{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing lipid membrane data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter notebook demonstrates the utility of the *refnx* for:\n",
    "\n",
    " - the co-refinement of three contrast variation datasets of a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) bilayer measured at the solid-liquid interface with a common model\n",
    " - the use of the `LipidLeaflet` component to parameterise the model in terms of physically relevant parameters\n",
    " - the use of Bayesian Markov Chain Monte Carlo (MCMC) to investigate the Posterior distribution of the curvefitting system.\n",
    " - the intrinsic usefulness of Jupyter notebooks to facilitate reproducible research in scientific data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in most Python scripts is to import modules and functions that are going to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use matplotlib for plotting\n",
    "%matplotlib inline\n",
    "from importlib import resources\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import refnx, scipy\n",
    "\n",
    "# the analysis module contains the curvefitting engine\n",
    "from refnx.analysis import CurveFitter, Objective, Parameter, GlobalObjective, process_chain\n",
    "\n",
    "# the reflect module contains functionality relevant to reflectometry\n",
    "from refnx.reflect import SLD, ReflectModel, Structure, LipidLeaflet\n",
    "\n",
    "# the ReflectDataset object will contain the data\n",
    "from refnx.dataset import ReflectDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for the analysis to be exactly reproducible the same package versions must be used. The *conda* packaging manager, and *pip*, can be used to ensure this is the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0.1.53.dev0+19c4b26', '1.15.2')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# version numbers used in this analysis\n",
    "refnx.version.version, scipy.version.version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ReflectDataset` class is used to represent a dataset. They can be constructed by supplying a filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth = resources.files(refnx.analysis)\n",
    "data_d2o = ReflectDataset(pth / \"tests\" / \"c_PLP0016596.dat\")\n",
    "data_d2o.name = \"d2o\"\n",
    "\n",
    "data_hdmix = ReflectDataset(pth / \"tests\" / 'c_PLP0016601.dat')\n",
    "data_hdmix.name = \"hdmix\"\n",
    "\n",
    "data_h2o = ReflectDataset(pth / \"tests\" / 'c_PLP0016607.dat')\n",
    "data_h2o.name = \"h2o\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `SLD` object is used to represent the Scattering Length Density of a material. It has `real` and `imag` attributes because the SLD is a complex number, with the imaginary part accounting for absorption. The units of SLD are $10^{-6} \\mathring{A}^{-2}$\n",
    "\n",
    "The `real` and `imag` attributes are `Parameter` objects. These `Parameter` objects contain the: parameter value, whether it allowed to vary, any interparameter constraints, and bounds applied to the parameter. The bounds applied to a parameter are probability distributions which encode the log-prior probability of the parameter having a certain value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "si = SLD(2.07 + 0j)\n",
    "sio2 = SLD(3.47 + 0j)\n",
    "\n",
    "# the following represent the solvent contrasts used in the experiment\n",
    "d2o = SLD(6.36 + 0j)\n",
    "h2o = SLD(-0.56 + 0j)\n",
    "hdmix = SLD(2.07 + 0j)\n",
    "\n",
    "# We want the `real` attribute parameter to vary in the analysis, and we want to apply\n",
    "# uniform bounds. The `setp` method of a Parameter is a way of changing many aspects of\n",
    "# Parameter behaviour at once.\n",
    "d2o.real.setp(vary=True, bounds=(6.1, 6.36))\n",
    "d2o.real.name='d2o SLD'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LipidLeaflet` class is used to describe a single lipid leaflet in our interfacial model. A leaflet consists of a head and tail group region. Since we are studying a bilayer then inner and outer `LipidLeaflet`'s are required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter for the area per molecule each DMPC molecule occupies at the surface. We\n",
    "# use the same area per molecule for the inner and outer leaflets.\n",
    "apm = Parameter(56, 'area per molecule', vary=True, bounds=(52, 65))\n",
    "\n",
    "# the sum of scattering lengths for the lipid head and tail in Angstrom.\n",
    "b_heads = Parameter(6.01e-4, 'b_heads')\n",
    "b_tails = Parameter(-2.92e-4, 'b_tails')\n",
    "\n",
    "# the volume occupied by the head and tail groups in cubic Angstrom.\n",
    "v_heads = Parameter(319, 'v_heads')\n",
    "v_tails = Parameter(782, 'v_tails')\n",
    "\n",
    "# the head and tail group thicknesses.\n",
    "inner_head_thickness = Parameter(9, 'inner_head_thickness', vary=True, bounds=(4, 11))\n",
    "outer_head_thickness = Parameter(9, 'outer_head_thickness', vary=True, bounds=(4, 11))\n",
    "tail_thickness = Parameter(14, 'tail_thickness', vary=True, bounds=(10, 17))\n",
    "\n",
    "# finally construct a `LipidLeaflet` object for the inner and outer leaflets.\n",
    "# Note that here the inner and outer leaflets use the same area per molecule,\n",
    "# same tail thickness, etc, but this is not necessary if the inner and outer\n",
    "# leaflets are different.\n",
    "inner_leaflet = LipidLeaflet(apm,\n",
    "                             b_heads, v_heads, inner_head_thickness,\n",
    "                             b_tails, v_tails, tail_thickness,\n",
    "                             3, 3)\n",
    "\n",
    "# we reverse the monolayer for the outer leaflet because the tail groups face upwards\n",
    "outer_leaflet = LipidLeaflet(apm,\n",
    "                             b_heads, v_heads, outer_head_thickness,\n",
    "                             b_tails, v_tails, tail_thickness,\n",
    "                             3, 0, reverse_monolayer=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Slab` Component represents a layer of uniform scattering length density of a given thickness in our interfacial model. Here we make `Slabs` from `SLD` objects, but other approaches are possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slab constructed from SLD object.\n",
    "sio2_slab = sio2(15, 3)\n",
    "sio2_slab.thick.setp(vary=True, bounds=(2, 30))\n",
    "sio2_slab.thick.name = 'sio2 thickness'\n",
    "sio2_slab.rough.setp(vary=True, bounds=(0, 7))\n",
    "sio2_slab.rough.name = name='sio2 roughness'\n",
