6vdbcJiwxzaoXxpUu1QePJiYsDTiVz+9cZ+fXrdNo0yoZKRpncZVDEv4jJY+gkHZqXaK7M77m1x+h2/snCHJ4/V5UVS5EkYT3x8hKBxXLciSQBgwbrNZnEelVORXuzETedG37q8hP8vcQJvYx7iU6/dkJEdITFfYRHhaP3+t7o9ls3hEWqpqBsHtnQulRrDK0xFC1LttS41Z14eAJ1l9VNU7D5zP0zlenGxRqjUbFGNvdlDa+Xex1Hhh5B9cDqmuV60iMqPgoxCTFK3BAARCdE48j9I/h4z8co/V1pfHXgK816hmEYhsmMsIKSRbG1inxkibW4/lRUBNRJOrQr007ZZlfILk3WqPTExwc4etS6fYy5hPXpo40/MRwEGyofpjC0sJiyoCSPmZELS9asKeJSihY13X+2bCIFKxHw5ImoHg+I+BoAKFgQuBl3CPdf3gcgvqOuFbqmKretFPUvisUdFivz58POo8OaDhYFzW+4tAGVvq+ksfb4uPvgs2af4eF7D7G171YsfX0ptvfbjltjb2FIDVVzfB77HK1/bq0pnmkp/4b8i203tinzUxtNtbqPtFAtsBqODj2Kn7v8jOYlmmssKuZ4FfcKk3dORr8N/RCbEOtgKRmGYTI+etLjdOhpzDsyD+O2jsPAjQMxfut4LDy2MNMUG86suKe+CZMZadJEuBOtWiUyVj16BCxbpsZA+PkJV6EhQ8SAXQ6of1buOyDJSDKg2gAsf3052qxuowT8fvHfF+hQtoNTzqlCBRFk/pfx+HQFX18gKipl6uH8+UV9EkMMrSAtSrRIVYZmJVQF5eLjiwiLDEOAb4BmmyFDhDuXzO+/C5lXrkyZuSs5UVGiydafc+e064cNA9ZdXKfMNy/RHPl886Uqd1p4s/KbOPvoLD7/73MAwMG7B9Hwh4bY8OYGlMlTJsX2D18+xJitY1LUi2lQpAFWdFphdJ8CfgWw7PVlaFO6Dfpv6I/ohGgk6BPQ+4/eiEmIwYDqlvkqJuoT8e72d5X5+kXqG83K5mg83DzQp2of9KnaBwn6BNx6dguxCbGQJAmebp7w9RCZHO69uIet17di7pG5eBr9FADwy7lfEJMQg3VvrLNb4gOGYZjMxPOY5/j+2PdYenIpQp6FmNyubqG6+KDhB3i93Osc7+dicJC8E3FmkHxoqHhTHx8vYi527jQ+OPbwEEHXW7YA7vluIGF0aWXd6RGnUS2wGo7c0wZknxx+EjUKWBjwYWeio4E8ecSnNWTLJgLXc+ZUl4VFhiH//1Rzyv1376OgX0Gz/RARiswpolgw1nZfa7Ruir+/mlK4alVREyX5tC08fEios6YY7r64CwBY0mEJhgUNS2WvtENEGLFpBJaeXKos83Lzwjt13kGvKr0Q4BuA60+vY/3F9Vh+ajmiE9QvyNvdGzObz8TYumMtKpJ49P5RtFvdDuHR4cqyhe0XYmStkWb2Eiw5sQQjNo1Q5g8NOYTgwsFm9nANIqIj8Ma6N7ArZJeybFqTafik6SfOE4phGMbFiEmIwfyj8zHrv1nKSx1LaF2qNRZ1WITiOYs7TrgsBgfJMzZhWEX+n39SVjNfuFBk7pILNQJA9d7rlf0r5auEaoGi7kfdwnU1g7xfzv2SXqeRAh8fkQ3L0nongIjlSK6cAMCJByeU6cDsgakqJ4CoKm9oRfk3xLibl2HtlrNnhUWkUiXg/PnU5ZUkIWvyavMBAUCE22VFOQFEzEN6IEkSFndYrHGXik2Mxf8O/Q9BS4JQZE4RNFvVDPOPzdcoJ42KNsKZkWfwbr13La7gXqdQHewZuEdJSAAAozaPwtzDc83udzPiJt7brgbD96rcK0MoJwCQyycXtvTZgral1QwOn+79lIs+ZjHCo8Jx8fFFnH10FlHxUc4Wh2FcitOhpxG0JAgTd0zUKCc6SYc6hepgVK1RmNJoCkYEjUCVgCqafbfd2Iaai2ty+ncXghUURkE2piX/lLnno8aXdKvQTbOub5W+yvSa82ucGshbqJCwEBlWmDdFjx6i/kty5QQQwdgyQQWCLD5+02JNlekDdw8Y3SZXLu28Xg9cuGBZqmQi4NkztWK8TOXK0DxcqwdWR/7s6VeKW5IkfNb8M6zvsT5VZS7ANwAL2y/EnoF7UDZPWauPVTmgMvYN2qcUQASA8dvG4/P9nxvdPjIuEr3X98aruFcARMHN2S1nW31cZ+Lp5ok13dagXJ5yyrLhfw9HdLyV5kImQ/Eq7hVmH5iNGotrIO/svKj0fSVUW1QNfrP8ELwsGEtOLOHfAJOlSdQnYvaB2aiztA4uPr6oLM/umR1TG03F7XG3cWToEXzf/nvMaD4DizoswtlRZ3Fs2DG0LtVa2T4iJgJtV7fF8pPLnXEaTDJYQcmihIaKiuSAWkX+8GFg1CigSxfxeeSIWN6uHQDPVwj1UAumGAbHA8Abld6AmyTegN9/eR/7b++HM8mdG7h+XcRoNG4sLCtubqLlzg2MGSOuwdq1gLe38T5sVVDqF1Fz/p4PO4+XsS+NypcaOp0InDdFcje2Fi2A7TdVBcXwwZuedK3QFZdGX8Kc1nNQt1Bd5XeR3TM7WpdqjYXtFyJkbAhG1hqZphiKsnnKYt/AfRqT/JTdUzD0r6F4EftCWfYy9iU6rOmAI/ePKMsWdViEQjkK2XxsZ+Hv7Y9fuv2iXLcbETcwY98MJ0vFOAIiwuqzq1Hy25KYtHMSToee1qzXkx5H7h/BiE0jUPH7iiaLwzJMZubO8zto8WMLTNo5SZNddHTt0bg55iY+a/6Z5kWWIbUK1sI/ff7Bqs6r4OPuA0DcV0P/HorvjnyXLvIzpuEYFCfiSoUaHz0Cli8XdU6MBcmX7/APrtQWSom/lz+eTHqiVBKXabu6LbZe3woAGF5zOBZ3XIyMTNE5RRV3qT97/mmxu5Se9MjzVR4lk9Wu/rtSZACTr7+l+PoCkZHmt7l9PxYVVuZWXD+MHdcZxCfGg0CaVMH25N6Le2jxYwtNRpY8PnnQu0pvZPPIhl/O/aJxexsRNAKLOixyiCzpxYTtE/D1oa8BiBie6+9cz5AKF2OcmIQYDP1rKFafW51inZebFwiEuMS4FOtG1RqFb1p/oxT9ZJjMzC/nfsFbm9/S1M4q5FcIqzqvQouSqSe1MeR06Gm0W90OD1+pqU3/1/J/eK++42tkZVbSGoPCWbyyKMkLNebPLyrEf/ih8e1LND2AK0kD5KbFm6ZQTgCgZ6WeioKy6domEFGGzYoRFhmmGdRaY0HRSTrULVRXSWV7+N7hFIrCkCHARx+ldKMzRu3awLFjauV5Y/j6AtdjDyjKSTaPbGhQpIHFMjsSe1R9N0fhHIWxb+A+dFjTAccfHAcAhEeH47