optimize_reflectometry_sim_exp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45bb3a21-90bc-49dd-befc-03ea3203147e",
   "metadata": {},
   "source": [
    "# Import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e7dd0ad-9e3a-41dc-9795-47ed9d71e408",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from refnx.reflect import SLD, Slab, ReflectModel\n",
    "from refnx.reflect import reflectivity, Structure\n",
    "from refnx.analysis import CurveFitter, Objective, Parameter, GlobalObjective\n",
    "from scipy.interpolate import interp1d\n",
    "from matplotlib import gridspec\n",
    "from refnx.dataset import Data1D\n",
    "import MDAnalysis as mda\n",
    "import pickle\n",
    "\n",
    "import string\n",
    "from tqdm import tqdm\n",
    "%matplotlib inline\n",
    "#%matplotlib qt\n",
    "#import hplusminus as hpm\n",
    "plt.rc('xtick', labelsize=16) \n",
    "plt.rc('ytick', labelsize=16) \n",
    "#matplotlib.rc('axes', labelsize=16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d748d56e-d68b-4f19-b3be-81a75d89fac3",
   "metadata": {},
   "source": [
    "# Functions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84765428-b5f0-400c-8f74-5f5116de1720",
   "metadata": {},
   "source": [
    "## 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95ca2b03-d21d-4b1c-a779-5790d01a4214",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Reflectivity_input2(sld, layer_thickness, water_thickness_front, water_thickness_back,roughness):\n",
    "    \n",
    "    dz = np.abs(np.diff(sld[:,0])[0])#(sld[30,0] - sld[0,0]) / 30\n",
    "    FrontWater = int(round( water_thickness_front / dz))\n",
    "    BackWater = int(round(water_thickness_back / dz))\n",
    "    Begin, End = FrontWater, len(sld)-BackWater \n",
    "    sld2 = np.zeros((End-Begin,2))\n",
    "    sld2[:,1] = sld[Begin:End,1]\n",
    "    sld2[:,0] = sld[Begin:End,0]\n",
    "    number_of_slabs = round((max(sld2[:,0]) - min(sld2[:,0])) / layer_thickness ) +1\n",
    "    number_of_slabs = int(number_of_slabs)\n",
    "    ave= int(round(len(sld2) / number_of_slabs))# sld_points_in_a_slab \n",
    "    \n",
    "    #################\n",
    "    s = len(sld2)-number_of_slabs*ave\n",
    "    d = int(np.floor((s/ave)))\n",
    "    SLD2=np.zeros((len(sld2)-ave,2))\n",
    "    SLD2[:,0] = sld2[0:len(sld2)-ave,0]\n",
    "    ###########---------------\n",
    "    j = 0\n",
    "    SLD_layer=[]\n",
    "    for i in range(0,number_of_slabs+d):\n",
    "        sld_slab = sum(sld2[j:j+ave,1])/ave\n",
    "        SLD2[j:j+ave,1]=sld_slab\n",
    "        j=j+ave\n",
    "        SLD_layer.append(sld_slab)\n",
    "        \n",
    "    \n",
    "    Ref = np.zeros((len(SLD_layer),4))\n",
    "    \n",
    "    for i in range(0,len(Ref)):\n",
    "        Ref[i][0] = layer_thickness \n",
    "        Ref[i][1] = SLD_layer[i]\n",
    "        Ref[i][2] = 0  #sld_slab[i]\n",
    "        Ref[i][3] = roughness  #2.0*random.random()-0.5#sld_slab[i]]\n",
    "        \n",
    "    #sld_slab2[0:Interface] = 0    \n",
    "    return Ref, SLD2\n",
    "\n",
    "def interpolate(sld,points):\n",
    "    size = np.shape(sld)\n",
    "    sldinterpolated = np.zeros((points,size[1]))\n",
    "    x = sld[:,0]\n",
    "    sldinterpolated[:,0] = np.linspace(min(x),max(x),points)\n",
    "    for j in range(1,size[1]):\n",
    "        \n",
    "        y=sld[:,j]\n",
    "        f = interp1d(x, y, kind='linear')\n",
    "        sldinterpolated[:,j]= f(sldinterpolated[:,0])\n",
    "        \n",
    "    return sldinterpolated\n",
    "\n",
    "def Add_Silicon_and_stuff(sld2,Ref,Sld_extra_layer,FrontorBack):\n",
    "    #Add extra layers to the existing SLD, the input of SLD_extra_layer is shown below\n",
    "    # Sld_extra_layer = [[thickness1,sld1,im_sld1,roughness],[thickness2,sld2,im_sld2,roughness]]\n",
    "\n",
    "    dz =  np.abs(np.diff(sld2[:,0])[0])\n",
    "    Points = []\n",
    "\n",
    "    for i in range(0,len(Sld_extra_layer)):\n",
    "        points = round(Sld_extra_layer[i][0] / dz)\n",
    "        Points.append(int(points))\n",
    "\n",
    "\n",
    "    Newsld =  np.zeros((len(sld2)+sum(Points),2))\n",
    "\n",
    "    if  FrontorBack == \"front\":\n",
    "        Start = 0    \n",
    "        Distance= np.linspace(min(sld2[:,0])-sum(Points)*dz,min(sld2[:,0]),sum(Points))\n",
    "\n",
    "        Newsld[Start:sum(Points),0] = Distance \n",
    "        Newsld[sum(Points):len(Newsld),0] = sld2[:,0]\n",
    "\n",
    "\n",
    "        for j, points  in enumerate(Points):\n",
    "\n",
    "            Newsld[Start:points+Start,1] = Sld_extra_layer[j][1]\n",
    "            Start+=points\n",
    "\n",
    "        Newsld[sum(Points):len(Newsld),1] = sld2[:,1]\n",
    "\n",
    "    if  FrontorBack == \"back\":\n",
    "        Start = len(sld2)   \n",
    "        Distance= np.linspace(max(sld2[:,0]),max(sld2[:,0])+sum(Points)*dz,sum(Points))\n",
    "        \n",
    "        Newsld[Start:Start+sum(Points),0] = Distance \n",
    "        Newsld[0:Start,0] = sld2[:,0]\n",
    "\n",
    "\n",
    "        for j, points  in enumerate(Points):\n",
    "            \n",
    "            Newsld[Start:points+Start,1]= Sld_extra_layer[j][1]\n",
    "            Start+=points\n",
    "\n",
    "        Newsld[0:len(sld2),1] = sld2[:,1]\n",
    "\n",
    "    if sum(Points)!=0 and FrontorBack == 'front':\n",
    "        Ref2 = np.zeros((len(Ref)+len(Sld_extra_layer),4))\n",
    "        for i in range(0,len(Sld_extra_layer)):\n",
    "            \n",
    "            Ref2[i] = Sld_extra_layer [i]\n",
    "        Ref2[i+1:len(Ref2)] = Ref \n",
    "          #2.0*random.random()-0.5#sld_slab[i]]\n",
    "\n",
    "    if sum(Points)!=0 and FrontorBack == 'back':\n",
    "        Ref2 = np.zeros((len(Ref)+len(Sld_extra_layer),4))\n",
    "        for i in range(0,len(Sld_extra_layer)): Ref2[len(Ref):i+len(Ref)] = Sld_extra_layer [i]\n",
    "        Ref2[0:len(Ref)] = Ref\n",
    "    if sum(Points) == 0: return Ref\n",
    "    return Ref2, Newsld"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e163320b-f80e-4798-8bac-0e9418b954a2",
   "metadata": {},
   "source": [
    "## 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1556ee2c-62a3-426b-8391-e7644e042e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Out_sld_at_this_deutration(SLD,deu_percentage):\n",
    "    \n",
    "    deu_percentage  /= 100\n",
    "    sld_total      =  np.zeros((len(SLD),2))\n",
    "    sld_total[:,0] =  SLD[:,0]\n",
    "    sld_total[:,1] =  (SLD[:,1] - SLD[:,2]) + (1-deu_percentage)*SLD[:,4] + deu_percentage*SLD[:,2]\n",
    "    \n",
    "    return sld_total\n",
    "\n",
    "def Reflectivity_From_SLD (SLD,remove_water,incident_intensity=1e8,AddSiliconInterface = True,\n",
    "                         All_atom=False,sld=False,deutration_level=38, sio2_thickness = 10,\n",
    "                          return_class=False,AnyDeutration=False,\n",
    "                          substrate_structure=False):\n",
    "    \n",
    "    \n",
    "    layer_thickness = 1.0\n",
    "    roughness, thickness = 0.6, sio2_thickness# of silicondioxide interface\n",
    "    remove_water_front = 0\n",
    "    remove_water_back  = 2 # zero \n",
    "    \n",
    "    q = np.linspace(0.0001,0.3,500)\n",
    "\n",
    "    Rq_noise = np.zeros((len(q),3))\n",
    "    Rq_noise [:,0] = q\n",
    "    bkg = 1e-8\n",
    "\n",
    "    try:\n",
    "           \n",
    "        sld_total = np.zeros((len(SLD), 2))  ## Deutrium percentage is defined here\n",
    "        sld_total [:,0] = SLD[:,0]\n",
    "        sld_total [:,1] = SLD[:,1]\n",
    "        sld_total [:,1] *= 10\n",
    "            \n",
    "        \n",
    "        ## The two numbers are back water and layer roughness\n",
    "        remove_water_front=remove_water\n",
    "        Ref, Sld2 = Reflectivity_input2(sld_total, layer_thickness, remove_water_front,remove_water_back,0.0)\n",
    "        \n",
    "        ###incase the rna is not on the side of majority\n",
    "\n",
    "        #################################################\n",
    "\n",
    "\n",
    "        if AddSiliconInterface:\n",
    "            \n",
    "            if substrate_structure: \n",
    "                \n",
    "                Substrate = substrate_structure\n",
    "                \n",
    "                s = np.shape(Substrate.slabs())\n",
    "                Sld_substrate = np.zeros((s[0]-1,s[1]-1))\n",
    "                for i in range(0,len(Substrate.slabs())-1): Sld_substrate[i] = Substrate.slabs()[i][0:4]\n",
    "\n",
    "                Ref[0][3] =  Substrate.slabs()[i+1][3]\n",
    "                Ref, Sld2 =  Add_Silicon_and_stuff(Sld2,Ref,Sld_substrate,'front')\n",
    "            \n",
    "            else:\n",
    "\n",
    "            #------_Adding silicon substrate\n",
    "                #roughness = 2.0# temporrary\n",
    "                Silicon = Slab(5.0, 2.07, roughness,name='silicon')\n",
    "                SiO2 = Slab(thickness, 3.41, roughness,name='Sio2')\n",
    "\n",
    "                Substrate = Silicon | SiO2 \n",
    "\n",
    "                #SLD_extra = Str.sld_profile();\n",
    "                s = np.shape(Substrate.slabs())\n",
    "                Sld_substrate = np.zeros((s[0],s[1]-1))\n",
    "\n",
    "                for i in range(0,len(Substrate.slabs())): Sld_substrate[i] = Substrate.slabs()[i][0:4]\n",
    "\n",
    "                Ref[0][3] = roughness\n",
    "                Ref, Sld2 = Add_Silicon_and_stuff(Sld2,Ref,Sld_substrate,'front')\n",
    "\n",
    "\n",
    "        Str=[]\n",
    "        \n",
    "        for i in range(0,len(Ref)):\n",
    "            c=Slab(Ref[i][0], Ref[i][1], Ref[i][3])\n",
    "            Str.append(c)\n",
    "\n",
    "        structure = Structure(Str)\n",
    "\n",
    "        R_calculated = ReflectModel(structure,dq=5,bkg=0)\n",
    "\n",
    "\n",
    "        Rq = R_calculated(q)\n",
    "        Rq_noise [:,1] = Rq\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "        Rq = R_calculated(q)\n",
    "        Var = ( Rq + Rq**2 ) / incident_intensity\n",
    "        Rq_new = []\n",
    "        Rq_new2 = []\n",
    "        for i in range(0,len(Rq)):\n",
    "            mu, sigma = Rq[i], np.sqrt(Var[i]) # mean and standard deviation\n",
    "            random_noise=-1.0\n",
    "            while(random_noise<0):\n",
    "                random_noise = random.choice(np.random.normal(mu, sigma, 1000))\n",
    "            #Rq_new.append(random.choice(np.random.normal(mu, sigma, 1000)))\n",
    "            Rq_new.append(random_noise)\n",
    "\n",
    "        Rq_noise[:,2] = np.array(Rq_new)# + 1e-9\n",
    "\n",
    "        if sld: return structure\n",
    "        if return_class: return R_calculated\n",
    "        return Rq_noise\n",
    "    \n",
    "    except(OSError):\n",
    "        \n",
    "        print('file %s not found in %s' %(filename, path))\n",
    "        return 0\n",
    "\n",
    "    \n",
    "def SLD_with_silicon_interface(sld_total):\n",
    "    \n",
    "    layer_thickness = 1.0\n",
    "    roughness, thickness = 0.6,5.0 # of silicondioxide interface\n",
    "    remove_water_front = 60#57\n",
    "    remove_water_back = 10\n",
    "    \n",
    "    \n",
    "    Ref, Sld2 = Reflectivity_input2(sld_total, layer_thickness, remove_water_front,remove_water_back,0.0) \n",
    "    #------_Adding silicon substrate\n",
    "\n",
    "    Silicon = Slab(5.0, 2.07, roughness,name='silicon')\n",
    "    SiO2 = Slab(thickness, 3.41, roughness,name='Sio2')\n",
    "\n",
    "    Substrate = Silicon | SiO2 \n",
    "\n",
    "    s = np.shape(Substrate.slabs())\n",
    "    Sld_substrate = np.zeros((s[0],s[1]-1))\n",
    "\n",
    "    for i in range(0,len(Substrate.slabs())): Sld_substrate[i] = Substrate.slabs()[i][0:4]\n",
    "\n",
    "    Ref[0][3] = roughness\n",
    "    Ref, Sld2 = Add_Silicon_and_stuff(Sld2,Ref,Sld_substrate,'front')\n",
    "\n",
    "\n",
    "    Str=[]\n",
    "    for i in range(0,len(Ref)):\n",
    "        c=Slab(Ref[i][0], Ref[i][1], Ref[i][3])\n",
    "        Str.append(c)\n",
    "\n",
    "    structure = Structure(Str)\n",
    "    \n",
    "    return structure\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def axis(ax,ref,fs=20,legend=True):\n",
    "    \n",
    "    font = fs\n",
    "    fonttick = fs\n",
    "    \n",
    "    ax.tick_params(direction='in', length=4, width=2, colors='k',\n",
    "               grid_color='r', grid_alpha=0)\n",
    "    if ref=='ref':\n",
    "        plt.xlabel('q [$\\AA^{-1}$]',fontsize=font)\n",
    "        plt.xticks(fontsize=fonttick)\n",
    "        plt.ylabel('R (q)',fontsize=font)\n",
    "        plt.yticks(fontsize=fonttick)\n",
    "        if legend:\n",
    "            plt.legend(fontsize=font)\n",
    "    if ref=='sld':\n",
    "        plt.xlabel('d $[\\AA]$',fontsize=font)\n",
    "        plt.xticks(fontsize=fonttick)\n",
    "        plt.ylabel('SLD [$x 10^{-6}\\AA^{-2}]$',fontsize=font)\n",
    "        plt.yticks(fontsize=fonttick)\n",
    "        if legend:\n",
    "            plt.legend(fontsize=font)\n",
    "        \n",
    "        \n",
    "    if ref=='fq':\n",
    "        plt.xlabel('q [$\\AA^{-1}$]',fontsize=font)\n",
    "        plt.xticks(fontsize=fonttick)\n",
    "        plt.ylabel('|F (q)| [$x 10^{-5}\\AA^{-1}]$',fontsize=font)\n",
    "        plt.yticks(fontsize=fonttick)\n",
    "        if legend:\n",
    "            plt.legend(fontsize=font)\n",
    "        \n",
    "    return plt\n",
    "\n",
    "\n",
    "def remove_water_alpha(sld, thickness, water_fraction1 = 1.0,water_fraction2 = 1.0,\n",
    "                       solvent ='D2O',AnyDeutration=False,deutration_level = 42,\n",
    "                      Full_profile=False):\n",
    "    \n",
    "    # 38% D2O corresponds to CMSi\n",
    "    \n",
    "    if solvent=='D2O' or solvent=='cmsi' or solvent==\"d2o\" : index1, index2 = 3, 4\n",
    "    \n",
    "    if solvent=='H2O' or solvent=='h2o': index1, index2 = 1, 2\n",
    "   \n",
    "    Sld_just_bilayer = sld [:,index1] - sld [:,index2]\n",
    "    dz = np.diff(sld[:,0])[3]\n",
    "    \n",
    "    remove_until = int(thickness / dz)\n",
    "    #print(remove_until)\n",
    "\n",
    "    Sld_water_removed = sld[:,index2]\n",
    "\n",
    "    Sld_water_removed [:remove_until] = 0\n",
    "\n",
    "    Sld_water_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "\n",
    "    Sld_water_removed [int(len(sld)/2):] *= water_fraction2\n",
    "\n",
    "    Final_sld = np.zeros((len(sld), 2))\n",
    "\n",
    "    Final_sld [:,0] = sld[:,0]\n",
    "\n",
    "    Final_sld [:,1] = Sld_water_removed + Sld_just_bilayer\n",
    "    \n",
    "    if AnyDeutration:\n",
    "        deutration_level /= 100\n",
    "        \n",
    "        Sld_h2o_removed   =  sld[:,2]\n",
    "        \n",
    "        Sld_d2o_removed   =  sld[:,4]\n",
    "        \n",
    "        # render removed water/d2o to zeros\n",
    "        Sld_h2o_removed [:remove_until] = 0\n",
    "        \n",
    "        Sld_d2o_removed [:remove_until] = 0\n",
    "        \n",
    "        Sld_h2o_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "        \n",
    "        Sld_d2o_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "        \n",
    "        if Full_profile:\n",
    "            Sld_h2o_removed  *=  water_fraction1\n",
    "        \n",
    "            Sld_d2o_removed  *=  water_fraction1\n",
    "            \n",
    "        # final sld\n",
    "        Final_sld = np.zeros((len(sld), 2))\n",
    "        Final_sld [:,0] = sld[:,0]\n",
    "        Final_sld [:,1] = Sld_just_bilayer + deutration_level * Sld_d2o_removed  + (1-deutration_level) *Sld_h2o_removed\n",
    "    \n",
    "    \n",
    "    return Final_sld\n",
    "\n",
    "def get_background (q, Rq, R_exp):\n",
    "    Chi_square = []\n",
    "    back = []\n",
    "    for i in np.linspace (0,10000,500): back.append (i*1e-9)\n",
    "    \n",
    "    for b in back:\n",
    "        R = Rq(q) + b\n",
    "        chi_square = (np.log (R_exp [:,1]) - np.log( R))**2 #/ np.log(R_exp[:,2])**2\n",
    "        \n",
    "        chi_square = chi_square[np.logical_not(np.isnan(chi_square))]\n",
    "        \n",
    "        Chi_square.append(chi_square.sum())\n",
    "    \n",
    "    return back[np.argmin(Chi_square)]\n",
    "\n",
    "\n",
    "def Silicon_position (path, filename=False, plot=False, SLD=False):\n",
    "    \n",
    "    silicon_t = 10 # Angstroms\n",
    "    \n",
    "    if not filename: sld = SLD\n",
    "    \n",
    "    else: \n",
    "        sld = np.genfromtxt(path+filename,skip_header=0)\n",
    "        \n",
    "        sld = interpolate(sld, int(1.5*len(sld)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    indices = sld [:,0] < -25\n",
    "    a = sld[indices,1] - sld[indices,2]\n",
    "    x = np.where(a==0)\n",
    "    index = max (x[0])\n",
    "\n",
    "    silicon_position = sld[index,0] - sld[0,0]\n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(sld[:,0], 10*(sld[:,1] - sld[:,2]))\n",
    "        plt.plot(sld[index,0], 10*a[index], 's',ms=5)\n",
    "        ymax = 0.05\n",
    "        plt.vlines(sld[index,0], 10*-0.04, 10*ymax, ls ='dashed')\n",
    "    \n",
    "    return silicon_position    \n",
    "\n",
    "\n",
    "def Water_fraction (sim_path, sim_filename, exp_filename, silicon_position, AnyDeutration=False, plot=False,\n",