    "sio2_slab.vfsolv.setp(0.1, vary=True, bounds=(0., 0.5))\n",
    "sio2_slab.vfsolv.name = 'sio2 solvation'\n",
    "\n",
    "solv_roughness = Parameter(3, 'bilayer/solvent roughness')\n",
    "solv_roughness.setp(vary=True, bounds=(0, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once all the `Component`s have been constructed we can chain them together to compose a `Structure` object. The `Structure` object represents the interfacial structure of our system. We create different `Structure`s for each contrast. It is important to note that each of the `Structure`s share many components, such as the `LipidLeaflet` objects. This means that parameters used to construct those components are shared between all the `Structure`s, which enables co-refinement of multiple datasets. An alternate way to carry this out would be to apply constraints to underlying parameters, but this way is clearer. Note that the final component for each structure is a `Slab` created from the solvent `SLD`s, we give those slabs a zero thickness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_d2o = si | sio2_slab | inner_leaflet | outer_leaflet | d2o(0, solv_roughness)\n",
    "s_hdmix = si | sio2_slab | inner_leaflet | outer_leaflet | hdmix(0, solv_roughness)\n",
    "s_h2o = si | sio2_slab | inner_leaflet | outer_leaflet | h2o(0, solv_roughness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Structure`s created in the previous step describe the interfacial structure, these structures are used to create `ReflectModel` objects that know how to apply resolution smearing, scaling factors and background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_d2o = ReflectModel(s_d2o)\n",
    "model_hdmix = ReflectModel(s_hdmix)\n",
    "model_h2o = ReflectModel(s_h2o)\n",
    "\n",
    "model_d2o.scale.setp(vary=True, bounds=(0.9, 1.1))\n",
    "\n",
    "model_d2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n",
    "model_hdmix.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n",
    "model_h2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `Objective` is constructed from a `ReflectDataset` and `ReflectModel`. Amongst other things `Objective`s can calculate chi-squared, log-likelihood probability, log-prior probability, etc. We then combine all the individual `Objective`s into a `GlobalObjective`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_d2o = Objective(model_d2o, data_d2o)\n",
    "objective_hdmix = Objective(model_hdmix, data_hdmix)\n",
    "objective_h2o = Objective(model_h2o, data_h2o)\n",
    "\n",
    "global_objective = GlobalObjective([objective_d2o, objective_hdmix, objective_h2o])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `CurveFitter` object can perform least squares fitting, or MCMC sampling on the `Objective` used to construct it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = CurveFitter(global_objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll just do a normal least squares fit here. MCMC sampling is left as an exercise for the reader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-2743.7355775855553: : 70it [00:12,  5.84it/s]/Users/andrew/miniforge3/envs/dev3/lib/python3.13/site-packages/scipy/optimize/_numdiff.py:596: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x1) - f0\n",
      "-2743.7355775855553: : 70it [00:12,  5.67it/s]\n"
     ]
    }
   ],
   "source": [
    "fitter.fit('differential_evolution');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_objective.plot()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Q / $\\\\AA^{-1}$')\n",
    "plt.ylabel('Reflectivity')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display out what the fit parameters are by printing out an objective:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "\n",
      "\n",
      "--Global Objective--\n",
      "________________________________________________________________________________\n",
      "Objective - 4884365728\n",
      "Dataset = d2o\n",
      "datapoints = 137\n",
      "chi2 = 411.1660258149095\n",
      "Weighted = True\n",
      "Transform = None\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "________________________________________________________________________________\n",
      "Parameters: 'instrument parameters'\n",
      "<Parameter:    'scale'    , value=1.0202 +/- 0.00356, bounds=[0.9, 1.1]>\n",
      "<Parameter:     'bkg'     , value=-1.37525e-07 +/- 1.21e-07, bounds=[-1e-06, 1e-06]>\n",
      "<Parameter:'dq - resolution', value=5  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  'q_offset'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters: 'Structure - ' \n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=2.07  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  ' - rough'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'sio2 thickness', value=12.7316 +/- 0.222, bounds=[2.0, 30.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=3.47  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'sio2 roughness', value=5.45692 +/- 0.576, bounds=[0.0, 7.0]>\n",
      "<Parameter:'sio2 solvation', value=0.142848 +/- 0.00743, bounds=[0.0, 0.5]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'inner_head_thickness', value=9.50411 +/- 0.154, bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'outer_head_thickness', value=5.79481  +/- 1.8 , bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   'd2o SLD'   , value=6.19043 +/- 0.00433, bounds=[6.1, 6.36]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'bilayer/solvent roughness', value=0.603799 +/- 8.55 , bounds=[0.0, 5.0]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "\n",
      "\n",
      "________________________________________________________________________________\n",
      "Objective - 4884252304\n",
      "Dataset = hdmix\n",
      "datapoints = 97\n",
      "chi2 = 114.40210335558325\n",
      "Weighted = True\n",
      "Transform = None\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "________________________________________________________________________________\n",
      "Parameters: 'instrument parameters'\n",
      "<Parameter:    'scale'    , value=1  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:     'bkg'     , value=2.88049e-07 +/- 1e-07, bounds=[-1e-06, 1e-06]>\n",
      "<Parameter:'dq - resolution', value=5  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  'q_offset'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters: 'Structure - ' \n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=2.07  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  ' - rough'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'sio2 thickness', value=12.7316 +/- 0.222, bounds=[2.0, 30.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=3.47  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'sio2 roughness', value=5.45692 +/- 0.576, bounds=[0.0, 7.0]>\n",