ujKd+ADag2AAvaLXCoPOnBp00/xepzqxH6KhQxCTGYvnd6hn8hwAiexTxDp187aQqJZvPIhrdrv42B1QeifN7y0JMeZx+dxdKTS7H05FIk6BMAAAuPL8SZR2fwd6+/kdvHAjMtYxdiEmJwNfwqQiJC8CzmGV7GvYS7zh3e7t7I45MHRf2Loqh/UeT0zplh/xNdiWcxz/DW5rc0KeoBoEelHljYfqFNv/3qgdWxb9A+tPixBe48vwMAmLBjArzcvfB2nbftIjdjHaygZFGsLdQYKh1Tpk0FFrcr0w4SJBAID14+wOnQ007L5pVWDAPk8/vmtyhA3pDgwsGKgnLo3qEU6wMDRc2S+/dN9xEYKNzQjiVd+uLFgRs3jG9bowaw7bpa26Np8abwcresvkZmIH/2/Dg4+CBm/TcLX/z3hSYQHwDcde74oOEHmNZkmsXB+K6Mr6cvPm78Md7a8hYAYPmp5ZjYYCJK5y6dyp6MKxMWGYbXfnwN58LU/OGNizXGj51/RLGcxZRlbpIbahSoge/bf4+367yN/hv6Ky6pcprvrX23oqi/maJKjM3oSY99t/fhn2v/YOuNrTgfdt6iuMu82fKiZoGaqBlYE8GFg9G0eFP4e/unuh+j8m/IvxiwcYDmBWIOrxxY0G4B+lTpkyYFsHTu0tg/aD+arWqGmxE3AQDv/PMOvN29MbTm0DTLzlgHKyhZFMNCjanVQCEiXIs8ruxbq2Ato33m882HuoXr4vC9wwCALde2ZFwFxTD+pGCQ1Q89QyXu8L3DRq1JQ4cCn35quo+wMO28KeUEEKmi17lA/Ikz8XDzwMdNPsaYumOw5twaHH1wFHrSo3K+yniz8puZbrA2tOZQfH3oa9yIuIFESsTXB7/Gwg4LnS0WYyNR8VHouKajRjkZVH0QFndYbNYKWTFfRRwYfACjt4zG8lMiuPfSk0uov7w+dvTbgQr5Kjhc9qzC0+inWHJiCZacWGK2toYpnkQ9wfYb25VkJnJ2qTal2uCNSm+gYr6K9hY50xCbEIuP/v0I/zv4PxBU14NGRRvhxy4/2i09cFH/otjdfzcar2ysWFKG/z0c3u7e6Fu1byp7W09kXCT239mPG09vICImArm8c6FMnjJoWLQhsnlks/vxMhIcg+JEXCEGJSAAiIgwXQNl0CBg3Kc3UXGxmhLr6aSnyOWTK+UOAGbsm4GP/v0IAFCvcD0cHHLQIfI7mi5ru2Dj5Y0AgI8af4TpzaZbtX9EdARyf6Wama+/cx2lcmvTioWGCitKardg3rwiI9mRI6a3OXfzEar8GKjMXxp9CeXzlrdKZibjsezkMgz7W9S58Xb3xu1xt1MUBmVcn0R9Irqv6648cwDg/Qbv4/MWn1v8coSIMH3vdHyy9xNlWd5sebGt7zbULGAm2waTKi9iX2DOoTn45vA3mgQchnjoPFA6d2nkyZYHfp5+0JMeUfFReBT5CHee30FMQkyqx6mYryJ6Ve6FQdUHcUyZAWdCz2DAxgE48+iMssxd547Pmn2GifUnOsQqfv3pdTRe0ViJSdFJOqztvhbdK3a3S/8H7hzA3CNz8deVv4zGk3m5eaFLhS4YV3cc6haua5djpjdpjUFhBcWJOFNBCQkRg175669XD+jfX7h8PXwoKswfFoYQFGm7Fnfr9gQAlMhRGjfHXzPZ76mHp1BzifgzlCAhbGIY8mbLa3J7V8UwQH7jmxvRqXwnq/uosKACLj+5DAD4qctPRt++1K4NHD+uXebuDiQkiCxekmQ67sSwjzHLfka/Df0AAEVyFMHtcbfZ1zkLEJMQg+Jzi+NR5CMAXLwxozJ+63jMPTJXmX+r1luY326+TffwouOL8Nbmt5S3zDm8cmBz781oWLShvcTNMhARfjn3C97d/i7CIrUmbZ2kw2slX0P7Mu3RokQLlMtbzmhsptzP46jHOPvoLE4+PIljD45hz609eBL1xOj2bpIbOpbriJFBI9GyVMs0ZTvMyMQnxuPLA19i+t7pmgxd5fOWx89dfkZQQctjQ23h0uNLaLKyCR5HPQYglKI/evyBjuU62tznned3MG7rOGy4vMHifd6o+AZmt5ytcfPMCHChRsYmZs1SlZNs2YSr0ciRQKdO4vPQIWD7drHuboI6gg4uWttsv9UDqyvxGgTCzps7HXYOjuJx5GNtgLyND8Hkbl7G+O23lMsSEoBixURNFEPlJHndE5l166DEuwDCvYuVk6yBt7s33qnzjjI//+h8LuCXwZh3ZJ5GOWlfpj2+bfutzffwyFojsbrramWw/CL2BVr91EpJYMJYxt3nd9Hq51bou6GvRjnJ5Z0L05pMw+1xt7Gt7zaMqTsGlQIqmVROAFEjKsA3AK+VfA2TGkzCujfW4dGERzg5/CQ+b/55iiQsiZSIjZc3os3qNig/vzyWnFhikQUmM3Eh7ALqLa+Hj/79SKOcvFXrLZwYfsLhygkAVMhXATv771SC7hP0Cei+rrsm3tNS4hLj8MV/X6DCggoplJMcXjnwWsnX0LNyT7Qo0QJ+nn6a9esurkP5BeUxa/8sxCcacXfJpLCCkgUJDQVWrhTTVasCUVFAq1bCirJokagkv2iRyDIVFQWgkBogX7ugeQVFkiS0LNlSmd9xY4f9T8DBGMafBPgGoJCfbab2eoXrKdOmFJQSJYDXjWQvvn075bK4lFZg9O4NFCmq11znVqVaWS0rk3EZVXsUfD18AYjsZatOr3KyRIyl/Hn5T4zbOk6Zr1mgJn7t/qvZwa4l9KrSCxve3AAvN5EoIzohGq+veZ0L0FnIugvrUHVRVc0LNh93H3zU+CPcHHsTnzT9xGRtDUvRSTrUKFADHzT6AMeHH8f1d67js2afoZi/9i35tafXMGLTCBSfWxyz9s9S0tdnVmITYjFz30zUXFJT819cJEcRbO+7HQvaL0jX2Iyq+atie9/t8PcSyQziEuPQeW1nrL+43uI+dofsRrVF1fDBrg80L5DqFa6HjW9uxJOJT7Cj3w6s6bYGO/vvRPikcKzvsV4T7xuTEIMPd3+IOsvqaJL4ZGZYQcmCLF8uYk6Cg4Ub1/Dhpgs1unsQpAKnlH0teWthOEDefnM7MpoboeHNH1TA+gB5GUMLyunQ0ybfbP/6qygkaQx/f9GMUaCA+C7PPTqnuPjoJJ3V+d+ZjE1un9wYUmOIMj/v6LwMd89lRY7dP4Ze63sprlhFchTBpl6bkN0zu13671C2A/7p84/SX7w+HkP/HorBfw62ufL885jnOPvoLPbf3o+TD0/iwcsHmeq39iruFQb/ORg9fu+hUQTalWmHC29dwPRm05HTO6dDjl0qdylMbTwVN8bcwJbeW/B6udc1rl2PIh/hw90fouicopiwfYISwJ2Z2HNrD6ovro6p/07VxGUMrj4Y50adQ8tSLc3s7TiCCgbhnz7/KC+CYhJi0H1dd7y37T1ExpkuUHb5yWV0+60bWvzYQnH3BoB82fJhZaeVODD4ADqV75QiCYaHmwe6VuiKI0OPYGWnlcjvm19Zdzr0NOosq4OJ2yemai0PiwxDvw398N+d/2w5bafDMShOxFkxKK1aATt2AAsXCncuwHShxnZv3keNn9U3RRGTI1J9QIdFhiH//9QbKqMFbHdd21UxwU5tNBWfNf/Mpn4S9Ynw/8IfkfHiAbZ/0H6TfuD37wtrirFkBcbInx+4fBnImRP46sBXmLxzMgCgbqG6ODzUuLWGybxcf3odZb8rqwx2t/fd7rQ/cyZ1QiJCELw8WHEdyuGVAwcGH0DlgMp2P9bR+0fR4ZcOih89AJTIWQJftfwKXcp3MRlgTES49ewWDtw9gAN3DuC/u//hQtgFTQYlQFiZW5VqhT5V+qBVqVYZNl7i+IPj6LW+F64/va4sy+6ZHfPbzkf/av2d4jZ7/el1fHPoG6w4vSKFi5eb5IbuFbvj3Xrvok6hOukumz15+PIh3t/1Pn4886NmeUG/gljacSnalWnnJMm07Lu9D6+veV1TGLJIjiIYW3csOpbriOI5i+Nl7EscuncIq8+txroL65BIqp+2BAmjao3CjOYzTCYaMsazmGeYtGMSlp5cqlleJEcRfNDwA/St2hd+Xqpb2MvYl1h0fBFm/TcLETERqJSvEk6OOOmwYsmm4CD5DIyzFJR69YS1ZONGEXNijp03d6LlT2Kg4xFTAHGzHlh0jBqLa+B06GkAwLdtvsWYumPSIHH6UmxuMeXt1IY3N6Bz+c4299V0ZVPsvS2qYqZWlfbpU6BOHfPphAGgRw+RxMA7qVj0az++hl0huwAAHzf+GJ82M5O7mMm0tP+lPbZc2wIA6Fi2I/7q9ZeTJWKMEREdgQY/NMClJ5cAiMDbrX22OtTyee/FPfRY1yNFTaYSOUugY9mOqFGgBnL75MaruFe4Gn4VZx+dxZH7R/DgpWXPe5lK+SpherPp6FK+S4aJg9OTHnMOzcH7u95XCl4C4mXP6q6rU2RfdAZhkWGYd2QeFhxbYNTFq0GRBhgeNBxdynfRDFRdnVdxr/C/g//D7IOzNdYACRJG1x6NGc1nuFydmCtPrqDL2i7K/WspdQrVwYJ2C0yWabCEf0P+xfBNwzVKNCBiEWsXrI0CfgUQFhmGQ3cPITYxVrPN0o5L072WC1eSZ6wme3YC8Bw3bgDPnpnf9vjN40CSN4Dv89J4ltoOSTQKaITTIacBAFvObUH/cv1tFTddCY8Kx51Q1XRe2sfyczZGNf9q2BstFJT9V/djSMUhJrfV6URGr4sXgYkTgZMn1biTHDmAN98E3n1XpIaOiREtMi4S+67sA5Je0tTLWy9N8jIZl0HlB2HLWaGg/H3mb5yud9putQEY+xCbEItuv3XDpbvq4Obb9t8iKHeQQ+/b7MiOjZ03Ysa+Gfj+2PdI1IsHRkh0COY9nGdVX97u3vD39kd0fHSKlLsX7lxAt1Xd0KZ0G8xuORuF/dMWp+FoHkc+xlub30qRzOW9+u9hcoPJ8NB5uMTz1BOemFBzAkZUGoGfz/6MhccX4u5zNZHLgasHcODqAYxwH4G2ZdqiTak2aFq8KQKyu2bK8ZexL7Hq9Cp8d+w7hL3SZkerGlgVc1vPRY0CNUAx5HIxN/nd82NXj12YfXA25h2Zp9xLpiiRqwQ+bvwxOpXvBEmS0vR7qpGrBvb13IcvD3yJBUcXKAp1DGKw/8p+o/tk98yOj5p8hIHVBtp8XGfBFhQn4iwLytSpzzBzpuXmRYZhGIZhGCZjEhERgZw5c6brMTnNMGM1/fo5WwKGYRiGYRiGMQ4rKFmQHDmcLQHDMAzDMAzDGIdjULIgH3/sDyACOp0oBggAtWoBvXqJ7FCPHok6KNfuPgXGqgGCl0ZfQqBfoMXH+e7Id/j4348BADUL1sSu/rvseRoOoerCqopv709dfkKHch3S3Gf/Df3x95W/AQDjgsdhWtNpae4TAO49v4cqC6so86dHns5wlWYZ+zNpu5rtpXze8jg45GCGCVjOjPx35z90/rWz4qteNk9ZbOu7DTl9cjpXMAcSEhGCARsH4Nyjc8qyvL55sbLTSjQo2iDd5Xn48iE+3fsp1p5fq1leNk9ZLO24FFUDq6a7TI6EiHD7+W2ce3QO58LO4d6Le3jw8gFCX4XiVdwrxCbGIi4hDjEJMZp0vulBiVwl0LdKXwyuMThT3wOuhr+pegUuDCsoWYzQUGDVKglATvz5J/D338CKFSI4+/hx7bZuJc4jMak+h79nTpQrXM6qgU6n6p3w8WGhoJyKOAW9l16pyOqKhEeF427cXSDpnBuXb4yc/jnT3G+jso3w9x2hoJx5fsZufqDrb65XZC2duzSqFa9ml36ZjM17zd/D0ktCQbkceRmnn51GsxLNnCxV1uRa+DX0/6c/Er2EchLgG4BtQ7dl+uQFNXLWwNF3juLtLW9j+SlRHPKJ/gk6beyE/7X6H8bWHZsuSvOruFf49vC3mPXfLJHuPel5qZN0mFR/EqY1nQZvd2+Hy+EMcuXKherFq6e6HREhLjEO0QnRiEmIQXR8NCLjIxEWGYYHLx/gwcsHuPfiHm4/v41bz24hJCIEL+NeWiVLpXyV0KZ0G3Qu3xkNijTgFyaMRbCCksUwLNLYoYNo06cbr4HiXf8y3hMJqOAfX8Hqh0qVgCrI75sfjyIfgUDYHbIb3St2t/s52QvDqrV5s+VFkRxF7NJv3cJ1leljD44hUZ9osvaANWy/uV2ZblWSq8czgnJ5y6F1qdbYdmMbAFG40VEKChEh5FkIzj46i9iEWGT3zI6ggkEIzG65pTWz8jT6KTqs6YCImAgAIvvVXz3/yvTKiYy3uzeWvb4MdQvVxdv/vI24xDgkUiLGbxuPo/ePYmnHpfD19HXIsZ/HPMf8o/Mx5/AchEeHa9ZVy18NSzouyfC1Q+yFJEnwcveCl7uXRdsTESJiInDr2S2l3Xl+B9Hx0YjXx8PTzRN5fPIgMHsgKgVUQpWAKsjnm8/BZ8FkRlhByWLsTVI4BgxQl+XPD3z4oWiGTN5xTZlOCC1n9bEkSULLUi3x89mfAQDbb2x3bQXFThXkkxNUIAhukhsSKRGv4l7h4uOLqJK/Suo7miFRn4gdN3Yo861Lt06rmEwm4p067ygKyl9X/sLtZ7ft6v4XkxCDZSeXYd6Rebj29FqK9VUCqqB/tf4YUmOIVQXJMgtxiXHo/lt3XA2/qixb1XmV5mVFVmFY0DBUC6yGbr91w70X9wAAa86vwbmwc1jddTWq5refe9XFxxex8NhC/Hj2xxQpkPP45MHM5jMxtOZQ4/FKowAAK4RJREFUu7wgyqpIkoTcPrmR2yc3ahao6WxxmEwMB8lnMV4mWWYLFEh92xsRasVA3XPbilW1LKlWs95+YztcOa21oQUlqECQ3fr19fTVKCRH7h9Jc58nHp5Q3sy669zRtHjTNPfJZB7almmLUrnEPasnPb4/9r3d+j509xCqL6qOd/55x6hyAgDnws5h4o6JKPFtCXy651M8j3ludLvMCBHhrc1v4d9b/yrLpjedjh6VejhRKudSp1AdnBh+As2Kq5a882HnEbQkCO/vfB+v4l7Z3PeTqCdYfHwxGq9ojErfV8L8Y/M1ykl2z+yY3GAyrr1zDSNqjWDlhGEyCKygZDH8korMPnwoguFnzgRatRLV5Vu1Aj7/XCwHtApKTkq7gnL7+e0UFVBdCY2CUtB+CgogqhLLHLmXdgVl2/VtynT9IvWRw4tTszEqOkmH0bVHK/NLTy7VVGq2laUnlqLRika4En5Fs7xUrlKoU6gOCvkV0ix/Hvscn+z9BCXnlcTXB79GTEJMmmVwdRYcW6DEXQBAnyp9MLXxVCdK5BoE+AZge7/tmFh/orIsQZ+ALw98iaJzimLav9MsqlxPRLgQdgFzDs1B659bo8DXBTBy80jsv6MtVOfv5Y+pjabi1thb+OK1L7KkJY9hMjLs4pXFaNIE2LEDmDYNGDNGxKMYsmMH8MknwMBBhBvFVQWlYUXbFJQCfgVQOaAyzoedF/3f3IEyecrYKr7DCI8Kx61nt5R5e1pQAKGgLD6xGIB9LCgcf8KkxqAag/DRvx8hMj4SETER+PnszxgeNNzm/qbvnY5pe9QMdBIkjKw1EhPrT0SJXCWU5VfDr2LV6VVYcGwBnscKy8nT6KeYsGMCvj3yLT5t+in6VesHd13m+/s5dPcQ3t32rjJfv0h9LHt9GQcFJ+Guc8dXLb9C8xLN8dbmtxDyLAQAEBETgen7pmP6vumoWaAmmhZrilK5S8HXwxfuOneER4fjwcsHOPvoLE48PIGwyDCTx6iWvxpG1x6N3lV6OyzGhWEYx8OV5J2IMyrJh4QApUoB8tderx7Qv79w+Xr4EFi1Cjh8GEC2x8CkAGW/CwMeo2LxvDYd871t7+Gbw98AADqV64SNPTem8Szsz44bO9DqZzHQz+OTB48nPrbroOLi44uo9L0opqqTdHg2+Rn8vPxs6utF7Avk/jI3EklkBjo27BhqFaxlN1mZzMNbm9/CwuMLAQBF/Yvi6ttXLQ6GNeTz/Z9jyu4pynxg9kD8/sbvZlPGPot5hm8OfYM5h+ekcOEpn7c83qnzDvpW7ZtprH+PIx+jxuIauP/yPgCgoF9BnBx+Evmz53eyZK5JVHwUPt//Ob498m2aXLwAoED2AuhRqQd6V+mN2gVrs0LIMC4AV5JnrGLWLFU5yZYN+PRTYORIoFMn8XnoELB9O+BVQLWeeOhzoEKxPDYfs2Up1c3r31v/Ij4x3szWziG5e5e9/+DK5y2vDMT0pMfxB8dT2cM0u0N2K8pJHp88qBFYwy4yMpmPifUnwkPnAQC48/wOlp1cZnUfXx/8WqOclM9bHkeGHkm1nkVO75yY3mw6boy5gbF1xypyAMDlJ5cxestoFPy6ILr91g0/nPoBd57fsVo2VyFRn4he63spyom7zh2/df+NlRMzZPPIhhnNZ+DOuDuY0WyG1dnNahaoifcbvI89A/bg7vi7mNtmLuoUqsPKCcNkEjKfjZ0xSWgosHKlmK5aFTh7VsSdBAeLrF6Bgeo2sdlUBaVs3lJpeug3LtYYnm6eiEuMw4vYFzh6/6hTinWZw1EB8jI6SYfaBWtjV4goVnnk/hGbU79uv6G6d71W8jUO+mRMUiJXCQytOVSxoszYPwP9qvWz2Gqx5MQSTNgxQZkvm6csdvffjQJ+FmTZSCLANwBz28wVRUr3TMNPZ34CQbwliYyPxB+X/sAfl/4AIKw8jYs1RuOijdG4WGOUzVM2Qww4vzzwpXJvA8BXr33lcs84VyWXTy5MaTwFHzb6EJeeXMLW61tx8fFF3Hl+RykkmCdbHgT6BqJU7lIIKhCEoIJByJvNNos+wzAZA1ZQshCGNVB27wbGjRNFGg8fTnLrMkDX7AaSisxDirAt/kQmm0c2NCraSPkD33R1k8v9eSdPMewI6haqq1yDw/cOp7K1aeT0sQDQuhSnF2bMM7XxVKw4vQIxCTEIfRWKqbunYl7beanut/b8WozcNFKZL5mrpNXKiSHFcxbHqs6rMKXRFCw6vggrTq/As5hnmm3uPL+Dn8/+rKQmD/ANQJNiTTC4xmC0LtXaJZWV06Gn8cmeT5T57hW7Y1zwOKfJk1GRJAkV81VExXwVnS0KwzAuALt4ZSEMa6D4+ACLFwN374pMXi1bCsWlZUsx322YakF5dTdtCgoAdCzbUZn+/dLvLpVu+Gn0UyVYE7B/Bi+Z+kXqK9P77+yHnvRmtjbOtfBruBlxU5k3dJ9jGGMU9CuIDxuqRY7mH52fqoL8z7V/0G9DP8XSUdCvIHb134VCOQqZ3c8SyuYpi29af4MH7z7A5t6b8Vatt1A6d2mj24ZFhmHdxXVou7otai6piYN3D6b5+PYkNiEW/Tb0Q7xeuK0WzlEYSzsudUlFimEYJiPBCkoWwlgNFLlI4/btavzJhx8CD6JVBUV6lnYFpWuFrsr09afXcS7sXJr7tBcnH55UpnP75EYxf/sVtDOkYdGG0Enilnsa/RQXwqxPjrD52mZlunJAZRTOUdhu8jGZl0kNJqFcHlFslUDo9ls3kyldN1zagM5rOyuD7jw+ebCj3w67V0D38fBBuzLtsKD9Alx75xruv3sfv3b7FaNrj0aVgJSFTE+HnkaDHxpg4vaJSNAn2FUWW5m2Z5qSoRAAVnRagZzeOZ0nEMMwTCaBFZQshGENFMB8HRTDt/S5kXYFpYh/EQQXDlbmf7/4e5r7tBeOqiCfHH9vf1QPrK7M77291+o+Nl3dpEwbWqUYxhxe7l5Y9voyuEkiXunBywdou7otHr58qGwTnxiPT/d8iu7ruiMuMQ6AKHL3T59/0sXtpqBfQbxZ+U3MbzcfZ0edRfikcGx4cwM6lO2g2e5/h/6Htqvb4mXsS4fLZI7D9w5j9sHZyvzbtd/GayVfc6JEDMMwmQdWULIQTZqIzxUrgOHDgSJFgKlTRe2Tw4fF55QpQOHiMXj4Sh24NK1ewkSP1tG9Qndl+rcLv7mMm9fxh2pGLUfFn8g0KdZEmbZWQXkR+0KzT/KBG8OYo2HRhpjfbr4yf/bRWVRZWAXvbXsPH+3+COXml8Mnez9RXA9zeufEtr7bULtQbafIm9snNzqX74y/e/2N/wb9p1GSdt7ciTar22gqhqcnCfoEjNg0QrlWZfOUxZctv3SKLAzDMJkRVlCyEEOGAB4ewNGjwNKlImC+Xj1g4UJg40bxGRwMJGS7p+5EEsYNso8bUbeK3ZTpK+FXXMaf/Nj9Y8q0owdjhgrKvtv7rFLStl3fpri25PHJo6lOzzCWMLLWSExuMFmZD48OxzeHv8GM/TM0cVjFcxbH3oF7NXFTzqRB0QY4OvQoelTqoSw7ePcgOv3aSbH2pCfzjszD2UdnlfkfXv8B2TyypbscDMMwmRVWULIQgYGiSKPMrFnAwYMp66AMfU+tR+AeXRCFC3oY6c16iucsjhYlWijzS08utUu/aeHRq0e4/fy2Ml+nUB2HHq9RsUaQIFzIwiLDcOnJJYv33XRNde9qV6YdpxdmbOKL177A4g6LjQ6odZIOQ2sMxZmRZ1A1f1UnSGcaX09frOm2BsNrDleW7bm1B6M2jUpXa+y9F/cwbc80ZX5ojaEul5WQYRgmo8NphrMQoaHA9evq/AcfAH/+mbIGypHYO0BnsU1CeFE8eiSC6e3BsJrDlFS7v134DXPbzHVqUOmxB6r1pED2Aijkl/YsRebI7ZMb1QKr4XToaQDCKmKJf3+iPhFbrm1R5tm9i0kLw4OGo2uFrvjl3C849uAY4hPjUTmgMgZUG4Ai/kWcLZ5JdJIOCzssRFRClJKK+IfTP6Bu4boYHjQ8lb3tw/ht45XK53l88uCL175Il+MyDMNkJVhBMYIkSb4AugKoA6AugGoAPAF8QEQZ9t9o+XIgIQGoUweoXt1MDZTmt5UaKHheFMuXi8xe9qBz+c7I45MH4dHhiE6IxrKTyzCh/oTUd3QQR+8fVabTqwpx29JtFQVly/UtGF9vfKr7HL1/FE+ingAQVapblWrlSBGZLEDebHkxpu4YZ4thNTpJh2UdlyEkIgQH7h4AAIzbOg4NizZ0eDD/1utbNQk+vmr5FfJky+PQYzIMw2RF2MXLOGUA/AjgbQC1IZSTDI9cB2XQIPM1UN4crrp44Vkx7NljPxm83L0wpMYQZf7LA18qbyOdQXIFJT1oV6adMr331l6Lzn/dxXXKdMOiDTmVKZOl8XL3wm9v/KZUE49OiEbP33siJiHGYceMSYjB21veVuYbFGmAgdUHOux4DMMwWRlWUIzzEsByACMA1AQw07ni2AfDOiiPHgHLlgF79ojlfn5A06YikP5xnIGC8ryosp+9eK/+e4r/+5OoJ/juyHf2PYCFEJHGxSu9FJTgwsGKghGvj8eum7vMbp+oT8Sv539V5ntU7GFma4bJGhT0K4iVnVYq8+fCzuGDnR847Hhf/vclbkSI+lBukhu+b/+9UteIYRiGsS/8dDUCEd0goqFEtISITgFwjapgaUSugzJ7tukUw0WKAMeuahUUeT97EeAbgHfqvKPMf/7f57jx9IaZPRzDjYgbeBr9VJmvVbBWuhw3uYuWYfFFY+y7vU9J++yuc8cbld5wqHwMk1FoX7Y9xtUdp8x/e+Rb7Lu9z+7HCYkIwRcHVO/esXXHulwSAYZhmMwEKyhZiPpJGUMPHDCdYjg+nvCctApK06b2l2Vi/YnI5Z0LAPAq7hX6buib7tWh99/er0xXyFshXd2m2pdpr0yvv7QesQmxJrf95dwvynSrUq0UtxaGYYBZr81SYk8IhIEbB9rdbXTctnGK+1hg9kBMazotlT0YhmGYtMAKShbCMIOXqRTDazc9BjxUP273yGIYMsRIZ2kkT7Y8WNRhkTJ/+N5h9N/QH9Hx0fY/mAn+u/OfMt2oaKN0Oy4gkgX4uPsAAJ5GPzVpRXkR+wJrL6xV5ntV7pUu8jFMRsHb3RurOq+CmyTSboc8C8HkHZNT2ctyNl/djL+u/KXMf93qa+TwymG3/hmGYZiUsIKSDkiSdMFYA1Aq1Z3tRGgo8Ntv6vysWcKtKzklaxhYT2L90LOLv91SDCenR6UeGFBtgDK/5vwa1FlWB39d+QuJ+kTHHNSA/XdUC0qjYumroOTwyoEuFboo86vOrDK63YpTK/AyTgQB5fTOic7lO6eHeAyToahVsBY+aKjGn3x//PtUY7ssISYhBmO2qpnOmhRrwi8JGIZh0gFWULIIy5cLt646dYAmTYAXL4BWrYSb16JFws1r0SKg9yite1fpUo5Nu7uw/UI0L9FcmT8fdh6dfu2EPF/lQYdfOuCrA1/h+IPj0JPeTC/WE/oqFNeeXlPmGxZtaNf+LcFQOdtybQsevXqkWZ+oT8S8o/OU+WE1hyG7Z/Z0k49hMhIfNflIExcy+K/BeBH7Ik19fnXgK9yMuAlABMbPbzc/XVKRMwzDZHUypYIiSdLvkiRdtrI5LIUTEVUy1gCkW2S4YYrhlSuBWrUASRIB8qNGAV26iM9rYWpVdTwvigMHHCuXj4cPtvTegvHB2logz2OfY/O1zZi8czJqL62NcvPLYe7huYhLjLPLcQ/cUU+scI7CKOZfzC79WkOLEi2UwpAJ+gTMOzJPs/6PS38ogyOdpMPo2qPTXUaGySh4unliVedVcNeJ8l53nt/Bu9vetbm/S48v4fP9nyvz79R5B5UDKqdZToZhGCZ1MqWCAqA4gHJWtmzOEDS9kFMF//EHULYscPw4QKTdRpKA/GW1NVDsnWLYGF7uXvim9Tc4OPggulfsrgwwDLn+9DrGbxuPmotr4viD42k+pqF7V8OiDZ3yVtRN54a366h1FeYdnYf7L+4DAKLio/DhbrU6ZrcK3VAsZ/orUQyTkageWB0fN/5YmV9+ajm2XNtidT8J+gQM/HMgYhNF8orA7IH4pOkn9hKTYRiGSYVMqaAQUS0ikqxse5wttyPx9RWfO3aYzuBFBDyKcWyKYXPUK1IP695Yh4jJEdjedzumNJqCeoXrQYKqPFx4fAGNVzTWBK3awrYb25TpxkUbp6mvtDCq1ijk9skNQGQzG7BxACLjIjHs72G4/lRkNXCT3DC92XSnycgwGYn3G76PoAJByvywv4chIjrCqj6+Pvi1pojrkg5L4O/tbzcZGYZhGPNkSgWFScnz5+LTzQ3Yvt14Bq/t2wFdbsenGE6N7J7Z0bJUS8xoPgMHhxxEyNgQjAwaqSgq0QnR6LK2CzZe3mhT/yERIbj85LIy36Z0G3uIbRP+3v74ooVaX2FXyC5kn5Vdk1p4fPB4lM9b3hniMUyGw8PNA6s6r4KnmycA4MHLBxi7dazF+598eBLT9qhphPtV7YeO5TraXU6GYRjGNKygZAFCQ4HTp8V0opnkWC1bAn6F1RgUt8iiDkkxbC3FchbDwg4LsaPfDiW9p5706L2+t+Ytp6X8c/0fZbp83vIokauE3WS1haE1h5rMDNSseDPMbDEznSVimIxNpYBKmN5UtTr+dPYnLD+5PNX9wqPC0XVtV8W1q0D2Avi2zbcOk5NhGIYxDisoWYDly4GEBCAgQMx37248xXB0fDSeJzxW5isVKuawFMO20KJkC+wbuA/+XsLVIjohGp1/7YwnUU+s6sfQJ71d6XZ2ldEWJEnCys4r8U6dd6CT1Fuyf7X++LvX38qbYIZhLGdC/QkILhyszI/cPBLbrm8zuf2ruFd4/dfXcfu5eEmjk3T4qctPyOWTy+GyMgzDMFpYQTGBJEkbJEk6LEnSYQBDkxa/JS+TJGmDM+WzBjmD15QpIvZETjHs5wdUqAD07Qt89RUQ3PquupNeh9weBZ0jsBmqBVbDhjc3wEPnAQB4+OohBv05CJQ84t8E0fHR2B2yW5lvV8b5CgogMhDNazsPd8ffxc5+O3F73G2s6rwKvp6+zhaNYTIkbjo3rO2+FoHZAwGIwPfXf30da8+vTbHto1eP0HZ1Wxy8e1BZ9nnzz9GiZIt0k5dhGIZRYQXFNDUA1E1qhZKWFTFYVsNJclmNnIlr0ybg2DF1+atXwOXLwOrVwOTJwNnbBvEnLwshJiplNi1XoFmJZviq5VfK/KarmzD/6HyL9t10dROiE0S1+uye2Z1S/8QcBf0KokXJFijqX9TZojBMhqeof1Fs6rUJvh5C0Y9LjEPP9T3Ra30vHH9wHHee38Hyk8tRfXF1/HfnP2W/YTWHYVKDSc4Sm2EYJsvjmiNQF4CIijtbBnthmMELEFaUzp2Bs2eBkyeBu3eFsgJ/bQ2U9MzgZS1j647Fjps7FHetCTsmoEnxJppCbcb48eyPynTXCl3h5e7lUDkZhnEuQQWDsLP/TrT/pT2eRj8FAPx6/lf8ev5Xo9sPrj4Yizos4oKMDMMwToQtKFkAYxm8Jk0Cfv4ZuHhRWFi2bwc8A7Q1UJyRwctSJEnCik4rFPeNuMQ49F7fG9Hx0Sb3CYsMwz/X1AD5/lX7O1xOhmGcT3DhYBwcfBB1Cpmux+vp5on5bedj2evLNLFgDMMwTPrDT+FMjjUZvJq+riooupeukcHLHAG+AVjVeZUyf+HxBUzeOdnk9r+c+wWJJC5C4RyF0bR4U0eLyDCMi1AubzkcGnIIP7z+A+oVrqfEsRX0K4iRQSNxftR5jK4zmi0nDMMwLgC7eGVyDDN4hYWJDF6//y4UkuSExakKSpEcRV0qg5cpWpVqhfHB4zHn8BwAwHdHv0Ob0m1SBL/HJ8bj2yNqutA+VfrATeeWrrIyDONcdJIOg2oMwqAag5CoT0SCPoHdPBmGYVwQtqBkcgwzeDVpombwqlcPWLRIVJJftEhUkj8dosag+EsZJ0j78xafa2JPBm4ciJsRNzXbLD6xGLee3QIAuOvcMbLWyPQUkWEYF8NN58bKCcMwjIvCCkomR87gVawY8M8/wPDhgIcHcPgwMGoU0KWL+DxyVA/kMEgz/CzjKCje7t74pesv8Hb3BgA8jnqMlj+1xIOXDwAAV8Ov4sNdHyrbD6w2EMVzFneGqAzDMAzDMEwqsIKSyZEzcT18CPj4AIsXi6xdM2cKN6/gYPH5/mdhgHucsl8e92JOktg2KgVUwqL2i5T5mxE3EbQkCOO3jkeTlU3wMk5oajm9c3JldoZhGIZhGBeGFZRMTpMm4nOVGkuO/PmBDz8UmbsOHRKfXQYaZPCK8cdrjXKkr6B2YED1AfjytS+V+dBXoZh7ZC5CX4Uqy5Z1XIYA3wBniMcwDMMwDMNYACsomZwhQ1SXLrkOijFuP9PWQHH1DF6mmNRgEua3nQ9PN0/NcjfJDYvaL0K3it2cJBnDMAzDMAxjCZzFK5MTGAgMHAgsXWo+g9fOY6oFpah/xsjgZYrRdUajQ9kO+PHMj7j29BoK+RVC/2r9USFfBWeLxjAMwzAMw6QCKyhZgG+/Ba5eFRm9WrUScScDBgjlJTQUWLkSOJLrDhAstm9bL2PFnxijWM5i+KjJR84Wg2EYhmEYhrESVlCyAD4+IoPXuHHAihXC3evwYe02Uq87oKTpErkzTgYvhmEYhmEYJnPBCkoWQc7gNX26KN64Z49IQeznBzRtCvzqfxvnnohti/qzgsIwDMMwDMM4B1ZQshhyBq8PP9Qu/+YrbQwKwzAMwzAMwzgDzuLFIDIuEuHR4cp8sZwZPwaFYRiGYRiGyZiwgsLg7gu1gryb5IYC2Qs4URqGYRiGYRgmK8MKCqOpgVI4R2G46dycKA3DMAzDMAyTlWEFhcGd5xx/wjAMwzAMw7gGrKAwuP1ctaCwgsIwDMMwDMM4E1ZQGNx6dkuZLpGzhPMEYRiGYRiGYbI8rKAwGgWleM7iTpODYRiGYRiGYVhBYTQuXpximGEYhmEYhnEmrKBkceIS43D/xX1lni0oDMMwDMMwjDNhBSWLc/f5XRAIACBBQpEcRZwsEcMwDMMwDJOVYQUli2MYf1LQryC83L2cJwzDMAzDMAyT5WEFJYvD8ScMwzAMwzCMK8EKShaHM3gxDMMwDMMwrgQrKFkcjYLiX9xpcjAMwzAMwzAMwApKloctKAzDMAzDMIwrwQpKFscwBoUVFIZhGIZhGMbZsIKShYlPjMe9F/eUeQ6SZxiGYRiGYZwNKyhZmHsv7kFPemW+qH9RJ0rDMAzDMAzDMKygZGkM408KZC8Ab3dv5wnDMAzDMAzDMGAFJUvD8ScMwzAMwzCMq8EKShbG0ILC8ScMwzAMwzCMK8AKShaGa6AwDMMwDMMwrgYrKFmYGxE3lGl28WIYhmEYhmFcAVZQsjDXwq8p02XzlHWiJAzDMAzDMAwjYAUli/Ii9gUeRT5S5svkKeNEaRiGYRiGYRhGwApKFuX60+vKtI+7Dwr6FXSiNAzDMAzDMAwjYAUli2Lo3lU6d2noJP4pMAzDMAzDMM6HR6VZlKvhV5Vpjj9hGIZhGIZhXAVWULIo156qFpQyuTn+hGEYhmEYhnENWEHJomgUFA6QZxiGYRiGYVwEVlCyKIYxKGxBYRiGYRiGYVwFVlCyIBHREQiPDlfm2YLCMAzDMAzDuAqsoGRBDN27/Dz9kN83vxOlYRiGYRiGYRgVVlCyIE+iniCHVw4AwnoiSZKTJWIYhmEYhmEYgbuzBWDSn3Zl2uHZ5GcIiwzDs5hnzhaHYRiGYRiGYRRYQcmiSJKE/NnzI392du9iGIZhGIZhXAd28WIYhmEYhmEYxmVgBYVhGIZhGIZhGJeBFRSGYRiGYRiGYVwGVlAYhmEYhmEYhnEZWEFhGIZhGIZhGMZlYAWFYRiGYRiGYRiXgRUUhmEYhmEYhmFcBlZQGIZhGIZhGIZxGVhBYRiGYRiGYRjGZWAFhWEYhmEYhmEYl4