    "                   structure=False, solvent=\"D2O\"):\n",
    "    \n",
    "    exp_path = \"./example-data/\"\n",
    "    \n",
    "    remove_water = silicon_position\n",
    "    \n",
    "    parameter_space = np.linspace(0,1.0,100) # water fraction\n",
    "    \n",
    "    R_exp           = np.genfromtxt (exp_path + exp_filename)\n",
    "    q               = R_exp [:,0]\n",
    "    Chi_square      = []\n",
    "    \n",
    "    for water1 in tqdm(parameter_space):\n",
    "        \n",
    "        sld = np.genfromtxt(sim_path+sim_filename,skip_header=0)\n",
    " \n",
    "        sld  = interpolate(sld,max(500, len(sld)))\n",
    "\n",
    "        sld2 = remove_water_alpha(sld, thickness=remove_water, water_fraction1= water1, \n",
    "                                  water_fraction2=1.0, solvent=solvent, AnyDeutration=AnyDeutration)\n",
    "        \n",
    "        Rq   =  Reflectivity_From_SLD (sld2, All_atom = True, remove_water=remove_water, incident_intensity=1e6,\n",
    "                                     return_class=True, substrate_structure=structure)\n",
    "        \n",
    "        a, b =  0,-1\n",
    "       \n",
    "        #chi_square = sum((R_exp [:,1] - Rq(q))**2/ R_exp [:,2]**2)/len(R_exp)\n",
    "        chi_square = sum((R_exp [:,1] - Rq(q))**2/ R_exp [:,2]**2)/len(R_exp)\n",
    "        Chi_square.append(chi_square)\n",
    "        \n",
    "    optimum_water_frac  = parameter_space [argmin(Chi_square)]\n",
    "    \n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(parameter_space, Chi_square)\n",
    "    \n",
    "        plt.plot(parameter_space [argmin(Chi_square)], Chi_square[argmin(Chi_square)],marker='s',\n",
    "                color='k', ms=8, alpha=0.5)\n",
    "\n",
    "    print (f\"min ($\\chi^2${min(Chi_square)})\") \n",
    "    \n",
    "    return optimum_water_frac, Chi_square, parameter_space\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2b6a57-dd8d-40d6-af78-ab4e19687014",
   "metadata": {},
   "source": [
    "## 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a416fdf-c2e3-40bd-8452-ae79435c7a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## A class to hold all the data after fitting\n",
    "class All_optimum:\n",
    "\n",
    "    def __init__ (self, Substrate_properties, Rq_global, Rq_local, Rq_exp, SLD_global,SLD_local, \n",
    "                  water_fraction, Optimum_water_fractions, Chi_squares):\n",
    "        \n",
    "        # Substrate properties\n",
    "        self.substrate_details = Substrate_properties\n",
    "        \n",
    "        # Rq_experimental\n",
    "        \n",
    "        self.Rq_exp_d2o      =   Rq_exp ['d2o']\n",
    "        \n",
    "        self.Rq_exp_h2o      =   Rq_exp ['h2o']\n",
    "        \n",
    "        self.Rq_exp_cmsi     =   Rq_exp ['cmsi']\n",
    "        \n",
    "        # q\n",
    "        self.q_d2o           =   Rq_exp ['d2o'][:,0]\n",
    "        self.q_h2o           =   Rq_exp ['h2o'][:,0]\n",
    "        self.q_cmsi          =   Rq_exp ['cmsi'][:,0]\n",
    "                \n",
    "        # Rq Results from global fitting\n",
    "        \n",
    "        self.Rq_d2o_global    =  Rq_global ['d2o']\n",
    "\n",
    "        self.Rq_h2o_global    =  Rq_global ['h2o']\n",
    "\n",
    "        self.Rq_cmsi_global   =  Rq_global ['cmsi']\n",
    "\n",
    "        # Rq Results from local fitting\n",
    "        self.Rq_d2o_local    =  Rq_local ['d2o']\n",
    "\n",
    "        self.Rq_h2o_local    =  Rq_local ['h2o']\n",
    "\n",
    "        self.Rq_cmsi_local   =  Rq_local ['cmsi']\n",
    "\n",
    "        # SLD result global\n",
    "        \n",
    "        self.sld_d2o_global   =   SLD_global ['d2o']\n",
    "        \n",
    "        self.sld_h2o_global   =   SLD_global ['h2o']\n",
    "        \n",
    "        self.sld_cmsi_global  =  SLD_global ['cmsi']\n",
    "        \n",
    "        # SLD result local\n",
    "        \n",
    "        self.sld_d2o_local   =   SLD_local ['d2o']\n",
    "        \n",
    "        self.sld_h2o_local   =   SLD_local ['h2o']\n",
    "        \n",
    "        self.sld_cmsi_local  =  SLD_local ['cmsi']\n",
    "        # Structures\n",
    "\n",
    "        self.global_chi  = Chi_squares ['global1']\n",
    "        self.d2o_chi     = Chi_squares ['d2o']\n",
    "        self.h2o_chi     = Chi_squares ['h2o']\n",
    "        self.cmsi_chi    = Chi_squares ['cmsi']\n",
    "        \n",
    "        # Water fraction\n",
    "        self.water_fraction   = water_fraction\n",
    "        \n",
    "        # Optimum water fractions\n",
    "        \n",
    "        self.water_fraction_global =  Optimum_water_fractions ['global1']\n",
    "        \n",
    "        self.water_fraction_d2o    =  Optimum_water_fractions ['d2o']\n",
    "        \n",
    "        self.water_fraction_h2o    =  Optimum_water_fractions ['h2o']\n",
    "        \n",
    "        self.water_fraction_cmsi   =  Optimum_water_fractions ['cmsi']\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bb22f0-0582-4454-8dd9-0efa386bf7ba",
   "metadata": {},
   "source": [
    "## 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb93a2a5-23d7-41d8-a8cf-832fcaf9bddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Silicon_position (path, filename=False, plot=False, SLD=False):\n",
    "    \n",
    "    silicon_t = 10 # Angstroms\n",
    "    \n",
    "    if not filename: sld = SLD\n",
    "    \n",
    "    else: \n",
    "        sld = np.genfromtxt(path+filename,skip_header=0)\n",
    "        \n",
    "        sld = interpolate(sld, int(1.5*len(sld)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    indices = sld [:,0] < -25\n",
    "    a = sld[indices,1] - sld[indices,2]\n",
    "    x = np.where(a==0)\n",
    "    index = max (x[0])\n",
    "\n",
    "    silicon_position = sld[index,0] - sld[0,0]\n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(sld[:,0], 10*(sld[:,1] - sld[:,2]))\n",
    "        plt.plot(sld[index,0], 10*a[index], 's',ms=5)\n",
    "        ymax = 0.05\n",
    "        plt.vlines(sld[index,0], 10*-0.04, 10*ymax, ls ='dashed')\n",
    "    \n",
    "    return silicon_position    \n",
    "\n",
    "\n",
    "def Water_fraction (sim_path, sim_filename, exp_filename, silicon_position, AnyDeutration=False, plot=False,\n",
    "                   structure=False, solvent=\"D2O\"):\n",
    "    \n",
    "    silicon_t = 10 # fixed\n",
    "    exp_path = \"./\"\n",
    "    \n",
    "    remove_water = silicon_position\n",
    "    \n",
    "    parameter_space = np.linspace(0,1.0,100) # water fraction\n",
    "    \n",
    "    R_exp           = np.genfromtxt (exp_path + exp_filename)\n",
    "    q               = R_exp [:,0]\n",
    "    Chi_square      = []\n",
    "    \n",
    "    for water1 in tqdm(parameter_space):\n",
    " \n",
    "        sld  = interpolate(np.genfromtxt(sim_path+sim_filename,skip_header=0),500)\n",
    "\n",
    "        sld2 = remove_water_alpha(sld, thickness=remove_water, water_fraction1= water1, \n",
    "                                  water_fraction2=1.0, solvent=solvent, AnyDeutration=AnyDeutration)\n",
    "        \n",
    "        Rq   =  Reflectivity_From_SLD (sld2, All_atom = True, remove_water=remove_water,\n",
    "                                      sio2_thickness = silicon_t, incident_intensity=1e6,\n",
    "                                     return_class=True, substrate_structure=structure)\n",
    "        \n",
    "        a, b =  0,-1\n",
    "       \n",
    "     \n",
    "        chi_square = sum((R_exp [:,1] - Rq(q))**2/ R_exp [:,2]**2)/len(R_exp)\n",
    "        Chi_square.append(chi_square)\n",
    "        \n",
    "    optimum_water_frac  = parameter_space [argmin(Chi_square)]\n",
    "    \n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(parameter_space, Chi_square)\n",
    "    \n",
    "        plt.plot(parameter_space [argmin(Chi_square)], Chi_square[argmin(Chi_square)],marker='s',\n",
    "                color='k', ms=8, alpha=0.5)\n",
    "\n",
    "    print (f\"min ($\\chi^2${min(Chi_square)})\")\n",
    "    \n",
    "    return optimum_water_frac, Chi_square, parameter_space\n",
    "    \n",
    "    \n",
    "\n",
    "def Obtain_Substrate_parameters (Rq):\n",
    "    \n",
    "    \n",
    "    ''' Rq: is the experimental reflectivity data taken for Si-D2O systems'''\n",
    "    \n",
    "    x, y, yerr = Rq [:,0], Rq [:,1],Rq [:,2] \n",
    "    data       = Data1D(data=(x, y, yerr))\n",
    "\n",
    "    ''' Set initial parameters '''\n",
    "    roughness =  2\n",
    "    thickness =  5\n",
    "    Silicon   =  Slab(5.0, 2.07, roughness,name='silicon') # fixed\n",
    "    SiO2      =  Slab(thickness, 3.47, roughness,name='Sio2') # fixed\n",
    "    D2O       =  Slab(thickness,6.2513,roughness,name='D2O') #varied \n",
    "\n",
    "\n",
    "    ''' Paramter bounds to vary within '''\n",
    "    Silicon.rough.setp(bounds=(0, 2), vary=True)\n",
    "    Silicon.sld.real.setp(bounds=(1.98, 2.1), vary=False)\n",
    "\n",
    "    SiO2.thick.setp(bounds=(5, 10), vary=True)\n",
    "    SiO2.rough.setp(2, bounds=(0, 2), vary=True)\n",
    "    SiO2.sld.real.setp(bounds=(3.3, 3.55), vary=True)\n",
    "\n",
    "    D2O.sld.real.setp(bounds=(6.0, 6.4), vary=True)\n",
    "    D2O.rough.setp(bounds=(0, 2), vary=True)\n",
    "\n",
    "    ''' Setup '''\n",
    "    structure = Silicon | SiO2 | D2O\n",
    "\n",
    "\n",
    "    ''' For fitting '''\n",
    "\n",
    "    model = ReflectModel(structure, bkg=3e-6, dq=5.0)\n",
    "    scale = np.average (Rq [:10,1]) \n",
    "    model.scale.setp(scale, bounds= (0.9, 1.1), vary=True)\n",
    "    model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)\n",
    "    objective = Objective(model, data, transform=Transform('lin'))\n",
    "\n",
    "    ''' Use differential evolution fitting procedure '''\n",
    "\n",
    "    fitter = CurveFitter(objective)\n",
    "    fitter.fit('differential_evolution', seed=42);\n",
    "\n",
    "    return structure, objective\n",
    "\n",
    "exp_path = \"./\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b659b22c-a57e-46e5-af24-55da01aa0c29",
   "metadata": {},
   "source": [
    "## 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ed25a44-7a24-4bdf-be3c-e907d2c9db45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sld_water_patch_fraction (sld, f):\n",
    "    \n",
    "    sld_new = np.zeros (np.shape (sld))\n",
    "    \n",
    "    sld_new [:,0] = sld [:,0]\n",
    "    \n",
    "    # Bulk Slds\n",
    "    \n",
    "    bulk_h2o_sld = np.average (sld [-10:,2]) * np.ones (len (sld))\n",
    "    bulk_d2o_sld = np.average (sld [-10:,4]) * np.ones (len (sld))\n",
    "    ##print (bulk_h2o_sld, bulk_d2o_sld)\n",
    "    \n",
    "    just_bilayer_sld = sld [:,1] - sld [:,2]\n",
    "\n",
    "    \n",
    "    sld_new [:,1]  = (1-f)*just_bilayer_sld + (1-f)*sld [:,2] + f*bulk_h2o_sld\n",
    "    \n",
    "    sld_new [:,2]  = (1-f)*sld [:,2] + f*bulk_h2o_sld\n",
    "    \n",
    "    \n",
    "    sld_new [:,3]  = (1-f)*just_bilayer_sld + (1-f)*sld [:,4] + f*bulk_d2o_sld\n",
    "    \n",
    "    sld_new [:,4]  = (1-f)*sld [:,4] + f*bulk_d2o_sld\n",
    "    \n",
    "    \n",
    "    return sld_new\n",
    "\n",
    "\n",
    "\n",
    "def Water_fraction_Water_patch (sim_path, sim_filename, exp_filename, silicon_position, AnyDeutration=False, plot=False,\n",
    "                   structure=False, solvent=\"D2O\"):\n",
    "    \n",
    "    exp_path = \"./example-data/\"\n",
    "    \n",
    "    remove_water = silicon_position\n",
    "    \n",
    "    water_patch = np.linspace(0, 0.5, 100)\n",
    "    parameter_space1 = np.linspace(0,1.0,100) # water fraction\n",
    "   \n",
    "    parameter_space = [ (water1, water2) for water1 in parameter_space1 for water2 in water_patch]\n",
    "\n",
    "\n",
    "    R_exp           = np.genfromtxt (exp_path + exp_filename)\n",
    "    q               = R_exp [:,0]\n",
    "    Chi_square      = []\n",
    "    \n",
    "    for water1, f in tqdm(parameter_space):\n",
    "        \n",
    "\n",
    "            sld  = np.genfromtxt(sim_path+sim_filename,skip_header=0)\n",
    "            sld  = interpolate(sld,max (500, len(sld)))\n",
    "            \n",
    "            sld2 = remove_water_alpha_water_patch (sld, thickness=remove_water,patch_frac=f, water_fraction1= water1, \n",
    "                                      water_fraction2=1, solvent=solvent, AnyDeutration=AnyDeutration)\n",
    "\n",
    "\n",
    "            Rq   =  Reflectivity_From_SLD (sld2, All_atom = True, remove_water=remove_water, incident_intensity=1e6,\n",
    "                                         return_class=True, substrate_structure=structure)\n",
    "\n",
    "            a, b =  0,-1\n",
    "\n",
    "            #chi_square = sum((R_exp [:,1] - Rq(q))**2/ R_exp [:,2]**2)/len(R_exp)\n",
    "            chi_square = sum((R_exp [:,1] - Rq(q))**2/ R_exp [:,2]**2)/len(R_exp)\n",
    "            Chi_square.append(chi_square)\n",
    "    \n",
    "        \n",
    "    optimum_parameters  = parameter_space [argmin(Chi_square)]\n",
    "    \n",
    "    \n",
    "    if plot:\n",
    "        plt.plot(parameter_space, Chi_square)\n",
    "    \n",
    "        plt.plot(parameter_space [argmin(Chi_square)], Chi_square[argmin(Chi_square)],marker='s',\n",
    "                color='k', ms=8, alpha=0.5)\n",
    "\n",
    "    print (f\"min ($\\chi^2${min(Chi_square)})\") \n",
    "    \n",
    "    return optimum_parameters, Chi_square, parameter_space\n",
    "\n",
    "    \n",
    "def get_chi_sqr (R_exp, R_sim, both_exp = False):\n",
    "    \n",
    "\n",
    "    chi_square = sum((R_exp [:,1] - R_sim [:,1])**2/ R_exp [:,2]**2)/ (len(R_exp)-1)\n",
    "    \n",
    "    if both_exp:\n",
    "        sigma12 = R_exp [:,2]**2 + R_sim [:,2]**2\n",
    "        chi_square = sum((R_exp [:,1] - R_sim [:,1])**2/sigma12 )/ (len(R_exp)-1)\n",
    "    \n",
    "    return chi_square\n",
    "\n",
    "\n",
    "def get_chi_array (R_object, solvent=False):\n",
    "\n",
    "    Chi_sq          =  np.zeros ((100,100))\n",
    "\n",
    "    index = 0\n",
    "    for i in range (100):\n",
    "\n",
    "        for j in range (100):\n",
    "            \n",
    "            if solvent:\n",
    "                \n",
    "                if solvent == \"h2o\" or solvent ==\"H2O\":  Chi_sq [i, j]  = R_object.h2o_chi [index]\n",
    "                if solvent == \"d2o\" or solvent ==\"D2O\":  Chi_sq [i, j]  = R_object.d2o_chi [index]\n",
    "                if solvent == \"cmsi\" or solvent ==\"CMSi\": Chi_sq [i, j] = R_object.cmsi_chi [index]\n",
    "                \n",
    "            else:\n",
    "                \n",
    "                Chi_sq [i, j] = R_object.global_chi [index]\n",
    "\n",
    "            index += 1\n",
    "            \n",
    "    return Chi_sq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e10a87e-c41a-4220-941d-59e78982ab28",
   "metadata": {},
   "source": [
    "## Remove water, scale and correct for patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c11ee354-188f-4b32-84dc-3218f4d19e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_water_alpha_water_patch (sld, thickness,patch_frac, water_fraction1 = 1.0,water_fraction2 = 1.0,\n",
    "                       solvent ='D2O',AnyDeutration=False,deutration_level = 42,\n",
    "                      Full_profile=False):\n",
    "    \n",
    "    # 38% D2O corresponds to CMSi\n",
    "    dz = np.diff(sld[:,0])[3]\n",
    "    \n",
    "    remove_until = int(thickness / dz)\n",
    "    \n",
    "    identity_array = np.ones (len(sld))\n",
    "    \n",
    "    identity_array [:remove_until] = 0\n",
    "    ## Obtain Bulk densities for each contrast\n",
    "    \n",
    "    deutration_level /= 100\n",
    "    \n",
    "    bulk_d2o   = np.average (sld [-10:,4])\n",
    "    \n",
    "    bulk_h2o   = np.average (sld [-10:,2])\n",
    "    \n",
    "    bulk_cmsi   = deutration_level*bulk_d2o + (1-deutration_level)*bulk_h2o\n",
    "                  \n",
    "                  \n",
    "    \n",
    "    if solvent=='D2O' or solvent=='cmsi' or solvent==\"d2o\":\n",
    "        index1, index2 = 3, 4\n",
    "        bulk_solvent_sld  = bulk_d2o*identity_array\n",
    "    \n",
    "    if solvent=='H2O' or solvent=='h2o':\n",
    "        \n",
    "        index1, index2 = 1, 2\n",
    "        \n",
    "        bulk_solvent_sld = bulk_h2o*identity_array\n",
    "    \n",
    "    if AnyDeutration:\n",
    "        \n",
    "        bulk_solvent_sld = bulk_cmsi*identity_array\n",
    "    \n",
    "                  \n",
    "    Sld_just_bilayer = (1-patch_frac)*(sld [:,index1] - sld [:,index2]) + patch_frac*bulk_solvent_sld\n",
    "    \n",
    "    sld [:, 2] *= (1-patch_frac)\n",
    "    sld [:, 4] *= (1-patch_frac)\n",
    "    \n",
    "    \n",
    "\n",
    "    #print(remove_until)\n",
    "\n",
    "    Sld_water_removed = sld[:,index2]\n",
    "\n",
    "    Sld_water_removed [:remove_until] = 0\n",
    "\n",
    "    Sld_water_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "\n",
    "    Sld_water_removed [int(len(sld)/2):] *= water_fraction2\n",
    "\n",
    "    Final_sld = np.zeros((len(sld), 2))\n",
    "\n",
    "    Final_sld [:,0] = sld[:,0]\n",
    "\n",
    "    Final_sld [:,1] = Sld_water_removed + Sld_just_bilayer\n",
    "    \n",
    "    if AnyDeutration:\n",