      "<Parameter:'sio2 solvation', value=0.142848 +/- 0.00743, bounds=[0.0, 0.5]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'inner_head_thickness', value=9.50411 +/- 0.154, bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'outer_head_thickness', value=5.79481  +/- 1.8 , bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=2.07  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'bilayer/solvent roughness', value=0.603799 +/- 8.55 , bounds=[0.0, 5.0]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "\n",
      "\n",
      "________________________________________________________________________________\n",
      "Objective - 4884252624\n",
      "Dataset = h2o\n",
      "datapoints = 104\n",
      "chi2 = 254.21572280767197\n",
      "Weighted = True\n",
      "Transform = None\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "________________________________________________________________________________\n",
      "Parameters: 'instrument parameters'\n",
      "<Parameter:    'scale'    , value=1  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:     'bkg'     , value=2.15154e-07 +/- 1.13e-07, bounds=[-1e-06, 1e-06]>\n",
      "<Parameter:'dq - resolution', value=5  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  'q_offset'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters: 'Structure - ' \n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=2.07  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:  ' - rough'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'sio2 thickness', value=12.7316 +/- 0.222, bounds=[2.0, 30.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=3.47  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'sio2 roughness', value=5.45692 +/- 0.576, bounds=[0.0, 7.0]>\n",
      "<Parameter:'sio2 solvation', value=0.142848 +/- 0.00743, bounds=[0.0, 0.5]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'inner_head_thickness', value=9.50411 +/- 0.154, bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:'area per molecule', value=57.5354 +/- 0.228, bounds=[52.0, 65.0]>\n",
      "<Parameter:   'b_heads'   , value=0.000601  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_heads_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_heads'   , value=319  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'outer_head_thickness', value=5.79481  +/- 1.8 , bounds=[4.0, 11.0]>\n",
      "<Parameter:   'b_tails'   , value=-0.000292  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - b_tails_imag', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   'v_tails'   , value=782  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'tail_thickness', value=13.5991 +/- 0.143, bounds=[10.0, 17.0]>\n",
      "<Parameter:' - rough_head_tail', value=3  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:' - rough_fronting_mono', value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=-0.56  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0  (fixed) , bounds=[-inf, inf]>\n",
      "<Parameter:'bilayer/solvent roughness', value=0.603799 +/- 8.55 , bounds=[0.0, 5.0]>\n",
      "<Parameter:' - volfrac solvent', value=0  (fixed) , bounds=[0.0, 1.0]>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(global_objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's example the scattering length density profile for each of the systems:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AsrKyGD58OLm5ubz//vtkZWWRkpJCSkoKNpsNgH//+98kJSVx2223sXnzZn788Ucee+wx7rrrLtXDiIh4MCUxlemVaEBWrVrF0qWOIbHWrVtXeGzXrl00b96cJk2a8PPPP3PvvffStWtXIiMjue6663j44YfdEbKIiNTS0enV+uouo1fCy8ybN6/SfT/88EP5daOsdL0GZ5xxBsuWLXNiVCIi4mq5ml5dicYPREREvEBZT4ySmKOUxIiIiHiBnEJHbaOGk45SEiMiIuIF8ou05cA/KYkRERHxAvnFjp4YLXR31CmdxNSmCFacR6+3iEjdlSUx/kpiynlsEvP4449jMpkqXNq3b++UtstWsM3Ly3NKe1I7Za+3VhAWETlx+UWOldkDfJXElPHo6qBOnTrx+++/l98u24n5ZFksFsLDw8v3GwoMDNQSzi5kGAZ5eXmkpqYSHh6OxaL/AEVETlSBhpMq8egkxsfHh9jYWJe0XdbuPzdOFNcJDw932fspItLQqSamMo9OYrZt20Z8fDz+/v7069ePSZMm0bRp0yqPLSwspLCwsPx2VlZWjW2bTCbi4uKIjo6muLjYqXFLZVarVT0wIiInoawnxs/qsZUg9c5jk5i+ffvy0Ucf0a5dOw4cOMATTzzBoEGDWL9+PSEhIZWOnzRpEk888cQJn8disejLVUREPJ56YiozGV4yZSQjI4NmzZrx0ksvcd1111V6vKqemMTERDIzMwkNDa3PUEVERJzunFcWsDklm0+v68OgNo3dHY7LZGVlERYWVqvvb4/tifmn8PBw2rZty/bt26t83M/PDz8/v3qOSkREpH6osLcyrxlYy8nJYceOHcTFxbk7FBERkXqndWIq89gk5p577mH+/Pns3r2bv/76i4suugiLxcLYsWPdHZqIiEi9yy8q7YnROjHlPHY4ad++fYwdO5bDhw/TuHFjBg4cyJIlS2jcuOGOA4qIiFSnoLh0sTv1xJTz2CRm6tSp7g5BRETEI5TY7BTZlMT8k8cOJ4mIiIhDQYm9/LqGk45SEiMiIuLhyuphAPx89NVdRq+EiIiIhzt2erX2+jtKSYyIiIiHK1+tV0NJFSiJERER8XDl06tV1FuBkhgREREPd3ShO31tH0uvhoiIiIfTcFLVlMSIiIh4uAINJ1VJSYyIiIiH075JVVMSIyIi4uHytYN1lZTEiIiIeDht/lg1JTEiIiIerkA9MVVSEiMiIuLhVBNTNSUxIiIiHi6/qHQHaw0nVaAkRkRExMMVlGg4qSpKYkRERDxc2ToxWrG3Ir0aIiIiHk5TrKumJEZERMTDqbC3akpiREREPJzWiamakhgREREPp3ViqqYkRkRExMOpJqZqSmJEREQ8XHlNjIaTKlASIyIi4uHKF7tTT0wFSmJEREQ8nGpiqqYkRkRExIMZhnG0JkbDSRUoiREREfFgxTYDm90AtE7MPymJERER8WBlvTCg4aR/UhIjIiLiwcrqYSxmE1aLyc3ReBYlMSIiIh6sfLVeqwWTSUnMsZTEiIiIeDDtm1Q9JTEiIiIe7OjMJH1l/5NeEREREQ9WUKQ1YqqjJEZERMSDad+k6imJERER8WCqiamekhgREREPVj47Sav1VqIkRkRExINp36TqKYkRERHxYAXF2sG6OkpiREREPFhZTYyfkphKlMSIiIh4MM1Oqp7XJDHPPvssJpOJO+64w92hiIiI1Jujhb1e85Vdb7ziFVm+fDlTpkyhS5cu7g5FRESkXqmwt3oen8Tk5OQwbtw43n33XSIiItwdjoiISL3SOjHV8/gkZsKECZx//vkMGzasxuMKCwvJysqqcBEREfF2Wiemej7uDqAmU6dOZdWqVSxfvvy4x06aNIknnniiHqISERGpPyrsrZ7H9sQkJSVx++238/nnn+Pv73/c4x944AEyMzPLL0lJSfUQpYiIiGupJqZ6HtsTs3LlSlJTU+nRo0f5fTabjQULFvDGG29QWFiIxXL0DfXz88PPz88doYqIiLhMeU2MhpMq8dgkZujQoaxbt67Cfddccw3t27dn4sSJFRIYERGRhqq8JkY9MZV4bBITEhJC586dK9wXFBREVFRUpftFREQaKm07UD2PrYkRERGRYwp7NZxUicf2xFRl3rx57g5BRESkXmk4qXrqiREREfFQhmFosbsaKIkRERHxUIUl9vLrGk6qTEmMiIiIhyobSgLw99FX9j95VU2MiLjOkdwidhzKYeehHPal53Mkt4j0vCLyimzY7AY2u0GA1UKwvw/Bfj6EBVhpEhFAk/AAEiICSIwMxM9HfymKOFPZUJKvxYyPRUnMPymJETlFpWYVMHdzKst2HWHFnnT2Hsk7qfYsZhMtGwXRLjaEDnGhdG8aTrfEcAJ99TEjUldH62GUwFRFny4ip5DDOYVM+zuZn9Yd4O+9GZUebxIeQMvGQTSNDKRRsB+RQb4E+lrwsZgwm0zkF9nIKSwhu6CE9LwiktPzSc7IJzk9n+zCEral5rAtNYeZaw8AjsSmc3wofVtGcWa7aHo1j8CqvyZFak2bP9ZMSYzIKWDlniN8sGg3v21IodhmlN/fNTGcwW0a0at5JN2bhhPqb61T+4ZhcDCrkE0pWWxJyWZ9ciYr96RzILOANfsyWbMvk3cW7CTE34cz2jZmZJc4zmofg6/G+EVqpH2TaqYkRqSBMgyDhdvSePOP7SzddaT8/i4JYVzaM4HhnWKJCT3+5qq1YTKZiA3zJzbMnzPbRZffn5yRz/JdR1i4LY0/tqRyJLeImWsPMHPtASKDfBndrQnXDGhOYmSgU+IQaWg0vbpmSmJEGqAN+zN5+qdN/LXjMABWi4mLeyRwVb9mdIoPq7c4moQH0KR7E0Z3b4LNbrBmXwazNqQwbVUyqdmFfLBoFx8v3s1F3Zvw7yGtaNk4uN5iE/EG5VsOaDipSkpiRBqQjLwiJv28ma9XJmEY4Otj5sq+zbhhcAviwgLcGpvFbKJH0wh6NI3g3uHtWLgtjQ8W7WLhtjS+XbmPaX8nc93AFtw+tA1BfvpoEoFjthxQT0yV9Ekh0gAYhsHP61J4bPp60nKKALigazz3jmjnkUM1PhYzZ7aP5sz20fy9N53X5mzjjy2HeGfBTn5ae4DXxnanZ7MId4cp4nYFRRpOqomSGBEvl5lXzAPT1vLzuhQAWkcH8+yY0+jVPNLNkdVO96YRfHhNH+ZuPsijP25gX3o+l01ZzAPntue6gS0wmUzuDlHEbdQTUzMlMSJebMXuI9w+dTXJGfn4mE38+8zWTDizlVcuOndW+xh6NY/kwe/XMXPtAZ76aRPJGfk8cn5HzGYlMnJqUmFvzZTEiHghwzB4/89dTPplMza7QbOoQF4f250uCeHuDu2khPpbS3+PMJ75eTMfLtpNTkEJz1/SRT0ycko6uk6MliOoygm9Kvn5+SQnJ1e6f8OGDU4LSERqVlhi475v1/LUT5uw2Q0u7BbPzNsGen0CU8ZkMnHj4Fa8+q9umE3wzcp9PPPzJgzDOP6TRRoYrRNTs1onMd9++y1t2rTh/PPPp0uXLixdurT8sauuusolwYlIRWk5hYx7dynfrNyH2QSPjuzIK5d3I6SOi9R5sgu7NeG5i7sA8O7CXXyyeI+bIxKpf6qJqVmtk5innnqKlStXsnr1aj788EOuu+46vvjiCwD9hSRSD3YeyuHCNxaxYk86If4+fHhNH65t4IWvl/ZK5P5z2wPw1E8bWbU33c0RidSvsuEkf60TU6Va18QUFxcTExMDQM+ePVmwYAEXXXQR27dvb9AfoiKeYH1yJld/sIzDuUU0jwrk/fG9aXWKLAx30+CWrN2Xwc/rUpjw+Sp+uX0Q4YG+7g5LpF6oJ6Zmte6JiY6OZu3ateW3IyMjmT17Nps2bapwv4g417JdRxj7zhIO5xbRKT6Ub2/pf8okMOCokXnu4i60aBTEgcwCnvl5k7tDEqk3qompWa2TmE8//ZTo6OgK9/n6+vLll18yf/58pwcmIjBvSypXvb+U7MIS+rSI5MsbT6dRsJ+7w6p3If5WXri0CyYTfL1iH4u2p7k7JJF6Ud4To+GkKtU6iUlISCA2Nrb8dkpKSvn1AQMGODcqEWHB1kPc+OlKCkvsDG0fzSfX9qnzLtMNQc9mkVx1ejMAHvh+HYUlNjdHJOJ6+Vqxt0Z1nng+fPhwZ8YhIsdYvOMwN3yygqISOyM6xTD5qp76EAPuO6c9MaF+7D2Sx6earSSngPyyDSD133+V6pzEaEaSiGus2H2E6z5eTmGJnbPaR/P62B5YLVroCiDYz4e7zm4LwBt/bCczv9jNEYm4VoGGk2pU509GzUgScb51+zIZ/+Fy8opsDGrTiLfG9cDXRwnMsS7ukUDr6GAy8oqZPH+Hu8MRcanyFXvVE1MlfTqKeIjdabmM/3AZOYUlnN4ykneu6qUhpCr4WMzcN6IdAB//tZvMPPXGSMOlvZNqpiRGxAOk5RRy9YfLyqdRv/t/vdR9XIOzO8bQPjaEvCIbny1VbYw0XJqdVLM6JzEWi15QEWfILSzhmg+Xs+dwHomRAXx4Te8GuY2AM5lMJm46oyUAHy7aVV43INKQ2OwGRSUq7K1JnZOYv//+25lxiJySSmx2bvl8FeuSM4kM8uWTa/sSHeLv7rC8wsgu8cSH+ZOWU8R3q/a5OxwRpzs2OVcSUzUNJ4m40ZMzN7Jg6yECrBY+GN+bFo2C3B2S17BazFw7sAUAny7eoxmT0uDkH5PE+KnAv0p6VUTc5NPFu/m4dK2TV/7VjW6J4e4NyAtd2jMRPx8zm1Oy+Tspw93hiDjV0YXuzJjNmhFcFacnMZmZmSxYsIDXXnvN2U2LNBgLtx3i8RkbAbjvnHaM6BR7nGdIVcICrZzfJQ6AL5fudXM0Is6VV5rEBPnWeq/mU06tk5gdO3YwduxYxo8fT0ZGBgC7du1i2rRpPP7444wePZoWLVoQGRnJkCFDeOihh1wVs4hX256aw78/X4XNbjCmRxNuOaOVu0Pyalf0aQrAzLUHyCrQdGtpOHKLSgDNTKpJrdO7cePGMW7cOJo1a0bnzp3JyckhKyuLsLAwOnbsSOfOnUlKSuL9999n6NChJCYmujJuEa+UkVfE9R8vJ7ughF7NIpg05jQtHHmSejaLoE10MNtSc5i+ej9Xlu6vJOLtyoaTApXEVKvWPTGpqal07tyZrl27kpKSwoQJE0hKSiI9PZ1FixYxZcoUTCYTffr0UQIjUgWb3eC2L/9m9+E8EiICmHJVT/x89OF0skwmE5f2SgBg+pr9bo5GxHnyypMYDSdVp9ZJzGuvvcYtt9zCuHHjmDx5MtOnT2fChAls3brVlfGJNBgvzd7Cwm1pBFgtvPt/vYgK9nN3SA3GyC7xACzffYQDmflujkbEOfJKh5PUE1O9WicxI0eOZPPmzfz5559cf/31rF69mmHDhjF48GAmTJhAamqqK+MU8Wq/bUjhzT8c+/w8e/FpdIgLdf1J847AwQ2wayHsWgBJyyBrP9jtrj93PYsPD6BP80gMA2auOeDucEScIk/DScdV5z4qi8XCrbfeyhVXXMHjjz9O+/btsdvt2GxaOVPkWDsP5XD312sAuGZAcy7s1sQ1J0rfDdt/h+1zYf8qyK7my9w3GBL7QKuhcNqlEBLjmnjq2ahu8SzbfYTpa/Zzw+CW7g5H5KTlFpb1xGg4qTonPcU6MjKS1157jT///JNhw4YxdOhQXnjhBfLz1aUrkltYwk2friS7sIQ+zSN58LwOzj1BfgYsexemDIZXu8JPd8OWn44mMIFR0KgtNG4PYU3BZIGiHNgxF357CF7qAN+Mh9TNzo3LDc7rHIvFbGJdcia70nLdHY7ISVNh7/E5Lb3r2LEjs2bNYubMmdxzzz28+OKLHDigbl05dRmGwX3frWVbag7RIX68Ma47VouTlmbK2AuLXoW/P4OSAsd9Jgs0PR1aD4XmgyC6A/iFVHyerQQObYbdC2H9d7BvOWyYBht+gF7XwLAnwL8ehrpcICrYj/6toli4LY3fNqRwk6aui5fLK1Zh7/HU+hM1Pz8f+zFj6XPmzOHFF1/k559/rnDcyJEjWb9+Pffdd99JBfb222/TpUsXQkNDCQ0NpV+/fvzyyy8n1aZIfXpv4S5+WnsAq8XE21f2cM6eSJnJ8MMEeK07LH/PkcBEd4RznoV7tsE1P8Ogux3DRf9MYAAsPhDbGU6/Ba7/HW5eBO1HAgas+ADe6gd7Fp98nG4yvKNjaGz2xoNujkTk5OUVqrD3eGqdxJx++ulkZWUBMGnSJJ5++mkMw2Dy5Mncc889FY718fHhzjvvPKnAEhISePbZZ1m5ciUrVqzgrLPO4sILL2TDhg0n1a5IfVi84zDP/uoYonlkZEd6Nos8uQaLcuGPSfB6T1j9GdhLoOUQuHoG3PKXIykJijrxdmM7w78+d7QT0Ryy9sHHIx1DVF5oaAdHErNybzppOYVujkbk5JQV9mqxu+rVuo+qpKSE8PBwAL777jv++usvfH19ueuuu+jWrZvTAxs1alSF208//TRvv/02S5YsoVOnTk4/n4izHMjM59YvSlfk7d6Eq05m8TXDgA3fw6yHIbt0DZTE02H4U5DY2zkBA7QY7EiGfrzVcb6f74GsZBj6GHjRYnzx4QF0bhLK+uQs5m5O5bJeWrNKvFfZcFKQkphq1bonJiYmhr/++guA+Ph40tLSAMjOznZNZMew2WxMnTqV3Nxc+vXrV+UxhYWFZGVlVbiI1LfCEhu3fLaKw7lFdIgL5emLTmJF3qwDMPUK+PZaRwIT3hQu/Qiu/dW5CUwZ3yC45AMY+qjj9p8vw8/3OhIpL3J2B8c+VBpSEm+Xp9lJx1XrV+aDDz5g/Pjx+Pr6EhQURNeuXenVqxepqan873//c0lw69ato1+/fhQUFBAcHMy0adPo2LFjlcdOmjSJJ554wiVxiNTWf2dsZHVSBmEBVqZc2bNu3cCG4SjYnfUQFGaC2QqD74EBd4DVCXU1NTGZHDU1AZEw805Y/q4juTnbe/7bGtYxmpd/38rCbYcoKLbhb9VfseKdNJx0fCbDOLE/szZu3Mi2bdsoKSmhSZMm9O7dG4vFNS9wUVERe/fuJTMzk2+//Zb33nuP+fPnV5nIFBYWUlh4dAw8KyuLxMREMjMzCQ31ztkW4l2+XpHEfd+uxWSCD8b35sx20SfeSO5hmH4rbCktmI/vARe+CTFVJ+8utepTRywAZz8JA/5T/zHUgWEY9H92LgcyC/jk2j4MbtvY3SGJ1MkFb/zJ2n2ZfDC+F2e1bxjrOdVG2b6Mtfn+PuE+qo4dO9KxY0dSUlKIjY2tc5C14evrS+vWrQHo2bMny5cv59VXX2XKlCmVjvXz88PPT8u4i3usT87k4R/WA3DH0LZ1S2B2zodpNznWeLH4wlkPw+kTHDOK3KHHVZB/BGY/6rg0bgdtR7gnlhNgMpkY1KYRX6/Yx8Jth5TEiNcqW+wuwKrhpOrUedGK4cOHOzOOWrHb7RV6W0Q8wZHcIm76dCVFJXaGto/mtrNan1gDthKY8yR8cqEjgYlq45j+POB29yUwZQbcDr2uBQz47gY4vMO98dTSoDaOxGXB1jQ3RyJSd1rs7vjq/Al5gqNQJ+yBBx7g3HPPpWnTpmRnZ/PFF18wb948Zs2a5dLzipwIm93gP1/+TXJGPs2jAnnp8m6YzSdQyJt7GL69BnbNd9zu8X+ONV98g1wTcF2c85xjD6akpY5C4+t/r3oNGg8ysHUjTCbYcjCbg1kFxIS6uJZIxAVyS5OYID8lMdWpc09MnWdc1FJqair/93//R7t27Rg6dCjLly9n1qxZnH322S49r8iJeOG3Lfy53bEz9eSrehIWYK39k5NXwTtnOBIYayBc/D5c8LpnJTAAPr5w2ScQHOtY7XfmXe6O6Lgignzp0iQMgIXb1Bsj3im/vLBXw0nV8dhX5v3333d3CCI1+nX9Ad6e5xheee6SLrSPPYEC8lWfOvY5shVCZCu4/DP3FO/WVkisI5H58FxY97WjNua0S9wdVY0GtWnMmn2ZLNh6iEt6Jrg7HJETUlRip8jmWCVf68RUz0kbuYicWranHt2Z+vqBLbiga3ztnmgrcay9Mv1WRwLT7jy48Q/PTmDKNO3rmOoNjt6YzH3ujec4BrVpBMCi7WkuH/4Wcbac0qJegGA/j+1vcLs6JzGumlYt4ulyCku46dMV5BbZ6NsikvvPbV+7JxZkwheXwbJ3ABOc+TBc/jn4h7k0XqcafC806elYv2bazXDMfmqeplvTcPytZg7nFrE9Ncfd4YickJyCsplJFnyctXFsA1TnV+bvv/92ZhwiXsEwDO75eg07DuUSG+rPG1f0qN0HTPpueH847JjjqH+5/FM4414we9mHk8UKY951/A67Fzo2ofRQfj4WejaLAGDJzsNujkbkxGQXFgMQ7K9emJro1RE5AW/P38GvG1LwtZh5+8oeNA6pxdpEe0tn9eSlQUgcjJ0K8d2cEk+xrZhtGdvYl72PfTn7OJx/mCJbEb4WX8L9wkkMTaR9RHuahTZzXjF+VCsY9gT8ci/M+S+0Px/CmjinbSc7vUUUi7YfZsnOI1zVr7m7wxGptbKemBAlMTWq86uTlpbGBx98wOLFi0lJSQEgNjaW/v37M378eBo31gJT0rDM3niQ/83aAsBjF3Ske9OI4z9p7dfw4wSwFUFcV0cCE1rL+pkqGIbBpiOb+CPpD1akrGBd2joKbcdfOyk+KJ5hzYZxebvLaRratM7nL9f7OkeB777ljs0i//WFR24UeXqrKJjt6IkxDMPlsypFnKWsJiZE9TA1qtOrs3z5ckaMGEFgYCDDhg2jbdu2ABw8eJDXXnuNZ599llmzZtGrVy+nBiviLptTsrhj6t8YBlx5elPG9T3OztR2O8ybBAued9xuPxLGvFPn6dO7Mncxbds0Zu2exf7c/RUeC/MLo3locxJCEmgc0Bhfiy/FtmKOFBxhZ+ZOthzZwv7c/Xyy8RM+2/QZw5sN5+5edxMbdBIrbpstMOo1mDLIsUXCpunQ8cK6t+ciXRLCKtTFtInx7PVtRMqUJTEaTqpZnV6d2267jUsvvZTJkydX+svGMAxuvvlmbrvtNhYvXuyUIEXc6XBOIdd95Cjk7d8qisdGdar5CcX58MMtsGGa4/aA22Ho4ydc/1JkK+K3Pb/x7dZvWXlwZfn9AT4BDGwykP7x/ekR04MWoS1q7GHIL8nnr/1/8d3W71iYvJBfd//K/H3zuaPHHYxtP7buvRMxHR2bUi58AX6+D1qcAQHhdWvLRcrqYhxDSoeVxIjXyCodTtLMpJrV6dVZs2YNH330UZUffiaTiTvvvJPu3bufdHAi7lZYYuPmz1aWr8j71rgeWGsq5M05BFPHOoZZzD4w8hXHHkQnIKcoh2+3fssnGz/hUP4hAMwmM4OaDGJ069EMaDKAAJ+AWrcX4BPA0KZDGdp0KFuObOGZpc+wKnUVk5ZNYvWh1TzR/4kTaq+Cwfc6krUjO+D3x2HUK3Vrx4VUFyPeKKc8iTmBBTRPQXVKYmJjY1m2bBnt21c9tXTZsmXExJw6O25Kw2QYBo/8sJ7lu9MJ8ffhvat7Ex7oW/0TUjfDF5dCxl7wD3