EVFIZhGIZhGIZhXAZWUBiGYRiGYRiGcRlYQWEYhmEYhmEYxmVgBYVhGIZhGIZhGJeBFRSGYRiGYRiGYVwGVlAYhmEYhmEYhnEZJCJytgxZFkmSXnh5efmVKlXK2aIwDMMwDMMwjF24ceMGYmNjXxJRDlv2ZwXFiUiSFAogG4C7zpbFicja2Q2nSsFkNvh3xTgC/l0xjoB/V4wjcPbvqgiAKCIKtGVnVlAYpyJJ0gUAIKJKzpaFyTzw74pxBPy7YhwB/64YR5DRf1ccg8IwDMMwDMMwjMvACgrDMAzDMAzDMC4DKygMwzAMwzAMw7gMrKAwDMMwDMMwDOMysILCMAzDMAzDMIzLwFm8GIZhGIZhGIZxGdiCwjAMwzAMwzCMy8AKCsMwDMMwDMMwLgMrKAzDMAzDMAzDuAysoDAMwzAMwzAM4zKwgsIwDMMwDMMwjMvACgrDMAzDMAzDMC4DKygMwzAMwzAMw7gMrKAwDMMwDMMwDOMysILCpCuSJGWTJKmzJEnLJUk6K0nSC0mSIiVJOiNJ0seSJGV3toxM5kCSpNySJIVJkkSSJF12tjxMxkaSpEBJkuZIknRVkqRoSZKeSpJ0QpKkr5wtG5MxkSQpWJKk9ZIkhUqSFJ/0m9olSVJ3Z8vGuDaSJAVJkvS+JEl/SJJ0P+l/LsaC/fpLknRUkqRXSb+3LZIk1U8Pma2FK8kz6YokSUMBLE2avQDgIoAcAOoD8ANwGUATIgpzjoRMZkGSpJUA+gOQAFwhovLOlYjJqEiSVA/AFgA5IZ5Z5yGeVxUBFCYid+dJx2REJEl6A8CvEC+KjwO4AaAggAZJy74kovedJyHjykiStBFAp2SLY4nI28w+3wAYDyAawHYA3gBaQPxHvkFEGxwjrW2wgsKkK5Ik9QcQDGAOEV0zWF4AwGYANQCsIaLeThKRyQRIktQCwE4ASwAMBysojI1IklQQ4mWKF4A+yf/EJUmqQ0RHnSIckyGRJMkdwAMA+QD0JKK1BuvqAdgN8XsrQ0Q3nCMl48pIkjQZQDYAx5JaKMwoKJIkNQewC0A4gHry+Cvp97YHQmkpQUQRjpfeMlhBYVyGpBvlIIBYADmIKM7JIjEZEEmSfACcBRAHoDOAq2AFhbERSZJ+BNAPwDtENN/Z8jAZH0mSKgM4B+AyEVUwsn4jxNvxN4not3QWj8mASJJEMK+gbAbQDsB4IpqbbN23AMYAmEBEXztaVkvhGBTGlTiT9OkFII8zBWEyNNMAlAIwCkC8k2VhMjCSJOUC0APAcwDLnCwOk3mItXC7pw6VgskSSJIku3IBwO9GNpGXdUwfiSyD/WYZV6Jk0mc8+MHM2IAkSVUBvAdgBRHtkySpuJNFYjI2DSBemOwEEJ8UvNwQgAdEvNxvRPTIifIxGZObSa28JEk9DK0kSZ4ErQGEANjnJPmYzEV5iOfYYyK6Z2T9yaTPquknUuqwgsK4EmOTPrcSkaVvmBgGACBJkg4iAcMzAJOcKw2TSaiU9PkIwH4A9ZKtnyVJ0iAiWpe+YjEZGSJKlCRpIIC/AayVJGkiRJB8AQgF+CiAfuzmzNiJokmfxpQTEFGkJEnPAOSSJMmPiF6mm2RmYBcvxiWQJKkdgCEQ1pOPnCwOkzF5B0AdABOJKNzZwjCZglxJn/0h3i4OgQhsLgHgGwC+AH5OstwxjMUQ0X4ATSAsJbUAvAmgMYBICIvdA+dJx2Qy5PINUWa2iUy2rdNhBYVxOpIkVQDwM0Squ4lEdCaVXRhGgyRJRQDMALCXiFY6WRwm8+CW9OkO4F0i+oGInhDRLSJ6D8J32xNssWOsRJKkXgCOALgDoC7EwLAsgDUApgLYKUmSh/MkZDIRUtKnuaxYkpl1ToEVFMapSJJUGMBWiDeV3xDRt04WicmYfA8xUBzlbEGYTIXs6qAHsMrI+h+SPpumizRMpkCSpDIQv6fHANoT0VEiiiSia0Q0AsL1qx6AQc6Uk8k0yM8xXzPbZEv6fOVgWSyGY1AYpyFJUl4AOyD8I1cAmOBciZgMTAeI2JOFkqR5ESSnXCwqSdIeeVsicpmHMOPS3Er6DDURFyevD0gXaZjMQk+IRAtbiSjSyPrfIDIqNYWo5cQwaeFO0mdhYyslSfKFKEL7zFXiTwBWUBgnIUmSH4B/ILJL/AFgGHFRHiZt5ITw6TaGj8E6fu4xlnIq6TOXJEmSkWeUnA6dFV7GGuSB4gsT6+XludNBFibzcwUitXU+SZIKG8nkVTPp82z6imUedvFi0h1JkrwA/AkRGLgNQC8iSnSuVExGhogkYw0imBkQhRrl5c+cKCqTgSCicxBBzD4QcQLJaZr0edLIOoYxRWjSZy0T62snfd5yvChMZoeIogHsTprtbmQTedmm9JHIMlhBYdIVSZLcIIIAm0Gk7ezKqRQZhnFhvkz6nJfklgoAkCQpCKLmDgAsSnepmIzMn0mfjSVJ0sTNSZIUDGB80qyxonoMYwvfJH1OTYqBAqDU3RkBYbVb7gzBTCGxVw2TnkiSNBbA3KTZDTBt4p5ARE/SRSgm05JUqDEEwoJS3sniMBmQpPo6vwJ4A6KA7EGIjEv1IRIzLCWi4c6TkMmISJI0G2rc5QUAFwEUhAiO1wFYkhQwzzApkCSpPbQlGepCZOk6arDsMyLabLDPXIh6c1EQ8b+eAFpC/N56ENF6B4ttFeyLzaQ3uQymu5jZ7hMArKAwDONUiEgvSVJPAHsADAXQHGIgcBzAIiL6yYniMRkUIpooSdJBACMBBAEoB5FtaS+AZUT0izPlY1yefEjpdiolW5bPcCURjZMk6TSAtyEUk3gAuwDMIKL/HCeqbbAFhWEYhmEYhmEYl4FjUBiGYRiGYRiGcRlYQWEYhmEYhmEYxmVgBYVhGIZhGIZhGJeBFRSGYRiGYRiGYVwGVlAYhmEYhmEYhnEZWEFhGIZhGIZhGMZlYAWFYRiGYRiGYRiXgRUUhmEYhmEYhmFcBlZQGIZhGIZhGIZxGVhBYRiGYRiGYRjGZWAFhWEYhmEYhmEYl4EVFIZhGIZhGIZhXAZWUBiGYRiGYRiGcRlYQWEYhmGyJJIkeUuStECSpHBJkkIkSRrmbJkYhmEYVlAYhmGYrMsMAEMB7APwGMASSZLaOFckhmEYhhUUhmEYJqvyBoDxRNSFiOoA2AugnZNlYhiGyfKwgsIwDMNkVfQAGkiS5C9JUgUAJQDccbJMDMMwWR5WUBiGYZhMhSRJ0yRJSpQkqWIqm84G0BvAMwAXAVwF8H2yvgpIkhQtSdIChwjLMAzDpEAiImfLwDAMwzB2QZKk/ACuA9hMRD1T2TY3hMXEN2lRWyLaamS7bwG8BaAyEV2xs8gMwzBMMtiCwjAMw2QmPgSQHcAsC7b9GEI5eZg0X9nEdl9B/F9OT7N0DMMwTKqwgsIwDMNkCiRJygZgAICzRHQmlW1LQ1hFLgD4MmlxFWPbEtF9AP8C6JJkoWEYhmEcCCsoDMMwTIZAkqTikiSRJEm7JEnKI0nSXEmSbkmSlCBJ0myIrFz+AFZb0N1XADwATAYgKzNGFZQkfknafqDtZ8AwDMNYAisoDMMwTEahetJnDICzAN4EcBTANgBHAHRIWr/HXCeSJDUC0AXAHiLaDGFFAYAKkiS5mdhN7rO9DXIzDMMwVsBB8gzDMEyGQJKkTwBMS5pdCFHDJNZg/UMAeQD4GS5P1ocEoczUAlCHiI4nLQ8DkA9AeVOB8JIkPQbgB8DfVP8MwzBM2mELCsMwDJNRqJb0uR/A28mUkwAAgQBupaI89AFQG8BaWTlJ4nzSpzk3rysAvACUs1ZwhmEYxnJYQWEYhmEyCtWTPicQkT7ZuoCkzwhTO0uS5A1gJoA4iGxfhshuXqYyeQHA06TPfKlKyjAMw9gMKygMwzCMyyNJkj+A4gDuAThmZBP/pM+XZrp5F0BRAAuIKCTZOllBMWdBeZHsWAzDMIwDcHe2AAzDMAxjAbJ71z4yHjz5POkzh7Gdk1zA3k+abSFJ0n/JNsmd9GlOQZEVk+dmtmEYhmHSCCsoDMMwTEagetLnaRPrw5I+c5tYPx0iwB0Aqpo5TilJknyIKNrIulxJn4/N7M8wDMOkEXbxYhiGYTIC1ZM+TxlbSURhAEIBFJMkycdwnSRJFQEMBfAAQDYikow1AHcg/hcrmpChPESKY6NZvhiGYRj7wAoKwzAMkxGQXbxOm9lmP4RnQI1ky/8HwA3AJyYsIzKXkz5TuHlJklQKIoXxUU4xzDAM41hYQWEYhmFcGkmS3AFUAnCfiJ6Y2XRz0mczg31bAmgL4CqAFakcSlZQjGXyapr0uSU1eRmGYZi0wQoKwzAM4+qUh6g/cjqV7X6DCGDvDQCSJOkgrCcAMIWIElLZ/1LSp7FA+d4A4gGsTF1chmEYJi1wJXmGYRgm0yBJ0hwA4wAEEdFJO/VZGMBtAL8T0Zv26JNhGIYxDVtQGIZhmMzELACvoKYUtgcTAegBfGzHPhmGYRgTsILCMAzDZBqSsnnNBtAtKXtXmpAkqQCA4QCWEhFn72IYhkkH2MWLYRiGYRiGYRiXgS0oDMMwDMMwDMO4DKygMAzDMAzDMAzjMrCCwjAMwzAMwzCMy8AKCsMwDMMwDMMwLgMrKAzDMAzDMAzDuAysoDAMwzAMwzAM4zKwgsIwDMMwDMMwjMvACgrDMAzDMAzDMC4DKygMwzAMwzAMw7gMrKAwDMMwDMMwDOMysILCMAzDMAzDMIzLwAoKwzAMwzAMwzAuAysoDMMwDMMwDMO4DKygMAzDMAzDMAzjMrCCwjAMwzAMwzCMy/B/Z8D+/5P7D74AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(dpi=150)\n",
    "plotRecipe(recipe, ax);\n",
    "ax.plot(r, gwave+1, label='wave')\n",
    "ax.plot(r, g1+2, label='cluster')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = recipe.pdf.profile.x\n",
    "g = recipe.pdf.profile.y\n",
    "gcalc = recipe.pdf.evaluate()\n",
    "pdf = np.column_stack([r,g, gcalc])\n",
    "np.savetxt(grdata + \"_\" + xyzfile1  + '_fit'  + '.cgr', pdf)    \n",
    "with open(grdata + \"_\" + xyzfile1 + '_fit' + '_parameters.txt', 'w') as outfile:\n",
    "    print(FitResults(recipe), file = outfile)\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export refined structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.03003354, 0.969981023, 1.03005248]\n"
     ]
    }
   ],
   "source": [
    "from ase.io import read, write\n",
    "import re\n",
    "\n",
    "with open('refined_cluster.xyz', 'w') as outfile:\n",
    "    print(len(atoms1), file = outfile)\n",
    "    print(' ', file=outfile)\n",
    "    for i in range(len(atoms1)):\n",
    "        print(atoms1[i], file = outfile)\n",
    "outfile.close()\n",
    "\n",
    "file_name = 'refined_cluster.xyz' \n",
    "structure = read(file_name)\n",
    "\n",
    "xyz_positions = structure.get_positions().copy()\n",
    "\n",
    "FitResults(recipe).saveResults('fitresults.txt')\n",
    "zoomscales = []\n",
    "with open('fitresults.txt') as f:\n",
    "    for line in f:\n",
    "        if len(re.findall('zoom',line)) != 0:\n",
    "            zoomscales.append(float(line.split()[-1]))\n",
    "f.close()\n",
    "\n",
    "print(zoomscales)\n",
    "\n",
    "xyz_positions[:,0] *= zoomscales[0]\n",
    "xyz_positions[:,1] *= zoomscales[1]\n",
    "xyz_positions[:,2] *= zoomscales[2]\n",
    "\n",
    "structure.set_positions(xyz_positions)\n",
    "\n",
    "write(file_name, structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}