    "                \n",
    "        Sld_h2o_removed   =  sld[:,2]\n",
    "        \n",
    "        Sld_d2o_removed   =  sld[:,4]\n",
    "        \n",
    "        # render removed water/d2o to zeros\n",
    "        Sld_h2o_removed [:remove_until] = 0\n",
    "        \n",
    "        Sld_d2o_removed [:remove_until] = 0\n",
    "        \n",
    "        Sld_h2o_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "        \n",
    "        Sld_d2o_removed [:int(len(sld)/2)] *= water_fraction1\n",
    "        \n",
    "        if Full_profile:\n",
    "            Sld_h2o_removed  *=  water_fraction1\n",
    "        \n",
    "            Sld_d2o_removed  *=  water_fraction1\n",
    "            \n",
    "        # final sld\n",
    "        Final_sld = np.zeros((len(sld), 2))\n",
    "        Final_sld [:,0] = sld[:,0]\n",
    "        Final_sld [:,1] = Sld_just_bilayer + deutration_level * Sld_d2o_removed  + (1-deutration_level) *Sld_h2o_removed\n",
    "    \n",
    "    \n",
    "    return Final_sld"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb209167-ae4c-48cb-bb10-74598e1aca7e",
   "metadata": {},
   "source": [
    "## 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ceb3bad3-f5a0-439a-9049-99445e7b96f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Get_all_parameters_water_patch (exp_filename_d2o, exp_filename_h2o, exp_filename_cmsi, R_cell, sim_path,\n",
    "                       sim_filename, exp_path=\"./\", substrate=False):\n",
    "\n",
    "    # Load experimental data\n",
    "    \n",
    "    R_exp_d2o    =  np.genfromtxt(exp_path + exp_filename_d2o)\n",
    "    R_exp_h2o    =  np.genfromtxt(exp_path + exp_filename_h2o)\n",
    "    R_exp_cmsi   =  np.genfromtxt(exp_path + exp_filename_cmsi)\n",
    "\n",
    "  #  print (exp_filename_d2o)\n",
    "    ''' Obtain substrate properties '''\n",
    "    if not substrate: # This required to use the same subsrate incase of different FF comparions\n",
    "        \n",
    "        structure, objective =  Obtain_Substrate_parameters (Rq = R_cell)\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        structure, objective = substrate ['structure'], substrate ['objective']\n",
    "        \n",
    "        \n",
    "    silicon_position     =  Silicon_position (sim_path, sim_filename, plot=False)\n",
    "    print (silicon_position)\n",
    "    ''' Obtain optimum water fractions '''\n",
    "\n",
    "    # H2o\n",
    "    \n",
    "    optimum_parameters_h2o, Chi_square_h2o, parameter_space =  Water_fraction_Water_patch (sim_path, sim_filename, exp_filename_h2o,\n",
    "                                                                                       silicon_position, AnyDeutration=False, plot=False,\n",
    "                                                                                       structure=structure, solvent=\"H2O\")\n",
    "\n",
    "    #D2O\n",
    "    \n",
    "    optimum_parameters_d2o, Chi_square_d2o, parameter_space =  Water_fraction_Water_patch (sim_path, sim_filename, exp_filename_d2o,\n",
    "                                                                                   silicon_position, AnyDeutration=False, plot=False,\n",
    "                                                                                   structure=structure, solvent=\"D2O\")\n",
    "    \n",
    "    print (optimum_parameters_d2o, \"D2O\")\n",
    "\n",
    "    # CMSi\n",
    "    optimum_parameters_cmsi, Chi_square_cmsi, parameter_space =  Water_fraction_Water_patch (sim_path, sim_filename, exp_filename_cmsi,\n",
    "                                                                                   silicon_position, AnyDeutration=True, plot=False,\n",
    "                                                                                   structure=structure, solvent=\"D2O\")\n",
    "\n",
    "\n",
    "\n",
    "    ''' Get Local Rq and SLD '''\n",
    "\n",
    "    # Get global chi square\n",
    "    Chi_global = np.array (Chi_square_cmsi) + np.array(Chi_square_h2o) + np.array(Chi_square_d2o)\n",
    "\n",
    "    # Global water fraction\n",
    "    optimum_parameters_global = parameter_space [np.argmin (Chi_global)]\n",
    "\n",
    "\n",
    "\n",
    "    Optimum_water_fractions = dict (h2o=optimum_parameters_h2o, d2o=optimum_parameters_d2o, cmsi=optimum_parameters_cmsi,\n",
    "                                   global1=optimum_parameters_global)\n",
    "\n",
    "    Rq_local                = dict (h2o=0, d2o=0, cmsi=0)\n",
    "\n",
    "    Rq_exp                  = dict (h2o = R_exp_h2o, d2o=R_exp_d2o, cmsi=R_exp_cmsi)\n",
    "\n",
    "    SLD_local               = dict (h2o = 0, d2o=0, cmsi=0)\n",
    "\n",
    "    Chi_squares             = dict (global1=Chi_global, h2o=Chi_square_h2o, d2o=Chi_square_d2o, cmsi=Chi_square_cmsi)\n",
    "\n",
    "    ''' Local Rq SLD assignments '''\n",
    "\n",
    "    for solvent in [\"h2o\", \"d2o\", \"cmsi\"]:\n",
    "\n",
    "\n",
    "        sld     = np.genfromtxt(sim_path+sim_filename, skip_header=0)\n",
    "        sld     = interpolate(sld, max(500, len(sld)))\n",
    "        \n",
    "        water_patch_fraction = Optimum_water_fractions [solvent] [1]\n",
    "                \n",
    "        water1  = Optimum_water_fractions [solvent][0]\n",
    "        \n",
    "\n",
    "        AnyDeutration = False\n",
    "\n",
    "        if solvent == \"cmsi\": AnyDeutration = True\n",
    "\n",
    "        sld2    = remove_water_alpha_water_patch (sld, thickness=silicon_position, patch_frac=water_patch_fraction, water_fraction1= water1, water_fraction2=1, solvent=solvent, \n",
    "                                  AnyDeutration=AnyDeutration)\n",
    "\n",
    "        Rq   =  Reflectivity_From_SLD(sld2, All_atom = True, remove_water=silicon_position, incident_intensity=1e6,\n",
    "                                     return_class=True, substrate_structure=structure)\n",
    "\n",
    "\n",
    "\n",
    "        SLD   =  Reflectivity_From_SLD(sld2, All_atom=True, sld=True,remove_water=silicon_position,\n",
    "                                         substrate_structure=structure)\n",
    "\n",
    "        q = Rq_exp[solvent][:,0]\n",
    "        background  = get_background (q,  Rq, Rq_exp [solvent])\n",
    "\n",
    "        Rq_ = np.zeros ((len(q), 2))\n",
    "        # Rq\n",
    "        Rq_ [:,0] = q\n",
    "        Rq_ [:,1] = Rq(q) + background\n",
    "\n",
    "        Rq_local [solvent] = Rq_\n",
    "\n",
    "        SLD_ = np.zeros ((len(SLD.sld_profile()[0]), 2))\n",
    "\n",
    "        SLD_ [:,0] = SLD.sld_profile()[0]\n",
    "        SLD_ [:,1] = SLD.sld_profile()[1]\n",
    "        SLD_local [solvent] = SLD_\n",
    "\n",
    "\n",
    "    ''' Global assigments '''\n",
    "\n",
    "    Rq_global              =  dict (h2o=0, d2o=0, cmsi=0)\n",
    "\n",
    "    SLD_global             =  dict (h2o = 0, d2o=0, cmsi=0)\n",
    "\n",
    "    Substrate_properties   =  dict (structure=structure, objective=objective, Rq_exp = R_cell)\n",
    "\n",
    "    for solvent in [\"h2o\", \"d2o\", \"cmsi\"]:\n",
    "\n",
    "\n",
    "        sld     = np.genfromtxt(sim_path+sim_filename, skip_header=0)\n",
    "        sld     = interpolate(sld, max(500, len(sld)))\n",
    "        water_patch_fraction = optimum_parameters_global [1]\n",
    "        \n",
    "        \n",
    "        water1  = optimum_parameters_global [0] # Use the global water fraction\n",
    "\n",
    "        AnyDeutration = False\n",
    "\n",
    "        if solvent == \"cmsi\": AnyDeutration = True\n",
    "\n",
    "        sld2    = remove_water_alpha_water_patch(sld, thickness=silicon_position, patch_frac= water_patch_fraction, water_fraction1= water1, water_fraction2=1, solvent=solvent, \n",
    "                                  AnyDeutration=AnyDeutration)\n",
    "\n",
    "        Rq   =  Reflectivity_From_SLD(sld2, All_atom = True, remove_water=silicon_position, incident_intensity=1e6,\n",
    "                                     return_class=True, substrate_structure=structure)\n",
    "\n",
    "\n",
    "\n",
    "        SLD   =  Reflectivity_From_SLD(sld2, All_atom=True, sld=True,remove_water=silicon_position,\n",
    "                                         substrate_structure=structure)\n",
    "\n",
    "        q = Rq_exp[solvent][:,0]\n",
    "\n",
    "        background = get_background (q,  Rq, Rq_exp [solvent])\n",
    "\n",
    "        Rq_ = np.zeros ((len(q), 2))\n",
    "        # Rq\n",
    "        Rq_ [:,0] = q\n",
    "        Rq_ [:,1] = Rq(q) + background\n",
    "\n",
    "        Rq_global [solvent] = Rq_\n",
    "\n",
    "        SLD_ = np.zeros ((len(SLD.sld_profile()[0]), 2))\n",
    "\n",
    "        SLD_ [:,0] = SLD.sld_profile()[0]\n",
    "        SLD_ [:,1] = SLD.sld_profile()[1]\n",
    "        SLD_global [solvent] = SLD_\n",
    "\n",
    "\n",
    "\n",
    "    All_params = All_optimum (Substrate_properties=Substrate_properties, Rq_exp=Rq_exp, Rq_global=Rq_global,Rq_local=Rq_local,\n",
    "                             Optimum_water_fractions=Optimum_water_fractions, Chi_squares=Chi_squares,\n",
    "                             water_fraction=parameter_space, SLD_global=SLD_global, SLD_local=SLD_local)\n",
    "    \n",
    "    return All_params\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c2782e-1690-47c4-b45f-217a4ef00287",
   "metadata": {},
   "source": [
    "# Effect of $\\gamma$ and $\\alpha$ on the SLD profile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "027891dd-8626-4c5a-920e-ad679c62a6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Amber\n",
    "out_path          = \"./example-data/\"\n",
    "#sim_filename      =  \"average.dat\"\n",
    "sim_filename      = \"nsld_mc3h_dopc.dat\"\n",
    "sim_path          = \"./example-data/\"\n",
    "silicon_position     =  Silicon_position (sim_path, sim_filename, plot=False)\n",
    "\n",
    "\n",
    "\n",
    "fig   = plt.figure(figsize=(12,4))\n",
    "\n",
    "gs    = fig.add_gridspec(nrows=1, ncols=2,width_ratios=[1,1] , wspace=0.3, hspace=0.3)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,0])\n",
    "#ax1.set_xticks([0, 25,50,75])\n",
    "#ax1.set_xlim(right=80, left=-9)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[0,1])\n",
    "#ax2.set_xticks([0, 25,50,75])\n",
    "#ax2.set_xlim(right=80, left=-9)\n",
    "ax1.set_ylabel('SLD [$x 10^{-6}\\AA^{-2}]$',fontsize=14)\n",
    "ax1.set_xlabel('d [$\\AA]$',fontsize=14)\n",
    "ax2.set_xlabel('d [$\\AA]$',fontsize=14)\n",
    "#plt.plot (sld [:,0], sld [:,1], '.')\n",
    "water1 = 1\n",
    "solvent = \"D2O\"\n",
    "for f in [0, 0.05, 0.1,0.15,]:\n",
    "    \n",
    "    sld     = np.genfromtxt(sim_path+sim_filename, skip_header=0)\n",
    "    \n",
    "    sld = get_sld_water_patch_fraction (sld, f)\n",
    "\n",
    "    #sld2 = remove_water_alpha_water_patch (sld, thickness=silicon_position,patch_frac=f, water_fraction1= water1, \n",
    "   #                       water_fraction2=1, solvent=solvent, AnyDeutration=False, Full_profile=True)\n",
    "    \n",
    "    ax1.plot (sld [:,0], sld [:,3]*10 , label = \"$\\gamma$ = \" + str (f*100) + \"%\")\n",
    "    \n",
    "    ax2.plot (sld [:,0], sld [:,1]*10 , label = \"$\\gamma$ = \" + str (f*100) + \"%\")\n",
    "\n",
    "#plt.xlim (0,30)\n",
    "plt.legend (fontsize=14, loc = (0.8, 0.3)) \n",
    "\n",
    "ax1.set_title (\"100 % D$_2$O\", fontsize=font,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),\n",
    "                                  pad=10)\n",
    "ax2.set_title (\"100 % H$_2$O\", fontsize=font,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),\n",
    "                                  pad=10)\n",
    "\n",
    "plt.savefig (\"./example-data/water_patch_demo.png\", dpi=300, bbox_inches='tight') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be840e06-cc8f-4946-925c-96f11100a200",
   "metadata": {},
   "source": [
    "# Vary $\\gamma$ and $\\alpha$ to optimize simulation reflectivity \n",
    "\n",
    "1. $\\gamma$ is the fraction of water patch area on the  bilayer <br>\n",
    "2. $\\alpha$ is the water fraction at the bilayer water interface \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eba6301b-dd42-4bd4-8aca-cec47f182b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18it [00:03,  5.24it/s]\n"
     ]
    }
   ],
   "source": [
    "path_exp = './example-data/'\n",
    "path_amber_mc3h  = './example-data/'\n",
    "sim_path = \"./example-data/\"\n",
    "#D2O\n",
    "\n",
    "# Load experimental data\n",
    "\n",
    "exp_filename_d2o  =   './example-data/rq_exp_d2o.txt' \n",
    "exp_filename_cmsi =   './example-data/rq_exp_cmsi.txt'\n",
    "exp_filename_h2o  =  './example-data/rq_exp_h2o.txt'\n",
    "\n",
    "# Load the Silica-D2o reflectivity data to obtain the substrate SLD\n",
    "R_cell =  np.genfromtxt('./example-data/rq_silica_d2o.txt')\n",
    "\n",
    "#D2O\n",
    "\n",
    "\n",
    "# Substrate\n",
    "structure, objective =  Obtain_Substrate_parameters (Rq = R_cell)\n",
    "\n",
    "substrate = dict (structure=structure, objective=objective)\n",
    "\n",
    "#print (structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b8733e1-9540-4b55-a84e-60e8ec7adb06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.899999999999999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [10:21<00:00, 16.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min ($\\chi^2$5.325326127489332)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [10:55<00:00, 15.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min ($\\chi^2$1.7889596806308616)\n",
      "(0.5050505050505051, 0.025252525252525256) D2O\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [10:52<00:00, 15.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min ($\\chi^2$1.1658385999725325)\n"
     ]
    }
   ],
   "source": [
    "# Amber\n",
    "ouput_object_name = \"output_data.pkl\"\n",
    "out_path          = \"./example-data/\"\n",
    "sim_filename      = \"nsld_mc3h_dopc.dat\"\n",
    "sim_path          =  path_amber_mc3h\n",
    "\n",
    "All_params = Get_all_parameters_water_patch(exp_filename_d2o, exp_filename_h2o, exp_filename_cmsi, R_cell, sim_path,\n",
    "                       sim_filename, exp_path=\"./\", substrate=substrate)\n",
    "with open(out_path +ouput_object_name, 'wb') as outp: pickle.dump(All_params, outp, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035c84b5-12fa-434b-96da-04f9d2af7864",
   "metadata": {},
   "source": [
    "# Plot optimum SLD and reflectivty at each contrast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80f76f48-7706-429b-9423-0abdc536e686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "water fraction =  (0.5353535353535354, 0.05050505050505051)\n"
     ]
    },
    {
     "data": {
      "image/png": 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asLCwpIkTJ25WSul58+YV+HbWihUr6vr5+SXfeOONRwFKlSqV/Oijj16+/dm1a9fokydPlgYICgpKrVOnzsEdO3bUjo2NzbaAfvnll/UAevfuvb+gMQnhTN5YjrOScizyIygoKLVu3boHtm/fXuvcuXO++X39HXfcsWX79u1XHT16tPz999+/xd7XNWvW7PwNN9ywKTU11WfNmjX5qsW1p+kbwODBgw/u3r378+eff37nTTfddPzZZ5/9d8WKFT+fO3duYlRU1BuXLl16bceOHbMLO1JOtWrV4sPCwk4fOXLk8lwRhw4dqlCtWrXjwcHBmWbkbdKkyeFLly4V+/fff4tndyxXfF5k/O7P7fMCCveZcf78+WIhISGXChOrI0hCLzLp1KnTroMHD1b566+/Lv9HX7NmTal9+/ZV7dix46787peXefPmVVixYkXzjz/++DeA1NRUBXD27NkAMB1VUlJS/Ap6O+yVV15psHXr1nrdu3ffEBYWlpTdPpMmTWrdqlWrf22PH3rooVWXLl0qNmjQoGuz7rtq1apS3333XfvatWsfHDx48OGCxCSEs3lbObaHlGORl8cee+yvuLi44FtvvfW67J5ftWpVqfnz518x4RHAqFGj9kZEROzo2rXrhj59+mT7YzeneQ327t0bBlC7du1sR8DJSb169U6mpKT4bNy4Mdf5EsLDwxNtQ2pmVbVq1QR7R8fKy44dO4qfOHEiLDw8/PKoWCVLlrwQFRVV8cKFC5l+JG3durWKn59fco0aNa640+eKz4u8vvuzfl5AwT8zYmJiSleuXPlUQWN1FMtvEQjXeemllxoCbN26NRzgyy+/rLN48eJL4eHhF4cPH34Q4LXXXts4b9681gMGDLj9kUceWaaUYtKkSV1LlSp17o033thoO5a9++UmNTWVJ5544vpbbrnlL1t7vMqVKydUq1bt8IQJE7qfOXNm5bx58xoopVJvuumm6LyOt2LFioqHDh0Kjo+P9927d2/JJUuW1N24cWPD+vXr781uGCqAIUOGdDh8+HDZDRs2fGHbNmrUqP0rV65c/s0333Rt3rx5qQEDBmwpV65cXGRkZPg333zToVixYglz5sz50Z73KISjeXs5Lggpx8Ie991338HIyMhFM2bM6Fm/fv2w/v37b65du/bZmJiYYsuXL6+5fPnyFhMmTPgh69CVYJrSrFu37ofcjt+hQ4dR9evXP9CjR4+d9erViz1z5kzgwoULr1q+fHlERETEjtatW+ereddNN9108H//+x+//fZbZXvGrHekZs2aDaxfv/7Rq6+++njp0qUTduzYUfabb75p6+Pjk/rCCy9cnp9i6NCh68aPHz+gdevWdwwdOnR98eLFk3/++ed669ata9y7d+/IrO3/nfF5kd/v/uw+L6BgnxlRUVFBJ0+eLDto0KDV+fjndQpJ6IuQ8ePHD8j4+KOPProeoE6dOgeHDx/+OUD58uWTfv3115n3339/zwkTJvTXWtOgQYP9U6ZMWWibNCI/++Vm9OjRzS9evFjs448/zlQQvvjii7kjRoy4YfTo0beXLl069n//+993V111VZ63s2zvz8/PLzkkJORi9erVj44bN+77cePG/ZNd7cWwYcParVixouGqVau+yPoL/uuvv17Zpk2bI9OmTWvz8ssv35iUlORfqlSps126dNkyadKkv2rVqpVn+2IhnMHby3F+STkW+TF9+vTIjh07Hp40aVKbyZMnX3fx4sXgwMDAxOrVqx958sknfxkzZszugh77vvvuW7Jy5cqrpk+f3vXChQshSqnUcuXKnRo6dOiSDz/8MDK/x2vbtm1stWrVDi9cuLDu888/vzPvVzhO06ZNo5cvX97op59+apeSkuJbqlSps40aNTrw2muvXU7EAcaNG/dPSEjI7ClTpnR45ZVX+iUnJ/uFhYWdeeCBB3597733NmQ9rjM+L/Lz3Z/b5wXk/zNj6tSpV/n6+qY89NBDLr0+2VH2jGYgPItSquETTzzxxMSJE51SG+YN7rnnnraLFy9u8ueff36R32G8svP+++9Xf+yxxz7VWv/liPiEUEo1Hzdu3EMTJkw4ZHUs7srR5bh3795VFy5c+KTWOtYB4QmLNW/e/NWvv/7av169epa3by6o0aNHN5s8eXKv6OjoiTk1GxX2cfTnBUCjRo0Gh4aGXlqzZk2mmvtbb721yg8//PCc1tquIUcdQdrQiyJn48aNoZ999tl1ly5dCmrfvv2wypUr31+1atURVsclhLCflGNRFLz22mtbSpYseX706NGtrI7Fkznj8+LHH3+suHv37ppvvPHGCkfFWRjS5EYUOS1btjyntZ5gdRxCiIKTciyKgoCAAD1x4sSfVq9eHW51LJ7MGZ8XBw8eDHn66afnde7c+XTeezufJPReSppSuZz8gwuHk3LsWvLvLdzRkCFDoocMGSJNaN3MY4899l9Oz1nxWSJNbrzTpaioKL+sw0gJ50hOTlZ79uzxAzy2naZwS5f279/vFx8fL5/TLnDgwIGg2NhYDUhHWS8RFxd3+p9//sl1yEchHO3o0aMBJ0+eVLj4s0Q6xXohpZSqUKFC/5o1a94QFBSUmvcrRGEkJiaq6OjotVFRUTO01tJpSTiEUsqnUqVKd1SvXv3awMBA+aB2Iq21unDhQsrOnTunX7x4ca3V8QjHUEpVatSo0RNhYWFllFJShoRLXLx4kZ07d35x/vx5l7atl4TeiymlApBmVa6QorVOsDoI4Z2UUoGA3G1zvkStdbLVQQjHUkr5AEFWxyGKFEs+SyShF0IIIYQQwoNJ20whhBBCCCE8mDTHyENYWJiuUaOG1WFcISHBtPAIDAy0OBLhSs667hs3bjyptS7n0IO6CXctw0I4kjeXYZByLIqGwpRjSejzUKNGDTZsuGL2Yst9/vnnAAwbNszSOIRrOeu6K6UOOvSAbsRdy7AQjuTNZRikHIuioTDlWBL6QkhNTeXw4cOXa02dwdfXl0qVKklNvBBOkpiYyJEjR0hOdl4fpoCAACpXroyvr/RtFcIZLly4wIkTJ0hNdd7AboGBgVSuXBkfH2mtLNyPJPQFlJqayjfffMPp06cJDXXeMLeJiYlcunSJu+++m5CQEKedR4ii6NKlS3z22WcEBgY69Ufz+fPnKVGiBIMGDZKkXggHO378OLNmzaJcuXJOTbbPnj1LhQoVuPXWW1FKOe08QhSEJPQFdOLECWJiYnjwwQed/gU9d+5cduzYwTXXXOPU8whR1Pz777+EhYUxcOBAp54nNTWVqVOncuTIEapWrerUcwlR1GzYsIHWrVvTqVMnp54nOTmZSZMmcerUKcLCwpx6LiHyS+4bFVBiYiLFixd3SW1baGgoSUkyX5EQjpaUlOTUO2w2Pj4+hISEkJiY6PRzCVHUJCYmuqQc+/n5Ubx4cfk+Fm5JEnoHe/jhh+nbt+8V27/88kuUUrRo0SLb1/39998EBwcTFRVl13luuOEGbrjhhkLFKjyPXHfXyFiODxw4gFIq0xIWFkbnzp1ZtGhRptfltxwLIZwnu+/jqKgoHnroIa666iqCgoIICQmhVatWvPrqq5w9exaAo0ePEhwczLp166wIW4gCKTIJvVKqj1JqpVLqglLqnFJqg1KqmyPPsXfvXj7++GPGjx9/xXMzZ84EzBf+tm3brni+efPm9OjRgxdeeMGuc4WFhcktvyJIrrvz5VSOx44dy5o1a1izZg2ffvopAQEB3HDDDURGRl7eJ7/lWAjhHNmV45UrV9K0aVN+//13HnnkEX777Tfmzp1L3759+fDDD3nxxRcBCA8PZ/jw4YwZM8ai6IXIvyKR0CulRgI/ARuBm4EBwHdAsCPP895773H11VcTERGRaXt0dDTLli2jT58+QHpyn9XIkSOZM2cOR44cyfNcu3btYteuXYUPWniEhAQ4cwZWrtzLTz8dYMcOiI+HpKT0JTk5fUlJSV9SU9MXrdMXkb2cynGtWrVo06YNbdq04cYbb+Snn37Cx8eHH3/8kdRUiI2F48dh2DD7y7EQwjmyluMzZ85w66230qBBA/7++28efvhhunbtynXXXce4cePYs2cPPXv2ISYGjhyBIUNGsnLlSqmlFx7D6zvFKqVqAO8BY7TW72V4alF2+xdUQkICX375JePGjbviuVmzZpGamsqLL77I2bNnmT17Nm+++eYV7e+vu+46QkND+fzzz3n22WdzPd+aNWsAqFevnuPehHArFy/CsGGwfz9s2wam+XVti6PybrmV46yCgoLw9/fnyJEk3nsPzp0z21NTryMwMJS33vqcd9/NvRwLIRwvu3L8ySefEBMTw6+//krx4sUz7Z+aCuvWFWfTph6k33BrSHh4E157bTpz57ZGRqoU7q4o/Be9B0gFpjrzJJGRkcTGxtKxY8crnvviiy9o0KABrVq14s477+TYsWP8/vvvV+zn5+dH27ZtWbhwoTNDFW5u9Wpo0QJCQuD772HjRpPMFy8OxYvHU7nyGSpXBj+/9MXXN33x8UlflEpfRN5yK8epqakkJyeTnJzMkSNHePLJJ4mPj8ff/xbOnTPXp1w5CAz0o1Kltsydu5AVKyx4E0IUcdmV4yVLllCxYsUr7rwB/PYbrFhhPmfLlIHwcAgKgipVOrF8+UK++src7RTCnRWFhL4D8C9wu1Jqr1IqWSn1n1LqQUeeJDIyEqUUTZs2zbR97dq1/PvvvwwdOhSAgQMHEhQUlGOzm+bNm7Nu3TqnTo4h3FNSEsyfD889B3//nb79uedg9264cAEmT/6aV175iejowjW3ybjIf7V0OZVjME3i/P398ff3p3LlykyZMoV+/aZRrVp7evWC0aPhwQfhqaegffvmHD68jmXLUpGWcUK4Vnbl+NChQ9SoUeOKfffvh/XrTWXI0KHwyCMwcqQpx/36NefcuUNs2nSERQ69py+E43l9kxugUtryFvAssBfThn6yUspPa/1+1hcopUYAIwCqVatm10mOHDlCaGgoAQEBmbbPnDkTHx8fhgwZAkDJkiUvt789e/YsJUuWzLR/uXLlSEhI4PTp09L5sYj59FN44IH0x4sWQaNGULmyc88rtffpcirHAM8//zw33ngjAKdPn2bKlJ+ZN28ElSsH06bN7Zf38/eH5s3L8fnnCcTFnebXX8OoU8dlb0GIIi+3cpzV6tVm3bEj1M7QotHHB5o2LQfAhQtHWL++Es2bOyNaIRyjKNTQ+wAlgJFa60+01su01g8AC4GxKpvp3rTW07TWEVrriHLlytl1kvj4+CtmmkxISOCbb76hbdu2lChRgtjYWGJjY7n55puJj4/n22+/veI4xYoVAyAuLi6/71N4uA0bzLpNG3j7bbjuOucn8yKz7MqxTfXq1YmIiCAiIoIePa6jffsPqFOnJ19//Rg6Sy9jWzkuUSKOs2fhn3+cHroQIk125bhq1aocOHAg07azZ2HPHtNssXXrK49jK8d168ahNfz1l7MiFqLwikJCfyptvTjL9t+BCkC4I05StmxZzpw5k2nbzz//zOnTp1m1ahWlS5e+vNx+u6nNy67ZzenTpwHyrJ2/+eabufnmmx0RurDYlClQqxbMnm0ev/wyPPlk9vvKdXeu7Mpxdo4cMU2gqlRpxMmTxzlx4kSm523luGtXU443bnR8rEKI7GVXjrt3786xY8fYmKEw7tlj1lddBcHZjHlnK8edOoXh4wM7d8KlS04LW4hCKQoJ/Y4ctttq5h3Sgrh+/fokJSURHR19edvMmTMJDg5myZIlLF++PNMybNgwVq1axd69ezMdZ//+/VStWvVyzUBOSpYseUVzHeGZpkwx7Tjj402HrJYtc95XrrtzZVeOs/Pff2Z99uxWAgICrrgmtnIcEVEMHx84eNBcXyGE82VXju+77z7CwsJ46KGHuHjxIpA5ob906RJLlizJdJz9+/cTEBBAkyY1qVvX9DfKUskvhNsoCgn9j2nrnlm29wSitdbHHHGSTp06AVwes/bEiRMsXLiQ2267jWuvvZYuXbpkWp5++mnAjICT0dq1ay8fKzfbt29n+/btjghdWOzoUbNetw6ioqB06Zz3levuXFnLcUb79u0jMjKSyMhIfvrpNxYsGMWmTYu47777CAoKyrSvrRwHBUHNmqbzsUweK4RrZFeOy5Qpww8//MDOnTtp0aIFH3wwmWXL/mDv3sX8+OOr1KtXjwULFmQ6ztq1a2nVqhVBQUHUr2+2HTzosrchRL4UhYT+V2A58LFS6n6l1HVKqWnAdYDDpnOsUaMGrVu35ueffwZg9uzZJCcnc88992S7f/369WnXrh1ffPHF5fa3hw4dYsuWLZeb5ORmw4YNbLA1uhYeKzERTp40HbBatDBDH+ZGrrtzZS3HGb3++uu0bduWtm3b8tFHtxMdHck773zI++9n7leftRxfdZXZfswhVQdCiLzkVI47derEli1b6NGjB++88y6ffdaTb7+9mYUL5/Hggw8yYcKEy/vGxcWxdOnSTOVYKVMBI81uhDvy+oRem2z5JuBrYALwC9AGGKy1/tyR53rggQeYO3culy5d4vHHH0drne141jarVq1i//792Prlzpkzh2rVqtG7d29HhiXc1KefQtWq5u/y5c2wacJ6GcsxmORAa315OXFC88wzZ3nqqU08/vgo/PwyDxaWtRzbrnGWZvZCCCfKWo5tqlevzuTJk/npp708/3wCX3xxgfXr1/PMM88QGhp6eb/58+ejtb48Ql3x4lCxoml2k3FYYSHchdcn9ABa63Na6we11hW01gFa66Za6zmOPs/QoUOpXLkyH330Ub5fGx8fz/vvv89LL710xQyyWUfQEN7hyy/Tk7wePayNpSjLWr7yKsdHjph1pUpXPpddOa5Y0QxlGRsLp05d+RohhOPlVY4PHzbr7MoxwJtvvsmYMWMoVarU5W3mx7lm/XqHhiqEQxSFceidomTJkpw8eZLDhw9TOkOj548++ojNmzdfUSuQl127dvHAAw9wyy23ZHptYmIi+/bto127dg6LXbiHmBizXrHCjIEsXK9kyZJs3LiRM2fOZBrmLrdyfOCAmcyrdOkrb73nVI6LFz/HiRPH2bu3ZLbD4wkhCq5UqVLs2rWLOnXq4OOTXk+ZWzmOijLluGTJK8vxsWPH6NOnD6NGjcr02oCAU5w6FcumTSWc9l6EKChJ6AuoZMmS9OrVi++++46EhIQrnv/ggw/yfcyAgAAmT56caZuvry9NmjShcePGBY5VuCdbQl+njkzuZJV69eoRHR3NjBkzSE5OvuL57Mrxzz+bjnExMdnfes+uHP/5ZwC7dnXj0CGZLE4IR+vQoQPz5s1jypQp2c6ynl05njEjPZHP0NLmshIlSvDpp59m2hYfH8j+/Tdw/nwIWsvntnAvSppz5C4iIkK7YydEW61BcHaD5wq3lpxsxiVv3x5SUiAhAeyY0BBw3nVXSm3UWkc49KBuwtFluGZNU0v/zz/QoIF9r5k2zUwnP3QoZBnYSgiH8OYyDI4tx6dPQ9myZuz58+fNoAT20NrcmTt71jS9C3fILDZCpCtMOXaLGnqlVEEGdNPA9VrrIjmGnyTynmvcOHj9dfN36dL2J/Mg191qly6Z2nk/P3NnxV5Nmpj1tm3OiUsIYb9//zXr+vXtT+bB1Mg3bQp//glbtkhCL9yLWyT0QBXM8JIxdu7vAwwB8pEKeZfNmzcD0KxZM0vjEPlnG0a+USN44IH8vVauu7V27za1dHXqmI6u9mrUyKx37jR3ZWREIyGss3OnWdt7hy2jq682Cf3WrdCrl2PjEqIw3CWhB3hJa33lbC7ZUEr5AUOdHI9bk8TOc508adZTp0KHDvl7bVG87kqpnsDTQEOgNOaH/2rgRa31P66MxTZDbN26+XtdaKipzTt6FA4dgho1HB6aEG7NG8oxpN9tk/n9hLtxl2ErXwAO2buz1jo57TWHnRaREE5iS+jDpH+kvcoAG4GHMBPCjQUaAZFKqequDGT/frOuWTP/r61d26z37nVcPEJ4EK8ox7YfAVKOhbtxixp6rfWrrniNEFY6dgw2bUqfMbRcOWvj8RRa66+ArzJuU0qtA/4FbgUmuiqWwiQCderAX3+ZRODaax0blxDuzpvKMcCePY6LRwhHcIuEXoiioGPH9Fu9fn6QYb4SkX+2KZqSXHlSqaEXwqE8rhxXqgRBQWbY2rNnzTj2QrgDd2lyI4RXS0oyybxS0Ls3TJwoHSPzSynlq5QKUEpdBXwMHAO+dmUMBw6YdUHawEtCL4T15fjiRZOMBwQUbJQaH5/0Wnopy8KdWFZDr5SqCUwHagLzgGe11vFpz63TWst8irkYPHiw1SGIfDiVVg8VFga//lrw4xTx674WaJn2939AN631iex2VEqNAEYAVKtWzSEn1zo9oS9MDf2+fQ4JRwhPZWk5tpXh6tXzN2RlRnXqmE6xe/ZAixYOCUuIQrOyhv4jYC4wANNZZqlSyjafcj4GhCua/P398c/PuHnCUrZZYQvbbr6IX/ehQBtgEHAOWKyUqpHdjlrraVrrCK11RDkHdVY4dgzi482ENCUKMPN7lSpmfVi68ouizdJyXJjmNja2GnpbE0oh3IGVCX0FrfWHWuuNWuthwC+YpL4kZtIokYv169ezfv16q8MQdti1Cz7/3Pxd2JFtivJ111rv1FqvTetcdy0QAjzjqvMXNhGoUME0szpxwswOLERR5OnlGOCqq8xaEnrhTqzsFBuY8YHW+nWlVBKwFChA/VfRsmPHDgBatWplcSQiL7fcAmmXq9AzC8p1N7TWsUqp/4B8zNdaOIVNBHx9zfWPjjbj0ctY9KKo88RyDOnN5yShF+7Eyhr63UqpHhk3aK3fBuYAta0JSQjH0jr9Q//hh2H8eGvj8RZKqQpAfcBl3dIK037epnJls46OLnQ4Qng8Ty3H1dNGzT9k9+w5QjiflTX0t2e3UWv9jlLqG1cHI4QznDtnmlcULw6TJlkdjWdSSv0IbAK2Ytrc1gUeB5LxkLGrbapUgbVrpR29KHq8rRyDKccpKTJimXAPliX0WutMrUiVUpW11ofTnpOvO+HxLl6ELVvM3xUqWBuLh4sEbgOeBAIws0r/AbyutT7gqiBsiUBhmspIDb0owrymHAcFmQEOYmLg+HEzNr0QVnOniaX+BspbHYQQjhATYzpOnT1rHktCX3Ba6zeBN62Ow9E1e0IUJe5Qjs+cMZ/JxYsXfoCCatXM53xUlCT0wj24U0KvrA7AkwwbNszqEEQuNm82XxxBQVCxItx3n2OOK9fdGsnJ6e1lbe1nC0Jq6IWwTsb286qQGUfVqrBxo/lcaNOm0KEJUWjulNDLUJXCa9hqYG+5Bb780tpYROEdPmySetu07wVlS+ilhl4I13PEXTabqlXNWjrGCndh5Sg3ohBWr17N6tWrrQ5DZOPbb9OTeFsTC0eR624NRyUCtv8PUkMvhOtJQi+8mST0Hmr37t3s3r3b6jCcKikpiYoVK14ee90TREfDwIGwdKl5XMfBoysXhevujvbtM+tatQp3HFtb26NHzZCmQgjXcVQ5BtOGHkwbeiHcgST0wm35+/vj7++PKmxjRxeytdGsXt0MUzlokKXhCAexJQKFrdkrVgxCQiApKb3DtBDCNRyZ0EsNvXA37pTQS7EQV3j44Yd5/fXXSU5OtjoUuxw5YtYtW5qJpIKDrY1HOIbtVr0jEgHbiEcnThT+WEII+0mTG+HN3KZTrNa6pdUxCPfz559/smLFCipXrkzjxo0pXrx4pufnz59vUWRXeuEF034eZBgzb+OoGnqA8uVh716T0NetW/jjCSHylprq2IS+YkUzUs6JEzK5lHAPbpPQi/zx8ysaly4sLIxbbrnF6jDydOoUvPJK+uOrr3bOeYrKdXc3jqyhL58224bU0AvhOkePQmKiKX9Z6oUKxN/fjGUfE2PKcnh44Y8pRGFIduChhgwZYnUILvHZZ59ZHYJdjh0z6+rVTS19RIRzzlNUrrs7uXjRzAYZEOCYOy+S0Avheo5sP28THm4S+qNHJaEX1nOnNvRC5GjDhg188803XLx4EYCLFy+6Vbt6W3JWvTq0bg0+UrK8Rsap4h1xXSWhF8L1nJXQg0nohbCa29fQK6WKAY2BpmlLE611N2ujst6KFSsA6Ny5s8WRONfx48fp168f69evRynFnj17qFWrFk888QRBQUG8//77lsaXmgoLF8Jvv5nHtg6PzlJUrrs7cWS7W5CEXggrOLocgyT0wr1YltArpVoBnwC+wBCt9RalVC3SE/emQBOgNqDSFpDRcADYn/bp5O2J3eOPP07FihU5deoU1WwD/wIDBgzg4YcftjAy49dfoW/f9MfO7gxbVK67O9m1y6wdNaeAJPRCuJ6jyzGYjrEgCb1wD1bW0H8BfAbsAFYopXyA4qQn7ueBf4C1wBDgAeBrrbWM3lyELF26lKVLl1K6dOlM22vXrk2UG8zoYRt3vn596NQJHnnE0nCEE/z7r1k3aOCY40lCL4Tr7dxp1o4qx5BeQ2/rQyWElaxM6CsDGzAJfSiggV+BKcB2rXUUgFKqFCah3ynJfNETFxdHQEDAFdtjYmIICgqyIKLMzpwx6/794dVXrY1FOIejEwFJ6IVwrdTU9Br6+vUdd1xpciPciZVd994DPgZ+BDYC24HrgXvI/ENDJkgvwjp16sTnn39++bFSipSUFN58802uvfZa6wJLExtr1qVKWRmFcBat0xN6RyUCktAL4VoHD0J8vEnAS5Z03HEloRfuxLIaeq31OKXUfKAMsBJIBEYALwH/KKUmp/3tUEqphUBP4FWt9fOOPr6rFCtWzOoQXOJ///sfnTt3Zv369SQkJPDkk0+yY8cOzp49y6pVq6wO73JCn6VFkNMUlevuLmJizF2Y0FDHDUtXtqyZkObUKUhOBplaQAjncvSPchtJ6IU7sfSrRGu9IcumqUqpr4DxwMPAXcC7OKiWXil1B+CkKX9ca+DAgVaH4BINGzZk69atTJ06lcDAQOLj4xkwYAAPPvgg4RYP/BsVld6G3lU19EXluruLTZvMumlTk4Q7gq9v+oQ0MTEyfrUQzmYrx46e8C9jQq+14z4jhCgIt6sbSmsn/4RSairwDvAKJqFvgqnJL5C0tvjvAo8DcwofqXCF5ORkwsPDmTBhgtWhZLJtm0nybKTJjXfakFbl4OiJwsqWNcn8qVOS0AvhbBs3mrWjy3FwsLl7d+6cuZNXpoxjjy9Efrjt9Dda691a6xuA3sC/wCSl1C9KqYKOIvs/YIfW+iuHBWmhJUuWsGTJEqvDcLrw8HBGjx7NTts9UzdhG/mkdGm4+WZo39415y0q191dOCsRKFvWrE+dcuxxhRBXsv0wb9nS8ceWZjciJ0lJ5ofe2bPmDo6zuW1Cb6O1XoQZk/4xoA1mVJx8UUp1AO4ERjk0OAtFR0cTHR1tdRhO99prr7F69WoaNWpE27Zt+fTTT7lw4YLVYWELoW9fmDsXXNW0vahcd3egNaxda/52dCIgCb0QrnHkCERHQ0gI1K3r+ONLQu/dUlLg+HGYMwc6dIBmzcxdmeBg870fFASBgRAQAP7+ZvHzM00rAwLMXZtSpUxn7DJlzFK6dPpSqlTmpTDcJqFXStVUSvVXSl2V9TmtdYrW+gOgLjA9n8f1x4ym87bWepedrxmhlNqglNoQExOTn9MJBxs+fDirV69mx44ddOjQgeeff57w8HDuueceSzvFXrxo1iEhloUgnGz7dvMlXbEi1Kvn2GNLQi+Ea/z+u1l37gw+Tsh4JKH3Tlqbkciuvtp8BwweDKtWwZYtcP48xMWZkZMSEiAx0dTGJyebJSXFDJXq42MS+cBA85ozZ8wSG5u+nD2beSkMyxL6tOYzFdL+7oeZQOp+4A+l1O3ZvUZrfVprnd+pe54GigF2jxKutZ6mtY7QWkeUK1cun6cTztCgQQPeeustoqOjee2115gzZw6dOnWifv36TJ06ldTUVJfGY0voixd36WmFCy1aZNY9ezq+s5sk9EK4RsZy7AyS0Hun/v2hQgXYkdYmpFw5aNsWVq+Gw4fNXfqLF+HSpczJfcYEPynJJO0XLsDJk+bz3racPp2+2BJ927w2BWVlp9jqWuvjaX8/BXTUWu9SSoVjJpj6urAnUEpVA54D7gMClVKBGZ4OTOsoe15rnVLYcwnnSkxMZO7cucyYMYNly5bRoUMH7r33Xo4cOcLLL7/MH3/8wddfF/q/jN1sTW4kofdOWoPtv1Pv3o4/viT0Qjjf+fOwYIH5u1cv55xDEnrvoTX873+wezfMn28qcipWhAkTYPjwgh/Xzy/9M9+ZrEzoMybXAbbmMFrro0opXwedoxYQBHyZzXOj05bmwGYHnc9lQkNDrQ7BJTZt2sSMGTP46quv8Pf358477+TDDz/kqqvSW2Zde+21dOzY0aVxWdXkpqhcd6tFRpoOsWXKQL9+jj++JPRCON/MmSap79QJrrqiMa9jSELvPTZuhGeeSX/crRssXWpdPPllZUK/IG3yqGeAxUqpYZjEuzfgqDkUNwNds9m+PO1cnwL/OehcLtW/f3+rQ3CJ1q1b06NHD6ZNm8aNN96IXzaz8DRo0IDbb8+2lZbTWNXkpqhc94yUUrcCdwARQHkgCpgLvKa1Pu/o850/Dw88YP4eMcI5HZ4loRdFjavL8b598MIL5u+HH3b00dPZEvpjx5x3DuF8y5eDbVL69u3hnnucd1fHWaxM6J8CXgcOA2eAapgOr4uAex1xAq11LPBH1u3KNIg9qLW+4jnhXvbu3Uv16tVz3ad48eJ89tlnLonn2DH44ov00U+kyY1LjMZ8+T8LRGPuqr0IdFVKtdNa59qB4sQJ+OADczs1NdWsc/r7+HGYN89MFV+rFjz3nHPekCT0oghyajnOuN6/H777znQy7NMHbrnFeW9Kaug93/Hj0KOH6cwKcOONJqH3NJYl9FrrJGC0UuoFoA7gj0my5SvODgsXLgSgl6f9hMynvJJ5V3v9dZg0Kf2xq/tMF5XrnkVfrXXG4aZWKKVOAzOBLsCy3F586BA8ks+u9I0bw48/Oq9JlST0oghyeTnu0wdmz3buDK6S0Hu21FRYv94k89WqwZAhhWsvbyXLZ4rVWscB21x8To+foPlYEbm/l5iYyKuvvspXX31FVFQUSUlJmZ5PSXFtf2bbKKY33wzXXgvdu7v09EXmumeUJQmwWZ+2rpzX68uVg9tuM0OIKWWWnP4uVcqMOd+tmxlH2FkkoRdFjTPLccZtto6M7dubCeGcmcxD5mEJL16Uu7aeJCUFWrWCv/82j3v3hlftHg/R/Vie0NsopSprrQ9bHYdwLy+88ALffPMNY8eO5fHHH+ett97iwIEDfP3117z88ssuj+fSJbMeOtQk9cIyndPWeU4hXK0aTJ7s5GjyyZbQnz5tmgk4O+kQwk15dDkGU3bDw+HAAVNLX6eO1REVXVqba2CbDCox0fy/qVLlyn3XrYNt20wyrxSEhcGAAa6P2ZHcJqEH/sZ0lClSjh+HTZvS//OlpJiZx4oVM0toKNSoAVWrmqGPippvv/2WqVOn0qtXL0aPHs2NN95I7dq1adCgAYsXL2bkyJEujScuzqxdNTOsuJJSqjLwErBEa70hh31GACMAqlWr5sLo7BMQYJrzXLhg2vkWdoZAITyNN5RjG0no3cPw4fDpp1duv/lmczd91CjTP+qzz8xQlDYDB8JXX7kuTmdxpxSxSNVRnTljet5/9ZVpw5UXPz8z5XCnTmYYvdRUhY+PdnqcVjt+/DgNGzYEICQkhNjYWMC0IX/66addHo8k9NZSSoUAPwHJwN057ae1ngZMA4iIiHDLglK2rEnoT52ShF4ULd5UjkHa0Vtt2zaYPj197pBKlUxTqN27TSXpjz+a5cEHr3xtv37w1FOujddZ3Cmhd9vC6mhxcaat1tq14O8PHTua20KBgabdbny82ScuziT+Bw6Ymck2bDDLO+9AmTIDadfuCPXqQZs23nvLvlq1ahw5coRq1apRp04dFi1aRMuWLVmzZg3FLMiqbQl9cLDLTw1AWVfMTuGmlFJBwHzM/BKdtdbRFodUKGXLmtqiU6egdm2roxHCNbytHIMk9FZ7/nkzERRAgwbwzz/m77g4+OknmDULfv3VbPP3h/r1TauH2bO9qzLFnRL6IuONN0wyX726mbTAni/zCxdgzRpYssQMx7V/fxC//FKLX36BFi1g2TLzi9Tb3HzzzSxdupQ2bdrw6KOPcscdd/DJJ59w+PBhxowZ4/J4bG3oraqh79u3rzUntphSyh/4AWgNdNdau7QjvTNIx1hR1HhjOQYZi95qBw+a9bPPwl13pW8vVgxuv90sly5BcrJp0hwQYE2cziYJvYudPAlvv23+/vJL+2vmQkLMOKk9epgfBBs2wDffmCEUN20ynT4efRReftm7autff/31y3/feuutVKlShdWrV1O3bl1uuOEGl8cjTW5cTynlA8wGrgWu11pHWhySQ0hCL4oSby3HYEbVAamht8qRI2b90EPpP66ysuquuitJQu9iX35pfin26gUdOhTsGErBsWM/07kz3HFHX264wdQMvPoqNGkC110HpUs7Nm530aZNG9q0aWPZ+a1O6H/++WegyNXUfwgMAF4FLiqlMv4HiPbUW/aS0IsixivLMUiTG6s895xptRATY4YuLV/khlXJzJ0S+kNWB+AKtqmFCztxwam0LKBvX4iOhnvvhZkzza0lPz9YvBi6dCncOawyd+5cu/ft37+/EyO5ktUJ/amimf31Tls/l7ZkNAEz26THkYReFDFeWY5BEnorxMXBa6+lP27WzLlzh3gCt0notdYtrY7B2Q4ehC1boEQJcGRrEV9feOkl8wt15UrYu9eMntOypTmXp7n11lvt2k8p5bKJpTZtMk2azp0zj6XJjetorWtYHYMzSEIvihJvLccgCb0V0ga8o2xZWLDAzO5d1PlYHUBR8ttvZt29u+M7ZVSrBjNmmDb1ANOmmZn1tmxx7HlcITU11a7FlbPEzpkDf/1lJq6oWdOMSCREYUhCL4R3KFfOVKidPGnmkxHOd+aMWYeFwTXXyAy94EYJvVKqmFLqMaXUcqXUcaVUYtpyPG3bY0opj+7WsHixWffunft+hdGtG9x4I5QpAwkJZvQbUXi2pjZPP21+JHlTx2NhjYyzxQohPJevL1SoYP4+ftzaWIoKW0Lvrf0FC8ItmtwopaoCy4AawCrge+A0ZrKp0kBD4H/Ag0qpa7XWURaFWijr1pl1x46FP1ZFW7f6LIKCYN48eP99eOwxU7N89izcfbcZJtMTzJ07l759++Lv759ne3pXtaGPjzfrOnWsbcaU03UXnkdq6IXwHhUrmiY3x46ZMc6Fc9ma3HjTOPKF5RYJPfAeEAdcpbU+kN0OSqkawDzgXeAWF8XlMEeOmM6roaFQt27hj9erV69cn2/WzKxtk1Ht2WMmUfAEt956K8eOHaN8+fK5tqd3ZRv6hASzDgpyyelylNd1F56jTBmzloReCM8XHg5//y3t6F3hlVfg00/N31JDn85dEvruwJCcknkArfUBpdQ4YJbLonKg9evNulUr09bO2Tp1Mh1j162Dd99NnznNE6Smpmb7t5VsNfTSdl44itTQC+E9pGOs67zxBly8aP5u2tTaWNyJu7Sh107a123Ymtu0auWY482dOzfX5ihKmSEsn3/ePN6+3ST5q1c75vyukmCrGgcOHz7M+PHjeeqpp/jzzz9dHIdZW11Dn9d1F56jZEnT9vbCBelIJ4Snk4TeNVJS0pP5HTtMvzZhuEtCvwR4VSlVM6cd0prcvAwsdlVQjmSroW/d2jHHO3fuHOdsYyjmokwZqF/fTHn855+mbb0n2LVrF40aNSI4OJjmzZvzzz//0Lp1a9555x0+/vhjunbtyrx581wWj7vU0Nt73YX7Uyq92Y10jBXCs0lC7xqXLpl1cDA0bCgDVGTkLgn9Y0AxYLdS6k+l1BSl1GtKqVfT/l4J7E7b53ErAy2I1FTHJ/T5sW4dfP21+XvBAjPh1N9/uz6O/Bg9ejTh4eHMnz+fxo0b06dPH3r27MnZs2c5c+YMI0eO5I033nBZPLaE3uoaeuFdpNmNEN5BEnrXsNXOh4RYG4c7cos29FrraKVUU2AE0Be4CUiru+IMsAMYA3yitb5kSZCF8N9/pkd2pUpQubLrz1+iBNx8s+mFf+wYrFgBQ4dCu3ZmCM127cyUye70SzcyMpLFixfTrFkzOnXqRMmSJRk1ahQ+aR0QHn74Ydq0aZPHURzHXZrcCO8iHWOF8A62AcgkoXcuW0Iv485fyS0SegCtdRzwftriVWzt562onbcJCDDtzdatM7PU7thhlk8+Mc+Hh5ulalWYMMEMz2hlgTl16hSVKlUCoESJEhQvXpwytuwHKF26NOfPn3dZPO7S5EZ4F6mhF8I72Grojx2zNg5vd+GCWUsN/ZXcpcmNV8s4wo2jVKlShSpVquTrNWXKQK9e8McfZibZQYPMHQMfH1OrsGkT/PSTGfKycWPT+cRKKsstg6yPXcldmtzkdd2TkpK45ppr2LVrlwujEgUlk0sJ4R1sNfTHjplmtsI5pIY+Z25TQ28PpVQn4EWtdTerY8kPZ9TQd+/evcCv7dDBLMOHm8dxcWZYy1Wr4LPPYNcuOHAA+veHWrXgtdegWDHHxJ0fQ4YMITCtSjw+Pp7hw4cTHGwmC844+o0r2E5ndQ19Xtfd39+f/fv3W/rjR9hPauiF8A5BQWZM9DNnTHkuV87qiLzPkSOwebP5W2ror+RpNfTlgM5WB5EfiYnpHVAjIqyNJSfFikHLlvDIIybWe+4x2+fPh/feg59/dn1Md911F5UqVaJs2bKULVuWIUOGULVq1cuPK1WqxJ133un0OKZMMXdWjhwxj62uobfHXXfdxSe2tlTCrUlCL4T3SGslyuHD1sbhjfbuNU2CH3zQPJaE/kpuUUOvlKpm564e95t32zZTu1uvnmOnKP7mm28AGDhwoOMOmub116FzZ5g1yyTze/c6/BR5+uyzz1x/0mxMnJj+/suVs35WOnuu+8WLF5k9ezaLFy+mZcuWFM9yb3LSpElOjVHYTzrFCuE9qlUzfdMOHkyfrV04xp49pilTiRJmuMqRI62OyP24RUIPHMC+CaOUnfu5jbVrzdqR7ecB4uLiHHvADEqUgAED4MQJk9DPnAnnzsHLL4Ofu/yPcRHbP/OCBdC2rfVNbuy57jt37qRFixYA7Nu3L9Nz0hTHvUgNvRDeo3p1sz540No4vJHtq69bN3DhFDQexV3SszhgJfB9HvtFYIa29Bhr1ph127bWxlEQtimVd+0yUy336mVq7osSW9v5Vq2sr5231/Lly60OQdhJOsUK4T0koXceW0JvRX8+T+EuCf0WIEVr/WluOymlYpGE3mU6dIDFi+GFFyAysmgOx+UunWEL4uTJk+zdu5dmzZpd7lws3IvU0AvhPSShdx5J6PPmLp1iNwIt7dzXY9oMnDhh2l8XLw5NmlgdTf4pBd27p7cFjImxNBxLeGJCf/78eQYMGED58uVp164dh9N6aN1///28+OKL1gYnMpGEXgjvIQm989gSek8YmMIq7pLQvwHcntdOWusftNbuEnOebLXzrVs7vu15zZo1qVmzpmMPmgPb8FsnT7rkdG4jNRWSkszfAQHWxmJjz3V/+umnOXLkCJs2baJYhuqMG264gR9//NHZIYp8yNgpVntU7yAhRFaS0DuP1NDnzS2a3GitDwNeN9DTn3+atTOa23R2YWP2sDCz/uQTiIoya19fl53eMomJZh0QYO5WuAN7rvv8+fP58ccfadasWaZOsA0aNLiik6ywVrFiZomLMxOmyFBsQniu8HDw9zd3s+PiJPl0JEno8+Z2td1KqRZWx+AoixaZ9bXXZv/8hg0b+Oabb7iYNvXZxYsXSU5OdlF09rM1FzpyxEw8tW2btfG4SsaE3pOcOXOGsra2HBmcP38e36LwS8zDSLMbIbyDj48ZKx1M5ZdwHNts7ZLQ58ztEnpguVKqq9VBFFZ0NGzfbmrcOnTI/Nzx48e55ppraN26NYMGDeL48eMAPPHEEzz55JN2Hf/LL7/kyy+/dHTY2eraFXbuTG9LHxvrktNazh3bz9tz3Vu1asX8+fMvP7bV0n/88ce0a9fOqfGJ/JOEXgjvYWt2c+CApWF4HWlDnze3aHKTxRzgV6XUEK31DxmfUEp1AN7QWnfI/qXu4/u0ATi7d7+yhvfxxx+nYsWKnDp1imrV0ufUGjBgAA8//LBdx3d1TX79+lClipl2+dw5l57aMu6Y0Ntz3V977TV69uzJjh07SE5O5p133mHHjh2sW7eOlStXuiBKkR+S0AvhPWrVguXLrZmQ0ZtJk5u8uV1Cr7V+QCl1FPhaKfWw1nqqUqoJ8BpwPbDT2gjzpjV8/rn5e8iQK59funQpS5cupXSWgc1r165NlBvfpwsNNWtJ6N1bu3btWL16NW+//Ta1a9dm6dKltGjRgjVr1tDEE4db8nKS0AvhPa66yqz/+8+xx01MhE2bzPdSSorZ1rq19/e7uXTJtHKwNfWVhD5nbpfQA2itX1JKHQamKKXuANoDh4B7gC/ycyyl1K3AHZhJqcoDUcBc4DWt9XmHBp5m0SLYssV8Ud9ww5XPx8XFEZBNw+yYmBiC3Ph+UsmSZn32rLVxuIqnJvQATZo0YebMmVaH4TBKqSrA05hyfDVQDKiptT5gZVyOYBvpRiaXEt7Om8uxTZ06Zr1nj2OP++CDMH165m033uj9s6b+8w/8/bf5OyQEIiKsjceduWMbepRSZYC6QArQEYgErtJaf661Ts3n4UanHedZoBcwBXgAWKyUcvj7P3MGHn3U/P3MM9kng506deJzWxU+po1zSkoKb775Jtfm1IPWDUgNvWfw9fXlxIkTV2w/deqUJ3eKrQPcBpwB/rQ4FoeSGnpRhHhtObZxdA39jz/CE0/A3LnmcZs20L69+Xv1asecw53Zmtq0aWM+I+Umc87croZeKTUeeBwT20TgP2Aq8A7wSAEO2VdrnXFKpBVKqdPATKALsCy3F1+4ACtXmmY0WpuxybP7OykJdu+GKVNM27kmTeChh7I/5v/+9z86d+7M+vXrSUhI4Mknn2THjh2cPXuWVatW2fWm6tata9d+jmSroX/9dZg1C774wnt/Lf/7L0ybZv52p4TenuuucxjQPCEhIds7Qx5ipda6AoBS6j7gOovjcRhJ6EUR4rXl2KZ2bbPet880jSlMHUpcHAwcmD4fSvnysGqVGUY5NNQMj1m2LLRqBQsWeOdw0rbRbUJCPG/EOVdzu4QeeA6YDkzQWh8HUEpFAT8qpSoAQ7TWSfYeLEsyb7M+bV05r9fv2gX5HfK9aVP45Zece2M3bNiQbdu2MWXKFAIDA4mPj2fAgAE8+OCDhIeH23UOK0YradXKfGCcP29GvfnpJ+9N6B98EJal/dSzNYlwB7ld93feeQcwd3ymTp1KSIbGlSkpKfz555/Ur1/f6TE6QwHuzHkMSehFUeHN5dimeHGoVMkM8xwVBYWZ/3HvXpPMV6wIY8ZAly5maEwwzW1mzzZN9RYtKvy53JWMbmM/d0zoG2itM/UP11ovSxvK8ldgIVDYdim2FD3PDrbFi0Pz5uYXsY+PWWf3t2382S5d4JZbcv8lGRUVRdWqVZkwYUK2z2Uc+caddO9uagTeeQdeecVMhOOtbLPi3n8/PFKQ+0IW+OCDDwBTQz99+vRMzWsCAgKoUaMGU6dOtSo8kQNJ6IXwLnXqmIT+v/8KnmS/9BLYukG1aGGa3WQ0axa8/74ZVnrbNvP54Y0JvYw/bz+3S+izJvMZtm9KG7ZyUWGOr5SqDLwELNFab8hhnxHACIBq1apdnvHVUWrWrMnRo0cpX758pu2nTp2iZs2apNi6sOfC1gZ/2LBhjg0uD6VLQ+W0+xrenNDbPkQefdQM2ekucrvu+/fvB6Br167MnTv3ilGUipKsZdidSadYIbLnSeU4o6uuMk11d++GHj3y//rUVFNpZmtqY2szn5FSpjLAlkZ4a4WA1NDbzy07xeZEa/0fUOC2JkqpEOAnIBm4O5fzTNNaR2itI8qVK1fQ0+VIa315sp+MLly44Naj3NgUL27WFy5YG4czeWqHWIDly5cX6WQenF+GHUlq6IXInieV44waNjTrrVsL9vqYGJPMlyplRnkZOzbnfb3980PGn7efW9TQK6WWAaO01v/mta/W+nja6DRLgJFaa7sGh1JKBQHzgVpAZ611dGFiLohH0tpuKKUYO3YswcHBl59LSUlh3bp1NLNNx+rGbE2zi0INvScm9AC7d+/m+++/JyoqisTExEzPzZgxw6KoRHa8/QtZiKLG1rdsQ7ZtAHJ34gSXWwVUrQoNGuS+v+3z48UXzVCZ48fn/5zuzPZd7AF1nZZzi4QeM9pMiXzsr/LzGqWUP/AD0BrorrXels/4HGJb2swIWmt27tyZacSRgIAAWrRowejRo60ILV+KUg29J36ILFiwgFtuuYXmzZuzceNGWrVqxd69e0lISKBjx45WhyeyKF3a3D6PjS38qBhCCOvZ+t1t22a+S+ytGNq71zTxtE0IXjnPYTugcWOz3rPHJPVPPeVdtdlSQ28/d0noAeYppRLysX/2Y/NlkVabPxvTkfZ6rXVkQYJzhOXLlwNw99138/777xNqG9jdwxSFGnpPbnIzbtw4xo8fz9ixYylRogSzZs2iUqVKDB06lLZt21odnsjC19fcWj9zxixhYVZHJIQojBIlTGK+c6eZZLJ1a/te9/ffJpkvWRJq1YJRo/J+zf33m+N3724mfTx/3ruSX6mht5+7JPQFndLypB37fAgMAF4FLiql2mR4LtqKpjefffZZoY/RqFEjB0RSMLYa+pMnTS9+28x43kJr921yY89137VrFwMHDgTA39+fS5cuERQUxLhx47j++ut5IutwCR4ibdZngJZp695KqRggRmu9wqKwHKJMGZPMnz4tCb3wbt5cjjNq394k9MuX25/QHz1q1oMGwUcf2fcaHx/TxKdMmfSEPst4Gx7rr79g7Vrztzf9SHEWt0jotdY5dlB1gN5p6+fSlowmAC868dw5Wr58OV999VW2bZyXLct1risAWrVq5azQ8lQiraHTf/+Z3vy//ALXX29ZOA6XnGySel9f8HOLEpLOnuteokQJ4tN+kYSHh/Pff//RuHFjkpOTOXPmjLNDdKbvsjy2feWtwDTB81hly5rb7dKOXhQBXluOM7r2Wpg+HZYuhaefznv/2FjTARbMuPP5ZftePn8+/691R6dPmyE5bc2PbBNbipx51Cg3BaG1rqG1VjksL1oR0+eff07v3r05f/48f/zxB+XKlePMmTNs2rSJhrbu8XlISkoiKcnu+bUcqmZNuPdeqFDBPP43z67MnsVda+fBvut+zTXX8NdffwFw/fXX8+STTzJhwgTuvvtuj25yk0s57mJ1bIUlHWNFUeHN5Tijbt3M+q+/0ptw5iQmBqpUAds0IYVJ6M+dy/9r3VFMTHrzo6efNjPmitx5fULvjt5++20mT57MV199hb+/P6+//jp///03Q4YMyTS7Z25mz57N7NmznRxp9pQyNQ/Dh5vH3taW3p07xNpz3d955x3atDEty1588UWuu+46fvjhB+rUqcP06dNdEabIJ1szm5js5rUWQnic8uWhSRPTqXPNmtz33bbNfI8GB5umOgW54+1tNfS2zrDVqsEbb5jBA0TuJKG3wL59++jevTsAgYGBXEgbLuahhx66PHGQJ7CNunnpkrVxOJqndoh98cUXWblyJVWqVKFp06YABAcHM2XKFLZu3cr333/vUZOzFCW2u13Hj1sbhxDCca5Nm9N+6dLc9ztyxKz79TM1+vaMbpOVLaHfvh0OHcr/692NjG6Tf5LQW6Bs2bKcT/sZXblyZbZv3w6YmWLjbP+LPYAtofe2GnpbxyR3rKHPzaeffkqXLl0oXbo03bt357XXXiMyMtKumYeFtSShF8L72BL6efNMv6yskpJg0aL0hD88vODnsg2a98wzplZ7/fqCH8sdSEKff5LQW6Bjx478/vvvANx222088sgj3H333dxxxx30KMg80RaxjXbjTTX0zz8Ptn6nnlZDf+jQIXbv3s27775L+fLlmTx5Mu3ataN06dJcf/31vP3222zatMnqMEU2bG1mJaEXwnv06AGVKpla859/vvL5KVOgVy+w3ZivUqXg5xo2DNq0SW+asnNnwY/lDmwJfYb5N0Ue3GwMj8szukYA4Zix5o8CG7XW8ZYG5kCTJ0++PArJ2LFj8fPzY9WqVdx22208//zzFkdnP29scmNr61i2LNx1l7WxFESdOnWoU6cOI0aMAODff//ljz/+YPny5bzyyis888wzJNuGDRBuw1ZDf+yY886RkGCG0Fu9GhYuNJ2/K1aEb76R9qlCOENgoJno6bHHzLpvX9MHzebvv826ZUszQdSgQQU/V4cO5vtr1CjzQ8HTJ36UGvr8c5uEXikVCPwPGA4EYmaDBZPUxyulpgDPaq0TcziER0hOTubrr7/mpptuAsDHx4en7RnTKotmzZo5NrACsNXQe1OTG9uHyLx55gPS3eTnup84cYKtW7eydetWtmzZwqVLlywd7lTkzBU19G++eeW08Nu2wYIFMGSI884rRFH2wAOmU+euXaZ5Ta9e5jvzo4/gzz/NPm+8YSaGcgTbuBqS0Bc9bpHQK6UU8AvQDfgJ+BWIwiT1VYEbgMeBhkAfi8J0CD8/P8aMGcP1hRy43R0Sem+sobcNWemuHyK5XfdTp06xYsUKli9fzrJly/jvv/+IiIigc+fOTJo0ifbt21Pc9itMuBVXtKHfsMGs27SB3r3N3YApU+Dbb80oO716Oe/cQhRVAQFmmOdXX4UbbzQ16Pv3w08/pe9Tr57jzicJfdHlFgk9cCvQFbhVa/1jNs9PV0r1B75VSvXXWs91bXiO1aZNGzZu3Ej16tULfIxLaVl0sIUNzGyn/vtvGDAAHnoIOne2LByHcPcPkdyue/ny5alWrRpDhgzh/fffp127dpb+/xD2K1vWzPh46pTpKOfv77hjHzoEL71kRs8AM9b11VebpjZTppi2vT//DDt2gJ3TYAgh8mHCBDOL6+TJ8N576dsHDoQ+faBqVcedy1ZnIwl90eMuCf0dwLc5JPMAaK3nKqW+AwYDHp3QDx8+nNGjRxMVFUXLli2vqDVt0aJFnsf49ttvARg2bJgzQrRLtWqmPeDp0/D992amO29J6N11hJvcrnuXLl2IjIzkiy++ICoqikOHDtG1a1dq1Kjh2iBFvvn6mnGrjx2DEycKNmxdTj75xMwbAab27qqrzN833WQ6gX/xBURFmVpDb03oV6wAW3/w+vVN58P69R37w0mInPj6wgcfmI6r06ebtu4lS5q/7Zx6xm6eXkN/7hy8/z4sXmweS0JvP3dJ6JsD9vQG/QV4xcmxON2gtJ4vTzzxxBXPKaU8ZpjBqlVh40b4/XczVNbZs1ZHVHju3uQmN0uXLiUpKYnIyEiWL1/OF198wahRoyhfvjxdunS5vNSsWdPqUEU2KlQwCf2xY45N6KOizPr++81dNNtNm8BAePll2LcP5szx3llqz52D666DxCy9r/r3hx9+sCYmUTS1bGkWZ/L0hP6rr2DcuPTHtuaIIm/uktCXw7SZz0sUUN7JsTjd/v37rQ7BYZo3Bz8/k9B7Q+dYT7/N5+/vT8eOHenYsSPjxo0jISGBNWvW8Ouvv/L4449z4cIFGeXGTVWsCFu2OLYd/aVL6Qn9TTdBo0ZX7mObpdZbE/r//jPJfIUKZhjBrVvNsmSJqYQICTE1qEJ4A1tC/+23sHkzrFyZXsY9gW227O7dTVPeO+6wNh5P4i4JfTCQYMd+iYCbNoaw38GDB2nXrh1+fpn/+ZOTk1m9enWh2tZbwZtGu/HkGvqMMtbU//HHH6xZs4aEhARq1apldWgiB47uGPvww6bNrk1Otf5ly5r133/DunUQEWHa83uD776Dzz4zf7dpA7Nmmb8rVDBNm0qVglq1zDjhnl7mhQBTyVaqlGkCu3MnrF0LhRyDw6VseUS3bpA2+rKwkzt9bFdWStXKbQEKMe2C++jatSunT5++YvvZs2fp2rWrBREVjq1GwNMT+pQUU5OnlBmZwNOsWbOG1157jR49elCqVCk6d+7MjBkzqF69Oh9//DEHDx7kv//+szpMkQNHj0X/669mHRRkktm6dbPfr1w5s541C665Bj780DHnt1pKCgwdCr/9Zh43bpz+3MiRZmZNHx/T5Gj3bmtiFMLRqlUzlQL9+5vH585ZG09+2ZoKObpvQVHgLjX0AN/bsY/CjEvv0bTWqIyzS6Q5deqU3cMKRkREODqsAvOWGnpb7XxQUObJP9xJbte9ffv2hIeH07VrVyZNmkTXrl2lRt6DVKpk1ocPO+Z4J06Y9bFjpgNeTm6+2XRA274d9uyBf/91zPmtdvKkmUyrRAmYNAluuSX9uZdeMst115n3fviwGflHCG8QEJB+500S+qLDXRL6u60OwBX69esHmI6vQ4YMITAw8PJzKSkpbN++nXbt2tl1rMYZq5ssZrtVHRdnasU8sT3q5Mme0as+t+u+c+dO6jlyQGPhUrah6w4dKvyxLl0yX4wBAaYmOjcVK8LcufDll6ZGOza28Od3B7amS1WrmtFFsmNrhvTDD+YHlRtM7yGEQ9jKvST0RYdbJPRa65lWx+AKZdN+MmutKV26NMUyZI4BAQF06NCB4cOH23Wss2lDypTMrerNRXx8zMgZly6ZpUQJqyPKn7g4eOQR0Gn3fhw5JrCj5XbdJZn3bI5K6Ldvh48/Nn+XL2//3aZSpczaGxL6o0fTJ+7JbZSMatXMesYMMy7/yZPuO2StEPkhCX3R4xYJvb2UUt2Bd7TWTa2OpSA+S+udVaNGDUaPHl2oWTt//NEM2W/lOPQZFS9ukvl+/eDFFz1rPPrz500yHxpqxgVu397qiHLmbtddOI4tuYyyZ7yvXDz5pBlKFiA/UxDYfiN6w/CzPXqYibIgvSlTdkaONPNozJxpPgeOHcvfv5kQ7srTEnqtzWzW0dHmsUxqnn/u1CnWHiWBbAZe8ywvvPBCptr5Y8eOMX36dFavXm1hVIVjm6zmjz9Me1VPYqsRKFvWDJOVWwIghLOUL28mOjp1yvw4LihbG/wxY+DTT+1/nbfU0Gud3sl1wADIZrqPyypVMhP+2G5u2fodCOHpbAn9lClm7Ht37+O2dCm0bm3uMELeTQXFlTwtofcK119/PR988AEAFy5cICIigjFjxtC5c2e++OILi6MrmN9+g7ffNn97Wg2f3OIT7sDHx8xgCum1VAVx8qRZP/ZYziPbZMeW0O/YYeJYu7bgMVjpwgVISjLNAL/9FuyYeJvyabObOHIOACGs1Lq1qeVOSjKzJG/ebHVEudu506yrVzdD7jb1yHYY1pKE3gIbN26kW7duAMydO5fQ0FBOnDjBJ598wtu2rNjDhIZChw7mb09L6M+fN2tvSej37t2b43NLly51YSQivwrbjl7r9AmibKNc2Cs8PH3iqcOH04d79DQFef+2dvb9+5sfM7t2OT4uIVypcWNTFnr1Mo9tP/TdlS2+u+4yd/m9ZS4MV5J/MgucP3+eUmnVYb///js333wz/v7+dOvWLddkzN3Z2uB6Sps9G2+rob/uuus4ls1g5kuWLOGmm25yfUDCboVJ6L/8Eh54AJKTTcf0DINo2cXPz8xU+/rr5vGZM/mPwR3YpvjIT0J/3XWmuVNysvkx88cfTglNCJcKDDQ/1CF9BlZ3ZUvoPWlWW3fjFp1i0yaNskdFpwbiItWqVWPVqlX07duXRYsW8d133wFw+vRpgoOD7TpG27ZtnRligXhaJxwbT0ro7bnuvXr1okePHvz111+XR8NZvHgxN998MxMnTnR2iKIQbAl9fjvGnjtnarZSU83jgnbs9PU1w1iC5yX0p0+bWW4PHDCPy5Sx/7W3327GqX/2WdN00N1rM4Wwly1BPnrUNL/x97c2nqzOnIGFC02zIJCEvjDcIqEH/sO+CaO8YmKpJ554gqFDhxISEkL16tXp1KkTACtXrqRJkyZ2HcMdhyi0JfSe1ORm/XozBjd4RkJvz3WfPHkygwYNok+fPixZsoS//vqLm266iXfffZcRMpe2WytoDX1MjEnmw8Jg/HgzyktB2RJhT0vo16+H/fvN3z4++Z/u3t8//ceMJPTCW9gS5HHj4JVX4LvvzGh07uKJJ+Dzz9MfV/SKaltruEtCXyQmlrIZOXIkLVu25NChQ/To0QOftMZitWvX5uWXX7brGCfTvnHC3OjnbPHi5os0Ls5MNf+//0HabxW3df316bci3eifMkf2XHelFLNmzaJv37507dqV7du3895779k9x4Gwjq1mfd++/L3O1sykenV46KHCxVC6tFlHRZmOag0aFO54rmJLwgcOhNmzCzbBna1YRUebzzF3nmROCHv06AHvvmvKR2IizJ/vXgn9xo1m3a+f6cPTsaO18Xgyt0joi8rEUhlFREQQERGRadv1+ahS+uWXXwD3Go9cKdMzffNmM0LGjBnundBfvGiSeX9/GD0aRo2yOqK85XTdN9nuV2bw7LPPMnjwYO68805atmx5eZ8W9gz74WaUUlWBd4EemDt1S4DHtNaFHLXdvdhuwOS3U2ZBO8Jmx3aMrVuhYUPTOdbWsc6d2X6Yly9f8NmqbQn999+b9711K9Syt0GoyFNRKcfupHlz09xm2TK49lpYsMDMv/DSS7lPuuZsv/8OTz8N//xjHs+aJUNVFpbbd4pVSpVUSkUopapYHYsjzZs3j06dOhEWFkZYWBgdO3a8PGmQJ1u9On0cencf09lWo1ehArz2WvqQgZ4oIiKCVq1aXf6hGBERQZcuXYiOjmbq1KmXn2vVqpXVoeabUioYWAbUB+4ChgJXAcuVUl41/UiNGhAQYDpm2vp25OX8+fT2p/lpN56T+vXhvvvSy8OWLYU/prMdOwbbtpm/C3OnrV07kwAFBJgf/LbaQ1F4Rakcu6OmTc3/62PHYNo007Tl9ttN+3VXOnzYzJExapSp/EtJMWVOkvnCc4saeqVUT6Cr1vqZLNufBcaTFqdS6hvgTq11suujdJyJEyfy7LPPcuedd16uaV2zZg2DBg3i5ZdfZvTo0dYGWAjFipnxb8H9e9Xb4itXzto4HGG/rfGwdxoO1ALqaa3/A1BKbQX2ACOBdyyMzaF8faFOHVNrtXt33mOoa232+e8/89gRNfQ+PvDJJzBxorlzlc2ASW4lJsbUosfFmceFKc+lS5sfRyNGmH8DW1Mm4RBFphy7o7AwWLMGvvnGNIcF8/c335jPnTvvNOO/167tnOR6wQJzrlmz0rcVKwZ//pk+XK4oHLdI6IH7ydLZVSnVA3gF2AZMBxpgCv1GwKOH6nj77beZPHlypjbN99xzD61bt2bcuHEendBD+iQthw7BkiXm17cjEg1HSUgwHXG2bjWPPaHtfF6qV69udQjO1A+ItCUBAFrr/UqpVcCNeFkiUK+eSeh37co7ob90ySTzPj7Qtq0Z6cZRbJ3T9u83Ey5ZeXs+N5s3m2S+ZEnzb3DjjYU/pu1OhyT0DlWkyrE7atHCLP37m3L9xhvmDlxKCnz2mVmqV4e9ewvebC2rCxdg3ToYNixzZ/PRo83IUi1bOuY8wn0S+uZA1t6gdwPxQE+t9TEwnf2AQXh4Qn/hwgW6du16xfauXbtywd777G4s46yLPXpAkybpybM7WLgQPvoo/XH9+tbF4ihzbUP12KF///5OjMQpGgE/ZbN9BzDAxbE4XYMG8OOPpgnJHXfkvq+t7XylSvDXX46Nw5bQ//STWZ56Ct5807HnKIz4ePjhB9MWF0ySMmOGY45t6xgsCb1DFaly7M6uucYst99u7vI9/zz8+qu5K3jwoJllesAAx/SBGzQIfv7Z/F2lCrz6qvmM88DWn27PXRL68kDWGZV6AH/Zkvk0CzDt7jzaTTfdxPfff88zz2RqYcQPP/xAPzu7n3dy496mxYub4bGWL4cVK0xicvZs+sRTVjp/Pn1K+5tugnvvhWx+W7mtnK77rbfeatfrlVKkpKQ4MiRXKANkN4jiaaC0i2NxOlutfDb9nK/gyM6wWV1zjanx3rvX9If58UczTrs7lGOADz4wPzJsHDmSr62G/vffTb+GUaNMp39RKEWqHHsKpUyS/eqrphZ95kyYPBm+/tqU+4L+vz92zHx2rFhhHl97LTz4INx8s8NCF1m4S0J/HrjcKUYpdRVQFojMst85wEE3glzrnXfS7ybWqVOHN954g+XLl1+eKCgyMpLIyEieeOIJu45Xy82HXnjuObM0b25uiVesaDra2TrMWvHlGBVl2urZboL07Ak33OD6OAojp+ueaptRyHtlN/9Ejv+LlFIjgBFgJnLzJBkTeq1zLitxcenDWzojoQ8JMZ3cExLM33v2QKlS0L27aWPevLlp7+9KsbGwYYP5bNm922zr08fU+N17r+POY5sPYOtWMwxomzbSNMBBikw59kSvv27K0iuvmOYxJ04UrKnd+fPmB7ZtkskKFUzzW+FkWmvLF2AFMC3D49FACtAhy353AvtdGVvLli21I9SoUcOupWbNmnYd7+jRo/ro0aMOic2ZXnlFa5OWpC/Vq2s9dqzW8+c799wpKVqvWaP1Dz9oXbOm1kFB5vyhoVq3aKH1oUPOPb8zOOu6Axu0G3wWZLcAx4GPs9n+ERCT1+sdVYZdJTVV69Klzf/VqKjs99mzR+vg4PQyNWCAc2N6+OH08pNx6dJF6ylTnHvuCxe0XrBA62eeufL8JUpoHRPj+HMmJ2s9c6bWDRua8zj7s8oR3LkM6yJYjj1Z69bp35VPP53/169bl14+27fX+tNPHR+jtypMOXaXGvp3gblKqTKYQj8M0xl2VZb9bgY8YBC1Kzl6FJKFaWNNudM49Nl57jl48kkzTNVHH5nZLA8eNDUBYGr8KlUys9c1bOiYc2ptbvO9955p+5tRQIBpz+fGLZZyZc91z6s9vQe2od+BaX+bVUPgHxfH4nRKmRrh336DpUvNbfCsIiNNh9iAAFOG7GxxVWCTJpnlww/hjz9MbBcvmr//+AOeecbUyC1ZAiVKOPbcjz9uRpzJqGlTU76bNnXO3QnbqB9LlpgOyramTaJQilQ59mT9+pmOrOfOmbL3xhv2v3bRItO5FuC668ycDsI13CKh11rPU0o9BjyJaWcXCdyf9msFgLRx6LsCT2V7EOG2goJMe9cPPjBt6T/+2CTaq1ebW+ixsaYpTPv2JrEPDy/4uS5dgrlzYWiGnhZlypgEafx4uPpqCAws7Dtybzm1p0/rVO6JbejnA28rpWpprfcBKKVqAO2BZ3J7oae6/nqTNP/8c/YJ/fHjZv3AAyaxdZUHHzTLpUtmCLxx40w5PnvWJABvv2068jqio/nXX8Pdd5vOrwC9e0PlymbWy5CQwh/fHjLajUMVuXLsqZ57zgxhWamS+b+/fj1cdZWpPMjNpUtmlKmEBPPYUZV0wj5ukdADaK0nAZNyeT4aKJXf47rjzHSPPPJIrs9PmpTjP4PHK1nSdGR76inTPm/HDujWzTy3apVJCO6/33x45Ncrr8ALL6Q/btXK1MS/8Qb4uc3/dOfL2p4+OTmZv//+mzFjxvDqq69aFFWhfAI8BPyklHoe0w73ZeAQ8LGVgTlL376m7faiRSahDQrK/Lxt0jarhpIMDjad3Lp1M7XXEyeacvbSS2YknOjowg0Hm5wMs2enJ/M9e5pxrF3d98ZW+79+venk36GDmVlaFEiRK8eeLDTUjEm/dauZWyYkxPTZyW2eh337TDJfvry5M3/ffa6LV3jATLGF4a4z023bti3TsmnTJubMmcMXX3zB9u3brQrL5cqXNyPMnDyZPhzeO++YW/e2WR/tceGCqZWfPt08LlbMHGPBAvMDoSgl89nx8/OjVatWvPbaa4waNcrqcPJNa30R6AbsBmYBs4H9QDetteeP85qNatWgWTPTrCVjZ7L4eDN28+efm8e2IWKtopRJ3B991AxPFxZmvtDzU36zGjLEJM2//GIe//uvGWrWio70tn/fr782P14++MD1MXiLoliOPd3jj5ta9pAQ8z2b08zJsbGm3N59t3kcEWEq7Rwxc7Wwn7enOm45M93y5cuv2BYfH8+9995Lx44dLYjIWmXLmlr59etN2+DoaBg71gyZd++96eNh52TsWDPMFphkPjbWtC0WmZUqVYq9e7OODusZ0u6o3WJ1HK50221mhKiPPkofjemPP8yPV5vGja2I7EoVK5oadduwd+PGmQT/gQfyd5yUFNPsDkw79o4dC3a3zlH69zfj+2/dapadO62LxRsUxXLsyYYNM8vIkTBtmulHExtrxq/P6PvvTfm3iYhwYZDiMm9P6D1mZrqgoCCee+45evbsyf3335/n/tdee60LonKd0FDzRT5rlumMtmCBWaKjYcqU7F/z3Xem5s5Wk9enDwwe7N3JvD3XfVOWAcy11hw9epQ333yT5s2bOys04WDDh5smLL/9ZqZMb9bMlAkwTXImTDBDR7qTFi1MQv/XX2a59dbcb9FnFBtr3mtiovmBcPSoU0O1S7ly5jNp7lxzZyQmxuqIhHC9Jk3M+rffzNK2rZlRNiXFNLezdXwdMcIk++3bWxdrUebtCb1HzUwXExNj90yxVW0DJXuZ2283Pet37TK3tz/7zIxYM306tGtn9jl50oxFPXRoeuebkiXNxDfenMyDfdc9IiICpRQZ+pQD0KZNGz6zDT8g3F5YGLz8shkhKmuNWLt27pfMg6nJq1zZ3DXbs8eMFW9vQn/rrWZUH7C2Vj47tqY3tr4LQhQl99xjmrx9/LFpTnfrrWbCtehoc1fd5vbbPWuiRm/j7Ql9gWamc/ZkFhknmYL0GtTZs2fTp08fu45x6NAhwPsSe39/M4pGYqIZ4ePAAXObu3176NLFfIjY2g+DmQTjySdNB1hvT+bBvuuedYhUHx8fypUrR1DWnpXC7T35pPn/P2NG+rbAQDO5kzsKDDQ12T/8YBL67t1Nx91Fi3KfyVVr0+QOoFcvsHN+PZex/SjZts3cQXztNTONvRBFQXCw+V5OSjLt6jdsMItN48ZmZJwuXSwLUeD9CT3kc2Y6AK31NGAaQERERHavL5QPsvSssiVcd999N2PHjrXrGEvTqrLcfRz6ggoIMInM33+n18z/8UfmfXr1MrWXtlFyigJ7rnv16tVdFI1wNqXMONDVq5s7UDNmmC9Pdx9p5frr4dtvTSfegwdNs7icEvpvvjF9Bc6dM3fafv3Vmg6wualSBYoXNx0DZ80y7+W556yOSgjXeughc2fQ1kH2xAnT8XXsWNN/TVjL2xP6M5ha+qxKk33NvUtkrUFNSkoiISGBEFcNruwhgoJMW739+81y4YIZFksp04a4Zk2rI3RPCQkJBKYNtn/48GGmTZvGpUuX6Nu3L508dUatIszHx3QyHTfO6kjsN3iw6VA6ebIZ7WLZMvPDvG3bzPv991/m5kQNG7pfMg8mmd+40YyB//HHpqmBEEWNnx907mz+vv56a2MRV/LqYStxs5npli5dyrfffptp2xtvvEGJEiUoVaoUvXr1IjY21tVhub0aNUy7vL59zfB4jzwiyXx2du3aRaNGjQgODqZ58+b8888/tG7dmnfeeYdp06bRrVs35s2bZ3WYoogoVszM5Aqm1r1dO/jzz/Tnt241yTGYieVeeunKGWHdSb16ZuZLcI8Ou0IIkZG3J/TzgTZKqVq2DRlmppvv6mDeeOMNojNU7axbt45nn32WoUOH8r///Y8tW7Z46sQ/wg2MHj2a8PBw5s+fT+PGjenTpw89e/bk7NmznDlzhpEjR/JGfubwFqKQunUzbW9tM8e+8oppfz5rlpm1+e23zfabbjKTwjXKrvrFjVSqZNYLF5pJdz780Np4hBDCxtub3LjVzHTbtm3LlFB99913tGvXjk/SqqWqVq3K888/z1tvveXq0IQXiIyMZPHixTRr1oxOnTpRsmRJRo0ahY+P+d3+8MMP06ZNG4ujFEWJv79pdmMb9vH3382S0X335X+8eqs0aGDaDJ8+bZr/ffCB+cEihBBW8+qEXmt9USnVDXgXMzOdApYCj1kxM11sbCzlM0ztuGrVqkyj2rRq1YrDhw/bdaxevXo5PD7h/nK77qdOnaJSWhViiRIlKF68OGUyTNVXunRpzp8/7/QYhcjqxhtNYh8TY2Z+3bvX9JF5+2245hqro7NfyZIQFQWHDpnkft8+2LTJjNPt7h2VhRDezasTenCvmenCw8PZu3cvVatWJSEhgb///puXX3758vPnz5+/3JkxLxXzmj5VeKW8rrvK0qMw62MhrODr6z012cWLmyZElSvD4cPQsqWZ0Xr6dKsjE0IUZV6f0LuT3r1789RTT/HGG28wf/58ihcvTseOHS8/v3XrVurUqWPXsfbt2wdArVq18thTeJO8rvuQIUMu/yiMj49n+PDhBAcHA2b0GyGEY7zwAkybZmro58wxtfWPPWbG3i9WzD1H6xFCeC9J6F3opZdeon///nTv3p2QkBBmzpxJQIbZkGbMmEGPHj3sOtbKlSsBSeiLmtyu+1133ZXp8ZAhQ67Y584773ROYEIUMSNHmqnumzaF7dth+XKzgBmZq00bM2FWamre66zbEhPhzBnzd+3aUKKEGdM/IcE8l5xs6VsXQrghSehdKCwsjJUrV3L27FlCQkLw9fXN9Px3330nY9GLAvvss8+sDkGIIkUpMxTn5s2mdn73boiLMzNcHzjgmHPs2OGY4wghvJsk9BYoWbJkttszdmAUQgjh/kqVMlPeb95sHp87B4sWQVKSmRRMKfvWGf/29TWj6Vy4ALt2mQ63QUEQGGgWX98rm/R07+7iNy6EcCuS0AshhBAOEhoKAwY47njt2zvuWEII7+XtE0sJIYQQQgjh1aSG3kPdcMMNVocgLCDXXQghhBBZKa211TG4NaVUDHDQAYcKA0464DjuxNveU1F+P9W11uWcGYxVHFiGoWj/H/EU3vae7H0/XluGQcpxHuT9uD+nl2NJ6F1EKbVBax1hdRyO5G3vSd6PyIu3/Zt62/sB73tP3vZ+3IG3/ZvK+3F/rnhP0oZeCCGEEEIIDyYJvRBCCCGEEB5MEnrXmWZ1AE7gbe9J3o/Ii7f9m3rb+wHve0/e9n7cgbf9m8r7cX9Of0/Shl4IIYQQQggPJjX0QgghhBBCeDBJ6IUQQgghhPBgktA7kVKqqlLqe6XUWaXUOaXUXKVUNavjsodS6lal1A9KqYNKqTil1C6l1OtKqRIZ9qmhlNI5LKUsDP8KSqkuOcQZm2W/0kqp6Uqpk0qpi0qpJUqpJhaFnSOl1B+5/NsvTNvHY66PO/PUcuxtZRikHHvCNXJXUo7dh5Rj51wjmSnWSZRSwcAyIAG4C9DAK8BypVRTrfVFK+Ozw2ggCngWiAaaAy8CXZVS7bTWqRn2fR2Yn+X1510RZAE8AqzP8DjZ9odSSmHeR03gYeAMMBZzzZppraNdGWgeRgGhWba1Bd7hymvhSdfHrXh4OfbWMgxSjm3c+Rq5DSnHbkvKseGYa6S1lsUJC/AokALUybCtJuY/7BNWx2dH/OWy2XYn5oOwW9rjGmmP77M6XjveT5e0WLvnss+Naft0zbCtJHAamGT1e7DjPX6K+cIq42nXx10XTy7H3laG0+KVcixLQf5NpRy70SLl2DnnlCY3ztMPiNRa/2fboLXeD6zC/Ed1a1rrmGw2235JV3ZlLC7UDziitV5u26C1Pgv8jJtfM6VUMWAA8LPW+rTV8XgRjy3HRbQMg5RjcSUpx55HynE+SULvPI2A7dls3wE0dHEsjtI5bb0zy/bXlVLJaW0T57tjG7cMZiulUpRSp5RSc7K0ocztmlVTSoW4JsQC6Q+UAGZm85wnXR93423l2BvKMEg59oRr5E6kHLsnKccOvEbSht55ymDafGV1Gijt4lgKTSlVGXgJWKK13pC2OQH4GPgdiAHqY9r5rVZKtdZaZ/2wsdJZYCKwAjiHaYf4LLBGKdVca30Cc80OZPNa2y/s0sAF54daIHcCJ4DfMmzzpOvjrrymHHtBGQYpx55wjdyRlGP3IuXYGdfI6nZG3roAicDr2Wx/FUi2Or58vpcQYANwBKiSx75VMQX0S6vjtuN9tcC0oXwl7fEe4Kts9huOaftW1eqYc3gflTDtQ9+xY1+PuT7usHhLOfbWMpwWr5RjWfL695Jy7OaLlOPCn1ua3DjPGcwvzKxKk31NgVtSSgVhemTXAnrqPHqWa60PAX8BrVwQXqForTcBu0mP9TQ5XzNw3+s2BNN8Lrvbe5l40vVxEx5fjr25DIOUY2cH5SWkHLs5KceFJwm98+zAtAHLqiHwj4tjKRCllD/wA9Aa6KO13mbvSzG/oD1Bxlhzu2ZRWmt3vr23RWu9xc79Pen6WM2jy3ERKcMg5VjkTsqxZ5ByXAiS0DvPfKCNUqqWbYNSqgbQnivHIHU7SikfYDZwLXCj1jrSztdVw7zHtU4MzyGUUhFAXdJjnQ9UVkp1zrBPKNAXN71mae+hEXbUBqTt7zHXx014bDkuCmUYpBw7My4vIuXYzUk5dsD509rxCAdTShUHtgBxwPOYX2AvY3o+N3XjX5cAKKWmAPdj2hj+kuXpaK11tFJqIuZH4RpMJ496mIkfSgLXaK13uTDkXCmlZgP7gU1ALKYTzljgEtBCa30y7YPzL0y7tjGkT2TRFLg67faYW1FKTQIewLSnPJ7lOY+5Pu7Kk8uxt5VhkHKMB1wjdyTl2L3+j0g5dtI1sroDgTcvQDXMbbJzmJnA5gE1rI7LztgPYD70slteTNvnHsx4uGcwnVmOAXOAelbHn837GQtsxfSuTwIOAdOA8Cz7lQFmYNrvXQKWYj48LH8P2bwn/7QPhp9zeN5jro87L55ajr2tDKfFK+XYza+Ruy5Sjt1nkXLsnGskNfRCCCGEEEJ4MGlDL4QQQgghhAeThF4IIYQQQggPJgm9EEIIIYQQHkwSeuFRlFIvKaXOK6V2KaWusToeIUT+STkWwvNJOXYvktALj6GUagUMAvoDn2F6xQshPIiUYyE8n5Rj9yMJvfAkZYCTwLa0xc/acIQQBSDlWAjPJ+XYzUhCL5xOKfWHUuqPPPb5XCml05ac9l2J+dA4CnwFDM1yjOczHCPaAaELIdJIORbC80k59l6S0At3cgxoC4zK4flEICDtbx/g7yzPf5b2+l+dEp0Qwh5SjoXwfFKOPYwk9MKdJGitI7XW/+Tw/HCgCeYDojhQO+OTWuvDWutIzGxtQghrSDkWwvNJOfYwktALh1JK3a6U+lcplaCU2qGUutlBxy0FvIyZrvujtM1NHXFsIURmUo6F8HxSjosWSeiFwyilugNzgD2Ynu9vAe8D9Rxw+HFASeApYHvaNvkAEcLBpBwL4fmkHBc90itZONIE4F/gRq11KoBSaicQCewq6EGVUnWBh4APtdZ7lFIKOI98gAjhDFKOhfB8Uo6LGKmhFw6hlPIFWgHf2z48ALTWa4EDhTz8O5gPjJfSjqmBfzDt94QQDiLlWAjPJ+W4aJKEXjhKGOAPHM/muey22UUp1RO4HngX0EqpUmnt9/YAtZVSxQt6bCHEFaQcC+H5pBwXQZLQC0c5CSQBFbJ5LrtteVJK+WFqA8B0wDmTYRkCKKBxQY4thMiWlGMhPJ+U4yJIEnrhEFrrFGA9cKtS6vL/K6XUNUCNAh72AaAh8CjQNctiGxtX2u0J4SBSjoXwfFKOiybpFCscaTzwOzBPKfUxUA7TMedYfg+klCoDvAgs1FpPyub5zZjhsuQDRAjHknIshOeTclzESA29cBit9RJgMGZYrLnAGOAxCtajfgJmsoqHczhXLBCNfIAI4VBSjoXwfFKOix5lOigLYS2l1OdAF6AOpuN8SgGOoQBf4FPgWq11FUfGKITInZRjITyflGPPJDX0wp1Ux3TkWVrA1z+X9vo7HRaRECK/pBwL4fmkHHsYqaEXbkEpVQMz1BbAea11vm8LKqXCgcppDxO11lsdFJ4Qwg5SjoXwfFKOPZMk9EIIIYQQQngwaXIjhBBCCCGEB5OEXgghhBBCCA8mCb0QQgghhBAeTBJ6IYQQQgghPJgk9EIIIYQQQngwSeiFEEIIIYTwYH5WB+DuwsLCdI0aNawOQwin2rhx40mtdTmr43AGKcOiKPDmMgxSjkXRUJhyXGQSeqVUbWAmUB64CAzXWm/I63U1atRgw4Y8dxPCoymlDlodQ16kDAuRM08owyDlWIjcFKYcF5mEHpgKfK61nq6U6gHMVkrV14WYWUtrzenTp0lISHBclDkoVaoUwcHBTj+PEG7M4WUYICUlhVOnTpGcnOyYKLPw8/MjLCwMHx9p4SgETirHiYmJnD59mtTUVMdEmYG/vz9ly5aVMizcmtsm9EqpKsDTQARwNVAMqKm1PpDNvlWBd4EegAKWAI9praPSni8HtAH6AGitFyulAFoCBfrJr7Vm3rx57Nu3j5CQkIIcIl/nOnv2LLfccgt16tRx6rmEcBR3L8MACQkJzJo1i7i4OAICAgp6mDzPUaJECQYPHuy0cwjhLJ5Qjk+fPs3nn39OsWLFnJJ0x8XFUb58eQYOHIivr6/Djy+EI7htQg/UAW4DNgJ/Atdlt5NSKhhYBiQAdwEaeAVYrpRqqrW+CFQDjmitkzK89GDa9gJ9iOzdu5fFixdz8803U65cORo0aEBgYGBBDmWXXbt2sXz5cknohSdx6zIMsHPnToKCgrj33ntJSywcTmvNzJkz2b17N40bN3bKOYRwIrcvx5GRkTRr1oxu3boV9BC5SklJ4ZNPPuHgwYPUqlXLKecQorDcOaFfqbWuAKCUuo8cPkSA4UAtoJ7W+r+0/bcCe4CRwDs5vK5Q395ffPEFO3acJDh4MwC+vosIDU3mqquuolevXoSGhhbm8FcoW7asS5r2COFAbl2GwdSely1b1mnJPIBSijJlykj5FZ7KI8pxpUqVCnuYHPn6+lK6dGkpw8KtuW2DMK21vQ3h+gGRtg+QtNfuB1YBN6ZtigIqKaX8M7yuetr2Avnuu+/488/ZTJw4hokTx/C//z3N3Lk9effdd+nevTsABw4cQCl1eQkMDKRixYpce+21vPfee5w/fz7TMbXWNG/enLfeequgYQnhNty9DOfm4Ycfpm/fvsCV5TjrsnnzZgCOHj1KcHAw69atc0ZIQliiKJTj559/HjBNa8LDw/nuu++cEY4QTuXONfT2agT8lM32HcAAAK11jFJqHTAM+CStI47C3EK8glJqBDACoFq1atmeNCEhAX//M/j7B5OYGEBSUgDbtzciOXk9zz33bKZ9x44dS79+/UhOTubEiROsXLmScePG8f7777No0SLq1q1rOy/jxo3jnnvu4d5776VMmTL5/9cQwvNYUoZzsnfvXj7++GNWr16dabutHGdlK7/h4eEMHz6cMWPGsGLFinydUwgv4LHluEqVKgAUK1aMp556irFjx3LTTTfh7++PEJ7CGxL6MsCZbLafBkpneHw/MFMpNQa4BAzOqVe91noaMA0gIiIi232aN29OePjN9OjRA63hjTfGkphYjIoVG9C8efNM+9aqVYs2bdpcfty/f38efPBB2rZty4ABA9i8efPlW/79+vUjKCiI6dOn89RTT9n5TyCER7OkDOfkvffe4+qrryYiIiLT9qzlODsjR46kUaNGrFu3jtatW+fntEJ4