csYNdiUK3Pdzj/MJ9v+pypW6aSXZQNQHRgNJe2vZTRrUef3PBPqXaR7fjwnA/5cvOXvLD8BX7Z9Qv7svcx+ezJhPrWYbd3qz+MehU+HgkrP4Ju4yCx90nH6Uy9mkcCsGLPEdXFiNfIKZ2dpMLemtXp1bnnnnu48cYbWblyJUOHDi1PWA4ePMicOXN49913eeGFF5waqEh9e2veDr5esQ+zCV4f253W0cHVH7xzHnz1f471UyJawLhvoFGbWp0nqyiLD9d/yGcbP6PAVgBAi7AWXNv5Ws5vcT5Wi3P/EjObzIzrMI52Ee24c96drEtbx42/3ciUs6cQ5leHNWtaDIKuV8CaL+CnO+GGee7fuPIYXRPDsJhNHMwqZH9mAU3C69jrJFKPNDupdur06kyYMIFGjRrx8ssv89Zbb2GzOfZ3sFgs9OzZk48++ojLLrvMqYGK1KfvV+0rn4n06MiODGkXXf3Bqz6BmXeCvQQST3fM1AmKOu45CkoK+GLzF7y/7n2yirIA6BzVmeu7XM+ZiWdiNrl2DZlesb14b/h73PDbDWw4vIH/zP0P7w5/F19LDb1N1Rn+pKPAN2WdY+2Y0292fsB1FOjrQ8e4UNYlZ7JqT7qSGPEK2YWqiamNOn9KXn755SxZsoS8vDySk5NJTk4mLy+PJUuWKIERr/bntjTu+3YtADcObsn4AS2qPtBud9SBTL/NkcCcdin834/HTWBK7CV8u/Vbzv/+fF5e+TJZRVm0Dm/Na2e+xhfnf8HQpkNdnsCUaRfZjneHv0uwNZhVqat4csmTdVuiP6gRDHvMcX3uU5Cd4txAT1LZoncr96S7ORKR2imviVFPTI1O+pPSarUSFxdHXFwcVqsKkMS7bdyfxc2fraTEbjCqazz3n1PNlgLF+fDtNfDny47bZ0wsXcnWv8b2lxxYwqUzLuWJxU+Qmp9KfFA8Tw98mm9HfcuZTc90S71Gu8h2/O+M/2E2mflh+w98ufnLujXUY7xjS4KibJj1oFNjPFk9SpOYVXuVxIh3yNbspFpxyZ97SUlJXHvtta5oWsRldqXlcvWHy8gpLKFvi0heuLQLZnMVSUXOIfh4FGz8AcxWGD0ZznywxsXe9mXv484/7uSG325ge8Z2wv3Cub/P/cy4aAYXtLoAi9m9e5ENbDKQu3veDcCLK15ka/rWE2/EbIbzXwKTGdZ/Bzv+cHKUdVfWE7NhfxZ5RSXHOVrE/crWiQn1V+dATVySxBw5coSPP/7YFU2LuMS+9DzGvbuEQ9mFtI8N4Z3/64WfTxWJxYG18O5ZjinU/uHwfz9At7HVtptXnMfrf7/OhT9cyO97f8disnBF+yuYedFMxnUYV7f6Exe5quNVDGoyiCJ7ERMXTKzVSsCVxHeD3jc4rv98D5TUoQ0XiA/zJzbUH5vdYO2+THeHI3JcWuyudur06kyfPr3Gx3fu3FmnYETc4WBWAePeW8r+zAJaNg7i0+v6EhZQxV8/G6bBD/+G4jyIbAlXfF3tDCTDMPhl1y+8tPIlDuYdBKBvXF8m9p5Im4jazVqqbyaTiScHPMmY6WPYnrGdd9a+w23dbzvxhs56yNFLdXg7LHrNsdGlm5lMJno2i+CndQdYuSed01sev/BaxJ2yC0o3gNRwUo3q9OqMHj0ak8lUYwGg1mIQb3A4p5Bx7y1lz+E8EiMD+Pz6vpU3dbTb4Y+nHSvTArQ6Cy75AAKq3jtp0+FNPLvsWValrgKgSXAT7u11L2c1Pcvj/7uICojiob4Pcff8u/lg/Qec3+J8Woa3PLFG/MNgxDPw3XWO1+y0SyCymuLoetSjNIlZpeJe8XCGYZCe50hiImpam0rqNpwUFxfH999/j91ur/KyatUqZ8cp4nQHswq4/J0lbE/NITbUny+uP524sH9Mvy3IcuxAXZbA9L8NrvimygTmSMERnlj8BJfPvJxVqasI8Angtu638ePoHxnabKjHJzBlzm52NoMTBlNiL+G/S/5bt9lKnS+GFoOhpAB+uQ/q0oaTlc9Q2ptet99JpJ5kFZRgszv+jYYHqiamJnVKYnr27MnKlSurffx4vTQi7pZ0JI9LJy8uT2A+v6EviZGBFQ9K3QzvDYOtv4DFDy56B4Y/VWkht2J7MZ9t/IyR34/k263fYmBwXovzmD56Ojd2uRE/yz96djycyWTiwb4P4m/xZ+XBlczeM7sujcB5LzoKn7f9Bpt/cn6gJ6hjXCh+PmYy8orZmZbr7nBEqpWRVwRAgNWCv9W9Rf+erk5JzL333kv//v2rfbx169b88YfnzEwQOdbOQzlcPmUxe4/k0TQykG9u7kerxv9YjXf1l/DumZC2BULi4dpfoOvlldpavH8xl06/lOeWP0d2cTYdIjvw8Tkf89zg55yyTYC7NAluwvjO4wF4ZdUrFNuKT7yRxm0dm18C/DIRCnOcF2Ad+PqY6ZLgWJFY68WIJzs6lKRemOOpUxIzaNAgzjnnnGofDwoK4owzzqhzUCKusjopg8umLGZ/ZgGtGgfx9U39KvbAFOXBjxPgh5sdBbwth8BN8x3rnxwjKTuJ2+fezo2zb2RH5g4i/CJ4rN9jfHn+l/SI6VG/v5SLXNPpGqL8o0jKTuKrLV/VrZFBd0N4U8jaBwued26AddAtMRyAtfsy3BqHSE3SS3tiatyrTQAXTbEW8US/rDvA5VMWk5ZTRMe4UL66qR+xYccsTndoC7w3FP7+DDDBkAfhyu8h+OiWA3nFeby26jVG/zCauUlzsZgsXNnhSmZcNINL2l7i9vVenCnQGsiE7hMAmLJ2CnnFeSfeiG8gnPs/x/XFb0LqJidGeOK6JIQDaJq1eLSy4aTIICUxx6MkRho8wzCYPH8Ht3y+isISO2e1j+brm/vRKLi0VsVuhyVvw5TBkLoRgqId2wcMmQilSYndsDNjxwxG/TCKd9e9S5G9iL5xffl21LdM7DOxbhsneoGLWl9Es9BmZBRm1H0l33bnQPuRjq0ZfrrbrUW+XUuTmE0HsigssbktDpGapOc6hpNU1Ht8SmKkQcsvsnH3N2t49pfNAIzv35x3rup5dO2FjL3wyQXw6/2OmTStzoKb/4SWR4dD/0r+i8tnXs6Dfz5Ial4qTYKb8MqQV3j37HdpHdHaHb9WvfEx+3BTl5sA+GjDR3XrjQE4ZxJYA2HPIlgz1YkRnpjEyADCA60U2wy2pGS7LQ6RmpT1xGh69fEpiZEGa+ehHC56axHfr0rGbILHR3Xk8Qs64WMxO3pflr8Hbw+A3QsdX7Dnv+gYPgqJARzrvdzw2w3c9PtNbD6ymWBrMLf3uJ0fLvzBq6ZMn6xzW5x78r0x4U3hjPsc1397GPLdU1hrMpk4rYmj12yNhpTEQ6mwt/bqlMQ8+uijNU6xFnEnwzD4duU+LnhjEZtTsmkU7Mfn159+dDfqlPXwwXDH0EZhFiT0dvS+9L4eTCZ2Ze5i4oKJXDbzMpYcWIKP2YerOl7FL2N+4frTrsffp+ZNHhsaH7MPN3a5EYBPN35Kka2obg2dPgEat4e8NJjzXydGeGLKhpTWJmW4LQaRmhxRYW+t1WnF3n379nHuuefi6+vLqFGjuOCCCxg6dCi+vnrBxb3Scgp58Pt1/LaxdKn/FpG8PrY70aH+kJ8BC/7nqH8xbOAbAkMfcSQvZgs7M3cyZc0Uft39K3bDDsB5Lc7jtu63kRCS4Mbfyv3ObXEur656ldS8VH7a+RMXtbnoxBvx8XX0dn10Pqz4ELpdCQk9j/88JyubZr0uWT0x4pnKh5OC1BNzPHXqifnggw9ISUnhyy+/JCQkhDvuuINGjRpx8cUX88knn3DkyBFnxylSo7Lel+EvL+C3jQexWkzcO6Idn1/fl+ggCyydAq91h8VvOBKYDhfArcug701sydjOfQvuY/QPo/l518/YDTtDEofw1civeG7wc6d8AgNgNVu5ssOVAHy84eO6L2bZfCB0+RdgwPRbobjAeUHWUtfSadZbD2ZrR2vxSEdyteVAbdW5JsZsNjNo0CCef/55tmzZwtKlS+nbty9TpkwhPj6ewYMH88ILL5CcnOzMeEUq2ZKSzeXvLOGeb9ZwJLeI9rEh/DBhABPOaIHPxu/hzb6Ope/zjziGM8Z9i/2yj1mYtYMbfruBS2Zcwi+7fsHA4KzEs/h65Ne8ftbrdIzq6O5fzaNc0vYSgqxB7MjcwZ/Jf9a9oRFPQ1Bjx0ywP55yXoC1FBPqT3SIH3YDNuzPqvfzixzPwSxHch8TemoNXdeF07bH7NChAx06dOC+++7j0KFDTJ8+vXy363vuucdZpxEpdyAzn5d+28p3q/ZhNxxLdN8+rA3X9UvAuvE7+O5Fx07K4PjSPPNBcjqN4ee9v/H5jxexM9Ox27rFZGFYs2Fcf9r1tI9s78bfyLOF+IYwps0YPt34KR9v/JhBCYPq1lBQI7jgdfjyX/DXG9D2HEcPTT3qkhDO75sOsiYpg97NI+v13CI1KSi2cSTXMZwUF6Yk5nhcssd348aNue6667juuutc0byc4pKO5PHewp1MXZ5EYYmjduXczrE8clYM8bu+g7fec0ydBgiIwOh7C6ta9ef7Pb8y+/vh5JfkAxBsDebiNhdzRYcriA+Od9ev41Wu7HAlX2z6gqUHlrLp8CY6RHWoW0PtzoXuV8Hfn8K0W+CWReAf6txga9A1IYzfNx1UXYx4nNSsQgD8rWbCAlQTczwuSWJEXGF9cibvLNjJT+sOlO/w2qdZBP/tnU/7fR/AB9861noBCGrM9p7jmB0Wwc9757B77mfl7bQMa8klbS/hotYXEewbXNWppBrxwfGc3exsft39K1O3TOWJ/k/UvbFzJsGuBZCxB359AEa/6bxAj6NL+fYDSmLEsxzIdPyRFRvqf8os43AylMSIRzuSW8SPq5P5ZsU+Nh44Wr9wWfM8bmu8moR9P2GauQsAO7AlriNzmp7G7IID7Ez6HpIcxwf4BHBO83MY02YMXRt31YfDSRjbfiy/7v6Vn3f+zF0976r7asV+IXDRZPjwPFj9GbQ5GzqNdmqs1elSulbMrrRcMvOL9ReveIyU0nqYWA0l1YqSGPE4qVkFzNmcyuyNB1m47RDFNgMfShjos41ro7cwwLYSv5QdkAKpFguLwyL5K7o5S0yFHCnOhtTFgGNGTf/4/pzd7GyGNRtGkDXIzb9Zw9A9ujutw1uzPWM7M3bM4MqOV9a9sWb9YeCd8OdLMP02iOsCkS2dF2w1IoJ8SYgIYF96Phv2Z9K/VSOXn1OkNlIyS5MYFfXWykknMXv37mXBggX4+fnRvXt3Wrdu2Muwi/NlFxSzYk86y3Yd4a8dh1mTlEEABXQ3b+ffpi0MCdlBZ/sWsOWzOceXb/19WRvdiLVBYezDMRWRkjTA0eNyetzpDG8+nDMSziDEN8SNv1nDZDKZ+Fe7f/HU0qf4astXjOsw7uR6ts58CPb8BUlL4Our4brZYHX9B3jn+DD2peezcX+WkhjxGAfKkpiwADdH4h1OKol57bXXuOuuuwgMDMRkMpGTk0PPnj1577336NKli7NilAbCMAwO5xax+UA2mw5kselAFlv2HyEndTetTMm0NyVxnXkvTf324uebRrLVzHZfK59brWz3DWOPtRElFb4sizFholNUJ/rF96NffD+6Ne6G1aKhAVcb2WokL618id1Zu1mWsoy+cX3r3pjFBy75AKYMgpS18NtDjkXxXKxTfCi/bkhhvYp7xYMc7Ynxc3Mk3uGkkpgnn3yS+++/n//+97+YzWa2b9/OW2+9Rb9+/Zg1axYDB9bvtElxH5vdICOviLScIg7nFHIou4DsjMNkHzlA9uEDFGQepDj3AFZTKqGWNPx90unok0U7Sz4ZcWZSfHxY4ePDdB8LmRYLEFPlecL8wujSqAtdGjsunRt1JtS3/ma1iEOQNYhRrUbx1Zav+GrLVyeXxACENYGL3oHPL3bsadVsAHQe45xgq9GpiePfjdaKEU+yMy0HgGZRGv6ujZNKYnJychg/fjxms2PNvNatW/PSSy8RGRnJ3XffzdKlS50SpCc5lL6fGX++W74sPThmyRgGGBiYMDAwoPQ2pSubGuVH2kuvGKX3la58apReM4wK93HMMcZxbleMxyi/fbTdYx4vjZHSeO0G2O127IBht2PYS8AoAbsNm70Yu1GCzbBRbBRjK71uM2zYjRKgBEwlGCYbxWY7hSY7hWbIM5vINZnJs5rIj65qXUW/0ktl4b6hNAlJpFV4K1qHty6/xAbFqijXQ1zW7jK+2vIVc/fO5VDeIRoHNj65BtsMg4F3ldbH/AdiT4NGbZwTbBU6xzuKe3ccyiG/yEaAr8Vl5xKpjWKbnV1puQC0iXHCzEm7DQqzHbM2SwqgpPDoT7sNDDuOLyt76cU4+vPY+4/1z9W6fQOh5ZCTj7WOTiqJ6dKlC4sXL65UB3PZZZfx9NNPn1RgnmrHvvW8fORbd4fhemU5xwl9rps53iLQ/piJ8gkk0jeUiIBGRAbHERkcT+PAaJoEN6FJSBPig+I19dkLtI1oS9fGXVlzaA3Td0znutOcsC7UmQ9B0jLY8yd8dRXcMAd8XfMXaXSoP42C/UjLKWRTShY9mka45DwitbXncB7FNoNAXwvxta2JKcqFXQshZZ1jcc/0XZB7