Oq8ox8OGDeOZZ57hxx9/5LbbbsvPKYWwlNs2ucmn7Ap6pnZ5Wus9Wut2Wuu6WutmWutC3Rd/6qmnqFKlCj4+PigFpUqZz7EGDXpSsWLFPF9/1VVX8cILL7B161aWLVt2ebuvry8DBgxg+vTphQlPCE/j8jKcnYSEBL788ksGDRpUoNc3bNiQJk2aSPkVRZXHl+PSpUvTs2dPKcPC43hDQn8GUzOQVWmyry1wiPDwcK677jrGjRtH586dCQw0nWVatuxCfHy8Xcfo06cPAKtWrcq0vVOnTuzZs4d9+/Y5Nmgh3JMlZTg7kZGRxMbG0rFjxyueS01NJTk5OdOSkpJyxX6dOnVi4cKFrghXCHfiseU4q06dOrFixQq7v8uFcAfekNDvwLTdy6oh8I8rAihdujTJyacACAoqT+nSpfN4hWFrE3j06NFM221NdiIjIx0YpRBuy/IybBMZGYlSiqZNm17x3MiRI/H398+0lCxZ8or9mjdvzqFDhzhy5IgrQhbCXXhsOc6a1Ddv3pzExEQ2bdrkqpCFKDRvaEM/H3hbKVVLa70PQClVA2gPPOOKABo0aIBSawHw8SlJgwY17Hqdrdlg1iHzypUrByAJgSgqLC/DNkeOHCE0NDTbCaCef/55brzxxkzbsptkJmP5deZQekK4GY8tx35+mVMh+Q4WnsitE3ql1K1pf7ZMW/dWSsUAMVpr2zASnwAPAT8ppZ7HtOF7GTgEfOyKOAMDAwkMNPNkhIZWtnuCqUOHDgGm+U5GxYoVA8wQWkJ4Mk8pwzbx8fE5lt/q1atf0cEuO1J+hbcpauVYyrDwRG6d0ANZB4P9KG29AugCoLW+qJTqhpluehamA85SzHTTF1wUJyEhZqjeHTui2Lw5kQYNGuT5mgULFgDQoUOHTNtPnz4NQFhYmIOjFMLlPKYMg5nA7cyZwjX3lfIrvFCRKsdShoUncuuEXmtt1wxyWuso4BYnh5Or0FATamxsKmfOnGHnzp2UKlUqx/337NnDq6++SvPmzenSpUum5/bv3w9AvXr1nBWuEC7hSWUYoH79+iQlJREdHX15bOr82r9/PwEBAdSsWdPB0QlhjaJWjuU7WHgit07oPUmVKsUBiI4+T0hICGfOnLmc0O/bt4/IyEhSUlKIiYlhxYoVfPrpp4SFhfHtt99e0YZ+7dq1+Pv75znmtRDCsTp16gTAunXrrkgEbOU4q7p162aaBG7t2rW0atWKoKAg5wYrhMhWbuXYHmvXrqVy5crUqlXL0aEJ4TTeMMqNW6hVy3yhHzp0gQsXLmQa6eb111+nbdu2dO3alZEjR7JlyxZeeukltmzZQp06da441i+//EK/fv0IDg52WfxCCKhRowatW7fm559/vuI5WznOumScRyIuLo6lS5dy++23uzJsIUQGuZVjeyxYsEDKsPA4UkPvIPXqlQfgxIlkgoJCaNCgAYGBgZdHsrHXkSNHWL58OYsWLXJGmEKIPDzwwAM8+uijfPjhhwQHB1OjRg27y/H8+fPRWjNkyBAnRymEyE1By/HatWvZu3cvI0aMcEGUQjiO1NA7SIUKZngsf/8wVq1aZfdIN1m99dZbdOrUiWuvvTbT9vz+MBBC2Cdr2Ro6dCiVK1fmo48+yuEVOXvzzTcZM2ZMrv1nhBDOV9By/MYbb3DXXXdRt27dTNvlO1i4O0noC6hEiRKcOnXqck/6EiWS09bVOX36NAkJCQU6bnh4OB9++GGmbVpr9u3bR0hISOGCFkJkEhISQnR0dKby6uvry4wZM/Ld5O3YsWPceOONjB49OtP2uLg4oqOjKVGihENiFkJkVqJECfbv359p5uaClOP4+HiaN2/Oq6++mmn7hQsXOHbsmHwHC7em5Fdn7iIiIvSGDRuyfS4yMpJly5aRmJjI5s1H+emnj/H1PcP11w+jRIkS2baPL6iyZctyxx13yDBawimUUhu11nkPsu6BcivDqampLFiwgM2bN2dKBhzJz8+PFi1a0Lt37ys6wAvhKN5chiH3chwfH88333zDgQMHnFKT7u/vf7kfnJRh4UyFKcfShr4Q2rRpc3kkmmeffZ6ffkomJaU03bv3ISbmMC+++KK1AQohcuXj40Pfvn3p27ev1aEIIQooKCiIu+66y+owhLCUNLlxkKpVK+PrGwPATz+tvWL2VyGEEEIIIZxBEnoHUUrh52cS+osXS8ltOSGEEEII4RKS0DvIkSNHKF/+EgBVq3bgyJEjFkckhBBCCCGKAknoHaRWrVoEBZ0AYPXqwxw5coRz585ZHJUQQgghhPB2ktA7SP/+/SlW7DgAFy6UJywsjLlz51oclRBCCCGE8HaS0DtIaGgolSvHAZCcXIOjR4+yc+dOi6MSQgghhBDeToatdKCAgIMAJCRU48CBA/j7+1sckRBCCCGE8HZSQ+9AVaqkAAkkJ4cTH+8ns8oJIYQQQgink4TegeLjL+Dvvx+AS5eqceHCBYsjEkIIIYQQ3k4SegcqW7YsAQF7AfD3b07ZsmUtjkgIIYQQQng7SegdqEGDBtSpYzrGHjpUilOnTsnQlUIIIYQQwqkkoXeg/v37U6KEqaFPTGwoQ1cK4eZWrjxGvXrLqVJlFvfddx/R0dFWhySEEELkmyT0DhQaGkq9ema22AsX6nDkiAxdKYQ7e+ut99m9uyuHD3dl5co/efbZZ60OSQghhMg3SegdTOvD+PicIDW1FP/8E8+pU6esDkkIkYPt278FYoAqJCdXJzIy0uqQhBBCiHyThN7BwsLKEhJiauWPHq3CkSNHpB29EG6qRIniwAoAjh9vSFBQkLUBCSGEEAUgCb2DNWjQgBIlTEJ/9mxDjh49yuzZsy2OSgiRnUaNGuHntxKA+Ph2FCtWTH6ACyGE8DiS0DtY//798fNbB0BiYivKlSvHwoULLY5KCJGdatWqUarUJgBSU7tw8uRp+QEuhBDC40hC72ChoaE0bHgeSCYpqRGpqcWtDkkIkYMGDRpQvPhh4BBQnvPna/Lzzz9bHZYQQgiRL5LQO0G/ftcSFLQd8GXHjlKUKlVKbuML4Yb69+9PSkoysASAs2dbs337dimvQgghPIok9E4waNAgKlT4F4D4+LacPHlSbuML4YZCQ0Np0qQJSpmEPimpK7GxsUyfPt3iyIQQQgj7SULvBKGhoZQpY9rlxsd3BJB29EK4qb59++LntwxIResOBASUYdasWVaHJYQQQtitSCT0SqkgpdQ8pdROpdRmpdQipVQtZ54zPHwvEEd8fAO2b48hKipKbuMLUUDOLMODBw+mZMlkYC0QSHJyZ86fP++IQwshMrDiu1iIosLP6gBcaIrWehGAUuohYDrQzVkn69u3B8uXryUurgunT7emceP9zJ07l2HDhjnrlEJ4O6eU4dDQUOrWrUts7O8kJ7fl/PmOhIWt4ty5c4SGhhb28EKIzFz6XewpUlNTOXnyJLGxsaSkpFgdjrDI4sWLm2zZsuVANk9ppdTZlJSUz1JTU6e0bNkyMesOliT0SqkqwNNABHA1UAyoqbU+kM2+VYF3gR6AwvRee0xrHWXv+bTW8cCiDJsigdEFjd8egwYNYuLEz/jvvy4kJ/emRIlZ7Ny505mnFMJlvK0Mt2rVii1blpKcPB6te1OmzCfyA1x4PW8rx54sOjoapRQ1atTA398fpZTVIQkLpKSkJDdu3Phk1u1aaxITE/2PHDny8Llz51oAd2Xdx6omN3WA24AzwJ857aSUCgaWAfUxwQ8FrgKWK6UKMx7kw8BPhXh9nkJDQ7n6avM5l5DQhejok5w6dcqZpxTClbyqDDdr1ozatc8Bh9G6MkePVmTz5s2OOrwQ7sqryrEnu3jxIpUrVyYgIECSeXEFpRSBgYFJ1atXPwt0yG4fqxL6lVrrClrrPsB3uew3HKgF3KS1nqe1/gnoB1QHRtp2UkptUkqdzGGpmvGASqmxQF1grMPfVRa1a/sRFLQDrYuzdWsF1q1bR3R0tLNPK4QreFUZ7t+/P5cuXQTmA3D8eFt+/fVX6fcivJ1XlWNP5+NTJLo1ikLw8fHRgG+2z7k4FgC01ql27toPiNRa/5fhtfuBVcCNGba10FqH5bAcsu2nlBoN3AL01lpfcsy7yVmDBg0IDjaj21y6dAPx8fG8+OKLzj6tEE7nbWU4NDSUcuXK4ePzS1o8/bh06RJz58511CmEcDveVo6FMW3aNKZNm2Z1GMLF3P3nYCNgezbbdwAN83MgpdQTwB1AD611bB77jlBKbVBKbYiJicnPaTLp378/ISG/ApCScgPJyf6sXLlSav1EUeIxZbhhw4aUK7cNOEtyckN8fetJvxchDLcvxxcvwunT+YlECO/i7gl9GUzbvqxOA6XtPUhax5+JQClMm7/NSqkNOe2vtZ6mtY7QWkeUK1cunyGnCw0NpUePmgQEbAZKcPp0B4oXLy61fqIo8Zgy/OKLL1K8uD/wMwDnzl3H0aNH7Q1RCG/m1uV4yhQoWxYmTrQ3ElEUfP7554SEhFgdhsu4e0IPoLPZlq8eI1rraK210lrX1lo3S1siHBRfrl588UXKlFkAwPnz/alVq5bU+omixiPKcJUqVahXrx4hIWYQjrNnr2P58uXS70UIw23Lca1akJAAv/ySyqVL0oLHlYYNG4ZS6oqlTZs2VofGwIED2bdvn9VhAK75ceHuCf0ZTM1AVqXJvrbA7VSpUoVrrz0BJJCa2p1Vq6JYs2aNNLsRRYVHlWF/f39SUhYA59G6OefPV5B+L0K4eTnu3Bl8fePZutWHli378/nnn8t3rAt1796do0ePZlp+/fVXS2NKSkqiWLFilC9f3tI4XMndE/odmLZ7WTUE/nFxLAVWuXIQJUsuB3yJjb2Vffv28cQTT8gHjigKPKoM9+7dm6AggHkAJCffKv1ehHDzchwUBCVK/AXA3r1N+PHHH5kzZ47FUVnj3LlzrF69mvnz57vsh01gYCAVK1bMtJQpU4YVK1bg7+/PH3/8cXnfqVOnEhoaernmvEuXLtx///08+uijlC5dmtKlSzNmzBhSU9P7aycmJvL0009TpUoVihcvTqtWrVi0KH06gz/++AOlFL/++iutW7cmICCARYsWXVEr/uKLL9K4cWNmzpxJjRo1CAkJ4e677yYxMZGPPvqIqlWrUrZsWZ544okCnX/p0qVcc801BAcHExERwaZNmy4/f/fdd3Px4sXLdzBsFUVz586ladOmFCtWjDJlynDXXXcFHTp0qEBzRLn7TLHzgbeVUrW01vsAlFI1gPbAM1YGlh8NGjSgatVfOXu2FwkJd1G69Lfs2bNHJq4RRYFHleFBgwYxZ84c1q2bS0LCUC5duom4uI+YPXs2DzzwgNXhCWEVty/HcXGzgO4kJd3Kvn338fPPP3P//fdbHVahFGSkmtWrV7Nx40aKFSvG999/zx9//EG7du3ydYwRI0bk+7zZ6dy5M2PGjGHo0KFs2bKFEydO8OSTT/LRRx9Rq1aty/vNnj2bYcOGsWbNGrZu3crw4cMJDw/niSeeAODuu+9m7969zJkzhypVqvDrr7/St29f1q9fz9VXX335OE8//TQTJ06kTp06lChRggULFlwR04EDB/jpp5/45ZdfOHz4MLfccgvHjh2jYsWK/P777/z777/cdttttG/fnltuuSVf5x87dixvvvkm4eHhPProowwePJh//vmHdu3a8d577/Hss8+yd+9eAEJCQjh27Bi33347r7/+OrfccgsXLlzgu+++Sy7ov7dlCb1S6ta0P1umrXsrpWKAGK31irRtnwAPAT8ppZ7HtOF7GTgEfOzKeAujf//+/PnnX+zcuZOUlAbs2dOcFi3+kbb0wqN5YxkODQ1lzpw5tGrVjmPHYtC6PhUr9mbhwoWS0Auv5C3lOClpLjAZuIbY2PL4+R2xOiRLnDx5kmLFiqGUIjQ0lJMnr5h01OEWLlx4RfvwBx98kDfffJMJEyawePFi7rvvPg4cOMANN9zAXXdlnuQ0PDycSZMmoZSifv367N69m3feeYcnnniCvXv38tVXX3HgwAGqVasGwEMPPcSSJUv4+OOP+eijjy4f58UXX+S6667LNdaUlBQ+++wzSpYsSePGjenVqxcrVqzg8OHDBAQE0KBBA9q3b8/y5cu55ZZb8nX+l19+ma5duwIwbtw4OnTowOHDh6lSpQolS5ZEKUXFihUv7797926SkpK49dZbqV69um1zctWqVQuU1FtZQ591Egvbv8oKoAuA1vqiUqobZrrpWZgOOEsx001fcFGchRYaGsq7777DypUf8t9/DUhOfhStR8rMscLTeWUZrlKlCq1aNWPhwrkkJY0kJqYXlSp9bnVYQjiLx5fjAwcOoPVF4Hvgbk6e7Evp0jM4d+4coaGhVodXYAWpKQ8ICOD7778nNDSUq666ipo1azq9JUCnTp2uuJtQqlQpwPRLmjNnDo0aNaJ8+fIsW7bsite3adMm0+y4bdu25YUXXuDcuXNs2rQJrTUNG2YeHTUhIYFu3bpl2hYRkXf/6mrVqlGyZMnLjytUqEDdunUJCAjItO3EiRMA+Tp/06ZNL/9dqVIlAE6cOEGVKlWyjeXqq6+me/fuNG7cmOuuu47u3btfcZ78sCyh11rb1Tteax2FmYDCo4WGhtKv3wXee+84KSlN+Oef6sTE/MHUqVMZNGiQR3/oiKLJm8tw165dWb36R06dGkl0dCcefDDK6pCEcApvKMdTpkxBaw1MB+4mPv4OkpNnFsmmcv379+ePP/7g5MmT1KxZk/79+zv9nMHBwdSpUyfH5yMjI0lNTSU2NpaYmJjLyb49UlNTUUqxfv16/P39Mz1XrFixTI+LFy+e5/GyHkMple22lJSUfJ8/4/O2HygZ2+Jn5evry++//05kZCS///47n376Kbt27Qr+/fffi7Vt2zYuzzeThbt3ivUqTZpcRc2aZgz6c+ce49Sp03z88cfMnj3b4siEEBkVK1aMgIBtwA5SUsry6adHZfhKIdxUelvp1cBWoAKxsT1ZuHChhVFZIzQ0lHbt2tGvXz+GDRtmeWXhgQMHeOihh/jwww/p0aMHgwcPJjk5c4uStWvXpv0gMyIjI6lUqRKhoaE0b94crTXHjh2jTp06mZbKlSs7PX5HnT8gIODyj4SMlFK0bduW8ePHs379esqVK6dnz56d3YhSeZKE3oX69+9PvXrLUeoEcA3JyX04f/58kfzQEcKdHTlyhIoVK+DjMxOAgwevleErhXBTiYmJGR69B8Dx40NITEyyJJ6iJiEhgWPHjmVaYmJiSElJYciQIXTu3JmRI0cyffp0oqOjmTBhQqbXHzlyhMcee4xdu3bx/fff89Zbb/H4448DULduXQYPHsywYcP4/vvv2bdvHxs2bODtt992ySSdjjp/jRo1iI+PZ/HixZw8eZJLly4RGRnJK6+8wvr164mKimL+/PkcP35cNWzYMN+18+D+o9x4ldDQUAYM6MP69dOIiXmeCxeexcenI1FRUR7f1k8Ib1KrVi1OnTpF8eJzOX/+FeLju7Fs2UtSToVwQy1btmTPnj1pj2YDL5Oa2pgDB5pImXWBJUuWEB4enmlb5cqVGTlyJP/99x/btm0DoGzZssycOZM+ffrQs2dPOnToAMDgwYNJSUnhmmuuQSnFvffeezmhB/jss8949dVXeeqpp4iOjqZMmTK0bt36cgdUZ3PE+du1a8f999/PHXfcwalTpxg/fjwDBw5k1apVfPDBB8TGxlK1alWGDx+eNGrUqNMFiVNlvM0hrhQREaE3bMhxZup8O3fuHKNGPcZXX71AampNlHqUmjV/oWvXrrzzzjvywSMsoZTa6KrZk12tIGX43Llz3HDDDWzZsoVz5z4BbiM0dCJvvBFc5NrkCs/gzWUYci/H77777uUhDo1HgPfx9d3OG2/8zujRT2T7Oneyc+dOGjRo4JBj2TqoOmr4SWfq0qULjRs3ZvLkyVaH4ha2b99+qXHjxrkOgbhly5awq6++ukbW7dLkxsVCQ0P56KP3aNnSTHqh9QQSE8uwePFimWxKCDdhG76yZMn/t3ff8VGU+QPHP8/WbLLZ9AZJCKFJJ/QiAoqKXSI/78SGBRXOO1FPr1jOU+/O0zv7qagonud5FhBFQEQUsSCI1FADgUAC6T3ZbJ3fH5usAQIEkrAp3/frNa+Y2dnJM4Pfme8885QwdLrXAaiuvoa5c+dJjArRxpSWlhIUFIROV5/SzAUO4vEM4G9/O9DpYva2225rF8m8aFmS0AeAzWZj1qyuhId/D4STm3s/VVVVfP7555LUC9FGJCYmkpaWhtW6DtiFx9OFgoLRZ6TdphCi6VJTU9E0zT8LJziABwEoKbmPO+64T+6rosOThD5ArroqnQsuWIRS1Wja/1FWNoXa2lr/DLJCiMC76KKLiIyMQKlXAKiqmiETwgnRxqSnp6PT6VBKERwcXLf2bWAd0JWPP06T0eTaqFWrVklzmxYiCX2A2Gw2XnvtQcaOXQCA1/sSZWUR7Nmzh/fff19qE4RoA6ZPn05SUhIm03+BSiorh/PFF/kSn0K0ITabDbPZTEhICA8//HDdrKUaMAvwUFMzk7/85Qvmz58vsSs6LEnoA8hms7FkyZVERa0GwnC5/kt5uZOcnByppReiDbDZbIwZM4ZevWIB3xCWmZmXSm2fEG2QUor777+fp556qm7mzw3A04CeQ4f+xq5dbfveKoOUiJPxer0KaHS2KknoAywszMa1136J0XgQGEZNzQtkZe3nb3/7G6+88orUJggRYH379sXhcGAw/AvwUll5OYsW/RDoYgkhjmP69On06dOnbubOh4AMNK037703ts02mTMajdjtpzX8uOhE7HZ7kFIqr7HPJKFvA9LSUhg37il0uio07ZdUV/+JQ4cO89RTT0knWSECLD09nZCQEIKDD6HUYsDE5s3jJS6FaENCQ0P9wz7bbDZmz55Nz5498XWQnQ7Y2bfvXF5/3dUmK8tiY2PJzc2lpqZGaurFMbxer6qurrbs37/f5Ha7/9zYNjKxVBuQnp6O3W4nM/M35Oa+AtxLdXUtBw8+yrJlywBkjHohAsRms3H77bfzyiuvkJHxDzyeKygs/D/uvPOPvPjiXyUuhWgD4uLiGgxb6aulX7hwIbt27cLr3QrcCcyjpOSvvPPOwwQFLWTGjBmBKu4x6q8jhw4dwuWSGW47q7y8PIPH44lu5COvUirP7Xb/eejQocsb++4pJ/RKqRRgNNAFsABFwC7gB03Tak91f6J+GMtZWCwW/vWvZ1i//l407QHc7hBKSx+UpF6IAJs+fTrLli1j//6vKS9fjdd7Dt9+25+FC9tWUiBEZ/XWW2/RvXt3/+82m43k5GSMRiNOpxNNewMYBszmu+/uQ6f7Henp6W3qnmqz2dpUecSZ169fv62nO0Fck5rcKKXClVK/VUrtBPYC/wX+ATwGvAx8CZQppd5XSk08nYIIX039zTfbSEn5A+BE0+ZQW/smVVVeli5dyrXXXiu99IUIAJvNxtSpU+nZsydG41MAZGdfxbvvLpJ4FKIN6NevHxaL5Yh1UVFRdOnSpUHN/V3AcjQthh9+eIQ33lh2xsspRGs5aUKvlLoXyALuAZYDVwM9gTDABMQDY4DfAeHACqXUF0qpPq1U5g6rvqZ+8+aHmDLlRZSqQNP+j8rK5RQUhPPll1/y2GOPMWHChDbZBlCIjiw9PZ3g4GC83s+A9Xi9sWzbNrZNj5ohRGfWt29frrnmGsaNG4fBYADcwDRgPU5nMg89NIq9eysDXEohWkZTauivBW4GEjVNu0vTtAWapmVpmlapaZpb07QCTdPWapr2nKZpFwDdgC3AZa1Z8I7MZrPx3nu3MnXqU1gsB9G0QXg867Dbr+fAgYPs3buXf/7zn9JhVogzqH4Iy7AwG76Xk3Do0PU888wr5OTkBLZwQohjpKen06tXL8LDwznrrLOIiYlBqWrgIpTaTlVVCsOHV7B7tyT1ov07aUKvadpQTdMWaZrW6LiXjWx/SNO0ezRN+0fzi9d52Ww23nzzPv75z9V07fo1YEXTXsHt/oyqqi7k5uaybNkySeqFOIP69u3LuHHjCA//BtiApiVw6NBlPPLII4EumhDiKDabjRkzZvD2228ze/ZsbDYber0eKELTJqHUdsrKujJwYBmPPfZfuZeKdk2GrWzDfE1wrmXbtjQmTpyLTlcKnIumbaG29s/U1BhYuXIl119/vbStF+IMSE9P56KLLkIpUOoRAMrLZ7Njx8HAFkwIcVz1zVkffPBBLr74YiIiIoACNO0clPoJpzOJxx+/mKeeWh3oogpx2k5plBulVPKpbK9p2oFTK45oTFiYjY8/voa5cxfwxBMRlJRciabdR1nZTdTUPI3Hs4CCgrm88cYbjBkzhr59+7a53vtCdAT1icFPP/3E+vU/kZGxDpdrJLm5/0dFRYXEnBBtWP0Q0T/99BOlpaVAMZo2EaUW4nSezxNPXEi/fnDNNYEuqRCn7lRr6PcD+05hES3EZrNx3303sW/fuVx++eOEhW0ConE6/8rBg1+zadN5bNiwm88++4w333xTmuII0YoeeeQRbLZQ4I8AHDp0LW+9tSSwhRJCnFD9A/m1115LWFgYAEpVo2kXo9TruN1Gpk+H88/fREmJ3D9F+3KqCf0sIBfYAfwZmA08CuwEcuo+v7nBIlqYzWbj7bd/wzPPbGLUqEcxmbYC8dTWPk519TYyMv6P0lIDn3/+ORMmTOCKK66QEXGEaGGJiYlMnDiRwYNLMRhW4HJZeOYZq8SZEO1A3759mT59Ot26dUMpBbjRtJn4Jp9y8cUXQxg1qgrp6y7ak1NN6PsCG4CBmqY9qmnaXE3T/gwMADYBfTVNe6t+aeGyijo2m42bbprB55/P4brrnqN//99htW4FovB6H2Tr1k/JyXmAbds87Nu3j5dfflna2QvRwlJTUwkLC8NgeBDwsm/fFG655QmJMSHauPT0dEaOHMngwYNJSUkhMjKybqz6fwHnotMdZs+eLvTuXcNvfrNSYlq0C6ea0F8DzNU0TWu4su73V4DpLVUwcXI2m41nnnma3/62L7Nm/YdBg35NWNg3gAVNux2Xawvbtj3Jzp3J7N+fzcKFCyWxF6KFpKenY7Va0ekygPmAkSVLJkhzNyHauIaj30yaNIno6GiCgoLQ6XQo9R063XD0+hXY7cG88MJ5XHppHhLSoq071YTeCsQc57NYIKR5xRGnqv7C9OSTf+ebb/7Cs8/uZdKk2URFfYhO58DrnYLTuZitW//Ld9/1JyMji3nz5knSIUQz2Ww20tPT62aP/TNQhd1+IevW2WSyKSHaAZvNxtNPP82cOXPo0aMHISEh6HQ63O5DeL0XYTLdj8Hg5ptvejNoEKxYEegSC3F8p5rQrwL+qpQa0XClUmok8Je6z0WA1Cf3ixY9wWOPFXL++beQkPACJlMRmtaPkpK/kZW1io0b/49PPlnPNddcI7X1QjRDeno6I0aMICioFKX+CsCePXeSkbErwCUTQjRFfUfZb7/9lkmTJpGYmIhOp0OnA6/3GSyWs7FYMsjOhgsugJtvhpKSQJdaiGOdakJ/J+AAflBK7VdKrVVK7QfWALV1n4sAq79AffbZf9i580b+9a8lpKU9hdG4CYiiuvo3FBau48svb+C551ZJMxwhTlN9Dd/AgQPR658H9mK3p/LNN0MCXTQhxCmw2WxMnTqVpKQkbDYbSincbjeVlWvRtDGEhDyOUg7efBP69PHyn//AkY2PhQisU0roNU3bB5wF3AGsBIrrft6Or0Ps/pYuoGgem83GrbfeyKpVt3P99S8yePBsQkOXA3pqa3/Bpk3zWbHit7zwwnauu04SeyFOlc1m4+yzzyYlJQGz+T4ANm68UkbIEKKdSU9Pp0+fPvTo0YPk5GRMJhM6nQ6Ho5qamocxm0cRFLSWoiId118P554L27cHutRC+JzyTLGaprk0TXtN07RbNE27uO7n65qmuVqjgKJl1HegnTNnJJMmvcR5580iPv59fO1+x7Nhw5N8/vnjPPvsAa677gZJ7IU4BX379uX8888nKmotQUHLcLnMzJoll0Qh2pP6N2533nknAwYMoHv37kRHR6OUQtM0ams343KdTXj4PZjNlaxaBYMHw29/i3SaFQF3ygm9aL8a9uy/7rqxjBz5DhdcMJP4+BdRqhCHYyCbNz/M8uVP8cwz+Vx33Y2S2AvRBOnp6TgcDioqKjAa78VorOXTT4189FGgSyaEOBUN75N33XUXXbp0ISIigpCQEPR6PR6Pm+rqF7FY0oiLW4Tb7eWf/4RevbzMnw9eb6CPQHRWJ03olVKblFJTlW/2hZNSSiUqpZ5XSt3f/OK1LKXUTUopTSl1ZaDLEkgNL1jXXHMhI0euYMqUO+ja9W8odRCnsxdbtvyO5cuf4T//8TJnzr08/PDDktyLNqEtxrHNZqNr16707t0bq7WMiRN9w2H86ldQWhrgwgnRBrXFOG6ovi/a119/zWOPPUavXr2wWq0YjUY8Hg9VVdkUFU0jOHgiRuN6Cgp03HQTpKYWsGJFdaCLLzqhptTQvw28BuQqpZ5RSqUrpXoopWxKKbNSKl4pNVYpNUcptRLYD/QGFrVesU+dUqobMBP4IdBlaSsaJvZXX30Zw4b9wJQpv6ZLlz+h1D6czhRWrryZt9/+LWvXJvD++x9IB1oRUG05jlNTU+nXrx9Wq5WffroFo3E9hw/D2Wf/KDEjRANtOY6P1jCxnzZtGjExMVitVpRSKKWw27/F7R6NUjcAuWRnx3LBBSGMHp3Ftm2VgS6+6EROmtBrmvZPoAfwT+BC4ENgN1AK1AC5wLfA34BC4DxN06Zomra7sf3V1eC/oJRao5SqqXtCTznOtklKqQ+VUuVKqQql1EKlVPKpHqRSSgfMA36Nb5Qe0cCRif2VDB++iQsu+A2jRr2GTpeD292Hzz+fxYoVj7J1aySZmZkyznYnJ3F8rPT0dPr160deXh4VFaUodTNgZ/v2ESxerCRmRJsjcdx09e3rL7roIoYMGcLAgQMJCQkhKCgIpTSUegedrl/d8LV21q5NJS3NwkMPQVVVoEsvOoMmtaHXNK1c07R/aprWD+iGb8bYe4EH8I14MwEI1zTtl5qmfX2S3fUErsb3QPDN8TZSSgUDX+IbVedG4HqgF/CVUupUJ7C6B/hO07SfTvF7nUrDxP6Xv7yKCy44yPXXP8qoUf9Bry/C7R7Gvn1v8uyz5/LUU59KrWPnJnF8FJvNRlxcHJqmodfrcbu34btEwkcfXcy7734p8SLaGonjU1Cf1N90001MnjyZQYMG0aVLF8LDwwkLCyM42IvB8Ahm8xAiI5fjchl4/HFISqrh5Zft0r5etCrDqX5B07SDwMGj19c1v7lL07TnTrKL1ZqmxdV951bgguNsNxNIBfpomranbvstQCa+YTKfrlu3ATheLUEaYAOmAeNPUi5Rpz6xB6ioqGDhwoV88MGvyMy8lL17r6Km5jy2b5/AX/+6gqqqxdx557WBLbAIBInjRuzfv5+IiAjy8/Pr1jwLXIqmncvGjb9mwYKF3HTTjMAVUIgjSRyfoqPvj++88w6LFy/m8OHDeL1eKioqyMs7QFXV5QQHj8ftfoqysjRmz4a5cz08/7yec84J7DGIjumURrlRSkUf3TlWKWVRSt2Lr+380yfbh6ZpTX1GvRz4of7iUffdfcB3wBUN1g3VNC36OMtB4Bx8bxUy6ybBGg28qpSa1cRydGr1F693332NP/7Rw4gR00lMXAIoMjMvYs6ci7nooo944w2pre9MJI4bl5qaSrdu3bBYLOh0OpQCo/FWoITCwhG8+258oIsohJ/EcfPUt69funQpGzdu5JtvvmHQoEGEhISglMLj+Ra3eyRm8y0olcvmzXomTIArr3Sxf3+gSy86mqaMcmNWSj2nlKoC8oHi+uBTSl0HZAFPAQeAKS1Ytv5ARiPrtwH9mroTTdNe1jQtQdO0FE3TUvB1wrlN07SXj/cdpdRtSqn1Sqn1hYWFp1ruDqk+sb/jjiu5+eZ1zJr1OmbzGjyeCD77bCq/+c1wLr1URsIRxwhIHAcqhtPT07nlllsYP348Xbt2JS4ujh49zERE3AvAF19M5oEHlkiMiPamU8Xx6aqfbfbWW29l2LBhWCwWYmKiMJk+QK/vj9H4OErZ+fhjI716ebjvPgfVMiCOaCFNqaF/GF/nle/xJe4rgOeUUi8A/wbKgSs0TRuladqKFixbJL52fUcrASJa8O8cQ9O0VzVNG65p2vCYmJjW/FPtTnp6et1kG7lMmvQYaWl/R6fLo7p6AN9880+ef74b7777SaCLKdqOgMRxoGK4/sH3nXfe4b777uOKK64gNTUVg2EJFsvLaJqB558fzxtvLDtjZRKiBXSqOG6O9PR0zjrrLM4++2wGDhxIamoqXbt2JTExguDgf2A2D0Knexe3W88//mGmd28v774Lmhbokov2rilt6H8BvKRp2p31K5Rv+IbX8SX3l2ma5myl8jX2v3iTxsM/7g41bWJzvt/ZNWw/OH/+fPbt24fFchPbtl1NefkNbNw4ibvuOsw337zF5Mka6enp2Gy2wBZaBFqni2ObzcavfvUrAHbv3s3FF19MUNA/yMsbTFXVWP7xj7HccQcEBQW4oEI0XaeL49PRWB+0j+pmmDt06BC7d+/G652BXj8Pp/NJDh0ayvTp8OKLbl5+2cCgQQEsvGjXmlJDnwQcPd9h/fhrT7diMl+Kr1bgaBE0XlMgzrCfa+tNTJiwiClTHkGvz8DhSOCdd27khRf68c47UlvfyXX6OI6KisJqtVJbW0lIyM0EBRWQm5vETTfJrJKi3ej0cXw6Go4cN3XqVLp06UJMTAzh4eF4PN+g043FbP4VQUEVfP+9gbQ0L6NHf8/LL78jzfLEKWtKQm8Ejp4dof731mzUtg1fu72j9QO2t+LfFU109MVqxAjFyJG/wmr9G+Bkw4aR3H33efzhD8vk4tR5dfo4ttlsJCYmYrPZCAurpW/f32E01vK//8HFF2dQXi6xIdq8Th/HzdHwXjlx4kT69OlDcnIywcFBGI3/JjJyLGbza3i9GmvXjmXOnCmce+4rvPmm9EkTTdfUUW66KqVS6xd8w1cds77us5byCTC64T7rJrwYV/eZaCPqL1aPPvoot912EzNmHCIx8Up0uk04HAk88cSFXHPNPpyt9S5HtGWdPo7tdju7d++mpKSEiIgIamvXEho6A6XcLF8+gOnTdwa6iEKcTKeP45ZQP479zTffTL9+/Rg/fjxJSUmUlOwFfk1Q0Nko9QNOZxQ//XQ/v/vdANLT75XBJkSTNDWh/xDfeLP1S/0daNFR6zObsjOl1DSl1DRgWN2qi+rWTWiw2Wv4hsL8WCl1hVLqcuBjfGPgz21iucUZlp6ezrBhw0hOLichYSrBwf8ENJYuHUxCQhZ///sCuTB1EBLHTbNw4UI8Hg8GgwGHw8HevXupqvoIm2024GHp0pFccsl6qakXASFxfGY1rK2/6qqriIqKIioqirCwMFJTKwgOvgCT6U6glMLC4Xz11fP8/e8urr32RknsxQk1pVPsTa3wdz846veX6n5+DUwE0DStWil1LvAM8Da+zjcrgTmapslEym1Uww5BWVlZVFXl8s47V1NY+DQlJak88EAse/d+z6uvHm/+EtGOSBw3QVZWFt27dyc7O5uQkBD/TLJ2+1uYTB6czldZunQ406Zt57PP+qHXB7rEopOROA6AhvfKffv2ERYWxtdff43JZMJi2UBm5nhKSx/G47manTtnsn//aIqLX+Cjjz5i6tSpMuCEOIbSZKykExo+fLi2fv36QBej3anv3Z+VlcWmTZuIju7JRx9dQlnZeQAMGPAVd911kKuvvlIuSm2AUuonTdOGB7ocrSHQMTx//nz+/e9/U11djcvlIisrC4fDgdvtRqfTYbXOoKTkecDM4MEH+eSTMJKTJSbEqenIMQyBj+PW0vBemZqayuTJk/niiy94//33OXjwIAcODKCy8kk0LQlwEhHxPIMGfUZYWIgk9h1Qc+JYEvqT6KgXkTOpfnjL4OAQ/vEPO0VFfwBMxMXt5fe/X8+cOb8IdBE7vY6cDAQ6hisqKnjvvffIyMigrKyMr7/+Gp1OR1lZGS6XC51Oh8MxCqfzPTQtgtjYCj791MaIEQErsmiHOnIMQ+Dj+EyrT/Tff/99Dh2qYvfuW7HbbwDAYNhAfPzv+OUvh9C/f39/Tb9o/yShb0Wd7SLSGo6ura+sPItvv70btzuBkJByVqwIY8yYQJeyc+vIyUBbiuHy8nIeeOABsrOzWbt2LUop3G43Xq+XmpoENO19XK4B6PUa99+vePBBCA4OdKlFe9CRYxjaVhyfSfX3zwULFrB/fw927LgPj6crYCcp6Xl69VrBuHFjSU1Nldr6DqA5cdzUTrFCnLaGo+Ckp6czfnwQkyf/HpPpB6qrwzj7bDczZ34jnX1EhxcWFsaLL77I3Xffjclkwuv1ctZZZ+H1etHp9mE2TyIx8X28Xvjb36BnTzdvvQVud6BLLoQIhIazT9977xDOP/9e4uKWARYOHvwd3333eyorg9m3bx8LFy486f5ExyUJvTij0tPTSU1NZejQRHr2vIOQkHl4vQZef3081167TybaEZ1CRkYGTqcTo9HIgAEDsFqt6PV6nM4KqqvvYPjwuwkLy+LwYQMzZkCvXl6efRbKygJccCFEQNQn9u+99ypPPJHPZZf9G52uDIdjMs8/fzOHDvUlKysr0MUUASQJvTij6i9Kf/nLX7j44vOJiXkcq/V+wM2nnw7mF78Auz3QpRSidRUVFREWFkZISAgDBgxgwIABmEwmdDodLpeLkpJPCQoaT2jor7BYcti/X8fdd0NcHEybBv/7H5SXB/oohBBnWv099JNPbuAvf/mU0NCf8Hpjef31q9m48Uo8nkCXUARKU4atFKJV9O/fH6fTyZo1q9i//yaKiv7Fhx/a2Lw5nxUrLHTrJm0BRceUmppKUFAQZrOZoqIirrzySnbt2oXdbsdsNmO326muriA6eilW63ekpPya2tpbWLkSFizwLQYDjBoF554L48fD6NEQGhroIxNCnCmzZ1+O2Tyfxx//npKSX/Hpp0Pp0mUz5503j6QkC3379pV29Z2I1NCLgElPTyctLY3x48cTHr6W0NAp6HSHyMyMY9w4Dzk5gS6hEK0jPT2duLg4lFJ0796da6+9lj/84Q907dqV4OBgKisrcblc1NTUkJ2dxY8/PsDVV7/Gtm2V/POfMG6cB03T+O47eOwxuOACCAuDgQPhlltg7lzYsAFcrkAfqRCitdhsNu6++zfMnl2AzXYVOl0RBQWD+d//7uOdd3azfft2aVffiUgNvQiYhhNrGAwGFi1aRGnpZVRUvEdubk/GjIHPP4e+fQNbTiFams1mY968eSilSE5OBuCaa65hyZIlrF27FqPRCEBtbS3gi48lS5ag1+u59dZpfPbZNOLichg58n66dr2GdevMbNwIGRm+5Y03fH/HbIYhQ2DkSF9t/tixkJICSgXgoIUQrUIpxYABBWRnX0pBwYu4XMPJzX2XTz75B0rtCHTxxBkiCb1