CPKOQEEmYBy3iZMS1Rpuc9+G0CeVxLz44ouMGTMGX19fLrvssvK/kJcuXUqbNif3F9SkSZP4/vvv2bx5MwEBAfTv35/nnnuOdu3anVS7JyvQP5S2hRW/qCv3C5gq3Vf5tqn8fuOYxx23jz7fZIBhovxok/HPdsqeZKp4H4DJVOF2heeajonRcBRrlh9nMmGYzJgwY5hM+JgsWEw++JgsWM1WrGYffC1Wx8Xqj581EH+/IIL8gwgOCCfQL5wg/wgCAxsTFBBBoDWQQJ9AgqxBp9wO0A3dmDZjWHNoDT9s/4FrO1978r1kx9bHHNoEM26HMe+W/1t2ts5NQpm35RAbkjOVxIjbbU/NBqB1dDBm83H+zR/aAvOehc0/ga2w5mNNZvAJAB8/x8XiCxYrYHI8ZjI7/hsr+/nP+6v4lisX5t695U4qiRk4cCAfffQRN998M7fddhvdunWjqKiI9evX8+mnn55UYPPnz2fChAn07t2bkpISHnzwQYYPH87GjRsJCnLfWGGXNqfzXZs1bju/iCcZ0XwEzy57lt1Zu1mVuoqeMU7YlTokBi79CD4aCeu+gcS+0OeGk2+3Cp3iS5MY1cWIB9h20FEP0yb6OLMql7/nWCDS5tiegPCm0GwgRLVyXIJjITASAiLBP8yxg3wDddJTrM877zy2bdvGnDlzmDdvHn///TcAI0eOJDIyktNOO40uXbrwyiuvnFC7v/76a4XbH330EdHR0axcuZLBgwdXOr6wsJDCwqPZaFaWPpREXC3IGsQ5zc9h2vZpfL/te+ckMeBYP+bsJ+C3hx0f1vHdIaGXc9o+RlldzPr9mqEk7re2dKZcu9gahtMXvgRzSlfKbn02nPUwxHV1WW+lp6vzLtbH8vPz47zzzuP5559n9uzZpKWlsXv3bj744AOGDBnCnj17TvocmZmONzcysurN2iZNmkRYWFj5JTEx8aTPKSLHN6aNYxbR7D2zySnKcV7D/W6FDqPAXuxYPyb3sPPaLtWpNInZmpJDsc1+nKNFXMdmN1i60/FvvE+LqKoP2jDtaAJz5kMw7huI73bKJjDgpCSmKomJiYwaNYpHH32UadOmnVRbdrudO+64gwEDBtC5c+cqj3nggQfIzMwsvyQlJZ3UOUWkdro27kqLsBbkl+Tz6+5fj/+E2jKZ4MK3ILIVZO2D7693zKhwosTIAEL8fSiy2cu78kXcYeP+LLIKSgjx86FzfBXLRqTvhh9vc1zvdyuccd8pnbyUcVkS40wTJkxg/fr1TJ06tdpj/Pz8CA0NrXAREdczmUyMae3ojZm27eT+YKnEPxQu/wysgbBjLsx/zqnNm0wmOpV+YWhISdxp4fZDAPRtGYmP5R9fzYYBM++ComxIPB2GncTGqw2Mxycxt956KzNnzuSPP/4gIcG9VdAiUrWRrUbiY/Jhbdpatqdvd27jMR1h5CuO6/Ofg62/ObX5srqYjSruFTcxDIPvVyUDcFb7Khb6XP8d7JgDFj8Y/ZZjFp8AHpzEGIbBrbfeyrRp05g7dy4tWrRwd0giUo1GAY0YnOAouJ+23cm9MQBdL4depevQfH8DpJ98nV2ZspV7tf2AuMuqvRlsT83B32pmVNe4ig/mHYFf73dcH3yvY/aRlPPYJGbChAl89tlnfPHFF4SEhJCSkkJKSgr5+fnuDk1EqlBW4DtjxwyKbcXOP8E5kyC+BxRkwNf/ByVFTmm2rLh344Es7HYXLwwmUoU3/3D0Xo7sEk+I/z/2fvv9McfidY3awYDb3RCdZ/PYJObtt98mMzOTIUOGEBcXV3756quv3B2aiFRhQJMBNA5oTHphOvP2zXP+CXz84LJPICACDqyGBf9zSrMtGwXhbzWTV2Rj1+Fcp7QpUltLdx5m7uZULGYTE86suPo9O/6AVZ84ro96tUGv91JXHpvEGIZR5WX8+PHuDk1EquBj9uHC1hcC8P22711zkvBEOP8lx/WFL0LyyS937mMx0z5Wm0FK/Su22Xnkx/UA/Kt3Ii0aHbOQa34G/DjBcb339dCsX/0H6AU8NokREe9zUeuLAPhr/1+k5Ka45iSdx0Dni8GwwbSbofjkh5g7l+1orboYqUfvLtzJ1oM5RAb5cu+IY7bUMQz4+V7ISobIlnD2f90XpIdTEiMiTtM0tCm9YnphN+z8uP1H153ovBcgOAbStsLcp066uU5auVfq2d7Debw2ZxsAD5/fgfDAY4aKFvwP1n3t2Lto9GSXbYLaECiJERGnKivwnbZ9GnbDRavgBkbCBa87ri95Gw5uOKnmytaK2bg/C8NQca+4lmEYPPzjegqK7fRvFcVF3ZscfXD1F/DH047r578ITfu6J0gvoSRGRJxqWLNhBFuDSc5JZnnKctedqO0I6HCBY1jpp7sdXfB1bSomBIvZRHpeMQcyC5wYpEhlM9ceYMHWQ/hazDw1uvPR3d93zoPppavyDrwTel3rthi9hZIYEXGqAJ8AzmtxHuDCAt8y50xyrOa7dzGsqX5F7+Pxt1poE+3YdE/FveJKeUUlPDlzIwD/PrMVLRuXbvZ4cAN8dRXYS6DzJXDWo26M0nsoiRERpysbUvp9z+9kFrqwziQswbGHDMDsRxwzOuqoY3zZDCXVxYjrvL9wF6nZhSRGBnDLkNKF67L2w2eXQGEWNBvoWJXXrK/n2tCrJCJO1zGqI20j2lJkL+LnXT+79mSnT4BGbR0Lgp3E2jFlxb3qiRFXScspZPL8HQDcO6I9fj4WKMqDqVdA9n7Hgnb/+syxJpLUipIYEXE6k8lU3hvj8iElH18YMclxfdk7kLG3Ts0cW9wr4gpvzN1ObpGNLglhjDwtzlHH9eME2P83BETCuK8dizlKrSmJERGXOL/F+VjNVjYf2czGwxtde7LWQ6H5ILAVwR+T6tREhzhHEpOckU9GnnO2NBApk5pVwBfLHAn2xHPaYzabHEn3hu/B7OPYrT2iuXuD9EJKYkTEJcL9wxnadChQD70xJhOc/YTj+povIXXTCTcRFmAlMTIAUG+MON87C3ZSVGKnV7MI+reKgpT18NsjjgeHPw3NB7g3QC+lJEZEXOaiNo4VfH/e+TMFJS6eutykJ3QYBRiOLQnqoFOc6mLE+Q7nFPL5UkcvzK1ntcZkK4LvrgdbIbQ9B/re5OYIvZeSGBFxmdPjTic+KJ7s4mx+3/u76084+F7Hz/XfweEdJ/z0TpqhJC7w0V+7yS+2cVqTMM5o2xj+fAUObYKgaLjwTRYfWMK4n8Yx9JuhvLX6LWx2m7tD9hpKYkTEZcwmM6NbjwZg2rZprj9hXFdoMwIMO/z58gk/vVMTbQQpzlVQbCvvhbllSCtMh3fAwhccD577LF/vm8uNs29kbdpaUvNSeXvN23y04SP3BexllMSIiEuNbj0aEyaWpSxjT9Ye159w8D2On2u+hIykE3pq2TTrHYdyyC/SX8Ny8n5cncyR3CKahAcwvEM0/HSXowC99TBmB4fw5JInAcfmqdd0vgaAjzd8TF5xnjvD9hpKYkTEpeKC4xiUMAiAr7Z85foTJvaBFoMdK58uevWEnhod4kejYF/sBmxOUW+MnBzDMPjgz90AXN2/GT47f4dd88Hix87Bd/DQoocB+Fe7f/FE/yf4T/f/kBiSSHphOr/t+c2NkXsPJTEi4nKXt7scgB+2/0B+Sb7rT1hWG/P3p5B3pNZPM5lM5VOtNx5QEiMnZ/HOw2w5mE2gr4XLe8SXz0Yq6nsD961+lfySfE6PO52JfSZiMpnwMftwTvNzAFy771gDoiRGRFxuYJOBJAQnkF2UzS+7fnH9CZsPctTHlBTAqo9P6KlauVec5ctljuHMi7o3IWzzl5C2BQIieS0kkC3pW4jwi+CZgc/gY/Ypf06v2F6AI4nRjurHpyRGRFzObDKX98ZM3TzV9R/OJhP0KZ22uvx9sJXU+qlHZygpiZG6S88tYtb6FACu6BkDCxzFvOv7XM0nWx3Dqk/0f4LGgY0rPK9b4274mHw4kHuA5Jzk+g3aCymJEZF6Mbr1aPwsfmw6som1aWtdf8LOF0NgFGQmwdba9/6UJTGbD2RRYrO7Kjpp4Kb9nUyRzU6n+FA6HZwBWcmUhMTx3+z1GBiMbDmSM5ueWel5gdZAOkR1AGD94fX1HbbXURIjIvUi3D+8fLz/y81fuv6EVn/ocbXj+tIptX5a86gggnwtFJbY2ZmW66LgpCEzDIOvljuGkq7oGVs+3X9qx7PYlL6FUN9Q7ul1T7XPbxnWEoDdmbtdHqu3UxIjIvVmbIexAMzaNYuU3BTXn7D3dWCywO6FcLB2+zeZzUeLe7XondTF6qQMthzMxs/HzEWWPyEzicyQGN5KXw3AHT3vICogqtrnNw9rDsDurN2uD9bLKYkRkXrTKaoTvWJ6UWKU8MWmL1x/wrAEaH++4/ryd2v9tPK6mGTVxciJK+uFOb9zLIErHb2AU1r1JLs4m7YRbRnTekyNz28R1gKAXZm7XBtoA6AkRkTq1fhO4wH4Zus35BTluP6EfW5w/Fz3LRTVbgGxshlKmmYtJyqvqIQZa/YDcEPCHji0iST/EL7M3gzA3T3vxmK21NhGi1BHErM7c7dmKB2HkhgRqVeDEgbRIqwFOcU5fLftO9efsNlAiGgOhVmwaXqtntLxmBlK+hKREzF740Fyi2w0jQyk/Z7PAXi7eSdK7CX0i+tH/yb9j9tGYkgiFpOFvJI8UvNSXR2yV1MSIyL1ymwyl/fGfLbpM4rtxS4+oRm6Xem4/vdntXpKm5hgfMwmMvOLSc6oh8X5pMH4cbWjF+aadsWYtv3GHh8rPxU66r9u73F7rdqwWqzEBcUBaJr1cSiJEZF6d37L84nyjyIlN4Vfd/3q+hN2GwuYHAW+R3Ye93A/HwttYkIArRcjtXckt4gFWw8BMKZoBgDvNuuAHTuDmgyiU6NOtW4rJigGgIN5B50faAOiJEZE6p2fxY9xHcYB8M7ad7DZXbzZYlgCtDrLcX117QqKteidnKif1x2gxG7QN9ZM2NZvSfKxMNPu+PdzS9dbTqitmEBHEqPhpJopiRERtxjbfixhfmHsztrNz7t+dv0Ju5cOKa3+AmqRNJUlMRs1zVpqaXrpUNJ/oldBcR6fxjbHhp0B8QM4rfFpJ9RWWU9MvSxF4MWUxIiIWwT7BpfXxkxZO4USe+23BqiT9udDQARkJcPOP457uPZQkhORnJHPst1HMJkM+hyZSabZxA++jqLw8Z3Hn3B7ZT0xGk6qmZIYEXGbse3HEu4Xzp6sPfy08yfXnszHD0671HF97dfHPbxDnKMm5kBmAUdyi1wZmTQAZb0wY5scxpq2ke9Cw8k3Smgb0Za+sX1PuD0lMbWjJEZE3CbIGlShN8blM5VOu8zxc9PM464ZE+JvpXlUIAAb1RsjxzFzbemspIAFFAOfRzpW5L2q41WYTKYTbq88iclVElMTJTEi4lZj248l0j+SpOwkvt36rWtPltALwptBcW6tNoU8ul6M6mKkeklH8tiwP4tgUwGtU35hdlAgqUYRUf5RnNfivDq1WVYTk5af5vrCdy+mJEZE3CrQGsi/u/4bgDdXv0lmoQsTBpPpmCGlb457uOpipDZmbXAU394asx6Kc/k0shEA/2r/L3wtvnVqM8o/CrPJjM2wcbjgsNNibWiUxIiI213c9mJah7cmszCTyWsmu/ZkXUqHlLbPhrwjNR6qnhipjV/XO5KYi4w5bPD1Zb0P+Jp9uazdZXVu02K2EO4XDkB6QbozwmyQlMSIiNv5mH24t/e9AEzdPJWdmcdfkK7OGreD2NPAXgIbf6jx0LJp1jvTcsktdPHsKfFKqdkFrNybToIplZjMNXwT6igIH9F8BJH+kSfVdtnzjxTUnGyfypTEiIhH6B/fnyEJQygxSnh++fOu3bOorMB3Xc01ONEh/sSE+mEY2gxSqjZ740EMA26MXE2OycQvIcEAXNL2kpNuW0nM8SmJERGPcU/ve7CarSxKXuTaBfA6XwyYYM8iyNxX46FdEsIBWJOU4bp4xGuVDSWdx1/8FBxEPgYtw1rSPbr7SbddlsRoOKl6SmJExGM0C23GTV1uAuDZZc+67i/QsCbQtJ/j+saad7bu0sRR3LsuWXUxUlFmfjGLdxymlSmZqNyt5UNJl7S9pE7Tqv9JPTHH57FJzIIFCxg1ahTx8fGYTCZ++OEHd4ckIvXg2tOupW1EWzIKM3h22bOuO1HHCx0/j1MX0yUxHIB1+5TESEVzNx+kxG4wPnQlG3x92eJrxdfsywWtLnBK+xH+EYCSmJp4bBKTm5tL165defPNN90diojUI6vZyn8H/Bezycwvu37hj73H3yKgTjqWftEkLYXM5GoPO620J2ZnWi6Z+S5ejE+8yqz1BwGD802L+CbUUQszvPlwwvzCnNK+emKOz2OTmHPPPZennnqKiy66yN2hiEg96xTVias7XQ3AY3895pqdfEPjIfF0x/VNM6o9LDLIl8TIAAA2aEhJSuUX2Zi3NZVOpt0EFO5jVpBjdecxbcY47RyqiTk+j01iTlRhYSFZWVkVLiLivW7tdivtI9uTXpjOAwsfcM2qpZ1GO34eb0ipSTgAazSkJKXmbz1EQbGdcYHLmBcYQK7ZTHxQPD1jejrtHOqJOb4Gk8RMmjSJsLCw8ktiYqK7QxKRk+Br8eX5wc8T4BPAspRlvLfuPeefpEPpkNLeJZB1oNrDuiSUFfdmOD8G8Uq/bUjBhJ3zLUuYGRwEwPktz8dsct7Xqnpijq/BJDEPPPAAmZmZ5ZekpCR3hyQiJ6lFWAsePv1hAN5a8xbLDixz7gnCmkBCH8CATdXPUjqtNIlZk6SeGIGiEju/bzpIT9NWSkoOsSjAH4CRrUY69Txlhb3ZxdkU21SPVZUGk8T4+fkRGhpa4SIi3u+CVhdwQasLsBt27p5/N8k51Rfh1knZkNKGH6o9pKy4Nzkjn8M5hc49v3idJTsPk1VQwmX+y/g1OBCbyUSnqE60DGvp1POE+IZgwjFVO7NICXRVGkwSIyIN1yOnP0LHqI5kFGbwn7n/Ia84z3mNl0213rsYslOqPCTE30rLxo4hg7Uq7j3lzdqQggUb51qWlg8ljWo1yunnMZvMhPo5/iB36caoXsxjk5icnBxWr17N6tWrAdi1axerV69m79697g1MROqdv48/r575KlH+UWxN38rDix7Gbtid03hYAiT0BowaF77rWrpyr9aLObXZ7AazNhykn3kjaaZs1vv5YTFZOKf5OS45X9kmkEpiquaxScyKFSvo3r073bs7lm6+66676N69O48++qibIxMRd4gNiuXlM1/Gx+zD7D2zmbJ2ivMar8XCd2VDSmv3ZTjvvOJ1/t6bTlpOIRf7LmZmkKMXpn98f6IColxyvjBfx787JTFV89gkZsiQIRiGUeny0UcfuTs0EXGT7tHdeeT0RwB4a/VbzNo9yzkNlyUxe/6qdkipW9NwAP7em+HazSnFo83akIIvxYwwL+en0qGkkS2dW9B7rLLhpIzCDJedw5t5bBIjIlKVMW3GcGWHKwF4+M+H2ZC24eQbDW8KTXrimKVU9cJ3neJD8bWYOZxbxJ7DTqzJEa9hGAa/bkjhDPMaNltLSLb6EGQN4symZ7rsnGXDSVlFWvusKkpiRMTr3NPrHgY1GUSBrYDb5t5GSm7VvScnpONox8+NP1b5sJ+Phc5NHH8Vr9qrdTtORZsOZJN0JJ8LfZYwI9ixQu+wpsMI8Alw2TnLtjBQT0zVlMSIiNexmC08P/h5Woe35lD+IefMWCofUloEOVVvc9CzmWPdjpV7lMScin7dkEIABZxhWcWs0noYZ68N809lSYxqYqqmJEZEvFKwbzBvDH2DSP9INh3ZxEN/PnRyM5YimkF8dzDs1S5816OpI4lZtTej7ucRrzVrfQrDzKtYGmAi22ImOjCa3jG9XXrOssJe9cRUTUmMiHitJsFNeOXMV7Carfy+93de//v1k2vwOENKPUp7YrakZJFTWHJy5xKvsistly0Hs7nA55htBlqcj8Vscel5y3pisgpVE1MVJTEi4tW6R3fnif5PAPDeuvf4cXvVCUitlK3eu/tPyDlU6eGYUH+ahAdgN2BNUkbdzyNeZ9aGFELJpbvPWuYHOmpgXD2UBMesE6MVe6ukJEZEvN6oVqO44bQbAHh88eOsOriqbg1FNIe4bo4hpc1Vz1Iq641ZpbqYU8qv61MYYVnO3CBfSkwm2ka0pW1EW5efV4W9NVMSIyINwq3db+XsZmdTYi/hjj/uICm7jpvAli98V3WPTs/S9WJWaobSKeNAZj6rkzK4wLKYmaWzkka1dP42A1VRYW/NlMSISINgNpl5euDTdIzqSHphOrfNua1uM5bKhpR2LYTcw5UeLuuJ+XtvBna7Fr07Fcxan0IUmTT13czf/v6YMHFey/Pq5dxlSUx+ST5FtqJ6Oac3URIjIg1GgE8Ar5/1OtEB0ezI3MGTS5488dV1I1tCbBcwbFUOKXWIC8XfaiYzv5idaTlOilw82S/rUzjPspRfgh21MH3j+hIdGF0v5w62BmM2Ob6q1RtTmZIYEWlQogOjef6M5zGbzMzcOZMftv9w4o2UDSltqPxcq8VMl9LNILVeTMOXllPI8t1HGGn5i5+CXLdjdXXMJrOmWddASYyINDg9Y3pyW/fbAHh66dNsTd96Yg10usjxc9cCyDtS6eFepUNKy3YpiWnofttwkFgjjUD/3ez2teJv8WNo06H1GoPqYqqnJEZEGqRrO1/LgPgBFNoKuWf+PSdWHxPVCmJOKx1Smlnp4b4tHTsWL91VuWZGGpZf1h/gfMsSZpSuDXNW06EEWYPqNQYlMdVTEiMiDZLZZOaZQc8QHRDNrsxdPLP0mRNroFP1Q0q9mkVgMZvYl57PvnRtBtlQZeYVs3jHYc6zLOLX0llJrtyxujrlSYzWiqlESYyINFiR/pE8N/g5TJj4cceP/LH3j9o/uWz13l3zKw0pBfn5cFoTxxfL0p2Vh5ukYZi96SDNjH1kBqWSbrEQ5RdBv/h+9R5H+YJ36ompREmMiDRovWJ7Mb7TeMCxEF56QS3rWBq1gehOYC+BLT9Xerhvy0hAQ0oN2a/rD3ChZRHTQ0q3GWg1Ch+zT73HEerr2D1dhb2VKYkRkQZvQvcJtA5vzZGCIyc27bpszZgqhpROL62LWaKemAYpp7CEBdsOMdTnL+aVbjNwQasL3BKLamKqpyRGRBo8P4sfTw18Ch+TD7P3zObX3b/W7ollU613/lFpSKlXswjMJth7JI8DmflOjljc7Y/NqXS2bWFDSB7FJhNtw1vTLrKdW2LRcFL1lMSIyCmhU1QnbuxyIwBPLXmKQ3mVN3ispHE7iOnsGFL6xzYEIf7W8rqYRds1pNTQzFy7nwsti8pnJV3QerTbYlFhb/WUxIjIKeP6LtfTMaojWUVZPLvs2do96bRLHT/XfVPpoYFtGgHw57ZaJETiNTLzi1m45QDd/Jax2t8PMybOa1E/2wxURZtAVk9JjIicMqxmK0/0fwKLycJve35jwb4Fx3/SaZcAJtizCDIqbio5sHVjAP7cflj7KDUgszak0Me+hj9DHLf7xfWjcWBjt8WjmpjqKYkRkVNK+8j2XNXxKsAxrHTcRfDCEqDZAMf19d9WeKhHs3ACfS2k5RSyOSXbFeGKG8xYs58LLH+WDyWNau2egt4yZdsOKImpTEmMiJxybul6C/FB8RzIPcCbq988/hO6lA4pra04pOTnYymfpbRQQ0oNwqHsQlZt30dM4DqSrT4EWvw5q+lZbo2prLC30FZIQUmBW2PxNEpiROSUE2gN5OHTHwbgs02fsfHwxpqf0PFCsPhC6gY4uKHCQwNbO+piFm5Lc0msUr9+WrufYaYV/BriC8DZzUcQ4BPg1piCrEH4mBzr06g3piIlMSJyShqUMIhzmp+D3bDzxOInKLGXVH9wQAS0Ge64vvbrCg8NbutIYpbtPkJ+kc1V4Uo9mb5mP6Os8/g1yLHNwMVtL3ZzRGAymQj104J3VVESIyKnrIl9JhLiG8LGwxv5cvOXNR/c5TLHz7Vfg+1owtOqcTBNwgMoKrGzaLt6Y7xZ0pE8DiVt4XDoXgrMZloGJ9KtcTd3hwUcLe7NKspycySeRUmMiJyyGgU04s6edwLwxt9vkJKbUv3Bbc+BwCjI3g875pTfbTKZGNYhGoDfNx10abziWt+s3Mdllvl8FxIMwMXt/4XJZHJzVA5ldTHqialISYyInNIubnMx3Rp3I68kj+eXP1/9gT5+0OVfjuurPqnw0LCOMQDM2ZyqqdZeymY3+H75broE/MkmP1+sJovbthmoimYoVU1JjIic0swmM4/0ewSLycLsPbNrXjumh2NqNlt/heyjvS59W0QR7OfDoexC1ibrS8Yb/bk9jdY5y5gX6qhrGtZ0GOH+4e4N6hiqiamakhgROeW1jWjL/3X8PwCeWfoM+SXV7IUU3QESeju2IVhztIbG18fMGe0ci6H9vlFDSt7o6xVJjPaZy8+la8Nc0u4yN0dUUdlwUlahamKOpSRGRAS4uevNxAXFkZyTzJQ1U6o/sIcj2eHvT+GY3bDL6mJmK4nxOum5RazasIW80G3kmc00C4yld2xvd4dVgbYeqJqSGBERHGvHPNDnAQA+3vAx29O3V31gp4vAGgSHt8Oev8rvPqtdDFaLiS0Hs9l2UKv3epNpfydzKb/xZZijF2Zs5/EeU9BbRjtZV01JjIhIqTObnsmZiWdSYpTw5JInsRv2ygf5hUDnMY7ry98rvzss0MrgNo4hpRlrD9RHuOIEhmEwbflO2obMZ7evlWCzH6PduGN1dcpqYrSTdUVKYkREjvFAnwcI8AlgVeoqftz+Y9UH9b3J8XPjjxU2hRzVNR6AmWv2YxiapeQNlu9Op9Wh35kR5vg6vKjtJQRZg9wcVWWanVQ1JTEiIseIC47j313/DcBLK18ivSC98kGxp0HzQWDYYPm75XcP6xiDn4+ZnWm5bNivAkxv8NGinZwd8At/BQZgxsQVHa90d0hV0nBS1ZTEiIj8w7iO42gT0YaMwgxeXvly1Qf1m+D4ufIjKMwBINjPh6GlBb4z1uyvh0jlZCRn5HNo05+sCM8A4Mz4gSSEJLg3qGocW9irXr6jlMSIiPyD1Wzl0dMfBWDa9mmsPLiy8kFtRkBECyjIrDDd+oKuTQD4/u9kim1V1NSIx/hsyR7GWGcyo3Ra9bjTrnFzRNUr64kpthdXvwTAKUhJjIhIFbpFd+PiNo7N/55c/CRFtqKKB5jNcPotjutLJ4PdkbCc1T6aRsG+HMouZO7m1PoMWU5AdkExC5csYV/ENgrNZrqEtaZXTC93h1WtAJ8AfMzayfqflMSIiFTjzp53EukfyY7MHUxeM7nyAd3GgV+YY7r15pmAY+G7i3s6hiSmLttbn+HKCfh86V4usX/DN6GOfZJu6nWnx02rPpbJZDpaF6MZSuU8Pol58803ad68Of7+/vTt25dly5a5OyQROUWE+YXx8OkPA/DB+g/YkLah4gF+wdD3Rsf1+c+X98b8q3dTx11bD7E/Q13/nqag2MZPC5aSEbmefLOZDsFNGdRkkLvDOi7NUKrMo5OYr776irvuuovHHnuMVatW0bVrV0aMGEFqqrpoRaR+nN3sbM5pfg42w8bDix6uPKx0+r/BNwQOroMtPwHQolEQp7eMxG7AF0vVG+Npvl6RxAUlX/JFmKMX5kYP74Upo1V7K/PoJOall17ihhtu4JprrqFjx45MnjyZwMBAPvjgA3eHJiKnkAf7PkikfyTbM7bz1uq3Kj4YGHl03Zh5z5X3xvxfv+YAfLpkD7mFJfUYrdQkr6iE6XPmczDK0QtzWkhzhjYd6u6waqUsiVFPzFEem8QUFRWxcuVKhg0bVn6f2Wxm2LBhLF68uNLxhYWFZGVlVbiIiDhDhH8Ej5z+COAYVlp6YGnFA/pNAL9QR2/M2q8AGNEplmZRgWTmF/P1iqR/Nilu8uGi3Yy2fch3oY4ZSXf2f9QremFASUxVPDaJSUtLw2azERMTU+H+mJgYUlJSKh0/adIkwsLCyi+JiYn1FaqInAKGNRvGxW0uxsDggYUPcKTgyNEHAyNh0F2O63P+C0V5WMwmbhjUEoD3Fu6iRNOt3S49t4jF835meeMkSkwmBjTq5nEbPdZEC95V5rFJzIl64IEHyMzMLL8kJekvHxFxrol9JtIyrCWH8g/xyKJHKi461vcWCGsK2fvhr9cBuKRnAlFBviRn5PP938luilrKvPzbBs4JeJe5QYH4YOKe/o+5O6QTUtYTk15YxSrSpyiPTWIaNWqExWLh4MGK29ofPHiQ2NjYSsf7+fkRGhpa4SIi4kwBPgH874z/4Wv2ZcG+Bby//v2jD1r9YVjpl+KfL8HhHfhbLdx0hqM35uXZWykotrkhagFYk5QBq97h80bFAFzZ5lJaR7R2b1AnqFFAIwDS8tPcHInn8NgkxtfXl549ezJnzpzy++x2O3PmzKFfv35ujExETmVtI9oysc9EAF5b9RrzkuYdfbDzxdByCJQUwMw7wTD4v37NaRIewIHMAj76a7cbIhab3eDl7+YR2Og39lmtRPsEc0vvu90d1glrHODYJf1Q/iE3R+I5PDaJAbjrrrt49913+fjjj9m0aRO33HILubm5XHON5y4NLSIN32XtLuPydpdjYDBxwUS2pW9zPGAywciXwScAds2Hvz/F32rhrrPbAvDmH9tJyyl0Y+SnpsnztjEk9xm+CAsA4JGBTxNoDXRzVCeurCfmcP5hN0fiOTw6ibn88st54YUXePTRR+nWrRurV6/m119/rVTsKyJS3yb2mUif2D7kleRx29zbOJRX+tdxZEs48wHH9V8mwqGtjO7ehE7xoWQXlPDY9A3VNypOt3ZfBofmvcjUmGwMk4kxicMY0uwsd4dVJ2VJzJGCIxTbi90cjWfw6CQG4NZbb2XPnj0UFhaydOlS+vbt6+6QRESwmq28eMaLJAQnkJyTzA2/3XB0xlK/W6HFYCjOg2+vwWIr4LmLu2Axm/hp7QFmbag8w1KcLzO/mCmff0FK3FySrT7EW0O5d+CT7g6rziL8I/AxOfZPUm+Mg8cnMSIinircP5x3hr9DdGA0OzJ3cNPsmxzTX80WGPMuBDaCg+vhxwl0jgvhpsGOIt+Hpq0jJbPAzdE3bCU2O499OotEv5dZFOiPHyZeHv4Owb7B7g6tzswmM5EBkYCKe8soiREROQmJIYm8N/w9ovyj2HxkMzf8doPjCyYkFi75AMw+sP47mPsk/xnahg5xoaTlFHHzZyspLNFsJVcwDIPnf1hC86wH+DzCH4DHTn+Ujo06uTmyk1dW3KskxkFJjIjISWoR1oJ3h79LhF8Em45s4qqfr2J35m5oeQaMes1x0J8v4b/4ZaZc2ZOwACurkzJ44Pt12O1GjW3LiTEMgxdmrCB0xy180Mjx2t7Y9nJGtbvEzZE5h2YoVaQkRkTECdpEtOHT8z4lITiBfTn7GPvTWObsnQPdx8FZji0LmPskTde8xKuXd8ViNvH9qmQe+mF9xUXzpM5KbHZe+G4+xrbrmBJjxzCZGNt0BLee/pC7Q3OaRoGO4t7yQvJTnJIYEREnaRbajE/P+5Qe0T3IKc7hjj/u4KklT5HX798w7HHHQQv+x5C19/HqmNaYTfDlsr3c+dVqLYR3kjLzinn83U84sv9mPi3tgbmi6XDuH/K81+yNVBtxQXEAJOdoBWhQEiMi4lSNAhrx3oj3uLLDlQB8teUrxkwfw++Jp2GMfAXMVtj4AyMXj+WDs2xYzCZ+WL2fy6YsZndarnuD91ILN+/n1deuY7N5EjPDrADc0eFq7h/yAmZTw/qaSwhOAJTElDEZDbQfMysri7CwMDIzM7UFgYi4xeL9i3nsr8c4kHsAgO7R3bk2ZgCD/3gZc/YBwMTBVpdw9c4z2Zwfjp+PmduHteHaAS3wt1rcG7wX2JGazTffTyaj8CN+CTNhN5kIx8Kzg59jQIsR7g7PJdYcWsOVP19JTGAMv1/6u7vDcYkT+f5WEiMi4kJ5xXm8v/59Pt7wMYU2x2q9zUOacmmxD8O3LiTWZsMw+7LQ/wxeSh/EaqMVjUP8uX5gC8b0SKBxiJ+bfwPPUlhiY+nGnSz5czIHS35jQYidArOjt+Xs0HY8MPwtGgdFuzlK1zmcf5ghXw/BhIkVV67A1+Lr7pCcTkkMSmJExLOk5KbwxaYv+Hbrt2QXZ5ff38XwpW/mIXoWFNK1oJAcI4pZJd1ZYO/CBlrTqmVLBrZuzMDWjWgXG4KvT8MaHjkem91gW0oGm9YvZOuun9lfsIpdgVns8rWWH9PREsbtAx6jf4uz3Rhp/TAMg75f9CW/JJ8Zo2fQPKy5u0NyOiUxKIkREc+UW5zL9B3T+XXXr/yd+jcGFT+Co0tKaFFcQvPiYuJKSrCUBFBQEkF+SSQZ9igIiCcsNJrg4GhCQhsTEhZFYFAwfv7+BPj74+/vT4CfHxazGbMZLGaT42Jy/DSXXj9RdfmiMAwDm92g2GZQYrNRYrNTXGKjxG6npLgQe0EOJUXZpGencST3MNk5h8jPP0h23gGOFB4i155GnjmbA9YSknx9KrTtY8BA/6aM63sHfZsPa1DFu8dz0Y8XsT1jO5OHTWZAkwHuDsfpTuT726fGR0VExKmCrEGMbT+Wse3HkpqXyoJ9C1h1cBWrUleRnJNMqo8PqT4+LA3wP+ZZ+UBy6WWt4y4b+BwxsB42MANmA8wYWMqvH73P8fXu+H9n/NVaU0vHphIGYDdBCWAzmcp/2oASk4mS6hIPH/7x7eS40chmppt/IsM7jqZ/h0sJ8ws7qd/DWyWEJLA9Yzt7svY0yCTmRCiJERFxk+jAaC5pewmXtHUsxJZZmMnurN3sytzF7szdHMrZT1rmHg7np5FenE2erYh8w4at9Lu/xkTAy/jaDYIMCDAsBOFLY0sQMX6NiY9oTuemvenU/CwiStdIOdW1CW/DvKR5bE3f6u5Q3E5JjIiIhwjzC6Nr4650bdy1xuOKbcXk2/LJL86n2F6M3W7DZivEbivCVlLg+Gkrcvw07FBaNWCz27EbBoYBJgwov5T9KLtedr8JTCZMpT859mf5dTDKH+OYxxzXfS1WfC0++Jit+Fh88DH5YDH7YPHxx+IbjNXHj0CfQKyWozUuUrMOUR0A2Hh4o5sjcT8lMSIiXsZqsWK1WAn1Vb3fqah9ZHsAtmdsp9hWfEongKdWmbuIiIiXSwhOIMQaQrG9mB2ZO9wdjlspiREREfEiJpOJTqU7ci87sMzN0biXkhgREREvMyRxCABzk+a6NxA3UxIjIiLiZc5MPBOAv1P/Ji0/zc3RuI+SGBERES8THxxPl8ZdsBt2pqyZ4u5w3Eazk0RERLzQ7d1v57rfruPrrV8TFRDFGQlnEOATQLG9mPyS/GovxfZiDMPAbtixG3YMDGyGrdJ9/1zQ/5+rSwNE+kdyc9eb6+tXrkTbDoiIiHipSUsn8cXmL9x2/uahzZlx0QyntqltB0RERE4B9/e5n7YRbflu23ccyD1AQUkBVrOVAJ+A8kugNbDCbavZislkwmwyYzaZMXHMdZMJM46fJiqvBv3PParC/cLr6TetmnpiRERExGOcyPe3CntFRETEKymJEREREa+kJEZERES8kpIYERER8UpKYkRERMQrKYkRERERr6QkRkRERLySkhgRERHxSkpiRERExCspiRERERGvpCRGREREvJKSGBEREfFKSmJERETEKymJEREREa/k4+4AXMUwDMCxpbeIiIh4h7Lv7bLv8Zo02CQmOzsbgMTERDdHIiIiIicqOzubsLCwGo8xGbVJdbyQ3W5n//79hISEYDKZ3B2OS2RlZZGYmEhSUhKhoaHuDkfQe+KJ9J54Hr0nnsXT3g/DMMjOziY+Ph6zueaqlwbbE2M2m0lISHB3GPUiNDTUI/7hyVF6TzyP3hPPo/fEs3jS+3G8HpgyKuwVERERr6QkRkRERLySkhgv5ufnx2OPPYafn5+7Q5FSek88j94Tz6P3xLN48/vRYAt7RUREpGFTT4yIiIh4JSUxIiIi4pWUxIiIiIhXUhIjIiIiXklJjBfavXs31113HS1atCAgIIBWrVrx2GOPUVRUVOG4tWvXMmjQIPz9/UlMTOT55593U8SnjjfffJPmzZvj7+9P3759WbZsmbtDOiVMmjSJ3r17ExISQnR0NKNHj2bLli0VjikoKGDChAlERUURHBzMxRdfzMGDB90U8ann2WefxWQycccdd5Tfp/ek/iUnJ3PllVcSFRVFQEAAp512GitWrCh/3DAMHn30UeLi4ggICGDYsGFs27bNjRHXTEmMF9q8eTN2u50pU6awYcMGXn75ZSZPnsyDDz5YfkxWVhbDhw+nWbNmrFy5kv/97388/vjjvPPOO26MvGH76quvuOuuu3jsscdYtWoVXbt2ZcSIEaSmpro7tAZv/vz5TJgwgSVLljB79myKi4sZPnw4ubm55cfceeedzJgxg2+++Yb58+ezf/9+xowZ48aoTx3Lly9nypQpdOnSpcL9ek/qV3p6OgMGDMBqtfLLL7+wceNGXnzxRSIiIsqPef7553nttdeYPHkyS5cuJSgoiBEjRlBQUODGyGtgSIPw/PPPGy1atCi//dZbbxkRERFGYWFh+X0TJ0402rVr547wTgl9+vQxJkyYUH7bZrMZ8fHxxqRJk9wY1akpNTXVAIz58+cbhmEYGRkZhtVqNb755pvyYzZt2mQAxuLFi90V5ikhOzvbaNOmjTF79mzjjDPOMG6//XbDMPSeuMPEiRONgQMHVvu43W43YmNjjf/973/l92VkZBh+fn7Gl19+WR8hnjD1xDQQmZmZREZGlt9evHgxgwcPxtfXt/y+ESNGsGXLFtLT090RYoNWVFTEypUrGTZsWPl9ZrOZYcOGsXjxYjdGdmrKzMwEKP9vYuXKlRQXF1d4f9q3b0/Tpk31/rjYhAkTOP/88yu89qD3xB2mT59Or169uPTSS4mOjqZ79+68++675Y/v2rWLlJSUCu9JWFgYffv29dj3RElMA7B9+3Zef/11brrppvL7UlJSiImJqXBc2e2UlJR6je9UkJaWhs1mq/I11+tdv+x2O3fccQcDBgygc+fOgOPfvK+vL+Hh4RWO1fvjWlOnTmXVqlVMmjSp0mN6T+rfzp07efvtt2nTpg2zZs3illtu4T//+Q8ff/wxcPS7wZs+x5TEeJD7778fk8lU42Xz5s0VnpOcnMw555zDpZdeyg033OCmyEU8x4QJE1i/fj1Tp051dyintKSkJG6//XY+//xz/P393R2O4Ejwe/TowTPPPEP37t258cYbueGGG5g8ebK7Q6szH3cHIEfdfffdjB8/vsZjWrZsWX59//79nHnmmfTv379SwW5sbGylKv+y27Gxsc4JWMo1atQIi8VS5Wuu17v+3HrrrcycOZMFCxaQkJBQfn9sbCxFRUVkZGRU+Mtf74/rrFy5ktTUVHr06FF+n81mY8GCBbzxxhvMmjVL70k9i4uLo2PHjhXu69ChA9999x1w9Lvh4MGDxMXFlR9z8OBBunXrVm9xngj1xHiQxo0b0759+xovZTUuycnJDBkyhJ49e/Lhhx9iNld8K/v168eCBQsoLi4uv2/27Nm0a9euQiW6OIevry89e/Zkzpw55ffZ7XbmzJlDv3793BjZqcEwDG699VamTZvG3LlzadGiRYXHe/bsidVqrfD+bNmyhb179+r9cZGhQ4eybt06Vq9eXX7p1asX48aNK7+u96R+DRgwoNLSA1u3bqVZs2YAtGjRgtjY2ArvSVZWFkuXLvXc98TdlcVy4vbt22e0bt3aGDp0qLFv3z7jwIED5ZcyGRkZRkxMjHHVVVcZ69evN6ZOnWoEBgYaU6ZMcWPkDdvUqVMNPz8/46OPPjI2btxo3HjjjUZ4eLiRkpLi7tAavFtuucUICwsz5s2bV+G/h7y8vPJjbr75ZqNp06bG3LlzjRUrVhj9+vUz+vXr58aoTz3Hzk4yDL0n9W3ZsmWGj4+P8fTTTxvbtm0zPv/8cyMwMND47LPPyo959tlnjfDwcOPHH3801q5da1x44YVGixYtjPz8fDdGXj0lMV7oww8/NIAqL8das2aNMXDgQMPPz89o0qSJ8eyzz7op4lPH66+/bjRt2tTw9fU1+vTpYyxZssTdIZ0Sqvvv4cMPPyw/Jj8/3/j3v/9tREREGIGBgcZFF11UIfEX1/tnEqP3pP7NmDHD6Ny5s+Hn52e0b9/eeOeddyo8brfbjUceecSIiYkx/Pz8jKFDhxpbtmxxU7THZzIMw3BPH5CIiIhI3akmRkRERLySkhgRERHxSkpiRERExCspiRERERGvpCRGREREvJKSGBEREfFKSmJERETEKymJEREREa+kJEZERES8kpIYERER8UpKYkSkQZs5cyYtWrSgT58+bNu2zd3hiIgTae8kEWnQ2rVrx5tvvsmGDRtYvHgxU6dOdXdIIuIk6okRkQYtKiqK1q1b07x5c3x9fd0djog4kY+7AxARqYtrrrmGJk2a8NRTTx33uFatWhETE8P69evrKToRqQ8aThIRr2Oz2YiNjeWnn36iT58+1R5XUlJCt27dGDVqFG+++SaZmZmYTKZ6jFREXEnDSSLiVrt378ZkMlW6DBkypNrn/PXXX1itVnr37l1j25MnT6Zly5ZMmDCB7Oxsdu7c6eToRcSdNJwkIm6VmJjIgQMHym+npKQwbNgwBg8eXO1zpk+fzqhRo2rsVTly5AhPPvkk8+bNIyEhgbCwMFavXk2rVq2cGr+IuI96YkTErSwWC7GxscTGxhIeHs7NN99Mv379ePzxx6t9zo8//sgFF1xQY7uPPfYYF110ER06dACgY8eOrFmzxpmhi4ibqSdGRDzGtddeS3Z2NrNnz8ZsrvpvrE2bNrF//36GDh1abTsbN27ks88+Y9OmTeX3de7cmdWrVzs7ZBFxIyUxIuIRnnrqKWbNmsWyZcsICQmp9rjp06dz9tln4+/vX+0xd955JxkZGSQkJJTfZ7fbSUxMdGrMIuJeSmJExO2+++47/vvf//LLL78ct2blxx9/5MYbb6z28ZkzZ7Jy5Ur+/vtvfHyOfsQtX76ca6+9lvT0dCIiIpwWu4i4j6ZYi4hbrV+/nr59+3LXXXcxYcKE8vt9fX2JjIyscGxqaioJCQns37+fRo0aVWqruLiYzp07c+211zJx4sQKj+3du5dmzZrxxx9/1DjzSUS8hwp7RcStVqxYQV5eHk899RRxcXHllzFjxlQ6dsaMGfTp06fKBAbg9ddfJyMjg1tvvbXSY4mJiQQGBqouRqQBUU+MiHiNCy64gIEDB3Lfffe5OxQR8QDqiRERrzFw4EDGjh3r7jBExEOoJ0ZERES8knpiRERExCspiRERERGvpCRGREREvJKSGBEREfFKSmJERETEKymJEREREa+kJEZERES8kpIYERER8UpKYkRERMQr/T8hbSMPoHB5cQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(*s_d2o.sld_profile(), label='d2o')\n",
    "ax.plot(*s_hdmix.sld_profile(), label='hdmix')\n",
    "ax.plot(*s_h2o.sld_profile(), label='h2o')\n",
    "\n",
    "ax.set_ylabel(\"$\\\\rho$ / $10^{-6} \\\\AA^{-2}$\")\n",
    "ax.set_xlabel(\"z / $\\\\AA$\")\n",
    "ax.legend();"
   ]
  }
 ],
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