oE/r27YtOp+P7778nO/tqiormkZOTxoQJvqR+yJBAl1CIltWtW7cjfrfZbCxatIjLLruMjRs3YjabCQ4OpqioCL1ez3fffcf69ev59ttvycjIwGAwkJX1DN27b2Ht2n/icCjWrHGwfr1i82YTP/4Iu3fD2rW+5YUXfH8nIcHXRGfiRJg0Cfr0kQRfiPYsNTUVTdOwWq188cWf2LJlJgUFV7Jr14O88sorhIT8hd/85leEh4cHuqiiFUmTG9EmpKen06dPH0aOHEnXrhaGDv09QUGrKCyEceMcfPlldaCLKESr0+l0REVFYTQaMZvNDBo0CE3TqKqqora2Frfbzfr166murkYpRW5uLq+99hp//OMf2bTpB+69dwyvvtqfhIT7+PDDrWzefIAFCyr4y1/g0kshKgoOH4b334fZs31vv7p1g1tvhY8/hmoJMyHanfT0dHr06EF1dTXp6Zfy5z/nkZz8BOCiouIOnnxyBJMmXcI999zD/PnzZYjoDkpq6EWb0LD5zYEDB7jqqqsIDb0eeJWamou45BIdy5fDOecEtJhCtLrhw4fz/fffY7FY6NmzJzt27CA3NxeTyYTBYMBut+N2u/3t7E0mE5999hmLFy+mtLQUg8HAvHnzeOONNwgKCsJoNHLBBRfw8MMzCQ4OYedO+PxzO59/7iYvry8HD9qYNw/mzYOgIF97/Kuvhssvl062QrQHDe+f9Q4depgPPriLXbv+SnX1BWzZEklJyWx0Ol89rswu2/FIDb1oc5KTkxk+fDgmk0Z09GzCw5dQW2vkoovg668DXTohWteMGTOIiIjAaDTSr18/Fi1axPjx4wkODsZut1NWVkZkZCQhISFYLBaCg4M5ePAgu3fvpra2lqqqKjweDzU1NVRUVFBRUcEHH3zAlClTmDXrDu6771Lef38ypaUXEhnZn0sueYj09A0MHeqkthY++QSuuw5iYzWuuQaWL0eGwhOinUlNTWXatCiuvfYl9PpsvN7h5OZ+yOrVh9ixQ9rVd0RSQy/apFGjRuFwOFi/fj0Wy29xOqupqbmaCy90sWiRkylTQgJdRCFaRX2H2dDQULp37w7ArFmzmDNnDkVFRZjNZuLi4pg6dSqLFi0iPz8fh8OB0WjE5XLh9Xr9Y9oHBQVRVVXln5U2KyuL6upqNE1D0zQcDju5uZ/SrdtW4uNdxMcfxum8DKfzSqqq0vjf/3xj3nft6mXWLB0zZ0JsbIBPkBDipNLT01m4cCFKZXHllX9nyZLbqa0dzIYNLxAU9BgbNmwgPz+fr776Cr1eT58+fWSIy3ZOaZoW6DK0acOHD9fWr18f6GJ0OhUVFSxcuJAvvviCb775htpaF5WVz2G3/x9BQS6++MLIuHGBLmXHoZT6SdO04YEuR2voCDHsdrsZN24cu3btIjw8nNDQUGbOnInJZOKJJ56gtraWkJAQgoODKSgoQNM09Ho9DocDp9OJ2WzGaDQSFBREZWUlRqMRp9OJ0+nEYPDV64SEhKDX69Hr9QAUFFgICrqN2tpfUFOTAIDJBL/4Bdx/PwwYELDTIRrRkWMYOkYcB0pFRQVvv/0RDz7Yl7KykShVSVTUzUREbKaiooIePXowefJkunfvLk1xAqw5cSxNbkSbVN8m8O2332bMmDEYjTpiYn5HRMSn1NYamTJFY+1a34Vq165dlJWVBbrIQrQag8HAsGHDiIuLw2Aw0Lt3b4qKirjjjju48847sVqtmM1mzjnnHB555BEeeOABLr30Ui644AKGDBmCyWQiPDwck8lEQkICl19+OUOGDMFqtaJpGjabDaUUVVVV6HQ6ampqCArKx2p9hqCgQQQHX0nfvrtxuTTefts33v1ll8GaNYE+M0KIk7HZbPzqVzeydWsKUVFfoGmhFBe/TUFBGi6XiwMHDmC1WsnKygp0UUUzSJMb0aYppbjwwgtxOp3s3buX0NAHUMpIScmFTJpUy7Rpr/Ljj/PweDzcc889TJ8+XV4Zig5p+PDhrFy5EpPJhM1mIzU1FYDbbruNsrIycnNzGTFixDGvzSsqKvjwww/JyMggJSUFk8nEoUOHGDt2LD/99BNffvklLpeL+Ph4KisrSUlJISsri5KSEqqrq3E6HZjNq7FYrmHOnBm4XL/m9dfh0099y6WXwuOPw+DBgTozQoimSEyM5bLL3mXBggIqK6dTUfE2JtN0goLW8NVXX3H11VcHuoiiGSShF23eVVddhcfj4d1336WkpASrdTY1NS9it1/Eu+/eTETEYkymvSxYsACdTsdtt90W6CIL0eKmTZvG+++/z86dOxkwYADp6emAr/bt8ccfP+73bDYbN99883E/3759O2VlZfTv359Ro0YxY8YM9u/fz7PPPsuHH36ITqfDYrGglOLrr+czd+4YHnpoOM8+C88//3NiP306PPEEJCW19JELIVrKhAnjqaz8iI0bw8nKuhin81007S7y89cyZsyYQBdPNIM0uRFtns1m49Zbb+Xdd98lJiYGl8tObOxdWK3f4nZHUlLyHpBKUVERixcvxiXz3YsOyGazceGFFzJ69Gh+/etft8ibqPT0dK6//nouueQSRo0a5X9ISElJ4dlnn+W3v/0tcXFx6PV6ampqyM7OZtq0acyd+xi//30FWVkwZ46vbf1//wtnneWrra+b4FYI0cakp6dz6aWXMH36D1x4YQaaZiQv7zmMxitYtmxZo99ZvXo1H3/8MV6v9wyXVpwK6RR7EtIRp225/fbbWblyJXq9nuTks/juuz9it49Crz9Ajx43ExFRxX333ceVV17p79wnTq4jd6jraDHscDgwm81n5G9VVFSwYMECFi9ezNatW6mursZoNBIfH89ll13Ggw8+iKZpbN9ezR/+oGfxYgsAXbs6mD17M1FRG6msrCQxMZGUlBTi4+NJTk72j4UtWk5HjmHoeHHcFmga3H23l+ee06HTOenRYw5XXBHCU0895d9mzZo1zJw5k5qaGq644goeeeQRwsLCAljqjq05cSwJ/UnIRaRtmT9/Pi+++CJVVVXExMSQlNSPb755iJycRIKCspk48U/s2fMdZrOZmTNnctNNN0mb+iboyMmAxHDz2e12rrzySjIzM9E0jdjYWPR6PTNmzCAvL4+1a9cCEBmZzscfn0tlZfe63xcRHv44AwcmkpyczFdffQXAzJkzmTFjhsRmC+rIMQwSx61F0+Cii/ayfHkPlLITFzeD665L5sEHH6SiooI77riDzZs3YzQaiYmJYcKECTz55JMopQJd9A6pOXEsbehFu5Keno7b7WbdunUMGTKE6667Do/HxpgxNeza1Y2vv36IsLArMBq9fPDBB+Tk5PD444+fsRpNIToii8XCpZdeyvPPP4/H46GyspLi4mKefvppBg8eTF5eHtXV1eh0i0hKeoHDh6+lrOw3lJRcSXn5WDyeP7F9+2c4nU4sFgsffPABhw8f5rHHHvMPm3kymqZJEiFEC1MK5s41MWLEYgoLL6Og4HU+//zXOBwPERsbS35+vj9GTSYT3377LV999RVjxozBYrEEuPSiIamhPwmpFWgfCgo00tLKOXQoHJNpCwkJ13PWWQlUV1dz2WWXcckll9C3b1951X8cHbl2T2K4ZVRUVPDiiy/yySefUFxcTHV1NRaLBafTSVVVFWFhYbjdbqqrqwkLC6OioguVlc/gdo8CwGKZS0zM0+h0Tnr27ElWVhZnnXUWEydOJCIigpiYGMaOHUtMTMwxf/uPf/wjWVlZvPzyy0RERJzpQ28XOnIMg8Rxa/J6vUyceB4//XQPNTWXYTKVEhc3jX79jJSXl3PgwAGCgoIIDg7G7XbTvXt3hg8fzqBBgxg6dCgpKSmtcm/1er0UFBQQHx/f4vtuq2QcetHpxcYqVqyA4ODDOJ2DOHz4LbKzKzGZTLz33ntcccUV3HDDDWzcuJGWeIh1u90B6yBUXl7Ojz/+KJ1/xRlls9n44x//yJw5c4iOjsZsNuP1evF6vWiahslkwmAwoNfrsdlsdO/uIDz8ChITX0QpN3b77eTlLcLh6EFubi41NTXs37+fTZs2sX//fv70pz8xbtw4nnrqKSoqKgBfrfzSpUv55JNP+PHHH5k1axYHDhwI8JkQomPR6XSMGTOS6Oh7CApajdMZQX7+2xQXB5GcnOyfpyI4OBi73c6ePXvIzs5m48aNzJ49m3vvvbfR/bpcLn8sn45HHnmEW2+9lZKSktPeR2ciCb3oMPr1C2fevP0YjTk4nUM4cOAtSkqMFBYWYrPZyMvL48EHH2T+/Pk4HI7T/jvZ2dlcc8013HPPPSfdtjWS7scee4z777+fF198kezs7BbfvxAncvHFFxMXF4fZbCYsLIzIyEisVivh4eGEhYURFBREUFAQFouFrl3jSUl5j3Hj7icsrACnsy95eR9TUjIZpRR2u52dO3eycOFCKioqMJvNfPzxxzz11FN4PB6WLl3KCy+8QEVFBR6Ph6ysLO6//36ZSE6IFjZz5kzCwizEx/+KkJAMnM4ubNr0N3Jzq7nooouYPn068fHxuFwuXC4X+/fvJycnh6ioKHbs2MGePXuO2J/b7eb222/nhhtuoLCw8JTLk5+fz+LFi9m2bRt/+tOfmvVg0FlIQi86lKuvHsUTT3xPVFQhNTU92bHjFbzeLpSVlWE0GgH44IMPjlujcCJ2u53Fixfz7LPPsnfvXpYsWcJLL7103AtNTU0N11xzDb/61a+oPcE4fm63m7Vr17J79+6TlqG8vJyvvvqK/Px81qxZw7PPPsuiRYuorq4+5eMR4nTYbDbuvvtuDAYDHo+HAQMGEBMTQ1lZGQkJCYSHh1NaWkpaWhr/+c9/+Oqrr/j44wf5+utKJkzIRdNCyM9/npqax/B6dej1eoqKivB6vVRXVxMZGcnKlSt56aWXWL16NSUlJeh0OpRSKKU4cOAA8+bNk6ReiBbUs2dP+vXrR1paL4YNexiDIRO3uy8ZGY+Tl1fGhRdeSElJiX+EG7vdzqZNm9ixYwcHDhzg/vvv5+DBgwB4PB4++ugjDh8+DMDrr79OcXHxKZXnySefpKioCE3T+OGHH3jrrbda9oA7IEnoRYei0+mYPftyfv/7ZcTF5eFwpJCf/wEOR3dycnKorKyksLCQZcuW8cQTTzTpqd/j8bB7926effZZ1q5dS0FBAaWlpXg8HlavXs3ChQsb/d6nn37K7t27Wb58OTfccANbtmw54nNN08jMzOS5557joYce4qGHHjppc6A33niDgoICXC4X5eXllJSUsGHDBp5++mm2bNnSIs2JhDiZ8ePHk5aWRmJiIr169WLWrFm8+OKLPPnkkyxdupQtW7bwwgsvMGDAAAwGA5GRkQwe3IMvv+zCHXdsRyk35eW3UFz8PyyWZJKTk1FK4fV6yc7OxuFw8MUXX6DT6dDpdLjdbsxmMzU1Neh0Or744gtmzZrVrsfF3rhxIxs3bgx0MYTwe+SRR+jTpw8hIbUkJ9+GwZBPefkQfvjh1wwdOoKEhATA91Dvcrmorq4mPz8fr9dLSUkJDz/8MC6XiyVLlrBmzRpKSkrYs2cP69ev51//+helpaVNKkd+fj5r167FYDCglEKn0/H111+35qF3CDLKjehwgoKCmD17Gpr2Ls88cwGHDydx+PBiQkKew2731RpERESwfPlywsPDueOOO474vtPp5PDhw+Tk5JCZmcmHH36Ix+Ph/PPPx2q1snTpUn/iXFRUxNatW48pQ05ODvPnz6empgabzUZpaSl/+MMfmD17Nueddx4VFRUsXbqUnTt3YrVaqaysJCsri+eff/6EQ22uX78epRRut5vKykoAxo0bR01NDe+88w6TJk1iypQpLXxGhTiSTqdj7ty5VFRUEB0d3eSRanQ6xb/+dRZxcSv5y18GU1U1nA0bXmLmzMWsWfM6eXl5mEwmioqKKC8vp6CggISEBGpqavzj2UdFRXHo0CG8Xi/79u2jR48erXy0x6qpqeHf//4vZWVuTKZIjMYIDIYw9PowevZMISrKjNUKoaFgtYLF4htNpKFnnnkGj8fDO++8c8bLL0Rj+vTpQ0pKCpmZmQQHH6R379+wa9c89u1L49574Z577mXmzFvxer0MGTKEPXv2cOjQIRwOB9HR0WRmZjJ//nz27t1LcXEx+fn5KKWoqqpi8+bNvPHGG9x4441ER0cftwyapvHpp58SEhKC0+nEaDRSU1NDTU0N+fn5xMXF1W0HRUWwaxfs3ev77/qluBgqKsDr9S0ej++nXv9zTNb/DA+H+HhISPAt8fG+xWQ6Qye9BUlCLzqk4OBgZs/+JZr2b55/vh+5uRPIzPwDkZHDCAm5F4fDQXBwMJ999hm33XabvxZwy5YtPProozidToYNG4bVaiUsLMxfS7B582bMZjMulwuj0UhZWdkxzV3cbjcfffQRxcXFuN1uysvLSU1NpaamhjVr1rB582aqqqqwWCx0796dNWvWUFxcjFKKtWvXEhYWxowZM445Jo/Hg91uR9M0NE2jsrLSP4NncHAwOTk5PPfccwwZMqRTjQogAiM4OJjg4OBT/p5Op+Ohh84lM/P3fPrpTCoqevOvf13LwIHZjBy5i7KyMg4dOoTRaKSqqgqPx8PFF19MUlIShw8f5uDBg+Tm5uJyuXj88cd57rnnGn0A3r9/P9HR0Vit1mYdZ2EhrF0Lu3dDZiZkZmps2qRRXHwzTX3JbTJBSgqkpkKPHhAfX8Xq1RF4vRm8/PIrXHvtdBmTXwScUoqLLrqIXbt2kZubS1HRBn7xi3dYsOA2Xn5ZERExksGDB1NdXc24ceNQSpGXl4fH4yE3NxdN0/joo4+47LLLWLdunX+/drsdg8HA6tWrWblyJVOnTmXs2LH06NGDoKCgI8qwfft2MjMz6datG5mZmTgcDkJCrISHp/HnP+/D7Y4jI8OXyLdWX1mloGtXX6zWL6mp0Lu3bzbs07jsnRGdIqFXSpmAJ4FLACeQrWnaxYEtlWhtISEhzJp1HUbjPL75ppSlSy+hpOQCysuXEB7+PNXVOwgJcbFu3TqsVivLli2jtLQUk8mE1WolKSkJu91OVlYWZWVl7N+/n65du9K/f3/sdjuFhYWEhoYSHBxCRkYuJlNX7HZYv34bP/5owOEYSVVVHkajh61boU+f3oSH9+b77z8nKEhj4sQJeDwedu7cidPpBKCsrIzMzMxGj+fgwYOkpqaSkZFBdXU1ERERJCQksGXLFgYOHIhSCpPJxJIlS7j55ps73JjdEscdh16v59//foL9+wu4665qliwJYePGPxMWtgST6SWsVitOp5O4uDgqKytJSUlh2rRpPPvss9TW1uJ0OjEYDGRkZDBv3jzuvvvuI/ZfUFDA7NmzMZlMPPPMM3Tv3r1J5XI6naxZs5fDh3vz7bd6Vq2CbduO3koBIYAXna4Ss9ldVwvvprq6AJ3ORHx8L6qqoKoKKiuhttb3QPBzNxkr8BwAd99dxcsvl3HVVTZGjYKRIyEy8rRPbZsncdy2JScnM2LECDIzM4mIiOCll37J1VcrrroK/vpXxUUX3YymPYXT6SQtLY3t27eTm5uLUoqEhASCg4NRSuHxeHC5XJhMJkpLS4mIiCA0NBSAkpIS/vvf/2IwGOjevTtxcXHEx8cTGhrKxx9/TFhYGPv2FZOQcC85OYPYurUfa9dGHVPW0FBfgt2rF8TGQnT0z0toqK9GXq8Hnc730+XiiLh8880PKC72MmbMVRQVGTh8GA4fhvx8yMnxLUe39FEKunWDfv2gb1/fz/79fUsz6w6arVMk9MBfARPQR9M0r1IqIdAFEmdGaGgoN998E17v6yQnv8aCBVeRk5NMcfE/+PbbaoYN28Pzz39NYmIh8fERpKSkHDEs3rp1m8jLM+BwnEV1dQ/y8vrQpctY8vOdlJdbWbs2mtWrbTzzjL7BXx1ct/wsL893M1+8GOAG9HoHn39ei8FQhNPZF4djPyZTIXv21JCYmMqBA9ClCzRsybB582ZsNhvx8fE4HA4uu+wyDhw4QGlpKVu2bMHhcBAaGsqePXvYuXMnffv2bc1TGwgSxx2IXq+nR48EFi+GRx+FRx5RfPnlpXTrZiIk5HeEhIT4J4S75JJLiIqKYvLkyaxatYqwsDBqamr8E900TOjdbjcLFizwD6M5d+5czj//fCZMmHDcpkFFRfD662W89loVWVlHxk1QEIwe7bthR0eXsn37x5SUrGX79qWYzTrCw8OJioqiX79+rFq1iurqan7zmz9w9dVX+2vdq6th3z5f04CsLJg7dwX79+txufrjcMSxdauVhi33BgyAKVN8y9lnQwebF0/iuI07//zzefPNN+nWrRthYWFccQXMnw833ADLlp3L/fencPDgXJKTk+ndu7f/vlRYWEhwcLB/7PhDhw6h0+mwWq3+OStMJhMRERFERESwatUqMjIyGDx4MA6Hg3XrdlFcPI7KyilkZfXE6zX6y2QyVZGSksPQodXcdtswzjrL1zSmsXqr0tJSKioq6Nat23GPMT8/n4UL3yAsDGbMiGPixIn+z1wuOHDAF6t798KWLdUsWLCR2toU7PZE9u+H/fth6dIj95ma6ovdAQNg4EDfz969z1zznTOe0CulEoHfAcPxZT0WoLumafsb2TYJeAY4H1+1yBfAHE3TmjwQsVIqGLgNSNQ0zQugadrhZh6GaEfCwsK45ZZbeO2117jllnksXRrFjh1jqaoayLp1g1m3zpd8m80ugoPtOBwTcLt1vP9+LC7Xbcfsb+/eY/+G2WwnPl6P11uF210LuHC7a3G7a9HpgtG0YBwOIx6PBafTjMdjprjYDIQBvjbANTVQVuZ7rf/aa74ahcREX21AUpKHAwciSEwcT0lJGZqWjdMZTN++/dixYzsFBQVs3LgRk8mE2+3m/fff5w9/+AOmVrqSSByLlqIU/OlPvtquG2/UyM6+gNDQKLp2vQuj0cjUqVNJTEwEYMyYMSQkJJCfn4+madTU1OBwOCgqKvK3y12zZg2ZmZn+2WuDg4NZsmQJWVlZXH755cTExKDT6XA6YcEC+M9/vCxfDh5POBCOXu8kImIbo0Y5uPPOAUyaZMVshqqqKl58cS7Dhinee28tJpMvkzAajeTn51NdXU1xcTF6vZ6tW7diMBj8TedCQn6+0QP89NNblJevxGQyEx8/kiFDbsdqPY+1a+GnnyAjw7f84x++7557ri+5v/lm3wNGy517iWNxrOjoaP785z8f8Wbruut8NduzZsFTT6UyZ87tHDgwF4/HQ3h4OOCbfC4iIoLs7GyMRiP9+vUjNDSUuLg48vPzycnJoWvXrv596nQ6LBYLtbUDWL26Hz/8kILHU/8/uJfk5H0kJa0nMXEXev0u+vTphd1uJz4+mISExiusSkpKmDlzJk6nk7fffttftqOtWLECk8lEcHAwX331FcOGDfO/QTAaf25qM3myxrx575Kbuxin08nrr79FZWUsO3bA9u2+JSMDduzwPQBkZcEnn/z8dwwG6NPHF/v9+8OIEb5Ybg2BqKHvCVwN/AR8A1zQ2EZ1gf8l4ABuBDTgceArpdQgTdOaOk5fT6AU+L1S6nzABfxd07SPm3UUol0JDw/nlltu4fXXX6dLl2VERCzEZBrM4cMXsWPHQOz2cBwOIw6HEWjYltWDXl+E0VhCWFgpUVEVJCW5SUz0UFm5A5frENHRGj17JqFpGiUlJaSkpLBu3TpcLhelpaWEh4dz9tlns3XrVoqLi/npp5+orlbExw8hMXEEsbGDWLlyO05nNF26jCEvz4zHk0heno7sbPANNa8Hzqkr0yQAHnwQjEY3NlsNLlcOTud+jMZyamtNbNpUQ0lJNkOH9iIsDCZPbvHXgRLHokVdfTV07664+GIXRUXDyMn5kPHjP+Dyyy/3b2Mymfjd737HjTfeSE1NDUFBQfTu3Zs1a9Zw2WWXkZeXx2effUZubi4HDx7EarVSXFyMTqdj8eLFfPTRR/TvP4k9eyazenV/SkpMgA6lPPTrl01a2k527nwCh6MIo7En69alMXToHURHR7Nw4UJqa2vR6/WYzWY8Hg/x8fFERETQrVs3Dh8+jNFo9I+8k5WV1ehxVldXEx8fj8ViweFwEBtr5447IklL833udMJ338Fnn8GyZbB1q+/N3rffwm3H1i80l8SxaNSYMWOOWXfHHb6k/r774JlnUpk27S7Ky28hOtpX4+71esnNzaVbt25YrVb/4BGpqank5+dz4MABf/+Y/v2Hs2fPBDIzz6OkJNX/N6KitpGa+iO9em1lwIBo8vLyiIiIYM8ejeLiYg4fPszdd9/No48+ysiRI48oX0lJCa+//joejweAxYsXc9111x3T/DQ7O5sNGzZQUFBAVVUVNTU1fP7551x11VXHHHP9GPuhoaGUlZWxfftmzj//fM46C6ZO/Xk7l8v3Fj4jwxezW7f6mutlZfl+1jfdq384bw2BSOhXa5oWB6CUupXjXECAmUAqvtdye+q23wJkArcDT9et2wAkH2cfaYCx7vO9mqb9USl1FvCNUipD07RG6lpFRxUZGcktt9xCeXk5MTExbNiwgfDwN+jRw4nBYGTw4LN5661PKSlR6HQeevYMQa8vw26voqqqimHDhtGnTx+8Xi8jR45k1apKcnLKqanxEBTUi88++wyr1eof0stqtRIXF0doaCh6vZ4BAwbwwQcfYLfbMZuNWCy5REYGM2pUONu3f4DX62XGjGoOHTrEBRdcwMiR4zl40JfQv//+WjIzndTWxrJ3r5uqqgjc7iicTjPFxTagH9CP2lpf20DwdRqqt29fiyf0EseixY0YARs2GBgyJJuSkm689dZs0tL03HHHz6/Whw4dyvDhw9m1axc9evTAarWybt06xo0bx4cffkhISAgHDx7EaDTidDr9/V1gMLt2TWHFinG43b4mcqGh+0hJWcENNwRhs9WyaNEiDh/eTXh4OC6Xi61bt/Lyyy8zdOhQtm/fTrdu3diwYQPJycnU1tZSW1tLQkICaWlprF27ln379mEymdi5cyfXXHNNo8eYm5uL2WwmJiam7phHcODAAdLqMnqTCSZN8i1//zvk5sLy5b5RO5o4mNCpkDgWp+S3v4W4OLjlFvjwwy4MHTqPUaPexmCopbCwEIfDgdvtZsqUKZSUlGC32zEajdjtdhwOBy5XLCtWTOR//7sKh8NXIx4cXMvYsbuxWN6munoDCQkJdO/eHbfbjdFoJDo6mj179lBaWoperyciIoIFCxZQVlbG5MmT0el0/mS+/t4LsG3bNrZs2cLgwT83gfV6vSxbtoyDBw+Sn58PgMPhYP78+UyaNInIBh1YHA4HixcvJjY2lpycHEJDQ1m7di2TJk06pvme0fhzW/pf/OLn9TU1vtr7+rduPXu21r9MABL6+tdsTXA58EP9xaPuu/uUUt8BV1B3AdE0beiJdqKUsuOrTXi7bvudSqlN+C4ucgHpZKKiooiMjEQpRUxMDPn5+ZSWlhIWFsbOneuw2YqpqjpETEwMoaHxREd3xe12A74ai3379nHDDTdQWlqKTqcjMjKSwsJCioqK/BeC+otEXFwcHo8Hp9OJpmkYDAYsFguhoaGYzWZSUlL8k200HD8+Ojqa77//nnHjxtGzp46uXe2sWvUpQ4Z0Qa/Xs2rVKtxuN127dqV794FUVATz3Xd7WbfuABBNdHQPzOYYgoLiMJtjiYhIafFOdhLHorUkJSm2bAlj+vTdrF7dm9mz4fPPYd48X2dRpRSPP/44jz/+OFVVVVRUVBAaGsoHH3zAoUOH6NatG3q9vm4kKhNZWb2pqrqd4mJfwqyUxuDB+znvvK3k5b2HUmCzncPBgwf9DwJ2ux2TyYTdbicjI4Pvv/+eyy+/nKKiIux2OzabjX79+qGUYuzYsQAMGjSIH374Abfbjclk4rzzzmv0+Pbs2eOf5A58FQ1btmzh0ksvRa/XH7N9166+pjatQeJYnI7rr/c1BZ06FTZs6MLevbfTu/dSXK4gjEYvbrebffv2MXjwYIqKiqitNbBv3wgqK2+nsnISvuc6CAraRlTUO0ycWMiIEQNYs+YgBQXVKKWIi4ujsLAQg8GAyWQiLCyM8vJyqqursdlspKSk8OWXX1JaWsqECRP497//jcvlIiYmhh07dgCQkJDAokWL6N69u78/S2ZmJtnZ2djtdrxeL0opjEYjFRUVrFq1ivT0dP9xrlmzhoqKCmJjY9m9ezc1NTVUVVWxZcsWhg494f/qfsHBMGyYb2ltbblTbH+gsddw24D/a+pONE0rUkotB6YAn9R1wBkAHDt4eB2l1G342vmRnHy8ygbRXkVERFBcXExiYiI5OTns3bsXh8NBUlISvXv3xuv1UlNTQ3R0NGlpafz4448A5OXlMWDAAPr27cv3338P+JLv+oQefB3y6pP92NhYDh8+TFxcHKWlpURGRhIbG8vBgwcxm83+Mbzr91P/qt5isVBQUMCBAwdISUkhKysLr9d7xM3eZrMRHByMplUSG+tmyhQrmZkrsdvtjBx5LsOHD8dsziU7O5vbb78dmy3lDJ7hIwQkjiWG27euXcP5+utw3n/f18xk0SL48Ue4/3646SZfjNhsNqqqqigrKyMvL481a9YwdepUysrKiIrqxa5d4ykqmoHT2RsAvd5Oz56ruf76EmJifK+x6p69KSgoYN++ff45IbxeL/v372fgwIF4PB5iYmLQ6/X+DvPJycmsW7cOg8GA2+3GYDAQFBRETEwMJpOJQYMGHXfSum3bthEeHu6PfZPJRG1tLYcOHSIpKal1T+zpkzgWRzjnHPjhBzj//Aqys8P58cfp6HSXYbMtwGo1smaNxoED3cjNjWDfvrNwuerTTTeRkSuwWF7F6/0eo9HEzp1R5OfvJzc3l/LyckJDQ/F4PP7YAoiJiaG8vJyqqipsNht6vZ6UlBS2bNnCpk2bsFqtxMTEYLfb2bt3L1VVVRgMBhISEli8eDHTp0/H4/Hw6aefEhUVRXR0tH/46aqqKlJSUli/fj3jxo0jLi6OkpISVq5cSZcuXVi3bh25ubkYjUaqq6t55ZVXePXVVwN38o+jLc8UG4mvrd3RSoCIU9zXLOAupdRW4DPgt5qm7Trexpqmvapp2nBN04bXvxYVHUdkZGTd2LYhFBcXU1tbi8Fg8CfUvXr1YvDgwYwdOxaLxQL4EnVN07j00kuPaI8XGRmJXq+noqICl8tFRUUFmqYRGRnpr4UbO3as/+aelpaG1WrFbrf7HxgqKyuPqLED3+RY9Q8S69at83fWqafT6Rg9enRdUwJfghMbG0tiYiKjR4/GYrGg0+nIyMjg6aefboWz2GQBiWOJ4Y7h6qth0yYYM8bX9OSuu3wdxR94IJiKiiQ0LZbqajN2u5HQ0P58++1gXnppKp988hqFhX/F6exNaGglgwa9Q9euo/F47mT37s+w2+3+v1FTU8PuuvEkzznnHOLj4/F6vZjN5iNevxcWFmK327FYLMTExDBo0CCmTp1KQUEB2dnZ/rHxwReP9bWEDZWWllJeXo7FYsHr9frfzOn1en8Z2iiJY3GMXr3g+edXM336e8TG7sLrDaWsbAbbtl3L7t3X8dVX49m9ewAul4GUlEN06/YcffpM4bLL3qFnz0JCQnwDuut0Onbs2EF5eTlGoxGPx3PMLMr1D8A1NTX+NvI6nY7s7Gz27dvnb8K2ceNG8vPz/U3tDh06xNatW8nIyGDr1q0UFRVhs9lIS0vzzykTERHB0KFDsVgsrFixAoDly5djMBgwGo3k5eVhNBr9bw7qy9rWtOUaevC9mjvaKQ+uXddjv/H3n6LTiYqK8o/7rtPpMJvNWCwWevToQXFxcaO94svKyrjkkkuO+Uyv1/ub3VRVVVFZWUlkZKR/NjuAHj16EBYWRnV1NSEhIf6ZLetf1e/bt4/4+Hiyfb1fAd/Fa8uWLYwfP549e/Y0WnPXv39/fvjhB7xeLzpd48/mbWQseoljcdpSUuCbb3wjRzz7LKxeDXPnWoBfH/c7SnmIjd1Kjx7fcd11BlasWMK6dUUoZWTz5s3k5+czcuRIduzYQX5+PklJSYwePZoePXqQm5tLYmIiFovFn8QHBQUdUTuv0+lQSnHNNdf4J9fZunUre/fuxWw2ExERwY4dO46JzfrJd44WGRnJhg0bOPfcc9tKzDZG4lgcQ6eDs87azciRebz22pPk548mPr4LCQkxuN21REa6SUj4kYEDI1i9+kcg3n/vKy4uxmw2Y7PZKCsr80/gGBcXR1FRkX90K/C9yap/e11SUoLZbPb3O2nYnj0vL8//sGyxWCguLiYtLY1Fixah1+v99+b6B3PAn8wHBQWxbds2vvnmGzZv3kxKSgrgyxnqa/MrKyuJiIhg+/btjXYcBl87/V27dlFaWkppaSllZWWUlZXhdruZMWMGYWFhLf3P4DsPrbLXllGKr1bgaBE0XlMgRJOEhYX5n/C7dOnC4cOHsVgs/iYw9W3m6/Xo0YNJkyYx7DiN4Oqb3ZSWluLxePzj7NbT6/VMmjSJRYsWERIScsR3CwsL6dGjBzk5OUck9Hq9Ho/Hw4oVK/wXuaPZbDb69+9PVlYWsbGxp30+WpnEsWg2vd7XXnfqVNi4EZ58spYlSxx4vQZcLvB4FDqdRs+eOYSHf8VZZ+3F6fSNhmgwnIPL5SIyMtLfNj43N5cVK1b4a97qR6iqFxQURFJSEgcOHCA/P5+IiAiUUkckAfUMBgOJiYlYrVYWL15MSUkJRqOR2tpa8vPzSUj4eZj1HTt2NDq7bnBwMNnZ2RQVFR2z/zZC4lickMViYcgQB/A155xzDpBLYWEh3bp1Y8eOCo5+kZOWlsauXbuoqqoiMTGR2NhYtm7disViobKykujoaJRSRzwA13e6NRqNFBUVHVOLD75hZBsm3/WTXX355Zd4vd4jRs06mlKK0NBQf7Oc+vtuff8Yh8NBdHQ0ffr04bvvvmP06NHHPIBrmsacOXPYtWsXI0eOxGQyYTKZMBqNfPHFF7hcLn7729+e9nk+kbbc5GYbvnZ7R+sHbD/DZREdSEhIiD8IG2sC01BBQQERERH88pe/PG4teH2zm/qHhNjY2GO2HThwoP9CU8/r9VJdXc3FF1/caK1cZGQkmzdvPuHT/OjRo49oPtAGSRyLFpWWBi++WM1dd/2DJ598i6uvvoPhw89l2rSZTJ78L1JTvyUpKdh/84efX9dHRkYSEeGbQK7+pm82m+nevbu/g3p9HCcnJ2M0Gjl06BAbNmxg7969jcb28Sil2L9/v/93r9fLzp07jzsutlKKPXv2NPpZGyBxLE6L1WolKiqKmpqaI9bXvxWvb946atSoE96LwTfqjMlkwuVyERwc7O+7Vk/TNCIiIvxNaaKiovz7sdlsx429hrZv387evXuP2La+f0xiYiJjx44lKiqK0tJScnJyjvn+V199xe7du4mOjiYpKYm4uDgiIiKwWq2t/vatLSf0nwCjlVL+AUqVUinAuLrPhDgtDRP6oy8q9W3mAf8QWTfeeOMxNesN6fV6goN9CUR9TfvRSbbFYmHs2LH+EXAADh8+zIgRI+jSpUuj+7XZbGRmZrJ58+bj/u2UlBTCw8Oprm7qMNBnnMSxaDUGg4HQ0FA0TfN3kK0fwWrEiBH06dMHOPLBPSEhgcmTJzNgwACsVithYWH+GkGXy4XFYuHss89Gp9P5h9qrr3Fv7AZ+PGFhYWRkZPh/Lygo8O+rMeHh4WzatKlZ56MVSRyL06KUom/fvpSVlZ1wuxPdi+vVN5fxer3k5OT4H9TrlZeX4/V6/cn3yJEjG93PyQQ1YfY2k8nETz/9dMS677//nuXLl/tH0jvTApLQK6WmKaWmAfVtGC6qWzehwWavAfuBj5VSVyilLsfXy/4gMPeMFlh0KCEhIY22Y23I4XBgt9u56aabjmg+czz1N+vg4GAqKysbfRU4fPhwf+fa+lrAc88994T7Pboz7NF0Oh3jx4/31y6eSRLHoi2oj8+ioiK8Xi8RERGYTCb/DdXr9TaaLKSlpREaGnpEjWB5ebl/eDvwzQ5b39wmKSnplOIsNDSUAwcOUFtbC3DSh4HQ0FBycnKOOzpOa5E4Fq2tZ8+eR7ydbqqj79NpaWn+9uxKKQYOHHjE5w0rzFpbTEwMGzdu9Mf3+vXr+fjjj/19bAIhUG3oPzjq95fqfn4NTATQNK1aKXUuvqmm38bX+WYlvqmmq85QOUUH5BvuUUPTtEafot1uN9XV1dx4443Ex8c3aZ8Oh4OoqChMJhM2m+2YV4Hg61gzYMAAtm3bhtPpZPLkyS3SOWbAgAF8+umnJ31IaQUSxyLg6l+NN2zyVi8qKoqysrIjRqupV5/kw88d1AsLC/2T0oCvqU52drZ/WNmjawRPRKfT+WsSe/bsybZt2074gF5/Ldq/fz+DBg3yr6+pqeHHH3+kT58+Tb4enSKJY9GqunTpclo11kffoy0WC2eddRZWq5XExMQjHn7dbrd/1Lf62GtN9UPW7t69G6UUH374IUlJScd9A3cmBOQxQtM0dZxl4lHbHdA07SpN02yapoVqmnZlXQ95IU6bXq/HarUet8agZ8+ePPLII3Tv3r3J+4yOjkbTNHQ63RHjyx9t3Lhx/qHvRo0adVrlP5rVaiUtLc1fU3CmSByLtsBgMFBcXExOTg779u07otPp4MGDm1XjXd9Up7a29rjtek/EaDSSmZmJy+Vi7969R9T+N8Zqtfqb2Hk8Hn766SeefvppHnnkEV555ZXTPo4TkTgWrS0kJITY2Fh/n5am8Hg8x63prn+IbzhqVGFhIV6vl/Dw8GNmcW0tYWFhrFy5knfffZcuXbpgMpnOyN89nrbchl6IVhMREXHci4tOpztiuKymOFnn2nrJycnEx8czaNCgFg3+ESNG+GsohehMNm/ejMfjwWg04vV62bJli/+zHj16YDAYmhQbtbW1hIaGHlMjeLJ2vScSERFBRkYGhw8fPmZyuMaEh4eTmZnJzp07eeGFF1iwYAGhoaGn1Q5YiLakX79+pzSAg8fjOW5iHhISgsVioba21t9/rOEM7aeiOTX5YWFhfPbZZ+zZs6dJ7e5bW1setlKIVhMREUFJSUmjr8A1TTvlpjCNvb5vjFKKN954o8VrEJKSkggKCjpmyE0hOrqioiJ/x/CuXbse0dwtKCiIgQMHsnPnzpPup7S0lOHDh7N27doWK1v9uNmbNm1qUrtavV6P2+3mrbfe8o/GI0RHkJqaekrJc30H9cbepCul6NKlC3v37qWsrAyTyURFRQV6vf6UmsV16dKFysrKJm/fWDna0jCzUkMvOqWGk0s1VD/V9IlGtWmu1ngdqJQ65VeaQnQE9XNHhIeH+5vGNNTU5mgul4tevXq1ePmUUmRkZJy0uU297OxsDh482KQh9oRoL+rnY2hqXy+3233CN1Px8fEYDAb/jLDg66h6srdgDdX3pesoJKEXnVJkZGSjT/52u/2UX9kFytGdjMaMGUNycnKASiNEYJysuVtKSgpBQUEnbHZTf2Pv2rVri5cvODiYvLy8IzrbnkgbnilWiNNmsVhO2NT1aB6P56TDRUdERFBYWMju3buPGTu+M5KEXnRKDceib8hut7fWSBIndKo3cbnpC+FzsnbuBoOBYcOGUVV1/MFYqqqq6NKlS6u8mdu2bRu5ubkBG8pOiLYiISGhyYM3uFyukz4EN5zbweFwtOWJ2c4IucKITul4N26Hw3HciZ7aOknyhWjcoEGDTlhDX1ZWxoABA1rlbyulpFOrEMCcOXMYPHhwk7Z1u90nfcAuLy/3d2SPj49vdLjozkQSetEpHa+Gvn7qaCFEx9GlSxeCgoKOO1StpmmnNEztiTS1Ta48gIvOJiEhgaCgoEb7rx3N7XaftIY+OjraP1u0UuqY/jMdqX18U0hCLzqlkJCQRnvcK6VaZLInIUTbodPpSE1NbbTZTf38EfWd9oQQrUOv1zNo0CBKSkqatL3ZbD7h5/WzPWuaRkxMzCnPE9HRSEIvOiWTyYTJZDpmmEev1ysJvRAd0IMPPsioUaP841bXs9vt9OjRI6AzPArRWfTr169JHWOVUidN6Js7T0RHIwm96LSO7nHvdDqxWq0nvYgIIdqf8PBwpk+fTn5+/hEP8rW1tfTv3z+AJROi80hOTkan0510sremJPTiSJLQi04rIiLiiLZ8drtdXrsL0YH17NmTCy+8kIMHDx6xXoZ7FeLMMJvN9O7dm/Ly8pNu25KzqXcGktCLTisyMvKIGvpADVkphDhzzjnnHPr27cuhQ4fweDwYjcZTml1SCNE8gwYNatIMrVJDf2okoRed1tGzxTocDknohejg9Ho96enpWCwWiouLSUhIkDHihTiDUlJSTrqNpmmS0J8iuYqJTis0NPSI33U6nXSIFaITsFqtXHvttej1enmIF+IMCwsLIyEh4YSdY5uT0LtcLux2++kWr90yBLoAQgRKY2PRS0IvROeQmJjI3LlzJaEXIgDS0tJYtmxZo0m71+tFr9djMJxeipqSktIpJ5mShF50WiEhIf6JJzRNQ9M0bDZbgEslhDhTpDOsEIHRo0eP40785Ha7sVgsMvnaKZKEXnRaDaeVdrvdREREnHaNgBBCCCGaJi4ujqCgoGPmggHf/bjh/bktqKmpQa/XB7oYJyRt6EWnVV8DoGkaLpdLhqwUQgghzgClFImJicdM9Aa+hD44ODgApTq+Ll26kJiYGOhinJAk9KLTUkoRHh6Ox+PB6XRKQi+EEEKcIbfffjuDBw8+Zn1brKFvDyShF51aeHg4LpcLr9dLTExMk7/XWm37jtemUAjR9p3qdaG+744QnVFoaGijMdMwoT8T7eg7SgyqjnIgrUUpVQhkA9FA5+s2faTOfg468vF30zSt6U807UiDGIaO/W/YVJ39HHTU4++wMQwSx0fp7McPHfccnHYcS0LfREqp9ZqmDQ90OQKps5+Dzn78HYH8G8o56OzH3xF09n/Dzn78IOegMdLkRgghhBBCiHZMEnohhBBCCCHaMUnom+7VQBegDejs56CzH39HIP+Gcg46+/F3BJ3937CzHz/IOTiGtKEXQgghhBCiHZMaeiGEEEIIIdoxSeiFEEIIIYRoxzp1Qq+USlJKfaiUKldKVSilFiqlkpv43SCl1FNKqcNKKbtSao1S6pzWLnNLa+Y5+KtS6nOlVLFSSlNKzWjl4raK0z0HSqnhSqlXlVI7lVI1SqkDSql3lFLdz0S5hY/EscQxSBy3dxLHEscSw83TaRN6pVQw8CVwFnAjcD3QC/hKKdWUOYfnATOBh4FLgcPAcqXUkFYpcCtogXPwa8ACfNpqhWxlzTwHvwT6A88DFwG/B4YC65VSSa1WaOEncSxxDBLH7Z3EscSxxHALqJ96urMtwF2AB+jZYF13wA3cc5LvDgY04KYG6wzALuCTQB/bmTgHddvq6n72rDsfMwJ9TGf4/4OYRtZ1A7zAo4E+ts6wSBxLHLfA/wcSx+3730/iWGv/cSwx3Pyl09bQA5cDP2iatqd+haZp+4DvgCua8F0X8F6D77qB/wEXKqXMLV/cVtGcc4Cmad5WLNuZctrnQNO0wkbWZQOFQNcWLqdonMSxxDFIHLd3EscSxxLDzdSZE/r+QEYj67cB/Zrw3X2aptU08l0Tvifk9qA556CjaNFzoJTqC8QCO5pZLtE0EscSxyBx3N5JHEscSww3U2dO6COB0kbWlwARzfhu/eftQXPOQUfRYudAKWUAXsFXKzCv+UUTTSBxLHEMEsftncSxxLHEcDMZAl2AAGtsVi3VhO+pZny3rekox9EcLXUOXgTGApdomtbYhUm0DonjjnMczSFx3L5JHHec4zhdEsPN0Jlr6Etp/Mk9gsafEhsqOcF36z9vD5pzDjqKFjkHSqm/AbcBN2ua9nkLlU2cnMSxxDFIHLd3EscSxxLDzdSZE/pt+NpsHa0fsL0J3+1eN8zS0d91AnuO/Uqb1Jxz0FE0+xwopR7AN0zWXZqmvd2CZRMnJ3EscQwSx+2dxLHEscRwM3XmhP4TYLRSKrV+hVIqBRhX99nJvmsE/q/Bdw3AL4DPNU1ztHhpW0dzzkFH0axzoJT6DfA48ICmaS+0ViHFcUkcSxyDxHF7J3EscSwx3FyBHjczUAsQgu/JfSu+IZEuBzYDWYC1wXbd8I2D+vBR3/8fvtdAtwLnAR8CtcDQQB/bGTwHE4BpwJ342r69WPf7tEAf25k4B/gms/ACy4DRRy39An1snWGROJY4bu45kDgO/CJxLHEsMdwC5zDQBQjw/0DJwAKgAqgEFgEpR22TUhccjxy13gI8DeTVXTjWAhMDfUxn+Bysqlt/zBLo4zoT5wCYf7zjB1YF+rg6yyJxLHHcnHMgcdw2FoljiWOJ4eYtqu5kCCGEEEIIIdqhztyGXgghhBBCiHZPEnohhBBCCCHaMUnohRBCCCGEaMckoRdCCCGEEKIdk4ReCCGEEEKIdkwSeiGEEEIIIdoxSehFm6GUelQpVamU2qWUGtWM/fyxbh9epdSVLVhEIcQJSAwL0f5JHLdPktCLNkEpNQKYDqQDbwKvNmN3K4GLgdUtUDQhRBNIDAvR/kkct1+S0Iu2IhIowjft81bAcLo70jRtraZpe1uqYEKIJpEYFqL9kzhupyShF61OKTVfKaXVLauOs9lqfBeOw8C7wPWtWJ4HG5Qnp7X+jhAdhcSwEO2fxHHHdtpPXkKcojxgKlBxnM+dgKnuv3XAxsY2UkptAJKPs480TdMONqEsbwJfAA8Bg5uwvRBCYliIjkDiuIOShF6cKQ5N0344weczgYHAUnxt7noAe47eSNO0oc0tiKZpuUCuUqqwufsSohORGBai/ZM47qCkyY1oMqXUL5VSO5VSDqXUNqXUVKXUqhO8umvqfsOBx4BFwEt1qwc1q7BCiGNIDAvR/kkci8ZIQi+aRCk1GfgvkImv9/tTwHNAnxbY/cNAGHA/kFG37rQvInXt8nKAMcDrSqkcpVR884spRPslMSxE+ydxLI5HmtyIpvozsBO4QtM0L4BSagfwA7DrdHeqlOoN3An8S9O0TKWUAippxkVE07THgcdP9/tCdFASw0K0fxLHolFSQy9OSimlB0YAH9ZfQMA3JBWwv5m7fxrfRePRun1qwHZ8bfiEEC1AYliI9k/iWJyIJPSiKaIBI5DfyGeNrWsSpdSFwCXAM4CmlAqva8OXCfRQSoWc7r6FEEeQGBai/ZM4FsclCb1oiiLABcQ18llj605KKWXAVyMAvk44pQ2W6wAFDDidfQshjiExLET7J3EsjksSenFSmqZ5gB+BaUop//8zSqlRQMpp7nYW0A+4C5h01DK7bhvpXS9EC5AYFqL9kzgWJyKdYkVT/Qn4HFiklJoLxODrnJN3qjtSSkUCjwCfaZr2fCOfb8I3ZJZcRIRoORLDQrR/EseiUVJDL5pE07QvgGvxDY21ELgPmMPp9ar/MxAC/Po4f6sMyEEuIkK0GIlhIdo/iWNxPMrXkVmI01M/kYWmaRNPsM18YCLQ07ep5jkDRTuuuuG49MA84DxN0xIDWR4hAkliWIj2T+JYSA29OFO64evMszLQBQEewFeWGwJdECHaEYlhIdo/ieMOSmroRbM0sVYgBd9wWwCVmqad9uQXLUEplQB0rfvVqWnalkCWR4hAkhgWov2TOBaS0AshhBBCCNGOSZMbIYQQQggh2jFJ6IUQQgghhGjHJKEXQgghhBCiHZOEXgghhBBCiHZMEnohhBBCCCHaMUnohRBCCCGEaMckoRdCCCGEEKId+3/yygB1HixCnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ouput_object_name =  \"mch_amber_15m.pkl\"\n",
    "ouput_object_name =  \"output_data.pkl\"\n",
    "out_path          =  \"./example-data/\"\n",
    "font              =  16\n",
    "\n",
    "with open(out_path + ouput_object_name, 'rb') as inp: R_amber = pickle.load(inp)\n",
    "\n",
    "#########\n",
    "\n",
    "\n",
    "annotate_pos = (0.05,0.9)\n",
    "\n",
    "fig   = plt.figure(figsize=(12,7))\n",
    "\n",
    "gs    = fig.add_gridspec(nrows=2, ncols=3,width_ratios=[1,1,1] , wspace=0.3, hspace=0.3)\n",
    "\n",
    "alpha = 0.4\n",
    "\n",
    "''' SLD '''\n",
    "# D2O\n",
    "for deu_index, deutration in enumerate(['d2o', 'h2o', 'cmsi']):\n",
    "\n",
    "    if deutration == \"d2o\": \n",
    "        \n",
    "        sld_amber = R_amber.sld_d2o_global\n",
    "        Rq_amber  = R_amber.Rq_d2o_global\n",
    "      \n",
    "        Rq_exp     = R_amber.Rq_exp_d2o\n",
    "        \n",
    "        chi_amber =  np.round (np.sqrt (get_chi_sqr (R_exp=R_amber.Rq_exp_d2o, R_sim=R_amber.Rq_d2o_global)),1)\n",
    "\n",
    "\n",
    "    if deutration == \"h2o\": \n",
    "        \n",
    "        sld_amber = R_amber.sld_h2o_global\n",
    "        Rq_amber  = R_amber.Rq_h2o_global \n",
    "        Rq_exp     = R_amber.Rq_exp_h2o\n",
    "        \n",
    "        chi_amber = np.round (np.sqrt (get_chi_sqr (R_exp=R_amber.Rq_exp_h2o, R_sim=R_amber.Rq_h2o_global)),1)\n",
    "\n",
    "    if deutration == \"cmsi\": \n",
    "        \n",
    "        sld_amber = R_amber.sld_cmsi_global\n",
    "        Rq_amber  = R_amber.Rq_cmsi_global\n",
    "        \n",
    "        Rq_exp     = R_amber.Rq_exp_cmsi\n",
    "        \n",
    "        chi_amber =  np.round(np.sqrt (get_chi_sqr (R_exp=R_amber.Rq_exp_cmsi, R_sim=R_amber.Rq_cmsi_global)),1)\n",
    "\n",
    "    ax1 = fig.add_subplot(gs[0,deu_index])\n",
    "    ax1.set_xticks([0, 25,50,75])\n",
    "    ax1.set_xlim(right=80, left=-9)\n",
    "\n",
    "   \n",
    "        \n",
    "\n",
    "    ax1.set_xlabel('d $[\\AA]$',fontsize=font,labelpad = -1.0) \n",
    "\n",
    "    ax1.plot(sld_amber [:,0], sld_amber [:,1],lw=2, color=\"blue\") \n",
    "\n",
    "    ax1.annotate (\"(\"+string.ascii_uppercase[deu_index] +\")\", xycoords = \"axes fraction\", \n",
    "              xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=16);\n",
    "    \n",
    "    if deu_index ==0:    \n",
    "       \n",
    "        \n",
    "        ax1.set_ylabel('SLD [$x 10^{-6}\\AA^{-2}]$',fontsize=font)\n",
    "        ax1.vlines (10, -2,7, linestyle = \"--\", color='k', alpha=0.5)\n",
    "        ax1.vlines (70, -2,7, linestyle = \"--\", color='k', alpha=0.5)\n",
    "        annotate_pos1 = (0.1,0.05) \n",
    "        ax1.annotate (\"Substrate\", xycoords = \"axes fraction\", xy =annotate_pos1,fontsize=14, rotation=90 );\n",
    "        \n",
    "        annotate_pos1 = (0.5,0.5) \n",
    "        ax1.annotate (\"Bilayer\", xycoords = \"axes fraction\", xy =annotate_pos1,fontsize=14, rotation=90 );\n",
    "        \n",
    "        annotate_pos1 = (0.92,0.05) \n",
    "        ax1.annotate (\"Bulk Water\", xycoords = \"axes fraction\", xy =annotate_pos1,fontsize=14, rotation=90 );\n",
    "\n",
    "\n",
    "    ax2 = fig.add_subplot(gs[1,deu_index])\n",
    "\n",
    "    ax2.set_xticks([0.0, 0.1,0.2,0.3]) \n",
    "\n",
    "    ax2.set_xlabel('q [$\\AA^{-1}$]',fontsize=font,labelpad = 1.0)\n",
    "\n",
    "    if deu_index ==0: ax2.set_ylabel('R(q)', fontsize=16)\n",
    "\n",
    "    ax2.errorbar(Rq_exp[:,0], Rq_exp[:,1],Rq_exp [:,2] ,lw=2, marker='o',color='k' ,ms = 4, alpha=alpha,\n",
    "            label='Experiments') \n",
    "    ax2.fill_between(Rq_exp[:,0], Rq_exp[:,1]-Rq_exp[:,2], Rq_exp[:,1]+Rq_exp[:,2],\n",
    "                     color='k', alpha=alpha)\n",
    "\n",
    "    if deutration == \"cmsi\":\n",
    "        \n",
    "        ax2.plot (Rq_amber[:,0], Rq_amber [:,1], lw=2, color=\"blue\", zorder=10)\n",
    "\n",
    "    else:\n",
    "        \n",
    "        ax2.plot (Rq_amber[:,0], Rq_amber [:,1], lw=2, color=\"blue\", zorder=10)\n",
    "\n",
    "    ax2.set_yscale('log')\n",
    "    \n",
    "   # ax2.set_xscale('log')\n",
    "\n",
    "    ax2.set_ylim(bottom=5e-8, top=2 )\n",
    "    ax2.annotate (\"(\"+string.ascii_uppercase[deu_index+3] +\")\", xycoords = \"axes fraction\", \n",
    "              xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None,\n",
    "                                         lw=1, alpha=0.5),fontsize=16);\n",
    "    \n",
    "    an_pos = (0.08, 0.15)\n",
    "\n",
    "\n",
    "    \n",
    "    if deutration == \"cmsi\": ax2.legend (fontsize=14, loc = (0.30, 0.58))\n",
    "    \n",
    "\n",
    "    if deutration=='d2o':\n",
    "        ax1.set_title (\"100 % D$_2$O\", fontsize=font,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),\n",
    "                                          pad=10)\n",
    "        ax1.set_ylim (-0.65, 6.5)\n",
    "\n",
    "    if deutration== 'h2o':\n",
    "        ax1.set_title (\"100 % H$_2$O\", fontsize=font,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),\n",
    "                                          pad=10)\n",
    "        ax1.set_ylim (-0.65, 3.8)\n",
    "\n",
    "    if deutration== 'cmsi': \n",
    "        ax1.set_title (\"CMSi ($\\sim$ 38% D$_2$O)\", fontsize=font,\n",
    "                                          bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),\n",
    "                                          pad=10)\n",
    "        ax1.set_ylim (-0.65, 3.8)\n",
    "#plt.savefig (\"./figures/fig2.png\", dpi=600, bbox_inches='tight') \n",
    "\n",
    "\n",
    "#plt.savefig (\"./pdf/fig2.pdf\", dpi=600, bbox_inches='tight')\n",
    "\n",
    "print ( \"water fraction = \",R_amber.water_fraction_global)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b98238-b622-4d6b-a203-7bcd03d9f42b",
   "metadata": {},
   "source": [
    "# Plot $\\chi^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3a560a60-8feb-4447-8e7e-13d2088ebdeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig   = plt.figure(figsize=(14, 8))\n",
    "annotate_pos = (0.05,0.9)\n",
    "ouput_object_name = \"output_data.pkl\"\n",
    "out_path          =  \"./example-data/\"\n",
    "width_ratios = np.ones (40)\n",
    "width_ratios [-1] = 1.2\n",
    "\n",
    "gs    = fig.add_gridspec(nrows=2, ncols=40, width_ratios=width_ratios,\n",
    "                         wspace=5, hspace=0.4)\n",
    "\n",
    "with open(out_path + ouput_object_name, 'rb') as inp: R_amber_mch = pickle.load(inp)\n",
    "# Xticks positions\n",
    "xticks = np.linspace (0.0, 0.5, 6)\n",
    "xticks = np.round (xticks,2)\n",
    "xticks_positions = np.linspace (0, 100, 6)\n",
    "## Yticks positions\n",
    "\n",
    "# Xticks positions\n",
    "yticks = np.linspace (0.0, 1.0, 4)\n",
    "yticks = np.round (yticks,2)\n",
    "yticks_positions = np.linspace (0, 100, 4)\n",
    "\n",
    "#######################\n",
    "plt.rc('xtick', labelsize=16) \n",
    "plt.rc('ytick', labelsize=16) \n",
    "plt.rc('axes', labelsize=16)\n",
    "vmax = 15\n",
    "end = 13\n",
    "ax1 = fig.add_subplot(gs[0, 0:end])\n",
    "\n",
    "\n",
    "\n",
    "Chi_sq = get_chi_array (R_object=R_amber_mch, solvent=\"d2o\")\n",
    "\n",
    "sns.heatmap(Chi_sq,  yticklabels=False, vmin=0, vmax=vmax,linewidths=0,\n",
    "            cbar=False, cbar_kws={\"aspect\":12, \"label\":\"$\\chi^2$\"},cmap=\"icefire\", ax=ax1);\n",
    "\n",
    "plt.xticks ( xticks_positions,xticks, fontsize=16);\n",
    "\n",
    "plt.yticks ( yticks_positions, yticks, fontsize=16);\n",
    "\n",
    "#ax1.set_title (\"$\\chi^2_{D_2O}$\", fontsize=20,\n",
    " #              bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "  #                       fc=\"white\", ec=None, lw=1, alpha=0.5), pad=10)\n",
    "\n",
    "        \n",
    "ax1.annotate (\"$\\chi^2_{D_2O}$\", xycoords = \"axes fraction\", \n",
    "          xy = (0.74, 0.85),bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "                                    fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=20);\n",
    "\n",
    "ax1.annotate (\"(A)\", xycoords = \"axes fraction\", \n",
    "          xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=16);\n",
    "\n",
    "plt.xlabel (\"$\\gamma$\", fontsize=20)\n",
    "plt.ylabel (\"$\\\\alpha$\", fontsize=20)\n",
    "plt.xlim (0, 60)\n",
    "\n",
    "start=end\n",
    "end += 13\n",
    "ax1 = fig.add_subplot(gs[0, start:end])\n",
    "\n",
    "Chi_sq = get_chi_array (R_object=R_amber_mch, solvent=\"h2o\")\n",
    "\n",
    "sns.heatmap(Chi_sq,  yticklabels=False, vmin=0, vmax=vmax,linewidths=0,\n",
    "            cbar=False, cbar_kws={\"aspect\":12, \"label\":\"$\\chi^2$\"},cmap=\"icefire\", ax=ax1);\n",
    "\n",
    "yticks = ['']*4\n",
    "plt.yticks ( yticks_positions, yticks, fontsize=0);\n",
    "plt.xticks ( xticks_positions,xticks, fontsize=16);\n",
    "plt.xlim (0, 70)\n",
    "#ax1.set_title (\"$\\chi^2_{H_2O}$\", fontsize=20,\n",
    "               #bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "                         #fc=\"white\", ec=None, lw=1, alpha=0.5), pad=0)\n",
    "        \n",
    "ax1.annotate (\"$\\chi^2_{H_2O}$\", xycoords = \"axes fraction\", \n",
    "          xy = (0.74, 0.85),bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "                                    fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=20);\n",
    "\n",
    "\n",
    "\n",
    "ax1.annotate (\"(B)\", xycoords = \"axes fraction\", \n",
    "          xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=16);\n",
    "plt.xlabel (\"$\\gamma$\", fontsize=20)\n",
    "\n",
    "\n",
    "start=end\n",
    "end += 13\n",
    "ax1 = fig.add_subplot(gs[0, start:end+1])\n",
    "\n",
    "\n",
    "Chi_sq = get_chi_array (R_object=R_amber_mch, solvent=\"cmsi\")\n",
    "\n",
    "sns.heatmap(Chi_sq,  yticklabels=False, vmin=0, vmax=vmax,linewidths=0,\n",
    "            cbar=True, cbar_kws={\"aspect\":12, \"label\":\"$\\chi^2$\",\"ticks\":[0,5,10,15]},cmap=\"icefire\", ax=ax1);\n",
    "\n",
    "#ax1.set_title (\"$\\chi^2_{CMSi}$\", fontsize=20,\n",
    "     #          bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "     #                    fc=\"white\", ec=None, lw=1, alpha=0.5), pad=0)\n",
    "\n",
    "\n",
    "ax1.annotate (\"$\\chi^2_{CMSi}$\", xycoords = \"axes fraction\", \n",
    "          xy = (0.74, 0.85),bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "                                    fc=\"white\", ec=None, lw=1, alpha=0.4),fontsize=20);\n",
    "\n",
    "\n",
    "plt.yticks ( yticks_positions, yticks, fontsize=0);\n",
    "ax1.annotate (\"(C)\", xycoords = \"axes fraction\", \n",
    "          xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=16);\n",
    "\n",
    "plt.xticks ( xticks_positions,xticks, fontsize=16);\n",
    "plt.xlim (0, 70)\n",
    "R_amber.water_fraction [ np.argmin (R_amber.h2o_chi)]\n",
    "\n",
    "\n",
    "#############Global\n",
    "\n",
    "\n",
    "xticks = np.linspace (0.0, 0.5, 6)\n",
    "xticks = np.round (xticks,2)\n",
    "xticks_positions = np.linspace (0, 100, 6)\n",
    "## Yticks positions\n",
    "\n",
    "# Xticks positions\n",
    "yticks = np.linspace (0.0, 1.0, 4)\n",
    "yticks = np.round (yticks,2)\n",
    "yticks_positions = np.linspace (0, 100, 4)\n",
    "\n",
    "#######################\n",
    "plt.rc('xtick', labelsize=16) \n",
    "plt.rc('ytick', labelsize=16) \n",
    "plt.rc('axes', labelsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[1, 13:29])\n",
    "\n",
    "Chi_sq = get_chi_array (R_object=R_amber_mch)\n",
    "\n",
    "sns.heatmap(Chi_sq,  yticklabels=False, vmin=10, vmax=45,linewidths=0,\n",
    "            cbar=True, cbar_kws={\"aspect\":12, \"label\":\"$\\chi^2$\"},cmap=\"icefire\", ax=ax1);\n",
    "\n",
    "plt.xticks ( xticks_positions,xticks, fontsize=16);\n",
    "\n",
    "plt.yticks ( yticks_positions, yticks, fontsize=16);\n",
    "plt.xlim (0, 60)\n",
    "plt.xlabel (\"$\\gamma$\", fontsize=20)\n",
    "plt.ylabel (\"$\\\\alpha$\", fontsize=20)\n",
    "\n",
    "#ax1.set_title (\"$\\chi^2_{global}$\", fontsize=20,\n",
    " #              bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5), pad=0)\n",
    "plt.xlabel (\"$\\gamma$\", fontsize=20)\n",
    "\n",
    "\n",
    "ax1.annotate (\"(D)\", xycoords = \"axes fraction\", \n",
    "          xy =annotate_pos,bbox=dict(boxstyle=\"round,pad=0.1\", fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=16);\n",
    "\n",
    "\n",
    "ax1.annotate (\"$\\chi^2_{global}$\", xycoords = \"axes fraction\", \n",
    "          xy = (0.74, 0.85),bbox=dict(boxstyle=\"round,pad=0.1\",\n",
    "                                    fc=\"white\", ec=None, lw=1, alpha=0.5),fontsize=20);\n",
    "\n",
    "#plt.savefig (\"./figures/S5.png\", dpi=600, bbox_inches='tight') \n",
    "\n",
    "\n",
    "#ax2.set_yticklabels([])\n",
    "#ax3.set_yticklabels([])\n",
    "\n",
    "plt.savefig (\"./example-data/metric_space.png\", dpi=800, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd0b06d-6b7f-4336-b6b8-aecb3573c950",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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